Principles of Molecular Oncology

Principles of Molecular Oncology

Principles of Molecular Oncology Third Edition

Edited by Miguel H. Bronchud, MD, PhD Hospital General of Granollers, Barcelona, Spain

MaryAnn Foote, PhD MA Foote Associates, Westlake Village, CA

Giuseppe Giaccone, MD, PhD National Cancer Institute, Bethesda, Maryland

Olufunmilayo Olopade, MD University of Chicago Medical Center, Chicago, IL

Paul Workman, PhD CRC Center for Cancer Therapeutics, Surrey, United Kingdom

Foreword by Karen Antman, MD Clinical Dean and Provost, Boston University School of Medicine Boston, MA

© 2008 Humana Press Inc. 999 Riverview Drive, Suite 208 Totowa, New Jersey 07512 humanapress.com

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by anymeans, electronic, mechanical, photocopying, microfilming, recording, or otherwise without written permission from the Publisher. All articles, comments, opinions, conclusions, or recommendations are those of the author(s), and do not necessarily reflect the views of the publisher. Due diligence has been taken by the publishers, editors, and authors of this book to assure the accuracy of the information published and to describe generally accepted practices. The contributors herein have carefully checked to ensure that the drug selections and dosages set forth in this text are accurate and in accord with the standards accepted at the time of publication. Notwithstanding, as new research, changes in government regulations, and knowledge from clinical experience relating to drug therapy and drug reactions constantly occurs, the reader is advised to check the product information provided by the manufacturer of each drug for any change in dosages or for additional warnings and contraindications. This is of utmost importance when the recommended drug herein is a new or infrequently used drug. It is the responsibility of the treating physician to determine dosages and treatment strategies for individual patients. Further it is the responsibility of the health care provider to ascertain the Food and Drug Administration status of each drug or device used in their clinical practice. The publisher, editors, and authors are not responsible for errors or omissions or for any consequences from the application of the information presented in this book and make no warranty, express or implied, with respect to the contents in this publication. Cover design by Nancy K. Fallatt For additional copies, pricing for bulk purchases, and/or information about other Humana titles, contact Humana at the above address or at any of the following numbers: Tel.: 973-256-1699; Fax: 973-256-8341; or visit our website at www.humanapress.com The opinions expressed herein are the views of the authors and may not necessarily reflect the official policy of the National Institute on Drug Abuse or any other parts of the US Department of Health and Human Services. The US Government does not endorse or favor any specific commercial product or company. Trade, proprietary, or company names appearing in this publication are used only because they are considered essential in the context of the studies reported herein. This publication is printed on acid-free paper. ∞ ANSI Z39.48-1984 (American National Standards Institute) Permanence of Paper for Printed Library Materials. Photocopy Authorization Policy: Authorization to photocopy items for internal or personal use, or the internal or personal use of specific clients, is granted byHumana Press Inc., provided that the base fee of US $30.00 per copy, plus US $.30 per page, is paid directly to the CopyrightClearance Center at 222 Rosewood Drive, Danvers, MA 01923. For those organizations that have been granted a photocopy license from the CCC, a separate system of payment has been arranged and is acceptable to Humana Press Inc. The fee code for users of the Transactional Reporting Service is: [978-1-934115-25-1/08 $30.00]. Printed in the United States of America. 10 9 8 7 6 5 4 3 2 1

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Foreword

The global shared vision for oncology is prediction and prevention of cancer rather than the paradigm of the past, diagnosis and treatment. Examples available today include vaccines for hepatitis and human papilloma virus to prevent hepatoma and cervical cancer respectively, avoidance of cervical cancer by removal of premalignant lesions found on Pap smear or colonoscopy and polypectomy for prevention of colon cancer. In the future, the use of genome wide scans to determine risks, followed by interventions to prevent or at least delay the emergence of disease provides a model not only for cancer but also for other life-threatening diseases. Many new molecularly targeted diagnostics and therapeutics described in this text, developed based on the rapid growth in our understanding of the molecular basis of cancer, already substantially improve survival of patients with previously lethal malignancies, and also improve quality of life because of fewer toxicities. Clearly research in academia and in the pharmaceutical industry is likely to continue to identify “drugable” targets and construct new diagnostics and therapeutics. Balancing this optimistic vision for the future control of cancer is the complexity of the cancer problem—both the cancer itself at the molecular level, but also globally at the social and political level. The public perceives cancer as a single disease and desires “a cure for cancer.” Actually, hundreds of malignancies have been identified by traditional diagnostic methods. Even within common cancer categories such as breast or colon cancer, however, subclassifications (and now molecular subclassifications) exist. Whether each patient’s cancer will ultimately prove to be unique, or whether cancers will fall into a reasonable number of groups for which treatment can be targeted based on affected molecular pathways is as yet unknown.

Past as Prologue Enthusiasts have compared current “targeted” therapies with “empiric” therapies of the past. Certainly the relative value of hypothesis generated research versus observations and empiricism has been debated. Each has provided major advances.

(Data mining of genome-wide scans is a current example of the value of observational studies compared to hypothesis driven research.) Observations of breast cancer responses to oophorectomy (Beatson, Lancet. 1896;2:104–107) and of responses of prostate cancer to estrogens (Huggins and Hodges, Cancer Res. 1941;1, 203) established systemic hormonal therapies of today. The use of alkylating agents in lymphoma derived from observations of lymphopenia in soldiers after accidental exposure to sulfur mustards stockpiled for use in World War II. Nevertheless, the characteristic of the cancer research of the past as exclusively “empiric” is inaccurate. The development of systemic treatment for cancer over the second half of the 20th century is a tribute to the creativity of cancer investigators and their application of the science of their time. Once the antitumor effect of nitrogen mustard was recognized, medicinal chemists constructed cyclophosphamide as a prodrug intended to be metabolized within tumor cells and created l phenylalanine mustard to target the production of melanin in melanoma cells. Both drugs proved effective in cancer despite metabolism and mechanisms in vitro that turned out to be different from those planned. Molecularly targeted drugs similarly do not always perform as planned. The quintessential example of targeted therapy, imatinib (Gleevec) was developed to target the PDGF (Platelet-derived Growth Factor) receptor tyrosine kinase but clinically proved most useful in CML (Chronic Myeloid Leukemia) (with translocations involving c-abl) and gastrointestinal stromal tumors (GIST, with mutated c-kit). Natural products, the class of drugs that were developed empirically based on screening have, in fact, proved effective in the treatment of many common malignancies. Both targeted hypothesis-driven drug development and empiric observations are likely to provide important leads in the future.

Essential Collaborators Despite progress in our understanding of carcinogenesis and the targets for its treatment, cancer remains a major public health problem globally in developed and developing nations. Patients v

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present with lesions comprising more than 1010 tumors cells. Sections of the tumor or metastases do not necessarily have identical molecular signatures. Clearly the wish for the simplicity of a single magic bullet targeting a single pathway seems unrealistic today based on our emerging knowledge of the regulatory intranet of the cell and the need for disruption of multiple pathways for a cancer to emerge. Combinations of systemic agents can target multiple components of the regulatory system of the cancer cell but also decrease the chance of emergence of resistant tumor clones. Avoiding the emergence of resistance provides the rational for combinations for both cancers and of infectious diseases such as TB or AIDS. To date combinations are generally required for the cure of malignancies.

Foreword

Solving the cancer problem will require collaborations of scientists both in academia and industry, individuals in government (research dollars, health policy and regulatory risk reduction), and public health as well as clinical oncologists. New effective anticancer agents will have to be affordable for both developed and developing countries. Cancer incidence and mortality are falling in many developed countries but rising in developing countries. Progress has been significant but considerable challenges remain. Karen Antman, MD Clinical Dean and Provost Boston University School of Medicine

Foreword to the Second Edition

The second edition of Principles of Molecular Oncology is published 200 years after the exposition of Dalton’s atomic theory of matter and 50 years after Watson and Crick described the basic structure of DNA. This edition comes less than four years after the first and is a consequence of the pace of discovery in such an exciting field of research. In the first edition it was anticipated that the publication of the sequenced human genome would appear in the middle of the first decade of the 21st century. It was published in 2001 and already sequenced genomes for several viruses, bacteria, plants, and animals are available. In 1844 Darwin wrote to a friend, “at last gleams of light have come and I am almost convinced (quite contrary to the opinion I started with) that species are not (it is like confessing a murder) immutable”. His The Origin of Species issued in 1859 provided evidence for the evolutionary theory of life and represented one of the most important discoveries in biology. The controversy surrounding Darwin’s theory resulted in the famous debate between Bishop Wilberforce and Thomas Huxley. When Wilberforce finished his long tirade against the theory, Huxley replied tersely “I have come here in the cause of science only” and went on to demolish the Bishop’s argument. The two men had very different backgrounds in education. Scientific method has continued to be the cornerstone in the study of life and human disease. The discovery of the structure and chemistry of DNA and the subsequent genetic research by many scientists have led to a much better understanding of the mechanisms of human biology and evolution and of the function of genes. The last 50 years has been a golden era in this important field with enormous consequences for applied medicine. Darwin of course knew nothing of genes; the processes he described were those of trial and error taking place over a vast time scale. Recent discoveries in human genetics have not been without controversy, but clinical research has benefited from the move away from trial and error to a more rational approach in the development of new patient management techniques for many medical conditions. The techniques involved are being applied in the study of human cancer and the molecu-

lar discoveries relating to the diagnosis, prevention, early detection, and new treatments are the subject of this book. Progress in the field of molecular oncology has been much faster than previously imagined because of the abundance of innovative technology. High throughput technology for gene sequencing and expression, including comparative genomic hybridization, proteomics, and proteoglycan research, has already allowed the study of biologic function using sequenced DNA, RNA, protein, and oligosaccharide molecules. We are already awash with data and the new subject of bioinformatics has been developed to bring some order to the problem. Poincaré, the famous French mathematician, knew from the work of Newton that the behavior of 2 bodies acting in a gravitational field could be explained with reasonable accuracy using simple mathematics but the behavior of 3 bodies was much more difficult to describe. He spent an important part of his working life on this problem and his eventual model was inaccurate. Understanding the function of genes is the key to the rational development of new treatments, but though some cancers are the result of an altered function of a single dominant gene, many arise from a more complex interaction between genes. New mathematics is being developed to help understand the complexity of these biologic systems. In spite of the complexity, important information has been provided using molecular techniques, allowing substantial improvement in management of patients with cancer. Improvements have included the identification of predisposition to some forms of cancer, more accurate diagnostic and prognostic information, new markers for analyzing tumor progression, a quantified assessment of minimal residual disease, and the rational development of new treatments and methods of prevention. Information on all these aspects of cancer care has been updated in this new edition. It is gratifying to see that a collaborative approach between scientists in many fields is being rewarded by so much progress in the field of human cancer care. As an undergraduate at Cambridge in the1950s, I had the advantage of contact with Crick, Brenner, vii

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Foreword to the Second Edition

Sanger, and Perutz, each of whom provided some insight into what was to come. Although since this time progress has been logarithmic, there is a great deal that remains a challenge for future editions of this book. Principles of Molecular Oncology provides valuable information for the continuing education of all oncologists. Derek Crowther, PhD, MB BChir, FRCP, FRCR Emeritus Professor of Medical Oncology University of Manchester and Christie Cancer Centre

References Charles Darwin “Recapitulation and Conclusion,” from The Origin of Species (1859): Appleton-Century-Crofts Inc. John Dewey “The Influence of Darwinism on Philosophy,” from The Influence of Darwinism on Philosophy and Other Essays (1910), reprinted from the Popular Science Monthly (July 1909), Henry Holt & Co.

Foreword to the First Edition

At the midpoint of the 20th century, our knowledge of cancer was based on epidemiology and pathology, and treatment consisted of surgery and radiation therapy. At mid-century, Medawar and colleagues initiated the understanding of transplantation immunology, Farber described the first use of an antifolic drug to treat leukemia, and Jacobson and coworkers described the irradiation-protection effect of spleen cells. These observations opened the door to the development of chemotherapy and transplantation in the treatment of cancer. Despite the rapid development of these new disciplines, progress was usually based on empiric observations and clinical trials. The rapid advances in molecular biology at the end of the 20th century mark a new era in our knowledge of cancer. Molecular immunology, molecular genetics, molecular pharmacology, and the Human Genome Project are in the process of providing a level of understanding of cancer undreamed of

in the past. Optimism is based on the firm belief that understanding at the molecular level will lead to better and earlier diagnosis, to new forms of treatment, and, most importantly, eventually to prevention ofmany types of cancer. Principles of Molecular Oncology provides a bold new look at the evolution of our knowledge of cancer. Authors from many disciplines are bringing together the facets that provide a comprehensive view of the whole. In a field progressing as rapidly as the understanding of cancer at the molecular level, any book must be regarded as a report of work in progress. The reader will enjoy the opportunity to pause and look at the whole field as it stands today. This book will prove both informative and intellectually satisfying. E. Donnall Thomas, MD Fred Hutchinson Cancer Research Center Nobel Laureate in Medicine/Physiology, 1990

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Foreword to the First Edition

A famous London surgeon is quoted as saying that a cure for cancer would not bediscovered by people in white coats working in laboratories, but rather by somebody leaning over a fence watching workmen digging a hole in the ground. Indeed, the ideathat malignant disease might have a single cause was rife until quite recently. But untilthe era of molecular biology, and the remarkable insights into cell biology that followed,the cancer field was in the doldrums. Viruses as the cause of human cancer had come and gone, chemical carcinogens and exposure to ionizing radiation seemed to be unlikelycauses of the bulk of human cancers, and it was not at all clear where to turn in cancer research. However, in the 1960s, two fields of investigation started to yield results that at least held some promise. Epidemiological studies showed quite unequivocally that there is a relationship between the development of certain cancers and cigaret smoking. And at least some forms of leukemia appeared to be associated with specificchromosomal changes. However, until the advent of recombinant DNA technology, there was no indication as to how these observations might be connected or about the cellularmechanisms of malignant transformation. When historians of science look back on the close of the 20th century and try toevaluate the fruits of the application of molecular and cell biology to the study of human disease, it is likely that they will pinpoint the better understanding of the biology of cancer as one of the highlights of this period. The discovery of oncogenes, together with improvements in cytogenetics, resulted in an amalgamation of these two fields of research and led to the dawning of an understanding of how cancers might result from the breakdown of normal cellular homeostatic mechanisms. Subsequently, the elucidation of the genetic control of the cell cycle, and how certain oncogenes monitor different aspects of cellular activity, allowing cells to go into cycle or directing them toward apoptosis, has started to provide some insights into the cellular mechanisms of malignant disease. Almost overnight, cancer has become less mysterious. It is clear that in many cases it results from the acquisi-

tion of mutations in one or more oncogenes that we acquire during our lifetime. Since at least some of these may result from specific chromosomal changes, or from the action of environmental carcinogens, these observations provide an elegant synthesis of several different fields of research. So although the final details of how a cell becomes cancerous still remain to be worked out, at last we have a blueprint of where to go in the future. Although it is true to say that the clinical impact of the remarkable advances in molecular medicine of the last few years may still be some time in the future, and that their immediate benefits have been oversold to the public, there seems little doubt that these new discoveries will play a major role in the cancer field in the future. The molecular approach is likely to provide a wide range of extremely valuable diagnostic agents for both the early recognition and assessment of the prognosis of different forms of cancer. It also seems likely that gene therapy, something that has been “just around the corner” for far too long, will find some of its early applications in cancer treatment. Thus, although molecular biology has shown us that cancer is an extremely complex disease, and that there are multiple routes to the neoplastic phenotype, there is little doubt that much of this work will find application in the clinic in the not too distant future. All these aspects of this complex and rapidly moving field are covered in this excellent book, Principles of Molecular Oncology. Clinical oncologists will find a series of balanced reviews of the current state-of-the-art of the diagnosis and treatment of cancer based on molecular technology, and, since cancer touches almost every field of clinical practice, specialists in other disciplines will find a very lucid and readable account of what is happening in one of the genuine success stories of today’s molecular medicine. Writing a foreword for a book for one of one’s former students, while a constant reminder of the closeness of personal dissolution, is still an enormous pleasure. If nothing else, it is reassuring to see that at least a few resistant xi

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human lines can survive all the potential damage of medical education and emerge relatively unscathed. I wish the editors and the excellent team of authors that they have brought together all the success with this book that it deserves. In a field that is moving so rapidly it is vital to have a bird’s eye view of the state of the art: I am sure that readers will

Foreword to the First Edition

obtain a balanced view of the potential and limitations of this exciting field. Professor Sir David J. Weatherall, MD, FRS Regius Professor of Medicine University of Oxford

Preface

The last 30 years or so has seen rapid growth in our understanding of the molecular basis of cancer. Many oncogenes and tumor suppressor genes have been discovered. The function of their protein products has been defined and they have been placed on signaling pathways and networks that are hijacked in cancer in ways that we can now comprehend. The three editions of Principles of Molecular Oncology have appeared during a period of unprecedented definition of the nature of molecular causation of cancer and the most rapid rational exploitation of this knowledge for the design of molecularly targeted cancer therapeutics. Although still incomplete, we have assembled a considerable “parts list” for normal and malignant cells. The current and future challenge is to complete this parts list and to assemble the components into a model of the whole that is intellectually satisfying and also allows robust and accurate predications of how biological systems respond to perturbation, including pinpointing the best therapeutic approaches. Of course, the sequencing of the human genome has made a major impact on the completion of the parts list. Technologies for gene resequencing and gene- and protein-expression profiling provide opportunities for genome-wide and proteome-wide searching. At the time of this writing, a debate rages, particularly in the United States, concerning the value for money of “big science,” “cancer genome anatomy” projects—which contribute further to the parts list and provide the tools for hypothesis generation—as distinct from individual investigator-led, hypothesis-driven “small science” that has been the traditional mechanism for basic research. This debate is healthy and is understandable in what is inevitably a cash-limited setting. However, in my view both approaches are important and are mutually beneficial. It is already almost impossible to imagine doing biomedical science without the human genome sequence. Similarly, accumulating the corresponding cancer genome sequences and other related information such as gene-expression patterns in cancer cells will make the ultimate understanding of the molecular basis of cancer much less difficult. At the same time, creative ideas generated by individual researchers remain crucial. A great

example of the mutual benefit of the two approaches is provided by the discovery of mutant B-RAF as an oncogene by high-throughput kinase mutation analysis and the subsequent functional and structural characterization, leading rapidly to drug discovery initiatives. The progression from gene to drug, along with the identification of the necessary biomarkers for diagnosis and prognosis and also pharmacodynamic endpoints for proof of concept, can now be accelerated by an array of powerful technologies [1]. These include RNA interference, high-throughput compound screening, chemical biology, and structural biology to name but a few. Over the last 5–10 years, translational cancer research has accelerated tremendously, particularly in area of targeted molecular therapeutics. The successes with trastuzumab, imatinib, gefitinib, erlotinib, bevacizumab, and others have clearly exemplified the ability to exploit our knowledge of the molecular biology of cancer to produce drugs that have a real impact on patients’ lives. This 3rd edition of the Principles of Molecular Oncology illustrates how far we have come in a short space of time. On the other hand, this edition also highlights the things that we need to do to move forward towards the goal of personalized medicine. Some have criticized the inevitable hype around the publication of the human genome sequence because of the somewhat inappropriate prediction that personalized medicines would simply fall into our hands. However, it is very clear that cancer is the therapeutic area in which individualized therapies based on genomic information on the particular patient concerned will be forthcoming over the next 5–10 years. To achieve this we need to complete the parts list and to understand the systems biology of cancer. Characterization of the multiple genes and proteins contributing to each individual cancer will lead to the potential for mathematical modeling of the responses of normal and pathologic systems when perturbed by cancer genes and therapeutic agents. Improved biomarkers will need to be developed. Ideally these will be minimally invasive markers, and the prospects for the use of molecular imaging are clearly very bright. The multiple drivers of malignant progression, together with the plasticity of cancer genomes that predispose to the development of therapeutic xiii

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resistance will necessitate the development of combinatorial treatments. We can now clearly envisage the scenario in which, at some finite time in the future, a cancer patient will undergo a set of analyses—perhaps involving a whole genome scan—and the individualized therapy will be selected on that basis. The cancer treatment will no longer be based on anatomical location and the appearance of the tumor under the light microscope, but will be directly linked to molecular causation. We will look forward to the rapid progress towards this ambitious scenario and reading about this in future editions of Principles of Molecular Oncology.

Preface

I would like to thank my co-editors, Miguel Bronchud, MaryAnn Foote, Giuseppe Giaccone, and Olufunmilayo Olopade. Particular thanks go Miguel for his tireless leadership of the project. Paul Workman, PhD

Reference 1. Collins I, Workman P. New approaches to molecular cancer therapeutics. Nature Chem Biol 2006;12:689–700

Contents

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Karen Antman Foreword to the Second Edition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Derek Crowther Foreword to the First Edition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Donall Thomas Foreword to the First Edition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sir David J. Weatherall Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Color Plates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 1

Selecting the Right Targets for Cancer Therapy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miguel H. Bronchud

Chapter 2

Clinical Importance of Prognostic Factors: Moving from Scientifically Interesting to Clinically Useful . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Lynn Henry and Daniel F. Hayes

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Chapter 3

Genetic Markers in Sporadic Tumors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elena Tamborini, Federica Perrone, Milo Frattini, Tiziana Negri, Antonella Aiello, Annunziata Gloghini, Antonino Carbone, Silvana Pilotti, and Marco A. Pierotti

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Chapter 4

Genetic Markers in Breast Tumors with Hereditary Predisposition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tatyana A. Grushko and Olufunmilayo I. Olopade

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Chapter 5

Circulating Tumor Markers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alan Horwich and Gill Ross

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Chapter 6

Antibody-Based Proteomics Analysis of Tumor Cell Signaling Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . Steven Pelech and Hong Zhang

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Chapter 7

Gene Expression Arrays for Pathway Analysis in Cancer Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiang Sean Yu, Raymond K. Blanchard, Yexun Wang, and Min You

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Chapter 8

Signaling Pathways in Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daniel Kalderon

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Chapter 9

Estrogen Receptor Pathways and Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jing Peng and V. Craig Jordan

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Contents

Chapter 10 Cyclin-Dependent Kinases and Their Regulators as Potential Targets for Anticancer Therapeutics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marcos Malumbres

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Chapter 11 Angiogenesis Switch Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jaume Piulats and Francesc Mitjans

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Chapter 12 Apoptosis Pathways and New Anticancer Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Frank A.E. Kruyt, Jose A. Rodriguez, and Giuseppe Giaccone

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Chapter 13 Genomic Instability, DNA Repair Pathways and Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gabriel Capellá, Josep Balart, and Miguel Angel Peinado

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Chapter 14 Epigenomics and Cancer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Isabel López de Silanes and Manel Esteller

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Chapter 15 Harnessing the Power of Immunity to Battle Cancer: Much Ado about Nothing or All’s Well That Ends Well? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Angelo A. Cardoso

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Chapter 16 Aurora Kinases: A New Target for Anticancer Drug Development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Teresa Macarulla, Francisco Javier Ramos, and Josep Tabernero

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Chapter 17 Emerging Molecular Therapies: Drugs Interfering With Signal Transduction Pathways . . . . . . . . . . . . . . . Alison H.M. Reid, Richard Baird, and Paul Workman

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Chapter 18 Suicide Gene Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Silke Schepelmann, Ion Niculescu-Duvaz, and Caroline J. Springer

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Chapter 19 Genotypes That Predict Toxicity and Genotypes That Predict Efficacy of Anticancer Drugs . . . . . . . . . . . Rosario García-Campelo, Miquel Tarón, Itziar De Aguirre, Pedro Méndez, and Rafael Rosell

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Chapter 20 A Personal Account of the Chemoprevention of Breast Cancer: Possible or Not Possible? . . . . . . . . . . . . . V. Craig Jordan

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Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Trade Names/Generic Names . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors

Karen Antman, MD, Clinical Dean and Provost, Boston University School of Medicine, Boston MA. Past President of ASCO (American Society of Clinical Oncology). Past President of AACR (American Association of Cancer Research) Antonella Aiello, PhD; Molecular Pathology, National Tumor Institute, Milan, Italy Richard Baird, PhD; Cancer Research UK Centre for Cancer Therapeutics, The Institute of Cancer Research and Royal Marsden Hospital, Sutton, Surrey, UK Josep Balart, MD, PhD; Translational Research Laboratory, IDIBELL-Catalan Institute of Oncology; Llobregat Hospital, Barcelona, Spain Raymond K. Blanchard, PhD; SuperArray Bioscience Corporation, Frederick, Maryland, USA Miguel H. Bronchud, MD, PhD; Division of Medical Oncology, Hospital General of Granollers, Barcelona, Spain Rosario Garcia-Campelo, MD, PhD; Juan Canalejo Hospital, A Coruña, Spain Gabriel Capellá, MD, PhD; Translational Research Laboratory, IDIBELL-Catalan Institute of Oncology; Llobregat Hospital, Barcelona, Spain Antonino Carbone, MD; Molecular Pathology, National Tumor Institute, Milan, Italy Angelo A. Cardoso, MD; Indiana University School of Medicine, Division of Hematology/Oncology, and Walther Oncology Center, Cancer Research Institute, Indianapolis, Indiana, USA Derek Crowther, MD, MB BChir, FRCP, FRCR; Emeritus Professor of Medical Oncology, University of Manchester and Christie Cancer Centre, Manchester, UK Itziar De Aguirre, MD; Catalan Institute of Oncology, Badalona, Spain Manel Estellar, PhD; Cancer Epigenetics Laboratory, Spanish National Cancer Centre (CNIO), Madrid, Spain MaryAnn Foote, PhD; MA Foote Associates, Westlake Village, California, USA Milo Frattini, PhD; Laboratory of Molecular Diagnostic Clinical Pathology; Cantonale Institute of Pathology, Locarno, Switzerland Giuseppe Giaccone, MD, PhD; National Cancer Institute, Bethesda, Maryland Annunziata Gloghini, PhD; Diagnostic Immunohistochemistry and Molecular Pathology Unit, Aviano, Italy Tatyana A. Grushko, PhD; Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, Illinois, USA

Daniel F. Hayes, MD; Breast Oncology Program, University of Michigan Comprehensive Cancer Center, Ann Arbor, Michigan, USA N. Lynn Henry, MD, PhD; Division of Hematology/Oncology, University of Michigan Comprehensive Cancer Center, Ann Arbor, Michigan, USA Alan Horwich, MD, PhD, FRCR, FRCP; The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK V. Craig Jordan, OBE, PhD, DSc; Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA Daniel Kalderon, PhD; Department of Biological Sciences, Columbia University, New York, NY, USA Frank A. E. Kruyt, MD; Department of Medical Oncology, VU University Medical Center, Amsterdam, The Netherlands Isabel López de Silanes, PhD; Cancer Epigenetics Laboratory, Spanish National Cancer Centre (CNIO), Madrid, Spain Teresa Macarulla, MD; Medical Oncology Department, Vall d’Hebron University Hospital, Barcelona, Spain Marcos Malumbres, PhD; Cell Division and Cancer Group, Centro Nacional de Investigaciones Oncológicas Madrid, Spain Pedro Mendez, MD; Catalan Institute of Oncology, Badalona, Spain Francesc Mitjans, Tiziana Negri, PhD; Molecular Pathology, National Tumor Institute, Milan, Italy Ion Niculescu-Duvaz, PhD; Institute of Cancer Research, CRC Centre for Cancer Therapeutics, Surrey, UK Olufunmilayo I. Olopade, MD; Section of Hematology/Oncology, Department of Medicine, University of Chicago, Illinois, USA Miguel Angel Peinado, PhD; Molecular Oncology Center, IDIBELL; Llobregat Hospital, Barcelona, Spain Steven Pelech, PhD; Canadian Institute for Health Research, Kinexus Inc, Canada Marco A. Pierotti, PhD; Deparment of Experimental Oncology; National Tumor Institute, Milan, Italy Silvana Pilotti, MD; Molecular Pathology, National Tumor Institute, Milan, Italy Jing Ping, PhD; Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA Jaume Piulats, PhD; Medical Oncology Department, Vall d’Hebron University Hospital, Barcelona, Spain Francisco Javier Ramos, PhD; Medical Oncology Department, Vall d’Hebron University Hospital, Barcelona, Spain Alison H. M. Reid, PhD; Bob Champion Stem Cell Laboratory, The Institute of Cancer Research, Sutton, Surrey, UK xvii

xviii Jose A. Rodriquez, MD; Department of Medical Oncology, MD; VU University Medical Center, Amsterdam, The Netherlands Rafael Rosell, MD; Juan Canalejo Hospital, A Coruña, Spain Gill Ross, MD, PhD, FRCR; The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK Silke Schepelmann, PhD; Institute of Cancer Research, CRC Centre for Cancer Therapeutics, Surrey, UK Caroline J. Springer, PhD; Institute of Cancer Research, CRC Centre for Caner Therapeutics, Surrey, UK Josep Tabernero, MD; Medical Oncology Department, Vall d’Hebron University Hospital, Barcelona, Spain Elena Tamborini, PhD; Molecular Pathology, National Tumor Institute, Milan, Italy Miguel Tarón, PhD; Catalan Institute of Oncology, Barcelona, Spain

Contributors E. Donnall Thomas, MD; Fred Hutchinson Cancer Research Center, Seattle, Washington, USA Yexun Wang, PhD; SuperArray Bioscience Corporation, Frederick, Maryland, USA David J. Weatherall, MD, FRS; Department of Medicine, University of Oxford, Oxford, UK Paul Workman, PhD, FMedSci; Cancer Research UK Centre for Cancer Therapeutics, The Institute of Cancer Research and Royal Marsden Hospital, Sutton, Surrey, UK Min You, PhD; SuperArray Bioscience Corporation, Frederick, Maryland, USA Xiang Sean Yu, PhD; SuperArray Bioscience Corporation, Frederick, Maryland, USA Hong Zhang, PhD; Canadian Institute for Health Research, Kinexus Inc, Canada

Color Plates

Color Plates follow p. 316 Color Plate 1

Fig. 3-1. The p53 pathway. (See discussion on p. 46)

Color Plate 2

Fig. 3-5. PTEN. The PI3K/PTEN/AKT pathway. (See discussion on p. 58)

Color Plate 3

Fig. 7-3. Gene-expression analysis by real time PCR array. The typical gene-expression analysis by PCR array starts with total RNA extraction, then reverse transcription to generate cDNA. The PCR reaction mixture and cDNA are dispensed onto PCR plates containing an array of prevalidated and optimized primer pairs. Using a real time PCR instrument, the amplification plots for all genes are generated and Ct values are compared between different samples. The relative gene expression change can be inferred by the ∆∆Ct methods. (See discussion on p. 139)

Color Plate 4

Fig. 7-4. Array microarray images from different pathway models used to analyze cancer genotype-specific and chemotherapeutic treatment responses. CDDO-Im induced upregulation of genes in both iMycEµ-1 and-2 cells. Shown are images of the cDNA arrays involved in cell cycling (top row), apoptosis (2nd row), stress and toxicity responses (3rd row), and NFκB signaling (bottom row). CDDO-Im treated and untreated samples are presented as array pairs by genotype. Squares indicate the CDDO-Im–induced genes and they are listed in the text box to the right of each array. Underlined gene names indicate genes that changed and are present on multiple arrays. Note that although the induction of some genes is visible by eye, others are not visible at these photographic settings but still detected by image analysis software. Reprinted with permission from S-S Han et al. Molecular Cancer, 2006, 5:22 [53]. (See discussion on p. 149)

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Fig. 10-6. Structural representation of CDK-cyclin interaction. Structural coordenates of CDK2 bound to cyclin A [84] or cyclin E [305] were obtained from the NCBI database and represented using Cn3d. (See discussion on p. 213)

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Fig. 13-1. Main types of DNA repair. Schematic representation of the main types of DNA repair pathways depicting origin of the damage, type of damage produced, and repair pathway involved. (See discussion on p. 270)

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Fig. 14-1. Schematic of the altered epigenetic pattern of cancer cells versus normal cells. An array of nucleosomes is shown where DNA (red line) is wrapped around histone octamers (grey circles). In the normal cell (top), CpG islands at the promoter of tumor-suppressor genes (TSG) are unmethylated (lack of red circles) and histone tails (protruding gray lines) show acetylated histone H3 (AcH3) and H4 (AcH4) and trimethyl-K4 of histone H3 (3mK4 H3), which represents a transcriptionally active environment and the gene will be expressed. In cancer cells (bottom panel), many TSG undergo aberrant hypermethylation (red circles) at their CpG islands and many different elements are recruited: DNA methylation is carried out by DNA methyltransferase proteins (DNMT) that participate in a multiprotein complex that contain histone deacetylases (HDAC) or histone methyltransferases (HMT) or both, and methyl-binding proteins (MBD) can be loaded onto methylated DNA through their interaction with both HDAC and HMT. Histone marks displayed by normal cells are lost and new marks as dimethyl-K9 at histone H3 (2mK9 H3) are gained. All these cooperative interactions are responsible for gene silencing of TSG in cancer cells. (See discussion on p. 283)

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Fig. 17-1. Subway map of cancer pathways. Reprinted by permission from Nature Reviews Cancer, copyright (2002) Macmillan Magazines Ltd. Available online at http://www.nature.com/nrc/journal/v2/n5/weinberg_poster/, with links to seminal papers and NCBI LocusLink entries for each gene product (Hahn WC, Weinberg RA. A subway map for cancer pathways. Nature Rev Cancer 2002;2(5):331–341). (See discussion on p. 317)

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Chapter 1 Selecting the Right Targets for Cancer Therapy Miguel H. Bronchud

1.1

Introduction

Molecular Oncology can be defined as that branch of medical science that looks at cancer from a molecular point of view. For several reasons, outlined in all of the chapters of this book, and globally reviewed in this first and initial chapter, “molecular oncology” represents the “heart of the matter” of cancer, and our best hope for developing more rational and safer new therapies for cancer throughout the multiple stages of cancer development, including its “pre-clinical” natural history, also known as “carcinogenesis.” These new anticancer drugs based on molecular oncology are also called “targeted therapies,” for it is precisely the “molecular targets” relevant to the cancer phenotype that are aimed at. But the impact of molecular oncology is not restricted to the development of new therapies. As I shall briefly review, and as is explained also in other chapters of this book, molecular oncology is helping us to define new methods for primary cancer prevention (for example, effective vaccines against carcinogenic viruses, like Hepatitis B virus and human papilloma viruses), or for secondary cancer prevention (like inhibitors of cyclo-oxygenase 2 (COX-2), to prevent colonic adenomas or carcinomas, or new antiestrogen molecules for breast cancer prevention, or antiandrogens for prostate cancer prevention). It is also allowing powerful new “molecular imaging” methods, that promise to detect and measure some of the key properties of malignant cells “in vivo,” as well as their response to therapy. In this first chapter, we shall try to see “the wood, rather than the individual trees,” even if, in light of present knowledge, it remains difficult to bridge the vast gulfs that open up on closer examination, and that cannot yet be spanned by the most audacious hypothesis. The evolution of most human cancers can be viewed as the operation of Darwinian selection, the processes among competing populations of dividing cells and the sequential accumulation of relevant genetic and epigenetic events. There are different types of tumor markers: (1) genetic markers in both hereditary tumors and nonhereditary tumors;

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

(2) cellular and tissue markers; (3) epigenetic markers, usually in nonhereditary tumors; and (4) circulating cancer markers. Some of these markers are already used routinely in clinical practice (e.g., several circulating cancer markers are useful for the diagnosis, prognosis, and follow-up of some cancer types), others are being investigated as a source of important prognostic information or even as predictors of response to chemotherapy or radiotherapy (e.g., several cellular and tissue markers), and others still are being explored, in the context of genetic counselling, as potentially useful in screening for hereditary cancer predisposition. Some epigenetic markers (see chapter on Epigenomics and Cancer by Manel Esteller and colleagues) promise to be useful in detecting premalignant changes, for example in the bronchial epithelium of heavy smokers. Regulatory pathways involved in the complex regulation of cell growth, differentiation, senescence, and cell death are being gradually understood. Although we are still largely unable to draw schematically precise cell-type-specific regulatory pathways, current knowledge and research efforts will be updated. In contrast, the classical metabolic regulatory pathways have been known for many years. Pathways such as, for example, the “citric acid cycle” (postulated by Krebs in 1937), the central role of ATP in the energy-transfer cycles (postulated mainly by Lipmann in 1939–1941), or the intriguing Mitchell’s hypothesis (1961) to explain the mechanism of oxidative and photosynthetic phosphorylation, to name but a few examples, have been part of biochemistry textbooks for decades. Some 29 years ago, the structure of DNA had already been known for over 2 decades and yet eminent scientists were pessimistic about real therapeutic progress in oncology. This book is a good demonstration that things have changed. Although there is still no treatment for any of the major lethal cancers that is as effective as the antibiotics are for infections, the knowledge that has accumulated on the fine regulatory mechanisms that are deranged in cancer cells is vast and undoubtedly promises new therapeutic insights. The introduction into routine clinical use of selective (though not entirely specific) tyrosine kinase inhibitors, for example for chronic myeloid leukemia or some solid tumors, like gastrointestinal stromal tumors (GIST), nonsmall cell 1

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lung cancer, renal cell carcinomas, and others, and the development of specific monoclonal antibodies for breast cancer or some lymphomas, are good examples. In contrast to the situation some 20 years ago, not only do we know many molecular targets to design new drugs for the chemoprevention or treatment of cancer, but, paradoxically, we have an apparent excess of targets for our current resources of drug development worldwide. The Human Genome Project has completed its first basic human genome map well ahead of schedule, and it is likely to give us further insights and more potential targets. It is now estimated that the human genome contains some 30,000 genes, less than originally thought by most researchers. Because of alternative splicing and other mechanisms these genes can code for up to 300,000 different proteins in human cells. Many of these genes and proteins are well characterized and their functions in various pathways are known. But the real function of the majority of these human genes and proteins remains speculative. In other words, the rate-limiting step in true progress against cancer is the amount of resources we can spend and the optimization and coordination of this huge research process, rather than a shortage or lack of therapeutic targets. Selecting the right targets for cancer therapy can make a big difference. If, for example, we were clever or lucky enough to correctly guess the right targets for the main human cancers, and if large multinational pharmaceutical companies agreed to focus their efforts and enormous resources on these targets, then revolutionary new cancer treatments might become available for clinical testing within 5 to 10 years. But, if we were wrong, or not enough importance was given to this war against cancer, then it might take another 20 or 30 years, or even more. The object of all cancer research is simply to stop cancer from being a major cause of death and human suffering, and it is in this light that all new research must ultimately be judged. In spite of progress there is still ample room for improvement, and many practicing oncologists still believe that new cancer drugs often deliver less than they promise. The problem may not only lie in the drugs themselves, but also in the way they are tested and used. The methodology of clinical development of new targeted drugs, coupled with new molecular imaging tools and a more precise understanding of what these drugs are actually doing in different cancers, will also need to improve if we want to reduce total costs for each new drug, still estimated to be between 600 and 800 million US dollars, and the overall time “from bench to regulatory approval” for clinical use, still around the 10 years mark. For example, it is commonly accepted that targeted drugs are generally very much less harmful and toxic than conventional cytotoxic agents, which probably means less need for phaseI studies (many trials are now directly coupling the phase I to a single phase-I/II study), but also more in-depth phase-II studies, with pharmacodynamics in well-defined populations of cancers, and ideally combination studies with two or more agents to hit the cancer-specific targets in the most efficient

M.H. Bronchud

and intelligent way. These methodological changes will need the support and adaptation of regulatory bodies, as well as new mathematical, imaging, and biochemical tools to assess response. In this initial chapter we review some new prospects in the prevention, early detection, and treatment of cancer, based on four basic truths of oncology: 1. cancer can be prevented; 2. cancer can be diagnosed, and the earlier the diagnosis the higher the chances of curative treatment; 3. cancer can be cured (by local or systemic therapies, or a combination of both), but the impact on mortality of present therapies is often limited; 4. cancer cannot always be cured, and it seems reasonable to predict that even in the year 2040 there will still be many cancers incurable at the time of clinical presentation.

1.2

The Evolution of a Cancer

All cells in an organism are, of course, derived from a single cell, the fertilized egg. At an early stage in development, the embryo consists of three types of cells: ectoderm, mesoderm, and endoderm. Mesoderm, at a very early stage, shows a further development, part of it becoming mesenchyme. Ectoderm gives rise to all the epithelia covering the outer surfaces of the body and extending into the mouth and nasal passages. Practically all the nervous system is also ectodermal. From the same source come the smooth muscle cells of the iris and a number of other structures, such as the anterior lobe of the pituitary gland. The endoderm gives rise to all epithelia lining the alimentary tract, the respiratory passages, and the lower part of the genitourinary passages, in all cases to the point where it meets with cells of ectodermal origin. It also gives rise to the parenchymal epithelial cells of the liver and pancreas. An organ may consist of many different tissues that intermingle with each other more or less intimately. Thus, for example, even a relatively simple tissue such as the liver may contain the following cell types: hepatocytes, fibrous tissue cells, blood cells of various types, blood vessels, bile ducts, nerve fibres, reticuloendothelial cells (Kupffer cells) etc. Modern gene expression profiling methods (e.g., DNA microarrays) are good molecular witnesses of such diversity and complexity of tissues. One can even dare to say that we shall never know exactly what is cancer, until the complexity of embryonic development and the precise mechanisms that regulate gene expression and DNA replication are finally understood. For cancer is, putting it in simple terms, the end-result of what happens when these mechanisms go progressively wrong in the adult. Cancers in the embryo are extremely rare, if they exist at all, although some neoplasms of children (e.g., some acute leukemias) may well originate in utero. In the adult, solid tumors can behave as a “parasitic organism” within an organism. A cancer is alive, obeys its own rules, but ignores most of the rules of the host, continues to grow and spread locally

1. Selecting the Right Targets for Cancer Therapy

or to distant sites, and eventually destroys its host organism, paradoxically destroying itself too.

1.2.1

Relatively Old Views on Carcinogenesis

For a long time the concept of “lineage” has been central to all our ideas about cancer. According to this concept, cancer starts as a local disease, in a given cell clone that, for reasons that remained unclear until some 20 years ago, could multiply faster than normal and displace its neighbors: families of cells can emerge that increase in numbers at the expense of their neighbors. Described in these terms, the evolution of a cancer can be viewed as the result of Darwinian selection among competing populations of dividing cells. The tissues of the body normally preserve their initial fine mosaicism even in old age, indicating that under normal circumstances there is “harmony” in tissues, equilibrium between cell death and cell proliferation, balanced regulatory exchanges between stroma and epithelial cells, and little or no competition between adjacent cells. However, according to this old view that remains widely accepted today, one of the earliest steps in the sequence leading to cancer is the emergence of families that are able to displace their neighbors [1–3]. No doubt this is partly the result of some “intrinsic change” in the cells (and mutations were suspected long before oncogenes and tumor suppressor genes were discovered), that enable them to compete for territory, but the whole process can presumably be accelerated by anything that causes cell death creating an opportunity for competition to occur. As a result, the normal equilibrium is lost and a “tumor” gradually develops. These “intrinsic changes” in the cells could induce cells to multiply without the usual restraints, e.g., they divide more frequently or are subject to less cell loss, but nevertheless keep within their normal territory and do not invade the surrounding tissues, thereby forming benign tumors. Alternatively, if these “intrinsic changes” somehow damage the normal regulatory networks that maintain “territoriality” and the normal cells acquire the ability to spread to alien sites, locally or to distant regions, then the tumors formed are called “cancers” because of their malignant behavior. Therefore, monoclonality is generally considered the hallmark of tumors, but situations exist in which clonality is not unequivocally associated to malignancy. Clonal markers are useful for the diagnosis or follow-up of disease progression for both solid and hematologic tumors. Modern methods of clonality determination include X-chromosome inactivation (in females), immunoglobulin and T-cell receptor gene rearrangement analysis, and specific chromosomal translocations or deletions. Benign conditions (e.g., benign monoclonal gammopathy), and some premalignant conditions (e.g., lymphomatoid granulomatosis, lymphomatoid papulosis, Langerhans cell histiocytosis, lymphoepithelial proliferations associated with Sjögren disease, etc.) may show monoclonal rearrangement without necessarily developing malignancy after prolonged follow-up [4, 5].

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Regarding the nature of these “intrinsic changes” that lead to cancer, several decades ago there was much debate as to whether their nature was “genetic,” secondary to mutations or chromosomal alterations, or “epigenetic,” because of abnormalities in gene expression without an underlying genetic lesion. Thus, although most known human carcinogens were proven to be mutagens, it was postulated that some cancers for which no cause had been discovered could be partly because of agents that were not mutagens, but acted instead by provoking cell division and errors in gene expression.

1.2.2

More Modern Views on Carcinogenesis

However, most of the evidence was clearly in favor of a “genetic” cause. Cancer incidence rises sharply with age and various models had been proposed to account for this [6, 7]. These models shared the view that a cancer cell arises as the end result of a series of steps that have occurred at some time in the life of the patient. They often postulated that each cell has several genes that independently restrain it from forming an ever-expanding family of cells, and that a cancer arises when a cell is created in which each of those genes has been inactivated by a separate, independent mutation. Logically, the probability of any particular one of our cells having a mutation in a particular gene will increase in direct proportion to our age. In 1958, Armitage and Doll [8] had calculated that for some of the common solid tumors the logarithm of cancer incidence should be linearly related to the logarithm of our age, and that, if such an interpretation were literally true, then we could deduce from the slope of the death rate from cancer of the large intestine in relation to age plotted logarithmically, that about six mutations are needed to produce a cancer of the large intestine. This guess is extraordinary if one thinks that more or less the same conclusion was reached, based on molecular genetic knowledge, by Fearon and Vogelstein in their classic work on colorectal tumorigenesis [9]. The “multi-step mutation” theory of cancer was also supported by other lines of epidemiological evidence [10, 11], and by the “initiation and promotion models of carcinogenesis.” A characteristic feature of most forms of carcinogenesis is the long period that elapses between initial application of the carcinogen and the time the first cancers appear. It is necessary to apply coal tar repeteadly to the skin of a mouse for several months before any tumors are detectable. Similarly, most common human cancers can take 3–30 years or more to develop. The chemical carcinogenesis models also helped to identify at least two classes of carcinogenic compounds: the initiators and the promoters. For example, if a group of mice are fed a small amount of the carcinogen dimethylbenzanthracene (DMBA) this produces widespread irreversible alterations (presumably mutations) in the cells of each mouse. Subsequent irritation of the skin by painting it twice a week with croton oil (the “promoter”) eventually results in the local appearance of tumors. These tumors will appear even if croton oil is not

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started until 16 weeks after the DMBA feeding, but no tumors arise if either DMBA or croton oil is given alone or if the order of the treatments is reversed. In several aspects, estrogens (for breast cancer) and testosterone (for prostate cancer) have also been regarded as potential tumor promoters. Other insights into the “genetic” nature of tumorigenesis came from studies on viral carcinogenesis [12–14] and from seminal observations in the uncommon retinal cancers in children [15]. The discovery of tumor oncogenes and tumor suppressor genes almost 20 years ago opened the way to the molecular epidemiology of cancer [16–20]. Thus, it soon became apparent that in general more than one somatic mutational event was needed for malignant transformation. The possible exception being the uncommon hereditary retinoblastomas, already described by the “two-hit model” proposed by Knudson [15]. Then it was also found that certain carcinogens are linked to selective (though not entirely “specific”) mutational events. For example, molecular linkage between exposure to carcinogens and cancer types have been described for p53 mutational spectra of hepatocellular carcinoma, skin cancers, and lung cancer [19]. Fearon and Vogelstein proposed a molecular model for colorectal carcinogenesis in 1990 [9] based on the sequential accumulation of genetic events in key regulatory genes along the sequence adenoma to carcinoma. More recently, in 1997, Kinzler and Vogelstein [21] proposed the concept of two different types of carcinogenic genetic events: those involving “gatekeeper” or “caretaker” genes characterized by their control of net cellular proliferation or maintenance of genomic integrity, respectively. Examples of gatekeeper genes include APC and beta-catenin in colon epithelium, Rb in retinal epithelial cells, NF1 in Schwann cells, and VHL in kidney cells. Thus, it is proposed that an alteration in APC leads to a derangement of the cellular proliferation pathway that is important for maintaining a constant cell population, at least in colonic cells. The identification of other gatekeeper genes is expected, and some may be genes crucial to morphogenetic events of specific tissues. Unlike gatekeeper genes, caretaker genes generally maintain genomic stability and are not involved directly in the initiation of the neoplastic process, but their mutations enhance the probability of mutations in other genes, including those in the gatekeeper class. Because multiple mutations are found in cancer cells, the existence of a “mutator phenotype” was suggested by Loeb in 1991 [22, 23] as an important step in tumor development, and candidate mutator genes are involved in multiple cellular functions needed for maintaining genetic stability, such as DNA repair, DNA replication, chromosomal segregation, cell cycle control, and apoptosis. Finally, some individuals may be predisposed to cancer because of inherited mutations of some key genes that may confer a familial predisposition to cancer. This has attracted considerable attention in recent times, particularly in relationship to breast cancer and colon cancer susceptibility genes [24, 25].

M.H. Bronchud

The genetic alterations in oncogenes generally lead to an increased function of the protein, whereas, in general, tumor suppressor genes are inactivated during carcinogenesis with apparent loss of function of the protein. However, the mechanisms of activation or inactivation are multiple, and the precise consequences on gain or loss of function are incompletely understood. K-ras and H-ras genes are examples of oncogenes preferentially altered by point mutation (codons 12, 13, and 61), generating a protein with constant GTPase activity. The c-myc gene can be activated by chromosomal translocation (in some leukemias) or by gene amplification (in some solid tumors). The p53 and Rb tumor suppressor genes are often knocked out by point mutation in one allele and by deletion (loss of heterozygosity) at the other. Others, like p16, have high rates of homozygous deletions or promoter hypermethylation. Some genetic defects are fairly characteristic for a given tissue type (most colorectal cancers have APC or beta-catenin mutations). But the “same players” are frequently involved in different tumors. Each human cancer can be regarded as a different molecular entity, with a different matrix of molecular targets, and it evolves with time (even as a result of systemic or local therapies) [26].

1.2.3 Cancers are Monoclonal, but the Carcinogenesis Process is Probably Polyclonal The recent progress in the physical mapping of the Human Genome, has already reached the “Postgenome Era.” Automated or semi-automated devices capable of reading thousands of genes are already available (see chapter by Sean Yu), and immuhistochemistry is also able to map regulatory pathways (see chapter by Steven Pelech). Genomics and proteomics are here to stay [27], but their routine use in the clinic obviously require proof of efficacy and judicious use. Cancer is a state, but carcinogenesis is a process. Key to the multistep genetic nature of cancer is that carcinogenesis is “progressive.” In most epithelial tissues, progression means the sequential accumulation of somatic mutations. In some cases of familial predisposition to cancer some of these mutations are inherited. Gradually, a given target tissue experiences a transition from normal histology, to proliferative and/or dysplastic changes, to so-called “intraepithelial neoplasia” (IEN), which can be early or severe, to superficial cancers (in situ), and finally to invasive disease. In some instances, the process may be aggressive and relatively rapid (e.g., in the presence of a DNA repair-deficient genotype or an aggressive human papilloma virus), but in general these changes occur over a long period of time. In the breast, for example, it is estimated that progression from atypical hyperplasia through ductal carcinoma in situ (DCIS) to adenocarcinoma may require 30 years or more [28, 29]. Similar observations have been made in other tissues, such as lung, head and neck, prostate, bladder, and colorectal tissue [30–34]. However it would be a mistake to believe that all common epithelial human cancers follow these clear-cut histological

1. Selecting the Right Targets for Cancer Therapy

sequential patterns from adenoma to carcinoma. In fact, only a minority of the commonest type of breast cancers (infiltrating ductal carcinomas, or IDC) arise from ductal carcinoma in situ. Thus, the molecular changes leading to IDC, that account for almost 70% of all breast cancers, can happen before the histological features associated with DCIS become evident. DCIS is characterized by a proliferation of malignant epithelial cells confined to the mammary ducts without light microscopic evidence of invasion through the basement membrane into the surrounding stroma; but IDC, by definition, show signs of invasion of stromal tissue, often with vascular and/or lymphatic vessel involvement. In other words, conventional histological and radiological techniques (e.g., bilateral mammograms) are not enough to detect with precision on-going carcinogenic risk in many cases of women at risk. Patients with a head and neck squamous cell carcinoma (HNSCC) often develop multiple (pre)malignant lesions, ranging from leucoplakia to other cancers, which led Slaughter et al. [35] back in 1953 to postulate the concept of “field cancerization.” The incidence rate of second primary tumors following a first diagnosis of HNSCC is 10–35%, depending on both the location of the first primary tumor and the age of the patient. The carcinogens associated with HNSCC (alcohol and tobacco smoking) are thought to induce mucosal changes in the entire upper aerodigestive tract (UADT), causing multiple genetic abnormalities in the whole tissue region. Similar arguments apply also to other tobacco-related cancers, like transitional cell carcinomas of the urogenital tract or bronchogenic carcinomas [36, 37]. An alternative theory for these observations is based on the premise that any transforming event is rare and that the multiple lesions arise because of the widespread migration of transformed cells through the whole UADT [38, 39]. However, most field changes appear to be induced by smoking, supporting the theory of carcinogen-induced field cancerization rather than field cancerization because of migrated transformed cells [40]. Other possible causes of “field carcinogenic events” can involve hormonal factors (e.g., changes in the ovaries, breasts, or prostate), inflammation and hyperemia (increased proliferative and angiogenic activity in chronic cystitis, gastritis, esophagitis, or colitis), chronic viral infections (e.g., Hepatitis B virus for hepatocarcinomas, Epstein-Barr virus for nasopharyngeal carcinomas, or some lymphomas), aberrant methylation linked to ageing, free-radical induced DNA damage (e.g., for cancers of the gastrointestinal tract), skin exposed to ultraviolet irradiation (e.g., actinic keratosis and squamous cell carcinomas), ionizing radiation-induced damage, or aberrant morphogenetic pathways. It is also possible that different carcinogenic pathways operate in different tissue fields belonging to the same organ. For example, adenocarcinomas of the right side of the colon are often associated with different clinical and molecular characteristics than adenocarcinomas of the colorectal region. Even in breast cancer, the reported incidence of multicentric or multifocal lesions in areas away from the primary tumor in

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mastectomy specimens ranges from 9 to 75%, depending on the definition of multicentricity, the extent of tissue sampling and different histological techniques of examination [41–43]. So that multifocality or multicentricity of breast cancers may in fact be a lot more common than currently acknowledged. In this context, the old “field cancerization” theory by Slaughter [35] and the more recent “multi-step carcinogenesis” model by Fearon and Vogelstein [9] can now come together in a single model: sequential field cancerization [44]. If it does require some seven sequential carcinogenic “genetic hits” in a single cellular clone for a malignant tumor to develop, it is mathematically more likely to occur in a tissue with a high background of genetic alterations in neighboring cellular clones, than in a tissue with a low background of such alterations, or with no detectable carcinogenic mutations at all (Figs. 1-1 and 1-2). The probability of a single clone accumulating seven independent but sequential genetic alterations leading to a malignant phenotype, without any similar events occurring in neighboring cells would seem to be rather low. This simple conclusion, and our ability to measure “background carcinogenesis” in different parts of the body, might lead to several unexpected implications. Technology is just beginning to be sufficiently sensitive to start testing the hypothesis. One potential technical problem is that in premalignant tissue, the “signal” (e.g., relevant oncogenetic lesions) might be diluted by the “noise” (normal genome of most of the cells in the tissue), until the premalignant clones have expanded enough to become more numerous locally than normal cells. However, it is only a matter of time before this goal is technically achievable. A possible future objective is the development of a combined histological and molecular staging system (Fig. 2). For example, after a follow-up of 5–10 years, one would expect more new cancers to developn in group IIIc of Fig. 1-2 (dysplastic changes and three or more than three significant mutations identified), than in group Ia (normal histology and no mutations identified). The clinical application of this concept and technology should then help to classify patients into various relative risk groups early on in the development of a malignant disease, allowing a tailor-made program for follow-up and screening, as well as more appropriate therapeutic and chemopreventive interventions [45–47]. For example [48, 49], a suitable combination of relevant biomarkers might help clinicians to identify smokers at high risk of developing lung cancers (approximately 10–15% of frequent smokers). Confrontation with personal cancer risk rather than general statistical risk, is a potent motivation to quit smoking and to undergo more frequent health checks (like high-resolution CT-scans to detect isolated pulmonary nodules). Some smokers might be protected because of genetic polymorphisms of enzymes involved in the molecular activation of precarcinogens present in tobacco, whereas others may be more vulnerable to the carcinogenic effects because of genetic defects in DNA repair enzymes. Some molecular changes associated with ageing and carcinogenesis might be epigenetic (e.g.,

6

M.H. Bronchud Multiclonality of Pre-Malignant Changes

FIELD CARCINOGENESIS

MULTISTEP CARCINOGENESIS

• transitional cell cancers of the urogenital tract • aerodigestive cancers (head and neck, lung, others) • multicentric, multifocal, synchronic, metachronic carcinomas (breast, prostate, colon, skin, others) Atleast 7 sequential oncogenetic “hits”

Multiclonality of premalignant changes

?

Fig. 1-1. Carcinogenesis is a “process” that is likely to be polyclonal and age-dependent. Cancer is a “state.” The word “cancer” has been for decades synonymous with “pernicious evil” in the figurative sense of the word. Partly because, unlike, for example, the infectious diseases, cancer is the “enemy within,” produced by our own body, not only because of environmental agents (external to us like tobacco, asbestos fibers, ionizing radiation, some viruses, or chemical carcinogens), but also because our own cells and our own genes can produce it. See text for explanation.

Fig. 1-2. Breast Ia is considerably less likely to develop a malignant tumor than breast IIIc. See text for explanation.

promoter hypermethylation) rather than genetic [50]. Even some pediatric malignancies might be secondary to abnormal morphogenetic events in utero [51]. It has been estimated that in the USA alone some 30% of people above the age of 60 can be found to have adenomas of the colon by colonoscopy, 70% or more of men above the age of 80 will have IEN of the prostate, 30% of people aged 60 or more have actinic keratosis on their skin, 20% of sexually active women above the age of 40 may have some degree of cervical IEN, at least 40% of heavy smokers can show metaplastic or dysplastic changes in their bronchial mucosa, and some 20% of women with dense mammograms and aged more than 50 may show atypical cells on ductal washings from the

nipple or ultrasound-guided fine-needle aspirates. The use of a battery of genetic or protein biomarkers relevant to each of the main cancer types may soon help us to better define individual cancer risks, and to measure background carcinogenesis in individual tissue samples. Perhaps, not too long from now, oncology units will be devoted to the treatment of carcinogenesis just as much as to the treatment of cancer. For example, DNA microarray-based sequence analysis uses comparative hybridization to obtain information ranging from mutational detection to polymorphism genotyping. Although further technical progress is needed to enable better detection of repeated sequences where changes may not always be distinguishable from wild-type sequences, some

1. Selecting the Right Targets for Cancer Therapy

initial experiments have found microarrays to be more sensitive, more accurate and faster than classical sequencing approaches [52, 53]. To increase the ability to detect genomic imbalance, including gains or losses of nuclei acid material, often associated with the carcinogenic process, like the occurrence of deletions in tumor suppressor genes or amplifications of oncogenes, new techniques are being developed. For example, several groups have adapted array technology to comparative genomic hybridization (CGH), leading to so-called array-CGH techniques. In CGH fluorescent signals along each chromosome are examined and analyzed to provide a cytogenetic pattern of gains and losses. In array-CGH experiments the mapping resolution can reach the kilobase level [54, 55]. Although technical limitations remain a problem, new developments proceed at a reasonably fast pace, for example to detect copy number changes in cancer cells isolated from routine paraffin-embedded tissues suitable for laser-capture micro-dissection samples [56, 57]. Single nucleotide polymorphisms (SNPs) are the most frequent form of DNA polymorphisms in the human genome, with over 1% differences among individuals, allowing the detection of specific genetic fingerprints, and have already been used to detect loss of heterozygosity (LOH) in several human tumor samples [58–60]. Finally, microarray-based gene expression profiling comparisons indicate a panel of up- or downregulated genes that can reveal candidate molecular markers for the disease in question, and help to classify tumors into novel tumor types, not previously known by conventional histological techniques, as well as help to predict clinical outcome and response to therapies [53]. Up- or downregulation of a gene can have dramatic genetic and epigenetic consequences, like the gain or loss of expression of other genes or alterations in the function of several gene-products; which, in turn, can relate to individual properties of cancer cells such as increased mutagenesis, loss of contact inhibition, independence from serum or exogenous growth factors, changes in the adhesion properties of the cell, resistance to conventional chemotherapy and radiotherapy, the capacity to induce angiogenesis and to invade surrounding tissues, and eventually to form metastasis. Gene expression profiling is already allowing the identification of specific molecular fingerprints for any given cancer [61], the identification of organ-related expression of peculiar classes of genes (often with still unknown function) [62], and cluster analysis of gene expression profiles for breast cancers [63–65], ovarian cancers [66], different types of lung cancers [67], soft tissue sarcomas [68], non-Hodgkin lymphomas [69], and prostate cancers [70], among others. Recently, Sabrina Spencer, from the Computational and Systems Biology laboratory at MIT (Massachusetts Institute of Technology in Cambridge, Boston) and other colleagues have published [71] an interesting computational model of carcinogenesis, based on the “hallmarks of cancer,” as defined by Hanahan and Weinberg (2000) [72].

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In the mathematical model proposed by Sabrina Spencer et al. [71], cancer progression is a form of somatic evolution in which certain mutations give a cell a selective proliferation advantage. Evidence strongly supports mutation as one of the dominant factors in setting rate-limiting steps in tumor progression, resulting in variation in the timing of progression between tumors. Tumorigenesis is thought to require four to six stochastic rate-limiting mutation events to occur in the lineage of one cell. Hanahan and Weinberg [72] suggest that six cellular alterations, or hallmarks, collectively drive a population of normal cells to become a cancer. The six hallmarks are: (i) self-sufficiency in growth signals, (ii) insensitivity to antigrowth signals, (iii) evasion of apoptosis, (iv) limitless replicative potential, (v) sustained angiogenesis, (vi) tissue invasion and metastasis. (vii) Genetic instability, that is defined as an “enabling characteristic” that facilitates the acquisition of other mutations because of defects in DNA repair. These hallmarks form a candidate set of rules that underlie the transformation of a normal tissue to a cancerous one. The quantitative ramifications of these rules are explored in this paper, and lead to a number of interesting phenomena and hypotheses. The author’s model [71] describes a simplified view of cancer progression using a stochastic model of tumorigenesis based on all these hallmarks. The complexity of cancer cannot be understood by considering individual mutations independent of their interactions. The effect of a mutation often depends on other mutations within the same cell, on other mutant cells within the tumor, and on the tumor microenvironment. The evolutionary dynamics of early mutations, which generally go undetected in clinical settings, act as the initial forces that drive neoplastic evolution. The key findings from this paper are the following four: (i) “Early-onset” cancers proceed through a different sequence of mutation acquisition than “late-onset” cancers. Specifically, genetic instability is the most common first mutation in early-onset cancers, whereas limitless replicative potential is the most common first mutation in later-onset cancers. (ii) Heterogeneity varies with early acquisition of genetic instability, mutation pathway, and selective pressures during tumorigenesis. (iii) There exists a range of optimal initial telomere lengths that lowers cancer incidence and raises the time of cancer onset. (iv) The ability to initiate angiogenesis is an important stagesetting mutation, which is often exploited by other cells and is therefore infrequently present in final tumors. This model presents a first step toward predicting the fate of early precancerous mutations computationally. Early events

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responsible for neoplastic progression are difficult to investigate experimentally for the very reason that they have not yet been detected. The main limitation of the model is obviously the difficulty of experimental validation. A thorough testing of the model would require periodically examining single cells for the presence of mutations in the hallmark categories, beginning before an animal has developed a clinically detectable cancer. Initiating testing for mutations once the animal has a palpable tumor ignores early mutation dynamics that, according to the model, are important for determining the timing and cellular makeup of the tumor that develops. A more feasible experimental alternative, for example in the case of colon cancers, would be to look at normal tissue around malignant and nonmalignat polyps, or apparently healthy colonic epithelia in patients undergoing prophylactive colonoscopies at various ages (say 50, 60, and 70 years of age), and measure in this tissue the frequency of genetic and epigenetic events in individual cells, or cell clusters, for example by laser-capture dissection with the help of genomic (e.g., microarrays) and proteomic (immunohistochemistry or tissue microarrays) tools. The mathematical computational model by Spencer et al. [71] relies on a number of simplifying assumptions, the most important relating to tissue architecture and molecular intracellular processes. For instance, they assume that all mutations fit into one and only one of the six hallmarks, whereas p53, for example, is known to be involved in cell cycle inhibition, apoptosis, genetic stability, and inhibition of blood vessel formation. If the precise biological behavior of a cell depends at any given time on the precise functional status of all the main cellular regulatory pathways combined, then it must necessarily follow that an obvious objective of cancer research is to use

all present and future molecular techniques to define a useful composite picture of all of these regulatory pathways in any given tumor at any given time. This overall picture should then provide us with the necessary information to determine prognosis and suitable treatment targets, as well as facilitate both target selection and in vivo target validation of new anticancer drugs (see also Table 1-1, or Matrix of Targets).

1.2.4 Towards a Definition of “Matrix of Targets” One can define a “Matrix of Targets” [26], as that graphic representation (two-dimensional, or three-dimensional) that gives an accurate picture of the functional implications of the molecular changes in all key regulatory pathways, for any given cancer (Table 1-1). The genetic alterations in oncogenes generally lead to a gain of function (GF) of the protein, whereas, in general, tumor suppressor genes are inactivated during carcinogenesis with apparent loss of function (LF) of the protein. However, the mechanisms of activation or inactivation are multiple, and the precise consequences on gain or loss of function are incompletely understood. K-ras and H-ras genes are examples of oncogenes preferentially altered by point mutation (codons 12, 13, and 61), generating a protein with constant GTPase activity. The c-myc gene can be activated by chromosomal translocation (in some leukemias) or by gene amplification (in some solid tumors). The p53 and Rb tumor suppressor genes are often knocked out by point mutation in one allele and by deletion (loss of heterozygosity) at the other. Others, like p16, have high rates of homozygous deletions or promoter hypermethylation. Some genetic defects are fairly characteristic

Table 1-1. Matrix of targets. A B C D E F

RP1

RP2

RP3

RP4

RP5

RP6

RP7

RP8

RP9

GF N N N GF N

N N N N GF N

N GF N GF N N

N N N LF N N

LF N N N N GF

N N N N LF LF

N N N LF N N

LF N N N N N

N GF N N N N

The precise aberrations of regulatory pathways involved in the control of growth, differentiation, cell death, developmental history, and invasive properties can provide a “matrix of targets” for any given cancer. This matrix represents a two-dimensional “molecular fingerprint,” but also a global functional map, of any individual cancer at a given time-point, and can eventually be used to select appropriate drugs and therapeutic strategies. Abbreviations: RP regulatory pathway; N normal gene; LF loss of function (mainly because of point mutation, methylation of the promoter, or deletion); GF gain of function (mainly because of point mutation or translocation); A to F regulatory elements in any given pathway (from upstream to downstream in each regulatory cascade), because of “cross-talk” between different pathways A-F do not necessarily imply different regulatory molecules, for the same molecule can play different roles in more than one pathway: a relatively small group of key regulatory molecules are responsible for the meaning and interpretation of multiple environmental signals; RP1-RP3 growth-factor dependent pathways operating in that specific tissue; RP4 hormone-dependent pathway; RP5 invasion and metastasis pathway; RP6 DNA repair pathway; RP7 cell-cycle regulatory pathway; RP8 apoptosis pathway; RP9 angiogenesis switch pathway. In some pathways, e.g., RP7, only one key molecular alteration is enough to contribute to the cancer phenotype, because of an “exclusivity principle.” See text for full explanation.

1. Selecting the Right Targets for Cancer Therapy

for a given tissue type (most colorectal cancers have APC or beta-catenin mutations). But the “same players” are frequently involved in different tumors. Each human cancer can be regarded as a different molecular entity, with a different matrix of molecular targets, and it evolves with time (even as a result of systemic or local therapies) (see Table 1-1). In this regard, it could be argued that each individual cancer is a “rare cancer,” because of its peculiar and perhaps unique “molecular signature” both at genomic and proteomic levels, and because of its individual clonal origins and development, and its own tumor-related stroma. In other words, when testing new targeted therapies it is important to know not only which “molecular target(s)” is hit, but also its precise molecular conformation (like the most vulnerable mutant variants of the target, e.g., EGFRs) and the full matrix of targets of any given tumor. Hence, patient selection in phase-II studies should be based not only on conventional inclusion/exclusion criteria (tumor type, histological grade, TNM stage, performance status, age, and sex of the patients), but also on a reasonably specific “matrix of targets” for one or preferably more than one regulatory pathways. This means that to achieve this very high and specific degree of “patient” and “cancer” selection, phase-II studies will probably require to be multicentric to allow for a larger patient candidate population. It can also follow from this that most of these new targeted agents will only be active in subgroups of patients (e.g., only some 25% of breast cancer cases are HER-2 positive and likely to respond to trastuzumab, Herceptin®), though suitable combinations of targeted agents hitting at different matrix-specific vulnerable sites can prove more effective and increase the chemosensitive population of patients. Table 1-1 represents the simplest graphic version of a “Matrix of Targets,” as it represents in a single twodimensional, strictly Cartesian, fashion the various regulatory pathways (RPs), in the x-axis, the various cellular levels of regulation (A to F: upstream to downstream), in the y-axis, and the functional status of each regulatory protein involved (N: normal; GF: gain of function; LF: loss of function). Regulatory pathways include: growth factor-dependent pathways (GF), hormone-dependent pathways, DNA-repair dependent pathways (DR), apoptosis (Ap) and cell-cycle control pathways (CCC), angiogenesis-switch pathways, etc. Up-stream elements are usually on the cellular membrane (receptors for growth factors, for example, but also cellular adhesion molecules), and are followed by cytosolic elements (second and third or fourth messengers, “cascading” in complex intracellular signalling pathways), and/or by regulatory proteins bound (or binding) to cytoplasmic organelles (like mitochondria, in the case of pro- or antiapoptotic regulatory proteins; or like proteasomes in the case of ubiquitin-degradation pathways). Downstream elements (e.g., transcription factors, tumor suppressor proteins like pRb and p53, specific DNA or RNA sequences) are mainly nuclear, and likely to convert on a limited number of regulatory events: the expression or repression of specific sets of genes, the coordinated production of

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DNA-replicating proteins, or specific three-dimensional conformational changes in chromatin. The latter are often the consequence of histone modification by regulatory enzymes. For example, steady-state levels of histone acetylation in the cell are maintained by a delicate balance between the action of histone acetyltransferases (HATs), and histone deacetylases (HDACs), which have become prime targets for cancer therapy in their own merit. The software of a cancer cell has gone badly and progressively wrong. Cancer cells do not have exactly the same underlying chemistry as the normal cells of the body. This makes them vulnerable to more specific and selective drugs. Most of these second, third, fourth etc. intracellular messengers are binary: they are either switched on or off (by phosphorylationdephosphorylation, for example). At any one time a number of + and - signals travel from the cell membrane to the nucleus (and, perhaps, also backwards). These signals are irreversibly altered in cancer cells. The normal balance between the on and off signals is altered in cancer cells. At present, the matrix of targets is a theoretical concept, rather than a practical reality. But, besides its conceptual value, it should eventually become possible to translate knowledge from genomic and proteomic tools into a two-dimensional matrix as shown on Table 1-1. Regarding genomics, there are three commonly used high-throughput methods to measure simultaneously the expression of thousand of genes in clinical specimens (see also the chapter by Sean Yu in this book). These include cDNA and oligonucleotide arrays, and multiplex quantitative real-time PCR. The latter, less publicized than cDNA microarrays, is based on the quantification of a fluorescent reporter generated during the PCR process. This signal increases in proportion to the amount of PCR product, that in turn reflects the abundance of the mRNA detected. Real-time PCR (RT-PCR) offers a wider dynamic range of detection compared with DNA microarrays, and also can be optimized to detect mRNA fragments recovered from formaldehyde fixed, paraffin-embedded tissues. A possible limitation of this technique is that it can measure only several dozen to a few hundred genes simultaneously, rather than several thousand genes like cDNA or oligonucleotide microarrays. Hence the need, for RT-PCR, to focuse on one or two regulatory pathways at a time. The emerging discipline of Proteomics will probably also be key to the definition of this matrix of targets. Kodadek and others have described two main practical applications of proteome arrays: protein function arrays and protein-detecting arrays [73–78]. The protein-detecting array consists of an arrayed set of protein ligands used to profile gene expression and draw proteosignatures of the cellular state. In protein-function arrays, a large amount of protein is spotted on a solid support and tested to characterize either a biochemical activity or a molecular interaction (protein binding, DNA binding, etc.). Further advances are expected to come also from so-called tissue microarrays. Gene microarray analysis generally relies on the availability of fresh frozen tumor samples. These

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M.H. Bronchud

specimens are often collected prospectively and rarely have significant follow-up data associated with them. In contrast, tissue microarrays, developed by Kallioniemi, Sauter, and Kononen [79] allow for the combination of several hundred tumor specimens in a single paraffin block and tumors with significant clinical follow-up can be obtained and analyzed by immunohistochemistry for specific gene products. Although they do not produce overwhelming amounts of data sets, like mRNA expression profiling arrays, they still generate a huge amount of immunohistochemistry data that again requires sophisticated bio-mathematical approaches for their interpretation. Several laboratories are pioneering these proteomic and tissue array techniques [73–81], and again the subject is thoroughly reviewed in a separate chapter of this book written by Steven Pelech. An exciting new development is the combination of Proteomics with conventional magnetic resonance imaging (MRI). For example, glioblastoma multiforme (GBM) has a classic appearance on MRI as an expansile mass with central necrosis, ring enhancement, and a large region with edema. By using MRI-guided proteomics, Hobbs et al. [82] were able to show spatial differences in protein expression patterns correlating with MRI contrast differences. In summary, Although we should not forget that relatively simple clinical and biochemical variables still remain the best prognostic indicators for most tumors (for example, age and sex of the patient, performance status, TNM stage, circulating marker levels), or that even the psychosocial circumstances of the patient and the choice of surgeon can be relevant prognostic factors [83], it is obvious that the introduction of these new molecular techniques will revolutionize clinical oncology. It seems also evident that, in general, it is still a little too early to say which of these techniques, or which combinations of techniques, will be most appropriate to allow for the identification of carcinogenic status of any given tissue, or for

therapeutic target selection and validation, and clinical follow up of cancer therapies. But progress is faster than anticipated by many. Let us take as an example two of the crucial pathways involved in the majority of carcinogenic processes of epithelial tissues: the retinoblastoma (Rb) pathway, and the p53 pathway (Table 1-2). Oncogene inhibitors (OIs) are targeted therapies (like several tyrosine kinase inhibitors, e.g., imatinib) that lock the conformational state of the oncogenic protein into its “inactive form” (either by binding to the active site, usually an ATP pocket, or by binding to an allosteric regulatory site) and can be active therapeutic agents, in general “cytostatic” rather than “cytotoxic,” against a tumor with a mutation of the targeted gene, or any gene “upstream” of the target. Tumor suppressor activators (TSAs) are potential targeted therapies that can lock the tumor suppressor activity in its “active form” (e.g., by inhibiting specific-site phosphorylation of the Rb protein and thereby preventing activations of the E2F family of growth stimulatory proteins), or can activate/reactivate latent tumor suppressor functions of wild-type tumor suppressor alleles (if there are any left in the cancer cell genome), or of mutated but still potentially functional tumor suppressor proteins (e.g., truncated mutant forms, or point mutations that do not destroy all of the tumor suppressor regulatory sites in the p53 protein). TSAs can , at least in theory, act on the normal tumor suppressor protein, or a mutant form that can be “reactivated,” or any gene “down-stream” of the target. The Rb pathway is mainly involved in the control of the transition from a resting stage of the cell cycle (Go or G1) to a replicating phase (S-phase). The main products of these genes include CDK4 (cyclin-dependent kinase 4), cyclin D1 (which interacts with and and activates CDK4, and is permanently activated, usually because of a chromosomal translocation, in some malignancies like the so-called “mantle non-Hodgkin

Table 1-2. Conceptual example of a matrix of targets in the Rb and p53 pathways. Rb pathway (p16INK4A): LF (CDK): N (Cyclin D1): N, or GF if p16 is N (Rb): N, or LF if above three are N (E2F): N, or GF if above 3 are N

P53 pathway (p53): LF (or GF if “negative-dominant”) (WT1 :familial Wilms tumor gene) : N (or LF) (HPV E6): N or GF (e.g., in cervical cancers) MDM2 : N or GF P21: N or LF

In parentheses, the identity of the regulatory protein. N normal function; GF gain of function; LF loss of function. In black: Tumor suppressor proteins. The p53 protein is a complex transcription factor that normally inhibits cell growth and stimulates cell death when induced by “cellular stress” and/or DNA damage. The most common way to disrupt the p53 pathway is through a point mutation that inactivates its capacity to bind specifically to its cognate recognition sequence in DNA, but there are many other ways to achieve the same effect, like the amplification of the MDM2 (mouse double minute 2 gene) or infection with human papilloma virus (HPV) whose E6 protein can bind to p53 to functionally inactivate it. Among many other functions, p53 can induce the expression of CKI (cyclin kinase inhibitors) like p21, that normally lead to cell cycle arrest. Some of the “cross-talk” points in the pathway are not graphically illustrated, but could be represented as loops that connect the pathways (e.g., p14ARF), and some mutant forms of tumor suppressors, as for example in the case of p53, can act in a “dominant-negative” fashion, resulting in a paradoxical “oncogene-like” effect. (See explanation in the text.)

1. Selecting the Right Targets for Cancer Therapy

lymphomas), Rb (essentially a transcription factor), and p16, which interacts with and inhibits CDK4 acting as a tumor suppressor, and often absent either because of promoter hypermethylation, or other epigenetic events, or because of point mutations, insertions or deletions in the so-called exon 1α of the CDKN2A locus (Table 1-2). This CDKN2A locus on chromosome 9 is unusual in that it codes for two different proteins, both of which appear to predispose, for example, to malignant melanomas. The Melanoma Genetics Consortium (www.genomel.org) has found, in rare families with four or more cases of individuals with malignant melanomas, mutations at this CDKN2A locus on chromosome 9 [84], impacting on the two alternative splice gene products, and hence on both the Rb and the p53 pathways (p16 and p14ARF). As discussed previously, p16 is a “cell cycle regulatory protein,” though recent evidence suggests that it might also play a role in human melanocyte senescence [85]. The additional product p14ARF is part of the p53 pathway where it acts by blocking MDM2 binding of p53 [86]. This can lead to an intranuclear accumulation of p53, that if mutated can lose its tumor suppressor functions, failing to prevent S-phase or to induce apoptosis. Curiously, at least in humans, germ-line mutations in this key CDKN2A locus all seem to predispose family members to malignant melanoma alone or at least preferentially to malignant melanoma, rather than to other cancers, with the exception perhaps of pancreatic cancers. The carcinogenic mutations within each major regulatory pathway very often but not always obey an “exclusivity principle,” in that, one and “only one” of the genes involved in each pathway (e.g., the Rb pathway) is generally mutated in a single tumor, exactly as predicted if the functional effect of each mutation was similar [87–89]. This “exclusivity principle” is likely to be the result of the strong selective pressures that determine the clonal expansion of premalignant and malignant cell clones, as two “contradictory” mutations in any pathway would mutually interfere with each other and add no obvious selective advantage to transformed cells. It is also of great interest that virtually all DNA tumor viruses that cause tumors in experimental animals or humans encode proteins that inactivate both the Rb and p53 tumor suppressor proteins or their pathways, suggesting that it might prove impossible for a tumor, at least of epithelial origin, to develop unless both the p53 and the Rb pathways have been altered and their tumor suppressor functions inactivated. In other words, therapeutic agents (small molecules, peptides, or nucleic acids) capable of restoring or at least partially reactivating these pathways could result in powerful anticancer effects, and perhaps even cures. The net result of any given altered regulatory protein (either because of loss of function, as seems to be the case for most tumor suppressors, or because of gain of function, as for most oncogenes) is always dependent on the context of other regulatory pathways. This can also help to explain why different effects of the same mutation are found in distinct cell

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types, because different cell types are likely to depend for their survival and proliferation on different combinations of growth factors, hormones and stromal-epithelial interactions. But differences in the net effects of a given oncoprotein can even be observed in the same cell type, depending on when the mutation occurred during the temporal sequence and spatial cellular clonal expansion of the carcinogenic process. An often quoted example of this property is K-RAS2 gene mutations. In normal mammalian pancreatic duct cells K-RAS2 gene mutations appear to initiate the neoplastic process, but when the same mutations occur in normal ovarian or colonic epithelial cells, they lead to self-limiting hyperplastic or borderline, rather than truly malignant or premalignat lesions. Unless, in the case of colonic cells, the cellular clone that acquires a K-RAS2 mutation already has an APC mutation, in which case the clonal expansion can progress to malignancy. APC is an important tumor suppressor protein inactivated (LF) in familial adenomatous poliposis coli (APC). In addition to the Rb and p53 pathways, as illustrated in Table 1-2, the “Matrix of Targets” concept could be applied to other important regulatory pathways, including: 1. Those involving SMADS (downstream effectors of the interesting family of transforming growth factor-beta or TGF-β), 2. Several receptor tyrosine kinases (RTKs) related to the activation of several growth factor receptors (the EGFR family, VEGFs, PDGFs, etc). 3. The adenomatous polyposis coli (APC) gene (part of the complex β-Catenin and ubiquitin-proteasome pathways that tag and degrade important regulatory proteins), 4. The glioma-associated oncogene (GLI) pathway, that can be activated if the tumor suppressor SUFU (a medulloblastoma cancer predisposition gene) is inactive, or if another tumor suppressor EXT1,2 (related to the rare syndrome of “hereditary multiple exostosis) is also inactivated. 5. The E-cadherin (CDH1) pathway, a tumor suppressor gene that codes for a cell membrane adhesion protein inactivated in several families with hereditary predisposition to familial diffuse gastric adenocarcinomas, and also and also linked to the APC pathway. 6. The phosphoinositide3-kinase (PI3K) pathway, inhibited by rapamycin-like drugs and some tyrosine kinase inhibitors (TKIs), a rather complex pathway, with several biofeedback loops that involve important regulatory molecules like the oncogene AKT (e.g., AKT2 often amplified in breast and ovarian cancers) and the tumor suppressor gene PTEN (a phosphatase rather than a kinase). 7. The Hypoxia Inducible Factor (HIF) pathway. In the latter, inactivation of the gene VHL (a classic tumor suppressor gene that when inactivated in the germ-line predisposes to the Von Hippel-Lindau syndrome) stimulates the growth of renal carcinoma cells through the control of angiogenesis. This is because the protein encoded by VHL is part of a

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M.H. Bronchud

ubiquitin ligase complex that degrades HIF-1α in the presence of oxygen. In the absence of oxygen, under normal circumstances or when VHL is mutated in tumors, the HIF1α transcription factor is stabilized (not degraded), leading to increased expression of cytokines like vascular endothelium growth factor (VEGF) and tumor angiogenesis. The complexity of the process leading to a clinical cancer cannot be underestimated. There are at least 16 ways to reduce or abolish the function of a gene product: delete the entire gene, loss of the relevant chromosome, delete part of the gene, disrupt the gene structure (by a translocation or an inversion), insert a sequence into the gene, inhibit or prevent transcription, promoter mutation reducing mRNA levels, decrease mRNA stability, inactivate donor splice sites (causing read-through into intron), inactivate donor or acceptor splice sites (causing exon to be skipped), activate cryptic splice sites, introduce a frameshift in translation, convert a codon into a stop codon, replace an essential aminoacid, prevent posttranscriptional processing, prevent correct cellular localization of product. Mutation of a gene is not the only way to abolish its function (e.g., long-range chromatin alterations, abnormal methylation and/or imprinting). For example, in human neoplasms p16 is silenced in at least three ways: homozygous deletion, methylation of the promoter, and point mutation. The first two represent the majority of inactivation events in most primary cancers. p16 is a very common early event in cancer progression and is frequently seen in premalignant lesions. The impòrtance of p16 is probably similar to that of p53. Mutations in the p53 gene have been found in some 30% of human tumors, and wild-type p53 has been reported to suppress tumorigenesis and promote apoptosis. The p53 protein is a potent transcription factor, and may promote transcription of genes also involved in carcinogenesis and angiogenesis. Loss-of-function mutations usually produce recessive phenotypes, so that as long as one allele remains normal there are no significant phenotypic changes. But, for a limited number of genes, a 50% reduction in the dosage of the gene can lead to phenotypic changes (dosage-effect). Certain regulatory functions are inherently dosage-sensitive: e.g., gene products that compete with each other to determine a developmental or metabolic switch or that cooperate with each other in interactions with fixed stoichiometry, or gene products whose function depends on partial or variable occupancy of a receptor or DNA-binding site. Less frequently, mutations can lead to gain of function, rather than loss of function. For example, they can result in the ability to acquire a new substrate, overexpression of the gene product, receptor turned permanently “on,” ion channel inappropriately open, structurally abnormal multimers, chimeric gene, ability to bind to new DNA sequences, or to trap and inactivate important regulatory molecules. If a protein has several catalytic and allosteric domains (e.g., at a regulatory network bottle neck) destruction or loss of function of only one of these domains can allow others to be inappropriately activated.

It is, at least theoretically, possible that some carcinogenic events may include both loss of the natural function of the gene product, and gain of a function not normally associated with that particular gene product. For example, a truncated protein might be unable to perform the original function of the native protein, but could still interact functionally with other regulatory proteins by exposing the remaining protein domains. An important issue in new drug development is whether to concentrate on abnormal oncoproteins (e.g., mutated forms of the regulatory proteins involved in carcinogenesis) or on the normal counterparts. Although some oncoproteins (e.g., Ras in pancreatic cancer) are frequently mutated at the same codon for a particular tumor type, many more derive from very large genes (e.g., BRCA1 and BRCA2) or relatively large genes (p53) with multiple different possible mutations along the gene, which may differ according to tumor type and epidemiological reasons (e.g., different ethnic group, contact with specific carcinogens, etc.). Thus, it could prove globally more rewarding to concentrate on normal regulatory proteins (e.g., at downstream “bottlenecks,” or points of “cross-talk”) than on mutated oncoproteins. The problem in this case, however, is that inhibition of normal downstream regulatory oncoproteins might prove more toxic than selective inhibition of mutated oncoproteins. In general, once the relevant oncoprotein is identified and purified, gene cloning allows the production of sufficient quantities to allow the determination of its main molecular mechanisms (catalytic or regulatory) and its three-dimensional structure. Appropriate molecules (e.g., developed by empirical methods like high-throughput screening, or by rational drug design) can then be tested in vitro and in preclinical models to find out activity and toxicity, and pharmacokinetics and pharmacodynamics. More and more oncological units will be devoted to clinical testing of new drugs, and cancer research is likely to undergo a rapid growth, provided enough resources are made available.

1.3 Cancer can be Prevented by both Primary and Secondary Prevention In recent years, age-adjusted death-rates from cardiovascular diseases in the developed world have decreased significantly in great part because of the early detection of treatable risk factors, like high serum cholesterol levels, diabetes, hypertension, obesity, and sedentarism. Smoking rates are also declining, helping to decrease both cardiovascular problems and smoking-related cancers, like carcinomas of the lung, transitional epithelium of the urinary bladder, and head and neck squamous carcinomas. But apart from smoking, another good example of the potential preventability of cancer comes from virus-related carcinogenesis. It is intriguing to find that virtually all known oncogenic retroviruses, key to the experimental identification of many oncogenes, affect species other than

1. Selecting the Right Targets for Cancer Therapy

humans, like, for example, rodents, cats, dogs, and poultry. Nevertheless, there are several clear cut examples of human viruses known to be carcinogenic. The hepatitis B virus (HBV) has been linked to human hepatocarcinomas, and an effective vaccine has already been available for many years. Some of the genital human papilloma viruses (HPV) are known to cause the majority of uterine cancers, both squamous and adenocarcinomas. Cancer used to be more frequent in women than in men in nearly all countries about a century ago, because of the great frequency of carcinoma of the cervix uteri and the rarity of smoking-related cancers in women (like lung, bladder, or head and neck cancers), and it is still more common in populations like several Latin American countries where these conditions still hold. Elsewhere, in great part because of population screening in sexually active women for cervical cancer in developed countries, with Papanicolau cytological smears and more recent colposcopic and hysteroscopic techniques, cancer is now more common in men. Cervical cancer in women is still prevalent all over the world (with about half a million cases per year) and it is the leading cause of cancer mortality and morbidity in countries like Mexico, Colombia, and Ecuador. The evidence, both epeidemiological and molecular, is now very strong that human papilloma viruses (HPV) are etiological agents associated with the majority of cervical cancer types (both squamous and adenocarcinomas) [90–93]. Several large cohort studies have consistently shown that HPV infections precede by some 10–15 years the development of cervical cancer. Besides HPV DNA detection, additional markers of carcinogenic progression include HPV type, estimates of the viral load, persistence of viral infection as determined by repeated sampling, viral integration into the DNA of the host cell, and possibly other environmental factors, like smoking and other sexually acquired infections. HPVs are small DNA viruses that usually cause warts (epidermal papillomas) in the skin, but some can also infect the genital tract. More than 30 HPVs have now been identified in the female genital tract, and four of these (HPV-16, HPV-18, HPV-31, and HPV-45) probably account for some 80–90% of cervical cancers and code for at least two oncogenes (E6 and E7) which are expressed once the viral genome integrates into the host’s DNA, disrupting some of the key pathways of cell-cycle control and apoptosis. Recent studies suggest that the ideal viral protein for therapeutic intervention in cervical pre-cancerous or malignant lesions is probably the E6 polypeptide, because a key element is the induction of host apoptotic pathways, thereby eliminating a primary viral infection as well as the virus-transformed cancer cell. E6 binds to E6AP, the prototype HECT domain protein, and forms an E3 ubiquitin-protein ligase that ubiquinates p53 resulting in rapid p53 degradation by the 26S proteasome [90–93]. The use of antisense oligonucleotides against E6AP [91] or of small peptides to block the activity of E6 [92] to degrade p53 might be valid therapeutic options for already established clinical cancers of the cervix, provided the p53 pathway

13

leading to growth arrest or apoptosis are still functional, or can be reactivated in infected cells. And this is a serious problem we shall return to, when we examine in closer detail the subject of targeted therapies. It is not only a particular molecular target that matters, but the global state of regulatory pathways in any given cancer, which we have called in the past the Matrix of Targets ( see also Table 1-1 and Table 1-2). The detection of HPV infection using molecular diagnostic methods such as the polymerase chain reaction can now be used, together with the traditional cytological Papanicolau smear analysis, in screening programs of early detection of cervical cancers. Theoretically, HPV vaccines should be able to prevent infection and protect against the malignant transformation. The path towards the development of effective HPV vaccines has been arduous. Partly because of the multiple serological sub-types, partly because the main protective immunity agent active in mucosal membranes (such as the cervix uteri) is the immunoglobulin A, which is only temporarily induced so that a putative HPV vaccine would have to be administered repeteadly to maintain an effective level of immunity. However, after many years of research an effective vaccine against the carcinogenic HPV has been finally approved in 2006 by both the American Food and Drug Administration (FDA) and the European Medical Evaluation Agency (EMEA) [94, 95]. On June 8, 2006, the FDA approved GARDASIL® to prevent cervical cancer and vaginal and vulvar precancers caused by human papillomavirus (HPV) types 16 and 18 and to prevent low-grade and pre-cancerous lesions and genital warts caused by HPV types 6, 11, 16, and 18. GARDASIL® is approved in the US for 9- to 26-year-old girls and women at high risk. GARDASIL® (quadrivalent human papillomavirus types 6, 11, 16, 18, recombinant vaccine), has also received approval by the European Regulatory Agency (EMEA) in 2006. The EMEA recommends that this HPV-vaccine be approved for the immunization of children and adolescents aged 9–15 years and of adult females aged 16–26 years for the prevention of cervical cancer, high-grade cervical dysplasia (CIN 2/3), high-grade vulvar dysplastic lesions (VIN 2/3), and external genital warts caused by human papillomavirus types 6, 11, 16, and 18. It has been shown to be an active prophylactic vaccine, but it is not intended to be used for the treatment of active genital warts; cervical cancer; cervical intraepithelial neoplasia (CIN), or vaginal intraepithelial neoplasia (VIN). It has not been shown to protect against disease because of nonvaccine HPV types. The health-care provider should inform the patient, parent, or guardian that vaccination does not substitute for routine cervical cancer screening. Women who receive this vaccine should therefore continue to undergo cervical cancer screening per standard of care. More information on this and other HPV-vaccines in development, or already approved, can be found on the following two internet web sites: 1. http://www.cancer.gov/ cancertopics/factsheet/risk/ HPV-vaccine 2. http://www.cdc.gov/od/oc/media/pressrel/r060629.htm

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Secondary cancer prevention, might also be possible in some cases. Cancer “chemoprevention,” can be defined as the “treatment of carcinogenesis, its prevention, inhibition or reversal.” It should be regarded as “secondary prevention,” as what it does is to stop or delay carcinogenesis, rather than to stop a disease from spreading. In other words, it is meant to delay or stop the pathogenetic process, rather than destroy the cancer-related etiological agent(s). The term “chemoprevention” is controversial. “chemo” may lead to a confusion with “chemotherapy,” and “prevention” may not be the best word to define “early detection” of cancer biomarkers. The subject is bound to grow very rapidly, both in terms of the identification, validation, and clinical relevance of cancer biomarkers, but also in terms of their impact on the quantitative estimation and prediction of individual human cancer risks. Since the publication of the Second Edition of “Principles of Molecular Oncology,” in the year 2004, the role of agents, like cyclooxygenase-2 (COX-2) inhibitors in chemoprevention of adenomas and colorectal cancers, has been tested in several important clinical trials. COX-2, a key enzyme for the production of prostaglandins from arachidonic acid, is overexpressed in colon carcinogenesis, but recent reports of serious adverse cardiovascular complications from therapy with COX-2 inhibitors, mainly because of excessive thromboxane production, has raised caution among the community of cancer physicians. Nevertheless, two reports at the 2006 American Association for Cancer Research in Washington, one from a study in the US and the other from an international study coordinated from Israel, have confirmed the definite inhibitory effect of long-term therapy with COX-2 inhibitors in patients with a history of colonic polyps or other risk factors for colorectal cancers. Again, results warned against the use of this chemopreventive strategy in patients also at risk of cardiovascular disease, and more studies need to be done to better define the population of ideal candidates for COX-2 inhibitors chemoprevention in terms of both general and specific inclusion and exclusion criteria [96]. COX-2 is a proinflammatory enzyme overexpressed in several types of cancer, and it has been approved by the FDA for cancer prevention in the rare syndrome of FAP (familial adenomatous polyposis coli), when youg patients, often in their teens, have thousands of polyps in their colonic mucosa. The Adenoma Prevention with Celecoxib (APC) Trial was a clinical trial in the US to determine if the arthritis drug celecoxib, which inhibits the enzyme COX-2, reduces the occurrence of new adenomas (precancerous polyps) in the colon and rectum of people who have already had such a polyp removed. Initial results of the APC Trial showed that those taking celecoxib had 33–45% fewer new adenomas and 57–66% fewer high-risk adenomas than those taking a placebo. For those people who took celecoxib and developed recurrent adenomas, the growths were fewer and smaller in number than those on placebo. Unfortunately, the use of celecoxib in the APC Trial was suspended in December 2004 because analysis by an independent US Data Safety and Monitoring Board showed that the risk of fatal and

M.H. Bronchud

major nonfatal cardiovascular events (cardiovascular death, heart attack, stroke, or heart failure) was 2.5 times higher for participants taking the drug compared to those on a placebo. Full analysis of the cardiovascular events, published in February 2005, showed that APC participants taking 200 mg of celecoxib twice a day had more than twice the risk of serious cardiovascular events compared to those taking the placebo. The APC participants taking 400 mg of celecoxib twice a day had more than three times the risk of serious cardiovascular events, compared to those taking the placebo. However, it must be stressed that initial results of the APC Trial showed that those participants taking celecoxib had indeed significantly fewer new adenomas and fewer new advanced adenomas than those on placebo after taking one of two doses of the drug twice a day for three years. (“advanced” adenomas are greater than 1 cm in diameter and have characteristics that make them more likely to become cancer.) For those taking the lower dose of celecoxib, they had 33% fewer new adenomas and 57% fewer advanced adenomas. For those taking the higher dose of celecoxib, they had 45% fewer new adenomas and 66% fewer advanced adenomas. When those who took celecoxib developed new adenomas, they were, on average, fewer in number and smaller in size than those on placebo. These results were presented at the American Association for Cancer Research (AACR) annual meeting on April 3, 2006 and should soon be published in full. To help identify who was at greatest risk of having a cardiac event because of celecoxib, the APC investigators compared the cardiac health history of participants upon entrance to the trial. They found that almost 9% of participants with a history of cardiovascular events (heart attack, stroke, coronary heart failure, or angina) had a second serious cardiac event while on celecoxib compared to only 3% of participants with a history of cardiovascular events who were on placebo. For those with no prior history of cardiovascular events, 2% of those on celecoxib had a serious cardiac event compared to 0.7% of those on placebo. So, although an individual’s risk of a severe cardiovascular event while taking celecoxib depends on their prior health history, the increased risk was seen in both those with a prior history and those without. More than a decade of epidemiologic research suggests that people who regularly take drugs that block COX enzymes have lower rates of certain precancers, cancers, and cancer-related deaths. The data are most consistent for colorectal cancer, but this reduction in risk is also seen for other cancers. Animal and laboratory studies using a variety of NSAIDs and COX-specific inhibitors show a decrease in cancer incidence with the use of these compounds. On a molecular level, studies have shown that the inhibition of the COX pathway changes the characteristics of cancer cells by reducing cell proliferation, increasing programmed cell death, reducing formation of blood vessels to feed cancer cells, and changing the body’s immune response. A more established, but still controversial, form of “chemoprevention” (particularly controversial in Europe) is the use of anti-estrogens in women at risk of breast cancer. Craig Jordan

1. Selecting the Right Targets for Cancer Therapy

and his colleagues will discuss this rapidly expanding subject in their chapter. As guidelines and preventive policies change constantly, it is worth consulting the “US Preventive Services Task Force” web pages for the most common cancers: 1. US Preventive Services Task Force: Screening for breast cancer. http://www.ahrq.gov/clinic/uspstf/uspsbrca.htm. 2. US Preventive Services Task Force: Screening for colorectal cancer. http://www.ahrq.gov/clinic/uspstf/uspscolo.htm. 3. USPSTF: The New US Preventive Services Task Force. http://www.ahrq.gov/clinic/uspstfab.htm. 4. US Preventive Services Task Force: Screening for lung cancer. http://www.ahrq.gov/clinic/uspstf/usplung.htm.

1.4

The Era of Targeted Therapies

The first edition of this book (Humana Press, 2000) was our first comprehensive presentation of the concept of cancer as a “disease process” involving key regulatory pathways. The second edition (Humana Press,2004) reaffirmed this dynamic concept, incorporating more recent evidence, and introducing such new topics of special interest as the combination of molecular diagnostics with developmental therapeutics, and the binary state concept “active/inactive” that seeks more relevant targets within the global molecular matrix of any given cancer. Here we take these concepts further, together with a growing body of evidence to support them. Thinking “binary” may help both target selection and target validation [97]. In Chinese ancient philosophy and medicine, the Universe we live in is clearly a model of the binary nature of things. For example, “Cold” was considered to be a “Yin” pathogenic factor, and its nature is to slow things down. On the other hand, Heat, or fire, was considered a “Yang” pathogenic factor, causing expansion and increased activity. Yin represents the negative, passive force. It is female in nature, dark, contractive, descending, and is symbolized by water. Yang symbolizes the positive, active force. It is male in nature, bright, high-flying, expansive, ascending, and is represented by fire. Of the two forces, Taoists believe yin to be superior and stronger. Citing the analogy of fire and water, they point out that fire, in spite of its brief appearance of great power, is easily extinguished by water. In a similar sort of way, all biochemical regulatory proteins can be present at any one time in an “on” or “off” molecular form, with regards to one or more of their functions. In concordance with Chinese philosophy, restoring tumor suppressor function (Yin) is probably more clinically meaningful than inhibiting oncogene function (Yang). This “Yin” or “Yang” effect will depend, for the normal wild-type forms of regulatory proteins, on the regulatory microenvironment, and the possible conformational changes (usually allosteric) produced by a number of enzyme activities, including phosphorylation/dephosphorylation, acetylation/

15

deacetylation, farnesylation/defarnesylation, methylation/ demethylation etc. Evolutionary pressures have worked against the ability of polypeptides to change randomly from one conformation to another. Instead of eliminating this ability altogether, selective pressures have limited it in a highly specific way so that many, if not most, protein molecules ara able to shift reversibly between several different but related stable conformations. Proteins with this property are known as “allosteric proteins”. Each distinct conformation of an allosteric protein has a somewhat different surface, and thereof a different ability to interact with other molecules. Usually only one of two conformations has a high affinity for a particular ligand, so that the presence or absence of the ligand determines the conformation that the protein adopts. When there are two distinct ligands, each specific to a different three-dimensional surface of the same protein, the concentration of one molecule commonly changes the affinity of the protein for the other. Such allosteric changes are fundamental to the regulation of many biological processes. For example, enzymes that act early in a pathway are almost always allosteric proteins that can exist in two different conformations; one is the active conformation that binds substrate at its “active site” and catalyzes its conversion to the next substance in the pathway. The other is the inactive conformation that tightly binds the final product of the same pathway at a different place on the protein surface known as the “regulatory site.” As the final product accumulates, the enzyme is converted to its inactivated conformation because this is stabilized by the binding of the product to the regulatory site. Alternatively, an enzyme involved in a metabolic pathway can be activated by an allosteric transition that occurs when it binds a ligand that accumulates when a cell is deficient in a product of the pathway. These simple mechanisms result in elegant feedback modes of regulation. Because the binding of a ligand to one site can affect another site by changing the protein’s conformation, any enzyme reaction or metabolic process can in principle be regulated by any other in the cell, regardless of its chemical nature. A classic example, is that of glycogen metabolism in muscle cells that is linked to the concentration of calcium ions by means of allosteric enzymes that alter their activity when the concentration of calcium ions change. Allosteric proteins often exist as aggregates of identical subunits. The conformation of one subunit can, following contact with a signal/ligand, influence that of neighboring subunits, producing an effect similar to amplification. Allosteric proteins of this kind act rather like a “switch-flipping” from one state to another, and the “cooperative binding” of substrate molecules produces a typical “sigmoidal response” curve. Conversion between one three-dimensional conformation to another may require an input of chemical energy, for example by transferring a phosphate group from ATP to a serine, threonine or tyrosine residue in the protein, forming a covalent linkage. About one tenth of all the different proteins made in

16

M.H. Bronchud

a mammalian cell contain covalently bound phosphate. Phosphate atoms are crucially involved in this complex biochemistry of cellular homeostasis. As early as 1937, Cori and Cori published their initial studies on glycogen phosphorylation, but it was not until 1988 that Tonks described the first partial sequence of a tyrosine phosphatase, and today we think that the human genome contains over 2,000 kinase genes, and over 1,000 phosphatase genes [98]. One of the key carcinogenic molecular changes, involved in about half of all human malignancies, refers to alterations in the p53 tumor suppressor (TS) protein, that is a complex protein with both DNA-binding domain and several regulatory proteins binding domains [99]. The TS p53, for example, interacts with many proteins implicated in regulation of protein function, including protein kinases and phosphatases, heat shock proteins, and DNA binding proteins, as well as with single stranded RNA or DNA. The functional p53 protein is usually a nuclear tetramer, and it is the ability to bind to specific DNA-sequences that is more tightly linked to TS function. Biochemical analysis of the regulation of wildtype p53 sequence-specific DNA binding has shown that the unphosphorylated tetramer has a cryptic sequence-specific DNA binding activity [100]. This cryptic or “latent state” of p53 depends upon a C-terminal negative regulatory domain, which locks the unphosphorylated tetramer into an inactive state. Phosphorylation of this C-terminal negative regulatory domain of latent p53 by a number of enzymes (e.g., protein kinase C or casein kinase II), or by deletion of this regulatory domain, or by binding to monoclonal antibodies can induce a conformational change in

the p53 tetramer capable of activating the sequence-specific DNA binding. About a decade ago, David Lane and colleagues [101], at the University of Dundee in Scotland, elegantly showed that the activation of p53 as a sequence-specific transcription factor following UV irradiation does not require increases in protein level and can be mimicked in vivo by the intranulcear microinjection of antibody directed to the C-terminal negative regulatory domain of p53. They proposed a model in which each C-terminal negative regulatory domain interacts with a motif in the core of the tetramer and must be displaced to permit the specific DNA binding activity of the protein. A prediction of this model, proposed long before the elucidation of the crystal structure of the phosphorylated and unphosphorylated p53 tetramer or the (still unclear) full molecular mechanisms of latency and activation, was that small peptides derived from the C-terminal negative regulatory domain might interfere with this intra- or intermolecular interaction, and activate the DNA binding function of latent p53. In very simple terms, see Fig. 1-3 (A and B) for graphic explanation, the new therapeutic drugs designed to restore the normal balance between Yin and Yang regulatory forces should, for example, either: 1. Lock or inhibit activated proto-oncogenes in their inactive state, like imatinib mesylate in the case of several tyrosine kinases, for example in the abl-bcr fusion protein of chronic myeloid leukemia, or the PDGFR tyrosine kinase in GIST (Fig. 1-3A); or:

A

B ONCOGENES

A

TUMOR SUPPRESSOR GENES

ACTIVE

INACTIVE

B

A

INACTIVE

B

AA

ACTIVE

B

A

B P

P D X

X D

AA

B

= ligand A,B = Subunits of Regulatory Molecules

A A

B

B

D = Blocker drug P = Phosphorylation

= ligand A,B = Subunits of Regulatory Molecules

A

B

D = Blocker drug P = Phosphorylation

Fig. 1-3. A Oncogenes as targets. A and B, subunits of regulatory molecules. D, drug that can lock the oncogene protein into its inactive form; P, phophorylation. Triangle, ligand. B Tumor suppressor as targets. Reproduced from Principles of Molecular Oncology (Bronchud M.H., Chapter 1), 2nd Ed., (2004), Humana Press, NJ.

1. Selecting the Right Targets for Cancer Therapy

2. Considering the binary states of most regulatory molecules (active/inactive) somehow lock or stabilize tumor suppressor proteins in their active state, which is the one normally responsible, for example, for inhibiting entry into S-phase or mitosis, or responsible for maintaining the normal differentiation and apoptotic pathways. An alternative, is a drug that deliberately activates “cryptic sites” in an inactive tumor suppressor protein (e.g., a truncated version of the wild-type protein) by inducing the suitable conformational changes (Fig. 1-3B). In other words, as regulatory proteins are usually present in normal cells in either an active form or an inactive form, and the chemical equilibrium between the two forms depends on the regulatory microenvironment of the cell, new targeted therapies should be able to correct, or al least limit, the malignant phenotype by restoring the normal balance between stimulatory and inhibitory signals within the cell.

1.4.1

Examples of Oncogene Inhibitors (OI)

Oncogenes are growth control genes present in the human genome (as “proto-oncogenes”), as well as in the genome of most if not all multicellular organisms. Incorrect expression or mutation of an oncogene usually results in “gain of function” and unregulated cell growth, as seen in malignant cells. Many of them are part of the complex cell’s signal transduction pathways, including cell membrane growth factor receptors, or intracytoplamic proteins. These signal proteins may be an intracytoplasmic piece of the receptor molecule, or another molecule activated just inside the membrane. Tyrosine kinase is an example of a signal molecule. The first signal may serve only as an intermediary, affecting a change in a second messenger. The G proteins, for example (belonging to the Ras oncogene family) are a group of intracytoplasmic secondmessenger molecules. The intracellular cascade of signals eventually reaches the nucleus, leading to changes in gene expression, and cell behavior. Of the over 100 oncogenes that have been identified so far, most are key players in these signal transduction pathways. Many examples of these new types of drugs [102–104] of “oncogene inhibitors” are already on the market; e.g., tyrosine kinase inhibitors (imatinib, erlotinib, trastuzumab), or EGFR inhibitors (cetuximab) , or VEGF inhibitors (bevacizumab). Many others are still undergoing clinical development, and will be reviewed by Paul Workman and his colleagues in this book. Indeed a “second generation” of tyrosine kinases is also already close to reaching the market, like dasatinib and lapatinib. Dasatinib (BMS-354825), for example, is a new, oral, small-molecule tyrosine kinase inhibitor (TKI) developed by Bristol Myers Squibb for the treatment of CML. Encouraging clinical trial data suggest it may have potential in the treatment of patients with CML whose cancer has become resistant to imatinib therapy. CML, one of the most common forms of leukemia, arises from the excessive production of abnormal stem cells in the bone marrow, which eventually suppress the

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production of normal white blood cells. The disease usually has three identifiable phases: the initial chronic phase, which is typically benign and lasts for an average 3 to 5 years from diagnosis, the accelerated phase and finally the blast-crisis phase. The vast majority of patients with CML have a genetic mutation called the Philadelphia chromosome, because of reciprocal translocation between the long arms of chromosomes 9 and 22. This leads to the creation of a bcr-abl fusion gene that encodes the production of the bcr-abl protein, a tyrosine kinase that influences cell growth, differentiation, and survival. Cells containing the Philadelphia chromosome replicate rapidly producing the characteristic pattern of abnormal cells seen in the bone marrow and blood of CML patients. Because the bcr-abl fusion protein is almost never seen outside leukemia cells, it presents an attractive therapeutic target and has been successfully exploited in the development of new treatments for CML. Conventional treatment options for patients with CML include conventional cytotoxic chemotherapy, interferon-alpha, allogeneic stem cell transplant (SCT), the only potentially curative therapy, and imatinib mesylate, the current gold standard. The development of imatinib (Glivec/Gleevec), a small-molecule TKI, was the first rationally designed drug for CML. It competitively inhibits bcr-abl tyrosine kinase activity. By blocking the effects of the bcr-abl fusion protein, imatinib helps destroy leukemic cells. It is currently indicated as a first-line treatment in patients with chronic Philadelphia-positive-chromosome CML as well as those who initially present in the accelerated or blastcell crisis phase. Although most patients with CML initially respond to treatment with imatinib, cases of imatinib resistance are increasingly being reported. Unmet clinical need therefore exists for drugs that can override imatinib resistance in patients with CML, especially in those who progress to the accelerated and blast-crisis phase. Preclinical and early clinical experience with dasatinib suggests that it possesses potent antileukemic activity in imatinibresistant cell lines as well as in malignant bone marrow cells isolated from patients with imatinib-resistant CML, and in mouse xenograft models of imatinib-resistant CML. Promising antileukemic activity has now been confirmed in an early clinical study in patients with imatinib-resistant and intolerant accelerated and blast phase CML who received twicedaily dasatinib. Major hematologic response, defined as the presence of less than 5% marrow blasts, were reported at the American Association of Cancer Researcc (AACR) meeting in Washington 2006 in 80% of patients in the accelerated phase and in 69% of patients in the blast-crisis phase of the disease. Corresponding cytogenetic response rates were 40% and 56% respectively, rates that compare very favorably with responses to imatinib. Typically about 46% of patients in the accelerated phase and 24% in the blast-crisis phase experience a major hematologic response to imatinib, whereas about 24% and 16% respectively have major cytogenetic responses. Lapatinib is an oral therapy targeting intracellular components of a receptor known as ErbB2 and a second receptor,

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ErbB1, which have been implicated in the growth of various tumor types. The phase-II trial (EGF 20009) tested lapatinib as first-line therapy for breast-cancer patients with tumors that express large amounts of ErbB2. An already marketed therapy, Herceptin® (trastuzumab), also works through its effect on ErbB2, but it is a monoclonal antibody administered by intravenous infusion. None of the patients in this lapatinib trial had been treated with Herceptin. Thirty-five percent of women (14 of 40) with locally advanced or metastatic breast cancer responded to lapatinib as first-line therapy, according to interim results of a study reported at the annual meeting of the American Society of Clinical Oncology (ASCO) in 2006. The data are the first to be reported on the use of lapatinib as a first-line therapy, but full publication is pending. The data presented at ASCO 2006 were derived from an interim analysis planned at the start of the trial and have been confirmed through an independent review. In the 35% of patients who experienced a partial response, tumor size was reduced by at least 30%. An additional 35% (an additional 14 of the 40 patients) showed stable disease through 12 weeks of therapy. All patients will continue to be followed for disease progression as part of the planned efficacy assessment. Full publication of the trial is eagerly awaited.The most frequently reported adverse events in this trial have been mild to moderate itching, rash, diarrhea, acne, and dry skin. No adverse events deemed drug-related, including cardiotoxicity, have been serious enough to cause clinical problems. At present all of these these “oncogene inhibitors” could be classified as follows (but the list is continuously growing): 1. Tyrosine kinase inhibitors: imatinib, gefitinib, erlotinib, lapatinib, dasatinib, etc. 2. EGFR inhibitors (other than 1): cetuximab (Erbitux®); panitumumab (Vectibix®). 3. VEGF inhibitors (other than 1): bevacizumab (Avastin®) 4. HER-2 inhibitors: trastuzumab (Herceptin®) It seems that most of the new drug entities acting on cell surface growth-factor receptors, or cytosolic down-stream regulatory elements, like for example inhibitors of the EGFR (epidermal growth factor receptor), or associated with their tyrosine-kinase activities, are mainly “cytostatic” in vivo rather than “cytotoxic”; they are fairly nontoxic (though, at times, with new unpredicted toxicities, like acneiform skin rashes), and are best given on a continuous long-term protocol. These considerations can lead to some methodological problems in clinical trials design. The primary end-points in phase-III randomized clinical studies should remain those reflecting survival of patients, but secondary endpoints should include measurable quality of life, disease-related symptoms, performance status, and standardized criteria to measure objectively and reproducibly time to disease progression. On the other hand, primary end-points in phase-I and II studies should include, besides toxicity and classical pharmacokinetics, data on relative efficacy and sophisticated in vivo pharmacodynamics, ranging from changes in genomics

M.H. Bronchud

(gene expression profiling) and proteomics (protein expression, intracellular localization, and posttranslational modifications) of key targets, and down-stream effector molecules, in tumors in response to the new drug. Additional surrogate end-points to be considered in phase-II studies include relevant in vivo dynamic changes on positive emission scanning imaging (PET), new dynamic molecular imaging like dynamic enhanced magnetic resonance imaging (DCE-MRI), and other methods to quantify changes in the growth kinetics of tumors, and for the subset identification of cancers that respond (versus those that do not respond) to the new therapies. There are still rather few published studies dedicated to examining in detail the various possible mechanisms of resistance to EGFR targeting, irrespective of the anti-EGFR drug considered. There is also some confusion as to the precise cytosolic and nuclear downstream effects following EGFR targeting, particularly when combined with conventional cytotoxic drugs. Moreover, although some consensual findings tend to suggest a link between the level of the EGFR protein target (as detected by IHC) and the intrinsic efficacy of the targeting drug, more pharmacodynamic studies in vivo are needed to establish convincing conclusions regarding EGFR levels and targeting efficacy on which clinical strategies can be based with confidence. It is however clear that some mutant forms of the EGFR molecule are linked to clinical response to some of these inhibitors. The best example is that of EGFR mutations that enhance the inhibitory activity of gefitinib (Iressa®) [105]. Responses to another tyrosine kinase inhibitor, erlotinib (Tarceva®) in non small cell lung cancer have led to significant though “marginal” (few months) improvements in survival, with oral therapy and little toxicity [106, 107]. This drug is now approved in both the USA and Europe for the treatment of locally advanced or metastatic nonsamll cell lung cancer. At times clinical and radiological responses are impressive (see Fig. 1-4 of a patient with bilateral bronchoalveolar carcinoma treated at our Divisions of Oncology in Granollers, Barcelona) with the conventional dose of 150 mg/ day of Tarceva® (Fig. 1-4). Whether these perhaps nonfrequent (around 12% of cases in our experience) , but truly impressive responses are caused by special molecular variants of the EGFR is under active investigation. The functional status of downstream effectors of the EGFR, like RAS and its pathway, is also of likely relevance to the tumour response to EGFR inhibitors. Activated oncogenes can also be inhibited by agents other than small molecular weight drugs, or monoclonal antibodies [108]. Progress is being made in the use of “antisense” (AS), technologies. Single-stranded antisense DNA forms a covalent bond with a specific sequence of messenger RNA (mRNA) inhibiting translation and rendering the complex susceptible to degradation by the enzyme RNaseH. ISIS 3521 ia a 20-base phosphorothioate antisense oligonucleotide targeted to the 3 untranslated region of the human PKC-α mRNA. Promising results in in vitro experiments and early phase-I clinical studies, have now led to phase-II studies, and some encouraging responses in low-grade non-Hodgkin lymphomas [109] with thrombocytopenia as the dose-limiting toxicity. The maxi-

1. Selecting the Right Targets for Cancer Therapy

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Fig. 1-4. A CT scans of the chest in a 56-year-old woman, nonsmoker, with bilateral bronchoalveolar carcinoma who had progressed following cisplatin-based chemotherapy. In spite of a rather poor performance status and serious respiratory insufficiency, she was treated at our center (Hospital General of Granollers, Barcelona) with oral Erlotinib (Tarceva®) at conventional doses, and her splendid radiological response is clearly visible comparing the pretreatment (A) (February 2006) and posttreatment (July 2006) CT scans. (Pictures published with written consent by the anonymous patient). B Radiological response is clearly visible following treatment with erlotinib (Tarceva®) in this patients with bilateral bronchoalveolar carcinoma.

mum tolerated dose was 2 mg/kg/day when ISIS 3521 was given as a continuous infusion. No phase-III studies have yet been reported with this antisense oligonucleotide.

1.4.2 Examples of Tumor Suppressor Activators (TSA) The case of tumor suppressor genes is more complex, because the ideal therapy in these cases, where the problem is usually a loss-of-function mutation (e.g., a deletion or a hypermethylation of the promotor), would be a genetic replacement of the wild type. But for mainly technical reasons (lack of suitable vector, inefficiency of the transformation systems, lack of stability of “transfected cells,” danger of oncogenetic transformation by retroviruses, etc.), the truth is that the original research enthusiasm of the 1990s for genetic therapy (GT) oncology programmes no longer exists, even if new GT approaches and new techniques are under constant active investigation. Thus it could well happen that if cancers are too advanced and evolved, there may not be any “latent” and “relevant” tumor suppressor function left as potential target for this new class of compounds or “tumor suppressor activators (TSAs). Hence the importance of combining earlier detection (e.g., research on preventive cancer medicine) with better and more effective drugs. The inability of molecules larger than 600 daltons to cross the plasma membrane has restricted the pharmacological use of proteins to those which function outside the cell, for example by binding to specific cell membrane receptors. Most tumor suppressors, on the other hand, are intracellular, and therefore cannot be administered to patients as recombinant proteins directly. Reversion of the malignant phenotype was

already demonstrated, over three decades ago, by the classical “cell fusion” experiments, with “Sendai virus” by Henry Harris, at Oxford. The loss of particular chromosomes in hybrid normal-malignant cells, or the gain of normal chromosomes when fusing normal cells with particular types of malignant cells in vitro, led to the first experimental data to back the hypothesis of “antioncogenes,” or tumor suppressor genes. Later experiments in the 1980s and 1990s, with the help of gene transfer methods including modified retroviruses and adenovirus, have given more support to the idea that in many cases the malignant phenotype of cancer cells can be reversed, in vitro and in vivo, by replacing a single key tumor suppressor gene. The technical difficulties, and potential dangers, of gene therapy in patients, however, have not yet led to clinically relevant forms of gene therapy in this context. Unlike oncogenes, tumor suppressor genes (formerly “antioncogenes”) display the function of suppressing cellular proliferation, and/or maintaining cellular differentiation, facilitating normal cellular adhesion mechanisms, stopping entry into the cell cycle (G1, S-phase , mitosis or G2) to allow DNA-repair mechanisms, maintaining normal cellular shape, cell contact inhibition mechanisms, etc. The tumor suppressor paradigm characteristically calls for loss of both functional copies of the gene and indeed many, but not all, tumor suppressor genes must undergo biallelic inactivation to sustain a true loss-of-function effect. However, a substantial number of cancer cells retain at least one “functional copy” (wild-type or mutant) of one or several key TS genes [110]. Tumor suppressor genes are either under-expressed in cancer cells, because of genetic deletions or promoter hypermethylation, or have sustained other forms of loss-of-function

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M.H. Bronchud

genetic or epigenetic changes. For example, abnormal p53 function can result through the acquisition of point mutations, posttranslation inactivation through binding to other regulatory proteins, like the so-called MDM2, enhanced degradation (e.g., by the activity of the E6 protein of carcinogenic human papilloma viruses), or other mechanisms like decreased translation of wild-type p53 by the folate-dependent enzyme thymidylate synthase. In these cases, depending on the cellular context and microenvironment, cancer cells are unable to undergo cell cycle arrest and/or apoptosis in response to the DNA damage produced by cytotoxic chemotherapy or radiotherapy. As “gene replacement” methods, by gene therapy of in vivo cell fusion, have not proved effective, and seem unlikely to do so in the near future, the possibility exists, at least in theory, of developing drugs capable of inhibiting the “inactivation of tumor suppressor function”; e.g., small molecular weight inhibitors of Rb phosphorylation, or capable of “restoring cryptic tumor suppressor function,” like d-peptide activators of p53. These new types of anticancer drugs could be called tumor suppressor activators (TSAs). Recent research work on the two main TS proteins identified so far (p53 and pRb), still in the preclinical phases of study, can serve as an example of the tremendous potential of these “tumor suppressor activating” (TSA) drugs as powerful anticancer agents.

1.4.2.1

p53 as an Example

Because of the presence of either wild-type or mutant p53 protein in most human tumors, restoration of endogenous p53 function in cancer cells, by small molecular weight compounds as shown by Bykov et al. [111], or by the in vivo delivery of transducible, proteolytically stable p53C′ d-peptides (like RITATp53C′), as recently shown by Snyder et al. [112] have become promising new approaches to cancer therapy. In other words, an important example of a TS that can prove a valid target for cancer therapy is p53. Underactivity of p53, because of loss-of-function mutations, encourages the growth of cancer, whereas overactivity can accelerate the aging process and cell death. Following some elegant pioneering work by Snyder and colleagues [113], a synthetic 34-amino-acid peptide, made of d-amino acids, rather than the conventional l-amino acids, and called “RI-TATp53C”, has been shown to be a potent activator of both wild-type and mutant p53 TS and DNA-Binding functions. In preclinical models, this synthetic peptide, that cannot be degraded by proteases and is internalized into cancer cells by a lipid-raft-dependent pinocytosis mechanism, has been shown to achieve complete cures in a mouse model of terminal peritoneal lymphomas. The use of d-amino acids is a clever trick. All proteins in living systems on this planet Earth are composed of l-amino acids. Is there any reason for the selection by Nature of the l-configuration at the Cα-atom? The ribosomal protein

chain-synthesizing machinery requires a standard and identical configuration, otherwise synthesis would be too complex, and therefore would not have a competitive chance. But there is no obvious reason, in this binary universe, for preferring the l-configuration over its mirror image. Presumably this “choice” occurred by chance, but this fact can provide us with a potential pharmacological opportunity. In fact, conventional l-peptides, even if of small molecular weight, are usually rapidly degraded by proteases, phagocytosed by immune cells or neutralized by antibodies. But synthetic d-peptides are not recognized by Nature, and may prove an effective “weapon” against cancer cells, however doubts remain over their pharmacokinetic, pharmacodynamic and safety profiles, as well as on their potential to generate inactivating immunological responses as these artificial peptides could prove to be rather immunogenic. Cα–aminoacids can be linked together by covalent “mirror image” peptide bonds, and the resulting “antipeptide” can be tailor made to act as an allosteric regulator. Alternative ways to activate p53 tumor suppressor functions are also under study. Some include a careful search for downstream mediators of these functions. For example, Boiko et al. [114], have developed a systematic approach for down stream mediators of the tumor duppressor function of p53 that reveal a major role for BTG2. Research led by Vassilev and colleagues (at ROCHE R&D laboratories in Nutley, N.J. and Penzberg, near Munich in Germany) has focused on MDM2 (mouse double minute 2) antagonists that can activate p53 and have already been shown to have synergistic antitumor activities with conventional genotoxic/cytotoxic drugs in animal models. These experiments have led, directly or indirectly, to a true revamping of the p53 tumor suppressor activator (TSA) area in recent months [115–137], and it is only a matter of time before promising small molecules (perhaps “Nutlins”) can enter the clinical development phase. Vassilev and colleagues [138] have elegantly shown in animal models with human tumor xenografts that the in vivo activity of Nutlins may affect not only tumor cells, but also their microenvironment. And they may induce cytostatic as well as cytotoxic effects not only in tumors with aberrant MDM2 expression, but also in those tumors that have retained wild-type p53 function. Hence, once again, the need to stress the importance of earlier detection of cancers, before they lose all of their TS genes, as well as the need for new TSA targeted drugs.

1.4.2.2

pRb as Another Example

At first sight, the retinoblastoma (Rb) protein is the true bottleneck of most cell cycle controlling pathways [139], even if it has been suspected for a long time that there is close molecular communication between p53 and pRb [140]. At least two “regulatory loops” are highly active in this Rb-p53 “cross-talk”: the already mentioned MDM2 and p14ARF. The additional product p14ARF is part of the p53 pathway where it acts by blocking MDM2 binding of p53 (86, 140).

1. Selecting the Right Targets for Cancer Therapy

In 1986–1987 the tumor suppressor Rb gene, first identified because mutated in hereditary retinoblastomas, was cloned by three different laboratories headed by eminent molecular biologists: Robert A. Weinberg and Thaddeus Dryja, William Benedict and Yuen-Kai Fung, and Wen-Hwa Lee. It is a rather large gene, over 200 kb and with 27 exons. It codifies for a nuclear protein of 928 amino acids, which is constitutively expressed during the cell cycle, but with characteristic cell cycle-dependent different degrees of phosphorylation. Phosphorylation of pRb is carried out initially by D-type cyclins (in protein complexes with CDK4 or CDK6) followed later by cyclin E/CDK2 . Virtually all human tumors tested so far show mutations that directly, or indirectly, alter the normal function of Rb. Nearly all tumor derived pRb mutants have lost the ability to repress E2F-responsive genes, and reintroduction by gene transfer of wild-type pRb into Rb −/− tumor cells leads to restoration of E2F control and cell cycle arrest. There are at least six human E2F genes (E2F1 to E2F6) and their protein products bind to specific DNA sequences as heterodimers with either DP1 or DP2 proteins. It is now clear that binding to pRb converts the E2F family from transcriptional activators to potent transcriptional repressors. E2F6, unlike the other E2F family members, is an intrinsic transcriptional repressor and does not apparently interact with pRb family members. Hyperphosphorylated pRb is unable to maintain the binding and inactivation of E2F, and free E2F molecules lead to transcription of multiple genes involved in the initiation of DNA synthesis and cellular proliferation. Using gene expression profiling methods (Affymetrix GeneChips) it has been possible to demonstrate changes in the expression of over 200 genes, most of them involved in cell cycle control, by CDK phosphorylation of wild-type pRb. Some of these genes are also involved with DNA repair, or changes in chromatin structure. As expected, a significant fraction of Rb-repressed genes have promoters that are bound/ regulated by E2F family members. However, targets were also identified that are distinct from genes known to be stimulated by overexpression of specific E2F proteins, suggesting that some of the multiple pRb effects are not directly related to E2F proteins. The conformation and activity of pRb is rather complex, and not fully understood. It is widely accepted that the activity of this large tumor suppressor protein is largely dependent upon the phosphorylation status of at least 16 potential CDK phosphorylation sites. But how exactly does phosphorylation of any of these sites change the full range of activities of pRb remains a matter of intense studies. Low molecular weight drugs designed to block “site-specific phosphorylation” of these sites is also under research, as they may ultimately lead to the growth arrest of populations of tumor cells [141]. Yuen Kai Fung’s laboratory, at the University of Southern California, has identified all the CDK sites (Thr-356, Ser-807/Ser-811, and Thr-821) the phosphorylation of which drastically modify the conformation of pRb. The so called

21

m89 strutural motif (identified in the m89 mutant of pRb) has greatly enhanced growth suppressing activity, similar to a mutant with alanine substitutions at Ser-807/Ser-811. Moreover, this m89 region is part of a structural domain, p5, conserved antigenically and functionally between pRb and p53. Rationally designed drugs capable of interacting with these key molecular sites [142–144] may exploit the coordinated regulation of the activity of these two tumor suppressors, or at least block the conformation of the tumor suppressor pRb into its active growth inhibitory hypophosphorylated structure (see also Fig. 1-3). One of the seemingly tragic, and yet unexplained, features of our human genome is that the key sensors that control many of the functional interactions between Rb and p53 are actually linked in the same gene, making them dually vulnerable to the same genetic attack. In general, mutational events that disable the Rb pathway and facilitate cell proliferation are counterbalanced by a p53-dependent response that eliminates, or at least inhibits, incipient cancer cells. Conversely, loss of p53 function enables cells sustaining oncogenic damage to survive and proliferate. Unfortunately for us, and as already pointed out, the INK4A-ARF locus encodes in the same genetic locus two distinct tumor suppressor proteins that regulate both the Rb and the p53 pathways. ARF (p19) is a sensor of inappropriate proliferation brought about by loss of Rb .The product of the p53 gene is part of the intrinsic mechanisms that monitor the cell cycle, for example inducing the expression of p21, and mutations, deletions, or underexpression of p19ARF facilitate Mdm2’s degradation of p53, leaving the cell unaware of the need to initiate cell death. It can be speculated (Table 1-1 and 1-2) that if effective and safe drugs were available to reactivate p53 (via MDM2 antagonists, or otherwise), in cancer cells with an intact pRb pathway or , in combination with effective inhibitors of pRb phopshorylation, if the upstream elements of this pRb pathway are also altered in the same cancer cells, then we could witness dramatic responses and perhaps even cures in up to 30–50% of the common solid cancers. Another potential mechanism to reactivate tumor suppressors is by epigenetic mechanisms. In this context, The US Food and Drug Administration (FDA) has approved Zolinza® (vorinostat) capsules for the treatment of advanced cutaneous T-cell lymphoma (CTCL), to be used when the disease persists in spite of conventional therapy, gets worse, or comes back during or after treatment (www.fda.gov/bbs/ topics/NEWS/2006). ZOLINZA®’s generic name is vorinostat, also known as suberoylanilide hydroxamic acid, or SAHA, is the first in a new class of anticancer therapies called histone deacetylase (HDAC) inhibitors. Histone deacetylation is thought to be a mechanism for silencing some tumor suppressor genes and other genes responsible for cell cycle progression, cell proliferation, programmed cell death (apoptosis), and differentiation (transformation of young cells into specialized cells), by altering the gene expression patterns secondary to chromatin three-dimensional structure. Vorinostat

22

was approved as part of FDA’s Orphan Drug program, which offers companies financial incentives to develop medications for diseases affecting fewer than 200,000 American patients a year. Evidence of Zolinza’s safety and effectiveness was developed in two clinical trials with 107 CTCL patients who received Zolinza after their disease had recurred following other treatments. A response, defined by improvements on a scale that scores skin lesions, occurred in 30% of patients who received Zolinza and lasted an average of 168 days. In summary, relevant molecular targets offer us a tremendous opportunity for both cancer prevention and cancer therapy. They are also of increasing use as predictive or prognostic markers of malignant disease, as reviewed “on balance” by Daniel Hayes in his chapter, and they have also entered the field of “molecular imaging” [145–150].With the aid of a cyclotron, for example, it is possible to develop positronemitting isotopes of elements that are easily incorporated into biological molecules such as fluorine-18, carbon-11, nitrogen-13, and oxygen-15, thereby allowing the direct in vivo detection and visualization of parts of the body with activated metabolic pathways (the classic is basic glucose metabolism with fluorine-18 linked to deoxyglucose. The disadvantage of PET radiopharmaceuticals is that they tend to have relatively short half-lives (from approximately 2 hours to some 20 minutes), and require for the generation an on-site cyclotron to produce them. An exciting development will be the production of efficient methods of generating and detecting fluorine-18 labeled nucleotides (like thymadine) that could be used to study cell tumor kinetics in vivo. In my opinion, this is crucial to the understanding of both human tumor biology and the assessment of clinical response to new cytostatic drugs, like many of the new “targeted therapies.” Thus when clinicians talk about “stable diease” or “stabilization” following some new drug trial, the doubt remains as to what was the natural cell kinetics in vivo of the tumor. To know beforehand the expected “doubling time,” “growth fraction” or even “mitotic index” would provide very valuable data, because there is great variability in the cell kinetics of tumors. In any case, the introduction of these more precise imaging techniques [145–150], and of these new targeted therapies have led to important changes in both response criteria and clinical trial design [151–154]. For example, new imaging techniques outdated most of the WHO criteria of response to therapy, first adopted in the late 1970s, and prompted the introduction of the so-called RECIST criteria for oncological clinical trials (Response Evaluation Criteria in Solid Tumors) from the year 2000. Neither these new diagnostic imaging tests [145–150] nor the new targeted drugs are “cheap”, and the relevance of financial considerations is of increasing importance to all health-care providers, including experimental oncologists, practicing oncologists, hematologists, radiotherapists, and cancer surgeons [153–154].

M.H. Bronchud

1.4.3 New Approaches to Targeting Loss-Of-Function Mutations in Tumor Suppressor Genes Loss-of function mutations in tumor-suppressor genes have been unfairly neglected so far for target-based drug discovery in cancer [97], mostly perhaps because it is easier to design small molecules to inhibit the gain of function in oncogenes than to restore or reactivate the function of tumor-suppressor genes. The case of Tumor Suppressor Genes is certainly more complex than the case of Oncogenes, because the ideal therapy in the former, where the problem is usually a loss-offunction mutation (e.g., a deletion in a gene or a hypermethylation of the promotor), would be a genetic replacement of the wild type. But gene therapy might not be the only answer to this problem. Drugs designed to target epigenomic changes [155] and, for example, re-activate the transcription of suitable tumor suppressor genes, or drugs that can reactivate cryptic proteins domains in tumor suppressor proteins or decrease the rate of degradation of tumor suppressors, thereby increasing their functional half-lives, are certainly possible, and already under study. Unfortunately, it could well happen that in cancers that are too advanced or evolved, there may not be any more “latent” or “relevant” tumor suppressor functions left as potential target for this new class of compounds that can be known as “Tumor Suppressor Activators (TSAs). Hence the importance of combining earlier detection of cancers (hence the need for more research on preventive cancer medicine), with the discovery and development of better and more effective drugs [97]. Unlike oncogenes, tumor suppressor (TS) genes (formerly “anti-oncogenes”) display the function of suppressing cellular proliferation, and/or maintaining cellular differentiation, facilitating normal cellular adhesion mechanisms, stopping entry into the cell cycle (G1, S-phase, mitosis or G2) to allow DNA-repair mechanisms, maintaining normal cellular shape, cell contact inhibition mechanisms, and senescence. The tumor suppressor paradigm characteristically calls for loss of both functional copies of the gene and indeed many, but not all, tumor suppressor genes must undergo biallelic inactivation to sustain a true loss-of-function effect. However, a substantial number of cancer cells retain at least one “functional copy” (wild-type or mutant) of one or several key TS genes. Wang et al. [156], at the Translational Genomics Research Institute in Phoenyx, AZ, directed by Daniel D. Von Hoff, have recently reviewed the targeting of loss-of-function mutations in tumor-suppressor genes as a strategy for development of cancer therapeutic agents. So called “synthetic lethal screening” has been a genetic technique used to identify mutations that are lethal in combination. Von Hoff’s recent research efforts are directed towards the discovery and development of what they have named “pharmacologic synthetic lethal screening” (PSLS), by cleverly using molecular techniques such as siRNA, RNAi or antisense molecules to identify genotypeselective anti-tumor agents.

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Chapter 2 Clinical Importance of Prognostic Factors Moving from Scientifically Interesting to Clinically Useful N. Lynn Henry and Daniel F. Hayes

2.1

Introduction

The term prognostic factor, when used regarding patients with malignancies, has taken on several meanings. In general, a prognostic factor is considered to be useful because its results serve to separate a large heterogeneous population into smaller populations with more precisely predictable outcomes. In theory, if this separation is both reliable and disparate, one can apply therapy more efficiently to the population by exposing those most likely to need and benefit from the therapy while ensuring that the other group avoids needless toxicities. In essence, the term tumor marker has come to describe a variety of molecules or processes that differ from the norm in the malignant cells, tissues, or fluids of patients with malignancies. Assessment of these alterations from normal can be used to place patients into categories that are distinguished by different outcomes, either in the absence of specific therapy, or after various treatments are applied. Tumor markers can include changes at the genetic level (e.g., mutations, deletions, or amplifications), the transcriptional level (e.g., over- or underexpression), the translational or post-translational level (e.g., increased or decreased quantities of protein, or abnormal glycosylation of proteins), and/ or the functional level (e.g., histologic description of cellular grade or presence of neovascularization). Each of these can be assessed by one or more assays, which uses one or more methods with differing reagents. This enormous heterogeneity of approaches is the root of considerable confusion regarding the true value, in clinical terms, of a given tumor marker. The “molecular revolution” is now well into its fourth decade. Yet, in spite of impressive advances in our understanding of the biology of human malignancy, and in the technology of investigating molecular processes, the number

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

of clinically useful products from these advances is disappointing. For example, in 1995, the American Society of Clinical Oncology (ASCO) first convened a panel of experts to establish guidelines for the use of tumor markers in colon and breast carcinoma. Although the expert panel reviewed many putative markers (including both tissue-based and circulating markers), its ultimate recommendations were surprisingly sparse (Table 2-1) [1, 2]. In its first deliberations, the panel felt that none of the newer molecular markers (e.g., erbB-2, p53, cathepsin D) was established in a scientifically rigorous fashion to be reliable and definitive. The most recent update from the year 2000, however, reflect some progress in the field, with recognition of erbB-2 (HER2) as a potential marker for sensitivity or resistance to certain standard therapies against breast cancer, and, more importantly, as a target of specific therapy itself [3, 4]. Why are the ASCO guidelines so conservative? In reviewing the available literature, the panel recognized that the science of clinical tumor marker investigation has been haphazard and relatively chaotic. Too often, studies of tumor markers are more inclined to “fishing expeditions” with the hope that something interesting will be detected with statistical significance, rather than being prospective, hypothesis-driven investigations. In light of this confusion, several authors of the guidelines separately developed a proposal for a framework in which previously published tumor marker studies might be critically evaluated. The authors also suggested that this framework might be used by investigators to plan future studies in a fashion that leads to more rapid acceptance, or refutation, of a given marker in the clinical arena. Details of this system, designated the Tumor Marker Utility Grading System (TMUGS), have been published elsewhere [5]. The contents of the current review will apply the principles of TMUGS to examples of evaluations of tumor markers in solid tumors, especially breast cancer, although these systems are certainly applicable to other malignancies in general. Recently developed reporting recommendations intended to guide researchers when designing and publishing tumor marker studies will also be discussed [6]. 27

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N.L. Henry and D.F. Hayes

Table 2-1. American Society of Clinical Oncology clinical practice guidelines for use of tumor markers in breast cancer (tissue factors only). Factor

Use

Guideline

Estrogen and progesterone receptors

Predictive factors for endocrine therapy

DNA flow cytometrically derived parameters erbB-2 (HER-2/neu)

Prognosis or prediction Prognosis Prediction for: trastuzumab CMF-like regimens doxorubicin taxanes endocrine Rx

p53 Cathepsin-D

Prognosis or prediction Prognosis

Measure on every primary breast cancer and on metastatic lesions if results influence treatment planning Data are insufficient to recommend obtaining results Data are insufficient to recommend obtaining results for this use erbB-2 should be evaluated on every primary breast cancer at time of diagnosis or at time of recurrence for use as predictive factor for trastuzumab; Committee could not make definitive recommendations regarding CMF-like regimens. erbB-2 may identify patients who particularly benefit from anthracycline-based therapy but should not be used to exclude anthracycline treatment. erbB-2 should not be used to prescribe taxane-based therapy or endocrine therapy Data are insufficient to recommend use of p53 Data are insufficient to recommend use of cathepsin-D

Modified from Bast RC Jr, Ravdin P, Hayes DF, et al. 2000 Update of recommendations for the use of tumor markers in breast and colorectal cancer: clinical practice guidelines of the American Society of Clinical Oncology. J Clin Oncol. 2001;19:1865–1878.

2.2 Importance of Tumor Markers: Adjuvant Systemic Therapy of Breast Cancer as a Case Study From the 1950s until about 1985, the annual odds of mortality because of breast cancer per 100,000 women increased steadily in the United States and other western countries (Fig. 2-1). In the mid-1980s, however, age-adjusted, breast cancer-mortality rates plateaued for women in the Western world, and, more recently, mortality from breast cancer has taken a rather dramatic decline [7]. Although screening and early application of local therapy (surgery, radiation) may have contributed to this decline, it is likely that these encouraging statistics are at least in part the result of widespread application of systemic therapy, including endocrine and chemotherapy [7, 8]. Indeed, several meta-analyses of worldwide data from prospective randomized clinical trials have confirmed that adjuvant systemic therapy reduces breast cancer recurrence rates by approximately 25% and, more importantly, mortality by approximately 15% in the population of women who participated in these trials, without further subgroup analyses [9–12]. These studies are not trials of treatment versus no treatment. Rather, they are trials of early treatment of the entire population versus later treatment of only those who have disease recurrence, if and when metastases occur. Because recurrent breast cancer is rarely if ever cured [13], these data illustrate the high stakes in making decisions about adjuvant systemic therapy. Given this dramatic and life-saving progress, should all patients with newly diagnosed breast cancer be treated with all available therapy to ensure maximum benefits? Application of systemic therapy to all patients with breast cancer would be inefficient, with the majority of patients being exposed to toxicities of therapy for little or no benefit. One might argue that the toxicities of endocrine therapies, such as

tamoxifen or aromatase inhibitors, are sufficiently tolerable that these therapies are acceptable to most if not all women. Tamoxifen is now used as a “chemopreventive” or “chemoprophylactic” to reduce risk of new breast cancers in women at high risk who have never had the disease [14]. Tamoxifen, however, causes occasional life-threatening toxicities (thromboses, second malignancies). Even the aromatase inhibitors, which may have fewer life-threatening toxicities compared with tamoxifen, at least with short follow-up, are not used indiscriminately because of side effects [15]. The side effects of chemotherapy are more dramatic, including nausea, vomiting, fatigue, and risk of infection and bleeding, and potential long-term complications such as second malignancies and congestive heart failure. Factors that might identify those patients most likely to have disease recurrence (designated prognostic factors), and factors that might identify those patients whose disease is most likely to respond to specific therapies (designated predictive factors), would be extraordinarily helpful; however, these factors need to be accurate. If they are not, women who are likely to benefit will be excluded from therapy, blunting the decline in mortality discussed previously.

2.3

Prognosis versus Prediction

Estimating a patient’s prognosis requires a complicated set of evaluations, which includes the propensity of a malignancy to expand in volume (proliferative capacity), its ability to escape its natural site of origin and establish growth in a foreign tissue (metastatic potential), and its relative sensitivity or resistance to therapy. Therapies for most solid tumors include surgery, radiation, systemic therapies, hormone therapies, or chemotherapies. In this regard, the terms prognostic and predictive have taken on separate meanings [16, 17]. The prognostic factor designation is usually reserved for those markers

2. Clinical Importance of Prognostic Factors: Moving from Scientifically Interesting to Clinically Useful Fig. 2-1. Age-standardized breast cancer death rate of women aged 35 to 69 years in the United States from 1950 to 2001. (From Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet. 2005;365:1687–1717. With permission.)

29

80

70

Lung

60

50

Breast

40

30

20

Colon and rectum Uberus

10

Stomach 0 1950

that specifically provide an estimate of the odds of a given cancer’s recurrence after local therapy alone. It is usually a measure of both proliferation and metastatic potential, and it usually implies the odds of systemic recurrence or death in a patient who does not receive systemic therapy. A schematic illustration of a pure prognostic factor is provided in Fig. 2-2A. In this case, in the absence of therapy, patients who are positive for the prognostic factor have a worse outcome than those who are negative. Therapy may be effective, but it is equally so (in relative terms) for both factorpositive and factor-negative patients, and therefore the curves from no treatment to treatment for factor-positive and factornegative patients are parallel. The prognosis for factor-negative patients is so favorable that only a few patients, at most, will benefit, even from very effective therapy. Therefore, a prognostic factor is most helpful in determining if a patient is likely to be cured by the prior therapy, such as local therapy alone (surgery or radiation therapy or both), or whether he or she is more likely to have a subsequent recurrence. If so, and if therapy is available that has demonstrated efficacy in that setting, knowledge of an individual’s prognosis permits reasonable decision-making regarding whether or not appli-

1960

1970 1980 Years

1990

2000

2010

cation of further therapy is indicated, especially if the therapy is associated with modest-to-severe toxicities. The best examples of prognostic factors for most solid tumors are the tumor-node-metastasis (TNM) staging systems [18]. A predictive factor is a tumor marker that helps select therapies most likely to work against a patient’s tumor. A predictive factor may be the precise target of the therapy, an associated molecule or pathway that modifies the effectiveness of the therapy, or simply an alteration that is an epiphenomenon linked to the target or pathway of the therapy (such as high levels of proliferation or coamplification of a neighboring gene). A factor that purely predicts benefit from therapy (a positive predictive factor) is illustrated in Fig. 2-2B. In this case, the prognosis in the absence of therapy is the same for factor-negative and factor-positive patients (i.e., the factor has no prognostic effects). Factor-positive patients, however, have a much better prognosis than factor-negative patients in the presence of the therapy for which the factor is predictive, and therefore the curves are not parallel. For example, it is clearly established that estrogen receptor (ER) content in breast cancer tissue is positively related to the odds of response and benefit from antiestrogen hormonal therapy, such as ovarian ablation,

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N.L. Henry and D.F. Hayes

A

PURE PROGNOSTIC FACTOR (Unfavorable)

Factor Neg

Good P r o g n o s i s

Factor Neg Factor Pos

Factor Pos

Poor

Factor Pos

Good P r o g n o s i s

Factor Neg Factor Pos Factor Neg

Poor

No Therapy

MIXED FACTOR (Favorable Prognostic/ Favorable Predictive)

Factor Pos

Good

Factor Pos

Factor Neg

Poor No Therapy

No Therapy

Therapy

C

P r o g n o s i s

PURE PREDICTIVE FACTOR (Favorable for response)

B

Factor Neg

Therapy

Therapy

MIXED FACTOR (Unfavorable Prognostic/ Favorable Predictive)

D

Good P r o g n o s i s

Factor Pos Factor Neg Factor Neg Factor Pos

Poor No Therapy

Therapy

Fig. 2-2. Schematic representation of prognostic and predictive factors. A Illustration of pure prognostic factor that is associated with unfavorable prognosis. B Illustration of pure predictive factor that is associated with response to specific therapy. C Illustration of mixed factor that is associated with favorable prognosis and favorable response to therapy. D Illustration of factor that is associated with unfavorable prognosis but favorable response to therapy. (Modified from Hayes DF, Trock B, Harris A. Assessing the clinical impact of prognostic factors: When is “statistically significant” clinically useful? Breast Cancer Res Treat. 1998;52:305–319. With permission.)

tamoxifen, or aromatase inhibitors, because ER plays a fundamental role in estrogen-dependent tumor growth and biology [19]. By contrast, p-glycoprotein content is a negative predictive factor for resistance to certain drugs, because this protein modulates multidrug resistance by increasing efflux of the antineoplastic agent from the cancer cell [20]. In real life, many if not most factors may be both prognostic and predictive (Fig. 2-2C). For example, in addition to serving as a strong predictive factor, ER is also a weakly favorable prognostic factor. Breast cancers with high ER content have generally slower growth potentials, and patients

with ER-positive tumors have a better prognosis, even if they receive no treatment [21, 22]. To further complicate this discussion, some markers may be associated with a poor prognosis independent of therapy, but they may predict for an improved outcome related to specific treatment modalities (Fig. 2-2D). One such marker in breast cancer is the erbB-2 (HER-2, c-neu) proto-oncogene. Since 1987, conflicting results from several studies have been reported regarding whether erbB-2 amplification or overexpression or both is a marker of poor prognosis [23–26]. erbB-2 is also a predictive factor. To add to the confusion, however,

2. Clinical Importance of Prognostic Factors: Moving from Scientifically Interesting to Clinically Useful

it may be a predictive factor for response to some therapies and resistance to others. For example, erbB-2 appears to predict relative resistance to hormone therapy and to alkylating agents, but sensitivity to anthracyclines [27–31]. More strikingly, erbB-2 serves as the target for a humanized monoclonal antibody, trastuzumab. Response to and benefit from trastuzumab is closely linked to erbB-2 amplification or overexpression or both, which was initially demonstrated in the metastatic setting [32, 33], and recently was shown to result in significantly improved outcomes in the adjuvant setting as well [34–36]. These considerations are often ignored in many prognostic factor studies. Rather, a population of patients is studied with a new, putative prognostic factor simply because the samples to be assayed are available and the outcome for the patients is known. Indeed, a prognostic factor can only be evaluated in the absence of systemic therapy, or at least in the absence of any therapy with which it interacts. A predictive factor can only be evaluated in the context of an untreated control group, preferably one that is prospectively identified and followed, as in prospective randomized trials. It is not surprising that studies of a marker that might have both prognostic and predictive capabilities, especially if these effects are in opposition (as may be the case with erbB-2), will provide relatively random and conflicting results if not carefully planned with both appropriate consideration of treatment effects and selection of satisfactory control groups.

2.4 How should Tumor Markers be Selected for Clinical Use? Ideally, a specific therapy will benefit all those to whom it is administered, and no patient will be exposed to toxicity needlessly. In an imperfect world, however, only a fraction of patients who receive a given treatment will benefit, whereas all are at risk for the side effects. Although identification of favorable and poor-prognosis subgroups is important, simply having a poor prognosis is not justification for treatment. Indeed, many patients will have tumors that are already resistant to specific treatments. In this case, predictive factors will permit selection of those patients who will benefit from the specific therapy. Unfortunately, treatment for the other patients may not be available or as effective. Therefore, even though their prognosis may be relatively poor, it is unreasonable to expose them to toxicity with no benefit. Do prognostic and predictive factors exist that permit such elegant selection of patients for treatment? Sadly, in most solid tumors, the answer is no. For patients with newly diagnosed solid malignancies, no prognostic factors predict subsequent recurrence and death with absolute certainty. Therefore, when they are applied in the clinic, both physician and patient must accept some margin of error. These decisions involve a careful assessment of several issues: the degree of separation

31

in outcomes between groups of patients defined by the marker results (marker strength), the reliability of the estimate of this degree of separation (assay methodology and statistical analysis), the magnitude of effectiveness of therapy for the patient’s condition (proportional reduction in risk of events), the degree of toxicity of that therapy, and the patient’s willingness (as well as the caregiver’s and society’s) to either forego potential benefit to avoid toxicity or to accept toxicity and cost to gain benefit. Part of the art and science of medicine is to determine which markers are most reliable in separating groups of patients who will do well from those who will not, and who will benefit from therapy from those who will not. If done appropriately, tumor-marker analysis should permit delivery of therapy as efficiently as possible, providing benefit to the greatest number of patients while avoiding exposure to toxicities as much as possible.

2.5 Recommending Therapy: How Much Benefit is needed to Justify Treatment? With an estimate of the odds of an event in the absence of therapy (the patient’s prognosis), and an understanding of the proportional reduction in the odds of an event (such as recurrence or death) because of application of therapy (prediction that a specific therapy will work for a given patient), one can calculate an approximate absolute chance of that patient benefiting from the therapy. Again, adjuvant therapy for breast cancer provides a useful example. One might estimate, using standard prognostic factors, that in the absence of systemic therapy a patient has a relatively high (e.g., 60%) chance of recurrence and death over the succeeding 10–15 years after diagnosis. Using predictive factors, one can also estimate the proportional reduction in this chance of recurrence (e.g., 30%) when adjuvant systemic therapy is applied to a population of women with similar characteristics. In this case, a 30% proportional reduction of a 60% absolute risk reduces the odds of an incurable recurrence by 20%. Put another way, 20% of women who would have had recurrent disease if untreated will not as a result of treatment. In this example, the odds of being cured increase from 40% to 60%. Consider another example: the same patient has a favorable prognosis (e.g., a 10% chance of recurrence over 10–15 years) in the absence of systemic therapy. Applying a similar predictive factor profile, the same therapy will still result in a 30% proportional reduction in events. In this case, only 3% of patients will benefit, because 90% are cured by local therapy alone. In a third example, if the same patient has a 10% chance of recurrence, but the patient’s predictive marker profile suggests a 70% proportional reduction in recurrence or death, then the absolute benefit is 7%. If you were the first patient, would you undergo 3–6 months of chemotherapy for a 20% improvement in the chances of being alive and disease free for the next 10 years? If you were

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N.L. Henry and D.F. Hayes

the second patient, would you agree to the same therapy for only a 3% improvement in survival? What if you had a favorable prognosis, but your chance of benefit was 7%? Several investigators have tried to address this subjective decisionmaking process with questionnaires that pose these dilemmas to respondents regarding adjuvant therapy for breast cancer [37–39]. Such studies are difficult to conduct, however, because an appropriately representative population is not readily identified. Unaffected subjects who are asked to serve as surrogates may not have the same perceptions as they might have if truly afflicted with the disease. Patients who must actually decide are often anxious and unsure, and their hypothetical answers may not reflect their true actions. Survivors who are separated in time from the point of making their decision may have considerable cognitive bias, because they may be more willing to accept the therapy that they perceive has led to their current state of well-being. Nonetheless, these studies have demonstrated remarkably similar and striking conclusions. For example, in one study, previously treated survivors were asked if they would reaccept adjuvant chemotherapy (cyclophosphamide, methotrexate, and 5-fluorouracil [CMF]) for 6 months, placed in the context of various prognostic scenarios [37]. As expected, most patients stated that would take therapy again when the gains were large (> 10% absolute benefit), and a decreasing fraction would be willing to do so as potential gains diminished. More than 50% said they would undergo chemotherapy for gains as small as 3–5%, and nearly 50% would be willing to accept therapy for as little as a 1% absolute improvement in outcome (Fig. 2-3) [37]. Nonetheless, given that a substantial proportion of patients would not accept therapy for an absolute benefit < 10%, accurate assessments of prognosis and prediction are essential. Similar scenarios can be generated for nearly all medical decision-making situations, assuming that the odds of event

occurrence, the proportional odds of reduction of the event, and the toxicities are well established. Computer models to help breast cancer patients estimate their absolute risks and benefits are now available on the World Wide Web [40–42].

2.6 How Can the Relative Strength of a Prognostic Factor be Determined? Prognostic and predictive factors can be placed into categories based on their relative strengths to divide a single population into two or more subgroups that have distinct outcomes (Figs. 2-4A and 2-4B) [43]. Let us consider 2 prognostic factors (Fig. 2-4A). One factor separates the population very strongly, so that factor-negative patients are very likely to be cured by A

PURE PROGNOSTIC FACTOR (Unfavorable) 100% cure

P r o g n o s i s

Factor 1 Neg

p<0.05

Factor 2 Neg 50% cure

10% cure

Factor 2 Pos

Factor 1 Pos No Therapy

Therapy

PURE PREDICTIVE FACTOR (For Sensitivity to Therapy)

B

100 100% cure P r o g n o s i s

% 50

0 1

?

2

3

5 10 ? 15 ? Scenario: % extra survival

25

Fig. 2-3. Fraction of patients with breast cancer who would accept 6 months of adjuvant chemotherapy according to added survival benefit. Previously treated breast cancer survivors were queried regarding whether they would be willing to be retreated with 6 months of adjuvant chemotherapy (cyclophosphamide, methotrexate, 5-fluorouracil) for different scenarios regarding added survival benefits. (Modified from reference 37 with permission.) Question marks represent different “cutoffs” that might be used to select therapy or not.

Factor 1 Pos p<0.05

50% cure

Factor 2 Pos Factor 2 Neg 10% cure

Factor 1 Neg No Therapy

Therapy

Fig. 2-4. Relative strengths of prognostic and predictive factors. A Relative strength of pure prognostic factor. B Relative strength of pure predictive factor. Factor 1 (—) is a strong factor while Factor 2 (— • — • —) is a relatively weak factor. Both factors reliably separate the population into 2 distinct groups not because of chance alone (p < 0.05).

2. Clinical Importance of Prognostic Factors: Moving from Scientifically Interesting to Clinically Useful

local therapy alone and factor-positive patients have a very poor prognosis. If effective therapy is available, a sufficient number of factor-positive patients will benefit so that most patients in that population will accept the therapy and its toxicities. The second factor may also reliably separate 2 groups of patients, with 1 group having a statistically significantly more favorable outcome than the other, but not by much. If effective therapy is available, a similar number of patients will benefit in both the negative and positive groups, exceeding the cutoff required for acceptance of therapy as described earlier. Thus, the clinician would likely use the first factor to help make decisions. Although recognition of the second factor might provide insight into the biology of the disease, it would not have clinical use. One can analyze predictive factors similarly (Fig. 2-4B). A strong predictive factor provides an indication that the therapy is so effective in factor-positive patients and unlikely to be very effective in factor-negative patients that, if the prognosis warrants therapy at all, the two groups of patients would be treated differently. By contrast, a weak predictive factor may provide an indication that factor-positive patients are a little more likely than factor-negative patients to benefit. The p-value suggests that the difference in efficacy between factor-positive and factor-negative patients is unlikely to be because of chance alone, but that the benefit for the factornegative patients is still likely sufficient to justify exposure to the therapy. For patients with newly diagnosed breast cancer, we have proposed 3 arbitrary categories for both prognostic and predictive factors, based on relative strengths: weak, moderate, and strong [43–45]. Let us assume that one can place a patient into 1 of 3 prognostic categories that fundamentally affects how he or she is treated. Patients with a very good prognosis might not accept any therapy, patients with a modest prognosis might accept some therapy, and those with a poor prognosis would be willing (assuming that effective therapy is available) to accept even more therapy or therapy with more toxicity (Fig. 2-5). A strong prognostic factor is one that moves a patient across 2 of these arbitrary prognostic categories, e.g., from very good to poor (Fig. 2-5). A modestly strong prognostic factor moves a patient less far. A weak factor may improve or worsen a patient’s prognosis, but by so little that it is clinically meaningless. These arbitrary categories will differ depending on the disease, the setting, and the investigator/clinician and the patient. Again, using breast cancer as an example, we have proposed that breast cancer prognostic factors that divide the population into subgroups that differ in outcomes (risk of recurrence over 6–10 years) by twofold or more are considered strong. Good examples of strong prognostic factors include clinical stage, pathologic identification of involved axillary lymph nodes, and estimation of tumor size. Prognostic factors that divide the population into subgroups that differ by 1.5- to 2-fold are considered moderately strong. These include tumor grade and perhaps levels of cellular proliferation. Weak prognostic factors divide

33

PROGNOSTIC FACTOR Strong Poor

Modest

Very Good

Modest

Weak

50-100%

10-50%

<10%

PROGNOSIS (Odds of Dying at Ten Years if No Adj Sys Rx) Fig. 2-5. Schematic model of relative strengths of strong, modest and weak prognostic factors. A strong prognostic factor moves patients across several prognostic categories. A modest prognostic factor might move a patient across 1 category whereas the weak prognostic factor does not move a patient outside of his/her original prognostic category. Prognostic categories are arbitrarily assigned. (Modified from Isaacs C, Stearns V, Hayes DF. New prognostic factors for breast cancer recurrence. Semin Oncol. 2001;28:53–67. With permission.)

the population into subgroups with outcomes that differ by 1- to 1.5-fold, and include estimates of ER expression and possibly erbB-2 overexpression or amplification or both. Likewise, one can also estimate the relative strengths of predictive factors. The strength of a predictive factor is best determined in the context of a prospective clinical trial in which patients are assigned randomly to the treatment of interest or not. The ratio of the likelihood that a factor-positive patient will benefit from treatment compared with a factornegative patient has been designated the relative predictive value (RPV) [46]. Estimations of the RPV are illustrated in Fig. 2-6 in which the risk of recurrence for treated patients is compared with the risk for untreated patients for each predictive factor category. As with prognostic factors, 3 categories of predictive factors have been proposed, based on RPV. For breast cancer, it is proposed that weak, moderate, and strong predictive factors have RPV of < 2-, 2- to 4-, and > 4-fold, respectively. The best example of a strong predictive value is ER for tamoxifen, with an RPV > 8-fold [10]. It also appears that erbB-2 amplification is a very strong predictor for benefit from trastuzumab, as has been demonstrated in both the metastatic and adjuvant settings [32, 34, 35, 47]. An additional concept in this discussion is the issue of residual risk after a selected course of therapy (Fig. 2-7). If multiple therapies are available, some patients may only need one to achieve a prognosis sufficient to avoid further or more therapy, whereas others may benefit from additional or more aggressive or more toxic approaches. The residual risk is a function of both original prognosis and the relative benefit from specific therapies. A group of patients may have an original

34

N.L. Henry and D.F. Hayes

Determining Relative Strength of Predictive Factors Therapy Better

Relative Predictive Value= RPV (pos)/RPV (neg)

Weak negative positive Moderate negative positive Strong negative positive 0.60

0.70

0.2/0.1 =

2

0.25/0.08 =

3.125

0.4/0.05 =

8

0.80 0.90 1.0

Relative Risk of Recurrence (Rx vs. No Rx)

Fig. 2-6. Schematic model for relative strengths of predictive factors. The relative benefit between groups of patients who are positive (solid bar) or negative (shaded bar) for the predictive factor are indicated. The difference in outcome, charted as proportional reduction in the odds of recurrence for treated versus untreated patients, is relatively small for groups of patients that are separated by a weak predictive factor. This difference becomes larger for those separated by a moderate predictive factor and is quite large for those treated by a strong predictive factor. The solid vertical line (unity) denotes no difference in recurrence between treated patients and untreated patients. (Modified from Hayes DF, Isaacs C, Stearns V. Prognostic factors in breast cancer: current and new predictors of metastasis. J Mammary Gland Biol Neoplasia. 2001;6:375–392. With permission.) RESIDUAL RISK 100% cure P r o g n o s i s

Factor 1 Neg

Would not give CTX even if available 10% cure

Would give CTX if available

Would not give more CTX even if available

Would give more CTX if available Factor 1 Pos

No Therapy

Therapy

Fig. 2-7. Schematic illustration of residual risk. See text and Figs. 2-2 and 2-4 for details of graphic. (—) = patients with little residual risk. (......) = patients with substantial residual risk. CTX: chemotherapy.

prognosis that is sufficiently poor to justify an initial therapeutic regimen. Some may respond so well and benefit so much that their post-treatment prognosis is so favorable that they would elect not to receive more treatment. Other patients might benefit less from the first approach. If further therapy is known to provide additional benefit sufficient to outweigh the risks, then these patients might accept it. Residual risk might

be estimated at baseline, before any therapy is given, using initial prognostic and/or predictive factors, as illustrated in the poor prognosis category in Fig. 2-5. For example, patients with node-positive breast cancer might be more willing to accept the increased toxicities of more therapy, such as addition of a taxane to treatment with doxorubicin and cyclophosphamide (AC), than patients with node-negative disease. Residual risk might better be assessed at the completion of the initial therapy, however, if markers are available that suggest residual disease burden exists. For example, recent studies of neoadjuvant therapy for breast cancer may permit clinicians to estimate the residual risk for patients after several rounds of chemotherapy (e.g., 4 cycles of preoperative AC) based on the presence or absence of residual invasive cancer in the operative specimen [48]. Because it has been established that these patients have a relatively poor prognosis, ongoing studies have been designed to determine whether these patients benefit from additional chemotherapy.

2.7 How Reliable are the Estimates of Relative Strengths of Tumor Markers? If clinicians use tumor markers to help patients avoid toxicities of therapy while still optimizing benefit, then they must be relatively confident of the estimates they have provided to patients. Clinical investigations of new cancer agents are carefully planned, using criteria and terminology that are generally agreed upon by most clinical scientists [49]. For example, new drugs are sequentially passed through phase 1, 2, and 3 studies, in which toxicity and dose, efficacy, and definitive use are determined, respectively. In these studies, scales have been developed to describe toxicities, responses, and overall outcomes. Such trials are prospectively planned, with detailed descriptions of the number and types of patients to be studied, how they will be treated, and how the statistical analysis will be performed. Indeed, these rules have been established so that the results of clinical studies approach the same veracity as those from laboratory investigations, in which variables and proper controls can be rigorously defined. Clinical studies that are not so rigorously defined, such as retrospective reviews of clinical experiences, may help generate hypotheses, but are rarely accepted as definitive. In the past, no such consensus system has existed to study tumor markers. More commonly, marker studies are performed using retrospectively available samples from patients treated in a nonuniform manner. Hypotheses are often generated after the data are analyzed, and then presented as fact. Even when multiple studies evaluating the same hypothesis are performed, the populations studied are often heterogenous and the methods often vary among investigators, which can be a source of bias, leading to invalid results, and the bias is frequently unrecognized [50]. Furthermore, negative

2. Clinical Importance of Prognostic Factors: Moving from Scientifically Interesting to Clinically Useful Table 2-2. Potential uses of tumor markers.

LIFE CYCLE OF A TUMOR MARKER

• Determination of Risk • Screening • Differential Diagnosis Benign vs Malignant Known Malignant: Tissue of Origin • Prognosis • Prediction • Monitoring Disease Course Detect Recurrence in Patient Free of Obvious Disease Patient with Established Recurrence

.

Positive Effect Benefit Neutral (RR=1)

First Paper

Second Third & Subsequent Paper Papers

35

Ultimate Estimate of RR (or Predictive Value)

Fig. 2-8. Hypothetical life cycle of a tumor marker.

results are usually not submitted for publication (unless to refute the results of a competing laboratory). It is not surprising that most tumor markers proceed through a typical life cycle before the true use is accepted or discarded (Fig. 2-8). In fact, progression through such a life cycle is common for new therapeutic ideas as well, but because the rules are better established the time required to reach consensus may be considerably shorter. We now return to the original TMUGS proposal [51]. Determination of relative strengths is only as good as the studies in which they are analyzed. In this regard, the relative quality of the studies is essential in reaching consensus about the strength of the marker. TMUGS was proposed to shorten the life cycle of tumor-marker analysis. One component of

TMUGS is the importance of a precise description of the tumor marker and the assays used to detect it. Tumor markers can be used for multiple purposes, ranging from screening for disease to monitoring progression (Table 2-2). A semiquantitative scale, which ranges from 0 to 3+, was developed to grade the clinical use of a tumor marker for any specific use (Table 2-3). For example, to assess whether a marker should be used to determine prognosis, users are urged to assign a score based on their interpretation of the available published data. A grade of 0 implies that sufficient data exist to conclude that the marker has no utility, whereas a grade of 2+ or 3+ implies that the marker should be considered or that it absolutely should be used, respectively, in routine clinical practice. More importantly, users are encouraged to support their evaluation by determining the level of evidence (LOE) on which their decision is based (Table 2-4). LOE I data are generated either from a prospective, highly powered study that specifically addresses the issue of tumor-marker use or from an overview or meta-analysis of studies, each of which

Table 2-3. Scale to evaluate use of tumor markers for favorable clinical outcomes. Use scale 0 NA +/−

+

Explanation of scale Marker has been adequately evaluated for a specific use and the data definitively demonstrate it has no use. The marker should not be ordered for that clinical use. Data are not available for the marker for that use because marker has not been studied for that clinical use. Data are suggestive that the marker may correlate with biological process and/or endpoint, and preliminary data suggest that use of the marker may contribute to favorable clinical outcome, but more definitive studies are required. Thus, the marker is still considered highly investigational and should not be used for standard clinical practice. Sufficient data are available to demonstrate that the marker correlates with the biological process and/or endpoint related to the use, and that the marker results might affect favorable clinical outcome for that use. However, the marker is still considered investigational and should not be used for standard clinical practice, for 1 of 3 reasons: 1. The marker correlates with another marker or test that has been established to have clinical use, but the new marker has not been shown to clearly provide any advantage. 2. The marker may contribute independent information, but it is unclear whether that information provides clinical use because treatment options have not been shown to change outcome. 3. Preliminary data for the marker are quite encouraging, but the level of evidence (see below) is lacking to document clinical use.

++

+++

Marker supplies information not otherwise available from other measures that is helpful to the clinician in decision making for that use, but the marker cannot be used as sole criterion for decision-making. Thus, marker has clinical utility, and it should be considered standard practice in selected situations. Marker can be used as the sole criterion for clinical decision making in that use. Thus, marker has clinical utility, and it should be considered standard practice.

From Hayes DF, Bast R, Desch CE, et al. A tumor marker utility grading system (TMUGS): A framework to evaluate clinical utility of tumor markers. J Natl Cancer Inst. 1996;88:1456–1466.

36

N.L. Henry and D.F. Hayes

Table 2.4. Levels of evidence for grading clinical use of tumor markers. Level I

II

III

IV V

Type of evidence Evidence from a single high-powered prospective study that is specifically designed to test marker or evidence from meta-analysis and/or overview of level of evidence II or III studies. In the former case, the study must be designed so that therapy and follow-up are dictated by protocol. Ideally, the study is a prospective randomized trial in which diagnostic and/or therapeutic clinical decisions in one group are determined based at least in part on marker results, and diagnostic and/or therapeutic clinical decisions in control group are made independently of marker results. However, may also include prospective but not randomized trials with marker data and clinical outcome as primary objective. Evidence from study in which marker data are determined in relationship to prospective therapeutic trial that is performed to test therapeutic hypothesis but not specifically designed to test marker use (i.e., marker study is secondary objective of protocol). However, specimen collection for marker study and statistical analysis are prospectively determined in protocol as secondary objectives. Evidence from large but retrospective studies from which variable numbers of samples are available or selected. Therapeutic aspects and follow-up of patient population may or may not have been prospectively dictated. Statistical analysis for tumor marker was not dictated prospectively at time of therapeutic trial design. Evidence from small retrospective studies which do not have prospectively dictated therapy, follow-up, specimen selection, or statistical analysis. May be matched case controls, etc. Evidence from small pilot studies designed to determine or estimate distribution of marker levels in sample population. May include “correlation” with other known or investigational markers of outcome, but not designed to determine clinical use.

From Hayes DF, Bast R, Desch CE, et al. A tumor marker utility grading system (TMUGS): A framework to evaluate clinical utility of tumor markers. J Natl Cancer Inst. 1996;88:1456–1466.

provides lower LOE. LOE II data are derived from companion studies in which specimens are collected prospectively as part of a therapeutic clinical trial, with pre-established endpoints and statistical evaluation for the marker as well as for the therapeutic intervention. Commonly, an early LOE III study will report an extraordinary difference between 2 groups delineated by a given tumor marker analysis (Fig. 2-8). Results from subsequent studies are often more inconsistent. Therefore, we have proposed that the relative strength of a marker for clinical utilities should only be determined within the context of LOE I (or at worse LOE II) studies. In these studies, the marker is the primary objective of a well-designed, highly powered, hypothesis-driven prospective clinical trial, or it is the objective of a statistically rigorous overview of LOE II or III studies or both. Furthermore, the strength of new prognostic or predictive factors can only be estimated by multivariate analytical methods, including pre-existing, accepted factors such as TNM staging and histopathology. It is possible that a marker may be quite prognostic or predictive when considered in a univariate fashion, but that it in fact is only reflecting information already achieved through other, established methods. In this case, acceptance of the new marker would only occur if it can be performed more easily, reliably, or less expensively. Unfortunately, most tumor-marker studies are LOE III, in which specimens happen to have been collected for a variety of reasons and are available for testing a given assay. In general, the authors of TMUGS suggested that results from LOE I studies are preferred to assign clinical use to a marker. Use of a system such as TMUGS to rigorously assess the reliability of assessment of the relative strengths of prognostic and predictive factors will substantially strengthen the clinicians’ confidence as they counsel their patients.

2.8 How Can the Relative Strengths of Prognostic and Predictive Factors be Applied Clinically? Outside a clinical trial, there is little value in determining that a patient has a poor prognosis unless therapy is available to change that prognosis. Moreover, if the patient or physician is unwilling to give up any benefit, regardless of how small and despite the risks, application of tumor markers is unnecessary unless the results are 100% accurate. Likewise, if the patient is unwilling to accept any therapy regardless of how large the benefit or how well tolerated the treatment, there is no point in applying tumor-marker data. In most cases, the patient and physician wish to apply therapy relatively efficiently. In this case, if the patient can judge how much benefit he or she is willing to forfeit to avoid toxicities, one can construct a model in which 1 marker might be used in some situations but not others [51]. Again, the example of application of adjuvant systemic therapy for patients with newly diagnosed breast cancer is used (Fig. 2-9). In this example, the following assumptions have been made: ●

●

●

Patients can be placed into 1 of the 3 prognostic categories based on the odds of systemic recurrence and death during the subsequent 10 years after diagnosis and local treatment in the absence of systemic therapy: very good (< 10% chance recurrence/death); moderate (10–50%); and poor (>50%); Patients would accept tamoxifen or an aromatase inhibitor for a small benefit (although not for no benefit at all), but that they would accept chemotherapy for only a 4% or higher absolute benefit; ER is a very strong predictive factor, such that tamoxifen and aromatase inhibitors proportionally decrease odds of

2. Clinical Importance of Prognostic Factors: Moving from Scientifically Interesting to Clinically Useful

Clinical Use of Prognostic and Predictive Factors

Clinical Use of Prognostic and Predictive Factors

PROGNOSIS

(Odds of Recurrence or Death at 10 Years if No Adj Sys Rx)

Poor

50-100%

PROGNOSIS 45 Year old woman with newly diagnosed breast cancer

(Odds of Recurrence or Deathat 10 Years if No Adj Sys Rx)

Very Good

40 Year old woman with newly diagnosed breast cancer

X

X

X Modest

37

X

10-50%

X

<10% ACx4 Pacl x 4

X

<10%

Very Good 4 + LN ERerbB2+

X

10-50%

Modest

Her x1y

2 +L N ER+ erbB2-

Tam

ACx4

X Pacl x4

Clinical Use of Prognostic and Predictive Factors PROGNOSIS (Odds of Recurrence or Death at 10 Years if No Adj Sys Rx)

Modest

Very Good

10-50%

65 Year old woman with newly diagnosed breast cancer

X

<10% 2cm Neg Nodes ER +

X Tam

X CTX

Fig. 2-9. Schematic illustration of use of prognostic and predictive factors to select appropriate treatments for individual patients with breast cancer (see text for details). CTX chemotherapy; Her trastuzumab; LN lymph node; Pacl paclitaxel; Tam tamoxifen.

●

●

recurrence by 40–45% in ER-positive patients and not at all in ER-negative patients [10, 15]; Different chemotherapy regimens can be applied in sequence with increasing benefits and toxicities, depending on predictive factors; and erbB-2 is strongly predictive for response to trastuzumab, resulting in approximately a 50% improvement in recurrence rate in women whose tumors overamplify erbB-2 [34, 35].

For example, 4 cycles of AC might proportionally decrease odds of recurrence by 33% in younger women (aged < 50 years), but by only 20% in older women (aged 50–69 years) [12]. We will also assume that 4 additional cycles of a taxane, such as paclitaxel, decrease the odds of recurrence proportionally by a further 20% in ER-negative patients, but perhaps not at all in ER-positive patients [52]. (Note: All of these assumptions are approximate estimates based on annual reduction of odds of recurrence calculations).

Figure 2-9 provides examples of how the combination of prognostic and predictive factors might be used. First, let us consider a 45-year-old patient with a 4-cm, poorly differentiated ER-negative, erbB-2 positive breast cancer with 4 of 10 involved axillary lymph nodes (Fig. 2-9A). This patient’s initial prognosis is poor. In the absence of systemic therapy, one might expect 60–70% of such patients to have a recurrence within the next 10 years. Endocrine therapy would not be expected to provide any benefit, and therefore would not be indicated. Four cycles of AC, with a proportional reduction of 33%, would prevent recurrence in 20–25% of patients, therefore reducing her absolute risk to approximately 50%. Four cycles of paclitaxel would be expected to further reduce her odds of recurrence proportionally by 20–30%, therefore reducing her absolute risk an additional 10–15%, to 35– 40%. Finally, 1 year of trastuzumab, started concurrently with paclitaxel therapy, would decrease her odds of recur-

38

rence proportionally by 50%, decreasing her absolute risk to approximately 20%, only one third of what her overall chance of recurrence was. In this case, most clinicians and patients would agree that the combination of chemotherapy and targeted therapy with trastuzumab is indicated. Next, consider a 40-year-old premenopausal woman with ER-positive breast cancer who has 2 positive axillary lymph nodes (Fig. 2-9B). In this case, in the absence of systemic therapy, one might estimate that her odds of recurrence over the next 10 years are approximately 50%. This patient would almost certainly find tamoxifen an acceptable adjuvant therapy, but would not be eligible for treatment with aromatase inhibitors because she is premenopausal. A proportional reduction of 40% would result in approximately 15–20 patients who would not have a recurrence, considerably exceeding the cutoff required for recommendation of the strategy. Even if the patient takes tamoxifen, however, her residual risk of recurrence over 10 years remains approximately 20–25%, still in the “moderate risk” category. Chemotherapy would result in an approximately 20–30% reduction of this 25% risk, and therefore approximately 5–7 additional patients would be alive and disease free because of the application of adjuvant AC. It would thus be reasonable to recommend 4 cycles of AC to this patient. Should this patient also receive 4 additional cycles of adjuvant paclitaxel? The answer depends on our confidence in the available data. Multiple prospective randomized clinical trials performed in the United States have addressed the use of sequential taxanes in this setting, but different dosing regimens and patient populations were evaluated. Two trials suggested that the addition of 4 cycles of paclitaxel after AC proportionally reduced the odds of recurrence and death by approximately 20% [52, 53]. An unplanned retrospective subset analysis of one of the trials suggested that this benefit was almost entirely confined to the ER-negative subgroup [54]. A third study compared AC plus 5-fluorouracil (FAC) with AC plus docetaxel (TAC), and found a 28% relative decrease in the risk of recurrence with the addition of the taxane. Other studies of taxanes have yielded contradictory or inconclusive information, however. Should the clinician wait for more mature data, probably pooled in a meta-analysis, before making decisions regarding this extra therapy? In this example, let us accept the data supporting the use of paclitaxel after AC. Furthermore, let us assume that all patients will have a further proportional reduction in recurrence of 20%, regardless of ER status. This patient’s residual risk of recurrence after tamoxifen and 4 cycles of AC is approximately 20% (Fig. 2-9B). A 20% proportional reduction of a 20% risk would result in a further absolute benefit of approximately 4% reduction of recurrence. Does this justify the therapy? This absolute benefit straddles the cut-off to treat or not, and the patient and her physician must discuss this issue carefully. Finally, consider a postmenopausal 65-year-old woman with a 2-cm, moderately differentiated ER positive breast

N.L. Henry and D.F. Hayes

cancer with no detectable axillary nodal involvement (Fig. 2-9C). In the absence of systemic therapy, her overall odds of recurrence over the next 10 years are approximately 20%. Therefore, she has an 80% chance of having been cured by local therapy alone. An aromatase inhibitor will proportionally reduce these chances by approximately 40%. Thus, for every 100 patients who are treated in this situation, 80 patients cannot benefit because they will not recur. Of the 20 patients who were destined to recur, 8 patients will not because of aromatase inhibitor therapy. Because aromatase inhibitors are relatively well tolerated, this percentage of absolute benefit exceeds our cut-off for recommending therapy, and most patients would accept it, resulting in an improvement of their expected cure rate from 80% to approximately 88%. Our assumptions suggest that chemotherapy would result in a further 20% proportional reduction in the risk of recurrence over 10 years for this group of patients. With aromatase inhibitor treatment, this patient has a residual recurrence risk of 12%. A proportional reduction by 20% of this risk represents 2–3 of 100 patients who might benefit. This number is below the cut-off that most, but not all, patients and clinicians consider worthwhile, especially given the toxicities of chemotherapy. However, some patients in this group are likely to have a difficult decision regarding whether or not to undergo chemotherapy. A promising new tool to determine prognosis has been developed for patients such as the one presented here, who have ER-positive, node-negative breast cancer. OncotypeDX is a multigene assay performed on fixed tumor tissue that is used to divide patients into 3 categories based on likelihood of disease recurrence (low, intermediate, and high) [55]. OncotypeDX is being evaluated as a predictive factor. Initial studies suggest that patients in the high-risk group are likely to benefit from chemotherapy, whereas those in the low-risk group are not [56]. It remains unclear how best to treat those in the intermediate group, however, and therefore a prospective, randomized clinical trial, designated TAILORx, has been recently opened. TAILORx will randomly assign patients in the intermediate group to chemotherapy plus endocrine therapy versus endocrine therapy alone. TAILORx and other LOE I trials may enable physicians and patients to make more informed decisions regarding the clinical use of new markers.

2.9 Are There Solid Tumor Markers that Fulfill the TMUGS Criteria for Routine Clinical Use? The previous examples regarding breast cancer illustrate how prognostic and predictive markers might be used to tailor patient care in the adjuvant setting. In all malignancies, markers might be used in one of several different situations

2. Clinical Importance of Prognostic Factors: Moving from Scientifically Interesting to Clinically Useful

(determination of risk, screening, differential diagnosis, prognosis, prediction, monitoring disease course) (Table 2-2) [5]. Different markers may perform differently in each situation for each disease (e.g., colon vs breast vs lung cancer). In general, the TNM staging system has been well accepted for prognosis for most if not all solid tumors [18]. The ASCO Guidelines Panel has made specific recommendations for breast and colon cancer based on data that they believe met criteria consistent with TMUGS (Table 2-1) [1–3]. In addition to those that have gained acceptance, newer assays such as OncotypeDX are being considered to determine if there is sufficient evidence to support routine use for prediction or prognosis or both. Few if any prognostic or predictive factors have been accepted for the other common solid malignancies, such as prostate, lung, and ovarian cancers [57–59]. For each, the TNM and grading scales are reliably prognostic. Serial circulating prostate specific antigen levels and CA125 levels are helpful in monitoring patients with prostate and ovarian cancers, respectively [58, 60]. For most solid tumors, however, better markers that have been well characterized using results from carefully designed and well performed studies are urgently needed.

2.10

Summary

In summary, the phrase “many are called, few are chosen” seems to reflect the current state of the art regarding tumor marker analysis in solid tumors. However, the field is evolving rapidly, with a convergence of molecular biology and technology and understanding of clinical trial design and analysis. Several of the large cooperative trialists groups have established separate correlative/biologic committees that are charged with designing hypothesis-driven LOE I and II studies, based on results from pilot studies. The emergence of erbB-2 in breast cancer as a predictive factor, in a manner similar to ER, may serve as a model of directed studies that lead to determination of the relative strength of the marker, and assignment of a TMUGS score that indicates whether or not it should be used clinically. In an attempt to standardize reporting of tumor-marker studies, and to guide design of the trials, the National Cancer Institute – European Organization for Research and Treatment of Cancer published REporting recommendations for tumor MARKer prognostic studies (REMARK) [6]. These guidelines outline concepts that should be considered when developing clinical studies, such as prospectively defining the question being addressed, choosing an appropriate patient population, determining endpoints, and identifying potential sources of bias. We hope that careful and thoughtful consideration of study design, such as is delineated in TMUGS and the REMARK guidelines, will considerably shorten the life cycle required to bring a tumor marker from the laboratory to the clinic.

