Tumor Models in Cancer Research, Second Edition (Cancer Drug Discovery and Development)

Cancer Drug Discovery and Development

Series editor Beverly A. Teicher Genzyme Corporation, Framington, MA, USA

For other titles published in this series, go to www.springer.com/series/7625

Beverly A. Teicher Editor

Tumor Models in Cancer Research Second Edition

Editor Beverly A. Teicher Genzyme Corporation Boston, MA USA [email protected]

ISBN 978-1-60761-967-3 e-ISBN 978-1-60761-968-0 DOI 10.1007/978-1-60761-968-0 Springer New York Dordrecht Heidelberg London © Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Humana Press, c/o Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of going to press, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Humana Press is part of Springer Science+Business Media (www.springer.com)

Preface

Progress in a given field is often dependent upon the development of appropriate, accurate models. In modern times, cancer research has been engaged in a focused search for such models for more than 50 years. The foremost problem in developing such models is that cancer is many, many diseases arising from nearly every tissue and metastasizing to many. A major breakthrough for model in cancer research was the development of transplantable rodent tumors. Many of the early tumor lines were carcinogen-induced, but other arose naturally in elderly animals from inbred strains of mice. These syngeneic tumors grown in the inbred host of origin allowed reproducible tumor growth and reproducible response to anticancer agents to be achieved. These tumor lines also frequently allowed the analysis of tumor metastasis in the host. The mutual needs for as large an array as possible of tumor types and expansion of true inbred strains of mice to carry these tumors lead to the identification of mutant mice with characteristics of deficient immunity suitable for the growth of human tumors as xenografts. The most frequently used of these mutant mouse strains are nude mice and SCID mice. Human tumor xenograft models were established from the many human tumor cell lines developed in the 1970s and 1980s and from fresh tumor explants. Since techniques for genetic manipulation have become more routine, animals expressing “oncogenes” or missing “tumor suppressor” genes have been developed, allowing a new level of understanding of the process of malignancy and new models for testing anticancer agent efficacy. Through the use of these techniques for some diseases and targets, it has been possible to establish specific animal models. Therapeutic index continues to be a critical variable for anticancer agents directed toward any cellular target related to proliferation. Animal models developed to determine potential normal tissue toxicities of new agents as well as the potential of normal tissue protectors have focused on proliferating normal tissues such as mucosa, gut, skin, and bone marrow although cardiac, renal, and lung toxi­ city can also be modeled. Still, it is the determination of meaningful experimental endpoints that defines the usefulness of models to a field. Increase-in-lifespan (survival) was an endpoint used by Dr. Howard Skipper and colleagues in their groundbreaking murine leukemia studies. Many current models, especially solid tumor models, are not amenable to a survival endpoint; therefore, other measures of tumor v

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response, usually involving tumor volume measurements are applied. Endpoints such as tumor growth delay and tumor growth inhibition closely mimic clinical endpoints, such as response time and time to recurrence. Other endpoints, such as ratio of treated group to control group, log kill, percent apoptosis, and tumor cell survival, depend upon the availability of an untreated or vehicle-treated control group in the experiment. The past 6 years since the first edition of this book have seen great progress in the development of genetically engineered mouse (GEM) models of cancer. These models are finding an important role in furthering our understanding of the biology of malignant disease. A comfortable position for GEM models in the routine conduct of screening for potential new therapeutics is slowly but surely coming. Increasing numbers of genetically engineered mice are available, some with conditional activation of oncogenes, some with multiple genetic changes providing mouse models that are moving closer to the human disease. While we wait for the perfection of the GEMs, the transplantable tumor remains the main resource for drug discovery and efficacy modeling. Though often maligned as models of human disease, antitumor activity in syngeneic mouse tumors and human tumor xenografts is a requirement for most therapeutics prior to entry into development. The criticism directed at these models is frequently a result of the differences between mice and humans. Drug pharmacokinetics in the mouse can be markedly different from pharmacokinetics for the same molecule in other species. The mouse is a remarkably resilient host often able to tolerate much higher doses of experimental therapeutics than human patients, thus allowing blood levels to be reached in mice that cannot be attained in humans frequently leading to disappointing clinical findings. These limitations of the host cannot readily be solved but are limitations which are recognized and are increasingly taken into account in decision making in selecting development candidates. An ideal tumor model would imitate in scale and mirror in response to the human disease. Though no such ideal models exist for the diseases that are cancer, the models described herein represent the efforts of many investigators for many years and approach with closer and closer precision examples that can serve as guides for the selection of agents and combinations for the treatment of human malignancy. 

Beverly A. Teicher

Contents

Part I  Introduction   1 Perspectives on the History and Evolution of Tumor Models........................................................................................ Shannon Decker and Edward Sausville

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Part II  Transplantable Syngeneic Rodent Tumors   2 Murine L1210 and P388 Leukemias........................................................ William R. Waud   3 Transplantable Syngeneic Rodent Tumors: Solid Tumors in Mice................................................................................. Lisa Polin, Thomas H. Corbett, Bill J. Roberts, Alfred J. Lawson, Wilbur R. Leopold III, Kathryn White, Juiwanna Kushner, Stuart Hazeldine, Richard Moore, James Rake, and Jerome P. Horwitz   4 B16 Murine Melanoma: Historical Perspective on the Development of a Solid Tumor Model.......................................... Enrique Alvarez

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Part III  Human Tumor Xenografts   5 Human Tumor Xenograft Efficacy Models............................................. Ming Liu and Daniel Hicklin

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  6 Imaging the steps of metastasis at the macro and cellular level with fluorescent proteins in real time......................... 125 Robert M. Hoffman   7 Patient-Derived Tumor Models and Explants......................................... 167 Heinz-Herbert Fiebig and Angelika M. Burger

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  8 The Pediatric Preclinical Testing Program............................................ 195 Christopher L. Morton and Peter J. Houghton   9 Imaging Efficacy in Tumor Models........................................................ 215 Vinod Kaimal, Wilbur R. Leopold, and Patrick McConville Part IV  Carcinogen-Induced Tumors 10 Mammary Cancer in Rats....................................................................... 245 Henry J. Thompson Part V  Disease and Target-Specific Models 11 Animal Models of Melanoma.................................................................. 259 Ene T. Fairchild and William E. Carson, III 12 Experimental Animal Models for Investigating Renal Cell Carcinoma Pathogenesis and Preclinical Therapeutic Approaches......................................................................... 287 Gilda G. Hillman 13 Animal Models of Mesothelioma............................................................ 307 Harvey I. Pass, Joseph B. Pincus, Michele Carbone, and Magdalena Plasilova 14 The Use of Mouse Models to Study Leukemia/Lymphoma and Assess Therapeutic Approaches...................................................... 325 William Siders 15 Spontaneous Companion Animal (Pet) Cancers................................... 353 David M. Vail and Douglas H. Thamm Part VI  Genetically Engineered Mouse Models of Cancer 16 Genetically Engineered Mouse Models of Pancreatic Ductal Adenocarcinoma...................................................................................... 377 Aram F. Hezel and Nabeel Bardeesy 17 Transgenic Adenocarcinoma of the Mouse Prostate: A Validated Model for the Identification and Characterization of Molecular Targets and The Evaluation of Therapeutic Agents...... 397 Sharon D. Morgenbesser

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18 The Utility of Transgenic Mouse Models for Cancer Prevention Research................................................................................ 423 Stephen D. Hursting, Laura M. Lashinger, Powel H. Brown, and Susan N. Perkins Part VII  Metastasis Models 19 Models for Evaluation of Targeted Therapies of Invasive and Metastatic Disease............................................................................ 447 Suzanne A. Eccles Part VIII  Normal Tissue Response Models 20 Animal Models of Toxicities Caused by Anti-Neoplastic Therapy...... 499 Stephen T. Sonis, Gregory Lyng, and Kimberly Pouliot 21 Bone Marrow as a Critical Normal Tissue that Limits Drug Dose/Exposure in Preclinical Models and the Clinic.................. 521 Ralph E. Parchment 22 Anesthetic Considerations for the Study of Murine Tumor Models........................................................................ 553 Thies Schroeder, Siqing Shan, and Mark W. Dewhirst Part IX  Experimental Methods and Endpoints 23 Preclinical Tumor Response End Points................................................ 571 Beverly A. Teicher 24 Tumor Cell Survival................................................................................. 607 Sara Rockwell 25 Apoptosis In Vivo..................................................................................... 625 L.C. Stephens, L. Milas, K.K. Ang, K.A. Mason, and R.E. Meyn 26 Transparent Window Models and Intravital Microscopy: Imaging Gene Expression, Physiological Function and Therapeutic Effects in Tumors............................................................... 641 Rakesh K. Jain, Lance L. Munn, and Dai Fukumura Index.................................................................................................................. 681

Contributors

Enrique Alvarez Biomodels, 313 Pleasant street, Watertown, MA 02472, USA K.K. Ang The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA Nabeel Bardeesy Massachusetts General Hospital Cancer Center, Boston, MA, USA Powel H. Brown University of Texas at Austin, Austin, TX, USA Angelika M. Burger School of Medicine, Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA Michele Carbone New York University Medical Center, New York, NY, USA William E. Carson, III The Ohio State University, Columbus, OH 43210, USA Thomas H. Corbett School of Medicine, Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA Shannon Decker University of Maryland Marlene & Stewart Greenebaum Cancer Center, Baltimore, MD, USA Mark W. Dewhirst Duke University Medical Center, Durham, NC, USA Suzanne A. Eccles McElwain Laboratories, CRC Center for Cancer, Institute of Cancer, Surrey, UK Ene T. Fairchild The Ohio State University, Columbus, OH, USA

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Heinz-Herbert Fiebig Oncotest GmbH Institute for Experimental Oncology 12. D-79108, Freiburg, Germany Dai Fukumura Department of Radiation Oncology, Massachusetts General Hospital Cancer Center, Boston, MA, USA Stuart Hazeldine School of Medicine, Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA Aram F. Hezel Massachusetts General Hospital Cancer Center, Boston, MA, USA Daniel Hicklin Schering-Plough Research Institute, Kenilworth, NJ, USA Gilda G. Hillman Department of Radiation Oncology, School of Medicine, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA Robert M. Hoffman AntiCancer, Inc., and Department of Surgery, University of California, San Diego, CA, USA Jerome P. Horwitz School of Medicine, Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA Peter J. Houghton Center for Childhood Cancer, Nationwide Children’s Hospital, Columbus, OH 43205, USA Stephen D. Hursting University of Texas at Austin, Austin, TX, USA Rakesh K. Jain Department of Radiation Oncology, Massachusetts General Hospital Cancer Center, Boston, MA, USA Vinod Kaimal Charles River Laboratories, Ann Arbor, MI, USA Juiwanna Kushner School of Medicine, Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA Laura M. Lashinger University of Texas at Austin, Austin, TX, USA Alfred J. Lawson School of Medicine, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48108, USA

Contributors

Wilbur R. Leopold III Charles River Laboratories, Ann Arbor, MI, USA Ming Liu Schering-Plough Research Institute, Kenilworth, NJ 07033, USA Gregory Lyng Biomodels, Watertown, MA, USA Katherine A. Mason The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA Patrick McConville Charles River Laboratories, Ann Arbor, MI, USA Raymond E. Meyn The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA Luka Milas The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA Richard Moore School of Medicine, Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA Sharon D. Morgenbesser Genzyme Corporation, Framington, MA, USA Christopher L. Morton St. Jude Children’s research Hospital, Memphis, TN, USA Lance L. Munn Department of Radiation Oncology, Massachusetts General Hospital Cancer Center, Boston, MA, USA Ralph E. Parchment Laboratory of Human Toxicology and Pharmacology, SAIC-Frederick Inc., NCI-Frederick, Frederick, MD 21702, USA Harvey I. Pass New York University Medical Center, New York, NY, USA Susan N. Perkins University of Texas at Austin, Austin, TX, USA Joseph B. Pincus New York University Medical Center, New York, NY, USA Magdalena Plasilova New York University Medical Center, New York, NY, USA Lisa Polin School of Medicine, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA

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Kimberly Pouliot Biomodels, Watertown, MA, USA James Rake School of Medicine, Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA Bill J. Roberts School of Medicine, Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA Sara Rockwell Yale Cancer Center, New Haven, CT, USA Edward Sausville University of Maryland Marlene & Stewart Greenebaum Cancer Center, Baltimore, MD, USA Thies Schroeder Duke University Medical Center, Durham, NC, USA Siqing Shan Duke University Medical Center, Durham, NC, USA William Siders Genzyme Corporation, Framington, MA, USA Stephen T. Sonis Harvard-Farber Cancer Center, Boston, MA, USA; Biomodels, Watertown, MA, USA L.C. Stephens The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA Beverly A. Teicher Genzyme Corporation, Framington, MA, USA Douglas H. Thamm The Animal Cancer Center, Colorado State University, Fort Collins, CO, USA Henry J. Thompson Cancer Prevention Laboratory, Colorado State University, Fort Collins, CO, USA David M. Vail University of Wisconsin, School of Veterinary Medicine, Madison, WI, USA William R. Waud Southern Research Institute, Birmingham, AL 35205, USA Kathryn White School of Medicine, Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA

Part I

Introduction

Chapter 1

Perspectives on the History and Evolution of Tumor Models Shannon Decker and Edward Sausville

Abstract  Modern cancer therapeutic research is at crossroads in evolving our approaches to discovering, developing, and entering novel therapeutics into earlystage clinical trials. This chapter endeavors to summarize the customary use and interpretation of animal models used for prioritization of cancer treatments for entry into clinical trials through the end of the last century. We then consider the novel screening paradigms currently in use which exemplify the diverse types of challenging lead compounds for in  vivo evaluation. Finally, we offer a strategic overview of steps to maximize utility of the animal model information in selecting agents for clinical study in the twenty-first century. Keywords  Targeted in vivo models • Cancer drug development

1.1 Introduction and Statement of the Problem Modern cancer therapeutic research is at crossroads in evolving our approaches to discovering, developing, and entering novel therapeutics into early-stage clinical ­trials. The sequencing of the human genome [1] and the increasing awareness of the detailed sequence of numerous cancer cell genomes raises the possibility that the empiricism so characteristic of past cancer drug development will give rise to an approach more analogous to current AIDS or cardiovascular disease-related ­paradigms, where a precise knowledge of the structure of a putative target guides all aspects of a drug’s conceptualization, development, and clinical testing. Yet we have not arrived there yet, as it is currently not feasible in most diseases to employ clinically applicable testing to predict the value of novel agents, outside of fairly specific

E. Sausville (*) University of Maryland Marlene & Stewart Greenebaum Cancer Center, 22 S. Greene St, Baltimore, MD 21201, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_1, © Springer Science+Business Media, LLC 2011

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examples relevant to antibody-based therapeutics. Indeed, as an example, while the recently observed lack of value of anti-epidermal growth factor antibody therapy in patients with colon cancer with mutated K-ras alleles is understandable post hoc, an appreciation of that reality was apparent only in retrospective analysis [2]. Had it been possible to model reliably that circumstance, it is conceivable that a more efficient and focused development strategy could have been designed. Thus, the challenges facing the use and interpretation of animal models for human cancer drug development center on the predictability of the models in forecasting effects on tumor cells as well as in predicting tolerability of the agent by the host. The model use should occur within a product lifecycle that usually offers no more than 2 or 3 years in most industrial development paradigms after a lead has been identified, and ideally has a direct relevance to how the agent will be studied in early clinical trials. A related issue that will always inject an element of empiricism into the use and interpretation of animal models used in prioritizing human therapeutics for clinical study is the intrinsic unpredictability of animal (usually rodent) vs. human pharmacology and metabolism. While algorithms exist to predict susceptibility to, for example, cytochrome p450 metabolism features [3] or bioavailability [4], since molecules for cancer treatment, at least the classical cytotoxics, are usually employed at close to their maximum tolerated dose (MTD), even minor differences from the human in rodent compound handling parameters (absorption, plasma protein binding, clearance mechanisms, intrinsic susceptibility of host tissues) can translate into decreased relevance of murine dosing and efficacy information as predicting clinical value. Table 1.1 lists points of model departure from rodent vs. human behavior. In contrast, it is interesting to consider that certain classes of agents, particularly monoclonal antibodies with intrinsic anti-signaling of tumor cell tropism properties have for the most part rather reliably defined useful effects that were eventually borne out in humans [5] perhaps in part because of the bland interaction of human antibodies with both mouse and ultimately human physiology, Table 1.1  Potential points of divergence between rodent and human drug features Property Example Plasma protein Camptothecins [61]; result in stabilization of lactone in mice and binding therefore increased perception of activity 7-OH staurosporine [62]; much more avid protein binding in humans prolong half-life and diminish potential for activity Half-life MS-275 [63]; human clearance much slower than mouse; correlates with mice tolerating more frequent dosing schemes while humans do not Intrinsic drug target Neriifolin and cardiac glycosides [64]; murine Na/K ATPase intrinsically susceptibility less susceptible to agents therefore mouse model over-predict capacity for anti-tumor activity H-ras farnesylation intrinsically more sensitive to certain farnesyltransferase inhibitors and therefore not appropriate model for human Ki-ras associated tumors [65] Differing end-organ Bizelisin [66] murine marrow cells intrinsically less susceptible to antisusceptibility proliferative effects than humans therefore under-predict human toxicity

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and the capacity of certain antibodies such as trastuzumab to down-regulate the action of a target to whose action the relevant cell type is “addicted.” This chapter endeavors to summarize the customary use and interpretation of ­animal models used for prioritization of cancer treatments for entry into clinical trials through the end of the last century. We then consider the novel screening paradigms currently in use which exemplify the diverse types of challenging lead compounds for in  vivo evaluation. Finally we will offer a strategic overview of steps to maximize utility of the animal model information in selecting agents for clinical study in the twenty-first century.

1.2 Tumor Models for Cancer Drug Development: Where We Were 1.2.1 Historical Basis Drug treatments for cancer arose from three distinct philosophical points of view. Classically, Ehrlich’s concept of “magic bullets” [6] that would cause toxicity to tumor cells while sparing normal cells arose from the observation that different dyestuffs had obvious affinity for different parts of the cell or different cell types. By this logic, therefore, screening for chemicals that might have a differential effect on tumor cells in comparison to normal cells might be a basis for deriving useful therapeutics related to cancer. A second potential direction was suggested by the observation of profound leucopenia as part of the symptom complex imparted by exposure to mustard gas during World War I. This suggested to some that in lower doses such chemicals might be useful in controlling tumors of (in that case) the hematopoietic system while not ablating all normal marrow elements [7]. Finally the observation that hormonal manipulation could cause useful regression of tumors derived from endocrine responsive organs [8] suggested that an understanding of the biological bases of tumor growth could impart strategies for treatment. This latter point of view, when coupled to the then emerging knowledge of the biochemistry of nucleic acids and the increase particularly in RNA content of tumor cells, led naturally to the efforts to develop what we now call anti-metabolites such as folate antagonists by Farber et al. [9] and purine and pyrimidine analogs by Elion, Hitchings, Heidelberger and a large number of colleagues [10]. Ironically, although such agents are now “lumped” into the category of “cytotoxics,” anti-metabolites were the rationally “targeted” therapeutics of the middle of the last century. The plethora of new chemicals potentially available for cancer treatment, along with relative indifference for cancer as a focus of opportunity by corporate pharmaceutical entities of the time created the perceived need to develop common platforms for evaluation of new molecules available for cancer treatment. This resulted in the evolution of tumor models that were geared for high throughput and mostly employed serially propagated tumor cells in syngeneic hosts. As recounted

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e­ lsewhere by Zubrod et al. [11], such screening efforts in academia exemplified by Memorial Sloan Kettering were helpful but to keep up with demand for compound evaluation, Congress in 1955 directed the U.S. National Cancer Institute (NCI) to develop a publicly funded and publicly accessible resource that would promote both clinical testing and pre-clinical evaluation of novel anti-cancer agents. The former initiative was the precursor of the current national Cooperative Group approach to clinical trials. The latter initiative resulted in the formation of the Cancer Chemotherapy National Service Center (CC-NSC), whose “NSC” accession catalog of compounds continues to the present day at NCI’s Developmental Therapeutics Program as the successor to the CC-NSC. Compounds studied by NCI for the most part were synthesized by contractors or solicited from academic or commercial parties by an active compound acquisition program [12]. Encouragement to industry as well as academic participants was provided by confidentiality agreements that assured protection of the submitting party’s intellectual property. Results generated by the NCI screening effort could then be the basis for development of the compound to clinical trials sponsored either by the NCI through its Cooperative Groups or privately funded ventures.

1.2.2 Early Screening Models The models employed in efforts at NCI and at academic screening centers included and were exemplified by the L1210 and P388 mouse leukemias serially transplanted by the peritoneal route and treated by intraperitoneal injection of drug. The endpoint of the screening assay was survival of the treated vs. untreated or vehicle-treated groups of animals. A compound was considered to show preliminary evidence of activity if the mean or median lifespan of the treated animals was increased by 125%, with the control group survival set at 100%, and with the important caveat that “positive” compounds had to have acceptable therapeutic index with evidence of maintained or increasing body mass in treated animals and no untoward short-term toxic phenomena. Among the advantages of this model as a screening tool were its relative speed, with experiment evaluation generally complete by 2–3 weeks; capacity for high throughput allowing many compounds to be evaluated; and reproducibility of the model owing to high take rate and uniform growth rate. Using these and related models, important clinically relevant principles of cancer chemotherapeutic development were elucidated and formed the basis for construction of human chemotherapy regimens and practices. These principles include the demonstration that active agents produced with each dose increment reduction in the tumor cell population a reduction in tumor cell mass by logarithmically increasing increments. This led to the concept that valuable agents had to be applied in successive “cycles” to cause tumor-free animals to emerge. The inverse relationship of tumor cell inoculum to curability at a constant dose led likewise to the theoretical underpinnings of “adjuvant” treatment programs [13, 14].

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Potential pitfalls of such models were numerous. Most obviously was the “same site” nature of the treated space, without a physiological barrier between the administration site and the locus of drug action. A second concern was the potential lack of relevance of such models to solid tumors. Both of these concerns were partially addressed by the use of syngeneic metastasizing murine models such as Lewis lung carcinoma or B16 melanoma. These models could be run either as solid allografts, with treatment intravenously or orally in a way that mimicked human treatment, or following a period of residence in a body part, generally an extremity, removal of the “primary” tumor could allow for observation of compound activity against the establishment or formation of metastases. Although such models were valuable adjuncts to evaluating positive compounds in the murine leukemia studies, an emerging concern throughout the later 1970s was that the paucity of agents emerging through such murine leukemia-based screens that ultimately had robust activity in human solid tumors. The limited value of agents detected in murine leukemia screening models when applied to human solid tumors resulted in enormous interest in the use of immunocompromised animals to study xenografts of human tumors through technology that was first applied on a large scale commencing ~1980 using athymic “nude” mice [15]. One initial hope was that agents thereby revealed to be active would be intrinsically more suitable for use in human solid tumors. An immediate problem in the use of these models, however, is their intrinsically less efficient throughput owing to a variety of factors including the mechanics of implanting and sizing tumors in a subcutaneous site; the fact that different human tumor cell lines had intrinsically different “take rates” and variable growth rates. This encouraged the development of prioritization criteria often after in  vitro screening to assure that compounds entering into in vivo study already had evidence of cytotoxic potential. The “NCI 60” cell line panel is representative of one such large-scale effort of this type whose historical basis and output has been described elsewhere [16, 17]. Moreover, criteria for value of an agent in athymic mouse xenografts are problematic in that tumor growth delay is more frequently encountered than actual responses of established tumors, and the meaning of this to the clinical setting remains undefined in a precise way to this day. Looking at the performance of predominantly classical cytotoxic agents studied at the NCI in a variety of murine syngeneic and prototypic human xenograft systems, one can conclude that agents irrespective of their level of in vitro activity which have activity in less than 33% of the models tested had no “positive” phase 2 clinical trials. In contrasts, agents with activity in at least 33% of such models had an approximately 50% likelihood of positivity in phase 2 clinical trials [18]. Noteworthily, there was little histology-specific correlation of activity in models with activity in the clinic. As described above, the reason for this disconnect between animal and human experiences when ultimately understood has in the examples cited in Table 1.1 largely related to differences in animal and human pharmacological features or target susceptibility or importance to the host organism. This and related experiences [19] has reinforced that from a purely stochastic viewpoint there is value in prioritizing compounds for entry into the clinic by their behavior in some number of animal models.

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The most profoundly dissatisfying aspect of this set of outcomes is that there is no tie on the part of the in vivo models used to evaluate the majority of screening experiences to the biology of the tumors studied. While one might argue that this is reasonable in light of the fact that most of the chemical entities selected from random screening experiences were not really designed around any key mechanism as relevant to the biology of a particular tumor, the present age has for the most part moved past the point where high enthusiasm for a compound arises solely by virtue of its behavior in a screening system. Rather novel approaches to cancer drug screening are generating lead compounds that will require distinctive approaches to further elicitation of activity in vivo, and will ideally be coupled to novel strategies to apply in early clinical trials.

1.3 Novel Screens Beget Novel In Vivo Model Challenges Traditionally as discussed above, lead compounds were selected for study in vivo based on evidence in vitro or expectation of cytotoxicity. The molecular target era has allowed the creation of a flood of new screening models. Importantly, some screens are aimed at identifying targets or pathways of interest as an initial step in then defining the effect of a compound on the target(s) or pathway(s) of interest, but not necessarily tied to initial evidence of cytotoxicity. Whether action of a lead against the target in one of these in vitro or non-traditional assay systems is enough to justify proceeding with the lead to in vivo models discussed throughout this volume is a key strategic issue to consider. These assay systems run the gamut from non-mammalian in vivo models in an array of organisms, to informationintensive screens capitalizing on the explosion of new “data mining” technologies, to cell-based in  vitro assays looking for non-classical endpoints such as angiogenesis or invasion.

1.3.1 Non-mammalian Models In the last 10 years, efforts utilizing non-mammalian models to actually identify targets and drugs have proliferated. In some cases, such as for yeast and Drosophila, the organisms have been used for many decades as biological models, but have not traditionally served as a source of anti-cancer leads. Other organisms such as zebrafish have arisen relatively recently as models. 1.3.1.1 Unicellular Yeast screens have been widely used in cell biology and genetics studies. It was the first organism to have its genome sequenced [20]. Yeast strains are easily grown

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and manipulated, allowing for facile studies of DNA damage repair, cell cycle ­progression and checkpoint control, among other well recognized utilities. Yeast screens have provided a facile way to identify sets of genes that contribute to sensitivity or resistance to particular drugs. For instance, for the synthetic tripeptide arsenical GSAO that inhibits angiogenesis and targets actively dividing but not quiescent endothelial cells, Hogg et al. identified 88 GSAO-sensitive Saccharomyces cerevisiae deletion strains by screening a genome-wide set of 4,546 such strains, thus identifying potential molecular targets of GSAO and allowing for confirmatory studies in mammalian cells [21]. Classical pathways well explored in yeast actually from a biological point of view have defined strains with alterations in cell cycle and cell cycle checkpoint control, particularly in response to DNA damage [22]. This observation has been capitalized on by numerous groups to screen for compounds that interfere with cell cycle control, thus potentially enhancing sensitivity to classical DNA-damaging chemotherapy or radiation therapy. The National Cancer Institute (NCI) Yeast Anticancer Drug Screen has screened tens of thousands of compounds in selected yeast strains mutated for cell cycle control or DNA damage repair [23]. One limitation of such screens though is the possibility that larger organisms do not rely on a single mechanism for repairing DNA damage. For example, mammals in some cases appear to have checkpoint-independent mechanisms for surviving radiation [24], so a compound identified in yeast as interfering with a checkpoint may be ineffective as a radiation sensitizer in humans. Thus, yeast serve to illustrate the caveat that a mammalian relevant in  vivo model may need to be carefully constructed to provide evidence that the yeast-related screen output is an accurate reflection of a human circumstance. 1.3.1.2 Multicellular In an attempt to overcome some of the shortcomings of unicellular organism screens as predictors of in  vivo activity, various groups have developed in  vivo models in non-mammalian organisms ranging from zebrafish to nematodes to flies. The potential advantages of such screens generally are that they are cheaper and proceed more quickly than mammalian in vivo models, but still have the capacity to provide information about the ability of a drug lead to act in a live host. Drosophila strains have been used for over a century for genetic studies, and have a relatively small genome, making it an attractive model for studying various biological processes. A number of different cancer-related screening campaigns have now been run in Drosophila models, including transgenic models. For example, extending from the observation discussed above that whole organisms have checkpoint-independent mechanisms for surviving DNA damage from chemotherapy and radiation, Tin Su et  al. ran a pilot screen for radiation sensitizers using wild-type and checkpoint mutants [24]. Drug candidates were mixed into food and placed in wells with Drosophila larvae, and survival was determined by counting the empty pupae cases. In another screening context, by looking at phenotypic

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changes from Drosophila developing leg imaginal discs, Phanstiel et al. screened for drug–polyamine conjugates with polyamine transporter (PAT)-selective targeting­ ability, deriving from the observation that PAT is elevated in many tumor types and hypothesizing that drug–polyamine conjugates may be able to selectively attack tumor cells [25]. While the limited genetic redundancy of Drosophila lends itself to phenotypic endpoints and is part of the basis of its attractiveness as a model, it is also potentially a limitation of the model as the hits identified in such models may fail in more complex mammalian systems where redundancy is more frequent. The Caenorhabditis elegans nematode has been used as a model system for several decades. The worm goes from egg to fertile adult in 3 days, and each adult can produce 300 progeny making it a quick and inexpensive model system. Numerous knockout mutants exist and strains can be frozen for decades [26]. In one recent cancer application, Salgia et al. described a C. elegans nematode model in which transgenic worms were generated harboring either wild-type c-Met or mutations of c-Met commonly seen in lung cancer [27]. The worms expressing the mutant c-Mets consistently displayed the phenotypic outputs of abnormal vulval development and low fecundity. While this model can be used to investigate the role of gene mutations in a whole organism, invertebrates may not be appropriate ­models for certain cancer-related processes such as apoptosis due to their lesser complexity [28]. Avian embryo models have also been used in developmental biology for many years, but only more recently in cancer research with any frequency, most likely due in part to the recent sequencing of the chick genome. Advantages include the speed of the model in reproducing human tumor growth and angiogenesis. Researchers have validated that human glioblastoma grafted onto the chorioallantoic­ membrane (vascularized extra embryonic tissue; CAM) displays similar patterns of gene expression changes as the human disease [29]. Although they still have ­efficiency advantages over mouse models, CAM models also have disadvantages over other vertebrate non-mammalian systems such as a relatively lengthy assay (~10 days), higher cost than other models and difficulties in quantitation of the output [30]. Proponents of Xenopus tadpole models point to rapid extra uterine development, the transparency of developing tadpoles, permeability of the skin, and similarities to mammals in certain organ development, anatomy and physiology as advantages [31]. To identify molecules affecting angiogenesis and lymphangiogenesis, Brandli et al. screened 1,280 compounds in a Xenopus model looking first for edema as a phenotype and then used whole-mount in situ hybridization of Xenopus embryos to visualize blood and lymphatic vessel development for the 66 positive hits from the initial stage of the screen, with confirmatory endothelial cell proliferation and tube formation assays then conducted on the second level hits. The original Xenopus model, Xenopus laevis has a pseudotetraploid genome and a relatively long generation time, making the development of stable transgenic lines lengthy relative to other non-mammalian models, however work has also been done to use the diploid Xenopus tropicalis as a model for experimental genetics [32].

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Zebrafish have been cited as having numerous advantages for screening, many of them shared with the advantages of Xenopus models above. The assays, while in vivo, are still relatively quick at approximately 3 days, relatively cheap, and have reasonably high throughput as they can be done on plates [9]. As ­demonstrated by Willett et al. using known angiogenesis inhibitors TNP470 and SU5416 [30], zebrafish, being transparent, lend themselves very well to ­angiogenesis-related assays as blood vessel formation can be assessed by visual inspection. Zebrafish have been used for models of drug sensitization and resistance. Transgenic models have been generated as well. Much less is known, however, about cancer-relevant issues such as DNA repair enzymes and the orthologs of human oncogenes and tumor suppressor genes in zebrafish than other model systems [33].

1.3.2 Technology-Intensive Screening Advances in fields such as computing technology, imaging, robotics, and miniaturization among others have helped spawn a range of new screening possibilities. All of these technology-intensive methodologies produce a wealth of information much more quickly than many classical screening techniques, but the challenge is in sifting through and capitalizing on the information. In many cases in vivo models applicable to the output of such screens will need to be constructed as a dedicated effort in parallel with the design and output of the ex vivo screen. 1.3.2.1 High-Throughput Screening High-throughput screening (HTS) methods became increasingly necessary as the number of potential molecular targets for cancer drugs grew virtually ­exponentially. In one possible format for an HTS assay, the activity of an enzyme is linked to an easily readable output, such as fluorescence or bioluminescence from luciferase. Cell-based HTS is also possible, many times with cell lines that have been transfected with a receptor or promoter of interest. Methodologies for HTS campaigns have been discussed extensively [34, 35] and the literature abounds with results from campaigns directed against particular enzymatic ­targets. In the fortunate circumstance where the role of the enzyme in a biological­pathway relevant to human disease is well understood, where structural biology can show the development candidate interacting with the binding site of the enzyme, where the candidate has favorable drug-like characteristics, and where the action of the drug on the target can be tracked in cell culture and in vivo models, the path for development can be relatively straightforward. In the case where the output of a screening campaign using a cell-based assay where ­pathway activation or inhibition is the ultimate readout, caution must be urged in  exploring activity in in  vivo models prior to deconvolution of the lead ­compounds mechanism of action.

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1.3.2.2 Chemogenomics Unlike a single HTS assay that has the ability to screen many compounds against a single target, chemogenomics represents the integration of a study of the effects of compounds on biological targets with modern genomics technologies, attempting to comprehensively discover and describe all possible drugs to all possible drug targets [36]. For instance, for a chemical genetics application, the function of proteins is probed by small molecules by adding a library of small molecules to cells, selecting those that produce the phenotype of interest and identifying the protein bound by the molecules [37]. Many of the newer applications of yeast screens fall into the chemogenomics category, helping to identify genes that can help explain the activity of known compounds [2]. 1.3.2.3 Proteome and Kinome Screens With the success of genome-wide screens, efforts next logically extended down to the proteome and a particular target class such as protein kinases (thus a “kinome” directed virtual screen) in the search for drug targets. In one such effort, Schreiber et al. combined a chemical genetics screen that identified small molecule modifiers of rapamycin activity with a probe of a yeast proteome chip to identify proteins that bound the small molecules [38]. One potential advantage of probing of the proteome over traditional affinity chromatography is the bias of chromatography toward high-abundance proteins. Some approaches have elected to limit the probe to the kinases rather than the whole proteome. Dagorn et al. screened the human kinome for all kinases involved in pancreatic cancer cell survival and gemcitabine resistance, identifying a set of potential targets for drug discovery campaigns [39]. Comprehensive screening of the whole yeast proteome has been undertaken to systematically identify protein–protein interactions, in an effort that might eventually assist in the development of small molecules that can disrupt key interactions [40]. Analysis of such protein–protein interaction data sets however requires significant bioinformatics resources, and the complexity will only increase when multicellular organism proteomes are screened. 1.3.2.4 Nanotechnology Considerable effort has been expended in recent years on integrating nanotechnology with more traditional biologically based methodologies. In one series of approaches nanoparticles such as quantum dots or magnetofluorescent particles are conjugated to peptides, antibodies, or small molecules to allow the targeting of the nanoparticle to specific cells, such as tumor cells. Some groups have had success in using such bioconjugates for imaging [41] and have demonstrated differential cellular uptake [42]. Others are using nanoparticles to produce formulations of compounds, ones with excellent in vitro activity but no systemic bioavailability, in an effort to make

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such compounds clinically viable [43]. As a therapeutic approach however, these bioconjugates remain unproven clinically and numerous scientific, cost, and regulatory hurdles exist. 1.3.2.5 RNA Interference Since its discovery approximately a decade ago, RNA interference (RNAi) has found application in many aspects of cancer drug discovery including target identification and validation, identification of drug resistance and sensitization mechanisms, and synthetic lethal screening. Genome-wide RNAi screens have been used successfully in C. elegans and Drosophila to understand biological processes and work toward a comprehensive characterization of gene function [44]. For example, Woo et al. identified “driver genes” in hepatocellular carcinoma, each of which can now be considered for screening to define hepatocellular carcinoma-related drugs [45]. Iorns et  al. suggest the utility of conducting chemical genetics and RNAi screens in parallel to simultaneously identify small molecule inhibitors and targets, giving as an example their use of an RNAi screen to identify the PDK1 pathway as a determinant of sensitivity to tamoxifen coupled with a screen to locate chemical inhibitors of the pathway [46]. Synthetic lethal screening is another potential application of RNAi. Two genes are “synthetic lethal” when cell death results from mutation of both genes even though the cell remains viable with mutation of either alone. One recent demonstration of the relevance of siRNA-related synthetic lethal screens arose from the observation that cells deficient in BRCA-1 were highly sensitive to concomitant PARP inhibition [47], based on the inability to repair DNA lesions utilizing homologous recombination.

1.3.3 In Vitro Models Cell-based in vitro models with cytotoxic endpoints that had the goal of identifying compounds for subsequent in  vivo testing were used for several decades as ­primary screens (e.g., the NCI60 described above). More recently, cell-based models are being employed to either further filter hits from the high throughput and mammalian models discussed above or to look for other endpoints such as angiogenesis. As discussed above, yeast screens have been employed to identify compounds that act against yeast strains with specific genetic mutations that are believed to be relevant to cancer. The number of hits obtained from such assays though still requires further filtering before an in vivo mammalian model can be contemplated. In vitro cell-based models, particularly those where activity in a knockout cell line can be compared to the wild-type, can act as a further filter. For instance, Lamb et al. used a three-stage screen to first identify compounds inhibiting the growth

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of double-strand break repair-deficient yeast cells, producing 28 hits, which were winnowed by looking for toxicity proportional to levels of topoisomerase I or II expression [48]. They then screened the remaining eight hits in two lines of chicken pre-B-cell line DT40, one wild-type and the other defective in doublestrand break repair. Other in  vitro assays assess look at endpoints other than cytotoxicity, such as endothelial cell migration or cord formation, looking for compounds that affect processes such as angiogenesis or metastasis. By themselves such assays are not necessarily sufficient to warrant pursuit of in  vivo models with identified compounds. In combination with other results, however, endothelial cell assays can be a source of lead compounds. For instance, Sekhar et al. combined observations from a chemistry-driven drug discovery screen for inhibitors of endothelial cell tubule formation with biochemical pathway screening and shRNA suppression to identify compounds to pursue as drug leads, and also validate ENOX1 as a target for enhancing radiation response of tumors [49]. Other endothelial cell strategies have looked to capitalize on differences between tumor and normal endothelial cells. Camussi et al. identified cyclic peptides that showed specific binding only to tumor but not normal endothelial cells to use as a mechanism for delivering antiangiogenic agents only to the tumor [50]. The integrin inhibitors can serve as an example, however, of how action on a molecular target coupled with endothelial cell assays for angiogenesis endpoints may not be enough to guarantee a drug candidate worthy of development. Screening for inhibitors of integrins, adhesion molecules considered important in angiogenesis­, has been conducted in conjunction with numerous other angiogenesis assays [51]. In this case, while data existed to support the search for integrin inhibitors, certain of the integrins are promiscuous and the biology considerably more complicated than suggested in primary screening assays, such that development of an integrin inhibitor has been thus far unsuccessful [52].

1.4 Tumor Models for Cancer Drug Development: Where We Need to Be The above examples emerging from modern biology-driven potential cancer ­relevant screens illustrate the wide diversity of premises that need to be embodied in the in vivo models that might ultimately be used to further evaluate the value of such lead molecules in vivo. Given the fact that many of these leads may not be intrinsically cytotoxic but directed to particular targets, either directly in a molecular sense or as part of a pathway a readout which formed the basis of the screening efforts, how would the clinical development process be informed and fortified by knowledge gleaned from in  vivo models exploring the activity of these agents? Following is a suggested series of steps that might be considered in the practice of in vivo models using such leads with the goal of pre-clinical evaluation of such a “targeted” compound. As illustrated in Table 1.2, it differs from the path that

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Table 1.2  Distinction between cytotoxic and “targeted” in vivo model usage Classical Targeted Maximum targeted dose driven Biologic dose bracketing an optimized concentration Pharmacology frequently deferrable Early PK and PD crucial and build into correlates of clinical value Number of models active key to prioritization Limited number of models, but target enriched Need to define host cell susceptibility Need to define effect on host target in relation to toxicity observed

might have been applied to the traditional “cytotoxic” drug candidate of the last century in that the latter agents were generally developed according to an MTD model where pharmacological information could reasonably be obtained after initial confirmation of in vivo activity on a particular schedule. In contrast, most efficient and useful development steps for targeted agents would have a more early integration of pharmacological information, both kinetic and dynamic, into the early development strategy, and may actually not embark on evaluation of uncharacterized models with respect to target expression or pathway activation status. With this reasoning, the following steps might be usefully be allied to the process of in vivo model use with screening leads in the age of biologically ­tailored cancer drug screens.

1.4.1 “In Vitro” Area Under the Concentration × Time Curve for Target Effect In a range of cell culture models expressing the target, definition of the time until target modulation as a function of compound addition and removal, and the relationship of this to secondary endpoints such as cytotoxicity is critical, and helps to define initial dosing strategies. Ideally controls with respect to secondary endpoints would include cell lines not expressing the relevant target or pathway. This would also provide valuable information about “off target” effects.

1.4.2 Qualification of Compound for In Vivo Study A series of related molecules active in  vitro can be further qualified for in  vivo study by application of algorithms suitable for selection of oral bioavailability [53], if continuous exposure is the intended strategy. Alternatively, “cassette” type dosing schemes [54] allow preliminary assessment of pharmacological properties of a series, thereby narrowing choices of molecules for in vivo evaluation.

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1.4.3 Initial Rodent Pharmacology and Model Selection Using a realistic dosing scheme, attempts to recreate at least the area under the concentration  ×  time curve (AUC) defined in  vitro by the results of studies described above using non-tumored animals should then occur. Transition to the use of tumored animals would initially use a tumor model with cells known to be dependent on the function of the target for growth, viability, or some easily assessed biologic readout. These may express the target endogenously or heterologously; in the latter event appropriate vector alone controls are necessary. In the event the target is expressed endogenously, consideration of a cell type related to the first where the target is absent or not functional would be an additional useful control.

1.4.4 Sample Size and Randomization of Animals Several considerations go into selecting the number of animals chosen for control and experimental groups, and consultation of a biostatistical expert in designing the experiments is useful. In part the sample size relates to the magnitude of the effect desired and the nature of the endpoint [55]. In the event that tumor is to be assessable at the initiation of the experiment, randomization of animals with different tumor sizes so that treatment groups are matched with respect to initial tumor size may be necessary.

1.4.5 Correlative Studies Ideally evaluation of efficacy in “hitting” the putative target should accompany in vivo evaluation of the compound, as well as in a most ideal case assessment of the pharmacologic properties of the agent achieving that effect (dose–response of effect on target in association with usual parameters such as plasma maximal concentration (Cmax), half life (t1/2), AUC, etc.). Determination of tumor drug levels corresponding to these phenomena would be a plus. Examples of successful integration of such information obtained in early in  vivo studies with value when applied to the clinic would include bortezomib anti-tumor effect correlated with effect on proteosome inhibition [56] or more recently effect of dasatinib on bcr-abl kinase substrate phosphorylation in relation to plasma concentrations in mice [57], a set of observations which assisted initial clinical development.

1.4.6 Additional Desirable Studies While one intensively evaluated model (with respect to pharmacodynamics and pharmacology) may be useful in setting the “boundary conditions” and expectations for benchmarking initial compound use and performance in humans, particularly

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if the tumor system studied is “artificial” with respect to the anticipated state of the target or pathway of interest in the clinic (e.g., heterologously expressed or otherwise manipulated cells), enthusiasm for the compound is increased generally if a range of non-manipulated cell types are exposed to the agent at the appropriate concentration dose and range with confirmation that in that circumstance there are expected effects on target function and consequences for cellular physiology. It may not be necessary to develop stable in vivo models from each cell type; such techniques as the “hollow fiber assay” [58] can be a way of usefully assessing in vivo effect without the time and expense of deriving independent models [59].

1.5 Conclusion The ultimate goal of in vivo model studies in the pre-clinical development of anticancer agents is to serve a variety of interests. First, from a strictly pragmatic standpoint, demonstration of unbiased, well understood in  vivo activity serves to increase confidence in investing the considerably more time-consuming and expensive effort in developing the safety database to allow human early phase clinical testing. Valuable activity in an in  vivo model should reflect pharmacological “action at a distance” across physiological and anatomical barriers in a way that has an acceptable therapeutic index on the clinical proposed dose range and schedule. Second, the in  vivo model experience from a scientific standpoint becomes that which the early clinical trials would ideally seek to emulate precisely as a “mirror image” accurate reflection. Third, from an ethical standpoint, clear demonstration of in vivo activity on the part of a candidate anti-cancer agent is a basis for potentially justifying in a prospective patient’s mind their participation in such a study. Although recent studies have documented that modern phase I anti-cancer drug clinical trials are extremely safe and for many of the newer molecular entities have the prospect of benefit in perhaps as much as 30% of participants [60], the initial in  vivo experiences in animals can serve as a talking point in assuring potential participants that there is the possibility of benefit at doses and schedules that have a modicum of expected safety and tolerability. The ideal for in vivo model use in therapeutics development is the assembly of a package of information that will guide in the design and ultimate interpretation of the initial human clinical trial. Conversely, it is also conceivable that once an initial appreciation of achieved human pharmacology emerges from the results of the initial early phase clinical trials in humans, a focused return to in  vivo animal ­models with the intention of conscientiously modeling the achieved human ­pharmacology in the animals may allow a more realistic strategy to emerge before committing to an extensive human phase 2 program. In this way in  vivo animal models can contribute not only to the initial qualification of a compound for human use but also to a more refined way of advancing it to having its best chance for ­positive later-stage clinical trial efforts.

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Part II

Transplantable Syngeneic Rodent Tumors

Chapter 2

Murine L1210 and P388 Leukemias William R. Waud

Abstract  L1210 and P388 leukemia models have been extensively used over the last 50 years. The models are rapid, reproducible, and relatively inexpensive (in comparison to human tumor xenograft and transgenic models). However, as with any experimental animal tumor model, there are limitations. Neither leukemia is a satisfactory model for either human cancer in general or human leukemia in particular. Despite the limitations of murine leukemia models, these models have been useful in making progress in anticancer drug development, in the development of a number of therapeutic principles, and in understanding the biologic behavior of tumor and host. These models are still useful today in conducting detailed evaluations of new candidate anticancer drugs (e.g., schedule dependency, route of administration dependency, formulation comparison, analog comparison, and combination chemotherapy). The greatest utility of murine leukemias today is derived from evaluations of drug-resistant sublines for crossresistance and collateral sensitivity. Crossresistance data, coupled with knowledge of resistance mechanisms operative in drug-resistant leukemias, may yield insights into mechanisms of action of agents. Similarly, crossresistance data, coupled with mechanisms of action of various agents, may yield insights into resistance mechanisms operative in drugresistant leukemias. Furthermore, crossresistance data may identify potentially ­useful guides for patient selection for clinical trials of new antitumor drugs. Keywords  Murine leukemia • L1210 • P388 • Drug resistance

W.R. Waud (*) Southern Research Institute, 2000 9th Avenue South, Birmingham, AL 35205, USA e-mail: [email protected]

B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_2, © Springer Science+Business Media, LLC 2011

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2.1 Introduction Mouse leukemia models were a central component of the initial drug discovery programs employed by the Division of Cancer Treatment and Diagnosis (DCTD) of the National Cancer Institute (NCI) during the early 1960s and 1970s. The L1210 and P388 leukemias, developed in 1948 [1] and 1955 [2], respectively, played a major role in both screening and detailed evaluations of candidate anticancer agents. Today, 50 years later, these models are still used to evaluate anticancer activity, although at a greatly reduced level, and to study mechanisms of drug resistance. This chapter reviews their past contributions and updates their present role in the evaluation of anticancer drugs. Data for the drug sensitivity of these two leukemias and various drug-resistant P388 sublines to clinically useful drugs are summarized.

2.2 Role in Drug Screening Spontaneous tumors in animals were first used as models for screening potential anticancer agents. In fact, these types of studies occurred even prior to the beginning of the twentieth century [3] and provided the basis for modern drug screening programs. However, large-scale screening and the ability to conduct detailed drug evaluation studies with anticancer agents increased greatly in the 1920s by the development of inbred strains of mice that allowed investigators to propagate tumor lines by serial transplantation in vivo [4]. The United States Congress became interested in cancer research when it was recognized in the 1940s that systemic cancer could be influenced by drug treatment. This was demonstrated at Memorial Sloan-Kettering, which was one of the first of several institutions in the United States and Europe that began drug screening programs. In that program, the mouse sarcoma SA-180 was used as its screening model. However, as drugs exhibited anticancer activity and the supply of new candidate agents exceeded the screening capacity of that program, the need for additional drug development capability became apparent. With this impetus, Congress directed NCI to implement a national drug development ­program, which went into effect in 1955 as the Cancer Chemotherapy National Service Center (CCNSC). Initially, the CCNSC primary screening program consisted of L1210 leukemia, SA-180, and mammary adenocarcinoma 755 [5]. Over the years, the composition of the primary screen changed several times, i.e., from the original three tumors to L1210 and two arbitrarily selected tumors; to L1210 and Walker 256 carcinosarcoma; to L1210 and P388 leukemia; and finally to L1210, P388, and either B16 melanoma or Lewis lung carcinoma [6]. Several additional models were also used during this period for special detailed drug evaluation. The primary screening program underwent a major change in 1976 when DCTD incorporated the use of three human tumor xenograft models. The new screen now

2  Murine L1210 and P388 Leukemias

25

consisted of a panel of colon, breast, and lung tumors, both murine and human. However, all drugs going through this screen were still evaluated initially for activity against the sensitive P388 leukemia model [7]. During this period, the low number of drugs discovered with marked antitumor activity against human solid tumors led to a radical change in the screening program that had used murine ­leukemia models as the primary screen. In the mid-1980s, NCI developed a new primary screen based on the use of established human tumor cell lines in vitro [8]. The new and old screen programs were to be conducted in parallel so as to permit a comparison; however, in early 1987, budget cuts at NCI forced an end to largescale P388 screening [9].

2.3 Characteristics Both L1210 and P388 leukemias were chemically induced in a DBA/2 mouse by painting the skin with methylcholanthrene [1, 2]. Propagation of the leukemia lines is in the host of origin by intraperitoneal (i.p.) implantation of diluted ascitic fluid containing either 105 (L1210) or 106 (P388) cells per animal. Testing is generally conducted in a hybrid of DBA/2 (e.g., CD2F1 or B6D2F1), because the hybrids are somewhat heartier. However, DBA/2 mice may be used for special studies and should be used for serial in  vivo propagation of the leukemias. Frequently used implant sites are i.p., subcutaneous (s.c.), intravenous (i.v.), or intracerebral (i.c.). For L1210 leukemia with an implant of 105 cells, the median days of death and the tumor doubling times for these implant sites are 8.8, 9.9, 6.4, and 7.0 days and 0.34, 0.46, 0.45, and 0.37 days, respectively. For P388 leukemia with an implant of 106 cells, the median days of death and the tumor doubling times for these implant sites are 10.3, 13.0, 8.0, and 8.0 days and 0.44, 0.52, 0.68, and 0.63 days, respectively. Skipper and coworkers at Southern Research Institute determined the rate of distribution and proliferation of L1210 leukemia cells using bioassays of untreated mice after i.p., i.v., and i.c. inoculation [10]. Following i.p. inoculation, most of the L1210 cells were found in the ascites fluid of the peritoneal cavity. Using the median day of death as the evaluation time point, the most infiltrated tissues were the bone marrow, liver, and spleen. Following i.v. inoculation, the majority of L1210 cells appeared in the bone marrow. On the median day of death from the i.v. implant, the most infiltrated tissues were also the bone marrow, liver, and spleen. After i.c. inoculation, most of the L1210 cells remained in the brain (for 3–5 days). On the median day of death from the i.c. implant, the spleen was heavily infiltrated (the extent of the leukemia in other tissues was not reported). Southern Research was one of the first institutions to become involved in the CCNSC screening program and was heavily involved in designing protocols for the program. One aspect essential to the operation of a screening program is the development of appropriate parameters for measuring antitumor activity. At Southern Research antitumor activity for leukemia studies is assessed on the basis of percent

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W.R. Waud

median increase in life span (% ILS), net log10 cell kill, and long-term survivors. Calculations of net log10 cell kill are made from the tumor cell population doubling time that is determined from an internal tumor titration consisting of implants from serial tenfold dilutions [11]. Long-term survivors are excluded from calculations of % ILS and tumor cell kill. To assess tumor cell kill at the end of treatment, the survival time difference between treated and control groups is adjusted to account for regrowth of tumor cell populations that may occur between individual treatments [12]. The net log10 cell kill is calculated as follows:

Netlog10 cellkill =

(T − C) − (durationof treatmentindays) 3.32 × Td

where (T – C) is the difference in the median day of death between the treated (T) and the control (C) groups, 3.32 is the number of doublings required for a population to increase 1-log10 unit, and Td is the mean tumor doubling time (in days) calculated from a log-linear least-squares fit of the implant sizes and the median days of death of the titration groups.

2.4 Sensitivity to Clinical Agents Many of the clinically useful compounds in current use were first detected in the murine leukemia models. The sensitivities of L1210 and P388 leukemias (implanted i.p.) to most of these agents (administered i.p.) are shown in Figs. 2.1 and 2.2 and Figs. 2.3 and 2.4, respectively. Overall, P388 leukemia is somewhat more sensitive than L1210 leukemia. For alkylating agents, the sensitivities are similar. Notable exceptions are chlorambucil, mitomycin C, and carboplatin, for which P388 is markedly more sensitive. For antimetabolites, the sensitivities are also similar. Exceptions are floxuridine (P388 being markedly more sensitive) and hydroxyurea (L1210 being markedly more sensitive). For DNA-binding agents, P388 leukemia is clearly more sensitive (e.g., actinomycin D, mithramycin, daunorubicin, teniposide, doxorubicin, and amsacrine). For tubulin-binding agents, P388 leukemia is again clearly more sensitive. The vinca alkaloids are active against P388 leukemia but ineffective against L1210 leukemia. Although most of the sensitivity data are for i.p. implanted leukemia and i.p. administered drug, valuable information can be obtained from separating the implant site and the route of administration. Table 2.1 shows the activity of melphalan, administered i.p., against both L1210 and P388 leukemias implanted i.p., i.v., and i.c. Melphalan given i.p. is very effective against both i.p. implanted leukemias. The activity is reduced to less than one-half when the implant site is changed to i.v. The activity is further reduced when the implant site is changed to i.c.; however, melphalan is able to cross the blood–brain barrier to some extent. This principle is shown more extensively with the data in Figs. 2.5 (L1210) and 2.6 (P388) for the leukemias implanted i.c. and various clinically useful agents administered i.p. Thiotepa, CCNU,

2  Murine L1210 and P388 Leukemias

27

NSC No. Rx Drug Alkylating Agents 750

D

Nitrogen Mustard 762

A

Chlorambucil

3088

D

Thiotepa

6396

A

Melphalan

8806

A

Hexamethylmelamine Cyclophosphamide Mitomycin C

13875

A

26271

A

26980

A

Dacarbazine

45388

A

Procarbazine

77213

D

CCNU

79037

A

Streptozotocin

85998

A

Busulfan

109724

A

Cisplatin

119875

A

Chlorozotocin

178248

A

Carboplatin

241240

D

BCNU

409962

A

Ifosfamide

Tubulin Binders Vinblastine

49842

G

Vincristine

67574

B

Miscellaneous Agents Gallium nitrate

15200

D

Bleomycin

125066

D

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

Net Cell Kill (log10 units)

Fig. 2.1  Sensitivity of i.p. implanted L1210 leukemia to clinically useful alkylating agents, tubulin binders, and other miscellaneous agents. L1210 leukemia (105 cells except for hexamethylmelamine, which used 106 cells) was implanted i.p. on day 0. Beginning on day 1, the agents were administered i.p. using the indicated schedules. Treatment schedule (Rx): A = day 1; B = day 1, 5, 9; C = day 1–5; D = day 1–9; E = day 1, 4, 7, 10; F = q3h × 8, day 1, 5, 9; G = day 1–15

BCNU, and ara-C/palmO-ara-C, administered i.p., exhibit ­comparable activity against either i.p. or i.c. implanted leukemias. In addition to melphalan, cisplatin, cyclophosphamide, ifosfamide, and 6-mercaptopurine (L1210) have reduced activity when the implant site is changed to i.c. Several agents become inactive when the implant site is changed to i.c. (e.g., methotrexate (P388), 5-fluorouracil, floxuridine, actinomycin D, vincristine, doxorubicin, and etoposide). Some comparisons using different treatment schedules can be misleading. Even though all values have been expressed as net cell kill (i.e., corrected for the treatment schedule), one schedule can

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W.R. Waud

NSC No. Drug Antimetabolites

Rx

Methotrexate

740

C

6-Thioguanine

752

A D

6-Mercaptopurine 755

A D

5-Fluorouracil

19893

C

Floxuridine

27640

D

Hydroxyurea

32065

F

Azacytidine

102816

D

2-Chlorodeoxy- 105014 adenosine 135962 PalmO-ara-C Deoxycoformycin 218321

A

Trimetrexate

249008

C

Fludarabine

312887

D

F F

DNA Binders Actinomycin D Mithramycin

3053

A

24559

D

82151

D

Teniposide

122819

B

Doxorubicin

123127

A

Etoposide

141540

B

Amsacrine

249992

D

Idarubicin

256439

D

Mitoxantrone

301739

D

Daunorubicin

-4

-3

-2

-1

0 1 2 3 4 Net Cell Kill (log10 units)

5

6

7

Fig. 2.2  Sensitivity of i.p. implanted L1210 leukemia to clinically useful antimetabolites and DNA binders. L1210 leukemia (105 cells, except for hydroxyurea, which used 104 cells and 6-thioguanine (day 1 only treatment) and daunorubicin, which used 106 cells) was implanted i.p. on day 0. Beginning on day 1 (day 2 for daunorubicin), the agents were administered i.p. using the indicated schedules. Treatment schedule (Rx): see legend for Fig. 2.1

be optimal, whereas another schedule is suboptimal. For nitrogen mustard, no ­conclusion can be drawn from the data about its ability to cross the blood–brain ­barrier. The agent is active against the i.p. implanted leukemia using a single i.p. injection (optimal) and is inactive against the i.c. implanted leukemia using 15 daily i.p. injections (suboptimal). This is further illustrated by chlorambucil which is active against i.c. implanted L1210 (using a single i.p. injection) and inactive against i.p. implanted L1210 (using nine daily i.p. injections). From work with these screening models, it became apparent that drug sensitivity was, in some cases, heavily dependent on drug concentration and exposure

2  Murine L1210 and P388 Leukemias Drug

29

NSC No. Rx

Alkylating Agents Busulfan

750

C

Nitrogen Mustard

762

C

Chlorambucil

3088

D

Thiotepa

6396

A

Melphalan Hexamethylmelamine Cyclophosphamide Mitomycin C

8806

A

13875

D

26271

A

26980

A

Dacarbazine

45388

A

Procarbazine

77213

D

CCNU

79037

A

85998

B

Ifosfamide

109724

A

Cisplatin

119875

B

Chlorozotocin

178248

B

Carboplatin

241240

B

BCNU

409962

A

Streptozotocin

Tubulin Binders Vinblastine

49842

B

Vincristine

67574

B

Paclitaxel

125973

C

Vinorelbine

608210

B

Gallium nitrate

15200

D

Mitotane

38721

D

Bleomycin

125066

D

Levamisole

177213

D

Miscellaneous

-3

-2

-1

0

1

2

3

4

5

6

7

8

Net Cell Kill (log10units)

Fig. 2.3  Sensitivity of i.p. implanted P388 leukemia to clinically useful alkylating agents, tubulin binders, and other miscellaneous agents. P388 leukemia (106 cells except for CCNU, which used 107 cells) was implanted i.p. on day 0. Beginning on day 1 (day 2 for CCNU, streptozotocin, and chlorozotocin), the agents were administered i.p. using the indicated schedules. Treatment schedule (Rx): see legend for Fig. 2.1

time which, in turn, was impacted by the in  vivo treatment schedule. As an example, studies conducted with 1-β-d-arabinofuranosylcytosine (ara-C) pointed out the need for concentration and time of exposure studies. Using L1210 leukemia in mice, it was shown that the optimal dosage and schedule for ara-C was 15–20 mg/kg/dose, given every 3 h for eight doses, then repeated three times at 4-day intervals [13]. This regimen was “curative.” The single-dose LD10 for mice was between 2,500 and 3,000 mg/kg, and using a single dose within that range

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W.R. Waud

Drug

Antimetabolites

NSC No.

Rx

Methotrexate

740

D

6-Thioguanine

752

D

6-Mercaptopurine

755

D

5-Fluorouracil

19893

D

Floxuridine

27640

D

Hydroxyurea

32065

F

Azacytidine

102816

D

PalmO-ara-C

135962

A

Deoxycoformycin 218321

D

Trimetrexate

249008

D

Fludarabine

312887

D

Gemcitabine

613327

E

DNA Binders 3053

A

Mithramycin

24559

D

Daunorubicin

82151

A

Teniposide

122819

B

Doxorubicin

123127

A

Etoposide

141540

B

Amsacrine

249992

B

Idarubicin

256439

B

Mitoxantrone

301739

B

Actinomycin D

-3

-2

-1

0

1

2

3

4

5

6

7

8

Net Cell Kill (log10 units)

Fig. 2.4  Sensitivity of i.p. implanted P388 leukemia to clinically useful antimetabolites and DNA binders. P388 leukemia (106 cells) was implanted i.p. on day 0. Beginning on day 1, the agents were administered i.p. using the indicated schedules. Treatment schedule (Rx): see legend for Fig. 2.1

Table 2.1  Activity of melphalan administered as a single i.p. injection against L1210 and P388 leukemias implanted i.p., i.v., and i.c.     Net cell kill (log10 units) Site Inoculum size L1210 P388 i.p. 106 4.7 >6.5 i.v. 106 2.0 2.9 1.2 2.4 i.c. 104

2  Murine L1210 and P388 Leukemias Drug

NSC No.

31

Rx

Alkylating Agents Busulfan

750 A

Nitrogen Mustard

762 G

Chlorambucil

3088 A

Thiotepa

6396 A

Melphalan

8806 A

CCNU Streptozotocin

79037 A 85998 D

Cisplatin

119875 A

BCNU

409962 A

Antimetabolites Methotrexate 6-Mercaptopurine

740 A 755 A

5-Fluorouracil

19893 D

Hydroxyurea

32065 F

Ara-C

63878 D

A

PalmO-ara-C

135962 A

DNA or Tubulin Binders Actinomycin D Vinblastine Daunorubicin Etoposide

3053 A 49842

G

82151

A

141540

B

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

Net Cell Kill (log10 units)

Fig. 2.5  Sensitivity of i.c. implanted L1210 leukemia to clinically useful agents. L1210 leukemia (104 cells except for CCNU, which used 105 cells) was implanted i.c. on day 0. Beginning on day 1 (day 2 for busulfan, chlorambucil, thiotepa, melphalan, hydroxyurea (single injection), cisplatin, BCNU, and daunorubicin), the agents were administered i.p. using the indicated schedules. Treatment schedule (Rx): see legend for Fig. 2.1

would effect a 3-log10-unit reduction in L1210 cells but was not “curative.” Although these in  vivo results might give the appearance of a concentrationdependent effect, in  vitro studies had clearly shown that cell kill of L1210 in culture was time dependent at the higher concentration levels employed. The apparent concentration dependence observed in vivo over a range of single doses had resulted from the extended time of exposure that resulted from those extremely high dosage levels.

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W.R. Waud Drug

NSC No.

Rx

Alkylating Agents Thiotepa

6396

A

8806

A

Cyclophosphamide

26271

A

Procarbazine

77213

D

CCNU

79037

A

Ifosfamide

109724

A

Cisplatin

119875

A

BCNU

409962

A

Methotrexate

740

D

6-Thioguanine

752

D

Melphalan

Antimetabolites

755

D

19893

D

276401

D

63878

D

135962

A

6-Mercaptopurine 5-Fluorouracil Floxuridine Ara-C PalmO-ara-C

DNA or Tubulin Binders Actinomycin D Vincristine Doxorubicin

3053

A

67574

B

123127

B

-4

-3

-2

-1

0

1

2

3

4

5

6

7

Net Cell Kill (log10 units)

Fig. 2.6  Sensitivity of i.c. implanted P388 leukemia to clinically useful agents. P388 leukemia (104 cells except for ifosfamide, methotrexate, 6-thioguanine, 6-mercaptopurine, 5-fluorouracil, and floxuridine, which used 103 cells and CCNU and ara-C, which used 105 cells) was implanted i.c. on day 0. Beginning on day 1 (day 2 for ifosfamide), the agents were administered i.p. using the indicated schedules. Treatment schedule (Rx): see legend for Fig. 2.1

2.5 Predictive Value Many investigators have questioned the use of experimental leukemias as primary screening models over the years. Some have argued that since L1210 or P388 leukemia was used for many years as the initial screening model, continued evaluation of compounds emerging from this screening configuration, even using solid tumor models for secondary evaluation, would only produce antileukemic drugs [14]. If compounds active against solid tumors were being missed by the primary screen

2  Murine L1210 and P388 Leukemias

33

composed of leukemias, it would appear reasonable that in order to obtain agents that are active against specific tumor types or solid tumors in general, then the primary screen should consist of specific tumor types or solid tumors. Even though this would appear to be a reasonable approach, it will depend on whether or not there are existing agents or that agents can be developed that will selectively kill specific cancer histotypes. The correlation between drugs active against L1210 or P388 leukemia and solid experimental tumor models has not been good. For example, only 1.7% of 1,493 agents that were active against P388 leukemia were also active against murine Lewis lung carcinoma. Further, only 2% of 1,507 agents active against P388 leukemia were also active against murine colon 38 adenocarcinoma. Finally, only 2% of 1,133 agents that were active against P388 leukemia were also active against human CX-1 (HT29) colon tumor. However, when comparing leukemias, a less than expected correlation was obtained – only 15% of 1,564 active agents against P388 leukemia were also active against L1210 leukemia [15]. One observation often referred to is that there are drugs active against experimental solid tumors that are inactive against P388 leukemia. For example, 15% of 84 agents that were inactive against P388 leukemia were active against at least one of eight solid tumors tested [15]. Flavone acetic acid has been cited as an example [14]. This compound was inactive in the initial P388 screen even though it was later shown to exhibit activity against the leukemia when the appropriate treatment schedule was used [16]. This example points out a problem with large-scale screening programs in that it is not logistically feasible to conduct preliminary schedule dependency trials. One other observation is that there are experimental solid tumors (e.g., murine pancreatic 02 ductal adenocarcinoma) that are not responsive in vivo to any clinically used agents, including many P388-active agents [14]. It may be noted, however, that this tumor is sensitive to numerous clinical agents in vitro after a 24-h exposure [17], suggesting that the in vivo insensitivity of this tumor may not be due to cellular characteristics but rather physiological or architectural constraints of the animal. Southern Research has evaluated a spectrum of compounds in the i.p. implanted P388 model in order to evaluate this model as a predictor for the response of human tumor xenografts to new candidate antitumor agents (unpublished data). The P388 data collected were compared to the data for various s.c. implanted human tumor xenografts, which were selected on the basis of the results of the NCI in vitro screen. In general, compounds that were active against P388 leukemia were active to a lesser degree in one or more of the xenografts in the in vivo tumor panel. However, there were isolated examples of a P388-active agent being inactive in the human tumor xenograft models tested and vice versa. There was no indication that the P388 model could predict compound efficacy for specific tumor xenografts. Whether or not the murine leukemias are poor predictors of activity in solid tumors is still somewhat questionable and will only be determined when drugs without antileukemic model activity but of proven value in the treatment of human solid tumors become available.

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W.R. Waud

2.6 Drug-Resistant Leukemias Panels of in vivo drug-resistant murine L1210 and P388 leukemia models have been developed at Southern Research for use in the evaluation of crossresistance and collateral sensitivity. These models have been used for the evaluation of new drugs of potential clinical interest. An extensive summary of in  vivo drug resistance and crossresistance data has been published by Schabel and coworkers [18]. Their initial manuscript included results of in vivo crossresistance studies on 79 antitumor drugs in seven drug-resistant L1210 leukemias and 74 antitumor drugs in 12 drug-resistant P388 leukemias. Previously we expanded this crossresistance database for the drugresistant P388 leukemias to include two new drug-resistant lines and more clinically useful drugs. Also, we updated the database to include new candidate antitumor agents entering clinical trials [19]. Three additional drug-resistant P388 leukemias were added to this database [20]. In this section, we report this crossresistance database for 16 drug-resistant P388 leukemias and many of the clinically useful agents.

2.6.1 Resistance to Alkylating Agents The crossresistance profile of cyclophosphamide-resistant P388 leukemia (P388/ CPA) to 14 different clinical agents is shown in Table 2.2. The P388/CPA line was crossresistant1 to one of the five alkylating agents, no antimetabolites, no DNAbinding agents, and no tubulin-binding agents. Crossresistance of P388/CPA has also been observed for two other alkylating agents (chlorambucil and ifosfamide) [20]. Interestingly, there are differences among these three agents. Chlorambucil and ifosfamide, like cyclophosphamide, each have two chloroethylating moieties, whereas mitomycin C is from a different chemical classes. Whereas ifosfamide, cyclophosphamide, and mitomycin C require metabolic activation, chlorambucil does not. Although P388/CPA is crossresistant to two chloroethylating agents, the line is not crossresistant to other chloroethylating agents (melphalan and BCNU). Therefore, P388/CPA appears to be crossresistant only to a select group of alkylating agents with differing characteristics. P388/CPA appeared to be collaterally sensitive to fludarabine. The effect of 15 different clinical agents on melphalan-resistant P388 leukemia (P388/L-PAM) is shown in Table 2.2. The P388/L-PAM line was crossresistant to approximately one-half of the agents – two of four alkylating agents, one of four antimetabolites, three of five DNA-binding agents, and one of two tubulin-binding

Crossresistance is defined as decreased sensitivity (by >2-log10 units of cell kill) of a drug-resistant P388 leukemia to a drug compared to that observed concurrently in P388/0 leukemia. Similarly, marginal crossresistance is defined as a decrease in sensitivity of approximately 2-log10 units. Collateral sensitivity is defined as increased sensitivity (by >2-log10 units of cell kill) of a drugresistant P388 leukemia to a drug over that observed concurrently in P388/0 leukemia. 1 

2  Murine L1210 and P388 Leukemias

35

Table 2.2  Crossresistance of P388 sublines resistant to various alkylating agents and antimetabolites to clinically useful agentsa Drug NSC no. Rxb CPA L-PAM DDPt BCNU MMCc MTX 5-FU ARA-C Alkylating agents Melphalan 8806 A – % – –       ± Cyclophosphamide 26271 A % – – – –     % Mitomycin C 26980 A ± % – – % –   % Procarbazine 77213 D –               ±d Cisplatin 119875 B – % % – –   % BCNU 409962 A – – – % –     – Antimetabolites Methotrexate 740 D     –   –e % % % 6-Thioguanine 752 A         –e –     6-Mercaptopurine 755 D           –     5-Fluorouracil 19893 D –   –   –e   % ∋ PalmO-ara-C 135962 A – – –   –e – – % Trimetrexate 249008 D   ± –     –   – Fludarabine 312887 D ∋ ∋ ∋     – ∋ % Gemcitabine 613327 E – – –     –   % DNA binders Actinomycin D 3053 A – ± –   ±       Doxorubicin 123127 A – – –   % –   – Etoposide 141540 B – – –   %d –   – Amsacrine 249992 B – % ∋   –d –   – Mitoxantrone 301739 B   % ∋     –   – Tubulin binders Vinblastine 49842 A         %       Vincristine 67574 B – % –   %d –   % Paclitaxel 125973 C   – –     –   – ARA-C, 1-β-D-arabinofuranosylcytosine; BCNU, N,N¢-bis(2-chloroethyl)-N-nitrosourea; CPA, cyclophosphamide; DDPt, cisplatin; 5-FU, 5-fluorouracil; L-PAM, melphalan; MMC, mitomycin C; MTX, methotrexate; NSC, National Service Center a  CD2F1 mice were implanted i.p. with 106 P388/0 or drug-resistant P388 cells on day 0. Data presented are for i.p. drug treatment at an optimal (≤ LD10) dosage. Symbols: resistance/crossresistance, %; marginal crossresistance, ±; no crossresistance, –; and collateral sensitivity, ∋ b  Treatment schedule (Rx): A = day 1; B = day 1, 5, 9; C = day 1–5; D = day 1–9; E = day 1, 4, 7, 10 c  Data from Ref. [23] d  Treatment schedule was day 1 e  Treatment schedule was day 1 and 5

agents. The alkylating agents involved in crossresistance represent different chemical classes. Similarly, the DNA-interacting agents involved in crossresistance include agents with different mechanisms of action – inhibitors of DNA topoisomerase II (amsacrine and mitoxantrone) and a DNA-binding agent (actinomycin D). However, the melphalan-resistant line did not exhibit crossresistance to other inhibitors of DNA topoisomerase II (e.g., doxorubicin and etoposide) or another DNA-binding agent (e.g., doxorubicin).

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The sensitivity of cisplatin-resistant P388 leukemia (P388/DDPt) to 17 ­different clinical agents is shown in Table 2.2. The P388/DDPt line was not crossresistant to any of these agents. Interestingly, the cisplatin-resistant line was collaterally sensitive to three agents (fludarabine, amsacrine, and mitoxantrone). Of these three agents, the latter two have been reported to interact with DNA topoisomerase II [21, 22]. The crossresistance data for N,N¢-bis(2-chloroethyl)-N-nitrosourea-resistant P388 leukemia (P388/BCNU) have been limited to the evaluation of alkylating agents. The crossresistance profile of P388/BCNU to four different clinical agents is shown in Table 2.2. The BCNU-resistant line was not crossresistant to melphalan, cyclophosphamide, mitomycin C, or cisplatin. The crossresistance profile of mitomycin C-resistant P388 leukemia (P388/ MMC) to 13 different clinical agents is shown in Table 2.2 [23]. The P388/MMC line was crossresistant to approximately one-half of the agents – one of three alkylating agents, zero of four antimetabolites, three of four DNA-binding agents, and two of two tubulin-binding agents. The pattern was similar to that observed for P388/L-PAM.

2.6.2 Resistance to Antimetabolites The effect of 14 different clinical agents on methotrexate-resistant P388 leukemia (P388/MTX) is shown in Table 2.2. The P388/MTX line was not crossresistant to any of these agents. The crossresistance data for 5-fluorouracil-resistant P388 leukemia (P388/5-FU) have been limited to antimetabolites. The sensitivity of the P388/5-FU to three different agents is shown in Table 2.2. The P388/5-FU line was not crossresistant to palmO-ara-C (a slow-releasing form of ara-C) or fludarabine (possible collateral sensitivity). Crossresistance was observed for methotrexate. The crossresistance profile of 1-β-d-arabinofuranosylcytosine-resistant P388 leukemia (P388/ARA-C) to 16 different clinical agents is shown in Table 2.2. The P388/ARA-C line was crossresistant to members of several functionally different classes of antitumor agents – four of five alkylating agents, three of five antimetabolites, zero of four DNA-binding agents, and one of two tubulin-binding agents. Interestingly, the line was collaterally sensitive to 5-fluorouracil.

2.6.3 Resistance to DNA- and Tubulin-Binding Agents The effect of 17 different clinical agents on actinomycin D-resistant P388 leukemia (P388/ACT-D) is shown in Table 2.3. P388/ACT-D was not crossresistant to any alkylating agents or antimetabolites. It was, however, crossresistant to all of the drugs tested that are involved in multidrug resistance except for amsacrine.

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Table 2.3  Crossresistance of P388 sublines resistant to various DNA and tubulin binders to clinically useful agentsa Drug NSC no. Rxb ACT-D ADR AMSA DIOHA VP-16 CPTc VCR PTX Alkylating agents Melphalan 8806 A – – –   –   –   Cyclophosphamide 26271 A – – – – –   –   Mitomycin C 26980 A   ± –     –f %   Procarbazine 77213 D – –         –   Cisplatin 119875 C – – –d   –d –f ±   BCNU 409962 A – –     –   –   Antimetabolites Methotrexate 6-Thioguanine 6-Mercaptopurine 5-Fluorouracil PalmO-ara-C Trimetrexate Fludarabine Gemcitabine

740 752 755 19893 135962 249008 312887 613327

D D D D A D D E

– – – – –      

– – – –e – – ∋ –

±     –e –      

               

–     –       –

               

– – – – – – – –

               

DNA binders Actinomycin D Doxorubicin Etoposide Amsacrine Mitoxantrone

3053 123127 141540 249992 301739

A A B B B

% ± % – %

% % % % %

% % % % %

– – – % %

% % % % %

–f –f   –f –f

– – – – –

  % %    

Tubulin binders Vinblastine 49842 B % % %   %   %   Vincristine 67574 B % % % % %   % % Paclitaxel 125973 C ± ± % –   –f – % ACT-D, actinomycin D; ADR, doxorubicin; AMSA, amsacrine; CPT, camptothecin; DIOHA, mitoxantrone; NSC, National Service Center, PTX, paclitaxel; VCR, vincristine; VP-16, etoposide a  CD2F1 mice were implanted i.p. with 106 P388/0 or drug-resistant P388 cells on day 0. Data presented are for i.p. drug treatment at an optimal (≤ LD10) dosage. Symbols: resistance/crossresistance, %; ­marginal crossresistance, ±; no crossresistance, –; and collateral sensitivity, ∋ b  Treatment schedule (Rx): A = day 1; B = day 1, 5, 9; C = day 1–5; D = day 1–9; E = day 1, 4, 7, 10 c  Data from Ref. [24] d  Treatment schedule was day 1, 5, 9 e  Treatment schedule was day 1–5 f  Treatment schedule was day 1 and 5

The crossresistance profile of doxorubicin-resistant P388 leukemia (P388/ADR) to 21 different clinical agents is shown in Table 2.3. The P388/ADR line was not crossresistant to any of the antimetabolites and was marginally crossresistant to only one alkylating agent (mitomycin C). Resistance was observed for all of the drugs tested that are reported to be involved in multidrug resistance (actinomycin D, doxorubicin, etoposide, amsacrine, mitoxantrone, vinblastine, vincristine, and paclitaxel). P388/ADR was collaterally sensitive to fludarabine.

