Biomarkers: In Medicine, Drug Discovery, and Environmental Health

This page intentionally left blank

BIOMARKERS

This page intentionally left blank

BIOMARKERS

In Medicine, Drug Discovery, and Environmental Health

Edited by

VishalS.Vaidya Joseph V. Bonventre Harvard Medical School Boston, Massachusetts

1WILEY A JOHN WILEY & SONS, INC., PUBLICATION

The cover art is called "Biofluid" and represents biological fluid with visible signs of biomarkers. Created by Dr. Ina Schuppe-Koistinen using watercolors, Dr. Schuppe-Koistinen is a senior principal scientist and molecular toxicologist at AstraZeneca, Sweden. Additional science watercolors by Dr. SchuppeKoistinen can be found at http://www.inasakvareller.se

Copyright © 2010 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com. Library of Congress Cataloging-in-Publication Data: Biomarkers : in medicine, drug discovery, and environmental health / edited by Vishal S. Vaidya, Joseph V. Bonventre. p. ; cm. Includes bibliographical references and index. ISBN 978-0-470-45224-0 (hardback) 1. Biochemical markers. I. Vaidya, Vishal S. II. Bonventre, Joseph V. [DNLM: 1. Biological Markers. 2. Diagnostic Techniques and Procedures. 3. Drug Discovery. 4. Environmental Monitoring—methods. QW 541 B616 2010] QH438.4.B55B555 2010 616.07'5-dc22 2010013135 Printed in the United States of America. 10

9 8 7 6 5 4 3 2 1

To: My parents, Sudhakar and Suhasini; my wife, Alka; and my sons, Ariv and Rian. Vishal Vaidya

To: My wife, Kristie; my daughter, Joanna; my son, Andrew; my son-in-law, Brian; and my grandson, Daniel. Joseph Bonventre

This page intentionally left blank

CONTENTS

Preface

xxii

Contributors

xxv

Biomarkers: An Evolutionary Perspective Michael A. Ferguson and Vishal S. Vaidya References

1 4

SECTION I: TOOLS FOR BIOMARKER DISCOVERY

5

2

7

Genomics Weida Tong and Donna L. Mendrick Introduction Evaluation of the Technology Clinical Applications Bioinformatics Challenges Applications to Drug Toxicology, Medicine, and Environmental Health Improve Understanding of Basic Cellular Architecture and Function Mechanism of Toxicity and Disease Algorithmic Models to Predict Toxicity or Disease Strengths, Weaknesses, and the Road Forward Conclusion Summary Points Disclaimer References

3

7 8 10 12 14 15 16 17 18 20 20 20 20

Proteomics for Biomarker Discovery Timothy D. Veenstra

25

Introduction

25 vu

CONTENTS

Vlll

4

Tissue or Biofluid Technology Protein Identification Using Mass Spectrometry Sample Preparation Protein Quantitation Examples of Biomarker Discovery and Evaluation Challenges in Proteomic Biomarker Discovery The Road Forward: Targeted Verification and Validation Conclusion Summary Points Acknowledgments References

26 28 29 30 33 35 38 39 43 44 44 44

Metabolic Profiling for Biomarker Discovery

47

Hector C. Keun

5

Introduction: What is Metabolic Profiling? Analytical Strategies for Metabolic Profiling Data Pre-Processing, Analysis, and Pattern Recognition Preclinical Toxicology: Models for Pathological Biomarker Discovery Disease Biomarker Discovery Using Metabolic Profiling Inborn Errors of Metabolism Neuroscience Cancer Infectious Disease Metabolic Syndrome: Insulin Resistance, Cardiovascular Disease, and Hypertension Environmental Health and Metabolic Profiling Conclusion: Strengths, Weaknesses, and the Way Forward for Metabolic Profiling in Biomarker Discovery Summary Points References

47 50 54 56 58 58 59 59 61

The Bittersweet Promise of Glycobiology

75

61 62 63 64 64

Padmaparna Chaudhuri, Rania Harfouche, and Shiladitya Sengupta Introduction Glycosylation in Pathological States Congenital Disorders of Glycosylation (CDG) Glycomics of Immune Disorders Glycomics in Cancer Other Acquired Diseases Glycans in Therapeutics and as Therapeutic Targets Tools to Analyze the Glycome

75 75 75 76 77 79 79 80

CONTENTS

Analytical Chemical Microarray Molecular Strengths, Weaknesses, and the Road Forward Conclusion Summary Points References

IX

80 81 82 83 84 84 84 85

SECTION II: BIOMARKERS OF INJURY/DISEASE

89

6

91

Biomarkers of Alzheimer's and Parkinson's Disease Walter Maetzler and Daniela Berg

Definition and Prevalence of Alzheimer's and Parkinson's Disease 91 Alzheimer's Disease 91 Parkinson's Disease 92 Pathophysiology and Mechanisms 92 Alzheimer's Disease 92 Genetic Aspects 93 Pathology 93 Pathophysiological Mechanisms 94 Parkinson's Disease 95 Genetic Aspects 95 Pathology 96 Pathophysiological Mechanisms 96 Concluding Remarks to Pathological and Pathophysiological Aspects 97 Current Means for Diagnosis/Prognosis of the Diseases and Their Limitations 98 Alzheimer's Disease 98 Clinical Markers 98 Genetic Markers 98 In Vivo Markers from Pathology 98 Pathophysiological Mechanisms 100 Further Diagnostic Assessments 100 Parkinson's Disease 100 Clinical Markers 101 Genetic Markers 101 In Vivo Markers from Pathology 101 Pathophysiological Mechanisms 102 Further Diagnostic Assessments 102 Novel Biomarkers 102 Alzheimer's Disease 102

CONTENTS

X

Clinical Markers Genetic Markers In Vivo Markers from Pathology Pathophysiological Mechanisms Further Diagnostic Assessments Combination of Markers Parkinson's Disease Clinical Markers Genetic Markers In Vivo Markers from Pathology Pathophysiological Mechanisms Further Diagnostic Assessments Combination of Markers Methods to Quantify Biomarkers Conclusion Summary Points References

102 102 103 104 105 105 106 106 106 106 107 107 108 108 109 109 110

Biomarkers of Cardiac Injury

119

Anthony S. McLean and Stephen J. Huang Introduction Definition and Prevalence Pathophysiology and Mechanisms Diagnosis Biomarkers of Cardiac Injury Inflammatory Markers of Cardiac Disease C-Reactive Protein (CRP) Interleukins (IL) Tumor Necrosis Factor (TNF) and Fas CD40 Ligand Matrix Metalloproteinases (MMPs) Myeloperoxidase (MPO) Markers for Myocardial Cell Injury Creatine Kinase-Myocardial Band (CK-MB) Troponins (cTn) Heart-Type Fatty Acid Binding Protein (H-FABP) Markers for Cardiac Stress B-Type Natriuretic Peptide (BNP) and N-Terminal ProBNP (NT-ProBNP) Adrenomedullin (ADM) ST2 Multimarker Approach? Conclusion

119 119 123 125 126 127 127 129 129 130 130 131 131 131 132 134 134 134 137 138 138 139

CONTENTS

XI

Summary Points References

139 140

Lung Injury Biomarkers

157

Urmila P. Kodavanti Introduction Causes of Lung Injury Morphological and Cellular Targets of Lung Injury Airway and Mucosa Alveolar Macrophage The Surfactant Covering Alveolar Epithelial Cells Alveolar Epithelium, Interstitium, and Capillary Endothelium Pathobiologic Processes Involved in Lung Injuries and Diseases Airway Epithelial Damage, Mucus Hypersecretion, and Goblet Cell Hyperplasia Airway Inflammation in Asthma Airway Inflammation in Bronchitis and Chronic Obstructive Pulmonary Disease Airway Fibrosis, Bronchoconstriction, and Hyperresponsiveness Alveolar Epithelial, Capillary Endothelial, and Terminal Bronchiolar Injuries Pulmonary Edema Neutrophilic Inflammation, Alveolar Apoptosis, and Emphysema Pulmonary Fibrosis and Granuloma Alveolar Phospholipidosis Pulmonary Surfactant and Surfactant Protein Abnormalities Sampling Techniques for Biomarker Analysis Induced Sputum Bronchoscopy and Lung Biopsy Bronchoalveolar Lavage for Analysis of Biomarkers of Lung Injury Biomarker Assessments and Their Involvement in Lung Injury and Disease Lung Injury Biomarkers in Bronchoalveolar Lavage Fluid (BALF) and Sputum Total Protein and Albumin Lactate Dehydrogenase Activity 7-Glutamyl Transferase Activity N-acetyl Glucosaminidase Activity Cells in Bronchoalveolar Lavage Fluid as Biomarkers of Lung Inflammation Cytokines and Chemokines in Sputum and Bronchoalveolar Lavage Fluid Biomarkers of Oxidative Stress in Bronchoalveolar Lavage

157 158 159 159 161 161 162 162 162 164 164 165 166 166 167 167 169 169 170 170 171 171 172 174 174 175 175 175 176 177

Xll

CONTENTS

Fluid, Sputum, Lung, and Plasma Ascorbate Glutathione Extracellular Superoxide Dismutase Ferritin, Lectoferrin, Transferrin, and Iron-Binding Capacities 4-Hydroxynonenal F(2)-Isoprostanes Exhaled Nitric Oxide Heme Oxygenase-1 Asymmetric and Symmetric Dimethyl Arginine Surfactant Proteins in Bronchoalveolar Lavage Fluid and Plasma Matrix Metalloproteases as Biomarkers of Lung Injury Collagen and Elastin Fragments as Biomarkers of Lung Injury Circulating Lung-Cell-Specific Proteins as Biomarkers Blood Coagulation and Thrombosis Markers in Lung Injuries Novel Approaches for Biomarker Identification Acknowledgments Disclaimer References

177 178 179 179 180 180 180 181 181 182 182 183 184 184 185 185 187 187 187

Translational Biomarkers of Acute Drug-Induced Liver Injury: The Current State, Gaps, and Future Opportunities

203

JosefS. Ozer, William J. Reagan, Shelli Schomaker, Joe Palandra, Mike Baratta, and Shashi Ramaiah Intrinsic and Idiosyncratic Drug-Induced Liver Injury: Terminologies and Background Histological Manifestations of Liver Injury Hepatic Steatosis/Fatty Liver and Steatohepatitis Cholestatic Liver Injury Common Mechanisms of Acute Liver Injury Mechanistic Manifestations of DILI Processes of Hepatocyte Cell Death The Role of Immune Responses in Liver Injury Metabolic Idiosyncrasy in Liver Injury Underlying Inflammation Mechanisms with Liver Injury Mitochondrial Oxidant Stress and Dysfunction Inhibition of Tissue Repair Response Disruption of Calcium Homeostasis and Cell Membrane Damage in Liver Disruption of Cytoskeleton in Liver Injury Traditional Preclinical and Clinical Biomarkers of Drug-Induced Liver Injury Gaps in Traditional Hepatic Biomarkers Considerations to Predict Acute Liver Injury: Anatomy and Time-Course

203 204 204 204 205 205 205 206 206 207 207 208 208 208 209 209 210

CONTENTS

xiu

New and Emerging Serum Enzyme Biomarkers of Liver Injury Discovery and Application of Purine Nucleoside Phosphorylase (PNP), Paraxonase (PON-1), and Malate Dehydrogenase (MDH) as Hepatic Biomarkers PON1 Is a Functional Marker of Chronic Liver Injury Malate Dehydrogenase (MDH) Activity Is a Candidate Biomarker of DILI-1 Biomarker Qualification by the Predictive Safety Testing Consortium (PSTC) ALT Isozymes: ALT1 and ALT2 Historical Background of ALT Biology Gene Expression of ALT Isoforms The Localization of ALT Protein in Tissues ALT Protein Levels in Serum Current Knowledge on Biology of ALT Does Metabolic Syndrome Illicit a Conflicting ALT Signal for DILI? Anorexia Shows Metabolic Indicators of Liver Injury Including Subtle ALT Elevations Biomarkers of Biliary Injury Authors' Opinion on Future Biomarkers of Liver Injury, Novel Approaches, and Platforms Reactive Oxygen Species (ROS) as Potential Markers for Liver Injury Mechanisms Inflammation Markers as Potential Indicators of Liver Injury Novel Hepatocellular Leakage Enzymes as Early Biomarkers of Symptomatic Change Hepatic Regeneration Markers to Supplement Injury Biomarkers Unification of Diagnostic Metrics of Liver Fibrosis Analytical Biomarker Platforms to Assay Serum Biomarkers of Liver Injury Mass Spectrometry Technologies Can Fill Gaps to Detect Biomarkers When Antibody Approaches are Limited An Overview of Mass Spectrometry Technologies Mass Spectrometry Technologies to Potentially Detect Biomarkers and Rare Protein Antigens of Injury Improved Tagging Techniques for Mass Spectrometry Detection of Proteins High-Throughput Chromatography Enhances the Downstream Detection of Rare Serum Proteins by Mass Spectrometry Mass Spectrometry Approaches to Distinguish Novel Biomarkers of Renal and Liver Injury Mass Spectrometry Approaches to Detect Novel Serum Biomarkers of Liver Injury Conclusion

212

212 213 214 214 214 214 216 216 217 217 218 218 219 219 219 220 221 222 222 223 223 224 224 224 225 226 226 226

XIV

CONTENTS

Acknowledgments Summary Points References 10 Biomarkers of Acute Kidney Injury Frank Dieterle and Frank D. Sistare Introduction Definition and Prevalence of Acute Kidney Injury Pathophysiology and Mechanisms Current Standards for Diagnosing Acute Kidney Injury Novel Kidney Safety Biomarkers Kidney Function Biomarkers Serum Cystatin C Functional Biomarkers Urinary Total Protein Urinary Albumin Urinary p2-Microglobulin Urinary Cystatin C Leakage Markers Urinary GST-a and GST-u/ir Urinary NAG Expression Markers Urinary Kim-1 Urinary Clusterin Urinary NGAL Urinary Osteoactivin Urinary Osteopontin Urinary L-FABP Urinary Trefoil Factor 3 Immune Markers Urinary IL-18 Newest Technologies and Achievements Around Kidney Safety Biomarkers Assays and Technologies Consortia Achieving the First Regulatory Qualification of Kidney Safety Biomarkers Conclusion Summary Points References 11 In Search of Biomarkers for Drug-Induced Vascular Injury

227 227 227 237 237 237 238 242 244 245 245 246 246 247 248 249 250 250 251 251 251 253 253 255 255 256 257 257 257 258 258 260 261 263 263 281

James R. Turk History and Background of DIVI Overview

281 281

CONTENTS

Types of Compounds Implicated Descriptive Pathology of Drug-Induced Vascular Injury Vascular Anatomy Rat Dog Primate Spontaneous Lesions in Preclinical Species Comparison with Human Vasculitides Progress in Biomarker and Model Development for Drug-Induced Vascular Injury Alpha-1-Acid Glycoprotein Calprotectin (S100A9/A8) Caveolin-1 Circulating Endothelial Cells/Particles Complement Component 3 Connective Tissue Growth Factor (CTGF) C-Reactive Protein (CRP) Endothelin-1 Fibrinogen GRO/CINC-1 Haptoglobin Metallothionein-1 (MT-1) Monocyte Chemoattractant Protein-1 (MCP-1) Neutrophil Gelatinase-Associated Lipocalin (NGAL) Osteopontin (OPN) Smooth Muscle Actin Thrombospondin-1 (TSP-1) Tissue Inhibitor of Metalloporteinases-1 (TIMP-1) Tissue Plasminogen Activator (tPA) Vascular Cell Adhesion Molecule 1 (VCAM-1) Vascular Endothelial Growth Factor (VEGF) Von Willebrand Factor Conclusion References 12 Biomarkers of Immunotoxicity

xv 282 283 283 284 284 285 285 285 286 286 286 286 288 288 288 289 289 289 289 289 290 290 290 290 291 291 291 291 291 292 292 292 292 307

Rodney R. Dietert Introduction History of the Use of Biomarkers in Immunotoxicity Assessment Establishing the Testing Paradigm A "Challenging" Issue for Immune Biomarkers Targets of Immunotoxicity

307 308 308 308 309

XVI

CONTENTS

Diseases of Primary Concern Increased Susceptibility to Infections and Tumors Chronic Diseases and Conditions Based on Immune Dysfunction Developmental Immunotoxicity: Increased Vulnerability in Early Life Differential Exposure-Outcomes Between Genders A Disease-Based Approach to Immune Biomarker Selection Toxicogenomic and In Vitro Approaches Conclusion Summary Points Acknowledgments References 13 Biomarkers in Obstetric Medicine

310 310 310 313 314 314 315 316 316 317 317 323

Manish Maski, Sarosh Rana, and S. Ananth Karumanchi Aneuploidies-Trisomies 21, 18, and 13 Alpha Fetoprotein Human Chorionic Gonadotropin Pregnancy-Associated Plasma Protein-A Unconjugated Estriol InhibinA Detection of Trisomy 21 Detection of Trisomy 18 Detection of Trisomy 13 Amniocentesis and Chorionic Villi Sampling Other Novel Markers for Aneuploidy Screening Preeclampsia and Fetal Growth Restriction Vascular Endothelial Growth Factor Placental Growth Factor VEGF Receptors VEGFRl/Fltl (Fms-Like Tyrosine Kinase 1) Endoglin Role of Angiogenic Factors in the Pathogenesis of Preeclampsia The Ability of Angiogenic Proteins to Predict Preeclampsia Other Potential Biomarkers for the Prediction of Preeclampsia Angiogenic Factors and Intrauterine Growth Restriction Preterm Labor and Other Pregnancy Complications Preterm Labor Abruption Gestational Diabetes Summary Points References

323 323 324 324 325 325 325 327 327 334 334 335 335 336 337 337 338 338 339 340 341 342 342 343 343 344 344

CONTENTS

Biomarkers in Cancer

xvn 355

Roopali Roy, Christine M. Coticchia, Jiang Yang, and Marsha A. Moses Introduction Cancer Biomarker Discovery Strategies Cancer Biomarkers Breast Cancer Prostate Cancer Ovarian Cancer Pancreatic Cancer Conclusion Summary Points Acknowledgments References

355 356 357 357 362 364 367 369 370 370 370

Biomarkers of HIV

381

Lewis Kaufman and Michael J. Ross Introduction Novel Biomarkers Host Genetic Determinants of Susceptibility to HIV Infection Chemokines/Chemokine Receptors CCR5 Variants CCR2-64I Variant SDF1-3'A Variant Other Chemokine Polymorphisms Human Leukocyte Antigens HLA Heterozygosity Protects Against Progression to AIDS Protective HLA Alleles HLA Alleles Associated with Rapid Progression to AIDS Other Host Genetic Factors Associated with HIV-Related Outcomes Host Factors Associated with Non-Opportunistic HIV-Related Diseases HIV-Associated Nephropathy HIV-Associated Dementia Clinical Markers CD4+ T-Cell Depletion Plasma Viral Load Combination of Viral Load, CD4+ Count, and Proviral DNA Levels Generalized Immune Activation Conclusion Summary Points References

381 382 382 382 384 385 385 385 386 386 386 387 387 387 387 388 389 389 389 390 390 391 393 393

XVlll

CONTENTS

16 Biomarkers of In Vitro Drug-Induced Mitochondrial Dysfunction James A. Dykens and Yvonne Will Introduction Magnitude of the Problem Mitochondrial Physiology Drag-Induced Mitochondrial Dysfunction (DIMD) Has Been Overlooked Novel Methods to Detect Mitochondrial Dysfunction In Vitro An Emerging Model of Idiosyncratic Drug Toxicity Mitochondrial Diseases Potential Biomarkers of Mitochondrial Dysfunction Animal Models Summary Points References SECTION III: TECHNOLOGY FOR BIOMARKER DETECTION

401 401 402 403 406 407 409 411 413 416 417 417 423

17 Immunoassay-Based Technologies for the Measurement of Biological Materials Used for Biomarkers Discovery and Translational Research 425 Vincent Ricchiuti Introduction Immunoassay and Immunochemistry Background Basic Principles Radioimmunoassays Overview Principle of Radioimmunoassay Enzyme-Linked Immunosorbent Assay and Enzyme Immunoassay Overview Principle of Enzyme Immunoassay Fluorescent and Chemiluminescent Immunoassays Fluorescent Immunoassays Heterogeneous Fluorescent Immunoassays Homogenous Fluorescent Immunoassays Fluorescence Polarization Immunoassay (FPIA) Chemiluminescent Immunoassays Multiplexing Using Antibody Array and Bead Immunoassays Planar Protein Array Formats Suspension or Bead-Based Arrays Example of Multiplexing Technology Simultaneous Multi-Analyte Detection Introduction Multiple Bead Particle Technology

425 426 426 426 427 427 428 431 431 432 433 433 433 433 434 435 436 437 440 440 441 441 441

CONTENTS

xix

Applications The Future Electrochemiluminescence (ECL) Microarrays ECL Diagram Detection Multi Arrays Technologies Biochip Array Technology Biochip Manufacturing Applications Future of Immunoassays Summary Points Acknowledgments References

443 445 445 446 446 447 447 448 448 448 449 450 450

18 Nanoscale Techniques for Biomarker Quantification

457

Madhukar Varshney and Harold G. Craighead Introduction Nanoscale Sensing Techniques for Biomarker Quantification Optical Detection Bio-Barcode Assay-Based Sensors Quantum Dots-Based Sensors Dye-Doped Nanoparticles-Based Sensors Surface Enhanced Raman Spectroscopy-Based Sensors Dynamic Light Scattering Mechanical Detection Nanomechanical Cantilever-Based Sensors Electrical Detection Field Effect Transistor-Based Sensors Liposomes-Based Sensors Magnetic Detection Giant Magnetoresistance-Based Sensors Future Trends Conclusion Summary Points References

457 458 459 459 461 464 465 468 470 470 473 473 475 478 478 482 483 484 485

19 Immunodiagnostics with a Focus on Lateral Flow Point-of-Care Devices 495 Roy R. Mondesire, Glen M. Ford, Hannie F Ford, and Stephen C. Mefferd Introduction Antibodies in Immunoassays Structure and Function of Antibodies Kinetics of Antibody-Antigen Reactions

495 496 497 499

XX

CONTENTS

Polyclonal Antibodies Hybridoma Technology Rapid Manual and Rapid Automated Immunoassays Elements of Immunoassays: Soluble Labels and Detection Homogeneous Enzyme Immunoassays Signal Measurement Methods Colorimetry Fluorometry Time-Resolved Fluorescence Luminescence Principles of Binding Non-Speciflc Interactions in Immunoassays Colloidal and Particle Immunoassays Flow-Through Assays Particle Capture Fluorochrome-Dyed Microspheres Point-of-Care Lateral-Flow Assay Technology Introduction to Traditional Lateral Flow Tests Nucleic Acid Detection and Lateral Flow Principle of the Lateral-Flow Procedure for Nucleic Acid Detection Haptenized Primers Haptenized Detection Probes Molecular Detection of Chlamydia Trachomatis—A Major Agent of Sexually Transmitted Infections Pathogenic Bacteria Detection with Bacteriophage Sensitivity of Lateral-Flow Technology Summary Points Useful Information for Future Trends Emerging Technologies References

500 500 501 502 503 504 504 504 505 505 505 506 506 506 506 506 507 507 510 511 511 512 512 512 513 513 513 513 514

SECTION IV: HOT TOPICS IN BIOMARKER RESEARCH

517

20 Biomarkers for Environmental Exposure

519

Jane E. Gallagher, Elaine A. Cohen Hubal, and Stephen W. Edwards Introduction Need for Biomarkers to Support Environmental Risk Assessment Considerations for the Use of Biomarkers in Environmental Risk Assessment Applications Biomonitoring Studies Interpretation of Biomonitoring Data

519 520 522 524 524 527

CONTENTS

xxi

Cumulative Risk Assessment Molecular Epidemiology Emerging Issues Toxicity Pathway-Based Risk Assessment Systems Biology to Support Risk Assessment Summary Points Acknowledgments References

532 534 537 537 539 540 541 541

Clinical Study Design in Biomarker Research

549

Orfeas Liangos and Bertrand L. Jaber Overview of Clinical Study Design Case Study Case Series Cross-Sectional Study Case Control Study/Nested Case Control Study Cohort Study Experimental Studies Uncontrolled Trial Controlled Trial Blinded/Un-blinded Design Parallel Two-Arm/Multiple-Arm and Crossover Design Biomarkers in Observational Studies Biomarkers for Disease Detection and Diagnosis Biomarkers for Disease Monitoring Biomarkers for Disease Prognostication Biomarkers in Interventional Studies Biomarkers for Treatment Response Biomarkers for Monitoring Toxicity Conclusion Summary Points References

549 549 549 549 550 550 551 551 552 553 553 553 554 556 556 557 557 558 558 558 558

Statistical Issues in Biomarker Research

561

Daniel Holder and Matthew Schipper The Role of Statistics in Biomarker Discovery, Development, and Qualification Types of Biomarkers Stages of Development Kidney Project Background Statistical Methods/Metrics for Assessing Biomarker Performance Sensitivity, Specificity, and Receiver-Operator Characteristic Curves

561 562 563 564 566 566

XXII

CONTENTS

Assessing Whether a Marker Adds Value to Other Markers Errors in the Reference Standard Planning Human Clinical Trials Prognostic Biomarkers and Other Topics Biases in Biomarker Studies Discussion Summary Points Acknowledgments References

569 572 575 577 578 578 579 580 580

23 Regulatory Perspective for Biomarker Qualification from the U.S. FDA 581 Federico Goodsaid Overview Regulatory Paths in Biomarker Evaluation and Qualification Evidentiary Recommendations Harmonization Summary Points References 24 The European Medicines Agency Approach

581 583 586 586 587 587 589

Marisa Papaluca Amati and Spiros Vamvakas Introduction European Medicines Agency and Biomarkers: Briefing Meetings and Scientific Advice New Procedure for the Qualification of Novel Methodologies Current Status Index

589 590 592 594 595

PREFACE

A biomarker is defined as a characteristic that can be objectively measured and evaluated as an indicator of normal biologic or pathogenic processes of pharmacological responses to a therapeutic intervention.' Examples of biomarkers are proteins; lipids; genomic, metabolomic, or proteomic patterns; imaging patterns; electrical signals; and cells present on a urinalysis. In medicine, disease processes are heterogeneous in their pathophysiology and clinical presentation, making diagnosis and prognosis challenging. In drug development, biomarkers are critical at a variety of stages of the process, with the need for informative determination of efficacy and toxicity that spans the preclinical-clinical spectrum. In commenting on a major initiative of the FDA that focuses on biomarkers, Janet Woodcock, MD, deputy commissioner for operations and head of FDA's Critical Path Initiative, said, "Most researchers agree that a new generation of predictive biomarkers would dramatically improve the efficiency of product development, help identify safety problems before a product is on the market (and even before it is tested in humans), and facilitate the development of new types of clinical trials that will produce better data faster."2 The FDA has provided guidance that a biomarker can be considered "valid" if 1) it is measured in an analytical test system with well-established performance characteristics, and 2) there is an established scientific framework or body of evidence that elucidates the physiologic, pharmacologic, toxicologic, or clinical significance of the test result.3 We need better biomarkers to predict clinical efficacy and toxicity in preclinical studies, diagnose disease earlier, predict outcome in a patient with disease, and identify who will respond to an intervention and whether the intervention is working. In addition, better biomarkers will permit better stratification of patients for clinical trials and potentially lead to definition of new therapeutic targets. A good predictive biomarker will have a significant effect on evaluation of potential therapies because it will enable the identification of subgroups of patients who will have a high incidence of injury and hence reduce the number of patients needed to study in order to test potential therapeutic strategies. A clinically useful new biomarker will improve the sensitivity and specificity for the detection of and characterization of disease. It is also likely that some of these biomarkers will be useful to monitor severity and progression of disease. xxm

XXIV

PREFACE

Translational biomarkers that can be measured in blood or urine in both experimental animals and man are of particular interest. Biomarkers that have been well studied and characterized as very sensitive biomarkers of injury in animals, if they function similarly in man, may make it possible to monitor safety and efficacy in clinical trials when the ability to obtain kidney tissue is severely constrained and when the severity of the injury early on is insufficient to result in obvious alterations in clinical state. Given the importance to the clinical, pharmaceutical, and regulatory communities motivated by more specific and timely diagnoses, early intervention, and safer therapies, there has been a great deal of activity devoted to discovery and "fit for purpose" qualification of various potential biomarkers in a number of diseases that affect many different organs. In this book we have tried to capture the excitement and potential of biomarkers over a wide variety of applications spanning medical diagnostics to safety monitoring in therapeutic and environmental exposures. The early chapters are devoted to individual treatments of applicability of genomics, proteomics, glycomics, and metabolomics to this rapidly evolving field of biomarker discovery. The next set of chapters takes specific organs or disease processes and considers in depth the state of the biomarker art in this specific area. Individual chapters are devoted to Alzheimer's and Parkinson's disease, cardiac injury, lung injury, drug-induced liver injury, acute kidney injury, drug-induced vascular injury, immunotoxicity, and obstetric medicine. These are followed by chapters discussing biomarkers in cancer, HIV, and drug-induced mitochondrial dysfunction. The book then moves to a more technical perspective incorporating chapters on immunoassay-based technologies, nanoscale techniques, and lateral flow immunodiagnostics at point of care. Chapters on environmental exposure, clinical trial design, and statistical issues in biomarker analysis then follow. The last two chapters deal with the regulatory perspectives of the FDA and the European Medicines Agency. The chapters are written by leaders in their respective fields and we are very grateful to them for their comprehensive chapters. We hope that the readers will agree with us that the material in this book is timely and will go far to advance the field of biomarker research and facilitate the development of new drugs that are safe, add new biological targets to our therapeutic armamentarium, and ensure environmental safety. Joseph V. Bonventre, MD, PhD and Vishal S. Vaidya, PhD Brigham and Women's Hospital, Harvard Medical School References 1. Group BDW. Biomarkers and Surrogate Endpoints: Preferred Definitions and Conceptual Framework. Clin Pharmacol Ther. 2001;69:89-95. 2. FDA. FDA Unveils Critical Path Opportunities List Outlining Blueprint to Modernizing Medical Product Development by 2010. Biomarker Development and Clinical Trial Design Greatest Areas for Impact. FDA News. P06-39, March 16, 2006. 3. FDA. Center for Drug Evaluation and Reseach (CDER) CfBEaRC, and Center for Devices and Radiological Health (CDRH): Guidance for Industry. Pharmacogenomic data submissions. 2005;l-22.

CONTRIBUTORS

Marisa Papaluca Amati, The European Agency for the Evaluation of Medicinal Products, London, United Kingdom Mike Baratta, Pharmacokinetics, Dynamics, and Metabolism, Pfizer, Andover, Massachusetts Daniela Berg, Center of Neurology, Department of Neurodegeneration and Hertie Institute for Clinical Brain Research, University of Tubingen, Tubingen, Germany Joseph V. Bonventre, Renal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Institutes of Medicine, Boston, Massachusetts Pamaparna Chaudhuri, Brigham & Women's Hospital, Harvard-MIT Division of Health Sciences & Technology, Cambridge, Massachusetts Christine Coticchia, Program in Vascular Biology and Department of Surgery, Karp Family Research Building, Children's Hospital Boston and Harvard Medical School, Boston, Massachusetts Harold G. Craighead, School of Applied and Engineering Physics, Cornell University, Ithaca, New York Frank Dieterle, Novartis Institutes of Biomedical Research, Translational Sciences, Basel, Switzerland Rodney R. Dietert, Department of Microbiology and Immunology, Cornell University, Ithaca, New York James A. Dykens, Pfizer, Drug Safety R&D, Sandwich, United Kingdom Stephen W. Edwards, National Health and Environmental Effects Research Laboratory, Immediate Office, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina Michael A. Ferguson, Division of Nephrology, Children's Hospital, Boston, Massachusetts XXV

XXVI

CONTRIBUTORS

Glen M. Ford, BioAssay Works, LLC, Ijamsville, Maryland Hannie F. Ford, BioAssay Works, LLC, Ijamsville, Maryland Jane E. Gallagher, Environmental Public Health Division, National Health and Environmental Effects Laboratory, U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, North Carolina Federico Goodsaid, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland Rania Harfouche, Brigham & Women's Hospital, Harvard-MIT Division of Health Sciences & Technology, Cambridge, Massachusetts Daniel Holder, Merck Research Laboratories, West Point, Pennsylvania Stephen J. Huang, Department of Intensive Care Medicine, Nepean Hospital, University of Sydney, Sydney, New South Wales, Australia Elaine A. Cohen Hubal, National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina Bertrand L. Jaber, Division of Nephrology, Department of Medicine, St. Elizabeth's Medical Center, Boston, Massachusetts S. Ananth Karumanchi, Beth Israel Deaconess Medical Center, Boston, Massachusetts Hector C. Keun, Department of Biomolecular Medicine, Faculty of Medicine, Imperial College London, South Kensington, London, United Kingdom Urmila P. Kodavanti, Environmental Public Health Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina Orfeas Liangos, Division of Nephrology, Department of Medicine, St. Elizabeth's Medical Center, Boston, Massachusetts Walter Maetzler, Center of Neurology, Department of Neurodegeneration and Hertie Institute for Clinical Brain Research, University of Tubingen, Tubingen, Germany Manish Maski, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts Anthony S. McLean, Department of Intensive Care Medicine, Nepean Hospital, University of Sydney, Sydney, New South Wales, Australia Stephen C. Mefferd, BioAssay Works, LLC, Ijamsville, Maryland

CONTRIBUTORS

xxvn

Donna L. Mendrick, Division of Systems Toxicology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas Roy R. Mondesire, RoMonics, LLC, Boulder, Colorado Marsha A. Moses, Program in Vascular Biology and Department of Surgery, Karp Family Research Building, Children's Hospital Boston and Harvard Medical School, Boston Massachusetts Josef S. Ozer, Pharmacokinetics, Dynamics, and Metabolism, PGRD, Pfizer St. Louis Laboratories, Chesterfield, Missouri Joe Palandra, Pfizer Biotech, Pharmacokinetics, Dynamics, and Metabolism, Andover, Massachusetts Shashi Ramaiah, Pfizer Global Research and Development, Drug Safety Research and Development, St. Louis, Missouri Sarosh Rana, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts William J. Reagan, Pfizer Biotech, Drug Safety Research and Development, Andover, Massachusetts Vincent Ricchiuti, Division of Endocrinology, Diabetes and Hypertension, Brigham and Women's Hospital, Boston, Massachusetts Roopali Roy, Program in Vascular Biology and Department of Surgery, Karp Family Research Building, Children's Hospital Boston and Harvard Medical School, Boston, Massachusetts Michael J. Ross, Division of Nephrology, Mount Sinai School of Medicine, New York, New York Matthew Schipper, Innovative Analytics, Kalamazoo, Michigan Shelli Schomaker, Drug Safety Research and Development, Pfizer, Groton Pfizer Groton/New London Laboratories, Groton, Connecticut Shiladitya Sengupta, Brigham and Women's Hospital, Harvard-MIT Division of Health Sciences & Technology, Cambridge, Massachusetts Frank D. Sistare, Merck & Co, Inc., Laboratory Sciences and Investigative Toxicology, Westpoint, Pennsylvania Weida Tong, Division of Systems Toxicology, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas James R. Turk, Amgen, Inc., Thousand Oaks, California

xxvm

CONTRIBUTORS

Vishal S. Vaidya, Laboratory of Kidney Toxicology and Regeneration, Renal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Institutes of Medicine, Boston, Massachusetts Spiros Vamvakas, The European Agency for the Evaluation of Medicinal Products, London, United Kingdom Madhukar Varshney, School of Applied and Engineering Physics, Cornell University, Ithaca, New York Timothy D. Veenstra, Laboratory of Proteomics and Analytical Technologies, Advanced Technology Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, Maryland Yvonne Will, Pfizer, Compound Safety Prediction, Groton Connecticut

CHAPTER

BIOMARKERS: AN EVOLUTIONARY PERSPECTIVE Michael A. Ferguson and Vishal S. Vaidya

The official National Institutes of Health (NIH) definition of a biomarker (biologic marker) is "a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention." 1 This definition is broad and, though not explicitly stated, encompasses laboratory tests, radiologic studies, as well as physical exam findings. Although the term biomarker is a relatively new one that dates back to the late 1960s,2 biologic assessments and measurements in the evaluation of human disease were practiced in antiquity, evident in the writings of the ancient Egyptians. Not surprisingly, clinically useful biomarkers have evolved over time, reflecting the scientific and technologic progress made over the centuries (see Figure 1.1). As a result, an increasing number of clinically relevant tests and procedures are available to estimate organ injury and guide treatment. Recent discoveries in genetics and molecular biology have resulted in impressive advances in our understanding of the pathophysiologic processes of individual diseases and yielded an abundance of prospective therapies directed against novel targets. This has brought about an increased focus on biomarker identification, validation, and quantification, as well as the development of analytical technologies for biomarker measurement. It is anticipated that further characterization of novel biomarkers will enable improvements in diagnostic and prognostic strategies and facilitate accelerated development of new pharmacologic and non-pharmacologic therapies, ultimately resulting in improved patient outcomes. In their earliest incarnation, biomarkers were confined to objective physical findings and observations, such as heart rate and tactile temperature and general features of the patient's body. The diagnostic and prognostic utility of such physical biomarkers can be traced to the earliest known medical manu1

2

BIOMARKERS

FIGURE I. I The evolution of biomarkers in medicine.Through the centuries, biomarkers have taken on different forms. Physical indicators of health and disease were the earliest biologic markers, dating back to the seventeenth century B.C. Qualitative analysis of biologic fluids followed, with uroscopy representing the major diagnostic modality employed from the sixth century B.C. through the eighteenth century A.D. Clinical chemistry and microscopy altered the scope of laboratory specimen and allowed for the routine assessment of analytes in bodily fluids in twentieth century medicine.The twenty-first century has brought with it the emergence of "-omic" technologies, vastly increasing the number of biomarkers in medicine.

scripts, the medical papyruses of ancient Egypt (seventeenth century B.C.). The Edwin Smith Papyri, believed to be written around 1500 B.C. and based on the earlier work of Imhotep (twenty-seventh century B.C.), details 48 cases of trauma as well as therapeutic and prognostic considerations. Breasted's translation of this document reveals "an ancient Egyptian surgeon... as a man with the ability to observe, to draw conclusions from his observations... and maintain a scientific attitude of the mind."3 The examination of biologic specimens can also be traced to ancient times, with reference to the qualitative inspection of urine and stools referenced in the Assyrian Book of Prognoses (650 B.C.).4 Not surprisingly, the prognostic and diagnostic significance of findings around this time was based on a combination of observation and spiritual mysticism. The utility of biologic specimens in the diagnosis and prognosis of disease was further advanced by the ancient Greeks. Hippocrates (350 B.C.) advocated a systematic approach to the patient that included physical exam procedures as well as careful inspection of bodily fluids.5 The Hippocratic physician employed his five senses to study the patient's secretions and excretions to determine the prognosis of a disease and aid in treatment. Par-

BIOMARKERS: AN EVOLUTIONARY PERSPECTIVE

3

ticular emphasis was placed on the evaluation of urine, and Hippocrates is credited with relating specific urinary characteristics, including sediments and surface foam, to chronic illness.5 In the centuries that followed, the practice of uroscopy became paramount in patient evaluation, and by the Middle Ages the matula (urine flask) emerged as the most recognizable symbol of medical practitioners. The importance of urinary diagnosis became exaggerated in the seventeenth century when it often superseded direct evaluation of the patient, leading to isolation of the physician and patient.6 This resulted in a backlash against those who practiced uroscopy; however, macroscopic examination of the urine remained the primary biologic marker in clinical diagnosis until the Victorian era.7 The nineteenth century brought impressive advances that allowed for the generation of increasing amounts of clinical data. Instruments, such as the stethoscope, ophthalmoscope, laryngoscope, spirometer, electrocardiogram, and sphygmomanometer, allowed for improved physical exam assessment and physiologic measurement. In addition, the X-ray, microscope, as well as new laboratory-based chemical and microbiologic techniques, vastly expanded the physician's diagnostic capabilities. These developments allowed for an unparalleled degree of objectivity with respect to the assessment of biologic indicators of normal and abnormal biologic function. As a result, physicians were able to establish standards and evaluate deviations of human physiology.5 By the turn of the twentieth century, clinical laboratories were growing in favor and influence and clinico-pathological laboratories opened in an increasing number of hospitals. Systematic analysis of blood and urine samples established reference levels for a variety of analytes, correlated variations in disease states, and clarified metabolic pathways in health and disease.8 Advances in analytic techniques, including chromatographic separation and colorimetric quantification of analytes, facilitated clinical usefulness and resulted in the ability to assay a growing number of biologic markers to monitor the changing condition of the patient.8 Increasingly, the medical provider became dependent on the chemical analysis of bodily fluids in the monitoring of health, diagnosis, and prognosis of disease, and assessment of response to therapeutic interventions. With increased understanding of the pathophysiologic processes involved in specific disease processes, the quest to identify and characterize biologic markers with improved sensitivity and specificity for a variety of illnesses and associated outcomes has followed. Efforts at biomarker discovery and validation have intensified since the turn of the twenty-first century. Advanced genomic, proteomic, and metabolomic techniques now permit comparative analysis of specimens from healthy and diseased individuals, facilitating biomarker identification. As a result, biomarker initiatives have become ubiquitous in the scientific landscape, with considerable private and public resources now dedicated to clarifying the utility of biologic markers in virtually all aspects of health care. Novel measures of biologic function will prove critical in the research setting, serving as surrogate endpoints in clinical trials that promise to streamline pharmacologic and non-pharmacologic therapeutic development.9 In addition, it is anticipat-

4

BIOMARKERS ed that selected biomarkers and/or biomarker panels will revolutionize drug development, environmental health screening, and medicine, facilitating the movement toward personalized patient care. Ongoing and future biomarker studies are likely to enable individualized assessment of disease susceptibility, response to therapy, as well as disease progression/regression. An emphasis on concurrent development of technologies for rapid biomarker analysis, ideally point of care modalities, will help ensure rapid assimilation into clinical practice. The use of biologic measures in the assessment of health and disease is not new; however, the concept of what constitutes a useful biomarker has evolved considerably, closely paralleling technologic advances of the time. The current era of scientific discovery has brought seemingly limitless opportunities for improvements in medical care. Coordinated efforts at biomarker discovery and validation, as well as technologies for biomarker measurement, will help ensure that the ultimate goal of safer drugs, a cleaner environment, and improved patient outcomes is realized.

REFERENCES 1. Biomarkers and Surrogate Endpoints: Preferred Definitions and Conceptual Framework. Clin. Pharmacol. Ther. Mar 2001 ;69(3): 89-95. 2. DeCaprio, A. Introduction to Toxicologic Biomarkers. In DeCaprio AP, Ed., Toxicologic Biomarkers. New York, NY: Taylor and Francis Group;2006:l-15. 3. Breasted, J. The Edwin Smith Surgical Papyrus. Chicago, IL: The University of Chicago Press; 1930. 4. Keele, K. D. The Evolution of Clinical Methods in Medicine. London. Pitman Medical Publishing Co., Ltd;1963. 5. Berger, D. A Brief History of Medical Diagnosis and the Birth of the Clinical Laboratory. Part 1—Ancient Times Through the 19th Century. MLO Med. Lab Obs. July 1999;31(7):28-30, 32, 34-40. 6. Pardalidis, N. Kosmaoglou E., Diamantis A., and Sofikitis N. Uroscopy in Byzantium (330-1453 A.D.), J. Urol. April 2008;179(4):1271-1276. 7. Armstrong, J. A. Urinalysis in Western Culture: A Brief History, Kidney Int. March 2007;71(5):384-387. 8. Rosenfeld, L. Clinical Chemistry Since 1800: Growth and Development. Clin. Chem. January 2002;48(1): 186-197. 9. Varmus, H. Foreword, In Downing G. J., Ed., Biomarkers and Surrogate Endpoints: Clinical Research and Applications. New York, NY: Elsevier; 2000.

SECTION I TOOLS FOR BIOMARKER DISCOVERY

This page intentionally left blank

CHAPTER

GENOMICS Weida Tong and Donna L. Mendrick

INTRODUCTION The study of changes in gene expression levels in tissue and cells has shown tremendous promise in the identification of novel biomarkers and mechanisms for clinical application and risk assessment. However, some early technical concerns hampered the field, from which it has not fully recovered. In the earliest days of microarray technology, the optimal study design (i.e., number of biological replicates, time points of study, analytical approaches, etc.) was unknown and protocols used in laboratories were not standardized. These issues caused groups to report less than stellar comparability results when different labs performed experiments, although appropriate statistical analyses and standardization of protocols did improve the extrapolation.12 This chapter first summarizes the progress that has been made in the past five years, with emphasis on the consensus for use of microarray technology and statistical methods for determining the differential expression to understand underlying mechanisms of disease and toxicity and the prediction of adverse events. Next, an approach to develop microarray-based diagnostic and prognostic tests is discussed, with a description of the FDA-led community supportive consortium effort, to address the issues and challenges associated with this approach. Given the important role of bioinformatics in genomic research, we advocate an integrated approach of data management, analysis, and interpretation through, for example, the FDA genomic tool, Array Track™. Lastly, the application of the genomic technologies is further illustrated with a number of examples. The chapter concludes with the authors' views on the issues and challenges remaining in this field and the way of moving this discipline forward.

7

BIOMARKERS

EVALUATION OF THE TECHNOLOGY The use of cDNA or oligonucleotide microarrays as a molecular tool to measure transcript abundance has been prevalent for more than a decade.3 During this time the production of microarrays and the associated laboratory methods have improved and become more standardized. The technical improvements have been achieved in the manufacturing of microarrays (resulting in improved inter- as well as intra-platform reproducibility), and also in the laboratory procedures used to generate transcript abundance data using microarrays. These process improvements have resulted in decreased requirements for starting material, greater sensitivity, and dynamic range, while simultaneously resulting in more standardized methods, all of which have contributed to improved data reproducibility.' ■2-4-5 One major application of microarray technology is to identify genes differentially expressed between different states, for example, changes between treated and control groups, or between diseased patients and healthy individuals. These so-called DEGs (differentially expressed genes) should be biologically informative, and importantly, be reproducible across different laboratories and platforms. Many statistical methods have been applied for DEG selection, ranging from the simple T-test (ANOVA for multiple groups)6 and SAM (statistical analysis of microarrays)7 to the Benjamini-Hochberg8 method that controls false discovery rate (FDR)9 and the conservative Bonferroni correction approach10 (Figure 2.1). To a greater or lesser degree, all of these methods explicitly or implicitly assume that genes are expressed independent of one another, such that each gene's selection constitutes a null hypothesis test. In reality, mRNA is extracted from tissues of different phenotypes and most genes differentiating phenotypes are expected to act interdependently through a number of complex biological, signaling, and metabolic pathways. True phenotype differentiating genes would be expected to exhibit expression in cascades or constellations with temporal dependency. While much is known

FIGURE 2.1 A summary of the main statistical methods used for determining differentially expressed genes (DEGs) for two-class comparisons using microarrays.The simple t-test method normally produces high false positives while the Bonferroni criterion has low specificity. Given the fact that the correlations among genes being analyzed are unknown, the methods such as false discovery rate, permutation testing, and volcano plot attempt to balance the specificity and sensitivity for DEGs identification.

GENOMICS

9

about pathways and gene associations, the knowledge regrettably remains qualitative and incomplete, precluding direct accounting for such expression covariance. All of these difficulties have led to many proposed DEG selection methods that, unfortunately, produce disparate gene lists. Not long ago, a number of publications11-15 raised concerns about the microarray technology based on the lack of agreement in DEGs or predictive signatures obtained from different laboratories and array platforms for highly similar study designs and experiments. Other performance issues for the array technology were also discussed, including 1) array quality—what degree of experiment quality and individual array platform technical performance should be deemed achievable and adequate? 2) data analysis issues—what results can be anticipated from different algorithms and approaches, and its corollaryxan consensus be reached for a baseline approach to microarray data analysis? 3) cross-platform issues—what consistency can be expected among different microarray experimental platforms? and 4) reliability issues—whether is it still necessary and required for the microarray results to be verified by alternative and well-established gene expression platforms such as real time PCR? On February 11, 2005, the FDA formally launched the Micro Array Quality Control (MAQC) project (http://edkb.fda.gov/MAQC) to address these concerns as well as other performance, standards, quality, and data analysis issues. Phase I of the MAQC project (MAQC-I, from February 11, 2005 to September 8, 2006) focused on assessing technical reliability of microarray technology with participation of 137 scientists from 51 organizations. Gene expression data on four titration pools from two distinct, commercially available reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. The resulting rich reference data set consists of over 1300 microarray hybridizations, and additional measurements for over 1000 genes with alternative technologies such as qPCR. The MAQC-I project observed, when standard operating procedures (SOPs) were followed and the data analyzed properly, high intraplatform reproducibility across test sites, as well as interplatform concordance in terms of genes identified as differentially expressed. Platforms with divergent approaches to the assay generated comparable results in terms of differential gene expression. In other words, the differential gene expression patterns reflected the same biology despite differences in platform technology. Similar results were observed from a realistic rat toxicogenomics experiment, in support of the major findings of data generated from the reference RNA samples. The MAQC-I results were published in six research papers in the September 2006 issue of Nature Biotechnology }^2X The MAQC-I project suggested that the common practice of ranking genes solely by a statistical significance measure like p-value from the simple T-test, and selecting DEGs using a stringently significant p-value threshold was the cause of an apparent lack of reproducibility in microarray experiments and thus recommended selecting genes using fold-change ranking together with a non-stringent p-value cutoff filter to balance specificity/sensitivity and reproducibility of DEGs. Several studies verified this recommendation.22'23

10

BIOMARKERS

It is now generally understood that reproducibility is a complicated issue that is affected by many factors, including the performance metrics used for assessing reproducibility and how to balance reproducibility with statistical power of a study. In general, the more samples for a study, the higher the reproducibility. Reproducibility has an inverse relationship with statistical significance, however this relationship is a complex one (i.e., not a simple trade-off). It appears that reproducibility is dependent on the number of DEGs pre-selected for assessing the reproducibility and, furthermore, the dependency is a complicated one and less understood. But most importantly, irreproducible gene lists resulting from different analysis approaches for the same data set could be all biologically relevant (i.e., true discovery).

CLINICAL APPLICATIONS Clinical diagnostic and prognostic assessments rely on, for example, accurate histopathology, cytomorphology, or immunophenotyping. Unfortunately, some diseases remain hard to classify by these current clinical techniques. The maturation of microarray technology provided the necessary groundwork for the recent deployment of two different microarray-based diagnostic tests that measure transcript abundance related to cancer.24,25 These recent advances highlight the utility of transcript-based molecular signatures (or classifiers) measured by microarrays in clinical applications and suggest their potential application to other fields, such as drug development and risk/safety assessment. Molecular classification uses supervised learning techniques to first identify a molecular signature that separates subjects (known as a training set) into known categories, such as diseases. The derived signature is then verified by predicting new subjects with known diseases (a test set).26-28 Once biologically qualified and validated, the molecular signature could be used to improve early detection of diseases, provide better diagnostic capabilities, etc. Developing classifiers from microarray data is often problematic because: 1) the predictor variables (i.e., genes from a microarray experiment) normally far outnumber the samples (i.e., the number of subjects), increasing the likelihood of a random solution with little or no predictive value; 2) the sample classes are often skewed between, for example, disease and healthy subjects; 3) microarray data tends to have a low signal to noise ratio (i.e., considerable random variability); and 4) diseases that are difficult to diagnose with clinical techniques could result in false positives and false negatives within the sample. Although MAQC-I20 demonstrated the technical reliability of microarray technology in detecting differential gene expression, questions remained regarding reliability of the technology in clinical applications such as disease diagnostics or prognostics, and for tailored patient treatment based on gene expression profiles. Specifically, the reliability and utility of classification models for the prediction of patient outcomes has been questioned in recent literature.14-1529 To investigate the capabilities and limitations of microarray technology in such practical applications, MAQC-II was launched on September 21, 2006 to address technical and scientific issues involved in the

GENOMICS

11

development and validation of predictive models or classifiers. The project aimed to develop best practices through comprehensively evaluating different machine learning approaches and modeling parameters for the development and validation of predictive models or classifiers for clinical and preclinical (toxicogenomics) applications. Multiple existing data sets were selected and distributed to participating organizations for independent data analyses (Table 2.1). A three-step approach was implemented to determine the best practices for gene expression based on predictive signature development (Figure 2.2): 1) Step 1 or initial discovery: predictive models were developed based only on the training set of each of the six MAQC data sets (Table 2.1) and were "frozen" (i.e., cannot be altered further) before receiving the test sets; 2) Step 2 or independent validation: prediction models were challenged by data sets that were set aside and unseen by the analysis teams in the previous step to determine best practices; and 3) Step 3: in this final step of the project, new sets of data will be generated from different labs or platforms to further challenge the "best practices." At the time of this writing, over 10 manuscripts were submitted to Nature Biotechnology and The Pharmacogenomics Journal. The manuscripts can be grouped into three areas of focus: 1) assessing the impact of modeling factors; 2) the process of qualifying a classifier; and 3) generating consensus documents on developing a classifier.

FIGURE 2.2 A three-step approach was implemented in the MAQC-II to develop the best practices for molecular classifiers. Six MAQC data sets (Table I) were divided into the training and test sets. In Step I, the training sets were distributed to 36 analysis teams and the resulting classifiers, signature genes, and the data analysis plans (DAPs) were locked down. In Step 2, the test sets with the samples' classification blinded were released to the analysis teams to challenge the classifiers and the best practices were constructed. In Step 3, new data will be generated to challenge the best practice. At the time of this writing, the manuscripts summarizing the first two steps have been submitted while Step 3 is proceeding in parallel.

12

BIOMARKERS

TABLE 2.1

MAQC-1 I data sheets. Datasets*

Clinical data

Step 1

Step 2

Training set

Test set

(n)

(n)

Breast cancer

130

100

Multiple myeloma

340

214

Neuroblastoma

246

253

Lung tumor

70

88

Toxicogenomics

Non-genotoxic hepatocarcinogenicity

216

201

data

Liver injury (necrosis)

214

204

* (I) The breast cancer data set was provided by the MD Anderson Cancer Center; (2) The multiple myeloma data set was provided by University ofArkansas for Medical Sciences; (3) The neuroblastoma data set was provided by University of Cologne; (4) The lung tumor data set was provided by the Hamner Institutes for Health Sciences; (5) The non-genotoxic hepatocarcinogenicity data set was provided by Iconix Biosa'ences, Inc, (now part of Entelos, Inc); and (6) The liver injury data set was provided by the National Institute of Environmental Health Sciences.

BIOINFORMATICS CHALLENGES While large community efforts, such as the MAQC projects, start to address the challenges remaining in the microarray field for clinical application and risk/safety assessment, a robust bioinformatics capability is also widely acknowledged as central to realizing the promises of the microarray technology. Successful application of microarray approaches inextricably relies on a bioinformatics solution for appropriate data management, the ability to extract knowledge from massive amounts of data, and the availability of functional information for data interpretation. Data management—The database and associated software organizes and enables access to all data from a study along with the microarray experimental design information. A microarray experiment involves multiple steps and the data in each step need to be appropriately managed, annotated, and, most importantly, stored in an appropriate data structure for ready analysis and correlation with the study observations. This enables efficient and reliable access for subsequent data analysis normally done by a multidisciplinary group of scientists. Furthermore, re-analysis is likely as new or more accepted analytic methods evolve, a process much more easily carried out with a well-managed and annotated database. Data analysis—With the price for the microarrays and reagents, as well as microarray service, continually declining, larger scale studies using microarrays become feasible—allowing a systematic test of the hypothesis. Consequently, a single microarray study yields a large amount of data and a formidable data analysis and visualization undertaking. The immensity of data analysis scales directly with the complexity of the experiment, such as the number of technical and biological replicates, and temporal and dose response parameters. In addition, since a plethora of potential sources of vari-

GENOMICS

13

ability inevitably complicate, and possibly confound, interpretation, public data can be important for comparison. Thus, the ability to search, filter, and apply mathematical and statistical operations and graphically visualize data quickly with an intuitive user interface becomes essential to facilitate the laborious process. Data interpretation—This is a highly contextual process incorporating known and unknown functions of genes, proteins, and pathways. Efficient and effective interpretation demands that relevant knowledge residing in public sources such as gene annotations, protein functions, and pathways are readily available and integrated with the data analysis process. In addition, the periodic re-examination of the data in light of the continual evolution of gene annotation information and pathways in the public domain is desirable, which further enforces the need of data and analysis results to be properly managed in a structured database. Several commercial vendors and institutes have developed bioinformatics solutions for microarray studies, such as Rosetta Resolver System (http:// www.rosettabio.com/products/resolver) and NIEHS CEBS (Chemical Effects in Biological Systems, http://cebs.niehs.nih.gov/cebs-browser/cebsHome.do). In addition, the public data repositories such as GEO (Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo) and Array Express (http://www.ebi. ac.uk/microarray-as/ae) are available with the primary goal of ensuring that microarray results published in peer-reviewed scientific journals are available for independent evaluation. However, a recent attempt to reproduce the work of 18 published studies in well-recognized prestigious journals met with great difficulty due to the lack of available data and/or accurate description of analytical methods in the papers or referenced public repositories.30 Thus, a data standard (i.e., ontology) is urgently needed beyond the MIAME guideline (Minimum Information About a Microarray Experiment, http://www.mged.org/workgroups/MIAME/miame.html) to report accurately the study design, microarray array experiment, and analysis methods. The data models corresponding to the standards need to be established with robustness to accommodate the evolving nature of the technology and other emerging molecular technologies. One effort with such potential is the bioinformatics solution developed at the FDA's National Center for Toxicological Research (NCTR) described below. The NCTR/FDA is developing a public data management, analysis, and interpretation software called ArrayTrack™31,32 (http://www.fda.gov/ScienceResearch/BioinformaticsTools/Arraytrack/default.htm). The tool is primarily used in the FDA for reviewing genomic data submitted by sponsors through the Voluntary Genomics Data Submission (VGDS) program (http://www.fda. gov/OHRMS/DOCKETS/98fr/2003d-0497-gdl0002.pdf). ArrayTrack stores all data and information related to DNA microarrays and the clinical and nonclinical study, as well as the processed data derived from proteomics and metabonomics experiments. In addition, ArrayTrack provides a rich collection of functional information about genes, proteins, and pathways drawn from various public biological databases for facilitating data interpretation. Many data analysis and visualization tools are available within ArrayTrack for individual

14

BIOMARKERS

FIGURE 2.3 A typical workflow using ArrayTrack to identify DEGs distinguishing treatment and control groups, followed by pathway and Gene Ontology (GO) analyses. (A) DEGs are identified using the Volcano plot or other means in ArrayTrack. DEGs can also be identified using other commercial or public tools and uploaded into ArrayTrack (B) DEGs are summarized in a table format and can be readily linked to ArrayTrack library functions for biological interpretation; (C) Significantly altered KEGG pathways are identified based on DEGs; (D) DEGs are submitted to the Gene Ontology tool in ArrayTrack to identify GO terms associated with significantly altered gene expression.

platform data analysis, multiple omics data integration and integrated analysis of omics data with study data. Importantly, gene expression data, functional information, and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. Using ArrayTrack, users can select an analysis method from the ArrayTrack toolbox, apply the method to selected microarray data, and the analysis results can be directly linked to individual gene, pathway, and gene ontology analysis (Figure 2.3). ArrayTrack is publicly available online.

APPLICATIONS TO DRUG TOXICOLOGY, MEDICINE, AND ENVIRONMENTAL HEALTH Genomics has been applied to a) improve our understanding of basic biological processes as well as the diversity of these processes, b) delineate mechanisms of efficacy and toxicity of xenobiotic compounds (e.g., drug, dietary supplements, and environmental agents), c) understand disease processes, and d) generate predictive models or molecular classifiers to provide better predictive and diagnostic accuracy.

GENOMICS

15

Improve Understanding of Basic Cellular A r c h i t e c t u r e and Function Examination of gene expression levels in various cells and tissue types has expanded our understanding of basic cellular function and opened many doors to new hypotheses. For example, the Japanese Toxicogenomics Project evaluated gene expression in discrete areas of the kidney, a highly heterogeneous organ with distinct functional units that are more or less susceptible to individual xenobiotic compounds.33 Although this can serve as a valuable source to improve our understanding of specific units' functioning, it is impractical routinely to isolate each of the functional units for analysis. Although genomics has been utilized most often to investigate an adverse event or disease, it can be employed to study basic issues such as animal husbandry, species, aging, and gender differences. Examination of genes expressed in the liver of normal rats can discriminate genders, fasted from rats fed ad libitum, and can distinguish Wistar from Sprague-Dawley rats, two outbred strains of albino rats.34 The Japanese initiative mentioned above also examined acetaminophen hepatotoxicity as an effect of aging and these investigators reported differences in the time course of response that may suggest older rats are more susceptible.35 Age also seems to play a role in human reactions to idiosyncratic drugs, compounds that fail to induce signs of hepatotoxicity in the classical tests performed in nonclinical species such as rodents.36 The same study found that gender seems to play a role in the extent of liver failure seen in such patients. As noted above, expression of genes in the liver of normal rats can discriminate the genders and thus may lead to clues as to the differential severity of hepatic injury seen in females.34 Gene expression in disease states that affect one sex more than another is a source of much research in areas other than liver disease, such as inflammatory diseases that affect women more than men,37-38 and vice versa in the case of hepatocellular carcinoma.3940 Another use of genomics is to identify the similarities and differences between cells in situ and those used in vitro. To reduce the use of animals and provide higher throughput assays, investigators would prefer to use in vitro hepatocyte systems rather than treating the rats in vivo. However, no clarity exists on which in vitro system most closely replicates the normal liver under normal situations or upon toxicant exposure. Some have used genomics to try and answer those questions. For example, Boess, et al. reported that liver slices were more similar to intact liver than primary hepatocytes.41 In contrast, Jessen, et al. studied the expression of genes within the rat liver, liver slices and primary rat hepatocytes prior to and after treatment with hepatotoxicants. Better correlation exists in the untreated state between the two in vitro systems (R2 = 0.87) than between cultured hepatocytes and liver slices with in vivo (R2 = 0.80 for both).42 They reported that both in vitro systems exhibited fewer gene expression alterations than did animals treated in vivo, an understandable reaction as neither in vitro system reflects circulatory aspects of the liver and replicates the pharmacokinetic aspects of the drug, to name a few differences. These authors concluded that they could not identify the best in vitro system to

BIOMARKERS

be used for the study of hepatotoxicity, although most utilize cultured hepatocytes versus tissue slices. Some of the different conclusions reached as to the best in vitro system to be used (i.e., slices versus hepatocytes) could be due to culture conditions, as there are no generally accepted standards of media and substratum used for such cells.

echanism of T o x i c i t y and Disease Examination of the mechanisms whereby drugs, dietary supplements, environmental agents, or disease processes alter cellular function and cause injury can lead to the discovery of new biomarkers and thus preventive intervention. In the area of drug or chemical development, such biomarkers can be incorporated into a screening assay and used to avoid similar liabilities with other compounds.43 Researchers at the EPA used a toxicogenomics approach to understand the different types of hepatic effects caused by triazole fungicides and derivatives of perfluoroalkyl acid. They found that this approach could be used to categorize the chemicals, provide mechanistic insight and predict downstream pathological damage.44 They concluded that toxicogenomics can be useful in the assessment of environmental risk. Much is being learned in terms of disease processes and such knowledge can lead to the identification of biomarkers that can span divergent needs such as diagnoses and providing new drug targets.45 A recent review by Margulies, et al. discussed the insight learned from applying genomics technology to the study of heart failure in animals and humans.46 Likewise, genomics approaches are leading us closer to a systems biology understanding of renal diseases.47 To assist in these endeavors, the European Renal cDNA Bank was created in 1998 to store samples of kidney biopsies for transcriptomics study. Such collaborative studies, for example, revealed genomic fingerprints that potentially can stage diabetic nephropathy, revealing potential new therapeutic targets to halt the progression of renal disease.48 Drugs are known to have heterogeneous effects on individual patients and between species. Since many xenobiotics undergo metabolic activation, genomics has been applied to the study of drug-metabolizing genes to help identify patient-specific susceptibility biomarkers and population (e.g., species) differences. Mattes, et al. examined the expression levels of genes involved in the glutathione pathway, a major route of detoxification, and reported major differences in basal tissue levels among mouse, rat, and canine.49 Several other groups have examined species similarities and responses to drugs and environmental agents to help explain in-life observations (reviewed in 50). Genomics can be used to study the pharmacology of drugs. For example, actions of immunosuppressive drugs were studied in mice and revealed common genes and pathways that can be used for future compound screening.51 The ability of drugs to inhibit the liver X receptor that controls cholesterol efflux from peripheral tissues can be studied with a genomics approach using peripheral blood from rodents, non-human primates, and humans.52 In an effort to find a noninvasive way to study cholesterol metabolism, a transcriptomics approach was utilized on circulating mononuclear cells and liver tissue

GENOMICS

17

from guinea pigs. The results indicated that the former could accurately reflect changes induced by drug therapy.53 The use of circulating white blood cells has shown great potential in the differential diagnosis of many human diseases and is discussed below. Another area of research involves mitochondria function. Mitochondrial dysfunction has been shown to contribute to disease, metabolic abnormalities, and drug induced toxicity. Desai and collaborators generated a microarray containing genes involved in the structure and function of the mitochondria (MitoChip)54 and have used this to identify dysfunction due to an idiosyncratic drug,55 nucleoside reverse transcriptase inhibitors,56 and a dietary supplement.57 Thus, genomics identifies many potentially useful biomarkers of normal cellular function, differences between sexes, response to xenobiotics, and disease processes. It also can provide an understanding of the mechanism of drug actions, environmental agents, and diseases. However, identification of individual biomarkers with understandable roles in such processes requires specialized training, subjective evaluation, and may lead to the lack of sufficient power to provide needed accuracy.

Algorithmic Models to Predict Toxicity or Disease Biologically-meaningful (i.e., easily interpretable) changes in gene expression provide context and thus are most acceptable to investigators. However, in some cases one does not obtain the needed accuracy if only utilizing genes with an understandable role to play in the adverse event or disease processes. Additionally, some biomarkers contribute more to the phenotype than others, thus requiring a weighting scenario employing a mathematical model. Thus, many investigators are using a statistical approach to identify and predict adverse events or disease. (Such approaches have been extensively evaluated in the aforementioned MAQC-II project.) For example, in the field of environmental chemicals, Thomas and colleagues at The Hamner Institute utilized a toxicogenomics approach to find methods to shorten the exposure time in animal studies used to identify lung tumor carcinogens. They identified six genes that could discern, after 13 weeks of exposure, chemicals known to induce lung tumors after a two-year exposure, thus suggesting a faster system to identify carcinogens.58 This data set was also extensively investigated and analyzed by the MAQC teams. Since hepatotoxicity remains a major issue during drug development and upon approval of drugs, many investigators have focused on a toxicogenomics approach to improve detection of such compounds using in vivo exposure in the rat and/or treatment of primary rat hepatocytes in culture. Successful predictive toxicogenomic algorithmic models have been generated in many pharmaceutical companies (e.g., AstraZeneca, GlaxoSmithKline, and Millennium Pharmaceuticals) and in commercial companies (Gene Logic and Iconix) yet, in most situations, the biomarker panels identified have not been subjected to public qualification since there is no business incentive to do so. Some predictive models, based on algorithms using a large number of genes in the rat, have been found capable of identifying compounds that can cause

18

BIOMARKERS

phenotypically-obvious damage in rats and humans and those of a more idiosyncratic nature, i.e., drugs for which no liver adverse event is seen in rats.59"61 This suggests that a toxicogenomics approach in drug discovery might 1) provide more complete compound information at a relatively early stage of drug discovery to assist in decision-making, and 2) offer clues as to potential biomarkers that might be useful to follow or prevent adverse events. The use of a multiplex algorithmic model has shown promise in multiple clinical indications. For example, as noted above, MammaPrint® is an FDA-approved test that examines gene expression in breast cancer tissue and provides the physician and patient with a report detailing the risk of cancer recurrence. Another FDA-approved assay, AlloMap®, monitors changes in 20 genes present in circulating white blood cells to assess the risk of cardiac allograft rejection. Using gene expression alterations as biomarkers of tissue injury is not restricted to allograft rejection although, to date, the other biomarkers have not been qualified for use. These include the examination of changes in the gene expression in circulating white blood cells as a measure of disease activity and/or disease type. Examples include distinguishing patients with ulcerative colitis from those suffering from Crohn's disease,62 identifying various forms of neurological disease,63 and predicting the future of allograft rejection.64 A very promising study suggests the use of such an approach may be more accurate than classical endpoints at detecting toxic levels of drugs in animals and humans.65

S T R E N G T H S , W E A K N E S S E S , A N D T H E ROAD FORWARD Since microarrays monitor the expression levels of tens of thousands of genes at one time, genomics is an ideal discovery tool to identify biomarkers of interest in understanding mechanisms of drug actions, exposure to environmental agents, and disease processes. However, in some settings, such as clinical medicine, access to the tissue itself is not feasible. Therefore, the search tends to focus on biomarkers accessible in body fluids. As discussed above, one method is to employ a genomics approach to study the effects of exogenous compounds and disease on circulating white blood cells since they 1) may have the same biological process as the tissue of interest and thus reflect the exact biological change, and 2) serve as a sentinel system in the body and may reflect abnormalities within the body. Another approach is to mine the genomics data in search of genes that encode secreted or cell surface proteins, thus providing starting points to pursue a proteomics approach.66 However biomarkers are discovered, there are major hurdles to their acceptance and use. Those identified and tested side-by-side with a drug can be qualified and the testing platform validated during this process. Even then, different regulatory centers at the FDA evaluate drugs and testing devices. Yet more challenging is when biomarkers are identified outside the clinical trial paradigm. Who will determine if biomarkers meet biological and testing standards?

GENOMICS

19

Who will pay for the appropriate testing? Goodsaid and Frueh at the FDA were instrumental in establishing a path forward at that agency.67 Working together with the Critical Path Institute's Predictive Safety Testing Consortium and its member pharmaceutical companies, a series of seven urinary protein biomarkers in rats were qualified for regulatory use by the FDA and EMEA68 (http:// www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/2008/ucml 16911. htm). The establishment of a working method to obtain regulatory approval of biomarkers not tied to a specific drug and a consortia-led effort to obtain the necessary data to do so seems to be the best method to successfully qualify biomarkers. Multiple consortia have been established to investigate both nonclinical and clinical biomarkers within the Critical Path Institute. However, each effort will need to be decided on a case-by-case basis. This can be a difficult task, particularly for clinical qualification as the costs likely will be high. Assuming clinical biomarkers are successfully qualified, the next hurdle they face will be patient/physician acceptance and third party reimbursement. The Genetic Information Nondiscrimination Act (GINA) of 2008 was written to protect individuals against discrimination based on their genetic information. However, it does not cover all types of insurance (e.g., life insurance) and covers genotype testing only.69 These gaps may discourage individuals from being tested with new genomic and proteomics biomarkers particularly if they provide predictive information as to eventual disease or likelihood of an adverse event. Another impediment is to obtain third-party payment. Most insurance companies follow the lead of the Centers for Medicare and Medicaid Services (CMS), a United States federal agency. However, coverage for tests may be limited by the law stating that reimbursement for services must be "necessary for the diagnosis or treatment." Thus, tests used to screen individuals without current complaints requires new legislation by Congress as was necessary, for example, for breast cancer screening.70 CMS and insurance carriers are requiring clear evidence that new biomarkers improve patients' health, lower costs, etc. and this can be difficult to obtain. Warfarin is a useful example to illustrate the complexity of the issue. It is a widely used anticoagulant with a narrow therapeutic window and large patient-to-patient variability. Warfarin causes 15 percent of all severe adverse events in the U.S. and a number of studies have found that variants of two genes along with other factors such as age can account for 31 to 79 percent of the inter-patient variability.71 Thus, the FDA revised warfarin labeling to include pharmacogenetic information and has approved multiple genetic tests. A recent clinical trial found that this algorithm approach using pharmacogenetic and clinical factors was successful in selecting a starting dose closer to the stable maintenance dose needed for a patient.72 However, CMS recently refused to cover the cost of the pharmacogenetic test even for this widely-used drug known to cause many serious adverse events. They stated that there was insufficient evidence to demonstrate it improved health outcomes (https://www.cms. hhs.gov/mcd/viewdraftdecisionmemo.asp?from2-viewdraftdecisionmemo. asp&id=224&), but will cover the cost of testing when in concert with a clinical trial to study the effectiveness of this approach.

20

BIOMARKERS

It is not clear who will pay for expensive clinical trials using generic drugs such as warfarin, particularly since diagnostic companies generally do not make sufficient profit to cover research costs and there is little or no incentive for drug companies to limit their markets with such tests. Once again, a consortium-type effort may be the best avenue, although it can be expected that only a limited number of biomarkers can be tested under such costly circumstances.

CONCLUSION Acceptance of microarray technology has benefited from the work of the MAQC consortia as discussed above. The work of the first effort showed, for example, that data generated in one laboratory can be replicated in another, addressing a major criticism at the time. It also demonstrated that similar biological networks and pathways are identified when different platforms are used. Newer consortia efforts should help establish appropriate methods to develop algorithmic models as well as methods to test their accuracy. Although the field of genomics is more standardized than some of the other omic technologies (metabolomics and proteomics), improvement is still needed, particularly in the discussion of when data are of sufficient quality and robustness to be analyzed. For example, if the RNA shows some signs of degradation, when is it deemed of insufficient quality to continue? There are no universally accepted pass/fail criteria for measurements of RNA integrity and data quality originating from microarrays, so the field will need to set some standards. Even after such technical considerations are met, it is no surprise that, once again, technology is outpacing human acceptance. It will take time and the efforts of many to address privacy concerns and resolve third-party payment issues before the usefulness of genomics will be fully appreciated.

SUMMARY P O I N T S 1. 2. 3. 4.

Genomics is achieving widespread acceptance. Great strides have been made in the discovery of potential genomic biomarkers in tissues and in circulating white blood cells thus enabling clinic use. Issues still remain in areas such as mining the data and determining when data is of sufficiently high enough quality to merit review. Privacy issues and the lack of third-party payment is hampering clinical acceptance of new biomarkers.

DISCLAIMER The views presented in this article do not necessarily reflect those of the U.S. Food and Drug Administration.

REFERENCES 1.

Bammler, T., Beyer, R. P., and Bhattacharya, S., et al. Standardizing Global Gene Expression Analysis Between Laboratories and Across Platforms. Nat. Methods. 2005;2:351-356.

GENOMICS 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.

21

Irizarry, R. A., Warren, D., and Spencer, R, et al. Multiple-Laboratory Comparison of Microarray Platforms. Nat. Methods. 2005;2:345-350. Schena, M., Shalon, D., Davis, R. W., and Brown, P. O. Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray. Science. 1995-,270:467^170. Kuo, W. P., Liu, E, and Trimarchi, J., et al. A Wequence-Oriented Comparison of Gene Expression Measurements Across Different Hybridization-Based Technologies. Nat. Biotechnol. 2006;24:832-840. Yauk, C. L., Berndt, M. L., Williams, A., and Douglas, G. R. Comprehensive Comparison of Six Microarray Technologies. Nucleic Acids Res. 2004;32:el24. Kerr, M. K., Martin, M., and Churchill, G. A. Analysis of Variance for Gene Expression Microarray Data. J. Comput. Biol. 2000;7:819-837. Tusher, V. G., Tibshirani, R., and Chu, G. Significance Analysis of Microarrays Applied to the Ionizing Radiation Response. Proc. Natl. Acad. Sci. USA. 2001; 98:5116-5121. Benjamini, Y. and Hochberg, Y Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. Roy Stat. Soc. B. 1995;57: 289-300. Reiner, A., Yekutieli, D., and Benjamini, Y Identifying Differentially Expressed Genes Using False Discovery Rate Controlling Procedures. Bioinformatics 2003;19:368-375. Dudoit, S., Yang, Y H., Callow, M. J., and Speed, T P. Statistical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Microarray Experiments. Statistica. Sinica. 2002;12:111-139. Marshall, E. Getting the Noise Out of Gene Arrays. Science. 2004;306:630-631. Ioannidis, J. P. Why Most Published Research Findings are False. PLoS Med. 2005;2:el24. Simon, R. Development and Evaluation of Therapeutically Relevant Predictive Classifiers Using Gene Expression Profiling. J. Natl. Cancer Inst. 2006;98: 1169-1171. Ein-Dor, L., Zuk, O., and Domany, E. Thousands of Samples are Needed to Generate a Robust Gene List for Predicting Outcome in Cancer. Proc. Natl. Acad. Sci. USA. 2006;103:5923-5928. Michiels, S., Koscielny, S., and Hill, C. Prediction of Cancer Outcome with Microarrays: A Multiple Random Validation Strategy. Lancet. 2005;365:488-492. Canales, R. D., Luo, Y, and Willey, J. C , et al. Evaluation of DNA Microarray Results with Quantitative Gene Expression Platforms. Nat. Biotechnol. 2006; 24:1115-1122. Shippy, R., Fulmer-Smentek, S., and Jensen, R. V, et al. Using RNA Sample Titrations to Assess Microarray Platform Performance and Normalization Techniques. Afar. Biotech. 2006;24:1123-1131. Mendrick, D. L. Translational Medicine:The Discovery of Bridging Biomarkers Using Pharmacogenomics. Pharmacogenomics. 2006;7:943-947. Patterson, T. A., Lobenhofer, E. K., and Fulmer-Smentek, S. B., et al. Performance Comparison of One-Color and Two-Color Platforms Within the Micro Array Quality Control (MAQC) Project. Nat. Biotechnol. 2006;24:1140-1150. Shi, L., Reid, L. H., and Jones, W. D., et al. The MicroArray Quality Control (MAQC) Project Shows Inter- and Intraplatform Reproducibility of Gene Expression Measurements. Nat. Biotechnol. 2006;24:1151-1161.

22

BIOMARKERS 21. 22. 23. 24. 25.

26. 27. 28. 29. 30. 31. 32. 33. 34.

35. 36. 37.

Guo, L., Lobenhofer, E. K., and Wang, C , et al. Rat Toxicogenomic Study Reveals Analytical Consistency Across Microarray Platforms. Nat. Biotechnol. 2006;24:1162-1169. Zhang, M., Yao, C , and Guo, Z., et al. Apparently Low Reproducibility of True Differential Expression Discoveries in Microarray Studies. Bioinformatics. 2008;24:2057-2063. Chen, J. J., Wang, S. J., Tsai, C. A., and Lin, C. J., Selection of Differentially Expressed Genes in Microarray Data Analysis. Pharmacogenomics J. 2007;7: 212-220. Van de Vijver, M. J., He, Y. D., and Van't Veer, L. J., et al. A Gene-Expression Signature as a Predictor of Survival in Breast Cancer. N. Engl. J. Med. 2002; 347:1999-2009. Dumur, C. I., Lyons-Weiler, M., and Sciulli, C , et al. Interlaboratory Performance of a Microarray-Based Gene Expression Test to Determine Tissue of Origin in Poorly Differentiated and Undifferentiated Cancers. J. Mol. Diagn. 2008;10:67-77. Tong, W., Welsh, W. J., Shi, L., Fang, H., and Perkins, R. Structure-Activity Relationship Approaches and Applications. Environ. Toxicol. Chem. 2003;22: 1680-1695. Simon, R., Radmacher, M. D., Dobbin, K., and McShane, L. M. Pitfalls in the Use of DNA Microarray Data for Diagnostic and Prognostic Classification. J. Natl. Cancer Inst. 2003;95:14-18. Slonim, D., Tamayo, P., Mesirov, J. P., Golub, T. R., and Lander, E. S. Class Prediction and Discovery Using Gene Expression Data. Annual Conference on Research in Computational Molecular Biology. Tokyo: ACM;2000;263-272. Ioannidis, J. P. Microarrays and Molecular Research: Noise Discovery? Lancet. 2005;365:454-^55. Ioannidis, J. P., Allison, D. B., and Ball, C. A., et al. Repeatability of Published Microarray Gene Expression Analyses. Nat. Genet. 2009;41:149-155. Tong, W., Harris, S., and Cao, X. et al. Development of Public Toxicogenomics Software for Microarray Data Management and Analysis. Mutat. Res. 2004; 549:241-253. Tong, W., Cao, X., and Harris, S., et al. Array Track—Supporting Toxicogenomic Research at the U.S. Food and Drug Administration National Center for Toxicological Research. Environ. Health Perspect. 2003;111:1819-1826. Tamura, K., Ono, A., Miyagishima, T., Nagao, T., and Urushidani, T. Comparison of Gene Expression Profiles Among Papilla, Medulla and Cortex in Rat Kidney. J. Toxicol. Sci. 2006;31:449^169. Mendrick, D. L. Toxicogenomics and Classic Toxicology: How to Improve Prediction and Mechanistic Understanding of Human Toxicity. In:Mendrick, D. L. and Mattes, W. B., Eds. Essential Concepts in Toxicogenomics. Volume 460 ed. Humana Press;2008;l-22. Morishita, K., Mizukawa, Y, and Kasahara, T, et al. Gene Expression Profile in Liver of Differing Ages of Rats After Single Oral Administration of Acetaminophen. J. Toxicol. Sci. 2006;31:491-507. Lucena, M. I., Andrade, R. J., and Kaplowitz, N., et al. Phenotypic Characterization of Idiosyncratic Drug-Induced Liver Injury: The Influence of Age and Sex. Hepatology. 2009;49:2001-2009. Kawasaki, M., Sekigawa, I., and Nozawa, K., et al. Changes in the Gene Expression of Peripheral Blood Mononuclear Cells During the Menstrual Cycle of Females is Associated with a Gender Bias in the Incidence of Systemic Lupus Erythematosus. Clin. Exp. Rheumatol 2009;27:260-266.

GENOMICS 38. 39.

40. 41.

42. 43.

44.

45. 46. 47. 48. 49. 50. 51. 52. 53. 54.

23

Hewagama, A., Patel, D., Yarlagadda, S., Strickland, F. M., and Richardson, B. C. Stronger Inflammatory/Cytotoxic T-Cell Response in Women Identified by Microarray Analysis. Genes. Immun. 2009. Liao, Y. J., Liu, S. P., and Lee, C. M., et al. Characterization of a Glycine N-Methyltransferase Gene Knockout Mouse Model for Hepatocellular Carcinoma: Implications of the Gender Disparity in Liver Cancer Susceptibility. Int. J. Cancer 2009;124:816-826. Rogers, A. B., Theve, E. J., and Feng, Y., et al. Hepatocellular Carcinoma Associated with Liver-Gender Disruption in Male Mice. Cancer Res. 2007;67: 11536-11546. Boess, E, Kamber, M., and Romer, S., et al. Gene Expression in Two Hepatic Cell Lines, Cultured Primary Hepatocytes, and Liver Slices Compared to the In Vivo Liver Gene Expression in Rats:Possible Implications for Toxicogenomics Use of In Vitro Systems. Toxicol. Sci. 2003;73:386-402. Jessen, B. A., Mullins, J. S., De, P. A., and Stevens, G. J. Assessment of Hepatocytes and Liver Slices as In Vitro Test Systems to Predict In Vivo Gene Expression. Toxicol. Sci. 2003;75:208-222. Buck, W. R., Waring, J. R, and Blomme, E. A. Use of Traditional End Points and Gene Dysregulation to Understand Mechanisms of Toxicity: Toxicogenomics in Mechanistic Toxicology. In: Mendrick, D. L. and Mattes, W. B., eds. Essential Concepts in Toxicogenomics. Volume 460 ed. Humana;2008;23-44. Martin, M. T., Brennan, R. J., and Hu, W., et al. Toxicogenomic Study of Triazole Fungicides and Perfluoroalkyl Acids in Rat Livers Predicts Toxicity and Categorizes Chemicals Based on Mechanisms of Toxicity. Toxicol. Sci. 2007;97:595-613. Woodcock, J. The Prospects for "Personalized Medicine" in Drug Development and Drug Therapy. Clin. Pharmacol. Then 2007;81:164-169. Margulies, K. B., Bednarik, D. P., and Dries, D. L. Genomics, Transcriptional Profiling, and Heart Failure. J. Am. Coll. Cardiol. 2009;53:1752-1759. Neusser, M. A., Lindenmeyer, M. T, Kretzler, M., and Cohen, C. D. Genomic Analysis in Nephrology—Towards Systems Biology and Systematic Medicine? Nephrol. Then 2008;4:306-311. Schmid, H., Boucherot, A., and Yasuda, Y, et al. Modular Activation of Nuclear Factor-KappaB Transcriptional Programs in Human Diabetic Nephropathy. Diabetes. 2006;55:2993-3003. Mattes, W. B., Daniels, K. K., Summan, M., Xu, Z. A., and Mendrick, D. L. Tissue and Species Distribution of the Glutathione Pathway Transcriptome. Xenobiotica. 2006;36:1081-1121. Mattes, W. B. Cross-Species Comparative Toxicogenomics as an Aid to Safety Assessment. Expert Opin. Drug Metab. Toxicol. 2006;2:859-874. Baken, K. A., Pennings, J. L., and Jonker, M. J., et al. Overlapping Gene Expression Profiles of Model Compounds Provide Opportunities for Immunotoxicity Screening. Toxicol. Appl. Pharmacol. 2008;226:46-59. DiBlasio-Smith, E. A., Arai, M., and Quinet, E. M., et al. Discovery and Implementation of Transcriptional Biomarkers of Synthetic LXR Agonists in Peripheral Blood Cells. J. Transl. Med. 2008;6:59. Aggarwal, D., Freake, H. C , Soliman, G. A., Dutta, A., and Fernandez, M. L. Validation of Using Gene Expression in Mononuclear Cells as a Marker for Hepatic Cholesterol Metabolism. Lipids Health Dis. 2006;5:22. Desai, V. G. and Fuscoe, J. C. Transcriptional Profiling for Understanding the Basis of Mitochondrial Involvement in Disease and Toxicity Using the Mitochondria-Specific MitoChip. Mutat. Res. 2007;616:210-212.

24

BIOMARKERS 55. 56. 57. 58. 59. 60. 61. 62. 63. 64.

65. 66. 67. 68. 69. 70. 71. 72.

Kashimshetty, R., Desai, V. G., and Kale, V. M., et al. Underlying Mitochondrial Dysfunction Triggers Flutamide-Induced Oxidative Liver Injury in a Mouse Model of Idiosyncratic Drug Toxicity. Toxicol. Appl. Pharmacol. 2009. Desai, V. G., Lee, T., and Delongchamp, R. R., et al. Nucleoside Reverse Transcriptase Inhibitors (NRTIs)-Induced Expression Profile of Mitochondria-Related Genes in the Mouse Liver. Mitochondrion. 2008;8:181-195. Joseph, A., Lee, T., and Moland, C. L., et al. Effect of (+)-Usnic Acid on Mitochondrial Functions as Measured by Mitochondria-Specific Oligonucleotide Microarray in Liver of B6C3F1 Mice. Mitochondrion. 2009;9:149-158. Thomas, R. S., Pluta, L., Yang, L., and Halsey, T. A. Application of Genomic Biomarkers to Predict Increased Lung Tumor Incidence in 2-Year Rodent Cancer Bioassays. Toxicol. Sci. 2007;97:55-64. Hultin-Rosenberg, L., Jagannathan, S., and Nilsson, K. C , et al. Predictive Models of Hepatotoxicity Using Gene Expression Data from Primary Rat Hepatocytes. Xenobiotica. 2006;36:1122-1139. Martin, R., Rose, D., Yu, K., and Barros, S. Toxicogenomics Strategies for Predicting Drug Toxicity. Pharmacogenomics. 2006;7:1003-1016. Kenne, K., Skanberg, I., and Glinghammar, B., et al. Prediction of Drug-Induced Liver Injury in Humans by Using In Vitro Methods: The Case of Ximelagatran. Toxicology in Vitro. 2008;22:730-746. Burczynski, M. E., Peterson, R. L., and Twine, N. C , et al. Molecular Classification of Crohn's Disease and Ulcerative Colitis Patients Using Transcriptional Profiles In Peripheral Blood Mononuclear Cells. J. Mol. Diagn. 2006;8:51-61. Sharp, F. R., Xu, H., and Lit, L., et al. The Future of Genomic Profiling of Neurological Diseases Using Blood. Arch. Neurol. 2006;63:1529-1536. Mehra, M. R., Kobashigawa, J. A., and Deng, M. C , et al. Clinical Implications and Longitudinal Alteration of Peripheral Blood Transcriptional Signals Indicative of Future Cardiac Allograft Rejection. J. Heart Lung Transplant. 2008;27:297-301. Bushel, P. R., Heinloth, A. N., and Li, J., et al. Blood Gene Expression Signatures Predict Exposure Levels. Proc. Natl. Acad. Sci. USA. 2007;104:18211-18216. Mendrick, D. L. and Daniels, K. K. From the Bench to the Clinic and Back Again: Translational Biomarker Discovery Using In Silico Mining of Pharmacogenomic Data. Biomarkers Med. 2007;1:319-333. Goodsaid, F. M. and Frueh, F. W. Biomarker Qualification Pilot Process at the U.S. Food and Drug Administration. AAPS J. 2007;9:E105-E108. Goodsaid, F. M., Frueh, F. W., and Mattes, W. Strategic Paths for Biomarker Qualification. Toxicology. 2008;245:219-223. Rothstein, M. A. Currents in Contemporary Ethics. GINA, the ADA, and Genetic Discrimination in Employment. J. Law Med. Ethics. 2008;36:837-840. Secretary's Advisory Committee on Genetics HaS. Realizing the Potential of Pharmacogenomics: Opportunities and Challenges. 2009. Kim, M. J., Huang, S. M., Meyer, U. A., Rahman, A., and Lesko, L. J. A Regulatory Science Perspective on Warfarin Therapy: A Pharmacogenetic Opportunity. J. Clin. Pharmacol. 2009;49:138-146. Anderson, J. L., Home, B. D., and Stevens, S. M., et al. Randomized Trial of Genotype-Guided versus Standard Warfarin Dosing in Patients Initiating Oral Anticoagulation. Circulation. 2007;116:2563-2570.

CHAPTER

PROTEOMICS FOR BIOMARKER DISCOVERY Timothy D. Veenstra

INTRODUCTION While it can be argued that genomics is the foundation of all "omics" that came subsequently, there is no denying the need for these other fields of study. Figure 3.1 shows that progression of complexity and regulatory events that occur as biological molecules are created from the DNA template. The human genome contains in the range of 25,000 genes. The sequence of bases within these genes dictates the protein that is ultimately translated and mutations can lead to errors that cause diseases such as cancer and neurological disorders. The transcription of these genes is regulated by outside agents such as transcription factors, but also by methylation and acetylation of individual bases within promoter regions. These genes are transcribed into mRNA transcripts that are post-transcriptionally regulated by microRNAs and events such as alternative splicing. Finally, these transcripts are translated into proteins that are further regulated by post-translational modifications such as phosphorylation, acetylation, glycosylation, etc. The net effect of all of these regulation events is 25,000 genes giving rise to potentially hundreds of thousands of proteins. The exact number is unknown and may never be known as the proteome complement of a cell is dynamic and sensitive to internal and external stimuli. While it is a daunting challenge, it is important to be able to piece together the puzzles of the proteome as these proteins play a major part in dictating the phenotype of the cell, tissue, or organism from which they are derived. This hope of being able to comprehensively scan complex mixtures has been the foundation of what drives the discovery of biomarkers for human diseases, such as neurological disorders and cancers. There are major tech25

26

BIOMARKERS

nologies that have enabled complex biological samples to be interrogated at the genomic, transcriptomic, proteomic, and metabolomics. For genomics, high-throughput sequencers (such as the 454 sequencer) are able to sequence 400-600 megabases of DNA per 10-hour run. This throughput allows whole genomes to be sequenced or sequences within large numbers of samples to be compared in genome-wide associated studies (GWAS) for the discovery of disease-specific mutations. For transcriptomics, DNA microarrays containing tens of thousands of probes enable comparative analysis of messenger RNA (mRNA) from various patients to discover differences in the abundances of transcripts within complex samples. For proteomics and metabolomics, mass spectrometry (MS) has been the driving technology as it possesses the capability of detecting thousands of proteins or metabolites in the time frame of hours. While genome sequencing has provided incredibly valuable insight into individual susceptibility to diseases such as cancer, and is now providing information directing the proper types of treatments, many scientists are looking to proteomics to provide biomarkers that indicate the early onset of disease or the response to a chosen therapy.

T I S S U E OR B I O F L U I D It is this ability to identify thousands of proteins within complex samples (such as blood, urine, tissue, etc.) that has spurred the hope of using MS to find

FIGURE 3.1

Flow of information from genes to proteins.

P R O T E O M I C S FOR BIOMARKER DISCOVERY

27

novel biomarkers. The goal is to simply identify and quantitate differences in proteins between comparative samples (e.g., healthy versus diseased) and hope that any of these proteins can be directly associated with the disease of interest. Before initiating any biomarker discovery project it must first be determined what types of samples will be studied and what need will the biomarker fill.1 For example, let's assume the disease of interest is renal cell carcinoma (RCC). This carcinoma arises within the proximal renal tube and is the most most common type of kidney cancer in adults.2 Detection of RCC most often occurs when noninvasive imaging is being used to evaluate non-specific symptoms. Tumors detected in this manner are generally smaller, early stage tumors than if they are detected in RCC patients that are exhibiting symptoms related to paraneoplastic disorders (e.g., hypertension, anemia, abnormal liver function, etc.), pain or mass related to metastatic disease. Fortunately, approximately 60% of RCCs are diagnosed while the disease is still localized.3 Renal cell carcinoma is, however, notoriously chemo-resistant.2 Therefore, while an early stage diagnostic marker would benefit RCC patients, what would be even more beneficial is a biomarker that would predict therapy response, enabling correct selection of the most effective therapies for each individual. On the other hand, ovarian cancer has a different need. Ovarian cancer affects over 22,000 women in the U.S. annually.4 This cancer is treatable when detected at an early stage; as reflected by the statistic that greater than 90% of women diagnosed with ovarian cancer prior to its spreading beyond the ovary live at least five years after detection. Unfortunately, less than 20% of ovarian cancers are detected at this early stage. Therefore, the greatest impact on individuals with ovarian cancer would come in the form of a biomarker that diagnoses early stage disease. For conducting the proteomic comparative studies, obtaining case and control samples that are properly matched based on parameters such as gender, age, ethnicity, and lifestyle is critically important. Another major decision that needs to be considered is whether to study biofluids or tissues.1,5 A partial list of advantages and disadvantages in working with either type of sample is provided in Table 3.1. In a large number of biomarker discovery efforts, biofluids such as serum, plasma, or urine have been the sample of choice for a number of reasons. Biofluids are easier to obtain than tissue samples. Urine collection, for example, is almost completely noninvasive, and blood samples are generally drawn as part of a routine physical. Tissue collection requires invasive procedures that may include general or local anesthesia. If the purpose is to discover a protein biomarker for early-stage diagnosis, it is much easier to obtain a biofluid sample to measure. It is impractical to acquire a tissue sample on an annual basis for a disease that may affect only a small subpopulation of individuals. The analysis of biofluids also has a number of disadvantages if the aim is to discover disease-specific biomarkers. The main disadvantage is the chance of finding a biomarker that is highly specific for the disease being investigated. Every cell in the body is within four cell units of the circulatory system. Taking this fact and the role of the circulatory system in molecular transport, it can be concluded that the proteome of these biofluids is made up of com-

BIOMARKERS

28

TABLE 3.1 Comparison of advantages and disadvantages of analyzing tissues and biofluids for the discovery of disease-specific biomarkers.

Characteristic Easy to acquire

Tissue

Biofluid

No

Yes

Higher

Lower

Can be localized to specific cell environment

Yes

No

Can study function in cell

Yes

No

Can compare levels in surrounding tissue

Yes

No

Useful for early disease detection

No

Yes

Useful for therapeutic monitoring

Yes

Yes

Concentration of biomarker

ponents originating from cells throughout the entire body.6 Discovery of a cancer-specific biomarker in serum for example, requires the ability to analytically detect a protein that has been secreted by a tumor and is diluted within a complex matrix prior to retrieving the sample. In addition, circulating proteins are subject to a number of proteolytic modifications that can occur during its transport from the site of disease. These modifications may render the protein unrecognizable depending on the analytical methods chosen. While the odds of finding a biomarker in any biofluid using a completely discovery driven approach are small, discovering one would be an enormous benefit to public health. This fact is what keeps scientists working in this area. The use of tissue samples also has its own advantages and disadvantages. Most biomarker discovery studies address a disease that is localized to a specific area in the body (e.g., tumor, neurological disorder), therefore examining the source tissue provides the greatest chance of recognizing a verifiable disease-specific biomarker. Determining other proteins with which the biomarker may interact can be performed to understand its function within a cellular context. The most obvious disadvantage when designing a study to analyze tissues is their availability as their procurement is invasive and the amount of material obtained is small. Even if a tissue-related biomarker was discovered, its use would be limited to therapeutic monitoring as biopsies cannot be routinely performed during yearly physical checkups for use in early-stage diagnostic testing.

TECHNOLOGY Finding disease-specific protein biomarkers can be the ultimate discoverydriven study. These studies are totally unbiased and are approached with no a priori knowledge of what the biomarker will be or to what class of proteins it belongs. Sometimes these experiments are referred to by the unflattering term "blind fishing expeditions," since the scientists have no idea what type of "fish" they are going to catch. If we build upon this analogy, MS-based biomarker discovery fishes with a net with the goal to capture and identify as many fish (i.e., proteins) as possible.7 Unfortunately, the mass spectrometer

P R O T E O M I C S FOR BIOMARKER DISCOVERY

29

(net) is not very selective and you end up identifying (catching) whatever proteins (fish) end up being selected (trapped). The major development in proteomics that has allowed the field to consider biomarker discovery has been the exponential increase in the size of the net and the number of fish that can be caught in a single experiment. How is it then that investigators believe that this unbiased approach will result in the discovery of disease-specific biomarkers? Their faith is built on the development of MS technologies that can identify thousands of proteins in a matter of hours. A subtle point that needs to be clarified is that mass spectrometers are primarily designed to identify peptides not proteins. It is important to understand that a protein that is identified using MS is in almost all cases identified through its peptides that are correlated back to their protein of origin. Therefore, most complex proteome samples are digested into tryptic peptides prior to MS analysis. Trypsin is the proteolytic enzyme of choice because of its high degree of specificity, which enables a defined search constraint to be added when identifying the peptides (i.e., every peptide must end with an Arg or Lys residue).

Protein I d e n t i f i c a t i o n Using Mass S p e c t r o m e t r y An illustration on how MS is able to identify thousands of proteins in complex proteome samples is shown in Figure 3.2. The base-peak chromatogram of a complex proteome sample that has been analyzed using an ion-trap mass spectrometer is shown in panel A. The base-peak chromatogram is a recon-

FIGURE 3.2 Schematic of how mass spectrometers select and identify peptides and proteins within complex biologic samples.

30

BIOMARKERS

struction of the peptide signals that are observed by the mass spectrometer. Examining a defined slice within the base-peak chromatogram (Panel B) reveals several peptide signals that were observed at this specific time point. To identify the peptides present, the mass spectrometer is instructed to sequentially isolate individual peptides and subject them to collisional induced dissocation (CID), popularly known as tandem MS (MS2). In most studies, peptides are sequentially selected based on their signal intensity (from highest to lowest). In the example provided in panel C, the peptide at m/z 1785 is isolated and subjected to MS2. The peptide at m/z 1821 is then isolated and subjected to MS2, followed by the peptide at m/z 1404, then 1912, etc. This sequence is conducted until a designated number of peptides (usually between 5 and 10) have been analyzed in this fashion. The mass spectrometer then records another mass spectrum to see if new peptide signals are detected before repeating the sequence of isolating peptides and recording their MS2 spectra. State-of-the-art mass spectrometers are able to repeat this sequence of peptide isolation followed by MS2 approximately 7000 times per hour. The entire set of recorded MS2 spectra is analyzed using software that converts the raw data into peptide identifications. While not every MS2 spectra provides a reliable identification, approximately 1000 peptides can be confidently identified in a single one-hour LC-MS2 experiment.

Sample Preparation Sample preparation prior to MS analysis is critical to the success of any proteome biomarker discovery project. The type of sample selected for the project will dictate many of the strategies used to prepare the sample. If plasma or serum is to be analyzed, it is critical to deplete the high abundant proteins. Approximately 99% of the protein content of serum and plasma is made up of 22 proteins, with albumin itself representing about 50% of their protein content (Figure 3.3A).8 If albumin and some of these other high abundant proteins are not removed from the sample, the mass spectrometer will not select peptides from lower abundant proteins and the net result will be a long list of identified peptides that originate from proteins such as albumin, transferrin, immunoglobulins, etc. Albumin depletion has been accomplished through its binding to Cibacron blue,9 specific antibodies,10 and peptides." Others have relied on chromatography12,13 or ultrafiltration14 to remove albumin, however, because of their lack of specificity, these methods can result in the loss of other potentially important proteins. Over the past two to three years immunoaffinity systems that remove large numbers of highly abundant proteins from serum and plasma have been developed and are commercially available. One of the first was the multiple affinity removal system (MARS) from Agilent that immunodepletes six high abundant proteins (i.e., albumin, IgG, IgA, transferrin, haptoglobin, and alpha-1-antitrypsin).15 An example of the depletion capabilities of this column is shown in Figure 3.3B. Studies of the robustness and reproducibility of MARS columns showed high reproducibility (standard deviation between runs >7%) over 250 serum samples analyzed during a six week period.15 Other commercial vendors have built upon

P R O T E O M I C S FOR BIOMARKER DISCOVERY

31

FIGURE 3.3 A) Relative abundance of proteins within human serum. B) Depletion of high abundance proteins in serum using the immunoaffinity-based multiple affinity removal system (MARS). Lane 2: serum standard; Lane 3: raw plasma; Lanes 4 and 7: plasma proteins elute from MARS column during first washing step; Lanes 5 and 8: plasma proteins that elute from MARS column during second washing step; Lanes 6 and 9: high abundant proteins that are retained by the MARS immunodepletion column. (See color insert for a full color version of this figure.)

the success of MARS technology to create columns capable of depleting up to 20 of the highest abundant proteins from serum or plasma.16 The effectiveness and ease of use of these columns have made them a standard procedure in the processing of serum or plasma prior to biomarker discovery by MS. Immunodepletion is typically not used with urine, however, it is useful for measuring CSF as this fluid can contain substantial amounts of albumin depending on the circumstances surrounding sample collection. Tissue samples are not generally immunodepleted, however, these can also contain albumin due to vascularization. Even if a sample is immunodepleted it is still much too complex to be directly infused into the mass spectrometer with the anticipation of obtaining thousands of peptide identifications. The basic strategy is to "divide and

32

BIOMARKERS

conquer." The peptides need to be effectively separated (i.e., divided) so that the mass spectrometer has the opportunity to identify (i.e., conquer) as many of them as possible. Probably the earliest separation technique used for biomarker discovery was two-dimensional polyacrylamide gel electrophoresis (2D-PAGE).17 State-of-the-art 2D-PAGE gels that utilize pi range focusing strips can resolve upwards of 3000 protein spots. While 2D-PAGE is routinely criticized for being laborious and low-throughput, the fact that it visualizes changes in protein abundance at the gel level requires only certain spots be selected for further MS analysis. One of the issues raised in the past in using 2D-PAGE for comparing complex biofluid proteomes for discovering biomarkers is the difficulty in aligning gels. While software programs for proper gel alignment have been developed and work quite effectively, nothing can replace reproducibility of separation within the gel itself. The development of 2D differential gel electrophoresis (DIGE) in 1997. has helped to ease the problem of gel-to-gel irreproducibility.18 This method has become increasingly popular, as illustrated by the over 200 manuscripts listed on PubMed for the year 2008 that used this technique. Up to three different proteome samples can be compared on a single 2D-PAGE gel using the DIGE method. In a typical 2D-DIGE experiment, three different samples (e.g., healthy, diseased, and internal control) are covalently labeled using different fluorophores, l-(5-carboxypentyl)-l'-propylindocarbocyanine halide Af-hydroxysuccinimidyl ester (Cy3), l-(5-carboxypentyl)-l'-methylindodicarbocyanine halide iV-hydroxysuccinimidyl ester (Cy5), and 3-(4-carboxymethyl)phenylmethyl)-3' -ethyloxacarbocyanine halide N-hydroxysuccinimidyl ester (Cy2). Equal amounts of the labeled proteomes and control sample are combined and resolved on a single 2D-PAGE gel. The resolved gel is scanned using different wavelengths corresponding to the excitation of the Cy2, Cy3, and Cy5 dyes. The individual fluorescent images are then merged to detect differences between the abundance levels of proteins from the three different proteomes from the different dyes to be compared. The ability to co-separate samples on the same gel ensures accurate quantitation of the same spots, eliminating confusion related to gel mis-alignment. As with conventional 2D-PAGE, differentially abundant protein spots, as determined by differences in the fluorescent images, are cored from the gel, enzymatically digested, and identified using MS. The other major prefractionation strategy used in biomarker discovery is referred to as solution-based. These "non-gel" based strategies rely on liquid chromatography (LC) methods to fractionate the proteome prior to MS analysis. A number of solution-based prefractionation methods have been proposed, however, they universally end with a reversed-phase (RP) LC separation that is coupled directly to the mass spectrometer for direct elution of peptides into the instrument. Most solution-based fractionations rely on at least two-dimensions of LC, however, strategies employing up to four dimensions have been utilized. The most commonly used fractionation to be combined with RPLC is strong cation exchange (SCX). This combination, popularly known as MudPIT for multidimensional protein identification technology, was popularized

P R O T E O M I C S FOR BIOMARKER DISCOVERY

33

by Dr. John Yates III (Figure 3.4).19 This combination has been used to analyze a variety of clinically relevant proteome samples including plasma, serum, urine, cerebrospinal fluid, plasma filtrate, and blood ultrafiltrate, often resulting in the identification of hundreds or thousands of proteins.20

Protein Quantitation A challenge in conducting solution-based separations with MS for comparative proteomics for the discovery of biomarkers is how to quantify differences between the peptides that are observed. While isotope-labeling methods such as isotope-coded affinity tags (ICAT) and 0 16 /0 18 labeling can be used in a solution-based approach, these methods only allow two samples to be directly compared with each other. Biomarker discovery requires several samples to be directly compared. To enable multiple samples to all be compared with each other, investigators have relied on methods that contrast the number of peptides identified for a specific protein or the actual peak intensities provided by specific peptides in different spectra. In one method, the relative abundance of proteins is based on the number of peptides identified for that specific protein in different samples.2' The hypothesis behind the utility of this method is illustrated in the analysis of a single serum sample. If tryptically digested serum is analyzed directly by LCMS2, large numbers of albumin peptides will be identified since this protein is present in the range of 40-80 mg/mL in serum. The number of peptides identified from a protein in the abundance range of ng/mL range (such as a chemokine) will probably not exceed one, if any are identified. It is unlikely, however, that even one peptide from a low-abundance protein, such as a chemokine, will be identified. In a practical example, the identification of nine peptides for cancer antigen-125 (CA-125) in serum sample A and two peptides for this protein from sample B, is taken to conclude that CA-125 is 4-5 fold more abundant in sample A. This approach, known as subtractive proteomics or peptide count, is a very attractive method for biomarker discovery because it requires minimal sample preparation and most proteomic laboratories have the capability of identifying thousands of proteins within serum/plasma. In addition, this quantitative measurement allows an unlimited number of comparative samples. Unfortunately, like most proteomic applications, it is still relatively low-throughput. The method does not have great quantitative precision with the minimum difference that can be reasonably measured being on the order of three-fold. This minimal difference means that low abundance proteins, which are generally only identified through one or two peptides, may not be accurately quantifiable. Quantitative measurements from the same type of data set can be made by directly comparing the peak areas of individual peptides identified in different samples.22 Using this approach, selected ion chromatograms of the peptides of interest are generated so that the peak area of each peptide can be measured. Many proteins will be represented by multiple peptides, therefore the abundance ratios provided are calculated by averaging the peptide peak area ratios for the same protein.

34

BIOMARKERS

FIGURE 3.4 Multidimensional protein identification technology for comprehensive analysis of complex proteome samples.

P R O T E O M I C S FOR BIOMARKER DISCOVERY

35

E X A M P L E S OF BIOMARKER DISCOVERY A N D EVALUATION A recent study comparing quantitative results provided by subtractive proteomics and measuring peak areas was conducted by analyzing plasma samples obtained from a human subject prior to (untreated) and nine hours after treatment with lipopolysaccharide (LPS).23 This endotoxin is known to induce inflammatory reactions, such as cell migration, cytokine production, and production of acute-phase proteins. The untreated and LPS-treated plasma samples were tryptically digested and the resultant peptides fractionated into 50 aliquots using SCX chromatography. Each of these aliquots was analyzed by reversed-phase LC-MS2. Combining the results obtained from analysis of both samples (i.e., treated and untreated) resulted in the identification of 5176 unique peptides corresponding to 804 proteins. The group plotted the number of peptides identified for 74 specific proteins against their literature documented concentrations in plasma to determine if the number of peptides identified for a specific protein correlated with their relative abundances. The correlation was quite good, suggesting that the number of peptides identified per protein provides a semiquantitative assessment of a protein's relative abundance in a complex mixture. The peak areas for peptides that were identified in both samples were also compared to identify proteins that were differentially abundant in LPStreated plasma and this calculated ratio was evaluated against that obtained using the number of peptide identifications. Eight out of the nine proteins in which a protein abundance ratio was determined showed an increase in concentration following LPS administration by both the protein abundance ratios and the ratios of peptide hits. Unfortunately, the overall correlation was not extensive as many of the up-regulated proteins were identified in only one of the two methods (and not by both methods). However, cases in which proteins were identified using both quantitative methods can provide a quick confirmation of the results without having to completely reproduce the entire study. At present, most comparative studies of serum and plasma are conducted using either of these two computational approaches as it is important to be able to measure the relative quantitation of proteins in numerous samples. As described later, the low-throughput nature of MS-based analysis of complex biologic samples hampers the confidence levels that are attainable when perceived abundance differences in proteins are observed. So how do we move forward in finding biomarkers? One possible solution is to increase the number of samples analyzed. If a difference between a protein's abundance is seen across a large number of samples, the confidence in that difference increases as well. This increased confidence, however, comes at the expense of time. Another option is to use a variety of proteomic platforms to conduct comparative proteomic analyses. In a study aimed at finding biomarkers for Down Syndrome (DS), first- and second-trimester maternal serum samples of DS were compared to gestational age-matched controls using 2D-DIGE,

36

BIOMARKERS

two-dimensional liquid chromatography-chromatofocusing (2D-CF), MudPIT, and MALDI-TOF-MS peptide profiling [15].24 Twenty-eight and 26 proteins were differentially abundant in first- and second-trimester samples compared to matched controls, respectively. Nineteen and 16 were specific for the first and second trimesters, respectively. Ten were differentially abundant in serum samples obtained from patients in either trimester. Twenty of these potential markers were identified by at least two of the four methods (Table 3.2), while a-1-acid glycoprotein 1 was observed using all four. The greatest overlap of potential biomarkers was observed when the results using 2D-CF and 2D-DIGE were compared. Somewhat surprisingly, only four out of the 20 markers were observed in both the 2D-CF and MudPIT analyses. Many of the potential biomarkers are serum glycoproteins that may play a role in cellular differentiation of fetal growth. As with increasing the total number of samples that are analyzed, using multiple platforms increases the confidence in the observed differences; however, it also substantially increases the study time. A recent, exciting development in the search for biomarkers using MS, has been the demonstration of the ability to compare proteomes extracted from formalin-fixed paraffin-embedded (FFPE) tissues.2526 Owing to the covalent crosslinking used to preserve the morphology of these samples, FFPE tissues had been considered intractable to MS analysis. What needs to be remembered is that biomarker discovery using MS does not rely on identification of the intact protein, rather tryptic surrogates are measured as a reflection of its protein of origin. Therefore, the protein extraction methods used for FFPE tissue only need to extract unmodified peptides in order to successfully identify and quantitate proteins within these samples. In a recent study, FFPE tissue sections obtained from poorly (PD), moderately (MD), and well-differentiated (WD) head and neck squamous cell carcinoma (HNSCC) tumors were compared using an LC-MS2 approach.27 Lasercapture microdissection (LCM) was used to extract approximately 20,000 cells from normal squamous epithelial tissue as well as tumors that were classified as PD, MD, or WD. The relative abundance of each protein was determined by measuring the number of peptides identified per individual protein. A number of significant abundance differences were seen between the cells. For example, cytokeratin 4 was found to be more abundant in normal epithelial tissue compared to tumor cells, while cytokeratin 16, vimentin, and desmoplakin were found to be more abundant in the tumor cells. The WD tumor cells showed a striking increase in the amount of the protein desmoplakin. As with any good study, the potential biomarkers found within the analysis of a complex mixture requires validation using an orthogonal method. While the MS discovery phase is slow, once the potential markers are identified, the validation methods (e.g., immuno assays) used to test the potential biomarkers are much faster and a large number of samples can be analyzed concurrently. For validation, archived tissues consisting of nor-

P R O T E O M I C S FOR BIOMARKER DISCOVERY

37

mal epithelial and tumor HNSCC were processed and used for immunohistochemical (IHC) detection with antibodies against cytokeratin 4, vimentin, and desmoplakin (Figure 3.5) using a tissue microarray (TMA). Ten normal tissue sections were used for each antibody tested, while for the PD, MD, and WD tumors between 33 and 105 tissues were analyzed. Scoring of the tissues was based on tissue differentiation and the intensity of staining by each individual antibody. For cytokeratin 4, light gray represents >5% and <25%; mid gray, 26% to 50% of the cells stained; dark gray, 51% to 75%; and black, 76% to 100% of the cells within the section stained, respectively. Most cells within the normal tissues were immunopositive for vimentin, while few tumor cells showed staining for this protein. For vimentin and desmoplakin, the percentage of positive tumors for each stage of differentiation {spotted box) compared with negative (white box) is depicted. None of the normal tissues stained for vimentin showed positive immunoreactivity, however, approximately 60-70% of the tumors showed positive

FIGURE 3.5 Tissue microarray (TMA) validation of head and neck squamous cell carcinoma biomarkers identified via global proteomic analysis of well- (WD), moderately- (MD), and poorly-differentiated (PD) HNSCC formalin-fixed paraffin-embedded (FFPE) tumor samples.Tissue sections immunostained for cytokeratin 16, vimentin, and desmoplakin using immunohistochemistry (IHC) are shown in the upper panels.The stained sections were evaluated based on tissue differentiation and intensity of IHC staining. For cytokeratin 4, light gray represents >5% and <25%; mid gray, 26% to 50%; dark gray, 5 I % to 75%; and black 76% to 100% of the cells stained positively for this protein. The scoring for vimentin and desmoplakin was based on the percentage of positive tumors for each stage of differentiation (checkered box) compared with negative (white box).Ten normal (i.e., non-neoplastic) tissues were analyzed in each experiment, while the number of HNSCC cancer tissues evaluated by IHC is indicated above each column. (See color insert for a full color version of this figure.)

38

BIOMARKERS

TABLE 3.2 Overlap in identification of potential markers for Down Syndrome using different proteomic analysis platforms, x indicates protein was identified by method listed above column, 2D-CF: two dimensional chromatofocusing; 2D-DIGE: two dimensional difference in-gel electrophoresis; MudPIT: multidimensional protein identification technology; MALDI-TOF: matrix assisted laser desorption ionization time-of-flight mass spectrometry.

Protein

Method 2D-CF

2D-DIGE

Afamin

X

X

Alpha-1 -acid glycoprotein 1

x

X

Alpha-1 -acid glycoprotein 2

x

Apolipoprotein A-l

X

X

Apolipoprotein A-4

X

X X

Apolipoprotein c-ll

X

Apolipoprotein D

X

Apolipoprotein E

X

Complement factor H

MudPIT

MALDI-TOF

X

X X X

X X X

Clusterin

X

X

Complement C3

X

X

Alpha-2HS-glycoprotein

X

X

Inter-trypsin inhibitor chain H4

X

X

X

X

Pregnancy specific glycoproteins

X

Sex hormone-binding globulin

X

X

Tetranectin

X

X

Transthyretin

X

X

X

Serum amyloid A

X

X

Complement C4

X

X

Ceruloplasmin

X

X

X X

immunostaining. All tissues were immunoreactive for desmoplakin, while approximately 5-15% of the tumors were negative. This study provides a good illustration of how a clinically relevant sample that is widely available can be used to find biomarkers for specific diseases.

C H A L L E N G E S I N P R O T E O M I C BIOMARKER DISCOVERY One of the major challenges in analyzing biologic samples is discovering statistically significant differences in biologically important proteins when comparing samples from healthy and disease-affected patients. To be clear, the difficulty is not in finding differences; a comparative biofluid study will find tens (if not hundreds) of differences. Obtaining statistical confidence in any of the observed difference so that the probability is high of any of the potential bio-

P R O T E O M I C S FOR BIOMARKER DISCOVERY

39

markers passing verification and validation studies is the challenge. There are a large number of published manuscripts that have validated their findings from a discovery study using methods such as immunoblotting and ELISA, etc., but this type of validation is a far cry from the time and cost associated with a clinical trial that requires the analysis of thousands of samples. Although a huge effort in time and resources has been put into biomarker discovery studies, very few proteins have successfully migrated the path of verification and validation and become clinically useful during the proteomics era. One of the reasons that it is difficult to obtain statistical confidence in differences found in comparative proteomic studies is that, outside of SELDI-TOF or MALDI-TOF profiling, MS-based analyses of complex biologic samples are low-throughput. Comprehensive analysis of a single plasma or serum sample takes days to complete. A cohort of 100 samples for instance, will require four to eight months from beginning of sample preparation until the end of the bioinformatic analysis even if an optimized process is developed. Even a cohort this large would probably not have statistical power to adequately address most clinical questions. This throughput has limited most comparative biofluid studies to a handful (e.g., 2-10) of samples. Even so, a typical study will find tens to hundreds of differences; depending on how significance is defined (which is often subjective). The difficulty is having enough confidence to pursue an extensive validation study based on data that has limited statistical power. When a small number of samples are compared, it is difficult to determine if a difference is statistically significant because the available information on the inter-individual variability of the myriad of proteins present in human biofluids is insufficient (i.e., what is "normal?"). It is well documented that long-term (age, physical condition, lifestyle, etc.) and short-term variables (time after eating, diurnal cycle, time of estrus, etc.) have an effect on the molecular content of biofluids.28 While variability in biological samples obtained from animals such as mice and rats can be minimized to a certain extent, it is nearly impossible to do within the human population. Another challenge is trying to recognize differences that are specific to the disease of interest. A comparison of any biological samples (e.g., diseased versus healthy) will reveal proteome-level differences—the challenge is to recognize which of these differences will adequately serve as a useful biomarker. The literature is "polluted" with manuscripts that identify acute-phase response proteins as potential biomarkers for specific cancers. While these proteins are undoubtedly upregulated in the circulation of cancer-affected individuals, they are not directly related to a specific type of cancer (such as CA-125 is for ovarian cancer), but are a general symptom of the disease.

THE ROAD FORWARD: TARGETED VERIFICATION AND VALIDATION Once a potentially useful biomarker has been discovered, verification and validation studies are required to quantitatively measure the proposedmarker in thousands of clinical samples. While antibody-based tests such as ELISAs

40

BIOMARKERS

and arrays will be a major part of this effort, MS is also expected to contribute when useful protein-specific antibodies are not available. Analytically, MS is an ideal tool for quantitating the absolute amounts of molecules in complex mixtures. Indeed, MS has been a standard technology in pharmaceutical companies for quantitating specific metabolites. The major advantage of MS is that it provides a direct measure of each compound, ensuring that only the analyte of interest is being quantitatively measured. The sensitivity of current MS technology enables compounds in the femtogram range to be quantitated with high accuracy and precision. Theoretically, quantitative measurements of specific peptides should show similar accuracy and precision shown for small molecules. Another advantage in using MS to perform verification and validation studies is that many of the potential biomarkers are being discovered using MS-based technologies. Therefore, most of the analytical parameters are already established for designing follow-up studies. In addition, MS is capable of quantitating a large number of different analytes in a single experiment. One of the prevailing hypotheses is that multiple biomarkers will be required for accurate disease diagnosis, making MS well suited as an important analytical diagnostic device in the future. The development of absolute quantification (AQUA) by Dr. Stephen Gygi's lab has been crucial for using MS to quantitate proteins in complex biologic samples.29 This study showed that by using stable-isotope labeled surrogates for a peptide of interest, accurate quantitative measurements could be acquired. Monitoring a specific peptide in a complex proteome sample is done using selected reaction monitoring (SRM) or multiple reaction monitoring (MRM). In these methods, the biologic sample is fractionated using LC directly on-line with MS analysis. Since the elution time of the analyte of interest is known, the mass spectrometer is instructed to isolate a specific mass-to-charge (m/z) value within the first quadrupole (Ql) region of the instrument (a triple quadrupole is the most conmmon type of instrument for this type of analysis) at a specific retention time. The isolated peptide ion proceeds to collision cell (Q2) and is subjected to MS2. Fragment (or transition) ions that are specific to the peptide are isolated in the Q3 region and move onto the detector. Fragment ions are monitored because they provide greater specificity and sensitivity than can be obtained if the parent peptide ion is analyzed directly. While a specific m/z value can be isolated at a designated retention time, a human biologic sample is very complex and the degeneracy of peptide masses is extremely high. The fragments, however, produced from a peptide are very unique. Monitoring fragment ions also increases the sensitivity of the measurement by reducing the amount of noise that is detected. The difference between SRM and MRM is that in SRM a single fragment ion is monitored, while in MRM, multiple fragments are measured. A study by Hunter and Anderson was critical in demonstrating the ability of MRM to quantitate multiple proteins within a complex proteome sample within a single experiment.30 This group took immunodepleted plasma and analyzed the sample using LC-MS2 with the mass spectrometer operating in a MRM mode. Quantitative MRM assays were designed to measure 53 high and

P R O T E O M I C S FOR BIOMARKER DISCOVERY

41

medium abundance proteins in human plasma. The quantitative data showed within-run coefficients of variation (CVs) between 2-22% for 47 of these proteins. Repeating the study with native plasma showed that, while the targeted analytes could still be detected in both sample types, prior immunodepletion resulted in significantly lower CVs. The experiments showed the ability to reproducibly measure proteins present at concentrations less than 1 ug/ml. The first step in designing an MRM assay is determining a peptide that can function as a surrogate to measure the absolute amount of a specific protein. Any protein will give rise to a number of tryptic peptides; however, not all of these will be suitable for an MRM assay. While there are a number of empirical rules that can be followed for selection (e.g., no methionyl residues within the peptide since these residues are prone to unpredictable oxidation), the optimal surrogate peptide needs to be identified experimentally. An example of the experimental details required in evaluating surrogate peptides for an MRM study is illustrated by a study designed to quantitate HER-2.31 HER-2 (also known as neu and ErbB2) is a member of the epidermal growth factor receptor (EGFR) family. The overexpression of HER-2, a cell membrane surface-bound tyrosine kinase, is associated with cancer initiation and progression. The first step in finding the optimal surrogate peptide for the protein was digesting the protein with trypsin and analyzing the resultant peptides using LC-MS2. Of the eighteen HER-2 tryptic peptides identified, six contained residues that were susceptible to oxidation (e.g., cysteine, methionine, or tryptophan) while another eight contained missed tryptic cleavage sites. Of the four remaining peptides, SGGGDLTLGLEPSEEEAPR was chosen since it provided good signal intensity and excellent chromatographic peak shape. This peptide, with an m/z of 957.5 (corresponding to the doubly charged version of the peptide ([M+2H]2+) produced several well resolved fragment ions that could be monitored during an MRM experiment (Figure 3.6A). The importance of using MRM to monitor a combination of parent and fragment ions having a distinctive, fragmentation pattern rather than a single peak is illustrated in Figure 3.6B. The MS2 spectrum of the peptide SGGGDLTLGLEPSEEEAPR is shown in the top panel along with three transition ions (m/z 914.4, 1043.5, and 1213.6) that were monitored during the MRM analysis. Monitoring of the m/z range 957.00-958.00 (the parent ion) produces a very intense peak at approximately 37 minutes, with two additional peaks within this m/z range observed at 30.8 and 43 minutes. It would appear to be reasonable to assume the peak at 37 minutes corresponds to the HER-2 peptide of interest based solely on this data. Further analysis, however, shows that MS2 of the peptide at 30.8 minutes gives rise to the three dominant fragment ions (m/z 914.4, 1043.5, and 1213.6) observed for the surrogate peptide of interest. Monitoring the parent ion and the m/z values of all three fragment ions is necessary to conclude that the peak at 30.8 minutes, not the more intense signal at 37 minutes, represents the surrogate peptide of interest. In a clinical setting, quantitative measurement of a specific protein will be used for, among other things, diagnostic or therapeutic monitoring. This use will necessarily require MRM methods to work with complex biologic

42

BIOMARKERS

FIGURE 3.6 A) Tandem mass spectrometry (MS2) spectrum of HER-2 peptide SGGGDLTLGLEPSEEEAPR showing the presence of three high abundance transition ions at m/z 914.4, 1043.5, and 1213.6. B) Importance of monitoring transition ions when conducting targeted quantitative studies.The four panels reveal a number of peaks that are detected throughout the chromatograms when the parent ion (m/z 957.5) and the three transition ions labeled in A) are monitored. The peptide can only be unambiguously assigned by monitoring the parent ion and each of the three transition ions.

43

P R O T E O M I C S FOR BIOMARKER DISCOVERY

samples such as serum plasma, urine, CSF, and tissues. One of the keys in developing an MRM/MS method is dealing with the ability to detect a specific species in the presence of all of the other components in the mixture (sometimes known as the matrix effect). The effect of adding increasing amounts of cell lysate on the signals obtained from a heavy isotopic version of the HER2 surrogate peptide ([M+2H]2+ m/z 962.5) is shown in Figure 3.7. The areas of the peaks representing the three transition ions all show a >50% decrease when the amount of matrix added to the peptide is increased from 100 to 250 ng. The addition of 1000 ng of cell lysate essentially makes each signal undeterminable. This study suggests that enrichment methods that extract the protein of interest from the biologic sample will be important in routine development of sensitive MRM assays. Taken collectively, the illustration for the HER2 peptide shows that an MRM assay will need to be optimized for each protein of interest.

CONCLUSION The number of disease-specific biomarkers identified using MS during the proteomic era has probably not met expectations. Great strides, however, have been made in our ability to comprehensively analyze important clini1 2 -,

• O V A

1.0co O rX CO CD C CO CO

8.0-

Total Area 924.5 1053.5 1224.5

6.0-

8. «■ 2.0-

0

^i^SS 200

400

600

800

1000

1200

Amount of cell lysate added (ng) FIGURE 3.7 Effect of the matrix concentration on the ability to detect a specific peptide within a complex biologic sample. As illustrated in this example, the addition of increasing amounts of cell lysates to a peptide internal standard results in a substantial decrease in the peak area of each transition ion that was being monitored. Note: each of the transition ions is 10 m/z units higher than those shown for the native peptide in Figure 3.6A because the internal standard peptide is labeled with 10 atoms of carbon-13.

44

BIOMARKERS

cal samples. Less than 10 years ago, the ability to identify 500 proteins in serum would be considered monumental; today it would be considered a failed experiment. Five years ago FFPE samples were considered intractable to MS analysis; today these samples are being used routinely for biomarker discovery.25 27 The capabilities of existing sample preparation methods and MS technology will continue to improve. The many biomarker discovery projects have resulted in a large number of "potentially" useful biomarkers. Collation of all of this available data may lead to potential biomarkers that could be selected for verification and validation studies. Beyond increasing capabilities at the discovery level, the rate at which MRM studies are conducted to monitor important biological molecules in biofluids is intensifying and will continue to expand. These studies may provide an alternative solution, beyond the development of affinity reagents, for verification and validation of diagnostic and therapeutic biomarkers.

SUMMARY P O I N T S 1. 2. 3. 4.

The rapid development of MS technology has enabled thousands of proteins within complex biologic samples to be identified within single studies. Quantitative proteomics allows protein abundances between samples obtained from healthy and disease-affected patients to be compared with the aim of finding novel disease-specific biomarkers. While large numbers of differences in protein abundances can be detected, it is often difficult to determine which ones are specific for the disease of interest. Methods that specifically target proteins within clinical samples have the potential to increase the accuracy and precision by which potential protein biomarkers are measured.

ACKNOWLEDGMENTS This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract NOlCO-12400. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the United States Government.

REFERENCES 1. 2. 3. 4.

Johann, D. J., Jr. and Blonder J., Biomarker Discovery: Tissue versus Fluids versus Both. Expert Rev. Mol. Diagn. 2007;7:473^t75. Jones J. and Pantuck A. J. Genomics and Proteomics in Renal Cell Carcinoma: Diagnosis, Prognosis, and Treatment Selection. Curr. Urol. Rep. 2008;9:9-14. http://seer. cancer.gov/statfacts/htmUkidrp. html. http://seer.cancer.gov/statfacts/html/ovary.html.

PROTEOMICS FOR BIOMARKER DISCOVERY 5. 6. 7. 8. 9. 10. 11. 12. 13.

14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

45

Issaq, H. J., Xiao, Z., and Veenstra, T. D. Serum and Plasma Proteomics. Chem. Rev. 2007;107:3601-3620. Zhang, H., Liu, A. Y., and Loriaux, P., et al. Mass Spectrometric Detection of Tissue Proteins in Plasma. Mol. Cell. Proteomics. 2007;6:64-71. Veenstra, T. D. Global and Targeted Quantitative Proteomics for Biomarker Discovery. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2007;847:3-11. Pieper, R., Su, Q., and Gatlin, C. L., et al. Multi-Component Immunoaffinity Subtraction Chromatography: An Innovative Step Towards a Comprehensive Survey of the Human Plasma Proteome. Proteomics. 2003;3:422-432. Zolotarjova, N., Martosella, J., Nicol, G., Bailey, J., Boyes, B. E., and Barrett, W. C. Differences Among Techniques for High-Abundant Protein Depletion. Proteomics. 2005;5:3304-3313. Bjorhall, K., Miliotis, T, and Davidsson, P., Comparison of Different Depletion Strategies for Improved Resolution in Proteomic Analysis of Human Serum Samples. Proteomics. 2005;5:307-317. Baussant, T., Bougueleret, L., Johnson, A., and Rogers, J., et al. Effective Depletion of Albumin Using a New Peptide-Based Affinity Medium. Proteomics. 2005;5:973-977. Sahab, Z. J., Iczkowski, K. A., and Sang, Q. X., Anion Exchange Fractionation of Serum Proteins versus Albumin Depletion. Anal. Biochem. 2007;368:24-32. Petric, T. C , Brne, P., Gabor, B., and Govednik, L., et al. Anion-Exchange Chromatography Using Short Monolithic Columns as a Complementary Technique for Human Serum Albumin Depletion Prior to Human Plasma Proteome Analysis. J. Pharm. Biomed. Anal. 2007;43:243-249. Tirumalai, R. S., Chan, K. C , Prieto, D. A., Issaq, H. J., Conrads, T. P., and Veenstra, T. D. Characterization of the Low Molecular Weight Human Serum Proteome. Mol. Cell. Proteomics. 2003;2:1096-1103. Darde, V. M., Barderas, M. G., and Vivanco, E, Depletion of High-Abundance Proteins in Plasma by Immunoaffinity Substraction for Two-Dimensional Gel Electrophoresis Analysis. Methods Mol. Biol. 2007;357:351. Gong, Y., Li, X., Yang, B., and Ying, W., et al. Different Immunoaffinity Fractionation Strategies to Characterize the Human Plasma Proteome. J. Proteome Res. 2006;5:1379. Maurya, P., Meleady, P., Dowling, P., and Clynes, M. Proteomic Approaches for Serum Biomarker Discovery in Cancer. Anticancer Res. 2007;27:1247-1255. Unlii, M., Morgan, M. E., and Minden, J. S., Difference Gel Electrophoresis: A Single Gel Method for Detecting Changes in Protein Extracts. Electrophoresis. 1997;18:2071-2077. Link, A. J., Eng, J., Schieltz, D. M., and Carmack, E., et al. Direct Analysis of Protein Complexes Using Mass Spectrometry. Nat. Biotechnol. 1999;17:676-682. Shen, Y, Kim, J., Strittmatter, E. F,. and Jacobs, J. M., et al. Characterization of the Human Blood Plasma Proteome. Proteomics. 2005;5:4034-4045. Ryu, S., Gallis, B., and Ah Goo, Y., et al. Comparison of a Label-Free Quantitative Proteomic Method Based on Peptide Ion Current Area to the Isotope Coded Affinity Tag Method. Cancer Inform. 2008;6:243-255. Bachi, A. and Bonaldi, T., Quantitative Proteomics as a New Piece of the Systems Biology Puzzle. J. Proteomics. 2008;71:357-367. Qian W. J., Jacobs J. M., Camp D. G., at el., Comparative Proteome Analysis of Human Plasma Following in Vivo Lipopolysaccharide Administration Using

46

BIOMARKERS

24. 25. 26. 27. 28. 29. 30. 31.

Multidimensional Separations Coupled with Tandem Mass Spectrometry. Proteomics. 2005;5:572-584. Nagalla S. R., Canick J. A., and Jacob T., et al. Proteomic Analysis of Maternal Serum in Down Syndrome: Identification of Novel Protein Biomarkers. J. ProteomeRes. 2007;6:1245-1257. Hood, B. L., Darfler, M. M., and Guiel, T. G., et al., Proteomic Analysis of Formalin Fixed Prostate Cancer Tissue. Mol. Cell. Proteomics. 2005;4:1741-1753. Hwang, S. I., Thumar, J., and Lundgren, D. H., et al., Direct Cancer Tissue Proteomics: A Method to Identify Cancer Biomarkers from Formalin-Fixed ParaffinEmbedded Archival Tissues. Oncogene. 2007;26:65-76. Patel, V., Hood, B. L., and Molinolo, A. A., et al., Proteomic Analysis of LaserCaptured Paraffin-Embedded Tissues: A Molecular Portrait of Head and Neck Cancer Progression. Clin. Cancer Res. 2008;14:1002-1014. Kasthuri, R. S., Verneris, M. R., Ibrahim, H. N., Jilma, B., and Nelsestuen, G. L. Studying Multiple Protein Profiles Over Time to Assess Biomarker Validity. Expert Rev. Proteomics. 2006;3:455^164. Gerber, S. A., Rush, J., Stemman, O., Kirschner, M. W., and Gygi, S. P. Absolute Quantification of Proteins and Phosphoproteins From Cell Lysates by Tandem MS. Proc. Natl. Acad. Sci. USA. 2003;100:6940-6945. Anderson, L. and Hunter, C. L., Quantitative Mass Spectrometric Multiple Reaction Monitoring Assays for Major Plasma Proteins. Mol. Cell. Proteomics. 2006; 5:573-588. Ye, X., Blonder, J., and Veenstra, T. D., Targeted Proteomics for Validation of Biomarkers in Clinical Samples. Brief. Fund. Genomic. Proteomic. 2009;8: 126-135.

CHAPTER

METABOLIC PROFILING FOR BIOMARKER DISCOVERY Hector C. Keun

I N T R O D U C T I O N : W H A T IS METABOLIC PROFILING? Metabolic profiling (also known as metabonomics or metabolomics) covers a range of techniques and technologies that are broadly aimed at characterizing the small-molecule (metabolite) content of a biological sample. The development of metabonomics and metabolomics arose at similar times in separate areas. Metabolomics as a concept encapsulates the extension of genomics and proteomics to the study of metabolism, in that it aims to define in some way the metabolome or "all the metabolites in a cell or organism."12 Its earliest applications focussed on functional genomics, specifically the detection of silent phenotypes in plants3 and microbes.4-5 Both NMR spectroscopy and gas-chromatography mass-spectrometry were used to detect metabolites. Metabonomics is defined as "the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification"6 and historically has "aimed at the augmentation and complementation of the information provided by measuring the genetic and proteomic response," i.e., aligned with the goals of systems biology.7 Metabonomics derives essentially from the work of Prof Jeremy Nicholson and his research group using NMR spectroscopy to characterize body fluids and tissues.8-11 In addition to phenotypic characterisation,12 both toxicology6, 13 14 - and disease detection or monitoring15-16 were major applications investigated during the early development of metabonomics, with a key objective being biomarker discovery. At the present time, both NMR and MS-based approaches to metabolic biomarker discovery remain popular and there is little 47

48

BIOMARKERS

practical distinction between many metabolomics and metabonomics studies in the literature. However, there is considerable variety in the specific ways in which the profiles are generated and analyzed, depending on the aims of each study (Figure 4.1). Some investigators apply statistical analysis to the analytical profile directly prior to spectral annotation and metabolite identification; this is sometimes referred to as "fingerprinting." Often multivariate or pattern recognition analysis of some kind is used with the aim of identifying combinations of metabolite changes that correlate to some biological or clinical parameter of interest, i.e., the definition of a "biomarker signature." Alternatively, the aim may be to investigate the possible mechanisms by which a particular intervention or genetic abnormality induces a pathological state or to understand the etiology of a chronic disease. In these cases often a "pathway-driven" approach is taken, using predefined metabolic models to infer changes in pathway flux or using informatic tools to associate known metabolic pathways to the differences in metabolic profiles observed. Here the unambiguous identification of metabolites is required, and experimentally specific metabolic pathways or classes of molecule can be targeted by the use of stable isotope labelled substrates or the use of particular extraction and chromatographic procedures. However the distinctions between biomarker discovery and mechanistic explorations are often more conceptual than practical; provided that the metabolic profiling strategy chosen measures many metabolites simultaneously in a largely unbiased fashion ("hypothesis generating"), the data generated have the potential to identify novel putative biomarkers for further investigation. In a typical metabolic profile, tens to thousands of metabolites are routinely detected, the relative levels of which are dependent on dietary intake, systemic metabolism (including the metabolism of symbiotic gut microbes) and organ function.17'18 It is the relative stability of the "normal" or "control" profile that affords metabolic profiling the sensitivity to detect even minor physiological perturbations. Even outside the laboratory setting, in uncontrolled human populations, the effects of homeostasis and metabolic regulation allow stable individual variation in metabolic phenotype ("metabotype") to be defined.1*"21 Upon treatment with a toxicant (or exposure to any other stressor), the metabolic profile can change for one of several reasons, including but not limited to the following: • Loss of metabolic function of the target organ • Effects on the transport of metabolites across membranes, or loss of the integrity of barrier membranes • Changes in the eating habits of the animal • Alterations to the gut microflora population • Perturbation to a metabolic pathway by the direct pharmacological action of the compound that may be coincident or causal of the lesion produced. In numerous studies it has also been shown that knowing which metabolic events occur simultaneously or in succession after exposure (i.e., metabolite

METABOLIC P R O F I L I N G FOR BIOMARKER DISCOVERY

49

FIGURE 4.1 A schematic illustrating a variety of approaches for applying metabolic profiling (metabonomics/metabolomics) in biomarker discovery.

50

BIOMARKERS

"dynamics" or "metabolic trajectories") can help to resolve which specific processes are responsible and to classify metabolic responses more accurately, highlighting the value of sequential or continuous sampling.22~27 Where the target organ is responsible for unique metabolic processes, or is the major site for certain pathways the resulting profile effect can be associated to a specific lesion. This has clearly been demonstrated for the kidney28,29 and liver,22'25 where some sub-organ specificity in response has also been reported. Metabolic profiling as a means of biomarker discovery and metabolic biomarkers themselves have several potential advantages over genomic and proteomic counterparts: • Metabolites are a defined chemical entity irrespective of species, genotype, localization, and biological matrix, facilitating the translation of analytical procedures between the laboratory and the clinic or field. • Changes in metabolism are a phenotypic and often functional endpoint. • Interaction between an organism and its environment, e.g., diet, is often at the level of small molecules. • Many metabolites can be readily measured with minimal invasion in body fluids, facilitating repeat sampling from the same animals/individuals, translation and screening. These factors, coupled with the fact that established and widely available analytical technologies such as NMR spectroscopy and mass spectrometry can be used, explain the explosion of research in the last decade using metabolic profiling as a strategy for biomarker discovery (Table 4.1).

A N A L Y T I C A L STRATEGIES FOR M E T A B O L I C PROFILING A wide range of analytical strategies have been used for generating metabolic profiles, however most are centred on either NMR spectroscopy or massspectrometry for detection, the latter usually coupled to gas chromatography or liquid (typically reverse-phase) chromatography. NMR and MS in broad terms are highly fit-for-purpose as metabolic profiling technologies: • Both can be used such that they are largely untargeted in the molecular structures that will be detected (for a given solvent system). • The signals produced are very sensitive to structure, so many compounds can be identified in the spectral data at once and the signals provide strong structural clues for the identification of unknown metabolites. • Both have technical extensions that give further detailed structural information, e.g., multidimensional NMR, MS/MS. • Both can distinguish isotopologues where stable isotopes are used to target one or several metabolic pathways. • Data are amenable to quantitative interpretation. • Both have a wide dynamic range.

METABOLIC PROFILING FOR BIOMARKER DISCOVERY

51

TABLE 4.1 Examples of biomarker studies using metabolic profiling. Only literature cited in this review is presented. Examples

Biomarker Toxicology: Toxicity screening Liver damage Steatosis Phospholipidosis Peroxisome proliferation Liver regeneration Renal damage Liver GSH depletion Testicular damage Vasculrtis Pancreatic damage Pharmacometabonomics Environmental heavy metals Cancer: Hyperpolarized ,3 C&' 5 N MRI Prostate cancer Colorectal cancer Ovarian cancer Breast cancer Choline kinase inhibition Metabolic syndrome: Coronary disease

Keun, et al. 200423; Ebbels, et al. 200724; Dieterle, et al. 200682 Beckwith-Hall, et al. I99825; Sanins, et al. I99292; Nicholls, et al. 2001' 3 ; Clayton, etal.2003 94 Skordi, et al. 200785 Nicholls, et al. 200013; Espina, et al. 2001 H; Hasegawa, et al. 200786 Ringeissen, et al. 200395 Bollard, et al. 200922 Nicholson, et al. 1985'; Holmes, et al. 199226; Holmes, et al. 199027; Anthony, et al. 199428; Holmes, et al. 199829; Holmes, et al. 199284 Soga,etal.200643 Ekman, et al. 200688; Nicholson, et al. 198687 Slim,etal.200289 Bohus, et al. 20099'; Bonus, et al. 200890 Clayton, et al. 2006'7 Bundy.etal.2007'45 Day, et al. 200753; Gallagher et al. 200854; Gabellieri, et al. 200855 Lynch, Nicholson 1997'°; Swanson, et al. 2006'25; Swanson, et al. 2003'26; Sreekumar,etal.2009'27 Backshall, et al. 200947; Chan, et al. 200948 Odunsi,etal.2005 ,2 ° Nimmagadda, et al. 2009" 8 ; Frickenschmidt, et al. 2008' 2I Nimmagadda, et al. 2009' l8 ; Gabellieri, et al. 200855

Hypertension Diabetes

Brindle, et al. 2002'6; Kirschenlohr, et al. 2006'38; Dunn, et al. 2007'37; Mora, et al. 2009'43; Otvos, et al. 2006'42 Holmes, etal.2008 2 ' Makinen, et al. 200866; Nicholson, et al. 19848

Genetic disease: Dystrophy Aminoacylase 1 deficiency GAMT deficiency Batten disease Huntington disease

Griffin, et al. 2001 l 5 Engelke, etal.2008' 08 Engelke,etal.2009109 Pears, et al. 2005"° Tsang,etal.2006"

Infectious disease: Bacillus cereus Plasmodium falciparum Schistosoma mansoni Typanosoma brucei brucei Plasmodium berghei Echinostoma caproni Meningitis

Bundyetal.2005' 29 Olszewski,etal.2009128 Li, et al. 2009 l3! ; Wang, et al. 2004'» Li, et al. 2008'33; Wang, et al. 2008 l32 Li, etal.2008' 34 Saric, etal.2008' 35 Coen,etal.2005' 36

Neurological disorders: Bipolar disorder Schizophrenia

Lan,etal.2009" 2 Holmes, etal.2006" 3

Dietary effects: Food restriction Gut microbial metabolism

Nicholson, et al. 19848; Connor et al. 2004'47 Dumas, et al. 200698; Martin, et al. 200959; Wikoff, et al. 200910°; Claus, et al. 2008'01; Swann, et al. 2009102

52

BIOMARKERS

However, they differ in several key respects: • MS is several orders of magnitude more sensitive than NMR. • NMR spectroscopy is non-destructive and can analyze the "native" tissue or biofluid with minimal preparation or even measure metabolites noninvasively in vivo. • NMR spectroscopy is more analytically reproducible and robust across platforms. • Metabolite signals are typically better resolved using MS, particularly when coupled to chromatography. • NMR is a better tool for de novo structure determination and for distinguishing isotopomers. Partly due to the fact that different types of online chromatography are frequently used, and partly due to a wide array of mass detectors and ionisation tech available, MS solutions to metabolic profiling vary widely and the precise form of data delivered is very dependent on the way a particular instrument is applied (reviewed by Dettmer, et al.30). Direct injection MS, without prior separation of components, is a rapid means of metabolite fingerprinting, which when using high resolution instruments such as time-of-flight (TOF) and Fourier-transform ion cyclotron (FT-ICR), mass spectrometers can allow the detection and identification of many species in a single experiment. The ability to identify unknown species in MS is very dependent on the accuracy of measured ion mass and isotope abundance patterns.31 Typically, electrospray ionisation (ESI) is used such that intact molecular ions can be detected. However, the lack of chromatography has many disadvantages, the most important being a) that isobaric species with the same mass but very different structures cannot be resolved, and b) that matrix dependent ion suppression or signal enhancement limits quantitative analysis. Liquid chromatography mass-spectrometry (LC-MS) provides one solution to these problems, again usually coupled with ESI (reviewed by Wilson, et al.32). Reverse phase C18 chromatography is frequently used, where highly polar metabolites are may not be well resolved and matrix effects can still be an issue, both in terms of ion suppression and retention time stability. The availabilty of ultra-performance liquid chromatography (UPLC) has had a significant impact in this area by exploiting the improved separation and hence speed and sensitivity afforded by small (1.7um) column matrix particle size.33 However, LC-MS protocols and platforms for metabolic profiling are still undergoing development.32'3^37 Gas-chromatography (GC)-MS methods and resources for metabolic profiling are somewhat more established.1838'39 Typically electron-impact ionisation (El) is used which causes molecular fragmentation. Derivatisation is also required prior to analysis to increase the volatility of metabolites, an important factor in determining what chemical classes are detected and a major source of intra- and inter-day experimental variability. Fortunately, fragmentation patterns are reproducible across platforms and so substantial EI-MS libraries have been

METABOLIC PROFILING FOR BIOMARKER DISCOVERY

53

established that can be used to great effect in annotation of metabolite profiles, e.g., the Golm Metabolome Database or GMD.4041 GC retention times are also consistent, with appropriate calibration, and provide further structural cues and library data, e.g., via AMDIS from NIST(http://chemdata.nist.gov/mass-spc/ amdis/). LC-ESI-MS often does not produce fragments and where additional collision cells are in place to do so the fragment patterns are often very instrument dependent, limiting progression and utility of LC-MS databases, although this is an area that is moving forward (e.g., METLIN42). Other separation techniques have been used successfully but to a lesser extent for metabolic profiling such as capilliary electrophoresis (CE)43,44 and gas-phase ion mobility.45,46 In contrast to MS, NMR instrument suppliers are few and present a relatively convergent and defined solution, with well-established and common platforms and processes across manufacturers (reviewed by 17). The most abundant elements in organic molecules (H, C, O, P, N) all have NMR-visible isotopes but 'H NMR spectroscopy dominates metabolic profiling because it is the most sensitive ('H nuclei are highly abundant (>99.9%) and have the highest gyromagnetic ratio). NMR spectroscopy typically requires little sample preparation other than the addition of a deuterated solvent and reference compound and can generate useful metabolic profiles from intact tissue biopsies without prior extraction via magic angle spinning (MAS)-NMR."47'48 The spectral profiles generated can be edited on the basis of molecular mobility, a technique frequently used to suppress signals from high molecular weight species such as lipoproteins in blood plasma.17 The low sensitivity of the technique means that typically hundreds of microlitres of a biofluid are used for analysis and most sub-micromolar species will not be detected. To compensate for this, NMR signal intensity is, to a good approximation in most experimental conditions, proportional to "free" concentration irrespective of the molecule examined or the exact biological mixture. This helps to reduce technical error and inter-laboratory variation, and means that NMR is unlikely to "miss" a large concentration change within the limits of detection.174950 The sensitivity of NMR-based metabonomics has been improved by recent developments such as cryogenic probe NMR51 and in particular dynamic nuclear polarisation, which can transiently increase signal to noise by >10,000 fold.52 Such enhancement has made in vivo 13C and 15N imaging practical5355 and could also be a route to exploiting putative biomarkers detected by conventional NMRbased metabolic profiling. Despite the fact that many metabolites produce very characteristic NMR spectra, and the recent arrival of metabolomic databases with substantial NMR content (e.g., the Madison Metabolomics Consortium Database (MMCD)56 and the Human Metabolome Database (HMDB)57), automated deconvolution of NMR-based metabolic profiles has not as yet proved a very successful strategy in biomarker applications. However "manual" or semiautomatic pattern matching and deconvolution can be routine in ID NMR data analysis and some commercial solutions are becoming more popular. NMR and MS are clearly different, varied, and complementary approaches. NMR is perhaps more limited in the scope of metabolism that can be covered but has many valuable features and a strong historical link to clinical

54

BIOMARKERS

applications. A mixture of multiple strategies is the best solution where possible. Both have off-the-shelf solutions but neither are trivial to implement. It is important to note that, as for other types of molecular profiling studies, great care must be taken to avoid introduction of sample bias58 during collection and analysis and indeed to assume that some bias is envitable and to characterize the effect of common problems such as freeze thaw cycles.50 Different biological matrices and platforms have different susceptibilities to bias, so such factors may need to be assessed separately in each study.

DATA P R E - P R O C E S S I N G , A N A L Y S I S , A N D PATTERN R E C O G N I T I O N Several aspects of pre-processing of raw spectral data are highly instrument dependent such as calibration, baseline correction, peak extraction, and alignment. The aim of all these steps is to produce a data table in which variations from instrumental artefacts are minimized. They are often intrinsic to the instrument software although many freeware options are available and popular in metabolic profiling (e.g., AMDIS or XCMS59). Metabolite identification and spectral annotation is often a distinct process which can occur either before or after statistical analysis, the latter approach, suitable for biomarker studies, providing the opportunity to reduce the task to just those metabolites that appear to be relevant to the study endpoint. However, other often necessary pre-processing steps such as variable transformation ("scaling") and normalization make a significant impact on the numerical analysis itself. Scaling is self-explanatory; the values for each variable in the analysis (a metabolite concentration or peak intensity) are mathematically transformed to make the distribution more normal or to reduce the dominance of highly abundant species over low concentration ones. Normalization in the context of metabolic profiling refers to a transformation that occurs across the values that constitute a profile, typically converting the metabolite levels into a relative value that has more biological relevance or removes some specific experimental artefact. A common example is the removal of urinary dilution or concentration by dividing the urine metabolic profile by the total metabolite intensity, the average fold-change or a more physiologically relevant quantity such as the osmolality or creatinine excretion. After these stages the data table is ready for whatever statistical analysis is deemed appropriate. From a statistical perspective there are no major reasons why metabolite data should be different from any other profile data. Within standard caveats, e.g., assumptions of normality should be tested, all standard methodologies are applicable. It is important to bear in mind however that as with all highly multivariate profiles, issues of multiple testing become very important60 and the risk of false positive associations is usually quite high and should be estimated, e.g., by the approach of Benjamini and Hochberg61 or resampling techniques. Pattern recognition (PR) techniques have and continue to be very important in the analysis of metabolic profile data (reviewed by Lindon, et al.62). The term PR, in the context of profiling analysis, refers to a range of statistical

METABOLIC PROFILING FOR BIOMARKER DISCOVERY

55

techniques that generate models that give a description of the statistical association (or more simply, similarity) between profiles or profile measurements (unsupervised PR) and/or between profiles and some target factor (supervised PR). One can then interrogate the model to find out those features of the profile that correlate to your factor of interest (potential biomarkers or collectively a "combination biomarker" or "biomarker signature") and one can use the model to predict information about new samples from the profiles alone, typically done to validate to the model. PR has played a particularly large role to date in applications where spectral data are analyzed directly prior to peak picking and annotation of metabolites (i.e., "fingerprinting"). The most widely used (and abused) techniques include bi-linear covariance modelling methods such as principal components analysis (PCA) and partial least squares regression (PLS) that are also synonymous with the terms "chemometrics" and "multivariate analysis" (reviewed by Trygg, et al.63). PCA and PLS produce parsimonious descriptions of multivariate data that allow quick visualization of clustering in the most important variables and can to some degree deal with interferences that coincide with signals of interest but vary in an uncorrelated (orthogonal) manner. Other PR techniques familiar across many scientific disciplines have been used in metabolic profiling including but not limited to: hierarchical clustering analysis and K-nearest neighbours;64 density estimation;24'65 self-organizing maps (Kohonen neural networks);66 and genetic algorithms.67'68 The main advantage of PR is that it can quickly extract patterns of response in metabolic profiles that are defined across many metabolites at once and are not straightforward to detect by visual inspection of the raw data. It can also be applied in an unbiased fashion, demonstrating a relationship between the profile as a whole and a factor of interest, rather than selecting just a few peaks that may have associations with the factor by chance. Finally, it is important to note that univariate analyses typically ignore correlations between predictors, whereas PR methods can reveal that many dozens of metabolites may respond to an interference in a common fashion while only one may appear to be significantly associated using standard methods of false discovery correction. Hence, even where we move to more well annotated datasets and away from fingerprinting, PR approaches remain popular and effective. PR approaches such as statistical total correlation spectroscopy (STOCSY) or statistical heterospectroscopy (SHY) can also assist metabolite annotation itself.69-72 These methods highlight the covariance in intensity between different spectral signals to facilitate structure determination. While the benefits of PR are well aligned with the goals of biomarker discovery and a top-down approach to systems biology, other often more basic research into metabolism and "bottom-up" approaches requires more detailed biochemical models that can provide information on flux through defined pathways and predict the behaviour of a living system from molecular parameters. Several groups are also currently seeking to extend established metabolic modelling methods to a genome-wide scale,73-76 sometimes including analytical approaches that can be referred to as "fluxomics."74'77 Metabolic fluxes can

56

BIOMARKERS

be inferred from the model or measured more directly via measurement of isotopomer dynamics after enrichment of specific pathways by an isotopicallylabeled substrate. While these approaches are not usually considered as biomarker discovery strategies, alterations in pathway flux can clearly act as biomarkers and are important considerations for metabolic biomarkers exploited by imaging of isotopologues (e.g., 18FDG-PET and 13C-pyruvate MRI). Identifying target pathways can also be achieved by more informatic approaches to data modelling and particular by the interrogation and statistical integration of multiple omics data. Techniques such as pathway over-representation and gene enrichment analysis can be applied irrespective of whether the entities measured are metabolites or genes. Genome-wide association or quantitative trait loci mapping which exploit simple models of genetic variation have been used to associate genotypes to global metabolic phenotypes in microbes,73,74 plants,78 and mammals,20'79including man. Such systems' biology approaches represent an important area for future development in metabolic profile data analysis.

P R E C L I N I C A L T O X I C O L O G Y : MODELS FOR P A T H O L O G I C A L BIOMARKER DISCOVERY There is a clear need for better translational biomarkers in toxicity studies during drug discovery and development, and metabolic profiling has the potential to address this "biomarker gap" (reviewed in 80, 81). Metabonomics can be readily incorporated from an early stage in preclinical investigation since, providing adequate care is taken with collection procedures, it can utilize samples that are typically generated during routine ADMET studies. It has been shown that metabonomics can reveal otherwise silent lesions, providing information for candidate selection.82 Beyond this, it can provide mechanistic clues that aid risk assessment.83 Much of the early development of metabonomics was in application of biofluid NMR spectroscopy to characterize the biochemical effects of toxicant exposure. Initially, work by Nicholson and co-workers showed that urinary NMR profiles were sensitive reporters of renal damage.9 Subsequent studies examined acute exposure to a number of model nephrotoxins and showed that proximal tubular damage produced aminoaciduria distinct from papilliary toxins that produced higher excretion of renal osmolytes such as betaine and methylamines.28'84 Furthermore, it was demonstrated that these profile differences could be classified by pattern recognition approaches, suggesting the possibility of toxicity screening by metabolic profiling approaches. Toxicity studies provided the opportunity to show that metabonomics is sensitive to a number of other pathologies, including but not limited to: liver steatosis;85 phospholipidosis;13,86 hepatobilliary damage;25 testicular necrosis,87-88 vasculitis;89 hepatic glutathione depletion;43 and pancreatic toxicity.90,91 A number of studies have provided evidence supporting the use of several specific metabolites as toxicity biomarkers. Increases in urinary taurine, observed by NMR-based profiling, has long been proposed as a marker of he-

METABOLIC PROFILING FOR BIOMARKER DISCOVERY

57

patotoxicity.92,93 Taurine is thought to have a protective role against oxidative stress and could reflect alterations to cysteine and glutathione metabolism.94 In a series of related publications, urinary metabolite profiles were shown elevated urinary levels of N-methylnicotinamide and N-mefhyl-4-pyridone3-carboxamide, both end products of the tryptophan-nicotinamide adenine dinucleotide (NAD+) pathway, and shown to correlate to the degree of the peroxisome proliferation induced by a number of PPAR agonists.95 Soga, et al.,43 using a CE-MS platform, shows that serum levels of opthalmic acid, an analogue of glutathione, specifically reflected hepatic glutathione depletion. Bollard, et al. have shown substantial increases in the metabolite betaine, in particular in urine, during liver regeneration using a partial hepatectomy model; betaine is converted to dimethylglycine (DMG) by the enzyme betainehomocysteine methyltransferase, which is largely liver specific. The partial hepatectomy study is one example of the work conducted as part of the Consortium on Metabonomics in Toxicology (COMET) project, an ongoing collaboration between the pharmaceutical industry and Imperial College London (ICL).96 In Phase I of the project (2001-2004) six industrial partners and ICL sought to establish a resource of metabonomic data to help evaluate the technology as applied to preclinical toxicological studies. In total >30,000 samples were analyzed by 'H NMR spectroscopy from 147 studies of exposure to model toxicants and other physiological stressors. Using these data it was possible to demostrate the high level of analytical49 and physiological17 inter-laboratory reproducibility of metabonomic responses using NMR spectroscopy of urine. A number of classification strategies and data analysis tools were explored, culminating in the development of an "expert system" that could match treatments based on the similarity of metabonomic response ("toxin-likeness").24 The second phase of the COMET project aims to focus more on developing metabonomics as a tool in mechanistic toxicology and in particular to understand individual variability in drug response. One of the model compounds investigated was the hepatotoxin galactosamine (galN), known to produce a lesion highly variable in severity.25 Coen, et al. used 'H NMR spectroscopy to explore why glycine protects against toxicity.83 Treatment with glycine alone was found to significantly increase hepatic levels of uridine, UDP-glucose, and UDP-galactose, and in view of the known effects of galactosamine, this suggested that the protective role of glycine against galN toxicity might be mediated by changes in the uridine nucleotide pool rather than by preventing Kupffer cell activation as widely believed. An important extension of earlier work on individual variability in galactosamine toxicity was to consider if baseline metabolic profiles were sensitive to any factors that could explain and/or predict variation in response to drug exposure. This notion—pharmacometabonomics—inspired the study by Clayton, et al., which examined the consequences of acetaminophen (paracetamol) exposure in individual rats.97 Using an intermediate dose level, a range of toxicity was produced together with considerable variation in the metabolic fate of paracetamol. Using NMR spectroscopic data of pre-treatment urines, it could

58

BIOMARKERS

be shown that: a) the paracetamol sulphate-to-glucoronide ratio could be convincingly predicted; b) a correlation was present between the metabolic profile and severity of hepatoxicity. The metabolite signals that allowed the prediction of pharmacokinetics (PK) included some that were likely to derive from glucoronides of dietary species such as cresol, perhaps indicating that such molecules affect endogenous capacity for various metabolic transformations leading to excretion and detoxification. The level of toxicity was correlated to baseline urinary excretion of taurine, trimethlamine-N-oxide (TMAO) and betaine. TMAO is a gut microbial metabolite of choline, which is also converted to betaine. Changes in both these metabolites have been associated with liver toxicity and susceptibility to fatty liver.98 Not only did the study by Clayton, et al. show the viability of pharmacometabonomics, it also suggested that metabolic profiling was a valuable probe for exploring the largely unknown role of gut microbial involvement in modulating each individual's drug metabolism and susceptibility to toxicity. Several other metabolic profiling studies have shown explicitly the effects of gut microbes on mammalian endogenous metabolic profiles99-101 and how they may affect drug toxicity.102

DISEASE BIOMARKER DISCOVERY U S I N G METABOLIC PROFILING Inborn Errors of Metabolism Perhaps the most obvious application for metabolic profiling in the clinical environment is in the diagnosis of inborn errors in metabolism. Since the pioneering work of Robert Guthrie, which led to the introduction of infant screening for phenylketonuria in the 1960s,103 LC-MS/MS (tandem MS) methods were established that by profiling a defined set of organic acids and amino acids could recognize up to 20 metabolic disorders simultaneously in a high-throughput manner.104 More recently, using adapted techniques, it has become possible to measure enzyme activities by reaction monitoring, and thus include the possibility of screening for other conditions such as lysosomal storage disorders.105' 106 Many countries now operate infant screening programs based on LC-MS/MC platforms with a throughput of 4 million samples annually in the U.S. alone. While clearly metabolic profiling of a kind, these screening protocols represent a very targeted and restricted output, measuring only pre-defined biomarkers. The use of less targeted profiling and systematic analysis with a view to biomarker discovery and definition of novel inborn errors of metabolism is also an important application. In this respect, NMR spectroscopy (reviewed in 107) has proved valuable, identifying new conditions such as aminoacylase I deficiency108 and guanidinoacetate methyltransferase deficiency.109 Even though the genetic lesion is incurable and such conditions are rare, screening biomarkers could be very important. In many types of metabolic disease, as in the classic example of phenylketonuria, dietary intervention started within the first month of life can be effective in preventing neurological damage and mental retardation.

METABOLIC PROFILING FOR BIOMARKER DISCOVERY

59

Neuroscience While metabolic profiles are sensitive to metabolic defects that can cause neurological effects, metabolomics has a wider role in characterizing neurological disorders and brain function. Metabolic profiling studies of animal models can clarify the consequences of genetic mutations that lead to neurological disease. For example, Pears, et al.110 characterized the metabolic alterations in brain tissue from a Cln3 knock-out mouse model of Batten's disease one of the neuronal ceroid lipofuscinoses. NMR spectroscopy of tissue from Cln3 null mutant mice was characterized by an increased glutamate concentration and a decrease in gamma-amino butyric acid (GABA) concentration, implying a change in neurotransmitter cycling between glutamate/glutamine and the production of GABA. Tsang, et al.111 investigated the metabolic consequences of Huntington's disease in the R6/2 mouse model using NMR spectroscopy. Global increases in relative brain concentrations of osmolytes, creatine, glutamine, and lactate, and decreases in acetate and N-acetylaspartate were found together with striatal-specific lower concentrations of GABA and choline. Clear differentiation of R6/2 and wild-type mice was also obtained for urine and blood metabolite profiles. The analysis of post-mortem tissue offers a more direct opportunity to discover clinically relevant biomarkers. Lan, et al." 2 used NMR spectroscopy to identify molecular changes in post-mortem brain tissue of patients with a history of bipolar disorder and compared discriminating features to the effect of chronic treatment with the mood-stabilising drugs lithium and valproate. Glutamate levels were increased in the post-mortem bipolar brain, while the glutamate/glutamine ratio was decreased following valproate treatment, and gamma-aminobutyric acid levels were increased after lithium treatment, suggesting that the balance of excitatory/inhibitory neurotransmission is central to the disorder. The level of N-acetyl aspartate, a clinically important metabolic marker of neuronal viability, was found to be unchanged following treatment. These findings show how metabolic profiling can provide new insight into the pathophysiology of bipolar disorder. In terms of direct clinical application, several metabolites can be measured noninvasively by MRS/MRI techniques, and in addition to blood and urine, a more directly relevant biofluid available is cerebrospinal fluid (CSF). Holmes, et al.113 showed that drug-naive or minimally-treated patients with first onset schizophrenia have a different CSF NMR profile compared to health controls (n=152 in total). In the disease group normalization of this profile was observed with treatment, prior to clinical improvement, whereas patients treated after more than one episode did not exhibit normalization, consistent with reports that suggest the efficacy of treatment is improved with earlier intervention.

Cancer It has long been known that tumour cells exhibit common metabolic phenotypes that can be exploited in diagnosis and therapy. For example, one of the manifestations of the Warburg effect, high glucose uptake and glycolysis to lactate, is the basis of 18-fluorodeoxyglucose (FDG) PET imaging of tumours.

60

BIOMARKERS

FDG uptake and accumulation detected by PET is a sensitive and specific noninvasive metabolic marker of cancer, and can be used for diagnosis, staging, prognosis and early detection of drug response."4 Warburg's discovery, dating from the beginning of the last century, has received renewed interest of late as there is growing evidence that the phenomenon, together with other aspects of the tumour metabolic phenotype, is regulated by well-established tumour suppressors and oncogenes such as p53 and hypoxia inducible factor (HIF) (reviewed by 115, 116). Other common manifestations of the tumor metabolic phenotype, such as alterations to choline metabolism, have been detected by NMR spectroscopy.117, " 8 Typically an increase in tissue phophocholine (PC) is observed, which is due in part to increased choline uptake and upregulation of choline kinase activity. A large number of studies have shown this phenomenon to occur across many tumor types, to be detectable noninvasively in vivo and to translate to animal models, although some exceptions have been reported.119 In cell models of tumourigenic progression, increases in PC and the PC:GPC (glycerophosphocholine) ratio have been frequently reported. Importantly, it has been shown that alterations to choline metabolism can preempt tumour formation in morphological normal tissue, discriminating tissue susceptible to tumor formation possibly by detecting "field effects."47 This is one possible route to improving early detection of cancer with diagnostic biomarkers. A key target for metabolic profiling in oncology is a plasma or urine biomarker that can be used for early detection of cancer. Odunsi and co-workers were able to achieve complete distinction between epithelial ovarian cancer patients and healthy controls using NMR spectroscopy.120 In breast cancer, mass spectrometry-based analysis of nucleosides in urine samples from breast cancer patients and healthy volunteers was able to identify patients with a sensitivity and specificity which improved upon current breast cancer biomarkers.121 However, there have been marked failures in the history of molecular profiling biomarkers for detection of cancer, including early metabolic studies. Fossel, et al. reported accurate detection of malignancies using proton NMR spectroscopy of blood plasma, results which were not supported by subsequent studies.123 Tissue-specific metabolic processess are also potentially a source of cancer biomarkers. In the prostate, zinc accumulates to inhibit aconitase and citrate oxidation via the TCA cycle,124 hence citrate accumulates in prostate glandular tissues to extremely high concentrations. Loss of this phenotype, which can be detected ex vivo in biopsy by MAS-NMR and in vivo by MRS, is associated with the presence of prostate cancer and can distinguish between benign and malignant disease.119,125,126 More recently in a high-profile study, Sreekumar, et al.127 identified the metabolite sarcosine as a potentially important metabolic mediator of prostate cancer cell invasion and aggressivity. Sarcosine was highly increased during prostate cancer progression to metastasis and could be detected noninvasively in urine. Reduction in sarcosine availability attenuated prostate cancer cell invasion while increasing availability induced an invasive phenotype in benign prostate epithelial cells.

METABOLIC PROFILING FOR BIOMARKER DISCOVERY

61

Infectious Disease Metabolomics has a role to play in understanding factors that influence pathogenicity and virulence, and to date metabolism is an under-explored facet of the host-pathogen interface. Olszewski, et al.128 highlight the uptake of extracellular arginine by the malarial parasite Plasmodium falciparum and its role proliferation. Bundy, et al.129 show that metabolic profiles were able to distinguish between avirulent strains of Bacillus cereus and clinical isolates from meningitis patients where screening of genomic DNA for the presence of genes encoding known toxins gave no candidate genes that were unambiguously able to distinguish between the two groups. A number of publications have characterized the metabolic consequences of infection in mouse models of human pathogenic parasitic disease including Schistosoma mansoni;130,131 Trypanosoma brucei brucei;132-133 Plasmodium berghei;128,134 and Echinostoma caproni.135 Marked alterations in plasma metabolic profiles have been observed including elevated plasma concentrations of lactate, branched chain amino acids, and acetylglycoprotein fragments.132 Urine from mice infected with P. berghei134 showed increased levels of pipecolic acid (unique to infection with this parasite), and decreased concentrations of TMAO, suggesting a disturbance to gut microbial populations. If these putative biomarkers translate to the clinical setting they may have important consequences for management of therapy. A clinical study by Coen, et al.136 showed that metabolic profiling of CSF could distinguish patients with bacterial or fungal meningitis from patients with viral meningitis and control subjects and clearly distinguished patients with postsurgical ventriculitis from postsurgical control subjects. Early diagnosis and selecting the appropriate treatment for patients with conditions such as meningitis or postsurgical ventriculitis using metabolic profiling could potentially save lives.

Metabolic Syndrome: Insulin Resistance, Cardiovascular Disease, and H y p e r t e n s i o n Perhaps the most active and ambitious area of metabolic profiling research is in the characterization of facets of the metabolic syndrome (MetS), in particular insulin resistance, cardiovascular disease, and hypertension. Diabetes and insulin resistance are obvious cases where we expect diagnositic and prognostic changes in a systemic metabolic profile and this has been extensively explored, not only in animal models,79'98 but also in human populations. For example, Korpela, et al. reported the prognosis of diabetic complications in sera of 613 patients by 'H NMR spectroscopy.66 Self-organising maps (Kohonen neural networks) were used to demonstrate that metabolic profiles correlated to patients with associated renal disease and as a result to patient mortality. Coronary disease is another condition with an expected metabolic phenotype. Brindle, et al.16 showed an NMR-based metabolic signature, in particular an altered lipid profile, associated with the presence and severity of atherosclerosis. GC-MS has also been used to define novel serum biomarkers of heart failure, e.g., pseudouridine.137

62

BIOMARKERS

While the diagnostic value of NMR profiles in coronary disease is still debated,138 a number of prolific studies support the prognostic value of lipid signatures. NMR spectroscopy gives a unique viewpoint into the lipid/lipoprotein particle distribution in blood serum or plasma139-141 that may provide better prognostic markers for coronary events than standard clinical chemistry. For example, in a prospective nested case-control study (n=1061) NMR measurements showed that gemfibrozil treatment significantly reduced LDL particle number and size relative to the placebo group, while conventional measurement of LDL-cholesterol (LDL-C) was not significantly altered.142 LDL-particle number was also significantly prognostic of a coronary event (fatal myocardial infarction or cardiac death) during follow-up in both treated and placebo groups, again where LDL-C was not. However, the most recent and largest study published reported that NMR-based lipoprotein measurements were comparable to, but not better than, standard lipid profile measurements for the prediction of cardiovascular disease in women (n=27,673 with 1015 incidents of cardiovascular events over an 11-year period).143 While disappointing, this study was only in apparently healthy women with a low overall risk; the possibility remains that NMR lipoprotein data could provide a diagnostic or prognostic advantage in other patient groups. As with the use of metabolic profiling for newborn screening, these large clinical studies are targeted to defined biomarkers in profile data analysis. Researchers are now attempting unbiased metabolic profiling for biomarker discovery in uncontrolled human population studies, as shown recently in a study by Holmes of 4630 individuals from 16 sites across four countries, part of the INTERMAP project.21 Two matched 24h urine samples were analyzed by 'H NMR spectroscopy and a range of pattern recognition analyses applied. This work showed that metabolic profiles could be identified that differentiated each population centre and different dietary habits. Importantly, the metabolites responsible for these discriminations, in particular formate, were associated with blood pressure (hypertension) across individuals, a key health indicator, a prognostic factor for coronary disease, and a component of the MetS phenotype.

ENVIRONMENTAL HEALTH AND METABOLIC PROFILING There is a substantial body of work applying metabolic profiling to environmental research (reviewed in detail by Bundy, et al.144), particularly in the area of ecotoxicogenomics. Several studies have shown that toxicant exposure can produce metabolic signatures in "sentinel organisms" (indicator species), both in laboratory experiments and in the field. For example, Bundy, et al.145 reported the effects of heavy metal contamination in earthworms (Lumbricus rubellus) on NMR-based metabolic profiles. Yet such studies have not translated into biomonitoring programs and metabolic profiling studies of human exposure are limited. There is currently a need to improve exposure assessment in order to get a clearer picture of how specific risk factors interact with genotype to produce effects on human health.146 Several studies exploring the potential of

METABOLIC PROFILING FOR BIOMARKER DISCOVERY

63

metabolic profiling for chemical exposure and risk assessment in human populations are underway (e.g., the FP7 Envirogenomarkers consortium) and this is likely to be one of the growth areas for metabolic profiling in years to come.

C O N C L U S I O N : STRENGTHS, WEAKNESSES, A N D T H E W A Y F O R W A R D FOR M E T A B O L I C P R O F I L I N G I N BIOMARKER DISCOVERY Metabolic profiling has several strengths that make it a good complementary tool to the many other approaches to biomarker discovery. Metabolites are easily measured in body fluids, which are more readily obtained than tissue, facilitate measurement of response dynamics, and can simultaneously report on effects to several organs. Metabolites are chemically unique across cell types, species, or physiological states and changes in metabolism frequently represent a functional endpoint that is the closest to phenotype. Metabolism is also often the interface by which an organism interacts with extra-genomic/environmental factors, giving a unique viewpoint into the health effects of diet, gut microbes, and xenobiotic exposure. However, many of these features are also at the root of some of the major challenges in application of metabolic profiling. The majority of metabolites in central metabolism are common across many cell types, making tissue-specific metabolites or biomarker signatures difficult to find and validate. Constant exogenous input from nutrition and gut microbial activity has a major influence on the metabolic profile (in systemic body fluids and most tissues) and thus it is very difficult to disentangle the primary effect of a particular stressor and inevitable secondary effects on dietary intake and gastrointestinal function.147 For many detectable small molecules the distinction between exogenous and endogenous is blurred.148 While genotype is essentially constant for an individual, the metabolome or metabotype is rapidly fluctuating and constantly evolving. Underlying stable features are detected but are not easy to define precisely without several measurements over time. Arguably, determining metabolic flux and not concentrations per se is the key to understanding genotype-phenotype relationships and the role of metabolism in the etiology of chronic disease. However, while rapid progress has been made in the development of genome-wide metabolic models for microbial organisms and single cells, there are no models at present that can adequately explain metabolic profiles at the whole-system level for multiorgan, multigenome communities such as mammals. Reactive flux is only one element of such a hypothetical model; metabolite transport and the integrity of physiological compartments are often not considered in detail but are vital for understanding the causes and consequences of tissue damage and organ failure. While the rapid evolution of metabolic profiling platforms and analytical techniques is paying dividends in improving the coverage of the metabolome, there has historically been little consensus as to common protocols, standards, and reporting structures, with a dearth of metabolic data in public repositories. After a decade of exponential growth, metabolic profiling has not yet yielded a clear biomarker success story with widespread use or defined regulatory approval.

64

BIOMARKERS

Yet the future looks bright for metabolomics and metabonomics. Initiatives to standardize many aspects of metabolic profiling studies are underway as part of the Metabolomic Standards Initiative (MSI)149 and data repositories are being established. The establishment of resources such as the Human Metabolome Database that can act as a portal integrating spectral libraries, data analysis tools, pathway data and literature information will accelerate the growth of a collective knowledge base for the entire community. Investment in systems biology is driving forward the development of more sophisticated models and algorithms for interrogating and integrating molecular profiles from "-omics" technologies. In particular, pathway-driven informatic approaches to identifying drug and biomarker targets will come to the forefront as more substantial public datasets become available. The completion and continued expansion of major population metabolome mapping projects, such as HUSERMET35 and other large cohort studies, will provide a wealth of information on metabolome composition, metabolite distributions, correlations and relationships to basic parameters such as age, gender, weight, diet, and smoking in normal human populations: vital reference data for understanding the potential of putative biomarkers from discovery projects. Moving beyond exploratory laboratory and clinical studies, metabolic profiling is well poised to make a significant impact on our understanding of how an organism's environment, in terms of nutritional, drug, toxicant and microbial exposures, interacts with genotype to modulate health and disease risk at an individual level.

SUMMARY POINTS 1. 2.

3. 4. 5.

Metabolic profiling (metabonomics/metabolomics) is the unbiased characterisation of metabolite content in biological samples. The principal technologies for generating metabolic profiles are NMR spectroscopy, GC- and LC-coupled mass spectrometry, and frequently multivariate pattern recognition techniques and metabolic models are used in data analysis and integration with other "-omics" data. Metabolite analysis is highly translatable from the laboratory to the clinic and metabolic biomarkers can be measured noninvasively in body fluids or by imaging. Metabolic profiling has been demonstrated as a viable approach in uncontrolled human populations on an epidemiological scale. The metabolic phenotype offers a unique viewpoint into how genotype, parasites, drugs, environmental exposures, and diet interact to determine pharmacology, health, and disease.

REFERENCES 1.

Oliver, S. G., Winson, M. K., Kell, D. B., and Baganz F. Systematic Functional Analysis of the Yeast Genome. Trends in Biotechnology. 1998;16(9):373-378.

METABOLIC PROFILING FOR BIOMARKER DISCOVERY 2. 3. 4. 5. 6.

7. 8.

9. 10. 11. 12.

13.

14. 15.

16.

65

Fiehn, O. Combining Genomics, Metabolome Analysis, and Biochemical Modelling to Understand Metabolic Networks. Comparative and Functional Genomics. June 2001;2(3):155-168. Fiehn, O., Kopka, J., Dormann, P., Altmann, T., Trethewey, R. N., and Willmitzer L. Metabolite Profiling for Plant Functional Genomics. Nature Biotechnology. Nov2000;18(ll):1157-1161. Raamsdonk, L. M., Teusink B., and Broadhurst D., et al. A Functional Genomics Strategy that Uses Metabolome Data to Reveal the Phenotype of Silent Mutations. Nature Biotechnology. Jan 2001;19(l):45-50. Tweeddale, H., Notley-McRobb, L., and Ferenci, T. Effect of Slow Growth on Metabolism of Escherichia Coli, as Revealed by Global Metabolite Pool ("Metabolome") Analysis. Journal of Bacteriology. Oct 1998;180(19):5109-5116. Nicholson, J. K., Lindon, J. C , and Holmes, E. Metabonomics: Understanding the Metabolic Responses of Living Systems to Pathophysiological Stimuli via Multivariate Statistical Analysis of Biological NMR Spectroscopic Data. Xenobiotica. Nov 1999; 29(11):1181-1189. Nicholson, J. K. and Lindon, J. C. Systems Biology—Metabonomics. Nature. Oct 23, 2008;455(7216):1054-1056. Nicholson, J. K., Oflynn, M. P., Sadler, P. J., MacLeod, A. F, Juul, S. M., and Sonksen, P. H. Proton-Nuclear-Magnetic-Resonance Studies of Serum, Plasma and Urine from Fasting Normal and Diabetic Subjects. Biochemical Journal. 1984;217(2):365-375. Nicholson J. K., Timbrell J. A., and Sadler P. J. Proton Nmr-Spectra of Urine as Indicators of Renal Damage—Mercury-Induced Nephrotoxicity in Rats. Molecular Pharmacology. 1985;27(6):644-651. Lynch, M. J. and Nicholson, J. K. Proton MRS of Human Prostatic Fluid: Correlations Between Citrate, Spermine, and Myo-inositol Levels and Changes with Disease. Prostate. Mar 1, 1997;30(4):248-255. Moka, D., Vorreuther, R., and Schicha, H., et al. Magic Angle Spinning Proton Nuclear Magnetic Resonance Spectroscopic Analysis of Intact Kidney Tissue Samples. Analytical Communications. Apr 1997;34(4): 107-109. Gavaghan, C. L., Holmes, E., Lenz, E., Wilson, I. D., and Nicholson, J. K. An NMR-Based Metabonomic Approach to Investigate the Biochemical Consequences of Genetic Strain Differences: Application to the C57BL10J and Alpk: ApfCD mouse. Febs Letters. Nov 10, 2000;484(3): 169-174. Nicholls, A. W., Nicholson, J. K., Haselden, J. N., and Waterfield, C. J. A Metabonomic Approach to the Investigation of Drug-Induced Phospholipidosis: An NMR Spectroscopy and Pattern Recognition Study. Biomarkers. Nov/ Dec 2000;5(6):410-423. Espina, J. R., Shockcor, J. P., and Herron, W. J., et al. Detection of In Vivo Biomarkers of Phospholipidosis Using NMR-Based Metabonomic Approaches. Magnetic Resonance in Chemistry. Sep 2001;39(9):559-565. Griffin, J. L., Williams, H. J., Sang, E., Clarke, K., Rae, C , and Nicholson, J. K. Metabolic Profiling of Genetic Disorders: A Multitissue H-l Nuclear Magnetic Resonance Spectroscopic and Pattern Recognition Study into Dystrophic Tissue. Analytical Biochemistry. Jun 1, 2001;293(1):16—21. Brindle, J. T., Antti, H., and Holmes, E., et al., Rapid and Noninvasive Diagnosis of the Presence and Severity of Coronary Heart Disease Using H-l-NMR-Based Metabonomics. Nature Medicine. Dec 2002;8(12): 1439-1444.

66

BIOMARKERS 17. 18. 19. 20. 21. 22. 23. 24.

25.

26. 27. 28.

29.

30. 31. 32.

Beckonert, O., Keun, H. C , and Ebbels, T. M. D., et al. Metabolic Profiling, Metabolomic and Metabonomic Procedures for NMR Spectroscopy of Urine, Plasma, Serum and Tissue Extracts. Nature Protocols. 2007;2(11):2692-2703. Weckwerth, W. Metabolomics: Methods and Protocols. Vol 358. New Jersey: Humana Press;2007. Assfalg, M., Bertini, I., and Colangiuli, D., et al. Evidence of Different Metabolic Phenotypes in Humans. Proceedings of the National Academy of Sciences of the United States ofAmerica. Feb 5, 2008; 105(5): 1420-1424. Gieger, C , Geistlinger, L., and Altmaier, E., et al. Genetics Meets Metabolomics: A Genome-Wide Association Study of Metabolite Profiles in Human Serum. Plos. Genetics. Nov 2008;4(11). Holmes, E., Loo, R. L., and Stamler, J., et al. Human Metabolic Phenotype Diversity and Its Association with Diet and Blood Pressure. Nature. May 15, 2008;453(7193):396-U350. Bollard, M. E., Contel, N. R., and Ebbels, T. M., et al. NMR-Based Metabolic Profiling Identifies Biomarkers of Liver Regeneration Following Partial Hepatectomy in the Rat. J. Proteome Res. Jun 22, 2009. Keun, H. C., Ebbels, T. M. D., and Bollard, M. E., et al. Geometric Trajectory Analysis of Metabolic Responses to Toxicity Can Define Treatment Specific Profiles. Chemical Research in Toxicology. May 2004;17(5):579-587. Ebbels, T. M. D., Keun, H. C., and Beckonert, O. P., et al. Prediction and Classification of Drug Toxicity Using Probabilistic Modeling of Temporal Metabolic Data: The Consortium on Metabonomic Toxicology Screening Approach. Journal of Proteome Research. Nov 2007;6(11):4407-4422. Beckwith-Hall, B. M., Nicholson, J. K., and Nicholls, A. W, et al. Nuclear Magnetic Resonance Spectroscopic and Principal Components Analysis Investigations into Biochemical Effects of Three Model Hepatotoxins. Chemical Research in Toxicology. Apr 1998;ll(4):260-272. Holmes, E., Nicholson, J. K., and Bonner, F. W, et al. Mapping the Biochemical Trajectory of Nephrotoxicity by Pattern-Recognition of Nmr Urinalysis. Nmr. in Biomedicine. Nov/Dec 1992;5(6):368-372. Holmes, E., Bonner, F. W., Gartland, K. P. R., and Nicholson, J. K. Proton Nmr Monitoring of the Onset and Recovery of Experimental Renal Damage. Journal of Pharmaceutical and Biomedical Analysis. 1990;8(8-12):959-962. Anthony, M. L., Sweatman, B. C , Beddell, C. R., Lindon, J. C , and Nicholson, J. K. Pattern-Recognition Classification of the Site of Nephrotoxicity Based on Metabolic Data Derived from Proton Nuclear-Magnetic-Resonance Spectra of Urine. Molecular Pharmacology. Jul 1994;46(1):199-211. Holmes, E., Nicholson, J. K., and Nicholls, A. W, et al. The Identification of Novel Biomarkers of Renal Toxicity Using Automatic Data Reduction Techniques and PCA of Proton NMR Spectra of Urine. Chemometrics and Intelligent Laboratory Systems. Dec 14, 1998;44(l-2):245-255. Dettmer, K., Aronov, P. A., and Hammock, B. D. Mass Spectrometry-based Metabolomics. Mass Spectrometry Reviews. Jan/Feb 2007;26(l):51-78. Kind, T. and Fiehn, O. Metabolomic Database Annotations via Query of Elemental Compositions: Mass Accuracy Is Insufficient Even at Less than 1 ppm. Bmc Bioinformatics. Apr 28, 2006. Theodoridis, G., Gika, H. G., and Wilson, I. D. LC-MS-Based Methodology for Global Metabolite Profiling in Metabonomics/Metabolomics. Trac-Trends in Analytical Chemistry. Mar 2008;27(3):251-260.

METABOLIC PROFILING FOR BIOMARKER DISCOVERY 33.

34.

35. 36. 37. 38. 39. 40. 41. 42. 43.

44. 45. 46. 47. 48.

49.

67

Plumb, R. S. and Wilson, I. D. High Throughput and High Sensitivity LC/MSOA-TOF and UPLC/TOF-MS for the Identification of Biomarkers of Toxicity and Disease Using a Metabonomics Approach. Abstracts of Papers of the American Chemical Society. Aug 22, 2004;228:U164-U164. Plumb, R. S., Johnson, K. A., and Rainville P., et al. UPLIC/MSE; A New Approach for Generating Molecular Fragment Information for Biomarker Structure Elucidation. Rapid Communications in Mass Spectrometry. 2006;20(13): 1989-1994. Zelena, E., Dunn, W. B., and Broadhurst, D. et al., Development of a Robust and Repeatable UPLC-MS Method for the Long-Term Metabolomic Study of Human Serum. Analytical Chemistry. Feb 15, 2009;81(4): 1357-1364. De Vos, R. C. H., Moco, S., Lommen, A., Keurentjes, J. J. B., Bino, R. J., and Hall, R. D. Untargeted Large-Scale Plant Metabolomics Using Liquid Chromatography Coupled to Mass Spectrometry. Nature Protocols. 2007;2(4):778-791. Bennett, B. D., Yuan, J., Kimball, E. H., and Rabinowitz, J. D. Absolute Quantitation of Intracellular Metabolite Concentrations by an Isotope Ratio-Based Approach. Nature Protocols. 2008;3(8):1299-1311. Lisec, J., Schauer, N., Kopka, J., Willmitzer, L., and Fernie, A. R. Gas Chromatography Mass Spectrometry-Based Metabolite Profiling in Plants. Nature Protocols. 2006;l(l):387-396. Fiehn, O. Extending the Breadth of Metabolite Profiling by Gas Chromatography Coupled to Mass Spectrometry. Trac-Trends in Analytical Chemistry. Mar 2008;27(3):261-269. Kopka, J., Schauer, N., and Krueger, S., et al. [email protected]: The Golm Metabolome Database. Bioinformatics. Apr 15, 2005;21(8):1635-1638. Schauer, N., Steinhauser, D., and Strelkov, S., et al. GC-MS Libraries for the Rapid Identification of Metabolites in Complex Biological Samples. Febs Letters. Feb 28, 2005;579(6): 1332-1337. Smith, C. A., O'Maille, G., and Want, E. J., et al. Metlin—A Metabolite Mass Spectral Database. Therapeutic Drug Monitoring. Dec 2005;27(6):747-751. Soga, T., Baran, R., and Suematsu, M., et al. Differential Metabolomics Reveals Ophthalmic Acid as an Oxidative Stress Biomarker Indicating Hepatic Glutathione Consumption. Journal of Biological Chemistry. Jun 16, 2006; 281(24): 16768-16776. Ramautar, R., Somsen, G. W., and de Jong, G. J., Ce-Ms in Metabolomics. Electrophoresis. Jan 2009;30(1):276-291. Dwivedi, P., Wu, P., Klopsch, S. J., Puzon, G. J., Xun, L., and Hill, H. H. Metabolic Profiling by Ion Mobility Mass Spectrometry (IMMS). Metabolomics. Mar2008;4(l):63-80. Kanu, A. B., Dwivedi, P., Tarn, M., Matz, L., and Hill, H. H. Ion Mobility-Mass Spectrometry. Journal of Mass Spectrometry. Jan 2008;43(l):l-22. Backshall, A., Allferez, D., and Telchert, R, et al. Detection of Metabolic Alterations in Non-Tumor Gastrointestinal Tissue of the Apc(Min/+) Mouse by H-1 MAS NMR Spectroscopy. Journal ofProteome Research. Mar 2009;8(3): 1423-1430. Chan, E. C. Y, Koh, P. K., and Mai, M., et al. Metabolic Profiling of Human Colorectal Cancer Using High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR-MAS NMR) Spectroscopy and Gas Chromatography Mass Spectrometry (GC/MS). Journal ofProteome Research. Jan 2009;8(1):352-361. Keun, H. C , Ebbels, T. M. D., and Antti, H., et al. Analytical Reproducibility in H-l NMR-based Metabonomic Urinalysis. Chemical Research in Toxicology. Nov2002;15(ll):1380-1386.

68

BIOMARKERS 50. 51. 52.

53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66.

Teahan, O., Gamble, S., and Holmes, E., et al. Impact of Analytical Bias in Metabonomic Studies of Human Blood Serum and Plasma. Analytical Chemistry. Jul 1, 2006;78(13):4307^318. Keun, H. C , Beckonert, O., and Griffin, J. L., et al, Cryogenic Probe C-13 NMR Spectroscopy of Urine for Metabonomic Studies. Analytical Chemistry. Sep 1, 2002;74(17):4588-4593. Ardenkjaer-Larsen, J. H., Fridlund, B., and Gram, A., et al. Increase in Signalto-Noise Ratio of > 10,000 Times in Liquid-state NMR. Proceedings of the National Academy of Sciences of the United States of America. Sep 2003; 100(18):10158-10163. Day, S. E., Kettunen, M. I., and Gallagher, F. A., et al. Detecting Tumor Response to Treatment Using Hyperpolarized C-13 Magnetic Resonance Imaging and Spectroscopy. Nature Medicine. Nov 2007; 13( 11): 1382-1387. Gallagher, F. A., Kettunen, M. I., and Day, S. E., et al. Magnetic Resonance Imaging of pH in Vivo Using Hyperpolarized C-13-labelled Bicarbonate. Nature. Jun 2008;453(7197):940-U973. Gabellieri, C , Reynolds, S., Lavie, A., Payne, G. S., Leach, M. O., and Eykyn, T. R. Therapeutic Target Metabolism Observed Using Hyperpolarized N-15 Choline. Journal of the American Chemical Society. Apr 2008; 130(14):4598-+. Cui, Q., Lewis, I. A., and Hegeman, A. D., et al. Metabolite Identification via the Madison Metabolomics Consortium Database. Nature Biotechnology. Feb 2008;26(2): 162-164. Wishart, D. S., Knox, C , and Guo, A. C , et al. HMDB: A Knowledgebase for the Human Metabolome. Nucleic Acids Research. Jan 2009;37:D603-D610. Ransohoff, D. F. Bias as a Threat to the Validity of Cancer Molecular-marker Research. Nat Rev Cancer. Feb 2005 ;5(2): 142-149. Benton, H. P., Wong, D. M., Trauger, S. A., and Siuzdak, G. XCMS2: Processing Tandem Mass Spectrometry Data for Metabolite Identification and Structural Characterization. Analytical Chemistry. Aug 15 2008;80(16):6382-6389. Broadhurst, D. I. and Kell, D. B. Statistical Strategies for Avoiding False Discoveries in Metabolomics and Related Experiments. Metabolomics. Dec 2006; 2(4):171-196. Benjamini, Y. and Hochberg, Y. Controlling the False Discovery Rate—A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B-Methodological. 1995;57(l):289-300. Lindon, J. C , Holmes, E., and Nicholson, J. K. Pattern Recognition Methods and Applications in Biomedical Magnetic Resonance. Progress in Nuclear Magnetic Resonance Spectroscopy. Jul 2001 ;39(1): 1-40. Trygg, J., Holmes, E., and Lundstedt, T. Chemometrics in Metabonomics. Journal ofProteome Research. 2007;6(2):469^179. Beckonert, O., Bollard, M. E., and Ebbels, T. M. D, et al. NMR-based Metabonomic Toxicity Classification: Hierarchical Cluster Analysis and K-NearestNeighbour Approaches. Analytica ChimicaActa. Aug 25, 2003;490(l-2):3-15. Ebbels, T., Keun, H., and Beckonert, O., et al. Toxicity Classification From Metabonomic Data Using a Density Superposition Approach: "CLOUDS." Analytica ChimicaActa. Aug 25, 2003;490( 1-2): 109-122. Makinen, V. P., Soininen, P., and Forsblom, C , et al. H-l NMR Metabonomics Approach to the Disease Continuum of Diabetic Complications and Premature Death. Molecular Systems Biology. Feb 2008.

METABOLIC PROFILING FOR BIOMARKER DISCOVERY 67. 68. 69.

70.

71.

72.

73. 74.

75. 76. 77. 78. 79. 80. 81. 82.

69

Goodacre, R. Making Sense of the Metabolome Using Evolutionary Computation: Seeing the Wood with the Trees. Journal of Experimental Botany. Jan 2005;56(410):245-254. Cavill, R., Keun, H. C , Holmes, E., Lindon, J. C , Nicholson, J. K., and Ebbels, T. M. Genetic Algorithms for Simultaneous Variable and Sample Selection in Metabonomics. Bioinformatics. Jan 1, 2009;25(1):112—118. Crockford, D. J., Holmes, E., and Lindon, J. C., et al. Statistical Heterospectroscopy, An Approach to the Integrated Analysis of NMR and UPLC-MS Data Sets: Application in Metabonomic Toxicology Studies. Analytical Chemistry. Jan 15, 2006;78(2): 363-371. Cloarec, O., Dumas, M. E., and Craig, A., et al. Statistical Total Correlation Spectroscopy: An Exploratory Approach for Latent Biomarker Identification From Metabolic H-l NMR Data Sets. Analytical Chemistry. Mar 1, 2005;77(5): 1282-1289. Keun, H. C , Athersuch, T. J., and Beckonert, O., et al. Heteronuclear F-19-H-1 Statistical Total Correlation Spectroscopy as a Tool in Drug Metabolism: Study of Flucloxacillin Biotransformation. Analytical Chemistry. Feb 15, 2008;80(4):1073-1079. Alves, A. C , Rantalainen, M., Holmes, E., Nicholson, J. K., Ebbels, T. M. D. Analytic Properties of Statistical Total Correlation Spectroscopy Based Information Recovery in H-l NMR Metabolic Data Sets. Analytical Chemistry. Mar 15, 2009;81(6):2075-2084. Reed, J. L., Vo, T. D., Schilling, C. H., and Palsson, B. O. An Expanded Genome-scale Model of Escherichia Coli K-12 (iJR904 GSM/GPR). Genome Biology. 2003;4(9). Wiback, S. J., Mahadevan, R., and Palsson, B. O. Using Metabolic Flux Data to Further Constrain the Metabolic Solution Space and Predict Internal Flux Patterns: The Escherichia Coli Spectrum. Biotechnology and Bioengineering. May 5, 2004;86(3):317-331. Wurtele, E. S., Li, J., and Diao, L. X., et al., MetNet: Software to Build and Model the Biogenetic Lattice of Arabidopsis. Comparative and Functional Genomics. Apr 2003;4(2):239-245. Zamboni, N., Kummel, A., and Heinemann, M. anNET: A Tool for Networkembedded Thermodynamic Analysis of Quantitative Metabolome Data. Bmc Bioinformatics. Apr 16, 2008. Cascante, M. and Marin, S., Metabolomics and Fluxomics Approaches. Essays in Biochemistry: Systems Biology, Vol45. 2008;45:67-81. Keurentjes, J. J. B., Fu, J. Y, and de Vos, C. H. R., et al. The Genetics of Plant Metabolism. Nature Genetics. Jul 2006;38(7):842-849. Dumas, M. E., Wilder, S. P., and Bihoreau, M. T., et al. Direct Quantitative Trait Locus Mapping of Mammalian Metabolic Phenotypes in Diabetic and Normoglycemic Rat Models. Nature Genetics. May 2007;39(5):666-672. Keun, H. C. and Athersuch, T. J., Application of Metabonomics in Drug Development. Pharmacogenomics. Jul 2007;8(7):731-741. Coen, M., Holmes, E., Lindon, J. C , and Nicholson, J. K. NMR-based Metabolic Profiling and Metabonomic Approaches to Problems in Molecular Toxicology. Chemical Research in Toxicology. Jan 2008;21(l):9-27. Dieterle, F, Schlotterbeck, G. T., Ross, A., Niederhauser, U., and Senn, H. Application of Metabonomics in a Compound Ranking Study in Early Drug Development Revealing Drug-induced Excretion of Choline into Urine. Chemical Research in Toxicology. Sep 18, 2006; 19(9): 1175-1181.

70

BIOMARKERS 83. 84.

85. 86.

87.

88. 89.

90. 91. 92. 93. 94. 95.

96.

Coen, M., Hong, Y. S., and Clayton, T. A., et al. The Mechanism of Galactosamine Toxicity Revisited; A Metabonomic Study. Journal of Proteome Research. 2007;6(7):2711-2719. Holmes, E., Bonner, F. W., and Sweatman, B. C , et al. Nuclear-MagneticResonance Spectroscopy and Pattern-Recognition Analysis of the Biochemical Processes Associated with the Progression of and Recovery from Nephrotoxic Lesions in the Rat Induced by Mercury(Ii) Chloride and 2-Bromoethanamine. Molecular Pharmacology. Nov 1992;42(5):922-930. Skordi, E., Yap, I. K. S., and Claus, S. P., et al. Analysis of Time-related Metabolic Fluctuations Induced by Ethionine in the Rat. Journal of Proteome Research. Dec2007;6(12):4572-4581. Hasegawa, M., Takenaka, S., Kuwamura, M., Yamate, J., and Tsuyama, S. Urinary Metabolic Fingerprinting for Amiodarone-induced Phospholipidosis in Rats Using FT-ICR MS. Experimental and Toxicologic Pathology. Oct 2007;59(2): 115-120. Nicholson, J. K., Drury, J. E., Timbrell, J. A., Higham, D. P., and Sadler, P. J. Biochemical Effects of Acute Exposure of Cadmium to Rats—Proton-Nmr Spectroscopy of Urine and Correlations with Testicular Damage. Human Toxicology. Mar 1986;5(2):115-116. Ekman, D. R., Keun, H. C , Eads, C. D., Furnish, C. M., Rockett, J. C , and Dix, D. J. Metabolomic Evaluation of Rat Liver and Testis to Characterize the Toxicity of Triazole Fungicides. Metabolomics. Jun 2006;2(2):63-73. Slim, R. M., Robertson, D. G., Albassam, M., Reily, M. D., Robosky, L., Dethloff, L. A., Effect of Dexamefhasone on the Metabonomics Profile Associated with Phosphodiesterase Inhibitor-induced Vascular Lesions in Rats. Toxicology and Applied Pharmacology. Sep 1, 2002;183(2):108-116. Bohus, E., Coen, M., and Keun, H. C , et al. Temporal Metabonomic Modeling of L-arginine-induced Exocrine Pancreatitis. Journal of Proteome Research. Oct 2008;7(10):4435-4445. Bohus, E., Racz, A., and Noszal, B., et al. Metabonomic Investigations into the Global Biochemical Sequelae of Exposure to the Pancreatic Toxin l-cyano-2hydroxy-3-butene in the Rat. Magn. Reson. Chem. Jul 28, 2009. Sanins, S. M., Timbrell, J. A., Elcombe, C , and Nicholson, J. K. Proton Nmr Spectroscopic Studies on the Metabolism and Biochemical Effects of Hydrazine Invivo. Archives of Toxicology. Aug 1992;66(7):489-495. Nicholls, A. W., Holmes, E., and Lindon, J. C , et al. Metabonomic Investigations into Hydrazine Toxicity in the Rat. Chemical Research in Toxicology. Aug 2001;14(8):975-987. Clayton, T. A., Lindon, J. C , and Everett, J. R., et al. An Hypothesis for a Mechanism Underlying Hepatotoxin-induced Hypercreatinuria. Archives of Toxicology. Apr 2003;77(4):208-217. Ringeissen, S., Connor, S. C , and Brown, H. R., et al. Potential Urinary and Plasma Biomarkers of Peroxisome Proliferation in the Rat: Identification of Nmefhylnicotinamide and N-methyl-4-pyridone-3-carboxamide by H-l Nuclear Magnetic Resonance and High Performance Liquid Chromatography. Biomarkers. May 2003;8(3-4):240-271. Lindon, J. C , Keun, H. C , Ebbels, T. M. D., Pearce, J. M. T., Holmes, E., and Nicholson, J. K. The Consortium for Metabonomic Toxicology (COMET): Aims, Activities and Achievements. Pharmacogenomics. Oct 2005;6(7):691-699.

METABOLIC PROFILING FOR BIOMARKER DISCOVERY 97. 98.

99. 100.

101. 102. 103. 104.