39

References 1. ASCO Expert Panel. Clinical Practice Guidelines for the Use of Tumor Markers in Breast and Colorectal Cancer: Report of the American Society of Clinical Oncology Expert Panel. J Clin Oncol. 1996;14:2843–2877. 2. ASCO Expert Panel. 1997 update of recommendations for the use of tumor markers in breast and colorectal cancer. J Clin Oncol. 1998;16:793–795. 3. Bast RC Jr, Ravdin P, Hayes DF, et al. 2000 Update of recommendations for the use of tumor markers in breast and colorectal cancer: Clinical practice guidelines of the American Society of Clinical Oncology. J Clin Oncol. 2001; 19:1865–1878. 4. Bast RC Jr., Ravdin P, Hayes DF, et al. Errata:2000 Update of recommendations for the use of tumor markers in breast and colorectal cancer: Clinical practice guidelines of the American Society of Clinical Oncology. J Clin Oncol. 2001;19:4185–4188. 5. Hayes DF, Bast R, Desch CE, et al. A tumor marker utility grading system (TMUGS): A framework to evaluate clinical utility of tumor markers. J Natl Cancer Inst. 1996;88:1456–1466. 6. McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM. Reporting recommendations for tumor marker prognostic studies. J Clin Oncol. 2005;23:9067–9072. 7. Peto R, Boreham J, Clarke M, Davies C, Beral V. UK and USA breast cancer deaths down 25% in year 2000 at ages 20–69 years. Lancet. 2000;355:1822. 8. Berry DA, Cronin KA, Plevritis SK, et al. Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med. 2005;353:1784–1792. 9. Early Breast Cancer Trialists Collaborative Group. Ovarian ablation in early breast cancer: Overview of the randomised trials. Lancet. 1996;348:1189–1196. 10. Early Breast Cancer Trialist’s Collaborative Group. Tamoxifen for early breast cancer: An overview of the randomised trials. Lancet. 1998;351:1451–1467. 11. Early Breast Cancer Trialist’s Collaborative Group. Polychemotherapy for early breast cancer: An overview of the randomized trials. Lancet. 1998;352:930–942. 12. Early Breast Cancer Trialist’s Collaborative Group. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: An overview of the randomised trials. Lancet. 2005;365:1687–1717. 13. Ellis M, Hayes DF, Lippman ME. Treatment of metastatic disease. In: Harris J, Lippman M, Morrow M, Osborne CK, eds. Diseases of the breast. 2nd ed. Philadelphia: Lippincott-Raven; 2000:749–798. 14. Fisher B, Costantino JP, Wickerham DL, et al. Tamoxifen for prevention of breast cancer: Report of the National Surgical Adjuvant Breast and Bowel Project P-1 Study. J Natl Cancer Inst. 1998;90:1371–1388. 15. Winer EP, Hudis C, Burstein HJ, et al. American Society of Clinical Oncology technology assessment on the use of aromatase inhibitors as adjuvant therapy for postmenopausal women with hormone receptor-positive breast cancer: Status Report 2004. J Clin Oncol. 2005;23:619–629. 16. McGuire WL, Clark GM. Prognostic factors and treatment decisions in axillary-node-negative breast cancer. N Engl J Med. 1992;326:1756–1761. 17. Gasparini G, Pozza F, Harris AL. Evaluating the potential usefulness of new prognostic and predictive indicators in node-negative breast cancer patients. J Natl Cancer Inst. 1993;85:1206–1219.

40 18. AJCC. Cancer staging manual. In: Greene FL, Page DL, Fleming I, et al., eds. American Joint Committee on Cancer, Cancer Staging Manual. 6th ed. New York: Springer-Verlag; 2002. 19. Osborne CK. Receptors. In: Harris J, Hellman S, Henderson I, Kinne D, eds. Breast diseases. 2nd ed. Philadelphia: J.B. Lippincott; 1991:301–325. 20. Trock B, Leonessa F, Clarke R. Multidrug resistance in breast cancer: A meta-analysis of MDR1/gp170 expression and its possible functional significance. J Natl Cancer Inst. 1997;89:917–931. 21. Fisher B, Costantino J, Redmond C, et al. A randomized clinical trial evaluating tamoxifen in the treatment of patients with node-negative breast cancer who have estrogen-receptor-positive tumors. N Engl J Med. 1989;320:479–484. 22. Fisher B, Redmond C, Dimitrov N, et al. A randomized clinical trial evaluating sequential methotrexate and fluorouracil in the treatment of patients with node-negative breast cancer who have estrogen-receptor-negative tumors. N Engl J Med. 1989;320:473–478. 23. Ravdin PM. Should HER2 status be routinely measured for all breast cancer patients? Semin Oncol 1999;26(4 Suppl 12):117–123. 24. Press MF, Bernstein L, Thomas PA, et al. HER-2/neu gene amplification characterized by fluorescence in situ hybridization: Poor prognosis in node-negative breast carcinomas. J Clin Oncol. 1997;15:2894–2904. 25. Hayes DF. Tumor markers for breast cancer. Ann Oncol. 1993;4:807–819. 26. Trock BJ, Yamauchi H, Brotzman M, Stearns V, Hayes DF. c-erbB-2 as a prognostic factor in breast cancer: A meta-analysis. Proc Am Soc Clin Oncol. 2000;372:97. 27. Ellis MJ, Coop A, Singh B, et al. Letrozole is more effective neoadjuvant endocrine therapy than tamoxifen for ErbB-1- and/or ErbB-2-positive, estrogen receptor-positive primary breast cancer: Evidence from a phase III randomized trial. J Clin Oncol 2001;19:3808–3816. 28. Yamauchi H, Stearns V, Hayes DF. 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 2001;19:2334–2356. 29. Muss HB, Thor A, Berry DA, et al. c-erbB-2 expression and response to adjuvant therapy in women with node-positive early breast cancer. N Engl J Med. 1994;330:1260–1266. 30. Thor A, Berry D, Budman D, et al. erbB2, p53, and adjuvant therapy interactions in node positive breast cancer. J Natl Cancer Inst. 1998;90:1346–1360. 31. Pritchard KI, Shepherd LE, O’Malley FP, et al. HER2 and responsiveness of breast cancer to adjuvant chemotherapy. N Engl J Med, 2006;354:2103–2111. 32. Slamon DJ, Leyland-Jones B, Shak S, et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med. 2001;344:783–792. 33. Mass R. The role of HER-2 expression in predicting response to therapy in breast cancer. Semin Oncol. 2000;27(6 Suppl 11):46–52; discussion 92–100. 34. Piccart-Gebhart MJ, Procter M, Leyland-Jones B, et al. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Engl J Med. 2005;353:1659–1672. 35. Romond EH, Perez EA, Bryant J, et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med. 2005;353:1673–1684.

N.L. Henry and D.F. Hayes 36. Joensuu H, Kellokumpu-Lehtinen PL, Bono P, et al. Adjuvant docetaxel or vinorelbine with or without trastuzumab for breast cancer. N Engl J Med. 2006;354:809–820. 37. Coates AS, Simes RJ. Patient assessment of adjuvant treatment in operable breast cancer. In: Williams CJ, ed. Introducing new treatments for cancer: Practical, ethical, and legal problems. New York, NY: John Wiley; 1992:447–458. 38. Ravdin P, Siminoff I, Harvey J. Survey of breast cancer patients concerning their knowledge and expectations of adjuvant therapy. J Clin Oncol. 1998;16:515–521. 39. Lindley C, Vasa S, Sawyer T, Winer E. Quality of life and preferences for treatment following systemic adjuvant therapy for early stage breast cancer. J Clin Oncol. 1998;16:1380–1387. 40. Ravdin PM, Siminoff LA, Davis GJ, et al. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol. 2001;19:980–991. 41. Loprinzi CL, Thome SD. Understanding the utility of adjuvant systemic therapy for primary breast cancer. J Clin Oncol. 2001;19:972–999. 42. Whelan TJ, Loprinzi C. Physician/patient decision aids for adjuvant therapy. J Clin Oncol. 2005;23:1627–1630. 43. Hayes DF, Trock B, Harris A. Assessing the clinical impact of prognostic factors: When is “statistically significant” clinically useful? Breast Cancer Res Treat. 1998;52:305–319. 44. Isaacs C, Stearns V, Hayes DF. New prognostic factors for breast cancer recurrence. Semin Oncol. 2001;28:53–67. 45. Hayes DF, Isaacs C, Stearns V. Prognostic factors in breast cancer: Current and new predictors of metastasis. J Mammary Gland Biol Neoplasia. 2001;6:375–392. 46. Hayes DF. Do we need prognostic factors in nodal-negative breast cancer? Arbiter. Eur J Cancer. 2000;36:302–306. 47. Mass RD, Press MF, Anderson S, et al. Evaluation of clinical outcomes according to HER2 detection by fluorescence in situ hybridization in women with metastatic breast cancer treated with trastuzumab. Clin Breast Cancer. 2005;6:240–246. 48. Fisher B, Bryant J, Wolmark N, et al. Effect of preoperative chemotherapy on the outcome of women with operable breast cancer. J Clin Oncol. 1998;16:2672–2685. 49. Simon R. Design and analysis of clinical trials. In: DeVita J, VT, Hellman S, Rosenberg S, eds. Cancer: Principles and practice of oncology. 6th ed. Philadelphia: Lippincott Williams and Wilkins; 2001:521–538. 50. Ransohoff DF. Bias as a threat to the validity of cancer molecular-marker research. Nat Rev Cancer, 2005;5: 142–149. 51. Hayes DF. Determination of clinical utility of tumor markers: a tumor marker utility grading system. Recent Results Cancer Res. 1998;152:71–85. 52. Henderson IC, Berry DA, Demetri GD, et al. Improved outcomes from adding sequential paclitaxel but not from escalating doxorubicin dose in an adjuvant chemotherapy regimen for patients with nodepositive primary breast cancer. J Clin Oncol. 2003;21:976–983. 53. Mamounas EP, Bryant J, Lembersky B, et al. Paclitaxel after doxorubicin plus cyclophosphamide as adjuvant chemotherapy for node-positive breast cancer: results from NSABP B-28. J Clin Oncol. 2005;23:3686–3696. 54. Berry DA, Cirrincione C, Henderson IC, et al. Estrogen-receptor status and outcomes of modern chemotherapy for patients with node-positive breast cancer. JAMA. 2006;295:1658–1667. 55. Paik S, Shak S, Tang G, et al. A multi-gene RT-PCR assay using fixed, paraffin-embedded tumor tissue to predict the

2. Clinical Importance of Prognostic Factors: Moving from Scientifically Interesting to Clinically Useful likelihood of breast cancer recurrence in node negative, estrogen receptor positive, tamoxifen-treated patients. N Engl J Med. 2004;351:2817–2826. 56. Paik S, Tang G, Shak S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptorpositive breast cancer. J Clin Oncol. 2006;24:3726–3734. 57. Strauss GM, Skarin AT. Use of tumor markers in lung cancer. In: Hayes DF, ed. Hematology/oncology clinics of North America: Tumor markers in adult solid malignancies. Philadelphia: WB Saunders; 1994:507–532.

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58. Kantoff PW, Talcott JA. The prostate specific antigen: Its use as a tumor marker for prostate cancer. In: Hayes DF, ed. Hematology/oncology clinics of North America: Tumor markers in adult solid malignancies. Philadelphia: WB Saunders; 1994:555–572. 59. Ozols R, Schwartz P, Eifel P. Ovarian cancer, fallopian tube carcinoma, and peritoneal carcinma. In: DeVita V, Hellman S, Rosenburg S, eds. Cancer: Principles and practice of oncology. 5th ed. Philadelphia, New York: Lippincott-Raven; 1997:1502–1539. 60. Fritsche HA, Bast RC. CA 125 in ovarian cancer: advances and controversy. Clin Chem. 1998;44:1379–1380.

Chapter 3 Genetic Markers in Sporadic Tumors Elena Tamborini, Federica Perrone, Milo Frattini, Tiziana Negri, Antonella Aiello, Annunziata Gloghini, Antonino Carbone, Silvana Pilotti, and Marco A. Pierotti

3.1

Introduction

The rate of mortality from cancer changed very little over the past 50 years; however, during the past decade, perceptions about this situation have changed rapidly, primarily because the description of cancer in molecular terms has significantly improved the ways in which human cancers are detected (often in early stages), classified, monitored and, more importantly, treated. As a consequence, we are witnessing the start of a new exciting era in cancer research. A milestone has been the formalization of the concept that cancer is a genetic disease. This concept, however, lumps together two types of genetic diseases with the same outcome: the first linked to an entirely somatic cell–gene deregulation and the second linked to genetic susceptibility. At the somatic cell level, deregulation of cancer genes that control the careful balance between increase in cell number and withdrawal from the cell cycle promotes neoplastic growth by disrupting this balance, which occurs as a result of circumvention of the apoptotic machinery, promotion of cell division and cell proliferation, loss of cell differentiation pathways, and disruption of cell–cell communication and interaction. Thus, cancer represents the endpoint of a multistep process involving cancer genes and stimulatory and inhibitory signals provided by and controlled by products of the cancer genes. An additional feature of the cancer phenotype is the capability of cancer cells to modify their environment. This concept includes the promotion of angiogenesis, the degradation of stroma organization, and the production of factors related to inflammatory processes. In the first type of genetic disease, alterations in cancer genes can involve either dominant, gain-of-function mutations within proto-oncogenes that result in abnormal positive signals for cell proliferation or recessive, loss-of-function mutations within the tumor suppressor genes (TSG) that interfere with the negative regulation of cell growth. Mutations within TSG also may have a dominant-negative effect,

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

one in which an altered protein is produced that competes with its wild-type counterpart and prevents its activity. Mutant versions of TSG TP53 provide an example of such a mechanism. A third type of cancer gene has been identified in colorectal tumors associated with hereditary nonpolyposis colorectal cancer (HNPCC). These genes control mismatch repair (MMR), a process associated with the fidelity of DNA replication, and have been designated mutator genes. Their alterations cause microsatellite instability (MSI), characterized by random contractions or expansions in the length of simple sequence repeats (SSR) or microsatellites, and may have important prognostic implications. The second type of genetic disease is based on the recognition of a genetic susceptibility in approximately 8–10% of patients with cancer. This latter disease results from the inheritance of altered alleles of genes, which are almost always TSG. With few relevant exceptions, such as RET proto-oncogene mutated in familiar medullary thyroid carcinomas and in tumors included in multiple neoplasia type-2 syndromes. Along with different penetrance, this tumor-suppressor type determines the genetic risk of cancer, which can be almost 100% during a lifetime. In all cases, including those genes that predispose to a genetic risk of cancer [1], alterations of cancer-associated genes define molecular markers Table 3.1. These markers are useful for novel diagnostic approaches and for genetic profiling of the tumor cell, with the aim of providing better prognostic evaluation and prediction of therapeutic drug response. In particular, the introduction into clinics of novel therapeutic molecules designed to interfere with target representing the altered genetic elements of the relevant pathogenetic pathway(s) in a given tumor, has increased the number of models of the so called “personalized or targeted therapy.” To provide a more rational view of the problems, this chapter organizes the subject by discussing genetic markers from cancer-associated genes that were altered by point mutation, deletion, or inappropriate expression at somatic cell level. The classes of genetic markers derived from chromosomal instability and from nonrandom chromosomal abnormalities and generating tumor-specific fusion product are also discussed. 43

44

E. Tamborini et al.

Table 3-1. Most relevant genetic markers in sporadic tumors. Gene

Acronyms

Location

TP53

-

17p13

HRas K ras N ras BRAF

-

7 12p12.1 11p15.5 7q34

Her2/neu

Her2 Erbb2

17q21

EGFR RET BCL2

Her1 -

7p11 10q11.2 18q21

BCL1 APC

PRAD1 CCNDd1 -

11q13 5q21

c-MYC

-

8q24

BCL6 P16INK4a

3q27 9p21

P15INK4b P14ARF

LAZ3 MTS1 INK4a CDKN2A CDKN2B ARF

KIT

-

4cen-q21

FLT3

13q12

PTEN

FMS like TK3 FLK2 STK1 MMAC1 TEP1

10q23.3

AKT 1

PKBα

14q32

AKT 2 AKT 3 COX-2

PKBβ PKBχ PTGS2 PGMS2

19q13.1–2 1q44 1q25.2–3

DCC, SMAD4, SMAD2 PAX5 TAL1 NPM ALK BCL 10 MALK 1 AP12

DPC4,2;MRAD4,2

18q21

-

BCR c-ABL PML RARA AML 1 ETO

c-IAP2, HIAP, MIHC

9p21 9p21

Tumors in which gene is deregulated Ovarian, colorectum, esophagus, head and neck, pancreas, lung, skin, stomach, liver, bladder, and breast cancer

Review (see reference list) 1

31 Pancreatic, colorectal, lung, follicular, and anapliastic tyroid cancers Melanoma, papillary thyroid cancer, endometrial, ovarian, prostate, lung, and bladder cancers Breast, ovarian, endometrial, gastric, salivary gland carcinomas, NSCLC Head and neck, colorectal carcinomas NSCLC Thyroid cancer Lymphoma, myeloid, ALCL, breast, prostate, thyroid, neuroblastoma, pancreatic cancers Lymphoma, breast, head and neck, esophageal cancer Colorectal, breast, cervical, endometrial, ovarian, prostate, lung, bladder cancers Lymphoma, breast, ovarian, gastric, melanoma, medulloblastoma, lung cancers Lymphoma Melanoma, bladder, head and neck, colorectum, lung, ovarian, hematopoietic cancers Ovarian, head and neck, colorectal, bladder cancers Colorectal, hepatocellular, glioma, primary central nervous system lymphoma, melanoma Mastocytosis/mast cell leukemia, germ cell tumors, SCLC, GI stromal tumors (GISTs), AML, neuroblastoma, melanoma, head-and-neck carcinomas, ovarian carcinoma, and breast carcinoma AML

32 49 56 57 68 69 70 75 83

88

115

122

Melanoma, glioblastoma, endometrial, colorectal, thyroid, hepatocellular, prostate cancers, tongue, breast, clear-cell renal cell carcinoma, cervical, Breast, prostate, thyroid, ovarian, pancreatic, colorectal cancers, melanoma

127

Colorectal, breast, gastric, cervical, ovarian, pancreatic, nonsmall-cell lung cancers, glioma, mesothelioma Colorectal, gastric, prostate, ovarian, esophageal, breast, testicular, endometrial cancers, glioma

138

9p13 1p32 5q35 2p23 1p22 18q21 11q21

Lymphoma T-ALL Lymphoma, inflammatory myofibroblastic tumors

158 171

MALT lymphoma MALT lymphoma MALT lymphoma

164, 165 164, 165 164, 165

22q11.21 9q34 15q22 17q21 21q22 8q22

Chronic myelogenous leukemias (CMLs), 10% de novo ALL Chronic myelogenous leukemias (CMLs), 10% de novo ALL Acute promyelocytic leukemia Acute promyelocytic leukemia Acute myelogenous leukaemia (M2) Acute myelogenous leukaemia (M2)

179, 180 178, 180 181, 182, 183 181, 182, 183 185 185

133

147

3. Genetic Markers in Sporadic Tumors

3.2 Genetic Markers Derived from Cancer-Associated Genes 3.2.1

TP53

The TP53 gene encodes a 393-amino-acid nuclear phosphoprotein, which contains phosphorylation sites at the amino (N) and carboxy C termini; a central zinc-binding core domain, which interacts with DNA; and a tetramerization domain at the C-terminus. P53 protein increases in response to a variety of stress signals including DNA damage, such as ionizing or ultraviolet (UV) irradiation and DNA-damage drugs. This increase induces the delay of cell cycle progression from G1 into S phase that allows DNA-damage repair. If DNA repair is unsuccessful, programmed cell death occurs. A p53-dependent pathway may also regulate the G2/M transition. Evidence of these effects on cellular growth and death has led to the view of p53 as “guardian of genome” [1]. The primary mechanism by which p53 negatively controls the cell cycle is the transcriptional activation of p21 that binds to cyclin/cyclin-dependent kinases (CDK) complex and inhibits RB phosphorylation with consequent arrest in the G1/S phase. Gadd 45alfa is also induced by p53 protein in response to DNA damage. The Gadd45 gene product interacts with various cell cycle-related proteins such as Cdc2 and PCNA and contributes to G2/M cell cycle arrest along with 14-3-3-omega, Reprimo, and B99. 14-3-3 omega belongs to an emerging family of protein that binds to Ser/Thr-phosphorylated residues and induces cell-cycle arrest in G2 by sequestering nuclear proteins required to enter into mitosis (e.g., Cdc2) in the cytoplasm. Reprimo is a glycosylated cytoplasmatic protein able to induce cell-cycle arrest by inhibiting Cdc2 activity without interfering with Cdc2/cyclin B1-complex. B99 protein may cause G2 arrest independently from Cdc2 through yet unknown pathway. It has been suggested that B99, interfering with microtubule rearrangements required to enter into mitosis (formation of a spindle) may trigger G2 arrest. Under extreme stress and severe DNA damage, p53 can induce activation of genes implicated in the apoptotic cascade, including bax, DR5, PIDD, NOXA, PUMA, and Fas/APO-1. It is reasonable to speculate that p53-mediated apoptosis is a consequence of the combined expression of the various target genes, the expression of which is modulated in the cell type and/or cell-stress specific manner. Wild-type p53 is known to repress a number of cellular promoters that may be important for an apoptotic response in the cell-cycle control, such as the survival factor bcl-2 or MAP4 and Stathmin, which are both involved in microtubule polymerization. The ability of p53 to exert regulation of such a large subset of genes can be attributed to its selective association with different transcription factors. This association is related to post-translational modifications of p53. More than 18 phosphoacceptor sites were reported for p53 protein, most of which are modified in response to damage or stress, although several

45

sites are phosphorylated under normal growth conditions. Phosphorylation of p53 differs during cell cycle progression in normal growing cells, and coincides with the ability of p53 to bind to some of its regulatory proteins including p300, mdm2, and JNK. The complex phosphorylation of p53 is mediated by multiple kinases, including ATM, ATR, and Chk2. In addition to the complex pattern of phosphorylation, acetylation appears to influence p53 activation and function. Different histone acetylases such as CBP/p300 and PCAF are known to modify p53 in several lysine residues in the C-terminus. Most importantly, the Lys residues 320 and 382 become selectively acetylated in vivo in response to DNA damage. It is likely that regulatory mechanisms of phosphorylation in the N-terminus-residues of p53 facilitate the acetylation of C-terminus sites, leading to activation cascade in response to DNA damage. TP53 mutations are involved in almost all tumor types and a database of these mutations is maintained at the International Agency for Research on Cancer. This database currently contains >25,000 entries representing the largest set of information on human mutations associated with cancer. More than 80–90% of the TP53 mutations reported in human cancers are clustered between exon 5 and 8 within the evolutionary conserved regions of the gene (codons 110–307). Exons 2 and 11 contain only 0.1% of the mutations identified from IARC TP53 database. The mutations are usually missense, giving rise to altered proteins. Several sites where mutations are detected at high frequency are referred to as hot-spot mutations, and these include codons 175, 248, 273, and 282. Most of these mutations are present in heterozygosity with the wild-type allele of the gene. The loss of wild-type p53 tumor suppressor function may occur by dominant-negative inhibition of wild-type p53 function. This effect corresponds to the capacity of the mutant protein to complex with the product of the remaining wildtype allele inactivating its function. Although mutations have been extensively discussed as a mean of wild-type p53 inactivation, mutation-independent mechanisms can inactivate p53 function. For example, overexpression of MDM2, which occurs in approximately 20% of human soft-tissue sarcomas in the presence of wild-type TP53 alleles, inhibits p53 function by targeting p53 for degradation. Furthermore, P14arf may bind MDM2 and neutralize its capacity to repress p53. Human papillomavirus (HPV) subtypes 16 and 18 express the E6 viral oncoprotein that also may target p53 through an ubiquitin-dependent process. TP53 has potential clinical applications in early detection strategies. Detection of TP53 mutations in sputum of patients with lung cancer, combined with K-ras mutation analysis, may be a suitable target for early detection strategies, although this method has a limitation: the percentage of tumor cells identified is very low [2]. In patients with breast cancer, the analysis of TP53 mutations in fine-needle aspirates [3] or core biopsy may be useful for prognostic assessment and prediction of response to adjuvant chemotherapy before radical surgery. Missense mutations in the L2 and

46

L3 loops from the zinc-binding domain of p53 protein were found to be associated with decreased disease-free and overall survival of the patients. These types of mutations were also found to be associated with resistance to doxorubicin therapy [3]. In patients with node-positive breast cancer, detection of p53 mutations, combined with the detection of her-2/neu gene amplification in surgical specimens, can be used as a molecular tool for prognostic assessment [4] and therapy planning. The most important subgroup of patients with breast cancer for which reliable prognostic factors are needed are women without axillary lymph node involvement. Introduction of routine TP53 mutation screening may be useful to assist in the selection of patients with node-negative breast cancer who can be considered for postoperative adjuvant treatment [5]. In colorectal cancer, detection of TP53 point mutations, particularly mutations in the conserved domains, may be a valuable prognostic adjunct in defining more-aggressive tumors, as survival of patients is strongly related to the presence of TP53 mutations alone or in combination with K-ras mutations [6]. TP53 mutations are frequent in late-stage ovarian cancer (50% of stage III and IV epithelial ovarian cancers) and have been associated with reduced overall survival rates [7]. The same holds true for p53 expression, which is higher in highgrade compared with low-grade ovarian cancer [8], and is associated with worse progression-free survival [9]. Interestingly, antibody (Ab) immunity to p53 protein seems to predict improved overall survival in patients with ovarian cancer [10]. The risk of ovarian cancer appears to be linked with TP53 codon 72 polymorphism. In particular, the TP53 Pro allele was associated with an increased risk of this tumor [11]. By contrast, the influence of this polymorphism on responsiveness to platinum-based chemotherapy is not well defined [12–14]. Studies suggest that there is a significant correlation between presence of TP53 missense mutations and resistance to cisplatin-based therapy in ovarian cancer [15]. Some findings suggest that inactivation of wild-type p53 may confer an increased sensitization to the chemotherapeutic agent paclitaxel and help overcome resistance in tumors that do not respond to platinum drug alone [16]. These results could make the detection of TP53 mutations a useful marker for therapy planning but conflicting findings suggest the need for further studies to elucidate the role of p53 in the response to chemotherapy in ovarian cancer. Preliminary results support the notion that in ethmoid sinus intestinal-type adenocarcinoma (ITAC), the presence of a TP53 mutation, which disables apoptosis, is a strong predictor for insensitivity to DNA-damaging chemotherapeutic agents, whereas a wild-type p53 correlates with a pathologic complete response [17]. Thus, in this tumor, p53 status seems to represent a promising biomarker to predict response to chemotherapy, and might be useful to design more tailored treatments to maximize the probability of response, possibly allowing organ-preserving approaches. The predictive value of wild-type p53 status was further

E. Tamborini et al.

confirmed comparing patients with ITAC who receive preoperative chemotherapy with patients with ITAC who are surgically treated. The results demonstrated a significantly improved disease-free survival in patients who received chemotherapy who had TP53 wild-type gene compared with patients who were surgically treated [18]. In bladder cancer, several findings support an influence of genetic polymorphisms of both DNA repair and defense genes on the occurrence of TP53 mutations [19]. In this tumor, p53 alterations are significantly associated with clinicopathologic features of poor prognosis [20] and TP53 analysis shows that patients harboring a normal p53 pathway have a low death rate and could be considered a low-risk category. By contrast, patients displaying an abnormal p53 pathway, with DNArepair gene defects [21], are found to have an aggressive course of their disease, a high death rate, and could be considered as high-risk cases [22]. In head-and-neck cancer, mutations in p53 protein DNAcontact-domain confer an accelerated tumor progression and reduce therapeutic responsiveness toward adjuvant radiant therapy [23] and toward cisplatin-fluorouracil neoadjuvant chemotherapy [24]. Taken together these data suggest that the loss of function of p53 protein and the type of mutation may lead to a more aggressive or more treatment-resistant tumor phenotype. A scheme of p53 interactions is shown in Fig. 3-1.

3.2.2

RAS

Ras genes belong to a multigene family that has a fundamental role in cellular proliferation. The human Ras genes encode for similar farnesylated or geranylgeranylated membrane-bound 21 Kd proteins (189 amino acids) involved in signal transduction, with a guanine nucleotide-binding activity and an intrinsic guanosine triphosphatase (GTP) activity. Normally, these proteins exist in equilibrium between an active and inactive state. The Ras proteins, characterized by a conformation that allows binding to guanosine diphosphate (GDP), remain inactive until they receive a stimulus from another protein upstream of the transdution pathway. This stimulus results in the exchange of GDP for GTP, followed by conformational change of Ras to its active state. The activated proteins transduce the signal by linking tyrosine kinases to downstream Ser/Thr kinases, such as raf, and mitogen-activated protein kinases (MAPK). They subsequently become inactivated by their intrinsic GTPase activity, which catalyzes the hydrolysis of GTP and permits the return to the inactive GDP-bound state (Fig. 3-2). Stabilization of Ras proteins in their active state causes a continuous flow of signal transduction, which results in malignant transformation. Ras proteins can acquire transforming potential secondary to a point mutation at codon 12, 13, 61, or 146 in the coding gene. Transformation can occur with mutations at or near the GTP-binding domain of Ras proteins, which prevent the inactivation of GTP and result in

3. Genetic Markers in Sporadic Tumors

Fig. 3-1. The p53 pathway. (see Color Plate 1 following p. 316.)

Fig. 3-2. The RAS-RAF signaling pathway.

47

48

E. Tamborini et al.

continuous Ras activity. Normal Ras genes can induce malignant transformation if highly overexpressed. Ras has potential clinical applications in the detection of pancreatic tumors; however, K-ras molecular analysis can only complement the cytologic evaluation, because K-ras mutation is not specific as it is present in the pancreatic juice of patients with chronic pancreatitis [25]. Detection of K-ras mutations in stool specimens could be a noninvasive presymptomatic indicator of colonic adenomas (mostly adenomas >1 cm) or early colorectal tumors [26]. Although limited by a low sensitivity, detection of K-ras mutations in sputum, bronchoalveolar lavage, or pleural fluid may improve cytologic diagnosis in both early-stage and recurrent lung adenocarcinomas [27]. A number of approaches aimed at abrogating K-ras activity have been explored in clinical trials. Usually, inhibitors directly affecting K-ras are too toxic for human cells. Currently, the most promising agents are represented by aminobiphosphonates, which are potent inhibitors of the synthesis of farnesyl and geranylgeranyl lipidic residues, thus disrupting Ras downstream pathways. Aminobiphosphonates have entered clinical practice in the treatment of bone metastases that arise from several neoplasms, including breast and prostate adenocarcinomas [28]. K-ras mutations are associated with resistance to epithelial growth factor receptor (EGFR) inhibitors gefitinib and erlotinib in patients with nonsmallcell lung cancer (NSCLC) [29] and cetuximab in patients with colorectal cancer [30].

3.2.3

BRAF

The Raf family genes encode for Ser-Thr kinases involved in the transduction of mitogenic signals from the plasma membrane to the nucleus. The most important of these, BRAF, encodes a protein that is recruited to the plasma membrane upon binding to Ras-GTP, and represents a key point in the signal transduction through the MAPK pathway (Fig. 3-2). BRAF has increased basal kinase activity because Ser446 is constitutively phosphorylated and two aspartic acids at residues 448–449. BRAF is activated by Ras, through phosphorylation of Thr599 and Ser602, by Rap-1 family genes, through lipid modifications of Rap1B, and by several growth factors. By contrast, BRAF is negatively regulated by Akt, through phosphorylation of Ser365, Ser429, Thr440, and by serum corticoid-inducible kinase on Ser365 [32]. BRAF mutations are found in various cancers. All mutations are represented by activating missense point mutations clustered in exons 11 and 15, particularly the T1799A transversion resulting in amino acid change V600E, the accounts that for more than 90% of all BRAF mutations. The BRAF V600E mutated protein show the insertion of a negative charge in the proximity of residues 599 and 602, thus mimicking the constitutive phosphorylation of the two residues [33]. In melanoma, BRAF mutations may precede the development of malignancy, because all types of nevi besides Spitz and blue nevi show BRAF alterations at a high frequency, with dysplastic

nevi showing a notable lower frequency [34]. In colorectal cancer, BRAF mutations are found in hyperplastic polyps and in serrated adenomas, suggesting that they represent an early and critical event in these types of lesions. Moreover, BRAF mutations are frequently present in sporadic colorectal cancer with methylated hMLH1, but not in HNPCC-related cancers, thus representing a possible strategy for exclusion criteria for HNPCC [35]. Another mechanism of BRAF activation has been identified, which involves chromosome 7 paracentric inversion that results in the AKAP9-BRAF fusion. This type of BRAF activation, which leads to a fusion protein with intact kinase domain and without the autoinhibitory N-terminal portion, is quite rare and associated with papillary thyroid carcinoma after radiation exposure [36]. Another mechanism of BRAF activation involves copy number gain, thus resulting in protein overexpression, a mechanism rare in papillary thyroid carcinoma while occurring in a consistent fraction of follicular cancers of conventional and oncocytic types [37]. A therapeutic approach targeted at the Raf kinases has been tested using specific inhibitors. Among these inhibitors, sorafenib seems to be the most promising and is in phase-III clinical trials [38].

3.2.4

her-2/neu

The her-2/neu gene encodes for a transmembrane receptorlike phosphoglycoprotein (185 kD) and is a member of the EGFR family. All the members of EGFR family except her2/neu bind specific ligands at their extracellular domain. Upon ligand binding, receptors can homodimerize or heterodimerize. her-2/neu is a preferential binding partner and, when expressed at very high level, it can homodimerize in absence of ligand. EGFR family receptors are characterized by a glycosylated extracellular N-terminus where the ligand binds, a hydrophobic transmembrane region, and, with the exception of HER-3, a functional kinase domain contained within the intracytoplasmic C-terminus. A truncated form of her-2/neu protein (95 kD), lacking the N-terminal extracellular domain, was found to be associated with nodal metastases in breast cancer [39] and to predict poor outcome [40]. This alteration is caused by post-translational cleavage of the receptor by metalloproteases [41]. her-2/neu has potential prognostic clinical applications in breast cancer: detection of her-2/neu amplification has use in identifying highly aggressive node-positive breast carcinomas. In patients with axillary node-negative breast cancer, her-2/neu amplification has been reported as an independent prognostic factor for risk of recurrence. In endometrial cancer, her-2/neu amplification represents a potential prognostic marker of poor outcome and may have clinical use in selecting patients for adjuvant therapy. In NSCLC, a negative impact of her-2/neu overexpression on survival was reported for patients who had radical resection. In brain tumors, her-2/neu overexpression and short-term mortality were related in primary malignant

3. Genetic Markers in Sporadic Tumors

brain tumors. her-2/neu gene expression has been reported as a factor for the prediction of treatment responsiveness in breast cancer. In particular, information regarding expression of this oncogene may become important for the prediction of response to anthracycline-containing chemotherapy and resistance to treatment with tamoxifen or chemotherapy with cyclophosphamide, methotrexate, and 5′-fluorouracil (5-FU) [42]. In a study of 386 patients with breast cancer with a 20-year follow-up, no differences in treatment response were found in patients with her2-positive or her2-negative tumors, suggesting that even patients with her2-negative tumors may benefit from chemotherapy treatment [43]; however, recently it was reported that amplification of her-2/neu is associated with clinical responsiveness to anthracycline-containing chemotherapy. In particular coamplification of her-2/neu and topoisomerase II genes may define a subgroup of high-risk breast cancer patients who benefit from anthracycline-based adjuvant chemotherapy [44]. her-2/neu has become a target for anticancer therapies. Trastuzumab, a monoclonal antibody (MAb) that binds the receptor, has been developed and used in clinical trials, prolonging survival of patients with metastatic disease overexpressing her-2/ neu. Trastuzumab has been used as adjuvant therapy in patients with her2-positive breast cancer and results showed that these women treated with chemotherapy plus trastuzumab did better than those treated with chemotherapy alone [45]. In NSCLC, trials of trastuzumab failed to demonstrate clinical benefit when administrated as monotherapy or combined with chemotherapy, except for the few her-2/neu 3+/FISH-positive patients, but activating mutations in her-2/neu reported in NSCLC offer the potential for targeted therapy. Increased her-2/neu copy number in NSCLC was associated with response to gefitinib therapy in EGFR-positive patients [46]. Similarly to NSCLC, her-2/neu seems to influence the EGFR tyrosine kinase inhibitor response in some head-and-neck cancers in which the her2/neu gene was mutated. By contrast, the lack of demonstrable benefit of trastuzumab with or without cytotoxic chemotherapy in phase-2 trials suggests that there is little role for trastuzumab for prostate cancer. Pertuzumab is a MAb directed against the dimerization domain of her-2/neu and interrupts the growth signals mediated by homodimers and heterodimers. Unlike trastuzumab, pertuzumab does not require high levels of her-2/neu protein expression to be effective. Four phase-2 trials of pertuzumab have been done in patients with relapsed solid tumors, including NSCLC, breast, prostate, and ovarian cancer [47]. Lapatinib and ZD6474 are multitargeted drugs that hit both her-2/neu and EGFR in testing in breast cancer and NSCLC clinical trials, respectively. Tumors can develop resistance to single-target drugs, so hitting a second target may overcome or circumvent that resistance [48].

3.2.5

EGFR

The human gene encoding EGFR expresses a full-length protein of 170 kD after glycosylation. EGFR binds several

49

ligands, including EGF, transforming growth factor alpha (TGF-α), and amphiregulin. After ligand-binding, EGFR can homodimerize or heterodimerize with the EGFR family members and trigger a cascade of events implicated in organ morphogenesis, maintenance, and repair of tissues. EGFR appears to promote solid tumor growth in a variety of human tumors including breast, ovarian, cervical, bladder, prostate, NSCLC, head-and-neck, colorectal, and glioblastoma. In glioblastoma, EGFR overexpression is often associated with gene amplification and this alteration has been shown to be accompanied in >50% of cases by gene rearrangement, the most common of which is the loss of the 2–7 exons of the extracellular EGFR domain, leading to the so called EGFR variant III (EGFRvIII), which is constitutively activated [50]. In prostate cancer, EGFR expression increases during the development of the androgen-independent tumors. In NSCLC, EGFR expression may be sustained by mutations in exons 18–21 of the tyrosine kinase domain or by gene amplification [51]. EGFR is overexpressed in approximately 50% of head-and-neck tumors and represents an early marker of cancerogenesis as it is upregulated even in the normal epithelium adjacent to the tumor. EGFR expression may differ between the sites of head-and-neck tumors: lower in laryngeal carcinoma compared with pharyngeal and oral carcinomas [52]. This overexpression results from an increased EGFR mRNA synthesis caused by different factors such as a dysregulated p53, polymorphisms in dinucleotide repeats in intron 1 of the EGFR gene, and EGFR amplification. EGFR activating mutations are rare in head-and-neck carcinoma, where the main mechanism for EGFR activation is autocrine or paracrine loop by EGFR ligands. In colorectal cancer, the principal mechanism of deregulation of EGFR is represented by amplification protein overexpression that is associated with disease progression and poor prognosis; point mutations in the tyrosine kinase domain of EGFR rarely occur. Targeting EGFR is a valuable molecular approach in cancer therapy, and EGFR-targeted therapies include those that act intracellularly, inhibiting the EGFR tyrosine kinase domain (gefitinib and erlotinib), and those that block ligand binding (cetuximab). Immunohistochemistry (IHC) is commonly used to evaluate EGFR protein levels but there are no standard scoring systems, and EGFR expression as detected by IHC is not an effective predictor of anti-EGFR therapy response. In glioblastoma, only 10–20% of patients have a response to tyrosine kinase inhibitor and coexpression of EGFRvIII and PTEN was reported as associated with a clinical response, despite the low sensitivity to gefitinib of the neoplastic EGFRvIII phenotype [53]. In NSCLC, only 10–20% of patients have a partial response to gefitinib/erlotinib. Although initial results indicated an excellent correlation between response and positive mutational status of EGFR in NSCLC, more recent papers have indicated an emerging role for increased copy number of both EGFR and her-2/neu and the status of HER-3 [54]. Patient characteristics associate with increased responsiveness to EGFR tyrosine kinase inhibitor are “never smoking”

50

E. Tamborini et al.

history, Asian ethnicity, female gender, and adenocarcinoma histology. Acquisition of drug resistance in patients initially responsive to EGFR tyrosine kinase inhibitor has been linked to a specific secondary somatic mutation T790M. Despite the overexpression of EGFR and its important role in the pathogenesis of head-and-neck tumors, EGFR inhibitors have a limited success as monotherapy in this malignancy and the resistance to EGFR inhibition probably is linked to EGFRindependent survival pathways. The clinical management of advanced colorectal cancer by the class of ligand-binding blockers (such as cetuximab) represents very promising targeted compounds as EGFR antagonists [55]. Nonclinical studies suggest that EGFR overexpression is required for cetuximab response. A patient with advanced colorectal cancer must show EGFR overexpression in the primary tumor, as detected by IHC, to be eligible for treatment. Such a methodology, however, does not seem to represent the best way to evaluate EGFR alterations. Moreover, it has been demonstrated that EGFR-negative patients, as determined by IHC, may respond to cetuximab-based therapies. Recent data indicate that the determination of EGFR copy number could be a promising approach to predict cetuximab efficacy in patients with advanced colorectal cancer [30].

3.2.6

RET

The RET proto-oncogene encodes a receptor tyrosine kinase (RTK) that is involved in the control of neural crest cell proliferation, migration, survival, and/or differentiation. The RET receptor comprises an extracellular ligand-binding domain that contains a cadherin homology region of unknown significance and a Cys-rich region, a transmembrane domain, an intracellular tyrosine kinase domain, and additional amino-acid sequences that function as regulatory domains [57]. Ligandbinding induces receptor dimerization and autophosphorylation in a trans fashion, and functions to recruit intracellular signaling proteins [57]. RET expression occurs predominantly in neural crest-derived cells. The RET intracellular domain contains at least 12 autophosphorylation sites. Tyr905 is a binding site for Grb7/10 adaptors, Tyr1015 for phospholipase Cγ, and Tyr981 for c-Src. Tyr1062 is the binding site for several proteins, including the Shc proteins, insulin receptor substrate-1/2, fibroblast growth factor receptor substrate 2, DOK 1/4/5, and Enigma, which, in turn, leads to the activation of many signaling pathways. Binding to Shc and FRS2 mediates recruitment of Grb2-SOS complexes, which leads to GTP exchange on RAS and RAS/ERK stimulation [57]. The RET proto-oncogene can be activated by two main mechanisms, gene mutation (including missense and in-frame duplication) and rearrangement with different partner genes that generate fusion proteins. Germ line mutations in the RET proto-oncogene are associated with multiple endocrine neoplasia type 2 (2A and 2B) and familial medullary thyroid carcinoma [58]. RET somatic mutations hotspots are V804, M918, and E768 that occur in approximately 50% of sporadic

medullary thyroid cancer [59]. The M918T mutation leads to ligand-independent activation of the kinase without causing a constitutive dimerization of the receptor and alters the substrate specificity of the kinase. Various kinds of therapeutic approaches, including tyrosine kinase inhibition, gene therapy with dominant negative RET mutants, MAb against oncogene products, and nuclease-resistant aptamers that recognize and inhibit RET, have been developed. The use of these strategies in nonclinical models has provided evidence that RET is a potential target for selective cancer therapy; however, a clinically useful therapeutic option for treating patients with RET-associated cancer is not available. An anilinoquinazoline, ZD6474, that also possesses an antiangiogenetic effect through VEGFR inhibition, seems promising [60]; however, thyroid cancers carrying the V804 mutations are resistant to this drug, and it seems that they could be treated with efficacy with novel chemotherapeutic options, that are in nonclinical evaluation.

3.2.7

BCL2

The BCL2 (B cell leukemia/lymphoma 2) gene spans more than 230 kb of DNA and consists of three exons of which exon 2 and a small part of exon 3 encode for the protein. Dependent on splicing of intron 2, BCL2 encodes for two mRNAs, BCL2α and BCL2β, of which only BCL2α seems to have biologic relevance. The BCL2α protein is a 26-kD membrane protein located at the cytosolic site of the nuclear envelope, endoplasmic reticulum, and outer mitochondrial membrane. BCL2 inhibits apoptosis under stress conditions and prolongs cell survival. In normal tissues, BCL2 protein displays a restricted topographic distribution within mature tissues that are characterized by apoptotic cell death. In secondary follicles, BCL2 is strongly expressed in the mantle zone, which comprises long-lived recirculating cells. In the thymus, BCL2 is present in the surviving mature thymocytes of the medulla. BCL2 is usually expressed in hematopoietic precursor cells, but it is absent in their most differentiated and terminal progeny [61]. BCL2 is also present in complex differentiating epithelium, where it is restricted to stem cell and proliferating zones. Because of its antiapoptotic function, the BCL2 gene initiated a new category of oncogenes, called regulators of cell death. Recently, BCL2 homologues, some of which bind to BCL2, have been identified, suggesting that BCL2 functions at least in part through protein–protein interaction. Site-directed mutagenesis of BCLl2 protein BH1 and BH2 domains showed that these two regions are important for binding of BCL2 to bax, a member of the BCL2-family that promotes cell death and whose interaction with BCL2 is necessary to regulate the apoptotic pathway. According to one study, BCL2 negatively regulates Beclin 1-dependent autophagy and Beclin 1-dependent autophagic cell death. These findings raise the possibility that BCL2 family members may function as oncogenes not only by blocking apoptosis but also by blocking autophagy [62].

3. Genetic Markers in Sporadic Tumors

The BCL2 gene was discovered by virtue of its involvement in the t(14;18) of follicular lymphomas. Although translocation is the main mechanism of BCL2 gene activation, BCL2 point mutations and amplification also have been reported. Mutations clustering in the BCL2 open-reading frame occur in high-grade B-cell lymphomas transformed from low-grade follicular lymphomas carrying BCL2 gene rearrangement. BCL2 gene amplification, which leads to increased protein production, has been detected in approximately 30% of high-grade diffuse large cell lymphomas (DLCL) lacking BCL2 translocation. BCL2 expression has been investigated both in lymphoid and nonlymphoid tumors. BCL2 protein is expressed in most cases of follicular lymphoma, both with t(14;18) and without it; however, a few cases lack both protein expression and rearrangement. BCL2 protein is also expressed in many lymphoid and myeloid neoplasms, although it is usually absent or expressed at low levels in Burkitt lymphoma, and anaplastic lymphoma kinase (ALK)-positive anaplastic large-cell lymphoma (ALCL), possibly because of the high rate of cell proliferation. BCL2 protein is often absent or present at low levels in large B-cell tumors at extranodal sites. BCL2 protein is found in Reed-Sternberg cells in 33–75% of all biopsy specimens from patients with Hodgkin disease; however, the neoplastic cells of lymphocyte predominance Hodgkin disease (lymphocytic and histiocytic or popcorn cells) tend to be BCL2 negative, thereby illustrating the distinct nature of this Hodgkin disease subtype [63]. Detection of BCL2 protein has diagnostic use and some prognostic value. The clinical applications of BCL2 include diagnosis of lymphomas where BCL2 protein expression is used as a marker for the differential diagnosis between reactive follicular hyperplasia and follicular lymphoma [64, 65]. Prognosis of patients with leukemia and lymphomas can be helped by BCL2. Because of the occurrence of BCL2 gene mutations in transformed high-grade B-cell lymphomas, this genetic lesion may represent a predictive marker of progression in BCL2-rearranged tumors. Clinical correlation studies in DLCL indicated that BCL2 amplification is associated with advanced-stage disease at presentation. In high-grade B-cell lymphomas, BCL2 protein expression is a strong, major predictor of overall survival, disease-free survival, and relapse-free survival either alone or in association with p53 expression [66], being related to a poor outcome. High BCL2 expression is associated with low remission rate in acute myeloid leukemia (AML) and is an indicator of poor response in acute lymphocytic leukemia (ALL). Clinical use has been shown in solid tumors. BCL2 expression is found in tumors of some hormonally responsive epithelium, such as tumors of the breast, prostate, and thyroid gland. In neuroblastoma and carcinoma of the prostate, BCL2 positivity is a poor prognostic marker, whereas breast cancer patients with BCL2 positive tumors have better survival. In the thyroid gland, BCL2 is overexpressed in adenomas and welldifferentiated carcinomas and is frequently lost in anaplastic

51

carcinoma [67]. In medullary thyroid cancer, lack of BCL2 immunoreactivity correlated significantly with a shorter survival, therefore, down-regulation of BCL2 expression in medullary thyroid cancer may identify a subset of tumors with a more aggressive clinical course. The association between IHC staining for BCL2 protein and the histologic type and prognosis of NSCLC is controversial. In cancers that overexpress BCL2, decreasing its expression, by targeting BCL2 directly or indirectly through an upstream regulator of BCL2, may render the neoplastic cells more sensitive to chemotherapeutic agents. In this view, the down-regulation of BCL2 protein expression by antisense oligonucleotides, which induce BCL2 mRNA degradation, may offer a new therapeutic approach for treatment improvements in patients with neoplasms resistant to traditional therapies. The results of in vitro and in vivo models are promising, particularly when combination of BCL2 ASO (G3139) with a cytotoxic agent is used. In humans, G3139 has been studied as a single agent in a phase-1 trial in 21 heavily pretreated patients with relapsed NHL, where one complete and two minor responses as well as nine disease stabilizations were observed. One study combining a chemotherapeutic agent with G3139 has been reported in metastatic melanoma, and studies are ongoing in a number of other solid tumors (melanoma, prostate carcinoma) and hematologic malignancies (myeloma, chronic lymphocytic leukemia [CLL], and AML). The development of a high-affinity, mechanistically validated small molecule antagonist of BCL2 has been reported. This small-molecule kills cancer in mouse models and primary human cancer cells in vitro [68].

3.2.8

BCL1–PRAD1–CCND1

The PRAD1 gene was first cloned from a parathyroid adenoma with inv(11) (p15;q13) and subsequently identified as BCL1. Transcription gives rise to two major mRNAs of 4.5 and 1.5 kb through alternative polyadenylation. The BCL1 gene encodes for a 36-kD nuclear protein of 295 amino acids, cyclin D1, which belongs to the cyclin G1 family. The bcl1–cyclin D1 protein binds and activates the CDK4 and CDK6 and seems to regulate the cell cycle G1–S checkpoint through phosphorylation of Rb protein. Accumulating evidence suggests that in addition to its original description as a CDK-dependent regulator of the cell cycle, cyclin D1 also conveys cell cycle or CDK-independent functions. Cyclin D1 associates with, and regulates activity of, transcription factors, coactivators, and corepressors that govern histone acetylation and chromatin remodeling proteins. The recent findings that cyclin D1 regulates cellular metabolism, fat cell differentiation, and cellular migration have refocused attention on novel functions of cyclinD1 and their possible role in tumorigenesis [69]. In normal tissue, the bcl1–cyclin D1 protein is expressed in the proliferating fraction of epithelial tissues, whereas it is absent in lymphoid tissues such as lymph node, spleen, and tonsil.

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The main mechanisms of BCL1 gene activation include translocation and amplification, both of which result in overexpression of normal RNA of 1.5 and 4.5 kb and of intact 36 kD cyclin D1 protein. The 11q13 region is involved in B-cell lymphomas, parathyroid adenoma, breast cancer, and squamous cell cancer of the head and neck. The BCL1 gene has diagnostic applications in patients with lymphoma. BCL1 protein expression is a marker that enables a differential diagnosis of mantle cell lymphoma being positive in >80% of cases. Expression of cyclin D1 has been observed in cases of multiple lymphomatous polyposis, the intestinal form of mantle cell lymphoma. In solid tumors, prognosis can be determined by the BCL1 gene. In cancers of the breast and of the head-and-neck region, 11q13 amplification is associated with poor clinical course of the disease: the relapse-free survival time of patients with BCL1-amplified breast tumors was demonstrated to be shorter than that of patients without BCL1 alteration. In a study performed in esophageal carcinoma, BCL1 was amplified in a subset of primary tumors and lymph node metastases. Metastases tended to be more common in patients with BCL1 amplification than in those without this abnormality. Moreover, BCL1 amplification was associated with decreased 1-year survival, thus providing useful prognostic information.

3.2.9

APC

Adenomatous polyposis coli (APC) is a TSG encoding for a large multidomain protein that plays a relevant role in the Wntsignalling pathway. APC is involved in intercellular adhesion, cell-cycle regulation, and apoptosis. APC contributes to mitosis by regulating microtubules dynamics and chromosomal segregation. The APC gene consists of 8535 bp spanning 21 exons on chromosome 5q21. Exon 15 includes >75% of the coding sequence. mRNA arising from alternate splicing have been described [70]. Somatic mutations result in both loss of the wild-type APC allele and in mutations that cumulatively occur in >80% of colorectal cancer. The somatic mutations encompass nonsense, nucleotide insertions, and deletions (all of which cause a premature stop codon determining an abnormal truncated protein), and can occur anywhere. More than 60% of somatic mutations, however, occur between codons 1286 and 1559, a region named the mutation cluster region. Within the mutation cluster region, two hotspot mutations are at codon 1309 and 1450. APC mutations in the mutation cluster region are associated with allelic loss whereas tumors with nonmutation cluster region mutations are coupled with truncating mutations, suggesting that there is a strong selective pressure on the second hit of inactivation [71]. Germline mutations in APC have been demonstrated in the most patients with familial adenomatous polyposis (FAP). In addition to somatic mutations, APC may be altered by silencing through promoter hypermethylation in a consistent

number of cancers. Although evidence for APC as an oncogene is very limited, its action as a homodimer suggests that dominant negative mutations might occur. APC mutations could cause chromosomal instability [72]. APC plays a pivotal role in early phases of colorectal cancer by modulating β-catenin/Tcf transcriptional activation. In the normal cells, APC is able to form a multiprotein complex with GSK-3β and axin, is phosphorylated by GSK-3β and binds to β-catenin, which in turn is phosphorylated by GSK3β and subsequently degraded by the proteasome pathway (Fig. 3-3). In tumor cells, when APC (as well as β-catenin or axin) is mutated, the multiprotein complex can not be formed and, therefore, β-catenin accumulates into the cytoplasm and then translocates to the nucleus, where it activates the Tcf factor, which in turn causes transcription of target genes [70]. The relevance of APC during colorectal carcinogenesis renders such a gene a good candidate for early diagnosis in bodily fluid, such as plasma, blood, or stool; however, the wide mutational spectrum makes handling such analyses difficult.

3.2.10

MYC

MYC is a member of the helix-loop-helix/Leu zipper superfamily, a gene family containing at least seven closely related genes. The most studied are C-MYC (cellular), N-MYC (originally isolated from neuroblastoma cells), and L-MYC (originally isolated from small-cell lung cancer cells [SCLC]). The MYC genes encode for nuclear DNA-binding proteins that are involved in transcriptional regulation. MYC proteins form homodimers or heterodimers through their C-terminal helix-loop-helix domains. MYC can heterodimerize with proteins such as max, mad, and MX11. Max can bind Myc to repress the transcriptional activation of MYC genes, whereas mad and MX11 can bind max and release MYC to function as a transcriptional activator. MYC is implicated in the control of normal cell proliferation, transformation, and differentiation. MYC expression is essential for progression through the cell cycle and is growth-factor dependent in untransformed cells [73]. A biologic function of MYC in different cell types is apoptosis induction in absence of specific growth factors. MYC abnormalities can be in the form of chromosome translocation, gene mutation, or gene amplification. The result is usually an increased MYC expression rather than change of the protein structure. Furthermore, MYC activation may be mediated by APC and/or β-catenin alterations in several tumors, leading to an increase of MYC transcription through an accumulation of β-catenin into the cytoplasm and the nucleus (Fig. 3-3). Gu et al. [74] suggested that tumor-associated MYC alteration may be related to an imbalance of the myc/max system. Although the neoplastic transforming activity of MYC generally lies in its ability to modulate the expression of a series of genes, a deregulated MYC expression contributes to carcinogenesis-inducing genomic instability (gene amplifica-

3. Genetic Markers in Sporadic Tumors

53

E-cadherin

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Fig. 3-3. APC/β - cetanin pathways and myc overexpression in normal (left) and tumoral cells.

tion, gene rearrangements, and karyotipic instability) in critical genes [75]. C-MYC, the major member of the MYC family, consists of three exons, the first of which does not code for a protein. In lymphomas, C-MYC gene mutations can occur in the gene transactivation domain and in the coding region after translocation into the Ig gene. Mutations can occur in the noncoding gene exon 1 and at the exon 1/intron 1 boundary with or without C-MYC gene translocation. This region is considered the C-MYC regulatory region and is responsible for its mRNA stability. In Burkitt lymphoma, mutations frequently occur at sites of phosphorylation, a finding that suggests that they may have a pathogenetic role. In DLCL, an aberrant hypermutation activity targets multiple loci, including C-MYC in >50% of cases. Mutations are distributed in the 59 untranslated or coding sequences, are independent of chromosomal translocations, and share features typical of V-region-associated somatic hypermutation [76]. In patients with DLBCL, C-MYC amplification, in association with other genetic lesions, occurs in approximately 20% of cases and is considered a progression marker. A multiparameter study used transcriptional and genomic profiling to define Burkitt lymphoma and to distinguish subgroups in other types of mature aggressive B-cell lymphomas. Of the 176 lymphomas without the molecular signature for Burkitt lymphoma, 155 were diffuse large-B-cell lymphomas. Of these 155 cases, 21% had a chromosomal breakpoint at the

MYC locus associated with complex chromosomal changes and an unfavorable clinical course [77]. Additionally, C-MYC amplification and overexpression have been reported to be associated with tumor progression from noninvasive to invasive and tumor relapse in breast carcinoma and the degree of malignancy in ovarian carcinoma metastasis development in intestinal-type gastric cancer. A nuclear accumulation of C-MYC may identify high-risk subsets of patients with synovial sarcoma of the extremities. C-MYC expression seems to be a useful prognostic marker able to identify high-risk melanoma and medulloblastoma. C-MYC overexpression, along with high serum platelet derived growth factor-BB (PDGF-BB) receptor levels, seems to be associated with melphalan resistance in patients with multiple myeloma [78]. It has been proposed that N-MYC might regulate angiogenesis because its overexpression leads to a down-regulation of leukemia inhibitory factor, a modulator of endothelial cell proliferation. N-MYC is frequently amplified/overexpressed in SCLC, retinoblastoma, and neuroblastoma. In the latter, N-MYC amplification has been correlated with increased metastases and poor outcome and encouraging results have been reported using a peptide nucleic acid-based antisense strategy for inhibition of N-MYC expression [79]. Human genomic DNA shows an EcoRI restriction fragment length polymorphism (RFLP) of L-MYC defined by the two alleles S and L. RFLP is a representative genetic trait

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associated with an individual’s susceptibility to several tumors as gliomas and esophageal and gastric cancer. A significant reduction in the L-MYC expression levels, related to loss of heterozygosity (LOH), may be associated with disease stage and course in bladder cancer, whereas L-MYC gene amplification and/or overexpression are involved in ovarian [80] and lung [81] carcinoma pathogenesis.

3.2.11

BCL6

The BCL6 gene consists of nine exons, the first two of which are noncoding. The gene is transcribed as a 3.8-kb message predominantly in normal adult skeletal muscle and in some patients with NHL carrying 3q27 chromosomal defects. BCL6 encodes for a 79-kD nuclear protein containing six C-terminal zinc finger domains and a N-terminal POZ domain, which mediates its sequence-specific transcriptional repressor function [82]. The BCL6 protein is predominantly expressed in the B-cell lineage, where it is found in mature B cells. In normal human lymphoid tissues, BCL6 expression is topographically restricted to germinal centers, including all centroblasts and centrocytes. This restriction indicates that BCL6 is specifically regulated during B-cell differentiation and suggests a role for BCL6 in germinal-center development and function. It has been suggested that BCL6 can directly repress the transcription of the p53 TSG in germinal center B cells. Therefore, in normal B-cell differentiation, BCL6 expression gives B cells an opportunity to diversify their Ab genes without hindrance from p53 [83]. The BCL6 gene can be activated by chromosomal translocation or somatic mutations. Breakpoints and mutations cluster in the BCL6.5 regulatory region, in a 3.3-kb EcoRI fragment that defines the major translocation cluster. BCL6 somatic mutations are multiple and often biallelic and they are found in tumors displaying either normal or rearranged BCL6 alleles, indicating their independence from chromosomal rearrangement and from linkage to Ig genes. BCL6 gene mutations have been found in >70% of DLCL and 45% of follicular lymphomas. Detection methods for BCL6 somatic mutations include polymerase chain reaction-single-strand conformational polymorphism and direct sequencing. BCL6 gene mutations have been found in a high proportion of normal B cells [84] and in most germinal center derived lymphomas, so this genetic abnormality does not seem to have a diagnostic use; however, transformation of follicular lymphoma to DLCL is associated with accumulation of new mutations in the 5′ noncoding regulatory region of the BCL6 gene that may regulate BCL6 mRNA expression and, in some cases, play a role in lymphoma transformation. From the prognostic point of view, it has been shown that BCL6 mutations predict shorter survival and refractoriness to reduced immunosuppression and/or surgical excision in posttransplantation lymphoproliferative disorders. Investigation of the prognostic value of BCL6 mutations is at early stages. In keeping with this, Vitolo et al. suggested that the pres-

ence of BCL6 gene mutation could predict a higher chance of being free of disease in patients with DLCL who were treated with standard chemotherapy, but not in patients who were treated with autologous stem cell transplantation [85]. Moreover, high BCL6 mRNA expression was a favorable prognostic factor in DLCL and its use in the stratification and design of risk-adjusted therapies for patients with DLCL was encouraged. Recent studies have demonstrated that B-cell differentiation patterns among patients with DLCL were associated with particular clinicopathologic features [86]. One such study observed that patients with DLCL expressing BCL6, but not CD10, more frequently had early-stage disease, normal lactate dehydrogenase levels, a primary extranodal origin, and a low or low/intermediate risk international prognostic index than did patients with DLCL expressing both BCL6 and CD10 [87].

3.2.12

9p21 Chromosomal Region

The 9p21 chromosomal region harbors a gene cluster consisting of the three genes physically proximate p14ARF, p16INK4a, and p15INK4b (Fig. 3-4A). p14ARF and p16INK4a both are encoded by the INK4a/ARF locus that shows a peculiar genomic organization containing two distinct promoters and alternative first exons, designed 1α and 1β, of which transcript are each spliced to two common exons. Exon 1α, 2,and 3 encodes p16INK4, whereas exon 1β, 2 and 3 encodes p14ARF which bears no homology to p16INK4. All three genes have a putative tumor suppressor role; p14ARF is a key component of the TP53 pathway, whereas p16INK4a and p15INK4b play an active role in the Rb pathway. On the basis of their cell-cycle inhibitor function, alterations of each gene can influence cellular growth regulation (Fig. 3-4B). The 9p21 chromosomal band is one of the major aberration hotspots in human cancers and its high susceptibility to genetic alterations is probably related to physical organization of this gene-cluster. In fact, the existence of tightly clustered breakpoints (close to the 1α and 1β exons and possibly also upstream of p15INK4b) in the 9p21 locus, as well as gene-specific deletion by illegitimate V(D)J recombinase activity [89] were reported. Furthermore, the promoter regions of all the three genes are rich in adjacent cytosine and guanine nucleotides in the DNA (CpG) islands that are target for gene silencing by methylation.

3.2.13

P16INK4a

p16INK4a is a G1-specific negative regulator of cell proliferation. The p16INK4a gene is composed of three exons coding for a 15.8-kD protein of 156 amino acids that show a four-tandem repeated motif structure. The p16 protein is the prototype of a family of nonfunctional-redundant CDK inhibitors, i.e., p15INK4b, p18INK4c, and p19INK4d. Their function is to block the association of CDK4/6 with cyclin D and then prevent the activation of the kinase activity of the CDK4-6/cycD complex. The

3. Genetic Markers in Sporadic Tumors

55 Fig. 3-4. Sturcture (A) and tumor-supression pathways (B) at the 9p21 locus.

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CDK4/6-cyclin D complex can phosphorylate the pRB protein, which concomitantly releases of E2F, a factor that permits transcription of the cell-cycle regulator genes and progression into S phase. On the contrary, binding of CDK4 or 6 with p16 protein blocks cell cycle in G1 phase. These functional relations are known as the p16INK4a/CDK4/cycD1/Rb pathway. The amount of p16 mRNA in nonpathologic human tissue is quite low, but accumulation of p16 transcript and protein has been shown in response to cellular senescence, oncogenic RAS gene stimulus, and inactivation of Rb gene. To date, three main mechanisms of p16INK4a inactivation, leading to deregulation of the G1-S transition, have been found: deletion of both alleles, deletion of one allele, and mutation in the remaining allele, and deletion of one allele and methylation-mediated silencing of the remaining allele. Deletions, point mutations, and methylation of the 5′ CpG island are molecular abnormalities that could affect p16INK4a function in human cancers. Potential clinical applications include risk assessment. LOH as well as mutations of p16INK4a have been reported in sporadic dysplastic nevi, thereby suggesting their role in malignant melanoma development [90]. Methylation of p16INK4a promoter in plasma and sputum is associated with lung cancer risk [91]. In diagnosis, sporadic pancreatic cancers have been found affected by p16INK4a mutations that seem to be useful as diagnostic markers but their biologic meaning has not been

defined. 9p21 LOH and p16INK4a alterations were significantly related to shorter survival, quicker relapse, and worse prognosis in Ewing sarcoma, cutaneous melanoma and NSCLC. Evidence of a role for p16INK4a inactivation in tumor progression was reported in meningioma, gastrointestinal stromal tumor (GIST), and in hematopoietic tumors. Alterations of p16 protein expression are generally not relevant in term of diagnostic or prognostic significance. Nevertheless, p16 expression may be of prognostic importance in GIST [92], primary malignant melanoma [93], and in the progression of cervical intraepithelial neoplasia (CIN) [94]. There is a tight correlation between p16 immunoreactivity and high-risk human papillomavirus (HR-HPV)-related cancer. Through a negative feedback loop with the Rb protein, the inactivation of Rb by HPV E7 oncoprotein results in enhanced immunoexpression of p16. Thus, p16 expression has been shown to be a surrogate marker of HR-HPV infection in oropharyngeal squamous cell carcinoma [95] and in CIN or cervical cancer [96]. p16INK4a inactivation correlates with poor response both in mouse models and in patients. Consistently, the action of traditional drugs, inducing DNA-damage and apoptosis, depends on effective programs of senescence and apoptosis controlled by p16 and p53 [96]. p16INK4a inactivation causes loss of checkpoint integrity making tumor cells unable to stop at predetermined points of the cell cycle and favoring an

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uncontrolled proliferation. This rationale led to the development of cyclin-dependent kinase inhibitors (CDKI) as promising new antitumor agents that suppress cell growth and then facilitate the induction of apoptosis [97]. The combination of CDKI and traditional chemotherapy seems to have a cooperative antitumor effect. In fact, tumor cells exclusively treated with DNA-damaging drugs may undergo cell-cycle arrest and DNA repair, but not necessary followed by apoptosis, leading to a cell cycle-mediated drug resistance that may limit the effectiveness of chemotherapy. Clinical observations suggest that the activity of DNA-damaging drugs could be improved by sequentially following them with the administration of CDKI, which can convert a cell from cell-cycle arrest to cell death [98, 99]. Thus, the molecular characterization of p16INK4a gene could be helpful to planning a more tailored cancer therapy.

3.2.14

p15INK4b

p15INK4b gene displays high homology to p16INK4a, particularly in exon 2, indicating its origin by a gene duplication event. The gene comprises 2 exons and encodes 2 protein isoforms, p15 and p15.5, that are synthesized from 2 alternative, in-frame, translation initiation codons. Although structurally distinct, both the proteins bind to CDK4 and CDK6 and suppress cell growth in response to extracellular stimuli such as TGF-ß1, at variance with p16, which is activated by intracellular stimuli. The role of p15INK4b in human tumorigenesis and definitive conclusions regarding the prognostic significance of its inactivation remain to be better defined. This gene is inactivated by homozygous deletion, involving or not the contiguous p16INK4a and p14ARF genes in different tumors such as esophageal squamous cell carcinoma, ALL, and bladder carcinoma. In ovarian cancer, p15INK4b deletion may be a potential indicator for poor chemotherapy response and adverse prognosis [100]. Similarly, in adult T-cell leukemia/lymphoma, patients with deleted p15INK4b and p16INK4a genes have a significant shorter survival than patients with both genes preserved [101]. Mutations of p15INK4b are not frequent, whereas promoter methylation was reported in some cancers as ovarian cancer [102]. In precursor B-cell acute lymphoblastic leukemia [103], the p15INK4b (but not p16INK4a) gene methylation is a potential marker of minimal residual disease and, with p16INK4a methylation, may be of prognostic significance in the early stages of colorectal cancer [104]. Finally, the possible correlation between p15INK4b inactivation by methylation with an aggressive transformation of B-cell and T-cell lymphomas, or an evolution of myelodysplastic syndromes (MDS) toward AML, provide evidence of a causal role of the promoter methylation in disease progression. On the contrary, mantle cell lymphoma with p15INK4b methylation tend to have lower proliferation and promoter methylation was also detected in normal stem cells. Therefore, this epigenetic change seems to represent a physiological mechanism of cell regulation rather than a primary oncogenic mechanism in mantle cell lymphoma [105].

3.2.15

p14ARF

In p19ARF (ARF = alternative reading frame) null mice model, lymphomas and sarcomas develop at early age, supporting the idea that human p14ARF functions as TSG. At variance with p16INK4a and p15INK4b, in mouse and human cells, p14ARF does not directly inhibit CDK, but stabilizes p53 by antagonizing its negative regulator mdm2. It has been accepted that p14ARF binding to mdm2, induces a conformational change that facilitates nucleolar import of the p14ARF-mdm2 complex. This interaction prevents the mdm2-mediated p53 degradation by nuclear ubiquitination leading to a p53-dependent cell-cycle arrest or apoptosis, depending on the cellular context; however, contrasting results have been reported about interplay between p14 and p53 in human cell. A p53-dependent cell-cycle arrest induced by p14 is sustained by some researchers, whereas others support that p14 overexpression may be enough to induce a p53-independent apoptosis. The occurrence of p14ARF multiple binding domains for E2F, which negatively affect the E2F-dependent transcription, speak in favor of a role of p14ARF, even in the Rb pathway. The role of p14ARF in human carcinogenesis is less clear than that of p16INK4a, because alterations of p14ARF are accompanied by p16INK4a deregulation in most cancers. Alterations exclusively of p14ARF, as point mutations uniquely targeting exon 1β, are rare. There is evidence that >40% of the INK4a/ARF mutations functionally impaired both p14ARF and p16INK4a, altering the subcellular distribution of p14ARF and decreasing its ability to activate the TP53 pathway in melanoma [106]. Homozygous deletion is the predominant mechanism of p14ARF inactivation in hepatocellular carcinoma, as well as in primary central nervous system lymphomas where this specific alteration is associated with a shorter patient survival [107]. An increase in homozygous deletion of p14ARF, always associated with codeletion of p16INK4a, correlates with increasing grade in primary gliomas [108]. p14ARF promoter methylation seems to be a biomarker for early detection of ulcerative colitis-associated colorectal cancer or dysplasia [109] and for the pathologic stage, clinical outcome, and prognosis of patients with bladder cancer [110]. Additionally, aberrant methylation of p14ARF gene correlates with poor survival in patients with osteosarcoma [111]. Regarding protein expression, in aggressive B-cell lymphomas, an abnormal p14 nuclear overexpression, not confined to the nucleoli and associated with TP53 and p16INK4a alterations, is a marker of a high tumor aggression, because it parallels an increased growth fraction as well as a more aggressive clinical course. Additionally, p14 expression is a predictor of both relapse and survival in squamous cell carcinoma of the anterior tongue [112]. In vulvar carcinomas, the presence of HPV significantly correlates with high p14 expression, whereas in HPV-negative tumors, low p14 expression predicts the poorest disease-specific survival [113]. A peculiar p14ARF inactivation mechanism is represented by the t(8;21) chromosomal translocation, the fusion protein of

3. Genetic Markers in Sporadic Tumors

which, consisting of the AML-1 transcription factor and the 8-21 corepressor (AML1 ETO), repress the p14ARF promoter transcription and reduces endogenous levels of p14 expression in AML [114].

3.2.16

KIT

The c-Kit proto-oncogene represents the cellular homolog of v-Kit, (Hardy Zuckerman 4 feline sarcoma virus) [115]. The gene spans >70 kb of DNA and includes 21 exons. The longest transcript is 5,230 bp and is alternatively spliced. The corresponding receptor is a 145-kD transmembrane tyrosine kinase glycoprotein, member of the RTK subclass III family, characterized by a similarly composed extracellular, juxtamembrane, and 2-part (split) intracellular tyrosine kinase domains (TKD-I and TKD-II). The KIT juxtamembrane domain contains α-helical elements whose proper configuration is essential to inhibitory regulation of tyrosine phosphorylation. KIT is expressed by hematopoietic progenitor cells, mast cells, and germ cells, and by the pacemaker cells of the gut. Steel factor is also known as KIT ligand or stemcell factor (SCF). Binding of SCF to KIT results in receptor homodimerization, activation of KIT tyrosine kinase activity, and resultant phosphorylation of a variety of substrates, including AKT and STAT3. In many cases, these substrates are themselves kinases and serve as effectors of intracellular signal transduction. Three general mechanisms of KIT activation in tumor cells have been described: autocrine and/or paracrine stimulation of the receptor by its ligand, SCF; acquisition of activating mutations; and cross-activation by other kinases and/or loss of regulatory phosphatase activity. KIT overexpression was found in SCLC, Ewing’s sarcoma, synovial sarcoma, and adenoid cystic carcinoma of the salivary glands. In all these tumors, KIT overexpression seems to be sustained by an autocrine/paracrine activation loop. The tyrosine kinase activity of KIT can be activated by mutation of several exons of the gene. These activating mutations cause ligand-independent kinase activity with resultant receptor autophosphorylation and stimulation of downstream signaling pathways, including MAPK and phosphatidyl inositol 3′ kinase (PI3K). c-kit mutations are most commonly found in mastocytosis/mast cell leukemia, AML, seminoma/dysgerminoma, and sinonasal natural killer/T-cell lymphoma. In all these tumors, mutations involve principally the exon 17 encoding for TKD-II. In GIST, more heterogeneous mutations are described, occurring in exons 11 (juxtamembrane domain), 9 (extracellular domain), 13 (TKD-I), and 17 (TKD-II), comprising point mutations, “inframe” insertions, and deletions. Approximately 15% of GIST express wild-type kit, 30% of which show activating mutations typically involving exon 12 (juxtamembrane domain) or 18 (TKD-II) of PDGFRA gene. PDGFRA and kit mutations are mutually exclusive. It was demonstrated that constitutively activated mutant PDGFRA can heterodimerize with KIT and activate it [117]. Imatinib, an inhibitor of tyrosine

57

kinase activity in BCR/ABL-positive leukemia, is effective in treating GIST [118]. With regard to kit gene status, the response is best in tumors with exon 11 mutations, moderate with exon 9 mutations, and expectedly poor with exon 17 mutations (because of primary resistance), as well as in presence of a wild-type kit [119]. If PDGFRA gene is mutated, exon 18 mutation is cause of primary resistance. Secondary resistance often develops within a few years in GIST treated with imatinib and mechanisms to explain it include development of secondary KIT mutations in exon 13 (V654A), exon 14 (T670I) [120], or exon 17 (eg, D816G, D820I, D820Y, N822K, Y823D), loss of KIT protein expression. and KIT gene amplification, as well as secondary PDGFRA mutation (D842V). Because most of the KIT mutations observed in mast cell neoplasms and germ cell tumors are located in the TKD-II, imatinib appears to be ineffective for these neoplasms. Similarly a remarkable effect of the drug has not been reported in patients carrying autocrine/paracrine loop activated wild-type KIT, such as SCLC and salivary gland adenoid cystic carcinoma. Several new drugs, such as dasatinib [121], sunitinib, PKC412, and nilotinib are under development or are being tested in clinical trials to inhibit KIT activation when there is primary/secondary imatinib resistance or when KIT/PDGFRA genes are wild-type.

3.2.17

FLT3

FLT3 belongs to the RTKIII family. In the human, FLT3 encodes a 993-amino acid protein expressed in immature hematopoietic cells, placenta, gonads, and brain. By immunoprecipitation studies, 2 bands can be identified, a major band of about 140 Kd and a less abundant band of 160 kD, localized to the plasma membrane and derived by post-translational N-linked glycosylation of the smaller one. In normal bone marrow, FLT3 expression appears to be restricted to early progenitors, including CD34+ cells with high levels of CD117 (c-KIT) expression. FLT3 is an important receptor in early hematopoiesis, involved in proliferation, differentiation, and apoptosis [122]. FLT3 is highly expressed in a spectrum of hematologic malignancies including 70–100% of AML of all FAB (French-American-British) subtypes, B-cell precursor ALL, a fraction of T-cell ALL, and chronic myeloid leukemia (CML) in lymphoid blast crisis. A common mechanism of FLT3 activation consists in internal tandem duplication (ITD) in the juxtamembrane domain, which contains the inhibitory signal for the tyrosine kinase. ITD leads to length polymorphism in the juxtamembrane region. Alternative and less-frequent mutations are localized in the activation loop of FLT3, which normally blocks access of ATP and substrate to the kinase domain. Altogether, these mutations are present in approximately 30% of patients with AML and result in constitutive activation of the FLT3 kinase [123]. FLT3 length mutations (LM) occur more frequently in AML patients with normal karyotype (70%), whereas, among patients with chromosomal aberrations, they are found

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in patients with t(15;17) (35%) and rarely in patients with t(8;21), inv(16), and complex karyotypes. In a study of a large cohort of AML cases, Schnittger et al. [124] showed that the complete remission rate and overall survival were not different between patients with or without FLT3-LM. In contrast, patients with FLT3-LM had a significantly shorter event-free survival with a higher relapse rate. In addition, the size of FLT3 ITD has been shown to have prognostic significance. Specifically, increasing ITD size has been associated with decreasing overall survival and relapse-free survival [125]. FLT3-LM may represent a new genetic marker in AML to monitor a subset of patients during follow-up, predict relapse, and plan early therapeutic intervention. Fusion of FLT3 to ETV6 has been reported in a patient with myeloproliferative disorder and a t(12;13)(p13;q12), suggesting this as a further molecular mechanism of inappropriate FLT3 activation and induction of leukemogenesis [126]. Several small molecule tyrosine kinase inhibitors, all of which work to compete with ATP binding, have been reported for the treatment of AML carrying FLT3 ITD. CEP-701 is a indolocarbazole tested in phase-2 trials. Treated patients have reduced bone marrow blasts (from 25% to <5% in 4 weeks). PKC412 is a staurosporine derivative that inhibits many tyrosine kinases, including VEGFR, PDGFR, KIT, and FLT3. In a phase-2 trial in adults with relapsed or refractory AML, six of eight patients had transient reductions in peripheral blast counts; however, it has been shown that clinical resistance to this compound can be conferred by N676K mutation or other point mutations in the FLT3 kinase domain. SU5416 is a indolidone compound studied as a VEGFR, KIT, and FLT3 inhibitor in refractory AML/MDS patients in phase-2 trials. It showed modest clinical activity, with 5% partial responses and 2% hematologic improvement. Another indolidone compound, SU11248, induced >50% inhibition of FLT3 phosphorylation in patients with AML at tolerable doses. Finally, Ab directed against FLT3 represents a promising approach for the treatment of leukemia [127].

3.2.18

mately 50% of PTEN mRNA. To date, ΨPTEN protein has not been detected, leading to the conclusion that analyses of PTEN protein expression do not show traces of contamination of ΨPTEN protein [128]. In addition to mutations and allele losses, PTEN could be inactivated by promoter hypermethylation that occurs in approximately 20% of endometrial carcinomas; in thyroid carcinoma, predominantly in follicular-type; and in melanoma patients, where this type of alteration may be detected also in paired sera samples [129]. PTEN can bind to cellular membranes and this association leads to correct positioning of the catalytic domain on the membrane. Mutations in exon 5, which encodes the phosphatase domain, as well as in exon 7 and 8, most of which determine premature stop codons, inactivate the PTEN protein. By its lipid phosphatase activity, PTEN dephosphorylates the phosphatidylinositol 3,4,5 triphosphate (PIP3) and the phosphatidylinositol 3,4 biphosphate (PIP2), determining the PI (4,5) biphosphate and the PI(4) phosphate formation, respectively (Fig. 3-5). The opposite biochemical reaction is catalyzed by phosphatidylinositol-3-kinase (PI3K), which is associated to cell growth and cell survival. Thus PTEN, which counteracts PI3K activity, is involved in cell death or modulation of arrest signal. PTEN prevents AKT phosphorylation, maintaining it in its inactive form, by dephosphorylation of PIP3 and PIP2, leading to block cell growth (Fig. 3-5) [122]. In addition to PI3K and Akt, PTEN negatively regulates the androgen receptor, collaborates with INK4a/ARF and MAGI-2, and causes increased expression of inducible cyclooxygenase-2 (COX-2) and c-myc [128]. PTEN is regulated by p53 by 2 mechanisms: at the transcriptional level, after several stimuli that directly act on p53; and at the protein expression level, through direct PTEN–p53 protein interaction, after p53 activation driven by PTEN itself [130]. PTEN mutations can also stimulate angiogenesis by influencing vascular endothelial growth factor (VEGF) activity and suppression of destabilization Growth factor

PTEN

PTEN (phosphatase protein homolog to tensin deleted on chromosome 10), is a TSG that encodes for a 403-amino acid protein that possesses lipid and protein phosphatase activities. With the exceptions of endometrial and prostate cancers, where PTEN plays a relevant role in early phases of cancerogenesis, PTEN inactivation seems to be involved in late stages of a number of tumors, both through somatic mutations or loss of region 10q23. Some inherited disorders, such as hamartomatous syndromes, are associated with mutations of PTEN gene. These syndromes include Cowden disease, Lhermitte-Duclos disease, and Bannayan-Riley-Ruvalcaba disorder [128]. The transcriptional analysis of PTEN is difficult for the presence of a pseudogene of PTEN, named Ψ PTEN located on 9p21 region. PTEN and ΨPTEN mRNA are both transcribed in all human tissues and ΨPTEN mRNA represents approxi-

Tyrosine Kinase receptor

OUT

PI3K AKT

P

PIP2

PIP3

P

AKT AKT

P P

PTEN cell death

cell survival

Fig. 3-5. PTEN. The PI3K/PTEN/AKT pathway. (see Color Plate 2 following p. 316.) of hypoxia-inducible factor-1 [128]. Studies have suggested

3. Genetic Markers in Sporadic Tumors

that PTEN expression is an important predictor of sensitivity to trastuzumab (a molecule that binds to and inhibit Her-2) in breast cancer [131].

3.2.19

AKT

The Akt family genes, which belongs to a subfamily of protein kinases named AGC protein kinases (encompassing PKA and PKC), encode for 3 Ser-Thr kinases and represent a major effector, mediating cell proliferation, survival, cell size, and response to nutrient availability, intermediary metabolism, and tissue invasion signals. The 3 isoforms of Akt family are ubiquitously expressed in mammals, but the levels of expression of the single member depend on tissue analyzed suggesting distinct roles [132]. Akt protein contains a pleckstrin homology (PH) domain, essential to bind PIP3 and PIP2, to trigger Akt to the plasma membrane and to induce a favorable conformation of Akt. Akt possesses two regulatory phosphorylation sites, Thr308 in the activation loop within the kinase domain, and Ser473 in the C-terminal regulatory domain. Phosphorylation of either Thr308 or Ser473 partially activates Akt, whereas phosphorylation of both sites is required for full activation. Thr308 is phosphorylated by PDK1, whereas the mechanism of phosphorylation of Ser473 is not completely understood; probably involving an autophosphorylation process and activation by ILK [132]. Reports describe the possible role of tyrosine phosphorylation in Akt regulation. For example, Tyr315 and Tyr326 are phosphorylated after receptor activation and are required for Akt activity [132]. Akt is phosphorylated and activated after cell stimulation from different growth factors and from a series of regulatory proteins [132]. Akt targets include Bad, Forkhead factors, IKKa, Mdm-2, caspase-9, GSK-3, eNOS, p27KIP1, and p21CIP1. Moreover, Akt is also involved in angiogenesis. Akt signaling has both proangiogenic (by stimulating eNOS, capillary formation, and endothelial cell proliferation) and antiangiogenic (by normalizing permeability, influencing extracellular matrix composition, regulating apoptosis) effects. The balance between signaling pathways under different conditions determines the angiogenic phenotype [133]. Akt acts not only pro-oncogenically, but also antioncogenically, by suppressing invasion and metastasis through alteration of cell motility [134]. At the end, deregulation of Akt expression seems to be involved in Akt drug response and radioresistance in several tumors [135], such as in metaplastic breast cancer, where it confers resistance to hormone therapy [136], and in ovarian cancer, where it confers resistance to cisplatin by modulating the direct action of p53 on the caspase-dependent mitochondrial death pathway [137]. Moreover, efforts have been made in the development of small molecule inhibitors that directly bind to Akt, such as triciribine and pyridine derivatives.

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3.2.20

COX

COX are the enzymes that catalyze the conversion of arachidonic acid to prostaglandin H2. At variance with COX-1, COX-2 is not expressed in most tissues but it can be rapidly induced by a wide number of extracellular and intracellular stimuli [138]. A putative Tcf-4 binding element was identified in the COX-2 promoter supporting the notion that COX-2 may be a downstream target of the APC-β-Catenin-Tcf-4 pathway [139]. Analysis of surgical specimens has shown that COX2 mRNA was expressed more in adenomas than in normal tissue and that this expression increased progressively with adenoma enlargement. Furthermore, COX-2 overexpression cooperates with K-ras in the progression of colorectal adenomas. COX-2 expression promotes cell proliferation by the activation of EGFR, inhibits apoptosis by up-regulating Bcl2, and facilitates metastatic potential by up-regulating MMP-2. COX-2 has been shown to directly promote angiogenesis by different mechanisms [140] (Fig. 3-6). Reports outline genetic variations in COX-2 gene might result in altered susceptibility to diseases. The −756G>C in the promoter has been associated with lower promoter activity. The 5937G>C, resulting in the Val511Ala amino-acid change, found in some Blacks, is associated with decreased susceptibility to colon cancer [139]. Nonsteroidal anti-inflammatory drugs (NSAID) inhibit prostaglandin production. Because COX-1 is involved in gastrointestinal protection and COX-2 is rapidly induced by several stimuli, attempts are being made to synthesize new COX inhibitors able to bind and block the COX-2 activity in a selective way. NSAID are able to induce apoptosis by COX-dependent and COX-independent mechanisms (Table 3-2) [141]. In humans, a large body of observational evidence suggests that the use of selective COX-2 inhibitors, which might avert the gastrointestinal bleeding complications associated with traditional NSAID, are able to reduce the incidence of colorectal adenoma and cancer by approximately 50%. Therefore, several trials are now evaluating the effect of anti-COX-2 and various cancers; however, randomized clinical trials have shown that the administration of two selective COX-2 inhibitors (celecoxib and rofecoxib) had led to increased risk of serious cardiovascular events, thus clearly indicating that these drugs, at the moment, cannot be routinely recommended for cancer prevention [142].

3.2.21

Chromosome 18q

The DCC gene (deleted in colon cancer) encodes for a transmembrane protein of about 190-kD, the netrin-1 receptor, belonging to the family of cell adhesion molecules. The DCC protein drives the migration of neuronal axons [143] and carries a significant homology with neural cell adhesion molecule and other members of the immunoglobulin gene superfamily. Hence, the DCC protein may be involved in the modulation of normal cell–cell and cell–matrix interactions.

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Fig. 3-6. COX-2 expression induces angiogenesis through different mechanisms.

Table 3-2. NSAIDs induce apoptosis through COX-dependent and COX-independent mechanisms. COX-dependent mechanisms

COX-independent mechanisms

Alteration of prostaglandin production

Inhibition of activation of NF-kB

Decreasing in angiogenetic factors

Alterations of Bax and Bak concentrations

Increasing level of arachidonic acid, that stimulates ceramide production (a mediator of apoptosis)

Alteration of Myc production through protein kinase C activation

Interference with the binding of PPARγ to DNA

Furthermore, the DCC protein induces apoptosis by proteolysis of specific receptors [144]. DCC alteration occurs by decrease of mRNA or protein expression, by promoter hypermethylation and, more rarely, by point mutations, all patterns associated with an increase of the metastatic potential of cancer cells and, consistently, DCC null immunophenotype has been proposed as prognostic marker in patients with colorectal cancer [144]; however, allelic imbalance analyses of chromosome 18q does not mirror the immunophenotype findings [145, 146]. This discrepancy seems to be caused by the fact that in the region 18q21-22, two other TSG, that cooperate with DCC in the colorectal carcinogenesis, are also located. These two genes are SMAD4 and SMAD2. SMAD4 encodes for a nuclear transcription factor involved in the TGF-β1 signaling and in angiogenesis [147]. SMAD2 interacts with the SKI protein and is involved in endodermal differentiation [147]. A recent meta-analysis of 27 key published studies revealed that the poor prognosis associated with chromosome 18q phenotype was maintained both in studies assessing DCC through microsatellites including the DCC region, and in those in

which markers mapped to a genomic region excluding DCC, indicating that the poor prognosis observed may not be entirely because of DCC [148]. Chromosome 18q LOH and/or decreased DCC expression have been associated with poor prognosis in patients with colorectal cancer who lack nodal or distant metastases at the time of their surgery (so-called stage II) as well as in patients who have nodal but not distant metastases at the time of surgery (stage III), thus indicating that stage-II colorectal cancer with alterations at chromosome 18q level may benefit from adjuvant chemotherapy [149].

3.3 Genetic Markers Derived from Nonrandom Chromosomal Abnormalities Recurring and highly consistent chromosomal aberrations have led to the identification of new proto-oncogenes at or spanning chromosomal breakpoints. Studies have shown that these genes are oncogenic and confirmed the pivotal role of chromosomal aberrations in tumor development. Specific

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Table 3-3. Nonfusion genes in hematopoietic tumors. Type

Translocation

Basic-helix-loop-helix Cystein-rich (LIM) proteins

Homeobox protein Others

t(7;19)(q35;p13) t(7;9) (q35;q34) t(11;14)(p15;q11) t(11;14)(p13;q11) t(7;11)(q35;p13) t(10;14)(q24;q11) t(7;10)(q35;q24) t(10;14)(q24;q32) t(14;19)(q32;q13) t(5;14)(q31;q32) t(7;9)(q34;q34) t(1;7)(p34;q34) t(X;14)(q28;q11) t(14;15)(q32;q11-13) t(2;7)(p12;q21) t(7;14)(q32;q21) t(1;14)(q21;q32) t(1;14)(q21;q32) t(1;14)(q21;q32) t(11;14)(q23;q32) t(12;22)(p13;q11) t(12;14)(q24;q32)

Affected gene

Rearranging gene

Disease

LYL1 TAL2 LMO1 LMO2 LMO2 HOX11 HOX11 lyt-10 BCL3 IL-3 TAN1 LCK C6.1B BCL8 CDK6 CDK6 BCL9 MUC1 MUM-2, MUM-3 DDX6/RCK CCND2 BCL7A

TCR-β TCR-β TCR-δ TCR-δ/α/β TCR-δ/α/β TCR-α/β TCR-α/β IgH IgH IgH TCR-β TCR-β TCR-α IgH IgK IgH IgH IgH IgH IgH IgL IgH

T-ALL T-ALL T-ALL T-ALL T-ALL T-ALL T-ALL B-NHL B-CLL Pre-B ALL T-ALL T-ALL T-PLL B-NHL s-MZL s-MZL Pre-B ALL, B-NHL B-NHL Myeloma, B-NHL B-NHL B-NHL B-NHL

TCR T-cell receptor; IgH immunoglobulin heavy chain; ALL acute lymphoblastic leukemia (T, B, or pre-B cell); B-NHL B non-Hodgkin lymphoma; B-CLL B chronic lymphocytic leukemia; T-PLL T prolymphocytic leukemia; LPL lymphoplasmacytoid lymphoma; sMZL splenic mantle zone lymphoma

Table 3-4. Fusion genes in hematopoietic tumors. Translocation t(1;19)(q23;p13) t(17;19)(q22;p13) t(4;11)(q21;q23) t(9;11)(q21;q23) t(11;19)(q23;p13) t(X-11)(q13;q23) t(1;11)(p32;q23) t(6;11)(q27;q23) t(11;17)(q23;q21) t(3;21)(q26;q22) t(3;21)(q26;q22) t(16;21)(p11;q22) t(6;9)(p23;q34) t(5;12)(q33;p13) ins(2;2)(p13;p11.2) inv(16)(p13;q22) t(3;5)(q25;q35)

Affected genes PBX1-E2A HLF-E2A AF4-MLL AF9-MLL MLL-ENL AFX1-MLL AF1P-MLL AF6-MLL MLL-AF17 EVI-1-AML1 EAP-AML1 FUS-ERG DEK-CAN PDGF-β-TEL REL-NRG CBFB-MYH11 NPM-MLF1

Disease Pre-B ALL Pro-B ALL ALL/Pre-B ALL/ANLL ALL/Pre-B ALL/ANLL Pre-B ALL/T-ALL/ANLL T-ALL ALL ALL AML CML Myelodisplasia AML AML CMML B-NHL AML AML

Abbreviations: ALL = acute lymphoblastic leukemia (T or B-cell); APL = acute promyelocytic leukemia; AML = acute myelogeneous leukemia; CMML = chronic myelomonocytic leukemia.

translocations initially have been identified in hematologic tumors, and subsequently they also have been demonstrated in a subset of solid neoplasms. In hematopoietic tumors, chromosomal translocations have two main consequences: the juxtaposition of a proto-oncogene to the gene for a Tcell receptor or an immunoglobulin (Ig) protein, inducing

oncogenic activation, and creation of a fusion gene encoding a chimeric protein. The genes involved often encode transcription factors, suggesting that disruption of transcriptional control plays a major role in oncogenesis. The main clinical application of these nonrandom chromosomal abnormalities is the diagnostic definition of several morphologically equivocal tumors followed by the assessment of minimal residual disease and therapeutic response. Because of the high number of chromosomal translocations identified in hematopoietic tumors, this chapter describes in detail only those with significant clinical relevance. Other translocations reported in hematopoietic tumors are listed in Table 3-3 and Table 3-4.

3.4 Hematopoietic Tumors: Proto-oncogene Activation 3.4.1

BCL2

BCL2 was one of the first oncogenes shown to be involved in nonrandom chromosomal translocations. It usually is rearranged with Ig heavy and light chain genes on chromosomes 14q32 (IgH), 2p11, and 22q11. In t(14;18)(q32;q21), approximately 70% of the breakpoints on chromosome 18 cluster within a major breakpoint region in the untranslated region of exon 3, 20% occur in the minor cluster region 20 kb downstream of BCL2; a few breakpoints cluster in the variant

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cluster region, 1.5 kb upstream or within the first noncoding exon and involve Ig light chain variant translocation. Recently, using new polymerase chain reaction (PCR) techniques, new clusters between major breakpoint region and minor cluster region, referred to as 3′ BCL2, 5′ mutation cluster region and intermediate cluster region, have been identified, that occur in approximately 20% of newly diagnosed follicular lymphoma. As a consequence of the breakpoint locations, the protein coding domain of BCL2 is maintained during translocation. Gene rearrangement therefore results in overexpression of intact BCL2 protein under the control of Ig enhancer sequences. The role of t(14;18) and BCL2 overexpression in tumorigenesis was demonstrated by in vivo studies. Transgenic mice bearing BCL2-Ig minigene harbor expanded B-cell compartments and developed follicular hyperplasia that eventually progressed to high-grade monoclonal lymphomas. When expression is directed to T cells, fully one-third of the mice develop peripheral T-cell lymphomas. Long latency and progression from polyclonal hyperplasia to monoclonal malignancy are consistent with the hypothesis that oncogenic events in addition to BCL2 overexpression are necessary for tumor formation. Accordingly, in lymphomas arising in BCL2-Ig transgenic mice, a common second tumorigenic hit is translocation of the C-MYC oncogene. t(14;18) is the molecular hallmark of follicular lymphomas and is associated with 60–80% of these tumors. The translocation is found in 20% of DLCL, likely transformed from low-grade follicular lymphomas, and about 10% of Hodgkin disease. The t(2;18) and t(18;22) variant translocations have been described in 10% of B-cell chronic lymphatic leukemias (B-CLL). Combined with morphologic and clinical observations, the finding of t(14;18) in lymph node aspirate may help define a differential diagnosis of follicular lymphoma [150]. t(14;18) is not exclusively associated with tumors. Using sensitive nested PCR, rare BCL2-JH harboring cells have been demonstrated in up to 50% of reactive tonsil and spleen and in peripheral blood of normal individuals, in whom the translocation frequency increases with age. The t(14;18) chromosomal translocation, has an increased prevalence in patients chronically infected with hepatitis C virus (HCV). It occurs with a strong bias for BCL2/JH6 joins. In this regard, HCV-associated t(14;18) more closely resemble t(14;18) in lymphomas than t(14;18) from normal patients [151]. Controversial data surround the prognostic impact of BCL2 translocation. Studies have demonstrated that BCL2-rearranged and germ-line tumors undergo the same clinical behavior, and a negative prognostic marker is represented by BCL2 protein overexpression [66]. Moreover, no correlation between BCL2 breakpoint location and either initial characteristics of the disease or survival of patients with follicular lymphoma patients could be demonstrated. The presence of t(14;18) provides a useful genetic marker to monitor patients after therapy. The PCR persistence of residual BCL2 rearranged cells in the peripheral blood and bone marrow of patients in clinical remission identifies a group of people at high risk of relapse.

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3.4.2

BCL1

t(11;14) (q13;q32) translocation involves the BCL1 locus on chromosome 11q13 and 1 of the joining regions of the IgH genes on chromosome 14q32, resulting in juxtaposition of BCL1 with the IgH enhancer. Only sporadically variable genes or switch IgM may be involved. t(11;14) probably reflects an error in normal variable diversity joining recombination during normal precursor B-cell development. More than 80% of the breakpoints on chromosome 11 cluster in a 300- bp region known as major translocation cluster, centromeric to BCL1. Recently, an extension of >400 bp of the major translocation cluster has been identified in a patient with mantle cell lymphoma with t(11;14), suggesting that rare breakpoints in the major translocation cluster can occur outside the previously defined region [152]. Two minor translocation clusters have been identified (mTc1 22 kb telomeric to BCL1, and mTc2, clustering in the 5′ flanking region of BCL1) that are less frequently involved in translocation. Although fluorescence in situ hybridization (FISH) and reverse-transcription polymerase chain reaction (RT-PCR) are reportedly the most sensitive assays, demonstrating the t(11;14) translocation in nearly 100% of mantle cell lymphoma, IHC remains the most widely adopted approach. Several Abs recognizing cyclin D1 have been described, but IHC detection of cyclin D1 has been considered a problem either for technical reasons (related to inadequate fixation/inefficacy antigen retrieval) or for intrinsic reasons (linking to other nuclear proteins). A novel IgG rabbit MAb (SP4) raised against a synthetic peptide from the C-terminus of human cyclin D1 has proven to be a sensitive and effective tool to detect cyclin D1. t(11;14) (q13;q32), the molecular hallmark of mantle cell lymphoma, is detectable by Southern blotting and PCR, in up to 50% of these tumors. The finding that the percentage of BCL1-positive cases increases by FISH analysis and the evidence that up to 90% of mantle cell lymphomas overexpress BCL1 protein suggest that deregulation of the BCL1 gene occurs in many more cases than originally thought. No clinical differences were observed between BCL1-rearranged and germ-line mantle cell lymphoma, an indication that BCL1 does not identify a clinically different mantle cell lymphoma subset. Sporadically, t(11;14) has been found in lymphoid malignancies other than mantle cell lymphoma, including B-CLL and multiple myeloma. Cyclin D1 (11q13) is one of the five recurrent chromosomal partners involved in IgH translocations in monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma. In some cases, however, reclassification of these t(11;14)-bearing tumors included them in the mantle cell lymphoma histotype. Translocation of C-MYC appears to be a very late secondary event occurring as a progression event in myeloma. The protein kinases inhibitor flavopiridol has been used in clinical trials to inhibit a variety of protein kinases including cyclin D1 [153]. Flavopiridol has been shown to induce growth arrest, cytotoxic cell death, and apoptotic changes in

3. Genetic Markers in Sporadic Tumors

a variety of tumor types, including leukemia and lymphomas. In nonclinical studies, the greatest activity of flavopiridol is observed in combination with chemotherapy agents. Because of its effect on cyclin D1, flavopiridol has been considered as a potential agent for the treatment of patients with mantle cell lymphoma. In two clinical trials, minimal activity or partial responses and stable disease were observed, respectively. These controversial results possibly reflect schedule-dependent differences in efficacy. Similar observations have been made in CLL trials.

3.4.3

C-MYC

The translocations involving the C-MYC gene on chromosomes 8 and one of the Ig loci are of three types. Approximately 80% of cases involve translocation t(8;14)(q24;q32), which occurs between C-MYC and the genes for the Ig heavy chain. The remainder involves translocation between C-MYC and Ig light chain sequences on chromosomes 2p11 and 22q11. In plasmacytomas, the breakpoints on chromosome 8 occur within the first noncoding intron of C-MYC, whereas in Burkitt lymphoma, the translocations are more variable and occur in the 5′ or 3′ sequences flanking the gene or up to 300 kb upstream from the gene. Owing to the relocation of C-MYC near or within the strong transcription controls of the Ig gene, the translocation results in a loss of normal gene regulation and leads to constitutive C-MYC expression. Translocation of C-MYC appears to be a very late secondary events occurring as progression event in myeloma. t(8;14)(q24;q32), and its variants t(8;22)(q24;q11) and t(2;8)(p11;q24), the molecular hallmark of Burkitt lymphoma, are observed in almost all cases of Burkitt lymphoma/leukemias. These translocations occasionally can be detected in 15% of other intermediate to high-grade B-cell lymphomas, and sporadically in low-grade B-cell lymphomas. In sporadic Burkitt lymphoma, translocation breakpoints cluster within the first exon or intron or immediately upstream from the gene, whereas in endemic Burkitt lymphoma, translocations with breakpoints dispersed over about 300 kb upstream from the gene are most frequent. Among other B-cell lymphomas, C-MYC rearrangement is observed in BCL2-positive follicular lymphomas undergoing high-grade transformation, and is considered a secondary genetic event involved in tumor progression.

3.4.4

BCL6

Chromosomal translocations with the Ig gene regions are among the most common rearrangements involving chromosome 3q27. The BCL6 gene can frequently rearrange with the IgH loci on chromosome 14 in the t(3;14) (q27;q32), but occasional rearrangement with the IgL loci, in t(3;22)(q27;q11) and t(2;3)(p11;q27), is also observed. In the t(2;3), the BCL6 and IgLκ genes are juxtaposed in a head-to-head configuration. Rearrangements of BCL6 with non-Ig genes have been described. Although many of the partner genes translocated

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with BCL6 are unknown, some have been identified, including a novel H4 histone gene located on chromosome 6p21, the B-cell transcriptional coactivator BOB1–OBF1 on chromosome 11q23.1, and the TTF gene that encodes a novel G protein on chromosome 4p11. Many variant rearrangements of BCL6, affecting chromosomes 1p32, 1p34, 3p14, 6q23, 12p13, 14q11, and 16p13, involve genes that have not been characterized. The gene for interleukin-21 receptor (IL21R) has been identified as the fusion partner with BCL6 in t(3;16)(q27;p11) in DLCL. A high frequency (15%) of intrachromosomal rearrangement of chromosome 3q27 has been observed as an under recognized mechanism of BCL6 translocation in B-cell NHL [154, 155]. BCL6 chromosomal translocations are associated to approximately 50% of cases of DLCL and 10% of follicular lymphomas. In DLCL, BCL6 rearrangement correlates with clinical presentation at extranodal sites, including the gastrointestinal tract. Some authors showed that patients with BCL6 gene rearrangement had a favorable overall survival and survival without disease progression. This finding was questioned by others, and, more recently, a multivariate analysis has shown that rearrangement of 3q27, together with BCL2 expression and the absence of a germinal-centre phenotype, was associated with a poor prognosis in nodal DLCL [156]. Moreover, within DLCL bringing BCL6 rearrangement, Akasaka et al. noted that cases characterized by non-Ig/BCL6 fusion had an overall survival significantly inferior to that of cases with Ig/BCL6 fusion [157]. The same authors demonstrated later that cell lines transfected with non-Ig/BCL6 fusion genes expressed high levels of BCL6 protein and showed characteristic punctuate nuclear staining, suggesting that non-Ig/BCL6 translocation may play a pathogenetic role in a proportion of DLCL.

3.4.5

PAX5 t(9;14)(p13;q32)

The PAX (for paired homeobox) 5 gene, mapping on chromosome 9p13, belongs to a transcription factor family that is involved in control of embryonic development and organogenesis. Members of this family contain two discrete DNA-binding domains (the paired box and the paired-type homeodomain) that display coordinate DNA-binding specificity. PAX5 is normally expressed in fetal brain and liver during development but becomes restricted to B cells and testis after birth. In the B-cell lineage, PAX5 undergoes downregulation during plasma-cell differentiation. Knockout mice experiments have demonstrated that PAX5 is important for midbrain development and that its loss of function results in maturation arrest of lymphocytes at the pro-B-cell stage. By contrast, PAX5 overexpression results in splenic B-cell proliferation. PAX5 targets have been proposed to be CD19; B-cell receptor component Ig alpha (mb-1); transcription factors. N-MYC and LEF-1 (positively regulated by PAX5); and the p53 tumor suppressor (which is downregulated). B-cell SRC-family tyrosine kinase BLK, which transforms lymphoid progenitors into an

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activated form but is dispensable for B-cell development and activation, is upregulated by PAX5 [158]. PAX5 seems to be required for normal IgH variable diversity joining recombination. Loss of PAX5 results in the substantial transition to a plasma-cell state, demonstrating a functionally significant role for PAX5 in the regulation of terminal B-cell differentiation. The function of PAX5 as a key inhibitor of plasma-cell differentiation has been elucidated [159]. t(9;14)(p13;q32) results in juxtaposition of the PAX5 gene with the IgH heavy-chain gene on chromosome 14 [160]. The translocation is present in approximately 50% of small lymphocytic lymphomas with plasmacytoid differentiation, the so-called lymphoplasmacytoid lymphomas. These tumors possess a plasma cell-like phenotype with serum paraprotein production, and follow an indolent course followed by large-cell transformation. Deregulation of PAX5 transcription by a translocated IgH promoter has been described rarely in NHL subtypes other than LPL and in myeloma [161]. An additional mechanism of PAX5 activation seems to be gene mutation. Pasqualucci et al. identified changes in the germline sequences of PAX5 in most cases of DLCL [76]. In these cases. an aberrant hypermutation activity targeted multiple loci, including PAX5, in >50% of cases. Mutations are distributed in the 59 untranslated or coding sequences, are independent of chromosomal translocations, and share features typical of V-region-associated somatic hypermutation [162]. Other PAX genes have been demonstrated to be activated and play a role in oncogenesis. Pax5 has been found in many B-cell lymphomas (with the strongest expression in follicular, mantle cell, and DLCL) but not in T-cell neoplasms and may prove to be a valuable diagnostic marker in paraffin-embedded biopsy specimens of B-lymphoblastic neoplasms because it is expressed strongly in such samples and is negative in T-cell lymphoblastic proliferations. Hodgkin disease sometimes mimics ALCL, and PAX5 may be useful in such cases because Reed-Sternberg cells can be positive, whereas ALCL (both ALK positive and ALK negative) are consistently negative for PAX5 [63].

3.4.6

TAL1

TAL1 (T-cell acute leukemia)–SCL (stem cell leukemia hematopoietic transcription factor) encoded gene product is homologous to a number of proteins that are involved in the control of cell growth and differentiation. The region of homology is restricted to a 56-amino-acid domain to form two amphipathic helices separated by an intervening loop. Such helix– loop–helix proteins are proposed to function as transcriptional regulatory factors based on their ability to bind in vitro to the E-box motif of eukaryotic transcriptional enhancers. It is suggested that the TAL1 protein may function as a transcriptional regulatory factor. Studies in mice indicate that TAL1 is essential for embryonic blood formation in vivo. In tissues, TAL1 is expressed in developing brain, normal bone marrow, and mast cells, leukemic T cells, and endothelial cells but not in normal

T cells. An antiapoptotic effect of ectopic TAL1 expression in response to cytotoxic agents was demonstrated. Tumor-specific alteration of TAL1 arises by either of two distinct mechanisms. One mechanism is represented by t(1;14)(p32;q11), which transposes TAL1 from its normal location on chromosome 1p32 into the T-cell receptor α/δ chain complex on chromosome 14q11. The second consists of a 90-kb deletion upstream of one allele of the TAL1 locus, probably because of aberrant Ig recombinase activity that results in the fusion between SCL–TAL1 and SIL (SCL interrupting locus, chromosome 1p33). Both mechanisms disrupt the 5 end of the TAL1 gene so that its expression is controlled by the regulatory elements of the TCRδ or SIL genes that are normally expressed in T-cell ontogeny. The consequence may be an ectopic TAL1 production that activates a specific set of target genes that are normally silent. Breakpoints affecting the 3′ side of TAL1 or occurring 25 kb downstream from the gene have been described. Alteration of the TAL1 gene is the most common genetic lesion known to be associated with T-cell ALL. Almost 25% of patients with T-cell ALL exhibit TAL1 deletions, and an additional 3% harbor the t(1;14) translocation. T-NHL or adult T-cell malignancies do not display TAL1 aberrations. Patients with T-cell ALL with TAL1 recombination have a significantly better outcome than other patients with T-cell ALL without the recombination. The results of an immunocytochemical study suggested that TAL1 protein is only rarely expressed by leukemic cells in T-cell ALL when the gene is not rearranged. In a study by [163] Chetty et al., samples from approximately 50% of T-cell ALL cases showed nuclear labeling, but the investigations were done in paraffin-embedded tissue, on which the available MAbs do not provide clean labeling; however, IHC labeling of fresh T-cell ALL samples that were studied with molecular biology techniques suggested that cases in which the TAL1 gene is rearranged can be detected by IHC.

3.4.7 BCL10 t(1;14)(p22;q32) and t(1;2)(p22;p12) The BCL10 gene was cloned from a t(1;14)(p22;q32) translocation breakpoint from a case of low-grade mucosa-associated lymphoid tissue (MALT) lymphoma. BCL10 is composed of four exons within an approximately 11.7-kb genomic segment. Its 2.8 transcript is expressed at relatively low levels in all normal tissues, with the highest expression levels in spleen, lymph node, testis, and developing central nervous system. The BCL10 gene encodes a predicted protein of 233 amino acids, which contains an amino-terminal caspase recruitment domain (CARD) from residues 13–101 homologous to that found in several proteins involved in apoptosis regulation. Its C-terminal 132 amino-acids contain no known motifs [164]. In normal cells, the BCL10 protein is primarily located in the cytoplasm and is essential for both the development and function of mature B and T cells. BCL10 functions downstream

3. Genetic Markers in Sporadic Tumors

of lymphocyte antigen receptors in conjunction with 2 other intracellular proteins, Carma1 and MALT1, to promote ubiquitination of the inhibitor of NF-κB (IκB) kinase subunit NEMO, leading to activation of the transcription factor NF-κB. Despite activation of NF-κB, wild-type BCL10 has been shown to be pro-apoptotic and behave as a tumor suppressor in vitro. In transgenic mice carrying a BCL10-Ig enhancer construct, a specific and consistent expansion of the splenic marginal zone B cells has been reported. This latter observation suggests that BCL10 deregulated expression may play an important role in MALT lymphomagenesis [165]. The t(1;14)(p22;q32) and variant t(1;2)(p22;p12) juxtapose the entire coding region of BCL10 to chromosome 14 under control of the Ig enhancer element (or IGLk region in the case of variant translocations). All BCL10 breakpoints thus far characterized cluster within the 5′ promoter region of the gene. In t(1;14)-carrying MALT lymphomas, BCL10 is strongly expressed in the tumor cell nuclei; however, BCL10 can have nuclear localization, although at much lower intensity, also in MALT lymphomas without t(1;14), where it is associated with advanced disease and correlates with t(11;18) [164]. These data suggest that nuclear BCL10 may confer oncogenic activity. Interestingly, BCL10 nuclear localization has been found in a subset of primary cutaneous marginal zone B-cell lymphoma in the absence of t(11;18) [166]. The t(1;14) translocation is frequently associated with BCL10 gene inactivating mutation. The regions more frequently mutated include the junction of exons 3 and 4 (with loss of a splice acceptor and deletions involving codons 116– 121 or −126) and 2 poliA and poliT stretches (beginning at codon 43 and 165, respectively) at which deletions or insertions of one or two bases result in frameshifts. Frameshift mutations produce two kinds of BCL10 truncation: CARDtruncation and C-terminal truncation distal to the CARD. CARD-truncation mutants fail to induce NF-κB, whereas mutants with C-terminal truncations retain NF-κB activation. Mutant BCL10 may confer a growth advantage on tumor B cells, and constitutive NF-κB activation could provide both antiapoptotic and proliferative signals by upregulating transcription of specific targets. BCL10 gene alteration in tumors is a rare event. t(1;14)(p22;q32) translocation (or its variant t(1;2)(p22;q22) involving the Igκ light chain locus) has been restricted to 5% of MALT lymphomas, whereas BCL10 gene mutation has been found in <10% of these tumors. A slightly higher frequency of BCL10 mutations in high-grade MALT lymphomas has been observed, suggesting that mutations may underlie histologic progression. Moreover, among low-grade MALT lymphomas, mutations were found in a subset of cases not responding to anti-Helicobacter pylori eradication therapy, but not in tumors that regressed completely; this observation may have important clinical implications. BCL10 amplification has been reported in a case of DLCL with IGH-BCL2 fusion and in pancreatic cancers. Extensive work excluded a significant involvement of BCL10 gene mutation in the

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pathogenesis of different solid cancers, including germ cell tumors, prostate carcinoma, SCLC, head-and-neck tumors, renal carcinoma, sarcomas, CLL, T-PL, multiple myeloma, and plasma cell leukemia. Therefore, BCL10 gene alteration is a genetic event mainly associated to a small percentage of the MALT subtype of NHL. If the preliminary clinical findings are confirmed in a larger controlled study, BCL10 gene mutation could be used to identify prospectively those MALT patients who would not benefit from H. pylori therapy and for whom a chemotherapeutic approach should be followed.

3.4.8

MALT1 t(14;18)(q32;q21)

MALT1 comprises an N-terminal death domain, which acts as a homotypic interaction module, followed by two immunoglobulin-like C2 domains and a caspase-like domain. MALT1 nucleotide sequence predicts an 813-amino acid protein that shows significant sequence similarity to the CD22β and laminin 5 α3b subunit. MALT1 protein has been identified as a paracaspase, a caspase-like protease with altered substrate specificity in comparison to caspases. Through yeast-twohybrid system experiments, it has been demonstrated that MALT1 binds BCL10, the protein involved in the t(1;14) MALT lymphoma translocation, by interacting with the two Ig-like domains. Under normal conditions, BCL10 and MALT1 form a tight complex that serves to oligomerize and activate the caspase-like domain of MALT1, leading to induction of NF-κB. Different genetic mechanisms can activate the MALT1 gene. Besides being involved in the t(11;18)(q21;q21) translocation, where it is fused to API2 in a chimeric product, MALT1 can be translocated to chromosome 14q32 and juxtaposed to the IgH gene. Under the effect of the IgH enhancer, its expression is deregulated resulting in a downstream activation of the NF-κB pathway. This t(14;18) translocation, which does not lead to a chimeric fusion product, occurs in 15–20% of MALT lymphomas, more frequently in nongastrointestinal sites such as liver, lung, and ocular adnexa [165]. In contrast to MALT lymphomas with t(11;18)(q21;q21), those with t(14:18) frequently harbor additional genetic aberrations, including trisomies 3 and/or 12 and 18. The MALT1 protein is highly expressed in the cytoplasm of t(14;18)/MALT1 MALT lymphomas but only weakly expressed or negative in those lacking the translocation. Concomitantly, BCL10 expression is restricted to the cytoplasm in the t(14;18)-positive MALT lymphomas and the amount of protein is higher than that of the translocation negative cases, suggesting that MALT1 overexpression may stabilize BCL10 in the cytoplasm where it is required to mediate MALT1 oligomerization and induce NF-κB activation [164]. In addition to chromosomal translocation, the MALT1 gene appears to be targeted by gene amplification, as demonstrated in cell lines from B-cell lymphomas as well as in several cases of t(11;18) negative gastric MALT lymphoma [164].

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3.5 Hematopoietic Tumors: Chimeric Proteins 3.5.1

NPM–ALK

The nucleolar phosphoprotein gene, nucleophosmin (NPM), is a highly conserved gene located on chromosome 5q35. Its protein product is involved in the late stages of ribosomal assembly and functions as a target for CDK2/cyclin E in the initiation of centrosome duplication. NPM is a crucial gene to consider in the context of the genetics and biology of cancer. NPM is frequently overexpressed, mutated, rearranged, and deleted in human cancer. Traditionally regarded as a tumor marker and a putative proto-oncogene, it has now also been attributed with tumor-suppressor functions [164]. The ALK gene, a gene located on chromosome 2p23, codes for a novel 200-kD transmembrane protein kinase that belongs to the insulin-receptor subfamily. Whereas NPM is expressed ubiquitously at high levels, the normal expression of ALK is restricted to neural tissues and is important for normal neural development and function. It has been hypothesized that ALK may serve as a receptor for an unidentified neurotropic factor. In this context, in vitro studies demonstrated high-affinity binding of ALK to pleiotrophin, a polypeptide growth factor which induces proliferation in a wide range of epithelial, endothelial, and mesenchymal cell lineages [167], and to its homologous midkine [168]. t(2;5)(p23;q35) generates a fusion NPM-ALK gene that encodes a chimeric protein. The NPM-ALK protein consists of N-terminal sequences derived from the NPM gene fused to C-terminal cytoplasmic sequences from the ALK gene, including the consensus protein tyrosine kinase residues. Therefore, t(2;5) results in the transcription of ALK driven off the strong NPM promoter, leading to inappropriate expression and constitutive activation of a truncated 80-kD ALK protein. Because of the breakpoint location, the fusion protein lacks extracellular and transmembrane domain and has intracellular localization. The NPM-ALK hybrid protein may have a key role in tumorigenesis by aberrant phosphorylation of intracellular substrates. Variant chromosomal translocations involving the ALK locus at chromosome 2p23 have also been observed, and the ALK fusion partners identified [169] (Fig. 3-5). TPM3-ALK is expressed from a t(1;2)(q25;p23) translocation fusing the N-terminal 221 residues of TPM3 to the cytoplasmic portion of ALK. TPM3 is a nonmuscle tropomyosin that has been shown to be fused with the truncated NTRK1 RTK in papillary thyroid cancer. This protein contains an N-terminal coiled-coil structure that allows self-association and leads to the activation of the TPM3-ALK fusion protein. The t(2,3)(p23;q21) translocation leads to the formation of three variant fusion proteins, TFG-ALKS (85 kd), TFG-ALKL (97 kd), and TFG-ALKXL (113 kd) [170], depending on the chromosomal breakpoint in the TFG gene (intron 3, 4, and 5, respectively). TFG, which stands for TRK-fused gene, was

originally cloned as a fusion partner for NTRK1 in papillary thyroid carcinoma. As for TPM3, it contains a coiled-coil domain that can mediate the oligomerization and constitutive activation of the fused tyrosine kinase. The ALK fusion partner involved in the inv(2)(p23;q35), ATIC, encodes an enzyme responsible for the final steps of de novo purine nucleotide biosynthesis. The ATIC-ALK chimeric kinase is driven to homo-oligomerization and constitutive activation by the N-terminal 229 residues of ATIC fused to ALK. The CLTC-ALK variant fusion protein is expressed from the t(2;17)(p23;q23) translocation. CLTC represents the heavy chain of the clathrin molecule contained in the vesicles that transport molecules through the cellular compartments. The homodimerization and activation of the CLTC-ALK chimera results from the fact that, normally, clathrin molecules oligomerize and form trimolecular complexes. The t(2;19)(p23;q13.1) leads to the expression of a TPM4-ALK fusion protein, where TPM4 is a nonmuscle tropomyosin highly related to TPM3. Additional ALK rearrangements have been cloned. Moesin, mapped on chromosome Xq1112, has been identified as the partner for ALK in the MSNALK fusion. The hybrid MSN-ALK protein has a molecular weight of 125 Kd and contains an active tyrosine kinase domain. In contrast to other translocations involving the ALK gene, the ALK breakpoint in this case occurs within the intronic sequence coding for the juxtamembrane portion of ALK. In addition, ALO17 (KIAA1618), a gene with unknown function, was fused to ALK in a case with t(2;17)(p23;q35); CARS, encoding the cysteinyl-tRNA synthetase, was fused to ALK in a case with t(2;11;2)(p23;p15;q31). In contrast to the NPM-ALK chimeric protein, which has nuclear and cytoplasmic localization, the other variant ALK fusion products are localized in the cytoplasm only, with the exception of MSN-ALK, which exhibits a distinctive membranerestricted pattern of ALK labeling. Because ALK protein is not expressed in adult normal tissues, the positivity and the staining pattern with ALK-specific Ab are relevant in the diagnosis of some tumors. The detection method for ALK rearrangements are twocolor FISH, RT-PCR, and IHC with MAb recognizing a formalin-resistant epitope in the chimeric and the 200-kD normal human ALK proteins. The presence of t(2;5) is specifically associated with 50–60% of CD30-positive ALCL, which represent a subset of high-grade NHL. This marker identifies a subgroup of morphologically heterogeneous ALCL with T/null phenotype that are characterized by a more favorable clinical course than NPM-ALK–negative ALCL. ALCL bearing alternative ALK rearrangements (10–20% of cases) are indistinguishable from ALCL with classical t(2;5) [171]. Some of the described rearrangements have been observed only in ALCL tumors, such as NPM-ALK itself, ATIC-ALK, the three forms of TFG-ALK, MSN-ALK, and ALO17-ALK; others have been found both in ALCL and inflammatory myofibroblastic tumors, like TPM3ALK, and CLTC-ALK, whereas TPM4-ALK and CARS-ALK

3. Genetic Markers in Sporadic Tumors

has been seen only in inflammatory myofibroblastic tumors. Rare cases of ALK-positive large B-cell lymphoma have been described. These are characterized by immunoblastic (rather than anaplastic) morphologic features, expression of fulllength ALK, in contrast to a chimeric protein characteristic of ALCL [172]. The ALK-positive lymphomas with a B-cell phenotype described by Gascoyne et al. may belong to this rare category, but there is insufficient information to confirm this. It has been reported that cases of ALK-positive DLCL are characterized by a simple or complex t(2;17)(p23;q23) involving the clathrin gene (CLTC) at chromosome band 17q23 and the ALK gene at chromosome band 2p23. These results demonstrating the presence of CLTC-ALK fusions in these tumors extended the list of diseases associated with this genetic abnormality to include classical T-cell or null ALCL, ALK-positive DLBCL, and inflammatory myofibroblastic tumors [173].

3.5.2

API2-MALT1 t(11;18)(q21;q21)

The API2 gene belongs to a family of inhibitors of apoptosis first identified in baculoviruses. It contains three copies of BIR (baculovirus inhibitor of apoptosis repeat) motifs, a middle CARD, and a C-terminal zinc-binding RING finger domain. The BIR domains are involved in inhibition of activated caspases (3, 7, and 9) through interaction with TNF-associated factor proteins. The API2 protein is highly expressed in lymphoid cells. The t(11;18)(q21;q21) chromosomal translocation fuses the A-terminal of the API2 gene product to the C-terminal of the MALT1 gene product, and generates a chimeric fusion product [174]. The API2-MALT1 fusion transcripts always comprise the N-terminal region of API2 with the three intact BIR domains in frame with the C-terminal region of MALT1 containing an intact caspase-like domain. Whereas full-length API2 and MALT1 are unstable and do not significantly activate NF-κB, the fusion protein becomes stable and significantly increases NF-κB activation. Thus, the signaling pathway that is maintained at basal level of expression in normal cells, can be perturbed by either t(1;14) or t(11;18) in MALT lymphoma resulting in marked over-expression of BCL10 or API2-MALT1 fusion proteins and in dramatic increase in NF-κB activity, which is likely to be critical in lymphoma progression [175]. A novel three-way variant translocation, t(11;12;18)(q21;q13;q21) has been described in a MALT lymphoma of the lung. The t(11;18)(q21;q21) translocation is specifically associated with 30–40% of extranodal marginal zone B-NHL of MALT type [164, 165] where it is the sole chromosomal aberration in most cases. This translocation is frequently found in advanced gastric MALT lymphomas, and almost exclusively in those that failed to respond to H-pylori eradication [176, 177]. Thus, detection of the translocation should help the clinical management of patients with tumors resistant to antibiotic therapy.

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3.5.3

BCR–ABL t(9;22)(q34;q11)

The normal cellular BCR gene spans a 135-kb region and contains 23 exons. The BCR gene is expressed as mRNA of 4.5 and 6.7 kb. It encodes a 160-kDa phosphoprotein associated with a Ser/Thr kinase activity and shows autophosphorylation activity as well as transphosphorylation activity for several protein substrates. The c-ABL gene is 225 kb and is expressed as either a 6- or 7-kb mRNA transcript. The ABL gene codes for a 145-kD tyrosine kinase with nuclear localization. The DNAbinding activity of the ABL protein is regulated by CDC2mediated phosphorylation, suggesting a cell cycle function for ABL. The gene is also implicated in processes of cell differentiation, cell division, cell adhesion, and stress response. The tyrosine kinase activity of nuclear ABL is regulated in the cell cycle through a specific interaction with Rb protein. ABL activity is negatively regulated by its SH3 domain, and deletion of the SH3 domain turns ABL into an oncogene. t(9;22)(q34;q11) translocation, which transposes the ABL gene from chromosome 9 to the center of the BCR gene on chromosome 22, results in a head-to-tail fusion of these two genes and the formation of the Philadelphia (Ph) chromosome. The 5′ exon of the ABL gene lies at least 300 kb upstream from the remaining ABL exons, and the very long intron is the target for translocation. Although the position of the breakpoint on chromosome 9 varies considerably, the breakpoint on chromosome 22 is clustered in a BCR. The BCR-ABL encoded product is a chimeric 210-kDa protein with cytoplasmic localization that has BCR information at its N-terminus and retains most of the normal ABL protein sequences. In some tumors, the ABL gene can be juxtaposed to the 5′ region of the BCR gene in a t(9;22) translocation cytogenetically indistinguishable from the Ph chromosome. In these cases, a unique ABL-derived tyrosine kinases of 190 kD is produced. The functional consequence of the BCRABL fusion is increased tyrosine kinase activity. Sequences within the first exon of BCR appear to be essential for this activation and probably work through direct physical binding to the kinase regulatory domain of ABL. Besides BCR-ABL rearrangement, point mutation in the ABL kinase domain and BCR-ABL amplification have been described in patients with advanced Ph+ leukemias [178] that may represent second mutational events during the course of CML. t(9;22) (Ph+) represents a diagnostic tumor-specific marker associated with >90% of CML, which have an unfavorable evolution to AML or ALL. The leukemia–specific BCR-ABL transcript is an excellent target for molecular monitoring by quantitative PCR and detection of minimal residual disease. There is no need for patient-specific primers because nearly all CML patients have one of the two transcript types (some patients have both), which differ by just 1 BCR exon. Early reduction of BCR-ABL transcript levels predicts cytogenetic response in patients with chronic phase CML who are treated with imatinib and the reduction of BCR-ABL correlate with prognosis [179]. Several studies have shown that PCR-nega-

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tivity indicates complete eradication of the leukemic clone with no risk of relapse, whereas PCR-positivity throughout the follow-up period has about 30% risk of relapse. Those patients who achieve a 3-log reduction by 12 months have a very low probability of disease progression in the subsequent 24 months of follow-up [180]. Inhibition of BCR-ABL tyrosine kinase activity has been introduced as a therapeutic approach in patients with CML. Imatinib administration resulted in antileukemic effect in CML patients in whom treatment with standard chemotherapy had failed to help them; however, primary refractoriness or relapse after imatinib treatment is observed in a significant proportion of patients with advanced-stage disease or with Ph+ ALL [180]. This result has been associated with either BCR-ABL gene amplification or single amino-acid substitutions affecting residues in the kinase domain of ABL that interfere with drug binding by eliminating crucial bonds or by stearic hindrance. Other mutations prevent structural adjustments required to accommodate imatinib. Strategies for overcoming resistance have been suggested by using alternative ABL inhibitors that exhibit increased potency or capture additional conformations of the ABL kinase. Two of these compounds are in phase-1 and −2 trials with encouraging results. Nilotinib, that was developed from the imatinib scaffold by eliminating two energetically unfavorable hydrogen bonds, is approximately 20-fold more potent than imatinib in kinase and cell-proliferation assays. Dasatinib, initially developed as a Src kinase inhibitor, turned out to be a potent inhibitor of ABL, with 300-fold greater activity than imatinib. Both agents are active against most imatinib-resistant kinase ABL mutants, with the exception of the T315I mutant, which is resistant to imatinib, AMN107, and dasatinib [180].

3.5.4

PML–RARA t(15;17)(q22;q21)

The PML (promyelocytic leukemia) gene codes for a DNAbinding zinc finger protein with a potential leucine zipper motif. The PML protein is expressed at significantly high levels in G1 phase of the cell cycle and at a lower level in S, G2, and M phases. PML exhibits multiple biologic functions. It is a mediator of interferon function and immune surveillance, and acts as a proapoptotic factor and as a tumor suppressor. PML upregulation by oncogenic RAS is involved in the control of p53 acetylation. In mice, PML regulates hematopoietic differentiation and controls cell growth and tumorigenesis. PML function is essential for the tumor-growth-suppressive activity of retinoic acid and for its ability to induce terminal myeloid differentiation of precursor cells. PML is needed for the retinoic acid-dependent transactivation of the p21 (WAF1–Cip1) gene, which regulates cell-cycle progression and cellular differentiation. The RARA gene is homologous to the receptors for steroid and thyroid hormones and codes for a nuclear receptor protein that binds the retinoic acid ligand and DNA through a zinc finger region, thereby activating a set of target genes.

t(15;17)(q22;q21) is an important example of a transcription fusion factor, in which the PML gene on chromosome 17 is fused with the RARA gene on chromosome 15. In the chimeric gene, the promoter and first exon of the RARA gene are replaced by part of the PML gene. The PML breakpoints are clustered in two regions on either side of an alternatively spliced exon. The translocation chromosome generates a PML-RARA chimeric transcript. Alternative splicing of PML exons produces multiple isoforms of the PMLRARA mRNA, even within a single patient. The PML–RARA fusion RNA encodes a predicted 106-kD chimeric protein that contains most of the PML sequences fused to a large part of the RARA gene, including its DNA- and hormonebinding domains. The oncoprotein PML-RARA suppresses transcription by recruiting histone deacetylase (HDAC) and rendering the nearby chromatin inaccessible to transcriptional activators. This results in interference with normal cell growth and differentiation. In addition to PML, RARA can form rare fusion proteins with other genes: promyelocyitc leukemia zinc finger gene (PLZF) t(11;17)(q23;q21), which is a zinc-finger transcription factor expressed in immature hematopoietic cells and implicated in the development of the central nervous system; nucleophosmin gene (NPM), the same gene rearranged with ALK in ALCL; STAT5b, and nuclear mitotic apparatus gene (NuMA). t(15;17)(q22;q21) is associated with almost 100% of cases of acute promyelocytic leukemia (APL) (AML3 or M3 in the FAB classification). The molecular characterization of PML– RARA has clinical prognostic impact. This genetic aberration represents a tumor-specific marker for a correct diagnosis of APL and because its presence is related to a good response to all-trans retinoic acid (ATRA), it permits the use of a specific therapy based on the use of this retinoid, which acts by overcoming the block of maturation at the promyelocytic stage and inducing terminal differentiation into granulocytes; however, treatment with ATRA in patients with APL induces disease remission transiently, and relapse occurs in approximately 30% of patients. This result might be explained by a faster reduction of the intracellular ATRA concentration after degradation or by the occurrence of missense mutations in the ligand-binding domain of PML-RARA that prevent interaction with ATRA [181]. In these cases, resistance to the differentiating action of ATRA could be overcome by cotreatment with HDAC inhibitor valproic acid [182, 183]. HDAC inhibitors may find a relevant clinical application in the treatment of PLZF-RARA-positive APL that are less sensitive to the action of ATRA and have unfavorable prognosis compared to PMLRARA APL [184]. Arsenic trioxide has been approved for the treatment of relapsed and refractory APL in the United States and Europe, with >80% achievement of hematologic and molecular remission in relapsed patients. In PML-RARA APL, it has been observed that the persistence of residual transcript during clinical remission allows identification of patients with high risk of relapse for whom further therapeutic treatment might be required [181].

3. Genetic Markers in Sporadic Tumors

3.5.5

AML–ETO t(8;21)(q22;q22)

The AML1 gene is the human homologue of Runt, an important gene in Drosophila that regulates segmentation. It consists of nine exons and the entire locus spans 260 kb. The expression of AML1 is regulated by alternative splicing and produces at least three proteins. The structure analysis of the AML1 gene showed that the 5′ portion of the gene contains the Runt homologous sequences, a DNA-binding domain, and dimerization sequences, whereas the 3′ portion contains gene transactivation sequences [185]. In adults, the AML1 gene is ubiquitously expressed in several tissues, particularly in bone marrow cells. Because AML1 knockout mice die during embryonic development, secondary to the complete absence of fetal liver-derived hematopoeisis, it is suggested that AML1-regulated target genes are essential for definitive hematopoiesis of all lineages. The ETO gene comprises 13 exons distributed over 87 kb of genomic DNA. ETO structurally belongs to the zinc-finger transcription factor genes. By Western blot analysis, the ETO product was identified as a 70-kD protein associated with the nuclear matrix. Its biologic function is unknown. ETO is expressed in several tissues, mainly during fetal life, with the highest mRNA levels occurring in brain and heart. ETO is also specifically expressed in CD34+ hematopoietic stem cells. t(8;21)(q22;q22) leads to the fusion of the AML1 and ETO genes. The resulting fusion gene transcribes a hybrid mRNA and is translated into a 94-kD AML1-ETO chimeric protein. The AML1–ETO chimeric gene contains in 5′ Runt but not transactivation sequences of AML1; in 3′, the gene contains the whole coding sequence of ETO, whose expression is regulated by the AML1 promoter [178]. In vitro transfection experiments suggest that the AML1– ETO fusion protein can suppress the normal AML1 protein function by inhibiting myeloid differentiation. Thus, the neoplastic transformation may result either by a dominant negative effect of the AML1-ETO hybrid protein, which blocks the transcription of specific genes involved in myeloid differentiation, or alternatively may be promoted by aberrant ETO transcription under the effect of AML1 promoter. Experimental in vivo models show that AML1ETO is not able, on its own, to induce leukemia. Additional genetic hits are necessary to induce overt neoplastic transformation, some of which being identified as FLT3LM [186], cKIT mutation/overexpression [187–188], WT1 overexpression [189], and an alternatively spliced isoform of t(8;21) transcript [190]. t(8;21), the most frequent cytogenetic alteration observed in AML, is associated with 12–15% of de novo AML cases and up to 40% of the AML subtype M2. It is also reported in a small portion of M0, M1, and M4 AML samples [178]. Except in rare pediatric cases, patients carrying this genetic abnormality usually have a favorable clinical course; however, relapses still occur in approximately 30% of the cases. Several studies show that molecular disease eradication is a

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prerequisite for long-term clinical remission and that real time quantitative RT-PCR may have prognostic value in predicting relapse risk in t(8;21) AML.

3.5.6

Other Translocations

A number of other chromosomal translocations have been described in hematopoietic tumors, which either juxtapose proto-oncogenes to antigen (Ag)-receptor genes or lead to the formation of fusion genes. These specific translocations are listed in Table 3-3 and Table 3-4.

3.6

Solid Tumors

Investigation of solid-tumor translocations has concentrated on sarcomas, whose cytogenetics have been well studied. In sarcomas, specific chromosomal translocations have generally been associated to distinct tumor histotypes, thus providing a clinical application in the differential diagnosis of sarcomas with difficult morphological diagnosis assessment (e.g., primitive peripheral neuroectodermal tumors [pPNET], synovial sarcoma, and rhabdomyosarcoma) and in some cases, a prognostic assessment. These markers can be potentially used for monitoring minimal residual disease. The gene more frequently involved in these specific chromosomal translocations is EWS. Molecularly, the oncogenic conversion of EWS follows a common scheme of activation that exchanges its putative RNA-binding domain with the DNAbinding domains of ETS-family transcription factor genes (FLI1, ERG, ETV1, E1AF, FEV, and ZSG) or other transcription factor genes (ATF1, WT1). This fusion may be necessary for the EWS-associated oncogenesis, and the transcription factor partner in the chimeric proteins may determine the specific tumor type. The fusion of a member of the ETS-family of DNA-binding proteins (FL1, ERG, ETV1, E1AF, FEV, and ZSG) with EWS gives rise to pPNET, ATF1 with EWS to clear cell sarcoma, and WT1 with EWS to intra-abdominal desmoplastic small-round-cell tumor. On the other hand, FUS and EWS proteins may functionally act as equivalents when fused with the transcription factor CHOP in myxoid liposarcoma (Fig. 3-6). These apparently opposite findings lead to the hypothesis that EWS and FUS proteins may be also interchangeable in the EWS-associated tumors. It is important to highlight that over the last 3 years, different pathologies often belonging to histologically different cellular lineages, i.e., sarcomas and leukemias, have been demonstrated to share the same translocation. Detection of these chimeric transcripts can be performed by conventional cytogenetics/molecular cytogenetics and by RT-PCR, and the best approach is the combination of both methodological approaches. Abs working at IHC level are efficiently used in diagnostic routine such as WT1, TFE3, and ALK. Regarding the pharmacologic treatment of sarcomas, data on the presence of deregulated RTK in several histotypes,

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might open new therapeutic possibilities especially in terms of target therapies.

3.7 3.7.1

of lifetime survival demonstrated that p53 mutations or p16/p14ARF homozygous deletions define a subset of patients with ES with highly aggressive behavior and poor chemoresponse [193].

Soft Tissues EWS-FLI1 t(11; 22)(q24; q12)

Karyotypic analyses have revealed a tumor-specific chromosomal translocation t(11; 22)(q24; q12) in 86% of both Ewing sarcoma and pPNET. The (11;22) translocation results in the fusion of the N-terminal region of the EWS gene rich in Glu, Ser, and Tyr residues to the ETS-like DNA-binding domain of the FLI1 (Friend leukemia integration site 1) gene. EWS is an ubiquitously expressed gene located on chromosome 22 that encodes for a RNA-binding protein, whereas FLI1, located on chromosome 11, is a member of the ETS family of transcription factors. The oncogenic effect of the t(11;22) translocation is caused by the formation of a chimeric protein. The protein has the potential to promote tumorigenesis by acting as an aberrant transcription factor that is functionally distinct from the normal FLI1. It has been demonstrated that the FLI1 COOH-terminal domain in addition to its DNAbinding domain is necessary to promote cellular transformation. A potential regulatory site within the EWS IQ domain at Ser266 has been identified, and it was demonstrated that phosphorylation of this Ser regulates the EWS transcriptional activity. Recently, a nuclear localization signal in FLI1 was identified and it is shared by all the fusion transcript types, suggesting that it is used for nuclear translocation of various fusion proteins. Several different EWS-FLI1 fusion types (up to 18 possible types) have been observed: the two main types, fusion of EWS exon 7 to FLI1 exon 6 (type 1) and fusion of EWS exon 7 to FLI1 exon 5 (type 2), account for approximately 85% of EWS-FLI fusions [191]. Type 1 EWS-FLI1 fusion has been shown to be a significant positive predictor of overall survival in patients with localized disease. This trend was confirmed by a subsequent study where it was demonstrated that differences in the C-terminal partner of EWS fusions are not associated with significant phenotypic differences, even if microarray data demonstrated that at least 41 genes are differently expressed between type I with respect to those nontype-I fusion transcript types [192]. Thus, molecular detection of the t(11;22) translocation and the molecular characterization of the fusion types are valuable in the differential diagnosis of small-round-cell tumors and provide information for the staging-related of Ewing sarcoma (ES). Nonrandom chromosomal aberrations were found associated to this translocation in >50% of these tumors. Chromosome gain is the most frequent event, of which trisomy 8 is the most common observed. Gain of chromosomes 2, 12, and 20 was reported along with the unbalanced translocation t(1;16) and deletion of the short arm of chromosome 1. Analysis

3.7.2

EWS-ERG t(21;22)(q22;q12)

t(21;22) is a variant translocation of EWS gene present in 5% of patients with ES. This translocation gives origin to the fusion of EWS to a member of ETS family on chromosome 21. In this translocation, identical EWS nucleotide sequences found in the EWS-FLI1 fusion transcripts are fused to portions of ERG encoding an ETS DNA-binding domain, resulting in the expression of a hybrid EWS-ERG protein. A variant with an insertion of a segment 22q21 into the long arm of 21q12 with a loss of a DNA segment around the breakpoint on the derivative chromosome 22 has been reported [194].

3.7.3

EWS-ETV1 t(7;22) (p22;q12)

This rare variant chromosomal translocation, identified in two cases of pPNET [195], fuses EWS to the ETV1 (for ETS Translocation Variant 1) gene, a member of the ETS family of transcription factors located on chromosome 7p22. Identical EWS nucleotide sequences found in most EWS-FLI1 and EWS-ERG chimeric transcripts are fused to a region of ETV1 encoding an ETS domain with sequence-specific DNA binding activity.

3.7.4

EWS-FEV t(2;22) (q33;q12)

EWS can be fused to FEV in the chromosomal translocation t(2;22) in a subset of patients with ES. The FEV gene is located on chromosome 2 and consists of thre exons. In the chimeric transcript, exon 10 of EWS fuses within intron 1 of FEV. FEV is an additional member of the ETS family that encodes a 238-amino-acid protein containing an ETS DNA-binding domain closely related to that of FLI-1 and ERG; however, compared with FLI-1 and ERG, FEV lacks transcription regulatory domains in its N-terminal part. The C-terminal part of FEV is Ala-rich, suggesting a potential transcription repressor activity. FEV expression is detected in adult prostate and small intestine, but not in other adult or fetal tissues [196]. A new fusion transcript type, in which exon 7 of EWS gene is fused with exon 2 of FEV, has been reported, supporting the existence of heterogeneity of molecular rearrangements.

3.7.5

EWS-E1AF t(17;22) (q12;q12)

The t(17;22) chromosomal translocation, leading to the fusion of EWS with E1AF, was described in an undifferentiated sarcoma of infancy. E1AF is a newly isolated member of ETS family of genes that is located on chromosome 17q21 and encodes for the adenovirus E1A enhancer-binding protein.

3. Genetic Markers in Sporadic Tumors

The breakpoint on chromosome 17 lies in the region upstream to the ETS domain of the E1AF gene. The human E1AF gene is organized in 13 exons distributed along 19 kb of genomic DNA. Its two main functional domains, the acidic domain and the DNA-binding ETS domain, are each encoded by three exons. The 3′-untranslated region of E1AF is 0.7kb. The 5′untranslated region is approximately 0.3 kb and is composed of a first exon upstream from the exon containing the first methionine [197]. As in other fusion proteins previously characterized in ES and Ewing family sarcoma, it is assumed that the RNA-binding domain of EWS may be replaced by the DNA-binding domain of E1AF.

3.7.6

EWS-ZGS t(1;22)(p36.1;q12)

A new translocation was detected in a MIC2-negative multidirectional differentiated small round cell sarcoma involving the EWS gene and a new gene located at 22q12. This new gene, named ZSG (zinc-finger sarcoma gene), is a putative Cys2-His2 zinc finger protein that contains a POZ transcriptional repressor-like domain at the N-terminus. The translocation rearranges intron 8 of EWS and exon 1 of ZSG generating a fusion sequence that comprises the transactivation domain of EWS fused to the zinc finger domain of ZSG. This product lacks the transcriptional repressor domain at the N-terminus of ZSG. This rearrangement, undetectable by cytogenetics, activates EWS in soft tissue sarcoma [198].

3.7.7

FUS-ERG t(16;21) (p11;q22)

All of the fusion genes reported in Ewing sarcoma have involved the NH2 terminus of EWS and the COOH terminus of an ETS family member. Four cases of Ewing sarcoma were not rearranged at 22q12 and showed a novel primary translocation t(16;21)(p11;q22), cytogenetically identical to that found in rare cases of AML. All of the four cases were characterized by the presence of a FUS/ERG fusion gene [199]. The same fusion transcript transforms hematopoietic cells and fibroblast by different pathways, suggesting that FUS/ERG has a particular role depending on the recipient cells in which it is expressed (Fig. 3-7) [200].

3.7.8

EWS-ATF1 t(12;22) (q13;q12)

This translocation is frequently and specifically found in malignant melanoma of soft tissues also named clear cell sarcoma and causes the fusion of EWS to the transcription factor ATF1. The chimeric EWS-ATF1 protein consists of the N-terminal domain of EWS linked to the βZIP DNA-binding domain of ATF1. The resulting fusion protein that causes malignant melanoma of soft parts, by trans-cooperating with small regions of the EWS activation domain (EAD approximately 30 residues), results in a potent transcriptional activation dependent on the conserved Tyr residues present in

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3’ gene

MLF1

5’ gene

NPM

Histologic diagnosis

Acute myeloid leukemia

ATIC

ALO17

MSN Anaplastic large cell lymphoma

ALK

CLTC

CARS

TPM4

Inflammatory myofibroblastic tumor

TPM3 NTRK1

Papillary thyroid carcinoma

TFG Fig. 3-7. ALK and NTRK1 rearrangments.

degenerate hexapeptide repeats (DHR, consensus SYGQQS). These findings provide evidence for a role of DHR in EADmediated transactivation, and demonstrate that the EAD represents a novel tyrosine-dependent transcriptional activation domain. Both in vitro and in vivo, EWS-ATF1 associates constitutively with CBP, a transcriptional coactivator, which links various transcriptional factors to basal transcription apparatus, participates in transcriptional activation, growth, cell-cycle control, and differentiation [201].

3.7.9

EWS-WT1 t(11;22) (p13;q12)

This translocation, recurrently associated with desmoplastic small-round-cell sarcoma, juxtaposes EWS to the Wilms’ tumor gene WT1 on chromosome 11p13. WT1 encodes a zinc-finger transcription factor that may play a crucial role in normal genitourinary development. It is expressed in the developing kidney, gonads, spleen, mesothelium, and brain. WT1 is an oncosuppressor gene specifically inactivated in a subset of Wilms’ tumors, and mutations have been found in the germ line of susceptible individuals. The translocation breakpoints within the EWS gene occur in introns 7–10 resulting in fusion of the A-terminal domain of EWS to the chimeric product. The breakpoint within the WT1 gene is invariant between exons 7 and 8 [202].

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The chimeric protein consists of the N-terminal domain of EWS and the DNA-binding zinc finger domain of WT1. The EWS-WT1 chimera may encode a transcriptional activator target gene which overlaps with those repressed by WT1. Expression of the fusion transcript induces the expression of endogenous PDGF, IL2 receptor beta, BAIAP3, Talla 1 and MLF1, myelodysplasia/myeloid leukemia factor 1 gene.

The presence of the EWS-CHOP chimeric gene in myxoid/ round cell liposarcomas indicates that the N-terminal part of FUS may be replaced by the N-terminal portion of EWS in a CHOP fusion oncoprotein and that the 2 N-terminal parts, when fused to certain transcription factors, have a common or very similar oncogenic potential.

3.7.12 3.7.10

TGF-CHN t( 9;3)

A recurrent translocation, t(9;22) (q22;q12), has been recognized in extraskeletal myxoid chondrosarcoma. In this specific translocation, the EWS gene becomes fused to CHN, a novel orphan nuclear receptor with a zinc-finger DNA-binding domain located at 9q22-31. CHN (also referred to as TEC, NOR-1, or MINOR) appears to be the human homologue of the rat gene NOR1, which was identified as a sequence overexpressed in brain cells undergoing apoptosis. The chimeric EWS-CHN gene encodes a EWS-CHN fusion protein in which the C-terminal RNA-binding domain of EWS is replaced by the entire CHN protein, including a large N-terminal domain, a central DNA-binding domain, and a C-terminal ligand-binding/dimerization domain. An alternative splicing of the 3′ end of the fusion transcript has been described by Brody et al. EWS-CHN fusion protein is supposed to induce tumorigenesis in EMC by activating the expression of CHN-target genes, but no putative target genes have been identified [203]. Three variants of EWS-CHN fusion transcript have been identified: type I, type II, and type III transcripts [204]. In a RT-PCR assay, using paraffin-embedded specimens, EWS-CHN or RBP56-CHN fusion gene transcripts could be detected in 15 (83%) of the 18 cases: EWS-CHN type 1 in 11 cases, EWS-CHN type 2 in 1, and RBP56-CHN in 3. The EWS/CHN type I transcript thus appear to be the most represented in EMC. Two cases of EMC expressed a novel translocation t(9;17)(q22;q11.2) where exon 6 of RBP56 gene (referred to also as TAF2N or hTAFII 68) was fused to the entire coding region of CHN (TEC). This transcript is structurally and functionally very similar to the EWS-CHN fusion.

3.7.11

EWS-CHOP t(12;22)(q13;q12)

t(12;22) was described in myxoid/round cell liposarcomas. This chromosomal translocation leads to the fusion between the Nterminal part of EWS and the CHOP gene (also named DDIT3), creating an EWS-CHOP chimeric gene. CHOP maps to chromosome 12q13, and was previously demonstrated to be consistently involved in rearrangements with the FUS gene in the t(12;16) in myxoid/round cell liposarcomas. At molecular level, the breakpoints on EWS occurred within intron 7, close to an ALU sequence, and similarly, the breaks on CHOP were observed to cluster in intron 1 near ALU sequences [205]. Two types of EWS-CHOP have been described, and a variant involving exon 13 of EWS and intron 1 of CHOP has been reported [206].

FUS-CHOP t(12;16)(q13;p11)

t(12;16)(q13;p11) is characteristic of the human myxoid/round cell liposarcomas. This chromosomal abnormality results from the fusion between a gene on chromosome 16 called FUS or TLS and a gene on chromosome 12 that encodes for a dominant inhibitor of transcription, CHOP/DDIT3. The FUS product contains a Glu-Ser-Tyr-rich segment and an RNAbinding domain, as in the EWS protein. After the rearrangement, the putative RNA-binding domain of FUS is replaced by the entire CHOP coding region, which contains a basic leucine zipper domain. As in the EWS fusion, the FUS domain provides a transcriptional activation domain to a presumptive DNA-binding activity of CHOP. To date, seven chimeric transcripts have been reported, among which the transcript types 1 and 2 are the most common variants [207]. In contrast to some other translocation-associated sarcomas, the molecular variability of FUS-CHOP fusion transcript structure does not appear to have a significant impact on clinical outcome of myxoid liposacomas [208]. By contrast, the presence of a round-cell component correlates with a worse prognosis for the patients, and the reduction of p14 protein expression and p53 mutation were related to poor prognosis.

3.7.13

FUS-ATF1 t(12;16)

One case of angiomatoid fibrous histiocytoma of low-grade malignant potential was reported carrying a translocation t(12;16) involving FUS gene on chromosome 16 and ATF1 on chromosome 12. The fusion transcript, detected by RT-PCR experiments and subsequent direct sequencing, revealed that FUS gene was interrupted at codon 175 and fused to codon 110 of ATF1, resulting in an in-frame junction with a Gly to a Val (GGT to GTT) transition. An identical fusion of FUS and ATF1 was detected in a large, deep-seated AFH, suggesting that the resulting chimera may be characteristic for these tumors [209]. In another case of AFH the presence of EWSR1-ATF1 chimera was demonstrated thus indicating that this fusion transcript can be associated with different tumor types.

3.7.14 PAX3-FKHR t(2;13)(q35;q14) and PAX7-FKHR t(1;13)(p36;q14) Alveolar rhabdomyosarcoma often harbors specific translocations, resulting in the fusion of a forkhead-domain gene FKHR at 13p14 with either the PAX3 or PAX7 developmental control genes at 2p35 and 13q14, respectively. PAX3 and PAX7 encode each one a transcription factor with DNA-binding domain

3. Genetic Markers in Sporadic Tumors

(paired box and homeodomain), which control development by activating specific target genes. A microarray analysis demonstrated differently expressed genes that identified different prognostic classes [210]. After translocation, the resulting chimeric transcription factor contains the DNA-binding domain, a truncated FKHR DNA-binding domain, and the C-terminal region of FKHR. The homeodomain is essential for transformation and the high level of fusion protein. A study, performed on 171 children with rhabdomyosarcoma (93 cases of embryonal and 78 of alveolar rhabdomyosarcomas) confirmed that these fusion transcripts are specific for the alveolar histotype only, and has showed that fusion status is not associated with outcome differences in patients with locoregional disease. By contrast, in patients with metastatic disease, the expression of PAX3-FKHR and PAX7-FKHR identifies a very high-risk subgroup and a favorable outcome subgroup, respectively. A novel translocation t(2;2)(q35;p23) involving PAX3 and NCO1 in a case of alveolar rhabdomyosarcoma negative for the classical rearrangements has been detected [211]. Embryonal rhabdomyosarcoma not associated with a typical translocation in the t(2;20)(q35;p12) has been reported [212].

3.7.15

SYT-SSX t(X;18)(p11.2;q11.2)

A characteristic SYT-SSX fusion gene resulting from the chromosomal translocation t(X;18)(p11;q11) is detectable in almost all (>90%) synovial sarcomas. As a result of this translocation, the SYT gene from chromosome 18 fuses to one of the three highly homologous genes, SSX1, SSX2, or, rarely, SSX4 at Xp11.2. Several variants of these translocations have been observed and among the very rare SSX4 fusion type, two distinct SYTSX4 fusion transcripts have been reported with a different breakpoint in the SSX4 gene. The formation of the corresponding chimeric genes, SYT-SSX1, SYT-SSX2, and SYTSSX4, in which the C-terminal amino acids of SYT are replaced by amino acids from the C-terminus of the SSX proteins, leads to the expression of fusion proteins the function of which is unclear but likely act as aberrant transcriptional regulators. Increasing evidence has implicated that SYT-SSX could play an important role in SS development through a mechanism by which E-cadherin expression, a prerequisite for epithelial differentiation, is aberrantly derepressed. This derepression is obtained through the binding of SYTSSX1 and SYTSSX2 [213]. A multi-institution study reported that overall survival was significantly better among cases localized at diagnosis, carrying the SYT-SSX2 transcript and among patients with primary tumors <5 cm in greatest dimension, whereas another study reported that histologic grade but not STYSSX fusion type is a strong predictor of survival [214].

3.7.16

PDGFB-COL1A1 t(17;22) (q22;q13)

This chromosomal translocation was identified in dermatofibrosarcoma protuberans, an infiltrative skin tumor of intermediate malignancy. This tumor, and its juvenile form, giant cell

73

fibroblastoma, (GCF) are cytogenetically characterized by the presence of supernumerary ring(s) derived from t(17;22). The breakpoints from translocations and rings in dermatofibrosarcoma protuberans and GCF contain the fusion of PDGFβ chain and the collagen type 1α1 (COL1A1) gene. PDGFβ (c-sis protooncogene) has transforming activity and is a potent mitogen for several cell types. COL1A1 is a major constituent of the connective tissue matrix. The gene fusion, deleting exon 1 of PDGFβ, leads to a deregulated production of PDGFβ generating a stimulation of PDGFR-beta and to malignant transformation. Dermatofibrosarcoma protuberans DNA transfection onto NIH3T3 fibroblast cells provided direct evidence of the transforming activity of COL1A1/ PDGFβ chimeric sequence [215]. Imatinib, an inhibitor of PDGFR kinase activity, has been used in vitro to test its effects on cell growth. The growth rate was reduced and the associated transformed phenotype changed to a flattened one. The effect could be reversed on removal of the compound. Many patients with dermatofibrosarcoma protuberans have been treated with imatinib and its clinical activity has been observed both in localized and metastatic tumors; however, fibrosarcomatous variants of DFSP lacking t(17;22) may not respond to the drug [216].

3.7.17 ETV6-NTRK3, TEL-TRKC t(12;15) (p13;q26) Congenital fibrosarcoma is an uncommon soft-tissue tumor mainly involving the extremities of neonates and cellular mesoblastic nephroma is a rare renal tumor generally diagnosed within the first 3 months of life. Despite the diverse tissue origin but in keeping with a very similar histologic appearance, these two tumors share two specific cytogenetic abnormalities: trisomy of chromosome 11 and a t(12,15) translocation. This rearrangement creates a transcriptionally active fusion gene that encodes a chimeric oncoprotein, ETV 6-NTRK3 (EN). This protein contains the helix-loop-helix dimerization domain of ETV6 (also referred to as TEL) fused to the tyrosine kinase domain of NTRK3. EN transforms NIH3T3 fibroblasts through a constitutive activation of both MAPK pathway and PI3K-AKT pathway (Fig. 3-8). The EN fusion protein has been detected in other human malignancies, such as secretory breast carcinoma and AML. In the latter case, a variant constituted by exons 1–4 of ETV6 fused to exons 13–18 of NTRK3 was detected [217]. EN constitutively phosphorylates insulin-receptor substrate, the major insulin-like growth factor I receptor substrate. EN-insulin-receptor substrate complexes bind Grb2 and PI3K p85 regulatory subunit, strongly suggesting that insulinreceptor substrate is functioning as the adapter molecule linking EN to Ras MAPK and PI3K AKT signaling pathways.

3.7.18

TPM3-ALK t(2;5) (p23;q35)

Inflammatory myofibroblastic tumor is a rare mesenchymal neoplasm composed of fascicles of bland myofibroblasts

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3’ gene

TLS/FUS

E. Tamborini et al.

5’ gene

Histologic diagnosis

CREB

Low grade fibromyxoid sarcoma

CHOP

Myxoid liposarcoma

ATF1

Angiomatoid fibrous histiocytoma Clear cell sarcoma

ERG

Myeloid leukemia

FEV

EWS

FLI1

3.7.20 Ewing sarcoma/ pPNET

ETV1

E1AF

ZSG

TAF2N

WT1

Desmoplastic small round cell tumor

TEC

Extraskeletal myxoid chondrosarcoma

Fig. 3-8. EWS and TLS/FUS rearrangements.

admixed with a prominent inflammatory component. In this tumor, ALK is fused with the gene TPM3. The C- terminal tyrosine kinase domain of ALK results when fused with the N-terminal coiled-coil domain of TPM3. The same translocation is shared by ALCL. As in ALCL, other partners of ALK have been identified, such as clathrin heavy chain CLTC gene, localized to 17q23 and detected only in one case of inflammatory myofibroblastic tumor, TPM4 t(2;19)(p23;q13.1), CARS t(2;11)(p23;p15) (which encodes the cysteinyl-tRNA synthetase and RANBP2 Inv(2)p23q 11-13) [218] (Fig. 3-5).

3.7.19

frame” fusion of a novel gene designated as ASPL at chromosome 17q25 with TFE3 (a transcription factor gene belonging to the helix-loop-helix Leu zipper family) on chromosome X. Depending on the DNA breakpoints, two transcript types have been reported, resulting in the transcriptional dysregulation of the pathogenesis of this neoplasm. Of the 12 cases reported [219], the translocation was caused by nonreciprocal translocation in 11 cases with only one case demonstrating a reciprocal translocation with respective fusion product, TFE3-ASPL. The reciprocal translocation between chromosomes 17q25 and Xp11 with demonstration of molecular fusion product between TFE3 and ASPL has been confirmed in another patient who initially presented with pulmonary metastases.

ASPL-TFE t(X;17) (p11;q25)

Alveolar soft part sarcoma is an unusual tumor with a controversial histogenesis and enigmatic clinical behavior. A peculiar morphologic characteristic is the presence of rectangular or rhomboid crystalline cytoplasmic deposits of unknown composition. This tumor is characterized cytogenetically by the “in

JAZF1-JJAZ1 t(7;17) (p15;q21)

Endometrial stromal tumor exhibits characteristic morphologic features and includes low-grade endometrial stromal sarcomas, undifferentiated endometrial sarcoma, and endometrial stromal nodules. Analyses of tumor RNA indicate that a JAZF1/JJAZ1 fusion is present in all types of endometrial stromal tumors; however, the fusion appears to be rarer among endometrial stromal sarcomas that would be considered highgrade according to certain classification schemes. These two genes encode for two DNA binding proteins containing zinc finger motifs. The fusion gene constituted by the 5′ JAZF1 and 3′ JJAZ sequence retain zinc-finger motifs from each gene. Recently three cases whose karyotypes were without the specific translocation or other rearrangements were reported, giving rise to the fusion of JAZF1 with PHF1 in the translocation t(6;7) and in the t(6p;10q;10p) the fusion of PHF1 with EPC1, the enhancer of polycomb gene [220].

3.7.21

FUS-CREB3L1 t(11;16) (p11;p11)

Low-grade fibromyxoid sarcoma (LGFMS) is a malignant softtissue tumor and is often confused with more benign as well as more malignant tumor types. The spectrum of genetic rearrangements in LGFMS is poorly investigated, but a pathognonomic ~ balanced translocation t(7;16) (q32 34;p11) has been reported to be a recurrent feature. This translocation leads to FUSCREB3L2 fusion gene, consisting of the 5′ part of the FUS gene in chromosome arm 16p and the 3′ part of the CREB3L2 gene (also known as BBF2H7) in 7q. Another translocation reported in this tumor histotype, the t(11;16) (p11;p11), revealed that FUS was fused to the CREB3L1 gene (cAMP responsive element-binding protein 3-like 1, also known as OASIS), located in chromosome band 11p11. The proteins encoded by these genes both belong to the same basic Leu-zipper family of transcription factors, and display extensive sequence homology in their DNA-binding domains [221]. In a sclerosing epithelioid fibrosarcoma, a rare low-grade mesenchymal neoplasm, a cytogenetic aberration corresponding to der(10)t(10;17) has been described in one patient [222].

3. Genetic Markers in Sporadic Tumors

3.8

Epithelial Tissue

Cytogenetic and molecular analyses of thyroid tumors have indicated these neoplasms as a good model for analyzing human epithelial cell multistep carcinogenesis. The thyroid gland manifests a wide spectrum of malignant neoplasms, including medullary thyroid cancer, which develop from the neural crest-derived C cells, and tumors arising from the epithelial follicular cells. The latter ones comprise several tumor types with different phenotypic characteristics and variable biologic and clinical behavior. Molecular studies have identified specific genetic alterations in these different tumor types. In particular, well-differentiated carcinomas of the papillary type, are characterized by the activation of the RTK RET and NTRK1 proto-oncogenes [223]. Somatic rearrangements of both RET and NTRK1 produce several forms of oncogenes. By contrast, well-differentiated carcinomas of the follicular type, characterized by the rearrangement PAX8-PPARγ. In all cases, RET or NTRK1 tyrosine kinase domains are fused to the N-terminus of different genes. Nearly all breakpoints in the RET gene occur within intron 11, leading intact the tyrosine kinase domain of the receptor and enabling the RET/PTC oncoprotein to bind SHC through Y1062 and activate the downstream cascade [60]. RET/PTC rearrangements activate the transforming potential of RET by multiple mechanisms. First, by substituting its transcriptional promoter with those of the fusion partners, they allow the expression of RET in the epithelial follicular thyroid cells, where it is normally transcriptionally silent. Secondly, the rearrangements generate constitutively active chimeric oncoproteins, which are distributed in the cytosolic compartment of the cell. Finally, activation of the RET kinase is mediated by fusion to domains that are capable of dimerization [223]. The most significant clinical relevance of RET rearrangements are the correlation with radiation exposure.

3.8.1

RET PTC1

The RET–PTC1 oncogene, a chimeric transforming sequence, originates by chromosome 10 inversion, inv(10)(q11.2q21.2), and is generated by the fusion of the tyrosine kinase domain of RET to the 5-terminal region of the gene H4–D10S170. H4–D10S170 contains a coiled-coil sequence that confers to the oncoprotein the ability to form dimers, resulting in a constitutive activation of the tyrosine kinase function [216].

3.8.2

RET-PTC2

In the case of the RET–PTC2 oncogene, the rearrangement involves the tyrosine kinase domain of RET and the gene of the regulatory subunit RIα of protein kinase A, which maps to chromosome 17q23. Cytogenetic analysis has revealed that this oncogene arises from a t(10;17)(q11.2;q23) reciprocal

75

translocation [223]. RIα, like the H4 gene, contains a dimerization domain involved in the activity of the oncogene.

3.8.3

RET-PTC3/PTC4

The RET–PTC3 and RET-PTC4 oncogenes are generated by the fusion of the tyrosine kinase domain of RET and a gene named ELE1α-ARA70 (also known as RFG) located in the same region, 10q11.2. In this case, a paracentric inversion of the long arm of chromosome 10 occurs, with breakpoints in exon 5 of ELE1α-ARA70 and exon 12 (RET-PTC3) and 11 (RET-PTC4) of RET [223, 60].

3.8.4 RET-PTC5-PTC9, RET-PCM1, ELKS-RET, and RFP-RET After the Chernobyl accident, an unusual higher frequency of thyroid cancers was observed in Belarus and Ukraine, and new forms of RET rearrangements were identified in papillary thyroid carcinomas from contaminated areas. In these new oncogenes, RET was fused to seven different donor genes. For example, RET-PTC5 fusion partner protein is GOLGA5, a coiled-coil protein expressed on the Golgi surface. RET-PTC6 and RET-PTC7 display rearrangements with the transcriptional intermediary factor 1-α and γ, respectively [223]. This protein family is able to bind the ligand-dependent activation function (AF2)-activating domain of the estrogen receptor, RARs, RXRs, vitamin D3 receptor, and regulate transcription. The RET fusion partner in RET-PTC8 is kinectin, whereas in RETPTC9, RET rearranges with REG9, a putative cytoplasmic protein possibly involved in intracellular transport processes. In RET-PCM-1, the activating sequences belong to a gene coding for a centrosomal protein that displays distinct cellcycle distribution. The ELKS gene (rearranged in the ELKSRET oncogene) is transcribed into a ubiquitously expressed mRNA with unknown function. Finally, in RFP-RET, an oncogene also present in the variant form ∆RFP-RET, RET is activated by juxtaposition with RET finger protein (RFP), a gene that belongs to the B-box zinc-finger protein family. RFP has been detected in the nuclei of various cells, including peripheral and central neurons, hepatocytes, adrenal chromaffin cells, and male germ cells. RFP is involved in transcriptional repression through its coiled-coil domain [233, 60].

3.8.5

NTRK1 and TRK Oncogenes

NTRK1, is a tyrosine kinase receptor for the nerve growth factor, which primarily regulates growth, differentiation, and programmed cell death of neurons in both the peripheral and central nervous system. NTRK1 was generated from the chromosomal rearrangement by fusing the NTRK1 tyrosine kinase domain to sequences of a tropomyosin gene, TPM3. The extracellular region of NTRK1 protein contains three Leu-rich motifs (LRM) flanked by conserved Cys residues. This extracellular domain also contains two C2 Ig-like loops similar to

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those present in neural cell adhesion molecules and in receptors for fibroblast growth factor, platelet-derived growth factor (PDGF), and colony-stimulating factor (CSF)-1 [224].

3.8.6

TRK

The TRK oncogene is generated by a 1q intrachromosomal rearrangement involving an isoform of the nonmuscle tropomyosin (TPM3) mapped to chromosome 1q22–q23 and NTRK1. Molecular analysis revealed the presence not only of the product of the oncogenic rearrangement (5′ TPM3-3′ NTRK1), but also of that related to the reciprocal event (5′ NTRK1-3′ TPM3).

3.8.7

TRK-T1

The TRK-T1 (T2; T4) oncogene, formed by the fusion of NTRK1 tyrosine kinase domain to sequences of the TPR (translocated promoter region) gene localized on chromosome 1q25, generates three chimeric-transforming sequences. TRK-T1 is encoded by a hybrid mRNA that contains 598 nucleotides of TRP and 1,148 nucleotides of NTRK1. An inversion of 1q is responsible for formation of TRK. TRKT2 and TRK-T4 rearrangements involve different genomic regions of the two partner genes, TPR and NTRK1, but occur in the same intron of both these genes. As a consequence, the same mRNA and oncoprotein are produced in both cases. The molecular characterization of these rearrangements indicates that the chromosomal mechanism leads to oncogenic activation as an inv(1q) [224].

3.8.8

TRK-T3

The TRK-T3 oncogene contains 1,412 nucleotides of NTRK1 preceded by 598 nucleotides belonging to a novel gene, TFG (TRK-fused gene) located on chromosome 3. The latter gene displays a coiled-coil region that could confer to the oncoprotein the ability to form complexes. The regions outside the coiled-coil domain of TFG affect TRK-T3 oncogenic activity. When deleted, complex formation is unaltered, whereas transforming activity is reduced. TFG, whose function is still unknown, has recently been suggested to be a novel member of the NF-κB pathway [225].

3.8.9

PAX8-PPARγ t(2;3)(q13;p25)

A translocation t(2;3)(q13;p25), detected in a subset of human thyroid follicular carcinomas, results in fusion of the DNAbinding domains of the thyroid transcription factor PAX8 to domains A to F of the peroxisome proliferator-activated receptor (PPAR)γ1. Because PAX8 gene can undergo splicing events, different transcripts can be originated after the fusion with PPARγ gene. PAX8-PPARγ1 mRNA and protein were first detected in five of eight thyroid follicular carcinomas but not in 20 follicular adenomas, 10 papillary carcinomas, and 10 mul-

tinodular hyperplasias. A subsequent investigation performed on 118 thyroid tumors both malignant and benign, confirmed that PAX8-PPARγ rearrangement segregated with follicular neoplasms, because it was not found in others types of malignant or benign thyroid lesions, including papillary carcinoma and its follicular variant, Hurtle cell carcinoma, and Hurtle cell adenoma. The resulting protein PAX8-PPARγ 1 inhibits the thiazolidinedione-induced transactivation by PPARγ1 in a dominant negative manner. PAX8-PPARγ1 may be useful not only in the diagnosis but also in treatment of thyroid carcinoma.

3.8.10

MECT1-MAML2 t(11;19)(q21;p13)

Mucoepidermoid carcinoma, presenting in the 5% of all salivary gland tumors, has been associated with a recurring chromosomal translocation, which is often the sole cytogenetic alteration. This translocation fuses exon 1 of a novel gene of unknown function, mucoepidermoid carcinoma translocated gene (MECT1) at 19p13 with exons 2 to 5 of a novel member of the mastermindlike gene family (MAML2) at 11q21. The fusion product has been shown to efficiently transform epithelial cells in vitro. It appears that the fusion transcript is associated with a distinct mucoepidermoid carcinoma subset showing favorable clinicopathologic features and an indolent clinical course [226].

3.8.11

ALPHA-TFBE t(6;11)(p21;q12)

The first description of a distinctive type of pediatric renal carcinomas with a t(6;11)(p21;q12) was published in 2001. The fusion results in fusion of the 5′ portion of the ALPHA gene, whose function is unknown and that does not encode for a functional protein, with the transcription factor gene, TFBE, a member of the MiT subfamily of bHLH-LZ transcription factor. The translocation results in an aberrant expression of full-length TFBE and the detection of its positivity at an ICH level could be helpful for the diagnosis [227].

3.8.12

BRD4-NUT t(15;19) (q13;p13.1)

t(15;19)(q13;p13.1) identifies a particularly aggressive form of a carcinoma in children and young adults. This carcinoma is poorly differentiated histologically and has an unfavorable prognosis. The molecular characterization revealed that BDR4 (bromodomain-encoding) gene is fused to NUT (nuclear protein in testis) and the fusion transcript is detectable by RT-PCR. Although the oncogenic mechanisms of BRD4-NUT have not been determined, the t(15;19) is highly characteristic of this tumor entity and the fusion protein plays a pivotal pathogenetic role in this extremely lethal form of carcinoma [228].

3.9

Conclusions

During the few last years, a gene-by-gene comparative analysis of normal and tumoral tissues allowed us to acquire new

3. Genetic Markers in Sporadic Tumors

insights into tumoral pathogenetic pathways and biologic functions of molecular markers. This analysis has used different approaches to select genes for mutational analysis in cancer, including those guided by information from linkage studies in cancer-prone families, identification of chromosomal abnormalities, or known functional features of individual gene or gene families. The elucidation of the human genome sequence and the improvement of sequencing and bioinformatics approaches makes now possible, in principle, to examine the cancer cell genome in a comprehensive and unbiased manner. This very new high-throughput approach has reveled, for example., that BRAF gene, not previously associated with cancer, is mutated in several cancer types. Moreover, a sequence analysis of nearly 13,000 genes in 11 breast and 11 colorectal cancers shows an average of 11 genes mutated per individual tumor, most of which was not known to be genetically altered in cancer and are predicted to affect a wide range of cellular functions. The latter ones included transcription, adhesion, and invasion. All this resultes in less empiric and more biologically orientated approaches to tumor classifications as well as to clinical use of biomarkers. Classifications based on genetic profile successfully complemented the morphologic ones, and several biomarkers introduced in the clinical setting to improve diagnosis and prognosis became or are becoming targets of new more tailored treatments. Currently, in fact, the interest is shifted from the molecular markers to their molecular pathways that can be targets for direct or indirect therapeutic interventions. Furthermore, because more cancers are treated at every stage of the disease and individual mutations may alter the response to therapy, the concept of a prognostic factor that is independent of therapy is disappearing. As consequence a number of molecular markers, or better the associated pathways of these molecules, are currently more regarded as targets for therapy than as diagnostic/prognostic predictors. A number of chromosomal translocations appear to be useful to correctly categorize a number of sarcomas and lymphomas lacking morphologic unequivocal features, particularly for the differential diagnosis of pPNET and alveolar rhabdomyosarcoma among the so-called small round cell sarcomas, and for synovial sarcoma among the spindle cell sarcomas (Fig. 3-9). Regarding hematopoietic tumors, the assessment of the specific translocation may assist the diagnostic definition of mantle cell lymphoma, LCAL, and acute promyelocytic leukemia. From the prognostic point of view, the presence of type 1 of EWS-FLI1 and EWS-ERG or SYT-SSX2 transcripts seems to predict a more favorable clinical outcome in pPNET and synovial sarcoma, respectively. Mounting evidence points out the possible application of the assessment of TP53 and K-ras mutations in sputum; and TP53, K-ras, and APC mutations in stool specimens for a noninvasive presymptomatic diagnosis of lung and colorectal carcinoma, respectively. Similarly, the detection of high levels of free circulating DNA in the blood of these patients has represented an unexpected new marker for tumor detec-

77

3’ gene

5’ gene

Histologic diagnosis Congenital fibrosarcoma

NTRK3

TEL

PDGF

COL1A1

Chronic myelomonocytic leukemia Dermatofibrosarcoma protuberans

Ewing’s tumor FUS

ERG Acute myelomonocytic leukemia Clear cell sarcoma

EWS

ATF1 Angiomatoid fibrous histiocytoma

Fig. 3-9. Examples of translocations shared by different tumor histotypes.

tion with a noninvasive approach. These approaches, however, although promising, are limited by a low sensitivity and the use of expensive and time-consuming technologies. Regarding the evolution of biomarkers from diagnostic/ prognostic tools to molecular targets for therapeutics, the more relevant examples are represented by her-2/neu, inhibitors of abnormally activated tyrosine kinases, ATRA, and COX2 inhibitors. In general, the concept of molecular targeting involves the interruption of interaction between cognate ligand-receptor and enzyme-substrate. her-2/neu exemplifies the former situation. A number of MAb were developed against this surface-located oncoprotein among which the humanized murine Ab trastuzumab has been successfully applied, alone or in association with chemotherapy, in the clinical setting. An example of the second type of inactivation is given by tyrosine kinase inhibitors such as imatinib. This agent, recognized as a paradigm of new agents for cancer therapeutics, specifically inactivates the abnormal kinase activity of BCR/ABL oncogene in CML, of c-kit activating mutations in GIST, and the autocrine-loop-induced activation of mature postfusion (COLI/1 PDGFB) PDGFB in dermatofibrosarcoma protuberans, exemplifying that the paradigm may be translated to other malignancies sustained by different molecular alterations but sharing the same enzymatic abnormality. ATRA therapy is another example of therapy targeting to a specific receptor system, the RARA receptor that retains its ligand-binding domain in the chimeric PML/RARA oncoprotein form, a molecular hallmark of APL. By binding to the chimeric oncoprotein ARA may restore the regulatory pathway permitting the terminal differentiation of myeloid cells. A further example is represented by COX-2 selective inhibitors, such as celecoxib and refecoxib that, through reduction in prostanoid concentrations, have been shown to inhibit apoptosis, angiogenesis, and cell proliferation and stimulate the

78

immune system, and which are successfully applied in cancer treatment and prevention. Finally, given the prominence of apoptosis in determining therapeutic responses, it cannot be disregarded that most chemotherapy schemes currently applied are apoptosis-dependent and that what in the past has been defined as drug resistance is, at least in part, caused by resistance to apoptosis. Thus, a more rational and effective application of current chemotherapy regimens cannot omit knowledge about the genetic make-up of a given tumor in terms of functional status of genes involved in cell death pathway such TP53 and p16INK4 and, to a less extent, BCL2.

E. Tamborini et al.

14.

15.

16.

17.

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E. Tamborini et al. 222. Ogose A, Kawashima H, Umezu H, et al. Sclerosing epithelioid fibrosarcoma with der(10)t(10;17)(p11;q11). Cancer Genet Cytogenet 2004;152:136–140. 223. Alberti L, Carniti C, Miranda C, et al. RET and NTRK1 ProtoOncogenes in Human Diseases. J Cell Physiol 2003;195: 168–186. 224. Pierotti MA, Greco A. Oncogenic rearrangements of the NTRK1/NGF receptor. Cancer Lett 2006;232:90–98. 225. Miranda C, Roccato, E., Raho, G. et al. The TFG protein, involved in oncogenic rearrangements, interacts with TANK and NEMO, two proteins involved in the NF-κB Pathway. J Cell Physiol. 2006;208:154–160. 226. Okabe M, Miyabe S, Nagatsuka H, et al. MECT1-MAML2 fusion transcript defines a favorable subset of mucoepidermoid carcinoma. Clin Cancer Res 2006;12:3902–3907. 227. Argani P, Lae M, Hutchinson B, et al. Renal carcinomas with the t(6;11)(p21;q12): Clinicopathologic features and demonstration of the specific alpha-TFEB gene fusion by immunohistochemistry, RT-PCR, and DNA PCR. Am J Surg Pathol 2005;29:230–240. 228. French CA, Miyoshi I, Kubonishi I, et al. BRD4-NUT fusion oncogene: A novel mechanism in aggressive carcinoma. Cancer Res 2003;63:304–307.

Chapter 4 Genetic Markers in Breast Tumors with Hereditary Predisposition Tatyana A. Grushko and Olufunmilayo I. Olopade

4.1

Introduction

Although epidemiological evidence supports certain risk factors (e.g., age, residence in Western countries, obesity, nulliparity, early menarche, alcohol consumption, ionizing radiation, hormone replacement therapy, mammographic density), a family history of breast cancer remains the strongest risk factor for the disease. Familial forms comprise approximately 15–20% of all breast cancers and apparently have a distinctive pathogenesis determined by the particular susceptibility-gene involved [1, 2]. While the susceptibility-genes in most breast cancers developing in familial clusters have not been identified (Fig. 4-1), it is estimated that between 40–46% are caused by germ-line mutations in the BRCA1 and BRCA2 genes, or by rare hereditary cancer syndromes caused by mutations in other tumor suppressor genes (TSG), CHEK2, p53, PTEN or ATM, or by germ-line mutations in BRIP1, PALB2, NBS1, RAD50 or mismatch repair genes MSH2 and MLH1, all of which are critical to genomic integrity [2, 3]. Recently, common breast cancer susceptibility alleles in CASP8 and TGFB1 have been identified [4] (Table 4-1). Inherited breast cancer has several distinctive clinical features: early age at onset, higher prevalence of bilateral breast cancer, presence of associated tumors (ovarian, colon, prostate, endometrial carcinomas, and sarcomas) [5]. Characterization of hereditary breast cancers at the molecular level is a research priority for those who wish to understand its etiology and pathogenesis and ultimately incorporate this knowledge into improved diagnosis and targeted treatment. The general information about known genes contributing to inherited breast cancer syndromes is provided in Table 4-1. For information about mutations in breast cancer susceptibility genes, their functions and interacting proteins, we direct the reader to recent comprehensive reviews [3, 6–14]. In this chapter, we focus on the secondary genetic alterations, cooperative oncogenes, TSGs, and other potential markers that are

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

characteristic of BRCA1/2-associated hereditary breast cancers, as these appear to be the best characterized.