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The sensitivity of amsacrine-resistant P388 leukemia (P388/AMSA) to 14 different clinical agents is shown in Table 2.3. P388/AMSA was not crossresistant to any of the alkylating agents and was marginally crossresistant to only one antimetabolite. Crossresistance was observed for all of the drugs tested that are involved in multidrug resistance. The crossresistance data for mitoxantrone-resistant P388 leukemia (P388/ DIOHA) have been limited mainly to agents involved in multidrug resistance. The sensitivity of P388/DIOHA to seven different clinical agents is shown in Table 2.3. The P388/DIOHA line exhibited mixed multidrug resistance – crossresistant to amsacrine and vincristine but was not crossresistant to actinomycin D, doxorubicin, etoposide, or paclitaxel. The crossresistance profile of etoposide-resistant P388 leukemia (P388/VP-16) to 13 different clinical agents is shown in Table 2.3. The P388/VP-16 line was not crossresistant to any of the alkylating agents or antimetabolites; however, it was crossresistant to all of the drugs tested that are reported to be involved in multidrug resistance. The sensitivity of camptothecin-resistant P388 leukemia (P388/CPT) to seven different clinical agents is shown in Table 2.3 [24]. P388/CPT was not crossresistant to any of these agents. The effect of 21 different clinical agents on vincristine-resistant P388 leukemia (P388/VCR) is shown in Table 2.3. The P388/VCR line was crossresistant to three of the agents – mitomycin C, cisplatin (marginal), and vinblastine. Unexpectedly, P388/VCR was not crossresistant to many of the drugs tested that are involved in multidrug resistance (e.g., actinomycin D, doxorubicin, etoposide, amsacrine, mitoxantrone, and paclitaxel). The crossresistance data for paclitaxel-resistant P388 leukemia (P388/PTX) have been limited to agents involved in multidrug resistance. The sensitivity of P388/PTX to three different clinical agents is shown in Table 2.3. The P388/PTX line was crossresistant to drugs that are involved in multidrug resistance (doxorubicin, etoposide, and vincristine).

2.7 Conclusions Currently, biotechnology continues to advance in an almost exponential fashion. Today, advanced techniques and tools allow us to conduct research not even imagined 50 years ago when the L1210 and P388 leukemia models were first used extensively (e.g., sequencing the human genome). Utilizing molecular biology techniques, the emphasis is now on the development of compounds designed for a specific target. Current NCI strategy suggests that models for evaluating these compounds contain the specific target either naturally or by gene transfection. Successful treatment of such models will theoretically provide the necessary proofof-concept required for continued development. This is a radical departure from the empirical approach to mass screening of compounds against murine leukemias.

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The L1210 and P388 leukemia models do have some advantages – they are rapid, reproducible, and relatively inexpensive (in comparison to human tumor xenograft or transgenic models). However, as with any experimental animal tumor model, there are limitations. Neither leukemia is a satisfactory drug discovery model for either human cancer in general or human leukemia in particular. Of course this could be said of any animal tumor model. Of the two leukemias, P388 is the more sensitive but overpredicts drug activity for both preclinical human tumor xenograft models and the clinic. However, the question of whether P388 leukemia (or L1210) is a poor predictor for solid tumor-active drugs has yet to be sufficiently answered. Although the murine leukemia models have severe limitations, these models have been very useful in anticancer drug development, in the development of a number of therapeutic principles, and in understanding the biologic behavior of tumor and host. These models are still useful today in conducting detailed evaluations of new candidate anticancer drugs (e.g., schedule dependency, route of administration dependency, formulation comparison, analog comparison, and combination chemotherapy). Perhaps the greatest utility of the murine leukemias today is derived from the evaluations of the drug-resistant sublines for crossresistance and collateral sensitivity. Analysis of the crossresistance data generated at Southern Research for clinical agents has revealed possible noncrossresistant drug combinations. The P388 leukemia lines selected for resistance to alkylating agents (e.g., P388/CPA, P388/L-PAM, P388/DDPt, P388/BCNU, and P388/MMC) differed in crossresistant profiles, both with respect to alkylating agents and other functional classes. Similarly, P388 leukemia lines selected for resistance to antimetabolites (e.g., P388/MTX, P388/5-FU, and P388/ARA-C) differed in crossresistance profiles, both with respect to antimetabolites and other functional classes. Clearly, the spectrum of crossresistance of an alkylating agent or an antimetabolite will depend on the individual agent. P388 leukemia lines selected for resistance to large polycyclic anticancer drugs (e.g., P388/ACT-D, P388/ADR, P388/AMSA, P388/DIOHA, P388/VP-16, P388/CPT, P388/VCR, and P388/PTX) were not generally crossresistant to alkylating agents or antimetabolites. However, the crossresistance profiles to DNA- and tubulin-binding agents were variable. Five of the 16 drug-resistant leukemias exhibited collateral sensitivity to one or more drugs. These observations of collateral sensitivity suggest that a combination of one of the five drugs plus one of the corresponding agents for which collateral sensitivity was observed might exhibit therapeutic synergism. Crossresistance data, coupled with knowledge of the mechanisms of resistance operative in the drug-resistant leukemias, may yield insights into the mechanisms of action of the agents being tested. Similarly, crossresistance data, coupled with the mechanisms of action of various agents, may yield insights into the mechanisms of resistance operative in the drug-resistant leukemias [19]. Furthermore, crossresistance data may identify potentially useful guides for patient selection for clinical trials of new antitumor drugs [19]. In conclusion, the role of L1210 and P388 leukemias in the evaluation of anticancer agents has diminished considerably. Nevertheless, many of the clinical agents

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now in use were first detected by the murine leukemias. These models are clearly still appropriate for answering certain questions, and the drug-resistant sublines can provide valuable information concerning crossresistance and collateral sensitivity. Acknowledgments  The majority of this work was supported by Contracts NO1-CM-07315 and NO1-CM-47000 and predecessor contracts with the Developmental Therapeutics Program, DCTD, NCI. The studies of gemcitabine and P388/VP-16 leukemia were supported by Eli Lilly and Company and by Burroughs Wellcome Company, respectively. The authors gratefully acknowledge the technical assistance of the staff of the Cancer Therapeutics and Immunology Department. J. Tubbs assisted with data management, and K. Cornelius prepared the manuscript.

References 1. Law LW, Dunn DB, Boyle PJ, Miller JH. Observations on the effect of a folic-acid antagonist on transplantable lymphoid leukemias in mice. J Natl Cancer Inst. 1949;10:179–92. 2. Dawe CJ, Potter M. Morphologic and biologic progression of a lymphoid neoplasm of the mouse in vivo and in vitro. Am J Pathol. 1957;33(3):603. 3. Griswold DP Jr, Harrison SD Jr. Tumor models in drug development. Cancer Metastasis Rev. 1991;10:255–61. 4. Zubrod CG. Historic milestone in curative chemotherapy. Semin Oncol. 1979;6(4):490–505. 5. Goldin A, Serpick AA, Mantel N. A commentary, experimental screening procedures and clinical predictability value. Cancer Chemother Rep. 1966;50(4):173–218. 6. Carter S. Anticancer drug development progress: a comparison of approaches in the United States, the Soviet Union, Japan, and Western Europe. Natl Cancer Inst Monogr. 1974;40:31–42. 7. Goldin A, Venditti JM, Muggia FM, Rozencweig M, DeVita VT. New animal models in cancer chemotherapy. In: Fox BW, editor. Advances in medical oncology, research and education. Vol. 5. Basis for cancer therapy 1. New York: Pergamon; 1979. p. 113–22. 8. Alley MC, Scudiero DA, Monks A, Hursey ML, Czerwinski MJ, Fine DL, Abbott BJ, Mayo JG, Shoemaker RH, Boyd MR. Feasibility of drug screening with panels of human tumor cell lines using a microculture tetrazolium assay. Cancer Res. 1988;48:589–601. 9. Cancer Lett. 1987;13(2):507. 10. Skipper HE, Schabel FM Jr, Wilcox WS, Laster WR Jr, Trader MW, Thompson SA. Experimental evaluation of potential anticancer agents. XVII. Effects of therapy on viability and rate of proliferation of leukemic cells in various anatomic sites. Cancer Chemother Rep. 1965;47:41–64. 11. Schabel FM Jr, Griswold DP Jr, Laster WR Jr, Corbett TH, Lloyd HH. Quantitative evaluation of anticancer agent activity in experimental animals. Pharmacol Ther. 1977;1:411–35. 12. Lloyd HH. Application of tumor models toward the design of treatment schedules for cancer chemotherapy. In: Drewinko B, Humphrey RM, editors. Growth kinetics and biochemical regulation of normal and malignant cells. Baltimore: Williams & Wilkins; 1977. p. 455–69. 13. Skipper HE, Schabel FM Jr, Wilcox WS. Experimental evaluation of potential anticancer agents. XXI. Scheduling of arabinosylcytosine to take advantage of its S-phase specificity against leukemia cells. Cancer Chemother Rep. 1967;51:1625–55. 14. Corbett TH, Valeriote FA, Baker LH. Is the P388 murine tumor no longer adequate as a drug discovery model? Invest New Drugs. 1987;5:3–20. 15. Staquet MJ, Byar DP, Green SB, Rozencweig M. Clinical predictivity of transplantable tumor systems in the selection of new drugs for solid tumors: rationale for a three-stage strategy. Cancer Treat Rep. 1983;67:753–65.

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16. Trader MW, Harrison SD Jr, Laster WR Jr, Griswold DP Jr. Cross-resistance and collateral sensitivity of drug-resistant P388 and L1210 leukemias to flavone acetic acid (FAA, NSC 347512) in vivo (Abstr). Proc AACR. 1987;28:312. 17. Wilkoff LJ, Dulmadge EA. Sensitivity of proliferating cultured murine pancreatic tumor cells to selected antitumor agents. J Natl Cancer Inst. 1986;77(5):1163–9. 18. Schabel FM Jr, Skipper HE, Trader MW, Laster WR Jr, Griswold DP Jr, Corbett TH. Establishment of cross-resistance profiles for new agents. Cancer Treat Rep. 1983;67:905–22 (see correction, Cancer Treat Rep. 1984;68:453–9). 19. Waud WR, Griswold DP Jr. Therapeutic resistance in leukemia. In: Teicher BA, editor. Drug resistance in oncology. New York: Marcel Dekker, Inc.; 1993. p. 227–50. 20. Waud WR. Murine L1210 and P388 leukemias. In: Teicher BA, Andrews PA, editors. Anticancer drug development guide: preclinical screening, clinical trials, and approval. 2nd ed. Totowa, NJ: Humana; 2004. p. 79–97. 21. Ho AD, Seither E, Ma DDF, Prentice G. Mitoxantrone-induced toxicity and DNA strand breaks in leukemic cells. Br J Haematol. 1987;65:51–5. 22. Nelson EM, Tewey KM, Liu LF. Mechanism of antitumor drug action: poisoning of mammalian DNA topoisomerase II on DNA by 4¢-(9-acridinylamino)methane-sulfon-m-anisidide. Proc Natl Acad Sci U S A. 1984;81:1361–5. 23. Rose WC, Huftalen JB, Bradner WT, Schurig JE. In vivo characterization of P388 leukemia resistant to mitomycin C. In Vivo. 1987;1:47–52. 24. Eng WK, McCabe FL, Tan KB, Mattern MR, Hofmann GA, Woessner RD, Hertzberg RP, Johnson RK. Development of a stable camptothecin-resistant subline of P388 leukemia with reduced topoisomerase I content. Mol Pharmacol. 1990;38:471–80.

Chapter 3

Transplantable Syngeneic Rodent Tumors: Solid Tumors in Mice Lisa Polin, Thomas H. Corbett, Bill J. Roberts, Alfred J. Lawson, Wilbur R. Leopold III, Kathryn White, Juiwanna Kushner, Stuart Hazeldine, Richard Moore, James Rake, and Jerome P. Horwitz

Abstract  As preclinical chemotherapists, we are often asked to identify experimental tumor models that can accurately predict for the drug response characteristics of all tumors of a given cellular subtype or molecular target. Unfortunately, it is impossible to give satisfactory answers to these inquiries. Because of the unique character of each independently arising tumor (whether spontaneous or induced), it does not take very long to realize that each tumor is a unique biologic entity with its own tumor growth behavior, histological appearance, drug response and molecular expression profiles. This is true whether the tumor is an experimental animal model or one originally derived from a patient. Further, many factors can influence the tumor growth and therapy response of experimental tumor models. Still, in vivo models are needed to adequately assess pharmacodynamics, toxicity and efficacy of any potential novel therapy. Presented herein is what we hope will be useful information regarding the transplant characteristics of tumor models, with some of the “pitfalls” to look out for when using any given tumor model for chemotherapy evaluations. Although most of the examples given use syngeneic models, the methodologies for assessing the predictive worth and maintaining model usefulness can be applied to almost any given transplantable tumor system (whether syngeneic or xenograft). Keywords  Preclinical model • Mice • Chemotherapy

L. Polin (*) Solid Tumor Drug Discovery Lab, Department of Oncology, Barbara Ann Karmanos Cancer Institute, Wayne State University, Prentis Building, Room 2232, 110 E. Warren Avenue, Detroit, MI 48201, USA e-mail: [email protected]

B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_3, © Springer Science+Business Media, LLC 2011

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3.1 Introduction For many decades, the results from transplantable tumor models have been viewed with considerable skepticism. The perception has long been that these models are excessively sensitive, and not predictive of the human disease. Although we do not intend to debate the many issues involved, it is our view that a better understanding of the transplant properties (e.g., take-rate) of the models – as well as a better understanding of the potential shortcomings in data presentation – will greatly aid the reader in the interpretation of these data. This chapter is an effort to summarize some of the basic operating characteristics of a wide range of solid-tumor models. Since this is a chemotherapy group, we can best explain some of the behavioral characteristics of these models within therapeutic experiments. Most of the data are drawn from the use of transplantable, syngeneic mouse tumors, but a few human tumors have been used for contrast.

3.2 Compatibility! Compatibility! Compatibility! Why consider using mouse-tumor models when there are so many human tumor models available? The reason is obvious: compatibility with the host animal. This one feature, above all else, allows the researcher a measure of dependability that can never be attained with the human tumor models in immune-deficient animals. Even if the researcher wishes to use the human tumor-xenograft models for a major portion of their studies, the ability to confirm a result in one or two syngeneic mouse-tumor models and healthy immune-competent mice will usually eliminate the many pitfalls awaiting the unwary [1–3].

3.3 Compatible But Not Perfect: Inbred Mice and Genetic Drift An inbred mouse has >99% homozygosity, and is defined as a product of 20 or more generations of brother–sister mating (each generation reducing heterozygosity by 19%). However, many of the inbred strains were developed between 1905 and 1915, and have undergone genetic drift in various breeding facilities. As an example, the first and third authors well recall the variations in C3H/He mice from various breeders that came into the laboratory in the early 1970s. These mice had different shades of coat-color and snouts of different shape from various suppliers. Since obvious physical attributes were so varied, it was clear that the quality control in breeding was lacking. During that period, the National Cancer Institute (NCI) undertook a program to standardize the common inbred strains (e.g., C3H/He, C57B1/6, DBA/2, Balb/c), and all are now a product of over 150 brother–sister matings. This standardization was accomplished, but some of our long-used

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C3H  mammary tumors failed to grow and metastasize adequately in the newly ­standardized C3H/He strain. Consequently, new tumor models were isolated and characterized – e.g., Mammary Adenocarcinoma-16/C and 13/C [4]. During the mid 1970s to early 1980s, we also developed various colon adenocarcinomas and pancreatic ductal adenocarcinomas for use in chemotherapy studies [5–7]. These tumor models have remained trouble-free in our laboratories and many other laboratories for over 30 years (using only the standardized mice purchased though NCI). This is not necessarily true for some of these same tumors used in other countries. In the late 1970s, we supplied Colon Adenocarcinoma-51 to a highly competent investigator in Europe, who found the tumor to be exceptionally ­drug-sensitive to a large ­number of different agents, and quite curable. In our hands, it was (and continues to be) among the least drug-sensitive tumor available (Table  3.1) [6], and only ­curable with two agents (e.g., PCNU, Piposulfan). Likewise, Colon Carcinoma-26 has remained a drug-insensitive tumor in our laboratories (Table 3.1) [6], but appears to be easily curable with a large number of agents in studies carried out in Japan. One is never sure if the problem is tumor– host incompatibility or the development of deviant lines of tumors. The implications are obvious: syngeneic tumor-model systems can be produced and maintained, but only with adequate quality controls and access to the mice of origin (discussed vide infra).

3.4 Evidence of Tumor–Host Incompatibility and Consequences This topic has been previously reviewed [8, 9], but must be expanded, because so many manipulations are used to overcome the many problems encountered with incompatible models (especially in human tumor-xenograft studies). One suspects that incompatible systems are most frequently used because the treatment results are much more impressive, since the host immune system contributes substantially to the tumor-cell-kill [8]. However, if such a tumor had a unique property required for study (and an appropriate syngeneic system could not be found), the investigator could use the mouse tumor in a (severe combined immunodeficiency [SCID]) mouse host and probably eliminate much of the compatibility issue.

3.4.1 No-Takes Failure to have 100% takes of 30–60-mg trocar tumor fragments may be ascribed to several factors: 1 . Infection 2. Use of an extremely slow-growing, low malignancy, early-passage tumor

Table 3.1  In-vivo activity of standard and investigational agents against transplanted tumors of mice Squam Mam Mam IV Lung IV AML Leuk Mam 16/C/ 16/C/ In-vivo agent Mam 44 Colon 38 Mam 16/C Adr Taxol Colon 51 Panc 02 Panc 03 Mam 17 17/Adr Colon 26 Mel B16 LC12 Leuk L1210 Adriamycin – ++ ++++ ± +++ ± – +++ ++++ – ± + ++++ ++++ + Taxol – ++++ ++++ – – + – ++++ ++++ – ± ± – – – Camp/CPT – + +++ +++ NA + – ++++ NA – NA + – NA NA VP-16 ++ ++ ++++ – + ± – ++ ++ – – – ++++ NA +++++ Vinbl/Vinc – +++ ++ – – – – – – – – – – –a + 5-FU – +++ +++ +++ NA – – + + – ++ ++ – +a ++++ Ara-C – +++ ++ NA NA – – – ++ +++ – – – ++++ ++++ Gemzar + +++ ++++ NA NA NA – + ++ NA +++ NA – ++++ +++++ Cytoxan + ± +++ ++++ NA ++ – ++ +++ +++ ++ ++ ++++ ++++ +++++ CisDDPt + ± + + NA ++ – ++ +++ ++++ ++ ++ ++++ NA ++ BCNU +++ – – ± NA ++ – – – – ++++ ++++ + NA +++++ Cryptophycin8 ++++ ++++ +++ +++ NA +++ +++ ++++ NA ++++ +++ ++ + +++ ++ SR271425 ++ ++++ ++++ + +++ ++ ++++ ++++ ++++ + ++++ ++++ ++++ ++++ +++++ XK469 +++ ++++ ++++ +++ +++ +++ ++++ ++++ ++++ ++++ ++ +++ ++++ +++ +++++ NA not available a SRI/NCI data for Vinc and 5-FU against AML1498. Cryptophycin-8 testing: L1210 data were with Crypto52, and Mamm-44 data were with a close analog being considered for second-generation clinical trials. The L1210 activity rating was expanded because of this tumor’s higher sensitivity to a variety of agents (see Sect. 3.11 for conversion of log kill to activity ratings).

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3 . Technical incompetence 4. Substantial tumor–host incompatibility The investigator can certainly identify and eliminate the problem of infection [9], as well as technical incompetence. Furthermore, it is unlikely that an investigator would be using an extremely slow-growing, low-malignancy, early-passage tumor for most efficacy studies. Thus, a substantial no-take frequency (5% or more) is usually a signal of tumor–host incompatibility. Interestingly, to get around the problem, one may see implanted tumors that are allowed to reach palpable size before being entered on study (e.g., 6–15 days post-implant, depending on the growth rate of the tumor). In this way, the no-takes are simply culled from the pool of trocared animals, and never entered onto the experiment (or mentioned).

3.4.2 Spontaneous Regressions or Tumors that Fail to Progress to Over 1,000-mg Size In a mouse-tumor model, the occurrence of either spontaneous regressions or a failure of the tumors to progress to over 1,000 mg in size (even at a low frequency of 1/100 mice) would suggest marked tumor–host incompatibility, and thus an invalid model. In human tumor-xenograft systems, a spontaneous regression and no growth thereafter may be the result of a mouse that has regained immune capacity (leaky). This can be confirmed by re-implantation of the tumor in the presumed leaky mouse. If it fails to grow, the leaky nature of the mouse can be verified, and the regression explained. Human tumors that fail to progress in immune-deficient mice are more common in athymic nude mice than in SCID mice (i.e., tumors that reach 250–500 mg and stay that size for many weeks, or even regress to zero) (Table 3.2). These tumors are usually caused by extensive diffuse necrosis within the tumor mass, which can be easily confirmed by histology (Table 3.2). Based on one of the definitions of malignancy (a malignant tumor must be able to grow and kill the host), such a non-progressing tumor model would be judged un-usable. Failure to see plots of all tumors in all treatment cages to >1,000 mg in size (or failure to provide data that include time to 1,000 mg, with range) may suggest that such an invalid human tumor model was used for data collection.

3.4.3 Excessive Curability Many tumor models of past decades were derived in random-bred mice or the original inbred strain subline is no longer available (e.g., Sarcoma 180, Ehrlich Ascites Carcinoma, Gardner Lymphosarcoma). Clearly, these models are excessively curable, with a variety of antitumor agents, because the immune system of the host provides additional cell-kill. The immune system kills by zero-order kinetics (meaning that it can kill a finite number of cells; e.g., 3 × 108 tumor cells). If the

Colon H116 80 (60–90) Colon H8 75 (70–80) Lung H125 35 (30–40) Prostate PC-3 20 (20) Breast MX-1 50 (40–60) Source: AACR 36:303, 1995

35 (10–50) 20 (15–20) 5 (0–5) 0 (0) 0 (0–20)

5.0 (3.0–5.0) 5 trials 5.5 (4.0–6.0) 4 trials 5.0 (3.0–8.8) 10 trials 6.0 (5.0–7.0) 2 trials 4.0 (2.2–6.0) 16 trials

4.0 (3.5–4.0) 5 trials 3.0 (3.0–4.0) 5 trials 3.0 (2.3–5.0) 14 trials 3.5 (3.0–5.0) 4 trials 2.7 (1.8–4.5) 13 trials

3/23 1/21 4/50 2/7 4/91

0/23 4/21 1/50 4/7 3/91

0/18 0/13 0/59 0/11 0/62

0/18 0/13 0/59 0/11 0/62

Table 3.2  Comparison of the behavior of transplanted human tumors in athymic nude mice and SCID mice Diffuse necrosis (determined by histology; H&E stained Exponential tumor-volume doubling time in sides) days SC tumors Progressive tumor growth SC and spontaneous regression Athymic nude mice SCID mice Athymic nude mice SCID mice Athymic nude mice SCID mice No. of tumors No. of No. of tumors reaching 250– No. of these reaching 250– tumors 500 mg but not these 500 mg but not tumors % Median regressing getting any regressing getting any necrosis % Median to zero larger to zero larger (range) necrosis (range) Median Td (range) Median Td (range) Human tumor

48 L. Polin et al.

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drug treatment can reduce the population only slightly, the immune system can often handle the rest. One of the indicators of such an immunogenic system is that it is more curable by chemotherapy if the tumor is allowed to grow for about 7 days before treatment than if treatment is started 1–3 days after implant (the total opposite of a syngeneic model). These 7 days of growth allow the mouse to see and process the cell-surface antigens and begin to develop an immune response. Some tumor models have given rise to deviant sublines that are highly immunogenic. The most famous case was the deviant line of Lewis Lung Carcinoma (LLC) that was unfortunately widely distributed by NCI. This subline was highly curable by agents that historically had no activity against the tumor [8]. At least one agent N-phosphoacylase-l-aspartate disodium was advanced to clinical trials on the strength that it was able to cure LLC at four dose levels, which should have provided skepticism by itself. Other lines of LLC behave as expected [8], and we still use this highly metastatic, drug-insensitive tumor for selected studies. However, the widespread use of the deviant line of LLC has given it such a bad reputation that few researchers believe any of the chemotherapy data derived from this famous old tumor model. This is unfortunate, since LLC is one of the most metastatic mouse tumors ever isolated. Other tumor systems may or may not be immunogenic, and cures could simply be the result of treatment with a very effective agent. This is easily sorted out. The cures are simply re-challenged with 30–60 mg1 subcutaneous (SC) trocar fragments of the original tumor. If they take and grow to 1,000 mg (footnote 1) with the expected exponential volume doubling time, it is clear that immune factors were not involved in the original cure. No-takes of tumors implanted in the challenged mice are proof that immune factors were involved in the original cure. It is interesting to note how often tumor-free mice are declared cures without re-challenge. The time to re-challenge is sometimes an issue. The time for one tumor cell to populate to 1 g = [3.32 Td × 9] can be added to the last day of treatment. If the mouse is still tumor-free, a cure can be reasonably certain (see footnote 1). This allows at least three doublings beyond detection (easy detection is 100 mg = 0.1 g = 108 cells). However, some investigators (e.g., H. Skipper) point to the possibility of the survival of a slow Td cell or greater cell loss that can occur with low cell numbers, and the fact that some tumors can repopulate from one cell. For these reasons, they suggest that the time for one cell to grow to 1 g of cells be increased by 50% = 1.5 (3.32 × Td × 9) (which is then added to the last day of Rx) before cure is declared and the mouse can be re-challenged. Regardless, re-challenge of tumor-free survivors should be a prerequisite before an animal can be declared cured. This would clarify the nature of the model being used.

There is a relationship between tumor size and cell number. With syngeneic, transplanted mouse tumors, the tumor mass is usually >85% tumor cells while in exponential growth. A 1 g mass (1,000 mg) = 1 × 109 cells; a 0.1 g mass (100 mg) = 1 × 108 cells; a 0.01 g mass (10 mg) = 1 × 107 cells; a 1 mg mass = 1 × 106 cells, and so on. Thus, a 30 mg mass = 3 × 107 cells (30 million cells). Human tumor cells and mouse tumor cells are approximately the same size, but only a few xenografted human tumors contain >80% tumor cells. Td = exponential tumor-volume doubling time. 3.32 = number of doublings per log. Cure is usually obtained when the population is reduced to approximately 10 or 100 cells for most solid tumors.

1 

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3.4.4 Lack of Invasion and Metastasis The failure to invade and metastasize is often a sign of strong host–tumor ­incompatibility. In addition, these tumors will often grow to an unusually large size (>6,000 mg) without affecting the health of the mouse. Most metastatic tumors will kill the mouse before they reach 6,000 mg, and some tumors will kill before the tumors reach 2,500 mg (e.g., Colon-26, Panc-02). A simple check for the metastatic capability of a tumor is as follows. The tumor (unilateral or bilateral) is allowed to reach approximately 1,500 mg in size (total burden). The mouse is euthanized, and the lungs are removed though the back (with care to avoid the primary tumor). The harvested lungs from each mouse are then implanted into a naive mouse of the same inbred strain. Growth of the lung tissue to a 1,000 mg mass will provide confirmation that metastatic cells were present and a histologic check of the mass will verify the tumor of origin. Highly metastatic tumors can be propagated in this manner with only a 30–60 mg-size trocar fragment of the lung tissue. The invasive nature of the tumor is also verified by the metastasis check, since non-invasive (often immunogenic) tumor models have little or no capacity to metastasize. Finally, the number of tumor cells required to establish tumor growth closely correlates with the metastatic and invasive nature of the tumor. In virtually all cases, 3 × 105 tumor cells are sufficient to establish SC growth with a highly metastatic and highly invasive mouse tumor, and often titers as low as 104 are adequate [10, 11]. Poorly metastatic tumors often require more than 106 tumor cells to establish SC growth in 100% of the mice [11]. All the mouse tumors we use are invasive and metastatic, although some are obviously more invasive and more highly metastatic (e.g., Pancreatic Ductal Adenocarcinoma-02, Mammary Adenocarcinoma-16/C, Colon Carcinoma-26, B16 melanoma, LLC, and Mammary Carcinoma-44). These six tumors can be used for surgery–chemotherapy adjuvant studies because lung metastases are present in nearly 100% of the mice by the time the tumor reaches 1,500 mg [4, 6, 7, 10–14]. The highly metastatic behavior can be encouraged and maintained by implanting lung fragments subcutaneously every passage or every few passages (instead of the primary SC tumor).

3.5 Compatibility Problems Unique to Human Tumors in Immune-deficient Mice Without a doubt, the most evident problem with human tumors implanted in athymic mice is a diffuse necrosis that can often make up 20–80% of the tumor mass, as determined by histology (Table 3.2). This diffuse necrosis (commonly referred to as shotgun necrosis) is evident even to the edges of the tumor mass, and even in tumors of small sizes (100–150 mg sizes). This type of diffuse necrosis is never seen in syngeneic mouse-tumor models, or in human tumors in humans. The diffuse

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necrosis is less evident in SCID mice than in athymic nude mice, but still occurs (Table 3.2). Obviously, the extent of necrosis will explain the growth behavior of many human tumors in immune-deficient mice. If the cell loss from the diffuse necrosis matches or exceeds the cell gain from replication, one can have a nonprogressing or even a regressing mass. Examples of the marked differences in tumor-volume doubling time of tumors implanted in SCID mice and athymic nude mice have been published [3], with other examples shown in Table 3.2. This lack of compatibility exerts considerable selective pressure on the tumor, and probably accounts for the marked biologic and drug–response variations in sublines of the same human tumor. For example, we have investigated four different sublines of human-prostate LNCaP from different sources, all totally dissimilar in terms of growth behavior and take-rate.

3.6 Passage of Tumors in Cell Culture: Maintaining Genotype, Histology, Biologic Behavior, and Drug–Response Characteristics The passage of tumors in cell culture may or may not alter the behavior of the tumors when re-implanted in mice. For example, one of the authors (Roberts) re-implanted Colon-26 in mice after 291 population doublings in culture (­culture passage carried out by E. Dulmadge and L. Wilkoff of Southern Research Institute). This culture-passaged subline behaved similarly to the ­preculture tumor with respect to histology, growth behavior, and drug response to three agents (sensitive to MeCCNU and CisDDPt, and insensitive to PalmoAraC). On the other hand, Roberts found that Colon Adenocarcinoma-38 changed markedly after culture passage (culture passage carried out by E. Dulmadge and L. Wilkoff at the same time). On re-implantation in mice, the culture-passaged subline of Colon-38 was markedly less differentiated histologically, grew faster, and was unresponsive to 5-fluorouracil (5-FU) and Anguidine (both drugs were highly active against the preculture tumor). In other studies, we have seen substantial changes in modal chromosome number, as well as a marked increase in the distribution of chromosome numbers within the cells of culture passage of tumors, unlike the mouse-passaged tumors that had no genotypic changes. Finally, malignancy properties can change with longer-term culture passage. We have found that culture passage reduced the take-rate more than 10,000-fold for L1210 and P388 leukemias, as well as slowing the growth rate and markedly decreasing invasive and metastatic behavior on re-implantation into the host of origin (DBA/2 mice). Obviously, there is little reason for tumors to maintain high-malignancy properties in a culture setting; they only need to be able to survive and replicate in the artificial cell-culture environment. The substantial increase in genetic instability in ­culture has obvious implications for the fidelity of the tumor model after

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r­ e-implantation in mice. With some effort, one can clone sublines of tumors from culture with widely differing properties. Some investigators (including the NCI) have used long-term-culture-passaged human tumor lines for new drug discovery since the early 1980s [15].

3.7 Cancer: a Cellular Disease, Take-Rate, Feeder Effect, Implications for Cure Many of the behavioral characteristics of transplantable tumor models are ­controlled by the take-rate behavior of the tumor. To illustrate this fact, it is necessary to discuss the establishment and behavior of tumors from counted cell implants. Counted cellimplant experiments (by the routes of administration planned for using the model) are one of the first steps that an investigator can use to characterize a tumor. An example of the take-rate of a highly metastatic mammary tumor implanted by various routes is shown in Table 3.3. It should be noted that the take-rate varies, depending on the location. Historically, intracerebral implants have the best takerates because the brain tissue acts as a feeder-layer. However, for this particular tumor, the intraperitoneal implants were almost as good (Table 3.3). An example of the take-rates of a low-viability, early-passage tumor is shown in Table 3.4. In this case, the (IC) take-rates were 3 logs better than the other three routes. Take-rates vary enormously depending on the tumor used. Examples are shown in Table 3.5. In general, the take-rate correlates closely with the invasive and metastatic capability of the tumor. In these examples, Mammary Carcinoma-44 is the most metastatic and the most invasive (>90% metastasis to the lungs from 1,000-mg-size SC tumors [11]. Colon 26 is virtually the same [6, 11]. Mammary 16/C is reasonably close, with >80% metastasis to the lungs from 800- to 1,000-mgsize SC tumors, and usually near 100% for tumors >1,200 mg [4, 10, 11]. Colon-51

Table 3.3  Comparison of take-rates of mammary adenocarcinoma-16/c by various implant routes Number of tumor-takes/number of mice implanted Number of cells implanted Subcutaneous Intravenous Intracranial Intraperitoneal 107 19/19 106 19/20 a 10/10 10/10 20/20 5 10 17/20 10/10 10/10 10/10 104 5/20 1/10 9/10 8/10 103 0/19 0/10 3/10 2/10 102 0/10 4 × 104 3 × 103 5 × 103 Take-rate level 4 × 104 One take-rate unit (by dilution) will establish a tumor in 63% of the mice [100 × (1 – 1/e)] This mammary adenocarcinoma is a highly invasive, highly metastatic, rapidly growing tumor a Leakage of the titered cell implant brei from the implant site probably accounts for the one notake. Three logs of incomplete takes should not occur with technically perfect implants. Data of B.J. Roberts

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is metastatic to both the lungs and regional lymph nodes from a 1,500-mg SC tumor in 80% of the mice, but has been used only occasionally for surgical studies [11]. Colon Adenocarcinomas-9, 10, and 36 were only marginally metastatic in the early passages studied (<10% from 1,000 mg tumors). It may be inferred from a casual reading of the literature that one tumor cell can be implanted, and will take and grow into a tumor mass that will kill the host. Although this is true for some tumors such as L1210 or P388 leukemias, these are exceptions. The lack of better tumor-takes at a particular location (other than the brain) is caused by 1–3 log-cell loss of tumor cells at the implant site, which occurs within 24 h of implant [16]. This cell loss can be prevented by admixing X-ray-killed tumor cells with the implants, or by admixing a brain-tissue brei with the tumor cells. Table 3.4  Comparison of take-rates of Colon Adenocarcinoma-10 (a low viability, poor t­ake-rate tumor) by various implant routes Number of tumor-takes/number of mice implanted Number of cells implanted Subcutaneous Intravenous Intracranial Intraperitoneal 8 10 8/9 10/10 107 2/10 6/9 6/10 106 1/10 1/10 10/10 0/10 105 0/10 1/10 9/10 0/10 104 6/9 0/10 103 3/10 0/9 102 Take-rate level 4 × 107 1 × 107 1 × 104 1 × 107 One take-rate unit (by dilution) will establish a tumor in 63% of the mice [100 × (1 – 1/e)] This colon adenocarcinoma was a slow growing, low viability tumor. It was in the 11th passage for this implant trial Note that the tumor-takes were 3 logs better by the IC route than by any other route of implant Table 3.5  Comparison of subcutaneous take-rates of different transplantable tumors Number of cells Number of tumor-takes/number of mice implanted implanted Mam-44 Colon-26 Mam-16/C Colon-51 Colon-09 Colon-10 Colon 36a 3/3 8/9 107 19/19 10/10 9/9 2/10 2/10 106 10/10 10/10 19/20b 10/10 2/10 1/10 0/10 105 10/10 10/10 17/20 3/10 0/10 0/10 0/10 104 10/10 9/10b 5/20 1/10 0/10 103 9/10 10/10 0/19 0/10 102 Not done 2/10 3 × 102 3 × 104 3 × 105 3 × 106 4 × 107 5 × 107 63% take-rate <103 level One take-rate unit (by dilution) will establish a tumor in 63% of the mice [100 × (1 – 1/e)] a In separate experiments, 2 × 107 cells SC produced 6/20 takes with IP implantation, confirming that this was a poor take-rate tumor b Leakage of the titered cell implant brei from the implant site probably accounts for the one no-take in each of these groups. Three logs of incomplete take should not occur with technically perfect implants (as occurred in Mam-16/C)

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An example is shown in Table 3.6. This phenomenon is known as the feeder effect or the Revesz effect, after the original investigator [17, 18]. The feeder cells (radiation-killed cells) can be different from the titered tumor cells being assayed, but work best if they are from the same inbred strain and are derived from a highly metastatic/fast-growing/high-take-rate tumor (Table 3.7). The only normal tissue Table 3.6  Comparison of B16 melanoma tumor-takes with and without added feeder layers Co60 killed B16 cells Brain Brei from No feeder layer C57Bl/6 mice added to titered tumor cells added to titered titered tumor cells only tumor cells B16 TumorB16 TumorB16 Tumortakes/ Median takes/ Median takes/ Median number number time to time to number Number of cells time to implanted a 1.0 g implanted a 1.0 g implanted a 1.0 g implanted 106 5/5 24 105 4/5 30 4/4 18 4/4 17 104 1/10 34 10/10 21 10/10 22 103 0/10 9/10 27 10/10 26 102 0/10 5/10 (27–35 6/10 (28–38 range) range) 101 0/10 1/10 Feeder layer only 0/10 One take-rate unit (by dilution) will establish a tumor in 63% of the mice [100 × (1 – 1/e)]. This is the take-rate level a B16 Tumors took and grew to 1.0 g in size. Feeder layers (approximately 5 × 107 killed cells/ implant) made with irradiated tumor cells or brain tissue and admixed with the titered tumor cells improved the take-rate by over 2 logs each Table 3.7  Comparison of Adenocarcinoma-755 tumor-takes in C57Bl/6 mice with and without added feeder layers: note, one feeder layer was made with Co60 killed B16 cells; the other was made from a brain tissue brei 10,000R Co60 killed B16 Brain Brei from C57Bl/6 mice added to titered No feeder layer titered cells added to titered tumor cells tumor cells tumor cells only CA755 CA755 CA755 Tumor-takes/ Median tumor-takes/ Median tumor-takes/ Median number time to number time to Number of cells number time to Implanteda implanted a implanted a 1.0 g 1.0 g implanted 1.0 g 106 5/5 18 5/5 11 5/5 16 105 5/5 24 5/5 14 5/5 17 104 0/10 10/10 16 5/5 22 103 0/10 10/10 20 5/5 33 102 7/8 26 3/5 38 101 0/10 0/5 Feeder layer only 0/10 Adenocarcinoma-755 tumors took and grew to 1.0 g in size. Feeder layers made with irradiated tumor cells (of a different tumor) or brain tissue and admixed with the titered tumor cells improve the take-rate by more than 2 logs each a

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that provides the feeder effect to all tumors is brain tissue. The feeder effect provided by brain tissue probably accounts for some of the difficulty in curing intracranial malignancies, since an additional 1–3 logs of tumor-cell-kill would be necessary to reduce the population below the take-rate level at that site. The feeder effect can only be provided by intact, metabolizing cells, and must be admixed with the titered cells and not at a distant location. Furthermore, greater than 106 X-ray killed cells are needed to produce the effect; with 5 × 107 to 1 × 108 used by most investigators. In selected cases, tumor cells killed by an antitumor agent can provide feeder activity [19]. It is likely that some feeder effect is taking place post-treatment with either chemotherapy or radiation treatment of any intact tumor mass. Immune-suppression does not change the take-rate of a syngeneic tumor model (Table 3.8). Thus the feeder effect is unrelated to subverting immune function. Other examples of the lack of influence of immune-suppression on take-rates are available [20]. As one approaches within 1 log of the take-rate level (or cure level), an accelerated tumor-cell loss is clearly taking place (compared to implants that are 3–4 logs above the take-rate). This is evident in virtually any end point or tumor location [10]. There is also a consequent shift in the shapes of the curves (survival or time to a specific tumor size). At 3–4 logs above the take-rate, curves are normal (i.e., Gaussian) (Fig. 3.1) [10]. With a Gaussian curve, the top half of the curve can be inverted and can then be superimposed on the bottom half of the curve. At approximately 0.5–1.5 logs above the take-rate, the curves become log-normal, in which case the data can be replotted using the log of time, and the curve will become normal or Gaussianshaped [10]. Near the take-rate level, which yields 20–50% no-takes or survivors after therapy, the curve is first-order kinetic (Fig. 3.1) [10]. This knowledge can be used to predict the outcome of a mouse experiment, or even a partially completed human trial. If the survival curve plunges in a Gaussian fashion, it is clear that one is far above the take-rate level, and no cures will be obtained. On the other hand, if the curve begins to bend-out in a first-order kinetic fashion, one can project that some cures will be obtained. Detailed examples of normal, log-normal, and firstorder-kinetic plots, with explanations and mortality curve analyses have been published [10]. Other examples of first-order-kinetic behavior are common in physics and biology: e.g., light-bulbs burn out by first-order kinetics (a constant fraction of the bulbs burn out per unit of time); most antitumor agents kill by first-order kinetics (a constant fraction of the cells are killed per given dose of a drug) [21]; and disinfectants kill by first-order kinetics (a constant concentration of the disinfectant in a test tube kills a constant fraction of the bacterial cells per unit of time) [21]. Once an investigator understands the take-rate behavior of a particular tumor, understands the relationship between tumor size and cell number, and understands the feeder effect, the meaning of cure for that particular tumor will become clear, regardless of the stage of the disease and the nature of the therapy. This knowledge can also be used in experimental planning and projecting the outcome of various treatment regimens. With this perspective, we can now examine the results from treatment of high take-rate tumors and poor take-rate tumors. The first example is shown in Table 3.9. A total of 21 cures were produced in 77 mice with poor take-rate tumors by therapies

17/17 2/19 1/20 0/19

Tumortakes/total

106 105 104 103

Syngeneic Syngeneic Syngeneic Syngeneic

ALS ALS ALS ALS

0.075 0.075 0.075 0.075

day –3 to day +7 day –3 to day +7 day –3 to day +7 day –3 to day +7

36 55.5 46 None

29–42 45–66 46 None

0/20 18/20 18/19 20/20

20/20 2/20 1/19 0/20

B16, 60 mg Allogeneic fragments SC B16, 60 mg Allogeneic fragments SC B16, 60 mg Syngeneic fragments SC No Rx

No Rx

No Rx

No Rx

0.075

ALS

No Rx

No Rx

day –3 to day +7

0/8

10/10

1/10

8/8

0/10

9/10

Immune-suppression was sufficient to allow B16 melanoma to take and grow in a foreign (allogeneic) host mouse (C3H) in 9/10 mice (8/10 of these grew to over 1,000 mg)

BDF1

C3H

C3H

Controls to determine the degree of immune-suppression

One take-rate unit by dilution will establish a tumor in 63% of the mice: For the immune-suppressed mice (ALS treated) shown above, the take-rate level was calculated to be 6 × 105 B16 tumor cells; identical with that of the saline treated controls

BDF1 BDF1 BDF1 BDF1

Immune-suppressed; antilymphocyte serum (ALS) started 3 days before tumor implant and continued for 7 days after tumor implant

One take-rate unit by dilution will establish a tumor in 63% of the mice: For the controls shown above, the take-rate level was calculated to be 6 × 105 B16 tumor cells

Table 3.8  Lack of influence of immune-suppression on the take-rate of B16 melanoma cells implanted intravenously in a syngeneic mouse Dosage; No.  of B16 Tumor free Median day of volume in Immunecells tumor death Range of tumor survivors on ml/mouse/ Schedule of suppressive implanted IV Nature of day 205 (dying only) deaths day treatment agent transplant Host strain on day 0 Controls BDF1 106 Syngeneic None (saline) 0.075 day –3 to day +7 34 26–41 0/17 BDF1 105 Syngeneic None (saline) 0.075 day –3 to day +7 52.5 45–60 17/19 BDF1 104 Syngeneic None (saline) 0.075 day –3 to day +7 56 56 19/20 BDF1 103 Syngeneic None (saline) 0.075 day –3 to day +7 None None 19/19

56 L. Polin et al.

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Fig. 3.1  Normal, log-normal, and first-order kinetic curves

that produced only one cure of 159 mice with high take-rate tumors; the log10 ­tumor-cell-kills were similar. A comparison of the curability, as a function of a threefold increase in tumor size of a poor take-rate tumor, is instructive (Table 3.10). Clearly, the tumor (Colon-36) was slightly less sensitive at a larger size (note growth delay and net cell-kill), but not markedly less. Curability, however, decreased dramatically, with only a threefold increase in tumor-cell number. The calculations can be used to approximate the cure level (take-rate level and the feeder effect). Starting with the 200-mg masses, the median is 2 × 108 cells to start (8.3 logs). Treatment killed 2.9 logs, and thus the median population is reduced to 5.4 logs = 2.5 × 105 (producing 10/15 cures). At the 600-mg size masses, the median is 6 × 108 cells to start (8.78 logs). Treatment killed 2.1 logs. Thus, the median population is reduced to 6.68 logs = 4.8 × 106, and only 1/8 cures. The data can be plotted on semi-log paper, and various results can be estimated. If the population is reduced to below 4 × 104, it should yield all cures. Above 1 × 107, there should be no cures. The 63% cure level is 3 × 105 cells. The feeder contribution for this dose-schedule of treatment and established tumors at the start of therapy was 2.22 logs (7.7 logs–5.48 logs). The cure of a poor take-rate tumor by chemotherapy is shown in Table 3.11. Again, the tumor-cell-kills were rather modest, considering the cures. Based on the last tumor to regrow in the treatment groups, it is possible to calculate that the cures produced by Procarbazine and Maytansine represent only slightly more than a 1.8 and 1.6 net log-kill, respectively. The cure of a high take-rate tumor by chemotherapy is clearly a challenge. We have never obtained cures of 30 mg SC trocar-implanted tumors such as Mam-44 or Panc-02 with chemotherapy, even when treatment begins the day after implant. A more responsive tumor, such as Mam-16/C, has had only an occasional chemotherapeutic cure in hundreds of trials. Examples of treatment of Mam-16/C with

Curesa 48 33 18

7/8 8/8 8/8 6/8

7/8 7/8 7/8 0/8

Curesa 0.8 0.55 0.3

Curesa 2.4 1.6 0.9

Curesa   2.2   1.45   0.65

5/8 2/8 2/8 0/8

Time for median control tumor Colon-10 to reach 750 mg post-trocar implant = 36 days. Exponential Td = 6.0 days63% take-rate level = 4 × 10 7 Curability was, in part, a function of the viability (take-rate) of a tumor. All tumor models were synegeneic a  Only two mice regrew tumors, growth delay not meaningful (five cured, one Rx death)

1/8 0/8 0/8 0/8

1,100 845 650 500

Colon Adenocarcinoma-10 at 100–400 mg

3,300 2,535 1,950 1,500

845 2,535 0/15 37 13/15 10/15 1.07 3.2   2.9 10/15 650 1,950 0/15 20 11/15 6/15 0.59 1.77   1.4 0/15 500 1,500 1/15 17 4/15 4/15 0.50 1.5   1.15 2/15 Time for median control tumor Colon-36 to reach 750 mg post-trocar implant = 22 days. Exponential Td = 3.4 days63% take-rate level = 5 × 107

Colon Adenocarcinoma-36 at 100–400 mg

Curable, poor take-rate tumors

845 2,535 0/8 17 3/8 1/8 0.77 2.3   1.8 0/8 650 1,950 0/8 12 3/8 0/8 0.55 1.64   1.2 0/8 500 1,500 0/8 10 0/8 0/8 0.46 1.37   1.0 0/8 Time for median control tumor Colon-51 to reach 750 mg post-trocar implant = 15 days. Exponential Td = 2.2 days (20 mice)63% takerate level = 3 × 105

Log10 kill per Log10 kill Log10 kill fraction Gross Net Cures

Colon Adenocarcinoma-51 at 100–400 mg

CRs excluding toxic deaths

1,000 3,000 3/26 13 12/26 8/26 0.9 2.8   1.7 0/26 750 2,250 1/42 12 20/42 16/42 0.86 2.6   1.5 0/42 500 1,500 0/49 7 14/49 6/49 0.5 1.5   0.4 1/49 200 600 0/18 3 0/18 0/18 0.23 0.7 −0.4 0/18 Time for median control tumor Mam-16/C to reach 750 mg post-trocar implant = 11–15 days. Exponential Td = 1.4 days63% take-rate level = 3 × 104

PRs excluding toxic deaths

Mammary Adenocarcinoma-16/C at 100–400 mg

Table 3.9  Treatment of transplantable tumors of mice with X-irradiation Treatment Median tumor RADS per growth delay Total dosage related Tumor (SC) size at first fraction in days deaths (Q2dx3) in RADS Rx Non-curable, high take-rate tumors

845

2,535

0/8

27.3

6/8

1/8

0.8

1.07

2.4

3.2

2.1

2.9

1/8

10/15

Log10 kill Log10 kill Log10 kill per fraction gross net Cures

function of take-rate and tumor size at first

Time for median control tumor Colon-36 to reach 750 mg post-trocar implant = 22 days. Exponential Td = 3.4 days Curability was, in part, a function of the viability (take-rate) of the tumor. Note that the net log tumor-cell-kill for Colon 36 did not change markedly with the change in tumor size at first Rx (and the log kill was not impressively large). The curability was simply related to the log kill necessary to reduce the population below the take-rate level with a feeder

300–1,000 mg (600 mg median = 6 × 10 8 cells

Table 3.10  Treatment of transplantable Colon Adenocarcinoma-36 in mice with X-irradiation curability as a treatment Treatment- Median tumor PRs RADS per Total CRs excluding growth delay excluding related dosage fraction toxic deaths toxic deaths in days Tumor (SC) size at first Rx (Q2dx3) in RADS deaths Colon-36 100–400 mg (200 mg 845 2,535 0/15 37 13/15 10/15 median = 2 × 108 cells) )

3  Transplantable Syngeneic Rodent Tumors: Solid Tumors in Mice 59

Not tabulated

0/10

10/10 CRs

0%

Not tabulated

46

–

Day 3,10 (IV) 0.44

– Not tabulated

0/10

–

0/10

–

1,000 mg on – day 23 63 mg on day 6% 23

1,000 mg on – day-25 100 mg on 10% day 25

37(27–52)

23(20–35)

36 (28–51)

25 (21–32)

55 (52–58)

14

–

11

–

34

27

Tumor growth delay in days –

1.2

–

0.83

–

3.2

2.5

Median log10 kill gross –

0.6

–

0.45

–

2.5

2.2

Median log10 kill net –

2/10 on day 93

0/10

2/10 on day 67

0/10

4/10 day 208 8/10 day 208

Tumor– free “Cures” 0/20

Td = 3.6 days: Trocar implanted SC on day 0 This is an excellent example of the utility of converting tumor growth delay data to log10 tumor-cell-kill. The cures do not represent a huge log kill. Only Ara-C and it=s depot form Palmo-Ara-C had substantial activity. The other two agents were only modestly active against this poor take-rate tumor.

Colon-36 No Rx (1499Q1) Maytansine

Not tabulated

Not tabulated

–

Td = 4.0 days. Trocar implanted SC on day 0.

Colon-36 No Rx – (1499J1) Procarbazine qd3–8 (PO)

The day of first treatment, the tumors were 231 mg median on day 16 (range 144–320) = 2.3 × 10 8 Exponential tumor-volume doubling time (Td) = 3.2 days. The 63% take-rate level = 5 × 107

q3hx8 (IP) days 16,20 Palmo-AraC qd16–23 (IP) 13.5

Ara-C

Tumor (Exp) Treatment Colon-36 No Rx (1564G1)

Schedule (route) –

Median time in days for tumors to Median % Body-weight reach 1,000 mg Drug tumor mass mg/kg/ loss at nadir % T/C mass (range) injection (day of nadir) deaths on (days) – 21 (17.5– – Not tabulated – 1,000 mg 27.5) on day 21 11.8 Not tabulated 0/10 9/10 CRs 0% 48 (45–65)

Table 3.11  Chemotherapeutic treatment of a poor take-rate tumor

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highly active agents are shown in Table 3.12. The log-kill values were substantial, but cures were not forthcoming, except in one case. This cure was re-challenged on day 182, and grew to over 2 g in 11 days, indicating that immune factors were not involved in the cure. The log-kill values – gross and net – were also consistent with a cure. It should be noted that other dose-schedules of this agent failed to produce cures or this impressive log-kill (thus, even this one cure is an outlier). In general, in order to obtain cures of such high take-rate tumors, surgery ­followed by chemotherapy has been successful, especially with highly active drug combinations [6, 7, 10, 11]. However, in order to produce cures of such models as Mam-16/C without surgery, we would need to resort to some tricks. For example, we could IP-implant the tumor as a counted cell brei, and start IP treatment the next day. This would improve the tumor-cell-kill by approximately 3 logs, and effect cures if the agent was highly active. This regimen has been successful for multiple agents, but does not reflect the usual clinical problem or treatment requirement. Likewise, we could implant the tumor only slightly above the take-rate, and begin treatment the next day by another route. Again, some cures could be expected if the agent could produce a 1 log net cell-kill, but to what aim? A casual reading of the literature may prompt the opinion that L1210 leukemia is a highly chemo-sensitive and curable tumor. It is indeed chemo-sensitive, but the cures have mainly been obtained with IP-implanted tumor and IP-drug ­administration the day after 105 cell implants. One can easily remove the possibility of a cure: the tumor can be IV-implanted with 106 cells, with treatment starting the next day. With this titer and implant route, cures are out of the question (Table 3.13). (In  many respects, cure is the worst and most misleading endpoint that an investigator­ can use. In most cases, the reader will have no concept of the actual log-kill required to produce the cure).

3.8 Results Tabulation of Chemotherapy Trials: Desired Information from a Tumor Model It is often said that a picture is worth a thousand words. However, the presentation of chemotherapeutic results in plots can be among the most misleading methods of presentation if all the information is not provided to the reader. Several examples follow. The first example (Fig. 3.2) would seem to indicate self-evident antitumor activity of Gemzar against a very slow-growing, early-passage pancreatic adenocarcinoma. However, the plot is truncated in time to pick the best appearance, and we have provided very little information (intentionally). The following data are obviously missing: (1) What was the body-weight loss from the treatment? (2) What happened to the treated mice later in time? If the reader is not told what happened to all of the treated tumors to a size of 1,000 mg, the presenter is remiss. Sacrifice for some assay or histology is not a valid explanation. Historically, if other findings are needed, extra mice are added to the trial, and the mice used for efficacy determination are not summarily killed until all the vital information is obtained. (3) What was the log-kill from

#1361 Passage 62

#1286 Passage 98

–

–

–

bid6,8,11,13 (IV) 124

d6,8,11,13 (IV)

No Rx

Taxol

VP-16

84

18

day 2,5,8 (IV)

Adriamycin

–

–

144

–

No Rx

Cryptophycin d1–8 (IV) #55

#2064 No Rx Passage 116

–

Loss 7%

Loss 8%

Gain 5%

Loss 4% day 9

Gain 6% day 8

0/7

0/7

–

0/5

–

Loss 7.5% (day0/5 8)

Unchanged day 8

1,790 (1,296– – 2,537) on day 9 0 (all zeros) 0% on day 9 0 (0–63) on 0% day 9

1,539 (446– – 2,440) on day 11 0 (all zeros) 0% on day 11

1,710 (723– – 2,232) on day 11 0 (all zero) on 0% day 11

13.5

21 (20–22)

21 (20.3–22.5) 13.5

–

16

–

13

–

7.5 (4.5–8.5)

24 (20–63)

8 (8–14.5)

23 (20.5–24)

10(8.5–13)

3.3

3.3

–

4.8

–

3.9

–

63% Take-rate level = 3 × 104; The exponential tumor-volume doubling time = 1.0 days; Tumors Implanted as 30–60 mg fragments on day 0

Table 3.12  Chemotherapeutic treatment Mammary Adenocarcinoma-16/C (a high take-rate tumor)

2.0

2.0

–

3.0

–

2.6

–

0/7

0/7

0/6

0/5

0/5

0/5

0/5

XK469

–

–

No Rx

0/5

Gain 4% day 7 –

Unchanged day 9

Gain 7% day 8 –

2,460 (309– – 3,560) on day 11 0 (0–95) 0%

1,263 (735– – 1,450) on day 9 0 (all zeros) 0% on day 9 8.5 (7.5–14)

22 (19.3–39)

8.6 (8.5–9.5)

–

13.4

–

–

4.0

–

d1,3,5,8,10, 350 Unchanged day0/5 41.7 (25.5–66.5)33.2 10 12,14 (IV) 14 With many dozens of trials, almost no cures could be found of this high take-rate tumor (indicating the difficulty in producing enough log kill)

#1620 Passage 134

420

–

qd 2–8 (PO)

–

SR271425

#2278 No Rx Passage 118

6

–

2.2

–

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Table 3.13  Non-curability of IV implanted L1210/0 A high take-rate tumora Activity L1210/0 rating leukemia MTD total Cures/no. against Gross dosage of mice L1210 log kill (mg/kg) Agent Exp no. implanted XK469 2473 300 6.4 +++++ 0/5 SR271425 2304 350 6.4 +++++ 0/5 VP-16 2473   70 8.0 +++++ 0/5 PALMO ARA-C 2443 140 5.7 +++++ 0/5 GEMZAR 2483   84 6.0 +++++ 0/5 BCNU 2483   28 6.7 +++++ 0/5 5-FU 2473 180 4.0 ++++ 0/5 CISDDPT 2473   12 2.4 ++ 0/5 ADRIAMYCIN 2304   17 1.6 + 0/6 VINBLASTINE 2473 6.0 1.6 + 0/5 TAXOL 2304   63 0.4 C 0/6 Mice were implanted IV on day 0 with 106 leukemic cells in the treatment groups (5 mice/group) and the corresponding Control group. Titered controls were also included (104 and 102) in order to verify the tumor doubling time and log10 kill. Treatment started on day 1 a  One cell will establish tumors with L1210

Fig. 3.2  Exp 2475: Treatment of a human pancreatic ductal adenocarcinoma

the treatment? An unwillingness to do the log-kill can indicate that the presenter is attempting to conceal how poor the activity really is. If the treatment affects the tumor growth after cessation of Rx (e.g., radiation treatment of the tumor and tumor bed can

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slow regrowth), the exponential Td is taken from the treated tumors – and not the control tumors – for the purpose of the log-kill calculation. This is the same trial presented in Fig. 3.2, but supplemented with additional information (including the details to the end of trial). The first important piece of information is the weight loss (-8.9% body-weight loss, with the nadir on day 89). Many antitumor agents and toxic agents make a mouse sufficiently sick that it fails to adequately eat or drink fluids. This causes a nonspecific weight loss. It is well known that weight loss alone will cause substantial growth inhibition of a solid tumor [22]. A -11% body-weight loss produced by 9 days of caloric restriction will produce a tumor-mass inhibition (T/C) = 47% for Mammary Adenocarcinoma16/C (with no deaths). Caloric restriction will have even greater effects on other solid tumors [22]. The NCI cutoff for a minimum indication of antitumor activity is a T/C of 42%. This cutoff was adopted because of the various caloric restriction studies (Ref. [22] is the most cited). Importantly, a T/C = 20% was possible with caloric restriction with no deaths [22], although the weight loss was punishing. A weight loss of -20% or more is considered frankly toxic by NCI standards, but weight losses of -15% are usually attended by some lethality. Historically, NCI requires a T/C (tumor mass) less than 10% to indicate high antitumor activity for a solid tumor. As an aside, weight loss does not add to survival improvement for a leukemia such as L1210 or P388, and is not an issue for survival testing. As Fig. 3.3 is examined, it is apparent that the treatment must have been near the lethal level, since the weight loss was -8.9% and the nadir occurred nearly 40 days after

Fig. 3.3  Exp 2475: Treatment of a human pancreatic ductal adenocarcinoma

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t­reatment (indicating the treated mice were probably in relatively poor condition for an extended period). The T/C (mass) = 50% on day 129, which is inactive by NCI standards. Statistical significance values are often applied to solid-tumor treatment data. A T/C in the 42–50% range would usually meet statistical significance at the 5% level, but is hardly of biologic importance in light of weight-loss information. In some cases, tumor-mass data can be presented as a mean value (average value). The mean provides greater proportional emphasis on the largest tumors in the group. In some cases it may be justified, but alternatively the presentation of a mean value may be done to place the best spin on the results, not the most conservative-spin. Historically, NCI prescribes that median values be used for tumor-mass information and mean values for weight-loss information. The next item to note is the dispensation of all the treated mice up to a size of 1,000 mg (Fig. 3.3). In this example, the median did not reach 1,000 mg. One mouse died from tumor burden on day 136. Two mice had tumors over 1,200 mg on day 143, and were sacrificed. The last measurement of all mice showed a median tumor mass of 580 mg (Fig. 3.3). The tumor-growth delay was 49 days at this time-point. This is a substantial delay, until the weight loss and the slow Td of the tumor (Td = 22 days) are considered. The last item is the conversion of the growth delay into gross log-kill (see Sect. 3.11 for additional details): Log − kill (gross) =

tumor growth delay in days 3.32 × (exponential tumor − volume doubling time )

Log − kill (gross) =

49 = 0.62(inactive ) 3.32 × (22 )

Now that the results are quantified and explained, the self-evident antitumor activity vanishes. This is a very good, highly active antitumor agent, but it does not work against this particular tumor. Figure 3.4 is another example of an incomplete plot (treated tumors not plotted up to 1,000 mg). On superficial examination, the agent may be considered active. If the mice are sacrificed on day 11, there would be no information to refute the purported activity. In actual fact, this is simply a truncated presentation of an agent that produced delayed lethality. The following actually occurred: 225 mg/kg total: all five mice were drug deaths, dying on day 11, 12, 13, 13, 14 150 mg/kg total: all five mice were drug deaths, dying on day 13, 14, 16, 16, 16 100 mg/kg total: there were two drug deaths (both day 16). The other three mice were sacrificed because of tumor burden. The optimum T/C mass = 700/1,830 = 38% on day 9 (marginally active). The log-kill (gross) of this treatment group = 0.75 (­marginally active). Thus, in the final analysis of the agent used in Fig. 3.4, all treatment groups were toxic, and even the dose that produced 40% drug deaths was only marginally active.

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In many cases, mice are sacrificed if they are dying from toxicity (and this is not explained to the reader). Delayed toxicity is an often overlooked topic in the use of tumor models [23]. In some cases, the delayed lethality can occur 30–120 days after treatment, even with clinically available agents [23]. It is for this reason that investigators will include non-tumor toxicity control mice in some trials; i.e., non-tumor normal mice injected with the maximum tolerated dosages (MTD based on short-term toxicity findings) of the drug and held for 150 days post-treatment to verify that no longdelayed toxicity exists [23]. Often, cured mice are held for the same purpose. One of the hallmarks of a delayed lethality drug is the failure of the mice to gain weight satisfactorily or to add skeletal mass. Normally, a mouse will continue to gain skeletal size throughout its life span, and at least reach 30 g within 3 months posttreatment with most antitumor agents. If the mice stay in the 19–21 g size for an extended period of time, one can suspect serious non-reversible organ toxicities (e.g., lung, kidney). The use of non-tumor toxicity-control mice to clearly separate tumor effects from treatment effects is standard practice [23]. It should be noted that the plot shown in Fig. 3.4, as well as the other plots, are done on Cartesian plots. This tends to give the appearance of more separation between the control and treated than there actually is (especially if the treatment groups are not plotted to 1,000 mg). Historically, it is better to use semi-log plots (with the median tumor masses plotted on the log axis). The exponential doubling-time portion of the growth curve will then be essentially linear, allowing the calculation of the exponential Td with more accuracy.

Fig. 3.4  Treatment of SC mammary adenocarcinoma 16/c

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Fig. 3.5  Treatment of MX-1 mammary tissue in female athymic nude mice (NCI data)

Figure 3.5 shows the NCI data. If it was not explained, it would appear that adriamycin was nicely active against the human breast adenocarcinoma MX-1 [T/C = 12%; 2/6 tumor-free mice cures; median of 1.7 log-kill among those not cured (++ activity rating)]. In fact, adriamycin is not active against this tumor in our testing [1, 3], and furthermore, it is markedly more toxic in immune-deficient mice than in conventional mice [3]. The dose used in the experiment shown in Fig. 3.5 is approximately three times the MTD in immune-deficient mice [3]. This is a superb educational example because the weights of all the mice were provided up to and including the day of sacrifice (day 39). The first item to note is the large body-weight loss (-12.4% of body weight) occurred with the nadir on day 39 (the day of sacrifice). At face value, the tumors could be considered to cause some of the weight loss, except that the mice were continuously losing weight post-treatment, including two mice that were listed as tumor-free. Furthermore, the tumors in this cage were too small to cause the mice to lose weight (based on the controls). However, the actual weight losses of the tumor-free mice essentially tell the whole story. One lost 5.0 g (-26% of its body weight), and the other mouse lost -4.9 g (-22% of its body weight). The mice were obviously sacrificed because they were moribund and dying of drug toxicity. In addition, it is unacceptable to kill tumor-free mice so soon after treatment. These mice would need to be held for at least 2 more months to be assured a cure. Lastly, the cures would have needed to be re-challenged with the tumor to prove that the cures were not the result of immune rejection in a leaky mouse. The next lower dose of adriamycin was also a toxic dose, and had no antitumor activity (T/C = 103%; no log-kill).

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3.9 Characterization of a Tumor Model and Quality-Control Monitoring To a large extent, the types of characterization end points that the investigator needs depend on the usage of the model. Furthermore, the tumor model is not a static entity. The exponential tumor-volume doubling time usually shortens substantially during the first 10 transplant generations, and can shorten more with additional passage [10]. For example, Mam-16/C had a 4.5-day Td in the 4th passage and a 1.25-day Td in the 29th passage. The cause is a decrease in the cell-loss factor [24], which is consistent with an observed improvement in the take-rate of a tumor with serial passage. Interestingly, the growth fraction and the intermitotic times do not change markedly with serial passage of new tumors [24]. Overall, it would appear that a transplantable tumor becomes fast-growing and less differentiated for two reasons: (1) the normal components that make up a portion of the original tumor and impart the highly differentiated character are lost in the first transplant (normal cells do not transplant well); and (2) the cell-loss factor decreases (either through an improvement in the fidelity of replication or by adapting to the implant environment). Either way, it is hard to envision that any of these changes could ­substantially alter the predictive worth of the model, other than making it more challenging. The Td has a bottom limit of 1.0 day for solid tumors of mice, and 0.35 days for leukemias of mice. We propose the following suggestions for using solid-tumor models for chemotherapy purposes. In most cases, this information will provide adequate characterization and quality-control monitoring. 1. Use tumors that have been in passage for over 10 serial passages (there is too much tumor to tumor-growth variability with fewer passages). 2. Use tumors from in vivo passage and not culture passage. Tumors directly from culture tend to magnify responses, because they are less adapted to the in vivo environment and thus more fragile. 3. Determine the take-rate of the tumor with titered tumor-cell implants at the various locations intended for use in the model. One should then use a titer for chemotherapy that is at least 3 logs above the 63% take-rate level (more if high antitumor activity is expected). 4. Obtain histologic sections of the tumor at least every three or four passages (every passage if it is a human tumor because of the diffuse necrosis problem). 5. Determine the exponential tumor-volume doubling time, and monitor this every few passages (every passage if it is a human tumor, since human tumors can become contaminated with spontaneous lymphoma cells, resulting in a marked decrease in the Td; or undergo excessive diffuse necrosis and have a marked increase in the Td). 6. Determine the metastatic behavior of the tumor at various sizes. 7. Passage the tumors at a relatively small size (e.g., 600–1,000 mg), while the animal is still healthy and the tumor is still in exponential growth. Passage at larger sizes increases the chance of passaging an infected tumor.

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8. Passage mouse tumors in the inbred strain of origin only (F1 hybrids should not be used). 9. Passage mouse tumors in mice that have been in the laboratory for at least 2 months (so that the mice are large and healthy and have well-developed immune systems). These older, larger mice have been exposed to, and have conquered, various extraneous bacteria and viruses. On the other hand, human tumors should be passaged in young immune-deficient mice (6–7 weeks) to minimize development of leaky mice; and also to avoid the possibility of spontaneous lymphomas that will contaminate human tumors (athymic mice and Balb/c SCIDs have less problems with spontaneous lymphomas than ICR SCIDs). 10. The texture and other appearance characteristics of the tumors should be noted. Photos are helpful if the tumor is being used for the first time (this is critical for human tumors which can, and do change easily over time). 11. Be very selective with tumor fragments used to maintain human tumors as xenograft transplants. A chosen fragment should possess a pearly appearance with clearly visible vascularity (red colored areas that include the tumor’s blood vessels). 12. Tumor fragments should be maintained in frozen storage vials in two different locations (10% dimethyl sulfoxide in media with 10% serum is standard). 13. Fragments of the tumor being passaged should be checked for bacterial contamination (incubate at 37°C for 72 h in a growth media). 14. The chemotherapeutic responses to standard agents should be determined. 15. For mouse tumors, only syngeneic models should be used. Verification can be done by treatment with IL-2, which is a simple and inexpensive technique [25]. More classic methods are also available [26]. 16. Simple vital statistics obtained: passage generation; histologic appearance and grade (degree of differentiation); host of origin; tissue and cell of origin; how induced; when induced; time required to reach 500 and 1,000 mg from a trocar implant; unusual characteristics (e.g., mucin-producing; common metastatic sites that vary with implant location), appropriate references. 17. Molecular characterizations of the tumor models will undoubtedly become increasingly critically important to a better understanding of the biologic and response behavior of the models. Since each independently arising tumor is a separate and unique biologic entity, no one tumor model is a perfect predictor for any other model or tissue type. For this reason, most investigators are forced to use multiple models for study [1]. Nonetheless, it is still better for the new investigator to understand a few models very well than many models superficially.

3.10 Summary Clearly, it is difficult to even introduce the topic of solid-tumor models of mice. Many of the details can best be learned by an apprenticeship in a laboratory with an experienced staff, because only a small portion of the practical information and

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reasons for the various methodologies is published. Lastly, many of the issues are not covered at all, or only superficially, because of previous publications. As such, the following references are suggested for additional details in the use of tumor models: [1, 3–11, 21, 23, 27–38]. Nonetheless, the information provided in this work concerning the transplant characteristics of the tumor models, as well as the few examples of data presentation pitfalls, should aid investigators in future readings. With these perspectives, the reader should not be misled into believing that transplantable mouse-tumor models are exceptionally sensitive to everything. In fact, there are very few core molecules with substantial antitumor activity. One should consider the numbers: Worldwide, over 2,000,000 agents have been tested in tumor-bearing animals, and countless additional agents have been tested in culture or other assays to reduce the number to two million. Only about 60 agents are currently available for clinical use, of which less than 15 are used with regularity. Even fewer have substantial activity against common solid tumors of humans. Likewise, in our experience, fewer than 15 agents (clinically available and investigational) from separate chemical cores have a high antitumor ++++ rating against more than one of the highly metastatic, highly invasive, high-take-rate syngeneic solid-tumor models of mice (Panc-02; Mam-16/C; Mam-44; Colon-51; Colon-26; B16 melanoma; LL Carcinoma); with the implant more than 3 logs above the takerate and the drug and tumor injected by different routes, or both (Table 3.1) [1, 4, 6, 7, 32, 34, 35] and unpublished results. It should be noted that a ++++ rating only requires >2.8 log-kill (gross); see Sect. 3.11 for rating scale.

3.11 Methods The methods of protocol design, drug treatment, toxicity evaluation, data analysis, quantitation of tumor-cell-kill, tumor-model systems, cross-resistance behavior, and the biological significance of the drug treatment results with transplantable tumors have been presented previously [1, 3, 9, 26–29, 31, 33–37]. A brief description of the methods as they apply to this work follows.

3.11.1 Tumor Maintenance Tumors were maintained in the mouse strain of origin and were transplanted into the appropriate F1 hybrid or inbred for the trials. Individual mouse body weights for each experiment were within 5 g, and all mice were more than 18 g at the start of therapy. The mice were supplied food and water ad libitum.

3.11.2 Origins of Mouse Tumors Used or Discussed Colon Adenocarcinomas-26,38,51 [5, 6], Mammary Adenocarcinoma-16/C [4], Mammary Adenocarcinoma-16/C/Adr [37, 39], Mammary Adenocarcinomas-17/0

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and 17/Adr [39, 40], Mammary Adenocarcinoma-16/C/Taxol (induced in vivo with repeated Taxol treatments over six passage generations, first usage published in Ref. [32]), Pancreatic Ductal Adenocarcinomas-02,03 [7], Squamous-Cell Lung LC-12 [41, 42], B16 melanoma (first chemotherapeutic treatment) [43], LLC (first chemotherapeutic treatment) [43], L1210 Leukemia [44], P388 Leukemia [45], AML-C1498 leukemia (arose spontaneously in a 10-month-old C57B1/6 female mouse at Jackson Memorial Labs in 1941 [46].

3.11.3 Chemotherapy of Leukemias: L1210/0 The animals were pooled, IV-implanted with counted numbers of leukemic cells (106, 104, 102) prepared from a spleen brei containing approximately 80% replacement of the spleen with leukemic cells (as judged by the eightfold enlarged size in the leukemic passage DBA/2 mice). Survival was the end point; with moribund mice sacrificed. Cause of death was verified by necropsy, and there were no drug deaths among the groups shown in Table 3.13. In all cases, treatment was started the day after implantation of the leukemic cells.

3.11.4 X-Irradiation of Solid Tumors X-irradiation was delivered through a Picker Vanguard X-ray unit operated at 280 kVp, 20 mA, 1.33 mm Cu HVL, and TSD of 47.5 cm. Anesthetized mice were irradiated (1.1 gy/min, 1 gy = 100 rads) through a treatment port of 1.2 × 4.1 cm that included the tumor site, axillary nodes, and 75% right-lung volume. Maximum scatter and leakage radiation to the shielded animal was less than 2.0%.

3.11.5 Chemotherapy of Solid Tumors The animals were pooled, SC-implanted bilaterally with 30–60 mg tumor fragments by a 12-gauge trocar, and again pooled before unselective distribution to the various treatment and control groups. For early-stage treatment, chemotherapy was started within 1–3 days after tumor implantation, when the number of cells was relatively small (107–108 cells). For more advanced staged disease, the tumors were allowed to reach 100–800 mg (depending on the trial) before the start of treatment. Tumors were measured with a caliper twice weekly. Mice were sacrificed when their tumors reached 1,500 mg. Tumor weights are estimated from two-dimensional measurements:

( )

Tumor weight (mg) = a b 2 ⁄ 2

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where a and b are the tumor length and width in (mm), respectively. In those cases in which bilateral SC tumors were used, both tumors on each mouse were added together, and the total mass per mouse was used for calculations of end points. Note: Some investigators present data as measurements (partly because tumor masses are obtained by measurements). However, all NCI protocols of the past have mandated the use of tumor weights in mg. We have often found that readers do not relate to measurements, and in some cases the measurements obscure the fact that unusually small tumors are being shown and end points are obtained at sizes that are not acceptable in standard NCI protocols.

3.11.6 End Points Assessing Antitumor Activity Solid Tumors The following quantitative end points are used to assess antitumor activity.

3.11.6.1 Tumor-Growth Delay (T–C Value) T is the median time (in days) required for the treatment group tumors to reach a predetermined size (e.g., 1,000 mg), and C is the median time (in days) for the control group tumors to reach the same size. Tumor-free survivors are excluded from these calculations (cures are tabulated separately). In our judgment, this value is the single most important criterion of antitumor effectiveness because it allows the quantitation of tumor-cell-kill.

3.11.6.2 Percent Increase Life Span For leukemic mice only: Percent Increase Life Span (%ILS) = [(T - C)/C] × 100, where C is the median day of death of the control group and T is the median day of death of the treated group. There were no cures in any of the trials shown herein.

3.11.7 Calculation Tumor-Cell-Kill For subcutaneously growing tumors, and leukemic survival trials, the log10 cell-kill is calculated from the following formula: Thelog10 cell − kill total (gross) =

T − C value in days (3.32)(Td )

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Thelog10 cell - kill total (net ) =

(T − C ) − (duration of treatment ) (3.32)(Td )

The log10 cell-kill/dose = log-kill gross/number of injections (if each injection has the same dose). For solid tumors, the T–C is the tumor-growth delay described here, and Td is the tumor-volume doubling time (in days), estimated from the best-fit straight line from a log-linear growth plot of the control group tumors in exponential growth (100–800 mg range). The conversion of the T–C values to log10 cell-kill is possible because the Td of tumors regrowing post-treatment (Rx) approximates the Td values of the tumors in untreated control mice. If the regrowing tumors do not (e.g., irradiation of tumor and tumor bed), the Td value is determined from the regrowing tumors. This equation for log-kill is also used for leukemia testing, where T is the median day of death for the treated group and C is the median day of death for the control group. The Td is determined from differences in the median days of death of the titered control groups. Four log10 dilution controls were included in all trials. There were five mice each in all treatment and control groups.