105. 106.

107. 108. 109. 110.

111.

71

Clayton, T. A., Lindon, J. C., and Cloarec, O., et al. Pharmaco-metabonomic Phenotyping and Personalized Drug Treatment. Nature. Apr 20, 2006;440(7087):1073-1077. Dumas, M. E., Barton, R. H., and Toye, A., et al. Metabolic Profiling Reveals a Contribution of Gut Microbiota to Fatty Liver Phenotype in Insulin-resistant Mice. Proceedings of the National Academy of Sciences of the United States of America. Aug 15, 2006;103(33):12511-12516. Martin, F. P. J., Sprenger, N., and Yap, I. K. S, et al. Panorganismal Gut Microbiome-Host Metabolic Crosstalk. Journal of Proteome Research. Apr 2009; 8(4):2090-2105. Wikoff, W. R., Anfora, A. T, and Liu, J., et al. Metabolomics Analysis Reveals Large Effects of Gut Microflora on Mammalian Blood Metabolites. Proceedings of the National Academy of Sciences of the United States of America. Mar 10, 2009;106(10):3698-3703. Claus, S. P., Tsang, T. M., and Wang, Y. L., et al. Systemic Multicompartmental Effects of the Gut Microbiome on Mouse Metabolic Phenotypes. Molecular Systems Biology. Oct 2008. Swann, J., Wang, Y, and Abecia, L., et al. Gut Microbiome Modulates the Toxicity of Hydrazine: A Metabonomic Study. Molecular Biosystems. 2009;5(4): 351-355. Guthrie, R. and Susi, A. A. Simple Phenylalanine Method for Detecting Phenylketonuria in Large Populations of Newborn Infants. Pediatrics. Sep 1963;32:338-343. Chace, D. H., Hillman, S. L., Millington, D. S., Kahler, S. G., Adam, B. W., and Levy, H. L. Rapid Diagnosis of Homocystinuria and Other Hypermethioninemias From Newborns' Blood Spots by Tandem Mass Spectrometry. Clin. Chem. Mar 1996;42(3):349-355. Li, Y, Scott, C. R., and Chamoles, N. A., et al. Direct Multiplex Assay of Lysosomal Enzymes in Dried Blood Spots for Newborn Screening. Clin. Chem. Oct 2004;50(10): 1785-1796. Li, Y, Brockmann, K., Turecek, F, Scott, C. R., and Gelb, M. H. Tandem Mass Spectrometry for the Direct Assay of Enzymes in Dried Blood Spots: Application to Newborn Screening for Krabbe Disease. Clin. Chem. Mar 2004;50(3): 638-640. Moolenaar, S. H., Engelke, U. F. H., and Wevers, R. A. Proton Nuclear Magnetic Resonance Spectroscopy of Body Fluids in the Field of Inborn Errors of Metabolism. Annals of Clinical Biochemistry. Jan 2003;40:16-24. Engelke, U. F. H., Sass, J. O., and Van Coster, R. N., et al. NMR Spectroscopy of Aminoacylase 1 Deficiency, A Novel Inborn Error of Metabolism. Nmr in Biomedicine. Feb 2008;21(2): 138-147. Engelke, U. F. H., Tassini, M., and Hayek, J., et al. Guanidinoacetate Methyltransferase (GAMT) Deficiency Diagnosed by Proton NMR Spectroscopy of Body Fluids. Nmr in Biomedicine. Jun 2009;22(5):538-544. Pears, M. R., Cooper, J. D., Mitchison, H. M., Mortishire-Smith R. J., Pearce, D. A., and Griffin, J. L. High Resolution H-l NMR-based Metabolomics Indicates a Neurotransmitter Cycling Deficit in Cerebral Tissue From a Mouse Model of Batten Disease. Journal of Biological Chemistry. Dec 30, 2005;280(52): 42508^2514. Tsang, T. A., Woodman, B., and McLoughlin, G. A., et al. Metabolic Characterization of the R6/2 Transgenic Mouse Model of Huntington's Disease by High-

72

BIOMARKERS

112. 113. 114. 115. 116. 117. 118. 119.

120. 121. 122. 123.

124. 125. 126. 127.

resolution MAS H-l NMR Spectroscopy. Journal of Proteome Research. Mar 2006;5(3):483^192. Lan, M. J., McLoughlin, G. A., and Griffin, J. L., et al. Metabonomic Analysis Identifies Molecular Changes Associated with the Pathophysiology and Drug Treatment of Bipolar Disorder. Molecular Psychiatry. Mar 2009;14(3):269-279. Holmes, E., Tsang, T. M., and Huang, J. T. J., et al. Metabolic Profiling of CSF: Evidence that Early Intervention May Impact on Disease Progression and Outcome in Schizophrenia. Plos Medicine. Aug 2006;3(8):1420-+. Frank, R. and Hargreaves, R. Clinical Biomarkers in Drug Discovery and Development. Nature Reviews Drug Discovery. Jul 2003;2(7):566-580. Kroemer, G. and Pouyssegur, J. Tumor Cell Metabolism: Cancer's Achilles' Heel. Cancer Cell. 2008;13(6):472-482. Vizan, P., Mazurek, S., and Cascante, M. Robust Metabolic Adaptation Underlying Tumor Progression. Metabolomics. 2008;4(1):1-12. Glunde, K., Jacobs, M. A., and Bhujwalla, Z. M. Choline Metabolism in Cancer: Implications for Diagnosis and Therapy. Expert Review of Molecular Diagnostics. 2006;6(6):821-829. Nimmagadda, S., Glunde, K., Pomper, M. G., and Bhujwalla, Z. M. Pharmacodynamic Markers for Choline Kinase Down-regulation in Breast Cancer Cells. Neoplasia. 2009;11(5):477-^184. Teichert, E, Verschoyle, R. D., and Greaves, P., et al. Metabolic Profiling of Transgenic Adenocarcinoma of Mouse Prostate (TRAMP) Tissue by H-l-NMR Analysis: Evidence for Unusual Phospholipid Metabolism. Prostate. Jul 1, 2008;68( 10): 1035-1047. Odunsi, K., Wollman, R. M., and Ambrosone, C. B., et al. Detection of Epithelial Ovarian Cancer Using H-1-NMR-based Metabonomics. International Journal of Cancer. Feb 20, 2005;113(5): 782-788. Frickenschmidt, A., Frohlich, H., and Bullinger, D., et al. Metabonomics in Cancer Diagnosis: Mass Spectrometry-based Profiling of Urinary Nucleosides from Breast Cancer Patients. Biomarkers. 2008;13(4):435^149. Fossel, E. T., Carr, J. M., and McDonagh, J. Detection of Malignant-Tumors— Water-Suppressed Proton Nuclear-Magnetic-Resonance Spectroscopy of Plasma. New England Journal of Medicine. 1986;315(22):1369-1376. Okunieff, P., Zietman, A., and Kahn, J., et al. Lack of Efficacy of WaterSuppressed Proton Nuclear-Magnetic-Resonance Spectroscopy of Plasma for the Detection of Malignant-Tumors. New England Journal of Medicine. 1990;322(14):953-958. Mycielska, M. E., Patel, A., and Rizaner, N., et al. Citrate Transport and Metabolism in Mammalian Cells: Prostate Epithelial Cells and Prostate Cancer. Bioessays. Jan 2009;31(l):10-20. Swanson, M. G.,Zektzer, A. S., and Tabatabai, Z. L., etal. Quantitative Analysis of Prostate Metabolites Using 'H HR-MAS Spectroscopy. Magn. Reson. Med. Jun 2006;55(6): 1257-1264. Swanson, M. G., Vigneron, D. B., and Tabatabai, Z. L., et al. Proton HR-MAS Spectroscopy and Quantitative Pathologic Analysis of MRI/3D-MRSI-targeted Postsurgical Prostate Tissues. Magn. Reson. Med. Nov 2003;50(5):944-954. Sreekumar, A., Poisson, L. M., and Rajendiran, T. M., et al. Metabolomic Profiles Delineate Potential Role for Sarcosine in Prostate Cancer Progression. Nature. Feb 12, 2009;457(7231):910-U176.

METABOLIC PROFILING FOR BIOMARKER DISCOVERY

73

128. Olszewski, K. L., Morrisey, J. M., and Wilinski, D., et al. Host-Parasite Interactions Revealed by Plasmodium falciparum Metabolomics. Cell Host & Microbe. Febl9,2009;5(2):191-199. 129. Bundy, J. G., Willey, T. L., Castell, R. S., Ellar, D. J., and Brindle, K. M. Discrimination of Pathogenic Clinical Isolates and Laboratory Strains of Bacillus Cereus by NMR-based Metabolomic Profiling. Ferns Microbiology Letters. Jan 1,2005;242(1):127-136. 130. Wang, Y. L., Holmes, E., and Nicholson, J. K., et al. Metabonomic Investigations in Mice Infected with Schistosoma Mansoni: An Approach for Biomarker Identification. Proceedings of the National Academy of Sciences of the United States of America. Aug 24, 2004;101(34):12676-12681. 131. Li, J. V., Holmes, E., and Saric, J., et al. Metabolic Profiling of a Schistosoma Mansoni Infection in Mouse Tissues Using Magic Angle Spinning-nuclear Magnetic Resonance Spectroscopy. International Journal for Parasitology. Apr 2009;39(5):547-558. 132. Wang, Y L., Utzinger, J., and Saric, J., et al., Global Metabolic Responses of Mice to Trypanosoma Brucei Brucei Infection. Proceedings of the National Academy of Sciences of the United States ofAmerica. Apr 22, 2008;105(16):6127-6132. 133. Li, J., Saric, J., Wang, Y. L., Utzinger, J., Balmer, O., and Holmes, E. Metabolic Profiling of Co-Infection of Trypanosoma Brucei Brucei Strains in Mice. American Journal of Tropical Medicine and Hygiene. Dec 2008;79(6):104. 134. Li, J. V., Wang, Y., and Saric, J., et al. Global Metabolic Responses of NMRI Mice to an Experimental Plasmodium Berghei Infection. Journal of Proteome Research. Sep 2008;7(9):3948-3956. 135. Saric, J., Li, J. V., and Wang, Y L., et al. Metabolic Profiling of an Echinostoma Caproni Infection in the Mouse for Biomarker Discovery. Plos Neglected Tropical Diseases. Jul 2008;2(7). 136. Coen, M., O'Sullivan, M., Bubb, W. A., Kuchel, P. W., and Sorrell, T. Proton Nuclear Magnetic Resonance-based Metabonomics for Rapid Diagnosis of Meningitis and Ventriculitis. Clinical Infectious Diseases. Dec 1, 2005;41(11): 1582-1590. 137. Dunn, W. B., Broadhurst, D. I., and Deepak, S. M., et al. Serum Metabolomics Reveals Many Novel Metabolic Markers of Heart Failure, Including Pseudouridine and 2-oxoglutarate. Metabolomics. Dec 2007;3(4):413^126. 138. Kirschenlohr, H. L., Griffin, J. L., and Clarke, S. C , et al. Proton NMR Analysis of Plasma is a Weak Predictor of Coronary Artery Disease. Nat. Med. Jun 2006;12(6):705-710. 139. Ala-Korpela, M., Makela, S. M., and Salminen, A., et al. 'H NMR Metabonomics of Serum to Identify and Classsify Lipoprotein Subclass Profiles. Atherosclerosis Supplements. Jun 2007;8(1):39. 140. Ala-Korpela, M., Lankinen, N., and Salminen, A., et al. The Inherent Accuracy of H-l NMR Spectroscopy to Quantify Plasma Lipoproteins is Subclass Dependent. Atherosclerosis. Feb 2007;190(2):352-358. 141. Otvos, J. D., Jeyarajah, E. J., Bennett, D. W., and Krauss, R. M. Development of a Proton Nuclear-Magnetic-Resonance Spectroscopic Method for Determining Plasma-Lipoprotein Concentrations and Subspecies Distributions from a Single, Rapid Measurement. Clinical Chemistry. Sep 1992;38(9): 1632-1638. 142. Otvos, J. D., Collins, D., and Freedman, D. S., et al. Low-density Lipoprotein and High-density Lipoprotein Particle Subclasses Predict Coronary Events and

74

BIOMARKERS

143.

144. 145.

146. 147.

148. 149.

are Favorably Changed by Gemfibrozil Therapy in the Veterans Affairs Highdensity Lipoprotein Intervention Trial. Circulation. Mar 28, 2006;113(12): 1556-1563. Mora, S., Otvos, J. D., Rifai, N., Rosenson, R. S., Buring, J. E., and Ridker, P. M. Lipoprotein Particle Profiles by Nuclear Magnetic Resonance Compared With Standard Lipids and Apolipoproteins in Predicting Incident Cardiovascular Disease in Women. Circulation. Feb 24, 2009;119(7):931-U944. Bundy, J. G., Davey, M. P., and Viant, M. R. Environmental Metabolomics: A Critical Review and Future Perspectives. Metabolomics. Mar 2009;5(1):3-21. Bundy, J. G., Keun, H. C , and Sidhu, J. K., et al. Metabolic Profile Biomarkers of Metal Contamination in a Sentinel Terrestrial Species are Applicable Across Multiple Sites. Environmental Science & Technology. Jun 15, 2007;41(12): 4458^1464. Wild, C. P. Environmental Exposure Measurement in Cancer Epidemiology. Mutagenesis. Mar 2009;24(2): 117-125. Connor, S. C , Wu, W., and Sweatman, B. C , et al. Effects of Feeding and Body Weight Loss on the H-1-NMR-based Urine Metabolic Profiles of Male Wistar Han Rats: Implications for Biomarker Discovery. Biomarkers. Mar 2004;9(2): 156-179. Nicholson, J. K., Holmes, E., Lindon, J. C , and Wilson, I. D. The Challenges of Modeling Mammalian Biocomplexity. Nature Biotechnology. Oct 2004;22(10):1268-1274. Fiehn, O., Robertson, D., and Griffin, J., et al. The Metabolomics Standards Initiative (MSI). Metabolomics. Sep 2007;3(3): 175-178.

CHAPTER

THE BITTERSWEET PROMISE OF GLYCOBIOLOGY Padmaparna Chaudhuri, Rania Harfouche, and Shiladitya Sengupta

INTRODUCTION Glycosylation is one of the most common post-translational modifications in eukaryotes, affecting more than half of all known proteins as well as many lipids (Figure 5.1).1,2 Glycosylation is involved in key developmental roles including cell differentiation and innate immunity and modulating signal transduction. Furthermore, several inherited and non-genetic diseases such as pathogenic infections, immunity, tumor invasion etc. arise as a result of aberrant glycosylation and alterations in their structures.3 As a result, glycomics (the study of sugars in an organism) has become a very active field of research that may lead to new approaches in the diagnosis and prognosis of human diseases. Glycans thus provide an alternative, though they are an under-explored class of cellular biomarkers for diseases, and are attractive targets for broad novel therapeutics. However, the glycome has proven extensively difficult to study, mainly due to its inherent structural (e.g., linear or branched) and chemical (e.g., N- or O-sulfation, epimerization, acetylation) complexities.4 For instance, there are 41 different types of glyco-modifications, comprising 13 different sugar building blocks.5

G L Y C O S Y L A T I O N I N P A T H O L O G I C A L STATES Congenital Disorders of Glycosylation (CDG) In the past decade over 30 genetic disorders have been identified that alter glycan synthesis and structure, ultimately affecting the function of most organs.6 The largest number of these disorders affects N-glycosylation and usu75

76

BIOMARKERS

FIGURE 5.1 The glycome represents the most abundant class of post-translational modifications, yet it is the hardest to decipher

ally leads to hypoglycosylated proteins, resulting in unoccupied glycosylation sites on proteins. Patients with this type of CDG exhibit developmental delays, particularly in the brain.7 Other aberrations include point mutations that create novel glycosylation sites on proteins, causing their misfolding and rapid degradation.6 For example, a mutation that creates an N-linkage site in fibrillin 1 causes Marian Syndrome, an incurable connective tissue disorder.8 Recently, patients with heightened susceptibility to mycobacterial infections were found to carry a pathological point mutation that creates a novel glycosylation site in the interferon receptor IFN7R2.9 Defective O-glycosylation has also been implicated in several CDGs, including congenital muscular dystrophy, Walker-Warburg syndrome being the most severe.6,10 Mutations that affect various stages in glycosaminoglycans (GAG) synthesis also cause several human disease phenotypes such as hereditary multiple exostosis, a benign bone tumor leading to deformity.6'"

Glycomics of Immune Disorders The development and function of the mammalian immune system are largely modulated by the glycome.12 For instance, the immune-cell glycome is altered during cell differentiation, activation, and apoptosis, and these alterations affect homeostatis, leading to various immune diseases.2 Studies that bridge immunology and glycobiology therefore continue to provide new insights into the diagnosis, prognosis, and therapeutic strategies for immune-related disorders. In auto-immune diseases like rheumatoid arthritis (RA), there is a marked increase in the percentage of serum IgG glycans lacking sialic acid and galactose residues. In RA, aggregated agalactosyl glycoforms of IgG (IgG-GO) are specifically recognized by mannose-binding lectin leading to inappropriate

T H E BITTERSWEET PROMISE OF GLYCOBIOLOGY

77

activation of the innate immune system. Abnormally high IgG-GO levels in the serum are also characteristic of other diseases, including Crohn's disease, juvenile onset chronic arthritis, systemic lupus eryfhematosus, and tuberculosis.13 Recent studies analyzing the glycosylation pattern of immunoglobulin Al (IgAl) in patients with IgA nephropathy (IgAN) provided new insights into the autoimmune nature of pathogenesis of this common renal disease. Specifically, aberrant glycosylation of O-linked glycans in IgAl resulted in galactose-deficient IgAl and their subsequent recognition by IgG or IgAl antibodies. The resulting immune complexes are not efficiently cleared by the liver and deposit in the renal mesangium, thus inducing glomerular injury.14 Aberrant glycosylation has also been implicated in other autoimmune diseases. For instance, deficiency in mannoside 3-1,6 N-acetylglucosaminyltransferase V (Mgat5), an enzyme in the N-glycosylation pathway which modifies T-cell receptors, lowers the T-cell activation thresholds by enhancing their clustering, resulting in kidney autoimmune disease, enhanced delayed-type hypersensitivity and increased susceptibility to autoimmune encephalomyelitis.15 The aberrant synthesis of endogenous glycans can result in the exposure of cryptic epitopes that are perceived by the immune system as non-self and induce chronic inflammation. For example deficiency of ct-mannosidase-II blocks N-glycan maturation and increases the expression of physiologically primitive hybrid N-glycans at the cell-surface, which are recognized by endogenous mannose-binding lectin receptors.16 Lastly, abnormal catabolism of glycans can affect the virulence of pathogens. For instance, hyaluronan is a glycan that consists of a disaccharide repeat that modulates innate immunity by interacting with Toll-like receptors (TLRs). Some pathogens express hyaluronidase that cleaves hyaluronan into fragments not recognized by TLR, thereby evading the immune response.17

Glycomics in Cancer The remodeling of cell surface receptors through the modification of their oligosaccharide structures is often associated with malignant cellular transformations and there are currently more than 100 tumor markers which are mainly glycoproteins and glycolipids (Figure 5.2).18,19 In the tumor environment, changes in glycosylation allow neoplastic cells to metastasize by modulating receptor activation, cell adhesion, and motility.19 The crucial importance of the glycome in cancer pathology becomes apparent as aberrant glycosylation occurs in essentially all types of human cancers, as is described in this section. Tumor cells tend to produce increased levels of glycoconjugates containing sialic acid, which is associated with the increased invasive potential of tumor cells and hence translates to poor prognosis. For example hypersialylation of pi integrins in colon and ovarian adenocarcinomas has been shown to lead to a more metastatic phenotype.20 Fucosylation is regarded as another important post-translational modification and is increased dramatically in tumors such as that of the liver, lung, and stomach. Interestingly, the best-known markers for hepatocellular carcinoma and metastatic lung cancer are fucosylated a-fetoprotein and core fucosylated E-cadherin, respectively.21'22

BIOMARKERS

78

FIGURE 5.2

Important glycans implicated in tumor progression.

Several N- and O-glycosylation alterations underlie many neoplastic changes and hence offer excellent potential as tumor markers and therapeutics. As such, one of the most common forms of glycosylation in human tumors is the upregualtion of (31,6-GlcNAc branched N-linked glycans, which lead to integrin and E-cadherin clustering, altered cell-cell and cell-matrix adhesion, and enhanced metastasis.2324 Cancer-related glycosylation changes that occur in prostrate specific antigen (PSA), interestingly, is the best current diagnostic marker for prostrate cancer.25'26 Aberrant O-glycosylation has also been frequently described as a tumor-associated alteration resulting in expression of novel carbohydrate epitopes. Altered mucosal glycosylation of O-linked oncofetal antigens such as Thomsen-Friedenreich (TF) disaccharide and sialyl-Tn have been implicated in epithelial cancers, including colon cancers.27 Underlying mechanisms include increased binding to adhesion receptors such as selectins, thus increasing tumor cell interactions with platelets, leukocytes, and endothelial cells, thus promoting tumor cell survival and metastasis.28 Interestingly, aberrant expression of glycans, especially heparan sulphate proteoglycans (HSPG), has also been implicated in tumor angiogenesis, shed-

T H E BITTERSWEET PROMISE OF GLYCOBIOLOGY

79

ding new light on the angiogenesis hypothesis of cancer.29,30 HSPG on tumor cells act as co-receptors to stabilize growth-factor receptor signaling complexes, thus promoting tumor proliferation and invasion in such diverse cancers as that of the pancreas, breast, ovaries, and liver. In a positive feedback loop, tumor cells upregulate genes that modulate sulfation of cell-surface HSPG, explaining why anti-angiogenic drugs eventually fail in the long run.31 Like proteins, lipid glycosylation is also involved in tumor pathology, playing a major role in tumor growth and metastasis.32 More specifically, colon cancer specimens showed three specific alterations in their glycosphingolipids compositions: increased ratio of acidic type-2 oligosaccharides, a2-3 and/or a2-6 sialylation, and a 1-2 fucosylation, opening the door for novel biomarkers.29

Other Acquired Diseases Altered protein glycosylation is also an attractive tool for noninvasive diagnosis of liver diseases such as cirrhosis. Modifications that continually appear in all liver diseases are hyperfucosylation, increased branching, abnormal sialylation, and a bisecting N-acetylglucosamine.33 Likewise, carbohydrate-deficient transferrin (CDT) is the most commonly used marker of alcoholic liver disease. In fatty liver diseases, aberrant glycosylation of apolipoprotein-B or fucosylation of N-glycans alter biochemical parameters of lipid metabolism in the liver.33

G L Y C A N S I N T H E R A P E U T I C S A N D AS T H E R A P E U T I C TARGETS As we have just described, glycans play a crucial role in differentiation, immunity, and signal transduction. However, owing to their structural complexity, their therapeutic potential has not been fully exploited, with a few notable exceptions. Glycans play an important role in two distinct but related areas of drug development.34 Firstly, as a component of therapeutic biotechnologically derived glycoproteins (e.g., antibodies and erythropoietin), glycans modulate their activity, stability, half-life, and immunogenicity.34 For example, modification of the glycan coat of a protein has led to the second-generation anaemia drug, Darbepoetin, which shows improved therapeutical benefits as compared to traditional therapeutics.35 Secondly, complex glycans that are isolated from natural sources or are chemically synthesized are themselves active pharmaceutical agents. For example, a synthetic version of a truncated heparin oligosaccharide (fondaparinux sodium) has successfully been used for the treatment of thrombosis.34 These advantages of glycan-based therapeutics are due to their more increased stability and ease of formulation as compared with protein-based drugs.34 Additionally, they are highly specific and less immunogenic than other protein or RNA-based therapies. Since the role of glycans in tumor proliferation, metastasis, and angiogenesis is well-known, concerted efforts have been put forward to develop

80

BIOMARKERS

therapeutics which target the following: (a) N-glycosylation; (b) sialylation pathways; (c) HSPG; and (d) chondroitin sulphate proteoglycans (CSPGs).18 For instance, Swainsonine, a plant-derived competitive inhibitor of N-glycan processing in the Golgi, is currently in Phase II trial, whereas heparin is widely used as an antigoagulant in clinics.336 Furthermore, CSPGs overexpression in tumors has been successfully targeted in mouse melanoma models using chondroitin-sulphate-binding cationic liposomes loaded with chemotherapeutics.37 Mucin overexpression in cancer induces rapid cellular growth and survival, making it another attractive target for tumor therapeutics.38 As such, MUC16 antibodies are in Phase II clinical trials, whereas a peptide-based vaccine therapy targeting MUC 1 is undergoing Phase III trials for ovarian and breast carcinomas, respectively.18 Finally, glycans are also potential targets for vaccines against many microbial pathogens such as Leishmania, HIV, and the influenza virus.3'39 Glycan-based vaccines are based on creating an immune response toward the surface glycan antigen of the pathogen.

TOOLS TO ANALYZE THE GLYCOME

Although there has been tremendous progress in elaborating tools to study the glycome in both experimental and clinical settings, this still presents a formidable challenge. This is due to the facts that sugars are present in much lower amounts than proteins in the cells, cannot be template-based amplified like nucleic acids, and have inherent structural and chemical heterogenicities which can yield millions of different modifications to proteins or lipids.40 Glycome analysis falls into four main categories: analytical, chemical, microarray, and molecular, as will be described in the following section.

Analytical Mass spectometry remains the main tool for glycome analysis, due to its high resolution and sensitivity in separating various glycoproteins from a complex mixture including body fluids and organs (Figure 5.3).2,29,41 This method, which enables precise primary structure determination to be achieved, requires first ionizing the test compounds in order to analyze the mass-to-charge ratio of gas phase ions. The two main types of ionizing methods, "soft" versus "hard," refer to the intensity of ionization generated by the spectrometer and is used to determine molecular weight or molecular composition, respectively. Hard ionization methods include fast electron impact (El) and chemical ionization (CI), whereas soft ionization comprises atom bombardment (FAB), electrospraty (ES) and matrix-assisted laser desorption ionization (MALDI), the latter being the most sensitive of all three.42 In order to improve resolution, soft and hard ionizing methods can be combined with each other or with chromatography and enzymatic techniques. A useful chromatography method includes using activated graphitized carbon and C18 chips in order to discriminate between hydrophilic and hydrophobic glycoproteins, respectively.43 This method also has the advantage of resolving glycoprotein

T H E B I T T E R S W E E T PROMISE OF GLYCOBIOLOGY

81

FIGURE 5.3 Analytical techniques for oligosaccharide profiling to distinguish between normal and cancerous cells. (See color insert for a full color version of this figure.)

anomers and isomers. Another method, regularly used in our laboratory, involves analyzing sulfated glycans using reverse polarity capillary electrophoresis, which can reliably yield the sugar signature of cells and zebrafish (Figure 5.4)4445

Chemical In the past few years, various synthetic fluorescent labeling techniques have been developed for molecular imaging of the cellular glycome. This method relies on metabolic labeling of target glycans with an unnatural monosach-

FIGURE 5.4 Capillary electrophoresis quantification of glycosaminogiycans between wild-type (bottom) and heparan sulfate-modifying enzyme-deficient (top) zebrafish.

BIOMARKERS

82

FIGURE 5.5 (A) Bioorthogonal chemical reporting strategy for imaging glycans. (B) Bioorthogonal reactions used to visualize chemical reporters appended to unnatural sugars. (C) Application of the bioorthogonal chemical reporter strategy for in vivo imaging of glycans in zebrafish. (See color insert for a full color version of this figure.)

haride substrate bearing a biologically orthogonal reactive group (such as azide or alkyne), and their subsequent ligation to fluorescent probes (Figure 5.5).46 For instance, in an interesting study, the versatile labeling technique based on Cu(I) catalyzed "click" chemistry between a fluorescent probe and azidomodified fucose was used to visualize protein fucolysation, a postranslational modification involved in metastasis and immunity.47 A similar chemical ligation strategy between alkynyl-glycans and biotin azides was employed to label and capture glycans in order to enrich the glycoprotein fraction of samples, thus facilitating subsequent analyses via mass spectrometry.48 Likewise, glycoprotein enrichment can be achieved by trapping them from biological samples onto synthetic polymers via a transoximization reaction.49 In an elegant study Bertozzi, et al. demonstrated for the first time glycome imaging in a living animal, the zebrafish (Figure 5.5).46M The authors metabolically labelled the zebrafish glycome with unnatural monosaccharide substrate reporters which subsequently fluoresced in the presence of bioorthogonal imaging probes. The ligation strategy used in the study, namely strain-promoted cyclo-addition of monosaccharide azides with fluoro-conjugated cyclooctynes (Cu(I)-free click chemistry), offers the advantages of improved kinetics under physiological conditions and superior analysis of glycan trafficking dynamics in live cells.

Microarray Like nucleic acids and proteins before them, lectins or antibodies have been used to build microarray s capable of monitoring specific glycoprotein-receptor interactions or determining the type of glycoprotein expressed under specific

T H E B I T T E R S W E E T PROMISE OF GLYCOBIOLOGY

83

conditions, respectively (Figure 5.6).2,51,52 Major limitations of this technique, however, are that lectins usually have low affinity for their ligands and glycodirected antibodies are scarce due to the lack of information about selective epitopes. To overcome these limitations, gold nanoparticles have been used to increase lectin binding signals, which can increase the avidity of the system.52 Some researchers are also developing synthetic oligosaccharide microarray chips made up of heparan sulfate or chondroitin sulfate oligosaccharides.53 An added advantage of this technique is that the shorter substrate used (e.g., oligosaccharides vs. lectins or antibodies) renders the arrays more selective, thus offering the most promise for clinical applications.

Molecular Antibodies have long been used to analyze the glycome of tissues by standard immunohistochemistry, although due to the limitation described above, this technique is severely limited.46 For this reason, a more sensitive approach is a modified ELISA (Biotin-NeutrAvidin adhesion assay, BNAA) where various biotinylated glycoproteins are adhered on the reaction plate and then incubated with a specific neutrAvidin-modified receptor in order to decipher the types of sugars this receptor binds.39 This technique shows important therapeutic

FIGURE 5.6

Schematic representation of the multiple modifications present in glycan microarrays.

84

BIOMARKERS

potential, as it helped establish the type of glycoproteins bound by HIV during infection and thus brought researchers a step closer to developing novel HIV therapeutics.39 Due to the glycome's uniquely high degree of complexity, most groups, including ours, lean toward using a combination of tools, rather than a single one, in order to get a more accurate representation of glycomic modifications.45' 54 The same approach should also be taken in clinical settings.

STRENGTHS,WEAKNESSES,ANDTHE FORWARD

ROAD

Due to the global role glycans play in all aspects of physiology, glycomicbased therapies are emerging as a crucial and novel concept to diagnose and target a plethora of diseases. The glycomic field has several advantages that make it well-suited and highly sensitive as a novel disease biomarker. Changes in glycosylation can be more distinct than changes in protein expression and affect many proteins functions. Specific glycans that are not present, or are present in low amounts, in normal state may be upregulated in diseased states. Also, the location of glycans on the cell surface and on the matrix gives them access to most proteins in the body.3 Since many signaling pathways are mutated in cancer and other diseases, targeting a single glycomic event would hamper the function of multiple proteins, translating to more potent and selective therapeutics. The major challenge of glycomic-based therapies lies in the lack of rapid and efficient analysis tools, which reflects the inherent structural and chemical complexities of the glycome, as compared with proteins, lipids, and nucleic acids.40 These limitations could be overcome by a better understanding of all possible glyco-modifications present in physiological and diseased states. To that end, glycoinformatic databases are being put forward to regroup all general knowledge deciphered thus far on the glycome.2,3

CONCLUSION Whereas glycosylation events were once merely thought of as superficial modifications, their crucial roles in all aspects of homeostasis are now clearly established. This has paved the way for using glycans as diagnostic tools and targeted therapeutics. Although the field of glycomics has come a long way, much progress needs to be made based on several criteria, mainly the need for better characterization, quantification, and isolation of glycans.

SUMMARY P O I N T S 1. 2.

Glycosylation of proteins and lipids is essential for their proper functioning. The glycome mediates all aspects of human development and homeostasis.

THE BITTERSWEET PROMISE OF GLYCOBIOLOGY 3. 4.

85

For this reason, alterations in the glycome underlies a plethora of diseases. Identifying and targeting disease glyco-biomarkers, as opposed to simply proteins or lipids, offers more therapeutic potential.

REFERENCES 1. 2. 3.

4. 5. 6. 7. 8.

9. 10. 11. 12 13. 14. 15. 16.

Turnbull, J. E. and Field, R. A. Emerging Glycomics Technologies. Nat. Chem. Biol. February 2007;3(2):741. Haslam, S. M., Julien, S., and Burchell, J. M., et al. Characterizing the Glycome of the Mammalian Immune System. Immunol. Cell Biol. October 2008; 86(7):564-573. Packer, N. H., Von der Lieth, C. W., and Oki-Kinoshita, K. R, et al. Frontiers in Glycomics: Bioinformatics and Biomarkers in Disease. An NIH white paper prepared from discussions by the focus groups at a workshop on the NIH campus, Bethesda, MD (September 11-13, 2006). Proteomics. January 2008;8(l):8-20. Prescher, J. A. and Bertozzi, C. R. Chemical Technologies for Probing Glycans. Cell. September 8, 2006;126(5):851-854. Honorat, M., Mesnier, A., and Di, P. A., et al. Dexamethasone Down-Regulates ABCG2 Expression Levels in Breast Cancer Cells. Biochem. Biophys. Res. Commun. October 24, 2008;375(3):308-314. Freeze, H. H., Genetic Defects in the Human Glycome. Nat. Rev. Genet. July 2006;7(7):537-551. Marquardt, T. and Denecke, J. Congenital Disorders of Glycosylation: Review of Their Molecular Bases, Clinical Presentations and Specific Therapies. Eur. J. Pediatr. June 2003;162(6):359-379. Lonnqvist, L., Karttunen, L., Rantamaki, T., Kielty, C , Raghunath, M., and Peltonen, L. A Point Mutation Creating an Extra N-glycosylation Site in Fibrillin-1 Results in Neonatal Marfan Syndrome. Genomics. September 15, 1996;36(3):468^175. Vogt, G., Chapgier, A., and Yang, K., et al. Gains of Glycosylation Comprise an Unexpectedly Large Group of Pathogenic Mutations. Nat. Genet. July 2005; 37(7):692-700. Van, R. J., Brunner, H. G., and Van, B. H. Glyc-o-Genetics of Walker-Warburg Syndrome. Clin. Genet. 2005 April; 67(4):281-289. Zak, B. M., Crawford, B. E., and Esko, J. D., Hereditary Multiple Exostoses and Heparan Sulfate Polymerization. Biochim. Biophys. Ada. December 19, 2002;1573(3):346-355. Rudd, P. M., Elliott, T., Cresswell, P., Wilson, I. A., and Dwek, R. A. Glycosylation and the Immune System. Science. March 23, 2001;291(5512):2370-2376. Axford, J. S. Glycosylation and Rheumatic Disease. Biochim. Biophys. Ada. October 8, 1999;1455(2-3):219-229. Novak, J., Julian, B. A., Tomana, M., and Mestecky, J. IgA Glycosylation and IgA Immune Complexes in the Pathogenesis of IgA Nephropathy. Semin. Nephrol. January 2008;28(l):78-87. Demetriou, M., Granovsky, M., Quaggin, S., and Dennis, J. W. Negative Regulation of T-Cell Activation and Autoimmunity by Mgat5 N-Glycosylation. Nature. February 8, 2001;409(6821):733-739. Marth, J. D. and Grewal, P. K. Mammalian Glycosylation in Immunity. Nat. Rev. Immunol. November 2008;8(ll):874-887.

86

BIOMARKERS 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28.

29.

30. 31.

32. 33.

Girish, K. S. and Kemparaju, K. The Magic Glue Hyaluronan and Its Eraser Hyaluronidase: A Biological Overview. Life Sci. May 1, 2007;80(21):1921-1943. Fuster, M. M. and Esko, J. D. The Sweet and Sour of Cancer: Glycans as Novel Therapeutic Targets. Nat. Rev. Cancer. July 2005;5(7):526-542. Taniguchi, N., Toward Cancer Biomarker Discovery Using the Glycomics Approach. Proteomics. August 2008;8(16):3205-3208. Christie, D. R., Shaikh, F. M., Lucas, J. A., Lucas, J. A., Ill and Bellis, S. L. ST6Gal-I Expression in Ovarian Cancer Cells Promotes an Invasive Phenotype by Altering Integrin Glycosylation and Function. J. Ovarian Res. 2008;1(1):3. Goldman, R., Ressom, H. W., and Varghese, R. S., et al. Detection of Hepatocellular Carcinoma Using Glycomic Analysis. Clin. Cancer Res. March 1, 2009; 15(5)1808-1813. Geng, E, Shi, B. Z., Yuan, Y. E, and Wu, X. Z. The Expression of Core Fucosylated E-Cadherin in Cancer Cells and Lung Cancer Patients: Prognostic Implications. Cell Res. October 2004; 14(5) :423^33. Zhao, Y Y, Takahashi, M., and Gu, J. G., et al. Functional Roles of N-Glycans in Cell Signaling and Cell Adhesion in Cancer. Cancer Sci. July 2008;99(7): 1304-1310. Zhao, Y, Sato, Y, and Isaji, T, et al. Branched N-Glycans Regulate the Biological Functions of Integrins and Cadherins. FEBS J. May 2008;275(9): 1939-1948. Arnold, J. N., Saldova, R., Hamid, U. M., and Rudd, P. M. Evaluation of the Serum N-Linked Glycome for the Diagnosis of Cancer and Chronic Inflammation. Proteomics. August 2008;8(16):3284-3293. Meany, D. L., Zhang, Z., Sokoll, L. J., Zhang, H., and Chan, D. W. Glycoproteomics for Prostate Cancer Detection: Changes in Serum PSA Glycosylation Patterns. J. Proteome Res. February 2009;8(2):613-619. Campbell, B. J., Yu, L. G., and Rhodes, J. M. Altered Glycosylation in Inflammatory Bowel Disease: A Possible Role in Cancer Development. Glycoconj. J. November 2001;18(ll-12):851-858. Kim, Y J., Borsig, L., Han, H. L., Varki, N. M., and Varki, A. Distinct Selectin Ligands on Colon Carcinoma Mucins can Mediate Pathological Interactions Among Platelets, Leukocytes, and Endothelium. Am. J. Pathol. August 1999; 155(2):461^72. Misonou, Y, Shida, K., and Korekane, H., et al. Comprehensive ClinicoGlycomic Study of 16 Colorectal Cancer Specimens: Elucidation of Aberrant Glycosylation and Its Mechanistic Causes in Colorectal Cancer Cells. J. Proteome Res. April 20, 2009. Fuster, M. M., Wang, L., and Castagnola, J., et al. Genetic Alteration of Endothelial Heparan Sulfate Selectively Inhibits Tumor Angiogenesis. J. Cell Biol. May 7, 2007;177(3):539-549. Chetrite, G., Le, N. E., and Pasqualini, J. R., Human Estrogen Sulfotransferase (hESTl) Activities and Its mRNA in Various Breast Cancer Cell Lines. Effect of the Progestin, Promegestone (R-5020). J. Steroid Biochem. Mol. Biol. September 1998;66(5-6):295-302. Surani, M. A. Reprogramming of Genome Function through Epigenetic Inheritance. Nature. November 1, 2001;414(6859):122-128. Blomme, B., Van, S. C , Callewaert, N., and Van, V. H. Alteration of Protein Glycosylation in Liver Diseases. J. Hepatol. March 2009;50(3):592-603.

THE BITTERSWEET PROMISE OF GLYCOBIOLOGY 34. 35. 36. 37.

38. 39. 40. 41. 42. 43.

44. 45. 46. 47. 48. 49. 50. 51.

87

Shriver, Z., Raguram, S., and Sasisekharan, R. Glycomics: A Pathway to a Class of New and Improved Therapeutics. Nat. Rev. Drug Discov. October 2004; 3(10):863-873. Egrie, J. C , Dwyer, E., Browne, J. K., Hitz, A., and Lykos, M. A., Darbepoetin Alfa has a Longer Circulating Half-Life and Greater In Vivo Potency Than Recombinant Human Erythropoietin. Exp. Hematol. April 2003;31(4):290-299. Goss, P. E., Baptiste, J., Fernandes, B., Baker, M., and Dennis, J. W. A Phase I Study of Swainsonine in Patients with Advanced Malignancies. Cancer Res. March 15, 1994;54(6):1450-1457. Lee, C. M., Tanaka, T., and Murai, T., et al. Novel Chondroitin Sulfate-Binding Cationic Liposomes Loaded with Cisplatin Efficiently Suppress the Local Growth and Liver Metastasis of Tumor Cells in Vivo. Cancer Res. August 1, 2002;62(15):4282^1288. Hollingsworth, M. A. and Swanson, B. J., Mucins in Cancer: Protection and Control of the Cell Surface. Nat. Rev. Cancer. January 2004;4(l):45-60. McReynolds, K. D. and Gervay-Hague, J. Chemotherapeutic Interventions Targeting HIV Interactions with Host-Associated Carbohydrates. Chem. Rev. May 2007;107(5):1533-1552. Raman, R., Raguram, S., Venkataraman, G., Paulson, J. C , and Sasisekharan, R. Glycomics: An Integrated Systems Approach to Structure-Function Relationships of Glycans. Nat. Methods. November 2005;2(11):817-824. Dove, A. The Bittersweet Promise of Glycobiology. Nat. Biotechnol. October 2001;19(10):913-917. Dell, A. and Morris, H. R. Glycoprotein Structure Determination by Mass Spectrometry. Science. March 23, 2O01;291(5512):2351-2356. Alley, W. R., Jr., Mechref, Y., and Novotny, M. V. Use of Activated Graphitized Carbon Chips for Liquid Chromatography/Mass Spectrometric and Tandem Mass Spectrometric Analysis of Tryptic Glycopeptides. Rapid Commun. Mass Spectrom. February 2009;23(4):495-505. Johnson, N. A., Sengupta, S., and Saidi, S. A., et al. Endothelial Cells Preparing to Die by Apoptosis Initiate a Program of Transcriptome and Glycome Regulation. FASEBJ. January 2004;18(1):188-190. Harfouche, R., et al. Glycome and Transcriptome Regulation of Vasculogenesis. 2009;Circulation 120:1883-1892. Laughlin, S. T. and Bertozzi, C. R. Imaging the Glycome. Proc. Natl. Acad. Sci. USA. January 6, 2009;106(l):12-7. Sawa, M., Hsu, T. L., and Itoh, T., et al. Glycoproteomic Probes for Fluorescent Imaging of Fucosylated Glycans In Vivo. Proc. Natl. Acad. Sci. USA. August 15, 2006;103(33):12371-12376. Hanson, S. R., Hsu, T. L., and Weerapana, E., et al. Tailored Glycoproteomics and Glycan Site Mapping Using Saccharide-Selective Bioorthogonal Probes. J. Am. Chem. Soc. June 13, 2007;129(23):7266-7267. Shimaoka, H. and Nishimura, S. I. One-Pot Solid Phase Glycoblotting and Probing by Transoximization for High Throughput Glycomics and Glycoproteomics. Chemistry—A European Journal 13. 2007; 1665-1673. Laughlin, S. T, Baskin, J. M., Amacher, S. L., and Bertozzi, C. R. In Vivo Imaging of Membrane-Associated Glycans in Developing Zebrafish. Science. May 2, 2008;320(5876):664-667. Cummings, R. and Etzler, M. E. Essentials of Glycobiology. Cold Spring Harbor Laboratory Press, 2nd Edition, Chapter 45. 2009.

88

BIOMARKERS 52. 53. 54.

Gao, J., Liu, D., and Wang, Z. Microarray-Based Study of Carbohydrate-Protein Binding by Gold Nanoparticle Probes. Anal. Chem. November 15, 2008; 80(22):8822-8827. de Paz, J. L. and Seeberger, P. H. Deciphering the Glycosaminoglycan Code with the Help of Microarrays. Mol. Biosyst. July 2008;4(7):707-711. Ito, H. and Narimatsu, H. Strategy for Glycoproteomics: Identification If Glyco-Alteration Using Multiple Glycan Profiling Tools. Journal ofProteome Research. 2009;8[3]: 1358-1367.

SECTION II BIOMARKERS OF INJURY/DISEASE

This page intentionally left blank

CHAPTER

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE Walter Maetzler and Daniela Berg

D E F I N I T I O N A N D PREVALENCE OF A L Z H E I M E R ' S A N D P A R K I N S O N ' S DISEASE During the last centuries, the world population has shown a continuous increase in the proportion of elderly subjects and consecutively neurodegenerative diseases occurring primarily with increasing age, such as Alzheimer's disease (AD) and Parkinson's disease (PD). These disorders are accompanied by a large personal, occupational, but also social burden, and the improvement of diagnostic measures to detect early and subtle symptoms to modify the disease course is one of the central challenges of clinicians, scientists, and governments. Interest has not only focused on the phase in which clinical symptoms have appeared (to measure disease progression) but also on the phase in which prevention efforts are expected to have their greatest impact: the preclinical phase, putatively lasting up to decades in both diseases, in which less neurons have degenerated compared to the time of diagnosis.

Alzheimer's Disease AD is the prototypical and, by far, the most common dementia. The diagnosis is based on the criteria of the National Institute of Neurologic and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA), and includes a persistent decline in cognitive function from a previously higher level.1 Memory loss is the principal cognitive deficit, and at least one other cognitive domain must also

91

92

BIOMARKERS

be affected, leading to aphasia (language disturbance), agnosia (failure to recognize people or objects in presence of intact sensory function), apraxia (inability to perform motor acts in presence of intact motor system), and/ or disturbance of executive function (plan, organize, sequence actions, or form abstractions).2 Mild cognitive impairment (MCI) is a preclinical phase of AD during which subjects have measurable cognitive deficits, which are not sufficient to fulfill criteria for any specific dementing disease. However, not all people diagnosed as having MCI will develop AD, pointing to the urgent need of state biomarkers, especially in preclinical and very early disease phases.3 Disease-specific neurodegeneration has been estimated to start about 20 years before the diagnosis can be made.4 At this stage, with a clinical dementia rating (CDR) score of 0.5 ("very mild dementia"), about 60 percent of neurons at specific brain sites (i.e., the layer II entorhinal cortex) are lost.5 Prevalence of AD doubles every five years, beginning with one percent at 60 years of age, and reaching over 30 percent at 85 years of age.6 Incidence rate also increases with age: for people with 65 years of age, the annual risk of developing AD is 0.6 percent, for those older than 85 years it is 8.4 percent.7 The economic burden of caring for patients with dementia exceeds that of more common illnesses like diabetes and arthritis.8

Parkinson's Disease Idiopathic Parkinson disease (PD) is basically a clinicopathologic diagnosis. Clinically, asymmetric manifestation of bradykinesia and at least one of the following symptoms: resting tremor, rigidity, and/or decrease of postural reflexes is observable. At the time when motor symptoms allow the clinical diagnosis, more than 50 percent of the dopaminergic neurons at the SN are lost,9 indicating not only the enormous compensatory capacities of the brain, but also the urgent need to detect subjects in this process before this large amount of neurons is lost. Besides essential tremor, PD is the most common movement disorder and affects up to 200,000 people in Germany, and approximately 500,000 people in the United States.10 Like AD, it is a disease of the elderly, about 1 percent of people older than 50 years suffer from this disorder. Lifetime risk tables report the risk of developing PD to be two percent for men and 1.3 percent for women."

PATHOPHYSIOLOGY AND MECHANISMS Alzheimer's Disease The cause for neurodegeneration in AD is not entirely clear and appears to be multifactorial, with several biochemical processes operating sequentially and/ or in parallel. It is hypothesized that similar or identical pathological lesions are the consequence of multiple environmental (toxic exposure, infection, inflammation) and genetic susceptibility factors.

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE

93

Genetic Aspects Monogenetic defects leading to AD are extremely rare, however, the affected proteins point to an involvement of the amyloid cascade in the AD pafhogenesis. Mutations of the Abeta precursor protein (APP) gene, or mutations in the presenilin 1 or 2 genes result in an increase of the amyloidogenic Abeta peptide (the 42 amino acid version) in the brain.12 There is some evidence that the abnormally phosphorylated tau protein (which is found in neurofibrillary tangles) is a secondary effect from the Abeta deposition. Also, the e4 allele of the apolipoprotein E (ApoE) gene is linked to frequency and onset of AD. ApoE is involved in cholesterol transport in the periphery but, maybe more important, also in central nutritive and pathogenic pathways. Isoform-specific effects on neurite outgrowth, neuronal plasticity, neurotoxicity, lipid peroxidation, oxidative injury, binding to cytoskeletal proteins, and interactions with Abeta including neuritic plaque formation, have been shown.13 Pathology On macroscopic examination, the AD brain appears atrophic with enlarged ventricles and sulci, and overall brain weight is reduced. The deep layers of the temporal cortex and the hippocampus are most severely affected. Most of the clinical features can readily be explained by a loss of cholinergic transmission in cortical brain regions innervated by neurons arising in the nucleus basalis of Meynert. The dopaminergic and serotoninergic neurotransmitter systems are also affected, and their dysfunction may explain, at least partly, many of the non-cognitive symptoms of AD, e.g., mood and motivation disturbances. Intrinsic classical neurotransmitters (e.g., gamma amino butyric acid and glutamate) and cortically localized neuropeptides (e.g., somatostatin and corticotropin releasing factor) are also altered.14 Neuritic plaques and neurofibrillary tangles are the distinguishing microscopic features used in the pathological diagnosis of AD (Figure 6.1).15,16 Neuritic plaques are located extracellularly and are composed of higher-order Abeta fibrils with diameters of up to 10 nm, mostly occurring in radiating, star-like assemblies. They are initially found in cortical areas, and may then distribute to deeper brain areas following a distinct hierarchical sequence.17 Neurofibrillary tangles are abnormal intracellular hyperphosphorylated filaments that form a distinctive paired helical structure (Figure 6.1). They are found throughout the neocortex and limbic nuclei, but are also strongly represented in the basal forebrain and in brainstem structures like the substantia nigra (SN), raphe nuclei, and locus ceruleus. The protein components of neurofibrillary tangles have been identified as microtubules, tau (microtubular associated protein), and ubiquitin. Plaques and tangles may result from destructive processes involving the disruption of microtubule assembly and synaptic loss, rather than its causes. They may contribute to further neuronal damage and disease progression: especially in animal models, the reversal or removal of this amyloid plaque and tangle pathology has been shown to be beneficial on clinical symptoms and may slow disease progression.18,19 The Abeta peptide forms soluble oligomers,

94

BIOMARKERS

FIGURE 6.1 The pathological hallmarks of Alzheimer's disease, i.e., neuritic plaques (Gallays stain, A) and neurofibrillary tangles (Gallays stain, B), and the pathological hallmarks of Parkinson's disease, i.e., alpha-synuclein-positive Lewy bodies (arrows) and Lewy neurites (arrowhead, C). Neuritic plaques are located extracellularly and are composed of Abeta fibrils. They are initially present in cortical areas. Neurofibrillary tangles are abnormal intracellular neuronal hyperphosphorylated filaments. They are found mainly in limbic areas. A major component is tau, a microtubulus-associated protein. Lewy bodies and Lewy neurites are eosinophilic proteinaceous neuronal inclusions which are located primarily in brainstem neurons. All these aggregates are thought to be crucially involved in the pathogenesis of the diseases, but rather not at early stages. By courtesy of Dr Jens Schittenhelm, Institute for Brain Research, University of Tuebingen. (A) and (C) x200, (B) x400. (See color insert for a full color version of this figure.)

which then aggregate into insoluble fibrils. Growing evidence suggests that the oligomeric forms are more toxic than the mature senile plaques and may be more related to the primary degenerating processes.20 Pathophysiological Mechanisms

Environmental exposures: Several epidemiologic studies have examined whether a link exists between aluminium exposure and AD, but the results are conflicting. There is some evidence that monomeric organic aluminium in drinking water may be associated with AD.21 An association was observed between an elevation of iron in the brain and AD, but no significant association was detectable for occupational exposure to lead or mercury, or for mercury from dental amalgams.22 Pesticides, especially organophosphates and carbamates, are known to cause neuronal damage. A significant association was observed between occupational exposure to pesticides in general, and for fumigants and defoliants in particular, and AD.22 There is clear evidence that oxidative stress contributes to AD pathogenesis. At the site of neuritic plaques, expression of inducible nitric oxide synthase (iNOS) is increased, as are oxidative markers.23 Reactive oxygen species (ROS) and reactive nitrogen species (RNS), along with many other substances with free radical character, can react with proteins, lipids, carbohydrates, DNA, and RNA, starting a "vicious cycle" which damages or destroys cells.24 Mitochondria are the main energy-generating organelles of human cells, and they are key players in oxidative stress phenomena as they generate more than 90 percent of the cell's endogenous oxidant species.25 Mitochondrial impairment has been suggested to contribute to AD, as energetic enzymes have been shown to be markedly impaired, and mitochondrial DNA shows

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE

95

abnormally elevated oxidation products in the temporal, parietal, and frontal lobes of the AD brain.26 A deficit of ATP production capacity inside the mitochondria seems to contribute to altered glucose metabolism and tolerance in AD patients.27 Disruption of calcium homeostasis and excitotoxicity—mediated via ionotropic glutamate receptors—are likely determinants of neuronal vulnerability in AD because neurons in brain regions with high Abeta load (entorhinal cortex, hippocampus, inferior parietal cortex) degenerate, whereas neurons in regions with little or no Abeta accumulation (cerebellum, striatum, motor cortex) typically do not.28 Neurons primarily affected in AD typically express high levels of NMDA receptors and have relatively low levels of calciumbinding proteins (like calbindin) compared to resistant neurons.29 Changes in the expression of glutamate receptors may also contribute to altered neuronal Calcium levels in AD, as a significant increase of the NR2A subunit of the NMDA receptor has been shown to occur in subjects with neurofibrillary tangle neuropathology.30 Insufficient Calcium homeostasis may lead to selective neuronal vulnerability in AD through perturbation of the energy metabolism, of antioxidant systems, and neurotrophic factor support.28 A striking feature of neuritic plaques is the presence of activated microglia, cytokines, and complement components, suggestive of a local inflammatory process in AD.31 There is evidence from epidemiological studies that regular consumption of non-steroidal anti-inflammatory drugs (NSAIDs) leads to a reduced risk of AD.32 In line with the observation that NSAID treatment studies with AD patients basically failed to show any benefit, it has been hypothesized that inflammation may be a key player rather at the very early/preclinical stages of AD.33 Diverse inflammation markers are increased in the brain and body fluids of AD, and inflammation inhibitors have been shown to inhibit fibril formation and to facilitate re-entering the cell cycle.33

Parkinson's Disease The cause of PD is unknown. Environmental factors seem to play a major role, as underlined by a large epidemiologic study which found that concordance rates for parkinsonism in monozygotic and dizygotic twins were indistinguishable.34 Genetic Aspects Five to ten percent of cases are directly linked to genetic defects. To date, 13 genetic loci, and nine genes are known to be capable of causing PD in the case of alterations.35 It seems obvious that the molecular pathways identified in these monogenic forms—primarily mitochondrial impairment, protein aggregation, and oxidative stress—may also be implicated in sporadic PD, as some of the proteins involved in monogenic Parkinsonism are also able to cause PD, i.e., solely by dosage effects (e.g., alpha-synuclein through duplication or triplication36).

96

BIOMARKERS

Pathology

PD motor symptoms are primarily related to the degeneration of dopaminergic cells of the pars compacta of the SN.37 However, other areas (e.g., pigmented brainstem nuclei, autonomic nuclei, pyramidal cells in the presupplementary cortex) are also affected. The degenerative process has been suggested to follow a distinct pattern starting in the deep brainstem and reaching high cortical areas within years or decades.38 The pathological hallmark of PD are Lewy bodies, i.e., eosinophilic cytoplasmic proteinaceous inclusions which are located primarily in brainstem neurons, but are also often found in the cortex (Figure 6.1). They consist of a dense core surrounded by a halo of 10 nm wide radiating fibrils. The primary structural component of these fibers is alphasynuclein, a protein physiologically located at the vicinity of pre-terminal synapses. Alpha-synuclein may cause impaired dopamine storage, influences dopamine transport activity, catalyzes the formation of hydrogen peroxide (a product known to damage neuronal membranes), and is very probably directly involved in the protein aggregation process which finally leads to Lewy bodies. This accumulation is associated with neuronal death in animal models.39 Pathophysiological Mechanisms

Environmental factors are heavily debated in PD. A substantial number of epidemiologic studies suggest an association between an exposure to pesticides and PD (see below, for a review see reference 22). This is underscored by pathological data which show increased levels of pesticides in the brains of PD cases versus controls.40 Many epidemiological studies also argue for an increased risk for PD when living in rural areas, working with wood or in other forms of construction, and use of well water. This may be associated with a runoff of pesticides or other environmental contaminants.41 There is evidence from epidemiological studies that the combination of an increased exposure to iron and copper, iron and lead, and iron and manganese (but not iron alone) leads to an increased risk for PD.22 Iron plays a crucial role in the pathogenesis of PD due to its increase in SN neurons and reactive microglia and its capacity to enhance production of reactive oxygen radicals.42 Considering epidemiological and pathological data, conflicting results are observable for the occurrence of other heavy metals, like mercury, copper, aluminium, and manganese, and the risk for PD.22 Oxidative stress has been suggested to play a major role in the pathogenesis of PD. Free radicals react with membrane lipids and lead to lipid peroxidation, membrane injury, and cell death. The dopaminergic system may be especially prone to oxidative stress as dopamine metabolized by oxidation reactions is capable of generating free radicals.43 Because the SN of PD patients contains high iron levels—which facilitates oxidation—and decreased glutathione levels—which protects against free radical formation—nigral cells may be selectively vulnerable to oxidative stress.42 The interaction of dopamine with metal ions may lead to further damage.

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE

97

Mitochondrial respiratory failure appears to be an important contributor to neuronal death in the SN of patients with PD. Complex I deficiency is basically related to this respiratory failure, but it is not clear whether this defect is primary and inherited or secondary to environmental influences. l-Methyl-4phenyl-pyridin (MPTP), a designer drug, causes a clinical syndrome closely resembling PD without relevant accompanying multifocal neurotoxicity. It was industrially developed as a potential herbicide with a structure resembling paraquat—a herbicide also associated with parkinsonism—but was never produced commercially. Rotenone is a further herbicide with a potent inhibitory effect on complex I mitochondrial activity which has the capability to provoke parkinsonism.44 Excitotoxicity is a pathological process with overactivation of NMDA-, AMPA- and/or metabotropic receptors leading to elevated calcium influx into the cell resulting in nerve cells damage by glutamate and similar substances. As mitochondria and the endoplasmic reticulum are the principal cellular calcium sinks, increased calcium influx is able to impair ATP synthesis, to induce free radical formation, and to lead to lipid peroxidation and finally to cell death.45 Excitotoxicity is discussed to be one of the major sources of neurodegeneration in PD,46 which may be underscored by the fact that midbrain dopamiergic neurons are selective autonomic pacemakers which are driven by calcium channels. These cells are especially prone to calcium overload.47 There is growing evidence from post-mortem and in-vivo studies that inflammation contributes to the pathophysiology of PD.48 Activation of glial cells has been consistently found in PD brains. Diverse cellular and molecular events are associated with neuroinflammation, and the final steps are mostly mediated by activated glial and peripheral immune cells. This cellular response to PD-associated neurodegeneration triggers, e.g., oxidative stress and cytokine-receptor-mediated apoptosis, which in turn might provoke disease progression.

C o n c l u d i n g Remarks t o Pathological and Pathophysiological Aspects It is remarkable that in both neurodegenerative diseases, AD and PD, similar pathophysiological processes lead to different clinical symptoms, which progress relentlessly. Only the pathological picture differs in the majority of cases: AD is defined by the occurrence of Abeta plaques and neurofibrillary tangles, whereas PD presents with Lewy bodies. However, about every fifth AD patient has Lewy bodies,49 and the same percentage of PD cases present with AD pathology.50 This may also point toward similarities of the diseases. One of the most strikingly similar aspects, however, is the long preclinical neurodegenerative phase of both disorders which may last at least one decade. This is the time frame when potent neuroprotective therapies may be most successful, and it should be a primary aim to define biomarkers for this phase.

98

BIOMARKERS

C U R R E N T MEANS FOR D I A G N O S I S / P R O G N O S I S OF T H E DISEASES A N D T H E I R LIMITATIONS The diagnostic accuracy of the presence (trait) and the severity (state), (Figure 6.2) of AD and PD is limited, and sensitive and reliable biomarkers that reflect the underlying disease process are urgently needed, as a prerequisite for a more certain diagnosis and the development of novel disease-modifying therapeutic strategies.

Alzheimer's Disease The diagnostic accuracy for AD reaches 80 percent sensitivity and 70 percent specificity, and even in specialized centres the current approach for diagnosing AD leads to misdiagnosis in up to 10 to 15 percent of cases.51 C l i n i c a l Markers

The current approach to the diagnosis is based on clinical aspects, with careful physical and neurologic examinations, and mental status testing to identify the characteristic memory, language, and visuospatial deficits.' In the medical history, potential risk factors like depression, hypertension, heart disease, diabetes, transient ischemic attacks, environmental exposure to toxins (particularly lead), low educational achievement, lack of intellectual and physical activity, and lack of social interaction should be assessed. G e n e t i c Markers

There is no genetic test or a simple blood or urine test that can detect AD. Mutations in the presenilins and the APP gene are able to cause early-onset AD and have strengthened the amyloid hypothesis. However, they are extremely rare and do not play a role in the clinically based diagnostic work-up. Fifty to 70 percent of AD patients have at least one ApoE4 allele, and only 15 to 25 percent of elderly controls. However, for routine diagnosis the accuracy of the ApoE genotype assessment is too low and cannot be recommended.52 In Vivo Markers f r o m Pathology

Definitive diagnosis of AD can only be made by autopsy with appropriate numbers of plaques and tangles determined from specific regions of the brain, in the presence of a clinical history consistent with dementia. Cerebrospinal fluid (CSF) assays for soluble Abeta, total tau, and phosphorylated tau are commercially available and are useful trait markers for the differential diagnosis between AD and non-AD causes of dementia. In AD, tau becomes abnormally phosphorylated, aggregates, and loses its ability to maintain the microtubule tracks. Abeta peptides result from enzymatic breakdown of amyloid precursor protein (APP) by presenilins and display the main part of amyloid deposits in

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE

99

FIGURE 6.2 Markers of Alzheimer's disease and Parkinson's disease. Note that both neurodegenerative diseases develop relatively similan especially in the way that both have a long preclinical phase in which large numbers of neurons are progressively lost, and discrete changes are already detectable. This time frame is the most promising phase in which neuroprotective therapies are supposed to be successful and it should be the primary aim to find state and trait markers for this period. Adapted from reference 98.

AD. According to a longitudinal study with pharmacologically untreated patients up to six years with repeated serial CSF measurements,53 CSF p-tau231 levels may decrease with disease duration, suggesting that phosphorylated tau CSF levels may have the potential to serve as a state marker. In addition, a high CSF total tau/Abeta ratio has been shown to be highly associated with increased risk of conversion from healthy to MCI54 and from MCI to AD.55 This is an example which may stand for future developments: the diagnostic accuracy of a combination of the three pathophysiologically widely independent CSF biomarkers (Abeta, total tau, and phospho tau) is superior to every "single CSF biomarker, by increasing both sensitivity and specificity.56 The best studied candidate biomarker in plasma so far is Abeta (40 and 42), but the findings are contradictory and thus no recommendation concerning the value of plasma Abeta as biomarker can actually be given. Acetyl- and butyrylcholinesterase are basically involved in the cholinergic neurotransmission by inactivating acetylcholine in the synaptic cleft and are suggestive biomarkers for cholinergic degeneration. CSF acetylcholine may be decreased in untreated AD patients, and showed a significant positive correlation with dementia scale scores.57 CSF acetylcholinesterase levels

100

BIOMARKERS

have been shown to be either decreased or unchanged compared to controls (reviewed in reference 58), and may therefore have limited potential to serve as a biomarker. Butyrylcholinesterase is detectable in diverse brain tissues and in the CSF. CSF butyrylcholinesterase levels have been shown to be decreased in AD59 and may correlate negatively with cognitive capacities.60 Pathophysiological Mechanisms

Markers associated with oxidative stress: Elevated homocysteine levels (associated with oxidative stress and excitotoxicity) and elevated low-density lipoprotein cholesterol levels are weakly associated with increased AD risk, but are not useful markers on an individual level (for a review see reference 61). Mitochondrial markers: ApoE is involved in mitochondrial impairment, lipid transport, and cholesterol homeostasis and is attracting a lot of attention in the context of AD, however, as there are conflicting results concerning CSF and serum levels in AD patients compared to controls, ApoE protein level seems not to be a promising marker for AD pathology. Markers associated with excitotoxicity: Tests focusing on excitotoxicity processes are not included in clinically-based diagnostic assessments. Markers associated with inflammation: Despite being increased in affected brain tissue, there is no convincing evidence that levels of molecules like tumor necrosis factor (TNF) or interleukin 1 and 6 change relevantly in body fluids during AD.61 F u r t h e r Diagnostic Assessments

Structural brain imaging, assessment for depression and laboratory testing with particular emphasis on thyroid function and vitamin B12 levels increase diagnostic accuracy.51 Structural MRI helps to exclude secondary dementia due to, e.g., vascular or metabolic pathology. Fluorodeoxyglucose (FDG) PET scanning on average achieves 90 percent sensitivity in identifying AD, although specificity in differentiating AD from other dementias is lower. Clusters of low patterns of cerebral glucose metabolism are typically observable in the posterior cingulate cortex and the precuneus, in the inferior parietal lobule, and the middle temporal gyrus of AD patients. This method may also provide specific and sensitive measure at very early disease stages: In MCI patients with increased risk for AD, reduced patterns of cerebral glucose metabolism were detectable in the posterior cingulate cortex.62,63 Automated analysis algorithms are already available, providing clinicians with z-score maps for metabolic deviation.64

Parkinson's Disease The thorough diagnostic work-up for PD enables correct characterization of about 80 to 90 percent of cases (high sensitivity but lower specificity) and requires clinical skills, but also some subjective clinical experience.

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE

101

C l i n i c a l Markers

A general neurologic examination focuses on the assessment of cardinal PD motor symptoms. Assessment of pyramidal and/or cerebellar function, eye movement, and orthostatic blood pressure determines whether further parkinsonian syndromes should be considered. Medication history will establish likely drug-induced disease, and for information about genetic causes the family history of first degree relatives is assessed. Amelioration of parkinsonism after an acute challenge dose of apomorphine, a short-acting dopamine agonist, or levodopa enhances the likelihood of a long-term diagnosis of PD.65 G e n e t i c Markers

Genetic tests for hereditary subtypes (i.e., PARK1-13) are the ideal biomarkers as they have been proven to correlate to pathopysiology, and to be highly sensitive and specific. However, the number of affected individuals is low. In V i v o Markers f r o m Pathology

Special attention has been placed on a breakdown product of central dopamine, i.e., homovanillic acid. Although CSF levels of homovanillic acid may not correlate with disease severity, the ratio of homovanillic acid/xanthine may significantly differ between patients with mild PD and controls.66,67 2-methyl6,7-dihydroxy-l,2,3,4-tetrahydroisoquinoline (2-MDTIQ), a dopamine derivate with striking similarities to MPTP, has been detected in PD but not in control CSF, and levels correlated negatively with disease duration.68 A decrease of dopamine receptors in lymphocytes has repeatedly been shown, and the decrease of the D3 mRNA expression correlated with the degree of clinical severity in PD patients.69 The development of radioligands permits the study of the dopaminergic system, which is basically affected in PD, these ligands provide powerful tools in the differential diagnostic of PD. Thanks especially to longitudinal studies on the dopamine transporters (e.g., [123I]beta-CIT, [uC]d-threo-methylphenidate-PET) and dopamine metabolism (e.g., [18F] Fluorodopa-PET) it is widely accepted that the maximal deterioration of this neurotransmitter system takes place at early or even premotor disease stages. These ligands are, in addition to clinical evaluation which is prone to bias and therefore of limited use as a "biomarker," the only established markers in PD which serve not only as trait but also as state markers. To separate PD from non-PD (including multiple system atrophy) at early stages, cardiac iodine-123 metaiodobenzylguanidine measures have been recommended.70 This tracer detects defects of the peripheral autonomic system. Lower CSF levels and higher plasma levels of alpha-synuclein have been observed in PD compared to controls, but the overlap was large and there is actually no evidence that CSF or plasma alpha-synuclein levels may have the potential to serve as biomarkers.71,72 Glutathione independent prostaglandin D synthase is very likely to play an important role in both maturation and maintenance of the central nervous system, and has been shown to be altered in CSF of PD patients.73

102

BIOMARKERS

Pathophysiological Mechanisms

There are, to our knowledge, so far no established markers associated with oxidative stress, mitochondrial dysfunction, excitotoxicity, and inflammation, which add any information to the current diagnosis of PD. F u r t h e r Diagnostic Assessments

Especially in younger patients, more extensive biochemical testing is indicated for exclusion of symptomatic parkinsonism, including Wilson disease. MRI is typically normal in PD patients, but should be initially assessed to exclude secondary forms of parkinsonism like normal pressure hydrocephalus and leucencephalopathy.

N O V E L BIOMARKERS The increasing prevalence of AD and PD, accompanied by the diagnostic uncertainties actually evident, motivate the drive to develop new biomarkers to reliably define the diseases and their course particularly at a very early, or even preclinical stages, and to identify the pathology associated with these disorders. A putative model of what a sufficient diagnostic procedure could look like in the future is presented in Figure 6.3. From a pathophysiological point of view, a further issue must be considered: neurodegeneration in AD and PD is very probably multifactorial, whereby several genetic and biochemical processes operate sequentially and/or in parallel, and similar pathological lesions can be the consequence of different causes and pathways. It is therefore inevitable to consider biomarkers which are capable of differentiating between the pathophysiology and/or localization of processes associated with the evolution of AD and PD. Biomarkers of high value must also act as surrogate endpoints for clinical outcomes, filling the gap for objective measurements to test new disease-modifying strategies. It is not always easy to distinguish between a "current" and a "novel" marker, and the classification may be to a certain degree subjective. We decided to include markers which may be most promising to serve as a part of future diagnostic panels.

Alzheimer's Disease C l i n i c a l Markers

The actual research focuses more on preclinical than on clinical symptoms of AD. There are hints that hyposmia, slight neuropsychological defects, and depression may be preclinical markers of AD (Figure 6.3).74-76 This is of particular interest as a combination of such markers may have the potential to serve as a relevant diagnostic markers on an individual level. This is matter of ongoing research. G e n e t i c Markers

In addition to ApoE, numerous putative genetic risk markers like monoamine oxidase A, myeloperoxidase, CYP46A1, ABCA1, alpha-1-antichymotrypsin,

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE

103

FIGURE 6.3 Neurodegenerative diseases such as Alzheimer's (AD) and Parkinson's disease (PD) lead to a progressive loss of abilities and increase of symptoms severity. Actual clinical diagnosis is mainly restricted to delineate patients from healthy subjects (i.e., to define that "something is wrong"), and to check for relevant differential diagnoses. For this purpose, trait markers are used (e.g., clinical parameters in A D and PD, cerebrospinal fluid (CSF) Abeta and tau levels, and neuropsychology in AD). State markers define progress/velocity of the disease course (e.g., CSF phospho tau levels in AD). As both A D and PD have a preclinical stage of more than a decade duration in which neurodegeneration takes place but symptoms are notyetvisible.it is of utmost importance to define both trait (e.g., hyposmia and depression in A D and PD, hyperechogenicrty of the substantia nigra in PD) and state markers, not only for the early clinical phase but also for the preclinical phase.These markers can define people at risk for developing the disease.

and ubiquilin 1 have been proposed for AD, although all suffer inconsistent replication suggesting that modest effect sizes are likely to be the norm (for a review see reference 77). However, it can be assumed that multiple weak genetic factors, together with ApoE4, account for a relevant genetic contribution to late-onset AD risk, and it should be the aim of future research to determine a panel of certain genetic risk factors that, as a whole, has the potential to increase diagnostic accuracy. In Vivo Markers from Pathology Molecular approaches to imaging the Abeta peptide with agents like Pittsburgh Compound B are in particular promising and argue for a preclinical appearance of amyloid deposits,78 but are actually limited to research settings. Furthermore, it is not clear at present whether the diagnostic accuracy of this

104

BIOMARKERS

method might be better than that of the more matured FDG PET as about 20-30 percent of healthy elderly with an age of 70 years also show cortical amyloid deposition, and it is not yet clear whether all of them will develop cognitive deterioration.79 One fundamental advantage of this method is its marker of a pathophysiologically relevant mechanism, making it a promising marker for application in treatment studies to investigate amyloid-modifying strategies. Beta-site APP-cleaving enzyme 1 (BACE1) is another promising biomarker: this protein is a transmembrane aspartyl protease with all the known characteristics of APP beta-secretase. Elevated CSF BACE1 protein levels in MCI were associated with an increased risk to concert to AD, and MCI subjects showed increased levels of BACE1 activity compared to healthy controls and AD patients.80 Thus, BACE1 may be able to detect very early changes, and may therefore serve as a marker for early detection, prediction, and for progression of AD. There is accumulating evidence that plasma concentrations of 24S-hydroxycholesterol reflect a mass of metabolically active neuronal cells. This oxysterol has been used as a marker of brain atrophy in patients with AD. A small fraction is entering the CSF; and CSF levels may reflect the rate of neuronal degeneration.81 Pathophysiological Mechanisms

Markers associated with oxidative stress: CSF levels of the oxidative stress markers 8-hydroxy-2'-deoxyguanosine, a DNA oxidation product, have been shown to be dramatically increased in AD patients, with no overlap to controls.82 In addition, 3-nitrotyrosine and isoprostanes are elevated in the CSF of AD (reviewed in reference 61). Isoprostanes are peroxidation products of arachidonic acid and structural isomers of prostaglandins, and 3-nitrotyrosine formation is a central event in nitrosative stress. An increase of isoprostanes was also found in the CSF of MCI subjects compared with controls, and levels increased over time.83 Thus, CSF isoprostanes and 3-nitrotyrosine levels are likely candidates for trait markers of oxidative stress in AD, and may at least partially dispose the capability to serve as state markers. In AD, CSF levels of (E)-4-hydroxy-2-nonenal (HNE), a neurotoxic product of lipid peroxidation, may be increased and positively correlated to in CSF homocysteine levels.84 Further important antioxidants are the vitamins A, B, C, and E. Serum levels of these vitamins can be influenced by dietary habits, and a low vitamin status may give an indication of the susceptibility of a subject to oxidative damage. Decreased vitamin E levels in AD compared to controls have been shown in the CSF and serum,85 and AD patients may, as a mean, also present with decreased serum levels of vitamins A, B6, B12, and C.61 Mitochondrial markers: Abeta may not only damage cells by the generation of plaques but may also have an influence on mitochondrial function as it has been found inside the mitochondria of AD neurons.86 Vice versa, mitochondrial dysfunction enhances Abeta accumulation in the neuronal cytoplasm.87 These phenomena might contribute to a "vicious cycle" involving

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE

105

amyloid deposition, mitochondrial failure, energetic failure, functional neuronal impairment, and cell death, and CSF Abeta levels may indirectly give an estimate about mitochondrial function. Markers associated with excitotoxicity: Abeta is able to induce calcium influx by inserting into the plasma membrane and forming ion-conducting pores.88 Vice versa, increase of intracellular calcium can cause tau phosphorylation and intracellular Abeta accumulation in neurons.89 Thus, CSF Abeta and phospho tau levels may give indirect information about excititotoxic pathways as they occur in AD. Markers associated with inflammation: To date, serum alpha(l)-antichymotripsin (ACT) concentration is the most convincing marker for CNS inflammation, and higher ACT levels have been observed to correlate positively with cognitive function (reviewed in reference 61). Further Diagnostic Assessments In particular magnetic resonance imaging (MRI) is useful in the diagnosis of presymptomatic and very early clinical symptomatic stages of AD. Even in preclinical stages of AD, significant atrophy of the hippocampal formation can be demonstrated by MRI, and predicts later conversion to AD with about 80 percent accuracy.90 This method may also serve as a useable state marker as annual atrophy rates of 3 to 7 percent have been demonstrated, compared to about 1 percent in healthy elderly (reviewed in reference 91). Automated and rater-independent methods like determination of cortical thickness, deformation-based (DBM) and voxel-based morphometry (VBM) have been shown to be associated with (the development of) AD and seem to have the potential to overcome confounding effects like general brain atrophy that occurs as a part of normal aging.92 A further promising approach of structural MRI is diffusion tensor imaging which detects white matter fiber tract alterations, a process occurring early in the AD disease course.93 Functional MRI (fMRI) allows for the measurement of brain activation during cognitive tasks at a high level of resolution without radiation exposure. In AD, fMRI studies suggest that one of the first factors that might be altered is the integration across neural networks. The changes in functional connectivity have been shown to precede differences in brain activation between the MCI and healthy control group.94 Combination of Markers With a molecular test for blood plasma, 18 signaling proteins were determined which classify AD, and patients who had MCI that progressed to AD, with very high accuracy. These proteins are associated with systemic dysregulation of hematopoiesis, immune responses, apoptosis, and neuronal support.95 This is an example how effective the combination of biomarkers may be to diagnose AD and persons at risk for AD. A combination of not only a specific set of different neurochemical markers in one specific compartment (CSF, blood), but also the combination of parameters obtained from different compartments and methods seems to be

106

BIOMARKERS

the most promising strategy to achieve a more accurate early and differential diagnosis. First examples are given: in MCI studies it has been shown that the combined measurements of the CSF markers Abeta, total tau and phospho tau, and regional cerebral blood flow96 or mediotemporal lobe atrophy97 have higher predictive power than either diagnostic approach alone. Furthermore, such combinations enable a cross-evaluation of the markers.

Parkinson's Disease C l i n i c a l Markers

There is evidence that a number of clinical premotor markers and risk factors exist, e.g., depression, olfactory dysfunction (in more than 50 percent of subjects in the premotor phase), autonomic dysfunction (like constipation, bladder dysfunction, orthostatic hypotension), executive dysfunction, rapid eye movement (REM) sleep behavior disorder (RBD, in more than 30 percent of subjects in the premotor phase), and slight motor signs (like reduced arm swing).98 These markers can be partially assessed by medical history and clinical examination, but there is a lack of accepted recommendations on how to define and use these markers in clinical practise. Currently conducted studies define the value of combinations of these symptoms in the preclinical and early diagnosis of PD (see also Figure 6.2). G e n e t i c Markers

A genetic approach which would be usable for the majority of patients suffering from PD is the determination of disease-associated polymorphisms. Polymorphisms of alpha-synuclein, N-acetyltransferase-2, monoamine oxidase B, glutathione transferase, and the mitochondrial gene tRNAGlu have been suggested to serve as promising targets of PD diagnosis.99' 10° With the advent of high density microarrays, gene expression profiling of human SN pars compacta showed down-regulation of 68, and up-regulation of 69 genes.101 The down-regulated genes referred to pathways including signal transduction, protein degradation (e.g., ubiquitin-proteasome subunits), dopaminergic transmission/metabolism, ion transport, protein modification/phosphorylation, and energy pathways/glycolysis functional classes. The up-regulated genes referred mainly to biological processes involving cell adhesion/cytoskeleton, extracellular matrix components, cell cycle, protein modification/ phosphorylation, protein metabolism, transcription, and inflammation/stress. It is actually possible to screen in families with genetic parkinsonism for mutation carriers who have not yet developed the disease. These persons are ideal candidates to study preclinical aspects of the disease and to define preclinical markers. In Vivo Markers f r o m Pathology

Oligomeric forms of alpha-synuclein may be increased in body fluids and brain extracts.102,103 Early amyloid aggregates like oligomers are most likely

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE

107

the pathogenic aggregation components that drive neurodegeneration and neuronal cell death, rather than mature amyloid fibrils,104 and are detectable in the plasma and CSF of PD patients and controls.102 Although not yet sufficiently investigated, there is great hope that these species may serve as potent biomarkers. Pathophysiological Mechanisms Markers associated with oxidative stress: Two cross-sectional studies found increased homocysteine plasma levels in PD compared to controls, which correlated positively with disease duration.105'106 An increased level of serum uric acid, a natural antioxidant and free radical scavenger, is associated with reduced risk of PD as shown in two independent studies.107,108 In addition, serum uric acid levels were associated with disease progression, making it an attractive predictive trait marker for disease course.107 Another study showed that both CSF levels of oxidized glutathione (but not reduced gluthatione) and vitamin E were reduced in PD patients compared to controls,109 suggesting them to be, in combination with other markers, attractive variables for a comprehensive diagnostic panel. Mitochondrial markers: Complex I and IV activity may be lower in PD patients than in controls, and a negative correlation of complex I and IV activity in platelet mitochondria with disease duration has been shown at very early disease stages.110 Markers associated with excitotoxicity: Glutamate uptake in platelets from PD patients has been shown to be reduced compared to controls by about 50 percent.1" This points to the multisystem character of PD as not only the brain may suffer from excitotoxicity, giving the intriguing perspective to include different compartments and tissue components into a comprehensive diagnostic panel. Markers associated with inflammation: With the advent of 2D gel electrophoresis, different serum levels of nine complement factors were detected between PD and control samples.112 The complement system is part of the non-specific immune system. Osteopontin (OPN) is a molecule with diverse functions including modulation of inflammatory response of microglia. It is detectable in the CSF in much higher levels than in the serum, and higher CSF and serum levels have been detected in PD compared to controls.113 These data give further support to evidence suggesting that the immune system is basically involved in the pathogenesis of PD. Further Diagnostic Assessments Promising MRI protocols focus mainly on the measurement of the iron content in the SN and the basal ganglia. Using a three tesla MRI with a gradient echo sequence, a correlation between clinical (motor) symptoms and iron content of the corresponding SN has been described.114 In PD, functional imaging may be associated rather with cognitive than with motor impairment. Using a mask generated from hypometabolic areas displayed by FDG PET (this mask comprised temporo-parietal and occipital

108

BIOMARKERS

regions), only the cognitive assessment but not the motor score was significantly associated with reduced glucose utilisation.115 Cortical PIB uptake is also strongly associated with dementia in PD.116 Increased echogenicity of the SN, as determined by transcranial sonography (TCS), is characteristic of PD and can help to differentiate PD from atypical parkinsonian syndromes.1 "About 8 to 10 percent of healthy subjects also show this hyperechogenicity of the SN, and there is increasing evidence that these subjects are at increased risk for PD, thus SN hyperechogenicity may be a premotor, or a "vulnerability" marker of PD." 8 Combination of Markers With a proteomics-discovered multianalyte profile (MAP), Zhang and colleagues"9 investigated CSF from patients with probable AD and PD, and from control subjects. Using the best fitting eight proteins, MAP agreed with expert diagnosis for 95 percent of PD, 95 percent of control subjects, and 75 percent of AD. The MAP consisted of the following (in decreasing order of contribution): tau, brain-derived neurotrophic factor (BDNF), interleukin 8, Abeta42, beta2-microglobulin, vitamin D binding protein, ApoAII, and apoE. This result suggests that combinations of proteins are highly effective at identifying PD (and moderately effective at identifying AD), and points to new approaches which combine markers to reach higher predictive power than either diagnostic approach alone. Microarray studies investigating the SN of PD patients underscore the reasonability of such protocols.101

M E T H O D S T O Q U A N T I F Y BIOMARKERS A wide range of methods are in use for the detection of subtle early signs of the two prominent neurodegenerative diseases AD and PD, and to differentiate between AD/PD, and non-AD/non-PD, as well as for prediction and description of disease progression. This includes genetic, clinical, biochemical, and imaging methods. Genetic approaches include sequencing and microarray technique, and especially the analysis of gene expression profiles may have the potential to contribute relevantly to a more sophisticated diagnostic procedure in late-onset AD and PD, as the technique may soon be available broadly and at reasonable prices, and have the advantage to provide an overview about many pathophysiologically relevant processes.101 However, the donor structure of the mRNA has to be determined, but cannot be brain tissue. A promising target is skin tissue as this is relatively easy to obtain, shows histological features comparable to brain tissue,120 and is an ideal donor tissue for stem cell experiments ("induced pluripotent stem cells, IPS"). New methods arising in the biochemical field enable measurement of more than one marker at the same time, i.e., multiplex analyses based on flow cytometry. Here, up to 100 (protein) markers can be detected with a relatively small amount of body fluid. First results are promising." 9 ' m New insights into the pathophysiology of these neurodegenerative diseases have also been pro-

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE

109

vided by results obtained with 2D gel electrophoresis, another method which enables analysis of a large number of proteins. However, methods like ELISA, western blot analysis, mass spectroscopy, high pressure liquid chromatography, electrochemical detection, and gas chromatography are still indispensable for the detection of new marker candidates as they are relatively broadly available, established, and comparably cheap. As a general remark, it must be stated that dealing with biological material has a main drawback compared to genetic (DNA), clinical, and imaging methods: assessment of samples is prone to bias and needs to be strictly standardized and optimized according to, e.g., time elapse from probe supply to freezing. Pre-analytical variability must be minimized with standardized procedures. Freezing period also influences some protein levels and is a relatively often neglected confounder in data analyses. Imaging methods may also have a great potential to refine diagnostic accuracy, especially as state markers, as they are noninvasive and can be repeatedly performed. MRI sequences enable, among others, detection of fiber tract pathologies (diffusion tensor imaging), composition of tissue (magnetic resonance imaging, gradient echo sequencing), and imaging of functional networks (functional MRI), but to date there is a lack of automated protocols which can be used in large cohorts. Radioligands and PET/SPECT provide, e.g., insights into metabolism (glucose utilisation) and pathophysiology (cholinergic system, amyloid deposition). The main drawback of these methods is that they are relatively cost-intensive and are not broadly available.

CONCLUSIONS Both neurodegenerative diseases, AD and PD, have a preclinical period of an undefined number of years, and a relentlessly progressive course. Both are not causatively treatable and suffer from a diagnostic procedure that is far from perfect. Efforts to identify biomarkers to assist with the early and differential diagnosis, as well as progression markers, are urgently needed, especially as neuroprotective therapies will hopefully soon be available. There exists a long list of promising candidate genes, proteins, and other biomaterials, and regions of interest, and there is broad acceptance that a single biomarker may not be adequate to image disease pathophysiology and progression. A panel of well-characterized biomarkers covering different pathophysiological aspects should be defined and validated in well-designed prospective studies, with the aim to serve for better diagnostic accuracy for all affected subjects and for subjects at risk.

SUMMARY POINTS 1.

Alzheimer's (AD) and Parkinson's disease (PD) are the two most common neurodegenerative diseases, and there is a strong correlation between age and the diseases' prevalence. Personal and societal burden is enormous.

110

BIOMARKERS

2.

3.

4.

5.

The pathophysiological mechanisms involved in AD and PD are widely comparable, and include genetic disposition (including monogenic forms), changes in neurotransmitter systems (mainly cholinergic in the former, dopaminergic in the latter disease), protein aggregation and dysfunction of elimination, oxidative stress, mitochondrial impairment (in particular in PD), and (chronic) inflammation processes. Current means for diagnosis of AD and PD under ideal conditions do only reach an accuracy of 80 to 90 percent and include an extensive medical history, neurological examination, exclusion of secondary forms of dementia/parkinsonism via imaging like MRI, and laboratory parameters (e.g., vitamin B12, folic acid, thyroid hormones). Up to now, most studies have emphasized discovery, characterization, and validation of several highly promising individual biomarkers, but their impact on different disease stages has hardly been extensively investigated. One of the primary goals of future studies on biomarkers of AD and PD should therefore be the evaluation and validation of given markers according to their impact on (a) diagnosis of subjects at risk (preclinical period), (b) differential diagnosis at early clinical stages, and (c) predication and description of disease course. Future research should also focus on the development and validation of cost-effective and broadly available high-throughput technologies for biomarker quantitation, as this seems the only way to come across the needs of highly accurate diagnosis and sufficient supervision of therapeutic strategies.

REFERENCES 1.

2. 3. 4. 5.

6. 7.

McKhann, G., Drachman, D., Folstein, M, Katzman, R., Price, D., and Stadlan, E. M. Clinical Diagnosis of Alzheimer's Disease: Report of the NINCDSADRDA Work Group Under the Auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology. Jul 1984;34(7): 939-944. Talwalker, S. The Cardinal Features of Cognitive and Noncognitive Dysfunction and the Differential Efficacy of Tacrine in Alzheimer's Disease Patients. J. Biopharm. Stat. Nov 1996;6(4):443-456. Chong, M. S. and Sahadevan, S. Preclinical Alzheimer's Disease: Diagnosis and Prediction of Progression. Lancet Neurol. Sep 2005;4(9):576-579. Blennow, K., de Leon, M. J., and Zetterberg, H. Alzheimer's Disease. Lancet. Jul 29, 2006;368(9533):387^103. Gomez-Isla, T, Price, J. L., McKeel, D. W., Jr., Morris, J. C, Growdon, J. H., and Hyman, B. T. Profound Loss of Layer II Entorhinal Cortex Neurons Occurs in Very Mild Alzheimer's Disease. J. Neurosci. Jul 15, 1996;16(14): 4491^500. White, L. R., Cartwright, W. S., Cornoni-Huntley, J., and Brock, D. B. Geriatric Epidemiology. Annu. Rev. Gerontol. Geriatr. 1986;6:215-311. Hebert, L. E., Scherr, P. A., and Beckett, L. A., et al. Age-Specific Incidence of Alzheimer's Disease in a Community Population. Jama. May 3, 1995; 273(17):1354-1359.

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE 8. 9.

10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25.

26.

111

Hebert, L. E., Scherr, P. A., Bienias, J. L., Bennett, D. A., and Evans, D. A. Alzheimer Disease in the U.S. Population: Prevalence Estimates Using the 2000 Census. Arch. Neurol. Aug 2003;60(8): 1119-1122. Bernheimer, H., Birkmayer, W., Hornykiewicz, O., Jellinger, K., and Seitelberger, F. Brain Dopamine and the Syndromes of Parkinson and Huntington. Clinical, Morphological and Neurochemical Correlations. J. Neurol. Sci. Dec 1973;20(4):415^55. Tanner, C. M., and Aston, D. A. Epidemiology of Parkinson's Disease and Akinetic Syndromes. Curr. Opin. Neurol. Aug 2000;13(4):427^30. Elbaz, A., Bower, J. H., and Maraganore, D. M., et al. Risk Tables for Parkinsonism and Parkinson's Disease. J. Clin. Epidemiol. Jan 2002;55(1):25-31. Hardy, J. and Selkoe, D. J. The Amyloid Hypothesis of Alzheimer's Disease: Progress and Problems on the Road to Therapeutics. Science. Jul 19, 2002; 297(5580):353-356. Fagan, A. M. and Holtzman, D. M. Astrocyte Lipoproteins, Effects of apoE on Neuronal Function, and Role of ApoE in Amyloid-Beta Deposition In Vivo. Microsc. Res. Tech. Aug 15, 2000;50(4):297-304. Lanari, A., Amenta, F., Silvestrelli, G., Tomassoni, D., and Parnetti, L. Neurotransmitter Deficits in Behavioural and Psychological Symptoms of Alzheimer's Disease. Mech. Ageing Dev. Feb 2006;127(2): 158-165. Thai, D. R., Rub, U., and Schultz, C , et al. Sequence of Abeta-Protein Deposition in the Human Medial Temporal Lobe. J. Neuropathol. Exp. Neurol. Aug 2000;59(8):733-748. Braak, H. and Braak, E. Neuropathological Stageing of Alzheimer-Related Changes. Ada Neuropathol. (Berl). 1991;82(4):239-259. Thai, D. R., Rub, U., Orantes, M., and Braak, H. Phases of a Beta-Deposition in the Human Brain and Its Relevance for the Development of AD. Neurology. Jun 25,2002;58(12):1791-1800. Taylor, J. P., Hardy, J., and Fischbeck, K. H. Toxic Proteins in Neurodegenerative Disease. Science. Jun 14, 2002;296(5575):1991-1995. Lewis, J., Dickson, D. W., and Lin, W. L., et al. Enhanced Neurofibrillary Degeneration in Transgenic Mice Expressing Mutant Tau and APR Science. Aug 24, 2001 ;293(5534): 1487-1491. Walsh, D. M. and Selkoe, D. J. A Beta Oligomers—A Decade of Discovery. J. Neurochem. Jun 2007;101(5):1172-1184. Gauthier, E., Fortier, I., Courchesne, R, Pepin, P., Mortimer, J., and Gauvreau, D. Aluminum Forms in Drinking Water and Risk of Alzheimer's Disease. Environ. Res. Nov 2000;84(3):234-246. Brown, R. C , Lockwood, A. H., and Sonawane, B. R. Neurodegenerative Diseases: An Overview of Environmental Risk Factors. Environ. Health Perspect. Sep2005;113(9):1250-1256. Floyd, R. A. Antioxidants, Oxidative Stress, and Degenerative Neurological Disorders. Proc. Soc. Exp. Biol. Med. Dec 1999;222(3):236-245. Hoyer, S. Models of Alzheimer's Disease: Cellular and Molecular Aspects. J. Neural. Transm. Suppl. 1997;49:11-21. Ames, B. N., Elson-Schwab, I., and Silver, E. A. High-Dose Vitamin Therapy Stimulates Variant Enzymes with Decreased Coenzyme Binding Affinity (Increased K(M)): Relevance to Genetic Disease and Polymorphisms. Am. J. Clin. Nutr. Apr 2002;75(4):616-658. Gabbita, S. P., Lovell, M. A., and Markesbery, W. R. Increased Nuclear DNA Oxidation in the Brain in Alzheimer's Disease. J. Neurochem. Nov 1998;71(5):2034-2040.

112

BIOMARKERS 27. 28. 29. 30.

31.

32. 33.

34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45.

Vanhanen, M. and Soininen, H. Glucose Intolerance, Cognitive Impairment and Alzheimer's Disease. Curr. Opin. Neurol. Dec 1998;ll(6):673-677. Mattson, M. P. and Magnus, T. Aging and Neuronal Vulnerability. Nat. Rev. Neurosci. Apr 2006;7(4):278-294. Spat, A., Szanda, G., Csordas, G., and Hajnoczky, G. High- and Low-CalciumDependent Mechanisms of Mitochondrial Calcium Signalling. Cell Calcium. Jul2008;44(l):51-63. Mishizen-Eberz, A. J., Rissman, R. A., Carter, T. L., Ikonomovic, M. D., Wolfe, B. B., and Armstrong, D. M. Biochemical and Molecular Studies of NMDA Receptor Subunits NR1/2A/2B in Hippocampal Subregions Throughout Progression of Alzheimer's Disease Pathology. Neurobiol. Dis. Feb 2004;15(l):8O-92. Butterfield, D. A., Griffin, S., Munch, G., and Pasinetti, G. M. Amyloid BetaPeptide and Amyloid Pathology are Central to the Oxidative Stress and Inflammatory Cascades Under Which Alzheimer's Disease Brain Exists. J. Alzheimers Dis. Jun 2002;4(3): 193-201. McGeer, P. L., Schulzer, M., and McGeer, E. G. Arthritis and Anti-Inflammatory Agents as Possible Protective Factors for Alzheimer's Disease: A Review of 17 Epidemiologic Studies. Neurology. Aug 1996;47(2):425-432. Pasinetti, G. M. The Role of Inflammation in Alzheimer's Disease Neuropathology and Clinical Dementia. From Epidemiology to Treatment. In: Beal, M. F., Lang, A. E., Ludolph, A. C, Eds Neurodegenerative Diseases. Cambridge: Cambridge University Press;2005:166-175. Tanner, C. M., Ottman, R., and Goldman, S. M., et al. Parkinson Disease in Twins: An Etiologic Study. Jama. Jan 27, 1999;281(4):341-346. Lesage, S. and Brice, A. Parkinson's Disease: From Monogenic Forms to Genetic Susceptibility Factors. Hum. Mol. Genet. Apr 15, 2009;18(Rl):R48-59. Singleton, A. B., Farrer, M., and Johnson, J., et al., Alpha-Synuclein Locus Triplication Causes Parkinson's Disease. Science. Oct 31, 2003;302(5646):841. Fearnley, J. M. and Lees, A. J. Striatonigral Degeneration. A Clinicopathological Study. Brain. Dec 1990;113(Pt 6):1823-1842. Braak, H., Del Tredici, K., Rub, U., De Vos, R. A., Jansen Steur, E. N., and Braak, E. Staging of Brain Pathology Related to Sporadic Parkinson's Disease. Neurobiol. Aging. Mar/Apr 2003;24(2):197-211. Kazantsev, A. G. and Kolchinsky, A. M. Central Role of Alpha-Synuclein Oligomers in Neurodegeneration in Parkinson's Disease. Arch. Neurol. Dec 2008; 65(12):1577-1581. Fleming, L., Mann, J. B., Bean, J., Briggle, T., and Sanchez-Ramos, J. R. Parkinson's Disease and Brain Levels of Organochlorine Pesticides. Ann. Neurol. Jul 1994;36(1): 100-103. Di Monte, D. A. The Environment and Parkinson's Disease: Is the Nigrostriatal System Preferentially Targeted by Neurotoxins? Lancet Neurol. Sep 2003; 2(9):531-538. Berg, D., Gerlach, M., and Youdim, M. B., et al. Brain Iron Pathways and Their Relevance to Parkinson's Disease. J.Neurochem. Oct 2001;79(2):225-236. Olanow, C. W The Pathogenesis of Cell Death in Parkinson's Disease—2007. Mov Disord. Sep 2007;22 Suppl 17:S335-342. Liu, B., Gao, H. M., and Hong, J. S. Parkinson's Disease and Exposure to Infectious Agents and Pesticides and the Occurrence of Brain Injuries: Role of Neuroinflammation. Environ. Health Perspect. Jun 2003;111(8): 1065-1073. Gerlach, M., Desser, H., Youdim, M. B., and Riederer, P. New Horizons in Molecular Mechanisms Underlying Parkinson's Disease and in Our Understanding

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE

46. 47. 48. 49. 50. 51.

52.

53. 54. 55.

56. 57.

58.

59.

60.

113

of the Neuroprotective Effects of Selegiline. J. Neural. Transm. Suppl. 1996; 48:7-21. Plaitakis, A. and Shashidharan, P. Glutamate Transport and Metabolism in Dopaminergic Neurons of Substantia Nigra: Implications for the Pathogenesis of Parkinson's Disease. J. Neurol. Apr 2000;247 Suppl 2:1125-35. Surmeier, D. J. Calcium, Aging, and Neuronal Vulnerability in Parkinson's Disease. Lancet Neurol. Oct 2007;6(10):933-938. Hirsch, E. C. and Hunot, S. Neuroinflammation in Parkinson's Disease: A Target for Neuroprotection? Lancet Neurol. Apr 2009;8(4):382-397. Ranginwala, N. A., Hynan, L. S., Weiner, M. F., and White, C. L., Ill Clinical Criteria for the Diagnosis of Alzheimer's Disease: Still Good After All These Years. Am. J. Gerlatr. Psychiatry. May 2008;16(5):384-388. Hughes, A. J., Daniel, S. E., Blankson, S., and Lees, A. J. A Clinicopathologic Study of 100 Cases of Parkinson's Disease. Arch. Neurol. Feb 1993;50(2): 140-148. Knopman, D. S., Dekosky, S. T, and Cummings, J. L., et al. Practice Parameter: Diagnosis of Dementia (an evidence-Based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. May 8, 2001;56(9):1143-1153. Mayeux, R., Saunders, A. M., and Shea, S., et al. Utility of the Apolipoprotein E Genotype in the Diagnosis of Alzheimer's Disease. Alzheimer's Disease Centers Consortium on Apolipoprotein E and Alzheimer's Disease. N. Engl. J. Med. Feb 19, 1998;338(8):506-511. Hampel, H., Buerger, K., and Kohnken, R., et al. Tracking of Alzheimer's Disease Progression with Cerebrospinal Fluid Tau Protein Phosphorylated at Threonine 231. Ann Neurol. Apr 2001 ;49(4):545-546. Li, G., Sokal, I., and Quinn, J. F, et al. CSF Tau/Abeta42 Ratio for Increased Risk of Mild Cognitive Impairment: A Follow-up Study. Neurology. Aug 14, 2007;69(7):631-639. Hansson, O., Zetterberg, H., Buchhave, P., Londos, E., Blennow, K., and Minthon, L. Association Between CSF Biomarkers and Incipient Alzheimer's Disease in Patients with Mild Cognitive Impairment: A Follow-up Study. Lancet Neurol. Mar 2006;5(3):228-234. Andreasen, N., Sjogren, M., and Blennow, K. CSF Markers for Alzheimer's Disease: Total Tau, Phospho-Tau and Abeta42. World J. Biol. Psychiatry. Oct 2003;4(4):147-155. Tohgi, H., Abe, T, Kimura, M., Saheki, M., and Takahashi, S. Cerebrospinal Fluid Acetylcholine and Choline in Vascular Dementia of Binswanger and Multiple Small Infarct Types as Compared with AlzheimerType Dementia. J. Neural. Transm. 1996;103(10):1211-1220. Sirvio, J. and Riekkinen, P. J. Brain and Cerebrospinal Fluid Cholinesterases in Alzheimer's Disease, Parkinson's Disease and Aging. A Critical Review of Clinical and Experimental Studies. J. Neural. Transm. Park Dis. Dement. Sect. 1992;4:337-358. Appleyard, M. E. and McDonald, B. Acetylcholinesterase and Butyrylcholinesterase Activities in Cerebrospinal Fluid from Different Levels of the Neuraxis of Patients with Dementia of the Alzheimer Type. J. Neurol. Neurosurg. Psychiatry. Nov 1992;55(11):1074-1078. Darreh-Shori, T., Brimijoin, S., Kadir, A., Almkvist, O., and Nordberg, A. Differential CSF Butyrylcholinesterase Levels in Alzheimer's Disease Patients

114

BIOMARKERS

61. 62. 63. 64. 65. 66. 67.

68.

69. 70. 71. 72. 73. 74. 75.

with the Apoe Epsilon4 Allele, in Relation to Cognitive Function and Cerebral Glucose Metabolism. Neurobiol. Dis. Sep 12, 2006. Teunissen, C. E., De Vente, J., Steinbusch, H. W., and De Bruijn, C. Biochemical Markers Related to Alzheimer's Dementia in Serum and Cerebrospinal Fluid. Neurobiol. Aging. Jul/Aug 2002;23(4):485-508. Mosconi, L. Brain Glucose Metabolism in the Early and Specific Diagnosis of Alzheimer's Disease. FDG-PET Studies in MCI and AD. Eur. J. Nucl. Med. Mol. Imaging. Apr 2005;32(4):486-510. Del Sole, A., Clerici, R, and Chiti, A., et al. Individual Cerebral Metabolic Deficits in Alzheimer's Disease and Amnestic Mild Cognitive Impairment: An FDG PET Study. Eur. J. Nucl. Med. Mol. Imaging. Jul 2008;35(7): 1357-1366. Minoshima, S. Imaging Alzheimer's Disease: Clinical Applications. Neuroimaging Clin. N. Am. Nov 2003;13(4):769-780. Albanese, A., Bonuccelli, U., and Brefel, C , et al. Consensus Statement on the Role of Acute Dopaminergic Challenge in Parkinson's Disease. Mov. Disord. Mar 2001; 16(2): 197-201. Lewitt, P. A., Galloway, M. P., and Matson, W., et al. Markers of Dopamine Metabolism in Parkinson's Disease. The Parkinson Study Group. Neurology. Nov 1992;42(11):2111-2117. Hartikainen, P., Reinikainen, K. J., Soininen, H., Sirvio, J., Soikkeli, R., and Riekkinen, P. J. Neurochemical Markers in the Cerebrospinal Fluid of Patients with Alzheimer's Disease, Parkinson's Disease and Amyotrophic Lateral Sclerosis and Normal Controls. J. Neural. Transm. Park Dis. Dement. Sect. 1992; 4(l):53-68. Moser, A., Scholz, J., Nobbe, R, Vieregge, P., Bohme, V., and Bamberg, H. Presence of N-Methyl-Norsalsolinol in the CSF: Correlations with Dopamine Metabolites of Patients with Parkinson's Disease. J. Neurol. Sci. Aug 1995; 131(2):183-189. Nagai, Y, Ueno, S., Saeki, Y, Soga, R, Hirano, M., and Yanagihara, T. Decrease of the D3 Dopamine Receptor Mrna Expression in Lymphocytes from Patients with Parkinson's Disease. Neurology. Mar 1996;46(3):791-795. Takatsu, H., Nagashima, K., and Murase, M., et al. Differentiating Parkinson Disease from Multiple-System Atrophy by Measuring Cardiac Iodine-123 Metaiodobenzylguanidine Accumulation. Jama. Jul 5, 2000;284(l):44-45. Tokuda, T, Salem, S. A., and Allsop, D., et al. Decreased Alpha-Synuclein in Cerebrospinal Pluid of Aged Individuals and Subjects with Parkinson's Disease. Biochem. Biophys. Res. Commun. Oct 13, 2006;349(1):162-166. El-Agnaf, O. M., Salem, S. A., and Paleologou, K. E., et al. Alpha-Synuclein Implicated in Parkinson's Disease Is Present in Extracellular Biological Fluids, Including Human Plasma. Faseb. J. Oct 2003;17(13):1945-1947. Harrington, M. G., Fonteh, A. N., Biringer, R. G., and Cowan, R. P. Prostaglandin D Synthase Isoforms from Cerebrospinal Fluid Vary with Brain Pathology. Dis. Markers. 2006;22(l-2):73-81. Devanand, D. P., Michaels-Marston, K. S., and Liu, X., et al. Olfactory Deficits in Patients with Mild Cognitive Impairment Predict Alzheimer's Disease at Follow-up. Am. J. Psychiatry. Sep 2000;157(9):1399-1405. Modrego, P. J. and Ferrandez, J. Depression in Patients with Mild Cognitive Impairment Increases the Risk of Developing Dementia of Alzheimer Type: A Prospective Cohort Study. Arch. Neurol. Aug 2004;61(8):1290-1293.

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE 76. 77. 78. 79. 80. 81. 82. 83. 84.

85. 86.

87. 88. 89. 90. 91. 92.

115

Rapp, M. A. and Reischies, F. M. Attention and Executive Control Predict Alzheimer's Disease in Late Life: Results from the Berlin Aging Study (BASE). Am. J. Geriatr. Psychiatry. Feb 2005;13(2): 134-141. Li, Y. and Grupe, A. Genetics of Late-Onset Alzheimer's Disease: Progress and Prospect. Pharmacogenomics. Dec 2007;8(12):1747-1755. Villemagne, V. L., Fodero-Tavoletti, M. T., Pike, K. E., Cappai, R., Masters, C. L., and Rowe, C. C. The ART of Loss: Abeta Imaging in the Evaluation of Alzheimer's Disease and Other Dementias. Mol. Neurobiol. Aug 2008;38(1):1-15. Aizenstein, H. J., Nebes, R. D., and Saxton, J. A., et al. Frequent Amyloid Deposition without Significant Cognitive Impairment Among the Elderly. Arch. Neurol. Nov 2008;65(11):1509-1517. Hampel, H. and Shen, Y. Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE1) as a Biological Candidate Marker of Alzheimer's Disease. Scand. J. Clin. Lab. Invest. 2009;69(1):8-12. Leoni, V. Oxysterols as Markers of Neurological Disease—A Review. Scand. J. Clin. Lab. Invest. 2009;69(l):22-25. Lovell, M. A. and Markesbery, W. R. Ratio of 8-Hydroxyguanine in Intact DNA to Free 8-Hydroxyguanine Is Increased in Alzheimer Disease Ventricular Cerebrospinal Fluid. Arch. Neurol. Mar 2001;58(3):392-396. De Leon, M. J., Desanti, S., and Zinkowski, R., et al. Longitudinal CSF and MRI Biomarkers Improve the Diagnosis of Mild Cognitive Impairment. Neurobiol. Aging. Mar 2006;27(3):394-401. Selley, M. L., Close, D. R., and Stern, S. E. The Effect of Increased Concentrations of Homocysteine on the Concentration of (E)-4-Hydroxy-2-Nonenal in the Plasma and Cerebrospinal Fluid of Patients with Alzheimer's Disease. Neurobiol. Aging. May/Jun 2002;23(3):383-388. Jimenez-Jimenez, F. J., De Bustos, F., and Molina, J. A., et al. Cerebrospinal Fluid Levels of Alpha-Tocopherol (Vitamin E) in Alzheimer's Disease. J. Neural. Transm. 1997;104(6-7):703-710. Manczak, M., Anekonda, T. S., Henson, E., Park, B. S., Quinn, J., and Reddy, P. H. Mitochondria Are a Direct Site of a Beta Accumulation in Alzheimer's Disease Neurons: Implications for Free Radical Generation and Oxidative Damage in Disease Progression. Hum. Mol. Genet. May 1, 2006;15(9):1437-1449. Busciglio, J., Pelsman, A., and Wong, C , et al. Altered Metabolism of the Amyloid Beta Precursor Protein Is Associated with Mitochondrial Dysfunction in Down's Syndrome. Neuron. Feb 28, 2002;33(5):677-688. Arispe, N., Rojas, E., and Pollard, H. B. Alzheimer's Disease Amyloid Beta Protein Forms Calcium Channels in Bilayer Membranes: Blockade by Tromethamine and Aluminum. Proc. Natl. Acad. Sci. USA. Jan 15, 1993;90(2):567-571. Mattson, M. P. Antigenic Changes Similar to Those Seen in Neurofibrillary Tangles are Elicited by Glutamate and Calcium Influx in Cultured Hippocampal Neurons. Neuron. Jan 1990;4(1): 105-117. Jack, C. R., Jr., Petersen, R. C , and Xu, Y. C , et al. Prediction of AD with MRIBased Hippocampal Volume in Mild Cognitive Impairment. Neurology. Apr 22, 1999;52(7): 1397-1403. Hampel, H., Burger, K., Teipel, S. J., Bokde, A. L., Zetterberg, H., and Blennow, K. Core Candidate Neurochemical and Imaging Biomarkers of Alzheimer's Disease. Alzheimers Dement. Jan 2008;4(1):38^18. Ries, M. L., Carlsson, C. M., and Rowley, H. A., et al. Magnetic Resonance Imaging Characterization of Brain Structure and Function in Mild Cognitive Impairment: A Review. J. Am. Geriatr. Soc. May 2008;56(5):920-934.

116

BIOMARKERS 93. 94. 95. 96.

97. 98. 99. 100. 101.

102. 103. 104. 105. 106.

107. 108.

Teipel, S. J., Stahl, R., and Dietrich, O., et al. Multivariate Network Analysis of Fiber Tract Integrity in Alzheimer's Disease. Neuroimage. Feb 1,2007;34(3): 985-995. Bokde, A. L., Lopez-Bayo, P., and Meindl, T., et al. Functional Connectivity of the Fusiform Gyrus During a Face-Matching Task in Subjects with Mild Cognitive Impairment. Brain. May 2006;129(Pt 5): 1113-1124. Ray, S., Britschgi, M., and Herbert, C , et al. Classification and Prediction of Clinical Alzheimer's Diagnosis Based on Plasma Signaling Proteins. Nat. Med. Nov2007;13(ll):1359-1362. Hansson, O., Buchhave, P., Zetterberg, H., Blennow, K., Minthon, L., and Warkentin, S. Combined Rcbf and CSF Biomarkers Predict Progression from Mild Cognitive Impairment to Alzheimer's Disease. Neurobiol. Aging. Feb 2009;30(2):165-173. Bouwman, F. H., Schoonenboom, S. N., and Van Der Flier, W. M., et al. CSF Biomarkers and Medial Temporal Lobe Atrophy Predict Dementia in Mild Cognitive Impairment. Neurobiol. Aging. Jul 2007;28(7): 1070-1074. Berg, D. Biomarkers for the Early Detection of Parkinson's and Alzheimer's Disease. Neurodegener. Dis. 2008;5(3-4): 133-136. Tan, E. K., Khajavi, M., Thornby, J. I., Nagamitsu, S., Jankovic, J., and Ashizawa, T. Variability and Validity of Polymorphism Association Studies in Parkinson's Disease. Neurology. Aug 22, 2000;55(4):533-538. Tan, E. K., Chai, A., and Teo, Y. Y., et al. Alpha-Synuclein Haplotypes Implicated in Risk of Parkinson's Disease. Neurology. Jan 13, 2004;62(1): 128-131. Grunblatt, E., Mandel, S., and Jacob-Hirsch, J., et al. Gene Expression Profiling of Parkinsonian Substantia Nigra Pars Compacta; Alterations in Ubiquitin-Proteasome, Heat Shock Protein, Iron and Oxidative Stress Regulated Proteins, Cell Adhesion/Cellular Matrix and Vesicle Trafficking Genes. J. Neural. Transm. Dec 2004;111(12):1543-1573. El-Agnaf, O. M., Salem, S. A., and Paleologou, K. E., et al. Detection of Oligomeric Forms of Alpha-Synuclein Protein in Human Plasma as a Potential Biomarker for Parkinson's Disease. Faseb. J. Mar 2006;20(3):419^25. Paleologou, K. E., Kragh, C. L., and Mann, D. M., et al. Detection of Elevated Levels of Soluble {Alpha }-Synuclein Oligomers in Post-Mortem Brain Extracts from Patients with Dementia with Lewy Bodies. Brain. Jan 20, 2009. Conway, K. A., Harper, J. D., and Lansbury, P. T., Jr. Fibrils Formed In Vitro from Alpha-Synuclein and Two Mutant Forms Linked to Parkinson's Disease are Typical Amyloid. Biochemistry. Mar 14, 2000;39(10):2552-2563. Dos Santos, E. E, Busanello, E. N., and Miglioranza, A., et al. Evidence That Folic Acid Deficiency Is a Major Determinant of Hyperhomocysteinemia in Parkinson's Disease. Metab. Brain Dis. Mar 18, 2009. Hassin-Baer, S., Cohen, O., and Vakil, E., et al. Plasma Homocysteine Levels and Parkinson Disease: Disease Progression, Carotid Intima-Media Thickness and Neuropsychiatric Complications. Clin. Neuropharmacol. Nov/Dec 2006; 29(6): 305-311. Schwarzschild, M. A., Schwid, S. R., and Marek, K., et al. Serum Urate as a Predictor of Clinical and Radiographic Progression in Parkinson's Disease. Arch. Neurol. Jun 2008;65(6):716-723. De Lau, L. M., Koudstaal, P. J., Hofman, A., and Breteler, M. M. Serum Uric Acid Levels and the Risk of Parkinson's Disease. Ann. Neurol. Nov 2005;58(5): 797-800.

BIOMARKERS OF ALZHEIMER'S AND PARKINSON'S DISEASE

117

109. Tohgi, H., Abe, T., Saheki, M., Hamato, E, Sasaki, K., and Takahashi, S. Reduced and Oxidized Forms of Glutathione and Alpha-Tocopherol in the Cerebrospinal Fluid of Parkinsonian Patients: Comparison Between Before and After L-Dopa Treatment. Neurosci. Lett. Jan 16, 1995; 184(1):21-24. 110. Benecke, R., Strumper, P., and Weiss, H. Electron Transfer Complexes I and IV of Platelets are Abnormal in Parkinson's Disease but Normal in Parkinson-Plus Syndromes. Brain. Dec 1993;116 (Pt 6): 1451-1463. 111. Ferrarese, C, Tremolizzo, L., and Rigoldi, M., et al. Decreased Platelet Glutamate Uptake and Genetic Risk Factors in Patients with Parkinson's Disease. Neurol. Sci. Feb 2001;22(l):65-66. 112. Goldknopf, I. L., Sheta, E. A., and Bryson, J., et al. Complement C3c and Related Protein Biomarkers in Amyotrophic Lateral Sclerosis and Parkinson's Disease. Biochem. Biophys. Res. Commun. Apr 21, 2006;342(4): 1034-1039. 113. Maetzler, W., Berg, D., and Schalamberidze, N., et al. Osteopontin Is Elevated in Parkinson's Disease and Its Absence Leads to Reduced Neurodegeneration in the MPTP Model. Neurobiol. Dis. Mar 2007;25(3):473^182. 114. Kosta, P., Argyropoulou, M. I., Markoula, S., and Konitsiotis, S. MRI Evaluation of the Basal Ganglia Size and Iron Content in Patients with Parkinson's Disease. J. Neurol. Jan 2006;253(l):26-32. 115. Liepelt, I., Reimold, M., and Maetzler, W., et al. Cortical Hypometabolism Assessed by a Metabolic Ratio in Parkinson's Disease Primarily Reflects Cognitive Deterioration—[18F]FDG-PET. Mov. Disord. 2009;Accepted. 116. Maetzler, W., Liepelt, I., and Reimold, M., et al. Cortical PIB Binding In Lewy Body Disease Is Associated with Alzheimer-like Characteristics. Neurobiol. Dis. Apr 2009;34(1):107-112. 117. Gaenslen, A., Unmuth, B., and Godau, J., et al. The Specificity and Sensitivity of Transcranial Ultrasound in the Differential Diagnosis of Parkinson's Disease: A Prospective Blinded Study. Lancet Neurol. May 2008;7(5):417^t24. 118. Berg, D. Transcranial Sonography in the Early and Differential Diagnosis of Parkinson's Disease. J. Neural. Transm. Suppl. 2006;(70):249-254. 119. Zhang, J., Sokal, I., and Peskind, E. R., et al. CSF Multianalyte Profile Distinguishes Alzheimer's and Parkinson's Diseases. Am. J. Clin. Pathol. Apr 2008; 129(4):526-529. 120. Ikemura, M., Saito, Y, and Sengoku, R., et al. Lewy Body Pathology Involves Cutaneous Nerves. J. Neuropathol. Exp. Neurol. Oct 2008;67(10):945-953. 121. Lewczuk, P., Kornhuber, J., and Vanderstichele, H., et al. Multiplexed Quantification of Dementia Biomarkers in the CSF of Patients with Early Dementias and MCI: A Multicenter Study. Neurobiol. Aging. Jun 2008;29(6):812-818.

This page intentionally left blank

CHAPTER

BIOMARKERS OF CARDIAC INJURY Anthony S. McLean and Stephen J. Huang

INTRODUCTION D e f i n i t i o n and Prevalence Although the term "cardiac injury" is used widely in cardiology, it is not well defined clinically. The term is used broadly to denote some form of cardiac insult, which may encompass myocardial cell injury and/or cell death (autophagy, apoptosis or necrosis). The causes of cardiac injury are multifarious, including ischemia, direct trauma to the heart,' drug-induced myocardial toxicity,2 myocardial depression as a result of severe sepsis, viral myocarditis, end-stage renal failure,4 and increased wall stress from congestive heart failure and pulmonary embolism.5,6 However, amongst all these, myocardial ischemia is the most prevalent cause of cardiac injury and commonly the term "cardiac injury" is used interchangeably with "myocardial infarction" (MI) or "acute coronary syndrome" (ACS). Pathologically, myocardial infarction (MI) is defined as myocardial cell death due to prolonged ischemia. Clinically however, the definition of MI has been changing over time and is inseparable from the development of cardiac biomarkers. Before the era of cardiac biomarkers, the clinical definition of MI was inconsistent and relied solely on clinical signs, ECG, and symptoms, which have poor sensitivities and specificities. In 1979, the World Health Organization (WHO) made the first attempt to unify the definition of acute myocardial infarction (AMI) globally based on history (or symptoms), ECG, and serum cardiospecific enzymes, including creatine kinase (CK), creatine kinase-MB isozyme (CK-MB), lactate dehydrogenase, and aspartate aminotransferase (AST) (Table 7.1).7 Subsequently however, in view of the inconsistent criteria 119

120

BIOMARKERS

used for denning AMI in various studies, the WHO Multinational Monitoring of Trends and Determinants in Cardiovascular Diseases (MONICA) code was introduced in 1984 for epidemiological purposes.8 Although ECG and symptom of chest pain remained as criteria in the MONICA code, they provided no prognostic value whatsoever. On the other hand, CK-MB was the strongest predictor of one-year and five-year mortality.9 Further, using the enzyme profiles provided by the WHO definition or the WHO-MONICA code resulted in a higher false positive rate of definite MI when compared to other criteria."^12 The seed for a redefinition of MI was thus planted. The discovery of troponin and its subsequent proven correlation with histological myocardial necrosis in the 1990s accelerated the redefinition process.13,14 In 2000, the criteria for the diagnosis of MI were redefined by a consensus group of the European Society of Cardiology and the American College of Cardiology (ESC/ACC).15 The new criteria represented a paradigm shift in the thinking process: instead of the two out of three criteria used in the WHO definition, the new definition requires specifically the rise and fall of cardiac biomarkers, preferably troponin, plus symptoms or ECG changes (Table 7.1). Since then, the role of biomarkers has been lifted to a new high level, making their measurements mandatory in the diagnosis of MI. Not surprisingly, the new definition has identified more patients with MI compared with the WHO criteria. Studies comparing the identification of patients with MI using the WHO and the ESC/ACC 2000 criteria clearly demonstrated that the latter was more sensitive and identified more patients with MI.16," However, these studies also found the ESC/ACC criteria were not universally accepted for the diagnosis of MI. Several concerns were expressed about the new definition, including: excessive reliance on troponins, insufficient attention to ECG changes, failure to identify early fatal cases in the first few hours after the onset of MI before there is time for troponins to be released, as well as non-fatal cases where troponin tests were unavailable, and the inability to maintain the consistency and comparability of historical (trend) epidemiological data.18-19 In view of the above concerns, new case definitions were published as an American Heart Association Scientific Statement in 2003. Following that, a universal definition of acute MI was released by the Joint European Society of Cardiology/American College of Cardiology Foundation/American Heart Association/World Health Federation Task Force in 2007.20 There were several major advancements made in the new universal definition. First, the new universal definition made it clear that the term myocardial infarction should only be used in the setting of myocardial ischemia and not from any other cause. Secondly, it designated different classes of myocardial infarction (Table 7.2). Thirdly, it re-emphasizes the role of troponin in the diagnosis of MI, recommending a more stringent cut-off value (99th percentile of upper range limit). Fourthly, while cardiac imaging was mentioned in the 2000 definition, it is only in the 2007 universal definition that cardiac imaging is included as one of the inclusion criteria. The 2007 universal definition will most certainly increase the sensitivity and specificity of the diagnosis, and the new classification will help clinicians manage patients with different classes of MI.

BIOMARKERS OF CARDIAC INJURY

TABLE 7.1

Year

121

The evolution of the definition of myocardial infarction since 1979.

Authority

Criteria

1979

World Health Organization (WHO)

Any two of the following three: 1. History of chest pain or any typical symptoms. 2. A typical ECG pattern with the development of Q waves. 3. Unequivocal change in serum enzymes (CK, CKMB, LD, or AST).

1984

WHO-MONICA code

1. Evolving diagnostic ECG; and/or 2. Diagnostic ECG and abnormal enzymes; and/or 3. Prolonged cardiac pain and abnormal enzymes.

2000

ESC/ACC

Either one of the following: 1. Typical rise and fall of troponin (or CK-MB, if troponin is not available) plus one of the following: • Ischemic symptoms; • Development of pathologic Q waves on the ECG; • ECG changes indicative of ischemia (ST segment changes); or • Coronary artery intervention. 2. Pathological findings of an acute Ml.

2007

ESC/ACCF/AHA/WHF

Any one of the following: 1. Typical rise and fall of troponin with at least one value above the 99th percentile of the URL, plus one of the following: • Ischemic symptoms; • Development of pathologic Q waves on the ECG; • ECG changes indicative of ischemia (ST segment changes or new LBBB); or • Imaging evidence. 2. Sudden, unexpected cardiac death before blood samples can be obtained or before biomarkers can appear in the blood, and accompanied by evidence of symptoms, ECG, coronary angiogram or autopsy. 3. For PCI patients with normal baseline troponin levels, troponin > 99th percentile of URL are indicative or peri-procedural myocardial necrosis. 4. For CABG patients with normal baseline troponin > 99th percentile of URL are indicative or peri-procedural myocardial necrosis. 5. Pathological findings of an acute Ml.

ACC: American College of Cardiology: ACCF: American College of Cardiology Foundation: AHA: American Heart Association; AST: aspartate aminotransferase; CK: Creatine kinase; CK-MB: creatine kinase-MB isozyme; ECG: electrocardiogram; ESC: European Society of Cardiology; LD: lactate dehydrogenase; LBBB: left bundle branch block; WHF: World Heart Federation.

122

BIOMARKERS

TABLE 7.2 Clinical classification of different types of myocardial infarction according to the 2007 universal definition.

Type 1

Spontaneous Ml related to ischemia due to a primary coronary event such as plaque erosion and/or rupture, Assuring, or dissection.

Type 2

Ml secondary to ischemia due to either increased oxygen demand or decreased supply, e.g., coronary artery spasm, coronary embolism, anaemia, arrhythmias, hypertension, or hypotension.

Type 3

Sudden unexpected cardiac death, including cardiac arrest, often with symptoms suggestive or Ml, accompanied by presumably new ST elevation, or new LBBB or evidence of fresh thrombus in a coronary artery by angiography and/ or at autopsy, but death occurring before blood samples could be obtained, or at a time before the appearance of cardiac biomarkers in the blood.

Type 4a

Ml associated with PCI.

Type 4b

Ml associated with stent thrombosis as documented by angiography or at autopsy.

Type 5

Ml infarction associated with CABG.

CABG: Coronary artery bypass graft sugery: LBBB: left bundle branch block; Ml: myocardial infarction: PCI: percutaneous coronary interventions.

Maintaining the consistency and comparability of historical data is a difficult task in epidemiological studies because of "diagnostic drift," which is largely driven by the availability of new cardiospecific biochemical markers. Examining a population-based MI registry data from 1997 to 2002, the 2003 definition, which included the use of troponins, identified 83% more definite Mis than the WHO-MONICA code.21 The Minnesota Heart Survey compared the incidence of MI for 1970 and 1980 with and without incorporating cardiospecific enzymes (CK or CK-MB) in the diagnostic criteria. When cardiospecific enzymes were not used, the incidences of definite MI were similar in 1970 and 1980 (0.174% vs 0.180%) regardless of whether or not autopsy findings were included in the algorithm. Inclusion of CK or CK-MB to the algorithm increased the rate of definite MI from 0.209% in 1970 to 0.277% in 1980.22 Examination of the Framingham Heart Study participants over four decades (1960 to 1999) revealed that the incidence rates of ECG-confirmed acute MI declined by approximately 50%, whereas the biomarkers-confirmed incidence increased by two-fold.23 While the decline in ECG-based diagnosis could be explained by improvements in primary prevention or intervention over that last few decades, as evident by a similar fall in MI case fatality, the increase in biomarker-based diagnosis confirmed the improved sensitivity of the diagnosis process. Despite the difficulties in defining MI, the Heart Disease and Stroke Statistics—2008 Update revealed that about one in three American adults have one or more types of cardiovascular disease. About 16 million Americans have coronary artery disease (CAD), of which 8.1 million had experienced an MI.

BIOMARKERS OF CARDIAC INJURY

123

The estimated annual incidence of MI is 600,000 new attacks and 320,000 recurrent attacks. In 2004, of the 451,326 deaths caused by CAD in the U.S., about one in five of all deaths, MI accounted for 156,816 deaths.24

Pathophysiology and Mechanisms The 2007 universal definition only confined its definition of MI to ischemic causes. In addition to coronary events, increases in myocardial oxygen demand or decreases in oxygen supply also constitute "ischemia" under the new definition. Hence, coronary artery spasm, coronary embolism, anaemia, arrhythmia, and hypoperfusion can all come under the ambit of "ischemia." The universal definition explicitly excludes other causes of MI, such as myocardial cell death associated with mechanical injury, renal failure, heart failure, cardioversion, sepsis, myocarditis, cardiac toxins, and infiltrative disease, although these can all lead to cardiac injury.20 CAD accounted for more than half of all cardiovascular events in Americans < 75 years of age and is the major cause of MI.24 CAD, which has previously been considered as a cholesterol disorder, is now recognized as an inflammatory disease. The cornerstone of CAD is the series of inflammatory responses triggered by various proinflammatory factors such as hypercholesterolemia, obesity, hyperglycemia, hypertension, and smoking. In hypercholesterolemia, the infiltration and retention of modified (oxidized) low-density lipoprotein (LDL) in the arterial intima leads to the release of phospholipids that can activate endothelial cells in large and medium-sized arteries.25 Segments of arteries exposed to hemodynamic strain are prone to develop atherosclerosis and have a higher endothelial cell expression of adhesion molecules and inflammatory genes.26 Adhesion of the platelets to the activated sites further enhances endothelial activation.27 The expression of leukocyte adhesion molecules is an important step in atherogenesis. In hypercholesterolemia, activated endothelial cells express vascular-cell adhesion molecule 1 (VCAM-1) which attracts monocytes and T lymphocytes to these sites.28 Once attached to the endothelium, these leukocytes transmigrate through the endothelial junction into the subendothelial layer in response to cytokines produced in this layer. The monocytes, now differentiated into macrophages under the effects of intima-released macrophage colony stimulating factor, will express scavenger receptor and start to internalize a wide range of molecules and particles, including bacterial endotoxin, apoptotic cell fragments, and oxidized LDL.29'30 The accumulation of cholesteryl esters inside the macrophages results in the formation of cytosolic lipid droplets and these macrophage acquire the foamylike appearance—and hence the name "foam cells." Also by upregulating the toll-like receptors, the macrophages will be activated by binding pathogen-like molecules, and produce inflammatory cytokines (tumor necrosis factor and interleukin 1), proteases, and cytotoxic oxygen radicals.31 Bacterial toxins, stress proteins, DNA motifs, heat-shock protein 60, and oxidized LDL can all activate the toll-like receptors.32 The T lymphocytes transform into T-helper 1 (Thl) effector cells after exposure to various antigens, including oxidized lipids, heat-shock protein 60, viral antigens, and lipid antigens.33 Activated

124

BIOMARKERS

Thl cells produce interferon-y that augments the production of inflammatory cytokines (tumor necrosis factor and interleukin-1) from macrophage.34 As the inflammatory process advances silently in the early stage, smooth muscles cells undergo a phenotypic modulation from a contractile, non-proliferative state to an activated proliferative and migratory state in response to growth factors.35 They make their way through to the intima from the tunica media. Acting in concert with endothelial cells and macrophages, the smooth muscle cells secrete matrix metalloproteinases (MMPs) in response to interleukin-18, a cytokine produced by macrophage and the endothelial cells.36 MMPs serve numerous functions in the vascular wall, including activation, migration, and cell death, as well as new vessel formation, remodelling, and destruction of extracellular matrix.37 As the inflammatory lesion progresses, calcification ensues.38 Cell death is common in established atherosclerotic lesions.39 The death of foam cells leads to accumulation of the extracellular lipid in the intima which coalesce to form the classic, lipid-rich necrotic core of the atherosclerotic plaque. The mature atherosclerotic plaque consists of a lipid-rich core separated from the vessel lumen by a fibrous cap composed of vascular smooth muscle cells, collagen, and extracellular matrix. Apoptosis of the smooth muscle cells leads to plaque destabilization and rupture, and the apoptotic process is promoted by various factors, including inflammatory cytokines (e.g., tumor necrosis factor) and proteases (MMPs and cysteine proteases) released by activated macrophage and oxidized LDL.40 Rupture of the fibrous cap exposes the underlying collagen, resulting in platelet activation, thrombosis, and potential occlusion leading to ischemia. While rupture is widely recognized as the main cause of MI, it only accounts for 40% to 65% of Mis.4' It is now believed that some of the episodes of MI could be due to massive endothelial cell apoptosis inducing thrombus formation.42 A number of pro-atherosclerotic factors can induce endothelial cell apoptosis, including elevated glucose concentration, oxidized LDL, and reactive oxygen radicals.43 MI is considered as necrotic myocardial cell death due to prolonged ischemia. Myocardial cell apoptosis is the first event in response to ischemia. It is characterized by cell shrinkage and aggregation of chromosomal DNA into small masses and preparation for exocytosis (seen as membrane blebbing under the microscope). Apoptotic bodies are rapidly removed by macrophages resulting in minimal inflammatory response.44 As apoptosis is an energy requiring process and, if energy (oxygen) supply fails to meet the demand, apoptosis will be stalled and followed by necrosis. Timely interventions aim to restore perfusion and significantly reduce infarct size and survival, possibly allowing apoptosis to complete.45 If necrosis prevails, the infarct areas are infiltrated by inflammatory neutrophils and macrophages. The release of inflammatory cytokines and proteases by these cells leads to inflammatory reactions as well as further cell loss and connective tissue disruption. In acute MI, the necrotic area is found predominantly at the center of the lesion and the apoptotic areas sandwiched between the necrotic and healthy tissues.46 MI is not the only form of cardiac injury (Figure 7.1). Cell death (apoptosis) is also a feature of cardiomyopathy, and contributes to the deteriorating

BIOMARKERS OF CARDIAC INJURY

FIGURE 7.1

125

Common causes of cardiac injury.

cardiac function observed.47"19 In the decompensated state of heart failure, volume overload is thought to be a causative factor of apoptosis.50 Other proapoptotic factors in heart failure include pressure overload, elevated angiotensin II, and catecholamine overproduction.51-53 Proinflammatory cytokines are also involved in heart failure. It is hypothesized that precipitating events, such as ischemic heart injury, trigger a series of inflammatory responses and the expressions of cytokines in the myocardium.54 These cytokines are associated with deleterious effects on the ventricular function and accelerate progression of heart failure.55 Heart failure aside, certain drugs and agents are also capable of inducing apoptosis and myocardial dysfunction, including doxorubicin,56 anthracyclin,57 arsenic trioxide,58 cyclophosphamide,59 alcohol,60 and bacterial toxins.61 Inhibition of the apoptotic mediator, caspase, reduces apoptosis and improves cardiac functions in various models.62

DIAGNOSIS The accuracy and reproducibility of a diagnosis depend on the clarity and validity of the definition of the disease. Prior to the introduction of cardiac biomarkers, the diagnosis of MI relied on clinical history, symptoms, and ECG changes. The most recent consensus for MI diagnosis is listed in Table 7.1.

126

BIOMARKERS

Although ECG is more objective than clinical history and symptoms, its value in the diagnosis of MI is questionable in that it lacks sensitivity and specificjty 63-65 fjjg di a g nos tic accuracy of the ECG depends upon the extent of myocardial necrosis and its localization. For example, while it is most sensitive in patients with occlusion of the left anterior descending artery, its sensitivity is only about 50% in detecting left circumflex occlusion.66 Furthermore, ECG diagnosis of acute MI is difficult in situations where the electroconduction pattern is altered, e.g., left bundle branch block and ventricular pacing, and often leads to missed diagnosis.67-68 Over the past 50 years, since the discovery of the release of aspartate aminotransferance (AST) by necrotic myocardial cells,69 biomarkers have assumed a crucial role in the diagnosis of MI.70 Biomarkers have substantially increased the detection of acute MI cases due to an improvement in sensitivity. The first generation of biomarkers for MI, namely AST and lactate dehydrogenase, suffer from poor cardiospecificity.69-71 It is now known that both enzymes are present ubiquitously in the body, most notably in the liver and red blood cells.7273 This lack of specificity renders these enzymes of little diagnostic value for acute MI.74-75 Creatine kinase (CK), due to its rapid appearance in serum after acute MI and its specificity for MI, soon became the choice of cardiac biomarker for MI in the 1970s.76 Although CK has superior specificity to AST and lactate dehydrogenase, a high level of CK is found in striated muscle and the serum CK level is subject to muscle injury.77 The false positive rate remains high even when the more cardiospecific isoform of CK, CK-MB, is used.78 Cardiac troponins, since endorsement in 2000 by the MI diagnosis consensus document, remain the mainstay for the diagnosis of MI despite the troponin levels being increased in various noncardiac pathological states.15 A distinction should be made between MI and other cardiac injuries when applying cardiac biomarkers. While MI and cardiac injury may both result in the elevation of cardiac biomarkers (often the same biomarkers) the two conditions are not synonymous. Etiologically, MI is the result of ischemia and cardiac injury can be due to various causes. In terms of biomarkers, the outcomes are similar—myocardial cell injury or cell death resulting in the release of intracellular cardiospecific contents (biomarkers). Therefore, the meaning of "specificity" in cardiac biomarkers may bear two different meanings: tissue specificity (specific to myocardial tissues), and disease specificity (specific to the type of cardiac disease). Much effort in the past decades has been invested to address these two issues of biomarkers.

BIOMARKERS OF C A R D I A C INJURY The applications of biomarkers fall into four main categories: screening, diagnostic, monitoring disease progression and guiding therapy, and prognostication. Depending on the use, the requirements for the biomarkers can be different. For example, while sensitivity and specificity are important characteristics for screening and diagnosis, they are less important for monitoring purpose. The desirable properties for an ideal cardiac biomarker intending to be used

BIOMARKERS OF CARDIAC INJURY

127

for diagnostic purpose are listed in Table 7.3.79 The etiological factors and the evolution of the disease are different for different cardiac disease, and hence to exhaustively list out all the cardiac biomarkers relating to different types of cardiac disease is impossible. The evolution of most cardiac diseases can be divided into three stages: Inflammation, myocardial cells injury or damage, and cardiac stress. While these stages are continuous, there is no clear demarcation for each stage and they may occur concomitantly. This chapter will discuss different cardiac biomarkers by grouping them into three categories: inflammatory markers, markers for cardiac injury, and for cardiac stress (Figure 7.2).

I n f l a m m a t o r y Markers of Cardiac Disease Inflammation plays a key role in CAD.80 All stages of plaque development and eventual rupture leading to ACS can be considered as inflammatory response.81 Inflammation also plays an important role in heart failure and myocarditis. 55,82,83 The detection of key inflammatory molecules or cytokines hence offers an attractive approach for detecting cardiac ischemia, heart failure, and predicting outcomes.84 C-Reactive Protein (CRP) CRP is produced mainly in the liver as an acute-phase reactant and is transcriptionally driven by interleukin-6. Recent data suggest that CRP is also produced locally in the atherosclerotic plaque especially by smooth muscle cells and macrophages, and is believed to have a direct role in the pathophysiology of atherosclerosis.85~87 CRP enhances macrophage uptake of low density lipoprotein and contributes to foam cell formation. It also causes plaque insta-

TABLE 7.3

Characteristics of an ideal cardiac biomarker

Sensitivity

High sensitivity to avoid missing diagnosis High concentration in myocardium Released rapidly for early diagnosis Reasonable half-life to permit adequate window for diagnosis

Specificity

Specific to myocardial tissue Specific to cardiac disease Specific to the type of cardiac disease

Assay

Accurate Reproducible Simple to perform and readily available Rapid turnaround time Favourable cost-benefit ratio Diagnostic cut-off well-defined

Other

Plasma or serum levels proportional to injury size

128

BIOMARKERS

FIGURE 7.2 The evolution of most cardiac diseases involves three stages: I) inflammation, 2) acute injury to myocardial cells, and 3) cardiac stress where the myocardium is subjected to pressure and/or volume overload.The myocardium responds to each stage by releasing different biomarkers.While these stages can be identified by various means, co-existence of these stages is to be expected. By measuring the concentrations (or relative concentrations) of these biomarkers at the same time, it is possible to identify the stage in addition to the type of cardiac disease. CRP: C-reactive protein; IL: interleukins;TNF: tumor necrosis factor; Fas: a member of theTNF-receptor family; MMP: matrix metalloproteinase; MPO: myeloperoxidase; CK-MB: creatine kinase-MB isozyme; cTn: cardiac troponin; H-FABP: heart-type fatty acid binding protein; BNP: B-type natriuretic peptide; NT-proBNP: N-terminal proBNP; ST2: a member of the IL-I receptor family and binds IL-33.

bility, induces adhesion molecule expression, and is associated with endothelia dysfunction.88'89 CRP was elevated only in patients with unstable angina and not those with variant angina caused by vasospasm, indicating that CRP is associated with inflammation in coronary artery rather than in the ischemic myocardium.90 CRP was also increased in other inflammatory conditions such as acute injury, infection, and chronic renal failure.91,92 CRP was also elevated in patients with heart failure and was first detected in the 1950s.93 Higher levels of CRP were found in patients with more severe heart failure. High CRP concentrations are associated with increased incidents of cardiovascular disease, and have similar magnitude as other risk factors such as LDL cholesterol, systolic blood pressure, and smoking.94 Inclusion of CRP into risk classification procedures provides more accurate assessments of

BIOMARKERS OF CARDIAC INJURY

129

cardiovascular disease risk.95'% High levels of CRP in unstable angina were associated with worsening outcome.97 Data from the JUPITER trial demonstrated that CRP can be used to target high risk patients who have typical LDL cholesterol and benefit from statin therapy. The study found that patients who achieved LDL cholesterol concentration of < 70 mg/dl (1.81 mmol/1) and CRP < 2 mg/L had the best long-term clinical outcomes.98 CRP is now a recognized independent marker of cardiovascular risk. The recommended cut-offs in clinical practice are < 1 mg/L for low-risk and > 3 mg/L for high-risk individuals.99 In heart failure, CRP is an independent predictor of adverse outcome in acute or chronic heart failure, and can predict the risk of future development of heart failure in asymptomatic older subjects.82,10° I n t e r l e u k i n s (IL)

IL-6, a pro-inflammatory cytokine produced by the macrophage in the atherosclerotic plaques, induces hepatic synthesis of all the acute phase proteins, including CRP.81,101 Elevated IL-6 (a 5 ng/ml) was associated with a 3.5-fold increase in one-year mortality in patients with ACS.9'102 IL-6 was also a predictor of mortality independent of troponin T and CRP. Healthy individuals with high IL-6 also had an increased risk for future myocardial infarction.103 IL-18 is also a pro-inflammatory cytokine that is highly expressed in atherosclerotic plaques (macrophages). Significantly higher levels of IL-18 mRNA were found in symptomatic (unstable) plaque than asymptomatic (stable) plaque, suggesting IL-18 destabilizes atherosclerotic plaque leading to ischemic syndromes.104,105 Adipocytes from obese patients secret a significant amount of IL-18, three-fold more than the non-obese counterparts, supporting the notion that adipocytes participate in innate immunity and that IL-18 contributes to the risks of development cardiovascular disease and type 2 diabetes.106 IL-18 was a strong predictor of death from cardiovascular causes in patients with CAD.107 Due to its high level in HF, IL-18 is not suitable for selectively diagnosing ischemic heart disease. IL-6 and IL-18 are also produced by nucleated cells in the heart and are significantly elevated in heart failure.108,109 Clinically, the peak IL-6 level correlates with the severity of decompensated heart failure. When compared to age-matched non-cardiac patients, IL-6 levels in acutely decompensated patients peaked at 12 hours and declined thereafter. The peak IL-6 levels significantly correlated with pulmonary artery wedge pressure on admission, and were higher in patients requiring mechanical ventilation.110 Treatment of acutely decompensated HF patients with levosimendan resulted in a decrease in IL-6 levels.1" IL-6 predicts one-year mortality and can identify patients at high risk for worsening of heart failure.112' " 3 T u m o r N e c r o s i s Factor ( T N F ) and Fas

TNF-ct levels are elevated in patients with HF.114TNF-a causes left ventricular dilatation, presumably via activation of matrix metalloproteinases (see be-

130

BIOMARKERS

low).115 High TNF-a levels were associated with higher incidence of HF in asymptomatic elderly subjects.116 Fas is a member of the TNF-a receptor family that is expressed on a variety of cell types, including cardiomyocytes. Fas is responsible for mediating apoptosis and plays a key role in the development of and progression of HF.117 On-going myocardial damage is related to activated TNF and the Fas system in patients with worsening heart failure.118 The soluble form (serum) of Fas is increased in patients with HF and the concentration is proportional to the severity of the disease."9 In congestive HF, soluble Fas levels were found to be a useful prognostic factor for 48-month mortality independent of neurohormonal factors.120 A recent genetic study found that a single nucleotide polymorphism (SNP) in the Fas promoter region (-670G/A) is associated with excess apoptosis of vascular smooth muscle cells in the atherosclerotic lesion and may be a risk factor for MI occurrence.121 CD40 Ligand Both CD40 and soluble CD40 ligand (sCD40L) are expressed by vascular cells and macrophages. Platelets also express CD40 ligands.122 CD40 promotes degradation of fibrous caps of atherosclerotic plaques and tissue factor production by stimulating macrophages.123 sCD40L is increased in patients with stable CAD documented by angiography.124 However, sCD40L does not identify subclinical atherosclerosis in the general population.125 Plasma sCD40L levels were higher among patients with evidence of intraplaque lipid in the carotid artery than among those without it.126 High plasma sCD40L levels are associated with increased cardiovascular risk in healthy women. When controlled for age and smoking history, patients with higher sCD40L concentrations have higher risks of MI, stroke, or cardiovascular death during a four-year follow-up.127 Examination of the data from the OPUS-IMI 16 trial demonstrated that patients with the highest levels of both sCD40L and cardiac troponin had a significantly higher cardiovascular risk (12:1) compared with patients with the lowest levels of both markers.128 Matrix Metalloproteinases (MMPs) MMPs are a family of proteinases that facilitate the degradation and reorganization of the extracellular matrix, and play a crucial role in the pathology of atherosclerosis and vascular disease.129 MMPs regulate various biological activities by activating mediators like TNF-a, growth factors and their receptors, plasminogen and its activators, and endothelin.129 The circulating concentration of one family member of the MMPs, MMP-9, is increased in patients with CAD.130 Plasma MMP-9 levels are transiently increased to two- to three-fold above normal during acute MI. The levels returned back to the control range within a week, suggesting an active role for MMP-9 in plaque rupture.131 Despite its roles in atherosclerosis, circulating MMP-9 concentrations are not associated with the severity of coronary stenosis as defined by angiography or with carotid atherosclerosis.130,132 However, MMP-9 correlates with cardiovascular risk as estimated by the Framingham

BIOMARKERS OF CARDIAC INJURY

131

risk score.'33 Increased in MMP-9 in subjects with a 50% carotid stenosis is associated with a two-fold increased risk of stroke or cardiovascular death.'34 As MMPs are affected by various comorbidities and drug therapies, diagnostic application MMPs is limited.135 Myeloperoxidase (MPO) MPO is a pro-inflammatory enzyme involved in LDL cholesterol oxidation and nitric oxide scavenging. It is mainly released by activated neutrophils, and its activity is abundant within atherosclerotic plaques.136 Interestingly, MPO activity was found to be lower in circulating neutrophils, but higher in circulation, in patients with acute MI and unstable angina as compared with those with chronic stable angina.137 This is indicative of their active release by neutrophils under these conditions, possibly under the stimulation of CRP.138 Plasma MPO levels were significantly elevated within two hours after the onset of symptoms (chest pain) and return to baseline levels within one week, including those with MI.137,139 This has significant advantage over troponin T which takes three to six hours to rise to measurable circulating levels after MI, suggesting MPO may be useful in triage and as a marker of unstable angina preceding MI—a predictor of vulnerable plaque. As the circulating MPO levels did not correlate with CK-MB and troponin (markers of myocardial damage), MPO may assume a role as a marker of instability and not simply a marker of oxidative stress and damage.137 MPO can be used for risk stratification for CAD. Compared to normal controls, blood and leukocyte MPO activity were higher in patients with CAD and in those apparently healthy but who developed CAD during an eightyear follow-up study.140,141 In the CAPTURE trial involving 1090 patients with ACS, serum MPO concentrations correlated with rates of death and MI, even in patients with undetectable cardiac troponin T (< 0.01 |xg/ml).142 A cut-off of 350 (ig/L was associated with an adjusted hazard ratio of 2.25 (95% CI, 1.32-3.82). In the emergency setting, MPO predicted the risk of MI in patients with chest pain even in the absence of cardiac necrosis.139 Plasma MPO was also significantly elevated in HF patients and was associated with worsening conditions.143144 However, MPO was not predictive of acute decompensated heart failure in patients presented with dyspnea in the emergency department.145 It is apparent that increased MPO is unlikely to be specific for cardiac diseases. Any disorder associated with dyspnea is sufficient to stimulate MPO release, as can activation of neutrophils and macrophages in any infections or inflammatory processes.146

Markers f o r Myocardial Cell Injury Creatine Kinase-Myocardial Band (CK-MB) Myocardial injury leads to the release of specific cytosolic substances which can be used as a marker for the injury. CK-MB is an enzyme present primarily in cardiac muscles and its intracellular level may increase in response to pres-

132

BIOMARKERS

sure overload or ischemia.147'I48 The enzyme is released rapidly (within four to six h) into the circulation after the onset of MI. It peaks at 24 h, and returns to normal by 36 to 72 h.149 While total CK is a sensitive marker of MI, it is clear from autopsy series and myocardial biopsy performed during coronary bypass surgery that CK-MB does not detect all myocardial necrosis in patients with suspected coronary ischemia.150'151 Further, increases in CK-MB do not occur in most patients with severe unstable angina.152 Since plasma CK-MB rise at about six hours after onset of symptoms, a single measurement on presentation has low sensitivity. Serial measurements over three hours in patients with nondiagnostic ECGs provides a better sensitivity (90%) and is more sensitive than serial ECG in patients with non-ST segment elevation MI (NSTEMI).153'154 Due to the transient change in concentrations, CK-MB cannot be used for late diagnosis of acute MI. However, a re-elevation of plasma levels may suggest infarct extension or re-infarction.155 Lack of specificity is also an issue for CK-MB. In patients with chest pain symptoms, about 10% had elevated CK-MB but normal troponin.156 Although CK-MB is present in small amounts (1-3% of total CK) in skeletal muscle, skeletal muscle injury can increase circulating CK-MB levels.157 In response to skeletal muscle damage, there is a re-expression of proteins that existed during ontogeny, resulting in excessive production of the CK-MB isozyme.158 Sufficient CK-MB can be released from damaged skeletal muscle to increase circulating levels, confusing the diagnosis of acute MI. Increased circulating CK-MB has been observed in patients who underwent surgery, with cardiac contusion after chest wall trauma and electrical injury.159'160 CK-MB is released for a longer period of time in response to skeletal muscle injury, and plasma levels decline more slowly than after an acute MI. Perhaps this prolonged appearance of plasma CK-MB can be used to differentiate injuries which are of myocardial or skeletal muscle origin. Serial CK-MB measurements have a reasonable sensitivity and specificity for diagnosing patients with ACS.156 However, CK-MB is less sensitive than troponin. It is not recommended as the first line of biomarkers for MI unless troponin is not available.20,161 In patients undergoing percutaneous coronary intervention, elevation in baseline CK-MB has no long-term prognostic value.162 Troponins (cTn) The troponins, troponin T and I, are part of the contractile apparatus of striated muscle, including cardiac myocytes. Cardiac troponins T (cTnT) and I (cTnl) are the most specific and sensitive markers of myocardial injury, and there is no clinical difference between cTnT and cTnl for diagnosing cardiac necrosis.163 The trigger for cTn release is necrosis, and cTn assays can detect as little as 1 g of myocardial necrosis.161 In myocardial injury where the cell membrane allows the escape of intracellular proteins, cTn begins to increase within two to four h after onset of symptoms, and remains elevated for 7 to 14 days depending on the extent of injury and reperfusion status. Early release in acute MI is attributable to the cytosolic pool, with subsequent release to the structural pool following degradation of the actin and myosin filaments

BIOMARKERS OF CARDIAC INJURY

133

in the area of damage. The latter degradation and release is responsible for the prolonged release. The transient increase is an important observation of acute injury insult. The lack of a "rise and fall" pattern often suggests false positives. There were concerns about the cardiospecificity of cTnT, especially in patients with renal failure and skeletal muscle disease.164 Subsequent studies on skeletal muscle from such patients established that isoforms of cTnT that are re-expressed in response to injury are not detected with the second- and third generations of cTnT assays.165 The cTnT are therefore highly specific for cardiac injury. When both skeletal muscle and cardiac injury are present, the improved specificity reduces the number of false-positives while maintaining high sensitivity. On the other hand, cTnl was not expressed in the skeletal muscles at any stage of neonatal development or during regenerative muscle disease processes like polymyositis and Duchenne muscular dystrophy.166 The ESC/ACC 2000 consensus recommends that cTn is the preferred cardiac marker for MI, and the upper limit be defined as the 99th percentile.167 The sensitivities and specificities for cTnl and cTnT measured at four to eight hours or beyond four hours are about 90%.168'169 As expected, the sensitivity for cTnT in early diagnosis (< six hours) is poor (about 55%) but the specificity remains high (> 90%).169' 17° Clinically, only the measurement of cTn is able to distinguish patients with unstable angina from those with NSTEMI.171 However, while increased cTn always indicates myocardial tissue damage, a positive test is unable to suggest the type of cardiac injury or the mechanism responsible for it. Studies in both symptomatic and asymptomatic patients have shown that renal failure is associated with chronic elevations of cTn.172 Sepsis or pulmonary embolism can also independently increase cTn.173 Other causes of cTn elevation include trauma, pericarditis, HF, hypertension, and inflammatory diseases (Table 7.4).174 Elevated cTn does not reflect the mechanism of myocardial damage and should not be used alone to diagnose myocardial infarction. Prognostically, elevation of serum cTnl or cTnT is associated with an increased risk of cardiac death or reinfarction at 30 days (OR 3.44,95% 2.944.03). Elevated cTn is also predictive of long-term (five months to three years) outcome in those with STEMI or non-STEMI.175 In the GUSTO-IIa trial, the 30-days mortality rate was 10% in those with a positive baseline cTnT test, 5% with a late positive test, and nil in those with a negative test.176 cTn is also useful in predicting outcomes in patients without or without ACS.177,178 cTn is also elevated in patients with heart failure (HF), even in the absence of overt ischemia.179-18° The percentage of HF patients with elevated cTn could be as high as 45%. The mechanism for this elevation is believed to be due to on-going myocyte injury and the progressive loss of cardiac myocytes, with on-going release of cTn into the circulation.182-183 cTn lacks both sensitivity and specificity for diagnosis of HF. As a prognostic tool, however, increased serum cTnl or cTnT in patients with HF has been demonstrated to be associated with increased risks of cardiac events, rehospitalization and mortality.179'181-184185

134

BIOMARKERS TABLE 7.4

Conditions commonly associated with cTn elevations.

Arrhythmias Congestive heart failure Coronary artery disease Coronary vasospasm Critically ill patient Hypertension Myocarditis Pericarditis, acute Pulmonary embolism Pulmonary hypertension, severe Renal failure Sepsis/septic shock Sepsis-related myocardial dysfunction Systemic inflammatory diseases Takotsubo cardiomyopathy Trauma

Heart-Type Fatty Acid Binding Protein (H-FABP)

H-FABP is a small cytosolic protein found in cardiomyocytes responsible for fatty acids transportation.186 H-FABP is rapidly released into the circulation following myocardial injury, and is detectable within two to three h of onset of clinical symptoms.187 The diagnostic sensitivity of H-FABP for cardiac injury is 93.1%, which is higher than CK-MB and cTn.188 Compared to CK-MB andcTn, H-FABP is abetter candidate for early detection of myocardial infarct. In a study involving 108 patients admitted to a mobile intensive care unit, H-FABP showed a better sensitivity to identify myocardial infarction than cTnl, myoglobin, and CK-MB.189 It also offers better sensitivity than TnT for early detection of acute MI and for detecting ongoing myocardial damage in congestive HF.169,190 H-FABP is now available as a rapid bedside test, and a study is currently underway to assess its diagnostic value outside the hospital in the general practice setting.191 Elevated serum H-FABP is associated with an increased risk of death and major cardiac events in patient with ACS despite a negative serum cTn and BNP.192In patients with dilated cardiomyopathy, the incidence of acute deterioration was significantly higher in patients with higher values of H-FABP than in those with lower values of the markers.193

Markers f o r Cardiac Stress B-Type Natriuretic Peptide (BNP) and N-Terminal ProBNP (NT-ProBNP)

The pre-prohormone BNP is a 134 amino-acid peptide synthesized in the ventricular myocytes and cleaved into the 108 amino-acid prohormone BNP. The prohormone is released into the circulation during hemodynamic stress.194

BIOMARKERS OF CARDIAC INJURY

135

Once in the circulation, the prohormone BNP is cleaved by a circulating endoprotease, corin, into the inactive 76 amino-acid NT-proBNP and the biologically active 32 amino-acid BNP. BNP causes arterial vasodilation, diuresis and natriuresis, which collectively reduce both afterload and preload. BNP also reduces the activities of the renin-angiotensin-aldosterone system and the sympathetic system. It plays an important role in counteracting the deleterious responses in heart failure by maintaining circulatory homeostasis and preventing the cardiovascular system from volume overload (Figure 7.3). Circulating levels of BNP and NT-proBNP differ, partly due to the different half-lives. NT-proBNP has a slightly longer half-life than BNP (60-120 min vs. 20 min), and may account for its higher concentrations that are approximately 20 times greater than BNP. While BNP is mainly cleared via internalization by cells that express the BNP receptors, renal clearance is the main mechanism for NT-proBNP. The concentrations of NT-proBNP are higher in patients with renal dysfunction, probably due to the reduced clearance. A number of clinical and epidemiology studies have demonstrated the relationship between HF and BNP or NT-proBNP195197 BNP is now commonly used to assist the diagnosis of HF, and has been endorsed as a useful diagnostic marker for HF. 198, '" BNP has been used to differentiate cardiac causes of dyspnea from that of pulmonary causes in the emergency setting.200 In the BREATHING NOT PROPERLY study, a plasma BNP level greater than 100 pg/ml was demonstrated to predict congestive HF (sensitivity = 90% and specificity = 73%).201 Similar findings were reported for NT-proBNP in the PRIDE study, although with different cut-off.202 The cut-off for patients younger than 50 years old is 450 ng/1 and for 50 years and older is 900 ng/1. BNP, however, fails to correlate with the New York Heart Association (NYHA) class of dyspnea and does not predict the severity of HF.203 The cut-off values may differ in patients with acute versus chronic HF. For example, about 20% of the patients with chronic HF exhibited plasma BNP levels below 100 pg/mg, which is the cut-off suggested for diagnostic purposes.204 BNP and NT-proBNP have been reported to be increased in patients with coronary artery disease.205 These increases are believed to be associated with both isolated left ventricular diastolic dysfunction and systolic dysfunction and are independent of hemodynamic overload.206-207 A single NT-pro BNP value at 96 hours after onset of symptoms proved useful for estimation of left ventricular ejection fraction.208 BNP was found to be useful in predicting mortality in acute MI.209 However, a recent systematic review casts doubt about the prognostic utility of BNP in these patients. The main issue of using BNP or BT-proBNP in patients with coronary artery disease is the lack of welldefined cut-off.210-211 What should be done to these patients once when one exceeds those cut-offs is also unclear. BNP can also be elevated in a number of conditions and is not specific to heart failure (Table 7.5). In the intensive care setting, as BNP is increased in a variety of cardiac conditions, it offers little help in differential diagnosis.212 BNP levels are also significantly confounded by age, gender, and fluid loading.213-215 The variation according to age and gender may be mediated par-

136

BIOMARKERS

FIGURE 7.3 Pathophysiology of cardiac disease and the physiological actions of B-type natriuretic peptide (BNP). Heart failure or ischemic events result in reduction in cardiac function (both systolic and diastolic), which in turn leads to a reduction in cardiac output (CO). Hypotension, tissue hypoperfusion, and reduced oxygen deliver (D0 2 ) are the main manifestations. The body compensates by increasing both preload and afterload: increasing salt and water retention via the rennin-angiotension-aldosterone system (RAAS), and vasoconstriction via baroreflex. Overcompensations result in deleterious effects such as pulmonary edema and increased cardiac workload. In response to volume overload, the myocardium releases BNP which partly counteracts the deleterious effects of overcompensation by inducing vasodilation, inhibiting the RAAS, and exerting some lusitropic effects. However the ability for BNP to compensate is limited. (See color insert for a full color version of this figure.)

BIOMARKERS OF CARDIAC INJURY

137

tially by the development of hypertension, renal disease, and vascular disease. Therefore, interpretation of BNP and NT-proBNP require the knowledge of age, gender, and the co-morbidities of the patients. Nevertheless, due to its high sensitivity and negative predictive value, BNP can be used to rule out cardiac disease. Adrenomedullin (ADM) ADM, a peptide of 52 amino acids, is a member of the calcitonin gene-related peptide family, which was originally discovered in human adrenal medulla as a hypotensive factor produced by pheochromocytoma cells.216 The precursor, pre-proADM, is synthesized and present in the heart, adrenal medulla, lungs, and kidneys. ADM is produced by the ventricular myocytes and fibroblasts in response to pressure and volume overload.217'218 Plasma ADM levels are significantly elevated in congestive heart failure, MI, and hypertension.219-221 Tissue levels of ADM and mRNA levels are also increased in ischemia.222 In acute MI, levels of ADM expressed in patients correlate with the severity of illness and are also a prognostic indicator for mortality in acute MI.223'224 The biological activities of ADM are similar to BNP—exerting a protective mechanism against the deleterious overcompensated response to failing hearts. ADM is a potent vasodilator, and can induce diuresis and natriuresis.216 Administration of ADM into patients with congestive heart failure increased urine output and urinary sodium excretion. ADM also improved cardiac index, hemodynamics, renal function, and hormonal parameters in the same group of patients.225 Part of these are mediated by its positive inotropic effect.226 The lack of standardization of the assay and a well-defined cut-off render hinders its utility as a clinical tool at present.

TABLE 7.5

Conditions or factors associated with BNP or NT-proBNP elevations.

Age Arrhythmias Cardiomyopathy: hypertrophic, ischemic, or dilated Congestive heart failure Coronary artery disease Gender Hypertension Left ventricular diastolic dysfunction Pulmonary embolism Renal failure Right heart failure Right ventricular overloading: fluid, or pressure overloading Sepsis or septic shock Sepsis-related myocardial dysfunction

138

BIOMARKERS

ST2 ST2 is a member of the IL-1 receptor family. Both the transmembrane isoform (ST2L) and the soluble form of ST2 (sST2) are expressed by cardiomyocytes in response to mechanical strain, and were found to be increased in patients day one after acute MI.227 Until the discovery of IL-33 in 2005, ST2 was believed to be a stray receptor without any significant physiological function.228 It is now clear that the IL-33/ST2 plays an important role in modulating various inflammatory processes. ST2 is associated with diseases such as asthma, pulmonary fibrosis, rheumatoid arthritis, and septic shock.229 IL-33 is known to possess some cardioprotective effect against pressure overload. In animal models, IL-33 blocks die effects of angiotensin II and reduces ventricular hypertrophy and fibrosis in the face of increased ventricular strain.230 IL-33 also possesses anti-atherosclerotic effects by reducing vascular inflammation of atherosclerosis.231 While the ST2L receptor mediates the effect of IL-33, the physiology of sST2 has yet to be determined.232 It is clear that sST2 binds to IL-33 and reduces the cardioprotective effects of IL-33—antagonizing the effects of IL-33 by acting as a "decoy receptor."233 The local tissue ratio of IL-33 and sST2 could regulate IL-33 mediated signalling. Serum sST2 levels were increased one day after MI. The levels correlated with creatine kinase and were inversely proportional to left ventricular ejection fraction.227 In patients with acute MI, baseline sST2 levels were associated with higher 30-day mortality and development of new congestive HE234 In an analysis of patients presenting to the emergency with acute dyspnea, sST2 concentrations were significantly higher in patients presenting with acute systolic heart failure than in patients presenting with other causes of dyspnea. A serum level above 0.23 ng/ml had an 11-fold increased risk of death at one year.235 In patients with acute HF, sST2 segregated with more severe New York Heart Association functional class symptoms, and correlated inversely with left ventricular ejection fraction. sST2 also strongly predicted morality from a few months after presentation to at least one year in this class of patients.236 A few aspects of ST2 need to be addressed before it can be proven to be clinically useful. First, the true pathophysiological meaning of serum sST2 levels in cardiac disease is not fully understood. Whether the ratio of IL-33 to sST2 would provide a better diagnostic and prognostic value than sST2 alone requires further investigation. Second, the specificity sST2 levels may be undermined as sST2 are also increased in various inflammatory diseases such as asthma, autoimmune disease, and sepsis. Third, the impact of treatmentinduced longitudinal changes in sST2 over time on the clinical outcome and long-term prognosis also requires investigation.

Multimarker Approach? The reliance on a single biomarker for diagnostic or prognostic purpose has in many cases proven to be unsatisfactory. For example, in the context of MI, ischemia can be due to a number of causes ranging from coronary flow obstructing thrombosis, supply-demand imbalance without thrombosis, to

BIOMARKERS OF CARDIAC INJURY

139

iatrogenic causes. To diagnose MI itself is already a difficult task, to determine the origin of the injury presents even more challenges. A number of studies have demonstrated the benefits of using multimarker approaches. For example, the prognostic value was better when CRP was used in conjunction with BNP or cTn than when used independently.237,238 Similarly, the use of several biomarkers of cardiovascular and renal dysfunction substantially improved the risk stratification for cardiovascular causes of death compared to the use of established risk factors.239 The combination of cTnT, ECG, and ischemia-modified albumin could identify 95% of patients whose chest pain was attributable to ischemic heart disease.240,241 However, a recent study has shown that the combined measurements of various biomarkers did not provide any additional clinically significant benefit than by using cTnl alone in the diagnosis of MI.242 The main reason for this observation is that the other additional biomarkers used, including NT-proBNP, CRP, MMP, MPO, as well as soluble CD40 ligand, have low specificities and that their levels are influenced by non-specific inflammatory and other stimuli in the absence of acute MI. Poor sensitivities of the other biomarkers are also a hindering factor. While the multimarkers approach seems to be attractive, further research is required to prove its value further. In particular, different combinations of markers are required for different types of cardiac dysfunction and clinical settings.

CONCLUSIONS The use and interpretation of cardiac biomarkers depends upon the specific clinical setting and what information is being sought. For example, biomarkers employed for diagnostic purposes have very different requirements than those used for prognostic purposes. The type, and stage of development, of the cardiac disease pose another level of requirement and difficulty for the development of biomarkers and their interpretations. Ideally, the biomarker should be specific for cardiac diseases, but this is both theoretically and practically impossible due to the sharing of common pathophysiological pathways in many diseases. To date, while a small number of biomarkers have proven clinical values, which are still limited, most are unfit to use clinically due to poor sensitivity and specificity. Despite the lack of sensitivity and specificity, this should not deter biomarkers from being used, provided they are used within the clinical context and the user is aware of their limitations. There is no doubt that knowledge about cardiac biomarkers will continue to evolve with technology, most notably proteomics, and with it, our understanding of their physiological roles. When properly used, biomarkers provide invaluable additional information in screening, diagnosis, monitoring, and prognosis of cardiac disease.

SUMMARY POINTS 1.

The term "cardiac injury" comprises miscellaneous cardiac insults that involve the processes of cell injury or cell death (autophagy, apoptosis, and necrosis). While myocardial infarction is a common cause of car-

140

BIOMARKERS

2.

3.

4.

diac injury, it is not the sole cause. Other causes include heart failure, myocarditis, sepsis-induced myocardial depression, trauma, and druginduced myocardial toxicity. It is estimated that about 16 million Americans have CAD and about half of these experienced an MI. The pathophysiological mechanisms for most cardiac diseases involve three stages: inflammation, myocardial cell injury (damage), and cardiac stress. While these stages are continuous, there is no clear demarcation for each stage and they may occur concomitantly. Traditional cardiac biomarkers mostly reflect the extent of myocardial cell damage. The most important biomarker in this category is cTn. A recent change of paradigm emphasizes the importance of inflammation and cardiac stress. Biomarkers for the latter two are now available in the clinical setting, and the more important ones are CRP and BNP. Biomarkers generally show poor sensitivity and specificity for diagnostic purpose. None of the biomarkers are ideal and multimarker approaches may provide better diagnostic and prognostic values. Further research are required in this area.

REFERENCES 1. 2. 3. 4. 5. 6. 7.

8. 9. 10.

Kron, I. L. and Cox, P. M, Jr. Cardiac Injury After Chest Trauma. Crit. Care Med. 1983:11:524-526. Yeh, E. T. Cardiotoxicity Induced by Chemotherapy and Antibody Therapy. Annu. Rev. Med. 2006:57:485^98. Fernandes, C. J., Jr., Akamine, N., and Knobel, E. Myocardial Depression in Sepsis. Shock 30. (Suppl 1). 2008:14-17. Collinson, P. O., Stubbs, P. J., and Morgan, S. H. Cardiac Troponin-I Accurately Predicts Myocardial Injury in Renal Failure. Nephrol. Dial. Transplant. 1999: 14:1030-1031. Del Carlo, C. H., Pereira-Barretto, A. C, Cassaro-Strunz, C, Latorre Mdo, R., and Ramires, J. A. Serial Measure of Cardiac Troponin T Levels for Prediction of Clinical Events in Decompensated Heart Failure. J. Card. Fail. 2004:10:43^18. Kaczynska, A., Szulc, M., Styczynski, G., Kostrubiec, M., and Pacho, R., et al. Right Ventricle Injury During Acute Pulmonary Embolism Leads to Its Remodeling. Int. J. Cardiol. 2008:125:120-121. World Health Organization. The Joint International Society and Federation of Cardiology /World Health Organization Task Force on Standardization of Clinical Nomenclature. Nomenclature and Criteria For Diagnosis oflschemic Heart Disease. Circulation 59; 1976:607-609. Gillum, R. F, Fortmann, S. P., Prineas, R. J., and Kottke, T. E. International Diagnostic Criteria for Acute Myocardial Infarction and Acute Stroke. Am. Heart J. 1984:108:150-158. Porela, P., Helenius, H., Pulkki, K., and Voipio-Pulkki, L. M. Epidemiological Classification of Acute Myocardial Infarction: Time for a Change? Fur. Heart]. 1999:20:1459-1464. Thompson, W. G, Mahr, R. G., Yohannan, W. S., and Pincus, M. R. Use of Creatine Kinase MB Isoenzyme for Diagnosing Myocardial Infarction When Total Creatine Kinase Activity Is High. Clin. Chem. 1998:34:2208-2210.

BIOMARKERS OF CARDIAC INJURY 11. 12.

13. 14. 15.

16.

17. 18. 19. 20. 21. 22. 23.

24.

25. 26.

141

Adams, J. E., Sicard, G. A., Allen, B. T., Bridwell, K. H., and Lenke, L. G., et al. Diagnosis of Perioperative Myocardial Infarction with Measurement of Cardiac Troponin I. N. Engl. J. Med. 1994:330:670-674. Packham, C , Gray, D., Weston, C , Large, A., and Silcocks, P., et al. Changing the Diagnostic Criteria for Myocardial Infarction in Patients with a Suspected Heart Attack Affects the Measurement of 30 Day Mortality but not Long Term Survival. Heart. 1984:88:337-342. Katus, H. A., Remppis, A., Looser, S., Hallermeier, K., and Scheffold, T., et al. Enzyme Linked Immuno Assay of Cardiac Troponin T for the Detection of Acute Myocardial Infarction in Patients. J. Mol. Cell. Cardiol. 21;1989:1349-1353. Ooi, D. S., Isotalo, P. A., and Veinot, J. P. Correlation of Antemortem Serum Creatine Kinase, Creatine Kinase-MB, Troponin I, and Troponin T with Cardiac Pathology. Clin. Chem. 46;2000:338-344. Alpert, J. S., Thygesen, K., Antman, E., and Bassand, J. P. Myocardial Infarction Redefined—A Consensus Document of the Joint European Society of Cardiology/American College of Cardiology Committee for the Redefinition of Myocardial Infarction. /. Am. Coll. Cardiol. 36;2000:959-969. Trevelyan, J., Needham, E. W., Smith, S. C , and Mattu, R. K. Impact of the Recommendations for the Redefinition of Myocardial Infarction on Diagnosis and Prognosis in an Unselected United Kingdom Cohort with Suspected Cardiac Chest Pain. Aw. J. Cardiol. 93 ;2004:817-821. Roger, V. L., Killian, J. M., Weston, S. A., Jaffe, A. S., and Kors, J., et al. Redefinition of Myocardial Infarction: Prospective Evaluation in the Community. Circulation. 114;2006:790-797. Tunstall-Pedoe, H. Redefinition of Myocardial Infarction by a Consensus Dissenter. 7. Am. Coll. Cardiol. 37;2001:1472-1474. Richards, A. M., Lainchbury, J. G., and Nicholls, M. G. Unsatisfactory Redefinition of Myocardial Infarction. Lancet. 357;2001:1635-1636. Thygesen, K., Alpert, J. S., White, H. D., Jaffe, A, S., and Apple, F. S., et al. Universal Definition of Myocardial Infarction. Circulation. 116;2007:2634—2653. Salomaa, V., Koukkunen, H., Ketonen, M., Immonen-Raiha, P., and Karja-Koskenkari, P., et al. A New Definition for Myocardial Infarction: What Difference Does It Make? Eur. Heart J. 2005:1719-1725. Burke, G. L., Edlavitch, S. A., and Crow, R. S. The Effects of Diagnostic Criteria on Trends in Coronary Heart Disease Morbidity: The Minnesota Heart Survey. J. Clin. Epidemiol. 42;1989:17-24. Parikh, N. I., Gona, P., Larson, M. G., Fox, C. S., and Benjamin, E. J., et al. Long-Term Trends in Myocardial Infarction Incidence and Case Fatality in the National Heart, Lung, and Blood Institute's Framingham Heart Study. Circulation. 119;2009:1203-1210. Rosamond, W., Flegal, K., Furie, K., Go, A., and Greenlund, K., et al. Heart Disease and Stroke Statistics—2008 Update: A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 117;2008:E25-146. Leitinger, N. Oxidized Phospholipids as Modulators of Inflammation in Atherosclerosis. Curr. Opin. Lipidol. 2003:14:421^130. Dai, G., Kaazempur-Mofrad, M. R., Natarajan, S., Zhang, Y, and Vaughn, S., et al. Distinct Endothelial Phenotypes Evoked by Arterial Waveforms Derived from Atherosclerosis-Susceptible and -Resistant Regions of Human Vasculature. Proc. Natl. Acad. Set USA. 101 ;2004:14871-14876.

142

BIOMARKERS 27. 28. 29.

30. 31. 32. 33. 34. 35.

36.

37. 38. 39. 40. 41. 42.

43.

Massberg, S., Brand, K., Gruner, S., Page, S., and Muller, E., et al. A Critical Role of Platelet Adhesion in the Initiation of Atherosclerotic Lesion Formation. J. Exp. Med. 196;2002:887-896. Cybulsky, M. I. and Gimbrone, M. A., Jr. Endothelial Expression of a Mononuclear Leukocyte Adhesion Molecule During Atherogenesis. Science. 251; 1991: 788-791. Clinton, S. K., Underwood, R., Hayes, L., Sherman, M. L., and Kufe, D. W., et al. Macrophage Colony-Stimulating Factor Gene Expression in Vascular Cells and in Experimental and Human Atherosclerosis. Am. J. Pathol. 140;1992: 301-316. Peiser, L., Mukhopadhyay, S., and Gordon, S. Scavenger Receptors in Innate Immunity. Curr. Opin. Immunol. 14;2002:123-128. Edfeldt, K, Swedenborg, J., Hansson, G. K., and Yan, Z. Q. Expression of TollLike Receptors in Human Atherosclerotic Lesions: A Possible Pathway for Plaque Activation. Circulation. 105;2002:1158-1161. Miller, Y. I., Chang, M. K., Binder, C. J., Shaw, P. X., and Witztum, J. L. Oxidized Low Density Lipoprotein and Innate Immune Receptors. Curr. Opin. Lipidol. 14;2003:437^45. Robertson, A. K. and Hansson, G. K. T Cells in Atherogenesis: For Better or For Worse? Arterioscler. Thromb. Vase. Biol. 26;2006:2421-2432. Szabo, S. J., Sullivan, B. M., Peng, S. L., and Glimcher, L. H. Molecular Mechanisms Regulating Thl Immune Responses. Annu. Rev. Immunol. 21;2003: 713-758. Risinger, G. M., Jr., Hunt, T. S., Updike, D. L., Bullen, E. C , and Howard, E. W. Matrix Metalloproteinase-2 Expression by Vascular Smooth Muscle Cells Is Mediated by Both Stimulatory and Inhibitory Signals in Response to Growth Factors. J. Biol. Chem. 281;2006:25915-25925. Chandrasekar, B., Mummidi, S., Mahimainathan, L., Patel, D. N., and Bailey, S. R., et al. Interleukin-18-Induced Human Coronary Artery Smooth Muscle Cell Migration Is Dependent on NF-Kappab- and AP-1-Mediated Matrix Metalloproteinase-9 Expression and Is Inhibited by Atorvastatin. J. Biol. Chem. 281;2006:15099-15109. Jones, C. B., Sane. D. C , and Herrington, D. M. Matrix Metalloproteinases: A Review of Their Structure and Role in Acute Coronary Syndrome. Cardiovasc. Res. 59;2003:812-823. Demer, L. L. Vascular Calcification and Osteoporosis: Inflammatory Responses to Oxidized Lipids. Int. J. Epidemiol. 31;2002:737-741. Geng, Y. J. and Libby, P. Progression of Atheroma: A Struggle Between Death and Procreation. Arterioscler Thromb. Vase. Biol. 22;2002:1370-1380. McCarthy, N. J. and Bennett, M. R. The Regulation of Vascular Smooth Muscle Cell Apoptosis. Cardiovasc. Res. 45;2000:747-755. Farb, A., Burke, A. P., Tang, A. L., Liang, T. Y., and Mannan, P., et al. Coronary Plaque Erosion without Rupture Into a Lipid Core. A Frequent Cause of Coronary Thrombosis in Sudden Coronary Death. Circulation. 93;1996:1354—1363. Durand, E., Scoazec, A., Lafont, A., Boddaert, J., and Al Hajzen, A., et al. In Vivo Induction of Endothelial Apoptosis Leads to Vessel Thrombosis and Endothelial Denudation: A Clue to the Understanding of the Mechanisms of Thrombotic Plaque Erosion. Circulation. 109;2004:2503-2506. Dimmeler, S. and Zeiher, A. M. Endothelial Cell Apoptosis in Angiogenesis and Vessel Regression. Circ. Res. 87;2000:434-439.

BIOMARKERS OF CARDIAC INJURY 44. 45. 46. 47. 48. 49.

50. 51. 52.

53. 54. 55. 56. 57. 58. 59. 60. 61.

143

Takemura, G., Ohno, M., Hayakawa, Y., Misao, J., and Kanoh, M., et al. Role of Apoptosis in the Disappearance of Infiltrated and Proliferated Interstitial Cells After Myocardial Infarction. Circ. Res. 82;1998:1130-1138. Gottlieb, R. A., Burleson, K. O., Kloner, R. A., Babior, B. M., and Engler, R. L. Reperfusion Injury Induces Apoptosis in Rabbit Cardiomyocytes. J. Clin. Invest. 94;1994:1621-1628. Ottaviani, G., Lavezzi, A. M., Rossi, L., and Matturri, L. Proliferating Cell Nuclear Antigen (PCNA) and Apoptosis in Hyperacute and Acute Myocardial Infarction. Eur. J. Histochem. 43;1999:7-14. Yaoita, H. and Maruyama, Y. Intervention for Apoptosis in Cardiomyopathy. Heart Fail. Rev. 13;2008:181-191. Olivetti, G., Abbi, R., Quaini, R, Kajstura, J., and Cheng, W., et al. Apoptosis in the Failing Human Heart. N. Engl. J. Med. 336;1997:1131-1141. Ibe, W., Saraste, A., Lindemann, S., Bruder, S., and Buerke, M., et al. Cardiomyocyte Apoptosis is Related to Left Ventricular Dysfunction and Remodelling in Dilated Cardiomyopathy, But Is Not Affected by Growth Hormone Treatment. Eur. J. Heart Fail. 9;2007:160-167. Dent, M. R., Das, S., and Dhalla, N. S. Alterations in Both Death and Survival Signals for Apoptosis in Heart Failure Due to Volume Overload. / Mol. Cell. Cardiol. 43 ;2007:726-732. Anselmi, A., Gaudino, M., Baldi, A., Vetrovec, G. W., and Bussani, R., et al. Role of Apoptosis in Pressure-Overload Cardiomyopathy. J. Cardiovasc. Med. (Hagerstown) 9;2008:227-232. Led, A., Claudio, P. P., Li, Q., Wang, X., and Reiss, K., et al. StretchMediated Release of Angiotensin II Induces Myocyte Apoptosis by Activating P53 That Enhances the Local Renin-Angiotensin System and Decreases the Bcl-2-to-Bax Protein Ratio in the Cell. J. Clin. Invest. 101;1998:1326-1342. Colucci, W. S., Sawyer, D. B., Singh, K., and Communal, C. Adrenergic Overload and Apoptosis in Heart Failure: Implications for Therapy. J. Card. Fail. 6;2000:l-7. Anker, S. D. and Von Haehling, S. Inflammatory Mediators in Chronic Heart Failure: An Overview. Heart. 90;2004:464-470. Seta, Y, Shan, K., Bozkurt, B., Oral, H., and Mann, D. L. Basic Mechanisms in Heart Failure: The Cytokine Hypothesis. J. Card. Fail. 2; 1996:243-249. Kalyanaraman, B., Joseph, J., Kalivendi, S., Wang, S., and Konorev, E., et al. Doxorubicin-Induced Apoptosis: Implications in Cardiotoxicity. Mol. Cell Biochem. 234-235;2002:119-124. Thorburn, A. and Frankel, A. E. Apoptosis and Anthracycline Cardiotoxicity. Mol. Cancer Ther. 5;2006:197-199. Zhao, X., Feng, T, Chen, H., Shan, H., and Zhang, Y, et al. Arsenic TrioxideIndnced Apoptosis in H9c2 Cardiomyocytes: Implications in Cardiotoxicity. Basic Clin. Pharmacol. Toxicol. 102;2008:419^125. Beranek, J. T. Apoptosis Contributes to Cyclophosphamide-Induced Cardiomyopathy. Bone Marrow Transplant. 29;2002:91. Fernandez-Sola, J., Fatjo, R, Sacanella, E., Estruch, R., and Bosch, X., et al. Evidence of Apoptosis in Alcoholic Cardiomyopathy. Hum. Pathol. 37;2006: 1100-1110. Buerke, U., Carter, J. M., Schlitt, A., Russ, M., and Schmidt, H., et al. Apoptosis Contributes to Septic Cardiomyopathy and Is Improved by Simvastatin Therapy. Shock. 29;2008:497-503.

144

BIOMARKERS 62. 63. 64.

65.

66.

67. 68. 69. 70. 71. 72.

73. 74.

75.

76.

Neviere, R., Fauvel, H., Chopin, C , Formstecher, P., and Marchetti, P. Caspase Inhibition Prevents Cardiac Dysfunction and Heart Apoptosis in a Rat Model of Sepsis. Am. J. Respir. Crit. Care Med. 163;2001:218-225. Schweitzer, P. The Electrocardiographic Diagnosis of Acute Myocardial Infarction in the Thrombolytic Era. Am Heart J 119;1990:642-654. Pozen, M. W., D'Agostino, R. B., Selker, H. P., Sytkowski, P. A., and Hood, W. B., Jr. A Predictive Instrument to Improve Coronary-Care-Unit Admission Practices in Acute Ischemic Heart Disease. A Prospective Multicenter Clinical Trial. N. Engl. J. Med. 310;1984:1273-1278. Rouan, G. W., Lee, T. H., Cook, E. E, Brand, D. A., and Weisberg, M. C , et al. Clinical Characteristics and Outcome of Acute Myocardial Infarction in Patients with Initially Normal or Nonspecific Electrocardiograms (A Report From the Multicenter Chest Pain Study). Am. J. Cardiol. 64; 1989:1087-1092. Schmitt, C , Lehmann, G., Schmieder, S., Karch, M., and Neumann, F. J., et al. Diagnosis of Acute Myocardial Infarction in Angiographically Documented Occluded Infarct Vessel: Limitations of ST-Segment Elevation in Standard and Extended ECG Leads. Chest. 120;2001:1540-1546. Rosner, M. H. and Brady, W. J. The ECG Diagnosis of Acute Myocardial Infarction in the Presence of Left Bundle Branch Block. Am. J. Emerg. Med. 16; 1998: 697-700. Ali, M., Cohen, H. C , and Singer, D. H. ECG Diagnosis of Acute Myocardial Infarction in Patients with Pacemakers. Arch. Intern. Med. 138;1978:1534— 1537. Ladue, J. S., Wroblewski, F., and Karmen, A. Serum Glutamic Oxaloacetic Transaminase Activity in Human Acute Transmural Myocardial Infarction. Science. 120;1954:497^199. Dolci, A. and Panteghini, M. The Exciting Story of Cardiac Biomarkers: From Retrospective Detection to Gold Diagnostic Standard for Acute Myocardial Infarction and More. Clin. Chim. Ada. 369;2006:179-187. Wroblewski, F. and Ladue, J. S. Lactic Dehydrogenase Activity in Blood. Proc. Soc. Exp. Biol. Med. 90;1955:210-213. Goldberg, D. M., Remtulla, M. A., and Lustig, V. The Diagnostic Accuracy of Three Recommended Methods for Serum Aspartate Aminotransferase Assays in Patients Suspected of Myocardial Infarction and Hepatobiliary Diseases. Clin. Biochem. 21;1988:323-328. Robinson, Y., Cristancho, E., and Boning, D. An Optimized Method for the Assay of the Red Blood Cell—Age-Related Enzyme Aspartate Aminotransferase. LabHematol. 10;2004:144-146. Pellar, T. G., Galbraith, L. V., Leung, F. Y, and Henderson, A. R. A Computer Program to Determine Diagnostic Decision Thresholds and Likelihood Ratios Illustrated with Aspartate Aminotransferase Activities After a Myocardial Infarction. Ann. Clin. Biochem. 26 (Pt 6); 1989:533-537. Galbraith, L. V., Leung, F. Y, Jablonsky, G., and Henderson, A. R. Time-Related Changes in the Diagnostic Utility of Total Lactate Dehydrogenase, Lactate Dehydrogenase Isoenzyme-1, and Two Lactate Dehydrogenase Isoenzyme-1 Ratios in Serum After Myocardial Infarction. Clin. Chem. 36; 1990:1317-1322. Hess, J. W. and MacDonald, R. P. Serum Creatine Phosphokinase Activity. A New Diagnostic Aid in Myocardial and Skeletal Muscle Disease. J. Mich. State Med. Soc. 62;1963:1095-1099.

BIOMARKERS OF CARDIAC INJURY 77'. 78. 79. 80. 81. 82.

83. 84. 85. 86. 87.

88.

89. 90. 91. 92. 93. 94.

145

Bruns, D. E. Diagnosis of Acute Myocardial Infarction When Skeletal Muscle Damage Is Present: A Caveat Regarding Use of Creatine Kinase Isoenzymes. Clin. Chem. 35;1989:705. Wu, A. H., Wang, X. M., Gornet, T. G., and Ordonez-Llanos, J. Creatine Kinase MB Isoforms in Patients with Skeletal Muscle Injury: Ramifications for Early Detection of Acute Myocardial Infarction. Clin. Chem. 38; 1992:2396-2400. Vasan, R. S. Biomarkers of Cardiovascular Disease: Molecular Basis and Practical Considerations. Circulation. 113;2006:2335-2362. Libby, P. Inflammation in Atherosclerosis. Nature. 420;2002:868-874. Hansson, G. K. Inflammation, Atherosclerosis, and Coronary Artery Disease. NewEngl. J. Med. 352;2005:1685-1695. Vasan, R. S., Sullivan, L. M., Roubenoff, R., Dinarello, C. A., and Harris, T., et al. Inflammatory Markers and Risk of Heart Failure in Elderly Subjects without Prior Myocardial Infarction: The Framingham Heart Study. Circulation. 107;2003:1486-1491. Cooper, L. T. Myocarditis. New Engl. J. Med. 360;2009:1526-1538. Larsson, P. T., Hallerstam, S., Rosfors, S., and Wallen, N. H. Circulating Markers of Inflammation are Related to Carotid Artery Atherosclerosis. Int. Angiol. 24;2005:43-51. Calabro, P., Willerson, J. T., and Yeh, E. T. Inflammatory Cytokines Stimulated C-Reactive Protein Production by Human Coronary Artery Smooth Muscle Cells. Circulation. 108;2003:1930-1932. Yasojima, K., Schwab, C , Mcgeer, E. G., and McGeer, P. L. Generation of C-Reactive Protein and Complement Components in Atherosclerotic Plaques. Am. J. Pathol. 158;2001:1039-1051. Bisoendial, R. J., Kastelein, J. J., Peters, S. L., Levels, J. H., and Birjmohun, R., et al. Effects of CRP Infusion on Endothelial Function and Coagulation in Normocholesterolemic and Hypercholesterolemic Subjects. J. Lipid Res. 48;2007:952-960. Pasceri, V., Cheng, J. S., Willerson, J. T., and Yeh, E. T. Modulation of C-Reactive Protein-Mediated Monocyte Chemoattractant Protein-1 Induction in Human Endothelial Cells by Anti-Atherosclerosis Drugs. Circulation. 103; 2001:2531-2534. Tanaka, A., Shimada, K., Sano, T., Namba, M., and Sakamoto, T., et al. Multiple Plaque Rupture and C-Reactive Protein in Acute Myocardial Infarction. J. Am. Coll. Cardiol. 45;2005:1594-1599. Liuzzo, G., Biasucci, L. M., Rebuzzi, A. G., Gallimore, J. R., and Caligiuri, G., et al. Plasma Protein Acute-Phase Response in Unstable Angina Is Not Induced by Ischemic Injury. Circulation. 94;1996:2373-2380. Wong, C. K., Szeto, C. C , Chan, M. H., Leung, C. B., and Li, P. K., et al. Elevation of Pro-Inflammatory Cytokines, C-Reactive Protein and Cardiac Troponin T in Chronic Renal Failure Patients on Dialysis. Immunol. Invest. 36;2007:47-57. Deodhar, S. D. C-Reactive Protein: The Best Laboratory Indicator Available for Monitoring Disease Activity. Cleveland Clin. J. Med. 56; 1989:126-130. Elster, S. K., Braunwald, E., and Wood, H. F. A Study of C-Reactive Protein in the Serum of Patients with Congestive Heart Failure. 51;1956:533-541. Mora, S., Musunuru, K., and Blumenthal, R. S. The Clinical Utility of HighSensitivity C-Reactive Protein in Cardiovascular Disease and the Potential Implication of JUPITER on Current Practice Guidelines. Clin. Chem. 55;2009: 219-228.

146

BIOMARKERS 95. 96.

97.

98.

99.

100. 101.

102.

103. 104. 105.

106. 107. 108.

Cook, N. R., Buring, J. E., and Ridker, P. M. The Effect of Including C-Reactive Protein in Cardiovascular Risk Prediction Models for Women. Ann. Intern. Med. 145;2006:21-29. Ridker, P. M., Paynter, N. P., Rifai, N., Gaziano, J. M., and Cook, N. R. C-Reactive Protein and Parental History Improve Global Cardiovascular Risk Prediction: The Reynolds Risk Score for Men. Circulation. 118;2008: 2243-2251. Lindahl, B., Toss, H., Siegbahn, A., Venge, P., and Wallentin, L. Markers of Myocardial Damage and Inflammation in Relation to Long-Term Mortality in Unstable Coronary Artery Disease. FRISC Study Group. Fragmin During Instability in Coronary Artery Disease. New Engl. J. Med. 343;2000:1139-1147. Mora, S. and Ridker, P. M. Justification for the Use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER)—Can CReactive Protein be Used to Target Statin Therapy in Primary Prevention? Am. J. Cardiol. 97;2006:33A^1A. Pearson, T. A., Mensah, G. A., Alexander, R. W., Anderson, J. L., and Cannon, R. O., Ill, et al. Markers of Inflammation and Cardiovascular Disease: Application to Clinical and Public Health Practice: A Statement for Healthcare Professionals From the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 107;2003:499-511. Anand, I. S., Latini, R., Florea, V. G., Kuskowski, M. A., and Rector, T., et al. C-Reactive Protein in Heart Failure: Prognostic Value and the Effect of Valsartan. Circulation. 112;2005:1428-1434. Schieffer, B., Schieffer, E., Hilfiker-Kleiner, D., Hilfiker, A., and Kovanen, P. T., et al. Expression of Angiotensin II and Interleukin 6 in Human Coronary Atherosclerotic Plaques: Potential Implications for Inflammation and Plaque Instability. Circulation. 101;2000:1372-1378. Lindmark, E., Diderholm, E., Wallentin, L., and Siegbahn, A. Relationship Between Interleukin 6 and Mortality in Patients with Unstable Coronary Artery Disease: Effects of an Early Invasive or Noninvasive Strategy. JAMA. 286;2001:2107-2113. Ridker, P. M., Rifai, N., Stampfer, M. J., and Hennekens, C. H. Plasma Concentration of Interleukin-6 and the Risk of Future Myocardial Infarction Among Apparently Healthy Men. Circulation. 101;2000:1767-1772. Mallat, Z., Corbaz, A., Scoazec, A., Besnard, S., and Leseche, G., et al. Expression of Interleukin-18 in Human Atherosclerotic Plaques and Relation to Plaque Instability. Circulation. 104;2001:1598-1603. Zirlik, A., Abdullah, S. M., Gerdes, N., MacFarlane, L., and Schonbeck, U., et al. Interleukin-18, the Metabolic Syndrome, and Subclinical Atherosclerosis: Results From the Dallas Heart Study. Arterioscler. Thromb. Vase. Biol. 27;2007: 2043-2049. Skurk, T., Kolb, H., Muller-Scholze, S., Rohrig, K., and Hauner, H., et al. The Proatherogenic Cytokine Interleukin-18 Is Secreted by Human Adipocytes. Eur. J. Endocrinol. 152;2005:863-868. Blankenberg, S., Tiret, L., Bickel, C , Peetz, D., and Cambien, R, et al. Interleukin-18 Is a Strong Predictor of Cardiovascular Death in Stable and Unstable Angina. Circulation. 106;2002:24-30. Mallat, Z., Heymes, C , Corbaz, A., Logeart, D., and Alouani, S., et al. Evidence for Altered Interleukin 18 (IL)-18 Pathway in Human Heart Failure. Faseb. J. 18;2004:1752-1754.

BIOMARKERS OF CARDIAC INJURY

147

109. Eslick, G. D., Thampan, B. V., Nalos, M., McLean, A. S., and Sluyter, R. Circulating Interleukin-18 Concentrations and a Loss-of-Function P2X7 Polymorphism in Heart Failure. Int. J. Cardiol. In Press;2008. 110. Suzuki, H., Sato, R., Sato, T., Shoji, M , and Iso, Y, et al. Time-Course of Changes in the Levels of Interleukin 6 in Acutely Decompensated Heart Failure. Int. J. Cardiol. 100;2005:415^t20. 111. Kyrzopoulos, S., Adamopoulos, S., Parissis, J. T., Rassias, J., and Kostakis, G., et al. Levosimendan Reduces Plasma B-Type Natriuretic Peptide and Interleukin 6, and Improves Central Hemodynamics in Severe Heart Failure Patients. Int. J. Cardiol. 99;2005:409-413. 112. Gwechenberger, M., Hulsmann, M., Berger, R., Graf, S., and Springer, C , et al. Interleukin-6 and B-Type Natriuretic Peptide are Independent Predictors for Worsening of Heart Failure in Patients with Progressive Congestive Heart Failure. J. Heart Lung Transplant. 23;2004:839-844. 113. Haugen, E., Gan, L. M., Isic, A., Skommevik, T., and Fu, M. Increased Interleukin-6 but Not Tumour Necrosis Factor-Alpha Predicts Mortality in the Population of Elderly Heart Failure Patients. Exp. Clin. Cardiol. 13;2008:19-24. 114. Levine, B., Kalman, J., Mayer, L., Fillit, H. M., and Packer, M. Elevated Circulating Levels of Tumor Necrosis Factor in Severe Chronic Heart Failure. N. Engl. J. Med. 323;1990:236-241. 115. Bradham, W. S., Moe, G., Wendt, K. A., Scott, A. A., and Konig, A., et al. TNF-Alpha and Myocardial Matrix Metalloproteinases in Heart Failure: Relationship to LV Remodeling. Am. J. Physiol. Heart Circ. Physiol. 282,2002: H1288-1295. 116. Lee, D. S. and Vasan, R. S. Novel Markers for Heart Failure Diagnosis and Prognosis. Curr. Opin. Cardiol. 20;2005:201-210. 117. Feng, Q. Z., Zhao, Y. S., and Abdelwahid, E. The Role of Fas in the Progression of Ischemic Heart Failure: Prohypertrophy or Proapoptosis. Coron. Artery Dis. 19;2008:527-534. 118. Shimizu, M., Fukuo, K., Nagata, S., Suhara, T, and Okuro, M., et al. Increased Plasma Levels of the Soluble Form of Fas Ligand in Patients with Acute Myocardial Infarction and Unstable Angina Pectoris. J. Am. Coll. Cardiol. 39;2002: 585-590. 119. Li, Y, Takemura, G., Kosai, K., Takahashi, T., and Okada, H., et al. Critical Roles for the Fas/Fas Ligand System in Postinfarction Ventricular Remodeling and Heart Failure. Circ. Res. 95;2004:627-636. 120. Tsutamoto, T., Wada, A., Maeda, K., Mabuchi, N., and Hayashi, M., et al. Relationship Between Plasma Levels of Cardiac Natriuretic Peptides and Soluble Fas: Plasma Soluble Fas as a Prognostic Predictor in Patients with Congestive Heart Failure. J. Card. Fail. 7;2001:322-328. 121. Hanasaki, H., Takemura, Y, Fukuo, K., Ohishi, M., and Onishi, M., et al. Fas Promoter Region Gene Polymorphism Is Associated with an Increased Risk for Myocardial Infarction. Hypertens. Res. 32;2009:Doi:10.1038/Hr.2009.2 122. Mach, E, Schonbeck, U., Sukhova, G. K., Bourcier, T, and Bonnefoy, J. Y, et al. Functional CD40 Ligand Is Expressed on Human Vascular Endothelial Cells, Smooth Muscle Cells, and Macrophages: Implications for CD40-CD40 Ligand Signaling in Atherosclerosis. Proc. Natl. Acad. Sci. USA 94;1997: 1931-1936.

148

BIOMARKERS 123. Mach, R, Schonbeck, U., Bonnefoy, J. Y, Pober, J. S., and Libby, P. Activation of Monocyte/Macrophage Functions Related to Acute Atheroma Complication by Ligation of CD40: Induction of Collagenase, Stromelysin, and Tissue Factor. Circulation. 96;1997:396-399. 124. Tayebjee, M. H., Lip, G. Y, Tan, K. T, Patel, J. V., and Hughes, E. A., et al. Plasma Matrix Metalloproteinase-9, Tissue Inhibitor of Metalloproteinase-2, and CD40 Ligand Levels in Patients with Stable Coronary Artery Disease. Am. J. Cardiol. 96;2005:339-345. 125. De Lemos, J. A., Zirlik, A., Schonbeck, U., Varo, N., and Murphy, S. A., et al. Associations Between Soluble CD40 Ligand, Atherosclerosis Risk Factors, and Subclinical Atherosclerosis: Results from the Dallas Heart Study. Arterioscler. Thromb. Vase. Biol. 25;2005:2192-2196. 126. Blake, G. J., Ostfeld, R. J., Yucel, E. K., Varo, N., and Schonbeck, U., et al. Soluble CD40 Ligand Levels Indicate Lipid Accumulation in Carotid Atheroma: An In Vivo Study with High-Resolution MRI. Arterioscler. Thromb. Vase. Biol. 23;2003:E11-14. 127. Schonbeck, U., Varo, N., Libby, P., Buring, J., and Ridker, P. M. Soluble CD40L and Cardiovascular Risk in Women. Circulation. 104;2001: 2266-2268. 128. Varo, N., De Lemos, J. A., Libby, P., Morrow, D. A., and Murphy, S. A., et al. Soluble CD40L: Risk Prediction After Acute Coronary Syndromes. Circulation. 108;2003:1049-1052. 129. Galis, Z. S. and Khatri, J. J. Matrix Metalloproteinases in Vascular Remodeling and Atherogenesis: The Good, the Bad, and the Ugly. Circ. Res. 90;2002: 251-262. 130. Tayebjee, M. H., Lip, G. Y, Tan, K. T, Patel, J. V, and Hughes, E. A., et al. Plasma Matrix Metalloproteinase-9, Tissue Inhibitor of Metalloproteinase-2, and CD40 Ligand Levels in Patients with Stable Coronary Artery Disease. Am. J. Cardiol. 96;2005:339-345. 131. Kai, H., Ikeda, H., Yasukawa, H., Kai, M., and Seki, Y, et al. Peripheral Blood Levels of Matrix Metalloproteases-2 and -9 are Elevated in Patients with Acute Coronary Syndromes. J. Am. Coll. Cardiol. 32;1998:368-372. 132. Olson, F. J., Schmidt, C , Gummesson, A., Sigurdardottir, V, and Hulthe, J., et al. Circulating Matrix Metalloproteinase 9 Levels in Relation to Sampling Methods, Femoral and Carotid Atherosclerosis. J. Intern. Med. 263;2008: 626-635. 133. Tayebjee, M. H., Nadar, S., Blann, A. D., Gareth Beevers, D., and MacFadyen, R. J., et al. Matrix Metalloproteinase-9 and Tissue Inhibitor of Metalloproteinase-1 in Hypertension and Their Relationship to Cardiovascular Risk and Treatment: A Substudy of the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT). Aw. J. Hypertens. 17;2004:764-769. 134. Eldrup, N., Gronholdt, M. L., Sillesen, H., and Nordestgaard, B, G. Elevated Matrix Metalloproteinase-9 Associated with Stroke or Cardiovascular Death in Patients with Carotid Stenosis. Circulation. 114,2006:1847-1854. 135. Clark, I. M., Swingler, T. E., Sampieri, C. L., and Edwards, D. R. The Regulation of Matrix Metalloproteinases and Their Inhibitors. Int. J. Biochem. Cell. S;o/.40;2008:1362-1378. 136. Mullane, K. M., Kraemer, R., and Smith, B. Myeloperoxidase Activity as a Quantitative Assessment of Neutrophil Infiltration Into Ischemic Myocardium. J. Pharmacol. Methods. 14;1985:157-167.