4.2

Molecular Pathology

BRCA1 and BRCA2 are the breast cancer susceptibility genes, the mutant forms of which predispose to both breast and ovarian cancers. BRCA1and BRCA2 function as classical TSGs on 17q1221, and 13q12-13 respectively, and loss of the wild-type allele is required for tumorigenesis in mutation carriers [5]. BRCA1/2 encode multifunctional proteins, which together with other proteins contribute to homologous recombination, DNA damage response, and transcriptional regulation [6–9, 11–15]. Current evidence demonstrates that BRCA1 and 2 are in the class of caretaker genes, which function to maintain genomic stability [5]. It is estimated that almost 5% of all cases of breast carcinoma are inherited in a dominant autosomal fashion, and most of them are associated with germ-line mutations at BRCA1/2 [2]. More than 1,000 mutations have been identified in BRCA1/2, and most of them result in protein truncations (Breast Cancer Information Core at http://research.nhgri.nih.gov/bic/ and Human Gene Mutation at http://archive.uwcm.ac.uk/uwcm/mg/hgmd0.html). Genetic testing for mutations in these genes in high-risk families is well established [16], and a variety of functional assays have been developed [17]. Mutations in BRCA do not directly result in tumor formation but instead cause genetic instability and trigger further alterations, including inactivation of other TSGs and/or activation of oncogenes, leading cell to malignant transformation [5]. Clinicopathologic and histologic characteristics of BRCA1/ 2-associated tumors differ from each other and both differ from age-matched breast cancers unselected for family history. BRCA1-associated tumors display distinct aggressive pathologic features, including early age of onset, high tumor grade, estrogen (ER) and progesterone (PR) receptor-negativity, lack of expression of estrogen-responsive gene pS2 and a high proliferation rate [18–20]. Histologically, the tumors are invasive ductal carcinomas, more with medullary subtype with features such as prominent pushing margins and lymphocytic infiltrate [18, 20–22], but with a scarce or absent ductal in situ component, decreased tubule formation, and the 85

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4.3

BRCA1 20% Emerging& Unknown Genes/ Polygenic Susceptibility (CASP8, TGFB1) 53-54%

BRCA2 20%

PTEN, ATM STK11/LKB1, MSH2/MLH1, BRIP1, PALB2, RAD50, NBS1 ²1%

CHEK2 5% TP53 ²1%

Fig. 4-1. Genetic susceptibility to breast cancer. BRCA1 and BRCA2 are two major genes associated with breast cancer. Mutations in each of these genes occur in approximately 20% of breast cancer families. Mutations in CHEK2 gene account for approximately 5% of all cases of familial breast cancer (defined by the diagnosis of breast cancer in ≥2 family members before the age of 60 years, but the risk for individual mutation carriers is probably <20%). Carriers of TP53 mutations develop Li-Fraumeni syndrome and are at high risk of developing early-onset breast cancer, but mutations are very rare. Susceptibility alleles in other genes, such as PTEN, ATM, BRIP1, PALB2, NBS1, RAD50, STK11/LKB1, and MSH2/MLH1, are even less common causes of breast cancer. Most familial clustering of breast cancer is unexplained. The residual genetic susceptibility in this group is presumed to be either caused by additional high-penetrance susceptibility genes, which still remain to be identified, or because of variants at many low-penetrance loci, each conferring a moderate risk of the disease (polygenic susceptibility), such as CASP8 and TGFB1, or because of exposure to hormonal and environmental factors, and stochastic genetic events. (Taken from references 1 and 2.)

disruption of the expected positive correlation between breast tumor size and lymph node status [23, 24]. BRCA1 carriers, in addition to having a high risk of breast and ovarian cancer, have an increased risk of prostate, colon, liver, and bone cancers [5]. The BRCA2 tumor phenotype is heterogenous and currently not well defined. Although typical BRCA2-associated tumors are a higher grade, the expression of ER/PR, pleomorphism, mitotic count, and age distribution are not always different from that in sporadic breast cancers [18, 25]. Histologically, BRCA2 tumors show reduction in tubule formation, the presence of continuous pushing margins, and an extensive intraductal component. Invasive lobular, pleomorphic lobular, and cribiform types have been reported more frequently in this group [20, 25, 26]. BRCA2 carriers have a lower risk of ovarian cancer when compared with BRCA1 carriers, but a higher risk of prostate, pancreatic, gall bladder, pharynx, stomach cancers, and melanoma, and most dramatic, a higher risk of breast cancer in men. The molecular markers contributing to multi-step carcinogenesis in BRCA1/2-deficient cells are summarized in Table 4-2.

Genomic Instability

Genomic instability is a broad term used to designate the genetic changes, which include aneuploidy, chromosomal and microsatellate instability, and chromosomal aberrations, DNA amplifications, centrosome amplifications, and micronuclei formation. Genomic instability likely results from a failure of coordination of S-phase and/or mitotic checkpoints and DNA repair. Genomic instability is a prominent feature of hereditary breast cancer. Aneuploidy is a marker of tumor progression and prognosis. Several studies using flow cytometry and cytogenetics evaluated the DNA content of BRCA1/2-related mammary tumor cells compared with tumor cells from sporadic cases [27–31]. Breast cancers occurring in BRCA1 mutation carriers were significantly more aneuploid than breast cancers occurring in sporadic cases. The study by Tirkkonen and colleagues [31] compared 21 patients with BRCA1-related breast cancers and 15 patients with BRCA2-related breast cancers with 55 unselected control patients, and showed that the total number of genetic changes was almost twice as high in patients with BRCA1/2 tumors. Ninety-two percent of BRCA1-associated and 62% of BRCA2-associated tumors were aneuploid, versus 49% of control cancers. Unlike the BRCA1 cases, the BRCA2associated tumors were of lower grade, more diploid, with low aneuploid S-fraction and were comparable to sporadic cases. The same conclusion was driven from a study of Johansson’s group [29]. They reported that BRCA1-positive tumors were more often DNA nondiploid (20/21) compared with (18/34) BRCA1-negative hereditary tumors, as well as being characterized by a higher S-phase fraction value. A hallmark of breast cancer as a solid tumor is genome-wide DNA copy number instability, resulting in large chromosomal gains and losses because of mutations in the DNA repair genes. Analysis of gains and losses may profile BRCA1/2-associated breast tumors and may yield a clue about the locations of other genes involved in the development of breast cancers associated with hereditary predisposition. Using comparative genomic hybridization (CGH) accompanied by fluorescence in situ hybridization (FISH), cDNA microarrays, and flow cytometry, a genome-wide analyses of chromosomal regions that are either amplified or deleted in BRCA1/2 breast cancers have been reported by several groups [28, 31–40]. In BRCA1associated tumors, losses of 2q, 4p, 4q, 5q, and 12q, and gains of 6p, 10p, and 17q22-24 were significantly more common than in the control group. BRCA2 cases were characterized by a higher frequency of losses in 13q (73%), 6q (60%), 11q and 9p and gains in 20q13 (60%), 8q22-24, and 17q22-ter as compared to the control group (12–18%) (39). Gains in the 17q22-24 segment were shared by both BRCA1- and BRCA2associated tumors and were observed in 40% and 87% of cases respectively, versus 8–31% in sporadic controls [31]. In hereditary tumors, chromosomal aberrations, including both structural and numerical changes, were particularly evident in chromosomes 1, 3, 16, and 17 [28].

BReast CAncer gene 2

Tumor Protein 53

BRCA2AD

TP53AD

ATMAR

MLH1AD STK11/ LKB1AD

MSH2

Ataxia-Telangiectasia mutated

MutS Homolog protein 2 MutL Homolog protein1 Serine/Threonine protein kinase 1

Phosphatase and TENsin PTEN1/ homolog deleted on MMAC1/ chromosome TEN TEP1AD

Cell-cycle-CHeckpoint hCHK2/ Kinase 2 CHEK2AD

BReast CAncer gene 1

Abbreviation

BRCA1AD

Gene DNA repair, Transactivation

Protein function Hereditary breast/ovarian cancer Bilateral/multifocal breast tumor; risk of prostate colon, liver, and bone cancer

Associated syndrome

Mutation frequency

Risk range

Disease penetrance

(continued)

~3% in Caucasians, <1% in blacks, 4–5% 60–85% (lifetime); 15–40% High in populations with founder mutations, risk of ovarian cancer 2–3% of all breast cancers, 12% of women by age 32, 20–40% of familial breast cancer 13q12– DNA repair, Hereditary breast/ovarian cancer 3% Caucasians, 1% in blacks, higher in 37–84% (by age 70), High 13 Transactivation Male breast cancer; risk of pancreas, gall populations with founder mutations, 60–85% (lifetime), bladder, pharynx, stomach, melanoma and 2–3% of all breast cancers, 14% of all 15–40% risk of prostate cancer. Biallelic germline male breast cancer, 2.7% of women ovarian cancer mutations of BRCA2 cause D1 Fancony by age 32, 20–40% of familial breast Anemia cancer 17p13.1 Cell cycle regula- Li-Fraumeni syndrome ≤1% of familial breast cancer 50–89% (by age 50), 90% High tion, DNA Soft tissue sarcoma, breast cancer, in Li-Fraumeni syndrome repair, apoptosis CNS-tumors, adrenocortical cancer, survivors leukemia, prostate cancer risk 22q12.1 DNA damage Li-Fraumeni syndrome (?) 5% of familial breast cancer, < 20% in Twofold increase in women, Low responce, Breast cancer, male breast cancer, risk of individual mutation carrier, 13.5% 10-fold increase in men cell cycle prostate and colorectal cancer in families having male breast cancer, regulation 1% of all breast cancers in women, 9% of all breast cancers in men 10q23.3 Protein tyrosine Cowden syndrome <1% 25–50% (by age 50) Moderate phosphatase 30–50% incidence of breast cancer; hamartoma, thyroid, oral mucosa, endometrial, and brain tumor 2p22–21 Muir-Torre syndrome <1% of familial breast cancer 12% (lifetime) High DNA MMR Colorectal carcinoma, gastrointestinal, 3p21.3 genitourinary, skin, and breast tumors 19p13.3 Serine/treonine Peutz-Jeghers syndrome <1% High High kinase Hamartous polips, breast, ovary, cervical, uterine, testicular, and colon carcinoma 11q22.3 DNA repair Ataxia-telangiectasia <1% Twofold risk of breast cancer Low Leukemia, lymphomas, immunodeficiency, (higher in women >50), ovarian cancer, breast cancer; inconclusive 15% of monoallelic carriers data on stomach, pancreas, and will develop breast cancer bladder cancer

17q12– 21

Location

Table 4-1. Breast cancer susceptibility genes.

4. Genetic Markers in Breast Tumors with Hereditary Predisposition 87

Location

8q21

Nijmegen breakage syndrome 1 (nibrin)

CASPase 8 (Cysteine2q33– ASpartic acid Protease 8) q34 Transforming growth 19q13.1 factor, beta 1

NBS1

CASP8

Associated syndrome

Proliferation, differentiation

Breast cancer

DNA repair, check- Breast cancer. Biallelic mutations cause point control subtype FA-J of Fanconi Anemia DNA repair Breast cancer, bilateral breast cancer, male breast cancer, Biallelic mutations cause subtype FA-N of Fanconi Anemia and predispose to childhood malignancies, including Wilms tumor and medulloblastoma, risk of prostate cancer DNA repair Breast, ovarian and prostate carcinoma, leukemia, malignant melanoma Hypomorphic mutations cause Nijmegen Breakage syndrome (NBS) and lymphomas DNA repair Breast, gynecologic, stomach and prostate carcinoma, leukemia, malignant melanoma. Hypomorphic mutations cause Nijmegen Breakage syndrome (NBS) and lymphomas Apoptosis Breast cancer

Protein function

AD

(4, 26, 38, 109, 145–152) autosomal dominant, AR autosomal recessive mode of inheritance; MMR mismatch repair

TGFB1

5q31

RAD50 homolog (S. cerevisiae)

BRCA1 interacting protein 17q22– C-terminus helicase 1 q24 Partner and localizer of 16p12.1 BRCA2

Abbreviation

RAD50

PALB2AR

BRIP1

Gene

Table 4-1. (continued)

1%

<1% of familial breast cancer

3.7% of familial breast cancer, <1% of all breast cancers, 1.3% of women by age 50 (Slavic cohort)

4.3% (Finnish cohort)

<1% of familial breast cancer

<1%

Mutation frequency

Low

Low

Twofold risk of breast cancer

Fourfold risk of breast cancer

Twofold risk of breast cancer (higher in women <50) Twofold risk of breast cancer (higher in women <50)

Risk range

Low

Low

Low

Low

Low

Low

Disease penetrance

88 T.A. Grushko and O.I. Olopade

Rare, 0%/0–3% vs, 15%/15–19% sporadic or 5%/4% non-BRCA 53% vs. 23 % sporadic 29% vs.2% sporadic 46–62% vs. 8% sporadic

Amplification/Overexpression

Expression Expression Expression Expression Expression Overexpression Overexpression Overexpression Amplification/overexpression Overexpression Expression Expression Overexpression

P27

Cyclin A Cyclin D1 Cyclin D3 Cyclin E CDK4 E2F-6 Active caspase 3 Bcl-2

Expression Mutation/Inactivation 70–79% vs. 0–26% non-BRCA or sporadic High, 41% vs. 11% sporadic 40%, similar to sporadic Data conflicting: either 35% vs.10% sporadic or similar to sporadic Data conflicting, presumably low, 20% vs. 41% sporadic High, 20% vs. 8% sporadic Low, 0–33% vs. 26–100% sporadic Low, 3% vs. 12% sporadic High, 16–71% vs. 9–25% sporadic Low, 7% vs. 28% sporadic 25% vs. 5% sporadic High, 32% vs. 3% sporadic Data conflicting: either low, 11–30% vs. 56% non-BRCA or 39–90% sporadic, or similar to sporadic

70–79% vs 0–26% non-BRCA or sporadic 42–68%/37–77% vs. 19–35%/22–35% sporadic

Low in GSTP1 and HIN1 genes

Hypermethylation of gene promoter CpG islands

Amplification Amplification Amplification/Overexpression

87–95% vs. 49–65% sporadic 6p, 10p, 17q22–24, 3q23.3–24.2 3p, 3q, 4p, 4q, 5q, 12q 25–30% (murine model) Specific set of 9–176 genes

BRCA1-mutated breast cancer

(continued)

Data conflicting, presumably high, 72% vs. 47% sporadic Moderate, 15% or similar to sporadic High, 27–70% High, 30% or similar to sporadic Low, 8–35% or similar to sporadic High, 47% or similar to sporadic 12% Similar to sporadic High, either 43–56% or similar to sporadic

Data conflicting: either 23% or similar to sporadic

Similar to non-BRCA and sporadic High, 40% Data conflicting, either high, 87% or similar to sporadic

High, 40% Data conflicting: either 29–64%/7–45% vs.17–19%/ 14–35% sporadic or similar to sporadic

Data conflicting: either up to 62% or similar to sporadic Low, similar to sporadic 15–85%

Data conflicting: either 0%/0–3% or similar to sporadic

High in p16 gene

Data conflicting: either 62% or similar to sporadic 7p, 8q22–24, 9p23–24, 17q24-ter, 17q23.3–24.2, 20q13 6q, 9p, 11q, 13q 44–65% (murine model) Specific set of 11–176 genes

BRCA2-mutated breast cancer

Alteration (destination or frequency)

High aneuploidy Specific gains Specific looses Amplification Downregulation/upregulation

Modification

P-cadherin CHEK2 P16 P21

Tumor suppressor genes, cell cycle and apoptotic proteins Rb TP53

C-MYC C-MYB TBX2

Centrosome aberrations Gene expression profiles Epigenetic lesions Gene methylation profiles Oncogenes HER2

Global genetic lesions Ploidy Chromosomal aberrations

Marker

Table 4-2. Specific somatic genetic alterations in hereditary BRCA1/2-mutated breast cancers.

4. Genetic Markers in Breast Tumors with Hereditary Predisposition 89

High, 12-58% vs. 3–7% sporadic High, 61% vs. 12% sporadic High, 53% vs. 10% sporadic High, 37% vs. 6% sporadic High, 43% vs. 19% sporadic High, 83% vs. 25% sporadic 22% vs. 4% sporadic Low

Low Low Low

High, 67% vs. 11–21% sporadic High, 83% vs. 48% sporadic

Expression Expression Expression

Overexpression Expression

BRCA1-mutated breast cancer

Expression Expression Expression Expression Expression Overexpression Expression Expression

Modification

Low, 8% Low or similar to sporadic

High, similar to sporadic High, similar to sporadic High

Low, 15% Low, 24% Low, 7% Low, similar to sporadic Low, 24% Low, 17% 10%, similar to sporadic High

BRCA2-mutated breast cancer

Alteration (destination or frequency)

Taken from references 6, 18, 19, 22, 24, 25, 27–43, 58–65, 67, 68, 69, 71, 72, 74, 78, 85–87, 91, 93–96, 105, 108, 111, 115, 123, 125, 126, 128, 139, 142, 144, 153–159) ER estrogen receptor; PR progesterone receptor; PS2 estrogen-responsive sequence; pS2 gastrointestinal trefoil protein.

Cytokeratin 8/18 Steroid hormones and genes involved in endocrine signaling ER PR PS2 Growth factor receptors and proliferation markers EGFR Ki-67

Markers of basal/myoepithelial and luminal phenotype Cytokeratins 5/6 Cytokeratin 14 Cytokeratin 17 Vimentin Osteonectin Fascin

Marker

Table 4-2. Specific somatic genetic alterations in hereditary BRCA1/2-mutated breast cancers.

90 T.A. Grushko and O.I. Olopade

4. Genetic Markers in Breast Tumors with Hereditary Predisposition

91

Based on CGH data by Tirkkonen et al. [31], that 4p, 4q, and 5q are frequent target for losses in BRCA1 breast cancers, Nathanson et al. [37] investigated the suggestion that the modifier genes of BRCA1 penetrance may be located in regions of allelic imbalance in the tumors of BRCA1 mutation carriers. Using nonparametric linkage analysis, they observed a significant linkage signal at D5S1471 on chromosome 5q (p = 0.009) in all the families analyzed together. The significance increased in a subset of families with an average age of breast cancer diagnosis <45 years (p = 0.003). These results suggest the presence of one or more genes on 5q3334 that modify breast cancer risk in BRCA1 mutation carriers such that they increase the age-adjusted penetrance of BRCA1 mutations. Of the 25 genes within the region, cyclin G1 (CCNG1) is of particular interest. It is a transcriptional target of p53, which is known to interact with BRCA1, localizes to nuclear foci after DNA damage, and is upregulated in breast involution [37]. Most recently, Johannsdottir and colleagues [35] applied a more refined approach to the 5q region, conducting loss of heterozygosity (LOH) analysis using 26 microsatellite markers and a high-resolution CGH platform. They confirmed a very high prevalence of 5q alterations in BRCA1/2 tumors and demonstrated particularly a high LOH frequency at the 5q34-q35.3 region. They suggested that the XRCC4, RAD50, RASA1, APC, and PPP2R2B genes of this region should be further investigated as putative TSGs. In addition, this analysis identified two new homozygous deletions in BRCA1 tumors, spanning regions of 0.7–1.5 Mbp on 5q12.1 and 5q12.3-q13.1. These regions contain two notable genes, respectively BRCC3/DEPDC1B (plecstrin/G protein interacting and RhoGAP domains) and PIK3R1 (PI3 kinase P85 regulatory subunit), which are good candidate TSGs in breast cancer [34, 38]. Although not supported by Nathanson et al. [37], the significant linkage on chromosome 4 to BRCA1 tumors detected by Tirkkonen et al. [31] has been confirmed recently by Jèonsson et al. [34] and van Beers et al. [40]. High-resolution CGH has been successfully used to build a classifier that allows differentiation of BRCA1/2 tumors [32, 34, 40]. Based on somatic genetic CGH profiles of 28 BRCA1 germ-line mutation carriers and 42 breast tumors from patients with family history with unknown BRCA status, Lodewyk and colleagues [36] developed a molecular classifier to distinguish BRCA1 mutation carriers from non-BRCA1 carriers with an accuracy of 84%. The chromosomal bands used by this classifier include regions on 3p, 3q, and 5q. A study by Lodewyk et al. [36] confirmed the losses of 5q and 12q found by Tirkkonen et al. [33], although the losses of chromosome 4q and 2p were not confirmed. One of the explanations of these discrepancies is that the goal of the Lodewyk et al. [36] study was to go beyond the identification of specific aberrations and to develop a classifier that would allow identification of individual BRCA1-associated tumors within a group of high-risk patients. Interestingly, preliminary data show that BRCA2-associated tumors displayed specific genetic similarities with BRCA1 tumors for some regions used by the

classifier. This finding is not completely surprising because BRCA1/2 are interacting proteins involved in overlapping molecular pathways. Alvarez and colleagues [32], based on a combination of the somatic genetic changes observed at the six most different chromosomal regions (2p11-21, 8p11-12, 12q11-21, 15q22-26, 18p, and 18q) plus ER status, developed a molecular classifier to distinguish between BRCA1/2 mutation carriers with accuracy of 76.7%. These data suggest that a genome-wide search to identify modifiers using a high resolution CGH approach in a larger number of families with BRCA mutations is feasible. A significant finding from murine models was that BRCA1/2 mutant cells carry out centrosome amplification [41–43]. Analyzing either BRCA1D11/D11 MEF cells or BRCA1 conditional mutant mammary tumor cells, Xu and colleagues [43], and Weaver and colleagues [42] found, that approximately 25–30% of BRCA1-deficient cells contained supernumerary functional centrosomes, instead of only one or two as observed in the control cells. In the mitotic phase, these centrosomes formed spindles with multiple poles. The multipolar spindles pulled chromosomes in different directions and led to unequal segregation of chromosomes and micronuclei formation. Consequently, the mutant cells were aneuploid. Centrosome amplification in mouse embryo fibroblast (MEF) derived from BRCA2D11/D11 mice impaired in DNA doublestrand break repair (DSBR) was observed in 44% of cells in passage 2 and 65% in passage 3 instead of only 10% in control cells [41]. In addition to chromosomal aberrations, mutant cells frequently develop micronuclei (32% in passage 2 and 52% in passage 3 versus 3–4% in controls). These abnormal DNA-containing bundles are formed through loss of acentric chromosome fragments and by chromosome missaggregation, which resulted in aneuploidy [41]. Considering that BRCA1 protein can physically associate with the centrosome [44], and that BRCA1/2 have both protein–protein interaction and transactivation activities [8, 9, 12], these first experiments provide genetic evidence implicating BRCA in the centrosome duplication process directly or indirectly, through transactivation of centrosome-specific genes. In human sporadic mammary carcinoma, centrosome defects were recorded in stages as early as preinvasive lesions and ductal carcinoma in situ (DCIS) [45, 46]. Although in hereditary breast cancer, an analysis of centrosome amplification has yet to be conducted, within the last several years, significant progress has been achieved in BRCA-centrosome functional studies [6, 14, 44, 47, 48] and murine models [6, 49]. These studies demonstrated that BRCA influence mitotic centrosomes through the regulation of centrosome duplication/ centrosome integrity, mitotic spindle formation, and proper segregation of chromosomes during mitosis, and help maintain the fidelity of cell division and preserve genomic stability. Progress has been made in identifying specific partners involved in BRCA1-dependent centrosome number regulation pathways, and several models for BRCA1-mediated regulation of centrosome function and duplication have been proposed.

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One model is based on the ubiquitin ligase function of BRCA1. It suggests that loss of BRCA1 abrogates its BRCA1-BARD1 E3 ligase activity leading to inappropriate accumulation of its substrates, nucleoplasmin B23 and γ-tubulin, in S-phase centrosomes. The centrosomes become hyperactive and repeat the duplication process [48]. The second model focuses on BRCA1’s involvement in the checkpoints controlling mitosis, which is under DNA damage control [47]. It is based on the finding that Aurora A, BRCA1, and GADD45a can functionally interact at the centrosome, and that following phosphorylation by Aurora A, BRCA1 arrests cells in the G2/M transition [49, 50]. Thus, centrosome amplification occurs secondary to the loss of BRCA1 during progression into mitosis. However, some investigators still consider the issue of the centrosomal role for BRCA1 controversial [51]. Nevertheless, data linking BRCA1 to centrosome function and finding that inhibition of BRCA1 in breast-derived cell lines resulted in centrosome amplification but had no effect in nonbreast cell lines [48], may shed light on the specific association of the BRCA1 mutant phenotype with tumors of breast and ovarian epithelial cells. Moreover, in hereditary breast cancers, some aspects of centrosome amplification may have clinical diagnostic or prognostic value or both, and may be a potential target for cancer therapy [6, 45, 52]. Thus, in view of the implication of BRCA1/2 gene product in the DNA repair process, homologous recombination, and cell-cycle control, a definitive increase in genomic instability, including aneuploidy, specific chromosome gains and losses, chromatid breaks and aberrant chromatid exchanges, deficient G1-S and G2-M checkpoints, centrosome amplification and aberrant mitoses, observed in BRCA-deficient cancer cells is consistent with the proposed role for the BRCA1/2 genes as caretakers of the genome [53].

4.4 4.4.1

Oncogenes HER2

HER2 (HER-2/neu, ERBB2) encodes a 185 kD transmembrane cell surface receptor glycoprotein with tyrosine kinase activity that belongs to the epidermal growth factor receptor (EGFR) family [54]. The HER2 proto-oncogene is involved in the regulation of normal cell growth and division, and is expressed at low levels in many normal epithelial cells. The HER2 oncogene is associated with tumor aggressiveness and enhanced chemoresistance of cancer cells through the mechanism of gene amplification, followed by increased transcription and higher levels of protein expression. A high level of HER2 protein expression is associated with accelerated cell growth and proliferation. HER2 amplification/overexpression has been reported in 20–30% of human breast cancers and in varying proportions of tumors of the ovary, endometrium, and other organs [54]. Breast cancers with HER2 amplification/ overexpression are biologically aggressive and are associated

with an increased risk of disease recurrence and shortened overall survival. In multiple studies, HER2 amplification/ overexpression has been shown to be an independent prognostic and predictive marker of response to targeted therapy with trastuzumab [55–57]. The HER2 oncogene and the BRCA1 TSG are located close to each other at 17q11-12 and 17q12-21, respectively. Both BRCA1-associated and HER2-amplified tumors occur in a subset of young women with histologically aggressive, hormone receptor-negative and highly proliferative breast cancers, suggesting a contribution of HER2 oncogene in BRCA1-associated tumor aggressiveness. Despite this association, several studies attempted to clarify this question, and using a small population of BRCA1 germ-line mutation carriers suggested a lower incidence of HER2 overexpression among BRCA1-associated cases compared with controls [29, 58, 59]. This particular feature of BRCA1-related breast cancer has been confirmed by a larger study in our laboratory [60]. We performed molecularcytogenetic FISH analysis of the HER2 gene in 53 BRCA1associated breast cancers, and showed that high levels of HER2 oncogene amplification do not occur in breast tumors from BRCA1 germ-line mutation carriers. In contrast, HER2 was highly amplified in 15% (6/41) of sporadic breast tumors. This result was consistent with findings of other investigators [61, 62]. We also showed by IHC that most nonamplified or low-amplified BRCA1-associated and sporadic tumors were negative for HER2 protein expression, but that high amplification of HER2 in sporadic tumors was invariably accompanied by a high level of protein expression. The results of our FISH study were supported later by a large collaborative study carried out on behalf of the Breast Cancer Linkage Consortium [25]. Samples of breast cancers from >100 BRCA1 mutation carriers were characterized by IHC and were HER2negative. Only 3% of BRCA1-mutated cases were positive for HER-2 protein expression, compared with 15% of sporadic cases. The same conclusion has been drawn from two publications [63, 64]. In a DNA microarray study comparing BRCA1/2-associated tumors to sporadic tumors, Hedenfalk and colleagues [65] showed that HER2 overexpression was not observed in BRCA1-associated tumors. CGH analysis has also revealed various losses and gains in BRCA1-associated tumors (losses of 3p, 3q, 5q, and 12q and gains of 6p, 10p, and 17q22-24) [31, 36] and in HER2-amplified breast carcinomas (18q losses, and 20q and 17q11-21 gains) [66]. Therefore, it has been confirmed that HER2 amplification/overexpression is not a feature of BRCA1-associated tumors. The precise mechanisms to explain why HER2 is never amplified or overexpressed in the background of a BRCA1 germ-line mutation are unknown [60]. HER2 and BRCA1 may participate in distinct molecular pathways. It is reasonable to postulate that once early inactivation of the normal BRCA1 allele and activation of specific oncogene(s) occurs in the breast tissue of germ-line BRCA1 mutation carriers [60, 67–69], there is little selective pressure for HER2 amplification [58]. Alternatively, the BRCA1 locus on chromosome 17 could

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potentially play a role in controlling amplification at the HER2 locus. Further dissection of these various pathways may allow elucidation of the conditions required for HER2 amplification in human breast cancer cells. The role of HER2 amplification in BRCA2-associated breast cancer is unclear at the current time. Two studies have examined the question [63, 64] and they did not find any HER2-amplified cases among mutations carriers. Studies that have examined HER2 overexpression by IHC reported either low frequency of HER2 in BRCA2 tumors, similar to BRCA1 cases, [25, 59, 70, 71], or frequent HER-2 positivity in BRCA2-associated tumor that was comparable to sporadic controls [19, 58, 72].

C-MYC protein by Western blot analysis [42]. BRCA1 protein structure (nuclear localization sequences and transactivation domain) suggests that BRCA1 might be involved in gene transcription. Consistent with this notion, Wang and colleagues [76] demonstrated that BRCA1 physically binds to C-MYC and represses its transcriptional and transforming activity. Furthermore, they showed that BRCA1 reverses the phenotype of rat embryonic fibroblasts transformed by c-myc-ras activation. Another group found that in addition to direct binding to C-MYC, BRCA1 specifically binds to Nmi (N-MYC-interacting protein), which functions as an adaptor molecule to recruit C-MYC to a complex with BRCA1 [77]. The authors showed that constructs made with BRCA1 mutations within Nmi binding sites are unable to suppress the transcriptional activation of C-MYC. We were interested in examining whether MYC amplification in sporadic breast cancer would be at least in part associated with loss of BRCA1 because of promoter methylation. Twenty of 62 sporadic tumors were BRCA-methylated, and eight of these demonstrated MYC amplification [67]. Of the 14 sporadic cases with MYC amplification, detected in our study, eight (57%) were BRCA1-methylated. In total, we found MYC amplification in a significantly higher proportion of tumors with BRCA1 dysfunction (29 of 60, 48% versus 6 of 42, 14%, p = 0.0003) [67]. All these data indicate that BRCA1 is a component of a transcription factor complex and may in part function as a tumor suppressor by regulating MYC activity [77]. Therefore, loss of BRCA1 in germ-line mutation carriers (and in some methylated sporadic tumors) may result in increased C-MYC activity and transforming potential, which in turn, can lead to gene amplification through abnormal autoregulation affecting its DNA replication [73]. Our observation that C-MYC activation occurs in a high proportion of BRCA1deficient cancers supports for a role for C-MYC in aggressive multistep tumor progression in hereditary BRCA1-mutated tumors. C-MYC status in BRCA-associated tumors has been reported in two other studies [63, 64], but with contradictory results, mainly because of differences in methodologies used and the small cohorts of cases analyzed.

4.4.2

C-MYC

The C-MYC oncogene encodes a proliferative nuclear DNAbinding protein, the deregulated expression of which has been shown to play an important role in the induction and progression of lymphomas, lung cancer, and breast cancer [73]. A number of studies have reported amplification and/or overexpression of C-MYC in sporadic breast cancers, ranging from 5 to 50% of cases studied [73]. C-MYC amplification and/or overexpression has been shown to associate with poor prognostic factors such as high tumor grade, ER-negativity and high proliferation rate, similar to BRCA1-mutated cancers [73]. The contribution of C-MYC to hereditary BRCA1mutated (and BRCA1-methylated sporadic breast cancers) was recently evaluated in our laboratory [67]. Using a MYC/CEP8 FISH assay on formalin-fixed paraffin-embedded tumor tissues from 40 women with known deleterious germ-line BRCA1 mutations and 62 sporadic cases, we showed C-MYC amplification in both BRCA1-associated and sporadic tumors. We found that 23% (14/62) of sporadic tumors had a MYC:CEP8 amplification ratio ≥2, a proportion of amplified tumors comparable to the approximately 25% of breast tumors that have been reported with C-MYC amplification in the literature [67]. However, in the BRCA1-mutated group the proportion of C-MYC-amplified tumors was significantly higher (21 of 40, 53%, p = 0.003). In a multivariable regression model, controlling for age, tumor size, and estrogen receptor status, BRCA1-mutated tumors demonstrated a significantly greater MYC:CEP8 ratio than sporadic tumors (p = 0.02). These data suggest that loss of BRCA1 in hereditary breast cancers is associated with C-MYC amplification and that the aggressive features of BRCA1-associated tumors are in part because of dysregulated C-MYC oncogenic activity. Our results are consistent with data from DNA microarray studies. A review of the set of genes published by Hedenfalk et al. [65] van ‘t Veer et al. [74], and Sorlie et al. [75] demonstrated that C-MYC on 8q24 was overexpressed in BRCA1 mutation carriers. These data are also supported in mouse model of carcinogenesis. Mice carrying a conditional BRCA1 mutation display gain of chromosome 15 (orthologous to human chromosome 8q24) by CGH and overexpression of

4.5 4.5.1

Tumor Suppressor Genes P53

The TP53 tumor suppressor gene is the most frequently altered gene in human malignancies. P53 is a transcription factor and it regulates cell proliferation and apoptosis. IHC assays revealed that approximately 15–40% of sporadic breast cancers showed a detectable expression of p53, resulting from the accumulation of mutant p53 protein [19]. A number of studies, using either IHC assays or DNA sequencing, have demonstrated a higher frequency of p53 mutations or immunopositivity for p53 protein in BRCA1/2-associated breast cancers compared with control tumors [58, 71, 78]. Fifty-three percent of the

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BRCA1/2-associated breast cancer cases overexpressed p53, compared to 24% of the sporadic cases (p < 0.0001). TP53 mutations have been characterized in 42% of the BRCA1associated tumors, 29% in BRCA2-associated cases and in 18–19% of sporadic cases (p = 0.0003 and p = 0.03 respectively; summarized by Chappuis et al. [19]. A frequency of p53 mutations and protein overexpression in breast tumors from BRCA2 mutation carriers is somehow intermediate between those of BRCA1 mutation carriers and nonhereditary cancers [58, 59]. Mutations in p53 occur at very high frequencies in typical medullary breast cancers [79], the observation bearing particular interest in view of the recognized pathobiologic similarities between medullary and BRCA1-associated breast cancers. Moreover, BRCA1/2-associated breast cancers carry novel p53 mutations not detected in a control series of tumors, suggesting that the spectrum of p53 mutations occurring in BRCA1/2-associated tumors is distinct from those in sporadic cases [78]. Because P53 interacts physically with BRCA1 and BRCA2, it was suggested that the presence of a BRCA1/2 mutation could be a determining factor in the selection of these mutations in breast cancer [80–82]. The precise biologic and clinical consequences of this increased frequency of p53 mutations in BRCA1/2-associated breast cancer cases are still elusive. The BRCA1 and BRCA2 proteins have been shown to interact with the Rad51 protein, which is involved in recombination and DNA double-strand repair [8, 13]. A cell lacking BRCA1/2 may have a decreased ability to repair DNA damage, which would lead to genomic instability. Functional p53 would prevent this abnormality either through cell cycle arrest or apoptosis. However, in the absence of functional gatekeeper, p53, the clonal population of cells could continue to proliferate and accumulate further somatic genetic abnormalities, resulting in tumor progression. Thus, loss of the p53 checkpoint control may be obligatory for the malignant transformation in cells with BRCA1/2 mutations [83]. This hypothesis is supported by data from murine models demonstrating that p53 loss leads to accelerated tumor formation, increased tumor incidence and a striking genomic imbalance in BRCA1/2-deficient animals [42, 84]. Armes and colleagues [58] were able to show that p53 overexpression consistently occurred at the preinvasive (DCIS) stage of disease, and suggested that p53 overexpression is an important and early event in the molecular pathogenesis of cancers arising in BRCA1/2 mutation carriers. These findings may have prognostic significance because breast carcinomas with p53 mutations tend to have a worse prognosis than patients without p53 mutations. Although p53 mutations occur more often in BRCA1/2associated tumors than in sporadic breast cancers, no absolute requirement for p53 mutations has been proven [42]. Some studies have not found the frequency of p53 alterations to be different from that found in sporadic breast cancers, as measured by IHC [22, 29, 59]. Analysis of a large data set of BRCA1/2-associated breast cancers in comparison to control cancers revealed a complex pattern of p53 immunostaining

T.A. Grushko and O.I. Olopade

[25]. The proportion of tumors showing strong p53 staining was higher in BRCA1-associated tumors than in controls, as was the proportion with staining in >50% of cells (44% versus 22%). However, the proportion with weak staining was actually lower in BRCA1 carriers. The authors were not able to show a clear relationship between p53 staining and BRCA1 status. In contrast, a study by Palacios and colleagues [64] revealed a high incidence of p53 immunostaining in BRCA1 (50%) tumors compared with BRCA2 (16%) and non-BRCA familial tumors (3.7%). There have been several studies examining the relationship of patient age to p53 expression, along with other histopathologic features, in BRCA versus familial [85] or sporadic [86] breast cancer. In a study by Eerola et al. [85], p53-positivity in BRCA1 patients aged <50 years (50%) differed significantly from that in patients diagnosed at age 50 years or older (7.7%, p = 0.024) and from familial non-BRCA patients (25, 9%, p = 0.024). Vaziri and colleagues [86] observed no differences in p53 staining between BRCA1-associated cancers and sporadic controls from older patients (aged >50 years). Further evidence for an important role for p53 in familial breast cancer is needed. Although loss of both the wild-type BRCA allele and the p53 pathway may be required for tumorigenesis, it is not known whether loss of the p53 pathway precedes or is followed by the loss of the normal allele of the BRCA gene. Either way, other mutations, perhaps those activating dominant oncogenes, would then arise because of the mutagenic environment caused by the DNA damage driven by mutant BRCA protein.

4.5.2

E-cadherin/catenin Cell Adhesion Complex

It is well established that the loss of the E-cadherin/catenin cell adhesion complex might promote tumor progression and metastasis in many cancers. E-cadherin is a TSG on 16q22.1. It is a transmembrane cell–cell adhesion molecule, which plays an essential role in the generation and maintenance of epithelial cell polarity. E-cadherin is mutated at a high frequency in invasive lobular breast carcinoma and in diffuse gastric cancer as a tumor-initiating or promoting event [87]. Studies have reported that E-cadherin alterations, e.g., germ-line and, more commonly, somatic mutations, and promoter methylation, occur at low frequency in familial breast cancer [88], and with slightly higher frequencies in ER-negative tumors and tumors that progress to develop distant metastasis and recurrence [89, 90]. Although no obvious differences in the anti-E-cadherin or ß-catenin immunophenotypes between BRCA1/2-associated and control groups of breast tumors were noted in two studies [58, 86], ß-catenin was lost more frequently (p = 0.05) in tumors of BRCA1 mutation cases diagnosed before 50 years of age than in matched controls [86]. In contrast, P-cadherin, encoded by another member of the cadherin gene family located on 16q, has emerged recently as a potential marker in breast cancer. It was reported that P-cadherin is a highly sensitive marker for normal myoepithelial cells [91].

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Its expression is preserved in tumors that have a basal-like phenotype (i.e., high-grade, ER-negative, medullary tumor type with expression of cytokeratin 5/6, cyclin E, and p53) [64, 87, 91], and is highly predictive of poor prognosis [87]. Most important, P-cadherin expression has been shown to strongly associate with the presence of a BRCA1 mutation [64, 87]. Palacios and colleagues [64] observed more frequent expression of P-cadherin in BRCA1-mutated breast tumors (15 of 19) than in either BRCA2 (1 of 15) or non-BRCA familial breast cancers (0 of 29).

is inversely correlated with p53 expression and high grade [94] apparently because of inability of the mutated p53 to activate p21 transcription. In BRCA1-associated tumors expression of p21 was detected in 9/26 tumors analyzed, these comprising four wild type and five mutants for p53. Three tumors wild type for p53 did not express p21 despite having high levels of p53 protein [95]. Similarly, in BRCA2-associated tumors p21 was expressed regardless of p53 status and, furthermore, some tumors expressing wild-type p53 did not express detectable p21 [95, 96]. Thus, IHC studies have failed to demonstrate a relationship between p21 and p53 in BRCA tumors, suggesting that p21 transactivation in hereditary tumors could be mediated by a p53-independent mechanism. This finding could be of practical significance, because an increase in p21 expression and apoptosis has been observed in cells with wildtype p53 exposed to chemotherapy [97].

4.6

Cell Cycle and Apoptotic Proteins

Checkpoint loss is a necessary precursor of BRCA1/2 genes inactivation in tumorigenesis. The tissue specificity of BRCAlinked disease might arise from a specific predisposition of the breast (and ovarian) epithelium to lose the function of such checkpoints [12]. Most cells that have inactivating mutations of BRCA will be unable to repair DNA damage sustained in the following cell cycle and thus will die. However, in the rapidly proliferating breast epithelium, some repair deficient cells may escape death, at least temporarily. Because these BRCAnull cells would be deficient in repair, they would sustain DNA damage at many sites, often affecting genes essential to cell cycle checkpoint activation. Mutation of a checkpoint gene would enable a BRCA-null cell to escape death permanently and to proliferate. Therefore, genetic instability caused by loss of BRCA1/2 may enable additional mutations, including alterations in checkpoint genes. In BRCA-mediated tumorigenesis, the alterations in p53, one of the key checkpoint genes, appears crucial. Cells that have successfully escaped death by checkpoint will accumulate multiple mutations and/or alterations in proteins controlling cell cycle and apoptosis.

4.6.1

p16

p16 (INK4a/ CdkN2A) is inhibitor of Cdk4/D cyclins. The normal p16 protein maintains cell cycle arrest. A nonfunctional p16 protein has lost its regulatory capacity and cannot constrain cells from passing through the cell cycle. Inherited mutations in p16 TSG and in Cdk4 gene confer susceptibility to cutaneous malignant melanoma and increased risk of breast cancer in CdkN2A-associated melanoma families [92]. However, the frequencies of down regulation or loss of p16 detected in familial tumors are similar to those demonstrated in sporadic breast tumors [93].

4.6.2

p21

The cyclin-dependent kinase inhibitor p21 (Cip1) blocks transition from G1 to S phase and suppresses cell proliferation. p21 is thought to be a major downstream effector of the wildtype p53-mediated growth arrest pathway that is induced by DNA damage. In sporadic breast tumors the expression of p21

4.6.3

p27

Another cyclin-dependent kinase complex inhibitor that plays an important role in breast cancer pathogenesis is p27 (Kip1). In normal cells p27 expression is crucial for G1-S transition. The p27 was demonstrated to bind to Cyclin E/Cdk2 complexes and inhibit the kinase function of Cdk2. A number of other functions have been suggested for p27 including as a promoter of apoptosis, as a regulator of drug resistance in solid tumors and having role in cell differentiation [98]. Decreased levels of the p27 in breast cancer are associated with a poor outcome. Patients whose tumors overexpress p27 may have significantly higher survival rates than patients without this overexpression [94]. Data regarding p27 expression, as well as other cell cycle proteins, in familial BRCA1/2-associated breast cancer are limited and contradictory. Robson et al. [59] reported that p27 expression does not differ between sporadic (33/40, 83%) and BRCA-associated (15/16, 94%, p = 0.28) breast cancers. This finding is contrary to observations by Osin et al. [94], where p27 was overexpressed in BRCA1/2 breast cancers (86% in familial tumors versus 65% in sporadic tumors). In a comprehensive study of 202 Ashkenazi Jews by Chappuis et al. [19], 32 tumors (16%) had been found to be positive for BRCA1/2 mutations. Low p27 expression was seen in 110 tumors (63%) and was significantly associated with BRCA1/2 mutations (p = 0.009). BRCA1/2 mutation carriers had a significantly worse 5-year distant disease-free survival compared with women without BRCA1/2 mutations (p = 0.003). Similar results were seen for women whose tumors expressed low levels of p27, compared with those with high levels (p < 0.0001). In a multivariate analysis, both BRCA1/2 mutations and low p27 expression were associated with a shorter distant disease-free survival (p = 0.05 and p = 0.01, respectively). In addition, a decreased p27 protein expression was observed in the BRCA1-mutant cell line HCC1937, compared with MCF-7 and other breast cancer cell lines expressing intact BRCA1 protein [99, 100]. It was concluded, that BRCA1/2 mutations are associated with low levels of p27 in

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breast cancer, and that both BRCA1/2 and p27 status can be identified as independent prognostic factors. BRCA1 transactivates expression of p27 through direct interaction with p27 promoter, and that the transcriptional regulation of p27 by BRCA1 may be a mechanism for BRCA1-induced growth inhibition [100]. At the same time, it was shown that BRCA1 binds to C-MYC and repress its transcriptional and transforming activity [76]. C-MYC in turn binds to p27 and repress p27 promoter activity [101]. Therefore, the p27 regulation by BRCA1 can be direct or indirect through C-MYC. In BRCA1-deficient cell, one would expect upregulation of C-MYC and down regulation of p27. Indeed, we observed C-MYC amplification in a significant proportion of BRCA1-deficient cells [67], and the down regulation of p27 was found even in higher, as would be expected, proportion of BRCA-mutated tumors by Chappuis et al. [19]. The novel concomitant finding by three independent research groups [102– 104] that p27 cytoplasmic re-localization rather then down regulation might be a mechanism of p27 alteration in breast cancer, may prompt more rigorous analysis of p27 expression in hereditary breast cancer and may eliminate discrepancies between existing results. Understanding the mechanisms controlling p27 expression in hereditary breast cancer may provide new strategies to inhibit tumor growth. p27 may have greater clinical use in BRCA-associated cancer prognosis then other candidate markers.

4.6.4

Cyclin D1

IHC analysis of cyclin D1 expression gave controversial results. Robson et al. [59] reported that the proportion of BRCA mutation positive cases was 50% (6/12) and was similar to mutation negative cases (15/32, 47%; p = 0.85). However, in another study, only 11% (1/9) cancers in BRCA1 mutation carriers showed strong or moderate staining of cyclin D1 that was significantly lower compared with 63% (12 of 19; p = 0.02) of control cancers [58]. The same group reported that the frequency of Cyclin D1 expression in BRCA2 mutation carriers was intermediate between BRCA1 mutation carriers and control cancers. A study by Osin et al. [94] detected significantly low frequency of Cyclin D1 in BRCA1-mutated tumors (1/21, 5%) than in BRCA2-mutated (4/15, 27%; p < 0.05) or sporadic tumors (24/69, 35%; p < 0.01). These results were confirmed by cDNA microarrays followed by tissue array study of BRCA1/2-associated breast cancers [65]. Latest FISH and IHC analyses revealed that Cyclin D1 is amplified in 19 of 74 (26%) of sporadic breast tumors but is not amplified or showed low frequency of protein expression in tumors of BRCA1 mutation carriers diagnosed before age 50 years [86]. However, high frequency of Cyclin D1 expression was detected in BRCA1 tumors diagnosed in patients aged ≥50 years and was comparable to matched controls. Based on the analysis of cyclin D1 and other markers, the authors showed that breast tumors of BRCA1 mutation carriers diagnosed with breast cancer before the age 50 years display a distinct tumor

phenotype compared with age-matched controls and tumor of carriers diagnosed at 50 years of age or older display a phenotype that is similar to that of age-matched sporadic breast cancers. The authors suggested that breast tumor phenotype of BRCA1 mutation carriers may be influenced by age at diagnosis. Cyclin D1 is a regulator of progression from G1 to S phase in cell cycle and is also known to be upregulated by estrogen and progestins, and down regulated by anti-estrogens. The transcription of ER-regulated genes is modulated by cyclin D1 [94]. Thus, it is not surprising that there could be an age related relationship with the ER and PR of BRCA1- and BRCA2-associated tumors.

4.6.5

Cyclin E

Cyclin E expression has been implicated as a marker of poor prognosis in breast cancer. Although the study by Vaziri et al. [86] showed no significant differences in tumors of BRCA1 mutation carriers compared with age-matched controls, a more recent study of Ashkenazi Jewish cohort suggested that a high level of cyclin E is a characteristic of BRCA1-mutated breast cancer [105]. Interestingly, neither hereditary, no sporadic breast cancers amplify Cyclin E (CCNE1). Data on BRCA2 tumors have not been reported. In the BRCA1 conditional mouse model, the expression of cell-cycle regulators was notably absent for p16 and cyclins A, B1, E; and present for cdc2, p21 (weak), cyclin D1, and p27 expression [106]. Disruption of BRCA2, in contrast to BRCA1, does not appear to have a marked effect on cell-cycle checkpoint enforcement. Instead, BRCA2 along with the BubR1 kinase inactivate the metaphase-to-anaphase surveillance mechanism, reverses proliferative arrest and fosters tumorigenesis in BRCA2-deficient cells. Mitotic checkpoint inactivation in BRCA2-deficient animals accompanied increased expression of p53 and p21 [41, 107].

4.6.6

Bcl-2

In hereditary breast cancer, an inverse correlation between loss of p53 expression and high proliferation index on one side, and low expression of the anti-apoptotic Bcl-2 on the other side, as has been demonstrated for sporadic tumors, has been expected [94]. Indeed, the reduction of the antiapoptotic gene Bcl-2 was a characteristic of most breast tumors from BRCA1 mutation carriers [108]. Surprisingly, two early studies have shown that BRCA-deficient tumors have the same level of Bcl2 expression as the control group, despite being highly proliferative and with frequent p53 mutations [58, 59]. Genes involved in the metabolism of steroid hormones have been hypothesized to modify breast or ovarian cancer risk in carriers of BRCA1/2 mutations. The growth regulatory effects of bioavailable steroid hormones may be modified by inherited genotypes at numerous loci including CYP1A1, CYP3A4, CYP17, or CYP19, as well as androgen receptor (AR) and ER [109]. Studies by Rebbeck et al. [109, 110] showed that

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A1B1 genotype and reproductive history may affect BRCA1/2associated breast cancer risk, and supported the hypothesis that pathways involving endocrine signaling may modify cancer risk in mutation carriers. Thus, most morphologic, FISH and IHC studies of familial breast cancers have identified specific characteristics associated with BRCA1 mutation, but have not identified specific features of BRCA2-associated tumors. Recently, however, using IHC profile of DNA repair proteins CHEK2 and RAD51, Honrado and colleagues [111] were able to differentiate BRCA2 tumors from familial non-BRCA and sporadic tumors with estimated probability of at least 76%. BRCA2 tumors demonstrated more cytoplasmic (50%) and less nuclear RAD51 staining versus BRCA1 tumors (17%), nonBRCA tumors (17%) and sporadic (15%, p < 0.001) tumors. The authors proposed an IHC profile for BRCA2-mutated tumors represented by negative RAD51 nuclear expression and positive RAD51 cytoplasm in combination with CHEK2 expression, which could help to select candidate families for genetic studies, especially those families with a male breast cancer.

mutations and 14/15 tumors without BRCA1 mutations were correctly identified in the BRCA1 classification. Five of eight tumors with BRCA2 mutations and 13/14 tumors without BRCA2 mutations were correctly identified in the BRCA2 classification. Thus, the accuracy of classification was significant for the identification of BRCA1-mutation-positive tumors and less significant for the identification of BRCA2 mutationpositive tumors. Using three statistical methods, the authors identified 176 genes that are most important in distinguishing a BRCA1-mutation-positive breast cancer from a BRCA2mutation-positive breast cancer. Within the list is a large block of genes that are upregulated in BRCA1-mutation-positive samples but not in BRCA2-mutation-positive samples. Examination of individual genes in this block suggests the coordinated transcriptional activation of two major cellular processes in BRCA1-mutation-positive samples: DNA repair and apoptosis. The DNA repair pathways are represented by genes (e.g., MSH2) that participate in the activation of the cellular responses to stress. In addition, BRCA1-mutation-positive tumors display increased expression of genes associated with apoptosis induction (e.g., PDCD5), and decreased expression of genes involved in apoptosis suppression (e.g., CTGF). The authors suggested that the mutation of BRCA1 leads to a constitutive stress-type state. Using a high-density tissue microarray, authors showed that levels of proteins encoded by selected genes (measured by IHC) correlated with the cDNA microarray results. Thus, data from the DNA microarray study by Hedenfalk et al. [65], suggest that breast cancers arising in the setting of germ-line BRCA1/2 mutations have unique gene expression profiles, and a heritable mutation influences the gene expression pattern of the cancer. Another group performed genome-wide gene expression profiles of 18 BRCA1-associated tumors and 2 BRCA2-associated tumors as a part of larger study of 117 primary breast cancers [74]. The aim was to identify a set of genes for predicting disease outcome and to develop a strategy for selecting patients who would benefit from adjuvant therapy. Microarray data were evaluated for approximately 25,000 human genes, and analyzed using an unsupervised hierarchical clustering algorithm. Approximately 5,000 genes appeared to be significant for tumor clustering. On the basis of these genes, tumors were divided into two groups with “good prognosis” and “poor prognosis.” The group of poor prognosis tumors had a dominant expression signature, which included downregulation of ERa (ESR1) and ER-targeted genes. A second dominant gene cluster was associated with lymphocytic infiltration and included several genes expressed primarily by B and T cells. It was shown that the poor prognosis signature consisted of genes regulating cell cycle, invasion, metastasis, and angiogenesis. In addition, they established a signature that identifies tumors of BRCA1 mutation carriers. Sixteen of 18 tumors of BRCA1 carriers were found within the group of poor prognosis that was consistent with the idea that most BRCA1 mutant tumors are ER-negative and manifest a higher amount of lymphocytic infiltrate. These findings

4.7

Gene Expression Profiles

Despite great efforts to identify unique molecular markers for breast cancer and, particularly, for hereditary breast cancer, that have a large impact both on estimation of prognosis and the choice of therapy for the individual patient, the results have been unimpressive. cDNA microarrays offer a systematic method to perform very extensive expression profiling for a single cancer specimen. Two pioneering studies aimed at identifying the distinct patterns of gene expression in BRCA1/2-associated breast carcinomas were published [65, 74]. In one study, RNA from primary tumors from seven BRCA1 mutation carriers, seven BRCA2 mutation carriers, and seven patients with sporadic cases of breast cancer were compared with a microarray of 6,512 complimentary DNA (cDNA) clones of 5361 genes, including 2905 known genes [65]. The BRCA1/2-associated tumors generally demonstrated disparate histologic and hormone receptor status. Fifty-one genes were identified that best differentiated among the three types of tumors using a modified F test (α = 0.001). In addition, the authors used a class-prediction method to determine whether the gene expression profiles of the 21 breast tumor samples accurately identified them as positive or negative for BRCA mutations. For the analysis of all 21 tumor samples, nine genes were differentially expressed in BRCA1 mutationpositive tumors and BRCA1 mutation-negative tumors. Those nine genes include KRT8, VLDLR, MCM7, RSTs (Hs.239666), HECH, ESTs (Hs.91604), BRF1, TP53BP2, and SPS. Eleven genes were differentially expressed in BRCA2 mutationpositive tumors vs BRCA2 mutation-negative tumors: ARP1, PCNA, HADHA, INTB8, PPP1CB, D123, CDK4, UGTREL1, ZNF161, ARVCF, and PDGFB. All seven tumors with BRCA1

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are in contrast to the Hedenfalk et al. [65] study, which was unable to appreciate the overlap in signatures between the ERnegative and BRCA1 mutated tumors. van’t Veer et al. [74] identified an optimal set of 100 genes that can distinguish BRCA1 tumors from sporadic cases. This set is enriched in lymphocyte-specific genes. However, the nine BRCA1 status reporter genes listed by Hedenfalk et al. [65] were not present in this set of 100 genes. Different data analysis and the different sets of hereditary tumors analyzed may account for these discrepancies. Nevertheless, both groups of authors showed that (1) gene expression profiling is a more powerful predictor of disease outcome in young patients with breast cancer than the standard system based on clinical and histologic criteria [74, 112], (2) genotype-phenotype correlations exist and, (3) the signature that reveals a BRCA1 status may help refine the diagnosis of hereditary breast cancer [65]. Another important finding from both studies is that sporadic tumors with methylated BRCA1 may be misclassified with the BRCA1-mutation-positive group. This suggests that BRCA1methylated sporadic tumors display pathologic features and gene-expression profiles similar to those of BRCA1-mutated hereditary breast cancers, and that promoter methylation may be an important mechanism for functionally inactivating BRCA1 in nonhereditary forms of breast cancer. These findings are consistent with our study of C-MYC amplification in BRCA1-deficient breast cancers (BRCA1-mutated hereditary and BRCA1-methylated sporadic), where we showed that the pattern of C-MYC amplification in BRCA1-methylated cases resembles to that of BRCA1-mutated cases rather than that of sporadic cancers [67].

4.8

Basal-like Phenotype

Human mammary glands contain two distinct types of epithelial cells, basal/myoepithelial and luminal, which easily can be distinguished by the pattern of expression of certain cytokeratins. The cytokeratins are a family of about 20 cytoskeletal intermediate filamentous proteins [113]. Basal-like breast epithelial cells express exclusively basal keratins 5, 6, 14, 15, 17, and other myoepithelial markers, like vimentin, whereas luminal cells express mainly the luminal cytokeratins 8, 18, and 19. The cytokeratin pattern is largely conserved after transformation of epithelial cells, allowing determination of the cell-type origin of primary carcinoma. Most breast cancers originate from luminal epithelium and express luminal cell-specific cytokeratins. Three to fifteen percent of all breast cancers originate from basal-like epithelium, express basal specific cytokeratins, and represent a more aggressive group of tumors. Tumor cell-type origin and the pattern of cytokeratin expression have been implicated in the prognosis of breast carcinoma [113]. Perou and colleagues [75, 114] used cDNA microarrays followed by IHC in an attempt to classify breast cancers based on variations in global gene expression patterns. They have

found that breast cancers encompass at least 5 biologically distinct subtypes of tumors: ER-negative/basal-like tumors, which are positive for keratins 5 and 17, ER-negative/basallike/HER-2-positive tumors, and ER-positive/luminal-like tumors, which are positive for cytokeratins 8 and 18. Given that BRCA1-associated tumors are negative for both ER and HER2 [24, 25, 60], we hypothesized that such tumors would have an ER-/basal-like pattern of gene expression. In collaboration with Perou, we conducted a pilot IHC study of cytokeratins in BRCA1-associated tumors. Six of seven tumors with BRCA1 mutations revealed positive staining for basal keratin 5, basal keratin 17, or both [115]. Consistent with our observations, Hedenfalk et al. [65] showed that the level of expression of cytokeratin 8 was low in tumors with BRCA1 mutations, whereas HER2 was not overexpressed. In contrast, cytokeratin 8 was highly expressed in tumors with BRCA2 mutations, a pattern of expression described for ER-positive sporadic tumors of luminal origin. Thus, it was proposed that cytokeratins, alone or in combination with the corresponding set of genes [65, 114] could be potential markers of cellular origin of BRCA1-associated tumors and prognosis. More recently, Sorlie and colleagues [116] re-analyzed cDNA microarray data for 18 BRCA1 and 2 BRCA2-mutated carcinomas from their previous study [74] and found that 80% of the BRCA1mutated tumors had a basal-like gene expression profile. Several other groups used IHC to confirm that the expression of cytokeratins 5/6 was statistically significantly associated with 78–88% of BRCA1-mutated breast cancers [117, 118]. The analysis of tumors from BRCA2 mutation carriers suggests that those tumors are rather of luminal epithelium phenotype, similar to most sporadic cancers [116, 118, 119]. Subsequent efforts have been directed toward the development the precise set of basal markers that define this type of carcinoma. Conventional histopathologic and molecular studies of breast cancers with the basal/myoepithelial pattern have shown that these tumors are often high grade, lymph node-negative, medullary type, have areas of necrosis, have a distinct pattern of genetic alterations, poor prognosis, have distinct population-based distribution, and respond differently to preoperative chemotherapy [120–122]. The development of a panel of additional markers that are not specific basal/myoepithelial markers but whose expression is specifically associated with basal-like cells, was based on several studies [21, 111, 117, 123–128]. The panel includes cytoskeletal proteins (osteonectin and fascin), cell adhesion molecules (P-cadherin), growth factor receptors (EGFR, NGFR/p75NTR), and cell cycle regulators (CCND1, CAV1, p21Cip1, p27Kip1), proliferation markers (Ki-67), apoptotic (BCL2) and heat shock proteins (αβ-crystallin) (Table 4-2). In addition to microarrays profiling, Perou’s group [129, 130] developed IHC profiling of invasive breast carcinoma based on the panel of 6 antibodies (ER, CK5/6, HER2, EGFR, vimentin, and CK8/18), and showed that the combination of these markers can accurately identify basal-like tumors. When analyzed for these markers, BRCA1-mutated tumors showed striking mor-

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phologic and IHC similarities with basal-like breast carcinoma (Table 4-2) compared with sporadic breast carcinomas and BRCA2-mutated tumors. The data confirming this observation continue to accumulate [127]. Therefore, it is not surprising that BRCA1-mutated tumors are negative for HER2 amplification and/or overexpression and have been shown to consistently segregate together with sporadic basal-like breast cancers in hierarchical clustering based on DNA microarray profiling [116]. The importance of this association between BRCA1 and the basal-like phenotype is the correlation with poor prognosis, the possibility of specific treatments, and the development of an accurate predictor of BRCA1 status in patients with aggressive breast cancer. To evaluate this question, a large collaborative study on behalf of the Breast Cancer Linkage Consortium (BCLC) (24) has been carried out. Samples of breast cancers from 182 BRCA1 mutation carriers, 63 BRCA2 mutation carriers, and 109 controls were characterized immunohistochemically for cytokeratins 5/6, 14, and 17, plus EGFR and osteonectin. This study showed that all five basal markers were more common in BRCA1-mutated tumors than in control tumors (CK5/6: 58% versus 7%; CK14: 63% versus 12%; CK17: 53% versus 10%; osteonectin: 43% versus 19%; and EGFR: 67% versus 21%; p < 0.0001 in each case). In a multivariate analysis, ER and cytokeratins 5/6 and 14 remained significant predictors of BRCA1 mutation status. In contrast, the frequency of basal markers in BRCA2 tumors did not differ significantly from that of the controls. Authors concluded, that the use of cytokeratin staining in combination with ER status and morphology provides an accurate predictor of BRCA1 mutation status, and may be useful in selecting patients for BRCA1 mutation testing. Within the last several years, a most intriguing new finding about BRCA1 is its implication in the maintenance of a normal, inactive X chromosome (Xi). At the Xi, BRCA1, along with phospho-H2AX, associates with facultative heterochromatin in late S-phase to maintain gene silencing [131]. The presence or absence of functional BRCA1 affects the XIST RNA localization on the X chromosome, and may alter the entire chromosome inactivation phenotype [132, 133]. Despite maintaining an XX karyotype, BRCA1-mutant cell lines and human and murine breast tumors lack an X chromosome with XIST RNA, macrohistone H2A 1.2, and histone H3mK27. Along the same line, it has been shown that most basal-like breast cancers lack the markers of a normal inactive X chromosome and display the duplication of the active X chromosome [134]. It appears that both BRCA1-mutated and basal-like tumors consistently display similar Xi abnormalities and increased expression of a small set of X chromosome genes. These results provide new insight into pathogenic mechanisms underlying both BRCA1-mutated and sporadic basal-like breast cancer and further confirm that BRCA1mutated tumors are restricted to the basal-like tumor subtype. Whether the role of BRCA1 in Xi maintenance and Xi abnormalities might specifically promote tumor of the breast or explain the aggressive basal phenotype in both inherited and sporadic breast cancer remains unknown.

It was proposed that BRCA1-mutated tumors are restricted to the basal-like tumor subtype because these tumors originate from true basal-phenotype cells. This suggestion was based on the findings that breast stem cells in both rodents [135] and humans [136] have CK5/6-positive basal/myoepithelial cell profiles. Foulkes [123] has hypothesized that the key function of BRCA1 is to be a stem cell regulator and promote the differentiation of glandular epithelium. For this reason, in a BRCA1-mutated cell, this transition can fail or abort and the basal cell phenotype gene expression pattern would be retained. Of note, the studies have also been published [137], indicating that Xi defects are common in breast cancer cells, irrespective of their BRCA1 status.

4.9

Epigenetic Lesions

Aberrant methylation of single or multiple genes was suggested as a marker for the detection of cancer cells, for tumor behavior and as a target for therapy [138]. BRCA1 itself undergoes CpG island hypermethylation in a subset of sporadic breast tumors [138, 139], whereas BRCA2 presumably does not [140]. The impact and significance of the epigenetic silencing of BRCA1 is functionally equivalent to carrying a germ-line BRCA1 mutation. Both events lead to the same disturbance of gene expression in a cancer cell [65, 74, 139]. Epigenetic changes in cancer are not limited to hypermethylation of gene promoter CpG islands, but also include a simultaneous global demethylation of the genome [93]. Global DNA hypomethylation of the malignant cell has been proposed as a cause for chromosomal instability, reactivation of endogenous viral sequences and up-regulation of certain genes [141]. Total levels of methylation in both inherited and sporadic forms of breast cancer have been studied to compare epigenetic processes in these alternate pathways of cancer development [93]. The methylation of CpG islands in the promoters of 10 genes (hMLH1, BRCA1, APC, LKB1, CDH1 (E-cadherin), p16/INK4a, p14/ARF, MGMT, GSTP1, and RARb2), 5-methylcytosine DNA content, and LOH were examined. In most tumors from BRCA1 carriers, a high rate of LOH at the BRCA1 locus was observed, accounting for the BRCA1 biallelic inactivation. None of the 21 BRCA1 familial tumors with LOH at the BRCA1 locus had BRCA1 methylation. However, of the two tumors that retained both alleles, one had methylated CpG islands. The authors concluded that BRCA1 promoter hypermethylation may play a role as a second inactivating event in BRCA1-linked families, but this mode of inactivation is infrequent because of the higher frequency of genetic deletions as a “second hit.” In addition, Alvarez and colleagues [32] found that 15/34 analyzed familial non-BRCA tumors had hypermethylation of the BRCA1 promoter. They suggested that somatic BRCA1 inactivation could modify the profile of tumor progression in familial non-BRCA cases. The pattern of methylation of different genes occurring in hereditary breast cancers was found to be distinct from that of sporadic tumors [93, 142]. Although resembling sporadic

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breast cancer in the methylation frequencies of p16/INK4a, CDH1 and retinoic acid receptor (RARb2), BRCA1-mutated tumors had a much lower frequency of glutathione S-transferase P1 (GSTP1) methylation (24% versus 4%, p = 0.0001) and lower high in normal-1 (HIN1) methylation (11% versus 69%, p < 0.0001). In contrast, BRCA2-associated tumors had a slightly higher frequency of p16/INK4a methylation (26% versus 15%, p = 0.13) and lower frequencies of methylation of the other genes (p = 0.02–0.07). Three BRCA2-mutated tumors displayed BRCA1 aberrant methylation. Simultaneous LOH of both BRCA1 and BRCA2 loci happened in sporadic breast tumors, and a de novo BRCA1 mutation has been found in a patient with a BRCA2 mutation [143]. The epigenetic profiles of familial breast cancers negative for BRCA1/2 appeared to be a merger of BRCA1- and BRCA2 -associated profiles. The hereditary tumors from BRCA1/2 patients displayed global hypomethylation, which did not differ statistically between hereditary tumors and their sporadic counterparts (p > 0.05). The authors drew several conclusions from this study. Familial tumors are “pushed” through the tumorigenic pathway because of their initial germ-line mutation, but other genetic and epigenetic lesions are also necessary. Epigenetic changes in familial breast cancers are similar to the sporadic forms, but with some qualitative and quantitative differences. Although overall methylation levels are comparable, the methylation of certain CpG islands differ between sporadic and inherited forms of cancer and deserve further analysis. Most notably, hypermethylation of gene promoters can frequently play a direct role in the silencing of wild-type genes either as a primary or second hit in both genetic and sporadic forms of the disease [93, 144]. Detailed analysis of gene methylation profiles of hereditary breast cancers may improve molecular monitoring of carriers, and is a likely potential target for future improved therapeutic approaches [138].

4.10

Conclusion

The data suggest that morphological features of BRCA1associated, BRCA2-associated and sporadic breast cancers are different, and that BRCA1-mutated tumors have unique features. BRCA2-associated cancer profiles have some similarities with BRCA1-associated tumor profiles. These findings are consistent with the arguments that cancer evolution proceeds down different routes in each group, but that the BRCA proteins perform distinct functions in overlapping biological processes. Thus, morphological parameters may be helpful when selecting cases to test for BRCA1/2 mutations. The unique features of tumors from germ line BRCA1/2-mutation carriers support a tumor progression model in which the early loss of BRCA causes defects in chromosome structure, cell division, and viability, so that a BRCA-deficient cell must acquire additional alterations that overcome these problems and presumably force tumor evolution down a limited set of pathways. The dissection of those pathways is very important in diagnosis, treatment and

prevention of this disease. The study of familial breast cancer is in its infancy. The current research suggests that the most familiar clustering of breast cancer is likely to be caused by variants at many loci, each conferring a moderate risk of the disease. The identification of new genes in hereditary breast cancer would make a major impact in risk prediction. Collaborative efforts of several research groups using high throughput methods and advanced technologies based on genome-wide association studies are required. Moreover, modern understanding of cancer susceptibility drives our efforts to studies with careful selection of appropriate controls, patient age, and ethnicity, and recognition of precursor lesions, which would require the development of novel interdisciplinary approaches. To conduct innovative and complex analyses of breast cancer, transdisciplinary research teams combining the efforts of medical oncologists, molecular geneticists, epidemiologists, biopsychologists, biostatisticians, and anthropologists are necessary. We believe that within the next decade, research on the molecular genetics and biology of cancer would need to incorporate the social world, and simultaneously, a greater emphasis on genetic factors should be promoted among those who study social relationships and health. We also suggest that behavioral and molecular genetics might be incorporated into epidemiologic and neurophysiologic studies that connect social environments to health and disease. Finally, it is necessary to ensure that research findings with clinical relevance are communicated clearly and thoroughly to clinicians, patients, and policy makers.

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82. Zhang H, Somasundaram K, Peng Y, et al. BRCA1 physically associates with p53 and stimulates its transcriptional activity. Oncogene. 1998;16(13):1713–1721. 83. Offit K. Are BRCA1- and BRCA2-associated breast cancers different? J Clin Oncol. 2000;18(21 Suppl):104S–106S. 84. Moynahan ME. The cancer connection: BRCA1 and BRCA2 tumor suppression in mice and humans. Oncogene. 2002;21(58):8994–9007. 85. Eerola H, Heikkilèa P, Tamminen A, et al. Histopathological features of breast tumours in BRCA1, BRCA2 and mutationnegative breast cancer families. Breast Cancer Res. 2005;7(1): R465–469. 86. Vaziri SA, Krumroy LM, Elson P, et al. Breast tumor immunophenotype of BRCA1-mutation carriers is influenced by age at diagnosis. Clin Cancer Res. 2001;7(7):1937–1945. 87. Arnes JB, Brunet JS, Stefansson I, et al. Placental cadherin and the basal epithelial phenotype of BRCA1-related breast cancer. Clin Cancer Res. 2005;11(11):4003–4011. 88. Salahshor S, Haixin L, Huo H, et al. Low frequency of Ecadherin alterations in familial breast cancer. Breast Cancer Res. 2001;3(3):199–207. 89. Putti TC, El-Rehim DM, Rakha EA, et al. Estrogen receptornegative breast carcinomas: a review of morphology and immunophenotypical analysis. Mod Pathol. 2005;18(1):26–35. 90. Rakha EA, Abd El Rehim D, Pinder SE, et al. E-cadherin expression in invasive non-lobular carcinoma of the breast and its prognostic significance. Histopathology. 2005;46(6):685–693. 91. Kovâacs A, Dhillon J, Walker RA. Expression of P-cadherin, but not E-cadherin or N-cadherin, relates to pathological and functional differentiation of breast carcinomas. Mol Pathol. 2003;56(6):318–322. 92. Borg A, Sandberg T, Nilsson K, et al. High frequency of multiple melanomas and breast and pancreas carcinomas in CDKN2A mutation-positive melanoma families. J Natl Cancer Inst. 2000;92(15):1260–1266. 93. Esteller M, Corn PG, Baylin SB, Herman JG. A gene hypermethylation profile of human cancer. Cancer Res. 2001;61(8):3225– 3229. 94. Osin PP, Lakhani SR. The pathology of familial breast cancer: Immunohistochemistry and molecular analysis. Breast Cancer Res. 1999;1(1):36–40. 95. Crook T, Brooks LA, Crossland S, et al. p53 mutation with frequent novel condons but not a mutator phenotype in BRCA1- and BRCA2-associated breast tumours. Oncogene. 1998;17(13):1681–1689. 96. Palacios J, Honrado E, Osorio A, et al. Phenotypic characterization of BRCA1 and BRCA2 tumors based in a tissue microarray study with 37 immunohistochemical markers. Breast Cancer Res Treat. 2005;90(1):5–14. 97. el-Deiry WS, Harper JW, O’Connor PM, et al. WAF1/CIP1 is induced in p53-mediated G1 arrest and apoptosis. Cancer Res. 1994;54(5):1169–1174. 98. Bloom J, Pagano M. Deregulated degradation of the cdk inhibitor p27 and malignant transformation. Semin Cancer Biol. 2003;13(1):41–47. 99. Elstner E, Williamson EA, Zang C, et al. Novel therapeutic approach: Ligands for PPARgamma and retinoid receptors induce apoptosis in bcl-2-positive human breast cancer cells. Breast Cancer Res Treat. 2002;74(2):155–165.

100. Williamson EA, Dadmanesh F, Koeffler HP. BRCA1 transactivates the cyclin-dependent kinase inhibitor p27(Kip1). Oncogene. 2002;21(20):3199–3206. 101. Yang W, Shen J, Wu M, et al. Repression of transcription of the p27(Kip1) cyclin-dependent kinase inhibitor gene by c-Myc. Oncogene. 2001;20(14):1688–1702. 102. Liang J, Zubovitz J, Petrocelli T, et al. PKB/Akt phosphorylates p27, impairs nuclear import of p27 and opposes p27-mediated G1 arrest. Nat Med. 2002;8(10):1153–1160. 103. Shin I, Yakes FM, Rojo F, et al. PKB/Akt mediates cellcycle progression by phosphorylation of p27(Kip1) at threonine 157 and modulation of its cellular localization. Nat Med. 2002;8(10):1145–1152. 104. Verhoog LC, Brekelmans CT, Seynaeve C, et al. Survival in hereditary breast cancer associated with germline mutations of BRCA2. J Clin Oncol. 1999;17(11):3396–3402. 105. Chappuis PO, Donato E, Goffin JR, et al. Cyclin E expression in breast cancer: predicting germline BRCA1 mutations, prognosis and response to treatment. Ann Oncol. 2005;16(5): 735–742. 106. Brodie SG, Xu X, Qiao W, Li WM, Cao L, Deng CX. Multiple genetic changes are associated with mammary tumorigenesis in Brca1 conditional knockout mice. Oncogene. 2001;20(51):7514–7523. 107. Jonkers J, Meuwissen R, van der Gulden H, Peterse H, van der Valk M, Berns A. Synergistic tumor suppressor activity of BRCA2 and p53 in a conditional mouse model for breast cancer. Nature Genet. 2001;29(4):418–425. 108. Freneaux P, Stoppa-Lyonnet D, Mouret E, et al. Low expression of bcl-2 in Brca1-associated breast cancers. Br J Cancer. 2000;83(10):1318–1322. 109. Rebbeck TR. Inherited predisposition and breast cancer: modifiers of BRCA1/2-associated breast cancer risk. Environ Mol Mutagen. 2002;39(2–3):228–234. 110. Rebbeck TR, Wang Y, Kantoff PW, et al. Modification of BRCA1- and BRCA2-associated breast cancer risk by AIB1 genotype and reproductive history. Cancer Res. 2001;61(14): 5420–5424. 111. Honrado E, Osorio A, Palacios J, et al. Immunohistochemical expression of DNA repair proteins in familial breast cancer differentiate BRCA2-associated tumors. J Clin Oncol. 2005;23(30):7503–7511. 112. van de Vijver MJ, He YD, van’t Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. New Engl J Med. 2002;347(25):1999–2009. 113. Malzahn K, Mitze M, Thoenes M, Moll R. Biological and prognostic significance of stratified epithelial cytokeratins in infiltrating ductal breast carcinomas. Virchows Arch. 1998;433(2):119–129. 114. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747–752. 115. Olopade OI, Grushko T. Gene-expression profiles in hereditary breast cancer. New Engl J Med. 2001;344(26):2028–2029. 116. Sorlie T, Tibshirani R, Parker J, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA. 2003;100(14):8418–8423. 117. Foulkes WD, Stefansson IM, Chappuis PO, et al. Germline BRCA1 mutations and a basal epithelial phenotype in breast cancer. J Natl Cancer Inst. 2003;95(19):1482–1485.

104 118. Laakso M, Loman N, Borg A, Isola J. Cytokeratin 5/14-positive breast cancer: True basal phenotype confined to BRCA1 tumors. Mod Pathol. 2005;18(10):1321–1328. 119. Lacroix M, Leclercq G. The “portrait” of hereditary breast cancer. Breast Cancer Res Treat. 2005;89(3):297–304. 120. Bertucci F, Finetti P, Cervera N, et al. Gene expression profiling shows medullary breast cancer is a subgroup of basal breast cancers. Cancer Res. 2006;66(9):4636–4644. 121. Carey LA, Perou CM, Livasy CA, et al. Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA. 2006;295(21):2492–2502. 122. Rouzier R, Perou CM, Symmans WF, et al. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res. 2005;11(16):5678–5685. 123. Foulkes WD, Brunet JS, Stefansson IM, et al. The prognostic implication of the basal-like (cyclin E high/p27 low/p53+/glomeruloid-microvascular-proliferation+) phenotype of BRCA1related breast cancer. Cancer Res. 2004;64(3):830–835. 124. Moyano JV, Evans JR, Chen F, et al. AlphaB-crystallin is a novel oncoprotein that predicts poor clinical outcome in breast cancer. J Clin Invest. 2006;116(1):261–270. 125. Reis-Filho JS, Pinheiro C, Lambros MB, et al. EGFR amplification and lack of activating mutations in metaplastic breast carcinomas. J Pathol. 2006;209(4):445–453. 126. Rodrâiguez-Pinilla SM, Sarriâo D, Honrado E, et al. Prognostic significance of basal-like phenotype and fascin expression in node-negative invasive breast carcinomas. Clin Cancer Res. 2006;12(5):1533–1539. 127. Turner NC, Reis-Filho JS. Basal-like breast cancer and the BRCA1 phenotype. Oncogene. 2006;25(43):5846–5853. 128. van der Groep P, Bouter A, van der Zanden R, et al. Re: Germline BRCA1 mutations and a basal epithelial phenotype in breast cancer. J Natl Cancer Inst. 2004;96(9):712–713. 129. Livasy CA, Karaca G, Nanda R, et al. Phenotypic evaluation of the basal-like subtype of invasive breast carcinoma. Mod Pathol. 2006;19(2):264–271. 130. Nielsen TO, Hsu FD, Jensen K, et al. Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma. Clin Cancer Res. 2004;10(16):5367–5374. 131. Chadwick BP, Lane TF. BRCA1 associates with the inactive X chromosome in late S-phase, coupled with transient H2AX phosphorylation. Chromosoma. 2005;114(6):432–439. 132. Ganesan S, Silver DP, Greenberg RA, et al. BRCA1 supports XIST RNA concentration on the inactive X chromosome. Cell. 2002;111(3):393–405. 133. Pageau GJ, Lawrence JB. BRCA1 foci in normal S-phase nuclei are linked to interphase centromeres and replication of pericentric heterochromatin. J Cell Biol. 2006;175(5):693–701. 134. Richardson AL, Wang ZC, De Nicolo A, et al. X chromosomal abnormalities in basal-like human breast cancer. Cancer Cell. 2006;9(2):121–132. 135. Smith GH, Chepko G. Mammary epithelial stem cells. Microsc Res Tech. 2001;52(2):190–203. 136. Bèocker W, Moll R, Poremba C, et al. Common adult stem cells in the human breast give rise to glandular and myoepithelial cell lineages: A new cell biological concept. Lab Invest. 2002;82(6):737–746. 137. Sirchia SM, Ramoscelli L, Grati FR, et al. Loss of the inactive X chromosome and replication of the active X in BRCA1-

T.A. Grushko and O.I. Olopade defective and wild-type breast cancer cells. Cancer Res. 2005;65(6):2139–2146. 138. Esteller M, Herman JG. Cancer as an epigenetic disease: DNA methylation and chromatin alterations in human tumours. J Pathol. 2002;196(1):1–7. 139. Wei M, Grushko TA, Dignam J, et al. BRCA1 promoter methylation in sporadic breast cancer is associated with reduced BRCA1 copy number and chromosome 17 aneusomy. Cancer Res. 2005;65(23):10692–10699. 140. Collins N, Wooster R, Stratton MR. Absence of methylation of CpG dinucleotides within the promoter of the breast cancer susceptibility gene BRCA2 in normal tissues and in breast and ovarian cancers. Br J Cancer. 1997;76(9):1150–1156. 141. Baylin SB, Esteller M, Rountree MR, Bachman KE, Schuebel K, Herman JG. Aberrant patterns of DNA methylation, chromatin formation and gene expression in cancer. Hum Mol Genet. 2001;10(7):687–692. 142. Krop I, Maguire P, Lahti-Domenici J, et al. Lack of HIN1 methylation in BRCA1-linked and “BRCA1-like” breast tumors. Cancer Res. 2003;63(9):2024–2027. 143. Tesoriero A, Andersen C, Southey M, et al. De novo BRCA1 mutation in a patient with breast cancer and an inherited BRCA2 mutation. Am J Hum Genet. 1999;65(2):567–569. 144. Esteller M, Fraga MF, Guo M, et al. DNA methylation patterns in hereditary human cancers mimic sporadic tumorigenesis. Hum Mol Genet. 2001;10(26):3001–3007. 145. Ahmed M, Rahman N. ATM and breast cancer susceptibility. Oncogene. 2006;25(43):5906–5911. 146. Cipollini G, Tommasi S, Paradiso A, et al. Genetic alterations in hereditary breast cancer. Ann Oncol. 2004;15 Suppl 1: I7–I13. 147. Gâorski B, Debniak T, Masojâc B, et al. Germline 657del5 mutation in the NBS1 gene in breast cancer patients. Int J Cancer. 2003;106(3):379–381. 148. Hall J. The Ataxia-telangiectasia mutated gene and breast cancer: Gene expression profiles and sequence variants. Cancer Lett. 2005;27(2):105–114. 149. Heikkinen K, Rapakko K, Karppinen SM, et al. RAD50 and NBS1 are breast cancer susceptibility genes associated with genomic instability. Carcinogenesis. 2006;27(8):1593–1599. 150. Meijers-Heijboer H, van den Ouweland A, Klijn J, et al. Lowpenetrance susceptibility to breast cancer due to CHEK2 (*)1100delC in noncarriers of BRCA1 or BRCA2 mutations. Nature Genet. 2002;31(1):55–59. 151. Rahman N, Seal S, Thompson D, et al. PALB2, which encodes a BRCA2-interacting protein, is a breast cancer susceptibility gene. Nature Genet. 2007;39(2):165–167. 152. Reid S, Schindler D, Hanenberg H, et al. Biallelic mutations in PALB2 cause Fanconi anemia subtype FA-N and predispose to childhood cancer. Nature Genet. 2007;39(2):162–164. 153. Carlson H, Ota S, Song Y, Chen Y, Hurlin PJ. Tbx3 impinges on the p53 pathway to suppress apoptosis, facilitate cell transformation and block myogenic differentiation. Oncogene. 2002;21(24):3827–3835. 154. Clarke CL, Sandle J, Parry SC, et al. Cytokeratin 5/6 in normal human breast: lack of evidence for a stem cell phenotype. J Pathol. 2004;204(2):147–152. 155. Funato T, Satou J, Kozawa K, Fujimaki S, Miura T, Kaku M. Use of c-myb antisense oligonucleotides to increase the

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sensitivity of human colon cancer cells to cisplatin. Oncol Rep. 2001;8(4):807–810. 156. Gronwald J, Jauch A, Cybulski C, et al. Comparison of genomic abnormalities between BRCAX and sporadic breast cancers studied by comparative genomic hybridization. Int J Cancer. 2005;114(2):230–236. 157. Pinilla SM, Honrado E, Hardisson D, et al. Caveolin-1 expression is associated with a basal-like phenotype in sporadic and hereditary breast cancer. Breast Cancer Res Treat. 2006;99(1):85–90.

158. Reis-Filho JS, Savage K, Lambros MB, et al. Cyclin D1 protein overexpression and CCND1 amplification in breast carcinomas: An immunohistochemical and chromogenic in situ hybridisation analysis. Mod Pathol. 2006;19(7):999–1009. 159. Savelyeva L, Claas A, Matzner I, et al. Constitutional genomic instability with inversions, duplications, and amplifications in 9p23-24 in BRCA2 mutation carriers. Cancer Res. 2001;61(13):5179–5185.

Chapter 5 Circulating Tumor Markers Alan Horwich and Gill Ross

5.1

Introduction

The concept of a circulating tumor marker applies to a secreted chemical product of a tumor cell such that the concentration of the chemical in the blood may in some way represent a quantifiable assessment of the tumor burden at that time. The earliest example is the protein produced from myeloma cells discovered by Bence Jones in the mid-19th century. Subsequently a number of oncofetal and other proteins have proved useful and are widely available from antibody (Ab)-based assays. This scene is set to expand dramatically with an increase in our knowledge of the molecular pathology of cancer subtypes and the application of genomic and proteomic analysis techniques. One example is the measurement of serum DNA concentration [1]. The DNA probably derives from necrosis and apoptosis [2]. The specificity can be increased by analyzing tumor DNA, such as that with allelic imbalance of sequences subject in that tumor type to frequent allelic losses [3, 4], or by analyzing the proportion of circulating DNA in long fragments (DNA integrity) [2]. A study of DNA integrity in the serum of patients with breast cancer suggested this test could predict lymph node metastasis [5]. Proteins or protein fragments may be released into the circulation from cancers and detected by surface-enhanced laser desorption-ionization time-of-flight mass spectroscopy (SELDI-TOF) [6]. An early report suggested this may form the basis of a test for ovarian cancer [7], however this has not been reproduced, and bioinformatics methodologies have also been questioned [8]. Despite a number of reports of high sensitivity and specificity [9, 10], at present the reliability of these proteomic profiles in cancer diagnosis remains controversial [11]. Currently, the range of possible tumor markers is broad; however, relatively few tumor markers have been incorporated in routine oncological practice (Table 5-1), possibly because any one marker is expressed in only a few tumors. This problem, however, can be addressed by a broad pre-

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

liminary screen. For example, we have conducted a study of human chorionic gonadotrophin (hCG), carcinoembryonic antigen (CEA), CA125, and CA19.9 in 74 patients with advanced bladder cancer and found that 43 (58%) had a significant increase of at least one serum marker [12]. This information was useful because of the close correlation between clinical and marker response to chemotherapy. A range of new serum markers is being investigated. YKL-40, a member of the “mammalian chitinase-like proteins” with some growth-factor activity is found in a number of cancers [13]. In melanoma, it is reported to be increased in 45% of patients and to be an independent prognostic indicator [14]. Serum chromogranin A may add to the assay of neuron-specific enolase in the detection of neuroendocrine differentiation in prostate cancer [15], or other sources. Mesothelin is highly expressed in ovarian cancers as well as mesothelioma, and has been detected in the serum of 67% and 71% of these cancers, respectively [16]. Tissue inhibitor of metalloproteinase-1 (TIMP-1) is over expressed in a range of malignancies including gastric, lung, breast, and colorectal cancers, and increased plasma concentrations have been reported in patients with colorectal cancer, the postoperative concentrations correlating with prognosis [17]. Finally, vascular endothelial Table 5-1. Circulating markers in oncology. Marker

Abbreviation

Tumor

Human chorionic gonadotrophin

HCG

Alfa fetoprotein Lactate dehydrogenase Placental alkaline phosphatase Prostate specific antigen Carcinoembryonic antigen Neuron-specific enolase

AFP LDH PLAP

Gestational trophoblastic; Germ cell, urothelial and gastrointestinal Germ cell; hepatocelluar Germ cell; lymphoma Germ cell

CA125 CA19.9

PSA CEA NSE

Prostate Gastrointestinal, especially colorectal; breast Small cell lung cancer; neuroendocrine tumor Ovarian Pancreas; gastrointestinal; ovarian

107

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growth factor (VEGF) is proangiogenic, and serum VEGF concentrations are prognostic in melanoma, lymphoma, esophageal, small-cell lung (SCLC), ovarian, and colon cancers. The report on a large series of patients with ovarian cancer showed that serum VEGF was an independent indicator of survival, and in particular, patients with stage I disease and serum VEGF concentrations >380 pg/ml had an eight-fold increased risk of mortality [18]. Macrophage inhibiting cytokine-1 (MIC-1) has been found to be better than other markers, including CA19-9, in the diagnosis of pancreatic cancer [19]. The clinical roles of circulating markers might include screening, diagnosis, staging, assessment of prognosis and monitoring of response, remission, and relapse. Additionally, as relatively specific tumor products, marker substances may confer tissue specificity for immunohistochemical (IHC) diagnosis and ligand-targeted techniques for imaging and therapy [20]. To be useful in clinical practice, an ideal marker should be both sensitive and specific. Furthermore, the marker test should reliably indicate the situation to which there is an appropriate therapeutic response. The sensitivity of a test is the probability of the test being positive in patients with the disease. Based on the symbols in Table 5-2, sensitivity equals A/(A + B). The specificity of the test is the probability of a normal test result in patients without the cancer. From Table 5-2 specificity equals D/(C + D). A further concept of value in judging markers is the positive predictive value, which is the probability of a patient having the cancer when the test is positive, i.e., the number of true positive results divided by the total number of positive results (i.e., A/[A + B]). These relatively simple concepts become more complex for marker tests in which no clear cutoff is seen between a normal and abnormal result, e.g., with prostate specific antigen (PSA) as a diagnostic test for prostate cancer. In this setting, higher values of the marker represent a greater probability of the presence of prostate cancer and the appropriate choice of cutoff level for finding cancer may depend upon patient-related factors such as the age [21], or the size of the normal prostate gland. The predictive ability of a test can be represented by the receiver operating curve (ROC), which is a plot of the sensitivity versus (1-specificity) for a binary classification as its discrimination threshold is varied.

Table 5-2. Evaluation of a marker test. Cancer present Cancer absent • Sensitivity = • Specificity = • Predictive value =

Marker positive

Marker negative

A C A A+B A C+D A A+C

B D

5.2 5.2.1

Use of Markers for Particular Cancers Testicular Cancer

The serum tumor markers α-fetoprotein (AFP) and hCG are in widespread clinical use to aid in the diagnosis and management of patients with nonseminomatous tumors, one or both of these markers being increased in approximately 75% of patients with metastatic disease [22–24]. More recently, lactate dehydrogenase (LDH) has proved useful in assessment of prognosis [25]. LDH has five isoenzymes, and LDH-1 may be a more specific marker, especially for seminoma [26]. Placental-like alkaline phosphatase (PLAP) has been evaluated as a tumor marker for seminoma; however, the sensitivity and specificity of assays developed to date have not encouraged widespread use of PLAP as a serum marker [27, 28]. The TRA-1-60 antigen (Ag) is expressed on embryonal cells and carcinoma in situ. It has been reported as sensitive, but not specific enough for clinical use [29].

5.2.2

AFP

AFP is an embryonic protein produced by the yolk sac and subsequently by the fetal liver. It has a MW of 70 Kd, is structurally similar to albumin, and probably serves a similarly diverse number of functions in the fetus. Serum concentrations decrease around the time of birth, but in adults it has been found in the serum of patients with hepatocellular carcinoma [30, 31] and subsequently in a proportion of patients with testicular nonseminoma and occasionally other tumors [32]. In nonseminoma it is usually associated by immunohistology with yolk sac differentiation. The general view is that AFP is not produced by pure seminoma despite a small number of case reports of the association.

5.2.3

Human Chorionic Gonadotrophin

hCG production is mainly from syncytiotrophoblastic cells. It is a hormone with a MW of 45 Kd and is produced normally by the placenta. It is comprised of two dissimilar subunits, α and β, and the amino acid sequence of the α-subunit is similar to some other human hormones, including luteinizing hormone (LH), follicle-stimulating hormone (FSH), and thyroid-stimulating hormone (TSH). The β-subunit is unique but shares some amino acid sequence with the LH subunit. The usual Abbased assays for hCG are directed at the β-subunit but measure both intact hCG and β-fragments.

5.3

Staging of Testicular Cancer

One or both of the tumor markers AFP and hCG are increased in the serum of approximately 75% of patients with metastatic nonseminoma. Moderate increases of hCG are found in 33% to 50% of patients with seminoma. In most cases, the

5. Circulating Tumor Markers

diagnosis of a testicular germ cell tumor is not difficult on clinical grounds although the presence of a palpably abnormal testis may indicate, as well as tumor, a possible diagnosis of local granulomatous infection, and when painful, there may be confusion with epididymoorchitis or torsion. The presence of an increased marker can complement further investigations such as local ultrasound. Furthermore, in approximately 5% of germ-cell tumors the primary site remains occult possibly because it is extragonadal, or alternatively, because the primary tumor has remained microscopic or infarcted. In these cases, the presentation may be with lymphadenopathy, retroperitoneal or mediastinal mass, an ovarian mass, or, rarely, a pineal or pelvic tumor. Additionally, tumor markers can help in staging assessments including assessment of prognosis. Typically staging occurs after orchidectomy and comprises assessment of tumor markers and a computed tomography (CT) scan of the thorax and abdomen. Increased concentrations of AFP or hCG after orchidectomy do not automatically indicate the presence of metastatic disease because of the time taken for clearing of these markers from the serum. The physiologic half-life of hCG determined by a standard immunoassay is approximately 36 hours, and for AFP, 5 to 7 days. Thus, especially for AFP, even patients whose tumor has been completely resected by orchidectomy may have abnormal AFP serum concentrations for some weeks, which are declining with an apparent half-life of 5 to 7 days. Therefore, staging assessments after orchidectomy require a sequence of markers for accurate interpretation. AFP, hCG, and LDH are tumor products that have contributed considerably to accurate assessment of prognosis and, therefore, appropriate management of patients with metastatic nonseminoma [33]. An International Germ Cell Cancer Collaborative Group prepared a database containing >5,000 patients with advanced nonseminoma who had been treated with platinum-based chemotherapy schedules. This compilation led to publication of a consensus stratification of germ-cell cancer prognosis (Table 5-3). Apart from a somewhat uncommon situation of primary mediastinal germ-cell cancer or the presence of nonpulmonary visceral metastases (usually liver, bone, or brain), the division of patients into three prognostic groups is based entirely on marker concentrations and these allow categorization of prognosis ranging from a group with an identified 48% 5-year survival to a group with a 92% 5-year survival with the presumption in this particular tumor that 5-year survivals equate to cure rates. A similar model can apply to extragonadal tumors [34]. In this study, prognosis of patients with metastatic seminoma was dominated by the rare adverse subgroup with nonpulmonary visceral metastases. A more detailed analysis of 286 of these patients [35] and also a series from the Memorial Sloan-Kettering Cancer Center [36] showed that increased serum LDH was also an independent adverse indicator.

109 Table 5-3. International germ-cell cancer collaborative group classification. Prognosis Good (5-year survival 92%)

Intermediate (5-year survival 80%)

Poor (5-year survival 48%)

Definition • Testis/retroperitoneal primary; and • no nonpulmonary visceral metastases; and • low serum markers (AFP <1,000 ng/ml, HCG <500 U/L, and LDH<1.5 × NUL) • As for good prognosis but with • intermediate serum markers (AFP=1,000–10,000 ng/ml, HCG 5,000–50,000 U/L, or LDH 1.5–10 × NUL) • Mediastinal primary or • nonpulmonary visceral metastases or • high markers (AFP >10,000 ng/ml, or hCG 50,000 U/l, or LDH > 10 × NUL)

AFP α-fetoprotein, hCG human chorionic gonadotrophin, LDH lactate dehydrogenase, NUL normal upper limit

5.4 Monitoring of Response in Testicular Cancer Because AFP, hCG, and LDH represent tumor products, it is anticipated that a decline in the number of marker-producing tumor cells would lead to a decline in the serum concentration of the marker. It should be recognized that a change in marker concentration could follow alteration in the rate of production of marker per cell, and that the concentration of marker in the serum represents a balance between production and metabolism or excretion. Thus, although a decline in serum marker is encouraging evidence of response, occasionally the pattern of decline can be complex [37]. Aspects that have been investigated include the following: • The surge phenomenon. This is a transient initial increase in marker after initiation of chemotherapy (Fig. 5-1) that has been thought to be caused either by release of stored marker or to the impact of chemotherapy on tumor differentiation [38, 39]. • The rate of serum marker decline after start of chemotherapies. Horwich and Peckham [37] found that this was not a precise prognostic factor based on a simple comparison of marker level on day 21 of chemotherapy compared to the level before chemotherapy on day 1, with the result expressed as an apparent half-life in days. It was found that the hCG half-life in 22 patients who subsequently remained relapse-free ranged from 2.5 to 9 days (mean: 4.4 days) whereas in the 7 patients who relapsed after chemotherapy, although the hCG half-life was within the same range in 6 cases, in 1 patient with very extensive disease, the half-life was prolonged at 34 days, and this patient never achieved clinical or marker remission. For AFP, a narrow range of a

110

Fig. 5-1. Marker surge phenomenon after chemotherapy for a germ cell tumor. Arrows indicate start of a chemotherapy cycle.

half-life of patients remaining disease free (5–9 days); for 11 patients who relapsed, the range of AFP half-life was 6–14 days. Three patients with a half-life >9 days relapsed. It seems that for most patients, the initial marker pattern was determined by tumor cells that were sensitive to chemotherapy such that even those destined to relapse after chemotherapy had a dramatic initial response. The possible exception is the population of patients with drug resistant disease and AFP-producing tumors. de Wit et al [40] studied a group of 669 patients treated with cisplatin combination chemotherapy. Sixty-three percent had abnormal AFP at the start of chemotherapy and 58% had abnormal hCG. In the half-time analysis confined to those patients with abnormal marker concentration 3 weeks after the start of chemotherapy it was found that prolongation of either hCG or AFP half-lives did not accurately predict treatment failure. Studies at the Memorial Sloan-Kettering Cancer Center, however, have identified marker regression rate as a useful predictor of outcome after chemotherapy. These studies were based on the rate of regression after 2 cycles of therapy as prolonged half-life defined for hCG as >3 days and for AFP >7 days. Marker regression was deemed satisfactory if less than these values or if the marker decreased to within normal limits. Satisfactory decline was associated with a median event-free survival of 20.7 months [41]. In a study that included 139 patients with poor prognosis tumors [42], a prolonged time to marker normalization was associated with inferior progression-free survival and overall survival, though the “favorable” time to marker normaliza-

A. Horwich and G. Ross

tion group also included all those whose markers became normal [42]. • Late change in marker regression slope (Fig. 5-2). The significance of this pattern of response is unclear. In general, continued regression is seen as equivalent to continued response though clearly a change in slope may be a harbinger of overt marker rise and relapse. For patients presenting with high serum hCG, a slowing in the rate of decline of marker concentration in the serum is common even in patients who are cured by their initial chemotherapy [43]. An isotope tracer study has suggested that this is physiologic [44]. • Residual mass. Just over half of patients treated with chemotherapy for bulky germ-cell tumors have evidence of a residual mass at the site of their previous disease when assessed by CT scanning after completion of the course of chemotherapy. For nonseminomas, these may represent fully differentiated or mature teratomas, areas of extensive necrosis, undifferentiated persisting germ-cell tumor, or a combination of these. For seminoma, the masses may be entirely fibrotic though a proportion contain residual viable seminoma. Tumor markers can have a valuable role in diagnosing the presence of persisting undifferentiated tumor in these settings and offer a useful guide to appropriate management.

Fig. 5-2. Late change in marker regression rate. Arrows indicate start of a chemotherapy cycle.

5. Circulating Tumor Markers

5.5 Monitoring of Remission in Testicular Cancer Serum markers can help in the continued monitoring of patients after completion of their initial treatment. Across the board, 5%–10% of patients who have had a satisfactory response to initial treatment will relapse. The expression of markers at the time of relapse is approximately equivalent in frequency to expression of markers at presentation. Often the pattern of marker expression changes within the individual patient and sensitive monitoring require an analysis of markers even in those whose original tumors were not apparently marker positive.

5.6

Tumor Markers in Prostate Cancer

The first marker used for prostate cancer was prostatic acid phosphatase (PAP). The acid phosphatases are found in a variety of tissues and the 5 isozymes have different properties and substrate specificities. PAP is predominantly composed of 2 of the isozymes that are also found in granulocytes and pancreas and thus may be abnormal in concentration in the serum in a range of conditions including polycythemia rubra vera, granulocytic leukemia, Gaucher’s disease, and pancreatic cancer. PAP has a MW of 100 Kd and is produced by the epithelial cells lining the prostatic acini. It is found in high concentration in prostatic fluid and in the serum of >75% of men with metastatic prostate cancer. To avoid false positive results, it is important that a blood sample not be taken immediately after rectal examination. Currently, serum PAP measurement has a limited role in view of the relatively low sensitivity and specificity of this marker. In practice, it has been replaced by that of serum PSA. PSA is an important marker for prostate cancer with relevance for population screening for diagnosis, for prognosis, for monitoring of treatment effects, and as a possible target mechanism in research on gene therapy. PSA is a glycoprotein of MW 34 Kd and is produced by prostatic epithelium. It is a Ser protease whose function is thought to be to liquefy seminal coagulum by proteolysis. The gene encoding PSA is on chromosome 19 and occupies approximately 6 kb. PSA is measured in the serum by radioimmunoassay. Increased concentrations are found in both men with benign prostatic conditions and men with prostate cancer. Attempts have been made to increase the sensitivity and specificity in PSA diagnosis of prostate cancer by refining the concentration using parameters such as PSA density (relating to size of the prostate gland), PSA velocity (rate of change with time), PSA relating to age, PSA fractionation (free vs bound) and also by measurement of cells in the circulation expressing PSA messenger RNA.

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5.6.1

PSA in Diagnosis

As with PAP, PSA can be increased by prior digital rectal examination although it is rare for this to cause the value to become out of the normal range [45]. The incidence of higher PSA concentrations increases with the stage of the tumor from approximately 40% in men with occult presentations to 70% in men with tumors penetrating the capsule, and to almost 100% in men with extension of the primary tumor to seminal vesicles or involvement of local lymph nodes [46, 47]. A number of large studies have evaluated PSA in the screening of prostate cancer. For example, in a study of >1,200 men over 50 years of age, serum PSA was found to be increased in 187 (15%) of whom 32 had cancer detected by biopsy (detection rate 2.6%) [48]. In a similar study based on 1,653 patients, PSA between 4 and 9.9 in 107 (6.5%) and >10 ng/ml in 30 (1.8%). Cancer was eventually diagnosed in 22% of the former but in 66% of the latter, with an overall detection rate of cancer in the study of 2.2% [49]. Not all screen-detected cancers are associated with an abnormal PSA, and in typical series 20% of such tumors are associated with a normal value [50, 51]. Although PSA offers a relatively inexpensive and highly acceptable screening test for prostate cancer, the rationale of this technology must be based on its specificity as well as on demonstration that early treatment of the disease improves the prognosis. A formal screening trial in prostate cancer has not yet been completed, and, therefore, PSA screening has not been adopted in all countries.

5.6.2

PSA and Staging

The incidence of an abnormal PSA increases with advancing stage of the cancer and mean value increases with advancing stage, probably as a consequence of the relationship between serum PSA and the volume of the prostate tumor [52]. In 1 study [53], the mean serum PSA was 5.6 ng/mL in men with organ-confined cancers, 7.7 ng/mL in men with localized cancers but capsular penetration, 23.2 ng/mL in men with seminal vesicle involvement, and 26.2 ng/mL in men with involved lymph nodes. The amount of PSA may be useful in the prediction of bone scan findings. In 521 men with newly diagnosed prostate cancer, of those with a PSA ≤ 20 ng/mL (n = 306), only 1 man had a positive bone scan and none with a PSA of <10 ng/mL had a positive bone scan [54].

5.6.3 5.6.3.1

PSA as Marker of Response to Treatment Radical Prostatectomy

A decrease in PSA to undetectable levels after radical prostatectomy defines a subset of patients with a good prognosis [55]. This finding has been confirmed by biopsy studies after prostatectomy, which are more frequently positive in those with an increased PSA [56]. This assay can provide a

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key indication for postprostatectomy irradiation. In this setting, the rate of rise of PSA can be helpful in distinguishing patients with locoregional from patients with advanced metastatic disease, because the latter tend to have a doubling time of <6 months. After radiotherapy for localized prostate cancer, the serum PSA has been found to decrease with a half-life of between 1 and 3 months [57, 58]. This slow regression reflects the known slow disappearance of malignancy after radiotherapy, thought to be a consequence of tumor cell death occurring only on attempted cell division; however, a longitudinal study has suggested that PSA concentrations regress very predominantly in the first year after treatment, and continued regression after 1 year appeared in only 8% of men [52]. In another study based on 143 patients followed for a median of 27 months after radiotherapy, 94% of those whose PSA normalized within 6 months remained relapse free compared with only 8% of those whose serum PSA remained increased after 6 months.

5.6.3.2 PSA After Hormonal Therapy and Chemotherapy PSA response and clinical response to hormone therapy are clearly connected [46]. The degree of decrease in PSA is an indicator of remission duration [59]. An increase in serum PSA after hormone therapy is a predictor of clinical progression with a mean lead-time of approximately 7 months. In the chemotherapy of hormone-refractory prostate cancer, a relationship between decrease in serum PSA by 50% and patient benefit has been seen [60–62]. Analysis has shown PSA response is a surrogate endpoint for survival [63]. There is increasing evidence that PSA can be used to monitor newly diagnosed patients who may not need radical treatment [64]. The rate of rise of PSA, expressed as a doubling time (PSADT) has emerged as a useful form of quantifying this rise [65]. It may also be useful in estimating the prognosis of patients whose disease has become hormone-refractory [66]. The Semeniuk analysis of 224 patients found those with a PSADT of ≤70 days had a median survival of 11 months, compared with 19 months for those with a longer PSADT (p < 0.0001).

5.7

Gastrointestinal Tumors

5.7.1

Carcinoembryonic Antigen

CEA is a 200 Kd glycoprotein and was isolated using an Ab raised by injection of an extract derived from human colonic carcinoma into rabbits. Immunochemical electron microscopy techniques demonstrate the presence of the protein in normal colonic columnar cells. Assays are available using both polyclonal and monoclonal antibodies. Serum CEA levels are increased in carcinomas of the gastrointestinal tract, but can be increased in a variety

of nonmalignant conditions, reducing the specificity: these include gastrointestinal inflammation, collagen disorders, infection, trauma, infarction, renal impairment, and smoking. Generally, however, concentrations achieved in these conditions do not reach those documented in colonic malignancy. The low sensitivity and specificity of serum CEA precludes its routine use in screening general populations for colorectal cancer, hence interest has turned to evaluating its use in assessing prognosis or monitoring therapy in established disease. The value of preoperative CEA concentration as an independent prognostic marker is not clear, although amounts broadly reflect tumor burden, and increase and decrease with response to therapy [67]. Large series have suggested that CEA is a predictor of outcomes in both primary colonic and rectal cancers [68] and for hepatic resections [69]. Serum CEA concentrations should decrease to normal within 6 weeks of complete tumor resection. Fewer than 5% of patients with Dukes A colorectal carcinoma will have increased serum CEA, increasing to 25% of Dukes B cases, 44% of Dukes C, and 65% of patients with metastatic disease. A number of investigators have reported that increased amounts of CEA predict an increased risk of recurrence [70–72], but others have reported its prognostic value to be limited [73, 74]. In a series of 377 patients with advanced colorectal cancer, serum CEA was an independent predictor of survival [75]. Monitoring for recurrent disease status by serum CEA is of limited value as up to 30% cases of recurrences will have normal concentrations [76]; however, an analysis of 530 patients with resected stage II and III colorectal cancer found that relapse was detected by symptoms in 65 cases, CEA in 45 cases, and routine annual tomography in 49 cases [77]. Recent advances in systemic therapy and hepatic surgery provide the indication for monitoring these patients. Increased amounts of serum CEA can be found in patients with advanced noncolorectal tumors, including breast, lung, cervix, endometrium, and ovarian cancer, and may be useful in monitoring response to therapy.

5.7.2

CA 19.9

CA 19.9 was derived from a human colonic adenocarcinoma cell line, and several commercial kits are available for its clinical measurement. Concentrations of this antigen are increased in up to 75% of patients with advanced colorectal malignancy, but its main value lies in its greater sensitivity than CEA in monitoring gastric, pancreatic, and biliary tumors [78]. CA19.9 concentrations are increased also in ovarian and hepatocellular cancer, with inflammatory conditions of the hepatobiliary system and in some benign illnesses such as thyroid disease. Research suggests that serial analysis of levels of CA19.9 can be used to predict response to radiotherapy in inoperable cases of pancreatic cancer [79], in which conventional imaging may have limited clinical sensitivity. It is also of value in determining responsiveness of pancreatic cancer to chemotherapy [80].

5. Circulating Tumor Markers

5.7.3

Guidelines

The tumor marker panel of the American Society of Clinical Oncology has published guidelines on use of CEA in colorectal cancer (81). These include the recommendations of pre-and post operative CEA for localized disease, as well as monitoring response of metastases.

5.8

Ovarian Cancer and CA125

CA125 was first reported in 1983 after a murine monoclonal antibody (MAb) was raised to a human ovarian cystadenomacarcinoma. CA125 is produced by tissues derived from coelomic epithelium, which includes the peritoneum, fallopian tube, endometrium, endocervix, pleura, and pericardium, but not the normal ovary. It is present as a cell-surface glycoprotein in approximately 80% of epithelial ovarian tumors, with a serum half-life of some 4 days. Although concentrations >35 IU/ml are seen during the first trimester of pregnancy, in a range of benign conditions (cirrhosis, endometriosis), and other advanced intra-abdominal malignancies, 99% of normal blood bank donors will have concentrations lower than this. Increases >35 IU/ml are seen preoperatively in >90% of women with stage III or IV ovarian carcinoma, but only 50% of women with stage I disease. Unfortunately, the low specificity of CA125 precludes its use in screening general populations, and its sensitivity is too low to use alone in the screening of high-risk women [82]. Combined with ultrasound and knowledge of menopausal status, CA125 levels provided 85% sensitivity and 98% specificity for the diagnosis of pelvic malignancy in a cohort of 143 women investigated for a pelvic mass [83, 84]. Serial CA125 concentrations are the best method of monitoring response to therapy. A decrease of >50% maintained for >28 days is highly predictive of response, and conversely, a serial increase indicates progression. In a detailed study [85], CA 125 was measured during early chemotherapy in 121 women with stage III or IV ovarian cancer to investigate whether the Ag could be used as a prognostic parameter. CA125 was determined before the start of chemotherapy and 1 month after the first, second, and third course. The Ag concentration before the start of chemotherapy held no prognostic information. CA125 was a significant prognostic parameter in all 3 courses but its correlation with survival improved with the number of courses. Women with high marker levels (>100 U/ml) 1 month after the third course had a median survival of 7 months, compared with a 50% 5-year survival in women who had ≤10 U/ml and a median survival of 22 months among women with intermediate CA125 levels. Cox regression analysis of the covariation among survival, CA125, and 5 variables (age, FIGO stage, histopathology, tumor grade, and bulk of residual tumor) showed that the CA125 value was the most significant prognostic parameter. As a consequence

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of this study, the investigators suggested that chemotherapy of women with high amounts of CA125 1 month after the third course could be discontinued and replaced by palliative therapy if other curative regimens are not available. Similar conclusions were reached in a large study involving 573 cases confirming that serum CA125 levels after 3 cycles of chemotherapy are predictive for the probability of achieving complete remission. The detection of relapse by CA125 was analyzed in 628 patients within a phase 3 chemotherapy trial assessable for relapse defined either clinically or by serum marker [86]. Five hundred fifty-six relapses were detected by clinical or radiological progression and 389 relapses by doubling of CA125 from the upper limit of normal or the nadir. The difference in progression-free rates in the 2 treatment groups was the same whichever method was used.

5.9

Breast Cancer-Related Markers

A number of MAbs have been raised to mucins, high-molecularweight glycoproteins produced by epithelial cells of the breast. The most heavily investigated mucin marker is CA15.3, which is increased in approximately 11% of women with operable breast cancer, and 60% of cases of metastatic disease. It is also increased in some 10% of women with benign breast disease. The lack of specificity and low sensitivity preclude the use of CA15.3 in screening or diagnosis of symptomatic breast disease; however, the preoperative level may be prognostically important. In a study of 600 patients followed for a median of just over 6 years, patients with preoperative CA15-3 of >30 U/L had a shorter overall survival [87]. Serial estimations may be of value in monitoring response of metastatic disease. A prospective study was undertaken to define the optimal combination of bone scan and tumor marker assays in staging a breast cancer cohort of 157 consecutive cases. The results suggest that in asymptomatic patients, a CA15-3 concentration of <25 U/ml (upper normal value chosen as the threshold) is strongly predictive of a negative bone scan; by contrast, high tumor marker concentrations are predictive of neoplastic bone involvement. When a doubtful bone scan is obtained in a patient with breast cancer, a normal marker level makes it highly probable that bone scan abnormalities are not related to malignancy [88].

Acknowledgment. This work was undertaken in The Royal Marsden NHS Foundation Trust who received a proportion of its funding from the NHS Executive; the views expressed in this publication are those of the authors and not necessarily those of the NHS Executive. This work was supported by the Institute of Cancer Research, the Bob Champion Cancer Trust and Cancer Research UK Section of Radiotherapy [CUK] grant number C46/A2131.

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Chapter 6 Antibody-Based Proteomics Analysis of Tumor Cell Signaling Pathways Steven Pelech and Hong Zhang

6.1

Introduction

The promise of personalized medicine is ultimately contingent on the successful identification of specific biomarkers for diseases and therapeutic modalities that can compensate for the molecular lesions that underlie these diseases. In the case of cancer, more than two decades of research have demonstrated the critical roles of a relatively small subset of proteins that are encoded by oncogenes and tumor suppressor genes. The gain of function of perhaps just a few oncoproteins and the loss of function of only a small number of tumor suppressor proteins in the right combinations may culminate in full neoplastic transformation. However, there may well be billions of such genetic change combinations so that every cancer patient has a unique form of the disease. Presently, just under half of cancer patients die from their disease within 5 years so there is a pressing need for development of new diagnostics and treatments. Like many chronic diseases associated with aging, cancer is a systems disorder. Most of the known oncogenes and tumor suppressor genes specify protein kinases, their regulators, or their target substrates. The human genome encodes at least 515 protein kinases (the kineome) [1, 2] and 140 protein phosphatases [3], which catalyze the reversible phosphorylation of over a third of all proteins at more than 1,000,000 sites (the phosphoproteome) [4]. Many of these phosphorylation events play key roles in the regulation of cell proliferation and survival. The phosphoproteome represents a relatively untapped source of potential biomarkers, and phosphoproteomics profiling should be extremely insightful for analysis of signaling pathways [5]. Our current knowledge of the composition and architecture of cell signaling systems is still extremely rudimentary. To elucidate these molecular communications webs, specific information is required concerning the spatial and temporal expression and activity of thousands of individual proteins in the nearly 200 different cell types in the organs and tissues of

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

the human body. One of the major challenges of this decade will be the elucidation of these regulatory networks and the development of technologies to track their protein components in tumor biopsies and bodily fluids for cancer diagnostics. Although cancer is commonly viewed as a genetics disease, its successful treatment will require the knowledge of malfunctioning signal transduction at the protein level and the application of small molecule drugs. A very powerful arsenal of protein kinase inhibitors is being developed by the pharmaceutical industry, which is now spending about a third of their annual research and development budgets on this class of enzymes [6]. We predict that within the next 10 years, most of the new drugs in clinical trials and entering the market place will be protein kinase inhibitors. One reason for this is because the industry is currently focused on only a few dozen of the protein kinases, and over 90% of them still remain to be explored for their therapeutic potential [4]. Another impetus is that over 400 other diseases have been linked to defective kinase signaling. Consequently, there will be an increasing demand to track signal transduction proteins in the near future.

6.2 Genomics versus Proteomics Profiling All humans are at least 99.9% identical in their genomic sequences, whereas the genetic differences known as single nucleotide polymorphisms (SNPs) are thought to underpin our individual susceptibilities to disease and treatment. Thousands of in-born genetic errors leading to metabolic diseases have already been catalogued. So called “functional genomics” often refers to the expression of genes as revealed in measurement of the amount of mRNA transcripts that are produced from these genes. This is fairly easily achieved, because it is relatively simple to produce a specific DNA or oligonucleotide probe that features a nucleic acid sequence with bases that are fully complementary to the sequence of the target gene of interest. Such oligonucleotide probes can be created cheaply for pennies and rapidly within hours. These probes can be deployed in several different procedures, 117

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including Northern blotting analysis on gels, quantitative PCR, and gene microarrays. Although gene microarrays are prone to less quantitative results with higher error rates, literally thousands of different mRNAs can be tracked on a single glass slide or silicon chip by this powerful technology. With the advent of gene microarrays, it has become feasible to track important SNPs and the expression of genes. Indeed, more than a $1 billion (US) is expended annually on the application of gene microarrays in biomedical research. However at the end of the day, we are under the opinion that such analyses will have only limited value in the accurate diagnosis and treatment of cancer. There are several reasons why we believe genomic profiling will prove to be inaccurate and potentially misleading. First, although certain genetic mutations can be directly correlated with the loss or gain of function in their protein targets, it is not clear that these proteins may be actually responsible for the disease phenotype. The very nature of cell signaling networks with their high degree of redundancy and elaborate feedback systems probably means that most malfunctioning signal transduction proteins can be compensated for. Furthermore, the majority of altered gene transcriptions in diseased cells more likely arise from compensatory measures than those changes that are actually responsible for the disease state. Second, it is well known that the correlation between mRNA and protein levels is quite poor, in the order of 50% for structural and metabolic pathway enzymes to much worse for signaling proteins [7]. In some cases, the mRNA level of a protein may even decrease in response to a treatment, but the actual level of that protein can increase several-fold. For example, docetaxel treatment of head and neck squamous carcinoma cells has been reported to produce a 56% decrease in the mRNA level of the p19 cyclin-dependent kinase inhibitor protein, but the protein level of p19 was increased 30-fold as assessed with a specific antibody [8]. Many mRNAs are not translated into proteins, and proteins often undergo different turnover rates than the mRNA for these proteins. Third, another limitation of indirect analysis of proteins by tracking their mRNA levels is that this provides no information about whether these proteins are subject to posttranslation modifications. Although protein phosphorylation is the major means of posttranscriptional regulation, it is only one example of more than 20 possible types of regulatory covalent modifications of proteins. These modifications are often extremely important in controlling the activity states and spatial distributions of signaling proteins in cells. The phenotype of a cell correlates more tightly with the amount of active signaling proteins than it does with their total expression levels. We have commonly observed inverse relationships between changes in the active phosphorylated forms of targets proteins and their overall levels. In hindsight, this is not surprising, because cells probably maintain a reserve of inactive protein that is poised for rapid stimulation within seconds after they

S. Pelech and H. Zhang

are needed. Once activated by phosphorylation, they may also become tagged for proteolysis. Fourth, genetic changes may be detectable in tumor tissue biopsies, but blood and other bodily fluids contain little or no mRNA for diagnostic purposes. By contrast, protein is readily found in serum, cerebral spinal fluid, saliva, urine, tears, milk, nipple aspirates, semen, vaginal secretions, and sweat.

6.3 Conventional Proteomics— Two-Dimensional Gel Electrophoresis Sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) has become the standard method in the field for separation of proteins on the basis of their size for analytical and preparative purposes [9]. This widely used technique relies on the sieving effect of a polyacrylamide gel when proteins coated with the negatively-charged detergent SDS are drawn through the gel in an electric field. Smaller sized proteins are able to migrate through the gel faster than larger sized proteins. Proteins that differ by as little as a few hundred Daltons can be resolved by this method. (Most proteins exhibit molecular masses in the forty to fifty thousand Dalton range.) Protein staining methods permit the visualization of discreet proteins in the gel as individual bands in a bar-codelike pattern. When these proteins are transferred from the SDS-PAGE gel onto a nitrocellulose membrane, the locations of specific proteins can be identified with antibodies by an immunoblotting procedure commonly referred to as Western blotting [10]. Most proteomics analyses are based on two-dimensional (2D) PAGE using the method Dr. Patrick O’Farrell [11] described more than 30 years ago. This 2D gel technique initially involves the separation of proteins based on their intrinsic charge in a pH gradient within a tube gel. Proteins migrate through the isoelectric focusing gel in the presence of an electric field until they encounter a pH at which they no longer possess a net charge. This pH is the isoelectric point of a protein, and it is a distinguishing characteristic. Following electrophoresis in the first dimension, the isoelectric focusing tube gel is applied length-wise to the top of an SDS-PAGE gel, and electrophoresis is continued into the second dimension. When the 2D gel is stained with sensitive-dyes (e.g., based on silver reagent), the various proteins inside a cell can be visualized as resolved spots. The greater amount of a given protein within a cell-derived sample, the larger and darker its specific spot appears. Silver-staining of a 2D gel can be used to track the expression of proteins and their covalent modification such as by phosphorylation. When a protein is phosphorylated, its intrinsic charge is altered and this results in a shift in the migration position of the protein in the 2D gel. If the protein samples have been obtained from cells that have been incubated with radioactive [32P]orthophosphate, then the 2D gel can be exposed to x-ray film, and the 32P–labeled phosphoproteins can be specifically detected. The more that a protein is

6. Antibody-Based Proteomics Analysis of Tumor Cell Signaling Pathways

phosphorylated or prevalent, the larger and more intense the spot on the x-ray film. Alternative methods for detection of phosphoproteins include the use of Pro-Q Diamond stain from Invitrogen (Hopkinton, MA) or phospho-site specific antibodies. For protein spots that can be detected and unambiguously identified, the O’Farrell 2D gel approach is a powerful way of monitoring the expression and regulation of potentially hundreds of proteins simultaneously. Public web-based databases have been created that document the identification of over a thousand different proteins on 2D PAGE proteomic maps [12]. However, the positions of scarcely more than a few dozen protein kinases have been deduced. This reflects the fact that like most signal transduction proteins, protein kinases are present at very minute levels in cells, and are often undetectable by even such sensitive protein dyes as silver-stain. Typically, signaling proteins are commonly produced at 100- to 1,000-fold lower levels than structural proteins and metabolic pathway enzymes. Consequently, these signal transduction proteins are usually overlooked using the traditional proteomics approaches. Therefore, it is often necessary to incorporate selective enrichment techniques as a preliminary step before 2D PAGE. In recent years, mass spectrometry (MS) such as MALDITOF has emerged as a very sensitive and powerful method to identify proteins that are resolved by one-dimensional (1D) or 2D gel electrophoresis [13]. It is now routine to use proteolytic enzymes such as trypsin to cleave eluted proteins from gels into smaller peptides that can be resolved by MS and accurately measured to four decimal places for their charge to mass ratios. Because the charge to mass ratio of all of the tryptic fragments of the proteins predicted to be encoded by the human genome can be calculated and is available in databases such as MASCOT (Matrix Science, London, UK), it is usually possible to immediately assign the identities of several proteins contained within a sample by mass fingerprinting. Although the combination of 2D PAGE and MS can be used to identify thousands of proteins within a cell or tissue lysate, this method is still laborious, expensive, nonquantitative, and highly impractical for comparisons of large numbers of biological samples. In fact, it is probably extremely misleading. The low abundance of signaling proteins poses a very serious issue for analyses by 2D PAGE [14]. The human genome encodes an estimated 30,000 proteins, and alternative splicing generates five isoforms on average for each gene. Furthermore, the typical phosphoprotein is likely to be phosphorylated at 10 or more separate sites. Moreover, there are many other forms of covalent modification of proteins that can alter their mobilities on 2D PAGE gels. Consequently, the estimated number of distinct protein species within any given cell or tissue type is likely to exceed 100,000. At best, 2D PAGE can resolve about 8,000 protein spots. Therefore, on average, each spot may contain more than 10 different proteins, and following trypsin treatment, could generate over 100 different peptides for resolution and detec-

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tion within a single mass spectrometry analysis. The signals from tryptic peptides generated from structural proteins and metabolic pathway enzymes are very likely to swamp any signals arising from low abundance signaling proteins. Another problem is that many proteins precipitate during the initial isoelectric focusing step. Other proteins possess very high or low isoelectric points such that they do not enter the second dimension of the 2D PAGE gel and are missed. Very high molecular mass proteins are also poorly resolved. Quantitation of protein expression by 2D PAGE and silverstaining can only be accurately performed if all of the spots that arise from a given target protein are tracked, and there is high confidence that essentially the entire signal is attributable to that protein in each spot. Except for only the most highly abundant proteins, this is pretty much impossible. It is tempting to speculate that the leftward mobility of proteins in some silver-stained spots on 2D PAGE arises from their progressive phosphorylation. However, it should be borne in mind that each spot contains a mixed population of phosphoforms that are differentially phosphorylated at multiple sites, even though they share a common net charge. Consequently, one cannot make any statement about the phosphorylation of proteins at specific sites following 2D PAGE where the proteins are nonspecifically visualized by silver stain, or directly for phosphorylation by autoradiography of resolved lysate proteins from [32P]orthophosphate-labeled cells or by detection with Pro-Q Diamond stain. These shortcomings seriously compromise the use of 2D PAGE for quantitative analyses despite its widespread use for proteomics studies. When specific enrichment and labeling methods are used to purify protein samples before MS, it is feasible to perform semiquantitative measurements using MS. For example, 462 proteins were analyzed in stably transfected cell lines overexpressing the ErbB2 (Neu, HER2) receptor-tyrosine kinase or an empty vector by using the SILAC (stable isotope labeling with amino acids in cell culture) method [15]. Of these, 198 showed a significant increase in tyrosine phosphorylation in ErbB2-overexpressing cells, and 81 showed a significant decrease in phosphorylation.

6.4 Antibodies—The Gold Standard for Proteomics Probes Often, it is only feasible to identify the locations of lowabundance cell signaling proteins on 1D and 2D gels by immunoblotting analysis. Western blotting analysis following electrophoresis is completely reliant on the availability of specific and potent antibodies. These are usually produced in rabbits, mice, or goats with short synthetic peptides of 10–20 amino acids long that correspond to a portion of the target protein. Alternatively, monoclonal antibodies are made by mouse and rabbit hybridoma B cells against whole target proteins. It typically takes 4–6 months to produce high affinity antibodies, and more often than not, the antibodies that are generated

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are nonspecific and/or impotent. Kinexus has independently tested more than 3,000 commercial antibodies with a rejection rate of greater than 75%. Nevertheless, antibodies are the best probes available for specific detection and quantification of proteins. The detection of phosphopeptides from phosphoproteins by MS is especially problematic because of their relatively low abundance when compared with nonphosphorylated peptides within samples [16]. Because the presence of other peptides can suppress the ability to detect phosphopeptides by MS, procedures must be employed to specifically enrich phosphoproteins and phosphopeptides prior to MS. In this regard, phosphosite-specific antibodies and immobilized metalaffinity columns have been particularly useful [17]. Over 1,000 phospho-site antibodies are commercially available for several hundred signaling proteins. However, these reagents are costly to purchase, in part because of the need to affinity purify these antibodies over two columns, i.e., one with the dephospho-form of the immunogenic peptide and the other column with the phosphorylated form of this peptide. This results in very low yields for phosphosite-specific antibodies. Less discriminating monoclonal pan-phosphotyrosinespecific antibodies such as 4G10 and PY20 have proven to be very useful for tracking protein-tyrosine phosphorylation in general, in part because of the extremely low levels of this form of phosphorylation. In most cell types, protein-tyrosine phosphorylation has been estimated to be 2,000-fold lower than protein-serine or protein-threonine phosphorylation [18], although in activated platelets the levels of protein-tyrosine phosphorylation are markedly higher [19]. There are also pan phospho-serine and phospho-threonine-specific antibodies that have been successfully used to concentrate phosphoproteins prior to their identification by MS [20]. An innovative approach to quantifying the expression and phosphorylation states of proteins is through the use of “Liquid Chip” technology from Luminex (Austin, TX). This instrument can in principle analyze up to 100 different target proteins simultaneously in a complex mixture using beads that can be coated with specific capture probes (typically antibodies). The presence of the captured protein is detected by its subsequent binding of a reporter probe (typically a biotinylated antibody, which could be bound to fluorescently labeled avidin). The amount of signal from the reporter antibody bound to each bead is quantified as it passes through a narrow orifice in a bead sorter with a two-laser beam detector (one beam identifies the bead, the other beam records the amount of reporter antibody bound). Several different companies (e.g., Invitrogen [Hopkinton, MA], Millipore [Billerica, MA], Becton Dickinson [Franklin Lakes, NJ]) offer specific assays for kinases and other signaling proteins that can be used with the Luminex instrument and related detectors. However, there are only a limited number of suitable antibody pairs (capture antibody and reporter antibody) that are commercially available. Another technical barrier appears to be a practical limit to the numbers of antibodies (a maximum of about 2 dozen)

S. Pelech and H. Zhang

that can be mixed together without extensive cross-reactivity that renders high backgrounds and high rates of false positive signals. The Luminex system is also not easily adapted for high throughput robotics-assisted analyses. Because of these limitations, we feel that this technology platform will have a more restricted use relative to protein microarrays.

6.5

Antibody Microarrays

Antibody microarrays are enticing because of the higher numbers of proteins that could be tracked simultaneously, their economy of scale, and their high throughput potential with automation [21, 22]. It can be estimated that 100 µg of an antibody may be sufficient for spotting tens of thousands of glass slides. In principle, antibody microarrays should be an order of magnitude more powerful than gene microarrays. Apart from being able to quantify the actual levels of proteins, antibody microarrays could also be used to track posttranslational modifications such as phosphorylation (with phospho-site-specific antibodies), subcellular location (by prefractionation of cellular extracts), protein–protein interactions and drug–protein interactions (by affinity chromatography prior to microarray analyses). Sample preparation for protein microarray analysis should also be faster and less expensive than gene microarrays, and require less biopsy material. Presently, only Clontech (Mountain View, CA) and SigmaAldrich (St. Louis, MO) sell antibody microarrays that can track cell-signaling proteins. For both of these commercial antibody microarrays, the lysates from control and experimentally treated cells are prelabeled with different dyes (e.g., Cy3 and Cy5), and then mixed together for incubation with the same antibody spots. If there are differences in the amounts of target proteins between the samples, then the competition for the various antibodies allows for the dye signal from one sample to predominate over the dye signal from the other sample. Because diverse dyes have different efficiencies in labeling proteins, it is a recommended practice to validate the initial experimental results with these antibody microarrays with a second experiment in which the dyes are reversed between the control and experimental samples. Presently, there are relatively few publications that describe the use of the Clontech [23, 24] and Sigma-Aldrich Panorama antibody microarrays [25, 26]. It is also feasible to avoid dye labeling of proteins prior to their incubation with antibody microarrays, and use surface plasmon resonance (SPR) for the detection of captured proteins instead [27]. The biggest challenge for antibody microarrays is to improve the accuracy of the results that are generated by this approach with better antibodies. In contrast to DNA or other oligonucleotide probes deployed in gene microarrays, antibody probes are often nonspecific, as well as expensive (i.e., thousands of dollars) and time consuming (several months) to produce. Validation of key antibody microarray results by an alternative strategy such as Western blotting is essential,

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study in which our proteomics discovery platform was applied to identify biomarkers for the action of a commonly investigated growth factor in a well-studied human tumor cell line.

6.7 Kinex™ Antibody Microarray Analysis of EGF-treated A431 Cells

Fig. 6-1. Different scenarios for protein binding to antibody microarrays. The antibodies are developed to recognize the proteins that are similarly labeled with an alphabet character.

because some signals from the microarrays may arise by antibody cross-reactivity. Another complication is that antibody microarrays rely on the use of nondenatured protein samples, and epitopes on proteins may be masked in their native forms. This is especially problematic if the target proteins are complexed with other proteins, which is likely to be very common. For example, at least 137 of the known protein kinases have been reported to occur in dimeric or multimeric forms, and based on their homology with related kinases, most protein kinases probably reside in complexes [28]. As shown in Fig. 6-1, these associated proteins may contribute to the signals detected on a microarray, because they are also dyelabeled.

6.6 Kinexus Antibody-based Integrated Discovery Platform Although some laboratories have the necessary specialized equipment and software to scan and analyze microarrays, most are not set up to conduct these types of experiments. Furthermore, as outlined above, there are many pitfalls and associated costs with performing antibody microarray analyses and follow-up validation studies. In view of this, Kinexus Bioinformatics Corporation launched its Kinex™ antibody microarray services in combination with its Kinetworks™ multi-immunoblotting services as a cost effective solution for academic and industrial laboratories to conduct systems proteomics research. Kinexus has provided its Kinetworks™ services to over 800 laboratories world-wide, and it has generated over 10,000 multi-immunoblots over the last 7 years. Much of the resulting data with quantification of the expression and phosphorylation levels of hundreds of signaling proteins is available to the scientific community on-line through our unique KiNET databank (www.kinexus.ca/kinet). In the balance of this chapter, we will provide an example of a case

Epidermal growth factor (EGF) is one of the best characterized of the growth factors that binds to receptor-tyrosine kinases, and there has been extensive studies of the signaling pathways that it evokes. Upon ligand binding, the EGF receptor dimerizes, autophosphorylates itself, and recruits a cascade of signaling proteins to transmit potent mitogenic signals in many cellular systems [29–32]. A431 cells were originally isolated from the vulva epidermoid carcinoma of an 85-year-old female. The EGF receptor is amplified, rearranged, and truncated in A431 cells, resulting in over 30-fold higher levels of mRNA for this receptor [33–35]. Like other cells that highly overexpress the EGF receptor [36], EGF treatment of A431 cells results in induction of apoptosis [37, 38]. Recently, a new and sensitive liquid chromatography-MS platform, Extended Range Proteomic Analysis (ERPA), was used to identify 13 phosphorylation sites and 10 extracellular domain N-glycan sites in the EGF receptor in A431 cells treated with EGF [39, 40]. In the same study [40], 19 proteins were identified that associated with the EGF stimulated EGF receptor. The SILAC method combined with MS has also been successfully used to identify 81 signaling proteins that became tyrosine phosphorylated in response to EGF activation of cultured cells [41]. We exploited the A431 cell system to explore the regulation of cell signaling in response to shortterm treatment with 20 nM EGF for 10 minutes using the Kinex™ antibody microarray. Protein microarrays have also been employed to study EGF signaling previously [42]. The Kinex™ antibody microarrays are printed in quadruplicate in 32 grids of 8 × 12 spots each on plastic microscope slide-sized chips with 603 antibodies from over 20 different commercial suppliers. These polyclonal and monoclonal antibodies were carefully selected, because they have been highly validated in-house at Kinexus to perform well on Western blots. They included 346 panspecific antibodies for measurement of the expressions of 240 protein kinases and 106 other signaling proteins, as well as 257 phospho-site-specific antibodies. To perform a Kinex™ analysis, the lysates with 50 µg protein each from both the untreated (control) and EGF-treated A431 cells were labeled with the same proprietary fluorescent dye. Each sample was separately applied to opposite sides of the antibody microarray that contains a dam to prevent mixing of the samples. Following incubation of the A431 cell samples with the Kinex™ chip, the unbound proteins were washed away, and the chips were scanned with a Perkin-Elmer Scan Array Express Reader. Image analysis of the TIF files that were produced was performed with ImaGene 7.0 software

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A

B

1-2

1-4

C

D

2-1

2-2

Fig. 6-2. Examples of changes in protein expression and phosphorylation as visualized on 4 of the 16 grids on a Kinex™ antibody microarray. Spots generated from lysates from A431 cells treated with 20 nM EGF for 10 minutes are shown in grey, whereas the corresponding spots from untreated controls are shown immediately in the row below in black. Each antibody was printed in adjacent duplicate spots. Antibody spots that revealed changes with EGF are boxed and labeled with the identity of the protein antigen. The %CFC values are indicated for selected antibody spots.

from BioDiscovery (El Segundo, CA). For the purposes of presentation in Fig. 6-2, the separate scan images of four of the grids with the control and EGF-treated samples were overlaid and slightly staggered so that they could be compared. The EGF-treated sample spots are shown in grey, whereas the untreated sample spots appear in black. Duplicate antibodies are printed as adjacent spots. Quantification of the signal intensity of all of the detected spots revealed that the difference between duplicates was within 10% for half of all of the antibodies used. Over 93% of the antibodies detected the binding of dye-labeled proteins from the A431 cell lysates with 100 or more counts. The highest signal observed was 2,636 counts, and the lowest reproducible signal was around 30 counts, so there was a 100-fold range of linear detection of protein binding to the Kinex™ antibody microarray. Table 6-1 provides a selective listing of all of the antibodies that revealed EGF induced changes in target protein expression or phosphorylation that were 33% or greater. For the generation of these findings, the percentage change from control (%CFC) was the averaged result from the analyses of two separate experiments performed on different occasions with two chips. Common to both experiments, 6.5% of the antibodies revealed greater than 25 %CFC increases and 5.8% of the antibodies revealed more than 25 %CFC decreases in signal

detection. Most of the EGF-induced increases in %CFC were in protein phosphorylation detection (8.2% of 257 phosphosite antibodies), whereas the EGF reductions in %CFC were mainly in protein expression (8.7% of 346 panantibodies). Roughly 67% of the phospho-site and panspecific antibodies showed less than 25 %CFC in both experiments. It is remarkable that EGF seemed to induce so many apparent changes in protein expression within 10 minutes of initial exposure to the A431 cells. The most likely correct interpretation of these findings is that the growth factor treatment did not alter the synthesis or degradation of these signaling proteins. Rather the treatment probably induced radical changes in complex formation amongst these signaling proteins. Because their associated proteins also contribute to the total signal recorded for many of the antibody spots, their binding to or dissociation from the target proteins would produce apparent changes in protein expression. Although this may undermine the use of antibody microarrays to accurately track protein expression, it demonstrates the power of this technology to sensitively reveal changes in protein–protein interactions. Such changes could still prove to be useful markers of drug action or disease. To more precisely track target proteins for expression changes, it would be desirable to use denatured proteins that are dissociated from other proteins. However, it

Phospho Site (Human)

S696 S645 S612 Y1248 S380/S386 S729 S807 T774 S463+S465 S540 S720 Y279/Y216 Y821 S19 Y577 S139 S19 T514 T410/T403 Y1230+Y1234+Y1235 S780 Y576 T696 T199 Y397 S530 T180+Y182 Y1003 T421+S424 S99 Y425 S363/S369 S45 S1981 S78 Y349+Y350 T538 S603 S63 Y507 S158+S162 Pan-specific

Target Protein Name

Ret Rad17 Rb ErbB2 [HER2] RSK1/2 PKCε Rb PRK1/2 [PKN1/2] Smad1/5/9 eIF2Bε Tau GSK3α/β Vinculin TyrHyd α FAK Histone H2A.X Crystallin αΒ PKCγ PKCζ/λ Met Rb FAK MYPT1 B23 (NPM) FAK Tau p38α MAPK Met S6Kα [p70 S6Kα] Bad FAK RSK1/2 Crystallin αΒ ATM PK Hsp27 Shc1 PKCθ Synapsin 1 Jun Lyn MARCKS PACSIN1

Ret receptor-tyrosine kinase Rad17 homolog Retinoblastoma-associated protein 1 ErbB2 (Neu, HER2) receptor-tyrosine kinase Ribosomal S6 protein-serine kinase 1/2 Protein-serine kinase C epsilon Retinoblastoma-associated protein 1 Protein kinase C-related protein-serine kinase 1/2 SMA- + mothers against decapentaplegic homologs 1/5/9 Eukaryotic translation initiation factor 2B epsilon Microtubule-associated protein tau Glycogen synthase-serine kinase 3 alpha/beta Vinculin Tyrosine hydroxylase isoform a Focal adhesion protein-tyrosine kinase Histone H2A variant X Crystallin alpha B (heat-shock 20 kDa like-protein) Protein-serine kinase C gamma Protein-serine kinase C zeta/lambda Hepatocyte growth factor receptor-tyrosine kinase Retinoblastoma-associated protein 1 Focal adhesion protein-tyrosine kinase Myosin phosphatase target 1 B23 (nucleophosmin, numatrin, NO38) Focal adhesion protein-tyrosine kinase Microtubule-associated protein tau Mitogen-activated protein-serine kinase p38 alpha Hepatocyte growth factor receptor-tyrosine kinase p70 ribosomal protein-serine S6 kinase alpha Bcl2-antagonist of cell death protein Focal adhesion protein-tyrosine kinase Ribosomal S6 protein-serine kinase 1/2 Crystallin alpha B (heat-shock 20 kDa like-protein) Ataxia telangiectasia mutated Ser/Thr kinase Heat shock 27 kDa protein beta 1 (HspB1) SH2 domain-containing transforming protein 1 Protein-serine kinase C theta Synapsin 1 isoform Ia Jun proto-oncogene-encoded AP1 transcription factor Yes-related protein-tyrosine kinase Myristoylated alanine-rich protein kinase C substrate PKC + casein kinase substrate in neurons protein 1

Full Target Protein Name P07949 O75943 P06400 P04626 Q15418 Q02156 P06400 Q16512 Q15797 Q13144 P10636 P49841 P18206 P07101 Q05397 P16104 P02511 Q02156 Q05513 P08581 P06400 Q05397 O14974 P06748 Q05397 P10636 Q16539 P08581 P23443 Q92934 Q05397 Q15418 P02511 Q13315 P04792 P29353 Q04759 P17600 P05412 P07948 P29966 Q9BY11

Swiss-prot Link 400 447 812 474 35 173 848 53 255 273 465 324 641 217 669 330 502 461 158 381 36 346 294 888 312 798 591 456 399 733 322 560 595 270 619 995 33 12 104 37 31 185

Control Average 300 229 478 33 3 13 463 1 3 17 25 134 39 11 5 157 87 45 0 5 7 3 37 184 102 99 106 27 33 44 23 74 1 116 23 60 5 12 51 21 3 128

245 105 97 87 84 84 82 80 77 77 77 71 68 60 59 56 54 48 44 42 41 40 40 40 39 39 38 37 36 36 36 36 35 −33 −33 −36 −38 −43 −51 −53 −66 337

244 98 99 56 0 25 70 6 51 1 13 54 7 18 22 61 15 19 17 17 35 11 5 37 38 12 28 24 30 13 33 5 17 38 0 6 24 43 19 16 23 277

EGF Aver- EGF Average Control Range age %CFC %CFC Range

(continued)

−24 2

461 166

1

0

INCR

−47 42

39

57 23

10 −8

131 −3 −41 82 −100 59 −44

EGF Immunoblotting %CFC

Table 6-1. Kinex™ antibody microarray detection of key changes in signal-transduction protein expression and phosphorylation induced by exposure of A431 cells to 20 nM EGF for 10 minutes

6. Antibody-Based Proteomics Analysis of Tumor Cell Signaling Pathways 123

Phospho Site (Human)

Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific

Target Protein Name

PTP1B PyDK2 PKA Cα/β CASP7 Syk Erk6 [p38γ] MEK6 [MAP2K6] p16 INK4 ROKα [ROCK2] MST1 RSK2 DNAPK p53 p35 Smac/DIABLO FasL MKP2 Grp78 ERP57 NME7 Nek4 SIRPα1 S6Kβ [p70 S6Kβ] ASK1 [MAP3K5] Aik CASP4 CaMKK [CaMKK2] TBK1 Bax Erk1 PP2B/Aα PKCβ1 MEK6 [MAP2K6] PAK3 Alk Mcl1 DFF45 HO1 HO2 KHS

Table 6-1. continued

Protein-tyrosine phosphatase 1B Pyruvate dehydrogenase kinase isoform 2 cAMP-dep. protein kinase catalytic subunit alpha/beta Pro-caspase 7 (ICE-LAP3, Mch3) Spleen protein-tyrosine kinase Mitogen-activated protein kinase p38 gamma (MAPK12) MAP kinase protein-serine kinase 6 (MKK6) p16 INK4a cyclin-dependent kinase inhibitor (MTS1) RhoA protein-serine kinase alpha (ROCK2) Mammalian STE20-like protein-serine kinase 1 Ribosomal S6 protein-serine kinase 2 DNA-activated protein-serine kinase Tumor suppressor protein p53 (antigenNY-CO-13) CDK5 regulatory subunit 1, p35 Second mitochondria-derived activator of caspase Tumor necrosis factor ligand, member 6 MAP kinase phosphatase 2 (VH2) Glucose regulated protein 78 ER protein 57 kDa (protein disulfide isomerase-assoc. 3) Nucleotide diphosphate kinase 7 (nm23-H7) NIMA (never-in-mitosis)-related protein kinase 4 Substrate of PTP1D phosphatase (SHPS1) p70 ribosomal protein-serine S6 kinase beta Apoptosis signal regulating protein-serine kinase Aurora/IPL1-related kinase 1 Pro-caspase 4 (ICH2 protease, ICE(rel)-II) Calmodulin-dependent protein-serine kinase Tank-binding protein 1 Apoptosis regulator Bcl2-associated X protein Extracellular regulated protein kinase 1 (p44 MAPK) Protein-serine phosphatase 2B - cat. subunit - alpha Protein-serine kinase C beta 1 MAP kinase protein-serine kinase 6 (MKK6) p21-activated protein-serine kinase 3 Anaplastic lymphoma kinase Myeloid cell leukemia differentiation protein 1 DNA fragmentation factor alpha (ICAD) Heme oxygenase 1 Heme oxygenase 2 Kinase homologous to SPS1/STE20 (MEKKK5)

Full Target Protein Name P18031 Q15119 P17612 P55210 P43405 Q05397 P52564 P42771 O75116 Q13043 P51812 P78527 P04637 Q13319 Q9NR28 P48023 Q13115 P11021 P30101 Q9Y5B8 P51957 P78324 Q9UBS0 Q99683 O14965 P49662 Q8N5S9 Q9UHD2 Q07812 P27361 Q08209 P05771 P52564 O75914 Q9UM73 Q07820 O00273 P09601 P30519 Q9Y4K4

651 265 518 22 350 151 104 957 732 421 431 287 397 286 281 885 704 817 126 522 449 801 363 337 404 705 285 423 513 252 66 472 381 476 1405 380 225 265 492 351

Swiss-prot Control Link Average 496 155 7 6 224 22 64 552 321 16 233 18 31 141 17 97 218 283 22 175 109 106 11 18 28 14 0 42 41 7 25 51 42 19 111 9 31 13 44 33

252 250 206 163 152 104 91 86 82 74 73 68 67 64 59 50 49 48 41 39 39 38 35 34 34 −33 −33 −33 −34 −34 −34 −34 −35 −35 −36 −37 −38 −38 −38 −39

247 158 209 24 160 23 117 72 60 66 65 27 23 55 15 12 28 40 46 22 18 27 6 9 13 25 38 2 31 1 32 4 37 16 52 17 13 8 12 7

EGF Aver- EGF Average EGF ImmunoControl Range age %CFC %CFC Range blotting %CFC

124 S. Pelech and H. Zhang

Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific Pan-specific

DnaJ homolog, subfamily B member 1 Tank-binding protein 1 14-3-3 protein zeta Bruton’s agammaglobulinemia tyrosine kinase ROR2 neurotrophic receptor-tyrosine kinase Janus protein-tyrosine kinase 1 Activated p21cdc42Hs protein-serine kinase Hematopoetic progenitor protein-serine kinase 1 Calcium/calmodulin-dep. protein-serine kinase 1 delta Pro-caspase 1 (Interleukin-1 beta convertase) Calcium/calmodulin-dep. protein-serine kinase 1 delta Heat shock 70 kDa protein 8 Extracellular regulated protein kinase 2 (p42 MAPK) Janus protein-tyrosine kinase 2 Hsp 70-related heat shock protein 4 (HSP70RY) Cyclin D1 (PRAD1) Inhibitor of NF-kappa-B protein kinase alpha (CHUK) BH3 interacting domain death agonist Checkpoint protein-serine kinase 1

P25685 Q9UHD2 P63104 Q06187 Q01974 P23458 Q07912 Q92918 Q8IU85 P29466 Q8IU85 P11142 P28482 O60674 P34932 P24385 O15111 P55957 O14757

397 508 428 381 177 151 382 504 414 298 201 297 507 256 376 231 430 430 193

14 50 91 61 88 16 46 3 99 21 162 32 42 40 65 180 79 65 37

−39 −40 −41 −41 −41 −42 −43 −43 −43 −45 −46 −48 −50 −52 −52 −55 −58 −61 −73

5 33 34 5 38 19 32 4 1 18 10 1 1 1 43 35 12 54 9

Values are the averages and ranges of signals recorded from two separate experiments. %CFC refers to the percent change from control (untreated with EGF). Only %CFC that were 33% or greater are shown for 101 target proteins and phospho-sites. For comparison, the %CFC in parallel studies performed by Kinetworks™ multi-immunoblotting are provided for some of the phospho-site antibodies in the rightmost column.

Hsp40 TBK1 14-3-3 ζ Btk ROR2 JAK1 ACK1[ACK] Hpk1 CaMK1δ CASP1 CaMK1δ Hsc70 Erk2 JAK2 APG2 Cyclin D1 IKKα Bid Chk1

6. Antibody-Based Proteomics Analysis of Tumor Cell Signaling Pathways 125

126

is tricky to find conditions to unfold proteins without inducing their precipitation, and the inclusion of detergents, for example, might interfere with the binding of target proteins to the antibodies on the microarray. Follow up Western blotting is necessary to evaluate the true nature of the EGF-induced changes observed with the panspecific antibodies.

6.8 Kinetworks™ Multi-immunoblotting Analysis of EGF-Treated A431 Cells Kinexus has developed a multi-immunoblotting process trademarked Kinetworks™, which permits the quantitative analyses of up to 50 or more target proteins at once on the same SDSPAGE gel [43]. With this method, a sample is loaded within a single lane that spans the width of the gel, and following SDSPAGE, the resolved proteins are electroeluted onto a nitrocellulose membrane. Subsequently, an Immunetics (Cambridge, MA) plexiglas manifold with typically 20 separate channels is overlaid over the membrane, and different mixtures of antibodies are applied to each slot of the immunoblotter. It is critical that none of the nonspecific cross-reactive proteins detected with antibodies in each mixture comigrate with target proteins in the same lane, otherwise this generates false positives. Therefore, exhaustive testing of the antibody mixtures with diverse cells and tissues is required. Following incubation of the immunoblot with secondary antibodies and detection by enhanced chemiluminescence (ECL), the resulting blot looks like a 2D gel, but with discreet bands rather than fuzzy spots. The multi-immunoblotting approach is cheaper, faster, more sensitive for the detection of protein kinases and other low abundance signal transduction proteins, more versatile and offers greater reproducibility than conventional 2D gel methods. This technique can be applied to cell or tissue samples, including patient biopsy material. With the Kinetworks™ mini-SDS-PAGE gel format, only 300 µg of crude cell lysate is required to probe the expression, state of phosphorylation, or cleavage of target proteins. The signals of immunoreactive proteins detected by ECL and a fluorescence scanner can be quantified over a 2,000-fold range with linearity. The results for the same sample analyzed by Kinetworks™ on different days typically vary by 5–20% depending on the signal intensity of each immunoreactive protein. Kinexus has performed over 10,000 Kinetworks™ analyses to examine the expression and phosphorylation of protein kinases, protein phosphatases, cell cycle, stress and apoptosis proteins. Figure 6-3 shows the results of five different Kinetworks™ KPSS Phospho-Site screens applied to the analyses of phosphoproteins in lysates from untreated A431 cells, and cells exposed to 20-nM EGF for 10 minutes. Each of these Kinetworks™ KPSS screens were capable of scanning 35–40 known phospho-sites. There was some redundancy in the coverage of phospho-sites by these screens, which together used over 133 distinct phospho-site antibodies. Approximately 19% of these antibodies did not detect their phosphoprotein targets

S. Pelech and H. Zhang

in the A431 cells. Only the results for the detected target phosphoproteins are quantified in Table 6-2. EGF treatment resulted in greater than 25 %CFC increases in phosphorylation levels for 30% of the target phospho-sites, and more than 25 %CFC reductions in phosphorylation for 17% of the target phospho-sites. Extensive Google searches revealed that of the 40 detected phospho-sites in Table 6-2 that showed greater than 25 %CFC increases, EGF has been found to stimulate the phosphorylation of 23 of them in previous studies in other tumor cell lines (e.g., Erk1, Erk2, RSK, PDK1, PKB/Akt). EGF also induces increases in the phosphorylation of the following phospho-sites that do not appear to have been reported previously: Grk2 S670; Hsp25 S86; Hsp27 S15; Hsp27 S78; IRS1 Y612; IRS1 Y1179; MEK1 T385; MLK3 T277+S281; MRLC2 S18; p53 S392; NR1 S896; PED15 S116; PKA Cβ S338; PKCγ T514; PKCγ T655; PKCη S674; and Rad17 S645. Of the 34 phospho-sites that EGF treatment of A431 cells caused greater than 25% reductions in their phosphorylation signals, 11 have actually been reported to be increased by EGF in other tumor cell lines. These conflicting phosphosites corresponded to: 4E-BP1 S65; cofilin 1 S3; CREB S133; FAK Y576; FAK S910; GSK3α S21; JNK T183+Y185; PKCε S729; Rac1/Cdc42 S71; S6K2 p85 T252; and STAT1 S727. The other observed EGF induced decreases in protein phosphorylation shown in Table 6-2 do not appear to have been previously reported. We can ascribe a high level of confidence that the target phospho-sites were accurately tracked in the experiments shown in Fig. 6-3, in part because the molecular masses of the detected phosphoproteins were monitored in parallel by the immunoblotting. However, it should be appreciated that Western blot analysis with phospho-site antibodies alone cannot differentiate whether the altered immunoreactivity signals reflected a change in the stoichiometry of phosphorylation of target proteins or an alteration in the total amount of the target proteins (i.e., the stoichiometry may be unaffected). It is necessary to evaluate whether there are changes in the overall expression levels of the target proteins to ascertain the true extent of their phosphorylation.

6.9 Comparison of Antibody Microarray and Immunoblotting Results for EGF-treated A431 Cells The previous Kinetwork™ multi-immunoblotting studies afforded the opportunity to critically evaluate the findings derived from the Kinex™ antibody microarray experiments with the EGFtreated A431 cell lysates. The rightmost column in Table 6-1 shows the %CFC induced by EGF treatment of the A431 cells in phospho-sites that were examined by immunoblotting. Of the 34 top phospho-site changes identified by the antibody microarray, 15 had positive correlations (i.e., similar trends in %CFC) and 14 had negative correlations (i.e., dissimilar trends such as

6. Antibody-Based Proteomics Analysis of Tumor Cell Signaling Pathways

127

Fig. 6-3. Examples of changes in protein phosphorylation induced by 20 nM EGF for 10 minutes in A431 cells as visualized by Kineworks™ multi-immunoblotting with KPSS Phospho-Site Screens 7.0, 8.0, 10.0, 11.0, and 12.0. The detected phosphoproteins in untreated cells are indicated in the leftmost panels. EGF induced increases in phosphorylation are boxed, whereas EGF induced decreases in phosphorylation are circled in the rightmost panels. Question marks are located to the left of unknown cross-reactive proteins that are EGF responsive.

PED15 (PEA15) MRLC2 p38α MAPK EGFR IRS1 IRS1 STAT1 Hsp25 Hsp27 Fos Erk1 Erk2 Shc1 AcCoA Carb. Rad17 GRK2 Raf1 PDK1 p53 Jun RSK1/2 PKCβ1/2 Hsp27 Rb PKCγ FAK PKCη PKA Cb Hsp27 RSK1/2 FAK NR1 Rb GSK3β PKBα (Akt1) eIF4E PKCγ Rb MEK1 MLK3 PKCζ/λ IR Erk5 MEK1/2 ATF2 Rb

Target Protein Name S116 S18 T180+Y182 Y1148 Y1179 Y612 Y701 S86 S78 T232 T202+Y204 T185+Y187 Y349+Y350 S80 S645 S670 S259 S244 S392 S73 S380/S386 T500 S15 S807 T514 S843 S674 S338 S82 S221/S227 Y397 S896 S780 Y216 S473 S209 T655 S807+S811 T385 T277+S281 T410/T403 Y999 T218+Y220 S217+S221 T51+T53 T826

Phospho Site (Human) Phosphoprotein-enriched in diabetes/astrocytes 15 Myosin regulatory light chain isoform 2 Mitogen-activated protein kinase p38 alpha Epidermal growth factor receptor kinase Insulin receptor substrate 1 Insulin receptor substrate 1 Signal transducer and activator of transcription 1 Heat shock 25 kDa protein (mouse) Heat shock 27 kDa protein beta 1 (HspB1) Fos-c FBJ murine osteosarcoma transcr. factor Extracellular regulated protein kinase 1 (p44 MAPK) Extracellular regulated protein kinase 2 (p42 MAPK) SH2 domain-containing transforming protein 1 Acetyl coenzyme A carboxylase Rad17 homolog G protein-coupled receptor kinase 2 (BARK1) Raf1 proto-oncogene-encoded protein kinase 3-Phosphoinositide-dependent protein kinase 1 Tumor suppressor protein p53 Jun AP1 transcription factor p39 Ribosomal S6 protein kinase 1/2 Protein kinase C beta 1/2 Heat shock 27 kDa protein beta 1 (HspB1) Retinoblastoma protein Protein kinase C gamma Focal adhesion protein kinase Protein kinase C eta cAMP-dep. protein kinase catalytic subunit beta Heat shock 27 kDa protein beta 1 (HspB1) Ribosomal S6 protein kinase 1/2 Focal adhesion protein kinase NMDA glutamate receptor 1 subunit zeta Retinoblastoma protein Glycogen synthase kinase 3 beta p39 Protein kinase B alpha (Akt1) Euk. transl. initiat. factor 4 (mRNA cap binding prot.) Protein kinase C gamma Retinoblastoma protein MAPK/ERK protein kinase 1 (MKK1) Mixed-lineage protein-serine kinase 3 Protein kinase C zeta/lambda Insulin receptor Extracellular regulated protein kinase 5 (BMK1) MAPK/ERK protein kinase 1/2 (MKK1/2) Activating transcription factor 2 (CRE-BP1) Retinoblastoma protein

Full Target Protein Name 0 0 0 0 0 0 0 453 706 1054 512 521 1082 370 4031 2058 1573 1470 811 468 6632 769 1434 4676 1463 415 707 3747 3593 9332 1161 1097 7648 1387 1012 764 356 5876 5937 939 1677 509 403 1397 3437 4978

Control 409 737 398 2456 1731 687 1095 2671 3957 4324 1466 1484 2882 961 9310 4682 3191 2862 1580 874 12074 1377 2558 7436 2296 645 1089 5655 5312 13702 1650 1544 10658 1927 1366 1010 469 7720 7661 1180 2068 627 482 1593 3794 5478

EGF Increase Increase Increase Increase Increase Increase Increase 489 461 310 186 185 166 160 131 128 103 95 94 87 82 79 78 59 57 55 54 51 48 47 42 41 39 39 35 32 32 31 29 26 23 23 20 14 10 10

%CFC 1 1 3 1 1 1 2 1 1 1 5 4 2 1 1 1 1 1 2 2 3 1 1 2 1 2 1 1 2 3 1 1 1 2 3 1 1 1 2 1 1 1 1 1 2 2

N

Table 6-2. Kinetworks™ multi-immunoblotting analysis of changes in signal-transduction protein phosphorylation induced by exposure of A431 cells to 20 nM EGF for 10 minutes

128 S. Pelech and H. Zhang

Adducin α Bad MEK1 MEK1 MARCKS RSK1/2 JNK Src S6 Rb PKCδ Rb Vinculin PRAS40 PTEN Paxillin 1 Adducin γ PKA Cα/β mTOR PKCα S6K2 p85 SOX9 STAT3 PKCα/β2 GSK3α eIF4G Integrin β1 Lyn NMDAR2B PKCγ Rb MEK2 CREB1 CDK1/2 CDK1/2 PAK1/2/3 Dok2 STAT1 ZAP70 Tau GSK3α Pax2 ErbB2 PRK2 eIF2α MAPKAPK2a B23 (NPM) FAK S726 S75 T291 S297 S158+S162 S363/S369 T183+Y185 Y529 S235 S612 S664 T821 Y821 T246 S380+T382+S385 Y31 S693 T197 S2448 S657 T444/S447 S181 S727 T638/T641 Y279 S1107 S785 Y507 Y1474 T674 T356 T394 S133 Y15 T161/T160 S144/S141/S154 Y142 S727 Y292 S712 S21 S394 Y1248 T816 S51 T334 T199 S910

Adducin alpha (ADD1) Bcl2-antagonist of cell death protein MAPK/ERK protein kinase 1 (MKK1) MAPK/ERK protein kinase 1 (MKK1) Myristoylated alanine-rich PKC substrate Ribosomal S6 protein kinase 1/2 Jun N-terminus protein kinase (SAPK) p38 Src proto-oncogene-encoded protein kinase 40S ribosomal protein S6 Retinoblastoma protein Protein-serine kinase C delta Retinoblastoma protein Vinculin Proline-rich Akt substrate 40 kDa (Akt1S1) PIP3 3-phosphatase + tensin homolog Paxillin 1 Adducin gamma (ADD3) cAMP-dep. protein kinase cat. subunit alpha/beta Mammalian target of rapamycin (FRAP) Protein kinase C alpha p85 ribosomal protein S6 kinase 2 SRY (sex determining region Y)-box 9 Signal transducer and activator of transcription 3 Protein kinase C alpha/beta 2 Glycogen synthase kinase 3 alpha p44 Eukaryotic transl. initiat. factor 4 gamma 1 Integrin beta 1 (fibronectin receptor beta sub., CD29) Yes-related protein kinase (NMD) glutamate receptor 2B subunit Protein kinase C gamma Retinoblastoma protein MAPK/ERK protein kinase 2 (MKK2) (human) cAMP response element binding protein 1 Cyclin-dependent protein kinase 1/2 Cyclin-dependent protein kinase 1/2 p21-activated protein-serine kinase 1/2/3 Docking protein 2 (mouse) Signal transducer and activator of transcription 1 Zeta-chain (TCR) associated protein kinase, 70 kDa Microtubule-associated protein tau Glycogen synthase kinase 3 alpha Paired box protein 2 ErbB2 (HER2, Neu) receptor-tyrosine kinase Protein kinase C-related protein-serine kinase 2 Eukaryotic transl. initiat. factor 2 alpha MAPK-activated protein kinase 2 alpha B23 (nucleophosmin, numatrin, NO38) Focal adhesion protein kinase

1443 612 8873 9231 1015 13798 92 1711 11237 933 939 2646 150 5705 1041 289 1637 2033 422 4527 1213 1221 1815 400 1394 4851 1785 3675 1289 163 6542 4562 2607 11841 2982 10282 6730 1731 686 1057 757 1954 370 2126 2191 2006 2501 1501

1571 657 9414 9614 1039 13898 91 1675 10988 908 879 2420 137 5124 928 252 1431 1773 364 3827 1017 1019 1511 331 1123 3899 1365 2804 972 122 4674 3204 1796 8150 2036 6858 4449 1110 438 641 455 1151 218 1248 1233 1121 1325 764

9 7 7 4 2 1 −1 −2 −2 −3 −6 −8 −8 −10 −11 −13 −13 −13 −14 −15 −16 −17 −17 −17 −19 −20 −24 −24 −25 −25 −29 −30 −31 −31 −32 −33 −34 −36 −36 −39 −40 −41 −41 −41 −44 −44 −47 −49 (continued)

1 1 2 2 1 3 4 2 1 2 1 2 1 2 2 1 1 1 1 1 1 2 2 1 2 2 1 1 1 1 2 2 1 1 1 2 2 1 1 1 1 1 1 1 2 1 1 2

6. Antibody-Based Proteomics Analysis of Tumor Cell Signaling Pathways 129

Y576 T234+T237 S3 T14+Y15 S65 T774 S4 T183+Y185 T451 T252 S722 S71 Y470 S729

Phospho Site (Human) Focal adhesion protein kinase B23 (nucleophosmin, numatrin, NO38) Cofilin 1 Cyclin-dependent protein kinase 1/2 Euk. transl. Initiat. factor 4E binding prot. 1 (PHAS1) Protein kinase C-related protein-serine kinase 1 B23 (nucleophosmin, numatrin, NO38) Jun N-terminus protein kinase (SAPK) p46 Double-stranded RNA-dependent protein kinase p85 ribosomal protein-serine S6 kinase 2 Focal adhesion protein kinase Ras-related C3 botulinum toxin substrate 1 Cortactin (amplaxin) (mouse) Protein kinase C epsilon

Full Target Protein Name 1029 4180 7392 9142 186 396 5013 109 269 1071 5104 3106 1099 334

Control 523 1954 3434 4017 81 167 2058 45 108 390 1701 989 215 0

EGF −49 −53 −54 −56 −56 −58 −59 −59 −60 −64 −67 −69 −80 −100

%CFC 1 1 2 2 1 1 1 4 2 2 2 2 2 1

N

Values are the averages of recorded ECL signals in counts per minute (cpm), with the number of determinations should in the rightmost column. %CFC refers to the percent change from control (untreated with EGF). One hundred and eight separate phospho-site antibodies successfully detected their target proteins in these tumor cells.

FAK B23 (NPM) Cofilin 1 CDK1/2 4E-BP1 PRK1 B23 (NPM) JNK PKR S6K2 p85 FAK Rac1/cdc42 Cortactin PKCε

Target Protein Name

Table 6-2. (continued)

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6. Antibody-Based Proteomics Analysis of Tumor Cell Signaling Pathways

an increase in %CFC from the microarray but no change or a decrease based on immunoblotting). Five of the target phosphosites showed no immunoreactivity signals on the immunoblots. Similar findings were observed when the overall results for the antibody microarray data were examined by validation studies by immunoblotting with 154 different phospho-site antibodies: 41% of the antibody microarray results were confirmed by immunoblotting; 42% of the antibody microarray results did not match the trends shown by immunoblotting; and 17% of the phospho-sites detected on the microarray could not be visualized by immunoblotting. In view of the higher concentrations of antibodies used in the microarray platform as compared to immunoblotting, it is not surprising that the antibody microarray was much more sensitive for protein detection. The high degree of false positives with the antibody microarray is also not unexpected in view of the considerations illustrated in Fig. 6-1. In particular, antibodies can demonstrate high cross-reactivity with other proteins. Furthermore, it is the amount of dye that is bound to proteins captured by the immobilized antibodies on the microarray that is specifically tracked. Because nondenatured proteins were examined with the antibody microarray, many of the target proteins should be expected to occur in complexes with other proteins. EGF induced changes in protein–protein interactions in these complexes will confound interpretation of the findings from the antibody microarray. The occurrence of the protein complexes also increased the probability of false negatives, because the epitopes recognized by the microarray antibodies may be masked by associated proteins. Furthermore, it is possible that some of the antibody epitopes may not be accessible in the native folded structure of monomeric target proteins depending on their state of activation. To get a sense of the false-negative rate, we examined the key changes in protein phosphorylation that were evident by immunoblotting and compared these to the corresponding results evident from the antibody microarray data. Of 89 phospho-site signal changes induced by EGF that were detected by the Kinetworks™ multi-immunoblots in A431 cell lysates by immunoblotting, only 15% of these EGF responses (i.e., greater than 25%CFC increase or decrease) were closely matched by the Kinex™ antibody microarray findings. By contrast, 80% of the phospho-sites that failed to show EGF-induced differences by multi-immunoblotting (i.e., less than 25%CFC increase or decrease), also demonstrate little (less than 25 %CFC) if any changes by the antibody microarray analysis. The multi-immunoblotting approach was much more accurate in picking up changes in protein phosphorylation than the antibody microarray. This inherent problem, which is associated with working with native, nondenatured proteins in antibody microarrays, is strongly demonstrated in the case of Erk1 and Erk2 phosphorylation. On the one hand, the Kinetworks™ multiimmunoblotting data shown in Table 6-2 clearly reveals EGF induced nearly threefold increases in the phosphorylations

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of both MAP kinases at their stimulatory phospho-sites in A431 cells. On the other hand, there were no indications of EGF increased phosphorylations of Erk1 and Erk2 from the Kinex™ antibody microarray, despite the fact that this microarray features anti-Erk1/2 T202+Y204/T185+Y187 phospho-site antibodies from four different commercial suppliers as separate spots. We did observe EGF triggered apparent decreases of 34–50% in the total levels of Erk1 and Erk2, respectively, with the antibody microarray. The explanation for these findings may arise from the fact that these MAP kinases occur in heterodimeric complexes with MEK1 and MEK2 in their inactive states, and upon their phosphorylation, they form homodimeric active complexes [28]. In their active, complexed forms, their phosphorylation sites may not be accessible to antibodies. Moreover, epitopes for the panspecific MAP kinase antibodies may also be masked in the Erk1-Erk1 or Erk2-Erk2 dimers. The ability to dissociate such complexes would markedly improve the reliability of antibody microarrays to quantify the expression and phosphorylation states of target proteins.

6.10 Antibody-driven Protein and Phospho-site Discovery One of the serendipitous benefits of the Kinetworks™ multiblot analysis is the detection of unknown cross-reactive proteins. Those unidentified immunoreactive proteins that change in expression or phosphorylation in response to a disease condition or treatment with a drug can be purified or at least tracked with the cross-reactive antibodies that detected them in the first place. This permits the identification of these proteins by MS or by direct protein sequencing by standard Edman degradation methodology. In the case of phospho-site antibodies, the epitope of the detection antibody is usually known, so it often possible to predict the location of the phosphorylation site within the identified protein. The same antibody could be useful for subsequent immunohistochemistry studies to precisely identify the cell type and subcellular compartment within which the phosphorylation has occurred. For example, we successfully used this approach to discover Ser-4 as a novel site for phosphorylation of B23 (also known as nucleophosmin) by its cross-reactivity with an antibody originally developed to recognize the MEK1 S217 + S221 phospho-sites [44]. From the amino acid sequences surrounding the B23 S4 site, we deduced pololike kinase as a possible candidate for catalyzing its phosphorylation, which we confirmed by multiple strategies, including RNAsi. By mutational analyses, we established that S4 phosphorylation of B23 is critical for centrosome duplication prior to mitosis. In the rightmost panels in Fig. 6-3, question marks have been placed next to more than 30 antibody cross-reactive proteins that demonstrated EGF-induced increases or decreases in

185 186 14 35 −17 103 31 INCR 9 39 0 87 −17 41 −15 −59 39 −19 −1 −31 −2 −13 0 −40 −60 −31 0 −100 −56 −30 −49 −67

370 477 360 66 131 −17 47 −19 43 62 5 −15 56 7 12 115 −42 21 −29 ND −9 −23 −41 −15 −32 −75 −50 −7 10 ND ND ND

106 117 ND 2 ND ND ND 262 ND ND ND ND ND ND ND −51 ND ND 47 ND −45 ND −56 ND 0* ND ND ND −53 −45 −51 −40

17 10 2

Colon adenocarcinoma

C2BBe1

178 260 −22 120 190 80 0* −46 −6 −52 47 0* 22 ND 116 0* −26 0* 0* −4 0* −17 0* 0* 0* −36 INCR 0* ND ND ND ND

5 5 1

Ovary endometrial

CO

2450 2100 INCR 44 53 −45 0* −86 −14 0* 0* 0* 22 −10 14 0* 0* −1 0* −46 8 0* 0* 0* 0* −59 0* 0* ND ND ND ND

5 5 2

Ovary endometrial

COA3

77 81 286 64 41 148 0* 0 INCR 0* 0* 5 12 47 −38 0 −8 −21 −30 26 −46 0* 0* 0* 0* −14 −16 −40 ND ND ND ND

5 5 2

Ovary adenocarcinoma

ishikawa

8 −16 0* 170 19 −44 36 67 43 56 0* 10 8 −27 31 33 6 −2 −12 −18 24 3 0* 23 0* −10 INCR −18 ND ND ND ND

5 5 2

Ovary Trophoblastoma

HTR8

−41 −45 −7 INCR 4 14 −6 2 64 −7 0* −1 −25 −6 −60 −26 34 17 13 44 20 ND 37 −40 34 76 ND 49 ND ND ND ND

17 10 1

Brain Glioblastoma

U87MG-EGFR

The data was retrieved by query of KiNET at www.kinexus/ca/kinet. The %CFC are shown. ND not determined. 0* no detectable signals. INCR corresponds to increses where there was no detectable phospho-site signals in the untreated control cells.

T185+Y187 T202+Y204 S217+S221 S473 S727 S259 S807+S811 T180+Y182 S726 S780 S9 S73 T638/T641 S896 S657 T183+Y185 Y216 Y279 T183+Y185 Y15 Y529 S693 Y418 S21 T451 S133 T308 S729 T14+Y15 T394 Y576 S722

Erk2 Erk1 MEK1/2 PKBα (Akt1) STAT3 Raf1 Rb p38α MAPK Adducin α Rb GSK3β Jun PKCα/β2 NR1 PKCα JNK p46 GSK3β GSK3α JNK p38 CDK1/2 Src Adducin γ Src GSK3α PKR CREB1 PKBα (Akt1) PKCε CDK1/2 MEK2 human FAK FAK

3 10 5

Tissue source cell type 17 10 1 to 4

Colon adenocarcinoma

Skin epidermoid carcinoma

EGF Conc. (nM) Time (min) Number of Determinations Protein name Phospho-Site

CaCo2

A431

Cell line name

Table 6-3. Summary of KiNET query results for EGF induced changes in 32 phospho-sites in eight different human tumor cell lines.

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6. Antibody-Based Proteomics Analysis of Tumor Cell Signaling Pathways

phosphorylation in the A431 cells. Enrichment of these phosphoproteins by immunoaffinity and their identification by MS could yield new missing links in EGF signaling pathways.

6.11 Variation of EGF Signaling Pathways in Diverse Cell Types As mentioned above, 11 of the phospho-sites that displayed reduced immunoreactivity signals by Kinetworks™ multiimmunoblotting in EGF-treated A431 cells were previously shown to undergo EGF induced increases in phosphorylation in other tumor cell types. This may reflect the cell-specific nature of signaling pathways. The same growth factor or drug treatment can elicit extremely different responses in diverse cells that differentially express signal transduction proteins. To explore this phenomenon, we took advantage of the KiNET on-line databank to examine how EGF affected protein phosphorylation in other tumor cell lines using the same antibodies and conditions used to investigate EGF action in A431 cells. From this query of KiNET, we found data for EGF’s effects on 32 phospho-sites in seven other tumor cell lines and this is summarized in Table 6-3. It is evident that there is a large diversity in the behavior of these phospho-sites to similar concentrations of EGF and time of exposure across the tumor cell lines. The most reliable biomarkers of EGF stimulation were the increased phosphorylations of Erk1 T185+Y187, Erk2 T202 + Y204, MEK1 S217 + S221, PKBα S473, STAT3 S727, Raf1 S259, Rb S807 + S811, p38α MAPK T180+Y182, adducin α S726 and Rb S780, and decreased phosphorylation of CREB S133. However, for each of these phospho-sites, there are examples of tumor cells where EGF had no or an opposite effect. This demonstrates the importance of working with a diversified panel of biomarkers to track the actions of any particular hormone or drug. The need to identify a panel of biomarkers to reliably diagnose a particular disease in patient biopsy material is revealed in another yet unpublished study that Kinexus has conducted to examine phosphoprotein patterns in human tumor cells by Kinetworks™ multiblotting analyses. Over 80 different phospho-sites were profiled across 40 well-characterized human tumor breast cancer cell lines. Not one of the cell lines showed a similar pattern of phospho-site signals with another. This supports the possibility that every human cancer is distinct at the molecular level, and underscores the need for personalized medicine approaches.

6.12

Conclusions

Antibody microarrays have the potential to transform proteomics studies and facilitate system biology research. The identification of reliable antibody probes and sample preparation remain significant obstacles in realization of the full potential of these protein microarrays. In the present study, it was

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estimated that for the assessment of EGF actions in the A431 tumor cell line, about 44% of the changes in protein phosphorylation evident from the Kinex™ antibody microarray could be validated by immunoblotting. However, 85% of the phosphorylation differences that were identified by Kinetworks™ multi-immunoblotting were missed by the antibody microarray. Nevertheless, in view of the high sensitivity, low cost, and wide scope of the analyses provided by the antibody microarray approach, this represents a very effective strategy for biomarker discovery, especially when this is accompanied by rapid validation by immunoblotting. Once a cell or tissue type becomes well characterized for the reliability of the antibody probes for that system, the antibody microarray should be a powerful tool for mapping cell signaling pathways and monitoring their disruptions in complex diseases such as cancer.

Acknowledgements. This work was supported by a grant from the Canadian Institutes for Health Research to SP and a grant from the National Research Council of Canada Industrial Research Assistance Program to Kinexus.

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Chapter 7 Gene Expression Arrays for Pathway Analysis in Cancer Research Xiang Sean Yu, Raymond K. Blanchard, Yexun Wang, and Min You

7.1 Hallmarks of Cancer and Cell-Signaling Pathways Cancer is a disease where anaplastic cells gradually acquire the capacity to become a malignant neoplasm. Tumorigenesis is a multistep process where genetic changes such as point mutation, gene amplification, translocation, deletion, and viral infection accumulate over time. These genetic changes, in turn, would cause downstream alterations in transcription, protein expression, and other cellular functions that eventually lead to the stepwise transformation of normal cells to malignant cells. During tumorigenesis, tumor cells acquire traits such as unlimited replicating potential, self-sufficiency of cell proliferation, and the ability to escape growth inhibition or apoptosis regulation [1]. Tumor cells would also accumulate changes that affect surrounding tissues; malignant cells acquire the ability to invade tissues, induce angiogenesis to support their own growth, and finally establish metastatic sites in remote organs (Table 7-1) [1]. In multicellular organisms, cells receive and process extracellular signals using intracellular signal transduction networks (Fig. 7-1A). The decision to survive, proliferate, or differentiate is determined by the cell’s genetic programming and environmental stimuli. The cellular signaling system is composed of a network of interacting signaling pathways, with each individual pathway responsible for receiving and processing a particular extracellular signal. A signaling pathway is composed of a series of interacting molecules that perform signaling functions by relaying or transducing a signal. The functional molecules include sensory components such as cell-surface receptors and their associated factors such as cytoplasmic adaptor proteins. In addition, other signaling components include protein modification enzymes, such as kinases, and functional components, such as transcription factors that can migrate into nuclei, bind to DNA, and modulate gene transcription activities. Since many biologic functions are related, biologic pathways share signaling components and often the outcome of cellular signaling is

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

dependent upon those interpathway interactions. For example, the oncogene Ras is a key signaling component in three signaling pathways: receptor tyrosine kinase (RTK), G-Protein coupled receptor (GPCR), and the integrin pathway. The changes in genetic programming during cancer progression rewire the cellular signaling pathways in a manner that eventually results in a series of gene expression changes. These changes are reflected by the multiple traits that cancer cells acquire during tumorigenesis, and the expression of many genes in different biologic pathways is altered. The path from normal to malignancy can also differ, because different cancers can take different routes by changing different components of a signaling network to achieve the same end result, and it has also been shown that cancer cells can acquire those traits in different order [1]. Therefore, to better understand the molecular mechanism of cancer, we need to study all levels of cell signaling: genetic profiles, gene expression profiles, protein interaction/modification, and signaling profiles. Here we focus on the usage of array tools for studying pathway-focused gene expression profiles in cancer.

7.1.1 DNA Microarrays for Gene Expression Analysis First introduced in the 1990s [2, 3], DNA microarrays have become a powerful tool in the study of gene expression profiles in all aspects of biomedical research for human diseases. As DNA microarray technology has matured and the sequences of entire genomes from human and other species have become available, genome-wide and focused DNA arrays have become available from both commercial and academic sources. Gene expression profiles generated from microarray experiments give a broad view of gene expression events that traditional low throughput molecular biology techniques such as Northern-blot analysis cannot deliver [2–4]. Additionally, a variety of other applications are being actively developed by both academic and commercial laboratories to obtain profiles of other biologic analytes. Examples of these new applications include: genotyping microarrays (e.g., single nucleotide polymorphism [SNP], comparative genome hybridization [CGH]), antibody (Ab)-based protein expression microarray, protein 135

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X.S. Yu et al. Table 7-1. Cellular signaling and hallmarks of cancer. Acquired capabilities 1 Autonomous cell growth (self-sufficiency in cell growth signaling) 2 Unlimited replication potential 3 Resistance to growth inhibition (insensitivity to antigrowth signals) 4 Resistance to apoptosis 5 Induction of angiogenesis 6 Tissue Invasion and metastasis

Biologic pathways

Biologic molecules involved

Genome stability Intracellular signaling pathways Telomere maintenance Cell senescence TGFb signaling

Growth factors and other extracellular ligands, cell surface receptors, intracellular signaling molecules, chromatins, promoters Telomerase, telomere, and telomere maintenance molecules

Apoptosis signaling cascade

Death receptor family and apoptosis signaling molecules (e.g., TNF/TNFR gene families) Angiogenesis inducer and inhibitors: VEGF, FGF, thrombosponding-1; ECM molecules Cellular adhesion molecules, extracellular protease and ECM molecules

Angiogenesis ECM Metastasis ECM Cell adhesion

Growth factors, growth inhibitor and receptors (e.g.,TGFb, ECM, Rb proteins), intracellular signaling network

ECM, extracellular matrix; FGF, fibroblast growth factor; TGFb, transforming growth factor beta; Rb, retinoblastoma; TNF, tumor necrosis factor; TNFR, TNF receptor; VEGF, vascular epithelial growth factor

microarrays to study protein–protein interaction, microarrays for transcription factor–promoter interaction (CHIP-on-chip), and tissue microarrays. At this time, however, oligonucleotide-based DNA microarrays are most commonly used to obtain profiles of genetic variations and gene expression.

7.1.2 Gene Expression Profiles as Markers for Tumor Classification and Prognosis One of the most remarkable advances of microarrays is using genome-wide gene expression profiling to generate a molecular signature of cancer. In this approach, the unique up- or down-expression pattern for genes can be obtained by screening expression patterns of thousands of genes and from this, a subset can be used as a biomarker panel. One application is to use DNA microarrays to identify expression signatures that can distinguish cancer cells from normal cells. A number of gene-expression profiles have been developed using genomewide DNA microarrays to distinguish normal cells from cancer cells [5–15]. One of the difficulties for this kind of study, however, is that a cancer tumor sample is a heterogeneous mixture of normal and cancer cells. It is, therefore, difficult to obtain a true expression profile signature for just cancer cells. This sample heterogeneity problem can be solved by using tissue or cell isolation techniques such as laser-capture microdissection (LCM) or fluorescence-activated cell sorting (FACS). Another microarray application is to use gene expression profiles to better classify cancer disease progression. Cancer is a highly heterogeneous disease; patients who have similar phenotypic tumors, characterized using histology or immunohistochemistry (IHC) and cytogenetic standards, may have different underlying genetic alterations. This heterogeneity is reflected clinically in their different responses to drug treatment, disease progression, and clinical prognosis. It is evident that current morphologic/pathologic diagnostic standards or individual molecular biomarkers such as carcinoembryonic antigen

(CEA), α-fetoprotein (AFP), and prostate-specific antigen (PSA) do not provide enough information to classify a diverse disease population. The multimark type of molecular signature that can offer better specificity and sensitivity for cancer diagnosis is much needed. To obtain a unique expression profile for a particular cancer disease, researchers would start using high-density microarrays to obtain genome-wide expression profiles of fresh or archived cancer samples from patients with well-documented clinical history. The high-density microarray data set is analyzed using statistical tools to generate an expression pattern of a subset of genes that matches different clinical classifications or outcomes. The information could lead to a multigene panel that would serve as the core assays for a clinical diagnostic test. Using this approach, Golub and his colleagues identified gene expression signatures that distinguish acute myeloid leukemia (AML) from acute lymphoblastic leukemia (ALL) in 1999 [16]. Since then, a number of other studies have been published that establish gene expression signatures for other solid tumors such as breast cancer [17, 18], melanoma [19], lung adenocarcinoma [20, 21], ovarian cancer [22, 23], and prostate cancer (4). Breast cancer, in particular, has been extensively studied using this global gene expression profiling approach. A number of independently developed breast cancer gene-expression signatures that contain 70–456 genes have been generated using patient samples and high-density microarrays. Several clinical trials are under way to validate those profiles as prognostic markers [24–26]. Burkitt’s and other B-cell lymphomas were reviewed by Dave [27]. In the Burkitt’s lymphoma study, it is interesting to note that the signature expression changes can be grouped into geneexpression aberrations by different biologic pathways [28]. Given that pathway alterations can occur in many points within a biologic pathway, pathway-guided analysis of gene-expression signature data may yield a higher-order signature (i.e., looking for pathway changes instead of changes of particular

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Fig. 7-1. Diagram of cell signaling in cancer cells and flow chart of pathway-array design. A Information flow in cancer cells where genetic changes cause cascading changes in intracellular signaling changes, gene expression changes, and finally the malignant traits of cancer. B The pathway-focused gene-expression tools such as oligonucleotide probe-based microarrays or RT-PCR based PCR arrays are designed based on pathway gene grouping extracted from multiple public data sources. The gene-specific probes/PCR primers are designed based on GenBank sequence information using bioinformatics tools.