3.11.8 Activity Rating Solid Tumors For comparison of activity with standard agents and comparisons of activity between tumors, the log10 kill values were converted to an arbitrary activity rating [35]. It should be noted that +++ or ++++ activity would be required to produce tumor regressions in most models. As such, ++ activity would not be scored by a clinician as more than stable disease. Duration of treatment 5–20 days Antitumor activity Gross log10 tumor-cell-kill Highly active ++++ +++ ++ + Inactive

>2.8 2.0–2.8 1.3–1.9 0.7–1.2 <0.7

3.11.8.1 Activity Rating for Leukemia L1210/0 Because the L1210 leukemia was markedly more sensitive to the antitumor agents than the solid tumors, an expanded activity rating was required (Tables 3.1 and 3.13).

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Duration of treatment 1–9 days Antitumor activity Gross log10 tumor-cell-kill Highly active +++++ ++++ +++ ++ + Inactive

³ 5.0 4.0–4.9 3.0–3.9 2.0–2.9 1.0–1.9 <1.0

3.11.9 Non-quantitative Determination Antitumor Activity Tumor-Growth Inhibition (T/C Value) The treatment and control groups are measured when the control-group tumors reach approximately 700–1,200 mg in size (median of group). The median tumor weight of each group is determined (including zeros). The T/C value in percent is an indication of antitumor effectiveness. A T/C value equal to or less than 42% is considered significant antitumor activity by the Drug Evaluation Branch of the Division of Cancer Treatment (NCI). A T/C value <10% is considered to indicate highly significant­ antitumor activity, and is the level used by NCI to justify a clinical trial if toxicity­, formulation, and certain other requirements are met (termed DN-2 level activity). A body-weight loss nadir (mean of group) of greater than 20% or greater than 20% drug deaths is considered to indicate an excessively toxic dosage. Acknowledgments  This work was supported by CA12623, CA82341, CA53001, Sanofi-Aventis previously Sanofi Synthelabo Research, and Eli Lilly Corporation.

References 1. Corbett T, Valeriote F, LoRusso P, Polin L, Panchapor C, Pugh S, et al. Tumor models and the discovery and secondary evaluation of solid tumor active agents. Int J Pharmacogn. 1995;33(Suppl.):102–22. 2. LoRusso P, Demchik L, Dan M, Polin L, Gross JL, Corbett TH. Comparative efficacy of DMP840 against mouse and human solid tumor models. Invest New Drugs. 1995;13:195–203. 3. Polin L, Valeriote F, White K, Panchapor C, Pugh S, Knight J, et  al. Treatment of human prostate tumors PC-3 and TSU-PRI with standard and investigational agents in SCID mice. Invest New Drugs. 1997;15:99–108. 4. Corbett TH, Griswold DP, Jr, Roberts BJ, Peckham JC, Schabel FM, Jr. Biology and therapeutic response of a mouse mammary adenocarcinoma (16/C) and its potential as a model for surgical adjuvant chemotherapy. Cancer Treat Rep. 1978;62(10):1471–88. 5. Corbett TH, Griswold DP, Jr, Roberts BJ, Peckham JC, Schabel FM, Jr. Tumor induction relationships in development of transplantable cancers of the colon in mice for chemotherapy assays, with a note on carcinogen structure. Cancer Res. 1975;35:2434–9.

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6. Corbett TH, Griswold DP, Jr, Roberts BJ, Peckham JC, Schabel FM, Jr. Evaluation of single agents and combinations of chemotherapeutic agents in mouse colon carcinomas. Cancer. 1977;40(5):2660–80. 7. Corbett TH, Roberts BJ, Leopold WR, Peckham JC, Wilkoff LJ, Griswold DP, Jr, et  al. Induction and chemotherapeutic response of two transplantable ductal adenocarcinomas of the pancreas in C57BL/6 mice. Cancer Res. 1984;44:717–26. 8. Corbett TH, Valeriote FA. Rodent models in experimental chemotherapy. In: Kallman RF, editor. The use of rodent tumors in experimental cancer therapy: conclusions and recommendations. New York, NY: Pergamon; 1987. p. 233–47. 9. Corbett T, Valeriote F, LoRusso P, Polin L, Panchapor C, Pugh S, et al. In vivo methods for screening and preclinical testing; use of rodent solid tumors for drug discovery. In: Teicher B, editor. Anticancer drug development guide: preclinical screening, clinical trials, and approval. Totowa, NJ: Humana Press Inc.; 1997. p. 75–99. 10. Corbett TH, Griswold DP, Jr, Roberts BJ, Schabel FM, Jr. Cytotoxic adjuvant therapy and the experimental model. In: Stoll BA, editor. New aspects of breast cancer. Vol. 4, Systemic therapy in breast cancer. London: William Heinemann Medical Books; 1981. p. 204–43. 11. Corbett TH, Roberts BJ, Lawson AJ, Leopold WR, III. Curative chemotherapy of advanced and disseminated solid tumors of mice. In: Jacobs JR, Al-Sarraf M, Crissman J, Valeriote F, editors. Scientific and clinical perspectives in head and neck cancer management: strategies for cure. New York, NY: Elsevier Scientific; 1987. p. 175–92. 12. Griswold DP, Jr, Corbett TH. Use of experimental models in the study of approaches to treatment of colorectal cancer. In: Lipkin M, Good RA, editors. Gastrointestinal tract cancer. New York, NY: Medical Book; 1978. p. 399–418. 13. Karrer K, Humphreys SR. Continuous and limited courses of cyclophosphamide (NSC26271) in mice with pulmonary metastasis after surgery. Cancer Chemother Rep. 1967;51:439–49. 14. Schabel FM Jr. Concepts for treatment of micrometastases developed in murine systems. Am J Roentgenol Radium Ther Nucl Med. 1976;126:500–11. 15. Boyd MR. The NCI in vitro anticancer drug discover screen, concept, implementation, and operation, 1985 to 1995. In: Teicher B, editor. Anticancer drug development guide: preclinical screening, clinical trials, and approval. Totowa, NJ: Humana; 1997. p. 75–99. 16. Wallace AC. Effect of delayed addition of irradiated cells to small viable tumor inocula. Cancer Res. 1965;25:355–7. 17. Revesz L. Effect of tumor cells killed by X-rays upon growth of admixed viable cells. Nature. 1956;178:1391–2. 18. Revesz L. Effect of lethally damaged tumor cells upon the development of admixed viable cells. J Natl Cancer Inst. 1958;20:1157–86. 19. Dykes DJ, Griswold DP, Jr, Schabel FM, Jr. Growth support of small B16 melanoma implants with nitrosourea-sterilized fractions of the same tumor. Cancer Res. 1978;36:2031–4. 20. Annual Progress Report to Division of Cancer Treatment, National Cancer Institute on Primary Screening and Development and Application of Secondary Evaluation Procedures for Study of New Materials with Potential Anticancer Activity. Lack of Influence of immune suppression on the number of cells required to establish takes of solid tumors. Southern Research Institute, Contract NO1-CM-43756, March 15, 1982. p. 1–9. 21. Skipper HE. The effects of chemotherapy on the kinetics of leukemic cell behavior. Cancer Res. 1965;25:1544–50. 22. Laster WR, Jr, Schabel FM, Jr, Skipper HE, Wilcox WS, Thomson JR. Experimental evaluation of potential anticancer agents IV. Host weight loss as it related to false positives in drug evaluation. Cancer Res. 1961;21:895–906. 23. Annual Progress Report to Division of Cancer Treatment, National Cancer Institute on Primary Screening and Development and Application of Secondary Evaluation Procedures for Study of New Materials with Potential Anticancer Activity. Section 27. Unusual toxicity problems ­encountered in the evaluation of new antitumor agents: implications for data analysis and protocol­ design. Southern Research Institute, Contract NO1-CM-43756, March 15, 1982. p. 1–32.

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24. Houghton JA, Taylor DM. Growth characteristics of human colorectal tumors during serial passage in immune-deprived mice. Br J Cancer. 1978;37:213–23. 25. LoRusso PM, Polin L, Aukerman SL, Redman BG, Valdivieso M, Biernat L, et al. Antitumor efficacy of Interleukin-2 alone and in combination with Adriamycin and Dacarbazine in murine solid tumor systems. Cancer Res. 1990;50:5876–82. 26. Motycka K, Bostik J, Danek PF, Chudomel V. Immunogenicity of the Gardner lymphosarcoma for the mice of the strain C3H (H-2k). I. The effect of the 60Co-irradiation of recipients and of the interval between the immunization and transplantation of the tumor on the antitumor resistance of immunized mice. Neoplasma 1986;33(2):167–75. 27. Simpson-Herren L, Corbett TH, Griswold DP, Jr. The cell population kinetics and response to an S-phase specific agent of three transplantable colon tumor lines. Cell Tissue Kinet. 1980;13:613–24. 28. Corbett TH, Leopold WR, Dykes DJ, Roberts BJ, Griswold DP, Jr, Schabel FM, Jr. Toxicity and anticancer activity of a new triazine antifolate (NSC-127755). Cancer Res. 1982;42:1707–15. 29. Corbett TH, Roberts BJ, Trader MW, Laster WR, Jr, Griswold DP, Jr, Schabel FM, Jr. Response of transplantable tumors of mice to anthracenedione derivatives alone and in combination with clinically useful agents. Cancer Treat Rep. 1982;66(5):1187–200. 30. Corbett T, Valeriote F, Baker L. Is the P388 murine tumor no longer adequate as a drug discovery model? Invest New Drugs. 1987;5:3–20. 31. Corbett TH, Bissery M-C, LoRusso P-M, Polin L. 5-Fluorouracil containing combinations in murine tumor systems. Invest New Drugs 1989;7:37–49. 32. Corbett TH, Valeriote FA, Demchik L, Polin L, Panchapor C, Pugh S, et  al. Preclinical ­anticancer activity of cryptophycin-8. J Exp Ther Oncol. 1996;1:95–108. 33. Corbett TH, Valeriote FA, Demchik L, Lowichik N, Polin L, Panchapor C, et al. Discovery of cryptophycin-1 and BCN-183577: examples of strategies and problems in the detection of antitumor activity in mice. Invest New Drugs. 1997;15:207–18. 34. Corbett TH, LoRusso P, Demchick L, Simpson C, Pugh S, White K, et al. Preclinical antitumor efficacy of analogs of XK-469: Sodium-(2-[4-(7-chloro-2-quinoxalinyloxy)phenoxy] propionate. Invest New Drugs. 1998;16:129–39. 35. Corbett TH, Panchapor C, Polin L, Lowichik N, Pugh S, White K, et al. Preclinical Efficacy of thioxanthone SR-271425 against transplanted solid tumors of mouse and human origin. Invest New Drugs. 1999;17:17–27. 36. Schabel FM, Jr, Trader MW, Jr, Laster WR, Jr, Corbett TH, Jr, Griswold DP, Jr. Cisdichlorodiammineplatinum (II): combination chemotherapy and cross-resistance studies with tumors of mice. Cancer Treat Rep. 1979;63:1459–73. 37. Schabel FM, Jr, Skipper HE, Trader MW, Laster WR, Jr, Griswold DP, Jr, Corbett TH. Establishment of cross-resistance profiles for new agents. Cancer Treat Rep. 1983;67:905–22. 38. Skipper HE, Schabel FM, Jr, Wilcox WS. Experimental evaluation of potential anticancer agents XIII: on the criteria and kinetic associated with curability of experimental leukemias. Cancer Chemother Rep. 1964;35:3–111. 39. Kessel D, Corbett TH. Correlations between anthracycline resistance, drug accumulation and membrane glycoprotein patterns in solid tumors of mice. Cancer Lett. 1985;28:187–93. 40. Biernat L, Polin L, Corbett T. Adaptation of mammary tumors of mice to a soft agar assay for use in drug discovery. Seventh NCI-EORTC Symposium on New Drugs in Cancer Therapy. Abstract #185, Amsterdam, March 1992. p. 17–20. 41. Smith WE, Yazdi E, Miller L. Carcinogenesis in pulmonary epithelia in mice on different levels of Vitamin A. Environ Res. 1972;5:152–63. 42. Tapazoglou E, Polin L, Corbett TH, Al-Sarraf M. Chemotherapy of the squamous cell lung cancer LC-12 with 5-fluorouracil, cisplatin, carboplatin or iproplatin combinations. Invest New Drugs. 1988;6(4):259–64. 43. Sugiura K, Stock CC. Studies in a tumor spectrum. III. The effect of phosphoramides on the growth of a variety of mouse and rat tumors. Cancer Res. 1955;15:38–51.

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44. Law LW, Dunn TB, Boyle PJ, Miller JH. Observations on the effect of folic-acid antagonists on transplantable lymphoid leukemias in mice J Natl Cancer Inst. 1949;10:179–92. 45. Dawe CJ, Potter M. Morphologic and biologic progression of a lymphoid neoplasm of the mouse in vivo and in vitro (abstr). Am J Pathol. 1957;33:603. 46. Bradner WT, Pindell MH. Myeoid leukemia C1498 as a screen for cancer chemotherapeutic agents Cancer Res. 1966;April(pt 2):375–90.

Chapter 4

B16 Murine Melanoma: Historical Perspective on the Development of a Solid Tumor Model Enrique Alvarez

The determination of weight of a factor in producing metastases can not be judged from single experiences on man, as it is impossible to eliminate conflicting conditions. Only by the use of a homogeneous material which the size of the cells, their histological and biological qualities, and the vascularity of the surrounding tissue, etc., are practically constant can valid conclusions be drawn, and this elimination of variables is possible to obtain only by the use of animal tumors of a long transplanted strain, so that the morphological and biological characters are well known. Dr. Leila C. Knox, 1922 [1]

4.1 Introduction The development of reliable models of disease mechanisms largely depends on our understanding of the characteristic processes of the disease being modeled. Syngeneic tumors in mice offer an important model for oncology research. Although the use of murine models of neoplastic disease has been raised to the level of a fundamental paradigm in oncology research, it is to be considered with all the care and diligence afforded to us by any biological model. Syngeneic murine tumors offer potential advantages as well as limitations, which should always be present in the mind of the investigator. Every year, countless studies are published describing new models and/or new cell lines available to scientists. Many of these are highly specialized, and have a narrow application to the broad community. Through time, several models are developed, which present us with a newer tool to truly advance our understanding. In many instances, these models fill a specific unmet need. If fortunate enough, the model is also relatively easy to reproduce, thus providing for rapid dissemination among the community. The overall relative importance of an experimental model depends largely on two important aspects.

E. Alvarez (*) Biomodels, 313 Pleasant Street, Watertown, MA 02472, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_4, © Springer Science+Business Media, LLC 2011

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First, how does the model recapitulate the process it attempts to emulate? Second, how does the model itself offer the flexibility to expand our knowledge relating to the pathologic process being evaluated? This chapter pinpoints how the B16 murine melanoma line has proven itself a valuable model on both points. This tumor line provides researchers with the ability to model the process of solid tumor formation and the following metastatic process seen in animals and man. Upon establishment of this model of metastasis, a more detailed understanding of the steps involved in tumor dissemination was gained. Astute manipulation of the cell line and a rational use of experimental animal models were essential for this process. Overall, this effort has helped the modern description of fundamental processes in metastasis, invasion, and antitumor drug development. The body of knowledge derived from the B16 murine melanoma line is ample, and even at present continues to grow. The emphasis of this overview is limited to its origin and to the seminal work attributable to the early work done with this tumor line and some of the tumor models that have arisen from it. In the span of biological research, this model is relatively new. But within one lifetime, it has helped shape our understanding of oncology. In science, many posed questions are old and numerous, but the tools we need to thoroughly explore them and ask new ones are in continuous evolution. These new tools encourage even more questions. The B16 melanoma is one of those tools.

4.2 Historical Context The process of scientific discovery never occurs outside the context of contemporary knowledge. Contemporary knowledge is relative to the time of the work itself. In the early 1970s, at the time of the establishment of the B16 as a model for metastasis research, the state of metastasis research from a technical standpoint was immature when compared to today’s standards. Although many recent developments in the area of metastasis have depended on the use of relatively recent technological advances (e.g., molecular biology, protein chemistry, and bioinformatics), many questions regarding the nature of metastasis are very old. Up to the early 1970s, there had been numerous qualitative (i.e., autopsies) and some quantitative studies (i.e., rodent models) relating to the natural history of the metastatic process, but a readily accessible murine model was lacking – specifically, a model that would offer a predictable metastatic pattern. A periodic survey of the historical record serves to focus our attention to the correct context of the research. Within this context, the significance of the questions asked becomes clearer, as well as the importance of the techniques being used. The development and current use of the B16 melanoma line should be incorporated into a larger scope of oncology research by looking at a previous generation of researchers and their professional contributions to the field. An often-quoted work by Dr. Stephen Paget was seminal in establishing our appreciation for the complexity of the processes involved in metastasis [2]. The concept of Seed and Soil, as it applies to the tumor embolus and its potential site of

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invasion, is firmly entrenched in the minds of researchers in the field of metastasis. Simple, allegorical, yet effective at setting the concepts of a complicated pathological process, the idea of seed and soil has been fundamental for over 110 years. The careful evaluation of human autopsies derived from breast-cancer patients and obvious nonrandom pattern of metastasis, noted by Paget, demonstrated that breast-cancer patients had a notable predisposition for bony metastasis. This bony metastasis was nonrandom in its distribution, since, as Dr. Paget commented: “Who has ever seen the bones of the hands or the feet attacked by secondary cancer?” This observation is critical in two important aspects. First, the bony tissue clearly presents the tumor with a preferred invasion site (soil to the seed). Second, bones are not just bones; there appears to be further discrimination by the tumor embolus as to which bone offers an optimal site for colonization. Effective tools, such as animal models that could be used to experimentally understand the human condition, were not yet available to Paget. It would still be many years until such specific disease models were widely available for use. Our contemporary description of the specific relationship between the cell and its host tissue uses the term “tumor microenvironment” to essentially name the same phenomena described in 1889. Successful tumor metastasis is a complex, nonrandom, multifactorial event that requires contributing components from both the cancer cell and its host. As with many areas in science, in oncology we also find a periodic re-evaluation of fundamental themes. The “Seed and Soil” concept has not been immune to this effect. In 1982, Hart specifically reflected on the mechanisms of metastasis as they apply to murine models [3]. This work reiterated the clear importance of circulation and physical-cell distribution on the outcome of a metastatic event. Hart concluded: Patterns of metastasis primarily appear to be a direct consequence of the delivery of an optimal dose of tumor cells to the first organ encountered along the lymphatic or venous pathway. Nonetheless, the very same tumors that use this mechanism as a predominant mode of spread will, on other occasions, exhibit true organ tropism.

This conclusion did not exclude the already noted metastatic preference of tumors to certain organs, but gave a stronger importance to circulatory parameters in the final disposition of tumor metastasis. This attempt at revisionism occurred almost 100 years after the publication by Paget. What had transpired in that interval to call for the reevaluation of the concept? In the first half of the 1900s, there was a general effort to describe the nature of circulating tumor cells in man and rodent models. In 1913, while studying a carcinoma model of the Japanese waltzing mouse, Tyzzer found a correlation between the tumor size, duration of tumor presence, peculiar conditions furnished by the host, and the number of metastases [4]. These important clinically applicable correlations were being laid down by scientists using syngeneic models. In 1915, Iwasaki presented a paper, which when carefully read can serve to introduce today’s researcher to many of the important areas of current interest [5]. By using microscopy to describe the tumor embolus–host interaction in necropsy cases, Iwasaki finely described the disposition of several tumor types in the vessels of patients. Most of the work presents the reader with a description of the relationship

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between the tumor embolus, thrombus formation/organization, and the formation of tumor metastasis. A particular description of tumor–vessel interaction by Iwasaki is of particular interest today: The tumour cell mass is here covered by a thin layer of endothelium continuous with the intima. Though a small number of leucocytes may be found between the tumour cells, the latter appeared to be normal: no sign of degradation can be seen. Some connectivetissue fibres from the wall, as well as cells from the covering endothelium, are thrust into the mass: these serve as stroma. I consider them as the first stage of metastases formation; at the same time, I consider that the penetration of the wall by the tumor cells from inside can sometimes occur without such an endothelial cover.

Eighty-five years ago we find an accurate description of the process of extravasation by a metastatic cell, while at the same time describing a relationship between tumor cells and endothelium. In the same paper, Iwasaki also studied the fate of intravenously (iv)-injected tumor cells in rats and mice: In the cases of mouse carcinoma I again observed the appearances of which I have described in cases of human sarcoma, that is, the tumour cell group on a vessel wall, having a single endothelial layer directly covering it. The carcinoma cells of such fixed emboli appear to be in healthy condition in every respect, and in the cases where connective tissue bundles thrust into and divide them up, they have the common appearance of alveolar carcinoma and are not to be considered as degenerating or organizing at all.

This statement serves to offer initial validation of the use of murine models from a comparative and histological perspective. When comparing his own tumor implantation results with that of fellow researchers (who obtained a lower tumor take-rate than himself) Iwasaki concluded: From my results I hold that it is not difficult to cause tumours by inoculating foci directly into vessels. Nevertheless, all tumor cells introduced into blood vessels do not necessarily disintegrate, as several authors believe, but if the technique is suitable, the lung will be attacked in a very considerable number of cases.

Iwasaki’s work was important in describing the lung as the preferred site of tumortakes after iv injection. Much work followed to determine the reasons for this apparent affinity. Warren and Gates clearly demonstrated that the successful establishment of tumors in rodent lungs after iv injection is strongly correlated with the cell viability of the sample being used [6]. At that time, the authors were attempting to standardize the procedure of iv tumor-cell implantation. This was demonstrated using the Walker 256 carcinoma line in rats. Cloudman in 1947 studied the – as they were formerly called – organophilic tendencies of murine hepatic tumors [7]. At that time, the author specifically looked at the dissemination patterns of two tumor lines (C954 liver carcinoma and C198 reticuloendothelioma). The work employed the use of subcutaneous (SC) implantations using a trochar as well as the use of parabiosis in mice. Interestingly, Cloudman’s method of tumor passage may have contributed to the organophilic tendencies noted. The mode of tumor maintenance for the C198 line required that tumor-affected pieces of liver from mice be SC-implanted in naive hosts. This SC tumor would lead to disseminated disease in the mice. When the initial line was being established, simple SC implantation led

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to poor tumor-take. But the hepatic metastatic spread of the tumor offered an optimal tissue for reproducibly metastatic tumor whenever this tissue was SC-implanted. In the case of the C954 tumor line, it was maintained via simple SC passage. The C954 had no metastatic properties. Retrospectively, it appears that Cloudman unintentionally selected for a tumor-line variant by only using the tumor stock from metastatic sites. The research compared the metastatic potential of the C198 line vs. the nonmetastatic C954 line. In addition to a direct comparison between both tumor lines, by using various mouse strains, there was a demonstration of the different metastatic patterns (organophilic tendencies) attributable to the mouse strain being used. This led to the conclusion: The appearance of tumor metastasis within a specific internal organ is probably dependent upon a host–tumor interrelationship rather than upon either the tumor type or the host type alone.

This is an important holistic approach to in vivo modeling. But strictly speaking, it was probably not a comparison of equal lines. The successful establishment of the tumor is attributed to both the tumor cell and the selected host. In 1949, Coman et al. presented work, which in the context of Paget’s hypothesis, discussed the apparent inability of the V2 rabbit carcinoma to metastasize to muscle [8]. By injecting tumor cells into the arterial circulation and demonstrating tumor growth in the muscle mass, the authors confirmed the ability of the tumor to recognize the tissue as favorable. In their conclusion, the reason for the apparent lack of tumors in certain organs was the result of a filtering effect by the lungs. In 1950 and 1952, Ziedman studied important parameters for the use of a murine tumor line in vivo at the University of Pennsylvania [9, 10]. Ziedman specifically studied the relationship between the number of viable tumor emboli iv-implanted and the total number of resulting metastatic nodules. In 1950, using the mouse Sarcoma 241 line, Ziedman concluded: That the number of metastases is directly proportional to the number of viable tumor cell emboli released into the circulation. The longer a primary tumor existed the greater the number of emboli released, as judged by the number of metastases appearing in the lungs [9].

In his studies, the author also compared two rabbit tumor lines (V2 squamous-cell carcinoma, Brown-Pearce carcinoma) and one rat tumor line (Walker 256 carcinoma). One matter that still had to be resolved regarding iv injections of tumor cells relates to the pulmonary disposition of tumor cells. Were the lungs acting as a mere sieve? To test this hypothesis, rabbits received an iv injection of tumor cells, while aortic outflow was captured. This collected blood was then iv-injected into a naive host; this second animal was then followed for tumor formation. Tumor formation in the second hosts demonstrated that tumor cells successfully passed through the pulmonary circulatory system. All three tumors were able to pass through the pulmonary vasculature and form tumors in the second host, thus negating the pulmonary sieve hypothesis. In 1961, Ziedman used microcinematic techniques to further evaluate the flow patterns of tumor cells in circulation [11]. In this work, he demonstrated the higher

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degree of membrane flexibility/deformability of the Brown-Pearce tumor vs. the V2 tumors. This work was done by studying the vascular patterns in the mesenteric vessels of rabbits. It illustrated how the Brown-Pearce cells, upon reaching an arteriolocapillary junction, could deform and pass more freely than the V2 cells. This added to the body of evidence for the tumor’s enhanced ability for transpulmonary passage, thus highlighting an intrinsic cellular difference between both lines. Descriptive pathology and in vivo work, as many of the works presented here have been, continue to be essential for the development of our understanding of metastasis. Even at the present time, in the era of genomics, an attempt to elucidate the underlying biological forces involved in the dissemination of prostatic carcinoma required the careful evaluation of autopsies over 19,000 patients [12]. Approximately 1,500 cases of prostatic carcinoma were identified in this cohort. Of those cases, 35% showed evidence of metastatic spread. Interestingly, over 100 years after the seminal work of Dr. Paget, this paper offers an attempt to describe in detail the common metastatic patterns of dissemination found in prostatic carcinoma patients using autopsy records.

4.3 B16 Melanoma In the context of the Seed–Soil hypothesis, the use of the B16 murine melanoma line as a model for both solid-tumor formation and metastasis was an important development in oncology research. Although the introduction of this model for metastasis research can be traced to 1970, the cell line itself had been identified and characterized as a tumorigenic line years before. The B16 murine melanoma cell line originated in 1954. The tumor spontaneously arose in a C57BL/6J mouse at the Jackson Laboratories in Maine. There is no record of the sex of the originating mouse. The initial neoplastic lesion arose in the skin at the base of the ear. The following is a histological description of the tumor from the Handbook on Genetically Standardized Jax Mice by Dr. Earl Green: Gross: soft gray tissue, frequently hemorrhagic. Microscopic: tumor cells polyhedral or spindle-shaped, arranged in perivascular mantles and diffuse masses; some cells contain fine pigmented granules, a few are obscured by large, very dark globules of pigment; stoma delicate and vascular. Pigment greatly decreased in comparison with early-transplant generation [13].

This offers the first histological description of the tumor line in its host. The tumor line was maintained at the Jackson Laboratories by continuous passage in vivo. At the time of the report, the tumor is described as metastatic to lung, liver, and spleen. This metastatic pattern was present after SC implantation. Using the B16 melanoma line from the Jackson Laboratories, Dr. Isaiah Fidler carefully documented the final disposition of the melanoma cells after iv injection in mice in 1970 [14]. A number of protocols used before this time to aid in the understanding of the metastatic process proved to be either cumbersome, unreliable, or both. There was an unmet need for a mouse line, and this line offered an important opportunity for the field. In the 1970 study, B16 murine melanoma cells

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were initially cultured in  vitro. The cells used for iv injection were labeled with 125 I-5-iodo-2-deoxyuridine. This radioactive label provided a clear and specific way to monitor the organs in which tumor cells arrested. This ability to monitor cells was specifically caused by the affinity of the radioactive label for viable cells. Cell death would lead to the excretion of the label from the animal, thus precluding the possibility of labeling the host’s tissues. The iv injection of killed labeled B16 cells served to demonstrate the inability of the host to reutilize the radioactive label, thus preventing a false-positive reading. In vitro, 200,000 labeled cells would produce an average of 40,000 counts/min. After inoculating the cells into naive hosts, the number of viable tumor cells in each organ was determined from radioactivity count, by the use of the ratio of cpms to cells in the original inoculum. Simple and efficient, the labeling of B16 murine melanoma cells could now open the door for the description of a complex tumor–host interaction. The B16 melanoma cells could now be iv-implanted and followed in the host animal, providing a faster, less cumbersome method than the previously used histopathological approaches. Tables 4.1 and 4.2 from the 1970 paper show the final organ of cellular arrest and temporal distribution of the viable/dead and labeled B16 murine melanoma cells after a single iv injection [14]. From the table, it can be easily established that in the earliest postinjection time-points, the majority of the cells find themselves in Table 4.1  Fate of 125IUDR-labeled tumor emboli organ distribution of 200,000 melanoma cells injected intravenously into C57BL/6J mice Number of cellsa Time of death Lung Liver Spleen Kidney Bloodb 1 min 2 min 5 min 7 min 10 min 15 min 30 min 45 min 1 h 2 h 4 h 8 h 12 h 1 day 2 days 3 days 7 days 14 days

136, 750 128, 500 106, 700 103, 500 130, 500 122, 800 117, 900 105, 000 100, 550 89, 590 46, 700 18, 000 5, 500 1, 700 610 450 450 400

2, 230 5, 500 7, 600 7, 570 9, 350 8, 390 4, 260 4, 140 3, 570 5, 830 5, 300 1, 340 700 600 260 200 230 0

200 230 260 280 270 310 250 390 330 680 870 320 580 140 160 90 40 0

Mean number of cells (20 mice per time interval) 1.0 cc of blood c Urinary bladder and contained urine a

b

300 270 270 290 310 310 370 460 370 530 770 300 230 130 130 20 0 0

3, 750 1, 590 2, 200 2, 300 2, 600 3, 340 3, 500 3, 900 3, 800 4, 300 4, 660 4, 500 1, 050 580 140 40 0 0

125

IUDR-labeled

Urinec – – – – 100 350 4,300 4,100 10,000 7, 000 33,900 23,500 10,500 1,700 1,400 20 0 0

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E. Alvarez Table 4.2  Fate of 125IUDR-labeled tumor emboli organ distribution of 200,000 125IUDRlabeled melanoma cells injected intravenously into C57BL/6J mice Number of cellsa Time of death Lung Liver Spleen Kidney Bloodb Urinec 1 min 96, 700 4, 860 40 120 2, 314 – 2 min 97, 250 5, 420 100 570 845 – 5 min 99, 600 4, 810 90 120 1, 190 – 10 min 91, 700 5, 700 300 270 2, 070 345 15 min 64, 400 4, 000 220 200 2, 600 380 30 min 41, 500 9, 300 420 590 5, 600 3, 530 45 min 19, 500 12, 430 900 930 6, 790 20, 060 1 h 2, 100 8, 800 940 740 9, 340 25, 000 2 h 550 1, 700 450 435 13, 200 24, 500 4 h 200 2, 310 640 360 4, 010 22, 290 8 h 0 750 330 0 1, 950 7, 260 24 h 0 40 120 0 100 190 2 days 0 30 40 0 0 120 7 days 0 0 0 0 0 60 14 days 0 0 0 0 0 0 Mean number of cells (10 mice per time interval) 1 cc of blood c Urinary bladder and contained urine a

b

the pulmonary tissue, but some are also localized in other organs. After 14 days, only the lungs contain labeled cells, now seen as tumor nodules. Liver, spleen, kidneys, and blood all showed the early presence of the labeled cells, but none of these tissues show the establishment of tumors at 14 days postinjection. Intravenously-injected B16 melanoma cells in mice could lead to rapid accumulation of cells in the pulmonary tissue, lead to early high levels of tumor cells in circulation caused by transpulmonary passage, or ultimately produce a very low rate of tumor formation in the animals. In the case of the B16 line, this tumor formation was limited to the pulmonary tissue. As a control, the author used killed B16 cells, which had had also been labeled with 125I-5-iodo-2-deoxyuridine. Some of the obvious questions presented by this work include: Why are so few cells if the injected cells able to form tumors? Why such apparent inefficiency? How do the cells select the target organ for colonization? Again, in the case of the B16 tumor, the lungs were not acting as a passive sieve. This work demonstrated that the mere presence of neoplastic cells in the circulation of the mice is not a guarantee of successful tissue colonization by the tumor, confirming earlier work done with mouse Sarcoma 241 tumors in 1950 by Ziedman [9]. The work with the B16 line is a good example of how research is done in the context of an already established scientific framework. But strictly speaking, the process recapitulated in the B16 model is only representative of what happens to neoplastic cells, which have escaped a primary tumor into circulation. At this point in time, the iv model was not truly representative of the

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entire set of steps now established as requirements for a cell to leave the primary tumor and successfully colonize a distal site. In 1973, Dr. Fidler published another paper, which described a model for metastatic neoplasia using B16 melanoma cells iv-injected in syngeneic mice [15]. Until this time, metastatic tumor models usually relied on the desegregation of cells from solid tumors (a heterogeneous mixture of neoplastic and normal cells). This study focused on the relationship between the number of implanted cells, tumor emboli size, and the resulting pulmonary metastatic nodules. Cell selection was aided by the use of in vitro culturing of the cells in order to select the most optimal cell population for implantation. Building upon prior work by Ziedman, the author studied the effects of cell viability and cell clumping in the formation of pulmonary nodules (Tables 4.3 and 4.4). The value of this effort was as important as it was simple. Using the B16 line, the results demonstrate a proportional increase in lung metastasis formation in animals injected with higher numbers of cells. Although this is a rather intuitive point (and one previously tested) it served to solidify a base of knowledge around the B16 as a murine model. In the context of the contemporary studies, this effort established a standard to be followed by contemporary scientists. The careful selection of viable single cells in this model was again shown to be essential for the establishment of a reproducible model of in vivo metastasis. These 1970 and 1973 papers presented what should be considered, an introduction of the B16 tumor line to the scientific community [14, 15]. Importantly, the works themselves clarified an optimal protocol for the establishment of metastatic lesion in the lungs of C57-black mice, using a relatively simple technical procedure. Also published in 1973, the process that gave rise to the B16 (F1, F10) sublines with different metastatic potentials. This was a short two-page paper with important ramifications [16]. The work describes the in vivo–in vitro selection process used Table 4.3  Relationship of the number of viable B16 melanoma cells injected iv in C57 mice with the number of resultant pulmonary metastases Number of viable B16 cells iv-injected Average number of pulmonary metastasesa ±SD 100 1, 000 10, 000 50, 000 100, 000

0.3 (0–1) 12.1 ± 3.4 (9–18) 71.6 ± 16.2 (52–96) 205.5 ± 34.3 (166–260) 394.8 ± 51.6 (338–502)

a Nine mice per each group. Pulmonary metastases were counted on day 14 post iv injection with the aid of a dissecting microscope

Table 4.4  The effect of B16 melanoma embolic size on resultant pulmonary metastases in C57 mice Total number of embolic Number of cells per Average number of resultant B16 cells iv-injected embolic clump pulmonary metastasesa ± SD 50, 000 10, 000–12, 000

1 4–5

11.5 ± 3.4 (5–14) 33.3 ± 8 (21–41)

a Eight mice per each group. Pulmonary metastases were counted on day 14 post iv injection with the aid of a dissecting microscope

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to identify cell variants with a high degree of preference for metastatic growth in the lungs. The experimental protocol started by SC implantation of the tumor cells into mice. The cells would grow into a tumor and spontaneously metastasize. The author then selected a resulting metastatic nodule from the lung. This tumor was dissociated and cultured as a monolayer in vitro. Upon expansion of the selected cells, these were then iv-implanted into a new host. Again, the resulting pulmonary nodules were harvested and cultured in vitro. After five cycles through the selection process, a line was derived. At this point the B16-F10 melanoma line had been specifically selected to metastasize to the lungs after iv injection. Table 4.5 shows the illustration used to explain the selection process. In 1973, Fidler concluded: As the tumour cell viability, size and homogeneity, and the syngeneic recipient did not change from one preparation to another, the differences in metastatic incidence could only be attributed to properties intrinsic to the various tumour cell lines. The clonal selection of tumours from successive metastases apparently results in cells better capable of survival and formation of secondary growths. This indicates that survival of circulating tumour emboli is not a random phenomenon [16].

Fifty years had passed since Dr. Leila Knox had proposed the criteria for an experimental murine model of metastasis. The B16 model system was incorporated into the American Type Culture Collection in June 1978 (Mr. Andrew Redman, personal communication). In the 1983 edition of the Catalog of Transplantable Animal and Human Tumors published by the Division of Cancer Treatment, National Cancer Institute (NCI) (Maryland, USA) a letter by Dr. Fidler is included [17]. This letter approves the distribution of the deposited B16 melanoma lines by the NCI. The catalog shows on the summary sheets for each tumor line that on October 11, 1979 the B16-F1, -F10, -F10LR-6, and -BL-6 were incorporated into the tumor repository. At this point, this important model was available to the entire scientific community. After the demonstration of the lung-colonizing ability of the B16-F10 lines, many in vivo lines were derived, using a similar selection approach. This approach of iv tumor cell administration would be followed for target organ seeding. Those tumors arising at the target organ would be selected and again reimplanted into fresh hosts, this would force a selection process. Examples of this methodology can be found in Brunson et al. [18], Nicolson et al. [19], and Nicolson [20]. These particular examples show how the selected lines would preferentially metastasize to the brains of C57 mice. The braincolonizing lines were shown to invade the meninges or the forebrain (lines were designated B16-B10b or B10n). Line B16-B10n showed preference for cerebral vasculature. By this route, the cells would gain access to the cerebral cortex. In 1979, Brunson and Nicolson selected another variant of the B16 melanoma line. This time the selected lines would invade the ovarian tissue when implanted in mice [21]. An observation was made that the selection process produced a less melanotic cell line than the originating cells. This ovary-colonizing variant was designated the B17-010 line. In 1980, Raz and Hurt used the cytochalasing B and colchicines as selection agents in the B16 melanoma to promote the cytoskeletal changes, which the authors suspected believed to be important in the development of differential patterns of metastases in this particular line [22]. The selected lines (designated B16-F10-B1, B2, and B3) the selected lines in addition to

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Table 4.5  Reproduction of serial lung implantation diagram Schematic representation of the tissue culture and animal transplantation system. The SC B16 melanoma tumor in the syngeneic mouse, C57BI/6J, was adapted to grow in tissue culture as described previously [2, 3]. Confluent monolayers of tumor cells were collected by 2-min treatment of 0.25% trypsin and vigorous shaking. Cell suspensions were diluted to give an inoculum dose of 50,000 viable cells (trypan blue excluding cells) in 0.25 ml Hank’s basic salt solution. Mice were injected iv and were killed with ether 3 weeks later, and submerged in consecutive washes of 7% iodine, 70% ethanol and sterile saline, and then placed in a laminar airflow hood. Their lungs, which contained melanoma nodules, were removed aseptically. Several pulmonary metastases were dissected free of the lungs, gently pressed through a number 70 stainless-steel mesh sieve and filtered through gauze. After centrifugation, the cells were resuspended in supplemented media2, plated in several Petri dishes, and incubated at 37°C, 5% CO2. Three to four days later, small colonies could be observed with the aid of an inverted microscope; one to five colonies with melanin granules were selected in each dish and their position marked. All other cells or colonies were removed by scraping and washed off with media. The selected attached colonies were incubated for an additional 3–5 days, then trypsinized, combined, and replated into 75C2 m Falcon flasks (Falcon Plastics). When the cultures became confluent, cells were collected, diluted to 25,000 cells (0.25 ml–1), and injected iv into new C57 mice. Three weeks later, these mice were killed, and their pulmonary metastases adapted to grow in culture as described above. This procedure was repeated five times. The original line was designated as line no. 26 (our melanoma clone 26), and its daughter lines and their progenies designated as lines 27, 28, 29, and 30. T.c., Grown in tissue culture; injected intravenously.

showing a higher capacity for brain colonization. The cells also showed an increased mean number of chromosomes when compared to the originating parental line. In addition to this selection process, which led to a line with higher propensity for brain colonization, the line also demonstrated a much higher rate of pulmonary growth when iv-injected. In 1988, another model of brain metastasis was presented using the B16 melanoma line [23]. This time, the B16 clones used produced metastatic spread to the brain parenchyma from the vessels found in the leptomeninges. In 1988, Arguello et al., using the B16 melanoma line, were able to modify its injection into mice to produce a model of bone-marrow metastasis [24]. Injection of a relatively small number of cells into the left ventricle produced a metastatic pattern strongly directed to the axial skeleton and bone marrow. In the description of the metastatic pattern of the line, the authors commented: “Metastases were also commonly found in the proximal large bones of the extremities. No metastases were ever seen in the most distal small bones such as carpals and tarsals.” This pattern of dissemination is reminiscent of the pattern described by Paget [2]. Now a model closely resembling the seminal work of Paget was available with the B16 melanoma line. In a publication by Dithmar et al., a novel selection of the B16 model was again demonstrated [25]. In this study, the B16-LS9 line selected by Rusciano et  al. is used to establish a model of uveal melanoma in mice [26]. The transcorneal implantation method used produces extra-ocular metastasis to lung and liver tissue. This pattern is similar to the human uveal melanoma condition. Again, the use of the B16 line helped in the development of a model for human pathology.

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Another area of cancer research that has directly benefited from the development of the B16 tumor line as a model for metastatic disease is cancer immunology. In some of the earliest work with the B16 melanoma line, Fidler and Ziedman studied the effect of host irradiation on the line’s metastatic rate [27]. At that time, the increased metastatic rate of the cells in mice that had received whole-body irradiation was not attributed to immunodepression, but to endothelial-cell damage. This increased colonizing ability was referred to as “enhanced trapping effect.” Interestingly, in their own discussion, the authors cite how previous work had demonstrated that the use of cortisone could also increase the metastatic ability of cells. It is likely that both the use of whole-body radiation and cortisone treatment of the animals also led to a degree of immunosuppression, which enhanced lung colonization by tumor cells. In some of the earliest studies of B16 melanoma cells and immune cells, the relationship between lymphocytes, macrophages, and B16 melanoma cells [28–31]. The direct repercussion of this work can be seen in the development of various methods used to activate the patient’s own immune system against the circulating cancer cells. Specifically, it is seen in protocols that use muramyl dipeptide or muramyl tripeptide in a liposome-based vehicle for iv administration [32–34]. Through activation of tissue macrophages, an immunemediated antitumor effect was elicited in vivo. This has led to a better understanding of the complex host–immune response to tumor cells. The B16 melanoma line was also instrumental in expanding our knowledge of the ability of metastases to spread. With the use of parabiosis systems in mice, it was demonstrated that metastatic spread can originate from a metastatic nodule. In a clever experiment, a mouse was sc-implanted with B16 melanoma, and when this first animal underwent surgical removal of the primary tumor site (leg amputation) and was then surgically joined to a naive host, the naive host developed lung tumors. These lung tumors originated from metastatic nodules present in the first animal. This finding offers proof that once a solid tumor has successfully spread to other tissues, the metastatic cascade perpetuates itself in the host. This problem can serve to emphasize the basic problem with solid tumors: metastases present the biggest therapeutic challenge.

4.4 Conclusions A recurring theme of this chapter is that the work that we now see as a final product is the summation of many people working at various points in time. This is evident from Paget’s hypothesis in the late 1800s to the current development of newer models of metastasis [2]. As a final example, I would like to offer an image giving a visual description of the now commonly accepted tumor-cell endothelialization extravasation steps for the B16-F10 melanoma line (Figs. 4.1 and 4.2). Lapis et al. published this sophisticated microscopy work in 1988 [35]. This is a clear example of how a new technology was able to shed light on ongoing scientific research. But microscopy work, specifically, studying the relationship between the endothelium

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and circulating tumor cells in a rodent model can be traced back to work presented in 1915 by Iwasaki (Figs. 4.3 and 4.4) [5]. While Iwasaki mostly studied human samples, a series of images were drawn of murine tumor cells interacting with the vasculature. Back in 1915, Iwasaki recognized that the importance of the tumor– endothelial interactions was essential for the establishment of the tumor embolus. In Lapi’s paper, newer technologies are applied to answer old questions. Over 70 years had passed since the Iwasaki’s description of extravasating tumor cells. At that time, two different reactions of the endothelium were noted, and contact of the tumor cells with the vessel wall would lead to either a separation of endothelial cells or an engulfment by the endothelial lining. The images here present us with a visual representation of works separated by many years, but of equal impact to our knowledge. In conclusion, the success of the B16 melanoma tumor model is clearly evident today. As an in vivo model for solid tumor formation and metastasis, the B16 has fulfilled two important criteria presented earlier. The model has been instrumental in the dissection of the many steps now associated with tumor establishment and the metastatic cascade. A wide range of disciplines have originated from the use of this tumor line. The eventual relevance of the many sublines that have been selected so far still remains unclear. The development of the solid-tumor model is a dynamic process, and one that attempts to produce a very close representation of the disease process itself. Currently, orthotopic tumor implantation appears to be gaining favor in the scientific community. Knowledge regarding embolization, tumor-cell viability, cell number, organ of arrest, and host strain are all important basic concepts required for the refinement of solid-tumor models. Another important topic in the ultimate development of metastatic models is how the site of implantation of tumor cells in the host animal will influence the metastatic pattern in murine models. Whereas many of the murine tumors that are SC-implanted spread to limited organs (mostly lungs), tumors implanted orthotopically generally behave in a manner more consistent with the natural metastatic progression of the human counterpart for in-depth coverage on this topic. An argument has recently been made regarding the need to use the advanced orthotopic tumor models for the screening and development of chemotherapeutic agents [36, 37]. While the orthotopic models might provide the researcher with a model that more accurately depicts the natural history of the tumor, the model itself is more technically demanding and costly than syngeneic metastatic systems. The knowledge gained from these efforts has directly led to the use of the B16 model to ask more questions regarding the nature of metastasis, with the added benefit of time to provide newer tools to answer such questions. It is also important to keep in mind that the overall selection and the acceptance of the model in the scientific community have been influenced by the already existing scientific framework of the time. The B16 melanoma tumor line has most definitely made a profound impact in the field of oncology research. It has served as a model for basic biological research, and has helped in the identification of distinct pathways now available as potential therapeutic targets.

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Fig. 4.1  An endothelialized thrombus attaching to the vessel wall (arrow heads), 24 h after inoculation of tumor cells. Part of a tumor cell (*) and endothelium

Fig. 4.2  A group of tumor cells completely surrounded by an endothelial covering (E) in the lumen of an arteriole, 2 weeks after inoculation of tumor cells. Scale bar: 10 µm, ×1,060

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Fig. 4.3  Experiment 37/189E: iv inoculation, March 3, 1915; killed 3 days later. Commencing growth of a young sarcoma embolus. Vacuolation of endothelial cells is seen at one point, and early distension of the artery is indicated by flattening of the folds of the elastic laminae. Cells resuming spindle shape (×525)

Fig. 4.4  Experiment 37/189B: iv inoculation, March 3, 1915; killed 5 days later. A later stage than Fig. 4.3. Increasing distension of vessel and entrance of capillaries into embolus on left upper surface (×525)

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4.5 Dedication To Olivia.

References 1. Charlton Knox L. The relationship of massage to metastasis in malignant tumors. Ann Surg. 1922;75(2):129–42. 2. Paget S. Distribution of secondary growths in cancer of the breast. Lancet. 1889;571–3. 3. Hart I. ‘Seed and soil’ revisited: mechanisms of site-specific metastasis. Cancer Metastasis Rev. 1982;1:5–16. 4. Tyzzer E. Factors in the production and growth of tumor metastases. J Med Res. 1913;28:309–33. 5. Iwasaki T. Histological and experimental observations on the destruction of tumor cells in the blood vessels. J Pathol Bacteriol. 1915;20:85–105. 6. Warren S, Gates O. The fate of intravenously injected tumor cells. Am J Cancer. 1936;27:485–92. 7. Cloudman A. Organophilic tendencies of two transplantable tumors of the mouse. Cancer Res. 1947;7:585–91. 8. Coman D, Eisneberg RB, McCutcheon M. Factors affecting the distribution of tumor metastases experiments with V2 carcinoma of the rabbit. Cancer Res. 1949;9:649–54. 9. Ziedman I, McCutcheon M, Coman DL. Factors affecting the number of tumor metastases. Experiments with a transplantable mouse tumor. Cancer Res. 1950;10:357–9. 10. Ziedman I., Buss JM. Transpulmonary passage of tumor cell emboli. Cancer Res. 1952;12:731–3. 11. Ziedman I. The fate of circulating tumor cells I. Passage of cells through capillaries. Cancer Res. 1961;21:38–9. 12. Bubendorf L, Schopfer A, Wagner U, Sauter G, Moch M, Willi N, et al. Metastatic patterns of prostate cancer: an autopsy study of 1589 patients. Hum Pathol. 2000;31(5):578–83. 13. Green E. Handbook of genetically standardized JAX mice. Bar Harbor, ME: The Jackson Laboratory; 1968. p. 57, 58. 14. Fidler I. Metastasis: quantitative analysis of distribution and fate of tumor emboli labeled with 125I-5-iodo-2¢-deoxyuridine. J Natl Cancer Inst. 1970;45(4):773–82. 15. Fidler I. The relationship of embolic homogeneity, number, size and viability to the incidence of experimental metastasis. Eur J Cancer. 1973;9:223–7. 16. Fidler I. Selection of successive tumour lines for metastasis. Natl New Biol. 1973;242:148–149. 17. National Cancer Institute, Division of Cancer Treatment Tumor Repository. Catalogue of transplantable animal and human tumors. Frederick: National Cancer Institute, 1983. 18. Brunson K, Beattie G, Nicolson GL. Selection and altered properties of brain-colonising metastatic melanoma. Nature. 1978;272:543–6. 19. Nicolson G, Bronson KW, Fidler IJ. Specificity of arrest, survival and growth of selected metastatic variant lines. Cancer Res. 1978;38:4105–11. 20. Nicolson G. Experimental tumor metastasis: characteristics and organ specificity. BioScience. 1978;28:441–7. 21. Brunson K, Nicolson GL. Selection of malignant melanoma variant cell lines for ovary colonization. J Supramo Struct. 1979;11:517–28. 22. Raz A, Hurt IR. Murine melanoma: a model for intracranial metastasis. Br J Cancer. 1980;42:331–41.

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23. Alterman A, Stackpole CH. B16 melanoma spontaneous brain metastasis: occurrence and development within leptomeninges blood vessels. Clin Exp Metastasis. 1989;7(1):15–23. 24. Arguello F, Baggs RB, Frantz CN. A murine model of experimental metastasis to bone and bone marrow. Cancer Res. 1988;48:6876–81. 25. Dithmar S, Rusciano D, Grossniklaus HE. A new technique for implantation of tissue culture melanoma cells in a murine model of metastatic ocular melanoma. Melanoma Res. 2000;10:2–8. 26. Rusciano D, Logenzoni P, Burger MM. Murine models of liver metastasis. Invasion Metastasis. 1994;14:349–61. 27. Fidler I, Ziedman I. Enhancement of experimental metastasis by x-ray: a possible mechanism. J Med. 1972;3:172–7. 28. Fidler I. Inhibition of pulmonary metastasis by intravenous injection of specifically activated macrophages. Cancer Res. 1974;34:1074–8. 29. Fidler I. Immune stimulation – inhibition of experimental cancer metastasis. Cancer Res. 1974;34:491–8. 30. Fidler I, Darnell JH, Budmen MB. Tumoricidal properties of mouse macrophages activated with mediators from rat lymphocytes stimulated with concanavalin A. Cancer Res. 1976;36:3608–15. 31. Fidler IJ, Bucana C. Mechanism of tumor cell resistance to lysis by syngeneic lymphocytes. Cancer Res. 1977;37:3945–56. 32. Fidler IJ. Therapy of disseminated melanoma by liposome-activated macrophages. World J Surg. 1992;16:270–6. 33. Killion J, Fidler IJ. Systemic targeting of liposome-encapsulated immunomodulators to macrophages for treatment of cancer metastasis. Immunomethods. 1994;4:273–9. 34. Killion J, Fidler IJ. Therapy of cancer metastasis by tumoricidal activation of tissue macrophages using liposome-encapsulated immunomodulators. Pharmacol Ther. 1998;78(3):141–54. 35 Lapis K, Paku S, Liotta LA. Endothelialization of embolized tumor cells during metastasis formation. Clin Exp Metastasis. 1988;6(1):73–89. 36. Kerbel R. What is the optimal rodent model for anti-tumor drug testing? Cancer Metastasis Rev. 1999;17:301–4. 37. Killion J, Radinsky R, Fidler IJ. Orthotopic models are necessary to predict therapy of transplantable tumors in mice. Cancer Metastasis Rev. 1999;17:279–84.

Part III

Human Tumor Xenografts

Chapter 5

Human Tumor Xenograft Efficacy Models Ming Liu and Daniel Hicklin

Abstract  Mouse models of cancer have consistently been used to qualify new anticancer drugs for development of human clinical trials. The most used models are xenografts of human tumors grown in immunodeficient mice. Retrospective preclinical–clinical correlation studies indicate that xenograft models are very useful and the models continue to make contributions to critical clinical development choices. However, improvements must be made to increase their values. Here, we review the value and the limitations of xenograft models, and discuss how to enhance their role in developing new anticancer treatments. Keywords  Xenograft model • Efficacy • Cancer drug discover

5.1 Introduction Before the initiation of human trials of novel anticancer agents, it is necessary to perform preclinical trials to evaluate the agent’s pharmacologic and toxic effects. Preclinical tumor models play an essential role in the evaluation of efficacy and optimization of lead compounds in the discovery and development of anticancer drugs. A robust, dependable animal model of human disease is critical to the evaluation of potential anticancer candidates. A typical example of an in-vivo screening in the development of targeted cancer therapeutics is shown in Fig. 5.1. Although other animal species (e.g. rat, hamster, rabbit dog) have been used as animal models for cancer research, the mouse has been the most important contributor due to its accessibility, short generation time, ease of propagation, lower consumption of test compounds, and the advances in mouse genetics.

M. Liu (*) Merck Research Laboratories, Schering-Plough Research Institute, K-15-2-2700, 2015 Galloping Hill Road, Kenilworth, NJ 07033, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_5, © Springer Science+Business Media, LLC 2011

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M. Liu and D. Hicklin Drug Candidate (post biochemical and cellular potency tests) Mouse PK Screening MTD testing in Mouse

Initial Efficacy Evaluation in Appropriate Xenograft Models

PK/PD profiling ADME profiling

Optimization of Dose & Schedule

Additional Efficacy testing in Models: • GEM • Primary Tumor Isolates • Orthotopic

PK/PD profiling

Combination Studies

PK/PD profiling

Toxicology Studies

Fig. 5.1  In-vivo flow of the development of targeted cancer therapeutics

The discovery of cancer drugs through in vivo screening methods traditionally utilized syngeneic transplantable murine tumors. Since 1955, the US National Cancer Institute (NCI) has provided screening support to cancer researchers worldwide [1]. The earliest in  vivo screens were the fast-growing murine leukemias, L1210 and P388, implanted intraperitoneally. These tumors were derived from leukemias originally induced chemically in the DBA/2 mouse by painting the skin with methylcholanthrene [2, 3]. Using survival as the endpoint, these tumors provided a rapid and reproducible means for identifying potential anticancer drugs [4]. From 1975 till 1985, the in  vivo P388 mouse leukemia model was used almost exclusively as the initial or primary screen at the NCI. It became evident that there were marked similarities in the drugs emerging from the murine leukemia screen. The classes of agents found active in the mouse tumor models were limited, mainly comprising alkylating agents and DNA interacting drugs [5]. Subsequently, panels of syngeneic murine solid tumors and human tumor xenografts have largely replaced the murine leukemias in anticancer drug screens. Syngeneic rodent tumor models provide an experimental model for evaluating the anticancer effects of therapeutic agents in animals with an intact immune system. However, their relevance to human cancer may be overestimated [6] as subsequent analysis revealed that almost 30% of the compounds that were disregarded by the syngeneic models were active in at least one human tumor xenograft, e.g. paclitaxel, and some classes

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of compounds, e.g. brefeldins and some minor groove DNA binders, seem to have less activities in syngeneic models [7]. Successful xenografting of human tumors into nude mice was first reported in the late 1960s [10, 11]. Nude mouse models are now extensively used in the development of potential anticancer drugs and studies of tumor biology Fig. 5.1. Moreover, mice with severe combined immunodeficiencies (e.g. SCID, beige, xid) have enlarged the spectrum of possible models and enabled engraftments of human tumors that were previously difficult to explant, such as those of the hematopoietic system. Thus, xenograft/human explants have become the workhorse in cancer drug development, and their use is highly recommended by various regulatory agencies [12]. Cancer is a complex family of diseases, characterized by the deregulation or dysregulation of normal control pathways for cellular growth, differentiation, and apoptosis. In the late 1980s and early 1990s, the focus of new drug development shifted to molecularly targeted/disease-directed treatment strategies [13]. In 1985, the NCI initiated a new project assessing the feasibility of employing human tumor cell lines for large-scale drug screening [14]. Cell lines derived from seven cancer types (brain, colon, leukemia, lung, melanoma, ovarian, and renal) were acquired from a wide range of sources and subjected to battery of in vitro and in vivo evaluations. In 1993, the composition of the cell line screen, often referred to as “the NCI panel of 60 cell lines,” NCI-60, was modified to include various prostate and breast tumor lines [15]. As part of the evaluation, in vivo tumor models derived from this panel were used to assess the antitumor efficacy of new compounds [16]. Because more than 85% of the screened compounds did not show activity, a highly sensitive three-cell-line prescreen was adopted in 1999 [7, 8]. In addition to the progress in the human xenograft models, the shift from “compound oriented” to “disease oriented” drug discovery at the NCI in 1989 also prompted a realization that there was a need to identify more target-defined ­models. Specifically designed and bred transgenic/knockout mice have proven very useful to satisfy this need. During the past 20 years, an impressive range of tools has become available to the mouse geneticist, as well as tumor biologists. Groundbreaking experiments in several laboratories established the first transgenic mouse tumor models by expressing viral [17, 18] or cellular [19, 20] oncogenes in specific tissues. Germline inactivation of the prototype tumor suppressors gene Rb [21] and p53 [22] using gene targeting technology in mouse embryonic stem cells provided additional tools to model the scope of mutations in human tumors [23]. Both xenograft and transgenic/knockout models are being increasingly utilized in the discovery of anticancer agents. However, both animal models have been criticized for failing to predict the response of human patients to new agents [24–26]. It is generally believed that requirements for successful preclinical animal-tumor models should include the following characteristics: reproduction of the biology of human cancer; objective and quantitative evaluation of cellular and molecular events associated with cancer progression; reliability, availability, and affordability. In this chapter, we compare the utility and the limitations of the xenograft models, particularly from the viewpoint of the pharmaceutical drug development, hoping to capture the advantages and disadvantages of the models for cancer drug evaluation.

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5.2 Xenograft Tumor Models for Efficacy Evaluation 5.2.1 Immunodeficient Mice The growth of human tumors in a different species (e.g. mouse) requires ­immunodeficiency in the host animal to prevent rejection of the transplanted ­foreign ­tissues. There are many strains of immunodeficient mice containing single mutations (e.g. nude, scid, beige, xid, rag-1 null, rag-2 null) or combined ­mutations, (e.g. bg/nu, bg/nu/xid, nude/scid, nod/scid) available for cancer research [6, 27]. These mice have mutations leading to different degrees of immunodeficiency in natural killer (NK) cells, lymphokine-activated killer (LAK) cells, macrophages, B cells, T cells, and blood immunoglobulin production. Table 5.1 includes an outline of some of the immunologic characteristics of the nude and severe combined immune deficiency (SCID) mice, with single mutations, in comparison to the NOD-SCID and the NIH III nu/beige/xid mice with combined mutations. Nude mice and SCID mice are the strains of immunodeficient mice most commonly used as the recipient for human tumor xenografts, with the nude mouse being more heavily utilized. Both nude mice and SCID mice are easily accessible Table 5.1  Immunodeficiencies in nude, scid, nod-scid and nude/beige/xid mice NIH III nude/ Nude Scid Nod-scid beige/xid Prkdc and nod on nu, beige on Gene defect Prkdc on Nu on chromosone chromosome chromosome chromosome 13, and xid on 18 16 11 X-chromosome Pre-B and B cells Pre-B and B cells Pre-B and B cells Defective absent absent B cells maturation/ absent precursor B cells normal Nonfunctional Nonfunctional Nonfunctional T cells Mature and functional T cells low or absent NK cells High Normal Nonfunctional Nonfunctional Macrophages Normal Normal Defective Defective LAK cells Normal Normal Absent IgG low IgG low IgG low IgG low Serum immuno­ globulin IgM normal IgM low IgM low IgM low IgA low IgA low IgA low IgA low Lymphoid Athymic Small lymphoid Small lymphoid Athymic organs organs organs Hair No Yes Yes No

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in large quantities from ­commercial sources (e.g. Charles River Laboratories, Jackson Laboratory, and Harlan Bio-products). The SCID mouse is in general more immunodeficient (reduced NK cell, nonfunctional T cell, and more defective B cell) than the nude mouse, therefore, higher take rates and more metastasis are achieved in SCID mice for many human tumor cell lines [28, 29]. Since the development of the human-SCID chimera mouse models [30], SCID mice have been broadly used in studies of anticancer immunotherapy by engrafting human tumor with various combinations of human peripheral blood leukocytes (HPBL), subsets of the HPBL, and/or intact human stromal tissue adjacent to the tumor tissue [31]. The nude mouse, due to its milder temper and hairless feature, is generally easier to handle and easier to observe/quantify the growth of transplanted tumors. The SCID mouse is more sensitive to the toxic effects of irradiation and some cytotoxic agents [32, 33]. The SCID mouse is also more expensive than the nude mouse, which may be a practical reason that it is used less frequently than the nude mouse for drug screening. Other immunodeficient mice, especially mice with combined mutations, are frequently used to explore the immunological mechanisms underlying tumor progression and compound efficacy [31, 34, 35]. Over the past ten years, NOD-SCID mice have been the “gold standard” for studies of human hematolymphoid engraftment in small animal models [36].

5.2.2 Cultured Tumor Cells Vs. Tumor Fragments Subcutaneous injection of cultured tumor cells into immunodeficient mice is widely practiced. In general, cultured tumor cells have a much higher take rate when ­inoculated as suspension into nude mice than human solid tumors of the same ­histological type that are transplanted directly from the patient [139]. A wide variety of human cancer cells can be procured from institutions such as American Type Culture Collection (ATCC, www.atcc.org), German Collection of Microorganisms and Cell Culture (DSMZ, www.dsmz.de), and European Collection of Cell Cultures (ECACC, www.ecacc.org.uk). Usually, the cryo-preserved cell lines are thawed and cultured in medium (e.g. DMEM or RPMI) supplemented with heat-inactivated fetal bovine serum, and expanded until the population is sufficient. Cells are harvested and implanted subcutaneously into various regions (e.g. axillary, flank, or back) of the immunodeficient mouse. Different cell lines require different cell number inoculums for optimal tumor growth. Between one and ten million cells per mouse is a suitable range for the majority of cell lines. To increase take rates, basement membrane matrix (e.g. BD Matrigel) is frequently incorporated in the inoculums. With or without the basement membrane matrix, proper in vivo growth kinetics induced by appropriate amount of inoculated cells needs to be titrated. Implanted animals are commonly monitored two to three times weekly for tumor growth using caliper measurements to determine the length (L), width (W), and height (H) of the tumor. The treatment of the implanted tumors can be started either immediately after inoculation (nonstaged model) or postponed until the tumor reaches a certain size (staged model).

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Tumor fragments have also been subpassaged in animals and used as a tool to evaluate anticancer therapeutics by the NCI [16] and others [12]. The initial solid tumors established in mice are maintained in the mouse by serial passage of 30–40 mg tumor fragments implanted subcutaneously. For each line of tumor, both range and mean values of tumor doubling time are provided to demonstrate the inherent variability of growth. Serial passage is not allowed to exceed a defined range with replacement starting from the frozen stocks around the tenth generation [16]. Xenografts derived directly from patient biopsies appear to have better retention of the morphological and molecular markers of the source tumors [7]. Issues with the use of tumor fragments include reports indicating contamination with malignant mouse cells, changes in hormone sensitivity, changes in histology patterns, and changes in response to anticancer agents following serial passage in mice [37–39]. Despite these issues, research groups using human tumor xenografts established in serial passage believe that such methodology has a higher correlation with clinical drug response. In addition, serial passage of tumor fragments from clinical specimens also allows for preselection of responsive tumor types for follow-up studies [12].

5.2.3 Subcutaneous Vs. Orthotopic Transplantation The subcutaneous xenograft model is easy to monitor and quantify, however, it is ectopic, (i.e. out of the native place). The inhibition of the growth of a tumor implanted in the subcutaneous tissue space after administration of a cytotoxic compound may be a reliable assay for antitumor activity in  vivo, but the same assay may be inappropriate to identify agents against cellular target molecules that are only expressed when the tumor resides orthotopically in original organs [40, 41]. In order to obtain improved models over subcutaneously growing human tumors, there have been efforts [42, 43] to develop techniques of surgical orthotopic implantation (SOI) to transplant histologically intact fragments of human cancers, including tumors taken directly from the patient, to the corresponding organ of immunodeficient rodents. The SOI models include spontaneous bone metastasis models of prostate cancer, breast cancer, and lung cancer; spontaneous liver and lymph node metastatic models of colon cancer; and spontaneous brain metastasis of melanoma and prostate cancers. Comparisons of the SOI models with transgenic mouse models of cancer have indicated that the SOI models have more features of clinical metastatic cancers. Xenograft tumors seem to yield a much higher frequency of metastases when implanted orthotopically [44]. Introduction of renal cancers, as well as tumors of other origins, into the subrenal compartment of immunodeficient mice seems to increase engraftment take rates and metastasis rates. After serial transplantations under renal capsule, xenografted tumors maintain genetic stability and responses to cytotoxic chemotherapy [45]. It is hoped that above models will increasingly be used in preclinical evaluation of potential therapeutics.

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One factor in selecting the orthotopic location of the transplanted xenograft is the hormonal dependency of the tumor. Many breast cancer (e.g. MCF-7, ZR-75-1, Br-10) and prostate cancer models (e.g. LNCaP and CWR-22) are dependent on estrogen or testosterone and they require supplementation with sex hormones or intact sexual organs to grow in the mouse [46, 47]. Usually hormone supplements are given by implanting time-release pellets of hormone (commercially available, e.g. Innovative Research of America, Sarasota FL) via trocar needle into the subcutaneous tissue of mice. The hormones in these pellets are designed to be constantly released and can last a defined period of time, from days to weeks. It is believed that it is better to have human breast cancers grow in ovariectomized female mice supplemented with extra estrogen due to the influence of fertility cycles on the primary tumor growth rate and metastasis rate [48]. One should note, however, that the extra estrogen can cause toxicity or even death to the mouse, so caution and especially close monitoring of the treated mouse (e.g. by physical signs or radioimmunoassays) needs to be exerted. Combinations of castration/ovariectomy and supplementation of testosterone/estrogen in various sequences have been utilized to mimic hormonal blocking therapies, hormonal supplementation therapies, and different drug and hormone resistant conditions [49–51].

5.2.4 Tumor Metastasis It is widely acknowledged that tumors grown subcutaneously are less likely to metastasize than those grown in the anatomically relevant or orthotopic sites, as mentioned earlier. Various studies have shown that transfection with human angiogenic cytokines and subcutaneous implantation of xenogeneic tumor cells with Matrigel and/or helper host cells such as fibroblasts can increase tumor growth and/ or malignant potential [52–54]. The simple “experimental metastasis” assays are intended to mimic late stage of metastasis (dissemination, extravasation, and colonization). The most commonly used method is the injection of cells into the tail vein of mice. In these cases, the resultant tumor colonies are most commonly confined to the lung – which harbors the first capillary bed encountered – due to mechanical trapping. Tumor cells growing in culture frequently have a good plating efficiency and are generally easy to clone; cloning efficiency is thought to be related to the ability to metastasize [55]. The intravenous inoculation of cultured cell suspensions has been widely used in the hope of obtaining pulmonary metastasis. Curiously, though, this rarely happens, and the majority of cell lines do not give rise to pulmonary metastases even following intravenous injection of several million cells. Only a limited number of cell lines produce metastases, and most of these do so with low frequency. It should be noted that there is no direct correlation between lung colonization and spontaneous metastasis, and in some tumor models there are significant discrepancies between these two functions [54, 56]. It is perhaps less surprising that tumor cells derived from cells that are naturally migratory, such as leukemias, lymphomas, and ­plasmacytomas,

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more readily metastasize and form colonies in multiple sites, including bone ­marrow, spleen, and liver. Other methods have been used to recapitulate the process of metastasis. Cells can be introduced into the portal circulation for liver colonies or the left ventricle of the heart for bone colonies. Inoculation of cultured cells into the left ventricle has proven to be a useful route for determining the specific organ tropism of the inoculated cells [57]. The spleen provides an alternative site for the injection of tumor cells than the portal or mesenteric vein. Injected cells pass almost immediately into the portal circulation. Inoculation of human tumor cell suspension into the mouse spleen can produce liver metastases using tumors (e.g. colorectal carcinoma) selected for liver implantation. A few metastases generated from the initial splenic implantation are isolated and cultured for further rounds of splenic inoculation/selection/cultivation. Cell lines generated this way will metastasize exclusively to liver and cause significant amount of micro-metastases [58]. Tumor cells can be injected directly into the liver parenchyma for circumstances where a small number of colonies are required [59]. Tumor cells have also been injected directly into the pleura, peritoneal cavity, bone marrow, and brain, but there is a risk of morbidity and mortality, and quantization of tumor burden is difficult [54, 60].

5.2.5 Monitoring Tumor Progression and Determining Efficacy The growth of the primary tumor is routinely quantified by in situ caliper ­measurements of the three perpendicular dimensions. Various formulas are used to determine the volume of the tumor, with L (length, longest diameter, in mm) × W (width, in mm) × H (height, in mm) × p/6 or (L)2 × W × p/6 the most commonly used formulas, both of which assume that tumors are ellipsoid, and both of which correlate well with the tumor weight measured upon necropsy [61]. Weight of the tumor can also be converted from measurements (mm) of two perpendicular dimensions, L and W, using the formula, L × (W)2 × 1/2, for a prolate ellipsoid that assumes a specific gravity of 1.0 g/cm3 [16, 62]. Parameters commonly used to quantify the effects of the test agents include the following: • • • • •

Dose levels Relative growth: % T (treated)/C (control) [1] Growth inhibition: 1 – % T/C [63] Growth delay: % (T – C)/C [63] Net log cell kill: [(T – C) – treatment duration] × 0.301/median doubling time [64] • Rate of partial or complete regression (PR or CR) [65] • Percent tumor free animals [65] • Increase in life span [65].

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Many researchers use computers in connection with electronic calipers, ­ eighing balances, and telemetry devices to facilitate and automate the operations. w There are various computer programs designed for in vivo tumor biology applications available commercially, e.g. LABCAT (www.LABCAT.com) and STUDYLOG (www.studylog.com). It is simple to evaluate efficacy by measuring subcutaneous tumors with calipers, thus accounting for their popularity. However, there are factors that, e.g. tumors of irregular shape, inconsistent measurement of tumor axes, and differences in tumor measurement between investigators, influence the validity of caliper measurements. Therefore, automated methodologies such as laser scanning are being developed [66] to reduce inter-operator variability and improve reproducibility. The therapeutic response in xenograft models is typically based on reduced tumor growth as opposed to tumor regression required in humans. Thus, the predictive value could be improved in mouse models by adopting tumor regressions as the efficacy end point. On the other hand, chronic slowness of tumor growth rate can be considered a relevant outcome as compared with tumor regression if our goal is to delay tumor progression [7]. To overcome the possibilities of leaving useful data due to setting arbitrary cutoff of T/C ratio, tumor biologists have been proposing the use of tools such as adjusted area under the curve ratio to quantify tumor inhibition [67]. Histology has traditionally been used to study tumor progression and both qualitative and quantitative evaluation of the changes of various tumor markers. Immunological techniques such as ELISA, immunoblotting, immunohistochemical staining, and nucleic acid based techniques such as Southern and Northern blottings, and quantitative PCR all have been utilized widely to study the changes of biological markers in the tumor samples. These techniques have been mostly used as end-point assays. It is uncommon to have biopsies done on tumors of preclinical studies. There have been efforts to overcome such limitations by transfecting tumor cell lines that are used to generate grafted tumors with a tumor marker gene (e.g. prostate specific antigen), and then monitoring the growth of the tumor in vivo by quantifying the tumor marker in the serum of the mouse host [31, 68]. However, despite such efforts, more sensitive technology such as molecular profiling of ­circulating tumor cells [9] for the study of tumor progression and metastasis would be desired. Recent work has established the feasibility of analyzing tumor growth and growth inhibition in live animals. Table 5.2 includes examples of in vivo techniques that have been utilized to monitor tumor progression, as well as efficacy by therapeutics, in xenograft models. In vivo imaging of cells tagged with light-emitting probes, such as green fluorescent proteins (GFP) [69] or firefly luciferase [70, 71] has been shown to be a powerful technology that enables imaging of single tumor cells and metastases and a wide range of other biological characterizations in tumor-bearing mice. GFP- or luciferase expressing tumors (e.g. colon, prostate, brain, liver, lung, bone, and others) were visualized externally through quantitative trans-cutaneous whole-body imaging. Since these technologies can be conducted in a real-time fashion on live animals, they provide particularly useful tools for the

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Table 5.2  In-vivo techniques used to monitor tumor progression/efficacy in xenograft models Subcutaneous Orthotopic tumor growth tumor growth Metastasis Angiogenesis Caliper measurement + − − − + + + + Optical imaging: bioluminescence and fluorescence MRI, Micro CT/PET/ + + + − SPECT Ultrasound + ± ± − Subcutaneous window + − − + chamber

study of the process of tumor progression. For example, direct observation of the metastatic process can be performed using in  vivo video microscopy, and early steps in the metastatic process related to cell survival or extravasation have been successfully imaged with this technique [72, 73]. Various transparent window preparations offer useful methods to allow noninvasive, continuous measurement of tumor growth, angiogenesis, blood flow, and expression of tagged genes in living tissue [74]. In addition, tumor tissue oxygen content can be measured using a polarographic needle microelectrode (pO2-Histograph, Eppendorf, Inc.) to document responses to various anticancer agents (e.g. cyclophosphamide, cisplatin) in combination with agents that aim at reversing hypoxia in tumor tissue [75]. Initial efforts are also being made to study tumor responses, such as angiogenesis, extracellular volume, and microvascular permeability, to anticancer agents with magnetic resonance imaging (MRI) [76, 77], positron emission tomography (PET) [78], ultrasound (US) [79], computed tomography (CT) [80], and single photon emission computed tomography (SPECT) [81] techniques. The area of in vivo tumor imaging technology, which offers exciting potential for studies of cancer physiology and treatment regimens, continues to develop quite rapidly.