BIOMARKERS OF CARDIAC INJURY

149

137. Biasucci, L. M., D'Onofrio, G., Liuzzo, G., Zini, G., and Monaco, C., et al. Intracellular Neutrophil Myeloperoxidase Is Reduced in Unstable Angina and Acute Myocardial Infarction, but Its Reduction Is Not Related to Ischemia. J. Am. Coll. Cardiol. 21 ;\996:6\ 1-616. 138. Singh, U., Devaraj, S., and Jialal, I. C-Reactive Protein Stimulates Myeloperoxidase Release from Polymorphonuclear Cells and Monocytes: Implications for Acute Coronary Syndromes. Clin. Chem. 55;2009:361-364. 139. Brennan, M. L., Penn, M. S., Van Lente, E, Nambi, V., and Shishehbor, M. H., et al. Prognostic Value of Myeloperoxidase in Patients with Chest Pain. N. Engl. J. Med. 349;2003:1595-1604. 140. Zhang, R., Brennan, M. L., Fu, X., Aviles, R. J., and Pearce, G. L., et al. Association Between Myeloperoxidase Levels and Risk of Coronary Artery Disease. Jama. 286;2001:2136-2142. 141. Meuwese, M. C , Stroes, E. S., Hazen, S. L., Van Miert, J. N., and Kuivenhoven, J. A., et al. Serum Myeloperoxidase Levels are Associated with the Future Risk of Coronary Artery Disease in Apparently Healthy Individuals: The EPIC-Norfolk Prospective Population Study. J. Am. Coll. Cardiol. 50;2007:159-165. 142. Baldus, S., Heeschen, C , Meinertz, T., Zeiher, A. M., and Eiserich, J. P., et al. Myeloperoxidase Serum Levels Predict Risk in Patients with Acute Coronary Syndromes. Circulation. 108;2003:1440-1445. 143. Tang, W. H., Brennan, M. L., Philip, K., Tong, W., and Mann, S., et al. Plasma Myeloperoxidase Levels in Patients with Chronic Heart Failure. Am. J. Cardiol. 98;2006:796-799. 144. Tang, W. H., Tong, W., Troughton, R. W., Martin, M. G., and Shrestha, K., et al. Prognostic Value and Echocardiographic Determinants of Plasma Myeloperoxidase Levels in Chronic Heart Failure. J. Am. Coll. Cardiol. 49;2007: 2364-2370. 145. Shah, K. B., Kop, W. J., Christenson, R. H., Diercks, D. B., and Kuo, D., et al. Lack of Diagnostic and Prognostic Utility of Circulating Plasma Myeloperoxidase Concentrations in Patients Presenting with Dyspnea. Clin. Chem. 55;2009:59-67. 146. Faith, M., Sukumaran, A., Pulimood, A. B., and Jacob, M. How Reliable an Indicator of Inflammation Is Myeloperoxidase Activity? Clin. Chim. Ada. 396; 2008:23-25. 147. Van Der Veen, K. J. and Willebrands, A. F. Isoenzymes of Creatine Phosphokinase in Tissue Extracts and in Normal and Pathological Sera. Clin. Chim. Ada. 13;1996:312-316. 148. Ingwall, J. S., Kramer, M. F, Fifer, M. A., Lorell, B. H., and Shemin, R., et al. The Creatine Kinase System in Normal and Diseased Human Myocardium. N. Engl. J. Med. 313;1985:1050-1054. 149. Sobel, B. E. and Shell, W. E. Serum Enzyme Determinations in the Diagnosis and Assessment of Myocardial Infarction. Circulation. 45; 1972:471^482. 150. Falk, E. Unstable Angina with Fatal Outcome: Dynamic Coronary Thrombosis Leading to Infarction and/or Sudden Death. Autopsy Evidence of Recurrent Mural Thrombosis with Peripheral Embolization Culminating in Total Vascular Occlusion. Circulation. 71;1985:699-708. 151. Gotlieb, A. I., Freeman, M. R., Salerno, T. A., Lichtenstein, S. V, and Armstrong, P. W. Ultrastructural Studies of Unstable Angina in Living Man. Mod. Pathol. 4; 1991:75-80.

BIOMARKERS 152. Katus, H. A., Diederich, K. W., Hoberg, E., and Kubler, W. Circulating Cardiac Myosin Light Chains in Patients with Angina at Rest: Identification of a High Risk Subgroup. J. Am. Coll. Cardiol. ll;1988:487-493. 153. Gibler, W. B., Lewis, L. M., Erb, R. E., Makens, P. K., and Kaplan, B. C , et al. Early Detection of Acute Myocardial Infarction in Patients Presenting with Chest Pain and Nondiagnostic Ecgs: Serial CK-MB Sampling in the Emergency Department. Ann. Emerg. Med. 19;1990:1359-1366. 154. Hedges, J. R., Young, G. P., Henkel, G. E, Gibler, W. B., and Green, T. R., et al. Serial Ecgs are Less Accurate Than Serial CK-MB Results for Emergency Department Diagnosis of Myocardial Infarction. Ann. Emerg. Med. 21;1992: 1445-1450. 155. Collinson, P. O. Troponin T or Troponin I or CK-MB (or None?). Eur. Heart J. 19(SupplN);1998:N16-24. 156. Lin, J. C , Apple, F. S., Murakami, M. M., and Luepker, R. V. Rates of Positive Cardiac Troponin I and Creatine Kinase MB Mass Among Patients Hospitalized for Suspected Acute Coronary Syndromes. Clin. Chem. 50;2004:333-338. 157. Collinson, P. O., Chandler, H. A., Stubbs, P. J., and Moseley, D. S., et al. Measurement of Serum Troponin T, Creatine Kinase MB Isoenzyme, and Total Creatine Kinase Following Arduous Physical Training. Ann. Clin. Biochem. 32; 1995:450-453. 158. Wolf, P. L. Abnormalities in Serum Enzymes in Skeletal Muscle Diseases. Am. J. Clin. Pathol. 95;1991:293-296. 159. Potkin, R. T., Werner, J. A., Trobaugh, G. B., Chestnut, C. H., 3rd, and Carrico, C. J., et al. Evaluation of Noninvasive Tests of Cardiac Damage in Suspected Cardiac Contusion. Circulation. 66; 1982:627-631. 160. Ehsani, A., Ewy G. A., and Sobel, B. E. Effects of Electrical Countershock on Serum Creatine Phosphokinase (CPK) Isoenzyme Activity. Am. J. Cardiol. 37;1976:12-18. 161. Alpert, J. S., Thygesen, K., Antman, E., and Bassand, J. P. Myocardial Infarction Redefined—A Consensus Document of the Joint European Society of Cardiology/American College of Cardiology Committee for the Redefinition of Myocardial Infarction. J. Am. Coll. Cardiol. 36;2000:959-969. 162. Miller, W. L., Garratt, K. N., Burritt, M. F., Lennon, R. J., and Reeder, G. S., et al. Baseline Troponin Level: Key to Understanding the Importance of Post-PCI Troponin Elevations. Eur. Heart J. 27;2006:1061-1069. 163. Jaffe, A. S., Ravkilde, J., Roberts, R., Naslund, U., and Apple, F. S., et al. It's Time for a Change to a Troponin Standard. Circulation. 102;2000:1216-1220. 164. McLaurin, M. D., Apple, F. S., Voss, E. M., Herzog, C. A., and Sharkey, S. W. Cardiac Troponin I, Cardiac Troponin T, and Creatine Kinase MB in Dialysis Patients without Ischemic Heart Disease: Evidence of Cardiac Troponin T Expression in Skeletal Muscle. Clin. Chem. 43;1997:976-982. 165. Ricchiuti, V., Voss, E. M., Ney, A., Odland, M., and Anderson, P. A., et al. Cardiac Troponin T Isoforms Expressed in Renal Diseased Skeletal Muscle Will Not Cause False-Positive Results by the Second Generation Cardiac Troponin T Assay by Boehringer Mannheim. Clin. Chem. 44;1998:1919-1924. 166. Bodor, G. S., Porterfield, D., Voss, E. M., Smith, S., and Apple, F. S. Cardiac Troponin-I Is Not Expressed in Fetal and Healthy or Diseased Adult Human Skeletal Muscle Tissue. Clin. Chem. 41;1995:1710-1715. 167. Apple, F. S., Quist, H. E., Doyle, P. J., Otto, A. P., and Murakami, M. M. Plasma 99th Percentile Reference Limits for Cardiac Troponin and

BIOMARKERS OF CARDIAC INJURY

168. 169. 170. 171.

172. 173. 174. 175. 176.

177. 178. 179. 180. 181. 182.

183.

151

Creatine Kinase MB Mass for Use with European Society of Cardiology/ American College of Cardiology Consensus Recommendations. Clin. Chem. 49;2003:1331-1336. Hetland, O. and Dickstein, K. Cardiac Troponins I and T in Patients with Suspected Acute Coronary Syndrome: A Comparative Study in a Routine Setting. Clin. Chem. 44;1998:1430-1436. McCann, C. J., Glover, B. M., Menown, I. B., Moore, M. J., and McEneny, J., et al. Novel Biomarkers in Early Diagnosis of Acute Myocardial Infarction Compared with Cardiac Troponin T. Eur. Heart J. 29;2008:2843-2850. Zimmerman, J., Fromm, R., Meyer, D., Boudreaux, A., and Wun, C. C , et al. Diagnostic Marker Cooperative Study for the Diagnosis of Myocardial Infarction. Circulation. 99;1999:1671-1677. Morrow, D. A., Cannon, C. P., Jesse, R. L., Newby, L. K., and Ravkilde, J., et al. National Academy of Clinical Biochemistry Laboratory Medicine Practice Guidelines: Clinical Characteristics and Utilization of Biochemical Markers in Acute Coronary Syndromes. Circulation. 115;2007:E356-375. De Zoysa, J. R. Cardiac Troponins and Renal Disease. Nephrology. 2004:9: 83-88. Donnino, M. W., Karriem-Norwood, V., Rivers, E. P., Gupta, A., and Nguyen, H. B., et al. Prevalence of Elevated Troponin I in End-Stage Renal Disease Patients Receiving Hemodialysis. Acad. Emerg. Med. 11;2004:979-981. Jaffe, A. S., Babuin, L., and Apple, F. S. Biomarkers in Acute Cardiac Disease: The Present and the Future. J. Am. Coll. Cardiol. 2006:48:1-11. Ottani, F., Galvani, M., Nicolini, F. A., Ferrini, D., and Pozzati, A., et al. Elevated Cardiac Troponin Levels Predict the Risk of Adverse Outcome in Patients with Acute Coronary Syndromes. Am. Heart J. 140;2000:917-927. Ohman, E. M., Armstrong, P. W., Christenson, R. H., Granger, C. B., and Katus, H. A., et al. Cardiac Troponin T Levels for Risk Stratification in Acute Myocardial Ischemia. GUSTO HA Investigators. N. Engl. J. Med. 335;1996: 1333-1341. Hamm, C. W., Giannitsis, E., and Katus, H. A. Cardiac Troponin Elevations in Patients without Acute Coronary Syndrome. Circulation. 106;2002:2871-2872. Aviles, R. J., Askari, A. T., Lindahl, B., Wallentin, L., and Jia, G., et al. Troponin T Levels in Patients with Acute Coronary Syndromes, with or without Renal Dysfunction. New Engl. J. Med. 346,2002:2047-2052. Nellessen, U., Goder, S., Schobre, R., Abawi, M., and Hecker, H., et al. Serial Analysis of Troponin I Levels in Patients with Ischemic and Nonischemic Dilated Cardiomyopathy. Clin. Cardiol. 29;2006:219-224. Wallace, T. W., Abdullah, S. M., Drazner, M. H., Das, S. R., and Khera, A., et al. Prevalence and Determinants of Troponin T Elevation in the General Population. Circulation. 113(16);2006:1958-1965. Liu, Z., Cui, L., Wang, Y, and Guo, Y Cardiac Troponin I and Ventricular Arrhythmia in Patients with Chronic Heart Failure. Eur. J. Clin. Invest. 36; 2006:466-472. Matsumori, A., Kawai, C, Yamada, T., Ohkusa, T, and Morishima, S., et al. Mechanism and Significance of Myocardial Uptake of Antimyosin Antibody in Myocarditis and Cardiomyopathy: Clinical and Experimental Studies. Clin. Immunol, lmmunopath 68;1993:215-219. Sato, Y, Kita, T., Takatsu, Y, and Kimura, T. Biochemical Markers of Myocyte Injury in Heart Failure. Heart. 90;2004:1110-1113.

BIOMARKERS 184. Latini, R., Masson, S., Anand, I. S., Missov, E., and Carlson, M., et al. Prognostic Value of Very Low Plasma Concentrations of Troponin T in Patients with Stable Chronic Heart Failure. Circulation. 116;2007:1242-1249. 185. Nishio,Y, Sato, Y., Taniguchi, R., Shizuta, S., Doi, T., et al. Cardiac Troponin T vs Other Biochemical Markers in Patients with Congestive Heart Failure. Circ. J. 71;2007:631-635. 186. Glatz, J. R, Paulussen, R. J., and Veerkamp, J. H. Fatty Acid Binding Proteins From Heart. Chem. Physics. 38;1985:115-129. 187. Tanaka, T., Hirota, Y., Sohmiya, K., Nishimura, S., and Kawamura, K. Serum and Urinary Human Heart Fatty Acid-Binding Protein in Acute Myocardial Infarction. Clin. Biochem. 24;1991:195-201. 188. Tanaka, T, Sohmiya, K., Kitaura, Y, Takeshita, H., and Morita, H., et al. Clinical Evaluation of Point-of-Care-Testing of Heart-Type Fatty Acid-Binding Protein (H-FABP) for the Diagnosis of Acute Myocardial Infarction. J. Immunoassay Immunochem. 27;2006:225-238. 189. Ecollan, P., Collet, J. P., Boon, G., Tanguy, M. L., and Fievet, M. L., et al. Pre-Hospital Detection of Acute Myocardial Infarction with Ultra-Rapid Human Fatty Acid-Binding Protein (H-FABP) Immunoassay. Int. J. Cardiol. 119; 2007:349-354. 190. Niizeki, T., Takeishi, Y, Arimoto, T, Takabatake, N., and Nozaki, N., et al. HeartType Fatty Acid-Binding Protein Is More Sensitive Than Troponin T to Detect the Ongoing Myocardial Damage in Chronic Heart Failure Patients. J. Card. Fail. 13;2007:120-127. 191. Bruins Slot, M. H., Van Der Heijden, G. J., Rutten, F. H., Van Der Spoel, O. P., and Mast, E. G., et al. Heart-Type Fatty Acid-Binding Protein in Acute Myocardial Infarction Evaluation (FAME): Background and Design of a Diagnostic Study in Primary Care. BMC Cardiovasc Disord. 2008:8:8. 192. O'Donoghue, M., De Lemos, J. A., Morrow, D. A., Murphy, S. A., Buros, J. L., et al. Prognostic Utility of Heart-Type Fatty Acid Binding Protein in Patients with Acute Coronary Syndromes. Circulation. 114;2006:550-557. 193. Sugiura, T, Takase, H., Toriyama, T, Goto, T., and Ueda, R., et al. Circulating Levels of Myocardial Proteins Predict Future Deterioration of Congestive Heart Failure./ Card. Fail. ll;2005:504-509. 194. Vanderheyden, M., Goethals, M., Verstreken, S., De Bruyne, B., and Muller, K., et al. Wall Stress Modulates Brain Natriuretic Peptide Production in Pressure Overload Cardiomyopathy. J. Am. Coll. Cardiol. 44;2004:2349-2354. 195. Davis, M., Espiner, E., Richards, G., Billings, J., and Town, I., et al. Plasma Brain Natriuretic Peptide in Assessment of Acute Dyspnoea. Lancet. 343; 1994: 440-444. 196. McDonagh, T. A., Robb, S. D., Murdoch, D. R., Morton, J. J., and Ford, I., et al. Biochemical Detection of Left-Ventricular Systolic Dysfunction. Lancet. 351; 1998:9-13. 197. Januzzi, J. L., Jr., Camargo, C. A., Anwaruddin, S., Baggish, A. L., and Chen, A. A., et al. The N-Terminal Pro-BNP Investigation of Dyspnea in the Emergency Department (PRIDE) Study. Am. J. Cardiol. 95;2005:948-954. 198. Cowie, M. R., Struthers, A. D., Wood, D. A., Coats, A. J., and Thompson, S. G., et al. Value of Natriuretic Peptides in Assessment of Patients with Possible New Heart Failure in Primary Care. Lancet. 350;1997:1349-1353. 199. Remme, W. J. and Swedberg, K. Guidelines for the Diagnosis and Treatment of Chronic Heart Failure. Eur. Heart J. 22;2001:1527-1560.

BIOMARKERS OF CARDIAC INJURY

153

200. Maisel, A. S., Mccord, J., Nowak, R. M., Hollander, J. E., and Wu, A. H., et al. Bedside B-Type Natriuretic Peptide in the Emergency Diagnosis of Heart Failure with Reduced or Preserved Ejection Fraction. Results from the Breathing Not Properly Multinational Study. /. Am. Coll. Cardiol. 41;2003:2010-2017. 201. McCullough, P. A., Nowak, R. M., Mccord, J., Hollander, J. E., and Herrmann, H. C , et al. 2002. B-Type Natriuretic Peptide and Clinical Judgment in Emergency Diagnosis of Heart Failure: Analysis From Breathing Not Properly (BNP) Multinational Study. Circulation. 106;2002:416-422. 202. Januzzi, J. L., Jr., Camargo, C. A., Anwaruddin, S., Baggish, A. L., and Chen, A. A., et al. The N-Terminal Pro-BNP Investigation of Dyspnea in the Emergency Department (PRIDE) Study. Am. J. Cardiol. 95 ;2005:948-954. 203. Maisel, A., Hollander, J. E., Guss, D., Mccullough, P., and Nowak, R., et al. Primary Results of the Rapid Emergency Department Heart Failure Outpatient Trial (REDHOT). A Multicenter Study of B-Type Natriuretic Peptide Levels, Emergency Department Decision Making, and Outcomes in Patients Presenting with Shortness of Breath. J. Am. Coll. Cardiol. 44;2004:1328-1333. 204. Tang, W. H., Girod, J. P., Lee, M. J., Starling, R. C , and Young, J. B., et al. Plasma B-Type Natriuretic Peptide Levels in Ambulatory Patients with Established Chronic Symptomatic Systolic Heart Failure. Circulation. 108;2003:2964-2966. 205. Goetze, J. P., Christoffersen, C , Perko, M., Arendrup, H., and Rehfeld, J. E, et al. Increased Cardiac BNP Expression Associated with Myocardial Ischemia. Faseb.J. 17;2003:1105-1107. 206. Celinski, R., Cholewinski, W., Stefaniak, B., and Tarkowska, A. Relationship Between Plasma BNP Levels and Left Ventricular Diastolic Function as Measured by Radionuclide Ventriculography in Patients with Coronary Artery Disease. Nucl. Med. Rev. Cent. East Eur. 7;2004:123-128. 207. Sakai, H., Tsutamoto, T., Ishikawa, C , Tanaka, T., and Fujii, M., et al. Direct Comparison of Brain Natriuretic Peptide (BNP) and N-Terminal Pro-BNP Secretion and Extent of Coronary Artery Stenosis in Patients with Stable Coronary Artery Disease. Circ. J. 71;2007:499-505. 208. Steen, H., Futterer, S., Merten, C , Junger, C , and Katus, H. A., et al. Relative Role of NT-Pro BNP and Cardiac Troponin T at 96 Hours for Estimation of Infarct Size and Left Ventricular Function After Acute Myocardial Infarction. J. Cardiovasc. Magn. Reson. 9;2007:749-758. 209. Katayama, T., Nakashima, H., Furudono, S., Honda, Y., and Suzuki, S., et al. Evaluation of Neurohumoral Activation (Adrenomedullin, BNP, Catecholamines, etc.) in Patients with Acute Myocardial Infarction. Intern. Med. 43;2004:1015-1022. 210. Jaffe, A. S., Babuin, L., and Apple, F. S. Biomarkers in Acute Cardiac Disease: The Present and the Future. / Am. Coll. Cardiol. 48;2006:1-11. 211. Oremus, M., Raina, P. S., Santaguida, P., Balion, C. M., and McQueen, M. J., et al. A Systematic Review of BNP as a Predictor of Prognosis in Persons with Coronary Artery Disease. Clin. Biochem. 41;2008:260-265. 212. McLean, A. S., Tang, B., Nalos, M., Huang, S. J., and Stewart, D. E. Increased BType Natriuretic Peptide (BNP) Level Is a Strong Predictor for Cardiac Dysfunction in Intensive Care Unit Patients. Anaesth. Intensive Care. 31;2003:21-27. 213. McLean, A. S., Huang, S. J., Nalos, M., Tang, B., and Stewart, D. E. The Confounding Effects of Age, Gender, Serum Creatinine, and Electrolyte Concentrations on Plasma B-Type Natriuretic Peptide Concentrations in Critically 111 Patients. Crit. Care Med. 31;2003:2611-2618.

BIOMARKERS 214. McLean, A. S. and Huang, S. J. The Applications of B-Type Natriuretic Peptide Measurement in the Intensive Care Unit. Curr. Opin. Crit. Care. 11;2005:406412. 215. McLean, A. S., Poh, G., and Huang, S. J. The Effects of Acute Fluid Loading on Plasma B-Type Natriuretic Peptide Levels in a Septic Shock Patient. Anaesth. Intensive Care. 33(4);2005:528-530. 216. Kitamura, K., Kangawa, K., Kawamoto, M., Ichiki, Y., and Nakamura, S., et al. Adrenomedullin: A Novel Hypotensive Peptide Isolated From Human Pheochromocytoma. Biochem. Biophys. Res. Commun. 192;1993:553-560. 217. Jougasaki, M., Rodeheffer, R. J., Redfield, M. M., Yamamoto, K., and Wei, C. M., et al. Cardiac Secretion of Adrenomedullin in Human Heart Failure. J. Clin. Invest. 97; 1996:2370-2376. 218. Nishikimi, T., Asakawa, H., Iida, H., Matsushita, Y, and Shibasaki, I., et al. Different Secretion Patterns of Two Molecular Forms of Cardiac Adrenomedullin in Pressure- and Volume-Overloaded Human Heart Failure. J. Card. Fail. 10;2004:321-327. 219. Kato, J., Kobayashi, K., Etoh, T., Tanaka, M., and Kitamura, K., et al. Plasma Adrenomedullin Concentration in Patients with Heart Failure. J. Clin. Endocrinol. Metab. 81;1996:180-183. 220. Kobayashi, K., Kitamura, K., Hirayama, N., Date, H., and Kashiwagi, T., et al. Increased Plasma Adrenomedullin in Acute Myocardial Infarction. Am. Heart J. 131; 1996:676-680. 221. Ishimitsu, T., Nishikimi, T., Saito, Y, Kitamura, K., and Eto, T., et al. Plasma Levels of Adrenomedullin, a Newly Identified Hypotensive Peptide, in Patients with Hypertension and Renal Failure. J. Clin. Invest. 94;1994:2158-2161. 222. Hofbauer, K. H., Jensen, B. L., Kurtz, A., and Sandner, P. Tissue Hypoxygenation Activates the Adrenomedullin System In Vivo. Am. J. Physiol. Regul. Integr. Comp. Physiol. 278;2000:513-519. 223. Miyao, Y, Nishikimi, T., Goto, Y, Miyazaki, S., and Daikoku, S., et al. Increased Plasma Adrenomedullin Levels in Patients with Acute Myocardial Infarction in Proportion to the Clinical Severity. Heart. 79;1998:39-44. 224. Katayama, T., Nakashima, H., Furudono, S., Honda, Y, and Suzuki, S., et al. Evaluation of Neurohumoral Activation (Adrenomedullin, BNP, Catecholamines, etc.) in Patients with Acute Myocardial Infarction. Intern. Med. 43;2004:1015-1022. 225. Nagaya, N., Satoh, T, Nishikimi, T, Uematsu, M., and Furuichi, S., et al. Hemodynamic, Renal, and Hormonal Effects of Adrenomedullin Infusion in Patients with Congestive Heart Failure. Circulation. 101;2000:498-503. 226. Szokodi, I., Kinnunen, P., Tavi, P., Weckstrom, M., and Toth, M., et al. Evidence for Camp-Independent Mechanisms Mediating the Effects of Adrenomedullin, a New Inotropic Peptide. Circulation. 97;1998:1062-1070. 227. Weinberg, E. O., Shimpo, M., De Keulenaer, G. W., MacGillivray, C , and Tominaga, S., et al. Expression and Regulation of ST2, an Interleukin-1 Receptor Family Member, in Cardiomyocytes and Myocardial Infarction. Circulation. 106;2002:2961-2966. 228. Schmitz, J., Owyang, A., Oldham, E., Song, Y, and Murphy, E., et al. IL-33, An Interleukin-1-Like Cytokine That Signals via the IL-1 Receptor-Related Protein ST2 and Induces T Helper Type 2-Associated Cytokines. Immunity. 23,2005:479^190. 229. Kakkar, R. and Lee, R. T. The IL-33/ST2 Pathway: Therapeutic Target and Novel Biomarker. Nat. Rev. Drug Discov. 7 ;2008:827-840.

BIOMARKERS OF CARDIAC INJURY

155

230. Sanada, S., Hakuno, D., Higgins, L. J., Schreiter, E. R., and McKenzie, A. N., et al. IL-33 and ST2 Comprise a Critical Biomechanically Induced and Cardioprotective Signaling System. J. Clin. Invest. 117;2007:1538-1549. 231. Miller, A. M., Xu, D., Asquith, D. L., Denby, L., and Li, Y., et al. IL-33 Reduces the Development of Atherosclerosis. J. Exp. Med. 205;2008:339-346. 232. Chackerian, A. A., Oldham, E. R., Murphy, E. E., Schmitz, J., and and Pflanz, S., et al. IL-1 Receptor Accessory Protein and ST2 Comprise the IL-33 Receptor Complex. J. Immunol. 179;2007:2551-2555. 233. Chackerian, A. A., Oldham, E. R., Murphy, E. E., Schmitz, J., Pflanz, S., et al. IL-1 Receptor Accessory Protein and ST2 Comprise the IL-33 Receptor Complex. J. Immunol. 179;2007:2551-2555. 234. Shimpo, M., Morrow, D. A., Weinberg, E. O., Sabatine, M. S., and Murphy, S. A., et al. Serum Levels of the Interleukin-1 Receptor Family Member ST2 Predict Mortality and Clinical Outcome in Acute Myocardial Infarction. Circulation. 109;2004:2186-2190. 235. Januzzi, J. L., Jr., Peacock, W. F., Maisel, A. S., Chae, C. U., and Jesse, R. L., et al. Measurement of the Interleukin Family Member ST2 in Patients with Acute Dyspnea: Results From the PRIDE (Pro-Brain Natriuretic Peptide Investigation of Dyspnea in the Emergency Department) Study. J. Am. Coll. Cardiol. 50;2007:607-613. 236. Rehman, S. U., Mueller, T., and Januzzi, J. L., Jr. Characteristics of the Novel Interleukin Family Biomarker ST2 in Patients with Acute Heart Failure. 7. Am. Coll. Cardiol. 52;2008:1458-1465. 237. Stolker, J. M. and Rich, M. W. The Combination of B-Type Natriuretic Peptide and C-Reactive Protein Provides Incremental Prognostic Value Among Older Patients Referred for Cardiac Catheterization. Am. J. Geriatric Cardiol. 16;2007:229-235. 238. Foussas, S. G., Zairis, M. N., Makrygiannis, S. S., Manousakis, S. J., and Anastassiadis, F. A., et al. The Significance of Circulating Levels of Both Cardiac Troponin I and High-Sensitivity C Reactive Protein for the Prediction of Intravenous Thrombolysis Outcome in Patients with ST-Segment Elevation Myocardial Infarction. Heart. 93;2007:952-956. 239. Zethelius, B., Berglund, L., Sundstrom, J., Ingelsson, E., and Basu, S., et al. Use of Multiple Biomarkers to Improve the Prediction of Death from Cardiovascular Causes. N. Engl. J. Med. 358;2008:2107-2116. 240. Peacock, F, Morris, D. L., Anwaruddin, S., Christenson, R. H., and Collinson, P. O., et al. Meta-Analysis of Ischemia-Modified Albumin to Rule Out Acute Coronary Syndromes in the Emergency Department. Am. Heart J. 152;2006: 253-262. 241. Sinha, M. K., Roy, D., Gaze, D. C , Collinson, P. O., and Kaski, J. C. Role of "Ischemia Modified Albumin," a New Biochemical Marker of Myocardial Ischaemia, in the Early Diagnosis of Acute Coronary Syndromes. Emerg. Med. J. 21;2004:29-34. 242. Apple, F. S., Smith, S. W., Pearce, L. A., and Murakami, M. M. Assessment of the Multiple-Biomarker Approach for Diagnosis of Myocardial Infarction in Patients Presenting with Symptoms Suggestive of Acute Coronary Syndrome. Clin. Chem. 55;20O9:93-100.

This page intentionally left blank

CHAPTER

LUNG INJURY BIOMARKERS Urmila P. Kodavanti

INTRODUCTION Biomarkers indicate quantitative and qualitative changes in molecules that reflect alteration in normal functioning of the cell and the organ system in response to various stimuli. The determination of biomarkers is essential in identifying and understanding the types of injuries, the consequent disease, and therapeutic targets. More importantly, biomarker analysis allows diagnosis of the disease. Depending on the physicochemical nature of the injury-causing substances and the organs impacted, the types of biomarkers vary. However, in all types of injuries, there are a few commonalities in the biomarkers between different diseases and the organ systems being affected, for example, those that measure inflammation, oxidative stress, and antioxidant compensation.15 These biomarkers, unlike tissue- and injury-specific biomarkers, individually may not reveal full details about the mechanisms of pathogenic processes; however, with the knowledge of the injury-causing agent and the organ system encountered, diagnosis of the disease can be made. In this chapter, lung injury biomarkers are described. For detailed information, readers are directed to recent review papers on pulmonary disease biomarkers.6-13 The lung is a unique organ system with highly metabolically active oxygen exchange process within the delicate epithelial and capillary endothelial layers having direct encounter with the outside environment. To counter the pressure differences between the capillary and the inhaled air and to prevent collapse caused by these pressure differences, unique surface lining fluid is secreted by type II cells, whereas the oxygen exchange occurs primarily through type I lining cells within the alveolar unit of the lung.14 The inhaled injury-causing substances are carried to these alveolar sacs via the trachea and respiratory bronchioles. 157

158

BIOMARKERS

Lung injury is induced by (1) the inhalation of one or more toxicants or microbial organisms, (2) substances that circulate through pulmonary capillaries, (3) as a result of left ventricular diastolic dysfunction, or (4) use of ventilators. Among all, the most prevalent contributing factors for acute and chronic lung injuries and diseases are environmental exposures; cigarette smoke being the primary environmental cause of lung disease.15-17 Depending on the causative factor, site-specific injury is inflicted on the airways and the lung parenchyma. Lung injuries are measured by physiological, morphometric, pathological, biochemical, and molecular analyses of the sputum, bronchoalveolar lavage fluid (BALF), plasma, urine, and tissues from humans and animal models. In clinical settings, lung injuries are examined by pulmonary function maneuvers, chest radiography, and computerized tomography (CT) scans.18 More detailed analysis often is made by bronchoalveolar lavage (BAL) for determination of inflammation and lung vascular leakage in patients,19,20 representing complex pulmonary CT abnormalities. Biopsy specimens are evaluated for more severe chronic disease and lung carcinoma.21 There are a few circulating biomarkers that are specific to the type of lung injury;22 those when analyzed in conjunction with radiographic examination provide diagnostic values. I will describe the causes of lung injuries, the cellular targets, the pathobiological processes involved, and then elaborate on biochemical and molecular biomarkers used for assessing lung injury in clinical and experimental studies.

CAUSES OF L U N G INJURY Inhaled reactive gases, respirable particulate matter, tobacco smoke, dusts, metals, silicates, other minerals, and fibers are the major contributors of lung injury and chronic pulmonary disease burden.23-26 Lung injury also occurs as a result of respiratory infections from bacteria.27,28 Viral infections to the upper respiratory tract often increase vulnerability to bacterial infection involving the lung parenchyma.29-31 In addition, lung edema can occur following left heart failure.32 Drug-induced pulmonary injury and phospholipidosis has been noted from long-term treatment with cationic amphiphilic, antipsychotic, and antiarrhythmatic drugs such as amiodarone.33-35 Clinical use of ventilators in infants also is associated with lung damage early in life that affects normal lung growth and increases subsequent vulnerability to adult lung diseases.36 In many cases of environmental lung diseases, the exposure occurs to mixtures of a variety of highly reactive inorganic or organic species, which may cause injury via different mechanisms.37 Therefore, the disease caused by such insults is complex and affects both airways and alveolar compartments with multiple pathologies and molecular mechanisms. The classical example of such complexity is exposure to cigarette smoke. Cigarette smoke contains thousands of chemical species, including gas components such as nicotine, carbon monoxide, benzene, formaldehyde, acetone, arsenic, ammonia, tar, cadmium, and a variety of poly cyclic aromatic hydrocarbons.37,38 Thus, the injuries that result from cigarette smoke exposures involve multiple biologi-

LUNG INJURY BIOMARKERS

159

cal signaling mechanisms and affect many cell types. The disease that results from cigarette smoke, thus, is actually a group of diseases, which is referred as COPD based on the functional outcome of obstruction of the airways.39,"° Some diseases occur following exposure to a single chemical, for example, berylliosis in beryllium-exposed individuals41'42 or fibrosis in bleomycin- or cadmium-exposed individuals.4344 Lung diseases from environmental exposures are modified by the contribution of heritable genetic and epigenetic abnormalities. One can inflict injury to the lung in a genetically homogenous population with no known genetic vulnerabilities and still find variation in response among individuals exposed, emphasizing how little is known about the genetic networks and biological mechanisms. It is possible that, if no known genetic or epigenetic factors regulating compensatory or adaptive processes are abnormal, the injury is repaired without producing chronic disease. In some instances, the contributing genetic polymorphisms or epigenetic deregulations have been associated with lung injuries, but the genetic bases for many lung diseases still remain largely unknown.45,46 One example that has been studied for decades is the presence of polymorphism in a-1 antitrypsin leading to increased emphysema in smokers.47 Similarly, the integrin (3-3 gene has been associated with asthma susceptibility.48 However, most of these are associations with only single genes. Many different gene targets are speculated to be different in chronic disease, emphasizing the complexities of lung pathogenesis resulting from environmental exposures. These very same genetic factors can serve as biomarkers of susceptibility variations in lung injury.

M O R P H O L O G I C A L A N D C E L L U L A R TARGETS OF L U N G INJURY The lung is composed of two major compartments: 1) airways for air passage and 2) parenchyma for gas exchange. Some chemical or microbial agents produce site-specific injury within pulmonary tissue, whereas other agents produce more widespread damage along the airways and the parenchyma. Standard histological and electron microscopy examination of experimental animal tissues or biopsies from human patients, in conjuction with immunohistochemistry and in situ hybridization approaches, allows one to identify the morphological targets within the lung. This information not only provides insights into where a given biomarker originates within the lung, and how it may be associated with impaired lung function, but also is critical in localized therapeutic interventions. Thus, a brief description of such lung structural components is warranted.

Airway and Mucosa The mucosal layer covers tracheal and bronchial epithelial cells along the airway surface. This mucosal layer and epithelial cells are the first to encounter inhaled toxicants that deposit on the airway surface. The depth of this layer is anatomically related to the diameter of the airway and the number of goblet cells

160

BIOMARKERS

contributing to secretion.4950 The composition of airway surface liquid may depend on secretion from airway glands, ion transport across the surface epithelium, transepithelial gradients in hydrostatic pressure, and surface tension. In addition to mucus-secreting goblet cells, the airway epithelium is comprised of the pseudostratified columnar ciliated cells. Intact and damaged airway epithelial structures are shown in Figure 8.1. The mucus layer functions by modulating innate immune response, detoxifying reactive inhaled substances, and removing particulates and pathogens via mucociliary clearance. Mucus is made up of large glycosylated proteins rich in serine and threonine to which large carbohydrate structures anchor." Some mucins remain associated with the cell membrane and function as receptors for pathogens and their components, whereas other mucins are secretory and layer airway surfaces. The composition of mucus can be modified by chemical exposures, which trigger mucus hypersecretion following airway injury and in a variety of diseases, such as asthma, bronchitis, COPD,

FIGURE 8.1 Airway structural alterations following inhalation of injury causing agents. A simplified sketch of major alterations is provided. Note that the physicochemical properties and the biological activities of inhaled substances will cause airway injuries by different mechanisms, and the end-results of pathologies will show a spectrum of alterations and temporality dependent on the nature of the inhaled single substance or mixtures. This airway damage shown in the figure is not representative of all injury processes; rather it illustrates major changes. Some of the figure components are copied from the slides obtained from Motifolio, Inc. (Ellicott City, MD).

LUNG INJURY BIOMARKERS

161

and cystic fibrosis.52-54 Mucin production is regulated by a number of genes and can be studied by the measurement of proteins and gene expression.52 The airway epithelium (Figure 8.1) is supported by the basement membrane, whereas smooth muscle layer surrounds the interstitial space supporting the basement membrane and the epithelium.55 Antigen-presenting dendritic cells project between these epithelial cells and capture antigens. On recognition of antigen (paniculate, microbial, or soluble), dendritic cells migrate to the draining lymph nodes and cause innate and humoral immune responses.56,57 The airway epithelium and also the deeper parenchyma are innervated by sensory C-fibers. These vagal C-fibers respond to inhaled noxious substances, such as capsaicin and, when stimulated, evoke classical defensive reflexes, such as bradycardia, systemic hypotension, increases in parasympathetic tone with bronchoconstriction, and cough.58

A l v e o l a r Macrophage Alveolar macrophages guard the air-blood interface by serving as the first line of defense against ingested particulates, bacteria, and other pathogens. Alveolar macrophages also clear degraded or excess surfactant material.59 They originate from blood monocytes and, when needed, migrate to the lung for host defense. In addition to killing, ingesting, and processing pathogens, alveolar macrophages play a significant role in innate immune response by synthesizing and secreting an array of cytokines and arachidonic acid metabolites.5960 These mediators are responsible for recruitment of neutrophils and other inflammatory cells into the airspaces and the subsequent inflammatory response. More recently it has been shown that macrophages play an equally important role in the resolution of inflammation through regulating the clearance of apoptotic neutrophils.61,62 These cells are recovered easily from the lung via bronchoalveolar lavage (BAL) in humans and laboratory animals, and serve as a major tool for identifying biomarkers that are responsible for a variety of lung injuries.

The Surfactant C o v e r i n g A l v e o l a r Epithelial Cells The surfactant is an essential component of the respiratory system. It is important for alveolar stability, prevention of collapse, and preserving patency. It also functions as the host defense system within the alveoli.63-66 The surfactant is made by the type II alveolar epithelial cells and is secreted at the apical surface in the form of lamellar bodies. Once secreted, it rapidly forms a thin layer over the alveolar surface. When alveoli are compressed during expiration, it reduces surface tension to prevent collapse.63-66 The surfactant material is composed of phospholipids (-80%) and other neutral lipids and proteins. While diplamitoyl phosphatidylcholine functions to reduce surface tension, the neutral lipids and proteins provide an appropriate backbone for its assembly and proper function. There are four surfactant proteins (SPs): SP-A, SP-B, SP-C, and SP-D. SP-A is the most abundant protein, followed by SP-D. SP-A and SP-D are large, water-soluble glycosylated proteins and play a role in

162

BIOMARKERS

host defense, whereas hydrophobic peptides SP-B and SP-C are important in the functioning of surfactant by enabling adsorption and spreading of the surfactant material along the alveolar lining.63-66 On injury to the airway lining, these proteins escape into the circulation and serve as important circulating biomarkers of lung injury, whereas their detection in the BALF provides more accurate information on the type of lung damage and alveolar defense.

A l v e o l a r Epithelium, I n t e r s t i t i u m , and Capillary Endothelium Respiratory bronchioles in humans terminate into small alveolar units called alveolar sacs, where the primary gas exchange occurs. Beneath a thin layer of surfactant, alveolar type I cells cover the most area of the alveolus, whereas type II cells, although the most abundant cell type of the alveoli, occupy hardly any space lining the alveolus (Figure 8.2). The pulmonary capillary network surrounds the alveolus, creating an extremely close distance between air and blood for diffusion of carbon dioxide from blood to air and oxygen from air to blood.67-69 Interstitial tissue supporting these structures is comprised of extracellular matrix, elastin, and pulmonary myofibroblasts. Abnormalities in these alveolar units are detected by a variety of biomarkers through BAL fluid analysis and histologically. Alveoli interlinked with connective tissue network and encapsulated by the pleural mesothelial layer, provide anatomical structure to the lung.

P A T H O B I O L O G I C PROCESSES I N V O L V E D IN L U N G INJURIES A N D DISEASES Pulmonary injuries can be classified based on where in the lung the injury occurs or what pathological processes may be involved in the development of the disease. One must be careful, however, because many injuries involve more than one structural or morphological units of the lung, and, thus, the specificity of the biomarkers selected to evaluate injury diminishes. However, as indicated previously, no single biomarker may be chosen for diagnosing a disease.

A i r w a y Epithelial Damage, Mucus H y p e r s e c r e t i o n , and G o b l e t Cell Hyperplasia There are several types of epithelial injuries that depend on the type of causative agent involved and the cell type primarily being injured. For example, ciliary damage by inhaled ozone or phosgene is associated with mucus hypersecretion and airway inflammation.70-72 In case of sulfur dioxide exposure, mucus hypersecretion is associated with airway inflammation.73,74 Inhaled reactive oxidant gases and respirable particles, including microbials, interact with the mucosal and ciliary components, chemically modifying proteins that render them nonfunctional. Cell epithelial membrane structures are injured, and ciliary beating is impaired. Often the entire layer of ciliary epithelia is sloughed off; rendering the basement membrane exposed to dam-

LUNG INJURY BIOMARKERS

163

FIGURE 8.2 Normal alveolus structure with capillary depicting interaction between pulmonary epithelial and endothelial cells. During injury, epithelial and endothelial cells are damaged, and macrophages are activated. Type II cell hyperplasia, protein leakage, neutrophilic inflammation, infiltration of macrophages, and secretion of inflammatory mediators are common features of acute injury. Depending on the nature of acute injury, macrophage accumulation, interstitial fibrosis, emphysema, granuloma, and other diseases including carcinoma are likely to occur Some of the figure components are copied from the slides obtained from Motifolio, Inc. (Ellicott City, MD). (See color insert for a full color version of this figure.)

aged cell debris and nonfunctional mucus components (Figure 8.1). This leads to the loss of epithelial integrity and basement membrane thickening.7577 Hyperplasia and mucus hypersecretion are triggered in goblet cells within the airway submucosal glands and airway epithelium.78-81 Mucus hypersecretion is a factor promoting increases in smooth muscle mass, airway vascularity, and airway fibrosis. The process of remodeling and repair may lead to functional impairment characterized by bronchoconstriction and airway hyperresponsiveness.82-84 Damaged airway epithelial and smooth muscle cells secrete cytokines and stimulate extravasation of inflammatory neutrophils, eosinophils, and monocytes at the damaged site.

164

BIOMARKERS

Impairment and chemical modification of mucus lining layers lead to a variety of pathogenic outcomes. Mucus secretion is a dynamic process and involves soluble N-ethyl-maleimide-sensitive factor attachment receptor (SNARE) proteins, myristoylated alanine-rich C kinase substrate (MARCKS), and Munc proteins for secretory granules processing and exocytosis.85 Excessive and defective mucus production has been associated with obstruction of small airways. The mechanism by which airway inflammation stimulates mucus production and transformation of Clara and ciliated cells into goblet cells involves epidermal growth factor receptor and IL-13 activation and coordinated involvement of FoxA2, TTF-1, SPDEF, and GABAAR.8687 These mediators are involved in transcriptional upregulation of mucin 5AC expression and increased mucin in goblet cell granules that fuse to the plasma membrane through actions of MARCKS, SNAREs, and Munc proteins.86,87 Thus, one or more of these proteins can be rate-limiting in the process and thus may serve as important biomarkers for diagnostic and therapeutic purposes.

A i r w a y Inflammation in Asthma Chronic inflammation and airway hyperresponsiveness are the central pathogenic processes involved in asthma.84-88~93 Asthma is a spectrum of abnormalities thought to be caused by interaction of environmental and genetic factors. Depending on the presence of an allergic or nonallergic component, the factors involved in the inflammatory process and the physiological and clinical outcomes vary.84-88-93 The primary contributors are the Th2 lymphocyte-mediated release of a set of cytokines, with recruitment and activation of mast cells, eosinophils, and macrophages. In the pathogenic process, neutrophil extravasation also is noted with involvement of CD4+ T lymphocytes and regulatory T cells.94-96 A variety of invasive and noninvasive biomarkers are available to diagnose the disease and to understand the pathogenic mechanisms of inflammation and bronchoconstriction in asthma. Inhaled particulates, including cellular components of gram-negative bacteria, and proteoglycans activate pattern-recognition receptors, including toll-like receptors that, through release of IL-7, stimulate dendritic cell receptors CD40, CD80, and OX40 to enhance Th2 polarization.97-99 Through production of Th2 cytokines IL-4, IL-5 and IL-9, IL-13, and cellular interactions between inflammatory cells and airway epithelium, fibroblasts, microvascular endothelial cells, smooth muscle cells, dendritic cells, and neuronal cells produce a chronic inflammatory phenotype associated with asthma.97-99 Mediators released in a temporal manner from the inflammatory cells have been, perhaps, the most extensively used biomarkers of inflammatory disorders.

A i r w a y Inflammation in Bronchitis and C h r o n i c O b s t r u c t i v e Pulmonary Disease Inflammation associated with chronic bronchitis resulting from cigarette smoking or inhalation of smoke from biomass burning presents with a different phenotypic expression than what is seen in asthma, however, with oc-

LUNG INJURY BIOMARKERS

765

casional overlap of both diseases.100,101 Mucus hypersecretion is moderate in asthma pathogenesis, whereas it is the predominant pathogenic factor in bronchitis.102-104 Through activation of nuclear factor (NFKB)-associated signaling, cigarette smoke components activate transcription of genes involved in production of inflammatory cytokines and subsequent infiltration of neutrophils and macrophages involved in innate immune response.102-104 The presence of adaptive CD8+ T lymphocytes has been central in inflammation associated with COPD.105 Alveolar macrophages play a central role in this inflammatory cascade. Lung epithelial cells secrete macrophage chemoattractant protein-1 and macrophage inflammatory protein-2, which attract and activate pulmonary macrophages and neutrophils. BAL of smokers with COPD contains more pulmonary macrophages than that of nonsmokers. These macrophages, however, are inefficient in phagocytosis and have longer half-lives. They express membrane glycoproteins essential for cell adhesion and phagocytosis and produce more oxygen radical species than do macrophages of nonsmokers. Macrophages of COPD patients have even greater elastolytic activity.106 CD8+ T cells also are increased in animal models of cigarette-smoke-induced inflammation and emphysema,107 suggesting that, in addition to modulating inflammatory response, these cells may play a role in alveolar destruction caused by imbalance between proteases and antiproteases. It is believed that CD8+ T cells secrete cytokines, such as interferon-7 and interferon-inducible protein-10, dominant features of the Thl phenotype.108 These mediators likely are involved in activation of proteases within macrophages, leading to destruction of the alveolar compartment. Because the pathobiologic and phenotypic presentation of COPD in different individuals is diverse, therapeutic approaches targeted to one particular phenotype have achieved limited success. Similarly, any one biomarker evaluation is less likely to be useful for diagnostic purpose. Numerous biomarkers have been analyzed in bronchial biopsies and sputum, including those involved in inflammation, mucus hypersecretion, and oxidative stress.

rway Fibrosis, B r o n c h o c o n s t r i c t i o n , d Hyperresponsiveness Bronchial smooth muscle hypertrophy and fibroblast proliferation likely occur as a consequence of chronic inflammation, mucus hypersecretion, and bronchoconstriction. More collagen is deposited along large and small airways affecting normal airway constriction. Because these multiple diseases coexist in different proportions in bronchitis, asthma, and COPD, it is not clear which initial stimuli perpetuate signaling events in causing multiple pathways to be stimulated. Subepithelial fibrosis occurs as a result of epithelial injury and mucus hypersecretion, leading to increased synthesis and deposition of extracellular matrix components, including collagens I, III, and V; fibronectin; laminin; tenascin; and biglacan. Collagen deposition is associated with smooth muscle cell migration, hypertrophy, and hyperplasia. A

166

BIOMARKERS

number of cytokines and co-stimulating factors specific to epithelial and smooth muscle cells might promote increased extracellular matrix production.82, 84,109' "° Myofibroblast migration has been shown to contribute to increased collagen deposition through transforming growth factor-(3 (TGF-P) signaling.111 It is believed that CD4+ T cells, along with other inflammatory cells, are central to airway remodeling and fibrosis.112 Cystic fibrosis is a classic example of an airway sodium-transport defect associated with a cascade of events leading to inflammation, fibrosis, mucus production, and airway dysfunction.113 Airway fibrosis associated with chronic bronchitis and asthma plays a significant role in producing bronchoconstriction.

A l v e o l a r Epithelial, Capillary Endothelial, and Terminal B r o n c h i o l a r Injuries There are a variety of environmental exposures, drugs, and idiopathic factors that inflict injuries to the deep lung with varying mechanisms, which are either persistent or reversible. Depending on the type of insult, the anatomical site, and the cell signaling pathway induction, the insult leads to specific pathological outcomes, such as inflammation, alveolar protein leakage, increased production of surfactants, defective surfactant production, stimulation of myofibroblasts through degradation of extracellular matrix (ECM) components, release of cytokines, stimulation of apoptosis, endothelial and type I cell injuries, and type II cell hyperplasia.62, n4-116 In a long-term situation, chronic fibrosis, emphysema, pulmonary hypertension, alveolar proteinosis, and often metastasis of the lung occur. However, there are marked variations in the degree of lung disease between individuals and laboratory animal species based primarily on multiple genetic differences.46'117,118 In subsequent sections, these processes will be discussed, along with focus on specific biomarkers. There are a limited number of circulating lung-specific biomarkers used clinically that provide insights into the type of pathology; these biomarkers in conjunction with patient exposure history, chest radiograms, and CT scans, make diagnoses possible in most cases, whereas in others, more invasive sampling of lung tissue is required via bronchoscopy.119_122BALF also is obtained and can be analyzed for a number of cytokines, inflammatory cells, and injury markers.

Pulmonary Edema Deep lung edema is a life-threatening complication that generally is associated with acute lung injury, infection leading to pneumonitis, and acute respiratory distress syndrome.123,124 Epithelial injury and disrupted function of sodium channels, along with increased endothelial damage, cause changes in alveolar and capillary permeability, which subsequently impairs gas exchange and causes secondary complications. Other than ventilation strategies, no standard treatment exists for permeability edema, making the search for novel regulators of endothelial and epithelial hyperpermeability and dysfunction important. A recent review provides an account of potential thera-

LUNG INJURY BIOMARKERS

167

peutic targets that attenuate oxidative stress, inflammation, epithelial barrier dysfunction, and hydrostatic and permeability edema. The understanding of these processes is critical in developing therapeutic strategies.125

N e u t r o p h i l i c I n f l a m m a t i o n , Alveolar A p o p t o s i s , and Emphysema The mechanism of airway and alveolar injuries involves receptor-mediated phosphorylation and cell signaling, leading to nuclear translocation of regulatory proteins, and transcription of genes that are responsible for inflammation and repair processes. Many proteins involved in these signaling events can serve as biomarkers of acute and chronic lung injuries. An example of how numerous pathological processes are regulated by NFKP-mediated signaling is provided in Figure 8.3. In the case of tobacco smoke, lung injury involves both airway and alveolar compartments, leading to bronchitis and emphysema. In the past decade or so, our understanding of the role of proteases, antiproteases, and vascular endothelial growth factor (VEGF) in cigarette-smoke-induced alveolar apoptosis has advanced significantly. The mechanisms that induce pulmonary apoptosis include growth factor deprivation, mitotic aberrations, extracellular matrix degradation, loss of cell-cell communication, activation of cell death receptors by soluble endogenous ligands, and epithelial and endothelial injuries.126,127 There are extrinsic and intrinsic pathways that trigger caspases (proteases) involved in the release of cytochrome C from mitochondria and ultimately formation of apoptotic bodies.62,128,129 These apoptotic bodies (Rho kinases) cause externalization of cell membrane phosphatidyl serine (normally associated with the inner side) and cell blabbing. Numerous antiproteases counteract the balance between proteases and antiproteases in maintaining structural integrity of the lung cell. Protease activated receptor-1, through inhibition of AKT phosphorylation, and other antiproteases such as tissue inhibitor of matrix metalloprotease-1, and inhibition of CD63-mediated ERK and AKT phosphorylation, inhibit apoptotic processes and protect cells.130 The antiprotease a-1 antitrypsin, implicated in protecting the lung from emphysema, directly can inactivate neutrophil elastase, which, through extrinsic and intrinsic mechanisms, cause apoptosis in smokers.131 The deficiency in a-1 antitrypsin has been associated with exacerbated emphysematous changes in smokers and in animal models of COPD and emphysema.131

Pulmonary Fibrosis and Granuloma A variety of environmental and occupational exposures, including ambient paniculate matter, reactive gases, asbestos, silica, and drug treatments induce fibrotic lung diseases and granulomas.132,133 Idiopathic pulmonary fibrosis, although rare, occurs in humans.134 In contrast to the destructive process of apoptosis induced in emphysema, the process of pulmonary fibrosis, especially bleomycin-induced, is associated with abnormal or aberrant tissue repair and dysregulated angiogenesis, likely involving similar pathways as in em-

168

BIOMARKERS

FIGURE 8.3 Major signaling events associated with nuclear factor-Kp} ( N F - K B ) activation and nuclear translocation in response to lung injury caused by inhaled substances. In addition to cytokines, growth factors, lipopolysaccharides (LPS), and lymphotoxins, oxidative stress infectious agents, inhaled particles, and reactive gaseous materials activate N F - K B family protein by dissociating inhibitor-KB via its phosphorylation through I-KB Kinase Complex. Upon activation, N F - K B homo- and heterodimers of Rel family including N F - K B (p50), N F - K B 2 (p52), RelA (p65), RelB, and c-Rel (Rel) translocate into the nucleus and induce gene expression. The phosphorylated I-KB is removed by proteosomal degradation. Note that some of the components of the figure and the basic pathways are extracted from the signaling pathways provided by SA Biosciences Inc. (http://www.sabiosciences.com/pathwaycentral.php) and Protein Lounge (San Diego, CA). VEGF, vascular endothelial growth factor;VEGFR.VEGF receptor; IL-1, interleukin-1; ILIR IL-1 receptor; LPS, lipopolysaccharide;TLRs,Toll-like receptors;Tumor necrosis factor,TNF;TNFR,TNF receptor;TCR,T-cell receptor; BCR, B-cell receptor; Lt-B, Lymphotoxin-S; Lt-B R, L t - p receptor; BAFFR, B-cell activating factor receptor;TRAFs,TNF receptor-associated factors; IKK-a, inhibitor kinase-a; IKK-a, inhibitor kinase-a; IKK-p, inhibitor kinase-p. (See color insert for a full color version of this figure.)

physema, but in an opposing manner.135,136 It is postulated that oxidative stress in bleomycin-induced lung injury activates PI3 kinase/AKT, which leads to increased transcriptional activation of collagen and fibroblast proliferation within the pulmonary interstitium.137 Activation of PI3 kinase/AKT also is known to activate HIF-1 and VEGF, which contribute to collagen- and fibroproliferative effects of bleomycin, unlike inhibition of AKT, and VEGF in emphysema.138 The role of TGF-(3 signaling through platelet-derived growth factor and its receptors in mediation of pulmonary fibrosis and aberrant tissue repair is well studied.139 A number of inflammatory cytokines, including tumor necrosis factor-a and interleukin-1, are involved in the signaling cascade that activates TGF-(3. Antioxidant interventions are known to reduce the propen-

LUNG INJURY BIOMARKERS

765

sity of fibrosis.140 Collagen deposition, viewed easily in histological sections of the lung, occurs in the interstitial space and at the terminal bronchiolar region often surrounding the area of proliferating myofibroblasts. In experimental studies, lung tissue hydroxyproline, a collagen component, is analyzed to determine in a quantitative manner the level of fibrosis.141 The evaluation of mediators, such as cytokines and growth factors such as platelet-derived growth factor and TGF-P, aids in determining fibrotic changes that might be occurring in the lung. Fibrosis and emphysema often coexist in the same lung at different locations, making targeted therapeutic interventions challenging. Depending on the injury-causing agent, the processes of aberrant tissue repair and morphology of collagen deposition differ; for example, in case of silica and asbestos exposure, granuloma surrounding toxic materials often is observed.

A l v e o l a r Phospholipidosis Dipalmitoylphosphatidyl choline and phosphatidyl ethanolamine are the primary surfactant phospholipids synthesized and assembled with surfactant proteins in lamellar bodies within type II alveolar epithelial cells and transported toward the apical surface of alveolar cells.65 Cationic amphiphilic drugs, such as amiodarone, gentamycin, and classes of other antiarrhythmic and antipsychotic drugs, when taken over a long period of time, cause pulmonary phospholipidosis characterized by increased accumulation of surfactant lipids in lamellar bodies within the alveoli and macrophages.34'35 These drugs are taken up selectively from circulation by lung cells in which they inhibit phospholipases, especially lysosomal phospholiases Al and A2, leading to reduced surfactant turnover and accumulation within the alveoli. Often, phospholipidosis is noted with silica exposure, in addition to granuloma development.142'143 Amiodarone is the most studied drug that, in addition to phospholipidosis, also induces fibrosis.144

Pulmonary Surfactant and Surfactant Protein A b n o r m a l i t i e s In an alveolus, the biochemical alterations are inevitable upon encounter of materials, such as proteins, hemoglobin, fatty acids, inflammatory cells, and exogenous substances that chemically modify surfactant and alter its function.145-147 Oxidation and nitration of surfactant molecules also can render it inactive. Surfactant deficiency during early lung growth predisposes newborn children to acute respiratory distress syndrome, which is treated by providing exogenous synthetic surfactant.145 Lysophospholipase from eosinophils, together with the enzyme phospholipase A2, catalyzes the hydrolysis of phosphatidylcholine, incapacitating the ability of the surfactant to maintain airway patency.148 Biochemical surfactant alterations and related functional abnormalities have been noted in a variety of obstructive lung diseases, cystic fibrosis, infections, pulmonary edema, and proteinosis. Surfactant abnormalities also accompany exposure to reactive gases and particulate matter.149-15°

170

BIOMARKERS

Given the chemical functionality of surfactant proteins and their interaction with alveolar macrophages and epithelial cells, it is possible that changes in surfactant proteins occurring with disease conditions may be detected in the serum, serving as biomarkers specific to lung cell injuries. Indeed, the levels of SP-A and SP-D in circulation are highly predictive of interstitial lung diseases and survival.151,152

S A M P L I N G T E C H N I Q U E S FOR BIOMARKER ANALYSIS Lung tissue sampling in humans may range from noninvasive collection and analysis of exhaled breath, sputum, blood, and urine to more invasive bronchoscopic techniques for BAL and biopsies. Site-specific fluid and biopsy samples of lung tissue are obtained using more advanced bronchoscopic methods assisted by endobronchial ultrasound. These sampling methods are selected based on the degree of complications and patient health. Induced Sputum Induced sputum is a noninvasive sampling technique that has been used to identify markers associated with injury in the airways.15'153,154 The use of sputum in identification of biomarkers of airways injury has improved our understanding of obstructive airway diseases, such as asthma and COPD. There are some differences in the consistency of biomarker identification between spontaneous and induced sputum. Induced sputum provides excellent consistency in identification of biomarkers associated with airway injury and disease.155 The determination of inflammatory cells in the induced sputum is highly accurate and reproducible and provides information about the severity of inflammation. Induced sputum also can detect bacterial infection in patients with tuberculosis156 or acquired immune deficiency syndrome.157 The protocol for collecting induced sputum includes administration of nebulized hypertonic saline at increasing volume, which provides consistent yields of inflammatory cells as opposed to those with normal saline.158 Generally, prior inhalation of albuterol is performed to produce bronchodilation, followed by inhalation of hypertonic or normal saline. Following administration of hypertonic saline, the patient is encouraged to expectorate sputum through voluntary coughing. In cases where coughing is not spontaneously elicited, the patient is asked to cough deeply. The specimen is collected in a sterile beaker and processed. The thick material contains mucus, cellular debris, and whole cells. The thick mucus is separated from saliva manually and analyzed for cellular markers following a quick staining protocol.159 Generally, patients with COPD and chronic bronchitis yield a sufficient quantity of sputum, whereas it is often difficult to collect sufficient material from healthy individuals. Inflammatory cells can be identified as biomarkers of underlying inflammation. Analysis of cytokine proteins can be accomplished using targeted antibodies for immunological techniques such as enzyme linked immunosorbant assay

LUNG INJURY BIOMARKERS

171

(ELISA) or western blotting. The sputum also can be examined in detail by microscopy and other techniques for the presence of inflammatory cells.160 Inhalation of hypertonic saline in some patients can induce a small degree of bronchoconstriction that is reversible. This potential bronchoconstriction can be reduced or prevented by prior inhalation of bronchodilators, such as albuterol.

Bronchoscopy and Lung Biopsy Airway epithelial cells are the first to encounter inhaled pathogens or toxicants. An endotracheal tube is used to sample upper airway cells without invasive bronchoscopy. However, most sophisticated cancer diagnosis is done by elaborate bronchoscopy-based technologies. Conventional white light bronchoscopy and forceps biopsy techniques have been modified to incorporate advanced computerized technologies. These are used in conjunction with the CT assessment and cryosampling techniques to provide better diagnostic field values and preserve sample integrity for molecular analysis.119'161,162 The bronchoscopic procedures are carried out with or without general anesthesia in patients. Different illumination modes, including blue-light filtering and auto-fluorescence mode, are used to get better views of the airways as deep as ninth-generation bronchioles. Medium- and large-airway wall dimensions have been measured reliably using CT. More recently, optical coherence tomography, a new micron-scale resolution imaging technique, has been employed that can image airways as little as 2 mm in diameter. This technique is more appropriate for understanding airway pathologies that are associated with changes in forced expiratory volume in individuals with obstructive airway disease.163 In the case of lung cancer, a solitary pulmonary nodule is sampled by means of transbronchial needle aspiration, brush, or transbronchial lung biopsy under fluoroscopy. Ultrasound technique allows more accurate localization and sampling of peripheral pulmonary, mediastinal, and hilar lesions. Generally, two-stage procedures are used to localize cancer within the lung and to obtain biopsy specimens. First, three-dimensional CT imaging is used, followed by interventional bronchoscopy.122 Because the specimens obtained from standard transbronchial lung biopsies lack sufficient quantity and quality due to crush artifact, flexible cryoprobes have been used in therapeutic bronchoscopy. These techniques are invasive and require special hospital procedures generally done under local or general anesthesia.

Bronchoalveolar Lavage f o r Analysis of Biomarkers of Lung Injury The BAL technique has been employed successfully in humans and in laboratory animals to sample lung lining fluid to determine a variety of injury and inflammation markers. In humans, BAL often is performed in conjunction with the use of a bronchoscope. After adequate sedation, a bronchoscope is advanced until wedged in a desired subsegmental bronchus at the desired loca-

172

BIOMARKERS

tion. 164 ' 165 Bronchial trauma needs to be avoided, especially for patients having hemorrhagic injury. At one time, generally 20 mL of sterile saline is infused with a syringe. The flow of saline is monitored through the bronchoscope tip. Then, the lavage fluid is withdrawn into a collection vessel by applying gentle suction (50 to 80 mmHg). This process is repeated with fresh saline infusion, followed by suction five or six times to collect a sufficient amount of fluid from a given lobular segment of the lung. Generally, 40% to 70% of the fluid is recovered. Ninety-five percent of sampled individuals experience no complications following lavage. However, occasionally cough, transient fever, or transient bronchospasm with a decrease in baseline partial pressure of oxygen occurs in nearly 5% of patients. Patients with persistent pneumonia, diffuse lung diseases, and alveolar hemorrhage are recommended for lavage. Lung lavage is also performed for clinical studies. This process can be diagnostic for infections, malignancies, and various types of inflammation. Generally, nearly 80% macrophages, 3% neutrophils, 1% to 2% eosinophils, and <15% lymphocytes are recovered in BALF from normal healthy adults.166 A variety of immunologic and interventional tests can be done on cells that are recovered and also on the cell-free fluid obtained by the lavage technique.19 In laboratory rats, a terminal lung lavage protocol is used to obtain lavage fluid. Often, a single lung lobe is lavaged, and the other lung lobe is used for different protocols.73 Numerous biomarkers of alveolar lining and injured airway cells have been analyzed to understand how and what type of lung injury might exist.167 Alveolar macrophages isolated by BAL can be cultured, and their functional impairment can be evaluated using molecular techniques in healthy and disease conditions. The injury markers analyzed in BALF range from vascular proteins; lysosomal enzymes; antioxidants; lipid mediators; surfactant proteins; and a variety of proinflammatory cytokines, and chemokines, which serve as biomarkers of various types of lung injuries. More common or injury-specific markers can be identified that provide information on the mechanisms underlying disease causation.

BIOMARKER ASSESSMENTS A N D T H E I R I N V O L V E M E N T I N L U N G INJURY A N D DISEASE This section covers the description of biochemical and molecular biomarkers shown to be altered in lung injury (Table 8.1). It provides background information about biomarkers in relation to lung injury, the analytical methods used to determine their levels, and the pathogenic roles in disease causation. This section is not meant to include all known biomarkers of lung injury or their biology; rather it describes most commonly used biomarkers with emphasis on those biochemical and molecular markers that are correlated with disease pathology and functional impairment.

173

LUNG INJURY BIOMARKERS TABLE 8.1

Lung injury biomarkers, their involvement in diseases, and analytical methods.

Injury type

Biomarker

Associated diseases

Biological Samples

Analytical Methods

References

Colorimetric* Western blotting

13,165, 167, 168, 173, 174 176

EBC* BALF Lung tissue

ELISA, LC-MS, Proteomic Immunohistochemistry Flow cytometry

1 1, 13, 186, 216,217, 218,221, 222

Bronchitis COPD Asthma Sarcoidosis Granuloma ARDS

Cystospin Histology

Cystospin Histology Immunohistochemistry

166, 177, 178, 180

Heme oxygenase-1 Thiobarbituric acidreactive materials Isoprostanes 4-hydroxynonenal Hydrogen peroxide 8-hydroxy guanicine

Bronchitis COPD Asthma Sarcoidosis Granuloma ARDS

Lung tissue Inflammatory cells BALF Plasma

ELISA 98, 123, HPLC 140,205, Colorimetric 206,215 Immunohistochemistry

Oxidant antioxidant imbalanceenzymes and activities

Antioxidant capacity Glutathione peroxidase Glutathione reductase Glutathione transferase Superoxide dismutase Extracellular superoxide dismutase

Bronchitis COPD Sarcoidosis Granuloma

BALF Lung tissue

Western blotting Colorimetric

140, 187, 195, 199, 200

Oxidant antioxidant imbalancebiochemical markers

Ascorbate Glutathione Uric acid Albumin Ferrrtin Transferrin Lactoferrin

Bronchitis COPD Asthma Sarcoidosis Granuloma

BALF Lung tissue Plasma

Colorimetric HPLC LC-MS Western blotting ELISA

140, 188, 201,204, 205

Mucus Muc5AC hypersecretion KL-6/MUCI

Bronchitis COPD Asthma Sarcoidosis Granuloma

BALF Lung tissue Serum

Western 7,73,78, blotting 79,81,244 ELISA Immunohistochemistry

Acute lung inuries

Serum BALF

Western blotting

Lung cell inury

Protein Albumin Lactate dehydrogenase activity -y-glutamyl transferase activity N-acetyl glucosaminidase activity

Acute lung injuries BALF Alveolar proteinosis Alveolitis Pneumonitis ARDS

Inflammationbiochemical (signaling)

Leukotrienes Cytokines and chemokines (IL-6, IL-IO, IL-ip, and others) Nitric oxide ICAM-I E-selection P-selection VEGF

ARDS Acute lung injury Bronchitis COPD Asthma Sarcoidosis Granuloma Pneumonitis Fibrosis

Inflammationcellular

Macrophages Neutrophils Eosinophils Lymphocytes

Oxidative stress

Sufactant abnormalities

SP-A SP-D

228,230, 231,233 234,235, 236

174

BIOMARKERS

TABLE 8.1 Injury type

Lung injury biomarkers, their involvement in diseases, and analytical methods, (continued,) Biomarker

Associated diseases

Biological samples

Analytical Methods

References

Epithelial injury

KL-6 CCI6 CK-19 SLX

Brohchrtis COPD Asthma Sarcoidosis Granuloma

Serum BALF

Western blotting

8,229,235. 236,245

Alveolar destruction

Desmosine Elastin fragment VEGF Matrix metalloproteases

Emphysema

Lung tissue Inflammatory cells Plasma

ELISA, Western blotting HPLC

10, 102, 237,238, 239,242, 243

Coagulation and thrombosis

TF PAI-I vWF-Ag ProteinC Thrombomodulin

ARDS Acute lung injury Sepsis

Plasma Lung tissue BALF

13,248, Western 249 blotting ELISA Turbidometric

Note that the list of biomarkers, analytical methods, and references are not all-inclusive or complete. Rather, selected references are used. Abbreviations: BALF, bronchoalveolar lavage fluid; IL-6, interleukin-6; IL-10, interleukin-10; IL-113, interleukin 10; /GAM- /, intracellular adhesion molecule-1; VEGF, vascular endothelial growth factor; ARDS, acute respiratory distress syndrome; COPD, chronic obstructive pulmonary disease; EBC, exhaled breath condensate; ELISA, enzyme-linked immunosorbant assay; LC-MS, liquid chromatography-mass spectometry; HPLC, high performance liquid chromatography; Muc5AC, mucin 5AC; KL-6/MUC-I, Krebs von den Lungen6/mucin I; SP-A surfactant protein-A; SP-D, surfactant protein-D; CC16, Clara cell protein 16; CK-19, cytokeratin fragment-19; SLX, carbohydrate antigen Sialyl Lewis;TF,tissuefactor; PAI-I, plasminogen activator inhibitor-1; vWF-Ag von Willebrand factor-antigen. *Colorimetric methods refer to spectrophotometric assays designed for clinical settings.

Lung Injury Biomarkers in Bronchoalveolar Lavage Fluid (BALF) and Sputum Total P r o t e i n and A l b u m i n

Perhaps the simplest and most accurate biomarker of acute and long-term lung epithelial or endothelial injury is the level of protein or albumin in BALF. Its measurement can be accomplished from all BALF samples obtained from patients, human clinical studies, and research experiments with laboratory animals.165,168 Generally, the fluid recovered from lavage is centrifuged at low speed (-1,000 x g), and cell-free fluid is subjected to total protein analysis. Protein is measured by standard colorimetric detection. A number of widely used methods are employed.169,170 Primarily, protein assay uses two general principles in colorimetric detection. In one, cupric ion is reduced to cuprous by protein in alkaline media, and cuprous then is detected colorimetrically by bicinchoninic acid. In the other, the Bradford assay, protein binds to Coomassie dye in an acidic environment, causing a spectral shift which is quantified. A variety of commercial kits use one of these methods. Depending on the chemical composition of the sample, an appropriate kit can be selected

LUNG INJURY BIOMARKERS

175

to avoid interference with the presence of alkali and other components, such as sodium or detergents. Because protein leakage may reflect the leakage of serum protein resulting from lung injury, and albumin is the primary protein component of serum, measurement of albumin can distinguish the presence of plasma protein in the lung lining from that of proteins leaked from epithelial cells. In most acute lung injuries, serum albumin is increased in BALF, which parallels the increase in total protein. Albumin is often measured using antibody-based immonoassays. Lactate Dehydrogenase Activity Airway and other cell injury and death can be measured by a number of techniques. The [3H]-thymidine incorporation and the [51Cr] release assays have been replaced by simpler, quicker, and less expensive but highly accurate assays. The study of cell proliferation and cell viability requires the accurate quantification of the number of viable cells in a cell culture. Lactate dehydrogenase (LDH) is a stable enzyme normally found in the cytosol of all cells, but rapidly releases into the media on damage of the plasma membrane.171 There are five isomeric forms of LDH, which are present in different proportions in different tissues. However, the release of total LDH generally has been associated with cell injury, and the activity of the enzyme is measured in the media or BALF to assess cell damage and survival. The test is based on conversion of lactate to pyruvate by lactate dehydrogenase in the presence of reduced nicotinamide adenine dinucleotide (NADH). The reaction velocity is determined by a decrease in absorbance at 340 nm resulting from the oxidation of NADH. This test also is employed to assess cell viability for in vitro cell culture experiments. This is also one of the preferred tests employed on BALF samples for determining lung epithelial cell injury.168 -y-Glutamyl Transferase Activity 7-Glutamyl transferase is a membrane-bound enzyme that catalyzes the transfer of the glutamyl moiety of glutathione (GSH) to a variety of amino acids and dipeptides. It is composed of a heavy chain and a light chain, which are derived from a single precursor protein, and is present in tissues involved in absorption and secretion. Thus, it is involved in the metabolism of GSH and GSH-substituted molecules.172 In the lung, the gene is expressed in two epithelial cells: (1) the bronchiolar Clara cell and (2) the alveolar type II cell.173 This enzyme has been shown to be released following acute lung injury, after which its levels are increased in BALF. The activity of this enzyme in BALF is used as a marker of lung cell injury and altered antioxidant homeostasis.174 The significance of its appearance in BALF in relation to lung cell GSH metabolism is not clear, especially its protective role in acute lung injuries. N-Acetyl Glucosaminidase Activity N-acetyl glucosaminidases are a group of commonly occurring enzymes involved in the degradation of polysaccharides and glycoconjugates containing

176

BIOMARKERS

N-acetylglucosamine residues. N-acetyl-fS-D-glucosaminidase is a lysosomal enzyme secreted by alveolar macrophages in response to phagocytosis of particulate material.175 This enzyme has been studied widely in BALF, and its increases are associated with acute lung injuries. It is not clear whether its release in the lung lining fluid is regulated transcriptionally or released constitutively from lysosomes resulting from cell injury. As a biomarker, increases in BALF NAG following lung injury of various kinds have been reported in rodents exposed to air pollutants. 73 ' m

Cells in Bronchoalveolar Lavage Fluid as Biomarkers of Lung Inflammation Cells recovered from BALF are assessed widely for the evaluation of the degree and the nature of inflammation.168 These cells have been used extensively as biomarkers of lung injury and also to identify the type of injury or disease because, depending on the disease condition and the involvement of immunological mechanisms during injury, the kinetics and the dynamics of cell recovery in BALF vary. Whole BALF is centrifuged using a specialized centrifuge (Cytospin), which enables the cells to adhere to the surface of a glass slide while the fluid is absorbed on a filter paper as the sample is spun at low speed. The layered cells on the slide are then dried and stained using standard cell-staining techniques. The types of cells present in BALF from healthy individuals or laboratory animal species vary depending on the species being studied. For example, human lavage fluid contains -92 macrophages; - 7 % lymphocytes; and - 1 % neutrophils, eosinophils, and basophils.166 The lavage fluid isolated from healthy Sprague-Dawley rats contains primarily macrophages and 1 % to 5% neutrophils, but rarely any lymphocytes or eosinophils,73 whereas the BALF of guinea pigs contain a large number of eosinophils.177 Similarly, mouse strains vary in their baseline BALF cell population. The inflammatory cells in BALF often have been sorted based on their surface expression of receptors using flow cytometry, especially in identification of the types of lymphocyte populations under varying immunologic involvement.178 Antibody-based multiplex bead technologies are used to simultaneously determine levels of tens of proteins in a single biological sample.179 Under a variety of acute and chronic lung injuries, the proportions of these cells change, and there is extravasation of inflammatory cells from the blood stream. Lung injury also has been shown to stimulate bone marrow to release band cells, which ultimately mature in the circulation and are migrated to the lung at the site of inflammation. During these processes, the expression of cell surface markers is modulated and reflects the type of insult and the chronicity of the disease.180 Noxious exposures to chemicals and pathogens are associated with inflammation in the lung, and the type of inflammation and immune reactions involved vary as indicated earlier.

LUNG INJURY BIOMARKERS

177

C y t o k i n e s and Chemokines in Sputum and Bronchoalveolar Lavage Fluid Inflammatory response induced by any kind of cell or tissue injury or infection is regulated by a variety of chemokines and cytokines that are produced by almost all epithelial, endothelial, smooth muscle, and inflammatory cells. Chemokines and cytokines are small secreted proteins that contain four cysteine residues in conserved sequences and are involved in directed chemotaxis.181 Based on the spacing of cysteine residues, different chemokines are classified in into different groups.182,183 Depending on the nature of the injury and the cell type affected, the profile of cytokine and chemokine induction differs, and, thus, the nature of lung pathologies differs. Some chemokines are proinflammatory and can be induced during an immune response, whereas others are considered homeostatic and are involved in controlling normal processes of tissue maintenance. Cytokines and chemokines interact with G-protein-linked transmembrane receptors on the surface of their target cells.182,183 Cytokine and chemokine production is regulated transcriptionally by a number of signaling pathways that involve toll-like receptors, mitogen activated protein (MAP)-kinases, and nuclear translocation of NFK|3, AP-1 and other transcription factors.184,185 MAP-kinase-mediated signaling and NFK(J signaling, perhaps, are the most studied signaling mechanisms that induce a variety of cytokine and chemokine gene expressions. Pulmonary injury response follows induction of multiple chemokines and cytokines with a differential temporal pattern as a result of receptor phosphorylation, signal transduction, and induction or transcription. So, their gene expression and protein concentrations in lung tissue components serve as biomarkers of underlying lung inflammation, infection, or chronic disease. Depending on the type of injury, the type and the pattern of cytokine induction vary. For example, lungkine (CXCL15) is detected specifically in lung epithelial cells and induces the in vitro and in vivo migration of neutrophils, while IL-5 is involved in infiltration of eosinophils.186 These chemokines, best measured in groups, are classified according to function because individual markers will not be sufficient to provide insight into the type and nature of the lung injury. They have been measured in induced sputum and also in BALF and lung tissue specimens. Immunohistochemical detection of chemokines can be achieved easily by using appropriate antibodies ^specific to the kind of marker and the species being examined.

Biomarkers of O x i d a t i v e Stress in Bronchoalveolar Lavage Fluid, Sputum, Lung, and Plasma The role of underlying oxidative stress in lung injury has been studied well and has relevance to its homeostatic mechanisms, as a variety of oxidative reactions are expected to occur in the presence of high levels of molecular

178

BIOMARKERS

oxygen. Lung lining antioxidant mechanisms involve chemical antioxidants (ascorbate, urate, and GSH), extracellular superoxide dismutase, iron-binding proteins (lectoferrin, transferrin, and ferritin), and a variety of cellular enzymes that leak from lung cells into the circulation. Ascorbate Ascorbate (ascorbic acid), commonly known as vitamin C, is a sugar molecule with antioxidant properties. It exists at high concentrations in the lung and lung lining fluid and has been shown to be the first line of defense in acute lung injury.187 Reactive oxygen species present in the lining fluid accept easily available electrons from ascorbate and are reduced to water, whereas ascorbic acid is oxidized to its nonreactive form, dehydroascorbic acid (DHA). This role of ascorbate as a critical antioxidant molecule has been appreciated for years, especially in lung antioxidant defense.188 However, recently, ascorbate has been shown to have a much broader role in the hydroxylation reactions that determine catalytic actions of thousands of cellular proteins.189 The iron moiety of a variety of dioxygenases (nonheme, iron-containing proteins), including prolyl, asparginyl, and lysyl hydroxylases and novel DNA repairing enzymes, human ABH2 and ABH3 proteins, is maintained in the active Fe(II) form by ascorbate.190,191 Thus, the activity of these enzymes depends on two factors: (1) iron and (2) ascorbate. Humans and some laboratory animals, such as guinea pigs and primates, lack the mechanism to synthesize ascorbate in the body, whereas, laboratory rodents (rats and mice) can produce ascorbic acid and do not require dietary supplementation. Ascorbic acid is transported across cells by two membrane transporters, SVCT1 and SVCT2, in a sodiumdependent manner.192 Analysis of ascorbic acid in BALF and lung tissue, as well as in plasma, is made by a number of biochemical techniques. Reduced ascorbic acid can be assayed conveniently using commercial kits in acid-deproteinized tissue extracts by a colorimetric procedure using ferrozine.193 The assay is based on the reduction of Fe(III) to Fe(II) by ascorbic acid, followed by chromogenic chelation of Fe(II) with ferrozine. The advantages of the ferrozine assay for reduced ascorbic acid include the assay's speed and ease. Accurate and reproducible analyses of ascorbate and its oxidized form are necessary for their application as biomarkers of oxidative stress. However, its proper quantification in biological samples poses a significant challenge because of its labile nature and the presence of chemically active molecules in biological fluids. Autooxidation can occur readily in the presence of oxygen, making it difficult to maintain it in reduced form. In vivo equilibrium between ascorbate and DHA cannot be successfully blocked; however, attempts are made to preserve its reduced form. The most commonly used method of stabilizing biological samples for ascorbate analysis is by acidic deproteinization with perchloric and meta-phosphoric acid.194 Ethylenediaminetetraacetic acid is used to bind metal iron in biological samples. In addition to its measurement by simple colorimetric method, electrochemical detection following high performance liquid chromatography (HPLC) separation, gas chromatography, and mass

LUNG INJURY BIOMARKERS

179

spectrometric methods also are used to quantify ascorbate and dehydroascorbate in acidified extracts of biological samples. BALF and lung tissues can be analyzed for the reduced form of ascorbic acid and the levels correlated with the degree of oxidative stress and lung injury. Glutathione GSH, a 7-glutamyl cysteinylglycine tripeptide, is an important cellular antioxidant, and its synthesis is regulated by phase II metabolism enzymes responsive to NRF-2.195 It has a variety of functions, in addition to being an antioxidant: it is involved in biotransformation and in leukotriene synthesis. Its intracellular concentrations of reduced form are maintained up to 5 mM, whereas extracellular concentrations are in the micromolar range. GSH reduces any disulfide bonds formed within cytoplasmic proteins to cysteines by acting as an electron donor. The reduced form of GSH is converted to glutathione disulfide or forms conjugate with electrophiles, while reducing other disulfide bonds. GSH also binds albumin and forms disulfide with other sulfhydryl-containing proteins.196 These disulfides then either are exported out of the cells by active cellular transport or re-reduced by enzymatic reaction involving GSH and other reductases. Although GSH is maintained in reduced form at very low levels in alveolar lining fluid, it has been shown to be involved in oxidative stress and acute lung injuries. A ratio of GSH to its disulfide form has been used as a measure of cellular oxidative stress.197 A variety of methods are available to measure levels of GSH in tissues and biological fluids, including fiuorometric, spectrophotometric, bioluminometric, and colorimetric, which often are applied to HPLC and mass spectrometry.196 The commonly used HPLC method for analysis provides measurements of reduced, oxidized and other disulfide forms.198 As with ascorbate, GSH is oxidized readily in biological fluids, so sample preservation and storage become highly critical in accurate measurement. There is little consistency in the literature regarding the procedures for sample preparation employed for the measurement of GSH in biological tissues. The change in the ratio of its disulfide over the reduced form can provide an index of oxidative stress and aid in identifying the nature of lung injury and chronicity. Extracellular Superoxide Dismutase Extracellular superoxide dismutase (EC-SOD) in alveolar lining fluid is a major extracellular antioxidant in the lung. In the human lung tissue, EC-SOD is located primarily in the extracellular matrix and may have a role in protecting matrix proteins from oxidation.199 It is also present in epithelial lining fluid at high concentrations.200 The human EC-SOD and mouse EC-SOD exist in tetrameric form, with molecular mass of 135,000, whereas, in the rat it exists as a diemeric molecule and is expressed in type II alveolar epithelial cells. EC-SOD is glycosylated and exhibits affinity for sulfated polysaccharides, such as heparin or heparan sulfate. Although detectable in plasma, EC-SOD mostly is bound to the extracellular matrix. During acute lung injury induced

180

BIOMARKERS

by cigarette smoke or hyperoxia, EC-SOD activity in BALF has been shown to increase and has been correlated with oxidative stress.199 EC-SOD in the lung and BALF has been detected using antibody-based western blotting and immunohistochemistry.'" Ferritin, Lectoferrin.Transferrin, and Iron-Binding Capacities Iron-binding proteins in BALF and lung tissues serve as markers of acute lung injury and chronic diseases associated with increased iron overload.201 A number of iron-binding proteins, normally present at high concentrations in BALF, are involved in regulation of iron homeostasis.201 The dissociation of iron from heme, its release by the cell using specialized transporters, and its binding to storage proteins are dynamic and tightly regulated processes that prevent free iron from catalyzing unwanted reactions.202'203 Heme degradation to biliverdin, iron, and carbon monoxide occurs by action of heme oxygenase-1 (HO1). The induction of HO-1 alters iron stores; the metal is released in a labile form within minutes to hours. Airway/alveolar lining iron homeostasis is best measured by a number of different analytical techniques that allow one to analyze total and unsaturated iron-binding capacities and associated measurement of ferritin, lactoferrin, and transferrin, the major iron-binding and storage proteins.204 A variety of commercial kits are available that colorimetrically measure the iron-binding capacities of a tissue homogenate or BALF. Proteins can be measured best by immunological techniques. Overall analysis will provide insights into the iron homeostasis under acute lung injury and oxidative stress in the alveolar lining fluid. 4-Hydroxynonenal Trans-4-hydroxy-2-nonenal (4-HNE), is generated in the oxidation of polyunsaturated lipids to produce an a, (3-unsaturated hydroxyalkenal under oxidative stress during a variety of lung injuries and injuries to other organs.205-206 Thus, it is used as a biomarker of oxidative stress. It has been implicated in numerous diseases, such as chronic inflammation, adult respiratory distress syndrome, atherogenesis, diabetes, and some types of cancers.205,206 It is produced endogenously during oxidative processes and has been shown to have dual effects on cells. At low concentrations (<5 uM), it has been shown to promote cell proliferation, whereas, at higher concentrations, it may induce apoptotic cell death by inducing death-related caspases.207 In cells, it is known to conjugate with GSH to form a more water-soluble conjugate by the action of GSH S-transferases (hGSTA4-4 and hGST5.8).208 Its ascorbate conjugate, ascorbylated 4-hydroxy-2-nonenal, has been detected in tissues as a biomarker of oxidative stress.209 F(2)-lsoprostanes F(2)-isoprostanes are a unique series of prostaglandin-like compounds formed in vivo via a nonenzymatic mechanism involving the free radical-initiated peroxidation of arachidonic acid. Isoprostanes containing an F-type prostane

LUNG INJURY BIOMARKERS

181

ring are members of a family of 64 prostaglandin F2-like compounds generated in v!V0.21o'211Isoprostanes are involved in many human diseases and inflammatory conditions. F2-isoprostanes (8-Iso-PGF2 ) are regularly formed in various tissues, and small amounts can be detected in plasma and in urine of normal subjects, but, under disease and injury conditions, their levels increase. It has been established that measurement of F2-isoprostanes is the most reliable approach to assess oxidative stress status in vivo.2n~2H As with 4-HNE, 8-isoprostane products have been shown to exert potent biological actions and, therefore, may be pathophysiologic mediators of disease. IsoPs 8-iso-PGF2 and 8-iso-PGE2 also serve as mediators of oxidant stress through their vasoconstrictive effect in pulmonary arteries and their inflammatory properties through activation of MAP kinases.215

Exhaled N i t r i c O x i d e The measurement of the fractional concentration of exhaled nitric oxide (FeNO) is a convenient, noninvasive test for airway inflammation216'2" Nitric oxide (NO) is a widely distributed endogenous regulatory molecule in the body synthesized from L-arginine by enzyme NO synthase (NOS). NO plays a role in vascular smooth muscle relaxation and is detected in exhaled breath at high concentrations in asthmatics.216,217 Elevation of FeNO is predominantly associated with eosinophilic inflammation and, although it is not unique for any particular injury or disease, it is associated closely with asthma in humans.217 NO is continuously produced in the airways by inducible calciumindependent NO synthase. NO syntheses are upregulated transcriptionally by inflammatory cytokines and, thus, are generally associated with airway injury and inflammation.217 The level of FeNO correlates with the degree of inflammation. The analysis of FeNO is helpful in establishing the correct diagnosis of inflammatory injury to the lung. NO-free air is inhaled to total lung capacity through the mouthpiece, and, while maintaining a steady flow rate, air is exhaled. The contamination of nasal NO, which is almost 10 times greater than exhaled NO, is avoided during this process by sampling during mouth breathing. Exhaled NO concentrations also have been measured in laboratory animals.218 In addition to FeNO, a number of other biomarkers have been attempted in exhaled breath.219

Heme Oxygenase-I Heme oxygenase-1 (HO-1), a rate-limiting enzyme in heme catabolism, has antioxidative, antiapoptotic, and anti-inflammatory effects.220 HO-1 is present ubiquitously in all cells and is a major inducible stress protein that is considered a standard biomarker of underlying oxidative stress in tissues. HO-1 is highly induced in a variety of lung injuries, including exposure to cigarette smoke, but, surprisingly, it is inhibited in lungs of patients with severe COPD.221 The levels of serum, BALF, and tissue HO-1 are measured by enzyme-linked immunosorbent assays.222 The HPLC assays for bilirubin and biliverdin also can be used to analyze HO-1 activity in cell culture systems.

182

BIOMARKERS

The carbon monoxide resulting from heme catabolism by action of HO-1 can be measured in controlled conditions by gas chromatography and titrated to measure heme oxygenase activity. HO-1 activity is measured spectrophotometrically, using a coupled assay employing biliverdin reductase.221

A s y m m e t r i c and Symmetric Dimethyl A r g i n i n e Posttranslational modifications resulting in methylation of arginine have been shown to regulate a variety of cellular processes, such as protein-protein interactions and histone modifications. This methylation of protein arginine has been shown to be dysregulated in a variety of diseases, including pulmonary, and, therefore, the by-products of arginine methylation have been suggested to serve as biomarkers of injury and diseases.223224 More importantly, the methylated arginines have been shown to be potent inhibitors of all NO synthases, which catalyze production of vasodilator NO, and, thus, their presence is associated with severe compromise in vaso- and bronchodilator function and in many other processes of signal transduction regulated by NO.225 Arginine methylation is accomplished by a group of protein arginine methyltransferases, which generate monomethyl arginine, symmetric dimethylarginine, and asymmetric dimethylarginine. Proteolytic cleavage of these methylated proteins results in the release of these methylated amino acid residues, which are detected in the BALF, plasma, and other body fluids and serve as biomarkers of disease conditions. It is not clear whether methylated arginine molecules remain in the cell, or if its cellular contents can serve as intracellular biomarkers. Elevated levels of asymmetric dimethyl arginine have been noted in pulmonary hypertension, sickle cell disease, and sclerosis.225 Asymmetric and symmetric dimethyl arginine is detected in plasma and other fluids by HPLC method involving precolumn derivatization with o-phthaldialdehyde, HPLC separation, and fluorescence detection. The GC-MS/MS analysis method seems to be more accurate and has been used as well.226-227

Surfactant Proteins in Bronchoalveolar Lavage Fluid and Plasma Surfactant proteins (SPs) in the plasma often have been associated with specific lung diseases. Because SPs are present only in the lung airway and on the alveolar surface, their detection in the circulation often provides specific information of ongoing pulmonary abnormality. For example, SP-A is detected in COPD patients but not in those with pulmonary fibrosis.228 Elevated levels of this protein also were found in the sputum specimens of COPD patients, and were associated with transcriptional induction and protein expression in the lung tissue of COPD patients. The levels of circulating SP-D and also Krebs von den Lungen-6 (KL-6), glycoproteins expressed by type II pneumocytes, correlate with alveolitis and Scleroderma.229 Serum SP-A has been associated with mortality in patients with idiopathic pulmonary fibrosis. Because the expression of SP-A and SP-D is abundant and restricted within the lung, these SPs are used clinically as biomarkers for lung diseases.230-231 The levels of SP-A

LUNG INJURY BIOMARKERS

183

and SP-D in BALF and pleural effusions reflect alterations in lung parenchyma and alveolar epithelium. Their determination in sera is useful for understanding the nature of lung injury and disease, including fibrosis, vascular diseases, pulmonary alveolar proteinosis, acute respiratory distress syndrome (ARDS), and pneumonitis. However, it should be noted that the SP homeostasis may be altered under other systemic diseases such as diabetes. Increased SP-A levels have been associated with insulin resistance in diabetic patients.232 Temporal increases in SP-A and SP-B in plasma have been shown to result from increases in alveolar capillary permeability in patients with ARDS.233 Although isolating purified surfactant material from the lung poses challenges, SP-A and SP-D are separated from BALF using maltosyl-agarose chromatography, followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis.234 In plasma, surfactant proteins can be analyzed using standard antibody-based immunological techniques. Clara cell secretory protein-16 (CC-16) also has been detected in the sera of ARDS patients.235 CC-16 has been proposed to be a sensitive biomarker of acute lung injury and has been shown to be increased after acute ozone exposure in humans.236

M a t r i x Metalloproteases as Biomarkers of Lung Injury ECM destruction is an important aspect of chronic obstructive pulmonary disease and other destructive diseases. Changes in matrix metalloproteases have been noted with a variety of pulmonary injuries. Metalloproteases catalyze orderly degradation of matrix proteins, including elastin and collagen, and impair structural integrity and contractile function of the lung. These enzymes serve as biomarkers of parechymal destructions associated with exposure to environmental agents, including cigarette smoke, and in disease conditions of emphysema.102 Different enzymes have different substrate specificities, and, thus, the consequence of their activation leads to degradation of various matrix proteins. To date, more than 20 matrix metalloproteases have been identified, including collagenases, gelatinases, stromolysins, elastases, matrilysin, and others. In addition to matrix degradation, these enzymes also regulate inflammation, cell migration, release of cytokines, and growth factors. They are synthesized in neutrophils, macrophages, and also airway epithelial cells. They digest a variety of structural matrix components, including collagen, elastin, fibronectin, proteoglycans, and laminin. These proteases have been detected in sputum, BALF, and lung cells. Standard approaches to analyze them include western blotting and ELISA techniques. Immunohistochemical techniques with appropriate antibodies can reveal their origin within the lung tissue. MMP-12 has been implicated in the pathogenesis of cigarette smoke-induced COPD.237 Mice lacking MMP-12 have reduced injuries from exposure to cigarette smoke. A number of MMPs have been shown to be increased in COPD, and have been implicated in asthma.102 In fibrosis, MMP 7, 8, and 9 have been shown to increase.102 Being able to analyze the levels and their localization can increase our understanding of the mechanisms of lung injury.

184

BIOMARKERS

Collagen and Elastin Fragments as Biomarkers of Lung Injury Detection of degradation by-products of matrix in the BALF and plasma not only reveal matrix destruction associated with lung injury, but these by-products are important regulators of subsequent inflammation and other pathogenic processes. Desmosine and isodesmosine, degradation products of elastin, have been identified in circulation and in the urine and have been considered fairly specific markers of emphysematous changes in the lung.238-239 These elastin by-products have been measured by chromatographic and immunological techniques. The development of highly sensitive and specific assays for elastin fragments has resulted in our increased understanding of elastolitic pulmonary disorders. Other degradation products of elastin are named elastokines based on their involvement in regulation of inflammatory cytokines.238'239 The cell membrane receptor S-Gal binds elastokines and stimulates expression of MMPs, thus, perpetuating the matrix degradation.240 These elastin fragments also have been shown to polarize lymphocytes toward a Th-1 response.241 As with elastin, collagen degradation products also have been implicated in acute lung injury, and those products have been detected in BALF and plasma. Endostatin, a proteolytic fragment of the basement membrane collagen XVIII, has been shown to inhibit angiogenesis via action on endothelial cells.242 Plasma and BAL procollagen aminoterminal propeptide type I and type III levels have been associated with acute respiratory distress syndrome.243 Thus, these by-products serve as biomarkers of underlying pulmonary disease. Because these markers in plasma also can originate from extrapulmonary organs, their analysis in BALF but not plasma can be confirmative of lung injury. The determination of these by-products needs to be made using highly specific antibodies. Because in normal circumstances these products will not be present in BALF or plasma, their presence will be indicative of degradation of matrix in lung injuries.

C i r c u l a t i n g Lung-Cell-Specific Proteins as Biomarkers Mucin-associated proteins, such as MUC1 or Krebs von den Lungen (KL)6 have been detected in BALF, sputum, and circulating blood in a number of airways injuries244 and in interstitial pulmonary diseases, pneumonia, and sarcoidosis.244 This mucin protein is made in epithelial cells of respiratory bronchioles and also in type II epithelial cells. Although it is made predominantly in airway epithelial cells, it is not entirely lung specific (as noted previously regarding surfactant proteins). This mucin also is made by pancreatic cells, esophageal epithelium, and stomach epithelium. Clara cell secretory protein-16 has been shown to be elevated in a number of lung diseases and serves as a specific biomarker of impairment in the air-blood barrier.245 Other lung-specific markers include cytokeratin fragment 19, which is an epithelialcell-specific protein released during lung injury and has been shown to be associated with lung fibrosis and other interstitial diseases (Table 8.1).

LUNG INJURY BIOMARKERS

185

Blood Coagulation and T h r o m b o s i s Markers in Lung Injuries The pulmonary capillary network and close physical attachment to lung epithelial cells render microvascular structures vulnerable to injury as a result of injury to lung epithelium. Increased coagulation and clot retention have been noted in a variety of pulmonary acute injuries, and these processes subsequently regulate inflammatory response and ultimately disease.246 Coagulation and fibrinolysis also play an important role in systemic toxicity.246 Normally, in an initial phase of coagulation cascade, the extrinsic pathway is triggered by activation of tissue factor (TF) and Factor VII, but, subsequently, the intrinsic pathway is triggered, which involves Factors VIII, X, and XI.247 TF is made by a number of pulmonary lung cells including macrophages, neutrophils, endothelial cells, and platelets under pathologic conditions.247 Activation of the TF pathway leads to the generation of thrombin, which catalyzes the conversion of fibrinogen to fibrin. The TF pathway alters inflammatory signaling by activation of protease-activated receptors. Under pathologic conditions, TF expression within the vasculature leads to thrombosis. A number of biomarkers of coagulation cascade have been determined in lung fluids, tissue, and plasma. Those include TF, tissue factor pathway inhibitor, thrombomodulin, von Willebrand Factor, Factor VII, and other factors.248 The fibrinolytic process is regulated by protein C and tissue plasminogen activator inhibitor-1, and these markers have been known to be elevated in BALF and plasma in chronic lung diseases as well as acute lung injuries.249 These markers are measured at the protein level by immunoassays, whereas the activities of these factors are measured in the plasma by turbidometric analysis for clotting time under various manipulations.

N O V E L A P P R O A C H E S FOR BIOMARKER IDENTIFICATION The evolution of high throughput molecular technologies over the past decade has led to rapid advancement in the field of biology, especially the identification of novel therapeutic targets and biomarkers. Microarray-based gene expression profiling for identification of new biomarkers has advanced our understanding of disease pathogenesis and the complex signaling networks in producing disease phenotypes.250,251 Wide use of these techniques also has resulted in the rapid increase of our understanding of gene networking in disease pathogenesis. Novel insights have emerged into the complexities of cellular genome regulation and dynamic changes that occur in response to signaling. High-throughput proteomic approaches have been employed to identify potential mechanisms and biomarkers associated with lung injuries.252 In the case of the lung, gene expression profiling is performed using lung tissue, lung cell cultures, or circulating polymorphonuclear cells to identify potential transcriptional markers associated with lung injuries. In general, dif-

186

BIOMARKERS

ferent commercially available, species-specific gene array platforms are used to determine gene expression of the entire genome of a cell or tissue specimen with small quantities of RNA materials. Epigenetic changes recently have been investigated in chronic lung diseases, such as asthma and air pollution.118 The primary known epigenetic mechanisms include those that regulate translational and transcriptional processes by miRNA, histone modifications, and DNA methylation. Numerous noncoding miRNAs exist in the cell that guide posttranscriptional degradation of complementary mRNAs and, thus, regulate translation to proteins or target them for destruction by the cellular machinery. Although the regulation of miRNA production and the mechanisms for targeting multiple genes by a single miRNA species are not uncovered fully, they are likely highly critical in nongenetic regulation of cellular processes of tissue growth, regeneration, repair, and death. One miRNA can regulate many, even hundreds, of gene targets, and, to date, hundreds of miRNAs have been identified. The role of miRNA in regulation of transcription in disease and cancer development has been recognized in the lung and other organ systems. 118253 ' 254 Thus, the analysis of miRNA levels in lung cells or tissue provides mechanistic insights into the disease processes and identification of novel biomarkers. Epigenetic regulation of gene expression also is accomplished by histone modifications involving methylation, acetylation, phosphorylation, ubiquitination, ADP-ribosylation, and other processes. These histone modifications affect DNA coiling and cause transcription silencing or activation.45,255,256 Thus, histone modifications regulate protein DNA interactions affecting transcription. These histone modifications, especially involving acetylation pattern, have been shown to play roles in pulmonary diseases and cancer.45,255,256 Studies of histone modifications in the pulmonary cells or tissue processes are likely to provide insight into the mechanisms by which developmental and tissue repair processes are regulated. Methylation of DNA also is involved in regulation of developmental and disease processes. DNA methyltransferase catalyzes covalent addition of methyl group to 5 carbon of cytosine in CPG dinucleotides. This modification has been shown to occur in a variety of injuries and pathologies.257 The developmental processes are regulated by rapid changes in DNA methylation. Alteration of DNA methylation has been shown to play a role in lung injuries and diseases.118 These genetic and epigenetic modifications, studied using high-throughput approaches, provide opportunities for new biomarker identification and development of novel therapeutic approaches. The continued use of high-throughput techniques to assess DNA methylation, miRNA profiling, and histone modifications will lead to identification of novel epigenetic biomarkers, and therapeutic targets in respiratory diseases. Global analysis of these epigenetic processes, together with gene expression profiling, is complex but provides comprehensive understanding of how biomarkers are altered in injuries and disease conditions.

LUNG INJURY BIOMARKERS

187

ACKNOWLEDGMENTS I thank Mr. John Barton (SRA International, Inc., Durham, NC) for editorial help in preparing the chapter and Mr. John Havel (SRA International, Inc., Durham, NC) for graphics help. I also thank Dr. Stephen Gavett, Dr. Andrew J. Ghio, and Dr. MaryJane Selgrade (U.S. EPA) for their critical review of this manuscript.

DISCLAIMER This article has been reviewed by the National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency and approved for publication. Approval does not signify that the contents necessarily reflect the views and the policies of the agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.

REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Gomez-Mejiba, S. E., Zhai, Z., and Akram, H., etal. Inhalation of Environmental Stressors & Chronic Inflammation: Autoimmunity and Neurodegeneration. Mutat. Res. Mar 31, 2009;674(l-2):62-72. Maes, M. The Cytokine Hypothesis of Depression: Inflammation, Oxidative & Nitrosative Stress (IO&NS) and Leaky Gut as New Targets for Adjunctive Treatments in Depression. Neuro. Endocrinol. Lett. Jun 2008;29(3):287-291. Nadeem, A., Masood, A., and Siddiqui, N. Oxidant-Antioxidant Imbalance in Asthma: Scientific Evidence, Epidemiological Data and Possible Therapeutic Options. Ther. Adv. Respir. Dis. Aug 2008;2(4):215-235. Prasad, A. and Tsimikas, S. Candidate Biomarkers for the Detection of Coronary Plaque Destabilization and Rupture. Curr. Atheroscler. Rep. Aug 2008; 10(4):309-317. Song, E, Poljak, A., Smythe, G. A., and Sachdev, P. Plasma Biomarkers for Mild Cognitive Impairment and Alzheimer's Disease. Brain Res. Rev. May 21, 2009. Tzouvelekis, A., Pneumatikos, I., and Bouros, D. Serum Biomarkers in Acute Respiratory Distress Syndrome an Ailing Prognosticator. Respir. Res. 2005;6:62. Levitt, J. E., Gould, M. K., Ware, L. B., and Matthay, M. A. The Pathogenetic and Prognostic Value of Biologic Markers in Acute Lung Injury. J. Intensive CareMed. May/Jun 2009;24(3):151-167. Turino, G. M. COPD and Biomarkers: The Search Goes on. Thorax. Dec 2008;63(12):1032-1034. Borrill, Z. L., Roy, K., and Singh, D. Exhaled Breath Condensate Biomarkers in COPD. Eur. Respir. J. Aug 2008;32(2):472-486. Rosas, I. O., Richards, T. J., and Konishi, K., et al. MMP1 and MMP7 as Potential Peripheral Blood Biomarkers in Idiopathic Pulmonary Fibrosis. Plos. Med. Apr 29, 2008;5(4):E93. Kharitonov, S. A. and Barnes, P. J. Exhaled Biomarkers. Chest. Nov 2006; 130(5):1541-1546.

188

BIOMARKERS 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30.

Smadja, D. M., Gaussem, P., and Mauge, L., et al. Circulating Endothelial Cells: A New Candidate Biomarker of Irreversible Pulmonary Hypertension Secondary to Congenital Heart Disease. Circulation. Jan 27, 2009;119(3):374-381. Frank, J. A., Parsons, P. E., and Matthay, M. A. Pathogenetic Significance of Biological Markers of Ventilator-Associated Lung Injury in Experimental and Clinical Studies. Chest. Dec 2006;130(6):1906-1914. Nkadi, P. O., Merritt, T. A., and Pillers, D. A. An Overview of Pulmonary Surfactant in the Neonate: Genetics, Metabolism, and the Role of Surfactant in Health and Disease. Mol. Genet. Metab. Jun 2009;97(2):95-101. Comandini, A., Rogliani, P., Nunziata, A., Cazzola, M., Curradi, G., and Saltini, C. Biomarkers of Lung Damage Associated with Tobacco Smoke in Induced Sputum. Respir. Med. Jul 14, 2009. Stampfli, M. R. and Anderson, G. P. How Cigarette Smoke Skews Immune Responses to Promote Infection, Lung Disease and Cancer. Nat. Rev. Immunol. May 2009;9(5):377-384. Vassallo, R. and Ryu, J. H. Tobacco Smoke-Related Diffuse Lung Diseases. Semin. Respir. Crit. Care Med. Dec 2008;29(6):643-650. Hohman, D. W., Noghrehkar, D., and Ratnayake, S. Lymphangioleiomyomatosis: A Review. Eur. J. Intern. Med. Jul 2008;19(5):319-324. Bargagli, E., Mazzi, A., and Rottoli, P. Markers of Inflammation in Sarcoidosis: Blood, Urine, BAL, Sputum, and Exhaled Gas. Clin. Chest. Med. Sep 2008; 29(3):445-458. Rose, A. S. and Knox, K. S. Bronchoalveolar Lavage as a Research Tool. Semin. Respir. Crit. Care Med. Oct 2007;28(5):561-573. Donati, S. Y. and Papazian, L. Role of Open-Lung Biopsy in Acute Respiratory Distress Syndrome. Curr. Opin. Crit. Care. Feb 2008;14(l):75-79. Doyle, I. R., Nicholas, T. E., and Bersten, A. D. Partitioning Lung and Plasma Proteins: Circulating Surfactant Proteins as Biomarkers of Alveolocapillary Permeability. Clin. Exp. Pharmacol. Physiol. Mar 1999;26(3):185-197. Clapp, R. W., Jacobs, M. M., and Loechler, E. L. Environmental and Occupational Causes of Cancer: New Evidence 2005-2007. Rev. Environ. Health. Jan/ Mar 2008;23(1): 1-37. Cohen, R. A., Patel, A., and Green, F. H. Lung Disease Caused by Exposure to Coal Mine and Silica Dust. Semin. Respir. Crit. Care Med. Dec 2008;29(6): 651-661. Kleeberger, S. R. and Cho, H. Y. Gene-Environment Interactions in Environmental Lung Diseases. Novartis Found. Symp. 2008;293:168-178; Discussion 78-83. Taskar, V. and Coultas, D. Exposures and Idiopathic Lung Disease. Semin. Respir. Crit. Care Med. Dec 2008;29(6):670-679. Goulding, J., Snelgrove, R., and Saldana, J., et al. Respiratory Infections: Do We Ever Recover? Proc. Am. Thorac. Soc. Dec 2007;4(8):618-625. Si-Tahar, M., Touqui, L., and Chignard, M. Innate Immunity and Inflammation—Two Facets of the Same Anti-Infectious Reaction. Clin. Exp. Immunol. May 2009;156(2): 194-198. Mallia, P. and Johnston, S. L. Influenza Infection and COPD. Int. J. Chron. Obstruct. Pulmon. Dis. 2007;2(l):55-64. Sethi, S. and Murphy, T. F. Infection in the Pathogenesis and Course of Chronic Obstructive Pulmonary Disease. N. Engl. J. Med. Nov 27, 2008;359(22): 2355-2365.

LUNG INJURY BIOMARKERS 31. 32. 33. 34. 35. 36. 37. 38.

39. 40. 41. 42. 43. 44.

45. 46. 47. 48. 49.

189

Van Ewijk, B. E., Van Der Zalm, M. M., Wolfs, T. R, and Van Der Ent, C. K. Viral Respiratory Infections in Cystic Fibrosis. J. Cyst. Fibros. Aug 2005 ;4 Suppl 2:31-6. Gehlbach, B. K. and Geppert, E. The Pulmonary Manifestations of Left Heart Failure. Chest. Feb 2004;125(2):669-682. Baronas, E. T., Lee, J. W., Alden, C , and Hsieh, P. Y. Biomarkers to Monitor Drug-Induced Phospholipidosis. Toxicol. Appl. Pharmacol. Jan 2007;218(1): 72-78. Halliwell, W. H. Cationic Amphiphilic Drug-Induced Phospholipidosis. Toxicol. Pathol. Jan/Feb 1997;25(l):53-60. Reasor, M. J., Hastings, K. L., and Ulrich, R. G. Drug-Induced Phospholipidosis: Issues and Future Directions. Expert. Opin. Drug Saf. Jul 2006;5(4): 567-583. Wiener-Kronish, J. P. and Dorr, H. I. Ventilator-Associated Pneumonia: Problems with Diagnosis and Therapy. Best Pract. Res. Clin. Anaesthesiol. Sep 2008;22(3):437^t49. Singh, N. and Davis, G. S. Review: Occupational and Environmental Lung Disease. Curr. Opin. Pulm. Med. Mar 2002;8(2): 117-125. Lu, X., Zhao, M., and Kong, H., et al. Characterization of Complex Hydrocarbons in Cigarette Smoke Condensate by Gas Chromatography-Mass Spectrometry and Comprehensive Two-Dimensional Gas Chromatography-Time-ofFlight Mass Spectrometry. J. Chromatogr. A. Jul 23, 2004;1043(2):265-273. Dahl, M. and Nordestgaard, B. G. Markers of Early Disease and Prognosis in COPD. Int. J. Chron. Obstruct. Pulmon. Dis. 2009;4(1): 157-167. Taylor, D. R. Risk Assessment in Asthma and COPD: A Potential Role for Biomarkers? Thorax. Mar 2009;64(3):261-264. Kreiss, K., Day, G. A., and Schuler, C. R. Beryllium: A Modern Industrial Hazard. Annu. Rev. Public Health. 2007;28:259-277. Santo Tomas, L. H. Beryllium Hypersensitivity and Chronic Beryllium Lung Disease. Curr. Opin. Pulm. Med. Mar 2009;15(2): 165-169. Kasper, M. and Barth, K. Bleomycin and Its Role in Inducing Apoptosis and Senescence In Lung Cells—Modulating Effects of Caveolin-1. Curr. Cancer Drug Targets. May 2009;9(3):341-353. Waalkes, M. P. and Rehm, S. Chronic Toxic and Carcinogenic Effects of Cadmium Chloride in Male DBA/2ncr and NFS/Ncr Mice: Strain-Dependent Association with Tumors of the Hematopoietic System, Injection Site, Liver, and Lung. Fundam. Appl. Toxicol. Jul 1994;23(1):21-31. Adcock, I. M., Tsaprouni, L., Bhavsar, P., and Ito, K. Epigenetic Regulation of Airway Inflammation. Curr. Opin. Immunol. Dec 2007;19(6):694-700. Risch, A. and Plass, C. Lung Cancer Epigenetics and Genetics. Int. J. Cancer. Jull,2008;123(l):l-7. Tuder, R. M., Yoshida, T., Arap, W., Pasqualini, R., and Petrache, I. State of the Art Cellular and Molecular Mechanisms of Alveolar Destruction in Emphysema: An Evolutionary Perspective. Proc. Am. Thorac. Soc. Aug 2006;3(6):503-510. Barthel, S. R., Johansson, M. W., Mcnamee, D. M., and Mosher, D. R Roles of Integrin Activation in Eosinophil Function and the Eosinophilic Inflammation of Asthma. J. Leukoc. Biol. Jan 2008;83(1):1-12. Voynow, J. A. and Rubin, B. K. Mucins, Mucus, and Sputum. Chest. Feb 2009; 135(2):505-512.

190

BIOMARKERS 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70.

Widdicombe, J. H. Regulation of the Depth and Composition of Airway Surface Liquid. J. Anat. Oct 2002;201(4):313-318. Widdicombe, J. Relationships Among the Composition of Mucus, Epithelial Lining Liquid, and Adhesion of Microorganisms. Am. J. Respir. Crit. Care Med. Jun 1995;151(6):2088-92;Discussion 92-93. Evans, C. M. and Koo, J. S. Airway Mucus: The Good, the Bad, the Sticky. Pharmacol. Ther. Mar 2009;121(3):332-348. Hauber, H. P., Foley, S. C , and Hamid, Q. Mucin Overproduction in Chronic Inflammatory Lung Disease. Can. Respir. J. Sep 2006;13(6):327-335. Rubin, B. K. Mucus, Phlegm, and Sputum in Cystic Fibrosis. Respir. Care. Jun 2009;54(6):726-732;Discussion 32. Bosse, Y., Pare, P. D., and Seow, C. Y. Airway Wall Remodeling in Asthma: From the Epithelial Layer to the Adventitia. Curr. Allergy Asthma Rep. Jul 2008; 8(4):357-366. Cook, D. N. and Bottomly, K. Innate Immune Control of Pulmonary Dendritic Cell Trafficking. Proc. Am. Thorac. Soc. Jul 2007; 4(3):234-239. Tsoumakidou, M., Demedts, I. K., Brusselle, G. G., and Jeffery, P. K. Dendritic Cells in Chronic Obstructive Pulmonary Disease: New Players in an Old Game. Am. J. Respir. Crit. Care Med. Jun 1, 2008;177(11):1180-1186. Pisi, G., Olivieri, D., and Chetta, A., Eds. The Airway Neurogenic Inflammation: Clinical and Pharmacological Implications. Jul 16, 2009. Lohmann-Matthes, M. L., SteinmuUer, C , and Franke-Ullmann, G. Pulmonary Macrophages. Eur. Respir. J. Sep 1994;7(9):1678-1689. Suarez, C. J., Parker, N. J., and Finn, P. W. Innate Immune Mechanism in Allergic Asthma. Curr. Allergy Asthma Rep. Sep 2008;8(5):451^159. Noble, P. W. and Jiang, D. Matrix Regulation of Lung Injury, Inflammation, and Repair: The Role of Innate Immunity. Proc. Am. Thorac. Soc. Jul 2006;3(5): 401^104. Chopra, M., Reuben, J. S., and Sharma, A. C. Acute Lung Injury: Apoptosis and Signaling Mechanisms. Exp. Biol. Med. (Maywood). Apr 2009;234(4): 361-371. Jobe, A. H. and Ikegami, M. Biology of Surfactant. Clin. Perinatol. Sep 2001; 28(3):655-669. Griese, M. Pulmonary Surfactant in Health and Human Lung Diseases: State of the Art. Eur. Respir. J. Jun 1999;13(6):1455-1476. Ghodrat, M. Lung Surfactants. Am. J. Health Syst. Pharm. Aug 15, 2006; 63(16):1504-1521. Enhorning, G. Surfactant in Airway Disease. Chest. Apr 2008; 133(4):975-980. Rennard, S. I. Epithelial Cells and Fibroblasts. Novartis Found. Symp. 2001 ;234: 104-112;Discussion 12-9. Herzog, E. L., Brody, A. R., Colby, T. V., Mason, R., and Williams, M. C. Knowns and Unknowns of the Alveolus. Proc. Am. Thorac. Soc. Sep 15, 2008; 5(7):778-782. Galambos, C. and Demello, D. E. Regulation of Alveologenesis: Clinical Implications of Impaired Growth. Pathology. Feb 2008;40(2):124-140. Pauluhn, J., Carson, A., and Costa, D. L., et al. Workshop Summary: PhosgeneInduced Pulmonary Toxicity Revisited: Appraisal of Early and Late Markers of Pulmonary Injury from Animal Models with Emphasis on Human Significance. Inhal. Toxicol. Aug 2007; 19(10):789-810.

LUNG INJURY BIOMARKERS 71. 72. 73. 74.

75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86.

87. 88. 89. 90.

191

Duniho, S. M., Martin, J., and Forster, J. S., et al. Acute Changes in Lung Histopathology and Bronchoalveolar Lavage Parameters in Mice Exposed to the Choking Agent Gas Phosgene. Toxicol. Pathol. May/Jun 2002;30(3):339-349. Harkema, J. R. and Wagner, J. G. Non-Allergic Models of Mucous Cell Metaplasia and Mucus Hypersecretion in Rat Nasal and Pulmonary Airways. Novartis Found. Symp. 2002;248:181-197;Discussion 97-200, 77-82. Kodavanti, U. P., Schladweiler, M. C, and Ledbetter, A. D., et al. The Spontaneously Hypertensive Rat: An Experimental Model of Sulfur Dioxide-Induced Airways Disease. Toxicol Sci. Nov 2006;94(1): 193-205. Wagner, U., Staats, P., Fehmann, H. C , Fischer, A., Welte, T., and Groneberg, D. A. Analysis of Airway Secretions in a Model of Sulfur Dioxide Induced Chronic Obstructive Pulmonary Disease (COPD). J. Occup. Med. Toxicol. 2006; 1:12. Ramos-Barbon, D., Ludwig, M. S., and Martin, J. G. Airway Remodeling: Lessons from Animal Models. Clin. Rev. Allergy Immunol. Aug 2004;27(1):3-21. Puchelle, E., Zahm, J. M., Tournier, J. M., and Coraux, C. Airway Epithelial Repair, Regeneration, and Remodeling After Injury in Chronic Obstructive Pulmonary Disease. Proc. Am. Thome. Soc. Nov 2006;3(8):726-733. Holgate, S. T. The Airway Epithelium Is Central to the Pathogenesis of Asthma. Allergol. Int. Mar 2008;57(1): 1-10. Voynow, J. A. What Does Mucin Have to Do with Lung Disease? Paediatr. Respir. Rev. Jun 2002;3(2):98-103. Rogers, D. F. Airway Mucus Hypersecretion in Asthma: An Undervalued Pathology? Curr. Opin. Pharmacol. Jun 2004;4(3):241-250. Rogers, D. F. The Airway Goblet Cell. Int. J. Biochem. Cell Biol. Jan 2003; 35(l):l-6. Rogers, D. F. Pulmonary Mucus: Pediatric Perspective. Pediatr. Pulmonol. Sep 2003;36(3): 178-188. Mehrotra, A. K. and Henderson, W. R., Jr. The Role of Leukotrienes in Airway Remodeling. Curr. Mol. Med. Apr 2009;9(3):383-391. Chung, K. F. The Role of Airway Smooth Muscle in the Pathogenesis of Airway Wall Remodeling in Chronic Obstructive Pulmonary Disease. Proc. Am. Thorac Soc. 2005;2(4):347-354;Discussion 71-2. Bergeron, C , Al-Ramli, W., and Hamid, Q. Remodeling in Asthma. Proc. Am. Thorac. Soc. May 1, 2009;6(3):301-305. Rogers, D. F. Physiology of Airway Mucus Secretion and Pathophysiology of Hypersecretion. Respir. Care. Sep 2007;52(9): 1134-1146;Discussion 46-9. Zhen, G., Park, S. W., and Nguyenvu, L. T., et al. IL-13 and Epidermal Growth Factor Receptor Have Critical but Distinct Roles in Epithelial Cell Mucin Production. Am. J. Respir. Cell Mol. Biol. Feb 2007;36(2):244-253. Cohn, L. Mucus in Chronic Airway Diseases: Sorting Out the Sticky Details. J. Clin. Invest. Feb 2006;116(2):306-308. Mauad, T. and Dolhnikoff, M. Pathologic Similarities and Differences Between Asthma and Chronic Obstructive Pulmonary Disease. Curr. Opin. Pulm. Med. Jan2008;14(l):31-38. Martinez, F. D. The Origins of Asthma and Chronic Obstructive Pulmonary Disease in Early Life. Proc. Am. Thorac. Soc. May 1, 2009;6(3):272-277. Kawada, M., Hachiya, Y., Arihiro, A., and Mizoguchi, E. Role of Mammalian Chitinases in Inflammatory Conditions. Keio J. Med. Mar 2007;56(l):21-27.

192

BIOMARKERS 91. 92.

93. 94. 95. 96. 97. 98. 99. 100.

101.

102.

103.

104. 105.

106.

107.

108.

Cockcroft, D. W. and Davis, B. E. Mechanisms of Airway Hyperresponsiveness. J. Allergy Clin. Immunol. Sep 2006;118(3):551-559;Quiz 60-1. Bloemen, K., Verstraelen, S., Van Den Heuvel, R., Witters, H., Nelissen, I., and Schoeters, G. The Allergic Cascade: Review of the Most Important Molecules in the Asthmatic Lung. Immunol. Lett. Oct 31, 2007;113(1):6—18. Betts, R. J. and Kemeny, D. M. CD8+ T Cells in Asthma: Friend or Foe? Pharmacol. Ther. Feb 2009;121(2):123-131. Fahy, J. V. Eosinophilic and Neutrophilic Inflammation in Asthma: Insights from Clinical Studies. Proc. Am. Thorac. Soc. May 1, 2009;6(3):256-259. Borish, L. and Culp, J. A. Asthma: A Syndrome Composed of Heterogeneous Diseases. Ann. Allergy Asthma Immunol. Jul 2008; 101(1): l-8;Quiz-ll, 50. Nouri-Aria, K. T. and Durham, S. R. Regulatory T Cells and Allergic Disease. Inflamm. Allergy Drug Targets. Dec 2008;7(4):237-252. Larche, M.. Regulatory T Cells in Allergy and Asthma. Chest. Sep 2007; 132(3): 1007-1014. Holgate, S. T. Pathogenesis of Asthma. Clin. Exp. Allergy. Jun 2008; 38(6): 872-897. Hamid, Q. and Tulic, M. Immunobiology of Asthma. Annu. Rev. Physiol. 2009; 71:489-507. Van Der Vaart, H., Postma, D. S., Timens, W., and Ten Hacken, N. H. Acute Effects of Cigarette Smoke on Inflammation and Oxidative Stress: A Review. Thorax. Aug 2004;59(8):713-721. Kim, V, Rogers, T. J., and Criner, G. J. New Concepts in the Pathobiology of Chronic Obstructive Pulmonary Disease. Proc. Am. Thorac. Soc. May 1, 2008; 5(4):478-485. Oikonomidi, S., Kostikas, K., Tsilioni, I., Tanou, K., Gourgoulianis, K. I., and Kiropoulos, T. S. Matrix Metalloproteinases in Respiratory Diseases: From Pathogenesis to Potential Clinical Implications. Curr. Med. Chem. 2009;16(10):1214-1228. Charokopos, N., Apostolopoulos, N., Kalapodi, M., Leotsinidis, M., Karamanos, N., and Mouzaki, A. Bronchial Asthma, Chronic Obstructive Pulmonary Disease and NF-Kappab. Curr. Med. Chem. 2009;16(7):867-883. Barnes, P. J. Immunology of Asthma and Chronic Obstructive Pulmonary Disease. Nat. Rev. Immunol. Mar 2008;8(3):183-192. Kemeny, D. M., Vyas, B., and Vukmanovic-Stejic, M., et al. CD8(+) T Cell Subsets and Chronic Obstructive Pulmonary Disease. Am. J. Respir. Crit. Care Med. Nov 1999;160(5 Pt 2):S33-7. Barnes, P. J., Shapiro, S. D., and Pauwels, R. A. Chronic Obstructive Pulmonary Disease: Molecular and Cellular Mechanisms. Eur. Respir. J. Oct 2003; 22(4):672-688. Maeno, T., Houghton, A. M., Quintero, P. A., Grumelli, S., Owen, C. A., and Shapiro, S. D. CD8+ T Cells Are Required for Inflammation and Destruction in Cigarette Smoke-Induced Emphysema in Mice. J. Immunol. Jun 15, 2007;178(12):8090-8096. Bochner, B. S., Hudson, S. A., Xiao, H. Q., and Liu, M. C. Release of Both CCR4-Active and CXCR3-Active Chemokines During Human Allergic Pulmonary Late-Phase Reactions. J. Allergy Clin. Immunol. Nov 2003; 112(5): 930-934.

LUNG INJURY BIOMARKERS

193

109. Yamauchi, K. and Inoue, H. Airway Remodeling in Asthma and Irreversible Airflow Limitation-ECM Deposition in Airway and Possible Therapy for Remodeling. Allergol. Int. Dec 2007;56(4):321-329. 110. Aceves, S. S. and Broide, D. H. Airway Fibrosis and Angiogenesis Due to Eosinophil Trafficking in Chronic Asthma. Curr. Mol. Med. Aug 2008;8(5): 350-358. 111. Sato, M., Hirayama, S., and Lara-Guerra, H., et al. MMP-Dependent Migration of Extrapulmonary Myofibroblast Progenitors Contributing to Posttransplant Airway Fibrosis in the Lung. Am. J. Transplant. May 2009;9(5): 1027-1036. 112. Doherty, T. and Broide, D. Cytokines and Growth Factors in Airway Remodeling in Asthma. Curr. Opin. Immunol. Dec 2007;19(6):676-680. 113. Sagel, S. D. Noninvasive Biomarkers of Airway Inflammation in Cystic Fibrosis. Curr. Opin. Pulm. Med. Nov 2003;9(6):516-521. 114. Van Der Vliet, A. and Cross, C. E. Oxidants, Nitrosants, and the Lung. Am. J. Med. Oct 1, 2000;109(5):398^121. 115. Lu, Q., Harrington, E. O., and Rounds, S. Apoptosis and Lung Injury. Keio J. Med. Dec 2005;54(4):184-189. 116. Bozza, F. A., Shah, A. M., Weyrich, A. S., and Zimmerman, G. A. Amicus or Adversary: Platelets in Lung Biology, Acute Injury, and Inflammation. Am. J. Respir. Cell Mol. Biol. Feb 2009;40(2):123-134. 117. Holgate, S. T., Davies, D. E., Powell, R. M., Howarth, P. H., Haitchi, H. M., and Holloway, J. W. Local Genetic and Environmental Factors in Asthma Disease Pathogenesis: Chronicity and Persistence Mechanisms. Eur. Respir. J. Apr 2007;29(4):793-803. 118. Bowman, R. V., Wright, C. M., Davidson, M. R., Francis, S. M., Yang, I. A., and Fong, K. M. Epigenomic Targets for the Treatment of Respiratory Disease. Expert Opin. Ther. Targets. Jun 2009;13(6):625-640. 119. Sheski, F. D. and Mathur, P. N. Endobronchial Ultrasound. Chest. Jan 2008; 133(l):264-270. 120. Ryu, J. H., Daniels, C. E., Hartman, T. E., and Yi, E. S. Diagnosis of Interstitial Lung Diseases. Mayo Clin. Proc. Aug 2007;82(8):976-986. 121. Greene, C. L., Reemtsen, B., Polimenakos, A., Horn, M., and Wells, W. Role of Clinically Indicated Transbronchial Lung Biopsies in the Management of Pediatric Post-Lung Transplant Patients. Ann. Thorac. Surg. Jul 2008;86(1): 198-203. 122. El-Bayoumi, E. and Silvestri, G. A. Bronchoscopy for the Diagnosis and Staging of Lung Cancer. Semin. Respir. Crit. Care Med. Jun 2008;29(3):261-270. 123. Ware, L. B. Pathophysiology of Acute Lung Injury and the Acute Respiratory Distress Syndrome. Semin. Respir. Crit. Care Med. Aug 2006;27(4):337-349. 124. Davis, I. C. and Matalon, S. Epithelial Sodium Channels in the Adult Lung— Important Modulators of Pulmonary Health and Disease. Adv. Exp. Med. Biol. 2007;618:127-140. 125. Lucas, R., Verin, A. D., Black, S. M., and Catravas, J. D. Regulators of Endothelial and Epithelial Barrier Integrity and Function in Acute Lung Injury. Biochem. Pharmacol. Jun 15, 2009;77(12): 1763-1772. 126. Tuder, R. M., Yoshida, T, Fijalkowka, I., Biswal, S., and Petrache, I. Role of Lung Maintenance Program in the Heterogeneity of Lung Destruction in Emphysema. Proc. Am. Thorac. Soc. Nov 2006;3(8):673-679. 127. Plataki, M., Tzortzaki, E., Rytila, P., Demosthenes, M., Koutsopoulos, A., and Siafakas, N. M. Apoptotic Mechanisms in the Pathogenesis of COPD. Int. J. Chron. Obstruct. Pulmon. Dis. 2006;1(2):161-171.

BIOMARKERS 128. Tuder, R. M., Yun, J. H., and Graham, B. B. Cigarette Smoke Triggers Code Red: P21cipl/WAF1/SDI1 Switches on Danger Responses in the Lung. Am. J. Respir. Cell Mol. Biol. Jul 2008;39(l):l-6. 129. Drakopanagiotakis, F., Xifteri, A., Polychronopoulos, V., Bouros, D. Apoptosis in Lung Injury and Fibrosis. Eur. Respir. J. Dec 2008; 32(6): 1631-1638. 130. Zhou, X., Hu, H., and Huynh, M. L., et al. Mechanisms of Tissue Inhibitor of Metalloproteinase 1 Augmentation by IL-13 on TGF-Beta 1-Stimulated Primary Human Fibroblasts. J. Allergy Clin. Immunol. Jun 2007;119(6): 1388-1397. 131. Hericks, A. J. andBhat, A. An Overview of Alpha-1 Antitrypsin Deficiency. Mo. Med. May/Jun 2007;104(3):255-259. 132. Warheit, D. B., Hart, G. A., and Hesterberg, T. W., et al. Potential Pulmonary Effects of Man-made Organic Fiber (MMOF) Dusts. Crit. Rev. Toxicol. Nov 2001;31(6):697-736. 133. Khalil, N., Churg, A., Muller, N., and O'Connor, R. Environmental, Inhaled and Ingested Causes of Pulmonary Fibrosis. Toxicol. Pathol. 2007;35(l):86-96. 134. Pardo, A., Selman, M., and Kaminski, N. Approaching the Degradome in Idiopathic Pulmonary Fibrosis. Int. J. Biochem. Cell Biol. 2008;40(6-7): 1141-1155. 135. Papaioannou, A. I., Kostikas, K., Kollia, P., and Gourgoulianis, K. I. Clinical Implications for Vascular Endothelial Growth Factor in the Lung: Friend or Foe? Respir. Res. 2006;7:128. 136. Elias, J. A., Kang, M. J., Crothers, K., Homer, R., and Lee, C. G. State of the Art. Mechanistic Heterogeneity in Chronic Obstructive Pulmonary Disease: Insights from Transgenic Mice. Proc. Am. Thome. Soc. Aug 2006;3(6):494-498. 137. Mehrad, B., Burdick, M. D., and Strieter, R. M. Fibrocyte CXCR4 Regulation as a Therapeutic Target in Pulmonary Fibrosis. Int. J. Biochem. Cell Biol. Aug/ Sep 2009;41(8-9):1708-1718. 138. Chaudhary, N. I., Roth, G. J., and Hilberg, F., et al. Inhibition of PDGF, VEGF and FGF Signalling Attenuates Fibrosis. Eur. Respir. J. May 2007;29(5): 976-985. 139. Krein, P. M. and Winston, B. W. Roles for Insulin-Like Growth Factor I and Transforming Growth Factor-Beta in Fibrotic Lung Disease. Chest. Dec 2002; 122(6 Suppl):289S-293S. 140. Day, B. J. Antioxidants as Potential Therapeutics for Lung Fibrosis. Antioxid. Redox. Signal. Feb 2008;10(2):355-370. 141. Wilberding, J. A., Ploplis, V. A., and McLennan, L., et al. Development of Pulmonary Fibrosis in Fibrinogen-Deficient Mice. Ann. NY Acad. Sci. 2001;936: 542-548. 142. Porter, D. W., Hubbs, A. F., and Mercer, R., et al. Progression of Lung Inflammation and Damage in Rats After Cessation of Silica Inhalation. Toxicol. Sci. Jun 2004;79(2):370-380. 143. Hook, G. E. Alveolar Proteinosis and Phospholipidoses of the Lungs. Toxicol. Pathol. 1991;19(4Pt 1):482-513. 144. Bedrossian, C. W., Warren, C. J., Ohar, J., and Bhan, R. Amiodarone Pulmonary Toxicity: Cytopathology, Ultrastructure, and Immunocytochemistry. Ann. Diagn. Pathol. Oct 1997;l(l):47-56. 145. Turell, D. C. Advances with Surfactant. Emerg. Med. Clin. North Am. Nov 2008;26(4):921-928. 146. Meyer, N. J. and Garcia, J. G. Wading Into the Genomic Pool to Unravel Acute Lung Injury Genetics. Proc. Am. Thorac. Soc. Jan 2007;4(l):69-76.

LUNG INJURY BIOMARKERS

195

147. Been, J. V. and Zimmermann, L. J. What's New in Surfactant? A Clinical View on Recent Developments in Neonatology and Paediatrics. Eur. J. Pediatr. Sep 2007;166(9):889-899. 148. Ackerman, S. J., Kwatia, M. A., Doyle, C. B., and Enhorning, G. Hydrolysis of Surfactant Phospholipids Catalyzed by Phospholipase A2 and Eosinophil Lysophospholipases Causes Surfactant Dysfunction: A Mechanism for Small Airway Closure in Asthma. Chest. Mar 2003;123(3 Suppl):355S. 149. Stenger, P. C , Alonso, C , Zasadzinski, J. A., Waring, A. J., Jung, C. L., and Pinkerton, K. E. Environmental Tobacco Smoke Effects on Lung Surfactant Film Organization. Biochim. Biophys. Acta. Feb 2009;1788(2):358-370. 150. Bakshi, M. S., Zhao, L., Smith, R., Possmayer, E, and Petersen, N. O. Metal Nanoparticle Pollutants Interfere with Pulmonary Surfactant Function in Vitro. Biophys. J. Feb 1, 2008;94(3):855-868. 151. Greene, K. E., King, T. E., Jr., and Kuroki, Y., et al. Serum Surfactant ProteinsA and -D As Biomarkers in Idiopathic Pulmonary Fibrosis. Eur. Respir. J. Mar 2002;19(3):439^46. 152. Cheng, I. W., Ware, L. B., Greene, K. E., Nuckton, T. J., Eisner, M. D., and Matthay, M. A. Prognostic Value of Surfactant Proteins A and D in Patients with Acute Lung Injury. Crit. Care Med. Jan 2003;31(l):20-27. 153. Fireman, E. Induced Sputum and Occupational Diseases Other Than Asthma. Curr. Opin. Allergy Clin. Immunol. Apr 2009;9(2):93-96. 154. Balbi, B., Pignatti, P., and Corradi, M., et al. Bronchoalveolar Lavage, Sputum and Exhaled Clinically Relevant Inflammatory Markers: Values in Healthy Adults. Eur. Respir. J. Oct 2007;30(4):769-781. 155. Economidou, F, Samara, K. D., Antoniou, K. M., and Siafakas, N. M. Induced Sputum in Interstitial Lung Diseases: Novel Insights in the Diagnosis, Evaluation and Research. Respiration. 2009;77(3):351-358. 156. Talat, N., Shahid, F, Dawood, G., and Hussain, R. Dynamic Changes in Biomarker Profiles Associated with Clinical and Subclinical Tuberculosis in a High Transmission Setting: A Four-Year Follow-up Study. Scand. J. Immunol. Jun 2009;69(6):537-546. 157. Zar, H. J., Dechaboon, A., Hanslo, D., Apolles, P., Magnus, K. G., and Hussey, G. Pneumocystis Carinii Pneumonia in South African Children Infected with Human Immunodeficiency Virus. Pediatr. Infect. Dis. J. Jul 2000;19(7):603-607. 158. Vatrella, A., Bocchino, M., and Perna, F, et al. Induced Sputum as a Tool for Early Detection of Airway Inflammation in Connective Diseases-Related Lung Involvement. Respir. Med. Jul 2007;101(7): 1383-1389. 159. Aitken, M. L., Greene, K. E., and Tonelli, M. R., et al. Analysis of Sequential Aliquots of Hypertonic Saline Solution-Induced Sputum from Clinically Stable Patients with Cystic Fibrosis. Chest. Mar 2003;123(3):792-729. 160. Fahy, J. V, Boushey, H. A., and Lazarus, S. C , et al. Safety and Reproducibility of Sputum Induction in Asthmatic Subjects in a Multicenter Study. Am. J. Respir. Crit. Care Med. May 2001 ;163(6): 1470-1475. 161. Gasparini, S. Evolving Role of Interventional Pulmonology in the Interdisciplinary Approach to the Staging and Management of Lung Cancer: Bronchoscopic Mediastinal Staging of Lung Cancer. Clin. Lung Cancer. Sep 2006;8(2): 110-115. 162. Bolliger, C. T., Sutedja, T. G., Strausz, J., and Freitag, L. Therapeutic Bronchoscopy with Immediate Effect: Laser, Electrocautery, Argon Plasma Coagulation and Stents. Eur. Respir. J. Jun 2006;27(6):1258-1271.

196

BIOMARKERS 163. Coxson, H. O., Mayo, J., Lam, S., Santyr, G., Parraga, G., and Sin, D. D. New and Current Clinical Imaging Techniques to Study Chronic Obstructive Pulmonary Disease. Am. J. Respir. Crit. Care Med. Jul 16, 2009. 164. Anzueto, A., Levine, S. M., and Jenkinson, S. G. The Technique of Bronchoalveolar Lavage. A Guide to Sampling the Terminal Airways and Alveolar Space. J. Crit. Illn. Nov 1992;7(11):1817-1824. 165. Hoffman, A. M. Bronchoalveolar Lavage: Sampling Technique and Guidelines for Cytologic Preparation and Interpretation. Vet. Clin. North Am. Equine. Pract. Aug 2008;24(2):423^135. 166. Barnes, P. J., Chowdhury, B., and Kharitonov, S. A, et al. Pulmonary Biomarkers in Chronic Obstructive Pulmonary Disease. Am. J. Respir. Crit. Care Med. Jul 1, 2006;174(1):6-14. 167. Wallenborn, J. G., Schladweiler, M. J., Richards, J. H., and Kodavanti, U. P. Differential Pulmonary and Cardiac Effects of Pulmonary Exposure to a Panel of Paniculate Matter-Associated Metals. Toxicol. Appl. Pharmacol. Aug 10, 2009. 168. Henderson, R. F. Use of Bronchoalveolar Lavage to Detect Respiratory Tract Toxicity of Inhaled Material. Exp. Toxicol. Pathol. Jul 2005;57 Suppl 1:155-159. 169. Polkinghorne, K. R. Detection and Measurement of Urinary Protein. Curr. Opin. Nephrol. Hypertens. Nov 2006;15(6):625-630. 170. Klein, B. Standardization of Serum Protein Analyses. Ann. Clin. Lab Sci. May/ Jun 1978;8(3):249-253. 171. Schwartz, M. K. Lactic Dehydrogenase. An Old Enzyme Reborn as a Cancer Marker? Am. J. Clin. Pathol. Oct 1991;96(4):441^t43. 172. Kinlough, C. L., Poland, P. A., Bruns, J. B., and Hughey, R. P. Gamma-Glutamyltranspeptidase: Disulfide Bridges, Propeptide Cleavage, and Activation in the Endoplasmic Reticulum. Methods Enzymol. 2005;401:426-449. 173. Jean, J. C , Liu, Y., and Joyce-Brady, M. The Importance of Gamma-Glutamyl Transferase in Lung Glutathione Homeostasis and Antioxidant Defense. Biofactors. 2003;17(1-4):161-173. 174. Shvedova, A. A., Kisin, E. R., and Mercer, R., et al. Unusual Inflammatory and Fibrogenic Pulmonary Responses to Single-Walled Carbon Nanotubes in Mice. Am. J. Physiol. Lung Cell Mol. Physiol. Nov 2005;289(5):L698-708. 175. Wereszczynska-Siemiatkowska, U., Dlugosz, J. W., Siemiatkowski, A., Chyczewski, L., and Gabryelewicz, A. Lysosomal Activity of Pulmonary Alveolar Macrophages in Acute Experimental Pancreatitis in Rats with Reference to Positive PAF-Antagonist (BN 52021) Effect. Exp. Toxicol. Pathol. May 2000;52(2): 119-125. 176. Vincent, R., Vu, D., and Hatch, G., et al. Sensitivity of Lungs of Aging Fischer 344 Rats to Ozone: Assessment by Bronchoalveolar Lavage. Am. J. Physiol. Oct 1996;271(4Ptl):L555-565. 177. Kodavanti, U. P., Hatch, G. E., Starcher, B., Giri, S. N., Winsett, D., and Costa, D. L. Ozone-Induced Pulmonary Functional, Pathological, and Biochemical Changes in Normal and Vitamin C-Deficient Guinea Pigs. Fundam. Appl. Toxicol. Feb 1995;24(2): 154-164. 178. Semenzato, G., Bortolin, M., Facco, M., Tassinari, C , Sancetta, R., and Agostini, C. Lung Lymphocytes: Origin, Biological Functions, and Laboratory Techniques for Their Study in Immune-Mediated Pulmonary Disorders. Crit. Rev. Clin. Lab. Sci. Oct 1996;33(5):423^155. 179. Schwenk, J. M., Lindberg, J., Sundberg, M., Uhlen, M., and Nilsson, P. Determination of Binding Specificities in Highly Multiplexed Bead-Based Assays for Antibody Proteomics. Mol. Cell Proteomics. Jan 2007;6(1): 125-132.

LUNG INJURY BIOMARKERS

197

180. Van Eeden, S. E, Kitagawa, Y., Klut, M. E., Lawrence, E., and Hogg, J. C. Polymorphonuclear Leukocytes Released from the Bone Marrow Preferentially Sequester in Lung Microvessels. Microcirculation. Sep 1997;4(3):369-380. 181. Chung, K. F. Inflammatory Mediators in Chronic Obstructive Pulmonary Disease. Curr. Drug Targets Inflamm. Allergy. Dec 2005;4(6):619-625. 182. Sallusto, F. and Baggiolini, M. Chemokines and Leukocyte Traffic. Nat. Immunol. Sep 2008;9(9):949-952. 183. Moser, B., Wolf, M., Walz, A., and Loetscher, P. Chemokines: Multiple Levels of Leukocyte Migration Control. Trends Immunol. Feb 2004;25(2):75-84. 184. Kucia, M., Jankowski, K., and Reca, R., et al. CXCR4-SDF-1 Signalling, Locomotion, Chemotaxis and Adhesion. J. Mol. Histol. Mar 2004;35(3):233-245. 185. Jiang, D., Liang, J., Li, Y., and Noble, P. W. The Role of Toll-Like Receptors in Non-Infectious Lung Injury. Cell Res. Aug 2006;16(8):693-701. 186. Zhao, J., Zhu, H., Wong, C. H., Leung, K. Y, and Wong, W S. Increased Lungkine and Chitinase Levels in Allergic Airway Inflammation: A Proteomics Approach. Proteomics. Jul 2005;5(ll):2799-2807. 187. Behndig, A. E, Blomberg, A., Helleday, R., Duggan, S. T., Kelly, F. J., and Mudway, I. S. Antioxidant Responses to Acute Ozone Challenge in the Healthy Human Airway. Inhal. Toxicol. May 21, 2009. 188. Panda, K., Chattopadhyay, R., Chattopadhyay, D. J., and Chatterjee, I. B. Vitamin C Prevents Cigarette Smoke-Induced Oxidative Damage In Vivo. Free Radic. Biol. Med. Jul 15, 2000;29(2): 115-124. 189. Bruegge, K., Jelkmann, W., and Metzen, E. Hydroxylation of Hypoxia-Inducible Transcription Factors and Chemical Compounds Targeting the HIF-Alpha Hydroxylases. Curr. Med. Chem. 2007;14(17):1853-1862. 190. Myllyla, R., Majamaa, K., Gunzler, V, Hanauske-Abel, H. M., and Kivirikko, K. I. Ascorbate Is Consumed Stoichiometrically in the Uncoupled Reactions Catalyzed by Prolyl 4-Hydroxylase and Lysyl Hydroxylase. / Biol. Chem. May 10, 1984;259(9):5403-5405. 191. Tschank, G., Sanders, J., Baringhaus, K. H., Dallacker, E, Kivirikko, K. I., and Gunzler, V. Structural Requirements for the Utilization of Ascorbate Analogues in the Prolyl 4-Hydroxylase Reaction. Biochem. J. May 15, 1994;300(Ptl): 75-79. 192. Wilson, J. X. Regulation of Vitamin C Transport. Annu. Rev. Nutr. 2005;25: 105-125. 193. Molina-Diaz, A., Ortega-Carmona, I., and Pascual-Reguera, M. I. Indirect Spectrophotometric Determination of Ascorbic Acid with Ferrozine by Flow-Injection Analysis. Talanta. Nov 1998;47(3):531-536. 194. Roginsky, V A., Barsukova, T. K., Bruchelt, G., Stegmann, H. B. Iron Bound to Ferritin Catalyzes Ascorbate Oxidation: Effects of Chelating Agents. Biochim. Biophys. Acta. Apr 17, 1997;1335(l-2):33-39. 195. Biswas, S. K. and Rahman, I. Environmental Toxicity, Redox Signaling and Lung Inflammation: The Role of Glutathione. Mol. Aspects Med. Feb/Apr 2009; 30(l-2):60-76. 196. Iwasaki, Y, Saito, Y, and Nakano, Y, et al. Chromatographic and Mass Spectrometric Analysis of Glutathione in Biological Samples. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. Jul 7, 2009. 197. Dalle-Donne, I., Rossi, R., Giustarini, D., Colombo, R., and Milzani, A. S-Glutathionylation in Protein Redox Regulation. Free Radic. Biol. Med. Sep 15, 2007;43(6):883-898.

198

BIOMARKERS 198. Mcmenamin, M. E., Himmelfarb, J., and Nolin, T. D. Simultaneous Analysis of Multiple Aminothiols in Human Plasma by High Performance Liquid Chromatography with Fluorescence Detection. J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci. May 29, 2009. 199. Fattman, C. L., Schaefer, L. M., and Oury, T. D. Extracellular Superoxide Dismutase in Biology and Medicine. Free Radic. Biol. Med. Aug 1, 2003;35(3): 236-256. 200. Gao, F., Kinnula, V. L., Myllarniemi, M., and Oury, T. D. Extracellular Superoxide Dismutase in Pulmonary Fibrosis. Antioxid. Redox. Signal. Feb 2008;10(2):343-354. 201. Ghio, A. J., Hilborn, E. D., and Stonehuemer J. G., et al. Particulate Matter in Cigarette Smoke Alters Iron Homeostasis to Produce a Biological Effect. Am. J. Respir. Crit. Care Med. Dec 1, 2008,178(11): 1130-1138. 202. Rouault, T. A. and Tong, W. H. Iron-Sulfur Cluster Biogenesis and Human Disease. Trends Genet. Aug 2008;24(8):398-W7. 203. De Domenico, I., McVey Ward, D., and Kaplan, J. Regulation of Iron Acquisition and Storage: Consequences for Iron-Linked Disorders. Nat. Rev. Mol. Cell. Biol. Jan2008;9(l):72-81. 204. Ghio, A. J., Turi, J. L., Yang, E, Garrick, L. M., and Garrick, M. D. Iron Homeostasis in the Lung. Biol. Res. 2006;39(l):67-77. 205. Syslova, K., Kacer, P., and Kuzma, M., et al. Rapid and Easy Method for Monitoring Oxidative Stress Markers in Body Fluids of Patients with Asbestos or Silica-Induced Lung Diseases. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. Aug 15, 2009;877(24):2477-2486. 206. Eder, E., Wacker, M., and Wanek, P. Lipid Peroxidation-Related l,N2-Propanodeoxyguanosine-DNA Adducts Induced by Endogenously Formed 4-Hydroxy2-Nonenal in Organs of Female Rats Fed Diets Supplemented with Sunflower, Rapeseed, Olive or Coconut Oil. Mutat. Res. Jul 31, 2008;654(2): 101-107. 207. Sunjic, S. B., Cipak, A., Rabuzin, F, Wildburger, R., and Zarkovic, N. The Influence of 4-Hydroxy-2-Nonenal on Proliferation, Differentiation and Apoptosis of Human Osteosarcoma Cells. Biofactors. 2005;24(l-4): 141-148. 208. Zhu, X., Gallogly, M. M., Mieyal, J. J., Anderson, V. E., and Sayre, L. M. Covalent Cross-Linking of Glutathione and Carnosine to Proteins by 4-Oxo-2Nonenal. Chem. Res. Toxicol. Jun 2009;22(6): 1050-1059. 209. Miranda, C. L., Reed, R. L., Kuiper, H. C , Alber, S., and Stevens, J. F. Ascorbic Acid Promotes Detoxification and Elimination of 4-Hydroxy-2(E)-Nonenal in Human Monocytic THP-1 Cells. Chem. Res. Toxicol. May 2009;22(5): 863-874. 210. Janssen, L. J. Isoprostanes and Lung Vascular Pathology. Am. J. Respir. Cell Mol. Biol. Oct 2008;39(4):383-389. 211. Janssen, L. J. The Pulmonary Biology of Isoprostanes. Chem. Phys. Lipids. Mar 2004; 128(1-2): 101-116. 212. Winnik, W. M. and Kitchin, K. T. Measurement of Oxidative Stress Parameters Using Liquid Chromatography-Tandem Mass Spectroscopy (LC-MS/MS). Toxicol. Appl. Pharmacol. Nov 15, 2008;233(1): 100-106. 213. Paredi, P., Kharitonov, S. A., and Barnes, P. J. Analysis of Expired Air for Oxidation Products. Am. J. Respir. Crit. Care Med. Dec 15, 2002;166(12 Pt 2):S31-7. 214. Michel, F., Bonnefont-Rousselot, D., Mas, E., Drai, J., and Therond, P. Biomarkers of Lipid Peroxidation: Analytical Aspects. Ann. Biol. Clin. (Paris). Nov/Dec 2008;66(6):605-620.

LUNG INJURY BIOMARKERS

199

215. Montuschi, P., Barnes, P., and Roberts, L. J., 2nd. Insights Into Oxidative Stress: The Isoprostanes. Curr. Med. Chem. 2007;14(6):703-717. 216. Montuschi, P. Analysis of Exhaled Breath Condensate in Respiratory Medicine: Methodological Aspects and Potential Clinical Applications. Ther. Adv. Respir. Dis. Oct2007;l(l):5-23. 217. Hoffmeyer, R, Raulf-Heimsoth, M., and Bruning, T. Exhaled Breath Condensate and Airway Inflammation. Curr. Opin. Allergy Clin. Immunol. Feb 2009; 9(l):16-22. 218. Yang, Y L., Huang, K. L., Liou, H. L., and Chen, H. I. The Involvement of Nitric Oxide, Nitric Oxide Synthase, Neutrophil Elastase, Myeloperoxidase and Proinflammatory Cytokines in the Acute Lung Injury Caused by Phorbol Myristate Acetate. J. Biomed. Sci. Jul 2008;15(4):499-507. 219. Esther, C. R., Jr., Boysen, G., and Olsen, B. M., et al. Mass Spectrometric Analysis of Biomarkers and Dilution Markers in Exhaled Breath Condensate Reveals Elevated Purines in Asthma and Cystic Fibrosis. Am. J. Physiol. Lung Cell Mol. Physiol. Jun 2009;296(6):L987-993. 220. Fredenburgh, L. E., Perrella, M. A., and Mitsialis, S. A. The Role of Heme Oxygenase-1 in Pulmonary Disease. Am. J. Respir. Cell Mol. Biol. Feb 2007; 36(2): 158-165. 221. Maestrelli, P., Paska, C , and Saetta, M., et al. Decreased Haem Oxygenase-1 and Increased Inducible Nitric Oxide Synthase in the Lung of Severe COPD Patients. Eur. Respir. J. Jun 2003;21(6):971-976. 222. Kitchin, K. T., Anderson, W. L., and Suematsu, M. An ELISA Assay for Heme Oxygenase (HO-1). J. Immunol. Methods. Jan 1, 2001;247(1-2):153-161. 223. Zakrzewicz, D. and Eickelberg, O. From Arginine Methylation to ADMA: A Novel Mechanism with Therapeutic Potential in Chronic Lung Diseases. BMC Pulm. Med. 2009;9:5. 224. Dweik, R. A. The Lung in the Balance: Arginine, Methylated Arginines, and Nitric Oxide. Am. J. Physiol. Lung Cell Mol. Physiol. Jan 2007;292(1):L15-17. 225. Ahmad, T., Mabalirajan, U., Ghosh, B., and Agrawal, A. Altered Assymetric Dimethyl Arginine Metabolism in Allergically Inflamed Mice Lungs. Am. J. Respir. Cell Mol. Biol. Jul 31, 2009. 226. Tsunoda, M., Nonaka, S., and Funatsu, T. Determination of Methylated Arginines by Column-Switching High-Performance Liquid Chromatography-Fluorescence Detection. Analyst. Oct 2005;130(10): 1410-3. 227. Meinitzer, A., Puchinger, M., and Winklhofer-Roob, B. M., et al. Reference Values for Plasma Concentrations of Asymmetrical Dimethylarginine (ADMA) and Other Arginine Metabolites in Men After Validation of a Chromatographic Method. Clin. Chim. Ada. Sep 2007;384(l-2): 141-148. 228. Ohlmeier, S., Vuolanto, M., and Toljamo, T., et al. Proteomics of Human Lung Tissue Identifies Surfactant Protein A as a Marker of Chronic Obstructive Pulmonary Disease. J. Proteome. Res. Dec 2008;7(12):5125-5132. 229. Hant, F. N., Ludwicka-Bradley, A., and Wang, H. J., et al. Surfactant Protein D and KL-6 as Serum Biomarkers of Interstitial Lung Disease in Patients with Scleroderma. J. Rheumatol. Apr 2009;36(4):773-780. 230. Shimizu, Y, Sunaga, N., and Dobashi, K., et al. Serum Markers in Interstitial Pneumonia with and without Pneumocystis Jirovecii Colonization: A Prospective Study. BMC Infect. Dis. 2009;9:47. 231. Mutti, A., Corradi, M., Goldoni, M., Vettori, M. V, Bernard A, and Apostoli P. Exhaled Metallic Elements and Serum Pneumoproteins in Asymptomatic Smokers and Patients with COPD or Asthma. Chest. May 2006;129(5): 1288-1297.

200

BIOMARKERS 232. Fernandez-Real, J. M., Chico, B., Shiratori, M., Nara, Y, Takahashi, H., and Ricart, W. Circulating Surfactant Protein A (SP-A), a Marker of Lung Injury, is Associated with Insulin Resistance. Diabetes Care. May 2008;31(5):958-963. 233. Schmidt, R., Markart, P., and Ruppert, C , et al. Time-Dependent Changes in Pulmonary Surfactant Function and Composition in Acute Respiratory Distress Syndrome Due to Pneumonia or Aspiration. Respir. Res. 2007;8:55. 234. Strong, P., Kishore, U., Morgan, C , Lopez Bernal, A., Singh, M., and Reid, K. B. A Novel Method of Purifying Lung Surfactant Proteins A and D from the Lung Lavage of Alveolar Proteinosis Patients and from Pooled Amniotic Fluid. J. Immunol. Methods. Nov 1, 1998;220(1-2):139-149. 235. Doyle, I. R., Hermans, C , Bernard, A., Nicholas, T. E., and Bersten, A. D. Clearance of Clara Cell Secretory Protein 16 (CC16) and Surfactant Proteins A and B From Blood in Acute Respiratory Failure. Am. J. Respir. Crit. Care Med. Nov 1998;158(5 Pt 1): 1528-1535. 236. Blomberg, A., Mudway, I., and Svensson, M., et al. Clara Cell Protein as a Biomarker for Ozone-Induced Lung Injury in Humans. Eur. Respir. J. Dec 2003;22(6):883-888. 237. Garbacki, N., Di Valentin, E., Piette, J., Cataldo, D., Crahay, C , and Colige, A. Matrix Metalloproteinase 12 Silencing: A Therapeutic Approach to Treat Pathological Lung Tissue Remodeling? Pulm. Pharmacol. Ther. Aug 2009;22(4): 267-278. 238. Viglio, S., Annovazzi, L., Luisetti, M., Stolk, J., Casado, B., and Iadarola, P. Progress in the Methodological Strategies for the Detection in Real Samples of Desmosine and Isodesmosine, Two Biological Markers of Elastin Degradation. J. Sep. Sci. Feb 2007;30(2):202-213. 239. Cantor, J. O. and Shteyngart, B. How a Test for Elastic Fiber Breakdown Products in Sputum Could Speed Development of a Treatment for Pulmonary Emphysema. Med. Sci. Monit. Jan 2004;10(l):RAl-4. 240. Robinet, A., Millart, H., Oszust, R, Hornebeck, W., and Bellon, G. Binding of Elastin Peptides to S-Gal Protects the Heart Against Ischemia/Reperfusion Injury by Triggering the RISK Pathway. FASEB J. Jul 2007;21(9):1968-1978. 241. Antonicelli, R, Bellon, G., Debelle, L., and Hornebeck, W. Elastin-Elastases and Inflamm-Aging. Curr. Top Dev. Biol. 2007;79:99-155. 242. Kanazawa, H. Role of Vascular Endothelial Growth Factor in the Pathogenesis of Chronic Obstructive Pulmonary Disease. Med. Sci. Monit. Nov 2007; 13(11):RA189-195. 243. Meduri, G. U., Tolley, E. A., Chinn, A., Stentz, R, and Postlethwaite, A. Procollagen Types I and III Aminoterminal Propeptide Levels During Acute Respiratory Distress Syndrome and in Response to Methylprednisolone Treatment. Am. J. Respir. Crit. Care Med. Nov 1998;158(5 Pt 1): 1432-1441. 244. Janssen, R., Sato, H., and Gratters, J. C , et al. Study of Clara Cell 16, KL-6, and Surfactant Protein-D in Serum as Disease Markers in Pulmonary Sarcoidosis. Chest. Dec 2003;124(6):2119-2125. 245. Mcauley, D. R and Matthay, M. A. Clara Cell Protein CC16. A New Lung Epithelial Biomarker for Acute Lung Injury. Chest. Jun 2009;135(6):1408-1410. 246. Nemmar, A., Nemery, B., Hoylaerts, M. P., and Vermylen, J. Air Pollution and Thrombosis: An Experimental Approach. Pathophysiol. Haemost Thromb. SepDec, 2002;32(5-6):349-350. 247. Well, S. Coagulation, Fibrinolysis, and Fibrin Deposition in Acute Lung Injury. Crit. Care Med. Apr 2003 ;31(4 Suppl):S213-220.

LUNG INJURY BIOMARKERS

201

248. Strukova, S. Blood Coagulation-Dependent Inflammation. Coagulation-Dependent Inflammation and Inflammation-Dependent Thrombosis. Front. Biosci. 2006;11:59-80. 249. Shetty, S., Padijnayayveetil, J., Tucker, T., Stankowska, D., and Idell, S. The Fibrinolytic System and the Regulation of Lung Epithelial Cell Proteolysis, Signaling, and Cellular Viability. Am. J. Physiol. Lung Cell Mol. Physiol. Dec 2008;295(6):L967-975. 250. Izuhara, K. and Saito, H. Microarray-Based Identification of Novel Biomarkers in Asthma. Allergol. Int. Dec 2006;55(4):361-367. 251. Reamon-Buettner, S. M., Mutschler, V., and Borlak, J. The Next Innovation Cycle in Toxicogenomics: Environmental Epigenetics. Mutat. Res. Jul/Aug 2008;659(l-2): 158-165. 252. Gottipolu, R. R., Wallenbom, J. G., and Karoly, E. D., et al. One-Month Diesel Exhaust Inhalation Produces Hypertensive Gene Expression Pattern in Healthy Rats. Environ. Health Perspect. Jan 2009;117(l):38-46. 253. Nana-Sinkam, S. P., Hunter, M. G., and Nuovo, G. J., et al. Integrating the Micrornome into the Study of Lung Disease. Am. J. Respir. Crit. Care Med. Jan 1, 2009;179(1):4-10. 254. Bartels, C. L. and Tsongalis, G. J. Micrornas: Novel Biomarkers for Human Cancer. Clin. Chem. Apr 2009;55(4):623-631. 255. Mroz, R. M., Noparlik, J., Chyczewska, E., Braszko, J. J., and Holownia, A. Molecular Basis of Chronic Inflammation in Lung Diseases: New Therapeutic Approach. J. Physiol. Pharmacol. Nov 2007;58 Suppl 5(Pt 2):453^60. 256. Bhavsar, P., Ahmad, T., and Adcock, I. M. The Role of Histone Deacetylases in Asthma and Allergic Diseases. J. Allergy Clin. Immunol. Mar 2008;121(3): 580-584. 257. Barros, S. P. and Offenbacher, S. Epigenetics: Connecting Environment and Genotype to Phenotype and Disease. J. Dent. Res. May 2009;88(5):400-408.

This page intentionally left blank

CHAPTER

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY: THE CURRENT STATE, GAPS, AND FUTURE OPPORTUNITIES Josef S. Ozer, William J. Reagan, Shelli Schomaker, Joe Palandra, Mike Baratta, and Shashi Ramaiah

INTRINSIC AND IDIOSYNCRATIC DRUGINDUCED LIVER INJURY:TERMINOLOGIES AND BACKGROUND In preclinical drug development, the chemical entities and exploratory toxicants that cause acute liver injury (drug-induced liver injury, DILI) at moderate to high doses (e.g., carbon tetrachloride, acetaminophen, thioacetamide, and limited new chemical entities) are most often predictable in nature, and exhibit a dose-response relationship and a short latent period (referred to as DILI type 1 or DILI-1). Compound doses that demonstrate DILI-1 with limited margins of efficacy compared to injury are typically discontinued prior to clinical development or their chemistry is redesigned to avoid hepatic liability at margins considered acceptable to regulatory bodies such as the FDA and EMEA.1 In contrast to DILI-1, there are developmental and even marketed compounds that are not readily predictable for hepatic signals, clearly lack dose-response relationship, and the latent period is often long and variable after repeated dosing (referred to as compounds exhibiting DILI type 2 or DILI-2). Compounds that induce DILI-2 (e.g., idiosyncratic DILI) in late stage clinical trials or post-marketing lead to program termination or regula203

204

BIOMARKERS

tory action with post-market warnings or black box designation added to the label (e.g., isoniazid, labetalol, trovafloxacin, felbamat, telithromycin, etc.), or even withdrawal from the market in certain instances (e.g., troglitazone, benoxaprofen, bromfenac, ticruynafen, etc.).2 The majority of human idiosyncratic acute liver failure cases that fall in this category occur with a 1:3000 to 10,000 frequency following drug administration2,34 and might also depend on multivariant patient susceptibility factors such as diet, metabolism, genetic, and perhaps less well understood factors when combined with drug treatment.5 DILI-1 and 2 show similar histologic manifestation as changes within hepatocytes (parenchymal) or biliary epithelium or a combination of both as discussed below. Chronic changes within human liver during complex disease processes include fibrosis, cirrhois, and neoplasia are not discussed at great length in this review.

H I S T O L O G I C A L M A N I F E S T A T I O N S OF LIVER INJURY Hepatic Steatosis/Fatty Liver and Steatohepatitis Hepatic steatosis, an accumulation of fat vacuoles within hepatocytes, is a common response noted with a variety of liver toxicants and represents an injury with potential reversibility.6 Compounds that induce microvesicular steatosis are associated with an increased mortality incidence and include metabolites of the antiepileptic drug valproic acid and the antiviral agent fialuridine.7 A syndrome noted in obese individuals, that also often show associated type II diabetes, is referred to as non-alcoholic fatty liver disease (NAFLD) where hepatocytes are markedly steatotic with a marked inflammatory component.8 Ethanol induced hepatic fatty change is clinically relevant because druginduced DILI shows similar hepatic signals to the background histologic change observed from alcohol abuse.9 There are no diagnostic or predictive biomarkers that prognostic for hepatic steatosis. Steatohepatitis is the next stage of progressive steatosis if left untreated.10 The hepatic inflammatory cells are usually neutrophils and mononuclear leukocytes.11 Conditions usually associated with steatohepatitis are alcoholic liver disease, NAFLD, and endotoxemia secondary to intestinal disease. Similar to hepatic steatosis, there are no predictive or diagnostic biomarkers for the inflammatory component of this hepatic pathology.

Cholestatic Liver Injury Cholestasis may be transient or chronic in nature.6 Canalicular cholestasis can be produced by drugs/chemicals that damage the bile canalicular structures and function. Cholestasis can occur simply as a result of physical obstruction of canaliculi within the liver parenchyma (intrahepatic) or outside the liver (extrahepatic). Disruption of actin filaments within the hepatocyte may cause cholestasis by preventing the normal pulsatile contractions that

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY

205

move bile through the canalicular system to the bile ducts. Transporters can be inhibited by drug treatment, causing cholestasis. Cholestatic injuries rarely manifest to DILI-2, do not rapidly progress clinically, are mostly reversible after withdrawal of drug treatment, and do not result in frequent mortality or liver transplantation.12

C O M M O N MECHANISMS OF ACUTE LIVER INJURY Mechanistic Manifestations of DILI Drug-induced liver injury has been divided into two classifications; predictive or intrinsic (DILI-1) and idiosyncratic (DILI-2). Intrinsic liver injury is a predictable, reproducible, dose-dependent reaction to a toxicant or drug candidate.13-16 A threshold dose exists for xenobiotics causing intrinsic liver injury.15 There is commonly a predictable latent period between the time of exposure and clinical evidence of liver injury.13 This type of liver injury accounts for the vast majority of toxic liver injury and is often caused by reactive products of xenobiotic metabolism, most commonly electrophiles and free radicals. Idiosyncratic responses are, by contrast, unpredictable responses to a drug or other toxicant. These responses are rare, show considerable dose independence, and can be associated with extrahepatic changes.14-17 Idiosyncratic drug reactions often occur after sensitization followed by re-exposure to a drug.6,13'15,16 Between the time of the first dosing and the time clinical signs become evident, there is usually a delay of one to five weeks,16 and occasionally several months. Thus, onset of DILI-2 is initiated with continued dosing with no definite timeline.13,15 Mechanisms of acute liver injury are described briefly below. It should be noted that in contrast to the intrinsic liver injury, the mechanisms for idiosyncratic liver injury are not fully characterized although mechanisms such as underlying inflammation,18 immune-mediated mechanisms,5 mitochondrial/ oxidative stress mechanisms19 and inhibition of tissue repair20-22 are of significant interest. Some less tested mechanisms such as calcium signaling and cytoskeletal changes merit further investigation.

PROCESSES OF H E P A T O C Y T E CELL D E A T H Hepatic cell death processes include apoptosis and necrosis.23 Apoptosis may be induced by xenobiotics due to oxidative stress mechanisms,14 decrease in apoptotic suppressor levels, or enhanced expression of apoptosis related genes.13 Necrosis is the predominant form of cell death in most toxic insults to the liver. Several drugs/chemicals such as carbon tetrachloride (CC14), acetaminophen, thioacetamide, allyl alcohol, and ethanol induce hepatocyte necrosis. Carbon tetrachloride, acetaminophen, and thioacetamide cause centrilobular necrosis, which is more common compared to the necrosis caused by allyl alcohol, found in the midzonal pattern, which is a rare form of damage. The majority of these compounds are currently employed as experimental

206

BIOMARKERS

hepatotoxicants and show predictable liver toxicity i.e., DILI-1. Compounds causing DILI-2 also induce mostly hepatocellular injury and a high incidence of mortality in affected patients (e.g., isoniazid, benoxaprofen, and troglitazone).

The Role of Immune Responses in Liver Injury One of the major mechanisms for idiosyncratic liver injury is centered on the specific immune response to drug exposures or to related drug metabolites.5 There is increased evidence, although controversial, for the role of a specific immune response (drug antibody reaction) as a mediator for idiosyncratic liver injury. This is based on the fact that there is an obvious lack of a relationship between the dose of the drug and subsequent idiosyncratic liver injury; and also due to the fact that the patients don't manifest reactions until they are treated for longer periods of time which could correlate with immune responsiveness (e.g., halothane mediated hepatotoxicity). Although antibodies to halothane metabolite associated proteins have been identified, there are also situations when certain patients with an antibody response do not develop liver injury. It is not clear if these antibodies cause tissue injury or if they arise as a consequence of hepatic injury. Although classic idiopathic autoimmune hepatitis is associated with anti-liver kidney microsomal LKM-1, anti-smooth muscle antibodies (SMA), or antinuclear antibodies, auto-immune hepatitis is heterogeneous and the lack of these antibodies does not preclude a strong autoimmune component.24 Recent studies have found serum factors in some patients with DILI-2 that are consistent with an autoimmune mechanism, but the patterns are heterogeneous and the same cytokine response can also be found in liver injury that is not autoimmune in nature. Another proposal surrounding an immune mediated mechanism for DILI-1 is the "hapten hypothesis."25 This mechanism also involves the bioactivation of a drug to a reactive metabolite,26 covalent modification of tissue macromolecules and cellular proteins, processing by the MHC antigen presentation pathway, and resulting recognition of the neoantigen by the immune system. Although the hypothesis of hapten neoantigen synthesis has been discussed,27 how the specific event occurs with known DILI-2 toxicants has not been investigated.

Metabolic Idiosyncrasy in Liver Injury The hypothesis that idiosyncratic reactions have a metabolic basis is based on polymorphisms in drug metabolism related genes. For some drugs, there is evidence that this may be the case. An important example is polymorphism(s) in acetylation catabolism with hydrazine and aromatic amine drugs such as isoniazid which show toxicity. Isoniazid causes hepatotoxicity in < 10% of the patients that are administered this drug. Human polymorphisms in isoniazid acetylases have been long recognized and epidemiologic studies indicate that fast acetylators of isoniazid are more susceptible to liver necrosis since elevated metabolites lead to a potent toxic acylating agent.28 Nevertheless, there

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY

207

has been controversy surrounding this issue. Some studies have failed to link acetylation status with hepatotoxicity and it is possible that additional mechanisms play a role in such toxicities.

U n d e r l y i n g Inflammation Mechanisms w i t h Liver Injury Several studies demonstrate the augmentation of DILI with underlying inflammation. Studies with rats injected with a small non-injurious dose of LPS and co-administered subtoxic doses of drugs leads to a combined effect that results in increased liver inury.18 It is not clear if similar types of mechanisms operate in human patients. It is unknown what the trigger would be to induce a similar DILI-2 response in humans. Underlying inflammation in internal organs may potentially act in this capacity, although no evidence has been gathered to support this hypothesis. However, the augmented response seen with underlying inflammation might bear resemblance to the idiosyncratic nature of liver injury where patients respond with liver injury.

M i t o c h o n d r i a l O x i d a n t Stress and D y s f u n c t i o n Free radicals are generated from hepatocytes in several ways, such as from ionizing radiation, oxidative metabolism by cytochrome P450, reduction and oxidation (redox) reactions that occur during normal metabolism, transition metals such as iron and copper, and from nitric oxide generated by a variety of inflammatory cells. The reactive species generated result in lipid peroxidation of membranes, oxidative modification of proteins, and lesions within DNA.29 Mitochondria appears to be an important target for oxidant stress leading to abnormal function and/or initiating death-inducing cascades by opening of the mitochondrial permeability transition (MPP) pore and triggering apoptosis and/or necrosis by both the caspase-dependent and independent pathways. Mitochondria are clearly targeted by oxidants, electrophiles, lipophilic cations, and weak acids and are critical to modulation of cell redox status, osmotic regulation, pH and cytosolic calcium homeostasis, and cell signaling.30 Compounds that may disrupt mitochondrial oxidative phosphorylation include bile acids and amiodarone. In addition, there are several drugs that inhibit beta oxidation of fatty acids in mitochondria leading to lipid accumulation, such as aspirin, valproic acid, and tetracyclines. In normal individuals, mitochondria have several inherent antioxidant mechanisms that neutralize oxidants; however when these mechanisms offer insufficient protection in susceptible individuals or modified preclinical species, manifestation of toxicity can result. Preclinical species typically do not exhibit signs of mitochondrial toxicity for most drugs, except when engineered to have a compromised antioxidant defense. Superoxide dismutase (SOD or MnSOD) deficient mice have been tested to address this hypothesis.19 Heterozygous Sod2 deficient mice administered DILI-2 drugs are associated with increased oxidant stress and mitochondrial dysfunction. This preclinical model appears to be somewhat promising in regards to exploring mechanisms in hepatic liability, yet further investigation is warranted.

208

BIOMARKERS

I n h i b i t i o n o f T i s s u e Repair Response Tissue repair is a dynamic response involving compensatory cell proliferation and tissue regeneration which is stimulated in response to acute toxicity, restoring hepatic structure and function. Extensive evidence in rodent models using structurally and mechanistically diverse hepatotoxicants such as acetaminophen, carbon tetrachloride, chloroform, thioacetamide, trichloroethylene, and allyl alcohol have demonstrated that tissue repair plays a critical role in determining the final outcome of toxic insult, i.e., recovery from injury and survival or progression of injury leading to liver failure and even death.20 The concept of adaptation or tissue repair is important because clinical patients who lack adaptability may face a risk of severe DILI. In addition, the theory that DILI patients may be divided into tolerators, adaptors, and susceptibles may be due to differential tissue repair response.31 For example, about 50% of the patients treated with tacrine, a drug proposed for treating Alzheimer's disease, developed ALT elevations (> 3 X ULN), but did not develop symptomatic liver injury or jaundice within U.S. clinical trials.4 Furthermore, there have been few post-approval reports of significant liver injury with tacrine. On the contrary, clinical trials with troglitazone (withdrawn from the market) used to treat type-2 diabetes resulted in lesser number of patients with ALT increases (> 3 X ULN) compared to tacrine, yet a significant fraction of patients developed serious DILI-2 liver injury.3 It is likely that patients in tacrine trials had an improved adaptative response compared to patients in troglitazone trials. Clearly, systematic studies are needed to address the role of tissue repair.

D i s r u p t i o n of Calcium Homeostasis and Cell Membrane Damage in Liver Membrane associated calcium and magnesium ATPases are responsible for maintaining the cellular calcium gradient.32 Significant and persistent increases in the intracellular calcium result from nonspecific increases in permeability of the plasma membrane, mitochondrial membranes, and membranes of the smooth endoplasmic reticulum. Elevated cytoplasmic calcium activates a variety of enzymes with damaging membrane effects. The major enzymes that are involved in activation by calcium include ATPases, phospholipases, proteases, and endonucleases. Thus, increased calcium causes increased mitochondrial permeability and induction of apoptosis and necrosis. The chemicals and drugs that cause liver damage by this mechanism include quinines, peroxides, acetaminophen, iron, and cadmium.

D i s r u p t i o n of C y t o s k e l e t o n in Liver Injury Changes in intracellular calcium homeostasis produced by active metabolites of xenobiotics may cause disruption of the dynamic cytoskeleton. There are a few toxins that cause disruption of the cytoskeleton through mechanisms independent of biotransformation, including microcystin. The hepatocyte is the specific target of microcystin, which enters the cell through a bile-acid trans-

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY

209

porter. Microcystin covalently binds to serine/threonine protein phosphatase, leading to the hyperphosphorylation of cytoskeletal proteins and deformation of the cytoskeleton.6

T R A D I T I O N A L PRECLINICAL A N D CLINICAL BIOMARKERS OF D R U G - I N D U C E D LIVER INJURY Traditional biochemical parameters are routinely tested in preclinical and clinical situations to detect hepatic signals (DILI-1). The serum hepatic enzyme activities resulting from leakage of damaged hepatocytes that are in common use include alanine aminotransferase (ALT) and aspartate aminotransferase (AST) (Figures 9.1 and 9.2). Other less widely used leakage enzymes include sorbital dehydrogenase (SDH) and glutamate dehydrogenase (GLDH), which are labile (SDH) or reagents are not widely available (GLDH) (Figure 9.1). These enzymes "leak-out" through the membrane into peripheral blood where they are measured in an activity assay following liver injury (for example, relatively more severe injury due to hepatitis and necrosis) or alterations in liver membrane permeability (for example hepatic glycogen and lipid accumulation). Alkaline phosphatase (ALP), gamma-glutamyl-transferase (or -peptidase) (GGT), and 5'-Nucleotidase (5'-NT) enzymes are considered cholestatic-induction parameters of the hepatobiliary origin and are elevated during biliary/hepatobiliary changes (Figure 9.1). Clinically, serum ALT elevations in combination with T bili elevations are considered to be the most specific predictor of the drug's potential for severe DILI-2 since T bili elevation represents the functional capacity of the liver. Hy Zimmerman (Zimmerman, 1968) originally noted that a patient with concurrent marked elevations in ALT and T bili has at least a 10% chance of mortality from liver failure. In a clinical trial, "Hy's Law cases" are identified as subjects in clinical trial who experience ALT > 3 X ULN and total bilirubin or T bili > 2 X ULN with mostly hepatocellular alterations and lack of concurrent disease component such as viral hepatitis among a broad list of noncausal developments1'3>4,16M (Figure 9.1).

GAPS IN T R A D I T I O N A L HEPATIC BIOMARKERS • Although ALT is considered the "gold" standard biomarker of liver injury, it has limitations as a specific predictor of acute liver injury. Serum ALT can increase in the absence of hepatocyte necrosis and in the presence of metabolic disease such as type 1 diabetes, NAFLD, or even skeletal muscle associated disorders. • No biomarker has been demonstrated to predict the potential of a compound to induce DILI-2 or determine the susceptibility of patients to develop DILI-2. • No biomarker differentiates between drugs that induce liver failure versus drugs that initiate transient liver injury despite continued drug treatment.

210

BIOMARKERS

FIGURE 9.1 Traditional and novel biomarkers of DILI. Widely employed historical predinical and clinical biomarkers include serum transaminases (ALT, AST) known to detect hepatocyte injury, while serum ALP andT bili are used to assess cholestatic or biliary injury. To address the gaps in the specificity of these traditional biomarkers, ongoing efforts are generating new biomarkers. Emerging enzyme biomarkers such as PON I, PNR MDH, and GLDH are being tested preclinically through a PSTC consortium and preliminary data shows promise. Furthermore, novel biomarkers continue to emerge based on exploratory studies reported in literature, although these analytes need additional testing. While efforts are ongoing to assess the usefulness of these emerging and new biomarkers, there is a need to test these analytes both in the predinical and clinical space to address translatability. In addition, the assay technologies and platforms employed to detect these biomarkers are also discussed in detail in this chapter

Nevertheless, ALT elevations alone still guide regulatory decisions in clinical trials to protect patient safety, even with some limited degree of uncertainty regarding what constitutes true injury. In this review, new hepatic biomarkers that can potentially translate to clinic to detect DILI-1 will be discussed. While ongoing biomarker efforts are geared toward understanding the specificity of current hepatic biomarkers, additional studies are needed to fill the existing gaps.

C O N S I D E R A T I O N S T O P R E D I C T A C U T E LIVER INJURY: A N A T O M Y A N D T I M E - C O U R S E Regarding the histologic designation of injury, biomarkers can differentiate between hepatocellular and biliary injury. Biomarkers to address regiospecific histologic change (e.g., centrilobular verses periportal) would certainly benefit compound developmental decisions, but these would not be considered nearly as critical as the biliary versus hepatocellular distinction. The time com-

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY

211

FIGURE 9.2 In spite of extensive studies to generate novel hepatic biomarkers, there appears to be a need for additional biomarkers. Although traditional and new hepatic biomarkers can detect hepatic injury, none of these address hepatic injury mechanisms. In addition, these markers do not assess hepatic regeneration, especially considering the role of hepatic regeneration/adaptability in the causation of DILI 2. The biomarkers listed in this figure are being assessed to address these gaps. While hepatic injury markers appear to be sensitive, based on the biology of these enzymes, additional information can also be obtained by refining the assays used to analyze these markers.

ponent for predictable liver injury is critical since developmental compounds and toxicants show effects that are both dose- and time-dependent and can not be readily ascertained prior to study execution. Biomarkers that show a relationship to the time course of liver injury would be extremely valuable for decision making when selecting compounds from a pool for further development. Truly predictive markers of liver injury have not yet been discovered and substantial evidence does not yet support or rule out their existence, although a consensus exists that these investigations would be resource intensive and difficult to perform. By predictive, we refer to biomarker changes that precede or are prodromal to anchored histopathology observations. For example, an algorithm of genomic markers of renal injury that are predictive at day five for injury manifested at day 28 have been reported, although the set of transcripts does not correlate to known disease mechanisms nor is their performance rigorously uniform across multiple platforms, which is a great limitation of such approaches.34 Soluble markers from serum and perhaps urine are translatable to the clinic, while genomic markers show mostly limited utility in this regard and will not be discussed further. In addition to biomarkers of degeneration and necrosis, biomarkers of reactive metabolites, inflammation, and recovery/liver regeneration are critical to obtain a comprehensive view of the relationship between dose and onset of hepatic injury, which will be discussed below.

212

BIOMARKERS

N E W A N D EMERGING SERUM E N Z Y M E B I O M A R K E R S OF LIVER INJURY To address the gaps in traditional hepatic biomarkers and to ensure efficient and effective drug discovery and development, it is critical that specific novel hepatic biomarkers are discovered and implemented. Both the pharmaceutical industry and regulatory agencies recognize the value in the development of novel biomarkers of drug toxicity and safety. The EMEA35 and the FDA36"38 have recently developed processes to guide the qualification of preclinical and clinical biomarkers for regulatory purposes. Pharmaceutical companies are working collaboratively with these regulatory agencies in consortia, like the Drug-induced Liver Injury Network (DILIN), the Innovative Medicines Initiative (Evil), and the Critical Path's Predictive Safety Testing Consortium (PSTC), with the common goal of identifying new biomarkers and qualifying both new and recognized biomarkers that are commonly employed in practice. These efforts will potentially benefit the development and subsequent acceptance of more reliable biomarkers of hepatic toxicity that can be utilized to monitor safety concerns in regulated preclinical and clinical studies. The utility and acceptance of these novel biomarkers will depend, to some extent, on their application across key model preclinical species and translation to humans, their presence in easily accessible tissues and/or biofluids such as blood or urine, and the ability for rapid analytical quantitation that sensitively and reproducibly correlates with well-defined preclinical histomorphologic changes. As mentioned previously, biomarkers of hepatic injury should be specific to liver, and outperform or add information to ALT and/or AST measurements.39-42

D i s c o v e r y and A p p l i c a t i o n of Purine Nucleoside Phosphorylase (PNP), Paraxonase ( P O N - I ) , and Malate Dehydrogenase ( M D H ) as Hepatic Biomarkers Utilizing proteomics as an approach to discover and develop novel biomarkers of hepatotoxicity is well documented in the literature.43-44^7 Three novel serum biomarkers, malate dehydrogenase (MDH), purine nucleoside phosphorylase (PNP), and paraoxonase (PON-1) were identified by proteomic methods as serum biomarkers associated with rat liver toxicity or hypertrophy48 (Figure 9.1). These authors used a series of archetypal hepatotoxicants to model specific modifications to the liver that are often encountered in safety evaluation studies and then searched for chemically induced alterations in the expression of highly specific gene products.48 Of particular interest in this study was the identification of biomarkers in the peripheral circulation that were quantitatively altered after exposure to liver toxicants. After dosing rats with either acetaminophen, a-naphthylisocyanate, Phenobarbital, or Wyeth (Wy)-14,632, four compounds that target the liver through different mechanisms, proteomic analysis of sera was completed and 19 possible biomarkers of altered liver function that correlated with actual hepatic effects were identified. After a critical evaluation of the 19 potential biomarkers, MDH, PNP, and PON1, were

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY

213

identified as proteins having the greatest potential to serve as early indicators of hepatotoxicity.48 Selection was based on a set of criteria which included a protein expression change at an early time point because of the potential for sensitivity, a known function or toxicity indication in published studies, origin primarily or exclusively in the liver, expression in human as well as rat, and the potential for adaptation of the analytical method to clinical chemistry autoanalyzers to allow rapid quantitation of enzymatic activities.48 Purine nucleoside phosphorlyase (PNP), a key enzyme in the purine salvage pathway, reversibly catalyzes the phosphorolysis of nucleosides to their respective bases and corresponding l-(deoxy)-ribose-phosphate. PNP is located mainly in the cytoplasm of endothelial cells, Kupffer cells, and hepatocytes and is released into hepatic sinusoids during necrosis. Serum PNP has been shown in the literature to be correlated with liver injury in the rat after treatment with galactosamine.49 Rat serum activities of PNP were also increased earlier than ALT following endotoxin treatment that resulted in cellular necrosis.50 Concurrent increases of PNP and ALT are indicative of hepatocyte damage following administration of several hepatotoxins.50 However, since ALT is found exclusively in hepatocytes and PNP is localized in hepatocytes, sinusoidal endothelial cells, and Kupffer cells, investigators proposed that PNP leakage may be a reliable marker of nonparenchymal cell injury in the liver when no concurrent ALT leakage is present.50 This premise was tested by measuring ALT and PNP concurrently after treatment with galactosamine and lipopolysaccharide. This study demonstrated that an elevation in PNP activity was an indicator of nonparenchymal cell damage in the absence of a change in ALT activity.49

P O N I Is a Functional Marker of C h r o n i c Liver Injury Paraoxonase-l (PON1) is a high density lipoprotein (HDL)-associated esterase secreted mainly by the liver that detoxifies organophosphates and protects low density lipoproteins from oxidative modifications. PON1 is released into normal circulation bound to HDL and it is a decrease in serum PON1 that is indicative of liver tissue damage. This is likely due to a reduction in PON1 synthesis and less secretion by the liver into blood.51 Decreases in serum PON1 have been reported after dosing male rats with phenobarbital52 and after endotoxin treatment in male hamsters.51 Decreases in PON1 have been linked clinically to chronic hepatic injury,53,54,55,56 but also to a number of other disease states including atherosclerosis57,58 and vasculitis.59 PON1 activity in humans displays a polymorphic distribution and can vary within a given population.60,61 Notwithstanding the variability in PON1 levels, a significant decrease (27%) in PON1 in patients with hepatosteatois was observed when compared to healthy controls,53 and PON1 was useful when testing patients for chronic hepatitis and cirrosis in conjunction with the following standard liver function tests: albumin, ALT, GGT, ALP, and bilirubin.54 PON1 has high diagnostic accuracy when distinguishing patients with liver disease from control subjects, increases the overall sensitivity without affecting specificity, and has a diagnostic accuracy equivalent to that of ALT in patients with chronic hepatitis.54

214

BIOMARKERS

Malate Dehydrogenase (MDH) Activity Is a Candidate Biomarker of DILI-1 Malate dehydrogenase (MDH) catalyzes the reversible conversion of malate into oxaloacetate utilizing NAD+ and is a constitutive enzyme in the citric acid cycle. MDH is a periportal enzyme that is released into the serum indicating tissue damage. While localized in two cellular compartments, the cytoplasm and the mitochondria, the enzyme found in the serum from leakage is primarily from cytoplasmic storage of the enzyme.41 Serum MDH has been reported as correlated with liver injury in the rat after treatment with acetaminophen,62 and thioacetamide.63 Clinically, this enzyme has been reported to be a useful measurement for estimating the severity of liver diseases64 and higher levels have been shown in cirrhotic patients when compared to noncirrhotic cases.65

Biomarker Qualification by the Predictive Safety Testing Consortium (PSTC) MDH, PNP, PON1, as well as glutamate dehydrogenase (GLDH) which has been considered for some time a sensitive measure of hepatotoxicity,66'67 are currently undergoing a regulatory qualification process being sponsored by the PSTC. These markers have been brought forward as putative safety biomarkers within the PSTC membership on the basis of evidence from peerreviewed literature and internal datasets. The sponsored qualification of these markers by the PSTC will include multi-site and company characterization and validation of technical attributes for each assay, an evaluation of the biological relevance, e.g., added value relative to aminotransferase activity, correlation in the preclinical setting to standardized histopathologic observations, and an extended evaluation in clinical settings with regulatory guidance. This qualification is anticipated to provide a substantial understanding of the performance characteristics for these biomarkers and determine how they might add value to currently employed markers in the detection and monitoring of hepatotoxicity in preclinical and clinical settings.

ALT I S O Z Y M E S : A L T I A N D ALT2 Historical Background of ALT Biology Alanine aminotransferase activity is a marker of hepatoxicity in humans, dogs, and rats based on high ALT levels in liver compared to other tissues.68 Although the liver contains greater levels of ALT, it is also present in skeletal muscle, heart, fat, intestines, and brain of rats, dogs, and humans.6871 The widespread distribution of ALT is probably related to the importance of this enzyme in carbohydrate and amino acid metabolism. Increases in liver ALT have been seen in conditions in rats that favor gluconeogenesis, such as in-

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY

215

creased protein intake, fasting, caloric restriction, diabetes, and treatment with corticosteroids.72'73 The exact origin of steady state ALT that is present in serum is not well understood, but is probably a result of normal cell senescence and associated release of enzyme. Historically, ALT increases in the circulation have been thought to be a markers of cellular necrosis, but there is some evidence that it can also be released from intact cells via a process referred to as membrane blebing during injury processes.74 These cytosolic packages of enzymes can then be released into the blood circulation with cellular injury and not reflecting necrosis per se.75 In drug development, significant increases in serum ALT without correlated histological evidence of hepatic necrosis or other organ injuries is observed infrequently. Thus, serum ALT may be increased without associated liver injury, which would indicate an adaptive response, which is not considered an adverse event.7677 Using classic biochemical and cellular fractionation techniques, it has been shown there are at least two different isoenzymes, and perhaps more, of ALT in rats.78 Historically, several investigators have shown that the major ALT isoenzyme is a soluble cytosolic form, but based on cellular fractionation, 4-20% of the ALT enzyme is mitochondrial in origin.78-80 Discrepancies in the amount of mitochondrial ALT form measured may be related to instability of this form of the enzyme. It has been shown that the ALT enzyme can be stabilized by glycerol or dimethylsulfoxide, which will ensure that activity is preserved.7881 Both ALT isoenzymes catalyze the reversible reaction of the transfer of the alpha amino group of alanine to the alpha keto group of ketoglutaric acid to form pyruvate and glutamate, and thus play an important role in amino acid metabolism and gluconeogenesis (Figure 9.3). The mitochondrial form favors the forward ALT reaction, thus favoring the pathway that can promote gluconeogenesis.78 It has also been shown that glucocorticoids, which stimulate gluconeogenesis, will increase the amount of the ALT mitochondrial form in the rat and mouse liver.78'82 The mitochondria form could also be purified from other tissues such as the pig heart.80 Until recently there have been limited tools for measuring and better understanding how these different forms of enzymes react to hepatocellular injury and adapt to different metabolic states in animals and humans.

FIGURE 9.3

ALT enzymatic pathway. Forward and reverse enzymatic reactions are shown.

216

BIOMARKERS

Gene Expression of ALT Isoforms The genes encoding different forms of ALT have been identified in the mouse,83 rats,71 dogs,84 and human.7085 In humans, the original ALT gene that was cloned was designated GPT which was the cytosolic form and encodes a 495 amino acid protein (54 Kd).70,85 More recently, cloning and expression of a homolog, designated GPT2 (hALT2), encodes for a protein 523 amino acid (58 Kd) in length.70 GPT2 (hALT2) shares 78% identity to GPT (hALTl) and shows ALT activity in recombinant systems. Expression of hALT2 occurs in the muscle, kidney, liver, brain and fat, and hALTl is expressed in the kidney, liver, heart and fat. Recently, two protein coding products from hALT2, designated as ALT2_1 and ALT2_2 were discovered (58 and 47 Kd, respectively).69 Comparison of the rat, human and murine sequences shows that rat ALT1 (rALTl) shares 97% and 88% homology to the murine and human sequences, respectively, with greater conservation present in rat ALT2 (rALT2), which is 98% and 94% identical to its murine and human sequences, respectively.71 The rALT2 gene encodes an N-terminal 28-amino acid extension which is a likely mitochondrial targeting sequence.86 How these different genes and other transaminases are regulated is starting to be more comprehensively understood.8789

The Localization of ALT Protein in Tissues Quantification of ALT protein in a panel of tissues by quantitative western blot analysis using antibodies generated against recombinant rat ALT isoenzymes showed very similar distribution patterns as seen with gene expression.71 Rat ALT1 and rALT2 are highly expressed in the liver and muscle. Rat ALT1 is highly produced in the small intestine and less in the colon. In contrast, rALT2 showed minimal production in intestinal tissue. Rat ALT 1 production is higher than rALT2 in heart and fat. Brown fat expresses more rALTl than the white fat tissue, but rALT2 was greater in white fat than brown fat. There were also some sex differences in rats where there was about 400% and 20% higher levels of rALT2 in liver and muscle, respectively in males than in females. Recently rALTl and rALT2 have been measured in normal rat livers with a kinetic based assay which uses D-cycloserine to differentially inhibit ALT1 versus ALT2 activity.81 Recombinant rALTl and rALT2 proteins are used as standards to measure the isoenzymes. Using this approach it has been determined that in 30 normal rat livers there is 134-381 and 26-118 mU/mg of rat ALT1 and ALT2 activity, respectively. It was shown by western blot that the majority of ALT in the liver is ALT1, which is present at an approximately seven-fold greater concentration than ALT2.71 Rat ALT2 is enriched 20-fold when rat liver mitochondria are isolated by subcellular fractionation. Distribution of human ALT proteins in tissue was characterized by western blot and immunohistochemistry using polyclonal antibodies against peptides of hALTl or hALT2.69In addition to the expression in human liver, hALTl was highly produced in skeletal muscle and kidney and lower levels in cardiac muscle and not in pancreas. In contrast, high levels of ALT2_2

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY

217

were found in cardiac and skeletal muscle, but not in kidney or liver, although recent unpublished data suggests that ALT2_1 is expressed in human liver (Reagan, WJ, unpublished observations). It has not been reported if ALT2_2 has enzyme activity. Immunohistochemistry results support data obtained by western blot.

ALT P R O T E I N LEVELS I N SERUM ALT isoenzymes in the serum of normal rats, mice, and humans has been detected by western blot,71 immunoprecipitation activity assays,69 and preliminary kinetic based assays. Using all three techniques it has been shown that the predominant form of ALT in the serum is ALT1. Via immunoprecipitation, 74-91% of normal ALT activity in human serum was depleted with the ALT1 specific antibodies.69 In contrast with the antibodies to hALT2, only 4-18% of the total ALT activity was depleted. Western blot analysis showed none or minimal amounts of ALT2 present in the blood of normal rats and mice.71

C U R R E N T K N O W L E D G E O N B I O L O G Y OF ALT Understanding the biology of ALT1 and ALT2 using new methodological tools is emerging. Experimental models of hepatoxicity in mice and rats with CCL4 or acetaminophen induced hepatotoxicity, respectively, have shown that ALT 1 and ALT2 are released into the blood.71 After a single dose of CCL4 given to mice, total serum ALT increased 66-fold as determined by a kinetic based assay with the ALT1 and ALT2 elevated 4.8-fold and 3.9-fold, respectively as determined by quantitative western blot.71 In rats treated with acetaminophen, total ALT activity increased 21-fold at 48 hours post dose, with ALT1 increasing four-fold and ALT2 increasing 16-fold. Total serum activity correlated better with protein levels of rALTl compared with rALT2. In contrast, there have been few in vitro and in vivo models tested to suggest the involvement of these isoforms in an adaptive response. Mice were treated with 25 to 75 mg/kg of dexamethasone for one to three days to induce a gluconeogenic state based on its ability to stimulate glycogen accumulation in the liver, but inhibit glycogen breakdown.82 Hepatic glycogen content peaked at 24 hr, with a 24-fold increase relative to controls and was elevated at 72 hr with a 19-fold increase. Total hepatic ALT levels were increased at 72 hr by 1.6-fold. Total serum ALT activity was increased twofold in the 75 mg/kg dose group at 24 hr. Elevations were due to ALT2 since hepatic ALT2 levels increased (up to three-fold) and ALT1 levels did not change from baseline as determined by quantitative western blot. There was no concurrent increase in serum GDLH, AST, or ALP at either time points nor any histological evidence of hepatic necrosis to suggest that the release of the ALT2 was associated with hepatocellular damage. Another study using methionine and a choline deficient diet as a model of non-alcoholic steatohepatitis given to mice was used to assess the changes in ALT isoenzymes.90 It was shown that after 12 wk of treatment there

218

BIOMARKERS

was a four-fold increase in serum ALT which correlated well to increases in ALT1 and ALT2 in the liver both at the protein and mRNA level with an approximate two times greater increase in ALT2 than ALT1 relative to control animals. The treatment induced hepatic steatosis with minimal inflammation, and no necrosis was found on liver histopathological examination. In this study there was no increase in hepatocyte apoptosis to explain the increase in the origin of the increase in serum ALT. Increases in ALT2 have also been noted in the livers of Ob/Ob deficient mice and increases in serum ALT in people have independently predicted type 2 diabetes.83,91 It is possible that this increase in human serum ALT is due mainly to ALT2. Clearly more studies need to be performed to understand the kinetics of ALT1 and ALT2 in different disease states in animals and man to determine how useful isoenzyme analysis may be to differentiate an adaptive versus cytotoxic state, but the evidence to date is intriguing that these types of assessments may be useful in an application to characterize drug-induced liver injury.

DOES M E T A B O L I C S Y N D R O M E I L L I C I T A C O N F L I C T I N G ALT S I G N A L FOR DILI? The incidence of metabolic syndrome associates with a variety of variables including: country of origin, ethnicity, and nutritional intake, and these patients are at increased risk for the development of cardiovascular disease (C VD) and type II diabetes. Plasma insulin and other components of metabolic syndrome show high correlation to simple waist measurement.92 Higher body mass index (BMI) associated with metabolic syndrome shows > 2 X ALT levels higher than upper limit of normal (ULN), while alcohol consumption also influences ALT elevations additively or even synergistically.93 Patients with metabolic syndrome showing ALT elevations might be falsely considered as individuals that show liver injury. Interestingly, obesity is reported to be associated with serum uric acid and ALT elevations.94 Based on these observations, serum uric acid associated with ALT elevations may be a potential signal that might differentiate metabolic syndrome from hepatotoxicity, although more investigations are recommended. No demonstrated relationship of uric acid elevations to liver injury has been reported in literature. Clearly, the high incidence of metabolic syndrome in the U.S. population underscores the complexity of new candidate drug evaluations in populations considered normal for first in human studies.

A N O R E X I A SHOWS METABOLIC INDICATORS OF LIVER INJURY I N C L U D I N G SUBTLE ALT ELEVATIONS Anorexic patients show a complex decrease in their glutathione homeostasis.95 Plasma glutathione levels are reduced and building blocks for its synthesis, homocysteine, glycine, and glutamine levels are elevated. Reduced capacity

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY

219

for detoxification is consistent with lower BMI and subtle ALT elevations in anorexic patients, revealing subtle liver injury.96 Drug-induced eating disorders have been documented,97 which can indirectly induce liver metabolic injury. Anorexia rarely induces acute liver insufficiency, where ALT > 50 X ULN in the absence of necrosis.98

BIOMARKERS OF BILIARY INJURY Histologic manifestations of biliary injury is usually accompanied by serum biochemical alterations including elevated T bili, alkaline phosphatase (ALP), gamma glutamyl transpeptidase/transferase (GGT) and 51 nucleotidase (5'-NT). In addition, total serum bile acids can also be elevated in cholestasis associated with biliary injuries in addition to hepatic function deficits. Other disorders, where elevations of serum bile acids is noted, include intrahepatic cholestasis of pregnancy (ICP),99 and gastro-intestinal disorders such as small intestinal bacterial overgrowth and pruritus.100 In addition to total serum bile acids (quantitative) used to assess biliary damage and hepatocellular function, there have been several reports on assessing individual bile acids as sensitive and specific markers of liver damage. In patients with steatohepatitis, levels of bile acids in the liver were elevated relative to control.101 Specifically, deoxycholic, chenodeoxycholic, and cholic acids were elevated in patients with steatohepatitis and liver damage. Elevation of serum bile acids can also be the result of xenobiotics inhibiting bile salt export pump (BSEP) transporter and subsequent liver damage as exemplified by nefazodone toxicity.102 Over 20 individual bile acids were detected in serum with CC14 and alpha-naphthylisothiocyanate (ANIT) treated rats using ultraperformance liquid chromatography-mass spectrometry.103 Untreated and CC14 and ANIT treated animals were discriminated by the unique bile acid profiles.103 Bile acids can be measured by liquid chromatography-electrospray tandem mass spectrophotometer or LC/MS/MS in a rapid one-step method that can be used for routine analysis.104

A U T H O R S ' O P I N I O N ON FUTURE BIOMARKERS OF LIVER INJURY, N O V E L A P P R O A C H E S , A N D PLATFORMS Reactive Oxygen Species (ROS) as Potential Markers for Liver Injury Mechanisms Oxygen free radicals are potent damaging agents to biomacromolecules and recent approaches are targeted toward measuring ROS in serum rather than within liver tissue. Total antioxidant response (TAR) can be measured in serum using a colorimetric clinical chemistry assay. Reduced uric acid, glutathione, and bilirubin in plasma suppresses color formation in a concentration dependent manner.105,106 For example, renal injury revealed reduced serum TAR levels,105' 106yet a broader evaluation taking into consideration other or-

220

BIOMARKERS

gan injuries has not been performed. TAR includes serum uric acid and its metabolites allantoin, 6-aminouracil, and triuret, which have been multiplexed in LC-MS/MS for urinary determination of purine metabolism endpoints and scavenging of singlet oxygen species107 (Figure 9.2). Measurement of serum uric acid and associated metabolites in a multiplex is recommended as a complement to TAR determinations. Also, metabonomic evaluation of urine samples by ultra-performance liquid chromatography/mass spectrometry UPLC/ MS and NMR reveal adenosylmethionine (SAMe) flux rate reductions and creatinine elevations in a panel of liver toxicity studies in rat.108 These urinary metabonomic data support the hapten carrier hypothesis stating that reactive metabolite formation and inability to detoxify are the initial steps of liver injury,108 which would be supported by more extensive investigations in blood. In addition to predicting early liver injury, potential mechanisms of DILI can also be potentially assessed by these approaches.