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X.S. Yu et al. Table 7-2. NFκB signaling pathway components and genes. Signaling pathway components

Genes in NFκB pathways

Extracellular ligands

TNF, IL1

Cell-surface receptors

TNFR, IL1R

Adaptor proteins

TRAF, TRADD, RAIDD, RIP, Myd88

Kinases and other signaling factors

TAK, TAB, MEDD, NIK, IKK, p38MAPK, NEMO

Transcription factor and cofactor

Iκβ, NFκβ, Rel A, RelB

NFκβ response genes

Cytokine, chemokine and other ligands (e.g., IL1, IL2, MIP-1, CCL5, TNF) Immunoreceptors (e.g., TLR, TNFR, FOS) Adhesion and ECM molecules (e.g., ICAM, VCAM) Acute phase and inflammatory response (e.g., AGT, C3, IL1, IL1R, TLR, EGR)

pathway components) for diseases than a set of individual gene alterations. Although these expression signature tests are still in the development phase, it is clear that disease specific molecular profiles of genetic, transcriptional, and proteomic variations will play an essential part of personalized medicine in the future. For this purpose, low-density, application-specific multimarker tests are more desirable than genome-wide tests because of simplicity and cost considerations.

7.1.3 Pathway-Focused Gene Expression Profiling In addition to using genome-wide microarray screening to generate cancer biomarker profiles, another major application of microarrays in cancer is to investigate the biologic pathways of tumorigenesis and to characterize the mechanisms of aberrant biologic pathways in cancer. Many research projects are done by studying specific hallmarks of cancer such as metastasis, angiogenesis, or apoptosis. For this type of mechanistic research that has a predefined scope, a genome-wide microarray may not be necessary and a focused-content microarray is designed so that only the relevant set of genes are included to study a specific biologic pathway of interest. These tools are applicationspecific or pathway-specific microarrays. Focused microarrays provide a bridge between single gene studies where it might be difficult to investigate the relationship between the gene members of a biologic pathway and the genome-wide high-density microarray approach where the volume of expression data comes with complexities of data analysis and molecular events that are not related to the scope of the study. This approach is made possible by developments on two fronts: a nearly complete genome sequence for human and other animal species, and years of cancer research resulting in the identification of major biologic pathways that are altered. One can research the rich biomedical literature to summarize the relevant information regarding pathway networks and, using GenBank and other DNA sequence databases, design contents for focused microarrays (Fig. 7-1B). Gene grouping for a biologic pathway can be summarized by studying both published research works on the

pathway and biologic pathway databases. Pathway information can be collected through various sources such as PubMed (www.ncbi.nlm.nih.gov/)or High Wire Press (www.highwire. org) and pathway annotation databases, such as Kyoto Encyclopedia of Genes and Genomes (KEGG) [29] and gene ontology (www.geneontology.org/). Sometimes high-density microarray data can be integrated into pathway microarray design using resources such as the Gene Expression Omnibus (GEO) [30]. With this information, one can group the components of signaling pathways to form a focused gene-expression profile that provides a valuable view of intracellular signaling networks in the context of tumorigenesis. For example, the NFκβ pathway is the key part of death receptor signaling. It contains cell-surface sensory molecules and their ligands, intracellular adaptor proteins, intracellular signaling kinases, transcription factors components, and target genes where transcription is regulated by NFκβ signaling. The functional components and gene examples are summarized in Table 7-2. Based on pathway grouping and sequence information from databases such as GenBank, gene specific reversetranscription polymerase chain reaction (RT-PCR) primers or oligonucleotide probes can be designed using sequence analysis and other bioinformatics design tools. Oligonucleotide-based microarrays or real-time PCR-based arrays can be produced. Pathway-focused approaches provide a simple and cost-effective tool to obtain pathway-related gene-expression profiles. It is an attractive alternative to the genome-wide microarray tool that is costly and complex to use. Pathwayfocused approaches have been successfully used in recent years in many areas of cancer research.

7.2 Tools for Pathway-Focused Gene Expression Profiling To get the most from gene-expression profiling experiments, it is necessary to have an understanding of the gene-expression analysis platforms and how they affect and are affected by the experimental goals, design, and analysis techniques selected by the investigator. These same considerations hold

7. Gene Expression Arrays for Pathway Analysis in Cancer Research

true for pathway-focused arrays although there are some additional factors to be considered. One of the most significant aspects of modern gene expression analysis is that the original sample mRNA molecules are not the ones measured by the instrumentation. Instead the original mRNA molecules are subjected to multiple enzymatic manipulations to produce replicas that incorporate a detection moiety or label into each molecule. If care is not taken to use high-purity RNA samples and maintain consistent enzymatic conditions during processing, the population of replicas will not accurately reflect the original population and will produce poor scientific results.

7.2.1 Pathway-focused Arrays for Gene Expression Analysis Microarrays are the most common format used for multigene expression profiling. Real-time PCR, which was once reserved for confirmatory analysis of a few genes, has developed to the point that PCR arrays are an alternative. The following sections will highlight the technical basis for each platform and review the strengths of each.

7.2.1.1

Hybridization DNA Microarrays

A DNA microarray is a solid support onto which gene-specific probes are immobilized at different locations. In their original form, microarrays were produced by spotting gene-specific cDNA fragments onto nylon membranes or glass slides. For genome-wide high-density arrays composed of thousands of genes, the cloning and maintenance of a high-quality collection of cDNA clones became a limiting factor. In recent years, shorter gene-specific oligonucleotides (25–70 nucleotides in length) have been increasingly used as the probes on microarrays [31]. Different immobilization methods have been developed to produce microarrays that have finer features within a single standard sized area. For example, Affymetrix (Santa Clara, CA) uses a photolithographic method to synthesize millions of oligo probes in situ onto a single quartz wafer, known as a GeneChip. Another major manufacturer, Agilent (Santa Clara, CA), uses a different in situ oligo synthesis technique, similar to inkjet printing, to produce high-density arrays on glass slides. An alternative to glass slide or quartz-wafer supports, is the nylon membrane that has been used as the solid support from the conception of DNA microarrays. As a solid support for immobilization of biomolecules, polymer-based membranes have been used for many years before the introduction of microarray technology. The membrane-based assay format provides excellent signal-to-noise performance in analyzing nucleic acids and proteins (e.g., Northern, Southern, and Western blots) for a wide range of detection methods. Sequence-specific nucleotide base-pairing between complementary strands of nucleic acid is the basis for target detection on DNA microarrays. The sequence-defined, immobilized “probe” is one strand and the labeled “target” is the other

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strand that produces the measured signal. The two-dimensional position of each probe is fixed on the matrix and provides a definition to associate signal strength with a particular gene. Fig. 7-2 presents an overview of the general process of using hybridization-based microarrays to obtain gene-expression profiles, which is a multistep process where total RNA is first isolated from biologic samples of interest creating the “target” material. Then the target is labeled using one of several labeling options available for microarray detection. The most common forms of target labeling are fluorescence (e.g., Cy3 or Cy5) or chemiluminescence, and target amplification is often desirable to increase overall signal strength. These methods usually start with the enzymatic conversion of mRNA into cDNA by reverse transcription. This step can also incorporate labeled nucleotides but by itself does not provide any enhancement of overall signal strength. Most often, the cDNA is transcribed, in vitro, to produce hundreds or thousands of labeled molecules from each single cDNA molecule, which enhances the sensitivity of microarray measurements and/or permits the use of smaller amounts of starting target material. A wide variety of options for these steps are available commercially, allowing users to balance both assay specificity and sensitivity. Labeled target is then hybridized to the gene-specific DNA probes on the microarray under conditions that promote only sequence specific annealing. After removal of unhybridized target molecules by washing, the amount of hybridized target on each probe element is measured. For fluorescent-labeled targets, a laser scanner is used to generate high-resolution fluorescent images. For chemiluminescent detection, high-quality array images are acquired using a cooled charged-coupled device (CCD) camera system, although x-ray film can also be used for semiquantitative measurements. To obtain a quantitative readout of array spots, array images are processed with array-image processing software that converts the brightness of array spots into an intensity value and associates it with a gene identity. These gene and sample specific intensity values are the raw data for the gene expression analysis.

7.2.1.2

Real-Time PCR arrays

A recent addition to pathway-focused array formats is the real-time PCR array. PCR is an enzymatic process whereby a DNA sequence (an amplicon) is exponentially amplified using a pair of oligonucleotide primers and a thermostable DNA polymerase [32]. PCR products should, in theory, double the number of amplicon molecules every cycle in a PCR reaction providing an excellent enhancement of target material. In reality, during the early phase of the PCR reaction, all the reagents are abundant and the kinetics of the reaction favor exact doubling of the amplicons (Fig. 7-3). The introduction of an instrument that can monitor PCR products during thermal cycling [33] revolutionized nucleic acid quantification by coupling amplification with real-time detection. Thus with a realtime graph of PCR product amount/cycle number plot (often called an amplification plot), one can make a more accurate

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Fig. 7-2. Studying of gene expression in cancer. Gene-expression profiles in cancer can be obtained by using total RNA extracted from tissue or cell culture samples. The analysis using a hybridization-based microarray typically involves labeling or amplifying the RNA target, hybridizing labeled targets with immobilized array of gene specific probes, imaging of hybridized target probes, and image data analysis. The gene-expression profile data is often validated by real-time RT-PCR technique.

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Fig. 7-3. Gene-expression analysis by real time PCR array. The typical gene-expression analysis by PCR array starts with total RNA extraction, then reverse transcription to generate cDNA. The PCR reaction mixture and cDNA are dispensed onto PCR plates containing an array of prevalidated and optimized primer pairs. Using a real time PCR instrument, the amplification plots for all genes are generated and Ct values are compared between different samples. The relative gene expression change can be inferred by the ∆∆Ct methods. (see Color Plate 3 following p. 316.)

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assessment on the number of molecules in a sample (Fig. 7-3). RT-PCR is a PCR method that uses a cDNA template generated from mRNA by reverse transcription (RT) [34] and is the basis for all RNA quantification by PCR [35]. It is a sensitive and flexible RNA analysis method that can measure samples as small as a single cell. The simplest and most universal way to quantify PCR products after each cycle is to use a fluorescent dye that binds double-stranded DNA, such as SYBR Green [36]. Introduced as a quantitative PCR reporter in 1998 [37], SYBR Green itself displays little fluorescence in solution yet it specifically binds double-stranded DNA over single-stranded DNA with a large fluorescent enhancement upon binding. SYBR Green binding to double-stranded DNA is sequence independent; therefore, two DNA molecules with similar length but different sequences will yield similar fluorescence upon SYBR Green binding. Thus, it is a mass reporter and requires a highly specific PCR reaction. An alternative to SYBR Green for real time monitoring of PCR is the use of fluorescence resonance energy transfer (FRET) methods such as TaqMan probes [3] and molecular beacon probes [16]. These methods use a third gene-specific oligonucleotide in the reaction that is not directly involved in the amplification but instead provides a sequence-specific fluorescent reporter. Fluorescence probes can offer greater specificity than the general DNA-binding dyes method, however, they are more expensive to make than unlabeled oligonucleotides and require additional gene-specific probe design for each assay. In real time PCR, the detectable fluorescent intensity increases as the amount of an amplicon accumulates after each cycle. The more abundant a particular cDNA (reverse transcribed from its mRNA) in the sample, the faster its amplification products accumulate and the sooner its fluorescent intensity will surpass a specified fluorescence signal threshold (Fig. 7-3). This threshold value is always chosen at the exponential phase of the PCR reaction where the kinetics of the amplification are the most precise. The number of PCR cycles taken to reach the threshold value is known as the “Ct value” and is inversely related to the amount of mRNA in the sample (i.e., the greater the abundance of mRNA, the smaller the Ct value). Traditionally, using a standard curve and the Ct value of the unknown sample, one can make an accurate measurement of the copy number within the unknown sample. While the standard curve method is quite accurate, it is cumbersome to set up. More commonly, researchers are more interested in comparative expression changes between samples, rather than the absolute mRNA levels. A simpler quantitation method, commonly referred as the “delta-delta Ct” (∆∆Ct) method, has been developed by Livak and Schmittgen [30]. Their method provides a means to calculate the fold-change ratio for a gene of interest (GOI) from two samples using only four Ct values. The first two Ct values are for the GOI and a reference gene from the control sample whereas the second two Ct values are for the same GOI and reference (housekeeping) gene from the experimental sample. For the experimental sample, the refer-

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ence gene Ct value is subtracted from the GOI Ct value to produce a ∆Ct for the GOI in that sample (CtGOI – Ctreference = ∆CtGOI:sample). This is repeated for the control sample to generate a ∆CtGOI:control. Since in a perfect PCR reaction, the amplicon DNA is increased twofold after each cycle, this equation returns a linear fold-change ratio from the two ∆Ct values: Fold-change ratio =2 − ( ∆ctGOI:sample-∆ctGOI:control) =2 −∆∆ct For gene-expression analysis, the quantitative RT-PCR method offers better assay sensitivity and larger dynamic range compared with hybridization microarray methods. Yet, RT-PCR is inherently a method of single analyte measurement where each reaction only detects one target. Given that RTqPCR is carried out in 96- or 384-well plates, it is possible to set up a single plate to run parallel PCR reactions that measure a group of pathway-focused genes simultaneously. For this type of purpose, we have developed a series of pathway-focused PCR arrays that cover various biologic pathways. A PCR array plate contains primers for 84 pathway genes, five reference gene candidates, and 7 other control reactions. The handling and processing of PCR arrays along with their data acquisition and analysis is generally much easier than traditional microarrays. In PCR array analysis, one only needs to perform two handling steps (RT then PCR) after which the assay reaction and data acquisition is combined in one instrument so the user can get the final gene expression result with much less effort. Because each real time PCR instrument only handles one PCR plate at a time, the sample handling throughput can also be a limiting factor. Overall, although PCR array gene-expression profiling is not a typical microarray assay where a single labeling reaction and hybridization can assay thousands of genes, a PCR array assay is capable of providing a focused gene expression analysis just like its spotted array counterpart.

7.2.1.3 Experimental Design and Analysis for Pathway-focused Arrays The design of any gene-expression analysis experiment is critically linked to the experimental goals and methods of data analysis. If the experimental purpose is not clearly defined, then appropriate experimental set-up and analysis techniques cannot be selected to complement each other and produce the desired answers. Failure to consider all three aspects (goals, design, and analysis) at the outset of the project will, at best, result in partial answers to the original questions and, at worst, be a significant waste of time and resources. While there are texts and articles available that provide discussions of this topic in sufficient detail to guide an investigator entering into these kinds of experiments [38–41], a brief overview of the most critical considerations follows. 7.2.1.3.1

Requirement for Biologic Replicates

The key considerations in good experimental design center on the desired sensitivity of expression change and the degree of confidence in that measurement. These factors allow for a

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calculation of the number of replicate measurements needed to meet these investigator-selected criteria. While straightforward when evaluating a single gene, the large numbers of genes on an array are a diverse population, each having its own characteristic variation in expression levels, and this substantially complicates the calculation. The wide variation in expression by different genes under different conditions means that although a 1.5-fold average change in expression for one gene may be significant, a fourfold average change for another may not, depending on the stability of each gene’s expression under the conditions being compared. In other words, for genes that have a very small variance in their expression under the conditions being studied, a smaller fold-change can accurately be measured than for genes that have a large variance in their expression levels. By defining the fold-change sensitivity and confidence level at the beginning of experimental design, statistical calculations can be used to determine the number of replicate samples that will be needed to meet these criteria for a specified percentage of the genes in the population. Constraints on resources, however, often call for a minimization of sample numbers and because the statistical minimum number for a legitimate calculation of variance is three, the use of three biologic replicates can provide a crude measure of a combined biologic expression and technical variance that may be acceptable for some basic research applications. In general though, increasing the number of biologic replicates in the study will improve the sensitivity to confidently detect small changes in gene expression. 7.2.1.3.2

Choosing an Appropriate Array Methodology

The array platform is also an important consideration as it will have both scientific and economic ramifications. For discovery work, whole genome microarrays provide an excellent tool because the entire population of genes can be monitored with no previous knowledge of the key genes that are likely to be involved. This format, however, is financially and computationally expensive to perform, which places limitations on the number of samples that can be examined. When examining a larger number of samples or experimental conditions (such as time course, dose response, or drug mechanism studies) and some previous knowledge is available regarding the biology, pathway-focused arrays can facilitate multigene throughput while conserving other resources. When selecting pathway-focused analysis, an additional consideration is whether to use hybridization microarrays or RT-qPCR array plates. The key scientific consideration between these two methods is the abundance of the mRNAs of interest in the samples and the total dynamic range needed to accurately quantify as many of the genes as possible. PCR arrays generally have better sensitivity for quantitatively measuring low levels, of expression such as those found for basal cytokine mRNA levels, plus qPCR has the potential for a 6- to 7-log dynamic range. On the other hand, hybridization microarrays provide faster processing of larger sample numbers and genes as well as a lower overall cost than qPCR.

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7.2.1.3.3

Sample Considerations

The RNA source is another consideration that takes into account the type of source (mixed cell type tissues, isolated individual cell types, or cultured cell lines), its RNA amount (few cells or many), and sample processing (snap-frozen fresh cells, formalin-fixed tissue sections, or microdissected cells). These will affect the both absolute and relative abundance of mRNAs within samples and, therefore, the sensitivity required to measure them as well as the choice of labeling system in the case of hybridization microarrays. The availability of sample material and processing investment, such as laser capture microdissection, can also be a factor in determining the number of samples to be evaluated. Additionally, these points become critical considerations for the isolation of high purity RNA samples that are required for all current gene expression systems, because of the fact that, unlike Northern blots, modern gene-expression analysis relies on highly controlled enzymatic manipulation of the RNA before detection can occur and anything that perturbs the enzymatic reactions can skew some or all of the gene expression results. 7.2.1.3.4

Data Analysis Considerations

The experimental design is also a significant consideration in the analysis of gene-expression data that can be broken down into two major parts: data acquisition and comparative analysis. While most of the analysis steps are similar for hybridization microarrays and qPCR arrays, they are very different in their methods of data acquisition. For microarrays, the hybridization signal is measured from photons (chemiluminescence or fluorescence) or electrons (β-particle radioactivity) emitted from the labeled cRNA or cDNA target using a CCD camera, photomultiplier tube, phosphor imager, or x-ray film. The amount of mRNA is directly proportional to the magnitude of the signal intensity. In PCR, a fluorescent signal from the geometrically amplified products is measured after each round of thermal cycling and the amount of mRNA is inversely proportional to the Ct value. Consequently, the issue of background subtraction is fundamentally different for the two methods and only applicable to hybridization-based systems. In this case, where the absolute signal intensity is the sum of both the specific signal and the nonspecific background signal for the array, it can be desirable, but not necessary, to subtract the background signal from the analysis. It is important to note that this transformation will have a much more profound effect on those genes with low signal intensities than those with high signal intensities. Additionally, any background subtraction will reduce the effective dynamic range for the assays and reinforces the need for technical proficiency during sample and microarray processing to keep the nonspecific signal levels as low as possible. Normalization of signal range between samples is applicable to both types of arrays and a critical expression analysis variable. A good consideration of normalization begins in the sample preparation phase to insure that all samples are handled

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and prepared equivalently. For pathway-focused microarray analysis, there are four basic types of arithmetic normalization methods that can be applied to the data. These include the population mean method, the population median method, the reference or housekeeping gene method, and no arithmetic normalization. The first two methods are generally limited to hybridization-based systems and can be used for arithmetic normalization of relatively small numbers of genes provided that the conditions being examined do not cause a global shift in expression levels for the selected subpopulation of genes. The reference or housekeeping gene method is the most commonly used method but relies on one fundamental premise: the expression of the reference gene is absolutely invariant under the conditions being examined. Because even so-called housekeeping genes do have altered expression levels under different conditions, it is extremely important to validate the choice of reference gene or genes before analyzing the expression data from other genes [42]. It is often desirable to determine multiple sets of gene expression results using different normalization settings to identify major effects within the experiment. In doing so, the use of no arithmetic normalization for one of these sets permits an observation of similarity between the raw microarray images. In hybridization microarrays, normalization is performed by dividing individual signal intensity values by a normalizing value (e.g., the signal intensity of the housekeeping gene(s) within a single sample). In PCR, however, because the Ct value is an exponential function, the Ct for the housekeeping gene(s) is subtracted (not divided) from the Ct for the GOI to create a ∆Ct value that becomes the normalized gene signal intensity. The net result of the data acquisition phase is a refined gene expression intensity value for each gene in each sample. Once all samples have been normalized, the refined expression intensities can be directly compared between samples and the comparative analysis phase has begun. For most experiments a control group is used to establish a reference baseline level of expression for each gene in the analysis. For microarrays the change in expression levels or fold-change ratio is calculated by dividing a gene’s normalized experimental expression intensity by the same gene’s control intensity value. For PCR, the difference in the ∆Ct, or ∆∆Ct, from two samples is a measure of the normalized gene expression levels as discussed above and a linear fold change ratio can be calculated by raising 2 to the power of the negative ∆∆Ct. The fold-change ratios and/or refined expression intensities can be subjected to a wide variety of different from analyses a simple t-test to hierarchical clustering depending on the goals of the experiment. One fundamental analysis technique is to filter the expression changes by statistical significance (pvalue) then rank the fold-change magnitudes. Use of a volcano plot provides an excellent visualization of these characteristics when comparing expression analysis results from two conditions. On this graph, the p-value is plotted as an exponential function on the y-axis and the log2 of each gene’s fold-change value is plotted on the x-axis [43]. (By using a log2 trans-

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formation of the fold-change a continuous and proportional scale is created on the x-axis that is centered on a onefold or no change axis of zero). Thresholds for statistical significance and fold-change can be superimposed on the plot to delineate the genes in the upper, outside regions that are most likely to be of interest to investigators. It is important to note that different methods of background subtraction and normalization may alter the results of the final analysis and because there is no one established standard for these settings, it is advisable to repeat the analysis with different selections to identify the presence of major trends that cross all settings. While different PCR and hybridization microarrays often produce different magnitudes of fold-change ratios (PCR generally larger than hybridization) the rankings of the fold-change ratios are usually highly concordant [44, 45].

7.3 Application of Specific Expression-Profiling Tools in Cancer Research The hallmark characteristics of cancer are manifestations of cellular functions related to growth, death, intracellular communication, and extracellular functions for cell-to-cell communication and tissue remodeling. A review of the acquired capabilities of cancer cells, as discussed by Hanahan and Weinberg [1], shows that there are a number of signal transduction pathways that play often overlapping roles in the manifestation of these traits (Table 7-1). These functions all involve signal transduction pathways that have been elaborated over the last several decades to the extent that many of the signaling steps and intermediaries are well characterized. Key members in these pathways have been identified and their status in different cancer cell types and treatment models is frequently and repeatedly assayed. Therefore, given the limited number of these key genes and the expense associated with assaying and analyzing large numbers of genes, limited content, pathway-focused gene expression arrays are often advantageous to streamline cancer investigations.

7.3.1 Studies of Individual Pathways using Focused Microarrays One of the most intensely studied pathways in the cancer field is the regulation of apoptotic cell death (See Chapter 12, this volume). While many of the intracellular mechanistic events have been documented as part of either the mitochondrial dependent or death receptor-mediated pathways, the initiating signals originate in distinct signaling pathways including tumor necrosis factor α (TNFα), fatty acid synthase (FAS)/death domain, and p53 pathways. Apoptosis focused microarrays contain key genes from all of these pathways, such as caspases, TNF receptors (TNFR), and MDM2, which make particularly good tools for monitoring

Fig. 7-4. Array microarray images from different pathway models used to analyze cancer genotype-specific and chemotherapeutic treatment responses. CDDO-Im induced upregulation of genes in both iMycEµ-1 and-2 cells. Shown are images of the cDNA arrays involved in cell cycling (top row), apoptosis (2nd row), stress and toxicity responses (3rd row), and NFκB signaling (bottom row). CDDO-Im treated and untreated samples are presented as array pairs by genotype. Squares indicate the CDDO-Im–induced genes and they are listed in the text box to the right of each array. Underlined gene names indicate genes that changed and are present on multiple arrays. Note that although the induction of some genes is visible by eye, others are not visible at these photographic settings but still detected by image analysis software. Reprinted with permission from S-S Han et al. Molecular Cancer, 2006, 5:22 [53]. (see Color Plate 4 following p. 316.)

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Fig. 7-5. Gene expression analysis of breast tumor and normal tissues using real-time PCR arrays. A The scatter plot compares expression of 84 cancer pathways genes between normal breast tissue and breast tumor. The Ct value of each gene is normalized against Ct of housekeeping gene. The solid diagonal line represents no change in gene expression. Any data points above the upper dotted line represent genes that are upregulated >twofold in tumor tissues. Any data points below the lower dotted line represent genes that are downregulated >twofold in tumor tissues. B Volcano plot of gene expression change and p-value of data points. The p-value is calculated using the two class Student’s t-test. Data points in the upper right quadrant represent genes that are upregulated >twofold with p < 0.01 in tumor tissues. Data points in the upper left quadrant represent genes that are downregulated >twofold with p < 0.01 in tumor tissues. C List of genes whose expressions are significantly (>twofold change of expression) changed in breast tumor tissues and their involvement in different signaling pathways.

global pathway effects resulting from treatments as well as identifying the pathway variants most affected. In the field of cancer medicine with the poor clinical outcome being death of the patient, many clinically effective therapeutics have been developed for which there is limited mechanistic information. Many of these therapies result in induction of apoptosis or growth arrest within tumor tissue cells. This situation leads to disparate pieces of information that should be connected in the context of cellular biology and sometimes a pathway-focused approach can be established by

reviewing the combined information. An example of this is the gene for a key regulator of cellular energy homeostasis, FAS that is over-expressed in a variety of malignancies including breast cancer and for which FAS inhibitors have demonstrated antitumorigenic qualities [46]. No known direct mechanism can explain how the amount of FAS regulates cell growth. In one report investigating the signaling mechanism by which FAS exerts its effects, Bandyopadhyay et al. used an siRNA approach in breast cancer cell lines to decrease the amounts of FAS protein without the use of

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small molecule pharmacologic agents, and determined how this effected tumor cell growth [47]. Because of the previously reported data indicating a focus on apoptosis and growth arrest, the investigators used the apoptosis and cell cycle-focused microarray to study the effects of FAS protein knock down on the expression of genes in these pathways. Their results showed a five- to ninefold upregulation of the proapoptotic genes BNIP3, TRAIL (TNFSF10), and DAPK2. These changes were confirmed by real-time RT-PCR for the mRNA level as well as by Western blot showing that the alteration of expression was manifest at the protein level. BNIP3 interacts with BCL2, an oncogene that protects cells from apoptosis by perhaps blocking this effect, although TRAIL has been shown to activate the apoptotic caspases 3 and 8. Increased amounts of DAPK2 have been shown to increase apoptosis. Taken together, these results demonstrate that FAS’s effects are to suppress apoptosis and they provide key focal points within this complex pathway for further investigation. Another example is to determine the details of signaling pathway interactions, which can aid in refining the target and intervention points for the development of chemotherapeutic agents. The drug doxazosin is used clinically to treat benign prostate hyperplasia. Research on prostate cancer intervention using this drug indicates that its mode of action is to induce apoptosis. To determine whether this action was exerted through the mitochondrial-dependent or death receptor apoptotic mechanisms, Garrison and Kyprianou [48] started by examining gene-expression changes using the apoptosis GEArray. Their results identified Bax, Bcl-xL, FADD, and Fas as significantly upregulated and indicating a death receptor-mediated mechanism. Considering that anoikis, a type of apoptosis associated with cellular shrinkage and separation from the extracellular matrix (ECM), is observed upon doxazosin treatment, they expanded their investigation to include genes for cell adhesion by using the ECM and adhesion molecule GEArray that demonstrated profound changes in expression for a wide variety of cell-attachment molecules. Overall, these results indicate that doxazosin’s anticancer activity may be due to an induction of death receptor-mediated apoptosis through anoikis. The third example is the use of pathway-focused gene expression to study a gene product’s function such as arachidonate 15-lipoxygenase, type 1 (15-LOX-1). In the colon, both tumorigenic and antitumorigenic characteristics are correlated with increased 15-LOX-1 expression depending on the colonic cell type. In an attempt to dissect this conflict, a functional analysis of 15-LOX-1 was performed by stably over-expressing it in the colonic tumor cell line, HCT-116 [49]. Initial results showed a decreased cellular proliferation rate because of increased 15-LOX-1 and directed the inquiry towards cellcycle regulation. Measurement of gene-expression levels for the key components of cell-cycle regulation led to use of the cell-cycle gene array, which identified three genes all closely related to the p53 signaling pathway with altered mRNA levels. This finding led to the use of the p53 signaling pathway

array that confirmed the regulation of two genes, CDKN1A and MDM2, as well as identifying nine other p53-regulated gene-expression changes. The known involvement of these genes in cell-cycle arrest and their regulation by p53 ultimately led to the determination that the increased 15-LOX-1 protein, but not its lipoxygenase activity, was directly correlated with increased phosphorylation of p53 at Ser15. In this way, identification of a specific subset of gene-expression changes within a larger pathway can guide investigators to more specific signaling mechanisms and regulator proteins for functional analysis.

7.3.2 Multipathway Studies using PathwayFinder Arrays Specific pathway-focused gene-expression arrays are used to study one pathway, which requires a researcher to know a priori which pathway is involved in the expression of the genes in question. The PathwayFinder Microarray (in both oligo or PCR format) is designed to assist researchers in identifying which of the major pathways has significantly altered gene expression in their experimental systems by monitoring multiple pathways simultaneously. One example is the Cancer PathwayFinder microarray (Table 7-3), which includes all key genes in biologic pathways that are involved in regulating major traits of cancer (Table 7-1). It has been used to aid the elucidation of the functional role of polyunsaturated fatty acids (PUFA), such as omega-3 (n-3) PUFA [50]. Using an adenovirus system to overexpress an exogenous n-3 desaturase, fat-1, researchers lowered the n6/n-3 fatty acid ratio in human lung carcinoma A549 cells to determine whether there was a change in their invasive potential. A profound difference in the cells’ adherence in culture and confirmatory decrease in migration on invasion assays, demonstrated a significant change in metastatic potential within the treated cells. After cell-proliferation assays found no change in growth rate and terminal transferese dUTP nick end labeling (TUNEL) assays showed an increase in apoptosis within lower n-6/n-3 ratio cells, gene expression analysis using the microarray identified eight downregulated genes. The most significant of these were MMP-1, ITG-2α, and NM23-H4 that were downregulated two-, three- and twofold, respectively, and focused the expression changes on the cell adhesion and tissue remodeling pathways. The downregulation of the three genes collectively indicated that the decreased metastatic potential of low n-6/n-3 PUFA cells is due in large part to changes in the ECM signaling pathways. The PathwayFinder microarray can be used in monitoring cell type-specific changes in gene expression within individual tumor types. Laser capture microdissection has become the premier method to harvest histopathologically consistent cell types for analysis from the tissue sections of cancerous tumors [51]. Given the cell-specific nature of signal transduction mechanisms, the microarrays are an excellent tool for the use of RNA isolated from microdissected tumor cells to determine

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X.S. Yu et al. Table 7-3. Gene grouping for Cancer PathwayFinder PCR array. Biologic pathways Cell-cycle control and DNA damage repair

Apoptosis and cell senescence

Signal transduction molecules and transcription factors

Cellular adhesion

Angiogenesis

Invasion and metastasis

Related cancer traits

Key genes in the pathways

Autonomous cell growth (selfATM, BRCA1, cyclin E1, sufficiency in cell growth CDC25A, CDK2, CDK4, signaling), unlimited replication CDKN1A, CDKN2A, CHEK2, potential E2F1, MDM2, RB1, S100A4, p53 Resistance to apoptosis; unlimited APAF1, BAD, BAX, BCL2, Bclreplication potential X, CASP8, CFLAR, GZMA, HTATIP2, Telomerase, TNF-α receptor, DR5, DR3 Resistant to growth inhibiAKT1, ERBB2, ETS2, FOS, tion; autonomous cell growth JUN, MEK, MYC, NFkB, (self-sufficiency in cell growth IkBα, PI3K, RAF1, SNCG. signaling) Tissue invasion and metastasis Integrin α1, Integrin α2, Integrin α3, Integrin α4, Integrin αV, Integrin β1, Integrin β3, Integrin β5, MCAM, MTSS1, PNN, SYK, UCC1 Induction of angiogenesis Angiopoietin-1, Angiopoietin-2, Endostatin, FGFR2, IFNα, IFNB1, IGF1, IL8, PDGFA, PDGFB, TEK, TGFB1, ALK5, Thrombospondin-1, TNF, VEGF Tissue invasion and metastasis MET, MMP1, MMP2, MMP9, MTA1, MTA2, NME1, NME4, PLAU, PLAUR, S100A4, SERPINB5, SERPINE1, TIMP1, TIMP3, TWIST1.

which pathways are most profoundly affected within specific cell types. For example, in breast cancer, individual focal disruptions of the myoepithelial (ME) cell layer, which is considered an absolute prerequisite for invasion of ductal tumors, are found generally surrounded by either estrogen receptor (ER) positive or negative tumor cells [49, 52]. The ER negative cells surrounding these ME disruptions are associated with a higher frequency of loss of heterozygosity and higher proliferation rates, leading Man and colleagues to hypothesize that they may represent more aggressive clones or be the direct precursors of invasive lesions and, therefore, have different gene-expression profiles than their ER-positive counterparts [52]. To test this hypothesis, they microdissected the ER-positive cells from 20-ER positive ME disruptions and ER- negative cells from 20 paired ER-negative ME disruptions for RNA isolation and analysis on a microarray. As is frequently the case because of the limited amount of material obtained from microdissection, the RNA-labeling process was performed using two rounds of in vitro transcription to amplify the labeled cRNA hybridization signal to a level adequate for quantitation. The results from this microarray, which included qPCR confirmation of three genes, indicated that cell-cycle control and apoptosis pathway members were most significantly altered, as were cell-adhesion genes. Overall, of the 11 genes identified as upregulated in ER-negative cell clusters, eight directly or indirectly promoted proliferation whereas three promoted apoptosis. The details of this simultaneous induction of coun-

teracting signaling pathway members remains to be determined, although most clearly correlate with the previously documented greater metastatic potential. Nonclinical evaluation of agents can be aided by pathwayfocused gene-expression analysis. Two such models for lymphoblastic B-cell lymphoma (LBL) and plasmacytoma (PCT) are the National Cancer Institute (NCI)-developed mouse c-myc over-expressing B-cell lines iMycEµ-1 and iMycEµ-2, respectively. To demonstrate their use as models for evaluating the growth inhibitory and death inducing potency of cancer drug candidates, Han et al. reported on the effects of dosing with 2-cyano-3,12-dioxoolena-1,9-dien-28-oic acid (CDDO)imidazole (CDDO-Im), a synthetic triterpenoid family member with documented antineoplastic activity in other types of cancer cell lines [53]. After verifying that CDDO-Im treatment inhibited proliferation and induced apoptosis that correlated with decreased level of myc protein by a factor of three to five, gene-expression analysis on pathway-focused microarrays was done. Based on previous knowledge of CDDO-Im activities, four pathway-model microarrays were used to monitor the drug’s effects and were chosen to cover four distinct modes of action. The mouse cell-cycle array monitored growth inhibitory effects genes, the apoptosis array monitored cell killing effects, the NF-κβ signaling pathway array was used to monitor anti-inflammatory effects, and the Mouse Stress and Toxicity PathwayFinder Array was used to monitor altered redox balance-related genes. Analysis of the microarray data

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for both cell lines produced a list of 30 genes that were concordantly induced by CDDO-Im. Five of these genes (Casp8, Creb1, Gadd45a, Lta, and Tnfrsf11a) also demonstrate the inter related nature of signaling pathways and the reproducibility of arrays by producing the same expression change on different array models (Fig. 7-4). Six concordant genes had a 10-fold or greater induction by CDDO-Im, of which five were oxygenases and may contribute to the previously documented perturbations of cellular redox levels. Not surprisingly, because this family of enzymes often plays a major role in drug metabolism

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and toxicity, three cytochrome P450 mixed function oxygenases, Cyp2a5, Cyp2b9, and Cyp2c29, were identified. The other two oxygenases include heme oxygenase 1 (Hmox1) and the mixed function oxidase, flavin-containing monooxygenase 4 (Fmo4), a gene target generally considered to be refractory to changes in mRNA levels. The sixth highly induced mRNA encodes Caspase 14 (Casp14) that is associated with inflammation and increased apoptotic cell death. While nine of the 30 concordantly upregulated genes are known to be direct myc binding target genes, the dual activator/repressor functions of

Fig. 7-6. Gene expression analysis of primary and metastatic breast RNA using real-time PCR arrays. A The scatter plot compares expression of 84 cancer pathways genes between matched primary and metastatic breast tumor samples. The Ct value of each gene is normalized against Ct of housekeeping gene. The solid diagonal line represents no change in gene expression. Any data points above the upper dotted line represent genes that are upregulated >twofold in metastatic tumor. Any data points below the lower dotted line represent genes that are downregulated >twofold in metastatic tumor. B The volcano plot of gene expression change and p-value of data points. The p-value is calculated using the two class Student’s t-test. Data points in the upper right quadrant represent genes that are upregulated >twofold with p < 0.01 in metastatic tumor. Data points in the upper left quadrant represent genes that are downregulated >twofold with p < 0.01 in metastatic tumor. C List of genes whose expressions are significantly (>twofold change of expression) changed in primary and metastatic breast tumor tissues and their involvement in different signaling pathways.

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myc as a transcription factor prevent a clear-cut view of cause and effect relationships between these genes’ altered expression and the genetic and/or drug altered levels of myc in these cancer cell models. Overall, the similarity of pathway responses between the two mouse cell lines and the known antineoplastic effects of CDDO-Im led the authors of this study to conclude that the iMycEµ-1 and iMycEµ-2 cell lines will be good mouse models for human LBL and PCT.

7.3.3 Application Examples of Real-time PCR Arrays Recently PCR arrays, i.e., multiple real-time PCR assays assembled in an array format, have emerged as a new tool in the pathway-focused gene-expression analysis. Peer-reviewed data describing this emerging technology is lacking, but its simplicity, sensitivity, and robustness point to a promising future. As an example to illustrate its application in screening candidate genes or biomarker discovery in cancer research, we studied several hundred genes involved in tumorigenesis in human breast cancer using a PCR array. Our PCR array contains 84 genes that are associated with pathways related to key cancer traits (Table 7-3). Using this array, a scientist can survey gene expression changes in key genes between, for example, matched human tumor and surrounding normal tissues. The protocol (Fig. 7-3) starts with the RT of total RNA to cDNA. Then the cDNA per sample is equally distributed across a 96-well plate containing an array of primer pairs specific for each gene of interest. The gross expression changes between two samples can be visualized using a scatter plot where ∆∆Ct of genes are plotted (Fig. 7-5A). In this particular experiment, three technical replicates of human breast tumor and normal tissue samples were performed. The expression ratio and statistical significance of the ratio were plotted as a volcano plot where upper left and upper right quadrants contain genes that meet both thresholds of expressional change and statistical significance (Fig. 7-5B). Those genes that show significant change between normal and tumor sample were then listed (Fig. 7-5C). It is interesting to observe that, whereas most angiogenesis, adhesion-related, and transcription factor genes are significantly downregulated in breast tumor, some other invasion, apoptosis, and cell-cycle–related genes are significantly upregulated. This distinct regulation of different pathways may offer insights on how the tumorigenesis process starts. Once a normal cell has acquired the primitive traits of a cancer cell, a different set of genes or pathways may be mobilized to move the primary cancer cell into the metastasis state. When we study the difference between primary and metastasic breast cancer samples, one might be interested to see how the genes related to tissue invasion and metastasis pathway behave (Fig. 7-6). In this example, matched primary and metastasic breast cancer samples were analyzed using a PCR array that contains PCR primers for 84 tissue invasion and metastasis genes.

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Significant up- and down-expressions are observed in many key genes in the category of ECM remodeling and cell adhesion (Fig. 7-6C). With this type of analysis, one can either narrow down the pathway of interest or select key genes in a particular pathway with a great deal of confidence. Taken together, this example demonstrates the ease and feasibility of using PCR arrays in cancer research.

7.4

Concluding Remarks

Pathway-focused microarrays are a flexible yet powerful tool to study pathway-related gene-expression profiles. Compared with genome-wide microarrays, the limited scope of focused microarrays offers simplicity of data analysis and interpretation. Another advantage is that functional gene grouping is based on the results of the research community as a whole. The pathway design itself contains valuable information reflecting current understanding of the molecular basis of the particular biologic pathway. While for the individual research scientist, this grouping can serve as a basis for new discovery, we must be mindful of its limitations. Because our understanding of biologic pathways is limited, focused arrays may not contain content that is relevant but outside the scope current knowledge. To study a biologic pathway in the context of the whole transcriptome, one may have to start with whole genome microarrays as the initial screening. Once a subset of genes of interest is identified, a focused grouping can be produced based on consideration of known biologic pathways. In this sense, focused microarrays are a complementary expression profiling tool that can be used to extend the results from the whole genome microarray. Any pathway focused design is a snapshot of our current understanding of genes in a biologic pathway. Since our understanding of biologic pathways is evolving and the scope of research projects changes, the pathway-focused expression tool also must be flexible to reflect this situation and one way to do this is to make customized pathway microarray tools based on a combination of basic pathway grouping and specific genes of interest to a particular research project. Gene-expression profiling is a tool where the consequence of genetic alterations is observed. In cancer cells, regulatory changes can also be observed before gene-expression change; for example, changes in protein phosphorylations can lead to transcription changes. Changes can also be observed after mRNA transcription, i.e., on the level of protein expression or protein modification. Pathway-focused expression profiling should be used as an integrated tool in conjunction with other profiling tools such as transcription factor/protein binding profiles and protein profiles to obtain a complete picture of disregulations occurring in cancer cells. The gene-expression profiling data can and should be validated using independent techniques such as protein expression measurement or quantitative real time PCR for individual genes. Overall, pathway focused or application focused expression profiling has a significant place in both basic research and clinical applications.

7. Gene Expression Arrays for Pathway Analysis in Cancer Research

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151 19. Celis JE, Kruhoffer M, Gromova I, et al. Gene expression profiling: Monitoring transcription and translation products using DNA microarrays and proteomics. FEBS Lett 2000;480:2–16. 20. Bhattacharjee A, Richards WG, Staunton J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA 2001;98:13790–13795. 21. Garber ME, Troyanskaya OG, Schluens K, et al. Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci USA 2001;98:13784–13789. 22. Ono K, Tanaka T, Tsunoda T, et al. Identification by cDNA microarray of genes involved in ovarian carcinogenesis. Cancer Res 2000;60:5007–5011. 23. Schwartz DR, Kardia SL, Shedden KA, et al. Gene expression in ovarian cancer reflects both morphology and biological behavior, distinguishing clear cell from other poor-prognosis ovarian carcinomas. Cancer Res 2002;62:4722–4729. 24. Garber K. Genomic medicine. Gene expression tests foretell breast cancer’s future. Science 2004;303:1754–1755. 25. Branca M. Genetics and medicine. Putting gene arrays to the test. Science 2003;300:238. 26. Weigelt B, Peterse JL, van’t Veer LJ. Breast cancer metastasis: Markers and models. Nat Rev Cancer 2005;5:591–602. 27. Dave SS. Gene expression signatures and outcome prediction in mature B-cell malignancies. Curr Treat Options Oncol 2006;7:261–269. 28. Dave SS, Fu K, Wright GW, et al. Molecular diagnosis of Burkitt’s lymphoma. N Engl J Med 2006;354:2431–2442. 29. Kanehisa M, Goto S, Hattori M, et al. From genomics to chemical genomics: New developments in KEGG. Nucleic Acids Res 2006;34:D354–D357. 30. Livak K, Schmittgen T. Analysis of relative gene expression data using real-time quantitative PCR and the 2−∆∆CT method. Methods 2001;25:402–408. 31. Kane MD, Jatkoe TA, Stumpf CR, Lu J, Thomas JD, Madore SJ. Assessment of the sensitivity and specificity of oligonucleotide (50mer) microarrays. Nucleic Acids Res 2000;28:4552–4557. 32. Saiki RK, Gelfand DH, Stoffel S, et al. Primer-directed enzymatic amplification of DNA with a thermostable DNA polymerase. Science 1988;239:487–491. 33. Orlando C, Pinzani P, Pazzagli M. Developments in quantitative PCR. Clin Chem Lab Med 1998;36:255–269. 34. Rappolee DA, Mark D, Banda MJ, Werb Z. Wound macrophages express TGF-alpha and other growth factors in vivo: Analysis by mRNA phenotyping. Science 1988;241:708–712. 35. Nolan T, Hands RE, Bustin SA. Quantification of mRNA using real-time RT-PCR. Nature Protocols 2006;1:1559–1582. 36. Schmittgen TD, Zakrajsek BA, Mills AG, Gorn V, Singer MJ, Reed MW. Quantitative reverse transcription-polymerase chain reaction to study mRNA decay: Comparison of endpoint and real-time methods. Anal Biochem 2000;285:194–204. 37. Morrison TB, Weis JJ, Wittwer CT. Quantification of lowcopy transcripts by continuous SYBR Green I monitoring during amplification. Biotechniques 1998;24:954–8, 960, 962. 38. Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nat Genet 2002;32 Suppl:490–495. 39. Simon R, Radmacher MD, Dobbin K. Design of studies using DNA microarrays. Genet Epidemiol 2002;23:21–36.

152 40. Knudsen S, Guide to analysis of DNA microarray data. 2nd ed. Hoboken, NJ, USA: John Wiley and Sons, 2004. 41. Simon RM, Korn EL, McShane LM, Radmacher MD, Wright GW, Zhao Y. Design and analysis of DNA Microarray Investigations. New York, NY, USA: Springer-Verlag, 2003. 42. Thellin O, Zorzi W, Lakaye B, et al. Housekeeping genes as internal standards: use and limits. J Biotechnol 1999;75:291–295. 43. Cui X, Churchill GA. Statistical tests for differential expression in cDNA microarray experiments. Genome Biol 2003;4:210. 44. Shi L, Reid LH, Jones WD, et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 2006;24:1151–1161. 45. Canales RD, Luo Y, Willey JC, et al. Evaluation of DNA microarray results with quantitative gene expression platforms. Nat Biotechnol 2006;24:1115–1122. 46. Pizer ES, Jackisch C, Wood FD, Pasternack GR, Davidson NE, Kuhajda FP. Inhibition of fatty acid synthesis induces programmed cell death in human breast cancer cells. Cancer Res 1996;56:2745–2747. 47. Bandyopadhyay S, Zhan R, Wang Y, et al. Mechanism of apoptosis induced by the inhibition of fatty acid synthase in breast cancer cells. Cancer Res 2006;66:5934–5940.

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Chapter 8 Signaling Pathways in Cancer Daniel Kalderon

8.1

Introduction

The ideal objective of this chapter would be to point out exactly how genetically altered signaling pathways contribute to cancer and how drugs might be used either to prevent these aberrant contributions or to redirect them to a different outcome such as cell death. At this point, we know that there are several signal transduction pathways that are frequently altered in cancer and that these alterations almost certainly make a major contribution towards development of many of the cancers in which they are found. In addition, we know of many situations where altered signaling pathways produce dramatic changes in cell survival, cell proliferation, morphology, angiogenesis, longevity, or other properties that characterize cancer cells. In some cases, these mechanistic connections between a mutation in a component of a signaling pathway and a characteristic change in the affected cell have been described in great detail and a causal connection has been thoroughly proven in one or more model systems. Despite this, I believe it is premature to conclude that even the best of these mechanistic connections is the full explanation of how a specific signaling pathway mutation leads to the development of a specific cancer. I have two basic reasons for this skepticism. First, signaling pathways are very versatile and are known to play a multitude of different roles in different contexts during normal development. Second, the development of a cancer is a very complex process involving interactions among many cells, regulated genomic instability, and strong selective pressures, and very little of this progression has been observed directly or inferred indirectly with confidence. One major objective of this chapter is, therefore, to explain why I believe it is crucially important to consider the cellular contexts of signaling pathways during cancer development to discern the roles of signaling pathways in cancer. A second objective is to summarize some generalities about signaling

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

pathways and to review some of the best established connections between specific signaling pathways and cellular behaviors. I should also make clear at the outset that my own research centers on signaling pathways in Drosophila development. My appraisal of signaling pathways in cancer draws on discrete examples and intuition from my area of greatest expertise and consequently emphasizes a developmental perspective of cancer, which is a perspective that has been poorly represented in the cancer literature before recent consideration of cancer stem cell theories, but it is a perspective that is essential in considering the etiology and treatment of cancer.

8.2 Signaling Pathways in Normal Development 8.2.1 External Signals Reset Interlocking Internal Pathways that Dictate Cell Behavior Cells have a variety of networks of interacting genes and gene products that are required for continued viability, growth, and cell division in a constant environment (Fig. 8-1). Although we do not appreciate the full extent of such networks or the full sophistication of their action, it is useful to label these networks as apoptosis pathways, cell-cycle pathways, growth pathways, genome integrity pathways, and so forth. Components of different pathways can interact, allowing these differently named pathways to coordinate their regulation of cell behavior. The intrinsic function of each pathway and the mechanisms for their coordination are remarkable products of evolution, not only because they perform homeostatic tasks of enormous complexity with near-perfect success but also because they are plastic and can accommodate a large number of steady-state cellular behaviors. Cells normally switch from one steady state to another in response to changes in their environment. This phenomenon is true for a yeast cell sensing a pheromone; a Drosophila cell sensing a developmental morphogen; and a human cell responding to a hormone, a secreted growth factor, a signaling 153

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and further discussion of how signaling pathways contribute to cancer.

8.2.2 Organization of Signaling Pathways

Fig. 8-1. Signaling pathways re-set the state of internal pathways to alter cell behavior. Large sets of molecules contribute to internal pathways that control major cell behaviors, such as cell survival, fate, shape, adhesion, movement, growth, and cycling. These internal pathways are co-ordinated by several interactions (thin arrows). These pathways can also be re-set by external signals (thick arrows) and by nutrients and oxygen availability. This is achieved through signaling pathways that lead to changes in transcription and the modification of some key regulatory proteins. It is hypothesized here that most signals re-set internal pathway in a digital manner, changing the cell from one stable cell state to another. Stabilization through chromatin modification and transcriptional feedback circuits (open arrow) is hypothesized frequently to stabilize a new state so that the external signal is no longer required and the cell is ready to respond to the next signal.

protein presented by a neighboring cell, or a change in adhesive properties of the surrounding extracellular matrix (ECM). I refer to all of these external cues as signals (or sometimes as ligands for receptors) and I reserve the term “signaling pathway” for the response of a cell to an external signal. Thus, environmental cues are transmitted by signaling pathways to control the expression and activity of gene products that alter the interlocking, internal cellular pathways that dictate cellular phenotypes. For a complex multicellular organism, two of the biggest challenges for signaling pathways are in sensory perception and in guiding development. We can expect signaling pathways to be organized to meet these challenges, both intrinsically and in the way they integrate with the rest of the cellular machinery. This chapter considers how signaling pathways are organized and especially how they contribute to development. Not enough is known to produce a defensible consensus on these issues. I, therefore, present a subjective viewpoint that is based on a mixture of evidence and intuition; it has the virtue of providing a useful framework for further experimentation

First of all, what are signaling pathways? Are there really distinct, easily defined signaling pathways, as opposed to networks of overwhelming complexity, and what is the variety of such pathways? My subjective answer is that there are very few basic pathways and that their general organization is similar. The basic elements of a signaling pathway are signal, receptor, transmission, and effector components. Receptor occupancy leads to changes in a variety of post-translational modifications and interactions among signal transduction transmission components, leading to a change in one or a small number of effector molecules. Although signaling pathways can have critical direct influences on cell shape, cell polarity, or other aspects of cell biology, by far the most significant effectors for most signaling pathways are transcription factors. Thus, at least in my definition here, the two termini for most signaling pathways are defined by the receptor(s) and the pathway’s transcriptional effector(s). What transpires between these termini in response to a signal can be very simple as in JAK/STAT, Notch, or transforming growth factor-beta (TGF-β)/ BMP (Bone Porphogenetic Protein) pathways, or quite complex as in receptor tyrosine kinase (RTK) pathways, Wingless (Wnt), and Hedgehog (Hh) pathways, which are still not thoroughly understood. Even for the simple pathways, signal transmission can be modulated by other factors, provoking the suggestion that signaling pathways behave as networks that perform sophisticated integration functions. Alternatively and in direct contrast, the apparent complexity might be present just to ensure that a simple function is performed efficiently and with minimal distraction from other cellular activities. There will undoubtedly prove to be some merit to each view, but which of these explanations is of predominant significance remains a matter of conjecture for now. My opinion is that the key construction principle for an effective signaling pathway is that it operates well as an insulated unit and that the in-built potential to respond to additional inputs is only used occasionally in specialized, predetermined circumstances. In other words, I strongly endorse the idea that the primary function of most signaling pathways is to provide a straightforward conversion of an external signal into a change in the activity of just one or two specific transcription factors.

8.2.3 Complex Developmental Patterns are Built by Simple Intercellular Interactions How are signaling pathways organized in humans and other complex organisms? A single-celled organism, such as a haploid yeast cell, must sense the presence of potential mating partners, nutrients of various types, and osmotic conditions inter alia to alter its transcriptional programs, morphology, and cell cycle accordingly [1–3]. The number of different signals

8. Signaling Pathways in Cancer

to sense is limited, but the potential responses to these signals are, to some degree, conflicting so it is important that the yeast assesses the strength of each signal and integrates these inputs in some way. It is understandable that there are some robust connections between different signaling pathways in yeast and that the degree of pathway activation is important in regulating these connections. In humans, signaling pathways that operate in sensory perception are similarly very sensitive to signal strength and their design clearly optimizes speed, sensitivity, accommodation, and fast recovery; integrative functions are delegated to neuronal circuitry [4–7]. In humans and other complex eukaryotes, we can look at a whole developmental program and acknowledge that the collective role of all cell–cell signaling interactions is far more extensive than for a haploid yeast cell. This greatly heightened demand could theoretically be met by a huge array of signaling pathways with complex integrative capabilities; however, studies in model genetic organisms suggest instead that each individual cell–cell interaction during development may be very simple and that complexity is built up hierarchically by sequences of cellular interactions that progressively refine cell fates according to their position within the developing organism [8–15] (Fig. 8-2). In this paradigm, the complexity of the whole developmental program translates largely into the need to present signals in precise spatially and temporally appropriate patterns. This complexity demands complex regulation of the expression patterns for signaling molecules. Some of the regulation can be built into transcriptional regulatory domains of a single gene encoding a signaling molecule, but it appears that an additional recourse is for an organism to encode a family of related signaling molecules that together encompass the extensive pattern of required expression patterns for that class of signaling molecule. Thus, in mice and humans, and to a lesser extent in simpler model organisms such as Drosophila, each basic signaling pathway can respond to a family of related signaling molecules. Sometimes this need requires the production of a small group of related receptor molecules, but the number of distinct receptors is almost always substantially lower than the number of signals. In other words, one receptor can commonly respond to several related ligands. Of course, when different, related receptors are used, the production of diverse downstream signals is allowed and can lead (in an evolutionary sense) to the development of a group of related pathways (as for activin, BMP, and TGF-β pathways) [16] or even to quite distinct responses to similar ligands (as might be true for the different responses to some members of the Wnt family mediated by distinct Frizzled (Fz) (family receptors) [17–19]. In general, however, a single receptor or a small group of related receptors produce an essentially equivalent signaling response to a family of ligands, allowing a given signaling pathway to be stimulated in very complex patterns during development, according to a family of ligand expression patterns. Although the expression of receptors and downstream signal transduction components could in theory

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Fig. 8-2. Successive cell interactions guide normal development. A hypothetical developmental sequence that produces patterned cell fates in one spatial dimension is depicted. Although hypothetical for reasons of space and simplicity the sequence is very similar in principle to known developmental sequences observed during Drosophila wing imaginal disc development. Imagine 11 columns of cells in an epithelium, where the first nine and last two columns are initially identical to each other in phenotype (cell state). Cells 10 and 11 produce a signal (“A”) that is distributed in a gradient and only cells 1–9 are responsive. Here, cells 7–9 perceive a strong signal, cells 4–6 register a weak signal and cells 1–3 do not see sufficient signal to change. Now cells 4–6 produce a signal (“B”) to which they cannot respond. Similarly, cells 1–3 and 7–9 produce different, paracrine signals (“C” and “D”, respectively) and all of these signals act only on immediate neighbors. Cell 3 and 7 adopt different states in response to signal “B” because they were initially in different states. Finally, cells 3 and 9 send local paracrine signals (“E” and “F,” respectively) that alter the states of cells 2 and 10, again producing different responses. In this example cells 5–8 are not responsive to these signals. Through this orderly sequence of signaling events 11 different cell states are produced. If signaling events occur during discrete time windows and induce self-stabilizing states, then individual signals can be re-used (so, for example, signal “B” might be the same as signal “E,” “F,” or both). In some cases, say for signal “A,” cells may need to respond continually over a long period to maintain the appropriate cell state. If there is cell proliferation during this period the gradient of “A” and the distribution of responses will change progressively. If the responses to signals are stabilized the cell proliferation will generally simply amplify the number of cells that have adopted a given state.

also be highly regulated to refine where and when productive signaling interactions take place, this strategy does not appear to be used extensively. Instead, receptors and signal transduction components for most major signaling pathways tend to be expressed ubiquitously, placing the major regulatory burden on signal production and movement. So far, I have presented a picture of a small set of core signaling pathways with one or a few minor variants diversifying some of the core pathways. Indeed, a half dozen conserved core pathways clearly accomplish most of the signaling that occurs during human, mouse, or Drosophila development. It is inevitable that each type of pathway is used in multiple situations and that no strict segregation of the roles played by each of these pathways exists. They can all participate in

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regulating patterns of transcription that influence cell fate just as they can all influence cell proliferation.

8.2.4

Signaling Resets Cell State

How are signaling pathways used during development? I put forward the widely accepted idea that the development of specific cell types is guided by progressive changes that are initiated by successive cellular interactions (Fig. 8-2). These changes are often described in terms of cell fate or differentiation depending on whether they can be reversed in extraordinary circumstances or whether they are intimately tied to cessation of proliferation. Regardless of terminology, the general point is that an individual cell moves from one state to another on a path towards a final phenotype. Each state could theoretically be characterized uniquely by a pattern of gene expression (and described more completely by including more aspects of cell biology). Initially, large groups of cells share very similar patterns of gene expression but these patterns become progressively more unique as smaller and smaller subsets of cells are exposed to the same sequence of signals (Fig. 8-2). It is extremely important to point out that the progress of a cell from one state to another not only moves it towards its ultimate destiny but also confers two crucial properties on the cell for the next step in this progression. First, it determines how the cell will respond if presented with a specific signal (or group of signals). Second, it determines which signals the cell itself will produce (or how it facilitates or impedes passage of long-range signals between cells on either side). In this way, groups of cells collaborate to send and receive signals that govern their development in a manner that is reproducible and leads to an organism-specific spatial plan of terminal cell types and cell numbers (Fig. 8-2). The whole progressive cellular interaction plan is, of course, built into each organism’s genome and is, in essence, the blueprint that distinguishes that particular organism from others. This plan is very difficult for us to decipher but we can infer some general properties from well-known characteristics.

8.2.5

A Robust and Versatile Design Principle

First, development of flies and mammals is regulative, meaning that the signaling plan tolerates significant fluctuations in cell numbers, geometries, sizes, and signaling efficiencies without affecting the essence of the outcome. Second, different organisms achieve different body plans and sizes using the same signaling pathways and principles. Thus, the signaling plan is both robust and versatile. Different plans presumably reflect changes in the sequence and spatial patterns of signals used rather than differences in the way any individual signaling pathway works or the types of response that can be elicited by any one pathway. These characteristics fit well with a general concept of modular use of subroutines in a developmental program and with only making demands of

any one subroutine that can be efficiently and reproducibly met. The subroutines I refer to here are the mechanisms that allow a signaling pathway to reset the internal status of a cell (transcriptional program and the status of internal growth, cell cycle, apoptosis, and other pathways). The general framework idea of successive, interdependent signaling interactions governing progressive cell-fate specification according to position is widely supported by available evidence, even though only a small number of paradigms (such as the development of the adult Drosophila wing and eye) where almost the entire sequence of relevant signaling events impose a spatial pattern of cell types and cell proliferation are reasonably well described [8, 12, 15, 20–23]. There is more room for conjecture when we try and impose a little more precision on this general framework of preordained iterative cell–cell signaling interactions to ask what information is really transmitted by a signaling pathway and how signaling pathways collaborate. Three issues are particularly significant. First, are responses to signals generally dose-sensitive? Some developmental signals clearly act as morphogens. That is, equivalent cells respond in at least two ways according to how much signal they sense. In the developing Drosophila wing, a BMP family molecule (Decapentaplegic [DPP]), a Wnt, and a Hh signal all act as morphogens [8, 24]. Those signaling roles affect only the earliest subdivisions of a large field of cells and they appear to impose only two, or perhaps three, discrete states on cells rather than a continuous dosedependent range (Fig. 8-2). In other words, most signals are perceived as either present or absent and the less common dose-dependent signals may still often be perceived simply as absent, weak, or strong. Thus, developmental signaling appears to be designed specifically to make digital choices, moving cells from one discrete cellular state to another rather than along a continuum. This digital interpretation of signals is due in part to properties of the signaling pathway within each cell and in part because of interactions among neighboring cells that allow for some degree of community decisionmaking, for example sharpening the borders between cells that do and do not register a specific digital response [12, 15, 24, 25]. The precise mechanisms by which signaling pathways and cell interactions produce such digital responses are not well understood but may be the underlying reasons for some of the apparent complexity of signaling pathways. Second, is the response to one signal influenced by the simultaneous presence of another, different signal? Not enough good examples exist to answer this question well, so instead I will offer an opinion. In several situations in Drosophila development, distinct signals are perceived simultaneously by a single cell. In most of those situations, each signal appears to be assessed independently. Thus, for example the identity of specific neuroblasts or muscle founder cells in Drosophila embryos is dictated by simultaneous signaling by Dpp, Wnt, and RTK pathways [13, 26]. Dpp levels (and presence) vary along the dorsal-ventral axis whereas the presence of Wingless, for example, varies over the anterior–posterior axis. Here, a cell

8. Signaling Pathways in Cancer

responds to each signal so that its new cell state is appropriate to its position along the anterior–posterior and dorsal–ventral axes. The response to the two signals has been integrated at some level but it might be as simple as adding the two responses together. In this way, two independent pieces of spatial information are transduced by using different pathways and crossregulation of pathways is avoided rather than being exploited. Obviously, this will not always be the case and there are certainly examples of a direct transcriptional target of one signaling pathway being inhibited or further induced by the activation of a second signaling pathway [27, 28]. In my opinion, however, cross-pathway modulation may represent the exception rather than the rule. Third, do cells generally assess developmental signals continuously? A prominent feature of some signal-transduction pathways is the incorporation of multiple mechanisms for negative feedback. Important potential consequences are that the signaling pathway shuts down rapidly after a transient signal disappears and that the signaling pathway response diminishes over time during continued exposure to a constant concentration of ligand. This situation is of obvious importance to sensory perception and in responding to transient hormonal signals or signals in the nervous system. Most such signals rely on G-protein–coupled receptors and many specific desensitization mechanisms have been elucidated for these pathways [29, 30]. The role of negative feedback is not so clear for Hh, Wnt, Notch, BMP, or RTK receptor pathways used during development. On one hand, such signals, especially Hh, Wnt, and BMP, often appear to elicit constant responses over long periods of time during development (but they do turn off when the ligand disappears). On the other hand, there are clearly situations where signaling episodes are restricted to short periods of time, as in Drosophila eye development [11, 12, 15]. Here, the specification of specific photoreceptor cell fates depends, inter alia, on temporally separable rounds of signaling by Notch and epithelial growth factor receptor (EGFR) pathways, where a different response is elicited at each round. This, of course, lends support to the framework idea that the response of a cell to a given signal is altered by successive signaling events, but it also sheds some light on how this is accomplished. First, in between signaling events, the cells appear to become refractory to stimulation of the pathway [31]. Although the mechanism of this desensitization is unknown it suggests that productive signaling is limited to discrete episodes or windows of developmental time. Second, during both Notch and EGFR signaling events, the cell producing the signal is generally refractory to stimulation. In other words, systems are in place to ensure that signaling is directional (Fig. 8-2). This restriction on autocrine signaling is also seen for Hh and Dpp signaling in wing development. The strategies that accomplish this are quite varied and contextdependent, so it is not possible to predict whether autocrine stimulation will necessarily be inhibited in a given setting. It is a widespread phenomenon and has obvious use in allowing cells to produce signals without necessarily changing their

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own status in response. Returning to the original question, it appears there are at least two types of time-frames for signaling. In one, a signal is present over a prolonged period of time and is continuously required to maintain a steady state response in a given cell. Many other signaling events may affect that same cell over this prolonged signaling period. Eventually, however, the changes elicited by the prolonged signal become self-stabilizing and the signal is no longer required. In a second scenario, a cell is offered only a limited developmental period in which to respond to a given signal. If the cell registers a response, the altered cell state is rapidly stabilized, and the cell restores the signaling pathway to a basal level so it can respond (in a different way) to the next round of signaling. The translation of a signaling episode to a stabilized cell state without requiring persistence of the signal is a key feature that allows cells to undergo a long sequence of signal-instructed cell fate changes without acquiring an impossibly complex signaling environment [32, 33]. It is a device that easily allows the same signal to be re-used many times to elicit distinct responses (Fig. 8-2).

8.2.6 Summary: Signaling Pathways in Normal Development In summary, I offer a model for signaling pathways in development, in which each signal elicits a simple response (off/on and perhaps also weak/strong). The response is stabilized by transcriptional circuits and chromatin modification, relieving further dependence on the initiating signal [34–36]. The response can include altered production of signals, an altered response to signals, and changes in fundamental cellular properties that dictate adhesion, migration, proliferation, or survival. Because a small number of pathways are used for many purposes, we cannot, in general, expect to find any robust linkage between a particular cellular response and a specific signaling pathway. This expectation is borne out by direct evidence that EGFR, Notch, and Wnt pathways can, for example, promote apoptosis in one setting and inhibit it in another, and promote proliferation in one setting but inhibit it in another. There may be preferential use of certain pathways for particular purposes. In fact, I will later argue that the phosphatidyl inositol 3′ kinase (PI3K) branch of RTK pathways is exceptional in being largely dedicated to promoting cell growth and survival. The principles that the response to a signaling pathway is generally context-dependent and that a change in signaling pathways in one cell alters its signaling and sensing properties to have a major knock-on effect in its neighbors, its daughters, and their neighbors are two key lessons of normal development that must be applied to the question of how signaling pathways contribute to cancer. In essence, these lessons lead us to expect that a single activated signaling pathway will make varied contributions to cell behavior as a cancer develops, that these contributions will be stochastic, producing distinct changes in cell state rather than a complex array of quantitative changes and that the development of a cancer will depend

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on the coordinated aberrant development of cells, driven by normal and aberrant developmental signals.

8.3

Origin of Cancer Cells

The origin of cancer cells is a huge subject area with many unanswered questions, so I cannot include an extensive or comprehensive discussion. It is crucial, however, to consider this issue to guess how signaling pathways contribute to cancer. At least three layers of complexity are intrinsic to cancer development. First, there are direct analogies to the logic of normal development, which is incompletely understood but includes the idea that outcomes depend on multiple progressive cellular communications and the way in which internal pathways are reset by these communications, as discussed above. Second, the genome of the prospective cancer cell changes during development. Hence, the normal developmental plan is modified several times during cancer ontogeny in a way that is not preordained and therefore potentially highly variable from one cancer to the next. Third, there are extensive selection processes at work so we only see a tiny, highly selected subset of the products of the developmental processes and genome changes that occurred during cancer development. Thus, cancer development is a much more complicated process than normal development and, worst of all, it is neither predictably reproducible nor readily observed. To make definitive progress, the last two issues must be addressed by direct experimentation. The most promising avenue is the development of sophisticated mouse models. Much progress is being made in this area, e.g., building conditional quantitative control of multiple oncogene and tumor suppressor gene (TSG) activities into a single mouse genome [37–40]. These models, however, fall short of simulating essential aspects of carcinogenesis. The models must also allow for clonal origins of cancer and for random mutations and selection processes to occur, while also allowing the experimenter to find developing cancers at their earliest stages. Simply combining several key genetic changes in relatively large groups of cells by targeted manipulation of the genome inevitably removes many of the normal and crucial steps of cancer development. Without new incisive models to ask open-minded questions, we have only very few and incomplete insights into cancer progression in humans and mice. On that basis, we can only construct hypotheses for how cancers might develop. It is crucial to be highly skeptical of these models because models survive and propagate inappropriately well in situations such as this, where acid tests cannot be conducted. I present some conjectures about how cancer develops to consider what roles might be played by mutations affecting signaling pathways.

8.3.1

Cancer Stem Cells

For a given cancer, key broad challenges are to define the cell of origin and the normal developmental progress of such cells,

to understand the sequence of mutations that can give rise to a cancer, and to understand how genome instability and selective pressures might allow such a sequence of mutations to arise and direct cancer development. The cellular origin of cancers is most commonly discussed in light of one form or another of the cancer stem cell hypothesis [41, 42]. At least two distinct issues are highlighted in the cancer stem cell hypothesis. First, the cancer stem cell gives rise to progeny that organize themselves into something resembling a tissue (or, in the case of blood cells, a lineage of at least partially differentiated progeny). In other words, a cancer cell lineage undergoes somewhat normal developmental processes, generating a cancer that is composed of clearly distinguishable cells. Second, the cancer stem cells may be the only component of the cancer that can efficiently reconstitute the whole cancer [43, 44]. Most commonly, this situation would be ascribed to a longer or even indefinite lifespan and constitutive telomerase activity. The similarity of these properties to those of normal stem cells suggests that the cancer stem cell is not a completely novel cell type but a modified version of a normal stem cell, with the obvious implication that normal stem cells may frequently be the origin of cancers. It is also, of course, possible that a normal stem cell derivative (that is not itself a stem cell) might adopt properties more akin to stem cells following specific mutagenic changes [45]. Indeed, there is direct evidence for even partially differentiated derivatives of stem cells to revert to normal stem cells in the Drosophila germline simply as a result of manipulation of one signaling pathway [46, 47]. It is reasonable to entertain the possibility that stem cells and their derivatives can exist in a dynamic flux where reversion to stem cell fate may be induced quite readily, especially in response to a significant genetic change. The conversion of a cell without long-term regeneration capacity to a cancer stem cell able to propagate the cancer by transplantation of a small number of cells has been directly demonstrated after introduction of a MLL-AF9 fusion protein transgene into mouse granulocyte-macrophage precursors [48]. If we accept that mature cancers may frequently be composed of distinguishable stem cell and nonstem cell derivatives, how does that affect our thinking about how cancers develop, how they progress, how they can be treated, and the roles played by specific carcinogenic mutations? First, a cancer may be advanced enough to be lethal without recourse to replenishment from stem cells. On the other hand, a small population of cancer stem cells may suffice to regenerate a full-blown cancer rapidly. Therapies may need to address both types of cancer cell populations and different therapies may be appropriate for each, even if they share the same genotype. Second, the principles that apply to cancer development likely extend to cancer progression. Even if we cannot detect most incipient cancers at their earliest stages and impair their development, therapies aimed at preventing progression can benefit from insights into cancer development. Third, consideration of the development of cancer broadens our view of how altered signaling pathways might contribute to cancer. There is a ten-

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dency to consider only how a mutationally altered signaling pathway alters the properties of cells of a frank cancer and to ignore possible contributions to earlier events and to possible future developments. To see how signaling pathways might contribute to cancer development and progression, we must understand at least the basic principles of those processes. Chief among those basic principles is how a single cell lineage can accumulate several specific mutations despite the facts that spontaneous mutation rates are generally very low, most cells have only limited replicative capacity, and most random mutations would incapacitate cells rather than improve their competitiveness and hence their proportional representation. I consider first what types of mutations are generally acquired by cancer cells and then some hypotheses about how developmental relationships among stem cells and their derivatives may allow the acquisition of those mutations.

8.3.2 Acquisition and Fixation of Multiple Mutations in Cancer Cells It is widely acknowledged that cancers generally require multiple genetic changes to arise [49–51]. Although the number of mutations required may be lower, e.g., in mice than humans and in blood cell tumors than solid tumors [51, 52], it is reasonable to take human colon carcinoma as an example (for the good reason that this is where a sequence of mutational events has been best studied to date). Here it is likely that 3 or 4 significant genetic changes must take place to produce a carcinoma. Even given the large number of epithelial stem cells in the colon and their potential longevity, it is numerically implausible that three or four specific types of mutation can accumulate in any one stem cell or its derivatives without any contribution from either increased rates of mutation or selection [49, 50]. A role for selection is clear simply by considering how a number of specific mutations could be preserved without being accompanied by numerous other cell lethal mutations. The question of mutation rates is not so easily addressed empirically or theoretically. Where measurements have been made, no clear increase is seen in the rate of induction of point mutations in colon carcinomas (with the notable exception of those arising in individuals with germline mutations in mismatch repair genes) [53]. Genomic instability, characterized by aneuploidy and gross chromosomal rearrangements, does appear to be increased [54]. It is well known that p53 and other guardians of genomic integrity are altered in most solid tumors so it is plausible that these types of mutation may enhance the frequency with which chromosomal rearrangements occur. Whether genome instability is present and required at an early stage of cancer development is not clearly resolved but has been discussed extensively elsewhere [55–60]. Here I simply summarize that there is likely a role for mutations that engender genome instability at some stage during cancer development. Good evidence exists that solid tumors generally include mutations that disable at least one component of a G1/S cell-

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cycle control point pathway [61–63] and at least one member of a pathway that activates p53 (including p53 itself) in response to loss of G1/S control [51]. Other significant mutations may promote cell growth, suppress apoptosis, and for tumors of epithelial origin, promote angiogenesis or promote epithelial to mesenchymal cell transitions. Relevant types of mutation include specific activating mutations, gene amplifications, or specific translocations to produce single dominant oncogene alleles. Loss of the second allele of a tumor suppressor (loss of heterozygosity [LOH]) is frequently accomplished by chromosome rearrangements, mitotic recombination, or chromatin silencing; however, these types of mechanism cannot generally silence both alleles without concomitant loss of adjacent genes and inevitable cell death. Given that there may be accelerated genome instability at some stage in cancer development and that LOH can be accomplished in many ways, it is possible that LOH occurs far more frequently than induction of a specific point mutation that inactivates a TSG (Tumor Suppressor Gene) or activates a proto-oncogene. Nevertheless, the collective requirement for a cancer cell to accumulate at least two specific “point” mutations and at least one or two LOH within the lifespan of a single genetic lineage and without compromising its viability is incredibly demanding and a fundamental issue that requires a plausible explanation [49, 51, 55, 60]. Do the cellular origins of cancer provide key insights into how cancer cells acquire multiple specific mutations?

8.3.3

Stem Cell Properties

If we assume that general mutation rates are low and occur mainly as a result of cell division, the key challenges to selective acquisition of a specific set of mutations are for a single lineage to undergo a very large number of cell divisions and for the cell’s environment to impose selective pressures that favor the fixation of specific mutations. Explicit consideration of a cancer developing from a stem cell (or a cell readily converted to a stemlike status) offers a number of hypotheses for possible genetic trajectories of cancer development (Fig. 8-3). These hypotheses are built on some basic lessons of developmental biology and basic ideas of a cancer stem cell hypothesis. First, genetic mutations can significantly alter the properties of both stem cells and their derivatives, altering their phenotypes towards those of cancer stem cells and cancer cells, respectively. Phenotypic changes in each of these cell types (stem cells and stem cell derivatives) can contribute to cancer development and progression [64]. Second, stem cells and stem cell derivatives initially have different properties (cell states) and initially inhabit different environments. Hence, mutations will frequently have different effects on stem cells and their derivatives. Third, mutations in each type of cell can alter their signaling behavior, abundance, or location, altering the environment of their neighbors, including cells of the same genotype. For example, a mutation in a stem cell may alter its properties indirectly by altering the signaling properties of

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the derivatives of that stem cell. Fourth, mutationally induced changes in intrinsic pathways of a cell or changes in a cell’s environment can provide strong selective pressure that favors further specific mutational changes.

8.3.4 Mutations that Increase Stem Cell Numbers

Fig. 8-3. Possible mechanisms for accumulation of early cancerpromoting mutations in stem cells. Each picture represents a developmental unit governed by a single stem cell niche. Stem cells (squares) self-renew and produce daughter transit-amplifying cells (circles) that proliferate and eventually differentiate into several cell types (triangles) that occasionally die and must therefore normally be replenished. The most potent type of mutation in a stem cell (black dot in A) is expected to be one that autonomously increases the number of those stem cells in niche (B). This creates more substrates for a possible second mutation and likely stresses some mutant stem cells because they inhabit an altered environment beyond the normal confines of the niche (grey arrowhead in B). This provides a selection for a second stem cell mutation (black squares in C) that promotes cell survival or proliferation under stress, leading to better survival and proliferation of those stem cells with both mutations (black squares in D). The second mutation may also favor survival and proliferation of transit-amplifying cells. A stem cell with a mutation that does not alter stem cell properties autonomously (E) may nevertheless duplicate and thereby become stabilized simply because random stem cell loss is generally followed by replacement by another stem cell to occupy all suitable positions in a niche (F). The mutation in question might elicit the production of a signaling molecule that promotes an increase in stem cell number nonautonomously (extra open squares in G), increasing the chance of a mutation in one of the amplified stem cells that will promote cell survival or proliferation (black square in H), just as described earlier (for C). In this case, there will often be no trace in the final cancer of the original mutation (black dot) that initiated the cancer. A mutation arising in a transit-amplifying cell that favors survival or proliferation (I) will transiently increase cell populations (J) but in most cases all mutant cells will eventually be lost (K). However, if there is normally some interconversion between transit-amplifying cells and stem cells or if the specific mutation incurred promotes reversion to stem cell behavior, then a mutation that either originated in transit-amplifying cells or was selected as promoting proliferation only of transit-amplifying cells can be fixed in the niche as a stem cell mutation (L). Several possible mechanisms are not depicted, including the possibility that aberrant signaling events induced by the altered cell environments depicted lead to epigenetic changes in each cell type that can, for example, silence key genes, inactivating checkpoints, cell cycle controls, or apoptotic pathways. Such epigenetic changes would likely be heterogeneous within the developing cancer and are reversible and may therefore not be recognized easily in the final cancer.

Stem cell characteristics may be key to creating a lineage that undergoes a very large number of cell divisions and there are various ways that selective pressures favoring carcinogenic mutations may be generated. A normal stem cell is characterized by unusual longevity and a high capacity for continued self-renewing divisions. Mutations that increase this replicative potential further, however, could have a dramatic impact as an early step in carcinogenesis, which could be achieved by accelerating stem cell divisions, especially for largely quiescent stem cells. Mutations that increase stem cell number would likely have a far greater impact and could be achieved by mutations that encouraged more frequent division of stem cells to give two stem cell daughters (symmetric divisions) or by mutations, which increase the likelihood that transitamplifying cells revert to a stem cell phenotype. These mechanisms would be more potent if stem cells could mobilize to occupy new niches, as is true for hematopoietic stem cells (HSC), or if stem cells could be maintained beyond the normal confines of a niche. Two types of mutation have the potential to promote stem cell amplification. First, it has been shown that disruption of organizers of cell polarity in Drosophila neuroblasts block asymmetric divisions and the expanded pool of neuroblasts form tumors (large masses with uncontrolled growth) [65–69]. Second, alterations in signaling pathways can promote stem cell expansion. The relevant signaling pathway would likely depend on the precise stem cell in question and its niche. From studies of normal development, mutational activation of the Wnt and Hh pathways appear to have such roles for mouse intestinal stem cells (Wnt), adult mouse neural stem cells (Hh), and Drosophila somatic ovarian stem cells (Hh) [70–73]. If such mutations were to occur in the appropriate stem cell, they would naturally become fixed in an expanded stem cell pool; in other words, they are self-selecting (Fig. 8-3). Although only altered stem-cell specific signaling pathways are likely to be able to induce large expansions of a single stem cell lineage, there are likely to be small stochastic expansions even for normal stem cells because loss of a stem cell from a niche that supports several stem cells is generally followed by duplication of a neighboring stem cell to occupy the vacant niche [73, 74]. This process will facilitate domination of a niche by stem cells of a single genotype even if the competitive advantage of that genotype (in terms of adhesion, longevity, or proliferation) is only slight. Thus, expansion of a single stem cell lineage may result primarily from alteration of a single dominant signaling pathway but might also be induced by more minor contributors to normal stem cell behavior. If an early mutation increased the size of the stem cell pool of a given lineage, we might expect this to have several addi-

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tional consequences that encourage the acquisition of further mutations in the same lineage (Fig. 8-3). First, some of the excess stem cells might find themselves projected beyond their normal niche into an environment that is less favorable, thereby decreasing the proliferation of such cells or increasing the likelihood that they will undergo apoptosis, which would create significant selective pressure favoring a mutation that inhibits apoptosis or promotes cell cycling. Such changes need not be dramatic (e.g., loss of a single allele of a TSG) to be fixed and spread through the expanded stem cell population. Such mutations may allow the stem cell population to spread even further beyond its normal niche and set up new selective pressures that demand more drastic changes in growth control or apoptotic pathways. It is possible that the expansion of a stem cell pool might not only create selective pressures but also engender an enhanced mutation rate through chromosome instability. It has been argued that insufficient telomerase activity engenders chromosome instability in the face of continued DNA replication [59]. Increasing the number of cell divisions of a given genetic lineage may exceed the capacity of telomerase in stem cells, which characteristically have levels of telomerase that are significant but considerably lower than found in many cancer cells [59]. Even small increases in chromosomal instability might significantly enhance the frequency of LOH mutations. Furthermore, once excessive divisions of a mutant stem cell lineage greatly exceed the maintenance capacity of telomerase, there will be sharply increased selective pressures in favor of increasing telomerase activity. It is easy to imagine how a mutation that affects a stem cell property (here causing an expansion in stem cell number) might not only provide more substrate stem cells for further mutation but might also create selective pressures and even increased mutation rates that favor fixing further mutations in the population. Some of these mutations, such as changes in intrinsic pathways that affect apoptosis, cell proliferation, or telomerase activity, would affect the properties of stem cells and their progeny and move the whole tissue towards a cancerous state.

8.3.5 Can Mutations be fixed in Stem Cells if they Only Benefit Stem Cell Derivatives? What about mutations that only affect stem cell derivatives (nonstem cells), e.g., enhancing their longevity or proliferation? Can such mutations be fixed in a stem cell population? A large population of transit-amplifying cells may well be limited in its growth potential either because of inhibitory signals from its normal environment or because competition among the expanded transit-amplifying cell population limits contact with positive signals from the normal environment. If a mutation that overcomes these limitations provides an advantage to transit-amplifying cells but not to stem cells, one might expect that such a mutation would rarely become fixed in the stem cell population and would eventually be lost from the transit amplifying cells; however, if there is significant natural

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or mutationally enhanced interconversion of stem cells and transient-amplifying cells, such mutations may readily become fixed in stem cells (Fig. 8-3). Thus, reversion to a stem cell state might allow selection of mutations that promote cancer even if those mutations do not alter stem cell properties.

8.3.6 Non-autonomous Mutational Contributions to Cancer Development In the hypothetical situations presented, I have considered the different impacts of mutations on stem cells and on their derivatives but I have only considered mutations that act cell autonomously. Can genetic changes in one lineage (A) affect the progression of another lineage (B) towards cancer? It certainly might if the cells of both lineages are intermingled. For example, lineage A could suffer a mutation that altered its signaling properties towards all neighbors, including those of lineage B. The change could affect growth rates, promote or relieve stress and thereby alter the selective conditions for future genetic variants of lineage B. This hypothetical scenario is most likely to be relevant in the context where the two lineages (A and B) derive from sister stem cells of a mutationally expanded pool. As a result of the subsequent accumulation of different mutations, only lineage B may eventually be preserved in the mature cancer, so there would be no evidence of the participation of lineage A and nonautonomously acting mutations (Fig. 8-3). Tumors are not always monoclonal, begging the question of whether interaction of two or more genotypes was causally important for development of the tumor [75–77].

8.3.7 Signal-induced Changes in Chromatin as Selectable Reversible Traits Many of the steps in cancer development are clearly irreversible genetic changes that are retained in the final cancer. It is well known that gene activity can be regulated epigenetically and that altered chromatin states of TSG in cancers often account for LOH [78]. Indeed, even transient germline DNA demethylation makes mice extremely prone to developing tumors [79]. Because changes in chromatin can be dynamic, we can ask whether alterations to chromatin might have played more roles during cancer development than evident from the chromatin status of a mature cancer. I believe this is likely to be the case based on analogies to normal development. Earlier I presented a picture that cell signaling moved cells from one state to another during development and that each state is stabilized partly by an altered chromatin state. The specific genes that are activated or repressed by such chromatin changes are precisely determined by the developmental choreography encoded in the genome. Furthermore, some of the chromatin modifications may be long lasting, as in the control of homeotic gene expression by Polycomb and Trithorax group complexes, whereas others are more transient and prone to be altered by subsequent signaling events [34, 80]. In a developing cancer, these precise

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programs will be greatly diversified because cells inhabit an abnormal environment and have suffered mutational alterations to their internal wiring. Nevertheless, discrete signaling events may reset, for example, histone modifications of varying sets of genes, thereby altering gene activities in a semi-stable manner. If one such reset is favorable, this epigenotype may be propagated at least transiently. The more signaling events a cell undergoes, the more variations in gene activity patterns it can test out for favorable outcomes. In other words, chromatin modifications may be important selectable traits that are reset by signaling events. The disorganized nature of a developing cancer, coupled with the possibility of signaling pathway mutations and altered production of signaling molecules within the developing cancer cell lineage or its sisters, may greatly increase the number of signaling events and hence the diversity of cell states from which to select. An example of this general hypothesis is the common silencing of the expression of a soluble Wnt antagonist, presumably mildly increasing Wnt signaling, at an early stage of colorectal cancer development [81].

8.3.8 Implications of Cellular Ontogeny of Cancer The discussion of possible genetic events and cellular interactions during cancer development is necessarily hypothetical because these events and interactions have not been studied directly. One could therefore choose to discard these hypothetical scenarios and stick to the more monolithic view that a cancer cell genotype includes all significant mutations that contributed to development of that cancer and that the effect of each significant mutation can be rationalized as having a specific consequence that is the same in the mature cancer as when the mutation first arose and throughout the course of cancer development. I think, however, it is wise to incorporate several modifications to this view. First, the final cancer does not necessarily provide a complete record of significant genetic changes that occurred during cancer development. There may have been both significant non-autonomous contributions, especially from former sister cells whose genotype is not preserved in the final cancer, and significant intermediate chromatin modifications that guided cancer development. Second, it is likely many genetic alterations that contributed to the step-wise development of the cancer cell lineage cannot readily be discerned from examination of terminal cancer cell genotypes and phenotypes. For example, the final cancer may lack any p53 activity but there may have been changes in gene dosage of p53 or numerous other p53-related genes that allowed cells to survive increased stress during cancer development before full inactivation of a p53 pathway. Third, and most important for further discussion, a given mutation that is recognized as characteristic of the mature cancer (and therefore inferred to be causally important and acting cell autonomously) may have played multiple different roles during development of the cancer. These roles may include an expansion of stem cells,

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promoting growth or survival of stem cell derivatives, increasing subsequent genetic variation, or creating selective pressures for the fixation of subsequent mutations. Some of these functions, such as maintaining an expanded pool of (now) cancer stem cells, may be retained in the mature cancer, whereas others, such as engendering selection conditions, may have changed or been lost as the cancer developed. Although we cannot chart all of these potential roles, it would clearly be short-sighted to assume that the role of an altered signaling pathway is confined to its effects on the bulk mature cancer.

8.4 Targets of Signaling Pathway Mutations that Drive Cancer It is generally acknowledged that mutations affecting signaling pathways frequently, indeed probably universally, contribute to cancer development. This conclusion is not a trivial conclusion, nor is it easily established beyond doubt, so it is worth summarizing the types of evidence behind it. First, for any specific gene the correlation between cancer and detection of mutations in the gene need not indicate a causal connection; however, causality is far more likely when the frequency of mutation of the gene is extremely high in cancer (perhaps of a specific type), when inherited mutations in the gene markedly predispose to earlyonset cancer and when mouse models indicate oncogenic or TSG activity. These criteria are amply fulfilled for a small core of genes including those encoding p53, Rb, Ras, PTEN, APC, and Ptc. Several others could presently be included in this list and doubtless new genes will be added as the scope of molecular forensics and validation tests increases with systematic genomic surveys. The listed gene products lie at the heart of known internal pathways for checkpoints that can promote cell cycle arrest or apoptosis (p53) or for G1/S arrest (Rb), and signaling pathways using RTKs (Ras, PTEN), Wnt (APC), or Hh (Ptc) ligands. Many of these proteins are, however, multifunctional, so it is important that additional gene mutations affecting p53, Rb, RTK, Wnt, and Hh pathways have also been implicated as causal in cancer development (even though the numerical incidence of some of these mutations may not be so overwhelming). Hence, there is little doubt that it is the actions of these altered gene products in internal regulatory pathways and in signaling pathways that is relevant to cancer. Furthermore, it is generally easy to rationalize why certain pathway mutations are most commonly found associated with cancer in terms of mutational target size and predicted consequences for the pathway. For example, the ptc gene is a relatively large target and its inactivation turns on the Hh pathway at high levels without any obvious mechanism for down-regulation. The pathway can also be activated by specific, and therefore relatively rare, activating mutations affecting the transmembrane Smo protein, by increased expression of a transcriptional effector of the pathway, Gli-1, or by inactivation of Su(fu), which normally restricts activity of Gli proteins. There are of course puzzles. APC (Adenomatous Polyposis Coli) and ∼ axin collaborate to ensure destruction of β -catenin and thereby

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silence the Wnt pathway in the absence of ligand, so mutation of either gene can strongly activate the Wnt pathway. Nevertheless, APC mutations are found in cancer far more frequently than axn mutations. Does this reflect important functional differences, some partial genetic redundancies or relative size of mutational targets? These are important issues to pursue but they do not detract from the overwhelming case that alteration of several signaling pathways contributes to a large fraction of all cancers. How then do signaling pathway mutations contribute to cancer? Three types of answer have been contemplated. First, signaling pathways normally control cell proliferation and cell death, so it has long been postulated` that aberrant signaling feeds into aberrant proliferation and survival behaviors. Much work has focused on the precise molecular connections between signaling pathways and internal growth and apoptosis pathways, and there clearly are multiple such connections. Second, and more recently, the role of a signaling pathway in stem cells has been emphasized. I have emphasized a mutation that increases stem cell numbers as potentially making critical contributions to cancer development. It also seems likely that the most potent such mutations will affect those signaling pathways that are essential within the stem cell niche but perhaps absent, or reduced in their influence in the environment of stem cell derivatives beyond the normal niche. Stem cell pathways will also likely feed into internal apoptosis and growth control pathways but additionally there may be very important influences on cell locomotion and adhesion, as these properties can influence where a cell resides relative to essential niche factors. Third, I have suggested that changes in signaling pathways might contribute to a huge number of unseen selections, accommodations to stress and changes in a cell’s receptive or signaling status (in part through resetting states of chromatin modification) that contribute to the genetic and developmental trajectory of cancer cells. For this proposed function, there would be too many potential scenarios to expect any general rules concerning which pathways are critical or how they alter internal regulatory pathways, cell receptivity or cell signaling activities. This category of potential actions may nevertheless be crucial in facilitating cancer development and progression. In discussing specific signaling pathways, it is important to consider whether and how the pathway acts on relevant stem cells, how it impinges on internal regulatory pathways and how it might relate to other mutations that commonly arise in specific cancers. The major internal pathways that regulate basic cell properties are discussed briefly to emphasize the most important concepts and introduce some of the primary molecular players.

8.4.1

Cell Cycles and the G1/S Transition

Cell proliferation requires an increase in cell mass, driven by protein synthesis, and continuous passage of cells through all phases of the cell cycle. The cell cycle can be arrested before S or M phase at checkpoints that ensure completion of

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previous phases, that all is in order, and that appropriate preparations have been made for the next phase [82–84]. DNA damage, incomplete DNA replication, or improper spindle assembly can all arrest cycling at a specific checkpoint. Aside from internal coordination and checking for errors, cells also make a decision of whether to engage in the cell cycle at all or remain quiescent. This decision depends on a cell’s internal status but is greatly influenced by external factors, which most commonly determine whether cells proceed through G1 to S phase. In other words, transition from G1 to S is the major externally influenced commitment point for entering and completing a cell cycle. Transition through G1 to S phase is normally accompanied by phosphorylation of Retinoblastoma (Rb) family proteins by two sets of cyclin-dependent kinases (CDK) [85, 86], which leads to the replacement of E2F/DP/Rb repressor complexes by E2F/DP activator complexes at regulatory regions of a large number of genes, several of which are essential for proper execution of S phase. CDKs require cyclin binding partners to be active and can be inhibited by two classes of cyclin-dependent kinase inhibitors (CKI). One class, consisting of p16Ink4a, p15Ink4b, p18Ink4c and p19Ink4d, bind to CDK4 and CDK6, thereby inhibiting cyclin D-dependent kinase activity. The other class of inhibitors, p21Cip1, p27Kip1, and p57Kip2, inhibit cyclin E-dependent kinases. During a normal cell cycle, cyclin D accumulates during G1, leading to increased cyclin D-dependent kinase assembly and activity that has two consequences. First, phosphorylation of Rb at select sites induces some E2F responsive genes, including cyclin E. Cyclin D-dependent kinases also bind p21, p27, and p57, thereby favoring their dissociation from cyclin E/CDK2. In these ways, cyclin E-dependent kinase activity is increased, leading to additional phosphorylation of Rb (at different sites) and greater activation of E2F-responsive genes, including cyclin E and cyclin A2. Continued passage through the mammalian cell cycle normally involves an extremely intricate interplay of multiple CDKs and their regulators, including control of mitotic entry and exit by regulation of cyclin A2/CDK1 and cyclin B/CDK1 [82–84]. Despite this complexity, most cell cycles can proceed relatively normally in mice without D-type cyclins, without CDK4/6, without E-type cyclins, or without CDK2 (to name a few such examples) [82–84]. Indeed, appropriate regulation of only a single CDK might suffice to drive cell cycles [87–89]. For the G1-S transition, this choice might be a complex of cyclin E with CDK2 or CDK1, or perhaps a cyclin D complex with CDK4 or CDK2. As for CDK, the Rb and E2F families exhibit enormously complex ranges of activities and interrelationships [85, 86, 90]. E2F1–3 are transcriptional activators, whereas E2F4–5 are repressors and each has a different affinity for the Rb family members, Rb, p107 and p130, whereas E2F6–7 do not interact directly with Rb proteins at all. E2Fs clearly have functions beyond the G1/S transition of the cell cycle and even beyond cell cycle control; likewise for Rb proteins. Mice lacking individual E2F members or Rb proteins display largely normal embryonic cell cycles, although

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mouse embryo fibroblasts (MEF) lacking E2F1–3 do fail to cycle normally. In Drosophila and Caenorhabditis elegans, there are fewer Rb and E2F family members, allowing simpler definitive tests. These tests have shown that Rb and E2F activities are not essential for cell cycles provided the genetic manipulations do not remove only transcriptional activators or only transcriptional repressors [85]. Nevertheless, as in mammalian cells, cell cycles can be altered through Rb and E2F proteins. Thus, for Rb, E2F, and CDK, it is clear that they normally participate in complex ways in cell cycling and that they provide important mechanisms for regulating cell-cycle decisions, but they are not the sole conduits for such regulatory input. To understand normal cell-cycle decisions, one must consider inputs that act through CDK, Rb, and E2F and inputs that act through other mechanisms [88, 91]. It is clear that most cancers include genetic aberrations affecting Rb/E2F regulation (85,86,92). These aberrations include loss of Rb, overexpression of cyclins or CDK, loss of CKI, or even occasionally E2F overexpression. The observation that only one such mutation is generally seen in each tumor is taken as strong evidence that the focus of all of these mutations is indeed the regulation of E2F activity by Rb, and it has been speculated that every solid tumor may have a mutation affecting Rb/E2F regulation [51]. What crucial property is endowed by such mutations? This question has no precise answer. One obvious, but imprecise, answer is that these mutations will tend to favor cell cycling under any given set of conditions. Thus, inhibitory factors can induce CKI but this will not arrest the cell cycle efficiently if Rb is absent or if CDK are overexpressed. Conversely, the requirement for positive growth factors that can induce cyclin D, leading to increased CDK4/6 activity and consequent cyclin E-dependent kinase activation, may be lowered by loss of Rb or CKI. Cell cycling is not necessarily rendered completely independent of external factors by this means.

8.4.2 Accelerated G1/S Transition can Trigger Senescence and Apoptosis E2F activation can promote senescence or apoptosis [85, 92]. An important, well-established intermediate in this activity is p14/p19ARF, which is transcriptionally induced directly by E2F [93, 94]. ARF binds directly to Mdm2, which normally sequesters and degrades p53 by promoting its ubiquitination. ARF increases p53 levels and activity, leading to apoptosis, cell cycle arrest, or senescence. The latter two responses are executed by CKI and cannot operate in the absence of Rb. Loss of Rb has the potential to activate cell cycling but also to activate apoptosis through ARF induction and p53 inactivation, which clearly can create selective conditions favoring loss of ARF or p53 function and, indeed loss of ARF or p53 enhances tumor formation in Rb heterozygous mice [93–95]. It should be stated, however, that the relationships among E2F, ARF, p53, and apoptosis or senescence are known to be much more complicated than I have portrayed. For example, E2F can induce p53-dependent apoptosis even in the absence of ARF and it can induce apop-

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tosis also in the absence of p53. Furthermore, it is not clear what level, duration, timing, or other quality of E2F activation leads to the different potential outcomes of cell cycling, senescence, or apoptosis. Rb/E2F axis mutations give us a big clue that a crucial step in cancer development involves changes in G1/S control and perhaps creating selective pressure for loss of p53 pathways. It is not clear exactly how much or how crucially cell-cycle dependence on external factors is changed by Rb pathway mutations, or why mutational alteration of Rb/E2F regulation induces apoptotic or other stress responses whereas normal cell-cycle dependent activation of E2F does not.

8.4.3

Regulation of Cell Growth

For cells to continue to proliferate, they must not only continue to divide but they must also increase in mass by synthesizing proteins and other components, which requires raw ingredients and can be limited by nutrient and oxygen availability. It also requires internal regulatory systems to be set in a distinct growth mode. In multicellular eukaryotes, no tight coupling is seen between the cell cycle and growth. Cell growth does generally lead to cell division but there is no strict connection between cell size and initiation of a cell division cycle [96]. Conversely, driving the cell cycle alone does not suffice to increase biomass accumulation (i.e., net proliferation); instead it produces smaller cells. The most incisive experiments investigating these connections have been performed in the developing Drosophila wing imaginaldisc. Here, increased cyclin E shortens G1 but G2 lengthens proportionately; increased Cdc25 phosphatase activity shortens G2 but G1 lengthens in response; and activation of E2F, which can induce both cyclin E and Cdc25 accelerates both G1 and G2 to increase the frequency of cell divisions [96]. These cells do not grow faster and therefore divide at a smaller size. These precise responses may not be shared in detail by other fly, mice, or human tissues but there is good reason to believe that they are laying down critical common principles. One such principle is that proliferation requires stimulation of both growth and cell cycling. Other principles relate to the mechanisms for stimulating growth. These mechanisms are still being worked out even in the wing imaginal disc model system but there are several important leads [97]. One set of pathways that regulate growth and integrate external signals with nutrient and oxygen availability involve PI3K, PKB, mTOR, and TSC1/2 [97, 98]. A few key targets of other signaling pathways, including cyclin D and Myc, are also known to induce growth. Thus, cyclin D-dependent kinase induces growth, most likely through a mechanism that is integrated with oxygen sensing [99]. Myc induces growth largely through effects on ribosomal RNA transcription and ribosome biogenesis [100, 101]. Finally, a very important pathway (or set of pathways) for regulating proliferation is emerging from studies in the Drosophila wing disc [97, 102, 103]. This pathway is named after Hippo, a STE20-family protein kinase, which acts in a complex with Salvador. Downstream is a second protein kinase

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complex, composed of Warts and Mats. Phosphorylation of the transcription factor coactivator Yorkie by Warts inactivates Yorkie. Yorkie activity induces the microRNA bantam, which stimulates growth and proliferation, while inhibiting apoptosis [104]; it also induces cyclin E and the inhibitor of apoptosis, DIAP1. Activation of the Hippo pathway suppresses proliferation and promotes apoptosis (and mammalian equivalents of several components have been found to act as tumor suppressors). The upstream regulators of this pathway are less certain but include Expanded and Merlin (neurofibromin 2; NF2), two interacting adhesion complex associated regulators of the actin cytoskeleton [102]. A separate input may come from the nonconventional cadherin, Fat [103, 105]. The connection between ligand-dependent signaling pathways and pathways that can affect cell growth are likely to be of critical importance in understanding normal cell proliferation and cancer development. This connection is clear for the PI3K pathway but our appreciation of how the Hippo pathway may contribute remains very primitive.

8.4.4 Terminal Limitations on Cell Proliferation: Cell Death and Senescence Apoptosis can be promoted through internal pathways and in response to external factors, such as tumor necrosis factor (TNF), Fas ligand, and TRAIL [95, 106]. The net outcome depends on the integration of many potential inputs and is highly context-dependent. Nevertheless, it is clear that a variety of genetic manipulations that potentially drive cell proliferation, such as overexpression of Myc or activation of E2F, can promote apoptosis. This situation imposes a selection in favor of mutations that reduce apoptosis and requires a change in the balance of pro- and antiapoptotic factors in order for many potentially proliferative signals to result in cell division and growth. The PI3K pathway figures prominently in such antiapoptotic accommodations and so do the surveillance pathways, centered on p53, that connect, for example, excessive E2F or Myc activity to apoptosis [95, 106]. The phenomenon of senescence may play an analogous but distinct role to that of apoptosis [95, 107]. The original senescent label was assigned to primary cells in culture that reached a cell division limit and permanently ceased division without dying. A related phenotype can be induced in cultured cells by some potentially proliferative signals, most notably Ras activation. Rb/E2F and p53 are important mediators of oncogene-induced senescence but terminal effectors have not been investigated extensively. A significant component of the response is thought to be maintained by chromatin silencing [108]. Senescence, like apoptosis, does appear to be a physiologically important limitation for cell proliferation during cancer development and needs to be understood in more detail [107]. Both apoptosis and senescence are incorporated into the broad conclusion that many genetic manipulations can provide selective pressure for loss of p53 pathways and that loss of p53 substantially reduces the likelihood of an apoptotic

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or senescent response in place of a proliferative response. p53 loss also permits proliferation in place of cell-cycle arrest or apoptosis in the face of DNA damage, spindle defects, and other stresses [109]. It is easy to rationalize how p53 can contribute to a multitude of paths to cancer development.

8.4.5 Cell Interactions that Regulate Proliferation within an Epithelium Many cancers develop in tissues composed predominantly of epithelial cells. It is important to consider how gene mutations allow cells to leave an epithelium to initiate invasive or metastatic growth, and to consider how cellular phenotypes are affected by being in an epithelium. Once again, the Drosophila wing disc provides a very useful model system, from which several generalizations can be drawn [97, 110, 111]. First, there are many cooperative interactions among cells that affect exposure to signaling molecules. Thus, the spread of key signaling molecules, such as Hh, Dpp, and Wg, is modulated by signal-dependent expression of receptors in cells that lie between a responding cell and the signal source. Local competitive interactions occur among neighboring cells, perhaps comparing signaling strength induced by widely distributed ligands or comparing intrinsic growth rates [112–114]. Furthermore, dying cells (artificially maintained by caspase inhibition) produce growth-stimulatory signals to their neighbors [115, 116]. Second, organization in an epithelium is likely essential for transmission of signals that regulate proliferation through the Hippo pathway. The initiation of such signals is uncertain but unconventional cadherins and adhesion junction associated proteins are certainly involved [97, 103]. Third, loss of several proteins associated with septate junctions (Lethal giant larvae, Scribble, and Lethal giant discs) alter normal epithelial polarity, so that apical surfaces are greatly increased and lead to extensive overgrowth, essentially because growth continues unabated well beyond the normal size and stage for cessation [97, 111]. A similar phenotype results from loss of specific components involved in endocytic pathways. Thus, normal polarity and epithelial organization play crucial roles in limiting cellular growth. It is not presently known whether this is because of epithelial interactions that limit signaling pathway activities or through other means. Although the limitations on growth imposed by confinement to an epithelium are not all that well understood mechanistically, this fact should not detract from their significance and the simple observation that overgrowth because of loss of normal epithelial character in wing discs is dramatic.

8.4.6 Angiogenesis, Epithelial-Mesenchymal Transitions, and Metastasis At some stage during cancer development, cells will begin to accumulate in environments that have a poor supply of oxygen and nutrients. Continued growth of these cells will be facilitated by an improved blood supply. Accordingly,

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solid tumors inevitably become vascularized at some point because the cancer cells secrete signals that promote the formation of new blood vessels. The VEGF (vascular endothelial growth factor) family of ligands play a major role in directing both normal and cancer-induced vascular development, and a major inducer of VEGF expression is the transcription factor HIF-1 (hypoxia-induced factor) [117, 118]. HIF-1 activity is increased by hypoxia but both HIF-1 and VEGF can be induced by many of the signaling pathways that are commonly activated in cancers [51, 118]. For example, both are independently induced transcriptionally by the Ras/MAPK branch of RTK pathways and HIF-1 translation is markedly increased by the PI3K branch of the same pathway. At a later stage of epithelial cell cancers, further spread is spatially constrained and continued growth through tissue invasion or metastasis requires that cells exit the epithelium. Epithelial-mesenchymal transitions can be regulated in many ways but a key step is the dissolution of adherens junctions that is often brought about by reducing the function or levels of epithelial-cadherin (E-cadherin) homotypic adhesion molecules [119, 120]. E-cadherin can be regulated in many ways by signaling pathways, including transcriptional repression through the PI3K pathway. The PI3K pathway can inhibit glycogen synthase kinase 3 (GSK3) activity through phosphorylation, which reduces GSK3-promoted ubiquitin-mediated degradation of the E-cadherin transcriptional repressor, Snail, and hence decreases E-cadherin expression [119]. Several other signaling pathways can alter Snail and E-cadherin activity as well as the expression of Integrins and other adhesion molecules [119, 121–123]. Partly through promoting epithelial– mesenchymal transitions, a number of signaling pathways have been shown to promote metastasis [120, 124]. Not enough is known to form an integrated picture of how altered signaling pathways in cancer cells control angiogenesis, epithelial–mesenchymal transitions, and metastasis. The communications that operate only within epithelia will, of course, change during epithelial–mesenchymal transitions and place different demands on proliferation and survival pathways after epithelial–mesenchymal transitions.

8.5

Signaling Pathways

Signaling pathways can be grouped into a small number of major families that are conveniently named either according to the receptor or ligand family. The history of discovery of these pathways led some to be characterized as growth-factor pathways (because they stimulated proliferation of cells in tissue culture) and others as developmental pathways (because they were uncovered by genetic mutations that produced aberrant development of model organisms). In the first edition of this text, I argued that there was no essential difference in the roles played by these differently named pathways, but now I believe it is an accepted idea that each type

of pathway can instruct developmental fates, inhibit growth, promote growth, or affect cell survival, all according to the developmental cellular context [14, 15, 17, 97, 125]. It is also evident, however, that genetic alterations of these pathways show characteristic, not random, associations with particular types of cancer, which, of course, leads us to focus on the pathway most frequently associated with a particular cancer and to try and understand what is the developmental context that makes that particular signaling pathway of overwhelming importance in cancer development. Accordingly, here I will deal only with signaling pathways firmly and frequently associated with common cancers. I will begin with RTK pathways, within which overexpression of RTK, activating mutations of Ras and loss-of-function mutations in PTEN are pervasive in many forms of cancer. I will then consider Wnt and Hh pathways, which have more characteristic cancer associations and for which there is some evidence of likely actions on stem cells. Finally, I will consider Notch, TGF-β, and JAK/STAT pathways more briefly.

8.5.1

Receptor Tyrosine Kinase Pathways

A large number of classically defined growth factors, including platelet-dervived growth factor (PDGF), epidermal growth factor (EGF), fibroblast growth factor (FGF), nerve growth factor (NGF), and TGFα family members signal by inducing dimerization and hence activation of receptors that are protein tyrosine kinases [126–129]. Dimerization enhances intermolecular receptor autophosphorylation, which stimulates enzymatic activity further and creates numerous binding sites for signal transduction components that contain SH2 (Src Homology 2) or PTB (Phospho Tyrosine-Binding) domains. Each SH2 domain recognizes phosphotyrosine itself and a short amino-acid sequence C-terminal to the Tyr that determines the specificity of SH2-phosphotyrosine binding. Similarly, PTB domain recognition of phosphotyrosine depends on the nature of the 3–5 N-terminal amino-acid residues. In some cases, most notably for insulin and FGF receptors (FGFR) [129, 130], docking proteins (IRS [insulin receptor substrate] and FRS [fibroblast growth factor receptor substrate], respectively) bind to the phosphorylated receptor, become phosphorylated themselves and thereby provide several additional phosphotyrosine-based binding sites that account for the bulk of further signal transmission. Tyrosine kinase receptors (plus associated docking proteins) have variable numbers and contexts of Tyr residues that are phosphorylated after activation, and can recruit different subsets of SH2/PTB domain molecules. The PDGF receptor (PDGFR), for example, has 12 such sites and can bind many SH2-domain containing proteins including phospholipase Cγ (PLCγ), cytoplasmic tyrosine kinases of the Src family, the p85 regulatory subunit of PI3K, the protein tyrosine phosphatase SHP-2, the adapter proteins Grb2, Shc, Nck, and Grb7, the Ras GTPase activating protein GAP, and transcription factors of the STAT family [131]. Binding to the receptor activates the

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recruited signaling molecule through a variety of mechanisms, including allosteric change, Tyr phosphorylation, or apposition to binding partners or substrates at the membrane. Hence, ligand binding to a single type of tyrosine kinase receptor can activate many types of molecules at the plasma membrane. This, of course, produces a large number of diverse responses. Many of these receptor-bound molecules feed into common pathways and it appears that several signaling routes serve similar, somewhat redundant purposes [129]. Indeed, when RTKs with different constellations of SH2 and PTB domains

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are activated the general pattern of immediate changes in gene transcription (the “immediate early response”) are quite similar; moreover RTKs can typically tolerate alteration of several docking sites without obvious loss of function [131, 132]. RTK pathways do not only alter patterns of gene expression but also lead to multiple changes in protein phosphorylation with diverse consequences that extend beyond changes in transcription. The major contributors to these diverse consequences are the Ras/ERK and PI3K branches of RTK pathways (Fig. 8-4).

Fig. 8-4. Receptor tyrosine kinase pathways. Binding of ligand promotes or stabilizes receptor tyrosine kinase (RTK) dimerization and transphosphorylation. This activates tyrosine kinase activity further and creates binding sites (P) for many molecules with SH2 or PTB domains. For the insulin receptor the IRS protein is recruited and phosphorylated to form additional phosphotyrosine motifs. Grb2 is recruited directly or indirectly by Shc, leading to SOS-catalyzed Ras activation at the plasma membrane. Ras activates a series of protein kinases, leading to activation of the major effector kinases, ERK and RSK, leading to the induction of key RNAs that are translated into protein products that can be stabilized by ERK and RSK. The induction of Myc and Cyclin D, amongst other products, facilitates G1/S transition. The second major branch of RTK signaling is initiated by PI3K recruitment and activation, leading to increased PIP3 production and activation of the key effector protein kinase B (PKB). PKB counters the proapoptotic action of BAD and FoxO and also inhibits TSC1/2 activity. TSC1/2 is activated via AMPK and GSK3 in response to low glucose and prevents activation of the Raptor Tor complex (mTORC1) by Rheb. mTORC1 stimulates protein translation, especially of selected mRNAs through phosphorylation of 4E-BP and S6 kinase, leading to cell growth. Phospholipase C is also recruited by RTKs, leading to increases in calcium ion concentration and PKC activity. Although the major responses are through Ras/Raf/ERK and PI3K/PBK and the former primarily affects transcriptional changes and the cell cycle, while the latter is antiapoptotic and growth promoting, there are also strong inputs from ERK and RSK that promote growth (thin, dotted lines) and inputs from PKB and GSK3 that stabilize major effectors of the Ras/Raf/ERK pathway (dotted lines). Also, Ras can activate PI3K (not shown).

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Ras/ERK Pathway

The Ras/ERK (extracellular signal regulated kinase) pathway leads to the activation of MAP kinases (ERK1 and ERK2 in mammalian cells) [133–135]. p21-Ras is associated with the plasma membrane as a result of farnesylation and is activated by binding GTP, a process catalyzed by GTP/GDP exchange factors, such as Son-of-sevenless (Sos) [129]. Sos is brought to its substrate by association with the adapter protein, Grb2, that binds activated tyrosine kinase receptors, such as PDGFR and EGFR (Fig. 8-4). The adapter protein, Shc, can also stimulate this process. Shc is recruited to signaling complexes by its SH2 domain and subsequently phosphorylated by RTK or by associated Src-family tyrosine kinases. Phosphorylated Shc then provides binding sites for the SH2 domain of Grb2 and recruits Sos to the membrane. GTP-bound Ras recruits the Ser/Thr protein kinase, Raf, to the plasma-membrane where it is phosphorylated and activated [135]. Raf, in turn, activates another protein kinase, MEK, by phosphorylation. Activated MEK phosphorylates the mitogen-activated protein kinases (MAPK), ERK1 and ERK2, by dual phosphorylation of Thr and Tyr residues. The Raf-MEK-ERK phosphorylation cascade is one of several MAPK phosphorylation cascades in mammalian cells [136]. ERKs can phosphorylate membrane-associated and cytoplasmic proteins and, especially during sustained activation, ERKs translocate to the nucleus and phosphorylate transcription factors of the TCF/Ets family, leading to activation of “immediate-early” genes including fos, jun, and myc, which also encode transcription factors [132, 137]. ERK can phosphorylate and activate p90RSK family kinases, which can phosphorylate cAMP-response element binding protein (CREB) and histone H3, among other substrates, to regulate gene transcription further [133, 138]. The initial transcriptional changes induced by ERK activation are quite extensive and are diversified further by the actions of immediate early gene products, such as Fos and Jun, which associate together in the AP1 complex (Fig. 8-4). The stability and activity of such secondary transcriptional effectors can also be modified by MAPK phosphorylation, as demonstrated most clearly for Fos. It has been argued that by virtue of initially inducing Fos and other gene products transcriptionally and then by promoting their subsequent accumulation through stabilization, MAPK activation can be translated into different outcomes depending on the duration of MAPK activation [133, 139]. Thus, the Ras/MAPK pathway has the potential to produce widespread changes in transcription that may have different patterns depending on a variety of parameters including the temporal profile of MAPK activation. The most widely cited transcriptional target of Ras/MAPK signaling relevant to cell cycling is the induction of cyclin D1 [140]. Activation of tyrosine kinase receptors, Ras, Raf, or ERK can activate cyclin D1 transcription, such that mRNA levels increase up to 20-fold, generally peaking about 6 hours after mitogen stimulation. Furthermore, Ras inhibition prevents

increased cyclin D1 transcription in response to mitogen and inhibits progression to S phase in an Rb-dependent manner [140, 141]. The transcription factors, AP-1 and c-Myc, can be induced by Ras/MAPK signaling, and antibodies (Ab) to Fos and Myc can inhibit cell cycling [142]. Conversely, overexpression or activated forms of Jun, Fos, and Myc can lead to growth-factor independent cycling in tissue culture cells. Although altered transcriptional programs are key responses to Ras/ERK signaling, there are also many examples of direct phosphorylation of substrates by ERK and RSK that can affect cell growth and apoptosis (Fig. 8-4) [126, 143, 144].

8.5.1.2

PI3K Pathway

A second major RTK signaling pathway is initiated by activation of PI3K (Fig. 8-4) [145]. Recruitment of the p110 catalytic subunit of PI3K through receptor association of the p85 regulatory subunit stimulates activity, perhaps largely by plasma membrane apposition, close to a source of phospholipid substrates. PI3K phosphorylates the 3′ position of the inositol residue in phosphatidyl inositol (PtdIns; PI), phosphatidyl inositol 4-phosphate (PtdIns 4-P; PIP) and phosphatidyl inositol 4,5 diphosphate (PtdIns[4,5]P2; PIP2). The PtdIns(3,4,5)P3 (PIP3) product, derived from PIP2 phosphorylation is especially important. PIP3 can be converted back to PIP2 by the lipid phosphatase PTEN (phosphatase and tensin homolog). PIP3 stimulates the activation initially of two Ser/Thr protein kinases: protein kinase B (PKB; also known as Akt) and PDK1 (3′ phosphoinositidedependent protein kinase 1). PKB activation requires binding of PIP3 to its pleckstrin homology (PH) domain as well as phosphorylation of its activation loop by PDK1 and phosphorylation of another Ser residue by mTORC2. PKB is a key mediator of multiple PI3K pathway effectors, including a central regulator of protein translation, mTORC1 [145–147]. mTORC1 is the TOR (target of rapamycin) complex, which is inhibited by the growth inhibitory drug, rapamycin. mTORC1 can phosphorylate and inhibit the activity of 4E-BP1 (eIF4E initiation translation factor binding protein) and, together with PDK1, mTORC1 can activate p70S6 kinase (S6K) by phosphorylation. These changes in 4E-BP1 and S6K1 activity alter protein synthesis in a highly significant manner. mTORC1 activity requires association with Rheb (Ras homolog enriched in brain) in its GTP-bound form. The tuberous sclerosis complex proteins TSC1/2 promote Rheb GTPase activity and therefore indirectly limit mTORC1 activity [145, 147, 148]. TSC1/2 is a key integrator of environmental signals. TSC1/2 activity is inhibited by the PI3K pathway (in part through phosphorylation by PKB) and activated in response to nutrient deprivation, hypoxia, and low energy levels. Low energy levels result in accumulation of AMP and activation of AMPK (AMP-activated protein kinase), which also requires phosphorylation on its activation loop by LKB1 (the protein kinase inactivated by familial mutations in PeutzJeghers syndrome). AMPK phosphorylation of TSC2 primes

8. Signaling Pathways in Cancer

further phosphorylation by GSK3, resulting in activation of TSC1/2 [98]. Energy shortage inactivates mTORC1 and reduces protein synthesis, provided LKB1 and GSK3 retain normal activities. PKB acts to counter this restriction not only by phosphorylating TSC1/2 and by reducing GSK3 activity through direct phosphorylation, but also by promoting glucose uptake and consequent maintenance of a high ATP/AMP ratio [149]. Thus, one major function of the PI3K pathway is to stimulate protein synthesis through phosphorylation of S6K and 4E-BP via PDK1, PKB, TSC1/2, Rheb, and mTORC1, which can counter nutritional, oxygen, and energy limitations that would otherwise limit protein synthesis. PKB is also a key intermediate in PI3K responses that affect the cell cycle and apoptosis (Fig. 8-4). PKB phosphorylation of FoxO Forkhead family transcription factors contributes to both responses [150]. Phosphorylated FoxO proteins bind to 14-3-3 proteins to promote their net nuclear export, leading to diminished activation of target genes. Among those target genes are negative regulators of the cell cycle, p27Kip1, and p130Rb2, the proapoptotic protein Bim and the proapoptotic Fas-ligand. These transcriptional changes are reinforced by numerous other consequences of PKB activity. PKB inhibits GSK3 activity by direct phosphorylation [145]. This stabilizes Myc and cyclin D1, which would otherwise undergo rapid proteolysis in response to phosphorylation by GSK3. PKB also reduces the activity of the proapoptotic Bad protein by phosphorylation and destabilizes p53 by Mdm2 phosphorylation [150]. Thus, activation of the PI3K pathway produces strong coordinated responses that oppose apoptosis and enhance protein synthesis and cell growth, while also contributing significantly to passage through the G1/S cell cycle restriction point (Fig. 8-4).

8.5.1.3 PI3K Pathway Connections to Translation and Cell Growth The effects of the PI3K pathway on protein translation have been thoroughly studied but some significant mysteries remain. Activation of the PI3K pathway leads to a modest immediate general increase in protein translation that is greatly enhanced for a subset of mRNAs (“TOP” mRNAs) that include a polypyrimidine stretch in their 5′ UTR (untranslated region) [146]. Among such mRNA are those encoding ribosomal proteins and other components of the protein translational machinery; their enhanced translation amplifies and sustains the initial increase in protein translation stimulated by PI3K. These effects were initially attributed to phosphorylation of ribosomal protein S6 by S6K but this hypothesis has since been contradicted [147]. Exactly how PI3K pathway activity selectively promotes translation of TOP mRNAs is not understood. The binding of eIF-4E to eIF-4G is critical for loading capped mRNA onto ribosomes. eIF-4E binds to the 5′ terminal cap structure of mRNA and to eIF-4G. The scaffolding protein eIF-4G, in turn, binds to the 40S ribosome and to eIF-4A, which collaborates with eIF-4B to unwind secondary RNA structure. 4E-BP competes with eIF-4G for binding to eIF4E, thereby

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inhibiting translation initiation, but 4E-BP is inactivated by mTORC1-dependent phosphorylation [146]. Phosphorylation of 4E-BP stimulates general translation about twofold but can stimulate translation of specific mRNA, such as c-myc, that contain highly structured 5′ UTR, more than 20-fold. Reinforcing this 4E-BP mechanism is an analogous mechanism for eIF-4A activation. PDCD4 (programmed cell death protein 4) inhibits eIF-4A but is degraded after phosphorylation by S6K [151]. Thus, a number of well-defined mechanisms (and perhaps more to be described in the future) connect PI3K activity to increased ribosome activity, with preferential activation of subsets of mRNA with characteristic 5′ UTR structures. In addition to this, ribosome biogenesis is enhanced in several ways, including increased transcription of rRNA genes [146]. Whether tested in tissue culture cells or whole organisms, the PI3K pathway consistently has robust antiapoptotic and growth stimulatory properties [130, 145, 146]. This situation is best illustrated by studies in Drosophila where the size of genetically manipulated clones in the wing disc and the size of entire genetically manipulated flies illustrate the universal nature of PI3K pathway responses and the remarkable paucity of effects on other developmental processes [152, 153]. Reduced signaling by the Drosophila insulin receptor, by the IRS protein, Chico, by PI3 kinase, by PKB, or by S6 kinase produces smaller flies with smaller wings and smaller wing-disc epithelial cells. Cell division rates are also generally slowed (producing fewer cells), but, obviously, not as much as growth because cell size decreases. Overexpression of these components or loss of PTEN activity produces increases in overall cell mass and cell size. Increased division rates are evident in response to loss of PTEN but not from most of the other manipulations. Clearly, the PI3K branch of RTK signaling primarily affects cell growth. Secondarily, there appears to be some stimulation of cell cycling, especially from components at the top of the PI3K pathway hierarchy, resulting in a shortened G1 phase in Drosophila wing-disc cells; however, this is frequently accompanied by an Rb/E2F-mediated increase in the length of G2, as observed in the response to activated Ras [152, 154]. The apparent dedication of the PI3K pathway to cell growth and survival contrasts with the diverse possible outcomes of the Ras/MAPK pathway and most other signaling pathways.

8.5.1.4

Phospholipase C Pathway

PLCγ bound to an activated tyrosine kinase receptor can be phosphorylated on Tyr and thereby activated to catalyze cleavage of phospholipids into diacylglycerol (DAG) and inositol triphosphate (IP3) (Fig. 8-4)[129]. Binding of IP3 to specific receptors on internal membranes leads to Ca2+ release from intracellular pools, leading to activation of Ca2+-dependent protein kinases. The combination of DAG and Ca2+ together activate conventional protein kinase C isozymes. Protein kinase C has many targets including transcription factors responsive to mitogens but it also can enhance activation of the Ras/ERK pathway.

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Interactions Between RTK Pathway Branches

RTK pathways also activate STAT and a variety of small Ras family GTPases (such as Ral, Rac, Rho, and Cdc42) that can impinge on transcriptional responses and important changes in the cytoskeleton. Furthermore, the activation of some of these RTK effectors can contribute to Ras or RTK-induced tumor formation in mouse models [145]. Nevertheless, it is reasonable to emphasize that Ras/MAPK and PI3K pathways appear to be the major effectors of RTK activation relevant to both normal development and cancer. RTK pathways can be triggered in a number of ways besides activation of RTKs. This includes the activation of non-receptor tyrosine kinases such as members of the Src and Abl families, after cytokine, T-cell receptor, or B-cell receptor activation of blood cells [155–158], and in response to ECM signaling by integrins [159, 160]. It also includes activation of many G-protein coupled receptors, which can activate the Ras/MAPK pathway in particular by recruiting scaffolding molecules of the Arrestin family [161, 162]. It is crucial to appreciate that the Ras/MAPK and PI3K pathways are in many ways interlocking pathways. They are interlocking in the sense that Ras/MAPK activation can also be antiapoptotic and promote protein translation [138, 143, 144, 146, 163], whereas the PI3K pathway can accentuate induction of cyclin D1 and Myc by Ras/MAPK by enhancing translation and stabilizing the protein products [145]. More importantly, activated Ras proteins can activate PI3K [134, 146]. In fact, only certain Ras proteins can efficiently activate PI3K directly but other Ras proteins still increase PI3K activity, whether by cross-activation of family members or by other means. This partial coupling between Ras and PI3K has been used to rationalize why in some cancers (especially melanoma) Ras activation and PI3K pathway activation (generally by PTEN mutations) are generally exclusive, whereas in other cancers both types of mutation are frequently found together [134].

8.5.1.6 Mutational Alteration of Receptor Tyrosine Kinase Pathways in Cancer Regulated presentation of ligands is the key mechanism for restricting the activity of signaling pathways during normal development, so it follows that inappropriate production of ligand could stimulate inappropriate growth, as recognized many years ago in the autocrine growth factor hypothesis [165]. During development, the temporally and spatially restricted production of ligands is generally itself a response to activation of a signaling pathway by another ligand, and this connection has been documented specifically for EGFR ligands induced by activation of the Ras/MAPK pathway [126, 166]. The frequent association of growth factor production with tumors is most likely secondary to internal disruption of a different signaling pathway, rather than being caused by mutational alteration of the promoter for the autocrine factor or of specific transcriptional activators or repressors. Inappropriate production of growth factors is common in tumors; it

may occasionally contribute to initiation of a tumor, but more commonly it will speed further development of cancers. Specific mutations affecting receptors can produce ligandindependent dimerization and activation [126, 128, 167, 168]. Such changes include loss of the extracellular region or C-terminal cytoplasmic regions, and more subtle changes in the transmembrane domain and around the active site, all of which lead to constitutive or enhanced tyrosine kinase activity. Inappropriate receptor activation (autonomously or through ligand production) has the virtue of activating all downstream RTK pathways but may not always be effective because of mechanisms of down-regulation. Feedback inhibition frequently occurs at multiple sites along a signaling pathway but the receptor is invariably a prime target [126, 128]. For RTK, activation frequently leads to receptor internalization and degradation. It can also promote binding of a phosphatase (SHP2) that can dephosphorylate receptor phosphotyrosines, and it can recruit and activate RasGAP, thereby limiting the extent of Ras activation. The EGFR family (ErbB1-4) illustrates some of these principles [126]. There are several ligands for the prototypical EGFR, ErbB1/EGFR (EGF, TGFα, Amphiregulin, heparin-binding EGF, betacellulin, and epiregulin), as well as for the related receptors ErbB3/HER3 and Erb4/HER4 (two families of alternatively spliced Neuregulins) but none for ErbB2/HER2. ErbB2 can be activated in response to ligands by forming heterodimers with other family members. Indeed, of all receptor isoforms these heterodimers are the most potent activators of downstream pathways and of cell proliferation in tissue culture. Thus, only overexpression of the ErbB2 receptor isoform suffices to transform established cell lines efficiently and, whereas erbB1 amplification is also seen in cancers, there is a particularly strong link between ErbB2 overexpression and rapid tumor growth. It seems that ErbB2 is particularly potent at stimulating growth for at least two reasons [126]. First, ErbB2 is preferentially incorporated into an activated heterodimer and produces the most stable, and therefore persistent signaling complex. Second, a homodimeric ErbB1 complex with EGF is rapidly internalized and degraded whereas an activated ErbB1/ErbB2 complex is less rapidly internalized and is recycled to the membrane after internalization. Thus, ErbB 2 overexpression enhances the normal response of other EGFR family members to normal low levels of ligand, in part because it is relatively insensitive to negative feedback. Collectively, EGFRs provide several examples of how constitutive pathway activation can be achieved by increasing tyrosine kinase activity [126]. This situation is also seen for non-receptor tyrosine kinases, whether caused by mutations that increase specific activity, overexpression, or both. A prominent class of such mutations in cancer are those that create fusion proteins, potentially altering transcriptional control, imposing dimerization, and removing intrinsic constraints on kinase activity [157]. Clearly, these abnormal tyrosine kinase activities could be countered by targeting the specific source

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of excessive tyrosine kinase activity. This has been done with some success by developing Ab to ErbB2 (trastuzumab) and tyrosine kinase inhibitors directed to the EGFR (gefitinib, erlotinib), and Bcr-Abl fusion protein (imatinib, which also can inhibit c-Kit and PDGFR) [167–170]. In keeping with the idea that Ras and PI3K are the most upstream components of the major branches of signaling pathways initiated by tyrosine kinases, these molecules are affected by mutations found in cancers. Activating ras mutations are common [134]. On the one hand, this can be attributed to the design of Ras as an “on/off” switch whereby specific mutations that compromise GTPase activity leave Ras in a permanent “on” state that is inert to negative feedback mechanisms, such as RasGAP activity. On the other hand, Ras also occupies a focal point, sufficient to activate the ERK pathway and also, when activated by mutation, the PI3K pathway. Loss-of-function mutations affecting the RasGAP protein NF1 (neorofibromin 1), which is one of several GAPs that can limit Ras activation, are also found in tumors. For certain cancers (especially melanomas) activating mutations in B-raf have also been found frequently (to the exclusion of ras mutations in the same cancer), providing evidence to augment analogous demonstrations in transgenic mice, that the Ras/MAPK pathway can be important for cancer development [134, 135, 145]. Although activating mutations in the catalytic subunit of PI3K have been found frequently, even more common are PTEN loss-of-function mutations, which also predispose mice to tumorigenesis [51, 150, 171]. Genetic activation or amplification of protein kinase B is also seen in cancers but alterations to more distal components of the PI3K pathway are not common. Loss of TSC1/2 function produces hamartomas, which characteristically do not progress further. The limited consequences of mTORC1 activation through loss of TSC1/2 and the elucidation of negative feedback mechanisms from mTORC1 and S6K that limit PI3K activity provide reasons for believing that only activation of the PI3K pathway at the level of protein kinase B or above is effective at promoting cancer [148].

8.5.1.7 Why do RTK Pathway Mutations Cause Cancer? What can be deduced about the contribution of RTK pathways to cancer from knowledge of pathway connections and the occurrence of specific mutations in cancer? Without tracking the progress of cancer development in molecular and cellular detail, we cannot actually deduce anything with certainty, but the following ideas are likely to be relevant. First, the PI3K pathway appears to play a major, conserved role in a wide variety of cancers. During normal development, the PI3K pathway is persistently linked to cell growth and survival rather than to determination of cell fates, strongly implying an analogous contribution to cancer development [145, 152, 153]. It is easy to see how such properties could

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benefit a wide variety of developing cancers and how initial, excessive proliferation of stem cells or other precancerous cells could create an environment where activation of the PI3K pathway provides a strong selective advantage. I suggest that the PI3K pathway is unique (or at least unusual) in its association with specific cellular behaviors that are more or less independent of the cellular context, and that this characteristic accounts for its widespread activation in multiple types of cancer. Second, activation of the PI3K pathway is likely an important consequence of Ras activation. In favor of this hypothesis, Ras activation and PI3K/PTEN mutations are exclusive in some tumors [134, 145]. Also, in melanomas, where the Ras/ MAPK pathway appears to be especially important, mutations are found that activate either Ras or Raf, but only the latter are accompanied by PTEN mutations. This situation can be rationalized as a requirement for activating both branches of RTK signaling in melanoma, with Ras activation sufficing for both tasks in this tissue. In other cancers, including colon cancer, there would appear to be contradictory evidence as Ras activation is often found together with mutational activation of the PI3K pathway [134, 145]; however, PI3K activation and PTEN loss are also seen together in some cancers. Thus, it is possible that a combination of mutations (PTEN loss plus activation of either Ras or PI3K) activates the PI3K pathway more strongly than loss of PTEN alone and that this may be critical during development of some cancers as growth conditions become increasingly demanding of antiapoptotic measures and changes that preserve growth in the face of hypoxia and nutrient limitation. The hypotheses that PI3K pathways make a generic contribution to growth and survival in the face of stress, and that Ras acts largely through the PI3K pathway could account for the strikingly high proportion of cancers harboring Ras and/or PTEN mutations. Third, activation of the Ras/MAPK pathway can also be critical, but probably only for a subset of cancers, including melanoma. The contribution of the Ras/MAPK pathway to cancer development is likely to be dependent on cell type, simply on the bases that the transcriptional consequences of Ras/MAPK activation depend on cell status and the observation of multiple, varied roles of this pathway in normal development [12, 15]. In this regard the Ras/MAPK pathway is similar to other signaling pathways but distinct from the PI3K pathway; however, there may also be a more generic contribution of the Ras/MAPK pathway to cancer because a number of consequences of Ras/MAPK activity (affecting growth, cyclin D, and Myc) augment or complement consequences of PI3K pathway activation. Many molecular targets of the Ras/MAPK pathway (TSC1/2, translation initiation factors, and death associated protein kinase) augment the antiapoptotic and growth-promoting effects of the PI3K pathway [143–145], and in Drosophila wing discs the primary role of the Ras/MAPK pathway is to stimulate growth and prevent apoptosis [154, 172]. The two most notable proteins induced by contributions from both the Ras/MAPK and PI3K pathways are cyclin D and Myc, so it is important to consider how

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these (likely) common responses to RTK pathway activation might contribute to cancer. The idea that cyclin D1 can be a crucial Ras target is clearly illustrated in mouse models, where breast cancer is induced by overexpressing Her2 or activated Ras in the mammary epithelium only if the mice contain a functional cyclin D1 gene [173]. If we accept that most tumors acquire mutations that drive G1/S progression by loss of Rb, CKI, or amplification of cyclin D [51, 140], then we need to ask why induction of cyclin D through the Ras/MAPK pathway might be significant in the context of Rb axis mutations. To this unresolved question, we can add the observation that Ras activation can also induce the CDKI p21 and p16Ink4a, thereby inducing cellular senescence rather than proliferation in some cell types [95, 107]. In considering these questions, two issues are especially important. First, Rb axis mutations do not completely uncouple cell cycles from extracellular inputs, so cyclin D may still influence the cell cycle, for example, through p107 and p130 in cancers that lack Rb [86, 140, 141]. Second, Ras mutations may sometimes arise before Rb axis mutations in a developing cancer and may promote cell proliferation at early stages and perhaps even produce selective conditions for Rb (or p53) axis mutations by inducing CKI. The potential role of Myc in cancer has been amply demonstrated by the frequent occurrence of Myc overexpression in human cancers and by a number of mouse models involving overexpression of Myc [174, 175]. From loss-of-function and overexpression studies in mice, flies, and tissue culture cells it is clear that Myc can promote G1/S cell-cycle progression and cell growth and also promote apoptosis [100, 154, 174, 175]. The net outcome of overexpression of Myc is highly tissue dependent. Although the proapoptotic role of Myc can limit cancer development [176], it is also possible that in other circumstances it creates a selective environment favoring mutations in PI3K or p53 pathways. Likewise, the necessity for Myc stimulation of the cell cycle or cell growth (which is probably accomplished largely by increased ribosome biogenesis and induction of translation initiation factors, including eIF-4E and eIF-2a) [100, 101, 175] is likely to depend on cell status and the signaling environment. Certainly, it is understandable that Myc induction can synergize with other consequences of RTK pathway activation to promote cell growth and proliferation. An interesting and potentially more specific role for Myc has emerged from studies in the Drosophila wing disc. Here, the relative levels of Myc in neighboring cells can dictate the outcome of cell competition [100, 112, 113]. Cells with lower levels of Myc are actively eliminated from the epithelium because their neighbors have higher Myc levels. Whether this results from local Myc-dependent signals or from Myc-dependent changes in signal receptivity is not yet clear. Such competitive consequences would clearly be very significant in the selection of specific genotypes during cancer development. In summary, it is possible that induction of cyclin D, Myc, and perhaps other gene products by the Ras/MAPK pathway alone suffices to promote development of some specific

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cancers. It seems likely that the most common effect of Ras/ MAPK pathway activation on cancer development is by augmenting PI3K pathway effects on proliferation and cell survival. Those effects are tightly linked to PI3K activity in all cell types and therefore by their nature and their generality are likely to contribute at several stages to the development of a wide variety of cancers.

8.5.2

Wnt Signaling

The Wnt name stems from the realization that Drosophila Wingless, which affects many developmental decisions, and mouse Int-1, which can induce tumors if overexpressed in response to insertion of a retrovirus, are similar in sequence and action [17]. The mechanisms of Wnt signaling have been studied largely in a developmental context in mice, zebrafish, Drosophila, C. elegans, and Xenopus. The primary receptors for Wnt are transmembrane proteins of the Fz family. Early studies on Wnt signaling and the actions most relevant to cancer involve alterations in gene expression through the Wnt/ β-catenin pathway. In this pathway, LRP5 or LRP6 low-density liporotein receptor-related protein family members act as essential co-receptors [17,177]. Other Fz- mediated pathways, collectively referred to as non-canonical, include those that act through phospholipids, cGMP, and calcium (the Wnt/Ca2+ pathway) [178, 179], or through Rho family GTPases and other means to control cell polarity or gastrulation movements (planar cell polarity pathway) [18, 180, 181]. Non-canonical pathways do not involve LRP5/6, β-catenin, or the TCF/LEF family of transcription factors, and in some cases are not even stimulated by a Wnt protein. There are situations where a Wnt signal can affect cell polarity and cell fate simultaneously [182] and situations where a non-canonical Wnt signaling pathway can affect transmission in the canonical Wnt/ β-catenin pathway [18], but in most cases there is no overlap or interference among these pathways despite the fact that they can sometimes use the same Fz receptor and the same signal transduction component, Disheveled (Dsh). I will therefore only discuss the Wnt/β-catenin pathway in detail. The central regulatory step in the Wnt β-catenin signaling pathway is the regulation of ubiquitin-mediated proteolysis of β-catenin [17]. In epithelial cells, the bulk of β-catenin normally associates with the homophilic, calcium-binding transmembrane adhesion molecule, cadherin, and with α-catenin, which can bind actin. This adhesion complex links the actin cytoskeletons of apposed cells and is required for maintaining the epithelium. When β-catenin is present in excess of cadherins, it is rapidly degraded by the proteasome after ubiquitination by an SCF (β-TRCP) complex that recognizes a short phosphorylated peptide motif. This motif is produced by multisite phosphorylation of the N-terminal region of β-catenin initiated by casein kinase 1 and continued by GSK3 [183]. Degradation of β-catenin can be inhibited by mutational alteration of the key phosphorylation sites or by inhibiting the activity of GSK3 or CK1. Wnt signaling also reduces degradation of β-catenin by

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Wnt LRP5/6

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Fig. 8-5. Wnt/b-catenin signaling pathway. In the absence of Wnt (left), Axin binds to ß-catenin, glycogen synthase kinase 3 (GSK3), casein kinase 1 (CK1) and APC (adenomatous polyposis coli) to promote phosphorylation of b-catenin at its N-terminus and phosphorylation of axin and APC. Phosphorylation of b-catenin creates a binding site for b-TRCP, which is part of an SCF complex that consequently ubiquitinates b-catenin, targeting it for complete proteolysis by the proteasome. APC phosphorylation enhances its binding to b-catenin. LEF/TCF DNAbinding proteins associate with corepressors to inhibit expression of Wnt target genes. In the presence of Wnt (right) the coreceptors Frizzled (Fz) and LRP5/6 are each bound and likely brought together, leading to phosphorylation of LRP5/6 at GSK3 and CK1 sites, the recruitment of Dsh to Fz and probably activation of Go and Gq family G-proteins. Whether through juxtaposition of axin complexes with active G-proteins or with Disheveled (Dsh) or through association with a GSK3-binding protein (GBP), GSK3 dissociates from axin, leading to reduced phosphorylation of all AXIN complex components, likely facilitated by an associated phosphatase 2A (not shown). This prevents b-catenin destruction and may reduce interactions with APC, so that cytoplasmic b-catenin accumulates and enters nuclei, where it associates with LEF/ TCF and other coactivators to induce Wnt target genes. Axin degradation also increases, perhaps as a consequence of its dephosphorylation.

inhibiting phosphorylation, but the precise mechanism for this action, which is discussed below, is not clear (Fig. 8-5). The regulation of β-catenin phosphorylation involves several additional players. Axin can bind GSK3, CK1, and β-catenin and thereby promotes phosphorylation of β-catenin. Adenomatous polyposis coli (APC) protein can bind to both Axin and β-catenin, especially after it is phosphorylated by GSK3, stabilizing the complex and further favoring β-catenin phosphorylation [71]. These interactions are seen in a variety of cell types and organisms, and are essential to keep the pathway silent in the absence of a Wnt signal. Wnt can bind to both Fz and LRP5/6 proteins, whereas Dsh binds to the C-terminus of Fz, and Axin binds to the C-terminus of LRP5/6 after ligand-stimulated phosphorylation of this region of LRP5/6 by casein kinases and GSK3 [17, 177,

184, 185]. Thus, the juxtaposition of Dsh with axin/APC/ β-catenin complexes is likely an important consequence of Wnt binding to its co-receptors. In one signaling model, Dsh brings a GSK3-binding protein (GBP/FRAT) to the axin complex, resulting in dissociation of GSK3 from axin; however, this cannot be the sole or universal mechanism as Drosophila has no recognizable GBP/FRAT homolog and mice develop normally in the absence of known FRAT proteins. Another postulated mechanism involves G-protein–mediated dissociation of GSK3 from axin. There is clear evidence for the involvement of Go and Gq family G-proteins in mammalian cell and Drosophila Wnt/β-catenin signaling but again there are reasons to believe that G-proteins are not the only means by which a Wnt signal is transmitted [186–188]. Wnt signaling also leads to axin degradation and it has been shown that Wnt can signal in the

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absence of GSK3 activity, suggesting that the regulation of axin degradation may be critical to signal transduction [189]. Axin degradation is slower than dissociation of GSK3 from axin, suggesting that axin degradation may consolidate signaling but cannot be the initiating mechanism [188]. It seems therefore that a small group of related mechanisms can be employed to spare β-catenin from phosphorylation and consequent degradation in the presence of a Wnt signal (Fig. 8-5). Stabilized cytoplasmic β-catenin can move to the nucleus and associate with Tcf/Lef family transcription factors [17, 190]. Tcf/Lef proteins bind to DNA but cannot activate transcription alone; β-catenin provides a strong transcription activation domain at its C-terminus. Hence, the β-catenin/Tcf complex can bind specific sites on DNA and stimulate transcription. In the absence of β-catenin, TCF proteins associate with Groucho family proteins to repress transcription. Wnt signaling can derepress as well as activate target genes with Tcf-binding sites. Many of the associated proteins that allow formation of TCF-based activator and repressor complexes have recently been described, and it is already clear that these factors have an important influence on the outcome of Wnt signaling [17, 190]. Perhaps most intriguing is evidence that APC, β-TRCP (responsible for β-catenin ubiquitination), and other factors thought to regulate β-catenin solely in the cytoplasm are found associated with DNA and may play a role in terminating signaling in the nucleus [191]. Wnt/β-catenin signaling can affect the expression of large numbers of genes but two targets, Myc and cyclin D1, have drawn particular attention as potential mediators of proliferative responses to Wnts [17, 51]. Wnts do indeed commonly stimulate proliferation of cells during development, but, as for other pathways, it is also possible to find circumstances where Wnt contributes to arrest of cellular proliferation instead [192].

8.5.2.1 Mutational Alteration of Wnt Pathways in Cancer Mutations in APC in colon carcinomas provide the most dramatic example of Wnt pathway mutations in cancer [17, 51, 193]. Such mutations are found not only in heterozygous form in the germline of individuals with predisposition to multiple colon polyps and cancer (familial adenomatous polyposis; FAP) but also in most spontaneous colon carcinomas, generally as truncation mutations accompanied by complete loss of the second allele. In such colon carcinomas and in melanomas, β-catenin is stabilized when APC mutations are present. In many of the cancers that retain APC function, there are either loss-of-function mutations in both alleles of an axin gene or there is a dominantly acting mutation in β-catenin that prevents its phosphorylation or recognition by β-TRCP. This finding suggests that stabilization of β-catenin and activation of Wnt/β-catenin pathway target genes is key to the tumorpromoting activity of APC mutations. APC may be the most common target for such mutations simply because it is a large target and has non-redundant functions, but it is also possible

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that there are additional relevant consequences of APC mutations beyond stabilization of β-catenin. These consequences might include changes in apoptosis pathways and cell migration behavior [194]. Because APC mutations are found very commonly even in the earliest stages of colon cancer, it is thought likely that such mutations initiate tumor development and hence the APC gene has been termed a gatekeeper for this tissue [17, 51, 193]. Mouse models in which the phosphorylated region of β-catenin has been removed also leads to adenomatous polyps in the intestine (although not in the colon), emphasizing this gatekeeper role. Aside from colorectal tumors initiated by mutations in mismatch repair genes, mutational activation of the Wnt/β-catenin pathway appears to be an essential step in development of colorectal cancers. This early, tissue-specific involvement of the Wnt pathway begs the question of whether the initial impact of altered Wnt signaling is on colorectal stem cells.

8.5.2.2 Do Cancers With Activated Wnt Pathways Result From Actions of the Wnt Pathway on Stem Cells? The gut epithelium includes numerous villi and associated crypts of Lieberkuhn, within which are proliferating cells [71, 193, 195]. Near the base of these crypts are some slowly replicating cells, which are postulated to be stem cells. The rest of the crypt may contain some differentiated Paneth cells and includes a large number of rapidly proliferating, transitamplifying cells. As there are no definitive stem cell markers for this tissue, stem cells and transit-amplifying cells are generally collectively referred to as progenitor cells, characterized by the presence of markers of replication and the absence of differentiation markers. Progenitor cells near the base of the crypt remain in place whereas the whole epithelial sheet distal to the progenitors moves up the villus as cells differentiate and are eventually lost from the villus tip in an indefinitely repeating 2- to 7-day journey. Wnt signaling is clearly required to maintain the proliferative crypt compartment because mice lacking Tcf4 or expressing the Wnt inhibitor Dickkopf in the gut lose most or all proliferating crypt cells [71, 193, 195]. In wild-type animals, nuclear β-catenin and expression of Wnt pathway reporter genes are normally seen close to the base of crypts, revealing localized active Wnt signaling. Furthermore, increasing the number of cells experiencing Wnt signaling and perhaps also the strength of Wnt signaling through APC mutations, β-catenin activation, or ectopic Wnt expression in genetically engineered mice produces a large expansion of crypts within a few days. These enlarged crypts can branch, as occurs also in normal development, to produce additional crypts harboring the mutant, overproliferating cells. Although it is not possible to distinguish whether Wnt signaling is acting on stem cells or transit-amplifying cells (or both), it is also not known to what extent transit-amplifying cells can revert to a stem cell fate. It is not clear if in this case excess Wnt signaling increases stem cell numbers, stem cell division

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rates or, for example, longevity of transit-amplifying cells. It is known that Wnt signaling normally inhibits expression of ephrin B and activates expression of EphB tyrosine kinase receptors and that the reciprocal signaling between ephrin B and its receptors defines the junction between the upwardly migrating epithelium and the crypt [71]. Excess Wnt signaling acts through ephrin B and its receptors to push this boundary further from the base of the crypts, allowing a larger proliferative compartment to be maintained without proliferating cells being sloughed off with the differentiated villus cells. The whole biology of the crypt and villus is far more complicated, involving contributions from Notch, Hh, and BMP pathways to determine and maintain spacing of villi and the behavior of differentiating and proliferating cells [71]. Many of these signals are themselves regulated by Wnt signaling (within regulatory loops). Thus, Wnt signaling is a key initiator of a set of cellular interactions that normally produce a very dynamic epithelium that is maintained by stem cell activity. It is understandable that excessive Wnt signaling activity can transform this normal epithelium into a similar developmental system that includes a greater number of proliferating cells. It is also likely that several of these excessive proliferating cells can act as stem cells in the appropriate environment and that these cells might create new crypts and invade adjacent crypts to greatly increase the number of active stem cells harboring mutations that activate the Wnt pathway. Although Notch signaling and Hh signaling may collaborate in this process, their activation alone cannot recapitulate the effects of the more central regulator of the stem cell-based developmental unit of the large intestine. Exactly why activation of Wnt signaling is so tightly associated with colorectal cancer and activated Hh signal is associated with epithelial cancers in other regions of the gut is not clear. Activated Wnt signaling is also associated with tumors in other tissues and cell types that are maintained by stem cells [51, 193]. Does Wnt normally play a central role in determining the behavior of those stem cells or does it act on derivatives that cannot reacquire a stem cell phenotype? For epidermal tumors, this turns out to be a complicated issue. First, there are some human tumors (pilomatricomas; characterized by a multiplicity of aberrant hair shafts) that are associated with stabilizing β-catenin mutations and can be phenocopied in mice by appropriately localized high-level expression of stabilized β-catenin [196]. Furthermore, stem cells in the bulge of a hair shaft require some Wnt/β-catenin activity to be maintained and to supply a stem cell to migrate down to the matrix at the base of a hair shaft, forming a population of transient-amplifying cells [197, 198]. These cells drive each cycle of hair growth. When this pool of cells is depleted the hair follicle contracts, slowly drawing mesenchymal cells at its base (the dermal papilla) into contact with the bulge, at which point bulge stem cell proliferation is activated to begin a new cycle. Activation of the bulge stem cells is accompanied by detectable Wnt/β-catenin signaling and modest activation of β-catenin can accelerate the start of a new hair cycle (but

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does not eliminate cycling) [197, 198], which implies that Wnt signaling is necessary but probably not sufficient to activate bulge stem cell proliferation and that Wnt antagonists in the bulge contribute to stem cell quiescence throughout most of the hair cycle. Wnt signals also have multiple effects on cell differentiation, with strong Wnt signaling promoting differentiation in the hair lineage [196, 197]. Conversely, mice with an engineered Lef1 gene product that cannot bind β-catenin produce sebaceous tumors. Analogous LEF-1 mutations that impair activation by β-catenin but retain repressor function are found in human sebaceous tumors [199]. Thus, activation of Wnt signaling can contribute to hair follicle cancers whereas loss of Wnt signaling can contribute to sebaceous cancers. Altogether, the role of Wnt signaling in epidermal tumors does not present a clear case of primarily acting on stem cells. Thus, although Wnt signaling is required for bulge stem cells to proliferate, excessive Wnt activation does not enforce continued stem cell proliferation or mobilization and does not appear to increase stem cell numbers. HSC can be defined more or less precisely by using a small group of antigenic markers and their function can be assayed by short-term and long-term reconstitution assays in lethally irradiated mice [41]. Using these approaches, it was initially found that in vitro treatment of HSC with Wnt or activated β-catenin–stimulated proliferation and increased the number of functional long-term stem cells, whereas overexpression of axin reduced HSC activity [200, 201]. Those studies pointed to Wnt signaling being a major determinant of HSC behavior but those conclusions have largely been contradicted by subsequent studies where gene activities were manipulated in vivo [202]. HSC remain functional in the absence of β-catenin and excessive β-catenin activity produced increased numbers of antigenically defined HSC, but these cells did not retain normal stem cell function. It is possible that the different outcomes in these studies resulted from different degrees of imposed Wnt pathway activity or from differences in the HSC environment (in vivo and in vitro) when suffering changes in Wnt pathway activity [193, 202]. Even without resolving those important issues, it seems clear that Wnt signaling plays some role in HSC behavior, that the appropriate level of Wnt signaling is important for normal HSC function, and that Wnt signaling is not a master regulator of HSC numbers as originally proposed. Accordingly, there are no clear cases of gatekeeper mutations in the Wnt pathway underlying cancers in the hematopoietic system but there are several instances where β-catenin appears to be activated (perhaps by autocrine signaling) in some leukemias and it is possible that this contributes to the maintenance of cancer stem cells. In summary, there is a very strong link between colorectal cancer and activated Wnt signaling that is consistent with enhancing a normal action of Wnt on gut stem cells or their immediate derivatives to produce an amplified population of mutant cells that can rapidly expand to occupy several clustered niches and support the accumulation of further cancer-promoting mutations.

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Excessive, and occasionally reduced Wnt signaling, is also associated with many other types of cancer. Although these effects may also relate to stem cell functions, they do not result from simply expanding stem cell pools and changes in Wnt signaling are unlikely to be the initiators of most such cancers. Instead, cell-type-specific patterns of genes induced by Wnt signaling, including cyclin D and myc, are likely to collaborate with other mutations at one or more stages of cancer development in a manner that is hard to predict at present.

8.5.3

Hedgehog Signaling Pathways

The Hh (Hedgehog) family of proteins was first uncovered in Drosophila, where its principal role is to control cell fate by inducing changes in gene transcription. In analogous fashion, vertebrate Hh proteins (Sonic, Desert, and Indian Hh in humans and mice) control many aspects of development, including patterning of the neural tube, somites, and limbs [203–206]. In many of these situations, Hh signaling induces the expression of other signaling molecules that regulate cell fate and cell proliferation but in a few instances Hh signaling also regulates cell proliferation directly, most notably in the case of stem cells. Hh signal transduction involves the relief of multiple inhibitory constraints on the activity of transcriptional activators of the GLI family of zinc-finger DNA-binding proteins (originally identified as being amplified in gliomas). The signaling pathway is best understood in Drosophila, where the GLI homolog is called Cubitus interruptus (Ci), but many of the interactions revealed in Drosophila have been found to be applicable to vertebrates also [203–206]. Binding of Hh to its receptor, Patched (Ptc) and a recently described coreceptor IHog (Cdo and Boc in mice) [207], releases an inhibitory constraint on the 7-pass transmembrane protein, Smoothened (Smo) (Fig. 8-6). The mechanism for this was originally thought to involve an allosteric change or complete disruption of Ptc:Smo complexes. It is now thought unlikely because Ptc acts catalytically on Smo and the two proteins neither colocalize extensively nor interact significantly. Unproven, postulated mechanisms currently involve regulated localization of Smo within the cell as well as regulation of Smo activity by low molecular weight compounds (presently unidentified) that might be pumped across membranes by Ptc. Smo is similar in structure to Fz proteins and G-protein coupled receptors. Nevertheless, there are only limited data supporting the involvement of G-proteins in Hh signal transduction [208]. Although the way Smo transduces a signal is unknown, much attention has focused on the role of the atypical kinesin family molecule Costal 2 (Cos2) because this protein can associate with both Smo and Ci, the transcriptional effector of the Hh pathway [204]. Ci activity is modulated in several ways by Hh signaling, basically converting it from a repressor to a potent transcriptional activator. In the absence of Hh, the primary Ci translation product (Ci-155) forms complexes with various proteins (including Costal-2, Fused, and Suppressor of Fused), binds to micro-

tubules, and undergoes partial proteolysis that produces a relatively stable product, Ci-75 (Fig. 8-6). Ci-75 has the same DNA-binding specificity as Ci-155 but acts as a transcriptional repressor. Although processing of Ci-155 to Ci-75 is slow (such that Ci-155 levels still exceed those of Ci-75 in the absence of Hh), the activity of Ci-155 is held in check by the stoichiometric binding partners Cos2 and Suppressor of Fused (Su[fu]) ). These proteins restrict access of Ci-155 (but not Ci-75) to the nucleus and may also limit transcriptional activation by Ci-155 in the nucleus. The proteolysis of Ci-155 requires phosphorylation of Ci at protein kinase A sites. Once phosphorylated at these sites, Ci can be further phosphorylated by GSK3 and casein kinase 1 (CK1). Phosphorylated Ci-155 can then bind to an SCF (β-TRCP) complex, leading to ubiquitination and partial proteolysis by the proteasome, sparing the N-terminal Ci-75 fragment [209]. Cos2 binds each of the kinases, PKA, GSK3, and CK1, thereby facilitating Ci-155 phosphorylation and proteolysis [204]. Hh signaling inhibits proteolysis of Ci-155 to Ci-75, frees Ci-155 from microtubules, and facilitates the conversion of Ci-155 into a transcriptional activator, stimulating accumulation of a small proportion of Ci-155 in the nucleus. Hh signaling causes de-repression of target genes that contain Ci-binding sites by eliminating Ci-75 and activation of the same target genes through Ci-155. How exactly Smo activation accomplishes these feats is unknown. It is evident that during Hh signaling there is some dissociation of PKA, GSK3, and CK1 from Cos2, which could suffice to inhibit Ci-155 proteolysis [204]. Partial release of Ci-155 from Cos2 and Su(fu) complexes has also been observed and this presumably involves altered interactions among these proteins and Smo that is initially triggered by Hh-induced changes in Smo (Fig. 8-6). The crucial Hh-induced changes in Smo may include increased localization to the plasma membrane and increased Smo phosphorylation, as well as activation of another Cos2-associated protein kinase, Fused (Fu). Many of the signaling interactions described above, including the roles of Ptc, Smo, phosphorylation, and proteolysis of Ci homologs, are conserved in mammals but there are also some clear differences [210]. The activities of Ci are distributed among three GLI proteins. GLI3 acts largely as a transcriptional repressor after partial proteolysis (that can be inhibited by Hh signaling). GLI2 acts largely as an activator that is regulated by Hh in part by proteolysis (that is generally complete rather than partial). GLI1 acts as an activator that can be regulated similarly to GLI2 but is most frequently regulated transcriptionally, being strongly induced in response to Hh signaling activity, thereby amplifying and maintaining an initial response mediated by the other GLI proteins. Other significant differences include the participation of a number of intraflagellar transport proteins and the implication that Smo activation involves accumulation at specific sites within cilia in mammals, rather than simply at the plasma membrane, as in Drosophila. Furthermore, no functional homolog for Cos2 has yet been defined in mice, inviting speculation that this

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Fig. 8-6. Hedgehog signaling pathway. In the absence of Hedgehog (left), Costal-2 (Cos2) binds to Ci-155 (GLI proteins in mammals), glycogen synthase kinase 3 (GSK3), casein kinase 1 (CK1) and protein kinase A (PKA) to promote phosphorylation of Ci-155. This creates a binding site for b-TRCP, which is part of an SCF complex that consequently ubiquitinates Ci-155, targeting it for partial (and some complete) proteolysis by the proteasome. The Ci-75 product accumulates efficiently in the nucleus and represses Hh target genes. Ci-155 that has not yet been proteolyzed is efficiently held in the cytoplasm by Cos2 complexes and by association with Su(fu). Su(fu) likely also prevents activation of Hh target genes by Ci-155 in the nucleus. In the presence of Hedgehog (Hh; right) the co-receptors Patched (Ptc) and Ihog (Cdo/Boc not shown) bind and Hh/Ptc complexes are internalized and degraded in lysosomes. Ligand binding alters the properties of Ptc so that Smoothened (Smo) becomes active, either through being routed to the plasma membrane or by other means. This is accompanied by accumulation of more highly phosphorylated Smo and perhaps by changes in the conformations or associations among Smo, Cos2 and Ci-155. The three protein kinases, PKA, CK1, and GSK3 dissociate partially from Cos2, allowing Ci-155 to escape proteolysis. Ci-155 remains largely cytoplasmic but a small fraction escapes to the nucleus. The activity of Ci-155 is greatly enhanced by the protein kinase Fused (Fu), which becomes more highly phosphorylated and likely activated by Smo. A potential target of Fu is Suppressor of fused (Su(fu)), which may dissociate or change its properties in response. In the absence of Fu kinase activity, Ci-155 activates target genes less strongly in response to Hh but Hh target genes are still de-repressed through loss of Ci-75. Activated Ci-155 is rapidly degraded after ubiquitination by a Cullin3-based SCF complex.

role may have been assigned to other proteins, perhaps including the intraflagellar transport proteins. Su(fu) also appears to carry a greater burden in pathway silencing in mammals because loss of Su(fu) substantially activates the Hh signaling pathway in mice but has only minor consequences in flies. Despite differences and uncertainties concerning the precise mechanisms of Hh signal transduction in Drosophila and

mammals, it is clear that, as for Wnt/β-catenin signaling, the principal effector is a small family of transcription factors that are converted from repressors to activators. Despite the fact that GLI proteins share a singular DNA-binding specificity, most of the genes induced by Hh signaling are tissue specific. The pattern of genes induced can also depend on the strength of Hh signaling, allowing Hh to act as a morphogen

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[203, 206]. How GLI proteins interact with other transcription factors to yield dose-dependent and tissue-specific patterns of expression has not been investigated extensively, but there can be no doubt that a response to Hh is defined by the state of the responding cell. In situations where Hh signaling appears to stimulate proliferation directly, myc, cyclin D, and cyclin E genes have been identified as significant targets [203, 211, 212].

8.5.3.1 Mutational Alteration of Hh Signaling Pathways in Cancer Because Hh signaling involves release of GLI proteins from a variety of inhibitory binding interactions, it is possible to activate the pathway ectopically by genetic inactivation of each inhibitory interaction. Some degree of activation can be achieved by loss of protein kinase A, Cos2 (in flies), or Su(fu) (in mice), but the strongest activation, equivalent to exposing cells to maximal levels of Hh, is achieved by loss of Ptc activity. Because GLI proteins are inhibited by stoichiometricbinding partners, excess GLI protein can activate Hh target genes ectopically. It is therefore not surprising that loss-of-function mutations in Ptc and Su(fu), activating mutations in Smo, and overexpression of GLI proteins have each been associated with human cancers [206, 213]. For some cancers, there is a particularly strong linkage to the Hh pathway, indicative of a gatekeeper function analogous to that of Wnt signaling in colorectal cancer. Basal cell carcinoma inevitably arises in individuals heterozygous for a ptc mutation (Gorlin’s syndrome) and activation of the Hh pathway is almost invariably associated with sporadic basal cell carcinoma [213–216]. Furthermore, overexpression of Hh from a keratin promoter in mice leads to very rapid and widespread development of tumors similar to basal cell carcinomas. These observations suggest that activation of the Hh pathway is a very early, obligatory step in development of basal cell carcinoma. Medulloblastomas are similarly highly prevalent in individuals with Gorlin’s syndrome and in mice with heterozygous ptc mutations, suggesting a special connection to the Hh pathway [72, 213, 216]. Are either of these tumors induced because of an effect of the Hh pathway specifically on stem cells? From examining the normal role of Shh in the relevant tissues the most likely answer is no, but there is room for doubt. In the skin, the exact target cells for basal cell carcinoma are not known but could include epidermal stem cells [214– 216]. Shh is known to collaborate with Wnt signaling in the development of hair follicles, acting in this capacity at the base of the hair follicle rather than in the bulge region where long-term stem cells reside. Shh also participates in defining the initial location of hair follicles. Aberrant Hh pathway activation in the epidermis between hair follicles might initiate an altered developmental program somewhat resembling hair follicle morphogenesis but instead producing basal cell carcinoma. In this regard it is notable that transient activation of the Hh pathway in mice produces basal cell carcinoma that regresses to leave a persistent remnant that can be reactivated, suggestive of stem cell properties [215].

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Medulloblastomas likely derive from granule cell precursors in the external germinal layer of the cerebellum [72, 203, 216]. These cells normally depend on Shh signals emanating from the relatively distant Purkinje layer for their proliferation and excess Shh has been shown to stimulate excessive proliferation. The granule cell precursors are progenitor cells rather than long-term stem cells but it is conceivable that excessive Hh pathway activity might permit long-term proliferation and acquisition of some stem-cell-like properties. In other regions of the brain (the subventricular zone of the lateral ventricles and the subgranular zone of the dentate gyrus), there are longterm neural stem cells that normally respond to Shh and are likely influenced in their maintenance and proliferation by Hh signaling. Although these stem cells are not known to be associated with tumors resulting from excess Hh pathway activity, they do provide an example implicating Hh signaling in neural stem cell behavior [70, 203, 217]. There are other more distant examples of Hh acting as a stem cell factor, including examples in the Drosophila ovary. Here, somatic stem cells depend on normal, low levels of Hh signaling and increase in number if Hh pathway activity is increased to high levels by loss of Ptc activity (73). In other cases, such as mouse HSC, there are indications that excess Hh signaling produces transient overproliferation but reduced long-term survival [218]. Although there are likely many situations where Hh family molecules serve as stem cell factors [219], it is not yet clear whether the cancers in which Hh signaling is most characteristically involved derive initially from altered stem cell behaviors. Beyond basal cell carcinoma, medulloblastoma, and other cancer predispositions that accompany inherited mutations affecting the Hh pathway, increased Hh pathway activity has been associated at great frequency with a large number of other tumors, including several in the upper digestive tract, lung, pancreas, and prostate gland [213, 219]. In some of these situations, excess pathway activity results from ectopic expression of a Hh ligand or the acquisition of a response to normal levels of ligand through derepression of Smo, rather than directly from mutations in the genes of pathway components. This, of course, illustrates the general principle that signaling pathways can contribute to cancer development without being affected directly by a genetic mutation that occurs simply because expression of signaling molecules is normally highly regulated during development and therefore readily altered as a result of mutations affecting other signaling pathways. Significantly, tumors with activated intact Hh signaling pathways and those resulting from Ptc inactivation frequently regress if treated with the potent Smo antagonist, cyclopamine, or related derivatives [213, 219, 220]. This finding is of general clinical significance and illustrates continued dependency of real tumors on aberrant signaling pathways. Important caveats to the general application of cyclopamine-related drugs are their inability to reduce signaling caused by direct genetic amplification or activation of GLI activators, the requirement for some level of Hh signaling to maintain several normal stem cell based developmental units and the observation that Hh signaling is

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incorporated into some developmental interactions in a manner that limits growth, as in the colon [221]. Nevertheless, this situation is one where the nature of developmental signals may favor drug efficacy. Cancer-associated mutations produce strong Hh pathway activity and this is likely necessary for cancer promotion. Most normal Hh signaling dependencies likely require only low levels of Hh signaling, based on a few examples investigated, such as Drosophila ovarian somatic stem cells [73], and the idea that morphogenetic actions, although spectacular and garnering great interest, are relatively rare. Furthermore, if developmental signals are truly perceived largely as simply off or on, even a two- or threefold drug-induced reduction in signaling efficacy may not impair the correct binary response to the attenuated message (although the aberrant strong signal may be attenuated down to a weak signal that allows normal development). This idea would, of course, apply to all drugs that inhibit signaling pathways, not just for the Hh pathway where highly effective inhibitors are already known.

8.5.4

Notch Signaling Pathway

The basic Notch signaling pathway is both simple in outline and highly conserved among multicellular eukaryotes [14, 222, 223]. Notch is a single-pass transmembrane receptor that can bind to Delta and Serrate family ligands, which are themselves generally membrane associated, limiting most signaling events to cells that are in direct contact. As a result of productive ligand–receptor interaction, Notch is cleaved first by a metalloprotease (either Kuzbanian/ADAM10 or TACE/ ADAM17) and then by a γ-secretase complex that includes presinillin, to release an intracellular fragment of Notch (Nicd or Nintra). Nintra is difficult to detect directly but potent in its action. It binds to a CSL protein (named after the vertebrate, Drosophila and C. elegans homologs, CBF1, Su(H), and LAG-1), which binds to specific DNA sequences and recruits a coactivator, Mastermind (Mam), which converts CSL proteins from transcriptional repressors to activators. There are important additional subtleties to this signaling system, especially concerning the features of ligands and receptors that determine when productive signaling takes place [14, 222, 223]. Delta and Serrate ligand expression is spatially regulated but the mere presence of ligand is not enough for signaling. Ligand must be modified by an E3 ubiquitin ligase (either Neuralized or Mind-bomb) and interact with Epsin, a ubiquitin binding protein, to be active, perhaps because this guides the ligand through an endocytic pathway that activates it in some way or presents ligand appropriately. Notch also must be modified by the addition of fucose groups to its extracellular EGF repeats to be active. Moreover, further glycosylation by the Fringe family of glycosyl transferases modifies its affinity for ligands, increasing activation by Delta and eliminating activation by Serrate in the best studied case in the Drosophila wing disc [14]. Several additional potential regulatory processes affect Notch stability and trafficking.

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As for Wnt and Hh signaling pathways, Notch signaling can produce a huge number of responses depending on the transcription factors that collaborate with the pathway’s transcriptional effector. For Notch signaling, an immediate target for active CSL is frequently the E(spl) complex of bHLH proteins (Hes in mammals), which then induce a second set of transcriptional changes much like the immediate early response gene products (Fos, Jun, and others) relay some of the responses of Ras/MAPK pathway activation. Notch responses are frequently transient because of a number of feedback mechanisms including the Mam-facilitated destruction of Nintra [224].

8.5.4.1 Mutational Alteration of Notch Signaling in Cancer Unlike Wnt and Hh pathways, there are no simple loss-offunction mutations that are known from studies in model organisms to activate the Notch pathway ectopically. Nevertheless, a high incidence of Notch pathway activation is seen in T-ALL (T-acute lymphoblastic leukemia/lymphoma) [225]. Some of the responsible mutations affect Notch, causing ligand-independent processing to release Nintra or, alternatively, stabilization of Nintra. Notch signaling is critical for progressing beyond the VDJ rearrangement step in the normal development of αβT-cells, after which signaling is normally turned off. It is thought that continued excess Notch activity exploits a normal developmental sensitivity to extend the survival and proliferation of immature T cells. Myc has been shown to be induced by Notch signaling in these cancers and is thought to contribute to the cancer phenotype. There are not many other clear instances of mutational activation of the Notch pathway serving as a likely primary contributor to cancer development [225], perhaps because strong activating mutations are not readily generated in cells that are not subject to genomic rearrangements. There are, however, many situations where there is evident activation of the Notch pathway in cancers and apparent participation of this pathway in their maintenance [99, 225, 226]. Such examples include intestinal cancers and medulloblastomas, where Notch signaling collaborates with other signals, such as Wnt and Hh to orchestrate altered developmental programs. Notch signaling also undoubtedly contributes to the normal regulation of several stem cells but neither loss nor gain of function of Notch signaling has been associated with a marked increase in stem cell number or activity. Altogether then, at present, the Notch pathway does not stand out as a gatekeeper for any specific cancer (with the possible exception of T-ALL) or as a primary regulator of any particular stem cell, and consequently merits less attention than Wnt and Hh pathways in terms of drug development.

8.5.5

TGFβ/BMP Family Signaling

Although TGFβ was first defined as an activity stimulating cell proliferation in culture, it is now clear that the TGFβ

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super-family is composed of two major branches (TGFβ /activins and BMP), each of which includes many ligands [227, 228]. All TGFβ family proteins are active as dimers, but heterodimeric partnerships are permissible, including association with ligands that form an inactive complex. The expression of BMP is highly regulated, but in many cases production of inhibitory ligands is also a key spatially restricted developmental event. Thus, the Spemann organizer (the most dorsal and earliest invaginating mesoderm of Xenopus) produces molecules, such as Chordin and Noggin, which bind to BMP4 and inhibit induction of epidermal cells, thereby leading to adoption of the default neural fate [229]. Furthermore, extracellular chordin and related molecules can be cleaved by specific proteases whether alone or in complex with BMP, modifying the spatial distribution of chordin and providing a means to transport inactive complexed BMP before activation by chordin proteolysis at a distant site. TGFβ family molecules are also antagonized extracellularly by binding to Follistatin, Inhibin, Lefty, and DAN/Gremlin/Cerberus family members in addition to Chordin and Noggin. Two types of receptor serine threonine kinases (types I and II) are required to respond to TGFβ family ligands [227, 228]. Initial binding of ligand to the type II receptor (sometimes enhanced by an ancillary glypican receptor such as Endoglin, Crypto, or Betaglycan) recruits type-I receptor, which also binds to the ligand. The type-II receptor is constitutively active and phosphorylates the type I receptor in a so-called GS region within a ligand-receptor complex that leads to its activation. Mutationally activated type-I receptor is sufficient to propagate the signal. Although both types of receptor contribute to ligand recognition, the specificity of the intracellular response is dictated solely by the type-I receptor. Furthermore, the several vertebrate type-I receptors only transmit two types of signal to their key targets, Smad proteins. The many members of the TGFβ /activin family of ligands activate two specific receptor-regulated Smads (R-Smads), Smad2 and Smad3, whereas all BMP family ligands activate the R-Smads, Smad1, 5 and 8. The receptor-regulated Smads have a conserved N-terminal MH1 domain and a conserved C-terminal MH2 domain, which interact with each other and are inert before pathway activation. Docking by the MH2 domain at the phosphorylated GS region of a cognate type I receptor allows the receptor to phosphorylate the R-Smad at its extreme C-terminus [227, 228]. This activation step promotes heterodimerization of the R-Smad with a second type of Smad protein, Smad4 (that does not interact directly with receptors) and also allows entry of the Smad complex into the nucleus. Both events are essential to elicit a transcriptional response. In most cases, the Smad complex associates with another DNA-binding protein (for example, FAST-1 for a specific functional site on the activin-responsive Mix-1 gene promoter); in other cases, the low affinity DNA-binding activity of the MH1 domain suffices to target the complex to important promoter regulatory elements. In each case, the MH2 domains of the Smads can

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provide transcription activation function. The Smad DNAbinding partners add further DNA-binding specificity, thereby influencing which target genes are affected by signaling, and transcriptional activation domains, coactivators or corepressors, thereby regulating to what extent genes are activated or repressed by TGFβ signaling. Those partners can be expressed differentially according to cell type and may themselves be subject to regulation by other signaling pathways. Hence, a cell’s history and exposure to additional signals greatly affects its response to TGFβ ligands. Inhibitory Smads are often transcriptionally induced by TGFβ/BMP family signaling. These Smad can act by competing with receptor-regulated Smad for association with type-I receptor or by inhibiting heterodimerization of activated R-Smads with Smad4. Although Smads are the major mediators of TGFβ signals, various MAPK pathways can be activated by TGFβ ligands through the MAPKKK, TAK1 without Smad participation [227, 228]. Furthermore, a second partner for activated R-Smads has been shown to mediate some Smad4independent responses [230]. Although the major signaling pathway from receptors to Smad is notably simple it can be modified to inhibit signaling by phosphorylation of the R-Smad linker region between MH1 and MH2 domains by MAPK and perhaps also by CDK. TGFβ/BMP proteins have many roles in vertebrate and invertebrate development, including instances of dose-dependent responses, which allow these ligands to act as morphogens, instructing cell fate according to spatial concentration gradients [8, 229, 231]. BMP can also promote apoptosis, for example, eliminating webbing between limb digits during vertebrate limb development; TGFβ ligands promote apoptosis in many circumstances in the immune system [228, 232, 233]. Members of the TGFβ family can stimulate proliferation of some cells in culture but more often exert an inhibitory role that can prevent growth, even of some tumor derived cells. The most prominent mechanisms by which TGFβ ligands are known to inhibit cell proliferation involve induction of CKI and repression of Myc [228]. Both p15INK4b and p21 are induced by TGFβ in a variety of cell types. This requires the association of activated Smad with FoxO transcription factors and hence induction of these CKI is prevented when the PI3K pathway is active, leading to export of FoxO proteins from the nucleus. The induction of CKI is only effective when Myc expression is repressed through Smad3/4/E2F/C-EB-P complexes; otherwise Myc in combination with Miz binds those CKI promoters to prevent their induction. TGFβ signaling represses expression of Id proteins through a Smad3/4/ ATF3 complex, thereby assisting differentiation of some cell types [228]. BMP signals can elicit the opposite response to prevent differentiation of mouse embryonic stem cells under certain growth conditions [234]. The overall picture that growth inhibitory effects of TGFβ ligands are largely channeled towards the G1/S cell cycle transition is supported by the observation that inhibition of cycling by TGFβ is much

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reduced in cells lacking Rb function. Clearly, loss of Rb or activation of the PI3K pathway or one of several pathways that induce Myc can each counter growth inhibitory effects of TGFβ to some degree.

8.5.5.1 Mutational Alteration of TGFb Pathways in Cancer TGFβ pathways can be inhibited generally by elimination of Smad4 and selectively by loss of individual type-I or -II receptors, although the latter might not always be effective because a single ligand often stimulates more than one pair of receptor types. Likewise, two or three R-Smads are generally available to respond to each ligand. Although constitutively active type-I receptors can be generated and there are phosphatases and ubiquitin-mediated degradation pathways for inactivating phosphorylated R-Smads, neither model genetic organisms nor cancer tissue provide clear precedents for mutations that turn TGFβ pathways on strongly and constituvely. Perhaps for this reason, the most notable examples of TGFβ pathway mutations in cancer involve loss of pathway activity. The most frequent known mutation associated with human tumors is loss of Smad4 function [51, 228]. This loss can be rationalized according to the essential role of Smad4 as a partner for all receptor-activated Smad and the generally growth inhibitory role of TGFβ signaling. LOH for the genomic region, including Smad4, is seen very frequently in human pancreatic carcinomas (approximately 50%) and in colorectal tumors (approximately 50%). In the intestinal crypts, TGFβ type-I receptors and TGFβ type-II receptors are expressed in cells near the lumen, implying a possible role in slowing proliferation and inducing differentiation as cells move up and mature from the base of the crypts toward the lumen. TGFβ type-II receptor mutations have been found in tumors, especially when genomic instability was induced by the absence of DNA repair enzymes, as in hereditary nonpolyposis colorectal cancer (HNPCC). TGFβ type-I receptors are also mutated in several cancers of different origin. Mouse models confirm the antitumorigenic roles of TGFβ signaling [235]. Mice lacking Smad3, an effector for TGFβ 1, develop lethal colorectal adenocarcinomas before 6 months, implying that failure of TGFβ signaling can suffice to promote tumor formation. Mice that are heterozygous for Smad4 (homozygotes die early) develop polyps and late-onset tumors at an enhanced rate, and can also markedly exacerbate the progression of polyps initiated by heterozygosity for the tumor suppressor APC [235]. Despite the thoroughly documented involvement of TGFβ pathways as tumor suppressors in humans and mouse models, there is also evidence that TGFβ signaling facilitates late stages of cancer development, involving cell migration and invasiveness [51, 228]. BMP in particular have been implicated as important stem cell factors, most clearly in the Drosophila ovary, where BMP signaling prevents differentiation of germline stem cells into cystoblasts [234, 236, 237]. Given these strong associations

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with stem cell biology, it is surprising that there are not more known causal associations between specific cancers and BMP signaling. Because BMP signaling is generally highly regulated extracellularly, e.g., by expression of antagonists, it may be that there is indeed enhanced BMP signaling at early stages of several cancers but such nonautonomous contributions may have escaped detection by looking only in the genomes of mature cancer cells.

8.5.6

JAK/STAT Signaling Pathway

The JAK/STAT signaling pathway has extensive similarities and intersections with RTK signaling pathways but both in mammals and Drosophila it is especially associated with signaling in the hematopoietic system [125, 238, 239]. Numerous cytokines, including many Interleukins, Colonystimulating factors, Interferons, and Erythropoietin, induce dimerization or multimerization of receptor subunits. This brings constitutively receptor-associated cytoplasmic tyrosine kinases known as JAK (Janus kinases) into close proximity, stimulating JAK trans-phosphorylation, consequent further activation of JAK tyrosine kinase activity and receptor phosphorylation, creating binding sites for SH2 domains of STAT proteins (signal transducers and activators of transcription). The recruited STAT proteins can then be phosphorylated on a tyrosine residue close to their C-termini that allows dissociation from the JAK/receptor complex and dimerization of STATs mediated by reciprocal interactions between their SH2 domains and their phosphorylated Tyr residues. Activated dimeric STATs then move to the nucleus and bind directly to DNA to activate transcription. Among the known transcriptional targets of several STATs are cyclin D and myc. This simple basic pathway is subject to a variety of modifying influences, of which the most important appear to be negative feedback controls and interactions with tyrosine kinase pathways [125, 238, 239]. SOCS (suppressors of cytokine signaling) proteins are among the proteins that are transcriptionally induced by STAT activation. SOCS proteins bind to phosphorylated JAK and JAKassociated receptors to reduce signaling by blocking access to STAT, inhibiting JAK activity or promoting ubiquitination and proteolysis of JAK/receptor complexes. PIAS (protein inhibitors of activated STAT) bind to activated STAT dimers to inhibit their activity, in part by promoting SUMOylation. PIAS proteins are not dedicated to JAK/STAT signaling; they also can direct SUMOylation of several other proteins, including p53. Several tyrosine phosphatases can reduce JAK/STAT signaling, especially nuclear phosphatases that can act on phosphorylated STAT. The induction of a subset of STATdependent genes can be inhibited by competitive binding of the Drosophila Ken protein to sequences that partially overlap STAT binding sites [125]. Ken is homologous to human Bcl-6, which is inactivated in many diffuse B-cell lymphomas. JAK/STAT signaling can activate RTK pathways to some extent because phosphorylated Tyr residues on associated receptors often include binding sites for Shc and other SH2

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domain RTK pathway components [125]. Furthermore, one of the SOCS proteins can bind and inhibit RasGAP, thereby enhancing Ras activity. Conversely, some activated RTKs including EGFR can recruit STAT and stimulate their phosphorylation and activation, probably through Src-family tyrosine kinases [125,238–240]. MAPK phosphorylation of STAT enhances the activity of STAT dimers. Hence, tyrosine kinase pathways and JAK/STAT pathways are almost always coactivated to some extent in response to either RTK ligands or JAK/STAT pathway ligands.