5.2.6 Examples of Single Agent and Combination Preclinical Trials Human tumor xenograft assays were successfully exploited at the NCI to facilitate the discovery of more than a dozen clinically useful cytotoxic anticancer drugs [16]. Current anticancer drugs that were evaluated in these systems include melphalan, cyclophosphamide, dacarbazine, BCNU, mitomycin C, cisplatin, actinomycin, doxorubicin, bleomycin, methotrexate, 5-fluorouracil, vinblastine, and paclitaxel. These drugs were evaluated in a panel of tumor lines that belong to different organ/disease types (colon, CNS, leukemia, nonsmall cell lung, small cell lung, melanoma, and ovary). Drugs were administered intraperitoneally, over different schedules such as:

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qd × 4 (once daily for consecutive 4 days) qd × 5 (once daily for consecutive 5 days) q4d × 3 (once every 4 days for three times) q7d × 3 (once every 7 days for three times)

At each dose or schedule level, each drug was ranked by five efficacy levels: • 0 = inactive, % T/C (change in tumor weight, each treated [T] or control [C] group of mice) > 40 • 1 = tumor inhibition, % T/C range 1–40 • 2 = tumor stasis, % T/C range 0 to −49 • 3 = tumor regression, % T/C range −50 to −100 • 4 = % T/C range −50 to −100 with > 30% tumor-free mice To screen and prioritize compounds for testing in the xenograft models, so-called hollow fiber assays were used, in which tumor cells are cultured in sealed hollow fibers and implanted either subcutaneously or intraperitoneally in the nude mouse. After drug treatment for 6–8 days, cell survival is quantified by MTT dye conversion measurements [82]. The in vivo drug sensitivity profiles of these human tumor xenografts have served as worldwide benchmarks for the testing of new agents. More recently, xenograft tumor models have been used to evaluate molecularly targeted therapies. Efforts devoted to such “target-oriented” drug discovery have produced fruitful results [83–86, 87]. Table 5.3 lists examples of therapeutics and xenograft models representing such achievements. One successful example of this effort has focused on the epidermal growth factor receptor tyrosine kinase (EGFR-TK). A variety of human tumor xenografts, including prostate (TSU-PRI, PC-3. DU-145, CWR-22), ovarian (OVCAR-3), breast (MCF-7, ZR-75-1), colon (GEO), vulva (A431), and lung carcinoma (A549, SK-LC-16, LX-1), grown in nude mice, were used to evaluate the EGFR-TK inhibitor (EGFR-TKI) termed gefitinib, also known as Iressa [130–132]. The EGFR signaling pathway contributes to a number of processes important to tumor progression, including cell proliferation and apoptosis. EGFR is highly expressed in many tumors and is associated with poor disease prognosis. Oral administration of gefitinib produced dose-dependent reversible growth inhibition in a wide range of tumor xenograft models. Gefitinib has also shown growth inhibitory activity against xenografts initiated from ductal carcinoma in situ tissues, indicating that EGFR inhibition may have a role in the treatment of early stage breast cancer [133]. In addition to small molecular chemicals, biologics such as monoclonal antibodies have also been shown widely to be efficacious in xenograft models. Anti-EGFR antibody cetuximab was validated to be efficacious in NSCLC models expressing both wide type and mutated EGFR [134]. Human tumor xenograft models have also been used to evaluate the ­combi­natorial efficacy of molecular therapeutics in combination with cytotoxic drugs [135, 136] or gene therapeutic agents, such as a p53 recombinant adenovirus [140]. Enhanced in  vivo efficacy was observed when farnesyltransferase

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Table 5.3  Examples of cancer therapeutics and xenograft responses Target Therapeutics mechanism Clinical indications Gefitinib EGFR Nonsmall cell (Iressa®) lung cancer Erlotinib EGFR Nonsmall cell lung (Tarceva®) cancer Pancreatic cancer Cetuximab EGFR Colorectal cancer (Erbitux®) Squamous cell carcinoma Sorafenib Renal cell Raf/Mek/Erk (Nexavar®) carcinoma pathway, PDGF, Hepatocellular VEGFRs carcinoma Sunitinib Advanced renal PDGFRs, (Sutent®) cell ca. VEGFRs, KIT, RET, Gastrointestinal flt3 stromal tumor Trastuzumab HER2/neu Metastatic breast (Herceptin®) cancer Lapatinib (Tykerb®)

EGFR, HER2/neu

Temozolomide (Temodar®)

Nucleotide methylation

Imatinib (Gleevec®)

Bcr-abl, c-Kit

Bevacizumab (Avastin®)

VEGF

Advanced or metastatic breast cancer Newly diagnosed glioblastoma multiforme Refractory anaplastic astrocytoma Philadelphia positive chronic myeloid leukemia, acute lymphoblastic leukemia Kit+ gastrointestinal stromal tumor Metastatic colorectal cancer Nonsquamous nonsmall cell lung cancer Metastatic breast cancer Glioblastoma Metastatic renal cell carcinoma

models that recapitulate clinical

Xenograft models A549, NCI-H1155, NCI-H23, NCI-H441 [89, 90] A549, NCI-H1299, NCI-H1975 [91, 92] BxPC-3 [93] GEO, HT-29, HCT-8, SW620, HCT-116 [94, 95] A431, UMSCC-1, SCC1, CAL27, FaDu [96–98] 786-O [99] PLC/PRF/5, LCI-D20 [100, 101] 786-O, KCI-18 [102] GIST-T1 [103] BT-474, MDA-MB-231-BR, JIMT-1, Her2(±)-MCF7 [104–107] MDA-MB-231-BR, MDA-MB-231-4173, MTLn3 [105, 108] U-87 MG, D-54 MG, D-245 MG, GBM6, GBM14 [109–111] U373 MG

KBM5, KBM7, K562 [112, 113]

GIST882 [114] GEO, LS174T, WiDr, HCT116, KM12SM [115–117] PC14PE6 [118]

KPL-4, MDA-MB-231 [119, 120] Hs683 [121] Caki-1 [122] (continued)

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Table 5.3  (continued) Therapeutics Docetaxel (Taxotere®)

Target mechanism Microtubules

Clinical indications

Xenograft models

Breast cancer

R-27, MX-1, SK-BR-3 [123, 124] A549, NCI-H460 [125]

Nonsmall cell lung cancer Prostate cancer Gastric adenocarcinoma Head and neck cancer

22Rv1, LNCaP, DU-145 [126] MKN-28, MKN-45, KKLS [127] UMSCC2, HNC-3, KB3-1 [128, 129]

inhibitor ­lonafarnib was combined with paclitaxel, cyclophosphamide, 5-FU, or vincristine in the human nonsmall cell lung cancer NCI-H460 xenograft model. Significantly greater combined efficacy for lonafarnib and the p53 adenovirus was also observed, compared to either agent alone, in both the intraperitoneal and subcutaneous DU-145 human prostate xenograft models. The EGFR-TKI gefitinib in combination with a range of cytotoxic agents has also shown promising activities in several human tumor xenografts. Gefitinib combination therapy was associated with a significant inhibition of tumor growth and a significant increase in survival of nude mice in the GEO colon model, especially when gefitinib was combined with paclitaxel [137]. Gefitinib in combination with cytotoxic agents (carboplatin, paclitaxel, or edatrexate) enhanced antitumor activity and in some cases produced tumor regression in nude mice bearing prostate tumor xenografts TSU-PrI and PC-3, [138]. It is also established that antibodies targeting the epidermal growth factor receptor, Cetuximab, and vascular endothelial growth factor receptor-2, DC101, are synergistic with regards to antitumor effects, in a BxPc3 subcuanteous xenograft model for pancreatic cancer [141]. Combination treatments with cytotoxics also increased the efficacy of cetuximab [134]. In addition, an enhancement of efficacy was shown in a head and neck cancer orthotopic model CAL33 with combined effects of anti-VEGF antibody bevacizumab, anti-EGFR inhibitor erlotinib, and irradiation [142]. These examples illustrate that, in addition to offering an efficacy screening tool, xenograft tumor models can also be used to analyze the action of molecular targeted therapeutics. Furthermore, xenograft models can be used not only to evaluate anticancer agents but also to facilitate target validation and proof of principle by exploring how manipulating the presumptive therapeutic target(s) in tumor cells alters their biological response to such agents. In conjunction with a variety of technologies (e.g. siRNA, functional blocking antibodies, dominant negative approaches), xenograft efficacy studies can help to identify responsive tumor subtypes, to stratify patient selection, and to guide clinical path [83]. The examination of human breast tumor lines in this way, contributed to the identification of trastuzumab, which is used clinically to treat breast cancers that overexpress the ErbB2 target protein [143].

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5.3 Pros and Cons No model is ideal. The most useful models would be those that reflect the natural history and histopathology of human disease, provide more accurate understanding of cellular and molecular mechanisms, and allow for discovery and development of therapeutics that are clinically effective. In the cancer arena, while not all chemotherapeutic agents that test positively in mouse models are efficacious in humans, agents that are efficacious in humans are generally effective in mice [25]. Both xenograft and transgenic models have strengths and limitations in this regard. A comparison of the general advantages and disadvantages of each type of model is listed in Table 5.4. However, we have to always bear in mind that both are models in the mouse. Mice tend to be more resilient to many xenobiotics and supranormal concentrations of protein molecules than are patients [139]. It is critical for a new drug to define the selectivity profile against human tumors of different organs. In this respect, human tumor xenografts are considered by many as the most relevant models [12]. In addition, xenograft models have been characterized and calibrated by many tumor biologists, and they have been widely used in large-scale screening for many years. Due to their extensive use, both the mouse hosts and the tumor cell lines are generally in the public domain and can be easily

Table 5.4  A comparison of xenograft (subcutaneous and orthotopic) and genetically engineered models Subcutaneous xenograft Orthotopic xenograft Transgenic/knockout Easy to set up Need surgical expertise Need TG/KO expertise Relatively inexpensive More expensive More expensive Labor economic Labor intensive Labor intensive Time economic Longer time needed Longest, months to years needed Widely used and better Database being Database being accumulated understood accumulated Easy to monitor tumor Not as easy to monitor tumor Not as easy to monitor tumor burden and progression burden and progression burden and progression Cannot study immune Cannot study immune Immune responses can be responses responses studied Gene expression is not organ Organ-specific gene Organ-specific gene expression specific expression available Relevant tumor cell and host Relevant tumor cell and host Least relevant tumor cell and host organ interactions organ interactions organ interactions Lack of natural metastasis Metastasis can be studied Metastasis can be studied Not as good for target Not as good for target Good for target validation and validation validation proof of principle Not as good for study of early Not as good for study Early tumorigenesis events can events of early events be studied Good for prophylactic therapy Not as good for Not as good for and chemoprevention prophylactic therapy prophylactic therapy and chemoprevention and chemoprevention

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obtained. Especially for the subcutaneous models, the methods are relatively easy to set up, less labor-intensive, less costly, and highly reproducible. Therefore, it is relatively straightforward to screen through a large panel of xenograft models representing various cancers of different organ types to evaluate tissue sensitivity of a particular series of anticancer drugs. Another advantage of xenografts is reflected in the naturally occurring mutational changes that take place during the process of carcinogenesis. Each cell line likely carries multiple mutations, and each set of mutations differ among cell lines, mirroring the clinical situation. The efficacy results may be more predictive if the anticancer candidates are tested against a panel of tumors within a given histology. Xenograft systems are not suitable for testing agents that work through immunebased or species-specific mechanisms that involve host cell interactions. Although the implanted cancer cells are of human origin, their growth depends on the mouse host (e.g. blood and nutrient supply, infiltration of extra-cellular matrix, interactions with host hormones and growth factors). Lack of spontaneous metastasis is another major drawback of xenograft models, although orthotopic implants can address this issue to some extent. However, while orthotopic procedures result in an environment that is more natural for the tumor, increasing the likelihood of metastatic progression, orthotopic models are far more labor intensive and require more surgical expertise. This feature makes it impractical to perform orthotopic assays for large-scale screening. Incorporation of green fluorescence protein or luciferase into xenografts make these models more powerful, by offering simple methods to monitor even very small and systemically distributed tumors by bioluminescence imaging techniques. It should be noted that, after the establishment of the new tumor cell line with the indicator gene, a thorough characterization needs to be conducted to make sure the new cell line still has the same phenotype as the original line. Although orthotopic xenograft models may be more representative than subcutaneous models, both share an intrinsic problem. The tumors usually are derived from cell lines that have been selected for in vitro and in vivo growth. They grow rapidly, so they can be used in higher throughput testing in a timely fashion; however, this rapid growth certainly does not reflect the progression of clinical cancers which are typically relatively slow growing. The interactions between xenograft tumor cells and vasculature or stromal microenvironment also are not well defined, and may not reflect the interactions that occur during cancer development in human patients.

5.4 Pharmacology and Pharmacokinetic Correlations Cancer drug discovery has entered a new era. Recent developments in molecular biology and chemistry (e.g. genomics and bioinformatics, improvements in cloning/expression technologies, structural biology, high-throughput screening, and combinatorial chemistry) have led to a significant increase of new drugable targets.

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In the year 2002, there were nearly 500 molecules undergoing clinical studies in cancer and this number could well reach 1,000 in 2003 [144]. Due to the rapid increase of candidates, new methods and criteria to prioritize the most promising candidates are needed before and during clinical trials. In addition to the traditional phase I clinical trial goals (maximal tolerated dose determination, phase II dose recommendation, safety and pharmacokinetic evaluation), current phase I studies also need to examine biomarkers and to stratify patients and clinical endpoints with these biomarkers [145]. Therefore, intense efforts are needed to correlate pharmacokinetic profiles and pharmacodynamic changes in the drug target with preclinical efficacy, such that adequate information can be gained with regard to the problems of dose determination and patient stratification. Current drug discovery programs commonly incorporate such integrated approaches at early phases of the program. Anticancer drugs traditionally have been given to patients by intravenous ­infusion; while during preclinical testing most experimental agents have been given by tail vein or intraperitoneal injections. However, there is a growing trend towards developing anticancer drugs that can be administered via additional routes of administration, in particular by oral routes. Therefore, pharmacokinetic studies of compounds delivered by various dosing routes and using various formulations (e.g. saline, 0.4% methylcellulose, 5–20% hydroxy-propyl-betacyclodextrin) should be tested before in vivo efficacy evaluations are started. Different pharmacokinetic parameters for experimental compounds, including half-life, AUC, Cmax, Cmin, and bioavailability can all be readily established in mouse models. For settings where constant delivery of a compound is desired, such as through the use of the Alzet Osmotic Pump, a pharmacokinetic profile utilizing appropriately sized pumps and compound formulations is also readily established. To determine the most suitable dose for reaching maximum in vivo efficacy, drug exposure should be compared with IC50 and IC90 concentrations required to achieve efficacy in cell-based assays. Mouse pharmacokinetic studies also provide important preliminary information which complements any subsequent studies in higher mammal species [86, 88, 146]. To correlate pharmacokinetic parameters with efficacy, and to help determine clinical dose levels/dose responses more accurately, changes in suitable molecular markers (pharmacodynamic markers) need to be monitored carefully. Taking an example, the EGFR-TKI gefitinib, a series of preclinical studies identified surrogate markers of EGFR activity, including EGFR phosphorylation and phosphorylation of the downstream molecules MAPK, AKT, and p27KIP1 [147]. In tumor xenograft models, including head and neck carcinoma, gastric adenocarcinoma, and breast adenocarcinoma, a relationship between gefitinib efficacy, EGFR level, and downstream markers (e.g. phosphorylated MAPK) was established. Preliminary analysis of serial skin biopsies from phase I patients has confirmed that gefitinib results in substantial changes in EGFR-dependent molecules such as phosphorylated MAPK and p27. However, the level of expression of EGFR in cells or tumors was not found to predict sensitivity to gefitinib [148]. Thus, additional biomarkers that can specifically indicate gefitinib sensitivity need to be further defined. As a matter of fact, one of the most important shortcomings in the quest of targeted

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therapy is the failure to identify and validate predictive markers. In order to identify predictive markers, it is necessary to understand the mechanisms of targeted agents as it is likely that the predictive markers may be specific for a disease type, biological process, and mechanism action of a specific targeted therapy [149]. A combination of multiple detection modalities to enhance the monitoring of biological marker changes have been exploring through the field [150]. The advancing developments in genomic, proteomic, molecular biology, chemistry, and imaging tools will help streamline marker identification and quantification, promoting a more rapid rate of drug testing. Collectively, murine tumor models are critical in drug development, but require a rational and hierarchical approach beginning with toxicology and pharmacology studies to identify therapeutic targets and to compare drugs using rigorous and clinically relevant outcome parameters, e.g. improved survival, better quality of life, and prevention of recurrence [151].

5.5 Future Perspectives It is estimated that the development of a new anticancer agent cost US$ 800 million to 1 billion and takes more than a decade between conception and approval [152]. However, 90% of novel anticancer drugs fail in the clinic despite evidence of antitumor efficacy in preclinical models [153]. In vivo tumor models have been frequently criticized for lack of predictive value. However, the failure of animal tumor systems to serve as predictive models for human cancer does not diminish their potential utility, given the absolute necessity of precisely defining the question that the model will be used to answer [154]. As every investigator is aware, the art of choosing an appropriate model follows upon the art of framing the most appropriate question. One should carefully select xenograft models that are relevant to the study objective, selecting models that represent the pathological or mechanistic setting which fits the goals of the study best. Well-established and/or simpler models still have their place. For example, while a bolus intravenous injection of enzymatically separated tumor cells may not be appropriate to study the process of metastasis, the tumor colonies of relatively uniform number, size, and organ location which form after injection may be invaluable to compare the access and activity of drugs or biological agents [54]. The NCI-60 is a set of human cancer cell lines derived from diverse tissues: brain, blood and bone marrow, breast, colon, kidney, lung, ovary, prostate, and skin. These cell lines have been subjected to a battery of xenograft experiments in numerous studies. Institute such as Wellcome Trust Sangers Institute has been sequencing NCI-60 for mutations in known human cancer genes and making the database available to public in their Web site (www.sanger.ac.uk). Such efforts will guide the use of xenograft models intelligently and efficiently. Table 5.5 lists what successful elements tumor models should possess. In the future, it is likely that a combination of both transgenic and xenograft models will allow investigators to reach a more complete understanding of tumor inhibition, thereby facilitating the drug discovery process. The transgenic model can be extremely

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M. Liu and D. Hicklin Table 5.5  Key requirements of successful preclinical tumor models Recaptulate the biology of human tumorigenesis Can be evaluated objectively and quantitatively Predictive of clinical response Readily available, reliable, and affordable

valuable to ask mechanism-related questions, however, its accessibility as a tool for large-scale drug discovery remains an issue. In contrast, the xenograft model is less defined in nature, but remains more accessible than the transgenic models. With increasing molecular characterization of each tumor line and the use of orthotopic methodology, the xenograft model will continue to make great contributions to target/ disease oriented cancer research, as well as remaining useful in large-scale drug screening. The marriage of mouse tumor models with rapidly evolving methods to profile genetic and epigenetic alterations in tumors, and to finely map genetic modifier loci, will continue to provide insight into the key pathways leading to tumorigenesis. The utility of both types of models continues to offer great promise for identifying relevant drug targets to treat human cancers [22]. Current anticancer drug discovery is focused on target-oriented and tumor cellspecific approaches; however, there is still an ongoing need for a better understanding of tumor biology. It is widely believed that the successful application of molecular cancer therapeutics will require accurate genetic profiling of tumors as well as the identification of novel, tractable, more promising, and more tumor-typespecific therapeutic targets. Enhanced efforts to identify and utilize pharmacodynamic markers during tumor progression and treatment would be expected to enable faster, more efficient, and more accurate studies of drug efficacy. Further advancement and availability of in  vivo imaging technologies will also be very helpful to the design, analysis, and interpretation of results generated from both transgenic and xenograft studies, especially for the detection of early events in tumor progression and metastasis. One benefit of these technologies will also be a reduction in the number of animals needed for experimental paradigms. It has been proposed that it would be fruitful for the NCI to partner with the FDA and the pharmaceutical industry to play a centralized role in identifying and establishing a standardized set of transgenic and knockout models of proven reliability and predictability to be used in preclinical cancer efficacy tests [25]. Collaborations such as The Mouse Models of Human Cancers Consortium (MMHCC) headed by NCI and the collaboration of the NIEDHS with the FDA, ILSI (International Life Science Institute), and several major pharmaceutical companies will be crucial to validate and promote such goals. Progress in these areas is already being made, and new resources including useful Web site references (http://emice.nci.nih.gov), databases for cancer models (http://cancermodels.nci.nih.gov), and cancer images (http://cancerimages. nci.nih.gov) are available to be freely shared by the cancer research community. A broadening of such centralized and concerted efforts to include an extensive panel of well-characterized human tumor xenograft models representative of different ­tissue types would be a welcome addition to the collaborations being pursued.

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136. Teicher BA. In vivo/ex vivo and in situ assays used in cancer research: a brief review. Toxicol Pathol. 2009;37:114–22. 137. Ciardiello F, Caputo R, Bianco R, Damiano V, Pomatico G, De Placido S, Bianco AR, Tortora G. Antitumor effect and potentiation of cytotoxic drugs activity in human cancer cells by ZD-1839 (Iressa), an epidermal growth factor receptor-selective tyrosine kinase inhibitor. Clin Cancer Res. 2000;6:2053–63. 138. Sirotnak FM, Zakowski MF, Miller VA, Scher HI, Kris MG. Efficacy of cytotoxic agents against human tumor xenografts is markedly enhanced by coadministration of ZD1839 (Iressa), an inhibitor of EGFR tyrosine kinase. Clin Cancer Res. 2000;6:4885–92. 139. Giovanella BC, Vardeman DM, Williams LJ, Taylor DJ, et  al. Heterotransplantation of human breast carcinomas in nude mice: correlation between nude takes, poor prognosis and overexpression of the HER-2/neu oncogene. Int J Cancer. 1991;47:66–71. 140. Nielsen LL, Gurnani M, Porter G, Trexler S, Emerson D, Tyler RD. Development of a nude mouse model of ras-mediated neoplasia using WR21 cells from a transgenic mouse salivary tumor. In Vivo. 1994;8:295–302. 141. Tonra JR, Prewett M, Corcoran E, Hicklin DJ, Zhu Z. In vivo method for establishing ­synergy between antibodies to epidermal growth factor receptor and vascular endothelial growth factor receptor-2. Methods Mol Biol. 2009;525:545–57. 142. Bozec A, Sudaka A, Fischel J-L, Brunstein M-C, Etienne-Grimaldi M-C, Milano G. Combined effects of bevacizumab with erlotinib and irradiation: a preclinical study on a head and neck cancer orthotopic model. Br J Cancer. 2008;99:93–99. 143. Shepard HM, Jin P, Slamon DJ, Pirot Z, Maneval DC. Herceptin. Handb Exp Pharmacol, Ther Antibodies. 2008;181:183–219. 144. Sikora K. Surrogate endpoints in cancer drug development. Drug Discov Today. 2002;7:951–56. 145. Wagner JA. Overview of biomarkers and surrogate endpoints in drug development. Dis Markers. 2002;18:41–46. 146. Kim H, Likhari P, Lin CC, Nomeir AA. High-performance liquid chromatographic analysis of the anti-tumor agent SCH 66336 in cynomolgus monkey plasma and evaluation of its chiral inversion in animals. J Chromatogr B Biomed Sci Appl. 1999;728:133–41. 147. Albanell J, Rojo F, Baselga J. Pharmacodynamic studies with the epidermal growth factor receptor tyrosine kinase inhibitor ZD1839. Semin Oncol. 2001;28(5 Suppl 16):56–66. 148. Wakeling AE, Guy SP, Woodburn JR, Ashton SE, Curry BJ, Barker AJ, Gibson KH. ZD1839 (Iressa): an orally active inhibitor of epidermal growth factor signaling with potential for cancer therapy. Cancer Res. 2002;62:5749–54. 149. Ellis LM, Hicklin DJ. VEGF-targeted therapy: mechanisms of anti-tumor activity. Nat Rev Cancer. 2008;8:579–91. 150. Cheung, AM, Brown, AS, Cucevic V, Roy M, Needles A, Yang V, Hicklin DJ, Kerbel RS, Foster FS. Detecting vascular changes in tumor xenografts using micro-ultrasound and micro-CT following treatment with VEGFR-2 blocking antibodies. Ultrasound Med Biol. 2007;33:1259–68. 151. Talmadge JE, Singh RK, Fidler IJ, Raz A. Murine models to evaluate novel and conventional therapeutic strategies for cancer. Am J Pathol. 2007;170:793–804. 152. Dimasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. J Health Econ. 2003;22:151–85. 153. Von Hoff DD. There are no bad anticancer agents, only bad clinical trial designs. Clin Cancer Res. 1998;4:1079–86. 154. Fidler IJ. The role of host factors and tumor heterogeneity in the testing of therapeutic agents. In: Fidler IJ, White RJ, editors. Design of models for testing cancer therapeutic agents. New York, NY: Van Nostrand Reinhold. 1982; p. 239–47.

Chapter 6

Imaging the Steps of Metastasis at the Macro and Cellular Level with Fluorescent Proteins in Real Time Robert M. Hoffman

6.1 Introduction In 2008, the Nobel Prize for chemistry was awarded for discovery and modification of green fluorescent protein (GFP) [1]. The Nobel announcement cited two uses of GFP (Nobel background), one of which was the use of GFP to track cancer cells in vivo, which was pioneered in our laboratory [2–6]. GFP was discovered in the bioluminescent jellyfish Aequorea victoria by Shimomura [7]. The GFP gene was cloned from A. victoria by Doug Prasher which enabled GFP to become the most powerful tool in cell biology [8–13]. The GFP cDNA encodes a 283-amino acid polypeptide with a molecular weight of 27 kDa [14, 15]. The monomeric GFP requires no other Aequorea proteins, substrates, or cofactors to fluoresce [16]. GFP gene gain-of-function mutants have been generated using various techniques [17–20]. For example, the GFPS65T clone has the serine-65 codon substituted with a threonine codon, which results in a single excitation peak at 490  nm [17]. Moreover, to develop higher expression in human and other mammalian cells, a humanized hGFP-S65T clone was isolated [21]. The much brighter fluorescence in the mutant clones allows for easy detection of GFP expression in transfected cells. Matz et  al. cloned six fluorescent proteins homologous to GFP. The proteins were isolated from the coral Discosoma. A red protein has been further developed for expression in mammalian cells and is now known as DsRed-2 [22]. Monomer variants of the Discosoma red protein have been developed with distinguishable colors from yellow-orange to red-orange with fruit names similar to their color, m-Cherry, m-Tomato, etc. [23]. Fluorescent proteins that can be converted from one color to another or become fluorescent upon light activation have also been developed [12, 24].

R.M. Hoffman (*) AntiCancer, Inc, Department of Surgery, University of California San Diego, 7917 Ostrow Street, San Diego, CA 92111 USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_6, © Springer Science+Business Media, LLC 2011

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6.2 The In Vivo Revolution Sparked by Fluorescent Proteins 6.2.1 Isolation of Stable High-Level Expression GFP and/or RFP Expressing Tumor Cell Lines We have isolated more than 100 GFP and red fluorescent protein (RFP) transformants of human and animal cancer cells that are stable in vitro and in vivo [2, 25–29] using the above GFP and RFP genes. The transformants are highly fluorescent in vivo in tumors formed from the cells. Using these fluorescent cancer cell lines, orthotopic transplant animal models [28–32] were developed for imaging the metastatic processes at the macro- and the single-cell level. The main techniques for producing bright stable fluorescent protein-expressing cells are described below. 6.2.1.1 Production of GFP Retrovirus The pLEIN retroviral vector (Clontech Laboratories, Inc., Palo Alto, CA) expressing GFP and the neomycin resistance gene on the same bicistronic message was used as a GFP expression vector. PT67, an NIH3T3-derived packaging cell line, expressing the 10 Al viral envelope, was purchased from Clontech Laboratories, Inc. PT67 cells were cultured in DMEM (Irvine Scientific, Santa Ana, CA) supplemented with 10% heat-inactivated fetal bovine serum (Gemini Bio-products, Calabasas, CA). For vector production, packaging cells (PT67), at 70% confluence, were incubated with a precipitated mixture of DOTAP reagent (Boehringer Mannheim, Indianapolis, IN) and saturating amounts of pLEIN plasmid for 18 h. Fresh medium was replenished at this time. The cells were examined by fluorescence microscopy 48 h after transfection. For selection, the cells were cultured in the presence of 500–2,000 mg/ml of G418 (Life Technologies, Inc., Grand Island, NY) for 7 days to select for a clone producing high amounts of a GFP retroviral vector (PT67-GFP) [33]. 6.2.1.2 Production of RFP Retroviral Vector For RFP retrovirus production, the HindIII/NotI fragment from pDsRed2 (Clontech Laboratories, Inc.), containing the full-length RFP (DsRed2) cDNA, was inserted into the HindIII/NotI site of pLNCX2 (Clontech Laboratories) that has the neomycin resistance gene to establish the pLNCX2-DsRed2 plasmid. PT67, an NIH3T3-derived packaging cell line (Clontech Laboratories) expressing the 10 Al viral envelope, was cultured in DMEM (Irvine Scientific) supplemented with 10% heat-inactivated fetal bovine serum (FBS; Gemini Bio-products). For vector production, PT67cells, at 70% confluence, were incubated with a precipitated mixture of LipofectAMINE reagent (Life Technologies, Inc., Grand Island, NY) and saturating amounts of pLNCX2DsRed2 plasmid for 18 h. Fresh medium was replenished at this time. The cells were examined by fluorescence microscopy 48 h after transfection. For selection of a clone

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producing high amounts of a RFP retroviral vector (PT67-DsRed2), the cells were cultured in the presence of 200–1,000 mg/ml of G418 (Life Technologies) which was increased stepwise in order to select for brighter cells [34]. 6.2.1.3 Production of Histone H2B-GFP Vector The histone H2B gene has no stop codon, thereby enabling the ligation of the H2B gene to the 5¢-coding region of the A. victoria EGFP gene (Clontech Laboratories). The histone H2B-GFP fusion gene was then inserted at the HindIII/ClaI site of the pLHCX (Clontech Laboratories) that contains the hygromycin resistance gene. To establish a packaging cell clone producing high amounts of a histone H2B-GFP retroviral vector, the pLHCX histone H2B-GFP plasmid was transfected in PT67cells using the same methods described above for PT67-DsRed2. The transfected cells were cultured in the presence of 200–400 mg/ml of hygromycin (Life Technologies) for 15 days to establish stable PT67H2B-GFP packaging cells [33, 34]. 6.2.1.4 GFP or RFP Gene Transduction of Cancer Cells For GFP or RFP gene transduction, 70% confluent cultures of cancer lines are used. Cancer cells were incubated with a 1:1 precipitated mixture of retroviral supernatants of PT67-GFP or PT67-RFP cells and RPMI 1640 (Mediatech, Inc.) containing 10% FBS for 72 h. Fresh medium was replenished at this time. Cells were harvested with trypsin/EDTA 72 h after transduction and subcultured at a ratio of 1:15 in selective medium, which contained 200  mg/ml of G418. The level of G418 was increased stepwise up to 800 mg/ml. GFP- or RFP-expressing cancer cells were isolated with cloning cylinders (Bel-Art Products) using trypsin/EDTA and amplified by conventional culture methods [34]. 6.2.1.5 Establishment of Dual-Color Cancer Cells For establishing dual-color cells, RFP-expressing cancer cells were incubated with a 1:1 precipitated mixture of retroviral supernatants of PT67H2B-GFP cells and culture medium. To select the double transformants, the cells were incubated with hygromycin 72 h after transfection. The level of hygromycin was increased stepwise up to 400 mg/ml. Clones of dual-color cancer cells were isolated with cloning cylinders under fluorescence microscopy. These clones were amplified by conventional culture methods. These sublines stably expressed GFP in the nucleus and RFP in the cytoplasm [34]. Nuclear GFP expression enabled visualization of nuclear dynamics, whereas simultaneous cytoplasmic RFP expression enabled visualization of nuclear cytoplasmic ratios as well as simultaneous cell and nuclear shape changes. Thus, total cellular dynamics can be visualized in the living dualcolor cells in real time. The cell cycle position of individual living cells was readily

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visualized by the nuclear–cytoplasmic ratio and nuclear morphology. Real-time induction of apoptosis was observed by nuclear size changes and progressive nuclear fragmentation. Thus, the dual-color cells are a useful tool for visualizing living-cell dynamics in vivo as well as in vitro. Drugs that could specifically perturb these processes can now be readily screened in real time in vivo [33, 35].

6.2.2 Imaging Sites of Metastasis 6.2.2.1 Patterns of Contralateral and Regional Lung Tumor Metastases Visualized by GFP Expression in Orthotopic Models After surgical orthotopic implantation (SOI) of GFP-expressing ANIP-973 human lung cancer in the left lung of nude mice, the primary tumor grew. GFP expression allowed visualization of the advancing margin of the tumor spreading throughout the ipsilateral lung. All animals explored had evidence of chest-wall invasion and local and regional spread. Metastatic contralateral tumors involved the mediastinum, contralateral pleural cavity, and the contralateral visceral pleura. While the ipsilateral tumor had a continuous and advancing margin, the contralateral tumor seems to have been formed by multiple seeding events. These observations were made possible by the stable, high-GFP fluorescence of the tumor cells [25, 26]. Contralateral hilar lymph nodes were also involved, as well as cervical lymph nodes visualized by GFP expression [25, 26]. When non-GFP-transfected ANIP was compared with GFP-transformed ANIP for metastatic capability, similar results were seen [25]. 6.2.2.2 GFP-Expressing Bone Metastases of Lung Cancer in Orthotopic Models SOI of H460-GFP human lung cancer in the left lung of nude mice was performed [28]. The implanted mice were sacrificed at 3–4 weeks at the time of significant decline in performance status. GFP fluorescence demonstrated metastases in the left lung, the contralateral lung, the chest wall, and the skeletal system. It was determined by GFP fluorescence that the vertebrae were the most involved skeletal site of metastasis. Metastasis could also be visualized in the tibia and femur marrow by GFP fluorescence [28]. 6.2.2.3 Prostate-Cancer Bone and Visceral Metastasis Visualized by GFP in Orthotopic Models A stable high GFP-expressing clone of human prostate carcinoma PC-3 was orthotopically implanted surgically in nude mice. Subsequent skeletal metastasis included the skull, rib, pelvis, femur, and tibia. All the tumors metastasized to the

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lung, pleural membrane, and kidney. Four of five tumors metastasized to the liver, and two of five tumors metastasized to the adrenal gland. In two mice, cancer cells or small colonies were seen in the brain, and in one mouse, a few cells could be seen in the spinal cord by GFP fluorescence [29]. 6.2.2.4 GFP-Expressing Melanoma Bone and Organ Metastasis Models We have characterized metastatic properties of bright, highly stable GFP-expressing B16 mouse-melanoma and LOX human melanoma [36]. The highly fluorescent malignant-melanoma cell lines allowed the visualization of skeletal and multiorgan metastases after intravenous (iv) injection of B16 cells in C57BL/6 mice and intradermal (id) injection of LOX cells in nude mice. The melanoma cell lines were transduced with the pLEIN-expression retroviral vector containing the GFP and neomycin resistance genes. Extensive bone and bone-marrow metastases of B16F0 were visualized by GFP expression when the animals were sacrificed 3 weeks after cell implantation. This was the first observation of experimental skeletal metastases of melanoma, which was made possible by GFP expression. For both cell lines, metastases were visualized in many other organs, including the brain, lung, pleural membrane, liver, kidney, adrenal gland, lymph nodes, muscle, and skin, by GFP fluorescence. 6.2.2.5 GFP-Expressing Brain Metastasis in Orthotopic Models With the use of GFP imaging, we have observed spontaneous metastasis to the brain in three orthotopic nude mouse model systems of human cancer: the PC-3 human prostate cancer cell line [29], the LOX human melanoma cell line [36], and spinal cord glioma model using the U87 human glioma cell line [37, 38]. 6.2.2.6 GFP-Expressing Experimental Multi-organ Metastases in Nude Mice CHO-K1 GFP transformants, injected via the tail vein, were visualized by GFP expression in the peritoneal wall vessels down to the single-cell level [39]. These cells formed emboli in the capillaries of the lung, liver, kidney, spleen, ovary, adrenal gland, thyroid gland, and brain. ANIP GFP cells were injected into the tail vein of nude mice, which were sacrificed at 4 and 8 weeks. In both groups, numerous micrometastatic colonies were detected in the lung by GFP expression in fresh tissue [39]. Even 8 weeks after injection, in most of the mice, colonies were not obviously further developed compared to that in mice sacrificed at 4 weeks [39]. Numerous small colonies, which ranged in size to less than 10 cells, were detected at the lung surface in both groups. After 8 weeks, metastases in the brain, the submandibular gland, the lung, the pancreas, the bilateral adrenal glands, the peritoneum, and the pulmonary hilum lymph nodes were visualized by GFP

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expression. Actively colonizing as well as dormant tumor cells were visualized in the lung [39]. Dormant micrometastasis is one of the most important steps in tumor progression [40]. In recent studies, the mechanism of dormancy was studied with regard to angiogenesis and other regulators of tumor colonization using GFP [41].

6.2.3 Whole-Body Fluorescence Optical Tumor Imaging of Tumor Growth and Metastasis We reported in 2000 for the first time, noninvasive imaging, in real time, of fluorescent tumors growing and metastasizing in live mice. The whole-body optical imaging system is external, simple, and noninvasive, and allows images to be captured from freely moving animals with internal GFP-expressing tumors. It affords unprecedented continuous visual monitoring of malignant growth and spread within intact animals. B16F0-GFP mouse melanoma cells were injected into the tail vein or portal vein of 6-week-old C57BL/6 and nude mice. Whole-body optical images showed metastatic lesions in the brain, liver, and bone of B16F0-GFP that were used for real-time, quantitative measurement of tumor growth in each of these organs. Whole-body optical images showed, in real time, growth of the GFPexpressing tumor on the colon and metastatic lesions in the liver and skeleton. Imaging was with either a trans-illuminated epifluorescence microscope or a fluorescence light box and thermoelectrically-cooled, color charge-coupled device (CCD) camera. The depth to which metastasis and micrometastasis could be imaged depended on their size and brightness. The simple, noninvasive, and highly selective imaging of growing tumors, made possible by strong GFP fluorescence, enables the detailed imaging of tumor growth and metastasis formation [3].

6.2.4 Whole-Body Imaging of RFP Pancreatic Cancer Progression A highly fluorescent, RFP-expressing pancreatic cancer model was orthotopically established in nude mice. The MIA-PaCa-2 human pancreatic cancer cell line was transduced with RFP and grown subcutaneously. Fluorescent tumor fragments were then surgically transplanted into the nude mouse pancreas. Groups treated with intraperitoneal gemcitabine or intravenous irinotecan were sequentially imaged to compare, in real time, the antimetastatic and antitumor effects of these agents compared with untreated controls. Rapid tumor growth and widespread metastases developed in untreated mice within 2 weeks, leading to a median survival of 21 days. Significant tumor growth suppression and consequent increase in survival (32.5 days, P = 0.009) were achieved with CPT-11. Gemcitabine highly improved survival (72 days, P = 0.004) by inducing transient tumor regression over the first 3 weeks. However, at this time, growth and dissemination occurred despite continued

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treatment, suggesting the development of tumor resistance. The antimetastatic efficacy of each drug was followed noninvasively in real time by imaging the RFPexpressing tumor and metastases, and was confirmed by fluorescent open imaging of autopsy specimens. This highly metastatic model reliably simulates the aggressive course of human pancreatic cancer. Noninvasive, sequential imaging permits the quantification of tumor growth and dissemination and, thereby, real-time evaluation of therapeutic efficacy [42].

6.2.5 Whole-Body Imaging of RFP Prostate Cancer Progression A whole-body imageable spontaneous metastatic model of human prostate cancer was developed by SOI and visualized by RFP expression. Human prostate cancer PC-3 cells were transduced with the pLNCX2-DsRed-2-RFP retroviral vector containing the RFP and neomycin resistance genes. A stable RFP-expressing PC-3 clone was injected subcutaneously in nude mice. Stable high-level expression of RFP was maintained in the subcutaneously-growing tumors. To utilize RFP expression for metastasis studies, fragments of the subcutaneously-growing tumor, which were comprised of RFP-expressing cells, were implanted by SOI in the prostate of nude mice. Primary tumor growth, progression, and subsequent lymphatic metastases were visualized in live, intact animals in real time by wholebody RFP fluorescence imaging. In total, 100% of the experimental animals developed lymphatic metastasis, the growth of which was monitored in real time by whole-body imaging. The aggressive lymphatic metastasis in this model reflects one of the major metastatic routes of prostate cancer in human patients. Intravital RFP imaging visualized single cancer cells in the lung and bladder. Open RFP imaging at autopsy visualized extensive primary growth and highly disseminated lymph node metastases [43].

6.2.6 Whole-Body Imaging of GFP Colon Cancer Progression Human colon cancer HCT-116 cells were transfected with GFP and subcutaneously injected into BALB/c nude male mice. Once subcutaneous xenografts were established, they were excised and orthotopically implanted into 32 other male BALB/c nude mice using SOI. The animals were serially imaged and euthanized at 6–8 weeks post-implantation. Tissues were procured and processed for hematoxylin and eosin analysis. All 32 animals demonstrated primary tumor growth, invasion, and peritoneal spread. Liver metastases were identified in 15/32 (47%), and lung metastases were confirmed in 13/32 (41%). In total, 19/32 (59%) animals demonstrated distant metastatic colony formation. This orthotopic model of colon cancer demonstrates local invasion and distant colony formation in the process of metastases [44].

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In summary, many different types of cancer models can be used for whole-body imaging, including orthotopic, colon [3], prostate [43], pancreas [42], bone [45], brain [46], and other cancer models [4]. The relative transparency of the footpad reduces the scatter of fluorescent light emitted from the tumor, and the presence of relatively few resident blood vessels makes it an excellent tumor transplantation site for whole-body tumor angiogenesis imaging [47]. Whole-body imaging of fluorescent cells is possible in essentially any organ. Examples include Fig. 6.1, which shows whole-body imaging of GFP-labeled cancer cells in the liver and skull [3]. Figure 6.2 is an example of “molecular imaging,” with whole-body images of a mouse lymphoma arising from a transgenic Em-Myc mouse. The virulence of the tumor is determined by genes controlling apoptosis, such as p53 and BCL2. Fluorescent tumor cells can be whole-body imaged in numerous lymphatic and non-lymphatic organs [48]. Figure 6.3 demonstrates the use of RFP (DsRed2). In this case, human glioma U87 is implanted in the brain of nude mice and whole-body imaging [46] demonstrates growth of the tumor over a 5-week period. Figure 6.4 demonstrates whole-body imaging of a highly metastatic pancreatic cancer-expressing RFP compared to open imaging of the same tumor. Comparison of the two-dimensional image with the measured volume of the tumor in the opened animal results in a straight line (r = 0.89). These results demonstrate that the easily acquired whole-body images correlate with tumor volume, thereby validating the imaging method for studying tumor growth and the efficacy of antitumor drugs [42, 49].

6.3 Imaging Bacterial Targeting of Tumors We have developed an effective bacterial cancer therapy strategy by targeting viable tumor tissue using Salmonella typhimurium auxotrophs that we have generated which grow in viable and necrotic areas of tumors. However, the auxotrophy severely restricts the growth of these bacteria in normal tissue. The S. typhimurium A1-R mutant, which is auxotrophic for leu-arg and has high antitumor virulence, was developed in our laboratory. In vitro, A1-R infects tumor cells and causes nuclear destruction. A1-R was initially used to treat metastatic human prostate and breast tumors that had been orthotopically implanted in nude mice. Of the treated mice, 40% were cured completely and survived as long as non-tumor-bearing mice. A1-R administered iv to nude mice with primary osteosarcoma and lung metastasis was highly effective, especially against metastasis. A1-R was also targeted to both axillary lymph and popliteal lymph node metastases of human pancreatic cancer and fibrosarcoma, respectively, as well as lung metastasis of the fibrosarcoma in nude mice. The bacteria were delivered via a lymphatic channel to target the lymph node metastases and systemically via the tail vein to target the lung metastasis. The metastases were cured without the need of chemotherapy or any other treatment. A1-R was administered intratumorally to nude mice with an orthotopically-transplanted human pancreatic tumor. The primary pancreatic cancer regressed without

Fig. 6.1  Comparison of external and internal images of bone metastasis. (a) External images of tumors in the skeletal system including the skull (small arrowheads), scapula (thick arrows), spine (fine arrows), and liver metastasis (hollow arrowheads) in a dorsal view of a live, intact nude mouse. (b–i) Series of external fluorescence images of metastatic lesions in the skull, rips, spine and tibia (b, d, f and h) compared with corresponding images of the exposed skeletal metastases (c, e, g and i) Bars 1,280 mm [49]

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Fig. 6.2  Whole-body fluorescence imaging allows the visualization of lymphoma dissemination. p53-null and Bcl2-overexpressing lymphomas are highly disseminated, infiltrating liver, kidneys, lungs (marked), and brain, whereas the control (ctrl.) lymphoma is restricted to the lymphoid compartment [49]

additional chemotherapy or any other treatment. A1-R was also effective against pancreatic cancer liver metastasis when administered intrasplenically to nude mice. The approach described here, where bacterial monotherapy effectively treats primary and metastatic tumors, is a significant improvement over previous bacterial tumor therapy strategies that require combination with toxic chemotherapy. Three promoter clones engineered in S. enterica typhimurium were identified to have enhanced expression in bacteria growing in tumors relative to those growing

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Fig. 6.3  Whole-body imaging of a brain tumor. Real-time whole-body imaging of a U87-GFP human glioma growing in the brain of a nude mouse at (a) 1 week, (b) 3 weeks, and (c) 5 weeks after surgical orthotopic implantation [49]

in the spleen. The expression of therapeutics in Salmonella under the regulation of one or more promoters that are activated preferentially in tumors has the potential to improve the efficacy of Salmonella tumor therapy. Exploitation of the tumorkilling capability of Salmonella has great promise for a new paradigm of cancer therapy [50].

6.4 Advantages of GFP Imaging The GFP approach has several important advantages over other optical approaches to imaging tumor growth in vivo. In comparison with the luciferase reporter, GFP has a much stronger signal, and therefore can be used to image unrestrained animals; irradiation with non-damaging blue light is the only step needed. Images can be captured with fairly simple apparatus and there is no need for total darkness. The fluorescence intensity of GFP is strong [13, 17–19] and the protein sequence of GFP has also been “humanized,” which enables it to be highly expressed in mammalian cells [21]. Importantly, unlike luciferase, fluorescent proteins come in a multitude of colors [23], allowing for multiple events to be imaged. In addition, GFP fluorescence is relatively unaffected by the external environment, as the chromaphore is protected by the three-dimensional structure of the protein [16]. A triple fusion reporter vector harboring a Renilla luciferase reporter gene, a reporter gene encoding a monomeric RFP, and a mutant herpes simplex virus type thymidine kinase was tested in vivo. A highly sensitive cooled CCD camera that is compatible with both luciferase and fluorescence imaging compared these two signals from the fused reporter gene expressed with a lentivirus vector in 293T cells implanted in nude mice. The signal from RFP was found to be approximately 1,000 times stronger than that from luciferase [51]. The weak signal from luciferase necessitates photon counting, with the construction of a pseudo-image in  vivo rather than true imaging, therefore greatly reducing resolution and precluding the

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Fig. 6.4  Correlation of whole-body and open images. (a) High-level expression of red fluorescent protein (RFP) in MIA-PaCa-2 cells in vitro. (b) External and (c) open images of a single, representative control mouse at autopsy on day 17 after surgical orthotopic implantation. A strong correlation can be observed between the fluorescence visualized externally and that obtained after laparotomy. (d) The liver, (e) spleen, and other solid organs were removed at autopsy and examined for evidence of metastatic disease. (f) Hematoxylin and eosin staining. (g) Intra-abdominal ascites. (h) Colonies of MIA-PaCa-2-RFP clones were easily retrieved and cultured from aspirated ascites fluid [49]

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Table 6.1  Comparison of optical imaging methods for tumor growth and metastasis [49] Parameter GFP/RFP Luciferase References Strength of signal 6.6 × 109 pixels/s/cm2/ 7.4 × 106 pixels/s/cm2/ [51] steradian steradian Minimum number of cells 1 300 [2, 83] imageable in vitro Minimum number of cells 1 3,000 [2, 53, 64,84] imageable in vivo Need for substrate No Yes [2, 53, 84] Need for anesthesia No Yes [53, 84] Method of visualization Direct imaging Photon counting (pseudo- [3, 84] image) Multi-color imaging Yes No [53] Stability of signal Yes No [4, 52] Need for excitation light Yes No [4, 84]

in vivo cellular imaging that is an important feature of GFP imaging. In addition, the rapid clearance of the injected luciferase results in an unstable signal that makes comparison of data difficult [52]. The stronger signals from fluorescent proteins allow much more cost-efficient instrumentation. To overcome limits on fluorescent protein imaging imposed by the skin, reversible skin-flap window models have been developed that allow single-cell imaging on most organs of the mouse [53]. The main advantage of luciferase-based imaging is that no excitation light is required. See Table 6.1 for a comparison of GFP and luciferase imaging [49].

6.5 Viral Labeling of Tumors with GFP in Live Animals 6.5.1 Selective In Vivo Tumor Delivery of the Retroviral Green Fluorescent Protein Gene to Report Future Occurrence of Metastasis The GFP gene was administered to intraperitoneally-growing human stomach cancer in nude mice in order to visualize future regional and distant metastases. GFP retroviral supernatants were ip-injected from day 4 to day 10 following ip implantation of the cancer cells. Tumor and metastasis fluorescence was visualized every other week using fluorescence optics via a laparotomy on the tumorbearing animals. No normal tissues were found to be transduced by the GFP retrovirus. Within 2 weeks after retroviral GFP delivery, GFP-expressing tumor cells were observed in gonadal fat, greater omentum, and intestine, indicating that these primary intraperitoneally-growing tumors were efficiently transduced by the GFP gene and could be visualized by its expression. At the second and third laparotomies at 4 and 6 weeks, respectively, GFP-expressing tumor cells were

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found spreading to lymph nodes in the mesentery. At the fourth laparotomy at 8 weeks, widespread tumor growth, including liver metastasis, was observed. Thus, reporter-gene transduction of the primary tumor enabled detection of its subsequent metastasis [54].

6.5.2 Telomerase-Dependent Adenovirus to Label Tumors In Vivo for Surgical Navigation Cancer surgery requires the complete and precise identification of malignant tissue margins, including the smallest disseminated lesions. Internal GFP fluorescence can intensely illuminate even single cells but requires GFP sequence transcription within the cell. Introducing and selectively activating the GFP gene in malignant tissue in vivo are made possible by the development of OBP-401, a telomerase-dependent, replication-competent adenovirus expressing GFP. This potentially powerful adjunct to surgical navigation was demonstrated in two nude mouse models that represent difficult surgical challenges – the resection of widely-disseminated cancer. HCT-116, a model of intraperitoneal disseminated human colon cancer, was labeled by virus injection into the peritoneal cavity. A549, a model of pleural dissemination of human lung cancer, was labeled by virus administered into the pleural cavity. Only the malignant tissue fluoresced brightly in both models. In the intraperitoneal model of disseminated cancer, fluorescence-guided surgery enabled resection of all tumor nodules labeled with GFP by OBP-401. These results suggest that adenoviralGFP labeling of tumors in patients can enable fluorescence-guided surgical navigation (Fig. 6.5) [55].

Fig. 6.5  Fluorescence-guided surgical removal of peritoneal disseminated HCT-116 tumors after GFP labeling with OBP-401 in vivo. Noncolored HCT-116 human colon cancer cells were injected into the abdominal space of nude mice. Ten days later, 1 × 108 PFU OBP-401 were ip injected. (a) Disseminated nodules were efficiently labeled and noninvasively visualized by GFP expression 5 days after virus administration. (b) Under general anesthesia, laparotomy was performed to remove intra-abdominal disease under GFP-guided navigation. (c) After removal of Disseminated nodules visualized by GFP-guided navigation. (Scale bars: a–c, 10 mm) [55]

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6.6 Color-Coded Imaging of the Tumor Microenvironment 6.6.1 Color-Coded Imaging of the Tumor–Host Interaction Using Colored Host Mice Fluorescent proteins have the properties of being very bright with high quantum yield and are available in many colors. Tumor–host models can be made with transgenic nude mice expressing GFP or RFP or cyan fluorescent protein (CFP) in almost all cells and tissues (Fig. 6.6). Colored transgenic nude mice are particularly useful, as they can accept human tumors cells. For example, when tumor cells expressing RFP are implanted in mice expressing GFP, various types of tumor–host interactions can be observed, including those involving host blood vessels, lymphocytes, tumor-associated fibroblasts, macrophages, dendritic cells, and others. The “color-coded” tumor–host models enable imaging and, therefore, a deeper understanding of the host cells involved and their function in tumor progression [56]. 6.6.1.1 Transgenic GFP Nude Mouse We have developed a transgenic GFP nude mouse with ubiquitous GFP expression. The GFP nude mouse was obtained by crossing nontransgenic nude mice with the transgenic C57/B6 mouse in which the beta-actin promoter drives GFP expression in essentially all tissues. In crosses between nu/nu GFP male mice and nu/+ GFP female mice, the embryos fluoresced green. Approximately 50% of the offspring of these mice were GFP nude mice. Newborn mice and adult mice fluoresced very bright green and could be detected with a simple blue-light-emitting diode

Fig. 6.6  Interactions of host stromal GFP-expressing fibroblast cell (short arrow) and Dunning RFPexpressing rodent prostate cancer cells (long arrows)in live tumor tissue. Scale bar, 20 mm [56]

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flashlight with a central peak of 470 nm and a bypass emission filter. In the adult mice, all the organs, including the heart, lungs, spleen, pancreas, esophagus, stomach, and duodenum, brightly expressed GFP. The following systems were dissected out and shown to have brilliant GFP fluorescence: the entire digestive system from tongue to anus; the male and female reproductive systems; brain and spinal cord; and the circulatory system, including the heart and major arteries and veins. The skinned skeleton highly expressed GFP. Pancreatic islets showed GFP fluorescence. The spleen cells were also GFP positive. RFP-expressing human cancer cell lines, including PC-3-RFP prostate cancer, HCT-116-RFP colon cancer, MDA-MB-435-RFP breast cancer, and HT1080-RFP fibrosarcoma, were transplanted to the transgenic GFP nude mice. All of these human tumors grew extensively in the transgenic GFP nude mouse. Dual-color fluorescence imaging enabled visualization of human tumor–host interaction by whole-body imaging and at the cellular level in fresh and frozen tissues (Fig. 6.7) [57]. 6.6.1.2 Transgenic RFP Nude Mouse The RFP nude mouse was obtained by crossing nontransgenic nude mice with the transgenic C57/B6 mouse in which the beta-actin promoter drives RFP (DsRed2) expression in essentially all tissues. In crosses between nu/nu RFP male mice and nu/+ RFP female mice, the embryos fluoresced red. Approximately 50% of the offspring of these mice were RFP nude mice. In the RFP nude mouse, all the organs, including the heart, lungs, spleen, pancreas, esophagus, stomach, duodenum,

Fig. 6.7  GFP expression in the tissues and cells of the transgenic GFP nude mouse. (a) Mice fluoresce brilliant, bright green under blue light excitation. The fluorescence could be detected with a simple blue light-emitting diode flashlight with a central peak of 470 nm and a bypass emission filter. (b) The dissected circulatory system, including the heart and major arteries and veins, had brilliant green fluorescence on blue light excitation. (c) The entire skeleton was dissected and could be seen to fluoresce brilliant green on blue light excitation. (d) Spleen cells also could be seen to fluoresce brilliant green on blue light excitation [57]

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the male and female reproductive systems, brain and spinal cord, and the circulatory system, including the heart, and major arteries and veins, brightly expressed RFP. The skinned skeleton highly expressed RFP. The bone marrow and spleen cells were also RFP positive. GFP-expressing human cancer cell lines, including HCT116-GFP colon cancer and MDA-MB-435-GFP breast cancer, were orthotopically transplanted to the transgenic RFP nude mice. These human tumors grew extensively in the transgenic RFP nude mouse. Dual-color fluorescence imaging enabled visualization of human tumor–host interaction (Fig. 6.8) [58]. 6.6.1.3 Transgenic CFP Nude Mouse The CFP nude mouse was developed by crossing nontransgenic nude mice with the transgenic CK/ECFP mouse in which the beta-actin promoter drives expression of CFP in almost all tissues. In crosses between nu/nu CFP male mice and nu/+ CFP female mice, approximately 50% of the embryos fluoresced blue. In the CFP nude mice, of all internal organs, the pancreas and reproductive organs displayed the strongest fluorescent signals, which varied in intensity. Orthotopic implantation of XPA-1 human pancreatic cancer cells expressing RFP, or GFP in the nucleus and RFP in the cytoplasm, was performed in female nude CFP mice. Color-coded fluorescence imaging of these human pancreatic cancer cells

Fig. 6.8  Transgenic RFP nude mouse. (a) Whole-body image of transgenic RFP nude mouse. (b) Digestive tract of RFP nude mice. (c) Whole skeleton of RFP nude mouse. (d) Bone marrow cells of the RFP nude mouse. (e) Splenocytes of the RFP nude mouse [58]

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implanted into the bright blue fluorescent pancreas of the CFP nude mouse afforded novel insight into the interaction of the pancreatic tumor and the normal pancreas, in particular the strong desmoplastic reaction of the tumor. The naturally enhanced blue fluorescence of the pancreas in the CFP mouse serves as an ideal background for color-coded imaging of the interaction of implanted cancer cells and the host (Fig. 6.9) [59].

Fig. 6.9  Imaging the orthotopic human pancreatic cancer CFP nude mouse model with XPA-1RFP cells. Orthotopic injection of human pancreatic cancer cells was performed in CFP nude mice; these images were obtained at week 6 post-implantation. Exposure time for CFP signals was 1 s. (a) In this series, whole-body dual-color imaging permits identification of the tumor in the whole animal. This brightly red fluorescent tumor had grown extensively into the blue pancreas. This could be seen after isolation of the GI tract, when only small slivers of blue pancreas could be seen around the red tumor. (b) The dual-colored XPA-1-GFP-RFP tumor was grown from human pancreatic cancer cells engineered to express GFP in nucleus and RFP in the cytoplasm. Wholebody tri-color imaging permitted identification of the tumor within the mouse. After isolation of the GI tract, a dual-colored tumor on a blue-fluorescent pancreas can been seen [59]

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6.6.2 Color-Coded Imaging in the Tumor Microenvironment We have developed a simple yet powerful technique for delineating the morphologic events of tumor-induced angiogenesis and other tumor-induced host processes with dual-color fluorescence. The method clearly images implanted tumors and adjacent stroma, distinguishing unambiguously the host and tumorspecific components of the malignancy. The dual-color fluorescence imaging is effected by using RFP-expressing tumors growing in GFP-expressing transgenic mice. This model shows with great clarity the details of the tumor–stroma interaction, especially tumor-induced angiogenesis and tumor-infiltrating lymphocytes. The GFP-expressing tumor vasculature, both nascent and mature, could be readily distinguished interacting with the RFP-expressing tumor cells. GFPexpressing dendritic cells were observed contacting RFP-expressing tumor cells with their dendrites. GFP-expressing macrophages were observed engulfing RFPexpressing cancer cells. GFP lymphocytes were seen surrounding cells of the RFP tumor, which eventually regressed. Dual-color fluorescence imaging visualizes the tumor–host interaction by whole-body imaging and at the cellular level in fresh tissues, dramatically expanding previous studies in fixed and stained preparations [60]. For example, when tumor cells expressing RFP are implanted into mice expressing GFP (as in Fig. 6.10), various types of tumor interactions can be observed [57, 60–62]. In fresh tissue specimens, tumor vessels expressing GFP can be visualized vascularizing tumors expressing RFP in primary and metastatic sites (Fig. 6.11). Dendritic cells expressing GFP can be seen in close contact with tumor cells with their dendritic processes (Fig. 6.12). Stromal fibroblasts expressing GFP can be seen in contact with multiple cancer cells through their pseudopodia. Lymphocytes, expressing GFP, can be observed in the process of rejecting tumor cells from growing in immunocompetent mice. Macrophages, expressing GFP, can be observed engulfing tumor cells expressing RFP (Fig. 6.13). The color-coded tumor–host models will enable imaging and, therefore, help elucidate to a much greater extent the function of the stromal host cells involved in tumor progression [56].

6.6.3 Noninvasive Color-Coded Imaging of the Tumor Microenvironment To noninvasively image cancer cell/stromal cell interaction in the tumor microenvironment and drug response at the cellular level in live animals in real time, we developed a new imageable three-color animal model. The model consists of GFP-expressing mice transplanted with dual-color cancer cells labeled with GFP in the nucleus and RFP in the cytoplasm. The Olympus IV100 Laser Scanning Microscope, with ultranarrow microscope objectives (“stick objectives”), was used for three-color wholebody imaging of the two-color cancer cells interacting with the GFP-expressing

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Fig. 6.10  Whole-body image of orthotopically-growing HCT 116 RFP+ human colon cancer in a nude GFP mouse. Image was acquired in a fluorescence light box with a CCD camera 10 weeks after orthotopic implantation of HCT 116 RFP+ cells. Original magnification, ×1 [56]

Fig. 6.11  Visualization of angiogenesis in live tumor tissue 3 weeks after sc injection of B16F10 RFP+ melanoma cells in a transgenic GFP mouse. Well-developed, host-derived, GFP-expressing blood vessels in an RFP-expressing mouse melanoma. Scale bar, 50 mm [56]

stromal cells. In this model, drug response of both cancer and stromal cells in the intact live animal was also imaged in real time. Various in vivo phenomena of tumor– host interaction and cellular dynamics were imaged, including mitotic and apoptotic tumor cells, stromal cells interacting with the tumor cells, tumor vasculature, and tumor blood flow. This new model system enables the first cellular and subcellular images of unperturbed tumors in the live intact animal (Fig. 6.14) [34].

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Fig. 6.12  Visualization of the interaction of host dendritic cells and tumor cells in fresh tumor tissue. Many host-derived GFP-expressing dendritic cells directly contacting B16F10 RFP-expressing melanoma cells with their dendrites (arrows) are visualized. Dendritic cell–lymphocyte clusters can be seen in certain regions of the image (arrowhead) 3 weeks after tumor implantation. Scale bar, 50 mm [56]

Fig. 6.13  Visualization of macrophage–tumor interactions. (a) Host GFP+ macrophage (arrowhead) contacting a PC-3 RFP+ human prostate cancer cell (arrow). (b) GFP+ macrophage (arrowhead) engulfing an RFP+ cancer cell (arrow). (c) RFP+ cancer cell (arrow) engulfed by a GFP+ macrophage (arrowhead). (d) RFP+ cancer cell (arrows) digested by a GFP+ macrophage (arrowhead). Scale bars, 20 mm [56]

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Fig. 6.14  Whole-body, noninvasive, subcellular imaging of drug response of dual-color mouse mammary cancer cells and GFP stromal cells in the live GFP nude mouse with and without doxorubicin. Dual-color MMT cells were injected in the footpad of GFP transgenic nude mice. (a) Whole-body image of untreated dual-color MMT cells in the footpad of a live GFP mouse. Note the numerous spindle-shaped dual-color MMT cells interdispersed among the GFP host cells. (b) Whole-body image of MMT dual-color cancer cells in a live GFP nude mouse 12 h after treatment with doxorubicin (10 mg/kg). The cancer cells lost their spindle shape and the nuclei appear contracted. (c) Whole-body image of dual-color MMT tumor. Numerous dual-color spindle-shaped MMT cells interacted with GFP-expressing host cells. Well-developed tumor blood vessels and real-time blood flow were visualized by whole-body imaging (arrows). (d) In vivo drug response of dual-color MMT tumor 12 h after iv injection of 10 mg/kg of doxorubicin. All of the visible MMT cells lost their spindle shape. Many of the cancer cells fragmented (arrows). Tumor blood vessels were damaged (dashed black lines) and the number of cancer cells was dramatically reduced 12 h after chemotherapy. Bar, 20 mm [34]

6.7 Imaging the Cell Biology of Metastasis In Vivo 6.7.1 Dynamic Imaging of GFP or RFP-Expressing Cancer Cells in Blood Vessels and Lymphatics Several new strategies now exist for imaging cancer cell interactions with both blood vessels and lymphatics in living animals. Signal attenuation by overlying tissue can be markedly reduced by opening a reversible skin flap in the light path, increasing detection sensitivity [63].

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6.7.2 Imaging Cancer Cell Trafficking in Blood Vessels The mechanism of cancer cell deformation and migration in narrow vessels is incompletely understood. In order to visualize the cytoplasmic and nuclear dynamics of cancer cells migrating in capillaries, RFP was expressed in the cytoplasm, and GFP, linked to histone H2B, was expressed in the nucleus of the cancer cells. Immediately after the cells were injected in the heart of nude mice, a skin flap on the abdomen was made. With a color CCD camera, we observed highly elongated cancer cells and nuclei in capillaries in the skin flap in living mice (Fig. 6.15). The migration velocities of the cancer cells in the capillaries were measured by capturing images of the dual-color fluorescent cells over time. The cells and nuclei in the capillaries elongated to fit the width of these vessels. The average length of the major axis of the cancer cells in the capillaries increased to approximately four times their normal length. The length of the nuclei in the capillaries increased 1.6 times. Cancer cells in capillaries over 8  mm in diameter could migrate up to 48.3 mm/h. The data suggest that the minimum diameter of capillaries where cancer cells are able to migrate is approximately 8 mm [64]. Dual-color cancer cells were also injected by a vascular route in an abdominal skin flap in nude mice. The mice were imaged with an Olympus OV100 small animal imaging system. We observed the nuclear and cytoplasmic behavior of cancer

Fig. 6.15  Classification of the deformation of HT-1080 dual-color cells in the vessels in the skin. (a) Nondeformed cells are within a microvessel. The cells in the microvessel are round and the nuclei oval. The cells occupy the full diameter of the vessel. (b) The cells and nuclei are elongated to fit a capillary. (c) The cells are arrested at the capillary bifurcation. Because of the difference of the deformability between cytoplasm and nucleus, only the cytoplasm is bifurcated. The nucleus is also deformed but remains intact. (d) Cytoplasmic fragmentation in very thin capillary; bar, 50 mm [64]

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Fig. 6.16  Intravascular trafficking of dual-color HT-1080 human fibrosarcoma cells labeled in the nucleus with histone H2B-GFP and in the cytoplasm with RFP. (c) Dual-color HT-1080 cell, trafficking at low velocity, advances between other cells and the vessel wall. The moving cancer cell contacts the other cells (arrow). The cell deforms slightly and continues to move without adhesion. Bar, 100 mm. Right, schematics (c). Images were taken every 1.04 s. Bar, 100 mm. Images were acquired in real time with the Olympus OV100 [16]

cells in real time in blood vessels as they moved by various means or adhered to the vessel surface in the abdominal skin flap (Fig. 6.16). Real-time dual-color imaging showed that during extravasation, cytoplasmic processes of the cancer cells exited the vessels first, with nuclei following along the cytoplasmic projections. Both cytoplasm and nuclei underwent deformation during extravasation. Different cancer cell lines seemed to vary strongly in their ability to extravasate. With the dualcolor cancer cells and the highly sensitive imaging system, the subcellular dynamics of cancer metastasis could be observed in live mice in real time [5].

6.7.3 Imaging Cancer Cell Trafficking in Lymphatic Vessels We have developed real-time imaging of cancer cell trafficking in lymphatic vessels. Cancer cells labeled with both GFP in the nucleus and RFP in the cytoplasm, or with GFP only or RFP only were injected into the inguinal lymph node of nude mice. The labeled cancer cells trafficked through lymphatic vessels where they were imaged via a skin flap in real time at the cellular level until they entered the axillary lymph node. The bright fluorescence of the cancer cells and the real-time microscopic imaging capability of the Olympus OV100 small-animal imaging system enabled visualization of the trafficking cancer cells in the lymphatics. Using this imaging strategy, two different cancer cell lines, one expressing GFP and the other expressing RFP, were simultaneously injected in the inguinal lymph node. Fluorescence imaging readily distinguished the two color-coded cell lines and their different abilities to survive in the lymphatic system. Using this imaging technology, we also investigated the role of pressure on tumor-cell shedding into lymphatic vessels. Pressure was generated by placing 25- and 250-g weights for 10 s on the bottom surface of a tumor-bearing footpad. Tumor cell fragments, single cells, and emboli shed from the footpad tumor were easily distinguished using the labeled cells and OV100 imaging system. Increasing pressure on the tumor increased the numbers of shed cells, fragments, and emboli. Pressure also deformed the shed emboli, increasing their maximum major axis [6].

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6.7.4 Determining Clonality of Metastasis Using Color-Coded Cancer Cells We used GFP-labeled and RFP-labeled HT-1080 human fibrosarcoma cells to determine clonality by simple fluorescence visualization of metastatic colonies after mixed implantation of the red and green fluorescent cells. Resulting pure red or pure green colonies were scored as clonal, whereas mixed yellow colonies were scored as nonclonal. In a spontaneous metastasis model originating from footpad injection in severe combined immunodeficient (SCID) mice, 95% of the resulting lung colonies were either pure green or pure red, indicating monoclonal origin, whereas 5% were of mixed color, indicating polyclonal origin. In an experimental lung metastasis model established by tail-vein injection in severe combined immunodeficient mice, clonality of lung metastasis was dependent on cell number. With a minimum cell number injected, almost all (96%) colonies were pure red or green and, therefore, monoclonal. When a large number of cells were injected, almost all (87%) colonies were of mixed color and, therefore, heteroclonal. We concluded that spontaneous metastasis may be clonal because they are rare events, thereby supporting the rare-cell clonal origin of metastasis hypothesis. The clonality of the experimental metastasis model depended on the number of input cells. The simple fluorescence method of determining clonality of metastases described here can allow large-scale clonal analysis in numerous types of metastatic models (Fig. 6.17) [65, 66]. Color-coded lung metastases were also visualized by external fluorescence imaging in live animals through skin-flap windows over the chest wall. Lung metastases were observed on the lung surface. SCID mice well tolerated multiple surgical procedures for direct-view imaging via skin-flap windows. Real-time metastatic growth of the color-coded clones in the same lung was externally imaged with resolution and quantification of green, red, or yellow colonies in live animals. The color coding enabled the determination of whether the colonies grew clonally or were seeded as a mixture with one cell type eventually dominating, or whether the colonies grew as a mixture. The simultaneous real-time dual-color imaging of metastatic colonies makes color-coded imaging of clones of cancer cells carrying various forms of genes of interest possible [66].

6.8 Imaging Lateral Gene Transfer Between Cancer Cells 6.8.1 Color-Coded Imaging of Circulating Cancer Cells Hormone-refractory human prostate carcinoma growing orthotopically efficiently delivers viable metastatic cells in the host circulation. This is in contrast to the ectopic tumors of the same lineage, which do not deliver live cells into the circulation. To investigate the malignant potential of viable circulating carcinoma cells,

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Fig. 6.17  Dual-color fluorescent images of metastatic colonies on the extracted lung. Most appeared green or red and, therefore, clonal. Numerous small metastases could be detected on the lung under fluorescence microscopy. (a) Low magnification view. in b1. b2 is a non-clonal metastatic coclony. (b1, b2) High magnification views. White arrows indicate the border of green and red colonies. Bars, 200 mm [66]

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we developed a novel dual-color orthotopic co-implantation model of PC-3 human prostate cancer metastasis in nude mice. This model is comprised of coinjection of an equivalent mixture of isolated and cultured GFP-expressing prostate cancer cells isolated from the circulation and parental RFP-expressing human prostate carcinoma cells. In the dual-color model, the selected GFP-labeled viable circulating cells have an increased metastatic propensity relative to the RFPlabeled parental cells [67].

6.8.2 Color-Coded Imaging of Gene Transfer Between Cancer Cells Interacting In Vivo Color-coded imaging visualized circulating yellow fluorescent prostate-cancer metastatic cells that were readily isolated from the circulation of tumor-bearing mice after mixtures of RFP- and GFP-expressing PC-3 human prostate carcinoma cells were implanted in the nude mouse prostate. The yellow fluorescent cells were purified from the circulation of nude mice to 99% homogeneity by FACS, expanded in culture, and re-implanted in the prostate of nude mice. The yellow fluorescent phenotype was heritable and stably maintained in vitro and in vivo by tumor cells for many generations. In the animals implanted with the yellow fluorescing cells, 100% developed aggressive metastatic cancer. Lung metastases were demonstrated in 100% of the animals as early as 4 weeks after injection of the yellow fluorescing cells in the mouse prostate. In contrast, when the GFP- and RFP-expressing parental cells were inoculated into the mouse prostate separately, none of the animals developed lung metastasis. All animals had almost exclusively yellow fluorescent cells in the circulation and bone marrow. These results are consistent with the idea that spontaneous genetic exchange between tumor cells in vivo contributes to genomic instability and creation of highly metastatic cells [68].

6.8.3 Color-Coded Imaging of Gene Transfer from High- to Low-Metastatic Osteosarcoma Cells In Vivo The 143B-GFP cell line with high metastatic potential and the MNNG/HOS-RFP cell line with low metastatic potential, both derived from the TE85 human osteosarcoma cell line, were either co-transplanted or transplanted alone in the tibia in nude mice. Upon mixed transplantation of the two differently-labeled sublines, resulting metastatic colonies were single colored, either red or green, thereby demonstrating their clonality and enabling facile color-coded quantification. When MNNG/HOS-RFP and 143B-GFP were cotransplanted in the tibia, the number of lung metastases of MNNG/HOS-RFP increased eightfold compared to that when MNNG/HOS-RFP was transplanted alone (P < 0.01). In contrast, no enhancement of MNNG/HOS-RFP metastases occurred when MNNG/HOS-RFP and 143B-GFP were transplanted separately in the right and left tibiae, respectively. This result

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suggests that the presence of 143B-GFP increased the metastatic potential of MNNG/HOS-RFP within the mixed tumor. We observed transfer of the Ki-ras gene from 143B-GFP to MNNG/HOS-RFP after they were co-implanted, suggesting that the Ki-ras played a role in increasing the metastatic potential of MNNG/HOSRFP in the presence of 143B-GFP [69]. These experiments further suggest the possible role of in vivo gene transfer in enhancing the metastatic potential of cancer cells. The data also further demonstrated the power of color-coded imaging to visualize cancer cell/cancer cell interactions in vivo.

6.9 Methods 6.9.1 Imaging Apparatus 6.9.1.1 In Vivo Imaging with an LED Flashlight and Filters Low-cost instrumentation and standard GFP and RFP biomarkers can be used to visualize tumors completely noninvasively. Utilizing a blue LED flashlight with a 470-nm excitation filter, tumors in – and on – several organs (including liver, pancreas, colon, bone, and brain) could be clearly imaged. The clearest demonstration of the power of this technique are the data showing that when the image of a surgically exposed colon tumor was analyzed and compared to the same tumor from a whole-body (unopened) image, the intensity of the GFP signal from the unopened mouse was 70% that of the opened. Furthermore, despite the expected distortion caused by light scattering, the image sizes were comparable [70]. A blue LED flashlight (LDP LLC, Woodcliff Lake, NJ, USA; www.maxmax. com/OpticalProducts.htm) with an excitation filter (midpoint wavelength peak of 470  nm) and an emission D470/40 filter (Chroma Technology, Brattleboro, VT, USA) for viewing could be used for whole-body imaging of mice with GFP- and RFP-expressing tumors growing in or on internal organs [3, 70]. Figure 6.18 shows whole-body imaging of two tumors, one expressing GFP and the other expressing RFP implanted in the brain. The image shows that the GFP and RFP tumors were simultaneously excited with the blue LED flashlight and readily imaged [70]. 6.9.1.2 Simple Light-Box Imaging Whole-body imaging that visualized the entire animal at lower magnification was carried out in a light box illuminated by blue light fiberoptics (Lightools Research, Inc., Encinitas, CA) and imaged using a thermoelectrically-cooled color CCD camera [3].

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Fig. 6.18  Whole-body imaging of green fluorescent protein (GFP) and red fluorescent protein (RFP) tumors in the brain in a nude mouse. GFP- and RFP-expressing tumors implanted in the brain in a single nude mouse. The excitation light was produced with a simple blue-LED flashlight equipped with an excitation filter with a central peak of 470 nm. The image was acquired with a Hamamatsu charge-coupled device (CCD) camera [70]

6.9.1.3 In Vivo Imaging with a Fluorescence Dissecting Microscope A Leica fluorescence stereo microscope, model LZ12, equipped with a 50-W mercury lamp, was used for high-magnification imaging of GFP-expressing tumors and metastasis in situ or for whole-body imaging of animals with GFP-expressing tumors. Selective excitation of GFP was produced through a D425/60 band-pass filter and 470 DCXR dichroic mirror. Emitted fluorescence was collected through a long-pass filter GG475 (Chroma Technology, Brattleboro, VT) on a Hamamatsu C5810 3-chip cooled color CCD camera (Hamamatsu Photonics Systems, Bridgewater, NJ). Images were processed for contrast and brightness and analyzed with the use of Image Pro Plus 3.1 software (Media Cybernetics, Silver Springs, MD). Images of 1,024  ×  724 pixels were captured directly on an IBM PC or continuously through video output on a high-resolution Sony VCR model SLVR1000 (Sony Corp., Tokyo, Japan). 6.9.1.4 In Vivo Cellular Imaging with a Variable Magnification Imaging Chamber An Olympus OV100 Small animal imaging system with a sensitive CCD camera and four objective lenses, parcentered and parfocal, enabling imaging from macrocellular to subcellular was developed (Fig. 6.19). Nuclear and cytoplasmic behavior of cancer

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Fig. 6.19  (a) Olympus OV100 Small animal imaging system. (b) OV100 covers a wide range of magnifications, from x0.14 (63 × 47 mm imaging area) to x16 (0.6 × 0.45 mm). Four individually optimized objectives, parcentered and parfocal, provide a 106-fold magnification range for seamless imaging of the entire body down to the subcellular level without disturbing the animal. The optics and antireflective coating ensure optimal imaging of multiplexed fluorescent reporters in small animal models. Subcellular structure can be visualized clearly by fluorescence imaging [5]

cells expressing GFP in the nucleus and RFP in the cytoplasm was observed in real time in blood vessels as they moved by various means or adhered to the vessel surface in the abdominal skin flap. During extravasation, real-time dual-color imaging showed that cytoplasmic processes of the cancer cells exited the vessels first, with nuclei following along the cytoplasmic projections. Both cytoplasm and nuclei underwent deformation during extravasation. With the dual-color cancer cells and the highly sensitive imaging system, the subcellular dynamics of cancer metastasis can be observed in live mice in real time [5]. 6.9.1.5 Imaging Chambers Designed for Whole-Body Imaging The FluorVivo small animal imaging system (INDEC Systems, Inc.) can be used for whole-body imaging in live mice. FluorVivo uses extremely bright, solid state, LED illuminators and a full color CCD camera to provide high-speed, multi-color imaging of up to three animals with single exposures. The instrument’s high speed acquisition permits in vivo monitoring of both static and dynamic processes, as well as real-time recordings of fluorescence-guided surgeries. FluorVivo’s fully integrated software provides complete control of the instrument, ease of use, and powerful analytical tools for extracting quantitative data from acquired images [58].

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The UVP iBox Small Animal Imaging System is capable of fluorescent protein imaging with a range of cameras that use front and back illuminated CCDs with sizes up to a 43-mm diagonal, greatly expanding the applications for high-resolution, large-field-of-view, and increased-throughput imaging. The iBox imaging system can be configured with both monochrome and color CCDs, with CCD resolution currently up to 8.3 megapixels and sensitive to a wide range of spectrum (CFP to near infrared). The range of fast lenses includes several interchangeable, fully automated optics: a 50-mm f1.2, 28-mm f1.8, and a 24–70-mm f2.8 zoom lens. These lenses give maximum imaging flexibility, with the field of view ranging from one to several animals. At f1.2, the typical exposures are less than 50 ms, minimizing the effect of animal movement. The camera, optics, sample platform position, and excitation and emission filters are under full software control, permitting reproducible and rapid imaging with software presets and macros [59].