Inflammation Markers as Potential Indicators of Liver Injury Hepatic inflammation appears to be common to the pathogenesis and progression of both toxic and metabolic hepatic disease. Hepatic inflammation can manifest as a histologic change with inflammatory cell infiltration or increased proinflammatory cytokines in the hepatobiliary milieu. Although such hepatic inflammation exists, there may not be serum biochemical changes such as elevated transaminases or parameters that detect lack of hepatic function. Thus, there is a need to develop reliable biomarker(s) to predict/detect hepatic inflammation states. An alternatively spliced and soluble form of TNF-ctreceptor 2 (TNFR2DS) can be measured by ELISA and discriminated from the proteolyzed form of membrane bound TNFR2.109 Multiple regression analysis of Caucasian Spanish populations revealed that ALT and AST elevations were inversely correlated to TNFR2-DS levels, independent of sex, age, body mass index, adiponectin, and homeostasis model assessment of insulin resistance.1M Although the function of TNFR2-DS is not well understood, it is hypothesized to be anti-inflammatory in nature by sequestering soluble serum TNFa.110 TNFR2DS is a potential biomarker to add information regarding inflammation state to a subtle preclinical ALT elevation in the absence of histopathology data (Figure 9.2). A better understanding of the TNFR2-DS signal preclinically is considered a prerequisite to application in a clinical translation model. The cytokine macrophage migration factor (MIF) mediates innate and adaptive immunity and contributes to inflammatory pathogenesis including rheumatoid arthritis (RA).111, " 2 MIF is constitutively expressed by macrophages and released into the blood by TNF and to a lesser degree by LPS.113 In addition to its role in innate immunity, MIF is also essential for adaptive immune response and recruits leukocytes to the site of inflammation. MIF is regarded as a general marker of inflammatory response and a possible target of therapeutic intervention.111112

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY

221

MIF is constitutively expressed in liver and is released to serum in patients with hepatitis.114 Idiosyncratic drug-induced liver injury may depend upon the balance between pro- and anti-inflammatory mediators. Using a guinea pig model of liver injury induced by halothane exposure, the level of liver toxicity associated with an increase in serum MIF.115 MIF leakage from the liver into the sera preceded peak increases in toxicity following APAP administration and MIF null (-/-) mice were less susceptible to this toxicity at early times and knock-out mice showed improved survival compared to wild type115 (Figure 9.2). MIF was demonstrated as a pro-toxicant signal in drug-induced liver injury and further studies are recommended. Thus, MIF is a candidate biomarker to monitor the progression of liver inflammation, yet lacks specificity for the origin of the inflammation source. Acute phase proteins can also be potentially employed for predicting hepatic inflammation. Acute phase proteins are induced by proinflammatory cytokines such as TNF, interleukin-6 (IL-6), IL-11, and IL-17, and include both the positive (increase with inflammation such as C-reactive protein in human) and negative phase (decrease with inflammation such as albumin) proteins (Figure 9.2). Both the local and systemic inflammation can alter acute phase proteins with the response time being from a few hours to days. These acute phase proteins have a very rapid response with return to baseline and remain elevated for a sufficient time period with varied kinetics of responses between acute phase proteins. Importantly, striking species-specific differences in acute phase responses has been reported (e.g., alpha 2 macroglobulin specific for rat, serum amyloid A for rat, and CRP for humans) which makes clinical translation quite challenging.116 Although the alterations of these acute phase proteins, MIF, and TNFa-DS may not be specific to the liver, they can be applied to hepatic inflammation when the investigator has ruled out inflammatory component in other organs (Figure 9.2). The role of osteopontin (OPN) in hepatic inflammation processes has been reported during alcoholic liver disease (ALD).117-120 The native and thrombincleaved form of OPN was induced within hepatocytes in the rodent ALD model with a strong correlation between cleaved form of OPN and hepatic neutrophil infiltration.121 These studies implicate OPN as an important chemotactic factor in the pathogenesis of hepatic inflammation during ALD. In the same study, the authors showed that uncleaved and cleaved OPN upregulated CDllb/CD18, which correlates quantitatively with neutrophil infiltration (Apte, et al., 2004; Banerjee, et al., 2006). Furthermore, a time course study also showed OPN to be an important early biomarker that can predict hepatic inflammation.117'121

Novel H e p a t o c e l l u l a r Leakage Enzymes as Early Biomarkers of Symptomatic Change Argininosucccinate synthase (ASS) and estrogen sulfotransferase (EST-1) are leakage enzymatic markers that have been shown to be significantly elevated in serum within hours following ischemia122 (Figure 9.1). ASS and EST-1 were not detected during chronic liver injury, while ALT elevations were profound.

222

BIOMARKERS

ASS was elevated with an acute toxicant six hours after insult, where ALT elevations were not yet detected. ASS is reduced in serum during cirrhosis of the liver.123 Although the data is intriguing, additional extensive evaluation of these markers is recommended with multiple hepatotoxicants and metabolic disease to assess their hepatic biomarker value. An early predictive set of markers within hours of injury, rather than days, would be a valuable tool for the lead optimization of developmental compounds.

Hepatic Regeneration Markers to Supplement Injury Biomarkers Analysis of traditional circulating biochemical markers of hepatic injury such as ALT and AST may not be adequate to assess the extent of hepatic damage. A decrease in the serum values for these leakage markers after drug-induced liver injury could be due to recovery from damage and associated increase in hepatic regeneration. A sustained decline in hepatic injury markers with a concurrent elevation in regenerative markers could potentially aid in the favorable outcome in clinical patients with acute liver injury. It is reported that analysis of hepatic growth factors using serum markers such as alpha fetoprotein (AFT*), retinol binding proteins, and des-gamma carboxy-prothrombin to be useful markers of hepatic regeneration in clinical situations such as with Amanita mushroom and acetaminophen toxicities, although additional studies are required to validate these biomarkers in hepatic regeneration models (Figure 9.2). Calcium metabolism and excretion is regulated in rodent bile duct systems by the Ca2+-binding protein regucalcin, which is expressed predominantly in kidney as well as liver.124 Over expression of hepatic regucalcin suppresses cell death and apoptosis making it a biomarker candidate of liver injury and regeneration.124 An acute D-galactosamine and lipopolysaccharide (GalN/ LPS) model of liver injury in mice revealed regucalcin serum elevations.125 CC14 treated rats showed two-fold ALT and AST elevations three-days posttreatment, whereas regucalcin serum elevations were observed 30-days posttoxicant treatment.126 Transient biomarkers of liver injury are a concern in clinical trials where sample collection may be as infrequent as monthly. Regucalcin elevations early after dosing are nearly 20-fold greater compared to one month later,126 nevertheless the detection of injury one month after dosing is a powerful signal to monitor reversible injury (Figure 9.2). Translation of regucalcin as a marker of liver injury has not yet been performed, but regucalcin is 93% conserved between rat and human. Although clinical threshold determinations for regucalcin elevations will be complex to determine and qualify, this marker would be a compelling addition to other available liver injury markers.

U N I F I C A T I O N OF D I A G N O S T I C METRICS OF LIVER FIBROSIS Liver fibrosis is a complex cascade of tissue remodeling following acute injury and repair that has been extensively investigated, although a unification of

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY

223

these observations has not yet been performed across pharmaceutical, medical, and academic centers of excellence. Consortia are initiating an exchange among many groups and there is great optimism for these new efforts. Serum soluble collagen fragments associated with liver fibrosis have been a focus of investigations for many years. The amino-terminal procollagen type III peptide is released into blood during collagen type III deposition in hepatic fibrosis, although this process is observed in other tissues that show similar disease progression. Elevations in PIIINP in rat serum were observed after just two weeks of CC14 treatment using RIA approaches, where hepatic tissue hydroxylproline was observed after six weeks of toxicant treatment.127 ELISA approaches detect PIIINP in bile duct ligation and dimethylnitrosamine models of liver fibrosis that develop one week after dosing.128'129 In chronic hepatitis C patients with biopsy examination, patients were screened with a large panel of biomarkers. PIIINP and MMP-1 combined showed diagnostic value in receiver operator curves (ROC) and logistic model analysis (Leroy score).130 Nearly 20 additional clinical numerical scores for liver fibrosis have been characterized using a diverse and broad collection of functional serum markers, clinical chemistry markers such as ALT, and clinical endpoints. Each score uses an independent algorithmic approach to assess clinical liver fibrosis injury.131 Notably, many of these scores contain overlapping biomarkers, although a comparison across the various scoring metrics has not yet been performed. A fibrosis multiplex containing PIIINP, hyaluronic acid, TIMP-1, a2-microglobulin, and MMP-1 would allow direct comparison of several tests including Leroy, Rosenberg, Patel, and Fibrometer.130,132-134 Early markers of liver fibrotic injury would be valuable in clinical trials that are lengthy with strict limits to the availability and frequency of patient biopsy. In CC14 treated rats, serum PIIINP elevation was observed after five to seven weeks, yet PIIINP mRNA was elevated 10-fold after one week.135 Whether a liver tissue RNA based clinical fibrosis model would perform better than the more traditional models is an approach to consider. PIIINP is an early predictor for joint destruction in RA, indicating that specificity is broader than for liver injury alone.136

A N A L Y T I C A L BIOMARKER PLATFORMS T O ASSAY SERUM BIOMARKERS OF LIVER INJURY Mass Spectrometry Technologies Can Fill Gaps to Detect Biomarkers When Antibody Approaches are Limited Immunoanalytical approaches to detect biomarkers, including MesoScale Discovery and Luminex (Millipore or Rules Based Medicine) are powerful approaches that allow multiplexing and parallel detection of analytes in a high throughput manner. These immune assays are also complex to analytically characterize and combine in a multiplex format since antibody interference or cross reactivity among assays might occur. Batch to batch variation in antibody reagent development can occur, making standardization approaches

224

BIOMARKERS

an extensive process when replenishing stocks from commercial sources. Immune reagents are often species specific and do not readily translate across preclinical species to human, although exceptions are noted. Analytical approaches, including HPLC coupled with tandem mass spectrometry (LC/MS/ MS), offer an attractive alternative to conventional antibody based approaches, at considerable equipment cost, maintenance, and required expertise.

An O v e r v i e w of Mass S p e c t r o m e t r y Technologies A tandem mass spectrometer has two mass analyzers. The mass analyzers are separated by a collision cell into which an inert gas (e.g., argon, nitrogen) is admitted to collide with the selected sample ions and bring about their fragmentation. The analyzers can be of the same or of different types, the most common combinations being quadrupole-quadrupole (or triple quadrupoles denoted as Qq-Q) and quadrupole-time of flight (denoted Qq-TOF or Q-TOF), where Q refers to a mass-resolving quadrupole, q refers to a collision cell, and TOF refers to a time-of-flight mass spectrometer. The selectivity of ion scans is superior on Q-TOF systems compared to triple quadrupoles because the high resolving power of the TOF mass analyzer permits high-accuracy fragment ion selection at no expense of sensitivity. This minimizes interferences from other peptide fragment ions (a-, b-, and y- type) of the same nominal mass but with sufficient differences in their exact masses. Triple quadrupoles, however, still remain the gold standard for targeted quantitative analysis (i.e., SRM analysis) due to their increased sensitivity and dynamic range compared to Q-TOF (Table 9.1).

Mass S p e c t r o m e t r y Technologies t o Potentially D e t e c t Biomarkers and Rare Protein Antigens of Injury Reports47 utilized Q-TOF technology for characterization of proteomic biomarkers of hepatotoxicity in rat liver, investigating the effect of carbon tetrachloride, acetaminophen, amiodorane, and tetracycline on protein elevations in the liver in vivo. Eight proteins were affected by the four toxicants including carbonic anhydrase III, 60 kD heat shock protein, glutamate dehydrogenase, adenylate kinase isoenzyme 4, NADP-dependent malic enzyme, 2-oxoisovalerate dehydrogenase, serotransferrin and N-Myc down regulated Gene 1, or NDRG1 related protein. A more common quantitative approach, utilized successfully for the quantitation of hidden allergenic peanut proteins, involves characterization and identification of proteins or peptides by Q-TOF and then subsequent targeted quantitation or single reaction monitoring (SRM) of select peptides using a triple quadrupole.137

Improved Tagging Techniques f o r Mass S p e c t r o m e t r y D e t e c t i o n of Proteins A new and novel mechanism helping the targeted SRM approach to quantitate peptides and proteins is the use of tagging reagents, such as the isotope coded

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY

225

TABLE 9.1 Comparison of mass spectrometry approaches. LC/MS/MS is the reference standard method for targeted analysis.

Quadrupole time of flight (Q-TOF)

Typically used for qualitative purposes (i.e., analyte scouting), taking advantage of the instrument's fast scanning speed and large dynamic mass range (for intact protein analysis)

Triple quadrupoles

Typically used for quantitative purposes (i.e., targeted quantitation) taking advantage of the instrument's sensitivity and quantitative dynamic range.

Ion trap hybrid (LTQ Orbitrap)

New technology recently introduced byThermo typically used for qualitative purposes. System has a lower dynamic mass range than Q-TOF, but generally a more powerful tool for smaller proteins or peptides < 4000 atomic mass units due to its high resolving power and accurate mass capabilities.

affinity taq (ICAT), isobaric tag peptide labeling (iTRAQ), and stable isotope labeled reagents (mTRAQ). Each labeling technique has its own distinct advantages, for instance ICAT is advantageous for cysteine containing peptides and proteins. Similar to ICAT, iTRAQ is based upon chemical tagging of N-terminus peptides generated from protein digests that have been isolated from cells in two different states. However iTRAQ suffers the same peptide overabundance problem and must be coupled with one or more dimensions of chromatographic or electrophoretic separation before MS analysis to limit the number of isobaric tagged peptides in the first MS dimension. mTRAQ is a non-isobaric variant of iTRAQ and is available in two labels. The ability to label the sample and reference peptides with either one of the two possible combinations is an inherent advantage of this method, as it provides a means for verification of the reported ratios. mTRAQ was used for absolute quantitation of the potential cancer marker pyruvate kinase M1/M2 in normal and diseased endometrial tissue.138

H i g h - t h r o u g h p u t C h r o m a t o g r a p h y Enhances the D o w n s t r e a m D e t e c t i o n of Rare Serum Proteins by Mass S p e c t r o m e t r y To characterize serum proteins, micro-chromatography approaches allow fractionation and removal of albumin and immunoglobulins from samples, allowing subsequent quantitation of trypic peptide analytes in liquid chromatography coupled to tandem mass spectrometry with electrospray ionization (LC/ ESI/MS/MS) for biomarkers that are in low ng/mL range.139 Use of disposable columns make this a high throughput approach to characterize analytes in the 1 ng/mL range, and a single antibody allows confirmation of the tryptic peptide rather than the two antibodies required for development of a sandwich as-

226

BIOMARKERS

say.139 Accurate measurement of low abundance peptides can also be detected by LC-ESI-Qq-TOF.139,14° Nearly all of the current biomarkers of liver injury discussed here have peptides that have been detected using this technological approach as part of serum proteome mapping approaches141'142 and personal communication, J. Marshall.

Mass S p e c t r o m e t r y Approaches t o Distinguish Novel Biomarkers of Renal and Liver Injury Cyclosporin A treatment blocks bile acid efflux resulting in concomitant cysteinyl leukotriene accumulation in kidney, thereby reducing glomerular filtration rate (GFR) and decreasing renal functional capacity.143 Bile acid accumulation in serum is an early marker for such injury and recent technology advances allow individual bile acid and conjugate measurements in addition to traditional total bile acid values from clinical chemistry methods. Ursodeoxycholic acid and conjugates are multiplexed in human plasma using a straightforward LC/ MS/MS methodology.144 A more comprehensive quantitative bile acid and conjugate profile in mouse plasma was demonstrated in LC/MS/MS indicating that detection of individual bile acids is routine enough to measure in drug development processes.145

Mass S p e c t r o m e t r y Approaches t o D e t e c t Novel Serum Biomarkers of Liver Injury Several candidate biomarkers of liver injury have been preliminarily characterized but have limited antibody reagent availability,41 making further analysis on these new platforms an attractive approach to the wider scientific community. Regucalcin, sorbitol dehydrogenase (SDH), hpd or 4-hydroxyphenylpyruvate dioxygenase (Serum Protein F), and ALT isoforms are all potential biomarkers of DILI-1 that are of high interest to investigators, yet a lack of available antibody reagents precludes further study. Since a large panel of serum protein are amenable to this approach using a standardized chromatography approach,141,142 mass spectrometry based proteomic approaches should be considered to develop multiplexes of proposed markers of DILI-1. Integration of multiple platforms for biomarker detection increases the rigor and confidence of both platform approaches. LC/MS/MS allows unprecedented quantitation of analytical standards for standard curve preparation, which cannot be as rigorously measured by antibody reagents alone. Having preliminary or validated antibody assays greatly reduces the cycle time for LC/MS/ MS assay development. Integration of technologies provides the most rapid and rigorous outcome for novel assay development.

CONCLUSIONS DILI is a complex injury process with multiple mechanisms and manifests either as intrinsic or idiosyncratic type. Idiosyncratic DILI is rare and is observed with marketed compounds clinically, while intrinsic DILI is observed

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY

227

throughout the entire drug development pipeline. Currently, ALT is considered the reference marker of predictable DILI, and in combination with T bili (in clinics), the reference marker for idiosyncratic DILI. While ALT is a sensitive biomarker of intrinsic DILI, additional biomarkers are sought to add value and enhance specificity. New serum enzyme biomarkers are being qualified to improve intrinsic DILI detection. Also, additional strategies under evaluation to add specificity to ALT include measuring ALT isoforms and individual bile acids. In addition to improving the value of ALT by the qualification of new hepatic biomarkers , there is also the need to understand precise mechanisms for DILI with the aid of undiscovered biomarkers. Parallel efforts in the area of new platform technologies will no doubt increase analytical rigor to assess DILI in conjunction with current technologies.

ACKNOWLEDGMENTS We sincerely thank PSTC Hepatic Working Group members and Pfizer Hepatic Biomarker Team for helpful discussions. We also thank Denise Robinson-Gravatt, Dale Morris, and Timothy Heath who provided constructive comments on the chapter; Rod Mathews, Jeff Prasakiewicz, and Jon Klover for helpful discussions on hepatic fibrosis; John Marshall for input on MS technologies and proteomics.

SUMMARY P O I N T S 1. 2.

3.

4. 5.

Traditional hepatic biochemical parameters (serum transaminases; ALT, AST) and histology fail to discriminate between DILI-1 and -2. Significant biomarker gaps exist with the traditional preclinical and clinical biomarkers of DILI-1. These gaps are being addressed by emerging serum enzyme biomarkers which are evaluated in a qualification process with regulators to add value to serum aminotransferases. Isozymes ALT1 and ALT2 are being analyzed to increase the specificity of ALT in response to extrahepatic injury and gluconeogenesis. New biomarkers such as paraxonase 1 (PON1), purine nucleoside phosphorylase (PNP), malate dehydrogenase (MDH), and glutamate dehydrogenase (GLDH) are also being qualified to add value to serum aminotransferases. New biomarkers of hepatic oxidant stress, inflammation, injury mechanisms, and regeneration will add significant value to understanding of DILI and predicting ultimate outcome of hepatic injury. Mass spectrometry technologies can fill gaps to detect biomarkers when antibody approaches are limited and are proposed to detect novel serum biomarkers of liver injury.

REFERENCES 1.

Zimmerman, H. J. Drug-Induced Liver Disease. Drugs. Jul 1978;16(1):25—45.

228

BIOMARKERS 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.

Watkins, P. B., Seligman, P. J., Pears, J. S., Avigan, M. I., and Senior, J. R. Using Controlled Clinical Trials to Learn More About Acute Drug-Induced Liver Injury. Hepatology. Nov 2008;48(5): 1680-1689. Senior, J. R. How Can 'Hy's Law' Help The Clinician? Pharmacoepidemiol Drug Saf Apr 2006;15(4):235-239. Lewis, J. H. "Hy's Law," The "Rezulin Rule," and Other Predictors of Severe Drug-Induced Hepatotoxicity: Putting Risk-Benefit Into Perspective. Pharmacoepidemiol Drug Saf. Apr 2006;15(4):221-229. Abboud, G. and Kaplowitz, N. Drug-Induced Liver Injury. Drug Saf. 2007; 30(4):277-294. Treinen-Moslen, M. Toxic Responses of the Liver. In: Klaasen, C. D., Ed. Casarette & Doull's Toxicology the Basic Science of Poisons. U.S.:McGraw Hill;2001:471-489. Stephens, J. R. and Levy, R. H. Valproate Hepatotoxicity Syndrome: Hypotheses of Pathogenesis. Pharm. Weekbl. Sci. Jun 19, 1992;14(3A):118-121. Diehl, A. M. Hepatic Complications of Obesity. Gastroenterol. Clin. North Am. Mar 2005 ;34(1):45-61. Nagata, K., Suzuki, H., and Sakaguchi, S. Common Pathogenic Mechanism in Development Progression of Liver Injury Caused by Non-Alcoholic or Alcoholic Steatohepatitis. J. Toxicol. Sci. Dec 2007;32(5):453^168. Lieber, C. S. Alcohol and the Liver: 1994 Update. Gastroenterology. Apr 1994; 106(4): 1085-1105. Bautista, A. P. Neutrophilic Infiltration in Alcoholic Hepatitis. Alcohol. May 2002;27(1):17-21. Watkins, P. B. and Seeff, L. B. Drug-Induced Liver Injury: Summary of a Single Topic Clinical Research Conference. Hepatology. Mar 2006;43(3): 618-631. Dahn, L. J. and Jones, D. P. Mechanisms of Chemically-Induced Liver Disease. In: Zakim, D., Boyer, T. D., Eds. Hepatology, a Textbook of Liver Disease. Philadelphia, PA: W.B. Saunders Company; 1996:875-890. Pineiro-Carrero, V. M and Pineiro, E. O. Liver. Pediatrics. Apr 2004;113(4 Suppi): 1097-1106. Sturgill, M. G. and Lambert, G. H. Xenobiotic-Induced Hepatotoxicity: Mechanisms of Liver Injury and Methods of Monitoring Hepatic Function. Clin. Chem. Aug 1997;43(8 Pt 2):1512-1526. Zimmerman, H. J. Drug-Induced Liver Disease. In: Schiff, E., Ed. Schiff's Diseases of the Liver. Baltimore (MD):Lippincott-Raven;1999:973-1064. Shenton, J. M., Chen, J., and Uetrecht, J. P. Animal Models of Idiosyncratic Drug Reactions. Chem. Biol. Interact. Nov 1, 2004;150(l):53-70. Deng, X., Stachlewitz, R. R, and Liguori, M. J., et al. Modest Inflammation Enhances Diclofenac Hepatotoxicity in Rats: Role of Neutrophils and Bacterial Translocation. / Pharmacol. Exp. Then Dec 2006;319(3): 1191-1199. Boelsterli, U. A. and Hsiao, C. J. The Heterozygous Sod2(+/-) Mouse: Modeling the Mitochondrial Role in Drug Toxicity. Drug Discov. Today. Nov 2008;13(2122):982-988. Mehendale, H. M. Tissue Repair: An Important Determinant of Final Outcome of Toxicant-Induced Injury. Toxicol. Pathol. 2005;33(1):41-51. Ramaiah, S. K., Bucci, T. J., Warbritton, A., Soni, M. G., and Mehendale, H. M. Temporal Changes in Tissue Repair Permit Survival of Diet-Restricted Rats From an Acute Lethal Dose of Thioacetamide. Toxicol. Sci. Oct 1998;45(2): 233-241.

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY 229 22.

23. 24. 25. 26. 27. 28. 29. 30. 31. 32.

33. 34. 35. 36. 37. 38. 39. 40. 41.

Ramaiah, S. K., Soni, M. G., Bucci, T. J., and Mehendale, H. M. Diet Restriction Enhances Compensatory Liver Tissue Repair and Survival Following Administration of Lethal Dose of Thioacetamide. Toxicol. Appl. Pharmacol. May 1998;150(1):12-21. Malhi, H., Gores, G. J., and Lemasters, J. J. Apoptosis and Necrosis in the Liver: A Tale of Two Deaths? Hepatology. Feb 2006;43(2 Suppl 1):S31^4. Uetrecht, J. Idiosyncratic Drug Reactions: Current Understanding. Annu. Rev. Pharmacol. Toxicol. 2007;47:513-539. Liu, Z. X. and Kaplowitz, N. Immune-Mediated Drug-Induced Liver Disease. Clin. Liver Dis. Aug 2002;6(3):755-774. Tang, W. Drug Metabolite Profiling and Elucidation of Drug-Induced Hepatotoxicity. Expert Opin. Drug Metab. Toxicol. Jun 2007;3(3):407^120. Uetrecht, J. P. New Concepts in Immunology Relevant to Idiosyncratic Drug Reactions: The "Danger Hypothesis" and Innate Immune System. Chem. Res. Toxicol. May 1999;12(5):387-395. Mitchell, J. R., Zimmerman, H. J., and Ishak, K. G., et al. Isoniazid Liver Injury: Clinical Spectrum, Pathology, and Probable Pathogenesis. Ann. Intern. Med. Febl976;84(2):181-192. Crawford, J. M. The Liver and the Biliary Tract. In: Cotran, R. S., Kumar, V., Collins, T., Eds. Robbins: Pathologic Basis of Disease. Vol 6. Philadelphia, PA: Saunders;1999:845-901. Sastre, J., Serviddio, G., and Pereda, J., et al. Mitochondrial Function in Liver Disease. Front. Biosci. 2007;12:1200-1209. Watkins, P. B., Kaplowitz, N., and Slattery, J. T., et al. Aminotransferase Elevations in Healthy Adults Receiving 4 Grams of Acetaminophen Daily: A Randomized Controlled Trial. Jama. Jul 5, 2006;296(l):87-93. Farrell, G. C , Duddy, S. K., Kass, G. E., Llopis, J., Gahm, A., and Orrenius, S. Release of Ca2+ From the Endoplasmic Reticulum Is Not the Mechanism for Bile Acid-Induced Cholestasis and Hepatotoxicity in the Intact Rat Liver. J. Clin. Invest. Apr 1990;85(4): 1255-1259. Zimmerman, H. J. The Spectrum of Hepatotoxicity. Perspect. Biol. Med. Autumn 1968;12(1):135-161. Fielden, M. R., Eynon, B. P., Natsoulis, G., Jarnagin, K., Banas, D., and Kolaja, K. L. A Gene Expression Signature That Predicts the Future Onset of DrugInduced Renal Tubular Toxicity. Toxicol. Pathol. 2005;33(6):675-683. EMEA. Biomarkers Qualification: Draft Guidance to Applicants. EMEA/ CHMP/SAWP/72894/2008-CONSULTATION. Goodsaid, F. and Frueh, F. Biomarker Qualification Pilot Process at the U.S. Food and Drug Administration. AAPS J. 2007 ;9(1):E 105-108. Goodsaid, F. and Frueh, F. W. Implementing the U.S. FDA Guidance on Pharmacogenomic Data Submissions. Environ. Mol. Mutagen. Jun 2007;48(5): 354-358. Goodsaid, F. M., Frueh, F. W., and Mattes, W. The Predictive Safety Testing Consortium: A Synthesis of the Goals, Challenges and Accomplishments of the Critica Path. Drug Discovery Today: Technologies. 2007;4(2):47-50. Amacher, D. E. A Toxicologist's Guide to Biomarkers of Hepatic Response. Hum. Exp. Toxicol. May 2002;21(5):253-262. Collings, F. B. and Vaidya, V. S. Novel Technologies for the Discovery and Quantitation of Biomarkers of Toxicity. Toxicology. Mar 20, 2008;245(3): 167-174. Ozer, J., Ratner, M., Shaw, M., Bailey, W., and Schomaker, S. The Current State of Serum Biomarkers of Hepatotoxicity. Toxicology. Mar 20, 2008;245(3): 194-205.

230

BIOMARKERS 42. 43.

44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57.

Tugwood, J. D., Hollins, L. E., and Cockerill, M. J. Genomics and the Search for Novel Biomarkers in Toxicology. Biomarkers. Mar/Apr 2003;8(2):79-92. Hong, H., Teital, C , James, L. P., Tong, W., Hinson, J. A., Fuscoe, J. C , and Dragon, Y. SELDI Based Proetomic Determination of Hepatic Biomarkers in Mouse Serum Following Acetaminophen Administration. Journal of Proteomics and Bioinformatics. 2008;l(8):424-436. Man, W. J., White, I. R., and Bryant, D., et al. Protein Expression Analysis of Drug-Mediated Hepatotoxicity in the Sprague-Dawley Rat. Proteomics. Nov 2002;2(11):1577-1585. Meneses-Lorente, G., Guest, P. C , and Lawrence, J., et al. A Proteomic Investigation of Drug-Induced Steatosis in Rat Liver. Chem. Res. Toxicol. May 2004; 17(5):605-612. Meneses-Lorente, G., Watt, A., and Salim, K., et al. Identification of Early Proteomic Markers for Hepatic Steatosis. Chem. Res. Toxicol. Aug 2006; 19(8): 986-998. Yamamoto, T, Kikkawa, R., Yamada, H., and Horii, I. Investigation of Proteomic Biomarkers in In Vivo Hepatotoxicity Study of Rat Liver: Toxicity Differentiation in Hepatotoxicants. /. Toxicol. Sci. Feb 2006;31(l):49-60. Amacher, D. E., Adler, R., Herath, A., and Townsend, R. R. Use of Proteomic Methods to Identify Serum Biomarkers Associated with Rat Liver Toxicity or Hypertrophy. Clin. Chem. Oct 2005;51(10):1796-1803. Ohuchi, T., Tada, K., and Akamatsu, K. Endogenous ET-1 Contributes to Liver Injury Induced by Galactosamine and Endotoxin in Isolated Perfused Rat Liver. Am. J. Physiol. Jun 1995;268(6 Pt 1):G997-1003. Arai, M., Mochida, S., Ohno, A., Ogata, I., and Fujiwara, K. Sinusoidal Endothelial Cell Damage by Activated Macrophages in Rat Liver Necrosis. Gastwenterology. May 1993;104(5):1466-1471. Feingold, K. R., Memon, R. A., Moser, A. H., and Grunfeld, C. Paraoxonase Activity in the Serum and Hepatic Mrna Levels Decrease During the Acute Phase Response. Atherosclerosis. Aug 1998;139(2):307-315. Kaliste-Korhonen, E., Tuovinen, K., and Hanninen, O. Effect of Phenobarbital and Beta-Naphthofiavone on Activities of Different Rat Esterases After Paraoxon Exposure. Gen. Pharmacol. Aug 1998;31(2):307-312. Atamer, A., Bilici, A., Yenice, N., Selek, S., Ilhan, N., and Atamer, Y. The Importance of Paraoxonase 1 Activity, Nitric Oxide and Lipid Peroxidation in Hepatosteatosis. J. Int. Med. Res. Jul/Aug 2008;36(4):771-776. Ferre, N., Camps, J., and Prats, E., et al. Serum Paraoxonase Activity: A New Additional Test for the Improved Evaluation of Chronic Liver Damage. Clin. Chem. Feb 2002;48(2):261-268. Gangadharan, B., Antrobus, R., Dwek, R. A., and Zitzmann, N. Novel Serum Biomarker Candidates for Liver Fibrosis in Hepatitis C Patients. Clin. Chem. Oct 2007;53(10): 1792-1799. Kilic, S. S., Aydin, S., Kilic, N., Erman, F., and Celik, I. Serum Arylesterase and Paraoxonase Activity in Patients with Chronic Hepatitis. World J. Gastroenterol. Dec 14, 2005;ll(46):7351-7354. Ng, C. J., Bourquard, N., and Grijalva, V., et al. Paraoxonase-2 Deficiency Aggravates Atherosclerosis in Mice Despite Lower Apolipoprotein-B-Containing Lipoproteins: Anti-Atherogenic Role for Paraoxonase-2. J. Biol. Chem. Oct 6, 2006;281(40):29491-29500.

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY 231 58. 59. 60. 61. 62.

63. 64.

65. 66. 67. 68. 69. 70.

71.

72. 73.

Rozenberg, O., Shih, D. M., and Aviram, M. Paraoxonase 1 (PON1) Attenuates Macrophage Oxidative Status: Studies in PON1 Transfected Cells and in PON1 Transgenic Mice. Atherosclerosis. Jul 2005;181(1):9-18. Rozek, L. S., Hatsukami, T. S., and Richter, R. J., et al. The Correlation of Paraoxonase (PON1) Activity with Lipid and Lipoprotein Levels Differs with Vascular Disease Status. J. Lipid. Res. Sep 2005;46(9):1888-1895. Davies, H. G., Richter, R. J., Keifer, M., Broomfield, C. A., Sowalla, J., and Furlong, C. E. The Effect of the Human Serum Paraoxonase Polymorphism Is Reversed with Diazoxon, Soman and Sarin. Nat. Genet. Nov 1996;14(3):334—336. Richter, R. J. and Furlong, C. E. Determination of Paraoxonase (PON1) Status Requires More Than Genotyping. Pharmacogenetics. Dec 1999;9(6):745-753. Zieve, L., Anderson, W. R., Dozeman, R., Draves, K., and Lyftogt, C. Acetaminophen Liver Injury: Sequential Changes in Two Biochemical Indices of Regeneration and Their Relationship to Histologic Alterations. / Lab. Clin. Med. May 1985;105(5):619-624. Korsrud, G. O., Grice, H. C , and McLaughlan, J. M. Sensitivity of Several Serum Enzymes in Detecting Carbon Tetrachloride-Induced Liver Damage in Rats. Toxicol. Appl. Pharmacol. Jul 1972;22(3):474-^183. Kawai, M. and Hosaki, S. Clinical Usefulness of Malate Dehydrogenase and Its Mitochondria! Isoenzyme in Comparison with Aspartate Aminotransferase and Its Mitochondrial Isoenzyme in Sera of Patients with Liver Disease. Clin. Biochem. Aug 1990;23(4):327-334. Misra, M. K., Khanna, A. K., Sharma, R., and Srinivasan, S. Serum Malate Dehydrogenase (MDH) in Portal Hypertension—Its Value as a Diagnostic and Prognostic Indicator. Indian J. Med. Sci. Feb 1991;45(2):31-34. Giffen, P. S., Turton, J., and Andrews, C. M., et al. Markers of Experimental Acute Inflammation in the Wistar Han Rat with Particular Reference to Haptoglobin and C-Reactive Protein. Arch. Toxicol. Jul 2003;77(7):392^K)2. O'Brien, P. J., Slaughter, M. R., Polley, S. R., and Kramer, K. Advantages of Glutamate Dehydrogenase as a Blood Biomarker of Acute Hepatic Injury in Rats. Lab. Anim. Jul 2002;36(3):313-321. Boyd, J. W. The Mechanisms Relating to Increases in Plasma Enzymes and Isoenzymes in Diseases of Animals. Vet. Clin. Pathol. 1983;12(2):9-24. Lindblom, P., Rafter, I., and Copley, C , et al. Isoforms of Alanine Aminotransferases in Human Tissues and Serum—Differential Tissue Expression Using Novel Antibodies. Arch. Biochem. Biophys. Oct 1, 2007;466(l):66-77. Yang, R. Z., Blaileanu, G., Hansen, B. C , Shuldiner, A. R., and Gong, D.W. Cdna Cloning, Genomic Structure, Chromosomal Mapping, and Functional Expression of a Novel Human Alanine Aminotransferase. Genomics. Mar 2002; 79(3):445^150. Yang, R. Z., Park, S., and Reagan, W. J., et al. Alanine Aminotransferase Isoenzymes: Molecular Cloning and Quantitative Analysis of Tissue Expression in Rats and Serum Elevation in Liver Toxicity. Hepatology. Feb 2009;49(2): 598-607. Hagopian, K., Ramsey, J. J., and Weindruch, R. Caloric Restriction Increases Gluconeogenic and Transaminase Enzyme Activities in Mouse Liver. Exp. Gerontol. Mar 2003;38(3):267-278. Rosen, F., Roberts, N. R., and Nichol, C. A. Glucocorticosteroids and Transaminase Activity. I. Increased Activity of Glutamicpyruvic Transaminase in Four Conditions Associated with Gluconeogenesis. J. Biol. Chem. 1959;234(3): 476-480.

232

BIOMARKERS 74. 75. 76. 77.

78. 79. 80. 81. 82. 83. 84.

85. 86. 87. 88. 89.

Gores, G. J., Herman, B., and Lemasters, J. J. Plasma Membrane Bleb Formation and Rupture: A Common Feature of Hepatocellular Injury. Hepatology. Apr 1990;ll(4):690-698. Hoffman, W. E. and Solter, P. F. Clinical Enzymology. In: Loeb, W. F, Quimby, F. W., Eds. The Clinical Chemistry of Laboratory Animals. Philadelphia:Taylor and Francis;1999:399^54. Amacher, D. E. Serum Transaminase Elevations as Indicators of Hepatic Injury Following the Administration of Drugs. Regul. Toxicol. Pharmacol. Apr 1998; 27(2): 119-130. Jackson, E. R., Kilroy, C , Joslin, D. L., Schomaker, S. J., Pruimboom-Brees, I., and Amacher, D. E. The Early Effects of Short-Term Dexamethasone Administration on Hepatic and Serum Alanine Aminotransferase in the Rat. Drug Chem. Toxicol. 2008;31(4):427^145. Swick, R. W., Barnstein, P. L., and Stange, J. L. The Metabolism of Mitochondrial Proteins. I. Distribution and Characterization of the Isozymes of Alanine Aminotransferase in Rat Liver. J. Biol. Chem. Aug 1965;240:3334-3340. Kafer, E. and Pollak, J. K. Amino Acid Metabolism of Growing Tissues. II. Alanine-Glutamic Acid Transaminase Activity of Embryonic Rat Liver. Exp. Cell. Res. Jan 1961;22:120-136. Katunuma, N., Mikumo, K., Matsuda, M., and Okada, M. Differences Between the Transaminases in Mitochondria and Soluble Fraction. I. Glutamic-Pyruvic Transaminase. J. Vitaminol. (Kyoto). Mar 10, 1962;8:68-73. Goldstein, R., Nettleton, D., Yang, R., Gong, D. W., and Reagan, W. J. Measurement of Alanine Aminotransferase Isoenzyme Activity by D-Cycloserine Inhibition. Society of Toxicology Meeting Abstract 2009. Reagan, W. J., Park, S., and Goldstein, R., et al. Hepatic ALT1 and 2 Proteins are Differentially Regulated by Dexamethazone Treatment in Mice. Society of Toxicology Meeting Abstract 2008. Jadhao, S. B., Yang, R. Z., and Lin, Q., et al. Murine Alanine Aminotransferase: Cdna Cloning, Functional Expression, and Differential Gene Regulation in Mouse Fatty Liver. Hepatology. May 2004;39(5): 1297-1302. Rajamohan, F., Nelms, L., Joslin, D. L., Lu, B., Reagan, W. J., and Lawton, M. Cdna Cloning, Expression, Purification, Distribution, and Characterization of Biologically Active Canine Alanine Aminotransferase-1. Protein Expr. Purif. Jul2006;48(l):81-89. Sohocki, M. M., Sullivan, L. S., and Harrison, W. R., et al. Human Glutamate Pyruvate Transaminase (GPT): Localization to 8q24.3, Cdna and Genomic Sequences, and Polymorphic Sites. Genomics. Mar 1, 1997;40(2):247-252. Habib, S. J., Neupert, W., and Rapaport, D. Analysis and Prediction of Mitochondrial Targeting Signals. Methods Cell. Biol. 2007;80:761-781. Edgar, A. D., Tomkiewicz, C, and Costet, P., et al. Fenofibrate Modifies Transaminase Gene Expression via a Peroxisome Proliferator Activated Receptor AlphaDependent Pathway. Toxicol. Lett. Sep 1, 1998; 98(l-2):13-23. Thulin, P., Rafter, I., and Stockling, K., et al. Pparalpha Regulates the Hepatotoxic Biomarker Alanine Aminotransferase (ALT1) Gene Expression in Human Hepatocytes. Toxicol. Appl. Pharmacol. Aug 15, 2008;231(l):l-9. Tomkiewicz, C , Muzeau, F., Edgar, A. D., Barouki, R., and Aggerbeck, M. Opposite Regulation of the Rat and Human Cytosolic Aspartate Aminotransferase Genes by Fibrates. Biochem. Pharmacol. Jan 15, 2004;67(2):213-225.

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY 233 90. 91. 92. 93.

94.

95. 96. 97. 98. 99. 100.

101. 102. 103.

104.

Liu, L., Zhong, S., and Yang, R., et al. Expression, Purification, and Initial Characterization of Human Alanine Aminotransferase (ALT) Isoenzyme 1 and 2 in High-Five Insect Cells. Protein Expr. Purif. Aug 2008;60(2):225-231. Hanley, A. J., Williams, K., and Festa, A., et al. Elevations in Markers of Liver Injury and Risk of Type 2 Diabetes: The Insulin Resistance Atherosclerosis Study. Diabetes. Oct 2004;53(10):2623-2632. Bauduceau, B., Vachey, E., and Mayaudon, H., et al. Should We Have More Definitions of Metabolic Syndrome or Simply Take Waist Measurement? Diabetes Metab. Nov 2007;33(5):333-339. Alatalo, P. I., Koivisto, H. M., Hietala, J. P., Puukka, K. S., Bloigu, R., and Niemela, O. J. Effect of Moderate Alcohol Consumption on Liver Enzymes Increases with Increasing Body Mass Index. Am. J. Clin. Nutr. Oct 2008;88(4): 1097-1103. Kang, Y. H., Min, H. G., Kim, I. J., Kim, Y. K., and Son, S. M. Comparison of Alanine Aminotransferase, White Blood Cell Count, and Uric Acid in Their Association with Metabolic Syndrome: A Study of Korean Adults. Endocr. J. Dec 2008;55(6): 1093-1102. Zenger, F., Russmann, S., Junker, E., Wuthrich, C , Bui, M. H., and Lauterburg, B. H. Decreased Glutathione in Patients with Anorexia Nervosa. Risk Factor for Toxic Liver Injury? Eur. J. Clin. Nutr. Feb 2004;58(2):238-243. Tsukamoto, M., Tanaka, A., and Arai, M., et al. Hepatocellular Injuries Observed in Patients with an Eating Disorder Prior to Nutritional Treatment. Intern. Med. 2008;47(16):1447-1450. Tanigawa, T, Higuchi, K., and Arakawa, T. Mechanism and Management of Drug-Induced Eating Disorder. Nippon. Rinsho. Mar 2001;59(3):521-527. Rautou, P. E., Cazals-Hatem, D., and Moreau, R., et al. Acute Liver Cell Damage in Patients with Anorexia Nervosa: A Possible Role of Starvation-Induced Hepatocyte Autophagy. Gastroenterology. Sep 2008;135(3):840-848, E841-843. Castano, G., Lucangioli, S., and Sookoian, S., et al. Bile Acid Profiles by Capillary Electrophoresis in Intrahepatic Cholestasis of Pregnancy. Clin. Sci. (Lond). Apr 2006; 110(4):459^165. Abrahamsson, H., Ostlund-Lindqvist, A. M., Nilsson, R., Simren, M., and Gillberg, P. G. Altered Bile Acid Metabolism in Patients with Constipation-Predominant Irritable Bowel Syndrome and Functional Constipation. Scand. J. Gastroenterol. 2008;43(12): 1483-1488. Aranhaa, M. M., Cortez, H., and Costad, A., et al. Bile Acid Levels Are Increased in the Liver of Patients with Steatohepatitis. European Journal of Gastroenterology & Hepatology. 2008;20(6):519-525. Kostrubsky, S. E., Strom, S. C , and Kalgutkar, A. S., et al. Inhibition of Hepatobiliary Transport as a Predictive Method for Clinical Hepatotoxicity of Nefazodone. Toxicol. Sci. Apr 2006;90(2):451^159. Yang, L., Xiong, A., and He, Y, et al. Bile Acids Metabonomic Study on the Ccl(4)- and Alpha-Naphthylisothiocyanate-Induced Animal Models: Quantitative Analysis of 22 Bile Acids by Ultraperformance Liquid ChromatographyMass Spectrometry. Chem. Res. Toxicol. Dec 15, 2008; 21(12):2280-2288. Tagliacozzi, D., Mozzi, A. F, and Casetta, B., et al. Quantitative Analysis of Bile Acids in Human Plasma by Liquid Chromatography-Electrospray Tandem Mass Spectrometry: A Simple and Rapid One-Step Method. Clin. Chem. Lab. Med. Dec 2003;41(12):1633-1641.

234

BIOMARKERS 105. Erel, O. A Novel Automated Direct Measurement Method for Total Antioxidant Capacity Using a New Generation, More Stable ABTS Radical Cation. Clin. Biochem. Apr 2004;37(4):277-285. 106. Erel, O. A Novel Automated Method to Measure Total Antioxidant Response Against Potent Free Radical Reactions. Clin. Biochem. Feb 2004; 37(2): 112-119. 107. Kim, K. M., Henderson, G. N., and Frye, R. F., et al. Simultaneous Determination of Uric Acid Metabolites Allantoin, 6-Aminouracil, and Triuret in Human Urine Using Liquid Chromatography-Mass Spectrometry. J. Chromatogr. BAnalyt. Technol. Biomed. Life Sci. Jan 1, 2009;877(l-2):65-70. 108. Schnackenberg, L. K., Chen, M., and Sun, J., et al. Evaluations of the Trans-Sulfuration Pathway in Multiple Liver Toxicity Studies. Toxicol. Appl. Pharmacol. Feb 15, 2009;235(l):25-32. 109. Esteve, E., Botas, P., and Delgado, E., et al. Soluble TNF-Alpha Receptor 2 Produced by Alternative Splicing Is Paradoxically Associated with Markers of Liver Injury. Clin. Immunol. Apr 2007;123(l):89-94. 110. Baumel, M., Lechner, A., Hehlgans, T., and Mannel, D. N. Enhanced Susceptibility to Con A-Induced Liver Injury in Mice Transgenic for the Intracellular Isoform of Human TNF Receptor Type 2. / Leukoc. Biol. Jul 2008;84(1): 162-169. 111. Morand, E. F. New Therapeutic Target in Inflammatory Disease: Macrophage Migration Inhibitory Factor. Intern. Med. J. Jul 2005;35(7):419^26. 112. Morand, E. F. and Leech, M. Macrophage Migration Inhibitory Factor in Rheumatoid Arthritis. Front. Biosci. Jan 1, 2005;10:12-22. 113. Calandra, T, Bernhagen, J., Mitchell, R. A., and Bucala, R. The Macrophage Is an Important and Previously Unrecognized Source of Macrophage Migration Inhibitory Factor. J. Exp. Med. Jun 1, 1994;179(6):1895-1902. 114. Ohkawara, T., Nishihira, J., Takeda, H., Asaka, M., and Sugiyama, T. Pathophysiological Roles of Macrophage Migration Inhibitory Factor in Gastrointestinal, Hepatic, and Pancreatic Disorders. J. Gastroenterol. Feb 2005;40(2): 117-122. 115. Bourdi, M., Reilly, T. P., Elkahloun, A. G., George, J. W., and Pohl, L. R. Macrophage Migration Inhibitory Factor in Drug-Induced Liver Injury: A Role in Susceptibility and Stress Responsiveness. Biochem. Biophys. Res. Commun. Jun 7, 2002;294(2):225-230. 116. Schreiber, G., Tsykin, A., and Aldred, A. R., et al. The Acute Phase Response in the Rodent. Ann. NYAcad. Sci. 1989;557:61-85;Discussion 85-66. 117. Apte, U. M., Banerjee, A., McRee, R., Wellberg, E., and Ramaiah, S. K. Role of Osteopontin in Hepatic Neutrophil Infiltration During Alcoholic Steatohepatitis. Toxicol. Appl. Pharmacol. Aug 22, 2005;207(l):25-38. 118. Ramaiah, S., Rivera, C , andArteel, G. Early-Phase Alcoholic Liver Disease: An Update on Animal Models, Pathology, and Pathogenesis. Int. J. Toxicol. Jul/Aug 2004;23(4):217-231. 119. Ramaiah, S. K. A Toxicologist Guide to the Diagnostic Interpretation of Hepatic Biochemical Parameters. Food Chem. Toxicol. Sep 2007;45(9):1551-1557. 120. Ramaiah, S. K. and Rittling, S. Pathophysiological Role of Osteopontin in Hepatic Inflammation, Toxicity, and Cancer. Toxicol. Sci. May 2008;103(1):4—13. 121. Banerjee, A., Burghardt, R. C , Johnson, G. A., White, F. J., and Ramaiah, S. K. The Temporal Expression of Osteopontin (SPP-1) in the Rodent Model of Alcoholic Steatohepatitis: A Potential Biomarker. Toxicol. Pathol. 2006;34(4): 373-384.

TRANSLATIONAL BIOMARKERS OF ACUTE DRUG-INDUCED LIVER INJURY 235 122. Svetlov, S. I., Xiang, Y., and Oli, M. W., et al. Identification and Preliminary Validation of Novel Biomarkers of Acute Hepatic Ischaemia/Reperfusion Injury Using Dual-Platform Proteomic/Degradomic Approaches. Biomarkers. Jul/Aug 2006;ll(4):355-369. 123. Igarashi, M., Kawana, S., Iwasaki, H., and Namiki, A. Anesthetic Management for a Patient with Citrullinemia and Liver Cirrhosis. Masui. Jan 1995;44(1): 96-99. 124. Yamaguchi, M. Role of Regucalcin in Maintaining Cell Homeostasis and Function (Review). Int. J. Mol. Med. Mar2005;15(3):371-389. 125. Lv, S., Wei, L., Wang, J. H., Wang, J. Y, and Liu, F. Identification of Novel Molecular Candidates for Acute Liver Failure in Plasma of BALB/C Murine Model. J. Proteome. Res. Jul 2007;6(7):2746-2752. 126. Yamaguchi, M., Tsurusaki, Y, Misawa, H., Inagaki, S., Ma, Z. J., Takahashi, H. Potential Role of Regucalcin as a Specific Biochemical Marker of Chronic Liver Injury with Carbon Tetrachloride Administration in Rats. Mol. Cell. Biochem. Dec2002;241(l-2):61-67. 127. Schuppan, D., Dumont, J. M., Kim, K. Y, Hennings, G., and Hahn, E. G. Serum Concentration of the Aminoterminal Procollagen Type III Peptide in the Rat Reflects Early Formation of Connective Tissue in Experimental Liver Cirrhosis. J. Hepatol. 1986;3(l):27-37. 128. Cho, J. J. and Lee, Y S. Enzyme-Linked Immunosorbent Assay for Serum Procollagen Type III Peptide in Rats with Hepatic Fibrosis. J. Vet. Med. Sci. Nov 1998;60(11):1213-1220. 129. Gerling, B., Becker, M., Waldschmidt, J., Rehmann, M., and Schuppan, D. Elevated Serum Aminoterminal Procollagen Type-III-Peptide Parallels Collagen Accumulation in Rats with Secondary Biliary Fibrosis. J. Hepatol. Jul 1996; 25(l):79-84. 130. Leroy, V., Monier, F, and Bottari, S., et al. Circulating Matrix Metalloproteinases 1,2,9 and Their Inhibitors TIMP-1 and TIMP-2 as Serum Markers of Liver Fibrosis in Patients with Chronic Hepatitis C: Comparison with PIIINP and Hyaluronic Acid. Am. J. Gastroenterol. Feb 2004;99(2):271-279. 131. Gressner, O. A., Weiskirchen, R., and Gressner, A. M. Biomarkers of Liver Fibrosis: Clinical Translation of Molecular Pathogenesis or Based on Liver-Dependent Malfunction Tests. Clin. Chim. Ada. Jun 2007;381(2): 107-113. 132. Cales, P., Oberti, F, and Michalak, S., et al. A Novel Panel of Blood Markers to Assess the Degree of Liver Fibrosis. Hepatology. Dec 2005;42(6):1373-1381. 133. Patel, K., Gordon, S. C , and Jacobson, I., et al. Evaluation of a Panel of NonInvasive Serum Markers to Differentiate Mild From Moderate-to-Advanced Liver Fibrosis in Chronic Hepatitis C Patients. J. Hepatol. Dec 2004;41(6):935-942. 134. Rosenberg, W. M., Voelker, M., and Thiel, R., et al. Serum Markers Detect the Presence of Liver Fibrosis: A Cohort Study. Gastroenterology. Dec 2004; 127(6):1704-1713. 135. Kauschke, S. G., Knorr, A., and Heke, M., et al. Two Assays for Measuring Fibrosis: Reverse Transcriptase-Polymerase Chain Reaction of Collagen Alpha(l) (III) Mrna Is an Early Predictor of Subsequent Collagen Deposition While a Novel Serum N-Terminal Procollagen (III) Propeptide Assay Reflects Manifest Fibrosis in Carbon Tetrachloride-Treated Rats. Anal. Biochem. Nov 15, 1999;275(2): 131-140. 136. Tebib, J. G., Viguier, P., Noel, E., Colson, F, Barbier, Y, and Bouvier, M. Serum N Terminal Procollagen III Fragment: A Predictive Marker of Joint Destruction In Rheumatoid Arthritis? Clin. Rheumatol. Dec 1992;ll(4):502-507.

236

BIOMARKERS 137. Careri, M., Costa, A., and Elviri, L., et al. Use of Specific Peptide Biomarkers for Quantitative Confirmation of Hidden Allergenic Peanut Proteins Ara H 2 and Ara H 3/4 for Food Control by Liquid Chromatography-Tandem Mass Spectrometry. Anal. Bioanal. Chem. Nov 2007;389(6):1901-1907. 138. Desouza, L. V., Romaschin, A. D., Colgan, T. J., and Siu, K. W. Absolute Quantification of Potential Cancer Markers in Clinical Tissue Homogenates Using Multiple Reaction Monitoring on a Hybrid Triple Quadrupole/Linear Ion Trap Tandem Mass Spectrometer. Anal. Chem. Mar 26, 2009. 139. Tucholska, M., Bowden, P., and Jacks, K., et al. Human Serum Proteins Fractionated by Preparative Partition Chromatography Prior to LC-ESI-MS/MS. J. Proteome. Res. Mar 6, 2009;8(3):1143-1155. 140. Tucholska, M., Scozzaro, S., and Williams, D., et al. Endogenous Peptides From Biophysical and Biochemical Fractionation of Serum Analyzed by MatrixAssisted Laser Desorption/Ionization and Electrospray Ionization Hybrid Quadrupole Time-of-Flight. Anal. Biochem. Nov 15, 2007;370(2):228-245. 141. Zhang, R., Barker, L., and Pinchev, D., et al. Mining Biomarkers in Human Sera Using Proteomic Tools. Proteomics. Jan 2004;4(l):244—256. 142. Marshall, J., Kupchak, P., and Zhu, W., et al. Processing of Serum Proteins Underlies the Mass Spectral Fingerprinting of Myocardial Infarction. J. Proteome. Res. Jul/Aug 2003;2(4):361-372. 143. Aleo, M. D., Doshna, C. M., and Fritz, C. A. An Underlying Role for Hepatobiliary Dysfunction in Cyclosporine a Nephrotoxicity. Toxicol. Appl. Pharmacol. Jul 1,2008;230(1):126-134. 144. Tessier, E., Neirinck, L., and Zhu, Z. High-Performance Liquid Chromatographic Mass Spectrometric Method for the Determination of Ursodeoxycholic Acid and Its Glycine and Taurine Conjugates in Human Plasma. J. Chromatogr. BAnalyt. Technol. Biomed. Life Sci. Dec 25, 2003;798(2):295-302. 145. Alnouti, Y., Csanaky, I. L., and Klaassen, C. D. Quantitative-Profiling of Bile Acids and Their Conjugates in Mouse Liver, Bile, Plasma, and Urine Using LC-MS/MS. / Chromatogr. BAnalyt. Technol. Biomed. Life Sci. Oct 1, 2008; 873(2):209-217.

CHAPTER

BIOMARKERS OF ACUTE KIDNEY INJURY Frank Dieterle and Frank D. Sistare

INTRODUCTION D e f i n i t i o n and Prevalence of A c u t e Kidney Injury Kidney disease and renal injury are prevalent serious health problems significantly impacting patient short and long-term survival due to alterations or even complete loss of the renal detoxification capacity, deregulation of salt and water balance, and disturbance of kidney endocrine function. Although renal injury and impairment have a broad pathophysiological spectrum, a classification into acute kidney injury (AKI) and chronic kidney diseases (CKD) is typically performed according to the speed of progression. CKD is characterized by a low glomerular filtration rate with a slow steady loss of renal function over time. CKD can have various causes, such as hypertension, diabetes, chronic glomerulonephritis, polycystic kidney disease, or tubulointersitital fibrosis.12 In contrast, AKI refers to a spontaneous and sustained decrease in renal function. AKI, which has also often been referred to as acute renal failure (ARF), has been reported to complicate 1-7% of all hospital admissions3-6 and 1-25% of intensive care unit (ICU) admissions,7,8 and is associated with a high mortality rate of up to 80% in the ICU setting.9-13 In addition, AKI is a considerable risk factor for the development of non-renal complications contributing to mortality.14'15 Although mortality rates seem to have decreased in recent years, the incidence of AKI, or at least the identification of AKI, has increased over time,16 and the lack of widely accepted tools to detect AKI early, renders a timely intervention difficult. Extensive clinical and nonclinical research to understand, define, and develop new tools 237

238

BIOMARKERS

to detect AKI has been conducted in the last decades. Although significant progress has been made in understanding the biology and mechanism of AKI in animal models, translation of this knowledge into improved management and outcomes for patients has been limited.17 Only recently, great expectations for better and earlier tools to improve the early detection of AKI have been created with the development of new renal safety biomarkers, which are reviewed in this chapter.

Pathophysiology and Mechanisms AKI can result from a number of factors, such as decreased renal or intrarenal perfusion, toxic or obstructive insults to the renal tubules, tulolointerstitial inflammation and edema, or primary reduction in the filtering capacity of the glomeruli,18 whereby ischemia and nephrotoxicity account for the largest number of cases of AKI.19 In particular in critically ill patients, nephrotoxicity and ischemia often add to other risk factors such as sepsis, hematologic cancers, renal impairment, or acquired immunodeficiency syndrome. As a consequence, nephrotoxicity has been shown to contribute to 8-60% of all cases of AKI, depending on the patient population and definition of AKI. For example, in a recent biopsy study of 104 patients suffering from AKI or chronic kidney failure, approximately 35% seem to be drug related.20 Looking at the anatomy and function of the kidney, it is straightforward to understand that drug-induced nephrotoxicity is not an uncommon phenomenon. The kidney is basolaterally exposed to blood circulating chemicals and metabolites, and its function is to increase the concentration and excretion of these entities, resulting in high luminal exposure. For example, cisplatin accumulates in the S3 segment of tubules and the subsequent high local concentrations of this cytotoxic drug causes direct damage to the tubular epithelial cells. Furthermore, even drugs with a short half-life and low toxicity to other organs can be potential nephrotoxicants since renal blood flow accounts for more than one fourth of the cardiac output. A considerable number of known marketed drugs induce kidney injury. Often toxicity is considered a class effect, for example certain antibiotics, analgesics, or immunosuppressants. Various modes of drug-induced nephrotoxicity in human have been reported. Some drugs and drug candidates have very specific effects leading to nephrotoxicity, and are beyond the scope of this chapter. The most frequently observed mechanisms are briefly summarized below. Acute tubular necrosis is not an uncommon phenomenon for cytotoxic drugs excreted via the kidney, since the role of the tubule to concentrate and reabsorb the glomerular filtrate renders it vulnerable to direct injury. A number of aminoglycosides, such as neomycin, gentamicin, tobramicin, amikacin, and streptomycin are not metabolized but excreted by glomerular filtration and bind to the tubuloepithelial membrane in the proximal tubule due to their cationic properties and may subsequently interfere with normal lysosomal cellular function, protein synthesis, or mitochondrial function. The combination with other drugs, which show a similar direct toxicity on the tubular epithelial cells such as cisplatin (agent for chemotherapy), causes more nephrotoxic-

BIOMARKERS OF ACUTE KIDNEY INJURY

239

ity than each agent alone. Also, a group of antiviral agents such as adefovir, cidofovir, and tenofovir are known to cause direct proximal tubular injury. They are actively reabsorbed by the human renal organic anion transporter-1, their structural similarity to naturally occurring nucleotides appearing to be an important factor for nephrotoxicity. A number of other drugs show similar direct tubular epithelial toxicity, such as the bisphosphonates ibandronate and zoledronate, contrast agents, amphotericin B, and many more. Direct tubular epithelial toxicity is one of the most frequent mechanisms observed in drug development leading to nephrotoxicity. Acute interstitial nephritis is an acute inflammatory condition that specifically affects the renal tubules and the interstitium caused by a cell-mediated hypersensitivity reaction to a drug. A number of drugs are associated with acute interstitial nephritis, such as vancomycin, penicillins, cephalosporins, diuretics, valproic acid, and many more. In most cases, acute interstitial nephritis is self-limited and reversible. Glomerular alterations and injury, also known as nephrotic syndrome or glomerulopathy, is marked by heavy proteinuria. Drugs such as gold therapy, doxorubicin, puromycin, penicillamine, and interferon can directly or indirectly affect mesangial cells or podocytes resulting in an altered permeability of the glomerular filtration wall. Glomerular injury is typically linked to subsequent tubular injury due to the protein overload resulting in poisoning of the tubules (see also Figure 10.1). Crystalline nephropathy, which is also known as obstructive uropathy, obstructive acute renal failure, or crystal nephropathy, can be traced back to the precipitation of crystals in the tubular lumens due to their relative insolubility in human urine. The risk of crystal deposition, which causes an obstruction of the tubular lumen, is increased by volume contraction caused by chronic diarrhea, anorexia with nausea/vomiting, adrenal insufficiency and renal salt wasting, but also pancreatitis, heart failure, and pleural effusions. Drugs associated with crystal deposition include antiretroviral agents such as indinavir, tenofovir, and acyclovir, but also the antibiotic sulfadiazine. Hemodynamic renal failure, which is also known as pre-renal nephropathy, is caused by modulation of the intra-renal blood flow. Intraglomerular pressure and consequently glomerular filtration rate are regulated by the vasomotor tone of the afferent and the efferent arterioles. In case of decreased renal blood flow, the intraglomerular pressure is maintained by vasoconstriction of the efferent arteriole and vasodilation of the afferent arteriole. Drugs that may interfere with the hemodynamic regulatory system can cause a critical further drop of the glomerular filtration rate and result in renal dysfunction. Angiotensin-convering enzyme (ACE) inhibitors and angiotensin receptor blockers inhibit the angiotensin II—mediated vasoconstriction of the efferent arteriole, whereas calcineurin inhibitors (cyclosporin A, tacrolimus) can cause a vasoconstriction, mainly of the afferent arteries. Nonsteroidal anti-inflammatory drugs (NSAIDS) and cyclooxygenase (COX) inhibitors can inhibit prostaglandin-induced vasodilation, affecting the afferent arteries. Contrast agents may impact renal perfusion in certain patients and precipitate nephropathy. Except

240

BIOMARKERS

FIGURE 10.1 Scheme of the glomerulus and tubules illustrating glomerular filtration and tubular reabsorption of low molecular weight (LMW) proteins and high molecular weight (HMW) proteins in a normal kidney, after glomerular injury, and after tubular injury. In the non-injured kidney, only low amounts of H M W proteins pass the glomerular filtration wall. LMW proteins pass freely the filtration wall and are reabsorbed to a great extent in the tubules with only a small fraction being excreted with the urine. In early stages of glomerular injury, H M W proteins pass the glomerular filtration wall and are reabsorbed in the tubules, competing with the reabsorption of the LMW proteins, which are subsequently excreted into urine to a large extent. With continued glomerular injury, the continuously reabsorbed H M W proteins "poison" the tubular reabsorption complex, and both LMW and H M W proteins are excreted into urine to a large extent.Thus, LMW proteins can be early and sensitive markers for glomerular injury or for a direct impairment of the tubular reabsorption complex. By contrast, with only tubular injury low amounts of H M W proteins appear in the urine while the LMW proteins continue to be reabsorbed and do not appear in the urine. (See color insert for a full color version of this figure.)

for the calcineurin inhibitors, which induce severe nephrotoxicity in a wide population limiting their clinical use, the other agents show their nephrotoxic potential mainly in individuals with reduced renal function, with other risk factors, and with co-medication of other potentially nephrotoxic drugs. The plurality of different modes of toxicity also has a consequence that nephrotoxicity is one of the major safety concerns in drug development and leads to the termination of a significant number of drugs from development in nonclinical and clinical stages. Further complications rendering the management of nephrotoxicity in drug development difficult are: Non-translatable nephrotoxicity: In some cases the nephrotoxicity observed in animal models does not exactly match the situation encountered in human. On the one hand, there are situations in which no nephrotoxicity in human is observed despite nephrotoxicity in one or several animal species at comparable doses. On the other hand, the nephrotoxicity observed in human may differ in terms of mechanisms, courses, and pathologies. An example is the acute toxicity observed with cyclosporine A. In rats, tubular degeneration, necrosis, and regeneration are observed with the distal tubules being more affected than the proximal tubules, whereas in human the same pathologies dominate more in the proximal tubules than in the distal tubules. Different modes of nephrotoxicity for the same drug: Various factors, such as dosing regimen (chronic or acute toxicity), individual renal performance, or metabolic activity can influence which mode of toxicity is domi-

BIOMARKERS OF ACUTE KIDNEY INJURY

241

nant and which renal pathology will manifest. An example is the drug lithium, which is used in the treatment of bipolar disorders. Treatment can cause a socalled nephrogenic diabetes insipidus (NDI), which is linked to the lithiuminduced down-regulation of the vasopressin-regulated water channel aquaporin-2 expressed in the collecting ducts. Similarly, the acute toxicity primarily causes necrosis in the collecting ducts. On the other hand, chronic lithium toxicity induces chronic tubulointerstitial nephropathy visible as cysts in the distal tubules. A second example is chronic treatment with NSAIDS. Besides the hemodynamic renal failure in patients at risk described above, chronic use of analgesics can lead to necrosis of the loop of Henle, to chronic interstitial nephritis, to papillary necrosis and ultimately to chronic renal failure. In contrast to the hemodynamic renal failure, patients at risk are mainly middle-aged women taking combination analgesics for various disorders. The third example is the acute versus chronic toxicity of cyclosporine A. Whereas the acute toxicity is directly linked to the vasoconstriction and is blood-level dependent and can be managed by reducing the dose levels, the chronic nephropathy is largely irreversible and can occur independent of acute toxicity. Risk factors: Several types of drug-induced kidney injury in humans occur in combination with certain risk factors, which are human specific and which cannot be modeled preclinically. For example, in clinical situations with an impaired renal perfusion such as renal artery stenosis, true volume contraction (duretics, diarrhea, vomiting), or effective volume depletion (nephrosis, cirrhosis decompensated heart failure), ACE inhibitors, COX2 inhibitors and non-steroidal anti-inflammatory drugs (NSAIDs) show a significantly increased risk of renal hemodynamic failure. The background is that in these clinical situations the kidney tries to maintain the glomerular filtration rate by a vasodilatation of the afferent arteries and a vasoconstriction of the efferent arteries. ACE inhibitors reduce the vasoconstriction, whereas the COX-2 inhibitors and NSAIDs inhibit the vasodilatation. For most of the clinical risk factors and underlying diseases, animal models do not exist and in those cases where animal models are available, their use in routine toxicology assessment is not feasible. These aspects also highlight the requirements of new tools for monitoring kidney safety in drug development and in routine use of drugs: The new tools need to be available for preclinical and clinical use to allow the translation of drug candidates from animals to human to evaluate kidney safety in all species, and they need to be able to cover various pathologies, biochemical and physiologic processes, modes and mechanisms of toxicity, and compartments of the kidney. Although most of the mechanisms of kidney injury described above involve a proximal tubular injury also as secondary toxicity, a monitoring of different compartments of the kidney would allow in many cases more precise understanding, greater sensitivity, and may enable a differential diagnosis by identifying the primary injury. On a molecular and cellular level, injury to the tubular epithelium results in a rapid loss of the cytoskeletal integrity and depolarization of the cell, disordering adhesion and membrane molecules.21'22 Further consequences are

242

BIOMARKERS

apoptosis, shedding of the tubular brush border and necrosis. If a significant number of cells are no longer viable, the filtrate can leak through the exposed basement membrane and result in severe inflammation and vasoconstriction through the action of vasoactive mediators.19,23 Repair mechanisms of the kidney include epithelial cell spreading and migration to cover the denuded areas of the basement membrane, cell de-differentiation, proliferation, and differentiation to restore the integrity and functionality of the nephron.24 Recent evidence proposes that there is a thin line between successful tissue repair and failure thereof leading to progression of the injury. Thus, early detection of injury and timely intervention are crucial to prevent severe injury, chronic kidney disease and kidney failure.25

C u r r e n t Standards f o r Diagnosing A c u t e Kidney Injury Definitions and criteria for AKI are mainly based on rapid decrease of renal function, i.e., glomerular filtration rate, typically identified by monitoring serum creatinine in patients. Numerous definitions of AKI based on serum creatinine, with or without oliguria, lead to a plurality of different clinical endpoints, criteria for patient selection, and classification, rendering clinical and epidemiologic studies difficult to compare.26 To simplify and standardize the clinical definition of AKI, the Acute Dialysis Quality Initiative (ADQI) has recently published a consensus definition of AKI entitled the RIFLE criteria (Risk for renal dysfunction, Injury to the kidney, Failure of kidney function, Loss of kidney function, and Endstage renal disease)17,27 with the criteria for the classification shown in Table 10.1. This classification system accounts for the linear relationship between minor elevation of creatinine and progression to kidney failure manifested by the need for renal replacement therapy. Although the utility of this classification system in determining risk of renal replacement therapy and hospital morality has been well documented,28,29 its utility in

TABLE 10.1 Rifle classification criteria for acute renal failure: Risk of renal dysfunction, injury to the kidney, failure of kidney function, loss of kidney function, end-stage renal disease; SCr = serum creatinine, UO = urine output.The criteria that lead to the worst possible classification should be used.

Stage

GFR Criterion

Urine Output Criterion

Risk (1)

Increased SCr x 1.5 or GFR decrease > 25%

UO < 0.5ml/kg/h x 6h

Injury (2)

Increased SCr x 2 or GFR decrease > 50%

UO < 0.5ml/kg/h x 12h

Failure (3)

Increased SCr x 3 or GFR decrease > 75% or SCr a4mg/dl (acute rise a 0.5mg/dl)

UO < 0.3ml/kg/h x 12h or Anuria x 12h

Loss (4)

Persistent ARF = complete loss of kidney function > 4 weeks

ESKD (5)

End-stage kidney disease (> 3 months)

BIOMARKERS OF ACUTE KIDNEY INJURY

243

treatment decisions and intervention might be limited due to the fundamental problem of using assessments of the glomerular filtration rate (GFR) for the identification and classification of kidney injury. The GFR is only indirectly linked to kidney injury and changes of the GFR reflect a late consequence in a chain of effects and changes associated with primary insult to the kidney. In addition, a large amount of the functioning renal mass can be lost without any significant changes of the GFR also often referred to as "renal reserve."3031 An illustrative example of the renal reserve is the situation of kidney donors. After donation of one kidney, which corresponds to the loss of 50% of the renal functioning mass, virtually no increases of serum creatinine as estimation of the GFR can be seen.32 34 The direct measurement of GFR by assessing the renal clearance of inulin is still considered the gold-standard.35-36 However, this approach is not suitable for routine clinical practice due to the need for continuous intravenous infusion, timed urine collection, and sometimes bladder catheterization. Also, other techniques based on iothalamate or other X-ray tracers37-39 allow measurement of GFR, but similarly to insulin are hampered by the need for urine collection and continuous intravenous infusion. An alternative method is GFR determination by plasma clearance of 51Cr-EDTA.40 Although it offers the advantage of not needing urine collection and continuous intravenous infusion, its use in clinical practice is very limited due to the special needs of handling radiolabled compounds. As a consequence, estimations of glomerular filtration rate by serum level measurement of endogenous constantly produced molecules, which are predominately cleared by glomerular filtration, have become the de facto standard in clinical practice, i.e., serum creatinine and blood urea nitrogen (BUN). Yet both methods have several drawbacks, complicating the timely and reliable detection of AKI. Serum creatinine is a small 113 Da molecule derived from the metabolism of creatine in skeletal muscle and from dietary meat intake. It is released into the plasma at a relative constant rate and is freely filtered by the glomerulus. Consequently, a decrease of GFR causes a rise of serum creatinine concentrations, showing an inverse relationship to GFR.41 Despite being the most widely accepted standard for assessing GFR, there are a number of limitations: 1) Production and release can be variable depending on sex, age, dietary intake muscle mass, and disease-related loss of muscle mass (e.g., rhabdomyolysis), resulting in significant variations of baseline serum creatinine. 2) In the case of normal kidney function, up to 25% of creatinine is secreted by the tubules into urine and is not filter by glomeruli, resulting in an overestimation of the GFR. If the GFR is decreased due to kidney impairment, the proportion of creatinine secretion is additionally increased, further contributing to an overestimation of GFR.42'43 Also, several drugs, such as cimetidine or trimethoprim, can competitively block the secretion of creatinine and consequently cause an increase of serum levels of creatinine in the absence of kidney injury, limiting its specificity.44 3) Rapid changes of GFR are not visible in real-time by changes of serum creatinine levels, but rather delayed, as creatinine needs time to accumulate to a new steady state, delaying the diagnosis of AKI by hours to days.45

244

BIOMARKERS

BUN: Serum urea is a low molecular weight waste product of protein metabolism and its serum levels are inversely correlated with GFR similarly to serum creatinine, but its use is problematic due to extra-renal factors: 1) The rate of urea production can be widely modified by protein intake, illness (chronic liver disease, sepsis, trauma) and drug intake, causing variable baseline BUN levels. 2) Approximately 40 to 50% of filtered urea is reabsorbed by proximal tubular cells. In the case of decreased circulating volume, more urea is reabsorbed, causing increased BUN levels under-representing GFR. Although urine output is routinely measured in crticially ill patients and can be helpful in assessing kidney function, its lack of specificity and sensitivity for AKI renders its use as a single criterion for detecting AKI doubtful. In summary, the deficiencies in current standards for detecting AKI, which have been used for decades, clearly show the urgent need for more sensitive and earlier tools for detecting AKI, and allowing for timely intervention and prevention of renal failure. In addition, more sensitive tools allow drug developers to assess better the potential of their compounds to induce kidney injury and to develop drugs that will show better overall renal safety.

N O V E L K I D N E Y SAFETY BIOMARKERS In recent years, kidney safety biomarkers have become promising tools to detect AKI in a much more sensitive and earlier way than the current standards of serum creatinine and BUN. Although the principle of measuring proteins in the urine or plasma to monitor kidney safety is not new, only recently has the availability of sensitive and reliable assays to measure many new biomarkers in systematic investigations involving preclinical studies, and clinical studies opened the door for these promising new tools and surfaced evidence of their utility. A number of proximal brush border enzymes, such as alanine aminopeptidase (AAP), alkaline phophatase (AP) or 7-glutamyltranspeptidase (GGT) have been measured in urine of patients for decades, but their instability and sometimes limited sensitivity have hindered their general breakthrough. In general, kidney safety biomarkers or a panel of biomarkers should have a number or properties to render them useful for different contexts: • The markers should be sensitive for detecting a diverse set of renal insults. A biomarker could be specific for a certain type of cellular injury; for a certain step in a pathologic process, physiologic function, or biochemical process; for only certain regional or anatomical compartments of the kidney; or could be a nonspecific marker of general kidney injury or dysfunction. Different complementary biomarkers might be combined on a kidney injury panel, offering the advantage that injury could be localized, more precisely characterized, and help to resolve ambiguities inherent in interpretations of more general traditional biomarkers. • The biomarkers should change early and sensitively allowing an early and accurate detection of AKI and thus a timely intervention. • The magnitude of change in the biomarker is expected to be proportional to the severity and extent of kidney injury or dysfunction.

BIOMARKERS OF ACUTE KIDNEY INJURY

245

• Specificity to kidney-related injury prevents the generation of false positive AKI diagnoses in the context of other organ injuries or other interfering events, in particular in critically ill patients. • Accessibility in peripheral body fluids such as blood or urine is important as invasive procedures such as biopsies are not generally acceptable and feasible in routine clinical practice. In addition to analyte stability in the stored sample, the absence of particular sampling requirements are a big plus. Knowledge of the kinetics of the biomarker in the body is needed and should suffice to match the practical needs for patient monitoring. A very transient biomarker signal, for example, may have limited practical utility. • Availability of technologies to measure these markers at reasonable technical efforts and prices is a practical consideration. Many of the most promising kidney biomarkers are discussed below. The biomarkers are ordered and characterized by the mechanism through which their levels are modulated and by the compartment of the kidney for which these biomarkers are specific; such as general kidney function biomarkers, de-novo expression injury response biomarkers, leakage markers, functional biomarkers of glomerular filtration, tubular re-absorption, and inflammatory biomarkers.

Kidney Function Biomarkers Serum C y s t a t i n C

Serum cystatin C is a general renal function marker that is rapidly gaining increased use in different clinical settings. Cystatin C used to be called 7-trace and is a non-glycosylated low-molecular weight 13 kDa protein. It is continuously produced by all nucleated cells and functions as a housekeeping factor.46 Cystatin C is directly and freely filtered from blood into the glomerulus, and therefore is considered an improved estimator of GFR due to several positive attributes: 1. Compared to serum creatinine, a greatly reduced impact of age, sex, muscle mass, dehydration state, and circadian rhythm on cystatin C serum levels has been observed. 2. An unhindered straightforward filtration of cystatin C by glomeruli. 3. In contrast to serum creatinine, an absence of tubular secretion or extrarenal clearance.47 A limitation for certain clinical contexts might be the modulation of serum cystatin C levels seen with corticosteroids, e.g., in transplantation.48 It has been demonstrated in numerous clinical studies and meta-reviews that serum cystatin C outperforms or at least equally performs to SCr for the estimation of the GFR in various different contexts of AKI and CKD, with a better performance in particular for glomerular function impairment, for critically ill patients, and for mild changes of GFR.49-54 The FDA approval of an assay

246

BIOMARKERS

to measure cystatin C also shows its increasing importance and value in clinical practice55 and it might be speculated that serum cystatin C may someday replace serum creatinine as a general renal function biomarker.

Functional Biomarkers U r i n a r y Total P r o t e i n

Total protein is the ensemble of all protein species measured together in urine. Abnormally high excretion of proteins is called proteinuria and has been highlighted in the literature as a clinical prognostic marker, as a preclinical and clinical diagnostic marker to detect AKI, and as a factor predicting progressive loss of renal function in the context of a variety of diseases.56"60 Alterations of the glomerular filtration barrier, such as damage to the glomerular podocytes, leads to leakage of plasma proteins into the ultrafiltrate.61The normal glomerular filtrate contains 10 mg protein/L, but only approximately 1% is normally present in the urine because of the strong re-absorption capacity of the proximal tubule. If this re-absorption reaches a saturation point due to excessive glomerular leakage with an often observed associated "poisoning" of the proximal tubules, or if the tubular protein reabsorption complex is directly damaged by toxic agents, proteinuria can be observed despite normal glomerular filtration rates.62 In progressive glomerular disease, dysfunctions of glomerular filtration and of tubular re-absorption are found together as tubuloglomerular proteinuria. Normal urinary proteins consist of low amounts of both high and low molecular weight proteins including albumin, other plasma proteins (beta 2-Microglobulin, alpha 1-microglobulin, retinol binding protein, haptoglobin, cystatin C) and Tamm-Horsfall glycoprotein secreted by tubular cells. As shown in Figure 10.1, glomerular injury and disease can result in a mixed picture of both high and low molecular weight proteins over the chronology of the injury; both types of protein contributing to the estimation of urinary "total protein." It has been postulated that the ratio between low and high molecular weight proteins in urine would allow a better prediction of the type and severity of damage than the quantity of proteinuria.63'M The clinical use of evaluating urinary total protein is many-fold, such as evaluating renal diseases including proteinuria, complicating diabetes mellitus, nephrotic syndromes (e.g., membranous proliferative glomerulopathies, lipoid nephrosis, systemic lupus erythematosus, amyloidosis, heavy metal poisoning by gold, lead, and cadmium), glomerulonephritis, Goodpasture's syndrome, Henoch-Schonlein purpura, kidney infection, polycystic kidney disease, multiple myeoloma, collagen diseases, drug-induced nephrotoxicity, renal tubular lesions, tubular proteinuria including Wilson disease and Fanconi syndrome, and hypertension (about 10-15% of patients with hypertension show proteinuria).65 Among these diseases, nephrotic syndromes cause the most severe proteinuria and nephrotic syndrome is defined usually by the extent of proteinuria. Proteinuria is diagnosed when total urinary protein excretion is greater than 300 mg/24h. For different laboratories, the reference ranges might vary slightly, but most commonly upper references ranges of 140-150mg/24h are

BIOMARKERS OF ACUTE KIDNEY INJURY

247

used for 24 hour urine collections, whereas an upper reference concentration of 45mg/mmol Creatinine is applied for a random spot urine collection specimen (Laboratory Corporation of America 2007). In the last decades, proteinuria has usually been diagnosed in a screening setting by simple dipstick tests, despite the possibility of the test being falsely negative even with nephrotic range proteinuria. False negatives can occur if the urine is diluted or if the proteinuria consists of mainly other proteins than albumin, since the reagent on the test strips, bromphenol blue, is highly specific for albumin. These qualitative assessments have significantly contributed to a mixed reputation of total protein as a marker of kidney injury as well as the fact that there has never been a formal assessment or qualification of total urinary protein as a biomarker for different pathologies, such as the glomerular injury, in a large-scale clinical context. The formal regulatory qualification of quantitative total urinary protein as a biomarker to monitor glomerular injury by the PSTC and its acceptance for specific preclinical use on a case-by-case translational contexts by the EMEA and FDA is expected to change the culture of using quantitative urinary protein in a more formal way in drug development and clinical settings (see "Consortia achieving the first regulatory qualification of kidney safety biomarkers"). A number of publications now highlight the diagnostic power of total protein for AKI induced by nephrotoxicants, such as cisplatin, thalidomide, pamidronate, aminoglycosides, ifosfamide, doxorubicin, or nonsteroidal anti-inflammatory drugs.66"71 Urinary Albumin

For more than four decades urinary albumin has been known to be a biomarker of kidney injury.7273 Albumin is a major high molecular weight serum protein larger than the pores of the glomerular filter, so albuminuria is best known as a biomarker of glomerulopathy, its appearance in urine presumed to represent a compromise to the integrity of the glomerular basement membrane.74 However a small but biologically significant fraction of albumin isfiltered,75*77and normally very efficiently absorbed by proximal tubule epithelium, degraded, and reutilized or excreted fragmented into the urine.78 The experimental data support therefore an interpretation that large quantities of intact albumin in urine in excess of 200 mg/g creatinine and referred to as macroalbuminuria exceed the absorptive capacity of normally functioning tubular epithelium and reflect dysfunction of the glomerular filter. The smaller quantities of intact albumin between 20 and 200 mg/g creatinine, and often referred to as microalbuminuria, may represent the significant fraction of albumin filtered by a normally functioning glomerulus that cannot be reabsorbed by tubular epithelium rendered dysfunctional by disease or chemical toxicants (see Figure 10.1). Concurrent dysfunction to both segments cannot be differentiated from albuminuria measurements alone. Furthermore, the traditional urinary dipstick protein detection method is insensitive to the lower critical ranges of urinary albumin useful for diagnosing renal tubular dysfunction. Clinically, urinary albumin has gained favor as a biomarker for monitoring chronic kidney disease progression and to monitor delay of progression with

248

BIOMARKERS

treatment intervention, especially for managing nephropathy, cardiovascular disease, and renal hypertension in diabetics.79-83 In human studies albuminuria has clearly been demonstrated to be a relevant functional biomarker of acute chemically induced injury to the tubular epithelium following, for example, treatment with gentamicin,84 carboplatin,85'86 ifosfamide,87 or cisplatin.88 Recently numerous studies were conducted in rats administered renal toxicants, including gentamicin and cisplatin, or non-toxicants and comparing the relative performance of albuminuria to several urinary biomarkers and to serum creatinine. Albuminuria was shown to outperform serum creatinine for detection of histopathologically confirmed drug-induced renal tubular injury,217 with albuminuria appearing in rats presenting with tubular histology at doses and times when serum creatinine and blood urea nitrogen were unchanged. Decades of data, together with recent studies and greater understanding of renal handling of this protein, point to the underutilization of this important and versatile renal function biomarker. Since albumin is such an abundant protein appearing in serum and animal feed, important practical considerations must be made regarding interpretations of potentially spurious findings in animal studies as a result of an occasional toe nail bleed or from food droppings contaminating overnight urine collections when fasting may not always be possible. U r i n a r y (32-Microglobulin

2-microglobulin is a low molecular weight (11.8 kDa) protein component of the MHC Class 1 molecule. In healthy subjects 150-200 mg are synthesized daily and eliminated via the kidney. It is readily filtered by the glomeruli, and almost completely reabsorbed and metabolized by the tubules. Only 0.1% of the (32-microglobulin filtered by the glomeruli is normally excreted into urine. It has been demonstrated that an impairment of the tubular uptake causes increased excreted urinary p2-microglobulin levels, up to several hundred folds. So far, there are two identified mechanisms proven to be responsible for this impairment. At first, glomerular alterations, damage, and/or diseases allow higher molecular weight proteins to pass through the filtration membrane causing a high protein load in the tubule. As a consequence, higher molecular weight proteins like albumin compete for common transport mechanisms decreasing the tubular uptake of (32-microglobulin and increasing the excretion into urine (see also Figure 10.1).89 Secondly, the tubular re-absorption complex is directly impacted by treatment with drugs or by different tubular diseases. For spot urine collections, the concentration of (3 -2-microglobulin in healthy subjects is typically s 160 ug/L or £ 300 |ag/L/ g creatinine. (3 -2-microglobulin may be unstable in urine of pH < 5.5 or in urine with enzymes present which may be proteolytic (e.g., urinary tract infection), therefore the urine should be alkalinized and frozen at -80 C within minutes of collection.90 Increases of urinary |3 -2-microglobulin with renal injury have been described in a number of settings including cardiac surgery,9192 renal transplantation,93 and in particular nephrotoxicity in more than 200 peer reviewed publications, such as HIV patients treated with tenofovir, disoproxil fumarate,

BIOMARKERS OF ACUTE KIDNEY INJURY

249

and other antiretroviral agents,94 patients with aminoglycoside treatments,95 cisplatin treatment regimens,9697 and gold treatment (sodium aurothiomalate) for rheumatoid arthritis98 to name only a few examples. In a review of 14 studies on urinary biomarkers in septic acute kidney injury, the authors noted that urine (3 -2-microglobulin was associated with a change in serum creatinine, can help to distinguish pre-renal azotemia from acute tubular necrosis, and can detect sub-clinical or predict acute kidney injury.99 However, they observed that the prognostic value of urine (3 -2-microglobulin in sepsis was unclear. Similarly, Herget-Rosenthal, et al. studied 73 patients with non-oliguric renal failure and found that while increased urine (3 -2-microglobulin was associated with the risk of requiring renal replacement therapy, it was not as predictive as markers such as NAG, RBP or alpha 1-microglobulin.100 Thus, despite its use for decades with a better performance than BUN and serum creatinine, there is a need to evaluate systematically the utility of |3 -2-microglobulin in different clinical contexts similar to the preclinical regulatory qualification of |32-Microglobulin by the PSTC, in a side-by-side comparison with urinary cystatin C, which shows better stability. Urinary Cystatin C

An impairment of re-absorption in proximal tubules by the same mechanisms as described for urinary (3 2-microglobulin can lead to several hundred-fold increases of urinary levels in humans and rats.101,102 Reported reference ranges and average control values of cystatin C are very consistent and indicate a normal urinary cystatin C concentration below 0.3mg/L including studies with nearly 2000 healthy subjects in total. For example, in a study with 1670 healthy subjects the average urinary cystatin C concentration was 5lug/1 ±25.2ugfl (Uchida, K. and Gotoh, A. 2002). Until now changes in urinary cystatin C levels have been characterized mainly in the context of different kidney diseases affecting glomerular integrity and proximal tubular re-absorption in humans.103'104 In a recent publication, urinary cystatin C levels were investigated in 50 patients with glomerular diseases and 22 patients with tubulointerstitial diseases, which were all proven by biopsy.101 Urinary cystatin/urinary creatinine ratios > 11.3 mg/mmol was highly associated with tubular proteinuria, biopsy-proven tubulointerstitial disease, and heavy proteinuria. The study identified both functional impairment due to protein overload in the case of heavy proteinuria (glomerular disease), as well as structural impairment due to tubulointerstitial disease as factors associated with increased urinary levels of cystatin C as also reported elsewhere. For instance, Uchida, et al identified increased urinary cystatin C concentrations for a number of subjects with proteinuria indicating tubular damage as a consequence of protein overload. In addition, highly increased urinary cystatin C levels were found in 56 patients with chronic renal failure.105 Tkaczyk, et al. showed that urinary cystatin C levels in 12 children with idiopathic nephrotic syndrome (INS) where higher than in children in clinical remission in the eighth week of INS treatment and higher than in healthy

250

BIOMARKERS

children.106 Also, in critically ill patients with AKI, urinary cystatin C showed a better performance than a number of other urinary biomarkers to predict the subsequent need for acute renal replacement therapy.100 Although urinary (32-microglobulin has a much longer track record of clinical and nonclinical use, cystatin C shows higher stability in urine and consequently might be the preferred marker for future use, in particular as both markers monitor the same pathologies and as assays for cystatin C have now become available.

Leakage Markers U r i n a r y GST-a and G S T - U / T T

Glutathione-S-transferases are soluble cytosolic enzymes with two subtypes of equal size which are located in different segments of the tubules, such as the asubtype in the proximal tubules and the p/Tr-subtypes j n the distal tubules (ufor rodents and IT for humans). GSTs play a role in detoxification processes in the kidney. In the case of tubular cell necrosis, the content of the cells leaks into urine. Thus, urinary GST-a is a leakage marker specific to proximal tubular cells and GST-JJ/TT protein levels are leakage markers indicating distal tubular cell injury. In rat studies, increased levels of GST-a and increased levels of GST-p were associated with proximal and distal tubular injury after treatment with cisplatin, gentamicin and N-phenylanthranilic acid (NPAA).107,108 In animals and in humans, increased GST-a levels were reported after treatment with compound A, a nephrotoxic degradation product of sevofluorane.109, "° Increases of both GST-a and GST-TT were associated with treatment with amphotericin B in male patients, but not in female patients.1" In a small study with 26 critically ill patients admitted to the intensive-care unit, of whom four developed AKI, both GST-a and GST-TT showed an AUC of the ROC of 0.9.117In another small study in kidney-transplanted patients, increased levels of GST-a were associated with cyclosporine A nephrotoxicity, whereas increased levels of GST-TT were associated with acute allograft rejection.112 Urinary GST-a has been reported to rise faster than serum creatinine and to be a very sensitive marker when the kidney is exposed to heavy metals."3 In diabetic patients GST-TT but not GST-a levels were correlated with the degree of albuminuria,114 whereas increased levels of GST-a, but only to a small extent increased levels of GST-TT were reported for obese patients with normal serum creatinine."5 In another study with 76 patients undergoing cardiac surgery, increased levels of GST-a and GST-TT were reported in 36 patients developing AKI as defined by the AKIN criteria.116 In conclusion, it can be said that both GST-a and GST-p AIT are promising markers in different clinical contexts. Yet more evidence about this clinical utility, in particular if only one of the different isoforms is evaluated, is needed. In particular the preclinical evaluation with a histopathology anchor is more limited than for other markers. In addition, GSTs are not stable in urine under certain conditions. Whether the recommendation to add stabilizing buffer to urine sample corrections turns out to be a clinical "show-stopper" needs to be seen, in particular when more clinical evidence of the utility of the GSTs and other "concurring" biomarkers may become obvious.

BIOMARKERS OF ACUTE KIDNEY INJURY

251

Urinary NAG Urinary NAG (N-Acetyl-ir-D-glucosaminidase) is a lysosomal brush-border enzyme of 140 kDa with two isoforms (A and B) and is mainly expressed in proximal tubules where its function is the breakdown of glycoproteins. Due to its size, plasma levels of NAG are normally not filtered by the glomeruli and its excretion into urine correlates with increased tubular lysosomal activity, tubular cell injury (leakage), and indirectly with increased proteinuria. NAG has been used for decades as it is stable in urine in constrast to many other urinary enzymes, and due to its specific localization in the proximal tubules. In the context of renal diseases (diabetic and hypertensive nephropathy, focal segmental glomerulosclerosis), AKI, and treatment with nephrotoxic compounds, increased urinary NAG levels have been observed typically before increases of serum creatinine and BUN.117-121 In hospitalized patients, increased NAG levels were associated with an adverse outcome (dialysis or death).122 Yet in the framework of exposure to metals and other nephrotoxicants and increased urinary urea, the activity of the enzyme is inhibited in urine by these molecules, therefore compromising its use.123-125 In addition, increased NAG levels have been reported in a variety of conditions without clinically significant renal injury, such as rheumatoid arthritis and hyperthyroidism.126,127 Finally, NAG has been criticized to be oversensitive in the absence of clinically relevant renal injury.118'128 This means that a broad utility of NAG, despite its high sensitivity under certain conditions, might be limited to a combination with other markers in a panel to compensate for its over-sensitivity, lack of specificity, and observed interferences.

Expression Markers Urinary Kim-1 Kidney injury molecule-1 (Kim-1 in rodents, KIM-1 in humans), which is also referred to as T-cell immunoglobulin mucin - 1 (TIM-1) and hepatitis A virus cellular receptor-1 (HAVCR-1), is expressed in very low levels in the body except on proximal tubular epithelial cells and lymphocytes. Kim-1 is a glycosylated type I cell membrane glycoprotein with a six-cystein immunoglobulin-like domain and a mucin domain in its extracellular region. An attractive characteristic of Kim-1 as a kidney safety biomarker is the fact that modulations of expression levels of Kim-1 in the body have only been reported upon proximal tubular injury (up to several folds induction of mRNA and protein levels) and to a much lower extent in the cochlea after cisplatin treatment.129 Kim-1 mRNA, and subsequently the protein, is expressed during dedifferentiation of proximal tubular epithelial cells. The protein is cleaved and the ectodomain is shed into the urine,130 and has been shown to be stable at room temperature for several hours. In numerous animal studies, the utility of Kim-1 as a biomarker to detect kidney injury has been demonstrated such as protein-overload nephropathy and aging-induced nephropathy,131 ischemiainduced renal injury,132 a model of polycystic kidney disease,133 and kidney injury induced by various nephrotoxicants such as cisplatin, folic acid, TFEC,134

252

BIOMARKERS

cadmium,135,136 contrast agents,137 gentamicin, mercury, chromium,138 ochratoxin A,139 cyclosporine,140 tacrolimus, lithium, furosemide, vancomycin, puromycin, and doxorubicin.240 The utility of Kim-1 as a biomarker to diagnose AKI and CKD in humans —and thus its utility as a translational marker—has been shown in different clinical contexts. In a study of 40 children undergoing cardiac surgery, for which originally NGAL levels were determined (see section about NGAL below), urinary KIM-1 levels could diagnose AKI 12 hours after surgery with an AUC of the ROC of 0.81, whereas rises of serum creatinine were observed between 24 and 72 hours only.141 The diagnostic performance of KIM-1 levels was lower than the diagnostic performance of NGAL, but repeated freezethaw cycles, a long-term storage of the samples between these measurements, and the small sample size may question the relevance of this difference. The same limitations in terms of sample size might apply to another study evaluating urinary KIM-1, NGAL, NAG, cystatin C, IL-18, and a 1-microglobulin in 103 patients undergoing cardiac surgery, with 13% of the patients developing AKI. KIM-1 showed the highest diagnostic performance (AUC 0.78) and was the only marker independently associated with AKI after adjusting for preoperative AKI score. The variance of reported results for the different markers in the context of cardiac surgery followed by AKI demonstrates that there is a pressing need to compile more evidence in different populations and assessing all markers together in these cohorts to obtain comparable evidence of their utility in different clinical contexts. A cross-section study is reported in which urinary KIM-1, MMP-9, and NAG levels of 29 patients with AKI (due to sepsis and hypoperfusion, nephrotoxins, and contrast-induced nephropathy) versus 45 control patients (healthy volunteers, CKD patients and patients with urinary tract infection, UTI) were determined.H1 The AUCs of the ROC were 0.74 for MMP-9, 0.90 for KIM-1, 0.97 for NAG, and 1.0 for all three biomarkers combined. In a study with 201 patients with clinically established AKI, urinary KIM-1 levels and NAG levels correlated with the clinical composite endpoint of death or dialysis requirement, also after adjustment for disease severity and comorbidity.142In a study with non-diabetic renal disease, urinary KIM-1 levels were increased in patients with proteinuria and decreased in those patients who were treated with a renin-angiotensin-aldosterone system inhibitor, sodium restriction, or diuretic therapies. In those patients, KIM-1 correlated with proteinuria decrease, rendering it a potential alternative clinical endpoint.143 The utility of KIM-1 was not only demonstrated as a peripheral marker, but also as a tool to assist the pathologist in evaluating kidney biopsies. For example, increased KIM-1 staining in kidney biopsies of patients with a pathology diagnosis of AKI was reported.144 Also increased KIM-1 staining in biopsies from 102 patients with a variety of kidney diseases was associated with tubulo-interstitial inflammation and fibrosis.145 In biopsies of transplanted kidneys, increased Kim-1 staining was detected in 100% of patients with deterioration of kidney function and pathological changes indicating tubular injury, in 92% of patients with acute cellular rejection, and in 28% of pa-

BIOMARKERS OF ACUTE KIDNEY INJURY

253

tients with normal biopsy readouts, which might indicate a higher sensitivity of KIM-1 staining compared to current standards of histopathology assessment.146 Also, in kidney transplantation, increased tertiles of urinary KIM-1 excretion were prognostic of graft loss.147 In conclusion, Kim-1 is proving to be one of the most promising biomarkers to monitor AKI impacting proximal tubular epithelial cells due to its unique specificity, its sensitivity to detect various forms of tubular injury earlier than current diagnostic standards, its stability, its translatability between different species, and finally thanks to rapidly increasing evidence of its preclinical and clinical utility in numerous contexts. Thus, Kim-1 might have the potential to become the "troponin of the kidney." Urinary Clusterin

Clusterin has a secreted and a nuclear isoform. Only the secreted isoform, which is a 76-80 kDa glycosylated protein with extensive post-translational modifications, is considered relevant in the context of kidney injury. During early stages of renal development it is highly expressed, but later only in the case of injury to proximal and distal tubules. Secreted clusterin has been suggested to play an anti-apoptotic role, to be involved in cell protection, lipid recycling, cell aggregation, and cell attachment.148 Clusterin gene over-expression was induced by different types of kidney injury in glomeruli, tubules, and papilla of rats and dogs as a result of drug nephrotoxicity,149-151 surgery and ischemia,152-155 and in animal models of different renal diseases.156 Changes of protein levels of clusterin have been observed in kidneys and in the urine of some of these animal studies.151,152,155'156 In human there are very limited and non-conclusive data available.157,158 In the PSTC regulatory qualification of biomarkers, urinary clusterin has proven to be a powerful diagnostic biomarker to monitor proximal tubular injury and regeneration with a performance nearly as good as urinary Kim-1 in rat studies (see section "Consortia achieving the first regulatory qualification of kidney safety biomarkers"). Urinary NGAL

Neutropil gelatinase-associated lipocalin (NGAL), which is also known as human neutrophil lipocalin, lipocalin-2, siderocalin, 24p3, or LCN2, is a 25kDa protein initially identified bound to gelatinase in specific granules of the neutrophil. It is expressed in various tissues at low levels, but induced in epithelial cells upon inflammation or other types of injury.159-16C In mouse models, strongly increased mRNA levels of NGAL and increased protein levels in the kidney and urine were seen shortly after cisplatin administration and renal ischemia.161' 162NGAL has been associated with an antiapoptotic and antioxidative role. In particular for ischemia-related AKI, NGAL could be translated into a sensitive human peripheral biomarker with increased levels upon injury measurable in blood and in urine. One of the most-cited studies of NGAL is a cardiopulmonary bypass study in children in which AKI occurred in 28% of the patients defined by a serum creatinine rise one to three days

254

BIOMARKERS

after operation. Urinary and serum NGAL level elevations measured only a few hours after surgery predicted AKI with a sensitivity and specificity close to 100%.163 In a second similar study in adults, increased urinary NGAL levels were observed though at lower specificity, which might be attributed to different co-factors.164 For trauma patients, urinary NGAL could differentiate between patients developing AKI and patients not developing AKI with high sensitivity and specificity.165 Urinary and plasma NGAL have also predicted contrast-induced AKI with a high diagnostic power two hours after contrast administration166-167,168 and similar to IL-18, increased urine levels collected the day after kidney transplantation could predict recipients with subsequent delayed graft function and who needed dialysis.226 Modulations of NGAL have also been reported for a number of kidney diseases. For example, increased serum and plasma NGAL levels were reported in patients with polycystic kidney disease. In addition, patients with higher cystic growth had higher urinary and plasma levels than patients with lower cystic development.169 In CKD, there is less coherence between urinary and blood levels of NGAL than in AKI settings, which might be explained by the mechanism of NGAL processing in the kidney. For example, in patients affected by IgA nephropathy, higher grade patients showed increased urinary NGAL levels but normal blood levels.170 Also, patients with idiopathic glomerulonephritis had increased urinary NGAL levels, which were correlated with the extent of proteinuria and urinary levels predicting worsening of renal function in patients for one year follow-up.171' 172In a recent study, patients with diabetic nephropathy showed increased serum and urinary NGAL levels even before other clinical signs such as albuminura, and the levels correlated with severity of disease.173 The role of NGAL in proteinuria-related CKD may be explained by the mechanism of how NGAL is processed in the kidney, and which also might raise some concerns in terms of specificity. NGAL is filtered by the glomeruli and under physiological conditions nearly completely reabsorbed in the promixal tubule by binding to the protein transporter complex cubilin-megalin. In the case of proteinuria, the non-specific binding of urinary proteins to this complex can lead to a saturation of the re-absorption capacity leading to increased urinary levels of NGAL. In addition, increased protein loads could lead to increased expression and release of NGAL as defensive mechanisms, as also shown for other "tubular stress" proteins such as Kim-1.174 As a consequence, conditions which lead to a saturation or impairment of the re-absorption complex or which lead to highly increased plasma levels due to non-renal expression of NGAL, could cause increases of urinary NGAL levels. Since NGAL can be expressed in different tissues and organs upon injury, such as muscle injury and liver injury, further mechanistic studies are proposed to investigate potential limitations of specificity of NGAL. Despite the excellent diagnostic performance of NGAL for ischemia-related AKI, the effect of drug-induced injury on the de novo expression of NGAL in kidney and on the impairment of tubular NGAL re-absorption might prove of high practical utility for use of NGAL useful as a kidney biomarker in the context of drug-development.

BIOMARKERS OF ACUTE KIDNEY INJURY

255

Urinary Osteoactivin Following discovery of osteoactivin (OA) in a rat model of osteopetrosis, OA has been shown recently to exist in transmembrane and glycosylated secreted isoforms acting as an anabolic factor regulating osteoblast differentiation and function.175 OA was initially identified as a transmembrane type I glycoprotein non-melanoma b in low metastatic human melanoma cell lines and is also referred to as gpnmb.176 OA was also identified in murine dendritic cells, termed cell heparan sulfate proteoglycan integrin dependant ligand (DC-HIL) and in human hematopoietic cells, termed growth factor inducible neurokinin (HGFIN). Additional studies suggest roles for OA as a receptor, ligand, or enzyme in regulating fibroblast differentiation and cancer metastasis, and attenuating degeneration induced in muscle, liver, and kidney.177 OA mRNA and protein expression has been shown to be robustly upregulated in ischemic rat kidney injury, localized to tubular epithelium and interstitial fibroblasts, suggesting a role for OA as an early trigger for renal interstitial fibrosis.178 OA has also been shown to be upregulated in the livers of a rat hepatic cirrhosis model, further suggesting a role of the protein in the pathogenesis of fibrosis.179 While at an early stage of evaluation, this promising biomarker's appearance in urine may prove to be a sensitive and insightful indicator of yet another unique aspect of the dynamic and complex renal injury response to acute kidney injury. Urinary OA may prove specific and useful for monitoring initial injury and continued kidney progression toward fibrosis if further studies conducted in animals and humans can demonstrate that acute damage to the liver and muscle do not yield OA secretions that will spill over from the blood compartment into the urine. Urinary Osteopontin Osteopontin is also known as secreted phophoprotein I (SPP1), 44kD bone phophoprotein, sialoprotein I, uropontin, and early T-lymphocyte activation-2 (Eta-1). It is synthesized at the highest levels in bone and epithelial tissues, but also expressed in macrophages, activated T cells, smooth muscle cells, and endothelial cells and it is widely distributed in normal adult human tissues, such as bone matrix, kidney, epithelial cells of gastrointestinal tract, gall bladder, pancreas, urinary and reproductive tracts, lungs, breasts, salivary glands, brain, arteries, urine, and milk.180 It plays a role in the regulation of osteoclast function during bone formation, tuomorigenesis, accumulation of marcrophages, and in the kidney in the protection versus NO, oxidative stress, and ischemia, and is also involved in regeneration processes. Osteopontin has also been associated with kidney stones, but it is unclear if it promotes or inhibits the formation of kidney stones.180'181 In the normal mouse, rat, and human kidneys, osteopontin is expressed in the thick ascending limbs of the loop of Henle and the distal convoluted tubules.182 Increased levels of osteopontin mRNA expression and protein expressions in the kidney have been observed in a number of diseases such as human progressive idiopathic membranous nephropathy, IgA neprhitis, lupus

256

BIOMARKERS

nephritis, cresentric glomerulonephritis, but also renal cell carcinoma. Similarly, increased mRNA levels and protein levels in kidneys of different animal models of renal injury have been reported, such as gentamicin, cisplatin, mercury chloride, cyclosporine, sevoflurane, angiotensin II, puromycin, bacitracin, ochratoxin, vancomycin, para-aminophenol, anti-thy-1 nephritis, unilateral urethral obstruction, and remnant kidneys in 5/6 nephrectomy-induced kidney injury.183-195 Although osteopontin has proven to be a very sensitive indicator of different forms of renal injury on a gene expression and a localized protein expression level, its value as a peripheral measurable urinary protein biomarker needs to be proven. The quantification of urinary osteopontin is complicated by the fact that in the kidney phosporylated and non-phosphorylated forms of osteopontin are secreted and that different fragments in urine are found, which are differently modulated by diseases, such as IgA nephritis.196 As recently commercially available protein assays for different platforms have been made available for the quantitation of osteopontin in the urine of rats, mice, and humans, it is expected that more evidence of the utility of osteopontin as a peripheral marker for detecting AKI will be generated soon. Urinary L-FABP Liver-type fatty acid-binding protein (L-FABP) is a 14-kDa protein normally expressed in the proximal tubules in the kidney.197 Cytoplasmic L-FABP in the proximal tubules bind free fatty acids, which are then transported to mitochondria or peroxisomes and metabolized there.198 Increased urinary levels of L-FABP have been associated with different types of kidney injury, in particular ischemia-related injuries, chronic kidney diseases, and some types of drug-induced acute kidney injury. Publications about the utility of L-FABP to detect drug-induced AKI include cisplatin, where urinary L-FABP was increased within 24 hours in contrast to serum creatinine (no increases until 72 hours) in mice and contrast-induced AKI, where increased urinary L-FABP levels also preceded increased serum creatinine levels, and after administration of cephaloridine urinary L-FABP levels indicated drug-induced renal injury seen in the absecene of rises in serum creatinine and BUN.199-201 In septic shock patients, only urinary L-FABP among a number of parameters showed a correlation with survival.202 Ischemia-related kidney injuries with subsequent increases of urinary L-FABP have been reported in the context of cardiac surgery where increases of L-FABP preceded increases of serum creatinine by a few days, and in kidney transplantation where increased urinary L-FABP levels were correlated with ischemia time and with peritubular capillary blood flow of the transplanted organ.203-204 In the context of chronic kidney diseases, increased urinary L-FABP levels have been identified in a number of studies such as non-diabetic chronic kidney disease, polycystic kidney disease, early diabetic nephropathy, and idiopathic focal glomerulosclerosis.205-209 Since L-FABP is predominantly expressed in the liver, urinary levels of L-FABP might not be specific to kidney injury and kidney diseases, but might be influenced also by changes of serum levels of L-FABP, if the proximal tubular re-absorption of the freely filtered L-FABP is saturated. Yet, until now no

BIOMARKERS OF ACUTE KIDNEY INJURY

257

non-renal injury or condition has been reported, which caused highly increased levels as described for the renal injuries (several fold). Some studies investigating urinary L-FABP levels for CKD patients with impaired renal reabsortion capabilities show only a low contribution of serum L-FABP levels to urinary levels.210-211 With the availability of commercial assays for measuring L-FABP in different species, it is expected that further data about the utility of L-FABP for detecting AKI, as well as possible limitations in terms of specificity, will become available soon. Urinary Trefoil Factor 3 Trefoil factors are small proteins secreted by mucus-producing epithelial cells. They are believed to play a role in mucosal surface homeostasis and protection against injury, possibly by inhibiting apoptosis and promoting epithelial cell differentiation and mucin secretion.212-215 Trefoil Factor 3 (TFF3) is reported to be widely distributed to human pancreas, brain, respiratory tract, gall-bladder, bile ducts, salivary glands, and gastrointestinal tract (ref 1). Rat kidney is also a major site of TFF3 expression.216 Recently217 in situ hybrization results in rat kidney have localized TFF3 mRNA to abundant tubules of the outer stripe of the outer medulla, a site enriched for proximal straight tubules, while histochemical localization data has detected TFF3 binding sites in the collecting ducts of the rat kidney,2'8 suggesting an important role of TFF3 for communication between cells of the proximal tubule and the downstream collecting duct. The discovery of a profound decrease in tissue and urinary TFF3 mRNA and protein levels, respectively in response to numerous kidney tubular toxicants has been made only recently in rats217 and its relevance to humans remains to be evaluated. The specific stimulus that regulates TFF3, in the context of acute renal tubular injury, is unknown. Results from studies in gastrointestinal models indicate that TFF3 is regulated by inflammatory cytokines,219' 22° suggesting that acute reduction of TFF3 may be mediated by inflammatory cytokines produced during the course of renal tubular injury. Further work is required to investigate the potential utility of TFF3 as a translational biomarker beyond the rat into other test species and its relative value in humans for reflecting events associated with cytokine signaling in monitoring drug induced and other causes of acute renal injury.

Immune Markers Urinary IL-18 Interleukin 18 (IL-18) is a proinflammatory cytokine that is converted from its pro-form to the active form by the intracellular cystein protease capsase-1.221 It is involved in inflammation, ischemic tissue injury, and T-cell mediated immunity and plays an important role in the activation of macrophages and natural killer cells.222223 In the kidney it is induced and cleaved mainly in the proximal tubules and released into the urine under different conditions of kidney injury: In mechanistic studies of mice models, it was been shown that IL-18 is a mediator of ischemic AKI in mice and with increased kidney and urinary protein

258

BIOMARKERS

levels as biomarker ischemia-related AKI.222,224 In patients, the utility of IL-18 to detect ischemia-related AKI has been shown in children undergoing cardiopulmonary bypass. Hereby increased IL-18 levels raised at four to eight hours after operation and remained increased for the next 40 hours, whereas serum creatinine detected AKI only 48 to 72 hours after cardiopulmonary bypass operation.225 Also in the context of kidney transplantation, increased IL-18 levels in the first 24 hours post-transplant were highly predictive of delayed graft function and of the need for dialysis.226 Also, increased mRNA levels in kidney biopsies of patients with acute kidney allograft rejection were reported.227 Two studies demonstrate a significant value of IL-18 as a biomarker to detect AKI: In a cross-sectional study, urinary IL-18 levels were significantly increased in patients with established AKI, but not in patients with urinary tract infection, CKD, nephritic syndrome, or prerenal failure, making it an ideal marker on a biomarker panel to differentiate different conditions, which can lead to increases of serum creatinine and other kidney biomarkers.228 In a study investigating the potential of IL-18 as an early marker of AKI in acute respiratory distress syndrome patients, IL-18 predicted AKI 24 hours before serum creatinine and was also predictive in mortality independent of severity scores of the illness, serum creatinine, and urine output. Yet, evidence of the utility of IL-18 as a biomarker to predict or diagnose AKI in other conditions, such as drug-induced kidney injury, has not been generated. More seriously, the utility of IL-18 has been challenged in several studies under conditions when increased IL-18 levels were expected, such as AKI in cardiac surgery in adults or contrast-induced nephropathy, despite other positive reports in these settings.229-232 As the underlying factors and reasons for these contradictions are currently unknown, interpretations of IL-18 should be made with caution and it is recommended to collect as much information about the studies, co-factors, and technical details together with the generated biomarker data to compile enough evidence about the utility and limitations of this promising kidney biomarker.

NEWEST TECHNOLOGIES AND A C H I E V E M E N T S A R O U N D K I D N E Y SAFETY BIOMARKERS Assays and Technologies The recent advances in the discovery and evaluation of kidney safety biomarkers have also stimulated the development of new assays as well as new technologies for the measurement of these biomarkers. The traditionally used tubular urinary enzymes (AP, AAP, G-GT, NAG) are typically spectrophotometrically measured enzyme-substrate-based colorimetric activity assays. With the new urinary and plasma protein biomarkers, ELISA assays have became the new workhorse for accurate quantitation. ELISA assays are based on the detection of an analyte with one or two epitopically distinct antibodies (depending on the assay format such as direct, indirect, sandwich, or com-

BIOMARKERS OF ACUTE KIDNEY INJURY

259

petitive assays) whereby one antibody is linked to an enzyme to generate a chromogenic, fluorogenic, or electrochemical detectable signal. A number of ELISA assays for the measurement of kidney biomarkers for human and several animal species have become commercially available, such as GST-a, clusterin, or NGAL.233"235 Elisa assays also have the advantage that no exotic hardware and training are required rendering them broadly usable, e.g., in hospital labs. Yet, ELISA assays also have certain drawbacks, in particular with respect to throughput and multiple-biomarker measurements. In particular a result is available only after several hours, a direct multiplexing of measurements is not supported (measurement of several biomarkers in the same sample/well), and the volume of sample needed can be limiting in nonclinical studies (typically 0.1-0.3 ml per biomarker). To overcome these limitations, kidney biomarker assays have been developed for two automated, user-friendly, high-throughput technologies, such as the Luminex® and the MesoScale Discovery® platforms. The principle of Luminex® xMAP® technology lies on the capture of antibodies conjugated to the surface of color-coded microspheres (beads), which react with specific antigens present in the sample similarly to sandwich assays. Then, detecting antibodies labeled with a fluorescent reporter molecule bind in proportion to the captured antigen. The quantification is performed by passing the suspended microspheres (beads) through the detection chamber of a flow-cytometer. A green laser detects the amount of analytes bound to the beads and a red laser identifies the color-coded beads (the nature of the target). Up to 100 colorcodes exist, which allow a theoretical multiplexing of up to 100 simultaneous assays, whereby only 10 jul sample volume is needed. For the Luminex technology, rules based medicine markets most of the kidney biomarkers mentioned in this chapter (rat and human) as well as kits (rat) for home use.236 The electrochemiluminescence-based MesoScale Discovery® assays also rely on the sandwich immunoassay format. Here, the antibodies are immobilized on planar arrays in microplate format and the readout is the light signal emitted by an electrochemiluminescence reaction. Each well is equipped with a working electrode and a counter electrode generating an electrical circuit, which initiates the electrochemical stimulation of the ruthenium-labeled detection antibodies, resulting in the emission of light. Different formats of plates are available. Multi-spotting up to 10 spots in 96-well format and up to 100 spots in 24-well format allows multiplexing.237 MesoScale Discovery® also offers a number of kidney safety biomarkers multiplexed on different plates for rat and for human. For both technologies, it is also possible for a customer to develop new ELISA assays and to transfer these and other existing ELISA assays onto these platforms. Whereas the MesScale Discovery® and Luminex® assays need specific platforms, which are not necessarily available in every clinical laboratory of medium-size and smaller hospitals, the transfer of biomarker assays on standardized clinical platforms ensures a broad availability of the assays in clinical units as recently reported for the urinary NGAL assay (ARCHITECT® analyzer from Abbott Diagnostics).238 Alternatively, the development of point-

260

BIOMARKERS

of-care devices will facilitate the distribution of the kidney biomarkers into smaller hospital units and to doctors' offices. An early example is the transfer of the serum NGAL assay onto the Triage® NGAL Device, from Biosite Incorporated. The device is deployable directly to the point of patient care and a measurement requires only micro liter quantities of whole blood or plasma and delivers results in approximately 15 minutes.238 Finally, the recent work by Vaidya and coworkers to develop a diagnostic dipstick for an easy Kim-1 quantitation (rat and human) is an exciting development in the direction of expanding the convenience of using the new kidney biomarkers for rapid diagnostic purposes. The dipstick test allows a visual readout within a few minutes and can be used directly by the patient. Current limitations are the absolute quantitation of Kim-1 by normalizing to urinary creatinine. At an attractive price, this dipstick test might not only revolutionize screening for renal injury in patients, but also animal studies in drug development due to its simple application without the need for complicated dedicated laboratory equipment.

C o n s o r t i a Achieving the First Regulatory Q u a l i f i c a t i o n of Kidney Safety Biomarkers The stagnation of medical product development has been pointed out in a widely recognized report from the U.S. Food and Drug Administration (FDA) 2004 entitled "Challenge and Opportunity on the Critical Path to New Medical Products."239 This signaled the start of the FDA Critical Path Initiative, aimed at increasing the awareness of the need for collaboration in particular for opportunities that imply considerable resources non-achievable for single entities (regulatory authorities, single companies, universities, or other government agencies) such as organ safety biomarker qualifications. As one of the first projects of the Critical Path Initiative, the C-Path Institute was founded in 2005 as neutral ground and a catalyst for programs. The initial project hosted by the C-Path Institute, the Predictive Safety Testing Consortium (PSTC), brought 16 pharma and companies, one patient organization, and advisors from academic institutions, the FDA, and the European Medicines Agency (EMA) to exchange data and methodologies with the goal to qualify organ safety biomarkers for regulatory decision making in preclinical, translational, and clinical contexts. Until recently, there had been no clear path forward on how organ safety biomarkers, such as the kidney safety biomarkers, could be qualified for regulatory decision making in drug development, such as adapting dosing regimens or stratifying patients for treatment. All new biomarkers, such as the kidney safety biomarkers, were considered as exploratory biomarkers for internal decision making only, despite evidence of superiority compared to the current BUN and serum creatinine standards, which had never been formally qualified but were the only accepted measures for kidney safety. The companies recognized the opportunity of collaboration under the PSTC and collected preclinical and clinical evidence for the utility of seven kidney safety biomarkers (urinary cystatin C, (32-microglobulin, Kim-1, clusterin, TFF3, albumin, and total protein). These data and claims about their performance relative to

BIOMARKERS OF ACUTE KIDNEY INJURY

261

BUN and serum creatinine, together with claims about the intended use of these biomarkers, were submitted to the FDA and EMA for formal regulatory qualification and approval for specific contexts in June 2007. In May 2008, the decision by the EMA and FDA for the acceptance of these biomarkers for use in specific preclinical contexts and for translational contexts on a case-by-case basis was published. Following the FDA and EMA first ever approval of new renal safety biomarkers, the first level of usefulness of these new markers in drug development is expected to be translational between toxicology studies and the first safety trials in healthy subjects and patients.240241 With the formal qualification of these seven biomarkers and with the availability of validated assays, it is anticipated that their preclinical and clinical use in drug development programs will increase further supplementing the nonclinical and clinical evidence of their utility, but also further highlighting their limitations. Future clinical use of these biomarkers will include the early identification of adverse renal effects of potentially nephrotoxic drugs, patient and medication selection, as well as punctual intervention for an optimized therapy on an individual basis, often also referred to as personalized medicine. Also, routine clinical care beyond drug development will profit from this expected exponentially growing use of these renal safety biomarkers, such as diagnosing and staging diseases as well as AKI. In May 2008, the HESI Development and Application of Biomarkers of Toxicity Technical Committee also submitted preclinical data for the urinary biomarkers RPA-1, GST-a and GST-(JL, and clusterin to the FDA and EMA as part of the biomarker qualification review process, demonstrating evidence of superior diagnostic performance of site-specific injury of some of these novel markers (e.g., GST-a and RPA-1) relative to reference markers in the nonclinical setting.242 An acceptance of the preclinical use of some of these biomarkers by the health authorities will further increase the toolbox of biomarkers accepted for preclinical contexts. Also, in Europe a public-private consortium was formed under the Innovative Medicine Initiative (IMI) in 2009 with the goal of accumulating clinical evidence, developing assays, and obtaining regulatory acceptance for biomarkers to monitor kidney, liver, and vascular safety in translational and clinical contexts. This consortium, called SAFE-T (Safer and Faster Evidence-based Translation), consists of 11 pharmaceutical companies, four small-medium enterprises, five academic institutions, and the EMA as founding members, and has a research budget of 34 million Euro for five years. SAFE-T and PSTC will jointly accumulate enough critical evidence for the utility and limitations of organ safety biomarkers, and in particular for kidney safety biomarkers, to obtain regulatory acceptance as well as broad scientific consensus for their utility in clinical contexts.

CONCLUSION AKI is not an uncommon event in routine clinical care and in drug development and becomes a devastating situation due to the fact that current diagnos-

262

BIOMARKERS

tic standards are late, insensitive, and cannot localize the injury in the kidney. Recently, a considerable number of promising new biomarker candidates have been proposed, which are not only more sensitive than the current diagnostic standards in different preclinical and clinical contexts, but sometimes are also site-specific and injury type-specific. Most of these biomarkers are not only useful in humans but also in different preclinical test species, which is of crucial importance when translating potentially nephrotoxic compounds into first in man trials in pharmaceutical drug development. Until recently the use of the new biomarkers in preclinical and clinical studies in drug development has been hindered by the lack of regulatory acceptance of these new biomarkers by the health authorities. In 2008, the first regulatory qualification of seven kidney safety biomarkers for preclinical use and for translational use on a case-by-case basis by EMA and FDA opened the door of safety biomarkers to drug development and will help to obtain more preclinical and translational evidence of the utility and limitations of the kidney biomarkers. For most of the kidney biomarkers, significantly more clinical evidence about their utility and limitations in different clinical contexts is needed before a regulatory qualification for wider clinical contexts will be achieved to enable their broad use in clinical trials. The increasing evidence of their utility will also promote their use outside of drug development, such as standard clinical care and diagnosis of kidney diseases. Differences of performances of single biomarkers, or differences of relative performances between biomarkers reported for different studies but in the same clinical context, clearly indicate a pressing need of large studies with assessments of not only one but of a whole panel of kidney biomarkers implemented. Such studies will assess the comparable utility of different biomarkers for certain clinical contexts, demonstrating the opportunities and limitations of each biomarker. This will enable understanding of which biomarkers should be combined on a panel for diagnosing kidney injury and disease in specific clinical contexts, and ultimately for developing decision algorithms for a panel of biomarkers. A first step in this direction was recently published for the diagnostic evaluation of nine urinary kidney biomarkers (Kim-1, NGAL, IL-18, HGF, cystatin C, NAG, VEGF, CXCL10, and total protein) for the cross-sectional comparison of 204 patients with and without AKI.243 The study revealed that different biomarkers showed different sensitivity for AKI but also different specificity versus the different control groups. Ultimately, the best diagnostic performance for the diagnosis of AKI was obtained by a logistic regression model which included NGAL, HGF, total protein and Kim-1 levels. Other studies including several promising biomarker candidates for preclinical and clinical contexts have been published for AKI after cardiopulmonary bypass244 and for different nephrotoxicants in preclinical studies.135,240 These studies will help to connect publications of small studies of single biomarkers by generating broader evidence of utility and limitations of these biomarkers, obtaining regulatory buy-in, supporting the development of biomarker technologies, and promoting their use in drag development and in daily clinical care. Compared to other organs, new biomarkers to monitor kidney safety are more advanced and their preclinical and

BIOMARKERS OF ACUTE KIDNEY INJURY

263

clinical application will expand exponentially in the coming years, completely changing the management of renal safety in drug development and clinical care.

SUMMARY P O I N T S 1. 2.

3.

4.

5.

Acute kidney injury is a common event in intensive care and can be a limiting factor in drug development. Current peripheral standards to detect kidney injury, blood urea nitrogen, and serum creatinine assess renal function and not kidney injury. Therefore these markers are late and insensitive and detect kidney injury only, when up to two thirds of the functional nephron mass has been lost. Therefore, AKI remains a clinical situation with a high mortality, despite advances in clinical care. A number of promising new kidney biomarker candidates have been proposed that monitor the function, integrity of different compartments of the kidney, and various molecular and biological processes induced by renal injury. Numerous publications demonstrate their utility (e.g., detecting AKI earlier than serum creatinine) and limitations in different preclinical and clinical contexts of detecting and prognosing AKI. Future research needs to be directed in compiling more coherent and comparative clinical evidence about the utility and limitations of new kidney biomarkers evaluated together in the same large studies for specific clinical contexts. In addition, technologies to measure several biomarkers in small laboratories and hospital units and simple bedside devices need to be developed. Already, assays for measuring the new biomarkers and the acceptance of seven biomarkers for preclinical and translational drug development contexts by EMA and FDA offer a unique toolset to manage kidney safety in drug development and in routine clinical care.

REFERENCES 1. 2. 3. 4. 5.

Wu, I. N. S. and Parikh, C. R. Screening Dor Kidney Diseases: Older Measures versus Novel Biomarkers. Clin. J. Am. Soc. Nephrol. Nov 2008;3(6): 1895-1901. Meguid, E. L., Nahas, A., and Bello, A. K. Chronic Kidney Disease: The Global Challenge. Lancet. 2005;365(9456):331-340. Hou, S. H., Bushinsky, D. A., Wish, J. B., Cohen, J. J., and Harrington, J. T. Hospital-Acquired Renal Insufficiency: A Prospective Study. Am. J. Med. 1983;74(2):243-248. Shusterman, N., Strom, B. L., Murray, T. G., Morrison, G., West, S. L., and Maislin, G. Risk Factors and Outcome of Hospital-Acquired Acute Renal Failure. Clinical Epidemiologic Study. Am. J. Med. 1987;83(1):65-71. Liangos, O., Wald, R., O'Bell, J. W., Price, L., Pereira, B. J., and Jaber, B. L. Epidemiology and Outcomes of Acute Renal Failure in Hospitalized Patients: A National Survey. Clin. J. Am. Soc. Nephrol. Jan 2006;1(1):43-51.

264

BIOMARKERS 6. 7. 8.

9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24.

Nash, K., Hafeez, A., and Hou, S. Hospital-Acquired Renal Insufficiency. Am. J. Kidney Dis. 2002;39(5):930-936. Chertow, G. M., Levy, E. M., Hammermeister, K. E., Grover, F, and Daley, J. Independent Association between Acute Renal Failure and Mortality Following Cardiac Surgery. Am. J. Med. 1998;104(4):343-348. De Mendonca, A., Vincent, J. L., Suter, R M., Moreno, R., Dearden, N. M., Antonelli, M., Takala, J., Sprung, C , and Cantraine, F. Acute Renal Failure in the ICU: Risk Factors and Outcome Evaluated by the SOFA Score. Intensive Care Med. 2000;26(7):915-921. Trof, R. J., Di Maggio, F, Leemreis, J., and Groeneveld, A. B. Biomarkers of Acute Renal Injury and Renal Failure. Shock. 2006;26(3):245-253. Mehta, R. L., Pascual, M. T., Soroko, S., Savage, B. R., Himmelfarb, J., and Ikizler, T. A., et al. Spectrum of Acute Renal Failure in the Intensive Care Unit: The PICARD Experience. Kidney Int. 2004;66(4):1613-1621. Radhakrishnan, J. and Kiryluk, K. Acute Renal Failure Outcomes in Children and Adults. Kidney Int. 2006;69(1):17-19. Ympa, Y. P., Sakr, Y., Reinhart, K., and Vincent, J. L. Has Mortality From Acute Renal Failure Decreased? A Systematic Review of the Literature. Am. J. Med. 2005;118(8):827-832. Bellomo, R. The Epidemiology of Acute Renal Failure: 1975 versus 2005. Curr. Opin. Crit. Care. Dec 2006;12(6):557-560. Levy, E. M., Viscoli, C. M., and Horwitz, R. I. The Effect of Acute Renal Failure on Mortality. A Cohort Analysis. JAMA. 1996;275(19):1489-1494. Parikh, C. R., McSweeney, P., and Schrier, R. W. Acute Renal Failure Independently Predicts Mortality After Myeloablative Allogeneic Hematopoietic Cell Transplant. Kidney Int. 200;67(5): 1999-2005. Waikar, S. S., Curhan, G. C , Wald, R., McCarthy, E. P., and Chertow, G. M. Declining Mortality in Patients with Acute Renal Failure, 1988 to 2002. J. Am. Soc. Nephrol. Apr 2006;17(4): 1143-1150. Mehta, R. L., Kellum, J. A., Shah, S. V, Molitoris, B. A., Ronco, C , Warnock, D. G., and Levin, A. et al. Acute Kidney Injury Network: Report of an Initiative to Improve Outcomes in Acute Kidney Injury. Crit. Care. 2007;11(2):R31. Thadhani, R., Pascual, M., and Bonventre, J. V. Acute Renal Failure. N. Engl. J. Med. 1996;334(22): 1448-1160. Vaidya, V. S., Ferguson, M. A., and Bonventre, J. V. Biomarkers of Acute Kidney Injury. Annu. Rev. Pharmacol Toxicol. 2008;48:463^193. Zhang, L., Wang, M., and Wang, H. Acute Renal Failure in Chronic Kidney Disease—Clinical and Pathological Analysis of 104 Cases. Clin. Nephrol. May 2005;63(5):346-350. Zuk, A., Bonventre, J. V, Brown, D., and Matlin, K. S. Polarity, Integrin, and Extracellular Matrix Dynamics in the Postischemic Rat Kidney. Am. J. Physiol. 1998;275:C711-31. Zuk, A., Bonventre, J. V., and Matlin, K. S. Expression of Fibronectin Splice Variants in the Postischemic Rat Kidney. Am. J. Physiol. Renal Physiol. 2001; 280(6):F1037-1053. Bonventre, J. V. and Zuk, A. Ischemic Acute Renal Failure: An Inflammatory Disease? Kidney Int. 2004;66(2):480-485. Bonventre, J. V Dedifferentiation and Proliferation of Surviving Epithelial Cells in Acute Renal Failure. J. Am. Soc. Nephrol. 2003;14 Suppl LS55-61.

BIOMARKERS OF ACUTE KIDNEY INJURY 25. 26. 27.

28. 29. 30. 31.

32. 33. 34. 35. 36.

37.

38. 39. 40. 41.

265

Vaidya, V. S., Shankar, K., Lock, E. A., Bucci, T. J., and Mehendale, H. M. Role of Tissue Repair in Survival From S-( 1,2-Dichlorovinyl)-L-Cysteine-Induced Acute Renal Tubular Necrosis in the Mouse. Toxicol. Sci. 2003;74(l):215-227. Lameire, N. and Hoste, E. Reflections on the Definition, Classification, and Diagnostic Evaluation of Acute Renal Failure. Curr. Opin. Crit. Care. 2004;10(6): 468-475. Bellomo, R., Ronco, C , Kellum, J. A., Mehta, R. L., and Palevsky, P. Acute Dialysis Quality Initiative Workgroup. Acute Renal Failure—Definition, Outcome Measures, Animal Models, Fluid Therapy and Information Technology Needs: The Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit. Care. Aug 2004;8(4):R204-212. Endre, Z. H. and Westhuyzen, J. Early Detection of Acute Kidney Injury: Emerging New Biomarkers. Nephrology. (Carlton). 2008;13(2):91-98. Uchino, S., Bellomo, R., Goldsmith, D., Bates, S., and Ronco, C. An Assessment of the RIFLE Criteria for Acute Renal Failure in Hospitalized Patients. Crit. CareMed. 2006;34(7): 1913-1917. Bosch, J. P. Renal Reserve: A Functional View of Glomerular Filtration Rate. Semin. Nephrol. 1995;15(5):381-385. Herrera, J. and Rodriguez-Iturbe, B. Stimulation of Tubular Secretion of Creatinine in Health and in Conditions Associated with Reduced Nephron Mass. Evidence for a Tubular Functional Reserve. Nephrol. Dial. Transplant. 1998;13(3):623-629. Jacobs, S. C , Ramey, J. R., Sklar, G. N., and Bartlett, S. T. Laparoscopic Kidney Donation From Patients Older Than 60 Years. J. Am. Coll. Surg. 2004; 198(6):892-897. Fehrman-Ekholm, I., Duner, F., Brink, B., Tyden, G., and Elinder, C. G. No Evidence of Accelerated Loss of Kidney Function in Living Kidney Donors: Results From a Cross-Sectional Follow-up. Transplantation. 2001;15;72(3):444-449. Coca, S. G. and Parikh, C. R. Urinary Biomarkers for Acute Kidney Injury: Perspectives on Translation. Clin. J. Am. Soc. Nephrol. 2008;3(2):481^190. Price, M. Comparison of Creatinine Clearance to Inulin Clearance in the Determination of Glomerular Filtration Rate. J. Uml. 1972;107(3):339-340. Gaspari, R, Mosconi, L., Vigano, G., Perico, N., Torre, L., Virotta, G., Bertocchi, C , Remuzzi, G., and Ruggenenti, P. Measurement of GFR With a Single Intravenous Injection of Nonradioactive Iothalamate. Kidney Int. 1992;41(4): 1081-1084. Prueksaritanont, T., Chen, M. L., and Chiou, W. L. Simple and Micro HighPerformance Liquid Chromatographic Method for Simultaneous Determination of P-Aminohippuric Acid and Iothalamate in Biological Fluids. J. Chromatogr. 1984;306:89-97. Boschi, S. and Marchesini, B. High-Performance Liquid Chromatographic Method for the Simultaneous Determination of Iothalamate and O-Iodohippurate. J. Chromatogr. 1981;224(1):139-143. Back, S. E., Krutzen, E., and Nilsson-Ehle, P. Contrast Media As Markers for Glomerular Filtration: A Pharmacokinetic Comparison of Four Agents. Scand. J. Clin. Lab Invest. 1988;48(3):247-253. Brbchner-Mortensen, J., Giese, J., and Rossing, N. Renal Inulin Clearance versus Total Plasma Clearance of 51Cr-EDTA. Scand. J. Clin. Lab. Invest. 1969; 23(4):301-305. Stevens, L. A. and Levey, A. S. Measurement of Kidney Function. Med. Clin. North Am. 2005;89(3):457^t73.

266

BIOMARKERS 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53.

54.

55. 56. 57. 58. 59.

Tomlanovich, S., Golbetz, H., Perlroth, M., Stinson, E., and Myers, B. D. Limitations of Creatinine in Quantifying the Severity of Cyclosporine-Induced Chronic Nephropathy. Am. J. Kidney Dis. 1986;8(5):332-337. Takubo, T., Kato, T., Kinami, J., Hanada, K., and Ogata, H. Effect of Trimethoprim on the Renal Clearance of Lamivudine in Rats. / Pharm. Pharmacol. 2000;52(3):315-320. Ixkes, M. C , Koopman, M. G., Van Acker, B. A., Weber, J. A., and Arisz, L. Cimetidine Improves GFR-Estimation by the Cockcroft and Gault Formula. Clin. Nephrol. 1997;47(4):229-236. Moran, S. M. and Myers, B. D. Course of Acute Renal Failure Studied by a Model of Creatinine Kinetics. Kidney Int. 1985;27(6):928-937. Mussap, M. and Plebani, M. Biochemistry and Clinical Role of Human Cystatin C. Crit. Rev. Clin. Lab Sci. 2004;41(5-6):467-550. Takuwa, S., Ito, Y, Ushijima, K., and Uchida, K. Serum Cystatin-C Values in Children by Age and Their Fluctuation During Dehydration. Pediatr. Int. 2002;44(1):28-31. Risch, L., Herklotz, R., Blumberg, A., and Huber, A. R. Effects of Glucocorticoid Immunosuppression on Serum Cystatin C Concentrations in Renal Transplant Patients. Clin. Chem. 2001,47(11):2055-2059. Madero, M., Sarnak, M. J., and Stevens, L. A. Serum Cystatin C As a Marker of Glomerular Filtration Rate. Curr. Opin. Nephrol. Hypertens. 2006; 15(6): 610-616. Dhamidharka, V. R., Kwon, C , and Stevens, G. Serum Cystatin C Is Superior to Serum Creatinine As a Marker of Kidney Function: A Meta-Analysis. Am. J. Kidney Dis. 2002;40(2):221-226. Shlipak, M. G., Praught, M. L., and Sarnak, M. J. Update on Cystatin C: New Insights Into the Importance of Mild Kidney Dysfunction. Curr. Opin. Nephrol. Hypertens. 2006;15(3):270-275. Herget-Rosenthal, S., Marggraf, G., Husing, J., Goring, E, Pietruck, E, Janssen, O., Philipp, T., and Kribben, A. Early Detection of Acute Renal Failure by Serum Cystatin C. Kidney Int. 2004;66(3):1115-1122. Herget-Rosenthal, S., Pietruck, F, Volbracht, L., Philipp, T, and Kribben, A. Serum Cystatin C—A Superior Marker of Rapidly Reduced Glomerular Filtration After Uninephrectomy in Kidney Donors Compared to Creatinine. Clin. Nephrol. Jul 2005;64(l):41-46. Seronie-Vivien, S., Delanaye, P., Pieroni, L., Mariat, C , Froissart, M., and Cristol, J. P. SFBC "Biology of Renal Function and Renal Failure" Working Group. Cystatin C: Current Position and Future Prospects. Clin. Chem. Lab. Med. 2008;46(12): 1664-1686. U.S. Food and Drug Administration Agency 510(K) Substantial Equivalence Determination Decision Summary Device Only, Rockville, USA (2007) http:// www.fda.gov/cdrh/reviews/k041878.pdf. Chesney, P. J., Davis, J. P., Purdy, W. K., Wand, P. J., and Chesney, R. W. Clinical Manifestations of Toxic Shock Syndrome. JAMA. 1981;246(7):741-748. Diamond, J. R. and Yoburn, D. C. Nonoliguric Acute Renal Failure. Arch. Intern. Med. Oct 1982;142(10):1882-1884. Richmond, J. M., Sibbald, W. J., Linton, A. M., and Linton, A. L. Patterns of Urinary Protein Excretion in Patients with Sepsis. Nephron. 1982;31(3):219-223. Herget-Rosenthal, S., Poppen, D., Husing, J., Marggraf, G., Pietruck, E, Jakob, H. G., Philipp, T., and Kribben, A. Prognostic Value of Tubular Protei-

BIOMARKERS OF ACUTE KIDNEY INJURY

60. 61.

62. 63. 64. 65. 66.

67. 68.

69. 70. 71.

72.

73. 74. 75. 76.

267

nuria and Enzymuria in Nonoliguric Acute Tubular Necrosis. Clin. Chem. 2004;50(3):552-558. Polkinghorne, K. R. Detection and Measurement of Urinary Protein. Curr. Opin. Nephrol. Hypertens. 2006;15(6):625-630. Schmid, H., Henger, A., Cohen, C. D., Frach, K., Grone, H. J., Schlbndorff, D., and Kretzler, M. Gene Expression Profiles of Podocyte-Associated Molecules As Diagnostic Markers in Acquired Proteinuric Diseases. J. Am. Soc. Nephrol. 2003;14(ll):2958-2966. Guder, W. G. and Hofmann, W. Markers for the Diagnosis and Monitoring of Renal Tubular Lesions. Clin. Nephrol. 1992;38 Suppl l:S3-7. D'Amico, G. and Bazzi, C. Pathophysiology of Proteinuria. Kidney Int. 2003; 63(3):809-825. Schieppati, A. and Remuzzi, G. Proteinuria and Its Consequences in Renal Disease. Acta Paediatr. Suppl. 2003;92(443):9-13. Rodicio, J. L. and Ruilope, L. M. Assessing Renal Effects and Renal Protection. J. Hypertens. Suppl. 1995;13(4):S19-25. Zomas, A., Anagnostopoulos, N., and Dimopoulos, M. A. Successful Treatment of Multiple Myeloma Relapsing After High-Dose Therapy and Autologous Transplantation with Thalidomide as a Single Agent. Bone Marrow Transplant. 2000;25(12):1319-1320. Desikan, R., Veksler, Y, Raza, S., Stokes, B., Sabir, T, Li, Z. J., and Jagannath, S. Nephrotic Proteinuria Associated with High-Dose Pamidronate In Multiple Myeloma. Br. J. Haematol. 2002;119(2):496-499. Koerbin, G., Taylor, L., Dutton, J., Marshall, K., Low, P., and Potter, J. M. Aminoglycoside Interference with the Dade Behring Pyrogallol Red-Molybdate Method for the Measurement of Total Urine Protein. Clin. Chem. 2001;47(12): 2183-2184. Loebstein, R. and Koren, G. Ifosfamide-Induced Nephrotoxicity in Children: Critical Review of Predictive Risk Factors. Pediatrics. Jun 1998;101(6):E8. Guo, X. and Nzerue, C. How to Prevent, Recognize, and Treat Drug-Induced Nephrotoxicity. Cleve. Clin. J. Med. Apr 2002;69(4):289-297. Benoehr, P., Krueth, P., Bokemeyer, C , Grenz, A., Osswald, H., and Hartmann, J. T. Nephroprotection by Theophylline in Patients with Cisplatin Chemotherapy: A Randomized, Single-Blinded, Placebo-Controlled Trial. J. Am. Soc. Nephrol. Feb 2005;16(2):452-458. Peterson, P. A., Evrin, P. E., and Berggard, I. Differentiation of Glomerular, Tubular, and Normal Proteinuria: Determinations of Urinary Excretion of Beta2-Macroglobulin, Albumin, and Total Protein. J. Clin. Invest. Jul 1969;48(7): 1189-1198. Vaidya, V. S. and Bonventre, J. V. Mechanistic Biomarkers for Cytotoxic Acute Kidney Injury. Expert Opin. Drug Metab. Toxicol. 2006;2(5):697-713. Gekle, M. Renal Albumin Handling: A Look at the Dark Side of the Filter. Kidney Int. 2007;71(6):479^81. Tojo, A. and Endou, H. Intrarenal Handling of Proteins in Rats Using Fractional Micropuncture Technique. Am. J. Physiol. 1992;263(4 Pt 2):F601-606. Sso, L. M., Sandoval, R. M., McKee, M., Osicka, T. M., Collins, A. B., Brown, D., Molitoris, B. A., and Comper, W. D. The Normal Kidney Filters Nephrotic Levels of Albumin Retrieved by Proximal Tubule Cells: Retrieval Is Disrupted in Nephrotic States. Kidney Int. 2007;71(6):504-513.

268

BIOMARKERS 77. 78.

79. 80. 81. 82. 83.

84.

85. 86. 87. 88.

89.

90. 91. 92. 93.

Christensen, E. I., Birn, H., Rippe, B., and Maunsbach, A. B. Controversies in Nephrology: Renal Albumin Handling, Facts, and Artifacts! Kidney Int. Nov 2007;72(10): 1192-1194. Greive, K. A., Nikolic-Paterson, D. J., Guimaraes, M. A., Nikolovski, J., Pratt, L. M., Mu, W., Atkins, R. C , and Comper, W. D. Glomerular Permselectivity Factors Are Not Responsible for the Increase in Fractional Clearance of Albumin in Rat Glomerulonephritis. Am. J. Pathol. 2001;159(3):1159-1170. Lane, J. T., Ford, T. C , Larson, L. R., Chambers, W. A., and Lane, P. H. Acute Effects of Different Intensities of Exercise in Normoalbuminuric/Normotensive Patients with Type 1 Diabetes. Diabetes Care. 2004;27(l):28-32. Bakris, G. Inclusion of Albuminuria in Hypertension and Heart Guidelines. Kidney Int. Suppl. 2004;(92):S 124-125. Weir, M. R. Microalbuminuria and Cardiovascular Disease. Clin. J. Am. Soc. Nephrol. 2007;2(3):581-590. Sarafidis, P. A. Proteinuria: Natural Course, Prognostic Implications and Therapeutic Considerations. Minerva Med. Dec 2007; 98(6):693-711. Selby, J. V., Karter, A. J., Ackerson, L. M., Ferrara, A., and Liu, J. Developing a Prediction Rule from Automated Clinical Databases to Identify High-Risk Patients in a Large Population with Diabetes. Diabetes Care. Sep 2001;24(9): 1547-1555. Rigden, S. P., Barratt, T. M., Dillon, M. J., Kind, P. R., De Leval, M., Stark, J. Renal Function Following Cardiopulmonary Bypass Surgery in Children: A Randomized Comparison of the Effects of Gentamicin and Cloxacillin with Cephalothin. Clin. Nephrol. May 1983;19(5):228-231. Pfaller, W., Thorwartl, U., Nevinny-Stickel, M., Krall, M., Schober, M., Joannidis, M., and Hobisch, A. Clinical Value of Fructose 1,6 Bisphosphatase in Monitoring Renal Proximal Tubular Injury. Kidney Int. Suppl. 1994;47:S68-75. Metz-Kurschel, U., Kurschel, E., Niederle, N., and Aulbert, E. Investigations on the Acute and Chronic Nephrotoxicity of the New Platinum Analogue Carboplatin.7. Cancer Res. Clin. Oncol. 1990;116(2):203-206. Heney, D., Wheeldon, J., Rushworth, P., Chapman, C , Lewis, I. J., and Bailey, C. C. Progressive Renal Toxicity Due to Ifosfamide. Arch. Dis. Child. 1991; 66(8):966 La Manna, G., Virzi, S., Deraco, M., Capelli, I., Accorsi, A., Dalmastri, V., Comai, G., Bonomi, S., Grassi, A., Selva, S., Feliciangeli, G., Scolari, M., and Stefoni, S. Tubular Dysfunction After Peritonectomy and Chemohyperthermic Treatment with Cisplatin. In Vivo. 2006;20(6A):703-706. Thielemans, N., Lauwerys, R., and Bernard, A. Competition between Albumin and Low-Molecular-Weight Proteins for Renal Tubular Uptake in Experimental Nephropathies. Nephron. 1994;66(4):453-^58. Davey, P. G. and Gosling, P. Beta 2-Microglobulin Instability in Pathological Urine. Clin. Chem. Jun 1982;28(6): 1330-1333. Fernandez, F , De Miguel, M. D., Barrio, V., and Mallol, J. Beta-2-Microglobulin As an Index of Renal Function After Cardiopulmonary Bypass Surgery in Children. Child Nephrol. Urol. 1988/1989;9(6):326-330. Dehne, M. G., Boldt, J., Heise, D., Sablotzki, A., and Hempelmann, G. TammHorsfall Protein, Alpha-1- and Beta-2-Microglobulin As Kidney Function Markers in Heart Surgery. Anaesthesist. 1995;44(8):545-551. Schaub, S., Wilkins, J. A., Antonovici, M., Krokhin, O., Weiler, T, Rush, D., and Nickerson, P. Proteomic-Based Identification of Cleaved Urinary Beta2-Mi-

BIOMARKERS OF ACUTE KIDNEY INJURY

94.

95. 96.

97. 98. 99. 100.

101.

102. 103. 104.

105. 106. 107. 108.

269

croglobulin As a Potential Marker for Acute Tubular Injury in Renal Allografts. Am. J. Transplant. 2005;5(4 Pt l):729-738. Gatanaga, H., Tachikawa, N., Kikuchi, Y., Teruya, K., Genka, I., Honda, M., Tanuma, J., Yazaki, H., Ueda, A., Kimura, S., and Oka, S. Urinary Beta2-Microglobulin As a Possible Sensitive Marker for Renal Injury Caused by Tenofovir Disoproxil Fumarate. AIDS Res. Hum. Retroviruses. Aug 2006;22(8):744-748. Schentag, J. J. and Plaut, M. E. Patterns of Urinary Beta 2-Microglobulin Excretion by Patients Treated with Aminoglycosides. Kidney Int. May 1980; 17(5):654-661. Jones, B. R., Bhalla, R. B., Mladek, J., Kaleya, R. N., Gralla, R. J., Alcock, N. W., Schwartz, M. K., Young, C. W., and Reidenberg, M. M. Comparison of Methods of Evaluating Nephrotoxicity of Cis-Platinum. Clin. Pharmacol. Ther. Apr 1980;27(4):557-562. Tirelli, A. S., Colombo, N., Cavanna, G., Mangioni, C , and Assael, B. M. Follow-up Study of Enzymuria and Beta 2-Microglobulinuria During Cis-Platinum Treatment. Eur. J. Clin. Pharmacol. 1985;29(3):313-318. Latt, D., Weiss, J. B., and Jayson, M. I. Beta 2-Microglobulin Levels in Serum and Urine of Rheumatoid Arthritis Patients on Gold Therapy. Ann. Rheum. Dis. Apr 1981 ;40(2): 157-160. Bagshaw, S. M., Langenberg, C , Haase, M., Wan, L., May, C. N., and Bellomo, R. Urinary Biomarkers in Septic Acute Kidney Injury. Intensive Care Med. 2007;33(7): 1285-1296. Herget-Rosenthal, S., Poppen, D., Hiising, J., Marggraf, G., Pietruck, F, Jakob, H. G., Philipp, T., and Kribben, A. Prognostic Value of Tubular Proteinuria and Enzymuria in Nonoliguric Acute Tubular Necrosis. Clin. Chem. 2004;50(3):552-558. Herget-Rosenthal, S., Van Wijk, J. A., Brocker-Preuss, M., and Bokenkamp, A. Increased Urinary Cystatin C Reflects Structural and Functional Renal Tubular Impairment Independent of Glomerular Filtration Rate. Clin. Biochem. 2007;40(13-14):946-951. Conti, M., Moutereau, S., Zater, M., Lallali, K., Durrbach, A., Manivet, P., Eschwege, P., and Loric, S. Urinary Cystatin C As a Specific Marker of Tubular Dysfunction. Clin. Chem. Lab Med. 2006;44(3):288-291. Tenstad, O., Roald, A. B., Grubb, A., and Aukland, K. Renal Handling of Radiolabelled Human Cystatin C in the Rat. Scand. J. Clin. Lab Invest. Aug 1996;56(5):409^14. Colle, A., Tavera. C , Laurent, P., Leung-Tack, J., and Girolami, J. P. Direct Radioimmunoassay of Rat Cystatin C: Increased Urinary Excretion of This Cysteine Proteases Inhibitor During Chromate Nephropathy. J. Immunoassay. 1990;11(2):199-214. Uchida, K. and Gotoh, A. Measurement of Cystatin-C and Creatinine in Urine. Clin. Chim.Acta. 2002;323(l-2): 121-128. Tkaczyk, M., Nowicki, M., and Lukamowicz, J. Increased Cystatin C Concentration in Urine of Nephrotic Children. Pediatr. Nephrol. 2004;19(11):1278-1280. Sadzuka, Y., Shimizu, Y, and Takino, Y Role of Glutathione S-Transferase Isoenzymes in Cisplatin-Induced Nephrotoxicity in the Rat. Toxicol. Lett. 1994;70(2):211-222. Harpur, E., Schuster, K., Betton, G., Bounous, D., Ennulat, D., Riefke, B., Mylecraine, L., Pettit, S., Hoffman, D., Gautier, J., and Beushausen, S. Comparative

270

BIOMARKERS

109. 110.

111.

112. 113. 114.

115.

116.

117.

118. 119. 120. 121. 122.

Performance of Novel Marker of Nephrotoxicity in the Rat. Poster, http://www. argutusmed.com/_fileupload/nephrotoxicity%20poster.pdf. Kharasch, E. D., Thorning, D., Garton, K., Hankins, D. C , and Kilty, C. G. Anesthesiology. 1997;86(1):160-171. Goldberg, M. E., Cantillo, J., Gratz, I., Deal, E„ Vekeman, D., McDougall, R., Afshar, M., Zafeiridis, A., and Larijani, G. Dose of Compound A, Not Sevoflurane, Determines Changes in the Biochemical Markers of Renal Injury in Healthy Volunteers. Anesth. Analg. 1999;88(2):437^t45. Pai, M. P., Norenberg, J. P., Telepak, R. A., Sidney, D. S., and Yang, S. Assessment of Effective Renal Plasma Flow, Enzymuria, and Cytokine Release in Healthy Volunteers Receiving a Single Dose of Amphotericin B Desoxycholate. Antimicrob. Agents Chemother. 2005;49(9):3784-3788. Sundberg, A. G., Appelkvist, E. L., Backman, L., and Dallner, G. Urinary PiClass Glutathione Transferase As an Indicator of Tubular Damage in the Human Kidney. Nephron. 1994;67(3):308-316. El-Safty, I. M. A., Gadallah, M., and Shouman, A. E. Effect of Silica Exposure on Urinary Excretion of Copper and Zinc. Am. J. Med. Sci. 2003;326(3): 122-127. Cawood, T, Bashir, M., Brady, J., Murray, B., and O'Shea, D. The Potential Use of Urinary Biomarkers for Detecting and Localizing Injury to the Nephron in Diabetes. Poster, http://www.argutusmed.com/_fileupload/diabetes%20poster%20 -%20June%202008%20ISN.pdf. Bashir, M., Cawood, T, Brady, J., Murray, B., and O'Shea, D. The Detection of Obesity-Related Nephropathy Using Novel Urinary Biomarkers. http://www.argutusmed.com/_fileupload/Image/obesity%20related%20nephropathy%20poster%20-%20june%202008.pdf. Koyner, J. L., Schuster, K., Trevino, S., and Murray, P. T. Alpha Glutathione S-Transferase and Pi Glutathione S-Transferase are Novel Biomarkers of Acute Kidney Injury Following Adult Cardiac Surgery, http://www.argutusmed.eom/_ fileupload/AKI%20in%20cardiac%20surgery%20poster%20-%202008%20 ASN%20renal%20week.pdf. Westhuyzen, J., Endre, Z. H., Reece, G., Reith, D. M., Saltissi, D., and Morgan, T. J. Measurement of Tubular Enzymuria Facilitates Early Detection of Acute Renal Impairment in the Intensive Care Unit. Nephrol. Dial. Transplant. 2003;18(3):543-551. Price, R. G. The Role of NAG (N-Acetyl-Beta-D-Glucosaminidase) in the Diagnosis of Kidney Disease Including the Monitoring of Nephrotoxicity. Clin. Nephrol. 1992;38 Suppl l:S14-9. Skalova, S. The Diagnostic Role of Urinary N-Acetyl-Beta-D-Glucosaminidase (NAG) Activity in the Detection of Renal Tubular Impairment. Ada Medica. (Hradec Kralove). 2005;48(2):75-80. Emeigh Hart, S. G. Assessment of Renal Injury In Vivo. J. Pharmacol. Toxicol. Methods. 2005;52(l):30-45. Ascione, R., Lloyd, C. T, Underwood, M. J., Gomes, W. J., and Angelini, G. D. On-Pump versus Off-Pump Coronary Revascularization: Evaluation of Renal Function. Ann. Thorac. Surg. 1999;68(2):493^98. Liangos, O., Perianayagam, M. C , Vaidya, V. S., Han, W. K., Wald, R., Tighiouart, H., Mackinnon, R. W., Li, L., Balakrishnan, V. S., Pereira, B. J., Bonventre, J. V, and Jaber, B. L. Urinary N-Acetyl-Beta-(D)-Glucosaminidase Activity and Kidney Injury Molecule-1 Level Are Associated with Adverse Outcomes in Acute Renal Failure. J. Am. Soc. Nephrol. 2007;18(3):904-912.

BIOMARKERS OF ACUTE KIDNEY INJURY

271

123. Wiley, R. A., Choo, H. Y., and Traiger, G. J. The Effect of Nephrotoxic Furans on Urinary N-Acetylglucosaminidase Levels in Mice. Toxicol. Lett. Nov 1982;14(l-2):93-96. 124. Bonventre, J. V. Diagnosis of Acute Kidney Injury: From Classic Parameters to New Biomarkers. Contrib. Nephrol. 2007;156:213-219. 125. Bondiou, M. T., Bourbouze, R., Bernard, M., Percheron, F., Perez-Gonzalez, N., and Cabezas, J. A. Inhibition of A and B N-Acetyl-Beta-D-Glucosaminidase Urinary Isoenzymes by Urea. Clin. Chim. Ada. 1985;149(l):67-73. 126. Iqbal, M. P., Ali, A. A., Waqar, M. A., and Mehboobali, N. Urinary N-AcetylBeta-D-Glucosaminidase in Rheumatoid Arthritis. Exp. Mol. Med. Sep 30, 1998;30(3):165-169. 127. Tominaga, M., Fujiyama, K., Hoshino, T., Tanaka, Y., Takeuchi, T., Honda, M , Mokuda, O., Ikeda, T., and Mashiba, H. Urinary N-Acetyl-Beta-D-Glucosaminidase in the Patients with Hyperthyroidism. Horm. Metab. Res. 1989;21(8): 438^40. 128. Higuchi, H., Sumita, S., Wada, H., Ura, T., Ikemoto, T., Nakai, T., Kanno, M., and Satoh, T. Effects of Sevoflurane and Isoflurane on Renal Function and on Possible Markers of Nephrotoxicity. Anesthesiology. 1998;89(2):307-322. 129. Mukherjea, D., Whitworth, C. A., Nandish, S., Dunaway, G. A., Rybak, L. P., and Ramkumar, V. Expression of the Kidney Injury Molecule 1 in the Rat Cochlea and Induction by Cisplatin. Neuroscience. May 12, 2006;139(2):733-740. 130. Zhang, Z., Humphreys, B. D., and Bonventre, J. V. Shedding of the Urinary Biomarker Kidney Injury Molecule-1 (KIM-1) Is Regulated by MAP Kinases and Juxtamembrane Region. Am. Soc. Nephrol. 2007;18(10):2704-2714. 131. Van Timmeren, M. M., Bakker, S. J., Vaidya, V. S., Bailly, V., Schuurs, T. A., Damman, J., Stegeman, C. A., Bonventre, J. V., and Van Goor, H. Tubular Kidney Injury Molecule-1 in Protein-overload Nephropathy. Am. J. Physiol. Renal Physiol. 2006;291(2):F456-464. 132. Ichimura, T., Bonventre, J. V., Bailly, V, Wei, H., Hession, C. A., Cate, R. L., and Sanicola, M. Kidney Injury Molecule-1 (KIM-1), a Putative Epithelial Cell Adhesion Molecule Containing a Novel Immunoglobulin Domain, Is Up-Regulated in Renal Cells After Injury. J. Biol. Chem. 1998;273(7):4135-4142. 133. Kuehn, E. W., Park, K. M., Somlo, S., and Bonventre, J. V. Kidney Injury Molecule-1 Expression in Murine Polycystic Kidney Disease. Am. J. Physiol. Renal Physiol. 2002;283(6):F1326-336. 134. Ichimura, T., Hung, C. C , Yang, S. A., Stevens, J. L., and Bonventre, J. V. Kidney Injury Molecule-1: A Tissue snd Urinary Biomarker for NephrotoxicantInduced Renal Injury. Am. J. Physiol. Renal Physiol. 2004;286(3):F552-563. 135. Prozialeck, W. C , Edwards, J. R., Vaidya, V. S., and Bonventre, J. V. Preclinical Evaluation of Novel Urinary Biomarkers of Cadmium Nephrotoxicity. Toxicol. Appl. Pharmacol. 2009, In Press. 136. Prozialeck, W. C , Vaidya, V. S., Liu, J., Waalkes, M. P., Edwards, J. R., Lamar, P. C , Bernard, A. M., Dumont, X., and Bonventre, J. V. Kidney Injury Molecule-1 Is an Early Biomarker of Cadmium Nephrotoxicity. Kidney Int. 2007;72(8):985-993. 137. Jost, G., Pietsch, H., Sommer, J., Sandner, P., Lengsfeld, P., Seidensticker, P., Lehr, S., Hiitter, J., and Sieber, M. A. Retention of Iodine and Expression of Biomarkers for Renal Damage in the Kidney After Application of Iodinated Contrast Media in Rats. Invest. Radiol. 2009;44(2): 114-123. 138. Zhou, Y., Vaidya, V. S., Brown, R. P., Zhang, J., Rosenzweig, B. A., Thompson, K. L., Miller, T. J., Bonventre, J. V, and Goering, P. L. Comparison of Kidney

272

BIOMARKERS

139. 140.

141. 142.

143.

144. 145. 146. 147.

148. 149.

150. 151.

Injury Molecule-1 and Other Nephrotoxicity Biomarkers in Urine and Kidney Following Acute Exposure to Gentamicin, Mercury, and Chromium. Toxicol. Sci. 2008;101(1):159-170. Rached, E., Hoffmann, D., Blumbach, K., Weber, K., Dekant, W., and Mally, A. Evaluation of Putative Biomarkers of Nephrotoxicity After Exposure to Ochratoxin A In Vivo and In Vitro. Toxicol. Sci. 2008;103(2):371-381. Perez-Rojas, J., Blanco, J. A., Cruz, C , Trujillo, J., Vaidya, V. S., Uribe, N., Bonventre, J. V., Gamba, G., and Bobadilla, N. A. Mineralocorticoid Receptor Blockade Confers Renoprotection in Preexisting Chronic Cyclosporine Nephrotoxicity. Am. J. Physiol. Renal Physiol. 2007;292(1):F131-139. Han, W. K., Waikar, S. S., Johnson, A., Betensky, R. A., Dent, C. L., Devarajan, P., and Bonventre, J. V. Urinary Biomarkers in the Early Diagnosis of Acute Kidney Injury. Kidney Int. 2008;73(7):863-869. Liangos, O., Perianayagam, M. C , Vaidya, V. S., Han, W. K., Wald, R., Tighiouart, H., MacKinnon, R. W., Li, L., Balakrishnan, V. S., Pereira, B. J., Bonventre, J. V., and Jaber, B. L. Urinary N-Acetyl-Beta-(D)-Glucosaminidase Activity and Kidney Injury Molecule-1 Level Are Associated with Adverse Outcomes in Acute Renal Failure. J. Am. Soc. Nephrol. 2007;18(3):904-912. Waanders, K, Vaidya, V. S., Van Goor, H., Leuvenink, H., Damman, K., Hamming, I., Bonventre, J. V., Vogt, L., and Navis, G. Effect of Renin-AngiotensinAldosterone System Inhibition, Dietary Sodium Restriction, and/or Diuretics on Urinary Kidney Injury Molecule 1 Excretion in Nondiabetic Proteinuric Kidney Disease: A Post Hoc Analysis of a Randomized Controlled Trial. Am. J. Kidney Dis. 2009;53(1):1^1. Han, W. K., Bailly, V, Abichandani, R., Thadhani, R., and Bonventre, J. V. Kidney Injury Molecule-1 (KTM-1): A Novel Biomarker for Human Renal Proximal Tubule Injury. Kidney Int. 2002;62(l):237-244. Van Timmeren, M. M., Van Den Heuvel, M. C , Bailly, V, Bakker, S. J., Van Goor, H., and Stegeman, C. A. Tubular Kidney Injury Molecule-1 (KTM-1) in Human Renal Disease. J. Pathol. Jun 2007;212(2):209-217. Zhang, P. L., Rothblum, L. I., Han, W. K., Blasick, T. M., Potdar, S., and Bonventre, J. V. Kidney Injury Molecule-1 Expression in Transplant Biopsies Is a Sensitive Measure of Cell Injury. Kidney Int. 2008;73(5):608-6T4. Van Timmeren, M. M., Vaidya, V. S., Van Ree, R. M., Oterdoom, L. H., De Vries, A. P., Gans, R. O., Van Goor, H., Stegeman, C. A., Bonventre, J. V, and Bakker, S. J. High Urinary Excretion of Kidney Injury Molecule-1 Is an Independent Predictor of Graft Loss in Renal Transplant Recipients. Transplantation. 2007 27;84(12): 1625-1630. Rosenberg, M. E. and Silkensen, J. Clusterin and the Kidney. Exp. Nephrol. 1995;3(1):9-14. Kharasch, E. D., Schroeder, J. L., Bammler, T., Beyer, R., and Srinouanprachanh, S. Gene Expression Profiling of Nephrotoxicity from the Sevoflurane Degradation Product Fluoromethyl-2,2-Difluoro-l-(Trifluoromethyl)Vinyl Ether ("Compound A") in Rats. Toxicol. Sci. 2006;90(2):419^131. Rached, E., Hoffmann, D., Blumbach, K., Weber, K., Dekant, W., and Mally, A. Evaluation of Putative Biomarkers of Nephrotoxicity after Exposure to Ochratoxin A In Vivo and In Vitro. Toxicol. Sci. 2008;103(2):371-381. Correa-Rotter, R., Ibarra-Rubio, M. E., Schwochau, G., Cruz, C , Silkensen, J. R., Pedraza-Chaverri, J., Chmielewski, D., and Rosenberg, M. E. Induction of Clusterin in Tubules of Nephrotic Rats. J. Am. Soc. Nephrol. 1998;9(l):33-37.

BIOMARKERS OF ACUTE KIDNEY INJURY

273

152. Tsuchiya, Y, Tominaga, Y, Matsubayashi, K., Jindo, T., Furuhama, K., and Suzuki, K. T. Investigation on Urinary Proteins and Renal Mrna Expression in Canine Renal Papillary Necrosis Induced by Nefiracetam. Arch. Toxicol. 2005; 79(9):500-507. 153. Yoshida, T., Kurella, M., Beato, R, Min, H., Ingelfinger, J. R., Stears, R. L., Swinford, R. D., Gullans, S. R., and Tang, S. S. Monitoring Changes in Gene Expression in Renal Ischemia-Reperfusion in the Rat. Kidney Int. 2002;61(5): 1646-1654. 154. Correa-Rotter, R., Hostetter, T. H., Manivel, J. C , Eddy, A. A., and Rosenberg, M. E. Intrarenal Distribution of Clusterin Following Reduction of Renal Mass. Kidney Int. 1992;41(4):938-950. 155. Ishii, A., Sakai, Y, and Nakamura, A. Molecular Pathological Evaluation of Clusterin in a Rat Model of Unilateral Ureteral Obstruction as a Possible Biomarker of Nephrotoxicity. Toxicol. Pathol. 2007;35(3):376-382. 156. Hidaka, S., Kranzlin, B., Gretz, N., and Witzgall, R. Urinary Clusterin Levels in the Rat Correlate with the Severity of Tubular Damage and May Help to Differentiate Between Glomerular and Tubular Injuries. Cell Tissue Res. 2002;310(3):289-296. 157. Rosenberg, M. E. and Silkensen, J. Clusterin: Physiologic and Pathophysiologic Considerations. Int. J. Biochem. Cell Biol. Jul 1995; 27(7):633-645. 158. Ghiggeri, G. M., Bruschi, M., Candiano, G., Rastaldi, M. P., Scolari, R, Passerini, P., Musante, L., Pertica, N., Caridi, G., Ferrario, R, Perfumo, R, and Ponticelli, C. Depletion of Clusterin in Renal Diseases Causing Nephrotic Syndrome. Kidney Int. 2002;62(6):2184-2194. 159. Nielsen, B. S., Borregaard, N., Bundgaard, J. R., Timshel, S., Sehested, M., and Kjeldsen, L. Induction of NGAL Synthesis in Epithelial Cells of Human Colorectal Neoplasia and Inflammatory Bowel Diseases. Gut. 1996;38(3):414-420. 160. Cowland, J. B. and Borregaard, N. Molecular Characterization and Pattern of Tissue Expression of the Gene for Neutrophil Gelatinase-Associated Lipocalin From Humans. Genomics. 1997;45(l):17-23. 161. Mishra, J., Ma, Q., Prada, A., Mitsnefes, M., Zahedi, K., Yang, J., Barasch, J., and Devarajan, P. Identification of Neutrophil Gelatinase-Associated Lipocalin As a Novel Early Urinary Biomarker for Ischemic Renal Injury. J. Am. Soc. Nephrol. 2003;14(10):2534-2543. 162. Mishra, J., Mori, K., Ma, Q., Kelly, C , Barasch, J., and Devarajan, P. Neutrophil Gelatinase-Associated Lipocalin: A Novel Early Urinary Biomarker for Cisplatin Nephrotoxicity. Am. J. Nephrol. May/Jun 2004;24(3):307-315. 163. Mishra, J., Dent, C, Tarabishi, R., Mitsnefes, M. M., Ma, Q., Kelly, C , Ruff, S. M., Zahedi, K., Shao, M., Bean, J., Mori, K., Barasch, J., and Devarajan, P. Neutrophil Gelatinase-Associated Lipocalin (NGAL) as a Biomarker for Acute Renal Injury After Cardiac Surgery. Lancet. 2008;365(9466):1231-1238. 164. Wagener, G., Jan, M., Kim, M., Mori, K., Barasch, J. M., Sladen, R. N., and Lee, H. T. Association Between Increases in Urinary Neutrophil Gelatinase-Associated Lipocalin and Acute Renal Dysfunction After Adult Cardiac Surgery. Anesthesiology. 2006; 105(3):485-491. 165. Makris, K., Markou, N., Evodia, E., Dimopoulou, E., Drakopoulos, I., Ntetsika, K., Rizos, D., Baltopoulos, G., and Haliassos, A. Urinary Neutrophil Gelatinase-Associated Lipocalin (NGAL) As an Early Marker of Acute Kidney Injury in Critically 111 Multiple Trauma Patients. Clin. Chem. Lab Med. 2009;47(1): 79-82.

274

BIOMARKERS 166. Bachorzewska-Gajewska, H., Malyszko, J., Sitniewska, E., Malyszko, J. S., and Dobrzycki, S. Neutrophil-Gelatinase-Associated Lipocalin and Renal Function After Percutaneous Coronary Interventions. Am. J. Nephrol. 2006;26(3): 287-292. 167. Bachorzewska-Gajewska, H., Malyszko, J., Sitniewska, E., Malyszko, J. S., Pawlak, K., Mysliwiec, M., Lawnicki, S., Szmitkowski, M., and Dobrzycki, S. Could Neutrophil-Gelatinase-Associated Lipocalin and Cystatin C Predict the Development of Contrast-Induced Nephropathy After Percutaneous Coronary Interventions in Patients with Stable Angina and Normal Serum Creatinine Values? Kidney Blood Press. Res. 2007;30(6):408-415. 168. Hirsch, R., Dent, C , Pfriem, H., Allen, J., and Beekman, R. H., Ill, Ma, Q., Dastrala, S., Bennett, M., Mitsnefes, M., and Devarajan, P. NGAL Is an Early Predictive Biomarker of Contrast-Induced Nephropathy in Children. Pediatr. Nephrol. Dec 2007;22(12):2089-2095. 169. Bolignano, D., Coppolino, G., Campo, S., Aloisi, C , Nicocia, G., Frisina, N., and Buemi, M. Neutrophil Gelatinase-Associated Lipocalin in Patients with Autosomal-Dominant Polycystic Kidney Disease. Am. J. Nephrol. 2007;27(4):373-378. 170. Ding, H., He, Y., Li, K., Yang, J., Li, X., Lu, R., and Gao, W. Urinary Neutrophil Gelatinase-Associated Lipocalin (NGAL) Is an Early Biomarker for Renal Tubulointerstitial Injury in Iga Nephropathy. Clin. Immunol. May 2007;123(2): 227-234. 171. Bolignano, D., Coppolino, G., Campo, S., Aloisi, C, Nicocia, G., Frisina, N., and Buemi, M. Urinary Neutrophil Gelatinase-Associated Lipocalin (NGAL) Is Associated with Severity of Renal Disease in Proteinuric Patients. Nephrol. Dial. Transplant. 2008;23(1):414-416. 172. Bolignano, D., Coppolino, G., Lacquaniti, A., Nicocia, G., and Buemi, M. Pathological and Prognostic Value of Urinary Neutrophil Gelatinase-Associated Lipocalin in Macroproteinuric Patients with Worsening Renal Function. Kidney Blood Press. Res. 2008;31(4):274-279. 173. Bolignano, D., Lacquaniti, A., Coppolino, G., Donato, V, Fazio, M. R., Nicocia, G., and Buemi, M. Neutrophil Gelatinase-Associated Lipocalin As An Early Biomarker of Nephropathy in Diabetic Patients. Kidney Blood Press. Res. 2009;32(2):91-98. 174. Bolignano, D., Donato, V, Coppolino, G., Campo, S., Buemi, A., Lacquaniti, A., and Buemi, M. Neutrophil Gelatinase-Associated Lipocalin (NGAL) As a Marker of Kidney Damage. Am. J. Kidney Dis. 2008;52(3):595-605. 175. Abdelmagid, S. M., Barbe, M. E, Rico, M. C , Salihoglu, S., Arango-Hisijara, I., Selim, A. H., Anderson, M. G., Owen, T. A., Popoff, S. N., and Safadi, F. F. Osteoactivin, an Anabolic Factor That Regulates Osteoblast Differentiation and Function. Exp. Cell. Res. 2008;314(13):2334-2351. 176. Weterman, M. A., Ajubi, N., Van Dinter, I. M., Degen, W. G., Van Muijen, G. N., Ruitter, D. J., and Bloemers, H. P. Nmb, a Novel Gene, Is Expressed in Low-Metastatic Human Melanoma Cell Lines and Xenografts. Int. J. Cancer. 1995;60(1):73-81. 177. Selim, A. A. Osteoactivin Bioinformatic Analysis: Prediction of Novel Functions, Structural Features, and Modes of Action. Med. Sci. Monit. 2009;15(2): MT19-33. 178. Nakamura, A., Ishii, A., Ohata, C , and Komurasaki, T. Early Induction of Osteoactivin Expression in Rat Renal Tubular Epithelial Cells After Unilateral Ureteral Obstruction. Exp. Toxicol. Pathol. 2007;59(l):53-59.

BIOMARKERS OF ACUTE KIDNEY INJURY

275

179. Onaga, M , Ido, A., Hasuike, S., Uto, H., Moriuchi, A., Nagata, K., Hori, T., Hayash, K., and Tsubouchi, H. Osteoactivin Expressed During Cirrhosis Development in Rats Fed a Choline-Deficient, L-Amino Acid-Defined Diet, Accelerates Motility of Hepatoma Cells. J. Hepatol. 2O03;39(5):779-785. 180. Xie, Y., Sakatsume, M., Nishi, S., Narita, I., Arakawa, M., and Gejyo, F. Expression, Roles, Receptors, and Regulation of Osteopontin in the Kidney. Kidney Int. 2001 ;60(5): 1645-1657. 181. Giachelli, C. M., Lombardi, D., Johnson, R. J., Murry, C. E., and Almeida, M. Evidence for a Role of Osteopontin in Macrophage Infiltration in Response to Pathological Stimuli In Vivo. Am. J. Pathol. Feb 1998;152(2):353-358. 182. Hudkins, K. L., Giachelli, C. M., Cui, Y, Couser, W. G., Johnson, R. J., and Alpers, C. E. Osteopontin Expression in Fetal and Mature Human Kidney. J. Am. Soc. Nephrol. 1999;10(3):444-57. 183. Wang, E. J., Snyder, R. D., Fielden, M. R., Smith, R. J., and Gu, Y Z. Validation of Putative Genomic Biomarkers of Nephrotoxicity in Rats. Toxicology. 2008;246(2-3):91-100. 184. Rached, E., Hoffmann, D., Blumbach, K., Weber, K., Dekant, W., and Mally, A. Evaluation of Putative Biomarkers of Nephrotoxicity After Exposure to Ochratoxin A In Vivo and In Vitro. Toxicol. Sci. Jun 2008;103(2):371-381. 185. Xie, Y, Nishi, S., Iguchi, S., Imai, N., Sakatsume, M., Saito, A., Ikegame, M., lino, N., Shimada, H., Ueno, M., Kawashima, H., Arakawa, M., and Gejyo, F. Expression of Osteopontin in Gentamicin-Induced Acute Tubular Necrosis and Its Recovery Process. Kidney Int. Mar 2001;59(3):959-974. 186. Hudkins, K. L., Giachelli, C. M., Eitner, F., Couser, W. G., Johnson, R. J., and Alpers, C. E. Osteopontin Expression in Human Crescentic Glomerulonephritis. Kidney Int. Jan 2000;57(1):105-116. 187. Hudkins, K. L., Le, Q. C , Segerer, S., Johnson, R. J., Davis, C. L., Giachelli, C. M., and Alpers, C. E. Osteopontin Expression in Human Cyclosporine Toxicity. Kidney Int. Aug 2001;60(2):635-640. 188. Thomas, S. E., Lombardi, D., Giachelli, C , Bohle, A., and Johnson, R. J. Osteopontin Expression, Tubulointerstitial Disease, and Essential Hypertension. Am. J. Hypertens. 1998;11(8 Pt 1):954-961. 189. Ramankulov, A., Lein, M., Kristiansen, G., Meyer, H. A., Loening, S. A., and Jung, K. Elevated Plasma Osteopontin As Marker for Distant Metastases and Poor Survival in Patients With Renal Cell Carcinoma. Cancer Res. Clin. Oncol. Sep2007;133(9):643-652. 190. Yang, A., Trajkovic, D., Illanes, O., and Ramiro-Ibanez, F. Clinicopathological and Tissue Indicators of Para-Aminophenol Nephrotoxicity in Sprague-Dawley Rats. Toxicol. Pathol. 2007;35(4):521-532. 191. Mezzano, S. A., Droguett, M. A., Burgos, M. E., Ardiles, L. G., Aros, C. A., Caorsi, I., and Egido, J. Overexpression of Chemokines, Fibrogenic Cytokines, and Myofibroblasts in Human Membranous Nephropathy. Kidney Int. 2000;57(1):147-158. 192. Okada, H., Moriwaki, K., Konishi, K., Kobayashi, T., Sugahara, S., Nakamoto, H., Saruta, T., and Suzuki, H. Tubular Osteopontin Expression in Human Glomerulonephritis and Renal Vasculitis. Am. J. Kidney Dis. 2000;36(3):498-506. 193. Verstrepen, W. A., Persy, V. P., Verhulst, A., Dauwe, S., and De Broe, M. E. Renal Osteopontin Protein and Mrna Upregulation During Acute Nephrotoxicity in the Rat. Nephrol. Dial. Transplant. 2001;16(4):712-724.

276

BIOMARKERS 194. Davis, J. W., II, Goodsaid, F. M , Bral, C. M., Obert, L. A., Mandakas, G., Garner, C. E., II, Collins, N. D., Smith, R. J., and Rosenblum, I. Y. Quantitative Gene Expression Analysis in a Nonhuman Primate Model of Antibiotic-Induced Nephrotoxicity. Toxicol. Appl. Pharmacol. 2004;200(1): 16-26. 195. Kharasch, E. D., Schroeder, J. L., Bammler, T., Beyer, R., and Srinouanprachanh, S. Gene Expression Profiling of Nephrotoxicity From the Sevoflurane Degradation Product Fluoromethyl-2,2-Difluoro-l-(Trifiuoromethyl)Vinyl Ether ("Compound A") in Rats. Toxicol. Sci. Apr 2006;90(2):419-431. 196. Gang, X., Ueki, K., Kon, S., Maeda, M., Naruse, T., and Nojima, Y. Reduced Urinary Excretion of Intact Osteopontin in Patients with Iga Nephropathy. Am. J. Kidney Dis. 2001;37(2):374-379. 197. Maatman, R. G., Van De Westerlo, E. M., Van Kuppevelt, T. H., and Veerkamp, J. H. Molecular Identification of the Liver and the Heart-Type Fatty Acid-Binding Proteins in Human and Rat Kidney. Use of the Reverse Transcriptase Polymerase Chain Reaction. Biochem. J. 1992;288(Pt l):285-290. 198. Sweetser, D. A., Heuckeroth, R. O., and Gordon, J. I. The Metabolic Significance of Mammalian Fatty-Acid-Binding Proteins: Abundant Proteins in Search of a Function. Annu. Rev. Nutr. 1987;7:337-359. 199. Negishi, K., Noiri, E., Sugaya, T., Li, S., Megyesi, J., Nagothu, K., and Portilla, D. A Role of Liver Fatty Acid-Binding Protein in Cisplatin-Induced Acute Renal Failure. Kidney Int. 2007;72(3):348-358. 200. Nakamura, T., Sugaya, T., Node, K., Ueda, Y, and Koide, H. Urinary Excretion of Liver-Type Fatty Acid-Binding Protein in Contrast Medium-Induced Nephropathy. Am. J. Kidney Dis. 2006;47(3):439^44. 201. Nakamura, K., Ito, K., Kato, Y, Sugaya, T., Kubo, Y, andTsuji, A. L-Type Fatty Acid Binding Protein Transgenic Mouse as a Novel Tool to Explore Cytotoxicity to Renal Proximal Tubules. Drug Metab. Pharmacokinet. 2008;23(4):271-278. 202. Noiri, E., Doi, K., Negishi, K., Tanaka, T., Hamasaki, Y, Fujita, T., Portilla, D., and Sugaya, T. Urinary Fatty Acid-Binding Protein 1: An Early Predictive Biomarker of Kidney Injury. Am. J. Physiol. Renal Physiol. 2009;296(4):F669-679. 203. Portilla, D., Dent, C , Sugaya, T., Nagothu, K. K., Kundi, I., Moore, P., Noiri, E., and Devarajan, P. Liver Fatty Acid-Binding Protein as a Biomarker of Acute Kidney Injury After Cardiac Surgery. Kidney Int. Feb 2008;73(4):465^72. 204. Yamamoto, T., Noiri, E., Ono, Y, Doi, K., Negishi, K., Kamijo, A., Kimura, K., Fujita, T, Kinukawa, T., Taniguchi, H., Nakamura, K., Goto, M., Shinozaki, N., Ohshima, S., and Sugaya, T. Renal L-Type Fatty Acid—Binding Protein in Acute Ischemic Injury. J. Am. Soc. Nephrol. 2007; 18(11):2894-2902. 205. Kamijo, A.,Sugaya, T.,Hikawa,A.,Yamanouchi,M.,Hirata,Y,Ishimitsu,T.,Numabe,A.,Takagi,M.,Hayakawa,H.,Tabei,F.,Sugimoto,T.,Mise,N.,andKimura,K. ClinicalEvaluationofUrinaryExcretionofLiver-Type Fatty Acid-Binding Protein As a Marker for the Monitoring of Chronic Kidney Disease: A Multicenter Trial. J. Lab. Clin. Med. 2005;145(3):125-133. 206. Nakamura, T., Sugaya, T., Kawagoe, Y, Ueda, Y, Osada, S., and Koide, H. Candesartan Reduces Urinary Fatty Acid-Binding Protein Excretion in Patients with Autosomal Dominant Polycystic Kidney Disease. Am. J. Med. Sci. 2005; 330(4):161-165. 207. Nakamura, T., Sugaya, T., Kawagoe, Y, Ueda, Y, Osada, S., and Koide, H. Effect of Pitavastatin on Urinary Liver-Type Fatty Acid-Binding Protein Levels in Patients with Early Diabetic Nephropathy. Diabetes Care. 2005;28(11): 2728-2732.

BIOMARKERS OF ACUTE KIDNEY INJURY

277

208. Nakamura, T., Sugaya, T., Kawagoe, Y., Ueda, Y, Osada, S., and Koide, H. Urinary Liver-Type Fatty Acid-Binding Protein Levels for Differential Diagnosis of Idiopathic Focal Glomerulosclerosis and Minor Glomerular Abnormalities and Effect of Low-Density Lipoprotein Apheresis. Clin. Nephrol. 2006;65(l):l-6. 209. Hofstra, J. M., Deegens, J. K., Steenbergen, E. J., and Wetzels, J. F. Urinary Excretion of Fatty Acid-Binding Proteins in Idiopathic Membranous Nephropathy. Nephrol. Dial. Transplant. 2008;23(10): 3160-3165. 210. Oyama, Y., Takeda, T., Hama, H., Tanuma, A., lino, N., Sato, K., Kaseda, R., Ma, M., Yamamoto, T, Fujii, H., Kazama, J. J., Odani, S., Terada, Y, Mizuta, K., Gejyo, F, and Saito, A. Evidence for Megalin-Mediated Proximal Tubular Uptake of L-FABP, a Carrier of Potentially Nephrotoxic Molecules. Lab. Invest. 2005;85(4):522-531. 211. Kamijo, A., Sugaya, T., Hikawa, A., Yamanouchi, M., Hirata, Y, Ishimitsu, T., Numabe, A., Takagi, M., Hayakawa, H., Tabei, E, Sugimoto, T., Mise, N., Omata, M., and Kimura, K. Urinary Liver-Type Fatty Acid Binding Protein as a Useful Biomarker in Chronic Kidney Disease. Mol. Cell. Biochem. Mar 2006; 284(1-2): 175-182. 212. Taupin, D. R., Kinoshita, K., and Podolsky, D. K. Intestinal Trefoil Factor Confers Colonic Epithelial Resistance to Apoptosis. Proc. Natl. Acad. Sci. USA. 2000;97(2):799-804. 213. Kinoshita, K., Taupin, D. R., Itoh, H., and Podolsky, D. K. Distinct Pathways of Cell Migration and Antiapoptotic Response to Epithelial Injury: StructureFunction Analysis of Human Intestinal Trefoil Factor. Mol. Cell. Biol. 2000 (13):4680-4690. 214. Lesimple, P., Van Seuningen, I., Buisine, M. P., Copin, M. C , Hinz, M., Hoffmann, W., Hajj, R., Brody, S. L., Coraux, C , and Puchelle, E. Trefoil Factor Family 3 Peptide Promotes Human Airway Epithelial Ciliated Cell Differentiation. Am. J. Respir. Cell. Mol. Biol. 2007;36(3):296-303. 215. Hoffmann, W. TFF (Trefoil Factor Family) Peptides and Their Potential Roles for Differentiation Processes During Airway Remodeling. Curr. Med. Chem. 2007;14(25):2716-2719. 216. Suemori, S., Lynchdevaney, K., and Podolsky, D. K. Identification and Characterization of Rat Intestinal Trefoil Factor: Tissue- and Cell-Specific Member of the Trefoil Protein Family. Proc. Natl. Acad. Sci. USA. 1991;88(24): 11017-11021. 217. Yu, Yan; Jin, Hong; Holder, Daniel; Ozer, Josef S.; Villarreal, Stephanie; Shughrue, Paul; Shi, Shu; Figueroa, David J.; Clouse, Holly; Su, Ming; Muniappa, Nagaraja; Troth, Sean P.; Bailey, Wendy; Seng, John; Aslamkhan, Amy G.; Thudium, Douglas; Sistare, Frank D.; and Gerhold, David L. Biomarkers of Kidney Tubule Injury: Urinary Trefoil Factor 3 and Albumin. Submitted (2009). 218. Chinery, R., Poulsom, R., Elia, G., Hanby, A. M., and Wright, N. A. Expression and Purification of a Trefoil Peptide Motif in a Beta-Galactosidase Fusion Protein and Its Use to Search for Trefoil-Binding Sites. Eur. J. Biochem. 1993;212(2):557-563. 219. Baus-Loncar, M., Al-Azzeh, E. D., Romanska, H., Lalani, El-N., Stamp, G. W., Blin, N., and Kayademir, T. Transcriptional Control of TFF3 (Intestinal Trefoil Factor) via Promoter Binding Sites for the Nuclear Factor Kappab and C/Ebpbeta. Peptides. 2004;25(5):849-854.

278

BIOMARKERS 220. Dossinger, V., Kayademir, T., Blin, N., and Gott, P. Down-Regulation of TFF Expression in Gastrointestinal Cell Lines by Cytokines and Nuclear Factors. Cell. Physiol. Biochem. 2002; 12(4): 197-206. 221. Melnikov, V. Y., Ecder, T., Fantuzzi, G., Siegmund, B., Lucia, M. S., Dinarello, C. A., Schrier, R. W., and Edelstein, C. L. Impaired IL-18 Processing Protects Caspase-1-Deficient Mice From Ischemic Acute Renal Failure. J. Clin. Invest. 2001 ;107(9): 1145-1152. 222. Lochner, M. and Forster, I. Anti-Interleukin-18 Therapy in Murine Models of Inflammatory Bowel Disease. Pathobiology. 2002/2003;70(3): 164-169. 223. Edelstein, C. L. Biomarkers of Acute Kidney Injury. Adv. Chronic Kidney Dis. 2008;15(3):222-234. 224. Melnikov, V. Y., Faubel, S., Siegmund, B., Lucia, M. S., Ljubanovic, D., and Edelstein, C. L. Neutrophil-Independent Mechanisms of Caspase-1- and IL-18-Mediated Ischemic Acute Tubular Necrosis in Mice. J. Clin. Invest. 2002;110(8):1083-1091. 225. Parikh, C. R., Mishra, J., Thiessen-Philbrook, H., Dursun, B., Ma, Q., Kelly, C , Dent, C , Devarajan, P., and Edelstein, C. L. Urinary IL-18 Is an Early Predictive Biomarker of Acute Kidney Injury After Cardiac Surgery. Kidney Int. 2006;70(1): 199-203. 226. Parikh, C. R., Jani, A., Mishra, J., Ma, Q., Kelly, C , Barasch, J., Edelstein, C. L., and Devarajan, P. Urine NGAL and IL-18 are Predictive Biomarkers for Delayed Graft Function Following Kidney Transplantation. Am. J. Transplant. 2006;6(7):1639-1645. 227. Simon, T., Opelz, G., Wiesel, M., Pelzl, S., Ott, R. C , and Siisal, C. Serial Peripheral Blood Interleukin-18 and Perform Gene Expression Measurements for Prediction of Acute Kidney Graft Rejection. Transplantation. 2004;77(10): 1589-1595. 228. Parikh, C. R., Jani, A., Melnikov, V. Y, Faubel, S., and Edelstein, C. L. Urinary Interleukin-18 Is a Marker of Human Acute Tubular Necrosis. Am. J. Kidney Dis. Mar 2004;43(3):405^114. 229. Bulent Gul, C. B., Gullulu, M., Oral, B., Aydinlar, A., Oz, O., Budak, E, Yilmaz, Y, and Yurtkuran, M. Urinary IL-18: A Marker of Contrast-Induced Nephropathy Following Percutaneous Coronary Intervention? Clin. Biochem. 2008;41 (7-8):544-547. 230. Ling, W., Zhaohui, N., Ben, H., Leyi, G., Jianping, L., Huili D, and Jiaqi Q. Urinary IL-18 and NGAL As Early Predictive Biomarkers in Contrast-Induced Nephropathy After Coronary Angiography. Nephron. Clin. Pract. 2008;108(3): C176-181. 231. Haase, M., Bellomo, R., Story, D., Davenport, P., and Haase-Fielitz, A. Urinary Interleukin-18 Does Not Predict Acute Kidney Injury After Adult Cardiac Surgery: A Prospective Observational Cohort Study. Crit. Care. 2008;12(4):R96. 232. Xin, C , Yulong, X., Yu, C , Changchun, C , Feng, Z., and Xinwei, M. Urine Neutrophil Gelatinase-Associated Lipocalin and Interleukin-18 Predict Acute Kidney Injury After Cardiac Surgery. Ren. Fail. 2008;30(9):904-913. 233. Argutusmedical Ltd Homepage, http://www.argutusmed.com. 234. R&D Systems Homepage, www.rndsystems.com. 235. Biovendor Homepage, http://www.biovendor.com. 236. Rules Based Medicine Website, http://www.rulesbasedmedicine.com. 237. Mesoscale Discovery Technology Website, http://www.mesoscale.com/catalogsystemweb/webroot/technology.htm.

BIOMARKERS OF ACUTE KIDNEY INJURY

279

238. Devarajan, P. Neutrophil Gelatinase-Associated Lipocalin (NGAL): A New Marker of Kidney Disease. Scand. J. Clin. Lab. Invest. Suppl. 2008;241:89-94. 239. Http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.html. 240. EMEA CHMP, Final Conclusions on the Pilot Joint EMEA/FDA VXDS Experience on Qualification of Nephrotoxicity Biomarkers. 2009, http://www.emea. europa.eu/pdfs/human/sciadvice/67971908en.pdf. 241. Http://www.fda.govtobs/topics/NEWS/2008/NEW01850.html. 242. HESI 2008 Annual Report, Page 10, http://www.hesiglobal.org/files/public/annual%20reports/HESI2008AnnualReportFinal.pdf. 243. Vaidya, V. S., Waikar, S. S., Ferguson, M. A., Collings, F. B., Sunderland, K., Gioules, C , Bradwin, G., Matsouaka, R., Betensky, R. A., Curhan, G. C, and Bonventre, J. V. Urinary Biomarkers for Sensitive and Specific Detection of Acute Kidney Injury in Humans. Clin. Transl. Sci. 2008; 1(3):200-208. 244. Liangos, O., Tighiouart, H., Perianayagam, M. C , Kolyada, A., Han, W. K., Wald, R., Bonventre, J. V., and Jaber, B. L. Comparative Analysis of Urinary Biomarkers for Early Detection of Acute Kidney Injury Following Cardiopulmonary Bypass. Biomarkers. 2009 In Press.

This page intentionally left blank

CHAPTER

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY James R. Turk

H I S T O R Y A N D B A C K G R O U N D OF D I V I Overview Rising attrition rates, especially in late phase drug development, are driving an unsustainable increase in expenditures required for research and development (R&D).1 The high cost of drug failure during clinical development, plus the requirement to minimize or prevent severe adverse events (AE) that occur after a drug has reached the market, emphasize an unmet need to develop translatable animal models and biomarkers that are predictive of toxicity in humans.2 There has been an increase in the incidence of an AE in preclinical toxicology studies that has been designated "drug-induced vascular injury" (DIVI). This AE is detected only by histology in animals that have exhibited no clinical signs or alterations of routine clinical pathology parameters at the time of sacrifice for standard toxicology studies. The morphological and pharmacological reversibility of DIVI is poorly understood. Noninvasive detection of DIVI is not currently possible due to the lack of specific and sensitive biomarkers of vascular cell injury.3,4-5 This lack of monitorable endpoints drives regulatory concern about the potential for DIVI to go undetected in early clinical trials in normal human volunteers while potentially promoting late phase or postmarketing progression of coronary and peripheral artery disease in human atherosclerosis, for which a number of circulating and functional biomarkers of endofhelial or vascular dysfunction have been proposed.6^16

281

282

BIOMARKERS

Types of Compounds Implicated Vascular injury in experimental animals has been associated with exposure to environmental toxins such as dioxin17 and compounds such as streptozotocin that is used to induce pancreatic beta cell death in animal models of diabetes.18 However, the focus of this chapter is DIVI that may halt the development of drugs to treat or prevent human disease. The observation that a compound as mundane as caffeine produces arteritis in rats19 illustrates species differences in cardiovascular pathophysiology20'21 and susceptibility to toxins, thereby challenging the translatability of preclinical DIVI to humans. Fenoldopam is a prototype compound that induces DIVI in rat mesenteric and canine coronary arteries22'23 for which there is no evidence of human AE. Indeed, fenoldopam is efficacious for indications that include increasing blood flow for cardio- and nephroprotection24-27 and the induction of controlled hypotension for oromaxillofacial and other surgery.28 During the 1980s and mid-'90s changes in mean arterial pressure (MAP) and heart rate (HR) were considered surrogate markers for DIVI in dogs.3'4i 29'30 If therapeutic doses of candidate drugs failed to induce hypotension and reflex tachycardia in humans, these drugs were considered to be safe. Using this guidance, many potent vasodilators that produced preclinical DIVI were developed due to monitorable hypotension with or without reflex tachycardia (Table ll.l). 5 ' 2 2 ' 2 3 ' 3 1 ^ 4 This paradigm shifted when endothelin receptor antagonists were shown to induce DIVI in the coronary arteries of dogs and non-human primates in the absence of significant alteration of HR or MAP, suggesting that these monitorable endpoints were insufficient criteria to exclude vascular injury in humans exposed to comparable doses.3'4'45"49

TABLE I I. I Pharmacological class, prototype drugs, and cardiovascular effects in preclinical druginduced vascular injury.

Pharmacological class

Prototype drug

Cardiovascular effect

A l receptor agonist

Adenosine

BP/reflex tachycardia

(3-adrenergic agonist

Isoproterenol

BP

Dopamine agonist

Fenoldopam

No change

Endothelin antagonist

Bosentan

No change

K-channel openers

Minoxidil

BP/reflex tachycardia

PDEIII inhibitor

Milrinone/theophylline

BP/reflex tachycardia

PDEIV inhibitor

Cilomilast/CI-1044

No change

PDEV inhibitor

Taladafil

BP

Vasoconstrictor

Angiotensin II

BP

Vasodilator

Hydralazine

BP/reflex tachycardia

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY

283

D E S C R I P T I V E P A T H O L O G Y OF D R U G I N D U C E D V A S C U L A R INJURY Vascular Anatomy The microscopic anatomy of the vasculature is relatively simple, consisting of three tunicae: (1) intima, composed of endothelial cells; (2) media, composed of smooth muscle cells and bounded by porous internal and external elastic laminae; and (3) adventitia, composed primarily of fibroblasts, that in epicardial conduit and other larger vessels are surrounded by and interdigitate with vasa vasorum and adipose tissue that contains not only adipocytes, but also capillaries, lymphocytes, macrophages, mast cell/basophils, and nerves.50-57 Additional cells from the circulating blood including erythrocytes, neutrophils, monocytes, eosinophils, and others influence vascular health and disease (Figure 11.1).

FIGURE I I. I Cells of the vasculature that may be involved in vascular injury. (See color insert for a full color version of this figure.)

BIOMARKERS

284

Rat In the rat DIVI primarily affects muscular resistance arteries of the mesentery with an external diameter of 100-800 microns. Necrosis and hemorrhage occur within the tunica media within the first 24 hours (Figure 11.2B). These lesions diminish over the next 3-14 days as leukocytes (macrophages, T-cells and B-cells) accumulate and fibroplasia occurs in the tunica adventitia (Figure 11.2C, D).22,23,32,35'37'u'52,58_67 The lesions are typically random and segmental. Inflammation may occur in the adjacent mesentery.63 DIVI also may occur in arteries of the heart, testis, and the pampiniform plexus.36-65

Dog In the dog DIVI typically affects 200-500 micron epicardial segments of the left and right coronary arteries, although smaller intramyocardial branches may be involved. The histologic changes and time frame are similar to those in the rat mesentery, consisting initially of necrosis of the tunica media with accumulation of hyaline droplets and pockets containing proteinaceous material and/or erythrocytes. In advanced lesions, the hemorrhage may bridge all vascular tunicae with pyknotic and karyorrhectic debris and a few leukocytes in the tunica media.The endothelium of affected arteries may become enlarged and overlain by adherent leukocytes. Hemorrhage, accumulation of mononu-

FIGURE I 1.2 A: Normal rat mesenteric artery; B: Necrosis and hemorrhage occur within the tunica media at 24 hours after fenoldopam; C and D: Accumulation of leukocytes and fibroplasia at seven days after fenoldopam. (See color insert for a full color version of this figure.)

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY

285

clear and polymorphonuclear leukocytes, and proliferation of fibroblasts also occurs in the tunica adventitia.29'30,33,34,39Jl3,49,68~74

Primate In non-human primates the lesions of DIVI are similar to those in the dog, consisting of medial necrosis with cellular debris and mixed inflammatory cell response in the intima, media, and adventitia. Arterial lesions are sometimes present in extracoronary sites including the small and large intestines and testis.75'76

Spontaneous Lesions in Preclinical Species Spontaneous necrotzing arteritis develops with age in hypertensive rats and occurs more frequently in males than females. Lesions are common in mesenteric and testicular arteries, with only the lungs, brain, and aorta spared in males. In females the lesions may be restricted to the tongue, parametrium, and mesentery.77,78 Spontaneous rupture of the pancreatoduodenal artery and hemoabdomen has been reported in ACI/SegHsd rats.79 Necrotizing arteritis of the extramural coronary arteries is a poorly understood spontaneous lesion in dogs. 29,33,34,80 This background lesion has been reported in nearly a quarter of studies performed during drug development.81, 82 Much like the rat, this lesion occurs more frequently in male than female dogs.83 Polyarteritis affecting the testis, epididymis, thymus, and other vascular beds is also observed in young dogs exhibiting febrile response (104-106°F), pain, and neutrophilia.84-86 Segmental necrotizing arteritis of variable severity with transmural mixed inflammatory cell infiltrate, fibrinoid necrosis of the tunica media, and loss of the internal elastic lamina affecting vessels of the kidney, small intestine, colon, heart, spleen, mesentery, urinary bladder, and pancreas have been reported in a cynomolgus monkey. Immunohistochemical staining showed that many of the infiltrating cells were T lymphocytes and histiocytes, suggesting a cell-mediated component to the pathogenesis.87 Spontaneous or background lesions that occur in vehicle control and dosed animals may confound the identification of DIVI as a toxicity that limits drug development. The clinical signs as well as the nature and distribution of lesions may aid in differentiation of background arteritis from DIVI. DIVI tends to be restricted to the mesenteric arteries of rats, and the coronary arteries of dogs and primates; whereas spontaneous arteritides involve medium to small arteries of many organs in addition to the mesenteric or coronary arteries, for which the term polyarteritiis nodosa may be applied. Hemorrhage in the tunicae media and adventitia is absent in spontaneous arteritides, but is consistently associated with DIVI. Animals with DIVI show no clinical signs.