8.5.6.1

Mutational Activation of JAK/STAT Pathway

The consequences of loss of JAK/STAT signaling has been analyzed in flies and in mice lacking a variety of STAT genes, JAK, and cytokine receptors [125, 241–243]. Some murine mutations produce serious defects in a variety of tissues but most disorders affect blood cells, leading to defects in erythropoiesis or, most commonly, dysfunction of T and B cells, producing severe combined immune deficiency (SCID) phenotypes. In Drosophila, the JAK/STAT pathway is used for assigning developmental fates, for example, in oogenesis, and in stimulating proliferation, for example, in eye and blood cell development. Correspondingly, excessive activation of JAK/STAT pathways is associated predominantly with blood cell cancers. Even in Drosophila, mutations that constitutively activate JAK signaling produce excessive blood cells that accumulate in melanotic tumors. In humans, JAK2 has been found to be activated in a variety of blood cancers, either through generation of fusion proteins that impose constitutive activation or, more commonly, especially in a subset of myeloproliferative disorders, by point mutations affecting JAK2 sequence. Other examples of cancers with increased JAK/STAT signaling resulting from loss of function of feedback inhibitors, including SOCS1 and 3 and PIAS3, are known. Studies in mice have shown that cancers can be promoted by activated STAT3 or STAT5, but also by loss of STAT1. To date, there is little evidence connecting the primary consequences of increased JAK/STAT signaling to actions in stem cells in human cancers, but there are several clear instances in model systems for JAK/STAT signaling affecting stem cells. Even mouse embryonic stem cells require JAK/STAT signaling under many in vitro culture conditions, although it is of course hard to relate this to the conditions of normal embryogenesis. In Drosophila male germline stem cells and their associated somatic support cell progenitors are in direct contact with (hub) cells that produce a ligand for the JAK/STAT pathway and they require JAK and STAT function to maintain these stem cells [125]. Ectopic production of ligand can increase stem cell numbers. Furthermore, reduced JAK/STAT activity leads to maturation of germline stem cells into spermatogonia but this process can then be reversed by restoring JAK/STAT signaling. Although the regulation of mammalian HSC in their normal environment is not thoroughly understood, JAK/STAT

signaling does not appear to be a dominant pathway regulating their behavior [242]. STAT5 activation has been associated with increased HSC renewal. STAT5 activation may normally result primarily from activation of the c-Kit tyrosine kinase receptor and in leukemias it is frequently activated by tyrosine kinase fusion proteins and may be required for their carcinogenic activities. Thus, as for Hh and TGFβ/BMP pathways, there is considerable evidence for effects of normal signaling on the regulation of specific stem cells and for participation of mutationally activated signaling in the development of characteristic cancers, yet there are no definitive links between these two roles. This may be because the roles of these pathways in cancer are not tied primarily to stem cell functions, but it is also possible that the links are not yet apparent simply because we cannot yet satisfactorily follow stem cell behaviors in normal tissues or during the development of cancers.

8.6

Summary

Normal development of multicellular eukaryotes is governed by successive sets of cellular interactions that signal changes in a cell’s state. Cell states can be approximated by patterns of gene transcription and are translated into specific behaviors by internal pathways that regulate growth, cell cycles, apoptosis, and progress towards a final cell fate. I suggest that most cell states are stabilized in between signaling events by a combination of chromatin modifications and transcriptional circuits, and that most signals are perceived simply as absent or present (and sometimes as weak or strong). In this way, a handful of basic signaling pathways can be used repeatedly to make robust digital decisions that move cells through a series of defined states that are effectively preprogrammed in the transcriptional circuitry of the genome. Thus, complexity is achieved largely by complex regulation of expression of signaling molecules and context-dependent interpretation of signals rather than by complicated integrations of the quantitative state of activity in several concurrently activated signaling pathways. Because signaling events normally reset cell behaviors, it is not surprising that genetic alterations of signaling pathways make important contributions to the development of most, indeed probably all, cancers. Unfortunately, there are no satisfactory ways to investigate rigorously the changes in a cells’ genome, state, or signaling behavior in developing cancers. We can only make hypotheses based on related developmental circumstances and the correlations between specific mutations and associated cancers. In making hypotheses, it is critical to consider the cell of origin for a cancer and to acknowledge the complexity of the multiple cell interactions that likely contribute to cancer development. Mutations that increase stem cell numbers or facilitate conversion of transit-amplifying cells back to cells with stem cell characteristics are expected to be crucial initiators of cancer.

8. Signaling Pathways in Cancer

This expectation may be fulfilled for Wnt pathway signaling and colorectal tumors but, in most cases, the lack of definitive stem cell and cancer stem cell markers has prevented extensive testing of this hypothesis. It also seems likely that stem cells in general require several signaling pathways for their normal function and that activation of a single pathway will in most cases cause only a small expansion of the functional stem cell pool that may be hard to detect directly, but which may nevertheless be critical for the initiation of cancer development. Once a stable progenitor or stem cell pool has been expanded by a mutational event all consequent cell interactions will be somewhat altered versions of normal development, creating selective pressures especially on survival and growth pathways and thereby favoring specific secondary mutations. Some of these mutational events may occur in siblings that are not preserved in the final cancer and some of the important signaling changes that confer a transient selective advantage during development may be induced by aberrant ligand production rather than by genetic alterations to pathway components. Hence, it is anticipated that a genetically altered signaling pathway will likely play several roles during cancer development and progression, and that genetically intact pathways may also contribute. To understand cancer development better requires improved mouse models and thorough examination of numerous model stem cell systems. We can anticipate that a thorough understanding is a long way off and that a separate line of research should therefore treat both the details of the responses to external signals and the aberrant sequence of signaling interactions that follow mutational alteration of a signaling pathway as complex “black box” modules that we can neither readily understand nor alter. Instead, we can develop drugs that act at the highest possible levels of signaling pathways to try and reduce aberrantly strong signaling in genetically altered pathways. The corresponding normal pathway may signal sufficiently strongly to support normal development of tissues maintained by stem cells if normal developmental signals are generally detected as simply absent or present. We can also anticipate that a mature cancer will contain at least two types of proliferating cells (stem cells and others), that more than one signaling pathway is likely to be altered genetically, that the different cancer cell populations may be affected differently by these altered pathways and that the developmental cancer unit is robust and capable of adapting to drug-induced changes by selecting for additional mutations. Thus, it will be essential to find effective drugs for several signaling pathways and administer them simultaneously. The most obvious pathways to be targeted are RTK pathways (especially the PI3K branch), which seem to contribute to cancer in virtually all tissues, and the one or two pathways that have the most clear-cut role in maintaining normal stem cells of the appropriate tissue,

Acknowledgment. Research in Dr. Kalderon’s laboratory is supported by grants from the National Institutes of Health.

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Chapter 9 Estrogen Receptor Pathways and Breast Cancer Jing Peng, PhD and V. Craig Jordan OBE, PhD, DSc

9.1

Introduction

Breast cancer is the most common cancer affecting women worldwide [1]. In the United States alone, an estimated 214,640 new cases of breast cancer will be diagnosed in 2006 [2]. It is estimated that one in every eight American women will be diagnosed with breast cancer within the course of her lifetime [2]. With more sensitive and accurate means of early detection and an ever-increasing number of drugs available to treat breast cancer, it is likely that women diagnosed today will live longer and may need more than one type of cancer therapy. Many cellular factors mediate breast transformation and tumor growth including growth factors, members of phosphorylation signaling cascades, oncogenes, and nuclear hormone receptors. Although each of these factors has a role in the development of breast cancer, the steroid hormone estrogen is the primary promotional factor. Epidemiologic evidence has shown that a woman’s overall lifetime exposure to endogenous estrogen, increased by early menarche, late menopause, and nulliparity, is the primary risk factor for developing breast cancer [3]. In 1896, George Beatson demonstrated that removal of the ovaries from a premenopausal woman with breast cancer could lead to a dramatic improvement in the course of the disease [4]. However by 1900, Stanley Boyd [5] had demonstrated, in perhaps the first clinical trial, that only one in three premenopausal women could anticipate disease control after oophorectomy. The reason for this conundrum, now known to be the selective hormonal sensitivity of breast cancer, would not be discovered until 60 years later, when Jensen and Jacobson [6] described the target-site specificity of estradiol in the immature rat. Their classic experiment showed that after an injection of [3H]estradiol, the radioactive steroid was bound to, and retained by, known estrogen target tissues, such as uterus, vagina, and pituitary gland. By contrast, estradiol

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

was not retained by nontarget tissues, such as skeletal muscle. These observations led Jensen to postulate that an estrogen receptor (ER) present in estrogen target tissues must sequester the steroid specifically and initiate the cascade of biochemical events associated with estrogen action in that tissue. Increased estrogen exposure is the most important risk factor for the initiation and progression of breast cancer. Therefore, ER-α and ER-β‚ which mediate estrogen action, have been well studied as both predictors of hormone sensitivity in breast cancer and crucial targets for anticancer drugs.

9.2

Biology of Estrogen Receptors

As the prime mediator of estrogen action in the body, ERs bind estrogen and activate transcription of estrogen-responsive genes in target tissues, resulting in growth and differentiation of cells. In the mid-1960s, ER-α was the first ER shown to be soluble and not membrane bound [7, 8]. However, the cloning of the ER-α gene [9,10], along with ensuing advances in molecular biology and genetics in the 1980s, led to a wealth of new knowledge concerning the structural biology and pharmacology responsible for ER-α-mediated gene transcription. The discovery, in 1996, of a second distinct ER (ER-β), expressed in a different profile of tissues, has led to more detailed studies of the role each of these receptors plays in breast cancer [11–14]. ER-β binds to DNA in a manner similar to ER-α, associates with coactivators, and activates estrogen response element (ERE)-dependent gene expression in transient transfection in a hormone-dependent manner. In addition, ER-β can form a heterodimer with ER-α on DNA that enhances ERE promoter activity [15]. However, ER-α and ER-β regulate different sets of gene expression, and have different ligand responses. They play different roles in breast cancer biology. ER-α is the main mediator of estrogen-induced proliferation, whereas ER-β either enhances or counteracts ER-α activity [16], and several reports suggest ER-β inhibits breast cancer cell proliferation and functions as a tumor repressor, as reviewed by Barkhem et al. [17]. Other than the 189

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classic ER-α and ER-β, some groups argue the presence of nonclassical ERs at the membrane of different cells, named ER-X and GPR30 (a G-protein coupled receptor) [18–21]. Differences in activity and tissue and cell distribution of two or more ERs may explain the wide-ranging activity of estrogens and the tissue-specific effects of selective ER modulators (SERMs) and estrogen-modulating drugs.

9.2.1

ing a highly conserved DBD (DNA Binding Domain) (97%) and LBD (Ligand Binding Domain) [12, 27, 28]. ER-α and ER-β are composed of multiple interactive functional domains, including the N-terminal A/B domain, referred to as activation function-1 (AF-1); the C domain, referred to as the DBD; and the C-terminal includes the E domain and the F domain. The E domain is referred to as the LBD, which also contains AF-2 region (Fig. 9-1) [29–31].

Structure of ER-α and ER-β 9.2.2

ER-α and ER-β are both members of the nuclear receptor superfamily, which includes steroid hormone receptors (glucocorticoid receptor, mineralocorticoid receptor, progesterone receptor [PR], androgen receptor, and ERs), thyroid and retinoid hormone receptors, vitamin D receptor, and a large number of orphan receptors for which no ligands have been identified [22–24]. Each receptor is named for its specific ligand, with the exception being orphan receptors, which have been placed in the nuclear receptor superfamily based on sequence homology but have no known endogenous ligand [24]. Each of these receptors functions as a ligand-inducible transcription factor that initiates mRNA transcription by binding to DNA response elements located in the promoter region of responsive genes. Studies tracing the evolutionary origins of receptors have shown that the ER was the first ancestral member of the nuclear receptor superfamily [25]. The human ER-α protein is composed of 595 amino acids with a molecular weight of approx 67 kD. The most abundant isoform of ER-β is a 54-kD protein, which binds estradiol with an affinity (0.5 nM) similar to ER-α (0.2 nM) [23, 26]. The two ERs are located on different chromosomes and share some similarities in function and protein structure includ-

DBD 104

ER-a

NH2

A /B 27% 1

ER-b

NH2

Trancription Activation

A /B AF-1

AF-1 is primarily involved in protein–protein interactions and transcriptional activation of target genes in a ligandindependent manner [32, 33]. For example, a study found that the transcription factor NFAT3, which was overexpressed in some breast tumors, interacted with the AF-1 domain of both ER-α and ER-β independent of ligand [34]. This interaction enhanced transcriptional activities of both ER forms in breast cancer cells probably through increasing binding of ER to ERE [34]. AF-1 is the domain that ER-α and ER-β proteins differ most markedly, which may explain why ER-α and ERβ activate different sets of genes and have different ligand responses [35–39]. The AF-1 domain of ER-α contains two distinct regions that are necessary for 17β-estradiol (estradiol) or tamoxifen-stimulated activation of ERE-reporter genes, whereas the AF-1 domain of ER-β contains neither of these two interacting regions and responds to tamoxifen only as an antagonist [37, 40]. The AF-1 domain of ER-α strongly activates transcription of ERE-containing genes whereas the AF-1 domain of ER-β has negligible activity in stimulating transcription [41] or even posses a repressive activity [42].

Hinge 250

C

LBD

311

D

547 595

E

97% 26% 140 214

C

AF-1 Domain

D AF-2a

61%

COOH

F 22% 498 530

E

F

COOH

AF-2

Nuclear Localization Dimerization DNA Binding Ligand Binding Co-activator Binding Co-repressor Binding Hsp90 Binding Fig. 9-1. Schematic comparison of human ER-α and ER-β structure. The structural domains are shown, and the percentage of amino acid identity shared by the 2 ERs is indicated for each domain. The horizontal bars highlight areas of different functions [31, 99].

9. Estrogen Receptor Pathways and Breast Cancer

Moreover, the AF-1 domain negatively regulates dimerization of ER-β [43]. A study indicated that the AF-1 domain of ER-α was required for tamoxifen-induced transcription activation of tumor repressor maspin, whereas ER-β had no activity to stimulate maspin transcription [44]. Deletion mutagenesis studies of ER-α AF-1 have shown that the first 40 amino acids are not required for transcriptional activity of ERα [45]. Further analysis showed that amino acids 41–64 are crucial for tamoxifen-stimulated ER-α activity but not for estradiol-stimulated activity [45]. The AF-1 domain activates transcription either independently or synergistically with the AF-2 domain. Many factors enhance ER-α activity by stimulating direct or indirect AF-1/AF-2 interactions, including the agonistic ligands such as estradiol or tamoxifen and coregulator proteins such as steroid receptor coactivator-1 (SRC-1) [42]. The AF-1 domain plays an important role in regulating nonclassic (independent of ERE or ligand) gene activation of ER. For example, ER can activate genes containing activating protein 1 (AP-1) elements in the promoter. The AF-1 domain of ER-α recruits SRC/p160 coactivators, which then interact with the CBP/p300 coactivators recruited by Jun/Fos proteins that bind to AP-1 sites [46]. In addition, adaptor proteins that facilitate ER membrane localization such as Shc and Striatin interact with the AF-1 domain, which is involved in membrane-associated nongenomic actions of estrogen [47, 48]. Moreover, the AF-1 domain of ER-α contains several serine residues such as S104, S106, S118, and S167, phosphorylation of which are critical in regulating ER activity. It has been shown that phosphorylation of those residues are induced by both estrogen-dependent and estrogen-independent pathways. For example, phosphorylation of serines 104, 106, and 118 is enhanced after estradiol binding to ER-α [49]. The most dramatic increase in phosphorylation, occurring at Ser118, has been shown to potentiate AF-1 function [50, 51]. In a recent study [52], glycogen synthase kinase-3 (GSK-3) was shown to regulate ER-α transcriptional activity by interacting and phosphorylating at different sites in the AF-1 region in response to estradiol. GSK-3 phosphorylates ER-α at Ser104, 106 (probably also Ser102) and interacts with ligand-free ER-α in unstimulated MCF-7 cells. This interaction stabilizes ER-α in the cytoplasm. Upon estrogen stimulation, GSK-3 is phosphorylated and dissociates from ER-α, which leads to dephosphorylation, nuclear translocation, and conformational change of ER-α. The Ser118 of ER-α is exposed and phosphorylated by another protein kinase, which leads to transcriptional activation. In the absence of estradiol, both ERs show an increase in transcriptional activity stimulated by mitogen-activated protein kinase (MAPK) phosphorylation of key residues in the AF-1 region [53, 54]. Researchers have shown that estradiol enhances the activity of c-Src, which then activates the MAPK pathway leading to phosphorylation of Ser118 [55–57]. Alternatively, in an estrogen-independent manner, epidermal growth factor receptor (EGFR) and other types of tyrosine kinase signaling activate the MAPK phosphorylation cascade, which then phosphorylates Ser118 [53,

191

58–60]. Ser167 is phosphorylated in an estrogen-independent manner by p90 ribosomal S6 kinase (Rsk), a member of the MAPK signaling pathway [59, 61, 62]. Ser167 can be simultaneously phosphorylated by AKT, owing to cross talk between signaling pathways in which PDK1 stimulates the activity of both Rsk and AKT [58, 60, 63–65]. Many signaling cascades lead to phosphorylation at the serine and threonine sites in the AF-1 domain and activate ER-α in the absence of estrogen. This phosphorylation mediates cross-talk between other signal transduction pathways and ER, and is one mechanism for endocrine therapy resistance [66].

9.2.3

DNA-Binding Domain

The C domain, functions as the DBD, which is the most highly conserved region of nuclear hormone receptor superfamily members [67]. The DBD contains two zinc finger structures that are crucial for receptor dimerization and specific DNA binding [68]. Six amino acids (CEGCKA) at the C-terminal base of the first zinc finger, shared by both ER-α and ER-β, comprise a region called P-box that is critical for ERE recognition [69]. The ERE is the specific DNA sequence located in the promoter region of responsive genes, where ER-α and ER-β bind. It is a perfect palindromic repeat with a consensus sequence of (5′-GGTCANNNTGACC-3′), which reflects the fact that the dimerization of receptors is an important component of DNA binding. A second zinc finger located at the N-terminus region of the DBD, the D-box, is the receptor dimerization interface and therefore also contributes to site-specific DNA binding. ER-α and ER-β can either homodimerize or heterodimerize. Dimerization leads to D-box interactions that stabilize DBDERE binding and can enhance binding to imperfect EREs. This expands the number of sequences with which the ER can interact [70]. The three-dimensional (3D) crystal structure of the ER-α DBD bound to an ERE [68, 71, 72] shows that two molecules of the DBD sit in the adjacent major grooves from one side of the DNA double helix. The side chains of Gln203, Lys206, Lys210, and Arg33 interact with the central 6 bp of AGGTCA by hydrogen bonds. Tyr195, His196, Tyr197, Arg211, Arg234, Lys235, Gln238, and Arg241 contact with phosphate backbone of ERE. The crystal structure data further support the results from biochemical and mutational studies. In addition, there is weak dimerization activity that occurs within the minimal region for DNA binding [73, 74], which is also observed in the DBD crystal structure. In addition to DNA binding, the DBD domain is a site for ER regulator proteins to interact, such as oncoprotein MUC1, which enhances ER-α activity by blocking ubiquitination and recruiting the coactivator p160, SRC-1, and GRIP1 [75]. In addition, the second zinc finger region in ER-α DBD is required for interaction and activation of STAT5b (transcription factor signal transducer and activator of transcription 5b) [76]. STAT5b is thought to be a major mediator of cross talk between ER and EGFR signal transduction pathway and thus a potential drug target for breast tumors overexpressing EGFR [77].

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Hinge Region

The D domain is a hinge region that interacts with a number of proteins. For example, the chaperone heat-shock protein 90 (hsp90) associates with ER at the hinge domain, rendering the unliganded ER inactive and stable, which is crucial for the high hormone-binding affinity of ER [30]. The hinge region has been shown to associate with a coactivator L7/SPA in the presence of the antiestrogen tamoxifen [78], which may enhance the partial agonist activity of tamoxifen. A recently identified protein that enhances ER-α transcriptional activity, ERBP, interacts with the DBD and hinge domains [79]. In addition, the coactivator PGC-1 interacts in a ligand-independent manner with the hinge region of ER and interacts in an agonist-dependent manner with AF-2, enhancing the transcriptional activity of ER and mediating cross talk with peroxisome proliferator-activated receptor γ (PPAR-γ) [80]. P21 activated kinase 6 (PAK6) interacts with the hinge domain and reduces transcriptional activity of ER-α. The binding is increased by 4-hydroxytamoxifen and might mediate the cross talk between steroid hormone receptor and Cdc42/Rac signal transduction pathways [81]. A recent study showed cyclin D1 competes with BRCA1 to bind to the hinge domain of ER-α and antagonizes BRCA1 repression of ER-α activity [82]. Studies have shown that sumoylation is a new mechanism that regulates activities of transcriptional factors including steroid hormone receptors, as reviewed by several groups [83, 84]. Sumoylation is a covalent modification of proteins leading to the attachment of small ubiquitin-like modifier (SUMO) to specific lysine residues of target proteins, which often results in recruitment of corepressors and inhibition of transcription [84]. ER-α is sumoylated in an estrogen-dependent manner, and the conserved lysine residues within the hinge domain is the sumoylation site [85].

9.2.5 Ligand-Binding Domain The C terminal includes the E domain and the F domain. The E domain is also known as the LBD, which is the largest domain of the ERs. The F-domain is not conserved in the nuclear receptor superfamily, and ER-α and ER-β share only 18% homology in this domain. The F domain has been implicated in interaction with nuclear cofactors and is required for ER-α/Sp1 action [86]. ER-α and ER-β share 59% genetic homology in the E domain, which contains regions responsible for specific ligand binding, coactivator and corepressor recruitment, and hormone-dependent transactivation activity (the AF-2 domain) [22, 87–89]. Crystal structures of the ER-α LBD bound to agonists such as estradiol and diethylstilbestrol (DES) and to SERMs such as raloxifene and 4-hydroxytamoxifen have been determined [90–92], giving a clear view of how agonists and antagonists induce different conformational changes in the LBD. The ER-α LBD has 12 α-helices (H1 to H12) and two-stranded antiparallel β-sheets (S1 and S2). The central core of three

helices is surrounded by two layers of helices creating a hydrophobic ligand-binding cavity, which is larger than that of most other nuclear hormone receptors [93, 94]. The comparative expansiveness of the ER-α ligand-binding cavity explains why estradiol does not occupy the entire cavity and why ER is able to bind a wide variety of steroids and environmental estrogens. When agonists such as estradiol and DES bind ER-α, helix 12 (H12) is positioned over the ligand and across the ligand-binding cavity in a groove created by H3, H5/H6, and H11. This configuration of the agonist-bound ER-α LBD exposes the coactivator-binding region of ER-α to the LXXLL recognition motifs found on most ER coactivators [13]. Antagonists and SERM, such as raloxifene and 4-hydroxytamoxifen, also bind ER-α, but induce very different conformations of the LBD in which H12 is placed in a position between H5 and H3, masking amino acids in the LBD that are critical for ER-α interaction with coactivators [90, 91]. Both raloxifene and 4-hydroxytamoxifen have alkylaminoethoxy phenyl side chains, which extend out of the ligand-binding cavity and interact with Asp 351 in H3 of ER-α, indicating that this residue may be important for the biologic activity of the SERM-ER complex. In fact, a mutation of Asp351 to Tyr has been discovered in breast cancer cells [95] that have become resistant to the antagonistic effects of tamoxifen and raloxifene [96, 97]. The differential displacement of H12 to cover an agonist ligand and allow the ER-α recognition site to be available for binding coactivators or for an antagonist ligand to displace H12 and obscure the coactivator recruitment site appears to be the key component of ER-α’s discrimination between agonist and antagonist ligands. In an x-ray crystallographic analysis, a second 4-hydroxytamoxifen binding site at the ER-β hydrophobic groove of the coactivator recognition surface was identified, which suggested that small antagonist could directly block receptor-coactivator interactions [98]. The amino acid residues located at the ligand binding cavity are very conservative between ER-α and ER-β, with a difference of only two amino acids. However, this small difference still allows the development of subtype selective ligands that preferentially bind to ER-α or ER-β [99]. Such ligands include propyl pyrazole triol, an ER-α-selective agonist that has been shown to exert only ER-α selective agonist effects in vivo [100], and methyl-piperidino-pyrazole, an ER-α-selective antagonist [101]. Another selective ligand is 5,11-cis-diethyl5,6,11,12-tetrahydrochrysene-2,8-diol (THC), which exerts opposite effects on the transcriptional activity of ER-α and ER-β [102]. THC is an ER-α agonist and an ER-β antagonist. THC does not have a bulky substituted side chain, as used by raloxifene and 4-hydroxytamoxifen to displace helix 12 from an agonist conformation. Rather, it has been shown that THC exerts its effects through differential coactivator (SRC-1, SRC-2, and SRC-3) recruitment to THC-bound ER-α and ER-β [102]. The 3D crystal structure of the ER-α LBD bound to THC and a fragment of the coactivator SRC-2 (GRIP-1) and ER-β LBD bound to THC has been determined [103].

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The 3D structure of ER-α and ER-β bound to THC shows that THC stabilizes the agonist conformation of the ER-α LBD that permits coactivator recruitment, whereas THC stabilizes the antagonist conformation of the ER-β LBD preventing coactivator recruitment [103]. Because ER-α and ER-β are thought to have different biologic functions, the subtype selective ligands are not only useful for examining the biologic functions of ER-α and ER-β but also important to design drugs that selectively enhance or block responses mediated by only ER-α or ER-β. In addition to AF-1 and AF-2, a third activation domain (AF-2a), located between amino acids 282 and 351 of ER-α, has been identified within the boundary of the D and E domains [104, 105]. In vitro studies have shown that human TATA binding protein–associated factor (TAFII30) directly interacts with the ER-α AF-2a domain in a hormone-independent manner to enhance ER-mediated transcription [106]. The enhanced transcription, owing to binding of TAFII30 to AF-2a, may be the mechanism for the autonomous transactivation activity of AF-2a in yeast and mammalian cell systems [104, 105]. Evidence indicates that transactivation of ER requires estrogen-dependent receptor ubiquitination and degradation [107–109]. Both AF-1 and AF-2 domain function in regulation of estrogen-induced ubiquitination and degradation of ER-α in addition to their transcription activities. Mutations in H12 of AF-2 that abolish the transactivation activity also disrupt proteolysis, which suggests that AF-2 is involved in coupling of estrogen-induced transcriptional activity and proteolysis [108]. On the other hand, Ser118 in the AF-1 region plays dual but separate roles in proteolysis and transcription. ER-α, with either a S118A or a S118E mutation, escapes degradation but the former has diminished transactivation activity whereas the latter has a higher transcriptional activity than the wild type [110].

9.2.6

ER Mutations in Breast Cancer

Because each domain of ER plays a specific role, mutations in these domains have great impact on ER activity. A number of ER splicing variants and point mutations have been identified in normal and diseased tissues including breast tumors. In addition, many ER mutants were generated in research labs to study functions of particular domains and amino acids. Many of these mutants can be found in an extensive review by Herynk and Fuqua [111]. A few naturally occurring splicing isoforms, ER-α-46, ER-α-36, and ER-βcx have been characterized in recent years that possess dominant negative activities against full-length ER-α and have potential clinical relevance in breast cancer. ER-α-46 is devoid of the AF-1 domain, and inhibits the proliferation of MCF7 cells by antagonizing full length ER-α AF-1 activity [112]. ER-α-36 lacks both the AF-1 and AF-2 domains but retains DBD and partial LBD domains [113]. It has three potential myristoylation sites at the N-terminal and is predominantly membrane

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bound. ER-α-36 inhibits estrogen-dependent and estrogenindependent transactivation of both full-length ER-α and ER-β. However, ER-α-36 can be activated by both estrogen and antiestrogen, such as tamoxifen and ICI182,780, to mediate membrane-initiated signaling activation of MAPK/ERK pathway and stimulate cell growth, which makes ER-α-36 a potential target for breast cancer therapy [114]. ER-βcx, or ER-β2, lacks essential amino acids in ligand-binding and AF2 domains, thus does not bind to estradiol nor activates estrogen-responsive genes. It prefers to heterodimerize with ER-α and inhibits binding of ER-α to ERE [115]. Analysis of ERβcx expression in breast tumors has suggested some correlation with tamoxifen resistance, but more specimens need to be evaluated to confirm this observation [115]. More studies are still needed to verify functions of those ER splicing variants in cell proliferation and cancer progression.

9.2.7

Mechanisms of Estrogen and ER Signaling

Current efforts are focused on unravelling the complex signaling network of estrogen action in breast cancer cells. The biologic functions of estrogen and ER signaling are mediated through at least four molecular mechanisms: 1. The classic ligand-dependent activation of ERE-containing target genes; 2. ERE-independent genomic actions; 3. Ligand-independent genomic actions; and 4. Membrane-mediated nongenomic actions (Fig. 9-2) [116]. These pathways are integrated at different levels and understanding them is critical to identify new therapeutic targets for the treatment of both hormone-responsive and hormone-irresponsive cancers.

9.2.8 Ligand-and-ERE-Dependent Activation of ER The classic ligand-dependent and ERE-mediated pathway is the best-characterized mechanism for ER activation of responsive genes. In brief, binding of estrogen leads to conformational change of ER; dissociation of inhibitory chaperon proteins such as Hsp70 and Hsp90; dimerization of ER and binding to the EREs at the promoter of target genes; and recruitment of coactivators to activate transcription. Many ER coactivators have been identified and the number is increasing, which are diverse in both structure and function. Most of the coactivators possess one or more LXXLL consensus sequences (L is leucine and X is any amino acid), named the nuclear recognition motif boxes (NR boxes), which bind to the coactivator-binding groove at the LBD or the AF-1 site of ER. Some coactivators are adaptors between ER and transcriptional machinery and some have enzymatic activities involved in histone modification such as histone acetyltransferases (HAT) and histone methyltransferases (HMTs),

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Fig. 9-2. Mechanisms of estrogen and ER signaling. ER functions through four mechanisms: (1) In the classic pathway, binding of estrogen leads to dimerization and nuclear translocation of ER. ER associates with ERE in the target gene promoter and activates transcription; (2) In the ERE-independent pathway, ER functions as a transcription activator by interacting with other transcription factors such as Jun/Fos. By this way ER simulates transcription of non-ERE-containing genes; (3) In the ligand-independent pathway, binding of growth factor (GF) to its corresponding receptor (GFR) activates protein kinase cascades, which lead to phosphorylation of ER. Phosphorylated ER activates transcription of target genes in the absence of ligand; (4) Unlike the 3 other mechanisms, membrane associated ER activates signal transduction pathways independent of mRNA and protein synthesis. This nongenomic response happens in the cytosol and only takes seconds to minutes [116].

chromatin remodeling, RNA processing and ubiquitin proteasome pathway. These coactivators have been extensively reviewed [42, 117, 118]. To name a few, the SRC family that consists SRC-1 (p160, NcoA), SRC-2 (TIF-2, GRIP-1, NcoA2) and SRC-3 (AIB1, p/CIP, ACTR, RAC3, TRAM-1) is the first coactivator identified. They contain a basic helixloop-helix-Per/ARNT/Sim domain at the N-terminal end that functions in DNA binding and protein–protein interactions, three LXXLL/NR box motifs in the center region involved in interaction with ER, and an intrinsic transcription activation domain (AD1 and AD2) towards the C-terminal. SRC-1 and SRC-3 also possess a HAT domain that provides them histone acetyltransferase activity (Fig. 9-3). SRC proteins can recruit additional coactivators and transcription machinery to the promoters of estrogen-regulated genes and their specificity and activity are regulated by phosphorylation induced by a variety of stimuli including hormones, cytokines, and growth factors [119]. CBP/p300 (cyclic adenosine monophosphate [cAMP] response element–binding protein, or CREB) and p/ CAF (p300 and CBP-associated factor) are among the vital coactivators recruited to ligand-bound ER by SRC proteins. CBP preferentially binds SRC-3 over SRC-1 and SRC-2 and serves to enhance ER transactivation via HAT activity and as a molecular scaffold for more extensive coactivator recruitment (Fig. 9-3). The N-terminus of CBP/p300 contains an

NR box that allows it to interact directly with nuclear receptors, and the C-terminal is responsible for SRC binding [42]. SRC’s C-terminus serves to recruit other coactivators such as coactivator-associated arginine methyltransferase-1 (CARM1) and protein arginine methyltransferase-1 (PRMT-1) [120, 121]. In addition to the basic complex of SRC, CBP/p300, HATs, and methyltransferases bound to liganded ER on the ERE/promoter region of estrogen-regulated genes, a host of other coactivators are recruited by these coactivators to the complex to enhance ER transcriptional activity. Recruitment of coactivators by ER to the target gene promoter is an orderly and cyclic process. Using chromatin immunoprecipitation (ChIP) assays, Metivier et al. [122] showed that recruitment of coactivators by ER-α to the EREcontaining promoter pS2 consists two steps: in the first 25 minutes of transcriptionally unproductive cycle, both in the absence and presence of estrogen, ER-α recruits SWI/SNF complex to remodel the nucleosomal organization of pS2 promoter, then HMT, HAT, and some proteins of the basal transcriptional apparatus join the complex. Subsequently, ER-α is targeted to proteasome by the APIS complex that is a subset of the 20S proteasome. The receptor is destined to degradation in the absence of estrogen. However, when estrogen binds, a transcriptional productive cycle (approximately 45 minutes) starts with the recruitment of p68 RNA helicase followed by

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Fig. 9-3. Structure of coactivators (SRC-1/p160 and CBP/p300) and corepressors (NCoR, SMRT). A The basic structure of SRC-1/p160 contains a basic helix-loop-helix (bHLH) motif and a Per-Arnt-Sim (PAS) homology region. The nuclear receptor interaction domain contains 3 LXXLL motifs. Areas of interaction with other cofactors as well as HAT activity are indicated below the structure [199–201]. B The basic structure of CBP/p300 also contains a nuclear receptor interaction domain with an LXXLL recognition motif. The structure also has 3 zinc-finger regions (CH/1, CH/2, CH/3), a bromodomain (Br), and a KIX domain that interacts with CREB [30, 91]. C The basic structure of the corepressors NCoR and SMRT contains three repressor domains (RD1, RD2, RD3) and 2 nuclear receptor interaction domains with the extended helical motif LXX I/H I XXX I/L. RD1 interacts with TBL-1, which then recruits HDAC1 and HDAC2. Other HDAC interact with regions of RD2 and RD3 and are indicated below the structure [200, 202, 203].

HMTs, p160, HATs, and additional components of transcriptional apparatus. RNA polymerase II is phosphorylated and transcription elongation proceeds. This cyclic regulation of ER-α transcription guarantees the effective cellular responses with respect to the environmental estrogen concentration. It has been revealed that the sequential addition of coactivators is essential to achieve maximum transcriptional activity [117]. A study [123] showed that the pure antiestrogen ICI182,780 promoted the interaction between ER-α and coactivator CBP/p300, but not SRC/p160, however, such recruitment of coactivator failed to activate transcription of target genes. This suggests that recruitment of the downstream coactivator (CBP/p300) without the upstream one (SRC/p160) is insufficient for transcriptional activation of target genes. An opposing group of coregulators, called corepressors, interacts with ER to inhibit transcription of target genes. The corepressors have been reviewed by several groups [42, 124, 125].

Interestingly, some coregulators can function as both coactivators and corepressors in a tissue-, cell-, promoter-dependent manner. Those coregulators include FKHR (forkhead homolog in rhabdomyosarcoma), ERRα (estrogen-related receptor α) and NSD1 (NR-binding SET-domain-containing protein 1) [125]. Most corepressors bind at the LBD/AF-2 domain of ER but some also bind at the AF-1 and DBD/hinge domains. The corepressors function prominently by recruiting histone deacetylase (HDAC) protein complex for chromatin modification, whereas they also inhibit transcription by competing with the coactivators, inhibiting ER dimerization or DNA binding, activating proteolysis of ER, and sequestering ER from nucleus. In addition, some corepressors function through more than one mechanism [125]. The most studied corepressors are SMRTs and NCoR, which interact with unliganded ER [126, 127] and also in the presence of ER antagonists such as tamoxifen and RU486 [128, 129]. NcoR and SMRT contain three

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independent repressor domains (RDs) at the N-terminal and a conserved bipartite nuclear-receptor-interaction domain (NRID) at the C-terminal. NRID contains critical L/I-X-X-I/V-I motif termed CoRNR box, which is similar to LXXLL/NR box but is predicted to form a longer helical structure compared with coactivators (Fig. 9-3) [124]. The ER-associated corepressors SMRT/NCoR then recruit other corepressors such as HDAC proteins to inhibit transcriptional activation by ER.

9.2.9

ERE-Independent Genomic Actions of ER

In addition to the classic ER/ERE-mediated activation of estrogen-responsive genes, on binding estradiol, ER-α and ER-β can activate the expression of a multitude of genes in an ERE-independent manner. This mechanism is often referred to as transcription factor cross talk in which ER functions as a coactivator [130]. To activate non-ERE-regulated genes, estradiol-bound ER either directly binds DNA sequence-specific elements within the promoter region of these genes, or indirectly contacts these alternate response elements through interactions with mediator proteins, such as activator protein (AP-1) and Sp1 transcription factor, that tether the ER to the promoter (Fig. 9-4). In fact, only a few genes have been identified whose expression is controlled by ER exclusively through the classic ERE-mediated mechanism [130]. Many estrogen responsive genes contain both an ERE and another specific transcription E2

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Fig. 9-4. ER as a transcription coactivator. A Transcription factor (TF) binds to the specific site (Site) in the promoter region. For example, AP-1 binds to AP-1 site and Sp1 binds to Sp1 site. Estradiol-bound ER interacts with those transcription factors and recruits other transcriptional coactivators (CoAct) to the promoter thus stimulates the transcription of non-ERE containing genes. B Some genes contain both an ERE and another specific transcription factor binding site in proximity. Estradiol-bound ER binds to the ERE and the specific factor binds to its corresponding sites. For maximum transcription activity, the 2 transcription complexes are integrated through interaction with coactivators (CoAct) [130].

factor binding site (AP-1 or Sp1 site) in proximity within the promoter, and interactions between transcriptional complexes formed on both sites through integrators maximally stimulate the transcription. For example, the pS2 gene has an ERE and an AP-1 site, and the retinoic acid receptor α1 (RARα1) gene has an ERE and a Sp1 site (Fig. 9-4) [131, 132]. AP-1 activation occurs when the N-terminus of ER interacts with the Jun/Fos transcription factor complex on AP-1 sites at the promoter regions. Such target genes include ovalbumin, insulin-like growth factor (IGF-1), cyclin D1, and collagenase [133]. ER-α and ER-β both activate transcription from AP1 sites after ligand binding, but they respond in an opposite manner to typical ER agonists and antagonists. In response to agonists such as estradiol and DES or the SERM tamoxifen, ER-α activates AP-1 sites. Conversely, ER-β activates AP-1 sites after binding the SERM tamoxifen or raloxifene, but estradiol binding antagonizes ER-β AP-1 activation [36]. The differential response of ER-α and ER-β to these ligands is thought to be cause by differences in the AF-1 and AF2 domains of the two ERs, or to disparities in coactivator recruitment [39, 46]. In addition, one mechanism is proposed that ER-β antagonizes ER-α activity by altering the estrogeninduced recruitment of c-Jun and c-Fos to estrogen-responsive promoters [134]. A number of ER/Sp1-mediated, estrogen-induced genes have been identified, including vascular endothelial growth factor (VEGF) [135], vascular endothelial growth factor receptor 2 (VEGFR2) [136], metastasis-associated protein 3 (MTA3) [137], and vitamin D3 receptor [138]. Such ER activation of target genes occurs through the tethered complex of ligand-bound ER-α and the Sp1 transcription factor at GC-rich promoter sequences (Fig. 9-4). Both ER-α and ERβ activate transcription of genes with GC-rich Sp1 promoter sites by forming a tethered complex of ER bound to Sp1 at the promoter site. Other than transcriptional activation, Sp1 has been implicated to function in ER-mediated gene repression. One study suggested Sp1 recruited ER-α and HDAC-1 at the p21/WAF1 promoter to maintain its repressed state [139]; another study proposed that Sp1 recruited estrogen-bound ERα at the half ERE site within the cyclin G2 promoter, resulting in joining of NCoR and HDACs and releasing of RNA polymerase II [140]. Another example to illustrate that ER negatively regulates the transcription of non-ERE containing target gene is through NF-κB. ER-α inhibits NF-κB-mediated interleukin-6 (IL-6) gene expression by interacting with the c-rel subunit of NFκB and preventing NF-κB binding to the responsive element in the IL6 promoter [141]. It is known that activation of NFκB is low in ER-positive but high in ER-negative breast cancer cells, where NF-κB induces genes that mediate cell proliferation and invasion. As a result, NF-κB has been implicated as a possible therapeutic target for treating ER-negative breast cancer [142]. Recent evidence has suggested that inhibition of NF-κB could reverse endocrine resistance in ER+ breast cancer as well [143, 144].

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ER influences receptor dimerization, coactivator or corepressor recruitment, and the effects of agonists and antagonists on ER-dependent gene activation. Therefore, signaling exchange that alters the phosphorylation status of ER influences every level of ER influence in the cell. Another mechanism through which other signing pathway modulates ER transcriptional activity is via phosphorylation of ER coactivator or corepressors [146]. For example, the activity of ER coactivator glucocorticoid receptor interacting protein 1 (GRIP1) is increased by phosphorylation at Ser736 induced by EGF pathway [147]. Cross talk between estrogen receptor and growth factor receptor signaling pathways, especially ER-EGFR cross talk, is one of the major mechanisms for resistance to endocrine therapy in breast cancer, as summarized in several reviews [66, 146, 148]. The mechanism of EGFR-ER cross talk is EGF signals to EGFR leading to a signaling transduction cascade through MAPK resulting in the phosphorylation of Ser118 of the ER-α AF-1 domain and ER transactivation [148]. In addition, MAPK phosphorylation of Ser106 and Ser124 at ERα (Ser87 and Ser105 at human ER-β) is thought to play an

9.2.10 Ligand-Independent Genomic Activation of ER In the absence of estradiol, other signal pathways induce phosphorylation of target proteins, which leads to ligandindependent activation of ER. Interaction between various cellular-signaling pathways is commonly referred to as “cross talk.” To date, many signaling pathways have been shown to cross talk with ER, including signaling pathways downstream of growth factor receptors such as EGF/EGFR, IGF/insulin receptor; factors that regulate cellular phosphorylation levels such as PKA and PKC; and transforming growth factor-β (TGF-β), as reviewed by several groups [66, 145, 146] (Fig. 95). Accordingly, many factors that activate those cross-talking signaling pathways have been shown to influence the activation of ER, including dopamine, cAMP, insulin, and IGF-1, heregulin, and TGF-α [146]. Ligand-independent activation of ER occurs primarily through signaling pathways downstream of growth factors that activate ER by enhancing phosphorylation of various Ser and Thre residues in the AF-1 and AF-2 domains of ER (Fig. 9-5). Increased phosphorylation of

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CoAct Activated transcription Fig. 9-5. A model of cross talk and signaling with ER. Estrogen activation of membrane-bound ER leads to fast cellular responses through activation of several signal transduction pathways such as MAPK and PI3K/AKT, which stimulates cell growth and proliferation. Although ERs do not contain a transmembrane domain, it could associate with the membrane through post-translational modification such as palmitoylation or through interactions with adaptor proteins. For example, ER forms complex with IGFR through interaction with Shc; ER associates with Src by interacting with PELP/MNAR; ER binds to PELP/MNAR or p130Cas which also binds to PI3K; ER forms complex with eNOS by binding to Striatin-Caveolin [156]. On the other hand, ER and its transcription coactivators are phosphorylated by different protein kinase cascades activated by growth-factor (GF)-bound growth factor receptor (GFR). The phosphorylation increases transcription activities of ER. Genomic and nongenomic actions of ER are integrated, and ER pathway cross-talks to other signaling pathways. As a result, overexpression of growth factor receptors or key factors in different signal transduction pathways often causes abnormal growth of cancer cells and resistance to endocrine therapy [66].

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important role in ligand-independent activation [145]. Various tyrosine kinase receptors are able to signal either through the ras, raf, MAPK cascade or via the PI3K/AKT cascade to effectively phosphorylate Ser118 and Ser167 and activate ER-α [148]. The MAPK pathway is central to signaling cross talk with ER. In addition to mediating the phosphorylation of ER-α Ser118, in vivo and in vitro evidence has shown that p90 ribosomal S6 kinase Rsk downstream of MAPK signaling is responsible for phosphorylating Ser167 [145]. Phosphorylation of Ser167 enhances interaction between ER-α and SRC3/AIB1 in the presence of tamoxifen, and leads to tamoxifen resistance [149]. In a recent study, p21 activated kinase 1 (PAK1), subject to regulation by PI3K/AKT pathway, was shown to phosphorylate Ser305 and Ser118 of ER-α and lead to tamoxifen resistance [150]. In addition to phosphorylating ER itself, EGFR signaling results in phosphorylation of ER coregulator SRC3/AIB1 and NcoR, which also augments the transcriptional activation potential of ER and enhances its effects on cell proliferation and survival [151]. Heregulin receptor (HER/cErB) is a member of EGFR family. Breast cancer cells overexpressing HER2 are insensitive to estradiol and tamoxifen, and the corresponding tumors are resistant to hormonal therapy. Simultaneous treatment with growth factor pathway inhibitors might be beneficial to reverse this process [66]. Cross talk between insulin growth factor-1 (IGF-1) system and ER is mediated by PI3K/AKT pathway, which leads to increased ER-α synthesis and activity [58]. Other than growth factors, transcriptional activity of ER is modified by cell-cycle-regulating proteins such as cyclindependent kinases (CDK), cyclin A, and cyclin D1. Experimental evidence has shown that the CDK2/cyclin A complex phosphorylates Ser104 and Ser106 and, increases estrogendependent and estrogen-independent transactivation of ER-α [152]. Additionally, cyclin D1 is frequently overexpressed in breast tumors and can activate ER-α in a ligand-independent fashion by recruiting SRC-family coactivators [153, 154].

9.2.11 Membrane-Mediated Nongenomic Action of Estrogen Binding of estrogen to ER not only triggers nuclear actions to regulate mRNA and protein synthesis but also results in rapid cellular responses within minutes that are independent of transcription. The latter is generally referred to as nongenomic estrogen signaling or extranuclear action of estrogen, which is thought to initiate at the cell surface [155, 156]. As early as 1977, researchers hypothesized the existence of a membraneassociated ER to explain rapid cellular responses to estradiol [157]. However, this membrane associated estrogen activity has not been the focus of many research groups until recently. The significance of cytoplasmic estrogen functions has now been increasingly accepted with accumulating evidence. Nongenomic action of estrogen has the following features:

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1. The responses are very rapid and only take seconds to minutes; 2. It does not require RNA and protein synthesis; 3. It often involves mobilization of second messenger such as Ca2+, cAMP, phosphotidylinositol, and nitric oxide (NO); and 4. It is frequently associated with activation of protein kinase cascades such as MAPK and PI3K/AKT [133, 156]. Much effort is focused on identifying the membrane-associated ER. Biochemical and microscopic analysis have shown that most ER localizes in the nucleus, while a small portion is at the mitochondria, endoplasmic reticulum, and plasma membrane [156]. The plasma membrane-associated ER appears to be identical to the nuclear ER, as suggested by circumstantial immunohistochemical evidences and studies using membrane-impermeable ligand and overexpression of nuclear receptors [158–162]. This has caused controversy because ER-α and ER-β have no transmembrane domains unlike other membrane receptors. Translocation of ER to the plasma membrane has been proposed through post-translational modifications and adaptor molecules. For example, estrogen-dependent palmitoylation of Cys447 at the LBD of ER-α localizes the receptor at the plasma membrane and is responsible for estrogen-induced MAPK and PI3K/AKT pathways [163]. Moreover, ER participates in various protein–protein interactions to mediate nongenomic actions. One example of such adaptor protein is Shc, whose interaction with ER-α is stimulated by estrogen and blocked by ICI182,780 [47]. As a result of estrogen induction, an ER-α-Shc-IGFR (insulin growth factor receptor) ternary complex forms, which leads to membrane association of ER-α and activation of MAPK pathway [164]. Another adaptor protein is called PELP1 (proline-, glutamic acid-, and leucine-rich protein 1) or MNAR (modular of nongenomic activity of estrogen receptor), which was first identified as a coactivator of ER functioning in the genomic pathway in the nucleus [165]. PELP1/MNAR also localizes in the cytoplasm and connects ER-α with c-Src, which in turn activates MAP kinases Erk1 and Erk2 [166]. Moreover, PELP1/MNAR interacts with the p85 subunit of PI3K and leads to constitutive activation of AKT, which causes tamoxifen resistance in MCF7 cells [167]. In addition to PELP1/MNAR, a cytoskeletal protein p130Cas also links ER-α to c-Src and the p85 subunit of PI3K in an estrogen-dependent manner in human T47D breast cancer cells [168]. The nongenomic activation of PI3K/AKT pathway by estrogen might be one of the mechanisms for estrogen’s antiapoptotic effects [169]. In endothelial cells, ER-α interacts with scaffold protein caveolin in a ligand-dependent manner and targets to caveolae, where it activates endothelial nitric oxide synthase eNOS [170] and leads to rapid release of NO [171]. Interaction of ER-α and caveolin might be mediated by another scaffold protein striatin. Lu et al. [48] showed that disruption of complex formation between ER-α and striatin

9. Estrogen Receptor Pathways and Breast Cancer

only blocked estrogen-induced rapid activation of MAPK, AKT, and eNOS but had no effect on transcriptional activity of ER. This provided conceptual support for potential development of “pathway dependent” selective ER modulators. The rapid nongenomic estrogen signaling could be induced by sequestration of ER in the cytoplasm. Metastatic tumor antigen 1 (MTA1) is a well-known ER corepressor to suppress transcriptional activity of ER. A naturally occurring short form of MTA1, MTA1s, sequestrates ER-α to the cytoplasm and enhances nongenomic responses of ER. Dysregulation of HER2 leads to overexpression of MTA1s and sequestration of ER in the cytoplasm, and expression of MTA1s in breast cancer cells prevents estrogen-induced nuclear translocation of ER and stimulates malignant phenotypes [172]. Therefore, MTA1s function as a repressor of ER genomic activity but an activator of ER nongenomic activity. Nevertheless, how MTA1 is related to understanding and treatment of breast cancer needs more clinical correlative study. Recent interest in estrogen regulation has resulted in the identification of “alternative estrogen receptors” that might mediate nongenomic actions of estrogen. One of them is named ER-X located at the plasma membrane of neurons, which mediates estrogen-induced activation of MAPK cascade [18]. However, ER-X has not been shown to be present in other tissues. The other protein is GPR30, an orphan G protein coupled receptor, which has been identified by several groups as membrane-associated estrogen receptor triggering rapid estrogen signaling independent of both ER-α and ER-β [19–21, 173]. It was shown that GPR30 bound to E2 with high affinity (dissociation constant 2.7 nM) and stimulated adenylyl cyclase activity in the breast cancer cell line SKBR3 lacking both ER-α and ER-β. In addition, GPR30 can be activated by antiestrogens such as ICI182,780 and tamoxifen [19, 173]. In another study, Revankar et al. [21] reported GPR30 located at endoplasmic reticulum instead of plasma membrane. They verified that both ER-α and GPR30 stimulated calcium mobilization but through different signaling pathways. Further, they found estrogen binding to GPR30 in SKBR3 cells resulted in generation of PIP3 in a process required EGFR activation. The discovery of GPR30 could explain the rapid actions of estrogen associated with membrane in certain ER-negative cells [21]. However, whether or not GPR30 is a genuine novel estrogen receptor mediating the rapid nongenomic estrogen activation remains controversial. One study indicated that micromolar not nanomolar E2 was needed to activate GPR30 in SKBR3 cells [174]. Pedram et al. showed the generation of second messenger by GPR30 was modest, and they did not observe significant E2 responses in SKBR3 cells [175]. In addition, knocking down GPR30 by expression of antisense oligo or siRNA in MCF7 cells seemed to have no effect on E2 activated cell proliferation [175, 176]. Further research to isolate and analyze the membrane-bound estrogen receptor and to examine whether the cellular effects

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are downstream physiologic effects of estrogen will be needed to confirm the functions of GPR30.

9.3 Conclusions and Clinical Applications The driving force for the intense interest in deciphering the interdependent and synergistic actions of estrogen is the potential to enhance survival from breast cancer. The recognition that the ER was an appropriate target to control the life and death of select breast tumors [177] has resulted in significant advances in targeted cancer therapy by the application of tamoxifen, a nonsteroidal antiestrogen originally destined to be a postcoital contraceptive but reinvented to become the “gold standard” for antihormonal therapy during the 1970s and 1980s [178]. It is now known that long-term adjuvant tamoxifen therapy, targeted to the ER, has saved the lives of 500,000 breast cancer patients and contributed significantly to the decreasing death rate from breast cancer in countries throughout the world [179]. A study of tamoxifen’s pharmacology and clinical applications opened the door to further improvements in therapeutics. Considerable research over two decades eventually resulted in the extensive development of third-generation aromatase inhibitors to prevent estrogen synthesis in postmenopausal women. Results with the competitive inhibitors letrozole and anastrozole and the suicide inhibitor exemestane now all demonstrate improvements in diseasefree survival and reductions in side effects when aromatase inhibitors are compared with tamoxifen in adjuvant clinical trials of patients with ER positive breast cancer [180–183]. Clearly, aromatase inhibitors more precisely target the ER without the side effects of blood clots and endometrial cancer noted with tamoxifen [184]. Nevertheless, the lessons learned with tamoxifen have had broader implications than are at first appreciated. Tamoxifen prevents mammary cancer in laboratory rats [185] and reduced the incidence of contralateral breast cancer when used as an adjuvant therapy, by 50% [186–188]. These preliminary data were the scientific rationale for testing the worth of tamoxifen to prevent breast cancer in high risk pre- and postmenopausal women [189, 190]. Tamoxifen is approved in the United States for reducing the incidence of breast cancer in high-risk women. The reason tamoxifen was even considered as a putative chemopreventive agent was the recognition that the drug was not in fact an “antiestrogen” at all sites but a selective estrogen receptor modulator (SERM); tamoxifen maintains bone density in laboratory animals and postmenopausal women [191, 192]. This knowledge resulted in the idea that tamoxifen-like compounds could be used to prevent osteoporosis [193, 194]. This data-based strategy has proved effective in the development of raloxifene, a failed breast cancer drug [195] as the

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first SERM to prevent osteoporosis in osteoporotic women [196] while simultaneously reducing the incidence of breast cancer [197, 198].

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Chapter 10 Cyclin-Dependent Kinases and Their Regulators as Potential Targets for Anticancer Therapeutics Marcos Malumbres

10.1

Introduction

The molecular mechanisms that control cell proliferation have been extensively studied during the last years. Since the discovery of Ras oncogenes in the early 1980s [1], it has become clear that many signaling pathways control the cell division cycle and that oncogenic alterations frequently result in accelerated or unscheduled cell proliferation. During the cell division cycle, cells grow in size, duplicate the genome (DNA synthesis or S phase) and finally segregate the two genomes (mitosis or M phase) into the two new daughter cells (Fig. 10-1). In normal cells, cell-cycle progression is controlled in response to diverse mitogenic and antimitogenic signals. These signaling cascades result in the modulation of diverse molecular mechanisms to regulate cell-cycle proteins, including gene expression, post-translational modifications, and protein degradation. Progression through G1 (a gap phase before DNA synthesis) mostly results in the expression of genes required for the replication of the genome during the S phase, and chromosome segregation during mitosis. Progression through these later phases additionally depends on reaching the appropriate protein levels and post-translational modifications of the existing proteins. Among these modifications, protein phosphorylation and dephosphorylation are especially relevant to control protein function during the cell cycle, as well as in many other aspects of cell life. Other protein modifications such as acetylation, methylation, and SUMOylation are also relevant for modulating protein activity. In addition, degradation of existing molecules is required when these proteins are not needed any longer or are an obstacle for the transition to the following phases. Among all regulators that control cell-cycle progression, some cell-cycle kinases have received especial attention because they play crucial roles in driving cells throughout the different phases of the cell cycle. Importantly, kinase activity is one of the preferred biochemical functions to be inhibited in therapeutic efforts. This chapter reviews the biology of a family of protein kinases that control progression throughout the mammalian cell

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

cycle, their regulation, and the therapeutic potential of these molecules in cancer treatment.

10.2 Cyclin-Dependent Kinases: A Historical View Early screenings in the 1960s and 1970s in the yeast Saccharomyces cerevisiae led Hartwell to the identification of a large series of genes that, in mutant form, arrested cell division [2, 3]. This identification led him to hypothesize the existence of “checkpoints” (Fig. 10-2) that regulate the sequence of events in mitosis [4]. Simultaneously, Nurse identified in a different yeast, Schizosaccharomyces pombe, a protein kinase named as Cdc2 involved in driving cell division [5,6]. In the early 1980s, Hunt et al. identified proteins whose concentrations increased and decreased throughout the cell cycle in sea urchin oocytes and frogs [7]. These proteins were named as cyclins and they were later found to be necessary in activating the Cdc2 kinase. These three investigators were awarded the Nobel Prize for Physiology and Medicine in 2001 for the confluence of different approaches to learning about the molecular machinery regulating the cell cycle. Different subsequent studies showed that this protein kinase, named Cdc2 in S pombe and Cdc28 in S cerevisiae, is required for both G1/S and G2/M transitions of the yeast cell cycle. Further studies in Xenopus revealed that Cdc2, in complex with a cyclin, is a component of the previously described maturation promoting factor (MPF) required for entry into mitosis [8, 9]. The human Cdc2 homologue was cloned in 1987 by Draetta et al. [10] and Lee and Nurse [11]. Early in the same year, Hanks had cloned a different member of the family that was named as PSK-J3 (putative serine/ threonine kinase; filter J colony 3), by hybridizing HeLa cDNA to oligonucleotide probes with similarity to known serine/threonine kinases [12]. More direct approaches to clone Cdc2-related proteins led to the cloning of several new Cdc2-related kinases in the next few years. Thus, a new member of the family was first cloned in Xenopus and human cells by complementation of yeast Cdc28 mutants, by differential display, or as a partner of cyclin A [13–16]. This new protein was called CDK2 (cyclin-dependent kinase 2). 207

208

M. Malumbres

although being very similar to CDK in their primary structure, have not acquired the “CDK” name because no cyclin partner is known [21]. One of these kinases, CrkRS, has been renamed as CDK12, as it interacts with cyclin L1 and cyclin L2 [27] (Table 10-1).

10.3 Cell Cycle CDK and Their Regulators 10.3.1

Fig. 10-1. The cell division cycle. Cells duplicate the genome in S phase and segregate chromosomes in M phase (mitosis). Two additional gap phases (G1 and G2) allow cells to grow in size and prepare the biochemical and cellular machineries required for cell division.

Fig. 10-2. Cell-cycle checkpoints. Progression throughout the cell cycle phases is monitored by different signaling pathways that arrest or delay cell-cycle progression upon inappropriate conditions. Some of the proposed checkpoints are shown in the figure. Some of them, such as DNA damage or the spindle checkpoints, are well characterized, whereas the physiologic roles of others are unclear.

The use of degenerated primers for amplification of human cDNA soon resulted in the cloning of additional members of the family [17–20]. Along with Cdc2 and CDK2, at least one more CDK member (named CDK3) was able to complement cdc28 mutants in S. cerevisiae, whereas other family members, such as PSK-J3, PSSALRE, or PLSTIRE were not [17–19]. During the Cold Spring Harbor Symposium on Cell Cycle in 1991 it was decided that members of this family of kinases would be called cyclin-dependent kinases (CDK). Mammalian Cdc2 then became CDK1, PSK-J3 was renamed as CDK4, and PSSALRE and PLSTIRE kinases corresponded to CDK5 and CDK6, respectively [21] (Table 10-1; Fig. 10-3). Further members of the mammalian CDK family were cloned and characterized in the following years: CDK7 [22]; CDK8 [23]; CDK9 [24]; CDK10 [25], and CDK11 [26]. Other CDKlike kinases such as the PCTAIRE or PFTAIRE proteins,

Cell Cycle versus Transcriptional CDK

Although the original CDK1 (Cdc2) kinase was identified by its involvement in cell cycle regulation, many mammalian CDK have no direct role in the cell cycle. In fact, the family has evolved with multiple family members with specific functions. At least 4 additional CDK, CDK2, 3, 4, and 6, have relevant functions in cell-cycle progression. However, the involvement of other CDK in cell-cycle regulation is not well understood. CDK5 was initially cloned by homology to CDK1 and it expressed in most cell types. It binds D-type and E-type cyclins although it is not activated by them and nonspecific cell-cycle role has been reported for these complexes [28]. In fact, its activity is mainly controlled by two activating partners, p35 and p39, which are almost uniquely expressed in brain. CDK5-p35/p39 complexes are involved in several aspects of brain function such as neuronal differentiation, migration, and synaptic transmission [29]. Most of the other CDK (CDK7, 8, 9, 10, and 11) have activities related to the control of the transcription although some of them display additional functions of relevance to the cell cycle (Table 10-1). CDK7 is responsible for the activating phosphorylation of at least CDK1, CDK2, CDK3, CDK4, CDK7, and CDK6, forming a complex with cyclin H and MAT1 (ménage a trois 1) known as the CDK-activating kinase (CAK). Together with six other subunits, CAK is part of the general transcription factor TFIIH where it is involved in promoter clearance and progression of transcription from the preinitiation to the initiation stage [30]. In fact, transcription by RNA polymerase II is not only regulated by CDK7cyclin H/Mat1 but also by CDK8-cyclin C and CDK9-cyclin T complexes. These three CDK complexes are known to phosphorylate the C-terminal domain (CTD) of the largest subunit of RNA polymerase II playing critical roles in the initiation, elongation, and processing of primary transcripts. CDK8cyclin C complexes are components of the RNA polymerase holoenzyme in which they function as negative regulators of transcription [31]. These complexes also phosphorylate cyclin H to inhibit its activity. CDK9, on the other hand, binds to cyclin T or cyclin K to form distinct positive-transcription elongation factors termed P-TEFb [32]. CDK10 is thought to have a role in regulating the G2/M phase of the cell cycle because CDK10 antisense and dominant negative mutants arrest cells in G2/M; however, little is known about the proteins that interact with this putative kinase and contribute to

10. Cyclin-Dependent Kinases and Their Regulators as Potential Targets for Anticancer Therapeutics

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Fig. 10-3. Human CDK. Table 10-1. Mammalian CDK. Symbol CDK1

Synonym Cdc2, cdc28

CDK2

CDK3 CDK4 CDK5

CDK6 CDK7

PSK-J3

MO15, CAK, STK1

Cyclin-binding domain

Main activating cyclin(other cyclins)

PSTAIRE

A1, A2, B1, B2 (E, B3)

PSTAIRE

A1, A2, E1, E2 (D1, D2, B1, B3)

PSTAIRE PISTVRE PSSALRE

E1, E2, A1, A2, C D1, D2, D3 p35, p39 (D-, Eand G-type cyclins)

PLSTIRE NRTALRE

D1, D2, D3 H

Actopaxin, Adenomatous Polyposis coli, Amphiphysin 1, anaphase promoting complex, BARD1, Caldesmon, Cdc7, Cdc20, Cdc25A, Cdc25 C, Cdh1, Cdk7, C/EBPbeta, CK II, Dynein, Dystrophin, EF-1, Eg5, EGFR, FANCG, Fos, GFAP, GM130, GRASP65, Histone H1, hHR6A, HMG-I(Y), IFAP300, KRC, Lamins A, B & C, Lamin B receptor, Lats1, MAP1B, MAP4, Marcks, MCM2, MCM4, MKLP1, Myb, NBP60, Neurofimalment H, NF-I, Nir2, NO38, Nuclear pore complex, Nucleolin, Nucks, Numatrin, Orc1, p18, p47, p53, p54NRB, PAP, Plectin, PP1-I2, pRb, R2, Rab4, Rap1GAP, RCC1, RIIalpha, S6K1, Sam68, Separase, Ski, Survivin, mSTI1, Tau, Vimentin and Thymidine kinase BARD1, B-Myb, BRCA1, CBP/p300, Cdc6, Cdc7, Cdk7, Cdt1, C/EBPbeta, DP1, hHR6A, HIRA, Ku70, Marcks, MCM2, MCM4, MyoD, NPAT, Nucleophosmin (B23), p107, p21Cip1, p27Kip1, p53, pRb, R2, RPA, Smad3, and Thymidine kinase Cables1 Cdt1, Marcks, p107, p130, pRb and Smad3 Amphiphysin 1, Cables, Disabled 1, Doublecortin, Munc 18, Nudel, p53, Pctaire1, Protein phosphatase inhibitor 1, PSD-95, Stat3, mSds3, Synapsin I and Tyrosine hydroxylase p107, p130, pRb Cdk1-6, p53, RARgamma, RNApolII

Substrates

CDK8 CDK9 CDK10

K35

SMSACRE PITALRE PISSLRE

C (K?) T1, T2, K Unknown

RNApoll pRb, RNApoll Unknown

CDK11

PITSLRE, Cdc2L1, Cdc2L2

PITSLRE

L1, L2 (D)

9G8, Cyclin L

CDK12

CrkRS, CRK7, CD2L7

PITAIRE

L1, L2

Cellular function Cell cycle (G2/M)

Cell cycle (G1/S)

Cell cycle (G0/G1/S) Cell cycle (G1/S) Senescence, Postmitotic neurons

Cell cycle (G1/S) Cdk activating kinase, transcription Transcription Transcription Transcription, Cell cycle (G2/M) Transcription, Cell cycle (M); possible role in apoptosis Transcription, RNA splicing

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its function in the cell. The only known function of CDK10 comes from its interaction with the Ets2 transcription factor, a regulator of CDK1 expression, inhibiting Ets2 transactivation in mammalian cells [33, 34]. CDK11, finally, binds to cyclin L and interacts with the general pre-mRNA splicing factors RNPS1 and 9G8 and also with the RNA polymerase II playing a role in transcript production and in regulation of RNA processing [35]. It has been reported that CDK11 has additional roles in centrosome maturation and bipolar spindle formation [36]. In general, this group of “transcriptional” CDK-cyclin complexes might link, perhaps in a cell-cycle dependent manner, various growth factor signaling pathways to transcription and RNA processing events [37].

10.3.2

Cyclins

CDK activity and cell-cycle regulation relies on the binding of the CDK subunit to their cyclin partners [21]. Cyclins form a family of proteins defined by their synthesis and destruction during each cell cycle. They share a 150-amino acid region of homology called the cyclin box (Fig. 10-4). It was soon established

that mammalian CDK are also activated upon binding of their cyclin partners (Table 10-1). Thus, CDK1 was early reported to bind and be activated by B-type cyclins, being the CDK1cyclin B kinase activity maximal during the G2/mitosis transition [38, 39]. Approximately 28 cyclins are represented in the human genome and their symbols incorporate the CCN (cyclin) prefix along the specific suffix for each family member (Fig. 10-4). At least four classes (cyclins A, B, D, and E) are clearly involved in cell-cycle control. D-type (CCND1, CCND2, and CCND3) and E-type (CCNE1 and CCNE2) cyclins bind to and activate CDK4/6 or CDK2, respectively, and coordinate G1/S transition. Cyclins A (CCNA1 and CCNA2) bind to and activate CDK2 during S/G2 and CDK1 during G2. B-type cyclins (CCNB1, CCNB2, and perhaps CCNB3) bind to and activate CDK1 during G2/M. Cyclin B3 forms a special subfamily well conserved in evolution that shares homology to cyclins B1 and B2 but also displays properties that resemble those of A-type cyclins and associate to CDK2. It has been proposed that cyclin B3 has a specific role in the mammalian meiotic cell cycle [40]. Four additional classes are involved in the regulation of transcription (cyclins C, K, H, and T) and cyclin L regulates mRNA splicing (Table 10-1; Fig. 10-4). Cyclin C protein levels peak in

Fig. 10-4. Human cyclins.

10. Cyclin-Dependent Kinases and Their Regulators as Potential Targets for Anticancer Therapeutics

mid-G1 and has been reported to bind and activate CDK3 during the G0/G1 transition [41]. The function of other cyclins is less understood although some of them might have distinct roles on cell-cycle regulation. Thus, cyclin J has been also described to bind and activate Drosophila CDK2 kinase and the inhibition of their interaction results in mitotic defects [42]. The corresponding mammalian protein (CCNJ) has not been analyzed. Cyclin F shares the greatest amino acid sequence similarity with cyclin A, and fluctuates during the cell cycle with a similar pattern to cyclin A and cyclin B [43]. It has been reported to interact with cyclin B1 to form CDK1-cyclin B1-cyclin F active complexes [44]. Cyclin F complements Cdc4 (a component of the Spk1, Cullin(-), F-box proteolytic complex) mutants in yeast also contain an F-box domain similar to that Cdc4. Although a direct CDK partner is not known yet, a recent genetic analysis in the mouse has shown that cyclin F is involved in the cell cycle re-entry from quiescence [45]. Cyclin G1 and G2 are one of the earliest p53 targets identified and they seem to the involved in the ATMp53-Mdm2 pathway [46]. Cyclin I shows the highest sequence similarity in the cyclin box to cyclins G and E, whereas the similarity between cyclins I and G also extends toward the C-terminus from the cyclin box; however, the expression of cyclin I mRNA does not correlate directly to the cell cycle, and it may therefore function independently of the cell-cycle control [47]. The involvement of other cyclins, such as M-type cyclins (M1, M2, M3, and M4), cyclin O (UNG2), cyclin P, or cyclin S, in cell-cycle regulation is not clear [21]. A new subfamily of cyclin-like proteins was characterized in 2000 and named after their binding to CDK3 (Cables/Ik3, Interactors with CdK3) [48]. These proteins bind to both CDK3 and CDK5.

10.3.3

CDK Inhibitors

10.3.3.1

INK4 Proteins

Regulation of CDK activity is tightly controlled at different levels including the described interaction with activating partners (cyclins) and the binding to negative regulators. In 1994, a new tumor-suppressor gene (TSG) was located on human chromosome 9p21 and designated MTS1 (i.e., multiple tumor suppressor 1) [49]. This gene had been originally described by Serrano et al. [50] as a cell-cycle inhibitory protein. Its ability to bind and inhibit the CDK4 kinase gave it the INK4 designation (Inhibitor of CDK4). The analysis of the 9p21 chromosomal region, deleted in multiple tumors, uncovered the presence of a similar gene (MTS2) [49], also described independently as a new member of the INK4 family (p15INK4b) [51]. A few years later, two new members of the family, p18INK4c and p19INK4d, were cloned by polymerase chain reaction (PCR)-based strategies or the two-hybrid screening [52–54]. The original INK4 gene (MTS1) is therefore designated as p16INK4a. The Human Genome Project has coined the new terms CDKN2A, B, C, and D to designate this family (Fig. 10-5). The locus that encodes the p16INK4a protein has special characteristics that differ from the other INK4-coding regions. Two proteins are

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encoded by this locus, p16INK4a and p19ARF (also called p14ARF in humans) [55]. Each transcript is composed of two common exons, E2 and E3, and two differentially expressed E1 exons, E1α (p16INK4a) and E1β (p19ARF), and the corresponding mRNAs are therefore transcribed from two different promoters. Exons E2 and E3 are read in different reading frames such that no sequence or structural homology is found between the two proteins; however, both of them are able to induce cellcycle arrest although through different pathways. Whereas p16INK4a activates retinoblastoma by inhibiting the cyclin D-CDK4/6 kinase activity, p19ARF blocks degradation of the tumor suppressor protein p53 mediated by Mdm2 [56]. In vitro, INK4 proteins are biochemically indistinguishable as CDK4/6 inhibitors. When ectopically expressed, they arrest cells in G1 if a functional pRb protein is present. Inhibition of CDK4/6 by INK4a proteins leads to hypophosphorylation of pRb that remains functional to repress transcription of genes required for S-phase entrance and progression [57, 58]. The structural basis of the inhibition of CDK4/6 by INK4 proteins has been very well established. p16INK4a, p15INK4b, p18INK4c, and p19INK4d share a structural motif, the ankyrin repeats, consisting of pairs of antiparallel α-helices, stacked side by side and connected by a series of intervening hairpin motifs. Four of these repeats are present in p16INK4a and p15INK4b and five in p18INK4c and p19INK4d (Fig. 10-5). These structural domains are involved in the binding of the inhibitors to the noncatalytic side of CDK4 and CDK6, opposite to the cyclin D binding site [59]. INK4 binding induces an allosteric change in CDK4/6 by rotating the two structural lobes of the kinase 15° around the vertical axis, altering both the binding site of cyclin D and preventing the cyclin D-CDK4/6 interaction, and altering the ATP binding site, reducing the affinity of the CDK subunit for the ATP [59–61]. By these two mechanisms, INK4 proteins reduce the kinase activity of CDK4/6. By preventing the formation of cyclin D-CDK4/6 complexes, they also force the redistribution of Cip/Kip inhibitors to the cyclin E-CDK2 complexes, causing also the downregulation of the cyclin E-CDK2 kinase activity [57, 62, 63]. INK4 proteins exhibit different expression patterns in vivo. In mice, p18INK4c and p19INK4d are widely expressed during embryonic development, whereas p16INK4a and p15INK4b expression is undetectable before birth. Early after birth p15INK4b, p18INK4c, and p19INK4d can be detected in many tissues, but expression of p16INK4a only increases with age [64]. In general, the different INK4 proteins are induced in response to different growth inhibitory pathways. Oncogenic stress and replicative cellular senescence specifically induce the expression of p16INK4a [56, 65]. p15INK4b is also upregulated in response to oncogenic stimuli [66]. Both p16INK4a and p15INK4b gene is progressively upregulated by the accumulation of cell doublings. One of the best characterized transcriptional regulations of an INK4 inhibitor is the induction of p15INK4b expression by transforming growth factor ß (TGFß) in epithelial cells [51]. Thus, although redundant in their activity as inhibitors of cyclin D-CDK4/6 kinases, cell-cycle arrest in response to spe-

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cific stimuli is mediated, to some extent, by specific INK4 proteins. A certain level of functional redundancy in vivo has been observed among INK4 proteins. Evidence has accumulated indicating a crucial role for p16INK4a as the effector of the antiproliferative consequences of ageing. Thus, p16INK4a induces an age-dependent decline in pancreatic islet and forebrain regenerative potential [67, 68] as well as in the self-renewal capacity of several stem cells [69]. These papers uncover a novel role for the p16INK4a tumor suppressor in promoting ageing, a role shared by the p53 tumor suppressor. Given that p16INK4a deficiency only partly mitigates most of the ageing effects studied, other cell-cycle regulators or unknown mechanisms may also participate in stem/progenitor-cell ageing.

10.3.3.2

Cip/Kip Proteins

Whereas INK4 proteins specifically inhibit CDK4 and CDK6, most other CDK are inhibited by members of the Cip/Kip family. These proteins include p21Cip1, p27Kip1, and p57Kip2, and their corresponding human genes have been designated as CDKN1A, CDKN1B, and CDKN1C, respectively (Fig. 10-5). p21Cip1 (also known as Waf1, Sdi1, or Pic1) is an inhibitor of CDK implicated in the negative regulation of the cell cycle [57, 70, 71]. This inhibitor is upregulated transcriptionally through both p53-dependent and p53-independent mechanisms. In response to γ-radiation or other forms of DNA damage, p21Cip1 is strongly upregulated in a p53-dependent manner. p21Cip1 is therefore a key mediator of p53-dependent growth suppression and is involved in cellular processes such as senescence and differentiation. p27Kip1, on the other hand,

was identified as an inhibitor in cells arrested by TGF-β and is regulated by growth inhibitory cytokines and by contact inhibition [72, 73]. p27Kip1 is strongly expressed in nonproliferating cells and plays important roles in the regulation of both quiescence and G1 progression. Because p27Kip1 increases during differentiation in many cell types both in tissue culture and in vivo, this protein might also function in cellular differentiation and development [74]. Much less is known about the function of p57Kip2. In vitro, this inhibitor is functionally similar to the other members of the Cip/Kip family [75]. In vivo, p57Kip2 displays some interesting properties that differ from p21Cip1 or p27Kip1. The p57Kip2 gene maps to human chromosome 11p15, a region paternally imprinted in mice and humans, and mutations in the expressed allele have been reported in patients with Beckwith-Wiedermann syndrome [76]. CIP-KIP members have an important role in the control of cell proliferation in response to antimitogenic agents. For example, p21Cip1 or p27Kip1 or both can regulate cell-cycle arrest induced by differentiation factors, DNA-damaging agents, and antimitogenic conditions such as cell-to-cell contact and loss of cell anchorage. Correlation of low levels of p27Kip1 expression in tumor samples with poor patient survival indicates that p27Kip1 regulation has an impact in normal development and in aberrant cell growth [77]. Several reports indicate that the cellular levels of p27Kip1 are regulated at the transcriptional, translational, and post-translational levels. Phosphorylation and ubiquitin-mediated degradation have been reported to be involved in regulating p27’s stability [78].

Fig. 10-5. Human CDK inhibitors of the INK4 and Cip/Kip families.

10. Cyclin-Dependent Kinases and Their Regulators as Potential Targets for Anticancer Therapeutics

The structure of CDK2/cyclin A in the presence of the inhibitory domain of p27 has been reported. This structure shows that the N-terminal region of p27 interacts with the small lobe of CDK2, thereby altering the conformation of this region and the ATP-binding site [79]. One of the key interactions outside the ATP-binding pocket involves the N-terminal coil of p27Kip1 and a highly conserved shallow groove on the cyclin molecule. This interaction is defined by the RRLFG motif of p27 that is also present in other proteins known to interact with CDK/cyclin complexes [59]. Whereas interaction of p21Cip1 or p27Kip1 with CDK2 complexes clearly blocks kinase activity, their role in CDK4/6 inhibition is unclear. Indeed, CDK4/6-cyclin D heterodimers can bind Cip/Kip inhibitors at stoichiometric concentrations without loosing their kinase activity (80). It has been proposed that this interaction titrates these inhibitors away from CDK2-cyclin E complexes, facilitating the activation of the CDK2 kinase. The physiologic relevance of this function, however, remains controversial [57, 81–83].

213

and catalysis to occur [84, 86]. The signature PSTAIRE loop rearranges to bring the E51 in proximity to the ATP-binding site, which stabilizes the position of the active site Lys to allow proper orientation of the ATP. In a second conformational change, the T loop moves away from the catalytic cleft (Fig.10- 6). CAK phosphorylation of the T-160 residue further stabilizes the T loop by eliminating the stearic hindrance that the T loop places on the catalytic site and allowing access to the substrate [87].

10.3.4 An Integrative View to CDK Activity Regulation CDK activity is tightly controlled by the balance between CDK activators, cyclins, and CDK inhibitors of the INK and Cip/Kip family. Because cyclins function as sensors of mitogenic signals and CDK inhibitors are the effectors of various antimitogenic signalling pathways, CDK activity is modulated to integrate all these signals and translate them into a cell division decision. The catalytic CDK subunits form a bilobular structure typical of known protein kinases [84–86]. The smaller lobe of the catalytic subunit contains approximately the first 100 residues of the protein and comprises a five-stranded β-sheet and a unique α-helix (Fig. 10-6). The α-helix contains the signature CDK PSTAIRE motif and is responsible for interaction with the regulatory cyclin subunit [84]. The larger lobe, defined by approximately 200 residues of the C-terminal, mainly comprises α-helices and is predicted to contain the peptidebinding site. The ATP-binding site lies in the cleft between the two lobes of the catalytic subunit. The small lobe contains the highly conserved Gly loop that provides the backbone amides that hydrogen bond to the β- and γ-phosphate of ATP and the highly conserved Lys residue (E51 in CDK2) involved in ion pairing with the α and β phosphates of ATP. Key Thr and Tyr residues (T-14 and Y-15, respectively, in CDK2) involved in the negative regulation of CDK activity lie in the Gly-rich region. The large lobe encodes the critical aspartic acid (D146 in CDK2) that establishes salt bridges with E51 and defines the correct configuration of the ATP-binding pocket. Another important motif of the CDK is the T loop, which contains the CAK phosphorylation site (T-160 in CDK2) required for kinase activation. The monomeric catalytic CDK subunit is catalytically incompetent. Binding the regulatory subunit induces a number of changes that allow proper ATP binding

Fig. 10-6. Structural representation of CDK-cyclin interaction. Structural coordenates of CDK2 bound to cyclin A [84] or cyclin E [305] were obtained from the NCBI database and represented using Cn3d. (see Color Plate 5 following p. 316.) inactive P

Cdk Cyc Cyc

Wee1 Myt1 Cdc25

Cdk

Cdk Cyc

inactive Cdk7 CycH INK4

CDK Inhibitors

Mat1

Cip/Kip

CAK

Cdk P

Cyc

active

Fig. 10-7. Regulation of CDK activity. Interaction with positive (cyclins) and negative (CDK inhibitors) partners and phosphorylation by inhibitory (WEE1, MYT1) or activating (CAK) kinases and phosphatases (CDC25).

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CDK kinase activity is also controlled by activating or inhibitory phosphorylations (Fig. 10-7). Wee1/Myt1 kinases inhibit kinase activity by phosphorylation of N-terminal residues that are removed by Cdc25 phosphatases [21]. In some cases, these activities modulate CDK activity in response to cell-cycle checkpoints such as the DNA damage checkpoint [88]. In addition to being devoid of these N-terminal phosphorylations, active CDK-cyclin complexes need to be phosphorylated in the T-loop of the kinase by CAK formed of CDK7, cyclin H, and Mat1 [30, 89]. Both activating and inactivating phosphorylations may exist in the same molecule resulting in inactive kinases. CDK activity is also regulated by mechanisms other than post-translational modifications and interaction with cellular inhibitors. As discussed earlier, synthesis and degradation of the regulatory cyclin subunit is another important factor. Ubiquitin-mediated protein degradation has a central role in regulating cyclin levels, which was originally shown for cyclin B [90] and later observed with cyclins E and D [91, 92]. Cellular localization of the enzymes with respect to the relevant substrate(s) is another level of regulation. For example, cyclin B1 is found in the cytoplasm until the initiation of mitosis, when it moves to the nucleus. A specific sequence, the cytoplasmic retention signal, is responsible for sequestering cyclin B1 in the cytoplasm [93, 94]. Similarly, cyclin A-CDK and cyclin E-CDK complexes also shuttle between the nucleus and the cytoplasm [95]. CDK inhibitors of the p16INK4a and p27Kip1 families are also subjected to regulated subcellular localization [96, 97]. Another level of regulation is represented by modulation of the CDKcyclin complex assembly, which requires mitogenic signals and is likely to involve modulation of interaction of CDK with molecular chaperons such as CDC37 [98–100]. Other CDK-binding proteins may be involved in the regulation of INK4-CDK4/6 interaction. p34SEI-1 is a novel CDK partner that binds to cyclin D-CDK4 complexes and renders them resistant to inhibition by p16INK4a. The expression of this protein is rapidly induced by serum in quiescent fibroblasts and its ectopic expression promotes fibroblast proliferation at low serum concentration, suggesting a role for this protein in facilitating the activation of the cyclin D-CDK4 complexes early in G1 [101].

10.4

Control of the Cell Cycle by Cdk

10.4.1 Entry into the Cell Cycle and DNA Replication In the last few years, biochemical examination of cell-cycle CDK, their regulators (cyclins and CDK inhibitors) and their substrates (mainly the retinoblastoma protein, pRb) has provided a general framework for the understanding of how mammalian cell-cycle progression is regulated. During the G1 phase of the cell cycle, cells may decide whether to

stay quiescent or to enter S phase, where the genome is duplicated. Progression throughout the G1 phase is regulated by a complex mechanism involving at least CDK4, CDK6, and CDK2 [57, 58]. An additional kinase, CDK3, can function at this level although its physiologic role is unclear [41, 102]. During the G1 phase, cells receive and evaluate mitogenic and antiproliferative signals. Mitogenic signals, such as those emitted by growth factors, frequently result in the induction of several mitogenic pathways such as Ras or PI3K signaling. Activation of these mitogenic pathways and the corresponding mitogen-activated protein kinases (MAPK) result in the induction and nuclear localization of D-type cyclins as reported by Sherr and colleagues in the early 1990s [103]. In fact, these D-type cyclins display some mitogen-response elements in their promoters that control their induction under the appropriate situations [104]. D-type cyclins (D1, D2, and D3) bind to and activate CDK4 and CDK6 proteins that become active (Fig. 10-8) and partially phosphorylate the retinoblastoma family members pRb, p107, and p130, the major substrates of these kinases (Fig. 10-9). This phosphorylation is thought to be partial because the Rb proteins have 16 putative CDK-phosphorylation sites and only a percentage of them are substrates of CDK4 and CDK6. So far, no major differences between CDK4 and CDK6 have been found in these studies and these proteins have been thought to have identical biochemical properties. Similarly, the three dimensional (3D)type cyclins seem to have similar activities in vitro, although they are expressed differentially in several tissues [105]. pRb family proteins function to repress transcription through the binding and inactivation of transcription factors, such as E2F members (E2F1-5), and the binding to histone deacetylases (HDAC) and chromatin remodeling complexes [106, 107]. Phosphorylation of pRb by these CDK is thought to partially inactivate the transcriptional repression by this protein, allowing the transcription of some target genes. Among them, E-type cyclins (E1, E2) are induced which, in turn, bind and activate CDK2. CDK2-cyclin E complexes are able to further phosphorylate the pRb protein canceling pRb-mediated repression of many genes whose activities are necessary for S-phase entry (Fig. 10-8). An additional kinase, CDK3, can function at this level. CDK3 is highly similar to CDK2 and CDK1 and it is able to complement cdc28 mutants of S. cerevisiae [17]. CDK3 interacts with E-type and A-type cyclins, and with the Cables/ Ik3 subfamily of cyclins. CDK3 also binds to cyclin C during G0 exit and stimulates pRb phosphorylation during the G0/G1 transition [41]. Thus, it has been proposed that during the G0/G1 transition there might exist a sequential activation of CDK3-cyclin C, CDK4/6-cyclin D, and CDK2-cyclin E complexes to phosphorylate and inactivate pRb. The requirement for this kinase activity however is not clear as CDK3 has been reported to be involved in G1 progression in a E2Fdependent but pRb-independent manner [108], and most “wild-type” laboratory mice do not express CDK3 because of a premature stop codon in its sequence [102].

10. Cyclin-Dependent Kinases and Their Regulators as Potential Targets for Anticancer Therapeutics

p16 INK 4a INK 4b p15 INK 4 p18 p18 INK 4c p19 INK 4d

C ip/K ip

215

p21C ip1 C ip1 p21 p27K ip1 K ip1 p27 p57K ip2 p57K ip2

C ip/K ip

C dk2

C dk4/6 P

P

C yc linD1,2,3

P

P

pR b

C yc lin E 1,2

pR b

DP E 2F

P

DP E 2F

P P P

P

pR b

P

DP E 2F

G1

G0

S

Fig. 10-8. Regulation of the G1/S transition by CDK4/6 and CDK2 complexes.

“P oc ket”

A pR b

N

p107

N

R b2/p130

N

S pacer

B C

C

C

Fig. 10-9. The retinoblastoma (Rb) family of proteins. Grey domains indicate regions of homology between Rb family members. The “pocket” domain involved in repression of transcription is also indicated.

Once cells are in S-phase, CDK2-cyclin E complexes need to be silenced to avoid re-replication of the DNA, and instead CDK2 binds to the newly synthesized cyclin A for proper completion and exit from the S-phase [21]. Thus, CDK2-cyclin A complexes phosphorylate some replication proteins such as Cdc6 which is required for late firing of origins. Phosphorylated Cdc6 is then transported to the cytoplasm to ensure that re-replication does not occur [109]. A-type cyclins also bind and activate CDK1 during the S/G2 transition to prepare cells for mitosis. At the end of S-phase, cyclin B synthesis begins

and CDK1-cyclin B complexes start to form. The two major isoforms of cyclin B (B1 and B2) bind and activate CDK1 during late S-phase and G2. These complexes also contribute to avoid re-replication by phosphorylating the minichromosome maintenance (MCM) replication proteins that then dissociate from the chromatin during late S or G2/M. CDK2 has been proposed to function in DNA repair during S phase because it phosphorylates some proteins (Table 10-1) involved in this pathway—such as BRCA1, BRCA2, p53 or Ku70—and CDK2-null cells display abnormal DNA repair activity [110–113]. After S phase, CDK2 phosphorylates BRCA2 blocking the interaction between this protein and Rad51, a protein that promotes the essential homologous-pairing and strand-exchange phases necessary for the recombinatorial repair of DNA damage [112]. Upon DNA damage, CDK2 is inactivated resulting in decreased BRCA2 phosphorylation and increased Rad51 recombination activity. Whether this activity might be compensated by other CDK such as CDK1 is not known. CDK2 might function in other diverse cellular functions such as meiosis [114] or apoptosis [115] in specific cell types.

10.4.2

Chromosome Segregation

CDK1-cyclin A/B complexes are thought to regulate many different steps in the G2/M transition and progression through mitosis by phosphorylation of a wide spectrum of substrates [21, 116]. CDK2 and CDK1 might share some substrates when

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bound to A-type cyclins, suggesting the important role of these partners in substrate selection [21]. Cytoplasmic CDK1-cyclin B complexes associate to centrosomes promoting centrosome separation by phosphorylation of the centrosome-associated motor protein Eg5. CDK1-cyclin B complexes are involved in different regulatory and structural processes, such as the fragmentation of the Golgi network, the breakdown of the nuclear lamina, and cell rounding [116]. Active CDK1 complexes phosphorylate numerous substrates (Table 10-1) including histones, nuclear lamins, kinesin-related motors and other microtubule-binding proteins, condensins, and Golgi matrix components among other proteins [21]. Furthermore, CDK1 contributes to regulation of the anaphase-promoting complex/cyclosome (APC/C), which is the core component of the ubiquitin-dependent proteolytic machinery that controls the timely degradation of critical mitotic regulators and finally permits chromatid to be segregated [117]. Activation of mammalian CDK1 depends on dephosphorylation of two neighboring residues in the ATP-binding site (Thr 14 and Tyr 15). This activation occurs at the G2/M transition when the activity of the dual-specificity phosphatase CDC25C towards CDK1 overcomes the inhibitory activity of the kinases WEE1 and MYT1. Exit from mitosis requires the inactivation of CDK1-cyclin B complexes by degradation of B-type cyclins through ubiquitination by the APC-dependent proteolytic pathway [117, 118]. Inactivation of CDK1 is absolutely required in this process as continuous CDK1 activity during the late stages of mitosis impedes cytokinesis and provokes a mitotic regression [119]. In addition to CDK1, at least two more CDKs, CDK10 and CDK11, might have a role in controlling specific processes in G2/M. For example, inhibition of CDK10 results in G2/M arrest [120] and the CDK11 locus expresses a mitosis-specific isoform (termed CDK11p58) through an internal ribosome entry site (IRES) [121]. Deregulated expression of CDK11p58 results in abnormal cytokinesis and increased cell death [36, 122]. In addition, CDK11p58 is required for microtubule nucleation and mitotic spindle formation as its absence induces short or monopolar spindles and the subsequent activation of the mitotic checkpoint [36]. The mechanism behind this activity remains unclear. Whereas the mouse CDK10 has not been genetically analyzed, targeted disruption of the mouse cdc2l gene encoding CDK11 results in embryonic lethality before implantation [123]. In fact, E3.5 CDK11-deficient blastocysts appeared to be growth retarded at the morula stage of development and die because of mitotic arrest and subsequent apoptosis.

10.5 Alteration of CDK and Their Regulators in Human Cancer 10.5.1

Cell Cycle CDK in Human Cancer

Some CDK and proteins involved in CDK regulation and some of their substrates have been linked to tumor development, underscoring the importance of these cell-cycle regulators in

maintaining appropriate proliferation rates. Genetic alteration of CDK in human cancer is not frequent although some of these genes are amplified and overexpressed in a wide variety of tumors and tumor cell lines. CDK4 is amplified in gliomas [124–126], sarcomas [127–131], astrocytomas [132], breast tumors [133], and carcinomas of the uterine cervix [134] along the HDM2 locus as part of an amplicon located in human chromosome 12 (12q13–14). A point mutation in CDK4 has been described in spontaneous and familiar melanomas [135, 136]. This mutation, substitution of Arg 24 by Cys (R24C), leads to misregulation of the kinase activity by preventing binding of the INK4 family of cell-cycle inhibitors without affecting the affinity of CDK4 for cyclin D1. Similarly, CDK6 is amplified in glioblastoma [137] and lung tumors [138]. CDK6 is frequently overexpressed in hematopoietic malignancies, in some cases as a consequence of a translocation that places the CDK6 locus under the control of strong promoters in these cells [139, 140]. CDK6 has been found translocated in melanoma [141] and its overexpression seems to be critical for pRb inactivation in these malignancies [142]. An independent prognostic value has been proposed for CDK6 overexpression in medulloblastoma [143]. Although CDK2 is not frequently altered in human cancer, two of their regulators, cyclin E and p27Kip1, have specific prognostic value in many different tumor types. Overexpression of CDK1 has been observed in some human tumors [144–146] and, in fact, this overexpression correlates with genomic instability and poor prognosis [147].

10.5.2 Tumor-Associated Alterations in CDK Regulators Human cyclins are frequently overexpressed in tumor cells, whereas CDK inhibitors (such as p16INK4a, p21Cip1, or p27Kip1) or CDK substrates (such as pRb) are frequently considered as tumor-suppressor proteins [56, 148–150].

10.5.2.1

Cyclins

A number of different mechanisms lead to the deregulated expression of cyclin D1 (CCND1) gene in human cancer. CCND1 maps to 11q13, a region that is altered in a variety of proliferative disorders. One of the characteristic translocations in a group of B-cell neoplasms (now collectively called mantle cell lymphoma, [MCL]) is the t(11;14) translocation in which the BCL-1 locus on chromosome 14 becomes juxtaposed with CCND1 on chromosome 11 (Fig. 10-10). As a result, most tumors with the t(11;14) translocations show increased expression of the cyclin D1 RNA, protein, or both, arguing that the primary target gene activated by the translocation is CCND1, previously known as PRAD1 or BCL1 (B-cell lymphoma 1) [151–154]. It appears that most MCL, which account for approximately 5% of all non-Hodgkin’s lymphomas (NHL), show cytogenetic or molecular evidence for the t(11;14) translocation; however, a significant number

10. Cyclin-Dependent Kinases and Their Regulators as Potential Targets for Anticancer Therapeutics

217

Translocation (B-cell lymphomas) C yc lin D1

14q32 IgH enhanc er

11q13 1

2

3

4

5

Amplification (breast, lung tumors, etc…)

1

2

3

4

5

Retroviral integration (mouse T-cell lymphomas)

MuL V

1

2

3

4

5

Fig. 10-10. Alteration of cyclin D1 gene (CCND1) in cancer through translocation, amplification or retroviral integration (frequent in mouse tumors).

of other tumor types without the apparent translocations expresses increased amounts of cyclin D1 [148]. CCND1 is also amplified in the 11q13 region in a significant portion of breast and squamous cell carcinomas as well as in other tumor types [155–157]. The average amplification frequency of CCND1 in primary breast tumors is 13–24% and the cyclin D1 protein is overexpressed in approximately 50% of breast and other tumor types [158–161]. Overexpression of cyclin D2 is not as frequent but has been observed in chronic B-cell malignancies [162], gliomas [163], gastric tumors [164], squamous cell carcinomas [ ]165, and germ cell tumors [166]. Some translocations have been found that express cyclin D2 under T-cell receptor in T-cell leukemias [167]. Cyclin D3 is also amplified, translocated and/or overexpressed in some tumors such as glioblastomas (163), lymphomas and leukemias [168, 169], colon carcinomas [171], and bladder cancer [171]. Aberrant localization of D-type cyclins is also found in human cancer [172]. Genetic alterations such as translocation or amplification are not frequent in other cyclins; however, cyclins E, A, and B are overexpressed in a variety of tumors, in some cases in the form of truncated forms [173]. This overexpression is especially relevant for the short forms of cyclin E, which are significantly overexpressed in many tumor types [174]. Many cancers overexpress cyclin E proteins or mRNA including breast, lung, and cervix and lymphomas, leukemias, sarcomas,

and endocrine tumors [175]. In most cases, deregulation of cyclin E comes from genetic alteration of its regulatory pathways, rather than mutation of the cyclin E locus itself. Cyclin E expression has been evaluated as a prognostic marker because it correlates with clinical outcome in different tumors including lymphomas [176], breast [177], and other tumor types [148, 175, 178].

10.5.2.2

CDK Inhibitors

Two independent lines of research led to the discovery of p16INK4a as an inhibitor of the CDK4/cyclin D kinase [50] and also implicated it as a candidate tumor suppressor located at the chromosomal position 9p21 [49]. This chromosomal region is frequently deleted in many human tumors [49, 179] and is linked to hereditary susceptibility to melanoma [180, 181]. It is now evident that p16INK4a/MTS1/CDKN2A alone, but not its close relative p15INK4b/MTS2/CDKN2B, can sustain tumor-specific mutations in a large number of tumors. The CDKN2A locus encodes two overlapping genes (Fig. 10-11), each regulated by its own promoter: p16INK4a and p14ARF [56]. Three major mechanisms of inactivation of the CDKN2A locus in human cancers are deletion of both alleles or deletion of one allele, and either intragenic mutation of the remaining allele or methylation of the remaining allele [149]. Deletions remove both p16INK4a and p19ARF (and occasionally p15INK4b),

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Fig. 10-11. Alteration of the CDKN2A and CDKN2B loci in human cancer. The CDKN2A-CDKN2B loci encode 3 tumor suppressor proteins: p16INK4a, p14ARF, and p15INK4b, as described in the text. These loci are deleted in a variety of tumors. Individual inhibitors are also inactivated by point mutations (arrow) or aberrant hypermethylation (star).

whereas intragenic point mutations that are frequent in the unique exon 1a of p16INK4a or in the common exon 2 appear to inactivate p16INK4a only function [182]. Therefore, deletions and intragenic mutations must be functionally distinct. One of the most striking differences between human tumors is the relative frequency of deletions and mutations in this locus. Homozygous deletions appear to predominate in gliomas (57%), mesotheliomas (56%), leukemias (40%), nasopharyngeal carcinomas (42%), sarcomas (8%), ovarian carcinomas (16%), and bladder carcinomas (18%). By contrast, esophageal (30%) and biliary tract cancers (58%) sustain only intragenic point mutations. Both deletions and mutations have been detected in head-and-neck carcinomas (8% mutations, 6% deletions) and nonsmall-cell lung cancer (NSCLC) (16% mutations, 14% deletions). Ninety-eight percent of the pancreatic cancers have inactivated p16: 48% homozygous deletion, 34% hemizygous deletion and intragenic mutation, and 16% hemizygous deletion and methylation-mediated silencing. Neither deletion nor mutation is detected in breast cancers, neuroblastomas, colorectal tumors, and nonacute lymphocytic leukemias [149, 183]. Frequently, p16INK4a is also inactivated by hypermethylation of its promoter and subsequent reduced expression [149] (Fig. 10-11). Although less frequently, specific alteration of other INK4 family members such as p15INK4b and p18INK4c has been described in hematopoietic tumors and a few other human

malignancies, frequently as a result of abnormal hypermethylation of their promoters [184–188]. p19INK4d seems to be downregulated in some cancers although the mechanism for its inactivation is unclear [189–191]. p21Cip1 is not a frequent target of genetic or epigenetic alteration in human tumors; however, its expression is frequently reduced in cancer cells as a target of the p53 TSG [192]. Similarly, p27Kip1 is not mutated in most human cancers although the gene itself is localized to the chromosome band 12p13, a locus known to be altered in leukemias and mesotheliomas, however, p27Kip1 protein levels are dramatically reduced in human tumors. More significantly, this reduction of p27Kip1 protein levels strongly correlates with tumor progression and poor survival in patients with breast, colon, or gastric carcinomas [148, 193–197]. The value of p27Kip1 as a prognostic marker is even higher when it is used in combination with cyclin E. Combinatorial analysis of p27Kip1 and cyclin E expression levels showed that patients with low cyclin E and high p27Kip1 levels have a considerably longer survival rate than patients with high cyclin E and low p27Kip1 levels [195]. These studies showed no correlation between p27Kip1 mRNA and protein levels, suggesting that a post-transcriptional mechanism(s) is responsible for the reduction of p27Kip1 in tumor cells. p27Kip1 protein levels are regulated by the ubiquitin-mediated degradation of p27Kip1 by the proteosome [198]. It has been suggested that the increased activity of a

10. Cyclin-Dependent Kinases and Their Regulators as Potential Targets for Anticancer Therapeutics

p27Kip1-specific degradative pathway may be responsible for the reduction of p27Kip1 in tumor cells (78). In fact, one of the proteins involved in p27Kip1 ubiquitination, Skp2, behaves as an oncogene in both human and mouse tumors [199, 200]. The third member of the Cip/Kip family, p57Kip2, is located on 11p15.5, a region implicated in sporadic cancers and Beekwith-Wiedemann syndrome (BWS), a inherited syndrome that predisposes to tumor development. p57Kip2 inactivating mutations have been found in 10–20% of BWS patients [150].

10.5.2.3

Alterations in Other CDK Regulators

Other proteins involved in the regulation of the G1/S kinases are also under investigation in cancer development although their causal role in human cancer is not clear yet. The CDC25 phosphatases (CDC25A, CDC25B, and CDC25C) activate CDK by removing inhibitory phosphates and are, therefore, candidate proto-oncogenes [201, 202]. In fact, CDC25A or B but not C phosphatases accelerate G1/S transition leading to premature activation of CDK2 and cooperate with both oncogenic Ras and loss of pRb in foci formation in rodent cells [148]. Human CDC25A maps to 3p21, a locus frequently involved in renal carcinomas, small-cell lung cancer (SCLC), and benign tumors of the salivary gland. CDC25A or CDC25B are overexpressed in several tumor types such as head-andneck cancer, gastric carcinomas, esophageal squamous cell carcinomas, lung tumors, colorectal carcinoma, ovarian cancer, NHL, and breast cancer [148].

10.5.3 Genetic Alteration of CDK Substrates: The Retinoblastoma Protein Within the pRb protein family, loss or inactivation of pRb is a rather frequent event in human tumors [148]. Children that inherit one defective autosomal allele have a high probability (>95%) of developing bilateral multifocal tumors of the retina (retinoblastoma) and are predisposed to other types of tumors later in life such as osteosarcomas, melanoma, and brain tumors. Somatic alterations of the pRb locus have been found in sporadic tumors such as lung, breast, and bladder carcinomas and, in fact, the loss of pRb function in sporadic cancer is more frequent than in inherited eye tumors [203]. In retinoblastoma, SCLC, bladder carcinoma, and many sarcomas, pRb function is lost directly through mutations within the pRb gene. In many cases, tumors with wild-type pRb protein lose other tumor suppressers in the same pathway such as the cellcycle inhibitor p16INK4a. In addition to genetic and epigenetic (promoter hypermethylation) alterations in the pRb locus, pRb function can also be altered by the effect of several viral proteins, such as the human papillomavirus E7 oncoprotein, E1A, large T antigen, and the locked nucleic acid (LNA) viral oncoprotein from Kaposi sarcoma-associated herpes virus. Among the other members of the family, p130 is less frequently lost and p107 inactivation is rarely reported [203, 204]. These

219

observations suggest that p107 and p130 may play a primary role in promoting differentiation rather than proliferation, a concept supported by results obtained with gene-targeted mice [205]. Similarly, recent evidence suggests that Rb gene family have differential roles in angiogenesis and can therefore modulate tumor growth [206]. All these data indicate that alteration of the cyclin/INK4/ CDK/pRb pathway is a universal feature of tumor cells, leading to the independence of these cells from mitogenic or antimitogenic signals [148].

10.6 Genetic Analysis of CDK and Their Regulators 10.6.1 Physiologic Roles of G1/S CDK and Their Regulators Although the general functions of mammalian CDK and their regulators have been extensively characterized in cultured cells, the exact role of each protein in vivo is obscured by the presence of multiple family members. The generation of loss-of-function (LOF) mutant mice for some of these proteins is now providing us with important information regarding the individual roles of mammalian G1 CDK (Table 10-2). Lack of either CDK4 or CDK6 is compatible with embryonic life in the mouse [207–209]. In fact, the absence of each of these kinases only affects specific cell types. Thus, only the pituitary gland and the beta cells of the endocrine pancreas seem to be severely affected by the lack of CDK4 [207, 208, 210], and the absence of CDK6 only results in abnormalities in the hematopoietic compartment [209]. An obvious explanation for these phenotypes comes from the high structural and functional homology between CDK4 and CDK6. In vivo, lack of one of these cyclin D-dependent kinases could be compensated by the remaining one, at least in those cells where both proteins are expressed (Fig. 10-12). Double CDK4;CDK6 deficient embryos display a more dramatic phenotype because they die during late embryonic development because of severe anemia [209]. These results demonstrate a partial compensation between the two CDK, at least in the control of hematopoiesis; however, the combined lack of CDK4 and CDK6 does not result in defective G1/S transition or decreased proliferation in most other cell types even in late embryonic development [209]. A similar phenotype is observed in embryos lacking the three D-type cyclins [211] underscoring the strong functional correspondence between both group of proteins. The fact that D-type cyclins can also form complexes with CDK2 [209] or CDK5 [28] does not seem to be sufficient to rescue the lack of the canonical D-type CDK, CDK4, and CDK6, although it could participate in the slightly increased survival of double CDK4;CDK6 mutant embryos (18.5 days of embryonic development versus 16.5 days in triple D-type cyclin mutant embryos) (Fig. 10-12). These D-type cyclinCDK2 complexes are also insufficient to rescue the defects

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M. Malumbres IK3-2

Cyclin L

Cdk5

Cdk10

Cdk11

Cyclin C

Cyclin T2a Cyclin T2b

Cdk8

Cdk9

Cyclin D1

IK3-1

Cyclin T1

Cyclin H

Cdk7

Transcriptional CDKs

Cdk3

Cyclin D2

Cyclin E1

Cyclin A1

Cyclin D3

Cyclin E2

Cyclin A2

Cdk6

Cdk2

Cdk4

Cyclin B3

Cyclin B1 Cyclin B2

Cdk1

Cell Cycle CDKs

Fig. 10-12. Interactions between CDK and cyclins. Apart from the canonical interactions described in the text, some additional CDK-cyclin complexes have been described (see text). All these interactions are summarized in this figure suggesting high promiscuity in the formation of CDK-cyclin complexes.

induced by the deficiency in CDK4 and CDK6, because CDK4;CDK6 double knock-out mouse embryonic fibroblasts (MEF) display an identical behavior to MEF lacking the three D-type cyclins [209, 211]. MEF deficient in CDK4 and CDK6 or cells deficient in the three D-type cyclins display a decreased rate of proliferation in culture, accompanied by diminished phosphorylation of pRb and delayed expression of S-phase and M-phase markers such as E-, A-, and B-type cyclins [209, 211]. The fact that all these cells are insensitive to the INK4 family of cell-cycle inhibitors highlights the specificity of these inhibitors versus D-type cyclin-CDK4/6 complexes. Yet, these MEF are able to respond to mitogenic stimuli after serum starvation, indicating that cyclin D-CDK4/6 complexes are not essential for mitogen-induced proliferation. Whether this ability is dependent on CDK2, other kinase or other kinase-independent pathways is unknown. The fact that absence of CDK2 is also compatible with life only affects germ cell development [114, 212]. In fact, the lack of mitotic phenotypes in the CDK2-null mice has questioned the importance of this protein in several of the processes such as DNA replication, centrosome maturation and segregation, and modulation of proteolysis [114, 212]. Not only that; the differences between the targeted deletion of CDK2 and those of the two E-type cyclins [213, 214] underscore the functional differences between CDK2 and these cyclins. Although the individual deficiency in one of these E-type cyclins has only minor effects in mouse development, combined deficiency in both cyclins results in embryonic lethality [213, 214]. Cells without cyclin E1 and E2 are able to proliferate although they display a dramatic defect in the G0/G1 transition because of problems loading the Mcm proteins onto the prereplication complexes [214], a defect not present in CDK2-null cells.

CDK2;CDK6 double mutants do not display any synergistic phenotype [209] (Table 10-2). The fact that all these three G1 CDK can phosphorylate pRb might suggest that these enzymes have overlapping roles (Fig. 10-12). Although it has been reported that CDK4/6 and CDK2 phosphorylate different residues in pRb, the absence of one of these kinases could alter the affinities for specific sites. In fact, CDK4 is quite effective in phosphorylating CDK2-specific sites in CDK2-depleted cells [215]. Not only these CDK, but also CDK1, CDK3, and CDK9 are able to phosphorylate pRb [21]. Alternatively, the control of pRb function might be modulated by the overall phosphorylation state rather than phosphorylation in specific sites, and a single CDK could achieve the appropriate levels of phosphorylation. Compensatory roles are not so obvious for those substrates that seem to be specific for each of these G1 CDK. Thus, it has been recently described that CDK4, but not CDK2, phosphorylate and inactivate Smad3 proteins [216]. Similarly, CDK2 (but not the other G1 CDK) phosphorylates a variety of proteins involved in DNA replication, centrosome duplication, and segregation and mitotic control [21, 57]. In many cases, they have neither been reported as CDK1 substrates, although given the promiscuity of these proteins [217], slight changes in affinities or subcellular localization could make these proteins available for phosphorylation by other kinases. Whether other CDK (such as CDK4 or even CDK1) might be able to phosphorylate these proteins in the absence of CDK2 is unknown. In a different scenario, one could assume that neither of these G1 CDK nor their substrates are essential for cellcycle progression. In yeast, a single CDK (most similar to human CDK1) is sufficient for cell-cycle progression. In fact,

10. Cyclin-Dependent Kinases and Their Regulators as Potential Targets for Anticancer Therapeutics

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Table 10-2. Mouse models of cell cycle CDK and their regulators. Genotype CDK Cdk4−/−

Cdk6−/−

Cdk4−/− Cdk6−/− Cdk2−/− Cdk2−/− Cdk6−/− Cdk2−/− Cdk4−/− Cyclins cyclin D1−/−

cyclin D1 replaced by cyclin E (knockin) cyclin D1 replaced by cyclin D2 cyclin D2−/−

cyclin D3−/− cyclin D1−/− cyclin D2−/− cyclin D1−/− cyclin D3−/− cyclin D2−/− cyclin D3−/− cyclin D1−/−, cyclin D2−/−, cyclin D3−/−

cyclin E1−/− cyclin E2−/− cyclin E1−/− cyclin E2−/−

cyclin A1−/− cyclin A2−/− cyclin B1−/− cyclin B2−/− cyclin F−/− cyclin G1−/− CDK Inhibitors p16INK4a−/−

p15INK4b−/− p18INK4c−/−

p19INK4d−/−

Phenotype

Reference

Decreased viability; reduced body size; pituitary atrophy; female sterility and decreased fertility in males; insulin-dependant diabetes because of a reduced number of beta cells. Decreased female fertility; decrease in the splenic hematopoiesis with megaloblastic erythrocytes; partial thymic atrophy and delayed T-cell response to stimulation. Late embryonic lethality (E14.5 to E18.5); severe megaloblastic anemia; multilineage hematopoietic failure in the liver. Sterility with atrophy of the gonads; defective spermatogenesis and oogenesis because of a block in the first meiotic division. Sum of the single knockout phenotypes. Embryonic lethality at mid gestation; defects in hematopoiesis and cardiac failure.

[207, 208, 210, 306–309]

Reduced size and viability; hypoplastic retina; pregnancy-insensitive mammary gland; neurologic disorders; malformation of the jaw; impaired proliferation of Schwann cells after injury. Rescues most of the cyclin D1−/− phenotype except for some defects in the breast epithelium. Rescues the cyclin D1−/− phenotypes to different extent; only the phenotype in the mammary gland is fully rescued. Female sterility; defective proliferation of ovarian granulosa and Sertoli cells in response to hormones; small testis; impaired proliferation of B lymphocytes; impaired proliferation of pancreatic beta-cells; developmental abnormalities in the cerebellum. Thymic atrophy with reduced expansion of immature T lymphoid cells; defects in B-cell development and granulocyte expansion. Reduced viability (die within 3 weeks); retarded growth; hypoplasia of the cerebellum; defective pancreatic beta-cell growth. Neonatal lethality; a fraction survive up to 2 months; reduced size; hypoplastic retina; neurologic disorders. Late embryonic lethality (E17.5–18.5); reduced embryo size; decreased erythropoiesis and megaloblastic anemia. Embryonic lethality (E15.5-E16.5); decreased size of the embryo; severe megaloblastic anemia and multilineage hematopoietic failure; developmental heart defects (in a fraction of the embryos). No defect. Testicular atrophy; reduced male fertility with abnormal meiotic features. Embryonic lethality (E10.5-E11.5); placental defect; failure of the trophoblast giant cells to undergo normal endoreplication; after tetraploid rescue, perinatal lethality, frequent cardiovascular abnormalities and reduced endoreplication of megakaryocytes. Testicular atrophy and male sterility because of a block in spermatogenesis Embryonic lethality (E5.5); death shortly after implantation. Embryonic lethality (earlier than E10.5); death shortly after implantation. No abnormalities. Embryonic lethality (E10.5); abnormal development of extra-embryonic tissues; delayed embronic development. Normal; reduced tumors in the liver

[311–313]

Some spontaneous tumors with ageing; thymic hyperplasia; soft tissue sarcoma, lymphoma, and melanoma; increased sensitivity to carcinogen-induced cancers; age-dependent decline in stem/progenitor self-renewal. Some spontaneous tumors with ageing; increased extramedullary hematopoiesis and lymphoproliferative disorders; angiosarcoma and lymphoma. Some spontaneous tumors with ageing; increased body size; multiple cysts in the kidney and mammary glands; Leydig cell hyperplasia; pituitary adenoma and some other malignancies (adrenal medulla tumors, thyroid tumors); haplo– insufficiency for carcinogen-induced tumor suppression. Testicular atrophy but conserved fertility; hearing impaired.

[209]

[209] [114, 212] [209] [310]

[314] [315] [316]

[317–319] [105, 320] [105] [105] [211]

[213, 214] [213, 214] [213, 214]

[222] [220] [221] [221] [45] [224] [67, 69, 232, 233]

[234] [234, 235, 321]

[322, 323] (continued)

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Table 10-2. (continued) Genotype INK4b−/−

p15

Phenotype INK4c−/−

p18

p18INK4c−/− p19INK4d−/− p16INK4a−/− and p19ARF−/− p21Cip1−/− p27Kip1−/−

p57Kip2−/− or p57Kip2m−/+ m−(imprinted) p21Cip1−/− and p27Kip1−/− p21Cip1−/− p57Kip2m−/+

p27Kip1−/− p57 Kip2m−/+ p18INK4c−/− p21Cip1−/− p18INK4c−/− p27Kip1−/− p19INK4d−/− and p27 Kip1−/−

Some spontaneous tumors with ageing; similar to the addition of single knock-out phenotypes; multiple cysts in pancreas and testis. Some spontaneous tumors with ageing; added phenotypes of the individual knockout strains. High level of spontaneous tumors; lymphoma and sarcoma. Some spontaneous tumors with ageing; normal development; histiocytic sarcoma, hemangioma, B-cell lymphoma, lung carcinoma. Some spontaneous tumors with ageing; iIncreased body size and organomegaly; female sterility; retinal dysplasia; pituitary hyperplasia and adenomas of the intermediate lobe; intestinal adenocarcinomas; haploinsufficiency for tumor suppression. Neonatal lethality; several developmental defects in the gastrointestinal tract and cleft palate; abnormal cell proliferation in placenta, cartilage, and lens. Similar phenotype to that of p27Kip1−/−; more pronounced hyperplasia of the ovaries (granulosa cells). Late embryonic lethality (E16.5 to E18.5); abnormal skeletal musculature (failure to form myotubes); abnormal development of the lung alveoli; abnormal skeletal development. Embryonic lethality (E12-E16.5); abnormal placental and lens development because of increased cell proliferation. Accelerated development of pituitary tumors; multifocal gastric neuroendocrine hyperplasia; bronchiolo-alveolar adenomas. Accelerated development of pituitary tumors; other hyperplasias or tumors in the thyroid, parathyroid, adrenal gland, endocrine pancreas, testis, and duodenum. Postnatal lethality (3 weeks); neurologic disorders; abnormal proliferation of neuronal populations in the central nervous system.

Combination of CDK and their regulators Reduced viability (up to 3 months); decreased size; curved spinal cords, Cdk4R24C/R24C p27Kip1−/− lordokyphosis; undifferentiated pituitary tumors. Spontaneous tumors with ageing; added phenotypes of each single Cdk4R24C/R24C p18INK4c−/− mutant strain. p18INK4c−/− and Cdk4−/− Similar to the Cdk4−/− phenotype. Increased body weight (but smaller than p27Kip1−/− alone). p27Kip1−/− Cdk4−/− Kip1−/− −/− p27 cyclin D1 Rescue of the cyclin D1−/− phenotypes but not of those of the p27Kip1−/− mice.

CDK1-cyclin B complexes, although initially responsible for the G2/M transition, have cryptic S-phase-promoting abilities that might be independent of the other CDK [218]. A comprehensive list of mouse models of other G1/S regulators such as the pRb family, E2F transcription factors, Cdc25 phosphatases, and CDK inhibitors has been summarized in recent reviews [150, 219] (Table 10-2).

10.6.2

Genetic Analysis of Mitotic CDK

The genetic analysis of individual G1/S kinases shows that individual ablation of these proteins has no major effects on cell cycle in most cell types. A different situation is found with CDK involved in mitotic progression. Whereas mice heterozygous for a CDK1-null mutation are viable and display minor defects in spermatogenesis, complete ablation of CDK1 results in early (before E3.5) embryonic lethality (Malumbres, Hunt, Caceres and Barbacid, unpublished results). Similarly, ablation of some mitotic cyclins such as cyclin A2 [220] or cyclin B1 [221] results in early embryonic lethality (Table 10-2).

Reference [234] [324] [232] [227, 228, 313] [229–231, 325]

[225, 226] [326] [327]

[328] [329] [235] [330]

[237, 239] [239] [331] [331] [208, 332, 333]

Unfortunately, the molecular basis for these phenotypes has not been established. Deletion of cyclin A1 only results in male sterility [222], suggesting a specific role of this cyclin in germ cells, whereas the absence of cyclin B2 does not have major effect on mouse viability or physiology [221]. Genetic ablation of CDK11 also results in embryonic lethality accompanied of mitotic defects of proliferating cells [123]. In addition to the models summarized above, only two additional cell-cycle cyclins have been targeted in the mouse (Table 10-2). Deficiency in cyclin F is embryonic lethal at E10.5 because of abnormalities in the yolk sac and in the development of the placenta [45]. Tissue-specific deletion of cyclin F revealed that it was not required for the development and function of a number of different embryonic and adult tissues. In contrast, MEF lacking cyclin F, whereas viable, do exhibit cell-cycle defects, including reduced population-doubling time and a delay in cell-cycle re-entry from quiescence, indicating that cyclin F plays a role in cell-cycle regulation. Ablation of the murine cyclin G1 does not result in any abnormality in embryonic or postnatal development up to 11 months

10. Cyclin-Dependent Kinases and Their Regulators as Potential Targets for Anticancer Therapeutics

of age [223]. Cyclin G1-null mice and MEF are deficient in the G2/M arrest after gamma irradiation suggesting that this protein plays a role in damage recovery and growth promotion after cellular stress. These mice are less susceptible to develop specific tumors probably because of an increase of p53 activity in the cyclin G1-deficient cells [224].

10.6.3

Tumor Mouse Models

Similarly to CDK deficiency, the lack of specific members of INK4 or Cip1/Kip1 proteins is compatible with life. The only exception being p57Kip2, a CDK inhibitor not well understood whose ablation results in embryonic lethality because of developmental defects [225, 226]. Importantly, in agreement with data from the molecular analysis of human tumors, most of INK4 and Cip/Kip-deficient mice display a tumor-prone phenotype (Table 10-2). P21Cip1-deficient mice [227] die of sarcomas and hematopoietic tumors late in life [228] whereas p27Kip1-null mice die of pituitary tumors [229–231]. Similar, p16INK4a, p15INK4b, and p18INK4c display increased susceptibility to tumor development with various penetrance (232–235). These data suggest a tumor suppressor role for CDK inhibitors in various tissues and highlight the importance of inhibiting CDK activity for arresting tumor development. Similarly, the use of genetic mutations that avoid INK4 functionality has been exploited by taking advantage of the CDK4 R24C mutation found in the CDK4 gene of melanoma patients [135]. Knock-in mice expressing normal levels of this mutant in the CDK4 endogenous locus (Table 10-3) develop normally and are fertile [207]; however, they are slightly larger (about 10–20%) than their wild-type littermates, and display progressive hyperplasia in the same endocrine cells that required CDK4 expression to proliferate: pancreatic β cells, Leydig cells, and adenohypophysis cells [207, 236, 237]. This hyperplastic growth often results in the appearance of insulinomas, Leydig cell tumors, and pituitary tumors after

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longer latencies (10–18 months). In addition, CDK4 R24C mice develop a wide range of other tumors that include epithelial (lung and liver tumors), mesenchymal (hemangiosarcomas), and lymphoid malignancies. As expected, the major malignancies observed in CDK4 R24C mice also occur in mice lacking one or more INK4 inhibitors. For example, soft-tissue sarcomas—responsible for more than half of the deaths in CDK4 R24C mice—are also the most frequent tumor type in p15INK4b-deficient animals [234] and p16INK4a-null mice [232, 233]. Similarly, pituitary tumors develop in most p18INK4c knock-out mice [234, 235]. The absence of INK4 activity in CDK4 R24C mice also generates susceptibility to tumor development upon carcinogenic insult (Table 10-3). Treatment of CDK4 R24C mice with classical skin tumoriginesis protocols results in more abundant and more aggressive skin carcinomas [238], suggesting that the CDK4 mutation harbors the propensity to act in concert with distinct oncogenic or chemical carcinogenic events to further worsen the cancerous state. In fact, this treatment leads to the rapid onset of invasive melanomas [238], a malignancy not observed in untreated CDK4 R24C mice. In a parallel treatment, p15INK4b- or p18INK4c-deficient mice did not develop invasive melanomas although p18INK4c-null mice showed premalignant lesions and faster proliferation of melanocytes [238]. p16INK4adeficient mice were subjected to similar carcinogenic protocols with dimethylbenzanthracene (DMBA) alone, resulting in a lower incidence of melanomas [232, 233]. Unfortunately, the differences in the carcinogenic protocol make difficult the direct comparison of the melanoma susceptibility between CDK4 R24C and p16INK4a-deficient mice. The fact that the absence of p18INK4c favors melanocyte proliferation suggests a compensatory role for INK4 proteins in preventing melanoma development. The oncogenic activity of CDK4 R24C strongly cooperates with p53 or p27Kip1 deficiency [237, 239], suggesting different mechanisms used by these proteins to regulate cell proliferation.

Table 10-3. Representative mouse models for preclinical evaluation of CDK as cancer targets. Kinase

Model

Phenotype

Reference

CDK4

Cdk4R24C/R24C

Mice expressing an endogenous INK4-insensitive CDK4R24C mutant develop a variety of tumor types with complete penetrance after long latency (12–28 months). This mutant cooperates with other oncogenes in melanoma development

[236, 237, 334, 335]

CDK4

Cdk4R24C/R24C + DMBA

Mice develop invasive melanoma with properties (S100 expression and progressive loss of melanin) similar to those observed in human patients

[238]

CDK4

Cdk4R24C/R24C; P27Kip1−/−

Mice develop aggressive pituitary tumors with short latency (8–10 weeks). These tumors are sensitive to treatment with flavopiridol.

[239]

CDK4

K5-Myc; Cdk4−/−

Lack of CDK4 inhibits skin tumor development induced by Myc

[336]

−/−

CDK4

Cdk4 ; MMTV-ErbB2

Resistant to ErbB2-induced breast tumors

[243, 244]

CDK4/6

Cyclin D1K112E/K112E; MMTV-ErbB2

Resistant to ErbB2-induced breast tumors

[242]

CDK2

Cdk2−/−; P27Kip1−/−

These mice develop tumors with similar incidence and latency than p27Kip1−/− mice suggesting that tumor suppressor function of p27Kip1 is independent of CDK2. Ablation of CDK2 does not interfere with tumor development.

[240, 241]

224

M. Malumbres

The dispensability of individual CDK in specific normal cells might be considered as an advantage for the lack of toxicity of putative specific CDK inhibitors. To what extent is the inhibition of a specific kinase useful as a therapeutic strategy? This question has been elegantly addressed in tumor models in which CDK2 or CDK4/6 kinase function has been specifically eliminated (Table 10-3). Thus, CDK2 has been ablated in tumors initiated by p27Kip1 deficiency. Since p27Kip1 inhibits CDK2, the absence of p27Kip1 in tumors is thought to cause high CDK2 activity and increased cell proliferation. These animals develop pituitary tumors with independence of the presence of CDK2 [240, 241], suggesting that inhibition of this kinase does not have any therapeutic advantage, at least in pituitary malignancies. Genetic manipulation of CDK4, on the other hand, has produced better results. Knock-in mice that express a cyclin D1 mutant form that binds, but does not activate, CDK4 or CDK6 display normal development of most tissues including mammary glands, indicating that these kinases are dispensable for normal development of the breast [242]. Strikingly, these knock-in mice are resistant to breast cancers initiated by ErbB-2, suggesting a differential requirement for CDK4/ 6-cyclin D1 activity in development versus tumorigenesis in the mammary gland. Similarly, CDK4-null mice do not show alterations in mammary gland development but are resistant to ErbB-2-driven breast tumorigenesis [243, 244].

These results strongly support CDK4/6 kinase activity as a specific therapeutic target in breast cancer [245].

10.7

Therapeutic Strategies

From human tumors and mouse models, it is clear that misregulation of G1 CDK activity by either overexpression of cyclins or loss of CDK inhibitory proteins almost invariably leads to hyperproliferative defects and eventually to tumor development. In particular, activation of the CDK4/6 pathway seems to dramatically decrease the requirements that allow cells to enter the cell cycle and participate in tumor development [148]. Similarly, activation of CDK2 and perhaps CDK1, through overexpression of E-, A-, or B-type cyclins or p27Kip1/p21Cip1 inactivation, seems to force the entry into S phase and commit cells to progress through the mitotic cell cycle. These data have been obtained from multiple research efforts including molecular analysis of human tumors, molecular and cellular biology, and the characterization of knockout and knock-in mice. These data have stimulated the design and development of small-molecule CDK inhibitors as new drugs for cancer therapy (Table 10-4). In the last few years, a plethora of CDK inhibitors have been analyzed in vitro, in mouse models, or in clinical trials [246–248]. More indirect methods such as the inhibition of DNA methyltransferase,

Table 10-4. Current CDK inhibitors in nonclinical or clinical studies. Compound

Target

E7070 PHA-533533 Hymenialdisine NU2058 & NU6027

CDK1/CDK2 CDK1/CDK2 CDK1/CDK2 CDK1/CDK2

AZ703 BMS-387032 CYC202 (R-roscovitine) CDKi277 UN-6140 PNU-252808 RO-3306 CVT-313 SU9516 Olomoucine ZK-CDK (ZK304709) JNJ-7706621 PD0332991 PD0183812 Fascaplysin CA224 CINK4 UCN-01

CDK1/CDK2 CDK1/CDK2 CDK1/CDK2 CDK1/CDK2 CDK1/CDK2 CDK1/CDK2 CDK1/CDK2 CDK1/CDK2 CDK1/CDK2 CDK1/CDK2 CDK1/CDK2 CDK1/CDK2 CDK4/CDK6 CDK4/CDK6 CDK4/CDK6 CDK4/CDK6 CDK4/CDK6 CHK1/2

SB-218078 JNJ-7706621

CHK1/2 Aurora kinases

*NA data not available

Other targets CDK5, GSK3β CDK4, CK1, GSK3β, CHK1, MEK1

CDK2, CDK9 CDK4,CDK5 CDK4, CDK5, CDK6 CDK5, CDK6, GSK3β

CDK4

VEGFR, PDGFR Aurora kinases CDK2 CDK5 CDK5 CDK1, CDK2, CDK4, CDK5, CDK6, PKC, MAPK, PDK1 CDK1 CDK1, CDK2, CDK3, CDK4

Cellular effect G1/S arrest G1 arrest N.A.* Growth inhibition of human breast-carcinoma cell lines G1, S, G2 arrest N.A. G1 and G2 arrest G1 and G2 arrest G2-M arrest

Reference [337] [259] [263]

G2 arrest at mitotic onset G1/S and G2/M arrest. G0/G1 and G2/M arrest G1/S and G2/M arrest. Induces apoptosis Proliferation inhibition and apoptosis inducer. G2/M phase arrest G1 arrest G1 arrest G1 arrest G1 arrest G1 arrest

[243, 338] [267, 273] [261] [272] [339] [340] [248] [341] [342] [343] [344] [262, 345] [304] [257] [254] [255] [346] [253]

G1/S and G2/M arrest. Induces apoptosis. G2 checkpoint abrogation G2/M phase arrest

[347] [348] [304]

10. Cyclin-Dependent Kinases and Their Regulators as Potential Targets for Anticancer Therapeutics

225

Inhibiting phos phorylation

CKI

C K I synthesis

6 C K I degradation

Fig. 10-13. Possible therapeutic approaches to inhibit CDK activity in tumor cells. These include inhibition of C dc 25 cyclin synthesis; increase of proteasome-dependent cyclin Wee1/Myt1 degradation; inhibition of CDK activating kinases; inhi4 bition of CDC25 phosphatases; re-expression of CDK 7 Y 15 T 14 5 inhibitory proteins; inhibition of proteolytic degradation P of CDK inhibitors such as p27Kip1; increase activity of C yc lin 1 8 WEE1/MYT1 inhibitory kinases; use of peptidomimetC dk C yc lin s ynthes is ics to block CDK-cyclin interaction; and small-molecule AT P ATP competitors. As described in the text, most current T 160 P 9 therapeutic efforts use this later mechanism although 2 alternative strategies are being evaluated in preclinical models. 3 C yc lin degradation

C dk7 Ac tivating phos phorylation

HDAC, or the proteasome might also modulate the protein levels of specific cell-cycle regulators (Fig, 10-13). As these strategies target many other signaling pathways in the cell, they are not discussed here. In general, all approaches discussed have been validated using molecular tools and nonclinical studies; however, most drugs that are being tested in clinical studies correspond to small-molecule ATP competitors directed against one or several CDK.

10.7.1 Therapeutic Inhibition of CDK4 and CDK6 Intensive screenings and drug design based on CDK/inhibitor co-crystal structure studies have led to the identification of a large variety of chemical inhibitors [246, 248–250]. Although all of them are competitive with ATP at the catalytic site, the kinase selectivity of these inhibitors varies greatly. The firstgeneration of pan-CDK inhibitory compounds (i.e., UCN01 and flavopiridol) has been tested in clinical trials [249, 251]; however, these molecules inhibit many other kinases and other cellular processes in the cell and are not specific CDK inhibitors. Selective inhibitors of CDK4/6 have been described in the literature more recently and it is noteworthy that higher selectivity can be achieved for inhibition of both CDK4/6 versus other CDK [252–256]. This selectivity is further reflected in cell-based assays in the form of a G1 cell-cycle block in pRb-positive cells that is maintained at high concentrations of these inhibitors. Among the new CDK4 and CDK6 inhibitors (Table 10-4), PD0332991 seem to display a superior overall profile including the combined attributes of potency, selectivity, and pharmaceutical properties. This compound is a highly specific inhibitor of CDK4 (IC50, 0.011 µmol/L) and CDK6 (IC50, 0.016 µmol/L), having no activity against a panel of 36 additional protein kinases [257]. It is a potent antiproliferative agent against Rb-positive tumor cells in vitro and in vivo, inducing marked tumor

regression [252, 258]. This compound entered into clinical trials in 2004 as an orally active inhibitor.

10.7.2

Inhibition of CDK2

CDK2 activity is deregulated in human cancer primarily through overexpression of cyclin E and cyclin A and inactivation of the CDK inhibitor p27Kip1 [148]. Given the relevance of these alterations in human cancer, CDK2 has been considered an important target for cancer therapy. Numerous CDK2 inhibitors have been described (Table 10-4) and their crystallographic structures either in complex with CDK2 or CDK2-cyclin A have been broadly analyzed [259]. R-Roscovitine (CYC202) (highly specific also for CDK1, CDK5, and other kinases) is currently in phase-2 trials for the treatment of breast cancer and NSCLC [260]. BMS-387032 (also active on several other CDK) and its derivative SNS-032 has been investigated in phase-1 trials for patients with advanced refractory solid tumors [261]. ZK-CDK (which also inhibits CDK1, the vascular endothelial growth factor receptor VEGFR, and the platelet-derived growth factor receptor PDFGR) is in phase-1 trials for the treatment of solid tumors [262]. Many of these CDK2 inhibitors also inhibit CDK1 and in certain cases a plethora of other kinases such as CDK5, CDK7, CDK9, GSK3β, MAPK, and ERK [263], complicating their biochemical profiling (Table 10-2). Therefore, further studies need to be accomplished to depict whether the antitumor effects are mainly because of the CDK2 inhibition or the synergism with other kinases. Genetic evidence has shown that CDK2-cyclin E activity is not essential for cell progression through the cell cycle and may be compensated by another kinases, possibly CDK4, CDK6, or CDK1. In addition, CDK2 inhibition by RNA interference fails to arrest proliferation of osteosarcoma cells and pRb-negative cervical cancer cells [215]. These results suggest that CDK2 may not be a good target for inhibition by

226

small molecules intended to treat cancer. This finding, along the fact that most efficient CDK2 inhibitors also inhibit other kinases, have shifted attention back toward CDK4 [245, 264] or CDK1 as the primary cell-cycle target for cancer drug discovery. Actual efforts are directed to obtain more specific CDK2 inhibitors, such as triazine-pyridine biheteroaryls [265].

10.7.3 Other CDK: CDK3, CDK7, and the Transcriptional CDK pRb family members are inactivated by successive phosphorylation by CDK4-cyclin D and CDK2-cyclin E kinase complexes; however, these kinase activities are totally absent in G0 cells, and therefore cannot account for the initial phosphorylation events inactivating pRb family proteins. CDK3, a CDK family member highly homologous to CDK2, has been suggested to participate in pRb phosphorylation in the cell-cycle entry forming active complexes with cyclin C [41]. CDK3 may also have pRb-independent roles because a dominant-negative mutant arrests the cell cycle in the presence of the SV40 T antigen, which is known to inactivate pRb proteins [108]. Hence, CDK3 might also be considered as a possible target for cancer therapy; however, CDK3 is only barely expressed in most human cell types and, noteworthy, is not functional in the mouse because the murine CDK3 gene carries a premature stop codon at position 187 that eliminates a third of the functional kinase domain [102]. Another member of the CDK family, CDK7, plays a critical roles in cell-cycle regulation as a CAK, and as a component of the general transcription factor TFIIH [21]. CAK is a complex composed of three subunits: CDK7, cyclin H, and MAT1. This kinase complex may phosphorylate and activate the major cellcycle CDK (CDK1, CDK2, CDK4, and CDK6) and it may control the progression through G1, S, and G2/M. CDK7 is ubiquitously expressed and, interestingly, its protein levels are moderately increased in tumor cells [266]. All this evidence suggests CDK7 for further consideration as an interesting cancer target [89]. In addition to modulating cell-cycle CDK activity, CDK7 is also involved in promoter clearance and progression of transcription by interacting with TFIIH components [30]. Other CDK family members such as CDK8 (in complex with cyclin C) and CDK9 (complexed to cyclin T and cyclin K) also regulate the transcriptional machinery. CDK9, interestingly, is also able to phosphorylate pRb similarly to other cell-cycle-specific CDK [21]. As described earlier, CDK10 and CDK11 are two additional kinases with specific roles in transcription, RNA splicing, G2/M transition, and centrosome cycle. Thus, although their therapeutic potential has not been firmly established, the inhibition of other less-known CDK might provide additional advantages to prevent cancer cell proliferation [248]. In fact, combined inhibition of CDK1/CDK2 and the transcriptional kinase CDK9 increases cell death in osteosarcoma cells [267], indicating the potency of concomitant compromise of cell cycle and transcriptional CDK activities.

M. Malumbres

10.7.4

Inhibition of CDK1

CDK1 has been long considered as the master regulator of mitosis [21, 116]. Loss of CDK1 activity results in G2 arrest and this protein seems to be essential for cell proliferation [21]. In addition, a number of primary tumors display aberrant expression of CDK1, and, in some cases, seems to correlate with patient survival rates [268–270]. Among the CDK, CDK1 has not received especial attention as a cancer target, possibly because of the essential role of this protein in the normal cell cycle and the predicted toxicity of specific inhibitors; however, the high homology with CDK2 and the intense search for CDK2 inhibitors in recent years has resulted in a large panel of small molecules that inhibit both CDK2 and CDK1 (Table 104). Similarly, the fact that CDK2 inhibition may also result in G2 arrest makes it difficult to discriminate between the effects of inhibiting these kinases using these compounds [271]. Attempts to identify CDK1-specific inhibitors have led to the characterization of new ATP competitors such as the synthetic 1-aza-9-oxafluorenes [272], imidizal pyridines [273], or the quinolinyl thiazolinone derivative RO-3306 [274]. RO-3306 inhibits CDK1-cyclin B1 activity with Ki of 35 nM, nearly 10fold selectivity relative to CDK2-cyclin E and >50-fold relative to CDK4-cyclin D. This compound clearly arrests cells at the G2/M phase border in a reversible manner, providing us with an interesting tool for molecular biology research. RO3306 arrests both tumor and normal cells similarly. Nonetheless, when treatment is extended, RO-3306 mediated CDK1 inhibition appears to be more pro-apoptotic in cancer cells, and normal cells do not die [274]. These results suggest that specific CDK1 inhibitors may also work as anticancer agents, although further work needs to be done to evaluate the therapeutic opportunities to inhibit tumor versus normal cells.

10.7.5 Selectivity versus Potency in CDK Inhibition The fact that genetic ablation of individual G1/S CDK has no major effects on cell-cycle progression has raised a note of caution in the design of small-molecule inhibitors against these kinases. The fact that cells can proliferate without CDK2, CDK3, CDK4, or CDK6, whether this depends on lack of function or compensation by other proteins (Fig. 1012), could make G1 CDK inhibitors less than attractive for cancer therapy. The molecular effects resulting from the enzymatic inhibition of one protein might differ from the effects caused by the absence of that protein. Thus, lack of CDK4 and CDK6 favor the presence of complexes between CDK2 and D-type cyclins and these complexes could function to promote G1 progression [209]. Small-molecule ATP analogues should provoke different results because they would allow the formation of inactive CDK4/6-cyclin D complexes without redistribution of the cyclins to other kinase-active complexes. Second, the requirements for these kinases in tumor cells have not been fully characterized in vivo. Although normal cells do not require these proteins, tumor cells that have

10. Cyclin-Dependent Kinases and Their Regulators as Potential Targets for Anticancer Therapeutics

increased proliferation rates could be more sensitive to the lack of these G1 kinases. In cultured cells, some tumor cell types are insensitive to the lack of CDK2 but require CDK4 [215]. These results also suggest that future design of smallmolecule CDK inhibitors could benefit if specificity towards a particular CDK is reduced. In general, one could assume that drugs inhibiting all cell-cycle CDK could be more effective than specific ones. Obviously, inhibition of many proteins might produce unexpected or out-of-control results unless we have a detailed characterization of the function of these proteins in vivo. The recent results using specific CDK4 kinase inhibition in vivo illustrate the fact that inhibition of specific CDK may have strong therapeutic applications in specific cell types. Thus, CDK4 kinase activity is required for Ras- or HER2induced breast tumors, but not for normal mammary gland development, suggesting CDK4 inhibitors as ideal drugs in these tumors (245). We hope that cancer patients will benefit from this knowledge if we are able to put together what we are learning from biochemical approaches, human tumors, clinical trials, and the genetic analysis of these proteins in mouse models.

10.7.6 Alternatives to Small-Molecular ATP Competitors In addition to inhibiting the kinase activity using smallmolecule ATP competitors for the different CDK, CDK regulation offers other opportunities such as avoiding CDKcyclin interaction, peptidomimetic design of CDK inhibitors targeting the recruitment site of the cyclin subunit, as well as gene or protein therapy approaches [148, 275] (Fig. 10-13). Molecules designed to inhibit the binding of these cyclins to their corresponding pockets in CDK (particularly CDK2 and CDK4) are being designed and developed. Structural studies have focused on how RXL (cyclin-binding motif) and LXCXE (part of cyclin structure) contribute to substrate selection. RXL motif in p27Kip1 is thought to bind to a hydrophobic surface on cyclin A, which is conserved between cyclins A, B, D, and E, that lies opposite to the CDK2-binding site [276]. Highly potent p21Cip1-derived peptide inhibitors of CDK-mediated pRb phosphorylation have been developed by several laboratories, and the molecular interactions governing cyclin-groove recognition and general rules for the development of peptidomimetic inhibitors of CDK are gradually being solved [277, 278]. Similar approaches have used peptides from the inhibitory domain of INK4 proteins [279]. These peptides arrest cells in the S-phase of the cell cycle in a CDK4/6-specific manner resulting in pRb hypophosphorylation, and suggest possible therapeutic uses of ankyrin peptidomimetics [280]. Additional approaches include the use of gene or protein therapy to re-introduce tumor suppressors absent in tumor cells. Thus, adenoviral vectors that direct the expression of p16INK4a, p21Cip1, and p27Kip1 have been constructed and tested both in tissue culture and in animal models. As expected, the overexpression of CKI inhibits CDK activity, G1 arrest, and cell proliferation in

227

both normal and tumor cells [281–286]. p16INK4a inhibits the proliferation of cells that express wild-type pRb (Rb+), and has little or no effect on the proliferation of cells with mutant pRb (Rb-), whereas p21Cip1 and p27Kip1 inhibit both Rb+ and Rb- cells equally well. Intratumoral injections of adenoviral- cyclin-dependent kinase-inhibitory (CKI) vectors into tumor xenografts inhibit tumor growth. The codelivery of p16 and p53, but not p16 or p53 alone, into tumor cells induces apoptosis and tumor regression in xenografts [286]. These results suggest that an optimal antitumor effect may be achieved only with adenoviral-CKI in intratumoral injections when the cytostatic effect of the inhibitors is combined with cytotoxicity and apoptosis through the cooperation of CKI with another gene product(s), or possibly with chemotherapeutic agent(s). The cellular concentration of CKI can be also increased by direct delivery of the inhibitory proteins. The inherent problem with this approach is that CKI are intracellular proteins and exogenous CKI are unable to cross the cell membrane and localize to the nucleus. Fortunately, a number of proteins have been described with the demonstrated ability to penetrate cell membranes and carry covalently linked cargo proteins inside the cell. Penetrin (a 16-amino-acid long peptide of Drosophila melanogaster antennapedia protein) can mediate the delivery of p16INK4a- and p21Cip1-derived peptides into cells and inhibit cell proliferation, although at relatively high concentrations (IC50: 10–50 mM) [287–290]. Different size peptides of the HIV-1 TAT protein have been used to deliver p16INK4a and p27Kip1 into normal and tumor cells. These exogenously added TAT-p16 and TATp27 proteins are potent inhibitors of cell proliferation [291, 292]. The IC50 of the various TAT-p27 fusion proteins is in the 0.8–5 mM range when added to human primary coronary artery smooth muscle cells [291].

10.7.7 Other Cell-Cycle Targets Involved in CDK Regulation 10.7.7.1

CDK Inhibitory Kinases

The delay in mitotic entry is controlled, at least in part, by inactivation of CDK1 through phosphorylation of two conserved residues (Thr14 and Tyr15) within the ATP-binding pocket. The kinases responsible for these two phosphorylation events include the MYT1 and WEE1 kinases. Expression of a CDK1 mutant unable to be phosphorylated by WEE1 and MYT1 causes premature mitosis [293]. Moreover, WEE1 is down-regulated in p53-positive cells after DNA damage [294] and its overexpression rescues cells from apoptosis [295], suggesting that inhibition of these kinases might abrogate the G2 checkpoint. A novel pyridopyrimidine class WEE1 inhibitor, PD0166 285, has been obtained using specific in vitro screening protocols [296]. In seven cancer cell lines tested, PD0166285 inhibits irradiation-induced Tyr15/Thr14 phosphorylation of CDK1 and is able to abrogate the G2 checkpoint and sensitize cells to radiation inducing apoptosis. Although these are promising results, the effect of WEE1 and MYT1 inhibitors should be further investigated in both normal and tumor cells.

228

10.7.7.2

M. Malumbres

Cell Cycle Phosphatases

The tight control of protein function by phosphorylation requires a proper balance between protein kinases and phosphatases. Some protein phosphatases such as CDC25A, B, and C are critical for proper cell-cycle regulation because they control CDK activity in response to specific signals or checkpoints or both. CDC25 phosphatases remove inhibiting phosphates —incorporated by the WEE1/MYT1 kinases—at the critical residues Thr14 and Tyr15 located within the CDK ATP-binding loop. Dephosphorylation of these residues by CDC25 proteins is the rate-limiting step for CDK activation in specific settings including the response to cellcycle checkpoints. Upregulation of these phosphatases leading to increased CDK activity is a feature of some human cancers and CDC25 inhibition may be an interesting strategy in cancer therapy [297, 298]. Available CDC25 inhibitors include diverse chemical classes such as vitamin K3 derivatives, the dysidiolides, sulfiricins, quinolinediones, and the napthofurandiones. Treatment with these compounds results in cell-cycle arrest in G1 and G2/M phases accompanied of increased phosphorylation of multiple CDK-cyclin complexes. Given the existence of three CDC25 proteins (A, B, and C) it is expected that a pab-CDC25 inhibitor would block cell-cycle progression efficiently. Other cell-cycle–specific phosphatases that control diverse phases of the cell division cycle include the CDC14A/B proteins. These molecules are responsible for the elimination from the target proteins of many phosphates that are incorporated in the early phases of mitosis by the cell-cycle kinases (such as CDK1 or PLK1) [299]. Inhibition of CDC14 phosphatases also results in cell-cycle arrest of cells that have incorporated mitotic abnormalities [300]; however, their use as cancer targets has not been evaluated.

10.8

Concluding Remarks

Lessons from the molecular analysis of human tumors as well as from the generation of gene-targeted mice have taught us that minor alterations in the balance that controls the activity of some cell-cycle regulators almost invariably result in neoplastic proliferation. In general, most, if not all, neoplasias show a deregulated cell cycle. Imbalances in the specific molecular sensors that control the progression through G1 and the G1/S transition, such as loss of cell-cycle inhibitors or overexpression of cyclins, may cooperate to trigger malignant transformation. Some of the alterations, such as overexpression of cyclin E1 or inactivation of p27Kip1, which have prognostic value, have an unknown genetic origin and the identification of the mechanistic basis for these defects is a challenge for the future. All these observations strongly implicate that G1 and G1/S CDK activity should be a primary target to control neoplastic proliferation. Targeting the cell-cycle regulatory machinery, and specifically the CDK, is being explored in both academia and pharmaceutical companies to develop new anticancer drugs [248]; however, despite the extensive molecular characteriza-

tion of cell-cycle control mechanisms, we do not know whether the “ideal” therapeutic drug should target CDK4/6, CDK2, or even CDK1 activity or a combination of them. Pan-CDK inhibitors display some activity against specific malignancies [248, 249], but their lack of specificity and toxic effects is preventing their general use as cancer drugs [248, 251, 301]. A few “second-generation” specific compounds have entered clinical trials with promising nonclinical results. In addition, some other protein kinases, such as Aurora or Polo kinases, might be of interest to design cell-cycle–targeted therapies directed against tumor cell growth or survival [302]. In addition to paying attention to these new drugs or targets, some questions need to be addressed to improve current strategies. For instance, recent results from mouse models of CDK [21] indicate some redundancy among family members that protect cells against the lost of one of these regulators. Given the dispensability of CDK2 in these models, it is predictable that CDK2-specific compounds that do not target CDK1 or CDK4 will be innocuous to the cells. Some exceptions may apply as CDK2 may be specifically relevant in melanocyte proliferation [303], but not in other cell types. Specific inhibition of CDK4, however, might be the best strategy for some tumor types such as HER2-positive breast tumors without having toxic effects in normal cells [245]. In general, some of these treatments may be cell-type specific although further research either from clinical studies or mouse models is required to shape these cellular preferences. Compounds that inhibit both CDK1 and CDK4 might display stronger efficacy although the side-effects of inhibiting CDK1 have not been evaluated in vivo. Concomitant inhibition of CDK and other cell-cycle kinases such as Aurora proteins might also provide a more consistent inhibition of tumor cell cycles [304]. In addition, the use of checkpoint abrogators might help to sensitize tumor cells to cytotoxic agents [302]. Several combination strategies are currently being evaluated in clinical trials and all will be looking forward their therapeutic outcome for a more detailed overview of their clinical use.

Acknowledgments. I thank J Lahti for advice on CDK11 and cyclin L genes and M Barbacid for helpful discussions. Work in my laboratory is funded by grants from the Fundación de la Asociación Española contra el Cáncer (AECC), Fundación Médica de la Mutua Madrileña Automovilística, Comunidad de Madrid, and Ministry of Education and Science (BMC2003-06098).

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Chapter 11 Angiogenesis Switch Pathways Jaume Piulats and Francesc Mitjans

11.1

Introduction

In the previous edition of this book, our chapter highlighted the large number of new therapeutic agents with antiangiogenic activity, which were in the clinical phase of research, and pointed out the preliminary discouraging results of some of these products. Currently, the situation at clinical level is very different as some of the agents have received marketing approval [1]; therefore, we are able to ask new questions about using antiangiogenic drugs in patients with cancer. The control of tumor neovascularization remains one of the most promising pharmacologic approaches, which could be transformed into therapeutic strategies. The pioneering works of Folkman’s group in the 1970s [2–4] established that tumors are dependent on angiogenesis and predicted a new basis for therapy of cancer [5, 6]. As basic research advances in the understanding of the angiogenesis pathways, scientists increase their knowledge of the mechanism of action of both the inducers and the blockers of the tumor-induced angiogenesis. In this sense, the emerging area of translational research is leading to a better and narrow interaction between the basic and the clinical researchers, which will eventually allow for a better understanding about how antiangiogenesis drugs work in vivo and will determine what kinds of clinical trials will best use the potential of these new weapons in the fight against cancer. The aim of this chapter is to provide a comprehensive and updated review of the current knowledge on tumor neovascularization mechanisms and analyze the current therapeutic approaches. We also analyze the first results obtained from clinical experience with antiangiogenic agents regarding tumor angiogenic processes.

11.2 Physiologic and Pathologic Angiogenesis Angiogenesis may be defined as the formation of new blood vessels from the existing vascular bed [7], whereas the term vasculogenesis defines the development of the vasculature From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

from structures in the early embryo [8]. Angiogenesis is a complex process mainly carried out by the extracellular matrix (ECM) and endothelial cells (EC) and regulated by angiogenic factors: inducers and inhibitors. Physiologic angiogenesis can be found in successful wound healing where macrophages, the cellular components of inflammatory responses, contribute to the healing response by releasing angiogenic factors. Physiologic angiogenesis is activated in the female reproductive system and during development of follicles, corpus luteum formation, endometrial vascularization during the menstrual cycle, and in embryo implantation. The sophisticated machinery of neovascularization is also an important component of many pathologic processes such as cancer, atherosclerosis, psoriasis, diabetic retinopathy, and endometriosis. This chapter concentrates on the mechanisms that direct the switch to the angiogenic phenotype of tumors. Intimate knowledge of angiogenesis pathways in cancer can, alternatively, offer two main advantages: first, the opportunity to establish the potential prognostic relevance of tumor angiogenesis in the evaluation of cancer, and second, to discover new pharmaceutic targets for therapy of malignant neoplasia. The angiogenesis switch pathways are related to the balance between positive and negative regulators of angiogenesis [9]. Among the positive angiogenic factors, we should emphasize vascular endothelial growth factor (VEGF) (also known as vascular permeability factor, VPF), which fulfills the criteria of a “direct-acting” angiogenesis growth factor [10], whereas two main endogenous negative regulators are angiostatin [11] and thrombospondin [12, 13]. Angiostatin is an angiogenesis inhibitor produced by the primary tumor, which mediates the suppression of angiogenesis in its metastases [9]. This role of angiostatin has demonstrated the remote influence of solid tumors on metastases [11]. A second negative regulator is thrombospondin, which is upregulated by wild-type p53 and downregulated during the switch to the angiogenic phenotype [12, 14]. Folkman has proposed that the primary tumor producing both angiogenic stimulators and inhibitors could direct the evolution of the tumor depending on the blood levels of these mediators, [9]. This apparent simplicity masks more complex processes in which many additional factors are involved. We would like to 239

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emphasize the role of vitronectin receptor (integrin αvβ3) and proteolytic enzymes in defining the angiogenesis pathways, because of their pivotal role in the design of new therapeutic strategies [15]. Many other players have also entered this exciting field of research, including circulating endothelial cells (CEC) and progenitor endothelial cells (PEC), which appear to became not only new potential targets in the treatment of tumor angiogenesis but are also being investigated as the most promising surrogate markers of the antiangiogenesis therapy [16].

11.3 Mechanisms of Tumor Neovascularization It is now well established that a tumor is unable to grow more than 1 mm3 without developing a new blood supply. Neovascularization is controlled by tumor cells, which may secrete angiogenic factors to attract EC. The activated EC, in turn, may also produce paracrine growth factors for the tumor. This crosstalk between tumor and EC is one of the major features in angiogenesis. The second factor is the delicate equilibrium between the endogenous inducers and inhibitors of neovascularization (Table 11-1). Normal cells secrete low levels of inducers and high levels of inhibitors; however, when progressing to malignancy, the tumor cells tip this balance to an angiogenic phenotype (Fig. 11-1).

11.3.1

Tumor Angiogenic Switch

The essential role of angiogenesis in tumor progression and metastasis and the balance between positive and negative regulatory factors lead to the idea of an angiogenic “switch” that is activated in tumor angiogenesis. Cells may switch to an angiogenic phenotype during progression toward tumorigenicity and this switch often takes place early, before tumorigenicity. In vivo

Table 11-1. Proangiogenic and antiangiogenic molecules. Proangiogenic Cell adhesion molecules E-selectin VE-cadherin, PECAM (CD31) VCAM-1 Canstatin Integrins (α vβ 3, α vβ 5,α 5β1) Chemokines & Growth Factors Interleukin-8 (IL-8) Monocyte chemotactic protein-1 (MCP-1) Ang 1 & Tie 2 Endoglin & receptors Acidic and basic fibroblast growth factor (a and bFGF) Neuregulin /heregulin PDGF-β1 TNF-α VEGF family/VEGFR Neuropilins PLGF Proteinases MMP Plasminogen activators Others COX-2 Nitric oxide synthase

Antiangiogenic Ang2 Angiostatin Anti-angiogenic antithrombin III (aaATHIII) Endostatin IFN - α, β, γ IL-4 IL-12 IL-18 Marpin Osteopontin PEDF Platelet factor 4 Prolactin fragment Proliferin-related protein Prothrombin kringle 2 Restin Retinoids SPARC fragment Thrombospondin −1 & −2 TIMPs, MMP inhibitors, PEX Vasostatin Vasculostatin

IFN, interferon; IL, interleukin; MMP, matrix metalloproteinase; PDGF, platelet-derived growth factor; PEDF, pigment epithelial-derived factor; PLGF, placental growth factor; SPARC, secreted protein acidic and rich in cysteine; TIMP, tissue inhibitor of metalloprotease; TNF, tumor necrosis factor; VEGF, vascular endothelial growth factor; VEGFR, VEGF receptor

switches develop angiogenesis in a graded fashion through several stages. In melanoma, for example, a significant increase in vessel counts is first observed in the progression from benign to dysplastic nevi. A further increase in vessel counts from radial to vertical melanoma has been correlated with greater risk of

Fig. 11-1. Angiogenic switch. Some cells in the developing tumor switch to an angiogenic phenotype. The angiogenic cascade starts with the initiation phase in which angiogenic factors are released by tumor and accessory cells. The balance between the endogenous inducers and inhibitors is a key feature in angiogenesis.

11. Angiogenesis Switch Pathways

recurrence, metastasis, and death. Similarly, angiogenesis in breast carcinoma is first noted in ductal carcinomas in situ (CIS). The angiogenic CIS being the previous stage to invasive carcinomas. In addition, transgenic mouse models have allowed researchers to study and define the angiogenic switch in early stages of tumor development preceding the appearance of the solid tumors [17, 18]. All these results suggest that activation of angiogenesis, the switching on, is a discrete event in tumor development. Potential switches could be any genetic alteration affecting oncogenes or tumor-suppressor genes (TSG), which may select for tumor cell clones with not only enhanced proliferation and survival potential but also with increased angiogenic growth factor production. In fact, one of the common features of a growing tumor mass appears because of the oxygen and nutrient consumption, which leads to a tumor microenvironment characterized by low oxygen tension. Hypoxia-inducible transcription factors (HIF) are activated in response to hypoxia. Hypoxia-inducible factor 1 (HIF-1) is a transcriptional activator that functions as a master regulator of oxygen homeostasis [19]. Once overexpressed, HIF signals through an expression increase of several proteins many of them involved in tumor angiogenesis [19]. Nevertheless, hypoxia is not the only potential switch, because HIF itself was reported to be activated under normoxic conditions [20], through cytokines [21], or even by the overexpression of the antiapoptotic bcl-2 protein [22]. It is clear that changes in the balance between positive and negative signals mediate the angiogenic switch. A net balance of inhibitors over activators would maintain the switch in the off position, whereas a shift to an excess of activating stimuli would turn angiogenesis on.

11.3.2 Endothelial Cells: Key Component in Angiogenesis Most of the tumor vessels come from the sprouting of new vessels from pre-existing ones, and are thus derived from

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normal, nonmalignant host cells. Although they are composed of normal cells, vessels elicited by tumors are frequently distinct from those in adjacent normal tissue: these vessels are leaky and abnormal in size and shape. EC appear to be fenestrated and they also increase specific cell adhesion molecules such as αvβ3 integrin [23, 24], which are essential for their viability during growth. Microcapillary EC from different organs exhibit a differential display of cell receptors, being possible to target them by specific peptide sequences [25]. Activated EC release a variety of growth factors such as fibroblast growth factor-β (βFGF), platelet-derived growth factor (PDGF), and insulin-like growth factor-1 (IGF-1), which can both maintain EC activation and act as paracrine stimulators of tumor cells. They are capable of producing a variety of factors that may inhibit tumor cell growth, such as interleukin-6 (IL-6), to which early-stage melanomas have been shown to be sensitive but late-stage melanomas are often resistant [26, 27]. It may be considered that angiogenesis generally occurs in at least three differentiated steps (Table 11-2): inductioninitiation, proliferation-invasion, and maturation-remodeling. In the first stage, angiogenic inducers, such as growth factors or cytokines, are released both by the tumor cells themselves and by the accessory cells recruited to the site. These factors stimulate vascular cell proliferation and invasion, thereby promoting blood vessel growth towards the tumor mass. One important result associated with cell invasion is that changes in the cell adhesion molecules enable EC to interact with surrounding stroma wherever the EC is proliferating and invading. In turn, the adhesion molecule-mediated signaling ensures continued cell survival, proliferation, and invasion. Later phases of angiogenesis involve a halt in proliferation, cell differentiation, and both tubular structure and lumen formation leading to blood circulation. The basal lamina is modified and the newly formed blood vessel is surrounded by differentiated pericytes and smooth muscle cells [28].

Table 11-2. Tumor angiogenesis as a complex multistep process.* 1. Initiation, where tumor cells from an incipient growing tumor mass experience the so-called angiogenic switch, which enables them to secrete soluble angiogenic growth factors. It is notable that this step is finely tuned by a change of the delicate balance between proangiogenic factors (VEGF, FGF, PDGF, and others), and antiangiogenic factors (endogenous inhibitors such as thrombospondin, endostatin, IFN-β, and others). 2. Proliferation–invasion step starts when the proangiogenic factors reach the corresponding receptor in neighboring quiescent endothelial cells and trigger signal transduction cascades. At this step, activated tumor EC secretes proteases that permit the dissolution of the vessel basal membrane and tumor microenvironment remodeling. Some integrins among other cell adhesion molecules are upregulated allowing EC invasion and migration towards the tumor mass. 3. In the maturation phase, the new vessel is stabilized by accessory cells like pericytes and lumen is formed to allow blood flow. An intimate crosstalk between endothelial cells and mural cells (supported by factors like angiopoietins and PDGF) is, indeed, very important to keep the new vessel mature and fully functional. EC, endotherlial cell; FGF, fibroblast growth factor; IFN-b, interferon β; PDGF, platelet-derived growth factor; VEGF, vascular endothelial growth factor. *Although tumor angiogenesis is clearly a dynamic process, it is possible and illustrative to split it in three defined steps. Key specific molecules participate in each step. These molecules, either soluble factors or receptors, represent important potential targets for therapeutic intervention.

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Angiogenesis may not be the only mechanism by which tumors acquire a microcirculation. Sood et al. [29] showed that the generation of microvascular channels by tumor cells, a process termed “vasculogenic mimicry,” does not imply the participation of EC. This phenomenon describes the embryoniclike ability of aggressive tumor cells to form vascular networks. It has been showed that vascular endothelial cadherin (VE-cadherin) was exclusively expressed by highly aggressive melanoma cells and its downregulation expression abrogated their ability to form those networks. These results suggested that VE-cadherin is critical in melanoma vasculogenic mimicry [30]. McDonald and Fors [32] have suggested that cancer cells contribute a small proportion of the lining of blood vessels in tumors (only 3% of the vessel surface in colon tumors grown orthotopically in mice). Some authors have named this phenomenon as mosaic vessels [33]. Some authors have depicted the important contribution of the circulating bone marrow-derived PEC in the tumor neovessel formation [34]. As a result of this participation the microvascular EC in the vascular bed of a tumor may be recruited both from the local neighborhood as well as from the bone marrow. The ratio of these EC from different sources may differ by tumor type [35]. A third type of microcirculation acquisition by the tumor has been observed in certain metastases where tumor cells exit from microvessels in the target organ, begin to grow around these vessels, cause the EC to undergo apoptosis, and finally induce neovascular sprouts from neighboring vessels. This process, called “co-option,” may represent an intermediate or alternative step in the switch to the angiogenic phenotype. Finally, in opposition to angiogenesis sprouting, there is the angiogenesis intussusception, where new vasculature is formed through the division of vessels by transluminal invagination and pillar formation [36]. These facts illustrate again the complexity of the tumor neovascularization process and gives additional cues to the angiogenic switch mechanisms.

11.3.3

Inducers of Angiogenesis

A number of both in vitro and in vivo bioassays have been developed to mimic the complex process of angiogenesis, especially two in vitro assays in which either EC proliferation or EC migration is studied. Both assays are often complemented by the use of an in vivo assay, such as implants into the normally avascular cornea of rabbits or rodents, referred to as the corneal pocket assay. Using these assays, a number of inducers of angiogenesis have been identified. Most of the angiogenesis inducers are part of a complex system that involves soluble ligands and EC receptors, such as the VEGF/ VEGFR and the FGF/FGR systems.

11.3.3.1

Vascular Endothelial Growth Factor

VEGF can be considered as one of the capital angiogenic factors because it is the first factor produced during embryogenesis

to control both vasculogenesis and angiogenesis [37]. Moreover, it is the only growth factor described to date whose null mice are not viable [38, 39]. VEGF was first identified by its ability to elicit vascular permeability, subsequently this factor, also called VPF, was shown to be a mitogen for EC and it has been described as a potent inducer of angiogenesis in vivo [40]. Several isoforms of VEGFr have been identified (VEGF-A, VEGF-B, VEGF-C, and VEGF-D) [41], and recently, VEGF-C and VEGF-D have been described as the specific inducers of lymphangiogenesis. VEGF is induced by hypoxia and hypoglycemia and it binds to specific receptors of the tyrosine kinase family (flk, flt-1, and flt-4), which may be upregulated on tumor EC [42]. Additionally, the VEFG/VEGFR system is highly specific: although VEGF may be expressed by a number of cells, its receptors are expressed mainly by EC and some malignant tumor cells. VEGF may be stored in the extracellular matrix as a heparin-binding protein bound to heparan sulfate proteoglycans. When angiogenesis is required, VEGF may be released from the ECM [43] or newly produced because its expression is often upregulated in many tumor cells. Indeed, some oncogenes such as mutated ras have been found to transcriptionally activate the expression of VEGF [44]. Actually, the VEGF/VEGFR system represents the most used target to design antiangiogenic therapeutic strategies. Currently, there are several marketed drugs: bevacizumab, an antibody (Ab) against VEGF, and sorafenib and sunitinib (tyrosine kinase inhibitors of VEGFR). Many other experimental drugs that also target this system are currently in clinical trials for various indications.

11.3.3.2

Fibroblast Growth Factors

Composed of at least nine forms, FGF constitutes a family of growth factors that are characterized by high-affinity binding to heparin; basic and acidic FGF are the forms that have been most widely studied and they are unusual in that they lack the signal sequence for secretion; however, both may be released from cells in certain conditions. Both acidic and basic FGF bind receptors on EC that are transmembrane tyrosine kinases and are thus coupled through the signal transduction cascade. There are at least four FGF receptors (FGFR1–4), which are widely expressed [45]. Like receptors, FGF are also expressed in a number of tissues, including tumors, and EC [46]. FGF possess an extremely strong affinity for heparin and then they are sequestered in the ECM until proteolytic enzymes degrade ECM during angiogenesis. FGF is a potent mitogen and chemotactic factor for EC. It also induces formation of capillary-like structures [47] and has shown angiogenic activities in vivo.

11.3.3.3

Angiopoietins and Tie-2 Receptors

At least six isoforms of angiopoietins have been described in the scientific literature that could potentially be involved in the neovascularization of tumors. While Ang-1 and Ang-3 are agonists of the Tie-2 receptor, Ang-2 and Ang-4 are described

11. Angiogenesis Switch Pathways

as antagonists of the Tie-2 receptor [48]. Ang-2 may have an agonistic effect at increased concentrations [49]. Although the physiologic relevance of this finding still remains to be defined, it clearly illustrates the complexity of this emerging system in angiogenesis [50]. To further increase this complexity, some reports describe opposite effects of a given agonist. Although Ang-1 was seen to promote tumor angiogenesis in vivo [51], recently it was reported that angiopoietin-1 could inhibit tumor growth and ascites formation in a murine model of peritoneal carcinomatosis [52]. In addition, other authors found the existence of the so-called angiopoietin-like molecules with homology to the angiopoietins, although their receptors are not well defined [53]. Like VEGFR, Tie-2 receptor is highly tissue specific because it is mainly expressed in EC. On the other hand, angiopoietins are mainly secreted by mesenchymal cells, especially pericytes [54] that participate in the differentiation of EC under a given stimulus. Although its concrete role in tumor angiogenesis is still under intense investigation, it is important to note the significant tumor growth inhibition achieved in mice models by means of adenoviral delivery of a recombinant soluble Tie-2 receptor [55].

11.3.3.4

Ephrin and Eph Receptors

The Eph receptor tyrosine kinase (RTK) family represents a new class of RTK, and its role in angiogenesis is just beginning to emerge. Unlike other families of RTK, which bind to soluble ligands, Eph receptors interact with cell surfacebound ephrin ligands activating signaling pathways in a bidirectional fashion, through both the Eph receptors and ephrin ligands [56]. This system has an even bigger complexity than others because there are 14 receptors and 8 ligands [57, 58]. Eph receptors and ephrin ligands play a critical role in vascular development during embryogenesis, although the function of these molecules in pathologic angiogenesis has not been well characterized. Preliminary data suggest a role of ephrin/ Eph molecules in promoting angiogenesis in tumors: soluble EphA2 receptor inhibited tumor neovascularization in a dorsal vascular window assay [59].

11.3.3.5

Transforming Growth Factor b

Transforming growth factor β (TGFβ) is a homodimeric polypeptide secreted in a biologically inactive, latent form. This form may be activated in vitro by heat, acidification, and proteases [60], thus providing a regulatory mechanism. TGFβ, similarly to TNFα, affects EC in a dual manner. They inhibit EC in vitro but stimulate angiogenesis in vivo [61]. It has been proposed that TGFβ induces angiogenesis by an indirect mechanism: it is highly chemotactic for monocytes and other accessory cells that, in turn, release angiogenic factors that are mitogenic for EC [62]. TGFβ and its receptors are expressed in many tissues but it seems that the differences in the response to TGFβ are attributable to differences in the surface expression of TGFβ receptors.

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11.3.3.6

Tumor Necrosis Factor a

Tumor necrosis factor α (TNFα) a secreted protein synthesized primarily by activated macrophages and by some tumor cells [63] was first described as causing solid tumor necrosis and regression. TNFα, like TGFβ, has paradoxical angiogenic activity. In vitro, TNFα has an antiproliferative effect on endothelial cells, whereas in vivo, it induces angiogenesis. The angiogenic activity in vivo is, in turn, also dual. When used at low concentrations TNFα induces angiogenesis: both vessel growth and EC proliferation. At high concentrations TNFα inhibits angiogenesis. Some authors [64] suggest that the mode of delivery of TNFα to EC might play a role in their response. 11.3.3.6.1 Platelet-Derived Growth Factor/Thymidine Phosphorylase Platelet-derived growth factor (PDGF) was first described in platelets as a new angiogenic factor; however, PDGF is not a mitogen for EC, so the name is inappropriate [65]. When cloned and sequenced, the gene for human thymidine phosphorylase matched that of PDGF. Many authors have described thymidine phosphorylase as an angiogenic enzyme, and it is now known that the angiogenic molecule is not the enzyme by itself but rather the product of thymidine phosphorylase action on thymidine: 2-deoxy-d-ribose is mainly responsible for the angiogenic activity [65]. PDGF/ thymidine phosphorylase is a particularly intriguing molecule to study: first, it is an angiogenic enzyme and not a classic growth factor; and second, its expression is exceptionally high in most solid tumors compared with normal tissues. 11.3.3.6.2 Transforming Growth Factor α and Epidermal Growth Factor Transforming growth factor α (TGFα) and epidermal growth factor (EGF) share 40% homology and both bind to the EGF receptor (EGFR). TGFα is expressed in macrophages and some tumor cells and, like EGF, it stimulates the proliferation of EC in vitro. They also induce migration in vitro and capillary-like tube formation and angiogenesis in vivo [66], although EGF is less potent. 11.3.3.6.3

Other Angiogenic Compounds

Finally, a number of other angiogenic molecules have been described, but in most cases the mechanism of action is either not completely known or understood, or appears to be indirect. Angiogenin, for example, a protein of the pancreatic ribonuclease family is angiogenic in vivo but not in vitro [67]. Interleukins (ILs) play a role in controlling angiogenesis either inducing it as IL-8 (which has been shown to potently stimulate angiogenesis [68]), IL-1α (which promotes angiogenesis by the upregulation of VEGF [69]), IL-17 [70], and IL-18 [71; or inhibiting angiogenesis as IL-4 [72] and IL-12

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[73]. Even osteopontin [74], thrombin [75], some prostaglandins [76], and nicotine [77] have been reported to have angiogenic activity. A biologic fragment of human tyrosyltRNA synthetase has the ability to induce angiogenesis in vitro and in vivo [78]. This finding has an interesting parallelism with the many antiangiogenic biologic fragments coming from inactive natural whole proteins.

11.3.4 Cell Adhesion Molecules and Angiogenesis It is well established that cell adhesion receptors mediate processes of cell adhesion, proliferation, migration, and invasion involved in the cascade of angiogenesis. Angiogenesis not only depends on growth factors but is also influenced by cell adhesion molecules, and they participate in both cell–cell interaction (tumor–endothelial) and cell–ECM interaction. This fact is clearly illustrated by the experimental studies where both, tumor and EC together are injected in nude animals [79]. The tumors formed were profoundly vascularized throughout by the tubular structures formed by the injected EC. Moreover, the tumor mass thus formed was on average 5.8-fold as large as control tumors, which were grown without exogenous EC [79]. In addition, the authors proved that the treatment of tumors coming from double cell injection, with antagonists of cell adhesion molecules involved in tumor–EC interaction could downmodulate tumor growth to the levels of the tumors arising from tumor cell injection only [79]. Similar results using a different experimental approach were obtained by Polverni and colleagues [80]. Together, the results indicate that the interaction of tumor cells and EC in orderly tumor angiomorphogenesis is highly dependent on the action of cell adhesion molecules mediating the adhesion of cancer cells to EC and also the adhesion of both tumor and EC to the surrounding tumor stroma, the inhibition of which remarkably retards tumor growth and angiogenesis. At least four families of cell adhesion receptors are classified depending on their biochemical and structural characteristics: the selectins, the immunoglobulin supergene family, the cadherins, and the integrins.

11.3.4.2

Selectins

Members of selectins, transmembrane receptors that mediate interaction to sialylated glycans, include P-selectin, L-selectin, and E-selectin. Furthermore, both P-selectin and E-selectin may be expressed in a soluble form. P-selectin and E-selectin are upregulated in endothelial cells after exposure to inflammatory agents such as TNF-α, lipopolysaccharide (LPS), and IL-1β [81]. E-selectin is also expressed in proliferating EC of the childhood hemangiomas (a benign tumor composed of EC).

11.3.4.3

Immunoglobulins

Studies implicate members of the immunoglobulin supergene family in angiogenic processes. These cell adhesion molecules

share the characteristic repetitive extracellular immunoglobulinlike domains and mediate heterophillic cell–cell adhesion [82]. Members of the family include ICAM-1, ICAM-2, ICAM-3, VCAM-1, and PECAM. Similarly to selectins, VCAM-1 and ICAM-1 can be expressed as soluble forms. Whereas ICAM-2 and PECAM are highly expressed in both resting and activated EC, ICAM-1 and VCAM-1 are upregulated after stimulation with inflammatory cytokines such as IL-1, TNFα, and interferon-γ (IFN-γ). I CAM-3 is highly expressed in tumor EC but not in sites of inflammation.

11.3.4.4

Cadherins

The cadherin family of cell–cell adhesion molecules, composed of E-cadherin, P-cadherin, L-cadherin, and VE-cadherin, are transmembrane proteins that mediate homophilic cell–cell adhesion in a calcium-dependent manner. Recently, Ab against VE-cadherin inhibited tumor angiogenesis and tumor growth without affecting vascular permeability [83]. The loss of cadherins, for instance, might promote increased invasion of activated EC as shown for invasive tumor cells.

11.3.4.5

Integrins

Integrins are heterodimeric transmembrane cell–ECM adhesion receptors composed of an α chain noncovalently associated with a β chain. At least 18 α subunits and 8 β subunits have been identified, which can combine to give at least 24 integrins [84]. This combination, in turn, defines their cellular and adhesive specificities. Integrins predominantly mediate cell– ECM interactions although some members may intervene in cell–cell adhesive events. Ligands for integrins include fibronectin, collagen, laminin, vitronectin, thrombospondin, fibrinogen, and others. Importantly, several integrins recognize the tripeptide sequence of Arg-Gly-Asp (RGD) within the ligands. A growing body of evidence has suggested a critical role for integrin receptors in the regulation of angiogenesis and vascular development [85, 86]. For example, the ECM molecules that are ligands for integrins are abundant in the surrounding vascular matrix and subendothelial basement membrane of blood vessels. This situation leads to inevitable changes in the integrin repertoire of new vessels, thus providing evidence of the importance of integrins in angiogenesis. EC express members of the β1, β3, and β5 subfamilies and stimulation of these cells with βFGF in vitro causes increased expression of β1 and β3 integrins. Integrin function during blood vessel formation in vivo has been studied most extensively for αvβ3.

11.3.4.6

Integrin avb3

The integrin αvβ3, also named the vitronectin receptor (VNR) is minimally expressed in quiescent blood vessels, but it is highly upregulated after stimulation by either angiogenic growth factors or tumors [87, 88]. Integrin αvβ3 can be considered as the most promiscuous member of the integrin

11. Angiogenesis Switch Pathways

family and may recognize several ligands: vitronectin, fibronectin, fibrinogen, laminin, collagen, von Willebrand factor, osteopontin, thrombospondin, tenascin, adenovirus penton base, bone sialoprotein, MMP2, and other RGD-containing proteins. This feature confers to any αvβ3-expressing cell the ability to adhere to, migrate on, and respond to almost any environment it may encounter. αvβ3 is expressed in several invasive tumors [89] such as late-stage glioblastomas [90], malignant melanomas [91, 92], renal carcinomas [93], ovarian carcinomas [94], osteosarcomas [95], and hemangiosarcomas [96]. Interestingly, αvβ3 overexpression has been well correlated with the degree of malignancy and invasion of those tumor types. For example, although normal melanocytes and nevi as well as noninvasive radial growth phase (RPG) melanomas are negative for αvβ3 expression, both invasive vertical growth phase (VGP) and metastatic melanomas are highly positive [97]. Indeed, this differential expression has been proposed as a prognostic factor [98]. Enenstein and colleagues reported that αv integrins were highly expressed on the tips of sprouting angiogenic blood vessels [99]. This highly restricted αvβ3 expression and its upregulation during neovascularization suggests that it may have a functional role in angiogenesis. Thus, antagonists of αvβ3 (both monoclonal antibodies [MAb) and cyclic RGD-containing peptides) prevented blood vessel formation in a number of in vitro and in vivo models of angiogenesis [86, 100–104]. Interestingly, a related integrin, αvβ5 is also involved in angiogenesis. In an elegant study using the rabbit corneal model, Friedlander and colleagues [105] showed that antagonists of αvβ3 integrin inhibited angiogenesis induced by βFGF but had little if any effect on VEGF-induced angiogenesis. In contrast antagonists of αvβ5 integrin were able to block VEGF- but not βFGF-induced angiogenesis. Most importantly, antagonists of αv integrins inhibited both cytokine- and tumor-induced angiogenesis. These findings define two distinct pathways leading to angiogenesis depending on the particular αv integrin involved. Recent studies have elucidated the possible mechanisms by which αvβ3 antagonists inhibit angiogenesis. Both MAb and cyclic-RGD peptides selectively induce programmed cell death (apoptosis) in angiogenically activated EC in vivo. First, Montgomery and co-workers showed that αvβ3 provides survival signals when it interacts with denatured collagen [106]. Subsequent studies demonstrated that systemic administration of αvβ3 antagonists promotes apoptosis in developing but not in resting blood vessels [102]. All these results support the hypothesis of a key role of the αvβ3 and αvβ5 integrins in angiogenesis, and it has been postulated that αvβ3 could provide a feedback mechanism, acting as a biosensor to facilitate integrin-mediated death when EC engage an inappropriate ECM [107]. Thus, unligated integrins could act as negative regulators of cell survival, initiating a process referred to as “integrin-mediated death.” Additional evidences proving the role of αv-integrins in tumor angiogenesis are provided by the fact that several snake

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venom disintegrins inhibit angiogenesis and tumor growth by a selective αvβ3 blockade of EC [108]. Other compounds, antagonists of αvβ3, like cilengitide (a cyclic-RGD peptide also named EMD 121974) [109] and synthetic peptidomimetics are also potent inhibitors of angiogenesis and tumor growth [110]. Cilengitide, for example, is currently in phase-2 clinical trials (Table 11-3). Also notable are the extracellular matrixderived peptides (fragments of natural not angiogenically active molecules), which inhibit αvβ3 eventually both tumor angiogenesis and tumor growth [111]. Examples are tumstatin [112], canstatin [113], arresten [111], angiostatin [114], and endostatin [115]. An additional and certainly important issue concerns to the fact that both nonsteroidal anti-inflammatory drug inhibitors of Cox2 [116], and clinical therapies using TNF and IFNγ [117], are associated with the suppression of integrin αvβ3 function and signaling in EC [118]. Finally, the αvβ3 integrin has also been used for both tumor targeting and tumor (angiogenesis) imaging [119].

11.3.5

Proteases

Angiogenesis is not only regulated by the action of growth factors and cell adhesion molecules but is also influenced by many other molecules. Among them, enzymes that degrade the ECM provide a suitable environment for EC migration through the adjacent stroma. At least three main families of proteolytic enzymes could play a role in angiogenesis and tumor progression: the serine proteases (including urokinase plasminogen activator [uPA]), the matrix metalloproteases (MMP), and the cysteine proteases (cathepsins B and L) [120]. Expression of uPA receptor (uPAR) on EC, for example, is increased by tumor cell-conditioned medium and VEGF [121]. In addition, uPAR in conjunction with integrins [122] could direct proteolysis at the leading edge of migrating EC, uPAR upregulation thus being a pivotal feature in angiogenic processes. Furthermore, antagonists of uPAR showed antiangiogenic activity both in vitro and in vivo [123] although uPA knockout mice have normal angiogenesis [38]. uPA could also be indirectly involved in angiogenesis regulation. Recent studies from Folkman’s lab showed that a fragment of plasminogen (angiostatin) acts as an endogenous inhibitor of angiogenesis [11]. MMP form a family of zinc-dependent endopeptidases with a broad spectrum of activity that is secreted as inactive zymogens [120]. MMP overexpression may be detected in tumor tissue or in adjacent stroma but not in surrounding normal tissue [124, 125]. Currently some MMP inhibitors are being tested in clinical trials for various indications (Table 11-3).

11.3.6

Lymphangiogenesis

The study of tumor angiogenesis should consider an additional mechanism that plays an important role in the spread of cancer cells within the body, we refer to lymphangiogenesis. Recently, several groups have shown that two members

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Table 11-3. Antiangiogenic agents in clinical trials. Agent

Ph

Mechanism of action

ABT- 510 ABX- IL8 Angiostatin Angiozyme

Abbott Abgenix EntreMed Ribozyme

2 2 2 2

Bevacizumab*

Genetech Roche Cephalon

2–3

TSP-1 analog Ab against IL-8 Multiple targets Ribozyme against VEGFR1 Ab against VEGF

CEP- 7055

Company

1

Combretasstatin A4 Oxigene CNTO 95 Centocor

1 2

EMD 121974

Merck KGA

2

Endostatin Cetuximab*

2 2–3

IMC-1121B Marimastat Prinomastat Valatinib

EntreMed Merck KgaA Imclone ImClone Genzyme Agouron Phar. Novartis

1 3 3 3

Sunitinib*

Pfizer

3

Sorafenib*

Bayer

3

Thalidomide*

Celgene Corp

2–3

Vitaxin/Abegrin

Med-Immune

1–2

VEGF trap

Regeneron

2

ZD6126 ZD6474

Astra Zeneca Astra Zeneca

2 3

Tyrosine kinase inhibitor (VEGFR1, R2, R3) Tubulin inhibitor Ab against avβ3 and avβ5 integrins Antagonist of avβ3 and avβ5 integrins Multiple targets Ab against EGFR Ab against VEGF-R2 MMP inhibitor MMP inhibitor Tyrosine kinase inhibitor (VEGFR2 and others) Tyrosine kinase inhibitor (VEGFR2, PDGFRβ, c-Kit, and others) Tyrosine kinase inhibitor (VEGFR2, RAF, and others) Multiple targets (reduction of TNF-α) Antibody against avβ3 integrin Soluble VEGF receptor Tubulin inhibitor Tyrosine kinase inhibitor (VEGFR2, EGFR)

Ab, antibody; EGFR, endothelial growth factor receptor; IL, interleukin; MMP, matrix metalloproteinase; RAF, serime/threonime-protein kinase; TNF, tumor necrosis factor; VEGFR, vascular endothelial growth factor receptor. * Drugs currently approved for certain indications, but also in clinical trials for other cancer indications.

of the VEGF family, VEGF-C and VEGF-D, are regulators of lymph vessel growth as well as enhancers of lymphatic metastasis [126]. These growth factors seem to be the ligands of VEGFR-3, which was found to be predominantly expressed on lymphatic vessels during development. [127]. Current investigation in this field suggests that VEGFR-3 and its ligands VEGF-C and D might play a crucial role in tumor-induced lymphangiogenesis. These data indicate that angiogenesis and lymphangiogenesis are induced and controlled by different members of

VEGF family. Thereby, VEGF-A and B and its two tyrosine kinase receptors VEGFR-1 and -2 mediate both vasculogenesis and angiogenesis; whereas VEGFR-3 and VEGF-C and -D modulate lymphangiogenesis. These findings suggest new approaches for limiting the spread of lymphatic metastasis by means of inhibiting the VEGFR-3 function.