6.10 Patient-Like Orthotopic Tumor Models 6.10.1 Surgical Orthotopic Implantation A most important development in mouse models of cancer was the availability of nude mice, which are athymic and T-cell deficient, enabling human tumors to grow in these mice [71, 72]. A second important development was the availability of orthotopic mouse models of cancer, which were first described by Miller et  al, 1981; Wang et  al, 1982; Ibrahiem et al, 1983; Naito et al, 1986; McLemore et al, 1987; Bresalier et al, 1987; and Fidler, 1990 [73–79]. These models yield significantly more information of novel compounds, especially on human tumors in orthotopic models than other models of cancer. Our laboratory developed orthotopic models of patient colon cancer in nude mice using SOI of histologically intact patient specimens, a major improvement since it allows higher metastatic rates [80–82]. 6.10.1.1 Ovarian Cancer Tumor fragments (1 mm3) derived from the nude mouse sc CHO-K1-GFP tumors were implanted by SOI on the ovarian serosa in six nude mice. The mice were anesthetized by isofluran inhalation. An incision was made through the left lower abdominal pararectal line and peritoneum. The left ovary was exposed, and part of the serosal membrane was scraped with a forceps. Four 1-mm3 tumor pieces were fixed on the scraped site of the serosal surface with an 8–0 nylon suture (Look, Norwell, MA). The ovary was then returned into the peritoneal cavity, and the abdominal wall and the skin were closed with 6–0 silk sutures. About 4 weeks later, the mice were killed, and the lungs and the other organs removed. All procedures of

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the operation described above were performed with a ×7 magnification microscope (Olympus) [82]. 6.10.1.2 Lung Cancer Tumor fragments (1 mm3) derived from the ANIP-GFP or H460-GFP sc tumor growing in the nude mouse were implanted by SOI on the left lung in nude mice. The mice were anesthetized by isofluran inhalation. The animals were put in a position of right lateral decubitus, with the four limbs restrained. A 0.8-cm transverse incision of the skin was made in the left chest wall. Chest muscles were separated by sharp dissection, and costal and intercostal muscles were exposed. A 0.4–0.5-cm intercostal incision between the third and fourth rib on the chest wall was made, and the chest wall was opened. The left lung was taken up by a forceps and tumor fragments were promptly sewn into the upper lung with one 8–0 suture. The lung was then returned into the chest cavity. The incision in the chest wall was closed by a 6–0 surgical suture. The closed condition of the chest wall was examined immediately, and if a leak existed, it was closed by additional sutures. After closing the chest wall, an intrathoracic puncture was made by using a 3-ml syringe and 25 and II gauge needle to withdraw the remaining air in the chest cavity. After the withdrawal of air, a completely inflated lung could be seen through the thin chest wall of the mouse. Then the skin and chest muscle were closed with a 6–0 surgical suture in one layer. All procedures of the operation described here were performed with a ×7 magnification microscope (Olympus) [82]. 6.10.1.3 Prostate Cancer Two tumor fragments (1 mm3) from a high GFP-fluorescent sc tumor from a single animal were implanted by SOI in the dorsolateral lobe of the prostate in five nude mice. After proper exposure of the bladder and prostate following a lower midline abdominal incision, the capsule of the prostate was opened, and the two tumor fragments were inserted into the capsule. The capsule was then closed with an 8–0 surgical suture. The incision in the abdominal wall was closed with a 6–0 surgical suture in one layer. The animals were kept under isoflurane anesthesia during surgery. All procedures of the operation described here were performed with a × 7 magnification microscope (Olympus) [82]. 6.10.1.4 Colon Cancer Colonic Transplantation For transplantation, nude mice were anesthetized, and the abdomen was sterilized with iodine and alcohol swabs. A small midline incision was made and the colorectal part of the intestine was exteriorized. Serosa of the site where tumor pieces were to

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be implanted was removed. Two to four pieces of 1-mm3 size tumor are implanted on the top of the animal intestine. An 8–0 surgical suture was used to penetrate these small tumor pieces and attach them on the wall of the intestine. The intestine was returned to the abdominal cavity, and the abdominal wall was closed with 7–0 surgical sutures. The animals were kept in a sterile environment. Tumors of all stages and grades can be utilized [82]. Intrahepatic Transplantation An incision was made through the left upper abdominal pararectal line and peritoneum. The left lobe of the liver was carefully exposed, and the liver was cut about 3 mm with scissors. Two to three tumor pieces of 1–2-mm3 size were put on the nude mouse liver and attached immediately with double sutures using 8–0 nylon with an atraumatic needle. After confirmation of no bleeding, the liver was then returned to the peritoneal cavity. The abdomen and skin were then closed with 6–0 black silk sutures [82].

6.11 Technical Details 6.11.1 RFP Retrovirus Production 1.  Insert the HindIII-NotI fragment from pDsRed2, containing full-length RFP cDNA, into the HINDIII-NotI site of pLNCX2, which contains a neomycin resistance gene, to establish the pLNCX2-DsRed2 plasmid. 2. Use PT67, an NIH3T3-derived packaging cell line expressing the 10 A1 viral envelope, to produce retrovirus. Culture 3 × 105 PT67 cells in a 25-mm2 flask with DMEM supplemented with 10% heat-inactivated FBS. It takes approximately 3 days for the cells to reach about 70% confluence. 3. For vector production, use PT67 packaging cells at 70% confluence. Plate PT67 cells on a 60-mm culture dish at 60–80% confluence 12 h before transfection. Use 10 mg of pLNCX2-DSRed2 DNA and the Lipofectamine PLUS transfection kit. Add 7 ml of pLNCX2-DsRed2 DNA to 87 ml serum-free medium in a tube and then add 6  ml Lipofectamine reagent, mix, and incubate for 15  min at 22–26°C (room temperature). 4. Dilute 4 ml of Lipofectamine reagent in 96 ml of serum-free medium in a second tube. Mix and incubate for 15 min at room temperature. 5. Combine the DNA prepared in Step 3 and diluted Lipofectamine reagent, then mix and incubate for 15 min at room temperature. 6. While the complexes are forming, replace the medium on the cells with 800 ml of serum-free DMEM. Add the DNA–Lipofectamine complex to the dish with cells containing fresh DMEM. Mix the complexes into the medium gently and incubate for 4 h at 37°C in 5% CO2.

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7. After 4 h of incubation, increase the volume of the medium to 5 ml and incubate for 24 h at 37°C. 8. After 24 h of incubation, clone the packaging cells by limiting dilution in 96-well plates. 9. For selection of a PT67 packaging cell clone producing large amounts of RFP retroviral vector (PT67-DsRed2), culture the cells in the presence of 100– 1,000 mg/ml of G418. Culture the cells for 1–2 days in each concentration of G418. Clones of PT67-DsRed2 cells with high viral titer production are identified with 3T3 cells used for virus titering. Clones with a titer higher than 1  ×  106 plaque-forming units per ml are used for RFP vector production.

6.11.2 GFP Retrovirus Production 1. Use the pLEIN or equivalent retroviral vector expressing enhanced GFP or equivalent GFP and the neomycin resistance gene, on the same bicistronic message, as a GFP expression vector. 2. Use PT67, an NIH3T3-derived packaging cell line expressing the 10 A1 viral envelope, to produce retrovirus. Culture 3 × 105 PT67 cells in a 25-mm2 flask with DMEM supplemented with 10% heat-inactivated FBS. It takes approximately 3 days for the cells to reach ~70% confluence. 3. For vector production, use packaging cells (PT67) (Clontech) at 70% confluence. Plate PT67 cells on a 60-mm dish at 60–80% confluence 12 h before transfection. Use 10 mg pFB-GFP (Clontech) with the Lipofectamine Plus transfection kit. Add 7 ml precomplexed pFB-GFP DNA in 87 ml of serum-free medium and then add 6 ml Lipofectamine reagent in a tube; mix and incubate at room temperature (22–26°C) for 15 min. 4. Dilute 4 ml Lipofectamine in 96 ml serum-free medium in a second tube. Mix and incubate at RT for 15 min. 5. Combine precomplexed DNA and diluted Lipofectamine reagent; then mix and incubate at RT for 15 min. 6. While the complexes are forming, replace the medium on the cells with 800 ml serum-free DMEM. Add the DNA–Lipofectamine reagent complex to the dish with cells containing fresh DMEM. Mix the complexes into the medium gently; incubate in a humidified incubator at 37°C and 5% CO2 for 4 h. 7. After 4 h of incubation, increase the volume of the medium to 5 ml. Incubate in the same conditions for 24 h. 8. After 24 h incubation, clone the packaging cells by limiting dilution in 96-well plates. 9. Examine the cells by fluorescence microscopy 48 h post-transduction. 10. For selection, culture the cells in the presence of 500–2,000 mg/ml G418 to select for a clone producing high amounts of a GFP retroviral vector (PT67-GFP). Culture the cells for 1–2 days in each concentration of G418. High-viral titer production clones of GFP PT67 cells are identified with 3T3 cells used for virus titering. Clones with titer higher than 106 plaque-forming units per ml are used for GFP vector production.

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6.11.3 RFP or GFP Gene Transduction of Tumor Cell Lines 1. For RFP or GFP gene transduction, use cancer cells that are 20% confluent. Plate cancer cells at a density of 1 × 105 to 2 × 105 cells per 60-mm plate 12–18 h before infection with RFP retrovirus. 2 . For retroviral infection, collect conditioned medium from packaging cells (PT67-DsRed2 or PT67-eGFP) and filter medium through a 0.45-mm polysulfonic filter. Add virus-containing filtered medium to the target cells. Add polybrene to a final concentration of 8 mg/ml. Incubate the cells for 24 h at 37°C. 3. Replace the medium with DMEM and 10% FBS after 24 h of incubation and check for RFP-expressing cells by fluorescence microscopy. 4. Collect tumor cells with trypsin-EDTA and subculture them at a ratio of 1:15 in selective medium, which contains 50 mg/ml G418. 5. To select brightly fluorescent cells, increase the concentration of G418 to 800 mg/ml in a stepwise way. Culture the cells for 1–2 days in each concentration of G418. 6. Isolate clones expressing RFP with cloning cylinders using trypsin-EDTA and amplify them in DMEM in the absence of the selective agent. Then select cells for brightness and stability.

6.11.4 Cell Injection to Establish an Experimental Metastasis Model 1. Collect fluorescent protein-expressing tumor cells by trypsinization for 3 min at 37°C with 0.25% trypsin. 2. Wash the cells three times with cold serum-free medium using a tabletop centrifuge at 500 × g. 3. Resuspend the cells in approximately 0.2 ml serum-free medium. 4. Within 30 min of collecting cells, inject 1 × 106 tumor cells in a total volume of 0.2 ml into 6-week-old C57BL/6 GFP mice or nude (nu/nu) GFP mice in the lateral tail vein, or subcutaneously using a 1-ml 27G2 latex-free syringe. 5. For liver colonization, inject fluorescent protein-expressing cells directly into the portal vein in anesthetized mice.

6.11.5 Surgical Orthotopic Implantation to Establish a Spontaneous Metastasis Model 1. Induce anesthesia with a “ketamine mixture” (10 ml ketamine HCl, 7.6 ml xylazine, and 2.4 ml acepromazine maleate, injected sc). 2. Use a microscope (Leica MZ6) with magnification of about x6 to about x40 for all procedures of the operation.

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3. Isolate fluorescent protein-expressing tumor fragments (1  mm3) from subcutaneously growing tumors, formed by injection of RFP-expressing tumor cells by mincing tumor tissue into 1-mm3 fragments. After proper exposure of the target organ, implant three tumor fragments per transgenic GFP mouse. 4. With 8–0 surgical suture, penetrate the tumor fragments and suture the fragments onto the target organ. 5. Keep the mice in a barrier facility under high-efficiency particulate air filtration.

6.11.6 Imaging 6.11.6.1 Fluorescence Microscopy 1. Use an Olympus BH2-RFCA fluorescence microscope equipped with a mercury 100-W lamp power supply or its equivalent. 2. To visualize both GFP and RFP fluorescence at the same time, produce excitation light using a D425/60 band-pass filter and a 470 DCXR dichroic mirror. 3. Collect emitted fluorescence light through a GG475 long-pass filter. 4. Capture high-resolution images of 1,024 × 724 pixels with a Hamamatsu C5810 three-chip cooled color CCD camera or its equivalent, and store directly on an IBM PC or its equivalent. 5. Process images for contrast and brightness using Image-Pro Plus 4.0 software or its equivalent.

6.11.6.2 Fluorescence Stereomicroscopy 1. Use a Leica fluorescence stereomicroscope (model LZ12) equipped with a mercury 50-W lamp power supply or its equivalent. 2. Produce selective excitation of GFP and/or RFP via a D425/60 band-pass filter and 470 DCXR dichroic mirror. 3. Collect emitted fluorescence through a long-pass filter (GG475) on a Hamamatsu C5810 three-chip cooled color CCD camera or its equivalent. 4. Process images for contrast and brightness with using Image-Pro Plus 4.0 software or its equivalent. 5. Capture high-resolution images of 1,024 × 724 pixels directly on an IBM PC or continuously through video output on a high-resolution Sony VCR model SLVR1000 or its equivalent. 6. For C57BL/6 mice, remove hair with Nair or by shaving before images are obtained

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6.11.7 Chamber Imaging Systems 6.11.7.1 Olympus OV100 1. Perform whole-body or intravital imaging with an Olympus OV100 imaging system using 470-nm excitation light originating from an MT-20 light source. 2. Collect emitted fluorescence through appropriate filters configured on a filter wheel using a DP70 CCD camera. Variable magnification imaging can be done with a series of four objective lenses for macro- or cellular and subcellular imaging in vivo. 3. Capture images on a PC (Fujitsu-Siemens) and process images for contrast and brightness with Paint Shop Pro 8 and cellR.

6.11.7.2 INDEC FluorVivo 1. Perform whole-body imaging with an INDEC FluorVivo imaging system using 470-nm excitation light. 2. Collect emitted fluorescence with the instrument’s full color CCD camera, using the appropriate emission filter. 3. Use the integrated FluorVivo software to adjust acquisition parameters. 4. Capture still and streaming images to the PC’s hard disc using the FluorVivo software. 5. Make required spatial and intensity measurements with the FluorVivo software.

6.11.7.3 UVP iBox 1. Turn on the power of the imaging system and (optional) turn on the warming pad. 2. Place the animal in the imaging system (optional) with their nose in the anesthesia cone. Illuminate the animal without excitation filter first to capture a wide spectrum (white-light) image. Set the F number of the camera lens to over 10 and the exposure time at 200 ms. Use the preview function of the imaging system while adjusting the height of the platform, supporting the animal to obtain a comfortable field of view. Adjust the focus of the lens until the image is clear. Reduce the intensity of illumination if horizontal strips are seen in the white-light image (blooming). Press the capture button when the preview image is satisfactory. 3. Change to the appropriate excitation and emission filter for fluorescence capturing. Adjust the focus with the aperture wide open (smallest number). The camera

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exposure time is typically about 1 s. The exposure time can be lengthened to increase the brightness. 4. (Optional) Use the VisionWorks LS software to create an overlay of the whitelight and the fluorescent images.

6.11.8 Tumor Tissue Sampling 1. Obtain tumor tissue biopsies from 3 days to 4 weeks after inoculation of tumor cells. Biopsies of tumor tissue can be obtained from anesthetized mice by removal of a small piece of tumor tissue (1 mm3 or less) with a scalpel. Staunch bleeding by pressing the wound with sterile gauze. Alternatively, the mouse can be killed and the tissue can be collected and processed for analysis. 2. Cut fresh tissue into pieces of about 1  mm3 and gently press onto slides for fluorescence microscopy. This procedure is done manually on normal slides. 3. To analyze tumor angiogenesis, digest the tissues with trypsin-EDTA for 5 min at 37°C before examination. 4. After trypsinization, place the tissues on precleaned microscope slides and cover with another microscope slide.

6.11.9 Measurement of GFP-expressing Tumor Blood Vessel Length and Evaluation of Antiangiogenetic Agents 1. Give mice daily ip injections of doxorubicin (5 mg per gram body weight in a 2-mg/ml solution of 0.9% NaCl) or other drugs or 0.9% NaCl solution (vehicle controls) on days 0, 1, and 2 after implantation of tumor cells. 2. Anesthetize mice with the ketamine mixture and obtain biopsies on days 10, 14, 21, and 28 after implantation (Step 18 provides biopsy sample details). 3. Gently flatten the tumor tissue between the slide and coverslip. 4. Quantify angiogenesis in the tumor tissue by measuring the length of GFPexpressing blood vessels in all fields using fluorescence microscopy. 5. Obtain measurements in all fields at x40 or x100 magnification to calculate the total length of GFP-expressing blood vessels. 6. Calculate the vessel density by dividing the total length of GFP-expressing blood vessels (in mm) by the tumor volume (in mm3).

6.11.10 Immunohistochemical Staining 1. “Snap-freeze” fresh tissue with liquid nitrogen, then orient and embed the frozen tissue in optimum cutting temperature blocks and store at -80°C. Cut the frozen sections to a thickness of 5 mm with a Leica CM1850 cryostat.

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2. Detect colocalization of GFP fluorescence, CD31, and nestin in the frozen skin sections of mice transgenic for nestin enhancer-driven GFP expression using the anti-rat immunoglobulin and anti-mouse immunoglobulin horseradish peroxidase detection kits following the manufacturer’s instructions. 3. Use monoclonal anti-CD31 (1:50 dilution) and monoclonal anti-nestin (1:80 dilution) as primary antibodies. To identify the GFP-expressing tumor-infiltrating natural killer cells, macrophages, and dendritic cells, detect localization of GFP together with cell surface markers using immunohistochemical staining with monoclonal antibodies to NK1.1, CD111b, and CD11c, respectively. 4. Use staining with substrate-chromogen 3,3¢-diaminobenzidine for antigen detection.

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67. Glinskii AB, Smith BA, Jiang P, et al. Viable circulating metastatic cells produced in orthotopic but not ectopic prostate cancer models. Cancer Res. 2003;63:4239–43. 68. Glinsky GV, Glinskii AB, Berezovskaya O, et al. Dual-color-coded imaging of viable circulating prostate carcinoma cells reveals genetic exchange between tumor cells in vivo, contributing to highly metastatic phenotypes. Cell Cycle. 2006;5:191–7. 69. Tome Y, Tsuchiya H, Hayashi K, et al. In vivo gene transfer between interacting human osteosarcoma cell lines is associated with acquisition of enhanced metastatic potential. J Cell Biochem. 2009;108:362–7. 70. Yang M, Luiken G, Baranov E, Hoffman RM. Facile whole-body imaging of internal fluorescent tumors in mice with an LED flashlight. Biotechniques. 2005;39:170–2. 71. Rygaard J, Povisen CO. Heterotransplantation of a human malignant tumour to “Nude” mice. Acta Pathol Microbiol Scand. 1969;77:758–60. 72. Rygaard J. Immunobiology of the mouse mutant “Nude.” Preliminary investigations. Acta Pathol Microbiol Scand. 1969;77:761–2. 73. Miller FR, Medina D, Heppner GH. Preferential growth of mammary tumours in intact mammary fatpads. Cancer Res. 1981;41:3863–7. 74. Wang WR, Sordat B, Piguet D, Sordat M. Human colon tumors in nude mice: implantation site and expression of the invasive phenotype. In: Sordat B, editor. Immune-deficient animals. Lausanne, Switzerland: Karger; 1982. p. 239–45. 75. Ibrahiem EH, Nigam VN, Brailovsky CA, et al. Orthotopic implantation of primary N-[4-(5-Nitro2-furyl)-2-thiazolyl] formamide-induced bladder cancer in bladder submucosa: an animal model for bladder cancer study. Cancer Res. 1983;43:617–22. 76. Naito S, von Eschenbach AC, Giavazzi R, Fidler IJ. Growth and metastasis of tumor cells isolated from a human renal cell carcinoma implanted into different organs of nude mice. Cancer Res. 1986;46:4109–15. 77. McLemore TL, Liu MC, Blacker PC, et al. Novel intrapulmonary model for orthotopic propagation of human lung cancers in athymic nude mice. Cancer Res. 1987;47:5132–40. 78. Bresalier RS, Raper SE, Hujanen ES, Kim YS. A new animal model for human colon cancer metastasis. Int J Cancer. 1987;39:625–30. 79. Fidler IJ. Critical factors in the biology of human cancer metastasis: twenty-eighth G.H.A. Clowes Memorial Award Lecture. Cancer Res. 1990;50:6130–8. 80. Fu X, Besterman JM, Monosov A, Hoffman RM. Models of human metastatic colon cancer in nude mice orthotopically constructed by using histologically intact patient specimens. Proc Natl Acad Sci U S A. 1991;88:9345–9. 81. Fu X, Guadagni F, Hoffman RM. A metastatic nude-mouse model of human pancreatic cancer constructed orthotopically from histologically intact patient specimens. Proc Natl Acad Sci U S A. 1992;89:5645–9. 82. Hoffman RM. Orthotopic metastatic mouse models for anticancer drug discovery and evaluation: a bridge to the clinic. Invest New Drugs. 1999;17:343–59. 83. Dusich JM, Oei YA, Purchio T, Jenkins DE. In vivo detection of lung colonization and metastasis using luciferase-expressing human A549 lung cells. Proc Am Assoc Cancer Res. 2002;43:1059. 84. Vooijs M, Jonkers J, Lyons S, Berns, A. Noninvasive imaging of spontaneous retinoblastoma pathway-dependent tumors in mice. Cancer Res. 2002;62:1862–7.

Chapter 7

Patient-Derived Tumor Models and Explants Heinz-Herbert Fiebig and Angelika M. Burger

Keywords  Human Xenografts • Patient derived tumor models • Molecular characterization • Drug sensitivity testing • Gene signature

7.1 Introduction 7.1.1 Historical Perspective Since the first report of the successful xenografting of a human tumor into nude mice in 1969, there have been numerous studies conducted throughout the world using the nude moue as a tool to answer a variety of questions regarding the cause, prevention, and therapy of cancer. Thus, the role of immunodeficient animals in oncology has continuously increased and the athymic nude mouse has proven to be an outstanding host for many human solid tumor xenografts [1, 2]. The latter are now extensively used in the development of potential anticancer drugs, new antineoplastic treatment modalities, and studies on tumor biology [3–7]. Moreover, mice with severe combined immunodeficiency (SCID) and non-obese diabetic (NOD)/SCID mice have enlarged the spectrum of possible applications in cancer research. SCID mice have enabled engraftments of human tumors which were previously difficult to explant such as those of the hematopoietic system [8]. Prior to development of immunodeficient mice, syngenic transplantable mouse tumor systems or autochthonous rat tumors were employed as the main – or the only – tools in the development of antitumor agents [5, 7, 10]. Most of the chemotherapeutic agents currently used in the clinic have been developed in these rodent tumor models (reviewed in Chaps. 1–4). The most frequently used murine tumors were the leukemias L1210 and P388, the melanoma B16, and the Lewis lung cancer H.-H. Fiebig (*) Oncotest GmbH Institute for Experimental Oncology, Am Flughafen 12, D-79108, Freiburg, Germany e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_7, © Springer Science+Business Media, LLC 2011

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model. Yet the classes of agents found active in the mouse tumor models, were limited and mainly comprised alkylating agents, antimetabolites, and other DNA interacting drugs [5, 11, 12]. Nevertheless, today transplantable syngeneic murine-tumor models remain particularly valuable for studying biological response modifiers or certain agents that need to be evaluated in a syngeneic environment, such as those targeting distant organ metastasis or requiring NK- or T lymphocytes [9]. In the late 1980s and early 1990s, new drug development moved from applying general cytotoxic principles to molecular target-directed treatment strategies [13]. Consequently, there was a need to identify tumor types/individual patient tumors that express the target and could benefit from more selective therapies in phase II/ III clinical trials. Thus, the in vivo models used in preclinical development today are “disease oriented” and target characterized and are either patient- or cell linederived xenografts or spontaneous tumors in specifically bred transgenic mice [14]. However, because of their high running costs and limited availability, transgenic mice are not suitable for large-scale drug testings. Thus, xenografts/human explants have become the gold standard in cancer drug development. Their use is highly recommended by regulatory agencies such as the EMEA (European Agency for the Evaluation of Medicinal Products) in the “note for guidance on the preclinical evaluation of anticancer medicinal products” [15, 16].

7.1.2 The Strength of Human Models Derived from Patient Explants While the spectrum of transplantable and autochthonous murine tumor models is confined to certain entities such as melanoma, colon, breast, bladder, or lung ­carcinomas and genetically engineered mouse models cannot cover all human ­cancers, patient explants and stably growing xenografts derived thereof can be generated from the vast majority of solid tumor types. Table 7.1 shows the broad panel of human tumor types included in the Freiburg xenograft collection. Moreover, the use of fresh surgical patient material or human tumor engraftments growing subcutaneously in nude mice enables chemosensitivity screening procedures in vitro and in vivo, and thus can be used to predict clinical response [11, 16–19]. Human tumor models established in serial passage have demonstrated a particularly high correlation of drug response compared to that in the clinic (Table  7.2). If evaluated in the nude mouse in  vivo, the correct prediction for response (positive predictive value) was 90% [18, 19]. If tested in vitro using the clonogenic assay, the correct prediction was 62%. The prediction for resistance was 97% in  vivo and 92% in  vitro. This allows for preselection of responsive tumor types in follow-up studies [20, 21]. It will be crucial for a new drug to demonstrate a differential selectivity against human tumors compared to the most sensitive normal tissue. In this respect, humantumor xenografts are being considered as the most relevant models because the

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Table 7.1  Patient-derived tumor models established in Freiburg Bladder CNS Leukemias Melanomas Prostate

Breast Colon Cervix uteri Gall bladder Gastric Head and neck Liver Lung Lymphoma Pleura mesothelioma Pancreas Ovarian Renal Sarcoma Testicle Uterus More than 1,700 tumors of cancer patients have been implanted s.c. into nude mice More than 300 human tumors of the following tumor types have been established 150–200 models have been molecularly characterized

Table 7.2  Comparison of drug response in human tumors growing subcutaneously in nude mice and in the patient

Nude mouse Patient Total Remission Remission 19 No remission Remission  2 No remission No remission 57 Remission No remission  2 Eighty comparisons were obtained in 55 tumors. Xenografts predicted correctly for response in 90% (19/21), resistance in 97% (57/59)

patient-derived tumors (a) grow as a solid tumor, (b) develop a stroma, vasculature­, as well as central necrosis, (c) show more or less differentiation, and (d) have clinically relevant response rates [19]. The tumor-xenograft architecture, the cell morphology, and molecular characteristics mirror the original patient cancers Fin most cases (Fig. 7.1). This is in marked contrast to xenografts derived from cell lines, which in general show an undifferentiated histology and as a result are very resistant to most of the standard agents [11] (Fig. 7.2). This is most likely a result of the high selection pressure in vitro during long-term culture resulting in ­aggressive subclones. In this chapter, we report on our experience with the growth of more than 1,700 patient tumors; the correlation of drug response against clinically used agents; the molecular characterization of parameters relevant for tumor development, growth, and dissemination; examples of target directed compounds discovered by our group; and the concept of discovering gene signatures predicting activity of a drug.

7.2 Materials and Methods 7.2.1 Establishment of Human Tumor Xenografts from Patient Explants All animal experiments were performed according to the project license number G-97/30 following German Animal License regulations (Tierschutzgesetz) which closely reflect the recently published UKCCCR guidelines for the welfare of ­animals in experimental neoplasia [22].

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Fig. 7.1  Histological appearance of the colon carcinoma CXF 158 and the large cell lung carcinoma LXFL 529 as original patient tumors depicted in a and b, respectively, and xenografts established thereof as s.c. solid tumors in serial passage shown in c–d. c represents the CXF 158 human tumor xenograft in passage 10, d the LXFL 529 explant in passage 5. Although stromal parts and blood supply are derived from the murine host, architecture and morphology of the tumors resemble closely the original patient specimens (H&E sections ×400)

Fig. 7.2  Histological appearance of cell line-derived human tumor xenografts established by subcutaneous injection of (a) COLO 205 colon-cancer cells obtained from the NCI Central Repository or (b) DU145 prostate cancer cells obtained from the American Type Culture Collection (ATCC). Both human tumor xenografts are undifferentiated and only sparsely vascularized. They have lost morphological elements which would enable to identify them as either colonor prostate cancer histologies (H&E sections ×400)

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7.2.1.1 Animals Outbred athymic nude mice of the NMRI genetic background from our own breeding­ facility or purchased from accredited breeders (Taconic, Charles River, Harlan, Janvier) were used for all studies described herin. The mice were kept in Macrolon™ cages on laminar-flow shelves and in recent years in individually ­ventilated cages from Tecniplast. In the first passage, tumors derived from male cancer patients were implanted into male nude mice, tumors from women into female mice. All therapeutic experiments with the exception of testicular and ­prostate cancers were carried out in female mice. 7.2.1.2 Tumors Over the past 20 years more than 1,700 resected human solid malignancies were implanted subcutaneously into nude mice. Tumor slices of approximately 5 × 5 × 0.5 mm to 1.0 mm in diameter were implanted in the flanks of the animals. Usually 16 fragments of patient material were grafted into four mice in the first passage. For therapeutic experiments, two tumor fragments of 1.5–2 mm in diameter were implanted subcutaneously between the fore and hind flank of each mouse near the arteria mammaria interna. 7.2.1.3 Tumor Growth Measurements Tumor growth was followed biweekly by serial caliper measurements determining two perpendicular diameters. The product of the two diameters was taken as a ­measure for tumor size. Drug treatment efficacy was evaluated as tumor volume inhibition. Tumor volume was calculated according to the formula: lw2/2, where l is the longer and w the perpendicular diameter. Relative tumor values were calculated for each individual tumor according to tumor size on dayX divided by tumor size on day0 at the time of ­randomization multiplied by 100. The median tumor size was taken for evaluation.

7.2.2 Experimental Design of Drug Testing 7.2.2.1 Study Design Drug testing was performed in serial passage when the tumor growth was regular. Drug response in the nude mouse versus that in the patient was compared when xenografts grew in passages between two and ten. In vivo testing of novel agents was performed with human tumor xenografts which had not been propagated beyond ten passages from the frozen master stock. Mice were selected randomly for vehicle control or test groups after 2–6 weeks when tumors were

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palpable and had reached mean diameters greater than 4 mm. Tumors with a yellow color indicating a high amount of fibrous tissue were excluded. Using these criteria, we did not observe spontaneous regressions or stationary growth behavior in the controls. Each test group consisted of five to six animals comprising between six and ten evaluable tumors. Experiments were terminated when tumors reached a diameter of 1.5 cm. 7.2.2.2 Chemotherapy Treatment regimens used in nude mice were adapted to clinically employed schedules with the exception that intermittent treatment was repeated in mice after 2 weeks instead of 3 to 4 weeks in patients. Drugs, dose, schedule, and route of administration are shown in Table 7.3. A dose around the LD10 after 14 days and around the LD20 28 days after initiation of therapy was considered the maximum tolerated dose (MTD) in tumor-bearing nude mice. If available, agents were used as clinical formulation, experimental drugs were either dissolved in normal saline or prepared, e.g. in 10% dimethylsulfoxide/phosphate-buffered saline (DMSO/PBS) and 0.05% Tween 80™. Table 7.3  Growth behavior of patient-derived tumors in nude mice Serial passageb Rapid growtha (%) Tumor origin Total (n) n (%) n Esophagus 10 7 70 6 60 Cervix uteri 10 7 70 6 60 Colorectal 152 88 58 78 51 Corpus uteri 8 4 50 4 50 Melanomas 63 39 62 27 43 Lung, small cell 39 14 36 16 41 Lung, NSC 227 118 52 87 38 Sarcomas, soft tissue 79 36 46 29 37 Ovary 22 7 32 8 36 Head and neck 47 16 34 16 34 Pancreas 39 16 41 13 33 Brain 6 2 33 2 33 Liver 3 1 33 1 33 Neuroblastoma 3 1 33 1 33 Osteosarcoma 13 5 38 4 31 Testicle 48 14 29 12 25 Pleuramesothelioma 36 14 39 9 25 Stomach 68 17 25 17 25 Bladder 44 17 39 10 23 Renal 124 37 30 24 19 Mammary 74 13 18 13 18 Prostate 41 7 17 2 5 Miscellaneous 129 42 33 34 26 Total 1,260 513 41 411 33 a 2 Tumor size (a × b) >60 mm 90 days after s.c. implant b At least three passages

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7.2.2.3 Evaluation Parameters for Tumor Response For comparing tumor responses in nude mice and in patients, the product of the two diameters was taken as a measure of tumor size. Tumors in nude mice were evaluated after maximum tumor regression or after 21–28 days in nonregressing tumors. The effect of treatment was classified in the xenograft system and in the patient as remission (the product of two diameters <50% of initial value), minimal regression (50– 75%), no change (>75–125%), and progression (>125%) of initial value. All patients enrolled in these studies had measurable lesions, evaluation was usually performed after two treatment cycles or after maximal tumor regression. The evaluation of tumor response in nude mice and in the patients was performed by different physicians. For testing new compounds, the in vivo evaluation was performed using tumor volume obtained by the formula 0.5 × length × (width)2 according to Geran et  al. [23]. Relative tumor volumes were calculated for individual tumors by dividing the tumor volume on dayt by the tumor volume on day0 multiplied with 100 for all time points. The minimum test/control in percent is considered as the optimal value (optimal T/C%).

7.2.3 Molecular Target Characterization of the Freiburg Patient-Derived Tumor Xenograft Panel A panel of 200 human tumor xenografts has been extensively studied for the expression of novel validated targets for cancer drug development by tissue microarray analysis and was profiled for mRNA expression of the whole annotated human genome [24–28]. Genomic Profiling. For mRNA preparation, tumors were grown in untreated mice. Following humane euthanasia, tumors were excised without delay and tumor pieces free of necrosis were flash frozen in liquid nitrogen. After mechanical tissue disruption, total tumor RNA was extracted using the RNeasy Mini kit (QIAGEN, Hilden, Germany). Prior to array analysis, one round of T7 promoter-based RNA amplification was performed. Affymetrix® HG-U133 Plus 2.0 mRNA expression arrays were used to determine the expression of 47,400 transcripts, corresponding to human 38,500 genes. The HG-U133 Plus 2.0 mRNA expression arrays have proven high reproducibility for mRNA expression analysis. CEL result files were preprocessed using the gc-RMA algorithm independently for training and validation sets. Chip normalization to the 50th percentile was performed afterwards. Predictive transcripts were identified by an iterative leave-one-out/intersection process utilizing Genespring BioScript SG2c-2 (Agilent, Santa Clara, USA). Support vector machines were used as the class prediction algorithm [31]. Tissue Microarrays. Microarrays were assembled from up to 150 paraffin embedded, formalin fixed human tumor xenografts by using a tissue microarrayer (Beecher Instruments, Sun Prairie, WI, USA). Fresh xenograft tissue was collected when tumors reached approximately 1.5 cm in size and immediately fixed in 10%

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PBS buffered formalin for 24 h followed by routine processing and embedding into paraffin [27, 28]. Whole tumor sections (4 mm) were cut and stained with H&E. Hematoxylin–Eosin sections of the xenografts were studied by light microscopy and representative areas marked on the slides. Xenograft biopsies, 0.6 mm in diameter, were taken from the corresponding area in the paraffin block and arrayed in duplicates into a new recipient block. Four micrometer sections of the microarray block were cut and transferred onto glass slides using the paraffin sectioning aid system (Instrumedics, Hackensack, NJ, USA). After rehydration, the endogenous peroxidase was blocked in 3% H2O2 solution. Antigen retrieval was accomplished through microwave pretreatment (20 min at 100°C) in citrate pH 6.0 or Tris/HCl pH 10 buffers, depending on the primary antibody. The detection system employed was based on streptavidin-peroxidase-diaminobenzidine and subsequent signal amplification with CuSO4 (Zymed, San Francisco, CA, USA) or strepavidin-peroxidase-Histogreen (Linaris, Wertheim, Germany). The arrays were stained with primary antibodies against targets of interest. Staining was analyzed by light microscopy (Zeiss Axiovert 100 Microscope, Darmstadt, Germany), according to the proportion of positive cells and intensity. A scoring system ranging from 0 to 3+ was used and the staining intensity evaluated by two independent observers [26–28].

7.2.4 Clonogenic Assay A modification of the double-layer soft-agar or clonogenic assay as described by Hamburger and Salmon was used [20, 30]. The cell population of this assay represents tumor stem and progenitor cells which are responsible for self-renewal and thus the unlimited growth of a tumor. An excellent correlation of drug response in the patient and in the clonogenic assay has been published by us and various other groups [17, 20, 21, 29]. 7.2.4.1 Preparation of Single-Cell Suspensions Solid human tumor xenografts were mechanically disaggregated and subsequently incubated with an enzyme cocktail consisting of collagenase 0.05%, DNAse 0.07%, and hyaluronidase 0.1% in RPMI 1640 at 37°C for 30 min. The cells were washed twice and passed through sieves of 200 and 50 mm mesh size. The percentage of viable cells was determined in a hemocytometer using trypan blue exclusion. 7.2.4.2 Culture Methods The tumor cell suspension was plated into 24-multiwell plates over a bottom layer consisting of 0.2 mL Iscove’s Modified Dulbecco’s Medium (IMDM) with 20%

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fetal calf serum and 0.7% agar. 20,000–100,000 cells were added to 0.2 mL of the same culture medium, in 0.4% agar, and were plated onto the base layer. Drugs were added after 24 h (drug overlay) in 0.2 mL medium. In each assay, six control plates received the vehicle only, drug-treated groups were plated in triplicate in three or six concentrations. Cultures were incubated at 37°C and 7% CO2 in a humidified atmosphere for 6–18 days and monitored closely for colony growth using an inverted microscope. During this period, in vitro tumor growth led to the formation of colonies with a diameter of >50 mm. At the time of maximum colony formation, vital colonies were stained with a tetrazoliumchloride dye 24 h before counting them with an automated image analysis system (Bausch & Lomb OMNICON FAS IV). Drug effects were expressed as percentage of survival, obtained by comparison of the mean number of colonies in the treated plates with the mean colony count of the vehicle treated controls (colony count T/C × 100). A compound was considered active if it reduced colony formation to less than 30% of the control group value (T/C £ 30%). Furthermore, inhibitory concentrations IC50, IC70, and IC90 were calculated corresponding to T/C values of 50, 30, and 10%. Using these evaluation parameters the majority of clinically established anticancer agents was active at a concentration of <1 mg/mL. 5-Fluorouracil (5-FU) was used as positive reference compound at ultra pharmacological doses of 100–1,000 mg/mL. The coefficient of variation in the control group was <50%. The average cloning efficiency increased markedly in recent years and is now 0.72% [20].

7.2.5 Gene Signatures Patient-derived tumor models were used to investigate the hypothesis that correlating drug response to gene expression would identify gene signatures that can predict the drug response of individual tumors to these agents. Between 74 and 28 xenograft tumors were characterized for their sensitivity towards 11 standard cytotoxic anticancer agents and the VEGF inhibitor Bevacizumab. The mean number of tumors treated with any of the various drugs was 54 (range 31–78). Tumors were considered as responsive if the drugs effected a tumor volume inhibition to less than 11–41% of control tumors. The median cut-off for all drugs was a T/C of 25%. Using these criteria, on average one-third of the test tumors were sensitive (responders) and two-thirds were resistant (nonresponders). The tumor xenografts’ gene expression profiles were determined using the Affymetrix HG-U133 plus 2.0 mRNA expression array representing ~38,500 human genes. Predictive gene signatures were found and subsequently validated using the leave-one-out cross-validation (LOOCV) technique and for three drugs in independent testing sets. To identify predictive transcripts, an iterative leave-one-out/intersection process was used as described by us before [31]. Briefly, in a first step all transcripts with

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a standard deviation >0.75 for at least one of the two classification groups (R, NR) were filtered out because they were unlikely to discriminate between the two groups. Gene lists consisting of the 300 most specific transcripts were generated by iteratively removing one tumor at a time from a training set. The final list of transcripts consisted of the intersection of the gene lists. The purpose of this iterative process was to reduce the risk of underestimating the prediction error by reducing the number of false positive transcripts. Welch ANOVA and Fisher’s rank tests were used to describe the rank of every transcript according to the hypergeometric Fisher’s exact test. This calculation was performed by Genespring’s BioScript SG2c-2 (Agilent, Santa Clara, USA). Support vector machines (SVMs) are powerful pattern recognition techniques assigning objects unequivocally to one of two classes. They build robust prediction models even when the dimensionality is high and the number of training samples is small. A Gaussian SVM (radial basis) was used as class prediction algorithm. This type of SVM gave the best prediction results in the LOOCV and, consequently, was used to predict the independent dataset as well [31].

7.3 Results and Discussion 7.3.1 Take Rates and Growth Behavior of Patient Tumor Explants in Nude Mice Of 1,700 patient tumors, the growth behavior of 1,260 different human malignancies growing subcutaneously in nude mice has been characterized in detail and is presented in Table 7.3. Histological examination of the tumors showed that 79% contained viable tumor tissue. However, only 41% of the tumors showed a rapid growth with tumor diameters of more than 8 mm after 90 days (Table 7.3). Thus, only these rapidly growing tumors were suitable for establishing stably growing models and for further investigations. Nonetheless, it must be noted that the growth of human tumors of the previously described category is still much slower than seen in established murine models. Four hundred and eleven (33%) of the 513 “rapid” growing human tumor explants were successfully transferred in serial passage and most of them were cryopreserved in liquid nitrogen with a recovery rate of 90%. Tumors in serial passage can be divided into three categories. The rate was ­highest (40–60%) in tumors of the esophagus, cervix and corpus uteri, colon, small cell lung, and melanomas. Intermediate growth rates between 20 and 40% were observed for carcinoma of the lung (non-small cell), soft tissue sarcoma, and carcinoma of the ovaries, head and neck, pancreas, testicle, pleuramesothelioma, stomach, and bladder. The lowest rates (5–19%) were observed in renal cancers and in the hormone dependent mammary and prostate carcinomas. The growth of breast and prostate cancers was increased when intramuscular estrogen or dihydrotestosterone depots were given every 2 weeks.

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Among the 400 models about 200 have been selected for further characterization­, e.g. the chemosensitivity profile and molecular characterization. These models showed a regular growth behavior and typical characteristics in terms of histology and differentiation. Rapid- and slow-growing tumors were included, as well as sensitive and resistant models against standard agents. All the tumors can and have been frozen in liquid nitrogen with a recovery rate of 90%. At the University of California, in a similar effort to establish human tumor models from surgical specimens a total of 323 patient explants were collected [4]. These yielded the development of only 27 (8%) of transplantable models. Although the California group had similar positive results with respect to retaining patient characteristics, their markedly lower success rate (8%) in obtaining stable, transplantable models versus that of our program (33%) might be a result of the processing of the specimens. The California group minced and then injected the tumor suspensions subcutaneously into nude mice without paying attention to the gender of the patient from which the tumors were derived or the age of the mice, but we established our models from tumor fragments and used mice of the same gender as the donor patient. Thus, we assured that the xeno-engrafted tumor specimens were provided with the most optimal environment such as their own extracellular matrix, appropriate hormonal conditions, and lowest probability of graft rejection. The Pediatric Preclinical Testing Program established by the NCI has recently reported that 95 pediatric xenografts were established that are currently used for preclinical drug testing [32]. They are suitable models representing very well the tumor type of origin. It was found that the characteristic expression patterns of the primary tumors were maintained in the corresponding xenografts for the majority of samples. The generated gene expression profiles as well as tissue microarrays for 75 models were used for the development of novel targeted therapies. However, it remains unclear how the xenografts were established and what the success rate was of tumors transplanted versus models established.

7.3.2 Comparison of Drug Response of a Tumor Growing in the Nude Mouse and in the Patient 7.3.2.1 Cytotoxic Agents In 55 tumors, 80 comparisons were performed (Table 7.2). The patients and the xenotransplanted tumors were treated with similar schedules at their respective MTD. In the nude mouse, two treatment cycles were given. In colorectal cancers, 24 comparisons were evaluated using single-agent chemotherapy with 5-FU or a nitrosourea and in three tumors combination chemotherapy. The other tumor types were treated in 40 cases with combination chemotherapy and in 13 cases with single-agent chemotherapy. Mammary cancers were treated initially with tamoxifen and after progression with the combination of cyclophosphamide, methotrexat, and 5-FU or adriamycin and cyclophosphamide. Stomach cancers were treated

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with 5-FU + adriamycin + mitomycin-C and small cell lung cancers (SCLC) with the ­combination of adriamycin + vincristine + cyclophosphamide and after progression with cisplatin and etoposide. The overall results are given in Table 7.2. A total of 21 patients reached a remission. The same result was observed in 19 tumors growing in the nude mouse. A total of 59 patients did not respond to treatment and the same result was found in 57 cases in the nude mouse system. Overall, xenografts gave a correct prediction for resistance in 97% (57/59) and for tumor responsiveness in 90% (19/21) of cases. Combination chemotherapy was more successful than single-agent therapy. Of the 43 combinations studied, 16 (37%) effected a remission in the patient as compared to 5 of 35 (14%) of single-agent chemotherapies. Single-agent therapy was successful in four patients with colorectal cancer and in one with stomach cancer. 7.3.2.2 Targeted Agents With targeted drugs, comparisons of drug response in the same tumor growing in the nude mouse and in the patient are difficult to obtain because the targeted agents Bevacizumab and the EGFR inhibitor Cetuximab are registered and given clinically in combination with cytotoxic agents. We have one example of single-agent therapy in a colon cancer which we established in the nude mouse. The established model CXF 1784 was treated with Cetuximab and Bevacizumab as single agents. Cetuximab effected a partial remission with a T/C of 10% of the control. Bevacizumab was less active with an inhibition to 44% of the controls. The patient had already received the FOLFOX and FOLIRI combinations and as third-line single-agent BV under which he showed a slow progression of the tumor markers CEA and CA 19–9 (Fig. 7.3). In the nude mouse the tumor showed a similar slow progression under Bevacizumab, however, delaying the tumor growth since the tumor volume of untreated controls was twice as large. The clinicians were informed that Cetuximab was very effective in this tumor. The patient was treated with 6 weekly injections under which the tumor markers CEA and CA 19–9 decreased to 35 and 53% (Fig. 7.4). In the example the tumor CXF 1784 responded exactly in the same way in the nude mouse as the patient in the clinic.

7.3.3 Clinically Used Cytotoxic Agents Active in the Freiburg Xenograft Panel 7.3.3.1 Cytotoxic Drugs A second approach to validate the xenograft system as a model for drug development is retrospective chemosensitivity profiling. Thus, xenografts were ­a nalyzed in view of their ability to identify compounds active in the clinic. A detailed analysis of ten standard agents of the older generation of cytotoxic drugs studied in 508 tumors was reported elsewhere [11, 19]. Here, we report on the response ­pattern of taxol, gemcitabine, taxotere, vindesine, and topotecan in clinically­

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responsive and resistant tumor types. Criteria for evaluation were the same as in the clinic. Each agent was evaluated in tumor types which are considered to be sensitive (CR + PR rate >20%) and in resistant tumor types (PR rate <10%). Overall, the five standard agents studied here (Table 7.4) induced remissions in 24% (45 of 187), and minor regressions or no change in 13% (25 of 187) of the cases examined. Yet 117 of 187 tumors (63%) progressed (Table 7.5). Thus, the overall response rate is similar to what is observed with monotherapy regimens in the patient. In the subgroup of sensitive tumors, the five compounds effected remissions in 37%. In contrast, only 4% remissions were seen in the resistant tumor entities. A progression was observed in 49% of the sensitive tumor types compared to 84% of the resistant tumor types (Table 7.5).

7.3.3.2 Clinically Used Targeted Agents As an example we investigated the angiogenesis inhibitor Bevacizumab [33], an antibody against human vascular endothelial growth factor (VEGF), in 99

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10

30

40

50

60

Days CA 19-9

Vehicle control

20

Erbitux therapy

ERBITUX; 30 mg/kg/day ip; Days 1,8,15

K514

Fig. 7.4  Effect of Cetuximab (Erbitux®) in the colon cancer CXF 1784 growing in the nude mouse and in the patient administered as fourth line therapy Table 7.4  Maximum tolerated dosesa of selected anticancer drugs in tumor-bearing nude mice Drug Dose (mg/kg/day) Schedule Route Gemcitabine 300 1,8,15 i.v. Taxol 20 1,8,15 i.v. Taxotere 20 1,8,15 i.v. Topotecan 2.5 1–3 i.v. Vindesine 1.5 1.5 i.v. a Until 7–10 days after last therapy

patient-derived tumor xenografts, growing subcutaneously in nude mice in order to compare the preclinical with the clinical activity pattern. Avastin was applied i.v. at a loading dose of 40 mg/kg/day on day 0, followed by 20 mg/kg/day on day 7 and 14, respectively. Antitumor activity was evaluated at the day of minimum T/C value or after 28 days. A marked inhibition (optimal T/C <20%) occurred in nine tumors (10%), 20 tumors (21%) showed T/C values between 20 and 34%, whereas 64 tumors (69%)

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Table 7.5  Anticancer activity of five drugs in clinically responsivea and resistant tumor types Responsive tumors

Resistant tumors

Drug

Total

CR + PR

MR + NC

P

Total

CR + PR

MR + NC

P

Gemcitabine Taxol Taxotere Topotecan Vindesine

17 13 3 8 72

1 7 2 3 29

5 1 1 1 8

11 5 0 4 35

10 6 6 11 41

0 0 1 0 2

2 0 5 0 2

8 6 0 11 37

Total

113 (100%)

42 16 55 74 3 (4%) 9 (12%) 62 (37%) (14%) (49%) (100%) (84%) a For definition see text; CR complete remission, PR partial regression, MR minor regression, NC no change, P progression, for further details see methods

were resistant to single-agent treatment with Bevacizumab with T/C values >34%. Therefore, Bevacizumab as a single agent showed moderate activity at the stronger cut-off point of T/C <20% and a broader activity at the weaker cut-off of T/C <34%. Antitumor activity (T/C <34%) was found in 4/20 colon, 12/36 NSCLC, 3/11 breast, 2/10 renal, and 7/22 miscellaneous tumor entities mimicking the activity pattern seen in the clinic. Nevertheless, most of the tumors grew progressively and doubled the initial tumor volume within 2–3 weeks. A regression of more than 50% was found only in one non-small cell lung cancer. In the clinic the remission rates observed in different tumor types were always <5% when Bevacizumab was administered as single agent. In conclusion, the preclinical antitumor profil and the degree of activity of Bevacizumab corresponded well to clinical experience with efficacy in colon, NSCL, breast, and renal cancers and also additional tumor entities responded. In the clinic, the real benefit was evident in combination with cytotoxic drugs when the progressionfree survival or the overall survival was significantly prolonged in colon, breast, lung, and renal cancers leading to registration in the United States, Europe, and Japan. 7.3.3.3 Comparison of Freiburg Experience to Other Groups These in vivo chemosensitivity data and the high correlation of drugs response in the same tumor growing in the nude mouse and in the patient demonstrate the strength of the patient-derived tumor xenograft model as a predictor of clinical response. Thus, this patient-derived model system provides a valuable tool to screen novel experimental agents to identify sensitive tumor types prior to entering phase II clinical trials. The sensitivity and predictivity data of patient-derived tumor explants generated with the Freiburg xenograft panel in recent years, however, have not always been mirrored in other programs using xenograft models like that of the U.S. National Cancer Institute [5, 6]. Status reports of latter institution about human tumor xenograft testings have not been favorable and as a result large-scale in vivo screening has been dramatically reduced. In the 1980s, the NCI used three patient-derived xenograft tumor models in nude mice (MX1, LX1, and CX1) which were partly

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able to identify compounds active in the clinic [12]: the mammary tumor MX-1 10 of 21, the lung cancer LX-1 7 of 21, and the colon cancer CX-1 0 of 22. Xenografts established from cell lines of the NCI 60 cell line panel were less successful. They responded well to alkylating agents, but the majority of drugs with other modes of action showed some growth delay only. Tumor remissions were an extremely rare event [11, 12]. An explanation for this phenomenon could be found in the undifferentiated histology of the vast majority of cell line-derived xenografts as shown in Fig. 7.2. Compared to patient-derived models, these tumors lost most differentiation and tissue architecture, vascularization is minimal, and it appears that only one homogeneous cell population persisted. The patient-derived xenografts maintained donor characteristics at the morphological and molecular level (Fig. 7.1) and the clinical response characteristics. Whenever xenografts were established from fresh patient material – such as in our labs or, for example, the Institute for Cancer Research in London where six different tumor types (teratoma, small cell lung, breast, melanoma, colon, and non-small cell lung) were studied for chemotherapeutic response – they were shown to be predictive and valuable for new drug development [7, 16, 34].

7.3.4 Molecular Characterization of Human Tumor Xenografts for Target-Oriented Drug Discovery Recent advances in molecular medicine have deepened our understanding of the pathological basis of oncogenesis. This development has had far reaching consequences for drug development in oncology. While traditional procedures for evaluation of drug efficacy were based on empirical screens measuring cytotoxicity, the current algorithms emphasize additional aspects of malignancy such as angiogenesis, apoptosis, cellular senescence, self-renewal, metastasis, and signal transduction [20, 21, 28, 34, 35]. The rapid characterization of new target proteins and their validation for new drug development would not have been possible without two key technologies that have eased the bottleneck of targeted drug development: (a) whole human genome gene expression profiling [25, 31] and (b) high-throughput tumor tissue microarrays [24–28]. 7.3.4.1 Significance of Gene Signatures for Anticancer Therapy Patient tumors established subcutaneously in serial passage in nude mice were characterized for their sensitivity towards one targeted and ten standard cytotoxic anticancer agents [25, 33]. The latter include the alkylating agents cyclophosphamide, ifosfamide, mitomycin-C, and cisplatin, the antimetabolites 5-FU, and gemcitabine; the topoisomerase II inhibitors adriamycin and etoposide as well as the tubulin binders paclitaxel and docetaxel. The mean number of tumors treated with any of the various drugs was 54 (range 31–78). The tumor xenografts’ gene

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expression profiles were determined using the Affymetrix HG-U133 plus 2.0 mRNA expression array representing ~38,500 human genes. The hypothesis was that the correlation of drug response to gene expression would identify gene signatures that can predict the drug response of individual tumors to these agents. Predictive gene signatures were found and subsequently verified using the LOOCV technique and for three drugs in independent testing sets. Tumors were considered as responsive if the drugs effected a tumor volume inhibition to less than 11–41% of control tumors. The median cut-off for all drugs was a T/C of 25%. Using these criteria, on average one-third of the test tumors were sensitive (responders) and two-thirds were resistant (nonresponders). The bioinformatic analysis yielded predictive gene signatures consisting of 20–129 genes (mean for the ten drugs 87 genes). On average, the response rate for predicted responders (83%) was 2.45-fold higher than that for all test tumors (random testing, 34%). This increase of response rates, following signature-guided testing, was consistent for all agents. Conversely, 94% of the predicted nonresponders (range 84–100%) proved to be nonresponders in the nude mouse [25, 31]. The majority of genes (59%) making up the predictive gene signatures had an unknown function. Known genes were implicated in cell proliferation, apoptosis, DNA repair, cell cycle, metabolism, and transcription. The predictive gene signatures presented here for 12 cytotoxic agents have the potential to substantially increase tumor response rates compared to empirical drug treatment. However, they need to be further validated. In a similar way a predictive gene signature was developed for the VEGF inhibitor Bevacizumab. The antibody was tested in vivo in 72 tumor models including tumor types in which Bevacizumab is registered in the clinic (colon, non-small cell lung, breast, and renal cancers) [36]. Bevacizumab was applied in 3 weekly injections i.v. when the tumors reached ~100 mm³. Using a T/C of <34% as a cut-off for activity 29% (21/72) of all tumors were sensitive, 12/34 NSCLC, 4/18 colon, 3/10 breast, and 2/10 renal cancers. A gene signature consisting of 35 genes was identified from which 21 genes are associated with angiogenesis. The signature was validated using the LOOCV and in the independent testing set. In the LOOCV, 15/47 tumors were predicted to respond, from which in the real testings ten responded. Therefore, the response rate increased from 30% (14/47) to 67%. From the predicted 32 nonresponders 28 (88%) were resistant in real testings. In the independent testing set (25 tumors), the correct prediction was 71% for the predicted responders and 89% for the nonresponders. This gene signature is currently being evaluated in a clinical pilot study. 7.3.4.2 Significance of Tumor Tissue Microarrays Since their introduction in the late 1990s, tissue microarrays have become a wellestablished method for the parallel evaluation of gene and protein expression in hundreds of tissue biopsies [37]. Fluorescent in situ hybridization (FISH) and immunohistochemistry allow a classification of tissues according to gene expression­,

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protein levels, and histology. Moreover, the relationship between gene expression, pathological variables, and clinical outcome data can be studied, which permits the assessment of the target’s relevance for therapy, diagnosis, and prognosis of cancer. Thus, tissue microarrays have proven to be a valuable tool for the study of the human oncoproteome. We have applied tissue microarray technology to our collection of human tumor xenografts [26–28]. For example, we have profiled the expression of five proteins involved in cell migration and/or angiogenesis: VEGF, matrix metalloproteinase 1 (MMP1), protease activated receptor (PAR1), cathepsin B, and b1 integrin and transferring receptor in a panel of over 150 tumors and compared their expression levels to available patient outcome data as reported elsewhere [28, 34]. The basic characterization of the angiogenic proteins included overall occurrence, associations with tumor histology, and prognostic significance. In the case of cathepsin B, a high expression was found in a wide range of tumors. Our tissue microarray results were compared to cathepsin B mRNA data (r = 0.48, p < 0.001, n = 94). The degree of correlation was moderate, which might reflect the inherent posttranslational processing between mRNA and intracellular protein. Cathepsin B overexpression was found in pleural mesotheliomas, urethral carcinomas, cancers of the prostate and lung adenocarcinomas. Overall, we have identified 22 xenografts in our tumor collection with highly elevated cathepsin B levels. These might be employed for preclinical testing of compounds targeting the enzyme or polymer prodrugs such as polymeric prodrug N-(2-hydroxypropyl) methacrylamide (HPMA) copolymer–Gly–Phe–Leu–Gly–doxorubicin conjugate PK1 or HPMA-conjugated geldanamycin that are aimed at utilizing tumor associated proteases for specific release of cytotoxic agents in the tumor cell [38, 39]. 7.3.4.3 Target Prevalence in the Freiburg Tumor Collection The development of anticancer agents is Focused on the discovery of novel targets or pathways which play a critical role in the pathogenesis of tumors. Therefore, we characterized our tumor collection on the genomic and proteomic level with different methods (Table 7.6). Gene expression was determined with the Affymetrix chip HG-U133 plus 2.0 mRNA expression array representing ~38,500 human genes. For protein analyses, immunohistochemistry is done on tissue microarrays or with bead suspension assays using protein lysates. The majority of the known signal transduction proteins (total and activated) were determined with these techniques. Mutation status of selected genes is ongoing. As an example gene expression for the AKT gene is shown in 220 tumor models (Fig. 7.5). At the protein level the data are shown for 150 tumor models (Fig. 7.6) and for the colon panel phospho-(p)AKT and total (t)AKT are presented (Fig. 7.7). The latter allows us to carefully select tumor models that are likely to respond to AKT pathway inhibitors and those that are not and thus can be used as “negative” controls. All of our molecular data are stored in a database, allowing the selection of models overexpressing targets of interest. Furthermore the expression of molecular

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Table 7.6  Patient-derived tumor models available for genomic and proteomic studies DNAGrowing in vivo Protein Gene repository lysate December expression Tissue for mutation 2007–2008 2008 Affy-chip microarray repository analyses Tumor Lung nonsmall cell 24 43 37 25 41 Colon 20 27 21 24 30 Melanoma 16 19 19 14 18 Kidney 11 17 12 7 12 Breast 10 11 11 11 10 Pancreas 8 9 9 10 10 Gastric 6 8 6 4 7 Lung small cell 3 8 5 4 8 Head and neck 5 7 5 4 7 Sarcomas 4 6 6 5 9 Ovary 3 7 4 5 9 Pleuramesothelioma 4 6 4 5 5 Bladder 4 6 6 5 4 Prostate 2 2 2 2 2 Uterus cervix 2 3 2 1 2 Uteruscorpus 0 1 1 1 2 Liver 1 1 1 1 1 Glioblastoma 1 1 1 1 1 GIST tumor 1 0 0 0 1 Testicular 1 1 2 1 2 Anal 1 1 0 1 1 Miscellaneous 3 0 6 5 4 Total >200 130 184 160 136 186 In addition, the gene expression data of 44 cell lines growing in vivo as xenografts are available

targets can be correlated with in  vitro or in  vivo activity of new drugs. This ­bioinformatic approach is based on the COMPARE algorithm developed by the U.S. National Cancer Institute [40]. This allows to determine if there is any statistical interaction (Spearman’s rank coefficient) with the potential molecular target, thus providing information regarding a possible mechanism of action of a drug.

7.3.5 Assessment of In Vivo Efficacy of Anticancer Agents Drug Discovery Using the Freiburg Xenograft System In recent years we have developed a combined in vitro/in vivo testing ­procedure for anticancer drug development [18–20]. Broad in  vitro testings using fresh human tumor xenograft tissues in the clonogenic assay or slow growing ­p ermanent cell lines derived thereof have been employed in our laboratory as

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10

AXF BXF CEXF CNXF CXF GXF HNXF LEXF LIXF LXFA LXFE LXFL LXFS LYXF MAXF MEXF MMXF OVXF PAXF PRXF PXF RXF SXF TXF UXF

mRNA expression

100

Fig. 7.5  Characterization of the Freiburg xenograft panel for AKT gene expression in 220 tumor models. AXF anal cancer, BXF bladder, CEXF cervix uteri, CNXF central nervous system, CXF colon, GXF gastric, HNXF head and neck, LIXF liver, LXF lung cancers adeno, epidermoid, large and small cell, MAXF mammary, MEXF melanomas, OVXF ovarian, PAXF pancreas, PRXF prostate, PXF pleuramesotheliomas, RXF renal, SXF soft tissue sarcomas, TXF testicule, UXF uterus cancers 100

normalized MFI

10

1

0.1

AXF BXF CEXF CNXF CXF GXF HNXF LIXF LXFA LXFE LXFL LXFS MAXF MEXF OVXF PAXF PRXF PXF RXF SKXF SXF TXF UXF

0.01

Fig. 7.6  Protein levels for Phospho-AKT in 150 human tumor xenografts growing s.c. in nude mice

a ­p rescreen preceding the in vivo testings in nude mice in order to identify the most sensitive tumor types and individual tumors, thus reducing the number of ­animal experiments. The clonogenic assay offers the advantage that tumor cells grow in three dimensions, and only tumor stem cell-like cells are capable of proliferating. Furthermore, the tumor source – the patient-derived in  vivo growing xenograft – resembles the tumor as it would grow in a patient. In ­addition,

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10

p-Akt t-Akt

8 6 4 2 0 CXF 1096 CXF 1788 CXF 647 CXF 676 CXF 1133 CXF 742 CXF 1034 CXF 233 CXF 260 CXF 504 CXF 280 CXF 269 CXF 1297 CXF 94LX CXF 1103 CXF 1784 CXF 243 CXF 1729 CXF 609 CXF 1783 CXF 533 CXF 1044 CXF 280 CXF 1086 CXF 158 CXF 1753 CXF 264 CXF 975 CXF 883

normalized mean fluorescence intensity (nMFI)

12

10

p-Akt t-Akt

1

0,1

0,01 CXF 1096 CXF 1788 CXF 647 CXF 676 CXF 1133 CXF 742 CXF 1034 CXF 233 CXF 260 CXF 504 CXF 280 CXF 269 CXF 1297 CXF 94LX CXF 1103 CXF 1784 CXF 243 CXF 1729 CXF 609 CXF 1783 CXF 533 CXF 1044 CXF 280 CXF 1086 CXF 158 CXF 1753 CXF 264 CXF 975 CXF 883

normalized mean fluorescence intensity (nMFI)

100

Fig. 7.7  Akt/PKB detection in patient-derived colon carcinoma xenografts. Native tumor lysates were analyzed for levels of Akt/PKB [total (t) and S473 (p)] by BioPlex® analysis. Values are given as normalized mean fluorescence intensity values. Normalization control is an Oncotest tumor lysate pool, generated from more than 100 lysates

the clonogenic assay has been well validated in the past in terms of response rates of fresh tumor explants versus that of patients in the clinic with a highly accurate prediction found for drug resistance and an intermediate predictivity for drug sensitivity [17, 19]. The first stage of the in vitro/in vivo xenograft model system is the evaluation of new compounds in 6–12 and the second stage in 24–48 human tumor xenografts for their inhibitory effects on colony growth in soft agar. The 3–6 most sensitive xenografts in vitro are used for subsequent in vivo testings of compounds against subcutaneously growing tumors in nude mice. One of the limitations of the clonogenic assay, however, is its labor intensity. Nevertheless, it is very valuable as a secondary screen for testing several hundred compounds per year, and for significantly reducing the number of animal tests.

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7.3.6 Selected Examples of Anticancer Agents in Clinical Trials Which Have Been Discovered in a Target-Oriented Approach For the past 20 years, our group has received compounds from academia, the European Organization for Research and Treatment of Cancer (EORTC), the NCI, and pharmaceutical industry [41, 42]. They belong to various structural classes and act by different mechanisms. An overview of compounds which showed activity in our models and where we contributed to the decision to develop the compounds clinically is shown in Table 7.7. Compounds that have entered clinical trials are methotrexate coupled to human serum albumin (MTX-HSA) as synthesized at the German Cancer Research Center or the telomere targeting agent RHPS4, and the minor groove binder DRH 417. The CDK-inhibitor flavopiridol, the DNA binder Quinocarmycin, the glycoprotein synthesis inhibitor Spicamycin, the HSP90 inhibitors 17-allylamino-geldanamycin (17AAG), and 17-dimethylaminoethylamino-17-demethoxy-geldanamycin (17DMAG) were obtained from the U.S. National Cancer Institute; the tubulin binder rhizoxin, and the bioreductive agent EO9 from the EORTC. From the pharmaceutical industry we obtained the platin derivative lobaplatin, the minor groove binder Trabectedin/ ET743, the recombinant human monoclonal antibody 2C4 (Omnitarg, pertuzumab), the telomerase inhibitor sodium metaarsenite/KML001, and various other compounds from companies. The minor groove binder Trabectedin was approved for the treatment of soft tissue sarcoma and lobaplatin for the treatment of SCLC.

Table 7.7  Anticancer drugs discovered as active in the Freiburg human tumor xenograft panel Mode of action References From academia

From the US-NCI

From the EORTC From companies

MTX–has RHPS4 DRH 417 Flavopiridol Quinocarmycin Spicamycin 17-AAG/tanespimycin 17-DMAG Rhizoxin EO9 Lobaplatin ET743/Trabectedin 2C4/pertuzumab KML001 Many discrete compounds

Antimetabolite, EPR effect Telomere targeting agent Minor groove binder Cyclin dependent kinase inhibition DNA binding Glycoprotein synthesis Heatshock protein 90 modulation Heatshock protein 90 modulation Tubulin binder Bioreductive alkylating DNA cross-linking and adducts DNA minor groove alkylation Anit-HER2 antibody Telomerase inhibitor/targets telomeres

[49–52] [53, 54] [55] [56] [57] [58] [43–46] [47, 48] [62] [63] [64] [59–61] [65] [66]

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7.3.7 Possible Future Impact of Patient-Derived Tumor Xenografts and Explants Inasmuch as new drug development is already focused on target-oriented and tumor cell specific approaches based on currently available knowledge, there is still an ongoing demand for a better understanding of tumor biology. Thus, basic research is directed toward genetic tumor profiling and the finding of novel more promising, tumor type specific therapeutic agents, or even individualizing cancer therapy. In accomplishing this, DNA array technology now provides a large amount of possible new gene candidates for therapeutic intervention, but the validation of these genes in a wide range of human tumor specimens seems still to prove a bottleneck. In particular, the lack of sufficient tumor tissue material to perform and thoroughly repeat a set of confirmatory analyses is often problematic. Here, our xenograft collection not only provides a rich source of the human genome, but also provides material of persistent quality. Tissues can be collected freshly and immediately flash-frozen, which results in extremely high total RNA, mRNA, protein, and phosphor protein quality. Moreover, the optimal tissue processing procedure can be evaluated, and later on, novel treatments – either small molecules or antibodies – can be tested in the same tissue from which a target was isolated and validated. In addition, pre- and posttreatment specimens for a pharmaco­ genomic approach can easily be collected. New biomarkers or gene signatures predicting the activity of new agents are continuously being developed. In view of these possibilities, patient-derived tumor xenografts and explants will retain their importance in preclinical cancer research and will continue to increase.

7.4 Summary In Freiburg, we have implanted more than 1,700 human tumors during the past 20 years and experimental models haven been developed for all major solid human tumor types by engrafting patient tumors into immunodeficient mice. The percentage of tumors established in serial passage was highest (40–60%) for cancers of the esophagus, cervix and corpus uteri, colon, SCLCs, and melanomas. Take rates between 20 and 39% were found for cancers of the lung (NSCLC), ovary, head and neck, pancreas, testicle, stomach and bladder, soft tissue sarcomas, and pleuramesothelimas. Only 5–19% of cancers of the kidney, breast, and prostate, however, could be transferred into serial passage. Two hundred tumors were characterized in detail for the sensitivity against standard agents as well as expression of oncogenes, suppressor genes, growth factors and their receptors, parameters of angiogenesis, invasion, and metastasis as well as resistance associated proteins. The gene expression profiles of 200 models were determined using the Affymetric HG-U 133 plus 2.0 expression array representing 38,500 genes. The response of human tumor xenografts to standard agents in the nude mouse in comparison to patient tumors was very similar, identical results were found in 90% (19/21) of sensitive and 97% (57/59) of resistant tumors. This highly accurate predictivity

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validates the xenograft system for drug development. Moreover, since xenografts retain most of the molecular and pathophysiological patient-tumor characteristics, they are suitable tools to study target-oriented therapeutic approaches. We have used these models for both biological and therapeutic investigations. Thus, initial testings are performed in vitro using the clonogenic assay and xenograft as donor material for fresh tissue specimens. Novel compounds are initially tested in 6–12 and up to a total of 24–48 human tumor xenografts. The in  vitro testings further allow the determination of the most sensitive tumors for subsequent in vivo studies, thus reducing the number of in vivo experiments. Compounds in clinical trial that have been found active by our patient-derived xenograft systems include a broad spectrum of agents with various modes of action and very specific targets such as methotrexate coupled to human serum albumin, RHPS4, DRH 417, flavopiridol, quinocarmycin, spicamycin, 17-AAG and 17-DMAG, rhizoxin, and EO9. The minor groove binder Trabectetin was registered in soft tissue sarcoma. Acknowledgments  We are grateful to our colleagues Julia Schüler, Thomas Metz, Veronika Jung, Elke Tetling, and Anke Behnke for their important contributions to this project. We thank Andre Korrat and Hildegard Willmann for software development and data evaluation. The work was supported by grants from the German Ministry for Research and Education BMBF and the National Cancer Institute, Biological Testing Branch.

References 1. Rygaard J, Povlsen CO. Heterotransplantation of a human malignant tumour to the mouse mutant “nude”. Acta Pathol Microbiol Scand. 1969;77:758–60. 2. Povlsen CO, Sordat B, Tamaoki N. Human tumors serially transplanted in nude mice. Report Copenhagen: The Nude Mouse Secretariat, 1977. 3. Houchens DP, Ovejera AA. Proceedings of the symposium on the use of athymic (nude) mice in cancer research. New York: Gustav Fischer Verlag; 1978. 4. Reid LM, Holland J, Jones C, et al. Some of the variables affecting the success of transplantation of human tumors into the athymic nude mouse. In: Houchens DP, Ovejera AA, editors. The use of athymic (nude) mice in cancer research. New York: Gustav Fischer Verlag; 1978. p. 107–22. 5. Ovejera AA. The use of human tumor xenografts in large scale drug screening. In: Kallman RF, editor. Rodent tumor models in experimental cancer therapy. Oxford: Pergamon Press; 1987, p. 218–20. 6. Venditti JM, Weseley RA, Plowman J. Current NCI preclinical antitumor screening in vivo: results of tumor panel screening, 1976–1982, and future directions. In: Garattini S, Goldin A, Hawking F, editors. Advances in pharmacology and chemotherapy. Vol 20. New York: Academic Press; 1984. p. 2–20. 7. Staquet MJ, Byar DP, Green SB, et al. Clinical predictivity of transplantable tumor systems in the selection of new drugs for solid tumors. Rational of a three-stage strategy. Cancer Treat Rep. 1983;67:753–65. 8. Fichtner I, Goan S, Becker M, et al. Transplantation of human haematopoietic of leukaemic cells into SCID and NOD/SCID mice. In: Fiebig HH, Burger AM, editors. Relevance of tumor models for anticancer drug development. Contrib Oncol, Vol 54. Basel: Karger; 1999. p. 207–17. 9. Bonnet D, Dick JE. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med. 1997;3:730–37.

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10. Bibby MC. Transplantable tumours in mice – the way forward. In: Fiebig HH, Burger AM, editors. Relevance of tumor models for anticancer drug development. Contrib Oncol, Vol 54. Basel: Karger; 1999. p. 1–13. 11. Fiebig HH, Dengler WA, Roth T. Human tumor xenografts: predictivity, characterization and discovery of new anticancer agents. In: Fiebig HH, Burger AM, editors. Relevance of tumor models for anticancer drug development. Contrib Oncol, Vol 54. Basel: Karger; 1999. p. 29–50. 12. Plowman J, Dykes DJ, Hollingshead M, et al. Human tumor xenograft models in NCI drug development. In: Teicher BA, editor. Anticancer drug development guide. Totawa: Humana Press; 1997. p. 101–25. 13. Sausville EA, Feigal E. Evolving approaches to cancer drug discovery and development at the National Cancer Institute. Ann Oncol. 1999;10:1287–92. 14. Malakoff D, Vogel G, Marshall E. The rise of the mouse, biomedicine’s model mammal. Science. 2000;288:248–57. 15. Note for guidance on the pre-clinical evaluation of anticancer medicinal products. (http:// www.eudra.org/emea.html). 16. Sausville EA, Burger AM. Use of human tumor xenografts in anti-cancer drug development. Cancer Res. 2006;66:3351–54. 17. Scholz CC, Berger DP, Winterhalter BR, et al. Correlation of drug response in patients and in the clonogenic assay using solid human tumor xenografts. Eur J Cancer. 1990;26:901–5. 18. Fiebig HH, Schmid JR, Bieser W, et al. Colony assay with human tumor xenografts, murine tumors and human bone marrow. Potential for anticancer drug development. Eur J Cancer Clin Oncol. 1987;23:937–48. 19. Fiebig HH, Berger DP, Dengler WA, et  al. Combined in  vitro/in  vivo test procedure with human tumor xenografts. In: Fiebig HH, Berger DP, editors. Immunodeficient mice in oncology. Contrib Oncol Vol 42. Basel: Karger; 1992. p. 321–51. 20. Fiebig HH, Maier A, Burger AM. Clonogenic assay with established human tumor xenografts: Correlation of in vitro to in vivo activity as a basis for anticancer drug discovery. Eur J Cancer. 2004;40:802–20. 21. Chumsri S, Phatak P, Edelman MJ, et al. Cancer stem cells and individualized therapy. Cancer Genomics Proteomics. 2007;4:165–74. 22. Workman P, Twentyman P, Balkwill F, et al. United Kingdom Co-Ordinating Committee on Cancer Research (UKCCCR) guidelines for the welfare of animals in experimental neoplasy (ed 2). Br J Cancer. 1998;77:1–10. 23. Geran RI, Greenberg NH, MacDonald MM, et al. Protocols for screening chemical agents and natural products against tumor and other biological systems. Cancer Chemother Rep. 1972;3:1–103. 24. Baumgarten AJ, Fiebig HH, Burger AM. Molecular analysis of xenograft models of human cancer cachexia – possibilities for therapeutic intervention. Cancer Genomics Proteomics. 2007;4:223–31. 25. Fiebig HH, Schüler J, Bausch N, et al. Gene signatures developed from patient tumor explants grown in nude mice to predict tumor response to 11 cytotoxic drugs. Cancer Genomics Proteomics. 2007;4:197–209. 26. Kuesters S, Maurer M, Burger AM, et  al. Correlation of ErbB2 gene status, mRNA- and ­protein expression in a panel of human tumour xenografts. Onkologie. 2006;29:249–56. 27. Wirth GJ, Schandelmaier K, Smith V, et al. Microarrays of 41 human tumor cell lines for the characterization of new molecular targets: expression patterns of cathepsin B and the transferrin receptor. Oncology. 2006;71:86–94. 28. Smith V, Wirth GJ, Fiebig HH, et al. Tissue microarrays of human tumor xenografts: characterization of proteins involved in migration and angiogenesis for applications in the development of targeted anticancer agents. Cancer Genomics Proteomics. 2008;5:263–73. 29. Von Hoff DD. In vitro predictive testing: the sulfonamide era. Int J Cell Cloning. 1987;5:179–90. 30. Hamburger AW, Salmon SE. Primary bioassay of human tumor stem cells. Science. 1977;197:461–63. 31. Korrat A, Greiner T, Maurer M, et al. Gene signature-based prediction of tumor response to cyclophosphamide. Cancer Genomics Proteomics. 2007;4:187–95.