C o m p a r i s o n w i t h Human Vasculitides Vascular toxicity associated with antineoplastic agents is clinically heterogeneous, ranging from asymptomatic arterial lesions to a fatal thrombotic

286

BIOMARKERS

microangiopathic syndrome.87" The term "systemic vasculitis" describes a heterogeneous group of rare diseases characterized by inflammation and fibrinoid necrosis of blood vessel walls. Vasculitis may be primary (with no identifiable cause) or it may be secondary to infection, malignancy, or autoimmune disease. Evidence suggests that accelerated atherosclerosis is a complicating feature of most, if not all, autoimmune diseases including rheumatoid arthritis (RA), scleroderma, sarcoidosis, and systemic lupus erythematosus.88-91 Polyarteritis nodosa is an idiopathic necrotizing vasculitis affecting smallto medium-sized arteries in multiple vascular beds in humans91,92 and may resemble spontaneous background lesions in preclinical species. As discussed above, DIVI is typically restricted to the coronary and mesenteric arteries. Rash is a common manifestation of human drug-induced cutaneous vasculitis in which eosinophils may be prominent in biopsies.93'94 Neither rash nor eosinophils are typical of DIVI.

PROGRESS I N B I O M A R K E R A N D M O D E L D E V E L O P M E N T FOR D R U G - I N D U C E D V A S C U L A R INJURY Noninvasive detection of acute DIVI is not currently possible due to the lack of specific and sensitive biomarkers; however, Table 11.2 contains a nonexhaustive shortlist of some of the most promising candidates that have been nominated in recent years.3"5-32- «• 46-58-59- M- «•69'95

Alpha-1-Acid Glycoprotein Alpha-1-acid glycoprotein, also known as orosomucoid 1, is an acute-phase serum protein produced primarily by hepatocytes, but also by vascular endothelial cells and adipocytes.96-97 Alpha-1-acid glycoprotein inhibits the effects of histamine on endothelial cells.98 Alpha-1-acid glycoprotein has been reported to increase in a dose-dependent fashion within 24 to 72 hours in the serum of rats with mesenteric DIVI.64 Alpha-1-acid glycoprotein can be detected by ELISA in rat and human,64,99 radioimmunoassay in dog100 and by proteomics with mass spectrometry in humans.101

Calprotectin (SI00A9/A8) Calprotectin, also known as S100A9/A8, is a serum biomarker of human vascular inflammation that is expressed by neutrophils, monocytes, and vascular smooth muscle cells.101-106 Calprotectin has been shown by immunohistochemistry to be expressed at day four in canine coronary DIVI.74 Calprotectin can be assayed by RIA in dog106 and ELISA in human.101-106

Caveolin-1 Caveolin 1 is expressed by multiple cell types associated with the vasculature including endothelial cells, smooth muscle cells, fibroblasts, and mac-

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY

TABLE I 1.2

287

Candidate biomarkers for drug-induced vascular injury.

Biomarker

Vascular expression

Detection

Species

Alpha-1 -acid glycoprotein (orosomucoid)

EC,Mac,Adp

ELISA, RIA, proteomics

Rat, dog, human

Calprotectin (S100A8/9)

Mon, Neu

ELISA, IHC, RIA,

Dog, human

Caveolin-1

EC,SMC,Mac,Adpa

IHC RT-PCR, western of PBMC

Brott, 2006; Dalmas 2008; Louden, 2006

Circulating endothelial cells/particles

EC

Ultracentrifugation and flow cytometry

Human

Complement 3

Mon/Mac

ELISA, microarray, proteomics

Rat, dog, human

Connective tissue growth factor

EC, SMC, uptake by Mac, pre-Adp

ELISA

Rat, human

C-Reactive protein

Hepatocyes, SMC

ELISA

Rat, dog, human

Endothelin-I

EC, SMC, Mac, Mst

ELISA

Rat, dog, human

Fibrinogen

Hepatocytes

ELISA

Rat, dog, human

ELISA

Rat

GRO/CINCI Haptoglobin

Hepatocytes.Adp

ELISA, proteomics

Rat, dog, human

Metallothionein-I

EC,Fbl,Mon,Adp

ELISA

Rat, dog, human

Monocyte chemoattractant protein-1

ECSMCMacAdp

ELISA

Rat, dog, human

NGAL

Neu

ELISA

Rat, dog, human

Osteopontin

EC, SMC, Mac, Fbl, Adp

ELISA

Rat, dog, human

Smooth muscle actin

SMC (EC)

Microarray

Rat, human

Thrombospondin-1

EC,SMC,Mac,Adp

ELISA

Rat, dog, human

Tissue inhibitor of metalloproteinases-1

EC,SMC,Mac,Adp

ELISA

Rat, dog, human

Tissue plasminogen activator

ECSMC, Fbl, Mac, Neu

ELISA

Rat, dog, human

Vascular cell adhesion molecule-1

EC, SMC, Mac

ELISA

Rat, dog, human

Vascular endothelial growth factor

ECSMCMacAdp

ELISA

Rat, dog, human

vonWillebrand factor

EC

ELISA

Rat, dog, human

288

BIOMARKERS

rophages.108-114 Decreased immunoreactivity for caveolin-1 has been shown at the onset of canine coronary and rat mesenteric DIVI.4 Caveolin-1 negatively regulates the activity of endothelial nitric oxide synthase (eNOS) and its absence is associated with excess basal release of NO and excessive vascular relaxation.110115 The observation that caveolin-1 induces smooth muscle cell apoptosis116 suggests that it may be a potential marker of DIVI-induced lesions in the tunica media as corroborated by its upregulation in smooth muscle cell-enriched fractions obtained by laser capture microdissection of rat mesenteric DIVI within one to four hours of induction.32 Noninvasive assay for caveolin-1 in humans has been performed by RTPCR, immunohistochemistry or western blotting of isolated peripheral blood mononuclear cells (PBMC),"7 or circulating microparticles.118 Antibodies against rat, dog, and human caveolin-1 are commercially available.

C i r c u l a t i n g Endothelial Cells/Particles Elevation of circulating endothelial cells (CECs) and microparticles has been proposed as a marker of human vasculitis.119'120 CECs and microparticles can be assayed by ultracentrifugation and flow cytometery.121 CEC in human vascular disease correlate with tissue factor and von Willebrand factor6'10,122 that are additional potential markers of DIVI as described by increased expression in endothelial cell-enriched samples of rat mesenteric DIVI discussed below.32 This technique has not yet been fully validated in the rat or dog.

Complement Component 3 Plasma complement component 3a (C3a) correlates with CEC in human autoimmune vasculitis.119 Complement component 3 also has been detected in human serum by proteomics with mass spectrometry.101 Elevated C3a has been assayed by microdialysis catheters in human thermal vascular injury.123 Complement component 3a receptor 1 (C3AR1) has been reported to increase by RNA microarray analysis of PBMC in human vascular thrombosis.124 Complement 3 is expressed by, and induces the conversion of, vascular smooth muscle cells from the contractile to synthetic phenotype associated with expression of osteopontin in rats.125 Complement component 3 expression detected by RNA microarray analysis is upregulated in canine coronary and rat mesenteric DIVI.32,58,59 69 In addition to assays for mRNA in tissues and PBMC, plasma C3a can be assayed by ELISA.118 Commercial ELISA kits are available for rat, dog, and human C3a.

C o n n e c t i v e Tissue G r o w t h Factor (CTGF) Connective tissue growth factor (CTGF) is expressed by multiple cell types associated with the vasculature including endothelial cells, smooth muscle, fibroblasts, and pre-adipocytes.126-131 Non-uniform shear induces CTGF expression in human endothelial cells129 and carotid atherosclerosis correlates

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY

289

with increased plasma CTGF as detected by ELISA.132Upregulation of CTGF has been detected by RNA microarray analysis of vascular smooth muscle cell-enriched samples of rat mesenteric DIVI, suggesting that further study of its potential as a plasma biomarker of DIVI is warranted.32 ELISA kits for rat and human CTGF are commercially available; however these are not reported to be cross reactive for canine CTGF, for which there are no commercially available antibodies.

C-Reactive Protein (CRP) C-reactive protein (CRP) is an acute phase protein produced primarily by the liver, but also by vascular smooth muscle cells.134 CRP has been proposed as a leading candidate biomarker for inflammation and human cardiovascular disease;134,135 however species differences may hamper translational use of CRP since it is a poor marker of acute inflammation in the rat.136 Plasma CRP has been shown by ELISA to be elevated in rat DIVI64 and in dogs with arteritis.137 Commercial ELISA kits are available for rat, dog, and human CRP.

Endothelin-1 Endothelin-1 (ET-1) is expressed by multiple cell types associated with the vasculature including endothelial cells, smooth muscle cells, fibroblasts, macrophages, and mast cells.138-141 Plasma ET-1 detected by ELISA is increased in human vascular disease.142 The induction of DIVI by endothelin receptor antagonists47-49,73,74 and the observations that the expression of endothelin and endothelin receptors are upregulated in rat mesenteric DIVI32-58> 59 suggest that further study ET-1 as a plasma biomarker of DIVI is warranted. Commercial ELISA kits are available for rat and human ET-1, for which cross-reactivity with canine ET-1 is likely.143'144

Fibrinogen Fibrinogen is a plasma glycoprotein synthesized in the liver. Plasma fibrinogen is increased in human peripheral arterial disease15 and increases in canine coronary and rat mesenteric DIVI.58,59, m Fibrinogen mRNA expression is upregulated in rat mesenteric DIVI.32'58'59 Commercial ELISA kits are available for rat, dog, and human fibrinogen.

GRO/CINC-I Rat CINC is a homolog of human GRO and interleukin-8 homolog that has recently been shown to increase as early as four hours post DIVI in the rat mesentery.64'145

Haptoglobin Haptoglobin is expressed primarily by hepatocytes; however ischemia upregulates haptoglobin mRNA expression in adipocytes.146 Haptoglobin is stored in neutrophil granules and is released on activation to exert anti-inflammatory

290

BIOMARKERS

effects.147 Plasma haptoglobin has been shown by ELISA to be elevated in rat DIVI.64 Plasma haptoglobin has been identified by proteomics and mass spectroscopy as a biomarker of vascular disease in humans101-148 and rats.149 Commercial ELISA kits are available for rat, dog, and human haptoglobin.

Metallothionein-I

(MT-I)

Metallothionein-1 (MT-1) binds heavy metals and exerts antioxidant activity. It is expressed in endothelial cells, macrophages, fibroblasts, and adipocytes, and in human vascular lesions.150-153 MT-1 expression is decreased in humans with low plasma HDL-cholseterol.154 MT-1 mRNA is upregulated in rat mesenteric DIVI.58,59 MT-1 expression in endothelial cells, smooth muscle cells, and fibroblasts was demonstrated by in situ hybridization as was upregulation of mRNA in endothelial cell-enriched samples obtained by laser capture microdissection of rat mesenteric DIVI.32 Commercial ELISA kits are available for rat, dog, and human metallothionein.

M o n o c y t e C h e m o a t t r a c t a n t Protein-1 ( M C P - I ) Monocyte chemoattractant protein-1 (MCP-1) is expressed by multiple cell types associated with the vasculature including endothelial cells, smooth muscle cells, fibroblasts, macrophages, mast cells, and adipocytes.54- "3-155-159 MCP-1 gene polymorphisms correlate with serum MCP-1 and prognosis in human vascular disease.160-161 MCP-1 mRNA is upregulated in canine coronary DIVI.69 Commercial ELISA kits are available for rat, dog, and human MCP-1.

N e u t r o p h i l Gelatinase-Associated Lipocalin ( N G A L ) Neutrophil gelatinase-associated lipocalin (NGAL) is expressed in endothelial cells, smooth muscle cells, and macrophages in human vascular disease.162 Vascular injury upregulates the expression of neutrophil gelatinase-associated lipocalin (NGAL) mRNA and protein in an NF-kappaB-dependent manner in rat and human vascular smooth muscle cells.163 Serum neutrophil gelatinaseassociated lipocalin (NGAL) correlates with relapse of vasculitis in humans.164 Commercial ELISA kits are available for NGAL in rat, dog, and human.

Osteopontin (OPN) Osteopontin (OPN) is expressed by endothelial cells, smooth muscle cells, macrophages, adventitial fibroblasts, and adipocytes.165-171 As described above, complement component 3 is expressed by and induces the expression of osteopontin in smooth muscle cells of rats.125 Plasma OPN is associated with the presence and extent of human vascular disease.160-172,173 Osteopontin (OPN) mRNA expression is upregulated in rat mesenteric DIVI.32-58-59 Commercial ELISA kits are available for rat, dog, and human OPN.

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY

291

Smooth Muscle A c t i n Alpha-smooth muscle actin is expressed primarily in SMC; however, it also may be expressed by microvascular endothelial cells and adventitial fibroblasts.174' "5 Increased numbers of large spindle cells expressing alpha-smooth muscle actin have been isolated from human peripheral blood mononuclear cells in human coronary artery disease.176 Smooth muscle actin is upregulated in smooth muscle cell-enriched fractions obtained by laser capture microdissection of rat mesenteric DIVI one to four hours after induction.32 At present there are no validated assays to assess smooth muscle actin in plasma or circulating PBMN for the rat or dog.

T h r o m b o s p o n d i n - 1 (TSP-I) Thrombospondin-1 (TSP-1) is expressed by multiple cell types of the vasculature including endothelial cells, smooth muscle cells, fibroblasts, macrophages, mast cells, and adiopcytes.177-180 Endothelial cell injury correlates with TSP-1.181 Thrombospondin-1 precursor mRNA expression is upregulated in canine coronary DIVI69 and in endothelial cell-enriched samples in rat mesenteric DIVI at the one-hour and/or four-hour time points.32 Commercial ELISA kits are available for rat, dog, and human TSP-1.

Tissue I n h i b i t o r of Metalloporteinases-1 ( T I M P - I ) Tissue inhibitor of metalloproteinases-1 (TIMP-1) is expressed by endothelial cells, smooth muscle cells, macrophages, fibroblasts, and adipocytes.182'184 TIMP-1 expression is greater in aortic smooth muscle cells from male than female rats.185 Plasma levels of TIMP-1 increase in human vascular disease.186- 187 TIMP-1 mRNA is upregulated in canine coronary69 and rat mesenteric DIVI.5859 Commercial ELISA kits are available for rat, dog, and human TIMP-1.

Tissue Plasminogen A c t i v a t o r (tPA) Tissue plasminogen activator (tPA) is expressed by endothelial cells, smooth muscle cells, fibroblasts, macrophages, and neutrophils.188-190 Serum tPA is increased in Henoch-Schonlein purpura, a childhood vasculitis.191 tPA mRNA is upregulated in rat mesenteric DIVI.32'58,59 Commercial ELISA kits are available for rat, dog, and human tPA.

Vascular Cell Adhesion Molecule I ( V C A M - I ) VCAM-1 is expressed by endothelial cells, smooth muscle cells, and macrophages.192, 193 Serum VCAM-1 increases in human vasculitis.194 VCAM-1 mRNA expression is upregulated in endothelial cell-enriched samples from rat mesenteric DIVI.32 Commercial ELISA kits are available for rat, dog, and human VCAM-1.

292

BIOMARKERS

Vascular Endothelial Growth Factor (VEGF) Vascular endothelial growth factor (VEGF) is expressed in endothelial cells, smooth muscle cells, fibroblasts, macrophages, mast cells, and adipocytes.195-198 Serum VEGF is an early marker of vascular damage that increases in acute human vasculitis and falls in remission.199-200 Serum VEGF increasd in rat mesenteric DIVI64 and VEGF mRNA was upregulated in endothelial cell-enriched samples microdissected from rat mesenteric DIVI.32 Commercial ELISA kits are available for rat, dog, and human VEGF.

Von Willebrand Factor Von Willebrand factor (VWF) is a plasma glycoprotein involved in platelet adhesion at the site of vascular damage, which acts as a bridge between the injured subendothelium and the platelet receptors.201 vWF in CEC correlates with human vascular disease.6202 In patients with preexisting vascular disease, VWF rises during acute coronary syndromes, and the extent of this VWF release is an independent predictor of adverse clinical outcome.203 vWF mRNA is upregulated in endothelial cell-enriched samples microdissection from rat mesenteri DIVI.32 Commercial ELISA kits are available for rat, dog, and human vWF.

CONCLUSIONS Drug-induced vascular injury in pre-clinical species currently halts the development of many drugs that have the potential to treat or prevent human disease. The noninvasive detection of DIVI is not currently possible due to the lack of specific and sensitive biomarkers. Table 11.2 contains a number of promising candidates that require further validation to facilitate their use for translation of preclinical findings to risk assessment of drugs for vascular injury in humans.2

REFERENCES 1. 2. 3. 4. 5.

Peck, R. W. Driving Earlier Clinical Attrition: If You Want to Find the Needle, Burn Down the Haystack. Considerations for Biomarker Development. Drug Discov. Today. 2007;12:289-294. Mattes, W. B. and Walker, E. G. Translational Toxicology and the Work of the Predictive Safety Testing Consortium. Clin. Pharmacol. Ther. 2009;85:327-330. Brott, D. A., Jones, H. B., Gould, S., Valentin, J. P., and Evans, G., et al. Current Status and Future Directions for Diagnostic Markers of Drug-Induced Vascular Injury. Cancer Biomark. 2005;1:15-28. Brott, D., Gould, S., Jones, H., Schofield, J., and Prior, H., et al. Biomarkers of Drug-Induced Vascular Injury. Toxicol. Appl. Pharmacol. 2005,207(2 Suppl): 441^145. Kerns, W., Schwartz, L., Blanchard, K., Burchiel, S., Essayan, D., and Fung, E., et al. Drug-Induced Vascular Injury—A Quest for Biomarkers. Toxicol. Appl. Pharmacol. 2005;203:62-87.

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY 293 6.

7. 8. 9. 10.

11. 12.

13. 14. 15. 16. 17.

18. 19. 20.

21.

Boos, C. J., Balakrishnan, B., Blann, A. D., and Lip, G. Y. The Relationship of Circulating Endothelial Cells to Plasma Indices of Endothelial Damage/Dysfunction and Apoptosis in Acute Coronary Syndromes: Implications for Prognosis. J. Thromb. Haemost. 2008;6:1841-1850. Halcox, J. P., Donald, A. E., Ellins, E., Witte, D. R., and Shipley, M. J., et al. Endothelial Function Predicts Progression of Carotid Intima-Media Thickness. Circulation. 2009;119:1005-1012. Koenig, W. and Khuseyinova, N. Biomarkers of Atherosclerotic Plaque Instability and Rupture. Arterioscler. Thromb. Vase. Biol. 2007;27:15-26. Koenig, W. Update on Integrated Biomarkers for Assessment of Long-Term Risk of Cardiovascular Complications in Initially Healthy Subjects and Patients with Manifest Atherosclerosis. Ann. Med. 2009;16:1-12. Lee, K. W., Blann, A. D., and Lip, G. Y Inter-Relationships of Indices of Endothelial Damage/Dysfunction (Circulating Endothelial Cells, Von Willebrand Factor and Flow-Mediated Dilatation) to Tissue Factor and Interleukin-6 in Acute Coronary Syndromes. Int. J. Cardiol. 2006;111:302-308. Packard, R. R. and Libby, P. Inflammation in Atherosclerosis: From Vascular Biology to Biomarker Discovery and Risk Prediction. Clin. Chem. 2008;54: 24-38. Pearson, T. A., Mensah, G. A., Alexander, R. W., Anderson, J. L., and Cannon, R. O. Ill, et al. Markers of Inflammation and Cardiovascular Disease: Application to Clinical and Public Health Practice: A Statement for Healthcare Professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 2003;107:499-511. Steffel, J. and Liischer, T. F. Predicting the Development of Atherosclerosis. Circulation. 2009;119:919-921. Tardif, J. C , Heinonen, T., Orloff, D., and Libby, P. Vascular Biomarkers and Surrogates in Cardiovascular Disease. Circulation. 2006; 113:2936-2942. Tzoulaki, I., Murray, G. D., Lee, A. J., Rumley, A., and Lowe, G. D., et al. Inflammatory, Haemostatic, and Rheological Markers for Incident Peripheral Arterial Disease: Edinburgh Artery Study. Eur. Heart J. 2007;28:354-362. Vasan, R. S. Biomarkers of Cardiovascular Disease: Molecular Basis and Practical Considerations. Circulation. 2006;113:2335-2362. Jokinen, M. P., Walker, N. J., Brix, A. E., Sells, D. M., and Haseman, J. K., et al. Increase in Cardiovascular Pathology in Female Sprague-Dawley Rats Following Chronic Treatment with 2,3,7,8-Tetrachlorodibenzo-P-Dioxin and 3, 3', 4, 4', 5-Pentachlorobiphenyl. Cardioovasc. Toxicol. 2003;3:299-310. Baczako, K. and Dolderer, M. Polyarteritis Nodosa-Like Inflammatory Vascular Changes in the Pancreas and Mesentery of Rats Treated with Streptozotocin and Nicotinamide. J. Comp. Pathol. 1997;116:171-180. Johansson, S. Cardiovascular Lesions in Sprague-Dawley Rats Induced by Long-Term Treatment with Caffeine. Ada Pathol. Microbiol. Scand. [A]. 1981; 89185-11. Maxwell, M. P., Hearse, D. J., and Yellon, D. M. Species Variation in the Coronary Collateral Circulation During Regional Myocardial Ischaemia: A Critical Determinant of the Rate of Evolution and Extent of Myocardial Infarction. Cardiovasc. Res. 1987;21:737-746. Turk, J. R. and Laughlin, M. H. Invited Review: Physical Activity and Atherosclerosis: Which Animal Model? Canadian Journal of Applied Physiology. 2004;29:657-683.

294

BIOMARKERS 22. 23. 24. 25.

26.

27. 28. 29. 30. 31. 32.

33. 34. 35.

36. 37. 38.

Kerns, W. D., Arena, E., Macia, R. A., Bugelski, P. J., and Matthews, W. D., et al. Pathogenesis of Arterial Lesions Induced by Dopaminergic Compounds in the Rat. Toxicol. Pathol. 1989;17:203-213. Kerns, W. D., Arena, E., and Morgan, D. G. Role of Dopaminergic and Adrenergic Receptors in the Pathogenesis of Arterial Lesions Induced by Fenoldopam Mesylate and Dopamine in the Rat. Am. J. Pathol. 1989;135:339-349. Ng, M. K., Tremmel, J., Fitzgerald, P. J., and Fearon, W. F. Selective Renal Arterial Infusion of Fenoldopam for the Prevention of Contrast-Induced Nephropathy. J. Interv. Cardiol. 2006;19:75-79. Cogliati, A. A., Vellutini, R., Nardini, A., Urovi, S., and Hamdan, M., et al. Fenoldopam Infusion for Renal Protection in High-Risk Cardiac Surgery Patients: A Randomized Clinical Study. J. Cardiothorac. Vase. Anesth. 2007; 21: 847-850. Landoni, G., Biondi-Zoccai, G. G., Marino, G., Bove, T., and Fochi, O., et al. Fenoldopam Reduces the Need for Renal Replacement Therapy and In-Hospital Death in Cardiovascular Surgery: A Meta-Analysis. 2008. J. Cardiothorac. Vase. Anesth. 2008;22:27-33. Shi, Y., Zalewski, A., Bravette, B., Maroko, A. R., and Maroko, P. R. Selective Dopamine-1 Receptor Agonist Augments Regional Myocardial Blood Flow: Comparison of Fenoldopam and Dopamine. Am. Heart J. 1992;124:418-423. Degoute, C. S. Controlled Hypotension: A Guide to Drug Choice. Drugs. 2007; 67:1053-1076. Greaves, P. Patterns of Drug-Induced Cardiovascular Pathology in the Beagle Dog: Relevance for Humans. Exp. Toxicol. Pathol. 1998;50:283-293. Greaves, P. Patterns of Cardiovascular Pathology Induced by Diverse Cardioactive Drugs. Toxicol. Lett. 2000;112-113:547-552. Chelly, J. E., Doursout, M. R, Begaud, B., Tsao, C. C , and Hartley, C. J. Effects of Hydralazine on Regional Blood Flow in Conscious Dogs. J. Pharmacol. Exp. Then 1986;238:665-669. Dalmas, D. A., Scicchitano, M. S., Chen, Y., Kane, J., and Mirabile, R., et al. Transcriptional Profiling of Laser Capture Microdissected Rat Arterial Elements: Fenoldopam-Induced Vascular Toxicity as a Model System. Toxicol. Pathol. 2008;36:496-519. Detweiler, D. K., Ratcliffe, H. L., and Luginbiihl, H. The Significance of Naturally Occurring Coronary and Cerebral Arterial Disease in Animals. Ann. N.Y. Acad.Sci. 1968;149:868-881. Detweiler, D. K. Spontaneous and Induced Arterial Disease in the Dog: Pathology and Pathogenesis. Toxicol. Pathol. 1989;17:94-108. Hanton, G., Le Net, J. L., Ruty, B., and Leblanc, B. Characterization of the Arteritis Induced by Infusion of Rats with UK-61, 260, an Inodilator, for 24 H. A Comparison with the Arteritis Induced by Fenoldopam Mesylate. Arch. Toxicol. 1995;69:698-704. Herman, E. H., Zhang, J., Chadwick, D. P., and Ferrans, V. J. Age Dependence of the Cardiac Lesions Induced by Minoxidil in the Rat. Toxicology. 1996;110: 71-83. Ikegami, H., Shishido, T, Ishida, K., Hanada, T, and Nakayama, H., et al. Histopathological and Immunohistochemical Studies on Arteritis Induced by Fenoldopam, a Vasodilator, in Rats. Exp. Toxicol. Pathol. 2001;53:25-30. Ikegami, H., Kajikawa, S., Ito, K., Nii, A., and Okamiya, H., et al. Immunohistochemical Study on Inducible Type of Nitric Oxide (Inos), Basic Fibroblast

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY 295

39. 40. 41. 42.

43.

44. 45.

46. 47. 48. 49. 50. 51. 52.

53.

Growth Factor (Bfgf) and Tumor Growth Factor-Betal (TGF-Betal) in Arteritis Induced in Rats by Fenoldopam and Theophylline, Vasodilators. Exp. Toxicol. Pathol. 2QQ2\5A:\-1. Mesfin, G. M., Shawaryn, G. G., and Higgins, M. J. Cardiovascular Alterations in Dogs Treated with Hydralazine. Toxicol. Pathol. 1987;15: 409-416. Mesfin, G. M., Piper, R. C , Ducharme, D.W., Carlson, R. G., and Humphrey, S. J., et al. Pathogenesis of Cardiovascular Alterations in Dogs Treated with Minoxidil. Toxicol. Pathol. 1989;17:164-181. Mesfin, G. M., Robinson, F. G., Higgins, M. J., Zhong, W. Z., and Ducharme, D. W. The Pharmacologic Basis of the Cardiovascular Toxicity of Minoxidil in the Dog. Toxicol. Pathol. 1995;23:498-506. Mesfin, G. M., Higgins, M. J., Robinson, F. G., and Zhong, W. Z. Relationship between Serum Concentrations, Hemodynamic Effects, and Cardiovascular Lesions in Dogs Treated with Minoxidil. Toxicol. Appl. Pharmacol. 1996; 140: 337-344. Liang, C. S., Thomas, A., Imai, N., Stone, C. K., and Kawashima, S., et al. Effects of Milrinone on Systemic Hemodynamics and Regional Circulations in Dogs with Congestive Heart Failure: Comparison with Dobutamine. J. Cardiovasc. Pharmacol. 1987;10:509-516. Nyska, A., Herbert, R. A., Chan, P. C , Haseman, J. K., and Hailey, J. R. Theophylline-Induced Mesenteric Periarteritis in F344/N Rats. Arch. Toxicol. 1998;72:731-737. Louden, C. and Morgan, D. G. Pathology and Pathophysiology of Drug-Induced Arterial Injury in Laboratory Animals and Its Implications on the Evaluation of Novel Chemical Entities for Human Clinical Trials. Pharmacol. Toxicol. 2001;89:158-170. Louden, C , Brott, D., Katein, A., Kelly, T, and Gould, S., et al. Biomarkers and Mechanisms of Drug-Induced Vascular Injury in Non-Rodents. Toxicol. Pathol. 2006;34:19-26. Albassam, M. A., Metz, A. L., Gragtmans, N. J., King, L. M., and MacAllum, G. E., et al. Coronary Arteriopathy in Monkeys Following Administration of CI1020, an Endothelin a Receptor Antagonist. Toxicol. Pathol. 1999;27:156-164. Albassam, M. A., Metz, A. L., Potoczak, R. E., Gallagher, K. P., and Haleen, S., et al. Studies on Coronary Arteriopathy in Dogs Following Administration of CI1020, an Endothelin a Receptor Antagonist. Toxicol. Pathol. 2001;29:277-284. Jones, H. B., Macpherson, A., Betton, G. R., Davis, A. S., and Siddall, R., et al. Endothelin Antagonist-Induced Coronary and Systemic Arteritis in the Beagle Dog. Toxicol. Pathol. 2003;31:263-272. Gonzalez, J. M., Briones, A. M., Somoza, B., Daly, C. J., and Vila, E., et al. Postnatal Alterations in Elastic Fiber Organization Precede Resistance Artery Narrowing in SHR. Am. J. Physiol. Heart. Circ. Physiol. 2006;291:H804-12. Birch, D. J., Turmaine, M., Boulos, P. B., and Burnstock, G. Sympathetic Innervation of Human Mesenteric Artery and Vein. Vase. Res. 2008;45:323-332. Boor, P. J., Gotlieb, A. I., Joseph, E. C, Kerns, W. D., Roth, R. A., and Tomaszewski, K. E. Chemical-Induced Vasculature Injury. Summary of the Symposium Presented at the 32nd Annual Meeting of the Society of Toxicology, New Orleans, Louisiana, March 1993. Toxicol. Appl. Pharmacol. 1995;132:177-195. Bruzzone, P., Cavallotti, C , Mancone, M., and Tranquilli Leali, F. M. AgeRelated Changes in Catecholaminergic Nerve Fibers of Rat Heart and Coronary Vessels. Gerontology. 2003;49:80-85.

296

BIOMARKERS 54. 55. 56. 57. 58. 59.

60. 61. 62. 63. 64.

65. 66.

67.

68. 69.

Chatterjee, T. K., Stall, L. L., Denning, G. M., Harrelson, A., and Blomkalns, A. L., et al. Proinflammatory Phenotype of Perivascular Adipocytes: Influence of High-Fat Feeding. Circ. Res. 2009;104:541-549. Hsieh, N. K., Liu, J. C , and Chen, H. I. Localization of Sympathetic Postganglionic Neurons Innervating Mesenteric Artery and Vein in Rats. J. Auton Nerv. Syst. 2000;80:1-7. Reifenberger, M. S., Turk, J. R., Newcomer, S. C , Booth, F. W., and Laughlin, M. H. Perivascular Fat Alters Reactivity of Coronary Artery: Effects of Diet and Exercise. Med. Sci. Sports Exerc. 2007;39:2125-2134. Sacks, H. S. and Fain, J. N. Human Epicardial Adipose Tissue: A Review. Am. Heart J. 2007;153:907-917. Dagues, N., Pawlowski, V., Guigon, G., Ledieu, D., and Sobry, C , et al. Altered Gene Expression in Rat Mesenteric Tissue Following In Vivo Exposure to a Phosphodiesterase 4 Inhibitor. Toxicol. Appl. Pharmacol. 2007;218:52-63. Dagues, N., Pawlowski, V., Sobry, C , Hanton, G., and Borde, F, et al. Investigation of the Molecular Mechanisms Preceding PDE4 Inhibitor-Induced Vasculopathy in Rats: Tissue Inhibitor of Metalloproteinase 1, a Potential Predictive Biomarker. Toxicol. Sci. 2007;100:238-247. Joseph, E. C , Rees, J. A., and Dayan, A. D. Mesenteric Arteriopathy in the Rat Induced by Phosphodiesterase III Inhibitors: An Investigation of Morphological, Ultrastructural, and Hemodynamic Changes. Toxicol. Pathol. 1996;24:436-450. Joseph. E. C. Arterial Lesions Induced by Phosphodiesterase III (PDE III) Inhibitors and DA(1) Agonists. Toxicol. Lett. 2000;112-113:537-546. Larson, J. L., Pino, M. V, Geiger, L. E., and Simeone, C. R. The Toxicity of Repeated Exposures to Rolipram, a Type IV Phosphodiesterase Inhibitor, in Rats. Pharmacol. Toxicol. 1996;78:44-^9. Mecklenburg, L., Heuser, A., Juengling, T., Kohler, M., and Foell, R., et al. Mesenteritis Precedes Vasculitis in the Rat Mesentery After Subacute Administration of a Phosphodiesterase Type 4 Inhibitor. Toxicol. Lett. 2006;163:54-64. Weaver, J. L., Snyder, R., Knapton, A., Herman, E. H., and Honchel, R., et al. Biomarkers in Peripheral Blood Associated with Vascular Injury in SpragueDawley Rats Treated with the Phosphodiesterase IV Inhibitors SCH 351591 or SCH 534385. Toxicol. Pathol. 2008;36:840-849. Westwood, F. R., Iswaran, T. J., and Greaves, P. Pathologic Changes in Blood Vessels Following Administration of an Inotropic Vasodilator (ICI 153,110) to the Rat. Fundam. Appl. Pharmacol. 1990;14:797-809. Zhang, J., Herman, E. H., Robertson, D. G., Reily, M. D., and Knapton, A., et al. Mechanisms and Biomarkers of Cardiovascular Injury Induced by Phosphodiesterase Inhibitor III SK&F 95654 in the Spontaneously Hypertensive Rat. Toxicol. Pathol. 2006;34:152-163. Zhang, J., Snyder, R. D., Herman, E. H., Knapton, A., and Honchel, R., et al. Histopathology of Vascular Injury in Sprague-Dawley Rats Treated with Phosphodiesterase IV Inhibitor SCH 351591 or SCH 534385. Toxicol. Pathol. 2008;36:827-839. Clemo, F. A., Evering, W. E., Snyder, P. W., and Albassam, M. A. Differentiating Spontaneous from Drug-Induced Vascular Injury in the Dog. Toxicol. Pathol. 2003;31Suppl:25-31. Enerson, B. E., Lin, A., Lu, B., Zhao, H., and Lawton, M. P., et al. Acute DrugInduced Vascular Injury in Beagle Dogs: Pathology and Correlating Genomic Expression. Toxicol. Pathol. 2006;34:27-32.

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY 297 70.

Harleman, J. H., Joseph, E. C , Eden, R. J., Walker, T. E, and Major, I. R., et al. Cardiotoxicity of a New Inotrope/Vasodilator Drug (SK&F 94120) in the Dog. Arch. Toxicol. 1986;59:51-55. 71. Isaacs, K. R., Joseph, E. C, and Betton, G. R. Coronary Vascular Lesions in Dogs Treated with Phosphodiesterase III Inhibitors. Toxicol. Pathol. 1989;17:153-163. 72. Joseph, E. C , Jones, H. B., and Kerns, W. D. Characterization of Coronary Arterial Lesions in the Dog Following Administration of SK&F 95654, a Phosphodiesterase III Inhibitor. Toxicol. Pathol. 1996;24:429^t35. 73. Louden, C. S., Nambi, P., Pullen, M. A., Thomas, R. A., and Tierney, L. A., et al. Endothelin Receptor Subtype Distribution Predisposes Coronary Arteries to Damage. Am. J. Pathol. 2000;157:123-134. 74. McDuffie, J. E., Yu, X., Sobocinski, G., Song, Y, and Chupka, J., et al. Acute Coronary Artery Injury in Dogs Following Administration of CI-1034, an Endothelin a Receptor Antagonist. Cardiovasc. Toxicol. 2006;6:25-38. 75. Albassam, M. A., Lillie, L. E., and Smith, G. S. Asymptomatic Polyarteritis in a Cynomolgus Monkey. Lab. Anim. Sci. 1993;43:628-629. 76. Albassam, M. A., Smith, G. S., and MacAUum, G. E. Arteriopathy Induced by an Adenosine Agonist-Antihypertensive in Monkeys. Toxicol. Pathol. 1999;26:375-380. 77. Saito, N. and Kawamura, H. The Incidence and Development of Periarteritis Nodosa in Testicular Arterioles and Mesenteric Arteries of Spontaneously Hypertensive Rats. Hypertens. Res. 1999;22:105-112. 78. Suzuki, T., Oboshi, S., and Sato, R. Periarteritis Nodosa in Spontaneously Hypertensive Rats—Incidence and Distribution. Ada Pathol. Jpn. 1979;29:697-703. 79. Cohen, J. K., Cai, L. Q., Zhu, Y. S., and La Perle, K. M. Pancreaticoduodenal Arterial Rupture and Hemoabdomen in ACI/Seghsd Rats with Polyarteritis Nodosa. Comp. Med. 2007;57:370-376. 80. Spencer, A. and Greaves, P. Periarteritis in a Beagle Colony. J. Comp. Pathol. 1987;97:121-128. 81. Hartman, H. A. Idiopathic Extramural Coronary Arteritis in Beagle and Mongrel Dogs. Vet. Pathol. 1987;24:537-544. 82. Hartman, H. A. Spontaneous Extramural Coronary Arteritis in Dogs. Toxicol. Pathol. 1987;17:138-144. 83. Son, W. C. Idiopathic Canine Polyarteritis in Control Beagle Dogs from Toxicity Studies. J. Vet. Sci. 2004;5:147-150. 84. Albassam, M. A., Houston, B. J., Greaves, P., and Barsoum, N. Polyarteritis in a Beagle. J. Am. Vet. Med. Assoc. 1989;194:1595-1597. 85. Hayes, T. J., Roberts, G. K., and Halliwell, W. H. An Idiopathic Febrile Necrotizing Arteritis Syndrome in the Dog: Beagle Pain Syndrome. Toxicol. Pathol. 1989;17:129-131. 86. Snyder, P. W., Kazacos, E. A., Scott-Moncrieff, J. C , Hogenesch, H., and Carlton, W. W., et al. Pathologic Features of Naturally Occurring Juvenile Polyarteritis in Beagle Dogs. Vet. Pathol. 1995;32:337-345. 87. Porter, B. E, Frost, P., and Hubbard, G. B. Polyarteritis Nodosa in a Cynomolgus Macaque (Macaca Fascicularis). Vet. Pathol. 2003;40:570-573. 87A. Doll, D. C , Ringenberg, Q. S., and Yarbro, J. W. Vascular Toxicity Associated with Antineoplastic Agents. J. Clin. Oncol. 1986;4:1405-1417. 88. Filer, A. D., Gardner-Medwin, J. M., Thambyrajah, J., Raza, K., and Carruthers, D. M., et al. Diffuse Endothelial Dysfunction Is Common to ANCA Associated Systemic Vasculitis and Polyarteritis Nodosa. Ann. Rheum. Dis. 2003 ;62: 162-167.

298

BIOMARKERS 89. 90. 91. 92. 93. 94. 95. 96. 97.

98. 99.

100. 101. 102.

103. 104.

105.

Guillevin, L. and Dorner, T. Vasculitis: Mechanisms Involved and Clinical Manifestations. Arthritis Res. Then 2007;9 Suppl 2:S9. Kallenberg, C. G., Heeringa, P., and Stegeman, C. A. Mechanisms of Disease: Pathogenesis and Treatment of ANCA-Associated Vasculitides. Nat. Clin. Pract. Rheumatol. 2006;2:661-670. Kane, G. C. and Keogh, K. A. Involvement of the Heart by Small and Medium Vessel Vasculitis. Curr. Opin. Rheumatol. 2009;21:29-34. Jennette, J. C , Falk, R. J., Andrassy, K., Bacon, P. A., and Churg, J., et al. Nomenclature of Systemic Vasculitides. Proposal of an International Consensus Conference. Arthritis. Rheum. 1994;37:187-192. Bahrami, S., Malone, J. C , Webb, K. G., and Callen, J. P. Tissue Eosinophilia As an Indicator of Drug-Induced Cutaneous Small-Vessel Vasculitis. Arch. Dermatol. 2006;142:155-161. Ramdial, P. K. and Naidoo, D. K. Drug-Induced Skin Pathology. J. Clin. Pathol. Jan 20, 2009;[Epub ahead of print] Tesfamariam, B. and Defelice, A. F. Endothelial Injury in the Initiation and Progression of Vascular Disorders. Vascul. Pharmacol. 2007;46:229-237. Castriota, G., Thompson, G. M., Lin, Y., Scherer, P. E., and Moller, D. E., et al. Peroxisome Proliferator-Activated Receptor Gamma Agonists Inhibit Adipocyte Expression of Alphal-Acid Glycoprotein. Cell Biol. Int. 2007;31:586-591. Martinez Cordero, E., Gonzalez, M. M., Aguilar, L. D., Orozco, E. H., and Hernandez Pando, R. Alpha-1-Acid Glycoprotein, Its Local Production and Immunopathological Participation in Pulmonary Tuberculosis. Tuberculosis (Edinb). 2008;88:203-211. Sorensson, J., Matejka, G. L., Ohlson, M., and Haraldsson, B. Human Endothelial Cells Produce Orosomucoid, an Important Component of the Capillary Barrier. Am. J. Physiol. 1999;276:H530-534. Maachi, M., Pieroni, L., Bruckert, E., Jardel, C , and Fellahi, S., et al. Systemic Low-Grade Inflammation Is Related to Both Circulating and Adipose Tissue Tnfalpha, Leptin and IL-6 Levels in Obese Women. Int. J. Obes. Relat. Metab. Disord. 2004;28:993-997. Yuki, M., Itoh, H., Tamura, K., Nishii, N., and Takase, K. Isolation, Characterization and Quantitation of Canine Alpha-1-Acid Glycoprotein. Vet. Res. Commun. 2008;32:533-542. Zhang, R., Barker, L., Pinchev, D., Marshall, J., and Rasamoelisolo, M., et al. Mining Biomarkers in Human Sera Using Proteomic Tools. Proteomics. 2004; 4:244-256. Altwegg, L. A., Neidhart, M., Hersberger, M., Miiller, S., and Eberli, F. R., et al. Myeloid-Related Protein 8/14 Complex Is Released by Monocytes and Granulocytes at the Site of Coronary Occlusion: A Novel, Early, and Sensitive Marker of Acute Coronary Syndromes. Eur. Heart J. 2007;28:941-948. Cagnin, S., Biscuola, M., Patuzzo, C , Trabetti, E., and Pasquali, A., et al. Reconstruction and Functional Analysis of Altered Molecular Pathways in Human Atherosclerotic Arteries. BMC Genomics. 2009;10:13. Inaba, H., Hokamura, K., Nakano, K., Nomura, R., and Katayama, K., et al. Upregulation of S100 Calcium-Binding Protein A9 Is Required for Induction of Smooth Muscle Cell Proliferation by a Periodontal Pathogen. FEBS Lett. 2009;583:128-134. Larsson, P. T., Hallerstam, S., Rosfors, S., and Wallen, N. Circulating Markers of Inflammation are Related to Carotid Artery Atherosclerosis. Int. Angiol. 2005;24:43-51.

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY 299 106. Miyamoto, S., Ueda, M., Ikemoto, M., Naruko, T., and Itoh, A., et al. Increased Serum Levels and Expression of S100A8/A9 Complex in Infiltrated Neutrophils in Atherosclerotic Plaque of Unstable Angina. Heart. 2008;94:1002-1007. 107. Heilmann, R. M., Suchodolski, J. S., and Steiner, J. M. Development and Analytic Validation of a Radioimmunoassay for the Quantification of Canine Calprotectin in Serum and Feces from Dogs. Am. J. Vet. Res. 2008;69:845-853. 108. Frank, P. G., Hassan, G. S., Rodriguez-Feo, J. A., and Lisanti, M. P. Caveolae and Caveolin-1: Novel Potential Targets for the Treatment of Cardiovascular Disease. Curr. Pharm. Des. 2007;13:1761-1769. 109. Frank, P. G., Pavlides, S., and Lisanti, M. P. Caveolae and Transcytosis in Endothelial Cells: Role in Atherosclerosis. Cell Tissue Res. 2009;335:41^7. 110. Murata, T., Lin, M. I., Huang, Y, Yu, J., and Bauer, P. M., et al. Reexpression of Caveolin-1 In Endothelium Rescues the Vascular, Cardiac, and Pulmonary Defects in Global Caveolin-1 Knockout Mice. J. Exp. Med. 2007;204:2373-2382. 111. Musicki, B., Liu, T., Strong, T., Jin, L., Laughlin, M. H., and Turk, J. R., et al. Low-Fat Diet and Exercise Preserve Enos Regulation and Endothelial Function in the Penis of Early Atherosclerotic Pigs: A Molecular Analysis. J. Sex Med. 2008;5:552-561. 112. Thompson, M. A., Henderson, K. K., Woodman, C. R., Turk, J. R., and Rush, J. W. E., et al. Exercise Preserves Endothelium-Dependent Relaxation in Coronary Arteries of Hypercholesterolemic Male Pigs. J. Appl. Physiol. 2004;96: 1114-1126. 113. Woodman, C. R., Turk, J. R., Rush, J. W E., and Laughlin, M. H. Exercise Attenuates the Effects of Hypercholesterolemia on Endothelium-Dependent Relaxation in Coronary Arteries from Adult Female Pigs. J. Appl. Physiol. 2004;96: 1105-1113. 114. Xu, Y, Buikema, H., Van Gilst, W. H., and Henning, R. H. Caveolae and Endothelial Dysfunction: Filling the Caves in Cardiovascular Disease. Eur. J. Pharmacol. 2008;585:256-260. 115. Hatakeyama, T., Pappas, P. J., Hobson, R. W., II, Boric, M. P., and Sessa, W. C , et al. Endothelial Nitric Oxide Synthase Regulates Microvascular Hyperpermeability In Vivo. J. Physiol. 2006;574:275-281. 116. Peterson, T. E., Guicciardi, M. E., Gulati, R., Kleppe, L. S., and Mueske, C. S., et al. Caveolin-1 Can Regulate Vascular Smooth Muscle Cell Fate by Switching Platelet-Derived Growth Factor Signaling from a Proliferative to an Apoptotic Pathway. Arterioscler. Thromb. Vase. Biol. 2003;23:1521-1527. 117. Tang, P. F., Burke, G. A., Li, G., and Wang, Y Patients with Long Bone Fracture Have Altered Caveolin-1 Expression in Their Peripheral Blood Mononuclear Cells. Arch. Orthop. Trauma Surg. Nov 12, 2008;[Epub ahead of print]. 118. Tesse, A., Martinez, M. C , Meziani, E, Hugel, B., and Panaro, M. A., et al. Origin and Biological Significance of Shed-Membrane Microparticles. Endocr. Metab. Immune Disord. Drug Targets. 2006;6:287-292. 119. Clancy, R., Marder, G., Martin, V., Belmont, H. M., and Abramson, S. B., et al. Circulating Activated Endothelial Cells in Systemic Lupus Erythematosus: Further Evidence for Diffuse Vasculopathy. Arthritis Rheum. 2001 ;44:1203-1208. 120. Erdbruegger, U., Grossheim, M., Hertel, B., Wyss, K., and Kirsch, T., et al. Diagnostic Role of Endothelial Microparticles in Vasculitis. Rheumatology (Oxford). 2008;47:1820-1825. 121. Lynch, S. F. and Ludlam, C. A. Plasma Microparticles and Vascular Disorders. Br. J. Haematol. 2007;137:36-48.

300

BIOMARKERS 122. Makin, A. J., Blann, A. D., Chung, N. A., Silverman, S. H., and Lip, G. Y. Assessment of Endothelial Damage in Atherosclerotic Vascular Disease by Quantification of Circulating Endothelial Cells. Relationship with Von Willebrand Factor and Tissue Factor. Eur. Heart J. 2004;25:371-376. 123. Machens, H. G., Pabst, A., Dreyer, M , Gliemroth, J., and Gorg, S., et al. C3a Levels and Occurrence of Subdermal Vascular Thrombosis are Age-Related in Deep Second-Degree Burn Wounds. Surgery. 2006;139:550-555. 124. Grond-Ginsbach, C , Hummel, M., Wiest, T., Horstmann, S., and Pfieger, K., et al. Gene Expression in Human Peripheral Blood Mononuclear Cells Upon Acute Ischemic Stroke. J. Neurol. 2008;255:723-731. 125. Lin, Z. H., Fukuda, N., Jin, X. Q., Yao, E. H., and Ueno, T., et al. Complement 3 Is Involved in the Synthetic Phenotype and Exaggerated Growth of Vascular Smooth Muscle Cells from Spontaneously Hypertensive Rats. Hypertension. 2004;44:42-47. 126. Che, Z. Q., Gao, P. J., Shen, W. L., Fan, C. L., and Liu, J. J., et al. Angiotensin II-Stimulated Collagen Synthesis in Aortic Adventitial Fibroblasts Is Mediated by Connective Tissue Growth Factor. Hypertens. Res. 2008;31:1233-1240. 127. Cicha, I., Yilmaz, A., Klein, M., Raithel, D., and Brigstock, D. R., et al. Connective Tissue Growth Factor Is Overexpressed in Complicated Atherosclerotic Plaques and Induces Mononuclear Cell Chemotaxis In Vitro. Arterioscler. Thromb. Vase. Biol. 2005;25:1008-1013. 128. Cicha, I., Yilmaz, A., Suzuki, Y, Maeda, N., and Daniel, W. G., et al. Connective Tissue Growth Factor Is Released from Platelets Under High Shear Stress and Is Differentially Expressed in Endothelium Along Atherosclerotic Plaques. Clin. Hemorheol. Microcirc. 2006;35:203-206. 129. Cicha, I., Goppelt-Struebe, M., Muehlich, S., Yilmaz, A., and Raaz, D., et al. Pharmacological Inhibition of Rhoa Signaling Prevents Connective Tissue Growth Factor Induction in Endothelial Cells Exposed to Non-Uniform Shear Stress. Atherosclerosis. 2008;196:136-145. 130. Sohn, M., Tan, Y, Wang, B., Klein, R. L., and Trojanowska, M., et al. Mechanisms of Low-Density Lipoprotein-Induced Expression of Connective Tissue Growth Factor in Human Aortic Endothelial Cells. Am. J. Physiol. Heart Circ. Physiol. 2006;290:H1624-634. 131. Tan, J. T., McLennan, S. V., Song, W. W, Lo, L. W., and Bonner, J. G., et al. Connective Tissue Growth Factor Inhibits Adipocyte Differentiation. Am. J. Physiol. Cell Physiol. 2008;295:C740-751. 132. Jaffa, A. A., Usinger, W. R., Mchenry, M. B., Jaffa, M. A., and Lipstiz, S. R., et al. Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study Group. Connective Tissue Growth Factor and Susceptibility to Renal and Vascular Disease Risk in Type 1 Diabetes. J. Clin. Endocrinol. Metab. 2008;93:1893-1900. 133. Kobayashi, S., Inoue, N., Ohashi, Y, Terashima, M., and Matsui, K., et al. Yokoyama M: Interaction of Oxidative Stress snd Inflammatory Response in Coronary Plaque Instability: Important Role of C-Reactive Protein. Arterioscler. Thromb. Vase. Biol. 2003;23:1398-1404. 134. Turk, J. R., Carroll, J. A., Laughlin, M. H., Thomas, T. R., and Casati, J., et al. C-Reactive Protein Correlates with Macrophage Accumulation in Coronary Arteries of Hypercholesterolemic Pigs. J. Appl. Physiol. 2003;95:1301-1304. 135. Verma, S., Szmitko, P. E., and Ridker, P. M. C-Reactive Protein Comes of Age. Nat. Clin. Pract. Cardiovasc. Med. 2005;2:29-36.

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY 301 136. Giffen, P. S., Turton, J., Andrews, C. M., Barrett, P., and Clarke, C. J., et al. Markers of Experimental Acute Inflammation in the Wistar Han Rat with Particular Reference to Haptoglobin and C-Reactive Protein. Arch. Toxicol. 2003;77:392-402. 137. Bathen-Noethen, A., Carlson, R., Menzel, D., Mischke, R., and Tipold, A. Concentrations of Acute-Phase Proteins in Dogs with Steroid Responsive Meningitis- Arteritis. J .Vet. Intern. Med. 2008;22:1149-1156. 138. An, S. J., Boyd, R., Zhu, M., Chapman, A., Pimentel, D. R., and Wang, H. D. NADPH Oxidase Mediates Angiotensin Il-Induced Endothelin-1 Expression in Vascular Adventitial Fibroblasts. Cardiovasc Res. Sep 1, 2007;75(4):702-709. 139. Lepailleur-Enouf, D., Valdenaire, O., Philippe, M., Jandrot-Perrus, M., and Michel, J. B. Thrombin Induces Endothelin Expression in Arterial Smooth Muscle Cells. Am. J. Physiol. Heart Circ. Physiol. 2000;278:H1606-1612. 140. Li, Z., Niwa, Y., Rokutan, K., and Nakaya, Y. Expression of Endothelin-1 in Macrophages and Mast Cells in Hyperplastic Human Tonsils. FEBS Lett. 1999;457:381-384. 141. Turk, J. R. Leading Edge of Medicine. Physiologic and Pathophysiologic Effects of Endothelin: Implications in Cardiopulmonary Disease. J. Am. Vet. Med. Assoc. 1998;212:265-270. 142. Videm, V., Wiseth, R., Gunnes, S., Madsen, H. O., and Garred, P. Multiple Inflammatory Markers in Patients with Significant Coronary Artery Disease. Int. J. Cardiol. 2007;118:81-87. 143. Biondo, A. W., Wiedmeyer, C. E., Sisson, D. D., and Solter, P. F. Comparative Sequences of Canine and Feline Endothelin-1. Vet. Clin. Pathol. 2003;32: 188-194. 144. Prosek, R., Sisson, D. D., Oyama, M. A., Biondo, A. W., and Solter, P. F. Plasma Endothelin-1 Immunoreactivity in Normal Dogs and Dogs with Acquired Heart Disease. J. Vet. Intern. Med. 2004;18:840-844. 145. Zagorski, J. and Delarco, J. E. Rat CINC (Cytokine-Induced Neutro-phil Chemoattractant) Is the Homolog of the Human GRO Proteins but Is Encoded by a Single Gene. Bichem. Biophys. Res. Commun. 1993;190:104-110. 146. Wang, B., Wood, I. S., and Trayhurn, P. Dysregulation of the Expression and Secretion of Inflammation-Related Adipokines by Hypoxia in Human Adipocytes. Pflugers Arch. 2007;455:479^92. 147. Theilgaard-Monch, K., Jacobsen, L. C , Nielsen, M. J., Rasmussen, T, and Udby, L., et al. Haptoglobin Is Synthesized During Granulocyte Differentiation, Stored in Specific Granules, and Released by Neutrophils in Response to Activation. Blood. 2006;108:353-361. 148. Pavon, E. J., Munoz, P., Lario, A., Longobardo, V, and Carrascal, M., et al. Proteomic Analysis of Plasma from Patients with Systemic Lupus Erythematosus: Increased Presence of Haptoglobin Alpha2 Polypeptide Chains Over the Alpha 1 Isoforms. Proteomics. 2006;6 Suppl l:S282-292. 149. Kiga, C , Sakurai, H., Goto, H., Hayashi, K., and Shimada, Y, et al. Proteomic Identification of Haptoglobin as a Stroke Plasma Biomarker in Spontaneously Hypertensive Stroke-Prone Rats. Life Sci. 2008;83:625-631. 150. Daskalopoulou, S. S., Daskalopoulos, M. E., Theocharis, S., Kavantzas, N., and Perrea, D., et al. Metallothionein Expression in the High-Risk Carotid Atherosclerotic Plaque. Curr. Med. Res. Opin. 2007;23:659-670. 151. Miyashita, H. and Sato, Y Metallothionein 1 Is a Downstream Target of Vascular Endothelial Zinc Finger 1 (VEZF1) in Endothelial Cells and Participates in the Regulation of Angiogenesis. Endothelium. 2005;12:163-170.

302

BIOMARKERS 152. Pauwels, M., Van Weyenbergh, J., Soumillion, A., Proost, P., and De Ley, M. Induction by Zinc of Specific Metallothionein Isoforms in Human Monocytes. Eur. J. Biochem. 1994;220:105-110. 153. Trayhurn, P., Duncan, J. S., Wood, A. M., and Beattie, J. H. Regulation of Metallothionein Gene Expression and Secretion in Rat Adipocytes Differentiated from Preadipocytes in Primary Culture. Horm. Metab. Res. 2000;32:542-547. 154. Sarov-Blat, L., Kiss, R. S., Haidar, B., Kavaslar, N., and Jaye, M , et al. Predominance of a Proinflammatory Phenotype in Monocyte-Derived Macrophages from Subjects with Low Plasma HDL-Cholesterol. Arterioscler. Thromb. Vase. Biol. 2007;27:1115-1122. 155. Demicheva, E., Hecker, M., and Korff, T. Stretch-Induced Activation of the Transcription Factor Activator Protein-1 Controls Monocyte Chemoattractant Protein-1 Expression During Arteriogenesis. Circ. Res. 2008;103:477-484. 156. Nakayama, T., Mutsuga, N., Yao, L., Tosato. Prostaglandin E2 Promotes Degranulation-Independent Release of MCP-1 from Mast Cells. J. Leukoc. Biol. 2006;79:95-104. 157. Simionescu, M. Implications of Early Structural-Functional Changes in the Endothelium for Vascular Disease. Arterioscler. Thromb. Vase. Biol. 2007;27: 266-274. 158. Takahashi, M., Suzuki, E., Takeda, R., Oba, S., and Nishimatsu, H., et al. Angiotensin II and Tumor Necrosis Factor-Alpha Synergistically Promote Monocyte Chemoattractant Protein-1 Expression: Roles of NF-Kappab, P38, and Reactive Oxygen Species. Am. J. Physiol. Heart Circ. Physiol. 2008;294:H2879-2888. 159. Tsuchiya, K., Yoshimoto, T, Hirono, Y, Tateno, T., and Sugiyama, T, et al. Angiotensin II Induces Monocyte Chemoattractant Protein-1 Expression via a Nuclear Factor-Kappab-Dependent Pathway in Rat Preadipocytes. Am. J. Physiol. Endocrinol. Metab. 2006;291:E771-778. 160. Brenner, D., Labreuche, J., Touboul, P. J., Schmidt-Petersen, K., and Poirier, O., et al. Cytokine Polymorphisms Associated with Carotid Intima-Media Thickness in Stroke Patients. Stroke. 2006;37:1691-1696. 161. De Lemos, J. A., Morrow, D. A., Blazing, M. A., Jarolim, P., and Wiviott, S. D., et al. Serial Measurement of Monocyte Chemoattractant Protein-1 After Acute Coronary Syndromes: Results from the A to Z Trial. J. Am. Coll. Cardiol. 2007;50:2117-2124. 162. Hemdahl, A. L., Gabrielsen, A., Zhu, C , Eriksson, P., and Hedin, U., et al. Expression of Neutrophil Gelatinase-Associated Lipocalin in Atherosclerosis and Myocardial Infarction. Arterioscler. Thromb. Vase. Biol. 2006;26: 136-142. 163. Bu, D. X., Hemdahl, A. L., Gabrielsen, A., Fuxe, J., and Zhu, C , et al. Induction of Neutrophil Gelatinase-Associated Lipocalin in Vascular Injury via Activation of Nuclear Factor-Kappab. Am. J. Pathol. 2006;169:2245-2253. 164. Chen, M., Wang, F, and Zhao, M. H. Circulating Neutrophil Gelatinase-Associated Lipocalin: A Useful Biomarker for Assessing Disease Activity of ANCAAssociated Vasculitis. Rheumatology (Oxford). 2009;48:355-358. 165. Campos, A. H., Zhao, Y, Pollman, M. J., and Gibbons, G. H. DNA Microarray Profiling to Identify Angiotensin-Responsive Genes in Vascular Smooth Muscle Cells: Potential Mediators of Vascular Disease. Circ. Res. 2003;92:111-118. 166. Dorheim, M. A., Sullivan, M., Dandapani, V., Wu, X., and Hudson, J., et al. Osteoblastic Gene Expression During Adipogenesis in Hematopoietic Supporting Murine Bone Marrow Stromal Cells. J. Cell Physiol. 1993;154:317-328.

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY 303 167. Li, G., Oparil, S., Kelpke, S. S., Chen, Y. F., and Thompson, J. A. Fibroblast Growth Factor Receptor-1 Signaling Induces Osteopontin Expression and Vascular Smooth Muscle Cell-Dependent Adventitial Fibroblast Migration In Vitro. Circulation. 2002;106:854-859. 168. Oki, Y., Watanabe, S., Endo, T., and Kano, K. Mature Adipocyte-Derived Dedifferentiated Fat Cells Can Trans-Differentiate Into Osteoblasts in Vitro and In Vivo Only by All-Trans Retinoic Acid. Cell Struct. Fund. 2008;33:211-222. 169. Partridge, C. R., Williams, E. S., Barhoumi, R., Tadesse, M. G., and Johnson, C. D., et al. Novel Genomic Targets in Oxidant-Induced Vascular Injury. J. Mol. Cell. Cardiol. 2005;38:983-996. 170. Seipelt, R. G., Backer, C. L., Mavroudis, C , Stellmach, V., and Cornwell, M., et al. Osteopontin Expression and Adventitial Angiogenesis Induced by Local Vascular Endothelial Growth Factor 165 Reduces Experimental Aortic Calcification. 7. Thorac. Cardiovasc. Surg. 2005;129:773-781. 171. Sugiyama, T., Yoshimoto, T., Hirono, Y, Suzuki, N., and Sakurada, M., et al. Aldosterone Increases Osteopontin Gene Expression in Rat Endothelial Cells. Biochem. Biophys. Res. Commun. 2005;336:163-167. 172. Minoretti, R, Falcone, C , Calcagnino, M., Emanuele, E., and Buzzi, M. R, et al. Prognostic Significance of Plasma Osteopontin Levels in Patients with Chronic Stable Angina. Eur. Heart J. 2006;27:802-807. 173. Ohmori, R., Momiyama, Y, Taniguchi, H., Takahashi, R., and Kusuhara, M., et al. Plasma Osteopontin Levels Are Associated with the Presence and Extent of Coronary Artery Disease. Atherosclerosis. 2003; 170:333-337. 174. Ando, H., Kubin, T., Schaper, W., and Schaper, J. Cardiac Microvascular Endothelial Cells Express Alpha-Smooth Muscle Actin and Show Low NOS III Activity. Am. J. Physiol. 1999;276:H1755-1768. 175. Stenmark, K. R., Gerasimovskaya, E., Nemenoff, R. A., and Das, M. Hypoxic Activation of Adventitial Fibroblasts: Role in Vascular Remodeling. Chest. 2002;122(6 Suppl):326S-334S. 176. Sugiyama, S., Kugiyama, K., Nakamura, S., Kataoka, K., and Aikawa, M., et al. Characterization of Smooth Muscle-Like Cells in Circulating Human Peripheral Blood. Atherosclerosis. 2006;187:351-362. 177. McLaughlin, J. N., Mazzoni, M. R., Cleator, J. H., Earls, L., and Perdigoto, A. L., et al. Thrombin Modulates the Expression of a Set of Genes Including Thrombospondin-1 in Human Microvascular Endothelial Cells. J. Biol. Chem. 2005;280:22172-22180. 178. Scott-Burden, T., Resink, T. J., Hahn, A. W, and Biihler, F. R. Induction of Thrombospondin Expression in Vascular Smooth Muscle Cells by Angiotensin II. J. Cardiovasc. Pharmacol. 1990;16 Suppl 7:S17-20. 179. Stenina, O. I., Krukovets, I., Wang, K., Zhou, Z., and Forudi, E, et al. Increased Expression of Thrombospondin-1 in Vessel Wall of Diabetic Zucker Rat. Circulation. 2003;107:3209-3215. 180. Varma, V, Yao-Borengasser, A., Bodies, A. M., Rasouli, N., and Phanavanh, B., et al. Thrombospondin-1 Is an Adipokine Associated with Obesity, Adipose Inflammation, and Insulin Resistance. Diabetes. 2008;57:432-439. 181. Reed, M. J., Iruela-Arispe, L., O'Brien, E. R., Truong, T., and Labell, T, et al. Expression of Thrombospondins by Endothelial Cells. Injury Is Correlated with TSP-1. Am. J. Pathol. 1995;147:1068-1080. 182. Castoldi, G., Di Gioia, C. R., Pieruzzi, F., D'Orlando, C , and Van De Greef, W. M., et al. ANGII Increases TIMP-1 Expression in Rat Aortic Smooth Muscle Cells In Vivo. Am. J. Physiol. Heart. Circ. Physiol. 2003;284:H635-643.

304

BIOMARKERS 183. Maury, E., Ehala-Aleksejev, K., Guiot, Y., Detry, R., and Vandenhooft, A. Adlpokines Oversecreted by Omental Adipose Tissue in Human Obesity. Am. J. Physiol. Endocrinol. Metab. 2007;293:E656-665. 184. Vaalamo, M., Leivo, T., and Saarialho-Kere, U. Differential Expression of Tissue Inhibitors of Metalloproteinases (TIMP-1, -2, -3, and -4) in Normal and Aberrant Wound Healing. Hum. Pathol. 1999;30:795-802. 185. Woodrum, D. T, Ford, J. W., Ailawadi, G., Pearce, C. G., and Sinha, I. Gender Differences in Rat Aortic Smooth Muscle Cell Matrix Metalloproteinase-9. J.Am. Coll. Surg. 2005;201:398^M)4. 186. De Leeuw, K., Freire, B., Smit, A. J., Bootsma, H., and Kallenberg, C. G., et al. Traditional and Non-Traditional Risk Factors Contribute to the Development of Accelerated Atherosclerosis in Patients with Systemic Lupus Erythematosus. Lupus. 2006;15:675-682. 187. West, M. J., Nestel, P. J., Kirby, A. C , Schnabel, R., and Sullivan, D., et al. The Value of N-Terminal Fragment of Brain Natriuretic Peptide and Tissue Inhibitor of Metalloproteinase-1 Levels as Predictors of Cardiovascular Outcome in the LIPID Study. Eur. Heart J. 2008;29:923-931. 188. Diamond, S. L., Eskin, S. G., and Mclntire, L. V. Fluid Flow Stimulates Tissue Plasminogen Activator Secretion by Cultured Human Endothelial Cells. Science. 1989;243:1483-1485. 189. Helenius, G., Hagvall, S. H., Esguerra, M., Fink, H., and Soderberg, R., et al. Effect of Shear Stress on the Expression of Coagulation and Fibrinolytic Factors in Both Smooth Muscle and Endothelial Cells in a Co-culture Model. Eur. Surg. Res. 2008;40:325-332. 190. Takamiya, M., Saigusa, K., Kumagai, R., Nakayashiki, N., and Aoki, Y. Studies on Mrna Expression of Tissue-Type Plasminogen Activator in Bruises for Wound Age Estimation. Int. J. Legal Med. 2005;119:16-21. 191. Besbas, N., Erbay, A., Saatci, U., Ozdemir, S., and Bakkaloglu, A., et al. Thrombomodulin, Tissue Plasminogen Activator and Plasminogen Activator Inhibitor-1 in Henoch-Schonlein Purpura. Clin. Exp. Rheumatol. 1998;16:95-98. 192. Hastings, N. E., Feaver, R. E., Lee, M. Y, Wamhoff, B. R., and Blackman, B. R. Human IL-8 Regulates Smooth Muscle Cell VCAM-1 Expression in Response to Endothelial Cells Exposed to Atheroprone Flow. Arterioscler. Thromb. Vase. Biol. 2009;29:725-731. 193. Trogan, E., Choudhury, R. P., Dansky, H. M., Rong, J. X., and Breslow, J. L., et al. Laser Capture Microdissection Analysis of Gene Expression in Macrophages from Atherosclerotic Lesions of Apolipoprotein E-Deficient Mice. Proc. Nat. Acad. Sci. USA. 2002;99:2234-2239. 194. Lee, A. B., Godfrey, T., Rowley, K. G., Karschimkus, C. S., and Dragicevic, G., et al. Traditional Risk Factor Assessment Does Not Capture the Extent of Cardiovascular Risk in Systemic Lupus Erythematosus. Intern. Med. J. 2006; 36:237-243. 195. Boesiger, J., Tsai, M., Maurer, M., Yamaguchi, M., and Brown, L. R, et al. Mast Cells Can Secrete Vascular Permeability Factor/Vascular Endothelial Cell Growth Factor and Exhibit Enhanced Release After Immunoglobulin E Dependent Upregulation of Fc Epsilon Receptor I Expression. J. Exp. Med. 1998; 188:1135-1145. 196. Inoue, M., Itoh, H., Ueda, M., Naruko, T., and Kojima, A., et al. Vascular Endothelial Growth Factor (VEGF) Expression in Human Coronary Atherosclerotic Lesions: Possible Pathophysiological Significance of VEGF in Progression of Atherosclerosis. Circulation. 1998;98:2108-2116.

IN SEARCH OF BIOMARKERS FOR DRUG-INDUCED VASCULAR INJURY 305 197. Jin, X., Ge, X., Zhu, D. L., Yan, C, and Chu, Y. E, et al. Expression and Function of Vascular Endothelial Growth Factor Receptors (Flt-1 and Flk-1) in Vascular Adventitial Fibroblasts. J. Mol. Cell. Cardiol. 2007;43:292-300. 198. Strande, J. L. and Phillips, S. A. Thrombin Increases Inflammatory Cytokine and Angiogenic Growth Factor Secretion in Human Adipose Cells In Vitro. J. lnflamm. (Lond). 2009;6:4. 199. Tsai, W. C , Li, Y H., Huang, Y. Y, Lin, C. C., and Chao, T. H., et al. Plasma Vascular Endothelial Growth Factor as a Marker For Early Vascular Damage in Hypertension. Clin. Sci. (Lond) 2005;109:39-43. 200. Wikman, A., Lundahl, J., and Jacobson, S. H. Sustained Monocyte Activation in Clinical Remission of Systemic Vasculitis. Inflammation. 2008;31:384-390. 201. Perutelli, P. and Molinari, A. C. Von Willebrand Factor, Von Willebrand FactorCleaving Protease, and Shear Stress. Cardiovasc. Hematol. Agents Med. Chem. 2007;5:305-310. 202. Blann, A. D. and McCollum, C. N. Von Willebrand Factor and Soluble Thrombomodulin as Predictors of Adverse Events Among Subjects with Peripheral or Coronary Atherosclerosis. Blood Coagul. Fibrinolysis. 1999;10:375-380. 203. Spiel, A. O., Gilbert, J. C , and Jilma, B. Von Willebrand Factor in Cardiovascular Disease: Focus on Acute Coronary Syndromes. Circulation. 2008;117: 1449-1459.

This page intentionally left blank

CHAPTER

BIOMARKERS OF IMMUNOTOXICITY Rodney R. Dietert

INTRODUCTION The immune system is universally recognized as playing a central role in the health and well-being of humans, wildlife, and domesticated animals. For this reason, identification of environmental health risks and application of the information for effective risk reduction can provide significant health benefits and should be a core of any safety screening strategy. But while protection of the immune system is an obvious and worthwhile goal, successful immunotoxicologic assessment is not necessarily a simple pursuit. The immune system represents one of the more daunting targets for toxicologic assessment. In part, this is directly related to its duality of function in which immune regulation of cell and tissue integrity and organ homeostasis is juxtaposed against a robust defense against external assault. The novel aspects of immunotoxicity evaluation are based on four fundamental features of the immune system: 1) the immune system is dispersed with representation in virtually every organ and tissue; 2) immune system components residing in different tissues may have their own range of toxicologic sensitivities; 3) the immune system is integrally wired to several other physiological systems (e.g., neurological, endocrine) so that both direct and indirect toxicologic effects can occur; and 4) the immune system is multifunctional, and significant alteration of any single function presents its own set of health risks. Immunotoxicology as the scientific basis for immune safety assessment has existed since as least the 1970s.1"1 During that interval, specific strategies have evolved for the identification and application of biomarkers to detect en307

308

BIOMARKERS

vironmental insult of the immune system. This chapter will consider both the background of existing biomarkers for immunotoxicity and more recent approaches to providing cost-effective safety assessment for the immune system.

HISTORY OF T H E USE OF BIOMARKERS I N I M M U N O T O X I C I T Y ASSESSMENT Establishing the Testing Paradigm Because of the complexity and multi-functional nature of the immune system, the challenge in immunotoxicity testing has been to determine: 1) what could be measured; 2) what should be measured and is sufficient for effective immune safety testing; and 3) what constitutes clinically- and/or biologically-significant alterations among the immune biomarkers measured? While not all of the questions have single definitive answers with universal agreement among scientists and regulators, there has been significant progress in defining the features of effective immunotoxicity testing strategies. Since the early 1980s, immune biomarkers have been used to replace what were then more specialized, cumbersome, and costly host resistance (HR) assays.5' 6 The HR assays (viral, bacterial, parasitic, and tumor challenges) were considered the gold standard. Sets of immune biomarkers were examined for their potential effectiveness in replacing HR assays. Additionally, a tieredapproach to testing was instituted whereby information on general immune status was obtained first. If these data collected caused concern or raised additional questions, more specific information using other immune parameters and assays could be collected in second or third tiers of assessment. In the early 1990s Luster and colleagues published a series of papers concerning the effectiveness of immune biomarkers for identifying immunotoxicants (also identified via HR evaluation).7'8 These studies utilized the National Toxicology Program database to enable a comparison of numerous xenobiotics as well as immune biomarkers. The fundamental concepts that developed from this analysis provided an important basis for immunotoxicity testing and continue to drive immunotoxicity testing strategies today. The concepts are: 1) no single immune parameter is sufficient as a screening tool to identify immunotoxicants and 2) functional tests (preferably multiple functional tests) are required to package biomarkers for achieving immunotoxic predictive success. For accurate identification of immunotoxicants, Luster, et al.7 found that eight different combinations, each employing three immune biomarkers, provided 100% predictive success (although the database was modest for some three-way combinations). Among those successful combinations, none were without immune functional measures. Five combinations included cell-mediated immune measures and three included humoral immune measures.

A "Challenging" Issue for Immune Biomarkers Remarkably, despite the observations of Luster, et al.7 that functionally-linked biomarkers were important for successful identification of immunotoxicants,

BIOMARKERS OF I M M U N O T O X I C I T Y

309

regulatory agencies have not been uniform in requiring challenge, vaccination, or immunization of the immune system before initial immune safety screening data are collected. In part, the argument for not challenging the immune system has been that immune-relevant safety data should be collected using test animals where developmental, reproductive, and/or neurological data will also be collected. Because immunization, challenge, or vaccination might change the physiological status of the animals, there has been concern it might alter reproductive or neurological baselines. But not challenging the immune system presents severe limitation in the opportunity to detect toxicant-induced immune dysfunction. It is akin to assuming that you can evaluate how well a car drives without ever starting it.

TARGETS OF IMMUNOTOXICITY One of the reasons that immunotoxicity testing and the use of biomarkers can be a significant challenge is that virtually every immune cell population and every immune maturational event represents a potential toxicologic target. The routes to potential pathology and diseases are numerous. This has been discussed in several reviews for the developing immune system where toxicants are known to disrupt critical immune maturation events during specific windows of prenatal or neonatal development.9-" Additionally, the existence of numerous pathways of xenobiotically induced immune damage-alteration is also reflected in the mode of action of therapeutic agents designed to correct immune dysfunction. Numerous cell signaling and receptor mediated pathways have been utilized to treat allergic and autoimmune disease alone. Xenobioticmediated immune damage is as diverse as the therapeutic pathways pursued in drug discovery. Examples of toxicant-induced specific immune disruption are: 1) the capacity of alcohol to impair macrophage maturation and function by depleting glutathione;12-13 2) the ability of lead to skew dendritic cells for promotion of T helper (Th) 2-biased immune responses;14 3) the ability of 2,3,7,8-tetrachlorodibenzo-jo-dioxin (TCDD) to cause exaggerated inflammation;15-16 4) the action of cyclosporine A to suppress natural T regulatory (Treg) cell population expansion;17 and 5) the capacity of tributyltin, an organotin, to induce apoptosis in thymocytes producing thymus atrophy.18 Additionally, a single toxicant is capable of disrupting multiple immune targets depending upon the exposure concentration, the timing of exposure, and the route of exposure. For example, TCDD is a potent immunotoxicant. However, it cannot be characterized simply as an immunosuppressor. While TCDD and similar chemicals can impair acquired immune responses,19-20 it also causes improper inflammatory responses15-21 and an elevated the risk of later life autoimmune responses.22-23 For this reason, it is most useful to think of immunotoxicants not as simply immunosuppressive compounds or alternatively, as immune enhancers, but rather as inducers of dysfunction. In reality, it seems likely that a majority of immunotoxicants are capable of causing more than one adverse immune outcome.

310

BIOMARKERS

DISEASES OF PRIMARY

CONCERN

Increased Susceptibility t o Infections and Tumors Immunotoxicology emerged as an interdisciplinary area of research and testing during a time when safety testing concerns were focused on immunosuppression (HIV- and chemically-induced) and the identification of occupationally-related sensitizing chemicals and drugs. Not surprisingly, application of biomarkers to identify immunotoxicants focused on these health concerns, and these health concerns are still important concerns today. The driving force was protection against infectious diseases and tumors. The resulting biomarkers used in routine screening (e.g., primary IgM production against sheep erythrocytes and histopathology of an unchallenged immune system) have utility for the detection of overt immunosuppression. This is good news relative to those goals established in prior decades. But there is a potential downside to the continued reliance on the application of historic biomarkers of immunotoxicity for safety testing. For example, Luebke et al. 24 pointed out that the spectrum of diseases associated with mild to moderate immunosuppression are quite distinct from those associated with more pronounced immunosuppression. Additionally, the prevalence of several chronic diseases has increased since the 1980s, and these required additional attention in terms of use of biomarkers in immunotoxicity safety testing. The disconnection that has emerged between prior versus future biomarkers involves the realization that a much wider spectrum of health risks needs to be identified during front-line immunotoxicity testing. A comparison of the derivation and application of original HR-defined biomarkers vs. a more relevant range of disease-based-defined biomarkers is illustrated in Figure 12.1. The problem that has arisen is that application of the flow chart shown on the left in Figure 12.1 utilizes only HR-derived biomarkers (with a possible addition of chemical sensitizer tests). These do not account for protection against the full range of immune-dysfunction based diseases known to have environmental risk factors (shown included in the flow chart on the right of Figure 12.1). The flow strategy based only on HR is effective for the detection of toxicant-induced overt immunosuppression. But this is not the concern with the environmental risk of asthma and other allergic diseases, type 1 diabetes, rheumatoid arthritis, autoimmune thyroiditis, lupus, celiac disease, multiple sclerosis, inflammatory bowel disease, and atherosclerosis. As discussed in the following subsection, it is becoming imperative that we protect against this broader group of immune-dysfunction based diseases.

C h r o n i c Diseases and C o n d i t i o n s Based on Immune Dysfunction Table 12.1 lists several different categories of immune-dysfunction based diseases that could be reduced via use of disease-based biomarkers for immunotoxicity testing. Many of the chronic diseases have known environmental risk factors and have increased in prevalence in the past two to three decades.

BIOMARKERS OF I M M U N O T O X I C I T Y

311

FIGURE 12.1 The flow chart illustrates a comparison of traditional immune biomarkers designed to represent surrogates of host resistance and their use (shown on the left) versus biomarkers derived from the spectrum of diseases linked to immune dysfunction and their application (shown on the right).

This suggests that additional environmental risk factors exist and need to be identified.25'26 Dietert and Zelikoff26 recently surveyed the prevalence of pediatric-onset chronic diseases having an immune dysfunction basis and environmental risk factors. These impact at least a quarter of the pediatric populations in some developed countries. Among those of concern are allergic diseases including asthma.27-32 Table 12.1 illustrates the problem in early safety detection for a potential elevated risk of allergic disease. The biomarker IgM antibody in a primary rodent antibody response against xenogeneic cell-immunogens like sheep erythocytes was never intended as a biomarker to detect an elevated risk of allergic disease. Therefore, it is not surprising that it is unlikely to perform well in that specific role. Instead, evaluation using relevant biomarkers such as IgE and IgG subclass levels, IL-4 levels (following appropriate challenge), and eosinophil activation for detection is precisely what is needed in current safety testing based on prevalence of human disease. Autoimmune diseases represent a second category of immune-based diseases that have increased in prevalence in recent decades.33 These diseases have the potential for multifactorial mechanisms of pathogenesis.34 The need for better prevention of these diseases has led to a search for predictive biomarkers35 and has become a significant concern within immunotoxicity testing.36Table 12.1 illustrates biomarkers such as quantitation of Treg and Thl7 populations and autoantibody measures that would be useful as indicators of

312

BIOMARKERS

TABLE 12.1

Predictive versus current biomakers o f immune-based health risks.

Predominant Health Concerns with Immunotoxicity

Potentially Predictive Biomarkers

Closest RoutinelyCollected Parameters*

Childhood or occupational asthma and other allergic diseases**

IgE responses, IgG subclasses, Eosinophil measures,Th2dependent function, inflammatory mediators (cytokines, metabolites)

IgM response

Autoimmune diseases (e.g., type 1 diabetes, inflammatory bowel disease, autoimmune thyroiditis, rheumatoid arthritis, lupus, multiple sclerosis, celiac disease)

IgG subclass and IgA responses, autoantibody screening.T regulatory (Treg) cell andThl7 analysis, B cell analysis.Tcell receptor usage

IgM response, CD4 and CD8T cell quantitation

Otitis media and other recurrent infections

IgG subclass and IgA responses, Inflammatory responses to host challenge.Thl vs.Th2 functional comparisons, marginal zone B cell function

IgM response, Natural Killer (NK) cell activity

Ineffective vaccine responses and unanticipated immune reactions after vaccination

Host responses to vaccine challenge and/or infectious agents including, antibody (multiple isotypes) and CMI/ CTL responses, and inflammatory profiles, autoantibody production

IgM response, NK activity, lymphocyte cell surface analysis; In humans: antibody titers to the vaccine agent are occasionally measured

Childhood and adult cancers

Th 1 ,Treg and Th 17-dependent NK cell activity, lymphocyte cell surface analysis functional profiles, CTL and NK activity in response to host challenge, regulation of inflammatory cell activity

Inflammatory-associated diseases (e.g., athlerosclerosis, schizophrenia, myalgic encephalomyelitis)

Inflammatory responses and proinflammatory cytokine profiles in resting and challenge states;Th and macrophage functional balance

NK cell activity

*8osed on 2008 USEPA and FDA routinely expected immunotoxicity data. **Note that the local lymph node assay (LIMA) is used to test the sensitizing potential of chemicals.

potential risk of autoimmunity. Examples of autoimmune diseases important as targets for improved safety evaluation include: type 1 diabetes,37,38 rheumatoid arthritis,39,40 autoimmune thyroiditis,41 celiac disease,42 multiple sclerosis,43 and lupus.44

BIOMARKERS OF I M M U N O T O X I C I T Y

313

Among other diseases and conditions associated with immunotoxicity are increased risk of infections,45 vaccine failures,46 unanticipated responses to vaccinations,47 and otitis media.45'48 Cancer is also a potential outcome of immunotoxicity and biomarkers should reflect this disease risk. Recently, it has been suggested that some forms of childhood leukemia involve immune dysfunctional responses to common pediatric infections.49 Using a rodent model, Ng, et al.50 showed that maternal smoking during pregnancy reduced the cellmediated immune cytotoxic lymphocyte (CTL) response and also produced increased tumor growth in the offspring. Several other diseases are linked with environmental factors, immune dysfunction and misregulated inflammation (Table 12.1). Calderon-Garciduenas, et al.51 recently found that children exposed to high levels of air pollutants exhibited misregulated inflammatory responses compared with urban children with lower air pollution exposures. Because misregulated inflammation can produce pathologies that can occur in virtually any tissue or organ, this category of immune-based diseases provides important biomarkers for use in safety testing. For this reason measurement of inflammatory cell function, including production of cytokines as well as oxygen radicals and nitric oxide, are useful for detecting potential inflammation-linked disease risk. Among the diseases of concern are atherosclerosis,52'53 schizophrenia,54-56 and myalgic encephalomyelitis.57-59 Other diseases/conditions such as autism and autism spectrum disorders are also candidates for possible environment-immune involvement."•60~62

DEVELOPMENTAL IMMUNOTOXICITY: I N C R E A S E D V U L N E R A B I L I T Y I N EARLY LIFE Developmental immunotoxicity (DIT) concerns exposure of the prenatal, neonatal, juvenile, and adolescent immune system to environmental factors that produce adverse health outcomes in later life. DIT warrants a special consideration for the selection and use of biomarkers in immunotoxicity evaluation. The developing immune system has been shown to be more sensitive to environmental insult than that of the adult.63-M Additionally, the early-life insult may take different forms and be more persistent compared with that seen in the adult.10 Because of the very nature of immune development and the occurrence of one-time maturational events,965 adult-derived safety data has limited relevance to the non-adult.1066 Prenatal-neonatal exposure to even low-levels of specific chemicals and drugs has been linked to an elevated risk of specific later-life diseases.25-26-28-67 Given the increased vulnerability of fetuses and neonates to environmentallyinflicted immune insult and the long-term implication of subsequent chronic disease, DIT testing may be more cost effective than adult-exposure immunotoxicity testing. Several different reviews have discussed possible strategies for collecting relevant DIT data in animal models68-73 and children.74 While suggested approaches and application of biomarkers may differ, there is widespread agreement on the utility of having DIT biomarker data for health protection.

314

BIOMARKERS

DIFFERENTIAL EXPOSURE-OUTCOMES B E T W E E N GENDERS Among recent findings in immunotoxicology for specific disease risk is the observation that major differences can exist between genders in exposure outcome. The gender-based differences in immunotoxic outcomes can be qualitative. In this case, one gender may experience a health risk that is not observed in the other gender following exposure to the same toxicant at the same dose during the same time frame. Alternatively, the comparison between genders may be more quantitative in nature. In this case, the genders differ in the toxicant dose range that produces a given immunotoxic outcome. The end result is that specific exposures to drugs, chemicals, or other environmental factors can present different health risks and/or impact the likelihood of different diseases for women versus men. This has implications for immunotoxicity testing in the need to examine both genders for exposure-assessment and for disease risk management. For example, a majority of autoimmune diseases occur predominately in females.75-77 Surprisingly, gender differences in immunotoxicity are particularly evident following early-life exposure to xenobiotics.73 These are life stages when one might have expected gender effects to be less prominent than those seen in the adult. In some cases, these differences can be explained by the potential endocrine disrupting nature of toxicants.78-80 But not all xenobiotics showing sex-based differences in immunotoxic outcomes are known endocrine disrupters.81,82

A DISEASE-BASED A P P R O A C H T O I M M U N E BIOMARKER S E L E C T I O N One of the concerns is that several categories of immune-based diseases (e.g., asthma and allergy, autoimmunity, inflammation-driven conditions) have risen in prevalence in recent decades despite ongoing immunological safety testing of chemicals and drugs. For some select diseases, a portion of this increased prevalence could be related to improved disease diagnosis (e.g., myalgic encephalomyelitis and some autoimmune diseases). Another part of the increase may reflect a less than adequate protection of populations to previously-identified immunotoxicants (e.g., heavy metals). However, currently identified immunotoxicants cannot account for the full extent of disease increases that have been observed. For this reason, it has become obvious that there are additional immunotoxic risk factors remaining to be identified. Because these diseases have increased in the face of ongoing immunotoxicity testing, a reduction in immune-based chronic diseases is likely to require a new approach to the use of biomarkers in immunotoxicity testing. As previously discussed, immunotoxicity testing has been based on biomarkers intended for the detection of overt immunosuppression (Figure 12.1, left half). But as shown in Figure 12.1, this is only one of several health risks that need to be detected. For this reason, a reverse engineered approach to

BIOMARKERS OF I M M U N O T O X I C I T Y

315

the selection of biomarkers for use in developmental immunotoxicity testing has been proposed.72 However, a similar approach would be useful whether the testing is performed following adult-exposure or for DIT. Such an approach is shown on the right side of Figure 12.1. This approach begins with the diseases of greatest concern and would drive testing using the biomarkers that are altered in association with those diseases. By including diseases from many different categories (allergy, autoimmunity, inflammation, cancer, infectious diseases), the subsequent immunotoxicity testing would move beyond immunosuppression to include problematic immune enhancement and misregulation. But to move beyond detection of simple immunosuppression, it may be necessary to modify traditional testing approaches. Different challenge methodologies are needed that would facilitate evaluation of a broader spectrum of immune and inflammatory parameters than is usually found in traditional tier one testing. For example, given that risk of allergic disease is a major concern, then measurement of antibodies involved in mast cell degranulation (e.g., IgE) as well as their promoting cytokines (e.g., IL-4) would seem to be a minimum testing expectation. Additionally, appropriate immune homeostasis is a major issue for inflammatory-related diseases of concern. Hence, there needs to be a better evaluation strategy for detecting the risk of misregulated inflammation. Presumably this would include measurement of such biomarkers as production of proinflammatory cytokines, their receptors, and tissue-damaging inflammatory mediators such as reactive oxygen species (ROS) and nitric oxide production. These should be measured early in immunotoxicity testing priorities and not relegated to second or third tiers of evaluation. To do this requires appropriate challenge models (e.g., influenza virus infection and airway evaluation). Additionally, if risk of autoimmune disease is to be adequately considered, then it is useful to screen for autoantibodies and to determine the status of regulatory cell populations such as the Tregs and Thl7 cells. In summary, while there are several options for ways to optimize these disease-associated biomarkers into a unified immunotoxicity testing protocol, protocols that are effective in identifying the needed spectrum of immunotoxicity-associated health risks are likely to have certain features: 1) the spectrum of parameters measured will be broader than those used in traditional testing emphasizing immunosuppression; 2) the immune system must be challenged in such a way as to elicit a broad range of acquired, innate, and inflammatory responses for evaluation; and 3) immunotoxicity safety data need to be relevant and predictive for the age group and gender under consideration.

TOXICOGENOMIC AND IN VITRO APPROACHES Most immunotoxicity evaluations to date have employed in vivo and in vivo/ex vivo strategies using biomarkers. However, there have been efforts to examine the potential for alternatives to animal testing or direct human evaluation. Two general alternative strategies have been examined: gene expression/toxicog-

316

BIOMARKERS

enomic systems83-85 and in vitro immune assays.86,87 At present, these alternative methodologies are in their infancy as potential substitutes. However, the best opportunity for which they may gain wider use is for initial screening of direct-acting (those not requiring metabolism) immunotoxicants.

CONCLUSIONS Biomarkers have been a pivotal component of immunotoxicity assessment since the origins of the interdisciplinary area. Originally, they were established as surrogates of host resistance to infectious agents and tumors. Additionally, identification of biomarkers of chemical sensitization has been an early goal within immunotoxicology. These have proved useful for the detection of xenobiotics that produce overt immunosuppression or sensitization. However, it is now recognized that health risks involve a wide range of diseases with underlying immune dysfunction rather than merely profound immunosuppression. Among these are allergic and autoimmune disease as well as leukocytic cancers and inflammatory conditions. These diseases impact more than a quarter of the population in some developed countries and detection of environmental risk factors for these diseases requires use of a broader set of biomarkers than is needed to detect overt immunosuppression. As a result, new immunotoxicity testing protocols capable of detecting any form of immune dysfunction/misregulation are being examined. Most utilize a challenged immune system to be able to detect any significant deviation from the expected response (suppression, enhancement, or misdirected responses induced by exposure to an environmental factor). Additionally, recent research has investigated the potential to apply toxicogenomic biomarkers and/or in vitro measures of immunotoxicity as substitutes for more complex in vivo/ex vivo assays.

SUMMARY POINTS 1. 2. 3. 4. 5. 6.

Immunotoxicity assessment using biomarkers is a core component of drug and chemical safety testing. Immune biomarkers were initially developed as substitutes for more complex measures of host resistance. Traditional immunotoxicity testing had a primary goal of detecting immunosuppression and utilized biomarkers designed to meet this goal. Under recent safety testing regulations, prevalence of environmentallyinfluenced immune-associated diseases (e.g., asthma, allergy, and type 1 diabetes) has risen. To reduce the prevalence of these diseases, immune biomarkers used in safety testing need to be more directly connected with those diseases of concern. To better address the full spectrum of health concerns including allergic and autoimmune diseases, more effective host challenge strategies and application of immune biomarkers are needed.

BIOMARKERS OF IMMUNOTOXICITY

317

ACKNOWLEDGMENTS The author thanks Janice Dietert for her editorial assistance and Burleson Research Technologies, Inc. (Morrisville, NC) for their continued support of immunotoxicity research and testing.

REFERENCES 1. 2. 3. 4. 5. 6. 7. 8.

9. 10. 11.

12. 13. 14.

Vos, J. G., Moore, J. A., and Zinkl, J. G. Effect of 2,3,7,8-TetrachlorodibenzoP-Dioxin on the Immune System of Laboratory Animals. Environ. Health Perspect. 1973;5:149-162. Luster, M. I., Faith, R. E., and Kimmel, C. A. Depression of Humoral Immunity in Rats Following Chronic Developmental Lead Exposure. J. Environ. Pathol. Toxicol. 1978;1:397-402. Dean, J. H., Padarathsingh, M. L., and Jerrells, T. R. Assessment of Immunobiological Effects Induced by Chemicals, Drugs or Food Additives. I. Tier Testing and Screening Approach. Drug Chem. Toxicol. 1919;2:5-11. Vos, J. G., Immunotoxicity Assessment: Screening and Function Studies. Arch. Toxicol. Suppl. 1980;4:95-108. Dean, J. H., Luster, M. I., and Boorman, G. A. Methods and Approaches for Assessing Immunotoxicity: An Overview. Environ. Health Perspect. 1982;43: 27-29. Dean, J. H., Luster, M. I., Boorman, G. A., and Lauer, L. D. Procedures Available to Examine the Immunotoxicity of Chemicals and Drugs. Pharmacol. Rev. March 1982;34(1): 137-148. Luster, M. I., Portier, C , Pait, D. G., White, K. L., Jr., Gennings, C , Munson, A. E., and Rosenthal, G. J. Risk Assessment in Immunotoxicology. I. Sensitivity and Predictability of Immune Tests. Fundam. Appl. Toxicol. 1992;18:200-210. Luster, M. I., Portier, C , Pait, D. G., Rosenthal, G. J., Germolec, D. R., Corsini, E., Blaylock, B. L., Pollock, P., Kouchi, Y., and Craig, W., et al. Risk Assessment in Immunotoxicology. II. Relationships Between Immune and Host Resistance Tests. Fundam. Appl. Toxicol. 1993;21:71-82. Dietert, R. R., Etzel, R. A., and Chen, D., et al. Workshop to Identify Critical Windows of Exposure for Children's Health: Immune and Respiratory Systems Work Group Summary. Environ. Health Perspect. 2000; 108 Suppl 3:483^190. Dietert, R. R. and Piepenbrink, M. S. Perinatal Immunotoxicity: Why Adult Exposure Assessment Fails to Predict Risk. Environ. Health Perspect. 2006; 114:477-483. Dietert, R. R. and Dietert, J. M. Potential for Early-Life Immune Insult Including Developmental Immunotoxicity in Autism And Autism Spectrum Disorders: Focus on Critical Windows of Immune Vulnerability. J. Toxicol. Environ. Health B Crit. Rev. 2008;11:660-680. Gauthier, T W., Ping, X. D., Harris, F. L., Wong, M., Elbahesh, H., and Brown, L. A. Fetal Alcohol Exposure Impairs Alveolar Macrophage Function via Decreased Glutathione Availability. Pediatr. Res. 2005;57:76-81. Ping, X. D., Harris, F. L., Brown, L. A., and Gauthier, T. W In Vivo Dysfunction of the Term Alveolar Macrophage After in Utero Ethanol Exposure. Alcohol Clin. Exp. Res. 2007;31:308-316. Gao, D., Mondal, T. K., and Lawrence, D. A. Lead Effects on Development and Function of Bone Marrow-Derived Dendritic Cells Promote Th2 Immune Responses. Toxicol. Appl. Pharmacol. 2007;222:69-79.

318

BIOMARKERS 15.

16. 17. 18. 19.

20.

21. 22. 23. 24. 25. 26. 27. 28. 29. 30.

Vorderstrasse, B. A., Cundiff, J. A., and Lawrence, B. P. Developmental Exposure to the Potent Aryl Hydrocarbon Receptor Agonist 2,3,7,8-Tetrachlorodibenzo-P-Dioxin Impairs the Cell-Mediated Immune Response to Infection with Influenza A Virus, But Enhances Elements of Innate Immunity. J. Immunotoxicol. 2004;1:103-112. Hogaboam, J. P., Moore, A. J., and Lawrence, B. P. The Aryl Hydrocarbon Receptor Affects Distinct Tissue Compartments During Ontogeny of the Immune System. Toxicol. Sci. 2008;102:160-170. Lim, D. G., Joe, I. Y., and Park, Y. H., et al. Effect of Immunosuppressants on the Expansion and Function of Naturally Occurring Regulatory T Cells. Transpl. Immunol. 2007;18:94-100. Gennari, A., Bol, M., Seinen, W., Penninks, A., and Pieters, R. OrganotinInduced Apoptosis Occurs in Small CD4(+)CD8(+) Thymocytes and Is Accompanied by an Increase in RNA Synthesis. Toxicology. 2002;175:191-200. Kerkvliet, N. I., Baecher-Steppan, L., Shepherd, D. M., Oughton, J. A., Vorderstrasse, B. A., and Dekrey, G. K. Inhibition of TC-1 Cytokine Production, Effector Cytotoxic T Lymphocyte Development and Alloantibody Production by 2,3,7,8-Tetrachlorodibenzo-P-Dioxin. J. Immunol. 1996;157:2310-2319. Johnson, C. W., Williams, W. C , Copeland, C. B., Devito, M. J., and Smialowicz, R. J. Sensitivity of the SRBC PFC Assay versus ELISA for Detection of Immunosuppression by TCDD and TCDD-Like Congeners. Toxicology. 2000; 156:1-11. Li, W. and Matsumura, F. Significance of the Nongenomic, Inflammatory Pathway in Mediating the Toxic Action of TCDD to Induce Rapid and Long-Term Cellular Responses in 3T3-L1 Adipocytes. Biochemistry. 2008;47:13997-14008. Mustafa, A., Holladay, S. D., and Goff, M., et al. An Enhanced Postnatal Autoimmune Profile in 24 Week-Old C57BL/6 Mice Developmentally Exposed to TCDD. Toxicol. Appl. Phamacol. 2008;232:51-59. Gogal, R. M., Jr. and Holladay, S. D. Perinatal TCDD Exposure and the Adult Onset of Autoimmune Disease. J. Immunotoxicol. 2008;5:413-418. Luebke, R. W, Parks, C , and Luster, M. I. Suppression of Immune Function and Susceptibility to Infections in Humans: Association of Immune Function with Clinical Disease. J. Immunotoxicol. 2004;1:15-24. Dietert, R. R. and Zelikoff, J. T. Early-Life Environment, Developmental Immunotoxicology, and the Risk of Pediatric Allergic Disease Including Asthma. Birth Defects Res. B Dev. Reprod. Toxicol. 2008;83:547-560. Dietert, R. R. and Zelikoff, J. T. Pediatric Immune Dysfunction and Health Risks Following Early Life Immune Insult. Curr. Pediatr. Rev. 2009;5:36-51. Moorman, J. E., Rudd, R. A., and Johnson, C. A., et al. National Surveillance for Asthma—United States, 1980-2004. U.S. Centers for Disease Control MMRW Surveilance Summaries. 2007;56(SS08) 1-14:18-54. Wang, L. and Pinkerton, K. E. Detrimental Effects of Tobacco Smoke Exposure During Development on Postnatal Lung Function and Asthma. Birth Def. Res. C. Embryo Today. 2008;84:54-60. Selgrade, M. K., Lemanske, R. F, Jr., and Gilmour, M. I., et al. Induction of Asthma and the Environment: What We Know and Need to Know. Environ. Health Perspect. April 2006;114(4):615-619. Yeatts, K.. Sly, P., and Shore, S., et al. A Brief Targeted Review of Susceptibility Factors, Environmental Exposures, Asthma Incidence, and Recommendations for Future Asthma Incidence Research. Environ. Health Perspect 2006; 114:634-640.

BIOMARKERS OF IMMUNOTOXICITY 31. 32.

33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44.

45. 46. 47. 48.

319

Nordling, E., Berglind, N., and Melen, E., et al. Traffic-Related Air Pollution and Childhood Respiratory Symptoms, Function and Allergies. Epidemiology. 2008;19:401^M)8. Pistiner, M., Gold, D. R., Abdulkerim, H., Hoffman, E., and Celedon, C. Birth by Cesarean Section, Allergic Rhinitis, and Allergic Sensitization Among Children with a Parental History of Atopy. J. Allergy Clin. Immunol. 2008; 122: 274-279. Shoenfeld, Y, Selmi, C, Zimlichman, E., and Gershwin, M. E. The Autoimmunologist: Geoepidemiology, a New Center of Gravity, and Prime Time for Autoimmunity. J. Autoimmun. 2008;31:325-330. Thewissen, M. and Stinissen, P. New Concepts on the Pathogenesis of Autoimmune Diseases: A Role for Immune Homeostasis, Immunoregulation, and Immunosenescence. Crit. Rev. Immunol. 2008; 28:363-376. Rose, N. R. Predictors of Autoimmune Disease: Autoantibodies and Beyond. Autoimmunity. 2008;41419^128. Dietert, R. R., Dietert, J. M., and Gavalchin, J. Risk of Autoimmune Disease: Challenges for Immunotoxicity Testing. In. Dietert, R. (Ed.) Immunotoxicity Testing. Humana Press, Towana, NJ;In Press. Myers, M. and Zimmet, P. Halting the Accelerating Epidemic of Type 1 Diabetes. Lancet. May 24, 2008;371:1730-1731. Kim, H. S. and Lee, M. S. Diabetes. Curr. Mol. Med. 2009;9:30-44. Thornton, J., Lunt, M., and Ashcroft, D. M. et al. Costing Juvenile Idiopathic Arthritis: Examining Patient-Based Costs During the First Year After Diagnosis. Rheumatology. 2008;47:985-990. Kobayashi, S., Momohara, S., Kamatani, N., and Okamoto, H. Molecular Aspects of Rheumatoid Arthritis: Role of Environmental Factors. FEBS J. 2008; 275:4456-4462. Stagi, S., Giani, T., Simonini, G., and Falcini, F. Thyroid Function, Autoimmune Thyroiditis and Coeliac Disease in Juvenile Idiopathic Arthritis. Rheumatology. 2005;44:517-520. Nadal, I., Donat, E., Ribes-Koninckx, C , Calabuig, M., and Sanz, Y Imbalance in the Composition of the Duodenal Microbiota of Children with Coeliac Disease./ Med. Microbiol. 2008;56:1669-1674. Sundstrom, P., Nystrom, L., and Hallmans, G. Smoke Exposure Increases the Risk for Multiple Sclerosis. Eur. J. Neurol. 2008;15:579-583. Cooper, G. S., Gilbert, K. M., Greidinger, E. L., James, J. A., Pfau, J. C , Reinlib, L., Richardson, B. C , and Rose, N. R. Recent Advances and Opportunities in Research on Lupus: Environmental Influences and Mechanisms of Disease. Environ. Health Perspect. 2008;116:695-702. Dallaire, R, Dewailly, E., and Vezina, C. et al. Effect of Prenatal Exposure to Polychlorinated Biphenyls on Incidence of Acute Respiratory Infections in Preschool Inuit Children. Environ. Health Perspect. 2006;114:1301-1305. Heilmann, C , Grandjean, P., Weihe, P., Nielsen, R, and Budtz-j0rgensen, E. Reduced Antibody Responses to Vaccinations in Children Exposed to Polychlorinated Biphenyls. Plos. Med. 2006;3:E311. Molina, V. and Shoenfeld, Y Infection, Vaccines and Other Environmental Triggers of Autoimmunity. Autoimmunity. 2005;38:235-245. Weisglas-Kuperus, N., Vreugdenhil, H. J., and Mulder, P. G. Immunological Effects of Environmental Exposure to Polychlorinated Biphenyls and Dioxins in Dutch School Children. Toxicol. Lett. 149:281-285.

320

BIOMARKERS 49. 50. 51. 52. 53. 54. 55.

56. 57. 58. 59. 60.

61. 62. 63. 64. 65. 66.

Greaves, M. Infection, Immune Responses and the Aetiology of Childhood Leukaemia. Nat. Rev. Cancer. 2006;6:193-203. Ng, S. P., Silverstone, A. E., Lai, Z. W., and Zelikoff, J. T. Effects of Prenatal Exposure to Cigarette Smoke on Offspring Tumor Susceptibility and Associated Immune Mechanisms. Toxicol. Sci. 2006;89:135-144. Calderon-Garciduenas, L., Macias-Parra, M., and Hoffmann, H. J., et al. Immunotoxicity and Environment: Immunodysregulation and Systemic Inflammation in Children. Toxicol. Pathol. 2009;In Press. Simeonova, P. P. and Luster, M. I. Arsenic and Atherosclerosis. Toxicol. Appl. Pharmacol. 2004;198:444-449. Wang, C. H., Hsiao, C. K., and Chen, C. L., et al. A Review of the Epidemiologic Literature on the Role of Environmental Arsenic Exposure and Cardiovascular Diseases. Toxicol. Appl. Pharmacol. 2007;222:315-326. Muller, N. Inflammation and the Glutamate System in Schizophrenia: Implications for Therapeutic Targets and Drug Development. Expert Opin. Ther. Targets. 2008;12:1497-1507. Martins-De-Souza, D., Gattaz, W. E, and Schmitt, A. et al. Prefrontal Cortex Shotgun Proteome Analysis Reveals Altered Calcium Homeostasis and Immune System Imbalance in Schizophrenia. Eur. Arch. Psychiatry Clin. Neurosci. 2009;In Press. Paterson, P. H. Immune Involvement in Schizophrenia and Autism: Etiology, Pathology and Animal Models. Behav. Brain Res. 2009;In Press. Dietert, R. R. and Dietert, J. M. Possible Role for Early-Life Immune Insult Including Developmental Immunotoxicity in Chronic Fatigue Syndrome (CFS) or Myalgic Encephalomyelitis (ME). Toxicology. 2008;247:61-72. Fuite, J., Vernon, S. D., and Broderick, G. Neuroendocrine and Immune Network Re-Modeling in Chronic Fatigue Syndrome: An Exploratory Analysis. Genomics. 2008;92:393-399. Lorusso, L., Mikhaylova, S. V., Capelli, E., Ferrari, D., Ngonga, G. K., and Ricevuti, G. Immunological Aspects of Chronic Fatigue Syndrome. Autoimmun. Rev. 2009;In Press. Ashwood, P., Enstrom, A., and Krakowiak, P., et al. Decreased Transforming Growth Factor Betal in Autism: A Potential Link Between Immune Dysregulation and Impairment in Clinical Behavioral Outcomes. J. Neuroimmunol. 2008; 204:149-153. Blaylock, R. L. and Strunecka, A. Immune-Glutamatergic Dysfunction as a Central Mechanism of the Autism Spectrum Disorders. Curr. Med. Chem. 2009;16(2):157-170. Li, X., Chauhan, A., and Sheikh, A. M. et al. Elevated Immune Response in the Brain of Autistic Patients. J. Neuroimmunol. 2009;207:111-116. Miller, T. E., Golemboski, K. A., Ha, R. S., Bunn, T, Sanders, F. S., and Dietert, R. R. Developmental Exposure to Lead Causes Persistent Immunotoxicity in Fischer 344 Rats. Toxicol. Sci. 1998;42:129-135. Luebke, R. W., Chen, D. H., and Dietert, R. et al. The Comparative Immunotoxicity of Five Selected Compounds Following Developmental or Adult Exposure. J. Toxicol. Environ. Health B Crit. Rev. 2006;9:1-26. Landreth, K. S. Critical Windows in Development of the Rodent Immune System. Hum. Exp. Toxicol. 2002;21:493^198. Dietert, R. R. Developmental Immunotoxicology: Focus on Health Risks. Chem. Res. Toxicol. 2009;22:17-23.

BIOMARKERS OF IMMUNOTOXICITY 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79.

80. 81. 82. 83. 84.

321

Selgrade, M. K. Immunotoxicity: The Risk Is Real. Toxicol. Sci. 2007;100: 328-332. Luster, M. I., Dean, J. H., and Germolec, D. R. Consensus Workshop on Methods to Evaluate Developmental Immunotoxicity. Environ. Health Perspect. 2003;111:579-583. Ladies, G. S., Chapin, R. E., and Hastings, K. L., et al. Developmental Toxicology Evaluations—Issues with Including Neurotoxicology and Immunotoxicology Assessments in Reproductive Toxicology Studies. Toxicol. Sci. 2005;88:24-29. Dietert, R. R. and Holsapple, M. P. Methodologies for Developmental Immunotoxicity (DIT) Testing. Methods. 2000;41:123-131. Burns-Naas, L. A., Hastings, K. L., Ladies, G. S., Makris, S. L., Parker, G. A., and Holsapple, M. P. What's So Special About the Developing Immune System? Int. J. Toxicol. 2008;27:223-254. Dietert, R. R. Developmental Immunotoxicity (DIT) in Drug Safety Testing: Matching DIT Testing to Adverse Outcomes and Childhood Disease Risk. Curr. Drug Saf. 2008;3:216-226. Dietert, R. R. Developmental Immunotoxicology (DIT): Windows of Vulnerability, Immune Dysfunction and Safety Assessment. J. Immunotoxicol. 2008; 5:401^112. Luster, M. I., Johnson, V. J., Yucesoy, B., and Simeonova, P. P. Biomarkers to Assess Potential Developmental Immunotoxicity in Children. Toxicol. Appl. Pharmacol. 2005;206:229-236. Zandman-Goddard, G., Peeva, E., and Shoenfeld, Y Gender and Autoimmunity. Autoimmun. Rev. 2007;6:366-372. Lleo, A., Battezzati, P. M., Selmi, C, Gershwin, M. E., and Podda, M. Is Autoimmunity a Matter of Sex? Autoimmun. Rev. 2008;7:626-630. Maul, R. W. and Gearhart, P. J. Women, Autoimmunity, and Cancer: A Dangerous Liaison Between Estrogen and Activation-Induced Deaminase? J. Exp. Med. 2009;206:11-13. Rooney, A. A., Matulka, R. A., and Luebke, R. W. Developmental Atrazine Exposure Suppresses Immune Function in Male, but Not Female Sprague-Dawley Rats. Toxicol. Sci. 2003;76:366-375. Karrow, N. A., Guo, T. L., and Delclos, K. B., et al. Nonylphenol Alters the Activity of Splenic NK Cells and the Numbers of Leukocyte Subpopulations in Sprague-Dawley Rats: A Two-Generation Feeding Study. Toxicology. 2004;196:237-245. Guo, T. L., Chi, R. P., Germolec, D. R., and White, K. L. Jr. Stimulation of the Immune Response in B6C3F1 Mice by Genistein Is Affected by Exposure Duration, Gender, and Litter Order. J. Nutr. 2005;135:2449-2456. Bunn, T. L., Parsons, P. J., Kao, E., and Dietert, R. R. Gender-Based Profiles of Developmental Immunotoxicity to Lead in the Rat: Assessment in Juveniles and Adults. J. Toxicol. Environ. Health A. 2001;64:223-240. Peden-Adams, M. M., Keller, J. M., Eudaly, J. G., Berger, J., Gilkeson, G. S., and Keil, D. E. Suppression of Humoral Immunity in Mice Following Exposure to Perfluorooctane Sulfonate. Toxicol. Sci. 2008;104:144-154. Luebke, R. W, Holsapple, M. P., and Ladies, G. S., et al. Immunotoxicogenomics: The Potential of Genomics Technology in the Immunotoxicity Risk Assessment Process. Toxicol. Sci. 2006;94:22-27. Baken, K. A., Vandebriel, R. J., Pennings, J. L., Kleinjans, J. C , and Van Loveren, H. Toxicogenomics in the Assessment of Immunotoxicity. Methods. 2007;41:132-141.

322

BIOMARKERS 85. 86. 87.

Baken, K. A., Pennings, J. L., and Jonker, M. J., et al. Overlapping Gene Expression Profiles of Model Compounds Provide Opportunities for Immunotoxicity Screening. Toxicol. Appl. Pharmacol. 2008;226:46-59. Gennari, A., Ban, M., Braun, A., and Casati, S., et al. The Use of In Vitro Systems for Evaluating Immunotoxicity: The Report and Recommendations of an ECVAM Workshop. J. Immunotoxicol. 2005;2:61-83. Carfi', M., Gennari, A., and Malerba, I., et al. In Vitro Tests to Evaluate Immunotoxicity: A Preliminary Study. Toxicology. 2007;229:11-22.

CHAPTER

BIOMARKERS IN OBSTETRIC MEDICINE Manish Maski, Sarosh Rana, and S. Ananth Karumanchi

A N E U P L O I D I E S - T R I S O M I E S 2 1 , 18, A N D 13 Maternal age is an inadequate screening criterion alone for the detection of autosomal trisomies. The utility of measurement of maternal serum biomarkers for the prenatal detection of autosomal trisomies was initially suggested in 1984 by the observation that levels of alpha fetoprotein were lower in mothers who subsequently delivered trisomic babies.1 The characterization of several other serum biomarkers over the next several years has lead to the routine use of various screening protocols for the prenatal detection of trisomies 21 (i.e., Down syndrome), 18 (i.e., Edward syndrome), and 13 (i.e., Patau syndrome.) Five maternal biomarkers are currently in routine clinical use, usually in combination: alpha fetoprotein (AFP); human chorionic gonadotropin (hCG), either in its total form or as its beta subunit (total hCG or free beta-hCG, respectively); pregnancy-associated plasma protein-A (PAPP-A); unconjugated estriol (uE3); and inhibin A (inh A). Each of these biomarkers will be discussed in turn, followed by a discussion of their use in combination, as well as in combination with ultrasonography, for the detection of Trisomies 21, 18, and 13.

Alpha Fetoprotein Alpha fetoprotein (AFP) is a 69-kDa glycoprotein synthesized in the fetal liver and yolk sac. AFP is a member of the Albuminoid superfamily and is located on chromosome 4. AFP binds and transports many ligands, including bilirubin, fatty acids, retinoids, steroids (including estrogens), and various

323

324

BIOMARKERS

drugs, but it has also been proposed to serve as a potential circulating reservoir of biologically-active peptides that can be produced via cleavage of the native protein. Furthermore, AFP contains several amino acid sequences that are homologous to cellular adhesion sequences found in other proteins.2 Investigation into the biologic activities of AFP (and its subfragments) is ongoing, but it has been shown to promote growth in a variety of cell and animal models, and, in some instances, has also been shown to inhibit proliferation.3,4 In addition to its use as a biomarker in the second trimester for the prediction of Trisomies 18 and 21, as will be discussed below, AFP is the test of choice in screening for neural tube defects.

Human C h o r i o n i c G o n a d o t r o p i n Human chorionic gonadotropin (hCG) is a glycoprotein hormone consisting of two subunits, alpha (92 amino acids) and beta (145 amino acids), joined noncovalently. It exists in three active forms: regular hCG, hyperglycosylated hCG, and the free beta-subunit of hyperglycosylated hCG. Regular hCG is made by fused villous syncytiotrophoblast cells of the placenta. The classically-recognized function of regular hCG is the promotion of corpus luteal progesterone production from gestation weeks three to six. However, more recent research suggests that regular hCG maintains angiogenesis in the myometrial spiral arteries throughout the length of pregnancy, and it has also been shown to promote the fusion of villous cytotrophoblast cells to form syncytiotrophoblast. Both of these more recently described functions are critical to effective placentation and represent the more logical prime functions of regular hCG over the length of gestation.5 Hyperglycosylated hCG is made by extravillous invasive cytotrophoblast cells and serves as an autocrine factor on these cells to initiate and control invasion, as occurs at implantation of pregnancy, and the establishment of hemochorial placentation, as occurs in malignancy, such as invasive hydatiform mole and choriocarcinoma.6 Hyperglycosylated hCG has been shown to inhibit apoptosis in extravillous invasive cytotrophoblast cells,7 thereby promoting cell invasion and growth. Screening tests for Down syndrome measure hyperglycosylated hCG or its free beta-subunit.

Pregnancy-Associated Plasma P r o t e i n - A Pregnancy-associated plasma protein-A (PAPP-A) is an 820-kDa homotetrameric glycoprotein (each monomer composed of 1547 amino acids), produced mainly by placental syncytiotrophoblast. A remote member of the alpha-mac roglobulin plasma protein family, PAPP-A can consistently be detected in maternal circulation four to six weeks after conception. PAPP-A is an inhibitor of bovine trypsin and human plasmin, and has been shown to bind a variety of cytokines. It specifically cleaves insulin-like growth factor binding protein-4 (IGFBP-4), which can increase IGF bioavailability and lead to stimulatory effects in a variety of systems.8 For example, PAPP-A knock-out mice demonstrate skeletal insufficiency in density, ar-

BIOMARKERS IN O B S T E T R I C M E D I C I N E

325

chitecture, and strength, likely via loss of cleavage of inhibitory insulin-like biding proteins that, in turn, decreases local IGF-1 bioavailability in bone.9 Moreover, PAPP-A is felt to be important in allowing maternal immunological tolerance of the fetus.10 The lymphocyte proliferation response to alloantigens and lectin, as well as expression of HLA-DR molecules on monocytes, is predominantly suppressed in vitro by PAPP-A.8

Unconjugated Estriol The biosynthesis of estrogens during pregnancy involves a coordinated effort between the fetal adrenal glands and the placenta. The fetal adrenal cortex provides the immediate androgen precursors to the placenta for conversion to estrogens. Estrogens have been postulated to have a number of important functions in maintaining pregnancy and preparing the reproductive tract for parturition, such as increasing blood flow in the vascular beds of the myometrium and endometrium, inducing neovascularization within the placenta, and inducing myometrial gap junction formation that coordinates contractions late in gestation. Because unconjugated estriol (uE3) is almost exclusively a fetal product that is secreted into the maternal circulation, maternal concentrations of uE3 may reflect abnormalities in fetal and placental development. For example, fetal death during the second or third trimester results in a significant drop in maternal uE3 concentrations within a few hours.11

Inhibin A Inhibin A (inh A) is a heterodimeric glycoprotein that consists of an alpha subunit (18 kDa) and a beta subunit (14 kDa) linked by disulfide bridges.12 It is a distant member of the transforming growth factor-beta (TGF-beta) superfamily. There also exists a molecule known as inhibin B (inh B) that consists of the identical alpha chain of inh A linked to a unique beta chain (termed inhibin beta B); inh B shares approximately 64% homology to inhibin A,13 but its regulation and temporal pattern in maternal serum is distinct from that of inh A.14 Inh A is produced by the granulosa cells of the developing follicle in response to FSH and LH, and its secretion tracks with gonadotropin-mediated dominant follicle growth and demise. Maternal serum levels of inh A begin to rise in the late follicular phase, reaching peaks in the midcycle and, subsequently, in the midluteal phase. The main function of inh A is in the negative regulation of FSH synthesis and secretion, and it plays a critical role in follicular development.14

D e t e c t i o n o f T r i s o m y 21 Down syndrome is the most common chromosomal abnormality among newborns, occurring in approximately one in 800 to 1000 live births.15'16 It is the result of three copies of chromosome 21, most often secondary to meiotic nondysjunction, which occurs at increasing frequency with advancing maternal age. Down syndrome results in a variety of dysmorphic features, con-

326

BIOMARKERS

genital malformations, and other health problems, such as brachycephaly with short stature, mental retardation, and congenital heart disease. Screening for trisomy 21, in one form or another, has been a well-established part of routine prenatal care in many countries for over 20 years. While previously performed most often in the early second trimester, since the early 1990s, screening for trisomy 21 has become routine in the late first trimester. The option for earlier screening is preferable to many women, as it allows more privacy in decision-making, more timely reassurance, or the option of earlier and safer termination of pregnancy. Several studies have evaluated the use of the aforementioned maternal serum analytes in the prenatal detection of trisomy 21 in singleton pregnancies, and the most important of them are summarized in Table 13.1. In the first trimester, two serum biomarkers stand out: free beta-hCG and PAPPA.17 These two biomarkers are usually combined with maternal age and fetal nuchal translucency to generate an overall risk of trisomy 21. Nuchal translucency is the lucent (i.e., hypoechoic) zone of fluid between the skin and soft tissues at the posterior fetal neck that is observed sonographically, and it is increased in fetuses affected by Down syndrome. Nuchal translucency is also often used in the determination of risk for trisomies 18 and 13,18 as well as other chromosomal abnormalities (e.g., Turner syndrome). It should be noted that in order to incorporate nuchal translucency into estimates of risk for aneuploidy, the sonographer must be stringently trained in the technique and gain sufficient experience, and external quality control is required.19 Furthermore, even despite adequate technical skill on the part of the sonographer, a small percentage of fetuses will be unable to be visualized secondary to fetal position or maternal body habitus. In the second trimester, inh A, AFP, uE3, and total hCG are commonly measured as the so-called "quadruple test." It is generally thought that free beta-hCG is the preferred form of hCG for screening in first trimester, while total hCG improves in performance in the second trimester. For example, Evans, et al.. performed a Monte Carlo simulation trial based on a literature review of the PUBMED database from 1966 to 2005.20 This group found that detection of Down syndrome increased by 4, 5, 6, and 7 percentage points when free beta-hCG was added to PAPP-A and nuchal translucency, as compared with 0, 0, 2, and 4 percentage points for total hCG, at 9-12 weeks gestation, respectively. Furthermore, these investigators found that free beta-hCG was associated with a greater reduction in false positive screening results at each week (9-12), as compared to total hCG. Each maternal biomarker is measured in units (usually ng/mL or IU/ mL) and then converted to a gestational age-specific multiple of the median value (MoM), based on a representative population of women with unaffected singleton pregnancies. Compared to unaffected pregnancies, levels of first trimester free beta-hCG and PAPP-A in Down syndrome are about 1.8 MoM and 0.4 MoM, respectively, and levels of second-trimester AFP and uE3 are, on average, 0.70-0.75 MoM. Second trimester levels of total beta-hCG and inh A are about 2.0 MoM in pregnancies affected by Down syndrome.

BIOMARKERS IN OBSTETRIC M E D I C I N E

327

Detection ofTrisomy 18 Trisomy 18 is the second most common autosomal trisomy in newborns, after Down syndrome, occurring in approximately 1 in 8000 live births. In the second trimester, the prevalence ofTrisomy 18 is approximately one in 2400, but there is a third trimester fetal loss rate of about 70% that results in the reduced prevalence of liveborn fetuses.21 As with Down syndrome, the majority of these cases are the result of meiotic nondysjunction. Trisomy 18 can produce abnormalities in any organ system, but congenital heart disease (most commonly ventricular septal defects and patent ductus arteriosus), gastrointestinal abnormalities (usually Meckel's diverticulum and malrotation), craniofacial abnormalities, and mental retardation occur in the majority of affected fetuses. Trisomy 18 is a largely lethal condition, with only 5 to 10 percent of newborns surviving past the first year. Several studies utilizing various combinations of the aforementioned biomarkers (minus inh A) have been performed to find an efficient screen for trisomy 18. The most important of these are summarized in Table 13.2. The most striking difference between a positive screen for Down syndrome and that for trisomy 18 lies in the levels of hCG and free beta-hCG, which are elevated in Down syndrome but reduced in trisomy 18. In affected pregnancies, the median free beta-hCG during the first trimester is 0.20 MoM, and the median total hCG during the second trimester is about 0.30 MoM.

Detection ofTrisomy 13 Trisomy 13 is a rare aneuploidy, with an observed incidence between one in 10,000 to 17,000 live births.2223 Trisomy 13 is almost uniformly lethal in the post-natal period, with median survival under three days and only 5% of affected infants surviving beyond six months. Again, meiotic nondysjunction related to increasing maternal age is the most common etiology, but Robertsonian translocation and mosaicism do occur. Trisomy 13 is usually phenotypically characterized by severe neurological defects, including holoprosencephaly, mental retardation, and, often, neural tube defects. Other common clinical features include severe eye defects, facial clefting, omphalocele, and congenital heart defects, such as patent ductus arteriosis, ventricular and atrial septal defects, and dextrocardia. Prenatal diagnosis of affected fetuses can often be suggested via ultrasonographic demonstration of the associated structural abnormalities. However, efforts have been made to determine a pattern of maternal serum biomarkers that reliably predicts trisomy 13. Sailer, et al. examined second-trimester maternal serum levels of AFP, uE3, and total hCG in 28 cases of fetal trisomy 13, each case matched with five unaffected pregnancies.24 They found that only uE3 levels (median MoM=0.71) were statistically different (p<0.01) between cases and controls. In the previously-noted study by Spencer and Nicolaides,25 they were unable to develop a specific algorithm that would differentiate between cases of trisomy 13 and trisomy 18, and hence developed a combined trisomy 13/18 algorithm (see above). Furthermore, in a study by Breathnach,

328 TABLE 13.1

BIOMARKERS Studies evaluating the prenatal detection of Trisomy 21. Comb. Risk Cutoff for Positive Screen

Design

Biomarkers Examined

% Detection Rate (DR) for Trisomy 21 at % False Positives (FP)

Wald, et al, 1996(126)

Multinational; Case-Control (4-5 controls matched per case); Samples drawn between 8 to 14 weeks gestation

AFRuE3, total hCG, free alpha-hCG, free beta-hCG, PAPP-A, inh A

Only free beta-hCG and PAPPA differed between groups sufficiently, resulting in DR=63% detection at FP=5.5% when both biomarkers combined with maternal age-related risk

1:300

Aitken, et al, 1996(127)

Case-Control (3-6 controls matched per case); Samples drawn between 7 to 18 weeks gestation

inh A, free beta-hCG, total hCG, AFP

DR (at FP=5%) rose from 53% to 73% when inhA was added to combination of free betahCG + AFP + maternal agerelated risk

Not given

Wald and Hackshaw, 1997(128)

Compilation of three data sets from three casecontrol series, all at 10 to 14 weeks gestation

Nuchal translucency (two data sets), free betahCG and PAPP-A (one data set)

Nuchal translucency+ free beta-hCG+ PAPP-A+ matemal age-related risk DR=80% at FP=5% (with correction for first trimester ascertainment bias)

1:400

Haddow, et al, 1998(129)

Case-Control; Measurements performed between 9 to 14 weeks gestation

AFRuE3, total hCG, free beta-hCG, PAPP-A, nuchal translucency

DR (with maternal age), at FP=5%: AFP=I7% uE3=4% total hCG=29% free beta-hCG=25% PAPP-A=42%;

Not given

Investigators

inh A levels only became abnormal from end of first trimester

Total hCG+PAPP-A=63% Free beta-hCG+PAPP-A=60% *Nuchal translucency significantly varied between study centers and was thus excluded from calculations HudererDuric.et al, 2000(130)

Case-Control; Samples drawn between gestation weeks 15 to 22, with 494% of samples collected during gestation weeks 17 or 18

AFR total hCG, uE3

3 biomarkers + maternal agerelated risk Sensitivity=75%, Specificity=79.5%

1:100

Sensitivity=83%, Specificity=69.8%

1:200

Sensitivity=92%, Specificity=63.3%

1:300

329

BIOMARKERS IN OBSTETRIC MEDICINE TABLE 13.1

Investigators

Krantz, et al, 2000(131)

Bindra, et al, 2002 (OSCAR study) (132)

Studies evaluating the prenatal detection of Trisomy 21. (continued)

Biomarkers Examined

Design

% Detection Rate (DR) for Trisomy 21 at % False Positives (FP)

Comb. Risk Cutoff for Positive Screen

Free beta-hCG, Case-Control; PAPP-A, nuchal Samples drawn between 9 weeks translucency + 0 days and 13 weeks + 6 days; Nuchal translucency measured between 10 weeks + 4 days and 13 weeks + 6 days

Combined biomarkers + nuchal translucency + maternal agerelated risk -in women<35 years: DR=87.5% at FP=4.5% -in women>35 years: DR=92%atFP=l4.3%; -in all women aged 14-49; DR=9l%atFP=5%

"Average risk for a woman >35 years,"

Case-Control; Measurements performed in One-Stop Clinic for Assessment of Risk (OSCAR) clinic in 14,383 women between gestation weeks 1 to 14 (median 12)

Maternal age-related risk + combined biomarkers + nuchal translucency: -DR=91.5% at FP=6.8% - D R =90.2% at FP=S%

1:300

Free beta-hCG, PAPP-A, nuchal translucency

actual cutoff not given

1

Crossley, et al, 2002(19)

Case-Control; Free beta-hCG, Over 17,000 PAPP-A, nuchal women from 15 translucency Scottish maternity units; Measurements performed between gestation weeks 10-14

Maternal age-related risk + combined biomarkers + nuchal translucency: DR=82% at FP=5%

1:250

Wapner, et al, 2003 (BUN Study Group) (133)

Free beta-hCG, Case-Control; Multicenter study PAPP-A, nuchal translucency in USA and Canada; Measurements performed between 10 weeks + 4 days and 1 3 weeks + 6 days gestation

Maternal age-related risk + combined biomarkers + nuchal translucency: -DR=85.2% at FP=9.4% -DR=78.7% at FP=5.0% -among subgroup of women a35,DR=89.8%atFP=l5.2%

1:270

330 TABLE 13.1

BIOMARKERS

Studies evaluating the prenatal detection of Trisomy 21. (continued)

Investigators

Design

Wald, et al, 2003, for the SURUSS Research Group (134)

Case-Control; Multicenter study in UK and Austria; >47,000 pregnancies; Measurements performed between 9 and 20 weeks gestation

Biomarkers Examined AFP, total hCG, free beta- hCG, uE3, PAPP-A, inhA, nuchal translucency

% Detection Rate (DR) for Trisomy 21 at % False Positives (FP) FP% for DR=85% (all combinations include maternal age):

Comb. Risk Cutoff for Positive Screen Not given

(1) free beta-hCG + PAPP-A at 10 weeks gestation + nuchal translucency=6.1 % (2) [AFP + uE3 + free beta-hCG + inh A] at 14-20 weeks gestation ("quadruple test")=6.2% (3) nuchal translucency + PAPPA at 10 weeks gestation + [AFP + uE3 + free beta-hCG + inh A] at 14-20 weeks gestation ("Integrated test")= 1.2% (4) "Serum integrated test" [same as (3) but without nuchal translucency]=2.7%

Malone, et al, 2005 (FASTER Research Consortium) (135)

Case-Control; Multinational; Over 38,000 women screened between gestation weeks 10 to 18; Fetuses found to have cystic hygroma were excluded

Free beta-hCG, 1:150 for (1) Maternal age-related risk PAPP-A, and + free beta-hCG + PAPP-A + 1st nuchal nuchal translucency ("combined trimester translucency, screening"): screening measured at 10 - D R at FP=5%: weeks + 3 days to 87% at 1 1 weeks gestation; 13 weeks+ 6 days 85% at 12 weeks; gestation 82% at 13 weeks. AND AFR total hCG, uE3, and inh A, at 15 through 18 weeks gestation (all calculations included maternal age-related risk)

(2) AFP+total hCG + uE3 + inh A at 15 to 18 weeks gestation ("second-trimester quadruple screen"): DR=8l%atFP=5%. (3) "Stepwise sequential screen" [risk results after each of (I) and (2) above]: DR=95% at FP=5%. (4) "Fully integrated screen" [single estimation of risk after (l)and(2)]: DR=96% at FP=5%, with first trimester measurements at 1 1 weeks gestation. (5)"Serum integrated screen" [same as (4) but without nuchal translucency]: DR=88% at FP=5%.

1:300 for 2nd trimester screening

BIOMARKERS IN O B S T E T R I C M E D I C I N E

TABLE I 3.1

331

Studies evaluating the prenatal detection of Trisomy 21, (continued)

Investigators

Design

Biomarkers Examined

Canick, et al, 2006 (for the FASTER Research Consortium) (136)

Case-Control: 79 cases of Down syndrome each matched to five controls, from 1 1 through 13 weeks gestation from the FASTER specimen bank (see previous)

Free beta-hCG, total hCG, inhibin A, PAPP-A (measured previously), and nuchal translucency (measured previously)

% Detection Rate (DR) for Trisomy 21 at % False Positives (FP) Maternal age-related risk + nuchal translucency + PAPP-A at 12 weeks gestation:

Comb. Risk Cutoff for Positive Screen Not given

-plus free beta-hCG: DR=84% at FP=5%; -plus total hCG: DR=83% at FP=5%; -plus inh A: DR=85% at FP=5%. (no statistically significant difference in screening performance among the three biomarkers)

Kagan, et al, 2008 (27)

Case-Control: >56,000 controls and 395 cases; Measurements performed between gestation weeks 1 1 and 13 + 6 days

Free beta-hCG, PAPP-A, nuchal translucency, and fetal heart rate

Maternal age + combined seNot given rum biomarkers + nuchal translucency + fetal heart rate: DR=90% at FP=3%

et al.,26 among the 15 cases of trisomy 13 identified among the more than 36,000 pregnancies screened, the following was observed: six were identified with cystic hygroma on ultrasound, and of the remaining nine cases, only four were identified using first-trimester nuchal translucency, maternal age-related risk, free beta-hCG, and PAPP-A, and only three of the seven cases still viable in the second trimester were identified using AFP, uE3, total hCG, inh A, and maternal age-related risk. More recently, in a study by Kagan, et al.,27 61 cases of trisomy 13 were identified out of nearly 57,000 pregnancies screened in the first trimester and were analyzed via a trisomy 13-specific algorithm. This algorithm incorporated maternal age-related risk, fetal nuchal translucency, fetal heart rate, free betahCG, and PAPP-A, and it identified 87% of affected pregnancies with a false positive rate of 0.2%, at a combined risk cutoff according to the distribution of pregnancies in England and Wales from 2000 to 2002. There are a number of approaches available to patients to assess their risk of aneuploidy. The various approaches include the first trimester combined tests (serum biomarkers and ultrasound), as well as integrated testing, which includes full integrated, serum integrated, step-wise sequential screening, and contingent sequential screening. The management of individual patients to

332 TABLE 13.2

Investigators

BIOMARKERS Studies evaluating the prenatal detection of Trisomy 18.

Design

Biomarkers Examined

Palomaki, et al, Case Series of 89 AFR uE3, total pregnancies; Multi- hCG 1995(137) national; Measurements performed between 13 and 22 weeks gestation (89% between 15-20 weeks)

% Detection Rate (DR) for Trisomy 18 at % False Positives (FP) Combined biomarkers + maternal age-related risk DR=53%at<0.l%FP DR=60% at 0.2% FP DR=65% at 0.3% FP DR=68% at 0.4% FP DR=70% at 0.6% FP DR=73% at 0.8% FP DR=76%at 1.0% FP

Tul.etal, 1999 (138)

Case-Control; Measurements performed between 10 to 14 weeks gestation

Free beta-hCG, PAPP-A, nuchal translucency

Maternal age + combined biomarkers + nuchal translucency: DR=89%at l%FP DR=86% at 0.5% FP

Brumfield, et al, 2000 (139)

Case Series of 30 pregnancies; Measurements performed between 14 and 22 weeks gestation

AFR uE3, total hCG, comprehensive ultrasonography

(1) 70% of affected fetuses had one or more abnormalities on comp. ultrasound, most often a choroid plexus cyst (43%);

Spencer and Nicolaides, 2002 (25)

Case-Control; 45 cases of trisomy 13 and 59 cases of trisomy 18 were combined to create a trisomy 13/18 risk algorithm; Measurements performed between 1 1 and

Comb. Risk Cutoff for Positive Screen

150 1 100 1 150 1 200 1 300 400 1500 Not given

(2) AFPs 0.75 MoM + uE3«; 0.60 MoM + total hCGs0.55 MoM=DRof43%

Free beta-hCG, PAPP-A, nuchal translucency

( l ) + (2)=DRof80%

1:190

Maternal age-related risk + combined serum biomarkers + nuchal translucency: DRfor 13/18= 95.4% at FP=0.30% 95.8% at FP=0.39% 96.2% at FP=0.47% 96.5% at FP=0.54%

1 150 1 200 1 250 1 300

14 weeks gestation Wapner et al, 2003 (BUN Study Group) (133)

Case-Control; Multicenter study in USA and Canada; Measurements performed between 10 weeks + 4 days and 13 weeks + 6 days gestation

Free beta-hCG, PAPP-A, nuchal translucency

Maternal age-related risk + combined biomarkers + nuchal translucency: -DR=90.9% at FP=2.0% -among subgroup of women a 35,DR=IOO%atFP=2.6%

1:150

333

BIOMARKERS IN O B S T E T R I C M E D I C I N E TABLE 13.2

Investigators

Studies evaluating the prenatal detection of Trisonny 18. (continued)

Design

Biomarkers Examined

% Detection Rate (DR) for Trisomy 18 at % False Positives (FP)

Comb. Risk Cutoff for Positive Screen

Palomaki, et al, Case Series: Compilation of six data 2003(140) sets totaling 129 affected pregnancies; Measurements performed between 8 to 15 weeks gestation

PAPP-A measured in 1 st trimester; AFRuE3,and total hCG (all measured in 2nd trimester)

Maternal age-related risk + PAPP-A (1 st trimester) + [AFP + uE3 + total hCG (all 2nd trimester)], so-called "integrated serum screen:" DR=87%at<0.l%FP DR=90%atFP=0.l% DR=9l%atFP=0.2%

1:50 1:100 1:150

Breathnach, et al, 2007 (for the FASTER Research Consortium) (26)

Free beta-hCG, PAPP-A, nuchal translucency, measured at 10 weeks + 3 days to 13 weeks + 6 days gestation

(1) Maternal age-related risk + free beta-hCG + PAPP-A + nuchal translucency ("combined screening"):

1:100

Case Series of 28 affected pregnancies from within the FASTER study population (fetuses with cystic hygroma excluded)

DR=60%atFP=0.l%;

AND

+ fetuses affected with cystic hygroma: DR=79% at FP=0.3%

AFR total hCG, and uE3 at 15 through 18 weeks gestation

(2) Maternal age-related risk + AFP + total hCG + uE3 at 15 to 18 weeks gestation: DR=l00%atFP=0.3%

1:100

(all calculations included maternal age-related risk) Kagan, et al, 2008(141)

Kagan, et al, 2008 (27)

Case-Control; Free beta-hCG, nearly 57,000 PAPP-A, nuchal women screened, translucency with 122 affected pregnancies; Measurements performed between 1 1 weeks + 0 days and 13 weeks + 6 days gestation

Maternal age-related risk + combined serum biomarkers + nuchal translucency:

(same as above)

Maternal age-related risk + combined serum biomarkers + nuchal translucency + fetal heart rate:

Free beta-hCG, PAPP-A, nuchal translucency, and fetal heart rate

DR=93% at 0.2% FP

DR=9l%atFP=0.2% DR=95% at FP=0.5%

1:50

1:50

334

BIOMARKERS

determine the most appropriate screening and diagnostic tests to determine the risk of aneuploidy is beyond the scope of this chapter.

Amniocentesis and C h o r i o n i c Villi Sampling The diagnostic test to confirm the fetal karyotype after a positive screening test can be done by either amniocentesis or chorionic villi sampling. Amniocentesis is a technique for withdrawing amniotic fluid from the uterine cavity using a needle via a transabdominal approach. Amniocentesis for genetic studies is usually performed between the 15th and 17th week of gestation. The amniotic fluid is sent to the cytogenetic lab for chromosome analysis. Conventional cytogenetic studies typically require at least seven days to obtain results. Interphase fluorescence in situ hybridization (FISH) provides faster results, within 24 to 48 hours, but only detects aneuploidy of chromosomes 13,18,21, X, andY, the most common causes of aneuploidy. Amniocentesis is associated with risks related to the procedure, such as membrane rupture and amniotic fluid leakage, amnionitis in 1% of cases,28 fetal loss of approximately 1 in 200,29 development of rhesus isoimmunization (1%),30 and very rarely needle puncture to the fetus. Uncomplicated amniocentesis, however, does not have long-term adverse effects on the children. Chorionic villus sampling (CVS) refers to a procedure for the prenatal diagnosis of genetic disorders in which small samples of the placenta are obtained for chromosomal or DNA analysis. It can either be done through a transcervical or a transabdominal route. CVS is generally performed during the first trimester (between 10 and 13 weeks), and reduces the at-risk couple's period of anxious waiting for test results and permits access to pregnancy termination at a safer and more discreet time. A larger amount of DNA is derived from CVS than from cells obtained at amniocentesis, thereby allowing reliable DNA analysis within hours or days of sampling. The complications include post-procedure bleeding, pregnancy loss,31 fluid leakage, and maternal cell contamination.32 The technique of CVS and the full scope of risks versus benefits of the procedure are beyond the scope of this chapter.

O t h e r Novel Markers f o r A n e u p l o i d y Screening Examination of fetal cells, cell-free circulating fetal DNA and mRNA in maternal blood, and measurement of urinary hyperglycosylated hCG to screen for fetal aneuploidy in the second trimester are under investigation.33-37 Circulating fetal DNA as measured by real-time PCR of maternal plasma samples is emerging as an important research tool for early diagnosis of aneuploidy (particularly for trisomies 13 and 21) and appears to improve the performance of the current standard maternal serum screen.38 Other promising technologies include measurement of placental DNA-methylation markers on chromosome 2139 and "shotgun" sequencing of cell free DNA from the plasma of pregnant women.40 More recently microRNA and cell-free circulating mRNA have been detected in maternal blood and are currently being explored as alternatives to current screening tests for aneuploidy.41,42

BIOMARKERS IN O B S T E T R I C M E D I C I N E

335

PREECLAMPSIA A N D FETAL G R O W T H RESTRICTION Preeclampsia, a systemic syndrome of pregnancy characterized by new-onset hypertension, proteinuria, and often (but not invariably) edema after 20 weeks gestation, affects 3 to 5% of all pregnancies and is a major cause of maternal, fetal, and neonatal morbidity and mortality worldwide.43 In developing countries where access to health care is limited, preeclampsia is estimated to result in > 60,000 maternal deaths per year.44 In the developed world, the burden of this disease largely falls on the neonate in the form of complications of prematurity, since delivery is the only known treatment for preeclampsia. Worldwide, preeclampsia is associated with a perinatal and neonatal mortality rate of 10%.45 It has long been recognized that preeclampsia will not resolve until after placental delivery, thus implicating the placenta in the pathogenesis of the disorder. Many now regard the pathogenesis of preeclampsia as a two-step process, the first of which is an asymptomatic stage of abnormal placentation, followed by a second, symptomatic stage in which the placenta elaborates soluble factors that enter the maternal circulation and cause widespread endothelial dysfunction46 (see Figure 13.1). In addition to the new-onset of hypertension and proteinuria after 20 weeks gestation that are the hallmarks of the disorder, preeclampsia may also be accompanied by pathological manifestations such as microangiopathic hemolytic anemia, acute renal failure, liver abnormalities, and CNS symptomatology (including convulsions, i.e., eclampsia). In fact, all of these clinical manifestations of preeclampsia can be explained by generalized maternal endothelial dysfunction in the vascular beds of the various involved organs. The last several years has seen the description of several of these placentally-derived soluble factors, and the results of many studies have led to the extremely plausible hypothesis that the clinical manifestations of preeclampsia result, in part, from an imbalance between circulating anti-angiogenic and pro-angiogenic factors in the maternal circulation.47-53 The two antiangiogenic factors implicated recently are soluble vascular endothelial growth factor receptor 1 (sVEGFRl), also known as soluble fms-like tyrosine kinase 1 (sFltl), and soluble endoglin (sEng), both of which show elevated levels in women with preeclampsia. The two pro-angiogenic factors implicated are vascular endothelial growth factor (VEGF) and placental growth factor (P1GF), with circulating concentrations (i.e., free levels) of both reduced in the disease. The following section focuses on the role of angiogenic factors in preeclampsia, but there are also a number of other potential biomarkers under investigation for use in preeclampsia.

Vascular Endothelial G r o w t h Factor VEGF represents a family of endothelial-specific mitogens involved in both vasculogenesis (i.e., the formation of new blood vessels de novo) and angiogenesis (i.e., the formation of new blood vessels from preexisting vessels.)

336

BIOMARKERS

FIGURE 13.1

Summary of pathogenesis of preeclampsia.

Furthermore, VEGF stabilizes endothelial cells in mature blood vessels and is particularly important in maintaining the health of the endothelium in the kidney, liver, and brain. This family consists of at least seven members: VEGF-A, VEGF-B, VEGF-C, VEGF-D, VEGF-E, PIGF, and snake venom-derived VEGFs (such as Trimeresurus flavoviridis, designated usually as svVEGF).54 All members except VEGF-E and svVEGF are encoded by the mammalian genome. The relevant VEGF family members are summarized briefly below. VEGF-A plays a central role in both angiogenesis and vasculogenesis. Studies in mice demonstrate that homozygous and heterozygous knockouts of the VEGF-A gene die in utero due to a variety of defects in angiogenesis, indicating that a basal level of VEGF-A supplied by two alleles is essential for complete formation of a mature and functioning vasculature.55'56 VEGF-A is abundantly expressed during normal placental development. It serves as an effective ligand of VEGF receptor type l(VEGFRl), also known as Fltl, and VEGF receptor type 2 (VEGFR2), otherwise designated as KDR/Flkl. Other isoforms of VEGF expressed in humans include VEGF-B, C, and D, but the role of these isoforms in placental angiogenesis has not been explored in detail.57-60 PIGF is further discussed below.

Placental Growth Factor The structure of PIGF is highly homologous to VEGF-A, as it contains a PDGF-domain that is 53% similar to the PDGF-domain within the VEGF

BIOMARKERS IN O B S T E T R I C M E D I C I N E

337

gene. Despite this similarity, its properties are somewhat different from those of VEGF-A. First cloned from a term placental cDNA library and mapped to chromosome 14,61 four isoforms of P1GF have since been identified. All isoforms bind tightly to Fit 1, but not to KDR/Flkl. It is thought that P1GF acts as a potent angiogenic growth factor by amplifying VEGF-signaling via displacement of VEGF from Fit 1, allowing it to instead bind in concert to KDR/Flk 1,6263 P1GF expression has been found in a variety of placentally-derived tissues, such as choriocarcinoma and human umbilical vein endothelial cells. P1GF has also been shown to be strongly expressed by the villous trophoblast.64

VEGF Receptors Currently, the family of VEGF receptors (VEGFR) consists of three members: VEGFR1, VEGFR2, and VEGFR3. VEGFR1 and VEGFR2 are bound and activated by VEGF-A and play a pivotal role in the regulation of angiogenesis, while VEGFR3, which has a high affinity to VEGF-C and VEGF-D, stimulates lymphangiogenesis.65,66

V E G F R I / F l t l (Fms-Like Tyrosine Kinase I) VEGFR 1 exists as both a cell membrane-bound form as well as a soluble, circulating form (sFltl) in which the transmembrane and cytoplasmic domains of the receptor are absent. Both forms are derived from alternatively splicing the mRNA and encoding the complete tyrosine kinase (membrane-bound Fltl). Fltl has an extremely high affinity for VEGF-A as compared to VEGFR2, while P1GF binds only to Fltl and not VEGFR2. Interestingly, the tyrosine kinase activity of Fltl is relatively modest, approximately 10-fold lower than that of VEGFR2, which renders Fltl only a modest stimulant of endothelial proliferation under normal physiological conditions. Fltl expression is most prominent on vascular endothelial cells. It is also expressed by cells of the monocyte/macrophage-lineage, in which stimulation of Fltl induces migration of these cells.67-69 Fltl protein has further been immunolocalized to the syncytiotrophoblast layer and endothelial cells in the placental villi. VEGFR1 has a limiting role on angiogenesis during the early stages of embryogenesis, as evidenced by the observation that Fltl-knockout mice die at E8.5-9.0 due to an overgrowth and dysfunction of blood vessels.70 VEGFR2 (also referred to as KDR/Flkl) exhibits an extremely strong tyrosine kinase activity, although its ability to bind VEGF-A is approximately one order of magnitude weaker than that of Fltl. Mice lacking both copies of Flkl die at E8.5 due to a lack of blood vessel development, a finding that indicates that VEGFR2-signaling is essential for the evolution of a functional vascular system in the developing embryo.71 VEGFR2 generates a variety of angiogenic signals and plays a pivotal role in endothelial proliferation and in cell migration/morphogenesis, including tubular formation. KDR/Flkl expression has been localized to the endothelial cells of the placental villi.72 In patients with severe preeclampsia, expression of Fltl was significantly higher in the placenta, but the expression of KDR/Flkl remained unchanged.72

338

BIOMARKERS

Endoglin More recently, the angiogenic factor endoglin has been implicated in the pathogenesis of preeclampsia.73 Endoglin (Eng) is a cell-surface co-receptor of transforming growth factors TGF-B 1 and TGF-B3.74 Both TGF-B 1 and TGFB3 belong to the TGF-B superfamily, which also includes activin, inhibin, and bone morphogenetic proteins. TGF-B signaling regulates a diverse array of cellular functions, such as cellular growth, differentiation, and development. It has been shown that TGF-B isoforms prevent trophoblast invasion, and there is an overexpression of TGF-B3 in preeclamptic placentas.75-77 Mutations in the ENG gene lead to a condition called hereditary hemorrhagic telangiectasia type 1 (HHT1), an autosomal dominant disorder characterized by arteriovenous malformations and focal loss of capillaries.78 Endoglin-null mice die in utero due to defective angiogenesis and cardiovascular development, while endoglin-heterozygous mice develop characteristics reminiscent of HHT1.79 Haploinsuffient mice (Eng+/-) demonstrate impaired vasodilation and decreased levels of eNOS in their kidneys and femoral arteries, implicating Eng as an important regulator of vascular homeostasis.80 Soluble endoglin (sEng), much like in the case of sFltl, is a truncated, circulating form of Eng that has been shown to be elevated in the serum of women with preeclampsia. The role of this protein in contributing to preeclampsia phenotypes was predicated on the hypothesis that sEng may impair TGF-B 1 binding to its intended cell surface receptors (i.e., membrane-bound endoglin), thereby decreasing endothelial nitric oxide signaling.

Role of Angiogenic Factors in the Pathogenesis of Preeclampsia The evidence implicating sFltl and sEng in causing preeclamptic phenotypes comes from the measurement of these anti-angiogenic proteins in the circulation of women with frank preeclampsia, from in vitro studies using sera from preeclamptic women, and from animal models of preeclampsia. In addition, the initial observations that placental expression and serum levels of sFltl are increased during preeclampsia compared to normal pregnancies have been confirmed by several groups.47,48,50,81 Other studies have shown that levels of sFltl are positively correlated with gestational age.5052 Clinically, sFltl levels have been observed to be directly proportional to the severity of proteinuria, but inversely correlated with platelet count, gestational age, and neonatal birth weight adjusted for gestational age.52 In women with preeclampsia, levels of sFltl are higher in those with early onset (less than 37 weeks),5052 more severe disease,48,50,52 and SGA neonates.50,82 Preeclamptics have also been shown to have decreased blood levels of free VEGF and P1GF, and decreased urinary P1GF.83-85 Two recent studies have helped elucidate the role of sEng in preeclampsia—showing that sEng is elevated in the sera of preeclamptic individuals, correlates with disease severity, and falls after delivery;73,86 and one of the reports demonstrates that sEng alterations antedate the clinical symptoms of preeclampsia by several months.86

BIOMARKERS IN O B S T E T R I C M E D I C I N E

339

The consolidation of these observations leads to the following proposed model of the pathogenesis of preeclampsia, as shown in Figure 13.1. A combination of genetic, immunologic, and/or environmental factors leads to impaired trophoblast invasion and differentiation, with resultant poor placentation characterized by insufficient vascular invasion of the endometrium and myometrium; this, in turn, leads to placental hypoperfusion. The ischemic placenta responds by releasing increased amounts of circulating sFltl resulting in decreased circulating levels of P1GF and VEGF, as well as by releasing increased amounts of circulating sEng that impairs TGF-pl binding to its intended cell surface receptors (i.e., membrane-bound endoglin); this results in systemic maternal endothelial dysfunction manifesting as the clinical phenotype(s) of preeclampsia.

The A b i l i t y of Angiogenic Proteins t o Predict Preeclampsia Although there is not yet any definitive therapeutic strategy (other than delivery) or preventative strategies for preeclampsia, clinical experience suggests that early detection, monitoring, and supportive care are beneficial to the mother and the fetus. Reliable prediction of preeclampsia would allow closer prenatal monitoring and timely intervention with steroids to enhance fetal lung maturity, magnesium for seizure prophylaxis, anti-hypertensive medications, bedrest, and expeditious delivery, as necessary. Furthermore, a robust biomarker for preeclampsia would provide a clear endpoint to simplify human studies of novel therapies and preventative strategies for preeclampsia. However, no screening test has yet proven accurate enough for widespread clinical use.87 Because alterations in circulating levels of angiogenic factors occur weeks prior to the clinical onset of preeclampsia, they represent promising biomarkers for screening and/or diagnosis. Significant elevations in maternal sFltl and sEng are observed from mid-gestation onward88,89 and appear to rise five to eight weeks prior to the onset of disease.86,90 The ratio of sFltl and sEng to P1GF is a better predictor of preeclampsia than any measure alone.86 Furthermore, recent retrospective studies demonstrating the feasibility of a urine screening test (P1GF) followed by a confirmatory blood test for circulating angiogenic proteins (sFltl and P1GF) for the prediction of preeclampsia appear promising.84 Levine and colleagues were the first group to study circulating angiogenic proteins in the sera of women enrolled in the Calcium for Preeclampsia Prevention (CPEP) trial.50 They found that concentrations of sFltl, which were previously reported to be increased in women with established preeclampsia,47 begin to increase steeply approximately five weeks before the onset of the clinical maternal syndrome. In parallel with the increase in sFltl levels, decreases in free P1GF and free VEGF levels were observed, suggesting that those reduced levels are the result of binding by sFltl. In addition, an association between sFltl and P1GF levels and the severity of preeclampsia was

340

BIOMARKERS

evident: in general, women who developed severe and/or early-onset preeclampsia had higher sFltl and lower P1GF levels at each time interval studied. Interestingly, this finding held true for women who simultaneously developed both preeclampsia and intrauterine growth restriction (IUGR). A similar study by Chaiworapongsa and colleagues compared levels of serum sFltl in normal pregnant women to those in patients with preeclampsia. This study determined that the mean plasma sFltl concentration was significantly higher in preeclamptics as compared to normal pregnancies up to six to ten weeks before the first clinical manifestation of the syndrome.52 In line with these findings, in a nested case-control study by Rana, et al.,91 levels of sFltl and sEng were already elevated at 17 to 20 weeks of gestation in those women who eventually developed preeclampsia later in pregnancy. The sequential changes in sFltl and sEng levels between the second and third trimesters were greatest in those patients who developed early-onset preeclampsia, a finding that has also been noted by other groups.92 However, levels of sFltl and sEng did not differ significantly between the control and preeclampsia groups at 11 to 13 weeks gestation.91 In a prospective study by Stepan, et al., high sFltl levels and lower-thannormal P1GF levels during the second trimester preceded preeclampsia development, with the most pronounced differences seen in women who developed early-onset preeclampsia.93 Of interest to note is that normotensive women enrolled in this study who delivered a growth-restricted newborn had higher sFltl and lower P1GF levels as compared to healthy pregnancies, but these differences were not statistically significant. Combining the measurements with uterine Doppler imaging analysis did not improve the prediction of adverse pregnancy outcomes in the case of sEng. However, when combining abnormal US-Doppler findings with sFltl levels, the sensitivity and specificity to predict preeclampsia increased to 79% and 80%, respectively. In another study, Romero and colleagues found that alterations in P1GF and sEng levels were already evident as early as 10 weeks gestation in those pregnancies who later developed IUGR, while sFltl levels were predictive only for preeclampsia and not IUGR.94 Various other studies have been able to reproduce the findings of measurement of circulating angiogenic factors for the prediction of preeclampsia.93,95"97

Other Potential Biomarkers for the Prediction of Preeclampsia More recently, placental protein 13 (PP13) has been reported to be a robust first trimester biomarker for predicting preeclampsia, especially when used in combination with first trimester uterine artery Doppler.98 The biological role of PP13 and its relationship with angiogenic factors, if any, remains unknown.99 Studies have also identified agonistic angiotensin II type I receptor (ATI) autoantibodies in women with preeclampsia.100 These ATI receptor autoantibodies, like angiotensin II itself, could lead to the production of tissue factor

BIOMARKERS IN O B S T E T R I C M E D I C I N E

341

by endothelial cells. Xia, et al. found that ATI receptor autoantibodies decreased invasiveness of immortalized human trophoblasts in an in vitro invasion assay.101 Furthermore, studies from Zhou, et al. indicate that ATI receptor autoantibodies recovered from the circulation of women with preeclampsia can replicate the key features of preeclampsia as well as increase both sFltl and sEng, in pregnant mice.102 The effects of these antibodies can be blocked with losartan, a pharmacologic ATI receptor antagonist, or by an antibodyneutralizing peptide.103 However, ATI receptor autoantibodies appear to be increased not only during pregnancy, but also in malignant renovascular hypertension and vascular rejection.103 Also, ATI receptor autoantibodies do not provide an explanation for the suppression of aldosterone production noted in preeclampsia.104 In summary, ATI agonistic autoantibodies may be one of several insults that can contribute to the placental damage that is proximallylinked to the production of anti-angiogenic factors. More recently, Poon, et al., in a large prospective study involving nearly 8000 subjects, demonstrated that angiogenic factors (P1GF) when used with PAPP-A and uterine artery dopplers in the first trimester demonstrated a sensitivity of 93% at a 5% false positive rate for the subsequent diagnosis of early-onset preeclampsia. This suggests that a combination of biomarkers may allow clinicians to accurately predict early-onset preeclampsia and its complications.105

Angiogenic Factors and I n t r a u t e r i n e Growth Restriction Normal fetal growth is a complex interplay of fetal, maternal, and placental health. An abnormality in any of these three systems leads to small for gestational age (SGA) babies. SGA babies that arise as a result of placental vascular insufficiency are referred to as exhibiting intrauterine growth restriction (IUGR). Although IUGR is frequently associated with severe premature preeclampsia, it can also occur in the absence of any evidence of preeclampsia. It has long been recognized that preeclampsia and IUGR share many common clinical and pathologic features. IUGR is a common complication of preeclampsia, and abnormal uterine blood flow by Doppler ultrasound in early pregnancy is associated with an increased risk for both disorders. Babies that are growth-restricted in utero usually display asymmetry of growth, with their head circumference being disproportionately large compared to their body length. Genetic variation among different populations can lead to some ethnic groups having smaller babies at birth, for example, among Asian populations, but these infants are not necessarily growth restricted in utero. In this section, we focus on IUGR due to placental insufficiency. It is unknown why some women with placental insufficiency develop the systemic syndrome of preeclampsia, while others have small-for-gestational-age babies without these maternal symptoms. Variability in clinical phenotype is probably attributable to individual environmental and genetic differences that alter the maternal response to the placental disease.

542

BIOMARKERS

With regard to angiogenic factors, studies have been performed measuring levels in placentas from normotensive pregnancies complicated by IUGR. There are some discrepancies in the study results. Using densitometric analysis of western blots, Yinon and colleagues found that placentas from pregnancies complicated by IUGR had increased levels of endoglin and its soluble form, sEng.106 A different study using the same approach did not find any alterations in endoglin levels in IUGR placentas, or in circulating sEng levels; however, this study was focused on late-onset IUGR.107 Levels of Fltl were found to be higher in IUGR placentas compared to normal placentas by immunostaining.72 It was also shown that severe IUGR placentas have an increased expression of sFltl protein.108 However, in the case of late-onset IUGR, in which the extent of placental ischemia is often less pronounced than in earlyonset IUGR, levels of placental sFltl protein, as well as HIF-lcprotein, were not altered compared to healthy placentas.109 These studies demonstrate that in cases of IUGR, a distinction between early-onset IUGR and late-onset IUGR can be made to explain discrepancies in data. In a different study, Romero, et al. have demonstrated prospectively that soluble endoglin, but not sFltl, may be useful to predict the subsequent onset of an SGA infant.94 Decreased levels of pregnancy associated plasma protein-A (PAPP-A) have also been associated with IUGR; however, its role in the pathogenesis is not well understood. 110Leptin and asymmetric dimethylarginine have also been reported to be elevated during placental insufficiency, but the cause-andeffect relationship of these molecules has not yet been established.111,112

PRETERM LABOR A N D O T H E R P R E G N A N C Y COMPLICATIONS Preterm Labor Preterm delivery (PTD) is one of the major neonatal problems of the developed world. Traditional methods for predicting women destined to deliver preterm relied upon obstetrical history, demographic factors, or premonitory symptoms that were neither sensitive nor specific.113 The development of various biochemical and biophysical tools has helped to distinguish between women who will and will not deliver preterm. However, even after caregivers have made an accurate diagnosis of preterm labor, the lack of effective interventions to prolong pregnancy remains a problem. Nonetheless, a number of biologic markers in serum, amniotic fluid, and cervical secretions have been evaluated for their potential to predict PTD. The most clinically useful biochemical approach in differentiating women who are at high risk for impending PTD from those who are not at high risk is measurement of fetal fibronectin (fFN) in the cervicovaginal secretions. 114~116 Fibronectins are large molecular weight (450 kD) glycoproteins found in the plasma and extracellular matrix. An epitope termed fetal fibronectin (fFN) is a unique fibronectin that has been identified in amniotic fluid, extracts of placental tissue, and malignant cell lines, and is recognized by the monoclonal antibody FDC-6. fFN is released into cervicovaginal secretions

BIOMARKERS IN OBSTETRIC M E D I C I N E

343

when the extracellular matrix of the chorionic/decidual interface is disrupted; this is the rationale for measurement of fFN as a predictor of PTD. Since the discovery of the link between the presence of cervicovaginal fFN and subsequent preterm delivery by Lockwood, Garite, and colleagues in 1991, the use of fFN has become widespread in day-to-day obstetrical practice."4 The principal utility of the fFN assay lies in its high negative predictive value. In one study, 99.5% of pregnant women presenting to their physicians with signs and symptoms of preterm labor, and who subsequently had a negative cervicovaginal fFN test, failed to deliver within seven days."7 Also, a combination of fFN with cervical length helps in identifying patients who are at low risk of preterm delivery. Subjects with negative fFN and cervical length > 3cm do not need any intervention as opposed to patients with positive fFN and cervical length < 2cm who are at high risk for preterm delivery and therefore may benefit from betamethasone and tocolysis.

Abruption The term abruptio placentae denotes seperation of normally implanted placenta before the birth of the fetus. The incidence of abruption varies widely in the published literature, but ranges from 0.49-1.29% or 1 inl20 deliveries."8 Abruption is associated with serious maternal and fetal risks including risk of maternal hemmorhage, shock, disseminated intravascular coagulation, risk of fetal asphyxia, and death. Various biomarkers tested for the prediction of abruption include C-reactive protein,"9 maternal serum alpha fetoprotein,120 uterine artery doppler velocimetry, maternal serum pregnancy-associated plasma protein-A,121 and angiogenic factors such as soluble endoglin.122- m However, none of the markers have been shown to be specific to abruption, and none are currently used in clinical practice.

Gestational Diabetes Gestational diabetes is defined as glucose intolerance that begins or is first recognized during pregnancy.124 All pregnant women in the United States are currently screened with a glucose challenge test (GCT) at 26-28 weeks gestation after ingestion of 50 g of glucose. If the plasma value at one hour exceeds a critical threshold (a 135 mg/dL), then a 100-g three-hour oral glucose tolerance test is administered. Although current screening guidelines for glucose intolerance during pregnancy provide an opportunity to offer management to those women diagnosed with gestational diabetes mellitus, there is a need to diagnose gestational diabetes earlier to prevent perinatal and obstetrical complications. In a recent study, a variety of serum biomarkers were studied in the first trimester including hormones, cytokines and chemokines, and surrogate markers of oxidative stress. This study found significant differences in plasma insulin and adiponectin concentrations at 11 weeks gestation in women destined to develop gestational diabetes.125 However, further studies are warranted to determine the utility of these biomarkers and to determine whether early identification results in an improvement in fetal outcomes.

344

BIOMARKERS

SUMMARY P O I N T S 1.

2.

3.

4.

Serum biomarkers in combination with fetal ultrasound have traditionally been used during pregnancy for aneuploidy screening. Circulating fetal nucleic acid markers hold promise to improve the performance of the current standard maternal serum screen for aneuploidy. There is emerging evidence that angiogenic factors are intimately related to the pathogenesis of preeclampsia. Prospective studies using standardized assays are needed to assess whether circulating angiogenic factors can be used to predict preeclampsia and whether this may lead to improved maternal and fetal outcomes. Automated measurement of serum biomarkers on platforms such as point-of-care devices that are easy to use and have a low coefficient of variation needs to be developed in the future, as the use of these biomarkers becomes widespread. There is a further need to develop reliable biomarkers for other pregnancy complications, such as abruption, preterm labor, and gestational diabetes.

REFERENCES 1. 2. 3. 4. 5. 6.

7.

8. 9.

Merkatz, I. R., Nitowsky, H. M., Macri, J. N., and Johnson, W. E. An Association Between Low Maternal Serum Alpha-Fetoprotein and Fetal Chromosomal Abnormalities. Am. J. Obstet. Gynecol. Apr 1,1984;148(7):886-894. Mizejewski, G. J. Alpha-Fetoprotein Structure and Function: Relevance to Isoforms, Epitopes, and Conformational Variants. Exp. Biol. Med. (Maywood). May 2001;226(5):377^108. Mizejewski, G. J. The Phylogeny of Alpha-Fetoprotein in Vertebrates: Survey of Biochemical and Physiological Data. Crit. Rev. Eukaryot. Gene Expr. 1995;5 (3-4):281-316. Mizejewski, G. J. Alpha-Fetoprotein as a Biologic Response Modifier: Relevance to Domain and Subdomain Structure. Proc. Soc. Exp. Biol. Med. Sep 1997;215(4):333-362. Cole, L. A. New Discoveries on the Biology and Detection of Human Chorionic Gonadotropin. Reprod. Biol. Endocrinol. 2009;7:8. Handschuh, K., Guibourdenche, J., Tsatsaris, V, Guesnon, M., Laurendeau, I., and Evain-Brion, D., et al.. Human Chorionic Gonadotropin Produced by the Invasive Trophoblast but Not the Villous Trophoblast Promotes Cell Invasion and Is Down-Regulated by Peroxisome Proliferator-Activated Receptor-Gamma. Endocrinology. Oct 2007;148(10):5011-5019. Hamada, A. L., Nakabayashi, K., Sato, A., Kiyoshi, K., Takamatsu, Y., and Laoag-Fernandez, J. B., et al.. Transfection of Antisense Chorionic Gonadotropin Beta Gene Into Choriocarcinoma Cells Suppresses the Cell Proliferation and Induces Apoptosis. J. Clin. Endocrinol. Metab. Aug 2005;90(8):4873-4879. Zhabin, S. G., Gorin, V. S., and Judin, N. S. Review: Immunomodulatory Activity of Pregnancy-Associated Plasma Protein-A. J. Clin. Lab Immunol. 2003; 52:41-50. Tanner, S. J., Hefferan, T. E., Rosen, C. J., and Conover, C. A. Impact of Pregnancy-Associated Plasma Protein-A Deletion on the Adult Murine Skeleton. J. Bone Miner. Res. May 2008;23(5):655-662.

BIOMARKERS IN OBSTETRIC MEDICINE 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.

20.

21. 22. 23. 24. 25. 26.

27.

345

El Farra, K. and Grudzinskas, J. G. Will PAPP-A Be a Biochemical Marker for Screening of Down Syndrome in the First Trimester? Early Pregnancy. Mar 1995;1(1):4-12. Parry Sea. In: Degroot, L. J., Ed. Endocrinology. 4th Ed. Philadelphia, PA.:WB Saunders Co;2001:2387-2390. Ying, S. Y. Inhibins and Activins: Chemical Properties and Biological Activity. Proc. Soc. Exp. Biol. Med. Dec 1987;186(3):253-264. Kingsley, D. M. The TGF-Beta Superfamily: New Members, New Receptors, and New Genetic Tests of Function in Different Organisms. Genes Dev. Jan 1994;8(2): 133-146. Welt, C. K. Regulation and Function of Inhibins in the Normal Menstrual Cycle. Semin. Reprod. Med. Aug 2004;22(3): 187-193. Driscoll, D. A. and Gross, S. Clinical Practice. Prenatal Screening for Aneuploidy. N. Engl. J. Med. Jun 11, 2009;360(24):2556-2562. Improved National Prevalence Estimates for 18 Selected Major Birth Defects— United States, 1999-2001. MMWR Morb. Mortal Wkly. Rep. Jan 6, 2006; 54(51):1301-1305. Canick, J. A. and Kellner, L. H. First Trimester Screening for Aneuploidy: Serum Biochemical Markers. Semin. Perinatol. Oct 1999;23(5):359-368. Nicolaides, K. H., Azar, G., Byrne, D., Mansur, C , and Marks, K. Fetal Nuchal Translucency: Ultrasound Screening for Chromosomal Defects in First Trimester of Pregnancy. BMJ. Apr 4, 1992;304(6831):867-869. Crossley, J. A., Aitken, D. A., Cameron, A. D., McBride, E., and Connor, J. M. Combined Ultrasound and Biochemical Screening for Down Syndrome in the First Trimester: A Scottish Multicentre Study. BJOG. Jun 2002;109(6): 667-676. Evans, M. I., Krantz, D. A., Hallahan, T. W., and Galen, R. S. Meta-Analysis of First Trimester Down Syndrome Screening Studies: Free Beta-Human Chorionic Gonadotropin Significantly Outperforms Intact Human Chorionic Gonadotropin in a Multimarker Protocol. Am. J. Obstet. Gynecol. Mar 2007;196(3):198-205. Hook, E. B. Population Cytogenetics: Studies in Humans. Hook, E. B., Porter, I. H., Eds. New York: Academic Press;1977. LYF, H. Genetic Disorders and the Fetus. 4th Ed. Baltimore: The John Hopkins University Press;1998. Parker, M. J., Budd, J. L., Draper, E. S., and Young, I. D. Trisomy 13 and Trisomy 18 in a Defined Population: Epidemiological, Genetic and Prenatal Observations. Prenat. Diagn. Oct 2003;23(10):856-860. Sailer, D. N., Jr., Canick, J. A., Blitzer, M. G., Palomaki, G. E., Schwartz, S., and Blakemore, K. J., et al.. Second-Trimester Maternal Serum Analyte Levels Associated with Fetal Trisomy 13. Prenat. Diagn. Sep 1999;19(9):813-816. Spencer, K. and Nicolaides, K. H. A First Trimester Trisomy 13/Trisomy 18Risk Algorithm Combining Fetal Nuchal Translucency Thickness, Maternal Serum Free Beta-Hcg and PAPP-A. Prenat. Diagn. Oct 2002;22(10):877-879. Breathnach, F. M., Malone, F. D., Lambert-Messerlian, G., Cuckle, H. S., Porter, T. E, and Nyberg, D. A., et al.. First- and Second-Trimester Screening: Detection of Aneuploidies Other Than Down Syndrome. Obstet. Gynecol. Sep 2007;110(3):651-657. Kagan, K. O., Wright, D., Valencia, C , Maiz, N., and Nicolaides, K. H. Screening for Trisomies 21, 18, and 13 by Maternal Age, Fetal Nuchal Translucency, Fetal Heart Rate, Free Beta-Hcg and Pregnancy-Associated Plasma Protein-A. Hum. Reprod. Sep 2008;23(9):1968-1675.

346

BIOMARKERS 28. 29. 30. 31.

32. 33.

34.

35.

36. 37.

38. 39.

40.

41.

Turnbull, A. C. and Mackenzie, I. Z. Second-Trimester Amniocentesis and Termination of Pregnancy. Br. Med. Bull. Oct 1983;39(4):315-321. Randomised Trial to Assess Safety and Fetal Outcome of Early And Midtrimester Amniocentesis. The Canadian Early and Mid-Trimester Amniocentesis Trial (CEMAT) Group. Lancet. Jan 24, 1998;351(9098):242-247. Tabor, A., Jerne, D., and Bock, J. E. Incidence of Rhesus Immunisation After Genetic Amniocentesis. Br. Med. J. (Clin. Res. Ed). Aug 30, 1986;293(6546): 533-536. Rhoads, G. G., Jackson, L. G., Schlesselman, S. E., De La Cruz, F. R, Desnick, R. J., and Golbus, M. S., et al.. The Safety and Efficacy of Chorionic Villus Sampling for Early Prenatal Diagnosis of Cytogenetic Abnormalities. N. Engl. J. Med. Mar 9, 1989;320(10):609-617. Boehm, F. H., Salyer, S. L., Dev, V. G., and Reed, G. W. Chorionic Villus Sampling: Quality Control—A Continuous Improvement Model. Am. J. Obstet. Gynecol. Jun 1993;168(6 Pt l):1766-1775;Discussion 75-77. Bianchi, D. W., Simpson, J. L., Jackson, L. G., Elias, S., Holzgreve, W., and Evans, M. I., et al.. Fetal Gender and Aneuploidy Detection Using Fetal Cells in Maternal Blood: Analysis of NIFTY I Data. National Institute of Child Health and Development Fetal Cell Isolation Study. Prenat. Diagn. Jul 2002;22(7): 609-615. Farina, A., Leshane, E. S., Lambert-Messerlian, G. M., Canick, J. A., Lee, T., and Neveux, L. M., et al.. Evaluation of Cell-Free Fetal DNA as a SecondTrimester Maternal Serum Marker of Down Syndrome Pregnancy. Clin. Chem. Feb 2003;49(2):239-242. Palomaki, G. E., Knight, G. J., Roberson, M. M., Cunningham, G. C , Lee, J. E., and Strom, C. M., et al.. Invasive Trophoblast Antigen (Hyperglycosylated Human Chorionic Gonadotropin) in Second-Trimester Maternal Urine as a Marker for Down Syndrome: Preliminary Results of an Observational Study on Fresh Samples. Clin. Chem. Jan 2004;50(1):182-189. Lo, Y. M., Tsui, N. B., Chiu, R. W., Lau, T. K., Leung, T. N., and Heung, M. M., et al.. Plasma Placental RNA Allelic Ratio Permits Noninvasive Prenatal Chromosomal Aneuploidy Detection. Nat. Med. Feb 13, 2007;(2):218-223. Dhallan, R., Guo, X., Emche, S., Damewood, M., Bayliss, P., and Cronin, M., et al.. A Non-Invasive Test for Prenatal Diagnosis Based on Fetal DNA Present in Maternal Blood: A Preliminary Study. Lancet. Feb 10, 2007;369(9560): 474-481. Wataganara, T. and Bianchi, D. W. Fetal Cell-Free Nucleic Acids in the Maternal Circulation: New Clinical Applications. Ann. NYAcad. Sci. Jun 2004;1022: 90-99. Chim, S. S., Jin, S., Lee, T. Y, Lun, F. M., Lee, W. S., and Chan, L. Y, et al.. Systematic Search for Placental DNA-Methylation Markers on Chromosome 21: Toward a Maternal Plasma-Based Epigenetic Test for Fetal Trisomy 21. Clin. Chem. Mar 2008;54(3):500-511. Fan, H. C , Blumenfeld, Y J., Chitkara, U., Hudgins, L., and Quake, S. R. Noninvasive Diagnosis of Fetal Aneuploidy by Shotgun Sequencing DNA from Maternal Blood. Proc. Natl. Acad. Sci. USA. Oct 21, 2008; 105(42): 16266-16271. Gilad, S., Meiri, E., Yogev, Y, Benjamin, S., Lebanony, D., and Yerushalmi, N., et al.. Serum Micrornas are Promising Novel Biomarkers. Plos. One. 2008;3(9):E3148.

BIOMARKERS IN OBSTETRIC MEDICINE 42. 43. 44. 45.

46. 47.

48.

49. 50. 51.

52.

53. 54. 55. 56. 57.

347

Heung, M. M , Jin, S., Tsui, N. B., Ding, C , Leung, T. Y., and Lau, T. K., et al.. Placenta-Derived Fetal Specific MRNA Is More Readily Detectable in Maternal Plasma Than in Whole Blood. Plos. One. 2009;4(6):E5858. Villar, J. Eclampsia and Preeclampsia Worldwide Health Problem for 2000 Years. Preeclampsia. 2003:189-207. World Health Report: Make Every Mother and Child Count. Geneva: World Health Org. 2005. Altman, D., Carroli, G., Duley, L., Farrell, B., Moodley, J., and Neilson, J., et al.. Do Women With Pre-Eclampsia, and Their Babies, Benefit From Magnesium Sulphate? The Magpie Trial: A Randomised Placebo-Controlled Trial. Lancet. Jun 1, 2002;359(9321):1877-1890. Roberts, J. M. Endothelial Dysfunction in Preeclampsia. Semin. Reprod. Endocrinol. 1998;16(1):5-15. Koga, K., Osuga, Y, Yoshino, O., Hirota, Y, Ruimeng, X., and Hirata, T., et al.. Elevated Serum Soluble Vascular Endothelial Growth Factor Receptor 1 (Svegfr-1) Levels in Women with Preeclampsia. J. Clin. Endocrinol. Metab. May 2003;88(5):2348-2351. Maynard, S. E., Min, J. Y, Merchan, J., Lim, K. H., Li, J., and Mondal, S., et al.. Excess Placental Soluble Fms-Like Tyrosine Kinase 1 (Sfltl) May Contribute to Endothelial Dysfunction, Hypertension, and Proteinuria in Preeclampsia. J. Clin. Invest. Mar 2003;lll(5):649-658. Eremina, V., Sood, M., Haigh, J., Nagy, A., Lajoie, G., and Ferrara, N., et al.. Glomerular-Specific Alterations of VEGF-A Expression Lead to Distinct Congenital and Acquired Renal Diseases. J. Clin. Invest. Mar 2003;111(5):707-716. Levine, R. J., Maynard, S. E., Qian, C , Lim, K. H., England, L. J., and Yu, K. F , et al.. Circulating Angiogenic Factors and the Risk of Preeclampsia. N. Engl. J. Med. Feb 12, 2004;350(7):672-683. Mckeeman, G. C , Ardill, J. E., Caldwell, C. M., Hunter, A. J., and McClure, N. Soluble Vascular Endothelial Growth Factor Receptor-1 (Sflt-1) Is Increased Throughout Gestation in Patients Who Have Preeclampsia Develop. Am. J. Obstet. Gynecol. 2004 Oct 2004;191(4):1240-1246. Chaiworapongsa, T., Romero, R., Espinoza, J., Bujold, E., Mee Kim, Y, and Goncalves, L. E, et al.. Evidence Supporting a Role for Blockade of the Vascular Endothelial Growth Factor System in the Pathophysiology of Preeclampsia. Young Investigator Award. Am. J. Obstet. Gynecol. Jun 2004;190(6):1541-1547;Discussion 7-50. Ahmad, S. and Ahmed, A. Elevated Placental Soluble Vascular Endothelial Growth Factor Receptor-1 Inhibits Angiogenesis in Preeclampsia. Circ. Res. Oct 29, 2004;95(9):884-891. Shibuya, M. and Claesson-Welsh, L. Signal Transduction by VEGF Receptors in Regulation of Angiogenesis and Lymphangiogenesis. Exp. Cell Res. Mar 10, 2006;312(5):549-560. Carmeliet, P., Ferreira, V, Breier, G., Pollefeyt, S., Kieckens, L., and Gertsenstein, M., et al.. Abnormal Blood Vessel Development and Lethality in Embryos Lacking a Single VEGF Allele. Nature. Apr 4, 1996;380(6573):435^139. Ferrara, N., Carver-Moore, K., Chen, H., Dowd, M., Lu, L., and O'Shea, K. S., et al.. Heterozygous Embryonic Lethality Induced by Targeted Inactivation of the VEGF Gene. Nature. Apr 4, 1996;380(6573):439^42. Kanda, M., Nomoto, S., Nishikawa, Y, Sugimoto, H., Kanazumi, N., and Takeda, S., et al.. Correlations of the Expression of Vascular Endothelial Growth

BIOMARKERS

58.

59. 60. 61. 62. 63.

64.

65. 66.

67.

68.

69.

70. 71.

Factor B and Its Isoforms in Hepatocellular Carcinoma with Clinico-Pathological Parameters. J. Surg. Oncol. Sep 1, 2008;98(3):190-196. Mccoll, B. K., Paavonen, K., Karnezis, T., Harris, N. C , Davydova, N., and Rothacker, J., et al.. Proprotein Convertases Promote Processing of VEGF-D, a Critical Step for Binding the Angiogenic Receptor VEGFR-2. FASEB J. Apr 2007;21(4): 1088-1098. Lyttle, D. J., Fraser, K. M., Fleming, S. B., Mercer, A. A., and Robinson, A. J. Homologs of Vascular Endothelial Growth Factor Are Encoded by the Poxvirus Orf Virus. J. Virol. Jan 1994;68(l):84-92. Kiba, A., Sagara, H., Hara, T., and Shibuya, M. VEGFR-2-Specific Ligand VEGFE Induces Non-Edematous Hyper-Vascularization in Mice. Biochem. Biophys. Res. Commun. Feb 7, 2003;301(2):371-377. Maglione, D., Guerriero, V, Viglietto, G., Delli-Bovi, P., and Persico, M. G. Isolation of a Human Placenta Cdna Coding for a Protein Related to the Vascular Permeability Factor. Proc. Natl. Acad. Set USA. Oct 15, 1991;88(20):9267-9271. Autiero, M., Waltenberger, J., Communi, D., Kranz, A., Moons, L., and Lambrechts, D., et al.. Role of Plgf in the Intra- and Intermolecular Cross Talk Between the VEGF Receptors Fltl and Flkl. Nat. Med. Jul 2003;9(7):936-943. Carmeliet, P., Moons, L., Luttun, A., Vincenti, V, Compemolle, V, and De Mol, M., et al.. Synergism Between Vascular Endothelial Growth Factor and Placental Growth Factor Contributes to Angiogenesis and Plasma Extravasation in Pathological Conditions. Nat. Med. May 2001;7(5):575-583. Ahmed, A., Dunk, C , Ahmad, S., and Khaliq, A. Regulation of Placental Vascular Endothelial Growth Factor (VEGF) and Placenta Growth Factor (PIGF) and Soluble Flt-1 by Oxygen—A Review. Placenta. Mar/Apr 2000;21 Suppl A:S 16-24. Alitalo, K. and Carmeliet, P. Molecular Mechanisms of Lymphangiogenesis in Health and Disease. Cancer Cell. Apr 2002;l(3):219-227. Veikkola, T., Jussila, L., Makinen, T., Karpanen, T., Jeltsch, M., and Petrova, T. V, et al.. Signalling via Vascular Endothelial Growth Factor Receptor-3 Is Sufficient for Lymphangiogenesis in Transgenic Mice. EMBO J. Mar 15, 2001; 20(6):1223-1231. Sawano, A., Iwai, S., Sakurai, Y., Ito, M., Shitara, K., and Nakahata, T., et al.. Flt-1, Vascular Endothelial Growth Factor Receptor 1, Is a Novel Cell Surface Marker for the Lineage of Monocyte-Macrophages in Humans. Blood. Feb 1, 2001;97(3):785-791. Barleon, B., Sozzani, S., Zhou, D., Weich, H. A., Mantovani, A., and Marme, D. Migration of Human Monocytes in Response to Vascular Endothelial Growth Factor (VEGF) Is Mediated via the VEGF Receptor Flt-1. Blood. Apr 15, 1996;87(8):3336-3343. Clauss, M., Weich, H., Breier, G., Knies, U., Rockl, W., and Waltenberger, J., et al.. The Vascular Endothelial Growth Factor Receptor Flt-1 Mediates Biological Activities. Implications for a Functional Role of Placenta Growth Factor in Monocyte Activation and Chemotaxis. J. Biol. Chem. Jul 26, 1996;271(30): 17629-17634. Fong, G. H., Rossant, J., Gertsenstein, M., and Breitman, M. L. Role of the Flt-1 Receptor Tyrosine Kinase in Regulating the Assembly of Vascular Endothelium. Nature. Jul 6, 1995;376(6535):66-70. Shalaby, F, Rossant, J., Yamaguchi, T P., Gertsenstein, M., Wu, X. F., and Breitman, M. L., et al.. Failure of Blood-Island Formation and Vasculogenesis in Flk-1-Deficient Mice. Nature. Jul 6, 1995; 376(6535):62-66.

BIOMARKERS IN OBSTETRIC MEDICINE 72.

73. 74.

75.

76.

77. 78.

79. 80. 81.

82.

83.

84.

349

Helske, S., Vuorela, P., Carpen, O., Hornig, C , Weich, H., and Halmesmaki, E. Expression of Vascular Endothelial Growth Factor Receptors 1, 2 and 3 in Placentas from Normal and Complicated Pregnancies. Mol. Hum. Reprod. Feb 2001;7(2):205-210. Venkatesha, S., Toporsian, M., Lam, C , Hanai, J, Mammoto, T., and Kim, Y. M., et al.. Soluble Endoglin Contributes to the Pathogenesis of Preeclampsia. Nat. Med. Jun 2006;12(6):642-649. Barbara, N. P., Wrana, J. L., and Letarte, M. Endoglin Is an Accessory Protein That Interacts with the Signaling Receptor Complex of Multiple Members of the Transforming Growth Factor-Beta Superfamily. J. Biol. Chem. Jan 8, 1999;274(2):584-594. Caniggia, I. and Winter, J. L. Adriana and Luisa Castellucci Award Lecture 2001. Hypoxia Inducible Factor-1: Oxygen Regulation of Trophoblast Differentiation in Normal and Pre-Eclamptic Pregnancies—A Review. Placenta. Apr 2002;23SupplA:S47-57. Caniggia, I., Grisaru-Gravnosky, S., Kuliszewsky, M., Post, M., and Lye, S. J. Inhibition of TGF-Beta 3 Restores the Invasive Capability of Extravillous Trophoblasts in Preeclamptic Pregnancies. J. Clin. Invest. Jun 1999; 103(12): 1641-1650. Jones, R. L., Stoikos, C , Findlay, J. K., and Salamonsen, L. A. TGF-Beta Superfamily Expression and Actions in the Endometrium and Placenta. Reproduction. Aug 2006;132(2):217-232. McAllister, K. A., Grogg, K. M., Johnson, D. W., Gallione, C. J., Baldwin, M. A., and Jackson, C. E., et al.. Endoglin, a TGF-Beta Binding Protein of Endothelial Cells, Is the Gene for Hereditary Haemorrhagic Telangiectasia Type 1. Nat. Genet. Dec 1994;8(4):345-351. Bourdeau, A., Dumont, D. J., and Letarte, M. A Murine Model of Hereditary Hemorrhagic Telangiectasia. J. Clin. Invest. Nov 1999; 104(10): 1343-1351. Jerkic, M., Rivas-Elena, J. V., Prieto, M., Carron, R., Sanz-Rodriguez, K, and Perez-Barriocanal, E, et al. Endoglin Regulates Nitric Oxide-Dependent Vasodilatation. FASEB J. Mar 2004; 18(3):609-611. Zhou, Y., McMaster, M., Woo, K., Janatpour, M., Perry, J., and Karpanen, T, et al.. Vascular Endothelial Growth Factor Ligands and Receptors That Regulate Human Cytotrophoblast Survival are Dysregulated in Severe Preeclampsia and Hemolysis, Elevated Liver Enzymes, and Low Platelets Syndrome. Am. J. Pathol. Apr 2002;160(4): 1405-1423. Shibata, E., Rajakumar, A., Powers, R. W., Larkin, R. W., Gilmour, C , and Bodnar, L. M., et al.. Soluble Fms-Like Tyrosine Kinase 1 Is Increased in Preeclampsia but Not in Normotensive Pregnancies with Small-for-Gestational-Age Neonates: Relationship to Circulating Placental Growth Factor. J. Clin. Endocrinol. Metab. Aug 2005;90(8):4895-4903. Buhimschi, C. S., Norwitz, E. R., Funai, E., Richman, S., Guller, S., and Lockwood, C. J., et al.. Urinary Angiogenic Factors Cluster Hypertensive Disorders and Identify Women with Severe Preeclampsia. Am. J. Obstet. Gynecol. Mar 2005;192(3):734-741. Levine, R. J., Thadhani, R., Qian, C , Lam, C , Lim, K. H., and Yu, K. F., et al.. Urinary Placental Growth Factor and Risk of Preeclampsia. JAMA. Jan 5, 2005;293(l):77-85.

350

BIOMARKERS 85. 86. 87. 88.

89.

90. 91.

92.

93. 94.

95.

96. 97.

Aggarwal, P. K., Jain, V., Sakhuja, V., Karumanchi, S. A., and Jha, V. Low Urinary Placental Growth Factor Is a Marker of Pre-Eclampsia. Kidney Int. Feb 2006;69(3):621-624. Levine, R. J., Lam, C , Qian, C , Yu, K. R, Maynard, S. E., and Sachs, B. P., et al.. Soluble Endoglin and Other Circulating Antiangiogenic Factors in Preeclampsia. N. Engl. J. Med. Sep 7, 2006;355(10):992-1005. Conde-Agudelo, A., Villar, J., and Lindheimer, M. World Health Organization Systematic Review of Screening Tests for Preeclampsia. Obstet. Gynecol. Dec 2004;104(6):1367-1391. Park, C. W., Park, J. S., Shim, S. S„ Jun, J. K., Yoon, B. H., and Romero, R. An Elevated Maternal Plasma, but Not Amniotic Fluid, Soluble Fms-Like Tyrosine Kinase-1 (Sflt-1) at the Time of Mid-Trimester Genetic Amniocentesis Is a Risk Factor for Preeclampsia. Am. J. Obstet. Gynecol. Sep 2005;193(3 Pt 2):984-989. Wathen, K. A., Tuutti, E., Stenman, U. H., Alfthan, H., Halmesmaki, E., and Finne, P., et al.. Maternal Serum-Soluble Vascular Endothelial Growth Factor Receptor-1 in Early Pregnancy Ending in Preeclampsia or Intrauterine Growth Retardation. J. Clin. Endocrinol. Metab. Jan 2006;91(1):180-184. Hertig, A., Berkane, N., Lefevre, G., Toumi, K., Marti, H. P., and Capeau, J., et al.. Maternal Serum Sfltl Concentration Is an Early and Reliable Predictive Marker of Preeclampsia. Clin. Chem. Sep 2004;50(9): 1702-1703. Rana, S., Karumanchi, S. A., Levine, R. J., Venkatesha, S., Rauh-Hain, J. A., and Tamez, H., et al.. Sequential Changes in Antiangiogenic Factors in Early Pregnancy and Risk of Developing Preeclampsia. Hypertension. Jul 2007; 50(1): 137-142. Vatten, L. J., Eskild, A., Nilsen, T. I., Jeansson, S., Jenum, P. A., and Staff, A. C. Changes in Circulating Level of Angiogenic Factors from the First to Second Trimester as Predictors of Preeclampsia. Am. J. Obstet. Gynecol. Mar 2007;196(3):239El-6. Stepan, H., Unversucht, A., Wessel, N., and Faber, R. Predictive Value of Maternal Angiogenic Factors in Second Trimester Pregnancies with Abnormal Uterine Perfusion. Hypertension. Apr 2007; 49(4):818-824. Romero, R., Nien, J. K., Espinoza, J., Todem, D., Fu, W., and Chung, H., et al.. A Longitudinal Study of Angiogenic (Placental Growth Factor) and AntiAngiogenic (Soluble Endoglin and Soluble Vascular Endothelial Growth Factor Receptor-1) Factors in Normal Pregnancy and Patients Destined to Develop Preeclampsia and Deliver a Small for Gestational Age Neonate. J. Matern. Fetal Neonatal. Med. Jan 2008;21(l):9-23. Stepan, H., Geipel, A., Schwarz, R, Kramer, T., Wessel, N., and Faber, R. Circulatory Soluble Endoglin and Its Predictive Value for Preeclampsia in SecondTrimester Pregnancies with Abnormal Uterine Perfusion. Am. J. Obstet. Gynecol. Feb 2008;198(2):175 El-6. Moore Simas, T. A., Crawford, S. L., Solitro, M. J., Frost, S. C , Meyer, B. A., and Maynard, S. E. Angiogenic Factors for the Prediction of Preeclampsia in HighRisk Women. Am. J. Obstet. Gynecol. Sep 2007;197(3):244 El-8. Wikstrom, A. K., Larsson, A., Eriksson, U. J., Nash, P., Norden-Lindeberg, S., and Olovsson, M. Placental Growth Factor and Soluble FMS-Like Tyrosine Kinase-1 in Early-Onset and Late-Onset Preeclampsia. Obstet. Gynecol. Jun 2007;109(6):1368-1374.

BIOMARKERS IN OBSTETRIC MEDICINE 98.

99. 100. 101. 102. 103. 104. 105. 106.

107.

108.

109.

110.

111.

351

Nicolaides, K. H., Bindra, R., Turan, O. M., Chefetz, I., Sammar, M., and Meiri, H., et al.. A Novel Approach to First-Trimester Screening for Early Pre-Eclampsia Combining Serum PP-13 and Doppler Ultrasound. Ultrasound Obstet. Gynecol. Jan 2006;27(1):13-17. Gonen, R., Shahar, R., Grimpel, Y. I., Chefetz, I., Sammar, M., and Meiri, H., et al.. Placental Protein 13 as an Early Marker for Pre-Eclampsia: A Prospective Longitudinal Study. BJOG. Nov 2008;115(12):1465-1472. Wallukat, G., Homuth, V., Fischer, T., Lindschau, C , Horstkamp, B., and Jupner, A., et al.. Patients with Preeclampsia Develop Agonistic Autoantibodies Against theAngiotensinATl Receptor. J. Clin. Invest. Apr 1999;103(7):945-952. Xia, Y., Wen, H., Bobst, S., Day, M. C , and Kellems, R. E. Maternal Autoantibodies from Preeclamptic Patients Activate Angiotensin Receptors on Human Trophoblast Cells. J. Soc. Gynecol. lnvestig. Feb 2003;10(2):82-93. Zhou, C. C , Zhang, Y, Irani, R. A., Zhang, H., Mi, T., and Popek, E. J., et al.. Angiotensin Receptor Agonistic Autoantibodies Induce Pre-Eclampsia in Pregnant Mice. Nat. Med. Aug 2O08;14(8):855-862. Fu, M. L., Herlitz, H., Schulze, W., Wallukat, G., Micke, P., and Eftekhari, P., et al.. Autoantibodies Against the Angiotensin Receptor (ATI) in Patients with Hypertension. J. Hypertens. Jul 2000; 18(7):945-953. Karumanchi, S. A. and Lindheimer, M. D. Preeclampsia Pathogenesis: "Triple A Rating"—Autoantibodies and Antiangiogenic Factors. Hypertension. Apr 2008;51(4):991-992. Poon, L. C , Kametas, N. A., Maiz, N., Akolekar, R., and Nicolaides, K. H. First-Trimester Prediction of Hypertensive Disorders in Pregnancy. Hypertension. May 2009;53(5):812-818. Yinon, Y, Nevo, O., Xu, J., Many, A., Rolfo, A., and Todros, T, et al.. Severe Intrauterine Growth Restriction Pregnancies Have Increased Placental Endoglin Levels: Hypoxic Regulation via Transforming Growth Factor-Beta 3. Am. J. Pathol. Jan 2008;172(l):77-85. Jeyabalan, A., Mcgonigal, S., Gilmour, C , Hubel, C. A., and Rajakumar, A. Circulating and Placental Endoglin Concentrations in Pregnancies Complicated by Intrauterine Growth Restriction and Preeclampsia. Placenta. Jun 2008;29(6):555-563. Nevo, O., Many, A., Xu, J., Kingdom, J., Piccoli, E., and Zamudio, S., et al.. Placental Expression of Soluble Fms-Like Tyrosine Kinase 1 Is Increased in Singletons and Twin Pregnancies with Intrauterine Growth Restriction. J. Clin. Endocrinol. Metab. Jan 2008;93(l):285-292. Rajakumar, A., Jeyabalan, A., Markovic, N., Ness, R., Gilmour, C , and Conrad, K. P. Placental HIF-1 Alpha, HIF-2 Alpha, Membrane and Soluble VEGF Receptor-1 Proteins Are not Increased in Normotensive Pregnancies Complicated by Late-Onset Intrauterine Growth Restriction. Am. J. Physiol. Regul. Integr. Comp. Physiol. Aug 2007;293(2):R766-774. Smith, G. C , Stenhouse, E. J., Crossley, J. A., Aitken, D. A., Cameron, A. D., and Connor, J. M. Early Pregnancy Levels of Pregnancy-Associated Plasma Protein A and the Risk of Intrauterine Growth Restriction, Premature Birth, Preeclampsia, and Stillbirth. J. Clin. Endocrinol. Metab. Apr 2002;87(4): 1762-1767. Lepercq, J., Guerre-Millo, M., Andre, J., Cauzac, M., and Hauguel-De Mouzon, S. Leptin: A Potential Marker of Placental Insufficiency. Gynecol. Obstet. Invest. 2003;55(3): 151-155.

352

BIOMARKERS 112. Savvidou, M.D., Hingorani, A. D., Tsikas, D., Frolich, J. C , Vallance, P., and Nicolaides, K. H. Endothelial Dysfunction and Raised Plasma Concentrations of Asymmetric Dimethylarginine in Pregnant Women Who Subsequently Develop Pre-Eclampsia. Lancet. May 3, 2003;361(9368): 1511-1517. 113. Lockwood, C. J. and Dudenhausen, J. W. New Approaches to the Prediction of Preterm Delivery. /. Perinat. Med. 1993;21(6):441-452. 114. Lockwood, C. J., Senyei, A. E., Dische, M. R., Casal, D., Shah, K. D., and Thung, S. N., et al.. Fetal Fibronectin in Cervical and Vaginal Secretions as a Predictor of Preterm Delivery. N. Engl. J. Med. Sep 5, 1991;325(10):669-674. 115. lams, J. D., Casal, D., Mcgregor, J. A., Goodwin, T. M., Kreaden, U. S., and Lowensohn, R., et al.. Fetal Fibronectin Improves the Accuracy of Diagnosis of Preterm Labor. Am. J. Obstet. Gynecol. Jul 1995;173(1):141-145. 116. Honest, H., Bachmann, L. M., Gupta, J. K., Kleijnen, J., and Khan, K. S. Accuracy of Cervicovaginal Fetal Fibronectin Test in Predicting Risk of Spontaneous Preterm Birth: Systematic Review. BMJ. 2002 Aug 10, 2002; 325(7359):301. 117. Peaceman, A. M., Andrews, W. W., Thorp, J. M., Cliver, S. P., Lukes, A., and lams, J. D., et al.. Fetal Fibronectin as a Predictor of Preterm Birth in Patients with Symptoms: A Multicenter Trial. Am. J. Obstet. Gynecol. Jul 1997;177(l):13-8. 118. Knab, D. R. Abruptio Placentae. An Assessment of the Time and Method of Delivery. Obstet. Gynecol. Nov 1978;52(5):625-629. 119. Tikkanen, M., Surcel, H. M., Bloigu, A., Nuutila, M., Hiilesmaa, V., and Ylikorkala, O., et al.. Prediction of Placental Abruption by Testing for C-Reactive Protein and Chlamydial Antibody Levels in Early Pregnancy. BJOG. Mar 2008;115(4):486-^91. 120. Tikkanen, M., Hamalainen, E., Nuutila, M., Paavonen, J., Ylikorkala, O., and Hiilesmaa, V. Elevated Maternal Second-Trimester Serum Alpha-Fetoprotein as a Risk Factor for Placental Abruption. Prenat. Diagn. Mar 2007;27(3): 240-243. 121. Pilalis, A., Souka, A. P., Antsaklis, P., Daskalakis, G., Papantoniou, N., and Mesogitis, S., et al.. Screening for Pre-Eclampsia and Fetal Growth Restriction by Uterine Artery Doppler and PAPP-A at 11-14 Weeks Gestation. Ultrasound Obstet. Gynecol. Feb 2007; 29(2): 135-140. 122. Signore, C , Mills, J. L., Qian, C , Yu, K. F, Rana, S., and Karumanchi, S. A., et al.. Circulating Soluble Endoglin and Placental Abruption. Prenat. Diagn. Sep 2008;28(9):852-858. 123. Tikkanen, M., Stenman, U. H., Nuutila, M., Paavonen, J., Hiilesmaa, V., and Ylikorkala, O. Failure of Second-Trimester Measurement of Soluble Endoglin and Other Angiogenic Factors to Predict Placental Abruption. Prenat. Diagn. Dec 2007;27(12):1143-1146. 124. Association, A. D. Gestational Diabetes Mellitus Position Statement. Diabetes Care. 2002c;25(Suppl 1)(S94):582. 125. Georgiou, H. M., Lappas, M., Georgiou, G. M., Marita, A., Bryant, V. J., and Hiscock, R., et al.. Screening for Biomarkers Predictive of Gestational Diabetes Mellitus. Acta. Diabetol. Sep 2008;45(3): 157-165. 126. Wald, N. J., George, L., Smith, D., Densem, J. W., and Petterson, K. Serum Screening for Down Syndrome Between 8 and 14 Weeks of Pregnancy. International Prenatal Screening Research Group. Br. J. Obstet. Gynaecol. May 1996;103(5):407^tl2.