11.3.7

Endogenous Inhibitors of Angiogenesis

A variety of molecules produced by normal mammalian cells can inhibit angiogenesis although they represent only 25% of all known inhibitors. Most inhibitors produced by mammalian cells are effective in the form in which they are secreted (e.g., thrombospondin) but some are proteolytic products of the extracellular cleavage of molecules that are angiogenically inactive when intact: examples include angiostatin [11], endostatin [128], tumstatin [112], arresten [111], and vasostatin [129]. Of the approximately 200 compounds with antiangiogenic activity described to date, we review the most representative. Additional information can be obtained in recent reviews [130, 131]. Thrombospondin, an ECM component, is lower in human breast cell lines than in control, or in immortal rat tracheal epithelial cells compared with primary cells. Thrombospondin is also downregulated when normal human fibroblasts immortalize as a result of loss of p53. In those cases, the decrease in thrombospondin shifts the phenotype of the cells from antiangiogenic to angiogenic [132]. As thrombospondin can also inhibit in vivo angiogenesis, it may be considered as an angiogenesis inhibitor. Angiostatin and endostatin molecules belong to a new family of antiangiogenic agents produced from the cleavage of natural nonangiogenic molecules. Angiostatin [11] derives from plasminogen and only the fragment but not the whole plasminogen showed both an antiproliferative effect on EC in vitro and blockade of neovascularization in vivo, and prevented the growth of primary tumor and metastases [11, 133]. Interestingly, angiostatin has no detectable direct effect on tumor cells. Since then, a number of authors have investigated the mechanism by which the primary tumor produces angiostatin from plasminogen [134–136]. Nevertheless, the exact mechanism of action of angiostatin is not yet clear [137, 138], although several authors reported binding to a given receptor like the ATP synthase on the surface of human EC [139], or the αvβ3 integrin [114]. Moreover, it seems that angiostatin is not selectively acting in EC because it also binds smooth muscle cells blocking their proliferation and migration in vitro [140]. Angiostatin, like many other antiangiogenic compounds has been used in vivo in antiangiogenic gene therapy experiments [141]. Endostatin, is a proteolytic fragment of collagen XVIII [128], with similar activity to angiostatin: it may block endothelial proliferation in vitro as well as in vivo angiogenesis and primary tumor growth [142], although with no signifi-

11. Angiogenesis Switch Pathways

cant direct activity against tumor cells. Endostatin has also been used in vivo in antiangiogenic gene therapy experiments [141, 143]. Concerning to its mechanism of action, many receptors have been described like VEGFR [144], integrins like α2β1 [145], α5β1, or αvβ3 [115], and cellsurface glypicans [146], although further research is needed to better understand it. Other members of this family of fragments coming from natural not angiogenically active molecules with proven antiangiogenic activity are tumstatin, a fragment of α3 type IV collagen [112]; canstatin, a fragment of the alpha2 chain of type IV collagen [113]; arresten, a fragment of the α1 chain of type IV collagen [111]; and PEX, a noncatalytic fragment of MMP2 [147]. Other authors have described both pro- and antiangiogenic peptides coming from fragments of 2 aminoacyl-tRNA synthetases [148]. In addition, the recently recognized, but steadily growing, knowledge of the relationship between the coagulation and angiogenesis pathways has research and clinical implications [149]. Cryptic domains can be released from hemostatic proteins through proteolytic cleavage, which may act systemically as angiogenesis inhibitors like angiostatin and antiangiogenic antithrombin III (aaATIII) [150]. Altogether these exciting findings are closely related to the initial hypothesis about the important role of endogenous inhibitors and the existence of a very delicate and finely tuned balance between inducers and inhibitors of tumor angiogenesis. Many other molecules have been proposed as antiangiogenic and they are currently under active investigation or even in clinical trials. IFN-γ, for example, was shown to inhibit both EC proliferation and angiogenesis in vitro and furthermore it had a dramatic effect in the treatment of hemangioendotheliomas. It is assumed to function through modulation of the FGFR. Another class of newly discovered angiogenesis inhibitors, which has been derived from fumagillin an antibiotic purified from fungal cultures, inhibit ECl proliferation in vitro. To avoid toxic effects of parent compound, AGM-1470/TNP-470 a synthetic analog with enhanced antiangiogenic activity has been synthesized [151] and is now being tested in clinical trials. Protamine, a cationic protein derived from sperm was shown to be a specific inhibitor of angiogenesis, probably by interfering with growth factors. Platelet factor-IV, released from platelets during aggregation, is able to inhibit the growth of solid tumors when used as a recombinant protein. A series of corticosteroids tested in a number of animal models in conjunction with heparin, which showed effective antiangiogenic activity. They have been termed angiostatic steroids. Some inhibitors of the signal transduction from the angiogenic factor receptors, such as genistein or herbimycin, are being investigated as angiogenic inhibitors [152]. Even additional natural compounds such as extracts from avascular tissues, have been shown to be antiangiogenic. Moses and co-workers, for example, identified an inhibitor of neovascularization from cartilage [153].

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11.4

Prognostic Value of Angiogenesis

As early as 1972, Folkman et al. [154] developed a microscopic angiogenesis grading system with which to quantify tumor angiogenesis. The main goal was to establish an objective method for measuring the tumor vasculature and its relationship with the clinical parameters of the disease. This goal entailed the search for a useful angiogenic index, not only for its prognostic value, but also to stratify patients for therapy [155]. The first results obtained by several groups showed a high level of variation related with the sample selection, inter- and intraobserver variation because of the limited experience in vessel counting, and the specificity of the marker used. These limitations delayed the achievement of this goal. Nevertheless, 20 years later, it was shown in breast cancer that microvessel density was an independent prognostic marker for both relapse-free and overall survival. These studies were done using factor VIII to identify the endothelium and established criteria for microvessel assessment [156–158]. Since the reproducibility of the method is poor, several improvements have been proposed. Fox et al. [159] proposed a counting system using a microscope eyepiece grid. Other groups are testing more sensitive endothelial markers such as CD31 or CD34. Thus, Kawaguchi et al. [160] examined the correlation between tumor angiogenesis and prognosis of lung adenocarcinoma (T1N0M0) using a MAb to CD31 and they showed that microvessel count might be a major prognostic factor and a useful tool to predict recurrence in patients with lung adenocarcinoma. CD34 has been used successfully on samples from ovarian cancer [161], gastric carcinomas [162], and malignant mesotheliomas [163]. Vermeulen et al. [164] proposed the standardization of angiogenic quantification to reduce interlaboratory variability and to confirm the prognostic value of intratumoral microvessel density (IMD) in solid tumors. This study proposed a detailed standard immunostaining (CD-3) marker method for IMD assessment and predicted the increased role of serum levels of angiogenic factors (βFGF, VEGF) as markers of tumor progression. Moreover, new specific markers for activated endothelium (e.g., Ab to endoglin [CD105] and integrins) are being studied to verify whether the ratio of activated/quiescent EC could add prognostic information to IMD assessment. Other investigators have demonstrated the positive correlation between the tumor neovascularization assessed by immunohistochemical (IHC) staining with anti-CD31 Ab and VEGF mRNA expression in breast tumors [165]. Further studies have shown the direct relationship between VEGF expression and tumor angiogenesis in cervical intraepithelial neoplasia and head-and-neck squamous-cell carcinoma [166, 167]. These reports are consistent with previous studies that reported the association of VEGF expression with early relapse in bladder carcinomas and its use

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Table 11-4. Key points in tumor angiogenesis therapy. 1. Since the oxygen diffusion limit in tissues is 100–200 µm, which corresponds to 3–5 cell layers surrounding a tumor vessel, growing tumor masses from 1–2 mm3 need to develop new microvessels. This neovascularization allows primary tumor growth and facilitates local invasion and metastasis. Therefore, tumor latency can be achieved by blocking tumor angiogenesis using specific inhibitors 2. Tumor endothelium loses the typical organized structure of vascular network and is phenotypically different from common capillaries. The abnormal structure of this new vasculature alters its permeability causing high interstitial pressures. Tumor EC divide up to 40 times more frequently than resting EC and overexpress, in a selective way, cell surface molecules such as integrin αvβ3, E-selectin, endoglin, endosialin, and VEGFR, which stimulate and capacitate the adhesion and migration of activated endothelium. 3. The angiogenic EC, activated by a tumor represent an important target in cancer therapies because they are genetically more stable than tumor cells. Antiangiogenic agents can act directly on tumor EC by inhibiting its response to one or multiple angiogenic factors (bevazicumab); or can act indirectly by inhibiting the synthesis of proangiogenic factors in the tumor cell (trastuzumab) 4. The treatment of both compartments, tumor cells and tumor EC, is much more effective than just treating tumor cells. Combination of antiangiogenic therapy with other oncologic therapies (chemo-, radio-, and immunotherapies) represents a promising future to fight against cancer. Currently, there are up to six marketed antiangiogenic drugs that will revolutionize the standard treatment of cancer

Table 11-5. Major advantage of antiangiogenesis therapeutic intervention in cancer. A single vessel provides the nutrition for thousands of tumor cells and has to be damaged at only one point to block blood flow upstream and downstream. A change of shape of local initiation of blood coagulation may be sufficient, other than killing the EC. The EC is a normal diploid cell, which is unlikely to acquire genetic mutations that render it drug resistant. Minimal side effects and expected low toxicity of the specific antiangiogenic agents because of the slow turnover rate of the EC in normal tissues in comparison to the turnover of cells involved in tumor angiogenesis. Tumor endothelium expresses specific antigens and targets. Therefore, through the inhibition of the overexpressed and specific antigens, the antiangiogenic therapy represents a targeted antitumor approach. Tumor endothelium is highly accessible, increasing the effectiveness of a systemic therapy. Blood flow, a surrogate marker for biologic activity, is measurable in the clinic. Temporary effects on vascular function may be sufficient to kill the EC. Because activated EC also promote tumor cell growth through paracrine effects, synergistic activity with conventional anticancer agents might be expected Dual activity of the antiangiogenesis therapy when using a common target expressed by both tumor and endothelial cells (e.g., metalloproteinases, αvβ3 expression on melanoma and EC). EC, endothelial cell

EC, endothelial cell; VEGFR, vascular endothelial growth factor receptor

as an independent prognostic marker in breast carcinomas [168, 169]. Despite the discrepancies observed in the literature, mainly because of the criteria used for microvessel counting, the results achieved until now seem to demonstrate a significant correlation between high tumor neovascularization and a reduction in patient survival [170]. The definitive angiogenic index may turn out to be a multiparametric factor instead of a single histologic measure of microvessel density in tumor tissue.

11.5

Therapeutic Approaches

Angiogenesis is a complex multistep process in which many potential key points might be susceptible to therapeutic intervention. Our current knowledge of the paracrine talk between tumor and EC allows us to define specific targets for therapy. Thus, the endothelial mitogens released by tumor cells; the tumor growth factors secreted by EC; the proteases released by both populations for degrading the local stroma; the pivotal role of some integrins, such as αvβ3 and αvβ5, in EC proliferation and migration and; finally, the natural endogenous angiogenesis inhibitors all contribute to neovascularization and are therefore potential targets for pharmacologic modulation (Table 11-4). The major advantages of antiangiogenesis therapeutic intervention in cancer are depicted in Table 11-5.

The antiangiogenic therapy is more than a simple promise. It is revealing itself as one of the most impacting approaches in the anticancer drug development of the last decade. Currently tumor angiogenesis blockers could be divided into two categories: direct and indirect inhibitors. Direct inhibitors of angiogenesis inhibit the response of EC to multiple angiogenic factors, blocking processes of proliferation, migration, and formation of neovessels, and is the mechanism of action of agents such as endostatin, angiostatin, bevacizumab, tumstatin, thrombospondin, and cilengitide. Indirect inhibitors of angiogenesis inhibit the synthesis of proangiogenic factors in the tumor cell, and is the mechanism of trastuzumab, cetuximab, IFN-α, and RTK inhibitors such as sorafenib, sunitinib, erlotinib, or gefinitib. In addition, a new family of antiangiogenic drugs, which are addressed to destroy the tumor blood vessels, have been described. A representative of the so-called vascular targeting agents (VTA) is combretastatin A4, which is currently in phase-2 studies (Table 11-3) [171]. From these clinical trials, several lessons for their application in future clinical trials can be extracted. In addition, through translational research, these lessons have also to be considered in the first stages of discovery of new therapeutic targets in the field of tumor angiogenesis [172]. When an angiogenic inhibitor is approved in a type of cancer, it is prudent to clinically try it in other types of cancer, which is the case of the MAb bevacizumab and cetuximab. In both cases, they were approved for administration in colorectal cancer

11. Angiogenesis Switch Pathways

[173, 174], bevacizumab in lung cancer [175], and cetuximab in head-and-neck squamous-cell cancer [176]. Nevertheless, both MAbs follow clinical studies in phase 2 and 3 for other tumors: breast carcinomas, pancreatic cancer, gastric carcinoma, esophageal cancer, and ovarian carcinoma. Another conclusion with implications in the treatment of patients and the development of new drugs is the necessity to apply the antiangiogenic agents in combinations with other therapeutic strategies. Combinations with chemotherapeutic agents, radiation therapy, and combinations of several antiangiogenic agents have demonstrated a clear superiority over the use of a single antiangiogenic drug [177–179]. In fact, targeting of multiple signaling routes that regulate the endothelial growth or promote apoptosis has synergistic antiangiogenic effects. The major effectiveness obtained in recent clinical trials with the combination of bevacizumab plus cetuximab with or without irinotecan in refractotory colorectal cancer is indeed remarkable [180]. Among all these combinations, trimodal therapy (antiangiogenic therapy plus chemotherapy plus radiotherapy) demonstrates much more activity and effectiveness than any one of them administered alone or even as a combination of two of them [181]. Currently, the simultaneous radiochemotherapy represents a standard treatment for several types of cancer. Therefore, a step further with the inclusion of trimodal therapy into our current antitumor armamentarium represents a logical continuity supported by excellent nonclinical results and preliminary clinical results [182]. Some examples exist of previously approved nonangiogenicrelated drugs, used to treat other conditions, but have recently been found to have antiangiogenic activity and can now be used for effective treatment in recurrent cancer. For example, doxycycline (chemically modified tetracycline) is an antibiotic that has recently demonstrated its antiangiogenic activity through the induction of thrombospondin. Doxycycline has shown activity in lung hemangioendotheliomas [183]. Likewise, another interesting contribution is provided by the so called antiangiogenic chemotherapy (metronomic therapy), based on the administration of low doses of chemotherapeutic agents during long periods of time, in contrast to the conventional chemotherapy based on high doses (often maximum tolerated doses) administered on intermittent schedules. These therapies have shown their activity in both nonclinical models and in clinical trials [184]. Protracted low doses of IFN-α have been used satisfactorily to treat both hemangiomas and giant-cell tumors [185]. The MAb bevacizumab received marketing approval from the Food and Drug Administration for the treatment of colorectal cancer, and other antiangiogenic drugs have also been approved in different countries and for several indications. In Table 11-5, these already marketed antiangiogenic drugs are summarized (either for oncologic or nononcologic indications). Bevazicumab, a humanized Ab against VEGF, was the first approved antiangiogenesis drug launched into the market for

249

oncologic indications. It has been approved to be used with intravenous 5-fluorouracil (5-FU)-based chemotherapy as treatment for patients with previously untreated metastatic cancer of the colon or rectum (first-line treatment). Bevazicumab in combination with carboplatin and paclitaxel is also approved for first-line treatment of advanced nonsmall cell lung cancer (NSCLC). Tumor cells release numerous angiogenic molecules and induce the expression of angiogenic receptors in tumor vessels (e.g., EGF induces EGFR and VEGFR in tumor-associated vessels). Thus, cetuximab and trastuzumab, an anti-EGFR antibody and anti-HER2 antibody, respectively, used to block the growth of neoplastic epithelial cells, also act as antiangiogenic drugs by lowering angiogenic factors and upregulating endogenous angiogenesis inhibitors. In fact, many other EGFR inhibitors could have similar antiangiogenic activities. Cetuximab is currently approved with or without irinotecan as a treatment for recurrent or metastatic colorectal cancer refractory to irinotecan. It is also approved, in combination with radiotherapy, for the treatment of head-and-neck cancer. Trastuzumab has been approved for the treatment of metastatic breast carcinomas overexpressing HER2 and as adjuvant treatment for operable breast cancer overexpressing HER2. Two additional marketed antiangiogenic drugs are orally available synthetic small molecules. Sorafenib and sunitinib are tyrosine kinase inhibitors of VEGFR2, although their activity is broader because they also block other key oncologic protein kinases (Raf, PDGFR-β, and c-Kit). Sorafenib and sunitinib have been approved for the treatment of patients with advanced renal-cell carcinoma. Sunitinib has also been approved for the treatment of patients with gastrointestinal stromal tumors (GIST) whose disease has progressed and are unable to tolerate treatment with imatinib. Thalidomide, the drug banned in the 1960s because of teratogenic effects, has been approved in combination with dexamethasone for the treatment of patients with newly diagnosed multiple myeloma. In one or the other way, directly or indirectly, almost all these antiangiogenic agents affect the VEGF system. They not only arrest EC proliferation, prevent vessel growth, and induce regression of existing vessels by increasing EC death, but in addition, VEGF inhibitors may also suppress the mobilization of PEC from the bone marrow. Antiangiogenesis treatment, in general, also improves cytotoxic drug delivery by normalizing the chaotic pattern and abnormal architecture of tumor vessels, and reducing vascular permeability and the interstitial fluid pressure, as it has been postulated and also experimentally proven through the so called “vasculature normalization process” [186, 187]. This novel mechanistic interpretation explains why these inhibitors act as a sort of chemosensitizer [188] and increase the efficacy of chemotherapeutics as has been previously discussed. Two additional issues should also be considered in view of the recent clinical trial data that has been obtained and that should be applied to future trials with new drugs: first, the establishment of specific treatment protocols for this type of

250

agent; and second, the definition of valid surrogate markers that allow measurement of the antiangiogenic activity of the drugs during the treatment. Regarding the first issue, it is necessary to emphasize that frequent and low dosing of some current chemotherapeutic drugs, such as vinblastine, has shown antiangiogenic activity and improved the control of tumor growth [189] through the metronomic therapy scheduling. Therefore, the design of new clinical protocols for antiangiogenic therapy is mandatory. With respect to the second issue, the measure of surrogate markers such as VEGF, VCAM-1, and CEC progenitors (CD133+, VEGFR3+) seem to be the optimal markers for following the antiangiogenic therapy [16]. Serious toxicity of antiangiogenic agents is generally assumed not to be a problem, because of the almost complete lack of angiogenesis in the adult; however, the effects of antiangiogenic therapy on physiologic angiogenesis are largely unknown because the differences between physiologic and pathologic angiogenesis are unclear. Therefore, one of the key questions about clinical antiangiogenesis therapies refers to the potential side effects of the treatment. Several studies addressed this issue early on. In a retinopathy model using postnatal mice, Drixler and co-workers demonstrated that angiostatin inhibits oxygen-induced intravitreal pathologic retinal angiogenesis without affecting the development of physiologic retinal vascularization, development, and growth

J. Piulats and F. Mitjans

of newborn mice [190]. This finding suggests that mechanisms of control of physiologic angiogenesis may differ from those of pathologic angiogenesis, and therefore supports the use of angiogenesis inhibitors in oncologic treatments.

11.6

Conclusion

The progression of tumors to malignancy and the establishment of metastasis clearly depend on the induction of neovascularization. In other words, cells in a developing tumor will progress only if they acquire the angiogenic phenotype necessary to attract the new vessels on which their malignancy depends. In this chapter, we reviewed the distinct pathways involved in such a process referred as the angiogenic switch. One key step is undoubtedly the delicate balance between natural inducers and inhibitors of angiogenesis. The tipping of this balance towards one or the other side would favor the inhibition of angiogenesis or promote neovascularization. Current antiangiogenesis research is engaged in the pursuit of novel, potent inhibitors, which may lead to new therapeutic drugs for cancer treatment. The new antiangiogenic agents launched during the last 2 years seem to confirm the positive data from the previous nonclinical and clinical research. MAb and tyrosine kinase inhibitors have played a pivotal role in this approach of cancer

Fig. 11-2. Model of combined therapy. Within a tumor, we may distinguish four different compartments: the tumor cells, the endothelial cells, the accessory cells, and the stroma. An antitumor therapy (affecting the proliferative rate of tumor cells) combined with an antiangiogenic therapy (affecting both the endothelial cell population and the apoptosis rate of the tumor cell population; see text) would lead to a more effective anticancer global therapy. This combination therapeutic approach might be also accomplished and eventually increased by targeting all the four compartments within a given tumor. Currently, antiangiogenesis therapy is the fifth modality for cancer treatment, in addition to surgery, radiotherapy, chemotherapy, and immunotherapy.

11. Angiogenesis Switch Pathways

therapy and the elegant technologies for “humanization” of Ab have, obviously, facilitated the success of these drugs. The aforementioned optimistic expectations of the current antiangiogenic therapy should not induce us to forget the open questions in this field. These questions, which will indicate the future paths of nonclinical and clinical research, can be summarized as: the study of the best protocols for clinical research on antiangiogenic agents; learning the effects of combitherapy treatments; focus on the research for defining biomarkers for an adequate pharmacologic evaluation of new agents; selection of the correct tumor indicator and patient population in the design of clinical studies; and introduction of predictive pharmacologic and toxicologic studies during the process of drug discovery, to detect potential side effects of the antiangiogenic agents. Antiangiogenic therapy would enhance the action of both, the classic cytotoxic chemoradiotherapy and the new immunotherapy in patients with cancer because of the action on the different compartments within the tumor, including the endothelium, the tumor, and the accessory cell populations, as well as the stromal compartment [191] (Fig. 11-2). Therefore, this approach may mimic the endogenous inhibitors of angiogenesis, such as angiostatin, maintaining tumor dormancy. Consequently, this therapy could facilitate a strategy that tends toward a chronicity of the disease in a way similar to the treatment of diabetes and AIDS.

Acknowledgments. We appreciate the helpful comments and criticisms of this manuscript by Dr Claudi Solà. The authors also wish to thank all the members of our laboratory for their fruitful comments, and Nuria Soriano and Neus Sanchez for expert editorial help.

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254 106. Montgomery AM, Reisfeld RA, Cheresh DA. Integrin alpha (v)beta 3 rescues melanoma cells from apoptosis in three-dimensional dermal collagen. Proc Natl Acad Sci USA 1994;91:8856–8860. 107. Cheresh DA, Stupack DG. Integrin-mediated death: An explanation of the integrin-knockout phenotype? Nat Med 2002;8:193–194. 108. Ramos OH, Selistre-de-Araujo HS. Snake venom metalloproteases– structure and function of catalytic and disintegrin domains. Comp Biochem Physiol C Toxicol Pharmacol 2006;142:328–346 109. Burke PA, DeNardo SJ, Miers LA, Lamborn KR, Matzku S, DeNardo GL. Cilengitide targeting of alpha(v)beta(3) integrin receptor synergizes with radioimmunotherapy to increase efficacy and apoptosis in breast cancer xenografts. Cancer Res 2002; 62:4263–4272. 110. Kumar CC, Malkowski M, Yin Z, et al. Inhibition of angiogenesis and tumor growth by SCH221153, a dual alpha(v)beta3 and alpha(v)beta5 integrin receptor antagonist. Cancer Res 2001;61:2232–2238. 111. Colorado PC, Torre A, Kamphaus G, et al. Anti-angiogenic cues from vascular basement membrane collagen. Cancer Res 2000; 60:2520–2526. 112. Maeshima Y, Manfredi M, Reimer C, et al. Identification of the anti-angiogenic site within vascular basement membrane-derived tumstatin. J Biol Chem 2001; 276:15240– 15248. 113. Kamphaus GD, Colorado PC, Panka DJ, et al. Canstatin, a novel matrix-derived inhibitor of angiogenesis and tumor growth. J Biol Chem 2000;275:1209–1215. 114. Tarui T, Miles LA, Takada Y. Specific interaction of angiostatin with integrin alpha(v)beta(3) in endothelial cells. J Biol Chem 2001;276:39562–39568. 115. Rehn M, Veikkola T, Kukk-Valdre E, et al. Interaction of endostatin with integrins implicated in angiogenesis. Proc Natl Acad Sci USA 2001;98:1024–1029. 116. Dormond O, Foletti A, Paroz C, Ruegg C. NSAIDs inhibit alpha (v)beta 3 integrin-mediated and Cdc42/Rac-dependent endothelial-cell spreading, migration and angiogenesis. Nat Med 2001;7:1041–1047. 117. Ruegg C, Yilmaz A, Bieler G, Bamat J, Chaubert P, Lejeune FJ. Evidence for the involvement of endothelial cell integrin alpha (v)beta3 in the disruption of the tumor vasculature induced by TNF and IFN-gamma. Nat Med 1998;4:408–414. 118. Ruegg C, Dormond O, Foletti A. Suppression of tumor angiogenesis through the inhibition of integrin function and signaling in endothelial cells: Which side to target? Endothelium 2002;9:151–160. 119. Meyer A, Auernheimer J, Modlinger A, Kessler H. Targeting RGD recognizing integrins: Drug development, biomaterial research, tumor imaging and targeting. Curr Pharm Des 2006;12:2723–2747 120. van Hinsbergh VW, Engelse MA, Quax PH. Pericellular proteases in angiogenesis and vasculogenesis. Arterioscler Thromb Vasc Biol 2006;26:716–728 121. Duffy MJ. The urokinase plasminogen activator system: Role in malignancy. Curr Pharm Des 2004;10:39–49 122. Kugler MC, Wei Y, Chapman HA. Urokinase receptor and integrin interactions. Curr Pharm Des 2003;9:1565–1574 123. Min HY, Doyle LV, Vitt CR, et al. Urokinase receptor antagonists inhibit angiogenesis and primary tumor growth in syngeneic mice. Cancer Res 1996;56:2428–2433.

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11. Angiogenesis Switch Pathways 145. Furumatsu T, Yamaguchi N, Nishida K, et al. Endostatin inhibits adhesion of endothelial cells to collagen I via alpha(2)beta(1) integrin, a possible cause of prevention of chondrosarcoma growth. J Biochem (Tokyo) 2002;131:619–626. 146. Karumanchi SA, Jha V, Ramchandran R, et al. Cell surface glypicans are low-affinity endostatin receptors. Mol Cell 2001; 7:811–822. 147. Pfeifer A, Kessler T, Silletti S, Cheresh DA, Verma IM. Suppression of angiogenesis by lentiviral delivery of PEX, a noncatalytic fragment of matrix metalloproteinase 2. Proc Natl Acad Sci USA 2000;97:12227–12232. 148. Ewalt KL, Schimmel P. Activation of angiogenic signaling pathways by two human tRNA synthetases. Biochemistry 2002;41:13344–13349. 149. Nash GF, Walsh DC, Kakkar AK. The role of the coagulation system in tumour angiogenesis. Lancet Oncol 2001;2:608–613. 150. Wojtukiewicz MZ, Sierko E, Klement P, Rak J. The hemostatic system and angiogenesis in malignancy. Neoplasia 2001;3:371–384. 151. Yamaoka M, Yamamoto T, Masaki T, Ikeyama S, Sudo K, Fujita T. Inhibition of tumor growth and metastasis of rodent tumors by the angiogenesis inhibitor O-(chloroacetylcarbamoyl)fumagillol (TNP-470; AGM-1470). Cancer Res 1993;53:4262–4267. 152. Fotsis T, Pepper M, Adlercreutz H, Hase T, Montesano R, Schweigerer L. Genistein, a dietary ingested isoflavonoid, inhibits cell proliferation and in vitro angiogenesis. J Nutr 1995;125:790S–797S. 153. Moses MA, Sudhalter J, Langer R. Identification of an inhibitor of neovascularization from cartilage. Science 1990;248:1408–1410. 154. Brem S, Cotran R, Folkman J. Tumor angiogenesis: A quantitative method for histologic grading. J Natl Cancer Inst 1972;48:347–356. 155. Fox SB, Harris AL. Markers of tumor angiogenesis: Clinical applications in prognosis and anti-angiogenic therapy. Invest New Drugs 1997;15:15–28. 156. Weidner N, Semple JP, Welch WR, Folkman J. Tumor angiogenesis and metastasis–correlation in invasive breast carcinoma. N Engl J Med 1991;324:1–8. 157. Weidner N, Folkman J, Pozza F, et al. Tumor angiogenesis: A new significant and independent prognostic indicator in early-stage breast carcinoma. J Natl Cancer Inst 1992;84: 1875–1887. 158. Bosari S, Lee AK, DeLellis RA, Wiley BD, Heatley GJ, Silverman ML. Microvessel quantitation and prognosis in invasive breast carcinoma. Hum Pathol 1992;23:755–761. 159. Fox SB, Leek RD, Weekes MP, Whitehouse RM, Gatter KC, Harris AL. Quantitation and prognostic value of breast cancer angiogenesis: Comparison of microvessel density, Chalkley count, and computer image analysis. J Pathol 1995;177:275–283. 160. Kawaguchi T, Yamamoto S, Kudoh S, Goto K, Wakasa K, Sakurai M. Tumor angiogenesis as a major prognostic factor in stage I lung adenocarcinoma. Anticancer Res 1997;17:3743–3746. 161. Heimburg S, Oehler MK, Kristen P, Papadopoulos T, Caffier H. The endothelial marker CD 34 in the assessment of tumour vascularisation in ovarian cancer. Anticancer Res 1997;17:3149–3151. 162. Tanigawa N, Amaya H, Matsumura M, Shimomatsuya T. Association of tumour vasculature with tumour progression and overall survival of patients with non-early gastric carcinomas. Br J Cancer 1997;75:566–571.

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Chapter 12 Apoptosis Pathways and New Anticancer Agents Frank A.E. Kruyt, Jose A. Rodriguez, and Giuseppe Giaccone

12.1

The Apoptotic Core Machinery

Apoptosis or programmed cell death is a physiologic process that determines tissue homeostasis and provides an effective way to remove unwanted cells, such as those that have accumulated oncogenic mutations. Inhibition of apoptosis disrupts the balance between cell proliferation and cell death and has been recognized as one of six key mechanisms that are essential for the generation of fully transformed malignant cells [1]. Cells that undergo apoptosis are characterized by morphologic changes that include cytoplasmic shrinkage, plasma membrane blebbing, and chromatin condensation in the nucleus, which facilitate the efficient inflammation-free removal of apoptotic cells by macrophages [2]. At the molecular level, proteolytic enzymes such as caspases play an important role as the executors of apoptosis leading to cell death. Apoptosis is distinct from passive nonregulated cell death (necrosis), and, in general, is referred to as caspase-dependent cell death. The term “classical apoptosis” has been coined to distinguish it from other forms of programmed cell death that display a mixture of morphology or molecular features or both representing caspase- or noncaspase-dependent cell death. We focus on the therapeutic exploitation of the core apoptotic machinery that regulates caspase-dependent cell death. Two main caspase activation pathways have been identified (see also Fig. 12-1). One route, known as the intrinsic or mitochondrial pathway, is triggered upon disruption of mitochondria, e.g., because of DNA damage induced by cytotoxic agents, and causes the release of cytochrome c into the cytoplasm [3, 4]. Together with dATP, cytochrome c is a cofactor for the assembly of the apoptosome, which contains Apaf-1 and procaspase-9, and leads to the processing and activation of caspase-9. The second route, the so-called extrinsic or death receptor pathway, is initiated through specific cell membrane receptors, such as Fas/CD95 and tumor necrosis factor (TNF)

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

family receptors, that upon ligand binding recruit the cytosolic death-domain-containing protein FADD (Fas-associated protein with death domain), which is able to bind and activate procaspase-8 in a complex named the death-inducing signaling complex (DISC). Both caspase-8 and -9 can activate the effector caspases-3, -6, and -7 provided that the caspase inhibitory effect of the inhibitor of apoptosis proteins (IAP) is relieved by Smac/Diablo, a proapoptotic protein that is also released from the mitochondria. The IAP family comprises proteins that contain one or more baculovirus IAP repeat (BIR) domains, which mediate in some IAP the interaction with caspases [5, 6]. The most potent caspase-inhibitory IAP is X-linked IAP (XIAP). When released in the cytosol, Smac binds to XIAP facilitating caspase activation. The activation of the effector caspases leads to the cleavage of various substrates, which results in the characteristic morphologic features of apoptotic cell death. An important class of regulators of apoptosis are the BCL-2 family proteins [7–9], comprising both antiapoptotic members, such as BCL-2, BCL-XL, and MCL-1, as well as proapoptotic members such as BAX and BAK, that share homology throughout four or three BCL-2 homology domains, respectively. Their primary mode of action has been assumed to be the regulation of mitochondrial integrity; however, they also appear to be involved in maintaining the integrity of other intracellular membrane structures, such as the endoplasmatic reticulum. Upon apoptosis activation, BAX and BAK translocate from the cytoplasm to the mitochondrial membrane where they oligomerize to form porelike structures, thereby causing mitochondrial outer membrane permealization (MOMP) and the release of apoptogenic factors, such as cytochrome c and Smac. The BH3-only proteins constitute a third class of proapoptotic BCL-2 proteins, which includes BID, BAD, BIK, PUMA, NOXA, BMF, and HRK. These proteins share homology in only one region, the BH3 domain. The BH3-only proteins appear to function as sentinels for the detection of cellular damage or aberrations; for example, BIM is activated by microtubule disarray, whereas 257

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Mitochondrial pathway

Death receptor pathway TRAIL

TARGET TARGET Cellular stresses e.g. cytotoxic agents

TRAIL-R1/ 2 FADD

BH3-only proteins Bcl-2 family multidomain proteins

Procaspase-8 activation

Bax homodimerization

Bid/ tbid Smac

TARGET

IAPs

Cyt c

Caspase-3,-6,-7

Apaf1/ procaspase-9 activation

Apoptosis Fig. 12-1. The apoptotic core machinery and therapeutic targets. The death receptor or extrinsic pathway is activated by the binding of ligand to their cognate death receptors; interaction between TRAIL and TRAIL-receptors are depicted. Upon receptor trimerization, FADD and procaspase-8 are recruited forming the death-inducing signaling complex (DISC) causing its processing into active caspase-8 that on its turn causes, in a cell type-dependent manner, a direct activation of the effector caspases (caspases-3, -6, or -7) or indirectly through the cleavage of Bid causing a proapoptotic shift in the Bcl-2 family balance resulting in the homodimerization of Bax and pore formation in the mitochondria, which enables the release of the apoptogenic factors cytochrome c and Smac form the mitochondrial intermembrane space into the cytoplasm. Cytochrome c induces the assembly of the apoptosome, consisting of Apaf-1 and procaspase-9, triggering caspase-9 activation and subsequently the activation of the effector caspases. Smac enhances the apoptotic signal by preventing the interaction of IAP with caspases thus facilitating their activation. Apoptotic targets currently evaluated in nonclinical and clinical studies include the TRAIL receptors, the Bcl-2 protein family, and IAP through different approaches, such as the use of recombinant protein of agonistic MAb in case of TRAIL, and antisense oligonucleotides or peptide and small molecule mimetics for Bcl-2 and IAP.

Noxa and Puma respond to DNA damage. On the other hand, Bid is activated by caspase-8-mediated cleavage, thereby connecting the death receptor and mitochondria routes, in a process known as the mitochondrial amplification loop. Heterodimerization amongst proapoptotic and antiapoptotic members of the BCL-2 family proteins was found to be at the basis of their MOMP regulatory activity; however, plausible models to explain their mode of action have been only recently proposed. The BH3-only proteins were shown to have different affinities for the other multidomain family members, with BIM and BID acting as direct activators of BAX and BAK, and others such as BAD, BIK, HRK, and NOXA acting as sensitizers by binding to antiapoptotic multidomain proteins [10–12]. Accumulating evidence supports a model in which sensitizer BH3-only proteins interact with the antiapoptotic molecules, and indirectly induce BAX or BAK activation by competitive displacement of activator BH3 proteins from the BCL-2 binding pockets [13, 14].

12.2

Apoptosis and Anticancer Therapy

Successful treatment with chemotherapeutic agents is largely dependent on their potency to trigger cell death in tumor cells, in which activation of apoptotic pathways plays an important role [15, 16]. In this context, chemoresistance, which is a common characteristic of tumor cells, is at least partially related to the intrinsic inhibition of apoptotic pathways that allows cancer cells to undergo malignant transformation [1], and to sustain the harsh conditions in tumor environment (e.g., low oxygen levels, shortage of nutrients). In particular, the role of the mitochondrial pathway in mediating cytotoxic agent-induced apoptosis has been demonstrated in many cell culture and mice models. Targeted disruption of caspase-9 or Apaf-1 in knock-out mice models resulted in resistance to cytotoxic drugs, whereas cells remained sensitive for death receptor-induced apoptosis [17, 18]. In a clinical setting, a direct relationship between apoptosis inhibition and

12. Apoptosis Pathways and New Anticancer Agents

response to conventional therapy has been more difficult to prove, but a large body of correlative data is now available. For example, mutations or altered expression of antiapoptotic Bcl-2 family proteins have been shown to have major effects on drug sensitivity in cell culture models [19, 20]; in line with this, overexpression of Bcl-2 in clinical samples from patients with diseases such as leukemia or prostate or brain tumors, correlated with poor responses and shorter overall survival [21–23]. Therefore, the design and development of drugs able to restore apoptosis in cancer cells has attracted major attention [24, 25]. Oncogene activity within tumor cells and the stress conditions present in the tumor environment (i.e., hypoxia, nutrient deprivation) lead to a low threshold for apoptosis activation in cancer versus normal cells. Apoptosis-targeted drugs are expected to demonstrate tumor-cell selectivity. We provide an overview of several proapoptotic strategies that are currently being tested in the clinic or that are likely to undergo clinical development in the next 1–2 years.

12.3

TRAIL

The tumor-necrosis-factor-related apoptosis-inducing ligand (TRAIL) receptor (TRAIL-R) family encompasses five members that interact with the TRAIL ligand [26, 27]. TRAIL-R1 (DR4) and -R2 (DR5) contain two cysteine-rich extracellular ligand-binding domains and a cytoplasmic region, called the death domain (DD), which is required for activation of the extrinsic apoptotic pathway after TRAIL binding. TRAIL-R3 (DcR1), R4 (DcR2), and circulating osteoprotegerin (OPG) lack a functional DD, and are possibly involved in negatively regulating apoptosis by sequestering TRAIL, acting as decoy receptors. TRAIL-Rs have different binding affinities for the ligand, TRAIL-R2 showing the highest and OPG the lowest affinity. TRAIL is a cytokine that has been shown to play a role in immune system surveillance and inflammatory responses [28], and has selective tumor cell killing activity, while sparing normal cells [29, 30]. This property has made the TRAIL pathway an attractive target for cancer treatment. The cellular and molecular basis for the selective sensitivity of tumor cells to TRAIL are not completely understood, but could be related to the greater expression of the TRAIL-R in tumor cells, or to the relative increase in decoy receptors in normal cells, or may also involve nonfunctionality of the pathway at more downstream levels [31]. Nonclinical studies revealed that TRAIL-induced apoptosis is independent of p53 status, which is a favorable characteristic because many tumors bear p53 mutations. Binding to TRAIL-R induces trimerization of receptors, causing the formation of the deathinducing signaling complex (DISC) and subsequent caspase-8 activation. Activation of the extrinsic pathway is sufficient to trigger apoptosis in some cells, whereas other cells require the activation of the mitochondrial route through caspase-8dependent BID cleavage [32].

259

In nonclinical models, recombinant soluble TRAIL has demonstrated impressive anticancer activity. TRAIL potently induced apoptosis in a broad spectrum of human tumor cell lines derived from leukemia, multiple myeloma, and neuroblastoma, and lung, colon, breast, prostate, pancreas, kidney, and thyroid carcinoma. Importantly, no systemic toxicity was observed in xenograft transplantation models in mice [33], in contrast to the earlier observed severe adverse effects seen in TNF- and FasL (Fas ligand)-based anticancer strategies, demonstrating systemic inflammation and important liver toxicity that hampered further clinical development [34]. Currently, the use of TNF in cancer is limited to the regional treatment (isolated limb perfusion) of locally advanced soft-tissue sarcomas and metastatic melanomas to avoid amputation [35]. TRAIL was shown to be effective as a combination therapy in cell lines and mice models. For example, the combination of TRAIL and 5-flurouracil (5-FU) was superior to either therapy alone in inhibiting the growth of established tumors in mice [36]. Similarly, TRAIL given in combination with paclitaxel or irradiation demonstrated synergistic activity in lung and breast cancer models, respectively [37, 38]. The potentiating effect of these treatments on TRAIL antitumor activity may be related to an increase of TRAIL-R2 levels induced by chemotherapy and γ-radiation, but an apoptosispriming effect on the mitochondrial pathway has also been described [39]. Besides TRAIL resistance caused by the overexpression of decoy receptors, other mechanisms have been found to influence sensitivity. For example, overexpression of c-FLIP, a protein that is enzymatically inactive and competes with caspase-8 binding during DISC formation, inhibits TRAIL-induced caspase-8 activation [40, 41]. On the other hand, in cancer cells where TRAIL-induced apoptosis depends on activation of the mitochondrial pathway, the overexpression of Bcl-2 or Bcl-XL, loss of Bax or Bak function, and high expression of IAP have been reported to result in TRAIL resistance [31]. Different agents have been developed for therapeutic purposes, including soluble recombinant TRAIL and agonistic antibodies (Ab) to the receptors [42] (Table 12-1). Different forms of recombinant TRAIL, both tagged and nontagged, have been generated of which nontagged versions appear to have the highest tumor specificity. Furthermore, toxicology studies indicated that tagged forms of TRAIL inflict hepatotoxicity, a side effect that was not observed with soluble nontagged TRAIL. The addition of zinc was found to further increase TRAIL activity by stabilizing its homotrimeric structure [43]. Apo2/TRAIL is being evaluated in phase-1 trails. A preliminary report in which 51 patients were enrolled and treated with escalating doses (0.5, 1.5, 4, 8, and 15 mg/kg) of Apo2/TRAIL for five consecutive days every 3 weeks did not show dose-limiting toxicities [44]. One patient with chondrosarcoma had a partial response at 8 mg/kg. Given the liver toxicity observed in nonclinical models with death receptor agonists, this study investigated two cohorts of patients, one with and one without liver metastases. Although mild

260

F.A.E. Kruyt et al.

Table 12-1. Anticancer agents targeting the death receptors. Drug

Company/Institution

Apo2/TRAIL HGS-ETR1 (AntiTRAILR1 MAb) HGS-ETR2 (AntiTRAILR2 MAb)

Genentech/Amgen Human Genome Sciences Human Genome Sciences

HGS-TR2J

Human Genome Sciences

Phase

Ref

1 1–2

44, 45 46, 50–56

1

57, 58

1

42

being planned. This Ab has been tested in a 3-weekly schedule, with similar results [58]. Gene therapeutic approaches with TRAIL-expressing adenoviral vectors are being explored. Efficient adenoviralexpressed TRAIL-dependent cell killing has been demonstrated in several tumor cell lines and tumor mice bearing NSCLC xenografts, [59, 60]. Although promising, currently the use of adenoviral vectors for cancer treatment has its limitations, inherent to viral delivery and poor cell infection efficiencies.

MAb, monoclonal antibody

12.4 increases in transaminases were observed in some patients, they were transient and not clinically relevant [45]. The development of TRAIL-R1 or -R2 agonistic monoclonal antibodies (MAb) is another approach to stimulate TRAILmediated apoptosis. The fully humanized MAbs HGS-ETR1 (mapatumumab) that targets TRAIL-R1 and HGS-ETR2 (lexatumumab) and HGS-TR2J that targets TRAIL–R2, respectively, have been studied. An advantage of MAb is that they have high affinity for their targets, thus limiting nonspecific binding to decoy receptors or OPG. In cell lines and mice models, these agents potently induced apoptosis [46, 47], and an enhanced antitumor activity was observed when combined with chemotherapy or radiation [48, 49]. Preliminary data from two phase-1 studies with mapatumumab, in which patients were treated with at doses ranging from 0.01 to 10 mg/kg every 4 weeks, demonstrated good tolerability. Toxicities possibly related to mapatumumab were thrombocytopenia, transaminitis, hyperbilirubinemia, and acute respiratory distress syndrome. Prolonged stable disease was recorded in patients with appendix carcinoma, hepatocarcinoma, and sarcoma, respectively [50– 52]. These data encouraged the start of a phase-2 study with mapatumumab at two dose levels (3 and 10 mg/kg) every 21 days in patients with relapsed or refractory non-Hodgkin’s lymphoma (NHL) [53]. Three of the 14 patients with follicular lymphoma had clinical responses, including one complete response. HGS-ETR1 is in phase-2 development as a single agent and it has been tested in 32 patients with advanced nonsmall-cell lung cancer (NSCLC) [54] and 38 patients with colorectal cancer [55]. Both studies reported that mapatumumab is well tolerated and approximately one third of patients had stable disease; however no major responses were observed. Additional phase-1b studies in combination with carboplatin/paclitaxel and cisplatin/gemcitabine have been initiated in patients with advanced solid tumors. Results of the combination with paclitaxel and carboplatin demonstrate that this combination appears to be safe and efficacious [56]. Preliminary results from dose-finding studies with HGSETR2 suggest good tolerability and no toxicity at doses up to 10 mg/kg every 14 days. Stable disease has been reported in several patients, but no major responses [57]. Dose escalation is continuing and combination studies with chemotherapy are

The BCL-2 Family

Bcl-2 was originally identified at the breakpoint of the chromosomal translocation t14-18 in follicular B-cell NHL [61]. Unlike other oncogenes identified at that time, Bcl-2 appeared not to stimulate cell proliferation, but instead to inhibit apoptosis [62]. After the identification of additional Bcl-2 family members, it became clear that protein interactions between pro- and antiapoptotic members through their BH3 domains determine whether the mitochondrial membrane is permeabilized and apoptosis is initiated [8]. In more recent models, the BH3-only members are believed to respond to different types of cellular stress or death-inducing signals, by acting as either direct activators of proapoptotic members, or as sensitizers by binding to antiapoptotic members, thereby releasing sequestered activators resulting in MOMP and initiation of the intrinsic apoptotic pathway [10, 14]. Regardless of the precise mechanism of action, nonclinical studies in many different tumor types in cell culture or in mice models have shown that Bcl-2 mediates resistance to apoptosis after treatment with cytotoxic agents, irradiation, and hormones [20, 63, 64]. Therefore, Bcl-2 is recognized as a major target for therapeutic intervention strategies for many malignancies.

12.5 Approaches for Targeting BCL-2 and Clinical Studies Currently, the main strategies to inhibit Bcl-2 protein expression or modulate its activity are: the use of antisense oligonucleotides to downregulate Bcl-2 expression; and the use of small-molecule inhibitors that antagonize Bcl-2 function (Table 12-2). Oblimersen is an 18-mer phosphorotioate antisense oligonucleotide directed against the first six codons of the Bcl-2 open reading frame [65]. In several tumor cell lines and xenograft mice models, including B-cell lymphoma and prostate cancer cells, oblimersen has demonstrated dose-dependent antitumor activity [66, 67]. Oblimersen enhanced the antitumor activity of a broad range of chemotherapeutic agents, such as cyclophosphamide, paclitaxel, and vinorelbine, in

12. Apoptosis Pathways and New Anticancer Agents

261

Table 12-2. Anticancer agents targeting the Bcl-2 family. Drug

Company/Institution

Oblimersen (antisense oligonucleotide) SPC2996 (antisense oligonucleotide) AT-101, Gossypol (broad spectrum Bcl2 family inhibitor) Apogossypol (broad spectrum Bcl2 family inhibitor) SAHB BH3 (BH3 only peptidomimetic) ABT-737 (small molecule)

Genta Inc.

3

73–89

Santaris Pharma

1–2 in CLL

94

Ascenta Therapeutics Inc.

1 in CLL

100

Burnham/ NCI

Nonclinical

101

Harvard Medical School Abbott Laboratories/ Idun Pharmaceuticals GeminiX Raylight Chemokine Pharmaceuticals, Inc.

Nonclinical

95

GX15-070 HA14-1 (small molecule Bcl2 inhibitor)

Phase

Ref

Nonclinical/ 1 106 1 Nonclinical

107 108

CLL, chronic lymphocytic leukemia

several xenograft models including lymphoma, prostate, and NSCLC [58–70]. Enhanced therapeutic activity was observed in combination with other therapeutics such as rituximab in lymphoma xenograft models in mice [71, 72]. The first studies in which the drug was either applied by continuous subcutaneous (SC) or intravenous (IV) infusion in patients with NHL or advanced solid malignancies, indicated that oblimersen was well tolerated without severe toxicities [73, 74]. Oblimersen is given by continuous IV infusion for several days, as it is thought that prolonged inhibition is advantageous. Infusions > 7 days have in general proven to be more toxic and more difficult to combine with other agents. A phase-1 study in patients with solid tumors showed increased transaminase and fatigue to be dose limiting and seen in continuous infusions of 14 and 21 days [74]. Current studies use 5- to 7-day continuous infusions. Phase-1 studies have been done in patients with hematologic malignances and solid tumors. A phase-1–2 study in patients with advanced chronic lymphocytic leukemia (CLL) who relapsed after fludarabine treatment was performed in 40 patients [75]. Dosing was limited in this patient population because of development of a cytokine release syndrome, characterized by fever, hypotension, and back pain, and activity was modest (8% of 26 assessable patients). Oblimersen appears to be able to be safely and effectively combined with chemotherapeutics. A phase-1 study showed that oblimersen can be safely given in untreated older patients with acute myeloid leukemia (AML) in combination with chemotherapy [76]. Of the 29 treated patients, 16 achieved a complete remission, and reduction of Bcl-2 transcripts in bone marrow samples collected at baseline and after 72 hours of oblimersen administration, correlated with response. The results obtained in these studies were the basis for the initiation of a large randomized phase-3 study in patients with untreated high-risk AML (aged 60 years or older). In another study, oblimersen and Fludarabine, Ara-C, and G-CSF (FLAG)

chemotherapy was administered to 20 patients with relapsed acute leukemias [77]. Downregulation of the target (Bcl-2) was detected in peripheral blasts. A phase-2 study in patients with relapsed AML at first relapse [78] suggests that oblimersen can be safely and effectively combined with gemtuzumab ozogamicin, a calicheamicin-conjugated antibody directed against CD33, an antigen highly expressed on AML cells. Furthermore, in a phase-2 study, oblimersen sodium was given at 7 mg/kg/ day as a 7-day continuous infusion together with dexamethasone and thalidomide in patients with relapsed multiple myeloma [79]. Of 33 patients enrolled, a response was obtained in 55% and clinical responses were obtained in patients who failed previous treatment with thalidomide. Several studies have been done in patients with solid tumors. Small-cell lung cancer (SCLC) is known to overexpress Bcl-2 in most cases, and some studies have been done in patients with this tumor type. A pilot study of 12 patients with refractory SCLC, however, did not demonstrate clinical activity when combined with paclitaxel [80]. On the other hand, when given in combination with carboplatin and etoposide as firstline treatment in 16 patients previously untreated for extensive disease SCLC, a partial response was observed in 86% of patients and the drug was well tolerated [81]. These results may be promising, although no downregulation of Bcl-2 could be detected in peripheral mononuclear cells in this study. Several studies established the feasibility of combining oblimersen with taxanes [82, 83] and with carboplatinpaclitaxel [84] in several solid tumor types. In patients with colorectal cancer, a phase-1 study in 20 patients suggested that oblimersen is well tolerated when administered with irinotecan [85]. A few studies have been done in patients with hormone-refractory prostate cancer. A phase-1 study was conducted with oblimersen and docetaxel in 20 patients. Fatigue became dose-limiting toxicity for doses of oblimersen >7 mg/ kg/day given for 6 days. Although 7of 12 taxane-naive patients had a response, there were no responses in taxane-refractory patients [86]. In a phase-2 trial of the same combination, a prostate-specific antigen (PSA) response was obtained in 55% of 28 patients treated. The concentration at steady state of oblimersen was higher in responders than in nonresponders [87]. The oblimersen has been tested with mitoxantrone, which represented the standard treatment for patients with advanced hormone-refractory prostate cancer, before the registration of docetaxel. Oblimersen was given in a 14-day continuous infusion, and no dose-limiting toxicities were observed in this study; 2 of 26 patients had a PSA response [88]. In a large phase-3 clinical trial, oblimersen combined with dacarbazine (oblimersen was given at 7 mg/kg/day by continuous IV infusion for 5 days; dacarbazine was given at 1,000 mg/m2 on day 6, every 3 weeks) was compared with dacarbazine alone in 771 patients with advanced melanoma as first-line treatment [89]. This study suggested that despite an improved response rate (13.5% versus 7.5%, p = 0.007) as well as improvement in progression-free survival (median 2.6 versus

262

1.6 months, p < 0.001) for the combination versus dacarbazine alone, no statistical significant improvement in overall survival, the primary endpoint of the study, was obtained in the population of patients with advanced melanoma. When taken together, the efficacy of the drug remains somewhat doubtful and the use of this drug is currently reconsidered; however, this finding does not disqualify Bcl-2 as a target because several questions regarding the suitability of oblimersen for cancer therapy have been raised [90]. Although several studies reported a decrease in Bcl-2 concentrations in peripheral mononuclear cells of treated patients [77, 91], others failed to observe such a decrease [81]. Thus, one of the main issues is that the expression of Bcl-2 in tumor cells may not be reduced by oblimersen to a sufficient extent to substantially abrogate its function. Moreover, the drug has been shown to have offtarget toxicity able to chemosensitize a melanoma cell line in a Bcl-2-independent manner [92]. The mitochondrial membrane channel-forming protein Voltage-dependent anion channel (VDAC) has been identified as a target for oblimersen [93]. Another antisense oligonucleotide is being investigated in patients with CLL in a phase-1–2 study. This agent, SPC2996, appears to have higher potency than oblimersen in nonclinical testing. SPC2996 is a novel high-affinity and biologically stable RNA analog in which the normally flexible ribose sugar ring is fixed in a rigid conformation through a methylene 2′-O, 4′-C linkage, a so called locked nucleic acid (LNA). This fixed conformation brings substantial advantages to the design of effective RNA-binding drugs, and enables single-stranded LNA oligonucleotides, termed “RNA antagonists,” to have superior efficacies in vivo in downregulating target mRNA compared with oligonucleotides based on other chemistries or to short interfering RNA [94]. Another approach to counteract the antiapoptotic function of Bcl-2 is by molecules that prevent its interactions with proapoptotic family members, thus shifting the balance to apoptosis activation. For this purpose, BH3 peptides derived from BAD or BID, which were modified to facilitate translocation through the cell membrane, were shown to be effective in killing leukemic cell lines [90]. The stabilization of the α-helix confirmation of BH3 peptides appeared to be one of the best approaches to obtain favorable characteristics, including high affinity for Bcl-2, effective cell entry, and protease resistance. A hydrocarbon-stapled stabilized BID BH3 peptide was able to effectively kill leukemia cells in vitro and in vivo, indicating that this approach has tumor selectivity [95]. From a mechanistic standpoint, BH3 peptides derived from activator BH3 only proteins (BID, BIM) will be able to directly trigger apoptosis, whereas those derived from BH3 sensitizers will act by releasing proapoptotic proteins that were sequestered by antiapoptotic family members [90]. The design or discovery of nonpeptide small molecule inhibitors of Bcl-2 and Bcl-XL based on the resolved structure of Bcl-2 and the Bcl-XL/Bak peptide complex [96] is an alternative approach for developing new cancer agents. Gossypol, a natural polyphenol derived from cotton plants, known as a

F.A.E. Kruyt et al.

male contraceptive and having potential anticancer properties [97, 98], was found to bind the BH3 pocket of Bcl-2 and Bcl-XL and thereby antagonize their activities [99]. A racemic mixture of gossypol, AT-101, is being evaluated in an ongoing phase-1 study in patients with CLL [100]. Seven patients have been treated with orally administered AT-101 at doses of 20–40 mg daily, and the drug was well tolerated and showed no severe toxicities. Apoptosis activation and Poly(ADP-ribose) polymerase (PARP) cleavage could be demonstrated in vivo in leukemia cells. Furthermore, an analog of gossypol missing the two aldehyde groups, called apogossypol, has been developed that binds both Bcl-2 and Bcl-XL and was shown to potently kill cancer cells [101]. A number of groups have reported the design and identification of different types of small molecule Bcl-2 antagonist using either structure-based computer screening methods or screens of chemical libraries for BH3 pocket-binding agents [102–105]. These agents usually do not only target Bcl-2, but also Bcl-XL and Mcl-1, although with varying affinities (Ki in the order of 100 to 1,000 nM). Several agents demonstrated promising antitumor activity in cancer cell cultures and tests in mice models are awaited. Recently, a novel small molecule was generated, ABT-737, with a two- to three– order-of-magnitude higher binding affinity (Ki < 1 nM) for Bcl-2, Bcl-XL, and Bcl-w compared with the other available small molecule inhibitors. ABT-737 had cytotoxic effects as single agent in lymphoma cells and in SCLC cell culture and mice models, whereas it enhanced chemotherapy-induced cell death in other tumor types [106]. GX15-070, an antagonist of the BH3 binding groove, has entered the clinic and dose escalation is still progressing with some hints of activity [107]. Furthermore, a number of compounds are in nonclinical testing including HA14-1 [108]. Taken together, although very promising, further testing of these drugs is needed to assess their value as new anticancer drugs.

12.6

Targeting the IAP Family

The IAP family is a group of structurally related proteins characterized by the presence of a zinc-binding motif termed the BIR domain. Eight human IAP proteins have been identified: • XIAP (hILP/MIHA/BIRC4), • cIAP1 (HIAP2/MIHB/BIRC2), • cIAP2 (HIAP1/MIHC/BIRC3), • hILP-2 (TS-IAP/BIRC8), • Livin (ML-IAP/KIAP1/BIRC7), • NAIP (BIRC1), • Apollon (BRUCE/BIRC6) • and Survivin (TIAP1/BIRC5) [6]. In addition to bearing one to three copies of the BIR domain, some IAP contain other structural motifs, such as the caspase recruitment domain (CARD) or the really interesting new gene (RING) domain, which may contribute to their function

12. Apoptosis Pathways and New Anticancer Agents

263

Table 12-3. Anticancer agents targeting inhibitor of apoptosis proteins and modulators. Drug

Company/Institution

Embelin (small molecule inhibitor of XIAPBIR3, Smac mimic) Compound 3 (Smac mimic, small molecule) AEG35156 (antisense to XIAP) LY2181308 (antisense to survivin) SPC3042 (antisense to survivin) YM155 (small molecule to survivin)

University of Michigan

Nonclinical

Phase

124

Ref

UT Southwestern

Nonclinical

125

Aegera Therapeutics

1

128, 129

Eli Lilly/ Isis Pharma- 1 ceuticals Santaris Pharma Nonclinical

137

Astellas Pharma

139, 140

2

138

and/or regulation. Although they constitute a functionally heterogeneous family, certain IAP have an important role in the negative regulation of apoptosis, and two members of the family, XIAP and survivin, have attracted attention in recent years as potential therapeutic targets [109, 110]. The current stages of development of these agents are summarized in Table 12-3. Several lines of evidence have contributed to validate XIAP and survivin as targets for anticancer treatment. In vitro experiments have shown that XIAP and, albeit less efficiently, Survivin may counteract chemotherapy-induced apoptosis when ectopically overexpressed in cultured tumor cells [5]. Conversely, artificially reducing the levels of expression of either IAP has been shown to sensitize tumor cells to treatment with anticancer agents [111, 112]. On the other hand, immunohistochemical analyses indicate that high expression levels of XIAP and survivin in cancer cells correlates, at least in some tumor types, with unfavorable patient prognosis [113, 114]. Importantly, overexpression of survivin is detected in a wide range of malignancies, but its levels are low in most adult human tissues.

12.6.1

by endogenous antagonists of XIAP, such as Smac/DIABLO [117, 118] In cells undergoing apoptosis, a processed form of Smac is released into the cytosol after permeabilization of the mitochondrial outer membrane. The extreme amino terminal end of this so-called “mature” Smac contains a sequence of four amino acids (Ala-Val-Pro-Ile [AVPI]), which is necessary for binding to XIAP and relieving XIAP-mediated caspase inhibition [119]. Two main strategies have been adopted to inhibit XIAP. With one, small molecules that interfere with XIAP antiapoptotic activity are undergoing nonclinical evaluation [114], and with the other, an antisense oligonucleotide aimed at reducing the expression levels of XIAP is in clinical trials.

12.6.1.1

Small Molecules Targeting XIAP Function

Building on the detailed information available on the XIAP function and regulation, several compounds that block XIAP have been identified. Using high throughput in vitro fluorogenic assays, a large number of molecules from chemical libraries were screened for their ability to de-repress caspase-3 enzymatic activity in the presence of recombinant XIAP. This approach led to the identification of two classes of compounds that target XIAP BIR2: the aryl sulfonamides [120] and the polyphenylureas [121]. In addition to having proapoptotic activity when applied to tumor cells in culture, the polyphenylureas have been shown to delay tumor growth in xenograft models of prostate, breast, and colon carcinoma [114]. On the other hand, several compounds that mimic the endogenous mechanism of Smac to induce caspase de-repression have been developed. These “Smac mimic” compounds include nonnatural tripeptides [122, 123], the natural benzoquinone embelin [124], and a symmetric tetrazoyl thioether termed compound 3 [125]. Nonclinical experiments using cultured cancer cells have shown that, depending on the compound and the model cell line used, Smac mimics may directly induce apoptosis in certain cases or, most commonly, sensitize tumor cells to treatment with chemotherapeutic agents, with TRAIL and TNFα [114, 126].

XIAP

XIAP contains three tandem BIR domains, termed BIR1-3, and a carboxy-terminal RING domain. XIAP is a potent caspase inhibitor that binds and inactivates caspase-9, -3, and -7. Accumulating evidence from structural and biochemical analyses indicates that XIAP is probably the only member of the IAP family that functions as a direct inhibitor of caspase activity, and the molecular mechanisms that underlie XIAPmediated caspase inhibition have been dissected to great detail [115, 116]. XIAP inhibits caspase-9 using an allosteric mechanism that requires its BIR3 domain. On the other hand, inhibition of caspase-3 and -7 involves the sterical occlusion of the caspase active site by XIAP, and is mediated by the BIR2 domain and BIR2-flanking sequences. Once the cell is committed to undergo apoptosis, the blockade of caspase activity imposed by XIAP needs to be relieved

12.6.1.2 Antisense-Mediated Targeting of XIAP Expression Decreasing the levels of expression of XIAP by using antisense oligonucleotides has been shown to lower the apoptotic threshold of tumor cells resulting in direct cell death or sensitization to chemotherapeutic agents [112, 127]. AEG35156/ GEM640 is a 19-mer second-generation antisense oligonucleotide directed against XIAP mRNA. In nonclinical studies, this compound efficiently reduced the levels of XIAP mRNA and protein, and showed antitumor activity in xenograft models of different types of human cancer [128]. AEG35156 is the only anti-XIAP agent to have advanced into clinical testing to date. The first phase-1 trial of AEG35156 as single agent given as a 7-day continuous infusion in advanced tumors began in 2004 [129]. Hints of activity have been observed in a patient with

264

F.A.E. Kruyt et al.

NHL and another patient with breast cancer. Dose-limiting toxicities have been thrombocytopenia and increased transaminases. Two other phase-1 studies are ongoing, the first combining AEG35156 with docetaxel in patients with solid tumors, and the second combining AEG35156 with idarubicin in patients with AML.

12.6.2

Survivin

Survivin is the smallest member of the IAP family, contains a single BIR domain, and it is unique among the IAP for its strong association with cancer [130, 131]. Whereas survivin is undetectable in most normal adult tissues, high levels of survivin are detected in many tumors, and survivin overexpression is frequently an indicator of poor patient prognosis [113]. Although expression of survivin has been observed in adult thymus and in some types of blood cells [132], the largely tumor-specific expression of this protein predicts that survivin-directed therapeutic agents may have a favorable toxicity profile. Thus, a considerable effort is being made towards the development of anti-survivin compounds [133]. The molecular mechanisms that underlie the antiapoptotic activity of survivin have been the subject of some controversy, and remain to be fully elucidated. In contrast to XIAP, survivin does not appear to act as a direct inhibitor of caspases and may instead rely on interactions with other cellular proteins to counteract apoptosis [134]. In addition to its function as a negative regulator of apoptosis, a role for survivin as a chromosomal passenger protein that regulates mitotic progression has been established [135]. Experimental interference with survivin expression or function in different model systems has been reported to induce apoptosis and sensitization to the chemotherapeutic drugs, but also to lead to mitotic aberrations, such as defective spindle checkpoint and altered cytokinesis [134, 135]. The different experimental settings may be partially responsible for the ongoing controversy regarding the physiological role of survivin. Attempts to obtain an integrative view of survivin function are further hampered by the existence of several survivin isoforms, which may be functionally different. From the point of view of anticancer therapy, the diverse roles of survivin in critical cellular processes often disrupted in tumor cells, such as apoptosis and cell division, suggest that targeting survivin may be advantageous compared with targeting other molecules involved in a single oncogenic pathway [136]. In addition to the multiple strategies (e.g., hammerhead ribozymes, small interfering RNA, and dominant negative mutants) that are being explored at the nonclinical level [134], two survivin-directed agents are presently undergoing clinical evaluation. The first agent, LY2181308, is a second-generation antisense oligonucleotide that targets survivin mRNA. After promising results in nonclinical studies, LY2181308 entered phase-1 clinical trials [137]. Another antisense oligonucleotide against survivin (SPC3042) is in the nonclinical phase of development [138]. Another compound, YM155, is

a small molecule that interferes with the expression of survivin. Phase-2 studies of YM155 are ongoing in patients with melanoma, NSCLC, hormone-refractory prostate cancer, and NHL [139]. The dose-limiting toxicity a phase-1 study was renal insufficiency [140]. The drug is administered by 7-day continuous infusion, because of its relative short half-life. Antitumor activity was observed in the phase-1 study. Two patients with chemotherapy-refractory NHL and one patient with prostate cancer had confirmed responses. Another phase-1 study is ongoing, where dose intensification is attempted by increasing hydration.

12.7

Conclusions and Future Perspectives

Targeting key apoptotic regulatory mechanisms in cancer cells is a promising strategy for the development of novel and improved therapeutic agents. Agonistic Ab or recombinant proteins have been developed that target apoptosis inducers located on the cell membrane such as the TRAIL-R. The targeting of intracellular apoptotic targets, including IAP and Bcl-2 family members, is usually based on the development of antagonistic molecules that interfere with protein-protein interactions. Antisense strategies, such as oblimersen, should be critically evaluated because it may have off-target activity. In nonclinical models, these strategies have shown promising results. Currently available data from clinical studies also hold promise as they showed mild toxicities. As for all targeted strategies, it will be important to preselect patients that will benefit from these approaches. Because cancer is highly heterogeneous and apoptosis can be deregulated in many different ways, a specific targeting strategy for each tumor type and perhaps within a tumor type may be required. Therefore, ongoing efforts are directed to further delineate the basic mechanism underling apoptosis (de)regulation in cancer cells, which will also provide additional drug targets. In addition, it will be essential to obtain reliable biomarkers that will greatly facilitate the patient selection process and contribute to the efficacy of these new therapeutics.

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Chapter 13 Genomic Instability, DNA Repair Pathways and Cancer Gabriel Capellá, Josep Balart, and Miguel Angel Peinado

13.1

The Genetic Basis of Cancer

Cancer cells share a number of characteristics including selfdependance on positive regulatory signals; lack of response to growth inhibitory signals, limitless proliferation, resistance to apoptosis, capability of getting nutrients and oxygen by angiogenesis, and the ability to invade and establish distal metastasis [1]. It is currently accepted that, underlying acquisition of the malignant phenotype, cells accumulate mutations in two classes of genes— proto-oncogenes and tumor suppressor genes (TSG)—through a multistage process [2, 3]. Most of the mutations that contribute to the development and behavior of cancer cells are somatic (i.e., arise during tumor development and are present only in the neoplastic cells of the patient). Only a small fraction of all mutations in cancer cells are present in the germline predisposing to cancer. TSGs have been defined as those genes inactivated by germline or somatic mutations in cancer [4]. Two types of suppressors can be envisioned: gatekeepers and caretakers [2, 5]. Gatekeepers act directly to regulate cell proliferation or apoptosis or both, and are rate limiting for tumorigenesis, an example being the Adenomatous Poliposis Coli (APC) gene in human colorectal tumorigenesis [6] or the RB gene in retinoblastomas. Caretakers, also affected by inactivating mutations in cancer, do not directly regulate proliferation. When mutated they lead to accelerated conversion of a normal cell to a neoplastic cell through an increased frequency of mutations in other cellular genes, particularly genes that are rate determining in tumor development [5]. It must be emphasized that distinguishing between what constitutes a growthregulating TSG versus a DNA repair-type TSG may be difficult.

13.2 Genomic Damage, Cell Heterogeneity, and Genetic Instability are Characteristic of Tumor Cells The relevance of DNA damage and repair to carcinogenesis became evident when it was recognized that agents causing cancer—carcinogens—can be also mutagens that change the DNA From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

sequence. All the effects of carcinogenic chemicals, ultraviolet (UV) radiation, or ionizing radiation on tumor production can be accounted for by the DNA damage that they cause and by the errors introduced into DNA during the cells’ efforts to repair this damage. Now, the contribution of endogenous DNA damage to cancer is increasingly recognized [7]. Undue DNA replication, loss of bases because of spontaneous disintegration of chemical bonds, and DNA damage secondary to endogenous reactants such as alkyl groups, metal cations, and reactive oxygen species (ROS) are important sources of DNA damage. When cells fail to adequately repair the acquired damage, carcinogenesis may occur. A link between carcinogenesis and failure of DNA repair was suggested when humans with inherited genetic defects in certain repair systems such as xeroderma pigmentosum were shown to have an enormously increased probability of developing certain cancers [7]. Multiple genomic alterations including aneuploidy, deletions, translocations, amplifications, and point mutations are characteristic of tumor cells. Although mutations are not the only processes affecting tumor progression—immune response, hormones, gene expression, and cell interaction may be also important—it is likely that they are the most relevant [3, 8]. Foulds et al. postulated a tumor dynamic model in which instability would result in increased mutation rate that in turn would facilitate progression from the earliest stages of the disease [9]. To account for the disparity between the rarity of mutations in normal cells and the huge number of alterations detected in tumor cells, Loeb proposed the mutator phenotype hypothesis [10–12]. He argued that an early step in tumor progression is the expression of a mutator phenotype resulting from mutations in genes that normally guarantee the fidelity of DNA synthesis or the adequacy of DNA repair. Mutations in these genetic stability genes could then produce additional mutations throughout the genome affecting both genes controlling growth and genes playing other roles in maintenance of DNA instability. Instability would result in constant appearance of heterogeneous cells that would be eventually selected based upon their phenotype: angiogenic ability, metastatic potential, or drug resistance, among others. The identification of aberrations in the mismatch repair (MMR) genes as responsible of microsatellite instability (MSI) in a subset of colorectal tumors is the first example of a mutator 269

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phenotype as an engine of tumor progression [13–15]. By analogy to MSI, Lengauer and coworkers postulated the existence of chromosomal instability in most sporadic colorectal tumors [16–18]. Nonetheless, the mechanisms underlying this specific type of genetic instability remain unknown although a putative role of APC (Adenomatous Poylposis Coli) mutations have been suggested [19]. The role of genetic instability in tumorigenesis is still a matter of controversy as it has been suggested that mutation accumulation could occur exclusively with selection in the absence of an increased mutation rate [18, 20–23]. Others have postulated that the peculiar dynamics of stem cells—that lacking effective DNA repair systems are highly prone to apoptosis—are key elements in our comprehension of tumor dynamics [24].

13.3

Responses to DNA Damage

DNA damage elicits a number or responses including: sensing and recognizing DNA damage by activation of cell cycle checkpoints, pauses that permit assessment and completion of DNA processing, either DNA damage repair or processing of DNA intermediates; ● the upregulation of a large number of genes; ● apoptosis when the cell is unable to repair the damage sustained; and ● the multiple distinct DNA repair responses [25]. ●

In this section, we briefly describe what is known about and the six major DNA repair pathways namely, base excision

repair (BER), nucleotide excision repair (NER), the two double-strand break repair systems: homologous recombination (HR) and nonhomologous endjoining (NHEJ), MMR, and translation DNA synthesis (Fig. 13-1). Also, another specific repair pathway, the methylguanine-methyltransferase (MGMT) will be described and the importance of DNA damage sensing summarized.

13.3.1 13.3.1.1

DNA Repair Pathways Base Excision Repair (BER)

Small chemical adducts (methylated or oxygenated bases), usually of endogenous origin, and regions of single-strand breaks can be repaired by this pathway that plays an important role in repair of ROS-induced damage [26] (Fig. 13-1). Notably, the oxidation product 8-oxoG is stable and readily mispairs with adenine (instead of cytosine). Unless repaired, this leads to G:C to T:A transversion at the next round of DNA replication. After recognition of the adduct, DNA glycosylases (up to 10 distinct enzymes have been identified in humans) excise the modified base leaving an apurinic or apyrimidinic site (AP). MutYH is a DNA glycosylase that plays a key role in BER of 8-oxoG:A (and G:A) mismatches by removing the mismatched adenine [27]. Other key components include OGG1 (8-oxoG DNA N-glycosylase 1), an orthologue of MuTM, that removes 8-oxoG from duplex DNA, and MTH1, a MutT orthologue that hydrolyses 8-oxoG to prevent its incorporation into nascent DNA [28]. Then a complex composed of APE1 (apurinic endonuclease), DNA

MAIN TYPES OF DNA REPAIR Endogenous origin (i.e. ROS)

Exogenous origin (i.e UV)

Ionizing radiation Chemicals

Replication errors

C

Oxo-G T T Non distorting adducts

BER

Helix distorting adducts

NER Tranlesion synthesis

T Double Strand Break

Homologous Recombination Non-homologous end joining

Mismatch Insertion deletion

MMR

Fig. 13-1. Main types of DNA repair. Schematic representation of the main types of DNA repair pathways depicting origin of the damage, type of damage produced, and repair pathway involved. (see Color Plate 6 following p. 316.)

13. Genomic Instability, DNA Repair Pathways and Cancer

polymerase β, and DNA ligase 3 in combination with XRCC1 targets the site to produce a short patch of repaired DNA [25]. Alternatively, in a few cases, a repair involving a long patch of DNA can also occur.

13.3.1.2

Nucleotide-Excision Repair (NER)

NER removes more than one base in response to adducts resulting in helix-distorting conformation, such as those of heterocyclic aromatic amines or polycyclic aromatic hydrocarbons, that impairs transcription and normal replication (Fig. 13-1). It is the most versatile system in terms of lesion recognition. Two NER subpathways have been proposed: global genome NER and transcription-coupled repair (TCR) that focus on damage that blocks elongating RNA polymerases [25, 29]. Although it is a controversial matter, the latter has been considered a distinct pathway of repair, a position supported by findings in Cockayne syndrome [25]. The global genome NER process includes: damage recognition (involving XP group A, XP group C, and RPA proteins and the TFIIH complex of transcription machinery); ● incision of the DNA strand (performed by XP group G and XP group F complexed with ERCC1); and ● removal of the adduct that is embedded in a DNA 25-mer. ●

Resynthesis is performed by polymerases δ or ε interacting with PCNA (proliferating cell nuclear antigen) followed by a final ligation step. Transcription-coupled NER machinery is quite different. Arrested transcription by RNA polymerase II is believed to result in recruitment of a multimeric complex composed of XP proteins, MSH proteins, CSA and CSB proteins, Breast Cancer 1 gene (BRCA1), and Breast Cancer 2 gene (BRCA2). Then NER or BER machinery is recruited according to the type of lesion encountered during transcription [29]. The presence in this complex of proteins involved in other types of repair reinforces the notion of overlapping between the distinct DNA repair pathways.

13.3.1.3 Double-Strand Breaks (DSB) Homologous Repair System DSBs are potent inducers of mutations and cell death [30]. They arise from ionizing radiation or X-rays, free radicals, and chemicals (Fig. 13-1), but they can also occur during replication of a single strand break (SSB) or in collapsed replication forks. Failure to repair DSB can lead to a number of consequences, including mutations, gross chromosomal rearrangements, other aberrations, and eventually cell death. Sensing of DSB is mainly performed by ATM (ataxia telangiectasia) or ATR (ataxia-telangiectasia and Rad53-related) proteins, DSBs are difficult to repair because the cell has to know which ends belong together without the other copy of the DNA molecule. When a second identical DNA copy is available, usually during G2/M phase when sister chromatids

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are close, homologous recombination seems to be preferred because it is an error-free system. Main components of the homologous recombination system include the multiprotein RAD50/MRE11/NBS1 nuclease complex [31] that resects flush DSB to generate single-strand DNA tracts amenable for repair [30]. This complex is likely to play other roles such as DNA damage signaling [31]. Then a RAD51 complex, that includes XRCC2 and XRCC3 as well as BRCA1 and BRCA2, forms a nucleoprotein filament that searches for the homologous duplex. DNA synthesis is followed by ligase action after gap filling and subsequent resolution of Holliday junctions by resolvases, such as members of the RecQ family of DNA helicases (i.e., BLM, WRN, and RecQL4) [32]. Although BRCA2 controls RAD51 recombinase activity, the role of BRCA1 is apparently a more general one, interacting both with sensing/ signaling functions and effector components (i.e., RAD51, RAD50, Rb (Retinoblastoma), and p53 proteins among others) of the DSB HR pathway [5].

13.3.1.4 Double-Strand Breaks (DSB); Nonhomologous End Joining (NHEJ) Repair System NHEJ is another major pathway used to repair DSB that is more error prone because it does not rely on an additional DNA copy [33] in which the DNA-dependent protein kinase plays a critical role [34]. It consists of the catalytic subunit and the regulatory subunit (the Ku70/Ku80 heterodimer). The DNA protein kinase catalytic subunit is a Ser/Thr kinase that belongs to the phosphatidyl inositol-3 kinase family. The Ku80/Ku70 heterodimer (Ku) exhibits sequence-independent affinity for double-stranded termini and on binding to DNA ends recruits and activates the catalytic subunit. Additional proteins are required for the completion of NHEJ, including the artemis protein and DNA ligase IV. The fusion of the blunt-ended DNA duplexes may result in deletion or insertion of base pairs. The role of the MRE11 nuclease complex in this process has not been clarified.

13.3.1.5

Mismatch Repair (MMR)

This system aims to repair single base substitutions usually secondary to errors occuring during DNA replication. These errors, that occur at a higher frequency at mono- or dinucleotide repeats because of DNA polymerase slippage, are thought to occur 1 per 1010 events (Fig. 13-1). This is the final outcome of the opposite effects of misincorporation of processive polymerase holoenzymes (1 per 105 errors) versus the combined efforts of the efficiency of editorial functions of polymerases (99.9%), mainly through their exonuclease function, and the efficiency of MMR system (99%). The MMR system in mammals is complex and is effected, in humans, by six known MutS Escherichia coli homologues and three MutL homologues [35]. The MMR system scans DNA in search for mismatches as well as insertion/deletion loops ranging from 1 to 10 or more bases [36]. MSH2 forms heterodimers with MSH3 and MSH6 that are able to recognize these errors.

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hMutSβ (hMSH2-hMSH3) and hMutSα (hMSH2-hMSH6) complexes may show distinct specificity in error recognition [37]. Once identified, a second complex formed by hPMS2 and hMLH1 completes DNA excision after strand discrimination. Recognition of the parental or correct strand is critical. Although, in E. coli, strand methylation plays a critical role in recognition, the molecular basis of this process in mammals remains unknown [38]. Finally, polymerases, endonucleases, and other proteins contribute to complete the repair process. Again, the components of this repair pathway show complex interactions with other DNA repair proteins participating in the NER system [39] and the HR repair of DSB [36] suggesting additional roles in genome maintenance.

13.3.1.6

Translation Synthesis

The process of translation synthesis is another mechanism for dealing with thymine dimers and bases with bulky chemical adducts. At a DNA replication fork, DNA adducts may cause a replicative polymerase, such as DNA polymerase ∆, to stall. Cells have therefore developed sophisticated mechanisms for switching off the replicative polymerase and switching on alternative polymerases (i.e., a polymerase such as pol ß, which will replicate past certain DNA lesions with high fidelity) [40]. Human cells have at least 10 DNA polymerases, although the mechanisms of their deployment are largely unknown [41]. Cancer cells may have a heightened dependence on one of the error-prone TLS polymerases, such as polymerases ß or κ, accounting for high rates of mutagenesis [42].

13.3.1.7 Other Types of Repair: Methylguanine- Methyltransferases (MGMT) or O6-Alkylguanine-DNA Alkyltransferase (ATase) In addition to these coordinate repair pathways involving several components, single-repair proteins can revert specific injuries. Alkylating agents induce mutations and promote carcinogenesis, cell death, chromosome damage, and cell cycle arrest. Alkylating agents transfer unsubstituted alkyl groups such as methyl or ethyl to nucleophylic sites in macromolecules. One of the many alkylation lesions in DNA is O6-alkylguanine [43]. O6-methylguanine is one of the most studied of alkyl-damaged bases. These lesions are highly mutagenic because A:T transition mutations arise at sites of 06-alkylguanine after two cycles of DNA replication. The activity in charge of repairing this system was initially characterized as alkyltransferase (ATase) because they remove all types of alkyl modification with distinct specificity. After repair, the enzyme is inactivated and committed to ubiquitination.

13.3.2

DNA Damage Sensing and Signaling

The first step in the response elicited by DNA damage includes sensing and recognizing DNA damage followed

by activation of cell-cycle checkpoints. The defining feature of a cell-cycle chekpoint [44] is that it serves as a quality-control system to couple sequential events within the cell cycle. Inadequate sensing and signaling of any DNA damage can be as harmful to genetic stability as any specific DNA repair defect [45]. An inappropriate repair can result in mutagenic repair of DNA damage associated with low apoptotic cell death [46]. Three distinct DNA damage checkpoints act during cell-cycle progression: the G1/S to prevent starting replication; intraS to prevent progression with replication; and G2/M to prevent going into mitosis. Sensing DSB is a crucial component of a DNA repair pathway [47]. The product of the Ataxia telangiectasia mutated (ATM) gene, the protein kinase ATM, which is mutated in the cancer prone disorder AT, appears to be a major sensor of DSB present after exposure to ionizing radiation. After activation, ATM phosphorylates a number of different substrates including: p53, mdm2, Chk2, Nbs1, Brca1, and Smc1, all of them putatively involved in downstream signaling of the DNA damage response. A homologue of ATM, ATR, seems to be involved in the response to UV-induced damage playing a role in the recognition and repair of DNA repair complexes that have stalled at sites of DNA damage [5]. We have previously discussed well documented examples of the connection between DNA damage checkpoint genes (ATM, BRCA1, BRCA2, NBS1, (Bloom Syndrome) BLM, and (Werne Syndrome) WRN genes) and tumorigenesis. Nevertheless, the list of these proteins is not complete until p53 is added. p53 is a major genome guardian molecule in response to DNA damage [48]. After phosphorylation of specific residues by other signaling molecules (i.e., ATM gene product or Chk2 protein [49]) it mediates cell-cycle arrest by inducing p21 expression [50] or apoptosis after increasing bax expression [51], thereby preventing propagation of cells that have accumulated DNA damage. Under some circumstances, p53 acts at the G1/S checkpoint to regulate the cell’s decision to synthesize DNA, although p53 also appears to have a critical function at G2/M [52]. p53 is believed to be among the most frequently mutated genes in human cancer.

13.4 Aberrations in DNA Repair Pathways and Human Cancer 13.4.1 High-Penetrance DNA Repair Gene Mutations and Hereditary Cancer Predisposition Syndromes The observation that germline aberrations in DNA-repair cancer genes underly the hereditary predisposition to cancer has been critical in strengthening the role of genetic instability in cancer development. We briefly summarize the evidence that supports this fact (Table 13-1 and Table 13-2).

13. Genomic Instability, DNA Repair Pathways and Cancer

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Table 13-1. Human hereditary diseases associated with DNA-repair defects and cancer predisposition. Protein Base excision repair MYH

Function DNA glycosilase

Global genome nucleotide excision repair XPA, XPC Damage recognition/binding XPB, XPD Helicase in transcription complex XPE DNA XPF, XPG Endonuclease single stranded DNA XPB, XPD Helicase in transcription complex Polymerase η

Translesion synthesis at adduct

Transcription-coupled nucleotide excision repair CSA Transcriptional activator/transcription coupling CSB Transcriptional activator/transcription coupling Homologous recombination MRE11 Nuclease belonging to the multiprotein MRE11 complex NSB1 Protein of unknown function belonging to the multiprotein MRE11 complex BRCA1 Sensing/signaling function Participates in Transcription Coupled NER BRCA2 Control RAD51 recombinase activity Participates in Transcription Coupled NER BLM Helicase of the RecQ family involved in resolving Holliday functions WRN Helicase of the RecQ family involved in resolving Holliday functions RECQL4 Helicase of the RecQ family involved in resolving Holliday functions Mismatch repair HMSH2 hMSH6 Mismatch recognition Transcription Coupled NER DSB repair DNA excision Transcription-coupled NER hMLH1 DSB repair Translesion synthesis Polymerase η Translesion synthesis at adduct DNA damage signaling ATM P53 CHK2

Protein kinase; related to PI(3)K DNA damage signal transduction Transcription factor Serine/Threonine kinases

Human disease

Cancer predisposition

MutY-associated adenomatous polyposis

Colorectal adenomas and carcinomas

Xeroderma pigmentosum

Skin carcinomas and melanomas

Xeroderma pigmentosum Trichothiodistrophy Xeroderma pigmentosum variant form

Skin carcinomas and melanomas None Skin carcinomas and melanomas

Cockayne syndrome

None; early ageing

Cockayne syndrome

None; early ageing

Ataxia telangiectasia-like syndrome

Unknown

Nijmegen breakage syndrome

Lymphoma

Hereditary breast cancer

Breast, ovary

Hereditary breast cancer

Breast, ovary

Bloom’s syndrome

Whole spectrum

Werner’s syndrome

Sarcoma

Rothmund-Thomson syndrome

Sarcoma, osteosarcoma

Hereditary nonpolyposis colorectal cancer

Colon, endometrial, small bowel, urinary tract

Xeroderma pigmentosum variant form

Skin carcinomas and melanomas

Ataxia telangiectasia

Lymphomas

Li-Fraumeni syndrome

Breast, soft tissue sarcomas, brain tumors, osteosarcoma, leukemia, adrenocortical carcinoma

DSB, double-stranded break; NER, nucleotide-excision repair

13.4.1.1 Base Excision Repair; MutY-Associated Polyposis Familial adenomatous polyposis (FAP) is a well-characterized autosomal dominant disorder in which hundreds or thousands

of colorectal adenomas develop, usually during late childhood or early adult life [1]. FAP is usually caused by inherited mutations in the APC gene. An attenuated form of the disease, AFAP (attenuated familial adenomatous polyposis) also occurs, associated with smaller numbers of adenomas

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Table 13-2. DNA repair aberrations in cancer risk assessment. Type of DNA repair

Gene/s

Aberration

Usefulness

Hereditary cancer BER

MYH

Biallelic germline mutation

HR

BRCA1, BRCA2

Germline mutation

MMR

MSH2, MLH1, PMS2,

Germline mutation

DNA damage sensing

P53 Chk2

Germline mutation Germline mutation

Cancer risk BER

MYH

HR MMR

OGG1 XRCC1 BLM MLH1

Y165C and G382D variants 326Cys/Cys genotype 194 Trp allele BLMAsh D132H

DNA damage sensing

ATM P53

Heterozygote variant carriers R72P and other variants

Chk2

1100delC variant

Molecular diagnosis and risk assessment of MYH-associated polyposis Molecular diagnosis and risk assessment of hereditary breast cancer Molecular diagnosis and risk assessment of hereditary nonpolyposis colorectal cancer Molecular diagnosis and risk assessment of Li-Fraumeni syndrome

Sporadic colorectal cancer susceptibility allele Sporadic lung cancer susceptibility allele Increased risk of tobacco-related cancer Increased risk of sporadic colorectal cancer in Ashkenazim Sporadic colorectal cancer susceptibility allele in Ashkenazim but not in white population Breast cancer-susceptibility allele Modest association with invasive cervical cancer. Inconclusive results for other cancers Breast cancer susceptibility allele

BER, base-excision repair; HR, homologous recombination; MGMT, methyltransferase; MMR, mismatch repair; MSI, microsatellite instability; NER, nucleotide-excision repair

and later clinical presentation. Recently, both FAP and AFAP were associated with biallelic inherited mutations of the BER gene, MutYH (human MutY homologue), mainly Y165C and G382D, in the absence of demonstrable inherited mutations of APC [27].

13.4.1.2 Nucleotide Excision Repair and Translation Synthesis: Xeroderma Pigmentosum (XP) The prototype hereditary syndrome associated with defects in NER, xeroderma pigmentosum, is an autosomal recessive disease that results in a dramatic increase in the risk of developing skin cancer secondary to alterations in one of seven genes (XPA–XPG). Mutations in the different genes lead to xeroderma pigmentosum lesions, all of them displaying overlapping phenotypes [40]. A variant form of the disease has been associated with a defective translation DNA polymerase η. This polymerase is highly error prone and can process damaged DNA at an acceptable error rate. Although in critical situations it may be a survival tool for a cell, the price for that is DNA error accumulation [41, 42]; however the relationship between NER DNA repair aberrations and cancer is not straightforward. The remaining two hereditary syndromes associated with inborn defects in other proteins involved in NER—Cockayne syndrome and trichothiodystrophy secondary to alterations in members of the XP proteins—do not result in increased cancer risk (Table 13-1).

13.4.1.3 Double-Strand Break Repair: Hereditary Breast Cancer, Hereditary Pancreatic Cancer, and Other Rare Cancer-Prone Syndromes In hereditary breast cancer, the relationship between alterations in components of the DSB repair pathway and cancer is more evident (Table 13-1). The proteins involved in the homologous recombination pathway—BRCA1 and BRCA2—are associated with a more common cancer-prone disorder, familial breast cancer. This syndrome roughly accounts for 10% of all breast tumors. Up to 20% of the observed risk in these families can be attributed to mutations in BRCA1 or BRCA2 genes. When families show more than six affected relatives, BRCAs mutations may explain 80% of the risk [53]. BRCA1 and BRCA2 gene mutations confer aprroximately 15-fold risk of breast cancer and >70% of mutation carriers will develop the disease. Loss of heterozygosity (LOH) for the genes is frequently found in breast and ovarian tumors arising in affected relatives, supporting its role as TSG. It is important to know that patients with an homozygous mutation in BRCA2 gene develop Fanconi anemia, an X-linked or autosomal recessive cancer susceptibility syndrome characterized by hypersensitivity to DNA crosslinking agents that associate with an increased risk of developing acute myeloid leukemia [54]. In hereditary pancreatic cancer, BRCA2 genes have been associated with the cancer. As many as 10% of sporadic pancreatic cancer patients and 15–20% of patients with family history harbor BRCA2 germline mutations [55–57].

13. Genomic Instability, DNA Repair Pathways and Cancer

Inborn defects in genes of the RecQ family of DNA helicases (i.e., BLM, WRN, and RecQL4) are responsible for three syndromes [58] (Table 13-1). Bloom syndrome is characterized by proportional dwarfism, birdlike facies, photosensitive rash, reduced fertility, and premature ageing. The characteristic cellular feature of Bloom syndrome is genetic instability associated with an increased rate of sister-chromatid exchanges and affected individuals are at increased risk of a whole spectrum of malignant disease. Mutations in the BLM gene are the underlying cause of the disease [59]. Werner syndrome and Rothmund-Thomson syndrome, secondary to WRN and RECQL4 gene mutations, respectively, share several features with Bloom syndrome [5, 60]. The latter two syndromes asociate with an increased risk for sarcoma development.

13.4.1.4 Mismatch Repair: Hereditary Nonpolyposis Colorectal Cancer A subset (10–20%) of colorectal [61] and a similar proportion of other types of human tumors (endometrial and gastric cancers) exhibit ubiquitous MSI. This discrete type of genomic instability is characterized by small deletions or insertions within short tandem repeats in tumor DNA compared with the matching normal tissue [13]. MSI is the molecular symptom of a defective MMR machinery [62], and characterizes a distinctive tumor progression pathway known as the microsatellite mutator phenotype (MMP) [63]. Germline mutations—mainly truncating point mutations but also gross deletions—in the MSH2 and MLH1 genes account for roughly for 40% of hereditary nonpolyposis colorectal cancer (HNPCC) as defined by Amsterdam criteria [64] (Table 13-1). Interestingly, mutations inactivating other MMR genes (PMS1, PMS2, and MSH6/GTBP) genes have been seen in a small fraction of patients with HNPCC [65]. Altogether, germline mutations in the known MMR genes have only been detected in 1–2% of colorectal cancer patients, although approximately 10–15% of all colon cancers display the MSI phenotype. In most sporadic cases, the phenotype is caused by epigenetic inactivation of the MLH1 gene that may occur through epigenetic changes, such as DNA methylation of MLH1 transcriptional regulatory sequences [65, 66].

13.4.1.5

DNA Damage Sensing

AT is a cancer prone, X-ray–sensitive syndrome secondary to mutations in the ATM gene. Affected individuals, usually compound heterozygotes or homozgotes for identifiable mutations, develop progressive cerebellar ataxia and have a 30–40% lifetime risk for lymphoid malignancies [32]. AT cells show spontaneous chromosomal instability and fail to suppress DNA synthesis in response to ionizing radiation. Mutations in the MRE11 multiprotein nuclease complex also result in an ataxia telangiectasia-like syndrome characterized by milder AT symptoms and lack of telangiectasias; however, its cancer risk is currently unknown [31].

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Germline mutations in the p53 gene have been seen in those affected by the Li-Fraumeni syndrome (LFS), a cancer-prone syndrome at a very increased risk for the development of a number of tumors, including soft-tissue sarcomas, osteosarcomas, brain tumors, breast cancers, and leukemias [67]. LFS provides genetic evidence for a common pathway of p53 and Chk2 genes: germline mutations in Chk2 have been reported in LFS families lacking p53 mutations [68]. In most of the previously described situations detection of germline aberrations is used in the clinical setting as an effective tool in assessing cancer risk in members of these cancer-prone families. The possibility of idenfifying susceptible carriers is critical to improved survival in affected families because new opportunities for early detection and prevention can be tested in the right population.

13.4.2 Low-Penetrance Variants of DNA Repair Genes as Cancer-Susceptibility Alleles Genetic susceptibility has been postulated as an important contributor to the etiology of cancer in general. It has been hypothesized that the presence of low-prevalence variants that modestly affect DNA repair ability may result in an increased risk to develop tumors by increasing the number of mutations accumulating in target cells in the earliest stages of tumorigenesis [69]. Obvious candidates are variants (attenuated alleles) of the genes responsible for the hereditary cancer syndromes that may underlie susceptibility to sporadic cancer. The recent identification of this association in some tumor types further supports the importance of genomic instability as a critical element in the pathogenesis of cancer (Table 13-2)

13.4.2.1

Base Excision Repair

Heterozygote carriers of MutY homology (MYH) variants Y165C and G382D are at increased risk of sporadic colorectal cancer [70, 71] although its role is still somewhat controversial. On the other hand, an increased risk of lung cancer among subjects carrying the OGG1 326Cys/Cys genotype has been observed [72]. A protective effect of the XRCC1 194 Trp allele for tobacco-related cancers has been reported [73].

13.4.2.2

Double-Stranded Break Repair

Carriers of the RecQ DNA helicase BLM mutation have been shown an increased risk for colorectal cancer in Ashkenazim [74].

13.4.2.3

Mismatch Repair

Association studies identified a new MLH1 variant (415G– >C, resulting in the amino acid substitution D132H) in approximately 1.3% of Israeli individuals confers clinically significant susceptibility to MSI(-) colorectal cancer [75] that was not confirmed in American population [76].

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13.4.2.4

DNA Damage Sensing

AT heterozygous variant carriers are present in up to 1% of the population and may be also responsible for a large amount of background genetic influence on the incidence of cancer in the population [77]. Nearly 20 years ago, epidemiologic surveys of relatives of AT cases suggested that female relatives were at modestly increased risk of breast cancer. Recently, large epidemiologic and molecular studies have finally provided conclusive evidence that heterozygote carriers of ATM mutations that cause AT are breast cancer-susceptibility alleles [78]. Fourteen polymorphisms have been identified in the TP53 gene, (http://www. iarc.fr/p53/Index.html), the most studied being R72P. Although the wild-type Arg form was found to be more susceptible than the variant Pro72 to degradation by the E6 onco-protein after human papilloma virus infection [79], epidemiologic evidence is suggestive of a modest association of the Arg/Arg homozygotes with invasive cervical cancer [80]. In other tumor types such as colorectal cancer, p53 variants may contribute to its development [81]. In addition, the Chk2 1100delC variant, associated with Li-Fraumeni syndrome is a low-penetrance breast cancersusceptibility allele [82] that is particularly common in families predisposed to combined breast and colon cancer [83]. A general consideration must be taken into account regarding evidence provided by association studies of low-penetrance variants. Low statistical power linked to limited sample size, heterogeneity across studies, or publication bias usually result in inconsistent results precluding drawing more definitive conclusions [84].

13.4.3 DNA Repair Biomarkers in the Prediction of Response 13.4.3.1 Markers of DNA Repair and Cisplatin Sensitivity Loss or increased activity of DNA repair pathways is likely to influence the response to DNA-damaging therapeutic strategies (Table 13-3). In a number of clinical settings, it

has been shown that failure of DSB repair pathways may sensitize to alkylating chemotherapeutic agents. Decreased expression levels of BRCA1 have been associated with better survival after cisplatin-based chemotherapy for nonsmallcell-lung cancer (NSCLC) [85]. Methylation-specific PCR, which indicates loss of gene expression through promoter methylation, correlates with cisplatin sensitivity in ovarian cancer in retrospective studies [86]. In testicular tumor cell lines, constitutive low levels of the nucleotide excision repair proteins XPA, XPF, and ERCC1 could be related to the favorable response of testis tumors to cisplatin-based chemotherapy [87, 88]. Adjuvant cisplatin-based chemotherapy improves survival among patients with completely resected NSCLC. Patients with completely resected NSCLC and ERCC1-negative tumors appear to benefit from adjuvant cisplatin-based chemotherapy, whereas patients with ERCC1-positive tumors do not [89]. The accumulation of polymorphic variants in NER (XPD and ERCC1) and BER (XRCC1) significantly increases the probability of achieving a complete response to treatment in advanced head-andneck squamous cell carcinomas [90].

13.4.3.2

MGMT and Gliomas

MGMT is frequently inactivated, mainly by epigenetic mechanisms, in several tumor types including colorectal [91] and central nervous system neoplasms, thus contributing to acquisition of additional genetic alterations. When adductpromoting chemotherapeutic treatments such as BCNU [92] or temozolamide [93, 94] are administered, MGMT lack, measured as mRNA or protein levels or through promoter hypermethylation, confers sensitivity to the chemotherapeutic treatment being used as the tumor cell is unable to repair the drug-induced DNA damage.

13.4.3.3

MSI and Response to 5-FU

MSI, as a symptom of a defective MMR system, has a strong impact on colorectal behavior. A meta-analysis showed that

Table 13-3. DNA repair aberrations in sporadic tumors. Type of DNA repair

Gene/s

Aberration

Usefulness

Prognosis MMR

MSH2, MLH1,

MSI (+) Lack of MSH2, MLH1 immunostaining

Better prognosis in colorectal cancer

Prediction of response HR

BRCA1

Decreased mRNA levels

HR

BRCA1

Promoter hypermethylation

NER

XPA, XPF, ERCC1 ERCC

Low protein expression Low protein expression

NER/BER

XPD, ERCC1, XRCC1

Accumulation of variants

Alkyl-damage repair

MGMT

Promoter hypermethylation

Better outcome in nonsmall-cell lung cancer after cisplatin treatment Better outcome in ovarian cancer after cisplatin treatment Cisplatin sensitivity in testis tumor cells Benefit from adjuvant cisplatin-based chemotherapy in nonsmall cell lung cancer Better response to cisplatin-based chemotherapy in advanced head-and-neck cancer. Better response to temozolomide in gliomas

13. Genomic Instability, DNA Repair Pathways and Cancer

colorectal cancers with MSI have a significantly better prognosis compared with cancers with intact MMR [95]. It has also been suggested that stage II or stage III colon cancers with high MSI may not benefit from fluorouracil-based adjuvant chemotherapy [96–98]; however, additional studies are needed to further define the benefit of adjuvant chemotherapy in locally advanced tumors with MSI [95]. Finally, it must be mentioned that the measure of accumulated genomic damage within tumor cells, a symptom of altered DNA repair, may be also of use in the clinical setting [99]. The measure of abnormal DNA content by flow cytometry has been evaluated as a prognostic marker although, in general, no consistent conclusions have been reached to date [100]. Comparative genomic hybridization either conventional or array-based is the technique most frequently used as an alternative to classic cytogenetics, allowing the investigation of specific chromosomal alterations together with global patterns of chromosomal disruption. Although showing most promising results, their potential use as prognostic markers needs validation in prospective investigations performed in larger series [101].

13.5

Conclusions

The relevance of genetic instability and DNA repair to carcinogenesis has been reviewed. Its contribution to cancer development is well established and the importance of the role of caretakers TSG to tumor development is evident. Specific functional defects in DNA repair can be associated with hereditary cancer syndromes and, in some cases, variants of the same genes can be considered as sporadic cancer susceptibility alleles. Although far from a comprehensive understanding of the DNA-repair-complex world, we are beginning to use this knowledge in the clinical setting. The molecular diagnosis of hereditary cancer syndromes is a reality. The profound impact of the types of genetic instability in tumor biology is having prognostic applications and may also influence response to specific treatments at the bedside. Meanwhile, we need to gain insight into the molecular basis of sensing DNA damage and learn how cells discriminate between distinct types of damage and why they choose to use one repair system or the other. Finally, because abnormal function of cell cycle checkpoints may be as critical as specific DNA-repair defects in generating genetic instability, efforts should be devoted to improve our knowledge about their function. A better comprehension of these mechanisms will certainly result in novel oportunities for better cancer prevention, management, and treatment.

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Chapter 14 Epigenomics and Cancer Isabel López de Silanes and Manel Esteller

14.1

Introduction

Cancer genes are recognized by their altered gene expression or activity, or both, leading to an abnormal phenotype. Nearly every tumor type presents an enormous complexity of altered gene functions, including activation of growth-promoting genes as well as silencing of genes with tumor growthsuppressing functions, all contributing to uncontrolled growth. These changes provide the cell with a competitive growth advantage that is realized through at least five cancercell phenotypes: enhanced cell division, resistance to apoptosis, sustained angiogenesis, invasion of tissues and metastasis, and evasion of antitumor immune responses (reviewed in Reference 1). Traditionally, only mutated genes have been considered as candidate cancer genes. However, clearly many more genes present altered gene expression in cancer cells than are mutated [2]. Epigenetic changes, mainly DNA methylation and, more recently, modification of histones, are now recognized as additional mechanisms with a major contribution to the malignant phenotype. Epigenetic inheritance involves the transmission of information not encoded in DNA sequences from cell to daughter cell or from generation to generation. Covalent modifications of the DNA or its packaging histones are responsible for transmitting epigenetic information. Functionally, epigenetic marks on the DNA and histones act to regulate gene expression, silence the activity of transposable elements and stabilize adjustments of gene dosage, as seen in X-chromosome inactivation and genomic imprinting. In mammals, epigenetic regulation is crucial for a variety of different processes such as development, cell differentiation, and proliferation. Additionally, epigenetic state of the genome is modulated by factors such as disease, nutrition, age, and sex. Whereas genetic alterations leave a permanent print in the genome, epigenetic alterations are reversible and the enzymes responsible for their maintenance are the potential target for a number of therapeutic compounds. Epigenetic modifications constitute the basis to establish the profiles of gene expression and nuclear organization for From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

a given set of genomic information. This information determines cell type identity. Basically, cells encode their epigenetic information in two groups of molecules: DNA and histones. In DNA, methylation of the 5-position of cytosine in CpG dinucleotides is the most common epigenetic modification [3]. The methyl group is transferred from S-adenosylmethionine to the C-5 position of cytosine by a family of DNA methyltransferases (DNMT). DNA methylation occurs almost exclusively at adjacent cytosine and guanine nucleotides in the DNA (CpG) nucleotides. CpG are unevenly distributed throughout the vertebrate genome, where this dinucleotide is relatively uncommon and has a tendency to cluster in regions known as CpG islands [4], many of which are coincident with the promoter of protein-coding genes. Most dispersed CpG in the genome are methylated, unlike in CpG islands, where methylation occurs rarely in normal cells [4] and results in transcriptional repression [5]. This situation is restricted to a small number of genes, including imprinted genes, X-chromosome genes in women, and a few tissuespecific genes whose expression is only required for a short period. The first observation of DNA methylation aberrations in human cancer was the finding that tumors were globally hypomethylated [6]. This discovery was found only 1 year after the first oncogene mutation was discovered in the Hras in a human primary tumor. The idea that the genome of the cancer cell undergoes a reduction of its 5-methylcytosine content compared with the normal tissue has been firmly corroborated [7, 8]; however, it does not associate with overexpression of oncogenes as originally thought and may be related with the generation of chromosomal instability. Not only is global DNA hypomethylation a common hallmark in cancer but also, paradoxically, hypermethylation of promoter regions of tumor suppressor genes (TSG) is also. To the best of our knowledge, the first discovery of methylation in a CpG island of a TSG in a human cancer was that of the retinoblastoma (Rb) gene in 1989 [9]. Not until 1994, the idea that CpG island promoter hypermethylation could be a mechanism to inactivate genes in cancer was fully restored as a result of the discovery that the Von Hippel-Lindau (VHL) gene also undergoes methylation-associated inactivation [10]. The origin of the current period of research in cancer epigenetic silencing 281

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was perhaps the discovery that CpG island hypermethylation was a common mechanism of inactivation of the TSG p16INK4a in human cancer [11, 12]. For many of these hypermethylated TSGs, it has been shown that their re-expression in tumor cells by demethylating drugs can lead to suppression of cell growth or altered sensitivity to existing anticancer therapies. As compounds have been identified that can readily reverse epigenetic silencing, there is increasing interest in epigenetic regulation of gene expression as a basis for new approaches to cancer treatment. For many years, epigenetic research focused on DNA methylation; this is changing and considerable attention is being given to histone modifications as well. DNA is wrapped around an octamer of histones called a nucleosome that constitutes the building unit of chromatin. Histone tails receive epigenetic information through a complex set of posttranslational modifications [13] including methylation, acetylation, phosphorylation, and sumoylation. Evidence exists that histone modifications help to determine higher-order chromatin structures, which may in turn influence the transcriptional status (e.g., see References 14 and 15). Additionally, there is increasing evidence that characteristic modifications patterns (the “histone code”) on histone tails are involved in gene regulation through changes in chromatin structure and condensation. The histone code is recognized by effector proteins that bind to the nucleosomes inducing changes on gene expression [16]. For example, methylation of H3 at lysine-4 [17] or arginine-17 [18] is closely linked to transcriptional competence, whereas methylation of H3 at lysine-9 or H4 at lysine-20 is associated with transcriptional repression [19, 20]. Initially, aberrations in post-translational modifications of histones in cancer cells were only shown to occur at individual promoters. These changes were reported to be associated with the presence of methyl-binding domain (MBD) proteins [21, 22]. In this context, hypermethylation of the promoter CpG islands of TSG was thought to be mechanistically linked to gene silencing through the recruitment of MBDs. The binding of MBDs to hypermethylated promoters would be followed by a change in the pattern of histone modifications that, in turn, would lead to a change in the chromatin structure compatible with gene inactivation. Furthermore, we have recently characterized post-translational modifications of histone H4 at a global level in a comprehensive panel of normal tissues, cancer cell lines, and primary tumors [23] and found that the global loss of monoacetylation and trimethylation of histone H4 is a common hallmark of human tumor cells. Besides having a direct effect on transcriptional activity, DNA methylation and histone modifications also play a key role in organizing nuclear architecture [15, 24], which in turn is also involved in regulating transcription and other nuclear processes. Therefore, epigenetic modifications are essential for defining the cellular transcriptome at several levels. Aberrant changes in the pattern of epigenetic modifications result in altered nuclear activity, and thereby altered transcriptome, transforming the identity of the cell.

I.L. de Silanes and M. Esteller

Although the importance of altered epigenetic regulation in tumorigenesis is clearly proven, little is known about its extent and genomic distribution. Epigenomics search to define the epigenetic pattern in a genome-wide scale. This term encompasses not only whole-genome studies of epigenetic processes but also the identification of the characteristic DNA sequences that specify where the epigenetic processes are targeted. Because it was realized that CpG dinucleotides in mammals represent the target for the covalent modification of DNA, it has been apparent that DNA sequence characteristic can influence that targeting of epigenetic processes. Historically, technology has limited large-scale approaches to epigenomics, but the emergence of highly reproducible quantitative highthroughput microarray technology is allowing nearly all epigenomics research to be read on microarray platforms. The field is nascent at present, and efforts to develop it include arraybased methylation analysis, array-based hybridization using probes prepared by immunoprecipitation with antibodies (Ab) to modified histones (so-called ChIP-on-CHIP), and highthroughput allele-specific expression analysis (for excellent reviews on epigenomics see References 25 and 26). In the following sections, we analyze all these aspects in greater detail.

14.2 DNA Methylation in Healthy Versus Cancer Cells The inheritance of information based on gene expression levels is known as epigenetics, as opposed to genetics, which refers to information transmitted on the basis of gene sequence. The main epigenetic modification in humans is the methylation of the cytosine located within the dinucleotide CpG. 5-Methylcytosine (5mC) in normal human tissue DNA constitutes 0.75–1% of all nucleotide bases and we should remember that about 4–6% of all cytosines are methylated in normal human DNA [7, 27]. DNA methylation results from the activity of a family of DNMTs that catalyze the addition of a methyl group to cytosine residues at CpG. To date, three members of the DNMT family have been described in mammalian cells. The first DNA cytosine-methyltransferase identified was revealed by purification and cloning [28]. This enzyme, now called DNMT1, is a protein that contains 1,620 amino acids and exhibits a 5- to 30-fold preference for hemimethylated substrates. This property led to the assignment of DNMT1 as the enzyme responsible for maintaining the methylation patterns after DNA replication. DNMT3a and DNMT3b were soon identified by searching expressed sequence tag (EST) databases [29] and were proposed to be the enzymes responsible for de novo methylation [30]. Mutations in the human DNMT3b gene are responsible for immunodeficiency, centromeric instability and facial anomalies (ICF) syndrome characterized by centromeric instability, indicative that global DNA hypomethylation affects chromatin organization. Since its discovery, DNA methylation has been associated with a transcriptionally inactive state of chromatin; however the mechanisms that lead to transcriptional silencing have

14. Epigenomics and Cancer

recently started to be unveiled. Initially, it was suggested that DNA methylation inhibited binding of transcription factors, leading to suppression of gene transcription. Indeed, several important transcription factors have been shown to be sensitive to methylation of CpG within their recognition sites [31]. However, in recent years, a more generally applicable mechanism by which DNA methylation can lead to transcriptional repression is gaining increasing attention. Thus, much evidence supports the idea that DNA methylation leads to the binding of a family of proteins known as MBD proteins. MeCP1 and MeCP2 were the first two members of the MBD family to show this activity [32]. Although MeCP1 was originally identified as a large multiprotein complex, MeCP2 is a single polypeptide with an affinity for a single methylatedCpG. MeCP2 germ-line mutations are responsible for the mental retardation disease Rett syndrome and the absence of a functional MeCP2 in these patients cause a specific deregulation of the gene expression [33]. Database searches led to the identification of additional proteins harboring the methylCpG binding domain, namely MBD1, MBD2, MBD3, and MBD4. MeCP2 was reported to repress transcription of methylated DNA through the recruitment of a histone deacetylase (HDAC)-containing complex (HDAC function to restore the positive charge of lysine residues in the amino tail of histones producing a compacted chromatin that is refractory to transcription) [34]. Thus, DNA methylation by the binding of MBD and recruitment of HDAC seems to have a direct influence on both histone acetylation and higher-order chromatin structure. This finding established for the first time a mechanistic connection between DNA methylation and transcriptional repression by the modification of chromatin. Ng and colleagues [35] reported that MBD2 is, in fact, a component of the formerly identified MeCP1 complex, which exhibits histone deacetylase activity. On the other hand, Wolffe’s laboratory identified MBD3 as a component of the Mi-2/NURD complex, which exhibits both histone deacetylase and ATPase-dependent nucleosome remodeling activities [36]. After the finding of the coupling between DNA methylation and histone deacetylation by MBD, additional mechanisms have been found. On one hand, DNMT were shown to be able also to recruit histone deacetylases [37, 38]; whereas on the other, both DNMT and MBD have been reported to recruit histone methyltransferases (HMT) that modify Lys 9 of histone H3 [39, 40]. Although these observations argue that DNA methylation is a key signal leading to histone modifications, chromatin remodeling, and gene silencing, this signaling can also operate in the opposite direction. For example, disruption of histone methylation in Neurospora crassa results in the elimination of DNA methylation. Similarly, increased histone acetylation in cells treated with HDAC inhibitors can also lead to demethylation of DNA. Taken together, these studies demonstrate the emerging concept of the crosstalk between the different mechanisms of epigenetic regulation: DNA methylation, histone modifications, and chromatin remodeling and their regulation are essential for the appropriate control of gene transcription.

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CpG dinucleotides are the subject for DNA methylation. CpG dinucleotides are not randomly distributed throughout the vast human genome and two main classes of CpG can be found. The first occurs throughout the body of genes that show tissue-specific expression, with methylation generally associated with gene silencing. The second class involves CpG islands (a cluster of CpG) that are uniformly unmethylated in normal cells, and frequently spans the 5′-end region (promoter, untranslated region and exon 1) of a number of genes. There are exceptions to this rule as it occurs in the X-chromosome’s genes in women and near imprinted genes [41]. Genomic imprinting is a special case of epigenetic modification in which the alteration occurs during germ-line reprogramming, leading to preferential expression of one of the parental alleles in somatic cells of the offspring. At least several hundred genes may show imprinting and aberrations can be found in rare human genetic disorders as well as common cancers [42]. A similar phenomenon of gene-dosage reduction can also be invoked with regard to the methylation of CpG islands in one X-chromosome in women, which renders these genes inactive to avoid redundancy. The perfect epigenetic equilibrium of the previously described normal cell is dramatically transformed in the cancer cell (Fig. 14-1). The epigenetic aberrations observed in cancer can be summarized into two categories: global genomic hypomethylation and transcriptional silencing of TSG by CpG island promoter hypermethylation.

14.2.1 Global Genomic Hypomethylation of Transformed Cells The first recognized epigenetic alteration in cancer cells was the finding of loss of DNA methylation at CpG dinucleotides [6]. The malignant cell can have 20–60% less genomic 5mC than its normal counterpart [7, 8]. Interestingly, most hypomethylation events occur in repetitive elements localized in satellite sequences or centromeric regions [7]. The extent of genome-wide DNA hypomethylation increases through all the tumorigenic steps, from benign proliferation to the invasive cancer [43]. Thus, hypomethylation may serve as a biologic marker with prognostic value. What is the evidence that hypomethylation might contribute directly to malignancy? Initially, it was believed that it might lead to the massive overexpression of oncogenes whose CpG islands were hypermethylated in normal cells [44]. Now, this is considered to be an unlikely or, at least, incomplete scenario and stronger hypothesis have been proposed including chromosomal instability, reactivation of transposable elements, and loss of imprinting. Hypomethylation of DNA might favor mitotic recombination, leading to loss of heterozygosity as well as promoting karyotypically detectable rearrangements. Additionally, extensive hypomethylation in centromeric sequences is common in human tumors and may play a role in aneuploidy. As evidence for this, it has been reported that patients with germ-line mutations in DNMT3b have numerous

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14.2.2 Hypermethylation-Associated Silencing of Tumor Suppressor Genes

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methylation because of a defect in DNMT1 is crossed with the colon adenoma-prone Min mouse (with a genetic defect in APC), the resulting mouse has fewer tumors [48]; but another DNMT1 defective mouse may have an increase risk of lymphomas [49]. This paradox is an important question that needs to be addressed.

Transcription

Fig. 14-1. Schematic of the altered epigenetic pattern of cancer cells versus normal cells. An array of nucleosomes is shown where DNA (black line) is wrapped around histone octamers (grey circles). In the normal cell (top), CpG islands at the promoter of tumor-suppressor genes (TSG) are unmethylated (lack of black circles) and histone tails (protruding gray lines) show acetylated histone H3 (AcH3) and H4 (AcH4) and trimethyl-K4 of histone H3 (3mK4 H3), which represents a transcriptionally active environment and the gene will be expressed. In cancer cells (bottom panel), many TSG undergo aberrant hypermethylation (red circles) at their CpG islands and many different elements are recruited: DNA methylation is carried out by DNA methyltransferase proteins (DNMT) that participate in a multiprotein complex that contain histone deacetylases (HDAC) or histone methyltransferases (HMT) or both, and methyl-binding proteins (MBD) can be loaded onto methylated DNA through their interaction with both HDAC and HMT. Histone marks displayed by normal cells are lost and new marks as dimethyl-K9 at histone H3 (2mK9 H3) are gained. All these cooperative interactions are responsible for gene silencing of TSG in cancer cells. (see Color Plate 7 following p. 316.)

chromosome aberrations [45] although it should be noted that patients with ICF do not have an increased incidence of cancer. Hypomethylation of malignant cell DNA can also reactivate intragenomic parasitic DNA, such as L1 (long interspersed nuclear elements, LINES) and Alu (recombinogenic sequence) repeats [46]. These, and other previously silent transposons, may now be transcribed and even “jump” to other genomic regions where they can disrupt normal cellular genes. Finally, the loss of methyl groups can affect imprinted genes. The best-studied case concerns the effects of the H19/ IGF-2 locus on chromosome 11p15 in certain childhood tumors [47]. We know very little about the real role of DNA hypomethylation in cancer cells. Is it really a “causative” factor, or just a “modulator of cancer risk,” or only a “bystander passenger”? This is one of the most frequently asked questions in the field. The studies in mouse models are extremely interesting, but puzzling: when the mouse deficient in DNA

DNA hypomethylation is not the only way in which methylation contributes to cancer. CpG islands located in the promoter region of TSG, are unmethylated in normal cells, but undergo a dense hypermethylation in cancer cells leading to gene silencing. The idea of the hypermethylation of CpG islands of TSG as a mechanism of gene inactivation in cancer was proposed in 1994 when methylation-dependent silencing of the VHL gene was demonstrated to be a mechanism of gene inactivation in renal carcinoma [10]. In the following years, parallel studies in the laboratories of Baylin and Jones established that CpG island hypermethylation is a common mechanism of gene inactivation in cancer. CpG island hypermethylation of TSG, which leads to their inactivation, is now considered the major epigenetic alteration in cancer [50]. It has been proposed that epigenetic inactivation of TSG by hypermethylation plays a key role by complementing genetic changes in the transformation from normal to malignant cells [45, 51]. Not every gene is methylated in every tumor type, but strong specificity is apparent with respect to the tissue of origin [41, 52]. For example, BRCA1 hypermethylation is exclusive of breast and ovarian neoplasias [53] whereas MLH1 hypermethylation of colon, endometrial, and gastric cancer [54–57]. Furthermore, the number of hypermethylated genes increases with the malignant potential [43] and can be useful to classify tumors of unknown origin [58]. Our laboratory has described the characteristic profile of hypermethylation that occurs in primary human tumors [59]. There are several lines of evidence that imply an active role of hypermethylation of TSG in the development of cancer. In the first place, hypermethylation is an early event in cancer. This is the case of p16INK4a, p14ARF, and MGMT [57] in colorectal adenomas and hMLH1 in endometrial hyperplasias [55] and gastric adenomas [60]. We do not know why some genes became hypermethylated in certain tumors, whereas others, with similar properties remain free of methylation. We can hypothesize, as researchers have done before with genetic mutations, that a particular gene is preferentially methylated with respect to others in certain tumor types because that specific inactivation confers a selective advantage, according to Darwinian rules. Another option is that aberrant DNA methylation is directly targeted. It has been proposed that fusion proteins such as PML-RAR can contribute to aberrant CpGisland methylation by recruiting DNMT and HDAC to aberrant sites [61]. This latter activity is somewhat controversial but, in any case, does not seem to be a general mechanism, at

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least in leukemia patients [50]. Selection and targeting are not exclusive events and are most probably happening all together in the generation and maintenance of hypermethylated CpG islands of TSG. TSG or tumor suppressor-like genes that undergo aberrant CpG island methylation in human cancers have been shown to affect critical cellular pathways with relevant consequences in the tumorigenesis [50]. A brief list of critical genes inactivated by DNA hypermethylation is shown in Table 14-1 and they can be classified according to their biologic functions:

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Cell-cycle genes: The cell cycle inhibitor p16INK4a is hypermethylated in a wide variety of human primary tumors and cell lines [8, 11], allowing the cancer cell to escape senescence and start proliferating. The retinoblastoma gene (Rb)

and the cell-cycle inhibitor p15INK4b can also suffer occasional aberrant methylation [9]. p53 network: p53 is the most frequently mutated TSG in human cancer, but nevertheless half of human primary tumors are wild-type p53. Another way to inactivate p53 is through the methylation-mediated silencing of the TSG p14ARF [62] because, in this way, the MDM2 oncogenic protein is not inhibited by p14ARF and is free to induce p53 degradation. p73, a gene that is a p53-homolog, is also hypermethylated in leukemias [63]. APC/β-catenin/E-cadherin pathways: APC is commonly mutated in sporadic colon tumors but little was known about the relevance of this particular pathway in noncolorectal tumorigenesis until recently. Now, it is recognized that aberrant methylation of APC is a common lesion in other

Table 14-1. Noncomprehensive list of genes silenced by CpG island hypermethylation in human cancer (Modified from Esteller, 2005). Gene hMLH1 BRCA1 p16INK4a p14ARF p15INK4b MGMT GSTP1 p73 DKK1 ER PR AR PRLR RARb2 RASSF1A WRN VHL Rb THBS-1 CDH1 CDH13 FAT HIC-1 APC SFRP1 COX-2 SOCS-1 EMP3 GATA-4 GATA-5 SCGB3A1 AhR RIZ1 DAPK TMS1 TPEF/HPP1 HOXA9 IGFBP3 EXT1 Lamin A/C

Function DNA mismatch repair DNA repair, transcription Cyclin-dependent kinase inhibitor MDM2 inhibitor Cyclin-dependent kinase inhibitor DNA repair of O6-alkyl-guanine Conjugation to glutathione p53 homologue Inhibitor of Wnt pathway Oestrogen receptor Progesterone receptor Androgen receptor Prolactin receptor Retinoic acid receptor β2 Ras effector homologue DNA repair, helicase Ubiquitin ligase component Cell cycle inhibitor Thrombospondin-1, anti-angiogenic E-cadherin, cell adhesion H-cadherin, cell adhesion Cadherin, tumour suppressor Transcription factor Inhibitor of β-catenin Secreted Frizzled-related protein 1 Cyclooxigenase-2 Inhibitor of JAK/STAT pathway Negative regulator of cell growth Transcription factor Transcription factor Negative regulator of cell growth Transcription factor Histone/protein methyltransferase Pro-apoptotic Pro-apoptotic Transmembrane protein Homeobox protein Growth factor-binding protein Heparin sulphate synthesis Structural protein

Tumor type Colon, endometrium, stomach Breast, ovary Multiple types Colon, stomach, kidney Leukemia Multiple types Prostate, breast, kidney Lymphoma Colon Breast Breast Prostate Breast Colon, lung, head and neck Multiple types Multiple types Kidney, hemangioblastoma Retinoblastoma Glioma Breast, stomach, leukemia Breast, lung Colon Multiple types Aerodigestive tract Colon Colon, stomach Liver, myeloma Glioblastoma, gliomas Colon, stomach Colon, stomach Testicular cancer Acute lymphoblastic leukemia Breast, liver Lymphoma, lung, colon Breast, glioblastoma Colon, bladder Neuroblastoma Lung, skin Leukemia, skin Leukemia, lymphoma

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neoplasms of the aerodigestive tract [64]. E-cadherin, Hcadherin, and FAT tumor-suppressor cadherin promoter hypermethylation is also important in the cancer biology of breast, colon, and other tumor types [58, 65]. Finally, methylation-associated silencing of the genes encoding secreted frizzled-related proteins (SFRP), that possess a domain similar to one in the WNT-receptor frizzled proteins, and can inhibit WNT-receptor binding by downregulating of the signaling pathway during development, has also been found in colorectal cancer [66]. DNA repair: DNA methylation has major consequences on the activity of DNA repair genes. Selected examples are the methylation-mediated silencing of the mismatch DNA repair gene hMLH1 in sporadic cases of colorectal, endometrial, and gastric tumors that cause the bizarre phenotype known as microsatellite instability (MSI) [56, 57, 67]. The promoter hypermethylation of MGMT [56] that prevents the removal of groups at the O6 position of the guanine and leads to the appearance of K-ras and p53 mutations [68]; the hypermethylation of the mitotic checkpoint gene CHFR [69]; the somatic inactivation of BRCA1 by aberrant methylation in breast and ovarian tumors, altering its role in the repair of double-strand breaks (DSB) in the DNA and leading to the same global expression changes that occur in carriers of BRCA1 germ-line mutations [70]. Hormonal response: Aberrant methylation of the estrogen, progesterone, androgen, and prolactin receptors occurs in breast and uterine tumors and may render cancer cells to be unresponsive to steroid hormones [22]. The differential action of the retinoids may also be abolished in tumors that show promoter hypermethylation of the retinoic acid receptor-β2 and the cellular retinol-binding protein I [71]. Cytokine signaling: The suppressor of cytokine signaling (SOCS) family of proteins has been implicated in the negative regulation of several cytokine pathways, particularly the receptor-associated tyrosine kinase/signal transducer and activator of transcription (Jak/STAT) pathways of transcriptional activation. SOCS-1 and SOCS-3 have found to undergo methylation-associated silencing in human cancer [72]. Other genes: This list is not exhaustive, but with the following examples we would like to emphasize the impact of promoter hypermethylation on a enormous variety of molecular routes. Promoter hypermethylation is found in the proapoptotic death-associated protein kinase (DAPK), and TMS1; the kidney tumor and hemangioblastoma-related von Hippel-Lindau gene (VHL); the Ser-Thr kinase LKB1/ STK11 in hamartomatous neoplasms, the ras-effector genes RASSF1A and NORE1A; the antiangiogenic factor thrombospondin-1 (THBS-1); the prostaglandin generator cyclooxygenase 2; the TPEF gene that contains epidermal growth factor (EGF) domains; the electrophilic detoxifier glutathione S-transferase P1 (GSTP1) in prostate, breast, and kidney tumors, and the transcription factors GATA-4 and GATA-5.

I.L. de Silanes and M. Esteller

14.2.3 Identification of New Hypermethylated Genes Classical DNA methylation research has concentrated on investigating the methylation status of cytosines occurring in known (or partially known) DNA sequences. However, alternative ways of investigating genome-wide methylation by searching for hitherto unidentified spots have been developed. All rely on the distinctive properties of the CpG islands to find new methylated sequences in the genome. The use of bisulfite modification of the DNA has been decisive in the expansion of the field. Until a few years ago, the study of DNA methylation was almost entirely based on the use of enzymes that distinguished unmethylated and methylated recognition sites. This approach had many drawbacks, from incomplete restriction cutting to limitation of the regions of study. Furthermore, it usually involved Southern blot technologies, which required relatively substantial amounts of DNA of high molecular weight. The popularization of the bisulfite treatment of DNA (which changes unmethylated “C” to “T” but maintains the methylated “C” as a “C”), associated with amplification by specific polymerase chain reaction (PCR) primers (methylation-specific polymerase chain reaction), taqman, restriction analysis and genomic sequencing [27] has made possible for every laboratory and hospital in the world to be able to study DNA methylation, even using pathologic material from old archives. We like to call this change the “universalization of DNA methylation.” Bisulfite modification techniques, which are ideal for studying biologic fluids and specific DNA methylation patterns of particular TSG, can also be coupled with global genomic approaches for establishing molecular signatures of tumors based on DNA methylation markers, such as CpG island microarrays, restriction landmark genomic scanning, and amplification of intermethylated sites [27]. Another approach that is widely used is the study of gene expression by microarrays comparing RNA from cancer cell lines before and after treatment with a demethylating drug [73]. This methodology has proven to be very useful in identifying new hypermethylated genes. However, we should point out that not all the genes that became re-expressed after the use of the demethylating agent will be methylated: a rigorous bisulfite genomic sequencing, expression, and functional analysis are always required. Other powerful methods for identifying novel epigenetically regulated genes use immunoprecipitation (ChIP) of methyl-binding proteins in combination with a CpG array island microarray (ChIP-on CHIP). The most promising method consists on the direct isolation of methylcytosine rich genomic DNA. This technique, called methylated DNA immunoprecipitation (M), uses a monoclonal antibody (MAb) directed against 5-mC to enrich the sample in methylated genomic DNA fragments [74]. Coupling MeDIP with arraybased comparative genomic hybridization (CGH) allows the construction of high-resolution maps of the human methylome [25, 74].

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14.3

“Histone Code” of Cancer Cells

Histone post-translational modifications constitute the second group of epigenetic modifications. Since the first reports of the occurrence of histone modifications, 40 years of chromatin research has resulted in the description of a variety of histone modifications and the specificity of certain modifications under particular physiologic conditions. Most histone modifications occur in their protruding N-terminal tails, including acetylation, methylation, phosphorylation, and sumoylation. Generally, the acetylation of histones mark active, transcriptionally competent regions whereas methylation can be a marker for both active and inactive regions of chromatin and depends on the Lys that is being modified [75]. There is specificity in the pattern of modifications under particular conditions. This fact has led to the proposal of the “histone code hypothesis,” according to which histone modifications act sequentially or in combination to form a code that may be read by nuclear factors [76]. During the past 10 years, increasing reports have evidenced the strong interplay between DNA methylation and histone modifications. These occur through different nuclear machineries including DNMT, which have been reported to recruit both HDAC [37, 38] and HMT that modify Lys 9 of histone H3 [39]. In addition, at least two groups of proteins bind methylated DNA and also recruit histone-modifying enzymes. These include methyl-CpG–binding domain proteins and the Kaiso family of proteins [77]. In cancer, aberrant acetylation and methylation of histones is widespread during the process of oncogenic transformation [78]. Similarly, the polycomb group proteins, that form transcriptionally repressor modules, are frequently overexpressed in cancer [79]. It is the recruitment of MBD onto the hypermethylated promoters that seems a major hallmark in cancer [22, 23]. Some of the first molecular evidence showing this aspect arose from the demonstration that methylation of a construct containing part of the 5′ BRCA1 CpG island inhibited gene expression in the presence of MeCP2 [80]. In vitro binding assays have shown the affinity of MBD for the methylated promoter sequences of TSG [81]. However, only by using ChIP assays, has it been possible to confirm the presence of MBD proteins in other aberrantly methylated genes in cancer [21, 22]. Most importantly, reversal of methylation by the use of DNMTs inhibitors results in both loss of methylation at the CpG island and release of the MBD protein [22]. One important question that arose from these studies was to determine whether the presence of MBD proteins on hypermethylated promoters was a general feature of cancer cells. We and other researchers have shown that MBD are truly associated with methylated DNA in vivo, by the presence of higher content in 5mC in the MBD-immunoprecipitated DNA compared with the whole genomic DNA [33]. In fact, we have found MBD in all methylated genes in cancer cells analyzed to date and their presence in the promoters of these genes is methylation dependent [33]. Therefore, the presence of MBD proteins is a major hallmark of TSG that are hypermethylated and therefore silenced in cancer [22, 33].

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Furthermore, we have characterized post-translational modifications of histone H4 at a global level in a comprehensive panel of normal tissues, cancer cell lines, and primary tumors [23]. In this study, we found that cancer cells exhibit a loss of monoacetylated and trimethylated forms of histone H4. Interestingly, these changes appeared early and accumulated during the tumorigenic process, as shown in a mouse model of multistage skin carcinogenesis [23]. By using mass spectrometry, these losses were attributed to occur predominantly at the acetylated Lys16 and trimethylated Lys20 residues of histone H4 and were associated with the well-characterized hypomethylation of DNA repetitive sequences. These data suggested that the global loss of monoacetylation and trimethylation of histone H4 is a common hallmark of human tumor cells. In summary, hypermethylated CpG islands of TSG display a characteristic “histone code” (or “histone index”) composed by several histone modifications that are compatible with gene silencing. Generally speaking, hypermethylated CpG islands of silenced TSG present overall histone hypoacetylation and histone methylation [76]. More specifically, hypermethylated promoter of TSG display deacetylation of histones H3 and H4, methylation of Lys9 of histone H3 and demethylation of Lys 4 of histone H3, loss of monoacetylation of Lys16 of histone H4, and loss of trimethylation of Lys20 of histone H4 [22, 23, 43]. It has o been shown that changes in global levels of individual histone modifications are predictive of clinical outcome of prostate cancer [82]. Through immunohistochemical (IHC) staining of primary prostatectomy tissue samples, Seligson and colleagues found two disease subtypes with distinct risks of tumor recurrence in patients with low-grade prostate cancer based on the differential staining for histone acetylation and demethylation of five residues at histones H3 and H4. These histone modification patterns were considered to be predictors of outcome independently of tumor stage, preoperative prostate-specific antigen (PSA) levels, and capsule invasion. Thus, widespread changes in specific histone modifications indicate previously undescribed molecular heterogeneity in prostate cancer and might underlie the broad range of clinical behavior in cancer patients. The identification of changes in the histone modification profile of cancer cells relies in the availability of techniques to study those changes. Mass spectrometry and specific antibodies raised against different histone modifications are the most powerful tools. Mass spectrometry would allow the identification of novel modifications. On the other hand, antibodies have helped, not only in determining global changes of histone modifications but also in identifying specific changes at defined sequences by using ChIP assays. The recent availability of genomic microarrays of different types (tiling arrays, promoter sequences) allows a genome-wide screening of histone modifications as well as the identification of the association of dedicated histone-modifying activities. It can be anticipated that great advances will be achieved in the next few years with the application of these novel technologies.

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14.3.1 Reversal of Epigenetic Modifications as a Cancer Therapy. Epigenetic modifications are reversible, whereas genetic alterations are not. This feature makes epigenetic modifications a perfect target for therapeutic interventions in patients with cancer. Because tumors present aberrant methylation levels and altered histone modifications, two types of epigenetic drugs directed against these alterations are being tested: DNA-demethylating agents and histone deacetylase inhibitors (HDACi) [83]. Many of these compounds are small molecules that have pharmacologic properties that enable easy delivery to tumors, which is a sharp contrast with the challenge of delivering gene therapy to reverse the effects of genetic silencing. To date, most attention has been centered on therapies that reverse methylation as a means of switching on genes that will suppress tumor growth. The lack of methylation of such genes in normal cells provides the potential for tumor specificity. The first drug used to inhibit DNA methylation was 5-azacytidine. This substance causes covalent arrest of DNMT, resulting in cytotoxicity. 5-Azacytidine was tested for its usefulness as an antileukemic drug before its demethylating activity was known [84]. The analogue 5-aza-2′-deoxycytidine is one of the most commonly used demethylating drugs in cultured cells assays. 1-(beta-D-Ribofuranosyl)pyrimidin-2-one is another recently developed cytidine analogue [85]. It forms a covalent complex with DNMTs (85). Furthermore, 1-(betaS-Ribofuranosyl)pyrimidin-2-one has also shown promising antitumoral effects in xenografts [85] and thymic lymphomas in mice [86]. Perhaps the most interesting feature of this DNA-demethylating agent is that it is chemically stable and of low toxicity [85, 86], and can be taken orally. It is in the field of hematologic malignancies that DNA-demethylating agents have had their greatest success so far, especially in high-risk myelodysplastic syndromes (MDS)by using 5-aza2-deoxycytidine [84]. In 2004, the FDA approved the use of 5-azacytidine for the treatment of all MDS subtypes. Epigenetic silencing is almost universally associated with histone deacetylation, which is catalyzed by at least three classes of HDAC in human cells. Naturally occurring and synthetic HDACi are therefore of great potential in cancer treatment. The driving force is that HDACi provoke an accumulation of acetylated proteins, particularly histones, resulting in the induction of genes and the upregulation of others that have become epigenetically silenced. Overall, HDACi manifest a wide range of activities against all HDAC and have been shown to induce differentiation, growth arrest or apoptosis or all three in transformed cells and in tumors. These compounds can be classified into the following groups according to their chemical nature: ●

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hydroxamic acids, such as trichostatin A, SAHA, PXD101, and NVP-LAQ-824; carboxylic acids, such as sodium valproate and butyrate; benzamides, such as MS-272; and others, including trapoxins and FK228 [83].

I.L. de Silanes and M. Esteller

It is believed that the anticancer effects of HDACi are mediated by the reactivation of the expression of TSG; however, the treatment of cancer cell lines with HDACi has pleiotropic effects inducing differentiation, cell-cycle arrest, and apoptosis. In this regard, the observation that cancer cells have lost monoacetylated Lys16 histone H4 [23] implies a new molecular pathway that may explain the beneficial effects of HDACi because these compounds may promote the restoration of normal histone H4 acetylation levels in the whole cell, restoring the normal chromatin status of repetitive DNA sequences [23]. It is clear from in vitro assays, nonclinical studies, and ongoing clinical trials that HDACi have enormous potential as anticancer drugs. In this regard, suberoylanilide hydroxamic acid has been approved for the treatment of cutaneous lymphoma in 2006.

14.4 Summary and Perspectives in an Epigenetic World Epigenetic changes have become established in recent years as being one of the most important molecular signatures of human tumors. The discovery of hypermethylation of the CpG islands of certain TSG in cancer links DNA methylation to the classic genetic lesions, affecting many cellular pathways, from DNA repair to apoptosis, cell cycle, and cell adherence. Promoter hypermethylation is now considered to be a bona-fide mechanism for gene inactivation. It affects genes involved in critical process for the correct regulation of the cell biology such as p16INK4a, BRCA1, or hMLH1. However, important questions await answers, such as the characterization of the factors involved in susceptibility and protection against CpG methylation and the intimate molecular routes that link CpG methylation to transcriptional silencing. The introduction of bisulfite methodology coupled with PCR techniques has popularized the studies of epigenetic lesions in human neoplasia, but the new genes that undergo aberrant methylation require close scrutiny, to select those that are genuinely important for human tumorigenesis. Additionally, CpG island hypermethylation has demonstrated its great versatility for the molecular monitoring of patients with cancer, and is a likely target for future and smarter therapeutic approaches. More importantly, all these questions need to be answered not only at the individual gene level but in a genome-wide scale. The optimization of techniques such as MeDIP and ChIP assays in combination with array platforms is helping in elucidating the epigenetic mechanisms globally and whole genome annotation of epigenetic patterns in normal and cancer cells is now underway. The large amount of data arising from these genome-wide experiments will need the interpretation and coordination of a group of scientist fully dedicated to this aim to be able to understand the biologic consequences of these finding. The launching of an International Human Epigenome Project should be the response to this necessity. Indeed, efforts toward this end have already been initiated in Europe with the participation of the Human

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Epigenome Consortium, the European Epigenome Network of Excellence, the High Throughput Epigenetic Regulatory Organization in Chromatin, and a private/public partnership involving eight centers funded by the German government. Knowledge of genome-wide epigenetics pattern and profiles will provide a novel resource for the understanding of fundamental biologic processes such as gene regulation, imprinting, development, genome stability, disease susceptibility, and the interplay between genetics and the environment.

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58. Paz MF, Fraga MF, Avila S, et al. A systematic profile of DNA methylation in human cancer cell lines. Cancer Res 2003;63:1114–1121. 59. Esteller M, Corn PG, Baylin SB, Herman JG. A gene hypermethylation profile of human cancer. Cancer Res 2001; 61:3225–3229. 60. Fleisher AS, Esteller M, Tamura G, et al. Hypermethylation of the hMLH1 gene promoter is associated with microsatellite instability in early human gastric neoplasia. Oncogene 2001;20: 329–335. 61. Di Croce L, Raker VA, Corsaro M, et al. Methyltransferase recruitment and DNA hypermethylation of target promoters by an oncogenic transcription factor. Science 2002;295:1079– 1082. 62. Esteller M, Tortola S, Toyota M, et al. Hypermethylation-associated inactivation of p14(ARF) is independent of p16(INK4a) methylation and p53 mutational status. Cancer Res 2000; 60:129–133. 63. Corn PG, Kuerbitz SJ, van Noesel MM, et al. Transcriptional silencing of the p73 gene in acute lymphoblastic leukemia and Burkitt’s lymphoma is associated with 5 CpG island methylation. Cancer Res 1999;59:3352–3356. 64. Esteller M, Sparks A, Toyota M, et al. Analysis of adenomatous polyposis coli promoter hypermethylation in human cancer. Cancer Res 2000;60:4366–4371. 65. Graff JR, Herman JG, Lapidus RG, et al. E-cadherin expression is silenced by DNA hypermethylation in human breast and prostate carcinomas. Cancer Res 1995;55:5195–5199. 66. Suzuki H, Watkins DN, Jair KW, et al. Epigenetic inactivation of SFRP genes allows constitutive WNT signaling in colorectal cancer. Nat Genet 2004;36:417–422. 67. Kane MF, Loda M, Gaida GM, et al. Methylation of the hMLH1 promoter correlates with lack of expression of hMLH1 in sporadic colon tumors and mismatch repair-defective human tumor cell lines. Cancer Res 1997;57:808–811. 68. Esteller M, Herman JG. Generating mutations but providing chemosensitivity: The role of O6-methylguanine DNA methyltransferase in human cancer. Oncogene 2004;8:1–8. 69. Mizuno K, Osada H, Konishi H, et al. Aberrant hypermethylation of the CHFR prophase checkpoint gene in human lung cancers. Oncogene 2002;21:2328–2333. 70. Hedenfalk I, Duggan D, Chen Y, et al. Gene-expression profiles in hereditary breast cancer. N Engl J Med 2001;344:539–548. 71. Esteller M, Guo M, Moreno V, et al. Hypermethylation-associated inactivation of the cellular retinol-binding-protein 1 gene in human cancer. Cancer Res 2002;62:5902–5905. 72. He B, You L, Uematsu K, et al. SOCS-3 is frequently silenced by hypermethylation and suppresses cell growth in human lung cancer. Proc Natl Acad Sci USA 2003;100:4133–4138. 73. Suzuki H, Gabrielson E, Chen W, et al. A genomic screen for genes upregulated by demethylation and histone deacetylase inhibition in human colorectal cancer. Nat Genet 2002;31:141–149. 74. Weber M, Davies JJ, Wittig D, et al. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet 2005;37:853–862. 75. Egger G, Liang G, Aparicio A, Jones PA. Epigenetics in human disease and prospects for epigenetic therapy. Nature 2004;429:457–463. 76. Bannister AJ, Kouzarides T. Histone methylation: recognizing the methyl mark. Methods Enzymol 2004;376:269–288.

14. Epigenomics and Cancer 77. Prokhortchouk A, Hendrich B, Jorgensen H, et al. The p120 catenin partner Kaiso is a DNA methylation-dependent transcriptional repressor. Genes Dev 2001;15:1613–1618. 78. Lund AH, van Lohuizen M. Epigenetics and cancer. Genes Dev 2004;18:2315–2335 79. Bracken AP, Pasini D, Capra M, Prosperini E, Colli E, Helin K. EZH2 is downstream of the pRB-E2F pathway, essential for proliferation and amplified in cancer. EMBO J 2003;22:5323– 5335. 80. Magdinier F, Billard LM, Wittmann G, et al. Regional methylation of the 5′ end CpG island of BRCA1 is associated with reduced gene expression in human somatic cells. FASEB J 2000;14:1585–1594. 81. Fraga MF, Ballestar E, Montoya G, Taysavang P, Wade PA, Esteller M. The affinity of different MBD proteins for a specific

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Chapter 15 Harnessing the Power of Immunity to Battle Cancer: Much Ado about Nothing or All’s Well That Ends Well? Angelo A. Cardoso

15.1

Introduction

The last decade has seen an unparalleled explosion in the search for novel, more specific and efficient therapies for the treatment of human cancer. The target specificity, potency, amplification potential, and memory properties of the immune system, in particular of its adaptive arm, has generated great interest on the development of cancer immunotherapy, mobilizing the efforts of immunologists and oncologists. This interest has been fueled by remarkable progress in genetics, molecular biology, cell biology, and biotechnology. These advances included, among others, the identification of tumor-associated antigens (Ag); the unveiling of mechanisms involved in Ag processing and presentation; the dissection of the molecular players mediating and regulating the priming, expansion, and effector phases of immunity; and, the development of effective methodologies for the identification, separation, amplification, targeting, and tracking of cells and molecules. This progress has generated great expectations for the therapeutic potential of cancer immunotherapy, often with unrealistic goals. Although some successes were observed (the adoptive transfer of target-specific T cells in viral-associated cancers and some antibody (Ab)-based approaches), most strategies tested showed limited results or even failed to show any clinical benefit. As with many other “magic bullets” to cure human cancer, the impressive observations seen in pre-clinical animal models have not resulted in significant successes when translated into the clinical practice. The important question is then whether the significant setbacks of tumor immunotherapy have propelled it into the large “unfulfilled potential” list of cancer therapeutics, or whether it stills holds promise as a more effective and specific therapy for human cancer. In this chapter, I discuss some of the advances in tumor immunology and their impact on the design and implementation of rationally-defined, more realistic approaches to harness the power of the immune system to treat human cancer. In particular, I address cancer immunoediting and how the sculpting of an From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

immune-evading tumor may condition immune interventions, the developments in the manipulation of antitumor T-cell immunity and of tumor-associated immunosuppression, and how the targeting of signal transduction pathways may open new avenues for cancer immunotherapy.

15.2 Cancer Immunoediting: Open Questions and Implications for Tumor Immunotherapy Although object of controversy, there is evidence supporting the idea that the human immune system is inherently built with safeguards against cancer. This “tumor immunosurveillance” stipulates that the host’s immunity is able to recognize and eliminate transformed cells at early stages (reviewed in References 1–3). Based on a prediction by Paul Erlich in the early 1900s, the cancer immunosurveillance hypothesis was proposed by MacFarland Burnet and Lewis Thomas, based on their thoughts on immunologic tolerance and homograft rejection, respectively (discussed in References 4, 5). The experimental work of many independent groups and remarkable progresses on genetics and tumor modeling, as well as the demonstration that immune cells may contribute to immune escape and the emergence of nonimmunogenic primary tumors, have led to the revision of the cancer immunosurveillance concept, and its integration into the broader concept of “cancer immunoediting.” In this new model, the immune system not only surveys for nascent tumors, which it attempts to eliminate, but also exerts selective pressure, sculpting the antigenic repertoire of the tumors emerging in immunocompetent hosts and their consequent immune evasion [2, 3, 6]. According with this hypothesis, nascent tumors are first subjected to the immune surveillance mechanisms (elimination phase), with the surviving malignant cells entering a period of immune-mediated latency (equilibrium phase), which is followed by the outgrowth of immunologically selected, immune-resistant tumors (escape phase) [3, 6]. A significant merit of this comprehensive concept is that it accounts for the diversity, complexity, and dynamic functionality of the 293

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immune system as well as for the complexity and adaptability of cancers and their supportive microenvironments. The tumor immunoediting model raises important questions not only on the role of immunity on cancer development and biology, but also for the design of successful strategies of immunotherapy. Dunn and colleagues put forth the necessity to not only elucidate the molecular and cellular mechanisms implicated in immunoediting, and particularly in the equilibrium and escape phases, but also to re-evaluate the role of immunity on cancers involving defined oncogenic molecules (which contain immunogenic epitopes), and the extent to which a “successful” tumor has been edited by the immune system [2, 3]. An obvious issue is whether the conceptual framework of cancer immunoediting can be used to design more efficient, mechanism-driven immunotherapeutic approaches. To that goal, many questions need to be resolved: ●

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If established cancers (which represent most of the diagnosed malignancies) have been antigenically selected and sculpted to escape immunity, how effective can be antitumor strategies that aim at boosting endogenous immunosurveillance mechanisms? Which mechanisms mediate or regulate the transition from immune-mediated latency into the outgrowth of less-immunogenic or antigenically-silent tumor variants? Can manipulation of such mechanisms be used as a means of tumor treatment, particularly to control drug-resistant clones and prevent cancer recurrence? How can immune-mediated latency and immune escape affect cancer relapse and activation of dormant metastasis? If so, do they play an active role or are they just passive players? How does conventional chemotherapy, which harms components and functions of the immune system, affects or alters immunoediting? Are mechanisms involved in immune-mediated latency still operative during and after chemotherapy? How do different drug regimens affect these functions? How are the tumor immunosurveillance and the immune mechanisms mediating immunoediting reconstituted after chemotherapy? Do different “rules” apply to cancer relapse or the development of secondary malignancies? Because immunoediting leads to the selection of poorly immunogenic tumors, is there a place for Ag-based strategies in cancer therapy? Is it possible to alter or broaden the immunogenic profile of such tumors? Can strategies directed at low-avidity or cryptic epitopes effectively target immunoedited tumors? Is epitope-binding optimization, as the heteroclitic approach, a valid strategy to expand the pool of immunogenic targets in these tumors? Are the immunotherapy approaches per se exerting a selective pressure on the tumor antigenic repertoire? In other words, can the immune interventions result in therapyinduced immunoediting?

A.A. Cardoso ●

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Is the use of poly- or multiepitope approaches indispensable to achieve more powerful antitumor responses, and to reduce the risk of therapy-induced tumor selection? Can repression or abrogation of tumor-associated immunosuppression restore or improve antitumor surveillance mechanisms and/or affect the latency and immune-evasion phases? How is it affected by approaches aimed at braking or reversing the immunological tolerance to dominant tumor-antigens? How can the transfer of highly avid, epitope-specific immune receptors (T-cell receptor [TCR], B-cell receptor [BCR]) improve the ability of immune effector cells to eliminate tumor targets? How effective they can be against an immunoedited tumor? What risks may it pose of further immune selection of nonimmunogenic tumor variants? How does immunoediting affect the cancer progenitor/stem cell pool in a tumor? Does immune pressure plays a significant role in the selection of the self-renewing cancer stem cell present in “mature” tumors?

Answers to these questions and a deeper understanding of the dynamic cross-talk between the immune system and the malignant cells within the ever-changing tumor microenvironment, and how they are affected by drugs and other cytotoxic interventions, are essential for the development of more effective immunotherapeutic strategies for human cancer.

15.2.1

Manipulating Antitumor T-Cell Immunity

T cells are critical players of immunity, namely in the response to microbial infections and in cancer. The cognitive and functional properties of the T cells make them optimal candidates for strategies of immune intervention, as they can provide help and execute effector functions, as well as play regulatory roles [7–10]. The two main categories of immunotherapeutic approaches sought after for cancer therapy are based on the recruitment, amplification, and functional differentiation of T cells with specificities for tumor-associated Ag—adoptive T-cell therapy and tumor vaccination. The main challenge, presently, is to define which strategies can be developed or implemented to improve the antitumor efficacy and, consequently, the clinical use of these approaches.

15.2.2

Adoptive T-Cell Therapy

A significant advantage of adoptive T-cell therapy is that the tumor host receives large numbers of ex vivo generated antitumor T cells instead of being “stimulated” by tumor vaccines into mobilizing and expanding in vivo tumor-specific effector cells from within their T-cell repertoire. Because tumors and their microenvironment frequently contain immunosuppressive mediators, the task of generating or amplifying antitumor T-cell immunity in the cancer hosts is not easy; so, potentially, adoptive transfer of T cells provides a straightforward

15. Harnessing the Power of Immunity to Battle Cancer: Much Ado about Nothing or All’s Well That Ends Well?

approach to surpass these tumor-associated roadblocks (reviewed in References 9, 10). Adoptive T-cell transfer has shown impressive results in the management of viral diseases, fueling great interest on its application for cancer therapy. It is well-established that the ex vivo expansion and the clinical use of epitope-specific T cells achieves significant results in viral infections and in bone marrow transplant (reviewed in Reference 11). Riddell and colleagues have shown, in a post-transplant setting, that the adoptive transfer of T cells was feasible, safe, and resulted in the reconstitution of specific immunity to cytomegalovirus (CMV) [12]; representing a valid approach to tackle the increasing risk for CMV infection in the post-transplant [13]. Importantly, this strategy restored the CMV-specific memory compartment responsive to later virus challenges [14]. Successful reconstitution of effective anti-CMV immunity was observed after the administration of donor-derived CMVspecific clones of CD8+ T cells, polyclonal T-cell lines, and purified CD8+ T cells isolated using human leukocyte Ag (HLA)-peptide tetramers [15]. Similar successes were seen on the use of T-cell adoptive transfer to reconstitute Ag-specific immunity to Epstein-Barr virus (EBV) in immunocompromised patients [16]. Strategies have been developed to effectively produce, within a single cell line, both CD4+ and CD8+ T cells with specificities to CMV, EBV, and several adenovirus serotypes, which could be expanded in vivo after injection in immunocompromised individuals [17]. Using these post-transplant applications as groundwork, adoptive T-cell immunotherapy was tested then to treat virusassociated malignancies, on the underlying principle that ex vivo expanded T cells specific for viral Ag should be equally effective in targeting the viral Ag present on these tumors. Clinical responses, with remission or control of disease progression were seen in patients with nasopharyngeal carcinoma who were receiving autologous EBV-specific cytotoxic T lymphocytes (CTL) [18, 19]; positive results were also observed in studies using allogeneic EBV-specific CTL [20, 21]. Adoptive transfer of virus-specific T cells (in most cases CTL) was also used in patients with relapsed EBV-positive Hodgkin’s lymphoma. Infused T cells were well tolerated, persisted for long periods and, importantly, retained their anti-EBV/antitumor reactivity. Reduction of viral load was generally observed in these patients, and complete remissions, partial responses, and disease stabilization have been reported [22–24]. Autologous or allogeneic EBV-specific T cells also were generated and transferred into patients with active relapsed angiocentric lymphoma, with disease stabilization observed in some cases [25]. Donor-lymphocyte infusion (DLI) is another form of adoptive immune transfer that has provided positive clinical results in some hematologic cancers. This approach is used in patients that relapse after allogeneic transplantation, and takes advantage of the direct reactivity of the donor immune cells against the tumor—the graft-versus-leukemia (GVL) effect. DLI is very effective for patients with relapsed chronic myeloid leu-

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kemia (CML), resulting in long-term clinical remissions and, possibly, cure. Although it has been used in other malignancies including acute myeloid and lymphoblastic leukemias (AML; ALL, respectively), multiple myeloma, and myelodysplastic syndromes (MDS), DLI seems to be less effective in the management of these cancers, albeit positive responses have been achieved in AML, MDS, and multiple myeloma (reviewed in References 26–29). However, a significant limitation of DLI is the toxicity related to graft-versus-host disease (GVHD), i.e., the immune response mediated by the donor’s immune cells against the host’s normal cells. The ideal scenario is then to elicit GVL activity without triggering undesirable side effects, as it seems to occur in patients in which the antitumor activity of DLI is not accompanied by significant GVHD. These cases suggest that these opposing effects are mediated by distinct populations of effector immune cells. This effect was shown for multiple myeloma with the demonstration that the antitumor effect of DLI was mediated by tumor-specific donor-derived CD8+ T-cell clones (which seem to be present before DLI) whereas the GVHD activity was exerted by T cells amplified after DLI [30–32]. The ability to discriminate between antitumor activity and GVHD, and the possibility of separating ex vivo the effectors of these distinct immune responses, should represent a major step not only for improving the efficacy and specificity of DLI, but also for the design and implementation of other strategies of adoptive T-cell therapy, not involving post-transplant intervention. Although the successes attained in virus-related malignancies and in post-transplant DLI are promising, the scenario is less encouraging on the use of adoptive T-cell therapy for solid tumors. The infusion of “highly-selected” T cells specific for a differentiation antigen overexpressed on malignant cells was used in patients with metastatic melanoma, resulting in tumor regression and in autoimmunity directed to normal melanocytes. The homing of clonal T cells to tumor sites and their persistence in patients was observed [33]. Although clinical responses and even some complete remissions were seen in some trials (particularly with melanoma), in many patients the results obtained were reduced or no clinical benefit could be reported. Moreover, adoptive T-cell immunotherapy proved significantly less effective in other solid tumors. Several important issues were raised by these studies: 1) No correlation was observed between Ag-avidity and potency of antitumor in vitro activity and clinical responses; 2) Highly tumor-reactive T-cell clones often show poor engraftment and/or failed to persist in the cancer hosts; 3) Although functionally potent tumor-specific tumor-infiltrating lymphocytes (TIL) can be found in tumor sites, these cells seem unable to effectively control tumor progression; and 4) The persistence of infused T-cell clonotypes correlates with clinical response [33–36]. Some of the mechanisms implicated in the “inefficiency” of adoptively transferred T cells include tumor immune evasion,

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and possibly immunoediting, (see page 288) and tumor- or microenvironment-mediated immunosuppression. ●

15.2.3

Improving Adoptive T-Cell Therapy

The expanding knowledge on the biology of the development, modulation, and functional properties of T cells, and advances in molecular and cell biology, has permitted the routine use of experimental approaches that, not long ago, were considered cumbersome or impractical [9–11]. Instead of simple T-cell amplification or mono-factorial approaches to improve their survival, strategies are being evaluated to modify the molecular and functional properties of these T cells, redirecting their Ag-specificity, providing them with built-in mechanisms to improve their long-term survival and maintenance of memory pools, and modifying the microenvironments where their antitumor functions are most needed (i.e., the tumor sites). The challenge, as elegantly put forth by Greenberg and collaborators, is to build or engineer better, more efficient tumor-specific T cells [10]. This area is the object of intense research, and several strategies have been proposed and are being investigated: ●

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Immunostimulatory cytokines—Strategies are being devised and explored that aim at improving the survival and longterm persistence of tumor-specific T cells. An approach that has been widely used, since the early times of “nonspecific” immunotherapy, is the administration of immunostimulatory cytokines, such as the infusion of interleukin-2 (IL-2); however, although IL-2 seems to play an important role in the lifespan of the infused T-cell populations, it has also been implicated in the expansion of immunosuppressive regulatory T cells (TReg; CD4+ CD25+) [37–39], potentially functioning as a double-sword approach. Other factors are being tested, such as other γ-chain-signaling cytokines (as IL-7, IL-15, and IL-21) that also mediate T-cell survival, and activating antiapoptotic molecules as Bcl-2 and Bcl-xL [40–43]. For example, it has been recently shown that IL15 can stimulate tolerant CD8+ T cells, rescuing them from their unresponsive state and re-establishing their ability to lyse tumor cells in an adoptive therapy model [44]. These approaches, however, require some caution, as immunostimulatory cytokines can also stimulate T cells with other Ag specificities (such as auto-reactive T cells), can exert different effects on central memory T-cell population, and sustained stimulation can disturb T-cell homeostasis. Also, cytokines such as IL-7 can stimulate lymphoblastic leukemia cells and, in a transgenic animal model, it has been shown to be leukemogenic [45, 46]. Gene transfer of prosurvival molecules—The strategies being tested include the transduction of genes coding for immunostimulatory cytokines (such as IL-2 and IL-15), antiapoptotic molecules (as Bcl-2 and Bcl-xL), and cell division potential (as the telomerase reverse transcriptase hTERT) [47–53]. Again, these approaches merit some caution, such as the risks of activation of auto-reactive clones, interfer-

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ence with T-cell homeostasis, unregulated lymphoproliferation and, in the case of hTERT transduction and genomic instability. Gene Transfer of T-Cell Receptors Specific for Tumor Epitopes—These strategies aim at genetically modifying T cells to arm them with TCR with specificities to know immunogenic epitopes, which are expressed by the tumor and thereby displayed on major histocompatability complexes (MHC). The principle here is then to redirect or retarget the antigen-specificity of T cells, thus “creating” or expanding the tumor-specific repertoire. One type of approach explores an old concept, and consists in the transduction into “recipient” T cells of genes encoding TCRα and TCRβ chains cloned from antitumor-specific T cells, with the transduced T cells acquiring the epitope-specificity of the “donor” TCR [54, 55]. Ideally, these TCR chains are cloned from patients’ T cells displaying high affinity for the tumor Ag. This strategy has been used for TCR recognizing different tumor Ag [56–61] and, because of its large potential, is the object of increasing interest in this field. Another approach is the transduction of genes encoding for chimeric Ag receptors or “T-bodies,” which consist of an ectodomain containing the heavy and light chains of a monoclonal antibody (MAb) (scFv; responsible for Ag-recognition) linked to an endodomain (usually the CD3ζ chain, CD28, or FcεRIγ) [62–67]. Important advantages of these chimeric receptors are that they can be tailored to include signaling domains for T-cell costimulation, and functional activation through receptor binding, and that they extend the range of immunogenic epitopes that can be recognized (larger peptides, glycolipids, and carbohydrates). Countering tumor or microenvironmental inhibitory cues—Because tumors use several and distinct mechanisms to subvert immunosurveillance and to escape immunity, is not easy to devise common strategies that may effectively improve the efficacy of T cell-based immunotherapy. The general idea is that the neutralization of mechanisms that can prevent or antagonize the functions of the adoptively transferred T cells should facilitate the antitumor activity of these cells. Strategies being tested include the neutralization of critical soluble inhibitors such as transforming growth factor (TGF)-β, and the inhibition of the tryptophan-depleting enzyme indoleamine 2,3-dioxygenase (IDO). In addition to stimulating some tumors and promoting tumor-associated angiogenesis, TGF-β inhibits T-cell proliferation and interferes with their effector functions, and promotes the induction and development of immunosuppressive TReg. Strategies have been devised or are being developed to counter TGF-β signals, such as drugs or small molecules specifically targeting its cognate receptors (TGFβ-R) or their downstream substrates in the TGF-β pathway (as Smad proteins), or dominant-negative forms of the receptors (as a TGFB-RII dominant negative form) [68–73]. Regarding IDO, its expression by tumor cells or by Ag-presenting cells (dendritic cells or macrophages)

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markedly affects T cells, blocking T-cell proliferation in the tumor sites, inhibiting effector functions, promoting tolerance, and indirectly mediating apoptosis of activated T cells (through kynurenine metabolites) [74–76]. Studies are necessary to determine whether IDO inhibition, such as through tryptophan analogues or small molecule inhibitors, can improve the antitumor efficacy of adoptive T-cell therapy. Another potential target is galectin-1, a Β-galactosidase-binding protein that mediates growth arrest and apoptosis of activated T cells, suppresses Tcell survival and TH1 responses, and inhibits immune effector functions [77–80]. Galectin-1 is found in sites of immune privilege, and is overexpressed in TReg [81, 82]. It has been shown that its expression on tumor cells can be regulated by TGF-β1 as well as by hypoxia, conditions often present in tumor sites [83, 84]. The targeted inhibition of galectin-1 in tumor cells results in tumor rejection by immune mechanisms, with immune protection to tumor rechallenge, suggesting a critical role for galectin-1 in tumor immune evasion [85]. Strategies that disrupt or inhibit galectin-1 or the immunosuppressive mechanisms that it generates may have a positive impact on the survival, long-term persistence and effector functions of infused antitumor T cells, and may represent a valid approach to improve the efficacy of tumor-directed adoptive T-cell therapy. Reversing cell-mediated immunosuppression—Another type of approach, which is closely linked to modifying the tumor microenvironment, is the abrogation or reversal of the immunologic tolerance to tumor Ag and/or of tumor-associated immunosuppressive mechanisms. This includes strategies to eliminate or neutralize TReg or other suppressive cells and immunosuppressive soluble factors, as well as to inhibit overexpressed enzymes that mediate the catabolism of key metabolic products. Manipulating T lymphopoiesis—A largely unexplored approach is the generation and expansion of T cells from precursors or from the stem cell pool (hematopoietic or common lymphoid stem cell), which seems now possible as more is known on the phenotype, biology, and development of these primitive/progenitor cells as well as on the mechanisms regulating extrathymic T-cell differentiation. Assays have been developed that allow for the ex vivo generation of T cells from their lymphoid precursors, offering great promise for its use for tumor immunotherapy [86–90]. Since the dynamics of the peripheral T-cell pool is exquisitely regulated, remaining relatively constant throughout adult life [91, 92], the goal is to generate large numbers of T lymphocytes guiding their selection processes through exposure to Ag of interest, thus expanding the repertoire of T cells with TCR specificities for tumor epitopes. The use of such strategies, coupled with other approaches such as target redirection and blockade of immunosuppression, may have great impact on the success of adoptive immunotherapy.

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15.2.4 Tumor Vaccines: Improving What is Not Working A significant number of clinical trials have been conducted assessing the clinical efficacy of multiple strategies of tumor vaccination, using tumor cells modified by activating factors or gene transduction; dendritic cells prepared using different approaches, pulsed with whole Ag or peptides, loaded with necrotic bodies or fused with tumor cells; using multiple immune adjuvants, proinflammatory stimuli, or immune factors; DNA vaccines; in autologous and allogeneic settings; as single agents or in combination with other approaches. If a general conclusion can be draw from these studies it is that therapeutic vaccination of patients with cancer have largely failed to make a significant impact on cancer management, with few positive responses and, most often, with lack of appreciable or durable clinical benefit. Increasing evidence indicates that main obstacles for success of vaccination approaches include the tumor-associated “immunodeficiency,” as most cancer patients are immunologically compromised, and the immunosuppression and immune tolerance mechanisms that thwart the ability of host’s immunity to respond efficiently against the tumor (reviewed in Reference 93, 94). Moreover, a possible, subversive effect of tumor vaccination is the stimulation or amplification of tumor-induced TReg, which can prevent the recruitment of naive T cells and inhibit effector T-cell functions, and potentially further expand T-cell tolerance to tumor Ag [95]. The future of vaccination strategies for cancer immunotherapy is dependent not only on the design of better vaccines but also, and in large part, on the use of approaches to effectively abrogate or inhibit the immunologic barriers. Several strategies to alter the immunosuppressive tumor microenvironment on to optimize the immunotherapeutic approaches are discussed in other sections (pages 291, 294, 295). Since dendritic cells are main mediators in the initiation and amplification of T-cell responses and are critical regulators of their functional outcome (productive immunity versus immune tolerance) they will continue to represent important tools for cancer vaccination. As discussed by Banchereau and Palucka, the challenge is to develop more efficient, rationally-designed strategies that may accentuate the immunostimulatory strengths of dendritic cells whereas eliminating or reducing their potential immunosuppressive, tolerance-inducing activities [96, 97]. The obvious hope is that a deeper knowledge of the distinct functions of dendritic cells in regulating immunity, and how they are influenced during their ex vivo manipulation, will lead to the development of interventions that can effectively recruit and augment antitumor immunity [96]. As tumor vaccination aims at mobilizing into tumor sites sufficient numbers of avid, tumor-specific effector immune cells capable of eliminating the malignant cells [80], interventions that may recruit a wider repertoire of antitumor cells are of great interest. An alternative is the use of polyepitope-based strategies, either the loading dendritic cells with multiple tumor immunogenic peptides or constructs encoding them into dendritic cells,

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or by directly using polyepitope DNA vaccines. By recruiting a more diverse, poly-specific antitumor T-cell repertoire, these vaccination approaches expand the antigenic targets of the immune attack and may prevent or diminish further immunoediting, i.e., the selection of immunotherapy-induced tumor variants. Early studies showed that polyepitope vaccination is feasible, reproducible, and can promote the expansion of tumor-specific T cells in both immunocompetent and immunocompromised hosts. They are an efficient method to prime multiple responses from naive T cells, and of inducing protective immunity.

15.2.5 Abrogation or Elimination of Negative Signals

that most of the cells in the T-cell repertoire remain naive or do not recognize the tumor (immune ignorance to tumor Ag). Another strategy that seems to be operative in tumors is the “split tolerance” or activation-induced nonresponsiveness affecting primarily CD8+ T cells, in which defective secretion of IL-2 impairs the ability of these immune cells to execute defined functions, such as cell proliferation and expansion [102–104]. Several strategies have been proposed or are being developed to alter tumor-induced tolerance or restore the antitumor reactivity and functionality of T cells with specificities for tumor Ag within the patients’ repertoire: ●

Two of the main obstacles limiting the effectiveness of T-cell immunotherapy for cancer are, in fact, two of the most frequent mechanisms used by tumors to escape or hinder the patient’s immunity, modifying their microenvironments into tumor-supporting, immune-adverse milieus [94, 98, 99]. They are: the induction of immunologic tolerance to tumor Ag, and the creation of tumor-mediated or tumor-induced immunosuppressive mechanisms. These tumor strategies actively participate and are instrumental for what Zitvogel et al denominated as “immunosubversion,” i.e., the evasion of tumor cells through the subversion of the endogenous antitumor immunity [98]. Increasing evidence supports the idea that the elimination of these cancer-associated immunologic barriers is not only desirable, but rather essential to permit the success of antitumor immunotherapy approaches.

15.2.6

Reversal of Immunological Tolerance

It is well established that cancers have the ability to induce immune cells to become unresponsive to malignant cells, by promoting epitope-specific T-cell tolerance to tumor-associated Ag—an active mechanism, as tolerent T cells are Ag-experienced cells. This functional inactivation has been demonstrated for both CD4+ and CD8+ T cells, thus showing that tumors can promote tolerance of both the helper and the cytotoxic compartments of the T-cell-mediated immunity [100, 101]. This immunologic tolerance primarily requires the involvement of specialized “tolerogenic” professional antigen-presenting cells, which process and display tumor epitopes to the T cells, promoting their functional inactivation. Both molecular and functional characteristics of these antigen-presenting cells (phenotype and differentiation/maturation status) and molecular cues on the tumor milieu (immunosuppressive cytokines; noninflammatory environment) do influence and determine the establishment of this immunologic tolerance to tumor (reviewed in Reference 94). An important aspect to consider is the efficiency of this tolerogenic effect, considering that only a reduced fraction of the T cells in the host’s repertoire is rendered tolerant to the tumor; more exactly through the differentiation of a set of tumor-induced CD4+ TReg that suppress both naive T cells and TH1 effector cells [95]. Interestingly, it seems

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Manipulation of dendritic cells— Professional Ag-presenting cells, and more specifically dendritic cells, play a decisive role in the fate of the overall T-cell response to the evolving tumor, influencing the functional outcome of T cells, i.e., induction of tolerance versus productive antitumor immunity (reviewed in References 105 and106). One approach consists in manipulating dendritic cells to alter their fate or their functional properties. For example, triggering of CD40 signaling in dendritic cells (or other Ag-processing cells) using activating Ab or CD40L fusion proteins has been pursued as a way to overcome tolerance and increase antitumor T-cell responses [107, 108]. Activation and maturation of dendritic cells through the engagement of toll-like receptors, and consequent secretion of inflammatory factors, has been proposed as an alternative for reversing tolerance, and for stimulating tumor-reactive T cells, rendering them refractory to immunosuppressive signals [109, 110]. Also, because tolerogenic dendritic cells upregulate IDO and consequent expansion of TReg (reinforcing T-cell tolerance), the pharmacologic blockade of this enzyme should provide clinical benefit. A different type of strategy is the blockade of activated signal transducer and activator of transcription 3 (STAT3) in dendritic cells, as it has been shown that it may lead to reversal of tolerance, and potent antitumor immunity [111–113]. This effect on dendritic cells can be potentiated further by the neutralization of this transcription factor in malignant cells, as its hyperactivation in tumors interferes with the Ag-presentation function of dendritic cells. Inhibition of negative regulators in T cells—In addition to molecules that are critical for their activation, T cells also express molecules that act as negative regulators, which in defined settings play significant roles in the induction and maintenance of T-cell tolerance. CTLA-4 (CD152), which like CD28 serves as a receptor for the B7-1/B7-2 molecules, negatively regulates T-cell activation and is involved in promoting T-cell tolerance, acting as an inhibitory checkpoint of T-cell immunity [114–116]. Therefore, blockade of CTLA-4 signals using neutralizing Ab or a CTLA-4 recombinant protein offers great potential as an approach to generate or augment antitumor T-cell responses in clinical settings [115, 117, 118]. An additional rationale for blocking CTLA-4 is that, through reverse signaling on B7-expressing dendritic

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cells, it activates tryptophan catabolism, thus contributing to inhibition of T-cell activation and to T-cell apoptosis [119]. Other approach is the blockade of PD-L1/PD-1 axis, which regulates negatively TCR signals, and seems to regulate peripheral T-cell tolerance [120–122]. As many cancers aberrantly express PD-L1/B7-H1, it has been proposed that blockade of its signals through PD-1 on T cells may reverse tolerance to tumor Ag. Modification of the protolerogenic tumor microenvironment—At early stages, tumors are largely noninflammatory environments, where dendritic cells can capture and present tumor Ag but that foster the induction of T-cell tolerance over productive T-cell immunity [105, 123]. Thus, tumor microenvironments are generally immunosuppressive and protolerogenic, and strategies that inactivate molecules or mechanisms involved in the induction and maintenance of T-cell tolerance offer great therapeutic promise. Approaches are being pursued to induce or potentiate an inflammatory phenotype in tumor sites (with immune adjuvants or proinflammatory agents), to neutralize cytokines directly involved in tolerance induction (as TGF-β1), or to inhibit mechanisms that regulate the function or survival/expansion of critical cellular mediators or regulators of T-cell tolerance (as TReg).

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15.2.7 Inhibition of Tumor-Associated Immunosuppression Tumors create conditions that subvert immunosurveillance and favor immune escape, using multiple and distinct mechanisms of immunosuppression. The cross-talk between the malignant cells and their microenvironment fosters and takes place in conditions in which immunosuppression is dominant, outweighing the efforts of the immunity to tackle the emerging cancer [93]. Since some of these immunosuppressive mechanisms are critically implicated in T-cell tolerance and represent obstacles to successful immunotherapy and some of the strategies aiming at abrogating or inhibiting them have been described in previous sections. Potential approaches to curtail or eliminate tumor-associated immunosuppression (reviewed in Reference 94) include: ●

Elimination or blockade of regulatory T cells or other tumorassociated suppressive cells. CD4+CD25+ TReg (expressing high CTLA-4, GITR and FoxP3) are potent inhibitors of T-cell activation, controlling the immunity directed to both self and foreign Ag, and exerting their suppressive activities both through direct cell-cell interaction mechanisms (CTLA4/B7 family members and PD-1/PD-L1) and through potent immunosuppressive cytokines (as TGF-β1 and IL-10) [124, 125]. In animal tumor models and in early clinical trials, elimination of TReg cells resulted in potent antitumor immunity [126–129]. Approaches being tested include the use of anti-CD25 antibodies for depletion of these cells, Ab specific for CTLA4 or PD-1 to block the inhibitory signals triggered through these molecules, and the neutralization of the

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chemokine CCL22/MDC to inhibit the chemoattraction of TReg cells to tumor sites [118, 130–133]. Other immune cells with suppressive activities, whose specific elimination may facilitate the development of antitumor immunity, include the Gr1+ CD11b+ myeloid suppressor cells, CD1d-restricted natural killer (NK) T cells, IDO-expressing dendritic cells, and regulatory dendritic cells. Neutralization of immunosuppressive cytokines or factors. TGF-β1 and IL-10 are important mediators of immunosuppression associated with tumors and, therefore, represent two potential targets for intervention. Strategies aimed at neutralizing TGF-β1 were discussed in page 291. Regarding IL-10, its inhibition using neutralizing Ab can result in enhanced T-cell immunity and tumor regression, although it has been suggested that this effect can be obtained through blockade of CTLA-4 signaling (which regulates IL-10 secretion) [134]. Because TReg cells are important producers of TGF-β1 and IL-10 in the tumor microenvironment, elimination of these suppressor cells may represent a more effective approach to purge or least reduce the levels of these inhibitory cytokines on tumor sites. Neutralization of other soluble factors that mediate or facilitate immunosuppression has been proposed, including: IL-13, which with TGF-β1 mediates the suppressive effects of inhibitory NK T cells on CTL responses [135]; prostaglandin E2, which suppresses the maturation and function of dendritic cells [136, 137]; CXCL12/SDF1or CCL12/MDC, which are chemokines that mediate the chemoattraction of suppressive immune cells [137]; and vascular endothelial growth factor (VEGF), which suppresses dendritic cell differentiation and functional maturation [138]. These approaches however warrant caution, because cytokines often mediate distinct functions, depending on the cell target and the contexts in which their activities are exerted. For example, in addition to its well-document immunosuppressive activities, IL-10 can also function as an immune-stimulator in defined contexts (particularly at high doses), facilitating tumor attack and rejection [139]. In fact, the administration of IL-10 has been proposed as an immunotherapeutic approach [140]. Modulation of “suppressive” enzymes—T-cell survival and function are critically dependent on products of the catabolism of tryptophan and arginine. The tryptophan-catabolizing enzyme IDO promotes tolerance to tumors, inhibiting effector T cells and contributing immune evasion, and thereby represents a promising target. Competitive antagonists as well as small-molecule IDO inhibitors are being developed and evaluated in pre-clinical studies [76, 99]. Other enzymes whose specific targeting is being considered include: the tumor cell- or myeloid cell-expressed enzyme arginase (ARG), which mediates the catabolism of arginine, and whose excess can result in suppression of T-cell responses; the nitric-oxide synthase iNOS, which also catabolizes arginine, and plays a role in immunity; and the cyclooxygenase 2 (COX2) enzyme that mediates prostanoid synthesis, and plays a central role in regulating the mechanisms of immune suppression, being involved in the generation of TReg.

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Targeting critical pathways transducing suppressive signals—As signals triggered by immune mediators are transduced into the cells through defined signaling cascades, and lead to the transcription regulation of multiple genes implicated in productive and suppressive immunity, increasing attention has been drawn to the therapeutic potential of targeting critical signaling molecules for cancer immunotherapy. These include transcription factors as STAT3 and NF-κB, tyrosine kinases as the VEGF-R1/FLT1, c-kit and BTK, and other signaling effectors.

15.2.8 Hyperactivation or Constitutive Engagement of “Suppressive” Signaling Pathways A novel, important aspect on the immune-evasion and stealth aptitude of tumors versus the cancer host’s immunosurveillance is the “manipulation” or aberrant expression of critical components of the signal transduction machinery of immune cells. This is exemplified by the recently unveiled role of the STAT3 on the suppression of antitumor immunity. STAT3 is a member of the STAT family of transcription factors, which are activated through phosphorylation by Jak tyrosine kinase molecules, in response to ligation of multiple cytokine receptors. STAT activation results in the formation of dimers, which translocate to the nucleus where they induce the expression of a variety of genes, thus regulating multiple cell functions [141–143]. In physiological conditions, activation of STAT3 and its tyrosine phosphorylation is triggered, among others, by IL-6-family cytokines (IL-6, LIF, and oncostatin M), epidermal growth factor, and HGF. Constitutive activation of STAT3 has been observed in distinct malignancies, including lymphoid and myeloid leukemias, breast, head-and-neck, and prostate cancers. Functionally, constitutive or aberrant engagement of STAT3 contributes to oncogenesis, resulting in dysregulated survival and proliferation of malignant cells, as well as promoting tumor-associated angiogenesis. Mechanistically, constitutive STAT3 engagement leads to changes in gene expression, affecting molecules regulating or mediating cell-cycle progression (as cyclin D1), cell apoptosis (as BclxL, Mcl-1, Survivin), transcription regulation (as c-Myc, NFkB), biosynthesis (as COX-2), cytokine secretion (as VEGF) and differentiation and cell fate decisions (as Notch ligands) [141, 144–147]. Recent studies have shown that the role of STAT3 in cancer biology is not only limited to its involvement in tumorigenesis and signaling dysregulation on malignant cells, but also that it exerts important effects on tumor immunity. Although it is well-established that STAT3 wields critical activities in the regulation of immunity (innate and adaptive) and inflammation, two seminal reports by Hua Yu’s group have shed light on the involvement of STAT3 in the initiation and development of antitumor immune responses, and its potential as a target for cancer immunotherapy. A first study [111] showed that constitutive activation of STAT3 on malignant cells downregulates or prevents the expression of proinflammatory

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factors, inhibiting the functional maturation of dendritic cells from progenitor cells, and dendritic cell activation. Consequently, tumor-induced STAT3 signaling hinders the priming and expansion of tumor-specific T cells, promoting immune tolerance. STAT3 inactivation on tumor cells (by a dominant-negative STAT3 construct or antisense oligonucletides) resulted in the upregulation of proinflammatory, immuneactivating chemokines and cytokines. Functionally, STAT3 neutralization restored dendritic cell maturation and enhanced their antigen-presenting cell (AgPC) function, leading to the generation of productive tumor-specific T-cell immunity. The second study [112] revealed that tumor-infiltrating immune cells also exhibit constitutively active STAT3. Using a model in which STAT3 is eliminated in hematopoietic cells (using Mx1-Cre-Stat3loxP/loxP mice), they observed potent and efficient antitumor immune responses, with inhibition of tumor growth and tumor metastasis. The effective antitumor response in these animals required immune cells, and correlated with improved function of both innate and adaptive immunity, with enhanced activities of dendritic cells, T cells, NK cells, and neutrophils. Importantly, they have shown that this antitumor effect does not occur from systemic autoimmune reactivity due to STAT3 ablation, but that is rather mediated through specific mechanisms of immunosurveillance. Finally, this study has shown that specific targeting of STAT3, using the platinumcontaining small molecule inhibitor CPA-7 [148], resulted in immune-triggered inhibition of established agent-resistant tumors, and that this antitumor effect was mediated by both T cells and NK cells. Taken together, these studies indicate that STAT3 activation on cancer affects the host’s antitumor immunity by sculpting the tumor/immune cross-talk towards a tolerogenic response, hampering the immunosurveillance to the evolving tumor, and by promoting immune escape through the interference with multiple elements of the immune system [111, 112]. Other studies have shown that the activation status of STAT3 plays a critical role in regulating the functional fate of dendritic cells. STAT3 signaling suppresses the expression of MHC class II expression in dendritic cells, with a decrease in MHC II dimers, invariant chain (Ii), and HLA-DM molecules and reduced levels of the endogenous cathepsin inhibitor cystatin C. This resulted in significant suppression of peptidespecific T-helper cell responses [149]. Nefedova et al showed that hyperactivation of JAK2/STAT3 by tumor factors affected normal dendritic cells differentiation and maturation. The selective blockade of JAK2/STAT3 (with the inhibitor JSI-124/ cucurbitacin I) resulted in the generation of mature dendritic cells and reduced numbers of immature myeloid cells, significantly enhancing antitumor efficacy. JAK2/STAT3 inhibition also resulted in upregulation of MHC II, costimulatory molecules, and improved function in triggering both allogeneic and antigen-specific T-cell responses [150–152]. STAT3 also plays important roles in antigen-specific T-cell tolerance, as antigen-presenting cells on AgPC lacking STAT3 effectively break T-cell anergy [113]. Furthermore, dendritic cells expressing

15. Harnessing the Power of Immunity to Battle Cancer: Much Ado about Nothing or All’s Well That Ends Well?

activated STAT3 secrete less IL-12, which is a critical mediator of the development, expansion, and activity of TH1 cells, and they also express increased levels of IDO, thus affecting T-cell proliferation [153]. STAT3 activation in malignant cells also seems to affect the migration and recruitment of immune cells to tumor sites, as well as the antitumor activity mediated by nitric oxide produce by tumor-associated macrophages [154]. Finally, the impact of microenvironmental cues on tumor-promoted activation of STAT3 in immune cells is supported by the demonstration that the STAT3-triggering cytokine IL-6, which is secreted by the tumor-supportive stroma, suppresses dendritic cell differentiation and maturation. STAT3 activation is required for the suppressive effects of IL-6 on bone marrow-derived dendritic cells, and IL-6 ablation and consequent STAT3 reduction resulted in enhanced dendritic cell-mediated T-cell activation [155]. Other signaling molecules or pathways potentially implicated in immune-evasion include the activation of mitogenactivated protein kinase (MAPK) signaling, the suppressors of cytokine signaling SOCS1 and SOCS3, activation of c-kit receptor tyrosine kinase, the negative regulatory functions of the Bruton’s tyrosine kinase (Btk), alteration of NF-κB binding activity, and Tob expression [156–163]. Additional studies are necessary to further dissect the mechanisms and functional consequences of the targeting of these signaling molecules as a means to improve antitumor immunity, as well as their validation in experimental models of human cancer and, ultimately, their clinical translation. Since some of these signaling pathways represent valid targets for strategies directly targeting the malignant cells and the tumor-associated angiogenesis, the potential effect of their blockade on eliciting or enhancing antitumor immunity or tumor immunosurveillance is of great therapeutic interest. It will be then important to include endpoints of immune function in the evaluation of clinical trials testing the antitumor efficacy of agents targeting defined “tumorigenic” signaling events or pathways, to determine the potential contribution of host’s immunity to the overall antitumor effect.