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32. Whiteford CC, Bilke S, Greer BT, et al. Credentialing preclinical pediatric xenograft models using gene expression and tissue microarray analysis. Cancer Res. 2007;67:32–40. 33. Fiebig HH, Metz T, Greiner T, et al. Antitumor activity of Avastin® in a panel of 100 patient derived tumor models in  vivo in relation to proteomic biomarker profiles. San Francisco: AACR-NCI-EORTC Molecular Targets and Cancer Therapeutics; 2007, p. A28. 34. Steel G. How well do xenografts maintain the therapeutic response characteristics of the source tumor in the donor patient? In: Kallman RF, editor. Rodent tumor models in experimental cancer therapy. Oxford: Pergamon Press; 1987, p. 205–8. 35. Klostermeyer A, Schüler JB, Fiebig HH, et al. Expression patterns of metastasis associated proteins(MMPs, CD44) in a panel of human tumor xenografts. Ann Hematol. 1998;77:220. 36. Fiebig HH, Schüler JB, Greiner T, et al. Determination of a 35 gene signature predictive for the effectiveness of Bevacizumab. San Francisco: AACR-NCI-EORTC Molecular Targets and Cancer Therapeutics, Congress; 2007, p. B10. 37. Kononen J, Bubendorf L, Kallioniemi A, et  al. Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat Med. 1998;4:844–7. 38. Satchi R, Connors TA, Duncan R. PDEPT: polymer-directed enzyme prodrug therapy. I. HPMA copolymer-cathepsin B and PK1 as a model combination. Br J Cancer. 2001;85:1070–76. 39. Borgman MP, Ray A, Kolhatkar RB, et al. Targetable HPMA copolymer-aminohexylgeldanamycin conjugates for prostate cancer therapy. Pharm Res. 2009;26:1407–18. 40. Paull KD, Shoemaker RH, Hodes L, et  al. Display and analysis of patterns of differential activity of drugs against human tumour cell lines: development of mean graph and COMPARE algorithm. J Natl Cancer Inst. 1989;81:1088–92. 41. Fiebig HH, Berger DP, Winterhalter BR, et  al. In-vitro and in-vivo evaluation of US-NCI compounds in human tumor xenografts. Cancer Treat Rev. 1990;17:109–17. 42. Hendriks HR, Berger DP, Dengler WA, et  al. New anticancer drug development: interim results of the cooperative program between the Freiburg preclinical anticancer drug development group and the EORTC new drug development office. Contrib Oncol. 1996;51:108–14. 43. Stebbins CE, Russo AA, Schneider C, et  al. Crystal structure of an Hsp90-geldanamycin complex: targeting of a protein chaperone by an antitumor agent. Cell. 1997;89:239–50. 44. Schnur RC, Corman ML, Gallachun RJ, et al. erbB-2 oncogene inhibition by geldanamycin derivatives: synthesis, mechanism of action, and structure-activity relationships. J Med Chem. 1995;38:3813–20. 45. Burger AM, Fiebig HH, Newman DJ, et al. Antitumor activity of 17-allylaminogeldanamycin (NSC 330507) in melanoma xenografts is associated with decline in Hsp90 protein expression. Ann Oncol. 1998;9 Suppl 2:132. 46. Burger AM, Fiebig HH, Stinson SF, et al. 17-(allylamino)-17-demethoxy-geldanamycin activity in human melanoma models. Anticancer Drugs. 2004;15:377–87. 47. Smith V, Sausville EA, Camalier RF, et al. Comparison of 17-dimethylaminoethylamino-17demethoxy-geldanamycin (17DMAG) to 17-allylamino-demethoxygeldanamycin (17AAG) in  vitro: effects on Hsp90 and client proteins in melanoma models. Cancer Chemother Pharmacol. 2005;56:126–37. 48. Hollingshead M, Alley M, Burger AM, et  al. In vivo anti-tumor efficacy of 17-DMAG (17-dimethylaminoethylamino-17-demethoxygeldanamycin hydrochloride), a water-soluble geldanamycin derivative. Cancer Chemother Pharmacol. 2005;56:115–25. 49. Stehle G, Sinn H, Wunder A, et al. Plasma protein (albumin) catabolism by the tumor itself – implications for tumor metabolism and the genesis of cachexia. Crit Rev Oncol Hematol. 1997;26:77–100. 50. Matsumara Y, Maeda HA. A new concept for macromolecular therapeutics in cancer ­chemotherapy: mechanism of tumoritropic accumulation of proteins and the antitumor agent smancs. Cancer Res. 1986;46:6387–92. 51. Hartung G, Stehle G, Sinn H, et al. Phase I trial of methotrexate-albumin in a weekly intravenous bolus regimen in cancer patients. Clin Cancer Res. 1999;5:753–59. 52. Burger AM, Hartung G, Stehle G, et al. Preclinical evaluation of a methotrexate-albumineconjugate (MTX-HSA) in human tumor xenografts in vivo. Int J Cancer. 2001;92:718–24.

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53. Cookson JC, Dai F, Smith V, et  al. Pharmacodynamics of the G-quadruplex-stabilizing telomerase inhibitor 3,11-difluoro-6,8,13-trimethyl-8H-quino[4,3,2-kl]acridinium methosulfate (RHPS4) in vitro: activity in human tumor cells correlates with telomere length and can be enhanced, or antagonized, with cytotoxic agents. Mol Pharmacol. 2005;68:15551–58. 54. Phatak P, Cookson JC, Dai F, et al. Telomere uncapping by the G-Qadruplex ligand RHPS4 inhibits clonogenic tumour cell growth in vitro and in vivo consistent with a cancer stem cell targeting mechanism. Br J Cancer. 2007;96:1223–33. 55. Burger AM, Loadman PM, Thurston DE, et al. Preclinical pharmacology of the pyrrolobenzodiazepine (PBD) monomer DRH-417 (NSC 709119). J Chemother. 2007;19:66–78. 56. Drees M, Dengler W, Roth T, et  al. Flavopiridol (L86-8275): selective antitumor activity in vitro and in vivo for prostate carcinoma cells. Clin Cancer Res. 1997;3:273. 57. Fiebig HH, Berger DP, Dengler WA, et al. Cyanocyclin A and the Quinocarmycin analog NSC 607 097 demonstrate selectivity against melanoma xenografts in-vitro and in-vivo. Proc Am Assoc Cancer Res. 1994;2794. 58. Burger AM, Kaur G, Hollingshead M, et al. Antiproliferative activity in vitro and in vivo of the spicamycin analog KRN5500 with altered glycoprotein expression in vitro. Clin Cancer Res. 1997;3:455–63. 59. Hendriks HR, Fiebig HH, Giavazzi R, et al. High antitumor activity of ET743 against human tumour xenografts from melanoma, non-small-cell lung and ovarian cancer. Ann Oncol. 1999;10:1233–40. 60. Grosso F, Sanfilippo R, Virdis E, et al. Trabectedin in myxoid liposarcomas (MLS): a longterm analysis of a single-institution series. Ann Oncol. 2009; 439–44. 61. Fiebig HH, Maier A, Bausch N, et al. Preclinical evaluation of Trabectedin to support the selection of tumor indications for further clinical development. Proc Am Assoc Cancer Res. 2009;2678. 62. Winterhalter BR, Berger DP, Dengler WA, et  al. High antitumors activity of rhizoxin in a combines in-vitro and in-vivo test procedure with human tumor xenografts. Proc Am Assoc Cancer Res. 1993;34:376. 63. Hendriks HR, Pizao PE, Berger DP, et al. E09, a novel bioreductive alkylating indoloquinone with preferential solid tumor activity and lack of bone marrow toxicity in preclinical models. Eur J Cancer. 1993;29:897–906. 64. Klenner T, Voegeli R, Fiebig HH, et al. Antitumor effect of the platinum complex D-19466 (Inn: Lobaplatin) against the transplantable osteosarcoma of the rat and other experiments. J Cancer Res Clin Oncol. 1992;118 Suppl:149. 65. Friess T, Bauer S, Burger AM, et al. In vivo activity of recombinant humanized monoclonal antibody 2C4 in xenografts is independent of tumor type and degree of HER2 overexpression. Eur J Cancer. 2002;38:S149. 66. Phatak P, Dai F, Nandakumar MP, et al. KML001 (sodium meta arsenite) cytotoxic activity is associated with its binding to telomeric sequences and telomere erosion in prostate cancer cells. Clin Cancer Res. 2008;14:4593–4602.

Chapter 8

The Pediatric Preclinical Testing Program Christopher L. Morton and Peter J. Houghton

Abstract  Cancer in children is rare, and tumors differ from more common ­epithelial carcinomas most frequently diagnosed in adults. Current multimodality treatments are curative in approximately 70% of children with cancer. Further, even for patients who ultimately succumb to their disease, initial responses to treatment may be good. Thus, the number of patients eligible for evaluating new agents is limited. Consequently, developing novel drugs, drug combinations, or strategies presents unique problems, as relatively few clinical trials can be conducted. The Pediatric Preclinical Testing Program (PPTP) is an experiment to determine whether panels of childhood cancers, either in vitro or in vivo growing as subcutaneous xenografts in mice, can accurately identify novel agents or combinations of agents that will have significant clinical activity. Here we describe the process of model selection, molecular characterization, in vivo drug evaluation, and summarize results to date. Keywords  Pediatric preclinical testing program • Human tumor xenografts • Brain tumors • Sarcomas • Neuroblastoma • Kidney tumors • Acute lymphoblastoid ­leukemia • Drug screening • Molecular characterization

8.1 Introduction Development of new therapies for children with cancer presents certain challenges unique to this population. For any given pediatric cancer type, the number of patients diagnosed annually is much smaller than for most adult cancers [1,

P.J. Houghton (*) Center for Childhood Cancer, Nationwide Children’s Hospital, 700 Children’s Drive, Columbus, OH 43205, USA e-mail: [email protected]

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2]. Overall, there are approximately 12,500 cases of cancer diagnosed in patients under 21 years old annually in the United States. For example, rhabdomyosarcoma is the most common sarcoma in children, and fewer than 400 cases are diagnosed each year among children and adolescents less than 20 years of age [3]. Cure rates, overall, are about 70% with current multimodality therapy, and the overall 5-year survival rate for children with cancer is approaching 80% [2]. This combination of low incidence and increasingly effective primary therapy results in relatively few children eligible for evaluating experimental therapies, and those that are available have generally been extensively treated and have highly resistant disease. Consequently, the population of patients available for Phase 1 and -2 clinical testing may not be sufficient and/or ideal for identifying novel therapies that may have significant benefit if used at diagnosis [4]. The challenge is to identify which novel agents (of the 400 plus being developed as cancer therapeutics) should be prioritized for pediatric testing. The magnitude of the problem is greatly amplified when one considers potential drug combinations, let  alone issues of drug scheduling, etc. The pioneering work by Skipper and Schabel, alluded to in the preface of this book, demonstrated that preclinical models of leukemia could serve as a guide to developing effective curative treatment for children with acute lymphoblastic leukemia. Several groups within the pediatric cancer community have systematically tested the validity of preclinical models to identify novel agents that may have clinical activity in childhood cancers [4–15] as an approach to overcoming the limitations in childhood cancer drug development noted above. On the basis of these experiences, the National Cancer Institute (NCI) implemented the Pediatric Preclinical Testing Program (PPTP), comprising a consortium of investigators who integrate in vivo and in vitro testing with expression and genomic profiling. A 2001 meeting organized by the Children’s Oncology Group (COG) and the NCI identified the need for a systematic approach to pediatric preclinical testing to allow the identification of preclinical models that can be used to reliably inform clinical prioritization decisions. Here, we describe the process used for selecting cell lines and xenografts for inclusion into the PPTP, and the current procedures and results from the in vivo screening component.

8.2 Selection of Preclinical Models The objective of the PPTP is to identify agents with significant activity in the panels of pediatric preclinical models as a potential mechanism for prioritizing agents for advancement to clinical trials for children with specific cancers, thereby expediting the discovery of more effective therapies for children with cancer – but how to best accomplish this? Certain syngeneic models or genetically engineered models of pediatric cancer were available, but by far the most comprehensive panel of tumors was represented by patient derived cell lines

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and xenografts in mice. The first initiative implementing the recommendations of the COG/NCI meeting was the Pediatric Oncology Preclinical Protein-Tissue Array Project (POPP-TAP), a collaborative effort between the NCI and the Children’s Oncology Group [16]. Xenografts and cell lines of pediatric tumors were solicited for the POPP-TAP project and a total of 75 high-quality xenograft samples representing eight tumor types were collected. Objectives of POPP-TAP included determining the gene expression profiles (cDNA arrays) of these preclinical models and developing xenograft tissue microarrays for protein expression of a panel of pediatric xenografts. In the course of this study, a few xenografts were identified that were not good representations of their primary tumors (i.e. their mRNA profile did not capture the characteristic RNA signature typical for the primary [patient] tumors), and these lines were excluded from the final PPTP tumor panel. Hierarchical clustering showed that the majority of the xenografts grouped according to their specific tumor types. Secondly, to formally validate that the expression profiles of the xenografts reflect those of the corresponding primary tumors, Ewing’s tumor, rhabdomyosarcoma, and neuroblastoma were used to test if the characteristic patterns discriminating different tumor types in primary tumors were preserved in the xenograft models. The artificial neural network trained with profiles of primary tumors accurately diagnosed the xenograft tumors for the majority of xenograft models. To determine whether xenografts or cell lines most closely represented the expression profiles of patient tumors, the Euclidean and Pearson’s distance of expression profiles was used as the most immediate way to measure profile similarity. The average distance of model systems from primary tumors indicates how well the model represents the primary tumor. Comparison of the results for the two model systems of neuroblastoma showed that xenografts were significantly closer to primary tumors than the cell lines were (Fig. 8.1). This latter point is of importance and the majority of models (75%) incorporated into the PPTP screen have been derived by directly engrafting patient tumor into mice and avoiding in vitro culture. These models have more recently been termed “tumorgrafts” [17]. As a result of the POPP-TAP initiative the Rh1 line was reclassified from rhabdomyosarcoma to Ewing’s sarcoma (it was subsequently shown to express the type 1 EWS/FLI transcript and t(11;22) translocation). The second round of characterization used the commercially available Affymetrix HG U133 plus 2 platform and 100K SNP arrays to detect genomic alterations. Profiling led to exclusion of two tumor lines that were of mouse origin and five osteosarcoma lines that did not cluster with human or xenograft osteosarcoma samples. Expression profiles of the remaining 87 models with profiles from 112 clinical samples representing the same histologies showed that model tumors cluster with the appropriate clinical histotype, once “immunosurveillance” genes (contributed by infiltrating immune cells in clinical samples) were eliminated from the analysis [18]. Profiling also led to reclassification of SK-NEP1 (putative anaplastic Wilms tumor) to Ewing sarcoma of the kidney [19].

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Fig. 8.1  (a) Hierarchical clustering of xenografts. All quality clones (N = 38,789) of the preclinical pediatric xenograft models were subjected to hierarchical clustering with average linkage using Pearson’s correlation coefficient as the metric. Distinct colors were used for each tumor type

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8.3 Molecular Characterization of Tumor Models The “major” difference between the gene expression of these models and their human counterparts was the signature contributed by stromal cells, which would be absent from cell lines, and contributed by murine elements in xenografts (these signals would not be identified on the HG U133 array). However, of equal importance is whether the models accurately represented the known genetic complexity of the respective childhood cancers. Preliminary data had shown that for several Wilms tumor xenografts similar copy number alterations (CNA) were represented in both patient derived tissue and in the models after heterografting for several passages (serial transfer from mouse to mouse). Although the models were not compared directly with patient tissue from which they were derived, analysis of copy number alterations using the Affymetrix 100K single nucleotide polymorphism GeneChip showed that the models have similar copy number alterations to their “generic” clinical counterparts. Several consistent copy number changes not reported previously were also found (e.g. gain at 22q11.21 that was observed in 5 of 7 glioblastoma samples, loss at 16q22.3 that was observed in 5 of 9 Ewing’s sarcoma and 4 of 12 rhabdomyosarcoma models, and amplification of 21q22.3 that was observed in 5 of 7 osteosarcoma models). Osteosarcoma models demonstrated the highest incidence of CNA, whereas acute lymphoblastoid leukemias and rhabdoid tumor demonstrated the lowest rate of CNAs. Importantly, consistent CNAs were non-random, and distributed differentially across the genome dependent on tumor type (Fig. 8.2). Extensive sequencing of these models is planned; however, one consistent feature of all human studies sequencing cancer genomes to date has been the diversity of genes mutated within individual tumor types. These data challenge the proposed gene-centric models of tumorigenesis [20] and caution one’s interpretation of the translational value of current “simplistic” genetically engineered rodent models.

Fig. 8.1 (continued) to enhance readability. (*) Xenografts that did not cluster with the majority of the same cancer type. Solid vertical lines, two pairs of samples each derived from a common cell line (SK-N-AS: NB-X75 and NB-X107; SMS-KCNR: NB-X75 and NB-X108) but obtained from different laboratories and derived from the s.c. (X75 and X107) or orthotopic (intra-adrenal; X108 and X64) route. (b) Artificial neural network (ANN) average committee votes from a feedforward resilient back-propagation multilayer perceptron ANN with three layers: an input layer of the top ten principal components of the data; a hidden layer with five nodes; and an output layer generating a committee vote for each of the three input classes. (c) clone cutter: sensitivity and specificity with increasing number of the top-ranking clones removed from the training and testing data sets. Quality-filtered clones were ranked by determining the sensitivity of prediction of the training samples with respect to a change in the gene expression level of each clone. Then, after classification using the clone-cutter ANN, the sensitivities (true positives) and specificities (true negatives) of the ANN to predict the xenograft samples were calculated with each successive removal of the top-ranking ANN clones and plotted. From Whiteford et al. [29]

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Fig. 8.2  Correlation of DNA copy and expression across the PPTP samples. (a) Correlation of expression variation and DNA copy variation according to genomic location. SNP markers are ordered across the X axis, whereas expression probe sets are ordered on the Y axis according to their mapped positions. The heat map displays Pearson’s coefficient (R) of the association between copy change and relative expression. Results from the 493 unique transcripts with R 0.6. (b) Distribution of the 493 copy-disrupted transcripts (R 0.6) across the genome. Y axis, frequency of observations after adjustment for chromosome length and number of genes per chromosome. Chromosomes are labeled by number and colored by their apparent grouping. (c) Distribution of the 493 copy-disrupted transcripts according to genome location and frequency of observation in tumor histotypes. The number of transcripts is plotted (Y axis) against chromosome position (X axis). Colors within the bars indicate the tumor histotype with the most frequent number of changes at a given chromosome location. From Neale et al. [16]

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The samples, from SNP and expression analysis of the models, were then integrated to determine whether changes in copy number were reflected by coordinate changes in gene expression. This analysis identified 493 copy number–altered genes that are nonrandom and appear to identify histotype-specific programs of genetic alterations. These data, although from relatively small sample sets, imply a genetic program leading to the complex genotypes of many childhood cancers. Again, this is an important consideration when developing genetically engineered models to recapitulate the complex genetic alterations found in childhood cancers. Full details of the molecular characterization of the models used in the PPTP are at http://pptp.stjude.org/demographcs. Genomic and expression files are available at http://pptp.stjude.org/affyData.php.

8.3.1 Model Fidelity On the basis of the molecular characteristics, as well as in vivo growth characteristics of the models, the PPTP in vivo panel constitutes 75% of models derived into mice directly from patient tumor (i.e. no in vitro culture), and 42% of the models were derived from previously treated patients. The methods for initiating primary tumors from patient samples [21] and tumor propagation methods used in the PPTP for both solid tumors and leukemia lines have recently been published [22]. The major concerns regarding tumor fidelity are through genetic drift, contamination with spontaneous lymphoma that occurs in SCID mice, and cross-contamination of tumor lines during transplant. Genetic drift can be minimized if tumors are serially passaged (mouse to mouse) for a limited duration (say 20 serial transfers), before reinitiating the line from cryopreserved tissue. Genetic drift can be monitored by high-resolution SNP analysis. More problematic is the potential overgrowth by murine lymphoma (particularly in NOD/SCID mice) or the potential for cross contamination. Recently a DNA fingerprinting technique to determining the fidelity of tumor lines using PCR has been adopted by the PPTP. DNA fingerprinting provides a simple, precise, reproducible method to characterize cell lines and tumors. This method determines the number of tandem repeat sequences (“variable number of tandem repeats” [VNTR]) in specific loci of the genome. The MCT118 locus (also termed D1S80) was characterized first, since this is a highly polymorphic site. The YNZ22 (also called D17S5) and COL2A1 were also chosen as potentially useful VNTR loci because they are composed of 70-base pair and 31–34-base pair repeats. Analysis of the MCT118 and YNZ22 loci was sufficient to distinguish 19 of the 21 lines. However, two cell lines (JR1 and SKNSH) contained the same number of repeat sequences at both the MCT118 and YNZ22 loci. Analysis of a third locus, COL2A1, readily distinguished these two cell lines from each other. We conclude that analysis of 2–3 VNTR loci is sufficient to characterize tumor and cell lines. This method gives added confidence that inadvertent cross-contamination between cell lines can be prevented.

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8.3.2 Primary Screening Because of the limited capacity to test more than 12 agents per year, the PPTP has focused on agents that are in early stage adult clinical trials, or where an IND has been filed. An overall schema for drug testing is shown in Fig. 8.3. The objective of the initial evaluation (Stage 1) is to define whether an agent has significant antitumor activity. For most of the agents screened the company or academic center submitting the agent has dose and schedule optima defined, and these data are factored into how the drug is administered, although preliminary toxicity testing may be required if existing data were obtained in different mouse strains. In addition, NOD/SCID mice used to propagate the ALL models tend to be more sensitive to many agents, and toxicity testing is done for most drugs prior to full screening. Where there is available drug pharmacokinetic data for human and mouse, a dose level and schedule that give relevant clinical drug exposure in mice are selected. Otherwise, for cytotoxic agents the maximum tolerated dose is used, and for noncytotoxic agents a “biologically effective dose” (BED) is administered. All agents are coded and screened “blinded” to avoid investigator bias, but also to allow screening of agents known to be active against certain childhood cancers (model credentialing). A third reason for blinded screening is that agents can be retested periodically to assess the stability of responses in the models.

Fig. 8.3  Schema showing the two stage testing process used by the PPTP. From Houghton et al. [6]

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8.3.3 Response Criteria: Solid Tumors The primary in vivo screen for new agents is against 44 solid tumor models and 8 ALL models [23]. Hence very large data sets are generated. To uniformly analyze experiments, we opted for a simple experimental design (treatment for two cycles of therapy over 6 weeks) and on use of several metrics to define tumor “response” using “pseudo-clinical” criteria. For cytotoxic agents where clinical objective response (i.e. tumor regression) is used for assessing drug activity, regressions in preclinical models would be an important determinant of activity. In contrast, for a cytostatic or antiangiogenic agent, prolonged tumor growth inhibition or disease stabilization (SD) would merit further testing. Response criteria for the solid tumor panels are summarized in Table 8.1. For individual tumors, progressive disease (PD) was divided into PD1 (<50% growth inhibition defined by T/C) and PD2 (>50% growth inhibition, but >25% increase in tumor volume). Other criteria are listed in Table 8.1, but mimic stable disease (SD), partial response (PR), complete response (CR) and maintained CR (MCR), as pseudo-clinical endpoints. Each individual mouse is assigned a score from 0 to 10 based on their response: PD1 = 0, PD2 = 2, SD = 4, PR = 6, CR = 8, and MCR = 10 (Table 8.1). The median scores for control and treatment groups are determined and overall group responses according to this median score are assigned. Studies in which the control group is not categorized as SD or PD are considered inevaluable and are excluded from analysis. Studies in which toxicity is greater than 25% are considered inevaluable and are excluded from analysis. Treatment groups with PR, CR, or MCR are considered to have had an objective response. Agents inducing objective responses are considered highly active against the tested line, while agents inducing stable disease or PD2 are considered to have intermediate activity, and agents producing PD1 are considered to have a low level of activity against the tested line.

Table 8.1  Tumor response definitions for solid tumor studies Response Definition PD1 Progressive <50% regression at all measurements and >25% disease 1 increase in tumor volume at the end of the study period, TGD value of £1.5 PD2 Progressive <50% regression at all measurements and >25% disease 2 increase in tumor volume at the end of the study period, TGD value of >1.5 SD Stable disease <50% regression at all measurements and £25% increase at the end of the study PR Partial response ³50% regression but with tumor volume ³0.1 cm3 during study CR Complete response Tumor volume <0.1 cm3 for at least one study measurement MCR Maintained complete Tumor volume <0.1 cm3 at the end of study response TGD tumor growth delay value

Score 0

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8.3.4 Response Criteria: Acute Lymphoblastic Leukemia Xenograft Models For the ALL xenograft models, percentages of human CD45 (hCD45) cells in the peripheral blood are measured relative to total mouse CD45 plus hCD45 cells. An event is defined as a tumor reaching 25% or greater of hCD45 cells in the peripheral blood. Event-free survival is defined as the time interval from initiation of the study to the first event or to the end of the study period for animals that do not event. Times to event are determined using interpolation. Response criteria for the ALL panel are summarized in Table 8.2. Responses are coded as progressive disease (PD), stable disease (SD), partial response (PR), or complete response (CR). A tumor is considered as having PD if the percentage of hCD45 cells never dropped below 1% and if the tumor has an event before the end of the study period. A response is classified as SD if the percentage of hCD45 cells never dropped below 1% and no event occurs before the end of the study. PR is assigned if the percentage of cells drops below 1% for any one time point regardless of whether the percentage reached 25%. A CR is assigned if the percentage of hCD45 cells drops below 1% for two consecutive weeks of the study and regardless of whether the percentage reached 25% or not. A CR is considered maintained if the percentage of hCD45 is less than 1% for the last three measurements of the study. For treatment groups, PD is further classified into PD1 and PD2 according to the tumor growth delay (TGD) value. 8.3.4.1 Event-Free Survival

An event in the solid tumor xenograft models is defined as a quadrupling of tumor volume from the initial tumor volume. Event-free survival is defined as the time interval from initiation of study to the first event or to the end of the study period for tumors that do not quadruple in volume. The time to event is determined using interpolation.

Table 8.2  Tumor response definitions for acute lymphoblastic leukemias Response Definition PD1 Progressive Disease 1 CD45% never drops below 1%, events before end of study, TGD* value of £1.5 PD2 Progressive Disease 2 CD45% never drops below 1%, events before end of study, TGD value of >1.5 SD Stable Disease CD45% never drops below 1%, no events before end of study PR Partial Response CD45% drops below 1% for only 1 week CR Complete Response CD45% drops below 1% for 2 consecutive weeks MCR Maintained Complete CD45% drops below 1% for last 3 consecutive Response measurements of the study *TGD = tumor growth delay value

Score 0 2 4 6 8 10

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8.3.4.2 Tumor Growth Delay Value

For treatment groups only, a TGD value is used to further classify mice with PD as PD1 or PD2. TGD values are calculated based on the numbers of days to event. For each individual mouse that has PD and has an event in the treatment groups, a TGD value is calculated by dividing the time to event for that mouse by the median time to event in the respective control group. Median times to event are estimated based on the Kaplan– Meier event-free survival distribution. If a mouse had a TGD value £1.5, that mouse is considered PD1. If the TGD value was >1.5, the mouse is considered PD2. Mice that have PD but do not have an event at the end of the study are coded as PD2. 8.3.4.3 Tumor Volume T/C Value A treated/control (T/C) value for tumor volume is calculated for each solid tumor study. Relative tumor volumes (RTV) for control (C) and treatment (T) mice are calculated at day 21 or when all mice in the control and treated groups still have measurable tumor volumes (if less than 21 days). The mean RTV for control and treatment mice for each study are then calculated and the T/C value is the mean RTV for the treatment group divided by the mean RTV for the control group. For the tumor volume T/C response measure, agents producing a T/C of £15% are considered highly active, those with a mean tumor volume T/C of £45% but >15% are considered to have intermediate activity, and those with mean T/C values >45% are considered to have low levels of activity [24]. The median RTV (or median hCD45% for ALL cell lines) at the last day of the study is also calculated. 8.3.4.4 EFS T/C Value The EFS T/C value is defined by the ratio of the median time to event of the treatment group and the median time to event of the respective control group. If no median time to event exists for the control group, then EFS T/C is undefined. If the treatment group does not have a median time to event, then EFS T/C is defined as greater than the ratio of the last day of the study for the treatment group divided by the median time to event for the control group. For the EFS T/C measure, agents are considered highly active if they meet three criteria: (a) an EFS T/C >2; (b) a significant difference in EFS distributions (p£0.050); and (c) a net reduction in median tumor volume for animals in the treated group at the end of treatment as compared to at treatment initiation (i.e. median RTV <1 at the end of study). Agents meeting the first two criteria, but not having a net reduction in median tumor volume for treated animals at the end of the study are considered to have intermediate activity. Agents with an EFS T/C <2 are considered to have low levels of activity. Xenografts in which the median EFS for the control line is greater than one-half of the study period or in which the median EFS for the control line does not exist are considered not evaluable for the EFS T/C measure of activity.

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8.4 Data Presentation Because the screen generates large data sets, we have used two approaches to present these in a “manageable” form. The first is the “Heat Map” (Fig. 8.4) where responses (PD1 ® MCR) are color-coded. While this has limited utility for single agents, it allows visualization of the activity in data sets obtained from agents having diverse mechanisms of action, and characteristics from small molecules to lytic viruses. For example, one can readily compare the activity of agents with specific mechanisms of action (e.g. antiangiogenic drugs, topoisomerase poisons etc.), or identify agents that have broadspectrum activity (cyclophosphamide, a known active agent, and standard component of many solid tumor protocols) or restricted histotype activity (e.g. bortezemib, or ABT263). The data set presented in Fig. 8.4 comprises 28 drugs/biologicals tested against 52 in vivo models (44 solid tumor, 8 ALL). There are 343 missing data points as some tumor models were not available for use during the early testing period, or data were excluded due to excessive toxicity (>25% deaths). For the 1113 data sets there were 163 objective responses for an overall response rate (defined as ³PR) of 14.7%. Excluding results from PR104 (see “false positives” below), the response rate was 12.2% (136/1,113). Notably, the screen identified the known active agents (vincristine, cyclophosphamide, cisplatin, topotecan) that contributed 46 of the responses in this data set.

Fig. 8.4  Cumulative “heat map” data for published studies. In vivo tumor panels are shown at the top, and agents tested are shown in the left column. Median group response for each treatment is color-coded according to the key (bottom). Black boxes indicate missing data

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Hence the response rate for “known active agents” was 30.7% (46/150), whereas the overall response rate for “novel” agents was 9.4% (90/963). The second method of graphic representation is the COMPARE format, modeled after the format used to present NCI in vitro data from the 60 cell line panel (Fig. 8.5). This format is more useful for visualizing individual agent activity, or for comparing differential or similar activity between two agents. The example shown demonstrates the single agent activity for cyclophosphamide and the ­experimental agent MLN8237, an inhibitor of Aurora kinase A. The point illustrated is that while cyclophosphamide has little activity against neuroblastoma models derived from patients that had received cyclophosphamide treatment [25], MLN8237 has significant activity against several neuroblastoma lines. The PPTP has also undertaken limited evaluation of agents where histotype selectivity was anticipated (anti-CD19 toxin against ALL models or cloretazine against glioblastomas), or where a “graded” approach was considered to ­demonstrate differential activity of a novel agent having the same target as an agent previously evaluated (e.g. topoisomerase 1) before evaluating the agent against the full in vivo panel.

Fig. 8.5  COMPARE representation of tumor sensitivity based on the difference of individual tumor lines from the midpoint response (stable disease). Bars to the right of the median represent lines that are more sensitive, and to the left are tumor models that are less sensitive. Red bars indicate lines with a significant difference in EFS distribution between treatment and control groups, while blue bars indicate lines for which the EFS distributions were not significantly different. Left: activity spectrum for cyclophosphamide; Right: activity of MLN8237. From Houghton et al. [23] and unpublished data

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8.5 Secondary Screening As mentioned above, the primary function of the Stage 1 evaluation is to identify significant antitumor activity, and to avoid “false negative” results. False negatives may be a consequence of low mouse tolerance to drug relative to humans, whereas false positives may be a consequence of high tolerance to drug of the tumor host (mouse) relative to humans [5, 26]. Hence, in secondary screening it is imperative to address this fundamental issue by defining the dose–effect relationship, and through pharmacokinetic determination of drug exposure in mice compared to that achieved at the MTD in phase 1 clinical trials [27, 28]. As shown in Fig. 8.4, the pre-prodrug PR104 demonstrated very high activity against multiple tumors when administered at its MTD. However, it lost essentially all activity at 50% MTD dosing, suggesting a very narrow therapeutic window. Pharmacokinetic modeling for PR104 and its active metabolite indicated that the mouse exposure relative to that achieved at the phase 1 MTD was ~10-fold greater per 21-day cycle of treatment. Hence, it is unlikely that effective drug exposures will be achieved to induce significant antitumor activity in the solid tumor models (false positive), although this is less clear with respect to the ALL panel. These results can at least be a one factor in considering whether to advance this agent for clinical trials in children. The problem of “false negative” results is no less problematic. For example, topoisomerase II poisons (epipodophyllotoxins, anthracyclines) are highly effective clinical agents, but tend to demonstrate only moderate antitumor activity in human xenograft models. In part, this is a consequence of low tolerance to anthracyclines (especially in SCID mice), and both low tolerance and also rapid clearance of etoposide in mice relative to that in children. As agents tested in the PPTP screen are either close to clinical evaluation, or have advanced to clinical trial, the great majority will have demonstrated some level of preclinical activity against mouse models (usually carcinoma models derived from adult tumor cell lines). Reference to Fig. 8.4 reveals many such agents that demonstrate minimal activity against childhood tumor models. For example small molecule inhibitors alvespimycin, lapatinib, vorinostat, AZD6244 that target HSP90, ERBB1/2, HDACs, and MEK1/2, respectively, or the TRAIL agonist antibody HGS-ETR2 show minimal activity in Stage 1 testing. This raises the issue of potential false-negative results as a consequence of under-dosing and not achieving adequate systemic exposure. Alternatively, the drug target may not be essential or critical to the growth or survival of these tumor cells in vivo. In the latter case the drug acts as a chemical probe to elucidate the underlying biology of these tumors. One approach to distinguishing between under dosing and the relevance of the drug target is to conduct pharmacodynamic studies to ascertain the degree and duration of target inhibition. For alvespicamycin, biomarkers for HSP90 inhibition are loss of client proteins, and induction of HSP70, and these changes were observed in tumors irrespective of their response to alvespimycin, suggesting that target inhibition was achieved. Similarly, the MEK1/2 inhibitor AZD6244 completely abrogated phosphorylation of its downstream substrates (ERK1/2), despite tumor progression, suggesting that

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this pathway has little relevance to tumor growth (at least alone). In contrast to its lack of activity against the primary in vivo panel, AZD6244 induced regressions in a pilocytic astrocytoma xenograft model where mutant BRAF (V600E) is constitutively activated, but demonstrated no activity against a pilocytic astrocytoma xenograft model that has amplified wild-type BRAF.

8.6 Combination Drug Testing The PPTP now evaluates approximately four experimental drugs in combination with standard agents per year. We considered experimental designs that would allow formal testing of synergy or antagonism between agents [29, 30]. However, such designs require multiple dose groups for each agent, severely restricting the number of tumor models that could be evaluated. Thus, rather than testing a combination rigorously in one or two models, the PPTP approach has been to determine whether a combination has a more general effect that can be extrapolated to multiple models in vivo. A typical experimental design used for testing rapamycin (Table 8.3), although sub-optimal for assessing true synergy or antagonism, allows the combinations to be tested against a reasonable number of models. The rationale for combining experimental agents with standard cytotoxic drugs used in the treatment of childhood cancer is that any new agent introduced into the clinic will almost certainly be added to standard of care protocols that incorporate these agents. The experimental design presented allows the determination of whether adding a novel agent enhances the antitumor activity of the standard agent over that achieved at the MTD for the standard agent alone. Testing the combination with the lower dose of standard agent (0.5 × MTD) allows for determination of enhanced therapeutic activity over that achieved at the MTD, and where the experimental agent may exacerbate Table 8.3  Experimental design for combination drug testing (example rapamycin) RAP @ VCR @ VCR @ Cytoxan Cytoxan @ Cisplatin Cisplatin Group 5 mg/kg MTD 0.5MTD @MTD 0.5MTD @MTD @0.5MTD A – – – – – – – B – X – – – – – C – – X – – – – D – – – X – – – E – – – – X – – F – – – – – X – G – – – – – – X H X – – – – – – I X X – – – – – J X – X – – – – K X – – X – – – L X – – – X – – M X – – – – X – N X – – – – – X

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host toxicity. For example, rapamycin enhanced the toxicity of cisplatin given at its MTD, but the ­combination with cisplatin administered at a lower dose level with rapamycin was superior to single agent cisplatin administered at the MTD in all models evaluated. The experimental design also allows determination of whether the cytotoxic agent impacts on the single agent activity of the experimental drug.

8.7 Secondary Models The PPTP Stage 1 and Stage 2 evaluations are conducted using subcutaneous xenografts for solid tumors, whereas the ALL panel is for disseminated disease. Potentially, the site of tumor growth could impact drug sensitivity, either positively or negatively. For example, a brain tumor growing in the flank of a mouse may respond to an agent that has poor penetration to the brain due to the blood–brain barrier. Within the PPTP there is capability to undertake secondary screening against orthotopic and disseminated models of brain tumors (intracerbral injection), rhabdomyosarcoma, neuroblastoma [31], and osteosarcoma models. Several transgenic models (neuroblastoma, rhabdomyosarcoma, and medulloblastoma) are also incorporated into the PPTP; however, use of these models is severely restricted due to the Dupont patent restrictions and licensing fees imposed on the usage of these genetically engineered mice for drug development. The PPTP is in a novel position to test the validity of these engineered models in the context of pediatric drug ­development, but it is unlikely that significant testing will be undertaken due to the restrictions imposed by Dupont, as described above.

8.8 Integrating Molecular Data with Drug Sensitivity The PPTP has established two databases for expression profiles and SNP analysis of untreated tumors. Potentially, it will be possible to interrogate these data with respect to tumor sensitivity with the object of identifying signatures that predict drug sensitivity. An example of this approach for analysis of MLN8237 is shown in Fig. 8.6. In this example a signature based on 261 probe sets distinguishes between tumors that regress from those that progress. Taking an approach similar to that used in the POPP-TAP [16], it may be possible to train an artificial neural network with such profiles of drug sensitive or resistant tumors and to potentially identify other xenograft tumors that would be sensitive to this agent.

8.8.1 Submitting Agents to the PPTP The PPTP has the capacity to test approximately 12 agents or combinations of agents annually in its in  vivo preclinical models. Agents are selected for PPTP ­testing based on their potential relevance in the childhood cancer setting and based

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Fig. 8.6  Expression signatures that distinguish responsive tumors from non-responders treated with MLN8237. Affymetrix U133 plus 2 expression profiles (untreated tumors) from ten tumor lines with objective responses (>PR) or nine non-responders (PD1) were analyzed. Left panel: differentiation based on 261 probesets that distinguish sensitive from resistant tumors. Top panel, Principal component analysis (PCA) representation of differential showing tumor histotypes (color key at bottom). Lower panel: PCA showing differentiation of sensitive (green) and resistant (red) tumors independent of histotype

on their stage of clinical development. Details of the submission process, and Materials Transfer Agreement (MTA) between the NCI and organization submitting the agent can be http://pptp.nchresearch.org/documents.html. The Pediatric Drug Development Group (PedDDG) is the group that recommends whether agents should be prioritized for evaluation by the PPTP. The PedDDG is primarily composed of NCI staff with drug development and pediatric oncology expertise, the Principal Investigator for the PPTP and the Chair of the Children’s Oncology Group Phase 1 Consortium. The application should address the specificity of the agent, preclinical studies (in  vitro and in  vivo), drug pharmacokinetics (preclinical and clinical where available), commitment to clinical development, as well as intellectual property issues.

8.9 Closing Remarks One significant concern is that the PPTP in vivo panel does not accurately represent the diversity of molecular subtypes for each histotype. At present the tumor panels are relatively small (for example only four glioblastomas), and additional models have been

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developed since inception of the PPTP that could be incorporated should resources become available. Under-represented tumors include anaplastic Wilms tumors, nonrhabdomyosaroma, soft tissue sarcomas, and leukemias other than ALL. Ideally, each panel should reflect clinical subtypes, and include at least 12 models representing a disease type would allow more informative “preclinical” phase 2 trials. A second challenge will be to integrate the molecular databases (expression profiling, SNP analysis) and drug response data. There exists the potential to identify markers of tumor response that are either histotype specific or cross histology groups. Identification and prospective validation of expression signatures, or copy number alterations that correlate with response could potentially prove informative for stratification of patient therapy. It is too early to determine whether the PPTP approach is valid for prioritizing agents for pediatric clinical trials. Importantly, results generated through the PPTP do not serve as a restriction point for drug evaluation in pediatric populations. Further clinical evaluation of PPTP “actives” and “inactives” will be required to ultimately determine the validity of this approach. On the basis of PPTP data, MLN8237, the Aurora kinase A inhibitor, was “fast-tracked” and has completed phase 1 testing in children. The lytic virus NTX-001 may be tested relatively soon. The rapamycin analog, temsirolimus, is in phase 2 testing, and combination testing has started. Lapatinib as a single has been extensively tested in patients with brain tumors, hence comparison of its clinical activity relative to the PPTP brain tumor data should soon be available.

References 1. Reis LA, Percy CL, Bunin GR. Cancer incidence and survival among children and adolescents. United States SEER Program. 1975–1995. NIH (Pub. No. 99–4649). Bethesda, MD: National Cancer Institute; 1999. p. 1–15. 2. Ries LA, Eisner MP, Kosary CL, et al. SEER Cancer Statistics Review 1975–2002. Bethesda, MD: National Cancer Institiute; 2005. 3. Gurney JG, Young JL, Roffers SD, Smith MA, Bunin GR. Cancer incidence and survival among children and adolescents: United States SEER Program. 1975–1995 NIH (Pub. 99–4649). Bethesda, MD: National Cancer Institute; 1999. p. 111–24. 4. Horowitz ME, Etcubanas E, Christensen ML, et al. Phase II testing of melphalan in children with newly diagnosed rhabdomyosarcoma: a model for anticancer drug development. J Clin Oncol. 1988;6:308–14. 5. Peterson JK, Houghton PJ. Integrating pharmacology and in vivo cancer models in preclinical and clinical drug development. Eur J Cancer. 2004;40:837–44. 6. Houghton PJ, Adamson PC, Blaney S, et  al. Testing of new agents in childhood cancer preclinical models: meeting summary. Clin Cancer Res. 2002;8:3646–57. 7. Boland I, Vassal G, Morizet J, et al. Busulphan is active against neuroblastoma and medulloblastoma xenografts in athymic mice at clinically achievable plasma drug concentrations. Br J Cancer. 1999;79:787–92. 8. Friedman HS, Colvin OM, Ludeman SM, et  al. Experimental chemotherapy of human medulloblastoma with classical alkylators. Cancer Res. 1986;46:2827–33. 9. Friedman HS, Houghton PJ, Schold SC, Keir S, Bigner DD. Activity of 9-dimethylaminomethyl-10-hydroxycamptothecin against pediatric and adult central nervous system tumor xenografts. Cancer Chemother Pharmacol. 1994;34:171–4.

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10. Lock RB, Liem N, Farnsworth ML, et al. The nonobese diabetic/severe combined immunodeficient (NOD/SCID) mouse model of childhood acute lymphoblastic leukemia reveals intrinsic differences in biologic characteristics at diagnosis and relapse. Blood. 2002;99:4100–8. 11. Liem NL, Papa RA, Milross CG, et  al. Characterization of childhood acute lymphoblastic leukemia xenograft models for the preclinical evaluation of new therapies. Blood. 2004; 103:3905–14. 12. Santos A, Calvet L, Terrier-Lacombe MJ, et  al. In vivo treatment with CPT-11 leads to differentiation of neuroblastoma xenografts and topoisomerase I alterations. Cancer Res. 2004; 64:3223–9. 13. Shusterman S, Grupp SA, Barr R, Carpentieri D, Zhao H, Maris JM. The angiogenesis inhibitor tnp-470 effectively inhibits human neuroblastoma xenograft growth, especially in the setting of subclinical disease. Clin Cancer Res. 2001;7:977–84. 14. Jaboin J, Wild J, Hamidi H, et al. MS-27–275, an inhibitor of histone deacetylase, has marked in  vitro and in  vivo antitumor activity against pediatric solid tumors. Cancer Res. 2002; 62:6108–15. 15. Peterson JK, Tucker C, Favours E, et al. In vivo evaluation of ixabepilone (BMS247550), a novel epothilone B derivative, against pediatric cancer models. Clin Cancer Res. 2005;11:6950–8. 16. Whiteford CC, Bilke S, Greer BT, et al. Credentialing preclinical pediatric xenograft models using gene expression and tissue microarray analysis. Cancer Res. 2007;67:32–40. 17. Garber K. From human to mouse and back: “tumorgraft” models surge in popularity. J Natl Cancer Inst. 2009;101:6–8. 18. Neale G, Su X, Morton CL, et al. Molecular characterization of the pediatric preclinical testing panel. Clin Cancer Res. 2008;14:4572–83. 19. Smith MA, Morton CL, Phelps D, Girtman K, Neale G, Houghton PJ. SK-NEP-1 and Rh1 are Ewing family tumor lines. Pediatr Blood Cancer. 2008;50:703–6. 20. Fox EJ, Salk JJ, Loeb LA. Cancer genome sequencing – an interim analysis. Cancer Res. 2009;69:4948–50. 21. Morton CL, Houghton PJ. Establishment of human tumor xenografts in immunodeficient mice. Nat Protoc. 2007;2:247–50. 22. Morton CL, Papa RA, Lock RB, Houghton PJ. Preclinical chemotherapeutic tumor models of common childhood cancers: solid tumors, acute lymphoblastic leukemia and disseminated neuroblastoma. Curr Protocol Pharmacol. 2007;39:14.8.1–.21. 23. Houghton PJ, Morton CL, Tucker C, et al. The pediatric preclinical testing program: description of models and early testing results. Pediatr Blood Cancer. 2007;49:928–40. 24. Plowman J CR, Alley M, Sausville E, Schepartz S. US-NCI testing procedures. In: Feibig HHBA, editor. Relevance of tumor models for anticancer drug development: Basel: Karger; 1999. p. 121–35. 25. Thompson J, Zamboni WC, Cheshire PJ, et al. Efficacy of systemic administration of irinotecan against neuroblastoma xenografts. Clin Cancer Res. 1997;3:423–31. 26. Kurmasheva RT, Houghton PJ. Pediatric oncology. Curr Opin Chem Biol. 2007;11:424–32. 27. Kirstein MN, Houghton PJ, Cheshire PJ, et  al. Relation between 9-aminocamptothecin systemic exposure and tumor response in human solid tumor xenografts. Clin Cancer Res. 2001;7:358–66. 28. Leggas M, Stewart CF, Woo MH, et al. Relation between Irofulven (MGI-114) systemic exposure and tumor response in human solid tumor xenografts. Clin Cancer Res. 2002;8:3000–7. 29. Tan M, Fang HB, Tian GL, Houghton PJ. Experimental design and sample size determination for testing synergism in drug combination studies based on uniform measures. Stat Med. 2003;22:2091–100. 30. Tan M, Fang HB, Tian GL, Houghton PJ. Repeated-measures models with constrained parameters for incomplete data in tumour xenograft experiments. Stat Med. 2005;24:109–19. 31. Thompson J, Guichard SM, Cheshire PJ, et al. Development, characterization and therapy of a disseminated model of childhood neuroblastoma in SCID mice. Cancer Chemother Pharmacol. 2001;47:211–21.

Chapter 9

Imaging Efficacy in Tumor Models Vinod Kaimal, Wilbur R. Leopold, and Patrick McConville

Abstract  Imaging modalities such as magnetic resonance imaging (MRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), X-ray computed tomography (CT), ultrasound imaging and optical imaging techniques are increasingly being used to assess disease progression and response to therapeutics in animal models of disease. In oncology, the applications of these modalities range from volumetric assessment of tumor burden to imaging of functional parameters such as tumor vascularity and permeability, cell proliferation and tissue hypoxia. Quantitative assessment of molecular events such as receptor occupancy or upregulation of enzymes (e.g.matrix metalloproteinases) is also possible. Further, the use of imaging has enabled the use of more realistic models of disease (e.g. orthotopic/metastatic models) and the generation of more predictive data in a noninvasive manner. This chapter aims to provide a brief overview of the various imaging modalities, followed by a discussion of the most relevant oncology endpoints that are accessible by imaging. Keywords  Imaging • MRI • PET • SPECT • CT • Bioluminescence • Optical • ultrasound

9.1 Introduction The past two decades have seen rapid development and adoption of imaging modalities for diagnosis and assessment of progression and therapeutic response in a multitude of human diseases. More recently, imaging has found widespread use in animal models, with oncology being the most prevalent area of focus. A broad

W.R. Leopold (*) Charles River Laboratories, Inc., 800 Technology Drive, Ann Arbor, MI 48108, USA e-mail: [email protected]

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array of imaging technologies are routinely used for anatomical detection of tumors and assessment of tumor growth and response in the full spectrum of cancer models and are relied on particularly in orthotopic, metastatic, and transgenic tumor models. Additionally, today’s molecular targeted therapies have increased the use of imaging technologies for quantifying drug-induced changes in physiology (commonly termed functional imaging) and for detecting drug-mediated cellular and molecular level events (commonly termed molecular imaging). Imaging methods can enable quantitative assessment of target modulation, and simultaneous measurement of traditional growth and lifespan based endpoints. The increasing focus on imaging endpoints to increase efficiency and predictive power in clinical trials further motivates the use of preclinical imaging to validate and optimize clinical imaging strategies. This chapter will begin with a description of the unique challenges posed by in vivo preclinical imaging, followed by an overview of the most prevalent preclinical imaging modalities and the basis for signal detection and generation of spatially resolved signal. The chapter will then discuss the most relevant oncology endpoints that are accessible by imaging.

9.2 Challenges in Preclinical Imaging Increased reliance on imaging technologies in clinical use has been one of the major driving factors in adaption of clinical imaging hardware and software standards to preclinical use. However, preclinical translation of even the most mature clinical imaging technologies has presented significant challenges related to achieving the higher resolution required in small animals (most commonly mice and rats) [1, 2]. The last decades have seen substantive advances in preclinical imaging hardware and the introduction of commercially available MRI, CT, PET [3], SPECT [4], and ultrasound imaging systems dedicated to small animals [5–7]. An increasing number of imaging technologies currently restricted to preclinical use have also recently been developed, many based on optical imaging technologies. Even though in vivo preclinical imaging technologies have been commercially available for the better part of the last decade, their widespread use in testing drug candidates has been more recent. Throughput, sensitivity, and resolution limitations have contributed to the cautious adoption of imaging technologies for testing efficacy [8], particularly by the pharmaceutical and contract research industry. Generally, optimization of any one of sensitivity, resolution, or imaging time requires sacrifice in at least one of the other two (Fig. 9.1). Despite these challenges, in the last 5 years, almost all large pharmaceutical companies have established their own imaging centers and imaging programs, and, even more recently, the major in vivo contract research organizations (CROs) have established in-house imaging programs. This trend is driving more rapid development and optimization of imaging technology, protocols, biomarkers, and probes

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ACQUISITION TIME Throughput Efficiency Statistical Power

SENSITIVITY Limit of detection Dynamic range Signal to Noise ratio

RESOLUTION Spatial information Tissue delineation

Fig. 9.1  Imaging is generally a compromise between the three parameters depicted above. An improvement in any one of these parameters results in some sacrifice in one or both of the others. For example, averaging of the MRI signal is a common approach to improving the SNR but results in longer scans. A higher resolution image leads to lower SNR, which has to be ­compensated by longer imaging time (i.e. averaging) assuming all hardware remains the same

for testing efficacy. The following sections of this chapter will highlight some of the more prevalent and accepted preclinical imaging technologies and approaches for determination of drug efficacy in oncology.

9.3 Imaging Modalities: Technical Overview and Use in Oncology Models 9.3.1 Magnetic Resonance Imaging Magnetic resonance imaging [9] is a clinically relevant imaging modality that relies on signals received from nuclei (most commonly protons) placed in a magnetic field and perturbed using radio-frequency (rf) pulses. The use of magnetic field gradients enables spatial resolution of signal and reconstruction of images in three dimensions. Major work in magnetic resonance began with Rabi et  al. [10] and the principle of nuclear magnetic resonance (NMR) was discovered independently by Bloch [11] and Purcell [12] in 1946. In the 1970s, several accomplishments led to the development of MRI [13, 14], with Lauterbur and Mansfield sharing the Nobel prize for its discovery in 2003. Systems specifically optimized for preclinical in  vivo imaging were introduced commercially about 20 years ago. MRI enables relatively high spatial resolution (~10 mm) but suffers from relatively poor sensitivity and motion artifacts (e.g. due to respiration and the cardiac cycle). Although prevention of motion artifacts can be achieved by a

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breath hold in clinical patients, animal imaging poses greater challenges with respect to motion. However, MRI enables unparalleled flexibility with choice of tissue contrast, leading to a broad spectrum of MRI-determined endpoints that are used in preclinical and clinical efficacy testing. For example, the MRI signal can be manipulated to measure tissue water content [9], water diffusion [15], oxygenation and blood flow [16], perfusion [17], temperature [18], elastivity [19], and metabolite concentrations [20]. An extensive array of exogenous probes (e.g. 19F, FeOx, Gd, CEST, Mn, Cr) [21–23] can also be ­targeted to specific cells, tissues or processes to enable molecular imaging [24] and cell tracking [25, 26]. The broad flexibility associated with tissue contrast in MRI has led to its use as a clinical standard in oncology in most major tissues, as well as in imaging the full spectrum of preclinical tumor models. In preclinical models, MRI is particularly well suited to imaging of the brain, and lower abdominal tissues. Liver MRI is also particularly well enabled due to the high natural iron content of normal liver tissue. This provides high contrast between the liver and lesions with abnormal pathology such as liver metastases or primary liver tumors [27]. The use of MRI in tissues near the thorax generally requires the use of respiratory and/or cardiac gating, to synchronize the MRI signal acquisition to the respiratory or cardiac cycle, thereby preventing motion-induced artifacts [28–30].

9.3.2 Computed Tomography Computed Tomography (CT) is based on the attenuation of X-rays as they pass through the subject. A three-dimensional image can be reconstructed from a series of projections acquired as an X-ray source and detector rotate together around the subject. Following their discovery by Wilhelm Rontgen around 1895, X-rays have been used in imaging applications. Although several individuals were instrumental in developing tomographic imaging using X-rays, Allan Cormack and Godfrey Housenfield are credited with the invention of the modern CT [31]. Preclinical CT, commonly known as microCT, allows resolution to approximately 5 mm and is generally not limited by sensitivity. Modern technologies with continuously moving source and detector allow rapid scanning at full resolution. Soft tissue contrast in CT is generally poor due to poorly differentiated X-ray attenuating properties of normal soft tissues. Therefore, traditionally, CT has been used for imaging the skeletal system. However, the development of CT contrast agents with differential tissue uptake properties and dual energy X-ray technologies has led to recent advances in soft tissue CT. In preclinical cancer models, CT imaging of bone invasion or remodeling by bone metastases [32] and multiple myeloma [33] is prevalent. Naturally high CT contrast in the airways has also led to extensive use of microCT in lung tumor models [34], commonly with respiratory and/or cardiac gating [35]. The application of high-resolution X-ray CT to soft tissue tumors (including liver [36], ­pancreas [37], spleen [36], kidney [38], and mammary fat pads [39]) has increased

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with the use of CT contrast agents [24, 40]. The introduction of rapid scan enabled preclinical CT systems and blood pooling contrast agents has increased the use of CT to image the vasculature [41] and tissue perfusion [42, 43].

9.3.3 Positron Emission Tomography Positron Emission Tomography is based on the detection of gamma photons that are emitted after positron–electron annihilation. Unstable nuclei containing a high number of protons (e.g. 18F) stabilize by the emission of positrons, which are captured by the surrounding electron cloud with the emission of photons. Coincidence counting and calculation of origin of the annihilation events based on trajectories of the gamma photons enable reconstruction of a spatially accurate image. Commonly used PET isotopes include 18F, 13N, 14C, 64Cu, and 124I. PET imaging was first discovered in the early 1950s and is primarily credited to Gordon Brownwell [44]. The first systems truly optimized for small animal imaging were released in the early 1990s [45, 46]. PET has relatively low resolution (~1–1.5 mm at best) but relatively high sensitivity. PET imaging is inherently quantitative as the PET signal is a direct measure of the tracer concentration, although accuracy depends on a complex series of corrections for detector non-idealities, random coincidences, scatter, and attenuation. Sensitivity and resolution are also highly dependent on the reconstruction algorithm, with iterative numerical methods often preferred [47, 48]. PET is used to study tumor biology using a wide variety of tracers with specific properties that enable differentiation of cancerous tissue from normal tissue, by preferential uptake into, or longer clearance from tumor tissue compared with surrounding normal tissues. Commonly available PET tracers include those used to measure cellular glucose metabolism (18F-fluorodeoxyglucose, FDG), cellular proliferation ([18F]-fluorothymidine, FLT), DNA synthesis ([18F]-1-(2¢-deoxy-2¢fluoro-b-d-arabinofuranosyl)thymine, FMAU) [49], cell membrane synthesis ([11C]- methionine), protein synthesis ([18F]-tyrosine, 18F-FTyr), and tissue hypoxia ([18F]-fluoromisonidasol, 18F-MISO]) [50–56]. Radioimmunotracers targeting tumor-specific antigens can also be used to differentiate normal and malignant tissues [57, 58]. The applications of PET imaging in preclinical models [59] are limited by (i) resolution, as tumors of less than several millimeters in dimension may not be robustly detected or may lead to partial volume errors [60], (ii) tumor uptake of the tracer (sensitivity), and (iii) tumor to background signal ratio (tumor specificity) which affects the accuracy of image segmentation. Beyond these technical limits, PET is limited only by the development, availability, and validation of PET-enabled radiotracer molecules. The use of traditional subcutaneous tumor models has been particularly successful and widespread in preclinical PET as this helps to overcome the resolution limitation (as tumor sizes of at least 3–4 mm in diameter are typically used) and the background signal limitation, as the tumor protrudes from the body and can be segmented with high accuracy.

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9.3.4 Single Photon Emission Computed Tomography Single photon emission computed tomography (SPECT) is based on detection of gamma photons emitted from the subject after systemic administration of a radioactively labeled tracer (analogous to PET). A three-dimensional distribution of radioactivity can be computed after generation of multiple two-dimensional projections (analogous to CT). SPECT imaging is accomplished by gamma camera detection of lower energy photons (compared with PET), but allows a much broader spectrum of energies and the detection of multiple isotopes of different energies simultaneously. Sub-millimeter resolution is feasible using modern small animal SPECT systems, through the use of pinhole collimators. Sensitivity is also relatively high, and can be improved by the use of multiple detectors. 99mTc is the most commonly used isotope in SPECT, although other isotopes such as 67Ga, 111In, and 123I are also prevalent [24]. The detector technology used in SPECT is essentially the same as that used in traditional gamma camera imaging, and therefore SPECT can be applied to a broad spectrum of approved and commonly used radiopharmaceuticals which is a key advantage. SPECT isotopes are also generally cheaper, more broadly accessible and longer lived than PET isotopes. Similar to PET, the reliance on exogenous radiotracers means that many of the same limitations of PET are present for SPECT as well. The accuracy of the image depends on correction for scatter, attenuation, and detector non-ideality. As with PET, the use of SPECT in preclinical oncology models can be limited by resolution (tumor size), tumor uptake, and background tissue uptake (tracer specificity). These factors can limit preclinical SPECT imaging in metastatic and orthotopic models, where tumor sizes are typically smaller than those in subcutaneously implanted tumors.

9.3.5 In Vivo Optical Imaging In vivo optical imaging is now widely prevalent in preclinical oncology efficacy testing through bioluminescence imaging (BLI) and fluorescence imaging (FLI). BLI is a relatively recently developed optical imaging method that relies on detection of light from luciferase-expressing cells in an animal [40, 61]. This is commonly achieved through implantation of cells engineered to express luciferase constituitively (e.g. tumor cells) or by use of transgenic animals that express luciferase in one or more tissues of interest. Emission of light at 460–630 nm [62] from these cells or tissues occurs following access of the luciferase substrate, luciferin, which is generally introduced by a systemic injection. In the presence of the luciferase catalyst, ATP and oxygen, luciferin is rapidly converted to oxyluciferin, producing light. FLI relies on light emission from a fluorophore, after excitation by a light source at the appropriate wavelength for the fluorophore in question. Excitation and emission wavelengths are typically in the 500–900 nm range. FLI can be applied to cell lines or transgenics expressing fluorescent ­proteins

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(analogous to luciferase) or by the use of exogenous probes labeled with fluorophores. In vivo optical imaging technologies rely on a light tight imaging box with a CCD camera array cooled to approximately liquid nitrogen temperature to minimize noise and maximize sensitivity for low levels of light [62, 63]. Resolution is limited to a few millimeters, but sensitivity is commonly not limiting, for BLI or FLI. In BLI, systemic luciferin at saturation levels (15 mg/kg in mice) results in the emission of up to 10–104 photons/sec/cell in cells engineered to express luciferase and can enable in vivo detection of a single cell [64]. Imaging times for BLI are typically seconds to a few minutes. In FLI, the emitted light intensity is measured as a proportion of the excitation light intensity, with imaging times of milliseconds to seconds typically adequate to obtain the required sensitivity. The limitations of optical imaging are related to the poor depth of penetration of light (~mm) in the visible to near-infrared spectrum, and the efficient scattering properties of normal tissue for these photons. While BLI has the advantage of the absence of a systemic background in normal animals, FLI suffers from high background autofluorescence in the visible to nearinfrared region.Excitations at multiple wavelengths coupled with spectral deconvolution software are generally required to correct for background autofluorescence. In vivo optical imaging is most commonly a two-dimensional modality, and images from multiple animal positions can be necessary to detect signals throughout the body and to unambiguously identify the anatomical location of a given signal. However, newer in vivo optical imaging technologies [65] enable three-dimensional imaging using tomographical techniques (e.g. by the use of mirrors) [62] or by rastering [66] techniques. The use of subcutaneous xenograft models can overcome the poor depth of penetration and lack of three-dimensional information. In FLI, the use of newer near-IR fluorophores that enable narrow bandwidth and well-separated excitation and emission minimizes background autofluorescence and maximizes tissue penetration of the light [67]. Both BLI and FLI can offer the advantage of imaging several animals simultaneously, making in  vivo optical imaging a relatively high-throughout modality. Both modalities are widely used for monitoring tumor burden using engineered tumor cell lines [68] or exogenous tumor-specific probes [69].

9.3.6 Ultrasound High-frequency sound propagates through tissue as pressure waves and is reflected at tissue interfaces. The reflected signal can be used to reconstruct images in real time, forming the basis for ultrasound imaging. Contrast between tissue types is determined by differences in acoustic impedance, which causes differential backscatter of the sound. Contrast can be improved using agents based on microbubbles [70, 71], or echogenic liposome formulations [72]. Ultrasound is a clinically prevalent imaging technology which was first used clinically over 50 years ago. The first specialized small animal ultrasound systems were delivered commercially in the last 5–10 years and commercial systems

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now offer real-time resolution down to approximately 30 mm. Traditionally, ultrasound has been a two-dimensional modality, but clinically developed technologies have evolved to enable three-dimensional imaging. The limitations of ultrasound have centered around poor soft tissue contrast and variability from manual positioning of the transducer, and the variable positions of internal organs. Despite this, ultrasound has seen increased use in oncology models and, in addition to anatomical detection of tumors, can be used for detection and quantification of tissue blood flow, through color Doppler [73], power Doppler [74], and pulsed wave Doppler imaging [75]. Ultrasound is therefore increasingly prevalent in the assessment of vascular development and response to therapy [76]. Microbubble contrast agents have also enabled dynamic assessment of tissue perfusion [71].

9.4 Imaging End Points in Oncology Models 9.4.1 Anatomical Imaging 9.4.1.1 Tumor Detection and Staging The ability for imaging techniques to distinguish and spatially localize cancerous tissue from normal tissue has led to it being a clinical standard for tumor diagnosis and detection of primary tumors and metastases. Preclinically, imaging has only become prevalent as a detection technique with the increasing use of orthotopic, metastatic, and transgenic mouse models. In these models, imaging is often the only non-invasive way to detect the appearance of a tumor, and in transgenic or metastasis models, detection and incidence can be the primary endpoint. Any imaging modality that can distinguish cancerous from normal tissue can be used for detection, and for this reason all the major modalities previously discussed in this chapter have commonly been used for this purpose. Due to the inherent flexibility of MRI contrast choice, it is often the modality of choice for detection of tumors in preclinical models, particularly in brain [77, 78], liver [79, 80], and kidney models [81]. 18 F-FDG PET can offer high tumor to background tissue contrast by the generally higher metabolic rates in tumor tissues and can therefore be used to detect tumors. Similarly, PET tracers such as 18F-FLT and 18F-MISO are frequently used for detection by their tumor specificities. In vivo optical imaging offers an attractive method for tumor detection, staging, and group matching prior to treatment due to its high throughput, and the relative ease of image generation and analysis, compared with other modalities. Luciferase or fluorescent protein expressing tumor lines are commonly used for this purpose [82, 83], particularly in metastatic models [84–86], and exogenous, tumor-specific fluorescent probes can also be used in this manner.

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9.4.1.2 Tumor Burden The adoption of major clinical imaging modalities for tumor diagnosis has undoubtedly positioned imaging techniques strongly for taking the next step: quantifying tumor growth or tumor burden and tracking it over time. Traditionally, tumor burden has been measured in terms of volume or weight. Other than lifespan, there is no more accepted method for assessing efficacy of a therapy than by quantification of tumor volume over time. The higher-resolution imaging modalities, MRI (Fig. 9.2a) and CT, have seen very common use for measuring tumor size, and are accurate methods for this purpose in a wide variety of models. To a lesser extent, ultrasound is used for similar means, with increased use in parallel with development of ultrasound transducer technology and software for automated definition of tissue boundaries. Contrast agents, such as gadolinium or iron-oxide based agents [23] for MRI, iodine-based agents for CT, and microbubble-based agents [71] for ultrasound can be used to improve tumor delineation and increase the accuracy of tumor burden assessment. Furthermore, imaging methods enable tracking of multiple tumors in a single animal over time. This is particularly important in transgenic models and metastasis models, which typically involve more than one, and commonly dozens of tumors [87, 88]. Not only does this allow assessment of total tumor burden, it also enables tissue specific effects to be differentiated. This is important for the development of tissue targeted agents. This approach can infer not only tumor growth inhibition, but successful tissue specific targeting [89]. While the traditionally anatomical modalities have been the mainstay for imagebased tumor burden assessment, the inherently functional and molecular imaging modalities are seeing increasing use for this purpose. For example, tumor-specific PET or SPECT tracer uptake can be used as a surrogate for tumor burden in certain cases (Fig 9.2b). However, increasing tumor size commonly results in differential vascularization (tracer access) and poorer viability or necrosis in the tumor core,

Fig. 9.2  Estimation of tumor burden and anatomy by a variety of imaging modalities. (a) MRI of an adrenal tumor in a PTEN-/- mouse (b) FDG PET in a PC-3 peri-tibial implant model (c) PC-3-luc bone metastasis imaging using BLI

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which can uncouple the tumor tracer activity from tumor size in a PET or SPECT study, ­particularly in subcutaneous tumors which are traditionally staged and followed to relatively large sizes, compared with deep tissue tumors [90]. Similar to their use in tumor detection, optical imaging methods are attractive for tracking tumor burden due to high efficiency and high sensitivity for doing so in a variety of models [82, 90–92] (Fig. 9.2c). However, since this methodology relies on a strong correlation between tumor size and light produced, the readout can become uncoupled with development of normal heterogeneities as tumors become larger. This can be a problem for genetically engineered luciferase and fluorescent protein expressing tumor lines which rely on supply, oxygenation, and tumor ATP levels, all of which commonly vary spatially in a tumor and in response to therapy. Exogenous substrate (e.g. luciferin) or probe access can also be an issue, as tissue perfusion is significantly heterogeneous in relatively large tumors. Additionally, increasing tumor size creates greater attenuation for light emanating from the center of the tumor, leading to decreasing total light per volume as tumor size increases. For the same reason, the positioning of the mouse and variability in internal organ position or even body weight can affect the optical readout. Despite these limitations, optical imaging can greatly improve the efficiency and accuracy of tumor burden assessment in orthotopic and metastasis models. Recently introduced luciferase vectors have improved expression levels and luciferase activity in mammalian cells and can result in detection of a single cell in vivo. This facilitates optical imaging in deep tissue models, particularly cell seeding models commonly used in studying metastasis.

9.4.2 Functional Imaging The rapid adoption of imaging technologies for drug discovery and development has truly occurred only with the development of imaging techniques that probe tumor physiology, known commonly as “functional imaging.” While these methods provide an opportunity to assess drug mechanism and prove method of action, they can also measure efficacy before traditional tumor growth or lifespan based endpoints can be quantified. Functional imaging methods may also increase specificity (correlation with response as determined by a traditional or clinically relevant end point) or ­sensitivity (magnitude of the response modulation) of the efficacy endpoint, compared with traditional end points. The following sections will discuss some of the prominent tumor physiological properties that can be accessed by imaging.

9.4.2.1 Imaging Tumor Metabolism and Metabolite Levels PET imaging using [18F]-FDG provides one of the most common imaging appro­ aches to in  vivo assessment of tumor metabolism. 18F-FDG is an ­FDA-approved

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tracer that is the mainstay of PET imaging in oncology. Since tumors generally have elevated glucose metabolism, [18F]-FDG can be used to quantify changes in tumor metabolism in response to therapy [93] by measurement of tumor 18F-FDG uptake. However, these types of images must be interpreted with care because elevated glucose metabolism may be associated with other physiological processes such as macrophage activity and inflammation. The additional dependence of FDG uptake on properties such as cell kill and vascular supply (which could be construed as a lack of specificity) has led to it being broadly successful in detecting response to therapy [94]. Since modulation of metabolism commonly precedes tumor size changes, 18F-FDG PET can provide a time-saving advantage. The limitations of 18 F-FDG PET include the generally high normal tissue background (decreases sensitivity for tumor), the variability associated with competition between the tracer and circulating glucose and variability introduced by muscular uptake in awake animals. Commonly, therefore, animals are fasted prior to imaging to deplete circulating glucose levels, and the uptake protocol must be carefully designed to minimize variability of tumor uptake from animal to animal [95] (Fig. 9.3b). Fluorescent reporters have also been coupled to deoxyglucose in place of the radionuclide 18F, enabling imaging of tumor metabolism using optical techniques [96, 97]. This approach addresses some of the issues with PET imaging, such as expensive instrumentation, availability, and short half life of the isotope.

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Magnetic resonance spectroscopy (MRS) (and related MRS imaging, MRSI) involves the combination of MR imaging techniques with conventional NMR spectroscopic methods to provide spatially localized spectra (Fig. 9.4). Since the MR signal is dependent on the local chemical environment of nuclei, MR spectroscopy can resolve nuclei from different metabolites by their chemical shift, enabling ­quantification of metabolite levels over time. Common MRS imaging protocols can provide information on metabolite levels from single volume elements as small as a few cubic millimeters. Pathologic tumor biology provides unique spectral signatures of various metabolites which distinguish tumors from normal tissue, enabling metabolite levels to be used as a surrogate for efficacy. For example, MRS methods can be used to quantify the levels of metabolites important to tumor function such as choline, lactate, and lipids using 1H MRS [98], ATP, and phosphocreatine using 31 P MRS [40, 99, 100]. The concentrations of these metabolites can be used as end points for tumor response to therapy.

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Fig. 9.4  Bioluminescence image overlaid on a T2-weighted anatomical MRI in a mouse ­intracranial glioma model (U87-MG-luc). Proton MR spectroscopy performed on the indicated voxel reveals reduction in tumor choline concentration in the treated tumor (below) compared to the untreated control (above). This example demonstrates the use of bioluminescence for relatively high throughput staging, screening and tumor burden tracking, while the anatomical MRI facilitates voxel placement for proton MRS-based quantification of tumor choline concentration and response to therapy

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9.4.2.2 Cell Proliferation DNA synthesis in the tumor can be quantified by PET imaging of an 18F labeled form of the nucleoside thymidine, 18F-Fluoro-thymidine (18F-FLT) [51, 101] (Fig. 9.5). 18F-FLT is the subject of a multicenter investigational new drug (IND) application and the subject of many ongoing clinical trials in oncology. 18F-FLT is phosphorylated by thymidine kinase and trapped in proliferating cells. Although it is not incorporated into DNA in the relatively short timeframe for a PET study, it is a measure of thymidine uptake and phosphorylation. Tumor 18F-FLT uptake can therefore be used as a biomarker for tumor proliferation [102]. One of the key advantages of FLT over the more commonly used PET tracer, 18F-FDG is its tumor specificity [103–105]. Bioluminescence imaging has also been used to image tumor cell proliferation in the transgenic Ef-luc mouse model, where the gene encoding luciferase is controlled by the human E2F1 promoter and the BLI signal correlates not only with cell number but also the proliferative capacity of the tumor [106]. 9.4.2.3 Vascular Imaging, Permeability, and Blood Flow Vascular end points including blood flow, blood volume, and vascular permeability can be assessed in tumors using a variety of MRI-based techniques such as dynamic contrast-enhanced MRI (DCE MRI) [107, 108] (Fig. 9.6), arterial spin labeling [109, 110], and iron-oxide-based contrast MRI [111]. These methods have been widely used to detect response to increasingly prevalent anti-angiogenic [112–114] and vascular disruption [115] therapies in mouse tumor models [40, 116].

Fig. 9.5  (a) A T2-weighted MR image in an intracranial tumor model showing definition of the tumor boundary. (b) Corresponding FLT PET image in the same tumor coregistered with the same MRI image filtered to highlight the edges for anatomical localization. This example highlights the inherently high resolution of MRI for defining tumor boundaries, volume, and heterogeneity, while FLT PET provides an inherently quantitative functional assessment of the tumor proliferation. Future use of integrated PET/MR systems will facilitate greater utility in tumor imaging studies

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Of these methods, DCE-MRI (Fig. 9.6) is the most clinically relevant and is a technique that employs rapid T1-weighted imaging coupled with the injection of a T1 modifying contrast agent such as Gd-DTPA in order to characterize blood flow, vascular surface area, and vessel permeability by the dynamics of tracer exchange between the vessels and the interstitial space [117, 118]. By manipulation of the tracer type and size, the sensitivity to flow vs. permeability can be manipulated [119, 120]. A variety of modeling approaches exist to generate meaningful end points that can quantify a vascular response [121]. With the introduction of microCT systems with rapid scanning capabilities, DCE imaging has also been used with CT contrast agents in preclinical models [122]. Advantages of CT-based DCE approaches include higher resolution than can be achieved through rapid MRI. In CT, tissue tracer concentration is more directly related to image signal than it is in MRI, where tissue relaxation times contribute. Small animal ultrasound [123] can also be used to assess tissue perfusion through dynamic uptake and clearance of microbubble contrast agents [73], and blood flow by Doppler methods [75]. Optical imaging techniques for dynamic contrast assessment of vasculature are also under development. Vessel size imaging [124, 125] can be used to quantify the distribution of microvasculature in tumors by using superparamagnetic iron-oxide contrast agents (SPIOs). SPIO-induced changes in tissue relaxivity have been shown to be proportional to the average vessel radius and can be used to calculate blood volume. Blood pooling contrast agents are also being used for generating vascular maps using MRI [126] and microCT [127], although commonly this is more easily accomplished in excised fixed tumor samples than in vivo where motion artifacts, contrast

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clearance, and sensitivity are limiting factors. Tumor blood flow measurements have also been performed using clinical PET imaging of [15O]-labeled water [128]. Arterial spin labeling (ASL) [129] is an MR imaging technique that can be used to quantify blood flow and tissue perfusion. ASL involves magnetically “tagging” water protons in the blood supply for a particular tissue and measuring their appearance downstream in the tissue of interest. ASL techniques have been used in ­preclinical models such as renal cell carcinoma [130] where high perfusion regions in the images were shown to correlate with viable tissue while regions of ­diminished perfusion was shown to be necrotic [129]. Targeted probes can also be used to assess the structure and integrity of the vasculature. One approach is to label a contrast agent with a vascular targeted antibody or peptide. For example, the integrin anb3 is expressed in angiogenic vessels and can be imaged by using probes that are coupled with anti-anb3 antibodies [131]. Such probes have been developed using both Gd and iron-oxide contrast agents [132]. Another target for imaging angiogenesis is vascular endothelial growth factor (VEGF), which is expressed by most solid tumors and plays a role in the formation of new vessels. PET has been used to image VEGF receptors by conjugating VEGF-121 with radioactive 64Cu [133–135]. Targeted optical imaging probes also exist for imaging vasculature [82]. These probes are distributed passively through blood vessels to image vascularity, blood pooling, and vascular leakage [136]. 9.4.2.4 Tumor Cellularity and Cell Kill Diffusion MRI (dMRI) is used in oncology applications for the early evaluation of tumor response to therapy. dMRI provides a measure of the apparent translational mobility of water in tissue, or apparent diffusion coefficient (ADC). The tissue water ADC is influenced by diffusion barriers such as cytoplasmic structures, organelles, cell membranes, and the extracellular matrix which break down in response to treatment. Several studies of different tumor types and models have shown that ADC is correlated with tissue cell density [137–140]. When a tumor responds to therapy, an early change in ADC can be observed, often before a measurable decrease in tumor volume [138, 139, 141]. Recent clinical trials suggest a positive prognostic value for early ADC change in brain tumors [142]. 9.4.2.5 Receptor Occupancy and Gene Expression Radio-labeled small molecule reporter systems can be used for direct imaging of molecular targets using PET or SPECT. For example, neuroendocrine tumors expressing somatostatin receptors (SSTRs) can be imaged using SST analogs like octreotide labeled with iodine (123I) using SPECT [143]. Radiolabeled antibodies and antibody fragments also enable receptor imaging. For example, the Her2/neu tyrosine kinase receptor is often expressed on the surface of breast cancer cells [144] and is a target for therapeutic agents such as Herceptin. Her2/neu expression

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and monitoring efficacy of therapy using PET has been accomplished using 68Ga, a positron emitter [145]. Herceptin has also been labeled with SPIO using a pretargeting approach and imaged using MRI [146] by exploiting the strong affinity of the biotin–streptavidin complex. In this work, the biotinylated antibody was first administered followed by streptavidin bound SPIO, leading to significant increase in the MRI relaxation times in Her2/neu expressing tumor tissue. 9.4.2.6 Tissue Hypoxia and pH Acidic and hypoxic microenvironments are typical of solid tumors, generally attributed to poor perfusion and elevated metabolic rates [147]. Hypoxia, in turn, drives processes such as angiogenesis-mediated by hypoxia-inducible factor-1 (HIF-1). MR techniques sensitive to blood or tissue oxygenation, e.g. BOLD (blood oxygen level dependant) contrast imaging [148] can be used to quantify hypoxia. However, BOLD images are also affected by tissue pH, the hematocrit and flow [149]. 19F MRI, following injection of perfluorocarbons (PFCs), has also been shown to be sensitive to pO2. This is due to the high solubility of molecular oxygen in PFCs, leading to T1 enhancement of the 19F nuclei [150]. Tissue hypoxia can be measured by the PET tracer 18F-Fluoromisonidazole 18 ( F-FMISO) [151] and a series of new analogs currently in development [152, 153]. This tracer is taken up by cells, where it is acted upon and trapped by nitroreductase enzymes expressed during hypoxia. Tissue PET signal is therefore a measure of hypoxia. Measurement of pHe (extracellular pH) and pHi (intracellular pH) is possible using various MR techniques including 31P MRS, and 1H MRS using imidazoles or aromatics [147, 154–158]. Optical techniques could also be used to image tissue oxygenation, potentially in tumors. For example, a transgenic mouse model, ROSA26 ODD-Luc/+ that ubiquitously expresses a bioluminescent reporter consisting of firefly luciferase fused to a region of HIF that is subject to oxygen-dependant degradation has been developed [159]. 9.4.2.7 Apoptosis Several in vivo approaches for imaging of apoptosis have been developed utilizing BLI [160], MRI [161–163], and PET/SPECT [164]. One technique using BLI makes use of tumor cells that have been transfected with a hybrid recombinant reporter consisting of luciferase linked to the estrogen receptor regulatory domain (ER) via a cleavage site for caspase-3 (DEVD) [160]. The presence of ER in the reporter renders the luciferase inactive. On activation of caspase-3 during apoptosis, the DEVD site is cleaved and the luciferase becomes active, signaling the onset of apoptosis in the presence of luciferin. Other luciferase-based approaches usually involving caspase 3 specificity have been reported [165]. Another approach to imaging apoptosis is to target phosphatidyl-serine (PS), which is normally present in the cytoplasmic leaflet of the cell membrane but is externalized to the outer

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l­eaflet in apoptotic cells [166]. Proteins like annexin-V bind with high affinity to PS. Therefore, labeling annexin-V with radioligands for PET/SPECT [160, 164, 167], fluorescent dyes [168], or MRI contrast agents such as SPIO [161–163] has been used to image the apoptotic cells.