BIOMARKERS IN OBSTETRIC MEDICINE

353

127. Aitken, D. A., Wallace, E. M., Crossley, J. A., Swanston, I. A., Van Pareren, Y., and Van Maarle, M., et al.. Dimeric Inhibin A as a Marker for Down Syndrome in Early Pregnancy. N. Engl. J. Med. May 9, 1996;334(19): 1231-1236. 128. Wald, N. J. and Hackshaw, A. K. Combining Ultrasound and Biochemistry in First-Trimester Screening for Down Syndrome. Prenat. Diagn. Sep 1997;17(9):821-829. 129. Haddow, J. E., Palomaki, G. E., Knight, G. J., Williams, J., Miller, W. A., and Johnson, A. Screening of Maternal Serum for Fetal Down Syndrome in the First Trimester. N. Engl. J. Med. Apr 2, 1998;338(14):955-961. 130. Huderer-Duric, K., Skrablin, S., Kuvacic, I., Sonicki, Z., Rubala, D., and Suchanek, E. The Triple-Marker Test in Predicting Fetal Aneuploidy: A Compromise Between Sensitivity and Specificity. Eur. J. Obstet. Gynecol. Reprod. Biol. Jan 2000;88(l):49-55. 131. Krantz, D. A., Hallahan, T. W., Orlandi, F., Buchanan, P., Larsen, J. W, Jr., and Macri, J. N. First-Trimester Down Syndrome Screening Using Dried Blood Biochemistry and Nuchal Translucency. Obstet. Gynecol. Aug 2000;96(2): 207-213. 132. Bindra, R., Heath, V., Liao, A., Spencer, K., and Nicolaides, K. H. One-Stop Clinic for Assessment of Risk for Trisomy 21 at 11-14 Weeks: A Prospective Study of 15 030 Pregnancies. Ultrasound Obstet. Gynecol. Sep 2002;20(3):219-225. 133. Wapner, R., Thorn, E., Simpson, J. L., Pergament, E., Silver, R., and Filkins, K., et al.. First-Trimester Screening forTrisomies 21 and 18. N. Engl. J. Med. Oct 9,2003;349(15):1405-1413. 134. Wald, N. J., Rodeck, C , Hackshaw, A. K., Walters, J., Chitty, L., and Mackinson, A. M. First and Second Trimester Antenatal Screening for Down Syndrome: The Results of the Serum, Urine and Ultrasound Screening Study (SURUSS). Health Technol. Assess. 2003;7(1 l):l-77. 135. Malone, F. D., Canick, J. A., Ball, R. H., Nyberg, D. A., Comstock, C. H., and Bukowski, R., et al.. First-Trimester or Second-Trimester Screening, or Both, for Down Syndrome. N. Engl. J. Med. Nov 10, 2005;353(19):2001-2011. 136. Canick, J. A., Lambert-Messerlian, G. M., Palomaki, G. E., Neveux, L. M., Malone, F. D., and Ball, R. H., et al.. Comparison of Serum Markers in First-Trimester Down Syndrome Screening. Obstet. Gynecol. Nov 2006;108(5):1192-1199. 137. Palomaki, G. E., Haddow, J. E., Knight, G. J., Wald, N. J., Kennard, A., and Canick, J. A., et al.. Risk-Based Prenatal Screening for Trisomy 18 Using Alpha-Fetoprotein, Unconjugated Oestriol and Human Chorionic Gonadotropin. Prenat. Diagn. Aug 1995;15(8):713-723. 138. Tul, N., Spencer, K., Noble, P., Chan, C , and Nicolaides, K. Screening for Trisomy 18 by Fetal Nuchal Translucency and Maternal Serum Free Beta-Hcg and PAPP-A At 10-14 Weeks of Gestation. Prenat. Diagn. Nov 1999;19(11): 1035-1042. 139. Brumfield, C. G., Wenstrom, K. D., Owen, J., and Davis, R. O. Ultrasound Findings and Multiple Marker Screening in Trisomy 18. Obstet. Gynecol. Jan 2000;95(l):51-54. 140. Palomaki, G. E., Neveux, L. M., Knight, G. J., and Haddow, J. E. Maternal Serum-Integrated Screening for Trisomy 18 Using Both First- and Second-Trimester Markers. Prenat. Diagn. Mar 2003;23(3):243-247. 141. Kagan, K. O., Wright, D., Maiz, N., Pandeva, I., and Nicolaides, K. H. Screening for Trisomy 18 by Maternal Age, Fetal Nuchal Translucency, Free BetaHuman Chorionic Gonadotropin and Pregnancy-Associated Plasma Protein-A. Ultrasound Obstet. Gynecol. Sep 2008;32(4):488^192.

This page intentionally left blank

CHAPTER

BIOMARKERS IN CANCER Roopali Roy, Christine M. Coticchia, Jiang Yang, and Marsha A. Moses

INTRODUCTION Cancer is a collection of diseases characterized by uncontrolled proliferation, local invasion into surrounding tissue, and in some cases, metastasis. Accounting for nearly a quarter of all deaths in the United States annually, this disease is the second leading cause of death in this country, second only to heart disease.1 In recent years there has been an unprecedented surge of interest within scientific, clinical, and financial arenas alike over the burgeoning field of cancer biomarker research. The popular press has even touted "...a revolution in cancer diagnosis: detection by means of biomarkers." The newly coined discipline "biomarker medicine" was said to offer "...the best hope of meeting the National Cancer Institute's goal of eliminating suffering and death from cancer by 2015."2This hope and enthusiasm for discovering new methods of detecting and treating cancer is easy to understand. It is now widely appreciated that early detection and treatment of this disease has the potential to save or, at the very least, extend the length and often improve the quality of patients' lives. Furthermore, the opportunity to accurately and specifically predict therapeutic efficacy during the course of cancer therapy can provide oncologists with the opportunity to quickly modify a therapeutic regimen in ways that would provide the best therapy for their patients. Biomarker medicine is also beginning to be applied to cancer risk assessment in novel and potentially meaningful ways. Although a number of cancer diagnostics are currently available for certain cancer types, there remain many human cancers for which no accurate or specific diagnostics or prognostics exist. Within the context of this chapter, we will review some of the most promising research in cancer diagnostics and prognostics with an emphasis on protein-based and genetic biomarkers. We 355

356

BIOMARKERS

focus here on breast and prostate cancer, two of the most common epithelial cancers for women and men, respectively, and for which some useful tests already exist. We also thought it important to include a review of cancers for which sensitive and reliable biomarkers are either limited or non-existent; ovarian and pancreatic cancers will be presented as representatives of this latter category. As the literature reporting the discovery of new potential cancer biomarkers continues to grow, the importance of validating these candidate markers in large test cohorts cannot be underestimated. In order to be as comprehensive as possible while still providing the reader with the most recent discoveries, we have specifically included only those markers for which validation studies have accompanied the preliminary discovery analyses.

C A N C E R BIOMARKER DISCOVERY STRATEGIES Perhaps no other area of translational research has benefited more from recent technological advances than biomarker medicine. These methods include, but are not limited to, whole-genome analysis, proteomic profiling, transcriptional profiling, and microRNA profiling. Comparative genomic hybridization (CGH) is a specific, whole-genome system that can detect gain or loss of gene copy numbers. Through its use, chromosomal regions with abnormal gene copy numbers can be identified and confirmed. Proteomic profiling is focused on the identification of proteins and/or protein peptides that are differentially present in the cancer patient's tissue or body fluid as compared to same from controls, or to the same individual's samples as part of a longitudinal study. Specific proteins/peptides or panels of same can provide useful clinical information regarding disease status, stage, and therapeutic efficacy. These proteins/peptides can also be multiplexed to provide greater predictive power. Mass spectrometry remains the experimental cornerstone of these proteomic strategies and a variety of different types of mass spectrometry have been exploited to discover novel cancer biomarkers. Transcriptional and microRNA profiling approaches are also being used for cancer biomarker discovery. In the first case, transcriptional profiling, microarrays are the most common tool used to collect and identify genes that are differentially expressed in samples of interest. Genes of interest are then verified and ultimately validated in larger sample cohorts. Ultimately, these potential biomarkers can then be used in the creation of a gene expression signature that can be used as a diagnostic or prognostic tool. Given that changes in mRNA levels are not always recapitulated at the protein level, proteomic studies are often preferred. Most recently, microRNA profiling has begun to be exploited for biomarker discovery. These single-stranded RNA molecules (21-24 nucleotides in length) have now been identified as being differentially expressed in a number of solid and hematopoietic tumors compared to control samples.3 The application of these and other experimental approaches to the discovery of new cancer biomarkers are included in the cancer-specific sections below.

FIGURE 3.3 A) Relative abundance of proteins within human serum. B) Depletion of high abundance proteins in serum using the immunoaffinity-based multiple affinity removal system (MARS). Lane 2: serum standard; Lane 3: raw plasma; Lanes 4 and 7: plasma proteins elute from MARS column during first washing step; Lanes 5 and 8: plasma proteins that elute from MARS column during second washing step; Lanes 6 and 9: high abundant proteins that are retained by the MARS immunodepletion column.

FIGURE 3.5 Tissue microarray (TMA) validation of head and neck squamous cell carcinoma biomarkers identified via global proteomic analysis of well- (WD), moderately- (MD), and poorly-differentiated (PD) HNSCC formalin-fixed paraffin-embedded (FFPE) tumor samples. Tissue sections immunostained for cytokeratin I 6, vimentin, and desmoplakin using immunohistochemistry (IHC) are shown in the upper panels.The stained sections were evaluated based on tissue differentiation and intensity of IHC staining. For cytokeratin 4, light gray represents >5% and <25%; mid gray 26% to 50%; dark gray 5 I % to 75%; and black, 76% to 100% of the cells stained positively for this protein.The scoring for vimentin and desmoplakin was based on the percentage of positive tumors for each stage of differentiation (checkered box) compared with negative (white box).Ten normal (i.e., non-neoplastic) tissues were analyzed in each experiment, while the number of HNSCC cancer tissues evaluated by IHC is indicated above each column.

FIGURE 5.3 Analytical techniques for oligosaccharide profiling to distinguish between normal and cancerous cells.

FIGURE 5.5 (A) Bioorthogonal chemical reporting strategy for imaging glycans. (B) Bioorthogonal reactions used to visualize chemical reporters appended to unnatural sugars. (C) Application of the bioorthogonal chemical reporter strategy for in vivo imaging of glycans in zebrafish.

FIGURE 6.1 The pathological hallmarks of Alzheimer's disease, i.e., neuritic plaques (Gallays stain, A) and neurofibrillary tangles (Gallays stain, B), and the pathological hallmarks of Parkinson's disease, i.e., alpha-synuclein-positive Lewy bodies (arrows) and Lewy neurites (arrowhead, C). Neuritic plaques are located extracellularly and are composed of Abeta fibrils. They are initially present in cortical areas. Neurofibrillary tangles are abnormal intracellular neuronal hyperphosphorylated filaments. They are found mainly in limbic areas. A major component is tau, a microtubulus-associated protein. Lewy bodies and Lewy neurites are eosinophilic proteinaceous neuronal inclusions which are located primarily in brainstem neurons. All these aggregates are thought to be crucially involved in the pathogenesis of the diseases, but rather not at early stages. By courtesy of Dr.Jens Schittenhelm, Institute for Brain Research, University of Tuebingen. (A) and (C) x200, (B) x400.

FIGURE 7.3 Pathophysiology of cardiac disease and the physiological actions of B-type natriuretic peptide (BNP). Heart failure or ischemic events result in reduction in cardiac function (both systolic and diastolic), which in turn leads to a reduction in cardiac output (CO). Hypotension, tissue hypoperfusion, and reduced oxygen deliver (D0 2 ) are the main manifestations. The body compensates by increasing both preload and afterload: increasing salt and water retention via the rennm-angiotension-aldosterone system (RAAS), and vasoconstriction via baroreflex. Overcompensations result in deleterious effects such as pulmonary edema and increased cardiac workload. In response to volume overload, the myocardium releases BNP which partly counteracts the deleterious effects of overcompensation by inducing vasodilation, inhibiting the RAAS, and exerting some lusitropic effects. However; the ability for BNP to compensate is limited.

FIGURE 8.2 Normal alveolus structure with capillary depicting interaction between pulmonary epithelial and endothelial cells. During injury, epithelial and endothelial cells are damaged, and macrophages are activated. Type II cell hyperplasia, protein leakage, neutrophilic inflammation, infiltration of macrophages, and secretion of inflammatory mediators are common features of acute injury. Depending on the nature of acute injury, macrophage accumulation, interstitial fibrosis, emphysema, granuloma, and other diseases including carcinoma are likely to occur Some of the figure components are copied from the slides obtained from Motifolio, Inc. (Ellicott City MD).

FIGURE 8.3 Major signaling events associated with nuclear factor-K(J ( N F - K P ) activation and nuclear translocation in response to lung injury caused by inhaled substances. In addition to cytokines, growth factors, lipopolysaccharides (LPS), and lymphotoxins, oxidative stress infectious agents, inhaled particles, and reactive gaseous materials activate N F - K P family protein by dissociating inhibitor-Kp via its phosphorylation through I-KP Kinase Complex. Upon activation, N F - K P homo- and heterodimers of Re I family including N F - K P (p50), NF-«p2 (p52), RelA (p65), RelB, and c-Rel (Rel) translocate into the nucleus and induce gene expression. The phosphorylated I-KP is removed by proteosomal degradation. Note that some of the components of the figure and the basic pathways are extracted from the signaling pathways provided by SA Biosciences Inc. (http://www.sabiosciences.com/pathwaycentral.php) and Protein Lounge (San Diego, CA).VEGF, vascular endothelial growth factor; VEGFR.VEGF receptor; 11 — I, interleukin-1; IL-IR, IL-I receptor; LPS, lipopolysaccharide;TLRs, Toll-like receptors; Tumor necrosis factonTNF;TNFR,TNF receptor;TCR,T-cell receptor; BCR, B-cell receptor; Lt-P, Lymphotoxin-p; Lt-0 R, Lt~P receptor; BAFFR, B-cell activating factor receptor;TRAFs, TNF receptor-associated factors; IKK-a, inhibitor kinase-a; IKK-a, inhibitor kinase-a; IKK-0, inhibitor kinase-p.

FIGURE 10.1 Scheme of the glomerulus and tubules illustrating glomerular filtration and tubular reabsorption of low molecular weight (LMW) proteins and high molecular weight (HMW) proteins in a normal kidney, after glomerular injury, and after tubular injury. In the noninjured kidney, only low amounts of HMW proteins pass the glomerular filtration wall. LMW proteins pass freely the filtration wall and are reabsorbed to a great extent in the tubules with only a small fraction being excreted with the urine. In early stages of glomerular injury, HMW proteins pass the glomerular filtration wall and are reabsorbed in the tubules, competing with the reabsorption of the LMW proteins, which are subsequently excreted into urine to a large extent. With continued glomerular injury, the continuously reabsorbed HMW proteins "poison" the tubular reabsorption complex, and both LMW and HMW proteins are excreted into urine to a large extent.Thus, LMW proteins can be early and sensitive markers for glomerular injury or for a direct impairment of the tubular reabsorption complex. By contrast, with only tubular injury low amounts of HMW proteins appear in the urine while the LMW proteins continue to be reabsorbed and do not appear in the urine.

FIGURE I I.I

Cells of the vasculature that may be involved in vascular injury.

FIGURE I 1.2 A: Normal rat mesenteric artery; B: Necrosis and hemorrhage occur within the tunica media at 24 hours after fenoldopam; C and D: Accumulation of leukocytes and fibroplasia at seven days after fenoldopam.

FIGURE 16.1 Mitochondnal function can fail in a variety of ways. Many drugs directly inhibit one or more of the four respiratory complexes of the Electron Transport System, or complex V, a.k.a. ATP Synthase (upper left panel). Several sites are capable of univalently reducing molecular oxygen to superoxide, notably complex I, ubiquinone, and complex III. Many antivirals and antibacterials also impede mtDNA synthesis or gene expression occurring in the matrix, resulting in erosion of mitochondrial capacity. Xenobiotics that undermine integrity of the inner membrane, or that serve as proton shuttles within it, uncouple the ETS from phosphorylation by ATP Synthase, and some inhibit mitochondrial pathways that fuel ETS, such as boxidation, Kreb's Cycle, or the transmembrane adenine nucleotide translocator (ANT). Most of the above deleterious effects precipitate the irreversible formation of the "permeability transition pore" (PT) that collapses membrane potential and permits release of cytochrome c and other pro-apoptotic factors into the cytosol.

FIGURE 16.2 Cos cell stained with a potentiometric dye that enters the mitochondria as a function of mitochondrial membrane potential (tetramethylrhodamine) and nuclear stain (Hoechst). Note the individual mitochondria shaped like beans and threads, and the fused reticulum. Image by Sandra Wiley.

FIGURE 17.8 A) In one-antibody (Ab) label-based assays, the targeted proteins are captured by an immobilized antibody and detected through labeling with a tag. B) In the sandwich label-based format, immobilized antibodies capture unlabeled proteins, which are detected by another antibody, with the signal for detection generated by several methods. C) Other antibody and protein array approaches are modifications of one-antibody and sandwich labelbased arrays. These alternative strategies of protein arrays allow detection of proteins on whole cells without protein isolation. D) A growing area of cancer research that uses protein arrays on serum specimens entails the development and design of tumor-associated antigen (TAA) arrays to enhance detection of autoantibodies against TAAs for cancer diagnosis. E) Complex protein extracts can also be spotted onto membranes and probed with antibodies targeting specific proteins on the so-called reverse-phase arrays. F) Proteins in suspension can also be detected by use of bead arrays.

FIGURE 17.10 Example of multiplex of several human cytokines/chemokines in one reaction vial as shown by the standard curves in serum matrix. (Kindly provided from Millipore.)

FIGURE 17.1 I The reader uses a 532 nm green laser ("assay" laser) to excite the phycoerythrin (PE) dye of the assay (streptavidin-PE).The 635 nm solid state laser (red "classify" laser) is used to excite the dyes inside the beads to determine their "color" or "region" and is also used for doublet discrimination by light scatter The reader has four detectors, one for each of the optical paths shown in the figure. Detectors are used to measure the fluorescence of the assay, to make bead determination (I-100), and lastly to discriminate between single and aggregate beads. (Kindly provided by Millipore.)

FIGURE 18.4 A. Fluorescence emitted from quantum dots. Blue fluorescence can be emitted from small particles of approximately 2 nm in diameter green from ~3 nm particles, yellow from ~4 nm particles, and red from large particles of ~5 nm.The wavelength of the excitation light is 365 nm. B. Fluorescence emission spectra depending on the size of quantum dots. (Image reproduced from http://www.aist.go.jp/aist_e/aist_today/2006_21 /hot_line/hot_line_22.html with permission from National Institute of Advanced Science and Technology.)

FIGURE 18.6 I-A. Schematic illustration of the procedure for the preparation of antibody conjugated Rubpy doped silica (RuDS) nanoparticles. l-B.The scheme of the RuDS labelbased fluorescent immunoassay of IL-6 on a protein microarray format, (a) Capture anti-IL-6 antibody was printed on the slide, (b) Antigen, IL-6, was attached to the slide via antibody/ antigen recognition, (c) Anti-IL-6 antibody-RuDS conjugates were coated on the slide to form a sandwich immunocomplex with RuDS as tags. 2-A. Fluorescence images of protein microarray with different concentrations of antigen, IL-6 (control, 0.1, I, 10,30,60, 100 ngmL-l). 2-B. Calibration curve of fluorescence intensity versus IL-6 concentration. (Image reproduced with permission from reference 23, Chapter 18.)

FIGURE 18.12 Structure of an FET nanobiosensor (a) Cross-sectional view: source and drain electrodes bridge the semiconductor channel. The gate electrode can be used to modulate the conductivity of the semiconductor channel. A receptor molecule attached to the surface of the semiconductor material can specifically recognize and capture a target molecule from a buffer solution. (b)Top view: SEM image of a typical nano-FET. In these structures, the channel length is the S-D distance and the channel width is the S or D electrode width. Examples of nano-FET fabricated using either (c) carbon nanotubes or (d) indium oxide NWs as semiconductor materials. (Image reproduced with permission from reference 60, Chapter I 8.)

FIGURE 18.16 Magnetic nanotag-based protein assay chip, (a) The chip has a 200 pi reaction well and is supported by an 84-pin ceramic base, (b) Embedded in the bottom of the reaction well are 64 sensors in an 8 x 8 array, (c) Each sensor has an active area of roughly 90 x 90 urn2 and consists of 32 linear giant magnetoresistive (GMR) segments, each 1.5 urn wide, which are connected in series, (d) The edge of one such sensor segment and bound nanotags are imaged with a scanning electron microscope. (Image reproduced with permission from reference 104, Chaper 18.)

FIGURE 19.9 Typical lateral-flow device for nucleic acid detection.

FIGURE 20.4 Biomarker framework for environmental applications. Conceptual framework that links environmental exposures, biomarkers, and health outcome.The framework can be used to evaluate the extent to which markers explain the pathway from exposure to outcome and whether biomarker data can assist in predicting outcome.This framework should also aid in estimating the association among multiple markers.

FIGURE 20.5 Graphical depiction of a mode of action for an environmental stressor. Key features include the toxicity pathway, which presents the pathway directly perturbed by the chemical, followed by a series of key events (blue circles) which are causally linked to the adverse outcome. Biomarkers which serve as surrogates for those key events highlighted by blue circles are considered bioindicators and can be used to parameterize quantitative models of the mode of action forthe chemical in question. In cases where a high throughput assay can be developed for the toxicity pathway, a single model predicting the adverse outcome relative to a perturbation of the toxicity pathway can then be used to predict toxicity for any chemicals perturbing that pathway solely on the basis of the assay results.

FIGURE 20.7 Systems modeling of mode of action. The mode of action concept outlined in Figure 20.5 is now extended to include an interconnected network of key event nodes. Perturbation of the network by environmental stressors is assumed to occur at toxicity pathway nodes within the network and propagate through the network through the other key event nodes. Perturbations which are sufficient to perturb an adverse outcome node would then result in an adverse outcome in the individual (depicted as a single node in the population network). Modeling mode of action in this fashion facilitates cumulative risk assessment by considering the aggregate impact of all perturbations from all stressors. It also allows other factors influencing susceptibility such as life stage, disease, and genetics to be modeled within the same framework.

FIGURE 22.2 Receiver-operator characteristic (ROC) plot for Kim-1, sCr, and BUN as markers of proximal tubule injury in rats. The curve shows the sensitivity estimate for all estimated specificity levels. The Kim-1 curve is generally above the other curves, indicating better performance.

FIGURE 22.4 Boxplots of Kim-1 values by kidney histopathology injury grade. A plot of the individual values sorted by Kim-1 value is superimposed over each, giving a finer scaled picture of the distribution of the data.The figure indicates that median Kim-1 values generally increase with an increased histopathology score. Also, some samples in the group of animals treated with a nephrotoxicant but with histopathology scores of zero have elevated Kim-1 levels.

BIOMARKERS IN CANCER

CANCER

357

BIOMARKERS

Breast Cancer Breast cancer is one of the most prevalent malignancies afflicting women in this country. Nearly one in 10 women will be diagnosed with the disease in their lifetime. In fact, nearly 200,000 new cases of invasive breast cancer are diagnosed within the United States each year, and in spite of recent advances and the availability of new treatment therapies, nearly 40,480 women were estimated to have died of the disease last year.4 Breast cancer is commonly classified into two groups based on their histological profile, with the majority of breast cancers falling under the category of ductal carcinoma, and the remainder, approximately 15%, of breast cancers categorized as lobular carcinomas. Ductal and lobular carcinomas of the breast are further defined using microscopic features of noninvasive (in situ) or invasive (infiltrating).5 Advanced metastatic breast cancers expand beyond the primary disease site within the mammary gland and disseminate to regional or distant lymph nodes (axillary, supraclavicular, and cervical) as well as bone, liver, and brain.5 The American Cancer Society Guidelines for Breast Cancer Screening recommend that women over the age of 40 receive a clinical breast exam and mammography screening every one to two years, and recommend a clinical breast exam at least every three years to women aged 20-39.6 Large meta-analysis studies and several large randomized controlled studies have demonstrated that the increased occurrence of routine mammography screening has markedly decreased breast cancer mortality.7-9 Any palpable tissue deemed abnormal during a clinical breast exam or any suspicious area visible on a radiogram warrants a biopsy and histopathology for a definitive diagnosis of breast cancer.6 While mammography has been useful in aiding in the early detection of treatable cancer, it is still costly to perform and the rate of patient compliance to screening recommendations is variable, pointing to a need for routine biomarkers that can accurately and reliably identify women who are at risk for breast cancer and who would benefit from additional screening.10 Currently there are no circulating biomarkers in blood, serum, or urine clinically available to aid in the early diagnosis of breast cancer, however potential biomarkers that assess breast cancer risk, and novel diagnostic markers, are in development, as discussed below. Upon diagnosis, primary treatment for breast cancer most often includes either surgical mastectomy or breast sparing lumpectomy with radiation.5 After primary surgical intervention, management of breast cancer is accomplished with adjuvant systemic chemotherapy, hormone therapy, or a combination of both treatments. While both chemotherapy and hormone therapy have been shown to reduce mortality and recurrence, not all women need or benefit from adjuvant therapy after primary surgical intervention. Specifically a subset of breast cancer patients without evidence of lymph-node involvement have a favorable prognosis and will not further benefit from chemotherapy. Meanwhile, a significant percentage of women will have their breast cancer recur

358

BIOMARKERS

or progress to metastatic disease despite the use of adjuvant systemic treatment modalities. This range of patient outcomes and response to therapy has created the need for accurate and informative tumor biomarkers that can aid in the management of breast cancer therapy. Of particular use are biomarkers that can identify disease with poor prognosis and predict response to therapy. Many advances in the understanding of the molecular mechanisms underlying breast cancer progression have assisted in the development of clinically useful tissue and serum biomarkers of breast cancer. Biomarkers that have been validated as having clinical usefulness are discussed here as are promising biomarkers in body fluids. The effect of the steroid hormones estrogen and progesterone on the mammary gland has been a focus of decades of research. The nuclear receptors, estrogen receptor (ER) and progesterone receptor (PR), respond to the steroid hormones estrogen and progesterone by directly regulating gene transcription and thereby affecting multiple aspects of mammary gland development and pathology.11'12 ER and PR are the most widely used biomarkers of breast cancer and several professional panels, including the American Society of Clinical Oncology and the European Society of Medical Oncology, have recommended that all newly diagnosed breast tumors be assayed for ER and PR status.13,14 ER and PR are powerful predictive markers and are highly selective for patients with early stage cancer, as well as those with metastatic disease who will benefit from endocrine therapy such as tamoxifen and aromatase inhibitors. For patients with early stage ductal carcinoma in situ (DCIS), studies examining the clinical utility of ER and PR as predictive biomarkers for guiding therapeutic management were not conclusive and therefore ER and PR are not explicitly recommended for this group. As a prognostic factor, the presence of ER and PR can be suggestive of favorable outcome when considered with other tumor factors. Such is not the case for lymph node-negative breast tumors, where ER and PR status was not deemed clinically useful in predicting outcome of this group. Determining the type of assay that most accurately and reproducibly measures ER and PR expression is an expressed priority of several oncology societies. ER and PR are currently measured by enzyme-linked immunosorbant assay (ELISA), ligand binding assay, and most commonly by immunohistochemistry (IHC). ErbB2 (or HER2) is a member of the epidermal growth factor receptor family that has been shown to be amplified or overexpressed in 15% to 30% of breast tumors where it contributes to inappropriate epithelial growth and survival. Assessment of ErbB2 expression is measured by IHC or fluorescence in situ hybridization (FISH). For use as a predictive marker to guide physicians' treatment management, several medical and professional societies recommend that every newly diagnosed breast tumor, whether early stage or metastatic, be screened for ErbB2 positivity. Tumors scoring 3+ on IHC for ErbB2 or having an ErbB2 amplification score of 2.0 or greater are deemed ErbB2-postitive, making it a predictive marker that selects for patients who will respond to ErbB2-targeted therapy such as Trastuzumab, the monoclonal

BIOMARKERS IN CANCER

359

antibody against ErbB2.15 Under current investigation is the potential utility of ErbB2 in predicting patient response to a variety of other treatment modalities. Several studies have suggested that ErbB2 expression may predict a relative resistance of early stage breast cancer to tamoxifen,16-" however insufficient evidence exists to justify the use of ErbB2 as a predictive biomarker for guiding treatment decisions involving the application of endocrine therapy.13'14 Similarly, as a prognostic indicator of both early stage, node-negative breast cancer as well as advanced disease that has not been treated with adjuvant therapy, ErbB2 has been associated with poor prognosis, but is not yet recommended for use as a clinical prognostic factor.13'H-18 In addition, numerous studies are investigating the potential of ErbB2 to predict patient response to a variety of chemotherapeutic agents and have demonstrated that ErbB2 positivity in early breast tumors may predict tumor response to therapy. Specifically, some studies have shown that ErbB2 positivity correlates with resistance to adjuvant cytotoxic chemotherapy consisting of cyclophosphamide, methotrexate, and 5-fluorouracil (CMF).16-19 Several large prospective clinical studies have suggested that ErbB2 overexpression might indicate sensitivity to anthracycline-based adjuvant therapies in breast cancer patients, although the evidence is not yet clear enough to recommend the biomarker for this specific use.20-23 When considering taxane-based therapies for breast cancer patients, ErbB2-postitivity is being examined as a marker of sensitivity. Studies ongoing are at present inconclusive and therefore, ErbB2 has not been recommended as a predictive biomarker for this particular clinical use.1314 The extracellular domain (ECD) of ErbB2 is frequently shed from the cell surface and is detectable in human serum. The serum levels of the ECD of ErbB2 is a biomarker candidate currently in development for use as a prognostic tool for monitoring disease recurrence and as a predictive indicator of response to therapy. The ECD of ErbB2 shows promise as an accessible, serially measurable biomarker that requires further validation before being recommended for clinical use.24-25 Urokinase plasminogen activator (uPA) is a trypsin-like serine protease that when active can contribute to a number of cellular processes involved in tumor cell migration, invasion, angiogenesis, and metastasis.26'27 Several inhibitors of uPA exist in vivo, including plasminogen activator inhibitor-1 (PAI-1) which is thought to be the primary endogenous inhibitor of uPA. PAI-1 has a variety of other extracellular matrix binding partners and cellular functions which contribute to cell survival and migration. Both uPA and PAI-1 expression has been shown to be increased in breast cancer tumors through an unknown mechanism. In a large prospective randomized clinical trial, as well as a pooled metaanalysis of smaller studies, both uPA and PAI-1 have been shown to be strong independent prognostic factors of newly diagnosed lymph node-negative breast cancers, where low levels correlate with a reduced risk of recurrence.28^30 High levels of uPA and PAI-1 are indicative of higher risk for cancer recurrence and may be able to identify a class of ER-positive breast cancer patients that

360

BIOMARKERS

will benefit from CMF adjuvant chemotherapy compared to clinical observation alone.31 The evaluation of several methods to quantitate uPA and PAI-1 expression levels has been extensively studied. Currently, ELISA of extracts from fresh tumor tissue of at least 200-300 grams is the only validated prognostic test method recommended.13 The promising prognostic value of uPA and PAI-1 highlights its potential as a meaningful target for future therapy.32 While the majority of genetic factors contributing to the onset and progression of breast cancer occur spontaneously, there are a number of familial inherited genes mutations that predispose women to a breast cancer. The most clinically relevant genetic biomarkers of breast cancer are the Breast Cancer 1 (BRCA1) and Breast Cancer 2 (BRCA2) genes which encode for tumor suppressor proteins with nuclear functions involved in DNA regulation, maintenance, and repair. While rarely mutated spontaneously, women with inherited mutation of BRCA1 and BRCA2 account for up to 5% of breast cancer cases. Due to their high susceptibility to breast and ovarian cancer, it is strongly recommended that women carrying BRCA1 or 2 mutations undergo routine cancer screening.5 The advancement of gene array-based technologies has spurred the development of a number of multi-gene based diagnostic tests that have the potential to tailor treatment modalities to individual tumors. The most validated of these multi-gene signature tests is Oncotype DX™.33 Using quantitative, reverse-transcription polymerase chain reaction (RT-PCR) technology to measure RNA expression of a 21 gene signature consisting of 16 cancer-related genes and five controls, Oncotype DX™ calculates a recurrence score for women with early stage breast cancer on adjuvant tamoxifen therapy which predicts a patient's risk for recurrence and distant metastasis.34 A large prospective clinical trial has validated the power of this test to identify women with Stage I or II, ER-postive, node-negative breast tumors on tamoxifen who have an excellent prognosis and who would not benefit from additional adjuvant chemotherapy.35-38 Currently no circulating diagnostic breast cancer biomarker exists, but several are clinically available for monitoring metastatic disease. Cancer Antigen 15-3 (CA 15-3) and Cancer Antigen 27.29 (CA 27.29) are members of the MUC-1 family of antigen proteins that are found in the circulation. CA 15-3 and CA 27.29 are currently being studied for their potential as prognostic markers in newly diagnosed, early stage disease and several prospective studies have suggested that serum CA 15-3 and CA 27.29 levels are significant predictors of poor outcome.39'40 Additionally, several large retrospective studies have demonstrated that serum levels of CA 15-3 and CA 27.29 may increase several months before clinical evidence of recurrent breast cancer and therefore might have potential as a marker of recurrence after primary and adjuvant therapy.13,4142 While the U.S. Food and Drug Administration has approved a CA 15-3 and CA 27.29 assay for use in determining recurrent cancer after primary and adjuvant therapy, large prospective clinical studies are needed to validate CA 15-2 and CA 27.29 for use as a marker of cancer

BIOMARKERS IN CANCER

361

recurrence, and to determine if treatment of the tumor marker, in the absence of clinically detected metastasis, improves outcome and overall survival.13 Currently, the measurement of serum CA 15-3 and CA 27.29 levels in patients with advanced, metastatic disease undergoing active therapy is the most clinical useful application of these biomarkers, as increase in CA15-3 and CA 27.29 levels is often indicative of treatment failure. Similar to CA 15-3 and CA 27.29, CEA levels in serum are sometimes measured in patients with advanced, metastatic disease actively undergoing therapy. While not as commonly found in the serum as the MUC-1 antigens in breast cancer patients, carcinoembryonic antigen (CEA) is a useful marker in patients with advanced metastatic disease who do not express CA 15-3 or CA 27.29.42 In the absence of measurable disease progression with imaging techniques, in patients with metastatic disease undergoing therapy, increases in serum levels of CEA can indicate a failure to respond to treatment.13'14 Matrix metalloproteinases (MMPs) are a group of extracellular enzymes that actively contribute to cancer progression. Their activities include proteolysis of extracellular matrix, processing of growth factors, and modulation of cell adhesion.43 Evidence is emerging that members of the MMP and/or ADAM (disintegrin metalloprotease) families can serve as potential markers for early detection and risk assessment as well as biomarkers of tumor recurrence, metastatic spread, and response to primary and adjuvant therapy for breast cancer. Specifically, a panel of MMPs in tumor tissue as well as serum, plasma, and urine were shown to be significantly elevated in patients with breast and other cancers compared to healthy controls.44-47 Urinary ADAM 12 levels have been shown to predict disease status and stage in breast cancer patients and ADAM 12 protein levels in urine increase with progression of disease.48 IHC analysis of human breast biopsy materials has shown that MMP-13 may be a useful prognostic indicator for invasive breast cancer. In this study, tumor-derived MMP13, correlated with expression of Her2/neu and TIMP-1 and with aggressive tumor phenotypes, inversely correlated with overall patient survival.49 The utility of MMP-9 activity to predict metastatic spread of disease as well as to monitor patient response to primary and adjuvant therapy and to evaluate outcome is of considerable interest.50,51 High levels of serum MMP-9 and TIMP-1 are associated with increased incidence of lymph node metastasis and decreased relapse-free and overall survival rates.47 MMPs may also be useful in predicting therapeutic efficacy. Plasma MMP-9 levels decrease after the surgical removal of primary breast tumors and a progressive decrease in plasma MMP-9 was observed in patients who responded well to adjuvant therapy.50 Importantly, in all patients who suffered a relapse of disease there was a gradual increase of plasma MMP-9 activity one to eight months prior to the clinical diagnosis of recurrence.50 Recently, efforts have focused on the use of MMPs and ADAMs as potential biomarkers of breast cancer risk and of early breast cancer detection. Recent evidence has shown that urinary MMP-9 and ADAM 12 may be useful as noninvasive breast cancer risk assessment tools.52

362

BIOMARKERS

Prostate Cancer Prostate cancer is one of the most common cancers among men in the United States, second only to skin cancer. Autopsy studies have revealed that the incidence of prostate cancer increases with age, with more than half of men over 70 years of age being detected with this disease.53 Prostate-specific antigen (PSA) is currently the most commonly utilized serum marker for prostate cancer, with elevated circulating PSA levels being associated with larger tumor size and higher cancer stage and Gleason grade.54 PSA is a kallikrein-like serine protease whose primary function is to liquefy the seminal fluid and aid in fertilization.55 Secreted by prostatic epithelial cells, PSA may also directly participate in prostate cancer progression. Evidence has suggested that PSA may perform both pro- and anti-cancer functions. It cleaves insulin-like growth factor (IGF) binding proteins (IGFBPs) and liberates IGF-I56 which stimulates growth and prevents apoptosis of prostate cancer cells.57 It has also been shown to activate transforming growth factor (TGF)-|3,58 another factor that regulates a variety of processes during cancer progression, including differentiation, proliferation, migration, and angiogenesis. However, PSA's proteolytic activity can also produce peptides that hinder the progression of cancer.59 Since its discovery, PSA has had a profound impact on prostate cancer patient management.14 It has been approved by the Food and Drug Administration both as a diagnostic marker and a marker to monitor therapeutic efficacy. In the context of early detection, patients who are older than 50 years with increased circulating levels of PSA (a 4 ng/ml) are advised to undergo prostate biopsy. For patients who are under surveillance or have had prior treatment for prostate cancer, failure of PSA to fall below the limits of detection or a rising trend in PSA concentration over time can prompt recommendations for additional treatments (National Comprehensive Cancer Network guidelines). However, the diagnostic specificity and sensitivity of PSA tests have not been satisfactory. Results from the Prostate Cancer Prevention Trial indicated that a PSA value greater than 4 ng/ml produced specificity of 93.8% and sensitivity of 20.5%. Lowering PSA cutoff values yielded higher sensitivity but at the expense of specificity.60 It has been shown that prostate cancer was detected in 15% of patients with PSA concentrations s 4 ng/ml (false negatives).61 Elevated PSA levels are also observed in benign prostate conditions (e.g., prostatitis and benign prostate hyperplasia, BPH) (false positives). Several methods have been studied to improve the specificity of PSA tests for the early detection of prostate cancer, such as the evaluation of PSA density (the ratio of serum PSA to prostate size), PSA velocity (the change in PSA level over a specified period) and percentage of free PSA (%fPSA).54 Only %fPSA has been formally incorporated into clinical practice.14 PSA exists in blood as both a complexed form (cPSA) with protease inhibitors, such as al-antichymotrypsin and a2-macroglobulin, as well as its free form. Conventional assays measure the concentrations of total PSA (tPSA). %fPSA, when multiplexed with tPSA data, can better distinguish between the presence of prostate cancer and benign prostate disease, thereby reducing unnecessary bi-

BIOMARKERS IN CANCER

363

opsies.62 Furthermore, development of additional and better biomarkers is also necessary to improve the specificity and sensitivity of PSA. Human kallikrein-related peptidase 2 (KLK2, also know as human kallikrein 2) is a member of the same family of serine proteases as PSA. It shares 80% amino acid sequence homology with PSA (also known as KLK3). KLK2 has most commonly been studied together with PSA species (e.g., free or total PSA). Serum KLK2 levels have been shown to predict prostate cancer in men with elevated PSA levels, suggesting that the combination of KLK2 and PSA may improve the selection process for patients in need of a tissue biopsy.63 Combination of serum KLK2 and free PSA levels also significantly improved the risk assessment for biochemical recurrence in men with moderate PSA values (s 10 ng/ml).64 KLK2 serum levels have also been demonstrated to predict an unfavorable prognosis in prostate cancer patients independent of PSA levels.65 In addition to KLK2, KLK11 has also shown promise in the detection of prostate cancer.66 Elevated levels of MMP-2 and -9, members of another protease family, have been associated with the presence of prostate cancer,44,67 prostate cancer stage,68 and clinical outcome.6971 MMPs have also been studied as markers of therapeutic efficacy in prostate cancer. Circulating levels of MMP-2 and -9 decreased significantly in metastatic patients after therapy.68 Growth factors that contribute to prostate cancer progression have also shown their utility as cancer markers. In a prospective study, men with the highest plasma IGF-I levels were shown to have a four-fold increased risk of developing prostate cancer.72 The association between plasma IGF-I levels and prostate cancer risk was also observed in another systematic review of published epidemiological reports.73 IGFBPs, which regulate the bioavailability of IGFs, have also been associated with prostate cancer. IGFBP-2 plasma levels were inversely correlated with aggressive disease in localized prostate cancer, whereas IGFBP-3 levels were inversely correlated with metastasis.74 TGF-pH plasma concentration was shown to be significantly elevated in patients with invasive prostate cancer compared to patients with benign disease or healthy controls75 and its postoperative levels after radical prostatectomy were reported to strongly predict disease progression.76 Tumors rely on angiogenesis, a process during which new blood vessels are recruited from the surrounding vasculature, thereby acquiring oxygen and nutrients to support their expansion and to gain access to other parts of the body.77 Increased microvessel density has been associated with prostate cancer metastasis78 and with progression after radical prostatectomy.79 The urinary level of the classic angiogenesis mitogen, vascular endothelial growth factor (VEGF), has been reported to be a predictive marker for survival in hormone-refractory prostate cancer patients,80 as well as in patients treated with radiation therapy.69 Plasma VEGF levels have also been shown to correlate with prostate cancer advancement, with an incremental increase from healthy controls to patients with localized disease, and to patients with metastatic disease. Preoperative VEGF levels have been associated with metastasis and biochemical progression after surgery.81

364

BIOMARKERS

Other promising prostate cancer markers currently under investigation include prostate cancer-specific molecules, such as prostate cancer antigen 3 (PCA3),82,83 prostate stem-cell antigen (PSCA),84 prostate-specific membrane antigen (PSMA),85 molecules modulating cell cycle and apoptosis (such as cyclins, p53, and Bcl-2),86 and metabolic enzymes such as a-methylacyl-CoA racemase (AMACR).87 High-throughput gene expression and proteomic profiling have become important approaches to identify novel prostate cancer markers. These profiling studies usually correlate the pattern of a panel of markers, as opposed to one single marker, with disease progression and patient outcome. For example, in a study comparing the gene expression patterns between prostate cancer and normal prostate specimens, a five-gene model was shown to predict recurrence following prostatectomy.88 It was found in another study that different gene expression patterns were associated with different subtypes of prostate cancer, including high grade, advanced stage, and tumor recurrence.89 Similarly, serum proteomic patterns have been reported to discriminate between patients with prostate cancer, benign prostate hyperplasia, and healthy men, and, when multiplexed with PSA, have been shown to improve its specificity.90-93 However, these profiling studies also have limitations. For example, many profiling studies are performed on a small sample size. The profiles identified in different studies show a limited overlap and the results are sometimes not reproducible.94 Measures must be taken to address these limitations. For example, greater numbers of samples will be needed to increase the discriminatory power of the candidate biomarkers which will eventually be validated using an independent sample set.95 Cancer cells disseminate into the circulation before metastasizing to secondary organs. Detection of circulating cancer cells has therefore been proposed as an aid in the detection of malignant prostate cancer and has already shown potential in prostate cancer diagnosis.96,97 Reverse transcription-PCR (RT-PCR) is the most common method used to detect the small number of circulating cancer cells. Primers used in RT-PCR to detect circulating prostate cancer cells include those specific for PSA, PSMA, PSCA, and KLK2. 98 "

Ovarian Cancer Diagnosed at a rate of 25,000 new cases each year, ovarian cancer accounts for approximately 16,000 deaths in this country annually.100 Unfortunately, nearly three-quarters of all patients with epithelial ovarian cancer are found to have clinically advanced stage III and IV disease, making ovarian cancer a neoplastic disease in dire need of sensitive and specific diagnostic biomarkers.3 In the absence of sensitive and specific diagnostics, ovarian cancer detection is currently accomplished using a combination of pelvic examinations, abdominal and/or transvaginal ultrasonography, and laproscopy.101 The biomarker CA125, discussed here, is often used to help distinguish between benign and malignant masses,101 with high levels of this marker predicting a poor prognosis.102 Prognostic tools available for this disease include the patient's disease stage at time of presentation and histological subtype. Among the most

BIOMARKERS IN CANCER

365

common of these are the serous, mucinous, clear cell, transitional cell, and endometrial subtypes. In the case of advanced stage II and IC cancer, successful cytoreductive surgery remains the best predictor of positive outcome.103 Cancer antigen 125 (CA125) is perhaps best known for identifying disease status in late-stage ovarian cancers.104' 105In up to 80% of diagnosed stage III and stage IV epithelial ovarian cancers, the level of serum CA125 is elevated, and correlates with disease status.106 As such, CA125 is often used to predict the likelihood of tumor malignancy and disease recurrence once treatment has begun. CA125, combined with transvaginal sonography, is being used as a screening tool in high-risk populations of patients, including those with BRCA1 mutations. CA125 has not yet been shown to be a truly reliable biomarker for earlystage ovarian cancer by itself, however, since it is only elevated in 50% of patients diagnosed with stage I and stage II ovarian carcinoma.107 In fact, up to 20% of later-stage ovarian cancer patients do not express the antigen at all.106 Additionally, the amount of CA125 in the serum can be affected by numerous outside factors, such as chemotherapy and non-gynecologic issues in the body, thereby making it impossible to make a diagnosis or prognosis for ovarian cancer based simply on its presence in the human body.108 When multiplexed with other biomarkers, however, it is a proven asset in the prognosis and treatment of early disease. One such biomarker is osteopontin, found in the extracellular matrix, inflammation sites, and body fluids. Elevated levels of this glycophosphoprotein have been reported to be present in the serum of patients with ovarian cancer (n=51), in comparison to serum from women suffering from other gynecological malignancies (n=47), benign ovarian disease (n=46), and normal controls (n=107).109 Another study of 234 patients with ovarian cancer was conducted in which their osteopontin levels were measured both before and after debulking primary surgery and throughout subsequent chemotherapy. Following the surgery, osteopontin levels decreased significantly, falling below the levels of normal controls.110 When tumors began to recur in the same patients, levels of osteopontin increased accordingly, leading to the conclusion that the protein is secreted by the tumors themselves.110 A direct correlation between increased levels of osteopontin in metastatic lesions and a decreased three-year survival rate in ovarian cancer patients has been reported, suggesting that the presence of osteopontin might be useful as an independent prognosticator of metastatic cancer.111 A study of presurgical patients with ovarian cancer has demonstrated that c-terminal fragments of osteopontin are present in the urine samples of patients with ovarian cancer and early-stage disease,112 suggesting that osteopontin may one day be used as a noninvasive biomarker for the early detection of ovarian cancer. One panel of biomarkers being studied both alone and in conjunction with other biomarkers, is kallikreins, a set of 15 genes located on chromosome 19ql3.4. This gene locus has proven extremely useful in the study of not only ovarian cancer, but breast and prostate cancers as well.113 Found in epithelial and endocrine tissue and detectable in the body fluids of cancer patients,

366

BIOMARKERS

certain kallikreins have already proven to aid in the screening of patients.113 Human kallikrein 10 (hK10), for example, has been detected in significantly elevated levels in the serum of patients with ovarian cancer, directly correlating with poor response to chemotherapy and unfavorable prognosis.114 Likewise, human kallikrein 14 (hK14) was detected in the serum of 20 patients with ovarian cancer, yet was not detected by ELISA in 28 normal control samples."5 When combined with other biomarkers, kallikreins may prove particularly efficient in the diagnosis and treatment of cancers. Interestingly, in a study of hK10 levels of 146 patients with ovarian cancer, 141 women with benign gynecological diseases and normal controls, the patients with benign disease did not show a significant elevation in serum hK10, while the cancer patients showed an extreme increase."4 When tested in conjunction with CA125, hK10 was found in the serum of 35% of cancer patients who had tested negative for CA125 alone. This finding is important, since only 50% of patients with early stage I and stage II disease have increased CA125 levels. When multiplexed with hK10, sensitivity increased 21%, with a specificity of 90%, a marked improvement.114 The possibility also exists that other kallikreins may be shown to have significant prognostic value when coupled with CA125 and other biomarkers. Unlike the ovarian cancer biomarkers discussed thus far, whose increased presence is often associated with advanced-stage disease and poor prognosis, an increased level of the glycosylated Kunitz-type protease bikunin in the plasma or serum of ovarian cancer patients is a positive sign. Bikunin inhibits invasion and metastasis, thereby slowing or, in some cases, halting, the spread of disease. In a study of 89 tumor samples that had been surgically removed from patients, elevated bikunin expression indicated an increased survival rate in 40 patients."6 Correlatively, 17 of 41 patients demonstrating reduced expression of the biomarker were diagnosed with late-stage ovarian cancer and poor prognosis.117 A larger immunoassay study of bikunin in the plasma of 327 presurgery ovarian cancer patients, 200 patients with benign gynecological disease and 200 normal controls further supports the results of the IHC study."8 HE4, human epididymis protein 4, is encoded on a gene that is typically amplified in ovarian tumors. In fact, though expressed in 100% of 16 human endometrioid epithelial ovarian cancers and 93% of serous ovarian carcinomas stained for its presence, it is absent in the ovarian surface epithelium of normal controls."9 When analyzed for its sensitivity compared to other biomarkers, HE4 was shown to have the highest sensitivity for stage I ovarian cancer, in comparison to CA125, osteopontin, and ErbB2.120 It has been demonstrated to be as specific as CA125, but without as many of the false-positives associated with the latter.121 When multiplexed, HE4 and CA125 have a 95% specificity in detecting disease as opposed to either of the two proteins by themselves.120 Angiogenic factors are also important in determining the prognosis of those suffering from ovarian cancer. One such factor, vascular endothelial growth factor (VEGF), is often elevated in the body fluids of ovarian cancer patients and has been found to contribute to the accumulation of ascites.122 Nu-

BIOMARKERS IN CANCER

367

merous groups have studied VEGF levels in the serum of women with ovarian cancer as a means of determining its potential prognostic abilities.123 In one such study, the amount of serum VEGF in 314 patients with both early and late-stage ovarian cancer was measured; stage I patients with high levels of VEGF in their serum faced an eight-fold increase in cancer-related death compared with those without high VEGF serum levels.124 A contrasting study showed, however, that the serum VEGF levels in both ovarian cancer patients, patients with benign disease, and normal controls were virtually the same. This study did, however, report that elevated levels of VEGF in ascites indicate a poor prognostic outcome in patients.125,126

Pancreatic Cancer Pancreatic adenocarcinoma is the fourth leading cause of cancer-related deaths in the United States and worldwide.127'128 In the United States, approximately 32,000 new patients are diagnosed with this disease and nearly the same number die from it each year. Accounting for only 2% of all new cancers diagnosed, pancreatic cancer is potentially lethal, with an overall five-year survival rate of just 4%.127 The dismal prognosis for this disease is largely due to late diagnosis and resistance to therapy. Pancreatic cancer is extremely difficult to diagnose in its early stages due to a lack of specific symptoms and the limitations of current diagnostic methods. Therefore, there exists an urgent need for the development of effective, early detection strategies for this disease. Risk factors for pancreatic cancer include smoking, familial chronic pancreatitis, and familial cancer syndrome. A number of inherited genetic alterations linked to familial cancer syndrome have been identified, including germ-line mutations in the BRCA2, pl6, PRSS1, STK11, hMLHl, and FANCG genes.129-131 In addition, spontaneous genetic mutations in KRAS, CDKN2A, TP53, and SMAD4/DPC4 have been described as a signature molecular profile for this malignancy.132 Sensitive and specific screening tests for early pancreatic cancer would provide the most benefit to individuals with these genetic mutations who have a heightened risk of developing pancreatic cancer. Neoplasms of the pancreas can be grouped according to their localization in the pancreas (head, body, or tail) or according to the type of cell from which the cancer originated (exocrine or endocrine). Exocrine pancreatic ductal adenocarcinomas (PDAC) are the most common and account for -75% of diagnosed pancreatic cancers. PDACs arise from lesions that begin in the pancreatic ducts, progress to pancreatic intraepithelial neoplasias (PanlNs), and ultimately form adenocarcinomas.132 Endocrine pancreatic tumors (also known as neuroendocrine tumors) are rarer. Neuroendocrine tumors (NETs) are characterized by the secretion of hormones and are typically named after the hormone they produce (e.g., gastrinomas, insulinomas, glucagonomas). Therefore, the hormones released by the tumor can be used as clinical biomarkers for these cancers. For example, two tumor markers currently in clinical use for NETs are neuron-specific enolase (NSE) and chromogranin A (CgA).133 Of these, CgA, a glycoprotein of the core of storage vesicles, is

368

BIOMARKERS

considered to be the best biomarker for diagnosis (86% specificity/69% sensitivity) as well as therapeutic efficacy of NETs.133,134CgA levels were found to correlate with disease status in patients with advanced disease, particularly in patients with liver metastases.133 Due to its higher prevalence in the population and a lack of effective diagnostic tools, we have focused our review on the biomarkers of PDAC. Several studies have evaluated differentially expressed biomarkers for PDAC in tissue, blood, or pancreatic juice. One of the major challenges is differentiating between PDAC and the more commonly occurring inflammatory condition, chronic pancreatitis. In fact, approximately 6-9% unnecessary resections for suspected pancreatic carcinomas are conducted for chronic pancreatitis patients. Serum carbohydrate antigen 19-9 (CA19-9) remains the only clinical biomarker in use for diagnosis and for assessing therapeutic efficacy of PDAC.135-137 CA19-9 has a sensitivity of -80% and specificity of -60-70%, 138 ' 139 however, CA19-9 levels can be elevated in patients with various benign pancreatobiliary disorders including chronic pancreatitis, cholestasis, and acute cholangitis, as well as in patients with extrapancreatic malignancies.135-136' 14° This lack of specificity may lead to a high false positive rate or misdiagnosis. In recent years several genomic and proteomic studies (for review see reference 141) have identified candidate biomarkers for PDAC including osteopontin,142 macrophage inhibitory cytokine l,143 MUC4,144 VEGF/soluble VEGFR1 ratio145 and hepatocarcinoma-intestinepancreas/pancreatitis-associated protein l,146 among others. Most of these reports, however, represent preliminary discovery phase results with systematic validation studies to determine clinical use of these potential biomarkers yet to be completed. Proteomic analysis of pancreatic juice via two-dimensional difference gel electrophoresis (DIGE) and tandem mass spectrometry (MS/MS) identified MMP-9, oncogene DJ-I, and alpha-lB-glycoprotein precursor (AIBG) as some of the upregulated proteins in samples from PDAC patients as compared to those from cancer-free controls.147 Validation studies indicated that serum levels of MMP-9, as measured by ELISA, were significantly higher in patients with PDAC than in patients with chronic pancreatitis and healthy controls.147 Concordantly, a significantly higher percentage of PDAC tissues stained positive for MMP-9, DJ-I, and AIBG as compared to normal pancreatic tissue.147 Yokoyama, et al. investigated the diagnostic value of MMP-2 analysis using gelatin zymography of pancreatic juices and found that the levels of active MMP-2 were universally upregulated in cancer patients (100%) in contrast to the levels in chronic pancreatitis patients (2%) and controls (0%).148 Plasma levels of another matrix metalloprotease, MMP-7, were reported to be significantly elevated in patients with pancreatic cancer149 and increased MMP-7 protein expression was also observed in PDAC tissue as compared to normal pancreatic tissue and predicted shortened survival of patients.150 Interestingly, expression of extracellular matrix metalloprotease inducer (EMMPRIN, CD 147) was also found to be upregulated in pancreatic neoplasms. EMMPRIN, designated as a general tumor collagenase stimulating

BIOMARKERS IN CANCER

369

factor, has been reported to stimulate the synthesis of MMPs from peritumoral fibroblasts in a variety of different tumor types including bladder,151 lung,152 melanoma,153 lymphoma,154 and pancreatic cancer.155 EMMPRIN mRNA levels were found to be two-fold higher in pancreatic tumor tissue but remained unchanged in chronic pancreatitis compared to normal pancreatic tissues. In the same study serum levels of EMMPRIN analyzed by ELISA were almost four-fold higher (4.13 ± 0.28 ng/ml) in pancreatic cancer patients compared to healthy volunteers (0.95 ±0.16 ng/ml).155 Recently, a member of the disintegrin metalloprotease family, ADAM9, has been analyzed in relation to pancreatic cancer. Immunohistochemical analysis of ADAM9 in tissue samples from PDAC, chronic pancreatitis, neuroendocrine cancer, and acinar cell carcinoma patients indicated that increased expression of this protease may contribute to disease aggressiveness.156 Additionally, cytoplasmic ADAM9 staining of PDACs correlated with poor tumor differentiation and shortened overall survival of the patients.156 Microarray studies have identified another class of biomolecule, a member of the carcinogenic embryonic antigen (CEA) family, carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAMl) whose gene expression was elevated in PDAC compared with normal pancreas or chronic pancreatitis.157 In a follow-up study, CEACAMl transcript and protein levels were found to be significantly higher in pancreatic intraductal neoplasia 3 (PanIN-3) lesions and PDAC as compared to noncancerous pancreatic tissue.158 Serum CEACAMl analyzed by ELISA was also detected in 91% of PDAC, 66% of chronic pancreatitis, and only 24% of normal controls. The combination of serum CEACAMl and CA19-9 gave a significantly higher diagnostic accuracy than CA19-9 alone.158 The detection of CEACAMl in PanIN-3 lesions representing pancreatic carcinoma in situ suggests that this marker may be useful for early diagnosis of this disease. Ultimately, the development of highly sensitive and specific biomarker/s for pancreatic cancer must consider the ability to: 1) differentiate between benign conditions such as pancreatitis and malignant disease; 2) detect early or resectable disease; 3) predict therapeutic efficacy; and 4) be useful as a screening test for individuals at a high risk of developing this disease.

CONCLUSIONS It is now clear that the discovery and validation of highly sensitive and specific biomarkers for human cancers has the potential to revolutionize the ways in which this disease is detected and treated. The impact of this nascent field of biomarker medicine on cancer patients' lives cannot be overstated. For example, early detection and treatment of cancer has the potential to save, or at the very least extend the length and often improve the quality of, patients' lives. Furthermore, the opportunity to accurately and specifically predict therapeutic efficacy during the course of cancer therapy can provide oncologists with the opportunity to expeditiously modify a therapeutic regimen in ways that would provide the best therapy for their patients. Recent advances in technologies

370

BIOMARKERS

utilized in the discovery of protein and molecular biomarkers have provided the unprecedented and exciting opportunity to identify novel biomarkers that were previously undetectable. As the field continues to grow exponentially, it will be critical that all discovery studies be supported by large scale validation efforts in order to provide patients and their physicians with reliable, sensitive, and specific cancer biomarkers.

SUMMARY P O I N T S 1. 2. 3. 4. 5.

Current studies reveal that cancer biomarkers may be useful, not only in early detection of cancer, but also in monitoring therapeutic efficacy as well as in cancer risk assessment. To date, there remain some human cancers, for example pancreatic and ovarian cancer, for which no sensitive, specific, or accurate diagnostic tests exist. Cancer biomarker discovery must ultimately be coupled with large scale validation studies to confirm clinical utility. Genomics, proteomics, metabolomics and other disciplines are commonly used in cancer biomarker discovery. "Biomarker medicine" is the newly coined title that describes this new discipline.

ACKNOWLEDGMENTS The authors gratefully acknowledge the editorial assistance of Ms. Caitlin Welsh and the support of NIH ROlCAl 18764, NIH P01CA4558, the Breast Cancer Research Foundation, and the support of Jane and Stuart Weitzman. We thank Kristin Johnson from Children's Hospital, Boston, MA for creating the figure on the cover page of this book representing our chapter.

REFERENCES 1. 2. 3. 4. 5.

6.

CDC National Vital Statistic Report, April 24, 2008. Center Stage In '06: Natural Gas, Iran, New Cancer Tests. The Wall Street Journal. 2005. Coticchia, C. M., Yang, J., and Moses, M. A. Ovarian Cancer Biomarkers: Current Options and Future Promise. J. Nad. Compr. Cane. Netw. Sep 2008; 6(8):795-802. American Cancer Society. Cancer Facts & Figures 2008. Atlanta: American Cancer Society;2008. Wood, W. C, Muss, H. B., Solin, L. J., and Olopade, O. I. Malignat Tumors of the Breast. In: De Vita, V. T, Jr., Hellman, S., and Rosenberg, S. A., Ed. Cancer: Principles and Practice of Oncology. 7th Ed. Philadelphia: Lippincott Willians &Wilkins;2005:1415-1475. Rimer, B. K., Schildkraut, J. M., and Hiatt R. R. Cancer Screening. In: De Vita, V. T, Jr., Hellman, S., and Rosenberg, S. A., Eds. Cancer: Principles and Practice of Oncology. 7th Ed. Philadelphia: Lippincott Williams & Wilkins;2005:569.

BIOMARKERS IN CANCER I. 8. 9. 10. II. 12. 13. 14.

15. 16. 17. 18. 19. 20. 21. 22. 23.

371

Hendrick, R. E., Smith, R. A., and Rutledge, J. H., Ill, Smart Cr. Benefit of Screening Mammography in Women Aged 40-49: A New Meta-Analysis of Randomized Controlled Trials. J. Natl. Cancer Inst. Monogr. 1997;(22):87-92. Miller, A. B., Baines, C. J., To, T, and Wall, C. Screening Mammography Re-Evaluated. Lancet. Feb 26, 2000;355(9205):747;Author Reply 752. Smart, C. R., Hendrick, R. E., Rutledge, J. H., Ill, and Smith, R. A. Benefit of Mammography Screening in Women Ages 40 to 49 Years. Current Evidence from Randomized Controlled Trials. Cancer. Apr 1, 1995;75(7):1619-1626. Ravdin, P. M., Cronin, K. A., and Howlader, N., et al. The Decrease in BreastCancer Incidence in 2003 in the United States. N. Engl. J. Med. Apr 19, 2007; 356(16): 1670-1674. Elledge, R. M. and Fuqua, S. A. W. Estrogen and Progesterone Receptors. In: Harris, L. M., Jr., Morrow, M., et al., Eds. Diseases of the Breast. 2nd Ed. Philadelphia: Lippincott Williams & Wilkins;2000:471. Dickson, R. B. and Russo, J. Biochemical Control of Breast Development. In: Harris, Jr., L. M., Lippman, M. E., and Morrow, M., et al., Eds. Diseases of the Breast. 2nd Ed. Philadelphia: Lippincott Williams & Wilkins; 2000. Harris, L., Fritsche, H., and Mennel, R., et al. American Society of Clinical Oncology 2007 Update of Recommendations for the Use of Tumor Markers in Breast Cancer. J. Clin. Oncol. Nov 20, 2007;25(33):5287-5312. Sturgeon, C. M., Duffy, M. J., and Stenman, U. H., et al. National Academy of Clinical Biochemistry Laboratory Medicine Practice Guidelines for Use of Tumor Markers in Testicular, Prostate, Colorectal, Breast, and Ovarian Cancers. Clin. Chem. Dec 2008;54(12):E11-79. Paik, S., Kim, C , and Wolmark, N. Her2 Status and Benefit from Adjuvant Trastuzumab in Breast Cancer. N. Engl. J. Med. Mar 27, 2008;358(13): 1409-1411. Yamauchi, H., Steams, V., and Hayes, D. F. The Role of C-Erbb-2 as a Predictive Factor in Breast Cancer. Breast Cancer. 2001;8(3):171-183. Yamauchi, H., Steams, V., and Hayes, D. F. When Is a Tumor Marker Ready for Prime Time? A Case Study of C-Erbb-2 as a Predictive Factor In Breast Cancer. J. Clin. Oncol. Apr 15, 2001;19(8):2334-2356. Andrulis, I. L., Bull, S. B., and Blackstein, M. E., et al. Neu/Erbb-2 Amplification Identifies a Poor-Prognosis Group of Women with Node-Negative Breast Cancer. Toronto Breast Cancer Study Group. J. Clin. Oncol. Apr 1998;16(4):1340-1349. Duffy, M. J. Predictive Markers in Breast and Other Cancers: A Review. Clin. Chem. Mar2005;51(3):494-503. Gennari, A., Sormani, M. P., and Pronzato, P., et al. Her2 Status and Efficacy of Adjuvant Anthracyclines in Early Breast Cancer: A Pooled Analysis of Randomized Trials. J. Natl. Cancer Inst. Jan 2, 2008;100(l):14-20. Pritchard, K. I., Shepherd, L. E., and O'Malley, F. P., et al. Her2 and Responsiveness of Breast Cancer to Adjuvant Chemotherapy. N. Engl. J. Med. May 18, 2006;354(20):2103-2111. Joensuu, H., Kellokumpu-Lehtinen, P. L., and Bono, P., et al. Adjuvant Docetaxel or Vinorelbine with or without Trastuzumab for Breast Cancer. N. Engl. J. Med. Feb 23, 2006;354(8):809-820. Buzdar, A. U., Ibrahim, N. K., and Francis, D., et al. Significantly Higher Pathologic Complete Remission Rate After Neoadjuvant Therapy with Trastuzumab, Paclitaxel, and Epirubicin Chemotherapy: Results of a Randomized Trial in Human Epidermal Growth Factor Receptor 2-Positive Operable Breast Cancer. J. Clin. Oncol. Jun 1, 2005;23(16):3676-3685.

372

BIOMARKERS 24. 25.

26. 27. 28. 29.

30. 31. 32. 33. 34. 35.

36. 37.

38. 39.

Leary, A. R, Hanna, W. M., and Van De Vijver, M. J., et al. Value and Limitations of Measuring Her-2 Extracellular Domain in the Serum of Breast Cancer Patients. J. Clin. Oncol. Apr 1, 2009;27(10): 1694-1705. Lennon, S., Barton, C , and Banken, L., et al. Utility of Serum Her2 Extracellular Domain Assessment in Clinical Decision Making: Pooled Analysis of Four Trials of Trastuzumab in Metastatic Breast Cancer. J. Clin. Oncol. Apr 1, 2009;27(10): 1685-1693. Andreasen, P. A., Kjoller, L., Christensen, L., and Duffy, M. J. The UrokinaseType Plasminogen Activator System in Cancer Metastasis: A Review. Int. J. Cancer. Jul 3, 1997;72(l):l-22. Annecke, K., Schmitt, M., and Euler, U., et al. uPA and PAI-1 in Breast Cancer: Review of Their Clinical Utility and Current Validation in the Prospective NNBC-3 Trial. Adv. Clin. Chem. 2008;45:31^15. Harbeck, N., Kruger, A., and Sinz, S., et al. Clinical Relevance of the Plasminogen Activator Inhibitor Type 1—A Multifaceted Proteolytic Factor. Onkologie. Jun2001;24(3):238-244. Janicke, F., Prechtl, A., and Thomssen, C , et al. Randomized Adjuvant Chemotherapy Trial in High-Risk, Lymph Node-Negative Breast Cancer Patients Identified by Urokinase-Type Plasminogen Activator and Plasminogen Activator Inhibitor Type 1. J. Natl. Cancer Inst. Jun 20, 2001 ;93( 12):913-920. Duffy, M. J. Urokinase Plasminogen Activator and Its Inhibitor, PAI-1, as Prognostic Markers in Breast Cancer: From Pilot to Level 1 Evidence Studies. Clin. Chem. Aug 2002;48(8): 1194-1197. Harbeck, N., Kates, R. E., and Schmitt, M., et al. Urokinase-Type Plasminogen Activator and Its Inhibitor Type 1 Predict Disease Outcome and Therapy Response in Primary Breast Cancer. Clin. Breast Cancer. Dec 2004;5(5):348-352. Ulisse, S., Baldini, E., Sorrenti, S., and D'Armiento, M. The Urokinase Plasminogen Activator System: A Target for Anti-Cancer Therapy. Curr. Cancer Drug Targets. Feb 2009;9(1):32-71. Paik, S. Development and Clinical Utility of a 21-Gene Recurrence Score Prognostic Assay in Patients with Early Breast Cancer Treated with Tamoxifen. Oncologist. Jun 2007;12(6):631-635. Paik, S., Shak, S., and Tang, G., et al. A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer. N. Engl. J. Med. Dec 30, 2004;351(27):2817-2826. Goldstein, L. J., Gray, R., and Badve, S., et al. Prognostic Utility of the 21Gene Assay in Hormone Receptor-Positive Operable Breast Cancer Compared with Classical Clinicopathologic Features. J. Clin. Oncol. Sep 1, 2008;26(25): 4063^071. Sparano, J. A. and Paik, S. Development of the 21-Gene Assay and Its Application in Clinical Practice and Clinical Trials. J. Clin. Oncol. Feb 10, 2008; 26(5):721-728. Gianni, L., Zambetti, M., and Clark, K., et al. Gene Expression Profiles in Paraffin-Embedded Core Biopsy Tissue Predict Response to Chemotherapy in Women with Locally Advanced Breast Cancer. J. Clin. Oncol. Oct 10, 2005; 23(29):7265-7277. Sparano, J. A. Tailorx: Trial Assigning Individualized Options for Treatment (Rx). Clin. Breast Cancer. Oct 2006;7(4):347-350. Ebeling, F. G., Stieber, P., and Untch, M., et al. Serum CEA and CA 15-3 as Prognostic Factors in Primary Breast Cancer. Br. J. Cancer. Apr 22, 2002;86(8): 1217-1222.

BIOMARKERS IN CANCER 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50.

51.

52. 53. 54. 55. 56.

373

Gion, M., Boracchi, P., and Dittadi, R., et al. Prognostic Role of Serum CA 15.3 in 362 Node-Negative Breast Cancers. An Old Player for a New Game. Eur. J. Cancer. Jun 2002;38(9):1181-1188. De La Lande, B., Hacene, K., Floiras, J. L., Alatrakchi, N., and Pichon, M. F. Prognostic Value of CA 15.3 Kinetics for Metastatic Breast Cancer. Int. J. Biol. Markers. Oct-Dec 2002;17(4):231-238. Valenzuela, P., Mateos, S., Tello, E., Lopez-Bueno, M. J., Garrido, N., and Gaspar, M. J. The Contribution of the Cea Marker to CA 15.3 in the Follow-up of Breast Cancer. Eur. J. Gynaecol. Oncol. 2003;24(l):60-62. Roy, R., Zhang, B., and Moses, M. A. Making the Cut: Protease-Mediated Regulation of Angiogenesis. Exp. Cell Res. Mar 10, 2006;312(5):608-622. Moses, M. A., Wiederschain, D., Loughlin, K. R., Zurakowski, D., Lamb, C. C , and Freeman, M. R. Increased Incidence of Matrix Metalloproteinases in Urine of Cancer Patients. Cancer Res. Apr 1, 1998;58(7): 1395-1399. Smith, E.R., Zurakowski, D., Saad, A., Scott, R.M., and Moses, M.A. Urinary Biomarkers Predict Brain Tumor Presence and Response to Therapy. Clin. Cancer Res. Apr 15, 2008;14(8):2378-2386. La Rocca, G., Pucci-Minafra, I., Marrazzo, A., Taormina, P., and Minafra, S. Zymographic Detection and Clinical Correlations of MMP-2 and MMP-9 in Breast Cancer Sera. Br. J. Cancer. Apr 5, 2004;90(7):1414-1421. Wu, Z. S., Wu, Q., and Yang, J. H., et al. Prognostic Significance of MMP-9 and TIMP-1 Serum and Tissue Expression in Breast Cancer. Int. J. Cancer. May 1, 2008;122(9):2050-2056. Roy, R., Wewer, U. M., Zurakowski, D., Pories, S. E., and Moses, M. A. Adam 12 Cleaves Extracellular Matrix Proteins and Correlates with Cancer Status and Stage. J. Biol. Chem. Dec 3, 2004;279(49):51323-51330. Zhang, B., Cao, X., and Liu, Y., et al. Tumor-Derived Matrix Metalloproteinase-13 (MMP-13) Correlates with Poor Prognoses of Invasive Breast Cancer. Bmc Cancer. 2008;8:83. Ranuncolo, S. M., Armanasco, E., Cresta, C , Bal De Kier Joffe, E., and Puricelli, L. Plasma MMP-9 (92 kDa-MMP) Activity Is Useful in the Follow-up and in the Assessment of Prognosis in Breast Cancer Patients. Int. J. Cancer. Sep 20, 2003;106(5):745-751. Zucker, S., Hymowitz, M., and Conner, C , et al. Measurement of Matrix Metalloproteinases and Tissue Inhibitors of Metalloproteinases in Blood and Tissues. Clinical and Experimental Applications. Ann. N.Y. Acad. Sci. Jun 30, 1999;878:212-227. Pories, S. E., Zurakowski, D., and Roy, R., et al. Urinary Metalloproteinases: Noninvasive Biomarkers for Breast Cancer Risk Assessment. Cancer Epidemiol. Biomarkers Prev. May 2008;17(5):1034-1042. Sakr, W. A., Grignon, D. J., and Crissman, J. D., et al. High Grade Prostatic Intraepithelial Neoplasia (HGPIN) and Prostatic Adenocarcinoma between the Ages of 20-69: An Autopsy Study of 249 Cases. In Vivo. May/Jun 1994;8(3):439-443. Lilja, H., Ulmert, D., and Vickers, A. J. Prostate-Specific Antigen and Prostate Cancer: Prediction, Detection, and Monitoring. Nat. Rev. Cancer. Apr 2008; 8(4):268-278. Lilja, H., Oldbring, J., Rannevik, G., and Laurell, C. B. Seminal Vesicle-Secreted Proteins and Their Reactions During Gelation and Liquefaction of Human Semen. J. Clin. Invest. Aug 1987;80(2):281-285. Cohen, P., Graves, H. C , Peehl, D. M., Kamarei, M., Giudice, L. C , and Rosenfeld, R. G. Prostate-Specific Antigen (PSA) Is an Insulin-Like Growth Factor

374

BIOMARKERS

57. 58.

59. 60. 61. 62.

63.

64.

65.

66.

67.

68. 69.

Binding Protein-3 Protease Found in Seminal Plasma. J. Clin. Endocrinol. Metab. Oct 1992;75(4): 1046-1053. Kojima, S., Inahara, M., Suzuki, H., Ichikawa, T., and Furuya, Y. Implications of Insulin-Like Growth Factor-I for Prostate Cancer Therapies. Int. J. Vrol. Feb 2009;16(2):161-167. Killian, C. S., Corral, D. A., Kawinski, E, and Constantine, R. I. Mitogenic Response of Osteoblast Cells to Prostate-Specific Antigen Suggests an Activation of Latent TGF-Beta and a Proteolytic Modulation of Cell Adhesion Receptors. Biochem. Biophys. Res. Commun. Apr 30, 1993;192(2):940-947. Heidtmann, H. H., Nettelbeck, D. M., Mingels, A., Jager, R., Welker, H. G., and Kontermann, R. E. Generation of Angiostatin-Like Fragments from Plasminogen by Prostate-Specific Antigen. Br. J. Cancer. Dec 1999;81(8):1269-1273. Thompson, I. M., Ankerst, D. P., and Chi, C, et al. Operating Characteristics of Prostate-Specific Antigen in Men with an Initial PSA Level of 3.0 ng/ml or Lower. JAMA. Jul 6, 2005;294(l):66-70. Thompson I. M., Pauler D. K., and Goodman P. J., et al. Prevalence of Prostate Cancer Among Men with a Prostate-Specific Antigen Level < or =4.0 ng per milliliter. N. Engl. J. Med. May 27, 2004;350(22):2239-2246. Catalona, W. J., Partin, A. W., and Slawin, K. M., et al. Use of the Percentage of Free Prostate-Specific Antigen to Enhance Differentiation of Prostate Cancer from Benign Prostatic Disease: A Prospective Multicenter Clinical Trial. JAMA. May 20, 1998;279(19): 1542-1547. Namm, R. K., Diamandism, E. P., and Toim, A., et al. Serum Human Glandular Kallikrein-2 Protease Levels Predict the Presence of Prostate Cancer Among Men with Elevated Prostate-Specific Antigen. J. Clin. Oncol. Mar 2000; 18(5):1036-1042. Steuber, T., Vickers, A. J., and Haese, A., et al. Risk Assessment for Biochemical Recurrence Prior to Radical Prostatectomy: Significant Enhancement Contributed by Human Glandular Kallikrein 2 (hK2) and Free Prostate Specific Antigen (PSA) in Men with Moderate PSA-Elevation in Serum. Int. J. Cancer. Mar 1, 2006;118(5):1234-1240. Steuber, T., Vickers, A. J., and Serio, A. M., et al. Comparison of Free and Total Forms of Serum Human Kallikrein 2 and Prostate-Specific Antigen for Prediction of Locally Advanced and Recurrent Prostate Cancer. Clin. Chem. Feb 2007;53(2):233-240. Stephan, C , Meyer, H. A., Cammann, H., Nakamura, T., Diamandis, E. P., and Jung, K. Improved Prostate Cancer Detection with a Human Kallikrein 11 and Percentage Free PSA-Based Artificial Neural Network. Biol. Chem. Jun 2006;387(6):801-805. Roy, R., Louis, G., and Loughlin, K. R., et al. Tumor-Specific Urinary Matrix Metalloproteinase Fingerprinting: Identification of High Molecular Weight Urinary Matrix Metalloproteinase Species. Clin. Cancer Res. Oct 15, 2008;14(20):6610-6617. Morgia, G., Falsaperla, M., and Malaponte, G., et al. Matrix Metalloproteinases as Diagnostic (MMP-13) and Prognostic (MMP-2, MMP-9) Markers of Prostate Cancer. Vrol. Res. Feb 2005;33(l):44-50. Chan, L. W., Moses, M. A., and Goley, E., et al. Urinary VEGF and MMP Levels as Predictive Markers of 1-Year Progression-Free Survival in Cancer Patients Treated with Radiation Therapy: A Longitudinal Study of Protein Kinetics Throughout Tumor Progression and Therapy. J. Clin. Oncol. Feb 1, 2004;22(3):499-506.

BIOMARKERS IN CANCER 70.

71.

72. 73.

74.

75. 76. 77. 78. 79.

80.

81.

82. 83.

375

Trudel, D., Pradet, Y., Meyer, R, Hard, P., and Tetu, B. Membrane-Type-1 Matrix Metalloproteinase, Matrix Metalloproteinase 2, and Tissue Inhibitor of Matrix Proteinase 2 in Prostate Cancer: Identification of Patients with Poor Prognosis by Immunohistochemistry. Hum. Pathol. May 2008;39(5):731-739. Miyake, H., Muramaki, M., Kurahashi, T., Takenaka, A., and Pujisawa, M. Expression of Potential Molecular Markers in Prostate Cancer: Correlation with Clinicopathological Outcomes in Patients Undergoing Radical Prostatectomy. Urol. Oncol. Oct 9, 2008. Chan, J. M., Stampfer, M. J., and Giovannucci, E., et al. Plasma Insulin-Like Growth Pactor-I and Prostate Cancer Risk: A Prospective Study. Science. Jan 23,1998;279(5350):563-566. Renehan, A. G., Zwahlen, M., Minder, C , O'Dwyer, S. T, Shalet, S. M., and Egger, M. Insulin-Like Growth Pactor (IGF)-I, IGF Binding Protein-3, and Cancer Risk: Systematic Review and Meta-Regression Analysis. Lancet. Apr 24, 2004;363(9418): 1346-1353. Shariat, S. R, Lamb, D. J., and Kattan, M. W., et al. Association of Preoperative Plasma Levels of Insulin-Like Growth Factor I and Insulin-Like Growth Factor Binding Proteins-2 and -3 with Prostate Cancer Invasion, Progression, and Metastasis. J. Clin. Oncol. Feb 1, 2002;20(3):833-841. Ivanovic, V., Melman, A., Davis-Joseph, B., Valcic, M., and Geliebter, J. Elevated Plasma Levels of TGF-beta 1 in Patients with Invasive Prostate Cancer. Nat. Med. Apr 1995;l(4):282-284. Shariat, S. R, Walz, J., and Roehrborn, C. G., et al. Early Postoperative Plasma Transforming Growth Factor-Betal Is a Strong Predictor of Biochemical Progression After Radical Prostatectomy. J. Urol. Apr 2008;179(4): 1593-1597. Folkman, J. Tumor Angiogenesis: Therapeutic Implications. N. Engl. J. Med. Nov 18, 1971;285(21):1182-1186. Weidner, N., Carroll, P. R., Flax, J., Blumenfeld, W., and Folkman, J. Tumor Angiogenesis Correlates with Metastasis in Invasive Prostate Carcinoma. Am. J. Pathol. Aug 1993;143(2):401^109. Silberman, M. A., Partin, A. W., Veltri, R. W., and Epstein, J. I. Tumor Angiogenesis Correlates with Progression After Radical Prostatectomy but Not with Pathologic Stage in Gleason Sum 5 to 7 Adenocarcinoma of the Prostate. Cancer. Feb 15, 1997;79(4):772-779. Bok, R. A., Halabi, S., and Fei, D. T., et al. Vascular Endothelial Growth Factor and Basic Fibroblast Growth Factor Urine Levels as Predictors of Outcome in Hormone-Refractory Prostate Cancer Patients: A Cancer and Leukemia Group B Study. Cancer Res. Mar 15, 2001;61(6):2533-2536. Shariat, S. R, Anwuri, V. A., Lamb, D. J., Shah, N. V., Wheeler, T. M., and Slawin, K. M. Association of Preoperative Plasma Levels of Vascular Endothelial Growth Factor and Soluble Vascular Cell Adhesion Molecule-1 with Lymph Node Status and Biochemical Progression After Radical Prostatectomy. J. Clin. Oncol. May 1, 2004,22(9): 1655-1663. Marks, L. S., Fradet, Y, and Deras, I. L., et al. PCA3 Molecular Urine Assay for Prostate Cancer in Men Undergoing Repeat Biopsy. Urology. Mar 2007; 69(3):532-535. Van Gils, M. P., Hessels, D., and Van Hooij, O., et al. The Time-Resolved Fluorescence-Based PCA3 Test on Urinary Sediments After Digital Rectal Examination; A Dutch Multicenter Validation of the Diagnostic Performance. Clin. Cancer Res. Feb 1,2007;13(3):939-943.

376

BIOMARKERS 84. 85. 86. 87. 88. 89. 90.

91. 92.

93.

94. 95. 96.

97. 98.

99.

Han, K. R., Seligson, D. B., and Liu, X., et al. Prostate Stem Cell Antigen Expression Is Associated with Gleason Score, Seminal Vesicle Invasion and Capsular Invasion in Prostate Cancer. J. Urol. Mar 2004;171(3):1117-1121. Elgamal, A. A., Holmes, E. H., and Su, S. L., et al. Prostate-Specific Membrane Antigen (PSMA): Current Benefits and Future Value. Semin. Surg. Oncol. Jan/ Feb 2000; 18(1): 10-16. Quinn, D. I., Henshall, S. M., and Sutherland, R. L. Molecular Markers of Prostate Cancer Outcome. Eur. J. Cancer. Apr 2005;41(6):858-887. Rogers, C. G., Yan, G., and Zha, S., et al. Prostate Cancer Detection on Urinalysis for Alpha Methylacyl Coenzyme A Racemase Protein. J. Urol. Oct 2004;172(4Ptl):1501-1503. Singh, D., Febbo, P. G., and Ross, K., et al. Gene Expression Correlates of Clinical Prostate Cancer Behavior. Cancer Cell. Mar 2002; 1(2):203-209. Lapointe, J., Li, C , and Higgins, J. P., et al. Gene Expression Profiling Identifies Clinically Relevant Subtypes of Prostate Cancer. Proc. Nad. Acad. Sci. USA. Jan 20, 2004;101(3):811-816. Adam, B. L., Qu, Y, and Davis, J. W., et al. Serum Protein Fingerprinting Coupled with a Pattern-Matching Algorithm Distinguishes Prostate Cancer from Benign Prostate Hyperplasia and Healthy Men. Cancer Res. Jul 1, 2002;62(13):3609-3614. Petricoin, E. R, III, Ornstein, D. K., and Paweletz, C. P., et al. Serum Proteomic Patterns for Detection of Prostate Cancer. J. Natl. Cancer Inst. Oct 16, 2002;94(20): 1576-1578. Qu, Y, Adam, B. L., and Yasui, Y, et al. Boosted Decision Tree Analysis of Surface-Enhanced Laser Desorption/Ionization Mass Spectral Serum Profiles Discriminates Prostate Cancer from Noncancer Patients. Clin. Chem. Oct 2002;48(10): 1835-1843. Ornstein, D. K., Rayford, W., and Fusaro, V. A., et al. Serum Proteomic Profiling Can Discriminate Prostate Cancer from Benign Prostates in Men with Total Prostate Specific Antigen Levels between 2.5 and 15.0 ng/ml. J. Urol. Oct 2004;172(4 Pt 1): 1302-1305. Baggerly, K. A., Morris, J. S., and Coombes, K. R. Reproducibility of SELDITOF Protein Patterns in Serum: Comparing Datasets from Different Experiments. Bioinformatics. Mar 22, 2004;20(5):777-785. Ransohoff, D. F. Rules of Evidence for Cancer Molecular-Marker Discovery and Validation. Nat. Rev. Cancer. Apr 2004;4(4):309-314. Ylikoski, A., Pettersson, K., and Nurmi, J., et al. Simultaneous Quantification of Prostate-Specific Antigen and Human Glandular Kallikrein 2 mRNA in Blood Samples from Patients with Prostate Cancer and Benign Disease. Clin. Chem. Aug 2002;48(8): 1265-1271. Gao, C. L., Rawal, S. K., and Sun, L., et al. Diagnostic Potential of ProstateSpecific Antigen Expressing Epithelial Cells in Blood of Prostate Cancer Patients. Clin. Cancer Res. Jul 2003;9(7):2545-2550. Hara, N., Kasahara, T., and Kawasaki, T, et al. Reverse Transcription-Polymerase Chain Reaction Detection of Prostate-Specific Antigen, Prostate-Specific Membrane Antigen, and Prostate Stem Cell Antigen in One Milliliter of Peripheral Blood: Value for the Staging of Prostate Cancer. Clin. Cancer Res. Jun 2002;8(6): 1794-1799. Kurek, R., Nunez, G., and Tselis, N., et al. Prognostic Value of Combined "Triple"-Reverse Transcription-PCR Analysis for Prostate-Specific Antigen,

BIOMARKERS IN CANCER

100. 101. 102.

103.

104. 105.

106. 107. 108. 109. 110. 111. 112.

113. 114.

377

Human Kallikrein 2, and Prostate-Specific Membrane Antigen mRNA in Peripheral Blood and Lymph Nodes of Prostate Cancer Patients. Clin. Cancer Res. Sep 1, 2004;10(17):5808-5814. American Cancer Society: Cancer Facts & Figures 2007. Atlanta: American Cancer Society; 2007. Ed 2007. Van Nagell, J. R., Jr., Depriest, P. D., and Reedy, M. B., et al. The Efficacy of Transvaginal Sonographic Screening in Asymptomatic Women at Risk for Ovarian Cancer. Gynecol. Oncol. Jun 2000;77(3):350-356. Paramasivam, S., Tripcony, L., and Crandon, A., et al. Prognostic Importance of Preoperative CA-125 in International Federation of Gynecology and Obstetrics Stage I Epithelial Ovarian Cancer: An Australian Multicenter Study. J. Clin. Oncol. Sep 1,2005;23(25):5938-5942. Bristow, R. E., Tomacruz, R. S., Armstrong, D. K., Trimble, E. L., and Montz, F. J. Survival Effect of Maximal Cytoreductive Surgery for Advanced Ovarian Carcinoma During the Platinum Era: A Meta-Analysis. J. Clin. Oncol. Mar 1, 2002;20(5): 1248-1259. NIH Consensus Conference. Ovarian Cancer. Screening, Treatment, and Follow-up. NIH Consensus Development Panel on Ovarian Cancer. JAMA. Feb 8, 1995;273(6):491-497. Riedinger, J. M., Bonnetain, F , and Basuyau, J. P., et al. Change in CA 125 Levels After the First Cycle of Induction Chemotherapy Is an Independent Predictor of Epithelial Ovarian Tumour Outcome. Ann. Oncol. May 2007;18(5):881885. Niloff, J. M., Knapp, R. C , Schaetzl, E., Reynolds, C , and Bast, R. C , Jr. CA 125 Antigen Levels in Obstetric and Gynecologic Patients. Obstet. Gynecol. Nov 1984;64(5):703-707. Bast, R. C , Jr., Siegal, F. P., and Runowicz, C , et al. Elevation of Serum CA 125 Prior to Diagnosis of an Epithelial Ovarian Carcinoma. Gynecol. Oncol. Sep 1985;22(1): 115-120. Sabbatini, P., Mooney, D., and Iasonos, A., et al. Early CA-125 Fluctuations in Patients with Recurrent Ovarian Cancer Receiving Chemotherapy. Int. J. Gynecol. Cancer. May/Jun 2007;17(3):589-594. Kim, J. H., Skates, S. J., and Uede, T., et al. Osteopontin As a Potential Diagnostic Biomarker for Ovarian Cancer. JAMA. Apr 3, 2002;287(13):1671-1679. Brakora, K. A., Lee, H., and Yusuf, R., et al. Utility of Osteopontin as a Biomarker in Recurrent Epithelial Ovarian Cancer. Gynecol. Oncol. May 2004;93(2): 361-365. Bao, L. H., Sakaguchi, H., Fujimoto, J., and Tamaya, T. Osteopontin in Metastatic Lesions as a Prognostic Marker in Ovarian Cancers. J. Biomed. Sci. May 2007;14(3):373-381. Ye, B., Skates, S., and Mok, S. C , et al. Proteomic-Based Discovery and Characterization of Glycosylated Eosinophil-Derived Neurotoxin and Coon-Terminal Osteopontin Fragments for Ovarian Cancer in Urine. Clin. Cancer Res. Jan 15, 2006;12(2):432^t41. Diamandis, E. P. and Yousef, G. M. Human Tissue Kallikreins: A Family of New Cancer Biomarkers. Clin. Chem. Aug 2002;48(8):1198-1205. Luo, L. Y, Katsaros, D., and Scorilas, A., et al. Prognostic Value of Human Kallikrein 10 Expression in Epithelial Ovarian Carcinoma. Clin. Cancer Res. Aug 2001;7(8):2372-2379.

378

BIOMARKERS 115. Borgono, C. A., Grass, L., and Soosaipillai, A., et al. Human Kallikrein 14: A New Potential Biomarker for Ovarian and Breast Cancer. Cancer Res. Dec 15, 2003;63(24):9032-9041. 116. Tanaka, Y., Kobayashi, H., Suzuki, M., Kanayama, N., Suzuki, M., and Terao, T. Upregulation of Bikunin in Tumor-Infiltrating Macrophages as a Factor of Favorable Prognosis in Ovarian Cancer. Gynecol. Oncol. Sep 2004;94(3):725-734. 117. Tanaka, Y., Kobayashi, H., and Suzuki, M., et al. Reduced Bikunin Gene Expression as a Factor of Poor Prognosis in Ovarian Carcinoma. Cancer. Jul 15, 2003;98(2):424-^30. 118. Matsuzaki, H., Kobayashi, H., and Yagyu, T, et al. Plasma Bikunin as a Favorable Prognostic Factor in Ovarian Cancer. J. Clin. Oncol. Mar 1, 2005; 23(7): 1463-1472. 119. Drapkin, R., Von Horsten, H. H., and Lin, Y., et al. Human Epididymis Protein 4 (HE4) Is a Secreted Glycoprotein That Is Overexpressed by Serous and Endometrioid Ovarian Carcinomas. Cancer Res. Mar 15, 2005;65(6):2162-2169. 120. Moore, R. G., Brown, A. K., and Miller, M. C , et al. The Use of Multiple Novel Tumor Biomarkers for the Detection of Ovarian Carcinoma in Patients with a Pelvic Mass. Gynecol. Oncol. Feb 2008;108(2):402^108. 121. Hellstrom, I., Raycraft, J., and Hayden-Ledbetter, M., et al. The HE4 (WFDC2) Protein Is a Biomarker for Ovarian Carcinoma. Cancer Res. Jul 1, 2003;63(13):3695-3700. 122. Nagy, J. A., Masse, E. M., and Herzberg, K. T., et al. Pathogenesis of Ascites Tumor Growth: Vascular Permeability Factor, Vascular Hyperpermeability, and Ascites Fluid Accumulation. Cancer Res. Jan 15, 1995;55(2):360-368. 123. Yamamoto, S., Konishi, I., and Mandai, M., et al. Expression of Vascular Endothelial Growth Factor (VEGF) in Epithelial Ovarian Neoplasms: Correlation with Clinicopathology and Patient Survival, and Analysis of Serum VEGF Levels. Br. J. Cancer. 1997;76(9):1221-1227. 124. Hefier, L. A., Zeillinger, R., and Grimm, C , et al. Preoperative Serum Vascular Endothelial Growth Factor as a Prognostic Parameter in Ovarian Cancer. Gynecol. Oncol. Nov2006;103(2):512-517. 125. Rudlowski, C , Pickart, A. K., and Fuhljahn, C , et al. Prognostic Significance of Vascular Endothelial Growth Factor Expression in Ovarian Cancer Patients: A Long-Term Follow-up. Int. J. Gynecol. Cancer. Jan/Feb 2006; 16 Suppl 1: 183-189. 126. Dehaven, K., Taylor, D. D., and Gercel-Taylor, C. Comparison of Serum Vascular Endothelial Growth Levels Between Patients with and without Ovarian Malignancies. Int. J. Gynecol. Cancer. Nov/Dec 2002;12(6):715-719. 127. Jemal, A., Murray, T., and Ward, E., et al. Cancer Statistics, 2005. Ca Cancer J. Clin. Jan/Feb 2005;55(1): 10-30. 128. American Gastroenterological Association Medical Position Statement: Epidemiology, Diagnosis, and Treatment of Pancreatic Ductal Adenocarcinoma. Gastroenterology. Dec 1999;117(6): 1463-1484. 129. Klein, A. P., Hruban, R. H., Brune, K. A., Petersen, G. M., and Goggins, M. Familial Pancreatic Cancer. Cancer J. Jul/Aug 2001; 7(4):266-273. 130. Van Der Heijden, M. S., Yeo, C. J., Hruban, R. H., and Kern, S. E. Fanconi Anemia Gene Mutations in Young-Onset Pancreatic Cancer. Cancer Res. May 15, 2003;63(10):2585-2588. 131. Klein, A. P., Brune, K. A., and Petersen, G. M., et al. Prospective Risk of Pancreatic Cancer in Familial Pancreatic Cancer Kindreds. Cancer Res. Apr 1, 2004;64(7):2634-2638.

BIOMARKERS IN CANCER

379

132. Bardeesy, N. and Depinho, R. A. Pancreatic Cancer Biology and Genetics. Nat. Rev. Cancer. Dec 2002;2(12):897-909. 133. Seregni, E., Ferrari, L., Bajetta, E., Martinetti, A., and Bombardieri, E. Clinical Significance of Blood Chromogranin a Measurement in Neuroendocrine Tumours. Ann. Oncol. 2001;12 Suppl 2:S69-72. 134. Baudin, E., Bidart, J. M., and Bachelot, A., et al. Impact of Chromogranin a Measurement in the Work-up of Neuroendocrine Tumors. Ann. Oncol. 2001 ;12 Suppl 2:S79-82. 135. Steinberg, W. The Clinical Utility of the CA 19-9 Tumor-Associated Antigen. Am. J. Gastroenterol. Apr 1990;85(4):350-355. 136. Dimagno, E. P., Reber, H. A., and Tempero, M. A. Aga Technical Review on the Epidemiology, Diagnosis, and Treatment of Pancreatic Ductal Adenocarcinoma. American Gastroenterological Association. Gastroenterology. Dec 1999; 117(6):1464-1484. 137. Goggins, M. Molecular Markers of Early Pancreatic Cancer. J. Clin. Oncol. Jul 10, 2005;23(20):4524-4531. 138. Yeo, T. P., Hruban, R. H., and Leach S. D., et al. Pancreatic Cancer. Curr. Probl. Cancer. JuVAug 2002;26(4): 176-275. 139. Ozkan, H., Kaya, M., and Cengiz, A. Comparison of Tumor Marker CA 242 with Ca 19-9 and Carcinoembryonic Antigen (Cea) in Pancreatic Cancer. Hepatogastroenterology. Sep/Oct 2003;50(53): 1669-1674. 140. Zhang, S., Wang, Y. M., Sun, C. D., Lu, Y, and Wu, L. Q. Clinical Value of Serum CA19-9 Levels in Evaluating Resectability of Pancreatic Carcinoma. World J. Gastroenterol. Jun 21, 2008;14(23):3750-3753. 141. Harsha, H. C , Kandasamy, K., and Ranganathan, P., et al. A Compendium of Potential Biomarkers of Pancreatic Cancer. Plos. Med. Apr 7, 2009; 6(4) :E 1000046. 142. Koopmann, J., Fedarko, N. S., and Jain, A., et al. Evaluation of Osteopontin as Biomarker for Pancreatic Adenocarcinoma. Cancer Epidemiol. Biomarkers Prev. Mar2004;13(3):487^91. 143. Koopmann, J., Rosenzweig, C. N., and Zhang, Z., et al. Serum Markers in Patients with Resectable Pancreatic Adenocarcinoma: Macrophage Inhibitory Cytokine 1 versus CA19-9. Clin. Cancer Res. Jan 15, 2006; 12(2):442-446. 144. Swartz, M. J., Batra, S. K., and Varshney, G. C , et al. MUC4 Expression Increases Progressively in Pancreatic Intraepithelial Neoplasia. Am. J. Clin. Pathol. May 2002;117(5):791-796. 145. Chang, Y T., Chang, M. C , and Wei, S. C , et al. Serum Vascular Endothelial Growth Factor/Soluble Vascular Endothelial Growth Factor Receptor 1 Ratio Is an Independent Prognostic Marker in Pancreatic Cancer. Pancreas. Aug 2008;37(2): 145-150. 146. Rosty, C , Christa, L., and Kuzdzal, S., et al. Identification of HepatocarcinomaIntestine-Pancreas/Pancreatitis-Associated Protein I As a Biomarker for Pancreatic Ductal Adenocarcinoma by Protein Biochip Technology. Cancer Res. Mar 15, 2002;62(6): 1868-1875. 147. Tian, M., Cui, Y. Z., and Song, G. H., et al. Proteomic Analysis Identifies MMP-9, DJ-1 and A1BG as Overexpressed Proteins in Pancreatic Juice from Pancreatic Ductal Adenocarcinoma Patients. Bmc Cancer. 2008;8:241. 148. Yokoyama, M., Ochi, K., and Ichimura, M., et al. Matrix Metalloproteinase-2 in Pancreatic Juice for Diagnosis of Pancreatic Cancer. Pancreas. May 2002;24(4):344-347.

380

BIOMARKERS 149. Kuhlmann, K. E, Van Till, J. W., and Boermeester, M. A., et al. Evaluation of Matrix Metalloproteinase 7 in Plasma and Pancreatic Juice as a Biomarker for Pancreatic Cancer. Cancer Epidemiol. Biomarkers Prev. May 2007;16(5):886-891. 150. Jones, L. E., Humphreys, M. J., Campbell, E, Neoptolemos, J. P., and Boyd, M. T. Comprehensive Analysis of Matrix Metalloproteinase and Tissue Inhibitor Expression in Pancreatic Cancer: Increased Expression of Matrix Metalloproteinase-7 Predicts Poor Survival. Clin. Cancer Res. Apr 15, 2004;10(8): 2832-2845. 151. Muraoka, K., Nabeshima, K., Murayama, T., Biswas, C , and Koono, M. Enhanced Expression of a Tumor-Cell-Derived Collagenase-Stimulatory Factor in Urothelial Carcinoma: Its Usefulness as a Tumor Marker for Bladder Cancers. Int. J. Cancer. Aug 19, 1993;55(l):19-26. 152. Polette, M., Gilles, C , and Marchand, V, et al. Tumor Collagenase Stimulatory Factor (TCSF) Expression and Localization in Human Lung and Breast Cancers. J. Histochem. Cytochem. May 1997;45(5):703-709. 153. Kanekura, T., Chen, X., and Kanzaki, T. Basigin (CD147) Is Expressed on Melanoma Cells and Induces Tumor Cell Invasion by Stimulating Production of Matrix Metalloproteinases by Fibroblasts. Int. J. Cancer. Jun 1, 2002;99(4): 520-528. 154. Nabeshima, K., Suzumiya, J., and Nagano, M., et al. Emmprin, A Cell Surface Inducer of Matrix Metalloproteinases (MMPs), Is Expressed in T-Cell Lymphomas. J. Pathol. Mar 2004;202(3):341-351. 155. Zhang, W., Erkan, M., and Abiatari, I., et al. Expression of Extracellular Matrix Metalloproteinase Inducer (EMMPRIN/CD147) in Pancreatic Neoplasm and Pancreatic Stellate Cells. Cancer Biol. Ther. Feb 2007;6(2):218-227. 156. Grutzmann, R., Luttges, J., and Sipos, B., et al. Adam9 Expression in Pancreatic Cancer Is Associated with Tumour Type and Is a Prognostic Factor in Ductal Adenocarcinoma. Br. J. Cancer. Mar 8, 2004;90(5): 1053-1058. 157. Logsdon, C. D., Simeone, D. M., and Binkley, C , et al. Molecular Profiling of Pancreatic Adenocarcinoma and Chronic Pancreatitis Identifies Multiple Genes Differentially Regulated in Pancreatic Cancer. Cancer Res. May 15, 2003; 63(10):2649-2657. 158. Simeone, D. M., Ji, B., and Banerjee, M., et al. CEACAMl, A Novel Serum Biomarker for Pancreatic Cancer. Pancreas. May 2007;34(4):436-443.

CHAPTER

BIOMARKERS OF HIV Lewis Kaufman and Michael]. Ross

INTRODUCTION More than 25 years since the first report of AIDS in 1981, nearly 21 million people have died from the infection, and another 33.2 million people worldwide are currently infected with HIV. Of these, 22 million live in Sub-Saharan Africa where the prevalence is over 5%. HIV/AIDS is clearly among the most urgent public health issues facing the world today. Identifying novel biomarkers that can be used as surrogates for clinical endpoints is not only valuable in evaluating an individual patient's prognosis, but is also critical for success in clinical trials, in particular for vaccine trials where shortening duration and reducing costs are essential to increase feasibility of these studies. The heterogeneity of the natural history of HIV infection between individuals spurred efforts by investigators to identify novel biomarkers. This includes heterogeneity in susceptibility to HIV-1 infection, viral transmission, and factors related to disease progression including rate of decline of CD4 cell count, level of viremia, viral set point, response to antiretroviral therapy, and development of opportunistic infections. Researchers have focused much attention on particular groups of patients that have non-classical responses to HIV infection. Some patients (known as "rapid progressors") progress to AIDS quickly after primary infection, typically within three years. Another group, (known as "long-term non-progressors") remain asymptomatic for many years without signs or symptoms of immune dysfunction and maintain normal CD4 counts and low plasma viremia in the absence of antiretroviral therapy (ART) in most cases. Long-term non-progressors make up approximately 5% of all HIV positive individuals. A third group (known as "highly exposed, but seronegative individuals") are people who remain HIV seronegative despite repeated documented exposures 381

382

BIOMARKERS

to HIV. This group includes sex workers, homosexual or heterosexual people that have unprotected sex with HIV positive partners, and injection drug users. Studies of cohorts of these naturally resistant individuals have identified host genetic factors that influence individual susceptibility and progression of disease.13 These data have important implications for the development of genetic screening tests that could be used to identify people at high risk of infection or disease progression and allow us to identify pathogenic mechanisms that could be exploited by investigators designing HIV vaccines. Most genetic variants that have been demonstrated to be associated with protection against HIV infection and/or progression to AIDS are polymorphisms in chemokines/ chemokine receptors, human leukocyte antigens, or other genetic factors, and are reviewed in this chapter. Besides host genetic factors, many other clinical factors have been identified as important predictive markers of HIV disease progression. It has been established that immunological events that occur early after infection are critical in determining and predicting rapidity of clinical progression. These include severity of acute seroconversion syndrome, CD4+ cell depletion, peak viral load, viral load set point, and others that will be reviewed in this chapter. Furthermore, these early immune and viral events often set the stage for a prolonged state of generalized immune activation. There is increasing evidence that the level of generalized immune activation in a particular patient is a critical factor that predicts disease progression.4'5 It is clear that chronic immune hyperresponsiveness predicts more rapid clinical progression and may be the most important difference between rapid progressors and long-term nonprogressors. Several novel biomarkers of this generalized immune activation have been found to be highly predictive of progression to AIDS. An individual person's genetic polymorphisms likely influence the degree and specificity of the immune response and impact many of these clinical biomarkers. This provides a logical link between the two parts of this chapter: host genetics and clinical markers.

N O V E L BIOMARKERS Host Genetic Determinants of Susceptibility to HIV Infection Chemokines/Chemokine Receptors Chemokines are a group comprised of over 40 low molecular weight proteins that are involved in leukocyte trafficking and modulation of inflammation and immune responses. The receptors for some of these chemokines also serve as co-receptors used by HIV to gain entry into the host cell (see Figure 15.1). The primary viral receptor, CD4, strongly binds the HIV envelope protein gpl20. For HIV to gain access to the cell, however, it requires subsequent interaction with a chemokine receptor, most commonly either CCR.5 or CXCR4, which induces a conformational change of the gp41 viral envelope protein, leading to fusion of viral and cell membranes.6'7 HIV viral isolates are able to use one or

BIOMARKERS OF HIV

383

FIGURE 15.1 Schematic of how HIV-1 enters cells, suggesting candidate genes that may effect clinical progression to AIDS. HIV entry requires an interaction with its primary receptor CD4 and a coreceptor (CCR5 for R5 virus or CXCR4 for X4 virus).The presence of CCR5 ligands Mip-1 a. Mip-10, or RANTES may limit access of R5 tropic virus to its coreceptor whereas the presence of SDF-1 may block X4 tropic virus. Polymorphisms in several of these candidate genes have strong associations with susceptibility and progression of HIV disease. Reprinted by permission from Macmillan Publishers Ltd: Nature Genetics 36, 566 (2004).

both of these coreceptors: viruses that are tropic for CCR5 are termed R5 viruses, viruses that are tropic for CXCR4 are called X4 viruses, and dual tropic viruses are referred to as R5X4 viruses. Most primary HIV isolates derived from recently infected patients are R5 (macrophage tropic) isolates, whereas X4 strains (lymphocyte tropic) become predominant later in the course of HIV infection.8 This is because the most common target cell for infection in the setting of primary infection is the macrophage, whereas T lymphocytes are the primary target cell late in the course of AIDS, leading to CD4 T cell depletion.8 Numerous other chemokine receptors are used by certain strains of HIV to gain entry into host cells including CCR2, CCR3, CCR8, CCR9, ChemR23, CX3CR1, STRL-33, and GPR1, among others.9"12 Despite the wide availability of potential coreceptors, CCR5 and CXCR4 are the coreceptors used by the great majority of viral isolates. The chemokine system is redundant, allowing a single receptor to interact with multiple ligands and for a particular ligand to bind to more than one receptor. The ligands for CCR5 are predominantly members of the (3-chemokine family including RANTES, MlP-la, and MIP-10. CXCR4 has only one known ligand: stromal derived factor 1 (SDF-1). Furthermore, high levels of CCR5 ligands can block entry of R5 strains,13 whereas high levels of SDF-1 can block entry of X4 tropic virus.14 Various monoclonal antibodies and blocking peptides directed at CCR5 or CXCR4 can block R5 and X4

384

BIOMARKERS

viral entry respectively.14-18 The mechanism likely involves both competitive inhibition with the HIV virus for receptor binding and decreased availability of the receptor either by internalization or dimerization after ligand binding.19 In 2007, maraviroc, an orally available small molecule CCR5 antagonist, was approved by the FDA for treatment of HIV infected patients with a history of previous ART exposure. The availability of chemokine receptors on the cell surface and levels of chemokine receptor ligands are therefore important factors that affect the ability of HIV to enter host cells. Researchers have identified numerous genetic polymorphisms that affect the abundance and function of these proteins, thereby altering host susceptibility to HIV infection. C C R 5 VARIANTS

The identification of CCR5 as the major coreceptor for macrophage-tropic HIV isolates stimulated a search for mutations or polymorphisms in the gene that may confer resistance to HIV infection. In addition to macrophages and monocytes, CCR5 is expressed on the surface of dendritic cells, microglial cells, and activated T cells 8.20 Individuals homozygous for CCR5-A32, a 32 base pair deletion in the CCR5 gene, are highly resistant to HIV infection.21 The deletion results in a frame shift mutation that causes translation of a truncated and non-functional isoform. Homozygous individuals have no expression of CCR5 and are resistant to HIV infection although they can rarely be infected by X4 strains.22 Heterozygotes can be infected but have slower progression toward AIDS in most studies and are more likely to be long-term non-progressors.21-2324 The allelic frequency of this mutation is 10-16% in Caucasians, particularly in those from northern Europe, with approximately 1% homozygotes; however, the allele is absent in Asian and sub-Saharan African populations25 despite the existence of numerous cohorts of highly exposed, seronegative individuals in these populations. A number of polymorphisms have also been identified in the CCR5 promoter region that can influence expression of CCR5 and have been linked to different rates of clinical progression to AIDS. Individuals with the CCR5P1 haplotype progress more rapidly to AIDS,26 even though expression levels of CCR5 on PBMCs was not significantly different comparing healthy controls who carried or did not carry the haplotype.27 Similarly, patients carrying a 59029-A/A polymorphism, which is located in the first intron of CCR5, have more rapid disease progression. In this case, individuals with the 59029-A/G allele show lower expression of CCR5 on PBMCs.28 Homozygosity for the 59356-T allele has been associated with increased perinatal transmission and is more common in African-Americans.28 Another allele, 59353C is more common in patients who have progressed to AIDS than in long-term nonprogressors, suggesting a role for this polymorphism in disease progression.27 The combination of CCR5A32 and CCR5-2459G causes low expression of CCR5 and is associated with resistance to HIV infection.29 Many other polymorphisms in the CCR5 gene have been identified, some of which are predict-

BIOMARKERS OF HIV

385

ed to affect its protein structure; however, since these variants are restricted to small populations and their prevalence is low,30 their role in altering HIV susceptibility is not well established. CCR2-64I VARIANT

The chemokine receptor CCR2b is a minor HIV coreceptor. A polymorphism in which a valine within the first transmembrane domain of the receptor is replaced by an isoleucine (CCR2-64I) has been associated with delayed progression to AIDS.24 Although this allele does not have a major effect upon susceptibility to infection, most, but not all cohorts studies have found that heterozygous or homozygous individuals have slower disease progression.31-33 Importantly, in contrast to CCR5A32, which is found exclusively in Caucasians, the CCR2-64I polymorphism occurs in all races with an allelic frequency of 10-25%. Since HIV rarely uses CCR2 as a coreceptor, the mechanism for the protective effect of this polymorphism is unclear and its influence upon disease progression remains somewhat controversial. SDFI-3'A VARIANT

The striking relationship between CCR5 mutations and HIV resistance led investigators to seek similar polymorphisms in the other main HIV coreceptor, CXCR4. Because both CXCR4 and its only known ligand SDF-1 are both essential proteins during embryogenesis,34 no mutations affecting the primary structure of either proteins have been identified. A mutation in the untranslated region of SDF-1, however, has been found to be protective against HIV.35 The polymorphism (SDF1-3'A) involves a G to A transition at position 801 relative to the start codon. This polymorphism is common in all geographic areas of the world, in particular in Asia. Some investigators have reported that SDF 1-3'A homozygous individuals have dramatically decreased rates of AIDS progression.35,36 Other studies, however, found that the allele either had no effect or was associated with accelerated disease progression.37-39 O T H E R CHEMOKINE POLYMORPHISMS

In addition to the above genes, important polymorphisms have been described for other chemokine ligands including RANTES, MlP-la, MIP-ip, and CCL3L1. 3 ' 4M2 High RANTES levels are found in long-term non-progressors40'43 suggesting a role for RANTES in limiting HIV disease. Several polymorphisms in the promoter region of RANTES (403G/A and 28C/G) are associated with increased RANTES levels and slower declines in CD4 counts. 4244 CCL3L1, the most potent agonist of CCR5, was increased in people with a segmental duplication of the gene and also caused a decrease in CCR5 expression on CD4+ T cells. Individuals with a fewer than average number of copies of CCL3L1 were more susceptible to HIV infection and tended to progress more quickly once infected. People with greater than average copy numbers for their ethnicity tended to be relatively resistant to HIV infection.41

386

BIOMARKERS

Human Leukocyte Antigens The major histocompatibility complex (MHC) represents a highly polymorphic set of genes that encode cell surface proteins that are critical for antigen presentation to CD4 and CD8 T cells. HLA class I (A, B, C) encodes molecules that bind to antigens derived from the processing of intracellular pathogens and then present them to CD8+ cytotoxic T lymphocytes. HLA class II genes (DR, DQ, and DP) encode proteins that bind antigen-derived peptides and thereby present these antigens to CD4+ T cells. The tremendous variability of these genes can strongly influence an individual's immune response to pathogens. Indeed, several studies have found associations between certain HLA alleles and progression of HIV disease. It has been demonstrated that HLA-B alleles, in particular, exert the greatest selection pressure on the virus and can impact many important clinical parameters including viral set point, absolute CD4 count, and rate of disease progression to AIDS.45 HLA

HETEROZYGOSITY PROTECTS A G A I N S T PROGRESSION TO A I D S

Heterozygosity at HLA loci is associated with slower HIV disease progression. Investigators have attributed slower progression in heterozygous individuals to their ability to present a more diverse assortment of antigens to T cells, thereby improving their ability to mount protective immune responses to pathogens.46 Carrington, et al. found that homozygosity at one or more HLA class I loci was closely associated with rapid progression to AIDS in African Americans and Caucasians.46 In another study, African heterosexual women and European homosexual men who were homozygous at HLA class I loci progressed more rapidly to AIDS.47 PROTECTIVE H L A

ALLELES

Two HLA alleles have been demonstrated to be consistently associated with slower progression of HIV to AIDS in multiple cohort studies; HLA-B *27 and HLA-B*57.48 HLA-B*27 occurs across most ethnicities at a frequency of 4-6% and is well known for its strong association with spondyloarthropathies. It has also been consistently reported to be associated with delayed HIV disease progression.49 In many studies of long-term non-progressors, the polymorphism is over-represented; at a frequency of 20% in the largest study,50 and even higher in other smaller cohorts. The protective effects imparted by the HLA*B27 allele is thought to be due to the stability of the interaction between the HLA-B pocket and a specific arginine residue of the HIV Gag protein (position 2). In fact, mutations of Gag at this location destabilize the interaction and are associated with more rapid progression to AIDS.51,52 Another HLA class IB allele, B*57, has also been strongly associated with low levels of viremia and slower progression to AIDS in multiple independent cohort studies. The protective effects of B*57 may be due to the enhanced ability of this particular HLA to present multiple HIV peptides, particularly those derived from Gag.5354 In some studies, up to 50% of long-term

BIOMARKERS OF HIV

387

non-progressors carry this allele.50 The protective effects of HLA-B*57 seem to occur early in the course of infection, unlike HLA-B*27 that seems to have its effect later in the course of the disease.55 Although certain HLA subtypes are increased in long-term nonprogressors and highly exposed seronegative individuals, it is clear that many other host genetic factors also contribute to these phenotypes. H L A ALLELES ASSOCIATED W I T H R A P I D PROGRESSION TO A I D S

Many studies have confirmed a strong correlation between HLA-B*35 with rapid progression to AIDS.48 There are many subtypes of HLA-B*35 and of these, B*35Px has been most strongly linked to rapid progression to AIDS. The Px variant differs from other variants by a single amino acid change at position 116. This greatly reduces the capacity of this allele to bind and present HIV peptides, preventing an effective anti-HIV immune response. Patients heterozygous for the B*35Px allele have more rapid AIDS progression than patients who are homozygous for any other HLA-B allele.48 O t h e r H o s t G e n e t i c Factors A s s o c i a t e d w i t h HIV-Related O u t c o m e s

Polymorphisms in several host genes that affect post-entry events in the HIV life cycle have also been identified. The most significant associations with HIV disease outcomes have been reported for two endogenous antiviral molecules; APOBEC3G and TRIM5a. APOBEC3G has broad antiviral activity and greatly reduces infectivity in v/f-deleted strains of HIV. However, its antiviral effect is largely mitigated by the HIV Vif protein, which promotes polyubiquitylation APOBEC3G, thereby targeting it for degradation by the 26S proteasome.56 Though investigators have not identified protective polymorphisms in the APOBEC3G gene, several alleles have been reported to be associated with more rapid progression to AIDS. These include the 186R allele,57 which is present in 37% of African Americans and only 3% of Caucasians, and the C40693T allele,58 which is also associated with an increased risk of acquiring HIV infection. TRIM5ads another endogenous antiviral factor that has some activity impairing HIV replication in vitro. Several polymorphisms in the second exon of the gene have been identified, including 136Q59 and 43Y.60 Patients who have at least one copy of the 43Y polymorphism have accelerated progression to AIDS compared to those without the polymorphism and patients with the 136Q allele may be associated with improved control of viral replication in selected patients.61 H o s t Factors A s s o c i a t e d w i t h N o n O p p o r t u n i s t i c HIV-Related Diseases HIV-ASSOCIATED NEPHROPATHY

An important complication of HIV infection is HIV-associated nephropathy (HIVAN), the most common cause of end-stage renal disease in HIV sero-

388

BIOMARKERS

positive patients. This disease occurs almost exclusively in African-Americans and Hispanics, whereas Caucasians appear almost completely resistant to disease development. Two major simultaneous recent studies62,63 identified non-coding polymorphisms in the gene myosin, heavy chain 9, non-muscle (MYH9) as being highly associated with increased risk for development of HIVAN, primary focal segmental glomerulosclerosis, and non-diabetic renal failure in African Americans. Kopp, et al. reported that the E-1 haplotype of MYH9 occurred in 60% of African Americans compared to only 4% of Caucasians and was strongly linked to the development of renal disease. MYH9 is highly expressed in glomerular visceral epithelial cells (podocytes), but its function in these cells remains largely unknown. The mechanism by which non-coding polymorphisms in MYH9 may confer increased susceptibility to disease is also unclear. Interestingly, coding mutations in MYH9 in humans leads to dominantly inherited syndromes which include proteinuric kidney disease, macrothrombocytopenia, and deafness.64-66 Furthermore, MYH9 knockout mice demonstrate embryonic lethality; importantly, MYH9 haploinsufficient heterozygous animals have morphologically normal appearing kidneys although these animals have not yet been tested for susceptibility to the development of nephropathy after injurious stimuli, including HIV.67 These data support a causal role for MYH9 polymorphisms in the strong predilection of HIVAN for persons of African ancestry. HIV-ASSOCIATED DEMENTIA

HIV-1 has been shown to induce central nervous system dysfunction in at least 30% of affected individuals.68 Cognitive disorders in these patients range from full dementia (HIV-associated dementia or HAD) to more mild impairments in executive function (HIV-associated neurocognitive disorders or HAND).69 With the advent of ART, the incidence of HAD has decreased, but as HIV positive patients live longer, the incidence of HAND has dramatically increased. Much recent work has focused on the early recognition of at risk individuals through the identification of genetic susceptibility alleles and via other clinical biomarkers. One highly studied gene is the ApoE plasma protein, which exists as three alleles known as e2, e3, and e4. Although primarily known for its role in lipoprotein metabolism, the e4 allele has been linked to numerous negative clinical outcomes including several CNS diseases, especially Alzheimer's disease.70 Corder, et al. found that in a study of 44 HIV positive patients, there was an increased incidence of mild dementia in HIV positive individuals carrying the e4 allele.71 More recently, Burt, et al. examined a larger wellcharacterized cohort of HIV positive individuals and reported that the e4/ e4 genotype was associated with accelerated HIV disease progression, especially increased risk of death compared with those of the e3/e3 genotype.72 However, an association between the e4/e4 genotype and HAD was not detected, although the authors suggest that an association with milder forms of neurologic dysfunction may still exist.

BIOMARKERS OF HIV

389

In addition, a number CSF biomarkers have been reported that may be of value in identifying patients with CNS pathology or impaired cognitive function (reviewed in 73). These include markers of glial activation such as MCP-1 and other chemokines, neopterin, quinolinic acid, FAS, and various reactive oxygen species, and the neuronal markers neurofilament light chain and tau protein.

Clinical Markers Progression to AIDS is determined by numerous factors, making the development of individual clinically predictive biomarkers challenging. However, several markers have proven valuable in monitoring the progression of disease. C D 4 + T-Cell D e p l e t i o n

A low CD4+ T cell count predicts short-term disease progression. CD4+ T cell count was the earliest recognized test that prognosticated the short-term risks of developing HIV-related complications including opportunistic infections. The absolute CD4 lymphocyte count is used to define AIDS according to the CDC case definition and is a primary criterion used to drive clinical decisions regarding HIV treatments. CD4+ T cell count can also be used to predict long-term risks of progression. In patients who recently seroconverted, low baseline CD4+ counts predict more rapid progression to AIDS, usually within a few years.74'75 However, higher CD4+ counts following seroconversion do not necessarily predict a better long-term outcome. In addition, CD4+T cells depletion in mucosal tissues occurs early after infection, particularly in the lamina propria of the gut. Testing for the loss of CD4+ T cells in the intestines a few weeks after infection accurately predicts the rate of disease progression.76,77 However, because this test requires endoscopic sampling of the colon early after seroconversion, its use as a biomarker is impractical for most patients. Plasma V i r a l Load

In the early 1990s, advances in basic molecular biological techniques allowed for quantitative measurement of plasma HIV RNA levels. Physicians quickly adopted this assay as a routine way to follow the effects of antiretroviral therapies. A greater than three-fold change in the HIV RNA viral load in any particular patient using the same assay would exceed any assay or diurnal variations and can be considered biologically significant. Viral loads can be used in varying ways to help predict disease progression. High levels of peak viral load measured in the first six months after seroconversion is most often associated with a poor prognosis.75 78 However, there is tremendous variability in peak RNA levels, partly because the peak usually occurs within one month after seroconversion, and because RNA measurements are often taken at various times after infection, usually greater than one month after conversion.79 In addition, rapid clearance of the virus after reaching the

390

BIOMARKERS

peak levels is associated with slower progression to AIDS, including longer viral suppression and a lower viral set point.80 The viral load at set point (or viral set point) is usually reached about four to six months after seroconversion. It is a measure of the balance between the virulence of the viral strain and the ability of the host immune system to contain the virus. Numerous cohort studies have demonstrated that the viral set point is an important predictor of long-term progression.75'81~84 The lower the viral set point, the better the overall prognosis. Furthermore, the viral set points of long-term non-progressors is many orders of magnitude lower on average than those who develop progressive disease toward AIDS.81,84~86 Lowering the viral load to less than 400 RNA copies/ml will minimize the lifetime risk of developing AIDS. This goal is often accomplished by the addition of antiretroviral therapies. A clear correlation also exists between rates of vertical transmission and viral load of HIV-infected mothers at the time of delivery.87'88 The correlation with viral load is also extremely strong for sexual transmission of the virus. This has been shown in multiple cohorts from around the world.89-93 The relative risk for sexual transmission of HIV-1 increases by 1.37 to 1.81-fold for each loglO increase in HIV viral load. Combination of Viral Load, CD4+ Count, and Proviral DNA Levels Although viral load can predict the rates of decline of CD4+ counts, an individual patient's clinical course can more accurately be predicted by combined measurements of viral load and CD4+ count.8494 It is clear that there are additional factors beyond viral load that can lead to decline in CD4 counts.95 Also, the ability of plasma viral load to predict progression to AIDS is decreased in patients who already have diminished CD4+ counts. Levels of HIV-1 proviral DNA in peripheral blood mononuclear cells (PBMC's) have also been proposed to be an early marker for predicting disease progression. The test detects all forms of intracellular proviral DNA and reflects the number of circulating HIV-infected cells, even those that are latently infected. A number of studies have concluded that HIV DNA levels directly correlate with risk of disease progression, independent of viral load and CD4+ count.74,75, % A combination of the three markers including CD4+ T-cell count, HIV viral load, and cellular HIV DNA level may provide the best estimate of risk for disease progression.75 Generalized Immune Activation Although most prognostic and therapeutic strategies are focused on HIV pathogenesis and reduction of viral load, HIV infection is also associated with a generalized state of immune activation and the markers of immune activation are predictive of disease development and progression.5 The deleterious role of immune activation is not a new concept, but in fact was recognized as an important pathogenic factor early on in the epidemic.97 HIV employs numerous strategies to evade immune surveillance including antigen variation and masking,

BIOMARKERS OF HIV

391

downregulation of host HLA expression, latency, and cell-cell transmission. It is therefore somewhat paradoxical that patients in whom HIV stimulates the highest degree of immune activation have the worst prognosis. In fact, in many patients, HIV induces activation of HIV-specific T cells and also T cells with unrelated specificities, B cells, natural killer cells, and monocytes.98 Several markers of generalized immune activation predict the progression toward AIDS in asymptomatic HIV positive individuals including 32-microglobulin, serum and urine neopterin, soluble CD8, soluble IL-2 receptor, interferon-a, and serum levels of IgA.99-103 This activation can then lead to anergy and apoptosis of uninfected cells that can eventually lead to loss of antigen-specific responses and ultimately to decline in CD4+ lymphocytes.104 Several recent studies have identified novel inflammatory biomarkers that may be particularly useful in predicting disease outcome. Levels of the acute phase reactant C-reactive protein (CRP) were highly predictive of HIV disease progression independent of CD4 count or HIV-1 viral load.105 Moreover, levels of CRP increased significantly over time regardless of progression to AIDS. In the Strategies for Management of Anti-Retroviral Therapy Trial (SMART), all cause mortality was more than 2.5-fold greater in those with relatively preserved CD4 counts that received episodic ART as compared to the current practice of continuous ART.106 In a subsequent analysis of the SMART study, levels of IL-6 and D-dimer were strongly linked to mortality in these patients.107 In fact, participants with D-dimer levels in the highest quartile had a remarkable 41-fold increased risk of death compared to individuals in the lowest quartile. Interestingly, other markers of systemic coagulation activation including profhrombin 1 and 2 were not associated with increased mortality. This suggests that the elevation seen in D-dimer levels is not simply a non-specific marker of increased thrombin activation. Interestingly, because patients in this trial did not have advanced HIV disease, deaths were due to non-HIV-related complications, suggesting the link between IL-6 and D-dimer levels to cardiovascular death is stronger in HIV patients than in the general population. In some studies, survival in AIDS patients has been more strongly linked to immune activation than to viral loads or CD4 counts.108 In both chimpanzees and human long-term nonprogressors, lack of progression correlates with absence of a generalized immune response even though viral loads are variable and in some cases very high.109,110 Some researchers argue that vaccinations that induce non-neutralizing antibodies may be a viable strategy to treat HIV infection.1" They postulate that though antibodies induced by such a vaccine would not eliminate HIV, they may decrease the state of overactive immune responses and inflammation.

CONCLUSION Associations between genetic polymorphisms and HIV disease progression may become clinically valuable predictors of HIV susceptibility and progression to AIDS. However, longer prospective trials are still needed to validate these markers and to correlate them with clinical makers such as CD4+count and viral load. HIV pathogenesis involves intricate interactions between host

392 TABLE 15.1

BIOMARKERS Genetic variants of chemokine genes that modulate HIV infection and progression.

Gene Variant

Association

Mechanism/(reference)

CCR5 32

Resistance to infection, delayed progression

Abolish or decrease CCR5 expression [21-25]

CCR5PI

Accelerate progression

Increase CCR5 expression?, promoter polymorphism [26-27]

CCR5 59029A/A

Accelerate progression

Increase CCR5 expression?, located in first intron [28]

CCR5 59356TAT

Increased perinatal transmission

Uncertain [29]

CCR5 59353C

Accelerate progression

Increase CCR5 expression? [27]

CCR5 32/CCR52459G

Resistance to infection

Reduced CCR5 expression [29]

CCR2-64I

Delayed progression

Uncertain, minor HIV coreceptor, [24, 31-33]

SDFI-3A

Delayed progression?

Increased SDF-I production?, [34-39]

RANTES 403G/A and 28C/G

Delayed progression

Increased RANTES production [42,44]

CCL3LI high copy number

Delayed progression

Increased CCL3LI expression [41]

TABLE 15.2

Other host genetic variants that modulate HIV infection and progression.

Association

Mechanism/(reference)

HLA class 1 homozygosity

Accelerated progression

Able to present narrow range of antigenic HIV-I peptides t o T cells [46^7]

HLAB*27

Delayed progression

Escape mutants develop later [49-52]

HLAB*57

Delayed progression

Enhanced ability to present HIV peptides [53-54]

HLAB*35

Accelerated progression

Reduced binding of HIV peptides [48]

APOBEC3G I86R

Accelerated progression

Unknown [57]

APOBEC3G C40693T

Increased risk of infection

TRIM5u l36Qand43Y

Protection against HIV infection

Decreased function of APOBEC3G? [58] Affect antiviral activity? [59-61]

Gene Variant HLAAIIeles:

Post HIV Entry:

Non-Opportunistic HIV-related Diseases: MYH9, non-coding polymorphisms

Increased susceptibility to HIVAN, alleles more common in Blacks

Effect podocyte cytoskeleton? [62-63]

ApoE e3/e3

Increased development of HAND?, accelerated HIV progression

Uncertain [70-72]

BIOMARKERS OF HIV

393

and viral factors. These factors change over the years following primary infection and are manifested differently in each person depending on host immunologic and genomic factors. It is therefore highly likely that no one biomarker will be able to accurately predict disease in all people, but that a combination of biomarkers will be required. These biomarkers, once adequately validated will have important implications in predicting prognosis, monitoring treatments, and designing preventive and/or therapeutic vaccines.

SUMMARY POINTS 1. 2. 3. 4. 5. 6.

Susceptibility to HIV infection and rates of HIV disease progression vary markedly between individuals. These complex traits are modulated by numerous host genetic and environmental factors. Polymorphisms in chemokine receptors and ligands, in particular those that HIV uses as co-receptors for viral entry, strongly influence HIV susceptibility and clinical outcomes. Genetic variants such as HLA polymorphisms that influence the ability of an individual's immune system to respond effectively to the HIV virus can significantly alter the rapidity of clinical progression. Polymorphisms in host genes can significantly influence the risk of nonopportunistic complications of HIV infection, including HIV-associated nephropathy (MYH9), and HIV-associated dementia (ApoE). Using a combination of clinical biomarkers including CD4+ T cell count, HIV viral load, and proviral DNA levels may improve clinicians' ability to predict short-term and long-term clinical outcomes. Several novel biomarkers, including markers of immune activation and coagulation may hold promise as accurate predictors of clinical outcomes including mortality in HIV positive patients.

REFERENCES 1. 2. 3. 4. 5. 6. 7.

Telenti, A. and Goldstein, D. B. Genomics Meets HIV-1. Nat. Rev. Microbiol. Nov2006;4(ll):865-873. Winkler, C., An, P., and O'Brien, S. J. Patterns of Ethnic Diversity Among the Genes That influence AIDS. Hum. Mol. Genet. Apr 1, 2004; 13 Spec No 1:R9-19. O'Brien, S. J. and Nelson, G. W. Human Genes That Limit AIDS. Nat. Genet. Jun 2004;36(6):565-574. Grossman, Z., Meier-Schellersheim, M., Sousa, A. E., Victorino, R. M, and Paul, W. E. CD4+ T-Cell Depletion in HIV Infection: Are We Closer to Understanding the Cause? Nat. Med. Apr 2002;8(4):319-323. Hazenberg, M. D., Otto, S. A., and Van Benthem, B. H., et al. Persistent Immune Activation in HIV-1 Infection Is Associated with Progression to AIDS. Aids. Sep 5, 2003;17(13):1881-1888. Rizzuto, C. D., Wyatt, R., and Hernandez-Ramos, N., et al. A Conserved HIV Gpl20 Glycoprotein Structure Involved in Chemokine Receptor Binding. Science. Jun 19, 1998;280(5371):1949-1953. Wu, L., Gerard, N. P., and Wyatt, R., et al. CD4-Induced Interaction of Primary HIV-1 Gpl20 Glycoproteins with the Chemokine Receptor CCR-5. Nature. Nov 14, 1996;384(6605): 179-183.

394

BIOMARKERS 8.

9.

10. 11. 12. 13.

14. 15. 16.

17. 18. 19.

20.

21. 22.

Moore, J. P., Kitchen, S. G., Pugach, P., and Zack, J. A. The CCR5 and CXCR4 Coreceptors—Central to Understanding the Transmission and Pathogenesis of Human Immunodeficiency Virus Type 1 Infection. AIDS Res. Hum. Retroviruses. Jan 2004;20(1):111-126. Choe, H., Farzan, M., and Sun, Y., et al. The Beta-Chemokine Receptors CCR3 and CCR5 Facilitate Infection by Primary HIV-1 Isolates. Cell. Jun 28, 1996;85(7):1135-1148. Doranz, B. J., Rucker, J., and Yi, Y, et al. A Dual-Tropic Primary HIV-1 Isolate That Uses Fusin and the Beta-Chemokine Receptors CKR-5, CKR-3, and CKR2b as Fusion Cofactors. Cell. Jun 28, 1996;85(7):1149-1158. Bjorndal, A., Deng, H., and Jansson, M., et al. Coreceptor Usage of Primary Human Immunodeficiency Virus Type 1 Isolates Varies According to Biological Phenotype. J. Virol. Oct 1997;71(10):7478-7487. Littman, D. R. Chemokine Receptors: Keys to AIDS Pathogenesis? Cell. May 29, 1998;93(5):677-680. Cocchi, E, Devico, A. L., Garzino-Demo, A., Arya, S. K., Gallo, R. C , and Lusso, P. Identification of RANTES, MIP-1 Alpha, and MIP-1 Beta as the Major HIV-Suppressive Factors Produced by CD8+ T Cells. Science. Dec 15, 1995;270(5243):1811-1815. Doranz, B. J., Grovit-Ferbas, K., and Sharron, M. P., et al. A Small-Molecule Inhibitor Directed Against the Chemokine Receptor CXCR4 Prevents Its Use as an HIV-1 Coreceptor. J. Exp. Med. Oct 20, 1997;186(8):1395-1400. Wu, L., Paxton, W. A., and Kassam, N., et al. CCR5 Levels and Expression Pattern Correlate with Infectability by Macrophage-Tropic HIV-1, In Vitro. J. Exp. Med. May 5, 1997;185(9): 1681-1691. Osbourn, J. K., Earnshaw, J. C , Johnson, K. S., Parmentier, M., Timmermans, V, and McCafferty, J. Directed Selection of MIP-1 Alpha Neutralizing CCR5 Antibodies from a Phage Display Human Antibody Library. Nat. Biotechnol. Aug 1998;16(8):778-781. Murakami, T., Nakajima, T, and Koyanagi, Y, et al. A Small Molecule CXCR4 Inhibitor That Blocks T Cell Line-Tropic HIV-1 Infection. J. Exp. Med. Oct 20, 1997; 186(8): 1389-1393. Schols, D., Struyf, S., Van Damme, J., Este, J. A., Henson, G., and De Clercq, E. Inhibition of T-Tropic HIV Strains by Selective Antagonization of the Chemokine Receptor CXCR4. J. Exp. Med. Oct 20, 1997;186(8): 1383-1388. Amara, A., Gall, S. L., and Schwartz, O., et al. HIV Coreceptor Downregulation as Antiviral Principle: SDF-1 alpha-Dependent Internalization of the Chemokine Receptor CXCR4 Contributes to Inhibition of HIV Replication. J. Exp. Med. Jul 7,1997;186(1):139-146. Van Der Meer, P., Ulrich, A. M., Gonzalez-Scarano, E, and Lavi, E. Immunohistochemical Analysis of CCR2, CCR3, CCR5, and CXCR4 in the Human Brain: Potential Mechanisms for HIV Dementia. Exp. Mol. Pathol. Dec 2000;69(3):192-201. Liu, R., Paxton, W. A., and Choe, S., et al. Homozygous Defect in HIV-1 Coreceptor Accounts for Resistance of Some Multiply-Exposed Individuals to HIV-1 Infection. Cell. Aug 9, 1996;86(3):367-377. Biti, R., Ffrench, R., Young, J., Bennetts, B., Stewart, G., and Liang, T. HIV-1 Infection in an Individual Homozygous for the CCR5 Deletion Allele. Nat. Med. Mar 1997;3(3):252-253.

BIOMARKERS OF HIV 23.

24.

25.

26. 27. 28.

29.

30. 31. 32.

33.

34. 35.

395

Dean, M , Camngton, M., and Winkler, C , et al. Genetic Restriction of HIV-1 Infection and Progression to AIDS by a Deletion Allele of the CKR5 Structural Gene. Hemophilia Growth and Development Study, Multicenter AIDS Cohort Study, Multicenter Hemophilia Cohort Study, San Francisco City Cohort, ALIVE Study. Science. Sep 27, 1996;273(5283):1856-1862. Smith, M. W., Dean, M , and Carrington, M., et al. Contrasting Genetic Influence of CCR2 and CCR5 Variants on HIV-1 Infection and Disease Progression. Hemophilia Growth and Development Study (HGDS), Multicenter AIDS Cohort Study (MACS), Multicenter Hemophilia Cohort Study (MHCS), San Francisco City Cohort (SFCC), ALIVE Study. Science. Aug 15, 1997;277(5328):959-965. Zimmerman, P. A., Buckler-White, A., and Alkhatib, G., et al. Inherited Resistance to HIV-1 Conferred by an Inactivating Mutation in CC Chemokine Receptor 5: Studies in Populations with Contrasting Clinical Phenotypes, Defined Racial Background, and Quantified Risk. Mol. Med. Jan 1997;3(l):23-36. Gonzalez, E., Bamshad, M., and Sato, N., et al. Race-Specific HIV-1 DiseaseModifying Effects Associated with CCR5 Haplotypes. Proc. Natl. Acad. Sci. USA. Oct 12, 1999;96(21):12004-12009. Martin, M. P., Dean, M., and Smith, M. W, et al. Genetic Acceleration of AIDS Progression by a Promoter Variant of CCR5. Science. Dec 4, 1998; 282(5395): 1907-1911. McDermott, D. H., Zimmerman, P. A., Guignard, K, Kleeberger, C. A., Leitman, S. R, and Murphy, P. M. CCR5 Promoter Polymorphism and HIV-1 Disease Progression. Multicenter AIDS Cohort Study (MACS). Lancet. Sep 12, 1998;352(9131):866-870. Hladik, P., Liu, H., and Speelmon, E., et al. Combined Effect of CCR5-Delta32 Heterozygosity and the CCR5 Promoter Polymorphism -2459 A/G on CCR5 Expression and Resistance to Human Immunodeficiency Virus Type 1 Transmission. J. Virol. Sep2005;79(18):11677-11684. Carrington, M., Kissner, T., Gerrard, B., Ivanov, S., O'Brien, S. J., and Dean, M. Novel Alleles of the Chemokine-Receptor Gene CCR5. Am. J. Hum. Genet. Dec 1997;61(6):1261-1267. Kostrikis, L. G., Huang, Y., and Moore, J. P., et al. A Chemokine Receptor CCR2 Allele Delays HIV-1 Disease Progression and Is Associated with a CCR5 Promoter Mutation. Nat. Med. Mar 1998;4(3):350-353. Anzala, A. O., Ball, T. B., Rostron, T., O'Brien, S. J., Plummer, R A., and Rowland-Jones, S. L. CCR2-64I Allele and Genotype Association with Delayed AIDS Progression in African Women. University of Nairobi Collaboration for HIV Research. Lancet. May 30, 1998;351(9116):1632-1633. Ioannidis, J. P., Rosenberg, P. S., and Goedert, J. J., et al. Effects of CCR5Delta32, CCR2-64I, and SDF-1 3'A Alleles on HIV-1 Disease Progression: An International Meta-Analysis of Individual-Patient Data. Ann. Intern Med. Nov 6,2001;135(9):782-795. Ma, Q., Jones, D., and Borghesani, P. R., et al. Impaired B-Lymphopoiesis, Myelopoiesis, and Derailed Cerebellar Neuron Migration in CXCR4- and SDF-1 Deficient Mice. Proc. Natl. Acad. Sci. USA. Aug 4, 1998;95(16):9448-9453. Winkler, C , Modi, W., and Smith, M. W, et al. Genetic Restriction of AIDS Pathogenesis by an SDF-1 Chemokine Gene Variant. ALIVE Study, Hemophilia Growth and Development Study (HGDS), Multicenter AIDS Cohort Study (MACS), Multicenter Hemophilia Cohort Study (MHCS), San Francisco City Cohort (SFCC). Science. Jan 16, 1998;279(5349):389-393.

396

BIOMARKERS 36. 37.

38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52.

Hendel, H., Henon, N., and Lebuanec, H., et al. Distinctive Effects of CCR5, CCR2, and SDF1 Genetic Polymorphisms in AIDS Progression. J. Acquir. Immune Defic. Syndr. Hum. Retrovirol. Dec 1, 1998; 19(4):381-386. Meyer, L., Magierowska, M., and Hubert, J. B., et al. CC-Chemokine Receptor Variants, SDF-1 Polymorphism, and Disease Progression in 720 HIV-Infected Patients. SEROCO Cohort. Amsterdam Cohort Studies on AIDS. Aids. Apr 1, 1999;13(5):624-626. Mummidi, S., Ahuja, S. S., and Gonzalez, E., et al. Genealogy of the CCR5 Locus and Chemokine System Gene Variants Associated with Altered Rates of HIV-1 Disease Progression. Nat. Med. Jul 1998;4(7):786-793. Van Rij, R. P., Broersen, S., Goudsmit, J., Coutinho, R. A., and Schuitemaker, H. The Role of a Stromal Cell-Derived Factor-1 Chemokine Gene Variant in the Clinical Course of HIV-1 Infection. Aids. Jun 18, 1998;12(9):F85-90. Zagury, D., Lachgar, A., and Chams, V, et al. C-C Chemokines, Pivotal in Protection Against HIV Type 1 Infection. Proc. Natl. Acad. Sci. USA. Mar 31, 1998;95(7):3857-3861. Gonzalez, E., Kulkarni, H., and Bolivar, H., et al. The Influence of CCL3L1 Gene-Containing Segmental Duplications on HIV-1/AIDS Susceptibility. Science. Mar 4, 2005;307(5714):1434-1440. Liu, H., Chao, D., and Nakayama, E. E., et al. Polymorphism in RANTES Chemokine Promoter Affects HIV-1 Disease Progression. Proc. Natl. Acad. Sci. USA. Apr 13, 1999;96(8):4581-4585. Gallo, R. C , Garzino-Demo, A., and Devico, A. L. HIV Infection and Pathogenesis: What About Chemokines? J. Clin. Immunol. Sep 1999;19(5):293-299. Wichukchinda, N., Nakayama, E. E., and Rojanawiwat, A., et al. Protective Effects of IL4-589T and RANTES-28G on HIV-1 Disease Progression in Infected Thai Females. Aids. Jan 9, 2006;20(2):189-196. Kiepiela, P., Leslie, A. J., and Honeybome, I., et al. Dominant Influence of HLA-B in Mediating the Potential Co-Evolution of HIV and HLA. Nature. Dec 9, 2004;432(7018):769-775. Carrington, M., Nelson, G. W., and Martin, M. P., et al. HLA and HIV-1: Heterozygote Advantage and B*35-Cw*04 Disadvantage. Science. Mar 12, 1999;283(5408): 1748-1752. Tang, J., Costello, C , and Keet, I. P., et al. HLA Class I Homozygosity Accelerates Disease Progression in Human Immunodeficiency Virus Type 1 Infection. AIDS Res. Hum. Retroviruses. Mar 1, 1999; 15(4):317-324. Carrington, M. and O'Brien, S. J. The Influence of HLA Genotype on AIDS. Annu. Rev. Med. 2003;54:535-551. Kaslow, R. A., Dorak, T., and Tang, J. J. Influence of Host Genetic Variation on Susceptibility to HIV Type 1 Infection. J. Infect. Dis. Feb 1, 2005; 191 Suppl 1: S68-77. Pereyra, F., Addo, M. M., and Kaufmann, D. E., et al. Genetic and Immunologic Heterogeneity Among Persons Who Control HIV Infection in the Absence of Therapy. /. Infect. Dis. Feb 15, 2008;197(4):563-571. Goulder, P. J., Phillips, R. E., and Colbert, R. A., et al. Late Escape from an Immunodominant Cytotoxic T-Lymphocyte Response Associated with Progression to AIDS. Nat. Med. Feb 1997;3(2):212-217. Kelleher, A. D., Long, C , and Holmes, E. C , et al. Clustered Mutations in HIV1 Gag Are Consistently Required for Escape from HLA-B27-Restricted Cytotoxic T Lymphocyte Responses. J. Exp. Med. Feb 5, 2001;193(3):375-386.

BIOMARKERS OF HIV 53.

54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65.

66. 67. 68.

397

Klein, M. R., Van Der Burg, S. H., and Hovenkamp, E., et al. Characterization of HLA-B57-Restricted Human Immunodeficiency Virus Type 1 Gag- and RTSpecific Cytotoxic T Lymphocyte Responses. J. Gen. Virol. Sep 1998;79 (Pt 9): 2191-2201. Gillespie, G. M., Kaul, R., and Dong, T., et al. Cross-Reactive Cytotoxic T Lymphocytes Against A HIV-1 P24 Epitope in Slow Progressors with B*57. Aids. May 3, 2002;16(7):961-972. Gao, X., Bashirova, A., and Iversen, A. K., et al. AIDS Restriction HLA Allotypes Target Distinct Intervals of HIV-1 Pathogenesis. Nat. Med. Dec 2005; 11(12):1290-1292. Chiu, Y. L. and Greene, W. C. The APOBEC3 Cytidine Deaminases: An Innate Defensive Network Opposing Exogenous Retroviruses and Endogenous Retroelements. Annu. Rev. Immunol. 2008;26:317-353. An, P., Bleiber, G., and Duggal, P., et al. APOBEC3G Genetic Variants and Their Influence on the Progression to AIDS. J. Virol. Oct 2004;78(20): 11070-11076. Valcke, H. S., Bernard, N. R, Bruneau, J., Alary, M., Tsoukas, C. M., and Roger, M. APOBEC3G Genetic Variants and Their Association with Risk of HIV Infection in Highly Exposed Caucasians. Aids. Oct 3, 2006;20(15): 1984-1986. Speelmon, E. C , Livingston-Rosanoff, D., and Li, S. S., et al. Genetic Association of the Antiviral Restriction Factor TRIM5alpha with Human Immunodeficiency Virus Type 1 Infection. J. Virol. Mar 2006;80(5):2463-2471. Javanbakht, H., An, P., and Gold, B., et al. Effects of Human TRIM5alpha Polymorphisms on Antiretroviral Function and Susceptibility to Human Immunodeficiency Virus Infection. Virology. Oct 10, 2006;354(l):15-27. Van Manen, D., Rits, M. A., Beugeling, C , Van Dort, K., Schuitemaker, H., and Kootstra, N. A. The Effect of Trim5 Polymorphisms on the Clinical Course of HIV-1 Infection. Plos. Pathog. Feb 8, 2008;4(2):E18. Kopp, J. B., Smith, M. W., and Nelson, G. W., et al. MYH9 Is a Major-Effect Risk Gene for Focal Segmental Glomerulosclerosis. Nat. Genet. Oct 2008;40(10): 1175-1184. Kao, W. H., Klag, M. J., and Meoni, L. A., et al. MYH9 Is Associated with Nondiabetic End-Stage Renal Disease in African Americans. Nat. Genet. Oct 2008; 40(10):1185-1192. Kelley, M. J., Jawien, W., Ortel, T. L., and Korczak, J. F. Mutation of MYH9, Encoding Non-Muscle Myosin Heavy Chain A, in May-Hegglin Anomaly. Nat. Genet. Sep 2000;26(1): 106-108. Heath, K. E., Campos-Barros, A., and Toren, A., et al. Non-muscle Myosin Heavy Chain IIA Mutations Define a Spectrum of Autosomal Dominant Macrothrombocytopenias: May-Hegglin Anomaly and Fechtner, Sebastian, Epstein, and Alport-Like Syndromes. Am. J. Hum. Genet. Nov 2001 ;69(5): 1033-1045. Seri, M., Cusano, R., and Gangarossa, S., et al. Mutations in MYH9 Result in the May-Hegglin Anomaly, and Fechtner and Sebastian Syndromes. The MayHeggllin/Fechtner Syndrome Consortium. Nat Genet. Sep 2000;26(1): 103-105. Mhatre, A. N., Li, Y, Bhatia, N., Wang, K. H., Atkin, G., and Lalwani, A. K. Generation and Characterization of Mice with Myh9 Deficiency. NeuromolecularMed. 2007;9(3):205-215. Sacktor, N., McDermott, M. P., and Marder, K., et al. HIV-Associated Cognitive Impairment Before and After the Advent of Combination Therapy. J. Neurovirol. Apr 2002;8(2): 136-142.

398

BIOMARKERS 69. 70. 71. 72.

73. 74.

75.

76.

77. 78.

79. 80. 81. 82. 83.

Antinori, A., Arendt, G., and Becker, J. T., et al. Updated Research Nosology for HIV-Associated Neurocognitive Disorders. Neurology. Oct 30, 2007;69(18): 1789-1799. Mahley, R. W., Weisgraber, K. H., and Huang, Y. Apolipoprotein E4: A Causative Factor and Therapeutic Target in Neuropathology, Including Alzheimer's Disease. Proc. Natl. Acad. Sci. USA. Apr 11, 2006;103(15):5644-5651. Corder, E. H., Robertson, K., and Lannfelt, L., et al. HIV-Infected Subjects with the E4 Allele for APOE Have Excess Dementia and Peripheral Neuropathy. Nat. Med. Oct 1998;4(10):1182-1184. Burt, T. D., Agan, B. K., and Marconi, V. C , et al. Apolipoprotein (Apo) E4 Enhances HIV-1 Cell Entry In Vitro, and the APOE Epsilon4/Epsilon4 Genotype Accelerates HIV Disease Progression. Proc. Natl. Acad. Sci. USA. Jun 24, 2008; 105(25):8718-8723. Price, R. W., Epstein, L. G., and Becker, J. T., et al. Biomarkers of HIV-1 CNS Infection and Injury. Neurology. Oct 30, 2007;69(18):1781-1788. Goujard, C , Bonarek, M., and Meyer, L., et al. CD4 Cell Count and HIV DNA Level Are Independent Predictors of Disease Progression After Primary HIV Type 1 Infection in Untreated Patients. Clin. Infect. Dis. Mar 1, 2006;42(5): 709-715. Rouzioux, C , Hubert, J. B., and Burgard, M., et al. Early Levels of HIV-1 DNA in Peripheral Blood Mononuclear Cells Are Predictive of Disease Progression Independently of HIV-1 RNA Levels and CD4+ T Cell Counts. J. Infect. Dis. Jul l,2005;192(l):46-55. Mehandru, S., Poles, M. A., and Tenner-Racz, K., et al. Primary HIV-1 Infection Is Associated with Preferential Depletion of CD4+ T Lymphocytes from Effector Sites in the Gastrointestinal Tract. J. Exp. Med. Sep 20, 2004;200(6): 761-770. Brenchley, J. M., Schacker, T. W., and Ruff, L. E., et al. CD4+ T Cell Depletion During All Stages of HIV Disease Occurs Predominantly in the Gastrointestinal Tract. J. Exp. Med. Sep 20, 2004;200(6):749-759. Katzenstein, T. L., Pedersen, C , Nielsen, C , Lundgren, J. D., Jakobsen, P. H., and Gerstoft, J. Longitudinal Serum HIV RNA Quantification: Correlation to Viral Phenotype at Seroconversion and Clinical Outcome. Aids. Feb 1996;10(2):167-173. Little, S. J., McLean, A. R., Spina, C. A., Richman, D. D., and Havlir, D. V. Viral Dynamics of Acute HIV-1 Infection. J. Exp. Med. Sep 20, 1999;190(6): 841-850. Blattner, W. A., Oursler, K. A., and Cleghorn, F, et al. Rapid Clearance of Virus After Acute HIV-1 Infection: Correlates of Risk of AIDS. J. Infect. Dis. May 15, 2004;189(10):1793-1801. Henrard, D. R., Phillips, J. F, and Muenz, L. R., et al. Natural History of HIV-1 Cell-Free Viremia.7ama.Aug 16, 1995;274(7):554-558. Sterling, T. R., Vlahov, D., Astemborski, J., Hoover, D. R., Margolick, J. B., and Quinn, T. C. Initial Plasma HIV-1 RNA Levels and Progression to AIDS in Women and Men. N. Engl. J. Med. Mar 8, 2001;344(10):720-725. Lavreys, L., Baeten, J. M., and Chohan, V, et al. Higher Set Point Plasma Viral Load and More-Severe Acute HIV Type 1 (HIV-1) Illness Predict Mortality Among High-Risk HIV-1-Infected African Women. Clin. Infect. Dis. May 1, 2006;42(9): 1333-1339.

BIOMARKERS OF HIV 84. 85. 86. 87.

88. 89. 90. 91.

92. 93. 94.

95. 96.

97. 98. 99.

399

Mellors, J. W., Munoz, A., and Giorgi, J. V., et al. Plasma Viral Load and CD4+ Lymphocytes as Prognostic Markers of HIV-1 Infection. Ann. Intern Med. Jun 15, 1997;126(12):946-954. Goudsmit, J., Bogaards, J. A., and Jurriaans, S., et al. Naturally HIV-1 Seroconverters with Lowest Viral Load Have Best Prognosis, but in Time Lose Control of Viraemia. Aids. Mar 29, 2002; 16(5):791-793. O'Brien, T. R., Blattner, W. A., and Waters, D., et al. Serum HIV-1 RNA Levels and Time to Development of AIDS in the Multicenter Hemophilia Cohort Study. Jama. Jul 10, 1996;276(2): 105-110. Mofenson, L. M., Lambert, J. S., and Stiehm, E. R., et al. Risk Factors for Perinatal Transmission of Human Immunodeficiency Virus Type 1 in Women Treated with Zidovudine. Pediatric AIDS Clinical Trials Group Study 185 Team. N. Engl. J. Med. Aug 5, 1999;341(6):385-393. Ioannidis, J. P., Tatsioni, A., and Abrams, E. J., et al. Maternal Viral Load and Rate of Disease Progression Among Vertically HIV-1-Infected Children: An International Meta-Analysis. Aids. Jan 2, 2004;18(1):99-108. Fiore, J. R., Zhang, Y. J., and Bjorndal, A., et al. Biological Correlates of HIV-1 Heterosexual Transmission. Aids. Jul 15, 1997;11(9): 1089-1094. Quinn, T. C , Wawer, M. J., and Sewankambo, N., et al. Viral Load and Heterosexual Transmission of Human Immunodeficiency Virus Type 1. Rakai Project Study Group. N. Engl. J. Med. Mar 30, 2000; 342(13):921-929. Pedraza, M. A., Del Romero, J., and Roldan, F., et al. Heterosexual Transmission of HIV-1 Is Associated with High Plasma Viral Load Levels and a Positive Viral Isolation in the Infected Partner. J. Acquir. Immune Defic. Syndr. Jun 1, 1999;21(2):120-125. Fideli, U. S., Allen, S. A., and Musonda, R., et al. Virologic and Immunologic Determinants of Heterosexual Transmission of Human Immunodeficiency Virus Type 1 in Africa. AIDS Res. Hum. Retroviruses. Jul 1, 2001;17(10):901-910. Tovanabutra, S., Robison, V, and Wongtrakul, J., et al. Male Viral Load and Heterosexual Transmission of HIV-1 Subtype E in Northern Thailand. J. Acquir. Immune Defic. Syndr. Mar 1, 2002;29(3):275-283. Lyles, R. H., Munoz, A., and Yamashita, T. E., et al. Natural History of Human Immunodeficiency Virus Type 1 Viremia After Seroconversion and Proximal to AIDS in a Large Cohort of Homosexual Men. Multicenter AIDS Cohort Study. J. Infect. Dis. Mar 2000;181(3):872-880. Rodriguez, B., Sethi, A. K., and Cheruvu, V. K., et al. Predictive Value of Plasma HIV RNA Level on Rate of CD4 T-Cell Decline in Untreated HIV Infection. Jama. Sep 27, 2006;296(12): 1498-1506. Kostrikis, L. G., Touloumi, G., and Karanicolas, R., et al. Quantitation of Human Immunodeficiency Virus Type 1 DNA Forms with the Second Template Switch in Peripheral Blood Cells Predicts Disease Progression Independently of Plasma RNA Load. /. Virol. Oct 2002;76(20): 10099-10108. Ascher, M. S. and Sheppard, H. W. AIDS as Immune System Activation: A Model for Pathogenesis. Clin. Exp. Immunol. Aug 1988;73(2):165-167. Lawn, S. D., Butera, S. T., and Folks, T. M. Contribution of Immune Activation to the Pathogenesis and Transmission of Human Immunodeficiency Virus Type 1 Infection. Clin. Microbiol. Rev. Oct 2001;14(4):753-777. Fahey, J. L., Taylor, J. M., and Detels, R., et al. The Prognostic Value of Cellular and Serologic Markers in Infection with Human Immunodeficiency Virus Type 1. N. Engl. J. Med. Jan 18, 1990; 322(3): 166-172.

400

BIOMARKERS 100. Lifson, A. R., Hessol, N. A., and Buchbinder, S. P., et al. Serum Beta 2-Microglobulin and Prediction of Progression to AIDS in HIV Infection. Lancet. Jun 13, 1992;339(8807): 1436-1440. 101. Liu, Z., Cumberland, W. G., Hultin, L. E., Prince, H. E., Detels, R., and Giorgi, J. V. Elevated CD38 Antigen Expression on CD8+ T Cells Is a Stronger Marker for the Risk of Chronic HIV Disease Progression to AIDS and Death in the Multicenter AIDS Cohort Study Than CD4+ Cell Count, Soluble Immune Activation Markers, or Combinations of HLA-DR and CD38 Expression. J. Acquir. Immune Defic. Syndr. Hum. Retrovirol. Oct 1, 1997;16(2):83-92. 102. Osmond, D. H., Shiboski, S., Bacchetti, P., Winger, E. E., and Moss, A. R. Immune Activation Markers and AIDS Prognosis. Aids. May 1991;5(5):505-511. 103. Ullum, H., Lepri, A. C , and Katzenstein, T. L., et al. Prognostic Value of Single Measurements of Beta-2-Microglobulin, Immunoglobulin A in HIV Disease After Controlling for CD4 Lymphocyte Counts and Plasma HIV RNA Levels. Scand. J. Infect. Dis. 2000;32(4):371-376. 104. Musey, L. K., Krieger, J. N., Hughes, J. P., Schacker, T. W., Corey, L., and McElrath, M. J. Early and Persistent Human Immunodeficiency Virus Type 1 (HlV-l)-Specific T Helper Dysfunction in Blood and Lymph Nodes Following Acute HIV-1 Infection. J. Infect. Dis. Aug 1999;180(2):278-284. 105. Lau, B., Sharrett, A. R., and Kingsley, L. A., et al. C-Reactive Protein Is a Marker for Human Immunodeficiency Virus Disease Progression. Arch. Intern. Med. Jan9,2006;166(l):64-70. 106. El-Sadr, W. M., Lundgren, J. D., and Neaton, J. D., et al. CD4+ CountGuided Interruption of Antiretroviral Treatment. N. Engl. J. Med. Nov 30, 2006;355(22):2283-2296. 107. Kuller, L. H., Tracy, R., and Belloso, W., et al. Inflammatory and Coagulation Biomarkers and Mortality in Patients with HIV Infection. Plos. Med. Oct 21, 2008;5(10):E203. 108. Giorgi, J. V, Hultin, L. E., and McKeating, J. A., et al. Shorter Survival in Advanced Human Immunodeficiency Virus Type 1 Infection Is More Closely Associated with T Lymphocyte Activation Than with Plasma Virus Burden or Virus Chemokine Coreceptor Usage. J. Infect. Dis. Apr 1999;179(4):859-870. 109. Choudhary, S. K., Vrisekoop, N., and Jansen, C. A., et al. Low Immune Activation Despite High Levels of Pathogenic Human Immunodeficiency Virus Type 1 Results in Long-Term Asymptomatic Disease. / Virol. Aug 2007;81(16): 8838-8842. 110. Gougeon, M. L., Lecoeur, H., and Boudet, F., et al. Lack of Chronic Immune Activation in HIV-Infected Chimpanzees Correlates with the Resistance of T Cells to Fas/Apo-1 (CD95)-Induced Apoptosis and Preservation of a T Helper 1 Phenotype.7. Immunol. Mar 15, 1997;158(6):2964-2976. 111. Cadogan, M. and Dalgleish, A. G. HIV Immunopathogenesis and Strategies for Intervention. Lancet Infect. Dis. Nov 2008;8(ll):675-684.

CHAPTER

BIOMARKERS OF IN VITRO DRUG-INDUCED MITOCHONDRIAL DYSFUNCTION James A. Dykens and Yvonne Will

INTRODUCTION It is increasingly evident that a wide variety of ethical pharmaceuticals have "off target" effects on mitochondrial function which, depending on potency and exposure, may be deleterious. Such dysfunction may arise not only from chronic exposure to drugs that impair mitochondrial replication or mitochondrial protein expression, such as certain antivirals (NRTIs) and aminoglycoside and oxazolidinone antibiotics, but also more acutely where the drug directly inhibits or uncouples oxidative phosphorylation (OXPHOS). In many instances clinical disposition of a drug is in direct accord with the magnitude of its mitochondrial effects, with the most potent effectors withdrawn from the market or dropped preclinically because of various toxicities arising from mitochondrial impairment per se. Although this book is focused on biomarkers of drug toxicity, pathognomonic biomarkers of acute mitochondrial impairment have not yet been developed. There are some new technologies that address erosion of mitochondrial capacity via failure to replicate or express genes, but not for acute OXPHOS inhibition. Lactic acidosis has been a traditional biomarker of mitochondrial impairment, and is indeed a symptom not only of many mitochondrial diseases, but also of drug-induced mitochondrial dysfunction, e.g., the biguanides. However, serum lactate fluctuates as a function of activity, among other causes, and therefore is not diagnostic of mitochondrial impairment. Nevertheless, lactic acidosis in a patient taking drugs known to have 401

402

BIOMARKERS

mitochondrial effects should prompt further exploration, especially in the absence of physical exertion. With these issues in mind, and given the emerging nature of this area, this chapter includes a preamble on mitochondrial physiology, plus discussion of new assays and cell culture techniques that may better reveal potential drug-induced mitochondrial dysfunction during drug development. We will also consider the symptoms found in patients having mitochondrial diseases, arising from both nuclear and mitochondrial mutations, in order to identify possible surrogate markers of mitochondrial insufficiency. This will also include consideration of current medical practice for diagnosis of mitochondrial syndromes and noninvasive assays that are currently being evaluated in the clinic. Compared to the pathologies in patients with systemic mitochondrial impairment, it is our sense that the heterogeneous distribution of plasma membrane transporters that bioaccumulate many drugs dictates organ selectivity and is another variable in the etiology of idiosyncratic toxicity. Nevertheless, current clinical practice in treating mitochondrial disease might illuminate some potential biomarkers.

MAGNITUDE O F T H E PROBLEM Hepatic toxicity is the primary cause of market withdrawals and denials of new drug applications, and this, plus striated muscle toxicity (both skeletal and cardiac) and nephrotoxicity are the major causes for the 386 Black Box warnings the FDA has issued since 1960.1,2The liver is the predominant target for drug injury because most drugs are orally dosed, and so absorbed from the intestine into the hepatic portal vein, which delivers them directly to the liver at much higher concentrations than those seen by other organs after systemic dilution. In addition, the liver is the site of active metabolism of a number of drug classes and therefore presents the potential for many drug-drug interactions (DDIs). Regardless of the anatomy, over 2.2 million serious adverse drug reactions (ADRs) occur in hospitalized patients annually, which translates into more than 105,000 deaths, making ADRs the fourth, fifth, or sixth leading cause of death in the U.S., depending on the year, ahead of AIDS, pneumonia, and accidents, including automobile deaths.3-5 Although these statistics are >10 years old, and the ranking varies from year to year, they highlight the fact that additional mechanistic insight into potential drug toxicities and DDIs is needed. One previously-overlooked potential source of drug toxicity may be offtarget mitochondrial impairment.^23 Mitochondrial toxicity is typically not assessed in drug-development programs, but even when it was evaluated, it was not detected because in most cases cell viability is not dependent on mitochondrial function (see below). Moreover, several new technologies and changes in cell culture protocols now reveal potential mitochondrial toxicity. Such toxicity may be due to inhibition of respiration (the electron transport system that generates mitochondrial membrane potential), and/or coupling to ATP production, (oxidative phosphorylation; OXPHOS). The detailed physiology of mitochondrial electron transport and coupling of membrane potential

IN VITRO DRUG-INDUCED MITOCHONDRIAL DYSFUNCTION

403

to ATP production exceed the scope of this review, and interested readers are directed to the comprehensive books by Nicholls and Ferguson24 and Scheffler.25 Nevertheless, the basic process warrants review, if only to underscore the physiological interdependence between the respiratory complexes and the integrity of the inner mitochondrial membrane.

MITOCHONDRIAL PHYSIOLOGY Biological energy is captured by removing electrons with high potential energy, and then allowing these electrons to cascade sequentially down a redox gradient via protein complexes in the inner mitochondrial membrane. Electrons enter the electron transport system (ETS) at complexes I and II, and are passed to the lipophilic carrier ubiquinone, which shuttles the electrons to complex III (Figure 16.1). From here, they pass to cytochrome c and thence to complex IV which accumulates four electrons to tetravalently reduce molecular oxygen to water. Together, ETS and oxygen consumption to yield water constitute respiration. At complexes I, III, and IV, the magnitude of the redox reaction is sufficient to translocate protons from the matrix across the inner membrane, which generates a membrane potential of approx. 220mV (inside negative). This potential energy is harnessed by complex V (aka, ATP synthase) where protons flow down their gradient coupled to the phosphorylation of ADP to ATP. Thus, OXPHOS depends not only on the integrity of coupled redox reaction centers of the ETS and function of complex V, but also on the impermeability of the inner membrane to protons. Should the inner membrane become porous to protons, the membrane potential dissipates, and ATP is not generated despite robust respiration; ETS is said to be "uncoupled" from phosphorylation.24'25 As an aside, generations of students learned this process as the electron transport chain, which connotes a one-to-one linkage between the various components. However, the molar ratios between the various components are not equal; for every mole of complex I, there are three moles of complex III, seven of complex IV, nine of cytochrome c, and 50 of ubiquinone.26 Only when the lateral diffusion coefficients are included in the analysis does the equation approach unity for rates of electron transport, which underscores the importance of integrity of the inner membrane for proper mitochondrial function. In this light, this process is more akin to an electron transport system (ETS) rather than a serial chain. Moreover, recent evidence indicates that the respiratory complexes aggregate into "super complexes" that facilitate the requisite redox reactions.27 Regardless of ETS architecture, mitochondria produce >90% of the energy required for viability of most aerobically poised cells. In so doing, they also produce the vast majority of the reactive oxygen- and nitrogen-centered free radicals that, because of high and indiscriminate reactivity, can damage and even kill cells. Moreover, mitochondria integrate a host of physiological pathways, so that when mitochondrial function declines, cell viability is imperilled. More catastrophically, when mitochondria die, the cell dies. When such failure

404

BIOMARKERS

is acute and profound, the cell dies via necrosis. When mitochondrial failure is less profound or less widespread, several pro-apoptotic proteins are released from the organelles, and the cell dies via apoptosis. These are the extremes of a continuum, but the key point is that mitochondrial viability is a proximate determinant of cell viability. Moreover, mitochondria are complex organelles, both anatomically and physiologically. The name comes from fusion of the Greek words mitos, "a thread," plus chondros, "a cereal grain," which accu-

FIGURE 16.1 Mitochondrial function can fail in a variety of ways. Many drugs directly inhibit one or more of the four respiratory complexes of the Electron Transport System, or complex V, a.k.a. ATP Synthase (upper left panel).6"23,30 Several sites are capable of univalently reducing molecular oxygen to superoxide, notably complex I, ubiquinone, and complex III. 2 " 7 Many antivirals and antibacterials also impede mtDNA synthesis or gene expression occurring in the matrix, resulting in erosion of mitochondrial capacity. Xenobiotics that undermine integrity of the inner membrane, or that serve as proton shuttles within it, uncouple the ETS from phosphorylation by ATP Synthase, and some inhibit mitochondrial pathways that fuel ETS, such as b-oxidation, Kreb's Cycle, or the transmembrane adenine nucleotide translocator (ANT).24-27 Most of the above deleterious effects precipitate the irreversible formation of the "permeability transition pore" (PT) that collapses membrane potential and permits release of cytochrome c and other pro-apoptotic factors into the cytosol.24'27 (See color insert for a full color version of this figure.)

IN VITRO DRUG-INDUCED MITOCHONDRIAL DYSFUNCTION

405

rately describes the constant fission and fusion they undergo to form "bean" or circular shaped individual organelles, which join to form thread-like organelles. These individual organelles can fuse to form a reticulum, which is typically simultaneously budding off organelles (Figure 16.2). Mitochondria contain their own DNA (with a genetic code different from that in the nucleus), all the components required for DNA replication and protein expression, for organellar replication, for the ability to fuse and then undergo fission, plus the capacity for OXPHOS. Mitochondria can fail via a variety of mechanisms, including repression of DNA replication and expression, loss of inner membrane impermeability, and inhibition of ETS and supporting metabolism (Figure 16.1). Mitochondrial ultrastructural anatomy underscores this notion that failure can arise from anatomical perturbations, such as loss of inner membrane stability, especially given the invaginations of it into cristae that serve to increase surface area (Figure 16.3). From a drug-safety perspective, mitochondria are a "targetrich" environment, which is also reflected by the large number of known inhibitors of respiration and OXPHOS, including rotenone, antimycin, oligomycin, and cyanide. Indeed, there are 60 classes of compounds that inhibit complex I alone.28 In this context, it should not be surprising to learn that many ethical pharmaceuticals also may have, to varying degrees, important effects on mitochondrial function.6-23' 26_3°

FIGURE 16.2 Cos cell stained with a potentiometric dye that enters the mitochondria as a function of mitochondrial membrane potential (tetramethylrhodamine) and nuclear stain (Hoechst). Note the individual mitochondria shaped like beans and threads, and the fused reticulum.21"27 Image by Sandra Wiley. (See color insert for a full color version of this figure.)

406

BIOMARKERS

FIGURE 16.3 Electron tomogram of an individual mitochondrion.The extensive invagination of the inner membrane to form cristae is evident in the left panel. The irregularities on the surface are the respiratory complexes of the Electron Transport System and complex V. The right image highlights four cristae to indicate anatomical diversity and show how they form multiple junctions with the inner boundry membrane.80

DRUG-INDUCED MITOCHONDRIAL DYSFUNCTION (DIMD) HAS BEEN OVERLOOKED Potential mitochondrial impairment has not been recognized as an important source of drug toxicity, in large measure, as a consequence of circumstances in cell culture methods.30 Heretofore, to avoid having to change media daily, most cells have traditionally been cultured in high glucose media containing 25 mM glucose, which is five times physiological. Eighty years ago, two principles of metabolic physiology were reported independently by Crabtree and Warburg.31,32 The Crabtree Effect describes repression of respiration in the presence of elevated glucose, while the Warburg Effect notes that aerobic glycolysis yields lactate despite competent mitochondria. As a result of these two effects, transformed cells in contemporary culture generate almost all of their ATP from glycolysis, not from OXPHOS. Such cells typically have low rates of respiration, and are correspondingly resistant to mitochondrial toxins. For example, cells grown in high glucose are not killed by rotenone, antimycin, oligomycin, or cyanide.33 Potential in vivo toxicity of most drugs in devel-

IN VITRO DRUG-INDUCED MITOCHONDRIAL DYSFUNCTION

407

opment has typically been evaluated in cells under these culture conditions where mitochondrial liabilities are least likely to be detected, especially using viability assays; that the nascent drug is not toxic to these cells has often lead erroneously to the conclusion that it lacks mitochondrial toxicity. However, retrospective surveys of drugs having organ toxicities continue to indicate that many xenobiotics undermine function of isolated mitochondria, and under some conditions in intact cells as well.6"23,2930 To render cells susceptible to mitochondrial toxicity, glucose in the media can be replaced by galactose. The net ATP yield from glycolysis using glucose as substrate is 2 ATP, whereas it is closer to 0 when galactose is substrate (an investment of 2 ATP equivalents is required for galactose to enter glycolysis).33 Using galactose, cells must use OXPHOS to survive, and respiration accelerates. Now that the cells are dependent on OXPHOS, they become susceptible to mitochondrial impairment For example, cells grown in galactose are completely killed by a concentration of oligomycin at which more than 80% of cells grown in glucose remain viable.33 In our laboratories, potential drug toxicity is now routinely evaluated in cells grown in either glucose or galactose, and increased susceptibility in the latter is considered prima facia evidence of mitochondrial liabilities that can be further defined using additional assays described below.

NOVEL METHODS TO DETECT MITOCHONDRIAL DYSFUNCTION IN VITRO Another reason drug-induced mitochondrial dysfunction has been overlooked is because the polarographic Clark electrode experiments required to detect it have been the purview of specialists able to isolate functioning organelles. As noted above, in the absence of cell toxicity, there has been no motivation to examine potential effects on isolated mitochondria. Moreover, monitoring respiration by isolated mitochondria usually takes 15-30 min per sample, hardly conducive to high throughput assessments needed in the drug development arena (Figure 16.4). To circumvent this bottleneck, new assays of mitochondrial respiration in 96-well formats have been developed based on quenching of Pt-based fluorescent probes by molecular oxygen; as respiration depletes 0 2 in the well, the signal increases34,35 Well over 650 drugs, ranging from "nontoxic" to those with known organ toxicities, have been evaluated in this type of assay, and approximately 33% of them have demonstrated some level of direct and acute effects on mitochondria, either inhibiting respiration, or uncoupling it from phosphorylation, with many drugs affecting both.36'37 In several important drug classes, the potency of mitochondrial toxicity reflects clinical disposition. For example, of the thiazolidinediones, three (ciglitazone, troglitazone, dargitazone) most potently uncouple electron transport from phosphorylation, and all three, plus muraglitizar, most potently inhibit respiration.38 All four of these compounds have either been withdrawn from the market because of hepatotoxicity, or were dropped in development for organ toxicity.38 Rosiglitazone and pioglitazone also significantly, but less potently, uncouple and inhibit respiration;38 both have received Black Box

408

BIOMARKERS

FIGURE 16.4 Polarographic Clark electrode to monitor mitochondrial oxygen consumption. Respiration by mitochondria is slow in the absence of substrate, and when basal respiration is added (state 2) proceeds until ADP is added, at which point respiration accelerates to the maximum rate (state 3) until all ADP is phosphorylated and respiration returns to state 2. Drug induced inhibition is detected by repressed respiration in either state, and uncoupling by accelerated respiration, or failure to return to basal after ADP is phoshorylated. A typical assay takes between 16-30 minutes, which prompted development of a higher-throughput assay.3"8

Warnings for congestive heart failure, and both are associated with hepatotoxicity. Mitochondrial dysfunction is a proximate determinant of the latter pathology, and bioenergetic crisis and remodelling also figure prominently in congestive heart failure.39 Although a justified inference, it remains to be determined whether the severity of congestive heart failure correlates with the magnitude of mitochondrial impairment, or whether such impairment presages onset. Classical biochemical methods can demonstrate which of the electron transport complexes is being inhibited. With new technologies, the individual respiratory complexes can now be immunocaptured intact and their activity determined in 96-well plates.40 In this format, respiratory inhibition can be localized to an individual complex, and structure-activity studies can be conducted. For example, rosiglitazone inhibits complex I, while muraglitizar inhibits complex V.38 However, darglitazone inhibits complex IV profoundly, but also complexes II/III and V, while both troglitazone and ciglitazone inhibit all the complexes.38 Given the diversity of the respiratory complexes, it seems

IN VITRO DRUG-INDUCED MITOCHONDRIAL DYSFUNCTION

409

unlikely that these drugs are interacting with a motif shared by them all. It remains to be determined how these pleotrophic inhibitors are interacting with each of the individual complexes, but once elucidated, this could generate more specific assays to support SAR studies and drug discovery. As the mitochondrial capacity is diminished by drug-induced impairment, the cell compensates by accelerating glycolyticflux.41"*3Since oxidation of the resulting pyruvate is increasingly impeded, lactate effluxes into the blood. As a result, lactic acidosis is a classical, but not a pathognomonic, marker for mitochondrial impairment both in vivo and in vitro. For example, extracts of the plants in the lily family have long been used to treat diabetes, and they do lower blood glucose. These biguanides have been associated with lactic acidosis, and the first two to reach the market, phenformin and buformin, were later withdrawn because of fatal lactic acidosis, while metformin remains on the market despite rare instances of lactic acidosis, likely attributable to its lower potency. 4142 For example, Wang, et al.42 determined the EC50 for lactic acidosis in rat for phenformin, buformin, and metformin to be ~ 5, 120, and 735 uM, respectively. Traditionally, lactate efflux can be assayed in culture media via enzyme-linked assays, and oxygen consumption can be determined polarographically. With newer technologies, both indices can now be monitored simultaneously via fluorescent probes encapsulated at the end of a light pipe that is inserted into the sample,43 and with this technology, biguanide-induced media acidification can be shown to increase just as oxygen consumption declines with the same rank order of potencies reported by Wang, et al.42 However, although suggestive, elevated blood lactate cannot be pathognomonic for DIMD because it is also affected by exercise and other physiological variables. Nevertheless, lactate can be interrogated via noninvasive imaging techniques, so it may well become more useful as DIMD becomes more widely studied in the clinic. As an aside, the biguanide studies illuminate the role of bioaccumulation in drug toxicity, and reciprocally, efficacy. Greater than lOOuM concentrations of the drugs were needed to accelerate media acidification and repress 0 2 consumption in HepG2 cells.43 Similarly, inhibition of immunocaptured respiratory complexes by phenformin, buformin, and metformin required concentrations orders of magnitude greater than those that yielded lactic acidosis in vivo.43 Both observations suggest that bioaccumulation is required to detect toxicity, and indeed, detection of effects on isolated mitochondria required 40 min preincubation, where the same rank order of potency was found.4M5 Note also that the increased glycolytic flux to compensate for loss of OXPHOS yields the desired clinical outcome, viz. decreased plasma glucose, albeit by an unanticipated mechanism of mitochondrial impairment.

AN EMERGING MODEL OF IDIOSYNCRATIC DRUG TOXICITY The question remains, why, given such potentially potent and acute deleterious effects on mitochondrial function, certain drugs are not universally toxic,

410

BIOMARKERS

i.e., why is frank organ toxicity idiosyncratic. Data like those discussed here support a novel model of idiosyncratic drug toxicity based on two concepts; threshold effects, plus heterogeneity of organ history and genetics. The threshold at which pathology emerges is relatively fixed and varies among different cell types. For example, an unstressed hepatocyte needs a set amount of ATP to conduct normal business such as albumin secretion, xenobiotic detoxification, glucose homeostasis, and a host of other basal processes. More aerobically demanding cells such as myocardiocytes and neurons have higher ATP turnover, but like hepatocytes and all other cells, they also require a minimum amount of energy for homeostasis. So although the basal energetic demand varies among different cell types, it is relatively constant across the population, and therefore is unlikely to underlie the idiosyncratic nature of many toxic drug responses. Rather, the bioenergetic reserve capacity above the threshold needed to maintain basal function varies more widely among individuals. For example, the mitochondrial capacity in a hepatocyte from an alcoholic is substantially less than that found in a drug-naive person.46 Cells with less reserve capacity (physiological scope) are closer to the bioenergetic threshold below which viability is compromised. As a result, such cells are more susceptible to drug-induced erosion of mitochondrial capacity, and hepatotoxicity will be apparent in some individuals under conditions that may be well tolerated by an individual with greater physiological scope. In this way, organ history, i.e., bioenergetic scope, plays a key role in the etiology of idiosyncratic drug toxicity. In an elegant review, Ulrich47 identifies a series of risk factors that converge in an individual to yield an idiosyncratic response. As several of these risk factors accumulate, the probability of drug-induced organ toxicity increases. The single factor that contributes the largest risk is increasing age,4748 although age in itself does not necessarily predict outcome. However, Ulrich also identified other risk factors including: inhibition of a key cellular function, extent of physical activity, genetics and inherited metabolic defects, plus concurrent drug exposures, heterogeneity in drug metabolism and bioactivation, presence of underlying disease, nutritional state, innate immune response, and gender (females more likely). Compellingly, there are parallel counterparts of all of these risk factors from the perspective of mitochondrial impairment. For example, mitochondrial capacity declines as we age, reducing the physiological scope and lowering the bioenergetic threshold for cell endangerment.48 As noted above, mitochondrial capacity is certainly a key cellular function, and its erosion compromises the cell's ability to respond to stressors or even to maintain basal function. Mitochondrial capacity increases with physical exercise, and conversely declines toward the minimal amount required for basal function with inactivity. Compared to a sedentary individual, an athlete has greater mitochondrial reserves, and hence can tolerate more drug-associated mitochondrial impairment before pathology emerges. This is particularly true for tissues where the bioenergetic capacity is subject to conditioning, such as skeletal muscle and heart, but less so for organs like liver where bioenergetic capacity reflects organ history more than conditioning.49 In terms of genetics and inherited metabolic defects, mu-

IN VITRO DRUG-INDUCED MITOCHONDRIAL DYSFUNCTION

411

tations or deletions in mitochondrial DNA cause a host of pathologies, and exposure to drugs with potential mitochondrial impairment can reveal previously silent mtDNA disorders,50 or to exacerbate already frank pathology.51 Concurrent exposures to several drugs with mitochondrial liabilities are at least additive,38 and surely, heterogeneity in drug metabolism will contribute to idiosyncratic responses, regardless of whether the toxicant is the parental or derivative molecule. Similarly, genetic variability in abundance and activity of plasma membrane transporters will also influence idiosyncratic responses to a given drug, with increased bioaccumulation predisposing toward toxicity. The risk factor of "underlying disease," in addition to mitochondrial disorders, clearly has bioenergetic sequelae, and "nutrition" also has direct influences on metabolic capacity and scope. Of the risk factors Ulrich identified, only "innate immune system" lacks a clear mitochondrial counterpart. But this risk factor is crucial to hapten-induced hypersensitivity reactions, not to cytotoxicity per se where the xenobiotic is a primary determinant of toxicity rather than the source of an inflammatory response. Given the relative high-throughput of the assays described here, especially in light of the pleotrophic effects of many drugs that are still poorly predicted by SAR, we propose that mitochondrial assessments be performed well before lead selection in the drug development process.36,37 At this stage, there is typically sufficient chemical diversity in the portfolio so that any observed mitochondrial impairment can be circumvented or minimized. It bears reiteration that assays with isolated organelles will likely yield more false positives than cell or intact animal models, and that the latter are required to characterize fully the nature of drug responses. However, in the latter models, the effects of bioaccumulation are in force, so that compounds with modest mitochondrial impairment that are substrates for transporters could be quite toxic at high localized concentrations, whereas in the absence of bioaccumulation, potent mitochondrial toxicants might never obtain sufficient concentrations in vivo to yield frank toxicity.

MITOCHONDRIAL DISEASES The fact that severe mitochondrial insufficiency translates into organ and systemic pathology is apparent on first principles by considering inherited mitochondrial syndromes and diseases. There are at least 75 such diseases, many of which are due to deletions and mutations in mtDNA, but also many are due to defects in nDNA encoded proteins destined for import in the mitochondria.50-52 An example of the latter is Freidreich's Ataxia (FA), where a triplet repeat in the gene for frataxin impairs mitochondrial iron homeostasis and hence Fe-S cluster assembly.53 FA typically has a pediatric onset, with progressive debilitating neuromuscular and CNS impairment, and is usually lethal via cardiomyopathy with median age of death at 35,52-53 Not all mitochondrial diseases are as relentless or severe, and such diseases range from symptomatically undetectable, to mild exercise intolerance, to sensory disturbances, to fatal ataxias. As might be expected, highly aerobi-

412

BIOMARKERS

cally poised and energy demanding tissues like CNS, sensory axis, and heart are frequently involved, but so also are metabolic pathologies like diabetes. For example, diabetes can be found in patients with MELAS (mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke), but typically not with MEERF (mycologist epilepsy and ragged-red fiber disease).52 Multi-organ system involvement is a hallmark of mitochondrial diseases.54 Using what we know about tissues affected by mitochondrial insufficiencies, we can extrapolate to DIMI and determine whether these are the same tissues first at risk, and in many instances they are. For example, hearing loss is associated with many mitochondrial insufficiencies and syndromes, and is among the first symptoms of aminoglycoside toxicity, plus mutations in mtDNA exacerbate the response.5155 Depending on which cells of the body are affected, symptoms might include: poor growth; loss of muscle coordination and muscle weakness; visual and/or hearing problems; developmental delays with learning disabilities or mental retardation; heart, liver, or kidney disease; gastrointestinal disorders such as severe constipation; respiratory disorders; diabetes; increased risk of infection; neurological problems including seizures; thyroid dysfunction; and dementia.52-56 However, in patients with mitochondriopafhies, distribution of defective copies of the causative genes is the result of several confounding processes, such as developmental segregation, and heteroplasmy, which is diversity of mtDNA within a cell and tissue. Plus, each mitochondrion contains multiple copies of mtDNA, which can also be heterogeneous. As a result, the same mutation can yield different symptoms in different patients, so called genocopies.50~54 Unfortunately, in the context of biomarkers there are no pathognomonic characteristics for mitochondrial diseases, and diagnosis is usually via family history, biopsy, histopathology, and detailed genetic analysis. However, this underscores the notion that bioenergetic capacity, fixed by genetics, mitochondrial disease, or organ history, is a prime determinate of idiosyncratic toxicity. For example, the mitochondrial capacity in a hepatocyte from an alcoholic is substantially less than that found in a drug-naive person.57 Therefore, the impaired hepatocyte will tolerate loss of mitochondrial capacity via drug exposure less than the cell with robust reserve capacity, and so will show organ toxicity sooner, and at a dose that does not yield pathology in a healthy person, i.e., idiosyncratically. We turn over our body weight in ATP everyday at rest.58 However, humans have aerobic scope and can increase metabolism between 10-20 fold over rest. This aerobic scope suggests that existing mitochondrial capacity would have to be eroded by any drug by more than 90% before resting metabolism in the cell would be imperilled. But note that the 20-fold range of aerobic scope largely reflects adaptations to training, where persistent increases in ATP turnover result in increased mitochondrial biomass. As such, the athlete has a higher bioenergetic threshold, and hence greater resistance to drug-induced mitochondrial impairment, but as noted above, only in those tissues subject to training, such as skeletal and cardiac muscle. However, it is in organs not capable of being trained, including liver (which is exposed to higher concentrations),

IN VITRO DRUG-INDUCED MITOCHONDRIAL DYSFUNCTION

413

kidney (which is enriched in transporters), peripheral and central nervous systems (assuming the drug can pass the blood-brain barrier), and sensory organs (e.g., aminoglycoside deafness), among others, where mitochondrial capacity is more static and the scope between the maximum ATP production and what is required to maintain viability is more constrained, that drug-induced toxicity is more typically observed. This is also influenced by the ability of mitochondria to replicate in response to reduced ATP availability so that drug-induced mitochondrial impairment would need to exceed the rate of replacement that is compensating for it. Interestingly, many drugs without organ toxicity often elicit transient increases in plasma liver enzymes ALT & AST, reflecting hepatocyte death. This "adaptation" seems a likely reflection of loss of cells with reduced aerobic capacity, and the lack of more severe organ toxicity a reflection of persistent viability of cells capable of replacing lost mitochondrial potential. In this way, if the ability to replace mitochondria exceeds the potency of the toxin, the cell will remain viable, although such replacement is expensive, i.e., requires energy and material that could have been used for other purposes, so that cell function and reserve capacity are correspondingly diminished.

POTENTIAL BIOMARKERS OF MITOCHONDRIAL DYSFUNCTION Chapters in this text provide information on biomarkers for various types of organ toxicity, such as muscle where myalgia, elevated serum muscle markers such as creatine kinase or myoglobin, and various troponins may signal rhabdomyolysis. However, any such biomarker reflects the final pathology, the organ toxicity, not mitochondrial dysfunction per se. Gradual mitochondrial erosion due to chronic repression of gene expression and/or mitochondrial replication can be monitored by assessing relative protein amounts. For example, monitoring the ratio of a nuclear-encoded protein and one encoded by mtDNA will reveal depletion of the mitochondrial capacity. This has been done using molecular biology techniques and peripheral blood samples.5960 A simple dipstick technology is available that can generate such information, and this technology works well for preclinical cell culture assessments. For example, repression of protein expression by several oxazolidinones and macrolide antibacterials, and nucleoside analogue antivirals can be detected after one cell population doubling.61 However, acute DIMD is difficult to detect in vivo. For example, uncouples dissipate the potential energy inherent in the mitochondrial membrane potential as heat, and can be detected as such in cells and under some circumstances in small animals.62 As noted above, this is the basis for many weight-loss dietary supplements. However, in intact animals, homeostatic mechanisms, such as increased sweating, compensate for extra heat production, so that core temperature is defended and is not a reliable biomarker of mitochondrial uncoupling. Moreover, depending on the extent of bioaccumulation and the tissue in question, mitochondrial uncoupling in one organ is unlikely to increase whole body temperature.

414

BIOMARKERS

Absent surrogate biomarkers, direct interrogation of mitochondrial function can be done by monitoring 0 2 consumption, C0 2 production, and heat production using direct and indirect technologies.63 Such metabolic assessments are well-established, and typically entail confining the patient in an airtight metabolic chamber where air input/output is finely controlled. Oxygen and C0 2 , and sometimes heat production, can be monitored in real time over extended periods. Portable devices, essentially a closed helmet, suffice for resting metabolism over shorter time periods, and the treadmill test where 0 2 consumption is monitored during exercise is well-known. Any of these techniques is likely capable of revealing DIMD, especially if a paired comparison of before-after drug exposure could be used, although to our knowledge none have been used in this capacity. However, without the benefit of paired-comparison experimental design that facilitates use of such technologies in the lab, intra-individual variability will substantially undermine utility for clinical evaluations of DIMD. Nevertheless, one could envision a pathological threshold for resting V0 2 , a surrogate below which the risk of organ toxicity from ETS inhibitors increases. Conversely, there could be a corresponding threshold of increased V0 2 for OXPHOS uncouplers above which the risk of frank lesion correspondingly increases. Similar indices could also be envisioned for C0 2 efflux and heat production. Another index of mitochondrial functional status is NADH/NAD reduction state, and for many years NADH fluorescence has served as a reliable indicator of cellular energetic and mitochondrial status. This technology is comprehensively reviewed by Mayevsky and Rogatsky,64 who discuss the historical development, and also state of the art in this area. For example, the advent of flexible light pipes and short-wavelength diodes has allowed development of compact fluorometers suitable for monitoring organ NADH fluorescence in situ.64 Although it is difficult to calibrate such systems in absolute terms, this issue can be circumvented by use of paired-comparison designs. The use of stable isotopes, i.e., non-radioactive atoms with the same number of protons, but different neutrons, for metabolic assessments has been the "gold standard" for years, being first described in 1949.65 The patient drinks water containing 2H2180, and using periodic blood tests over days to weeks, the clearance rates of the two isotopes is quantified via mass spec.66 The 0 2 equilibrates with C0 2 via carbonic anhydrase, and with water, which is lost as urine, sweat, etc. However, 2H2 depletion accounts for water loss, so that C0 2 production serves as a surrogate index of 0 2 consumption. Note, however, that it really is a surrogate for mitochondrial Krebs Cycle function, not OXPHOS, which under normal circumstances is a valid assumption.66 But during oxidative stress, C0 2 is also produced by the hexosemonophosphate shunt (HMS), branching off from glycolysis, that generates NADPH required as a cofactor by many enzymes, such as glutathione reductase. As such, HMS flux can serve as a surrogate index of glutathione turnover, which accelerates during oxidative stress, and correspondingly confounds C0 2 as a surrogate for OXPHOS. This confounding effect would be exacerbated by inflammation, where transmembrane NADPH oxidases generate free radicals necessary for

IN VITRO DRUG-INDUCED MITOCHONDRIAL DYSFUNCTION

415

antibacterial activity. However, the long duration of the test and the repeated examinations serve to moderate this variable. Currently, elegant, noninvasive exhalation breath techniques for monitoring mitochondrial function based on stable isotope analysis are under clinical evaluation.6768 Substrate selection and pattern of 13C labelling allow this technology to selectively interrogate several cellular processes in different tissues. For example, 13C-methionionine is preferentially metabolized via a transmethylation pathway in the liver that is not found, or has very low activity, in other tissues. When 1-carbon labelled methionine is used, the activity of a-ketobutyrate decarboxylase is interrogated, since this liver mitochondrial enzyme is a limiting step of substrate oxidation to CO r 68 Using 3- or 4-carbon labelled methionine it is possible to assess the trans-sulphuration pathway via release of the carbon as a-ketobutyrate, further metabolized into C0 2 via the tricarboxylic acid cycle. In addition, methyl-13C-labelled methionine can also be used to interrogate hepatic mitochondrial status. The latter is metabolized to sarcosine, which is oxidized by sarcosine-dehydrogenase to produce a one carbon fragment that can be converted into CO r 6 9 In rat liver, the sarcosine oxidase system is present exclusively in the mitochondria.69 Stable isotope C0 2 exhalation for mitochondrial assessment is also done by monitoring decarboxylation of ct-ketoisocaproate, and although in patients with primary biliary cirrhosis the signal is the same as that in normal patients, it is significantly lower in patients with alcoholic liver disease.70 Another stable isotope 31P can also be used to assess mitochondrial function using NMR. For example, using a surface coil to monitor NMR signals from the three phosphates of ATP, phosphocreatine and inorganic phosphate, one can follow bioenergetic status of a muscle noninvasively. These signals decline when the muscle is paced or rendered hypoxic, and the rate of adenylate recovery after exercise ceases, or the muscle is reperfused, reflects mitochondrial capacity (OXPHOS remains at maximum rates until all available ADP is phosphorylated).71,72 Recent advances in visible-wavelength spectroscopy permit noninvasive determination of haemoglobin and myoglobin oxygenation states, and when combined with 31P monitoring, provide noninvasive 0 2 consumption and hence determination of coupling efficiencies.73 Such a protocol should be able to detect DIMD, but to our knowledge has not been applied in this manner. However, as noted above, not having the advantage of a paired-comparison design, determining the adenylate recovery rate of a patient with suspected mitochondrial impairment forces reliance on population thresholds. Another potential biomarker of DIMD could be an index of free radical production. For example, deoxyguanosine in DNA is preferentially oxidized by hydroxyl radicals to 8-OH-deoxyguanosine (8-OH-dGUA), which is excised and excreted intact in the urine.74 As such, 8-OH-dGUA can serve as a noninvasive biomarker for endogenous radical production in patients undergoing radiation therapies, and also for radicals generated during an inflammatory response.74 Whether it, or some other marker of oxidative stress, can be developed as a biomarker for DIMD will depend on its variability and poten-

416

BIOMARKERS

tial thresholds, but also on whether the drug in question increases radical production, a result of how the drug is undermining mitochondrial function. For example, inhibitors of complexes III and IV, which serve to more fully reduce the "up stream" components of ETS and correspondingly increase the probability of their autoxidation to yield superoxide, will yield more robust signals from an oxidative biomarker than inhibitors of complex II, or some other component of mitochondrial metabolism, such as a Kreb's Cycle enzyme.24-27

A N I M A L MODELS As noted above, all the technologies discussed have been used in animal models, but such preclinical models offer additional benefits, including the possibilities of genetic manipulation. For example, Ong, et al.75 have described use of a heterozygous mouse where the manganese form of superoxide dismutase found in mitochondria (SOD2) is knocked down by 50%. In these Sod2(+'") animals, the previously silent hepatotoxicity of troglitazone is detected as increased serum alanine aminotransferase activity and co-occurrence of midzonal areas of hepatic necrosis.75 In hepatocytes isolated from Sod2(+/>, but not wild-type mice, troglitazone caused a concentration-dependent increase in superoxide production detected using a mitochondrial-targeting fluorescent probe. It is instructive in this context to reconsider the convergence of risk factors discussed previously; potential DIMI needs to be evaluated in animal models most likely to reveal it. Typically, potential in vivo toxicity of nascent drugs is evaluated in young, drug-naive animals with full mitochondrial capacity, precisely the circumstances under which it is least likely to be detected. Rather, old animals, preferably with prior chronic alcohol or hepatotoxic drug exposure to decrease hepatic mitochondrial capacity, would be a more physiologically—and pathologically—realistic model. Finally, genetic variability can be manipulated in preclinical animal models to increase, or decrease, susceptibility to various agents. Although the initial assumption is that all in-bred strains of rats or mice should be comparable physiologically or behaviourally responsive to xenobiotic exposure, this is not the case.7677 For example, mice from 14 standard inbred strains were evaluated for sensitivity to pentobarbital (PB) by monitoring low-dose stimulation and highdose depression of locomotor activity, reduced rearing, hypothermia, and ataxia assessed via rotarod. The strains significantly differed in all responses, with a > 4-fold range in the amount of PB present in the brain when failing the rotarod test, and a > 5-fold range in latency of response.77 Such rodent strains, thought to derive from a mixture of four subspecies, have been inbred for over 100 years, which has also fostered the tacit assumption that variations between mtDNA should be inconsequential. But this is also not the case. Recently, the complete sequence of mtDNA from 16 strains indicates that they all descend from the same wild type Mus musculus domesticus female ancestor.78 Moreover, the rate of accumulation of replacement substitutions in mtDNA is faster in the inbred strains of both mice and rats than in the wild types.78-79 As a result, there is more diversity in mtDNA from inbred strains, and hence larger range of possible phenotype in the in-

IN VITRO DRUG-INDUCED MITOCHONDRIA!. DYSFUNCTION

417

bred animals compared to their outbred counterparts. More recently, these data have been extended and corroborated; 50 of the 52 inbred mice strains directly descended from the same initial female ancestor, with mtDNA mutations in 26 strains.79 These researchers then generated conplastic strains on the C57BL/6J background for 12 mtDNA variants with one to three functional mtDNA mutations, plus comparable strains with the four M. musculus subspecies, to yield a panel of 16 mtDNA variants. Phenotypic analysis of these conplastic strains revealed that mtDNA variations alter susceptibility to experimental autoimmune encephalomyelitis and anxious behavior. In this case, contrary to expectations, mtDNA apparently affected complex traits. In this light, it might be informative to evaluate these conplastic strains for differential susceptibility to known hepatotoxicants and to drugs associated with idiosyncratic hepatoxicity, or other organ toxicity.

SUMMARY POINTS 1.

2. 3. 4.

5.

The evidence is rapidly accumulating that many marketed drugs have direct "off target" mitochondrial liabilities, either inhibiting ETS and/or uncoupling OXPHOS, which are increasingly implicated in the development of idiosyncratic drug toxicities. Organ toxicity is a function of potency, but also of bioaccumulation, with aerobically poised organs typically at highest risk, as is the case of inherited mitochondrial diseases. The availability of animal strains with diversity in mtDNA provides potentially useful tools to help illuminate how phenotypic diversity might contribute to idiosyncratic responses. The current absence of biomarkers for drug-associated mitochondrial dysfunction can be circumvented by determining mitochondrial capacity directly. Several techniques are available, although they have not yet been used in this capacity. This situation will undoubtedly change as the importance of xenobiotic mitochondrial impairment gains wider appreciation, and such data along with thorough preclinical mitochondrial assessments early in the drug discovery and development process will help improve the safety profile for future drugs.

REFERENCES 1. 2. 3. 4.

http://www.fda.gov/cder/drug/drugreactions/default.htm#adrs: %20prevalence%20and%20incidence. http://www.fda.gov/cder/livertox/default.htm. Committee on Quality of Health Care in America: Institute of Medicine. To EnIs Human: Building a Safer Health System. Washington, D.C. National Academy Press, 2000. Lazarou, J., Pomeranz, B., and Corey, P. N. Incidence of Adverse Drug Reactions in Hospitalized Patients: A Meta-Analysis of Prospective Studies. JAMA. 1998;279:1200-1205.

418

BIOMARKERS 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.

20. 21. 22.