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components of the immune system that respond to, interact with, or modulate the developing tumor, often exerting opposing functions towards an undesirable outcome—to facilitate the survival and expansion of the cancer. The obvious challenge is how to manipulate the immune system to effectively contain or eliminate the tumor, overcoming adverse conditions that impede or limit its antitumor activities. As amply discussed in this text, some of these hurdles are laid by the immunity itself. Considering the mechanistic simplicity of the immune interventions being used, which mostly are designed to exploit one single immune activity or targeting a single immune mechanism, it is not surprising that these approaches have been largely unsuccessful. The time has come for rationally-designed strategies that take into account the complexity and diversity of the immunologic events that take place within any given tumor. Early combination studies showed some promise, such as the use of tumor vaccination and blockade of CTLA-4, or combinatory tumor vaccination and adoptive T-cell therapy. Many combinations are possible, and several are being proposed or tested, or can be definitely evaluated, provided that specific therapeutic agents are available, and suitable clinical protocols and patient selection can be made. For example, the use of IDO antagonists or STAT3 inhibitors as part of chemotherapeutic regimens; the neutralization of TGF-β1 and/or elimination of TReg in combination with adoptive T-cell transfer (with or without in vivo T-cell boosting with tumor vaccines); the neutralization of tolerogenic dendritic cells and TRegs combined with adoptive T-cell therapy (with ex vivo expanded multi-specific T cells or TCR-derived T cells); and many other approaches that the ingenuity of researchers and clinicians can devise. A common denominator is that, ideally, immune interventions for cancer therapy should merge means to optimize and boost antitumor immunity while inactivating tumor-associated immunosubversive mechanisms. The success of these “immune cooperativity” approaches will help to define the place of tumor immunotherapy in the long journey for the cure of human cancer. It is hoped that this will be a case in which “all’s well that ends well”.

15.3 Conclusions or How Tackling Complex Processes Require Cooperativity

References

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Chapter 16 Aurora Kinases: A New Target for Anticancer Drug Development Teresa Macarulla, Francisco Javier Ramos, and Josep Tabernero

16.1

Introduction

The cell division process is one of the hallmarks of every living organism. Within the complete cell-cycle process, mitosis constitutes one of the most critical steps, by which a copy of the duplicate genome is segregated by the microtubule spindle system into the two resulting cells. Errors in this process can lead to genomic instability, a condition associated with tumorigenesis [1]. This process is tightly regulated by several proteins, some of them acting as check-points that ultimately ensure the correct temporal and spatial coordination of this critical biologic process. Among this network of mitotic regulators, Aurora kinases (AK) play a critical role in cellular division by controlling chromatid segregation. AK is the name given to a family of Serine/threonine (Ser/thr) protein kinases that regulate many processes during cell division [2–5]. Three AK family members have been identified in mammalian cells: A, B, and C. These proteins are involved in the regulation of multiples steps of mitosis. The expression levels of human AK are increased in certain types of cancer including breast, colon, pancreatic, ovarian, and gastric tumors [6]. In experimental models, overexpression of AK can induce spindle defects, chromosome mis-segregation, and malignant transformation. Conversely, downregulation of AK expression cause mitotic arrest and apoptosis in tumor cell lines. This observation has lent an interest to this family of kinases as potential drug targets for development of new anticancer therapies.

16.2

Biology of Aurora Kinase Family

Yeasts have a single AK, however, metazoans have at least three highly related AK with numerous names in the literature (Table 16-1). We will refer to the mammalian kinases as Aurora-A (AKA), Aurora-B (AK-B), and Aurora-C (AK-C). The three kinases

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

share a similar catalytic domain located in the carboxy terminal region, but the three AK differ in the length and sequence of the amino terminal domain, and it seems to be responsible for the specificity of each kinase. AK-A, -B, and −C have 402, 344, and 309 amino acids, respectively. AK-A and -B have 57% of identity in their sequences, AK-B and -C have high degree of similarity with 75% of identity in the sequence, and there is a 60% identity in the sequence between AK-A and -C [4]. Despite a high degree of sequence homology, especially in the catalytic domains, the subcellular localization and functions of the three AK are largely nonoverlapping. In a typical somatic cell cycle, M phase compromises mitosis and cytokinesis. Mitosis is the process of nuclear division by which a complete copy of duplicate genome is segregated into two daughter cells, whereas cytokinesis is the process of cytoplasmatic division that occurs at the end of mitosis. Mitosis is conventionally divided into five phases —prophase, prometaphase, metaphase, anaphase, and telophase—on the basis of changes in the structure and behavior of the spindle and chromosomes. During prophase, the nuclear membrane breaks down, the chromatin condenses into chromosomes in the nucleus, the centrosome enlarges, and microtubule nucleation activity (referred to as centrosome maturation) increases in the cytoplasm. During prometaphase, microtubules are captured by kinetochores and chromosomes attach to the spindle fibers. In metaphase, chromosomes move to an equatorial plane. In anaphase, the duplicated chromosomes have attached to the spindle fibers and start separation. In telophase, the chromosomes reach the mitotic poles and the chromatin condensation begins, and finally a contractile ring is formed and cytokinesis is completed [5]. Mitosis activation is associated with the recruitment of multiple proteins, including γ-tubulin, pericentriolar components, cycline B1, cyclin-dependent kinase 1 (Cdk1), and several mitotic kinases that include Polo kinase, NIMA-related kinase, WARTS-related kinases and AKs [5]. The level of these kinases is reduced in G1 cells, and these proteins and their activity levels peak at the G2/M transition [7]. During M phase of mitosis, AK-A and -B localize to key mitotic structures. The different AK 307

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Table 16-1. The three Aurora kinases in mammals are known by multiples names in the literature. Name

Alternative names

Aurora-A

Binding proteins

Aurora 2, AIRK1, ARK1, BTAK, STK6, STK15, AYK1, IAK1 Aurora 1, AIRK2, ARK2, IAL1, AIK2, STK12, AIM 1. Aurora 3, AIRK3, AIE2, STK13, AIE1, AIK3

Aurora-B Aurora-C

TPX2, TACC1 INCENP, borealin, survivin, TACC1

Table 16-2. Aurora kinases have different functions during cell division. Family member Aurora -A Aurora-B Aurora-C

Localization Centrosome Spindle poles Kinetochores Spindle midzone Centrosome

Cellular function Mitotic spindle formation Centrosome maturation Chromosome biorientation Spindle checkpoint function Functions remain unclear

exhibit strikingly different subcellular localization and functions that have been extensively reviewed elsewhere [2, 4, 5]. Here, we briefly review the recent advances in the understanding of AK function and their biologic role (Table 16-2).

As cells enter into mitosis, the TPX2 protein is located in a complex with importin-α and importin-β. Ran-GTP is a small GTPase that during mitosis liberates TPX2 from importin-α and importin-β. The liberated TPX2 then binds to AK-A at the centrosome and targets it to the microtubules proximal to the pole [16]. Biochemical studies have confirmed an important role of AK-A in spindle assembly, maintenance or both [17]. Another function conferred to AK-A is centrosome maturation and separation. The centrosome is the main microtubuleorganizing center of cells. To become fully functional after their duplication and separation, centrosomes must recruit a number of different proteins (maturation). In the absence of AK-A, recruitment of several components to the centrosome is deficient [18, 19]. Regulation of AK-A is complex and involves both processes of phosphorylation/dephosphorylation and degradation. Phosphorylation stimulates kinase activity and three phosphorylation sites have been identified in human AK-A: Ser51, Thr288, and Ser342. Phosphorylation of Thr288 is important for regulation of the kinase activity, both for its function and its stability [20]. The phosphatase PP1 negatively regulates AK-A. AK-A is degraded in late mitosis/early G1 phase [21, 22].

16.2.2 16.2.1

Aurora Kinase A

Like AK-B and -C, AK-A is only expressed during mitosis. During prophase, AK-A localizes to the centrosomes, whereas in later stages of mitosis it is located at the spindle poles [8, 9]. Repression of AK-A activity in human cells delays their entry into mitosis [10]. AK-A overexpression produces genetic instability and tumorigenesis by disrupting the proper assembly of the mitotic checkpoint complex at the level of the Cdc20-BubR1 interaction [11]. It has been shown that AKA overexpression produces resistance to apotosis induced by paclitaxel in human cancer cell lines [12]. Similarly, ectopic expression of AK-A renders cells resistant to cisplatin, etoposide, and paclitaxel-induced apoptosis and stimulates AKT activity in wild-type p53 but not in p53-null ovarian cancer cell lines [13]. AK-A is a key regulator of the p53 pathway and as its overexpression induces Mdm-2-mediated destabilization and inhibition of p53 [14]. Although AK-A activation seems to depend on the cdc2/cyclin B activity, the kinase that activates AK-A has not been identified. The kinase activity of AK-A is regulated by a protein called TPX2, a prominent component of the spindle apparatus, which is also required for mitotic spindle assembly. Some studies have demonstrated that NH2-terminal of TPX2 can directly interact with the COOH-terminal catalytic domain of AK-A. Upon small interfering RNA (siRNA)-mediated elimination of TPX2 from cells, the association of AK-A with the spindle microtubules was abolished, although its association with spindle poles was unaffected. Conversely, depletion of AK-A by siRNA had no detectable influence on the localization of TPX2 [15].

Aurora Kinase B

AK-B is a chromosomal passenger protein. It localizes to the centromeric regions of chromosomes in the early stages of mitosis. During prophase, AK-B is a nuclear kinase. In metaphase, AK-B is located in the inner centromere. The centromere is a specialized region of the chromosome that assembles the two kinetochores and maintains sister-chromatid cohesion until anaphase. Later in mitosis, in anaphase, AK-B undergoes a dramatic relocalization from the centromeres to the microtubules at the spindle-equator. As the spindle elongates and the cell undergoes cytokinesis, AK-B accumulates in the spindle midzone before finally concentrating at the midbody [4]. Once the kinase associates with central spindle microtubules during anaphase, its motility is highly reduced. A subpopulation of AK-B seems to be transported by astral microtubules to the equatorial cell cortex. AK-B is essential for a number of processes during mitosis. AK-B expression and activity in proliferation tissues are cell-cycle regulated: expression peaks at the G2-M transition, and kinase activity is maximal during mitosis. AK-B binds three other chromosome passenger proteins, i.e., inner centromere protein (INCENP), surivivin, and borealin. The complex of AK-B, INCENP, and surivivin has essential regulatory roles at centromeres and the central spindle in mitosis [23]. Chromosomal passenger shows a dynamic localization pattern during mitosis, appearing on the chromosome arms and in inner centromeres from prophase through metaphase and then transferring to the spindle midzone from anaphase through cytokinesis [24]. Ak-B seems to have an important role in regulating chromosome biorientation

16. Aurora Kinases: A New Target for Anticancer Drug Development

[25]. Normally, the first attachment of chromosomes to the spindle microtubules is monotelic (one kinetochore bound and one kinetochore free). Eventually, both kinetochores bind to the same spindle pole (syntelic attachment), or the chromosomes become attached to both spindle poles (amphitelic attachment). Some studies have demonstrated that interfering with AK-B function can cause defects in chromosome alignment at the equator during metaphase [26, 27]. During mitosis, AK-B plays a critical function in chromosomal condensation by phosphorylating the H3 histone [28–30]. AK-B is required for correct cytokinesis. If the protein kinase is inactive, the nuclear division is not affected, but cytokinesis fails, resulting in cell polyploidy and cell death. These events strongly suggest that AK-B is required for proper progression of cytokinesis in mammalian cells [31]. Overexpression of an inactive form of AK-B prevents the last step of cytokinesis, and a similar phenotype is produced by exogenous expression of a dominant mutant of INCENP [32]. Finally, AK-B is required for spindle-assembly checkpoint function. This checkpoint delays sister-chromatid separation until all chromosomes are properly aligned on the spindle, and monitors the attachment of microtubules to kinetochores or the generation of tension that results from bipolar attachment of sister chromatids or both. AKs are required for stable activation of the checkpoint as a result of a loss of spindle tension [33, 34].

16.2.3

Aurora Kinase C

AK-C is expressed at a moderate level, although than at lower degree than AK-B in diploid human fibroblasts; however, the level of AK-C is highly expressed in normal testis and several human cancer cell types. Little is known about the exact function and regulation of AK-C. Immunofluorescence studies using a specific antibody (Ab) have shown that AK-C is located to the centrosome during mitosis from anaphase to cytokinesis, these results suggesting that AK-C may play a role in centrosome function at the late stage of mitosis [35]. Like AK-A, AK-C also localizes to spindle poles, but only in late mitosis [35]. AK-C protein level in M phase of

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mitosis is higher than that of S phase. Like AK-B, AK-C is a chromosome passenger and interacts with the INCENP [36]; however, the affinity of AK-C for INCENP is lower than the affinity of AK-B. Additionally, AK-C-dead mutants induce multinucleation and siRNA-mediated silencing of both AKB and -C produce multinucleated cells (No entenc aquesta frase??). Interestingly, AK-C is able to rescue the AK-B silenced multinucleation phenotype, this result suggesting that AK-C function overlaps and complements AK-B function in mitosis [37]. In summary, AK-C is a chromosomal passenger protein that localizes first to centromeres and later in mitosis to the midzone of the mitotic cells and cooperates with AK-B to regulate mitotic chromosome segregation and cytokinesis in mammalian cells.

16.3

Aurora Kinases and Cancer

AKs are frequently overexpressed in human tumors. Misregulation of the cell-cycle machinery can have an important impact on cellular proliferation. Based on the prevalence of genetic abnormalities in human cancer, it is plausible that proteins involved in maintaining the integrity of chromosome segregation may also play a role in cellular transformation.

16.3.1

Aurora Kinase A and Cancer

AK-A has attracted intense interest after the discovery that the chromosomal region (20q13.2) in which it is located commonly undergoes amplification in epithelial cancers [38]. AK-A is overexpressed in 12–50% of breast, colorectal, and gastric cancers [39–47]. Bischoff et al. have shown that AK-A RNA is expressed in a variety of human tumor cell lines while having limited expression in normal tissue [6]. AK-A DNA was found to be amplified, and its RNA overexpressed, in 52% of a cohort of primary colorectal cancers examined in this study. Katayama et al. obtained fresh tumor tissues and their normal counterparts from 12 patients who had surgical resected primary colorectal cancers. They reported that the expression levels of AK-A and -B were higher in human colorectal cancer than in normal mucosa. Additionally, the expression levels

Table 16-3. Aurora kinase inhibitors in clinical development. Compound (Manufacturer) Chemical class AK-A IC50 AK-B IC50 AK-C IC50 Other targets

ZM447439 (AstraZeneca) quinazoline derivative 110 nM 130 nM NA MEK (1790 nM) SRC (1030 nM) LCK (880 nM)

Hesperadin MK0457/VX-680 (Merck (Boehringer Ingelheim) MSD/Vertex) MLN8054 (Millenium) AZD1152 (AstraZeneca) Indolinone NA 259 nM NA AMPK, LCK, MKK1, MAPKAP-K1, CHK1, PHK (1000 nM) CycB/ CDK1 (2800 nM)

4,6 diamino-pyrimidine 0.6 nM 18 nM 4.6 nM FLT3 (30 nM)

pyrimidoaminobenzoate 34 nM 5700 nM NA NA

AK-A, Aurora kinase A; AK-B, Aurora kinase B; AK-C, Aurora kinase C; IC50, 50% inhibitory concentration; NA, not available

quinazoline derivative 27 nM 3.7 nM NA NA

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of AK-B showed a tendency to be higher in more aggressive cancers, indicating that AK-B expression might serve as a marker of prognostic evaluation of malignant tumors [48]. The AK-A gene is amplified and overexpressed in breast cancer cell lines, playing a critical role in oncogenic transformation of breast tumor cells [46]. In addition, amplification of the AK-A locus (20q13) correlates with poor prognosis in patients with node-negative breast cancer [50]. Tanaka et al. performed an inmunohistochemistry (IHC) analysis with normal tissue and invasive ductal adenocarcinoma of the breast, and showed that overexpression of AK-A was observed in 94% of the tumors, irrespective of the histopathologic types, whereas the protein was not detected in normal ductal and lobular cells [46]. Gritsko et al. examined the kinase activity and protein levels of AK-A in 92 patients with primary ovarian tumors [41]. This study showed high AK-A activity in 44 (48%) specimens and increased AK-A protein levels in 52 (57%) cases; however, the relationship between overexpression of AK-A and tumor grade/stage was controversial, AK-A being preferentially activated/overexpressed in low-grade and early stage ovarian cancer, although there was no statistical significance at the kinase level between lowgrade/early stage and high-grade/late stage tumors [41]. A previous study demonstrated amplification of AK-A centrosome in 10–25% of ovarian cancers [51]. Cytogenetic and molecular studies have shown that many human pancreatic cancers exhibit chromosome abnormalities and gain of chromosome 20q, where the AK-A gene is localized. Li et al. investigated the expression and copy number alteration of AK-A in pancreatic carcinoma cells and primary tumors, as well as their association with size, degree of differentiation, and metastasis status of the tumor. Increased AK-A protein expression was detected in 22 of 38 sections (58%) from patients with pancreatic cancer. The extent of AK-A expression was not significantly correlated with the size, degree of differentiation, and metastasis status of the tumor [52]. Sen et al. examined whether increase of AK-A copy numbers and protein levels were linked to aneuploidy in bladder cancers and if AK-A expression levels were related to histologic grade and tumor stage. They concluded that AK-A gene amplification and associated increased expression of mitotic kinase were associated with aneuploidy and more aggressive clinical behavior in human bladder cancer [49]. Another tumor type in which AK-A has been demonstrated to be overexpressed is gastric cancer. Sakakura et al. evaluated 72 unselected primary gastric tumors and found AK-A overexpression in >50% of them [45]. Anand et al. showed that AK-A amplification may constitute an alternative route to mitotic checkpoint dysfunction during carcinogenesis, and increased AK-A expression can cause resistance to apoptosis induced by some chemotherapeutic agents in human cancer cell lines [12]. Meraldi et al. elucidated the origin of the centrosome amplification phenotype that is frequently observed in a variety of genetically altered cells and tumors. They showed that overexpression

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of AK-A did not regulate centrosome duplication but gave rise to extra centrosomes through defects in cell division and consequent tetraploidization. Indeed, the absence of a p53 checkpoint exacerbated this phenotype. They concluded that errors during cell division, combined with the inability to detect the resulting hyperploidy, constitute a major cause for numerical centrosome aberrations in tumors [53]. All these observations implicate AK-A as a potential oncogene in many colon, breast, and other solid tumors, and a promising protein target for cancer drug intervention.

16.3.2

Aurora Kinase B and Cancer

Unlike AK-A, the role of AK-B in tumorigenesis has been less studied. The reasons for this less extensive evaluation are not clear, although the pattern of expression of AK-B is not much different from that of AK-A. This reality is starting to change as several new compounds targeting AK-B are in nonclinical and clinical development. AK-B has been implicated in cancer development; although it has not been shown to be oncogenic; however, a few studies have examined expression of AK-A and AK-B in parallel [6, 48], and none of them have included an analysis of AK-C. It has been shown that expression levels of AK-B increase as a function of Duke’s stage in colorectal cancer [48]. Increased levels of phosphorylation of histone H3 were also shown to correlate with overexpression of AK-B in some human colorectal tumor cell lines [54]. Marchet et al. have shown in a small study that the expression of AK-B in evaluated gastric cancer specimens from 32 patients correlated with nodal involvement in a multivariate model [55]. Lopez-Rios et al. have shown that the expression of AK-A and -B is correlated with survival in mesothelioma. The authors analyzed 99 pleural mesotheliomas by gene expression profiling, and showed that more-aggressive mesotheliomas express higher levels of both AK-A and -B and functionally related genes involved in mitosis and cell-cycle control. Moreover, they independently confirmed the negative effect of the expression of AK-B protein by immunohistochemical (IHC) in a separate cohort of patients [56].

16.3.3

Aurora Kinase C and Cancer

AK-C has been shown to be overexpressed in multiple tumor cell lines; however, little data are known about the role of its expression in human cancer specimens and prognosis. Takahashi et al. performed an IHC analysis of AK-C expression in 78 primary colon cancer, 36 colorectal adenomas, and 15 normal colon specimens. Increased expression of AK-C was observed in 51% of colon cancers. Furthermore, colon cancer adenomas showed high expression of AK-C, 19% of cases, being intermediate between colorectal cancers and normal colorectal mucosa. These results suggest that overexpression of AK-C might be involved in tumorigenesis, progression, or both in colorectal cancer [57].

16. Aurora Kinases: A New Target for Anticancer Drug Development

16.4 Development of Aurora Kinase Inhibitors As the Auroras are clearly implicated in tumorigenesis, and are also kinases, they are thought to represent promising targets for anticancer drug development [58–62]. In normal cells, AK-A inhibition results in delayed, but not blocked, mitotic entry [10, 63], centrosome separation defects [63, 64], and failure of cytokinesis [63]. Antitumor effects have been seen with AK-A inhibition by siRNA in human pancreatic cell lines, with growth suppression in in vitro models, and almost total abrogation of tumorigenicity in mouse xenograft models [65]. AK-B inhibition produces abnormal kinetochore microtubule attachments, failure of the process of chromosomal biorientation, and of cytokinesis [66, 67]. Although there is initial recruitment of checkpoint proteins, such as BubR1 and Mad2, to kinetochores at initial mitosis in prophase, they subsequently dissociate as mitosis progresses with the abrogation of AK-B function. This dissociation weakens and compromises the checkpoint, allowing cells to progress on mitosis from metaphase to anaphase, despite incorrect microtubule–kinetochore attachments. Recurrent cycles of aberrant mitosis without cytokinesis conduct to massive polyploidy, finally leading to apoptosis [27, 28, 30, 68, 69]. The effects of AK-A and -B inhibition in malignant cells impair chromosome alignment, disrupt the mitotic checkpoint, cause polyploidy, and lead finally to cell death. These effects have been shown in vitro to be greater in transformed cells than in nontransformed or quiescent cells [68]. This selectivity for malignant cells shows, in principle, that the side effects of AK inhibition would be reasonably acceptable; however, rapidly dividing cells, e.g., cells in the hematopoietic and gastrointestinal systems, may be affected by AK inhibition. Several AK inhibitors are in nonclinical and clinical development (Table 16-3). Most of the compounds have the capacity to inhibit both AK-A and -B, although there is an increasing interest to develop compounds more specific for one AK. It has been shown that compounds designed to be dual AK-A and -B inhibitors produce a cellular phenotype entirely consistent with AK-B inhibition alone. Yang et al. have shown that the kinases activity of both AK-A and -B are equally inhibited in cells treated with ZM447439 and hesperadin, dual AK-A and -B inhibitors. Additionally, they have demonstrated that AK-B must be present and active to achieve the expected phenotype of AK-A inhibition, and that the inactivation of AK-B function by siRNA overrides the required functions of AK-A in mitosis. Moreover, they have shown that AK-B is responsible for mitotic arrest in the absence of AK-A, providing a molecular explanation why dual inhibitors generate a phenotype consisting with loss of AK-B function but not of AK-A [70]. It has been shown by siRNA and antisense studies that a specific phenotype is achieved by single AK-A inhibition [71]. Whether this AK-A inhibition by an AK-A specific inhibitor will translate into a different phenotypic pattern in the

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clinics is unclear; however, single AK-A inhibitors, such as MLN8054, are in clinical development. We briefly review the main characteristics of the AK inhibitors in clinical development. The first three compounds developed as AK inhibitors included ZM447439, hesperadin, and MK0457 (former VX-680). All three compounds were nonspecific inhibitors: ZM447439 inhibited AK-A and -B; hesperadin primarily AK-B; and MK0457 AK-A, -B, and -C. Selective compounds were developed later on, including MLN8054, a selective inhibitor of AK-A; compound 677, an inhibitor of AK-B; and AZD1152, also a selective AK-B inhibitor.

16.4.1

ZM447439

ZM447439 is a quinazoline derivative, ATP competitor that inhibits AK-A and -B in vitro with IC50 values in the 100 nM range [68, 72]. ZM447439 inhibits other kinases but is at least 10-fold more potent against AK, although the inhibition of three of them is in the micromolar range (MEK, SRC, and LCK). This compound occupies the ATP-binding pocket and an adjacent cleft in AK-A. The sequence homology between the three AK suggests that ZM447439 probably inhibits AK-B and AK-C in a similar manner. siRNA experiments provide evidence that cell phenotype described after the ZM447439 treatment is because of inhibition of AK-B, and not of AK-A or other kinases [70]. Specifically, neither the centrosome separation defect nor delayed mitotic entry, both characteristic of AK-A inhibition, are seen. ZM447439 induces incorrect microtubule-kinetochore interaction, failure of chromosome biorientation, abrogation of the mitotic checkpoint, failure of cytokinesis, and tetraploidy. Cells treated with ZM447439 either undergo apoptosis with the next cell cycle, because of the inherited tetraploidy, or G1 arrest, possibly induced by a p53-dependent G1 checkpoint. Therefore, ZM447439 causes cell growth inhibition, a small increase in the level of apoptotic cells, and p53-dependent polyploidy. ZM447439 has shown different effects on proliferating and nonproliferating cells. Proliferating tumor cells treated with this compound were killed, whereas nonproliferating cells were not affected [68]. Given the fact that most normal cells do not proliferate rapidly, the treatment with ZM447439 could show a broader therapeutic index than other agents. Moreover, ZM447439 has shown different effects on proliferating tumor and normal cells, depending on the p53-mediated postmitotic checkpoint. Normal cells treated with this compound underwent a postmitotic cell cycle arrest, characterized by a tetraploidy status. On the contrary, tumor cells lacking a functional p53 pathway were unable to activate this postmitotic checkpoint arrest, and therefore they underwent additional cell cycles with aberrant mitosis and failure of cytokinesis leading to cell death [59, 68, 73]. These observations together suggest that the AK inhibitor ZM447439 may be selectively toxic to proliferating tumor cells, and therefore can represent a new opportunity to develop novel anticancer agents [59].

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16.4.2

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Hesperadin

Hesperadin is a small indolinone molecule [69, 74]. Hesperadin specificity was tested against AK-B, with an IC50 value of 259 nM, and a diverse panel of 25 kinases in vitro, seven of which were in the micromolar range (AMPK, LCK, MKK1, MAPKAP-K1, CHK1, PHK, and CycB/CDK1). The specificity against AK-A was not tested, however. Hesperadin induces aberrant microtubule–kinetochore interactions, with an increase in the formation of syntelic attachments. Despite failing to achieve proper chromosome biorientation, treated cells evade the mitotic checkpoint, proceeding from metaphase to anaphase, failing to undergo cytokinesis, leading to tetraploidy. It is unclear whether hesperadin is a useful drug because it generates highly polyploidy cells but without apparent loss of viability.

16.4.3

MK0457

MK0457 is a 4,6 diaminopyrimidine that targets the ATPbinding site common to all AK [75]. MK0457 potently inhibits all three AK, with IC50 of 0.6, 18, and 4.6 nM for AK-A, -B, and -C, respectively, but had >100-fold selectivity for AK over a diverse panel of 55 kinases tested, with the exception of FLT3, which is well inhibited with IC50 of 30 nM. FLT3 kinase has a role in hematopoiesis and is frequently mutated in patients with acute myelogenous leukemia (AML), correlating with poor prognosis. MK0457 additionally inhibits the imatinib- and dasatinib-resistant ABL(T351I) kinase [76]. Harrington et al. examined the survival of a wide panel of tumor cell lines after MK0457 treatment [75]. Leukemia, lymphoma, and colorectal cancer cell lines were particularly sensitive, MK0457 treatment resulting in polyploidy, the cell death being attributable to apoptosis. Studies with mouse xenografts models of AML, colon cancer, and pancreatic cancer confirmed the therapeutic effect of MK0457 in vivo. MK0457 caused a marked reduction (98%) in tumor volume in a human AML xenograft model resistant to the standard chemotherapy, this tumor growth reduction being dose dependent. MK0457 also induced tumor regression in human pancreatic and colon cancer xenograft models. In all these studies, inhibition of tumor growth was parallel with reduction in histone H3 phosphorylation and with increasing apoptosis. Based on these exciting nonclinical data, MK0457 started clinical development in solid malignancies and in refractory/relapsed leukemia. From the phase-1 studies, the dose-limiting toxicity has been shown to be neutropenia. Interestingly, prolonged stabilization of the disease has been observed in patients with advanced pancreatic cancer and nonsmall-cell lung cancer (NSCLC) [77].

16.4.4

MLN8054

MLN8054 is a selective, orally administered, small molecule inhibitor of AK-A being developed. It is an ATP competitive and reversible inhibitor. MLN8054 has a relative speci-

ficity for AK-A (IC50 0.034 µM) over AK-B (IC50 5.7 µM) [78]. In in vitro models, treatment with low concentrations of MLN8054 (≤2 µM) results in aberrant mitotic spindle formation consistent with AK-A inhibition, whereas higher concentrations (4 µM) produce loss of phosphorylation of histone H3, characteristic of AK-B inhibition. This drug displays antitumor activity against a broad spectrum of human tumor xenografts, including colon, prostate, and NSCLC [79]. Toxicity in animals was reversible, basically myelosupression, cataracts, and gastrointestinal mucosa damage. This drug also has shown to have reversible central nervous system sedative effects in animals.

16.4.5

Compound 677

Compound 677 is a selective AK-B inhibitor being developed and has shown activity in nonclinical studies as a single agent or in combination with cytotoxic drugs with a synergistic effect [80]. Although polyploidy has been induced after treatment with 677 in all cells independently of the p53 status, the sensitivity is increased in those cells with nonfunctional p53.

16.4.6

AZD1152

AZD1152 is an acetanilide-substituted pyrazole-aminoquinazolone prodrug, selective AK-B inhibitor. AZD1152 hydroxy-QPA (active drug) inhibits AK-A, -B, and -C with IC50 of 687, 3.7, and 17 nM, respectively, indicating selectivity for AK-B over -A. Nonclinical studies have shown that AZD1152 reduces the phosphorylation of histone H3 and cell-cycle progression with aberrant mitosis, resulting in polyploidy and cell death. Human tumor xenografts models have demonstrated AZD1152 activity in colorectal cancer and NSCLC [81]. Two phase-1 studies are being conducted, the preliminary results of one of them showing prolonged stabilization of the disease in patients with melanoma, nasopharyngeal carcinoma, and adenocystic carcinoma, the dose-limiting toxicity being neutropenia [82]. Other new AK inhibitors are in nonclinical and clinical development, including AT9283, NCED#17, MLN8024, MP235 and -529, PHA-680632 and -739358, R763, and SNS-314. Some of these compounds are single AK inhibitors whereas others are pan-AK inhibitors.

16.5

Conclusion

It is estimated that at least 6.2 million deaths per year are cancer related. Despite recent advances in new chemotherapeutic agents and new molecular approaches, the prognosis in many tumors remains poor. An urgent need exists for new therapies with improved efficacy over current treatments. Among all the targeted therapies, drugs directed to inhibit one or multiple AKs constitute one of the most exciting approach

16. Aurora Kinases: A New Target for Anticancer Drug Development

in cancer therapy. Some of these compounds are in clinical development having shown clinical efficacy in refractory solid tumors and AML. In the next years, some of these compounds may hopefully achieve meaningful clinical results and, therefore, regulatory approval. These clinical studies will also help in elucidating the best way to target AK, either individually (AK-A or AK-B) or globally (pan-AK).

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314 chromosomal passenger protein, Aurora-C. J Biol Chem 2004;279:47201–47211. 37. Sasai K, Katayama H, Stenoien DL, et al. Aurora-C kinase is a novel chromosomal passenger protein that can complement Aurora-B kinase function in mitotic cells. Cell Motil Cytoskeleton 2004;59:249–263. 38. Bar-Shira A, Pinthus JH, Rozovsky U, et al. Multiple genes in human 20q13 chromosomal region are involved in an advanced prostate cancer xenograft. Cancer Res 2002;62:6803–6807. 39. Fraizer GC, Diaz MF, Lee IL, Grossman HB, Sen S. Aurora-A/ STK15/BTAK enhances chromosomal instability in bladder cancer cells. Int J Oncol 2004;25:1631–1639. 40. Goepfert TM, Adigun YE, Zhong L, Gay J, Medina D, Brinkley WR. Centrosome amplification and overexpression of aurora A are early events in rat mammary carcinogenesis. Cancer Res 2002;62:4115–4122. 41. Gritsko TM, Coppola D, Paciga JE, et al. Activation and overexpression of centrosome kinase BTAK/Aurora-A in human ovarian cancer. Clin Cancer Res 2003;9:1420–1426. 42. Jeng YM, Peng SY, Lin CY, Hsu HC. Overexpression and amplification of Aurora-A in hepatocellular carcinoma. Clin Cancer Res 2004;10:2065–2071. 43. Kamada K, Yamada Y, Hirao T, et al. Amplification/overexpression of Aurora-A in human gastric carcinoma: Potential role in differentiated type gastric carcinogenesis. Oncol Rep 2004;12:593–599. 44. Moreno-Bueno G, Sanchez-Estevez C, Cassia R, et al. Differential gene expression profile in endometrioid and nonendometrioid endometrial carcinoma: STK15 is frequently overexpressed and amplified in nonendometrioid carcinomas. Cancer Res 2003;63:5697–5702. 45. Sakakura C, Hagiwara A, Yasuoka R, et al. Tumour-amplified kinase BTAK is amplified and overexpressed in gastric cancers with possible involvement in aneuploid formation. Br J Cancer 2001;84:824–831. 46. Tanaka T, Kimura M, Matsunaga K, Fukada D, Mori H, Okano Y. Centrosomal kinase AIK1 is overexpressed in invasive ductal carcinoma of the breast. Cancer Res 1999;59:2041–2044. 47. Tong T, Zhong Y, Kong J, et al. Overexpression of Aurora-A contributes to malignant development of human esophageal squamous cell carcinoma. Clin Cancer Res 2004;10:7304–7310. 48. Katayama H, Ota T, Jisaki F, et al. Mitotic kinase expression and colorectal cancer progression. J Natl Cancer Inst 1999;91:1160–1162. 49. Sen S, Zhou H, Zhang RD, et al. Amplification/overexpression of a mitotic kinase gene in human bladder cancer. J Natl Cancer Inst 2002;94:1320–1329. 50. Isola JJ, Kallioniemi OP, Chu LW, et al. Genetic aberrations detected by comparative genomic hybridization predict outcome in node-negative breast cancer. Am J Pathol 1995;147:905–911. 51. Tanner MM, Grenman S, Koul A, et al. Frequent amplification of chromosomal region 20q12-q13 in ovarian cancer. Clin Cancer Res 2000;6:1833–1839. 52. Li D, Zhu J, Firozi PF, et al. Overexpression of oncogenic STK15/ BTAK/Aurora A kinase in human pancreatic cancer. Clin Cancer Res 2003;9:991–997. 53. Meraldi P, Honda R, Nigg EA. Aurora-A overexpression reveals tetraploidization as a major route to centrosome amplification in p53-/- cells. Embo J 2002;21:483–492. 54. Ota T, Suto S, Katayama H, et al. Increased mitotic phosphorylation of histone H3 attributable to AIM-1/Aurora-B

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Chapter 17 Emerging Molecular Therapies: Drugs Interfering With Signal Transduction Pathways Alison H.M. Reid, Richard Baird, and Paul Workman

17.1

Introduction

In recent years, anticancer drug discovery and development has undergone rapid and unprecedented change. Increased understanding of the molecular basis of cancer has led to the development of innovative treatments that are active and often less toxic than “traditional” cytotoxic chemotherapy [1]. A number of these novel agents have completed clinical trials and are licensed for use (Table 17-1). The development of more selective, target-based therapies is made possible through a detailed understanding of the molecular differences in structure and function between cancer versus normal cells [2–7]. This understanding has been achieved mainly in the last quarter of the 20th century by painstaking, hypothesis-driven molecular biology and genetic research [8, 9]. The characterization of animal viruses in the 1960s and 1970s was followed by the discovery of cancer-causing oncogenes and tumor suppressor genes in the 1970s and 1980s. The 1990s witnessed an increased understanding of how cancer genes interact with intracellular signal transduction pathways [10], and 2003 saw the effective completion of the sequencing of the human genome, 50 years after Watson and Crick elucidated the structure of DNA and hence the molecular mechanism for DNA replication and for heredity [11, 12]. The impact of the new wealth of genomic information will be enormous, and includes an emphasis on integrating traditional hypothesis-driven research with genomics, proteomics, and other “-omic” technologies. Discovering critical nodes in tumor cell re-wiring is clearly one of the roles for “omics” research – the study of biological systems on a global, massively parallel basis [3–5, 13–15]. In this chapter, we describe the rationale for targeting signal transduction pathways, particularly in relation to our increased understanding of the cancer genome. We highlight some of the technologies that are being implemented to accelerate drug

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

discovery. Finally, we discuss the progress made with signal transduction inhibitors that are approved for clinical use and those that are in nonclinical and clinical development, including the challenges of clinical trials with such agents.

17.2 Rationale for Targeting Signal Transduction Pathways The term signal transduction describes the processes involved in the communication between the cell and its environment, and in the regulation of cell fate [16]. These pathways are commonly hijacked by the genomic abnormalities that drive malignant progression [17–19]. Genes and pathways whose importance has been clearly established in cancer are illustrated in Fig. 17-1 in the form of a “subway map of cancer pathways” [20]. The rationale for developing signal transduction inhibitors as anticancer agents is clear: drugs that are targeted to crucial molecular abnormalities and biochemical pathways exploited by cancer cells should be more effective and significantly less toxic to normal tissues than the broadly antiproliferative cytotoxic drugs that dominate current therapy [2, 21]. Therapeutic selectivity for tumor versus normal cells is explained by the concept of oncogene addiction [22, 23]. According to this concept, for which there is increasing experimental support from laboratory and clinical studies, cancer cells develop an accentuated dependence on the molecular pathways that drive their malignant properties. Thus, inhibition of these pathways has a much more damaging effect on the cancer cell compared with normal cells. New molecular targets selected for drug development should be those that are important for the initiation and progression of cancer, e.g., those targets causing: activation of proliferative signal-transduction pathways; activation of antiapoptotic, cell-survival pathways; and pathways involved in the initiation of angiogenesis, invasion, and metastasis [9, 17, 24–26]. Imatinib targets the BCR-ABL fusion protein in chronic myeloid leukemia (CML) and the mutated KIT receptor in gastrointestinal stromal tumors (GIST) [27]. Trastuzumab targets 317

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Table 17-1. Examples of current genomic targets and drugs in nonclinical and clinical development. Data accurate as of January 2007. Target Bcr/Abl kinase Mutated c-KIT RTK PDGF RTK Bcr/Abl kinase Src kinase inhibitor PML-RARγ EGF (ErbB1) RTK

Drug Imatinib (Gleevec, STI571)

Nilotinib (AMN107) Dasatinib (Sprycel, BMS-354825) ATRA (Vesanoid) Monoclonal antibodies Cetuximab (IMC-C225, Erbitux) Panitumumab (ABX-EGF; Vectibix) Small molecule inhibitors Erlotinib (Tarceva, OSI-774, CP358774) Gefitinib (Iressa®) PKI166

Stage in development FDA approval: 2001 for CML 2002 for GIST phase 2 FDA approval: 2006 for imatinib-resistant CML FDA approval: 1995 for APL FDA approval: 2004 for metastatic colorectal cancer FDA approval: 2006 for colorectal cancer FDA approval: 2004 for metastatic NSCLC FDA approval: 2003 for NSCLC (second-line therapy) phase 1

EGFR and HER2 RTK Lapatinib (GW-572016)

EGFR, HER2, and ErbB4 RTK HER2 (ErbB2) RTK

HER dimerization inhibitors Farnesyl transferase

Farnesyl transferase/ Geranylgeranyltransferase Ras-Raf-MEK-MAP kinase pathway CRAF and BRAF tyrosine kinases MEK kinase

C-RAF

PI3 kinase pathway PI3 kinase

mTOR

IGF-1R Met Cell cycle targets Cyclin-Dependent Kinases (CDKs)

BIBW 2992 HKI-272 Canertinib (CI-1033) Monoclonal antibodies Trastuzumab (Herceptin)

Monoclonal antibodies Pertuzumab (2C4, Omnitarg) Tipifarnib (R115777, Zanestra) Lonafarnib (SCH66336, Sarasar) L-778123 L-744832 AZD3409 Sorafenib (BAY 43-9006, Nexavar) CI-1040 (PD184352) PD0325901 AZD6244 (ARRY-142886) U0126 Antisense oligonucleotides ISIS 5132 LErafAON LY294002 PI-103 ZSTK474 Everolimus (RAD-001) Temsirolimus (CCI-779)

FDA approval: 2007 for HER2 positive advanced breast cancer in combination with capecitabine phase 2 phase 2 phase 2 FDA approval: 1998 for metastatic HER2 positive breast cancer 2006 for adjuvant treatment of HER2 positive breast cancer phase 2 phase 3 phase 3 phase 1 phase 1 phase 1 FDA approval: 2005 for advanced renal cell carcinoma phase 2 phase 2 phase 1 nonclinical phase 2 phase 1

AP23573 CP-751,871 XL880 ARQ 197

nonclinical nonclinical nonclinical phase 2 FDA approval: 2007 for advanced renal cell carcinoma phase 2 phase 2 phase 1 phase 1

nonspecific kinase inhibitors Flavopiridol UCN-01 (7-staurosporine) E7070

phase 2 phase 2 phase 2 (continued)

17. Emerging Molecular Therapies: Drugs Interfering With Signal Transduction Pathways

319

Table 17-1. (continued) Target

Drug selective CDK inhibitors CDK 1/2: Seliciclib (CYC202, (R)-Roscovitine) BMS-387032 (SNS-032) PNU-252808 AZ703 NU6120 NU6140 GW297361 CDK 4/6: PD0332991

Stage in development

phase 1 phase 1 nonclinical nonclinical nonclinical nonclinical nonclinical phase 1

Polo-like kinase HSP90

Histone deacetylases (HDAC)

DNA methyl transferase (DNMT) Proteasome Aurora kinase

Apoptotic pathways TRAIL-R2 Survivin XIAP Pan-Bcl2 family inhibitor

Integrins Alpha-2 integrin Alpha v-integrin PARP-1 and 2, DNA strand break and base damage repair enzymes Direct Angiogenesis Inhibitors

BI 2536 17-AAG 17-DMAG VER49009 Depsipeptide (FK228) Phenylbutyrate Vorinostat (Zolinza, SAHA) PXD101 LAQ824 LBH589 MS-275 CI-994 (tacedinaline) MGCD0103 Decitabine (2’-deoxy-5-azacytidine) Bortezomib (Velcade; PS-341) AZD1152 MK-0457 (VX-680) AT9283 monoclonal antibodies HGS-ETR2 Recombinant human Apo2L/TRAIL RhApo2L YM155 XIAP antisense AEG35156 GX15-070 Small molecule inhibitor (binds to nucleic acid motifs) CX-3543

phase 2 phase 1 phase 2 nonclinical phase 2 phase 2 FDA approval: 2006 for percutaneous T-cell lymphoma phase 2 phase 1 phase 1 phase 1–2 phase 3 phase 1 phase 2 FDA approval: 2003 for multiple myeloma phase 1 phase 1 phase 1 phase 1 phase 1 phase 2 phase 1 phase 1 phase 1

E7820 inhibitor of alpha-2 integrin expression Cilengitide (EMD 121974) alpha v-integrin antagonist

phase 2 phase 2

KU-0059436 AGO14699 Angiostatin Endostatin TNP-470 Bevacizumab (Avastin)

phase 1 phase 2 phase 2 phase 1 phase 2 FDA approval: 2004 for metastatic colorectal carcinoma FDA approval: 2006 for renal cell carcinoma and GIST FDA approval: 2005 for renal cell carcinoma phase 2 phase 1 phase 2 phase 3

Indirect angiogenesis inhibitors VEGF-A VEGFR2, PDGFR-ß, c-KIT, Flt-3

Sunitinib (Sutent, SU11248)

VEGFR2, PDGFR-ß, RAF1, c-KIT, Flt-3

Sorafenib (Nexavar, BAY 43-9006)

VEGF-A, PIGF, VEGF-B VEGF-A, VEGF-C, VEGF-D VEGF, VEGF-C, VEGF-D VEGFR2, RET, EGFR

Aplidin (Dehydrodidemnin B) VEGF-AS (Veglin) VEGF TRAP ZD6474 (Zactima)

(continued)

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Table 17-1. (continued) Target VEGFR1, VEGFR2, VEGFR3, PDGFR-ß, c-KIT VEGFR2, VEGFR1, Flt-4, c-kit, PDGFR-ß MMP, VEGF-VEGFR binding EGFR, VEGFR2 (KDR), ErbB2, and EphB4 VEGFR2/FGFR VEGFR, PDGFR, FGFR, Src, Lck, Lyn

Drug Vatalinib (PTK787/ZK222584) AZD2171 Neovastat (AE-941) XL647 BMS-582664 BIBF 1120

Stage in development phase 2 phase 2 phase 3 phase 2 phase 2 phase 2

APL, acute promyelocytic leukemia; ATRA, all-trans-retinoic acid; CDK, cyclin-dependent kinase; CML, chronic myeloid leukemia; EGF, epidermal growth factor; FDA, (US) Food and Drug Administration; FGF, fibroblast growth factor; GIST, gastrointestinal stromal tumor; IGF-R, insulin-like growth factor receptor; NSCLC, non-small-cell lung cancer; PDGF, platelet-derived growth factor; RTK, receptor tyrosine kinase; VEGF, vascular endothelial growth factor.

Fig. 17-1. Subway map of cancer pathways. Reprinted by permission from Nature Reviews Cancer, copyright (2002) Macmillan Magazines Ltd. Available online at http://www.nature.com/nrc/journal/v2/n5/weinberg_poster/, with links to seminal papers and NCBI LocusLink entries for each gene product (Hahn WC, Weinberg RA. A subway map for cancer pathways. Nature Rev Cancer 2002;2(5):331–341). (see Color Plate 8 following p. 316.)

17. Emerging Molecular Therapies: Drugs Interfering With Signal Transduction Pathways

the ErbB2 (HER2, neu) receptor commonly found in breast cancer. These agents have achieved high clinical response rates when used in patients with the targeted molecular defect, and are considerably less toxic than conventional combination cytotoxic chemotherapy. These landmark agents clearly show the clinical potential of new drugs targeted to cancer-specific, deregulated signal-transduction pathways. The first wave of molecular therapeutics, led by agents like imatinib and trastuzumab, has swiftly been followed by other molecular therapeutics including gefitinib, erlotinib, cetuximab, bevacizumab, sunitinib, and sorafenib. Potential drug targets or drugs in development are not in short supply. New challenges lie in trying to develop those therapies most likely to achieve clinical success, and in rationally selecting combinations of these agents with the hope of increased efficacy.

17.3 Strategies for Hitting Signal Transduction Targets Although the main focus of this chapter will be the development of small-molecule drugs, generally defined as having a MW < 500 Da, selected antibodies (Ab) are considered where these have mechanistic relevance. In addition, a number of alternative strategies show considerable promise, including the use of antisense oligonucleotides [28], protein therapies (including monoclonal antibodies [MAb]) [29], gene therapy [30], cancer vaccines [31], and RNA interference [32]. In comparison to the other strategies mentioned, smallmolecule drugs have a number of advantages and disadvantages. They have attractive pharmacokinetic properties, particularly when it comes to oral administration and tumor penetration, and are relatively easy to produce. On the other hand, it is technically very difficult to design small molecules to successfully disrupt large domain protein–protein interactions (e.g., SH2 domains), or to interfere with transcription factor–DNA complexes. Nonetheless, small-molecule cancer drugs have a proven track record of successfully targeting enzymes, including classical targets such as dihydrofolate reductase and novel kinases (BCR-ABL, EGFR and others).

17.4

Contemporary Drug Development

Historically, drug development has been a notoriously expensive, slow, and risky business. In fact, taking into account the cost of failed drugs, the cost of successfully developing a drug from nonclinical discovery to marketing approval was estimated to be US $802 million in 2003 [33]. Current costs are likely to exceed US $1 billion. The whole process has, in the past, taken an extremely long time; the average development period is approximately 15 years to progress a drug from initial discovery through to regulatory approval, and can take much longer. A stark example is provided by the development of paclitaxel, which received

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regulatory approval in 1992, a staggering 29 years after the crude extract from the bark of the Pacific yew tree was discovered to have antitumor activity. A further example is the delay between the discovery of the RAS oncogene in 1982 and the start of clinical trials with a RAS antisense oligonucleotide and farnesyl transferase inhibitors in 1999. The regulatory approval of imatinib came fully 41 years after the report of the chromosomal abnormality that is responsible for CML [34]. The risk inherent in drug development is apparent from the fact that only 1 in 20 drugs entering clinical trials will gain regulatory approval [35, 36]. The failure rate in nonclinical development is considerably higher with perhaps only one in thousands of potential drugs making it into the clinic. Frequent reasons for failure include poor pharmacokinetics, and now more commonly, toxicity, and lack of efficacy [35]. How then, do we go about developing signal transduction inhibitors for use as anticancer agents? How do we create more effective, less toxic drugs, more quickly (in 5–7 years or less), more efficiently (with > 5% success rate), and less expensively (cheaper than US $1 billion)? The key to recent successes has been to focus our efforts on important molecular targets, and to take advantage of a range of new technologies at our disposal to accelerate the drug discovery process [2, 3, 36–38].

17.4.1 New Technologies Enhancing the Efficiency of Drug Discovery and Development Figure 17-2 illustrates the phases of contemporary drug development with the new key technologies that facilitate the process. The completion of the Human Genome Project, together with the Cancer Genome Project [39] will accelerate the stream of new targets flowing into cancer drug discovery. The systematic, high-throughput sequencing of the genomes of cancer cell lines and patient tumors should uncover all the remaining cancer genes, and will be a rich resource to mine for drug development targets over the next 5–10 years [39–41]. One early success for the Cancer Genome Project was the identification of activating mutations in the B-RAF gene found to be present in 70% of melanomas, 10% of colorectal cancers, and a number of other tumors [41]. This discovery provides strong evidence to support the development of inhibitors of the BRAF kinase. How will researchers know which genes in the rich torrent of information flowing from cancer genomes are the most important on which to focus their efforts? Not only are there > 350 known cancer genes, but large numbers of abnormalities in known or putative cancer genes can be found in an individual tumor [40]. It is possible that many mutated or deregulated cancer genes will be downstream consequences of genomic instability and occur as relatively late changes in the stepwise process of multistep carcinogenesis. Neutralizing the effects of such genes may well not “turn round” the cancer juggernaut, whereas targeting those genes driving early carcinogenesis could be more effective, especially for early

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Fig. 17-2. Contemporary drug development. ADME: Absorption, Distribution, Metabolism and Excretion. Note: Rodent-only toxicology has been advocated based on successful experience by Cancer Research UK and the European Organisation for the Research and Treatment of Cancer. (Modified from Garrett MD, Workman P. Discovering novel chemotherapeutic drugs for the third millennium. Eur J Cancer 1999;35:2010–2030.)

intervention and chemoprevention. Many factors influence the choice of which molecular target to work on [42]. Table 17-2 outlines the criteria for selecting and validating new targets for contemporary drug development. A few points are

worth highlighting. First, validation of the role of the target and the corresponding pathway in the disease is crucial [43]. Genetic evidence of mutations or altered expression can be very important; however, not all cancer drug targets are the

17. Emerging Molecular Therapies: Drugs Interfering With Signal Transduction Pathways Table 17-2. Criteria for validation and selection of new drug targets. • Frequency of genetic or epigenetic deregulation of the target or pathway in human cancer • Demonstration in a model system that the target contributes to the malignant phenotype • Evidence of reversal of the malignant phenotype (e.g., by gene knockout, dominant negative, antisense, RNAi, antibodies, peptides, or drug leads) • Practical feasibility, tractability or “druggability” of the target (e.g., enzymes are commonly more tractable than are most protein-protein interactions) • Availability of a robust, efficient, and informative biologic test cascade to support the drug discovery program • Ability to build and run a cost-effective high-throughput screen • Availability of a structure-based design approach • Potential for the use of pharmacodynamic endpoints and other biomarkers for diagnosis and outcome prediction Modified from Workman P. The impact of genomic and proteomic technologies on the development of new cancer drugs. Ann Oncol 2002;13:115–124.

products of mutant oncogenes. They may be downstream of the bona fide oncogene or play a supporting role, as in the case of the molecular chaperone HSP90 [44]. Identification of sensitive nodes in the oncogenic signaling pathway is a key aim. Second, the technical druggability of the target is critical to the likely success of the drug discovery project [36]. Gene expression microarrays have helped speed up the process of target identification and validation, and the ability to profile the messenger RNA transcribed from thousands of genes simultaneously enables genomic and mechanistic questions to be addressed at all stages of drug development [45]. Proteomic technology is less well advanced for routine use at present, but clearly the ability to measure the expression of thousands of genes at the functional protein level will be extremely valuable [46, 47]. Metabonomic studies seek to examine in vivo metabolic profiles. Ultimately the goals of systems biology are to integrate different levels of knowledge (molecular, cellular, and physiologic) and to generate computational models of these relationships such that optimal strategies for treating complex disease like cancer can be found [48–51]. The application of high throughput “omics” technologies provides a global picture of the genes and pathways that are deregulated in oncogenesis. Their integration with a traditional hypothesis-driven approach has particularly powerful potential [4, 8, 14–16, 52]. Having identified a new signal-transduction target, the next step is to discover a small-molecule inhibitor that represents a starting point for chemical modification into a drug [36]. Automated high-throughput screening is now the major source of novel drug leads, and is used to rapidly identify inhibitors of a specific target. Identification is achieved by screening large, diverse, chemical libraries (typically 20,000–100,000 compounds, up to a million or more) against either recombinant proteins or cells that have been engineered, for example, to produce a reporter gene readout of a particular signal transduction pathway. In addition to “real,” robotic high-throughput screening, virtual “in silico” screening methods [53], rapid fragment screening by nuclear magnetic

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resonance, and high-throughput crystallography [54], can all be used together synergistically to increase the likelihood and efficiency of finding hits. The ability to generate large numbers of molecules for further high-throughput screening has been enabled by modern chemistry techniques, including solid-phase, combinatorial, and parallel synthesis [55]. Initial hits often have low potency and selectivity for the target, and will usually require chemical modification to generate improved lead compounds (as in the initial development of imatinib). To this end, the “hit to lead” and lead optimization process involves close interaction of bioscientists with medicinal chemists who refine the structure of the hit compound in multiple rounds of making and testing, guided by biologic feedback [36]. Optimization of small-molecule inhibitors is greatly aided by a detailed knowledge of the atomic structure of the target. The use of X-ray crystallography has been particularly important in this regard, contributing greatly, for example, to the development of kinase and HSP90 inhibitors [36, 56]. It is important to note at this stage that a great proportion of compounds that fail in the drug development pipeline do so because they are unable to make the transition from biochemical or cell-based assays to successful use in animal or human studies. This failure is usually because, despite having good potency and selectivity against the molecular target and exhibiting promising properties in cells, they have poor pharmacokinetic properties, and hence will never make it to the target in sufficient concentrations in vivo. One key to successful drug development is to focus at a fairly early stage on the pharmacokinetics of new compounds as well as their potency and selectivity on the target. Indeed the high-throughput screening and synthetic techniques mentioned above are supported by high-throughput cassette- or cocktail-dosing techniques, where the pharmacokinetics of a mixture of compounds, administered to animals in low doses, can be determined using high-performance liquid chromatography coupled to mass spectrometry [4, 57–59]. In addition to optimizing pharmacokinetic properties, the establishment of robust relationships between pharmacokinetics and pharmacodynamics is very important before the demonstration of efficacy in in vivo animal models of cancer [60, 61], which requires the development of appropriate pharmacodynamic markers that can be used to help construct an “audit trail” of drug behavior [62]. Selection of appropriate molecularly derived animal models is crucial to the development of targeted signal transduction inhibitors. Both human tumor xenografts in immune-suppressed mice and transgenic mouse models may be useful [63, 64]. The human predictiveness of animal tumor models remains somewhat uncertain, but the laboratory–clinical transition is aided by selection of tumors with the appropriate molecular pathway and oncogenic drivers [65]. The integrated use of a whole series of powerful new technologies accelerates the development of potent, specific, and druglike compounds designed to modulate specific molecular targets, including various components of cancer signal transduction pathways. This in turn increases the likelihood

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Fig. 17-3. Selection of new drug and chemical probe structures: a) Kinase inhibitors b) HSP90 molecular chaperone inhibitors and the histone deacetylase inhibitor SAHA.

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of clinical success. The chemical structures of representative signal transduction drugs that show activity in the clinic and in the laboratory are shown in Fig. 17-3.

17.5 Clinical Trial Design for Molecular Therapeutics Once a potent, selective, and druglike signal-transduction inhibitor is designed, how are clinical trials started? The drug has shown anticancer activity in vitro and antitumor activity in animal models (e.g., human tumor xenografts), but how do researchers know what dose and schedule to use, and how will they know if it is inhibiting the target in patients?

17.5.1 Traditional Clinical Drug Development for Cytotoxic Agents It has become clear that the traditional model of early clinical trials [66] (summarized in Table 17-3) is not particularly wellsuited to the assessment of molecularly targeted agents. It is important to explore why. Traditional phase-1 trials of cytotoxic drugs are designed to find the maximum tolerated dose, to identify the range of toxicities seen, including the dose-limiting toxicity, and to describe the pharmacokinetics of the drug. Any hints of tumor response are documented, but investigated in subsequent trials. Phase-2 trials are done in specific tumor types, treating patients with the maximum tolerated dose that was identified in phase-1 studies. The main endpoint is tumor response, which is based on tumor size. Guidelines developed by the World Health Organization (WHO) in the late 1970s defined Table 17-3. Traditional clinical drug development. Phase 1 Objective

Disease Dose End point

Design

Phase 2

What is the RD to Is there antitutake forward to mor activity in phase 2/3? selected tumor types ? Range of toxicities and MTD Pharmacokinetics All tumor types Tumor-specific Escalated RD Toxicity Tumor response

Dose escalation in Two-stage (early small cohorts stopping rule) (3–6 patients)

Phase 3 Is the new treatment better than current standard therapy?

Tumor-specific RD Survival Symptoms + quality of life Randomized with or without blinding

MTD, maximum tolerated dose; RD, recommended dose Modified from Fox E, Curt GA, Balis FM. Clinical trial design for targetbased therapy. Oncologist 2002;7:401–409.

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tumor response in four categories. Complete response requires disappearance of the tumor. Partial response is defined as a reduction in size of the tumor of ≥ 50%. Progressive disease is defined as an increase in tumor size of ≥ 25% or more. Stable disease means neither partial response nor progressive disease criteria have been met, indicating tumor stasis but no significant change in tumor size. The WHO guidelines were reviewed in the light of three decades of clinical use, and have been updated in the RECIST (Response Evaluation Criteria in Solid Tumors) guidelines [67]. Important differences in response criteria are outlined in Table 17-4. Several key features of the RECIST criteria were based on analysis of retrospective clinical data. Prospective validation studies have been done and this information, combined with experience acquired thus far, will be incorporated in RECIST 2.0 [68]. Phase-3 trials are designed to answer the question: is this new therapy better than the current standard therapy? These definitive studies are usually randomized, controlled trials involving hundreds of patients, often recruited from many different hospitals, and are expensive and time-consuming to do. The endpoints in phase-3 studies are survival, patient symptoms, and quality of life. As such, they represent “true” endpoints (as opposed to surrogate endpoints like tumor shrinkage).

17.5.2 Clinical Trial Design for Molecular Therapeutics A number of important differences in the properties of molecularly targeted agents demand a new approach to clinical trials (Table 17-5). First is the question of dose. Drugs acting on highly specific molecular targets that are differentially expressed or activated in cancer cells may result in relatively low tissue toxicity, and where toxicity is seen, this may involve nonproliferating tissues. Such drugs may reach an optimum biologic dose that is significantly below the maximum tolerated dose, and this can be assessed by measuring pharmacodynamic markers of biochemical or biologic activity in tissue samples from patients [62]. Second is the question of schedule. In many current frontline treatment regimes, cytotoxics are administered at their maximum tolerated dose in a pulsed fashion, each pulse havTable 17-4. Definition of best response according to WHO and RECIST criteria. Best response CR PR SD PD

WHO change in sum of products Disappearance 50% decrease Neither PR nor PD criteria met 25% increase

RECIST change in sums of longest diameters Disappearance 30% decrease Neither PR nor PD criteria met 20% increase

CR, complete response; PD, progressive disease; PR, partial response; SD, stable disease.

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Table 17-5. Clinical drug development of target-based therapy. Phase 1

Phase 2

Phase 3

Is there antitumor activity in selected tumor types?

Is the new treatment better than current standard therapy?

Dose

Optimum biologic dose Pharmacokinetics Pharmacokineticpharmacodynamic relationship Range of toxicities Target-bearing tumors Escalated

End point

Inhibition of target

Target-bearing tumors Optimum biologic dose Survival

Design

Guided dose escalation

Target-bearing tumors Optimum biologic dose Time to progression Controlled

Objective

Disease

Randomized (+/− blinded)

Modified from Fox E, Curt GA, Balis FM. Clinical trial design for targetbased therapy. Oncologist 2002;7:401–409.

ing the maximum antitumor effect, but also causing considerable toxicity. Breaks between cycles allow for recovery of normal tissues, particularly bone marrow. In contrast, molecular therapeutics are likely to be cytostatic rather than cytotoxic, and may be more effective if administered continuously (preferably by the oral route) rather than in intermittent intravenous pulses. Third is the question of patient selection. Who should receive the drug? Traditional phase-1 studies looked at patients with a whole range of different tumor types, while a phase-2 study examined tumor response in patients with a particular histological type (e.g., non-small-cell lung cancer [NSCLC]). With molecularly targeted agents, it may be necessary in many cases to identify a subset of patients who are most likely to respond, for example based on expression or mutation of the target deregulated signal transduction pathway [65]. The most prominent examples of this in current practice, are the use of trastuzumab in patients with breast cancers that overexpress the ErbB2 (HER2, neu) receptor [69]; and the use of imatinib in the treatment of patients with leukemia targeting the BCRABL translocation, as well as patients with GIST with activating KIT mutations [93]. Insufficient data may be available to restrict entry in phase-2 or 3 studies to patients with a high level of expression or mutation of the target, but it should be essential that studies collect data on the target status to correlate this with outcome. The development of an understanding of the relationship between response and the status of the molecular target, or indeed other molecular biomarkers, may lead to a closer definition of the appropriate patient populations for later trials. Fourth is the question of tumor response. Phase-2 studies have, in the past, used tumor shrinkage as a surrogate outcome measure for clinical benefit, but given the predominantly cytostatic effects of most molecular therapeutics, we are unlikely to see significant tumor shrinkage. If, however, these

agents can offer patients an increase in time-to-progression, or stabilization of their disease, we must use these criteria as endpoints in phase-2 and 3 trials. Finally, more flexible and creative trial designs, those that are tailored to the specific properties of the drug being studied, such as combined phase 1 and 2 trials and randomized discontinuation studies, may be needed [71].

17.5.3 The Importance of Pharmacokinetic and Pharmacodynamic Endpoints To properly evaluate molecularly targeted drugs, it is necessary to define both what the body does to the drug (pharmacokinetics) and what the drug does to the body (pharmacodynamics) [72–74]. Pharmacokinetic and pharmacodynamic endpoints are an absolutely critical component of contemporary drug development in the postgenome era, as we try to establish the best ways to use the new agents at our disposal. It is only with such endpoints that we can follow the biochemical and biologic effects of a drug from simple model systems (e.g., enzyme assays, cancer cells in tissue culture) through animal testing (e.g., human xenografts and transgenic models) to clinical outcomes in patients. Pharmacokinetic and pharmacodynamic endpoints enable the assembly of a pharmacologic audit trail, referred to earlier, which can help answer questions arising at all stages of the drug development process [62, 74] (Fig. 17-4). The audit trail documents the answers to a number of important questions that must be posed during development of a drug: ●

●

●

●

Is the target expressed and is the pathway active? Answers to this question are helpful in selecting the best models to use in nonclinical studies and which patients to include in clinical trials. It is valuable to understand the relationship between target expression, pathway activity, and response to the therapeutic agent. Are sufficient concentrations achieved in plasma, blood, and tissues? This information is critical, since if concentrations required for activity of the drug are not achieved in animal studies, or in patients, it is pointless to proceed further. Time and effort can then be focused on modifying the chemical structure of the drug to overcome the problem, or diverted to other projects if there is no way forward. Is there activity on the desired molecular target? For example, inhibition of a hypothetical kinase could be measured by assessing the phosphorylation of a downstream substrate. It is crucial to know the answer to this question, since lack of activity on the target is clearly a major reason for lack of therapeutic benefit. Is there modulation of the desired biochemical pathway? This question can be answered by, e.g., using phosphospecific Ab to monitor the activation of downstream components of the pathway (e.g., phospho-ERK 1/2 in the mitogen-activated protein kinase pathway [MAPK] pathway). It would also be valuable to assess the “off-target”

17. Emerging Molecular Therapies: Drugs Interfering With Signal Transduction Pathways Can we detect

Expression of the target and/or activity of the pathway

Are we achieving

Active blood or tissue concentrations

Can we show

Activity on the desired molecular target

Can we measure

Can we demonstrate

Does all this translate into a

Modulation of the desired biochemical pathway

The desired biological effect

Disease response

Fig. 17-4. Pharmacological audit trail: The importance of pharmacokinetic (PK) and pharmacodynamic (PD) endpoints. Modified from Workman P. How much gets there and what does it do?: The need for better pharmacokinetic and pharmacodynamic endpoints in contemporary drug discovery and development. Curr Pharma Design 2003; 9: 891–902.

●

●

effects on other signaling pathways, particularly those pathways of key therapeutic or toxicologic importance. To this end, gene expression microarrays and proteomic techniques can be used to profile changes to the transcriptome and proteome, respectively. Have we achieved the desired biologic effect? For example, do treated cells or tumors undergo apoptosis, necrosis, or cytostatic growth arrest? It is particularly important to distinguish between a cytostatic and cytotoxic tumor response for drugs in development, not least because cytostatic drugs will likely require continual administration to maintain tumor growth suppression, whereas more cytotoxic drugs will be best administered at intervals. Is a clinical benefit seen? Ultimately, this question can only be answered through the conduct of large, randomized, phase-3 drug trials, which cannot be done for all drugs in development. Researchers need to short-list the most promising drugs based on surrogate endpoints of efficacy (e.g., time to progression or evidence of biologic response) to select compounds with the highest likelihood of success.

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Invasive and Noninvasive Biomarkers

To answer the questions posed by the audit trail, biomarkers must be incorporated into trial design at the earliest opportunity. A survey has shown a disappointingly poor uptake of pharmacokinetic/pharmacodynamic biomarker endpoints in clinical trials [75]. This trend must change if drugs that hold the greatest promise in cancer therapeutics are to reach their full potential. It is important to remember that biomarkers reflecting drug action may not necessarily be biomarkers of clinical benefit for patient [76]. Both types of biomarkers can be extremely useful. Techniques for assessing pharmacodynamic endpoints can be considered in two groups: invasive or minimally invasive techniques. Clearly, the latter are preferable from the patient’s perspective and are logistically and ethically more acceptable [77]. A clear link must exist between the surrogate tissue, the endpoint, and the primary biologic effect of the drug. Prior demonstration of the selected tissues as valid surrogates for the tumor should be established. Invasive techniques include tumor biopsy, or collection of surrogate tissues, e.g., skin biopsies; measurement of circulating tumor cells; or measurement of effects on peripheral blood mononuclear cells (PBMC). Circulating tumor cells have been shown to provide prognostic information in breast cancer [78] and there is considerable interest in their use in breast, prostate, and colorectal cancer (Fig. 17-5). Minimally or noninvasive techniques include buccal mucosal sampling and acquisition of hair follicles. Target modulation can be assessed with radiologic modalities such as magnetic resonance spectroscopy (MRS), magnetic resonance imaging (MRI), and positron electron tomography (PET) (Fig. 17-6, 17-7). MRI, and in particular dynamic contrast-enhanced MRI (DCE-MRI), together with MRS are increasingly being incorporated into anticancer drug trials to measure pharmacodynamic endpoints [79, 80]. MRI is considered the gold standard for morphologic assessment of various tumor types, providing information on location, size, extent of invasion, and presence of metastases. MRI also provides functional measures of tumor physiology and the local tissue matrix. DCE-MRI involves taking a series of images obtained every few seconds after injection of a contrast agent. Quantitative methods derived from these serial measurements provide a means of assessing the kinetics of the contrast agent in the tumor. As MRI technology is widely accessible, there has been considerable interest in the use of DCE-MRI for the evaluation of tumor vasculature before and after treatment. Recommendations for the use of DCE-MRI in the evaluation of novel antiangiogenic and antivascular targeting agents have been published [81]. DCE-MRI has been incorporated into a number of phase-1 clinical trials of antivascular agents including 5,6-dimethylxanthenone-4-acetic acid [82], combretastatin-A4-phosphate (CA4P) [83], and ZD6126 [84].

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Fig. 17-5. Circulating tumor cells. Printed by permission from Dr J De-Bono from phase-1b trial of CP-751,871 in combination with docetaxel. A 7.5 ml blood sample was taken for enumeration of total CTCs and IGF-1R expressing CTCs from all subjects at base on days 1 and 8 of each cycle and at the end of the study. Samples were processed using the (Immunicon) Celltracks systemTM. The illustration shows cells isolated from peripheral blood by immunomagnetic cell selection and analyzed with a fluorescence microscope. Cells that stain positive for 4′,6-diamidino-2-phenylindole (DAPI; nuclear stain), positive for CK (epithelial cell), and negative for CD45 (lymphocyte) are identified as a circulating tumor cells.

Fig. 17-6. GIST patient with multiple liver and peritoneal metastases prior to the start of treatment on positron emission tomography (PET) (a) and computerized tomography (CT) (c). A complete remission was achieved on PET 8 days after the start of imatinib treatment (b). On CT at 4 weeks, no major volume changes were observed (d). Only after 24 weeks of treatment (e) was an objective tumor response observed on CT.

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Fig. 17-7. GIST patient with multiple abdominal metastases on PET prior to treatment (a). A major reduction in [18F]-fluorodeoxyglucose (FDG) uptake at 48 hours after the start of treatment was observed (b), and correctly predicted the achievement of a complete remission on PET at day 8 (c). (Stroobants S, Goeminne J, Seegers M et al. 18FDG-Positron emission tomography for the early prediction of response in advanced soft tissue sarcoma treated with imatinib mesylate (Glivec®). Eur J of Cancer 2003; 39: 2012–2020. With permission.)

With CA4P, DCE-MRI was able to define a minimum effective dose, but not an optimal biologic dose [83, 85]. New approaches for tumor assessment in MRI technology include attachment of functional contrast agents to specific ligands or targeting moieties [86]. Another approach uses contrast agents that act as a substrate for an existing cellular process or can be activated in situ, e.g., by being designed as a substrate for a specific enzyme [87–90]. Diffusion weighted MRI does not use contrast, but measures parameters associated with the rate and distance of water molecule diffusion [91]. Alterations in these parameters may reflect drug access [92]. By incorporating the aforementioned elements into trial design, it is hoped that drugs and molecular targets that are unlikely to do well are identified promptly, and resources shifted to those drugs most likely to do well in the clinic. Strategies at government level are being implemented to accelerate the early clinical development of new cancer therapies. In 2004, a report was submitted to the US Food and Drug Administration (FDA) entitled “Innovation/Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products.” This document detailed the high

costs and disappointing results of the current biomedical development process, and led to collaboration between the US National Cancer Institute and the FDA called the NCI-FDA Interagency Oncology Task Force (IOTF). Two guidance documents have been released: “Exploratory Investigational New Drug (IND) Studies” aiming to give researchers an improved understanding of drug distribution, pharmacokinetics, and target localization before commencing large-scale trials. The accompanying document “INDs-Approaches to Complying with CGMP during Phase I” provides information on complying with good manufacturing practice, detailing the manufacture of small amounts of drug for testing before starting phase-1 studies. A key aim of the collaboration is to develop a standard approach for evaluating biomarkers, aiming to use them as surrogate endpoints. In the UK, the National Cancer Research Institute (NCRI) announced funding for 17 new experimental cancer medicine centers located across the country. Grants were awarded to centers based on scientific and clinical excellence. The funding will support translational research with the aim of bringing together

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laboratory and clinical research, with the overall objective of accelerating drug development for cancer. It is hoped that changes in clinical trial design and increased monetary and governmental support will impact the number of oncology drugs successfully completing clinical development. At present, of those drugs entering phase 2, 70% will not be developed further, while 59% will fail in phase 3. A key objective is not only to decrease the overall attrition rate but in particular, to avoid late stage, expensive failures.

17.6 Imatinib as a Paradigm for Cancer Therapy Imatinib mesylate (Fig. 17-3) represents one of the most dramatic successes for the first wave of molecular therapeutics. Its precursor was discovered in the late 1980s using high-throughput screens at Ciba-Geigy (now Novartis), searching for compounds with kinase inhibitory activity. One of the hits found during the screen was a compound of the 2-phenylaminopyrimidine class; this compound had low potency and poor specificity, but was the starting point for the synthesis of a number of improved analogues [93]. Based on an iterative exploration of structure–activity relationships, this series of compounds was optimized to inhibit a variety of targets. Imatinib emerged as the lead compound optimized against the platelet-derived growth factor receptor (PDGFR), based on its selective activity and druglike pharmacokinetic properties. It was also noted to be a potent inhibitor of BCR-ABL, a fusion protein tyrosine kinase found in CML, and c-KIT, a tyrosine kinase found mutated in most GIST [94].

17.6.1

Imatinib in CML

CML is a clonal hematopoeitic stem cell disorder that accounts for approximately 20% of all leukemias. Clinically, the disease follows three distinct phases: chronic, accelerated, and blast. The chronic phase lasts for approximately 5 years and is characterized by an excess of normally differentiated myeloid cells; however, the disease subsequently transforms through an accelerated phase to an acute leukemia (blast crisis), which is invariably fatal. Progression of CML through the three clinical phases can also be characterized at the molecular level by an accumulation of abnormalities, which eventually leave the cells unable to differentiate normally. Chief among these abnormalities is the BCR-ABL fusion protein, formed by a reciprocal translocation between the long arms of chromosomes 9 and 22, t(9:22)(q34;q11) [34, 95, 96]. The resultant translocation is commonly referred to as the Philadelphia chromosome and can take a number of forms, depending on the breakpoint in BCR, but 95% of patients with CML have the p210 BCRABL form. BCR-ABL is crucial for the pathogenesis of CML,

causing activation of a variety of intracellular signaling pathways that lead to alterations in cell proliferation, adhesion, and survival. All these events are dependent on the tyrosine kinase activity of the fusion protein. In addition, transduction of BCR-ABL into murine hematopoeitic stem cells followed by transplantation into syngeneic mice, causes a CML-like syndrome. Thus BCR-ABL is an ideal target for treatment of the high proportion of patients that have the corresponding genetic abnormality. Imatinib entered phase-1 trials, initially in patients with chronic phase CML for whom therapy with interferon-alpha (IFN-α) had failed to help them. Incredibly, at doses >300 mg, 53 of 54 patients (98%) achieved a complete hematologic response, which was maintained in 51 of 53 patients (96%). Doses at this level were extremely well-tolerated, with side effects that included nausea, vomiting, fluid retention, muscle cramps, arthralgias, and myelosuppression. The pharmacokinetics of imatinib are good; its half-life of 13–16 hours is sufficiently long to permit once-daily oral dosing [97]. With such impressive levels of activity in patients with chronic phase CML who had been failed by IFN-α, the phase-1 studies were rapidly broadened to include patients with CML in accelerated phase and blast crisis, and also patients with relapsed or refractory Philadelphia chromosome-positive acute lymphocytic leukemia (ALL) [98]. Response rates of 55% were obtained in both patient groups; however, these responses tended not to be durable. The patients with myeloid blast crisis fared better with 18% continuing on imatinib in remission up to 1 year, but unfortunately, nearly all of the lymphoid phenotype patients relapsed between 1 and 4 months. Nonetheless, imatinib has remarkable single-agent activity in these difficultto-treat patients. Phase-2 studies tested imatinib further in patients with IFN-refractory, accelerated phase, and myeloid blast crisis disease, confirming the pattern of response seen in the smaller phase-1 studies [99–109]. The recommended dose was based on pharmacokinetic studies showing that a trough level of 1 µM was reached at a dose level of 300 mg. This dose corresponded to the concentration needed for maximum cell kill in vivo, as well as the dose threshold for significant therapeutic benefits. The main outcome criteria used in these studies were hematologic response, cytogenetic response, and relapse rate at 18 months. In patients with chronic phase CML, the response rates were 95%, 60%, and 9%, respectively. In accelerated phase, it was 53%, 26%, and 40%. In myeloid blast crisis it was 29%, 15%, and 78%. These studies formed the basis for accelerated FDA approval of imatinib for the first-line treatment of patients with CML in December 2002.

17.6.2

Resistance to Imatinib in CML

The dramatic efficacy of imatinib as first line therapy for patients with CML has been overshadowed by the emergence of clinical resistance. Despite high hematologic and cytogenetic

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response rates, primary refractoriness and acquired resistance are increasingly seen in patients with CML, particularly in more advanced stages of the disease. Two principle mechanisms of resistance exist: most frequently the acquisition or the selection of specific point mutations within several critical regions of the ABL kinase domain [110–118], and the overexpression of BCR-ABL, mainly as a result of gene amplification [111–119]. X-ray crystallographic studies have shown that the high selectivity and efficacy of imatinib are a result of binding and locking BCR-ABL in its inactive, autoinhibited conformation [120, 121]. Mutations seem to cause resistance by inducing a transition from the inactive to the active state, a form in which imatinib cannot bind, or by disturbing critical contact points between imatinib and BCR-ABL. An increasing number of mutations responsible for imatinib resistance have been characterized (reviewed in Reference 122).

17.6.3

Overcoming Resistance

Imatinib resistance often coincides with the reactivation of kinase activity within the leukemic clone. Therefore, therapeutic targeting of BCR-ABL and its downstream pathways remains a valid therapeutic strategy. Different ABL mutants display different degrees of resistance to imatinib. While some mutations confer a highly resistant phenotype, suggesting the strategy of stopping imatinib and trying a different therapeutic approach, others may be overcome simply by increasing dose [123, 124]. Th us, several approaches are being evaluated including: the use of higher dose imatinib; imatinib in combination with chemotherapeutic agents; more potent inhibitors of BCR-ABL (e.g., nilotinib), and dual BCR-ABL/SRC kinase inhibitors (e.g., dasatinib) that maintain the ability to bind to and inhibit the mutant form. Routine testing of BCR-ABL sequences present in the tumor are increasingly being incorporated into clinical practice to enable rational, individualized therapeutic management of patients with CML. An analogy can be drawn with the treatment of HIV infection with reverse transcriptase inhibitors. Testing for viral mutations allows tailoring of HIV therapy to each individual, selecting therapeutic strategies that will be most effective against the prevailing viral mutations. In the same way, individualization of anticancer therapy promises to yield more impressive results, than the “one size fits all” therapeutic approach of the premolecular era. BCR-ABL can activate multiple signaling pathways, including those of the SRC kinase family. BCR-ABL has been shown to interact with and activate SRC kinases independently of BCR-ABL kinase activity. These findings suggested that therapeutic benefit might be achieved by dual inhibition of either ABL or SRC. A number of novel ABL/SRC inhibitors have emerged. These include SKI-606, AZD0530, AP23464, and dasatinib. Of these, dasatinib is in the most advanced stages of development. In addition, a novel BCR-ABL inhibitor, nilotinib, with higher affinity

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than imatinib is also showing promise for imatinib-resistant patients.

17.6.4

Nilotinib

Nilotinib is a novel, selective BCR-ABL inhibitor that is more potent than imatinib (IC50< 30 nM) against wild-type BCR-ABL but is also active against 32/33 imatinib-resistant BCR-ABL mutants [125, 126]. It was rationally designed based upon the structure of the imatinib-ABL complex [120, 127, 128]. Crystallographic studies of nilotinib highlight subtle differences between it and imatinib in the mechanism of binding to ABL. A better fit of nilotinib to the ABL protein may account for its increased potency [126]. A phase-1 study in patients with imatinib-resistant CML (chronic, accelerated, and blast crisis) and 13 patients with Philadelphia chromosome-positive ALL established the maximum tolerated dose to be 600 mg twice daily [129]. Side effects included myelosuppression, skin rash, and transient indirect hyperbilirubinemia. In patients with chronic, accelerated, and blast crisis, hematologic/cytogenetic responses were seen in 92%/53%, 72%/48%, and 39%/27%, respectively. Two of the patients with Philadelphia chromosome positive ALL also responded. Encouragingly, early phase clinical trials suggest that nilotinib may be able to rescue those patients who develop imatinib resistance secondary to point mutations. Trials continue evaluating nilotinib in CML patients intolerant of or refractory to imatinib.

17.6.5

Dasatinib

Dasatinib (see Fig. 17-3) is an orally bioavailable ABL kinase inhibitor with 2-log greater potency than imatinib. In nonclinical studies, it demonstrated activity against 14 of 15 imatinib- resistant BCR-ABL mutants [130]. Dasatinib prolonged survival in mice with BCR-ABL–driven disease and inhibited proliferation of bone marrow progenitor cells from patients with imatinib-sensitive and imatinib-resistant CML [130]. A phase-1 study in patients with various phases of CML or Philadelphia chromosome positive ALL with progression or intolerance to imatinib showed promising results. Dasatinib at doses of 15 to 240 mg/day was given in 4-week treatment cycles. In patients with chronic phase CML, hematologic and cytogenetic responses were seen in 92.5% and 45 % of patients, respectively. In patients with accelerated-phase CML, blast crisis with CML, or Philadelphia chromosome-positive ALL, hematologic and cytogenetic responses were seen in 77.5% and 25% respectively. Responses were maintained in 95% of patients with chronic-phase disease, followed up for 12 months; and in 82% of patients with accelerated-phase disease followed up for > 5 months. Nearly all patients with lymphoid blast crisis and Philadelphia chromosome-positive ALL had a relapse within 6 months. Responses were seen in all BCRABL genotypes except the T3151 mutation, which is resistant to both imatinib and dasatinib in vitro. A collection of phase-2

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studies, the “START” studies, show encouraging initial results of activity in imatinib-resistant patients [131–134] and dasatinib has now been FDA approved a treatment for CML that no longer responds to imatinib. A multitude of signaling pathways are activated by BCRABL. Nonclinical studies in imatinib-resistant cell lines have demonstrated synergy between imatinib and drugs targeting the RAS or PI3K pathways. It will be interesting to see if this nonclinical work can lead to combinations of agents achieving results in the clinic. A further analogy can be drawn with HIV therapy in terms of mono versus combinatorial therapy. Monotherapy of HIV patients with reverse transcription inhibitors provides a selective pressure in which the virus mutates acquiring resistance. Therefore, despite initial clinical responses, nearly all patients will relapse with resistant infection. A more successful strategy with superior results is achieved with combinatorial therapy, and many HIV patients treated with modern combinatorial therapy can now look forward to near-normal life expectancy [135]. CML, like HIV, has a high frequency of genetic evolution, and acquired resistance frequently caused by mutations in the target, BCR-ABL. Therefore, a combinatorial strategy that attacks BCR-ABL and downstream targets, may prevent, delay, or overcome imatinib resistance.

17.7

Imatinib in GIST

Although originally derived from a screen for PDGFR tyrosine kinase inhibitors and tested in patients with BCR-ABL-driven CML, imatinib was subsequently shown to inhibit mutated cKIT tyrosine kinase associated with GIST, a malignancy highly refractory to standard chemotherapies. GIST are characterized by gain-of-function mutations in the KIT proto-oncogene commonly in exon 11, but also seen in exons 9,13, or 17 [136]. In GIST lacking KIT mutations, mutation in PDGFRα may be an alternative oncogenic mechanism [137, 138]. Phase 1-2 studies in patients with metastatic or unresectable GIST have shown high overall response rates and suggested improved quality of life [139–142]. Given the limited treatment options for patients with metastatic or unresectable GIST, and the impressive early trial results, imatinib was promptly approved by the FDA for GIST in 2002. Two phase-3 randomized studies showed an increase in progression-free and overall survival [143, 144]. The phase-3 studies assessed imatinib doses of 400 mg/day versus 800 mg/ day, with 746 patients treated in the NCI-Intergroup S0033 trial [143] and 946 patients treated in the European-Australian phase-3 randomized trial [144]. Overall partial response and stable disease rates were 48%/26% for the NCI study and 51%/ 33% for the European-Australian study. Two-year progression-free survival rates assessed at a median followup period of 25 months for 400 mg/day versus 800 mg/day were as follows: 50%/53% (p > 0.05) for the NCI study and 50%/56% (p = 0.026) for the European-Australian study.

Imatinib 400 mg/day is the recommended starting dose for treating patients with metastatic GIST [145]. However, given the European-Australian study has reported a statistically significant advantage in progression-free survival in the 800mg/day group, the recommended starting dose in metastatic disease may need to be revised. Crossover was permitted in the phase-3 studies from 400 mg/day to 800 mg/day when patients progressed, which induced further disease responses, with 18.1% of patients still alive and progression free after 1 year following dose increase in the European-Australian study [146]. Therefore on progression, patients should have their imatinib dose increased to 800 mg/day. The drug is safe and well-tolerated with mild to moderate side- effects including anemia, periorbital edema, nausea, diarrhea, fatigue, neutropenia, and skin rash. Activating KIT mutations and PDGFRa mutations are important in determining response to imatinib. Mutations in the KIT gene occur in decreasing order of frequency in exons 11, 9, 13, and 17 [147–151] and in the PDGFRa gene involving exon 18 or 12 [148, 150, 152]. No untreated GIST has mutations in >1 KIT exon, and all PDGFR-mutant GIST are in tumors without KIT mutations [147, 148, 152]. Thus, KIT and PDGFRa mutations appear to be alternative and mutually exclusive oncogenic mechanisms in GIST. A proportion of GIST will have both wild-type KIT and PDGFRa genes [147, 148]. The type of mutations has a bearing on outcome with respect to imatinib response and progression-free survival. For example, patients with a KIT exon 11 mutation have a statistically significantly better progression-free survival than those patients with exon 9 mutations or no mutations [147, 148], which provides prognostic information. A phase-3 study showed that for patients with expression of an exon 9 KIT oncoprotein, treatment with a high-dose regimen statistically significantly increased progression-free survival [153]. These results suggest that treatment decisions regarding imatinib dose may be differential depending on mutational status.

17.7.1

Imatinib Resistance in GIST

Mechanisms of imatinib resistance in GIST include acquisition of a new KIT or PDGFRa point-mutation, coexpressed with the preimatinib mutation in the same genes; amplification of the KIT gene with resultant KIT oncoprotein overexpression, without new point mutation; activation of an alternate tyrosine-kinase oncoprotein with loss of KIT oncoprotein expression; and finally KIT or PDGFRa activation outside the transmembrane hotspot region, without secondary genomic mutation [154]. Future challenges lie in determining a possible role for adjuvant/neoadjuvant imatinib in GIST, and in treating primary or acquired imatinib-resistant disease. All GIST at progression show activation of pathways downstream of KIT or PDGFRa, including PI3K/Akt/mTOR [154]. Sunitinib, a multitargeted kinase inhibitor of KIT, FLT3, PDGFR, and vascular

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endothelial growth factor receptor (VEGFR) has shown activity in imatinib-resistant GIST. A randomized, double-blind, placebo controlled phase-3 study recruited 312 patients with advanced GIST. Patients were randomly assigned in a 2:1 ratio to receive sunitinib (n = 207) or placebo (n = 105). Sunitinib was given as a 50 mg once-daily dose in 6-week cycles with 4-weeks-on and 2-weeks-off drug. The trial was unblinded early when a planned interim analysis showed a significantly longer time to tumor progression with sunitinib than placebo (27.3 versus 6.4 weeks, p < 0.0001) [155]. Other drugs being evaluated in the post-imatinib setting include everolimus (mTOR inhibitor), oblimersen (antisense oligonucleotide to BCL-2 mRNA), bevacizumab (Ab to VEGF), and temsirolimus (rapamycin analogue, mTOR inhibitor).

tyrosine kinase or MAb targeting the extracellular domain and inhibiting kinase activity indirectly. In addition, antisense and toxic immunoconjugates to ErbB1 are under investigation [163]. Advantages and disadvantages can be found for each of the main approaches. Small-molecule inhibitors acting at the ATP-binding site of EGFR tyrosine kinase probably have better tumor penetration and the potential to inhibit the tyrosine kinase activity of other ErbB family members. On the other hand, it is argued that MAb not only block ErbB signaling, but induce an anticancer cytotoxic immune response. This latter point is supported by evidence of greater antitumor effect in vivo than in vitro, but this may also be caused by antiangiogenic effects of the Ab.

17.8

17.8.2

Targeting ErbB Receptor Signaling

ErbB receptors are a family of structurally related tyrosine kinase receptors that are important mediators of the proliferation, differentiation, and survival of normal cells (156). Four ErbB members have been identified, namely ErbB1 (also called HER1 or EGFR), ErbB2 (also called HER2 or NEU), ErbB3 (HER3), and ErbB4 (HER4). Most therapeutic efforts to date have focused on ErbB1 and ErbB2, hereafter referred to as epidermal growth factor receptor (EGFR) and HER2/ neu, respectively. The importance of ErbB receptors in cancer has long been recognized; they have been implicated in cellular proliferation, apoptosis, differentiation, angiogenesis, motility, and invasion [157]. The dysregulation of ErbB function is known to occur by a number of mechanisms including gene amplification causing receptor overexpression and ErbB mutations that increase receptor activity. Nonclinical studies have established that blocking ErbB receptor activity results in blockade of downstream signaling through the RAS→RAF→MEK→MAPK pathway and delayed tumor growth or tumor shrinkage in vivo [157]. As would be expected, ErbB signaling is not simply a linear pathway; extensive “cross-talk” occurs between the 4 ErbB receptors and between the ErbB family and other transmembrane receptors (e.g., G-protein coupled receptors and estrogen receptors) [158]. In addition to inhibiting growth driven by the MAPK pathway, activation of ErbB signaling inhibits apoptosis through the activation of the phosphatidylinositol 3′kinase (PI3K) pathway [159], and alters cell motility, migration, and adhesion through its effects on focal adhesion kinase [160]. Expression of EGFR in tumors has been correlated with poor response to therapy, the development of resistance to cytotoxics, disease progression, and poor survival [161, 162].

17.8.1 Targeting EGFR: Small Molecule or Antibody? The two most important strategies to target EGFR overactivity have been small-molecule inhibitors of the intracellular

Gefitinib

Gefitinib (for chemical structure see Fig. 17-3) is a 4-anilinoquinazoline inhibitor of EGFR tyrosine kinase with high specificity and potency (IC50 23 nM). It also inhibits downstream signaling, causing an increase in p27Kip1, a decrease in CDK2 activity, and the induction of a G1 cell-cycle arrest [164–166]. Apoptosis is seen at higher doses, and it increases the proapoptotic effects of all cytotoxics evaluated on EGFR-overexpressing cells [163]. In particular, combination studies in vitro have demonstrated greater cytotoxicity of cisplatin, more DNA-adduct formation, and less DNA repair of platinum-DNA adducts when combined with gefitinib [168]. Gefitinib has excellent oral bioavailability, with little toxicity at active doses in mice, causing 50% tumor reduction at a daily oral dose of 10 mg/kg and complete responses at 200 mg/kg; however, tumors usually regrew when gefitinib was stopped. Interestingly, in the presence of EGFR, low gefitinib concentrations also inhibit the growth of HER2overexpressing breast cancer cells, showing greater growthinhibitory effects than trastuzumab [166]. In phase-1 clinical trials, gefitinib was given as a daily oral dose over 14 or 28 days. Dose-limiting toxicities were diarrhea and acneiform skin rash, thought to be related to high EGFR expression in skin [169–171]. Skin was proposed as a surrogate tissue for pharmacodynamic studies because of ease of access and high EGFR expression. The pharmacodynamic studies confirmed inhibition of EGFR signaling by IHC (decreased Ki67, increased p27Kip1, increased keratin 1, and increased phospho-STAT3 expression). In the phase-1 studies, the rash was dose-dependent with increased drug exposure resulting in higher incidence and severity of rash [169, 171–173]. Rash may therefore be a surrogate marker of target inhibition and activity of anti-EGFR agents. A number of questions, however, remain unanswered at this time, such as why some patients develop rash without clinical benefit [174]. Phase-2 studies showed promising clinical antitumor activity, with objective responses seen in patients with NSCLC and squamous cell carcinomas of head and neck [175–179].

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17.8.3 Trials in First-Line NSCLC Treatment (INTACT)-1 and (INTACT)-2 Early nonclinical and clinical progress led to two large, multicenter, randomized controlled phase-3 trials of gefitinib in combination with cytotoxic chemotherapy. Chemotherapy, either gemcitabine/cisplatin [180] or paclitaxel/carboplatin [181], was given with or without gefitinib. The trials’ results showed that the addition of gefitinib made no difference to outcome [182]. These results were somewhat surprising, given the evidence of activity using gefitinib as a single agent in NSCLC and head-and-neck malignancy. One reason for the apparent lack of activity is that gefitinib targets the same tumor cell population as cytotoxic chemotherapy, thus losing any opportunity for additive, let alone synergistic effects. A second possibility is that if gefitinib causes cell-cycle arrest, it will antagonize the effects of cytotoxic therapy, which requires cycling cells.

17.8.4

Single Agent Trials

Additional trials in patients with NSCLC have been done to establish whether any survival benefit could be gained by giving gefitinib as a single agent, compared with placebo. The Iressa Survival Evaluation in Lung Cancer (ISEL) trial included 1,692 patients who had progressed or were not deemed fit for further chemotherapy. The results showed statistically significant greater tumor shrinkage in the gefitinib group, but this did not translate to an overall survival benefit. The overall survival durations were similar in the two groups: 5.6 months in treated patients versus 5.1 months with placebo. A very similar drug, erlotinib, did demonstrate an overall survival advantage in the secondand third-line setting [185].

17.8.5

Erlotinib

Erlotinib (see Fig. 17-3) is a second small molecule with very similar physicochemical and pharmacologic properties to gefitinib [167]. It is a highly potent, specific, reversible, ATPcompetitive inhibitor of EGFR tyrosine kinase (IC50 2nM). In vitro studies showed that 50% inhibition of EGF-mediated autophosphorylation occurred at 20 nM, and that this caused 50% growth inhibition in HN5 head-and-neck tumor cells. In vivo studies using HN5 xenografts demonstrated that tumor shrinkage occurred with an oral dose of 100 mg/kg. During phase-1 trials, erlotinib was well tolerated with common toxicities being diarrhea and acneiform rash. On an uninterrupted, oral, daily dosing schedule, diarrhea was dose limiting, precluding escalation beyond 150 mg/day. At this dose, steady-state concentrations were approximately double the active concentrations seen in animal studies. A number of partial responses in patients with metastatic renal and colorectal carcinomas and more patients with relatively long periods of stable disease were seen [173, 175].

Erlotinib, like gefitinib, was tested in first-line NSCLC in combination with chemotherapy. The TALENT and TRIBUTE trials were conducted in chemotherapy-naive patients with advanced NSCLC. The chemotherapy regimens were identical to INTACT-1 and -2 trials. Patients were treated with gemcitabline/cisplatin with or without erlotinib in the TALENT trial [183]; and carboplatin/paclitaxel with or without erlotinib in the TRIBUTE trial [184]. Disappointingly, as was found with gefitinib, no benefit was seen from adding erlotinib to standard chemotherapy in patients with NSCLC; however, unlike gefitinib, erlotinib showed an overall survival benefit as a single agent in the second- and third-line NSCLC setting. The BR.21 trial compared erlotinib with best supportive care plus placebo. The trial included 731 patients, randomly assigned 2:1 to receive either erlotinib at 150 mg/day or placebo (488 erlotinib, 243 placebo). Study endpoints included overall survival, response rate, and progression-free survival. The response rate was 9% with erlotinib compared with <1% with placebo. A statistically significant increase in median progression-free survival was seen for erlotinib compared with placebo (9.9 versus 7.9 weeks). Overall survival time was statistically significantly increased by 2 months compared with placebo (6.7 versus 4.7 months) [185]. Subgroup analyses were similar to the gefitinib study, with increased tumor response rate in patients of Asian origin, women, lifetime nonsmokers, and patients with adenocarcinoma. Why erlotinib should have shown a survival benefit whereas gefitinib did not, despite similar response rates, is not completely understood. A possible explanation is that erlotinib and gefitinib may cross-react with other kinases, or may bind slightly differently to the kinase domain of EGFR resulting in different levels of activity. It has been suggested that the doses of the two agents used may have resulted in different inhibitory activity against EGFR. Whether the dose of erotinib was used at a higher equivalent dose than that for gefitinib remains to be established.

17.8.6 EGFR Mutations and Response to EGFR Tyrosine Kinase Inhibitors As the trials of gefitinib and erlotinib unfolded, it was apparent that certain patients derived benefit from these agents while others did not. A great deal has been learned about the clinical and molecular determinants of response and survival in patients with NSCLC treated with EGFR tyrosine kinase inhibitors. In the phase-3 ISEL trial of gefitinib in preplanned subgroup analyses, patients of Asian origin and never-smokers showed significantly longer survival with gefitinib, despite no overall survival benefit seen in the trial as a whole. A higher objective tumor response rate was seen for gefitinib compared with placebo in never-smokers, patients of Asian origin, women, and patients with adenocarcinoma [186]. In the phase-3 trial (BR.21) of erlotinib, female sex, adenocarcinoma histology, and never having smoked predicted for

17. Emerging Molecular Therapies: Drugs Interfering With Signal Transduction Pathways

response [185]. What was different about these tumors that rendered them sensitive to tyrosine kinase inhibitors? An answer to this question came from the discovery and subsequent analysis of EGFR mutations in patient tumor biopsies: 90% of EGFR mutations in lung adenocarcinomas occur in exons 19 and 21, corresponding to the ATP-binding site of the tyrosine kinase domain [187, 188]. The mutations were detected in diagnostic tissue samples taken from most patients who demonstrated a response to an EGFR tyrosine kinase inhibitor. The mutations affect the ATP-binding cleft of the EGFR, where the tyrosine kinase inhibitor binds. The two mutations account for 90% of EGFR mutations in lung adenocarcinomas. The most common mutation is a short in-frame deletion of 9, 12, 15, 18, or 24 nucleotides in exon 19. The second most common mutation is a point mutation in exon 21 resulting in the substitution of Leu by Arg at codon 858 [189]. In cell lines, the EGFR mutants display markedly increased susceptibility to EGFR tyrosine kinase inhibitor activity. Importantly, the frequency of mutations is increased in patient groups associated clinically with increased sensitivity to these agents. EGFR mutations were observed in 13% of patients in the TRIBUTE trial. Response rate to chemotherapy plus erlotinib was significantly higher in patients with EGFR mutations than in those without. Median survival was 10 months for patients with wild-type tumors, whereas it was not reached for those harboring EGFR mutations [190]. In vitro studies have shown that the presence of EGFR mutations confers resistance to several cytotoxic drugs, including cisplatin [191]. Some have hypothesized, therefore, that those patients with EGFR mutations may be better served by treatment with an EGFR inhibitor alone rather than in combination with chemotherapy. There are data to suggest that the presence of an EGFR mutation may be prognostically favorable, irrespective of the treatment chosen. This is a question that will need to be addressed by appropriately designed randomized clinical trials.

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in bronchoalveolar carcinoma showed that the presence of EGFR exon 19 or 21 mutation was a powerful predictor of response to erlotinib and time to progression but not overall survival; that the presence of K-RAS mutation predicts resistance to erlotinib; and that patients with both EGFR mutation and amplification do well, which may support the theory of “oncogene addiction” [194].

17.8.8

Resistance Mutations

Mutations in K-RAS, a mediator of EGFR signaling, are mutually exclusive with EGFR mutations, and are associated with resistance to EGFR tyrosine kinase inhibitors. In addition, secondary mutations, conferring resistance to EGFR tyrosine kinase inhibitors, occurring in patients with primary mutations that initially rendered them sensitive to EGFR tyrosine kinase inhibitors, are being identified. Two studies examined tumor specimens from patients with EGFR mutations who had significant initial clinical benefit from either gefitinib or erlotinib but subsequently developed progressive disease. In four of seven patients analyzed, a common secondary mutation, a substitution of Met for Thr at position 790 (T790M), was identified only in the recurrent tumor specimen [195, 196]; however, in the remaining patients, a secondary mutation was not found in the EGFR tyrosine kinase domain in the recurrent tumor specimens, suggesting that other mechanisms of resistance must also exist.

17.9

Anti-EGFR Monoclonal Antibodies

The second prominent strategy for targeting EGFR tyrosine kinase has been to raise MAb to the extracellular receptor domain, which induces a conformational change that prevents activation of tyrosine kinase and downstream signaling. In addition, Ab may induce an immune response.

EGFR Mutations: Not the Whole Story

Exon 19 and 21 mutations are not the whole story. The prevalence of EGFR-activating mutations does not appear to entirely account for the benefit seen with EGFR tyrosine kinase inhibitors. In support of this is the finding that approximately 14% of EGFR tyrosine kinase inhibitor—responsive tumors analyzed to date do not have mutations in exons 19 or 21. Conversely, 14% of the EGFR tyrosine kinase inhibitor nonresponsive tumors contain mutant EGFR [192]. Therefore, patient selection for drug therapy with EGFR tyrosine kinase inhibitors cannot be dictated by receptor mutation alone. Other molecular mechanisms such as EGFR gene amplification and receptor ligand overexpression can give the receptor “gain of function” that may lead to EGFR dependence and in turn sensitivity to EGFR inhibitors [193]. These findings have fuelled the search for other molecular markers of activity. Large prospective trials incorporating tissue acquisition are underway. One such phase-2 study

17.9.1

Cetuximab

Cetuximab is a human-to-murine chimeric MAb that binds EGFR with an affinity 10 times that of the EGFR ligands, EGF, and transforming growth factor-alpha (TGFα). It has been shown to block EGFR signaling, inducing p27Kip1 expression and to arrest EGFR-expressing tumors in G1 [197]. Cetuximab inhibits the growth of a wide range of EGFR-expressing human tumor xenografts, actually causing shrinkage in some. It has been shown to enhance the activity of radiotherapy, cytotoxic chemotherapies, and MAb raised against HER2 [198–200]. In phase-1 clinical trials, cetuximab was given as a single weekly intravenous dose, and a dose of 200 mg/m2 was considered optimal, based on saturation of clearance of the drug [172]. Toxicity was minimal, and included the now familiar acneiform rash. A number of phase 2 and 3 trials have been conducted with cetuximab. The most promising results to date

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have been seen in squamous cell carcinoma of the head and neck and colorectal carcinoma.

17.9.2 Cetuximab for Squamous Cell Carcinoma of the Head and Neck EGFR is ubiquitously overexpressed in squamous cell carcinoma of the head and neck (SCCHN). In SCCHN, antiEGFR Ab has been tested alone and in combination with chemotherapy and radiotherapy. Cetuximab has been tried to overcome chemotherapy resistance. In patients with advanced disease, refractory to platinum-based chemotherapy, cetuximab was added to the same dose and schedule of platinum chemotherapy that the patient had been receiving before disease progression. A response rate of 10% and a median survival of 6 months were observed. In a second study of a similar patient population, cetuximab was administered alone when patients progressed on platinum-based chemotherapy. The response rate was 12%, indicating that cetuximab alone may be just as active as the cetuximab/platinum combination but less toxic [201]. A study of second-line chemotherapy in this population achieved response rates of 3.4% and median survival of 3 months, suggesting that cetuximab may have superior activity in this context [202]. In the first-line setting, a phase-3 trial of 424 patients with locally advanced SCCHN examined the effect of combining cetuximab with high-dose radiation on locoregional control and survival. Patients received radiation alone for 6–7 weeks or radiation with cetuximab. The combination provided a significant overall survival benefit (54 versus 28 months; p = 0.02) and improved locoregional control at 2 years (56 versus 48% p = 0.02). This benefit was achieved with a minimal increase in toxicity in the combination group [203].

17.9.3

Cetuximab for Colorectal Carcinoma

Cetuximab has been licensed by the FDA, as monotherapy or in combination with irinotecan, for second-line therapy in refractory colorectal carcinoma. The registrational trials included a phase-2 study of cetuximab plus irinotecan in 120 patients with EGFR-positive colorectal carcinoma refractory to irinotecan, where the response rate was 22.5% [204]. Another phase-2 study, the “BOND” trial, compared irinotecan plus cetuximab with cetuximab alone in patients with irinotecan-refractory metastatic colorectal cancer with EGFR-expressing tumors. The response rate was 10.8% for cetuximab alone and 22.9% for cetuximab plus irinotecan [206]. The authors concluded that EGFR status did not predict cetuximab response. A number of phase-3 studies continue in metastatic colorectal cancer including the “CRYSTAL” trial comparing cetuximab with irinotecan in first-line treatment of EGFRexpressing patients [206] and the “EPIC” trial comparing cetuximab plus irinotecan to irinotecan alone in patients with EGFR-positive tumors who have progressed on first-

line oxaliplatin therapy [207]; and the “EXPLORE” study that randomly assigns patients with EGFR-positive tumors to either FOLFOX4 (5-fluorouracil, leucovorin, and oxaliplatin), or FOLFOX4 plus cetuximab. Preliminary results from this study have demonstrated high (>80%) response rates to cetuximab with oxaliplatin-based chemotherapy as firstline therapy [208]. The design of these clinical trials was based on the assumption that the presence of EGFR would be necessary before seeing drug activity; however, unlike the HER2/neu story with trastuzumab, quantitative levels of receptor measured by immunohistochemistry (IHC) have not correlated with response.

17.9.4 Cetuximab in Non-small-Cell Lung Cancer Cetuximab has shown activity in NSCLC. Two single-cohort, phase-2 trials in previously untreated patients tested cetuximab in combination with a platinum-based doublet with responses in the range of 26–29% and median survival times of 10–11 months [209, 210]. A European phase-2 randomized trial tested cisplatin/vinorelbine with or without cetuximab as first-line therapy in 86 patients with advanced NSCLC. Patients treated with cetuximab had a higher response rate (31.7% versus 20.0%) and prolonged time to disease progression (4.7 versus 4.2 months) compared with patients who did not receive cetuximab [211].

17.9.5

Panitumumab

As the most serious reported toxicity of cetuximab is hypersensitivity reactions, fully humanized Ab to the EGFR extracellular domain have been developed. Panitumumab is a fully human antiepidermal growth factor-receptor Ab. Phase-1 clinical trials confirmed safety, tolerability, and dosing schedule [212]. A number of phase-2 studies have been completed and panitumumab has been tested in a phase-3 study in patients with advanced colorectal cancer versus best supportive care. Results showed a 46% reduction in the risk of disease progression and a partial response rate of 8% [213].

17.10

Targeting HER2

An important therapeutic strategy against HER2 is the use of Ab to the extracellular domain of the receptor, including trastuzumab, one of the most successful molecularly targeted agents to date [214]. Other strategies under investigation include anti-HER2 Ab coupled to immunotoxins and cytotoxics; bispecific Ab binding both HER2 receptor and antitumor immune effector cells; straight immunization with HER2 protein; HER2 antisense; and targeted adenoviral gene therapy. In addition, several specific, small-molecule inhibitors of HER2 tyrosine kinase are under development [163, 215].

17. Emerging Molecular Therapies: Drugs Interfering With Signal Transduction Pathways

17.10.1

Trastuzumab

Trastuzumab is a humanized MAb with high affinity for HER2 (Kd = 0.1 nM). It potently inhibits the proliferation of HER2overexpressing breast cancer cells in vitro, promoting accelerated HER2 internalization and degradation. HER2-positive tumors comprise 20% of breast cancers and are associated with an aggressive natural history. Three phase-1 clinical trials were conducted with trastuzumab and were primarily designed to determine the safety and pharmacokinetics of trastuzumab (10–500 mg) administered intravenously as single or weekly doses. Treatment was well tolerated, with side effects including chills, asthenia, fever, and cardiotoxicity. Phase-2 studies have examined the use of fixed-dose trastuzumab either as a single-agent or in combination with cytotoxic chemotherapy. In one pivotal trial, single-agent trastuzumab was administered to 222 patients with HER2-positive metastatic breast cancer who had relapsed after one or two lines of chemotherapy [216]. The overall response rate was 21% when assessed in evaluable patients by the investigators and 15% when analyzed on an intent-to-treat basis. Side effects commonly observed with chemotherapy, like alopecia, mucositis, and neutropenia, were rarely seen. Trials with trastuzumab in combination with chemotherapy were pursued based on evidence of synergy in xenograft studies. It should be noted that the cardiotoxicity seen occasionally with single-agent trastuzumab is significantly worse when the drug is given in combination with anthracycline-containing regimes (seen in 27% of those patients receiving doxorubicin/ cyclophosphamide and trastuzumab) [214]. Additional phase2 studies have explored the effect of trastuzumab in a range of other cancers, including ovarian, NSCLC, and prostate cancers [217–224]. Phase-3 randomized controlled trials [214, 225] showed that combination therapy with anthracycline or taxane-based regimes significantly prolonged the median time to disease progression, increased the overall response rate and increased the duration of response. Crucially, addition of trastuzumab improved the overall survival of patients with advanced metastatic breast cancer from 20.3 to 25.1 months (p = 0.046) [69]. As with previous studies, the benefit for the addition of trastuzmab was particularly marked for patients whose tumors were strongly positive for HER2. In early trials, trastuzumab was given weekly with initial pharmacokinetic data suggesting a half-life of 8 days. Subsequently, the half-life has been shown to be close to 21 days, and trastuzumab can be given every 3 weeks at three times the weekly dose (a loading dose of 8 mg/kg followed by 6 mg/kg every 3 weeks), with equal efficacy. Clinical trials of trastuzumab in the adjuvant setting have produced very significant results. Five randomized clinical trials have shown that adjuvant trastuzumab reduces the risk of recurrent HER2-positive disease by approximately 50%: the Herceptin Adjuvant (HERA) trial, the combined North

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American National Surgical Adjuvant Breast and Bowel Project (NSABP) B31, North Central Cancer Treatment Group (NCCTG) N9831 trials, the Breast Cancer International Research Group (BCIRG) 006 trial, and the Finnish Trial [226–229]. The five trials had different designs, and the chemotherapy regimens, and in particular the use of taxanes, and timing of the adjuvant trastuzumab varied. The proportion of women with node-negative disease and geographic distribution also varied between the trials. Consistently, all women with highly significant cardiac risk factors were excluded from all trials because of the recognized negative interaction of trastuzumab with anthracyclines in advanced disease. Across all five trials, highly statistically significant reductions in rates of recurrence from 39% to 52% were demonstrated. These results were observed at median follow-up times ranging from 1 to 3 years. A statistically significant overall survival benefit has been achieved in the B31-N9831 trial [227], with the data for the other trials awaited. Trastuzumab has revolutionized the treatment of breast cancer both in the metastatic and the adjuvant setting. The use of preoperative trastuzumab in combination with chemotherapy or alone is being considered. The preoperative setting provides an opportunity for tissue collection before, during, and after therapy; however, the rate of primary resistance to single-agent trastuzumab for women with metastatic HER-2 overexpressing tumors is 66–89%. Most women who achieve an initial response to trastuzumab will develop resistance within 1 year. Collection of serum and tumor blocks continues in the adjuvant trials, with the goal of characterizing molecular signatures that correlate with response or failure to trastuzumab. Translational research conducted as part of the NSABP-B31 trial showed that patients with coamplification of c-MYC and HER2 seemed to derive the greatest benefit from adjuvant trastuzumab [227]. This outcome may be as a result of trastuzumab switching on the proapoptotic function of deregulated c-MYC. The topoisomerase II α gene (TOP2A) is either amplified or deleted with equal frequency in most HER2-amplified breast cancers and in some breast cancers without HER2 amplification [230]. Amplification (or deletion) of TOP2A may account for sensitivity or resistance to cytotoxic agents depending on the specific genetic defect at the TOP2A locus [231]. Diagnostic tests to ascertain TOP2A status, may help to select ideal patients for the combination therapy of an HER2-targeting drug with a cytotoxic, such as a topoII-inhibitor, particularly in the case of TOP2A amplification. Delineating the mechanisms of primary or acquired (i.e., treatment-induced) resistance and therapeutic approaches to tackle them is paramount. Potential mechanisms of trastuzumab resistance include altered receptor–antibody interaction; increased Akt activity; increased cell signaling from other HER receptors; reduced PTEN level; reduced p27kip1; and increased insulin growth factor-1 receptor (IGF-1R) signaling. Combinations of trastuzumab with other biologic agents such as bevacizumab are ongoing, and a number of novel compounds targeting HER2 are in development.

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The results obtained with trastuzumab are some of the most striking ever seen in the history of breast cancer trials. They illustrate clearly that even in complex solid tumors, it is possible to identify and treat an important molecular “driver” of cancer progression. Ironically, the success of trastuzumab, imatinib, cetuximab, and other molecular targeted agents has led to the wider socio-economic problem of affordability. Many of these treatments can cost upwards of US $40,000/year, and healthcare systems in rich countries are struggling to cope with this, let alone the poorer countries of the world. Interestingly, returning to the example of adjuvant trastuzumab, the Finnish study looked at the administration of only 9 weeks of trastuzumab after adjuvant chemotherapy and found a similar improvement in recurrence-free survival at 3 years as the HERA, NCCTG, and BCIRG studies [228]. Further studies and longer follow-up are awaited.

17.11 Dual Inhibition of EGFR and HER2 In addition to drugs targeting individual ErbB receptors, drugs targeting two or more of the ErbB receptors are in development. These include both small molecules and Ab. Sound scientific rationale exists for dual kinase/receptor targeting. A number of nonclinical in vitro and in vivo studies have shown superior antitumor activity using a dual ErbB approach rather than single receptor targeting [166, 223– 234]. For example, in HER2-overexpressing breast cancer cell lines, treatment with the EGFR tyrosine kinase inhibitor gefitinib plus the anti-HER2 receptor Ab trastuzumab produced an increased apoptotic effect when compared with either agent alone [235]. These in vitro results translated into positive in vivo results in HER2-positive BT-474 breast cancer xenografts, where the combination of trastuzumab and gefitinib produced enhanced antitumor activity compared with trastuzumab alone [235]. Nonclinical work with lapatinib, an EGFR and HER2 inhibitor, demonstrated greater inhibition of cell proliferation and induced apoptosis at lower concentrations in GEO colon cancer cells than antagonists targeting either receptor alone [236]. Targeting of >1 receptor may have the advantage of blocking redundant signaling that may be used to bypass more-specific ErbB tyrosine kinase inhibitors, and may be more effective at preventing the emergence of drug resistance.

17.11.1

Lapatinib

Lapatinib is an orally active, quinazoline small molecule that reversibly inhibits EGFR and HER2 tyrosine kinases. It has been shown to inhibit EGFR and HER2 phosphorylated (phospho)-tyrosine, phospho-Erk1/2, phospho-AKT, and cyclin D in tumor cell lines and in xenograft models, and is a potent inhibitor of tumor cell growth in vitro and in vivo (IC50 < 0.2 µM) [237, 238].

A range of toxicology studies supported the oral administration of lapatinib to humans. The first studies with oral lapatinib were conducted in healthy volunteers and showed lapatinib to be safe. In patients with cancer, 2 phase-1 trials using a dose range from 175 mg up to 1800 mg once daily or 500 mg up to 900 mg twice daily reported no significant toxicity at the maximum doses [239, 240]. Most common sideeffects were mild and consisted of gastrointestinal and skin toxicity. The maximum-tolerated dose study did not select patients on the basis of ErbB receptor status although receptor status was tested in patients on trial. Clinical activity was demonstrated with one complete response (CR) in an EGFR-overexpressing SCCHN, and 22 patients with various tumors overexpressing either EGFR or HER2 with stable disease for a median duration of 4 months (range: 1–13 months) [240]. The phase-1B study, recruited patients with heavily pretreated metastatic cancer with biopsiable disease and EGFR or HER2 overexpression on IHC, HER2 overexpression on gene amplification, or evidence of activated EGFR and HER2 receptors on IHC. Significantly, four patients with trastuzumab-resistant metastatic breast cancer, two of whom were classified as having inflammatory breast cancer, had partial responses. A further 24 patients with a variety of different carcinomas achieved stable disease for ≥ 6 months [239]. Phase-2 studies have been completed in advanced or metastatic breast cancer. Lapatinib was generally well tolerated at 1,250 mg once daily and 1,500 mg once daily as monotherapy. The most common adverse events were gastrointestinal toxicities (diarrhea, nausea, vomiting, and anorexia), rash, and asymptomatic left ventricle ejection fraction problems. The first phase-2 trial recruited 44 trastuzumabrefractory metastatic breast cancer patients overexpressing HER2 on IHC [241]. The response rate for the first evaluated 36 patients was 22% (3 partial responses and 5 stable disease). A second phase-2 trial of lapatinib, this time as first-line treatment in patients with metastatic breast cancer with HER2 amplification detected by FISH (fluorescence in situ hybridization), demonstrated a response rate of 38% [242]. In another phase-2 study, patients with HER2 overexpression (HER2 2/3 positive or FISH positive) alone and not patients with EGFR overexpression alone predicted for sensitivity to lapatinib in relapsed/refractory inflammatory breast cancer [243]. Data from another phase-2 trial demonstrated preliminary activity with lapatinib against brain metastases from patients with trastuzumab-refractory breast cancer [244]; however, despite two partial responses, the study failed to reach the four objective responses required to reject the null hypothesis. After these results, other phase-2 and 3 trials with lapatinib and chemotherapy and/or hormonotherapy were started. Encouraging results from a phase-3 randomized study in metastatic renal cell cancer with lapatinib versus hormono-

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therapy in patients who expressed EGFR and/or HER2 by IHC showed statistically significant prolonged median overall survival for the group of EGFR 3+ patients treated with lapatinib [245].

17.11.2

BIBW-2992 and HKI-272

Other small-molecule inhibitors of EGFR and HER2 include HKI-272 and BIBW-2992. Both agents are irreversible inhibitors and may have the advantage of eliminating kinase activity until new receptors are synthesized; however, this seems to happen rather quickly, probably within 2 days. Nevertheless, it is probably only necessary to maintain effective concentrations of the drug for a shorter time period. BIBW-2992 has demonstrated encouraging activity in phase-1 [246–249]. A phase-2 study of BIBW- 2992 in combination with an antiangiogenic agent is underway in men with hormone-refractory prostate cancer, and the combination of BIBW-2992 with docetaxel is currently being evaluated in two other trials. Recent data from the phase-1 trial of HKI-272 given as a continuous, once-daily, oral treatment demonstrated activity in breast cancer [250]. Two phase-2 studies of HKI-272 in patients with HER2-overexpressing breast cancer and advanced NSCLC are ongoing.

17.11.3

Canertinib (CI-1033)

Canertinib (see Fig. 17-3) is an irreversible pan-ErbB tyrosine kinase inhibitor. Targeting all four receptors ErbB receptors has the theoretical advantage of blocking redundant signaling that might be used to bypass more specific ErbB tyrosine kinase inhibitors; such agents could be more effective at preventing the emergence of drug resistance. CI-1033 was relatively well tolerated in phase-1 studies [251, 252], but phase-2 studies have not shown sufficient antitumor activity to warrant phase-3 testing [253].

17.11.4

Pertuzumab

Pertuzumab is a fully recombinant humanized Ab that binds to the HER2 receptor at domain II, sterically blocking dimerization of HER2 with EGFR and ErbB3 [254– 258], thereby inhibiting intracellular signaling. In phase-1 studies, pertuzumab was well tolerated with principal side effects of fatigue, nausea, and vomiting [259]. The maximum tolerated dose was not reached with dose escalation to 15 mg/kg. Pharmacokinetic studies showed a terminal halflife of approximately 21 days, supporting every-3-weeks dosing. Pertuzumab infusions given every 3 weeks at doses ≥ 5.0 mg/kg maintained serum concentrations in excess of 20 µg/mL. Dose-response studies in nonclinical models have shown 80% tumor growth suppression at steady state trough concentrations of 5–25 µg/mL [260]. The recommended regimen for phase-2 testing was fixed at a dose of 420 mg (equivalent to 6 mg/kg for a 70-kg patient) every 3 weeks [260]; however, with this schedule, steady-state concentra-

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tions were only achieved after 90 days, and a loading dose of 840 mg was therefore recommended. A number of phase-2 trials have been conducted in prostate [261, 264], ovarian [262], breast, and NSCLC [263]. Clinical responses were seen in a phase-2 trial of patients who had been heavily pretreated for ovarian cancer, with five partial responses and eight stable diseases for at least 6 months. The overall response rate was 4.3% [262]. No clinically significant activity was seen in patients with chemotherapy-naive [261] or chemotherapy-resistant hormone-refractory prostate cancer [264]. The interim results of a phase-2 trial of singleagent pertuzumab in patients with advanced NSCLC who had progressed through at least one line of chemotherapy were presented. While no complete responses have been observed, 42% of patients had disease stabilization at 6 weeks [263].

17.12

The IGF-1 Receptor

In addition to Ab directed against the ErbB receptor family, clinical trials are in progress with a Ab against the IGF-1R. IGF-1R regulates cell proliferation, differentiation, and motility and is antiapoptotic [265]. An IGF-2 receptor exists but it does not have tyrosine kinase activity. The tyrosine kinase domain of the IGF1-R is highly homologous with the tyrosine kinase domain of the insulin receptor, sharing 84% amino acids. The insulin receptor is responsible for control of glucose uptake and metabolism, and one might hypothesize that any attempts at targeting the IGF1-R may cause dysfunction of glucose control. Epidemiologic studies suggest high end normal levels of IGF-1 increase the risk of cancer (breast, prostate, and colorectal). Other studies suggest expression levels of IGF-1R correlate with clinical outcome. In tumor models, IGF-1R modulates cell proliferation, survival, and metastasis and induces resistance to targeted therapies. CP-751, 871 is the first fully human Ab with high specificity to IGF-1R to enter clinical trials. In vitro, CP-751, 871 inhibits IGF-1 binding and down-regulates IGF-1R by receptor internalization. In vivo, it significantly increases the antitumor activity of cytotoxic agents, including the taxanes. A phase-1 study of CP-751, 871 with docetaxel has been conducted [266]. CP-751, 871 was given as an every-3-week infusion with a fixed dose of docetaxel, 75 mg/m2. The dose of CP-751, 871 was escalated from 0.1 mg/kg to 10 mg/kg. No grade-3 or 4 toxicities were attributed to CP-751, 871 alone, and a dose-limiting toxicity was not reached. Grade-3 hyperglycemia was seen in two patients but was attributed to corticosteroids rather than CP-751, 871. In this study, one of the secondary endpoints was to measure circulating tumor cells and to assess the effect of CP-751, 871 on IGF1-R expression. Loss of detection of IGF-1-R expression on circulating tumor cells was seen by day 8 at doses of ≥1.5 mg/kg. Expression of IGF-1R was detected again by day 1 of cycle 2 in some patients (Fig. 17-5). While these data are for a small group of patients, circulating tumor cells hold promise as a possible

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tool for predicting response and assessing biologic effects of targeted therapies.

17.13 Retinoids—Targeting the PML-RARa Fusion Protein in APL Acute promyelocytic leukemia (APL) is a distinct subtype of acute myeloid leukemia (AML) in which a balanced reciprocal translocation between chromosomes 15 and 17 results in the formation of a chimeric gene that encodes the formation of the promyelocytic leukemia-retinoic acid receptor alpha (PML-RARα) fusion protein. In normal mammalian cells, the promyelocytic leukemia protein (PML) is primarily localized in multiprotein nuclear complexes called PML nuclear bodies. Indeed, PML protein is a negative-growth regulator capable of causing growth arrest in the G1 phase of the cell cycle, transformation suppression, senescence, and apoptosis. The fusion protein PML-RARα, causes deregulation of growth control, and has a crucial importance in driving and maintaining malignant progression in APL. Although most patients with APL achieve a complete remission with anthracycline-based chemotherapy regimes, up to 20% will have hemorrhagic syndrome either before or during chemotherapy. This bleeding disorder is attributed to disseminated intravascular coagulation, fibrinolysis, and proteolysis. The introduction of all-trans-retinoic acid (ATRA) for patients with newly diagnosed APL, either alone or combined with chemotherapy in induction, has improved overall survival up to 80% [267–270]. ATRA works by differentiating leukemic promyelocytes into mature cells. The European APL Group compared therapy with ATRA plus chemotherapy versus concurrent ATRA until complete remission, both groups then followed by further chemotherapy. The study group found a significantly reduced relapse rate at 2 years with a significantly reduced relapse rate at 2 years among patients who received concurrent therapy (6% versus 16%, p = 0.04) [270].

17.14

Farnesyl Transferase Inhibitors

An alternative potential strategy to tackle the dysregulated receptor tyrosine kinase→RAS→RAF→MEK→ ERK kinase pathway is to target RAS (Fig. 17-1). The RAS proteins were some of the first proteins identified that possessed the ability to regulate cell growth, and are often constitutively active because of point mutations in RAS genes [271]. Importantly, approximately 20% of all tumors have an activating mutation in one of the RAS genes [272]. In these tumors, mutant RAS drives several aspects of the malignant phenotype, including the deregulation of cell growth, apoptosis, and angiogenesis. Continuous activation of RAS protein can occur as a result of activated upstream signals, particularly by the ErbB family of receptors. Therefore, therapeutic modulation of RAS

should not target tumors purely on the basis of mutation. Despite some success with anti-RAS antisense therapy and agents designed to block RAS in the inactivated state, these approaches are practically difficult. The development of farnesyltransferase inhibitors (FTI), was based on the supposition that inhibition of posttranslational modification and membrane localization of RAS would block proliferative signal transduction [273]. The precise mechanism of action of FTI has received considerable debate. Three highly conserved prenyltransfereses exist: farnesyltransferase (FTase), geranyltransferase-1 (GGT-1), and geranyltransferase-2 (GGT-2). FTase and GGT-1 can cross-prenylate many proteins, which provides a means of escape for a cancer cell exposed to an FTI alone. If the therapeutic aim is to disrupt a protein like K-RAS, while FTase preferentially prenylates K-RAS, GGT- 1 can also prenylate K-RAS resulting in a protein that is similarly oncogenic. Despite this knowledge, FTI still made their way into the clinic. While mutant RAS is important in a number of cancers, clinical success with FTI has paradoxically been seen in tumors with low levels of RAS mutations, such as breast cancer in which < 2% of tumors have RAS mutations. In fact, the antitumor responses in breast cancer with FTI were independent of RAS status and of EGFR and estrogen/progesterone receptors [277]. It has become clear that the critical downstream target of FTI may not be RAS proteins, or may include other polypeptides in addition to RAS. A number of farnesylated proteins have been proposed to account for the RAS-independent activity of the FTase inhibitors in breast cancer and hematologic malignancies including RhoB and other Rho proteins [274], components of the phosphoinositide 3′ kinase/Ser/Thr kinase Akt-2 pathway [275], and CENP-E and CENP-F [276]. The jury is still out on the question of how exactly the FTI work. The future of FTI development will need to address why some tumors but not others respond and how exactly these agents are mediating their antiproliferative and proapoptotic effects. The methylquinolone tipifarnib FTI has progressed furthest through clinical trials.

17.14.1

Tipifarnib

Phase II and III trials of tipifarnib as monotherapy for colorectal, lung, and pancreatic cancers have all been disappointing [278, 279, 281, 282]. Modest clinical activity was seen in patients with advanced hormone-resistant breast cancer [277], and nonclinical work demonstrating greater tumor regression with the combination of tipifarnib and tamoxifen than with either agent alone, suggested that FTI may be more efficacious combined with hormonal therapy; however, results of a phase2 study of letrozole with or without tipifarnib in 121 women with tamoxifen-resistant advanced breast cancer showed no benefit for the combination over letrozole alone [280]. The most promising results with tipifarnib have been in untreated, poor-risk, AML and myelodysplastic syndromes (MDS) in which a 33% response rate was seen (eight complete responses and two partial responses) [283]. Patients

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were treated with 600 mg twice daily for 21 days. An accelerated approval application was made to the FDA to use tipifarnib in elderly patients with AML. This application, however, was unsuccessful and results of the Phase III study are awaited. FTI are being tested in combination with hormone therapy, chemotherapy, and radiation therapy. New drugs which are inhibitors of both FTase and GGT have entered the clinic, and the most important objective will be demonstrating the proof of concept for clinical activity.

17.15

RAF and MEK Inhibitors

It has been technically difficult to inhibit the RAS gene or its protein product directly, and inhibition of FTase has been more complex than originally thought. An alternative approach to block pathologic signaling through the MAPK pathway is to inhibit downstream RAF or MEK. Because both of these are kinases, they are suited to inhibition by drugs. After the activation of RAS, the next critical step in the pathway is activation of the family of Ser-Thr kinases known as RAF kinases. This family consists of three isoforms, A-RAF, B-RAF, and C-RAF (also called Raf-1). Each RAF species has a distinct expression profile in tissues, suggesting unique functions [284]. C-RAF is ubiquitously expressed whereas A-RAF and B-RAF have more restricted expression profiles. The activation of RAF is a complicated multistep process. Activated RAS recruits inactive RAF from the cytosol to the plasma membrane, a step which is essential for RAF activation. After several further modifications, RAF induces a downstream signal transduction cascade beginning with the activation of MEK [285]. Sorafenib (Fig. 17-3) was the prototype RAF kinase inhibitor. Sorafenib’s greatest efficacy to date has been as an antiangiogenic agent in renal cell carcinoma, because of effects on vascular endothelial growth factor receptors (VEGFR).

17.15.1

ISIS 5132/ LErafAON

Antisense oligonucleotides are synthetic nucleic acids designed to hybridize to a selected region in a target mRNA transcript. This results in degradation by RNase H or steric inhibition of translation, and therefore subsequent inhibition of target protein synthesis [286]. Second-generation phosphorothioate antisense oligonucleotides have been developed with a more favorable profile than their predecessors; however, there remain some difficulties in their development. In particular, the delivery of sufficient active drug into tumor tissues is a concern. Attempts have been made at modifying antisense oligonucleotide backbones or using different forms of drug delivery such as liposomal encapsulation. ISIS 5132 is a potent and specific antisense inhibitor targeting the 3′-untranslated region of C-RAF mRNA. Tumor

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cell growth inhibition was demonstrated in vitro and in vivo and led to three phase-1 studies, which explored continuous and intermittent intravenous administration, and established mild-to-moderate thrombocytopenia as the principal hematologic toxicity [286]. In phase-2 trials in patients with hormone-resistant prostate cancer, colorectal cancer, and ovarian cancer, no clinically significant tumor activity was seen, although sustained stable disease in some patients suggested a cytostatic effect [287–289]. Further development of ISIS 5132 has been halted owing to the absence of clinical activity, but a liposomal formulation of a C-RAF antisense oligodeoxynucleotide, LErafAON, designed to overcome degradation and improve intracellular delivery has entered the clinic. A phase-1 trial in patients with advanced solid tumors treated with 8 weekly intravenous infusions showed dose-independent hypersensitivity and dose-dependent thrombocytopenia, at a dose of 6 mg/ kg/ week, with thrombocytopenia being the dose-limiting toxicity [290]. Another phase 1 showed the product to be well tolerated in combination with radiotherapy at doses of 2 mg/kg given twice weekly, using steroid and antihistamine premedication [291]. Other approaches to RAF inhibition include targeting HSP90 with drugs such as 17-AAG. HSP90 is a molecular chaperone that forms a multimolecular complex with C-RAF. Inhibition of HSP90 leads to C-RAF destabilization and degradation through cellular proteolytic mechanisms. In contrast, wild-type B-RAF does not require HSP90 for stability, but mutated, activated B-RAF binds to an HSP90-cdc37 complex that is required for its stability. Mutant B-RAF is more sensitive to degradation by 17-AAG treatment than wild-type B-RAF [292, 293]. Other key cellular proteins are degraded when HSP90 is inhibited which may further contribute to its anticancer activity (see section HSP-90).

17.15.2 CI-1040, PDO325901 and ARRY-142886 Moving further down the signaling pathway, CI-1040 was the first MEK-targeted agent to enter the clinic. It is a potent and selective allosteric inhibitor of both MEK isoforms, MEK1 and MEK2 [294]. After successful nonclinical studies [294], a phase-1 study suggested that the drug was safe and well-tolerated with side-effects of mild skin rash, diarrhea, and fatigue. A partial response was seen in a patient with pancreatic cancer, and stable disease for > 3 months in 25% of the patients [295]. A phase-2 study in NSCLC, breast, colorectal, and pancreatic cancers was negative [296]. Poor pharmacokinetic characteristics were found in the phase-2 study and the drug has not been developed further; however, MEK is believed to be a valid target for cancer therapy, given its place in the MAPK pathway and the importance of inappropriate MAPK pathway activation to many cancers. Phase-2 studies are underway with second-generation agents [297, 298]. Work with one of the second-generation compounds demonstrated complete

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abrogation of tumor growth in B-RAF mutant xenografts associated with MEK inhibition. Enhanced and selective sensitivity to MEK inhibition was seen in the B-RAF mutant tumors compared with “wild-type.” Mutant RAS tumors were only partially inhibited. Tumors frequently have mutation of B-RAF or RAS, tending to show mutual exclusivity, suggesting that each mutation confers a similar selective advantage. Nevertheless, this study suggests that B-RAF mutated cancer cells may have a dependency on MEK-ERK rendering them more sensitive to MEK inhibition than RAS mutants [299].

17.16 Phosphatidylinositol 3-Kinase (PI3 Kinase) Pathway Inhibitors The PI3 kinase pathway has a pivotal role in cellular processes, including growth, cell survival, differentiation, chemotaxis, and metabolism [300]. After activation by receptor tyrosine kinases and RAS, second messengers are generated by PI3 kinase, particularly phosphatidylinositol-3,4,5-triphosphate (PIP3), and downstream pathways are activated. The family of PI3 kinases is composed of 16 members [301], and there are four class-1 lipid kinase isoforms, which collectively form PIP3. One member of this quartet of lipid kinases, p110α, is frequently overexpressed and mutated in many cancers [301, 302]. Other deregulated members of the PI3 kinase pathway involved in cancer include loss of the PTEN phosphatase and overexpression and activation of the upstream receptor tyrosine kinases and downstream Ser/Thr kinase PKB/Akt [300]. In addition to PI3 kinase inhibitors, small-molecule inhibitors of PKB/Akt are in nonclinical development. The prototype PI3 kinase pathway inhibitors are LY294002 and wortmannin. Wortmannin is potent but chemically unstable [303]. LY294002, a flavone, has activity at very high doses, but is rapidly metabolized and displays significant toxicities, including dermatitis and wasting; further development has not been pursued [304]. A number of highly potent isoform-selective direct PI3 kinase inhibitors are being developed and include the imidazopyridines and the pyridofuropyrimidines [305, 306]. PI-103 is an example of a pyridofuropyrimidine, which is undergoing optimization before entering the clinic. The attraction of this compound is its ability to inhibit both mTOR and PI3 kinase p110α, thereby blocking the feedback activation of Akt that occurs with mTOR inhibitors [307, 308]. The impact of feed-back or feed-forward loops in responses seen to cancer therapeutics must not be underestimated. More sophisticated drugs blocking different elements of the same pathway, or different pathways simultaneously, offer a “2-hit” strategy that should help to abrogate compensatory mechanisms. An orally active s-triazine PI3 kinase inhibitor, ZSTK474, has been identified from a compound screen of agents with

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similar antiproliferative activity to LY294002 and wortmannin [309]. This agent again is in nonclinical development, but many inhibitors of PI3-kinase and related downstream kinases (e.g., AKT, mTOR, PDK1, and ILK) on the pathway are poised for utilization as therapeutic targets. It would be expected that these agents would be most effective against cancers with a deregulated PI3 kinase pathway. The effects on insulin signaling and glucose homeostasis may be of concern, since the PI3 kinase pathway, and particularly P110 Pα, is important in regulating this process [310, 311].

17.17 Inhibitors of the Mammalian Target of Rapapmycin (mTOR) mTOR is a key kinase downstream of the activation of PI3K. It has a central role in controlling cell anabolism and catabolism, and also influences the regulation of apoptotic cell death [312]. Inhibition of mTOR signaling leads to G1-to S-phase cell-cycle arrest. The main downstream targets of mTOR are p70 S6 kinase and the eIF-4E- binding protein (4E-BP1). Four mTOR inhibitors exist: the prototype rapamycin (see Fig. 17-3) and three rapamycin derivatives everolimus (RAD-001), temsirolimus (CCI-779), and (AP23573). Each of these inhibitors forms a complex with the intracellular immunophilin FKPB12, and the resulting complex inhibits mTOR. Rapamycin was originally isolated as an antifungal agent [313], but showed antitumor and immunosuppressive activity in nonclinical trials, and was licensed for suppression of transplant rejection [314]. The development program of rapamycin as an anticancer agent was given low priority and was halted completely in 1982 when temsirolimus, a soluble rapamycin derivative with a safe toxicologic profile in animals, was developed. Everolimus, temsirolimus, and AP23573 have entered clinical trials. Dose-limiting toxicities are relatively consistent between the three compounds and include reversible mucositis, thrombocytopaenia, weakness, and fatigue. Severe psychiatric side-effects were seen at very high doses of temsirolimus. No significant immunosuppression was seen with any of the three drugs. At the recommended doses, side-effects were mostly cutaneous and included acneiform rash, herpes lesions, maculopapular rash, and nail disorders.

17.17.1

Everolimus

Everolimus is an oral macrolide analogue of rapamycin that targets mTOR to inhibit the downstream signaling events involved in the regulation of G1-to S-phase transition. The main downstream targets of mTOR are p70 S6 kinase and the eIF-4E-binding protein (4E-BP1). Everolimus has demonstrated in vitro and in vivo antiproliferative activity against a number of human tumor cell lines [315]. In vivo studies have shown that a single administration of everolimus (5 mg/kg) causes significant inactivation of

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p70 S6 kinase in tumor biopsies, skin biopsies, and peripheral blood lymphocytes. In nonclinical models, significant inhibition of tumor p70 S6 kinase was maintained up to 48 hours, and inhibition over 72 hours was seen in skin and peripheral lymphocytes [315, 316]. In another study, a human cancer cell line (the B16/BL6 melanoma model) was more sensitive in vivo than in vitro. Tumor-associated blood vessel density was decreased > 50% after treatment with everolimus, suggesting that additional antiangiogenic effects might explain the difference in sensitivity. These observations provide in vivo validation for using the inhibition of p70 S6 kinase inhibition in tumor, skin, or lymphocytes as a pharmacodynamic marker in clinical trials. Two phase-1 studies have been conducted exploring weekly and daily oral schedules of 5–75 mg and 5–10 mg of everolimus, respectively [317, 318]. In one study, significant inhibition of phospho-S6K1 in PBMC was durable at doses ≥ 20 mg/week [317]. In the other study, major inhibition of phospho-S6K1 was seen at all dose levels and schedules. A dose-related inhibition of phospho-4EBP1 was noted and phospho-AKT expression increased in a dose-dependent manner [318], owing to a feedback loop effect. Recommended doses from the two studies were 20 mg/week and 10 mg/day, respectively. Responses were seen in patients with colon cancer and NSCLC, and stable disease for ≥ 4 months in patients with renal and breast cancers. A combination phase 1 and 2 study with imatinib-refractory GIST recommended a dose of 2.5 mg/day with imatinib at a dose of 600 mg/day. Partial responses were seen in two patients and some disease stabilization [433]. Everolimus has been tested in combination with hydroxyurea in glioblastoma multiforme; with gefitinib in a phase 1 and 2 trial in patients with advanced NSCLC; and has been combined with cytotoxics. A phase-1 study of weekly everolimus and gemcitabine indicates myelosuppression as the principle toxicity, which appears to be synergistic. Therefore, the maximum dose of gemcitabine in this combination is far lower than might have been hoped for at 600 mg/m2 [319]. Phase-2 studies are ongoing in several tumor types.

17.17.2

Temsirolimus

Two phase-1 studies have been conducted with temsirolimus. Given as a weekly intravenous infusion, the recommended dose was found to be ≤ 220 mg/m2. Dose-limiting toxicities of depression, mucositis, thrombocytopaenia, and hyperlipidemia were noted at this dose level. Partial responses were seen in two patients: one with renal cell cancer, and one with breast cancer adenocarcinoma [320]. A second phase-1 study established a daily 19.1 mg/m2 dose given for 5 days every 2 weeks as the maximum tolerated dose. Dose limiting toxicities included thrombocytopaenia and mucositis [321]. A number of phase-2 trials have been performed with temsirolimus in patients with endometrial, renal cell carcinoma,

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breast cancer, glioblastoma, and mantle cell lymphoma [322–326]. The 2-stage, phase-2 study in patients with recurrent or metastatic endometrial cancer was based on sound scientific rationale, as PTEN is frequently lost in endometrial cancer. In the 19 patients evaluable for response, 23% showed a partial response and 63% stable disease. Of note, responses were independent of PTEN status [322]. These results suggest that monotherapy with temsirolimus could be a treatment option for endometrial carcinoma, a cancer for which no standard of care is currently established. The other tumor type in which there has been success with temsirolimus is renal cell carcinoma. Results from a phase-2 study demonstrated improved survival in those patients with intermediate or poor-prognosis (22.5 months for the intermediate group, and 8.2 months for the poor prognosis group). These results compared favorably with historical controls treated with IFNα (13.8 and 4.9 months for the intermediate and poor prognosis groups, respectively) and led to a large, randomized multicenter phase3 study in patients with advanced, metastatic renal cell carcinoma of poor prognosis. An interim analysis of 626 patients randomly assigned to receive either IFNα alone, IFNα and temsirolimus, or single-agent weekly temsirolimus, showed that patients treated with temsirolimus alone had a statistically significant longer median survival (10.9 months) than patients who received IFNα (7.3 months). The combination of the two agents did not improve survival in this group of patients. Finally, significant activity has been seen in mantle cell lymphoma. In a phase-2 study of 35 patients, who had relapsed after chemotherapy and rituximab therapy, an overall response rate of 38% was seen. The median response duration was 6 months [326]. Nonclinical studies have shown that mTOR inhibitors can downregulate cyclin D1 in mantle cell lymphoma. Mantle cell lymphoma is caused by a chromosomal translocation between chromosomes 11 and 14, whereby cyclin D1 is overexpressed. This may therefore explain the impressive, albeit early, activity in clinical trials in this disease.

17.17.3

AP23573

The most recent rapamycin analogue to reach the clinic, AP23573, has been tested in three phase-1 studies [327–329], and is being tested at phase-2 level in patients with hematologic malignancies and soft tissue and bone sarcoma [330, 331]. The chosen schedule for the phase-2 studies to date, has been daily administration for 5 days in a 2-weekly schedule. In the sarcoma study of 193 evaluable patients, 28% of patients experienced a complete or partial response or stable disease for at least 16 weeks duration. In 26% patients, imaging showed rapid reduction of partial metabolic responses 3–5 days after treatment. Combination studies of mTOR inhibitors with other drugs are being tested in the hope that this will reduce resistance. Nonclinical studies have shown that increased Akt activity in breast cancer is correlated with a poorer prognosis and

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resistance to tamoxifen, which suggests that tamoxifen resistance, may, at least in part, be related to increased mTOR pathway signaling [332]. Other studies have shown that increased Akt phosphorylation and reduced PTEN expression in NSCLC cells may be linked to gefitinib resistance [333]. These examples demonstrate how resistance to one drug may be caused by signaling through an alternative pathway. A rationale exists for combining mTOR inhibitors with other targeted therapies to combat drug resistance.

17.18 Cyclin-Dependent Kinase Inhibitors A number of signal transduction pathways converge on the cellcycle control apparatus, which has become a major area of targeted therapeutic research. Cyclin-dependent kinases (CDKs) are frequently deregulated in cancer by a number of means and represent an significant area of targeted drug development. These drugs can be classified based on their effects against the cell cycle CDKs as either pan-CDK inhibitors or more selective inhibitors, with varying degrees of activity against the transcriptional CDKs. Debate is ongoing in this field regarding the likely utility of highly selective versus pan-CDK inhibitors. Laboratory studies have indicated that inhibition of CDK2 alone may be insufficient to cause an antitumor effect in many cancers [334, 335]. Flavopiridol is a relatively broadly acting pan-CDK inhibitor that is in phase-2 clinical trials. Based on nonclinical activity in cancer this agent is being tested clinically in patients with CLL, lymphoma, and multiple myeloma. Novel drug schedules using bolus rather than infusional flavopiridol appear to be overcoming pharmacokinetic difficulties [336, 337]. Combinations of flavopiridol with fludarabine and rituximab have high response rates with significant but manageable toxicity [338]. Flavopiridol has been shown to potentiate imatinib-mediated apoptosis in BCR-ABL–positive leukemic cells [339], and a phase-1 study of imatinib and flavopiridol is ongoing. Flavopiridol has been shown to prevent the hypoxiamediated induction of VEGF, which may explain responses and stable disease seen in renal cell carcinoma [340, 341]. PD 0332991 is a CDK4/6 inhibitor, selected from other compounds for its pharmacokinetic properties, and has entered phase-1 clinical trial [342–344]. Several compounds potently inhibit CDK2 and CDK1, with significantly lower activity against CDK4/6 [345]. These compounds include seleciclib (CYC-202; (R)-Roscovitine; refs. 58 and 348) BMS-387032, an amine thiazole; PNU252808, also a thiazole derivative; SU9516; and imidazo(1,2-a)pyridines, including AZ703, amino imidazo (1,2-a) pyridine-1d, and the purine-based NU6102 and NU6140. In a phase-1 study of seliciclib, patients were treated at doses of 100, 200, and 800 mg twice daily orally, for 7 days, repeated every 3 weeks. Doses were increased from 200 to 800 mg because of pharmacokinetic data suggesting low drug exposure. Dose-limiting toxicities at the 800-mg dose included fatigue, skin rash, electrolyte disturbances, liver enzyme increases,

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and hyperglycemia. In addition, at 800 mg, a sudden reversible increase in creatinine concentration was seen in the drug administration period, and there were safety concerns about continuing to treat patients with increasing creatinine concentrations over the 7-day period. Disease stabilization was achieved in a patient with ovarian cancer for 18 weeks [346]. Shorter duration regimens have been possible at higher doses [347]. As with other mechanism-based agents, the use of pharmacokinetic-pharmacodynamic endpoints will be extremely important. Pharmacodynamic markers have been shown to be altered in vitro and in vivo in the HCT116 human colon cancer carcinoma xenograft model in response to novel CDKs Inhibitors [60, 348]. Pharmacodynamic markers may include Rb and p27Kip1 phosphorylation as markers of CDK4 and/or CDK2 inhibition [60, 348], and CDKs phosphorylation; depletion of cyclin D1 or McI-1, or induction of p53 as markers of CDK7/9 inhibition. Pharmacodynamic markers have been used in clinical trials of flavopiridol [349] and E7070 [350].

17.19

Inhibitors of HSP90

HSP90 is a molecular chaperone which functions to stabilize a number of mutated and overexpressed “client” signaling proteins that promote the proliferation and survival of cancer cells [44, 351]. Prominent among the HSP90 client proteins are a number of oncogenic kinases, including C-RAF [352], HER2 [353, 354], CDK4 [355], BCR-ABL (119) Polo-1, and Met. Other client proteins relevant for the development and maintenance of cancer include mutant p53 [356], estrogen and androgen receptors [357–360], AKT, and the catalytic component of telomerase, hTERT. Inhibition of HSP90 leads to incorrect folding and subsequent degradation of oncogenic client proteins through the ubiquitin proteasome pathway. Thus, inhibition of HSP90 is an extremely attractive means for tackling multiple deregulated genes and pathways simultaneously; killing many oncogenic birds with one stone. In this way, HSP90 inhibitors could have the potential to tackle all six of the so-called “hallmark traits” of malignancy [44]. In the early 1990s, certain natural products (notably radicicol, Fig.17–3, and the benzoquinone, ansamycin, geldanamycin) were found to bind to HSP90. These agents compete with ATP for binding at the nucleotide docking site located in the N-terminal domain of HSP90 [361, 362]. In doing so, they prevent the correct assembly of mature HSP90/client protein/ cochaperone complexes, which leads to inhibition of the essential ATP hydrolysis and hence to the proteasomal degradation of the client by recruitment of a ubiquitin ligase. Geldanamycin (see Fig. 17-3) itself proved too hepatotoxic for clinical use, but one of its derivatives, 17-AAG (Fig. 17-3) was found to be less hepatotoxic while retaining the antitumor activity of geldanamycin [363]. Various techniques including Western blotting and gene expression microarray analysis have been used to define a molecular signature of HSP90 inhibition [364], including, in particular, the depletion of client proteins

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Fig. 17-8. Pharmacokinetic-pharmacodynamic-“response” relationship for 17-allylamino, 17-demethoxygeldanamycin (17-AAG) in a malignant melanoma patient with prolonged stable disease. (a) Peak plasma concentration of >10µmol/L and concentrations of approximately 100 nmol/L are achieved at 24 hours. (b) Tumor biopsies taken before and 24 hours after drug administration shows CDK4 depletion and heat shock protein 70 (HSP70) induction (the tumor did not express c-RAF-1 in this case). (c) Computed tomography scans of a left-sided submandibilular metastasis of malignant melanoma 34 months apart. (Banerji U, O’Donnell A, Scurr M, et al. Phase I pharmacokinetic and pharmacodynamic study of 17-allylamino, 17-demethoxygeldanamycin in patients with advanced malignancies. J Clin Oncol 2005;23:4152–4161. With permission.)

such as C-RAF, HER2, and CDK4; and the simultaneous induction of HSP70 (Fig. 17-8). Proteomics techniques were used successfully to identify a further marker, AHA1, which represents the first HSP90 co-chaperone to be identified that acts as an activator of the ATPase activity of the chaperone [365]. 17-AAG was found to have good xenograft activity [366– 368] and acceptable toxicity and therefore entered the clinic.

Several phase-1 clinical trials of 17-AAG have been completed [369–372]. These studies evaluated different drug schedules including weekly, twice weekly, daily for 5 days (21-day schedule), and daily for 3 days (14-day schedule). Dose-limiting toxicity was hepatic toxicity in the 5-times and 3-times/ day schedules. Other side effects included gastrointestinal (diarrhea) and constitutional symptoms (fatigue). A particular

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problem with 17-AAG was the odor of the vehicle required for solubility of the drug. Dimethyl sulfoxide (DMSO), used as part of an egg phospholipid formulation, is malodorous and induced nausea in patients. Banerji et al. commented that the odor persisted in patients’ secretions for variable lengths of time after drug administration, and appeared to have a negative impact on quality of life, although this was not formally assessed [373]. No complete or partial responses were seen in the phase-1 studies, but several patients with melanoma and renal cell cancer had sustained stable disease, and activity was seen in patients with prostate cancer. Phase-2 studies are ongoing in various tumor types including melanoma, prostate, breast, thyroid, ovarian, renal, malignant mast cell, and mantle cell lymphoma. Nonclinical work suggested that 17-AAG might sensitize tumors to cytotoxics, in particular, taxanes, possibly because of its ability to inhibit Akt activity [374, 374a]. Maximum tolerated dose was not reached in the study of weekly 17-AAG owing to formulation issues. Tumor biopsies were obtained before and after treatment for 12 patients treated at doses of 320–450 mg/m2. The decision to biopsy at these two dose levels was guided by reproducible pharmacodynamic changes in peripheral blood lymphocytes at these dose levels, and also because plasma concentrations of the drug at these levels correlated with plasma concentrations in nonclinical studies where growth inhibitory activity was noted. The molecular signature of reduced expression of CRAF and CDK4 and increased expression of HSP70 has been shown to correlate with target inhibition of malignant cells in vitro and in vivo [61, 369, 375]. The difficulties encountered with 17-AAG in terms of its poor solubility and limited oral bioavailability prompted the development of improved 17-AAG formulations and also novel agents [351]. A Cremophor-based 17-AAG formulation referred to as KOS-953 was tested in a phase 1 and 2 trial of 17AAG and trastuzumab. The phase-1 part was open to patients with any solid tumor type and phase-2 part to patients with trastuzumab-refractory breast cancer only. In the phase-1 section, four cohorts of patients (25 in total) received doses ranging from 225–450 mg/m2 and were treated with both 17-AAG and trastuzumab weekly. Again, the principal toxicities were gastrointestinal and fatigue. One patient with trastuzumab-refractory breast cancer had a partial response, and three other trastuzumab-refractory breast cancer patients had tumor regressions of 25%, 22%, and 21%. Sound scientific rationale for investigation of HSP90 inhibitors in HER-2 positive breast cancer can be found, as HER-2 is a highly sensitive client protein of HSP90. In addition to improved formulations, novel agents have been developed, including the 17-AAG analogue 17-DMAG. 17-DMAG is a more water-soluble derivative of geldanamycin with good oral bioavailability and phase-2 studies are in progress. Other geldanamycin analogues in development include the hydroquinone IPI-504 [376]. Small-molecule synthetic HSP90 inhibitors including purine and diarylpyrazole resorcinol (such as VER49009, Fig. 17-3) agents are also being developed [377–382].

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17.20

Inhibiting Angiogenesis

Angiogenesis is a critical developmental and adult physiologic process that results in new blood vessel formation in normal tissues. It has been demonstrated that human tumors are associated with increased vascularity and that angiogenesis plays a role in tumor formation, growth, survival, and metastasis [383]. The discovery that tumors cannot grow beyond 1–2mm3 without forming new blood vessels highlighted angiogenesis as a potential cancer therapeutic target [384, 385]. Extensive research has uncovered and isolated growth factors and receptors controlling angiogenesis, some of which are proving to be validated therapeutic targets. One of the principal players in pathologic angiogenesis is VEGF. VEGF blockade has been shown to inhibit angiogenesis and suppress tumor growth in nonclinical models [386]. The VEGF gene family consists of six related glycoproteins: VEGF-A, VEGF-B, VEGF-C, VEGF-D, VEGF-E, and placental growth factor. VEGF-A is thought to be the most important for angiogenesis and is subject to several mechanisms of control. VEGFs mediate their effects on the vascular endothelium through high-affinity tyrosine kinase receptors. Three receptors have been identified to date. VEGFR-2 is thought to mediate most of the proangiogenic effects of VEGFs [387]. A pivotal pathway for angiogenesis control is regulation by oxygen concentration. VEGF expression is regulated by the product of the von-Hippel-Lindau (VHL) gene. In normal conditions, the VHL protein binds to hypoxia-inducible factor 1-alpha (HIF1α) causing ubiquitin attachment and HIF1α degradation in the proteasome (388). Under hypoxic conditions, as found in solid tumors, or with abnormal VHLproduct function, constitutively expressed HIF1α dimerizes with HIF1ß leading to the increased transcription of hypoxiainducible genes, such as VEGF and PDGF [388]. Angiogenesis inhibitors can be considered as direct, indirect, or miscellaneous inhibitors. Direct inhibitors target endothelial cells, inhibiting their ability to proliferate, migrate, or form new blood vessels, e.g., endostatin and angiostatin. This review will concentrate on indirect inhibitors that block the production of endothelial growth factors or downstream angiogenic signaling pathways. Two principle indirect therapeutic strategies exist for targeting angiogenesis namely, neutralizing anti-VEGF Ab and small-molecule multitargeted kinase inhibitors, which have activity against VEGFR and other signaling pathways. Numerous drugs are in varying stages of development, including bevacuzimab, sunitinib, sorafenib, PTK787/ ZK222584, ZD6474, AE-941, and AZD-2171, of which the first three agents will be discussed in greater detail.

17.20.1

Bevacizumab

Bevacizumab was the first antiangiogenic therapy approved by the FDA for the treatment of cancer. A humanized variant of a

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murine anti-VEGF-A MAb, it binds to all biologically active isoforms of VEGF. A number of phase 2/3 studies of bevacuzimab with chemotherapy were undertaken in patients with advanced colorectal cancer [389, 390], stage IIIB-IV NSCLC [391, 392], advanced breast cancer [393], and metastatic renal cell cancer [394]. The endpoints of increased response rate and prolonged time to progression were only significantly better than current therapy in advanced colorectal cancer [389, 390] and nonsquamous NSCLC [392]. The clinical trial that led to the licensing of bevacuzimab was a double-blind, randomized, phase-3 trial of bevacuzimab given with IFL (irinotecan, 5-FU, and leucovorin), as first-line therapy for metastatic colorectal cancer [395]. Median duration of survival was increased from 15.6 in the bolus IFL-plus-placebo cohort to 20.3 months in the bolus IFL-plus-bevacuzimab cohort. Similar increases were seen in progression- free survival, response rate, and duration of response. Bevacuzimab was generally well tolerated, with hypertension the most frequent side effect. Some serious toxicities were observed, albeit at low frequency. Gastrointestinal perforation, wound healing complications, hemorrhage, thrombosis, hypertension, and proteinuria have been reported with this drug [396–398]. An Eastern Oncology Cooperative Group (ECOG)-sponsored trial evaluated bevacuzimab plus FOLFOX4 regimen in patients who had progressive disease after FOLFOX4. A significant survival advantage was demonstrated in the bevacuzimab cohort compared with FOLFOX4 alone [399]. Bevacuzimab has been combined with paclitaxel/ carboplatin in patients with nonsquamous NSCLC. Eight hundred seventy-eight patients were enrolled and randomized to receive either bevacuzimab with paclitaxel/caroplatin or palitaxel/carboplatin alone. Statistically significant advantages were seen in the arm where bevacuzimab was combined with paclitaxel and carboplatin, in response rate (27% versus 10%, p < 0.0001), progression-free survival (6.4 versus 4.5 months,(p < 0.0001), and median overall survival (12.5 versus 10.2 months, p = 0.0075) [392]. Bevacuzimab provided the “proof of concept” for an antiangiogenesis approach, and has been followed by the development of several other VEGF inhibitors. A variety of small-molecule receptor tyrosine kinase inhibitors targeting VEGFR have been developed and show particular promise in renal cell carcinoma, a tumor type for which systemic treatment options have been very limited. Advances in our understanding of the molecular biology of clear cell carcinoma (one of the most common forms of renal cell cancer) has aided drug discovery. The VHL tumor suppressor gene is mutated in most cases of clear cell carcinomas leading to deregulation of HIF, which in turn causes to upregulation of a number of genes including VEGF, platelet derived growth factor (PDGF), carbonic anhydrase IX, TGF-a, and CXCR4 [388, 400]. VEGF is known to be overexpressed in renal cell carcinoma and it is thought that the upregulation of these genes contribute to tumor angiogenesis and growth.

17.20.2

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Sunitinib

Sunitinib (see Fig. 17.3) is an orally active multitargeted receptor tyrosine kinase inhibitor with activity against VEGFR, PDGFR, c-KIT receptor, and FLT3 kinase. Nonclinical studies demonstrated significant activity against a variety of xenograft models [401]. In phase-1 testing, clinical responses were seen in renal cell carcinoma, neuroendocrine tumors, and thyroid cancer [402]. Because expression of VEGFR and PDGFR is upregulated in renal cell carcinoma, this was a rational tumor type in which to pursue development. Two phase-2 studies (169 patients) have assessed sunitinib activity in metastatic clear cell renal cell carcinoma. In the first study, 87% of patients had clear cell histology and in the second study, clear cell carcinoma histology was an entry criterion as was prior nephrectomy. Entry criteria for both studies included failure of previous cytokine therapy. Sunitinib 50 mg was given daily on a 4-week-on, 2-week-off cycle [402]. The results of both studies have been combined in a meta-analysis, showing a 42% overall response rate with a further 24% of patients with stable disease for > 3 months. Median progression-free survival was 8.2 months and the progression-free survival for those patients who responded was substantially greater than those achieving stable disease (14.8 versus 7.9 months). Median overall survival was 16.4 months for the first study and had not been reached for the second study at the time of publication [403, 404]. The results of a phase-3 study of sunitinib as first-line therapy in metastatic renal cell carcinoma have been published [405]. Patients with metastatic renal cell carcinoma with no prior systemic therapy were randomly assigned to receive 50 mg sunitinib daily on a “4-weeks-on, 2-weeksoff” schedule or 9 MU IFNα administered three times weekly. The primary endpoint was progression-free survival with secondary endpoints of response rate, overall survival, safety, and patient-reported outcomes. Median progressionfree survival was significantly improved in the sunitinib group compared with the IFNα group (11 months versus 5 months, p < 0.001). The advantage was seen across all clinical subsets of patients. The median overall survival had not been reached for either group, but overall response rate by RECIST classification was significantly better for the sunitinib group (31%) than for the IFNα group (6%, p<0.001). This study represents the first demonstration of benefit over cytokine therapy in a randomized phase-3 study in first-line therapy of renal cell cancer.

17.20.3

Sorafenib

Sorafenib (see Fig. 17-3) is a bi-aryl urea, originally developed as a RAF kinase inhibitor but found to have an IC50 in the nanomolar range against VEGFR-2, VEGFR-3, PDGFR, flt-3, c-kit, and C-RAF and B-RAF kinases [406]. Principle side effects of sorafenib include diarrhea, fatigue, and skin toxicity [407–410]. Activity has been demonstrated in renal cell cancer in phase-2/3 trials [411, 412].

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The recommended phase-2 dose of 400 mg sorafenib was explored in a novel phase-2 study design, a randomized discontinuation study [413]. All patients (202) received 400 mg oral sorafenib twice daily in a 12-week run-in period. After 12 weeks, patients with <25% tumor shrinkage from baseline were randomly assigned to receive sorafenib or placebo for a further 12 weeks. Patients with >25% shrinkage from baseline continued open-label sorafenib and patients with >25% tumor growth discontinued drug. The primary endpoint was the percentage of randomly assigned patients remaining progressionfree at 24 weeks after initiation of sorafenib. This trial design has the advantage of trying to delineate the proportion of patients with stable disease secondary to drug from those with disease stabilization owing to the biology of their disease. Sorafenib was well tolerated and demonstrated disease stabilization in a number of patients. Of 202 patients, 73 had tumor shrinkage of >25%; 65 had stable disease and were randomly assigned to receive sorafenib (32 patients) or placebo (33 patients). At 24 weeks, 50% of sorafenib-treated patients were progression-free compared with only 18% of placebo patients (p = 0.0077). Median progression-free survival from randomization was significantly longer with sorafenib (24 weeks versus 6 weeks, p = 0.0087). Median overall progression-free survival was 29 weeks for the entire renal cell population. Sorafenib was restarted in 28 patients whose disease had progressed on placebo. These patients remained on drug for a median of 24 weeks before further progression. A phase-3, randomized, double-blind, placebo-controlled trial demonstrated an estimated 39% improvement in survival for patients with metastatic renal cell carcinoma receiving sorafenib versus placebo (p = 0.018). All patients had received one prior systemic therapy within 8 months of study entry. Progression-free survival of 24 weeks in the sorafenib cohort was double that of 12 weeks in the placebo cohort (p < 0.000001) [412]. Sorafenib received FDA approval for the treatment of advanced renal cell carcinoma in December 2005, becoming the first oral approved therapy for renal cell carcinoma in over a decade. As the number of antiangiogenic compounds increases, important questions arise regarding the clinical application of these compounds. For example, what is the optimal clinical setting for these drugs? Trials to date have examined the metastatic setting, but adjuvant studies are underway. What is the optimal duration of therapy in responsive patients? What is the optimal chemotherapeutic schedule to combine with antiangiogenics in colorectal cancer and should these therapies be given concurrently or sequentially? VEGF inhibition has been shown to transiently “normalize” the disorganized and dysfunctional tumor vasculature in some experimental models. Therefore combining antiangiogenic drugs with cytotoxic drugs may improve the delivery of oxygen and cytotoxic drugs to tumor cells [414, 415]. Whether the “vascular normalization” theory will have a long-term meaningful impact on anti-VEGF Ab-chemotherapy combinations is unknown, but this is just one of several examples where increased under-

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standing of drug interactions should aid the maximization of therapeutic effect.

17.21

Histone Deacetylase Inhibitors

In addition to the genetic mutations that drive cancer, growing evidence suggests that gene expression is controlled by epigenetic changes [416, 417]. These epigenetic changes can be critical for cancer development and progression and are, therefore, a potential therapeutic target. The opposing actions of histone acetyltransferase enzymes (HAT) and histone deacetylase enzymes (HDAC) tightly regulate gene expression through chromatin modification and are involved in the epigenetic control of gene expression. HDAC are also responsible for deacetylating core nucleosomal histones and other proteins that are part of transcription factor complexes regulating the cell cycle and apoptosis [416, 417]. Histone deacetylase inhibitors (HDACi) are novel anticancer agents that target the HDAC. They can induce tumor cell death, differentiation, and cell-cycle arrest [418]. Evidence suggests that they may affect host immune responses and tumor vasculature. Four main classes of HDACi: hydroxymates (e.g., vorinostat, suberoylanilide hydroxyamic acid [SAHA, Fig. 17-3], LAQ824, and PXD101; butyrates or short chain fatty acids (e.g., phenylbutyrate); the cyclic peptides (e.g., depsipeptide); and the benzamides (e.g., MS-275). The HDACi that has made the greatest impact in the clinic thus far is vorinostat, SAHA. A phase-1 study in solid tumors and hematologic malignancies explored three dose schedules [419]. A 2-hour intravenous infusion was given for 3–5 days/ week, either weekly, every 2 weeks, or every 3 weeks. A doselimiting toxicity was not reached in patients with solid tumors, but myelosuppression was dose-limiting in patients with hematologic malignancies. Side-effects included nausea, diarrhea, fatigue, constipation, nonspecific ST changes on electrocardiogram, and reversible renal impairment. An oral preparation was developed to allow daily dosing and has been tested in >70 patients with solid tumors or hematologic malignancy. Daily and twice-daily dosing has been tested, and once-daily dosing for 3 days/week [420, 421]. The maximum tolerated doses were 400 mg once daily or 200 mg twice daily for continuous dosing and 300 mg twice daily for 3 days/week. Dose-limiting toxicities were anorexia, fatigue, dehydration, and diarrhea. Also noted were nonspecific ST wave changes and QT prolongation. Importantly, in the intravenous and oral studies, Western blot analysis of PBMC showed an increase in histone acetylation present for up to 10 hours after a 400-mg or higher dose [419, 420]. In the intravenous study, five patients had biopsies before and after treatment, with three of five showing an increase in accumulation of acetylated histones in post-treatment samples [420]. Clinical activity was seen in these studies. In the phase2 setting, vorinostat has demonstrated activity in cutaneous Tcell lymphoma [422]. At a 400-mg, once-daily dose, a response rate of 30%, including one complete response, was achieved

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in a group of 74 patients. Symptoms improved, with 30% of patients noticing significant relief from pruritus. Cardiac toxicity with these agents has been a concern. Those agents with significant cardiac toxicity such as LAQ824 have been withdrawn. Trials of depsipeptide have incorporated strict monitoring of cardiac parameters. In > 4,000 electrocardiographs (ECG), ST-T wave changes and changes in heart rate have not been associated with cardiac damage [423]. Alterations to QTcB (a measure of electrical conduction in the heart) have been within safe limits. HDACi can affect nonhistone proteins in addition to histone proteins and therefore they may have a much broader effect on cellular physiology than was first realized. Toxicity with HDACi may be improved by the production of isoform-specific inhibitors. An HDAC6-specific inhibitor has already been developed but remains in nonclinical development at present [424]. The challenge for the future development of this class of drugs is in optimizing their activity, as single agents, or in combination with other therapies. A number of mechanisms have been proposed as to how HDACi could be successfully combined with other drugs to give synergistic effects, and combination studies based on sound scientific rationale are eagerly awaited.

17.22 Poly ADP-Ribose Polymerase (PARP) Inhibition PARP-1 is a member of the PARP enzyme family, responsible for DNA strand break and base damage repair [425]. The enzyme binds directly to the area of DNA damage and produces large branched chains of poly ADP-ribose attracting other DNA repair proteins. Inhibition of PARP-1 particularly affects base-excision repair pathways. Some tumors have DNA repair defects, particularly those cells with dysfunctional BRCA1 or BRCA2. The loss of BRCA1 or BRCA2 function results in defective double-strand break repair by homologous recombination and, therefore, these cells are exquisitely sensitive to PARP-1 inhibition [426, 427]. This finding is an example of the attractive approach of “synthetic lethality.” KU-0059436 is a potent, oral small-molecule inhibitor of PARP-1 and 2 enzymes with a mean IC50 of 2nM. A phase-1 trial of KU-0059436 in patients with advanced solid tumors incorporated recruitment provision for BRCA carrier enrichment and “triple-receptor negative” breast cancer. KU0059436 was initially given daily on an intermittent schedule, moving on to twice daily continuously. Thirty-four patients have been treated, with no dose-limiting toxicities noted at time of publication [428]. Of these patients, five were BRCApositive and a further patient had a family history strongly suggestive of possible BRCA positivity but declined testing. Adverse effects have been mild and include nausea, anorexia, constipation, and fatigue. No difference in toxicity was seen in BRCA1 mutation carriers at the time of publication. Pharmacodynamic analysis was performed on PBMC and tumors where possible. A poly ADP-ribose (PAR) dot-blot assay was used to measure PAR formation by PARP-1. Plucked hair

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follicles were collected to assess changes in phospho-H2AX. More than 50% inhibition of PARP-1 was seen in PBMC, even in early cohorts. PARP-1 inhibition was detectable 6 hours after dose with a suggestion of an increase in degree and duration of inhibition with dose escalation. Approximately 50% PARP-1 inhibition was seen in tumor biopsies in two patients. A patient with an ovarian carcinoma had a partial response; she had a strong family history of ovarian and breast cancers but declined BRCA mutation status testing. Two patients (pleural sarcoma and renal carcinoma) had stable disease for 6 months, having progressed on previous therapies [428].

17.23 Targeting Single versus Multiple Signal Transduction Pathways The oncogene addiction hypothesis [22, 23, 429] emphasizes the importance of oncogenes or tumor suppressor gene in the maintenance and the initial development of cancer. It is an important concept because it provides a potential means to explain how taking out a single key oncogenic lesion in a cancer that has undergone a number of activating oncogenic events may nevertheless provide an anticancer effect. Furthermore, oncogene addiction may explain how inhibitors of oncogenic signal transduction can exhibit selective effects in tumor versus normal cells. Elegant conditional expression studies in transgenic mice showed that expression of c-MYC led to the development of an aggressive sarcoma. When c-MYC expression was turned off, the malignant cells differentiated into normal bone cells. Furthermore, restoration of c-MYC expression resulted in apoptosis of the osteocytes rather than reversion to malignancy. A number of other experimental studies suggest that correction of a single oncogenic defect can produce an important anticancer effect, even in the presence of multiple oncogenic abnormalities [23]. The thesis is that malignant cells are becoming physiologically dependent on continued activity of specific activated or overexpressed oncogenes for maintenance of the malignant phenotype. It is likely that the multistage process of carcinogenesis is not just a simple summation of the individual effects of oncogene activation and tumor suppressor repression. The molecular circuitry of cancer that regulates signal transduction and gene expression is likely to be very different compared to that of normal cells. Only when we have a more detailed understanding of the deranged dynamic circuitry of cancer cells will we be able to pinpoint with certainty which signaling pathways or nodes are the crucial ones to tackle for each cancer. Nonetheless, the oncogene addiction hypothesis is supported in the clinic, for example, by activity of imatinib against BCR-ABL in CML and ATRA against PML-RARα in APL. Oncogene addiction can help explain why a selective anticancer effect can be obtained with molecular therapeutics that hit signal transduction pathways activated in cancer cells but that are required by normal cells. For example, mTOR inhibitors (e.g., temsirolimus) and PI3 kinase inhibitors (e.g.,

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LY294002) have been shown to have potent activity against cancer cells that have lost PTEN tumor suppression [430]. However, CML, APL, and c-KIT–mutant GIST are diseases that appear to be driven mainly by a single genetic defect, whereas most solid cancers are the result of the accumulation of numerous genetic abnormalities arising from disruption on a genomic scale [40]. Although oncogene addiction and tumor suppressor gene hypersensitivity are still likely to apply to such tumors, it seems likely that multiple lesions will be involved in driving malignancy, leading to a degree of redundancy in oncogene signal transduction pathways. This hypothesis probably explains why, despite having such impressive activity in the earlier, chronic phase of CML, imatinib produces only temporary responses in the later accelerated and blast crisis stages of disease and also in AML, where other oncogenic lesions in addition to BCR-ABL are important for driving malignancy. The significant but limited activity seen with agents such as trastuzumab and gefitinib may also be explained by the need to take out multiple important oncogenic pathways in most solid tumors. For the majority of human cancers, it seems unlikely that correction of a single molecular defect will be sufficient to achieve real clinical benefit, and that a combination of agents will be required. In the short- to medium- term, however, new molecular therapeutics are finding their place alongside traditional cytotoxic chemotherapies.

17.24

Concluding Remarks

Proof-of-principle has been established that targeting signal transduction pathways can be clinically beneficial. The longterm goal is that of effective and less-toxic therapies matched to the specific molecular pathology of each individual patient [36]. The sequencing of the normal human genome together with cancer genomes has accelerated the discovery and validation of new molecular targets for cancer drug development. The use of global gene expression and proteomic profiling is of major importance. The process of drug discovery and development can be done more quickly, more efficiently, and cheaper than ever before through a twofold strategy of focusing on important molecular targets and using modern high-throughput technologies to complement more traditional hypothesis-driven research [36]. Clinical trials of molecular therapeutic agents will increasingly involve early hypothesistesting, pharmacokinetic and pharmacodynamic endpoints and careful selection of patients for treatment based on molecular pathological criteria [36, 65]. Cancer drug development is a multidisciplinary process, the success of which depends on the close collaboration between a number of disciplines including: molecular biology, medicinal chemistry pharmacology, and medicine. Figure 17-9 illustrates how effective translational research can be fostered by the rapid flow of information back and forth between lab and clinic. Table 17-6

Knowledge of the molecular differences between normal + cancer cells on a genomic scale

New molecular diagnostics, outcome prediction, pharmacodynamic endpoints + pharmacogenomic biomarkers for use in clinical trials

Laboratory studies of clinical drug resistance

Multi-agent, genomebased treatments targeted to the detailed genomic / proteomic profile of individual patients

Fig. 17-9. Translational research in molecular therapeutics.

New cancer drugs acting on novel molecular targets

Drug development

DNA microarrays + Gene expression

Signal transduction

Bioinformatics resources

http://www.nature.com/genomics/

Genomics resources

http://dtp.nci.nih.gov/

http://www.icr.ac.uk/research/research_sections/cancer_therapeutics/ index.shtml

http://discover.nci.nih.gov/

http://www-genome.wi.mit.edu/cancer/index.html

http://brownlab.stanford.edu

http://www.affymetrix.com

http://www.crukdmf.icr.ac.uk/

http://www.ebi.ac.uk/microarray/

http://www.cellsignal.com/

http://cgap.nci.nih.gov/Pathways

http://www.ncbi.nlm.nih.gov

http://www.nature.com/genomics/ http://www.ebi.ac.uk/services/index.html

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Books

http://www.sciencemag.org/feature/plus/sfg/

http://cgap.nci.nih.gov/

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Unigene

http://www.ncbi.nih.gov/SNP/

http://genome.ucsc.edu/cgi-bin/hgTracks http://www.celera.com

http://www.ncbi.nlm.nih.gov/mapview/map_search.cgi

http://www.ensembl.org/Homo_sapiens/

Website

Cancer genome project

Human genome

Topic

Table 17-6. Useful Web sites. Web sites were operative as of January 2007.

The Institute of Cancer Research, UK Developmental Therapeutics Program NCI, NIH, USA (continued)

Ensemble Human Genome Browser European Bioinformatics Institute (EBI), Sanger Centre, UK National Center for Biotechnology Information (NCBI) Human Genome Browser National Institutes of Health (NIH), Bethesda, USA University of California Santa Cruz (UCSC), USA, Human Genome Browser Genome sequencing company Rockville, MD, USA Database of human single nucleotide polymorphisms (dbSNP) NCBI, NIH, USA Unigene database for nonredundant, gene-orientated clusters NCBI, NIH, USA Cancer Genome Anatomy Project National Cancer Institute (NCI), NIH, USA Nature Genome Gateway excellent resource of free original research papers, news, and links to other sites Science Functional Genomics similar quality and scope to the Nature site NCBI Handbook NCBI, NIH, USA Users Guide to the Human Genome Genomic, proteomic, structural databases, and bioinformatics tools European Bioinformatics Institute, Sanger Centre, UK National Center for Biotechnology Information National Institutes of Health, USA Cancer Genome Anatomy Project; BioCarta, and KEGG pathways link from pathways to genes, proteins, and expression in different tissues Cell Signaling Technology Inc. produces reagents (eg, phosphoantibodies) to study signal transduction European Bioinformatics Institute tools for managing, storing, and analyzing microarray data Cancer Research UK DNA Microarray Facility Institute of Cancer Research, UK Affymetrix Leading producer of oligo-gene expression microarrays Brown Lab Howard Hughes Medical Institute, Stanford University, USA Golub Lab Center for Genome Research, Whitehead Institute, USA Weinstein Lab Laboratory of Molecular Pharmacology, NCI, USA Cancer Research UK Centre for Cancer Therapeutics

Description

17. Emerging Molecular Therapies: Drugs Interfering With Signal Transduction Pathways 351

Regulatory bodies

http://clinicaltrials.gov/

Clinical trials

http://www.fda.gov/cder/cancer/druglistframe.htm

http://www.mca.gov.uk/

http://www.emea.eu.int/

http://www.fda.gov/cder/

http://ctep.info.nih.gov/reporting/ctc.html

http://www.eortc.be

http://ctep.info.nih.gov/index.html

http://www.nature.com/cgi-taf/dynapage.taf?file=/nbt/journal/v16/ n2s/index.html

http://www.nature.com/nrd/

http://www.phrma.org/

Website

Pharmacogenetics + pharmacogenomics

Topic

Table 17-6. (continued)

useful starting point for exploration of contemporary pharmacogenomics Clinical Trials – site for public + professionals NIH, USA Cancer Therapy Evaluation Program NCI, NIH, USA European Organisation for Research and Treatment of Cancer (EORTC) helps develop, conduct and co-ordinate laboratory + clinical research in Europe Common Toxicity Criteria standard terminology to name and grade severity of adverse events Center for Drug Evaluation and Research Food and Drug Administration (FDA), USA European Agency for the Evaluation of Medicinal Products agency co-ordinating the regulatory policies of European states Medicines Control Agency UK regulatory body List of FDA-approved oncology drugs lists drug, approved use, manufacturer, and approval date

Pharmaceutical Research and Manufacturers of America (PHRMA) USA pharmaceutical and biotech companies Nature Reviews Drug Discovery timely reviews from Nature Publishing Group Nature Biotechnology Pharmacogenomics Supplement

Description

352 A.H.M. Reid et al.

17. Emerging Molecular Therapies: Drugs Interfering With Signal Transduction Pathways

lists some useful Web sites covering the areas of genomics, signal transduction, and drug development. An exciting future lies ahead for cancer drug discovery and therapy. As an ever increasing number of molecular therapeutics are discovered, new challenges arise as to how best to use and combine these therapies, both with traditional cytotoxics and with other targeted therapies. In recent years, we have witnessed a departure from the “one size fits all” therapeutic regimens of the premolecular era, to more tailored treatment based on prevailing biology. Characterization of individual tumor biology and personalized anticancer therapy may soon be realized. A continued rational exploitation of our growing molecular knowledge of cancer combined with a pragmatic, realistic approach to nonclinical and clinical drug development remains critical for future progress.

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Chapter 18 Suicide Gene Therapy Silke Schepelmann, Ion Niculescu-Duvaz, and Caroline J. Springer

18.1

Introduction

A major advance of the 20th century was the deciphering of the genetic code. As a result, novel technologies emerged, which led ultimately to the development of gene therapy. These advances raised hopes that many human diseases, including cancer, would become curable. Gene therapy for cancer can target both malignant or the nonmalignant, supporting cells (e.g., the vasculature) within a tumor. Different types of therapeutic proteins can be used, including tumor-suppressor proteins, cytokines, toxins, or prodrug-activating enzymes. This chapter deals with the latter, gene-directed enzyme prodrug therapy (GDEPT), a form of suicide gene therapy. This approach uses DNA or RNA technology to deliver a gene to cancer cells that encodes for a foreign prodrug-activating enzyme. After administration of a relatively nontoxic prodrug, the foreign enzyme catalyzes the conversion of the prodrug into a cytotoxic drug, which kills the cancer cells. The aim of GDEPT is to improve conventional cancer chemotherapy by selectively activating the prodrug at the tumor site, thus minimizing toxicity in nontumor tissues. GDEPT is one of the more popular areas of cancer gene therapy as evaluated by 52 clinical protocols (10.4% of total protocols) with a total of 567 patients (16.5% of total patients) in 2001, with some protocols reported as combination suicide gene therapy (83 protocols). In GDEPT, the gene expressing the enzyme is transduced into the cancer cell using a vector or vehicle. The gene needs to be expressed selectively and efficiently in the tumor cells to spare normal cells. The biggest challenge for GDEPT remains the selective targeting of the prodrug-activating enzyme to malignant cells.