9.4.2.8 Imaging of Oncogenic Pathways As drug discovery has evolved to be a target driven endeavor, strategies have been developed in parallel to enable imaging of target modulation, many based on approaches originally developed for in  vitro assays. Some of the imaging strategies already discussed demonstrate the use of molecular imaging reporters to enable quantitative imaging of cellular and molecular processes including receptor occupancy [143–146], apoptosis [160–166], and angiogenic targets [131–135]. However, there has been broader use of reporters that drive imaging signal conditionally based on modulation of oncogenic pathways, with many of the most prevalent oncology targets being the focus of image-based reporters [169]. For example, proximity assays based on fluorescence or bioluminescence resonance energy transfer (FRET/BRET) rely on protein interactions to bring the donor and acceptor fluorophores into close proximity [170]. Another approach is a two-hybrid system, in which interaction between proteins leads to reconstitution of an active transcription factor, leading to expression of a reporter gene [171–173]. In protein-­complementation assays (PCA), two fusion proteins are expressed, each carrying a fragment of the split reporter such as luciferase [174] or a fluorescent protein [175]. The interaction of these proteins leads to a functional reporter [176–180]. These approaches can enable imaging of modulation of targets such as Akt [181–183], EGFR [184], mTOR [185], RAS [186], Raf [187], and ERK/MEK [188].

9.4.2.9 Other Imaging Applications in Oncology Imaging proteolytic activity. A wide variety of diseases, including cancer and metastases formation, show an upregulation of proteases such as MMPs (matrix metalloproteinases) and excessive protein degradation [189]. Fluorescence-based imaging approaches have been used to quantify MMP activity [69]. Imaging of tumor stem cells. Tumor stem cells, also known as cancer initiating cells, are hypothesized to be a sub-population of solid tumor cells that are capable of repopulating an entire tumor, but with their progeny lacking this ability [190]. These cells can be identified based on an increasing number of cell surface markers using techniques such as fluorescence activated cell sorting (FACS). Emerging in  vivo fluorescence imaging techniques have been used to image these cells after genetically modifying the cells to express ZsGreen fluorescent protein [191].

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9.5 Imaging Efficacy in Oncology Models: Future Outlook Comprehensive imaging centers in pharmaceutical and contract research industry drug discovery programs have become prevalent only in the last 5 years. The increasingly rapid adoption levels and parallel optimization of image-based applications have moved preclinical imaging beyond the anatomical to routine use in quantifying drug function and tumor physiology. Oncology drug discovery now critically relies on image-based methodology for increasing the speed, sensitivity, and specificity in generation of endpoints determining drug efficacy. In parallel with this evolution, use of imaging is increasingly “modality independent.” With the introduction of extensive multimodality core imaging facilities in academic and teaching environments and more recently in industry, the imaging approach has evolved to be driven as much by the pharmacologists, biologists, clinicians, and chemists as by the imaging scientists themselves. This has provoked an endpoints based approach where imaging is used only if it can provide a tangible advantage over traditional methods. The modality or imaging protocol is chosen based on suitability and prior validation, rather than preferred use of a specific modality due to modality-centric expertise. Preclinical MRI, PET, SPECT, CT, optical, and ultrasound imaging technologies are now widely available, and underlying technologies have largely reached the point of stability and strong performance. Major advances are now focusing increasingly on new applications of these technologies and novel software to enable better automated and more quantitative endpoints. New software approaches for leveraging inherent image heterogeneities that have traditionally been viewed as confounding are under development and have been shown to increase sensitivity and specificity of image-based endpoints [192, 193]. New imaging probes and tracers across all modalities are increasing the spectrum of applications and the specificity of image-based approaches. Supporting hardware such as benchtop radiopharmaceutical production systems and multiple animal imaging approaches [194] are also headed toward wide use. PET/CT [195, 196] and SPECT/CT [197] integrated multimodality imaging systems that are now the preclinical as well as clinical standard for PET and SPECT have highlighted the advantage of integrated multimodality approaches. MRI/PET [198, 199] and MRI/SPECT [200] and integration of optical detectors with MRI, PET, SPECT, and CT comprise new approaches that will further potentiate imaging in oncology drug discovery and development.

References 1. Balaban RS, Hampshire VA. Challenges in small animal noninvasive imaging. ILAR J. 2001;42:248–62. 2. Riemann B, Schafers KP, Schober O, Schafers M. Small animal PET in preclinical studies: opportunities and challenges. Q J Nucl Med Mol Imaging. 2008;52:215–21. 3. Levin C, Zaidi H. Current trends in preclinical PET system design. PET Clin. 2007;2:125–60.

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Part IV

Carcinogen-Induced Tumors

Chapter 10

Mammary Cancer in Rats Henry J. Thompson

Abstract  A need exists for a useful, practical, model for breast cancer not only to rapidly produce invasive, autochthonous tumors that can be used for screening of new compounds for treatment of advanced disease, but also for evaluation of candidate agents for the prevention of breast cancer. Such a model exists, is easy to set up in the laboratory, relatively inexpensive in that it requires only a single dose of a carcinogen, reproducible when adequately powered statistically, and relevant to human disease. This chapter will focus on this rat model for breast cancer induced by injection of the carcinogen, 1-methyl-1-nitrosourea, as the initiating agent. Keywords  Mammary carcinogenesis • 1-Methyl-1-nitrosourea • Model for breast cancer • Rat

10.1 Introduction Human breast cancer is a very heterogeneous disease and presents itself in many forms [1]. As recently reviewed, one important lesson derived from the use of animal models is that each model serves to illuminate some aspect of breast cancer and mimics a subset of the many subsets of the human disease [1, 2]. Thus, many different models will be needed to fully enable the understanding of the cellular and molecular basis for breast cancer and eventually provide the critical preventive and therapeutic approaches being developed to conquer this cancer [1, 3–8]. The 1-methyl-1-nitrosurea (MNU)-induced rat model for breast cancer is a useful, practical, model that has been deployed not only to produce invasive, autochthonous tumors that can be used for screening of new compounds for treatment of advanced disease, but also for evaluation of candidate agents for prevention of breast cancer.

H.J. Thompson (*) Cancer Prevention Laboratory, Colorado State University, Fort Collins, CO, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_10, © Springer Science+Business Media, LLC 2011

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This latter approach to cancer control has seen major clinical advances, with the demonstration of the clinical efficacy of agents such as tamoxifen, raloxifene, and fenretinide, in chemoprevention of breast cancer in selected groups of women at high risk for development of the disease [9–13]. These experimental clinical trials in chemoprevention have been extremely costly to perform, since they have involved the administration of the above drugs to thousands of women over many years. It is therefore of the utmost importance to be certain that there is a strong pre-clinical rationale for the use of a new chemopreventive agent before it is introduced into clinical trials. In addition, negative outcome from the large clinical trials that attempted to use b-carotene to prevent cancer before there was strong evidence that this compound was truly an effective chemopreventive agent in experimental animals, underscores the importance of pre-clinical animal studies prior to clinical testing. The above considerations serve to highlight the importance of having a useful experimental model for breast cancer. It is indeed fortunate that such a model exists, and that it is easy to set up in the laboratory, relatively inexpensive as it requires only a single dose of a carcinogen, reproducible when adequately powered, and above all, relevant to human disease. This last criterion has been demonstrated by the experimental data which have shown that all three of the chemopreventive agents that have been found to be clinically effective in preventing breast cancer were first shown to be highly active agents in the animal model [9, 14, 15] which uses the carcinogen, MNU, as the initiating agent. This chapter will focus on the use of this model.

10.2 Historical Perspective Modern studies of mammary carcinogenesis in rats began with the introduction of the use of polynuclear hydrocarbons as carcinogens, most notably in the pioneering work of Huggins et al. [16]. In a classic paper published in 1961, data were reported on the effective use of 3-methylcholanthrene (3-MC) and 7,12-dimethylbenz(a) anthracene (DMBA). Both agents were shown to be potent mammary carcinogens when administered orally at appropriate single doses to Sprague–Dawley rats; tumor incidence of 100% was readily attained. However, although some of the mammary tumors induced by DMBA are indeed carcinomas, most of the tumors developing in rats treated with DMBA or 3-MC are benign, and should be classified as fibroadenomas [17]. Moreover, the mammary carcinomas induced by these polynuclear hydrocarbons in rats do not metastasize. These important limitations on the usefulness of the DMBA model therefore led to a search for a more effective mammary carcinogen that would induce tumors that are almost exclusively carcinomas. This search culminated with the introduction of MNU in 1975, in a classic paper by Gullino et al. [18]. Although the model, as presently used, has been modified to make it simpler and easier to implement, its basic elements remain unchanged, and it remains the system of choice at the present time. Unless one is specifically

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i­nterested in the effects of carcinogenic hydrocarbons on the mammary gland, i.e., modulation of the initiation of carcinogenesis­, there is essentially no good justification for the continued use of DMBA to induce mammary cancer in rats. The original system proposed by Gullino et al. used three intravenous injections of MNU, each at a dose of 50 mg/kg, given 4 weeks apart, beginning at 50 days of age. Inbred BUF/N rats were the strain of choice (mean latent period, 77 days), although the authors also showed high tumor yields in Sprague–Dawley and F344 rats (mean latent periods, 86 and 94 days, respectively). In sharp contrast to the tumors induced by DMBA, all of the 60 tumors from rats treated with MNU that were examined histologically were adenocarcinomas or papillary carcinomas. Rats bearing tumors induced by MNU were hypercalcemic, and bony metastasis was seen in some animals. Tumors were transplantable, and sensitive to estrogen. In several hundred animals with primary mammary carcinomas induced by MNU, the authors did not find tumors of other organs growing to a macroscopic size. The need for a total of three injections of MNU was not evaluated in this initial paper. As other investigators became interested in using this model for chemoprevention studies, it became important to shorten the time required for initiation of carcinogenesis, thus allowing a wider window for evaluation of the anti-promoting activity of new agents. A first modification was to eliminate the third intravenous dosing with MNU, and also to give the second dose 1 week (rather than 1 month) after the first [19]. Then it was shown that a single intravenous dose of MNU was just as effective as two doses [20]. Finally, to make the model even easier to use, it was shown that intraperitoneal injection of MNU was just as effective as intravenous administration of the carcinogen [21]. Thus it is now possible to give the complete initiating dose of carcinogen to a cohort of 100 rats in only a few hours, with a minimum of technical skill required. Furthermore, the marked instability of MNU in dilute alkaline solution makes this an ideal carcinogen to handle. Finally, the model has been standardized in the readily available Sprague–Dawley rat. The use of this outbred rat as a model remains relevant even though inbred strains have shown promise in studying genetic susceptibility to breast cancer [22, 23].

10.2.1 Induction of Mammary Carcinomas Using MNU Mammary carcinomas can be induced in female Sprague–Dawley rats by administering a single dose of MNU intraperitoneally [21, 24]. The use of this outbred rat strain is strongly recommended since it is both readily available and highly susceptible to chemically induced mammary carcinogenesis. The characteristics of the carcinogenic response are also best characterized in this strain. A discussion of the genetic susceptibility of this and other rat strains to chemically induced mammary carcinogenesis can be found in [25, 26]. An extensive description of the technical details by which MNU is prepared, injected, and inactivated has been reported recently [27]. A summary of the most critical aspects of this protocol is presented in Table 10.1.

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Table 10.1  Protocol for the induction of mammary carcinomas using MNU Storage and analysis of MNU

• MNU, obtained from a commercial source, is stored on desiccant below –10°C prior to use. • A solution of 14 mg/ml dissolved in water is prepared prior to use for the carcinogenesis protocol for spectral analysis to confirm purity of the compound (use a 1:1,000 dilution). • Concentration of MNU can be confirmed by measuring the absorbance at lmax = 231 nm; the extinction coefficient for MNU is log e3.77 (in water).

Preparation of MNU for injection

• MNU for animal injection is prepared in 0.9% NaCl acidified to pH 5.0 using acetic acid. • A satisfactory working concentration is 14 mg MNU/ml; more dilute solutions can be prepared, but this increases the injection volume required. • Care must be exercised to insure that MNU is completely dissolved in the saline solution; this is achieved by gentle heating with hot tap water accompanied by vigorous shaking. This process should be carried out in a sealed injection vial. • It is usually recommended that MNU be used immediately after preparation. However, spectral analysis indicates that the MNU solution is >95% stable for a period of 8 h at room temperature.

Injecting rats with MNU

• MNU is injected intraperitoneally using a 1 ml disposable plastic syringe equipped with a 26 gauge, 3/8″ needle. • The amount injected is dictated by carcinogen dose and weight of the animal. There is no need for any type of animal restraining device during the injection procedure. • It is helpful to prepare an injection schedule with body weight listed in 5 g increments and the appropriate volume of carcinogen to be injected listed next to the body weight. Animals can be weighed at the time they are injected.

Cleanup following injection

• MNU in excess of that used during carcinogen administration should be chemically inactivated in an institutionally designated chemical fume hood. Typically, a saturated solution of sodium carbonate is used for this purpose. • A dilute solution of alkali can be used to decontaminate work surfaces. • In general other materials used during carcinogen administration can be disposed of via incineration in compliance with an institution’s Biosafety guidelines.

10.2.2 Carcinogen Dose and Age of Administration Currently there are two additional factors that must be considered in using MNU to induce mammary carcinomas: the dose of carcinogen to be administered and the age at which carcinogen is injected. The specific research question being addressed will, in part, dictate the choices made. The following information should be of value in making these decisions. A dose-dependent induction of mammary carcinomas is observed in response to MNU. The typical range of doses that has been used is from 12.5 to 50 mg MNU/kg body weight. At 50 mg/kg dose, which is the dose most typically injected, a high incidence and multiplicity of carcinomas is observed, and the latent period is short;

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mean time to tumor depends on the age at which MNU is injected. References [21, 24, 28] provide quantitative information relative to the changes in incidence and multiplicity of palpable mammary carcinomas over time following carcinogen administration at various doses of carcinogen injected at different ages. In general, if the research question simply requires the rapid induction of palpable mammary carcinomas to test a therapeutic agent, or if the research question is best tested when a robust carcinogenic process is induced with a short latent period and the occurrence of carcinomas in essentially all injected animals, then 50 mg MNU/kg is the dose of choice. At the other extreme is the carcinogenic response observed when a dose of 12.5 mg MNU/kg body weight is injected. In general <30% of injected animals are observed with palpable mammary carcinomas after a latency in excess of 6 months. The age at which MNU is injected has a significant impact on the rapidity of the carcinogenic response. From the time of the initial report of Huggins et al. [16], the accepted age at which to inject, at least the initial dose of carcinogen if multiple injections were used, was 50 days of age. Clearly, the animal is very responsive to carcinogenic insult at this age. However, reported data have shown that the injection of MNU at ages prior to 50 days results in robust tumor development [24]. We have reported on the carcinogenic response when rats were injected with 50 mg MNU/ kg at 21 days of age [28]. Greater than 90% incidence of invasive carcinomas was detected in wholemount preparations within 35 days post-carcinogen, and at this time >60% of the animals had mammary carcinomas that were detectable by palpation. This is in contrast to the longer latent periods (noted above) when MNU was injected at 50 days of age. Moreover, animals can be sacrificed at time points ranging from 14 to 35 days post-carcinogen, and a high incidence of pre-malignant lesions identified and evaluated in mammary gland wholemount preparations [28, 29]. The procedure for preparing wholemounts is described in detail in [30]. Based on our experience, if a high incidence of palpable mammary carcinomas with a short latent period is desired, then the model of choice is 50 mg MNU/kg body weight injected at 21 days of age. The added advantage of this model is that it also permits the investigation of the genesis and prevention of pre-malignant lesions. Whether MNU is injected at 21 or 50 days of age, there are many practical advantages to this method of mammary carcinoma induction. They are summarized in Table 10.2.

10.2.3 Typical Animal Protocols To facilitate the adaptation of the MNU model for those less experienced in experimental carcinogenesis, we describe two typical experimental protocols. 10.2.3.1 Chemoprevention Protocol In a typical chemoprevention experiment, the effects of a potential chemopreventive agent on the carcinogenic response are contrasted to the carcinogenic

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Table 10.2  Advantages of inducing mammary carcinomas using a single i.p. injection of 50 mg MNU/ kg body weight Category Attributes Induction • Minimal supplies are needed; readily available methodology • Convenient i.p. injection protocol requiring minimal manipulation of the animal • MNU has a short half life in the animal (<2 h); this reduces the management issues and costs associated with containment of carcinogen treated animals and disposal of animal-related carcinogen waste • Chemical inactivation of MNU is easily accomplished • Overall, the procedures are convenient and economical Carcinogenic • Short latency to tumor emergence response • High incidence and multiplicity of mammary carcinomas and low prevalence of benign mammary tumors • Low incidence of tumors at other organ sites • Mammary tumors can be detected by palpation throughout the time course of an experiment • Rate of tumor growth and/or regression can be monitored via caliper measurements Experimental • Ability to operationally distinguish between carcinogenic initiation and design promotion/progression • In general experimental groups comprised 25–30 animals provide adequate statistical power • Ability to complete a study in as little time as 5 weeks following MNU injection

response observed in a control group of animals that are administered a placebo treatment. The endpoints of the carcinogenic response that are usually compared are the incidence and multiplicity of carcinoma and the latency to their detection as assessed by palpation. For statistical reasons, we recommend studying the effects of an agent using 30 animals per experimental group. If rats are injected with 50 mg MNU/kg body weight at 21 days of age, the experiment can be usually completed within 7–8 weeks if the occurrence of palpable mammary tumors, histopathologically classified, is used as an endpoint. If the carcinogenic response is assessed in wholemount preparations, the study can be completed within 5–6 weeks of carcinogen treatment. However, this approach incurs more effort and resources and is recommended only if there is an interest in quantifying the occurrence of pre-malignant as well as malignant mammary gland pathologies. If MNU treatment is delayed until 50 days of age, the typical duration of an experiment is 6 months, although some investigators have reduced this interval by 1–2 months. Given that similar information is obtained from both protocols relative to screening a compound for potential chemopreventive activity, the savings in time, effort and resources can be considerable if the 21 days of age injection protocol is used. This experimental timeframe is especially important if custom diet formulations containing chemopreventive agents are of limited supply [31]. Additional information related to the design of experiments and their execution is provided in [27, 30].

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10.2.3.2 Therapeutic Protocol A primary objective of a tumor induction protocol to test a therapeutic agent is to rapidly generate animals bearing palpable mammary tumors of measurable dimensions. It is presumed that the shorter the time period between carcinogen administration and the detection of tumors with measurable dimensions, the more expediently the experiment can be completed. The injection of 21-day-old female Sprague–Dawley rats with 50 mg MNU/kg body weight should yield a majority of the animals with palpable tumors within 7–8 weeks following carcinogen administration. As reported in [24], it is possible to further shorten latency by injecting up to 75 mg MNU/kg body weight. What is generally more variable than latency is the time that it takes for palpable mammary tumors to reach dimensions that can be reproducibly measured using vernier calipers. Animals are randomized to treatment regimes as they develop tumors with measurable dimensions. Some investigators wanting a small number of tumor-bearing rats frequently inject only a small number of animals, and at times are disappointed by failure to rapidly obtain tumors that are of measurable size. We generally recommend injecting up to twice the number of animals required for a particular experimental protocol so that animals with tumors of measurable dimensions can be randomized to a treatment protocol over a narrow time frame. We also note that some investigators have not reported tumor yields equivalent to those mentioned above. Through many personal communications with investigators adapting this model system, we have learned that other than errors in carcinogen preparation or administration, that two other factors can impact tumor latency and yield: excessive noise in an animal facility and disruption of the animals’ normal routines within an animal room, particularly if such disruptions include frequent transport of animals to different rooms within the facility and/or the frequent administration of anesthesia. Care should also be taken to adjust the light cycle to which animals are exposed; a 12 h light/12 h dark cycle is generally recommended in the animal holding room.

10.2.4 Biological Characteristic of Mammary Carcinomas Induced by MNU While the biological characteristics of chemically induced mammary carcinomas in rats have been reviewed [32, 33], we briefly draw attention to several of these characteristics and summarize them in Table 10.3. Chemically induced mammary carcinomas in the rat originate from ductal mammary epithelial cells, most likely from cells in the terminal end bud. This is significant since the majority of human breast cancers have a ductal histogenesis [34]. Moreover, most evidence indicates that these carcinoma have a similar pathogenesis to the human disease, i.e., they progress from ductal hyperplasias without or with atypia to ductal carcinoma in situ to invasive carcinoma. As reported by Thompson et al. [28], pre-malignant as well as malignant

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Table 10.3  Biological characteristics similar to the human disease process Biological characteristics Features Histogenesis Ductal epithelial cells Pathogenesis Ductal hyperplasia, ductal carcinoma in situ, invasive carcinoma Histopathology Carcinoma with cribiform, papillary, and mixed morphology are most common Ovarian hormone dependence Greater than 70% of mammary tumors that reach a palpable size regress in response to ovariectomy Pregnancy Full term pregnancy prior to carcinogenic initiation protects against tumor formation Metastasis While infrequent, metastases to the lung have been noted Pathogenetic characteristics Altered expression of TGFa, ErbB2, cyclin D1, and gelsolin has been reported

stages of the disease can be studied in animals injected with MNU at 21 days of age. Histologically, the carcinomas induced and their pre-malignant precursors have many similarities to the human disease, although there are also obvious differences [34]. A  histological comparison of the lesions induced in this model relative to those observed in the human disease has been published recently [35]. Other characteristics of the human disease process reflected in the MNU model are the occurrence of both ovarian hormone-dependent and hormone-independent carcinomas, and the protection against tumor development conferred by a full term live birth prior to carcinogenic initiation. One characteristic that is of major concern in the human disease and that is observed infrequently in this model, is the occurrence of metastases. The lung appears to be a primary site of occurrence when metastases are observed. An understanding of the molecular biological characteristics of mammary carcinomas induced by MNU is only beginning to emerge. This topic has been reviewed in [1, 36]. To date it appears that MNU-induced mammary carcinomas, like their human counterparts, have altered expression of TGFa, ErbB2, cyclin D1, and gelsolin. Cytogenetic analyses clearly demonstrate that tumor cells are aneuploid, and structural alterations of chromatid, iso-chromatid breaks and translocations involving chromosome 1, 2, and 4 are detectable [37]. A G to A transition mutation in codon 12 of the Ha-ras gene is observed in MNU-induced mammary carcinomas; however, a common misconception is that most MNU induced mammary carcinomas harbor this mutation. At the dose of MNU most commonly used, they do not, an observation of importance since this mutation is uncommon in human breast cancer [38]. Interestingly MNU-induced mammary carcinomas do not appear to have misregulated p53 activity as is commonly seen in the human disease, although the signaling pathway of which phosphatidyl inositol-3 kinase (PI-3 kinase) is a component is activated in MNU-induced mammary carcinomas [39] and this pathway is misregulated in the majority of breast cancers in women [40, 41]. It is clear that additional work is required to clarify the pathogenetic basis of the disease process induced in the mammary gland by MNU. The continued study of the genetic susceptibility, especially regarding the study of quantitative trait loci (QTLs) remains an important area [22, 42].

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10.2.5 Value of this Model Relative to Genetically Engineered Models for Mammary Cancer The advantage of the MNU-induced model is that autochthonous mammary carcinomas can be induced rapidly, reproducibly, and relatively inexpensively. These carcinomas have many characteristics similar to the human disease, and this model has been shown to have value in identifying agents that are effective in the control and treatment of the human disease process. While the availability of transgenic and knockout models for the study of mammary carcinogenesis clearly provides an invaluable and complementary set of tools to investigate the genesis, prevention, and treatment of breast cancer, currently, none of these models can fully replace the advantages offered in the MNU-induced mammary carcinogenesis model in the rat. Acknowledgement  The contribution of Michael B. Sporn to the writing of the original version of this chapter is gratefully acknowledged.

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14. Anzano MA, Peer CW, Smith JM et al. Chemoprevention of mammary carcinogenesis in the rat: combined use of raloxifene and 9-cis-retinoic acid. J Natl Cancer Inst. 1996;88(2):123–5. 15. Moon RC, Thompson HJ, Becci PJ et al. N-(4-Hydroxyphenyl)retinamide, a new retinoid for prevention of breast cancer in the rat. Cancer Res. 1979;39(4):1339–46. 16. Huggins H, Grand LC, Brillantes FP. Mammary cancer induced by a single feeding of polymucular hydrocarbons, and its suppression. Nature. 1961;189:204–7. 17. McCormick GM, Moon RC. Effect of pregnancy and lactation on growth of mammary tumors induced by 7,12-dimethylbenz(a)anthracene (DMBA). Br J Cancer. 1965;19:160–6. 18. Gullino PM, Pettigrew HM, Grantham FH. N-Nitrosomethylurea as mammary gland carcinogen in rats. J Natl Cancer Inst. 1975;54(2):401–14. 19. Moon RC, Grubbs CJ, Sporn MB, Goodman DG. Retinyl acetate inhibits mammary carcinogenesis induced by N-methyl-N-nitrosourea. Nature. 1977;267(5612):620–1. 20. McCormick DL, Adamowski CB, Fiks A, Moon RC. Lifetime dose–response relationships for mammary tumor induction by a single administration of N-methyl-N-nitrosourea. Cancer Res. 1981;41(5):1690–4. 21. Thompson HJ, Adlakha H. Dose-responsive induction of mammary gland carcinomas by the intraperitoneal injection of 1-methyl-1-nitrosourea. Cancer Res. 1991;51(13):3411–5. 22. Adamovic T, McAllister D, Rowe JJ, Wang T, Jacob HJ, Sugg SL. Genetic mapping of mammary tumor traits to rat chromosome 10 using a novel panel of consomic rats. Cancer Genet Cytogenet. 2008;186(1):41–8. 23. Lan H, Kendziorski CM, Haag JD, Shepel LA, Newton MA, Gould MN. Genetic loci controlling breast cancer susceptibility in the Wistar-Kyoto rat. Genetics. 2001;157(1):331–9. 24. Thompson HJ, Adlakha H, Singh M. Effect of carcinogen dose and age at administration on induction of mammary carcinogenesis by 1-methyl-1-nitrosourea. Carcinogenesis. 1992;13(9):1535–9. 25. Shepel LA, Gould MN. The genetic components of susceptibility to breast cancer in the rat. Prog Exp Tumor Res. 1999;35:158–69. 26. Shull JD. The rat oncogenome: comparative genetics and genomics of rat models of mammary carcinogenesis. Breast Dis. 2007;28:69–86. 27. Thompson HJ. The induction of mammary carcinogenesis in the rat using either 7,12 dimethylbenz[a]anthracene or 1-methyl-1-nitrosourea. In: Ip M, Asch B, editors. Methods in mammary gland biology and breast cancer research. New York: Kluwer/Plenum; 2000. p. 19–30. 28. Thompson HJ, McGinley JN, Rothhammer K, Singh M. Rapid induction of mammary intraductal proliferations, ductal carcinoma in situ and carcinomas by the injection of sexually immature female rats with 1-methyl-1-nitrosourea. Carcinogenesis. 1995;16(10):2407–11. 29. Thompson HJ, McGinley JN, Wolfe P, Singh M, Steele VE, Kelloff GJ. Temporal sequence of mammary intraductal proliferations, ductal carcinomas in situ and adenocarcinomas induced by 1-methyl-1-nitrosourea in rats. Carcinogenesis. 1998;19(12):2181–5. 30. Thompson HJ, Singh M, McGinley J. Classification of premalignant and malignant lesions developing in the rat mammary gland after injection of sexually immature rats with 1-methyl1-nitrosourea. J Mammary Gland Biol Neoplasia. 2000;5(2):201–10. 31. Thompson MD, Thompson HJ, Brick MA et al. Mechanisms associated with dose-dependent inhibition of rat mammary carcinogenesis by dry bean (Phaseolus vulgaris, L.). J Nutr. 2008;138(11):2091–7. 32. Welsch CW. Host factors affecting the growth of carcinogen-induced rat mammary carcinomas: a review and tribute to Charles Brenton Huggins. Cancer Res. 1985;45(8):3415–43. 33. Russo J, Russo IH. Experimentally induced mammary tumors in rats. Breast Cancer Res Treat. 1996;39(1):7–20. 34. Russo J, Gusterson BA, Rogers AE, Russo IH, Wellings SR, van Zwieten MJ. Comparative study of human and rat mammary tumorigenesis. Lab Invest. 1990;62(3):244–78. 35. Singh M, McGinley JN, Thompson HJ. A comparison of the histopathology of premalignant and malignant mammary gland lesions induced in sexually immature rats with those occurring in the human. Lab Invest. 2000;80(2):221–31.

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36. Medina D, Thompson HJ. A comparison of the salient features of mouse, rat and human mammary tumorigenesis. In: Ip M, Asch B, editors. Methods in mammary gland biology and breast cancer research. New York: Kluwer/Plenum; 2000. p. 31–6. 37. Goepfert TM, Moreno-Smith M, Edwards DG, Pathak S, Medina D, Brinkley WR. Loss of chromosomal integrity drives rat mammary tumorigenesis. Int J Cancer. 2007;120(5):985–94. 38. Zhang R, Haag JD, Gould MN. Reduction in the frequency of activated ras oncogenes in rat mammary carcinomas with increasing N-methyl-N-nitrosourea doses or increasing prolactin levels. Cancer Res. 1990;50(14):4286–90. 39. Jiang W, Zhu Z, Thompson HJ. Modulation of the activities of AMP-activated protein kinase, protein kinase B, and mammalian target of rapamycin by limiting energy availability with 2-deoxyglucose. Mol Carcinog. 2008;47(8):616–28. 40. Velculescu VE. Defining the blueprint of the cancer genome. Carcinogenesis. 2008;29(6):1087–91. 41. Gustin JP, Cosgrove DP, Park BH. The PIK3CA gene as a mutated target for cancer therapy. Curr Cancer Drug Targets. 2008;8(8):733–40. 42. Shepel LA, Lan H, Haag JD et al. Genetic identification of multiple loci that control breast cancer susceptibility in the rat. Genetics. 1998;149(1):289–99.

Part V

Disease and Target-Specific Models

Chapter 11

Animal Models of Melanoma Ene T. Fairchild and William E. Carson, III

Abstract  A wide variety of animal models of malignant melanoma are currently available to the researcher interested in this disease process. The Xiphophorus fish represents a genetic form of melanoma in which tumor formation is tied to the activity of a tyrosine kinase and specific tumor-suppressor genes. The South American opossum Monodelphis domestica possesses the photoreactivation pathway of pyrimidine dimer repair, and has been used extensively in photobiology studies of melanoma. Melanomas arise spontaneously in dogs, and this model has been used to evaluate novel cytokine combinations and gene-therapy protocols. The Sinclair swine also develops melanoma tumors, and the tumor regressions seen in this model provide a unique model for the study of interactions between tumor cells and immune effectors. Murine models are by far the most widely employed animal model of melanoma, and include the induction of tumors via application of physical agents or transgenic manipulations as well as the inoculation of mice with tumor-cell lines such as the spontaneously occurring Harding–Passey, Cloudman, and B16 cell lines. Finally, the more recently described models which employ immunodeficient mice provide a method for studying human tumors under ­controlled conditions. Keywords  Melanoma • Xiphophorus monodelphis • B16 • MT-RET • Sc10

11.1 Introduction The worldwide incidence of malignant melanoma is rising faster than any other cancer, and in the United States alone, almost 69,000 new cases of melanoma were projected for the year 2009. While thin primary melanomas are highly curable with surgery, the prognosis for patients with advanced disease is poor: Over 70% of patients with primary melanomas thicker than 4 mm or metastasis to the regional W.E. Carson, III (*) The Ohio State University, Doan Hall, N924, 410 W. 10th Avenue, Columbus, OH 43210, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_11, © Springer Science+Business Media, LLC 2011

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lymph nodes will die of disseminated disease within 5 years of diagnosis. The treatment of metastatic disease has undergone significant changes over the past three decades with the introduction of high-dose cytokine therapy (e.g. interferon-alpha and interleukin-2), biochemotherapeutic regimens, novel vaccination strategies, and sentinel lymph node biopsy techniques. Despite these advances and a better understanding of the molecular lesions underlying the development of the malignant phenotype, the therapeutic options for patients with metastatic disease remain limited. High-dose cytokine therapy infrequently produces durable responses and is often poorly tolerated by patients with advanced disease. Aggressive chemotherapeutic regimens have not consistently proved superior to therapy with single agents, and the improved response rates achievable with biochemotherapy come at the price of significant toxicity. Other approaches to the patient with advanced disease, such as peptide and anti-ganglioside vaccines, remain investigational. Thus, it is critical that novel regimens with favorable toxicity profiles enter into clinical development. In order to move new treatments rapidly into the clinic and to gain a better understanding of their mechanism of action, it is essential that experiments be conducted in animal models of malignant melanoma. This chapter reviews those models that are currently in use and explores their strengths and disadvantages.

11.2 Xiphophorus Species Central American freshwater fish of the genus Xiphophorus do not normally develop pigmented tumors [1]. However, hybrids between different species of this genus may spontaneously develop melanomas in skin structures called “macromelanophores” that are formed by terminally differentiated melanocytes. Tumor development is markedly enhanced following treatments with ultraviolet radiation (UVR), X-rays, carcinogens, or differentiating agents (reviewed in Ref. [2]). A series of studies in Xiphophorus hybrids has revealed that the development of melanomas is genetically determined [2, 3]. The responsible genes (termed “Tu” for tumor) map to the sex chromosomes and are necessary for the formation of normal black spots within the skin as well as melanoma lesions. The Tu genes exhibit oncogenic potential in the absence of any moderating factors and thus act in a dominant fashion. Tumor development is normally inhibited via the action of tumor-suppressor genes (“R” for regulating) which must be present in two copies in order to be fully functional. The R genes may or may not be linked to the Tu genes; however, R genes are not found in species that lack Tu genes. Melanoma results when the R genes of a Tu-bearing species are deleted from the genome by hybridization with a species lacking Tu genes [2, 4]. Wittebrodt et  al. have cloned and characterized an important Tu gene (Xmrk) that is homologous to a number of other Tu alleles in Xiphophorus [5–8]. Xmrk apparently arose as the result of non-homologous recombination involving a highly related proto-oncogene that is present as a single copy in all species of Xiphophorus [8]. The recombination event resulted in a second copy of the gene that was placed

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under the control of a completely separate promoter region and thus was not subject to the same controls as the parent proto-oncogene. Overexpression of the Xmrk oncogene is necessary and sufficient for neoplastic transformation of pigment cells in Xiphophorus, and levels of Xmrk expression correlate with the malignancy of the melanoma tumor that eventually develops [9]. The protein encoded by the Xmrk gene is a novel receptor tyrosine kinase that is closely related to the receptor for human epidermal growth factor [5]. This kinase is constitutively autophosphorylated and can induce tumor formation in the embryos of medakafish when driven by a strong promoter sequence [10]. There is evidence to suggest that transformation occurs in response to the binding of an unknown ligand to the extracellular domain of the Xmrk gene product [10]. Furthermore, Xmrk kinase induction results in signaling through multiple pathways by activating STAT5, phophotidylinositol 3-kinase, and Ras-Raf-ERK. These pathways have also been implicated in the pathogenesis of human melanoma [11–13]. In addition to altering cell proliferation, survival, and motility, Xmrk suppresses differentiation by interfering with signaling via the micropthalmia-associated transcription factor (MITF) [14]. Certain hybrids and backcrosses are particularly sensitive to melanoma induction by carcinogens or tumor promoters, and others readily develop tumors following exposure to UVR wavelengths in the range of 290–304 nm. The mechanism involved appears to be inactivation of tumor-suppressor genes (such as R gene), which are present in a single copy in these fish [2]. Malignant melanomas arise from the macromelanophore: a flattened, multinucleated, terminally differentiated cell that is 100–400 mm in diameter, contains a number of mature melanosomes, and is derived from the melanocyte population of the skin. Malignant lesions ­contain macromelanophores and small, spindle-shaped melanocytes with few melanosomes but numerous mitotic figures. Microscopic analysis reveals that these melanocytes differentiate from skin melanoblasts [15]. These tumors are locally aggressive, and invasion of the underlying muscle is common. Malignant melanomas are rapidly fatal to affected fish. Xiphophorus are easily maintained, are possessed of a short generation time, and are genetically well-characterized. This system is therefore well-suited for the identification of oncogenes and tumor-suppressor genes involved in the development of malignant melanoma. However, the highly aggressive and invasive nature of these tumors and their genesis in pigmented structures unique to fish must be taken into account when interpreting any results obtained in this model.

11.3 South American Opossum The South American opossum Monodelphis domestica possesses the photoreactivation pathway of pyrimidine dimer repair and has been used extensively in photobiology studies. Pyrimidine dimers induced by UVR are recognized by the photolyase enzyme, which catalyzes the enzymatic monomerization of dimers using the energy of long-wavelength visible light. Thus, exposure of animals to photoreactivating

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light (PRL) can remove 80–90% of pyrimidine dimers induced by suberythemal doses of UVR [16, 17]. In fact, exposure of UVR-treated Monodelphis to PRL reduces the incidence of melanoma from near 40% to well under 10% – clear evidence that UVR-induced DNA damage is important in the induction of melanoma [18]. Experimental comparisons can be made between UVR-exposed animals that have been treated with PRL and those that have received a control treatment of equal duration. In this manner, the effects of UVR-induced DNA damage can be specifically evaluated. UVR alone can induce melanoma tumors in Monodelphis. This is the only nontransgenic animal in which UVR alone acts as a complete carcinogen [19]. Genetic analysis of these UVR-induced melanoma tumors has revealed that, similar to lesions arising in humans, mutations in p53 rarely occur and that these lesions progress in a p53-independent manner [20]. However, unlike human melanomas, UVR-induced primary lesions in Monodelphis retain wild-type copies of both H-ras and N-ras. It has been hypothesized that this difference is due to the dermal origin of the Monodelphis tumors and the absence of the vertical growth phase found in human lesions [21]. Interestingly, application of carcinogenic compounds such as 9,10-dimethyl-1,2-benz(a)anthracene are also highly effective in the induction of malignant melanoma in this species [22]. Monodelphis is a small (approximately 100 g) pouchless opossum that is easily maintained and has been well-characterized via numerous studies relating to the damaging effects of UVR [23]. It is clear that the development of tumors is not genetically determined. Melanomas do not arise spontaneously in this species with any great regularity; however, application of UVR (e.g. 125–250 J/m2, three times per week, for 70–80 weeks) will induce tumors in 10–20% of animals, of which about one-third will metastasize to lymph nodes [18]. All tumors, regardless of their origin (e.g. naturally occurring, carcinogen-induced, and UVR-induced) possess the ability to metastasize to lymph node basins, whereas only spontaneous and carcinogen-induced tumors can spread to visceral sites [2]. Melanomas arising in Monodelphis are dermal in location and have their origins in lesions of melanocytic hyperplasia [19, 22, 24]. Dendritic melanocytes enlarge during the development of malignant lesions and assume a more polygonal shape. Eventually, they will give rise to clusters of infiltrative cells exhibiting deep pigmentation and epithelioid characteristics. The resulting lesions are characterized by cellular atypia, nuclear pleomorphism, and areas of depigmentation. Kusewitt et al. have also demonstrated the presence of S-100 immunoreactivity in the foci of melanocytic hyperplasia, UVR- and carcinogen-induced primary tumors, as well as in metastatic lesions [24]. Tumors can be established in tissue culture and even implanted into immunodeficient mice for further analysis. Additionally, murine and human melanoma cell lines can be injected subcutaneously (sc) into young (0–4 days) opossums. Due to the immunological immaturity of these young animals, the cell lines are able to engraft, form tumors, and in some cases metastasize. However, when the pups reach 5 weeks of age, the engrafted tumors begin to show signs of regression as their immune systems mature [25, 26]. Older animals appear to be more susceptible to the effects of UVR exposure and develop melanocytic lesions with much greater frequency than younger animals.

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Interestingly, approximately 40% of newborn opossums exposed to UVR will later develop metastatic melanoma as adults. This suggests that early exposure to sunlight may act as an initiator in the progression to melanoma. The susceptibility to UVR-induced melanomas can be modulated by dietary factors. Animals receiving a diet high in saturated fat do not develop skin lesions following exposure to UVR, whereas no protection is observed in animals receiving high levels of unsaturated fat in their feed [2]. The susceptibility of Monodelphis to UVR, the ability to modulate DNA repair by photoreactivation, and the presence of large, easily characterized chromosomes makes this animal model attractive for studies of photobiology. Disadvantages include the dermal location of melanoma lesions, the infrequent rate of spontaneous regression, and the lack of information regarding the immune system of this species.

11.4 Canine Melanoma Canine melanomas arising in the oral cavity, mucocutaneous junction, and distal extremities are highly aggressive tumors that grow rapidly, invade locally, and metastasize readily to lymph nodes and distant organs; cutaneous canine melanomas are usually benign. Melanoma may arise in any breed, but it is especially prevalent in purebred dogs such as Golden Retrievers, Irish Setters, Scottish Terriers, Doberman Pinschers, and Schnauzers [27, 28]. Canine melanomas are difficult to control even with radical surgery, and chemotherapy and radiation therapy are not considered effective in the control of either primary tumors or metastatic disease, although significant palliation may occasionally be achieved. In the absence of distant metastases, the median survival following aggressive surgical resection of primaries located in the oral cavity or on the digits is approximately 3–12 months [29–32]. Radiation treatments may be useful as an adjuvant to surgery, but not in the face of significant residual disease [33–35]. Chemotherapeutic regimes employing dacarbazine (DTIC) with or without cyclophosphamide are employed most commonly in the setting of advanced disease but generally yield only partial response of short duration [28]. The lungs are the most common site of metastasis, and spread to this organ is the usual cause of death. Thus, it should come as no surprise that 1 year survival rate for canine oral melanoma is just 25%. In the absence of distant disease, the size of the primary lesion and the ability to obtain local control are the most important determinants of survival [31]. A high mitotic rate and presence of aneuploidy may also possess prognostic significance; however, other factors such as breed, age, sex, and anatomic location do not appear to affect prognosis [27]. Like their human counterparts, metastatic canine melanomas tend to be resistant to conventional therapies. Numerous in vitro studies have indicated that immunologic approaches to the treatment of this tumor may be effective [27]. Therefore, canines with malignant melanoma have been employed in several large studies of biological response modifiers and other immunotherapeutic modalities. For ­example, ­administration of inter-

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leukin-2 (IL-2) in combination with recombinant tumor necrosis factor alpha has resulted in tumor regression in five of 13 dogs bearing malignant melanoma tumors. One of the five dogs exhibited a complete response that persisted for >3 years [36]. In another study, MacEwan et al. compared surgical resection of malignant melanoma tumors to surgery followed by therapy with Corynebacterium parvum. The heatkilled bacteria acted as a nonspecific immune stimulant, and it was hypothesized that this treatment might elicit an anti-tumor response. It was determined that the addition of this treatment led to a significant increase in overall survival (7.5 vs. 12 months) in animals with advanced-stage tumors (involvement of lymph nodes or distant organs) [37]. Other trials have targeted the monocyte/macrophage compartment with nonspecific stimulants, such as liposome-encapsulated muramyl tripeptide phosphatidylethanilamine (L-MTP-PE). L-MTP-PE stimulates the tumoricidal activity of canine macrophages and induces the secretion of proinflammatory factors. In a recently completed study it was found that the use of L-MTP-PE as an adjunct to surgical resection of Stage I oral melanomas resulted in a statistically significant increase in overall survival as compared to surgery alone, with 80% of the dogs treated with L-MTP-PE still alive at >2 years [38]. Canine oral melanomas also lend themselves to studies of gene therapy. Treatments that have been studied in this model include the intratumoral administration of a cDNA encoding recombinant human GM-CSF and an autologous tumor vaccine composed of irradiated tumor cells transfected with the GM-CSF gene ex vivo [39, 40]. Canines that had been injected with a combination of lipoplexes containing a plasmid coding the suicide gene herpes simplex thymidine kinase and irradiated xenogeneic cells secreting human GM-CSF and human IL-2 and simultaneously treated with ganciclovir exhibited prolonged survival compared to canines receiving control therapy [41]. Another study examined the efficacy of intratumoral injections of plasmid DNA-encoding staphylococcus enterotoxin B and either canine GM-CSF or IL-2 [42]. The overall response rate was 46%, although responses were more frequently seen in dogs with smaller tumors. Importantly, the survival times for animals bearing stage III tumors were significantly prolonged by the intratumoral administration of these genes as compared to animals receiving surgical treatment alone. The results obtained in these trials serve to highlight the importance of this model and its utility in the investigation of novel immunotherapeutic modalities. Canine melanomas are locally aggressive and are resistant to systemic treatments, just like their human counterparts. Therefore, it is quite likely that this model will continue to be used in the analysis of innovative biologic treatments.

11.5 Sinclair Swine The Sinclair, Hormel, Munich troll and melanoblastoma-bearing Libechov (MeLiM) breeds of miniature pig exhibit a strong predisposition toward the development of cutaneous melanoma [43]. This is a genetic trait that can be enhanced through

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selective breeding, so that over 50% of swine will exhibit melanoma at birth and 85% will develop lesions by 1 year of age [44, 45]. Melanocytic nevi are also observed in these species. Two separate loci appear to be involved in the development of tumors. One is the B haplotype located within the swine leukocyte antigen (SLA) complex. Animals homozygous for this gene exhibit a strong tendency toward the development of melanoma tumors. A second gene family, unlinked to the SLA complex, is fully expressed only in animals exhibiting the B haplotype [46]. The second locus may actually be related to the retinoblastoma (Rb) locus in humans. Susceptible animals have inherited either an inactive form of this allele or are missing it altogether. In this case, tumor development occurs following somatic mutation of the second allele [47]. The inheritability of melanoma susceptibility has made these animals useful for genomic studies. Genome-wide quantitative trait loci (QTL) mapping in the MeLiM model detected several QTLs associated with ulceration, presence of melanoma at birth, lesion type, and number of aggressive melanomas. Comparative mapping has helped to reveal new regions of the human genome that may harbor melanoma susceptibility genes [48]. Interestingly, animals that have undergone oophrectomy (but not orchiectomy) exhibit reduced tumor growth, which can be reversed via the administration of estradiol. Despite these observations, it is clear that melanoma inheritance is not sex-linked [49]. Melanomas may develop in utero and animals are frequently born with congenital flat or raised black skin lesions, a proportion of which may represent malignant tumors. Malignant melanomas also arise throughout the life of the animal. These may develop from pre-existing pigment lesion and may exhibit areas of ulceration. Malignant lesions are frequently large and exophytic. Metastasis to lymph nodes and vital organs occurs in up to 25% of animals, but is rarely a significant cause of morbidity because most primary and metastatic lesions eventually undergo significant regression characterized by shrinkage of tumors and loss of pigmentation [49–51]. Tumor development has been carefully characterized by several investigators, and occurs in five distinct stages [2, 50, 51]. Stage I lesions are flat black macules that contain heavily pigmented melanocytes. The melanocytes are located singly or in nests just superficial to the epidermal basement membrane. Stage II lesions are raised pigmented nodules composed of melanocytes that have begun to invade the superficial dermal structures. Stage III lesions are exophytic and frequently ulcerated. These lesions are invasive and highly proliferative in nature. Melanocytes exhibit significant cellular atypia and may exhibit an epithelioid or spindle-shaped morphology. Mitotic figures are commonly seen. These tumors are composed of pigmented melanocytes that deeply infiltrate the dermis and epidermis and give rise to metastatic lesions. Careful histologic analysis of these tumors reveals that approximately 70% have features consistent with acral lentiginous melanomas of humans, with the remainder resembling superficial spreading melanomas (10%), or nodular mealnomas (20%) [52]. The ultrastructure of tumor cells found in Sinclair swine melanomas is very similar to that of human tumors. Thus, it is not surprising that these cells stain positively for S100 [52].

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Stage IV represents the first stage of regression. Tumors are smooth and bluish in color, and may have a depigmented halo. T-laden macrophages invade the tumor mass. In the final phases of regression (Stage V), there is extensive infiltration of the tumor by lymphocytes and pigment-laden macrophages. Tumor cells disappear from the lesion, and depigmentation and dermal fibrosis become prominent histologic features. These findings suggest that tumor regression is largely mediated by immune mechanisms and may be related to an increased activity of host immune effectors [50]. Indeed, those animals with malignant melanomas exhibit enhanced leukocyte reactivity to tumor-cell lysates [53]. Other studies suggest that the cytotoxic response to the melanoma is mediated in part by gd T cells [43]. Experimental strategies such as analysis of gene expression microarrays and suppressive subtractive hybridization comparing regressing tumors and actively proliferating tumors from these miniature pigs have been employed to identify changes in the tumor that may contribute to regression. These studies have revealed a decreased expression of genes involved in cell cycle progression, DNA replication, DNA recombination, and DNA repair as well as an increase in the retinoic acid receptor-responsive gene RARRES1 [54, 55]. RARRES1 appears to be expressed in differentiated melanocytes and expression is lost as the tumor de-differentiates. Advantages of the Sinclair swine melanoma model include the observed genetic tendency, the high level of spontaneous transformation, and pathologic parallels to human disease. This model also provides an interesting experimental system for the study of spontaneous tumor regression.

11.6 Murine Models There are a number of murine models of malignant melanoma, which may vary with respect to several parameters, such as the origin of the tumor line employed, the site of inoculation, and the genetic background of the host. Each model has distinct strengths and weaknesses, and these must be taken into consideration prior to the selection of a murine model for use in a specific experimental system. In the following section, we will review models which employ chemical and physical carcinogens, transgenic mice, naturally occurring murine melanoma cell lines, and immunodeficient mice.

11.6.1 Induction with Physical Agents Melanoma tumors rarely develop spontaneously in rodents (e.g. mice, rats, hamsters, gerbils, or guinea pigs). Two general approaches are available to the researcher who wishes to induce the formation of melanomas or pigmented preneoplastic tumors in experimental animals. The first involves the repeated application of a carcinogen with or without subsequent applications of a tumor-promoter.

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Alternatively, animals may be exposed to UVR followed by applications of a reagent with the ability to promote tumor development [2]. Carcinogens that have been employed in this fashion include 7,12-dimethylbenz(a)anthracene (DMBA), trimethylanthracene, nitrogen mustard, and nitrosurea compounds [2]. Initially there is transient hyperpigmentation of the epidermis. This is followed by the development of lesions derived from the dermis that have little or no metastatic potential. Application of tumor promoters such as croton oil or TPA can markedly enhance the development of carcinogen-induced melanomas. In fact, a single application of DMBA can induce the formation of melanomas if animals are treated repeatedly with one of these tumor-promoters [2]. A basic protocol involved the application of 100 mL of 0.4% DMBA in acetone to shaved skin followed by twice-weekly applications of 25 mL of 2.5% croton oil in acetone of dimethyl sulfoxide until the appearance of raised black skin lesions occurs [56]. The resulting tumors are locally aggressive, metastasize to multiple organs, and may be transplanted to new hosts or established in culture. The ability to obtain nevi and malignant melanomas with the application of a simple chemical carcinogen implies that UVR-induced DNA damage is not an essential step in the development of this cancer [2, 56]. This approach to tumor induction has been applied to multiple species, including transgenic murine strains, and has led to the development of several useful murine melanoma cell lines. The JB/MS and JB/RH melanomas were induced in C57BL/6 mice via a single application of DMBA to the scapular region of 4-day-old mice, followed by twice-weekly application of croton oil. These tumors developed at 16 and 23 weeks, respectively. The tumors continued to display a melanotic phenotype following transplantation to normal C57BL/6 mice, and metastasized spontaneously in these new hosts [56]. The K1735 cell line was induced in a C3H/HeN mouse via the application of UVR (ten 1-h exposures to a FS40 sunlamp over a 2-week period) followed by 92 weekly treatments with croton oil [57]. The K1735 cell line expresses MHC class I and class II molecules, and is capable of inducing a specific yet ineffective immune response in syngeneic mice [58]. This cell line may be grown in culture and implanted via the sc injection of a single-cell suspension, or may be passaged via the sc implantation of tumor fragments derived from a solid tumor raised in a mouse. This line has been used to great effect in studies examining the role cell-surface molecules such as B7 and Fas ligand that are able to influence the host-immune response to tumor [58, 59]. Other investigators have applied these protocols to transgenic mice in the hopes of producing a murine melanoma cell line with a specific genetic alteration. For example, utilizing a DMBA protocol, melanomas were successfully induced in mice transgenic for an activated human H-ras gene. Subsequent chromosomal analysis revealed translocations of chromosome 4 that led to reduced expression of the p16 gene product, a situation similar to that reported for human melanomas [60]. UVR may be employed in a variety of ways to induce melanoma formation. Interestingly, UVR can act as a promoter for carcinogen-induced melanoma in mice. Benign blue nevi induced with DMBA may be converted into malignant lesions via exposure of the pigmented areas to chronic low-dose UVR [61]. These results have been confirmed in other murine strains and UVA, UVB, and UVA plus

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UVB appear to be equally effective promoters [62]. UVR also increases the rate of melanoma induction following applications of DMBA plus croton oil [63]. In general, the effect of UVR appears to be a local one, since application of UVR to nontreated skin is ineffective in the induction of tumors [63]. Despite its reputation as a complete carcinogen in humans, UVR alone cannot induce malignant melanoma in normal murine strains [64]. However, UVR treatment of normal skin can induce the formation of malignant melanomas if the treatment is followed by the application of a standard tumor-promoter [57]. These observations tend to confirm the carcinogenic potential of UVR seen in sun-exposed human populations, and validate the use of these models to study malignant melanoma. Chemically induced and UVR-induced melanomas of mice exhibit histologic features similar to those described for melanomas that arise spontaneously [62]. Hyperplasia of dermal melanocytes with dendritic characteristics is believed to be the first step in tumor development. Examination of benign tumors reveals the presence of large, polygonal cells that are heavily pigmented. Malignant lesions contain cells of similar morphology that in addition may exhibit nuclear atypia and variable pigmentation. Other rodent species (i.e. rat Chinese hamster, gerbil, and guinea pig) only rarely develop spontaneous melanoma. Rats and guinea pigs are relatively resistant to chemical carcinogens, whereas melanoma tumors can be reliably induced in Chinese hamsters and gerbils using standard protocols [2]. In contrast, malignant melanomas develop spontaneously with some frequency in the Syrian hamster (Mesocricetus auratus) [65]. This species (especially the golden and white variety) is also quite susceptible to the induction of tumors by chemical carcinogens, although they appear to be resistant to the effects of UVR [66]. Although the mouse is relatively resistant to the development of carcinogeninduced melanomas, there are several distinct advantages to the use of this laboratory animal in these protocols. There is a wealth of information relating to the murine immune system, immunologic and molecular reagents are widely available, and tumors may be induced in a variety of inbred and transgenic strains. The disadvantages of this approach are less obvious, but must be taken into consideration when contemplating this technique. Foremost, the tumors and cell lines obtained via DMBA treatments (e.g. JB/MS and JB/RH) are frequently nonpigmented, and this may represent a distinct phenotypical difference between these lesions and the majority of tumors that arise in humans. In addition, the incidence of melanoma formation following treatment with carcinogenic compounds appears to be strainand species-dependent. This suggests that the mechanism responsible for carcinogenesis may not be fully generalizable to humans [2].

11.6.2 Tumors Arising in Transgenic Mice The limitations associated with the induction of melanoma tumors in mice via the use of carcinogens have led investigators to develop transgenic mice with the ­potential to develop these tumors. One such model employs mice transgenic for

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expression of the simian virus 40 (SV40) early region under the control of the melanocyte-specific tyrosinase gene. These mice express the small and large transforming (T) antigens and develop ocular melanomas, skin melanomas, and hyperplasia of neural-crest-derived pigmented cells [67]. The T antigen is expressed to a different degree in various inbred lines of mice derived from individual founders. Mice with light coats develop eye tumors that are rapidly growing and fatal at an early age, whereas mice with darker coats develop slow-growing eye tumors later in life. Thus, the cutaneous melanomas that do arise in susceptible transgenics are rare and usually benign at the time the animal dies because of the ocular lesions. To circumvent this problem, skin can be taken from the susceptible strains and grafted onto low-susceptibility transgenics with a longer lifespan. Interestingly, donor pigment cells in these grafts exhibited selective proliferation in those areas nearest the healing wound edges. This finding suggested that growth and malignant conversion of melanocytes expressing the T-antigen [67]. The T antigen functions to inactivate important tumor-suppressor genes (e.g. p53 and Rb), and the results obtained with this model suggest that these wellstudied pathways may be important in the development of melanoma in humans. The T antigen has been shown to potentiate the development of invasive and metastatic melanoma in other models including transgenic mice that express the ret oncogene in retinal epithelium. In the absence of T antigen expression, these mice only exhibit micropthalmia and benign tumors [68]. Thus, the T antigens have powerful effects on a number of pathways that might influence cell proliferation. This explains why mice transgenic for the T antigens may develop melanoma, but this also makes it difficult to pinpoint key steps in the oncogenic process. Interestingly, p53 is infrequently inactivated in human melanomas [69]. In contrast, Rb function may be inhibited in melanoma via loss of specific regulatory pathways [70]. Cell-cycle entry and progression are regulated by the cyclin-dependent kinases (cdks). Cdk4 and cdk6 mediate the phosphorylation of Rb, which inactivates it and permits transcription factors to translocate to the nucleus, where they induce the transcription and expression of specific growth-related genes. The (INK)4a and INK4b gene products (p16INK4a and p16INK4b) are specific inhibitors of cdk4 and cdk6. In the absence of functional INK4 proteins, cdk4 and cdk6 are driven towards a more activated state. Rb becomes hyperphosphorylated, with subsequent dysregulation of the cell cycle. In addition, other cdk inhibitors (e.g. the Cip/Kip proteins encoding p21 and p27, respectively) become sequestered from the cdk2/cyclin E in an indirect manner following the loss of INK4a function [71]. Interestingly, the INK4a gene can code for an unrelated protein through the use of an alternate open reading frame. The resulting gene product (p19ARF) functions as a potent growth-suppressor via its ability to stabilize p53 [70]. Serrano et al. have developed a mouse model with a deletion of exons 2 and 3 of the INK4a gene that eliminates expression of both p16INK4a and p19ARF. These mice are essentially dysfunctional for both the Rb and p53 pathways, and develop spontaneous cancers at an early age. Fibrosarcomas and B-cell lymphomas are the most common tumors that develop in these mice, yet despite the link between INK4a and melanoma, these transgenic mice fail to develop this particular tumor [72].

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One gene that can accentuate the oncogenic potential of cells expressing an inactivated INK4a gene is oncogenic ras. Mice transgenic for the activated H-ras gene (H-rasV12G) under the control of the tyrosinase promoter develop cutaneous melanomas at a very low rate and only after a long period of observation. Interestingly, both INK4a alleles become deleted in these rare tumors [70]. In contrast, INK4aD2/3 mice expressing activated H-ras develop large numbers of cutaneous melanomas spontaneously after only a few weeks. A similar result was obtained with mice expressing activated N-ras [73]. These tumors are amelanotic, highly vascular, and similar in many respects to nodular melanomas [74]. It was subsequently shown that melanoma genesis and maintenance in this model are strictly dependent upon expression of H-rasV12G. These experiments were performed in INK4a-null mice that were transgenic for doxycycline-inducible H-rasV12G. Withdrawal of doxycycline and downregulation of inducible H-ras led to the clinical and histologic regression of primary and explanted tumors in this murine model. The initial stages of regression were marked by extensive apoptosis of tumor cells and host-derived endothelial cells; however, enhanced expression of vascular endothelial growth factor (VEGF) could not substitute for the loss of activated H-ras [75]. Additional models exploring the consequences of constitutively active oncogenes include transgenic mice that express the ret oncogene under the control of the ubiquitous metallothionein-1 (MT) promoter. Surprisingly, these MT-RET mice exhibited melanocytic tumors in the choroid of the eyes, the dermis around the nose and neck, and in the leg muscles, mediastinum, and retroperitoneal space. Also, mice with the MT-RET transgene expressed on a BALB/c background had a decreased incidence in these tumors as compared to mice with the transgene expressed on the C57Bl/6 background [76]. Interestingly, transgenic mice expressing RET under the melanocyte-specific promoter Tyrp1 did not develop melanoma tumors [77]. The clinical relevance of melanoma models over-expressing ret however is questionable as mutations in ret have not been identified in human melanomas. Transgenic mice have been created that over-express hepatocyte growth factor/ scatter factor (HGF/SF), a pleiotropic cytokine that stimulates melanocytes as well as other cell types. These mice have a unique distribution of melanocytes, which can be found in the dermis, the epidermal/dermal junction, and the basal layer of the epidermis as well as the base of hair follicles like wild-type mice [78]. Spontaneous melanomas arise in this model with a mean onset of 21 months and metastasize in 15% of animals. These melanomas demonstrate a dermal morphology that does not reflect that of human melanomas. However, when these mice are exposed to neonatal UV irradiation, the resulting melanomas exhibit a different histology with extension of tumor cells into the epidermis. These lesions also exhibit both junctional and dermal components [79]. These patterns are reflective of those in human melanomas and are likely due to the more superficial pattern of melanocyte distribution which leads to greater exposure to UV radiation and hair follicles. Further studies of UV radiation and HGF/SF mice have shown that UVB and not UVA is responsible for initiating melanoma formation in these mice [80].

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11.6.3 Spontaneously Arising Murine Melanomas Malignant melanomas are a rare occurrence in mice. There are three melanomas that arose in mice and could be propagated either as a cell line or as a transplantable tumor. The Harding–Passey cell line is derived from a dermal melanoma that developed on the ear of an ICR mouse and S91 (or Cloudman) melanoma cell line developed in a DBA mouse. The transplantable B16 melanoma arose spontaneously in a C57BL/6J mouse in the 1950s and several different subclones are now available [81–83]. These lines can be maintained either in vitro under standard culture conditions or can be passaged in vivo as sc tumors. Many innovative murine models of melanoma have been developed utilizing these three cell lines. Some of the most important ones are described here. The Harding–Passey cell line has been utilized in numerous preclinical studies of novel anticancer therapies, including boron neutron capture therapy, hyperthermia, strategies employing attenuated herpes simplex virus I (HSV1), and radioiodinated antibodies [84–87]. This cell line can be implanted subcutaneously or intramuscularly [84, 88]. It has been used to generate a mouse brain-tumor model in which cells are injected intracranially into C57BL/6 [86]. Brain tumors develop in 100% of animals, and may be imaged in as few as 5 days. Melanotic and amelanotic variants of the Harding–Passey cell line also exist, thus it has been used in studies that correlate melanin content with biologic behavior [89]. The Cloudman line can be used to generate sc tumors [90] for use in the evaluation of novel anticancer strategies [90, 91]. In addition, these tumors can be harvested, mechanically disrupted (e.g. by forcing tissue fragments through a wire screen), cleared of cellular proteins and debris, and utilized as a single-cell suspension for injection [92]. An intracutaneous model of malignant melanoma was also developed using this cell line [93].

11.6.3.1 Models Employing the B16 Cell Line The B16 murine melanoma cell line arose spontaneously in the C57BL/6 mouse in 1954, and like most tumors, was probably monoclonal in origin. However, significant heterogeneity would likely have been generated within the primary tumor in a short period of time as the result of genetic instability and unique selective pressures (e.g. in  vitro culture conditions and/or subsequent site of implantation). Subclones of the B16 line have been generated that exhibit an enhanced rate of proliferation, superior ability to colonize specific organs such as the lung following iv injection, and increased metastatic potential following sc implantation in a syngeneic host [94]. The ability to generate this variety of sublines suggested to some researchers that the B16 cell line may have accumulated a greater degree of heterogeneity than more recently developed melanoma cell lines. However, newer murine melanoma lines appear to exhibit just as much phenotypic diversity as ones developed in the early portion of the twentieth century. Studies by Fidler et al. revealed

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that the K-1735 melanoma cell line (first isolated in 1979) is quite heterogeneous and contains subpopulations of cells exhibiting diverse biologic behavior. In fact, of the 22 subclones derived from the parental line, only two were indistinguishable from the original cell line [95]. Also, Stackpole et  al. evaluated the phenotypic diversity of the B16 melanoma line using tumor fragments that had been kept in the frozen state for over 20 years. They were able to isolate a large number of clones that varied widely in their potential for dissemination (e.g. growth rate, metastatic potential, and ability to colonize organs). Moreover, cells readily converted their phenotype during growth in vitro and in vivo [96]. The subclones of the B16 cell line most commonly employed in murine experimental systems at the present time are B16F1 (low metastatic potential) and B16F10 (high metastatic potential). The differences in metastatic potential appear to relate to variations in cell-surface properties [97]. These clones were developed in experiments performed in the laboratory of J. Fidler in the 1970s [98]. The B16F10 tumor was obtained from Jackson Laboratories in 1970 and passaged in syngeneic mice as a sc tumor prior to being established in cell culture. After four to five in vitro passages, the tumor was frozen and stored in liquid nitrogen. Years later, the line was thawed, grown subcutaneously, and once again established in vitro. Two aliquots of cells were then prepared. One was further divided and used to inject a group of mice directly. The other was used to produce several clones. Aliquots of the unmanipulated cells and the distinct clone were then injected intravenously into syngeneic mice, and pulmonary metastases were counted at 18 days. The unmanipulated cells exhibited a metastatic potential similar to that of the parental line (median number of metastases = 40, range 8–131), whereas the clones differed markedly in their ability to colonize the lung (3–500). These experiments suggested that primary tumor-cell populations are enormously diverse with respect to their proclivity to disseminate to visceral organs. Another B16 variant with high metastatic potential is the BL6 subline [99]. The B16-BL6 cell line was generated by the injection of B16F10 cells into the urinary bladder of male C57BL/6 mice through the vas deferens. The bladder was then excised and cultured in  vitro on semi-solid agar (37°C, 5% CO2). Tumor cells that invaded through the bladder wall into the agar were recovered and repassaged. This entire process was repeated six more times, and the resulting variant was designated BL6. As might be expected, the BL6 variant is highly invasive and highly tumorigenic, yet poorly immunogenic. In fact, this clone can invade through the bladder wall in just 24 h. Other variants that metastasize preferentially to the ovary (B16-O10), brain (B16-B15b), and liver have also been developed [100, 101]. The B16 variants – B16F1, B16F10, and B16-BL6 – may be grown subcutaneously via the injection of 106 cells in a volume of 20–100 mL followed by therapeutic manipulation in 7–10 days [102]. Alternatively, tumors may be treated after they achieved a given size (e.g. 5–10 mm in diameter). The B16F1 cell line will not metastasize to visceral organs, whereas visceral spread (primarily to the lungs) can be expected uniformly in mice receiving sc implants of the B16F10 or B16 BL6 variants. Tumor volume may then be measured using calipers and standard ­formulas, or survival may be used as an end point. Antimetastatic therapies may be

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evaluated in mice that have been iv-injected (via the lateral tail vein) with 2 × 105 B16F10 cells [102, 103]. Treatment may then be started 1 d later. Following a period of treatment (e.g. 2 weeks), mice are euthanized, their lungs are harvested, and pulmonary surface colonies are enumerated with the aid of a dissecting microscope. Further metastasis to other visceral sites probably arise from the lung via hematogenous spread. In fact, most terminal-stage lung metastases themselves develop from lung lesions measuring only 1–2 mm in diameter [104]. The generation of visceral metastases via the iv injection of tumor cells or sc implantation of unique subclones that metastasize directly to the lungs does not accurately reflect the normal sequence of events in the clinical setting, in which nodal metastases play a prominent role. To address this problem, several unique models have been developed that employ the B16 melanoma cell line. Nathanson et al. injected B16 cells subcutaneously into the left foot pad of 6- to 8-week-old C57BL/6 mice [105]. They used the F1, F10, and BL6 variants, injecting 5 × 104 viable cells in a volume of 0.05 mL. Animals were inspected daily for the development of tumors, which generally became visible approximately 2 weeks following inoculation. At 18 days, the affected limb was amputated and the popliteal lymph nodes were isolated. Visceral metastases were enumerated at day 18 or at the time of death from systemic disease. Analysis of tumor-bearing mice demonstrated a direct correlation between the development of lymph node metastases and the size of the primary tumor. Also, pulmonary metastases correlated with tumor size with the BL6 variant and they exhibit a greater tendency to spread to nodal basins and then to the lung than either the F1 or F10 strains. The incidence of pulmonary metastases in mice whose regional lymph nodes did not contain tumor also correlated with increased size of the primary tumor, an apparent indicator of hematogenous spread. A similar model was developed by Markovic et al. utilizing 1 × 106 B16F10L cells implanted as single-cell suspension into the right food pad of mice [106]. The tumorbearing limb was amputated when the tumors routinely measured 6–8 mm in diameter (approximately 18 days). Animals with palpable inguinal lymphadenopathy were removed from the experimental group. In the absence of further therapy, mice characteristically died of metastatic pulmonary disease at about 35 days post-surgery [106]. An alternative site of injection is the web space of the hind foot (106 cells/0.1 mL), which gives rise to tumors of similar behavior [107]. Wanebo et  al. have developed a variation of these models in which B16F10 tumor cells are injected subcutaneously in the mid-tail of syngeneic mice [108]. 5 × 105 cells are injected in a total volume of 0.025 mL. Fully 100% of mice exhibit local tumor growth within 2–3 weeks of inoculation, and the majority develop inguinal adenopathy caused by lymphatic spread by 5–7 weeks. Mean survival time for tumor-bearing mice was found to be approximately 54 days. At autopsy, inguinal lymph nodes were noted to be markedly enlarged, and multiple metastatic lesions were visible in the lungs. If desired, the tumor can be resected at 2 weeks post-inoculation via amputation of the tail 5–10 mm distal to the base. If tumors are allowed to grow, they will reach a diameter of 15–20 mm by 6 weeks, displaying areas of necrosis and ulceration. This model is particularly useful for examining the effects of specific treatments on residual nodal disease following resection of the

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primary lesion. All of these local models of melanoma are valuable because they recreate the major clinical stages of malignant melanoma; local tumor growth, (Stage I), involvement of regional lymph nodes (Stage II), and metastases to distant organs such as the lungs (Stage III). Several other models of B16 melanoma with unique characteristics have been described. B16 melanoma cells may also be injected subcutaneously into the dorsal surface of the ear [109]. This results in the formation of black tumor nodules that are visible within 3 days. Tumor growth proceeds rapidly in this site, and all mice are dead by day 22. An alternative site of tumor inoculation is the peritoneal cavity. Intraperitoneal (ip) injection of B16F1 melanoma cells (106/0.1 mL) into 6- to 8-week-old C57BL/6 mice will give rise to tumor nodules that grow rapidly and develop into solid intra-abdominal tumors [110]. In the absence of treatment, mice succumb within 3 weeks. Treatment with interferon-alpha consistently prolongs survival of tumor-bearing mice by 7–10 d. The B16F1 or the B16 F10 clones may be employed, depending on the need to induce the formation of distant metastases. This model has been used with success by Fleischmann et al. in the analysis of the effects of interferon-alpha on the growth of the B16 line. Cytotoxic treatments may given intraperitoneally beginning 1–5 days post-inoculation and one obvious advantage of this model is the ability to deliver cytokines and chemotherapeutic agents directly to the site of tumor growth [111]. Another ip model involves the implantation of gelatin sponges containing B16F10 melanoma cells and recombinant human basic fibroblast growth factor (bFGF) onto the serosal surface of the left lateral hepatic lobe of syngeneic C57BL/6 mice. Initially, tumor growth is localized within the gelatin sponge. However, peritoneal implants eventually develop, giving rise to peritoneal carcinomatosis. This model permits the evaluation of the cellular infiltrate induced by cytokine combinations, as well as characterization of the pattern of vascularity before and after treatment [112]. Induction of tumors in mice through the application of physical agents is a powerful approach to the study of melanoma but is hampered by the long period of time required for the establishment of transplantable tumors. The use of transgenic approaches is gaining greater popularity, especially as we gain a better understanding of the molecular basis of malignant melanoma. The obvious disadvantage of this approach is the need to identify suitable molecular targets for manipulation and the requirement for transgenic capabilities. Spontaneously arising tumors represent an important resource for the animal researcher, and numerous models are available. Unfortunately, no one model precisely recapitulates the metastatic cascade observed in human tumors. Other concerns relating to models that employ murine cell lines involve the potential for alterations to occur following prolonged in vitro culture or in vivo passage.

11.6.4 Tumor Models that Employ Immunodeficient Mice It has long been a goal of cancer researchers to propagate human tumors in other species in order to facilitate the study of tumor biology and evaluation of novel

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therapeutic modalities. Early attempts focused on the implantation of tumors into privileged sites, such as the anterior chamber of the eye, the hamster cheek pouch, and the parenchyma of the brain. Tumors have also been implanted into fetuses and newborn animals which are naturally immunocompromised, as well as adult animals that have been rendered immune-deficient via thymectomy and total body irradiation (TBI) [113]. An alternative approach became available in the mid-1960s with the description of the congenitally athymic nude mouse, which arose on Balb/c background as the result of a mutation in a winged helix protein gene on chromosome 11 (Hfh11) [114]. The thymus is almost totally absent in nude mice because of failure of development of the thymic anlage, which arises from the ectoderm of the third pharyngeal pouch [115]. Functional T cells cannot develop to maturity in the absence of the thymic microenvironment, and therefore the nude mouse cannot efficiently reject human xenografts consisting of normal or neoplastic tissue. Another mouse that is equally useful is the severe combined immunodeficiency (SCID) mouse which arose as the result of a mutation in the C.B-17 strain. This syndrome is characterized by a complete lack of functional T cells and B cells in the adult mouse. It was later determined that the SCID phenotype resulted from an inactivating (nonsense) mutation in the gene encoding DNA-activated protein kinase (Prkdc) located on mouse chromosome 16. DNA-activated protein kinase functions in double-stranded DNA break repair and in recombination among the variable, diversity, and joining segments of immunoglobulin and T-cell receptor genes. Loss of this gene results in arrested development of T and B cells. Immunodeficient mice may be injected intracranially, intradermally, subcutaneously, intraperitoneally, or intravenously with cultured melanoma cell lines or cell suspensions derived from primary melanoma tumors or experimental tumors. Fragments of human tumors may also be implanted in various anatomic sites, with the expectation that engraftment will occur in a significant percentage of cases. The histological, molecular, and biochemical characteristics for human tumor xenografts are generally maintained in the nude mouse. Melanomas and colon carcinomas grow well in the nude mouse, whereas prostate carcinomas and leukemic tumors often fail to engraft [113]. Previous work by Fogh et  al. has demonstrated that xenografts derived from recurrent tumors or metastatic lesions are more likely to engraft (50% and 39%, respectively) than are primary tumors (approximately 20%) [116]. A large number of cells or a large volume of primary tumor are generally required in order to ensure successful tumor take in the sc position, however, sc xenografts rarely metastasize unless special techniques are employed [113]. Most human tumors grown in nude mice exhibit a proliferative pattern similar to that seen in original tumor, especially if the cells are grown orthotopically (i.e. within the same organ from which they originated). The SCID mouse has been useful for studies of human tumor specimens and tumor-cell lines that grow with difficulty in other strains. This system also provides a much-needed mechanism for developing improved models of tumor metastasis. In addition, SCID mice may be reconstituted with immune cells from syngeneic mice or with human hematologic tissues (adult peripheral blood lymphocytes) to generate the so-called hu-PBL-SCID model [117]. Human immune cells can survive and remain functional for a considerable period of time, as

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­ easured by the production of human immunoglobulin by implanted B cells. PBL m and tumor-infiltrating lymphocytes derived from cancer patients may be engrafted along with the primary tumor to create a host-tumor model [118]. This model is complicated by the potential for reactivation of latent Epstein–Barr virus (EBV) infection if EBV+ donors are employed, and EBV-induced human B-cell lymphomas routinely develop unless specific measures are employed [119]. 11.6.4.1 Nude Mouse Models Numerous nude mouse models of malignant melanoma have been described. These vary primarily according to the nature of the tumor inoculums and the details of the implantation procedure (e.g. sc or iv injection, or orthotopic implantation). Recent reviews of this topic offer a comprehensive survey of this topic [113, 120]. Of particular interest are nude mouse models, which closely approximate specific clinical entities, and models which accurately reflect the metastatic cascade that progresses from primary lesion to sc sites and nodal basins and then on to visceral organs. Most melanoma cell lines, such as the MeWo human melanoma cell line, will grow subcutaneously only if large numbers of cells are injected. However, tumortake and lethality are markedly enhanced when mice received sc implants of lung cubes that had been impregnated with small numbers of MeWo cells as a result of prior in vitro coincubation. Numerous, large lung nodules were found in one mouse receiving such implants and sc transfer of the metastatic spread to the lungs. Cell lines from such metastases or from primary tumors that arose from implanted long cubes were remarkable for their ability to colonize the lung following iv injection [121]. Also, iv injection of MeWo sublines derived from metastatic lesions consistently gave rise to extrapulmonary metastases. Other investigators have isolated sublines of parental tumors that exhibit a distinct tendency toward pulmonary metastasis following sc implantation or iv inoculation. Examples of these lines include the HSR+ MeWo variant, MM-RU, 451Lu, and CRML [122–125]. Another approach to the development of metastatic models in nude mice involves the use of cells derived from particularly aggressive human tumors. The (BRO) human melanoma cell line was derived from a tumor that exhibited very rapid growth, a distinct tendency for local recurrence, and rapidly fatal metastasis to visceral organs. Nude mice inoculated intraperitoneally with 106 BRO cells survived only 14 d. BRO cells metastasized to the lung and diaphragm following ip or sc injection, and exhibited a doubling time of just 2.3 days [126]. Cell lines with increased metastatic potential may also be established via the iv-injected human tumor cells and selection of tumor deposits that have colonized visceral sites. The LOX-L amelanotic human melanoma cell line was established in this fashion from the LOX parental line normally, which does not spread to visceral sites after sc inoculation [127]. LOX-L cells metastasize rapidly to the lungs following ip or sc injection and have been used to evaluate novel chemotherapeutic regimens in vivo. Fodstad et  al. utilized this same general strategy to develop the FEMX-1 human melanoma cell line that preferentially metastasizes to sc sites [128].