Gurwitz, J. H., Field, T. S., Avorn, J., Mccormick, D., Jain, S., and Eckler, M., et al. Incidence and Preventability of Adverse Drug Events in Nursing Homes. Am. J. Med. 2000;109:87-94. Boelsterli, U. A. and Lim, P. L. Mitochondrial Abnormalities—A Link to Idiosyncratic Drug Hepatotoxicity? Toxicol. Appl. Pharmacol. 2007;220:92-107. Brunmair, B., Staniek, K., and Gras, F, et al., Thiazolidinediones, Like Metformin, Inhibit Respiratory Complex I: A Common Mechanism Contributing to Their Antidiabetic Actions? Diabetes. 2004;53:1052-1059. Cardoso, C. M., Custodio, J. B., Almeida, L. M., and Moreno, A. J. Mechanisms of the Deleterious Effects of Tamoxifen on Mitochondrial Respiration Rate and Phosphorylation Efficiency. Toxicol. Appl. Pharmacol. 2001;176:145-152. Dykens, J. A., Jamieson, J. D., and Marroquin, L. D., et al. In Vitro Assessment of Mitochondrial Dysfunction and Cytotoxicity of Nefazodone, Trazodone, and Buspirone. Toxicol. Sci. 2008;103:335-345. Fau, D., Eugene, D., and Berson, A., et al. Toxicity of the Antiandrogen Flutamide in Isolated Rat Hepatocytes. J. Pharmacol. Exp. Ther. 1994;269:954-962. Haasio, K., Koponen, A., Penttila, K. E., and Nissinen, E. Effects of Entacapone and Tolcapone on Mitochondrial Membrane Potential. Eur. J. Pharmacol. 2002; 453:21-6. Keller, B. J., Yamanaka, H., and Thurman, R. G. Inhibition of Mitochondrial Respiration and Oxygen-Dependent Hepatotoxicity by Six Structurally Dissimilar Peroxisomal Proliferating Agents. Toxicology. 1992;71:49-61. Krause, M. M., Brand, M. D., and Krauss, S., et al. Nonsteroidal Anti-Inflammatory Drugs and a Selective Cyclooxygenase 2 Inhibitor Uncouple Mitochondria in Intact Cells. Arthritis Rheum. 2003;48:1438-1444. Masubuchi, Y., Yamada, S., and Horie, T. Diphenylamine as an Important Structure of Nonsteroidal Anti-Inflammatory Drugs to Uncouple Mitochondrial Oxidative Phosphorylation. Biochem. Pharmacol. 1999;58:861-865. Maurer, I. and Moller, H. J. Inhibition of Complex I by Neuroleptics in Normal Human Brain Cortex Parallels the Extrapyramidal Toxicity of Neuroleptics. Mol. Cell. Biochem. 1997;174:255-259. Nulton-Persson, A. C , Szweda, L. I., and Sadek, H. A. Inhibition of Cardiac Mitochondrial Respiration by Salicylic Acid and Acetylsalicylate. J. Cardiovasc. Pharmacol. 2004;44:591^t95. Pessayre, D., Mansouri, A., Haouzi, D., and Fromenty, B. Hepatotoxicity Due to Mitochondrial Dysfunction. Cell. Biol. Toxicol. 1999;15:367-373. Souid, A. K., Tacka, K. A., Galvan, K. A., and Penefsky, H. S. Immediate Effects of Anticancer Drugs on Mitochondrial Oxygen Consumption. Biochem. Pharmacol. 2003;66:977-987. Tay, V. K., Wang, A. S., Leow, K. Y, Ong, M. M., Wong, K. P., and Boelsterli, U. A. Mitochondrial Permeability Transition as a Source of Superoxide Anion Induced by the Nitroaromatic Drug Nimesulide In Vitro. Free Radic. Biol. Med. 2005;39:949-959. Wallace, K. B. and Starkov, A. Mitochondrial Targets of Drug Toxicity. Ann. Rev. Pharmacol. Toxicol. 2000;40:353-388. Zhou, S. and Wallace, K. B. The Effect of Peroxisome Proliferators on Mitochondrial Bioenergetics. Toxicol. Sci. 1999;48:82-89. Benbrik, E., Chariot, P., and Bonavaud, S., et al. Cellular and Mitochondrial Toxicity of Zidovudine (AZT), Didanosine (Ddi) and Zalcitabine (Ddc) on Cultured Human Muscle Cells. /. Neurol. Sci. 1997;149:19-25.

IN VITRO DRUG-INDUCED MITOCHONDRIAL DYSFUNCTION 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33.

34. 35. 36. 37. 38.

39. 40.

419

Riesbeck, K., Bredberg, A., and Forsgren, A. Ciprofloxacin Does Not Inhibit Mitochondrial Functions but Other Antibiotics Do. Antimicrob. Agents Chemother. 1990;34:167-169. Nicholls, D. G. and Ferguson, S. J. Bioenergetics 3. p. 317. 2002, Academic Press; London. Scheffler, I. Mitochondria, 1st Ed., p. 367. 1999;John Wiley, New York. Chazotte, B. and Hackenbrock, C. R. The Multicollisional, Obstructed, LongRange Diffusional Nature of Mitochondrial Electron Transport. J. Biol. Chem. 1988;263:14359-14367. Dykens, J. A. Redox Targets: Enzyme Systems and Drug Development Strategies for Mitochondrial Dysfunction, p. 1053-1087 In Comprehensive Medicinal Chemistry II (Triggle, D. J., Taylor, J. B., Eds.), 2007, Elsevier, Oxford. Chan, K., Truong, D., Shangari, N., and O'Brien, R J. Drug-Induced Mitochondrial Toxicity. Expert Opin. Drug Metab. Toxicol. 2005;1:655-669. Mokhova, E. N. and Khailova, L. S. Involvement of Mitochondrial Inner Membrane Anion Carriers in the Uncoupling Effect of Fatty Acids. Biochemistry Mosc. 2005;70:159-163. Drug-Induced Mitochondrial Dysfunction. (Dykens, J. A., Will, Y, Eds.) 2008; p. 616:Wiley, New York, NY. Rodriguez-Enriquez, S., Juarez, O., Rodriguez-Zavala, J. S., and Moreno-Sanchez, R. Multisite Control of the Crabtree Effect in Ascites Hepatoma Cells. Eur. J. Biochem. 2001;268:2512-2519. Warburg, O., Geissler, A. W., and Lorenz, S. On Growth of Cancer Cells in Media in Which Glucose Is Replaced by Galactose. Hoppe Seylers Z. Physiol. Chem. 1967;348:1686-1687. Marroquin, L. D., Hynes, A., Dykens, J. A., Jamieson, J. D., and Will, Y Circumventing the Crabtree Effect: Replacing Media Glucose with Galactose Increases Susceptibility of Hepg2 Cells to Mitochondrial Toxins. Toxicol. Sci. 2007;97:539-547. Hynes, J., Marroquin, L. D., and Ogurtsov, V. I., et al. Investigation of DrugInduced Mitochondrial Toxicity Using Fluorescence-Based Oxygen-Sensitive Probes. Toxicol. Sci. 2006;92:186-200. Will, Y, Hynes, J., Ogurtsov, V. I., and Papkovsky, D. B. Analysis of Mitochondrial Function Using Phosphorescent Oxygen-Sensitive Probes. Nat. Protoc. 2006;1:2563-2572. Dykens, J. A. and Will, Y. The Significance of Mitochondrial Toxicity Testing in Drug Development. Drug Dis. Today. 2007;12:777-785. Dykens, J. A., Marroquin, L. D., and Will, Y Strategies to Reduce Late Stage NCE Attrition Due to Mitochondrial Toxicity: Development of a High Throughput Respiration Screen. Exp. Opin. Mol. Diag. 2007;7:161-175. Nadanaciva, S., Dykens, J. A., Bernal, A., Capaldi, R. A., and Will, Y Mitochondrial Impairment by PPAR Agonists and Statins Identified via Immunocaptured OXPHOS Complex Activities and Respiration. Toxicol. Appl. Pharmacol. 2007;223:277-287. Ingwall, J. S. Energy Metabolism in Heart Failure and Remodelling. Cardiovasc. Res. 2009;81:412^tl9. Nadanaciva, S., Bernal, A., Aggeler, R., Capaldi, R., and Will, Y Target Identification of Drug Induced Mitochondrial Toxicity Using Immunocapture Based OXPHOS Activity Assays. Toxicol. In Vitro. 2007;21:902-911.

420

BIOMARKERS 41. 42. 43.

44. 45. 46. 47. 48. 49. 50. 51.

52. 53. 54. 55.

56. 57.

58. 59.

60.

Chan, N. N., Brain, H. P., and Feher, M. D. Metformin-Associated Lactic Acidosis: A Rare or Very Rare Clinical Entity? Diabet. Med. 1999;16:273-281. Wang, D. S., Kusuhara, H., Kato, Y, Jonker, J. W., Schinkel, A. H., and Sugiyama Y. Involvement of Organic Cation Transporter 1 in the Lactic Acidosis Caused by Metformin. Mol. Pharmacol. 2003;63:844-848. Dykens, J. A., Jamieson, J., Marroquin, L., Nadanaciva, S., Billis, P. A., and Will, Y. Biguanide-Induced Mitochondrial Dysfunction Yields Increased Lactate Production and Cytotoxicity of Aerobically-Poised Hepg2 Cells snd Human Hepatocytes In Vitro. Toxicol. Appl. Pharmacol. 2008;233:203-210. El Mir, M. Y, Nogueira, V, Fontaine, E., Averet, N., Rigoulet, M., and Leverve, X. Dimethylbiguanide Inhibits Cell Respiration via an Indirect Effect Targeted on the Respiratory Chain Complex I. J. Biol. Chem. 2000;275:223-228. Owen, M. R., Doran, E., and Halestrap, A. P. Evidence That Metformin Exerts Its Anti-Diabetic Effects Through Inhibition of Complex 1 of the Mitochondrial Respiratory Chain. Biochem. J. 2000;348:607-614. Sastre, J., Serviddio, G., and Pereda, J., et al. Mitochondrial Function in Liver Disease. Front Biosci. 2007;12:1200-1209. Ulrich, R. G. Idiosyncratic Toxicity: A Convergence of Risk Factors. Annu. Rev. Med. 2007;58:17-34. Cortopassi, G. A. and Wong, A. Mitochondria in Organismal Aging and Degeneration. Biochim. Biophys.Acta., 1999;1410:183-93. Fromenty, B. and Pessayre, D. Impaired Mitochondrial Function in Microvesicular Steatosis. Effects of Drugs, Ethanol, Hormones and Cytokines. J. Hepatol. 1997;26:43-53. Dimauro, S. Mitochondrial Diseases. Biochim. Biophys. Ada. 2004;1658:80-88. Schon, E. A., Hirano, M., and Dimauro, S. Drug Effects in Patients with Mitochondrial Diseases, p. 311-324. In Drug-Induced Mitochondrial Dysfunction (Dykens, J. A. and Will, Y, Eds.), 2008;Wiley, New York, NY. Finsterer, J. Mitochondriopathies. Eur. J. Neurol. 2004;11:163-186. Delatycki, M. B. Evaluating the Progression of Friedreich Ataxia and Its Treatment. J. Neurol. 2009;256 Suppl 1:36-41. Naviaux, R. K. Mitochondrial DNA Disorders. Eur. J. Pediatr. 2000;3: S219-226. Bindu, L. H. and Reddy, P. P. Genetics of Aminoglycoside-Induced and Prelingual Non-Syndromic Mitochondrial Hearing Impairment: A Review. Int. J. Audiol. Nov 2008;47(11):702-707. Schapira, A. H. V. Mitochondrial Disease. The Lancet. 2006;368:70-82. Sastre, J., Serviddio, G., Pereda, J., Minana, J. B., Arduini, A., Vendemiale, G., Poli, G., Pallardo, F. V, and Vina, J. Mitochondrial Function in Liver Disease. Front. Biosci. 2007;12:1200-1209. Dykens, J. A. and Will, Y Preface, pp. xiii-xvii. In Drug-Induced Mitochondrial Dysfunction (Dykens, J. A. and Will, Y, Eds), 2008; Wiley, New York, NY, 616. Saitoh, A., Fenton, T., Alvero, C, Fletcher, C. V, and Spector, S. A. Impact of Nucleoside Reverse Transcriptase Inhibitors on Mitochondria in Human Immunodeficiency Virus Type 1 -Infected Children Receiving Highly Active Antiretroviral Therapy. Antimicrob. Agents Chemother. 2007;51:4236-4242. Shikuma, C. M., Gerschenson, M., and Chow, D., et al. Mitochondrial Oxidative Phosphorylation Protein Levels in Peripheral Blood Mononuclear Cells Correlate with Levels in Subcutaneous Adipose Tissue Within Samples Differ-

IN VITRO DRUG-INDUCED MITOCHONDRIAL DYSFUNCTION

61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77.

421

ing by HIV and Lipoatrophy Status. AIDS Res. Hum. Retroviruses. 2008;24: 1255-1262. Nadanaciva, S., Willis, J. H., and Barker, M. L., et al. Lateral-Flow Immunoassay for Detecting Drug-Induced Inhibition of Mitochondrial DNA Replication and mtDNA-Encoded Protein Synthesis. J. Immunol. Methods. 2009;343:1-12. Walsberg, G. E. and Hoffman, T. C. Direct Calorimetry Reveals Large Errors in Respirometric Estimates of Energy Expenditure. J. Exp. Biol. 2005 ;208: 1035-1043. Hamilton, B. F, Stokes, A. H., Lyon, J., and Adler, R. R. In Vivo Assessment of Mitochondrial Toxicity. Drug Dis. Today. 2008;13:785-790. Mayevsky, A. and Rogatsky, G. G. Mitochondrial Function In Vivo Evaluated by NADH Fluorescence: From Animal Models to Human Studies. Am. J. Physiol. CellPhysiol. 2007;292:C615-640. Lifson, N., Gordon, G. B., Vissher, M. B., and Nier, A. O. The Fate of Utilized Molecular Oxygen and the Source of Heavy Oxygen of Respiratory Carbon Dioxide, Studied with the Aid of Heavy Oxygen. J. Biol.Chem. 1949; 180:803-811. Schoeller, D. A. Measurement of Energy Expenditure in Free-Living Humans by Using Doubly Labeled Aater. J. Nutr. 1988;118:1278-1289. Milazzo, L., Menzaghi, B., Massetto, B., Sangaletti, O., and Riva, A. 13C-Methionine Breath Test Detects Drug-Related Hepatic Mitochondrial Dysfunction in HIV-Infected Patients. J. Acquir. Immune Defic. Syndr. 2006;41:252-253. Banasch, M., Goetze, O., and Hollborn, I., et al. 13C-Mefhionine Breath Test Detects Distinct Hepatic Mitochondrial Dysfunction in HIV-Infected Patients with Normal Serum Lactate. J. Acquir. Immune Defic. Syndr. 2005;40:149-154. Milazzo, L. Clinical Assessment of Mitochondrial Function via [ 13C] Methionine Exhalation, pp. 493-506. In Drug-Induced Mitochondrial Dysfunction (Dykens, I. A. and Will, Y., Eds.). 2008;Wiley, New York, NY. 616 pp. Lauterburg, B. H., Liang, D., Schwarzenbach, F. A., and Breen, K. I. Mitochondrial Dysfunction in Alcoholic Patients as Assessed by Breath Analysis. Hepatology. 1993:17:418^122. Arnold, D. L., Matthews, P. M., and Radda, G. K. Metabolic Recovery After Exercise and the Assessment of Mitochondrial Function In Vivo in Human Skeletal Muscle by Means of 31P NMR. Magn. Reson. Med. 1984;1:307-315. Dykens, J. A., Wiseman, R. W, and Hardin, C. D. Preservation of Phosphagen Kinase Function During Transient Hypoxia via Enzym Abundance or Resistance to Oxidative Inactivation. /. Comp. Physiol. B. 1996;166:359-368. Amara, C. E., Marcinek, D. J., Shankland, E. G., Schenkman, K. A., Arakaki, L. S., and Conley, K. E. Mitochondrial Function In Vivo: Spectroscopy Provides Window on Cellular Energetics. Methods. 2008;46:312-318. Dykens, J. A. and Baginski, T. J. Urinary 8-Hydroxydeoxyguanosine Excretion as a Non-Invasive Marker of Neutrophil Activation in Animal Models of Inflammatory Bowel Disease. Scand. J. Gastroenterol. 1998;33:628-636. Ong, M. M., Latchoumycandane, C , and Boelsterli, U. A. Troglitazone-Induced Hepatic Necrosis in an Animal Model of Silent Genetic Mitochondrial Abnormalities. Toxicol. Sci. 2007;97:205-213. Boyer, C. S., Ross, D., and Petersen, D. R. Sex and Strain Differences in the Hepatotoxic Response to Acute Cocaine Administration in the Mouse. J. Biochem. Toxicol. 1988;3:295-307. Crabbe, J. C , Metten, P., Gallaher, E. I., and Belknap, J. K. Genetic Determinants of Sensitivity to Pentobarbital in Inbred Mice. Psychopharmacol 2002;161:408^H6.

422

BIOMARKERS 78. 79. 80.

Goios, A., Pereira, L., Bogue, M., Macaulay, V., and Amorim, A. Mtdna Phytogeny and Evolution of Laboratory Mouse Strains. Genome Res. 2007;17:293-298. Yu, X., Gimsa, U. and Wester-Rosenlof, L., et al. Dissecting the Effects of mtDNA Variations on Complex Traits Using Mouse Conplastic Strains. Genome Res. 2009;9:159-165. Frey, T. G., Renken, C. W., and Perkins, G. A. Insight Into Mitochondrial Structure and Function From Electron Tomography. Biochim. Biophys. Ada. 2002;1555:196-203.

SECTION III TECHNOLOGY FOR BIOMARKER DETECTION

This page intentionally left blank

CHAPTER

IMMUNOASSAY-BASED TECHNOLOGIES FOR THE MEASUREMENT OF BIOLOGICAL MATERIALS USED FOR BIOMARKERS DISCOVERY AND TRANSLATIONAL RESEARCH Vincent Ricchiuti

INTRODUCTION This chapter summarizes various methods employed to characterize and quantify biological materials from human and animal sources. The measurement of biological compounds in body fluids and tissues is a critical component of clinical diagnostics, clinical research and translational, and represents an objective endpoint for many clinical trials, especially those involving therapeutic interventions and biomarkers of toxicity. Over the past decade, there have been significant technological advances made to characterize and quantify biological compounds from in vivo sources and many of these can be exploited in translational research and biomarkers of toxicity. The purpose of this chapter is to provide an overview of select methods that are available to the clinical researcher to assess biological compounds from human or animal material. The six areas of technologies that will be discussed in this chapter are: 1. Immunoassay and immunochemistry 2. Radioimmunoassays 3. Enzyme-linked immunoabsorbent assay 4. Chemiluminescence and fluorescence immunoassays 425

426

BIOMARKERS

5. Multiplex microarrays and beads immunoassays 6. Future of immunoassays Alternative technologies used to characterize and quantify biological materials such as chromatographic methods (including high pressure liquid chromatography and gas chromatography), mass spectrometry used for genomics (gene expression and genotyping), proteomics, and metabolomics are discussed in separate chapters of this book (see Chapters 1, 2, and 3). Immunoassays, chromatography, and mass spectrometry are significantly more established technologies than are genomics, proteomics, and metabolomics. Additionally, immunoassays, chromatography, and mass spectrometry methodologies provide quantitative results as opposed to the three latter methods that are more semi-quantitative or qualitative.

IMMUNOASSAY AND IMMUNOCHEMISTRY Background Immunoassay methodologies represent, perhaps, the most frequently used approach to measure biological compounds in translational, clinical research and biomarkers discoveries. Immunoassays are either approved by the Food and Drug Administration (FDA-approved) or for "research use only" (non FDAapproved). Either way, assays must be fully validated by laboratory prior to being utilized for precision, accuracy, reproducibility, analytical sensitivity, and linearity verification of dynamic range of assay. When validated, immunoassays can be used for the detection of small and large molecules such as hormones and lipids, as well as larger peptides and proteins that are present in human body fluids and tissues.1 In addition, a number of synthesized molecules such as therapeutic agents can be measured by immunoassays. Immunoassays can measure antigens and antibodies as well. Many immunoassays are extremely sensitive and can detect as little as 0.1 pg of compound/ml of body fluid.2 Basic Principles Regardless of the method used, all immunoassays rely upon the interaction of an antigen with an antibody.1 The extent to which this interaction occurs (the amount of antigen that is bound to antibody versus free) allows one to measure, either qualitatively or quantitatively, the amount of that particular antigen that is present in a biological fluid or tissue. Detection methods for particular assays vary and depend on the approach used to detect the antigenantibody complex. Antigens are defined as any substance that possesses antigenic sites (epitopes) that produce corresponding antibodies.1 Antigens can be small molecules such as peptides or steroids hormones, etc. or they can be very large compounds such as glycolipids and proteins. Antibodies that are generated in response to antigens (haptens) can be one of five types and include IgG, IgM, IgA, IgE, and IgD. Antibodies consist of heavy chain and either

T H E MEASUREMENT OF BIOLOGICAL MATERIALS

427

K or X light chains and possess constant and variable regions. The hypervariable region can be assembled to recognize a wide variety of epitopes (Figure 17.1).2 Although antibodies can serve as antigens, for purposes of immunoassays, they are reactants used to detect antigens. Different types of antibodies can be obtained from several sources. Polyclonal antibodies are generated by immunizing an animal with an antigen. In this case, multiple antibodies are generated which recognize different epitopes. As a consequence, the affinity of polyclonal antibodies for a complex antigen is usually stronger than is that of a monoclonal antibody.3 Monoclonal antibodies are generated using somatic cell fusion and hybridoma selection.4 The resulting established cell line generates a homogeneous antibody population that represents a single epitope.2 While specific for a certain epitope, monoclonal antibodies may cross-react with different antigens that possess the same epitope. Nonetheless, the development of monoclonal antibodies has revolutionized immunoassay methodologies because monoclonal antibodies are well defined and specific reagents and their production can yield a nearly limitless supply of antibody.5 Further, monoclonal antibodies can be prepared through immunization of a non-purified antigen. A more recent approach to the development of antibodies for use in immunoassays is phage display in which antibody fragments of predetermined binding specificity are encoded in phage and expressed in bacteria.6 Figure 17.2 shows the classification of the various immunoassays available and their characteristics. Each of these methods (except chromatography) is discussed in this chapter.

RADIOIMMUNOASSAYS Overview Radioimmunoassay (RIA) was first described in 1960 for measurement of endogenous plasma insulin by Solomon Berson and Rosalyn Yalow of the Veterans Administration Hospital in New York.7 Yalow would later be awarded the 1977 Nobel Prize for Medicine for the development of the RIA for peptide hormones,8 but because of his untimely death in 1972, Berson could not share the award. Also in 1960, Dr. Roger Ekins of Middlesex Hospital in London published his findings on saturation analysis used to estimate thyroxine in human plasma.9 The immunoassay technique with a radioactive label immediately caught the imagination of many researchers and clinicians, and in the ensuing decade RIA for new analy tes were published at a rapid pace and variants of the method were rapidly developed. In 1968, Miles and Hales published their first results of an immunoradiometric technique with radioactive labeled antibodies rather than labeled antigen for measuring insulin in human plasma.10 In many laboratories around the world, special facilities were built in which investigators could safely work with the amounts of radioactivity required for the labeling of antigens or antibodies, but concern persisted with regard to the safety of laboratory personnel, the radioactive waste problem, the requirements of building special laboratory facilities, and the procurement of expensive counting equipment. It should be recalled that in the original studies,7-9 iodine-131 (131I) (6 and

428

BIOMARKERS

FIGURE 17.1 Antibodies consist of heavy chain and/either/or light chains and possess constant and variable regions.The hypervariable region can be assembled to recognize a wide variety of epitopes.

7 radiation) was used for the labeling because no alternatives were available at that time. The potential health problems related to the use of radioactive materials were greatly diminished when manufacturers such as Amersham began marketing iodine-125 (l25I) (weak radiation) preparations of sufficiently high specific activity and purity.

Principle of Radioimmunoassay RIAs are heterogeneous assays, meaning they require a washing step to separate antibody bound and free radiolabel. Typical radioisotopes used in RIAs include 125 131 3 1, 1, H, 14C, and 32P, although the majority of assays utilize 125I because of its ready ability to conjugate antigens without altering their biological activity. RIAs can be either competitive or non-competitive. Competitive assays are very common and utilize conditions of antigen excess as opposed to non-competitive assays that employ an excess of antibody.1-2 To some extent radioimmunoassays have been replaced by the enzyme immunoassay (EIA) method, which we discuss later in this chapter.

T H E MEASUREMENT OF BIOLOGICAL MATERIALS

429

FIGURE 17.2 Classification of various assays from the most sensitive (chemiluminescence) to the lesser sensitive (chromatography). RIA: radioimmunoassay; IRMA: immunoradiometric assay; ELISA: enzyme-linked immunosorbent assay; EIA: enzyme immunoassay.

Various competitive RIA methods have been developed to measure a plethora of different biological compounds. Figure 17.3A shows one of the general methods that is routinely employed.2 Briefly, a known amount of labeled antigen and antigen from a biological specimen are combined and reacted with a known amount of antibody that is usually coated on a solid phase such as sepharose beads or on the inner wall of plastic tubes. After the mixture equilibrates, it is washed to remove unreacted antigens and the immune complex containing both labeled and unlabeled antigen is trapped in the solid phase. The washing step is referred to as bound versus free (B/F) separation.2 Radioactivity can be detected by scintillation counting and is expressed as counts per minute (CPM). Applying the concept of competition between labeled and unlabeled antigen, the antigen-bound percentage of total radioactivity against logarithmic concentration of the antigen can be compared to a standard curve as shown in Figure 17.3B. The CPM plot on the standard curve gives the concentration of antigen. To prepare a standard curve, known amounts of both labeled and unlabeled antigen are reacted as above. Various other competitive RIA methods exist in which a second antibody is utilized to capture antigen-antibody complexes in the solid phase. In addition, non-competitive assays are available

430

BIOMARKERS

FIGURE 17.3 A) Antigen and labeled antigen were added to antibodies that are bound to a solid phase. Antigen-antibody went through a reaction where both antigen and labeled antigen were bound to antibodies. Both bound and free antigens were washed and reaction was measured by counter B) The CPM plot on the standard curve gives the concentration of antigen. (Modified from Ashihara, et al. 2001, reference 2).

that employ conditions of antibody excess. These include techniques that are termed immunoradiometric assays (IRMAs) or sandwich type assays. These latter approaches can increase greatly the level of sensitivity of detection of compounds that are present in biological samples at very low concentrations." In summary, radioimmunoassays offer a number of advantages over other immunoassays in that they are highly sensitive and precise. Radioimmunoassays are the most precise method to assess steroids and endocrine hormone levels over more recent technologies such as chemiluminescence methods. The disparity in the hormonal values obtained from different assay methods warrants clinicians to be aware of their clinical interpretation. Using the same reference range for different assay methods is not appropriate. A comparative study between the new and standard assays is essential.12 In addition, radiolabeled compounds are easily prepared. Disadvantages include the fact that radioisotopes must be utilized within a few weeks (125I labeled), which may have a short half-life of 4-6 weeks, laboratory must have a radiation permit in good standing and an adequate facility to ensure the safety of laboratorians and proper disposal of wastes. Radioimmunoassays are heterogeneous assays and therefore cannot be easily fully automated.

T H E MEASUREMENT OF BIOLOGICAL MATERIALS

431

E N Z Y M E - L I N K E D I M M U N O S O R B E N T ASSAY A N D ENZYME IMMUNOASSAY Overview Enzyme-linked immunosorbent assay (ELISA) and enzyme immunoassay (ElA) were developed independently and simultaneously. Between 1966 and 1969, the group in Villejuif (Paris, France) reported their successful results of coupling antigens or antibodies with enzymes such as alkaline phosphatase (EC 3.1.3.1), glucose oxidase (EC 1.1.3.4), and others.13-14 Avrameas and colleagues13-14 described the optimal coupling of these molecules by means of glutaraldehyde. Their purpose was to use the enzyme-labeled antigens and antibodies to detect antibodies or antigens by immunofluorescence, and they applied their tools to histopathology. In Los Angeles, Pierce and colleagues15 had successfully developed the same line of research, also for histochemical purposes. The Uppsala group had developed a so-called (radio)immunosorbent technique in which antibodies were insolubilized by coupling them to cellulose or Sephadex beads. Engvall and Perlmann published their first paper on ELISA in 197116 and demonstrated quantitative measurement of IgG in rabbit serum with alkaline phosphatase as the reporter label. In the same year, van Weemen and Schuurs17 published their innovative work on enzyme immunoassay (EIA) and reported that it was possible to quantify human chorionic gonadotropin concentrations in urine. They used the enzyme horseradish peroxidase (EC 1.11.17), coupled by means of glutaraldehyde, as the reporter label.18,19 Perlmann's further research included cytotoxicity of human lymphocytes20 and immunogen selection and epitope mapping for malaria vaccine development.21 Engvall's group applied the ELISA measurement tool to parasitology, (e.g., malaria22 and trichinosis23), microbiology,24 and oncology.25-27 Engvall then focused her scientific interests on the biochemistry of tissues, e.g., fibronectin, laminin, integrins, and muscular dystrophies. Engvall's laboratory is currently investigating the use of differentiation factors for muscle regeneration and myogenic cells from non-muscle tissues for muscle cell replacement.28 During the late 1960s and early 1970s, many RIA test systems were essentially "home-brew" methods developed by individual researchers who could not keep pace (particularly financially) with the possibilities and facilities of commercial manufacturers such as Boehringer-Mannheim (Germany), Abbott (United States), and Organon Teknika (The Netherlands), to name only a few. Commercialization of EIA/ELISA test kits had started. Solid-phase techniques29'30 were used in the development of microtiter plates (96 wells) in which either an antigen or an antibody is noncovalently bound to a solidphase support. Technical advances led to automated pipetting devices (Micromedics; Hamilton), multichannel pipettes (Lab Systems), and microtiter plate readers and washers. In the 1980s fully automated test instruments were manufactured by Boehringer-Mannheim and Abbott, among others. Such automated systems have come to stay in medical laboratories. The spectacular invention of EIA/ELISA generated a whole series of test formats, from the

432

BIOMARKERS

immunoenzymometric (already mentioned in ref. 13) to the many variants of "sandwich" test procedures. For a comprehensive review of the possibilities the reader is referred to reference 31. The Dutch group at Organon/Organon Teknika successfully developed EIA systems in the field of reproductive endocrinology, including assays for human chorionic gonadotropin,32'33 total estrogens, and human placental lactogen34 in plasma. However, the new tests did not become commercially successful until the late 1970s and early 1980s, when they matched the exquisite sensitivity of existing RIA systems for the same analytes. In the early 1970s, blood-bank screening for virologic diseases such as hepatitis B antigen was done either by (semi) automated RIA or nonradioactive, but rather cumbersome, hemagglutination tests. In 1976, Organon Teknika developed and marketed a highly successful EIA system for the hepatitis B surface antigen (HbsAg),35 featuring a 96-well microtiter plate format. This test became the first commercially available EIA. Other microbiological and virologic diagnostic tests soon followed, e.g., for hepatitis B "e" (HBe) antigens,36 rubella antibodies, toxoplasma antibodies, and in the 1980s, an EIA system for detection of human immunodeficiency virus antibodies.

Principle of Enzyme Immunoassay In terms of methodologies, heterogeneous EIAs are similar to RIAs, although detection of antigen-antibody interactions is afforded by cleavage of substrates by enzymes linked to antibodies. Heterogeneous EIAs are at least as sensitive as RIAs, and in some cases is more sensitive (Figure 17.2). Various enzymes can be utilized in EIAs. The most common are alkaline phosphatase, x-galactosidase, glucose oxidase, urease, and catalase. The development of substrates cleaved by enzymes initially employed colorimetric and fluorometric detection and later chemiluminescent methods. EIAs are readily amendable to adaptation to fully automated techniques. An important advantage of EIAs over RIAs is that the former can be developed as homogeneous assays in which the tedious washing step to remove free antigen is eliminated, although homogeneous EIAs are frequently less sensitive than RIAs or heterogeneous EIAs. The first homogeneous EIA developed was enzyme multiplied immunoassay technique (EMIT).37 In summary, the number of analytical and clinical investigations relying on EIA/ELISA measurement procedures worldwide is exceedingly large. Thus, the numbers of measurements and determinations using immunoassay for routine patient care are astronomical. The clinical impact of EIA/ELISA as nonradioactive variants of immunoassays is indeed overwhelming. Perlmann, Schuurs, Engvall, and van Weemen were honored for their inventions when they received the German scientific award of the "Biochemische Analytik" in 1976 in Germany, five years after they had published their first papers. Given the impact that their inventions have had on clinical diagnosis and healthcare in general, as well as on the development of a well-established in vitro diagnostic industry, these inventors deserve to be honored again.

T H E MEASUREMENT OF BIOLOGICAL MATERIALS

FLUORESCENT AND IMMUNOASSAYS

433

CHEMILUMINESCENT

Fluorescent Immunoassays Several decades ago, it was demonstrated that antibodies could be labeled with molecules that fluoresce.38Jl0 These fluorescent compounds are called fluorophores or fluorochromes. They have the ability to absorb energy from an incident light and convert that energy into light of a longer wavelength and lower energy as the excited electrons return to the ground state. Fluorophores are typically organic molecules with a ring structure, and each has a characteristic optimum absorption range.39 The time interval between absorption of energy and emission of fluorescence is very short and can be measured in nanoseconds. Ideally, a fluorescent molecule should exhibit high intensity, which can be distinguished easily from background fluorescence. It should also be stable and have a high molar extinction coefficient (a measurement of how strongly a chemical species absorbs light at a given wavelength). The two compounds most often used are fluorescein and rhodamine, because these can be readily coupled with antigen or antibody. Fluorescein absorbs maximally at 490 to 495 nm and emits a green color at 517 ran.41 It has a high intensity, good photostability, and a high quantum yield. Tetramethylrhodamine absorbs at 550 nm and emits red light at 580 to 585 nm. Because their absorbance and emission patterns differ, fluorescein and rhodamine can be used together. Newer compounds that are beginning to be used are phycobiliproteins derived from algae, porphyrins, and chlorophylls, all of which exhibit red fluorescence at over 600 nm.42 H e t e r o g e n e o u s Fluorescent Immunoassays

Heterogeneous Fluorescent Immunoassays (FIAs), which require a separation step, include the following: indirect, competitive, and sandwich assays. These are based on the same principles as those of EIAs, but in this case the label is fluorescent. Such a label can be applied to either analyte or antibody. Use of solid phase is the typical means of separation in heterogeneous assays. Microbeads made of polysaccharides and polyacrylamides have been used by a number of manufacturers. Either analyte or antibody can be attached to the beads and reacted with analyte and a fluorescent labeled analyte. Then the reaction mixture is centrifuged, the supernatant is discarded, and the beads are analyzed for fluorescence. More recently, the use of magnetic particles allows separation by applying a magnetic field.43 H o m o g e n o u s Fluorescent Immunoassays

Homogenous FIAs require no separation of procedure, so they are rapid and simple to perform.44'45There is only one incubation step and no wash step, and usually competitive binding is involved. The basis for this technique is the change that occurs in the fluorescent label on analyte when it binds to a specific antibody. Such changes can be related to wavelength emission or polarity.

434

BIOMARKERS

There is a direct relationship between the amount of fluorescence measured and the amount of analyte in the patient sample. As binding of patient analyte increases, binding of the fluorescent analyte decreases and hence more fluorescence is observed. Typically, homogenous assays including enzyme assays have suffered from low sensitivity. Hence, most research has aimed at increasing sensitivity and newer procedures have been developed that include florescence polarization immunoassay (FPIA),46 florescence excitation transfer immunoassay, and time-resolved fluorescence immunoassay.44'45 All of these require specific instrumentation. Fluorescence P o l a r i z a t i o n Immunoassay (FPIA)

FPIA is based on the change of polarization of fluorescent light emitted from a labeled molecule when it is bound by antibody.4M1 Incident light directed at the specimen is polarized with a lens or prism so the waves are aligned in one plane. If a molecule is small and rotates quickly enough, when it is excited by polarized light, the emitted light is unpolarized. If, however, the labeled molecule is bound to antibody, the molecule is unable to tumble as rapidly, and it emits an increased amount of polarized light. Thus, the degree of polarized light reflects the amount of labeled analyte that is bound. In FPIA, labeled analytes compete with unlabeled analyte in the patient sample for a limited number of antibody binding sites (Figure 17.4). The more analyte that is present in the sample, the less the fluorescence labeled analyte is bound and the less the polarization that will be detected (Figure 17.4). With competitive binding, analyte from the specimen and analyte-fluorescein (AF) labeled reagent competes for binding sites on the antibody. As a homogeneous immunoassay, the reaction is carried out in a single reaction solution, and the bound Ab-AF complex does not require a wash step to separate it from "free" labeled AF. FPIA is utilized to provide an accurate and sensitive measurement of small toxicology analytes such as therapeutic drugs, and drugs of abuse, toxicology, and some hormones. FPIA utilizes three key concepts to measure specific analytes in a homogeneous format: fluorescence, rotation of molecules in solution, and polarized light. Fluorescence: Fluorescein is a fluorescent label. It absorbs light energy at 490 nm and releases this energy at a higher wavelength (520 nm) as fluorescent light. Rotation of molecules in solution: Larger molecules rotate more slowly

FIGURE 17.4 Competitive fluorescence polarization immunoassay. Competitive binding, analyte from the specimen and analyte-fluorescein (AF) labeled reagent compete for binding sites on the antibody.

T H E MEASUREMENT OF BIOLOGICAL MATERIALS

435

in solution than do smaller molecules. This principle can be used to distinguish between the smaller analyte-fluorescein molecule, AF, which rotates rapidly, and the larger Ab-AF complexes, which rotate slowly in solution. Polarized Light: Fluorescence polarization technology distinguishes antigen-fluorescein (AF) label from antibody bound-analyte-fluorescein (Ab-AF) by their different fluorescence polarization properties when exposed to polarized light (Figure 17.5). Polarized light describes light waves that are only present in a single plane of space. When polarized light is absorbed by the smaller AF molecule the AF has the ability to rotate its position in solution rapidly before the light is emitted as fluorescence. The emitted light will be released in a different plane of space from that in which it was absorbed and is therefore called unpolarized light. With the larger sized Ab-AF complex, the same absorbed polarized light is released as polarized fluorescence because the much larger Ab-AF complex does not rotate as rapidly in solution. The light is released in the same plane of space as the absorbed light energy, and the detector can measure it (Figures 17.5 and 17.6A). Measurement of large complexes using fluorescence, rotation, and polarized light in FPIA is shown in Figure 17.6A. FPIA results in an inverse dose response curve such that lower levels of patient analyte result in a higher signal (in this case, the signal is polarized light) (Figure 17.6B). High signal at low patient analyte levels results in a highly sensitive assay. Table 17.1 shows the advantages and disadvantages of florescence assays.

Chemiluminescent Immunoassays Several recently developed immunoassays use the principle of chemiluminescence to follow analyte antibody combination.52 Chemiluminescence is the emission of light caused by a chemical reaction producing an excited molecule that decays back to its original ground state. A large number of molecules are capable of chemiluminescence, but some of the most common

FIGURE 17.5 Polarized Light. Fluorescence polarization technology distinguishes analyte-fluorescein (AF) label from antibody bound-AF (Ab-AF) by their different fluorescence polarization properties when exposed to polarized light.

436

BIOMARKERS

FIGURE 17.6 A) The light is released in the same plane of space as the absorbed light energy, and the detector can measure it. Measurement of large complexes (AF) is performed using fluorescence, rotation, and polarized light in fluorescence polarization immunoassay (FPIA) 490nm. B) Measurement of large complexes using fluorescence, rotation, and polarized light in FPIA. FPIA results in an inverse dose response curve such that lower levels of patient analyte result in a higher signal (in this case, the signal is polarized light).

substances used are luminol,53 acridium esters,54 peroxyoxalates55,56 ruthenium derivative, and dioxetanes57 (Figure 17.7A). When these substances are oxidized, typically using hydrogen peroxide and an enzyme for a catalyte, intermediates are produced that are of a higher energy state. These intermediates spontaneously return to their original state, giving off energy in the form of light (Figure 17.7B). Light emissions range from a rapid flash of light to a more continuous glow that can last for hours. Different types of instrumentation are necessary for each kind of emission.58Table 17.1 shows advantages and disadvantages of chemiluminescence assays.

M U L T I P L E X I N G U S I N G A N T I B O D Y ARRAY A N D BEAD I M M U N O A S S A Y S Innovation in immobilization surfaces and detection strategies has increased the number of planar arrays and bead-based technologies. Planar antibody arrays are the most common type of protein arrays. This section describes the main formats of planar arrays and the differences between planar arrays and bead-based assays (Figure 17.8).

T H E MEASUREMENT OF BIOLOGICAL MATERIALS

TABLE 17.1

437

Advantages and disadvantages of chemiluminescence versus florescence assays.

Method

Florescence

Chemiluminescence

Advantages

• Sensitivity is higher than those of radiolabels and enzyme reactions.

• An excellent sensitivity comparable to EIA and RIA.

• The methodology is simple and there is no need to deal with and dispose of hazardous substances

• Reagents are stable and relatively nontoxic. • The sensitivity of some assays has been reported to be in the range of attamoles (10-18) to zeptomoles (10-21). • Because very little reagent is used, they are inexpensive to perform. • Detection systems basically consist of photomultiplier tubes which are simple and relatively inexpensive.

Disadvantages

• The main problem is the separation of the signal on the label from background fluorescence because of different organic substances normally present in serum. • Nonspecific binding to substances in serum can cause diminishing of the signal and change the amount of fluorescence generated.

• False results may be obtained if there is lack of precision in injection of the hydrogen peroxide. • Some biological materials such as urine or plasma may cause diminishing of the light emission.

• Any bilirubin or hemoglobin present can absorb either the excitation or emission energy. • It requires expensive dedicated instrumentation, which may limit its use in smaller laboratories.

Planar Protein Array Formats The main planar label-based assays are 1-antibody assays, which use one antibody to capture the target molecule, and sandwich assays, which use two antibodies to capture the target protein.59-61 One-antibody and sandwich assays both have advantages and pitfalls. In one-antibody label-based assays, the targeted proteins are captured by an immobilized antibody and detected through labeling with a tag (Figure 17.8A). In direct labeling, proteins are labeled with a fluorophore such as cyanine (Cy3 or Cy5). In indirect labeling, proteins are labeled with a tag that is later detected by a labeled antibody. One-antibody label-based assays allow the incubation of two different samples, each labeled

438

BIOMARKERS

FIGURE 17.7 A) Shows luminol as an example of chemiluminescence where the emission of light caused by a chemical reaction produces an excited molecule that decays back to its original ground state. B) Illustrates when these chemiluminescence molecules (such as luminol) are oxidized, typically using hydrogen peroxide and an enzyme for a catalyst, intermediate products are produced that are of a higher energy state.These intermediates spontaneously return to their original state, giving off energy in the form of light.

with a different tag on the arrays. These types of assays, therefore, allow the use of a reference sample that is co-incubated with a test sample and facilitates normalization.59-61 Another advantage of these types of assays is that they are competitive as the analytes in the test and reference solutions compete for binding at the antibodies,59-61 leading to improvement in linearity of response and dynamic range compared with noncompetitive assays.61 The main disadvantage of these types of assays is the disruption of the analyte-antibody interaction by the label, which may limit detection as well as sensitivity and specificity. In the sandwich label-based format, immobilized antibodies capture unlabeled proteins, which are detected by another antibody, with the signal for detection generated by several methods (Figure 17.8B). The use of two antibodies targeting each analyte confers greater specificity than label-based assays. The reduced background of these assays also increases the detection limit. The sandwich format allows only non-competitive assays, because only one sample can be incubated on each array.59-61 Noncompetitive assays have sigmoidal binding responses, which are linear in competitive formats, and require standard curves of known concentrations of analytes to achieve accurate calibration of concentrations.61 Sandwich assays are more difficult to develop in a multiplexed manner than label-based assays because matched pairs of antibodies and purified analytes may not be available for each target,

T H E MEASUREMENT OF BIOLOGICAL MATERIALS

439

FIGURE 17.8 A) In one-antibody (Ab) label-based assays, the targeted proteins are captured by an immobilized antibody and detected through labeling with a tag. B) In the sandwich label-based format, immobilized antibodies capture unlabeled proteins, which are detected by another antibody, with the signal for detection generated by several methods. C) Other antibody and protein array approaches are modifications of one-antibody and sandwich label-based arrays. These alternative strategies of protein arrays allow detection of proteins on whole cells without protein isolation. D) A growing area of cancer research that uses protein arrays on serum specimens entails the development and design of tumorassociated antigen (TAA) arrays to enhance detection of autoantibodies againstTAAs for cancer diagnosis. E) Complex protein extracts can also be spotted onto membranes and probed with antibodies targeting specific proteins on the so-called reverse-phase arrays. F) Proteins in suspension can also be detected by use of bead arrays. (See color insert for a full color version of this figure.)

and the potential cross-reactivity among detection antibodies increases with additional analytes.60'6I The size of multiplexed sandwich assays is limited to 30 to 50 different targets,59-61 in contrast to one-antibody assays, for which

440

BIOMARKERS

only the availability of antibodies and the space on the substrate limit the number of targets analyzed. Other antibody and protein array approaches are modifications of one-antibody and sandwich label-based arrays. These alternative strategies of protein arrays allow detection of proteins on whole cells without protein isolation (Figure 17.8C).60,62 A growing area of cancer research that uses protein arrays on serum specimens entails the development and design of tumor-associated antigen (TAA) arrays to enhance detection of autoantibodies against TAAs for cancer diagnosis (Figure 17.8D). The rationale is related to the presence of antibodies in the cancer sera that react with a unique group of autologous cellular antigens or TAAs.63-M Complex protein extracts can also be spotted onto membranes and probed with antibodies targeting specific proteins on the so-called reverse-phase arrays65,66 (Figure 17.8E). Proteins in suspension can also be detected by use of bead arrays (Figure 17.8F)62,67-70

Suspension or Bead-Based Arrays Suspension or bead-based arrays use different fluorescent beads. Each bead is coated with a different antibody, and all beads are spectrally resolvable from each other.62,67-70 The beads are incubated with a sample to allow protein binding to capture the antibodies, and the mixture is incubated with a cocktail of detection antibodies, each corresponding to one of the capture antibodies. The detection antibodies are tagged to allow fluorescent detection. The beads are passed through a flow cytometer system, and each bead is probed by two lasers, one to read the color or identity of the bead, and another one to read the amount of detection antibody on the bead.62,67~70 Multiplexed bead-based flow-cytometry assays represent an active area of development. Differentially identifiable beads coated with proteins, autoantigens, or antibodies use a cytometer system to identify a variety of bound antibodies or proteins.62,67_7° Advances in instrumentation and bead chemistries will probably make this approach valuable for the detection of circulating diseases markers and cancer cells in clinical practice. In another version of this method, suspensions of cells are incubated on antibody arrays, and the number of cells that bind each antibody is quantified by dark-field microscopy. These arrays enable characterization of multiple membrane proteins in specific cell populations or changes in cell surfaces induced by drug therapies.

Example of M u l t i p l e x i n g Technology The use of protein array technology over conventional assay methods has advantages that include simultaneous detection of multiple analytes, reduction in sample and reagent volumes, and high output of test results. The susceptibility of ligands to denaturation, however, has impeded production of a stable, reproducible biochip platform, limiting most array assays to manual or, at most, semi-automated processing techniques. Such limitations may be overcome by novel biochip fabrication procedures.

T H E MEASUREMENT OF BIOLOGICAL MATERIALS

441

Simultaneous Multi-Analyte Detection Introduction The world of Healthcare and Drug Discovery encompasses many arenas including understanding the nature of the problem—discovery research; targeting the potential candidates for regulation and amendment—target profiling & lead optimization; testing dose response and effects—ADME; and finally clinical testing to prove efficacy—clinical trials & diagnostics. In each phase, several biomarkers may need to be screened. Cytokines, chemokines, and cell signaling targets are a common area of biomarker analysis in many fields like cardiovascular disease, diabetes research and treatment, immunological disorders, or even the cosmetic industry. One may also target more specific analytes like osteocalcin for bone metabolism or adiponectin for obesity research. Conventional assays like radioimmunoassays and ELISAs are restricted in sample volume and number of tests that can be conducted to get the adequate information. Most assays may not address the complete dynamic range to measure both basal and elevated concentrations, requiring repetition. This requires a large amount of time and cost involvement, and may also challenge the integrity of the sample, and hence data generated. The ideal testing method would be a convenient, simple tool that enables measurement of all appropriate analytes, in a small sample volume, to provide a relevant conclusion. It would need to be comparable and compatible with the conventional assays to enable effortless adoption. It should be flexible to incorporate new tests and be easily transferred for reproducibility. In the following section of the chapter we will present three commercially available platforms for multiplexing: bead particles, arrays electro-chemiluminescence, and biochip array technology. M u l t i p l e Bead Particle Technology

The xMAP technology used in the Luminex (Luminex Corporation, Austin, TX) and equivalent instruments meets the requirements described above. Luminex's xMAP® (Multi Analyte Profiling, "x" being the variable depicting the type of assay) technology is built on proven, existing methods of flow cytometry, microsphere-based assays, and traditional chemistry, combined in a unique way71 (Figure 17.9). xMAP technology can be configured to perform a wide variety of bioassays quickly and could be cost-effective when used as multiple (save operator time) (Figure 17.10). However, the sensitity of assays may vary. The technology is based on uniformly sized 5.6 micron carboxylated polystyrene hydrophobic beads. Each bead contains a mixture of two fluorescent dyes that provide a "unique signature" which can be identified by the Luminex instrument. Thus, when a specific antibody is attached to a specific bead, the instrument can define and quantify the analyte being measured (Figure 17.11). These beads are dyed by adding them to an organic solvent containing the two fluorescent dyes in a defined ratio. The dyes are absorbed into the beads.

442

BIOMARKERS

FIGURE 17.9 The multiplex Luminex® assay format differs from conventional ELISA in one significant way—the multiplex capture antibody is attached to a polystyrene bead, whereas the ELISA capture antibody is attached to the microplate well. The use of the suspension bead-based technology enables the multiplexing capabilities of the Luminex® assays.The xMAP® technology uses 5.6 micron polystyrene microspheres (Luminex beads), which are internally dyed with red and infrared fluorophores of differing intensities. Each bead is given a unique number or bead region, allowing differentiation of one bead from another Beads covalently bound to different antibodies (capture antibodies) can be mixed in the same assay, utilizing a 96-well microplate format. At the completion of the sandwich immunoassay, beads can be read, using the Luminex® detection system, in single-file by dual lasers (633 nm and 532 nm wavelength) for classification and quantification of each analyte. (Kindly provided by Millipore.)

The beads are then dropped into an aqueous solution whereby the dyes are now trapped inside each bead set due to their hydrophobic nature. The basic and most commonly used instrument can detect a hundred different bead sets, where each has a designated unique number. Theoretically, this enables assaying up to 100 different analytes from the same sample; however, so far the highest number of tests multiplexed is closer to 31 analytes. Here is an example of a multiplexed immunoassay format. A specific antibody is attached to a specific bead set. Thus, to measure five analytes, the specific capture antibody for each analyte would be covalently conjugated tofivedifferent carboxylated bead sets. These beads are mixed together and added to the standard or sample containing the analytes in a 96-well plate format. This is followed by incubating with the biotinylated detection antibody mixture which binds to the bead immobilized analyte, followed by the reporter fluorescent dye phycoerythrin, conjugated to streptavidin forming an analyte-antibody sandwich. The Luminex instrument (or equivalent) collects the reaction mixture from each well and, following the fluidics principle of flow cytometry, transports these beads into the pathway of two different lasers in a flow cell. One laser identifies the specific bead, which in turn identifies the analyte being assayed; while the other laser measures the intensity of the fluorescent signal from the reporter dye. The data is analyzed and reported in real time by dedicated analysis software.

T H E MEASUREMENT OF BIOLOGICAL MATERIALS

443

FIGURE 17.10 Example of multiplex of several human cytokines/chemokines in one reaction vial as shown by the standard curves in serum matrix. (Kindly provided from Millipore.) (See color insert for a full color version of this figure.)

Applications

Since its inception in 1999, the Luminex instrument has played a significant role in both human and veterinary drug discovery research, genetic analysis, pharmacogenomics, clinical diagnostics, and the general healthcare industry.72-77 These liquid microarrays have been used in neonatal blood screening. A drop of blood from a toe prick can be used directly in the assay to measure multiple analytes, thus ensuring the health of newborns.73 Alternately, a blood spot may be dried onto a filter and sent to any global location. This can be eluted in a small volume of buffer and used for screening. This has proven to be a useful, safe, and effective method for population studies across many demographics. It has proven to be a valuable tool in clinical trials for the Pharmaceutical Industry. A complete array of biomarkers may be screened when selecting a potential drug target. From this, a select few targets and diagnostic biomarkers may be chosen to continue testing, in vitro and in vivo, in small

444

BIOMARKERS

FIGURE 17.1 I The reader uses a 532 nm green laser ("assay" laser) to excite the phycoerythrin (PE) dye of the assay (streptavidin-PE).The 635 nm solid state laser (red "classify" laser) is used to excite the dyes inside the beads to determine their "color" or"region" and is also used for doublet discrimination by light scatter. The reader has four detectors, one for each of the optical paths shown in the figure. Detectors are used to measure the fluorescence of the assay, to make bead determination (I -100), and lastly to discriminate between single and aggregate beads. (Kindly provided by Millipore.) (See color insert for a full color version of this figure.)

animal models. The biomarker arrays may be expanded to include the upstream or downstream regulators to understand their mechanism of action and ADME. As drug development progresses, toxicity panels may be included to assess negative effects. Once the target drug is selected in its appropriate formulation for administration, this moves into clinical trials where hundreds of subjects may need to be tested before the drug can enter the marketplace. The xMAP technology helps to accomplish this in a very short time, as opposed to the many years and resources it would take using conventional methods. The flexibility of the Luminex technology has enabled multi-analyte testing in sample matrices that would have been impractical to test in the past, e.g.,

T H E MEASUREMENT OF BIOLOGICAL MATERIALS

445

tears,78,79 breath condensates,80,81 gingival crevicular fluids,72 and cerebral fluid, among others.73 The Future While the Luminex instruments have been ideal for high-content screening, they were not considered high-throughput. They were not easily adaptable to liquid handling systems for automation. A maximum of 6-8 plates could be run in a typical day. It was also susceptible to sample matrix effects as insoluble paniculate matter could not be washed away. However, these limitations have been overcome by the introduction of two innovations in this technology. The use of magnetic beads in place of the standard polystyrene beads now enables use of automated liquid handlers and reduces matrix-related interferences in the assay. FlexMAP 3D—an upscale Luminex model that can detect up to 500 bead sets—can analyze 384 well plates and takes less than 20 minutes to read a plate. It also provides higher sensitivity and dynamic range. The software is compatible with automation and liquid handling instrumentation. With these improvements, one may significantly reduce the level of technical errors in sample handling and assay setup, which may be a significant application in studies requiring large numbers of test samples. The Luminex xMAP technology and instrumentation is highly adaptable and flexible. It has been effectively used in small labs and academic settings, as well as in the biopharmaceutical industry. Its applications range from in-depth cell-based studies to clinical diagnostics. One can visualize this becoming an integral part of global healthcare.

Electrochemiluminescence (ECL) Microarrays Electrochemiluminescence (ECL) methods use electric current to generate light-emitting reactions.82 Upon application of a voltage, the ruthenium label on the detection molecule and the co-reactant tripropylamine are oxidized. A high-energy electron transfer from the tripropylamine radical to ruthenium puts ruthenium in an excited state. Relaxation of the excited state ruthenium to the ground state generates chemiluminescence emission at 620 nm.82,83 Like other chemiluminescent assay methods,84 ECL methods have a high signal and low background, since no external light source is used to generate signals. In addition, this ECL technology requires that the ruthenium be in close proximity to the electrodes, thus further reducing background produced by unbound detection molecules. An ECL methodology using magnetic beads and a fluidics system (BioVeris, Gaithersburg, MD) has been used for immunoassays.82,83,85 However, it is not amenable to intact cell-based assays. Recently, an ECL methodology using microwell plates with carbon electrodes built into the bottom became available. A charge-coupled device camera within the reader records ECL signals (Meso Scale Discovery, Gaithersburg, MD). The carbon surface electrodes, originally used to coat soluble capture molecules for immunoassays, were later found to also bind cells tightly.86 Consequently, suspension cells can be immobilized on the carbon surface plates and the plates

446

BIOMARKERS

can be washed on a microplate washer. This eliminates the tedious centrifugation steps used in the live suspension cell-based ELISAs. MSD's assays are based on MULTI-ARRAY® technology, a proprietary combination of ECL detection and patterned arrays. ECL detection offers a unique combination of sensitivity, dynamic range, and convenience. Arrays bring speed and high density of information to discovery through miniaturization, organization, and parallel processing of biological assays. ECL Diagram Electrochemiluminescence detection uses labels that emit light when electrochemically stimulated. Background signals are minimal because the stimulation mechanism (electricity) is decoupled from the signal (light). Labels are stable, non-radioactive, and offer a choice of convenient coupling chemistries. They emit light at -620 nm, eliminating problems with color quenching. Few compounds interfere with electrochemiluminescent labels so you can use large, diverse libraries with confidence. Multiple excitation cycles of each label amplify the signal to enhance light levels and improve sensitivity (Figure 17.12). Detection MSD's instruments use custom-designed optics and photodetectors to collect and quantitatively measure light emitted from the microplates. Proprietary electronics and signal processing algorithms convert the measured signal into useful data.

FIGURE 17.12 Multiple excitation cycles of each label amplify the signal to enhance light levels and improve sensitivity. (Kindly provided by Meso Scale Discovery.)

THE MEASUREMENT OF BIOLOGICAL MATERIALS

447

Multi Arrays Technologies MULTI-ARRAY® microplate footprints are integrated electrodes and arrays form conveniences and performance. The technology offers different formats of arrays: the multi-array or multi-spot plates. The multi-array plates feature flexibility to coat on surface in either 96 or 384 wells format. This is very useful for high throughput screening. The multi-spot offers the flexibility of high throughput multiplexing of biomarkers in a 96-well single spot or 4-, 7-, and 10-spot array (Figure 17.13).

Biochip A r r a y Technology The biochip array technology (Figure 17.14) may be novel, but the methodology is familiar, featuring competitive and sandwich immunoassays.87 The capture agents can be antigens or antibodies. Antibodies are covalently bound in the correct orientation to the test regions on the surface of the biochip. Analytes in the patient sample are bound by their complementary antibodies on the biochip. The enzyme-labeled detection agent also binds to the analytes. The signal reagents produce a chemiluminescent reaction that is used to quantify the amount of each analyte present. The biochip is the foundation of biochip array technology. A single 9 mm biochip acts as the reaction vessel, replacing multiple cuvettes. Randox biochips (Randox Laboratories Ltd, United Kingdom) currently hold up to 25 tests but are capable of carrying over 100 different assays. Evidence uses one biochip per patient sample to produce a panel of test results: the patient profile.

FIGURE 17.1 3 The multi-spot offers the flexibility of high thoughput multiplexing of biomarkers in a 96- and 384-well single spot (A) or 4-, 7-, and 10-spot array (B). (Kindly provided by Meso Scale Discovery.)

448

BIOMARKERS

FIGURE 17.14 The biochip is the foundation of biochip array technology. A single 9 mm biochip acts as the reaction vessel, replacing multiple cuvettes. Randox biochips currently hold up to 25 tests but are capable of carrying over 100 different assays. (Kindly provided by Randox.)

Biochip M a n u f a c t u r i n g

Biochip manufacturing requires a multidisciplinary approach and strict quality control procedures. The surface of the ceramic biochip is activated using silanation and a polymer method technique to create a uniform hydrophobic surface. Spots are precisely created using piezoelectric nano-dispensing of 330 pL droplets sequentially to achieve a total volume of 10 nL of ligand solution. The hydrophobic surface of the biochip prevents the fluid from spreading out, containing it in a uniform drop with a diameter of 0.3 nm. Dispense-head cameras monitor production to automatically detect deviations in size, shape, and position.87 The charge-coupled device (CCD) camera is used to quantify the chemiluminescence generated. The image and numerical data are archived along with QC and calibration data (Figure 17.15). Applications

Evidence assays are arranged into disease-orientated test panels (e.g., fertility) or related groups of tests (e.g., drugs of abuse, renal function profile). The tests have been carefully selected to offer established, emerging, and novel assays of clinical significance arrays in the fields of cytokines,88'89, n-93 adhesion molecules,90,91and cardiac markers.94,95 Customized arrays can be manufactured to customer requirements.

F U T U R E OF I M M U N O A S S A Y S The first immunoassay was described by Berson and Yalow in 1959. Their work resulted in their receipt of the Nobel Prize in Medicine in 1977. Since this introduction, immunoassays have evolved considerably. There have been several milestones that have led to the proliferation of modern immunoassays.

T H E MEASUREMENT OF BIOLOGICAL MATERIALS

449

FIGURE 17.15 The charge-coupled device camera is used to quantify the chemiluminescence generated.The image and numerical data are archived along with QC and calibration data. (Kindly provided by Randox.)

The development of monoclonal antibodies from mouse hydridoma cells by Millstein and Kohler (Nobel Prize in 1984) enabled the production of high quantities of antibodies with well characterized epitope specificity. The first homogenous immunoassay (no separation step required) was the EMIT, which enabled adaptation of this assay onto automated chemistry platforms. EMIT was also one of the first immunoassays that made use of non-isotopic labels. Other non-isotopic labels became available, such as chemiluminescence, to improve the analytical sensitivity of immunoassays. The advantages of highsensitivity immunoassays have created expanded diagnostic roles for some existing assays such as thyroid stimulating hormone for hyperthyroidism, C-reactive protein for cardiovascular risk assessment, and other applications. The development of instrumentation capable of automated heterogeneous immunoassays (separation step to improve sensitivity) has enabled movement of this technology from the "special chemistry" sections of a clinical laboratory into the "core" laboratory with other high-volume testing. Today, immunoassays play a prominent role in the analysis of many clinical laboratory analytes such as proteins, hormones, drugs, and nucleic acids. The future involves development of assays with higher sensitivities which will enable the discovery of new biofnarkers for disease diagnosis, and technology that will enable simultaneous multimarker analysis of tests whose needs are naturally grouped together (e.g., cytokines and allergens).

SUMMARY P O I N T S 1. 2.

Immunoassay methodologies represent the most frequently used approach to measure biological compounds in translational, clinical research and biomarkers discoveries. The new generation of non-isotopic immunoassays using chemiluminescence and fluorescence labeling molecules are safer than isotopic immunoassays.

450

BIOMARKERS 3.

4.

5.

Sensitivity is higher than those of radiolabels and enzyme reactions. The methodology is simple and there is no need to deal with and dispose of hazardous substances. Microarray, bead-based multiplex technologies and instrumentation are highly adaptable and flexible. They have been effectively used in small labs and academic settings; as well as in the biopharmaceutical industry. The future of immunoassay-based technology involves development of assays with higher sensitivities which will enable the discovery of new biomarkers for disease diagnosis, and technology that will enable simultaneous multimarker analysis of tests whose needs are naturally grouped together (multiplex panels).

ACKNOWLEDGMENTS I would like to thank Sonali Nayak (Millipore Corporation), Karma Carrier (Meso Scale Discovery), and Rajnesh Mathur (Randox) for providing material for the figures. Also, a particular thanks to Loc Tran (Brigham and Women's Hospital) for the tremendous help in the preparation of this chapter.

REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Davies, C. (1994) Principles. In: The Immunoassay Handbook (D. Wild, Ed.), Pp 3 ^ 7 . New York:Stockton Press. Ashihara, Y., Kasahara, Y, and Nakamura, R.M. Immunoassays and Immunochemistry. In: Clinical Diagnosis and Management by Laboratory Methods, 20th Ed. (J. B. Henry, Ed.), pp. 821-849. W. B. Saunders; Philadelphia, PA. 2001. Van Regenmortel, M. H. Protein Antigenicity. Mol. Biol. Rep. Jun 1992;16(3): 133-138. Koehler, G. and Milstein, C. Continuous Cultures of Fused Cells Secreting Antibody of Predefined Specificity. Nature. 1975;256:495^197. Zola, H. Monoclonal Antibodies: A Manual of Techniques. CRC Press; Boca Raton, FL. 1987. Winter, G., Griffiths, A. D., Hawkins, R. E. and Hoogenboom, H. R. Making Antibodies by Phage Display Technology. Annu. Rev. Immunol. 12;1994;433-455. Yalow, R. S. and Berson, S. A. Immunoassay of Endogenous Plasma Insulin in Man. Clin. Invest. 1960;39:1157-1175. Nobel Prize Home Page, http://nobelprize.org/medicine/laureates/1977/index. html (Accessed June 2005). Ekins, R. P. The Estimation of Thyroxine in Human Plasma by an Electrophoretic Technique. Clin. Chem. Ada. 1960;5:453-459. Miles, L. E. M. and Hales, C. N. Labelled Antibodies and Immunological Assay Systems. Nature. 1968;219:186-189. Espinosa, R. J., Brugues, M. J., and Llanos, O. J. Technical and Clinical Performances of Six Sensitive Immunoradiometric Assays of Thyrotropin in Serum. Clin. Chem. 1987;33:1439-1445. Rojanasakul, A., Udomsubpayakul, U., and Chinsomboon, S. Chemiluminescence Immunoassay versus Radioimmunoassay for the Measurement of Reproductive Hormones. Int. J. Gynaecol. Obstet. May 1994;45(2): 141-146.

THE MEASUREMENT OF BIOLOGICAL MATERIALS 13. 14. 15. 16. 17. 18. 19. 20.

21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33.

451

Avrameas, S. and Uriel, J. Methode de Marquage D'antigenes Et D'anticorps Avec Des Enzymes Et Son Application en Immunodiffusion. CR Acad. Sci. Hebd. Seances Acad. Sci. D. 1966;262:2543-2545. Avrameas, S. Coupling of Enzymes to Proteins with Glutaraldehyde. Immunochemistry. 1969;6:43-52. Nakane, P. K. and Pierce, G. B. Enzyme-Labeled Antibodies for the Light and Electron Microscopic Localization of Tissue Antigens. J. Cell. Biol. 1967;33:307-318. Engvall, E. and Perlmann, P. Enzyme-Linked Immunosorbent Assay (ELISA). Quantitative Assay of Immunoglobulin G. Immunochemistry. 1971;8:871-874. Van Weemen, B. K. and Schuurs, A. H. W. M. Immunoassay Using AntigenEnzyme Conjugates. FEBS Letts 1971;15:232-236. U.S. Patent 762120. United States Patent and Trademark Office Home Page. http://www.uspto.gov (Accessed June 2005). Patents 7016396 snd 7018838. Deutsches Patent-Und Markenamt. http://depatisnet.dpma.de (Accessed June 2005). Pape, G. R., Troye, M., Axelsson, B., and Perlmann, P. Simultaneous Occurrence of Immunoglobulin-Dependent and Immunoglobulin-Independent Mechanisms in Natural Cytotoxicity of Human Lymphocytes. J. Immunol. 1979; 122: 2251-2260. Perlmann, P., Berzins, K., Perlmann, H., Troye-Blomberg, M., Wahlgren, M., and Wahlin, B. Malaria Vaccines: Immunogen Selection and Epitope Mapping [Review]. Vaccine. 1998;6:183-187. Voller, A., Huldt, G., Thors, C , and Engvall, E. New Serological Test for Malaria Antibodies. Br. Med. J. 1975;1:659-661. Ljungstrom, I., Engvall, E., and Ruitenberg, E. J. Proceedings: ELISA, Enzyme-Linked Immunosorbent Assay—A New Technique for Sero-Diagnosis of Trichinosis. Parasitology. 1974;69:xxiv. Engvall, E. Quantitative Enzyme Immunoassay (ELISA) in Microbiology. Med. Biol. 1977;55:193-200. Seppala, M., Rutanen, E. M., Heikinheimo, M., Jalanko, H., and Engvall, E. Detection of Trophoblastic Tumour Activity by Pregnancy-Specific 61 Glycoprotein. Int. J. Cancer. 1978;21:265-267. Sipponen, P., Ruoslahti, E., Vuento, M., Engvall, E., and Stenman, U. CEA and CEA-Like Activity in Gastric Cancer. Ada. Hepatogastroenterol. (Stuttg) 1976;13:276-279. Uotila, M., Ruoslathi, E., and Envall, E. Two-Site Sandwich Enzyme Immunoassay with Monoclonal Antibodies to Human Alphafetoprotein. J. Immunol. Methods. 1981;42:11-15. Engvall, E. and Wewer, U. M. The New Frontier in Muscular Dystrophy Research: Booster Genes. FASEB J. 2003;17:1579-1584. Wide, L. and Porath, J. Radioimmunoassay of Proteins with the Use of Sephadex-Coupled Antibodies. Biochem. Biophys. Ada. 1966;30:257-260. Catt, K. and Tregear, G. W. Solid-Phase Radioimmunoassay in Antibody-Coated Tubes. Science. 1967;158:1570-1572. Schuurs, A. H. W. M. and Van Weemen, B. K. Enzyme-Immunoassay: A Powerful Analytical Tool [Review]./. Immunoassay. 1980;1:229-249. Van Weemen, B. K. and Schuurs, A. H. W M. Immunoassay Using AntigenEnzyme Conjugates. FEBS Letts. 1971;15:232-236. Van Weemen, B. K. and Schuurs, A. H. W. M. Immunoassay Using AntibodyEnzyme Conjugates. FEBS Lett. 1974;43:215-218.

BIOMARKERS 34.

35. 36. 37. 38. 39. 40. 41. 42. 43.

44. 45. 46. 47. 48. 49.

50.

51.

Bosch, A. M. G., Van Hell, H., Brands, J. A. M., Van Weemen, B. K., and Schuurs, A. H. W. M. Methods for the Determination of Total Estrogens (TE) and Human Placental Lactogen (HPL) in Plasma of Pregnant Women by Enzyme-Immunoassay. Clin. Chem. 1975;21:1009 Wolters, G., Kuijpers, L. P. C , Kacaki, J., and Schuurs, A. H. W. M. EnzymeImmunoassay for Hbsag. The Lancet. 1976;II:690. Van Der Waart, M., Snelting, A., Cichy, J., Wolters, G., and Schuurs, A. H. W. M. Enzyme-Immunoassay in Diagnosis of Hepatitis with Emphasis on the Detection of "E" Antigen (Hbeag). J. Med. Virol 1978;3:43^19. Reubenstein, et al., 1972. Wood, B. T., Thompson, S. H., and Goldstein, G. Fluorescent Antibody Staining. Preparation of Fluorescein-Isothiocyanate-Labeled Antibodies. J. Immunol. 1965Aug;95(2):225-229. Borek, F. and Silverstein, A. M. A New Fluorescent Label for Antibody Proteins. Arch. Biochem. Biophys. Apr 1960;87:293-297. Christopoulos, T. K. and Diamantis, E. P., Fluorescence Immunoassays, Immunoassay. Academic Press. 1996;309-335. Wisdom, G. B. Conjugation of Antibodies to Fluorescein or Rhodamine Methods Mol. Biol. 2005;295:131-134. Gray, B. H. and Gantt, E. Spectral Properties of Phycobilisomes and Phycobiliproteins from the Blue-Green Alga-Nostoc. Sp. Photochem. Photobiol. Feb 1975;21(2):121-128. Taber, L. K., O'Brien, P., Bowsher, R. R., and Sportsman, J. R. Competitive Particle Concentration Fluorescence Immunoassay for Measuring 5,10-Dideaza-5,6,7,8-Tetrahydrofolic Acid (Lometrexol) in Serum. Clin. Chem. 1991 ;37: 254-260. Mathis, G. Rare Earth Cryptates and Homogeneous Fluoroimmuno-Assays with Human Sera. Clin. Chem. 1993;39:1953-1959. Barnard, G., Kohen, E, Mikola, H., and Lovgren, T. Measurement of Estrone3-Glucuronidc in Urine by Rapid, Homogeneous Time-Resolved Fluoroimmunoassay. Clin. Chem. 1989;35:555-559. Dandiker, W. B., Kelly, R. J., Dandiker, J., Farcuhar, J., and Levin, J. Fluorescence Polarization Immunoassay. Theory and Experimental Methods. Immunochemistry. 1973;10:219-227. Dandiker, W. B., Hsu, M. L., Levin, J., and Rao, R. R. Equilibrium and Kinetic Inhibition Assays Based Upon Fluorescence Polarization. Methods Enzymol. 1981;74:3-28. Dandiker, W. B. and De Saussure, V. A. Fluorescence Polarization in ImmunoChemistry. Immunochemistry. 1970;7:799-828. Jolley, M. E., Stroupe, S. D., Wang, C. H. T., Panas, H. N., Keegan, C. L., Schmidt, R. L., and Schwenzcr, K. S. Fluores-Cence Polarization Iminunoassay. I. Monitoring Aminoglycoside Antibiotics in Serum and Plasma. Clin. Chem. 1981;27:1190-1197. Popelka, S. R., Miller, D. M., Holen, J. T., and Kelso, D. M. Fluorescence Polarization Immunoassay. II. Analyzer for Rapid Precise Measurement of Fluorescence Polarization with Use of Disposable Cuvettes. Clin. Chem. 1981 ;27: 1198-1201. Jolley, M. E., Stroupe, S. D., Schwenzer, K. S., Wang, C. J., Lu-Steffes, M., Hill, H. D., Popelka, S. R., Holen, J. T., and Kelso, D. M. Fluorescence Polarization Immunoassay. III. An Automated System for Therapeutic Drug Determination. Clin. Chem. 1981;27:1575-1579.

THE MEASUREMENT OF BIOLOGICAL MATERIALS 52. 53. 54. 55. 56. 57. 58.

59. 60. 61. 62. 63. 64. 65.

66. 67. 68.

69. 70.

453

Christopoulos, T. K. and Diamantis, E. P., Chemiluminescence Immunoassays. Immunoassay. Academic Press. 1996;337-353. Kricka, L. J. Ligand-Binder Assays. New York, Dekker, 1985. McCapra, R, Tutt, R. E., and Topping, R. M. Assay Method Utilizing Chemiluminescence. UK Patent 1977; 1,461,877. Masuya, H., Kondo, K., Aramaki, Y., and Ichimori, Y. Pyridopyridazine Compounds and Their Use. Eur. Patent Appl. 1992,491.477. Iim, Yoshida H. and Aramaki, Y, et al. Improved Enzyme Immunoassay for Human Fibroblast Growth Factor Using a New Enhanced Chemiluminescence System. Biochem. Biophys. Res. Commun. 1993;193:540-545. Hummelen, J. C , Luider, T. M., and Wynberg, H. Stable 1, 2-Dioxetanes as Labels for Thermochemiluminescent Immunoassay. Methods Enzymol. 1986;133: 531-557. Stanley, P. E. Commercially Available Luminometers and Imaging Devices for Low-Light Measurements and Kits and Reagents Utilizing Bioluminescence or Chemiluminescence: Survey Update I. J. Biolumin. Chemilumin. 1993;8: 237-240. Haab, B. B., Dunham, M. J., and Brown, P. O. Protein Microarrays for Highly Parallel Detection and Quantitation of Specific Proteins and Antibodies in Complex Solutions. Genome Biol. 2001;2:RESEARCH0004. Chan, S. M., Ermann, J., Su, L., Fafhman, C. G., and Utz, P. J. Protein Microarrays for Multiplex Analysis of Signal Transduction Pathways. Nat. Med. 2004;10:1390-1396. Barry, R., Diggle, T., Terrett, J., and Soloviev, M. Competitive Assay Formats for High-Throughput Affinity Arrays. J. Biomol. Screen. 2003;8:257-263. Kingsmore, S. F. Multiplexed Protein Measurement: Technologies and Applications of Protein and Antibody Arrays. Nat. Rev. Drug Discov. 2006;5:310-321. Wang, X., Yu, J., Sreekumar, A., Varambally, S., Shen, R., and Giacherio, D., et al. Autoantibody Signatures in Prostate Cancer. N. Engl. J. Med. 2005;353: 1224-1235. Anderson, K. S. and Labaer, J. The Sentinel Within: Exploiting the Immune System for Cancer Biomarkers. J. Proteome Res. 2005;4:1123-1133. Nishizuka, S., Charboneau, L., Young, L., Major, S., Reinhold, W. C , and Waltham, M., et al. Proteomic Profiling of the NCI-60 Cancer Cell Lines Using New High-Density Reverse-Phase Lysate Microarrays. Proc. Natl. Acad. Set USA. 2003;100:14229-14234. Petricoin, E. R, III, Bichsel, V. E., Calvert, V. S., Espina, V., Winters, M., and Young, L., et al. Mapping Molecular Networks Using Proteomics: A Vision for Patient-Tailored Combination Therapy. J. Clin. Oncol. 2005;23:3614-3621. Pang, S., Smith, J., Onley, D., Reeve, J., Walker, M., and Foy, C. A Comparability Study of the Emerging Protein Array Platforms with Established ELISA Procedures. J. Immunol. Meth. 2005;302:1-13. Lash, G. E., Scaife, P. J., Innes, B. A., Otun, H. A., Robson, S. C , and Searle, R. R, et al. Comparison of Three Multiplex Cytokine Analysis Systems: Luminex, Searchlight, and FAST Quant. J. Immunol. Meth. 2006;309:205-208. De Jager, W. and Rijkers, G. T. Solid-Phase and Bead-Based Cytokine Immunoassay: A Comparison. Methods. 2006;38:294-303. Waterboer, T., Sehr, P., and Pawlita, M. Suppression of Non-Specific Binding in Serological Assays. J. Immunol. Methods. 2006;309:200-204.

454

BIOMARKERS 71. 72.

73.

74.

75.

76.

77. 78.

79.

80.

81.

82. 83. 84.

Fulton, R. J., McDade, R. L., Smith, P. L., Kienker, L. J., and Kettman, J. R., Jr. Advanced Multiplexed Analysis with the Flowmetrix System. Clin Chem. Sep 1997 ;43(9): 1749-1756. Twetman, S., Derawi, B., Keller, M., Ekstrand, K., Yucel-Lindberg, T., and Stecksen-Blicks, C. Short-Term Effect of Chewing Gums Containing Probiotic Lactobacillus Reuteri on the Levels of Inflammatory Mediators in Gingival Crevicular Fluid. Ada. Odontol. Scand. 2009;67(1): 19-24. Buttram, S. D., Wisniewski, S. R., Jackson, E. K., Adelson, P. D., Feldman, K., Bayir, H., Berger, R. P., Clark, R. S., and Kochanek, P. M. Multiplex Assessment of Cytokine and Chemokine Levels in Cerebrospinal Fluid Following Severe Pediatric Traumatic Brain Injury: Effects of Moderate Hypothermia. J. Neurotrauma. 2007; 24(11):1707-1717. Lambeck, A. J., Crijns, A. P., Leffers, N., Sluiter, W. J., Ten Hoor, K. A., Braid, M., Van Der Zee, A. G., Daemen, T., Nijman, H. W., and Kast, W. M. Serum Cytokine Profiling as a Diagnostic and Prognostic Tool in Ovarian Cancer: A Potential Role for Interleukin 7. Clin. Cancer Res. 2007;13(8):2385-2391. Ray, C. A., Bowsher, R. R., Smith, W. C , Devanarayan, V., Willey, M. B., Brandt, J. T., and Dean, R. A. Development, Validation, and Implementation of a Multiplex Immunoassay for the Simultaneous Determination of Five Cytokines in Human Serum. J. Pharm. Biomed. Anal. 2005;36(5): 1037-1044. Hildesheim, A., Ryan, R. L., Rinehart, E., Nayak, S., Wallace, D., Castle, P. E., Niwa, S., and Kopp, W. Simultaneous Measurement of Several Cytokines Using Small Volumes of Biospecimens. Cancer Epidemiol. Biomarkers Prev. 2002; 11 (11): 1477-1484. Dunbar, S. A. and Jacobson, J. W. Application of the Luminex Labmap in Rapid Screening for Mutations in the Cystic Fibrosis Transmembrane Conductance Regulator Gene: A Pilot Study. Clin. Chem. Sep 2000;46(9): 1498-1500. De Paiva, C. S., Villarreal, A. L., Corrales, R. M., Rahman, H. T., Chang, V. Y, Farley, W. J., Stern, M. E., Niederkorn, J. Y, Li, D. Q., and Pflugfelder, S. C. Dry Eye-Induced Conjunctival Epithelial Squamous Metaplasia Is Modulated by Interferon-Gamma. Invest. Ophthalmol. Vis. Sci. Jun 2007;48(6):2553-2560. Malvitte, L., Montange, T., Vejux, A., Baudouin, C , Bron, A. M., CreuzotGarcher, C , and Lizard, G. Measurement of Inflammatory Cytokines by Multicytokine Assay in Tears of Patients with Glaucoma Topically Treated with Chronic Drugs. Br. J. Ophthalmol. Jan 2007;91(l):29-32. Epub Aug 30, 2006. Komakula, S., Khatri, S., Mermis, J., Savill, S., Haque, S., Rojas, M., Brown, L., Teague, G. W., and Holguin, F. Body Mass Index Is Associated with Reduced Exhaled Nitric Oxide and Higher Exhaled 8-Isoprostanes in Asthmatics. Respir. Res. Apr 16, 2007;8:32. Rosias, P. P., Robroeks, C. M., Kester, A., Den Hartog, G. J., Wodzig, W. K., Rijkers, G. T., Zimmermann, L. J., Van Schayck, C. P., Jobsis, Q., and Dompeling, E. Biomarker Reproducibility in Exhaled Breath Condensate Collected with Different Condensers. Eur. Respir. J. May 2008;31(5):934-942. Epub Jan 9, 2008. Yang, H., Leland, J. K., Yost, D. and Massey, R. J. Electrochemiluminescence: A New Diagnostic and Research Tool. Biotechnology 12. 1994;193-194. Liang, P., Sanchez, R. I., and Martin, M. T. Electrochemiluminescence-Based Detection of e-Lactam Antibiotics and e-Lactamases. Anal. Chem. 68. 1996; 2426-2431. Roda, A., Pasini, P., Mirasoli, M., Michelini, E. and Guardigli, M. Biotechnological Applications of Bioluminescence and Chemiluminescence, Trends Biotechnol. 22. 2004;295-303.

THE MEASUREMENT OF BIOLOGICAL MATERIALS 85.

86. 87. 88.

89. 90.

91.

92.

93. 94.

95.

455

Li, Y. M, Lai, M. T., Xu, M, Huang, Q., DiMuzio-Mower, J., Sardana, M. K., Shi, X. P., Tin, K. C , Shafer, J. A., and Gardell, S. J. Presenilin 1 Is Linked with c-Secretase Activity in the Detergent Solubilized State, Proc. Natl. Acad. Sci. USA91. 2000;6138-6143. Lu, Y., Wong, W. L., and Meng, Y. G. A High Throughput Electrochemiluminescent Cell-Binding Assay for Therapeutic Anti-CD20 Antibody Selection, J. Immunol. Methods. 314. 2006;74-79. Fitzgerald, S. P., Lamont, J., McConnell, R. I., and Elbenchikh, O. Development of a High Throughput Automated Analyser Using Biochip Array Technology. Clin. Chem. 2005;51:1165-1676. Berrahmoune, H., Lamont, J. V., Herbeth, B., Fitzgerald, P. S., and Visvikis-Siest, S. Biological Determinants of and Reference Values for Plasma Interleukin-8, Monocyte Chemoattractant Protein-1, Epidermal Growth Factor, and Vascular Endothelial Growth Factor: Results from the STANISLAS Cohort. Clin. Chem. Mar 2006;52(3):504-510. Epub Jan 19, 2006. Berrahmoune, H., Herbeth, B., Lamont, J. V., Masson, C , Fitzgerald, P. S., and Visvikis-Siest, S. Heritability for Plasma VEGF Concentration in the Stanislas Family Study. Ann. Hum. Genet. Jan 2007;71(Pt l):54-63. Kavsak, P. A., Ko, D. T., Newman, A. M., Palomaki, G. E., Lustig, V., Macrae, A. R., and Jaffe, A. S. "Upstream Markers" Provide for Early Identification of Patients at High Risk for Myocardial Necrosis and Adverse Outcomes. Clin. Chim. Ada. Jan 2008;387(1-2):133-138. Epub Oct 3, 2007. Kavsak, P. A., Ko, D. T., Newman, A. M., Lustig, V, Palomaki, G. E., Macrae, A. R., and Jaffe, A. S. Vascular versus Myocardial Dysfunction in Acute Coronary Syndrome: Are the Adhesion Molecules as Powerful as NT-Probnp for Long-Term Risk Stratification? Clin. Biochem. Apr 2008;41(6):436-439. Epub Dec 27, 2007. Banfi, G., Migliorini, S., Pedroni, R, Galliera, E., Dogliotti, G., Malavazos, A. E., and Corsi, M. M. Strenuous Exercise Activates Growth Factors and Chemokines Over-Expression in Human Serum of Top-Level Triathlon Athletes During a Competitive Season. Clin. Chem. Lab. Med. 2008;46(2):250-252. Roh, M. I., Kim, H. S., Song, J. H., Lim, J. B., and Kwon, O. W. Effect of Intravitreal Bevacizumab Injection on Aqueous Humor Cytokine Levels in Clinically Significant Macular Edema. Ophthalmology. Jan 2009;116(l):80-86. Horacek, J. M., Tichy, M., Pudil, R., Jebavy, L., Zak, P., Ulrychova, M., Vavrova, J., Maly, J., and Palicka, V. New Biomarkers of Myocardial Injury and Assessment of Cardiac Toxicity During Preparative Regimen and Hematopoietic Cell Transplantation in Acute Leukemia. Clin. Chem. Lab. Med. 2008;46(1): 148-149. Horacek, J. M., Tichy, M., Jebavy, L., Pudil, R., Ulrychova, M., and Maly, J. Use of Multiple Biomarkers for Evaluation of Anthracycline-Induced Cardiotoxicity in Patients with Acute Myeloid Leukemia. Exp. Oncol. Jun 2008; 30(2): 157-159.

This page intentionally left blank

CHAPTER

NANOSCALE TECHNIQUES FOR BIOMARKER QUANTIFICATION Madhukar Varshney and Harold G. Craighead

INTRODUCTION Miniaturized analytical devices and "labs-on-a-chip" are developing based on microfabrication technology. These systems are typically based on existing methods for biochemical analysis made more compact and rapid by the use of small fluid volumes and microfluidic systems for performing chemical reactions. Nanostructure science and engineering utilizing structures and devices with dimensions typically measuring in micrometers and nanometers, enables access to new physical length scales, and enables new approaches for molecular detection and analysis. The use of nanostructures can enable identification, detection, enumeration and isolation of small numbers of analyte molecules in complex mixtures. The small size of the materials and structures is comparable to the size of most biological materials, such as proteins, nucleic acids, cells, viruses, etc. (Figure 18.1). Chemical recognition may be combined with optical, electrical, mechanical, or magnetic signal transduction for very sensitive detection of biomarkers or other chemical compounds. These nanoscale techniques are being explored for a range of new analytical approaches rather than simply miniaturizing existing chemical analysis methods. These approaches are still the topic of research and are rapidly evolving as the technical capabilities and understanding of the nanoscale processes are developing. In this chapter we will provide a review of some of the methods and research directions in these areas. The specificity of the overall detection system is based on attaching biorecognition elements (antibodies, nucleic acid probes, aptamers, enzymes, and proteins) to nanomaterials or nanostructures, which can bind with target 457

458

BIOMARKERS

FIGURE 18.1 Size comparison between biomolecules and several nanomaterials. (Image reproduced with permission from reference 60.)

analytes in a complex medium consisting of non-targeted analytes along with other interfering chemical moieties. The existing surface modification and protein modification techniques are ready to apply to nanoscale techniques. Some detection techniques are only sensitive at nanometer distance. These techniques are complemented by the small size of nanomaterials. For example, giant magneto resistance (GMR) based detection techniques and surface enhanced Raman scattering (SERS) are most sensitive for detecting material at nanometer distance from the device surface. When nanomagnetic particles are specifically attached to the surface of the GMR sensor, it has shown that even a single magnetic nanoparticle can be detected. Shrinking of sample or interrogation volume has enabled the study of an individual molecule, otherwise obscured by ensemble averaging. Reduction in volume is achieved by using nanostructures or by confining samples in the nanofluidic channels. Observing a single molecule provides an opportunity to measure the distribution of behavior as opposed to examining only the average behavior. These single molecule detection techniques not only make it possible to detect extremely low number of molecules, but also significantly enhance signal to noise ratio.

NANOSCALE SENSING TECHNIQUES FOR BIOMARKER QUANTIFICATION This chapter includes a survey of nanoscale sensing techniques used for the quantification of biomarkers. Other techniques used specifically for imaging and studying biological systems are beyond the scope of this chapter. Conventionally, nanotechnologies refer to techniques that employ nanomaterials with at least one critical dimension in the range of 1-100 nm. In this chap-

NANOSCALE T E C H N I Q U E S FOR BIOMARKER Q U A N T I F I C A T I O N

459

ter we have categorized nanoscale sensing techniques based on the detection technique (optical, electrical, mechanical, and magnetic) used for the system. In some cases there is more than one detection technique that can be used for a particular assay, in which case we have categorized that assay based on the currently prevalent detection technique used for that assay or the detection technique that is used for the most sensitive detection.

OPTICAL

DETECTION

Bio-Barcode Assay-Based Sensors A combination of large amplification and high specificity of a typical immunoassay or DNA hybridization lends bio-barcode assays significant importance. The working principle of bio-barcode involves using two types of particles— one is used for the separation of target analyte, while the other is primarily used for amplification and specific binding with target analyte. Generally, a micro-sized magnetic particle conjugated with the biorecognition elements is used for separation. In the case of nucleic acid, the biorecognition element is an oligonulceotide, complementary to the statistically unique region of the target,' while in the case of individual protein or cell surface protein, the recognition element is an antibody (polyclonal or monoclonal), peptide, or aptamer. The particle used for amplification is a nanoparticle coated with another biorecogniton element to sandwich target analyte with the microparticles. In addition to biorecognition elements, the surface of nanoparticles is coated with hundreds of oligonucleotides (used for amplification) referred to as bio-barcodes. These bio-barcodes are used for the amplification of signal and can also be used for multiplex detection of analytes. Bio-barcodes typically comprise 15-20 mer oligonucleotides, allowing the user to pair a unique barcode with every conceivable recognition element, since for a 20-mer there are 420 unique combinations. After choosing the appropriate combination, these micro and nanoparticles are added to the sample concurrently. Following incubation, target analytes along with nanoparticles and magnetic particles are separated by applying magnetic force and unattached nanoparticles and analytes are washed off. The barcodes are released in buffer chemically (e.g., by dithiothreitol, DTT)2 or by heating the solution,2'* and are detected using microarray via scanometric using nanoparticle probes (Figure 18.2).7 If biomarkers carry fluorescent tags, then in situ fluorescence-based approaches are applied. However, in principle, any appropriate readout mechanism can be integrated with the system. Until now, the scanometric method has provided the lowest detection limit for both nucleic acid (high zeptomolar, 1021 M)1 and protein targets (low attomolar, 10~18 M).8 The potential of bio-barcode assay for multiplex detection is exploited by detecting four types of DNA using synthetic oligonucleotide sequences of 30-33 base long associated with (a) hepatitis B virus surface antigen gene, (b) variola virus, (c) ebola virus, and (d) human immunodeficiency virus at concentrations as low as 5 pmol/L in 40 min using capillary DNA Analyzer.9

460

BIOMARKERS

FIGURE 18.2 Schematic representation of a bio-barcode assay.The target analyte is sandwiched between magnetic microparticles conjugated with the biorecognition elements and nanoparticles coated with biorecognition elements and bio-barcodes.The bar-code oligonucleotides are released, and detected using the scanometric method. (Image reproduced with permission from reference 10.)

The bio-barcode assay is up to 106 times more sensitive than ELISA-based technology and is comparable to PCR in terms of its sensitivity.10 However, bio-barcode assay is much simpler in use and thus is viewed as an efficient alternative to PCR that will soon be available for widespread use in research and clinical applications. Due to the extremely low detection limit of these assays, they are also suitable for pre-mortem tests, where most non-PCR methods are not applied due to the extremely small amount of analytes present in the body fluid. In one such application, the bio-barcode method is used to detect amyloid-derived-diffusible ligands (ADDL, responsible for Alzheimer's disease through study of the brain)3 in the cerebral spinal fluid of subjects afflicted with the disease. This is a pre-mortem method and can be used to monitor the progress of Alzheimer's disease. Sometimes the use of sophisticated tools such as microarrays and chipimaging limits the portability of scanometric based bio-barcode assays. This

NANOSCALE TECHNIQUES FOR BIOMARKER QUANTIFICATION

461

issue can be overcome by a simple electrical readout from the oxidative release of metals from nanoparticles. One such electrochemical bio-barcode assay is used to detect human-fetoprotein (AFP), a tumor marker related to heptocellular cancer, yolk sac cancer, and liver metastasis from gastric cancer, testicular cancer, and nasopharyngeal cancer. Here, oligonucleotide bio-barcodes used in optical methods were replaced by CdS nanoparticles. This electrochemical biobarcode method was used to detect a minimum of 9.6 pg/ml of AFP.11 Bio-barcode assays offer several advantages that make them suitable for biomarker detection. They provide necessary specificity, sensitivity, multiplex detections and low detection limit required to detect extremely small amounts of biomarkers. This is beneficial not only in providing better diagnosis, but also in monitoring various stages of disease, which is imperative for early detection and cure. Due to these advantages, the use of these assays will extend and play a critical role in diagnosing a wide variety of fatal diseases such as cancer, HIV, and neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. A comparison between bio-barcode assay and conventional techniques is shown in Figure 18.3 in the area of medical diagnostics.

Q u a n t u m Dots-Based Sensors The use of organic fluorophores is well established. During the past decade, advances in synthesis and biofunctionalization of colloidal semiconductor nanocrystals called quantum dots (QDs) have replaced organic fluorophores

FIGURE 18.3 The comparison of bio-barcode assay with other conventional detection technologies for medical diagnostic applications, (Image reproduced with permission from reference 10.)

462

BIOMARKERS

in some cell biology applications. These nanometer sized crystalline particles are composed of elements from periodic groups II-IV (e.g., CdSe) or III-V (e.g., InP) and are extremely bright and resistant to photobleaching that is a limitation of organic fluorphores. Advantages of QDs, including their intense brightness, makes them suitable for single molecule detection,12 and their size turnable emission wavelengths and single excited wavelength for multiple emission QDs make them suitable for the design of a simple and compact system to simultaneously detect multiple biomolecules (Figure 18.4).13 Additionally, owing to their robust optical properties in complex biochemical media, QDs are extensively used in the area of fluorescence immunolabeling for probing structures and locating signal transduction-related molecules. Ness and coworkers developed an immunohistochemical (IHC) protocol that combines conventional enzymatic signal amplification and QD labeling to detect intracellular antigens in rat and mouse brain tissue sections. Their study showed that QD IHC labeling resulted in greater sensitivity as compared to similar IHC approaches using conventional dyes.14Wu and coworkers developed reliable and specific QD probes to localize breast cancer cell surface marker Her2, cytoskeleton fibers, and nuclear antigens in fixed cells, live cells, and tissue sections, with a substantial increase in brightness and photostability as compared to organic dyes.15

FIGURE 18.4 A. Fluorescence emitted from quantum dots. Blue fluorescence can be emitted from small particles of approximately 2 nm in diameter, green from ~3 nm particles, yellow from ~4 nm particles, and red from large particles of ~5 nm. The wavelength of the excitation light is 365 nm. B. Fluorescence emission spectra depending on the size of quantum dots. (Image reproduced from http:// www.aist.go.jp/aist_e/aist_today/2006_2l/hot_line/hot_line_22.html with permission from National Institute of Advanced Science and Technology.) (See color insert for a full color version of this figure.)

NANOSCALE T E C H N I Q U E S FOR BIOMARKER Q U A N T I F I C A T I O N

463

Although QDs are used in numerous applications for the quantification of bacterial cells and nucleic acids,16-18 a limited amount of work has been done in the use of QDs for quantifying biomarkers in medical diagnostics. Most QD based assays employ optical detection techniques, but few researchers focus on developing QD-based electrochemical sensors. Ho and coworkers developed sandwich immunoassay to capture carcinoembryonic antigen (CEA) between anti-CEA antibodies and alpha-CEA antibodies-CdS quantum dots. Anti-CEA antibodies were functionalized on carbon nanoparticle/ poly(ethylene imine)—modified screen printed graphite electrodes. The immobilized QDs were released by acid from the sandwich complex and square wave anodic stripping voltammetry was used to amplify the signal response obtained from the dissolved CdS QDs. The detection limit of the sensor was 32 pg/ml with a linear detection range of 0.032-10 ng/ml of CEA.19 In place of solid surface for the capture of analyte, magnetic particles were used to immobilize organophosphorylated acetylcholinesterase (OP-AChE) along with anti-phosposerine conjugated QDs (CdS ZnS).20 Following incubation, excess QDs were removed by washing and then square wave voltammetry was used to quantify the amount of Cd released from QDs. The detailed schematic of the methodology is shown in Figure 18.5. This magnetic immunoassay electrochemical assay was able to detect OP-AChE over a broad concentration range of 0.3-300 ng/ml in human plasma with a detection limit of 0.15 ng/ml.

FIGURE 18.5 Schematic illustration of magnetic electrochemical immunoassays of OP-AChE:A. Plasma samples, B. Magnetic capture of OP-AChE using amorphous MP-Ab I conjugates, C. Selective recognition of bound OP-AChE using QD-Ab2 labels, D. Electrochemical SWV analysis of cadmium released by acid from the captured QDs, and E. Representative SWV signal output. (Image reproduced with permission from reference 20.)

464

BIOMARKERS

Finally, more efforts are required to apply quantum based optical detection for the quantification of biomarkers in medical diagnostics. Additionally, in order to broaden the use of quantum dots, it can be combined with other assays, such as liposomes, and doped nanoparticles.

D y e - D o p e d Nanoparticles-Based Sensors Numerous types of luminescent probes are used in cell biology. These include quantum dots (QDs), fluorescent polymer particles and dye-doped nanoparticles (also called FloDots). While QDs have advantages compared to organic dyes in imaging and detection of biomolecules because of brightness, resistance to photobleaching and fluorescent linewidth, they display poor solubility, agglutination, blinking, and low quantum yield. Similarly, fluorescent particles, such as dye containing polystyrene particles and fluorescent polymethacrylic nanoparticles, used in biological applications2122 also exhibit some shortcomings. Owing to limited agglomeration, swelling and dye leaking, these polymer particles are not highly suitable for bioanalysis. FloDots as coined by the researchers are superior to some of the contemporary luminescent probes and have been studied extensively by Tan group at the University of Florida. These particles are dye-doped silica nanoparticles, which consist of a large number of luminescent organic or inorganic dye molecules dispersed inside the silica matrix. It has been shown that at optimal excitation and emission wavelengths, the luminescence intensity of a single 70-nm Rubpy FloDot is equivalent to that of many quantum dots or thousands of dye molecules. They are highly photostable because of the shielding effect of silica protecting dye-doped molecules from environmental oxygen. Additionally, silica is a desirable matrix for its role in the dispersion of particles in water and can be functionalized with a variety of functional groups suitable to conjugate FloDots with biotin, antibodies, nucleic acid, and enzymes. Wu and coworkers23 reported a tris (2,2'-bipyridyl) ruthenium (II) chloride hexahydrate (Rubpy) dye encapsulated silica nanoparticles based immunoassays for the detection of inflammatory biomarker IL-6. In this microarray approach primary anti-IL-6 antibodies were printed on an amino-functionalized slide followed by incubation with IL-6 and dye-doped silica particles functionalized with secondary anti-IL-6 antibodies. Following washing, quantitative analysis of the fluorescent images was performed by Scan Array Express HT microarray scanner. The detection limit was linear over a range of 0.1 to 10 ng/ml of IL-6 with a detection limit of 0.1 ng/ml.23 The schematic of the methodology and the measurement curves are shown in Figure 18.6. The Wiesner group24,25at Cornell University is also making monodisperse fluorescent core-shell silica nanoparticles (C dots) with enhanced brightness and photostability as compared to parent free dye for the development of molecularly targeted probes that exhibit low toxicity, high biostability, biocompatibility, and efficient clearance profiles through biological barriers in the body. They have been used for the in-vivo specific target of tumor and treatment,24-25 but have great potential to develop some quantitative detection of biomarkers similar to FloDots.

NANOSCALE TECHNIQUES FOR BIOMARKER QUANTIFICATION

465

FIGURE 18.6 I -A. Schematic illustration of the procedure forthe preparation of antibody conjugated Rubpy doped silica (RuDS) nanoparticles. l-B.The scheme of the RuDS label-based fluorescent immunoassay of IL-6 on a protein microarray format, (a) Capture anti-IL-6 antibody was printed on the slide, (b) Antigen, IL-6, was attached to the slide via antibody/antigen recognition, (c) Anti-IL-6 antibody-RuDS conjugates were coated on the slide to form a sandwich immunocomplex with RuDS as tags. 2-A. Fluorescence images of protein microarray with different concentrations of antigen, IL-6 (control, 0.1, 1, 10, 30, 60, 100 ng mL-1). 2-B. Calibration curve of fluorescence intensity versus IL-6 concentration. (Image reproduced with permission from reference 23.) (See color insert for a full color version of this figure.)

Surface Enhanced Raman Spectroscopy-Based Sensors Surface enhanced Raman spectroscopy (SERS) is an enhancement of Raman scattering by molecules absorbed on rough metal surfaces. The collective resonant excitation (surface plasmon excitation) of free electrons in metal nanostructures can enhance electromagnetic fields near the particle surface by many orders of magnitude.26 In short range plasmonic interactions, the field

466

BIOMARKERS

may be enhanced by a factor of 1014-1015 and is useful in identifying or detecting single molecule under ambient conditions.27-29 In a typical format of a SERS-based sensor, an oligonucleotide probe with a Raman dye is conjugated on gold nanoparticles while another probe is conjugated on the chip. The target oligonucleotide sequence is sandwiched between the chip and nanoparticles. Raman spectroscopic finger print reading is taken after impregnating gold with silver metal. Silver impregnation is known to enhance the signal30 however this is not always required. The number of commercially available Raman dyes makes it possible to identify or detect multiple oligonucleotide probes in a single test. Cao and coworkers, reported the detection of six nucleotide sequences for hepatitis A virus vall7 polyprotein gene, hepatitis B virus surface antigen gene, human immunodeficiency virus, ebola virus, variola virus (smallpox), and bacillus anthracis protective antigen gene using six Raman dyes (Cy3, TAMRA, Texas-red, Cy3.5, rhodamine 6G, and Cy5). In this work, nanoparticles functionalized with oligonucleotides and Raman labels were used to perform multiplex detection of oligonucleotide targets (Figure 18.7a). The unoptimized detection limit of the system was 20 fM.30 In short, six types of Raman dye-labeled and oligonucleotide-modified gold nanoparticles (diameter 13 nm) were prepared with sequences that were complementary to 30-36 bases oligonucleotide sequence for the target analyte. Initially, a chip spotted with 15 mer oligonuleotide sequence probes was used to hybridize the target sequence. Following this, excess unhybridized target sequences were washed off and the overhang of the target sequences were hybridized with Raman active oligonucleotide modified gold particles. Silver enhancement around gold particles was used before measuring Raman spectrum. The Raman spectra of six dye-labeled nanoparticle probes after silver enhancing on a chip is shown in Figure 18.7b. Although SERS is commonly used for short range plasmonic coupling interactions, it is also used for long-range plasmonic coupling as it allows the detection of proteins, clustered receptors on cell membranes, and intact viruses based on the coupling of adjacent metallic NPs in a no-wash/single step format. Qian and coworkers used gold nanoparticles modified with malachite green and thiolated nucleotide probes to prepare SERS NP beacons.26 These beacons were turned on and off by biomolecular binding and dissociation events. Figure 18.8 shows the design and preparation of SERS NP beacons by using monodispersed colloidal Au in two sizes (40 and 60 nm) and their operating principles. The NPs were first encoded with a reporter molecule such as malachite green (with distinct Raman signatures) and then functionalized with thiolated DNA probes. Long range plasmonic coupling was formed between two or more gold particles conjugated with complementary nucleotide sequence (direct sandwich assay) or a target nucleotide sequence sandwiched between two or more gold nanoparticles conjugated with the complementary oligonucleotide sequence for the target nucleotide (indirect sandwich assay). The resulting NP aggregates caused long-range plasmonic coupling interactions and enhanced the Raman signals. The direct sandwich formed between 60 nm SERS beacons and NP aggregates caused a SERS contrast ratio of 40-50 (calculated by

NANOSCALE T E C H N I Q U E S FOR BIOMARKER Q U A N T I F I C A T I O N

467

FIGURE 18.7 A. Schematic illustration of nanopartides functionalized with oligonucleotides and Raman labels, coupled with surface-enhanced Raman scattering (SERS) spectroscopy to perform multiplexed detection of oligonucleotide targets, B.The Raman spectra of six dye-labeled nanopartides probes after Ag enhancing on a chip (after background subtraction). Each dye correlates with a different color in our labeling scheme (see rectangular boxes).TAMRA,tetramethyl rhodamine, and C. Six D N A sandwich assays with corresponding target analysis systems. AlO is an oligonucleotide tether with 10 adenosine units. (Image reproduced with permission from reference 30.)

using the areas of Raman peaks before and after hybridization). For indirect sandwich assay, the target sequence from cDNA of a cancer biomarker CD97 was sandwiched between two probes on different NPs. The SERS NP beacons aggregates showed excellent sequence specificity and were able to discriminate single-base mismatches with an improved on/off intensity ratio of 200-300. Nanoparticles-based SERS detection offers several advantages when compared to fluorescence based chip detection. The Raman signal can be extracted by excitation with single-laser excitation (also common with quan-

468

BIOMARKERS

FIGURE 18.8 Schematic diagrams showing the design and operating principles ofSERS NP beacons. A. Colloidal Au nanocrystals are encoded with a Raman reporter molecule, functionalized with thiolated D N A probes, and are stabilized and protected with low MWPEGs. B. Long-range plasmonic coupling induced by direct binding between two D N A sequences. C. Long-range plasmonic coupling induced by one target molecule binding to two NPs. (Image reproduced with permission from reference 26.)

turn dots). Secondly, the number of available Raman dyes are much more than available fluorescent dyes.31 Newer Raman dyes can be easily designed by chemically modifying two similar dyes.32 Therefore, nanoparticle-based SERS method offers potentially greater flexibility, a larger pool of available and non-overlapping labels, and higher multiplexing capabilities than conventional fluorescence-based detection approaches.30

Dynamic Light Scattering Gold and silver nanoparticles, including spherical particles, nanorods, and nanoshells within the size range of tens of nanometers to hundreds of nanometers are known to have a large absorption and scattering cross section in the surface plasmon resonance wavelength regions.33"35 The magnitude of light scattering from gold particles can be higher than light emission from fluorescent dyes.36 This unique property of metal nanoparticles has enabled a wide range of applications in the biomedical field. These include molecular

NANOSCALE T E C H N I Q U E S FOR BIOMARKER Q U A N T I F I C A T I O N

469

and cell imaging, biosensing, and photothermal therapy.37-39 Dynamic light scattering (DLS), also known as photon correlation spectroscopy or quasielastic light scattering, is a technique conventionally used to determine the size distribution profile of small particles in a solution. Because of the established protocols of gold conjugation chemistry and biomolecular interactiondirected nanoparticle assemblies, the DLS technique can be directly applied to quantitative immunoassays. The capabilities of DLS to differentiate between free nanoparticles vs. nanoparticle dimers and clusters (due to nanoparticleanalyte conjugation) based on size difference make them a potential analytical tool for quantitative immunoassay. Liu and coworkers employed nanosized gold nanoparticles (diameter 37 nm) and nanorods (10 nm x 40 nm) for the quantitative detection of free prostate specific antigen (PSA).36 A pair of anti-PSA antibodies (capture and detection antibodies) was conjugated on nanoparticles, and mixed with f-PSA in the solution. The binding of f-PSA caused nanoparticles to form dimers, oligomers, or aggregates depending on the concentration of the antigen. Through DLS anlaysis, the relative ratio of nanoparticles dimers, oligomers, or aggregates vs. individual nanoparticles was measured. In principle, this ratio should increase with an increase in the concentration of antigen and such correlation would measure the concentration of f-PSA in the solution. In this study, the results showed the correlation between the scattering light intensity of gold nanoparticles and nanorods and the concentration of f-PSA in the picomolar range. A detection limit of 0.02 pM for gold particles and 0.4 pM for gold nanorods was established. Figure 18.9 shows the schematic of the immunoassay involving gold nanoparticles and nanorods and detection curve for the PSA. Scattering properties of gold nanoparticles have often been used in microscopic imaging for qualitative evaluation, but not as frequently in quantitative analysis. Other areas where DLS has been successfully applied include analyzing size and size distribution polymers, proteins, and nanoparticles. In some cases DLS has shown to be useful in monitoring the concentration of gold nanoshell in blood samples after intravenously injecting nanoparticles in a murine tumor model (Xie, et al., 2007).

FIGURE 18.9 A. Schematic of the formation of aggregates of gold nanoparticles, nanorods in an immunoassay. B.TEM micrographs of (a) gold nanoparticles (scale bar: 50 nm), (b) gold nanorods (scale bar: 60 nm),and (c) their dynamic light scattering intensities and linear regression curves for the detection of PSA. (Image reproduced with permission from reference 36.)

470

BIOMARKERS

MECHANICAL

DETECTION

Nanomechanical Cantilever-Based Sensors Detection of extremely small forces using micro- and nanoelectromechanical systems (MEMS and NEMS) is well established. The adsorption of molecules on the surface resulting in nanomechanical forces offers an exciting opportunity for the development of highly sensitive and miniaturized label-free biological sensors.41-42 In the past MEMS and NEMS have been developed as sensitive chemical and biological sensors capable of detecting small amounts of analytes,43 as light as 7 zeptogram (1 zg = 1021 g) in vacuum.44 In general, sensors built with this technology are operated in either static or dynamic sensing mode. In static sensing mode, a micro-sized cantilever undergoes bending due to surface stresses created by molecular adsorption confined to one side of the cantilever. The surface stress change can be read in the form of nanometer displacement. Dynamic mode sensors are excited at natural resonant frequencies, and shifts in resonant frequency as a result of analyte binding signify detection. The adsorption-induced bending and frequency variations can be measured by using several techniques such as variations in optical beam deflections, piezoresistivity, piezoelectricity, capacitance, and using optically interferometric techniques. Static deflection-based sensors are suitable for in-situ detection of analytes, while in most cases, dynamic sensors require measurements performed in air or vacuum to improve sensitivity, which is strongly limited by viscous damping effects in fluids. However, there has been some concern that the drying process and transport to and from solution may result in increased noise and non-specific binding. Recently, Burg and co-workers demonstrated a novel approach for operating dynamic resonators in the solution while maintaining the advantage of high quality factor by working in vacuum.45 They designed a suspended cantilever with a built-in microfluidic channel used to flow solutions. The measurement was done in the vacuum while the solution was flowing through the microchannels. The effects of stiffness and thickness on resonant frequency have been experimentally demonstrated and analyzed in recent works .45~48 The location of the bound analyte on the sensor can also determine to what extent changes in mass or stiffness affect the resonant frequency. Tamayo and co-workers have shown that bacteria adsorbed at the free end of a cantilever, where the motion is maximum, results in negative frequency shifts due to mass related effects while adsorption near the clamped end, where the motion is minimum, gives way to positive frequency shifts due to increased stiffness.46 Resonant sensors for mercury vapors have also shown positive and negative shifts, depending on how the mercury is adsorbed on the sensor.49 For cantilevers entirely coated in gold, the frequency increases, however, if gold is coated only at the tip of the resonator, the same mercury vapor will decrease the frequency. With proper experimental design, one or more of these effects can be neglected, thus facilitating data analysis and interpretation of results.

N A N O S C A L E T E C H N I Q U E S FOR BIOMARKER Q U A N T I F I C A T I O N

471

Although cantilever-based sensors are extremely sensitive, they offer no intrinsic selectivity for biomolecules. Selectivity is achieved by affinity binding of biomolecules on the surface of cantilevers functionalized with selfassembled monolayers, nucleic acids, antibodies, or peptides. While the absolute mass sensitivity is an advantage point for resonant sensors, sensitivity to a particular concentration of analyte in a biologically relevant medium can be more important for early detection of disease or trace constituent analysis. For low analyte concentrations, static deflection-based sensors are limited by the need for a minimum surface coverage required to bend the cantilever by a measurable distance.50 On the other hand, dynamic sensors are limited by the total amount of mass bound to the devices.51 One method which may overcome some limitations of resonant sensors is secondary mass labeling for signal amplification. If additional mass is added to only those devices with target analytes, then the frequency shift will be enhanced and the detection limit improved.52-53 Gerber group demonstrated the first nanomechanical cantilever arraybased sensor for multiple differential gene expression without the use of sample amplifications or labels. The cantilever was able to detect specific transcripts without employing amplification steps in total RNA derived from human or rat cell lines. The markers that are upregulated to a high expression level upon drug exposure were detected and the cantilever array sensors were used as a tool for the fast detection of expression of significant marker genes in the field of personalized medical diagnostics. Specific target hybridization events were monitored by cantilever bending as a result of changes in surface stress. Figure 18.10 shows the working principle of a cantilever-based detection system. The detection limit of the device is -10 pM and is directly comparable with conventional gene-chip technologies where the detection limit is 1-6 pM, applying fluorescent probes.54 Other label-free techniques such as SPR55 are not capable of detecting as small as 12 mers nucleic acid sequences. Nanowire sensors56 were shown to be very sensitive with detection limits of fM range, however detection was performed in a non-competitive environment.

FIGURE 18.10 A. Schematic of the working principle of the static mode of cantilever B. Biofunctionalized cantilever array with different types of thiol-functionalized ssDNA using microcapillaries. (Image reproduced with permission from reference 54.)

472

BIOMARKERS

Craighead group at Cornell University used arrays of cantilever for the detection of prostate specific antigen (PSA), a protein biomarker associated with prostate cancer.57 Cantilevers in the form of trampolines (diameter 6 urn and thickness 90 nm) supported with four arms were used for the capture of PSA alone with anti-PSA antibodies on the device, or sandwich capture of the PSA between anti-PSA capture antibodies in the device and anti-PSA antibodies for the capture of nanoparticles as mass labels. PSA alone was detected with a detection limit of 50 ng/ml, while with mass amplification the detection limit was improved by six orders of magnitude to 50 fg/ml. A different shape of the cantilever, paddle lever, was used for the detection of prion protein, responsible for mad cow disease. The working principle and assay format was the same as discussed above. With mass amplification the detection limit was 2 ng/ml of prion protein in pure buffer and 200 pg/ml in blood serum.53, "Different shapes of the cantilevers were used in order to maximize the frequency change for the small change in mass on the cantilever. A comparative study of experimental results and modelling has been presented for different shapes and sizes of cantilever for the point mass detection.59 Several challenges must be overcome before cantilever array sensors can be widely used. Advances in developing superior bio-affinity molecules and

FIGURE 18.1 I SEM of A.An array oftrampolines, B.Trampoline resonator with nanoparticles mass tag for the detection of 50 ng/ml of PSA, C.Trampoline resonator for the control sample with no PSA, and D. Calibration curve for the detection of PSA. Scale bars in SEM are 6 pm.

NANOSCALE T E C H N I Q U E S FOR BIOMARKER Q U A N T I F I C A T I O N

473

improved functionalization techniques are required in order to enhance analyte binding, thereby improving detection limits. Advances in nanofabrication and nanoscale motion detection techniques and miniaturization of optical detection systems are crucial for the viability of nanomechanical detection platforms. While technology for designing electronic chips has progressed, the integration of electronic, mechanical, and fluidic design still requires much work before integrated devices can be used as a viable sensing platform.

ELECTRICAL

DETECTION

Field Effect Transistor-Based Sensors A diversity of sensor architectures such as cantilevers, quantum dots-based fluorescent detectors, giant magneto-resistance, and others have been designed and fabricated at nanoscale dimensions. Most of them require integration of optics, and/or some labels to translate the surface-binding event into a readable signal. In contrast, sensors designed to operate likefieldeffect transistors (FET) are label-free detection techniques as they do not require any labels and are capable of translating analyte-binding directly into a readable signal, without using elaborate optical components. These devices utilize the electronic properties of the sensing element, such as conductance, to produce signal output.60 The use of ID nanomaterials such as nanowires (NWs) and nanotube(NTs) into electrical devices offers substantial advantages for biological detection. The diameter of NWs and NTs is comparable to the size of biological entities, and they are several micrometers long, providing a size compatibility between electrical components and biological analyte, as well as convenient interface with micrometer scale device components. The presence of surface charge on the surface of biological analyte or the charge transfer during a biological process could be directly detected by electronic nanocircuits based on NWs and NTs.61 Therefore, electronic devices based on NWs and NTs can serve as one of the most efficient strategies for the integration of biology and electronics into a common platform in biological sensing and detection.62 A typical structure of an FET sensor is illustrated in Figure 18.12. All FET sensors have gate, drain, and source terminals and there is a slight variation in the working of FET based on NWs and NTs depending on their physical and material properties. In the case of single walled carbon NTs, every atom is on the surface and exposed to the environment, and thus even a small change in the environment can cause drastic changes to the electrical properties of NTs. NWs are equally sensitive due to high surface to volume ratio as all electrical current flows through the nanometer-scale cross section. In principle, the devices based on NTs and NWs should have a detection limit at a single particle level. Lieber group demonstrated the use of silicon NWs for the detection of virus and was able to improve the sensitivity to a single virus.63 The signal transduction in NWs is caused by the depletion or accumulation of charge carriers as a result of charged biomolecules that are bound at the surface. There are two types of charge carriers in the NWs- holes for a p-type

474

BIOMARKERS

FIGURE 18.12 Structure of an FET nanobiosensor (a) Cross-sectional view: source and drain electrodes bridge the semiconductor channel. The gate electrode can be used to modulate the conductivity of the semiconductor channel. A receptor molecule attached to the surface of the semiconductor material can specifically recognize and capture a target molecule from a buffer solution. (b)Top view: SEM image of a typical nano-FET In these structures, the channel length is the S-D distance and the channel width is the S or D electrode width. Examples of nano-FET fabricated using either (c) carbon nanotubes or (d) indium oxide NWs as semiconductor materials. (Image reproduced with permission from reference 60.) (See color insert for a full color version of this figure.)

semiconductors or electrons for n-type semiconductor. The carrier density in the NWs is proportional to the conductance of the wire, which can be determined from the source-drain current of the device. Depending on the charge of the anlayte molecules the charge carriers will accumulate or deplete, causing a respective increase or decrease in the conductivity of the device. When a negatively (such as DNA) or positively charged molecule (such as protein below its isoelectric point) binds to the p-type NWs, it causes an increase or decrease in the conductivity of the NWs, respectively.61 The mechanism leading to signal transduction in single wall nanotube (SWNT) biosensors has, until recently, been poorly understood. For example, some researchers have reported that SWNT will show an increase in conductivity for every protein tested, independent of the overall charge on the protein.64 This contradicts the observation made in the case of NWs65,66 and cannot be explained as an electrostatic gating effect caused by the charged analyte perturbing the charge carries in the NTs. In order to resolve this anomaly, it was suggested that the dominant sensing mechanism is a modulation of the Schottky barrier at the electrodes-nanotube

NANOSCALE T E C H N I Q U E S FOR BIOMARKER Q U A N T I F I C A T I O N

475

interface caused by binding of analyte and receptor.64 However, Heller and coworkers believed that electrostatic gating can also play an important role depending on the nature of the analyte.67 Lieber group demonstrated the capabilities of silicon nanowires-based FET sensors for multiplex detection of PSA, carcinoembryonic antigen (CEA), and Mucin-1. They used a bottom-up fabrication technique to fabricate p- and n-type doped silicon NWs. A linker molecule, aldehyde propyltrimethoxysilane (APTMS), was used to functionalize anti-PSA, anti-CEA, and anti-Mucin-1 antibodies. The detection limit for PSA, CEA, and Mucin-1 was 2 fM, 0.55 fM, and 0.49 fM, respectively.68 Some other work performed by Zhou group demonstrated the use of n-type ln 2 0 3 NWs and p-type CNTs in detecting PSA. They claimed to have developed a novel approach to link anti-PSA antibodies to ln 2 0 3 NWs via onsite synthesis of a succinimidyl linking molecule. The system detected a minimum of 5 ng/ml of PSA under unoptimized conditions.69 Both NTs and NWs are extensively used to detect protein,66' 70~72 nucleic acids,73-75 and cancer biomarkers.68

Liposomes-Based Sensors Liposomes are versatile structures used for labeling, drug delivery, and other therapeutic applications. Structurally liposomes are the vesicles whose membranes are made of phospholipids with hydrophobic chains forming the bilayer. The polar head groups of the lipids are oriented toward the extravesicular solution and inner cavity (Figure 18.13). Liposomes offer large surface area, large entrapment volume, and lipid bilayer can be conjugated with a variety of biorecognition elements. A wide variety of hydrophilic molecules can be encapsulated within the inner cavity of liposomes, including enzymes, DNA, vaccines, fluorescent dyes, electrochemical and chemiluminescent markers, and pharmaceutical compounds.76 Liposomes provide wider use in encapsulating drug molecules inside vesicles and also control the release of the drugs to minimize toxic effects of drugs and maximize their therapeutic

FIGURE 18.13 Structure of a liposome modified with biorecognition elements. Lipids form a bilayer entrapping an aqueous core entrapping highly water-soluble marker molecules. (Image reproduced with permission from reference 76.)

476

BIOMARKERS

index. This chapter will limit its discussion to the applications of Hposomes in numerous detection techniques. There are a number of different lipids that can be used in the lipid bilayer and can be easily modified to attach numerous biorecognition elements. Typically, modified lipids include phosphatidylethanolamine (PE)77 (amine modified), N-glutaryl-PE (carboxyl modified),78-80 maleimidomethyl cyclohexane-carboxamide (MCC)-PE or maleimidophenyl butyramide (MPB)-PE (maleimide modified),81"83 pyridyldithio propionate (PDP)-PE (disulfide modified),84 and cholesterol,85 or polyethylene glycol86 (hydroxyl modified). The modified lipid membrane is conjugated to biorecognition elements covalently using commonly used heterobifunctional and homobifunctional cross linkers, or with non-covalent interactions such as those provided by the biotin-streptavidin interaction, or protein A/G mediated protein association. Because Hposomes can entrap various hydrophilic molecules, they can be suitably modified as a signal enhancer for optical and electrochemical detection techniques. The detection based on lipososomes is performed by either keeping Hposomes intact during signal generation or lysing them before signal readout. The intact form of liposome is generally used in optical detection while the lysed form is used for the optical as well as electrochemical detection. Electrochemical detection systems based on Hposomes are increasingly getting attention from the researchers due to their high sensitivities, which at times are better than the sensitivities of the compared optical detection systems. Therefore, we have decided to discuss Hposomes based sensors under this section although they are equally used with optical detection systems. Baeumner group developed an electrochemical biosensor based on a PMMA substrate with interdigitated electrodes and microfluidic channels.87 The hsp70 mRNA was isolated from Cryptosporidium parvum and amplified using nucleic acid sequence-based amplification (NASBA). The amplified target sequences were detected by a sandwich hybridization between capture probes on superparamagnetic beads and reporter probes on tagged Hposomes. The electrochemical marker potassium ferro/ferrihexacyanide present inside the liposome was lysed for the amperometric quantification of the target DNA sequence. Amplified mRNA from only 1 oocytes was detectable with the electrochemical biosensors based on tagged Hposomes. Figure 18.14 shows the SEM of the gold interdigitated electrodes, micrograph of an embossed channel on PMMA with gold electrodes, and the microfluidic chip with channels and interconnects. An optical biosensor was developed using a membrane-based lateral flow system for the detection of 10 viable Mycobaterium avium subsp. paratuberculosis (MAP).88 The assay was based on the extraction of RNA from MAP and amplified using reverse transcriptase PCR. A nucleic acid hybridization sandwich format was used for the capture of target DNA sequence between capture DNA probes on the membrane surface and reporter DNA probes on the Hposomes encapsulating sulforhodamine B dye. The presence of dye inside Hposomes was quantified using a hand-held reflectometer or a fluorescence reader. This format has been used for the detection of astrovirus,89 Bacillus anthracis,90 Dengue virus,9192 and E. coli.93

NANOSCALE T E C H N I Q U E S FOR BIOMARKER Q U A N T I F I C A T I O N

477

FIGURE 18.14 A. SEM of gold interdigitated microelectrodes formed on a PMMA substrate. B. A PMMA sheet containing a hot embossed channel was then bonded to the PMMA containing the electrodes. The finished device contained a 500pm channel positioned along the electrodes. C. The chip with channels and interconnects. (Image reproduced with permission from reference 87.)

In the past, liposomes have been encapsulated with several electrochemical markers such as potassium ferrocyanide used for the assessment of poreforming toxin,94 horseradish peroxidase used for the detection of theophylline,95 and ascorbic acid used for the detection of atrazine.96 The use of liposomes as signal enhancing labels in detection techniques has proven to improve the sensitivity,77,97 detection limit,98 and in most cases their use has reduced the total detection time99 as compared to conventional biosensing techniques. Researchers have found one to three orders of magnitude improvement in the sensitivity and detection limit when tagged liposomes are compared with antibody tagged fluorophores,100 analyte tagged fluorophores,10' HRP tagged

478

BIOMARKERS

antibodies,97 and biotin tagged enzymes.77 However, in most cases the enhancement was not proportional to the number of molecules entrapped inside liposomes. This has been attributed to the steric hinderance and multivalency of liposomes. The relatively large size of liposomes prevents binding of multiple liposomes with the adjacent antigens.76-98,102 Moreover, liposomes have multiple biorecognition elements on their surface, which prevents them from binding with one to one molar ratio of the antigen, possible with the smaller labels.7698 Although liposomes have been used for a variety of applications due to their flexibility in encapsulating numerous labels, the search for newer labels and using them with other formats, such as bio-barcode assays, FET, magnetic, and others will further broaden their use.

MAGNETIC

DETECTION

Giant Magnetoresistance-Based Sensors Giant magnetoresistive (GMR) is a spin-dependent transport effect observed in thin film structures composed of alternating ferromagnetic and non-magnetic layers. The term "giant" refers to the large change in electrical resistance in a magnetic field. In the absence of an external magnetic field, the direction of magnetization of adjacent ferromagnetic layers is antiparallel due to weak anti-ferromagnetic coupling between layers. Current is sent through the device and the first magnetic layer spin polarizes the current. Since the layers are antiparallel there will be a high degree of spin scattering of the electron current resulting in high resistance. On the contrary, when an external magnetic field is applied, the magnetization of the adjacent ferromagnetic layers is parallel resulting in lower spin scattering and resistance. More recently, the GMR effect, which is widely used in the read heads of modern hard disk drives, is used in numerous biodetection techniques based on the labeling of protein and nucleic acids with magnetic tags. GMR was discovered in 1988 by a research team led by Peter Griinberg of the Jiilich Research Centre. It was also simultaneously but independently discovered by the Albert Fert group at the University of Paris-Sud (FR). For this discovery, Griinberg and Fert won numerous prestigious awards including the 2007 Nobel Prize in Physics. Two variations of GMR, namely, spin valve (SV) and magnetic tunnel junction (MTJ), have been used for various readout mechanisms and sensing arrays. In SV configuration, a conductor is used between magnetic layers while an insulator layer is used in MTJ. Neither of them has shown an absolute superiority over the other.103 However, SV biosensors are easy to fabricate and they are in an advanced stage of development, while MTJs have a high magnetoresistance ratio (MR) resulting in a potentially high single to noise ratio. The signal generation on GMR sensors is due to the attachment of magnetic tags to the protein or nucleic acids in close proximity to the sensor surface. The size of the magnetic tag ranges from as small as tens of nm to as large as 3 um. This includes paramagnetic polystyrene beads and similar sized paramagnetic particles. The larger tags mismatch in size with nucleic acids or

NANOSCALE T E C H N I Q U E S FOR BIOMARKER Q U A N T I F I C A T I O N

479

protein targets in biological assays, thus prejudicing the quantitative capabilities of the system. The use of magnetic nanoparticle tags (also called nanotags) in the diameter of 100-1000 A, which are comparable to the size of the target biomolecules to be assayed, are expected to enhance the performance in real biological assays. Typically, this type of measurement is performed in the form of an array of devices on a microchip and is also categorized as magnetic microarray. The basic methodology of such a magnetic microarray in the case of nucleic acid detection is shown in Figure 18.15. In short, (a) SV and MTJ sensors are bound with known DNA probes which are complementary to the target DNA fragments, (b) Unknown DNA fragments are labeled with magnetic nanotags using binding mechanisms as biotin and streptavidin, and

FIGURE 18.15 Principle of magnetic DNA microarray with direct labeling of target nucleic acid with the magnetic nanotags I. Schematic illustrations of a. the top view of a MagArray SV sensor and b. its cross section. A single magnetic nanoparticle label is shown bound to the sensor through hybridized probe and target DNAs in the biologically active area. The aluminum leads define the electrically active area where an electrical sense current passes in the SV. (Image reproduced with permission from reference 103.)

480

BIOMARKERS

(c) Tagged DNA fragments are selectively captured by binding to the surface of the sensor by DNA hybridization mechanism.103 Eventually, the presence of magnetic nanotags is detected by measuring the change in the resistance before and after attaching magnetic nanotags, and by applying voltage or current across the device. Osterfeld and coworkers designed a magnetic nanotag-based protein assay chip consisting of 64 sensors in an 8 x 8 array. Each sensor has an active area of 90 x 90 (am2 consisting of 32 linear GMR segments, each 1.5 um wide connected in series.104 This SV stack of materials Ta 3/Seed layer 4/PtMn 15/ CoFe 2/Ru 0.85/CoFe 2/Cu 2.3/CoFe 2/Cu 1/Ta 4 (all thicknesses in nm) were deposited on an Si/SiO2 substrate (Figure 18.16). The chip was manually as well as robotically spotted with capture antibodies while the control experiment was performed using spotted BSA. This was followed by incubation of samples with single or multiple analytes. Following the capture of target analyte by respective capture antibodies, biotinylated antibodies specific to target analyte were incubated and used to attach magnetic nanotags. Seven analytes (TNF-a, IL-la, G-CSF, lactoferrin, CEA, eotaxin, and IFN-7) present in the same sample were simultaneously detected at 1 pg/ml level. In order to improve the detection limit, the signal was amplified by adding more nanotags. By doing so, the detection limit was as small as 57 fM, 56 fM, 53 fM, 13 fM, 5 fM, 119 fM, 59 fM of TNF-a, IL-la, G-CSF, lactoferrin, CEA, eotaxin,

FIGURE 18.16 Magnetic nanotag-based protein assay chip. The chip has a 200 pi reaction well and is supported by an 84-pin ceramic base a. Embedded in the bottom of the reaction well are 64 sensors in an 8 x 8 array, b. Each sensor has an active area of roughly 90 x 90 um2 and consists of 32 linear giant magnetoresistive (GMR) segments, each 1.5 um wide, which are connected in series. c.The edge of one such sensor segment and bound nanotags are imaged with a scanning electron microscope (d). (Image reproduced with permission from reference 104.) (See color insert for a full color version of this figure.)

NANOSCALE T E C H N I Q U E S FOR BIOMARKER Q U A N T I F I C A T I O N

481

FIGURE 18.17 GM- based multiplex protein assay with nanotag amplification. Seven different capture antibodies were used to functionalize different regions of a chip to detect seven different protein analytes. A mixed sample containing each of the seven analytes at a concentration of I pg/ml in PBS was incubated on the chip followed by linker incubation and two rounds of nanotag amplifications. The graph shows signal response after second round of nanotag amplification. The numbers above the bars indicate the average signals for each set of sensors. (Image reproduced with permission from reference 106.)

and IFN-7, respectively (Figure 18.17). When compared to other magnetic detection, use of 50 nm diameter nanotag combined with 30 nm passivation layer on SV valve was credited to have improved the detection limit of the current work.104 In another study conducted by Martin and coworkers,105 it was observed that magnetically assisted hybridization improved the detection limit of GMR sensors by three orders of magnitude as compared to diffusion controlled hybridization. In addition, when compared to diffusion kinetics, the packing density of the particles was also improved by field assisted kinetics. The magnetic field assisted separation and concentration of magnetically labeled biomolecules have shown promising results with a detection time of less than 30 min.106,107The ssDNA from the conservative region of 16S rDNA from Escherichia coli was biotinylated at 5' end and was tagged with 250 nm streptavidin coated magnetic nanotag. Measurements were made using an in-plane transverse external excitation field of 1.1 (kA/m)rms (211 Hz) in combination with a DC bias field of 2.4 kA/m to magnetically attract nanotag tagged target ssDNA to the sensor surface functionalized with complementary probe. The presence of magnetically assisted hybridization gave a 25% higher saturation signal and an improved detection limit by three orders of magnitude (from pM to fM) as compared to diffusion assisted hybridization. The capability of GMR sensors in terms of high sensitivity, multiplex detection, ease of use, scalability, and system integration make them ideal for use in portable instruments in medical diagnostics, and point-of-care applicaitons. In the near future, capture agents with higher affinity to the analyte, and analyte-sized nanotags with higher magnetic moment are expected to enhance the analytical sensitivity of nanotag-based GMR sensors.104

482

BIOMARKERS

FUTURE TRENDS This chapter was intended to elucidate a variety of ways the nanomaterials, structures, and devices are being utilized for the quantification of biomarkers. Some of the approaches are being used in diagnostic devices or in research while others are still being studied for their full potential. It is likely that continuing technology development and possibilities for further integration of techniques will result in the emergence of new approaches. For example, total internal reflection fluorescence (TIFR) microscopy has been extensively used for understanding the action of enzymes or inhibitory molecules in the progression of disease or biological systems;108-110 visualization of molecular dynamics;111-113 transportation of proteins;"4'115 and binding of molecules to the membrane surface in a complex matrix.116,117 Surprisingly, TIRF has not been used for quantification of biomarkers except very few reports.118, " 9 Single molecule techniques are very sensitive and can be applied for the quantification of biomarkers, but not all of them are suitable for high throughput analysis. For example, trapping of molecules (optically and magnetically), dynamic force spectroscopy, and fluorescence correlation microscopy (FCS) are appropriate for single molecule interrogation but they are not yet high throughput techniques. However, they could become part of future analytical concepts. Additionally, the sorting of molecules could potentially be used for the quantification of biomarkers. In our group, we have extensively studied the analysis of nucleic acids in nanofluidic channels, and these techniques can be used for the quantitative measurement of nucleic acid-based biomarkers.120-123 In this technique, DNA molecules are driven electrophoretically through nanofabricated cavities to confine and dynamically elongate them beyond their equilibrium length for repeated detection via laser-induced fluorescence spectroscopy. This technique would be a significant improvement over measurement techniques in bulk by exposing all biomarkers along the length of nucleic acid which would otherwise not be detected in coiled or aggregated form when measured in the bulk. In a variant of this technique, nanofluidic channels consisting of narrow constrictions and wider regions are used to create size dependent trapping of DNA and as a result separate different sizes of DNA into bands.124-126 Single molecule confocal microscopy-based techniques are used to visualize DNA strands in both of the above mentioned techniques. By confining molecules in the nanoliter volumes, more uniformly illuminated probe volume is created and the molecular resolution is permitted beyond the capabilities of a diffraction limited system.123 Single molecule approaches are also used for the sorting, counting, and color co-incidence measurement of labeled biomolecules as they pass through the optically excitation volume.127131 The exact concentrations of different species can be obtained by counting the number of biomolecules in real-time. The signal-to-noise ratio is improved by reducing the excitation volume in order to reduce the background noise from scattering or intrinsic fluorescence of unlabeled biomolecules in the excitation volume. The optical excitation reduced below the width of the channel makes it possible to detect only one

NANOSCALE T E C H N I Q U E S FOR BIOMARKER Q U A N T I F I C A T I O N

483

molecule at a time. Nanofluidics channels with the width of a few hundred nanometers are fabricated and comparable size of laser excited volume is used for the single molecule detection of labeled biomolecules. The multiple channels in a single chip and use of spectrally distinguishable fluorescent labels could be used for multiplex detection and high throughput real-time quantitative measurement of biomarkers. In place of physically confining sample to a small volume in nanofluidic channels, the optical confinements using zero-mode waveguides are also used for the optically excited and interrogated studies of bound and unbound biomolecules. In this case, subwavelength metallic holes are used for the attenuation of the incident light. With the diameter of holes in few tens of nanometers, this attenuation length becomes less than the film thickness.132-133 The net effect of this is an optical excitation volume which is of the order of zeptoliters (1021), significantly smaller than could be obtained, for example, by total internal reflection illumination, which can confine light to the evanescent field in one direction only. The FCS can be used to determine the concentration of entities by measuring the diffusion constant of freely diffusing fluorescently labeled biomolecules as they pass through nanoholes. A million holes can be fabricated on a single coverslip and that could be used for the highly parallel and high throughput quantitative analytical tool. Not only the flow of molecules on the surface is used for the single molecule analysis, the flow through the nanoscale pores is also pursued to study molecular structures.134-139 In this case, as the molecule passes through the pore, the ion current through the pore is modulated and it can lead to the knowledge of the structures of molecules, such as single strand, double strand DNA, or any other combination. Although, nanopores are simple but powerful biophysical probes of molecular confirmation, some separation and purification steps may be required to make this technique applied for the quantification of biomarkers.

CONCLUSION Nanoscale techniques are providing new opportunities for sensitive bio-chemical detection. Their utility will likely increase as they are incorporated in more integrated and miniaturized systems. The scope of the nanoscale technologies for the biomarker quantification can be improved either by developing highly sensitive detection techniques or making systems with massive parallelization. Nanomaterials are being used for the sensitive and also multiplex detection to some extent, but fabrication techniques will be highly appropriate for the multiplexing or parallelization. In general, fairly simple structures are fabricated to improve the sensitivity of detection techniques for handling small volumes or for the high throughput parallel detection. Microfabrication techniques scaled down to make nano-fabricated structures can be used for the massive parallel replication of identical units with 105,106, or more individual reactions performed on a single unit. It is clear that the scale and levels of integration are compatible with active optical and electronic devices in today's

484

BIOMARKERS

integrated electronic and optoelectronic systems, and carrying out millions of parallel optical or electronic measurements is a realistic possibility. In this respect, optical systems are best suited as they do not require direct contact with the analyte necessitating the use of interconnects and wires complicating the integration of a massive number of nanostructures with the external electrical measurement system. Other potential detection systems based on magnetic field either require specially designed structures, such as GMR or electo-magnets, and lend themselves to the same level of complexity as with electrical systems or require a huge mismatch in the size of permanent magnets, rendering them unfit for the highly parallel systems on a small chip. In order to greatly improve disease diagnostics, the advances in detection technologies are as important as the advances in the field of genomics and proteomics. The availability of multiple biomarkers is necessary for the diagnosis of highly complex diseases like cancer, where the heterogeneity of the disease makes a single test inadequate. Detection of multiple biomarkers might provide not only the information required for the robust diagnosis of the disease, but also about the stages of the disease that could facilitate early detection and cure of the disease.

SUMMARY P O I N T S 1.

2.

3.

4.

5.

6.

Nanostructure science and engineering utilizing structures and devices with dimensions typically measured in micrometers and nanometers enables access to new physical length scales and enables new approaches for molecular detection and analysis. Conventionally, nanotechnologies refer to techniques that employ nanomaterials with at least one critical dimension in the range of 1-100 nm. In this chapter we have categorized nanoscale sensing techniques based on the detection technique (optical, electrical, mechanical, and magnetic) used for the system. Shrinking of sample or interrogation volume has enabled the study of an individual molecule, otherwise obscured by ensemble averaging. Reduction in volume is achieved by using nanostructures or by confining samples in the nanofluidic channels. The specificity of the overall detection system is based on attaching bio-recognition elements (antibodies, nucleic acid probes, aptamers, enzymes, and proteins) to nanomaterials or nanostructures, which can bind with target analytes in a complex medium consisting of non-targeted analytes along with other interfering chemical moieties. While technology for designing electronic chips has progressed, the integration of electronic, mechanical, and fluidic design still requires much work before integrated devices can be used as a viable sensing platform. It is likely that continuing technology development and possibilities for further integration of techniques will result in the emergence of new approaches.

NANOSCALE TECHNIQUES FOR BIOMARKER QUANTIFICATION 7.

8.

485

Single molecule techniques are very sensitive and can be applied for the quantification of biomarkers, but not all of them are suitable for high throughput analysis. However, they could become part of future analytical concepts. In the future, nanofluidics and nanostructure-based single molecule analysis such as stretch of DNA, sorting of molecules, color coincidence measurement, diffusion through nanoholes, and zero mode waveguides would become part of the unltrasensitive and high throughput analytical tools applied for the quantification of biomarkers.

REFERENCES 1. 2. 3.

4. 5.

6. 7. 8. 9. 10.

11. 12. 13.

Nam, J. M., Stoeva, S. I., and Mirkin, C. A. Bio-Barcode-Based DNA Detection with PCR-Like Sensitivity. J. Am. Chem. Soc. 2004;126:5932-5933. Thaxton, C. S., Hill, H. D., Georganopoulou, D. G., Stoeva, S. I., and Mirkin, C. A. A Bio-Barcode Assay Based Upon Dithiothreitol-Induced Oligonucleotide Release. Anal. Chem. 2005;77:8174-8178. Georganopoulou, D. G., Chang, L., Nam, J. M., Thaxton, C. S., Mufson, E. J., Klein, W. L., and Mirkin, C. A. Nanoparticle-Based Detection in Cerebral Spinal Fluid of a Soluble Pathogenic Biomarker for Alzheimer's Disease. Proc. Nat.Acad. Sci. USA. 2005;102:2273-2276. Mirkin, C. A., Letsinger, R. L., Mucic, R. C , and Storhoff, J. J. A DNA-Based Method for Rationally Assembling Nanoparticles into Macroscopic Materials. Nature. 1996;382:607-609. Elghanian, R., Storhoff, J. J., Mucic, R. C , Letsinger, R. L., and Mirkin, C. A. Selective Colorimetric Detection of Polynucleotides Based on the DistanceDependent Optical Properties of Gold Nanoparticles. Science. 1997;277: 1078-1080. Park, S. J., Taton, T. A., and Mirkin, C. A. Array-Based Electrical Detection of DNA with Nanoparticle Probes. Science. 2002;295:1503-1506. Taton, T. A., Mirkin, C. A., and Letsinger, R. L. Scanometric DNA Array Detection with Nanoparticle Probes. Science. 2000;289:1757-1760. Nam, J. M., Thaxton, C. S., and Mirkin, C. A. Nanoparticle-Based Bio-Barcodes for the Ultrasensitive Detection of Proteins. Science. 2003;301:1884-1886. He, M., Li, K., Xiao, J., and Zhoua, Y. Rapid Bio-Barcode Assay for Multiplex DNA Detection Based on Capillary DNA Analyzer. J. Virol. Methods. 2008;151:126-131. Cheng, M. M., Cuda, G., Bunimovich, Y L., Gaspari, M., Heath, J. R., Hill, H. D., Mirkin, C. A., Nijdam, A. J., Terracciano, R., Thundat, T., and Ferrari, M. Nanotechnologies for Biomolecular Detection and Medical Diagnostics. Curr. Opi. Chem. Biol. 2006;10:11-19. Ding, C , Zhang, Q., and Zhang, S. An Electrochemical Assay for Protein Based Bio Bar Code Method. Biosens. Bioelectron. 2009;Doi:10.1016. J. Bios. Dec 23, 2008. Bruchez, M., Moronne, M., Gin, P., Weiss, S., and Alivisatos, A. P. Semiconductor Nanocrystals as Fluorescent Biological Labels. Science. 1998;281:2013-2016. Yu, W. W., Qu, L. H., Guo, W. Z., and Peng, X. G. Experimental Determination of the Extinction Coefficient of Cdte, Cdse, and Cds Nanocrystals. Chem. Mater. 2003;15:2854-2860.

486

BIOMARKERS 14. 15.

16. 17.

18.

19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29.

Ness, J. M., Akhtar, R. S., Latham, C. B., and Roth, K. A. Combined Tyramide Signal Amplification and Quantum Dots for Sensitive and Photostable Immunofluorescence Detection. J. Histochem. Cytochem. 2003;51:981-987. Wu, X., Liu, H., Liu, J., Haley, K. N., Treadway, J. A., Larson, J. P., Ge, N., Peale, R, and Bruchez, M. P. Immunofluorescent Labeling of Cancer Marker Her2 and Other Cellular Targets with Semiconductor Quantum Dots. Nat. Biotechnol. 2003;21:41^16. Xue, X., Pan, J., Xie, H., Wang, J., and Zhang, S. Fluorescence Detection of Total Count of Escherichia Coli and Staphylococcus Aureus on Water-Soluble Cdse Quantum Dots Coupled with Bacteria. Talanta. 2009;77:1808-1813. Liu, Y. J., Yao, D. J., Chang, H. Y, Liu, C. M., and Chen, C. Magnetic BeadBased DNA Detection with Multi-Layers Quantum Dots Labeling for Rapid Detection of Escherichia Coli 0157:H7. Biosens. Bioelectron. Dec 1, 2008;24(4): 558-565. Ikanovic, M., Rudzinski, W. E., Bruno, J. G., Allman, A., Carrillo, M. P., Dwarakanath, S., Bhahdigadi, S., Rao, P., Kiel, J. L., and Andrews, C. J. Fluorescence Assay Based on Aptamer-Quantum Dot Binding to Bacillus Thuringiensis Spores. J. Fluoresc. 2007;17:193-199. Ho, J. A., Lin, Y. C , Wang, L. S., Hwang, K. C , and Chou, P. T. Carbon Nanoparticle-Enhanced Immuoelectrochemical Detection for Protein Tumor Marker with Cadmium Sulfide Biotracers. Anal. Chem. 2009;81:1340-1346. Wang, H., Wang, J., Timchalk, C , and Lin, Y. Magnetic Electrochemical Immunoassays with Quantum Dots Labels for Detection of Phophorylated Acetylcholinesterase in Plasma. Anal. Chem. 2008;80:8477-8484. Bourel, D., Rolland, A., Le Verge, R., and Genetet, B. A New Immunoreagent for Cell Labeling. CD3 Monoclonal Antibody Covalently Coupled to Fluorescent Polymethacrylic Nanoparticles. J. Immunol. Methods. 1988;106:161-167. Adler, J., Jayan, A., and Melia, C. D. A Method for Quantifying Differential Expansion within Hydrating Hydrophilic Matrixes by Tracking Embedded Fluorescent Microspheres. J. Pharm. Sci. 1999;88:371-377. Wu, H., Huo, Q., Varnum, S., Wang, J., Liu, G., Nie, Z., Liu, J., and Lin, Y. DyeDoped Silica Nanoparticles Lables/Protein Microarrays for Detection of Protein Biomarkers. Analysts. 2008;133:1550-1555. Burns, A. A., Vider, J., Ow, H., Herz, E., Penate-Medina, O., Baumgart, M., Larson, S. M., Wiesner, U., and Bradbury, M. Fluorescent Silica Nanoparticles with Efficient Urinary Excretion for Nanomedicine. Nano. Lett. 2009;9:442-448. Choi, J., Burns, A. A., Williams, R. M., Zhou, Z., Flesken-Nikitin, A., Zipfel, W. R., Wiesner, U., and Nikitin, A. Y. Core-Shell Silica Nanoparticles as Fluorescent Labels for Nanomedicine. J. Biomed. Opt. 2007; 12:064007. Qian, X., Zhou, X., and Nie, S. Surface-Enhanced Raman Nanoparticle Beacons Based on Bioconjugated Gold Nanocrystals and Long Range Plasmonic Coupling. /. Am. Chem. Soc. 2008;130:14934-14935. Nie, S. M. and Emory, S. R. Probing Single Molecules and Single Nanoparticles by Surface-Enhanced Raman Scattering. Science. 1997;275:1102-1106. Michaels, A. M., Nirmal, M., and Brus, L. E. Surface Enhanced Raman Spectroscopy of Individual Rhodamine 6G Molecules on Large Ag Nanocrystals. J.Am. Chem. Soc. 1999;121:9932-9939. Kneipp, K., Wang, Y, Kneipp, H., Perelman, L. T., Itzkan, I., Dasari, R., and Feld, M. S. Single Molecule Detection Using Surface-Enhanced Raman Scattering (SERS). Phys. Rev. Lett. 1997;78:1667-1670.

NANOSCALE TECHNIQUES FOR BIOMARKER QUANTIFICATION 30. 31. 32. 33. 34. 35. 36.

37. 38.

39. 40. 41. 42. 43. 44. 45. 46. 47.

487

Cao, Y. W., Jin, R. C , and Mirkin, C, A. Nanoparticles with Raman Spectroscopic Fingerprints for DNA and RNA Detection. Science. 2002;297:1536-1540. Graham, D., Mallinder, B. J., and Smith, W. E. Surface-Enhanced Resonance Raman Scattering as a Novel Method of DNA Discrimination. Angew. Chem. Int. Ed. 2000;39:1061-1064. Kneipp, K., Kneipp, H., Itzkan, I., Dasari, R. R., and Feld, M. S. Ultrasensitive Chemical Analysis by Raman Spectroscopy. Chem. Rev. 1999;99:2957-2976. Nikoobakht, B. and El-Sayed, M. Preparation and Growth Mechanism of Gold Nanorods (Nrs) Using Seed-Mediated Growth Method. Chem. Mater. 2003;15:1957-1962. Link, S. and El-Sayed, M. A. Spectral Properties and Relaxation Dynamics of Surface Plasmon Electronic Oscillations in Gold And Silver Nanodots and Nanorods. J. Phys. Chem. B. 1999;103:8410-8426. Sun, Y. and Xia, Y. Shape-Controlled Synthesis of Gold and Silver Nanoparticles. Science. 2002;298:2176-2179. Liu, X., Dai, Q., Lauren, A., Coutts, J., Knowles, G., Zou, J., Chen, H., and Huo, Q. A One-Step Homogeneous Immunoassay for Cancer Biomarker Detection Using Gold Nanoparticles Probes Coupled with Dynamic Light Scattering. J. Am. Chem. Soc. 2008;130:2780-2782. Katz, E. and Willner, I. Integrated Nanoparticle-Biomolecule Hybrid Systems: Synthesis, Properties and Applications. Angew. Chem. Int. Ed. 2004;43: 6042-6108. El-Sayed, I. H., Huang, X., and El-Sayed, M. A. Surface Plasmon Resonance Scattering and Absorption of Anti-EGFR Antibody Conjugated Gold Nanoparticles in Cancer Diagnostics; Applications in Oral Cancer. Nano. Lett. 2005;4:829-834. Huang, X., El-Sayed, I. H., Qian, W., and El-Sayed, M. A. Cancer Cell Imaging and Photothermal Therapy in the Near-Infrared Region by Using Gold Nanorods. J. Am. Chem. Soc. 2006;128:2115-2120. Xie, H., Gill-Sharp, K. L., and O'Neal, D. P. Quantitative Estimation of Gold Nanoshell Concentrations in Whole Blood Using Dynamic Light Scattering. Nanomedicine. 2007;3:89-94. Thundat, T. and Majumdar, A. Microcantilevers for Physical, Chemical, and Biological Sensing. Sensors Sensing Biol. Eng. 2003:338-355. Ziegler, C. Cantilever-Based Biosensors. Anal. Bioanal. Chem. 2004;379: 946-959. Waggoner, P. S. and Craighead, H. G. Micro- and Nanomechanical Sensors for Environmental, Chemical, and Biological Detection. Lab Chip. 2007;7: 1238-1255. Yang, Y T., Callegari, C , Feng, X. L., Ekinci, K. L., and Roukes, M. L. Zeptogram-Scale Nanomechanical Mass Sensing. Nano. Lett. 2006;6:583-586. Burg, T. P., Godin, M., Knudsen, S. M., Shen, W., Carlson, G., Foster, J. S., Babcock, K., and Manalis, S. R. Weighing of Biomolecules, Single Cells and Single Nanoparticles in Fluid. Nature. 2007;446:1066-1069. Tamayo, J., Ramos, D., Mertens, J., and Calleja, M. Effect of the Adsorbate Stiffness on the Resonance Response of Microcantilever Sensors. Appl. Phys. Lett. 2006;89:224104-224107. Ramos, D., Tamayo, J., Metens, J., Calleja, M., and Zaballos, A. Effect of the Adsorbate Stiffnesson the Resonance Response of Microcantilever Sensor. J. Appl. Phys. 2006;100:106105-106108.

488

BIOMARKERS 48. 49. 50.

51. 52. 53. 54.

55.

56. 57. 58. 59. 60. 61. 62. 63. 64.

Gupta, A. K., Nair, P. R., Akin, D., Ladisch, M. R., Broyles, S., Alam, M. A., and Bashir, R. Anomalous Resonance in a Nanomechanical Biosensor. Proc. Natl. Acad. Sci. 2006;103:13362-13367. Thundat, T., Wachter, E. A., Sharp, S. L., and Warmack, R. J. Detection of Mercury Vapor Using Resonating Microcantilevers. Appl. Phys. Lett. 1995;66: 1695-1697. McKendry, R., Zhang, J., Arntz, Z., Strunz, T., Hegner, M., Lang, H. P., Bailer, M. K., Certa, U., Meyer, E., Giintherodt, H., and Gerber, C. Multiple Label-Free Biodetection and Quantitative DNA-Binding Assays on s Nanomechanical Cantilever Array. Proc. Natl. Acad. Sci. 2002;99:9783-9788. Ilic, B., Yang, Y, and Craighead, H. G. Virus Detection Using Nanoelectromechanical Devices. Appl. Phys. Lett. 2004;85:2604-2606. Su, M., Li, S., and Dravid, V P. Microcantilever Resonance-Based DNA Detection with Nanoparticle Probes. Appl. Phys. Lett. 2003;82:3562-3564. Varshney, M., Waggoner, P. S., Tan, C. P., Montagna, R. A., and Craighead, H. G. Prion Protein Detection Using Nanomechanical Resonator Arrays and Secondary Mass Labeling. Anal. Chetn. 2008;80:2141-2148. Zhang, J., Lnag, H. P., Huber, E, Bietsch, A., Grange, W., Certa, U., McKendry, R., Guntherodt, H. J., Hegner, M., and Gerber, C. H. Rapid and Label-Free Nanomechanical Detection of Biomarkers Transcripts in Human RNA. Nat. Nanotechnol. 2006;1:214-220. Su, X. D., Wu, Y. J., Robelek, R., and Knoll, W. Surface Plasmon Resonance Spectroscopy and Quartz Crystal Microbalance Study of Streptavidin Film Structure Effects on Biotinylated DNA Assembly and Target DNA Hybridization. Langmuir. 2005;21:348-353. Zheng, G. F., Patolsky, E, Cui, Y, Wang, W. U., and Lieber, C. M. Multiplexed Electrical Detection of Cancer Markers with Nanowire Sensor Arrays. Nat. Biotech. 2005;23:1294-1301. Waggoner, P., Varshney, M., and Craighead, H, G. Prostate Specific Antigen Using Nanomechanical Cantilever Array. Lab Chip. 2009;21:3095-3099. Varshney, M., Waggoner, P., Montagna, R. A., and Craighead, H. G. Prion Protein Detection in Blood Serum Using Resonator Array. Talanta. 2009; 80:593-599. Waggoner, P. S. and Craighead, H. G. The Relationship Between Material Properties, Device Design, and the Sensitivity of Resonant Mechanical Sensors. J. Appl. Phys. 2009; 105:054306. Curreli, M., Zhang, R., Ishikawa, E N., Chang, H., Cote, R. J., Zhou, C , and Thompson, M. E. Real-Time, Label-Free Detection of Biological Entities Using Nanowire-Based Fets. IEEE Trans. Nanotechnol. 2008;7:651-667. Allen, B. L., Kichambare, P. D., and Star, A. Carbon Nanotube Field-Effect-Transistor-Based Biosensors. Adv. Mat. 2007;19:1439-1451. Willner, I. Biomaterials for Sensors, Fuel Cells, and Circuitry. Science. 2002; 298:2407-2408. Patolsky, E, Zheng, G., Hayden, O., Lakadamyali, M., Zhung, X., and Lieber, C. M. Electrical Detection of Single Viruses. Proc. Natl. Acad. Sci. USA. 2004;101:14017-14022. Chen, R. J., Choi, H. C , Bangsaruntip, S., Yenilmez, E., Tang, X. W., Wang, Q., Chang, Y. L., and Dai, H. J. An Investigation of the Mechanisms of Electronic Sensing of Protein Adsorption on Carbon Nanotube Devices. J. Amer. Chem. Soc. 2004;126:1563-1568.

NANOSCALE TECHNIQUES FOR BIOMARKER QUANTIFICATION 65.

66. 67. 68. 69.

70.

71. 72. 73. 74. 75. 76. 77. 78. 79. 80.

489

Stern, E., Klemic, J. E, Routenberg, D. A., Wyrembak, P. N., Turner-Evans, D. B., Hamilton, A. D., Lavan, D. A., Fahmy, T. M , and Reed, M. A. LabelFree Immunodetection with CMOS-Compatible Semiconducting Nanowires. Nature. 2007;445:519-522. Cui, Y., Wei, Q. Q., Park, H. K., and Lieber, C. M. Nanowire Nanosensors for Highly Sensitive and Selective Detection of Biological and Chemical Species. Science. 2001;293:1289-1292. Heller, I., Janssens, A. M., Mannik, J., Minot, E. D., Lemay, S. G., and Dekker, C. Identifying the Mechanism of Biosensing with Carbon Nanotube Transistors. NanoLett. 2008;8:591-595. Zheng, G. R, Patolsky, F, Cui, Y, Wang, W. U., and Lieber, C. M. Multiplexed Electrical Detection of Cancer Markers with Nanowire Sensor Arrays. Nature Biotechnol. 2005;23:1294-1301. Li, C , Curreli, M., Lin, H., Lei, B., Ishikawa, F. N., Datar, R., Cote, R. J., Thompson, M. E., and Zhou, C. W. Complementary Detection of Prostate Specific Antigen Using In(2)0(3) Nanowires and Carbon Nanotubes. J. Am. Chem. Soc. 2005;127:12484-12485. Balavoine, F., Schultz, P., Richard, C , Mallouh, V., Ebbesen, T. W., and Mioskowski, C. Helical Crystallization of Proteins on Carbon Nanotubes: a First Step Towards the Development of New Biosensors. Angew. Chem. Int. Ed. 1999; 38:1912-1915. Bradley, K., Briman, M., Star, A., and Griiner, G. Charge Transfer from Adsorbed Proteins. Nano. Lett. 2004;4:253-256. Boussaad, S., Tao, N. J., Zhang, R., Hopson, T, and Nagahara, L. A. In Situ Detection of Cytochrome C Adsorption with Single Walled Carbon Nanotube Device. Chem. Commun. 2003;1502-1503. Hahm, J. and Lieber, C. M. Direct Ultrasensitive Electrical Detection of DNA and DNA Sequence Variations Using Nanowire Nanosensors. Nano Lett. 2004; 4:51-54. Li, J., Ng, H. T., Cassell, A., Fan, W, Chen, H., Ye, Q., Koehne, J., Han, J., and Meyyappan, M. Carbon Nanotube Nanoelectrode Array for Ultrasensitive DNA Detection. Nano Lett. 2003;3:597-602. Nguyen, C. V., Delzeit, L., Cassell, A. M., Li, J., Han, J., and Meyyappan, M. Preparation of Nucleic Acid Functionalized Carbon Nanotube Arrays. Nano Lett. 2002;2:1079-1081. Edwards, K. A. and Baeumner, A. J. Liposomes in Analyses. Talanta. 2006; 68:1421-1431. Jones, M., Kilpatrick, P., and Carbonell, R. Preparation and Characterization of Bifunctional Unilamellar Vesicles for Enhanced Immunosorbent Assays. Biotechnol. Prog. 1993;9:242-258. Weissig, V, Lasch, J., Klibanov, A., and Torchilin, V. A New Hydrophobic Anchor for the Attachment of Proteins to Liposomal Membranes. FEBS Lett. 1986;202:86-90. Kung, V. and Redemann, C. Synthesis of Carboxyacyl Derivatives of Phosphatidylethanolamine and Use as an Efficient Method for Conjugation of Protein to Liposomes. Biochim. Biophys. Ada. 1986;862:435^139. Maruyama, K., Kennel, S., and Huang, L. Lipid Composition Is Important for Highly Efficient Target Binding and Retention of Immunoliposomes. Proc. Natl. Acad. Sci. USA. 1990;87:5744-5748.

490

BIOMARKERS 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98.

Rule, G., Montagna, R., and Durst, R. Characteristics of DNA-Tagged Liposomes Allowing Their Use in Capillary-Migration, Sandwich-Hybridization Assays. Anal. Biochem. 1997;244:260-269. Bredehorst, R., Ligler, R, Kusterbeck, A., Chang, E., Gaber, B., and Vogelt, C. Effect of Covalent Attachment of Immunoglobulin Fragments on Liposomal Integrity. Biochemistry. 1986;25:5693-5698. Katoh, S., Kishimura, M., and Tomioka, K. Immune Lysis Assay of Antibodies by Use of Antigen-Coupled Liposomes. Colloids Surf. A. 1996;109:195-200. Hansen, C , Yao, G., Moase, E., Zalipsky, S., and Allen, T. Attachment of Antibodies to Sterically Stabilized Liposomes: Evaluation, Comparison and Optimization of Coupling Procedures. Biochim. Biophys. Ada. 1995;1239:133-144. Carroll, T., Davison, A., and Jones, A. Functional Cholesteryl Binding Agents: Synthesis, Characterization, and Evaluation of Antibody Binding to Modified Phospholipid Vesicles. J. Med. Chem. 1986;29:1821-1826. Zalipsky, S., In: Lasic, D., Martin, F. (Eds.). Stealth Liposomes. CRC Press. 1995:93-102. Nugan, S. R., Asiello, P. J., Connelly, J. T., and Baeumner, A. PMMA Biosensors for Nucleic Acids with Integrated Mixer and Electrochemical Detection. Biosens. Bioelectron. 2009;24:2428-2433. Kumanan, V., Nugen, S. R., Baeumner, A., and Chang, Y. A Biosensor Assay for the Detection of Mycobacterium Avium Subsp. Paratuberculosis in Fecal Samples. J. Vet. Sci. 2009;10:35^12. Tai, J., Ewert, M., Belliot, G., Glass, R., and Monroe, S. Development of a Rapid Method Using Nucleic Acid Sequence-Based Amplification for the Detection of Astrovirus. J. Virol. Method. 2003; 110:119-127. Hartley, H. and Baeumner, A. Biosensor for the Specific Detection of a Single Viable B. Anthracis Spore. Anal. Bioanal. Chem. 2003;376:319-327. Baeumner, A., Schlesinger, N., Slutzki, N., Romano, J., Lee, E., and Montagna, R. Biosensor for Dengue Virus Detection: Sensitive, Rapid, and Serotype Specific. Anal. Chem. 2002;74:1442-1448. Zaytseva, N., Montagna, R., Lee, E., and Baeumner, A. Multi-Analyte SingleMembrane Biosensor for the Serotype-Specific Detection of Dengue Virus. Anal. Bioanal. Chem. 2004;380:46-53. Baeumner, A., Cohen, R., Miksic, V, and Min, J. RNA Biosensor for the Rapid Detection of Viable Escherichia Coli in Drinking Water. Biosens. Bioelectron. 2003;18:405-413. Xu, D. and Cheng, Q. Surface-Bound Lipid Vesicles Encapsulating Redox Species for Amperometric Biosensing of Pore-Forming Bacterial Toxins. JACS Commun. 2002;124:14314-14315. Haga, M., Sugawara, S., and Itagaki, H. Drug Sensor: Liposome Immunosensor for Theophylline. Anal. Biochem. 1981;118:286-293. Baeumner, A. and Schmid, R. Development of a New Immunosensor for Pesticide Detection: A Disposable System with Liposome-Enhancement and Amperometric Detection. Biosens. Bioelectron. 1998;13:519-529. Suita, T. and Kamidate, T. Preparation of Antibody-Coupled Liposomes Containing Horseradish Peroxidase as a Marker Molecule. Anal. Sci. 1999;15:349-352. Singh, A., Kilpatrick, P., and Carbonell, R. Application of Antibody Fluorophore-Derivatized Liposomes to Heterogeneous Immunoassays for D-Dimer. Biotechnol. Prog. 1996;12:272-280.

NANOSCALE TECHNIQUES FOR BIOMARKER QUANTIFICATION 99. 100. 101. 102. 103. 104.

105.

106. 107.

108. 109. 110.

111. 112. 113. 114.

491

Jones, M., Kilpatnck, P., and Carbonell, R. Competitive Immunosorbent Assays for Biotin Using Bifunctional Unilamellar Vesicles. Biotechnol. Prog. 1994;10:174-186. Lee, M., Durst, R., and Wong, R. Comparison of Liposome Amplification and Fluorophor Detection in Flow-Injection Immunoanalyses. Anal. Chim. Acta. 1997;354:23-28. Choquette, S., Locascio-Brown, L., and Durst, R. Planar Waveguide Immunosensor with Fluorescent Liposome Amplification. Anal. Chem. 1992;64:55-60. Singh, A., Kilpatrick, P., and Carbonell, R. Noncompetitive Immunoassays Using Bifunmctional Unilamellar Vesicles or Liposomes. Biotechnol. Prog. 1995;11:333-341. Wang, S. X. and Li, G. Advances in Giant Magnetoresistance Biosensors with Magnetic Nanoparticle Tags: Review and Outlook. IEEE Trans. Magnet. 2008;44:1687-1702. Osterfeld, S. J., Yu, H., Gaster, R. S., Caramuta, S., Xu, L., Han, S., Hall, D. A., Wilson, R. J., Sun, S., White, R. L., Davis, R. W., Pourmand, N., and Wang, S. X. Multiple Protein Assays Based on Real-Time Magnetic Nanotag Sensing. Proc. Natl.Aca. Sci. USA. 2008;105:20637-20640. Martin, V. C , Cardoso, F. A., Germano, J., Cardoso, S., Sousa, L., Piedada, M., Feitas, P. P., and Fonseca, L. P. Femtomolar Limit of Detection with a Magnetoresistive Biochip. Biosen. Bioelectron. 2009;Doi:10.1016. J. Bios. 2009.01.040. Ferreira, H. A., Feliciano, N., Graham, D. L., Clarke, L. A., Amaral, M. D., and Freitas, P. P. Rapid DNA Hybridization Based on AC Field Focusing of Magnetically Labeled Target DNA. Appl. Phys. Lett. 2005;87:013901-013903. Graham, D. L., Ferreira, H. A., Feliciano, N., Freitas, P. P., Clarke, L. A., and Amaral, M. D. Magnetic Field-Assisted DNA Hybridisation and Simultaneous Detection Using Micron-Sized Spin-Valve Sensors and Magnetic Nanoparticles. Sens. Actuators B. 2005;107:936-944. Hodgkinson, G. N., Tresco, P. A., and Hlady, V. The Influence of Sub-Micron Inhibitory Clusters on Growth Cone Substratum Attachments and CD44 Expression. Biomaterials. 2008;29:4227^1235. Schmidt, P. M., Lehmann, C , Matthes, E., and Bier, F. F. Detection of Activity of Telomerase in Tumor Cells Using Fiber Optical Biosensors. Biosen. Bioelectron. 2002;17:1081-1087. Morin, N. A., Oakes, P. W, Hyun, Y. M., Lee, D., Chin, Y E., King, M. R., Springer, T. A., Shimaoka, M., Tang, J. X., Reichner, J. S., and Kim, M. Nonmuscle Myosin Heavy Chain IIA Mediates Integrin LFA-1 De-Adhesion During T Lymphocyte Migration. J. Exp. Med. 2008;205:195-205. Fu, G., Wang, C , Liu, L., Wang, G. Y, Chen, Y. Z., and Xu, Z. Z. Heterodimerization of Integrin Mac-1 Subunits Studied by Single Molecule Imaging. Biochem. Biophys. Res. Commun. 2008;368:882-886. Sohn, H. W., Pierce, S. K., and Tzeng, S. J. Live Cell Imaging Reveals That the Inhibitory Fcgammariib Destabilizes B Cell Receptor Membrane-Lipid Interactions and Blocks Immune Synapse Formation. J. Immunol. 2008;180:793-799. Keller, P., Toomre, D., Diaz, E., White, J., and Simons, K. Multicolour Imaging of Post-Golgi Sorting and Trafficking in Live Cells. Nat. Cell Biol. 2001 ;3: 140-149. Varadi, A., Tsuboi, T, and Rutter, G. A. Myosin Va Transports Dense Core Secretory Vesicles in Pancreatic MIN6 Beta-Cells. Mol. Biol. Cell. 2005;16: 2670-2680.

492

BIOMARKERS 115. Kolin, D. L., Ronis, D., and Wiseman, P. W. K-Space Image Correlation Spectroscopy: A Method for Accurate Transport Measurements Independent of Fluorophore Photophysics. Biophys. J. 2006; 91:3061-3075. 116. Gesty-Palmer, D. and Thompson, N. L. Binding of the Soluble, Truncated Form of an FC Receptor (Mouse FC Gamma RII) to Membrane-Bound Igg as Measured by Total Internal Reflection Fluorescence Microscopy. J. Mol. Recognit. 1997;10:63-72. 117. Poglitsch, C. L. and Thompson, N. L. Interaction of Antibodies with FC Receptors in Substrate-Supported Planar Membranes Measured by Total Internal Reflection Fluorescence Microscopy. Biochemistry. 1990;29:248-254. 118. Kapoor, R., Kaur, N., Nishanth, E. T, Halvorsen, S. W., Bergey, E. J., and Prasad, P. N. Detection of Trophic Factor Activated Signaling Molecules in Cells by a Compact Fiber-Optic Sensor. Biosens. Bioelectron. 2004;20:345-349. 119. Conboy, J. C , McReynolds, K. D., Gervay-Hague, J., and Saavedra, S. S. Quantitative Measurements of Recombinant HIV Surface Glycoprotein 120 Binding to Several Glycosphingolipids Expressed in Planar Supported Lipid Bilayers. J. Am. Chem. Soc. 2002;124:968-977. 120. Levy, S., Mannion, J., Cheng, J., Reccius, C , and Craighead, H. G. Entropic Unfolding of DNA Molecules in Nanofiuidic Channels. Nano. Lett. 2008;8: 3839-3844. 121. Reccius, C. H., Mannion, J. T., Cross, J. D., and Craighead, H. G. Compression and Free Expansion of Single DNA Molecules in Nanochannels. Phy. Rev. Lett. 2005;95:268101-268104. 122. Reccius, C. H., Stavis, S. M., Mannion, J. T, Walker, L. P., and Craighead, H. G. Conformation, Length snd Speed Measurements of Electrodynamically Stretched DNA in Nanochannels. Biophys. J. 2008;95:273-286. 123. Mannion, J. T, Reccius, C. H., Cross, J. D., and Craighead, H. G. Conformational Analysis of Single DNA Molecules Undergoing Entropically Induced Motion in Nanochannels. Biophys. J. 2006; 90:4538-4545. 124. Han, J. and Craighead, H. G. Entropic Trapping and Sieving of Long DNA Molecules in a Nanofiuidic Channel. /. Vac. Sci. Technol. A. 1999;17:2142-2147. 125. Han, J. and Craighead, H. G. Separation of Long DNA Molecules in a Microfabricated Entropic Trap Array. Science. 2000;288:1026-1029. 126. Han, J. and Craighead, H. G. From Microfluidics to Nanofiuidics: DNA Separation Using Nanofiuidic Entropic Trap Array Device. Proc SPIE. Microfluidic Devices and Systems III, Santa Clara, CA. 2000:42-48. 127. Stavis, S. M. Single Molecule Studies of Quantum Dot Conjugates in a Submicrometer Fluidic Channel. Lab Chip. 2005a;5:337-343. 128. Stavis, S. M. Detection and Identification of Nucleic Acid Engineered Fluorescent Labels in Submicrometre Fluidic Channels. Nanotechnology. 2005b; 16: S314-S323. 129. Stavis, S. M. Single-Molecule Mobility and Spectral Measurements in Submicrometer Fluidic Channels. J. Appl. Phys. 2005c;98:044903-1-044903-5. 130. Foquet, M. DNA Fragment Sizing by Single-Molecule Detection in Submicrometer-Sized Closed Fluidic Channels. Anal. Chem. 2002;74:1415-1422. 131. Tegenfeldt, J., Prinz, C , Cao, H., Huang, R., Austin, R., Chou, S., Cox, E., and Sturm, J. Micro- and Nanofluidics for DNA Analysis. Anal. Bioanal. Chem. 2004;378:1678-1692. 132. Levine, M. J., Korlach, J., Turner, S. W., Foquet, M., Craighead, H. G., and Webb, W. W. Zero-Mode Waveguides for Single-Molecule Analysis at High Concentrations. Science. 2003;299:682-686.

NANOSCALE TECHNIQUES FOR BIOMARKER QUANTIFICATION

493

133. Moran-Mirabal, J. M. and Craighead, H. G. Zero-Mode Waveguides: SubWavelength Nanostructures for Single Molecule Studies at High Concentrations. Methods. 2008;46:11-17. 134. Lemay, S. G. Nanopore-Based Biosensors: The Interface Between Ionics and Electronics. ACS Nano. 2009;3:775-779. 135. Kasianowicz, J. J., Brandin, E., Branton, D., and Deamer, D. W. Characterization of Individual Polynucleotide Molecules Using a Membrane Channel. Proc. NatLAcad. Sci. USA. 1996;93:13770-13773. 136. Storm, A. J., Storm, C , Chen, J., Zandbergen, H., Joanny, J. E, and Dekker, C. Fast DNA Translocation Through a Solid-State Nanopore. Nano Lett. 2005; 5:1193-1197. 137. Heng, J. B., Ho, C , Kim, T., Timp, R., Aksimentiev, A., Grinkova, Y. V., Sligar, S., Schulten, K., and Timp, G, Sizing DNA Using a Nanometer-Diameter Pore. Biophys. J. 2004;87:2905-2911. 138. Li, J., Gershow, M., Stein, D., Brandin, E., and Golovchenko, J. A. DNA Molecules and Configurations in a Solid State Nanopore Microscope. Nature Mater. 2003;2:611-615. 139. Mathe, J., Visram, H., Viasnoff, V., Rabin, Y., and Meller, A. Nanopore Unzipping of Individual DNA Hairpin Molecules. Biophys. J. 2004;87:3205-3212.

This page intentionally left blank

CHAPTER

IMMUNODIAGNOSTICS WITH A FOCUS ON LATERAL FLOW POINT-OF-CARE DEVICES Roy R. Mondesire, Glen M. Ford, Hannie F. Ford, and Stephen C. Mefferd

INTRODUCTION Before the advent of immunodiagnostics, the diagnosis of infectious diseases required the demonstration of the presence of an organism by conventional methods. In many instances, these procedures were not useful because of technical difficulty and/or time required to achieve results. Immunodiagnostics provided a new and important set of tools for infectious disease diagnosis indirectly by detection of antibodies or directly by detection of antigen associated with the presence of the organism. Many immunodiagnostic tests are accurate, affordable, and user-friendly with a fairly rapid turnaround time. Infection by foreign antigen or organism results in antibody responses to antigens recognized by and appropriately presented to the body's immune system. Detection of antibody responses is accomplished by measurement of blood or serum reactivity to a known antigen reagent system. This involves production of polyclonal or monoclonal antibodies to the antigen of interest and this is used as the reagent detection system. The detection of either antibody or antigen in patient blood or serum is the basis for immunodiagnostic tests. These types of immunodiagnostic technologies have been employed for many years and are still the most important diagnostic tools for infectious disease. A limitation of the technology is that detection of antibody does not necessarily equate to active infection. This interpretation of an antibody response depends on the biology of the specific disease or host-agent interaction. There are many infectious diseases in which an antibody response clears the body of 495

496

BIOMARKERS

infection. In these instances, the interpretation of positive antibody tests is that the animal has been infected by the organism, but the status of active infection is not certain. For infectious agents that are not effectively eliminated by antibody responses, the detection of antibody responses may correlate better with an interpretation of active infection. Detection of antigen is a more direct determination of the presence of active infection. Rosalyn Yalow and Solomon Berson1 were the first to describe the principles of immunoassay technology. By the 1970s this technology evolved from research and development into incorporation in many large central and local hospital laboratories. Immunoassays have been applied to both qualitative and quantitative analyses and have been applied to most biomedical research. The application of impressive scientific and technological innovations into the in vitro medical device industry resulted in a dramatic increase in the use of immunodiagnostic products.

ANTIBODIES IN IMMUNOASSAYS Antibodies are secreted by B lymphocytes and comprise the primary arm of the humoral immune response. B lymphocyte development occurs in specific inductive microenvironments and includes both antigen-independent and dependent processes. Antigen specific naive B lymphocytes are retained in secondary lymphoid organs upon recognition of receptor-specific antigen. Secondary lymphoid organs contain B lymphocyte-rich follicles in which these naive antigen-specific B lymphocytes undergo clonal expansion which results in the generation of memory B lymphocytes or antibody-secreting plasma cells. Vertebrate immune systems have evolved a variety of strategies to achieve diversification of antigen receptor molecules. DNA recombination events result in mature antigen receptor genes from separate gene segments. Three gene segments are involved in this event. These genes have been designated variable (V), diversity (D), and joining (J). As the B cell matures, it rearranges or shuffles these gene segments and selects among hundreds of DNA segments. Specific sequences of DNA are cut and then selected pieces spliced together (Figure 19.1). For antibody, the V gene segment encodes the majority of the variable domain including complementarity-determining regions 1 and 2 (CDR1 and CDR2). The D segments and the DJ junctions encode CDR3. In the mouse, an extensive Ig repertoire is achieved by a large group of genes encoding the variable regions of the heavy (H) and light (L) chains (VH and VL). In humans however, the fewer VH and VL are compensated for by the relatively longer CDR3. For the heavy chain of antibody, the variable regions are derived from gene rearrangement and recombinations of the VH, DH, and JH. In the case of the light chain the variable regions are derived from the VL and JL gene segments. In humans there are approximately 105 V(D)J germline exons and 103 VL exons. Thus there are 108 different possibilities in the germline. Maturation and selection of B -cell clones in the germinal center results in the production of high affinity antibodies. During this time, antigen-specific B-cell clones

497

IMMUNODIAGNOSTICS

Germ-line DNA

5'—]

v

|

H8enes

1 D H ssgments

C H genes

JH segments

1

r-3'

11 Gene rearrangement

1

B-cell DNA

V J t Transcription and RNA splicing

1

B-cell mRNA

Heavy chain

FIGURE 19.1 chain.

NH 2 _|

ID

v

|| CDRl

1!

vH

CDR2

j

j

1

rn

1

J \ Translation

II

|

CH

| — COOH

CDR3

General pattern of rearrangement of genes in the production of an antibody heavy

undergo isotype switches and somatic hypermutation. In the T-cell-dependent regions of the peripheral lymphoid organs, B cells react with specific antigen and proliferate with the assistance of T cells and accessory cells.

S t r u c t u r e and Function of A n t i b o d i e s Antibodies are glycoproteins belonging to the immunoglobulin (Ig) supergene family. Five major isotypes occur within this family and are present in the majority of higher mammals. These have been designated IgG, IgM, IgA, IgD, and IgE. Size, charge, amino acid composition, and carbohydrate content distinguish the isotypes. IgG, usually depicted as the basic immunoglobulin molecule (H2L2), is a monomeric glycoprotein composed of two identical heavy chains (H) and two identical light chains (L). It is the predominant antibody in normal serum and accounts for 70-75% of the total Ig content. Human IgG has a sedimentation coefficient of 7S and a molecular mass (MM) of approximately 160 kDa. Four subclasses are recognized for human IgG. These have been designated IgGl, IgG2, IgG3, and IgG4. Figure 19.2 illustrates the general structure of IgG. IgM is the first immunoglobulin isotype detected early in a primary immune response and accounts for about 10% of the total antibody pool. It is pentameric, each unit consisting of a MM of 180 kDa. The relative MM is thus approximately 900 kDa. Monomers of IgM are linked by disulfide bonds in a circular array. A cysteine-rich "J chain" joins two of the monomers to complete the circle (Figure 19.3). IgA is a carbohydrate-rich immunoglobulin. In humans it is the second most abundant immunoglobulin. In most other mammals, IgA forms a relatively small part of the plasma pool. Dimeric IgA predominates in mammals and consists of two IgA molecules in association with a secretory component (SC). A J chain links the dimeric form via the Fc. IgA is the predominant immunoglobulin at the mucosae and plays an important role in protection at these surfaces. IgD (MM =170 kDa) accounts for a small fraction of circulating Ig.

498

BIOMARKERS

Antibody Combining Site (Paratope)

1 vL Q Hinge Region

1

v„

Fab —

1 s

11 s

1 s -S-S-

H^

1

-s-s-s-s,,

CH2 Fc CH3

1

s

1i CH1

J

1

s

1 1

r

1

s

*\.

Interchain disulfide bonds Intrachain disulfide bonds

s

1 FIGURE 19.2 Schematic of an antibody molecule showing the domains which make up the heavy (H) and light (L) chains.The variable region of the molecule is designated V. Note theVL andVH regions together form the antibody combining site. In IgG, there are three constant (CH) domains designated C I, C 2, and C 3,The domains C 2 and C 3 constitute the Fc region of the molecule.There are intra and inter-chain disulfide bonds (-s-s-). The hinge region, which imparts segmental flexibility to the molecule, is shaded.

FIGURE 19.3 General schematic of IgM showing its pentameric configuration, inter-chain disulfide bonds, and J-chain.

IMMUNODIAGNOSTICS

499

The structure of IgD confers a high susceptibility to proteolysis and heat. It is present primarily on the surface of circulating B lymphocytes in association with IgM and although its function has not been established, it may play a role in B cell differentiation. IgE (MM = 200 kDa) is primarily found associated with the high affinity IgE receptor (FccRI) on the membranes of mast cells and basophils. It mediates Type I hypersensitivity reactions and may play a role in immunity to some parasitic diseases. Two distinct functions, antigen binding and effector functions, are associated with antibodies. The binding site on an antibody is located on the hypervariable domain of the Fab region of the Ig. This binding site or "paratope" is located in the N-terminal portion of the molecule. This paratope is capable of recognizing and binding to an epitope on a corresponding antigen. The effector functions reside in the C-terminal portion of the molecule in the Fc constant domain region. Examples of effector functions are complement fixation and cell surface binding by means of Fc receptors expressed on phagocytic or other effector cells. The binding of antigenic epitopes to antibody paratopes involves multiple non-covalent bonds. The attractive forces involved in binding include hydrogen bond, electrostatic, van der Waals, and hydrophobic interactions. Hydrophobic interactions account for approximately 50% of the total strength of bonding and are primarily due to the interaction of non-polar hydrophobic residues where contact with water molecules is greatly reduced. These non-covalent interactions are very dependent on the distance between the interacting residues.

Kinetics of A n t i b o d y - A n t i g e n Reactions Immunoassays are based on the reversible binding reaction of an antibody molecule with a corresponding antigen where there is a significant amount of binding energy. These antigen-antibody reactions can be described by the law of mass action. The kinetics of this reversible reaction can be represented thus: [Ag]+[Ab]

-

K

[AgAb]

(19.1)

Where: [Ag] = free antigen (Ag) concentration [Ab] = free antibody (Ab) concentration [AgAb] = Ag complexed with Ab kj = the association rate k2 = the dissociation rate Equation 1 assumes a single antigenic epitope binding to a single Fab on an antibody. The rate of formation of the AgAb complex and law of mass action can be represented thus: d[AgAb] dt

=k 1 [Ag][Ab]-k 2 [AgAb]

(19.2)

500

BIOMARKERS

From equation 1, at equilibrium (i.e., when the net rate is zero): k,

[AgAb]

k2

[Ag] [Ab]

Where Keq is the equilibrium constant in liters mole-1 (LM-1). For analytes which exist at sub-nanomolar concentrations, for example 10-11 M levels, it is important to have antibody at greater than 100-fold excess (> 10-9 M), in order to provide a reasonably high frequency of interaction between the antigen and antibody. Typically a Keq of > 109 LM-1 is required for immunoassays. Antibody affinity, a measure of the strength of the bond between a single antigen-combining site and an antigenic determinant, is traditionally determined with the Scatchard equation: r/[Ag] = -Kr + nK

(19.4)

Where r is the number of occupied sites on the antibody, [Ag] is the concentration of free antigen, and n is the antibody valency. In the case of polyclonal antibodies, the affinity measurement is a reflection of the average affinities of the different antibodies present in the sample.

Polyclonal Antibodies A specific antibody reacts only with a small region (epitope) contained on the molecular structure of the antigen. Thus, the physical size and complexity of the antigen influences the number of different specific antibodies produced. Larger and more complex immunogens contain many different epitopes, each producing and reacting with its own specific antibody. The technology used to develop polyclonal antibodies involves injecting animals (rabbits, sheep, goats, donkeys) with the antigen of interest and collecting the serum portion of the blood. Of the total serum immunoglobulins from an immunized animal only 0.1-10% contain specific antibodies reacting with the injected material. Various protein purification techniques are used to isolate specific antibody from nonspecific antibody and other serum proteins. In affinity purification, the serum is applied to a solid phase column containing a chromatographic resin with immobilized antigen to which the antibody was raised. This results in absorption of the antibody from the liquid to solidphase. By various chemical treatments, the specific antibody is eluted and separated from nonspecific serum proteins.

Hybridoma Technology In 1976, immunology was revolutionized by Kohler and Milstein 2 when they showed that individual antibody producing cells could be immortalized when fused with a myeloma cell line, making it possible to produce a virtually unlimited supply of antibodies with the same specificity. These are called monoclonal antibodies (MAbs). Large quantities of specific antibodies can be produced, purified, and adapted as tools for in vitro diagnostics, in vivo diag-

IMMUNODIAGNOSTICS

501

nostics, or therapeutics. They are generally derived by immunizing mice with an antigen, isolating antibody-producing cells from the spleen or lymph nodes and fusing them with an immortalized plasmacytoma cell to obtain a hybridoma cell that can be cultured in vitro or grown in mice. As the hybridoma cells replicate, continuous production of large quantities of monoclonal antibody occurs. The ability of monoclonal antibodies to react with a single epitope allows development of immunoassays that have high specificity. Monoclonal antibodies have been used for cancer immunotherapy, where a radioactive or cytotoxic compound is attached to the antibody and injected into a patient. Thus, the toxic material concentrates primarily at the cancer site, leading to death of the cancerous cells. Anti-CD3 monoclonal antibodies have been used for allograft rejection treatment.

RAPID M A N U A L A N D RAPID A U T O M A T E D IMMUNOASSAYS With the desire of health practitioners to obtain clinical results in minutes rather than hours, a major milestone occurred in the past two decades with the development and optimization of rapid immunoassays. These can be manual, automated, or machine run. While the rest of the clinical diagnostic market expands at rates below 10%, the use of rapid immunoassays is growing at an annual rate of greater than 15% for the past few years, with revenues in the billions by biotechnology companies in 2008. Rapid manual immunoassays are used in point-of-care testing, where the need to have a result is of critical importance. Two types of rapid point-of-care assays have been developed. These are lateral-flow and flow-through assays. Many rapid automated-machine run tests are performed using membrane or nanoparticle solid surfaces. Because these have significantly greater surface areas than the wells, tubes or macroparticles used in conventional enzyme-linked immunosorbent assay (ELISA), more capture antibody or antigen can be immobilized. Combined with the inherent property of membranes to channel analytes into close proximity with the coated solid-phase, reaction rates occur significantly faster. Nanoparticles have the advantage of a mobile colloidal liquid-phase that also brings the reactants into close proximity, thereby increasing the reaction rate. Since the reaction of analyte with the solid-phase is usually complete after 10 to 30 minutes, high degrees of precision and reproducibility are realized. Nanoparticles used in rapid automated immunoassays include magnetic particles or latex particles coated with antigen or antibody. A robotic arm removes a sample from a primary collection tube, dispenses a precise amount into a reaction well containing the nanoparticles, the reaction mixture is incubated, and the particles washed automatically. Magnetic particles are easier to wash since a magnet is used to pull the particles to the side of the reaction tube or well during aspiration and washing. Latex nanoparticles are trapped on glass or cellulose fibers and washed. After the washing step, the particles are exposed to conjugate, washed again, substrate added, and the extent of reaction measured optically.

502

BIOMARKERS

The sensitivity of rapid automated tests for their corresponding analyte is in the sub-picogram range with linear dose-responses over a four-log range.

E L E M E N T S OF I M M U N O A S S A Y S : SOLUBLE LABELS A N D D E T E C T I O N The ELISA is a heterogeneous enzyme immunoassay (EIA). Heterogeneous enzyme immunoassays are those that have at least one separation step to distinguish reacted from unreacted reagents. Figures 19.4 and 19.5 illustrate some of the principles of heterogeneous enzyme immunoassays. This technique, first described by Engvall and Perlmann,3 applies to all immunoassays in which one or more of the reactants is immobilized onto a solid phase. This solid phase is typically used to immobilize specific antibody or antigen depending on the assay configuration. Other components of immunoassays are the enzyme-labeled antibody or antigen. These conjugated reagents are used to probe any molecules that have reacted with the surface-bound antibody or antigen. Verification of the reaction sequence, in the case of a colorimetric assay, is achieved with a chromogenic substrate. Enzymes are more widely used than any other label in immunoassays. They generate colored, fluorescent, or luminescent compounds from neutral substrates. The various enzymes used in ELISA include horseradish peroxidase (HRP), alkaline phosphatase (AP), glucose oxidase, c-galactosidase, glucoamylase, carbonic anhydrase, and acetylcholinesterase. Several covalent conjugation methods are available for the coupling of enzymes to antigens or antibodies. The enzymes HRP (44 kDa) and AP (140 kDa) are the most commonly used in heterogeneous immunoassays. HRP catalyzes the conversion of the substrate H 2 0 2 to H20 and 0 2 . It then oxidizes another substrate resulting in a colored, fluorescent, or luminescent derivative, depending on the nature of the substrate. The enzyme AP catalyzes

Sandwich ELISA for the detection of specific IgG

+Substrate

1

2

3

Wash

4

5

Wash

FIGURE 19.4 Typical sandwich ELISA for the detection of specific IgG. In this configuration, species specific antibody (I) is bound to a solid phase. A sample containing IgG, (2) is added.The antibody (Y shaped molecule) in the sample is captured by the bound antibody (3). Following a wash step.anti species IgG enzyme (E)-conjugate is added (4).The conjugate binds to the captured IgG, forming a "sandwich"(5). Following a wash step, the addition of the appropriate chromogenic substrate will result in a color change, thus revealing binding of the specific antibody of interest.

IMMUNODIAGNOSTICS

503

FIGURE 19.5 Typical sandwich ELISA for the detection of antigen. In this configuration specific antibody (I) is bound to a solid phase. A sample containing antigen (2) is added.The antigen in the sample is captured by the bound antibody (3). Following a wash step, an antibody enzyme conjugate is added (4). The conjugate binds to a site on the captured antigen, forming a 'sandwich' (5). Following another wash, the addition of the appropriate chromogenic substrate will result in a color change, thus revealing binding of specific analyte of interest.

the hydrolysis of phosphate esters of primary alcohols, phenols and amines. Another commonly used approach employs biotin-avidin binding reactions with one of the components complexed with a chromogenic enzyme. Avidin (MM = 67 kDa) can be isolated from purified egg white. This molecule has a very high affinity (association constant = 1015 LM-1) for the small water soluble vitamin biotin (MM = 0.244 kDa). Four biotin molecules can bind to one avidin molecule. In a typical ELISA, biotinylated antibody and avidin-labeled enzyme are used instead of the enzyme-labeled antibody. This combination offers a significant enhancement in signal.

HOMOGENEOUS ENZYME IMMUNOASSAYS In homogeneous EIAs, the immunological reactions and the detection of changes in enzymatic activity are carried out in the same solution. There is no need for the separation of bound and free labels. As for the heterogeneous EIAs, the substrates used can be chromogenic, fluorogenic or chemiluminescent. Enzymes used for homogeneous EIAs include c-D-galactosidase, glucose6-phosphate dehydrogenase (G6PDH), hexokinase and glucose oxidase. The substrates for these labels are the fluorogenic substrate 4-methylumbelliferyle-D-galactopyranoside, D-glucose 6-phosphate + NAD+, D-hexose + ATP and glucose, respectively. Homogeneous EIAs can be competitive or noncompetitive binding assays. Competitive assays are based on the modulation of enzyme activity due to the competitive reaction of antibody with labeled and free antigen. Here the enzyme activity is either activated or inhibited as a result of immune complex formation. Noncompetitive assays utilize enzyme-labeled antibody conjugates.

504

BIOMARKERS

One approach involves a proximal linkage assay based on substrate channeling due to the close proximity of the one enzyme with a corresponding coupling enzyme. In another method changes in enzyme activity take place due to the binding of antigen to the corresponding antibody enzyme conjugate.

SIGNAL MEASUREMENT METHODS Colorimetry The most common and simplest detection system is colorimetry. This can be determined visually or with the aid of a spectrophotometer. A common chromogenic substrate for peroxidase is 3,3',5,5'-tetramethylbenzidine (TMB), described earlier.

Fluorometry Fluorescence begins with the absorption of photons by fluorophores. At the appropriate wavelength, the electrons are energized from a ground energy state to an excited singlet state. As the molecule returns to the ground state, it emits a photon of light at a lower energy (i.e. longer wavelength). The Stokes' shift is the difference between the excitation maximum and the emission maximum (Figure 19.6). The standard label, fluorescein isothiocyanate (FITC) has an absorptionemission time interval of only one nanosecond (ns). Many other compounds, however, exhibit delayed fluorescence with much higher time intervals. The use of fluorescence techniques in immunoassays has been reviewed.4

Relative fluorescence

Emission Excitation

Wavelength (nm) FIGURE 19.6 Principle of fluorescent measurement. The Stokes' shift represents the difference between the maximum excitation and the maximum emission.

IMMUNODIAGNOSTICS

505

Fluorometric enzyme immunoassays (FIAs) are more sensitive than the colorimetric immunoassays and therefore may be used to detect or measure small concentrations of analyte. A disadvantage is increased complexity of the procedure and the need for instrumentation. A common substrate for AP in fluorometric El As is 4-methylumbelliferyl phosphate (4-MUP). AP dephosphorylates 4-MUP to the form the fluorophore 4-methylumbelliferone (4-MU).

Time-Resolved Fluorescence Background interference by light scattering and intrinsic fluorescence of sample components are some of the limitations of the traditional fluorescent compounds. Time-resolved fluorescence is based on the principle that some lanthanides, such as europium (Eu3+), form fluorescent chelates with certain organic ligands. These fluorophores have very large Stokes' shifts and decay times (200 nm and >500 nanoseconds respectively). Time-resolved fluorescence takes advantage of these long decay times and large Stokes' shifts. Thus any short-lived fluorescence background signals or scattered excitation radiation are eliminated. Signals are thus measured under conditions of virtually no background. The use of time-resolved fluorescence techniques in immunoassays has been reviewed.5 Several homogeneous and heterogeneous FIAs for the detection and quantitation of various diseases markers have been developed and many of them have been automated. Luminescence In chemiluminescent immunoassays, luminescent compounds emit light during the course of a chemical reaction. Luminol derivatives or acridinium esters have been used as labels. The kinetics are very fast and light is emitted within seconds of substrate oxidation. These assays are generally very sensitive with high dynamic ranges. In an electrochemiluminescence (ECL) technique,6 a ruthenium metal chelate and tripropylamine are utilized. Both of these molecules become oxidized at the surface of an electrode where they react to form an excited state of ruthenium that decays, releasing a photon at 620 nm. This technology is easily adapted to immunoassays and molecular diagnostics.

P R I N C I P L E S OF B I N D I N G Biological macromolecules can bind to plastic surfaces in various ways. The adsorption of molecules to surfaces is primarily due to intermolecular attraction forces. These forces are of two types—alternating polarities, known as hydrophobic interaction, and stationary polarities. Alternating polarities occur when molecules in close proximity create disturbances in their electron clouds. Molecules which possess stationary polarities can bind to each other through dipoles. Hydrogen bonding can result between two dipoles. Chemical groups which mediate H bonding include -OH, =0, -NH2, =NH, and gN. Prior to the immobilization of a molecule to a surface, it is useful to know the properties of both the solid phase and the molecule to be bound. For example, some plastics

506

BIOMARKERS

are available in either hydrophobic or hydrophilic forms, and others have both properties. The geometry of a globular molecule will dictate the maximum number of molecules that can be packed in the densest monolayer on a surface. The packing density is a function of the orientation of the molecule after binding. For an elongated molecule such as IgG (approximately 150 A x 3 A), it can be shown that vertical packing will result in more molecules bound per unit area when compared to a horizontal packing of the molecules.

NON-SPECIFIC INTERACTIONS IN IMMUNOASSAYS Non-specific interactions can have adverse effects on immunoassays. This effect is manifested primarily in reduced specificity. Many factors can contribute to this effect. The elimination of non-specificity is central to the performance of a reliable test. Several rational and empirical approaches are used to reduce or eliminate non-specific interactions. One approach requires the chemical modification of the immunoglobulin. In order to circumvent this effect, F(ab)<£ and Fab IgG fragments may be used. F(ab)<£ and Fab Ig fragments are produced by the enzymatic actions of pepsin and papain respectively (Figure 19.7).

C O L L O I D A L A N D PARTICLE I M M U N O A S S A Y S Flow-Through Assays Flow-through rapid manual assays are membrane tests where the sample flows transversely through the membrane and analyte is trapped on the anti-analyte coated membrane surface. After sample has passed through the membrane, colloidal conjugate or enzyme conjugate containing anti-analyte is added in drop form and acts as the reporter molecule. For enzyme conjugates, a substrate addition is required to visualize the reaction. The total assay time for flow-through tests is 2-15 minutes. Sensitivities approach the upper picogram range.

Particle Capture Following agglutination, colored agglutinated microspheres can be applied to a membrane. The complexes will be caught on the filter due to their larger size. Unreacted single microspheres will pass through. The results may be visually assessed. Reflectometry or densitometry may also be used for a more objective assessment of results. Particle capture may be followed by detection with an enzyme-labeled antibody and chromogenic substrate. In this approach, uncolored microspheres labeled with antibody are attached to a membrane. Sample containing antigen is added followed by appropriate conjugate and precipitable substrate (Figure 19.8).

IMMUNODIAGNOSTICS

507

FIGURE 19.7 Effect of pepsin and papain digestion of IgG.The F(ab) 2 fragment is used more frequently for immunoassays than the Fab fragment.

Fluorochrome-Dyed Microspheres Fluorochrome-dyed microspheres have become more widely used in diagnostics and there have been many unique applications that use this technology. For example, a flow cytometric capable of discriminating microspheres by size and fluorescent color has been used to simultaneously perform realtime analysis of several assays on the surfaces of the microspheres. This assay system can simultaneously perform several different assays in a single tube with a single specimen.

P O I N T - O F - C A R E L A T E R A L - F L O W ASSAY TECHNOLOGY Introduction to Traditional Lateral Flow Tests Rapid, manual lateral flow immunoassays are used in point-of-care testing, where technician time is substantially reduced from hours, typical of laboratory-based ELISAs, to minutes by use of combined membrane and

508

BIOMARKERS

FIGURE 19.8 Particle entrapment with chromogenic detection.The extent of color formation is proportional to the amount of antigen in the sample. This approach works equally well for antibody detection where microspheres are coated with antigen.

nanoparticle technologies.7 Particles are employed as a solid phase in many important immunodiagnostic procedures. A comprehensive approach to understanding the history and role of particles in immunodiagnostics has been published.8 Colloidal gold or latex particles are coated with antibody or antigen and act as the reporter molecules, enabling a visual detection of the captured analyte. Typically, optimal coating of the gold particles is at or near the iso-electric point of the substance being coated, allowing for enhanced interaction of the substance with the negatively charged surface. For colloidal gold chemistries, binding via sulfur groups contained in cysteine and methionine amino acid residues results in a dative covalent bound. After coating of the particles, the resultant conjugate is dried down on polyester or glass fiber membranes. For latex particles, covalent coupling of coating proteins is achieved using functional carboxyl moieties contained on the surface of the latex. In these assays, coated polymer membranes act as the solid-phase, capturing bound analyte (the target of interest). The membrane is typically impregnated with 0.5 to 1.5 micrograms of capture reagent per cm2 and dried. At this coating concentration, reaction time is greatly reduced from hours to minutes. For example, the maximum coated capture protein in a microwell is 400 nano-grams per cm2. Lateral-flow assays have an advantage over flow-through assays in that these devices can be stored at elevated temperatures (up to 50°C) and require minimal hands-on manipulation—a single step is all that is needed to run the tests. Flow-through test reagents often need to be stored refrigerated, and the tests involve several steps to complete the assay. In lateral-flow assays, the sample is added to an absorbent pad containing a dried colloidal-gold conjugate (or other colored nanoparticle, such as latex, selenium, or carbon). If analyte is present in the sample, it reacts with the conjugate. Conjugate-bound analyte migrates by capillary action through

IMMUNODIAGNOSTICS

509

the membrane in a chromatographic fashion. If analyte is present in sufficient concentration, the conjugate-analyte complex binds to the coated membrane, forming a visually detectable colored line on the membrane (Figure 19.9). Conversely, the test strip can be read with a reflectance reader for both qualitative and quantitative results. 1. Sample medium which may contain a prefilter to trap red blood cells 2. Conjugate medium containing a dried mixture gold colloid or nanoparticles 3. Analytical nitrocellulose membrane with immunoglobulin capture stripes 4. First antibody test line 5. Second antibody control line 6. Absorbent medium 7. Polyester film backing attached to analytical membrane 8. Pressure sensitive acrylic laminated to vinyl or polypropylene. Figure 19.10 is a photograph of a commercial lateral flow device showing test and control lines. Many rapid, manual tests are performed using membrane, or nanoparticle, solid surfaces. Because these have significantly greater surface areas than the wells, tubes, or macro plastic beads used in conventional ELISA, more capture antibody or antigen can be immobilized. Since the reaction of analyte with the solid-phase is usually complete after a few minutes, high degrees of precision and reproducibility are realized. Nanoparticles used in rapid, manual immunoassays include metal colloids such as gold, silver, and selenium as well as polymers such as latex, coated with antigen or antibody. This technology has been used for a variety of human and animal targets. Some of these include, pregnancy, fertility, drugs of abuse, sexually transmitted diseases, HIV, TB, malaria, and numerous other diseases prevalent in the industrialized and non-industrialized world. Lateral or tangential flow immunoassays are conducted in single-use test devices.

FIGURE 19.9 Typical lateral-flow device for nucleic acid detection. (See color insert for a full color version of this figure.)