18.2 Background to Suicide Gene Therapy In suicide gene therapy, there are two strategies to render the cancer cells more sensitive to drugs or toxins; toxin gene therapy, in which the genes for toxic products are transduced into From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

tumor cells or GDEPT, and prodrug activating gene therapy in which the transgenes encode enzymes that activate specific prodrugs to create toxic metabolites [1, 2]. The latter is sometimes also referred to as gene prodrug activation therapy (GPAT) [3] or, if recombinant viruses are used for gene delivery, virus-directed enzyme prodrug therapy (VDEPT) [4]. GDEPT is a two-step treatment. In the first step, the gene for a foreign enzyme is targeted to the tumor cells. After expression of the foreign gene at the tumor site, a relatively nontoxic prodrug is administered, which is converted into an active, cytotoxic drug by the foreign enzyme. Ideally, the gene for the enzyme should be expressed exclusively in the tumor cells and not in normal tissues or body fluids. Furthermore, the enzyme must reach a concentration sufficient to activate prodrug for clinical benefit. The catalytic activity of the expressed protein must be adequate to activate the prodrug under physiologic conditions. Because expression of the foreign enzymes will not occur in all cells of a targeted tumor in vivo, a bystander effect is required, whereby the prodrug is cleaved to an active drug that kills not only the tumor cells in which it is formed, but also neighboring tumor cells that do not express the foreign enzyme [5]. The main advantages of suicide gene therapy systems are: an increased selectivity for cancer cells, reducing side effects; ● an amplification effect as one molecule of enzyme can activate many prodrug molecules; ● higher concentrations of active drug at the tumor, compared with the concentrations achievable by conventional chemotherapy; ● generation of bystander effects; and ● induction of an immune response by tumor cells enzyme transduction and kill. ●

18.3

Vectors in Suicide Gene Therapy

Suicide gene therapy requires vectors or vehicles that are capable of efficient and selective delivery of the therapeutic genes to tumor cells. For applications such as ex vivo gene 367

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therapy, intratumoral administration of the vector or locoregional delivery, the ability to target specific cells may not be necessary. If systemic delivery is required, vector targeting is of major importance. Various vector systems have been proposed for gene therapy. These include viral vectors such as adenoviruses [6], adeno-associated viruses (AAV) [7], Herpes simplex virus (HSV) [8], lentiviruses [9–12], retroviruses [13–15], RNA viruses [16, 17], or vaccinia virus [18, 19]. Nonviral gene delivery vectors include naked DNA (with or without electroporation) [20], bacteria [21], and cationic lipids, liposomes, polyethylene imine (PEI), poliamino acids, peptides, or dendrimers [21–24]. In any gene therapy approach, it is important that the vehicles deliver the genes efficiently and that they are safe to administer to humans. Adenoviruses have achieved better infection rates (10%–50%) in vivo than retroviruses (0.9%-14.6%). Nonviral vectors with electroporation have achieved up to 8% transfection in vivo. Unusually high values (up to 59%) have been reported for nonviral vector transfection in vivo, however the highest values (>80%) were reported for a combination of viral and nonviral vectors (adenovirus complexed with PEI or diethylaminoethyl [DEAE]-dextran).

18.3.1

Nonreplicating Viral Vectors

The major clinical concern in the use of viral gene therapy vectors is the control of their dissemination. One way to reduce the risk is the use of nonreplicating viruses, such as replication-defective adenoviruses, retroviruses, or HSV. Replication-defective adenoviruses have been tested in numerous phase-1 clinical GDEPT trials, most of which used the thymidine kinase/ganciclovir (TK/GCV) or the cytosine deaminase/5-fluorocytosine (CD/5-FC) enzyme prodrug systems. Various tumor types have been treated in these studies, including malignant mesothelioma [25], glioma [26], retinoblastoma [27], melanoma [28], metastatic colorectal liver carcinoma [29, 30], ovarian [31], and prostate cancer [32]. Adenoviruses are not intrinsically tumor-selective and in all of these studies, the vectors were delivered by direct injection into the tumors or near the tumor site to target the vectors to the cancer cells. Other commonly used vectors for GDEPT are replicationdefective retroviruses, including lentiviral or murine leukemia virus (MLV)-based vectors. Lentiviral vector for GDEPT have not been tested in clinical trials. In contrast, there are substantial nonclinical and clinical data for MLV vectors. These viruses have an intrinsic selectivity for dividing cells, which makes them suitable for gene therapy in the brain, where the only dividing cells are those within a tumor. Nonclinical studies with MLV viruses have been done in glioma models [33–35], but MLV vectors have been used to deliver prodrug-converting enzymes to tumor endothelial cells [36] or to thyroid [37], colon, breast [38], or liver cancer xenografts

[39]. Clinical GDEPT studies have been done in patients with breast cancer or melanoma [15] or glioblastoma multiforme [13, 40, 41]. There has been one phase 3, controlled trial of retroviral GDEPT therapy for glioblastoma multiforme [42]. After 4 years of follow-up, no benefit of the GDEPT was observed, which was probably because of poor transduction efficiencies [42]. Finally, replication-defective HSV vectors have been engineered for GDEPT. They have been used to deliver TK to glioma models [43–46]. In addition, a replication-defective HSV virus has been used to co-express TK and CD for double suicide gene therapy [45].

18.3.2

Replication-Selective Viruses

One way to enhance gene transfer efficiencies of viral vectors is to use replicating viruses [5, 47–50]. These vectors have the advantage that each virus particle can infect a tumor cell that generates progeny capable of spreading to other cells. Thus, replicating viruses achieve higher efficiencies of gene delivery than replication-defective viruses [51]. Some replicating viruses, such as adenoviruses, HSV, vaccinia, vesicular stomatitis virus (VSV), replicate in tumor cells and lyse them. These vectors are oncolytic and so have intrinsic antitumor activity. They spread throughout tumors in vivo, killing more malignant cells in successive rounds of infection compared with nonreplicating viruses. Some replicating viruses, such as vaccinia [18, 19] or VSV [16] are inherently tumor-selective, whereas others, including adenoviruses [52] or HSV [12] do not naturally target tumor cells and have to be genetically modified using gene deletion/modification or transcriptional targeting strategies to achieve tumor-selectivity. An oncolytic HSV vector was the first virus designed to replicate selectively in tumor cells because of a deletion in the tk gene [53]. For clinical use, it is desirable to retain the viral TK-activity of HSV as it provides an inherent safety mechanism against uncontrolled viral replication [12]. Replicating viruses can be “armed” with therapeutic genes, thus enhancing the potential for the eradication of the tumor [5, 54]. A variety of replicating vectors has been employed for GDEPT, including adenoviruses [55] and HSV [56]. The incorporation of the tk gene into the genomes of oncolytic adenoviruses enhanced the antitumor effect in models of malignant glioma, colon, lung and ovarian cancer when the virus was given in combination with GCV compared with virus alone [57–59]. In other studies, GCV did not improve the efficacy of TK-expressing oncolytic adenoviruses [60– 62], which was probably because of inhibition of viral replication by the TK/GCV system [63]. Oncolytic adenoviruses have been armed with CD [64, 65], nitroreductase (NR) [66, 67], carboxypeptidase G2 (CPG2) [68] or carboxylesterase (CA) [69]. Using a double suicide gene therapy approach, an oncolytic adenovirus has been armed with a TK/CD fusion enzyme [70]. This virus is the best-characterized oncolytic GDEPT virus to date and was the first oncolytic vector to be

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used in a clinical trial to deliver a therapeutic gene [71]. The treatment was found to be safe with no dose-limiting toxicity and evidence of some tumor responses [71]. Concerns have been raised over the dependence of adenoviral gene delivery vectors on the coxsackievirus and adenovirus receptor (CAR). Some reports described downregulation of CAR in human tumors [48]. Other studies, however, found high amounts of CAR in various human cancer xenografts [32] and human metastatic prostate cancer has been shown to re-express CAR to high levels after expression is lost in local cancer [72]. Adenoviral vectors have been designed for CAR-independent gene delivery by retargeting them to alternative receptors that are selectively expressed on tumor cells [73]. For example, insertion of an Arg-Gly-Asp acid (RGD) motif into the HI-loop of the adenoviral fiber knob resulted in efficient CAR-independent GDEPT vectors that infect tumor cells through integrins [65, 74]. CAR expression on the surface of cancer cells can be pharmacologically manipulated. The deacetylase inhibitor FR901228, sodium butyrate and trichostatin A are all able to increase the CAR concentration. Cells treated with FR901228 before infection showed a 4- to 10fold increase in transgene expression from a β-galactosidase (β-gal)-expressing adenovirus [75]. Furthermore, inhibitors of the Raf-Ser-Thr kinase (rapidly growing fibrosarcomas in mice, RAF)/mitogen-activated protein kinase (RAF/MAPK) pathway have been shown to increase CAR expression and restore infectivity of refractory cancer cells [76]. These findings remain to be confirmed in vivo; however, they raise the possibility that drugs blocking the Ras-protein (isolated from rat with sarcoma, RAS) signaling pathway may enhance the efficacy of adenoviral GDEPT vectors in the future. With HSV viruses, the endogenous viral TK expression can be used for GDEPT [77]. It has been shown that the TK/GCV system inhibits not only tumor growth, but also HSV replication [78, 79]. Therefore, oncolytic HSV vectors have been successfully developed for enzyme/prodrug combinations, in which the cytotoxic drug affects HSV-replication to a lesser extent than TK/GCV [78–81]. These findings show that when using replicating viruses for GDEPT, it is important that the activated prodrug is not toxic to the replicating vector [68, 82, 83]. To date, replicating HSV vectors and other replicating viruses for GDEPT, such as vaccinia [19, 84], VSV [16] or replication-competent retroviruses [14] have not been tested in clinical GDEPT trials.

18.3.3

Bacterial Vectors

Bacterial vectors have been developed for enzyme prodrug therapy (BDEPT). One example, Salmonella typhimurium, localizes to tumors after systemic injection in mice. The wildtype pathogen led to death in the mice. Attenuated hyperinvasive auxotropic mutants made by deletion of the mbH that leads to lipid A metabolism showed melanoma targeting after intravenous (IV) administration, with tumor:liver ratios ranging from 250:1 to 9,000:1. When these vectors were

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administered to C57BL6 mice bearing B16F10 melanomas, tumor growth was suppressed, resulting in prolonged animal survival. A Salmonella vector expressing the tk gene under the control of a β-lactamase (β-L) secretion signal was developed and showed efficacy after GCV treatment in vivo [85, 86]. Salmonella expressing TK was proposed as an imaging agent because [14C]-2′fluoro-2′-deoxy-1-β-d-arabinofuranosyl-5iodouracil ([14C]-FIAU) accumulation was bacterial-dependent in mice tumors [87]. Other bacteria, such as Clostridium acetobutylicum and Bifidobacterium longum, have been shown to germinate selectively and grow in hypoxic regions of tumors after IV administration. A tumor:liver ratio >103 was reported for Bifidobacterium [88]. Bifidum bacteria harboring marker genes were constructed and this gene delivery system was claimed to be tumor-specific and nontoxic. Clostridium was genetically engineered to express the tumor necrosis factor-α.. (TNF-α..) and CD genes. The specificity of Clostridia was further improved by using a radiationinduced promoter to control the therapeutic genes [89, 90]. A report describes the use of the clostridial strain Clostridium sporogenes for the expression of CD. This strain has the highest reported colonization efficiency and can be systemically injected as spores. Another advantage is that this way of delivery does not elicit an immune response [91].

18.3.4 Nonviral and Viral/Nonviral Hybrid Vectors The alternative to viral vectors is the nonviral strategy [22–24], including transfection procedures such as injection of naked DNA and the use of physical devices such as gene guns, jet injection, and electroporation [92]. More common systems are based on noncovalent complexes of carrier molecules and plasmid DNA. Such systems are suitable for systemic gene delivery to tumors or metastases or both. The development of such carrier molecules is difficult because of biologic barriers, which must be overcome. Major advantages are linked to reliability, safety, and the fact that large expression cassettes can be transferred by this procedure. Using plasmid in conjunction with electroporation, an in vivo transfection efficiency of 3–8% was obtained compared with 0.1% for the same plasmid without electroporation [93, 94]. The approach is plagued by low transfection efficiencies especially in vivo. Differences in gene expression between rodents and human have been reported. Most nonviral vectors used in gene delivery are those that form complexes with DNA. These include lipoplexes (such as cationic lipids or cytofectins) and polyplexes (such as polyl-lysine [PLL], PEI, peptides, dextrans, and dendrimers). For the lipoplexes, transfection efficiencies in vitro in the range 0.2–35% have been reported [23, 95, 96]. An exciting development has been to combine viral with nonviral strategies. One possibility is to use a viral/nonviral hybrid vector. Accordingly, liposomes in conjunction with

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the hemagglutinating Japan virus (HVJ-liposomes) were constructed, which showed low immunogenicity and good in vivo transfection ability. The same system used to transfer the CD gene to nude mice bearing BXPC3 human pancreatic tumor xenografts showed a transfection efficiency of approximately 30% at day 3 (using lacZ as marker gene). At day 7, however, almost no positive β-gal cells were found [97]. After administration of repeated dose of 5-FC and also HVJ-CD liposomes, the tumor size was reduced by 72% at day 28. In contrast to cationic liposomes, which do not penetrate tissues because of their net positive charge and large size, the hemagglutinating virus of Japan-artificial viral envelope (HVJ-AVE) anionic liposomes can penetrate tissues and exhibit higher efficiency of transfection. HVJ-AVE anionic liposomes with the envelope that mimics the human immunodeficiency virus (HIV) have been constructed and the LacZ gene was transfected by intrathecal administration to the central nervous system of nonhuman primates. Transfection efficiencies of 29–59% in neurons were reported [98]. An alternative strategy uses polycations to increase the adenoviral-mediated expression of the transgenic protein. Complexation of adenovirus harboring the lacZ gene with PEI allows the selective transfection of biliary epithelia through biliary canulation. Administration in vivo of 1 × 109 pfu of adenovirus co-complexed with PEI led to >80% infected epithelial cells, whereas adenovirus alone infected <5% [99]. Finally, a variety of ligands have been examined for their liposome-targeting abilities, including folates and transferrins. Folate-containing cationic liposomes were optimized for the systemic delivery of p53 gene to mice carrying JSQ-3 xenografts derived from tumors of the nasal vestibule, which had failed radiation therapy. Transfection efficiencies of 40– 50% were achieved after systemic administration of the vector [100].

18.3.5

Targeting Cancer Cells

The targeting of cancer cells is important for the success of suicide gene therapy. A number of targeting possibilities has been described: structural changes of the viral envelopes that allow selective interactions with specific receptors overexpressed on the surface of the cancer cells [101]; ● specific transcriptional regulation using tissue specific or inducible promoters; ● alternative splicing [102]; ● translational control [103, 104], gene deletion strategies that restrict the replication of viral vectors, and transgene expression to malignant cells [105]; or ● specific delivery strategies. The focus here is mainly on the transcriptional regulation using specific promoters. ●

One way to achieve targeting specificity towards cancer cells is the use of tissue-specific promoters. This procedure is also

known as tissue-targeted expression [101, 106]. If a tumor cell overexpresses a particular protein because of increased specific transcriptional activity of its promoter (rather than gene duplication), and a therapeutic gene (or a gene that regulates the expression of the therapeutic gene) is inserted downstream of this promoter, then introduction of this DNA-sequence into these tumor cells should allow specific expression of the gene. Here, normal tissue that is also transduced would express much lower levels of the gene product and express none in an ideal system. This methodology (transcriptional selectivity) does not enhance transfection efficiency but it is able to increase the expression of a therapeutic gene in cancer cells and to prevent or minimize the expression of the same gene in normal (surrounding) cells. A number of promoters have already been investigated with positive results, e.g., human telomerase promoter to target cancer cells (68), α-fetoprotein (AFP) promoter to target hepatocellular carcinoma [10, 107], prostate-specific antigen (PSA) to target prostate cancer, MUC-1 promoter to target breast cancer cells [108], tyrosinase promoter to target melanoma cells [109], or the osteocalcin promoter to target osteosarcoma [101]. Recently, the midkine promoter (MK) was suggested for the treatment of pediatric tumors (Wilms tumor and neuroblastoma). MK is a newly identified heparin-binding growth factor that is transiently expressed in the early stages of retinoic acid-induced differentiation of embryonal carcinoma cells and is overexpressed in many human malignant tumors. A recombinant replication-defective adenovirus containing the TK gene under the control of the MK promoter, followed by GCV administration achieved high activity in Wilms tumors (G-401) and neuroblastoma (SK-N-SH). In contrast to adenoviral vectors harboring the TK gene under the cytomegalovirus (CMV) promoter control, this system did not produce any liver toxicity after GCV administration. The promoter of the vascular endothelial growth factor (VEGF) that is activated by hypoxia, was found to be useful in killing highly metastatic Lewis lung carcinoma A11 cells under hypoxic conditions. A retroviral vector was constructed harboring the HSV-TK gene under the control of the VEGF promoter [110]. Several examples showed that the use of enhancers in conjunction with specific promoters in viral constructs may be beneficial. Placing a 1455-bp PSA enhancer sequence upstream of either PSA or glandular kallicrein promoter (hKLK2) increased the expression of the marker gene in the PSA-positive prostate cancer cell line LNCaP by 20-fold [111]. Tandem duplication of the PSA enhancer increased expression levels 50-fold, while retaining tissue specificity. Furthermore, the expression of all enhancer constructs was increased 100-fold above basal levels when induced with dihydrotestosterone. Adenoviral vectors produced on this basis and harboring either epidermal growth factor promoter (EGFP) or NR genes were evaluated in LNCaP cells, showing selective expression in PSA-positive cells [111]. On a similar basis, a hypoxia inducible enhancer (a fragment of a human

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VEGF containing a hypoxia responsive element) was coupled to an AFP promoter in a retroviral vector with an HSV-TK gene. After transfection into hepatoma cells and after exposure to 1% O2 and GCV, specific toxicity was reported [112]. The l-plastin promoter has been proposed for use in gene therapy. It belongs to a family of genes encoding actin-binding proteins. Infection of ovarian carcinoma cells (OvCar-5 and SK-OV-3) with the recombinant replication-defective adenovirus containing the CD gene under the control of the l-plastin promoter, followed by 5-FC treatment proved to be effective both in vitro and in vivo [113]. Telomerase-specific suicide gene therapy vectors expressing bacterial NR or CPG2 were designed using hTER or hTERT telomerase transcriptional regulatory sequences. These constructs were able to sensitize human cancer cells to CB1954 or ZD2767P, respectively (68). The latency-associated promoter (LAP) was proposed for sustaining long-term gene expression in neurons. In vivo data indicate that although the HSV-1-LAP vector can drive the expression of the TK gene in a variety of central nervous system neurons, there is a reduction in the regulation of the promoter [114]. The Myc-Max binding motif (which activates the transcription of an adjacent promoter) was proposed for the treatment of small-cell lung cancer, which overexpressed myc-family oncogenes [115]. A Cre/loxP approach was also suggested in conjunction with the CD/5-FC suicide gene therapy system for the treatment in vivo of gastric carcinoma models [116]. Finally, the osteocalcin promoter has been used in a conditionally replication-competent adenoviral vector. The recombinant Ad-OC-E1a vector harboring the osteocalcin promoter proved to be effective in inhibiting the growth of prostatespecific albumin PSA-producing and nonproducing human prostate cancer cell lines [117].

18.4 Enzyme/Prodrug Systems for GDEPT GDEPT consists of two entities comprising a foreign enzyme and a corresponding prodrug. In 2001, a table containing the main 20 GDEPT systems was collated [118]. Since then, improved and novel combinations have been described. They include alkaline phosphatase/etoposide phosphate [102], CA/1-carboxylate etoposide [119], hypoxanthine-guanine phosphoribosyl transferase/allopurinol [120], horseradish peroxidase (HRP)/indole-3-acetic acid [121, 122], tyrosinase/ hydroxyphenylpropanol and N-acetyl-4-S-cysteaminylphenol [123], linamarase/linamarin-releasing hydrogen cyanide [124], folylpolyglutamyl synthetase/edatrexate producing edatrexate polyglutamate [80, 125], cytochrome P450 1A1/AQ4N [1,4bis[2-(dimethylamino-N-oxide)ethyl]amino-5,8-dihydroanthracene-9,10-dione] [126], CYT 1B1/resveratrol and aryloxime [127, 128], and β-2-alcohol dehydrogenase/ethanol [129]. The systems are in different stages of development, from in vitro nonclinical studies [121] to phase-3 clinical trials [128].

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There are specific requirements of the enzymes used in GDEPT. They should have high catalytic activity (preferably without the need for cofactors), should be different from any circulating endogenous enzymes, and should be expressed in cancer cells in sufficient concentration for therapeutic efficacy. The enzymes must catalyze scission or other types of reactions such as phosphorylation, rybosyl transfer, redox reactions or β-eliminations. The reactions catalyzed must be different from those of any circulating endogenous enzyme. The enzymes proposed for suicide gene therapy can be characterized into two major classes. The first class comprises enzymes of nonmammalian origin with or without human counterparts. Examples include viral TK, rabbit CA, bacterial CD, bacterial CPG2, purine nucleotide phosphorylase (PNP), thymidine phosphorylase (TP), and NR, d-amino-acid oxidase (DAAO), xanthine-guanine phosphoribosyl transferase (XGPRT), penicillin G amidase (PGA), β-l, multiple drug activation enzyme (MDAE), β-gal, rabbit cytochrome P450 (CYP 4B1), HRP, and deoxyribonucleotide kinase (DRNK). Those enzymes that do have human homologues have different structural requirements with respect to their substrates compared with the human counterparts. Their main drawback is that they are likely to be immunogenic. The second class of enzymes for suicide gene therapy comprises enzymes of human origin, which are absent from or are expressed only at low concentrations in tumor cells. Examples include deoxycytidine kinase (dCK), carboxypeptidase A (CPA), β-glucuronidase (β-glu), and CYP450 isoforms (CYP 1A2, CYP 3A, CYP 2B, CYP 1B1, CYP 2B1, and CYP 2D6). The advantages of such systems reside in the reduction of the potential for inducing an immune response. Their presence, however, in normal tissues is likely to preclude specific activation of the prodrugs only in tumors unless the transfected enzymes are modified for different substrate requirements. Some of these enzymes are absent or expressed at low concentration in the tumor cells such as dCK, CA, TP, or CYP. The genes encoding for GDEPT enzymes can be engineered to express their products either intracellularly or extracellularly in the recipient cells [2]. The site of enzyme expression and therefore prodrug activation is an important factor in suicide gene therapy [130]. The extracellularly expressed variants are either tethered to the outer cell membrane [2, 131–133] or secreted from the cells [131, 134]. There are potential advantages to each approach. Where the enzyme is intracellularly expressed, the prodrug must enter the cells for activation and subsequently, the active drug must diffuse through the interstitium across the cell membrane to elicit a bystander effect. Cells in which the enzyme is expressed tethered to the outer surface or secreted are able to activate the prodrug extracellularly. A more substantial bystander effect could therefore be generated in the latter system, but spread of the active drug into the general circulation is a possible disadvantage [2, 134]. There are a number of ways to increase the efficiency of the enzyme used in GDEPT systems. The discovery of

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orthologous enzymes with similar profiles from different species and improved kinetic of activation is one possibility. For example, the rabbit CA is 100- to 1,000-fold more active than the human homologue [135, 136] or the canine CYP 2B11 exhibits a Km 10- to 20-fold lower than CYP 2B6 used to activate cyclofosphamide and ifosfamide in gene therapy [137]. The crystallographic investigation of the catalytic site of the enzyme allows a rationale design of better substrates represents and alternative strategy. A third possibility to increase the catalytic efficiency of the system toward a given substrate is to mutate the enzyme. The engineering of the HSV-TK by site-directed mutagenesis produced mutants that displayed a substantial increase in the GCV and ACV activity with respect to the wild-type enzyme [138].

18.5 Prodrugs and Drugs for Suicide Gene Therapy A prodrug designed for a GDEPT system should be a good substrate for the activating enzyme. Favorable characteristics of prodrugs for GDEPT include efficient prodrug activation even at low concentration of prodrug (low Km) and rapid conversion of the prodrug to the active drug (high kcat) [139, 140]. The prodrug should not be a substrate for any endogenous enzyme, to avoid cytotoxic activation outside the tumor in normal tissues [140, 141]. Good physiologic stability of prodrug is required to prevent premature release of cytotoxic drug or alternatively deactivation of the prodrug before it reaches the expressed enzyme. A suitable pharmacokinetic profile in terms of bioavailability, biodistribution, area-under-the-concentration curve (AUC), and half-life in plasma is necessary. The cytotoxicity differential between prodrug and drug should be as high as possible to allow a comfortable therapeutic window. A minimum of 100-fold differential is considered by some authors [141–143] to be necessary for significant therapeutic gains, although lower values have been reported to produce good biologic effects [134, 144, 145]. Two parameters are important in describing the cytotoxicity differentials: the potential of activation and the degree of activation [146]. The first (measured as the IC50 ratio between the prodrug and drug cytotoxicities in the absence of the activating enzyme) describes how much of the drug has been deactivated by the conversion into the prodrug. The degree of activation (measured as the IC50 ratio between prodrug and drug cytotoxicities in the same cell line expressing the activating enzyme) describes the efficiency of the enzyme/prodrug system. Differentials of 100to 2,500-fold have been reported for nitrogen mustard prodrugs. In the HSV-TK/GCV system in osteosarcoma, an activation of 44,000-fold has been reported [147] and a 10,000-fold activation was claimed for N-(ethoxy-4-β-glucuronyl)butyl anthracyclin by β-glu [148]. With the emergence of replication-competent viral vectors, bacterial vectors and engineered macrophages, the interaction between prodrug and vector is also a consideration. The

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prodrug should not release a drug that kills the transfection vector prematurely. On the other hand, prodrugs can be used to control the spread and adverse responses because of these new vectors [149, 150]. The released drug should also fulfill a number of criteria. Ideally, it should be active against both dividing and quiescent cells [139, 141, 151]. Examples of drugs fulfilling this requirement are alkylating agents, 6-methylpurine, and 2fluoroadenine [152, 153]. Its cytotoxicity should be as high as possible to overcome potential limitations in prodrug penetration of tumors and in the capacity of the activation mechanism [154]. An example of a highly potent drug is the CC-1065 analog released from seco-CBI-Q galactoside, with an IC50=0.13 nM [155, 156]. Another important parameter for the drug is its half-life in physiologic conditions, which should achieve the right compromise between tumor diffusion and prevention of systemic escape [139, 140, 157]. A suggested suitable half-life varies from many seconds to several minutes [151]. The bystander effect is required for the drug, as only a percentage of tumor cells will be transfected or transduced and will therefore express the activating enzyme. A highly diffusible drug is likely to mount a stronger bystander effect. To both diffuse freely in the interstitial space and to cross the cells membranes, the drug should ideally be a neutral, uncharged compound; however, if a drug is too diffusible and stable, leakage into the general circulation will occur, with corresponding systemic toxicity. Drugs that are acting directly without the requirement for extra endogenous enzymatic activation steps have an advantage in circumventing potential resistance because of low expression of endogenous enzyme. For most GDEPT systems described to date, the enzyme is expressed intracellularly. Consequently, the prodrugs must be able to cross the cell membrane, either by passive diffusion or by active transport. If the expressed enzyme is tethered on the outer membrane of the cells [131, 133] or secreted [131, 134], this requirement is waived. Two basic types of prodrugs have been used in GDEPT: the directly linked prodrugs and the self-immolative prodrugs (pro-prodrugs). The direct prodrugs can be defined as a pharmacologic inactive derivative of a drug, which requires chemical transformation to release the active drug. In terms of anticancer activity, the conversion of the prodrug to an active drug results in a sharp increase in its cytotoxicity. In a directly linked prodrug, the active drug is released directly after the activation process. A self-immolative prodrug can be defined as a compound generating an unstable intermediate which, following the activation process, will extrude the active drug in a number of subsequent steps. The most important feature is that the site of activation is normally separated from the site of extrusion. The activation process remains an enzymatic one. The extrusion of the active drug relies on a supplementary spontaneous fragmentation. Potential advantages of self-immolative

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prodrugs are the possibility of altering the lipophilicity of the prodrugs with minimal effect on the activation kinetics and the possibility to improve unfavorable kinetics of activation because of unsuitable electronic or steric features of the active drug. The range of drugs that can be converted to prodrugs is greatly extended and is unrestricted only by the structural substrate requirements for a given enzyme. Two ways of developing prodrugs for GDEPT have been investigated. The first is based on known prodrugs with various spectra of activity: antiviral (e.g., GCV and acyclovir), antibacterial (e.g., 6-methylpurine-2 -deoxyribonucleoside and 6-thioxanthine), antiparasitic (e.g., allopurinol), antifungal (e.g., 5-FC), antitumoral (e.g., 5 -deoxy-5-fluorouridine, arabinosylcytosine, cyclophosphamide, and ifosfamide), compounds that are known to produce toxic metabolites but are poorly activated in the tumors (e.g., selenomethionine [158], 4-ipomeanol [159]; 2-aminoanthracene [160], and 2, 4-dinitro-5-aziridinyl-benzamide), or drugs for other indications (e.g., paracetamol) [122]. Transduction of the tumor with the corresponding activating enzyme achieves the tumorselective cytotoxic effect. The first generation of enzyme/prodrug systems used wellknown anticancer prodrugs of clinical use. The advantages were that their behavior, kinetics, and pharmacokinetics were known and some of them are approved as drugs by the regulatory authorities, which was useful for establishing the proof of principle that GDEPT systems were working. The second route for the generation and development of enzyme/prodrug systems starts from enzymes with no known prior prodrug substrate. The specificity for the natural substrates and the structural requirements is well researched. The enzyme should catalyze a reaction with distinct substrate specificity, but should allow modifications in certain parts of the substrate without large alteration of the activation kinetics. This site of accepted variability is exploited either to attach a known drug to the enzyme specifier, or to modify the substrate in a convenient way such that it will generate a cytotoxic drug after enzymatic activation. Examples of enzymes for which prodrugs have been designed and synthesized for GDEPT include NR [161, 163], CPG2 [164–168], β-glu [132], β-gal [169], CPA [131], phosphatase [102], tyrosinase [123], and CA [119]. Many prodrugs tailored for GDEPT attempt to take advantage of both types of approach: the prodrug is derived from a known antitumor drug modified to become deactivated and substrate for the foreign enzyme. Examples are anthracyclins, 5-fluorouracil (5-FU), methotrexate, and etoposide prodrugs. Other prodrugs have a more radical design: both the prodrug and the released drug are new entities with respect to clinical use. This category takes advantage of cytotoxic moieties, that cannot be used as systemic drugs because of undesired side effects, but that become relatively nontoxic after suitable dramatization to prodrug. Activation of these prodrugs by the expressed enzyme releases the cytotoxic moiety locally, at the tumor site, minimizing side effects. Examples in this class

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are alkylating agents, pyrrolobenzodiazepine, enedyine, and amino-seco-cyclopropylindole. An alternative strategy to increase the efficiency of the GDEPT systems is the use of synergistic drug combinations based on the understanding of the activation step and the mechanism of action of the released drugs. Addition of compounds with apoptotic properties (butyrate, camptothecin, and hydroxystaurosporine) [170] or hydroxyurea [171] to the HSV-TK/GCV system is reported to produce a synergistic effect. Methimazole, an antithyroid drug, which reduces the P450-reductase gene expression in liver was shown to increase the therapeutic index of cyclophosphamide (CP) in CYP 2B1/CP gene therapy [172]. An important feature of solid human tumors is hypoxia. Use of a bioreductive drug, tyrapazamine, in combination with CYP 2B6/CP was able to increase its efficacy under hypoxic conditions [173]. Another way of tackling hypoxia is the development of hypoxia-induced expression vectors. An example is the development of a hypoxia inducible cytosine deaminase expression vector. The percentage of conversion of 5-FC to 5-FU was 63% in hypoxia versus 13% in normoxia [174]. A different approach to design more efficient GDEPT systems is based on the use of vectors expressing more than one GDEPT enzyme. Several strategies have been based on the use of transgene cassettes encoding for either multiple suicide genes [175–177] or on combinations of suicide genes with cytokine genes [178, 179] or other genes [180].

18.6

The Bystander Effect

The bystander effect in a suicide therapy system can be defined as the cytotoxic effect on nongenetically modified cells after prodrug administration, when only a fraction of the tumor mass is genetically modified to express an activating enzyme [181]. The successes described in GDEPT would surely not be possible without the existence of such an effect. Although the bystander effect is difficult to quantify, particularly in vivo, models have been devised to examine the bystander effect. Other phenomena, for example the immune response, can contribute strongly to the overall effect.

18.6.1

Mechanisms of the Bystander Effect

The prodrugs activated in GDEPT systems can release active drugs that are either diffusible or nondiffusible across cell membranes. In the case of diffusible toxic metabolites formed after prodrug activation and released from dead and dying genetically modified cells, they will spread, according to diffusion laws, within the tumor cell population. This mechanism is postulated for most GDEPT systems such as 5-FU formed from 5-FC; for the metabolites of CP or isofosphamide (IP), aldophosphamide, phosphoramidic mustards, or acrolein; for benzoic acid mustard released from CMDA and for

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6-mercapto-purine (6-MeP), formed from the corresponding deoxynucleoside. The most relevant feature of such a mechanism is that cell-to-cell contact is not required for the killing of untransfected cells either in vitro or in vivo. The bystander effect is relying only on the diffusibility of the active drugs in the tumor interstitium and across the tumor cell membranes and on their cytotoxic potential. A number of examples support this assumption. For purine or pyrimidine nucleoside prodrugs, the toxic metabolites are generally phosphorylated and are therefore not diffusible across cell membranes, so the mechanism of the bystander effect is different. The HSV-TK/GCV system requires cell-to-cell contact to display a bystander effect. The transfer of toxic metabolites from cell-to-cell mainly requires the existence of gap junctional intercellular communications (GJIC), but other mechanisms could also be involved. The GJIC broadly vary among the types of cell lines and are measured using dye (e.g., Lucifer yellow) diffusion through gap junctions. It was demonstrated that the level of GJIC is predictive of the extent of the bystander effect in vitro, whatever the origin of the cancer cell lines. Consistent with this model, a number of reports showed that tumor cells resistant to bystander effect did not show dye transfer from cell-tocell, whereas bystander effect-sensitive tumor cells did [182]. Some exceptions suggest that the bystander effect is not completely mediated by gap junctions, even if cell-to-cell contact is necessary [183]. The bystander effect has been observed in vivo and generally there is a relationship between in vitro and in vivo behavior. The bystander effect in vivo can be enhanced by collateral immune effects. Another effect called the “good Samaritan effect” has been described. This effect refers to the observation that transfected cells can be protected from the active drug, presumably by lowering the concentration of the cytotoxic metabolites through GJIC [184, 185], which could be considered as beneficial because the transgenes will last longer producing more toxic metabolites, thus enhancing the bystander effect. On the other hand, it could be regarded as detrimental, making the eradication of the whole cell population more difficult. In the GDEPT systems involving diffusible metabolites, it is difficult to pinpoint methods to enhance the local transfer of the active drug. One possibility is to express the enzyme extracellularly on the surface of tumor cells. Another way is to release drugs that can cross the cell membrane by active transport. There are a number of options for the improvement of the bystander effect based on the GJIC hypothesis for the GDEPT systems releasing nondiffusible toxic metabolites. It was noticed that the GJIC can be controlled pharmacologically by using dieldrin, a drug that decreases cell-to-cell communication. The dye transfer was diminished and dieldrin inhibited the bystander effect. c-AMP, foskolin, corticoids, carotenoids, and flavanoids (such as flavanone, apigenin, and tangeretin) are able to induce GJIC in vitro. This effect may be cellspecific or connexin-specific. The bystander effect was also induced in vivo with c-AMP and retinoids [182]. Apigenin

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and lovastatin, an inhibitor of HMG-CoA reductase, both upregulate gap junction function and dye transfer in tumors expressing gap junctions [186, 187 ]. In one report studying human lung tumor cell lines of different origins, significant cell killing occurred when only 10% of cells expressed HSV-TK. In this system, GJIC were not apparent from measuring the rapid intercellular transport of Lucifer Yellow, that detect “rapid-transfer” gap junctional communications, although it could be seen by the slow transfer of a different dye, calcein-AM, which measures the “slowtransfer” gap junctions. Neither an inhibitor (1-octanol) nor an enhancer (all-trans retinoic acid) of gap junctions affected the extent of the bystander effect, suggesting either that low levels of gap junctions can produce a maximal bystander effect or that bystander cell killing occurs by other means [183]. GJIC are heavily dependent on the activity of connexins. The type of connexin expressed does not appear to be crucial for the bystander effect because similar results were obtained with cells expressing different types of connexins. It was, however, demonstrated that the transfection of connexin genes into a number of different tumor cells (i.e., PC12 adrenal pheochromocytoma, HT-116 colon carcinoma, N2A neuroblastoma, and C6 glioma) significantly increased the bystander effect for the HSV-TK/GCV system. It was shown that transfection of C × 43-gene in MDA MB 435 breast cancer cell line restores GJIC and that high expression of C × 43 enhanced the bystander effect of the HSV-TK/GCV system. On the other hand, the noncommunicating MDA MB 435 breast cancer cell line triggered a significant bystander effect both in vitro and in vivo with the HSV-TK/GCV system, suggesting that mechanisms other than GJIC may be involved in the bystander effect [188]. Another explanation may be that the TK-enzyme or the toxic metabolites can be transported by apoptotic vesicles in nontransfected cells. The fact that a bystander effect can be induced in the absence of apoptotic death in hepatocarcinoma cells and that the transfer of GCV-TP occurs before apoptotic degeneration opposes this assumption [189]. Phagocytosis of material from dying TK-positive cells (e.g., hydrolases or other lytic enzymes) to bystander cells has also been suggested as a mechanism for the bystander effect. Apoptosis was detected in bystander cells and it was found that this event could be inhibited by Bcl2 expression. During the apoptosis induction period, in bystander cells cocultured with HSV-TK expressing cells, no phagocytosis was observed. It has also been suggested that killing of tumor cells by apoptosis could heighten the immune response to wild-type tumor cells by a priming effect.

18.6.2

Immune Effects (in GDEPT)

It is generally accepted that the immune response improves the efficacy of GDEPT systems in vivo. Several lines of evidence strengthen this view. The first is that although the bystander effect has been observed in immunocompromised animals,

18. Suicide Gene Therapy

a number of data suggest that the bystander effect in vivo is mediated largely through the release of cytokines [190] and therefore GDEPT systems are more efficient in immunocompetent animals. The second is the existence of the “distant bystander effect.” A distant bystander effect has been reported in a number of situations when the tumor is anatomically separated with no possible metabolic cooperation, were both inhibited after suicide gene therapy was administered only to one tumor [3, 191]. An immune related response has been proposed to explain this effect. Conflicting opinions exist because previous reports described the occurrence of the distant bystander effect in immunodeficient animals. A new model was devised by implanting colorectal tumor cell in two lobes of the liver, followed by HSV-TK/GCV therapy to only one tumor. After GCV administration, the distant tumors regressed partially or totally, the distant bystander effect being observed in 92% of animals. This study clearly demonstrated that the distant bystander effect was because of an immune response [191]. The third line of evidence is given by the cotransfection of both suicide genes and immune enhancing genes. Therapies using both a suicide gene and the GM-CSF or IL gene proved to be more effective compared with the suicide gene therapy alone. A HSV-TK suicide gene therapy system in conjunction with GM-CSF was administered in BALB/c mice bearing M-26 colon carcinoma, followed by GCV administration. Although there were no differences in the size of tumors compared with HSV-TK/GCV alone, tumors regrew only in mice receiving the TK/GCV-therapy alone. Such combined systems are also able to induce complete or partial resistance to a tumor rechallenge [192]. Higher efficacy as well as antimetastatic activity was shown by the same HSV-TK+GM-CSF/GCV system in a model of metastatic breast cancer [193]. Similar observations were reported for the CD/5-FC system. It was found that intraperitoneal administration of AdSCF/AdGM-SCF in mice bearing CT26 colon adenocarcinoma followed by treatment with AdCF/5-FC could suppress tumor growth and prolong the survival period [194]. It was suggested that some drugs released during suicide gene therapy in vivo could produce tumor necrosis and an inflammatory response, which may break down the immunologic isolation and elicit an immune response [195]. Such drugs may have a definite advantage in comparison with those inducing apoptosis. It was believed that for the CYP2B1/IF system, the phosphoramide mustard resulting after activation causes DNA cross links inducing cell death by apoptosis. A study showed a necrotic mechanism of cell death, which may have important implications for the activation of the immune system [196].

18.7

Clinical Evaluation

Gene therapy is a rapidly expanding area. Its progress can be measured in the number of clinical trials generated because its

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conception in the late 1980s. To date, a total of 1,192 protocols have been reported from which 743 phase-1 trials (62.3%), 242 phase 1/2 trials (20.3%), 169 phase-2 trials (14.2%), 12 phase 2/3 trials (1%), and 26 phase-3 trials (2.2%). From these protocols, 797 (67%) addressed cancer diseases, from which 94 (7.9%) were designed for suicide gene therapy (www. wiley.co.uk/genmed/clinical). Clinical trials with ONYX-015 showed that this replicating viral vector is generally well tolerated at doses up to 2 × 1012 particles by intrathecal, intraperitoneal, and intravenous administration. Viral replication was tumor-selective and also transient (<10 days) [106]. A number of clinical trials have different suicide gene therapies. An important consideration are the side effects of the different components. These may be elicited by the vectors, the enzyme, and/or the prodrugs. Clinical phase-1, phase-2, and phase-3 trials are ongoing with HSV-TK/GCV, CD/5-FC, CYP450-CP, and NR/CB1954. A number of recent reviews analyzed the progress made in clinical trials for carcinoma of the breast [197], brain tumors [198], gynecologic cancers [199], gastrointestinal tumors [200], and malignant glioma [201] and report the latest progress in this area.

18.8

Conclusions

Major improvements are needed in vector design to enhance the targeting and delivery of suicide genes. Multiple options are available, including nonviral vectors, more complex systems involving coexpression of suicide genes with immunologic or tumor-suppressor genes, and selectively replicating viruses. The combination of GDEPT with radiotherapy or immunotherapy has been investigated. Such approaches may involve either a sequential treatment schedule (GDEPT/radiation therapy or GDEPT/immunotherapy) or the transfection of suicide gene(s) together with genes able to increase the sensitivity of the tumors to radiation or enhance the potential of the host immune system with cytokine genes. GDEPT systems have already shown efficacy in vivo. Future developments in this technology should use mutagenesis to obtain more efficient activation of a given prodrug, or to adapt the active site so that it binds better to prodrugs that are not substrates for endogenous enzymes. The prodrugs, too, could be redesigned to create better substrates for the enzymes, to maximize drug release or the bystander effect to take advantage of self-immolative strategies of activation, or to allow the active drug to accumulate more readily in tumor cells. The simultaneous release of active drugs that can act by different mechanisms, leading to a synergistic effect on tumor cells and the design of more effective new types of prodrugs is another way to progress. Modalities to enhance and to control the bystander effect, particularly if cell-permeable and

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cell-impermeable active metabolites can be released together, may be useful to improve the therapies. The occurrence of resistant populations is less likely for drugs with different mechanisms of action. In conclusion, major improvements to the vectors and the enzyme/prodrug systems have already occurred. The focus for the future is to combine these aspects into one effective therapy.

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381 Sustained antitumor immunity prolongs animal survival. Cancer Res. 1996;56:3758–3762. 179. Toda M, Martuza RL, Rabkin SD. Combination suicide/cytokine gene therapy gene therapy as adjuvants to a defective herpes simplex virus-based cancer vaccine. Gene Ther. 2001;8:332–339. 180. Zhang J-H, Wan M-X, Pan B-R, Yu B. (2006) Cytotoxicity of HSVtk and hrTNF-a fusion genes with IRES in treatment of gastric cancer. Cancer Lett. 2006;235:191–201. 181. Huber BE, Austin EA, Richards CA, Davis ST, Good SS. Metabolism of 5-fluorocytidine to 5-fluorouracil in human colorectal tumor cells transduced with the cytosine deaminase gene: Significant antitumor effects when only a small percentage of tumor cells express cytosine deaminase. Proceed Natl Acad Sci USA. 1994;91:8302–8306. 182. Mesnil M, Yamasachi H. Bystander effect in herpes simplex virus-thymidine kinase/ganciclovir cancer gene therapy: Role of gap-junctional intercellular communications. Cancer Res. 2000;60:3989–3999. 183. Imaizumi K, Hasegawa Y, Kawabe T, et al. Bystander tumoricidal effect and gap junctional communication in lung cancer cells. Am J Respir Cell Mol Biol. 1998;18:205–212. 184. Andrade-Rosental AF, Rosental R, Hopperstad MD, et al. Gap junctions: The “kiss of death” and the “kiss of life”. Brain Res Rev. 2000;32:308–315. 185. Wygoda MR, Wilson MR, Davis MA, Trosko JE, Rehemtulla A, Lawrence TS. Protection of herpes simplex virus thymidine kinase-transduced cells from ganciclovir-mediated cytotoxicity by bystander cells: The Good Samaritan effect. Cancer Res. 1997;57:1699–1703. 186. Touraine RL, Ishii-Morita H, Ramsey WJ, Blaese RM. The bystander effect in the HSVtk/ganciclovir system and its relation to gap junctional communication. Gene Ther. 1998;5:1705–1711. 187. Touraine RL, Vahanian N, Ramsey WJ, Blaese RM. Enhancement of the herpes simplex virus thymidine kinase/ganciclovir bystander effect and its antitumor efficacy in vivo by pharmacologic manipulation of gap junctions. Human Gene Ther. 1998;9:2385–2391. 188. Grignet-Debrus C, Cool V, Baudson N, Velu T, Calberg-Bacq C-M. The role of cellular- and prodrug-associated factors in the bystander effect induced by the Varicella zoster and Herpes simplex viral thymidine kinases in suicide gene therapy. Cancer Gene Ther. 2000:7:1456–1468. 189. Grignet-Debrus C, Cool V, Baudson N, et al. Comparative in vitro and in vivo cytotoxic activity of BVaraU against tumor cells expressing either the Varicella zoster or the Herpes simplex virus thymidine kinase. Cancer Gene Ther. 2000;7:215–223. 190. Ramesh R, Marrogi AJ, Munshi A, Abboud CN, Freeman SM. In vivo analysis of the “bystander effect”: A cytokine cascade. Exp Hematol. 1996;24:829–838. 191. Agard C, Ligeza C, Dupas B, et al. Immune-dependent distant bystander effect after adenovirus-mediated suicide gene transfer in a rat model of liver colorectal metastasis. Cancer Gene Ther. 2001;8:128–136. 192. Jones RK, Pope IM, Kinsella AR, Watson AJM, Christmas SE. Combined suicide and granulocyte-macrophage colonystimulating factor gene therapy induces complete tumor regression and generates antitumor immunity. Cancer Gene Ther. 2000;7:1519–1528. 193. Majumdar A, Zolotorev A, Samuel S, et al. Efficacy of herpes simplex virus thymidine kinase in combination with cyto-

382 kine gene therapy in an experimental metastatic breast cancer model. Cancer Gene Ther. 2000;7:1086–1099. 194. Cao X, Huang X, Ju DW, Zhang WP, Hamada H, Wang J. Enhanced antitumoral effect of adenovirus-mediated cytosine deaminase gene therapy by induction of antigen-presenting cells through stem cell factor/granulocyte macrophage colony-stimulating factor gene transfer. Cancer Gene Ther. 2000;7:177–186. 195. Rivas C, Chandler P, Melo JV, Simpson E, Apperley JF. Absence of in vitro or in vivo bystander effects in a thymidine kinase-transduced murine T-lymphoma. Cancer Gene Ther. 2000;7:954–962. 196. Karle P, Renner M, Salmons B, Gunzburg WH. Necrotic, rather than apoptotic death caused by cytochrome P450-activated ifosfamide. Cancer Gene Ther. 2001;8:220–230.

S. Schepelmann et al. 197. Stoff-Khalili MA, Dall P, Curiel DT. Gene therapy for the carcinoma of the breast. Cancer Gene Ther. 2006;13: 633–647. 198. Cutter JL, Kurozumi K, Chiocca EA, Kaur B. Gene therapeutics: The future of brain tumor therapy? Expert Rev Anticancer Ther. 2006;6:1053–1064. 199. Brooks RA, Mutch D. Gene therapy in gynecological cancer. Expert Rev Anticancer Ther. 2006;6:1013–1032. 200. Lohr M. Gene therapy for gastrointestinal tumors. Zeitschrift Gastro. 2006;44:333–340. 201. Barzon L, Zanusso M, Columbo F, Palu G. (2006) Clinical trials of gene therapy, virotherapy, and immunotherapy for malignant tumors. Cancer Gene Ther. 2006;13: 539–554.

Chapter 19 Genotypes That Predict Toxicity and Genotypes That Predict Efficacy of Anticancer Drugs Rosario García-Campelo, Miquel Tarón, Itziar De Aguirre, Pedro Méndez, and Rafael Rosell

19.1

Introduction

Lung cancer is the leading cause of death from cancer in most industrialized countries [1]. Most patients are diagnosed with advanced disease and median survival remains poor with ranges between 8 and 10 months. Cisplatin combination chemotherapy is the standard of care for patients with advanced disease, but little progress has been made in the clinical outcome of this population during the last 20 years [2, 3]. Traditional oncology still targets the broad patient population, but a better understanding of lung cancer at the molecular level will allow an individualized approach using customized chemotherapy or targeting specific traits of a particular tumor. In this setting, pharmacogenomics can be used to predict drug response and clinical outcomes to reduce adverse events and to determine the dosage of drugs [4], helping to prolong time to progression and survival through tailored chemotherapy. DNA repair is a very complex system of defense, and deficiencies in this system likely lead to the development of cancer. DNA repair capacity is genetically determined and modulates lung cancer susceptibility and treatment response [5]. Suboptimal DNA repair capacity has been associated with increased risk of lung cancer and DNA repair capacity has been found to be significantly lower in women, younger patients, and people who have never smoked [6, 7]. On the other hand, effective DNA repair capacity may be associated with poor survival in patients with lung cancer who were treated with chemotherapy [8]. Several major DNA repair pathways involve different DNA repair genes (Fig. 19-1), whose products have been linked to recognition and repair of damaged DNA [9]. DNA repair pathways are a common denominator in cisplatin resistance as well, because cisplatin exerts its toxicity through the formation of intrastrand adducts and interstrand cross-links. Nucleotide excision repair (NER) has been strongly linked to cisplatin resistance [10] and it coordinates the activity of >20 enzymes to exert its repair activity [11]. NER removes many DNA helix-distorting lesions, including cisplatin and ultraFrom: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

violet (UV)-induced photo products [12]. Inherited defects in the NER process cause serious clinical consequences, such as xeroderma pigmentosum, or Cockayne syndrome [13]. Differential expression levels in some of the repair genes involved in cisplatin resistance may provide differential chemosensitivity in patients with lung cancer who are treated with cisplatinbased combination.

19.2 Using mRNA Expression Profiling of DNA Repair Genes to Determine Anticancer Drug Efficacy: Potential Markers of Response to Chemotherapy 19.2.1

ERCC1 and Cisplatin Resistance

Excision repair cross complementing 1 (ERCC1) has a critical role in the NER pathway. ERCC1, together with its XP group F (XPF) partner, is responsible for the 5′ side incision of the damage strand at the phosphodiester bonds between nucleotides 22 and 24 [14]. As cisplatin-induced intrastrand adducts are primarily repaired by NER genes, high ERCC1 levels are associated with increased removal of these adducts and relative cisplatin resistance [15]. It has been reported that those resected and chemonaive patients with nonsmall-cell lung cancer (NSCLC) with high ERCC1 expression had significant better survival compared with patients with low expression levels [16], suggesting that a functional repair system confers less aggressive tumors with better outcome and decreased risk of relapse in the setting of resected lung cancer. The potential use of ERCC1 mRNA expression as a predictive marker of activity of cisplatin has been an important area of research. High tumor tissue levels of ERCC1 mRNA in tumors from patients with gastric or ovarian cancer have been associated with cisplatin resistance [17, 18]. ERCC1 mRNA expression is also a predictor of minor histopathologic response to cisplatin-based neoadjuvant radiochemotherapy in patients with esophageal cancer [19]. 383

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DNA repair systems Cancer

Mutations

DNA damage

Replication errors

Replication

Genomic instability

Persistent DNA damage

DNA repair

Aging MMR BER TC-NER GC-NER XRCC1 BRCA1 OGG1 CSA CSB

HRR OSR

ERCC1 BRCA1 MGMT XPD XRCC3 RRM1 RAD51

Fig. 19-1. DNA repair systems.

In patients with metastatic colorectal cancer who were treated with oxaliplatin/ 5-fluorouracil (5-FU), high levels of ERCC1 significantly correlated with poor response and shorter survival [20]. In 56 patients with lung cancer treated with gemcitabine/ cisplatin, ERCC1 expression was assessed [21]. Impressive and significant differences were observed in median survival (15 months for patients with low levels of ERCC1 versus 5 months for patients with high levels, p = 0.009). Cox multivariate analysis showed that ERCC1 expression was an independent predictive variable. In a Spanish Lung Cancer Group (SLCG) trial, a strong correlation, although not statistically significant, was found between low ERCC1 expression levels in pretreatment tumor biopsies from gemcitabine/cisplatintreated patients with advanced NSCLC and median survival (13.7 months for patients with low levels and 9.5 months for those with high levels, p = 0.190) [22]. Based on these promising clinical data, the SLCG initiated the Genotypic International Lung Trial (GILT), a prospective, randomized, multicenter phase-3 trial to assess the role of ERCC1 gene transcripts as a predictive marker of chemotherapy response in patients with stage IV NSCLC who had available tumor biopsy samples. RNA was isolated from pretreatment biopsies, and quantitative real-time reverse-transcription polymerase chain reaction (RT-PCR) assays were done to determine ERCC1 mRNA expression. Results were available in 8 days. Patients were randomly assigned in a 1:2 manner to a control group (A) and they received docetaxel/cisplatin or to the genotypic group (B), where patients with low levels of ERCC1 received docetaxel/cisplatin (B1) and patients with high levels received docetaxel/gemcitabine (B2) (Fig. 19-2). Preliminary results from 264 patients showed that the response rate for patients with low ERCC1 levels was 56.6% whereas for patients in the control group, it was 40.4% (p = 0.02). When patients in the control group were categorized according to the ERCC1

R A N D O M I Z E

Control arm docetaxel / cisplatin GENOTYPE B1 docetaxel / cisplatin (low ERCC1 mRNA)

Experimental arm ERCC 1 levels

GENOTYPE B2 docetaxel / gemcitabine (high ERCC1 mRNA)

Fig. 19-2. GILT trial design. Patients were randomly assigned in a 1:2 ratio to control group (A) or to the genotypic groups, where patients with low levels of ERCC1 receive docetaxel/cisplatin (B1) and those with high levels receive docetaxel/gemcitabine (B2).

levels, those with low levels had a response rate of 47.3%, whereas those with high levels had a response rate of 26.1% (Table 19-1). No major differences in toxicity according to chemotherapy arm, although patients with low ERCC1 levels had greater neurotoxicity (Table 19-2) [23]. Olauseen et al. [24] reported the results of ERCC1 expression using immunohistochemical analysis in the IALT (International Adjuvant Lung Cancer Trial) trial, which compared adjuvant cisplatin-based chemotherapy with observation among patients with resected NSCLC. Seven hundred sixty-one tumors were analyzed and ERCC1 expression was positive in 335 (44%) and negative in 426 (56%). A benefit for cisplatin-based adjuvant chemotherapy was associated with the absence of ERCC1 expression (p = 0.009). Adjuvant chemotherapy, compared with observation, significantly prolonged survival among patients with ERCC1-negative tumors (adjusted hazard ratio for death, 0.65; 95% CI, 0.50–0.86;

19. Genotypes That Predict Toxicity and Genotypes That Predict Efficacy of Anticancer Drugs Table 19-1. Response according to ERCC1 levels in the GILT Trial. Control

Genotype

A1 Low A2 High B1 Low ERCC1 (n/%) ERCC1 (n/%) ERCC1 (n/%) CR PR CR + PR (95% CI) SD PD

55 (100%) 4 (7.3%) 22 (40%) 26 (47.3%) (40.7–53.8) 23 (41.8%) 6 (10.9%)

23 (100%) 1 (4.3%) 5 (21.7%) 6 (26.1%) 18.2–33.9) 13 (56.5%) 4 (17.4%)

B2 High ERCC1 (n/%)

99 (100%) 1 (1%) 55 (55.6%) 56 (56.6%) 51.7–61.4) 32 (32.3%) 11 (11.1%)

61 (100%) 2 (3.3%) 21 (34.4%) 23 (37.7%) (31.8–43.6) 26 (42.6%) 12 (19.7%)

CI, confidence interval; CR, complete response; PD, progressive disease; PR, partial response; SD, stable disease

Table 19-2. Toxicity in docetaxel/cisplatin-treated patients according to ERCC1 mRNA levels. Odds ratio of grades 3 and 4 toxicities. Toxicity Hematologic Gastrointestinal Neurologic

ERCC1 Level (Group)

N (%)

Low (A1 + B1) High (A2) Low (A1 + B1) High (A2) Low (A1 + B1) High (A2)

54 (34.8%) 8 (34.7%) 28 (18.1%) 4 (17.4%) 38 (24.5%) (13.0%)

OR (95% CI) P value 1.0 (0.4–2.5) 1.0 (referent) 1.1 (0.3–3.3) 1.0 (referent) 2.2 (0.6–7.7) 1.0 (referent)

0.99 0.94 0.23

p = 0.002). The 5-year survival rates among patients with ERCC1-negative tumors were 47% in the chemotherapy group and 39% in the control group. Median overall survival was 14 months longer in the adjuvant chemotherapy group (56 months) than in the control group (42 months). Diseasefree survival was also longer in the chemotherapy group among patients with ERCC1-negative tumors than in the control group (adjusted hazard ratio for recurrence or death, 0.65; 95% CI, 0.50–0.85; p = 0.001). Among those patients with ERCC1-positive tumors, no significant difference in survival was seen between the adjuvant chemotherapy group and the control group. Other genes could be examined in conjunction with ERCC1. The human myeloid zinc finger (MZF1) functions as a transcription repressor regulator of ERCC1 transcription in response to cisplatin-induced DNA damage [25]. MZF1 mRNA is constitutively expressed in human ovarian cancer cells and quantitative PCR in these cells showed that MZF1 mRNA was decreased with exposure to cisplatin [26].

19.2.1 BRCA1 as a Predictive Marker for Platinum and Antimicrotubule Agents BRCA1 is recognized as one of the genes conferring genetic predisposition to breast and ovarian cancer [27]. In sporadic breast or ovarian cancer, in which germ line

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mutations are not identified, BRCA1 expression is reduced in approximately one third of patients [28]. The decrease in BRCA1 function is associated with the conversion to a malignant phenotype [29]. Aberrant methylation of the BRCA1 promoter may be the other mechanism of transcriptional inactivation of BRCA1 in sporadic mammary carcinogenesis [30]. BRCA1 is also a component of multiple repair pathways, and it can function as a sensor of DNA damage interacting with Rad51, activating homologous recombination repair (HRR) [31]. It is also implicated in transcription-coupled NER TC-NER [32] pathway. BRCA1 is directly related to apoptosis and abrogates the apoptotic phenotype induced by a range of DNA-damaging agents, such as cisplatin, etoposide, and bleomycin, and induces dramatic responses to antimicrotubule agents including taxanes and vinorelbine [33–35]. Egawa et al. showed in patients with locally advanced or recurrent breast cancer, a nonstatistically significant trend of lower expression of BRCA1 mRNA being associated with increased sensitivity to docetaxel [36]. The SLCG conducted one of the first studies in NSCLC, where 55 patients with resected NSCLC were treated with neoadjuvant gemcitabine/cisplatin chemotherapy. Median survival was significantly higher for patients with the lowest BRCA1 mRNA levels and it was very poor in patients with the highest levels [37]. These findings suggest that BRCA1 levels could identify patients who are candidates for treatment with noncisplatin–containing regimens but also patients who are candidates for treatment with antimicrotubule treatment as well.

19.2.2

Ribonucleotide Reductase

Ribonucleotide reductase catalyzes the rate-limiting step of deoxyribonucleotide formation [38], essential for DNA synthesis and repair. Ribonucleotide reductase plays a role in DNA repair, increasing DNA repair efficiency through activation of the RRM1/p53R2 DNA damage repair complex [39]. The specific role of RRM1 as predictor of response to specific drugs such as gemcitabine, may be explained because the gemcitabine antiproliferative activity is dependent on DNA incorporation into one of its triphosphate metabolite (gemcitabine-triphosphate) and subsequent function as a DNA synthesis chain terminator [40]. Overexpression of ribonucleotide reductase has been described as a mechanism of resistance to 2,2-difluorodeoxycytidine in the human oropharyngeal cancer KB cell lines, in variants of the human erythroleukemic line K562, as well as in NSCLC cell lines [41–43]. RRM1 expression was assessed in 77 patients with resectable NSCLC. Patients with high levels of expression lived longer and had disease recurrence later than patients with low levels of expression [44]. The predictive value of RRM1 mRNA expression as predictor of response to chemotherapy was analyzed in 75 patients with NSCLC who were treated with gemcitabine/cisplatin, vinorelbine/cisplatin, or paclitaxel/carboplatin [45]. In the gemcitabine/cisplatin group, low RRM1 mRNA levels conferred

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a highly significant effect on survival, 15.5 months for those patients with low RRM1 expression levels and 6.8 months for those with high levels (p = 0.0028) as well as an effect on time to progression (i.e., 7.6 months for patients with low RRM1 levels and 4.3 months for patients with high levels; p = 0.05) [46]. RRM1 and ERCC1 were analyzed in 100 patients with NSCLC who where part of a large randomized trial comparing gemcitabine/cisplatin versus gemcitabine/cisplatin/vinorelbine versus gemcitabine/vinorelbine, followed by vinorelbine/ ifosfamide [47]. In the gemcitabine/cisplatin cohort, patients with low RRM1 mRNA expression levels had longer median survival than patients with high levels. Median survival was longer among gemcitabine/cisplatin-treated patients with low mRNA expression levels of both RRM1 and ERCC1, than among patients with high levels of both genes [48]. Results have been reported on RRM1 expression as a predictor of response to gemcitabine in 30 patients with locally advanced NSCLC who were treated with induction chemotherapy based on gemcitabine plus carboplatin. A significant inverse correlation (p = 0.013; r = −0.474) was seen between RRM1 expression and disease response. Grouping patients as responders or nonresponders, RRM1 expression was significantly associated with response (p = 0.027) [49].

19.3 Single Nucleotide Polymorphisms in DNA Repair Genes The most common sequence variation in the human genome is the stable substitution of a single base, the single nucleotide polymorphism (SNP). SNPs arise because of point mutations that are selectively maintained in populations. Most SNP are silent and do not alter the function or expression of a gene [50]. SNP may also have a role in molecular cancer epidemiology: Forty-six SNPs in 39 cancer-related genes from 166 case-controlled molecular epidemiologic studies have been described. SNPs altering the conserved amino acids are more likely to be associated with cancer susceptibility, and most of these SNPs are those involved in DNA repair, metabolism, and cell-cycle checkpoints [51]. Polymorphic variants in DNA repair genes may explain interindividual differences in survival in patients with NSCLC treated with cisplatin. Polymorphisms conferring suboptimal DNA repair in tumors could lead to more biologically aggressive tumors, and these same could favorably influence response to platinum agents through inefficient removal of platinum adducts.

19.3.1

XPD SNP

XPD, also known as excision repair cross complementing 2 (ERCC2), is involved in NER and basal transcrip-

tion repair (BER) pathways. The basal transcription factor TFIIH, contains two helicases, XPB and XPD, which open a 30-base-long segment around the DNA damage area [52]. Rare XPD mutations diminish NER capacity, resulting in increased risk of skin cancer, due to high sensitivity to UV light, leading to different autosomal recessive syndromes like xeroderma pigmentosum group D, Cockayne syndrome, and trichothiodystrophy [53]. XPD SNPs have been associated with altered DNA repair capacity, increasing the risk of cancer [54]. The most commonly studied polymorphisms are the XPD Lys751Gln (exon 23) and Asp312Asn (exon 10). Effects of XPD SNPs on risk of carcinoma remain unclear and although findings (not statistically significant) have been reported, interesting results were observed in some studies. The largest study was a patient-control study (1,092 lung cancer patients, 1,240 control patients); the authors reported an overall adjusted overall response (OR) of 1.47 (95% CI, 1.1–2.0) for the Asp312Asn polymorphism (Asn/Asn versus Asp/Asp); and 1.06 (95% CI, 0.8–1.4) for the Lys751Gln polymorphism (Gln/Gln versus Lys/Lys). Gene:tobacco-smoking interaction analyses revealed that the adjusted OR for each of the two polymorphisms decreased significantly as pack-years increased. A stronger gene-tobacco-smoking interaction was observed for the Asp312Asn polymorphism than for the Lys751Gln polymorphism, suggesting that cumulative cigarette smoking modifies the association between XPD polymorphism and lung cancer risk [55]. Similar results were reported by Hou et al. [56] in 185 patients with lung cancer matched with 162 control patients. The presence of one or two variant alleles was associated with increased risk for lung cancer among never-smokers only, in particular, younger (< 70 years) never-smokers (OR, 2.6) for exon 10, and OR 3.2 for exon 23. SNP in coding regions of DNA repair genes may contribute to individual variations to platinum compounds. In patients with colorectal cancer who were treated with oxaliplatin/5fluouracil (5-FU), responses were higher in the Lys751Ly2 genotype group, with a median survival of 17.4 months, compared with 12.8 months for patients with Gln715Gln genotype (p = 0.002). The polymorphism Asp312Asp had no effect on survival or response [57]. XPD Asp312Asn SNP was analyzed in 103 patients with stage III and stage IV NSCLC. Genotypes were not associated with stage, but individuals with the wild-type genotype (Asp/ Asp) had a median survival of 16.3 months, quite similar to the heterozygote (Asp/Asn) with median survival of 15.2 months, whereas patients with the homozygote variant genotype (Asn/ Asn) had a median survival of 6.6 months (p = 0.003). In the analysis stratified by stage, the association between XPD and overall survival seemed to be stronger in stage III than in stage IV patients [58]. De Las Peñas et al. [59] published the results of XPD SNP in 135 patients with advanced NSCLC who were treated with cisplatin/gemcitabine. Carriers of XPD Ln751Gln had a

19. Genotypes That Predict Toxicity and Genotypes That Predict Efficacy of Anticancer Drugs

higher risk of death than carriers of LysGln (HR 2.02; 95% CI 0.99–4.09; p = 0.05).

19.3.2

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one ERCC1 8092A allele have a significantly increased risk of grade 3 or 4 gastrointestinal toxicity (AOR, 2.33; 95% CI, 1.07–5.05; p = 0.03) [65].

ERCC1 SNP

Two common SNP of ERCC1, at codon 118 C/T and C8092A are well recognized. It has been reported in a case-control study (1,752 patients with lung cancer and 1,358 control patients) that no overall association existed between ERCC1 polymorphisms and lung cancer risk. However, stratified analyses revealed that the adjusted odds ratios (AOR) for the C8092A polymorphism (A/A versus C/C) decreased significantly as pack-years increased, with the AOR of 2.11 (95% CI, 1.03–4.31) in patients who never smoked and 0.50 (95% CI, 0.25–1.01) in patients who were heavy smokers. These results strongly support the role of ERCC1 C8092A polymorphism that may modify the associations between cumulative cigarette smoking and lung cancer risk [60]. The codon 118 C/T has been found to be associated with shorter overall survival in patients with advanced colorectal cancer who are treated with platinum-based chemotherapy [61]. In 128 patients with advanced NSCLC who were treated with platinum-based chemotherapy, there was a significant association between C8092A SNP and overall survival, with median survival times of 22.3 (C/C) and 13.4 (C/A or A7A) months, respectively (p = 0.006), suggesting that any copies of the A allele were associated with poor outcome. No statistical association was found for the codon 118 polymorphism and overall survival (p = 0.41) [62]. In NSCLC as well, longer survival was observed for patients who were stage IV carriers of ERCC1. One hundred eighteen patients with 118 CC were treated with cisplatin/docetaxel and were compared with carriers of CT or TT genotypes. Median survival was 9.67 months for 34 patients with ERCC1 118C/T, 9.74 months for 17 patients with T/T, and not reached for 11 patients with C/C (p = 0.004) [63]. The largest study reporting the effect of ERCC1 SNP in patients with NSCLC was presented by Tarón et al. ERCC1 SNP (118 C/T and C8092A) were analyzed in 706 patients with stage IV NSCLC who were treated with cisplatindocetaxel combination. No significant differences in outcome were observed according to ERCC1 118 C/T SNP. The uncommon ERCC1 8092A/A genotype predicted poor survival in patients with advanced NSCLC: median survival of 7.2 months compared with 9.3 months for patients with the C/C genotype, and 10.2 months for the C/A genotype respectively (p = 0.05). The association between C8092A and median survival was stronger for patients with a performance status of 0 [64]. Regarding toxicity, ERCC1 C8092 SNP may predict gastrointestinal toxicity in patients with NSCLC who are treated with platinum-based chemotherapy. Patients carrying at least

19.3.3

XRCC3 SNPs

The X-ray repair cross-complementing group 3 (XRCC3) gene belongs to the homologous recombination pathway, with repairs the DNA double-strand breaks induced by chemotherapy. The polymorphism in codon 241 (Thr to Met) of XRCC3 has been associated with the level of bulky DNA adducts in leukocytes of healthy subjects [66]. Carriers of XRCC3 241 MetMet have higher levels of DNA adducts regardless of smoking status (mean levels: MetMet 11.44, ThrMet 7.69, ThrThr 6.94; p = 0.04) (66). De las Peñas et al. (59) reported the results of the analysis of 14 polymorphisms in 13 genes in 135 patients with advanced NSCLC who were treated with gemcitabine plus cisplatin. No other polymorphism in any of the genes included in the study except XRRC3 was significantly related to survival. Median survival was significantly increased in those patients harboring XRCC3 241MetMet polymorphism (16 months versus 10 months for patients with ThrMet and 14 months for those with ThrThr, p = 0.001). XRCC3 SNP were assessed in 878 patients with advanced NSCLC who were treated with different cisplatin combinations (162 treated with gemcitabine/cisplatin and 716 treated with docetaxel/cisplatin). Median survival in younger patients (aged <55 years) was not reached in patients with XRCC3 241 MetMet genotype treated with gemcitabine/ cisplatin and median survival was 9.2 months for patients treated with docetaxel/cisplatin (p = 0.02), which translated into a 60% difference in survival in 2 years. This survival benefit disappeared in older patients, as expected, due to the interaction between age and DNA repair capacity. The results suggest that younger patients with MetMet genotype might benefit from other combinations, such as cisplatin/ etoposide [67].

19.4

Conclusions

Currently available drugs show limited efficacy in the treatment of patients with advanced NSCLC, with no few adverse reactions because of the failure to correct population selection. Genetic information on DNA repair SNP and DNA repair gene expression have been found in patients with NSCLC that can be incorporated as efficient markers of response to standard chemotherapy regimens (Fig. 19-3). Clinical trials based on molecular tumor characteristics would help to clarify and identify the best and most useful predictive markers, leading to the long-awaited individualized treatment approach.

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Low ERCC Ovarian cancer Better response

Low ERCC1 NSCLC Longer survival No differences in response

Low RRM1 Longer survival No differences in response

Dabholkar et al J Clin. Invest

Lord et al CCR

Rosell et al Oncogene

Low RRM1 Better than ERCC1 CC: 0.410 P < 0.001 Rosell et al CCR

2002

2003

2004

1994

2001

Low ERCC1 Colorectal cancer Longer survival No differences in response Shirota JCO

BRCA1 Differential modulator Quinn et al Cancer Res

Low ERCC1 NSCLC Better response Rosell et al ASCO

2005

Low BRCA1 Better than RRM1 CC:0.815 P<0.001 Taron et al Hum Mol Genet

Fig. 19-3. Development of Molecular Assays for Rapid Assessment of Cisplatin Response.

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11. De Boer J, Hoeijmakers JH. Nucleotide excision repair and human syndromes. Carcinogenesis 2000;21:453–460. 12. De Laat WL, Jaspers NG, Hoeijmakers JH. Molecular mechanism of nucleotide excision repair. Genes Dev 1999;13:768–785. 13. Hasty P, Campisi J, Hoeijmakers J, Van Steeg H, Vijg J. Aging and genome maintenance: lessons from the mouse? Science 2003;299:1355–1359. 14. Houtsmuller AB, Rademakers S, Nigg AL, Hoogstrten D, Hoeijmakers J, Vermeulen W. Action of DNA repair endonuclease ERCC1/XPF in living cells. Science 1999;284:958–961. 15. Li Q, Yu JJ, Mu C, et al. Association between the level of ERCC1 expression and the repair of cisplatin-induced DNA damage in ovarian cancer cells. Anticancer Res 2000;20:645–652. 16. Simon GR, Sharma S, Cantor A, Smith P, Bepler G. ERCC1 expression is a predictor of survival in resected patients with nonsmall cell lung cancer. Chest 2005;127:978–983. 17. Metzger R, Leichman CG, Danenberg KD, et al. ERCC1 mRNA levels complement thymidylate synthase mRNA levels in predicting response and survival for gastric cancer patients receiving combination cisplatin and fluorouracil chemotherapy. J Clin Oncol 1998;16:309–316. 18. Dabholkar M, Bostick-Bruton F, Weber C, Bohr VA, Egwuagu C, Reed E. ERCC1 and ERCC2 expression in malignant tissues from ovarian cancer patients. J Natl Cancer Inst 1992;84:1512–1517. 19. Warnecke-eberz U, Metzger R Miyazono F, et al. High specificity of quantitative excision repair cross-complementing 1 messenger RNA expression for prediction of minor histopathological response to neoadjuvant radiochemotherapy in esophageal cancer. Clin Cancer Res 2004;10:3794–3799.

19. Genotypes That Predict Toxicity and Genotypes That Predict Efficacy of Anticancer Drugs 20. Shirota Y, Stoehlmacher J, Brabender J, et al. ERCC1 and Thymidylate Synthase mRNA levels predict survival for colorectal cancer patients receiving combination oxaliplatin and fluorouracil chemotherapy. J Clin Oncol 2001;19:4298–4304. 21. Lord R, Brabender J, Gandara D, et al. Low ERCC1 expression correlates with prolonged survival after cisplatin plus gemcitabine chemotherapy in non-small cell lung cancer. Clin Cancer Res 2002;8:2286–2291. 22. Rosell R, Danenberg KD, Alberola V, et al. Ribonucleotide reductase messenger RNA expression and survival in gemcitabine/cisplatin-treated advanced non-small cell lung cancer patients. Clin Cancer Res 2004;10:1318–1325. 23. Rosell R, Cobo M, Isla I, et al. ERCC1 mRNA-based randomized phase III trial of docetaxel doublets with cisplatin or gemcitabine in stage IV non-small-cell lung cancer. J Clin Oncol 2005;23: Suppl16:A7002.abstract. 24. Olaussen KA, Dunant A, Fourte P, et al. ERCC1 expression and benefit of adjuvant cisplatin-based chemotherapy in patients with resected non-small-cell lung cancer. N Engl J Med 2006;355:983–991. 25. Gaboli M, Kotsi PA, Gurrieri C, et al. Mzf1 controls cell proliferation and tumorogenesis. Genes Dev 2001;15:1625– 1630. 26. Yan QW, Reed E, Zhong XS et al. MZF1 posseses a repressively regulatory function in ERCC1 expression. Biochem Pharmacol 2006;71:761–771. 27. Miki Y, Swensen J, Shattuck-Eidens D. A strong candidate for the breast and ovarian cancer susceptibility gene, BRCA1. Science 1994;266:66–71 28. Thompson ME, Jensen RA, Obermiller PS, Page DL, Holt JT. Decreased expression of BRCA1 accelerates growth and is often present during sporadic breast cancer progression. Nat Genet 1995;9:444–450. 29. Seery LT, Knowlden JM, Gee JM, et al. BRCA1 expression level predicts distant metastasis of sporadic breast cancers. Int J Cancer 1999;84:258–262. 30. Rice J, Ozcelik H, Maxeiner P, Andrulis I, Futscher BW. Methylation of the BRCA1 promoter is associated with decreased BRCA1 mRNA levels in clinical breast cancer specimens. Carcinogenesis 2000;21:1761–1765. 31. Thompson LH, Schild D. Homologous recombinational repair of DNA ensures mammalian chromosome stability. Mutat Res 2001;477:131–153. 32. Gowen LC, Avrutskaya AV, Latour AM, Doller BH, Leadon SA. BRCA1 required for transcription-coupled repair of oxidative DNA damage. Science 1998;281:1009–1012. 33. Quinn JE, Dennedy RD, Mullan PB, et al. BRCA1 functions as a differential modulator of chemotherapy-induced apoptosis. Cancer Res 2003;63:6221–6228. 34. Tassone P, Tagliaferri P, Perricelli A, et al. BRCA1 expression modulates chemosensitivity of BRCA1-defective HCC1937 human breast cancer cells. Br J Cancer 2003;88:1285–1291. 35. Fedier A, Steiner RA, Schwarz VA, Lenherr L, Haller U, Fink D. The effect of loss of BRCA1 on the sensitivity to anticancer agents in p53-deficient cells. Int J Oncol 2003;22:1169– 1173. 36. Egawa C, Miyoshi Y, Takamura Y, Taguchi T, Tamaki Y, Noguchi S. Decreased expression of BRCA2 mRNA predicts favorable response to docetaxel in breast cancer. Int J Cancer 2001;95:255–259.

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390 54. Spitz MR, Wu X, Wang Y, et al. Modulation of nucleotide excision repair capacity by XPD polymorphism in lung cancer patients. Cancer Res 2001;61:1354–1357. 55. Zhou W, Liu G, Miller AP, et al. Gene-environment interaction for ERCC2 polymorphisms and cumulative cigarette smoking exposure in lung cancer. Cancer Res 2002;62:1377–1381. 56. Hou S, Falt S, Angelini S, et al The XPD variant alleles are associated with increased aromatic DNA adduct level and lung cancer risk. Carcinogenesis 2002;23:599–603. 57. Park D, Stoehlmacher J, Zhan W, Tsao-Wei D, Groshen S, Lenz HJ. A xeroderma pigmentosum group D gene polymorphisms predicts clinical outcome to platinum-based chemotherapy in patients with advanced colorectal cancer. Cancer Res 2001;61:8654–8658. 58. Gurubhagavatula S, Liu G, Park S, et al. XPD and XRCC1 genetic polymorphisms are prognostic factors in advanced nonsmall-cell lung cancer patients treated with platinum chemotherapy. J Clin Oncol 2004;22:2594–2601. 59. De las Peñas R, Sanchez-Ronco M, Alverola V, et al. Polymorphisms in DNA repair genes modulate survival in cisplatin/gemcitabine-treated non-small-cell lung cancer patients. Ann Oncol. 2006;17:668–675. 60. Zhou W, Liu G, Park S, et al. Gene-smoking interaction associations for the ERCC1 polymorphisms in the risk of lung cancer. Cancer Epidemiol Biomarkers Prev 2005;14:491–496. 61. Park DJ, Zhang W, Stoehlmacher J, et al. ERCC1 gene polymorphism as a predictor fro clinical outcome in advanced colorectal

R. García-Campelo et al. cancer patients treated with platinum-based chemotherapy. Clin Adv Hematol Oncol 2003;1:162–166. 62. Zhou W, Gurubhagavatula S, Liu G, et al. Excision repair crosscomplementation group 1 polymorphism predicts overall survival in advanced non-small cell lung cancer patients treated with platinum-based chemotherapy. Clinical Cancer Res 2004;10:4939– 4944. 63. Isla D, Sarries C, Rosell R, et al. Single nucleotide polymorphisms and outcome in docetaxel-cisplatin treated advanced non-small-cell lung cancer. Ann Oncol 2004;15:1194–1203. 64. Tarón M, Alverola V, López-Vivanco G, et al. Excision repair cross-complementing group1 (ERCC1) single nucleotide polymorphisms and survival in cisplatin/docetaxel-treated stage IV non-small-cell lung cancer patients: a Spanish Lung Cancer Group Study. J Clin Oncol 2006;24:Suppl18:A7053. 65. Suk R, Gurubhagavatula S, Park S, et al. Polymorphisms in ERCC1 and grade 3 or 4 toxicity in non-small cell lung cancer patients. Clin Cancer Res 2005;11:1534–1538. 66. Matullo G, Palli D, Peluso M, et al. XRCC1, XRCC3, XPD gene polymorphisms, smoking and (32) P-DNA adducts in a sample of healthy subjects. Carcinogenesis 2001;22:1437– 1445. 67. Rosell R, Alberola V, Camps C, et al. Clinical outcome of gemcitabine/cisplatin versus docetaxel/cisplatin treated stage IV non-small-cell lung cancer patients according to X-ray repair cross-complementing group 3 polymorphism and age. J Clin Oncol 2006;24:Suppl18:A7055.abstract.

Chapter 20 A Personal Account of the Chemoprevention of Breast Cancer: Possible or Not Possible? V. Craig Jordan

20.1

Introduction

Thirty-five years ago, the answer to the simple question “is the chemoprevention of breast cancer possible?” would have been “most unlikely.” Thirty-five years ago, there was almost no constructive strategy to prevent breast cancer. This approach to cancer therapeutics was not considered necessary because combination cytotoxic chemotherapy was predicted to cure cancer. Regrettably, this clinical prophecy did not materialize. Nevertheless, the link between estrogen and the development and growth of breast cancer became an established fact throughout the early years of the 20th century. Beatson [1] and Boyd [2] demonstrated that some women with advanced breast cancer responded to oophorectomy but subsequent advances in laboratory investigations would create a new strategy that would transform an idea – i.e., chemoprevention, into a practical reality for thousands of women at high risk for developing breast cancer. Lathrop and Loeb [3] used inbred strains of mice with a high incidence of mammary cancer to demonstrate that early oophorectomy would prevent carcinogenesis. Twenty years later in 1936, Lacassagne, in an address to the American Association for Cancer Research concluded that to prevent breast cancer “a therapeutic antagonist was necessary to stop the congestion of oestrone in the breast” [4]. However, there was no appreciation of a subcellular therapeutic target until Jensen identified the estrogen receptor (ER) [5] as the mediator of estrogen action in estrogen target tissues, including the breast [6]. The story of the practical application of chemoprevention for women at high risk would start with the assembly of apparently unrelated pieces of scientific evidence to develop not one, but ultimately two, approaches to reducing breast cancer incidence. But first, existing compounds needed to be taken from the shelves of the pharmaceutical industry where they had been placed. The so-called nonsteroidal antiestrogens, tamoxifen and raloxifene, were never intended by the pharmaceutical

From: Principles of Molecular Oncology, Third Edition Edited by: Miguel H. Bronchud et al. © Humana Press Inc., Totowa, NJ

scientists to be chemopreventives, and both had failed in their initial applications. Only with the recognition of the potential of a new drug group, the selective estrogen receptor modulators or SERMs [7], would both tamoxifen and raloxifene be reinvented as the first agents to reduce the incidence of breast cancer in targeted populations of high-risk women.

20.2

ICI 46,474 to Tamoxifen

ICI 46,474 started in the Pharmaceutical Division of Imperial Chemicals Industries (ICI) as an agent that was an exceptional postcoital contraceptive in laboratory rats [8, 9]. The fertility control team led by Walpole was tasked with finding molecules that would modulate the reproductive cycle in animals and humans; however, the antifertility properties of tamoxifen in the laboratory would not translate to women. Quite the opposite. Tamoxifen (ICI 46,474) was subsequently developed as a profertility agent in subfertile women; tamoxifen induced ovulation [10, 11]. Tamoxifen also was tested [12, 13] as an agent to treat breast cancer based on the earlier connection between estrogen and breast cancer growth [1, 2]. Initially, the early small clinical studies did not show significant therapeutic advantages over the standard endocrine therapies available in the late 1960s/early 1970s, but tamoxifen had fewer side effects than these products [14, 15]. Overall, the financial prospects for tamoxifen were bleak in the early 1970s [12], but a strategy was put in place [12, 13] that would not only elevate tamoxifen to become the “gold standard” as an endocrine therapy for the treatment of breast cancer throughout the 1980s and 1990s [16], but would also permit tamoxifen to evolve and become the first medicine to reduce the incidence of breast cancer in women without the disease [17]. The work that created a roadmap for the clinical development of tamoxifen as a treatment and chemopreventive in the 1970s was not conducted at ICI’s Pharmaceuticals Division (now AstraZeneca) because the company was not, at that time, focused on research to discover anticancer drugs. A friendship between me and Arthur Walpole (he was the examiner of my PhD on antiestrogens in 1972) resulted in the reinvention of tamoxifen as the first targeted breast cancer drug. This 391

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reinvention was made possible in my laboratory through unrestricted financial support from Walpole, Roy Cotton (the head of the clinical effort with tamoxifen in the United Kingdom), and Lois Trench, the drug monitor for tamoxifen at ICI Americas. Three simple laboratory principles would ultimately translate to clinical practice and improve the prognosis of millions of women with breast cancer: tamoxifen prevented the development of carcinogen-induced rat mammary tumors [18, 19]; ● tamoxifen blocked estrogen binding to the human and animal tumor ER [20–22]; and ● long-term, early treatment of carcinogen-induced rat mammary tumors is superior to short-term late treatment once tumors appear [23–26]. ●

Overall, these principles provide the basis for current practice with endocrine therapy by targeting the patient with an ERpositive tumor and deploying long-term adjuvant antihormone therapy to create optimal antitumor actions [27, 28], and the extension of these principles to chemoprevention in women at high risk [17, 29]. It is recognized that at least 500,000 women are alive today because of long-term tamoxifen therapy targeted to the ER. However, the road to chemoprevention with tamoxifen was not straightforward and tangential laboratory discoveries created a new dimension in therapeutics with the recognition of SERM action [7].

20.3

Recognition of SERM Action

The key to recognizing SERM action was the peculiar pharmacology of tamoxifen in the laboratory mouse. Until the mid-1980s, tamoxifen and its related family members were referred to as nonsteroidal antiestrogens [30] based on their pharmacology in rats and humans. The compounds were partial agonist/antagonists in the rat uterus but antiestrogenic in rat vagina and mammary gland (tumor) [8, 21, 31]. This pharmacology appeared to translate to humans with antiestrogenic actions in the breast [14] but with some estrogen-like actions noted in the vagina [32] and circulating gonadotrophins [33, 34] in postmenopausal women. In contrast, tamoxifen was classified as an estrogen using the standard Doisey vaginal assay [35] and the 3-day mouse uterine weight test [36]. It was the need to address the pharmacology of tamoxifen in the mouse that created an unanticipated opportunity to advance chemoprevention. The change in fashion of cancer research to focus on human disease directly during the 1980s, resulted in abandoning the study of the carcinogen-induced rat mammary tumor model and focusing on human tumors and cell lines transplanted in athymic (immune deficient) mice. The MCF-7 breast cancer cell line has been an invaluable laboratory model to decipher the basic actions of estrogeninduced growth [37, 38]. The cell line can be inoculated into athymic mice and tumors will grow in response to estrogen

administration [39]. Controversy surrounded these observations because the results were believed to be an indirect action of estrogen in vivo [39]. The hypothesis that estrogen activated breast cancer growth through the ER in the breast cancer could not be replicated in vitro. Nevertheless, the athymic model seemed to replicate human disease as tamoxifen administration blocked tumor growth [40]. These technical issues were resolved in the mid 1980s when the Katzenellenbogens discovered that an estrogenic contaminant in culture media was stimulating MCF-7 cells maximally so exogenous estrogen was ineffective [41]. However, it was the realization that tamoxifen was stimulating increases in uterine weight in the athymic mouse while preventing the growth of MCF-7 tumor that led to the idea of target site-specific actions the nonsteroidal antiestrogens [42]. The metabolites of tamoxifen were the same in the human breast tumor and the mouse uterus so the conclusion was that the tissue was interpreting the tamoxifen-ER complex differently in the breast and uterus [42]. These conclusions were further supported by bitransplanting a human ER-positive endometrial carcinoma [43] and an MCF-7 breast tumor in the same athymic mouse [44]. Estrogen caused the growth of both human tumors but tamoxifen blocked only the growth of the breast tumor and enhanced the growth of the endometrial cancer [44]. The spectrum of metabolites was the same in breast and endometrial cancers so the tamoxifen-ER complex was again interpreted differently in human breast and endometrial cancers. The consequences of these laboratory data were profound for women being treated for breast cancer with tamoxifen and, indeed, for the limited future of tamoxifen as a ubiquitous chemopreventive agent in high-risk women. The possibility that tamoxifen could enhance endometrial cancer growth while, at the same time, block estrogenstimulated breast cancer growth [44, 45] was rapidly addressed by the clinical community [46, 48]. It is now established that tamoxifen will enhance the detection of endometrial cancer in postmenopausal women but not in premenopausal women [17, 49]. This important advance, which informed clinicians about an important side effect of tamoxifen, not only improved health care but also focused attention on the development of new strategies to prevent breast cancer. Fortunately, a new strategy was in place to broaden the effectiveness of chemoprevention as a healthcare policy.

20.4 Preparing to Use Nonsteroidal Antiestrogens as Chemopreventive Agents The fact that tamoxifen prevented rat mammary carcinogenesis [18, 19, 26] provided a scientific foundation to translate these data from the laboratory to clinical practice. Another critical piece of clinical data was the observation that adjuvant tamoxifen treatment resulted in a reduction in contralateral breast cancer [50]. However, the accuracy of establishing a

20. A Personal Account of the Chemoprevention of Breast Cancer: Possible or Not Possible?

true high-risk population and the knowledge that the received wisdom of the time was that estrogen was essential to maintain bone density and prevented coronary heart disease (CHD) created a dilemma. Even if tamoxifen prevented breast cancer, the long-term administration of an “antiestrogen” would increase the risk of osteoporosis and CHD in a larger population. The advance would be a step backwards in public health. However, the finding that tamoxifen and its chemical cousin, keoxifene (LY156758), could prevent rat mammary carcinogenesis [51] but at the same time maintain bone density in ovariectomized rats [52] created a new dimension in therapeutics. Most importantly, keoxifene, a drug that had failed to become established as a breast cancer drug because of cross resistance with tamoxifen [53, 54] was less estrogenlike than tamoxifen in the rat uterus [55] and was shown to be less effective than tamoxifen at stimulating the growth of endometrial cancer [56]. Overall, the SERM action of tamoxifen was now a class effect so a new approach to the chemoprevention of breast cancer was possible. The significance of the laboratory findings was immediately recognized. An analog of tamoxifen that was safer could be used to prevent osteoporosis but prevent breast cancer as a beneficial side effect [57, 58]. Keoxifene was about to be reinvented by the Eli Lilly Company as raloxifene, a drug to treat and prevent osteoporosis, and also possibly CHD, but with fewer side effects than tamoxifen. Most importantly, raloxifene was anticipated to display none of the endometrial side effects documented with tamoxifen.

20.5 The Indirect Approach to Breast Cancer Chemoprevention A plan to prevent breast cancer as a public health initiative was initially described at the First International Chemoprevention meeting in New York in 1987 [57]. It is reasonable to simply state the proposal, published from the 1987 meeting and subsequently refined and presented at the annual meeting of the American Association for Cancer Research in San Francisco in 1989 [58]. In 1988, I wrote “The majority of breast cancer occurs unexpectedly and from unknown origin. Great efforts are being focused upon the identification of a population of high risk women to test “chemopreventive” agents. But, are resources being used less than optimally? An alternative would be to seize upon the developing clues provided by an extensive clinical investigation of available antiestrogens. Could analogs be developed to treat osteoporosis or even retard the development of atherosclerosis? If this proved to be true then a majority of women in general would be treated for these conditions as soon as menopause occurred. Should the agent also retain antibreast tumor actions then it might be expected to act as a chemosuppressive on all developing breast cancers if these have an evolution from hormone dependent to hormone independent disease. A bold commitment to drug discovery and

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clinical pharmacology will potentially place us in a key position to prevent the development of breast cancer by the end of this century [57].” The concept was refined by 1990 [58]: “We have obtained valuable clinical information about this group of drugs that can be applied in other disease states. Research does not travel in straight lines and observations in one field of science often become major discoveries in another. Important clues have been garnered about the effects of tamoxifen on bone and lipids so it is possible that derivatives could find targeted applications to retard osteoporosis or atherosclerosis. The ubiquitous application of novel compounds to prevent diseases associated with the progressive changes after menopause may, as a side effect, significantly retard the development of breast cancer. The target population would be postmenopausal women in general, thereby avoiding the requirement to select a high risk group to prevent breast cancer.” This concept [57, 58] is exactly what has been translated to clinical practice: use a SERM (raloxifene) to treat osteoporosis and reduce the incidence of breast cancer as a beneficial side effect.

20.6 The Indirect Approach to Chemoprevention in Practice It is now possible to test this evidenced-based [57, 58] hypothesis by examining clinical studies of raloxifene used to treat osteoporosis while monitoring the impact of breast cancer incidence at the same time. The first proof of principle was noted by Cummings and coworkers [59], but the initial 4-year osteoporosis study with raloxifene referred to as the Multiple Outcomes of Raloxifene Evaluation (MORE) has now been extended out to 8 years in the Continuing Outcomes Relative to Evista (CORE). Martino and coworkers [60] found that in the placebo group (2,576 women with osteoporosis) there were 4.2 cases of breast cancer per 1,000 women per year. In contrast, in the women treated with raloxifene (5,129 women with osteoporosis), there were only 1.4 cases of breast cancer per 1,000 women per year. It is now possible, using these figures, to evaluate the progress made in chemoprevention over the last 20 years by calculating the approximate incidence of breast cancer in women being treated for osteoporosis (Fig. 20-1). It is estimated that 500,000 women are taking raloxifene worldwide [61] so this is a reasonable starting point to discover the potential impact on public health. If 500,000 women use bisphosphonates to treat osteoporosis over a 10-year period, then 21,000 women would develop breast cancer. If these same women had been appropriately treated for osteoporosis during the 1990s, using hormone-replacement therapy (HRT), there would be a significant increase in the incidence of breast cancer based upon the Women’s Health Initiative [62] and the Million Women’s Study [63]. On average, 500,000 women would develop 34,230 breast cancers over a 10-year period. In contrast, if those same women now

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V.C. Jordan

New Breast Cancers 1990’s-2000’s 1990’s

BISPHOSPHONATES

21,000

AVERAGE HORMONE REPLACEMENT THERAPY USE

2000’s RALOXIFENE

34,230

7,000

27,230 FEWER BREAST CANCERS

Fig. 20-1. An estimation of breast cancer incidence in a population of 500,000 postmenopausal women with the same risk for osteoporotic fractures as participants in the CORE trial [60] treated for a ten year period with a bisphosphonate, hormone replacement therapy (HRT) based on the average breast cancer risk between the Women’s Health Initiative [62] and the Million Women’s Study [63] or currently with raloxifene. The overall change in prescribing practices from the former practice of HRT would be the standard treatment to the current practice of prescribing raloxifene will produce a net decrease of 27,230 breast cancers. (Jordan VC. Optimising endocrine approaches for the chemoprevention of breast cancer. Beyond the Study of Tamoxifen and Raloxifene (STAR) Trial. European Journal of Cancer 2006; (in press). Reproduced with permission from European Journal of Cancer.)

take raloxifene for 10 years, there would only be 7,000 women who will develop breast cancer; a net decrease of 27,320 breast cancers from those women that would have been taking HRT during the 1990s. The indirect approach to breast chemoprevention is already providing a valuable enhancement of public health.

20.7 The Direct Approach to the Chemoprevention of Breast Cancer The National Surgical Adjuvant Breast and Bowel Project (NSABP) P-1 Study to test the worth of tamoxifen to reduce breast cancer incidence in high-risk women, opened in the United States and Canada in May 1992 with an accrual goal of 16,000 women to be recruited at 100 North American sites [17]. The study closed after accruing 13,338 in 1997 because of the high-risk status of the participants. Those patients eligible for entry included any women aged greater than 60 years, or women aged between 35 and 59 years whose 5-year risk of developing breast cancer, as predicted by the Gail Model [64] was equal to that of a 60-year old woman. Additionally, any woman aged more than 35 years who had a diagnosis of lobular carcinoma in situ (LCIS) treated by biopsy alone was eligible for entry to the study. In the absence of LCIS, the risk factors necessary to enter the study varied with age, such that a 35-year-old woman must have had a relative risk (RR) of 5.07, whereas the required RR for a 45-year-old woman was 1.79. The breast cancer risk of women enrolled in the study was extremely high, with no age group having an RR of less than 4—including women aged more than 60 years. Recruitment was balanced, with about one-third younger than 50 years, one-third between 50 and 60 years, and one-third older than

60 years. Secondary endpoints of the study included the effect of tamoxifen on the incidence of fractures and cardiovascular deaths. The study also planned to provide the first information about the role of genetic markers in the etiology of breast cancer; however, the evaluation of whether tamoxifen has a role to play in the treatment of women who are found to carry somatic mutations in the BRCA-1 gene could not be accomplished satisfactorily because so few women, even in this high-risk population carried the imitated gene [65]. The first results of the NSABP study were reported in September 1998 after a mean follow-up of 47.7 months [17]. A total of 368 invasive and noninvasive breast cancers were diagnosed in the participants; 124 in the tamoxifen group and 224 in the placebo group. A 49% reduction in the risk of invasive breast cancer was seen in the tamoxifen group, and a 50% reduction in the risk of noninvasive breast cancer was observed. A subset analysis of women at risk because of a diagnosis of LCIS demonstrated a 56% reduction in this group. The most dramatic reduction was seen in women at risk because of atypical hyperplasia, where risk was reduced by 86%. The benefits of tamoxifen were observed in all age groups, with a relative risk of breast cancer ranging from 0.45 in women aged 60 years and older to 0.49 for those aged 50–59 years, and 0.56 for women aged 49 years and less. A benefit for tamoxifen was observed for women with all levels of breast cancer risk within the study, indicating that the benefits of tamoxifen are not confined to a particular lower-risk or higher-risk subset. Benefits were observed in women at risk on the basis of family history and those whose risk was because of other factors. As expected, the effect of tamoxifen occurred on the incidence of ER-positive tumors, which were reduced by 69%

20. A Personal Account of the Chemoprevention of Breast Cancer: Possible or Not Possible?

per year. The rate of ER-negative tumors was not affected by tamoxifen treatment. Tamoxifen did, however, reduce the rate of invasive cancers of all sizes, but the greatest difference between the groups was the incidence of tumors 2.0 cm or less. Tamoxifen reduced the incidence of both node-positive and node-negative breast cancer. The beneficial effects of tamoxifen were observed for each year of follow-up in the study. After year 1, the risk was reduced by 33%, and by year 5 by 69%. Tamoxifen also reduced the incidence of osteoporotic fractures of the hip, spine, and radius by 19% as an expression of SERM action; the difference approached, but did not reach, statistical significance. This reduction was greatest in women aged 50 years and older at study entry but the actions of tamoxifen as an antiestrogenic drug were not being tested in high-risk populations as noted with raloxifene in the MORE trial [66]. No difference in the risk of myocardial infarction, angina, coronary artery bypass grafting, or angioplasty was noted between groups. Raloxifene was subsequently not found to prevent death from coronary heart disease in the trial, Raloxifene Use for the Heart (RUTH), but there was no increased risk for endometrial cancer and breast cancer incidence was reduced by 50% [67]. The NSABP P-1 study confirmed the association between tamoxifen and endometrial carcinoma. The relative risk of endometrial cancer in the tamoxifen group was 2.5. The increased risk was seen in women aged 50 years and older, whose relative risk was 4.01. All endometrial cancers in the tamoxifen group were grade 1 and none of the women who received tamoxifen died of endometrial cancer (one endometrial cancer death in the placebo group was noted). The evaluation of endometrial cancer was followed up in a subsequent publication [49]. Endometrial cancer incidence for postmenopausal women taking tamoxifen was 3.08/1,000 per year compared with 0.58/1,000 per year in placebo control. There was no increase in endometrial cancer relative to placebo in premenopausal women. Although there is no doubt that tamoxifen increases the risk of endometrial cancer, it is important to recognize that this increase translates to an incidence of 2.3 women per 1,000 per year who develop endometrial carcinoma. More women in the tamoxifen group developed deep vein thrombosis (DVT) than in the placebo group. Again, this excess risk was confined to women aged 50 years and older. The relative risk of DVT in the older age group was 1.71. (95% CI 0.85–3.58). An increase in pulmonary emboli was seen in the older women receiving tamoxifen, with a relative risk of approximately 3. Three deaths from pulmonary emboli occurred in the tamoxifen group, but all were in women with significant comorbidities. An increased incidence of stroke (RR 1.75) was seen in the tamoxifen group, but this did not reach statistical significance. An assessment of the incidence of cataract formation was made using patient self-report. A small increase in cataracts was noted in the tamoxifen group—a rate of 24.8 women per

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1,000 compared with 21.7 in the placebo group. There was also an increased risk of cataract surgery in the women receiving tamoxifen. These differences were marginally, statistically significant, and observed in the older patients in the study. These findings emphasize the need to assess the patient’s overall health status before making a decision to use tamoxifen for breast cancer risk reduction. An assessment of quality of life showed no difference in depression scores between groups. Hot flashes were noted in 81% of the women in the tamoxifen group compared with 69% of the women in the placebo group, and the tamoxifen-associated hot flashes appeared to be of greater severity than those in the placebo group. Moderately bothersome or severe vaginal discharge was reported by 29% of the women in the tamoxifen group and 13% in the placebo group. No differences in the occurrence of irregular menses, nausea, fluid retention, skin changes, or weight gain or loss were reported. The NSABP-2 trial, referred to as the Study of Tamoxifen and Raloxifene (STAR), started in 1999 but excluded premenopausal women as raloxifene has not been evaluated or tested in this population. A total of 19,747 postmenopausal women (mean age: 58.8 years) with an increased 5-year Gail risk (mean: 4.03) were randomly assigned to receive either tamoxifen 20 mg or raloxifene 60 mg for 5 years. A final analysis was initiated after at least 327 invasive breast cancers were diagnosed. There were 163 and 168 invasive breast cancers observed in women assigned to tamoxifen and raloxifene, respectively. Thus, the conclusion of the study was that raloxifene was equivalent to tamoxifen at reducing the risks of breast cancer in postmenopausal women at high risk. However, the side effect profile favored raloxifene. There were 36 and 23 cases of uterine cancer with tamoxifen and raloxifene, respectively; fewer hysterectomies with raloxifene; and fewer thromboembolic events occurred with raloxifene compared with tamoxifen. Similarly, there were fewer cataracts and cataract surgeries noted in women taking raloxifene, but there was the same number of osteoporotic fractures in both groups. Thus, raloxifene, a drug that has been extensively investigated and used for the treatment and prevention of osteoporosis for the past 7 years, has now been shown to be a useful agent with reduced side effects to prevent breast cancer in high-risk postmenopausal women. However, to achieve the goal of practical progress in the chemoprevention of breast cancer, which can truly enhance public health, three interdependent issues must be addressed satisfactorily: whom to treat, what agent to use, and is the process affordable?

20.8

The Practice of Chemoprevention

The question of whether the chemoprevention of breast cancer is possible can now be answered satisfactorily based on appropriate translational research conducted over the past three decades. The SERMs have been evaluated in clinical

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trial and proven to reduce the risk of breast cancer in select populations where side effects are known to be reduced. The groups where the chemoprevention of breast cancer is reasonably demonstrated are: women who are being treated with raloxifene to prevent osteoporosis [60]; ● premenopausal women with a Gail high risk of breast cancer can be treated with tamoxifen [17, 49]; and ● postmenopausal women with a Gail high risk of breast cancer can be treated with raloxifene [61]. ●

Thirty years ago there was nothing physicians could do except institute a “wait and see” policy and even close monitoring was not possible because mammography was not a practical possibility either. Physicians are now able to intervene successfully to reduce the incidence of breast cancers in select populations and monitor populations adequately to detect early disease and reduce the risk of dying from breast cancer. This advance in molecular oncology, i.e., targeting the ER to block cancer development and growth [68] merged with the new science of selective ER modulation [7, 69] have created practical advances in patient care. But can healthcare systems afford this advance? A recent cost-effectiveness study [70] demonstrated that if the group selected for study is at high enough risk, i.e., Gail score 3–5 and the cost of the drug is low enough, then the value of chemoprevention as a healthcare intervention is appropriate. This is particularly true for tamoxifen in premenopausal women in countries such as the UK where the drug is extremely inexpensive [71]. Raloxifene, too, is appropriate when used for the prevention of osteoporosis where the prevention of breast cancer is a beneficial side effect. There is no other alternative medicine that affects two separate diseases. Lastly, what is the appropriate strategy for the chemoprevention of breast cancer in the largest group at risk—postmenopausal women? Raloxifene is clearly the current therapy of choice for reducing the risk of high-risk postmenopausal women. The ability to reduce the incidence of breast cancer by an equivalent amount as tamoxifen [61] but at the same time reduce the risk of endometrial cancer, hysterectomy, cataracts, and cataract surgery associated with tamoxifen adds a new dimension to the physicians’ armamentarium. However, the question of whether an aromatase inhibitor will be superior to raloxifene is one that will be answered over the next 5 years. Unfortunately, there is only one trial, NSABP P-4, that is addressing this important question directly. The trial will compare and contrast the SERM raloxifene against the aromatase inhibitor letrozole to reduce the incidence of breast cancer in high-risk postmenopausal women. The issue of side effects and patient compliance will be critical in this comparison. The aromatase inhibitor will also decrease bone density so bisphosphonates will need to be administered thereby increasing costs. In contrast, raloxifene will not decrease bone density thereby enhancing cost benefit estimates.

V.C. Jordan

The present opportunity to exploit molecular targets for carcinogenesis has now been shown to be effective and improve medical practice. The new science of chemoprevention first advanced by Sporn in the mid-1970s [72, 73] led the way for the current success in breast chemoprevention. It remains for others to advance the science of targeted therapeutics and apply the same successful paradigm, demonstrated for the chemoprevention of breast cancer, to other organ sites.

Acknowledgements. Dr. Jordan is supported by the Department of Defense Breast Program under award number BC050277 Center of Excellence (Views and opinions of, and endorsements by the author(s) do not reflect those of the US Army or the Department of Defense), SPORE in Breast Cancer CA 89018, R01 GM067156, FCCC Core Grant NIH P30 CA006927, the Avon Foundation and the Weg Fund and the Alfred G. Knudson Chair in Cancer Research of the Fox Chase Cancer Center.

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Appendix A Abbreviations

5-FC 5-FU 5-mC 6-Me-P A aaATIII Ab AAV AC

5-fluorocytosine 5-fluorouracil 5-methylcytosine 6-mercapto-purine

AFAP AFP Ag AK ALCL ALK ALL AML AMPK AOR AP APC APC/C APL ASCO AT ATR ATRA AUC AVE

antiangiogenic antithrombin III antibody adeno-associated virus chemotherapy with doxorubicin and cyclophosphamide attenuated familial adenomatous polyposis α-fetoprotein antigen Aurora kinase anaplastic large-cell lymphoma anaplastic lymphoma kinase acute lymphoblastic/lymphocytic leukemia acute myeloid leukemia AMP-activated protein kinase adjusted odds ratios apyrimidinic anaphase-promoting complex anaphase-promoting complex/cyclosome acute promyelocytic leukemia American Society of Clinical Oncology ataxia telangiectasia ataxia telangiectasia and Rad53-related all-trans retinoic acid area-under-the-concentration curve artificial viral envelope

B B-cell CLL BAC BCIRG BCR BDEPT BER BIR bp Btk BWS

B-cell chronic lymphatic leukemia Bacterial antigen complex Breast Cancer International Research Group B-cell receptor bacteria-directed enzyme prodrug therapy base excision repair baculovirus IAP repeat base-pair Bruton’s tyrosine kinase Beekwith-Wiedemann syndrome

C CA CAK CAR

carboxylesterase CDK-activating kinase coxsackievirus and adenovirus receptor

CARD CCD CD CDDO CDK CDKI CEA CEC CGH CHD CHiP CIN Ci CIS CKI CLL CMF

CPG2 CR CREB CT CTD CTL CYP

caspase-recruitment domain charged-coupled device cytosine deaminase 2-cyano-3,12-dioxooleana-1,9-dien-28-oic acid cyclin-dependent kinase cyclin-dependent kinase inhibitor carcinoembryonic antigen circulating endothelial cell comparative genomic hybridization coronary heart disease chromatin immunoprecipitation cervical intraepithelial neoplasia cubitus interruptus carcinoma in situ cyclin-dependent kinase-inhibitor chronic lymphocytic leukemia chemotherapy with cyclophosphamide, methotrexate, and 5-fluorouracil chronic myeloid leukemia cytomegalovirus Continuing Outcomes Relative to Evista cyclooxygenase-2 cyclophosphamide carboxypeptidase A adjacent cytosine and guanine nucleotides in the DNA carboxypeptidase G2 complete response cAMP-response element binding protein computed tomography C-terminal domain cytotoxic T lymphocytes cytochrome

D DAAO DAG DAPK DCE-MRI DCIS DD DLI DSB dCK DEAE DHR DISC

d-amino-acid oxidase diacylglycerol death-associated protein kinase dynamic contrast-enhanced MRI ductal carcinoma in situ death domain donor-lymphocyte infusion double-strand break deoxtcytidine kinase diethylaminoethyl degenerate hexapeptide repeats death-inducing signaling complex

CML CMV CORE COX-2 CP CPA CpG

399

400

Appendix A

DLCL DMBA DMSO DNMT DPP DRNK DVT

diffuse large-cell lymphoma dimehylbebzanthracene dimethyl sulfoxide DNA methyltransferases Decapentaplegic deoxyribonucleotide kinase deep vein thrombosis

E EBV EC ECG ECM ECOG EGF EGFR EGFP ER ERCC EST

Epstein-Barr virus endothelial cell electrocardiograph extracellular matrix Eastern Cooperative Oncology Group epidermal growth factor epidermal growth factor receptor epidermal growth factor promoter estrogen receptor excision repair cross complementing Expressed Sequence Tag

F FAB β-FGF FAC

FRET FRS FSH FTase FTI Fz

French-American-British fibroblast growth factor-β chemotherapy with doxorubicin and cyclophosphamide plus 5-fluorouracil fluorescence-activated cell sorting Fas-associated protein with death domain familial adenomatous polyposis Fatty acid synthase Fas ligand Food and Drug Administration fibroblast growth factor receptor familial adenomatous polyposis 2′fluoro-2′-deoxy-1 β-d-arabinofuranosyl-5iodouracil fluorescence in situ hybridization fludarabine, ara-C, and G-CSF chemotherapy with 5-fluorouracil, leucovorin, and oxaliplatin fluorescence resonance energy transfer fibroblast growth factor receptor substrate follicle-stimulating hormone farnesyltransferase farnesyltransferase inhibitors Frizzled

G β-gal β-glu GCV GDEPT GDP GEO GGT GILT GIST GJIC GOI GPAT GPCR GSK3 GSTP GTP GVHD GVL

β-galactosidase β-glucuronidase ganciclovir gene-directed enzyme prodrug therapy guanosine diphosphate Undefined in Yu chapter geranyltransferase Genotypic International Lung Trial gastrointestinal stromal tumor gap junctional intercellular communication gene of interest gene prodrug activation therapy G-Protein coupled receptor glycogen synthase kinase 3 glutathione S-transferase P guanosine triphosphatase graft-versus-host disease graft-versus-leukemia

FACS FADD FAP FAS FasL FDA FGFR FAP FIAU FISH FLAG FOLFOX4

H HAT hCG HCV HDAC HDACi HERA Hh HIF HIV HJV HLA HMT HNPCC HPV HR HR-HPV HRP HRR HRT HSC HSV

histone acetyltransferase enzyme human chorionic gonadotrophin hepatitis C virus histone deacetylase histone deacetylase inhibitor Herceptin Adjuvant trial Hedgehog hypoxia-inducible transcription factor human immunodeficiency virus hemagglutinating Japan virus human leukocyte antigen histone methyltransferases hereditary nonpolyposis colorectal cancer human papillomavirus homologous recombination high-risk human papillomavirus horseradish peroxidase homologous recombination repair hormone-replacement therapy hematopoietic stem cells Herpes simplex virus

I IALT IAP IC50 ICI IDO Ig IGF-1 IFL IFN IHC IL IMD INCENP IND IOTF IP IP3 IRES IRS ISEL ITAC ITD IV

International Adjuvant Lung Cancer Trial inhibitor of apoptosis proteins 50% of inhibitory concentration of a drug Imperial Chemicals Industries indoleamine 2,3-dioxygenase immunoglobulin insulin-like growth factor-1 chemotherapy with irinotecan, 5-FU, and leucovorin interferon immnohistochemical, immunohistochemistry interleukin intratumoral microvessel density inner centromere protein Investigational New Drug Interagency Oncology Task Force isofosphamide; intraperitoneal, intraperitoneally inositol triphosphates internal ribosome entry site insulin receptor substrate Iressa Survival Evaluation in Lung Cancer Intestinal-type adenocarcinoma internal tandem duplication intravenous, intravenously

J JM

juxtamembrane

K kb kD

kilobase kilodalton

L β-L LAP LBL LCIS LCM LDH LFS LH LINES LM

β-lactamase latency-associated promoter lymphoblastic B-cell lymphoma lobular carcinoma in situ laser-capture microdissection lactate dehydrogenase Li-Fraumeni syndrome luteinizing hormone long interspersed nuclear elements length mutation

Appendix A

401

LNA LOE LOH LPS LXCXE

locked nucleic acid level of evidence loss of heterozygosity lipopoysaccharide part of cyclin structure

M MAb MALT MAPK MBD MCL MCM MDAE MDS ME MedDIP MGMT MHC MIC MK MLV MMP MMR MOMP MORE MPF MRI MRS MSI mTor MZF

monoclonal antibody mucosa-associated lymphoid tissue mitogen-activated protein kinase methyl-binding domain mantle cell lymphoma minichromosome maintenance multiple drug activation enzyme myelodysplastic syndromes myoepithelial methylated DNA immunoprecipitation methylguanine-methyltransferase major histocompatability complexes macrophage inhibiting cytokine midkine promoter murine leukemia virus matrix metaloprotease mismatch repair mitochondrial outer membrane permealization Multiple Outcomes of Raloxifene Evaluation maturation promoting factor magnetic resonance imaging magnetic resonance spectroscopy microsatellite instability mammalian target of rapapmycin myeloid zinc finger

N NCCTG NCI NCRI NER NGF NHEJ NHL NK NR NSABP NSAID NSCLC

North Central Cancer Treatment Group National Cancer Institute National Cancer Research Institute nucleotide excision repair nerve growth factor nonhomologous endjoining non-Hodgkin lymphoma natural killer nitroreductase National Surgical Adjuvant Breast and Bowel Project nonsteroidal anti-inflammatory drugs non-small-cell lung cancer

O OPG OR

osteoprotegerin overall response

P PAP PARP PBMC PCNA PCR PCT PD PDGF PDGF-BB PDGFR PEC PEGF PEI PET

prostatic acid phosphatase poly (ADP-ribose) polymerase peripheral blood mononuclear cell proliferating cell nuclear antigen polymerase chain reaction plasmacytoma progressive disease platelet-derived growth factor Undefined in Pierotti chapter platelet-derived growth factor receptor progenitor endothelial cell pigment epithelial-derived factor polyethylene imine positron electron tomography

PGA PGF PH PI3K PIP2 PIP3 PLAP PLC PLL PML PML-RARα PNP pPNET PR PSA PSADT PUFA R RAF

RAS RB RECIST REMARK RFLP RGD RING ROC ROS RPG RPV RR RT-PCR RTK RUTH RXL S SC SCCHN SCF SCID SCLC SD SELDI-TOF SERM SFRP siRNA SLCG SNP SSB SSR SOCS SoS SPARC STAR T TReg TAC

penicillin-G amidase placental growth factor pleckstrin homology phosphatidyl inositol 3′ kinase Phosphatidyl inositol 3,4 biphosphate Phosphatidyl inositol 3,4′,5′ triphosphate placental-like alkaline phosphatase phospholipase C poly-l-lysine promyelocytic leukemia promyelocytic leukemia-retinoic acid receptor alpha purine nucleotide phosphorylase primitive peripheral neuroectodermal tumor partial response; progesterone receptor prostate-specific antigen rate of rise of PSA, expressed as a doubling time polyunsaturated fatty acid

Raf-Ser-Thr kinase (rapidly growing fibrosarcomas in mice, RAF)/mitogen-activated protein kinase rat with sarcoma retinoblastoma Response Evaluation Criteria in Solid Tumors REporting recommendations for tumor MARKer prognostic studies restriction fragment length polymorphism Arg-Gly-Asp really interesting new gene receiver operating curve reactive oxygen species radial growth phase relative predictive value relative risk reverse-transcription polymerase chain reaction receptor tyrosine kinase Raloxifene Use for the Heart cyclin-binding motif

subcutaneous, subcutaneously squamous cell carcinoma of the head and neck stem cell factor severe combined immune deficiency small-cell lung cancer stable disease surface-enhanced laser desorption-ionization time-of-flight mass selective estrogen receptor modulators secreted frizzled-related proteins small interfering RNA Spanish Lung Cancer Group single nucleotide polymorphism single strand break simple sequence repeats suppressor of cytokine signaling son of sevenless Secreted Protein Acidic and Rich in Cysteine Study of Tamoxifen and Raloxifene

regulatory T cell chemotherapy with doxorubicin and cyclophosphamide plus docetaxel

402

Appendix A

T-ALL TC-NER TCR TGF THBS TIL TIMP TK TK/GCV TMUGS TNF TNFR TNM TP TRAIL TRAIL-R TSG TSH TUNEL

T-acute lymphoblastic leukemia/lymphoma transcription-coupled NER T-cell receptor transforming growth factor thrombospondin tumor-infiltrating lymphocytes tissue inhibitor of metalloproteinase thymidine kinase thymidine kinase/ganciclovir Tumor Marker Utility Grading System tumor necrosis factor tumor necrosis factor receptor tumor-node-metastasis staging system thymidine phosphorylase tumor-necrosis-factor-related apoptosis-inducing ligand TRAIL receptor tumor-suppressor gene thyroid-stimulating hormone in situ DNA nick end labeling

U uPA uPAR UV

urokinase plasminogen activator urokinase plasminogen activator receptor ultraviolet

V VDAC VDEPT VE VE-cadherin VEGF VEGFR receptor VGP VNR VPF VSV VTA W Wnt X XGPRT transferase XIAP XPF XPD

voltage-dependent anion channel virus-directed enzyme prodrug therapy vascular endothelial vascular endothelial cadherin vascular endothelial growth factor vascular endothelial growth factor vertical growth phase vitronectin receptor vascular permeability factor vesicular stomatitis virus vascular targeting agent

Wingless

xanthine-guanine phosphoribosyl X-linked IAP XP group F excision repair cross complementing 2

Appendix B Trade Names/Generic Names

We have tried to standardize the use of generic names in this edition of Principles of Molecular Oncology. Often drugs have one name during development, a name that is often a series of numbers in an effort to protect a company’s research activity. Trade names have been omitted, as is standard for scientific communication. In some cases, while the generic name is universal, trade names vary by country and, thus, their use does not add to clarity. Generic names begin with a lower case letter, while trade names are capitalized, with a trademark (™) or registration mark (®). Many drugs enter the market with a trademark, which is later changed to a registration mark. Generic names provide some information as to the class of drugs. For example, monoclonal antibodies are designated with ‘-mab’, taxanes with ‘xel’, and hematopoietic growth factors with ‘-stim’. Generic Name

Trade Name

A 5-azacytidine 5-aza-2′-deoxycytidine alemtuzumab arsenic trioxide

Vidaza Doctabine CamPath Trisenox

B bevacizumab bleomycin busulfan

Avastin Blenoxane Myleran

C carboplatin cetuximab cilengitide cisplatin cyclophosphamide

Paraplatin Erbitux None assigned Platinol, Platinol-AQ Cytoxan

D dacarbazine dasatinib dexamethasone docetaxel doxazosin doxorubicin doxycycline

DTIC-Dome Sprycel Decadron, Hexadrol Taxotere Cardura Adriamycin, Rubex Vibramycin

Generic Name

Trade Name

E endostatin epratuzumab erlotinib etoposide (VP-16) everolimus

None assigned Tarceva VePesid, Etopophos, Toposar None assigned

F 5-fluorouracil finasteride flavopiridol fludarabine

None assigned Proscar None assigned Fludara

G gefitinib gemcitabine gemtuzumab ozogamicin

Iressa Gemzar Mylotarg

H hesperadin

None assigned

I ibritumomab tiuxetan idarubicin imatinib mesylate (STI 571) irinotecan

Zevalin Idamycin Gleevec Camptosar

K keoxifene

None assigned

L lapatinib leucovorin letrozole lexatumumab lonafarnib

Tykerb Wellcovorin Femara None assigned Sarasar

M mapatumumab melphalan (L-Pam) methotrexate mitroxantrone MK0457

None assigned Alkeran Folex, Rheumatrex Novantrone None assigned

403

404

Appendix B

Generic Name

Trade Name

Generic Name

N nilotinib

None assigned

T tamoxifen

O oblimersen ONYX-015 oxaliplatin

Genasense None assigned Eloxatin

P paclitaxel pantitumumab pertuzumab

Taxol, Onxol Vectibix Omnitarg

R RO-3306 R-roscovitine raloxifene rapamycin (sirolimus) rituximab

None assigned None assigned Evista Rapamune Rituxin

S SAHA seliciclib sorafenib sunitinib

None assigned Nexavar Sutent

Trade Name

temsirolimus thalidomide tipifarnib topotecan tositumomab trastuzumab

Nolvadex, Apo-Tamox, Gen-Tamoxifen, Nolvadex-D, Novo-Tamoxifen, PMSTamoxifen, Tamofen, Tamone None assigned Thalomid None assigned Hycamtin Bexxar Herceptin

U UCN-101

None assigned

V vinorelbine vorinostat

Navelbine Zolinza

Z ZD 1839 ZD6126 zebularine ZM447439

None assigned None assigned None assigned None assigned

Index

A AACR. See American Association for Cancer Research Abl families, 170 Acetylated histone H3 (AcH3) and H4 (AcH4), 284 Activation function-1 (AF-1) domain, 189–190, 192, 194 Activator protein (AP-1), 190, 195 T-Acute lymphoblastic leukemia/lymphoma (T-ALL), 177 Acute lymphocytic leukemia, 51, 330 Acute myeloid leukemia, 51, 340 Acute promyelocytic leukemia (APL), 68, 77. See also EGFRand HER2, dual inhibition Adeno-associated viruses (AAV), 368 Adenoid cystic carcinoma, 57 Adenomatous polyposis coli (APC) protein, 11, 52, 162, 173, 174, 181 Adenoviral-cyclin-dependent kinase-inhibitory (CKI) vectors, 226 Adjuvant cisplatin-based chemotherapy, 276 Adoptive T-cell therapy, 294–297 AF-1/AF-2 Interactions, 190 AFAP. See Attenuated familial adenomatous polyposis AFP. See Serum tumor markers a-fetoprotein AGM-1470/TNP-470 Synthetic drug, 247 AKAP9-BRAF fusion, 48 Akt expression, 59 ALL. See Acute lymphocytic leukemia Allosteric proteins, 16 All-trans-retinoic acid, 68, 77, 340, 349 American Association for Cancer Research, 14 American Society of Clinical Oncology, 18 AML. See Acute myeloid leukemia AMP-Activated protein kinase (AMPK), 168 cAMP-Response element binding protein (CREB), 168, 193, 194 Anaphase-promoting complex/cyclosome (APC/C), 215 Anaplastic lymphoma kinase (ALK) and NTRK1 rearrangments, 71 positive anaplastic large-cell lymphoma, 51 positive large B-cell lymphoma, 67 positive lymphomas, 67 Androgen receptor, 189 Aneuploidy marker of tumor progression and prognosis, 86 Angiogenesis, 165–166 advantages of, 248

angiogenic switch, 240 cell adhesion molecules and cadherin, 244 immunoglobulin, 244 integrins, 244, 245 selectins, 244 endogenous inhibitors of, 246, 247 inducers angiopoietins and Tie-2 receptors, 242, 243 ephrin and Eph receptors, 243 fibroblast growth factors, 242 transforming growth factor b, 243 tumor necrosis factor a (TNFa), 243 vascular endothelial growth factor, 242 interleukins (ILs), 243 lymphangiogenesis and, 245, 246 pathways, 9 physiology and pathology of, 239, 240 prognostic value of, 247, 248 proteases, 245 therapeutic approaches of, 248–250 Angiopoietin-1, 243 Angiostatin angiogenesis inhibitor, 239 and antiangiogenic antithrombin III inhibitors, 247 Anilinoquinazoline ZD6474, 50 Antiapoptotic bcl-2 protein overexpression, 241 Antibody-driven protein and phospho-site discovery, 131 Antibody microarrays, 119, 120 Anticancer drug efficacy BRCA1, for platinum and antimicrotubule agents, 385 DNA repair systems, 384 ERCC1 and cisplatin resistance, 383–385 ribonucleotide reductase, 385–386 Anti-EGFR monoclonal antibodies cetuximab, 335–336 panitumumab, 336 Anti-Helicobacter pylori eradication therapy, 65 APC/b-cetanin pathways E-cadherin pathways, 285–286 and myc overexpression, 53 and Tcf-4 pathway, 59 APC-Dependent proteolytic pathway, 215 405

406 APIS complex, 193 Apoptosis (Ap), 9, 165 and anticancer therapy, 258, 259 G1/S transition and, 164 Apoptotic core machinery and therapeutic targets, 257 Array-CGH techniques, 7 Arsenic trioxide, 67 ASCO. See American Society of Clinical Oncology Astrocytomas, 215 Ataxia telangiectasia (ATM), 271 ATMp53-Mdm2 pathway, 210 ATP/AMP ratio, 169 ATP competitors, CDK and, 226 ATRA. See All-trans-retinoic acid Attenuated familial adenomatous polyposis, 273 Aurora kinases (AK), 307 A, B and C, 308, 309 and cancer, 309–310 inhibitors development, 311–312 1-Aza-9-Oxafluorenes, 225 AZD1152 acetanilide-substituted pyrazole-aminoquinazolone prodrug, 310 B Baculovirus IAP repeat, 257 Basal keratins, 98 Basic helixloop-helix-Per/ARNT/Sim domain, 193 BCL2 expression, 51 family and anticancer agents targeting, 260, 261 overexpression, 62 positive follicular lymphomas, 63 a protein, 50 BCL1 amplification, 52 BCL6 chromosomal translocations, 63 BCL10 gene mutation, 65 BCL6 gene mutations, 54 BCL6 mRNA expression, 54 BCL10 protein, 64 Bcl-XL/Bak peptide complex, 262 BCR-ABL fusion protein, 171 fusion protein, for Imatinib targets, 317 positive leukemia, 57 tyrosine kinase activity, 68 Beekwith-Wiedemann syndrome, 218 Benign monoclonal gammopathy, 3 Bevacizumab in lung cancer, 249 Bilateral bronchoalveolar carcinoma, 19 Biomarkers of cancer, 14 as CEA, AFP, and PSA, 135 DNA repair in, 276 and EGF stimulation, 133 evolution of, 77 invasive and noninvasive (See Molecular therapeutics) and phosphoproteomics, 117 BIR. See Baculovirus IAP repeat Bleomycin, 385 Bloom syndrome, 275

Index B-NHL B non-Hodgkin lymphoma, 61 Bone Porphogenetic Protein (BMP) pathway, 153, 157 Bone sialoprotein, 245 BRAF activation, 48 BRAF V600E mutated protein, 48 BRCA1/2, 191, 214 BRCA1 and BRCA2 homologous recombination pathway, 274 BRCA2 germline mutations, 274 BRCA1 positive and negative hereditary tumors, 856 Breast cancer, 4 adjuvant systemic therapy, 28 age-standardized death rate of, 29 BRCA1 and BRCA2 susceptibility genes in, 85 cell cycle and apotopic proteins, 95–97 chemoprevention, indirect approach, 393 estrogen receptor (ER) biology and, 188 AF-1 domain, 189–200 DNA-binding domain, 200 ER-a and ER-b, structure of, 189 ERE-independent genomic actions of ER, 195 ER mutations in, 192 estrogen and ER signaling mechanisms, 192 hinge region, 190 ligand-and-ERE-dependent activation of ER, 192–195 ligand-binding domain, 191–192 ligand-independent genomic activation of ER, 196–197 membrane-mediated nongenomic action, 197–198 gene expression analysis and signature, 136, 146 genetic susceptibility and, 86 genomic instability and, 86 hereditary, 274 luminal epithelium and, 98 oncogenes C-MYC, 93 HER2, 92 and predictive factors, 31 prognostic and predictive factors treatments in, 37 somatic genetic alterations in hereditary BRCA1/2-mutated, 89, 91 susceptibility genes and, 87, 88 tumor suppressor genes E-cadherin/catenin cell adhesion complex, 94, 95 P53, 93, 94 Breast Cancer International Research Group (BCIRG), 337 Bristol Myers Squibb for treatment of CML, 17 Bronchogenic carcinomas, 5 Burkitt lymphoma, 51, 136 BWS. See Beekwith-Wiedemann syndrome Bystander effect. See Suicide gene therapy C E-Cadherin (CDH1) pathway, 11 P-Cadherin cell adhesion molecules, 98 Caenorhabditis elegans, 163, 172, 179 CAK phosphorylation site (T-160), in CDK2, 212 Cancer and carcinogenesis, 3, 4 cell signaling in cancer cells and flow chart of pathway-array design, 137 cellular ontogeny implications, 162

Index Cancer (continued) cellular signaling and hallmarks of, 136 chemoprevention, 14 epigenetic modifications in therapy, 288 evolution of, 2 gene expression study in, 140 genetic basis of, 269 Hh signaling pathways in, mutational alteration of, 178–179 immunosurveillance concept of, 293 and intrinsic changes in cells, 2 lineage concept, 3 multi-step mutation theory of, 3 non-autonomous mutational contributions and, 161 Notch signaling, mutational alteration of, 179 PathwayFinder microarray, 147, 148 pathways, 320 research, expression-profiling tools, 144–150 RTK pathways, mutational alteration of, 170–171 TGFb pathways in, mutational alteration of, 181 Wnt pathway and, 172–176 Cancer cells and DNA methylation, 282 epigenetic alteration in, 283–284 histone code of, 287 Cancer Genome Project, 321 Cancer stem cells, 158 cancerpromoting mutations in, 160 mutations and, 160–161 properties, 159–160 Carboplatin, 246, 260, 261, 334, 347, 385, 386 Carcinoembryonic antigen, 107 Carcinogenesis process, 6 Carcinomas uterine cervix, 215 CARD. See Caspase recruitment domain CARM-1. See Coactivator-associated arginine methyltransferase-1 Casein kinase 1 (CK1), 176 Caspase activation pathways, 257 Caspase recruitment domain, 64 b-Catenin phosphorylation, 173 b-Catenin/Tcf complex, 174 transcriptional activation, 52 CBP/p300 coactivators, 190 CDC25A/ B/C, protein phosphatases, 227 CDC14A/B proteins, 227 cdc28 mutants, 207 Cdc2 protein cyclin B1-complex, 45 kinase, 206, 207 CDC25 proteins, 227 Cdc42/Rac signal transduction pathways, 191 Cdc6, replication proteins, 214 CDDO-Im induced upregulation of genes, 145 CDK11, 221 CDK activity regulation, 212–213 CDK4 and CDK6 therapeutic inhibition, 213, 224 CDK and regulators in cell cycle activity regulation, 212–213 checkpoints, 207, 213 chromosome segregation, 214–215

407 cyclins, 209–210 DNA replication and, 213–214 transcriptional CDK and, 207–209 CDK3/ CDK7 and transcriptional CDK, 213, 225 CDK4-6/cycD complex, 54, 55 CDK-Cyclin complexes, 227 CDK1-Cyclin A/B complexes, 214, 215 B1-cyclin F active complexes, 210 CDK2-Cyclin, 197, 212 A complexes, 214 E complexes, 66, 213, 214 CDK4-Cyclin D complexes, 225 D kinase, 216 CDK4/6-Cyclin, 95 D complexes, 213, 226 kinase, 223 CDK8-Cyclin C complexes, 207, 213 CDK7-Cyclin H/Mat1, 207 CDK inhibitors, 216–218, 222 Cip/Kip proteins, 211–212 INK4 proteins, 210–211 p21Cip1, 211, 226 CDKN2A locus on chromosome 9, 11 in human cancers, 216 CDK11p58, mitosis-specific isoform, 215 CDK5-p35/p39 complexes, 207 CDK PSTAIRE motif, 212 CDK4 R24C mutation, 222 CDK regulators, tumor-associated alterations cyclins, 215–216 inhibitors, 216–218 B-Cell acute lymphoblastic leukemia, 56 chronic lymphatic leukemias, 62 lymphomas, 181 transcriptional coactivator BOB1-OBF1, 63 tumors, 51 Cell-cycle control pathways (CCC), 9 Cell death and senescence, terminal limitations on cell proliferation, 165 Cell growth protein translation, PI3K pathway and, 169 regulation, 164–165 Cells type(s) abT-cells, 179 A431 Cells, 120 basal/myoepithelial cells, 62 BCL2-JH harboring cells, 62 blood cells, 2 cancer cells, origin of, 158–162 CD4+cells, 298 CDK2-null cells, 214 CD8+ T cells, 295 circulating tumor cells, 328 colonic cells, 4 EBV-specific T cells, 295 ectoderm, 2

408 Cells type(s) (continued) endoderm, 2 fibrous tissue cells, 2 germ cells, 57, 109 Gr1+ CD11b+ myeloid suppressor cells, 299 haploid yeast cell, 154 hepatocytes, 2 iMycEm-1 and-2 cells, 145 kidney cells, 4 leukemic T cells, 64 luminal cells, 62 mantle cell, 62 mast cells, 58 MCF-7 cells, 190, 192, 198 mesoderm, 2 migrated transformed cells, 5 neural cell, 76 n-6/n-3 PUFA cells, 147 n-6/n-3 ratio cells, 147 pacemaker cells, 57 Paneth cells, 174 peripheral blood mononuclear cells, 327 progenitor cells, 57 Reed-Sternberg cells, 64 reticuloendothelial cells/Kupffer cells, 2 Schwann cells, 4 SKBR3 cells, 198 stem cell, 17 T cells, 64, 294 T47D breast cancer cells, 197 testicular germ cell, 109 TH1 cells, 300 transient-amplifying cells, 161 transit-amplifying, 174, 175 transitional cell, 5 TReg cells, 298–299 Cellular proliferation pathway, 4 Cellular signaling system, 135 Cervical intraepithelial neoplasia, 13, 55 Cervix uteri carcinoma, 13 Cetuximab, 17, 18, 47–49, 315, 329, 335–336 as antiangiogenic agents, 246 for colorectal carcinoma, 50, 336 molecular therapeutics, 321 in nonsmall-cell lung cancer, 336 for squamous cell carcinoma of head and neck, 336 CHEK2 and RAD51, DNA repair proteins, 97 Chemoprevention breast cancer direct approach, 394–395 indirect approach, 393–394 practice of, 394–395 Chordin protein, 180 Chromatin immunoprecipitation (ChIP) assays, 193 Chromatin, signal-induced changes, 161–162 9p21 Chromosomal region, 54 Chromosome 18q LOH/decreased DCC expression, 60 Chronic lymphocytic leukemia, 51 Chronic myeloid leukemia, 16–17, 317, 321 CIN. See Cervical intraepithelial neoplasia

Index Cip/Kip proteins, 211–212, 222 Circulating endothelial cells (CEC), 240 Cisplatin combination chemotherapy, 110 Cisplatin response, molecular assays for, 388. See also DNA repair genes, SNPs in CLL. See Chronic lymphocytic leukemia Clostridium acetobutylicum, 369 CML. See Chronic myeloid leukemia C-MYC expression and mutations, 53, 63 Coactivator-associated arginine methyltransferase-1, 193 Cockayne syndrome, 271 Colon cancer, 4 Colonic polyps, 14 Colonystimulating factors, 181 Comparative genomic hybridization, 7 Compound 677 inhibitor, 312 Continuing Outcomes Relative to Evista (CORE), 393 CoRNR box, 195 Coronary heart disease (CHD), 391 Costal 2 (Cos2), 176, 178 COX-dependent and COX-independent mechanisms, 59, 60 COX-2 expression and angiogenesis, 60 CpG island hypermethylation, 282–284 Critical aspartic acid, 212 Crypts of Lieberkuhn, 174 ab-Crystalline heat shock proteins, 98 CSL protein, 179 CTCL. See Cutaneous T-cell lymphoma Cutaneous T-cell lymphoma, 21 Cyclic adenosine monophosphate (cAMP), 196 Cyclin B1/B2 in cytoplasm, 213–214 Cyclin D1, 189, 195, 216 Cyclin D-CDK4 complexes, 213 Cyclin D-CDK4/6 kinases, 210 Cyclin-dependent kinase (CDK), 163, 164, 206 alteration of, human cancer and, 215–218 CDK4, 10 CDK1 inhibition, 207, 219 CDK2 inhibition, 206, 212, 213, 219, 224–225 cell cycle, and regulators, 207–213 control of cell cycle by, 213–215 genetic analysis of, 218–223 inhibition, selectivity vs. potency in, 225–226 inhibitor p21 (Cip1), 95 inhibitors, 56, 163, 197 inhibitory kinases, 226–227 therapeutic strategies, 223–227 Cyclin D1 expression, 96 Cyclin E CDK complexes, 213 CDK2 complexes, 210 dependent kinase activity, 163 expression, 96 Cyclin/INK4/CDK/pRb pathway, 218 Cyclins, 209–210 Cyclo-oxygenase 2 (COX-2) prostaglandin generator, 1, 14, 283 Cys447 , estrogen-dependent palmitoylation of, 197 Cytokeratins cytoskeletal intermediate filamentous proteins, 98 Cytokine signaling (SOCS) family of proteins, 284 Cytosolic death-domain-containing protein FADD, 256

Index D DAN/Gremlin/Cerberus family, 180 DBD-ERE binding, 190 DBD/hinge domains, 194 DCC null immunophenotype, 60 DCE-MRI. See Dynamic contrast-enhanced MRI DCIS. See Ductal carcinomas in situ Death-associated protein kinase (DAPK), 284 Death-inducing signaling complex, 256 Death receptor/extrinsic pathway, 256 Deep vein thrombosis (DVT), 392 Delta-delta Ct (DDCt) method, 142 Delta family ligands, 179 Detoxifier glutathione S-transferase P1 (GSTP1), 286 Diacylglycerol (DAG), 169 Dickkopf, Wnt inhibitor, 174 Diethylstilbestrol (DES), 191, 195 Dimethylbenzanthracene (DMBA) carcinogen, 3, 4, 224 DISC. See Death-inducing signaling complex Distinct positive-transcription elongation factors (termed P-TEFb), 207 DNA-binding dyes method, 142 DNA cytosine-methyltransferase (DNMT1), 282 DNA damage drugs, 56 repair pathways and, 270 sensing and signaling, 272 DNA doublestrand break repair, 91 DNA repair dependent pathways (DR), 9 gene mutations, 272 markers of, 276 DNA repair genes, SNPs in. See also Anticancer drug efficacy ERCC1 SNP, 387 XPD SNP, 386–387 XRCC3 SNPs, 387 DNA repair pathways base excision repair (BER), 270 double-strand breaks (DSB) homologous repair system, 271 nonhomologous end joining (NHEJ) repair system, 271 mismatch repair (MMR), 271, 272 nucleotide-excision repair (NER), 271 translation synthesis, 272 Donor-lymphocyte infusion (DLI), 295 Docetaxel, 38, 116, 258, 261, 322, 333, 377, 378, 380 Drosophila melanogaster, 155, 156, 160, 164, 172, 176, 177, 181, 226 CDK2 kinase, 210 cell sensing, 153 Hh signaling pathway and, 178, 179 insulin receptor, 169 JAK/STAT pathway in, 181, 182 Ken protein, 181 wing disc, 165, 171, 172, 179 Drug development new drug structures, selection of, 323 phases and new key technologies, 321–322 validation and selection of targets, 323 Drug metabolism and toxicity, oxygenases function, 149

409 DSBR. See DNA doublestrand break repair D-Type cyclins CDK4/6 complexes, 219 D1/D2/D3 complex, 213, 219 Ductal carcinomas in situ, 4, 91, 241 Dynamic contrast-enhanced MRI, 327 E 4E-BP1 and eIF-4E initiation translation factor binding protein, 168, 169, 172 EBV-positive Hodgkin’s lymphoma, 295 E2F/DP/Rb repressor complexes, 163 E2F families (E2F1-5), 163–165, 213 Eg5, centrosome-associated motor protein, 215 EGFRand HER2, dual inhibition BIBW-2992 and HKI-272, 339 CI-1033, 339 IGF-1 receptor, 339–340 lapatinib, 338–339 pertuzumab, 339 tipifarnib, 340–341 EGF-treated A431 cells Kinetworks™ multi-immunoblotting analysis of, 126 Kinex™ antibody microarray analysis of, 121–125 EMEA. See European Medical Evaluation Agency Endostatin proteolytic fragment of collagen XVIII, 246, 247 EphB, tyrosine kinase receptors, 175 Eph receptor tyrosine kinase (RTK) family, 243 Ephrin and Eph receptors, 243 Epidermal growth factor (EGF), 166, 170 expression, 49 family (ErbB1-4), 170 overexpression, 50 signaling pathways in tumor cell types, 133 tyrosine kinase inhibitor, 50 Epithelial-cadherin (E-cadherin) homotypic adhesion molecules, 166 Epithelial growth factor receptor (EGFR) pathways, 157, 168, 190 Epithelial-mesenchymal transitions, 165–166, 166 Epithelial ovarian tumors, 113 Epithelial tissue ALPHA-TFBE t(6;11)(p21;q12), 76 BRD4-NUT t(15;19) (q13;p13.1), 76 MECT1-MAML2 t(11;19)(q21;p13), 76 NTRK1 and TRK Oncogenes, 75 PAX8-PPARg t(2;3)(q13;p25), 76 RET PTC1, 75 RET-PTC2, 75 RET-PTC3/PTC4, 75 RET-PTC5-PTC9, RET-PCM1, ELKS-RET, and RFP-RET, 75 TRK oncogene, 76 TRK-T3 oncogene, 76 TRK-T1 (T2; T4) oncogene, 76 ER-a and ER-b proteins, 191 AF-1 domain and, 190 structure of, 189 ER-a-Shc-IGFR (insulin growth factor receptor) ternary complex, 197 ER-a/Sp1 action, 191 ErbB1/ErbB2 complex, 170 ErbB2 (HER2, NEU) receptor, breast cancer, 170, 321, 326 ErbB2-overexpressing cells, 119

410 Erb4/HER4 receptor, 170 ERCC1-positive and negative tumors, 276 Erlotinib treatment, 19 Erythropoietin, 181 Estrogen-induced transcriptional activity, AF-2 domain and, 192 Estrogen-modulating drugs, 189 Estrogen receptor (ER), 188 AF-1 domain, 189–190 DNA-binding domain, 190 ER-a and ER-b, structure of, 189 ERE-independent genomic actions of, 195 ER mutations and, 192 estrogen and ER signaling mechanisms, 192 hinge region, 191 ligand-and-ERE-dependent activation of, 192–195 ligand-binding domain, 191–192 ligand-independent genomic activation of, 196–197 membrane-mediated nongenomic action of, 197–198 mutations, in breast cancer, 192 negative/basal-like tumors, 98 positive tumors, 30 Estrogen-related receptor a (ERRa), 194 Estrogen response element (ERE)-dependent gene expression, 188, 189, 192 AF-1 domain, role of, 190 Estrogens tumor promoters, 4 Ethmoid sinus intestinal-type adenocarcinoma, 46 Etoposide (VP-16), 258, 303, 364, 366, 378, 380, 396 E-Type cyclins (E1/E2), 206, 213 E3 ubiquitin ligase, 179 European Medical Evaluation Agency, 13 Ewing sarcoma (ES), 70 EWS and TLS/FUS rearrangements, 74 EWS-Aassociated oncogenesis, 69 EWS-Associated tumors, 69 EWS-ERG protein, 70 EWS-FLI1 fusion transcripts, 70 EWS-FLI1 fusion types, 70 Extracellular matrix (ECM) signaling, 121, 153, 170 F FACS. See Fluorescence-activated cell sorting Familial adenomatous polyposis (FAP), 52, 174, 273 Farnesyl transferase inhibitors, 315. See also EGFRand HER2, dual inhibition Fas ligand, 165 FAST-1, DNA-binding protein, 180 Fibroblast growth factor-b (bFGF), 241 Fibroblast growth factor (FGF), 166, 241 Fibroblast growth factor receptors (FGFR), 166 Fibronectin protein, 245 Field cancerization theory, 5 Flavopiridol protein kinases inhibitor, 62, 63 FLT3 activation and induction of leukemogenesis, 58 FLT3 expression, 57 Fludarabine, 258, 338 Fluorescence-activated cell sorting, 135 Focused microarrays, 144–147 Forkhead factors, 60 Forkhead homolog in rhabdomyosarcoma (FKHR), 194

Index FoxO Forkhead family transcription factors, 169, 180 Fz proteins, 173 G Gastrointestinal stromal tumors, 1, 55, 250, 317, 330 Gastrointestinal tumors CA 19.9 antigen, 111 carcinoembryonic antigen, 111 GATA-4 and GATA-5 transcription factors, 286 Gaucher’s disease, 111 GBM. See Glioblastoma multiforme GBP/FRAT homolog, 173 Gefitinib, 18, 47, 48, 169, 312, 315, 327–329, 338, 344 Gemcitabine, 257, 328, 337, 377–380 Gemtuzumab ozogamicin, 258 Gene-directed enzyme prodrug therapy (GDEPT), 367 Enzyme/Prodrug Systems for, 371–372 Gene expression microarrays, 323 Gene Expression Omnibus, 139 Gene expression profiling pathway-focused, 138 DNA hybridization microarrays, 139 experimental design and analysis for, 139–144 real-time PCR arrays, 139, 141 Gene prodrug activation therapy (GPAT), 367 Gene replacement methods, 20 Genes ABL, 67 AKAP9-BRAF, 48 Akt, 59 ALO17-ALK, 66 AML1, 69 AML1-ETO chimeric, 69 APC, 100, 162, 163, 174, 269 APC and beta-catenin in colon epithelium, 4 API2, 67 API2-MALT1, 67 Ataxia telangiectasia mutated (ATM), 272 ATIC-ALK, 66 axin, 174 bantam, 165 bax, 45 BCL2, 50, 51, 61, 62 BCL6, 54, 63 BCL10, 64, 65 BCR-ABL, 67 BER, 274 BLM, 275 BNIP3, 147 BRAF, 48 B-raf, 171 B-RAF gene, 321 BRCA1, 99, 385 BRCA1 and BRCA2, 12, 274 BRIP1, 85 c-ABL, 67 CARS-ALK, 66 Casp8, 149 Casp14, 149 CASP8 and TGFB1, 85 CCND1/2/3, 209, 215, 216

Index Genes (continued) CCNE1/2, 209 cdc2l, 215 CD/5-FC suicide gene, 371 CDH1, 99 CDK4, 222 CDKN1A/B/C, 211 CHEK2, 85 Chk2, 275 clathrin (CLTC), 67 CLTC-ALK, 66 C-MYC, 52, 63 c-myc, 4, 168, 169 coamplification of C-MYC and HER2, 337 Creb1, 149 cyclin A2, 163 cyclin D, 176, 178, 181, 182 cyclin D1, 168, 172, 174 cyclin E, 178 DAPK2, 147 DCC, 59 DNMT3b, 282 DR5, 45 E1AF, 70 (EGFP) or NR, 370 EGFR and ErbB3, 339 EGFR-overexpressing SCCHN, 338 ER-a, 188 ERa (ESR1), 97 ER-b, 188 erbB-2, 27 ERCC1, 383, 384, 387 ERCC2, 386 ERG, 69 ER-targeted genes, 97 ETO, 69 ETV1, 69, 70 EWS, 70 Fas/APO-1, 45 FEV, 69, 71 FLI1, 69 FLI1 (Friend leukemia integration site 1), 71 FLT3, 57, 58 Fmo4, 149 fos, 168 Gadd45a, 149 Gadd 45alfa, 45 gatekeeper/caretake, 4 GSTP1, 99 HER2, 336, 337 her-2/neu, 46, 48 HER2/neu, 337 hMLH1, 99 Hmox1, 149 H-ras, 281 HSV-TK, 370 IgLk, 63 INK4, 210 ITG-2a, 147 jun, 168

411 Kit, 57 KIT mutations, 326 KIT/PDGFRA, 57 K-ras and H-ras, 4 lacZ, 370 LEF-1, 175 LKB1, 99 L-MYC, 52 Lta, 149 MALT1, 65 MDM2, 46 MGMT, 99 mismatch repair (MMR) genes, 270 Mix-1, 180 MMP-1, 147 MSH2 and MLH1, 275 MSH6/GTBP, 275 MSN-ALK, 66 MTS1, 210 Mutations in K-RAS, 335 MutYH, 274 Myc, 164, 165, 172, 174, 179, 181 myc, 168, 176, 178, 181 myc-family oncogenes, 371 NBS1, 85 NF1 in Schwann cells, 4 NM23-H4, 147 N-MYC, 52 non-Ig, 63 non-Ig/BCL6 fusion, 63 NOXA, 45 NPM-ALK, 66 nuclear mitotic apparatus gene (NuMA), 67 nucleophosmin (NPM), 66 p16, 4 p53, 159, 162, 370 p107, 218 p130, 218 PALB2, 85 p53 and Rb tumor suppressor, 4 P14arf, 45 p14ARF, 216, 217 p14/ARF, 99 PAX, 63, 64 PAX5, 64 p21Cip1, 217 PDGFRA, 57 PDGF-Ra, 332 PIDD, 45 p16INK4a, 54, 55, 216, 217 p16/INK4a, 99 p15INK4b, 54, 56, 57 p18INK4c, 54, 55 p19INK4d, 54, 55 PML, 67 PMS1, 275 PMS2, 275 p14/p19ARF, 164 PRAD1, 51 pRb, 218

412 Genes (continued) promyelocyitc leukemia zinc finger gene (PLZF), 67 pS2, 195 Ptc, 162, 178 PTEN, 57, 162 PUMA, 45 p21 (WAF1–Cip1), 67 RAD50, 85 Raf, 48 Rap-1, 48 RARA, 67 RARb2, 99 Ras, 46, 162, 206 ras-effector genes RASSF1A and NORE1A, 286 RAS oncogene, 321 Rb, 162 Rb in retinal epithelial cells, 4, 281 RECQL4, 275 RET, 50 retinoic acid receptor a1 (RARa1), 195 RRM1/p53, 385–386 SMAD2, 60 STAT5b, 67 TAL1, 64 TFG-ALK, 66 TK, 370 Tnfrsf11a, 149 TOP2A, 337 TP53, 45 TPEF, 286 TPM3-ALK, 66 TPM4-ALK, 66 TRAIL (TNFSF10), 147 TTF, 63 VHL in kidney cells, 4 von Hippel-Lindau gene (VHL), 286 WRN, 272 XPD, 386 XRCC3, 387 ZSG, 69 Genetic analysis of CDK, and regulators G1/S CDK, physiologic roles of, 218–221 mitotic CDK, 221–222 tumor mouse models, 222–223 Genomic targets and drugs, 318–320 Genotypic International Lung Trial (GILT), 384 ERCC1 levels in, 385 GEO. See Gene Expression Omnibus Germ-cell cancer prognosis, 109 G0/G1 transition, 213, 218 GIST. See Gastrointestinal stromal tumors Glioblastoma multiforme, 10 Glioma-associated oncogene (GLI) pathway, 11 GLI proteins, 162, 177, 178 Glucocorticoid receptor, 189 Glucocorticoid receptor interacting protein 1 (GRIP1), 196 Glycogen synthase kinase-3 (GSK-3), 166, 169, 173, 174, 176, 190 Glypican receptor, 180 G2/M transition, 214 Gorlin’s syndrome, 178

Index G-protein coupled receptor (GPR30), 157, 176, 189, 198 Graft-vs.-host disease (GVHD), 295 Graft-vs.-leukemia (GVL) effect, 295 Granulocytic leukemia, 111 Grb2, adapter proteins, 166, 168 Grb2, Shc, Nck, and Grb7, adapter proteins, 166 Grb2-SOS complexes, 50 GRIP1, coactivator, 190 Groucho family proteins, 174 Growth factor-dependent pathways (GF), 9 GSK3-binding protein (GBP/FRAT), 173 G1/S transition cell cycles and, 163–164 senescence and apoptosis, 164 GTP/GDP exchange factors, 168 H Hardy Zuckerman 4 feline sarcoma virus, 57 HATs. See Histone acetyltransferases HDACi classification, 288 HDACs. See Histone deacetylases HDM2 locus, 215 Head and neck squamous cell carcinoma, 5 Heat-shock protein 70 (hsp70) and (hsp90), 191, 192 Hedgehog (Hh) signaling pathways, 176–179 Hemagglutinating Japan virus (HVJ-liposomes), 370 Hematopoietic stem cells (HSC), 160, 175, 178, 182 Hematopoietic tumors chimeric proteins AML-ETO t(8;21)(q22;q22), 69 API2-MALT1 t(11;18)(q21;q21), 67 BCR-ABL t(9;22)(q34;q11), 67, 68 NPM-ALK, 66, 67 PML-RARA t(15;17)(q22;q21), 68 nonfusion genes, 61 proto-oncogene activation BCL1, 62, 63 BCL2, 61, 62 BCL10 t(1;14)(p22;q32) and t(1;2)(p22;p12), 64, 65 C-MYC and BCL6, 63 MALT1 t(14;18)(q32;q21), 65 PAX5 t(9;14)(p13;q32), 63, 64 TAL1, 64 Hepatitis B virus (HBV), 1, 13 Hepatocellular carcinoma, 4 HER2 protein induced breast tumors, 226 negative tumors, 48 targeting trastuzumab, 337–338 HER2 amplification/overexpression, 92 Hereditary cancer predisposition syndromes, 272–275 Hereditary nonpolyposis colorectal cancer (HNPCC), 43, 181, 275 Hereditary tumors, 1 Heregulin receptor (HER/cErB), 197 her-2/neu gene expression, 49 Hesperadin indolinone molecule, 312 High-grade cervical dysplasia (CIN 2/3), 13 High-grade vulvar dysplastic lesions (VIN 2/3), 13 High-risk human papillomavirus (HR-HPV)-related cancer, 55

Index Hippo pathway, 165 Histone acetyltransferases, 9, 192, 193 Histone deacetylases, 9, 213 containing complex, 283 inhibitors, 21 protein complex, 194, 224 Histone H3, 168 Histone methyltransferases (HMTs), 192–194 HIV-1 TAT protein, 226 bHLH proteins (Hes in mammals), 179 HNPCC-related cancers, 48 HNSCC. See Head and neck squamous cell carcinoma Homologous recombination repair (HRR), 385 Hormone-dependent pathways, 9 Hormone replacement therapy (HRT), 393–394 Human cancer, CDK alteration and their regulators in CDK substrates, genetic alteration of, 218 cell cycle CDK in, 215 tumor-associated alterations, 215–218 Human chorionic gonadotrophin (hCG), 107, 108 9p21Human chromosome, 210 Human colon carcinoma, 159 Human Genome Project, 2, 210 Human IAP proteins, 262 Human papilloma viruses (HPV), 1, 13 Human TATA binding protein–associated factor (TAFII30), 192 4-hydroxytamoxifen, 191 Hypoxia inducible factor (HIF) pathway, 11, 166, 241 I IARC TP53 database, 45 ICI182,780, antiestrogen, 194, 197, 198 IDC. See Infiltrating ductal carcinomas IEN. See Intraepithelial neoplasia IGF/insulin receptor, 196 ImaGene 7.0 software, 121, 122 Imatinib for cancer therapy CML, 330 dasatinib, 331–332 in GIST EGFR mutations and, 334–335 ErbB receptor signaling, 333 erlotinib, 334 gefitinib, 333 imatinib resistance, 328–333 NSCLC treatment, 334 targeting EGFR, small molecule/antibody, 333 resistance in, 330–331 resistant CML, 17 Immunoglobulin supergene family, 244 Immunostimulatory cytokines, 296 Imperial Chemicals Industries (ICI), 391 Infiltrating ductal carcinomas, 5 Inflammatory myofibroblastic tumors, 67 Inhibitor of apoptosis proteins (IAP), 257 INK4 family, 219, 222 cell-cycle inhibitors, 215 proteins, 211 Inositol triphosphate (IP3), 169 Insulin-like growth factor (IGF-1), 195, 197

413 Insulin receptor substrate (IRS), 166 Interagency Oncology Task Force (IOTF), 329 Interferons, 181 Interleukins, 181 Internal ribosome entry site (IRES), 215 International Germ Cell Cancer Collaborative Group, 109 Intra-abdominal desmoplastic small-round-cell tumor, 69 Intraepithelial neoplasia, 4 Intrinsic transcription activation domain (AD1 and AD2), 193 Investigational New Drug (IND), 329 Iressa Survival Evaluation in Lung Cancer (ISEL), 334 Irinotecan, 246, 258, 330, 341 IRS protein, 169 J JAK2/STAT3 inhibition, 300 JAK/STAT signaling pathway, 181–182 JAK tyrosine kinase, 181 Janus kinases (JAK), 181 Jeghers syndrome, 168 Jun/Fos proteins, 190 transcription factor complex, 195 K Kaiso family of proteins, 287 Kinase inhibitors Aurora kinase, 309, 311 cyclin-dependent kinase, 344 EGFR tyrosine kinase, 334–335 ErbB tyrosine kinase, 338–339 Kinase and HSP90, 323 PI3 kinase, pathway, 342, 349 Kinetworks™ multi-immunoblotting analysis of EGF-treated A431 cells, 126 Kinexus antibody-based integrated discovery platform, 121 Kinexus Bioinformatics Corporation, 121 KIT mutations, 57 c-Kit proto-oncogene, 57 c-Kit receptor tyrosine kinase, 301 c-Kit tyrosine kinase receptor, 182 KPSS Phospho-Site Screens, 126, 127 K-RAS2 gene mutations, 11 Kyoto Encyclopedia of Genes and Genomes (KEGG), 138 L Laminin protein, 245 Langerhans cell histiocytosis, 3 Lapatinib oral therapy, 17 Laser-capture microdissection, 136, 332, 333 Latency-associated promoter (LAP), 371 LBD/AF-2 domain, 194 LCM. See Laser-capture microdissection Level of evidence (LOE) data, 36, 37 Li-Fraumeni syndrome (LFS), 86, 275 Ligand Binding Domain (LBD), 189, 191–192 Ligand-inducible transcription factor, 189 Liquid Chip technology from Luminex, 120 Lobular carcinoma in situ (LCIS), 394

414 Locked nucleic acid (LNA), 218, 262 9p21 LOH and p16INK4a alterations, 55 Long interspersed nuclear elements (LINES), 283 Loss-of-function (LOF) mutant mice, 218 Loss of heterozygosity (LOH), 7, 159, 161, 181 LPL lymphoplasmacytoid lymphoma, 61 LRP5/6, low- density liporotein receptor-related protein family, 172, 173 LRP5/6 proteins, 173 L7/SPA, coactivator, 191 Lung cancer, 4 LXXLL consensus sequences, 192 LXXLL/NR box, 195 LXXLL recognition motifs, 191 Lymphomatoid granulomatosis, 3 Lymphomatoid papulosis, 3 Lymphoplasmacytoid lymphomas, 64 M MALT lymphomas, 65, 67 Mammalian CDK, 208 Mantle cell lymphoma (MCL), 62, 215 Mantle non-Hodgkin lymphomas, 11 MAP4 and Stathmin survival factor, 45 MAPK/ERK pathway, 192 MAPK signaling pathway, 190 Marker surge phenomenon, 108, 109 MASCOT databases, 119 Maspin, tumor repressor, 190 Mastermind (Mam), coactivator, 179 MAT1 (ménage a trois 1), 205 Matrix of targets graphic representation, 8 Maturation promoting factor (MPF), 206 MeCP2 germ-line mutations, 283 Melphalan, 52 Memorial Sloan-Kettering Cancer Center, 109 Metalloproteinase-1 (TIMP-1), 107 Metastatic tumor antigen 1 (MTA1), 198 Methyl-binding domain (MBD) proteins, 282 Methylguanine-Methyltransferases (MGMT), 272 Methyl-piperidino-pyrazole, ER-a-selective antagonist, 191 MGMT and Gliomas, 276 MGUS. See Monoclonal gammopathy significance Midkine promoter (MK), 370 Mineralocorticoid receptor, 189 Minichromosome maintenance (MCM) replication proteins, 214 Mi-2/NURD complex, 283 Mitochondrial outer membrane permealization (MOMP), 257 Mitogen-activated protein kinases (MAPK), 168, 182, 213, 224 phosphorylation, 190, 197 Mitotic CDK, genetic analysis of, 221–222 MK0457 4,6 diaminopyrimidine, 312 MLH1 transcriptional regulatory sequences, 275 MLL-AF9, fusion protein transgene, 158 MLN8054 inhibitor, 312 Modern gene expression profiling methods, 2 Molecular beacon probes, 142 Molecular chaperone HSP90 inhibitors, 323–325 Molecular targets, 1, 9

Index Molecular therapeutics clinical trial design for, 325–326 cytotoxic agents, traditional clinical drug development for, 325 invasive and noninvasive biomarkers, 327–330 pharmacokinetic and pharmacodynamic endpoints, 326–327 pharmacological audit trail, 327 target-based therapy, clinical drug development of, 326 Monoclonal gammopathy significance, 62 Mouse embryonic fibroblasts (MEF), 164, 219, 221, 222 Mouse models, of cell cycle CDK and regulators, 220–221 MRE11 multiprotein nuclease complex mutations, 275 MSN-ALK protein, 66 M918T mutation, 50 mTORC1, target of rapamycin (TOR) complex, 168, 169, 171 MUC1, oncoprotein, 190 Multi-immunoblotting approach, 126 Multi-step carcinogenesis model, 5 Murine leukemia virus (MLV), 368 Mutator phenotype hypothesis, 269 MutY-associated polyposis, 273 Myc-dependent signals, 172 MYC expression, 52 Myelodysplastic syndromes (MDS), 56, 288 Myoepithelial markers, 98 N National Cancer Institute and FDA (NCI-FDA), 329 National Cancer Research Institute (NCRI), 329 National Surgical Adjuvant Breast and Bowel Project (NSABP), 394 Neorofibromin 1 (NF1), RasGAP protein, 171 NER-Cockayne syndrome, 274 Nerve growth factor (NGF), 166 Neuroblastoma, 53 Neurofibromin 2 (NF2), 165 NFAT3, transcription factor, 189 Noggin protein, 180 Non-BRCA familial breast cancers, 95 Nonhereditary tumors, 1 Non-Hodgkin’s lymphomas (NHL), 215 Nonrandom chromosomal abnormalities, genetic markers, 60, 61 Nonsmall-cell lung cancer (NSCLC), 216, 249, 260, 312, 326, 336, 383 Nonsteroidal antiestrogens, as chemopreventive agents, 392–393 Nonsteroidal anti-inflammatory drugs, 59 North American National Surgical Adjuvant Breast and Bowel Project (NSABP), 337 North Central Cancer Treatment Group (NCCTG), 337 Notch signaling pathway, 175, 179 NPM-ALK protein, 66, 67 NR-Binding SET-domain-containing protein 1 (NSD1), 194 NSAID. See Nonsteroidal anti-inflammatory drugs Nuclear-receptor-interaction domain (NRID), 195 Nucleotide excision repair (NER), 383 O O6-Alkylguanine-DNA Alkyltransferase (ATase), 272 Oblimersen, 257–259, 261, 327 Oblimersen 18-mer phosphorotioate antisense oligonucleotide, 260

Index O’Farrell 2D gel approach, 119 Oncogene inhibitors (OIs), 10 bevacizumab, 17 cetuximab, 17 EGFR, 18 HER-2, 18 tyrosine kinase, 17, 18 VEGF, 18 Oncogene protein, 16 OncotypeDX, 38 Oropharyngeal squamous cell carcinoma, 55 Osteonectin and fascin cytoskeletal proteins, 98 Osteopontin protein, 245 Ovarian cancer and CA125, 113 Oxaliplatin, 330, 377, 379 8-OxoG DNA N-glycosylase 1(OGG1), 270 P Pancreatic cancer, 111 Paclitaxel, 37, 38, 45, 256–258, 303, 315, 328, 341 p300 and CBP-associated factor (p/CAF), 193 Papillary thyroid carcinoma, 66 p14ARF-mdm2 complex, 56, 210 Patched (Ptc), Hh receptor, 176 PathwayFinder arrays, 147 Pathway-focused arrays array platform for, 143 biologic replicates and, 142, 143 sample and data analysis, 143, 144 PAX5 overexpression and transcription, 63, 64 PBMC. See Peripheral blood mononuclear cells PDGF-BB. See Serum platelet derived growth factor-BB PDGFRA and kit mutations, 57 PDGF receptor (PDGFR), 166, 168 PELP1 (proline-,glutamic acid-, and leucine-rich protein 1), 197 Peripheral blood mononuclear cells, 327 Perkin-Elmer Scan- Array Express Reader, 121 Peroxisome proliferator-activated receptor g (PPAR-g), 191 Pertuzumab, 48, 312, 333 p16 expression, 55 p27 expression, 95 PGC-1, coactivator, 191 Pharmacologic synthetic lethal screening, 22 Philadelphia chromosome, 17 Phosphatase and tensin homolog (PTEN), 168, 169, 171 Phosphatidyl inositol 4,5 diphosphate (PtdIns[4,5]P2; PIP2), 168 Phosphatidylinositol 3-kinase pathway inhibitors bevacizumab, 346–347 cyclin-dependent kinase inhibitors, 344 histone deacetylase inhibitors, 348–349 of HSP90, 344–346 inhibiting angiogenesis, 344 Mammalian Target of Rapapmycin (mTOR), 340–342 Poly ADP-Ribose Polymerase (PARP) inhibition, 347 sorafenib, 345–346 sunitinib, 345 Phosphatidyl inositol 34 kinase (PI3K), 157 Phosphatidyl inositol 4-phosphate (PtdIns 4-P; PIP), 168 Phosphatidyl inositol (PtdIns; PI), 168

415 3¢ Phosphoinositidedependent protein kinase 1 (PDK1), 168 Phosphoinositide3-kinase (PI3K) pathway, 11 Phospholipase Cg (PLCg), 166, 169 Phospholipase C pathway, 169 Phospho Tyrosine-Binding (PTB) domain, 166, 167 PI3K/AKT pathway, 197 PI3K pathway, 165, 166, 168–169, 171, 180, 181 protein translation and cell growth and, 169 PI3K/PTEN/AKT pathway, 58 PI3K/PTEN mutations, 171 PI3K signaling., 213 Pilomatricomas, human tumor, 175 p16INK4a/CDK4/cycD1/Rb pathway, 55 p16INK4a/MTS1/CDKN2A complex, 215 p27Kip1 inhibitor of CDK, 211, 213, 215 protein, 217, 218 Placental-like alkaline phosphatase (PLAP), 108 Platelet-derived growth factor receptor (PDFGR), 224 Platelet-dervived growth factor (PDGF), 166 Pleckstrin homology (PH) domain, 59, 168 PLSTIRE kinase, 207 PML-RARA chimeric transcript, 67 Polycythemia rubra vera, 111 Polymerase chain reaction (PCR)-based strategies, 210 p53 pathway, 47, 164, 165, 172 p21-Ras, cellular protein, 168 pRb family proteins, 213 Predictive factors relative strengths, 34 p85, regulatory subunit, 168 Pre-mRNA splicing factors RNPS1, 209 p90 ribosomal S6 kinase (Rsk), 190 Primary malignant melanoma, 55 Proangiogenic and antiangiogenic molecules, 240 Progenitor endothelial cells (PEC), 240 Progesterone receptor (PR), 189 Prognostic and predictive factors, 30 clinical use of, 36–38 relative strengths of, 32 Prognostic factor definition of, 27 relative strengths of, 33 Programmed cell death protein 4 (PDCD4), 169 Proliferation markers (Ki-67), 99 Pro-Q Diamond stain, 119 Prostate cancer, tumor markers in diagnosis, 111 hormone therapy and, 112 PSA as marker in, 111, 112 and staging, 111 Prostate carcinoma, 51 Prostate specific antigen, 111 Prostatic acid phosphatase (PAP), 111 Protein arginine methyltransferase-1 (PRMT-1), 193 Protein, gain (GF) and loss (LF) of function, 8 Protein inhibitors of activated STAT (PIAS ), 181 Protein kinase B (PKB), 168, 169, 171 Proteins 2D PAGE proteomic maps, 119 Protein translation and cell growth, PI3K pathway related to, 169

416 Proteomic technology antibody-based proteomics analysis, 117–133 conventional proteomics, 117–119 genomics vs proteomics profiling, 117–119 to identify marker, AHA1, 345 in molecular therapies, 323, 327 Prototype hereditary syndrome, 274 p90RSK family kinases, 168 p70S6 kinase (S6K), 168, 169, 171 PSK-J3 (putative serine/threonine kinase; filter J colony 3), 206, 207 PSSALRE kinase, 207 Ptc:Smo complexes, 176 PtdIns(3,4,5)P3 (PIP3), 168 PTEN mutations, 58, 59 p53 Tumor suppressor (TS) protein, 16 p21/WAF1 promoter, 195 R Radical prostatectomy, 111, 112 RAF and MEK inhibitors CI-1040, PDO325901 and ARRY-142886, 341–342 ISIS 5132/ LErafAON, 341 Raf-MEK-ERK phosphorylation, 168 Raloxifene, 191 Raloxifene Use for Heart (RUTH), 395 Ras and loss-of-function mutations, 166 Ras downstream pathways, 48 Ras/ERK (extracellular signal regulated kinase) pathway, 168 RAS/ERK stimulation, 50 RasGAP activity, 171 Ras homolog enriched in brain (Rheb), 168, 169 Ras/MAPK pathway, 166, 168–172, 179 ras mutations, 171 RAS-RAF signaling pathway, 47 Ras signaling, 213 Rb and p53 pathways matrix of targets, 10 Rb/E2F axis mutations, 164, 165 Receptor-regulated Smads (R-Smads), 180 Receptor tyrosine kinase (RTK) pathways, 154, 156, 157, 166, 167, 181 branches, interactions between, 170 in cancer, mutational alteration of, 170–171 mutations, cause of cancer, 171–172 phospholipase C pathway, 169 PI3K pathway, 168–169 Ras/ERK pathway, 168 translation and cell growth, PI3K pathway related to, 169 RECIST. See Response Evaluation Criteria in Solid Tumors RECQL4 genes mutations, 275 Reprimo glycosylated cytoplasmatic protein, 45 Response Evaluation Criteria in Solid Tumors, 22, 325 criteria for oncological clinical trials, 22 RET expression, 50 Retinal cancers in children, 4 Retinoblastoma (Rb) family proteins, 53, 163, 164, 218, 225 Retinoid hormone receptors, 189 RET proto-oncogene, 50 RET somatic mutations, 49 Rituximab, 258, 338 RNA polymerase II, 194, 207, 209

Index Robust and versatile design principle, 156–157. See also Signaling pathways RO-3306, quinolinyl thiazolinone derivative, 225 Rothmund-Thomson syndrome, 275 RRLFG motif, of p27, 212 R-Roscovitine (CYC202), 224 RT-qPCR array plates, 143 RXL (cyclin-binding motif), 226 S Saccharomyces cerevisiae (yeast), 206, 207, 213 SAHA, histone deacetylase inhibitor, 21, 288, 324, 348 Salivary gland adenoid cystic carcinoma, 57 Salmonella typhimurium, 369 SCF. See Stemcell factor SCF (b-TRCP) complex, 176 Schizosaccharomyces pombe, 206 SDS-PAGE. See Sodium dodecyl sulphate-polyacrylamide gel electrophoresis g-Secretase complex, 179 Secreted frizzled-related proteins (SFRP), 284 Selectins transmembrane receptors, 243 Selective ER modulators (SERMs), 189, 191, 195 Selective estrogen receptor modulators, 391 Semeniuk analysis, 112 Seminoma, 110 Serine/threonine kinases, 206 Serine threonine kinases (types I and II), 180, 181 SERM action, recognition of, 392 SERM-ER complex, 191 SERMs. See Selective estrogen receptor modulators Serrate family ligands, 179 Ser-Thr kinase LKB1/ STK11 in hamartomatous neoplasms, 286 Ser/Thr protein kinase, 168 Serum markers, 111 Serum platelet derived growth factor-BB, 53 Serum tumor markers a-fetoprotein, 108 Severe combined immune deficiency (SCID) phenotypes, 182 Shc, adaptor protein, 197 Sigma-Aldrich Panorama antibody microarrays, 120 Signaling pathway mutations for cancer, 162 angiogenesis, epithelial-mesenchymal transitions, and metastasis, 165–167 apoptosis, accelerated G1/S transition and, 164 cell cycles, G1/S transition and, 163–164 cell death and senescence, 165 cell growth regulation, 164–165 cell interactions and, 165 Hh, Dpp, and Wg, singnaling molecules, 165 Signaling pathways Hedgehog (Hh), 176–179 JAK/STAT, 181–182 in normal development cell state and, 156 complex developmental patterns, by simple intercellular interactions, 154–156 external signals reset interlocking internal, 153–154 organization, 154 robust and versatile design principle, 156–157

Index Signaling pathways (continued) Notch, 179 receptor tyrosine kinase, 166–172 TGFb/BMP family, 179–181 Wnt signaling, 172–176 Signal transducer and activator of transcription 3 (STAT3), 181, 298 Signal transduction, 317 rationale for targeting pathways, 317, 321 strategies for hitting targets, 322 SILAC. See Stable isotope labeling with amino acids in cell culture Single nucleotide polymorphism (SNP), 386 Sjögren disease, 3 Skin cancers, 4 Smad3/4/ ATF3 complex, 180 Smad3/4/E2F/C-EB-P complexes, 180 Smad4 function loss, 181 Smad proteins, 180 Small-cell lung cancer, 218 Small ubiquitin-like modifier (SUMO), 191 Smo protein, 162, 178 SMRTs and NCoR, 194, 195 sMZL splenic mantle zone lymphoma, 61 Sodium dodecyl sulphate-polyacrylamide gel electrophoresis, 118, 119 Soft tissues ASPL-TFE t(X;17) (p11;q25), 74 ETV6-NTRK3, TEL-TRKC t(12;15) (p13;q26), 73 EWS-ATF1 t(12;22) (q13;q12), 71 EWS-CHOP t(12;22) (q13;q12), 72 EWS-E1AF t(17;22) (q12;q12), 70, 71 EWS-ERG t(21;22) (q22;q12), 70 EWS-ETV1 t(7;22) (p22;q12), 70 EWS-FEV t(2;22) (q33;q12), 70 EWS-FLI1 t(11; 22) (q24; q12), 70 EWS-WT1 t(11;22) (p13;q12), 71, 72 EWS-ZGS t(1;22) (p36.1;q12), 71 FUS-ATF1 t(12;16), 72 FUS-CHOP t(12;16) (q13;p11), 72 FUS-CREB3L1 t(11;16) (p11;p11), 74 FUS-ERG t(16;21) (p11;q22), 71 JAZF1-JJAZ1 t(7;17) (p15;q21), 74 PAX3-FKHR t(2;13) (q35;q14) and PAX7-FKHR t(1;13) (p36;q14), 72, 73 PDGFB-COL1A1 t(17;22) (q22;q13), 73 TGF-CHN t( 9;3), 72 TPM3-ALK t(2;5) (p23;q35), 73 Solid-phase, combinatorial, and parallel synthesis, 323 Solid tumors, 51 Son-of-sevenless (Sos), 168 Sorafenib, 47, 239, 243, 245, 246, 312, 313, 315, 335, 340–342 Spk1-Cullin-F-box proteolytic complex, 210 Squamous cell carcinoma of head and neck (SCCHN), 336, 338 SRC family, 193 coactivators, 197 tyrosine kinases, 168, 170 Src Homology 2 (SH2) domain, 166–168 SRC/p160 coactivators, 190 Stable isotope labeling with amino acids in cell culture, 119 STAT5b. See Transcription factor signal transducer and activator of transcription 5b

417 Steel factor, 57 STE20-family protein kinase, 164 Stemcell factor, 57 Stem cell leukemia hematopoietic transcription factor, 64 Stem cell transplant (SCT), 17 Steroid hormone receptors, 189, 191 Steroid receptor coactivator-1 (SRC-1), 190 Study of Tamoxifen and Raloxifene (STAR), 395 Su(fu) complexes, 176–178 Suicide gene therapy advantages of, 367 bystander effect, 373–375 immune effects, 374–375 mechanisms of, 373–374 efficient and selective delivery of bacterial vectors, 369 nonreplicating viral vectors, 368 nonviral and viral/nonviral hybrid vectors, 369–370 replication-selective viruses, 368–369 targeting cancer cells, 370–371 prodrugs and drugs for, 372–373 SUMOylation, of proteins, 181 Sunitinib, 56, 239, 243, 245, 246, 313, 315, 326, 327, 340, 341 Suppressive signaling pathways, 300–301 Suppressors of cytokine signaling (SOCS) proteins, 181, 182 Surface plasmon resonance (SPR), 120 Surivivin, and borealin inner centromere protein (INCENP), 308 Survivin of IAP family, 264 SWI/SNF complex, 193 SYBR green dye, 142 Synovial sarcoma, 57 T TaqMan probes, 142 Targeted therapies era, 1, 15–17 TATp27 proteins, 226 T-Cell acute leukemia, 64 immunity and tumor regression, 299 receptor, 170, 216 receptors specific gene transfer for tumor epitopes, 296 tolerance, 298–299 TCF/Ets family, 168 TCF/LEF family of transcription factors, 172 Tcf/Lef family transcription factors, 174 Tenascin protein, 244 Terminal transferese dUTP nick end labeling (TUNEL) assays, 147 Testicular cancer, 108 remission monitoring in, 111 response in, 109, 110 stages in, 108, 109 Testicular germ cell tumor, 109 Testosterone tumor promoters, 4 TGFa family, 166 TGFb /activin family, 180 TGFb/BMP family signaling, 179–181 TGFb type-I/type-II receptors, 181 thalodomide, 243, 247, 258 Three dimensional (3D)-type cyclins, 212 Thrombospondin angiogenesis inhibitor, 239

418 Thrombospondin protein, 245 Thrombospondin-1 (THBS-1) antiangiogenic factor, 286 Thyroid hormone receptors, 189 Tie-2 receptor, 242, 243 T790M somatic mutation, 50 TMUGS. See Tumor Marker Utility Grading System T-PLL T prolymphocytic leukemia, 61 TPM4-ALK fusion protein expression, 66 TP53 pathway in melanoma, 56 TRA-1-60 antigen, 108 TRAIL. See Tumor-necrosis-factor-related apoptosis -inducing ligand Transcriptional CDK, cell cycle and, 207–209 Transcription-coupled NER machinery, 271 Transcription factor signal transducer and activator of transcription 5b, 190 Transforming growth factor a and Epidermal growth factor, 243 Transforming growth factor-b (TGF-b), 154, 196, 210, 2211, 243 Trastuzumab, 9, 17, 18, 31, 33, 37, 38, 48, 58, 76, 91, 169, 245, 246, 311, 315, 320, 327, 330–332, 340, 344 Trastuzumab monoclonal antibody (MAb), 49 Trimethyl-K4 of histone H3 (3mK4 H3), 284 TSC1/2 function loss, 171 TSC1/2, tuberous sclerosis complex proteins, 168, 169 TSG p16INK4ainactivation in human cancer, 282 Tumor-associated immuno suppression inhibition, 299, 300 Tumor cells and DNA damage, 269 Tumor classification, gene expression profiles and prognosis, 136 Tumorigenesis, role of genetic instability, 270 Tumor immuno editing model, 294 Tumor markers circulating markers, 107 clinical use of, 36 for clinical use selection, 31 evaluation of, 108 life cycle of, 35 relative strengths of, 34–36 significance of, 28 testicular cancer for, 108 types of, 1 Tumor Marker Utility Grading System, 27 Tumor-necrosis-factor-related apoptosis-inducing ligand, 258–260 Tumor necrosis factor (TNF), 165 Tumor neovascularization mechanisms, angiogenic switch, 240, 241 Tumor-node-metastasis (TNM) staging systems, 29 Tumor suppressor activators (TSAs), 10 E2F, 21 p53 protein, 19 pRb, 19 Tumor-suppressor genes (TSG), 3, 7, 8, 16, 19, 22, 43, 93, 158, 161, 210, 241, 281, 284, 317 hypermethylation-associated silencing of, 284–286 Tumor-supression pathways at 9p21 locus, 55

Index Tumor vaccines, 297–298 Two-hit model, 4 Tyrosine kinase inhibitors (TKIs), 11, 17 U Up/downregulation of gene, 7 Upper aerodigestive tract, 5 V Vaginal intraepithelial neoplasia (VIN), 13 Vascular endothelial cadherin, 242 Vascular endothelial growth factor receptor 2 (VEGFR2), 195 Vascular endothelial growth factor (VEGF), 11, 166, 195, 224, 333, 370 Vascular targeting agents (VTA) combretastatin A4, 248 Vasculogenic mimicry, 242 VE-cadherin. See Vascular endothelial cadherin Vector systems, for gene therapy, 368 VEFG/VEGFR system, 242 Vesicular stomatitis virus (VSV), 368 Vimentin myoepithelial markers, 98 vinorelbine, 257, 330, 378, 379 Virus-directed enzyme prodrug therapy (VDEPT), 367 Vitamin D receptor, 189 Vitronectin protein, 245 Vitronectin receptor (VNR), 244, 245 V804 mutations, 50 von Hippel-Lindau syndrome, 11 von Willebrand factor, 245 vorinostat, 21 W WEE1 inhibitor, 226, 227 WEE1/MYT1 kinases, 215, 227 Werner syndrome, 275 Wildtype p53 sequence-specific DNA binding, 16 Wnt d-catenin signaling pathway, 172, 174, 177 Wnt pathway, 163 WNT-receptor frizzled proteins, 286 Wnt signaling pathway, 172–176 X Xenopus, 172, 180, 206 Xeroderma Pigmentosum (XP), 274 XIAP (hILP/MIHA/BIRC4), function and regulation, 263 X-linked IAP (XIAP), 257 X-ray crystallographic analysis, 191 Y Yang pathogenic factor, 15 Yin pathogenic factor, 15 Yorkie activity, 165 Z ZM447439 quinazoline derivative, 311