510

BIOMARKERS

FIGURE 19.10 Commercial lateral-flow device showing test and control lines, Shown is a rapiad format isotyping kit used in the monitoring of class and sub-class characteristics of monoclonal antibodies often used in the development of immunoassays. The three cassettes pictured demonstrate the reaction line at the control line (C) indicating the test was performed properly. Also shown is a reaction line in the left cassette with a visible line at the G2a position indicating this is a subclass G2a monoclonal antibody, a reaction line in the center cassette at the A position indicating this is an IgA antibody, and a reaction on the right cassette at the K position indicating that this is a Kappa light chain monoclonal antibody. This rapid format isotyping kit represents a significant time saving for the laboratory as this test is completed in five minutes, while the traditional ELISA format assay takes eight hours from beginning to end.

Nucleic A c i d D e t e c t i o n and Lateral Flow Nucleic acid amplification testing (NAAT) is capable of single copy target detection with discrimination of single base pair mismatches. The methods include thermal cycling by the polymerase chain reaction (PCR),9 as well as numerous isothermal amplification strategies such as nucleic acid sequencebased amplification (NASBA),10 strand displacement amplification (SDA),11 or loop-mediated isothermal amplification LAMP.12 NAAT enables superior diagnostic sensitivity and specificity for genetic and clinical testing but has been hampered by the expensive equipment and complex procedures needed to detect the target of interest. This lateral flow strategy can be applied to either PCR or isothermal amplification. A complete, one-step, fully functional, ready-to-use lateral-flow assay device for the rapid, accurate detection of a

IMMUNODIAGNOSTICS

511

FIGURE 19.1 I General principle of lateral flow detection of nucleic acid targets.These devices are generally stable at room temperature and its performance is not compromised by elevated temperatures. This method can be applied in routine research laboratories as an alternative to gel electrophoresis and can be a convenient single nucleotide polymorphism (SNP) analysis.

target nucleic acid in a fluid sample, wherein the device contains all reagents necessary for the assay in an anhydrous format, has been described.13 A general scheme for detection is shown in Figure 19.11. 1. Reagent pad containing dried, fluoresceinated, and biotinylated detection probes 2. Conjugate pad containing dried NeutrAvidingianoparticles 3. Analytical nitrocellulose membrane with mmunoglobulin capture stripes 4. Absorbent medium

Principle of the Lateral-Flow Procedure f o r Nucleic A c i d D e t e c t i o n The fundamental strategy for lateral-flow detection of nucleic acid amplification product is to incorporate two different haptens, one that binds to a conjugated nanoparticle, and the other that anchors dyed nanoparticles in a linear array upon wicking past a membrane bound antibody that recognizes the other hapten.14,15 Here we describe biotin that binds to NeutrAvidin conjugated nanoparticles, and fluorescein isothiocyanate (FITC) that is recognized by an anti-FITC line deposited onto a nitrocellulose membrane. Haptenized Primers One approach is to haptenize each of the primers so that as amplification occurs the product becomes labeled with both haptens. This is particularly suited to amplification methods such as PCR that generate double stranded product that incorporates the primers. The advantage of this approach is that it is a homogeneous labeling strategy that does require hybridization on the strip and allows for multiplexing (Gerdes, J. C. 2001. Personal Communication).

512

BIOMARKERS

Haptenized Detection Probes Amplification methods such as NASBA or asymmetric PCR that generate single stranded intermediates can be bifunctionally haptenized by hybridization to two adjacent haptenized probes. Following amplification, separate biotinylated and fluoresceinated oligonucleotide detection probes specifically hybridize to the same strand of the amplified target DNA or RNA molecules that is present. Alternatively, the probes may be added post amplification or dried on the lateral flow laminate construct. When the probe-target complexes encounter NeutrAvidin-coated nanoparticles in the conjugate medium, the biotinylated portion of probe will bind to the NeutrAvidincattached to the nanoparticles. The resulting complexes continue to migrate through the nitrocellulose until they encounter the immobilized anti-Fluorescein isothiocyanate antibody line where the antibody binds the fluoresceinated portion of the probe. This interaction arrests further migration of the nanoparticle-haptenized duplexes and rapid accumulation of conjugates occurs at the antibody line and is visible with the unaided eye. Molecular Detection of Chlamydia Trachomatis— A Major Agent of Sexually Transmitted Infections Successful detection of specifically amplified chlamydia trachomatis has been achieved. Amplification reactions were designed using NASBA. The reaction parameters were designed to target specific regions of the 16s Ribosomal RNA for these organisms (Medline Accession # D85722). Two DNA oligomers were designed to hybridize adjacent to each other on the minus-sense RNA NASBA products demarcated by the primer binding sites. In each system, one of these oligomers had a fluorescein isothiocyanate (FITC) moiety at the 5c end, and a phosphate blocking group at the 3c end. The second oligomer of each set has a biotin at the 39 end (5c-FITC-AAATAGAGCAAGACGCAGAT-3cand 5c-BIOTIN-TTGATGAAGTGGCAGTTACT-3). Lateral-flow detection following nested PCR and CT asymmetric PCR has also been achieved for chlamydia. One elementary body (EB) was easily detected with these formats. In the study of 60 male urine specimens, a relative sensitivity and specificity of 100% was achieved.16

Pathogenic Bacteria Detection with Bacteriophage Bacteriophages play a critical role in bacterial biology and may contribute significantly to microbial evolution.17 The coat protein of bacteriophage MS2, a 27-34 nm is an icosahedral bacteriophage that has been detected by lateral flow following propagation in Escherichia coli. In an optimized system, the presence of a line in the test region of the lateral strip is a result of the presence of phage coat protein and thus indicative of the presence of E. coli.18 The same principle has been applied to the detection of methicillin resistant staphylococcus aureus (MRSA) using bacteriophages specific to this pathogen.19

IMMUNODIAGNOSTICS

513

S E N S I T I V I T Y OF L A T E R A L - F L O W TECHNOLOGY To date, lateral-flow detection sensitivity levels of 0.2 to 1.0 ng/ml are typical for average molecular-weight proteins and substances. Examples of these assays include clostridium difficile toxin A, with detection levels of 500 pg/ ml and 10 to 50-mer DNA-oligonucleotide detection, with sensitivity limits below 200 pg/ml. Lateral flow assays for whole bacteriophage, virus, and bacteria including bacteriophage will detect 5 X104 to 1 X 105 organisms per ml. Much of the increased sensitivity realized in lateral flow is due to the incubation of sample with the dried gold or nanoparticle conjugate within the test platform prior to the start of the assay reaction. The resultant mixture allows for thorough mixing of reactants, allowing for near liquid-phase kinetics; both capture antigen and analyte are highly mobile, allowing for rapid attachment of analyte to the high surface-area solid-phase (about 100-1000-fold more surface area than is contained in an ELISA microwell). Thus, gold particles are evenly dispersed in a sample containing analyte; the analyte is likewise evenly dispersed, allowing for uniform binding of analyte to the solid-phase.

SUMMARY P O I N T S 1.

2. 3.

4. 5.

Rapid assay test technologies have evolved, from the detection of a single analyte, to the detection of multiple targets from the same sample. The trend to multiplexing is likely to continue as the need for multiple nucleic acid probe detection and proteomic analysis expands. Today, the worldwide point-of-care market is estimated at $4 billion in annual revenue, with a compounded annual growth rate of 10%. Additionally, the over-the-counter market is expanding rapidly, as a growing number of low-cost, easily administered tests reach people, not only in homes and physicians' offices, but also in remote areas of developing countries. Advances in rapid assay test technologies have, among other things, simplified the test process by permitting the use of oral swabs in lieu of more difficult to obtain body fluids. The point-of-care technology is not only restricted to humans but can also be utilized in animal, food, and plant testing.

U S E F U L I N F O R M A T I O N FOR F U T U R E T R E N D S Emerging Technologies New emerging technologies include biosensor electronic field amplification and surface light scattering technologies. In addition, new tools from molecular biology are being developed. The detection of nucleic acid targets specific for an infectious agent provides a sensitive alternative technique for infectious disease diagnosis.

514

BIOMARKERS •

Point-of-care (POC) market including physician office laboratory (POL) testing markets worldwide http://www.devicelink.com/ivdt/archive/08/01/003.html.

•

Examples of the POC industry include hospital bedside, home/self-testing, and physician's office lab (POL).

•

The POC industry will expand by 10.4%, with some individual sectors growing in excess of this forecast. The projections indicate that by 2011, the POC industry will maintain 4 1 % of the market.

•

Sales of hospital critical care products and POL tests will grow at an 11.7% compound annual rate by 2011. The POL product increase will net $2.64 billion by 2011, up from $1.66 billion in 2005.

•

The combined market for hospital bedside POC testing products totaled $1.3 billion in 2005, and is forecast to grow to $2.6 billion by 2011.

REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

Yalow, R. S. and Berson, S. A. Assay of Plasma Insulin in Human Subjects by Immunological Methods. Nature. (London). 1959;184:1648-1649. Kohler, G. and Milstein, C. Continuous Culture of Fused Cells Secreting Antibodies of Pre-defined Specificity. Nature. (London). 1976; 256:495^197. Engvall, E. and Perlmann, P. Enzyme Linked Immunosorbent Assay (ELISA). Quantitative Assay of Immunoglobulin G. Immunochemistry. 1971;871—875. Hemmila, I. L. Applications of Fluorescence in Immunoassays. In J. D. Winefordner, Ed. Chemical Analysis. Volume 117. John Wiley & Sons, Inc. 1991. Yang, H., Leland, J. K., Yost, D. and Massey, R. J. Electrochemiluminescence: A New Diagnostic and Research Tool. Bio/Technology. 1994;12:193-194. Jameison, F, Sanchez, R. I., Dong, L., Leland, J. K., Yost, D. and Martin, M. T. Electrochemiluminescence-based Quantitation of Classical Clinical Chemistry Analytes. Anal. Chem. 1996;68:1298-1302. Charlton, D. E., 2002. U.S. Patent No. 6,485,982. Bangs, L. 1996. The Latex Course. Princeton, New Jersey. Roche U.S. PCR U.S. Patents No. 4,683,195, No. 4,683,202, and No. 4,965,188. Davey, C. and Malek, L. T. 1989. Nucleic Acid Amplification Process. European Patent No. EP 0329822. Walker, G. T., Little M. C , Nadeau J. G. and Shank, D. D. Isothermal In Vitro Amplification of DNA by a Restriction Enzyme/DNA Polymerase System. Proc. Natl.Acad. Sci. USA. 1992;89:392-396. Notomi, T., Okayama, H., Masubuchi, H., Yonekawa, T., Watanabe K., Amino N. and Hase T. Loop-mediated Isothermal Amplification of DNA. Nucleic Acids Res. 2000;28(12), e63. Gerdes, J., et. al. 2003 U.S. Patent Application 20040110167. Gerdes, J. 1999 U.S. Patent No. 5,989,813. Mondesire, R. R., Kozwich, D. L., Johansen, K. A., Gerdes, J. C. and Beard S. E. Solid-phase Nucleic Acid Extraction, Amplification, and Detection. IVD Technology. May/June 2000;9-13. Mondesire, R. and Gerdes, J. 2002. Unpublished data.

IMMUNO DIAGNOSTICS 17. 18. 19.

515

Kwan, T., Liu J, DuBow, M., Gros, P. and Pelletier, J. The Complete Genomes and Proteomes of 27 Staphylococcus Aureus Bacteriophages. PNAS. Vol. 102. 2005;14:5174-5179. Mondesire, R. 2000. Unpublished work. Wheeler, et al. 2005. U.S. Patent Application 20050250096.

This page intentionally left blank

SECTION IV HOT TOPICS IN BIOMARKER RESEARCH

This page intentionally left blank

CHAPTER

BIOMARKERS FOR ENVIRONMENTAL EXPOSURE Jane E. Gallagher, Elaine A. Cohen Hubal, and Stephen W. Edwards

INTRODUCTION The potential impact to human health from environmental exposures is of interest to scientists, clinicians, regulators, and the general public. Available information on the range of possible exposures to multiple chemicals in air, water, and soil is increasing as is the understanding of the impacts of these chemical exposures and how these can be modified through interactions with other biological, physical, and psychosocial stressors. During the last decade, biomarkers have been developed and used in environmental health sciences to enhance exposure assessment, gain insight into disease mechanism, and better understand acquired or inherited susceptibility. In addition, biomarkers are being developed and applied to address the growing awareness1 that population vulnerability factors must also be considered in evaluating cumulative health risk resulting from exposures to multiple environmental stressors. This chapter focuses on issues relevant to the development, application, and interpretation of environmental biomarkers with a particular focus on recent advances in the ways in which these are used for human risk assessments.2 While many of the concepts discussed herein are applicable to risk management2 as well, extensive treatment of that subject is beyond the scope of this chapter. The definition of biomarker varies slightly across scientific fields, (toxicology, occupational hygiene, medicine and epidemiology) but generally biomarkers are classified into biomarkers of exposure to xenobiotics (e.g., intact parent chemicals, metabolites), biomarkers of effect (i.e., an endogenous compound, a measure of functional capacity, or any indicator of actual

519

520

BIOMARKERS

or potential health impairment), and biomarkers of susceptibility (e.g., a genetic characteristic or a preexisting disease).3,4 Ideally, biomarkers are most valuable for environmental applications if they 1) predict disease risk, 2) reflect target tissue responses, 3) demonstrate a dose-response relationship, and 4) are specific, sensitive, and reproducible.5

Need f o r Biomarkers t o Support Environmental Risk Assessment The risk assessment process is applied to estimate potential risks to a given individual or population following exposure to a particular agent. This process considers inherent characteristics of the agent of concern as well as the characteristics of the specific individual or population. Risk assessment includes four steps: hazard identification, dose-response assessment, exposure assessment, and risk characterization. Risk assessments often form the basis for regulation and mitigation of chemical exposures. To help prevent and clean up chemical contamination of air, water, and soil, environmental laws are enacted, often as a result of legislative mandates, and "chemicals of concern" are identified for further toxicity evaluation. Regulatory agencies must then review the existing toxicological data pertinent to human and environmental health and derive "acceptable" exposure levels for each chemica.16 In the United States, certain organizations and government agencies routinely derive exposure criteria for chemicals of concern. For example, the American Conference of Governmental and Industrial Hygienists (ACGIH) derives threshold limit values (TLVs),7 and the federal Agency for Toxic Substances and Disease Registry (ATSDR) determines minimal risk levels (MRLs).8 The U.S. Environmental Protection Agency (EPA) derives reference doses/concentrations (RfDs/RfCs),9 maximum contaminant levels and goals (MCLs/MCLGs, http://www.epa.gov/safewater/ contaminants/index.html), and tolerance definitions for pesticides (http://www. epa.gov/opp00001/regulating/tolerances.htm). Traditionally, these reference values have been derived for a single chemical at a time using apical endpoints from high-dose animal studies or human epidemiological studies. The first generation of biomarkers contributing to our understanding of risk and susceptibility related largely to genotoxic carcinogens.10 Consequently, interventions and policy changes have served to reduce risk from several important environmental carcinogens. For example, as of 2007 there are 51 agents that were reclassified for potential carcinogenicity by the International Agency for Research on Cancer (IARC) based in part on mechanistically relevant biomarkers10 (Table 20.1). The IARC monographs report the assessment made by groups of experts of the weight of evidence in humans and experimental animals based on detailed guidelines (see the IARC Web site http://www. iarc). For example, the weight of evidence supporting the upgrade of ethylene oxide from 2A (probably carcinogenic to humans) to 1 (carcinogenic to humans) included the presence of hemoglobin adducts measured in humans.11 More recently, the EPA has developed guidance documents and synthesis reports 12-14, and white papers4 15-17 to advance a framework for conduct-

521

BIOMARKERS FOR E N V I R O N M E N T A L EXPOSURE

TABLE 20.1

Summary of IARC changes in carcinogenicity status based on biomarker data.

Change

Number of Chemicals

Mechanistic evidence used to upgrade hazards from 2A (probably carcinogenic to humans) to 1 (carcinogenic to humans)

3

Mechanistic evidence used to upgrade hazards from 2B (possibly carcinogenic to humans) to 2A (probably carcinogenic to humans)

36

Mechanistic evidence used to upgrade hazards from 3 (not classifiable as to carcinogenicity to humans) to 2B (possibly carcinogenic to humans)

4

Mechanistic evidence used to downgrade hazards from 2B (possibly carcinogenic to humans) to 3 (not classifiable as to carcinogenicity to humans)

8

Total

51

ing cumulative risk assessment by characterizing the combined risks to human health or the environment from multiple agents or stressors (Figure 20.1). As biomonitoring efforts (e.g., Center for Disease Control [CDC]; National Health and Nutrition Examination Survey [NHANES]: http://www.cdc.gov/nchs/ nhanes.htm) include panels of biomarkers of exposure, effect, and susceptibility, they will increasingly provide important insights that link exposure to health outcome. This is important because it is unlikely that there is a single "ideal" biomarker with all the important characteristics for relating health outcomes with a particular exposure and because a panel of biomarkers will be needed to reflect both short-term and long-term exposures. A second requirement for accurate cumulative risk assessment is an understanding of what biomarkers and biomonitoring data tell us about disease in the community and risk to the population. Finally, the utility of biomarkers for understanding risk to multi-factorial diseases (e.g., asthma, neurodegenerative diseases) should be evaluated since biomarkers represent the only way to monitor the integrated response to the environmental and genetic factors in the target population. In addition, there is considerable international emphasis on the use of mode of action (MOA) for risk assessment with the hope that it will allow more accurate prediction of risk at low doses. A unified framework for the use of MOA in risk assessment has been developed jointly by the U.S. EPA, the International Life Sciences, Risk Sciences Institute (ILSI RSI)18,19 and the International Programme on Chemical Safety (IPCS).20,21 This framework has been incorporated into national22 and international23-25 guidelines for risk assessment. The use of MOA for quantitative risk assessment does not address another challenge for risk assessment, however, which is that the dose-response relationships are generally driven by apical endpoints measured in animal models. This in turn requires extrapolation between species in order to predict risk for humans or sensitive ecological species and adds an extra degree of uncertainty to those risk predictions.26 The

522

BIOMARKERS

FIGURE 20.1 Representation of data elements that impact cumulative risk Cumulative risk is defined as the combined risks from aggregate exposures to multiple agents or srtressors (i.e., chemical, biological, or physical agents).14

increased identification and use of molecular biomarkers in the target populations can allow quantitative measurements of effects in the target population and thereby increase the reliability of quantitative assessment of risk in those populations.

C o n s i d e r a t i o n s f o r the Use of Biomarkers in Environmental Risk Assessment Informative biomarkers are those that are persistent, have a long half-life, are easily collected using noninvasive procedures, and can be used to establish a link between exposure and disease. It is also important that the biomarker is detectable in a substantial fraction of the study population with a broad spatial distribution and temporal occurrence. Biomarkers should have sufficient sensitivity to give information on regional differences in populations (e.g., secular trends) and differences in time scales (e.g., seasonal or long-term) of interest.4'27 Additional factors which should be considered when validating new biomarkers are: 1) significance with regard to mechanisms or mode of action of the chemicals, 2) specificity and sensitivity for the chemical or endpoint being tested, 3) potential for confounding of analyses by other factors (e.g., diet), 4) magnitude of both inter and intra-individual variation of the marker, and 5) cost and ease of analysis.28

BIOMARKERS FOR E N V I R O N M E N T A L EXPOSURE

523

In contrast to the use of biomarkers in pharmaceutical studies, environmental toxicant exposures are typically episodic and vary in magnitude over time. Consequently, the timing of the measurement of an environmental chemical specific biomarker is problematic given the temporal changes in biomarker formation and removal. Figure 20.2 is an example of chemicals that can be DNA reactive and the issues encountered if one relies on a single time point measurement in a single body compartment when there is a varied time course for formation and elimination of DNA reactive chemicals.29 The identical sampling issues are relevant for non-DNA reactive chemicals (i.e., pesticides or pesticide metabolites). When interpreting biomarker measurements, assumptions are frequently made that the chemical is at steady state levels in the body, however, toxicant exposures rarely occur under steadystate conditions, and biomarkers are typically most sensitive to recent toxicant exposures.30 Most exposure, effects, and susceptibility markers in humans are analyzed in biological fluids rather than target organs, yet the relevance of these changes to the disease pathways is typically not clearly elucidated. Human biomonitoring has been used in occupational medicine since the early 1930s, with the main matrices being urine and blood.31 Blood is an ideal matrix for most chemicals because the blood plasma is in contact with all tissues and is in equilibrium with the organs and tissues where chemicals are deposited. The main disadvantage of using blood in human biomonitoring is that it is an invasive matrix and thus can have an adverse effect on the participant response in volunteer epidemiological studies.32 In addition, only limited quantities of

FIGURE 20.2 Time course of exposure biomarkers following acute exposure. Hypothetical relationships among different biomarkers of exposures with respect to their relative levels and time of appearance after a single dose of a chemical.29

524

BIOMARKERS

blood can be drawn at any given time point, particularly in pediatric populations and requires personnel certified to obtain samples. As will be discussed, many effects cannot be monitored non-invasively. As a result, the judicious use of laboratory animal studies in conjunction with biomonitoring is important for optimal use of effects data for risk assessment. Because chemicals are stored or excreted in different tissues and organs, there are many other matrices. Examples of these alternative matrices are: urine, saliva, hair, nails, adipose tissue, feces, semen, breath, cord blood, meconium, and breast milk. Neri, et al.33 provide a review of chemicals that have been effectively measured in these matrices in various human biomonitoring studies. Barr, et al.34 prioritize, for each life stage, preferred biological matrices for assessing exposure to different classes of environmental chemicals. This prioritization scheme is based on matrix availability, time period of concern for a particular exposure or health effect, and properties of the environmental chemicals to be monitored. For example, assessing potential exposure to non-persistent organic chemicals such as organophosphate pesticides during the fetal period is facilitated by analysis of maternal blood and urine during gestation as well as analysis of cord blood at birth. Lipophilic persistent organic pollutants (POPs) accumulate in breast milk of lactating women. As a result, breast milk is an important matrix for measuring exposure of infants to compounds such as dioxins and polybrominated diphenyl ethers (PBDEs). Exposure to bioaccumulative inorganic chemicals including some forms of lead, mercury, and cadmium is best characterized in whole blood while non-bioaccumulative inorganics such as arsenic are better measured in urine. A longer term, integrated measure of arsenic can be obtained by analyzing hair or nails. A final and critical consideration is the need to plan for efficient use of limited biological matrices such as blood and urine to meet study objectives and minimize participant burden. The authors point out that the scheme was developed specifically to address the chemicals and developmental time points of interest to the National Children's Study.35 However, this type of prioritization scheme is well conceived, and could be extended to address chemicals and time points of interest for any sensitive population.

APPLICATIONS Biomonitoring Studies Biomonitoring is the direct measurement of human exposure to chemicals in the environment by measuring the parent chemicals or their metabolites (biomarkers of exposure) in specimens, such as blood or urine. Biomonitoring measurements are the most direct assessments of exposure because they indicate the amount of the chemical that actually gets into people from all environmental sources (e.g., air, soil, water, dust, food) combined.36 As public and private demands for biomonitoring increase, population-scale programs are being developed and implemented across the United States, Europe, and Asia. Recent biomonitoring studies have characterized the levels of over 200

BIOMARKERS FOR ENVIRONMENTAL EXPOSURE

525

parent compounds or metabolites from a variety of pollutant classes in human tissue. Potential application of these data to inform risk assessment and public health decision making is being widely discussed.37-40 Biomonitoring data have many important applications for assessing environmental health. These data can be used to: estimate exposures; identify fate of substances in the body; determine exposure trends; provide early warning indicators of exposures; establish associations between environmental exposures and potentially adverse health effects; develop reference ranges for public health decisions; and provide guidance for design of health studies (e.g., toxicology, epidemiology).41 Figure 20.3 summarizes the wide range of applications for biomonitoring data in risk assessment, risk management, and disease prevention.37'42 Biomonitoring data have been used successfully to track population trends, to identify susceptible groups, to evaluate results of exposure mitigation, and to provide indications of emerging environmental health issues. Biomonitoring for lead and mercury have shaped prevention strategies, identified susceptible groups, and improved the scientific basis for health risk estimates.43 In the case of dioxin and PCBs, biomonitoring has also enabled sci-

FIGURE 20.3 Applications for biomonitoring data. Graphical depiction summarizing the wide range of applications for biomonitoring data in risk assessment, risk management, and disease prevention.42

526

BIOMARKERS

entists and public health professionals to track population trends in measured levels of these compounds and to evaluate progress in reducing exposures.44 More recently, biomonitoring for perchlorate, brominated flame retardants, and persistent fluorinated compounds has provided important indications of emerging environmental health issues as a result of exposures.45 Continued biomonitoring allows for the identification of emerging environmental chemicals of concern due to their presence in human tissues and fluids, and their potential relationships to health issues.46"18 Major investments are being made to collect biomonitoring data. Significant government sponsored biomonitoring programs and studies include: • CDC National Biomonitoring Program36 • CDC Public Health Tracking Program49 • California Environmental Contaminant Biomonitoring Program50 • National Children's Study35 • Agricultural Health Study51 • The Canadian Health Measures Survey52 • German Environmental Surveys (GerES I-III).53 The CDC-sponsored National Biomonitoring Program36 publishes the "National Report on Human Exposure to Environmental Chemicals" with results of its ongoing assessment of the U.S. population. The Third Report54 (published in 2005) presents biomonitoring exposure data for 148 environmental chemicals (Table 20.2, black print) over the period from 2001 to 2002. The Fourth Report (http://www.cdc.gov/exposurereport/default.htm) with population biomonitoring data covering the years 2003 and 2004 and analysis for 75 new chemicals (Table 20.2, gray print) was just released in 2009. The CDC report provides valuable exposure information to scientists, physicians, and health officials on exposure of the U.S. general population to environmental chemicals. The report highlights intended uses of these data: 1) to track, over time, which chemicals get into the U.S. population and at what concentrations, 2) for chemicals with known toxicity levels, to determine the proportion of the population with levels above those known to be associated with adverse health effects, 3) to establish reference ranges that can be used by physicians and scientists to determine whether a person or group has an unusually high exposure, 4) to assess the effectiveness of public health efforts to reduce exposure of the U.S. population to specific chemicals, and 5) to set priorities for research on human health effects of exposure. In addition to the nationally-based biomonitoring program, the CDC has recently launched a planning grant program to support the building of biomonitoring capacity for public health laboratories. Approximately $10 million was distributed to 25 state and regional programs, supporting 33 states in biomonitoring planning. In 2003, CDC funded New Hampshire, New York, and the Rocky Mountain Biomonitoring Consortium to implement biomonitoring programs. The consortium comprises six states—Arizona, Colorado, Mon-

BIOMARKERS FOR E N V I R O N M E N T A L EXPOSURE

527

tana, New Mexico, Utah, and Wyoming (http://www.cdc.gov/biomonitoring/ state_grants.htm). As these state-based studies are designed and data become available, policy makers will have increasing capacity to inform public health decisions based on sound science. Despite the promise of biomonitoring, there are significant limitations and challenges associated with realizing the full potential of these data. Biomonitoring can be invasive and specimens may be difficult to collect from young children. As such, exposure information for a particularly vulnerable subset of the population is limited or missing. In addition, without better information on some of the key molecular events associated with chemical exposures, risk assessors cannot relate biomonitoring data to exposure-dose-response relationships or estimate the range of risks for the general population or for specific sensitive groups. As with many other exposure biomarkers, the analysis is conducted in biological fluids rather than target organs and the relevance of these changes to the disease pathways needs to be elucidated.

Interpretation of Biomonitoring Data The ability to measure chemicals in humans (biomonitoring) is far outpacing the ability to reliably interpret these data for public health purposes, creating a major knowledge gap.55 Though low levels of environmental contaminants can be measured in human tissues, it is not always known whether the measured exposure leads to an adverse health outcome. In addition, information on exposure pathways is often required to link biomonitoring results to contaminant sources for exposure mitigation and risk reduction. These and related challenges associated with using biomonitoring data for exposure and risk assessment have been discussed by Albertini, et al.37 Measurement of an environmental chemical in an individual's blood or urine does not indicate a risk for disease.36 Research is required to determine which levels of a chemical may cause a health effect and which levels are likely of no significant health concern. For some chemicals, such as lead, research studies provide a good understanding of health risk associates with various blood levels. For most of the environmental chemicals for which information is presented in the third CDC report, more research is needed to determine whether exposure at the reported levels presents potential health concerns. While the CDC36 has stated that biomonitoring is "the most health-relevant method of determining human exposure to environmental hazards," others recommend caution in use and interpretation of these data. For example, due to current limitations of data collected in population-level biomonitoring studies, it has been recommended by some environmental health risk assessors that these data be carefully interpreted,38 with the goal of establishing baseline exposure information, rather than for conclusions about human health risk. In addition, the use of biomonitoring data to design and evaluate public health interventions for compounds such as phthalates and flame retardants (polybrominated diphenyl ethers) requires additional information on potential sources, temporal and spatial patterns of exposure, as well as a mechanistic understand-

528

BIOMARKERS

TABLE 20.2 List of chemicals measured in selected participants for NHANES 2003-2004. Most, but not all, chemicals are included in the "Fourth National Report on Human Exposure to Environmental Chemicals" released in 2009 (http://www.cdc.gov/exposurereport). Chemicals in gray are new and appear for the first time in the fourth report.The source table is available at http://www.cdc.gov/exposurereport/ pdf/NHANES03-04List_03_2007.pdf

Metals Antimony Barium Beryllium Cadmium Cesium Cobalt Lead Mercury Molybdenum Platinum Thallium Tungsten Uranium Arsenic Arsenous (III) acid Arsenic (V) acid Monomethylarsonic acid Dimethylarsinic acid Arsenobetaine Arsenochoiine Trimethylarsme oxide

Organochlorine Pesticides Hexachlorobenzene Beta-hexachlorocyclohexane Gamma-hexachlorocyclohexane Pentachlorophenol 2,4,5-Trichlorophenol 2,4,6-Trichlorophenol p,p'-DDT p,p'-DDE o,p'-DDT Oxychlordane trans-Nonachlor Heptachlor epoxide Mirex Aldrin Dieldrin Endrin Monohydroxy methoxychJor Dihydroxy methoxychlor Endosulfan-ether Endosulfan-lactone Endosulfan-sulfate

Phthalates

Organophosphate Insecticides: Dialkyl Phosphate Metabolites

Mono-methyl phthalate Mono-ethyl phthalate Mono-n-butyl phthalate Mono-isobutyl phthalate Mono-benzyl phthalate Mono-cyclohexyl phthalate Mono-2-ethylhexyl phthalate Mono-(2-ethyl-5-oxohexyl) phthalate Mono-(2-ethyl-5-hydroxyhexyl) phthalate Mono-n-octyl phthalate Mono-(3-carboxypropyl) phthalate Mono-isononyl phthalate Mono-(2-ethyl-5-carboxypentyl) phthalate Polycyclic Aromatic Hydrocarbons 1 -Hydroxybenz[a]anthracene 3- and 9-Hydroxybenz[a]anthracene

Dimethylphosphate Dimethylthiophosphate Dimethyldithiophosphate Diethylphosphate Diethylthiophosphate Diethyldithiophosphate Organophosphate Insecticides: Specific Metabolites Malathion dicarboxylic acidf 3,5,6-Trichloro-2-pyridinol 2-lsopropyl-4-methyl-6-hydroxypyrimidine para-Nitrophenol 2-(Diethylamino)-6-methylpyrimidin-4-ol/one 3-Chloro-7-hydroxy-4-methyl-2H-chromen-2-one/ol

BIOMARKERS FOR ENVIRONMENTAL EXPOSURE TABLE 20.2

529

List of chemicals measured in selected participants for NHANES 2003-2004. (continued)

1 -Hydroxybenzo[c]phenanthrene 2-Hydroxybenzo[c]phenanthrene 3-Hydroxybenzo[c]phenanthrene 1 -Hydroxychrysene 2-Hydroxychrysene 3-Hydroxychrysene 4-Hydroxychrysene 6-Hydroxychrysene 3-Hydroxyfluoranthene* 2-Hydroxyfluorene 3-Hydroxyfluorene 9-Hydroxyfluorene 1 -Hydroxyphenanthrene 2-Hydroxyphenanthrene 3-Hydroxyphenanthrene 4-Hydroxyphenanthrene 9-Hydroxyphenanthrene 1 -Hydroxypyrene 3-Hydroxybenzo[a]pyrene 1 -Hydroxynapthalene 2-Hydroxynapthalene Phytoestrogens Daidzein Enterodiol Enterolactone Equol Genistein O-Desmethylangolensin Other Chemicals Perchlorate Acrylamide Glycidamide Environmental Phenols Bisphenol A 2-Hydroxy-4-methoxybenzophenone (Benzophenone-3) 4-tert-Octyl phenol 2,4,4'-Trichloro-2'-hydroxyphenyl ether (Triclosan) Herbicides: Substituted Ureas Diuron

5-Chloro-1,2-dihydro-1 -isopropyl-[3H]-1,2,4-triazol-3-one Ace p hate Methamidaphos Pyrethroid Pesticides cis-3-(2,2-Dichlorovinyl)-2,2-dimethylcyclopropane carboxylic acid trans-3-(2,2-Dichlorovinyl)-2,2-dirnethylcyclopropane carboxylic acid 3-Phenoxybenzoic acid 4-Ruoro-3-phenoxybenzoic acid cis/trans-Dimethylvinylcyclopropane carboxylic diacid cis-3-(2,2-Dibromovinyl)-2,2-dimethylcyclopropane carboxylic acid Other Pesticides 2-lsopropoxyphenol Carbofuranphenol N,N-Diethyl-3-methylbenzamide (DEET) 2,5-Dichlorophenol Fungicides ortho-Phenylphenol Chlorothalonil Metalaxyl Dichloran Ethylenethio urea (ETU) Propylenethio ur'ea (PTU) Phthalimide Tetrahydrophthalimide Perfluorinated Compounds Perfluorooctanoic acid Perfluorooctane sulfonic acid Perfluorohexane sulfonic acid 2-(N-Ethyl-peifluorooctane sulfonamido) acetic acid 2-(N-Methyl-peifluorooctane sulfonamido) acetic acid Pefluorodecanoic acid Petfluorobutane sulfonic. acid Perfluoroheptanoic acid Perfluorononanoic acid Perfluorooctane sulfonamide Perfluoroundecanoic acid Perflurododecanoic acid

530 TABLE 20.2

BIOMARKERS List of chemicals measured in selected participants for NHANES 2003-2004. (continued)

Linuron Dimethoxy pyrimidine Dimethyl pyrimidine Methyl methoxytnazine Bensu!furon-methy!:|: Chloroimuron ethyl+ Foramsulfuron^: Halosulfuron:|: Nicosulfuronj: Primisu!furon-methyl:|: lodosulfuron^; Rimsulfurord; Sulfometuron-methyU: Sulfosulfuron.j: Chlorsulfuron^: Ethametsulfurorvrnethytt Metsulfuron-methylj Prosuifuron.j. Thifensulfuron-rnethyhJ: Tnasulfuron^: Triflusu!furon-methyl+ Other Herbicides 2,4,5-Trichlorophenoxyacetic acid 2,4-Dichlorophenoxyacetic acid 2,4-Dichlorophenol Acetochior Acetochlor mercapturate Alachlor Alachlor mercapturatef Atrazine Atrazine mercapturate Diaminochlorotriazine Desethylatrazine Desisopropylatrazine Hydroxyatrazine Metolachlor Metolachlor mercapturate Dacthal Trifluralin Volatile Organic Compounds 1,1,1-Trichloroethane !. 1,2,2-Tetrachloroethane 1,1,2-Trichloroethane

Polychlorinated Dibenzo-p-dioxins, Dibenzofurans, Coplanar and Mono-Ortho-Substituted Biphenyls 1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin (OCDD) 1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin (HpCDD) 1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin (HxCDD) 1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin (HxCDD) 1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin (HxCDD) 1,2,3,7,8-Pentachlorodibenzo-p-dioxin (PeCDD) 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) 1,2,3,4,6,7,8,9-Octachlorodibenzofuran (OCDF) 1,2,3,4,6,7,8-Heptachlorodibenzofuran (HpCDF) 1,2,3,4,7,8,9-Heptachlorodibenzofuran (HpCDF) 1,2,3,4,7,8-Hexachlorodibenzofuran (HxCDF) 1,2,3,6,7,8-Hexachlorodibenzofuran (HxCDF) 1,2,3,7,8,9-Hexachlorodibenzofuran (HxCDF) 1,2,3,7,8-Pentachlorodibenzofuran (PeCDF) 2,3,4,6,7,8-Hexachlorodibenzofuran (HxCDF) 2,3,4,7,8-Pentachlorodibenzofuran (PeCDF) 2,3,7,8-Tetrachlorodibenzofuran (TCDF) 2,4,4'-Trichlorobiphenyl (PCB 28)f 2,3',4,4'-Tetrachlorobiphenyl (PCB 66) 2,4,4',5-Tetrachlorobiphenyl (PCB 74) 3,4,4',5-Tetrachlorobiphenyl (PCB 81) 2,3,3',4,4'-Pentachlorobiphenyl (PCB 105) 2,3,3',4',6-Pentachlorobiphenyl (PCB 1 10) 2,3',4,4',5-Pentachlorobiphenyl (PCB 1 18) 3,3',4,4',5-Pentachlorobiphenyl (PCB 126) 2,3,3',4,4',5-Hexachlorobiphenyl (PCB 156) 2,3,3',4,4,,5'-Hexachlorobiphenyl (PCB 157) 2,3',4,4,,5,5'-Hexachlorobiphenyl (PCB 167) 3,3',4,4',5,5'-Hexachlorobiphenyl (PCB 169) 2,3,3',4,4',5,5,-Heptachlorobiphenyl (PCB 189) Non-dioxin-like Polychlorinated Biphenyls 2.2',5-Trichloro biphenyl (PCB 18) 2,2'3,5'-Teti-achloi-o biphenyl (PCB 44) 2.2',4,5'-Tetrachloro biphenyl (PCB 49) 2,2',5,5'-Tetrachlorobiphenyl (PCB 52) 2,2',3,4,5'-Pentachlorobiphenyl (PCB 87) 2,2',4,4',5-Pentachlorobiphenyl (PCB 99) 2,2',4,5,5'-Pentachlorobiphenyl (PCB 101) 2,2',3,3',4,4'-Hexachlorobiphenyl (PCB 128) 2,2',3,4,4',5' and 2,3,3',4,4',6-Hexachlorobiphenyl (PCB I38& 158) 2,2',3,4',5,5'-Hexachlorobiphenyl (PCB 146)

BIOMARKERS FOR E N V I R O N M E N T A L EXPOSURE TABLE 20.2

531

List of chemicals measured in selected participants for NHANES 2003-2004. (continued)

1,1 -Dichloroethane 1.1 -Dichloroethene 1,2-dibromo-3-chloropropane 1,2-Dichlorobenzene 1,2-Dichloroethane 1,2-Dichbropropane 1,3-Dichlorobenzene 1,4-Dichlorobenzene 2,5-Dimethylfuran Benzene Bromodichloromethane Bromoform Carbon tetrachloride Chiorobenzene Chloroform cis-1,2-Dichloroethene Dibromochloromethane Dibrornomethane Ethylbenzene Hexachloroethane m-/p-Xylene Methylene chloride Methyl-tert-butyl ether (MTBE) Nitrobenzene o-Xylene Styrene Tetrachloroethene Toluene trans-1,2-Dichloroethene Trichloroethene

2,2,,3,4',5',6,-Hexachlorobiphenyl (PCB 149) 2,2',3,5,5',6-Hexachlorobiphenyl (PCB 151) 2,2',4,4',5,5,-Hexachlorobiphenyl (PCB 153) 2,2',3,3',4,4',5-Heptachlorobiphenyl (PCB 170) 2,2',3,3',4,5,5,-Heptachlorobiphenyl (PCB 172) 2,2',3,3',4,5',6,-Heptachlorobiphenyl (PCB 177) 2,2',3,3',5,5',6-Heptachlorobiphenyl (PCB 178) 2,2',3,4,4',5,5'-Heptachlorobiphenyl (PCB 180) 2,2',3,4,4',5',6-Heptachlorobiphenyl (PCB 183) 2,2',3,4',5,5',6-Heptachlorabiphenyl (PCB 187) 2,2',3,3',4,4,,5,5'-Octachlorobiphenyl (PCB 194) 2,2',3,3',4,4',5,6-Octachlorobiphenyl (PCB 195) 2,2',3,3',4,4',5,6' and 2,2',3,4,4',5,5',6-Octachlorobiphenyl (PCB I96&203) 2,2',3,3',4,5,5',6-Octachlorobiphenyl (PCB 199) 2,2',3,3',4,4',5,5',6,-Nonachlorobiphenyl (PCB 206) 2,2,,3,3',4,4'.5,5',6,6'-Decachloro biphenyl (PCB 209) Polybrominated Diphenyl Ethers 2,2',4'-Tribromodiphenyl ether (BDE 1 7) 2,4,4'-Tribromodiphenyl ether (BDE 28) 2,2',4.4'-Tetrabromodiphenyl ether (BDE 47) 2,3',4.4'-Tetrabromodiphenyl ether (BDE 66) 2,2',3.4,4'-Pentabromodiphenyl ether (BDE 85) 2,2',4,4'.5-Pentabromodiphenyl ether (BDE 99) 2,2',4,4',6-Pentabromodiphenyl ether (BDE 100) 2,2',4,4',5,5'-Hexabromodiphenyl ether (BDE 153) 2.2',4,4',5,6!-Hexabromodiphenyl ether (BDE 154) 2,2',3,4,4',5',6-Heptabromodiphenyl ether (BDE 183) 2,2',4.4',5,5'-Hexabromobiphenyl (BB 153}

Tobacco Smoke Cotinine *Resu/ts available for 1999-2000 only. f Results available (or 1999-2000 and 2003-2004, not for 2001-2002. ^Pending availability of acceptable standards.

ing of the source-to-outcome continuum. In the context of human health risks, Calafat and McKee56 outline research needs for using Di(2-ethylhexyl)phthalate (DEHP) biomonitoring data to inform exposure assessment. Their recommendations include the need to identify vulnerable segments of the population that may be more highly exposed to phthalates than is the general population, and to identify sources of exposure to these vulnerable groups. To articulate and begin to address the challenges associated with interpretation of biomonitoring data, the National Research Council's (NRC) Com-

532

BIOMARKERS

mittee on Human Biomonitoring for Environmental Toxicants published a report in July 2006 titled, "Human Biomonitoring for Environmental Chemicals."57 The report provides a reference guide for considering best practices in design, conduct, and reporting on results of biomonitoring studies. The committee presents four research recommendations: (1) Develop a coordinated strategy for biomarker development and population biomonitoring based on the potential for population exposure and public-health concerns. (2) Develop biomonitoring-based epidemiologic, toxicologic, and exposure-assessment investigations and public-health surveillance to interpret the risks posed by low-level exposure to environmental chemicals and, where possible, improve interpretation of results of such studies. (3) Advance individual, community, and population-based strategies for reporting results of biomonitoring studies. (4) Review the bioethical issues confronting the future of biomonitoring, including confidentiality, informed consent, reporting of results, and publichealth or clinical follow-up. As interest and application of biomonitoring is also increasing rapidly in Europe, the European Center for Ecotoxicology and Toxicology of Chemicals (ECETOC) established a dedicated task force on biomonitoring with broad representation from academia, non-governmental organizations (NGOs), and industry. This task force published a "white report" titled "Guidance for the Interpretation of Biomonitoring Data."58 The report includes a framework for the interpretation of human biomonitoring data that incorporates four principal considerations: analytical integrity, ability to describe exposure (pharmacokinetics), ability to relate to effects, and overall evaluation (weight-ofevidence). Creative research is currently being conducted by a variety of investigators to address many of the NRC (National Research Council) and ECETOC recommendations for developing approaches to evaluate biomonitoring data in a risk assessment context.59"62 As demand for, and implementation of, biomonitoring programs and studies continues to increase, new methods that integrate multiple stressors with the resulting health effects are needed for interpreting these data to realize the potential for biomonitoring to improve public health.

Cumulative Risk Assessment In response to the increasing focus on cumulative risk and the combined risks from aggregate exposures to multiple agents or stressors, the U.S. EPA has expended significant effort to explore cumulative approaches to risk assessment. Cumulative risk assessment is an analysis, characterization, and possible quantification of the combined risks to human health or the environment from multiple agents or stressors.'4 The concept of cumulative risk assessment has gained credence as a useful and viable framework by which to assess environmental exposures. It places exposures in the context of the continuum beginning with pollutant emissions to the environment and ending with human health consequences. The framework presented in Figure 20.4 provides

BIOMARKERS FOR E N V I R O N M E N T A L EXPOSURE

533

the conceptual basis for considering biomarker data and other health metrics to assess potential risks. In this framework, biomonitoring and biomarkers of exposure, effects, and susceptibility, along with other health data, are used to characterize the receptor (individual, community, or population), potential exposures, and health outcomes. By considering an array of metrics across the source-exposure-outcome continuum, this framework begins to address the multi-factorial nature of environmental disease and cumulative risk that are of increasing interest to public health decision makers. The framework suggests that no single biomarker will likely satisfy risk assessment needs. Instead, the use of multiple biomarkers representing various components of complex disease pathways as shown in Figure 20.4 may yield endpoints that are more comprehensive in the ability to assess effects of exposure. Biomarkers offer the potential advantage of integrating the net effect of all of these factors in producing a given internal dose for a given individual. For example, DNA adducts can be useful markers of polycyclic aromatic hydrocarbon (PAH) exposure by providing an integrated measurement of intake, metabolic activation, and delivery to the DNA in target tissues. The aryl hydrocarbon receptor, known to bind and be activated by a variety of compounds, is thought to initiate the cellular responses by altering gene expression and inhibiting cell cycle for both halogenated (dioxin) and nonhalogenated PAHs.63 Certain chemical classes of pesticides, such

FIGURE 20.4 Biomarker framework for environmental applications. Conceptual framework that links environmental exposures, biomarkers, and health outcome.'1 The framework can be used to evaluate the extent to which markers explain the pathway from exposure to outcome and whether biomarker data can assist in predicting outcome. This framework should also aid in estimating the association among multiple markers. (See color insert for a full color version of this figure.)

534

BIOMARKERS

as organophosphates and carbamates, inhibit cholinesterase,64 which is an important enzyme for the proper functioning of the nervous system. Shields, et al.5 provide an excellent example using tobacco smoke exposure and measures of exposure, effect, and susceptibility, that when taken together provide critical linkages from exposure to health outcome. Emerging technologies that impact validation of biomarkers from the chromosomal levels to the specific genes related to the disease process include inductively coupled plasma mass spectroscopy (ICP-MS) and fluorescence in situ hybridization (FISH) as well as DNA microarray, proteomics, and metabolomics, all of which measure changes on the molecular scale. Results from imaging technologies including positron emission tomography (PET) scanning, single photon emission computed tomography (SPECT), computer tomography (CT), and magnetic resonance imaging (MRI) have been applied as biomarkers, in particular for drugs acting at the central nervous system.65 Complex mixtures represent a special challenge in conducting cumulative risk assessments. EPA's guidance for the health risk assessment of chemical mixtures66 presents approaches for combining the toxicities and cumulative impact of multiple chemical stressors. These approaches necessarily involve a number of simplifying assumptions when the mixtures are complex. Although the current methods provide a valuable resource for assessing cumulative risks, future cumulative risk assessment will need a more complete understanding of the interactions among chemicals in complex mixtures. Some current research efforts are seeking to identify toxicological principles of joint action that are applicable to mixtures involving many chemicals. The National Toxicology Program's Annual Report on Carcinogens67 lists 11 mixtures, including environmental tobacco smoke, coal tar, diesel exhaust particulates, mineral oils, polychlorinated biphenyls (PCBs), polybrominated biphenyls (PBBs), and polycyclic aromatic hydrocarbons (PAHs). Increasingly, it is recognized that many real-life exposures involve complex mixtures, such as coke-oven emissions, diesel exhaust, asphalt fumes, or welding fumes. In such instances the exact composition of the mixtures often is not even characterized. Lewtas outlines strategies that allow for the identification and comparative assessment of complex mixtures containing genotoxic chemicals68 using bioassay directed fractionation to identify potential carcinogens or by comparative potency methods that provide an approach to evaluate the relative toxicity of a series of mixtures.

Molecular Epidemiology Molecular epidemiology is a branch of public health that deals with the contribution of potential genetic and environmental risk factors identified at the molecular level, to the etiology, distribution, and control of the disease in groups of relatives and populations. Molecular epidemiology improves our understanding of the pathogenesis of disease by identifying specific pathways, molecules, and genes that influence the risk of developing disease.

BIOMARKERS FOR ENVIRONMENTAL EXPOSURE

535

The field of molecular epidemiology, established in the 1970s, matured in the early 1980s in the study of cancer.69 The incorporation of biomarkers of exposure, biological changes preceding disease, and susceptible subgroups was hoped to enhance epidemiologic studies by incorporating causal mechanistic information about the process of carcinogenesis. As reviewed by Vineis and Perera,10 the outcomes resulting from tobacco smoke/PAHs exposure can be characterized in terms of biomarkers of biological effect (as measured by DNA adducts and p53 mutations) and individual susceptibility (single nucleotide polymorphisms). These markers have been valuable in elucidating the steps that link tobacco smoke/PAHs to the onset of lung cancers. As MOA figures more prominently in environmental risk assessment, molecular epidemiology studies incorporating mechanistic biomarkers are essential for addressing the issues with species extrapolation.26 The use of biomarkers for MOA-based risk assessment requires a special type of biomarker,70 which has typically been referred to as a bioindicator. These bioindicators are distinguished from biomarkers of exposure or biomarkers of general toxicity because they are causally linked with a specific key event in the mode of action (Figure 20.5, dark gray circles). Biomarkers of general toxicity (Figure 20.5, light gray circles) can be considered effects markers since they do represent biological effects resulting from exposure to an environmental stressor.

FIGURE 20.5 Graphical depiction of a mode of action for an environmental stressor Key features include the toxicity pathway, which presents the pathway directly perturbed by the chemical, followed by a series of key events (dark gray circles) which are causally linked to the adverse outcome. Biomarkers which serve as surrogates for those key events highlighted by dark gray circles are considered bioindicators and can be used to parameterize quantitative models of the mode of action for the chemical in question. In cases where a high throughput assay can be developed for the toxicity pathway, a single model predicting the adverse outcome relative to a perturbation of the toxicity pathway can then be used to predict toxicity for any chemicals perturbing that pathway solely on the basis of the assay results. (See color insert for a full color version of this figure.)

536

BIOMARKERS

If they are not causally linked to an adverse outcome (i.e., apical endpoint), however, they are not appropriate for use in a quantitative risk assessment as they are not guaranteed to quantitatively predict that adverse outcome under all circumstances. In practice, not all key events will have bioindicators that can be measured non-invasively, so quantitative models based on the MOA will require data from laboratory animal models, clinical studies, and molecular epidemiology studies. As the number of bioindicators measured in the target population increases, however, the confidence in the risk predictions will also increase. In addition to the role for bioindicators in establishing key event parameter values in the target organism, they also serve a role in evaluating risk management decisions and post-market surveillance. Incorporation of mechanistic bioindicators into large biomonitoring studies as previously discussed would allow ongoing evaluation of the risk assessment decisions and allow mechanistic models to be used in interpretation of the predicted outcomes of chemical exposures. This not only has direct impacts on risk assessment, but it also provides valuable information for refining MOA models for the future. Several new and exciting biomarkers becoming available for epidemiologic studies are due largely to the development of high-throughput technologies and theoretical advances in biology. However, most of these markers have not yet been validated, and their role in the causal paradigm is not clear. This area has been the subject of many critical reviews,72"76 particularly for gene expression and toxicogenomics as they relate to disease induction or causation. Gene expression microarrays, developed over the past decade, represent powerful tools for investigating biological, mechanistic, and disease processes.77 With regard to the application of gene expression in molecular epidemiological studies, van Leeuwen, et al.78 found genome-wide differential gene expression in children exposed to air pollution in the Czech Republic. Jost-Albrecht and Hofstetter79 used human monocytes from peripheral blood to study differential expression following exposure to metal ions. Fry, et al.80 demonstrated the robust impact of a mother's arsenic consumption on fetal gene expression, as evidenced by transcript levels in newborn cord blood. Wild, et al.81 provide examples where the application of gene technology has provided insight into effects associated with complex mixtures. Sen, et al.82 recently summarized the published literature on transcriptional profiles resulting from exposure of cells or organisms to complex environmental mixtures such as cigarette smoke, diesel emissions, urban air, motorcycle exhaust, carbon black, jet fuel, and metal ore and fumes. Overall, these preliminary transcriptomic data indicate that environmental exposures elicit changes in gene expression and that the nature of the changes varies depending on the type of exposure. This encourages further exploration of the sensitivity, specificity, and stability of these types of measurements. The stability of conclusions in the face of ever changing analytical methods is also a concern. Much of this instability is due to the low power to detect subtle changes when measuring expression of 25,000 transcript levels simultaneously. This would suggest that a tiered approach is needed that couples genomics

BIOMARKERS FOR E N V I R O N M E N T A L EXPOSURE

537

driven discovery experiments with targeted follow-up studies using informative biomarkers with the characteristics described in this chapter. Despite the challenges associated with using gene expression directly as a biomarker for effects, perturbation of some interesting pathways are consistently seen, perhaps surprisingly, in terms of inflammation, oxidative stress, cell proliferation, and apoptosis for several different exposures.

E M E R G I N G ISSUES Three developments over the past decade have dramatically changed the way in which biomarkers must be used for risk assessment. As previously discussed, the increasing use of the mode of action framework83 for risk assessment requires increased emphasis on bioindicators70 to fully evaluate the proposed mode of action (MOA) in target organisms (e.g., humans). Second, the proposed use of toxicity pathways for risk assessment71 requires quantitative, mechanistic models to move beyond screening and prioritization. These computational models in turn require quantitative bioindicators for a subset of key events in the species of interest to better define the uncertainty of the model. Finally, the application of systems biology to risk assessment84 has a strong reliance on informative biomarkers for all the reasons listed above.

Toxicity Pathway-Based Risk Assessment An enormous challenge faced by risk assessors is how to use high throughput screening data for risk assessment. The number of chemicals of concern to regulatory agencies across the globe is unmanageable without a high throughput screening and prioritization system.85 In addition, the regulatory requirements regarding the number of chemicals requiring data and data required per chemical are increasing. For example, the European Union has established a regulation on Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) which requires reports on all substances produced or imported in quantities of one ton or more per year.86 This has resulted in a number of efforts throughout the world to establish high throughput alternatives to animal testing. Examples include the European, Japanese, and Korean centers for validation of alternative methods, ECVAM, JaVAM, KoCVAM respectively. In the U.S., efforts in this regard were spearheaded by the development of the program in Computational Toxicology at the U.S. EPA87 and the publication of the National Toxicology Program roadmap88 and culminated in a report from the National Research Council of the National Academy of Sciences on "Toxicity Testing in the Twenty-first Century: A Strategy and a Vision."71 The central premise behind the NRC vision is the concept of a toxicity pathway. These are normal biological processes that result in an adverse health outcome when sufficiently perturbed by a chemical stressor. Since these events will typically be driven by direct effects of the chemical, they represent a specific and sensitive readout of the activity of that chemical. In addition, since

538

BIOMARKERS

they will typically be intracellular and molecular events, they are amenable for high-throughput screening. Therefore, once the relationship between the perturbation of these pathways and one or more adverse health outcomes has been established, thousands of chemicals can be evaluated purely on the basis of the high-throughput screening results. For toxicity pathway-based risk assessment to become a reality, however, it must integrate with the MOA-based risk assessment discussed above. As a result, bioindicators of downstream key events are critical to establishing the quantitative relationship between the perturbation of the toxicity pathway and the resulting adverse outcome (Figure 20.5), and the development of the toxicity pathway assays must be done in conjunction with studies to determine the MOA linking each toxicity pathway to its adverse outcomes. As molecular level toxicity pathway events are used to drive risk assessment, there is a critical need for exposure assessments at the molecular level as well89 (Figure 20.6). Biomarkers of exposure will play a critical role in this process by providing the same level of quantitative information in the target organism for exposure modeling that bioindicators provide from effects modeling. Because analysis of chemicals and their metabolites in accessible matrices may not provide the necessary information about cellular dose inside an internal organ, surrogate effects markers may be needed. In contrast to requirements for MOA modeling, biomarkers of general toxicity which are specific for the tissue of interest can be very valuable for this purpose. This merging of exposure and effects science highlights the need for more integrated research between the two fields.90,91

FIGURE 20.6 Integration of exposure and response. Cascade of exposure-response processes for integrating exposure science and toxicogenomic mode-of-action information.89

BIOMARKERS FOR E N V I R O N M E N T A L EXPOSURE

539

stems Biology t o Support Risk Assessment Many of the recent developments in risk assessment require systems approaches to be successful. The large number of chemicals and even larger number of potential interactions among them make cumulative risk assessment via direct testing impossible, even with high throughput screening. The very process of defining an MOA for a chemical or toxicity pathway requires discounting all effects deemed irrelevant to the adverse outcome(s).92 The increased reliance on upstream events as surrogates for adverse outcomes comes with the risk of unknown modulators of the process. Known modulators such as genetics, life stage, disease, or cultural influences must be considered in light of the mechanistic model. Systems approaches address many of these issues by explicitly modeling interactions mechanistically, providing global measurements of molecular events to evaluate completeness of the MOA, and allowing additional risk factors to be incorporated mechanistically into the model. One example of this approach is a Key Event Network (Figure 20.7), which extends the concept of isolated MOAs for chemicals or toxicity pathways to a connected network.84 Toxicity pathways would be special types of nodes in the network in that they represent the events most proximal to the environmental stressor and are ideally linked to high throughput assays for chemical evaluation. Nodes directly linked to adverse outcomes would also be anchor points in that they represent the apical endpoints used in risk assessment. A MOA for a toxicity pathway can now be represented as the propagation of a perturbation through this network that perturbs one or more adverse outcome nodes. This adverse outcome is then modeled for each individual at the population level with additional considerations for other population vulnerabilities (Figure 20.1) that may influence susceptibility. While life stage, disease, and genetic susceptibility could be incorporated at this level as well when the biological basis is ill-defined, the ultimate goal of this approach is to model those events mechanistically at the appropriate points in the underlying networks (Figure 20.7). This approach has several advantages over considering MOAs in isolation. The incorporation of global biology ("Omics") measurements at the level of the underlying molecular network aids in characterizing genetic susceptibility (Figure 20.7) and objectively evaluating the process of defining the MOA.84 Reverse engineering approaches for molecular networks based on Omics data are becoming quite advanced,93-95 and these networks are being used extensively in characterizing disease networks.96,97 In addition, the network framework provides a mechanistic basis for predicting cumulative risk. For example, consider the two toxicity pathways highlighted in Figure 20.7. Assume the toxicity pathway on the right causes significant changes in the adverse outcome on the right via the MOA shown, but it also has a much smaller impact on the adverse outcome on the left via the alternate route. While the impact on this alternate adverse outcome may not be significant in isolation, it may be when coupled with a perturbation of the toxicity pathway on the left. By modeling all perturbations to the network for each toxicity pathway, it becomes possible

540

BIOMARKERS

FIGURE 20.7 Systems modeling of mode of action.84The mode of action concept outlined in Figure 20.5 is now extended to include an interconnected network of key event nodes. Perturbation of the network by environmental stressors is assumed to occur at toxicity pathway nodes within the network and propagate through the network through the other key event nodes. Perturbations which are sufficient to perturb an adverse outcome node would then result in an adverse outcome in the individual (depicted as a single node in the population network). Modeling mode of action in this fashion facilitates cumulative risk assessment by considering the aggregate impact of all perturbations from all stressors. It also allows other factors influencing susceptibility such as life stage, disease, and genetics to be modeled within the same framework. (See color insert for a full color version of this figure.)

to identify these potential interactive effects prior to testing specific chemical mixtures. This paradigm can be further expanded to the development of virtual tissues and systems as the knowledge base and understanding of the biological systems improves.98'" The systems approaches described build upon the MOA framework for risk assessment and the use of toxicity pathways for high throughput evaluation of chemical toxicity. Just as biomarker and bioindicator data are critical for those building blocks, the systems approaches to risk assessment will only be as successful as the identification and use of informative biomarkers and bioindicators. Therefore, integrated research projects linking toxicity pathways assay results to in vivo outcomes and the identification of appropriate biomarkers and bioindicators along the exposure-dose-response continuum will help to bring the vision of "Toxicity Testing in the Twenty-first Century"71 into focus.

SUMMARY P O I N T S 1.

Biomarkers for environmental health applications fall into three classes: biomarkers of exposure, biomarkers of effect, and biomarkers of susceptibility.

BIOMARKERS FOR ENVIRONMENTAL EXPOSURE 2. 3. 4. 5.

541

Biomonitoring data allow environmental researchers to track population trends, identify susceptible subpopulations, evaluate exposure mitigation, and identify emerging chemicals of concern. Biomonitoring could be improved by increasing the number of bioindicators causally linked to the mode of action for the environmental stressor(s) being studied. In order to facilitate cumulative risk assessment, panels of biomarkers which integrate the net exposures and effects from all stressors being studied will be needed. New approaches to risk assessment such as toxicity pathway screening and systems modeling will require all three classes of biomarkers and an understanding of the connections among them.

ACKNOWLEDGMENTS We would like to thank R. Julian Preston for helpful discussions and critical review of the manuscript. We appreciate the critical review of the manuscript by Michael Madden and Jennifer Orme-Zavaleta. We would also like to thank Chuck Gaul and Keith Tarpley from the SRA International Creative Team for the graphic design of Figures 20.5 and 20.7.

REFERENCES 1. 2.

3. 4. 5. 6. 7. 8.

NRC. Science and Decisions: Advancing Risk Assessment. Washington, D.C.: National Academy of Sciences;2009. Available at: http://www.nap.edu/catalog. php?record_id= 12209. NRC. Risk Assessment in the Federal Government: Managing the Process. Washington, D. C: NRC Committee on the Institutional Means for Assessment of Risks to Public Health;July 2006. Available at: http://www.nap.edu/openbook.php?record_id=366. NRC. Biological Markers in Environmental Health Research. Committee on Biological Markers of the National Research Council. Environ. Health Perspect. Oct 1987;74:3-9. Ryan P. B., Burke, T. A., Cohen Hubal, E. A., Cura, J. J., and McKone, T. E. Using Biomarkers to Inform Cumulative Risk Assessment. Environ. Health Perspect. May 2007;115(5):833-840. Shields, P. G. Tobacco Smoking, Harm Reduction, and Biomarkers. J. Nad. Cancer Inst. Oct 2, 2002;94(19): 1435-1444. Mumtaz, M. M., Ruiz, P., and De Rosa, C. T. Toxicity Assessment of Unintentional Exposure to Multiple Chemicals. Toxicol. Appl. Pharmacol. Sep 1, 2007;223(2):104-113. ACGIH. Annual Reports of the Committees on Tlvs® and Beis®: American Conference of Governmental and Industrial Hygienists;2006. Available at: http:// www.acgih.org/tlv. ATSDR. Minimal Risk Levels (Mrls) for Hazardous Substances (2006): Agency for Toxic Substances and Disease Registry; 2006 Available at: http://www.atsdr. cdc.gov/mrls.html.

BIOMARKERS 9. 10. 11.

12.

13. 14.

15. 16.

17. 18. 19. 20. 21. 22. 23. 24.

USEPA. Reference Dose Rfd: Description and Use in Health Risk Assessments. "U.S. EPA/ORD/NCEA, Washington, D.C." 2006 Available at: http://www.epa. gov/iris/rfd.htm. Vineis, P. and Perera, F. Molecular Epidemiology and Biomarkers in Etiologic Cancer Research: The New in Light of the Old. Cancer Epidemiol. Biomarkers Prev. Oct 2007;16(10):1954-1965. Schettgen, T., Broding, H. C , Angerer, J., and Drexler, H. Hemoglobin Adducts of Ethylene Oxide, Propylene Oxide, Acrylonitrile and Acrylamide-Biomarkers in Occupational and Environmental Medicine. Toxicol. Lett. Aug 5, 2002;134 (l-3):65-70. USEPA. Guidance on Cumulative Risk Assessment of Pesticide Chemicals That Have a Common Mechanism of Toxicity.: "U.S. EPA/OPP, Washington, D.C." 2002 Available at: http://www.epa.gov/oppfeadl/trac/science/cumulative_guidance.pdf. USEPA. Framework for Cumulative Risk Assessment.: U.S. EPA/ORD/NCEA, Washington, D.C.;2003. Available at: http://cfpub.epa.gov/ncea/cfrn/recordisplay.cfm?deid=54944. USEPA. "Concepts, Methods and Data Sources for Health Risk Assessment of Multiple Chemicals Exposures and Effects": "U.S. EPA/ORD/NCEA, Cincinnati, OH";2007. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm? deid=190187. Callahan, M. A and Sexton, K. If Cumulative Risk Assessment Is the Answer, What Is the Question? Environ. Health. Perspect. May 2007;115(5):799-806. Defur, P. L., Evans, G. W., Cohen Hubal, E. A., Kyle, A. D., Morello-Frosch, R. A., and Williams, D. R. Vulnerability as a Function of Individual and Group Resources in Cumulative Risk Assessment. Environ. Health Perspect. May 2007; 115(5): 817-824. Menzie, C. A., MacDonell, M. M., and Mumtaz, M. A Phased Approach for Assessing Combined Effects from Multiple Stressors. Environ. Health Perspect. May2007;115(5):807-816. Meek, M. E., Bucher, J. R., and Cohen, S. M., et al. A Framework for Human Relevance Analysis of Information on Carcinogenic Modes of Action. Crit. Rev. Toxicol. 2003;33(6):591-653. Seed, J., Carney, E. W, and Corley, R. A., et al. Overview: Using Mode of Action and Life Stage Information to Evaluate the Human Relevance of Animal Toxicity Data. Crit. Rev. Toxicol. Oct/Nov 2005;35(8-9):664-672. Boobis, A. R., Cohen, S. M., and Dellarco, V., et al. IPCS Framework for Analyzing the Relevance of a Cancer Mode of Action for Humans. Crit. Rev. Toxicol. Nov/Dec 2006;36(10):781-792. Boobis, A. R., Doe, J. E., and Heinrich-Hirsch, B., et al. IPCS Framework for Analyzing the Relevance of a Non-cancer Mode of Action for Humans. Crit. Rev. Toxicol. 2008;38(2):87-96. USEPA. Guidelines for Carcinogen Risk Assessment (Final): U.S. Environmental Protection Agency;2005. Available at: http://cfpub.epa.gov/ncea/cfm/ recordisplay.cfm?deid=l 16283. EC. Technical Guidance Document on Risk Assessment: European Commission;2003. Available at: http://ecb.jrc.ec.europa.eu/documents/TECHNICAL_ GUIDANCE_DOCUMENT/EDITION_2/Tgdpart 1 _2ed.pdf. OECD. Guidance Notes for Analysis and Evaluation of Chronic Toxicity and Carcinogenicity Studies: Organisation for Economic Co-Operation and Devel-

BIOMARKERS FOR ENVIRONMENTAL EXPOSURE

25. 26. 27. 28. 29. 30. 31. 32.

33. 34.

35. 36. 37. 38. 39. 40.

543

opment; 2002 Available at: http://www.olis.oecd.org/olis/2002doc.nsf/linkto/ env-jm-mono(2002)19. UNECE. Amendments to the Globally Harmonized System of Classification and Labelling of Chemicals (GHS): Geneva: United Nations. Document;2007. Available at: http://www.unece.org/trans/danger/publi/ghs/ghs_rev01/01amend_e html. Preston, R. J. Extrapolations are the Achilles Heel of Risk Assessment. Mutat. Res. May 2005;589(3): 153-157. Groopman, J. D. and Kensler, T. W. The Light at the End of the Tunnel for Chemical-Specific Biomarkers: Daylight or Headlight? Carcinogenesis. Jan 1999;20(1):1-11. Hulka, B. S. ASPO Distinguished Achievement Award Lecture. Epidemiological Studies Using Biological Markers: Issues for Epidemiologists. Cancer Epidemiol. Biomarkers Prev. Nov/Dec 1991;1(1): 13—19. Henderson, R. K, Bechtold, W. E., Bond, J. A., and Sun, J. D. The Use of Biological Markers in Toxicology. Crit. Rev. Toxicol. 1989;20(2):65-82. Bartell, S. M., Griffith, W. C , and Faustman, E. M. Temporal Error in Biomarker-Based Mean Exposure Estimates for Individuals. J. Expo. Anal. Environ. Epidemiol. Mar2004;14(2):173-179. Angerer, J., Ewers, U., and Wilhelm, M. Human Biomonitoring: State of The Art. Int. J. Hyg. Environ. Health. May 2007;210(3-4):201-228. Rockett, J. C , Burczynski, M. E., Fornace, A. J., Herrmann, P. C , Krawetz, S. A., and Dix, D. J. Surrogate Tissue Analysis: Monitoring Toxicant Exposure and Health Status of Inaccessible Tissues Through the Analysis of Accessible Tissues and Cells. Toxicol. Appl. Pharmacol. Jan 15, 2004;194(2):189-199. Neri, M., Ugolini, D., and Bonassi, S., et al. Children's Exposure to Environmental Pollutants and Biomarkers of Genetic Damage. II. Results of A Comprehensive Literature Search and Meta-Analysis. Mutat. Res. Jan 2006;612(l):14-39. Barr, D. B., Wang, R. Y., and Needham, L. L. Biologic Monitoring of Exposure to Environmental Chemicals Throughout the Life Stages: Requirements and Issues for Consideration for the National Children's Study. Environ. Health Perspect. Aug2005;113(8):1083-1091. NCS. The National Children's Study Research Plan—Version 1.3.: National Children's Study;2007. Available at: http://www.nationalchildrensstudy.gov/ research/studydesign/researchplan/pages/appendices.aspx. CDC. National Biomonitoring Program: "Department of Health and Human Services, Centers for Disease Control and Prevention";2009. Available at: http:// www.cdc.gov/biomonitoring. Albertini, R., Bird, M., and Doerrer, N., et al. The Use of Biomonitoring Data in Exposure and Human Health Risk Assessments. Environ. Health Perspect. Nov 2006;114(11): 1755-1762. Paustenbach, D. and Galbraith, D. Biomonitoring and Biomarkers: Exposure Assessment Will Never Be the Same. Environ. Health Perspect. Aug 2006; 114(8): 1143-1149. Sexton, K. and Hattis, D. Assessing Cumulative Health Risks From Exposure to Environmental Mixtures—Three Fundamental Questions. Environ. Health Perspect. May 2007;115(5):825-832. Williams, B. L., Barr, D. B., Wright, J. M., Buckley, B., and Magsumbol, M. S. Interpretation of Biomonitoring Data in Clinical Medicine and the Exposure Sciences. Toxicol. Appl. Pharmacol. Nov 15, 2008;233(l):76-80.

544

BIOMARKERS 41.

42. 43. 44. 45. 46. 47.

48. 49. 50. 51. 52. 53. 54.

55. 56.

ACC. Summary Report: International Council of Chemical Associations (ICCA) Workshop: Making Sense of Human Biomonitoring Data. Paper Presented at: International Council of Chemical Associations (ICCA) Workshop: Making Sense of Human Biomonitoring Data; July 26-27, 2006; Minneapolis, Minnesota. Robison, S., Needham, L., Faustman, E., Zenick, H., and Sheldon, L. Abstract #1291: Integration of Biomonitoring Data Into the Risk Assessment Process. Paper Presented at: Society of Toxicology Annual Meeting, 2005;New Orleans, LA. USEPA. Air Quality Criteria for Lead, Vol I and II of II.: U.S. Environmental Protection Agency;2006. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=15882. Lakind, J. S., Hays, S. M., Aylward, L. L., and Naiman, D. Q. Perspective on Serum Dioxin Levels in the United States: An Evaluation of the NHANES Data. J. Expo. Sci. Environ. Epidemiol. May 2009;19(4):435-441. Birnbaum, L. S. and Cohen Hubal, E. A. Polybrominated Diphenyl Ethers: A Case Study for Using Biomonitoring Data to Address Risk Assessment Questions. Environ. Health Perspect. Nov 2006;114(11):1770-1775. Braverman, L. E., He, X., and Pino, S., et al. The Effect of Perchlorate, Thiocyanate, and Nitrate on Thyroid Function in Workers Exposed to Perchlorate LongTerm. / Clin. Endocrinol. Metab. Feb 2005;90(2):700-706. Guruge, K. S., Taniyasu, S., and Yamashita, N., et al. Perfluorinated Organic Compounds in Human Blood Serum and Seminal Plasma: A Study of Urban and Rural Tea Worker Populations in Sri Lanka. J. Environ. Monit. Apr 2005;7(4):371-377. Kaiser, R., Marcus, M., and Blanck, H. M., et al. Polybrominated Biphenyl Exposure and Benign Breast Disease in a Cohort of U.S. Women. Ann. Epidemiol. Jan2003;13(l):16-23. CDC. National Environmental Public Health Tracking: Centers for Disease Control and Prevention;2005. Available at: http://www.cdc.gov/nceh/tracking/ biomonitoring.htm. CDPH. California Environmental Contaminant Biomonitoring Program: California Department of Public Health;2007. Available at: http://www.cdph.ca.gov/programs/biomonitoring/pages/default.aspx. AHS. Agricultural Health Study. National: National Institutes of Health (NCI and NIEHS) and the U.S. EPA;2006. Available at: http://www.aghealth.org. HC. Canadian Health Measures Survey. Chemical Substances: An Ecoaction Initiative: Health Canada;2008. Available at: http://www.chemicalsubstanceschimiques.gc.ca/bio_e.html#5. UBA. Health and Environmental Hygiene: German Environmental Survey (Geres);2009. Available at: http://www.umweltbundesamt.de/gesundheit-e/survey/index.htm. CDC. Third National Report on Human Exposure to Environmental Chemicals: "National Center for Environmental Health, Division of Laboratory Sciences, Centers for Disease Control and Prevention, Atlanta, GA"; 2005 Available at: http://www.cdc.gov/exposurereport/pdf/thirdreport.pdf. Bahadori, T., Phillips, R. D., and Money, C. D., et al. Making Sense of Human Biomonitoring Data: Findings and Recommendations of a Workshop. J. Expo. Sci. Environ. Epidemiol. Jul 2007;17(4):308-313. Calafat, A. M. and McKee, R. H. Integrating Biomonitoring Exposure Data Into the Risk Assessment Process: Phthalates [Diethyl Phthalate and Di(2-Ethylhexyl) Phthalate] as a Case Study. Environ. Health Perspect. Nov 2006; 114(11): 1783-1789.

BIOMARKERS FOR ENVIRONMENTAL EXPOSURE 57. 58.

59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71.

72.

545

NRC. Human Biomonitoring for Environmental Chemicals: NRC Committee on Human Biomonitoring for Environmental Toxicants; July 2006. Available at: http://www.nap.edu/catalog.php ?record_id=l 1700. ECETOC. Guidance for the Interpretation of Biomonitoring Data: European Centre for Ecotoxicology and Toxicology of Chemicals;2005. Available at: http:// www.ecetoc.org/index.php?mact=mcsoap,cntnt01, details, 0&cntnt01by_ category=l&cntnt01order_by=reference%20desc&cntnt01template=display_ Iist_v2&cntnt01 display_template=display_details_v2&cntnt01 document_ id=70&cntnt01 returnid=94. Clewell, H. J., Tan, Y. M., Campbell, J. L., and Andersen, M. E. Quantitative Interpretation of Human Biomonitoring Data. Toxicol. Appl. Pharmacol. Aug 15, 2008;231(1):122-133. Georgopoulos, P. G., Sasso, A. E, and Isukapalli, S. S., et al. Reconstructing Population Exposures to Environmental Chemicals from Biomarkers: Challenges and Opportunities. J. Expo. Sci. Environ. Epidemiol. Feb 2009;19(2):149-171. Hays, S. M. and Aylward, L. L. Using Biomonitoring Equivalents to Interpret Human Biomonitoring Data in a Public Health Risk Context. J. Appl. Toxicol. May 2009;29(4):275-288. Tan, Y. M., Liao, K. H., and Clewell, H. J., III. Reverse Dosimetry: Interpreting Trihalomethanes Biomonitoring Data Using Physiologically Based Pharmacokinetic Modeling. J. Expo. Sci. Environ. Epidemiol. Nov 2007;17(7):591-603. Denison, M. S., Pandini, A., Nagy, S. R., Baldwin, E. P., and Bonati, L. Ligand Binding and Activation of the Ah Receptor. Chem. Biol. Interact. Sep 20, 2002;141(l-2):3-24. Clark, R. F. Insecticides: Organic Phosphorus Compounds and Carbamates. In: Flomenbaum, N. E., Goldfrank, L. R., and Hoffman, R. S., Eds. Goldfrank's Toxicologic Emergencies. 8th ed. New York: McGraw-Hill;2006:1497-1512. Bhatnagar, A., Hustinx, R., and Alavi, A. Nuclear Imaging Methods for NonInvasive Drug Monitoring. Adv. Drug Deliv. Rev. Mar 15, 2000;41(l):41-54. USEPA. Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures.: "U.S. EPA/RAF, Washington, D.C." 2000. Available at: http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001 .pdf. NTP. Report on Carcinogens (2006): National Toxicology Program;2006. Available at: http://ntp.niehs.nih.gov/roc/toclO.html. Lewtas, J. Genotoxicity of Complex Mixtures: Strategies for the Identification and Comparative Assessment of Airborne Mutagens and Carcinogens from Combustion Sources. Fundam. Appl. Toxicol. May 1988;10(4):571-589. Perera, F. P. and Weinstein, I. B. Molecular Epidemiology and Carcinogen-DNA Adduct Detection: New Approaches to Studies of Human Cancer Causation. J. Chronic. Dis. 1982;35(7):581-600. McCarty, L. S., Power, M., and Munkittrick, K. R. Bioindicators versus Biomarkers in Ecological Risk Assessment. Human and Ecological Risk Assessment. Jan 2002;8(1): 159-164. NRC. Toxicity Testing in the Twenty-First Century: A Vision and a Strategy. Washington, D.C.:National Academy of Sciences;2007. Available at: http://dels. nas.edu/best/reportdetail.php?link_id=4286&session_id=9aeebe6aab89fl66a4 2c0c2ad3269c37. Crowell, J. A. The Chemopreventive Agent Development Research Program in the Division of Cancer Prevention of the U.S. National Cancer Institute: An Overview. Eur. J. Cancer. Sep 2005;41(13):1889-1910.

546

BIOMARKERS 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87.

88. 89. 90. 91.

Potter, J. D. At the Interfaces of Epidemiology, Genetics and Genomics. Nat. Rev. Genet. Feb 2001 ;2(2): 142-147. Potter, J. D. Epidemiology, Cancer Genetics and Microarrays: Making Correct Inferences, Using Appropriate Designs. Trends Genet. Dec 2003;19(12): 690-695. Waters, M. D. and Fostel, J. M. Toxicogenomics and Systems Toxicology: Aims and Prospects. Nat. Rev. Genet. Dec 2004;5(12):936-948. Webb, P. M., Merritt, M. A., Boyle, G. M., and Green, A. C. Microarrays and Epidemiology: Not the Beginning of the End but the End of the Beginning. Cancer Epidemiol. Biomarkers Prev. Apr 2007;16(4):637-638. Ballman, K. V. Genetics and Genomics: Gene Expression Microarrays. Circulation. Oct7, 2008;118(15):1593-1597. Van Leeuwen, D. M., Van Herwijnen, M. H., and Pedersen, M., et al. GenomeWide Differential Gene Expression in Children Exposed to Air Pollution in the Czech Republic. Mutat. Res. Aug 30, 2006;600( 1-2): 12-22. Jost-Albrecht, K. and Hofstetter, W Gene Expression by Human Monocytes from Peripheral Blood in Response to Exposure to Metals. J. Biomed. Mater. Res. B Appl. Biomater. Feb 2006;76(2):449-455. Fry, R. C , Navasumrit, P., and Valiathan, C , et al. Activation of Inflammation/ NF-Kappab Signaling in Infants Born to Arsenic-Exposed Mothers. Plos. Genet. Nov2007;3(ll):E207. Wild, C. P. Environmental Exposure Measurement in Cancer Epidemiology. Mutagenesis. Mar 2009;24(2):117-125. Sen, B., Mahadevan, B., and Demarini, D. M. Transcriptional Responses to Complex Mixtures: A Review. Mutat. Res. Nov/Dec 2007;636(l-3): 144-177. Meek, M. E. Recent Developments in Frameworks to Consider Human Relevance of Hypothesized Modes of Action for Tumours in Animals. Environ. Mol. Mutagen. Mar 2008;49(2): 110-116. Edwards, S. W and Preston, R. J. Systems Biology and Mode of Action Based Risk Assessment. Toxicol. Sci. Dec 2008;106(2):312-318. Judson, R., Richard, A., and Dix, D. J., et al. The Toxicity Data Landscape for Environmental Chemicals. Environ. Health Perspect. May 2009; 117(5): 685-695. Williams, E. S., Panko, J., and Paustenbach, D. J. The European Union's REACH Regulation: A Review of Its History and Requirements. Critical Reviews in Toxicology. 2009 DTBS-Pubmed MEDLINE. 2009;39(7):553-575. USEPA. Framework for a Computational Toxicology Research Program: U.S. Environmental Protection Agency, Washington, DC 20460; 2003. EPA/600/R-03/065. Available at: http://www.epa.gov/ncct/comptox_framework.html. NTP. NTP Vision & Roadmap National Toxicology Program: National Toxicology Program, Research Triangle Park, NC 27709;2004. Available at: http://ntp. niehs. nih.gov/fi les/ntprdmp .pdf. Cohen Hubal, E. A., Richard, A. M., and Shah, I., et al. Exposure Science and the U.S. EPA National Center for Computational Toxicology. J. Expo. Sci. Environ. Epidemiol. Nov 5, 2008;lll(2):226-232. Cohen Hubal, E. A. Biologically-Relevant Exposure Science for 21st Century Toxicity Testing. Toxicol. Sci. Jul 14, 2009;lll(2):226-232. Sheldon, L. S. and Cohen Hubal, E. A. Exposure as Part of a Systems Approach for Assessing Risk. Environ. Health Perspect. Aug 2009; 117(8): 119-1194.

BIOMARKERS FOR ENVIRONMENTAL EXPOSURE 92. 93. 94. 95. 96. 97. 98.

99.

547

Guyton, K. Z., Barone, S., Jr., Brown, R. C , Euling, S. Y., Jinot, J., and Makris, S. Mode of Action Frameworks: A Critical Analysis. J. Toxicol. Environ. Health B Crit. Rev. Jan 2008; 11(1): 16-31. Bansal, M., Belcastro, V., Ambesi-Impiombato, A., and Di Bernardo, D. How to Infer Gene Networks from Expression Profiles. Mol. Syst. Biol. 2007;3:78. Feist, A. M , Herrgard, M. J., Thiele, I., Reed, J. L., and Palsson, B. O. Reconstruction of Biochemical Networks in Microorganisms. Nat. Rev. Microbiol. Feb 2009;7(2): 129-143. Hecker, M., Lambeck, S., Toepfer, S., Van Someren, E., and Guthke, R. Gene Regulatory Network Inference: Data Integration in Dynamic Models—A Review. Biosystems. Apr 2009;96(1):86-103. Gohlke, J. M., Thomas, R., and Zhang, Y, et al. Genetic and Environmental Pathways to Complex Diseases. BMC Syst. Biol. 2009;3:46. Loscalzo, J., Kohane, I., and Barabasi, A. L. Human Disease Classification in the Postgenomic Era: A Complex Systems Approach to Human Pathobiology. Mol. Syst. Biol. 2007;3:124. Kavlock, R. J., Ankley, G., Blancato, J., Breen, M., Conolly, R., Dix, D., Houck, K., Hubal, E., Judson, R., Rabinowitz, J., Richard, A., Setzer, R. W., Shah, I., Villeneuve, D., and Weber, E. Computational Toxicology, a State of the Science Mini Review. Toxicol. Sci. May 2008;103(l):14-27. Knudsen, T. B. and Kavlock, R. J. Comparative Bioinformatics and Computational Toxicology. In: Abbot, B., and Hansen, D., Eds. Developmental Toxicology. 3rd ed.:Informa Healthcare USA, Inc.;2008:311-360.

This page intentionally left blank

CHAPTER

CLINICAL STUDY DESIGN IN BIOMARKER RESEARCH Orfeas Liangos and Bertrand L. Jaber

OVERVIEW OF C L I N I C A L STUDY

DESIGN

Case Study The case report is the smallest unit of every patient-oriented research study design. Although statistical inferences are impossible with this design and the effectiveness of diagnostic tools or safety of therapeutic interventions cannot be tested, it remains a valuable method for the report of rare conditions, unusual presentations of more common conditions, emerging new diseases, or novel therapeutic interventions for rare diseases. In addition, the case report is valuable in the discovery of rare cases of toxicity or adverse effects due to pharmaceutical or environmental compounds.

Case Series The case series is the next step in the evolution of study design. In the case series, a group of cases, typically with common features and usually consecutive, is summarized.1 Summary statistics can be used to describe the series of cases and further specify unique characteristics or responses to certain treatments. Depending on the size of the series, overlap with the next level of observational study design, the cross-sectional study, may exist.

Cross-Sectional Study The cross-sectional study design consists of identifying at the same point in time a population with characteristics of interest, and examining other characteristics. In this study design, it is possible to estimate the frequency of 549

550

BIOMARKERS

disease or health outcome within the population sample.1'3'4 This design is sometimes useful for suggesting possible associations between risk factors and disease. The main limitation is that since the presence of a risk factor and disease are assessed at the same point in time, the temporal relation between the risk factor and the disease is blurred and unclear. In summary, although it is relatively inexpensive and simple to carry out and no follow-up is required, the cross-sectional study is unable to establish causality but rather association of certain characteristics with disease prevalence for instance, and it does not provide information about long-term outcomes, and has no predefined comparison group. Nevertheless, it remains an important, valuable epidemiological survey tool for monitoring the health status and potential health care needs of populations over time.

Case Control Study/Nested Case Control Study The case control study or nested case control study is a design in which two groups are compared, one with a specific disease or pre-specified outcome, and the other without.1-35 The statistical inference usually entails looking back and determining exposure to possible risk factors or causes of disease. These cases and controls can be compiled de novo from a population, or can be derived from a preexisting cohort or registry (see next section), in which case the design is referred to as a nested case control study. Both these designs allow for statistical matching of coexisting characteristics or covariates between the cases and controls which can be based on individual (one to one) or group matching of the covariates, in an attempt to eliminate confounding. The apparent problem arises, however, that unknown confounding factors may remain unaccounted for and the exposure variable to be studied, although associated with disease, may still not be causal. Thereby, in a case control study design, the same limitations of association versus causality exist, equal to the cross-sectional studies. Therefore, conclusions from case control study designs must be drawn with caution and conservatively. These designs should only be used to generate hypotheses that would have to be confirmed with higher levels of evidence, as discussed below, for example with prospective cohort studies. On the other hand, a recent comparative study suggests that well-designed case control studies provide reliable estimates of treatment effects.6 Of note, generally, case control studies also cannot calculate the incidence rate of disease. These limitations not withstanding, case control studies are suitable when occurrence of disease is rare. They are relatively inexpensive and require a relatively small number of subjects, and can be conducted quickly. This design remains extremely valuable for the study of chronic and slowly progressive diseases where there is a long time lag between an exposure variable and outcome.

Cohort Study A cohort study is characterized by a large cohort of patients or healthy populations defined either by a geographic characterization, a common pathologic condition or disease, therapeutic intervention or other characteristic to be

CLINICAL STUDY DESIGN IN BIOMARKER RESEARCH

TABLE 21.1

551

Overview of clinical study design.

Descriptive studies Populations (ecologic) Individuals • Case reports • Case series • Cross-sectional surveys Analytic studies Observational • Case-control studies • Cohort studies (retrospective and prospective) Experimental • Uncontrolled trial • Controlled trial (randomized controlled trial)

studied.4 A registry of cases is a cohort study that is typically used for postmarketing surveillance of pharmaceutical toxicity or adverse effects.7 Cohort studies can be retrospective (also known as historical or non-concurrent) or prospective (also known as concurrent). The latter is more expensive to conduct but provides a more robust design. The observational nature of these designs does not allow for a systematic evaluation of a single intervention or condition and statistical inferences can only be made based on associations, a major drawback of these designs. Nevertheless, for an initial characterization of a disease or condition, for the study of the natural history and prognosis of a condition, and for the identification of potentially important factors that could later be tested under controlled conditions, the cohort study remains an important and valuable design for the study of human disease.3 For instance, to determine causes of disease in a population sample free from disease that is followed over time, the cohort study design allows the determination of incident cases of new disease. Similarly, to determine natural history of disease, using a population of patients with known disease that is followed over time, the cohort study design allows the determination of pre-specified outcomes such as death.4

Experimental Studies Uncontrolled Trial In a single arm uncontrolled trial, an intervention is made in a group of cases (similar to a case series or cohort design) and the response is observed. Comparisons and even statistical inferences can be made when comparing the prewith the post-intervention group. Although a relatively expedient and intuitive design, this approach has a number of important limitations and flaws. Enrollment of subjects with a condition that is temporary or fluctuating in nature, at the time when the condition is severe, i.e., at the peak of its severity, increases

552

BIOMARKERS

the possibility that subsequent improvement was not due to the intervention, but to a random effect, a phenomenon known as "regression to the mean."8 Other causes of improvement over time that could be misattributed to a specific treatment effect is the placebo effect, and lastly, the natural history of disease itself. Enrolling a control group and comparing the intervention group with the control group can address this problem. Once this is accomplished, treatment allocation becomes paramount.9-10 If the latter is not controlled, it remains subject to selection and/or allocation biases. In addition, an uncontrolled interventional study, even with a two-arm design including a control group, is subject to confounding, in case there is an imbalance of the distribution of a confounding variable between the treatment and control arms. This illustrates how under uncontrolled conditions the quality of the evidence derived from a prospective two-arm interventional study may not be superior to a retrospective case control study, in some instances. C o n t r o l l e d Trial A controlled trial is characterized by the inclusion of a control group for comparison to the intervention group. The former group may consist of concurrent or non-concurrent controls, and may receive no intervention, usual care, or administration of a placebo." A randomized controlled trial is currently considered to be the clinical study design that provides the highest level of evidence to assess treatment effect.1 In a randomized controlled trial, the allocation to a treatment or control group is performed prospectively and at random to eliminate bias in the allocation process and to minimize confoundj n g i(W2 Frequently, a placebo or other mock intervention (even a sham invasive procedure such as surgery) is applied to the control group to account for the placebo effect. Clinical trials are used to evaluate safety and effectiveness of novel therapeutics. Clinical trials typically are conducted in phases: phase I to determine tolerability and dose range in healthy individuals; phase II to determine the therapeutic index and effectiveness in patients affected with the condition to be treated; and phase III to compare the novel treatment to standard therapy to demonstrate superiority or non-inferiority.13 A minority of investigators have disputed the magnitude of the placebo effect,14 which may lend credit to open label randomized trials, especially if concealment of the treatment allocation is not possible or ethical, for example when comparing surgical versus percutaneous repair of an abdominal aortic aneurysm.15 However, several caveats have to be considered for this design. The randomized controlled trial can usually only address one specific and predefined question, which has to be selected and formulated carefully. Other ethical or practical problems that may arise in randomized controlled trials include the following: the condition to be studied is rare, leading to underpowered study results; the condition has an acute course not allowing for enough time for enrollment before it has resolved or the predefined outcome has occurred; the condition requires immediate treatment to prevent an adverse outcome, which would make administration of placebo unethical;16 and finally, the condition may

CLINICAL STUDY DESIGN IN BIOMARKER RESEARCH

553

affect special populations where informed consent may be difficult to obtain, such as in children and individuals with mental or physical disabilities. B l i n d e d / U n - b l i n d e d Design

In randomized controlled trials, in addition to controlling for the placebo effect, blinding has to be discussed separately. Although self evidently, blinding of the study participants is paramount in order for a placebo controlled randomized controlled trial to be successful, additional blinding, specifically of the study coordinators or investigators is often performed.15 This second layer of blinding allows investigators to control for potential observer bias and improves the quality of the results.17 Parallel T w o - A r m / M u l t i p l e - A r m and C r o s s o v e r Design

Most randomized controlled trials employ a parallel design where the study arms refer to the number of groups into which the subject eligible for enrollment will be assigned. A two-arm study has one intervention and one control (placebo) arm; additional intervention arms can be designed in which escalating doses of the same intervention, drug, or two or more competing, distinct therapies are administered. The inclusion of a control arm however remains the standard design, although comparative research designs may omit this in favor of a comparison of an established therapy with a novel competing treatment. The latter design may be necessary, especially if the use of placebo is not ethically justifiable, due to existence of a proven, effective standard therapy for the condition being studied. Finally, the crossover study is a special two-arm design in which both arms are randomly assigned to the intervention or control and vice versa.18 This has the benefit that both groups will be treated equally and receive the benefit of being assigned to treatment at some point during the observation period. Compared to the traditional parallel two-arm study design, the crossover study usually requires a smaller sample size. However, not all conditions can be studied in such a way, especially acute conditions where a short natural history or time course of the disease is not amenable to this design. (See Table 21.1 for an overview of clinical study designs.)

BIOMARKERS I N O B S E R V A T I O N A L S T U D I E S Biomarkers are defined as parameters or findings of either structural, biochemical, physiologic, or genetic nature that indicate the presence, severity, or progress of a physiologic or pathophysiologic process. For simplicity, in the ensuing sections, the discussion of biomarker validation is focused on disease as opposed to physiologic or pathophysiologic processes. As such, ideally, a biomarker should be undetectable whenever the disease of interest is absent. Once a disease develops, the biomarker should become detectable and the intensity of the biomarker expression should be quantitatively associated with the degree of disease severity, allowing for quantitative measurement

554

BIOMARKERS

TABLE 21.2 mission.")

Components of biomarker-based model validation. (Adapted from Taylon et al., with per-

Ascertain true outcome Build model properly including internal validation Avoid over fitting the model Consider biomarker assay reproducibility Perform external validation Choose appropriate performance measures dependent on outcome and prediction type

and severity assessment and ultimately prognostic stratification and treatment allocation. Most biomarkers cannot fulfill all these prerequisites and may only be suitable for one of the aforementioned goals, for example disease detection. Careful consideration has to be given toward the design of studies performed for validation of biomarkers for any of these applications19 (Table 21.2). It is important to recognize, however, that in studies involving biomarkers, consistent sampling, storage, and assay techniques are required to minimize analytical sources of error,20 but these are not the subject of this section. Analytical method validation for biomarker measurement to ensure adequate precision of reproducibility of the assay is another important precondition for a successful study design.21

Biomarkers f o r Disease D e t e c t i o n and Diagnosis The process of development and establishment of a novel biomarker for disease detection, especially in reference to cancer screening, can be structured into a five-step process.22 A preclinical exploratory phase in which the biomarker is discovered; a clinical assay and validation phase in which the assay is developed and its diagnostic performance for established disease is evaluated; a retrospective longitudinal phase in which early detection of incident disease is evaluated; a prospective screening phase in which the extent and nature of disease detected through screening is described; and a clinical, postintroductory surveillance phase in which the impact of biomarker application on the burden of disease in the population is quantified.22 In a case report for example, the coinciding detection of a biomarker with a disease can be reported. Building on this basic unit, a case series can report a number of cases in which the detection of a biomarker coincides with the disease in question. Cohorts offer the possibility of comparing positive and negative cases in which the hypothesis testing can be performed. This requires a sufficient frequency of the disease in the cohort and an accepted and reliable method for detecting the disease against which the biomarker can be tested, a so-called "gold" or "reference standard." Sensitivity, specificity, positive, and negative predictive values can be derived from the frequencies of true and false positively and negatively identified disease while using the biomarker

C L I N I C A L STUDY DESIGN IN BIOMARKER RESEARCH

555

and reported in a standard two-by-two table (Table 21.3). Finally, due to the observational nature of cohort studies, the effects of confounders have to be carefully assessed and adjusted for.23 A case control study design can also serve as the substrate for this exercise and it has the advantage that the subgroups can be balanced. A disadvantage of a case control design is that it allows for the selection of cases and controls TABLE 21.3

Classic 2 x 2 table summarizing index test result. Disease (based on gold standard test)

Positive Index test result Negative

Present

Absent

True positive

False positive

(TP)

(FP)

False negative

True negative

(FN)

(TN)

Sensitivity = TP I (TP + FN) Specificity = TN/(TN + FP) Positive predictive value =TP I (TP + FP) Negative predictive value -TN I (FP + TN)

FIGURE 21.1 Conceptual diagram displaying the introduction of selection bias in a case -control study design aimed at validating a diagnostic biomarker Exclusion of study participants with mild disease expression from entry in the study leads to overestimation of the biomarker's diagnostic precision.

556

BIOMARKERS

which may lead to an overestimation of the precision of the biomarker tested due to selection bias, by selecting out the "grey-zone" with mild disease to be detected in which the biomarker is less likely to be precise (Figure 21.1).

Biomarkers for Disease Monitoring A biomarker that displays a proportional association with the extent of a disease can be used as a surrogate to monitor its severity or detect a relapse. Examples of such biomarkers are tumor and vasculitis markers such as the prostate specific antigen24 and the anti-neutrophil cytoplasmic antibody,25 respectively. For validating this potential function of a biomarker, a cohort study may be used in which subjects with a known disease are prospectively followed over time with consecutive biomarker measurements and monitoring for development of the outcome of interest. The link between biomarker and disease remission and relapse, for instance, can then be statistically inferred, as well as timing of biomarker increase in relationship to the outcome of interest can be assessed (Figure 21.2). The enrichment effects of clinical severity or biomarker expression that are evident in case control studies, however, do not only represent a shortcoming, but may aide in the detection of potential candidate biomarkers in the early phases of the biomarker discovery process.26

Biomarkers for Disease Prognostication The link of a biomarker with a subsequent pre-specified clinical outcome may be useful for estimating the probability of developing that outcome. In addition, prognostic biomarkers may also be used to estimate treatment response, for example to chemotherapy for certain malignancies.27 In this context, the "marker by treatment interaction design" is a study design in which detection of the biomarker separates the population into groups with distinct responsive-

FIGURE 21.2 Conceptual diagram displaying use of the cohort study design to validate a diagnostic or disease severity biomarker Both temporal and quantitative association of the biomarker with the disease can be evaluated.

CLINICAL STUDY DESIGN IN BIOMARKER RESEARCH

557

ness to the treatment regimen to be tested.27 A different, more complex approach described as "marker-based strategy design" measures the biomarker first and, after the marker status is known, assigns study participants randomly to the treatment or control arm.27 In theory, the link between biomarker and outcome could be qualitative (present or absent) or quantitative, and thus similar to the behavior of a severity marker (see Bidmarkers for Disease Monitoring). In the latter, a higher biomarker concentration or expression in a biological sample (e.g., serum, plasma, urine, or pathological specimen) indicates a higher probability for the clinical outcome to occur. Again, a longitudinal study design in the form of a case series, or even better, a cohort, would be most suitable for examining this question.28 A case control study design is also possible, but is more likely to overestimate the discriminative precision of the marker (Figure 21.1).

BIOMARKERS I N I N T E R V E N T I O N A L S T U D I E S Biomarkers for Treatment Response Biomarkers linked to disease severity or clinical outcomes may be used as measures of severity or as surrogates for clinical outcomes in interventional trials.29 As such, the biomarker becomes a surrogate endpoint replacing the clinical outcome, or a marker to monitor changes in a disease process. Thus, the biomarker represents in itself an endpoint in the trial. To fulfill this function, the candidate biomarker needs to be tightly associated with the outcome, be independent of other factors or confounders, and should have demonstrated robustness throughout a spectrum of various studies.28 Prior internal and external validation, avoidance of model over-fitting, and over-reliance on inference statistics, choice of sufficient sample sizes, and pre-specification of the validation are paramount for the development process.30 At present, such stringent methodological criteria are infrequently employed as recent systematic literature reviews suggest.31 One example for such a biomarker could be urinary albumin excretion and its link to progression of diabetic kidney disease. Treatments or interventions that reduce albuminuria would also be expected to reduce the clinical outcome of kidney disease progression. Traditionally, regulating bodies such as the U.S. Food and Drug Administration have required "hard" clinical end points such as mortality or need for renal replacement therapy for approval of a treatment. More recently, surrogate outcomes, such as biomarkers, are increasingly accepted in order to accelerate and streamline the approval process,3233 a practice that has not remained without controversy.34-35 A statistical association of a biomarker with a clinical endpoint is not sufficient evidence that the biomarker is a reliable surrogate. Ideally, a functional role of the biomarker in the pathophysiological processes that leads to the clinical endpoint should also be required. The benefits of the use of biomarkers in this setting are evident, especially when chronic, slowly progressive conditions are studied, which may require several years for the clinical endpoint to develop. Biomarkers that reliably predict the outcome would therefore greatly speed up drug development.32

558

BIOMARKERS Biomarkers could also be incorporated into trials as stratification tools for the early identification of high-risk populations and target early therapeutic intervention aimed at modifying disease course.

Biomarkers f o r M o n i t o r i n g Toxicity Similar to biomarker surrogates for clinical endpoints in therapeutic trials, biomarkers can also be used as surrogates for drug-related adverse effects in therapeutic trials. In addition to serving as surrogate endpoints, biomarkers representing intermediate pathophysiological steps in a disease process also allow for the resolution of more detail in the study of an exposure-disease association, for example identify and quantify the importance of confounders such as cigarette smoking and occupational aniline-exposure for urological malignancies.23 High prognostic discrimination and predictive performance is required, in order to minimize bias that could result from the use of biomarkers as surrogate outcomes. Strict criteria dictate that a surrogate should yield unambiguous information about differential treatment effects on the true endpoint.36

CONCLUSION The choice of a study design has a substantial impact on the quality of the results derived from the research to be conducted. The increased utilization of biomarkers in human disease provides an opportunity to enhance and accelerate clinical research. However, rigorous methods and proper choice of study design remain prerequisites for generating reliable and valid, high quality results.

SUMMARY P O I N T S 1.

2.

The potential value of biomarkers in clinical medicine includes insight into disease mechanism, early disease detection, prediction of disease severity, prognosis, recovery, and relapses, as well as the promise of serving as therapeutic efficacy surrogate endpoints. The various clinical study designs offer advantages and disadvantages to the emerging field of biomarker research, and require use of rigorous methods that are study design-specific.

REFERENCES 1. 2.

Lader, E. W., Cannon, C. P., and Ohman, E. M., et al. The Clinician as Investigator: Participating in Clinical Trials in the Practice Setting: Appendix 1: Fundamentals of Study Design. Circulation. 2004;109(21):E302-304. Backman, C. L. and Harris, S. R. Case Studies, Single-Subject Research, and N of 1 Randomized Trials: Comparisons and Contrasts. Am. J. Phys. Med. Rehabil. 1999;78(2): 170-176.

CLINICAL STUDY DESIGN IN BIOMARKER RESEARCH 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

559

Morgenstern, H. and Thomas, D. Principles of Study Design in Environmental Epidemiology. Environ. Health Perspect. 1993;101 Suppl 4:23-38. Mann, C. J. Observational Research Methods. Research Design II: Cohort, Cross Sectional, and Case-Control Studies. Emerg. Med. J. 2003;20(l):54-60. Wacholder, S., Silverman, D. T., McLaughlin, J. K., and Mandel, J. S. Selection of Controls in Case-Control Studies. III. Design Options. Am. J. Epidemiol. 1992;135(9):1042-1050. Concato, J., Shah, N., and Horwitz, R. I. Randomized, Controlled Trials, Observational Studies, and the Hierarchy of Research Designs. N. Engl. J. Med. 2000;342(25): 1887-1892. Willett, W. C. and Colditz, G. A. Approaches for Conducting Large Cohort Studies. Epidemiol. Rev. 1998;20(l):91-99. Davis, C. E. The Effect of Regression to the Mean in Epidemiologic and Clinical Studies. Am. J. Epidemiol. 1976;104(5):493-498. Hagino, A., Hamada, C , Yoshimura, I., Ohashi, Y., Sakamoto, J., and Nakazato, H. Statistical Comparison of Random Allocation Methods in Cancer Clinical Trials. Control Clin. Trials. 2004; 25(6):572-584. Kalish, L. A. and Begg, C. B. Treatment Allocation Methods in Clinical Trials: A Review. Stat. Med. 1985;4(2): 129-144. Sibbald, B. and Roland, M. Understanding Controlled Trials. Why Are Randomised Controlled Trials Important? Bmj. 1998; 316(7126):201. Roberts, C. and Sibbald, B. Understanding Controlled Trials. Randomising Groups of Patients. Bmj. 1998;316(7148):1898-1900. Giacinti, L., Lopez, M., and Giordano, A. Clinical Trials. Front. Biosci. 2006;11:2918-2923. Kienle, G. S. and Kiene, H. The Powerful Placebo Effect: Fact or Fiction? J. Clin. Epidemiol. 1997;50(12):1311-1318. Lader, E. W., Cannon, C. P., and Ohman, E. M., et al. The Clinician as Investigator: Participating in Clinical Trials in the Practice Setting: Appendix 2: Statistical Concepts in Study Design and Analysis. Circulation. 2004;109(21):E305-307. Edwards, S. J., Lilford, R. J., and Hewison, J. The Ethics of Randomised Controlled Trials from the Perspectives of Patients, the Public, and Healthcare Professionals. BMJ. 1998;317(7167):1209-1212. Chalmers, T. C , Smith, H., Jr., and Blackburn, B., et al. A Method for Assessing the Quality of a Randomized Control Trial. Control Clin. Trials. 1981;2(1): 31-49. Sibbald, B. and Roberts, C. Understanding Controlled Trials. Crossover Trials. Bmj. 1998;316(7146):1719. Taylor, J. M., Ankerst, D. P., and Andridge, R. R. Validation of Biomarker-Based Risk Prediction Models. Clin. Cancer Res. 2008;14(19):5977-5983. Tworoger, S. S. and Hankinson, S. E. Use of Biomarkers in Epidemiologic Studies: Minimizing the Influence of Measurement Error in the Study Design and Analysis. Cancer Causes Control. 2006;17(7):889-899. Lee, J. W., Hulse, J. D., and Colburn, W. A. Surrogate Biochemical Markers: Precise Measurement for Strategic Drug and Biologies Development. J. Clin. Pharmacol. 1995;35(5):464-470. Pepe, M. S., Etzioni, R., and Feng, Z., et al. Phases of Biomarker Development for Early Detection of Cancer. J. Natl. Cancer Inst. 2001;93(14):1054-1061. Schulte, P. A. Methodologic Issues in the Use of Biologic Markers in Epidemiologic Research. Am. J. Epidemiol. 1987;126(6): 1006-1016.

560

BIOMARKERS 24. 25. 26. 27. 28. 29. 30. 31. 32.

33. 34. 35. 36.

Lin, K., Lipsitz, R., Miller, T., and Janakiraman, S. Benefits and Harms of Prostate-Specific Antigen Screening for Prostate Cancer: An Evidence Update for the U.S. Preventive Services Task Force. Ann. Intern. Med. 2008; 149(3): 192-199. Kyndt, X., Reumaux, D., and Bridoux, F., et al. Serial Measurements of Antineutrophil Cytoplasmic Autoantibodies in Patients with Systemic Vasculitis. Am. J. Med. 1999;106(5):527-533. Mandrekar, S. J. and Sargent, D. J. Clinical Trial Designs for Predictive Biomarker Validation: One Size Does Not Fit All. J. Biopharm. Stat. 2009;19(3):530-542. Sargent, D. J., Conley, B. A., Allegra, C , and Collette, L. Clinical Trial Designs for Predictive Marker Validation in Cancer Treatment Trials. J. Clin. Oncol. 2005;23(9):2020-2027. Rigatto, C. and Barrett, B. J. Biomarkers and Surrogates in Clinical Studies. Methods Mol. Biol. 2009;473:137-154. Biomarkers and Surrogate Endpoints: Preferred Definitions and Conceptual Framework. Clin. Pharmacol. Ther. 2001;69(3):89-95. George, S. L. Statistical Issues in Translational Cancer Research. Clin. Cancer Res. 2008;14(19):5954-5958. Vickers, A. J., Jang, K., Sargent, D., Lilja, H., and Kattan, M. W. Systematic Review of Statistical Methods Used in Molecular Marker Studies in Cancer. Cancer. 2008;112(8):1862-1868. Challenge and Opportunity on the Critical Path to New Medical Products. U. S. Department of Health and Human Services; Food and Drug Administration. Available at: http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.html. Accessed May 30, 2004. Amur, S., Frueh, F. W., Lesko, L. J., and Huang, S. M. Integration and Use of Biomarkers in Drug Development, Regulation and Clinical Practice: A U.S. Regulatory Perspective. Biomarkers Med. 2008;3(2):305-311. Avorn, J. FDA Standards—Good Enough for Government Work? N. Engl. J. Med. 2005;353(10):969-972. Freemantle, N. and Calvert, M. Composite and Surrogate Outcomes in Randomised Controlled Trials. BMJ. 2007;334(7597):756-757. Prentice, R. L. Surrogate Endpoints in Clinical Trials: Definition and Operational Criteria. Stat. Med. 1989;8(4):431-440.

CHAPTER

STATISTICAL ISSUES IN BIOMARKER RESEARCH Daniel Holder and Matthew Schipper

T H E ROLE OF STATISTICS I N BIOMARKER DISCOVERY, D E V E L O P M E N T , A N D QUALIFICATION Although there is a spike in the enthusiasm to discover, develop, and qualify biomarkers, the fundamental desire to find assays that are useful for assessing diagnosis, prognosis, and/or monitoring disease state is not new. Advances in technology, together with more sophisticated informatics, have heightened expectations for finding useful markers. However, in many ways, the basic scientific paradigm for discovering and establishing markers has not changed. Although some have viewed the role of statistical analysis in the biomarker development process as limited to confirming preconceived hypotheses, in fact, statistical reasoning can and should play many roles. Too often the notion of biomarker discovery, development, and qualification is oversimplified into a direct line relationship between hypothesis generation and testing. A more realistic description, however, might start with a vague idea, which only through experience and experimentation can be transformed into a testable hypothesis. The important point is that the kind of question that can be addressed by confirmatory statistical testing is rarely apparent in the earliest stages of a biomarker program. What designs are feasible and how likely a given design is to give a useful answer, and how to measure performance, are parts of question formulation that cannot be overlooked. Together with insight and exploration of past data, they are a critical part of understanding what can be hoped to be accomplished. To understand the role of statistics, we must first recognize that just as there are multiple stages in biomarker development, there are different styles 561

562

BIOMARKERS

of data analysis. Following Mallows and Tukey (1982),1 we might first distinguish exploratory data analysis (EDA) from critical or confirmatory data analysis (CDA). EDA is detective work aimed at discovery of "what seems to be going on." As much as anything else, it is an attitude which is built on a willingness to look for what can be seen, whether or not it was anticipated. "Seen" might be taken literally, since the mind together with eye are often the best tools for discovering patterns and novelty. Pervasive use of graphics is often the hallmark of high quality EDA. Another essential feature of EDA is flexibility. Here we mention not only flexibility around statistical techniques, but data transformation, normalization, adjustments, and consideration of a broad range of models. CDA should probably be reserved for instances for which the data quality is high and we already have a sense of what is going on, often expressible in the form of a hypothesis. Again following Mallows and Tukey, CDA can be divided further into OCDA (overlapping critical data analysis), SCDA (separated confirmatory data analysis), and CCDA (careful confirmatory data analysis). The idea here is not to make alphabet soup, but to emphasize that the attitudes we adopt and methods we deem appropriate change depending on the situation in which we find ourselves. OCDA is based on recognition, that once we have explored a set of data, we might carry it further and try to squeeze as much confirmatory information as we can from it. Although this is often prudent, it does not carry as much evidentiary weight as an SCDA, in which new data is analyzed in a way suggested by other data. "Confirmation comes from repetition. Any attempt to avoid this statement leads at least to failure and more probably to destruction."2 Probability models, especially those based on randomization and resampling, can give us some idea of how likely a particular result is to repeat. However, these are much inferior to the confirmation that comes from obtaining similar results in an independent experiment. CCDA is the last step down the road toward confirmation. In these studies, design of data gathering, prior choice of what is to be assessed, and the most careful methods of critical data analysis are pre-specified and combined into the most trustworthy assessment of evidence we know. For the kidney toxicity example discussed below, EDA and OCDA were used liberally to gather initial evidence for biomarkers of acute kidney injury. EDA was particularly useful in microarray screening experiments. OCDA was vital for defining a rat gene expression assay for kidney toxicity. Below we discuss analysis of urine protein data that has more of an SCDA flavor when considered for the objective of establishing markers of acute kidney toxicity in rats.We also discuss some of the statistical challenges involved in planning human trials. We would expect that the initial human study would be suitable for EDA and some OCDA, with a progression toward generating studies worthy of SCDA as the program progresses.

TYPES OF BIOMARKERS Biomarkers may serve many purposes in research and are often classified according to their intended use. The most common types of biomarkers are

STATISTICAL ISSUES IN BIOMARKER RESEARCH

563

diagnostic, prognostic, and progression. Diagnostic biomarkers are those that classify subjects as having or not having a certain condition or disease, or more generally, assess disease severity. Prognostic biomarkers add the element of time and predict risk of a future event. A progression biomarker measures the change in disease state or severity over time. Although the same biomarkers may be valuable for multiple purposes, most often different study designs, data structures, and methods are needed to assess performance for each type of use. For example, the performance of diagnostic markers can typically be assessed using data from a single time point, while prognostic marker assessment usually requires at least one measurement of a disease-related response sometime after measurement of the marker, and progression markers normally require multiple measurements of the marker and a response over time. A good biomarker development program needs to remain cognizant of the proposed biomarker use and tailor the assessment of the marker to be relevant to that use. Researchers should carefully consider the intended context of use for a candidate biomarker. The intended population is a contextual element of particular importance to statistical performance assessment of a biomarker. The results of most statistical assessments center around a set of estimates of performance metrics along with some measure of how much the estimates might be different from a population parameter, and/or an evaluation whether there is credible evidence to rule out an arbitrary population parameter (i.e., a hypothesis test). We need to be realistic about the extent to which our intended use population might differ from the populations that have actually been studied. One specific use for a biomarker is as a surrogate endpoint in clinical trials. Many definitions of surrogate endpoints have been given. A useful operating definition given by Temple (1999)3 is "a laboratory or physical sign that is used in therapeutic trials as a substitute for a clinically meaningful endpoint that is a direct measure of how a patient feels, functions or survives and that is expected to predict the effect of the therapy." While all surrogate endpoints fit within the usual definition of a biomarker, the converse is not true. Much has been published regarding the statistical criteria a biomarker must meet in order to be considered a surrogate endpoint beginning with Prentice (1989).4 Because our focus in this chapter is on toxicity biomarkers and especially on diagnostic markers we will not discuss surrogate endpoints further here. The kidney biomarkers we discuss later in the chapter were developed to detect damage or injury to the kidney. These biomarkers are intended to be used in a diagnostic manner to detect drug toxicity. Studies from multiple laboratories with many different nephrotoxicants suggest that they are applicable to the population of laboratory rats in a wide variety of settings. Plans to study applicability in humans will be discussed in a later section.

STAGES OF D E V E L O P M E N T Developing and validating a biomarker for use typically involves multiple studies. Table 22.1 describes five phases of medical test development given

564

BIOMARKERS

TABLE 22.1

Five phases of medical test development.

Phase

Description

Typical Objectives

1

Exploratory Investigations

Identify promising tests and settings for application.

2

Retrospective Validation

Determine if some minimally acceptable sensitivity and specificity are achieved.

3

Retrospective Refinement

Define criteria for positivity. Determine covariates affecting test performance. Develop algorithms for combining tests.

4

Prospective Application

Determine positive predictive values, detection, and false positive probabilities when the test is applied in practice.

5

Disease Impact

Determine effects of testing on cost and mortality associated with disease.

by Pepe (2003)4 in the context of cancer biomarker research. Although this structure may not apply precisely to the development of biomarkers for drug induced toxicity, it provides a useful framework to consider.

K I D N E Y PROJECT B A C K G R O U N D To breathe life into some of the statistical concepts surrounding biomarkers, in this chapter we will reference an initiative by the Predictive Safety Testing Consortium (PSTC) to qualify accessible biomarkers that improve monitoring of specific kidney safety concerns in rats, and early human clinical trials sufficiently to enable early drug development.6 Specifically, the group focused on discovering and developing improved biomarkers for drug-induced tubular injury since historically, among compounds that were dropped due to animal toxicity studies, this has been one of the leading contributors. The decision was made early to focus on establishing biomarker performance metrics in the rat test species and to bridge to human studies from knowledge gained with that species. In the rat studies, the goal was not to replace the need for animal histopathology, but rather to define the strengths and limitations for biomarkers that could add valuable information to the classical markers blood urea nitrogen (BUN) and serum creatinine (sCr) and improve on their ability to monitor for kidney injury and proper function. The specific clinical objective was focused on acute drug-induced kidney injuries that could be seen soon after drug administration and not necessarily general medical uses such as monitoring for progression of kidney injury associated with diseases such as hypertension, transplant rejection, or diabetes. It was important to acknowledge that success would lead to further opportunities to

STATISTICAL ISSUES IN BIOMARKER RESEARCH

565

expand upon the potential utility of these markers in a progressive qualification framework. We will illustrate statistical methods and metrics for assessing biomarker performance at the pre-clinical stage using a subset of the data collected for urinary Kim-1. Details of the study can be found elsewhere,7 however the data used in this chapter are a subset of the data discussed there. Briefly, male Sprague Dawley rats received one of four nephrotoxicants (gentamicin, cisplatin, thioacetamide, or cyclosporine), Gentamicin sulfate was administered at 0, 20, 80, or 240 mg/kg/day (n=5 rats/dose group/time point) and the animals were necropsied on days 3, 9, or 15. In the cisplatin groups a single dose of cisplatin was administered intraperitoneally (i.p.) to male Sprague Dawley rats (n=5 rats/dose group/time point) at doses of 0, 0.5, 3.5, or 7 mg/kg and necropsy was performed on day 3 or 8 post-treatment. Cyclosporine A was administered subcutaneously (s.c.) at 0, 6, 30, or 60 mg/kg/day to rats (n=5/ dose/time point) and necropsy was performed on day 3,9, or 15. A single dose of thioacetamide (TAA) was administered by oral gavage at 0, 50, 100, or 200 mg/kg (n=5 rats/dose group/time point) and necropsy was performed on day 2 (24 hr post-dose) or day 3 (48 hr post-dose). Urine was collected prior to necropsy and assayed for Kim-1 and creatinine. Blood was also collected prior to necropsy and assayed for toxicity evaluation which included serum clinical chemistry (BUN, creatinine). Concentration of urinary Kim-1 was divided by urinary creatinine in an attempt to normalize for the varying excretion rates between individual animals. We evaluated each of the analytes on the log (base 2) scale since their variability tended to increase with concentration and to facilitate expression of results in terms of ratios. To adjust for the possibility of a drift in experimental conditions and differences in vehicles, Kim-1, sCr, and BUN were each adjusted by subtraction of the mean value (log scale) for control animals measured at the same time point in the same study. Although we found them useful in this analysis, experimenters should not automatically assume that such adjustments are always helpful. Each bias adjustment brings error with it, so it is prudent to weigh whether the tradeoff is beneficial. At necropsy, kidneys were collected for histomorphologic evaluation (H&E staining) soon after the last blood collection and exsanguination. A trained pathologist blinded to the biomarker data scored the slides for histomorphologic alterations assigning grades of 0 (no observable pathology), 1 (very slight), 2 (slight), 3 (moderate), 4 (marked), or 5 (severe). Alterations for individual animals were grouped into composite categories for statistical analysis. The construction of a composite score to measure tubule alterations was a difficult process by which pathologists made careful decisions about which alterations would contribute to the score. Moreover, it is important that such decisions were blind to the biomarker data so as to not bias performance measures. However, since biomarker performance metrics are measured directly against this reference standard, we cannot overemphasize the importance of constructing it carefully.

566

BIOMARKERS

S T A T I S T I C A L M E T H O D S / M E T R I C S FOR ASSESSING BIOMARKER P E R F O R M A N C E In this section, we discuss some of the more technical statistical aspects of biomarker performance assessment. Most often there is not a known set of optimal statistical methods or a single analysis script that works best. Nonetheless, there can be great benefit from the application of statistical thinking and the use of appropriate methods in biomarker studies. We do not have the space or the wisdom to address all of the statistical issues that might arise. Instead, we comment on a few challenges we have faced in the hope that our experiences may be a benefit to others and recommend that experimenters seek specific advice from a trained statistician for biomarker studies of any consequence.

Sensitivity, Specificity, and ReceiverOperator Characteristic Curves When the reference standard has only two outcomes, like injured and not injured, it is natural to think of performance in terms of the proportion of the reference positive samples that the biomarker correctly predicts as positive (sensitivity) and the proportion of the reference non-positive samples that the biomarker correctly predicts as non-positive (specificity). For a biomarker that takes on more than two values this typically requires creating a test by dichotomizing the biomarker values at a threshold. Results from such a test are displayed in cross classification Table 22.2. If high values of the biomarker are considered evidence of a positive response, then typically the sensitivity for such a test is estimated as P1/(P0+P1) and the specificity as N0/(N0+N1). Calculating proportions along the other margin, the positive predictive value (PPV) is the proportion of samples called positive that actually are positive, PI/ (Nl+Pl) and the negative predictive value (NPV) is the proportion of samples called negative that actually are negative, N0/(N0+P0).

TABLE 22.2 Cross classification table of a dichotomous response and biomarker with a threshold. Biomarker

Reference

^Threshold

>Threshold

Total

Negative

NO

Nl

NO+NI

Positive

PO

PI

PO+PI

Total

NO+PO

Nl+Pl

NO+NI+P0+PI

Table gives counts of samples classified by a dichotomous response and relative to a biomarker threshold. If high values of the biomarker are considered evidence of a positive response, then simple formulas for estimates of sensitivity (PI /(PO+P I)), specificity (N0/(N0+N I)), positive predictive value (PI l(P l+NI)) and negative predictive value (N0/(NO+P0)) can be applied.

STATISTICAL ISSUES IN BIOMARKER RESEARCH

567

Choice of a specific threshold is an essential part of the creation of a test and hence the table. Occasionally researchers have particular reasons to favor a particular threshold, for instance, they might concentrate their interest on a threshold that has particular biological significance. Alternatively, researchers might choose a threshold based on statistical performance, for example one that yields 95% specificity with the current data. However, most often in biomarker development, there is no single threshold of paramount importance. In such cases, researchers are well advised to assess the performance of a marker over a range of possible thresholds. The desire to assess performance of a marker over a variety of thresholds is one way to motivate analysis of a receiver-operator characteristic (ROC) curve. One can contemplate creating a 2x2 cross classification table like Table 22.2 for every possible threshold, and calculating the estimates of sensitivity and specificity. The long list of tables is cumbersome to express, however, the sensitivity estimates can easily be plotted against the thresholds. Figure 22.1 depicts such a plot for kidney toxicity marker urinary Kim-1, in studies described above. We might note that at a Kim-1 value of about 3, the estimate of sensitivity is about 0.83 and specificity about 0.95, and at Kim-1 of about 1, the sensitivity is about 0.99 with specificity of about 0.80. A traditional ROC curve takes the values depicted in Figure 22.1 and plots the sensitivity versus 1-specificity as depicted by the solid black line of Figure 22.2. Note that the solid line passes through 1-specificity = 0.05, sensitivity = 0.83, and 1-specificity = 0.20, sensitivity =

FIGURE 22.1 Plot of sensitivity and specificity for Kim-1 thresholds.The sensitivity and specificity estimates for urinary Kim-1 for thresholds at all observed Kim-1 values are plotted. Note Kim-1 values are log (base2) and normalized by mean of controls from the same study.The response variable is whether the kidney histopathology score is greater than zero.

568

BIOMARKERS

0.99, which corresponds to the 1 and 3 thresholds mentioned above. In this way, the ROC curve plots sensitivity estimates versus 1-specificty estimates for all relevant thresholds, without the threshold usually being made explicit on the plot. An astute reader might notice that for all thresholds below the lowest value of the marker that gave a positive result, the sensitivity is uniformly estimated to be 1. On the ROC plot, this is the long plateau at 1.0 that starts at a 1-specificity of about 0.25. Note that if we took all of the marker values below this cutoff (Kim-1 value of 0.83) and artificially lowered them, say subtracted 5 from each, this would change the specificity and sensitivity curves plotted in Figure 22.1, by pushing the left tail of the curves further to the left. However, note that this would have no effect on the ROC curve, since the plot of sensitivity vs. 1-specifcity would remain the same. This phenomenon is an example of a more general characteristic, namely, that the ROC curve depends on the marker values only through their ordering. Two sets of markers values that yield the same ordering of the samples will have the same ROC curve. An important corollary to this is that any monotonic transformation of the biomarker values (e.g., scaling, centering, taking logs) will result in the same ROC curve. Figure 22.2 shows the wisdom of looking over multiple thresholds when comparing the performance of markers. While the sensitivity estimates for Kim-1 and sCr are both 0.48 at 0.99 specificity, Kim-1 has greater sensitivity (0.91) than sCr (0.75) when compared at 0.9 specificity. One strategy to

FIGURE 22.2 Receiver-operator characteristic (ROC) plot for Kim-1, sCr, and BUN as markers of proximal tubule injury in rats.The curve shows the sensitivity estimate for all estimated specificity levels. The Kim-1 curve is generally above the other curves, indicating better performance. (See color insert for a full color version of this figure.)

569

STATISTICAL ISSUES IN BIOMARKER RESEARCH

TABLE 22.3 AUCROC estimates and 95% confidence intervals for urinary markers of kidney toxicity. AUCROC

SE

95% Cl

Kiml

0.967

0.012

(0.944,0.989)

sCr

0.870

0.028

(0.816,0.925)

BUN

0.843

0.031

(0.784,0.903)

Marker

p-values (Kim I vs. sCr: 0.0007, Kim / vs. BUN: 0.00008, sCr vs. BUN: 0.46) Table displays estimates of area under the ROC curve, its standard error (SE), and a 95% confidence interval for the data described in this section. Estimates and tests based on method ofDeLong (1988). Statistics address the question of relative performance of the biomarkers.

combat the arbitrariness of comparing at a single, or even a small set of thresholds, is to average the sensitivity estimates over all the thresholds. The area under the ROC curve (AUCROC) is one way to collapse the information of the curve into a single performance assessment statistic. AUCROC is essentially a weighted average of the sensitivity estimates across the 1-specficity values, where the weights are based on changes in 1-specificity. Thinking geometrically, the weights are the lengths of the bases of rectangles for a given sensitivity. Note that the ROC curve we have generated and AUCROC we have calculated are actually sample estimates which have theoretical counterparts. We have used a particular nonparametric procedure to obtain our estimates, but note that parametric estimation procedures such as the binormal method could also be used (chapter 5 of Pepe, 2003). Suppose we put the Kim-1 values for all of the samples with a positive response in one hat and the Kim-1 values for samples with a non-positive response in a different hat. Taking all possible pairs of drawings, one from each hat, we can calculate the proportion of times that the Kim-1 value from the positive hat exceeds the Kim-1 value drawn from the non-positive hat. This proportion is called the concordance probability and it can be shown that for a binary response is equivalent to the AUCROC. One advantage of the concordance probability view is that it readily generalizes to responses that take on more than two values. Using the concordance viewpoint, DeLong (1988)8 showed how to perform a significance test of whether two AUCROCs are different. Such a test can be the basis for the claim that one biomarker outperforms another biomarker. In our current example AUC estimates are given in Table 22.3. The results clearly indicate that the urinary Kim-1 marker outperforms the sCr and BUN markers.

Assessing W h e t h e r a Marker Adds Value t o O t h e r Markers Since the non-parametric estimate of a marker's ROC curve is based only on the dichotomous response and the ordering (ranks) of the marker values, it is a technique which enjoys widespread applicability for characterizing marker

570

BIOMARKERS

performance and the comparison of one marker to another. However, in biomarker development, the most relevant question is often not the performance of a single marker in isolation, but rather whether a marker adds new information to the body of information that is already available. For instance, in our example we might ask how much better off are we by adding the information obtained from urinary Kim-1 to the blood information we get by assaying sCr and BUN? The basic strategy to answer this question is to identify a set of metrics that quantify the discriminatory ability of a given set of markers, and compute the metrics for the current set of markers with and without the new candidate marker(s) and then judge whether the gain in performance is satisfactory. There are many ways to combine values from markers. Perhaps the most straightforward way is a linear score bO + bl*markerl + b2*marker2 + etc., where the b's are a set of numeric coefficients. In some instances, non-linear methods for combining markers can be much more powerful. Some examples would be if marker 2 was only important when marker 1 was high or if the response depended on the ratio of markers 1 and 2. For simplicity we will focus on linear scores. To find a score with good performance it is often useful to construct a model. When a mechanistic model is not available, as is most often the case, a mathematical model can be useful. Linear logistic regression is a common mathematical model used for binary outcomes. One simple view of the logistic equation (24.1) is that it maps any score (range may be from negative infinity to positive infinity) onto the probability scale between 0 and 1. Pr (Response is Pos) = 1/(1 + exp(-Score based on markers))

(24.1)

Statistical algorithms give us ways to find scores that optimize the fit of the model to experimental data. Figure 22.3 gives boxplots for scores found by fitting logistic regression models to the Kim-1 data. In Panel A, we observe a small number of samples with large BUN scores, but overall substantial overlap in BUN scores between the positive and non-positive samples. Panel B shows substantial overlap between the positive and non-positive samples for a sCr score. Combining BUN and sCr, panel C shows some improvement over the individual analytes. Panel D shows that the score which combines Kim-1 with sCr and BUN does a much better job of separating the positive samples from the non-positive samples. A few summary statistics that capture the performance of these models are given in Table 22.4. The likelihood ratio (LR) gives a model-based assessment of the fit that is often used to create hypothesis tests. Note that the difference in LR statistics for nested models can be referred directly to a chi-squared distribution for a statistical test of whether the marker(s) added to the model, result in a significantly better fit. The increase in LR from 97.2 to 114.3 with the addi-

STATISTICAL ISSUES IN BIOMARKER RESEARCH

571

FIGURE 22.3 Boxplots and estimates of probability of postive hisotopath for logistic regression models based on BUN, sCr, (BUN and sCr), and (BUN, sCr, and Kim-1). Figure 22.3 depicts the fit of four logistic regression models to the kidney histopathology data. Panels A and B show the fit of models for BUN and sCr individually, Panel C depicts a model using a score that combines BUN and sCr, and Panel D shows a model that combines BUN, sCr, and Kim-1. In each panel, the modeled probability of positive histopathology is plotted versus the score. Horizontal boxplots are given for the scores of the non-positive (lower box) and positive (upper box) samples. Each box extends from the 25th to the 75th percentile with a bar at the median. Whiskers are drawn from the largest and smallest values that are not considered extreme based on a variability estimate based on the quartiles. Values considered extreme are plotted as individual points.

tion of BUN to the SCr model yields an increase of 17.1 yielding p=3e-5. The addition of Kim-1 to the model including sCr and BUN gives an increase of 60.3 in the LR with corresponding p=8e-15. Thus, there is strong evidence that Kim-1 adds more information to the model than would be expected by chance. The R2 statistic (Nagelkerke, Craigg, and Uhler (see Harrell 2001, p2479)) is somewhat analogous to the R2 from ordinary linear regression in that it compares each score with the maximum attainable score. C is the concordance probability, the probability that for a pair of observations, the observation with the higher predicted response has the higher observed response. For binary response, C is equivalent to the AUCROC. Metrics suggested by Pencina (2008)10 are given in the remaining columns. The difference in the mean predicted value for positive and non-positive samples gives the integrated discrimination improvement (IDI), which is a measure of the predictive ability of each score and thus can be used to compare across models. All of

572

BIOMARKERS

TABLE 22.4 Summary statistics for binary linear logistic scores combining BUN, sCn and Kim-1. Mean

Mean

Integrated

Predicted Predicted Discrimination LR Score

Chisq

C

R2

Value

Value

Improvement

(POS)

(nonPos)

(IDI)

-l.5+3.4*BUN

77.8

0.844 0.475

0.648

0.269

0.379

-l.l+7.8*sCr

97.2

0.870 0.565

0.701

0.228

0.472

-l.8+7.2*sCr+2.8*BUN

1 14.3 0.908 0.636

0.739

0.199

0.540

-3.8+3.5*sCr+2.1 *BUN+1.1 *Kim 1 174.6 0.979 0.839

0.872

0.098

0.775

The likelihood ratio (LR Chisq) gives a model-based assessment of the fit that is often used to create hypothesis tests.The R2 statistic (Nagekerke, Craigg, and Uhler (see Harrell 2001, p247)) is somewhat analogous to the R2 from ordinary linear regression. C is the concordance probability, the probability that for a pair of observations, the observation with the higher predicted response has the higher observed response. For binary responses C is equivalent to the AUCROC, which is a measure of sensitivity integrated over all levels of specificity. Mean Predicted Values are the average predicted value in the positive and non-positive group. As explained by Pencina (2008), the difference of these yields the integrated discrimination improvement (IDI), which is a measure of the predictive ability. The performance statistics are given for linear scores based on fitting a linear logistic regression model to the data described. Estimated coefficients for the models are given in the first column.

these performance metrics show sizeable increases with the addition of Kim1 to the model. Re-sampling methods like the bootstrap or jackknife may be used to assess variability and bias in these statistics.

Errors in t h e Reference Standard Performance of a biomarker is best measured against a reference standard, which we expect to reflect truth with a high degree of accuracy. However, in some experimental contexts, our reference standard may be measured with error. In these cases it is worthwhile to consider the effect of error in the reference standard on the assessment of biomarker performance. The standard for determination of kidney injury in the rat is careful examination of kidney morphology by a trained pathologist. Although highly accurate, this determination is not perfect, since pathologists cannot examine every section of both kidneys, some level of variability between the subjective evaluations of pathologists is expected, and molecular signals may precede the ability to observe structural damage. This last potential bias is of great importance since we would like to avoid penalizing a biomarker for being more sensitive than the reference standard. Figure 22.4 shows the distribution of Kim-1 urine levels by kidney histopathology grade. For animals without detectable kidney injury, we separated the samples based on whether or not the animals received a nephrotoxicant or not. The plot suggests an elevation in urinary Kim-1 for some animals that received a known nephrotoxicant even though they did not have positive histopathology. To this point, we have treated the histopathology response as if it were 100% accurate. We might want to consider the possibility that in some

573

STATISTICAL ISSUES IN BIOMARKER RESEARCH

n=46

55

21

i

30

26 i

—

—

—, —

x

CO -i

-* -

X

,****

—L-3

x^

r


<

nr

y 1 /

^

O " X

CM „ i

#"

if

x* x

X

X

XX

X

X

_ i _

X

xX

x*

)?

3K

T~

y:

xx

?

x

X X

x

x*

J?

X

x< X

*

_ l _

X

X

1

Control

Nephro Tox Hist=0

Hist=1

Hist=2

Hist23

FIGURE 22.4 Boxplots of Kim-1 values by kidney histopathology injury grade. A plot of the individual values sorted by Kim-1 value is superimposed over each, giving a finer scaled picture of the distribution of the data.The figure indicates that median Kim-1 values generally increase with an increased histopathology score. Also, some samples in the group of animals treated with a nephrotoxicant but with histopathology scores of zero have elevated Kim-1 levels. (See color insert for a full color version of this figure.)

cases our markers might be more sensitive than histopathology and explore the effect on marker performance. Focusing on the samples from animals treated with a nephrotoxicant which did not have positive histopathology, we might consider several different possibilities for their true injury status. Previously (Sistare 2010) we have described an analysis which takes the histopathology score as absolute truth as an "inclusion" analysis to distinguish it from an "exclusion" analysis which excludes samples from animals treated with a nephrotoxicant that did not have positive histopathology. Summary performance metrics for the sCr and BUN models, with and without Kim-1, for both the inclusion and exclusion analysis are given in Table 22.5. The results show that Kim-1 adds significant information to the sCr and BUN model for both the inclusion and exclusion analysis. For the LR Chisq, R2, and integrated discrimination improvement (IDI) = mean of the predicted values for the positive samples — mean of the predicted values for the nonpositive samples, the improvement observed with the addition of Kim-1 to the model was greater in the exclusion analysis than in the inclusion analysis. For the concordance probability, C, the improvement with the addition of Kim-1 was 0.071 in the inclusion analysis but slightly smaller (0.060) in the exclusion analysis.

574

BIOMARKERS

TABLE 22.5 Comparison of statistics for analysis methods that handle samples from nephrotoxicant treated non-positive histopathology score animals differently.

Analysis

Model

n

LR Chisq

C

Mean Mean Integrated Predicted Predicted Discrimination Value Value Improvement R2 (POS) (nonPos) (IDI)

Inclusion

sCr+BUN 178 1 14.3 0.908 0.636 sCr+BUN+KIMI 178 174.6 0.979 0.839 Difference 60.3 0.071 0.203

0.739 0.872 0.133

0.199 0.098 -0.101

0.540 0.775 0.234

Exclusion

sCr+BUN 123 88.6 0.940 0.700 sCr+BUN+KIMI 123 157.7 1.000 0.985 69.1 0.060 0.285 Difference

0.848 0.989 0.141

0.255 0.018 -0.237

0.593 0.971 0.378

3 Ordered sCr+BUN 178 100.2 0.825 0.487 Categories sCr+BUN+KIMI 178 188.4 0.915 0.739 Difference 88.1 0.090 0.252

0.799 0.851 0.052

0.607 0.444 -0.163

0.193 0.407 0.215

5 Ordered sCr+BUN 178 175.2 0.834 0.656 Categories sCr+BUN+KIM 1 178 255.2 0.903 0.798 80.0 0.069 0.142 Difference

0.691 0.851 0.160

0.240 0.1 II -0.129

0.451 0.740 0.289

Table of performance statistics for analyses assessing value of information added by urinary Kim I. Analyses differ in the way they handle the histopathology score. Inclusion uses the dichotomous score including all samples, exclusion treats histopathology scores from animals treated with a nephrotoxicant without kidney injury as missing 3 Ordered Categories assigns these animals a score of 0.5. The 5 Ordered Categories analysis is like the 3 Orderd Categories with the a/ category expanded into 1, 2, and z3.The performance statistics are the same as in Table 22.4.

Another alternative is to consider the histopathology grades as ordered categories. In this framework, we might entertain the notion that instead of treating samples from animals treated with a nephrotoxicant that did not have positive histopathology as zeroes (inclusion analysis) or missing (exclusion analysis), we assign them a score between 0 and 1. Although for the analysis we envision, the value of the grade assigned does not matter so long as it is between the positives and non-positives, we will give them a grade of 0.5 for concreteness. In this setup, we can no longer fit a binary logistic regression model, because we now have three categories. Instead, we fit a generalization of the logistic regression model, called an ordinal logistic regression model. This model essentially fits a logistic regression model on the dichotomy that compares {0} with {0.5, 1} and a second regression model that compares {0, 0.5} with {1}. However, the coefficients in the models are constrained so that the score assigned to the markers is the same for both comparisons except for an intercept. In our current Kim-1 example, fitting only the Kim-1 marker the score to compare {0} with {0.5, 1} is 0.05 + 1.17*Kim-l, while the score to compare {0, 0.5} with {l}is -2.90 + 1.17*Kim-l. Note that for this technique we interpret the C index in terms of the concordance probability, since for three categories the AUCROC is not defined. The rows marked "3 Ordered Categories" in Table 22.5 show that this analysis gives convincing

STATISTICAL ISSUES IN BIOMARKER RESEARCH

575

evidence that Kim-1 adds information to sCr and BUN. When Kim-1 is added to the model, the improvement in the concordance probability C is 0.09, and improvement in R2 is 0.252. The IDIs reported are for the comparison of {0, 0.5} with {1}. Once we have moved to the more general ordinal regression model, there is no particular incentive to continue with the positive histopath grades collapsed, so we might as easily fit the model to ordered category grades {0,0.5, 1, 2,2:3} with 4's and 5's folded into the s=3 because of their low prevalence. These results are given in the "5 Ordered Categories" rows of Table 22.5 and support the conclusion that Kim-1 adds kidney toxicity information to sCr and BUN.

Planning Human Clinical Trials The statistical methods utilized in human clinical trials are in many ways similar to those used in animal studies. Distinctions sometimes exist when data that would be available in animal studies, for instance histopathology results are not available in human studies. In addition, as noted earlier there are important distinctions between exploratory data analysis and confirmatory data analysis. Confirmatory trials require a higher level of prospective specification. Among other things, this includes whether one biomarker or several will be used (if several, how they will be combined), values to be used as thresholds, and whether a threshold in terms of absolute value or within subject change will be utilized. These changing methods coincide with the changing objectives as one progresses through the stages of medical test development in Table 22.1. The reason for prospectively specifying analyses is that otherwise estimates will likely be optimistically biased. One way to view how the bias arises is to recall that for practically all statistical estimates of variability we consider the present observations as a sample from a larger population. When we make choices for the analysis model based on the current sample, (e.g., model parameter estimates, which variables to include in a model, transformations, best thresholds), the statistical model becomes optimized for our observed sample. The assessment of biomarker performance using a model that has been optimized to give maximum performance for the current sample is likely to be too optimistic compared to the performance we are likely to observe when the biomarker is used in a new sample. Sometimes this is called bias due to overfitting. When possible, the easiest way to limit or avoid this bias is to prespecify the analysis. When it is not possible to pre-specify a large chunk of the analysis methods, re-sampling methods like the bootstrap and cross-validation can be used to estimate and adjust for the bias associated with the types of choices that can be automated. As we saw above, the AUCROC is very useful in exploratory trials as a global metric to assess sensitivity and specificity across the entire range of thresholds. However, for confirmatory trials we often have sufficient information before the trial to specify a single or small number of thresholds and prefer to directly assess performance in the context of a specific use. For instance, in some situations a dichotomous appraisal or decision is required

576

BIOMARKERS

for each subject. An example would be whether to continue or stop dosing. Or the goal might be to estimate the proportion of the population for whom the drug causes toxicity. Some of the statistical issues that arise in clinical evaluation of biomarkers may be illustrated in the context of the kidney project mentioned earlier. As discussed, several biomarkers were shown to perform well in animal models. Early clinical trials have also shown these urinary biomarkers to be promising. In particular, a recently reported cross-sectional study (Vaidya, et al., 2008)" showed good discriminatory ability of nine urinary biomarkers at distinguishing subjects with diagnosed AKI from normal controls. Two new studies have recently been proposed and discussed within the PSTC. The first study is an observational study in healthy patients and in patients with impaired kidney function. Biomarker levels will be measured in each subject at various time points under fasted and non-fasted conditions over a one-year period. The inclusion criteria are designed to ensure that patients in the study will not be exposed to any nephrotoxic agent. The primary goal of the study is to characterize "normal" within and between subject variability in the biomarkers and to assess whether the mean value or variability of the biomarkers varies by age, gender, or baseline kidney function. The second study is planned to be in a patient population indicated for a diagnostic cardiac catheterization. As a result of the contrast agent given with this procedure, some fraction of the patients will experience contrast induced nephropathy (CIN). Biomarker levels will be measured in patients prior to the procedure and at various times after the procedure. The goal of the study is to assess the diagnostic accuracy of each biomarker in correctly classifying subjects with CIN. CIN is typically defined as a 25% increase from baseline in sCr. Clearly, it is unethical to produce kidney injury in humans for the sole purpose of measuring biomarker performance. In the study mentioned above, we attempt to make use of data from subjects who accept the risk of CIN for the potential benefits of the catheterization procedure. CIN is in some ways the same and in some ways different from nephropathy induced by a pharmaceutical agent. Thus, it is important to think carefully about the extent to which the study population is a good stand in for the intended use population. Note, however, that unless we have data by which to compare the two populations, the extent to which a sample from the CIN population is representative of the intended use population is a matter more for medicine and biology than statistics. As in the preclinical studies, statistical issues can arise in the clinical setting when the reference standard for detecting disease is known to give incorrect results for some non-negligible fraction of patients. Although histopathology would be the ideal reference standard for many toxicity biomarkers, ethics often preclude its measurement in human studies. In the kidney example, the standard way to detect CIN, or more generally AKI, in the clinic is through a rise in serum creatinine (sCr). However, sCr is an imperfect reference standard since it may rise for reasons unrelated to AKI and may also fail to rise when

STATISTICAL ISSUES IN BIOMARKER RESEARCH

577

there has been damage to the kidneys. Although the AKIN criteria have been developed to address these shortcomings, they are still based largely on sCr. The question then naturally arises, how can one show that a new biomarker is "better" than sCr, the reference standard to which it is being compared? One answer is that a biomarker could be better by exhibiting a rise in the presence of kidney injury earlier in time than sCr. Knowledge of possible kidney injury early on could allow some action to be taken to prevent further injury. In studies in which subjects receive multiple doses of a potentially nephrotoxic drug, it is desirable to detect any injury when it is still reversible, that is when if dosing is stopped, the kidney will fully recover. New biomarkers could also be better in a strictly practical manner. For instance, they might be obtained via a urine collection rather than a blood draw or they might be less costly to assay. Because sCr may not rise even when there has been damage to the kidneys, the estimate of specificity calculated from the group of patients in the CIN study who did not exhibit a rise in sCr may be biased. The observational study, described above, provides an alternate population of patients from which to estimate specificity. For a given biomarker, patient population, and threshold, an estimate of specificity can be calculated as the proportion of subjects with biomarker values above (or below) the threshold. The advantage of this estimate of specificity is that the subjects included in the calculation are much more likely to be true "negatives" (i.e., those who do not have AKI) since they were not exposed to a nephrotoxic agent.

Prognostic Biomarkers and O t h e r Topics Most of the discussion on statistical methods above has focused on diagnostic biomarkers. For diagnostic biomarkers, metrics such as sensitivity, specificity, and AUC are most relevant as they summarize the ability of a biomarker to discriminate between those with and without some condition. Additional considerations are necessary when evaluating prognostic biomarkers. Here it is important to assess how well predicted risk agrees with observed proportions, often referred to as "calibration." Because the AUC depends only on the ranks of biomarker values or the ranks of predicted probabilities from a logistic regression model and not on the actual values, it can be insensitive to changes in predicted probabilities (risk). Thus, when assessing whether a new biomarker "adds value" in a prognostic sense to an existing biomarker panel, AUC may not "tell the whole story." When the data are summarized by familiar 2x2 table, like Table 22.2, the negative and positive predictive value are useful in this regard. It is important to keep in mind however that predictive values depend on the prevalence of the condition being detected. Thus, predictive values estimated from a case-control study in which 50% of the subjects have the condition of interest by design do not apply to a prospective study in which the prevalence is expected to be much lower. Because our focus in this chapter is primarily on diagnostic markers, we do not discuss this issue further. Two other topics we only have space to mention are time dependent ROC curves and regression modeling to assess the effect of covariates on diagnostic accuracy measures. Time dependent ROC curve analysis is useful

578

BIOMARKERS

when biomarkers are used to predict who will develop disease in the future. Because the predictive ability of markers often decreases as the length of time between biomarker measurement and manifestation of disease increases, the ROC curve can be estimated as a function of this time lag. Interested readers are referred to Heagerty, et al., 2000. n In many situations, the diagnostic ability of a biomarker may vary between males and females or between the levels of some covariate. Regression models to assess this have been developed and are described in chapters 3 and 6 of Pepe, 2003.

BIASES I N BIOMARKER S T U D I E S It is generally straightforward to calculate estimates of sensitivity and specificity or other accuracy measures from a dataset. Interpretation of these estimates however, depends on how the data were collected. There are many sources of bias which can easily creep into a biomarker trial. Some of the more common potential sources of bias are listed below. Imperfect reference standard—occurs when the reference standard is incorrect for a non-negligible fraction of the population. A good example of this is use of serum creatinine as the gold standard for acute kidney injury. As discussed earlier, subjects with injury to kidney may not exhibit a rise in serum creatinine due to reserve capacity of the kidney, and subjects with no kidney injury may exhibit increased sCr. Verification bias—occurs when there is a non-random selection of patients for the gold standard test. For example, this bias could arise in a study assessing the value of kidney biomarkers, if only the subjects with increased sCr were followed for occurrence of a clinical event such as initiation of dialysis. Spectrum/extrapolation bias—occurs when subjects in the study are not representative of the population in which biomarker will be applied. An example would be if only the most severe cases of kidney injury were included in a case control study. Overdiagnosis bias—occurs when preclinical disease may regress and never become clinical but may still be diagnosed by the biomarker. For example, after undergoing a contrast enhanced CT scan, some patients will exhibit increased sCr, however most of these subjects will recover without any intervention and the increased sCr will be the only sign of kidney injury.

DISCUSSION In this chapter, we have discussed the role of statistics in the discovery, development, and confirmation of biomarkers. We have described some of the broad statistical issues and illustrated some of the challenges they present in a urinary kidney toxicity marker example. We have emphasized the importance of considering the statistical framework and indeed all notions of biomarker performance assessment, within the context of the proposed biomarker use. Although most of the stochastic models applied to biomarker data are based

STATISTICAL ISSUES IN BIOMARKER RESEARCH

579

on random sampling from a population in an idealized way, calculations from such models are often useful. Nonetheless, in our abstraction from the specifics of a study design to a stochastic model we ought to pay particular attention to the sampling population. Differences in assay values between individuals based on gender, age, and species are common. Generalization across such differences usually requires supporting data. In general, we should consider the results from any single trial within the context of other studies in the field. In so doing, we should keep in mind the sobering calculations made by loannidis (loannidis, 2005)13 on why most published research findings are false. Substituting the specific case of biomarker findings for his more general result on research findings, we note that the probability that a biomarker finding is true, given a positive statistically significant result, is a function of the level of statistical significance, the power of the study, the pre-study odds of the biomarker finding being true, and bias. Study power, in turn, is a function of sample size and effect size, so large studies and biomarkers with large effect sizes should command a higher level of acceptance. Conversely, situations in which the markers have low pre-study odds of being true markers, for instance when a number of markers or complex combinations of markers are selected from a large set based solely on performance in a small study, have a higher probability of yielding false positive findings, even when statistical significance is achieved. Lastly, bias is a major factor in the lack of replication of many biomarker results. We have enumerated a few major sources of biases above, however, there are many others that may lead us astray. To convince us that these are not simply theoretical concerns we might point to work in the assessment of genetic risk for acute coronary syndrome (ACS) (Morgan, et al., 2007).14 In a large scale replication study of 85 putative genetic variant risk factors, each of which had been previously associated with ACS, the researchers found that only one variant was nominally significant, and less than 50% of the variants were even marginally more frequent in cases than controls. These results should remind us that our desire and enthusiasm for biomarkers is not cause for unrestrained optimism.

SUMMARY P O I N T S 1. 2. 3. 4. 5.

Statistical analysis can and should play more than a confirmatory role in biomarker research. Analogously to drug development, biomarker development is a process that typically involves multiple studies from preclinical to confirmatory human trials. Statistical methods need to be appropriately matched to the stage of development and the biomarker's intended use. The extent to which a biomarker adds value to existing markers is often more relevant than its standalone performance. Study design parameters, including study population and sampling scheme, must be carefully chosen to avoid biased results.

580

BIOMARKERS

ACKNOWLEDGMENTS The authors would like to acknowledge the help of David Gerhold and Josef Ozer for conduct and oversight of the animal experiments, Frank Sistare, Frank Dieterle, Frederico Goodsaid, and the PSTC for many helpful discussions around biomarker qualification, and Vishal Vaidya and Joe Bonventre for their work with kidney toxicity in general and Kim-1 in particular.

REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

11. 12. 13. 14.

Mallows, C. L. and Tukey, J. W. (T. deOliviera, et al., Eds.). Some Recent Advances in Statistics. New York, NY: Academic Press, Inc.; 1982. Tukey, J. W. Analyzing Data: Sanctification or Detective Work? American Psychologist. 1969;24;83-91. Temple, R. Are Surrogate Markers Adequate to Access Cardiovascular Disease Drugs? JAMA. 1999;282:790-795. Prentice, R. L. Surrogate Endpoints in Clinical Trials: Definitions and Operational Criteria. Statistics in Medicine. 1989;431^140. Pepe, M. S. The Statistical Evaluation of Medical Tests for Classification and Prediction. New York, NY: Oxford University Press; 2003. Sistare R, et al. Establishing Best Practices and Performance Expectations for Qualifying New Safety Biomarkers for Drug Development and Regulatory Decision-Making. Nature Biotechnology. 2010. Vaidya, V. S., Ozer, J. S., and Dieterle, et al. Kidney Injury Molecule-1 Outperforms Traditional Biomarkers of Kidney Injury in a Multisite Preclinical Biomarker Qualification Studies. Nature Biotechnology. 2010. DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. Comparing the Areas Under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. Biometrics. 1988;44:837-845. Harrell, Jr, F. E. Regression Modeling Strategies. New York, NY: Springer; 2001. Pencina, M. J., D'Agostino, R. B., Sr., D'Agostino, R. B., Jr., and Vasan, R. S. Evaluating the Added Predictive Ability of a New Marker: From Area Under the ROC Curve to Reclassification and Beyond. Statistics in Medicine. 2008;27:157-172. Vaidya, V. S., Waikar, S. S., and Ferguson, M. A., et al. Urinary Biomarkers for Sensitive and Specific Detection of Acute Kidney Injury in Humans. Clinical Translational Science. 2008;l(3):200-208. Heagerty, P. J., Lumley, T., and Pepe, M. S. Time-dependent ROC Curves for Censored Survival Data and a Diagnostic Marker. Biometrics. 2000;56(2): 337-344. Ioannidis, J. P. A. Why Most Published Research Findings Are False. PLoS Medicine. 2005;2(8):el24. Morgan, T. M., Krumholz, H. M., and Lifton, R. P., et al,. Nonvalidation of Reported Genetic Risk Factors for Acute Coronary Syndrome in a Large-Scale Replication Study. Journal of the American Medical Associaton. 2007;297: 1551-1561.

CHAPTER

REGULATORY PERSPECTIVE FOR BIOMARKER QUALIFICATION FROM THE U.S. FDA Federico Goodsaid

OVERVIEW Biomarkers are tools to be qualified in close association with individual submissions for drug approvals. The Drug-Diagnostic Co-Development Concept Paper1 described a specific example of this association, where the approval of a drug and a test are closely linked with each other, both in their product concepts and in the timelines for their regulatory approvals. Several examples of biomarkers approved through this co-development process are shown in the table of valid biomarkers in the context of drug labels (Table 23.1).2 Biomarkers in this table include both genetic as well as translational entries. Genetic biomarkers are often integrated in drug development as clinical or nonclinical markers of drug efficacy or safety for the purpose of patient selection in clinical trials, response prediction through stratification or enrichment, or dose optimization. Translational biomarkers have applications similar to those of genetic biomarkers, but may also be useful for response monitoring and as early indicators of toxicity or adverse reactions. Novel approaches are continuously being tested for the successful integration of biomarkers in drug development. Their applications range from early compound selection through post-marketing applications. However, the integration of these novel biomarkers into routine nonclinical and clinical practice and regulatory submissions has often been slow. Hesitation in the application of these tests is often associated with fear, not only about how comprehensiveness of the data supporting these applications, but also about the regulatory interpretation of the context of use for these applications.3 Biomarker tests can 581

BIOMARKERS

582

TABLE 23.1 Excerpt for CYP 2C19 from the Table of Valid Genomic Biomarkers in the Context of Approved Drug Labels.

Biomarker

Examples of other Drugs Reference Associated w/ (PubMed ID) this Biomarker

Label Context Representative Label

Drug

CYP2CI9 Variants

CYP2C19 poor metabolizer status is Clopidogrel associated with diminished response to Clopidogrel.The optimal dose regimen for poor metabolizers has yet to be determined. (Dosage and AdministrationPharmacogenetics) Based on literature data, patients with genetically reduced CYP2C19 function have lower systematic exposure to the active metabolite of clopidogrel and diminished antiplatelet responses and generally exhibit higher cardiovascular event rates following myocardial infraction than do patients with normal CYP2C19 function. (PrecautionsPharmacogenetics) CYP2C19 is involved in the formation of both the active metabolite and the 2-oxo-clopidrogrel intermediate metabolite. Clopidrogrel active metabolite pharmacokinetic and antiplatelet effects as measured by ex vivo platelet aggregation assays, differ according to CYP2C19 genotypes.The prevalence of CYP2C19 alleles that result in intermediate and poor CYP2C19 metabolism differs according to race/ethnicity (Clinical Pharmacological - Pharmacogenetics)

CYP2CI9 Variants with alternate context

CYP2C19 Variants (Poor Metabolism-PM Voriconazole Omeprazole 12867215 and Extensive Metabolizers-EM) with (ml) 11866669 genetic defect leads to change in drug Pantoprazole exposure. "In vivo studies indicated that (m2) CYP2C19 is significantly involved in the Esomeprazole metabolism of voriconazole.This enzyme (m3) exhibits genetic polymorphism. For exdiazepam ample, 15-20% of Asian populations may (m4) be expected to be poor metabolizers. For Nelfinavir Caucasians and blacks, the prevalence of (m5) poor metabolizers is 3-5%. Studies conFlabeprazole ducted in Caucasians and Japanese healthy (m6) subjects have shown that poor metabolizers have, on average, four-fold higher voriconazole exposure (AUC^) than their homozygous extensive metabolizer counterparts. Subjects who are heterozygous extensive metabolizers have, on average, two-fold higher voriconazole exposure than their homozygous extensive metabolizer counterparts."

19636246 19576320 19537521 19463375 19429918 19414633 19106083 19268736 19193675 19108880 19487603 19106084 19414633 18482659

REGULATORY PERSPECTIVE FOR BIOMARKER QUALIFICATION FROM THE FDA 583

TABLE 23.1 Excerpt for CYP 2C19 from the Table of Valid Genomic Biomarkers in the Context of Approved Drug Labels, (continued)

Biomarker

Label Context Representative Label

CYP2CI9 Variants with alternate context (no effect ofVariants)

Examples of Reference other Drugs (PubMed Associated w/ ID) this Biomarker Drug

Prasugrel Metabolic Status In healthy subjects, patients with stable atherosclerosis, and patients with ACS receiving prasugrel, there was no relevant effect of genetic variation in CYP2B6, CYP2C9, or CYP3A5 on the pharmacokinetics of prasugrel's active metabolite or its inhibition of platelet aggregation. (8. Use in Special Populations) There is no relevant effect of genetic variation in CYP2B6, CYP2C9, or CYP3A5 on the pharmacokinetics of 352 prasugrel's active metabolite or its inhibition of platelet aggregation. (12. Clinical Pharmacology-12.5 Pharmacogenomics) Whereas the pharmacokinetics of prasugrel's active metabolite are not known to be affected by genetic variations in CYP2B6, CYP2C9, CYP2C19, or CYP3A5, the pharmacokinetics of clopidogrel's active metabolite are affected by CYP2CI9 genotype, and approximately 30% of Caucasians are reduced-metabolizers (14 clinical studies).

1953752 19429918 19414633 18094219 17900275 17361128

be integrated into drug development when we have a consensus about the context in which we are measuring the biomarker and the evidence supporting this measurement. These levels of consensus need to be reflected in the regulatory review of biomarker data.

REGULATORY PATHS I N BIOMARKER EVALUATION AND QUALIFICATION The path from an exploratory biomarker to a biomarker qualified for a specific application context can be long and unpredictable.4 Application of these biomarkers requires an objective record for their nonclinical or clinical context and supporting qualification evidence. Information from the development of exploratory biomarkers has been shared between the pharmaceutical industry and the FDA through Voluntary exploratory Data Submissions (VXDS).5 Submissions of exploratory biomarker data have allowed reviewers at the FDA to share with scientists in the pharmaceutical industry study designs, sample isolation and storage protocols, technology platforms, analysis algorithms, bio-

584

BIOMARKERS

logical pathway interpretation, and electronic data submission formats. This experience has been valuable in training our reviewers for the analysis and interpretation of biomarker data. VXDS have reached a milestone in 2009 of five years since their initial implementation. Over 40 submissions have been received in this program, which is supported by the Interdisciplinary Pharmacogenomic Review Group (IPRG) at the FDA. The IPRG is an FDA-wide, interdisciplinary group created to ensure high-quality review of these voluntary submissions, while also having the responsibility to ensure proper partitioning of voluntary from non-voluntary data. The VXDS program was launched to discuss exploratory biomarker data sets, addressing a need for informal interaction between sponsors and regulators to evaluate such exploratory data. VXDS submissions have not only helped communicate exploratory biomarker data between pharmaceutical companies and the FDA, but have also had a positive impact on the content of these data in regulatory submissions. As we have approached the fortieth VXDS submission, we have also had increasing numbers of consults for regulatory submissions by the Genomics Group in the Office of Clinical Pharmacology at CDER, indicating that industry is moving from exploratory to formal regulatory submission applications. Approximately two-thirds of these VXDS submissions have focused on clinical study design issues. The remaining submissions include both toxicogenomic as well as other genomic data, including data from prototype pharmacogenetic devices. Clinical submissions have had data associated with multiple types of oncology therapies as well as with Alzheimer's disease, hypertension, hypoglycemia, depression, obesity, and rheumatoid arthritis. Joint VXDS meetings with the European Medicines Agency (EMEA) have helped generate consensus on opportunities and limitations of genomic data in drug development and regulatory review. VXDS submissions have stressed the need for a regulatory path from exploratory biomarkers to biomarkers qualified for a specific context. Such a path is currently being tested at the FDA through a Pilot Process for Biomarker Qualification.6 This Pilot Process is focused on the specific needs of the regulatory environment to ensure scientifically accurate and clinically (or pre-clinically) useful decision making. In the first use of this new joint-agency review process put in place by U.S. and European drug regulators, the FDA and the European Medicines Association (EMEA)7 allow drug companies to submit the results of seven new tests as evidence of nephrotoxicity by new drugs. The qualification of these biomarkers covers at this time voluntary submission of these data for rat studies. On a case-by-case basis, the FDA will also consider possible application of these biomarkers in phase-1 human trials. The tests measure levels of seven key proteins or biomarkers that scientists from the FDA and EMEA believe provide important new safety information about the effects of drugs on the kidney. When reviewing INDs, NDAs, or BLAs, both regulatory agencies will now consider the test results in addition to BUN and creatinine. The development of the new renal toxicity biomarkers was led by the Predictive Safety Testing Consortium (PSTC),8 whose members include scientists

REGULATORY PERSPECTIVE FOR BIOMARKER QUALIFICATION FROM THE FDA

585

from 16 pharmaceutical companies. The PSTC was organized and led by the Critical Path Institute.9 Researchers from Merck and Novartis identified the new biomarkers, tested them to prove their sensitivity, specificity, and positive and negative predictive value, and then shared their findings with the other consortium members for further study. The consortium then submitted applications for their qualification to the FDA and EMEA. This is a unique example of how a group of drug companies can work together to propose and generate qualification data for new safety tests and then present them jointly to the FDA and EMEA for qualification. The FDA and EMEA laid the groundwork for such joint-agency reviews in 2004 with the development of the VXDS framework. The VXDS review served as the baseline model around which to design the Pilot Process for Biomarker Qualification in 2006. A similar Biomarker Qualification Data Submission (BQDS) meeting is held in this Pilot Process to allow an exchange of questions with the sponsor about scientific and clinical information submitted for qualification. This Pilot Process for Biomarker Qualification allowed the PSTC to submit a single application for biomarker qualification to both regulatory agencies, and then to meet jointly with scientists from both agencies to discuss it in detail and to address additional scientific questions posed by the regulators. Each regulatory agency reviewed the application separately and made independent decisions on whether each would allow the new biomarkers to be used. The new biomarkers qualified by FDA and EMEA are KIM-1, Albumin, Total Protein, a2-microglobulin, Cystatin C, Clusterin, and Trefoil Factor-3. Testing for these proteins will help scientists assess whether a drug is likely to cause damage to the kidneys, a toxic side effect of some drugs. At this time, both the FDA and EMEA require drug companies to submit the results of two other tests, called BUN and serum creatinine, to show whether such kidney damage has occurred. The seven new tests may provide important advantages over these two tests. For example, in the rat model, once kidney damage has begun to occur, it takes a week before the two current tests can detect it.10 The new tests are more sensitive and can reveal cellular damage within hours." BUN and serum creatinine show that damage has occurred somewhere in the kidneys, but the new tests can also pinpoint which parts of the kidney have been affected.12 While additional studies are needed, the new biomarkers may one day allow promising drugs to advance into clinical trials that otherwise would have been abandoned, because currently there are no tests available to detect early-onset renal injury. The seven new tests were developed and will be carried out initially in rats, but they were selected because other studies have shown that similar biomarkers are produced in human kidney cells.13 While the FDA and EMEA will consider only data from rat studies initially, the PSTC will begin work to qualify these biomarkers in human studies. If these studies are successful, the PSTC will present a new application seeking acceptance of the human biomarkers over the next two years. The need for an accurate, comprehensive, and efficient process for biomarker qualification is closely linked with our ability to quickly integrate new

586

BIOMARKERS

biomarkers in drug development and regulatory review. The Biomarker Qualification Pilot Process at the FDA is testing the scientific, clinical, and regulatory components for a biomarker qualification process. Experience gained with this pilot process will be useful in the development of a formal regulatory process for biomarker qualification.

EVIDENTIARY RECOMMENDATIONS The most difficult part of this process will be to define incremental contexts of use and the corresponding evidence with which biomarkers may be qualified. An industry goal may very well be qualified biomarkers capable of managing new drugs in the clinic with nonclinical or clinical findings such as those of nephrotoxicity somewhere in their previous developmental stages. In contrast, the goal as far as public health is concerned may be to obtain better biomarkers such as those of nephrotoxicity for routine clinical use, as quickly as the data will allow. Intermediate qualification contexts and data need to be defined so investment in biomarker qualification studies will be both productive for the clinic as well as for the pharmaceutical industry. Initial studies proposed by consortia are unlikely to match a clear context for qualification for a full clinical application of biomarkers. What intermediate contexts for qualification can we define, and what study characteristics can we propose for qualification in these intermediate contexts of use? Several authors14,15 have proposed evidentiary recommendations for biomarker qualification. Unlike the incremental process for biomarker qualification embodied in the Pilot Process for Biomarker Qualification at the FDA,7 papers on evidentiary recommendations often propose all-or-nothing qualification contexts, where if the ultimate goal is a clinical qualification, no intermediate qualification contexts are expected to be defined or qualified. This approach is not only time-consuming, but also not likely to encourage the investment needed to generate data for biomarker qualification. At each stage, whether the context of use for a biomarker is to be in vitro, in a nonclinical animal model, or in the clinic, a company or consortium proposing a qualification will likely seek a quick return on the qualification of a biomarker after data are available to qualify a biomarker in a specific context in drug development. An effective process for biomarker qualification should include incremental application context steps, so that these incremental steps can quickly benefit the drug development process.

HARMONIZATION The application of biomarkers to drug discovery and development has the potential of improving the efficacy and speed of bringing more effective and safer new drugs to market. This requires that biomarkers that may be applicable to such uses be qualified for a specific application context. To achieve this, both the process of qualification and the evidentiary criteria and standards for qualification will need to be described and defined. ICH E1616 is a harmonization effort to define the context, structure, and format of the biomarker quali-

REGULATORY PERSPECTIVE FOR BIOMARKER QUALIFICATION FROM THE FDA

587

fication submission. It is based on the previous experience by the FDA and EMEA regarding biomarker qualification. This harmonization effort does not address the evidentiary requirements for biomarker qualification. The structure, format, and content of a submission of biomarker data for qualification depend on the context in which the biomarker is intended to be used. The first step in drafting a submission for qualification of a biomarker is to determine its context of use, preceding specific decisions on applicable structure and format. The context of use for a biomarker is 1) the general area of biomarker application, 2) the specific applications/implementations, and 3) the critical factors which define where a biomarker is to be used and how the information from measurement of this biomarker is to be integrated in drug development and regulatory review. In order to demonstrate the alignment between proposed context and data, the initial context proposal must be supported by data available at the initial application step or expected to be available throughout the data evaluation process in biomarker qualification. There is a convergent relationship between an initial qualification context and the data supporting it. The initial gap between proposed context and data may need to be filled throughout the qualification process. Initial context proposals, however, should project a significant improvement over currently available biomarkers and/or endpoints. The context of a biomarker drives data requirements to demonstrate its qualification for the intended application. The structure of a submission document ensures that the context and data can be submitted in a package consistent for consortia submitting qualification as well as for reviewers in regulatory agencies evaluating a qualification package. The structure of a qualification submission is independent of the context of this submission, but must also be flexible enough to deal with the specific requirements of each context. On the other hand, the format of data required to qualify a biomarker may vary significantly with the context in which it is to be used. It is therefore only possible to harmonize general regulatory guidelines on data format for biomarker qualification submissions.

SUMMARY P O I N T S 1. 2. 3.

The qualification of novel biomarkers for drug development and regulatory review is made possible by the development and testing of regulatory mechanisms for biomarker qualification. The urgency for getting more and better biomarkers to improve new drug development is clear to the pharmaceutical industry and regulatory agencies. Harmonized processes for biomarker qualification are being actively developed through the ICH harmonization procedures.

REFERENCES 1.

FDA. Drug-Diagnostic Co-Development Concept Paper, http://www.fda.gov/ cder/genomics/pharmacoconceptfn.pdf. Accessed on October 19, 2008.

588

BIOMARKERS 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

12.

13. 14.

15. 16.

FDA. Table of Valid Genomic Biomarkers in the Context of Approved Drug Labels. http://www.fda.gov/cder/genomics/genomic_biomarkers_table.htm. Accessed on October 19, 2008. Wagner, J. A. Strategic Approach to Fit-for-Purpose Biomarkers in Drug Development. Annu. Rev. Pharmacol. Toxicol. 2008;48:631-651. Rifai, N., Gillette, M. A., and Carr, S. A. Protein Biomarker Discovery and Validation: The Long and Uncertain Path to Clinical Utility. Nat. Biotechnol. Aug 2006;24(8):971-983. Goodsaid, F. and Frueh, F. W. Implementing the U.S. FDA Guidance on Pharmacogenomic Data Submissions. Environ. Mol. Mutagen. Jun 2007;48(5): 354-358. Goodsaid, F. and Frueh, F. Process Map Proposal for the Validation of Genomic Biomarkers. Pharmacogenomics. Jul 2006;7(5):773-782. Goodsaid, F. and Frueh, F. Biomarker Qualification Pilot Process at the U.S. Food and Drug Administration. AAPS J. Mar 23, 2007;9(1):E105-108. Goodsaid, F. M., Frueh, F. W., and Mattes, W. Strategic Paths for Biomarker Qualification. Toxicology. Mar 20, 2008;245(3):219-223. Public Consortium Efforts in Toxicogenomics. Methods Mol. Biol. 2008;460: 221-238. Duarte, C. G. and Preuss, H. G. Assessment of Renal Function-glomerular and Tubular. Clin. Lab. Med. 1993;13:33-52. Vaidya, V. S., Ramirez, V, Ichimura, T, Bobadilla, N. A., and Bonventre, J. V. Urinary Kidney Injury Molecule-1: A Sensitive Quantitative Biomarker for Early Detection of Kidney Tubular Injury. Am. J. Physiol. Renal Physiol. Feb 2006;290(2):F517-529. Zhang, J., Brown, R. P., Shaw, M., Vaidya, V S., Zhou, Y., Espandiari, P., Sadrieh, N., Stratmeyer, M., Keenan, J., Kilty, C. G., Bonventre, J. V, and Goering, P. L. Immunolocalization of Kim-1, RPA-1, and RPA-2 in Kidney of Gentamicin-, Mercury-, or Chromium-treated Rats: Relationship to Renal Distributions of iNOS and Nitrotyrosine. Toxicol. Pathol. 2008;36(3):397-409. Dieterle, F, Maurer, E., Suzuki, E., Grenet, O., Cordier, A., and Vonderscher, J. Monitoring Kidney Safety in Drug Development: Emerging Technologies and Their Implications. Curr. Opin. Drug Discov. Devel. Jan 2008;11(1):60—71. Altar, C. A., Amakye, D., Bounos, D., Bloom, J., Clack, G., Dean, R., Devanarayan, V, Fu, D., Furlong, S., Hinman, L., Girman, C , Lathia, C , Lesko, L., Madani, S., Mayne, J., Meyer, J., Raunig, D., Sager, P., Williams, S. A., Wong, P., and Zerba, K. A Prototypical Process for Creating Evidentiary Standards for Biomarkers and Diagnostics. Clin. Pharmacol. Then Feb 2008;83(2):368-371. Wagner, J. A., Williams, S. A., and Webster, C. J. Biomarkers and Surrogate End Points for Fit-for-Purpose Development and Regulatory Evaluation of New Drugs. Clin. Pharmacol. Then Jan 2007;81(1):104-107. International Committee on Harmonization (ICH) E16:Genomic Biomarkers Related to Drug Response: Context, Structure and Format of Qualification Submissions. http://www.ich.org/LOB/media/MEDIA5518.pdf. Accessed on September 2, 2009.

CHAPTER

THE EUROPEAN MEDICINES AGENCY APPROACH Marisa Papaluca Amati and Spins Vamvakas

INTRODUCTION In recent years, the pharmaceutical industry has been confronted with a large number of potential candidates in the early discovery phase that was never observed before, as a result of new manufacturing technologies and the exploitation of genomic and molecular biology knowledge. It is now therefore critical that robust methods for selection of the most appropriate candidates for further clinical development are identified and used. In this context, a key role may be played by new non-clinical and translational biomarkers, which might assist in candidate selection and be further exploited where appropriate all over the life-cycle of a medicinal product. However, it appears that the benefits of biomarker discovery to become tangible requires new models and new study designs in the preclinical and early clinical phases. In fact, an increase in voluntary submission of new toxicology approaches, as well as in scientific advice requests on preclinical studies, has been observed at the European Medicines Agency. In addition, we observed considerable changes in the last two years in the scope of scientific advice for clinical development, with a significant increase of requests (more than 60 procedures) for advice on early phase human studies, whilst traditionally the companies in scientific advice asked questions on the main "phase III trials" that are supposed to provide the confirmatory data for benefit/risk assessment. To support early studies, and keeping in mind the safety of patients first, the European Medicines Agency has published a new key guideline on the first exposure in man, holding a workshop to engage the scientific community and industry. Further work on translational medicine is in the work plan of the CHMP for 2009/2010. 589

590

BIOMARKERS

Another very hot area for biomarkers research concerns the impact they may have on novel models for clinical development, including modelling clinical trials designs. In this respect, taking into account the global connotation of pharmaceutical biomarkers research, the European Medicines Agency has developed close interactions within the bilateral confidentiality arrangements with the U.S. FDA, Japan Ministry of Health Labour and Welfare and PMDA, and Health Canada. Significant progress is also ongoing in the area of genomic biomarkers within the International Conference on Harmonisation of requirements for medicines evaluation (ICH) where regulators and industry scientists elaborated two guidelines of consensus on definitions applicable to pharmacogenomic biomarkers and samples (ICH El5) and on regulatory submissions context and format (ICH El6—currently under public consultation). It is expected, in addition, that the European Medicines Agency will be involved substantially in biomarkers science both with the C-Path and the Innovative Medicines Initiative where biomarkers of safety and efficacy of medicines play an important role. The newly established scientific advice procedure dedicated to biomarker qualification provides a major opportunity and concrete regulatory tool for supporting industry and academia in biomarkers R&D.

EUROPEAN MEDICINES AGENCY A N D BIOMARKERS: BRIEFING M E E T I N G S A N D SCIENTIFIC ADVICE Since 2001 informal platforms have been offered by the European Medicines Agency both to industry and academia for discussing scientific issues relevant to innovative medicines development in the form of briefing meetings with the innovation task force (ITF), (http://www.ema.europa.eu/htms/human/mes/itf. htm). Such free-of-charge briefing meetings focus on innovative therapeutic approaches and facilitate the informal exchange of scientific information and the provision of informal guidance early in the development process. In 2003 the CHMP set up a special group of academia and regulators scientists, the Pharmacogenomics Working Party, with the mandate of discussing scientific and technical information on the emerging topic of genomics and genomic biomarkers with academic and industrial sponsors, in close collaboration with international partners in the field (http://www.ema.europa.eu/htms/human/ mes/emergingtechnologies. htm). It soon became evident that new methodologies were growing rapidly and affecting global drug development. As a result, the joint dialogue with the FDA soon started, culminating in 2006 with the establishment of the joint guiding principles "Processing Joint FDA European Medicines Agency Voluntary Genomic Data Submissions" (VGDSs) within the framework of the Confidentiality Arrangement. The joint FDA/European Medicines Agency briefing meetings are still available and offer an opportunity to prepare thoroughly for formal procedure such as the qualification submission (see below).

T H E EUROPEAN M E D I C I N E S AGENCY APPROACH

591

Questions on key scientific choices during drug development are addressed by the European Medicines Agency in a formal way, expressing consensus at the EU level via the CHMP Scientific Advice procedure as established in the founding Regulation 2003/726/EC. The scientific advice is a prospective advice related to a specific product(s), indication(s), or technology within a product-specific development program (http://www.ema.europa. eu/htms/human/sciadvice/advice.htm). However, since the review of the legislation in 2004 (Regulation726/2004/ EC) a novel legal instrument and opportunity for more in-depth and greater scope of scientific advice became available at the European level. In this context, it has been considered essential to involve the regulators as third party (vis-a-vis of industry and academia) for the independent evaluation of biomarkers suitability for a proposed use in drug development. In order to address such a need, the European Medicines Agency expanded the previously available informal discussions in the context of the joint briefing meetings/Voluntary Genomic Data Submissions (VGDS) into a new scientific regulatory evaluation process leading to the "qualification" of novel development methods such as the biomarkers. The first pilot process of non-product specific biomarkers qualification was based on assessment of the data generated by the PSTC in support of novel acute drug-induced nephrotoxicity biomarkers. This joint FDA/EMA assessment, which saw for the first time in the two agencies the nomination of two "Biomarker Qualification Teams"—one at the FDA and one at the EMA—included nine supplementary data submissions by the PSTC, and was concluded with the two agencies' positions being aligned and concordant on the role of the proposed biomarkers as acceptable for acute drug-induced nephrotoxicity detection in nonclinical context. In view of the potential impact of biomarkers on innovative drug development and for the purpose of formalizing their qualification process (so to give an unequivocal sign of commitment from the agency side in support of novel methods, and a well defined regulatory position), the European Medicines Agency has convened a group to develop a new formal procedure to give sponsors the possibility to discuss with the regulatory experts the development of these new approaches and receive, agreed at the EU level, the scientific opinion of the SAWP and CHMP. This drafting group included the European Medicines Agency executive director, the CHMP and SAWP chairs, and several senior regulators, including the authors of this paper. The resulting procedure, which is published on the European Medicines Agency Web site (http://www.ema.europa.eu/htms/human/sciadvice/biomarkers.htm), is to be considered as a guide only and is in practice adapted to the needs of each particular application on a case-by-case basis. The principles valid across applications are outlined below. We are describing below some examples of biomarkers or candidate biomarkers, which stakeholders have discussed with us in previous years. The level of expression of a certain receptor as parameter for suitability for treatment with a specific receptor antagonist is the most obvious example

592

BIOMARKERS

and the one which has already resulted in several regulatory opinions. New approaches may include qualification of measuring tumour stem cells in peripheral blood cells as early signs of metastasis. Another interesting approach is the Surrogate Threshold Effect methodology: establish a certain level of change of a biomarker that enables the conclusion that there is e.g., a 70-80% probability that it will translate into clinical benefit. Examples could include imaging biomarkers (e.g., FDG-PET), histopathology biomarkers (e.g., Ki67), and blood biomarkers (PSA). In the cardiovascular area examples include imaging methods such as quantitative coronary angiography/measurement of the lumen diameter, and intravascular ultrasound/measurement of both lumen and plaque dimensions for evaluating activity and progression of atherosclerosis, assessing the potential incremental benefit of new therapies. Also, biochemical methods as biomarkers for atherosclerosis are under very intensive research and development currently, such as the C-Reactive Protein (CRP). Numerous examples can also be found in the CNS area. In Alzheimer's disease, candidate biomarkers include structural MRI to measure brain volume, PET to detect amyloid in the brain, tau and phospho-tau protein levels in cerebrospinal fluid to be correlated with cognitive or functional deterioration, especially to diagnose and monitor early disease. For multiple sclerosis, the correlation of MRI with clinical deterioration has been discussed for a number of years, but has not yet been conclusively established. Hence, the problem is not a lack of possibilities from the scientific point of view. It is important though that these scientific ideas are developed and substantiated in a way that can make them useful also from a regulatory point of view.

NEW PROCEDURE FORTHE QUALIFICATION OF NOVEL METHODOLOGIES The European Medicines Agency qualification process is a new, voluntary, scientific pathway leading to either a CHMP opinion or scientific advice on innovative methods or drug development tools, irrespective of specific products (http://www.ema.europa.eu/pdfs/human/sciadvice/7289408en.pdf): • CHMP qualification advice on future protocols and methods for further method development toward qualification, based on evaluation of the scientific rationale and on preliminary data submitted. • CHMP qualification opinion on the acceptability of a specific use of the proposed method (e.g., use of a novel methodology or an imaging method) in a research and development (R&D) context (nonclinical or clinical studies), based on the assessment of submitted data. As the scientific knowledge and the intended use of a new method may change in line with the generation of additional data, the qualification process may encompass an ongoing interaction between the CHMP and the applicant.

T H E EUROPEAN M E D I C I N E S AGENCY APPROACH

593

Prior to final adoption of a qualification opinion, the CHMP evaluation, being open to public consultation of the scientific community, will ensure that CHMP shares information open to an enlarged scientific scrutiny and discussion. The impact of the qualification on the regulatory technical standard also requires that the international dimension of the scientific evaluations is accommodated within the available confidentiality arrangements. The European Medicines Agency envisages a truly broad scope of the new activity. The qualification process addresses innovative drug development methods and tools. It will focus on the use of novel methodologies developed by consortia, networks, public/private partnerships, learned societies, and the pharmaceutical industry for a specific intended use in pharmaceuticals R&D. The existing scientific advice/protocol assistance procedure is prospective advice related to a specific product(s), indication(s), or technology within a development program, and is not affected by the new qualification procedure. It is acknowledged that highly specialized scientific knowledge is needed for each qualification procedure. Therefore, a specialized group appointed by the CHMP for each procedure on a case-by-case basis, named "qualification team," led by a coordinator who is a CHMP and/or SAWP member, is in charge of the preparatory assessment of data and protocols, ensuring that efficient use is made of the resources available in the European experts' network. The procedure applicable to provide this broad scientific advice is based on the existing scientific advice procedure adapted to host the activity of the qualification team and to incorporate international collaboration. In addition, a public consultation step will be implemented to hear the views of the scientific community prior to a final qualification opinion. The timing of the public consultation will be agreed to by the applicant, who will also have the opportunity to remove any confidential information from the document to be published. The operational sustainability of the process will require the levy of appropriate assessment and advice fees. The process will be reviewed after completion of 10 procedures and the European Medicines Agency will consider revising the procedure to adjust to the needs of all participants. Briefing meetings are possible and recommended at an early stage of biomarkers development as a way to promote the preliminary exchange of views on the science underpinning the biomarkers. The sponsor is recommended to seek the new qualification procedure after the briefing meeting. The European Medicines Agency is currently discussing possibilities to better structure briefing meetings, incorporating them in the early stage of the qualification procedure, so to ensure full knowledge transfer. In the event that new scientific information relevant to the qualified novel methodology/ies becomes available after final adoption of the qualification opinion, or if the applicant wishes to, a follow-up procedure can be initiated. The follow-up procedure will follow the same standard time lines as the qualification opinion/qualification advice.

594

BIOMARKERS

C U R R E N T STATUS The first formal qualification procedure addressed predictive biomarkers for drug induced nephrotoxicity in the non-clinical setting. The final conclusions can be found on the European Medicines Agency Web site (http://www.ema. europa.eu/pdfs/human/sciadvice/67971908en.pdf). According to discussions with stakeholders, applicants are going to seek regulatory opinions for other major toxicities in the non-clinical setting such as liver toxicity. Several other qualification procedures in the clinical setting are currently under evaluation or in the pre-submission phase. The authors are available for informal orientation discussions with sponsors.

4-Hydroxynonenal, 180

A

Aberrant glycosylation, 77 Abruption, 343 Acute coronary syndrome, 119 Acute kidney injury, 237, 262 Acute liver injury, 210 Adrenomedullin (ADM), 137 AIDS, 381 rapid progression, 387 Airway, 159 epithelial damage, 162 fibrosis, 165 inflammation, 164 Albumin, 174 Algorithmic models, 16 Alpha fetoprotein, 323 Alpha-1-acid glycoprotein, 286 ALT isoforms, 216 ALT isozymes, 214 ALT protein, 216 Alveolar apoptosis, 167 Alveolar epithelial cells, 161, 166 Alveolar macrophage, 161 Alveolar phospholipidosis, 169 Alzheimer's, 91, 105, 109 environmental exposures, 94

Amniocentesis, 334 Analytic strategies, 50 Anatomy, vascular, 283 Aneuploidies-trisomies, 323 Aneuploidy screening, 334 Angiogenic, 338 Angiogenic proteins, 339 Antibody-antigen reactions, 499 Anorexia, 218 Antibodies, 496 monoclonal, 427, 449 Antibody paratopes, 499 Antibody-based tests, 39 Antigen binding, 499 Antigen-antibody reactions, 499 Antigenic epitopes, 499 Antigens, 426 Apoptosis, 205 Ascorbate, 178 Ascorbic acid, 178 Asthma, 164 Asymmetric dimethyl arginine, 182 ATP, 412

B

B lymphocytes, 496 B- type natriuretic peptide (BNP), 134 595

596 Bacteriophages, 512 Bead-based arrays, 440 Biliary injury, 219 Binding assays, 503 Bio-barcode assays, 459 Biochip array, 447 Biochip, manufacturing, 448 Biofluid proteomes, 32 Biofluids, 27 Bioinformatics, 13 Biological materials, 425 Biomarker dualification data submission, 585 Biomarker: performance metrics, 566 pilot process, 585, 591 qualification, 581 Biomarkers, 1, 16, 126, 138, 157, 209,425,457,519,536, 549,561,569,581 development, 564 discovery, 56, 58 genomic, 582 methods to quantify, 108 prognostic, 577 regulatory perspective, 581 research, 425, 549, 561 sensitivity, 566 specificity, 566 types, 563 Biomonitoring, 523, 532 Blinded design, 553 Blood coagulation, 185 Blood urea nitrogen, 244, 564 Breast cancer, 357 Bronchitis, 164 Bronchoalveolar lavage fluid, 174, 176, 182 Bronchoalveolar lavage, 158, 171 Bronchoconstriction, 165 Bronchoscopy, 171

c

Calcium homeostasis, 208 Calprotectin, 286 Cancer antigen, 129, 365 Cancer, 59, 77, 355, 369, 592

INDEX

biomarkers, 357 breast, 357 growth factors, 362 ovarian, 364 pancreatic, 367 prostate, 362 Cantilever, 470 Capillary endothelial, 166 Capillary endothelium, 162 Carcinoembryonic antigen, 463 Cardiac disease, 127 Cardiac injury, 119 Cardiac stress, 134 Cardiomyopathy, 124 Cardiovascular disease, 61 Careful confirmatory data analysis, 562 Case control study, 550 Caveolin-1, 286 CCR2-64I, 385 CCR5, 284 CD4+, 389 CD40, 130 C-fibers, 161 Chemiluminescent immunoassays, 435, 505 Chemokine polymorphisms, 385 Chemokine receptors, 382 Chemokines, 177, 382, 392 Chlamydia trachomatis, 512 Cholestatic liver injury, 204 Chorionic villi sampling, 334 Chromotography, 50, 52, 225 Chronic disease, 310 Chronic kidney injury, 237 Chronic obstructive pulmonary disease (COPD), 165 CINC-1,289 Circulating endothelial cells, 288 Clark electrode, 407 Clinical study, 549 Clinical trials, 18 human, 575 Clusterin, 253 Cohort study, 550 Collagen, 184 Collisional induced disassociation (CID), 30

597

INDEX

Colorimetry, 504 Complement component 3, 288 Compounds, 282 Computer tomography, 534 Confirmatory data analysis, 562 Congenital disorders, glycosylation (CDG), 75 Connective tissue growth factor, 288 Controlled trial, 551 Conventional assays, 441 Coronary artery disease, 123 Crabtree effect, 406 C-reactive protein (CRP), 127, 289, 592 Creatine kinease-myocardial band, 131 Cross sectional study, 549 Crossover, 553 Crystalline nephropathy, 239 Cumulative risk, 522, 532 Cytokines, 177 Cytoskeleton, 208

D

Data analysis, 12 Data interpretation, 13 Data management, 12 DEGs, 8 Developmental immunotoxicity, 313 Diabetes, gestational, 343 Diagnosis, 2, 10 Differential exposure, immunotoxicity, 314 Disease, 15, 58, 61,79,91,554 prognostication, 556 Disease monitoring, 556 Dog, DIVI, 284 Dopaminergic cells, 96 Drug-induced, 281, 286, 292, 401 Dye-doped, 464

E ECG, 119, 125 Ecitotoxicity, 97 Effector functions, 499

Elastin, 184 Electrochemiluminescence, 445 Electron transport system, 399 Emphysema, 167 Endogenous plasma, 427 Endoglin, 338 Endothelin-1, 289 Environmental chemical, 527 Environmental exposure, 519 Environmental health, 62, 525 Environmental risk, 520 biomarkers, 522 Enzyme immunoassay, 432 Enzyme-linked immunosorbent assay, 431 Epigenetic, 186 Estrogen receptor, 358 European medicines, 589 European Medicines Agency, 591 Exhaled nitric oxide, 181 Experimental studies, 551 Exploratory data analysis, 562 Expression markers, 251 Extracellular domain, 359 Extracellular matrix, 166 Extracellular superoxide dismutase, 179

F F(2)-isoprostanes, 180 Fas, 129 Fatty liver, 204 FDA, 581 Ferritin, 180 Fetal growth, 335 Fibrinogen, 289 Field effect transistor, 473 FloDots, 464 Flow-through assays, 506 Fltl, 337 Fluorescence, 504 Fluorescence in situ hybridization, 534 Fluorescence polarization immunoassay, 434 Fluorescent immunoassays, 433 Fluorescent labeling, 81

598 Fluorochrome-dyed microspheres, 507 Fluorometry, 504 Free radicals, 207

G

Generalized immune activation, 390 Genes, 7 differently expressed, 8 Genomic biomarkers, 582 Genomics, 25 Gestational diabetes, 343 Giant magneto resistance, 458 Giant magnetoresistive, 478 Glomerular filtration, 240 Glutamyl transferase activity, 175 Glutathione, 179 Glutathione-S-transferases, 250 Glycans, 75, 84 therapeutics, 79 Glycome analysis, 80 Glycomics, 75, 84 cancer, 77 immune disorders, 76 Glycoproteins, 497 Glycosylation, 75 congenital disorders, 75 Glycosylation alterations, 78 Goblet cell hyperplasia, 162 Gonadotropin, 224 Granuloma, 167 GRO, 289 GST, 250

H

Haptenized detection probes, 512 Haptenized primers, 511 Haptoglobin, 289 Harmonization, 586 Heart failure, 125 Heart-type fatty acid burning protein (H-FABP), 134 Heavy chains, 497 Heme oxygenase-1, 181 Hemodynamic renal failure, 239 Hepatic regeneration markers, 222

INDEX

Hepatic steatosis, 204 Hepatic toxicity, 402 Hepatocellular leakage enzymes, 221 Hepatocyte cell death, 205 Heterogeneous EIAs, 432 Heterogeneous enzyme immunoassay, 502 Heterogeneous fluorescent immunoassays, 433 Histopathology grades, 574 HIV, 381, 391 dementia, 388 genetic factors, 387 nephropathy, 387 non-opportunistic, 387 viral load, 389 HLA alleles, 386 HLA, heterozygosity, 386 Homogenous enzyme immunoassays, 503 Homogenous fluorescent immunoassays, 433 Human chorionic gonadotropin, 224 Human vasculitides, 285 Hybridoma, 500 Hypercholesterolemia, 123 Hyper responsiveness, 165 Hypertension, 61

I Identification, biomarkers, 3 Idiosyncratic, 203, 205, 410 Immune activation, generalized, 390 Immune disorders, glycomics, 76 Immune dysfunction, 310 Immune markers, 257 Immune system, 307 Immune biomarkers, 308, 312, 316 disease-based approach, 314 in vitro approach, 315 toxicogenomic, 315 Immunoassays, 425

599

INDEX

chemiluminescent, 435, 505 enzyme-labeled antibody, 502 enzyme-labeled antigen, 502 fluorescent, 433 non-specific interactions, 506 Immunochemistry, 426 Immunodiagnostics, 495 Immunoglobulin Al (IgAl), 77 Immunoglobulin supergene, 497 Immunohistochemistry, 83 Immunoradiometric assays, 430 Immunotoxicity, 307, 316 developmental, 313 differential exposure, 314 targets, 309 Induced sputum, 170 Infections, 310 Infectious disease, 61 Inhibin A, 325 Innovation task force, 590 Insulin resistance, 61 Interleukins, 129 Internal reflection fluorescence, 482 International Conference on Harmonisation, 590 Interstitium, 162 Interventional studies, 557 Intrauterine growth restriction, 341 Intrinsic liver injury, 203 In vitro, mitochondrial dysfunction, 401 Iron-binding, 180 Ischemia, 123, 126 Isotope, 414 Isotopologues, 50

Lactic acidosis, 401 Lateral flow, 495, 507, 510, 513 Leakage markers, 250 Lectoferrin, 180 Leukocyte antigens, 386 Lewy bodies, 96 Light chains, 497 Light scattering, 468, 505 Likelihood ratio, 570 Lipid glycosylation, 79 Lipophilic persistent organic pollutants, 524 Liposome, 475 Liver fibrosis, 222 Liver injuries, inflammation markers, 220 Liver injury, 203, 226 biomarkers, 209 cholestatic, 205 drug-induced, 203 immune responses, 206 metabolic idiosyncrasy, 206 underlying inflammation, 207 Liver, cell membrane, 208 Luminescence, 505 Luminescent probes, 464 Luminex, 443 Lung biopsy, 171 Lung disease, environmental, 159 Lung injury, 158, 162, 171, 183 biomarkers, 172, 174 Lung, 157, 177 Lung-cell-specific proteins, 184

K

Macromolecules, 505 Magnetic resonance imaging, 534 Malate dehydrogenase, 212 MAQC, 10, 16 Mass spectrometry (MS), 26, 29, 35, 43, 50, 80, 224 Matrix metalloproteinase, 124, 130, 183,361 Metabolic idiosyncrasy, 206 Metabolic profiles, 59, 62 Metabolic syndrome, 61, 218 Metabolites, 50, 63

Kallikreins, 363 Kidney biomarkers, 244 Kidney injury, 237, 564 diagnosis, 242 Kidney project, 564 Kidney safety, 258

L Labeling techniques, 81 Lactate dehydrogenase activity, 175

M

600

Metallothionein-1, 290 Methods to quantify biomarkers, 108 Methylation, 186 Metabolic profiling, 47 Microarray, 8, 20, 82, 369, 445, 447 tissue, 37 Microfabrication, 457, 483 Mitochondria, 16, 207 Mitochodrial diseases, 411 Mitochondrial dysfunction, 401 animal models, 416 biomarkers, 413 drug-induced, 406 Mitochondrial oxidant stress, 207 Mitochondrial physiology, 403 Mitochondrial respiration, 407 Mitochondrial tomogram, 406 Mitochondrial toxicity, 402 Molecular classification, 10 Molecular epidemiology, 534 Monitoring fragment ions, 40 Monoclonal antibodies, 427, 449 Monocyte chemoattractant protein-1,290 Mucus, 160 hypersecretion, 162 Multi-analyte, 441 Multimarker approach, 138 Multiple affinity removal system (MARS), 31 Multiple arm, 553 Multiplexing, 440 Myeloperoxidase, 131 Myocardial cell injury, 131 Myocardial infarction, 119

N

N-acetyl glucosaminidase activity, 176 Nanoelectromechanical, 470 Nanofluidics channels, 483 Nanomaterials, 482 Nanoparticals, 464, 501 Nanoscale, 457, 483 Nanostructures, 457

INDEX

Nanotag, 480 Nanotubes, 473 Nanowires, 473 Necrosis, 205 Nephrotoxicity, 240 Nested case control study, 550 Neurodegenerative diseases, 91 Neurofibrillary tangles, 93 Neuroscience, 59 Neurotic plaques, 93 Neutrophil gelatinase-associated lipocalin, 290 Neutrophilic inflammation, 167 NMR spectroscopy, 50, 52, 62 Normalization, 53 Novel biomarkers, 102, 185, 244, 334, 382, ,407, 581 N-terminal ProBNP, 134 Nucleic acid, 510

o

Observational studies, 553 Obstetric medicine, 323 One-antibody, 437 Optical detection, 459 Orosomucoid 1, 286 Osteoactivin, 255 Osteopontin, 255, 290 Ovarian cancer, 364 Overlapping critical data analysis, 562 Oxidative stress, 177, 205

P

Pancreatic adenocarcinoma, 367 Parallel two-arm, 553 Paraxonase, 212 Parkinson's disease, 92, 107, 109 environmental factors, 97 markers, 101, 106 Partial hepatectomy study, 57 Particle capture, 506 Pathological discovery, 56 Pharmaceutical industry, 589 Placebo effect, 553 Placental growth factor, 336

601

INDEX

Planar antibody arrays, 436 Planar protein array, 437 Plasma, 177, 182 complement component 3, 288 mass spectroscopy, 534 Plasma protein-A, 324 Plasma viral load, 389 Point-of-care diagnostics, 495 Polarographic, 407 Polyarteritis nodosa, 286 Polybrominated biphenyls, 534 Polybrominated diphenyl ethers, 524,527,531 Polyclonal antibodies, 500 Polycyclic aromatic hydrocarbons, 534 Polymorphisms chemokine, 385 genes, 387, 391 Positron emission tomography, 534 Potentiometric dye, 405 Preclinical species, 285 Preeclampsia, 335, 338 biomarkers, 340 Predictive Safety Testing Consortium, 214 Prefractionation strategy, 32 Pregnancy, 325, 342 Pre-processing, 54 Preterm labor, 342 Primate, DIVI, 285 Profile measurements, 55 Progesterone receptor (PR), 358 Prognosis, 10 Prognostic biomarkers, 577 Prostate cancer, 362 Prostate-specific antigen, 362 Protein, 174 Protein identification, 29 Proteomics, 25 Proviral DNA levels, 390 Pulmonary edema, 166 Pulmonary fibrosis, 167 Pulmonary surfactant, 169 Purine nucleoside phosphorylase, 212

Q

Qualification, 581 Qualification team, 593 Quantum dots, 461

R Radioimmunoassay, 427 Radioisotopes, 429 Raman dyes, 466 Rapid immunoassays, 501 Rat, DIVI, 284 Reactive oxygen species, 219 Receiver-operator characteristic curve, 567 Reference standard, 572 Regulatory path, 583 Regulatory perspective, 581 Respiratory bronchioles, 162 Risk assessment, systems biology, 539 Risk assessment, toxicity pathwaybased, 537

s

S100A9/A8, 286 Sample preparation, 30 Sandwich type assays, 430 SDFl-3'A, 385 Self-organising maps, 61 Separated confirmatory data analysis, 562 Serum creatinine, 564 Serum cystatin C, 245 Serum enzyme biomarkers, 212 Serum proteins, 225 Sexually transmitted infections, 512 Single photon emission computed tomography, 534 Smooth muscle actin, 291 Soluble labels, 502 Spontaneous lesions, 285 Sputum, 174, 177, 182 ST2, 138 Statistical confidence, 38 Statistical issues, 561, 576 Statistical methods, 8

602 Steatohepatitis, 204 Stokes' shifts, 505 Subtractive proteomics, 33 Surface enhanced Raman scattering, 460 Surface enhanced Raman spectroscopy, 467 Surfactant proteins, 182 Surfactant protein abnormalities, 169 Surrogate endpoint, 563 Suspension, 440 Symmetric dimethyl arginine, 182 Symptomatic change, 221 Systems approaches, 539

T

Target pathways, 56 T-cell depletion, 389 Technology, 8, 28, 258 Terminal bronchiolar injuries, 166 Therapeutics, glycans, 79, 84 Thrombosis, 185 Thrombospondin-1, 291 Time-resolved fluorescence, 505 Tissue inhibitor of metalloporteinases-1, 291 Tissue microarray, 37 Tissue, 28, 36, 60, 208 Tissue plasminogen activator, 291 Toxicity, 15, 57 Toxicant-induced, 309 Toxicity pathway, 537 Toxicology, 56, 558 Toxiogenomic, 315 Transcriptomics, 26 Transferrin, 180 Treatment response, 557 Trefoil factors, 257 Trisomy 13, 327 Trisomy 18, 327 prenatal, 332

INDEX

Trisomy 21, 325 prenatal, 328 Troponins, 132 Tubular reabsorption, 240 Tumor necrosis factor (TNF), 129 Tumors, 310

u

Un-blinded design, 553 Unconjugated estriol, 325 Uncontrolled trial, 551 Urinary albumin, 247 Urinary cystatin C, 249 Urinary interleukin 18, 257 Urinary Kim-1,251 Urinary liver-type fatty acidbinding protein, 256 Urinary microglobulin, 248 Urinary NAG, 251 Urinary total protein, 246 Urinary trefoil factor 3, 257 Urokinase plasminogen activator, 359

V

Vascular anatomy, 283 Vascular cell adhesion molecule 1, 291 Vascular endothelial growth factor, 292, 335 receptors, 336 Vascular injury, 281 biomarkers, 287 VEGFR1, 337 Villi sampling, 334 Von Willebrand factor, 292

w

Warburg effect, 59,406

X

Xenobiotics, 205