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Other investigators have determined that orthotopic (i.e. intradermal) injection of human melanoma cell lines increases the likelihood that the xenografted tumors will behave in a manner similar to the parent tumor. Rofstad established cell lines from sc deposits obtained from four different patients [129]. These cells were injected intradermally and evaluated for several parameters. It was found that the growth rate, histopathologic character, and angiogenic potential of the parent tumors were maintained in the orthotopically located xenografts. In addition, the organ-specific metastatic pattern of the xenografts closely resembled that seen in the donor patients. Lines with organ-specific metastatic patterns may also be generated via in  vitro manipulations. Human melanoma cells with the propensity to metastatsize to the brain following iv inoculation were developed by culturing the parental melanoma line in increasing concentrations of wheat germ agglutinin (a toxic lectin compound). The resulting subline (70-W) consistently gave rise to brain and sc metastases as well as to lesions in the bone marrow, ovaries, muscle, and intra-abdominal organs [130]. Other models of interest include cell lines that induce severe cancer cachexia in nude mice when grown subcutaneously [131], and a cell line that induces a syndrome of diffuse hyperpigmentation (melanosis) caused by the release of pigment granules from the xenografted tumor and uptake of the structures by macrophages throughout the body [132]. 11.6.4.2 SCID Mouse Models Human melanoma tumors engraft readily into SCID mice, and exhibit a tendency to metastasize more readily and grow more rapidly than when implanted into nude mice. As with the nude mouse, various cell lines have been adapted to this model, and a variety of ingenious methods for the inoculation of tumor have been devised [120]. SCID mouse models have been used extensively in the analysis of basic tumor biology as well as the evaluation of various therapeutic modalities [133, 134]. It was discovered early on that cell lines that metastasize spontaneously from sc tumors in nude mice will do so more rapidly and with greater frequency when implanted into SCID mice [120]. Other investigators have evaluated the behavior of freshly isolated tumor specimens from metastatic lesions [135]. 100% tumor take was observed when cell suspensions were injected subcutaneously into SCID mice, and two-thirds of these tumors could be transplanted successfully into new hosts. As few as 5 × 105 cells were required to yield a 100% incidence of tumor formation. Interestingly, seven of nine tumors metastasized to distant sites on the first or second passage. The lungs were the primary site of metastasis, but spread to the abdominal viscera and thoracic lymph nodes was also noted. The expression of specific surface antigens was found to be maintained over the course of passaging in SCID mice. Tumor-associated lymphocytes were identified in the original tumor inoculums, but the presence of these immune cells did not appear to influence the outgrowth or metastatic potential of sc tumors. A more extensive study conducted by Taylor et al. investigated the engraftment and dissemination of human melanoma cells obtained from various sources [136].

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They examined two cell lines, four early-passage cell lines, and fresh or cryopreserved cells from nine patient biopsies. SCID mice were inoculated via the ip, sc, and iv routes. The take-rate was highest for established cell lines (77%), and early-passage cell lines and fresh tumor cells engrafted in 65% and 53% of mice, respectively. Administration of tumor cells via the ip route resulted in tumor growth in 77% of mice, as compared to 41% for sc injection and 48% for iv injections. A distinct correlation was noted to exist between the number of cells injected and the percent of mice developing tumor: Only 26% engraftment was obtained when 1 × 106 cells were injected, whereas 69% take could be achieved with the injection of very large numbers of cells (50 × 106). Each tumor engrafted in at least one animal. Dissemination of tumor cells to distant organs via hematogenous or lymphatic spread was common and reproducible, with the number of metastases per animal averaging 16.3. The lung, liver, spleen, abdomen (viscera and peritoneum), and kidneys were the most common sites of spread. Moreover, the histologic character of the patient biopsy specimens was maintained after extensive passage in SCID hosts. A very sophisticated orthotopic model was reported by Juhasz et  al. in 1993 [137]. SCID mice and nude mice were given full-thickness human skin grafts measuring approximately 1.5 cm in diameter. After the grafts were completely healed, 2 × 106 melanoma cells were injected intradermally in a volume of 50 mL into the skin transplants. It was theorized that the human skin grafts would provide the melanoma cells with the unique dermal environment necessary for optimal engraftment. The skin grafts themselves were successful in over 90% of mice. Seven different cell lines were employed in this study, and all seven were engrafted without difficulty. Interestingly, several of the melanoma lines (WM164, WM9, and 451Lu) grew as multiple nodules that infiltrated the grafts without effecting major changes in the overall architecture of the dermis. Other cell lines (WM582, WM793, and 1205Lu) appeared to infiltrate the human dermis along collagen fibrils, and seemed to have induced the formation of endothelial vessels. In each case, the overall pattern of invasion was found to be quite similar to that of the original patient biopsy. Moreover, cell lines that produced metastases when implanted subcutaneously in SCID mice also formed metastases in this model. For example, the metastatic cell lines 1205Lu and 405Lu possessed the ability to invade human dermis and disseminated to the lungs in the majority of animals. Chudnovsky et al. have developed a variant of this model wherein freshly isolated human keratinocytes were mixed with melanocytes that had been genetically manipulated to express mutations found in human melanomas, overlaid on human dermis, and then implanted into immunocompromised mice [138]. The contribution of individual mutations could then be assessed in a three-dimensional tissue environment. Immunodeficient mice provide an important model for the study of melanoma in which human tumors may be studied directly. Tumors with different clinical behaviors may be employed, and the effect of novel treatments may be assessed prior to the initiation of clinical trials. The use of human skin grafts and intradermal injections of tumor cells is a novel approach that may help us to understand the earliest stages of tumor invasion. It is important to remember that these mice are not

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entirely immune-deficient, and the effects of the remaining effector cells (e.g. macrophages and NK cells in SCID mice) cannot be completely ignored. The cost of acquiring and housing immunodeficient mice is another limitation that prevents these models from being more widely employed.

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Chapter 12

Experimental Animal Models for Investigating Renal Cell Carcinoma Pathogenesis and Preclinical Therapeutic Approaches Gilda G. Hillman

Abstract  Advanced metastatic renal cell carcinoma (RCC) is poorly responsive to conventional treatment including most chemotherapeutic drugs, hormones and radiation therapy. Although recent developments in anti-angiogenic therapy have improved targeting these highly vascularized tumors, the treatment of metastatic disease has been and remains a difficult clinical challenge. Pre-clinical studies in animal tumor models of RCC are essential to address new therapeutic approaches for metastatic disease. Previous studies have shown that animal models are also useful to improve our understanding of the progression and molecular genetics of the disease in order to tailor the proper therapy to each type and stage of RCC. The goals of this chapter are to review various experimental RCC models used in numerous preclinical studies and provide an update on recent developments in this field. Keywords  Renal cell carcinoma • Animal models • Pathogenesis • Therapy

12.1 Introduction The incidence of renal cell carcinoma (RCC) is continuing to increase with approximately 57,760 new cases each year in the United States of America [1]. This increased RCC incidence may be linked to certain risk factors including smoking, obesity, high protein diets and hypertension [2, 3]. The disease is responsible for an estimated 12,980 deaths each year as updated by ACS for 2009 [1]. Nearly half of the patients present only with localized disease that can be treated by surgical removal [2–5]. However, one-third of the patients have metastatic disease at first presentation, and 30–50% of the patients treated for localized RCC subsequently

G.G. Hillman (*) Department of Radiation Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI 48201, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_12, © Springer Science+Business Media, LLC 2011

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develop metastatic disease [4, 5]. Patients with metastatic RCC frequently present with pulmonary metastases [4, 5]. Historically, the median survival of patients with metastatic RCC ranged from 8 to 11 months [4–6]. Debulking nephrectomy has a positive survival impact in patients with metastatic RCC, as demonstrated in two randomized trials [6, 7]. Within the patients benefiting from debulking nephrectomy, the subgroup of patients with metastasis only to the lung appeared to have a survival benefit with a median survival of 14 months vs. 10 months in patients not undergoing nephrectomy. Metastatic RCC is poorly responsive to conventional treatment including most chemotherapeutic drugs, hormones and radiation therapy [2, 4, 5]. Although recent developments in anti-angiogenic therapy have improved targeting these highly vascularized tumors, the treatment of metastatic disease has been and remains a difficult clinical challenge. To develop new and alternative therapeutic modalities for metastatic disease, various animal models were developed and used in numerous preclinical studies. These models were also useful to investigate the metastatic progression of RCC and the molecular genetics of the disease. The properties of an ideal tumor model for RCC are spontaneous origin, histologically proven adenocarcinoma, predictable growth rate and ability to metastasize similarly to human RCC in a reasonable time frame [8, 9]. This chapter reviews several experimental models, including the murine Renca renal adenocarcinoma in Balb/c mice, the rat kidney carcinoma in Wistar–Lewis rat, the Eker rat model of hereditary RCC and human RCC tumor xenograft models in athymic nude mice. These models have been well characterized and extensively used to study the pathogenesis of RCC disease and to assess the efficacy and safety of novel treatment modalities. Novel recent animal models will be discussed in this updated chapter.

12.2 Murine Syngeneic Renal Adenocarcinoma: The Renca Model In the early 1970s, the Renca murine renal adenocarcinoma model was isolated and characterized by Hrushesky and Murphy [10]. The Renca tumor arose spontaneously in the kidney of a Balb/c mouse, and was found to induce metastatic kidney tumors when injected under the renal capsule of Balb/c mice [10]. Renca was histologically characterized as a poorly differentiated renal cortical adenocarcinoma of the granular type, pleomorphic with large nuclei [8–10]. Renca can be maintained by either in vitro culture or in vivo passage by intraperitoneal (i.p.) injection or by subcapsular renal injection in syngeneic Balb/c mice. The progression of Renca tumor following subcapsular implantation mimics that of human RCC because of the formation of a primary tumor mass on the kidney followed by the development of spontaneous metastases [10–12]. Metastases develop in the regional lymph nodes, lung, liver and peritoneum; thus Renca can be staged similarly to human RCC [10–12]. The renal Renca model allows evaluation of the therapy, on the primary tumor as well as on metastatic deposits. A nephrectomy of the tumor-bearing

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kidney can be performed [12], therefore, the model is applicable for the development of therapeutic protocols for advanced metastatic disease, similar to the clinical situation of postnephrectomy metastatic RCC patients. The mean survival time of Renca-bearing mice is approximately 46 days when 105 cells are implanted intrarenally, thus allowing for a therapeutic evaluation in a reasonable time frame. On the basis of these properties, Renca does represent an ideal RCC tumor model and indeed has been used extensively as a preclinical model to investigate various therapeutic approaches for metastatic RCC. In the 1970s, early studies in this model have included hormonal therapy, and chemotherapy with single or combined drugs that were found of limited efficacy [8, 9]. The immunogenicity of this tumor is relatively low, although some protection to rechallenge with viable Renca cells following immunization with crude membrane preparations from Renca cells has been reported [13]. This model is a valuable tool in testing immunotherapy approaches. Immunotherapy is a novel therapeutic approach developed in the 1980s for the treatment of disseminated cancers refractory to conventional treatments. This approach utilizes biological response modifiers (BRMs)/cytokines and immune cells capable of enhancing immune mechanisms directed against the tumor that may be present although ineffective in cancer-bearing hosts [14]. The most commonly used BRMs are interferons (IFN) and the lymphokine interleukin 2 (IL-2). In the 1980s, Wiltrout and co-workers have developed chemoimmunotherapy combining the administration of chemotherapeutic agents (doxorubicin hydrochloride or flavone acetic acid) with adoptive immunotherapy (IL-2 and lymphokine activated killer cells) for the treatment of Renca that resulted in significant antitumor responses [11, 12, 15]. These preclinical studies were translated into clinical trials for RCC patients [16]. Other cytokines such as IL-7, IL-1 or combination of cytokines including IFNa/IL-2 or IFNa/IFNg were tested [8, 9, 15]. Gregorian and Battisto have demonstrated the existence of immunosuppressive effects induced by the tumor cells in Renca-bearing mice [17, 18]. Generation of specific cytotoxic T lymphocytes to Renca cells is particularly difficult when using irradiated Renca cells in in vitro assays due to their immunosuppressive activity (personal communications). In the 1990s, we had further developed the Renca model to address the efficacy and mechanism of action of cytokine therapy alone or combined with radiation therapy [19–29]. We have defined the kinetics of the tumor model following Renca cell implantation in various sites. A concentration of 105 cells can be administered in 0.1-ml Hank’s balanced salt solution (HBSS) for i.p. or flank subcutaneous (s.c.) injections or in 0.5-ml HBSS for i.v. injection via a tail vein. For kidney implantation, the right kidney of anesthetized mice is exposed through a right flank incision and injected subcapsularly with 105 Renca cells in 50-µl HBSS using a 27-gauge needle. Intraperitoneal injection of Renca induced metastases in the mesenteric lymph nodes starting by day 16 and progressing to carcinomatosis [21]. Liver metastases were observed in 38% of the mice, and lung metastases were detected in 5% [21]. Following s.c. injections of Renca cells in the right flank, small tumors were detectable by day 14 and grew progressively reaching a size of 1–1.5 cm3 [21]. Large tumors showed a tendency to become ulcerative and necrotic. Tumors remained localized at the site of injection and metastases were not detectable in other organs.

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Following kidney implantation, a macroscopic primary tumor was detectable by days 7–10, which then grew progressively to 1–2 cm3 by day 20–21 [19]. Large tumors of 7–8 cm3 were present by days 25–35. Pulmonary metastases were first noted by days 15–20 following renal implantation. However, large variations in the number of lung metastases were observed from mouse to mouse. Metastases to the liver, hemorrhagic ascites and/or carcinomatosis were also observed in most animals after 21 days, confirming previous studies. Similar to previous reports mice began to die on day 21, with a 50% survival rate by day 37 and less than 10% mice survived more than 45 days [19]. To increase the incidence and number of pulmonary metastases, we have i.v. injected Renca cells and observed visible tumor nodules of <1 mm diameter by day 15. By day 20, numerous lung metastases (100–200) were consistently enumerated in both lobes and increased in size [20, 21, 23]. Tumor was not detectable in other organs. Mice became moribund by day 30, and a median survival of 38 days was observed. This more predictable model of lung metastases was essential for quantitative evaluation of new therapeutic approaches for the treatment of lung metastases [20, 23, 24, 27, 29]; thus mimicking the clinical situation of postnephrectomy patients presenting with lung metastases. Using these various Renca models, we have studied the interaction between local tumor irradiation and systemic (i.p.) IL-2 immunotherapy. In the kidney tumor model, a greater therapeutic effect was demonstrated on the primary tumor and distant metastases by local irradiation of the tumor-bearing kidney followed by IL-2 therapy than with each modality by itself [19]. In the pulmonary metastases model, irradiation of the left tumor-bearing lung followed by systemic IL-2 therapy resulted in increased tumor reduction in both lungs suggesting that radiation enhances the systemic effect of IL-2 [20, 23]. We demonstrated the requirement for T cell and NK cell functions in the mechanism of action of the combined therapy [23]. In histological studies of lung tumor nodules, we found that radiation caused not only tumor cell apoptosis but also vascular damage allowing for an influx of macrophages and mononuclear cells in the tumor nodules and surrounding tissues [23, 24]. The combination of both therapies induced a greater vascular damage and massive infiltration of immune cells that may play a role in tumor destruction [23, 24]. Our data in the Renca models suggest that radiation therapy causes changes in the tumor cells and the tumor environment which increase the tumor susceptibility to destruction by the immune system activated by IL-2. The Renca system was an ideal animal model to investigate these complex issues, and these studies were translated into a clinical trial for metastatic RCC [25]. Independent studies recently demonstrated enhanced therapeutic effect of intratumoral administration of dendritic cells combined with localized radiotherapy in subcutaneous Renca tumors, confirming the role of radiation in enhancing immunotherapy approaches [30]. Amplified antitumor responses were associated with enhanced systemic cytokine production from splenocytes in response to tumor cells. A new original study combined i.p. IL-2 injections with oral administration of the histone deacetylase (HDAC) inhibitor MS 275 which have shown antitumor activity and immunomodulatory properties [31]. These studies were performed in mice bearing orthotopic Renca kidney tumors expressing luciferase to image mice

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at different time points after treatment and assess tumor response in the kidneys and inhibition of lung metastases [31]. This bioluminescence approach to follow in situ tumor growth in different organs is a novel and well accepted technique. These studies demonstrated a synergistic effect of IL-2 with HDAC inhibitor and decreased Foxp3+ regulatory T cells [31]. The Renca kidney model and lung metastases model were also helpful in the evaluation of other cytokines, including IL-4 and IFNg [27–29]. Following in vitro studies on human RCC cell lines demonstrating growth inhibition induced by IL-4 and IFNg [26], we have tested the therapeutic effect of these cytokines in different Renca models in vivo. We found that systemic treatment of either cytokine-induced regression of pulmonary metastases in a dose-dependent manner and increased mouse survival [27, 29]. We showed that IL-4 and IFNg have different antitumor mechanisms of action; consequently we found that sequential administration of IFNg followed by IL-4 resulted in a greater therapeutic effect [29]. During these studies, we developed a new treatment approach of intratumoral injection of IL-4 in Renca kidney tumors [28]. On day 16 following Renca cell renal implantation, the tumor-bearing kidney was re-exposed through a midline abdominal incision, and the kidney tumor (of 4–6 mm diameter) was injected with 50-µl IL-4. This treatment was safely performed and resulted in a marked inhibition of tumor growth in the kidney, with minimal effect on the progression of metastases in other sites [28]. Thus, the Renca kidney model can be adapted to therapeutic manipulations. Other studies used IL-4 for transfection of Renca tumor cells and showed that IL-4 transduced Renca cells injected s.c. caused systemic immunity [8, 9, 15, 32]. In the 1990s, the newly discovered cytokine IL-12 seemed to be the most promising, because it was found to be particularly effective at mediating a specific immune antitumor response in various tumor models including the Renca model [33, 34]. Subsequently, the Renca model was extensively used to study IL-12 mechanism of action, IL-12 gene therapy and IL-12 combination with other cytokines. IL-12 antitumor effects were shown to be increased by tumor irradiation [34], similar to our findings with IL-2 and tumor irradiation [19–24]. Using the Renca kidney model, Wiltrout et al. showed that IL-12 administered with pulse IL-2 was safe and induced a rapid and complete regression of primary and metastatic Renca tumors that was greater than with each cytokine alone [35]. This effect was found to be related to enhanced macrophage production of nitric oxide following studies in the s.c. Renca model [36]. Furthermore, in the same s.c. Renca model, the mechanism of IL-12 antitumor activity was found to depend on induced expression of IFNg by T and NK cells, leading to IFNg induced expression of chemokines IP-10 and Mig within tumor tissue. These chemokines act as chemoattractants for activated T lymphocytes [37, 38]. IL-12 was also found to potentiate the cytolytic effector function of recruited CD8+ T cells [37]. These studies in the Renca model led to monitoring IFNg, IP-10 and Mig mRNA in biopsies of RCC tumors and peripheral blood mononuclear cells from IL-12-treated patients demonstrating augmented levels of those molecules after therapy [39]. Thus, the Renca model is helpful for testing new treatments for translational purpose, and it also allows the investigation of mechanism of these treatments and determination of response parameters for patient monitoring.

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A novel Fas-mediated antitumor pathway involving also IFNg was discovered in studies performed with the Renca model [40, 41]. Renca cells express low levels of Fas that can be enhanced by IFNg and TNFa, making the cells susceptible to apoptosis induced by Fas ligand (FasL)-expressing hybridomas (dIIS), cross-linking of anti-Fas Abs or soluble Fas (FasL) [40]. The Fas/FasL pathway was found to be involved in cell-mediated killing of Renca cells by activated T cells, whereas a granule-mediated pathway predominated in killing of Renca cells by activated NK cells [40]. When Fas-overexpressing Renca cells (post-IFNg and TNFa treatment) were injected into mice, there was a consistent and significant delay in tumor progression, reduced metastasis and prolonged survival [41]. These findings show that therapeutic manipulation of Fas expression may represent a means of tumor treatment as well as a novel mechanism for IFNg-mediated antitumor effect. More recently, a new Renca model expressing a defined human cancer-associated antigen was generated [42]. The Renca/CA-IX model was developed by transduction of Renca cells with human carbonic anhydrase (CA-IX) gene coding for a cell surface tumor-associated antigen expressed by most clear RCC [42]. This model maintains antigen expression, forms metastases and has tumor growth kinetics equivalent to that of the original Renca cell line. This new model will be useful to study immunotherapeutic approaches and specific immune response to a human RCC antigen in immuno-competent Balb/c mice. The Renca model was also very useful for addressing various approaches of cytokine gene therapy to circumvent the toxicity caused by systemic injection of cytokines. For generation of cancer vaccines, Renca cells were transfected with cytokine genes. Renca cells transfected with IL-2, IFNg, granulocyte–macrophage colony-stimulating factor (GM-CSF) or IL-12 failed to produce s.c. tumors [43, 44]. We found that s.c. vaccination with cytokine transfected Renca cells combined with lung radiation significantly reduced the number of pulmonary metastases in the Renca lung metastases model [44]. Others showed that IL-12 transfected Renca cells used as a cancer vaccine inhibited the growth of parental Renca cells injected at a distant site and were synergistic with systemic IL-18 treatment [43]. We and others investigated the direct use of cytokine gene therapy for established Renca tumors using different vectors. An IL-12 adenovirus construct, administered i.v., inhibited Renca hepatic metastases in a Renca hepatic metastasis tumor model induced by intrasplenic Renca cell injection followed by splenectomy [45]. This effect seemed to be mediated by macrophages and neutrophils [45]. Recently, we demonstrated the efficacy of adenoviral vectors expressing cytokine cDNA genes encoding the human IL-2 cDNA (Ad-IL-2) and murine IFN-g gene (Ad-IFN-g) [46]. These constructs were particularly effective when three intratumoral injections were administered in Renca s.c. tumors compared to single dose gene therapy. We found that tumor irradiation enhanced the therapeutic efficacy of Ad-IL-2 and Ad-IFN-g intratumoral gene therapy. Tumor irradiation, administered one day prior to three doses of Ad-IL-2 treatment, was more effective than radiation or Ad-IL-2 alone, resulting in tumor growth arrest in all mice, increased survival and a consistent increase in complete tumor regression response rate [46]. Complete responders rejected Renca tumor challenge and had specific cytotoxic T cell activity;

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demonstrating induction of specific tumor immunity. The effect of radiation combined with three doses of Ad-IFN-g was less pronounced and did not lead to tumor immunity [46]. Histological observations showed that irradiation of the tumor prior to gene therapy increased tumor destruction and inflammatory infiltrates in the tumor nodules [46]. These findings demonstrate that tumor irradiation improves the efficacy of Ad-IL-2 gene therapy for induction of antitumor immune response [46]. These studies also showed the increased effectiveness of intratumoral IL-2 gene therapy preceded by tumor irradiation. This therapeutic approach did not cause toxicity and resulted in complete responders with induction of specific tumor immunity in about 50% of the mice while our previous studies on tumor irradiation and systemic injections of IL-2 did not lead to complete responses [19, 20, 23, 24]. Recently, we found that tumor irradiation enhanced the level and duration of IL-2 cytokine gene expression, both in tumor lysates and serum of mice treated with radiation and Ad-IL-2 (Hillman et  al., manuscript in preparation). Therefore, we suggest that tumor irradiation administered prior to gene-mediated immunotherapy is an effective strategy to debulk the tumor, enhance the probability of cell transfection and increase the level and duration of gene expression in the tumor to generate effective and potent therapeutic cancer vaccines. In another study, we used intratumoral injection of adenoviral vectors to modify the phenotype of Renca tumors to express MHC class II antigens with an IFN-g gene and suppress the invariant Ii chain with an antisense reverse Ii-RGC gene construct [47, 48]. This approach converted, in situ, Renca tumor cells into antigenpresenting cells (APC) in order to stimulate T helper cells by expression of class II antigens and by suppression of Ii protein that normally blocks the antigenic peptide-binding site of MHC class II molecules during synthesis in the endoplasmic reticulum [47, 48]. A single recombinant adenoviral vector containing IFN-g gene and an antisense Ii-RGC gene (rAV/IFN-g/Ii-RGC) efficiently induced the MHC Class II+/Ii- phenotype in Renca tumor nodules, and when combined with a suboptimal dose of Ad-IL-2 induced a potent antitumor immune response [47, 48]. This study shows that the phenotype of tumor cells can be modified by gene therapy in situ to induce an immune tumor response. A different approach involves the use of plasmids containing cytokine cDNA inserts. Intradermal injections of murine IL-12 plasmid DNA-induced systemic biological effects characteristic of IL-12, including enhanced NK activity and IL-12 inducible IFNg genes [49, 50]. Pretreatment of mice with IL-12 plasmid DNA induced tumor growth delay when mice were re-challenged with s.c. Renca [49]. Another approach used IL-2 plasmid DNA complexed with a cationic lipid for intratumoral injection of Renca tumors and showed antitumor activity and induction of specific immunity [51]. In conclusion, the Renca system has been of great value for testing multiple new therapeutic approaches for RCC-including novel gene therapies – and in the investigation of their mechanism of action, as well as in studying the role of several components of the immune system involved in the antitumor response. Other issues related to progression and angiogenesis of RCC were addressed in the Renca system including ways to inhibit these processes. Transfection of Renca

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cells with basic fibroblast growth factor (bFGF) gene increased their metastatic potential when injected i.v. or intrarenally, probably through the production of metalloproteinase 2 (MMP-2) [52]. The ratio of MMP-2 and tissue of inhibitor of metalloproteinase-2 (TIMP-2) was found to be a critical factor in the invasion and metastases of RCC based on Renca studies [53]. Absence of expression of TGFb type II receptor in Renca was also associated with an aggressive growth pattern [54]. Transfection of Renca cells with this receptor conferred tumor suppressive activity emphasizing the role of TGFb type II receptor as a mediator in Renca tumorigenicity [55]. Transfection of mouse endostatin into Renca cells decreased the rate of tumor growth when these cells were injected s.c. or i.v., suggesting that gene delivery of the angiogenesis inhibitor endostatin may be an effective strategy to prevent progression of RCC disease [56]. VEGF Trap is a newly developed VEGF blocking agent that is a soluble decoy receptor comprising parts of VEGFR-1 and VEGFR-2 based on a human IgG1 backbone. VEGF Trap binds VEGF and suppresses VEGF signaling and angiogenesis. This agent was shown to be a potent inhibitor of primary tumor growth and lung metastasis in the orthotopic Renca model and induced a decrease in vascular leaking into the tumor microenvironment [57].

12.3 Rat Renal Carcinoma Models 12.3.1 The Wistar–Lewis Rat Renal Adenocarcinoma The Wistar–Lewis rat renal adenocarcinoma, like murine Renca, also arose spontaneously in the kidney of a Wistar–Lewis rat and originated in the renal cortical tubules. Its growth and expansion were characterized by White and Olsson and found to be nonhormonal dependent [reviewed in [9]]. This tumor is maintained by transplantation in the flank of syngeneic rats. The s.c. tumor takes 3 weeks to develop and does not metastasize, and the mean survival is unpredictable [9]. To induce metastases, rats were first splenectomized. Then, 24 h later a tumor fragment was placed into the peritoneal cavity. Nine weeks later, all animals displayed ascites and metastatic disease into the diaphragm, bowel and muscle throughout the abdominal cavity [9]. Alternatively, the rat cancer cells could be injected into the renal capsule and resulted into 80% tumor occurrence. However, the time required for widespread metastases was unpredictable. Chemotherapeutic studies were performed on s.c. tumors as well as on the i.p. metastatic tumors and showed selective responses to drugs [8, 9]. Thus, although this rat RCC tumor model was less predictable and required a longer time frame study than Renca, it was useful to test chemotherapeutic agents. Renal implantation of the rat tumor showed a rapid growth rate of the primary kidney tumor, and within 90 days metastasized to the lungs even when the primary tumor was resected. Electron microscopic studies in this model revealed that the rat RCC cells shared many ultrastructural features with human RCC cells, such as the presence of large nuclei, abundant glycogen granules and numerous large vacuoles

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[58]. The primary tumor was used to study the tumor cholesterol metabolism, and also showed some similarity with human RCC tumors in its accumulation of an excess of esterified cholesterol due to an increased rate of cholesterol synthesis [58].

12.3.2 The Eker Rat Model In 1954 and 1961, Eker and Mossige described a dominantly inherited cancer syndrome in Wistar rats in which bilateral multicentric RCC develops at an early age [59]. The hereditary tumors that develop in the Eker rat model have many similarities to their human counterparts. They have similar histology, are bilateral, over express transforming growth factor (TGFa) and do not exhibit a high frequency of ras oncogene activation [60, 61]. Loss of sequences on rat chromosomes 4 (q11 through qter), 5 (monosomy) and 6 (q24) occur in these tumors and tumor-derived cell lines. Animals carrying the Eker mutation develop hemangiosarcomas in the spleen (males and females) and uterine leiomyosarcomas as second primary tumors later in life [60, 61]. Vascular neoplasms (hemangioblastomas) and second primary tumors are also associated with RCC in human von Hippel–Lindau (VHL) disease [61]. Genetic analysis of Eker rats showed that the familial tumors were due to an alteration in a single gene, which caused heterozygote carriers of the mutation to develop spontaneous RCCs between 4 and 12 months of age, whereas rats that are homozygous for the wild-type allele rarely develop spontaneous RCC (<0.5%) [60]. When homozygous, the mutation is lethal prenatally at 9–10 days of gestation [60]. Eker rats were found to have a 70-fold increase in the susceptibility to chemical carcinogenesis [61] and increased sensitivity to radiation [60, 62], resulting in a greater cancer incidence. A carcinogen that targeted both renal epithelial and mesenchymal cells caused an increase in tumors of epithelial origin in susceptible animals; the number of carcinogen-induced mesenchymal tumors was unaffected by the presence of the mutation at the susceptibility locus [61]. The cancer susceptibility gene was identified in the 1990s as the rat homolog of the tuberous sclerosis gene 2 (Tsc2) and the mutation involved a 6.3-kb insertion within an intron of the gene on chromosome 10q [63, 64]. This region was found to be homologous with human chromosome band 16p13.3, the site of human Tsc 2 gene [63]. Rat Tsc2 functions as a tumor suppressor gene as normal kidneys of heterozygote carriers express both normal and abnormal Tsc2 mRNA, whereas primary tumors and tumor-derived cell lines exhibit only the mutant transcript [60]. Tumor suppressor genes represent a class of cancer susceptibility genes in humans. Inheritance of a mutation in one allele of a tumor suppressor gene predisposes individuals to develop tumors after sustaining an additional spontaneous mutation in the remaining normal allele of that gene. In humans, several tumor suppressor genes including the VHL gene, the WTI gene and the Tsc2 gene have been implicated in the development of sporadic as well as hereditary tumors in the kidney [2]. In the Eker rats, a single gene mutation (Tsc2) predisposes to multiple bilateral RCCs with an autosomal-dominant pattern of inheritance. Therefore, animals carrying the Eker mutation serve as a model for hereditary RCC.

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In the 1990s, the Eker rat model was extensively studied to identify key genetic components in the pathogenesis of RCC and to dissect the molecular mechanisms underlying tubular epithelial carcinogenesis. Very early preneoplastic stages in tubular transformation in hereditary RCC can be identified in Eker rats. By laser microdissection procedure, loss of heterozygosity was detected in the region of Tsc2 locus on rat chromosome 10 in preneoplastic renal tubular lesions in Eker rats [65]. These studies suggested that, in heterozygotes, at least two events (one inherited and one somatic) are necessary to produce large adenomas and carcinomas [65]. Studies on patients with tuberous sclerosis complex (TSC) documented that it is an autosomal-dominant disorder characterized by seizures, mental retardation and hamartomas. These TSC patients have increased frequency for developing RCC at an early age, similar to Eker rats [66]. The role of Tsc2 gene was further elucidated using the Eker rat model. This gene encodes a large membrane-associated GTPaseactivated protein (GAP) designated tuberin, and its biological activity was determined by transfection of Eker rat-derived RCC cells with wild-type Tsc2 gene. These cells demonstrated a decreased ability to form colonies in vitro and tumors in vivo providing evidence for the tumor suppressor function of Tsc2 gene [67]. The Tsc2 gene may also contribute to regulation of the cell cycle and cell survival [68]. Recently, a Tsc2 knockout mouse was generated to further characterize the tuberin function in  vivo [69]. Tsc2 heterozygous mice developed renal carcinomas, and Tsc2 homozygous mice died at 10-day embryonic age, similar to the observations made in Eker rats [69]. These studies emphasize an essential function for tuberin in mouse and rat embryonic development as well as in RCC incidence. Other studies have investigated the role of the VHL gene in the rat RCC pathogenesis. The rat VHL gene was identified and found to be 90% homologous to the human VHL gene [70]. However, alterations were not detected in the rat VHL gene in Eker rat RCC cell lines, suggesting that RCC development in the rat is independent of VHL mutation or deletion in contrast to human sporadic RCC [70]. The influence of hormonal and dietary factors on RCC incidence was also investigated in the Eker rat model. Estrogen treatment was found to enhance the development of renal tumors in Eker rats [71] and a high fat diet increased renal preneoplasia [72]. In conclusion, the Eker rat model is an excellent example of a Mendeliandominant predisposition to RCC cancer in an experimental animal that allows the study of genes and molecular events involved in the development of hereditary RCC and secondary tumors.

12.4 Xenografts of Human RCC Tumor Cell Lines in Immunodeficient Mice Athymic nude mice are deficient in immune T cell functions and do not reject xenografts therefore they are used for implantation of human tissues. In the beginning of the 1990s, human RCC cells have been successfully heterotransplanted in nude mice and grew locally as a solid tumor after s.c. injection [73].

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The tumorigenicity of tumor cells in nude mice is used as a criterion for characterizing the malignancy of the cells isolated from a tumor specimen [73]. In addition, human RCC xenografts established by s.c. or i.p. injection in nude mice have been tested for their responsiveness to therapy. Human RCC tumors growing in nude mice were studied for localization and treatment by monoclonal antibodies (MAbs) including the G250 and DAL K29 MAbs directed specifically against human renal tumor lines. van Dijk et al. have demonstrated that G250 MAb or the bispecific MAb CD3/G250, injected i.v., preferentially localized in human renal tumor transplanted s.c. in nude mice [74]. In a subsequent study, G250 administered i.p. induced a significant growth inhibition of this tumor, and this effect was enhanced by intratumoral injection of IFNa and TNFa resulting in macrophage infiltrates into the tumor stroma. Singh et al., found that human RCC cells administered i.p. in nude mice developed into ascites tumor and could be targeted by specific MAb DAL K29 linked to liposomes containing methotrexate resulting in a higher drug uptake [75]. Other groups explored the subcapsular renal implantation of human RCC cells in the nude mouse kidneys. Based on the concept that neoplasms are heterogeneous and contain subpopulations of cells with different biological behavior patterns, including metastatic potential, Fidler has demonstrated the usefulness of orthotopic implantation human RCC in the kidney of nude mice [76]. Fidler has implanted human RCC cells, obtained from surgical specimens, into different organs of nude mice and showed that growth at different organs selected for different subpopulations of human RCC cells. However, the cells did not metastasize unless they were implanted orthotopically in the kidney [76]. The injection of human RCC cells into the kidney produces the highest incidence of tumorigenicity, suggesting that the kidney is a better environment than the skin, spleen or peritoneum. Some of these tumors spontaneously metastasize to the lung, pancreas, diaphragm and mesenteric lymph nodes [76]. Tumor cells were isolated from the kidney tumors to establish RCC cell lines in culture and study their biological activity in vitro and in vivo [76]. Other studies have confirmed the tumorigenicity and metastatic potential of human RCC cells obtained from cell lines or surgical specimens [77, 78]. Not every cell line or cells from different surgical specimens are tumorigenic. However, established human RCC tumors in the mouse kidney usually showed histological characteristics of the original primary tumor, including positivity to cytokeratins and vimentin [78, 79]. Orthotopic xenograft RCC models were used to test new anticancer drugs (BCH-4556) or gene therapy [80–82]. Human RCC cells transfected with IFNb did not induce localized tumors, kidney tumors and spontaneous lung metastases when injected in the kidney, or s.c., or i.v. [81]. These IFNb-transfected RCC tumor cells stimulated a high level of nitric oxide production by murine macrophages in vitro and in vivo that was cytotoxic to tumor cells [81]. This study led to testing retroviral vectors encoding murine macrophage inducible nitric oxide synthase (iNOS) gene for the treatment of RCC [82]. Infection of metastatic human RCC cells with iNOS retrovirus decreased their tumor growth and metastasis when injected i.v. or in the kidney of nude mice by producing nitric oxide that enhanced the rate of apoptosis of the tumor cells [82]. More recently, treatment of s.c. or i.p.

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human clear RCC xenografts with intratumoral injections of capsid-modified oncolytic adenoviruses reduced tumor growth and increased survival [83]. We have developed a new xenograft RCC experimental tumor model in nude mice. We have established a tumor cell line designated KCI-18 RCC from a primary renal tumor specimen obtained from a patient with papillary RCC (nuclear grade III/IV) [79]. Chromosome analysis of KCI-18 RCC cell line (passage 8 in vitro) revealed a hypertriploid karyotype with multiple clonal aberrations: 75–85, XX, -X, add (1) (p36) x2, +2, +3, +5, +i (5) (q10), +6, +del (7) (q11), +8, der (9;14) (q10;q10), -9, +10, +12, -14, add (15) (q26), +16, +17, +21, +mar, ace [cp6]. Cells were injected into the kidney (intra kidney, IK) of Balb/c nude mice. Renal tumors were resected, recultured in vitro, and repassaged in the kidney in vivo to produce KCI-18/IK cell lines [79]. Cells from the KCI-18/IK lines preserved the karyotype of the original KCI-18 RCC cell line. The KCI-18/IK lines were highly tumorigenic, and grew in the kidney with faster kinetics than the original KCI-18 RCC cell line. Tumor nodules of 0.2 cm were detectable on the kidney by day 11 postcell injection and grew to large tumors of 2–2.5 cm invading the kidney by day 44. Metastases were detectable by day 37 and observed mostly in the lungs and occasionally in the liver [79]. Mouse survival was about 44–50 days. The tumor presence in kidney and lungs was histologically confirmed and defined as a high-grade carcinoma with a sinusoidal vascular pattern. Tumor cells were characterized by large pleiomorphic nuclei, prominent nucleoli and abundant eosinophilic cytoplasm. Tumor cells co-expressed cytokeratin and vimentin. These features resembled those of the original human tumor specimen [79]. These studies demonstrate that orthotopic models of heterotransplanted human RCC cells are representative of human RCC disease progression, and preserve the karyotypic and histological characteristics of the human primary tumors. These models can be used to study human RCC progression and metastasis and in vivo responsiveness of human RCC tumors to treatment. The KCI-18 tumor model was used to test if the soy isoflavone genistein promotes metastasis but when combined with tumor irradiation could enhance radiotherapy. We had previously reported the potentiation of radiotherapy by pure genistein for prostate cancer in  vitro and in orthotopic prostate tumor models in vivo [84]. However, when genistein was used as single therapy in murine prostate tumor models, it promoted metastasis to regional para-aortic lymph nodes [84]. To clarify whether these intriguing adverse effects of genistein are intrinsic to the orthotopic prostate tumor model, we tested the effect of pure genistein alone or combined with kidney tumor irradiation in our KCI-18 RCC model [79]. Treatment of established kidney tumors with genistein demonstrated a tendency to stimulate the growth of the primary kidney tumor and increase the incidence of metastasis to the mesentery lining the bowel. In contrast, when given in conjunction with kidney tumor irradiation, genistein significantly inhibited the growth and progression of established kidney tumors. These findings confirm that, in an orthotopic RCC model, genistein treatment alone promotes metastasis, whereas it acts as a radiosensitizer when combined with tumor irradiation, similar to its effects in orthotopic prostate cancer models [79, 84, 85].

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In a novel and recently published study, we demonstrated that orthotopic RCC xenograft models are also useful to investigate the effect of anti-angiogenic drugs on tumor vasculature using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) [86]. Human RCC tumors are highly vascularized and recent developments in anti-angiogenic therapy have improved the targeting of RCC metastatic disease. The drug sunitinib is a small molecule receptor tyrosine kinases (RTK) inhibitor that has demonstrated antitumor and anti-angiogenic activities in mouse xenograft models [87–89]. Sunitinib has been particularly effective in recent clinical trials with an objective response rate of 47% and 11 months median progression-free survival [90–92]. However, long-term control of the disease is still not achieved and adverse effects of cardiotoxicity were documented in some of the patients, probably as a result of alterations to normal vasculature [93]. To improve the efficacy of sunitinib and decrease its impact on healthy vital organs, we investigated the effect of lower and less toxic doses of sunitinib on tumor vasculature in the KCI-18 RCC model [86]. Histological observations of KCI-18 kidney tumors showed the typical morphology of highly vascularized human RCC tumors, exhibiting abnormal vessels that are enlarged, disorganized and leaky as a result of defective basement membrane [86]. These structural defects of tumor vessels cause increased interstitial tissue pressure, impaired blood supply and decreased oxygen supply in tumors compromising the delivery and efficacy of cytotoxic drugs and radiotherapy [94, 95]. The challenge is to develop imaging technologies to monitor early vascular changes and induction of tumor vasculature “normalization,” namely regularization of vessels and blood flow, by anti-angiogenic drugs for scheduling cytotoxic therapy. Imaging of KCI-18 kidney tumor-bearing mice using DCE-MRI was successful at evaluation of vascular changes induced by various doses of sunitinib [86]. These vascular changes were observed in the right tumor-bearing kidney, in which KCI-18 cells were implanted, as well as in the left contralateral normal kidney by DCE-MRI. Sunitinib, at a suboptimal but effective dose of 20 mg/kg/day, caused increased tumor perfusion and decreased vascular permeability associated with thinning and regularization of tumor vessels while mildly affecting normal vessels in the normal contralateral kidney [86]. Sunitinib inhibited KCI-18 kidney tumor growth and caused modulation of its molecular targets VEGFR-2 and PDGFR-b, which are important receptors involved in signaling pathways of angiogenesis and found to be highly expressed in human RCC [86]. These preclinical studies emphasize the potential of human xenograft RCC tumors to assess the clinical use of DCE-MRI for selecting the dose and schedule of anti-angiogenic compounds to combine with cytotoxic therapies. Other anti-angiogenic compounds or approaches were also recently investigated in different xenograft preclinical RCC models. RCC is characterized by the loss of VHL tumor suppressor gene resulting in dysregulation of growth factor signaling including VEGF, PDGF and Raf pathways. Sorafenib, a multikinase inhibitor drug, also targets RTKs including VEGFR-1, -2, and -3 and PDGFR-b and serine/threonine kinases including Raf-1 and B-Raf that are molecules involved in angiogenesis and tumor progression. Sorafenib inhibited angiogenesis and tumor growth in both Renca (VHL+/+) and human 786-O (VHL-/-) RCC xenografts in athymic nude mice, confirming the key role of angiogenesis in supporting RCC tumor growth [96].

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These findings corroborate previous studies demonstrating that drugs which disrupt established tumor blood vessels (ZD6126) and drugs which interfere with neovascularization (ZD6474) are effective at causing tumor growth delays in the human clear RCC Caki-1 s.c. xenograft model [97]. Blockade of VEGFR-2 signaling pathway by TSU-68 (SU6668) inhibitor caused inhibition of kidney tumor growth but not of bone tumor xenografts generated in nude mice using the human RBM1-IT4 RCC cell line, obtained from an RCC bone lesion, that has mutated VHL [98]. The role of VHL gene in angiogenesis is further emphasized in a study showing that hypomethylating agents causing re-expression of VHL gene in human UOK 121 clear RCC s.c. xenografts reduced tumor growth [99]. A novel metastatic xenograft tumor model expressing a defined human tumor antigen was established from a primary tumor of a patient with sporadic type 2 chromophil (papillary) RCC who responded well to IL-2 immunotherapy [100]. Serial passages in SCID mice under the renal capsule generated a metastatic RCC model LABAZ1 expressing the membrane-bound tumor-associated antigen carbonic anhydrase type 9 (CA IX) [100]. This model is valuable to study the role of CA IX tumor antigen in RCC progression and metastasis [100]. In conclusion, animal models generated by xenograft implantation of human tumor RCC cell lines in immunodeficient mice have proven to be adequate for investigating novel chemotherapeutic or angiogenic drugs, gene therapy and imaging of human RCC tumors in vivo. The disadvantages of using heterotransplants in nude mice or SCID mice reside in their deficient immune system, which does not allow the study of T cell responses and induction of antitumor-specific immune responses. For demonstrating efficacy of cancer vaccines and immunotherapybased approaches, the Renca model in immunocompetent mice has been valuable in our studies and others. New generations of syngeneic animal models that express human tumor antigens could also be promising for studying these therapeutic approaches, dependent on an integral immune system. The Eker rat model has been helpful to investigate the genetic basis and molecular events involved in the development of hereditary RCC and secondary tumors. Acknowledgments  We thank Dr. A. Konski, Chairman of Radiation Oncology Dept., for supporting our studies. We thank Dr. F. Sarkar, Dr. M. Haacke, Dr. V. Singh-Gupta, Dr. J. Raffoul, Dr. H. Zhang, C. Yunker, A. Al-Bashir and A. Patel for excellent contribution to our new studies. Our studies were supported by grants from the Pardee Foundation, American Institute for Cancer Research, American Cancer Society, The Fund for Cancer Research, and Karmanos Cancer Institute; and grants from the companies Transgene SA (Strasbourg, France), Antigen-Express, Inc. (MA, USA), and Pfizer, Inc. (NY, USA).

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Chapter 13

Animal Models of Mesothelioma Harvey I. Pass, Joseph B. Pincus, Michele Carbone, and Magdalena Plasilova

Abstract  Animal models for mesothelioma mainly stem from exposure of different species to environmental fibers that are carcinogenic. The majority take months for a mesothelioma to develop in the abdomen or the chest. The models are invaluable, however, for studying molecular pathways. Other models in transgenic animals have used novel promoters or other molecules involved in mesothelioma generation to produce the disease. Keywords  Mesothelioma • Rodents • Asbestos • Fibers

13.1 Introduction Mesotheliomas are tumors that originate from the serosal lining of the pleural, pericardial, and peritoneal cavities [1]. Malignant pleural mesotheliomas (MPMs) are among the most aggressive human tumors, with a survival of less than 1 year after diagnosis. None of the current therapeutic approaches has been shown to alter the natural history of this disease [2]. The continued rise in the incidence of mesotheliomas in the United States and abroad (2,000–3,000 cases per year in the United States) has been related to the widespread commercial use of asbestos during the last 50 years and its latency in manifestation [3–5]. The exact biochemical mechanism that causes asbestos to induce mesothelioma is unclear [6–10]. In tissue culture, asbestos fibers can cause mutagenic events, including DNA-strand breaks and deletion mutations through the production of hydroxyl radicals and superoxide anions. They can also alter chromosome morphology and ploidy by mechanically interfering with their segregation during mitosis. Furthermore, macrophages produce DNA-damaging oxyradicals H.I. Pass (*) Department of Cardiothoracic Surgery, New York University Medical Center, Suite 9V, 530 First Avenue, New York, NY 10016, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_13, © Springer Science+Business Media, LLC 2011

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following phagocytosis of asbestos fibers, and will elaborate lymphokines that may depress the host immune response. Although the association between asbestos and mesothelioma is indisputable, less than 10% of the people exposed to high doses of asbestos will develop ­mesotheliomas. Moreover, approximately one-half of the reported cases of ­mesothelioma have no documented exposure to asbestos [10]. In areas where asbestos mines exist, more than 70% of mesothelioma patients have a positive exposure history, but in areas with no substantial asbestos-using industries, as few as 10% of patients with mesothelioma have a positive exposure history. These findings and reports of mesothelioma developing in childhood – or even in utero – as well as the continued description of the disease in individuals too young to have the usual latency period (25–40 years) from asbestos exposure to MPM development suggest that there are probably other unknown factors involved in the pathogenesis of mesothelioma. Thus, researchers have long sought additional carcinogens that may be responsible for mesotheliomas in nonasbestos-exposed individuals, or that could render particular individuals more susceptible to the carcinogenic effect of asbestos. Nonasbestos fibers and ionizing radiation have been described to cause the malignant transformation of mesothelial cells. This chapter focuses on the development of animal models for mesothelioma. Since conventional wisdom dictates that the etiology of most mesotheliomas is asbestos, a large part of the discussion will focus on animal models of ­mesothelioma using asbestos fibers. However, since there is interest in viral-induced mechanisms for mesothelioma in humans, viral-induced mesotheliomas in hamster and murine models are also discussed. Finally, orthotopic transplantation of established ­mesothelioma cell lines in various positions in immunocompromised animals is described. The individual sections of the chapter discuss the individual means by which the tumor can be induced or propagated in the animal. Routes of delivery and species specificity are highlighted.

13.2 Asbestos-Induced Mesothelioma in Animal Models 13.2.1 Types of Asbestos Fibers There are two families of asbestos fibers: serpentine (chrysotile), which is curly and pliable, and the rod-like amphiboles, which include crocidolite, amosite, and anthrophylite, tremolite, and actinolyte [11]. The size and thickness of these fibers will vary considerably, and the carcinogenic effects are related to their physical characteristics, with greater tumor promotion seen in the long, thin fibers that are readily phagocytosed and are stable in tissue [12]. Inhaled fibers are cleared from the tracheobronchial tree by macrophages and ciliary action, and the remaining fibers accumulate in the lower one-third of the lungs, adjacent to the visceral pleura [13]. As opposed to crocidolite and other amphiboles, the serpentine fibers break

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down into smaller subunits that deteriorate or dissolve in the lung, and are then eliminated by lymphatics. The relative carcinogenicity of the fibers remains controversial. It is safe to say that in humans there seems to be a “gradient” from crocidolite to chrysotile in potency in order to induce mesothelioma and – despite the ability of chrysotile to induce mesothelioma in laboratory animals – it is generally agreed that crocidolite is of greater carcinogenicity in humans [14]. Nevertheless, chrysotile constitutes the majority (90%) of the asbestos used worldwide, and there are marked differences in the disease incidence in chrysotile workers [15–17]. Brake-lining workers chiefly exposed to chrysotile fibers and other automobile maintenance workers are estimated to represent 20,000 deaths from asbestos-related cancer over the next 40 years. This finding may be a function of difference in fiber size or contamination with crocidolite fibers. The necessity to delineate the risk of chrysotile-induced mesothelioma is even clearer when one considers that most buildings use this fiber for cement products and insulation. Attempts at delineating the dose intensity and relation of fiber type to mesothelioma development have been performed using fiber analysis of tissues. This fiber content, measured by light and electron microscopic techniques, depends on the amount of fiber deposition (a function of the duration and dose intensity) as well as fiber clearance. Recent data suggest that the fiber concentration exceeding 1 million fibers per gram of dried tissue may be associated with increased mesothelioma risk. Moreover, epithelial mesothelioma may be associated with significantly lower fiber content than sarcomatoid mesothelioma [18].

13.2.2 Asbestos-Induced Animal Models of Mesothelioma: General Comments Animal models for mesothelioma have been developed using all types of asbestos fibers [19–22]. Just as in the human disease, the animal models have demonstrated that fiber length, diameter, shape, and durability are more important than the type of fiber. All types of asbestos can induce tumor formation in animals through intraperitoneal (i.p.), intrathoracic, or intratracheal inoculation. The observation that some fibers are more mesotheliomatogenic than others is not coincidental. There seems to be a relationship between the carcinogenicity of a specific fiber and the proportion of long, thin fibers it contains. The general consensus is that long, thin fibers over 8 µm in length and thinner than 1 µm have the strongest carcinogenic effects because they cannot be readily phagocytosed by alveolar macrophages for mechanical clearance via the mucociliary escalator. However, threshold toxicity has yet to be defined, because asbestos dust comes in variety of particle lengths and it is difficult to administer a fiber of some precise length without also injecting fibers of other lengths. As a result, most studies have attempted to characterize carcinogenic potential as a function of number of particles over or under a certain length.

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13.2.3 Intraperitoneal Asbestos Injection Intraperitoneal (i.p.) injections have the disadvantage of introducing the asbestos into the body in an unnatural manner. It has been argued that the conclusions drawn from carcinogenic studies are not applicable to humans because the physiological route of entry into the respiratory tract via the conducting airways is effectively bypassed. Thus, mesothelial cells are exposed to massive local doses of fiber that are not relevant for human exposure. Advocates of this method of induction point to it as being less labor-intensive than inhalation studies, and view it as an excellent opportunity to test the carcinogenic potential and potency of a fiber. Studies using the peritoneal mode of induction also offer the advantage of producing tumors in a large number of the injected animals.

13.2.4 Intraperitoneal Asbestos Injection: Rats Tumor induction rates vary between 56% and 97.5% in the rat mesothelioma model. A variety of naturally occurring asbestos fibers – such as crocidolite, standard chrysotile, Canadian chrysotile, Rhodesian chrysotile, and amosite – have been studied for their ability to induce mesothelioma [23, 24]. In these studies, where a fixed, notadjusted-for fiber-length dose in milligrams of a fiber has been administered, there is conflicting evidence as to whether standard chrysotile or standard crocidolite is more tumorigenic. Davis found chrysotile and amosite to be more carcinogenic than crocidolite [24]. However, a study by Minardi and Maltoni suggests that standard crocidolite is the most hazardous material used in the study, inducing mesothelioma in 97.5% of animals tested, followed by amosite (90%), Rhodesian chrysotile (82.5%), Canadian chrysotile (80%), and Californian chrysotile (72.5%) [23]. Davis demonstrated that the hazard of a particular fiber (e.g. chrysotile, amosite, crocidolite) is related to the number of long fibers it contains [24]. Thus, chrysotile with over 60 million fibers >10 µm produced mesothelioma in 68% of injected animals at the 2.5-mg dose. In contrast, amosite with only 10 million fibers >10 µm induced mesothelioma in 59.4% and crocidolite with 9 million fibers >10 µm in 56.3% of animals at the same dose. Wagner similarly showed that the length of the crocidolite fiber is directly proportional to its tumorigenic ability [21]. Samples that were milled for 4 and 8 h, and as a result contained far fewer crocidolite fibers >6.5 µm than 1- and 2-h milled fibers, induced mesothelioma in 34% of the animals compared to 80% for the 1- and 2-h group. A rat study by Miller found that injecting a dose containing 109 amosite particles >5 µm led to an incidence of mesothelioma in 88% of the animal group, confirming that a dose containing a large number of long fibers will be successful in promoting tumor growth [25]. In the rat species, the appearance of effusions and solid-tumor nodules after inoculation is often associated with the development of mesothelioma. When these are present, the tumors have been characterized as predominantly biphasic and

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spindle-shaped (sarcomatous), but few reports indicate that all the patterns observed in humans – including tubular, papillary, solid, and spindle cell – are possible [23, 25]. In addition, several different patterns have been seen in the same tumor, and metastases are common. Intraperitoneally injected rats had a mean survival time between 509 and 1,002 days. In general, there is a direct relationship between administered dose, the ability of the fiber to induce mesothelioma, and average survival time (i.e. high doses and very toxic fiber administration resulted in a decrease of the life span of the animal). Amosite administration resulted in an average survival time between 462 and 889 days, crocidolite in 416–1,002 days, and chrysotile in 476–903 days – all depending on the administered dose, type of chrysotile, and milling time [23, 27].

13.2.5 Intraperitoneal Asbestos Injection: Mice Various rates of tumor induction have been reported in the murine mesothelioma model, ranging from 25% to 45% [20, 28]. Davis reported tumor growth in 25% of Balb/c mice and 45% of CBA mice treated with Wittenoon George crocidolite [28]. A study on the carcinogenic potential of amosite, chrysotile, and calindra chrysotile by Suzuki showed amosite to be the most mesotheliomatogenic fiber, inducing tumors in 40.5% of Balb/c mice, followed by Calidria chrysotile (25%) and standard chrysotile (0%) at the administered dose [20]. The relationship between fiber toxicity and number of long fibers, which has been observed in the rat mesothelioma model, holds true for the murine model as well. In the Suzuki study discussed here, amosite was the most mesotheliomatogenic fiber – causing tumors in 40% of the treated group – presumably because it had the highest percentage of long fibers (6% >7.5 µm). Calindra chrysotile, with the second highest percentage of long fibers (4.6% >5 µm), induced mesothelioma in just 25% of animals, and chrysotile with only 2% of fibers >3 µm was found to be nontumorigenic at the given dose [20]. In the murine animal model, the latency period has been established at 7 months [20, 28]. It has been speculated that this period is shorter in the mouse than in many other species, because of the animal’s relative short life span. Ultrastructural analysis of mesothelioma in the mouse species provides a blurred picture on the predominant cell type. Davis reports that while all three histological forms of human malignant mesothelioma were present, as with the human disease, the majority of malignant cells identified in the ascites were epithelial tumors exhibiting typical mesothelial differentiation, with long, thin microvilli, intermediate filaments, numerous microscopic vesicles, and much glycogen [28]. No ­evidence of metastases was noted. Suzuki similarly notes the presence of ascites in most mesothelioma cases, but the vast majority of fibrous tumors and the small minority of biphasic tumors reported are in stark contrast with both the history of human disease and Davis’ findings [20]. Tumors grew preferentially in the ­omentum, mesentery, and serosae of the gastrointestinal and

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genital organs, the diaphragm, the capsule of liver and spleen, and the abdominal wall peritoneum. Mesothelioma cell lines from rats and mice have been established in vitro, with the majority established in mice as described by Davis [28]. Mesothelial tumors of the Balb/c strain were more likely to be established than tumors of the CBA strain of mouse. All cell lines achieved greater than 32 passages, were in culture for at least 7 months, and exhibited a wide range of morphologies ranging from stellate-shaped cells to fibroblast-like cells. No correlation was found between morphology and doubling time, which ranged from 16 to 30 h. The tumorigenicity of all cell lines was tested by inoculation in syngeneic mice. The kinetics of tumor development varied substantially among cell lines, with the most tumorigenic lines (AB1 and AC29) producing ascites in 27 and 24 days, respectively, and solid tumors 34 and 25 days after subcutaneous (s.c.) ­inoculation. All cell lines produced solid-tumor growth, at times without ­concurrent ascite formation. In vivo aggressiveness did not correlate with in vitro morphology or growth rate.

13.2.6 Intrapleural Asbestos Injection Because most human mesothelioma is manifested in the pleural cavity, the intrapleural mode of induction is cited as being more relevant to the human disease than studies done in the peritoneum. However, its applicability to the human disease fibers introduced in this manner still bypasses the body’s natural defenses, and subjects mesothelial cells to doses of fiber that would not be encountered under normal circumstances with the human cases.

13.2.7 Intrapleural Asbestos Injection: Rat Tumor induction rates appear to be somewhat lower in the pleura than in the peritoneum, with chrysotile viewed as a more potent tumor initiator than crocidolite, inducing mesothelioma in 65% of injected animals compared to 45% induction for crocidolite [23]. Whitaker reports successful induction rates in 56% of the animals inoculated with Western Australian crocidolite [26]. Although few analyses have been performed to link the carcinogenicity of a fiber to its length, it is logical to assume that the same relationship holds true as with peritoneal studies. The time it takes to develop tumor in the pleura of rats is relatively long. Whitaker reports a latency of 56 weeks in animals treated with Western Australia crocidolite asbestos. The average survival time is longer in animals afflicted with pleural mesothelioma than in their peritoneal counterparts, with rats injected in the pleura surviving between 105 and 111 weeks [23]. Crocidolite administration resulted in an average survival time of 105 weeks, and animals injected with Canadian chrysotile lived an average of 111 weeks.

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Malignant cell lines have been developed from rat pleural ascites. Whitaker reports establishing cell cultures in two of five pleural effusions for 2 and 8 months [26]. The presence of collagen fibers using Gieson’s stain. Examination of the cell junctions, profuse microvillous borders, and intermediate filaments further ­confirmed the mesothelial nature of the culture.

13.2.8 Inhalation of Asbestos in Animal Models Inhalation studies offer the advantage of introducing the carcinogen through the only significant pathway for humans because almost all exposures to asbestos in our species occur through breathing. However, these studies are often expensive, pose a hazard to the researchers conducting them, and have a very low incidence of mesothelioma induction, which makes them impractical for discriminating the fibers’ potential for mesothelioma production.

13.2.8.1 Inhalation Studies in Rats It has been known for some time that inhalation studies are not as efficient in ­producing mesothelioma as intracavital injections. Miller reported mesothelioma induction in 2 of 42 (5%) of animals, yet a study by Botham in which rats were exposed to high concentrations of Northwest Cape crocidolite failed to induce any mesothelioma in the animal group [25, 29]. In the Miller study, amosite asbestos with a significant proportion of fibers >25 µm was used to induce mesothelioma in 5% of the treated group.

13.2.8.2 Inhalation Studies: Guinea Pig A study by Botham et al. shows that West Cape crocidolite and Transvaal ­crocidolite are capable of producing mesothelioma in albino Guinea pigs [29].

13.2.8.3 Intratracheal Asbestos Administration Intratracheal instillations are considered to be nonphysiological and unsuitable for risk characterization because of the frequent, uneven distribution of fibers within the different lobes. One common result of this mode of introduction is the ­formation of areas of greater deposition leading to high local doses and acute inflammatory responses. However, like intracavital injections, this method can be considered for comparative risk assessment among different fiber types.

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13.2.8.4 Intratracheal Asbestos Administration: Syrian Golden Hamster There is some evidence that long fibers (i.e. fibers >8 µm) are not required for the induction of mesothelioma intratracheally. Mohr et  al. reported an incidence of mesothelioma of 5.6% (8 of 142) in the Syrian golden hamsters after intratracheal instillations of crocidolite fibers, with 50% <2.1 and <0.2 µm in diameter [19]. Light microscopy observation has revealed tumors consisting of large, polyhedral cells of the epitheloid type, with abundant amphophylic cytoplasm and sharply defined cell contours [19]. Areas of papillary formations have also been observed. Scanning electron microscopy analysis has shown individual, minute polypoid alterations, with surfaces composed of normal mesothelial cells, and polymorphic cells which varied in size and shape. The microvilli of these cells displayed clear pleomorphism.

13.3 Spontaneous Models of Mesothelioma Spontaneous mesotheliomas are generally rare in experimental animals. No spontaneous mesotheliomas have been reported in mice and hamsters. However, Fisher 344 rats have been reported to have a 3–4% natural occurrence of tumors by a number of investigators, and it is generally accepted that spontaneously occurring tumors are quite similar to those induced by asbestos. Tanigawa reported that spontaneously induced mesothelial tumors were specific to males, with an incidence of 4.3%, or 17 of 395 [30]. The majority of tumors (16 of 17) were confined to the genital serosa and peritoneum, with one tumor occurring in the pleural cavity. Expansion of the peritoneal cavity caused by ascites is typical in this disease, as is protrusion of the scrotum. Microscopically, the tumors are notable for complex papillary growth and sessile nodular growths resembling sarcomas. Immunohistochemical localization of keratin proteins and histological patterns of the tumors examined suggest that spontaneous mesothelioma essentially resembles the epithelial type of human mesothelioma.

13.4 Other Agents for Animal Production of Mesothelioma 13.4.1 Chemical Although most cases of mesothelioma can be linked to asbestos exposure, some cannot, which raises the question of whether other agents cause mesothelioma or augment the effects of asbestos [31]. Rice reports that the polycyclic aromatic hydrocarbon carcinogen 3-methylcholanthrene (MC) is capable of inducing mesothelioma when injected intragastrically in certain strains of mice. The C3H strain was the most susceptible of the six

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strains tested, with a mesothelioma incidence of 39% (12 of 31 treated animals) and an average survival time of 8.3 months. The Balb/c strain had an incidence of 28% (9 of 32) and average survival of 10.4 months. Because both groups of animals sustained high rates of peritoneal injury, there is some uncertainty as to whether the mesothelial proliferation was caused primarily by the mechanical insult caused by the 12 injections received by each group. A few of the mesotheliomas were predominantly mesothelial. However, most were mixed and fibrous in nature with epithelial elements. The neoplasms grew within the peritoneal cavity over the surfaces of the viscera, and were frequently invasive of other organs, including the diaphragm. Methyl (acetoxymethyl) nitrosamine (DMN-OAc) has been established as an agent capable of inducing mesothelioma in male Sprague–Dawley and Buffalo rats. Berman and Rice report the induction of 25 testicular mesotheliomas in 78 treated rats (32%) [32]. It is notable that intraperitoneal (i.p.) administration of the chemical agent did not produce mesotheliomas in any other location except the testes, and there was no evidence of metastases or local invasion. Although some tumors contained massive amounts of stroma, there were no examples of the sarcomatoid variant of tumor found in humans among the examined mesotheliomas. All examined tumors stained positive by the colloidal iron method.

13.4.2 Man-Made Fibers Experimental studies suggest that certain man-made fibers have a greater toxicity than naturally occurring asbestos fibers. This greater mesotheliomatogenic effect is believed to be a result of the greater proportion of long fibers and their long durability. In intracavital injection studies in rats, erionite-induced mesothelioma at rates of 54.5–93% of treated animals percentages considerably higher than the incidence produced by either amosite, crocidolite, or chrysotile [20, 28]. Suzuki suggests that erionite’s toxicity is linked to its high content of long fibers (4% >9.5 µm) – more than any of the tested asbestos fibers. The median survival time for the animals treated with erionite, which can be indicative of the toxicity of a fiber, was also significantly shorter (513 days) for animals treated with erionite than for rats treated with crocidolite, chrysotile, and amosite [28]. Davis suggests that doses of less than 160,000 fibers >8 µm seldom produce mesothelioma, and that about 600,000 fibers of this length are needed to produce substantial levels of mesothelioma. Ultrastructural and histological characteristics of erionite-induced mesothelioma were similar to those of asbestos fibers. Man-made vitreous fiber 21 (MMVF 21), or stonewool, has similarly been shown to be very mesotheliomatogenic. Miller reports a 95% incidence of mesothelioma in rats treated with MMVF 21 (versus 88% for amosite), and a mean survival time of 284 days (versus 509 days for amosite) [25]. The results correlate well with the hypothesis that long fibers are more toxic than shorter ones – MMVF 21 contains more than seven fold more fibers >10 µm than amosite.

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13.5 Novel Viral-Induced and Transgenic Knockout Models of Mesothelioma An association between mesothelioma and viruses has been previously reported. Malignant mesotheliomas, immunohistochemically and architecturally identical to those seen in humans, have been induced in chickens when a DNA fragment of the oncogene of the sarcoma virus was introduced intraperitoneally [33]. Our group was the first to report that 60% (25 of 48) of human mesotheliomas contain DNA for the amino terminus region of T-antigen, a protein associated with various DNA viruses, including SV40, which is capable of causing malignant transformation in some human cancers, including malignant mesothelioma [34]. Humans were exposed to SV40 through the administration of SV40-contaminated polio vaccines between 1955 and 1963. Today, SV40 continues to be transmitted horizontally and vertically, despite eradication of the contaminated vaccines. SV40 is a DNA-tumor virus that infects monkeys and causes malignant transformation of hamster and murine cells. SV40 produces two transforming proteins: the large T antigen (TAg), which is responsible for binding and inhibiting tumor suppressor genes, e.g. NF2, Rb, p53, CDKN2A/ARF, p107, p130 and transcription factors such as p300 and CBP leading to uncontrolled DNA replication and cellular proliferation. The second transforming protein called small T antigen (tAg) binds and inhibits cellular phosphatase which further contributes to malignant cell transformation (1A). In nonpermissive hosts, SV40 has been shown to be oncogenic [35]. No productive infection and virions result in nonpermissive hosts. SV40 is capable of transforming a number of different mammalian cells in  vitro. Murine cells transformed by SV40 infection in vitro are capable of producing lethal tumors in vivo when transplanted back into the syngeneic host. Thus, SV40 murine transformed cells can be oncogenic in syngeneic hosts, and the tumors induced in vivo express SV40 tumor-specific transplantation antigens. These transplantation antigens include SV40 TAg and tAg, both of which are derived from a single early gene-product transcript [36].

13.5.1 SV40 Viral Hamster Models Newborn hamsters are also extremely susceptible to SV40-induced tumors. In 1962, Gerber and Kirschstein reported that SV40 induced ependymomas in hamsters. Since that discovery, wild-type SV40 has been known to be highly oncogenic in hamsters [37]. Newborn animals are particularly susceptible, and will develop fibrosarcomas at the injection site following subcutaneous inoculation of a low dose of SV40 [38]. When newborn hamsters are inoculated with intracerebral SV40, they develop ependymomas [37]. Weanling and adult animals may develop ­fibrosarcomas if injected subcutaneously with a high dose of virus [>109 plaque-

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forming units (pfu)], but only with a low tumor incidence and after prolonged incubation periods [39]. When SV40 (>108.5 pfu) is injected intravenously into weanling hamsters, subjecting many cell types to high concentrations of virus, lymphocytic leukemia, lymphoma, and osteosarcoma will develop at sites distant from the injection [40]. Carcinomas – the most common tumors in humans – never develop following SV40 injection, suggesting that epithelial cells may be resistant to SV40 transformation. These data indicate that only specific types of cells are susceptible to SV40 transformation, mesothelial cells, osteoblasts/osteocytes, macrophages, and B-lymphocytes, and that the specific routes of SV40 inoculation used apparently play a key role in the induction of specific hamster tumors. It is actually fortuitous that a relationship between SV40 and mesothelioma was discovered in the hamster. Lipotich reported the use of an SV40-induced hamster mesothelioma-cell line (800TU) [41]. Before this study, mesotheliomas were not observed following s.c., intracerebral, and intravenous (i.v.) inoculation of SV40 in the hamster. Indeed, the development of the 800 TU line was an inoculation accident, for newborn hamsters in these experiments were injected between the scapulae with SV40, and all of the other animals in the experiment developed in situ fibrosarcomas. The development of the mesothelioma was caused by accidental pleural injection of the SV40 (R. C. Moyer, personal communication). Stimulated by these isolated yet intriguing pieces of data, Carbone investigated the oncogenicity of wild-type SV40 and SV40 small t-deletion mutants when injected into the hearts, pleura, or peritoneum of 21-day-old hamsters [42]. Mesotheliomas that could be characterized as macroscopically, microscopically, ultramicroscopically, and histochemically identical to those seen in humans, developed within a 3-month period in 30 of the 43 hamsters injected with wild-type SV40. All (n = 34) the hamsters injected with the small t-mutant SV40 developed true histiocytic or B-cell lymphomas, yet only 1 developed a mesothelioma. The decreased oncogenicity in the deletion mutant group was puzzling, because small T-antigen binds and inhibits the activity of the cellular phosphatase 2A that will subsequently prevent dephosphorylation of large T-antigen and the p53 protein product [43, 44]. It is theorized, therefore, that in addition to physical binding between large T-antigen and the products of p53 and retinoblastoma (Rb), alteration of the Rb and p53 phosphorylation state by small T-antigen may be required to completely inactivate their function, and thus allow the cell to progress to S phase during which transformation could occur. The SV40-induced hamster mesotheliomas spread along the pleural, pericardial, and peritoneal surfaces obliterating the cavities and infiltrating the diaphragm and the chest wall in the absence of distant metastases. Histologically, epithelial, spindlecell, and more often mixed-type mesotheliomas are seen. Ultramicro-scopically, the tumors and derived cell lines showed long, branching microvilli without core filaments, basal lamina, intracellular lumens, perinuclear tonofilaments, ­intercellular junctions, the absence of secretory granules, and limited cytoplasmic organelles, especially rough endoplasmic reticulum. These mesotheliomas are associated with

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hyaluronic acid, contain cytokeratins, and are immunohistochemically similar to human mesotheliomas. Southern blot hybridization of the DNAs extracted from the tumors reveals SV40 DNA sequences, and the cell lines derived from these tumors contain and express the early region of SV40 DNA. Immunohistochemical staining of the cell lines and tumors reveals the presence of intranuclear T-antigen.

13.5.2 SV40 Transgenic Mouse Models SV40 TAg transgenic mouse models have been developed to study the SV40 viral oncogenesis and co-carcinogenesis with asbestos exposure in the context of the development of malignant mesothelioma. Robinson et al. developed four lines of MexTAg mice that express SV40 TAg selectively in the mesothelial cells of the pleural, pericardial, and peritoneal cavities [45]. The four lines carry different numbers of the viral genome: high (100 copies), intermediate (32, 15 copies) and single copy. All high transgene copy mice showed some spontaneous tumor development (5%). Mesothelial cells from the mutant mice, cultivated in  vitro, demonstrated immortality, with logarithmic growth for >100 doublings, as opposed to logarithmic growth in just six to eight doublings for normal mesothelial cells. These cells, isolated from high copy MexTAg mice, were able to form anchorage-independent colonies and grow in low-serum conditions, typical characteristics of a transformed cell line. After intraperitoneal injection of asbestos fibers, high-copy mice developed mesotheliomas that extremely aggressive, with a median survival rate of 35 weeks. This compares to 63 weeks for the wild-type and 55 weeks for the single copy line. The study demonstrated a direct relationship between the number of SV40 copies in the genome and the survival after exposure to asbestos. Additionally, increasing the dose of asbestos also accelerated the development of mesothelioma in high copy mice. The low level of spontaneous tumors suggests a two stage model, in which asbestos causes irritation, leading to mesothelial replication. The mesothelial cells that express TAg are more likely to become transformed in the proliferation process. These SV40 Tag transgenic mice, demonstrating a model of mesothelioma akin to the human disease, are good for studying early molecular changes during malignant transformation and can be used to test novel therapies.

13.5.3 Transgenic Murine Models, Utilizing Nf2, Ink4a/ARF, and P53 Knockout Mice Somatic gene changes involving inactivation of genes such as neurofibromatosis type 2 (Nf2), deletions of CDKN2A/Arf gene loci, and mutations of p53 gene are well described in human mesothelioma. Experimentation with the inactivation of

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Nf2, along with various other tumor suppressor genes, was investigated in the hope of finding a murine parallel to the human disease. Altomare et al. reported that Nf2 (+/–) knockout mice exposed to asbestos develop malignant mesotheliomas at a much higher rate than wild-type mice [46]. Similar to human malignant mesothelioma, tumors from Nf2 (+/–) mice demonstrated homozygous deletions of tumor suppressor genes p16 (Ink4A), p14 (Arf)/p19 (Arf), p15 (Ink4B) and inactivation of p53/Arf. This suggests that these mutations permit malignant mesothelial cells to skip crucial cell-cycle checkpoints, proliferating without control. Additionally, the Nf2 (+/–) mice also displayed lower survival times because of the more aggressive nature of their malignant mesotheliomas, as compared to the wild-type mice. Recent development of conditional Nf2 combo knockout (CKO) mice further advanced transgenic mouse models of malignant mesothelioma. Jongsma et  al. developed conditional mouse models by introducing mesothelial-specific loss of Nf2, Ink4A/Arf, and p53 transgenes by an intrathoracic injection of a viral AdenoCre recombinase specific to those genes [47]. This group also created compound homozygous and heterozygous Nf2/Ink4a/Arf; Nf2/p53 and Nf2/Ink4a/P53 knockout littermates. Without the introduction of any other carcinogenic elements, such as asbestos, thoracic tumors developed in 80–100% of homozygous combo knockout mice. The murine model does not demonstrate the same tendency to develop epitheloid tumors as the human model. Malignant mesothelioma was most common in Nf2;p53 or Nf2;InK4a/Arf CKO mice. The Nf2; p53 and Nf2;Ink4a/Arf had some epitheloid but none were seen in the Nf2;p53,Ink4a mice. Sarcomatoid and mixed were seen in all three groups. Nf2;Ink4a/Arf CKO tumors demonstrated to be the most aggressive genotype. These tumors are contrasted to the homozygous Nf2;p53 CKO tumors, which were not extremely aggressive. The evidence supports the idea that the loss of Ink4a is the factor that significantly reduces the latency period and contributes to more invasive behavior.

13.6 Orthotopic Transplants and Xenograft Xenografts offer the advantage of producing mesothelial tumors in the animal model at a faster rate and with higher success than asbestos animal models. Furthermore, the resulting tumors are of a similar histological type as their human counterparts and retain their functional and morphological features during several generations, and can therefore provide accurate information on the chemosensitivity of the human tumor. Mice have been successfully used to replicate human MPM. Chahinian reports the original transplant of human mesothelial tumor from two patients into nude mice of the Balb/c strain [48]. Intraperitoneal (i.p.) transplants did not grow, but s.c. xenografts/implants were able to produce tumor in an average of 46 days in the animal. The tumor transplants of the first generation grew in 6 of 20 mice (30%), with a take-rate of implants of 53%. Overall, tumors grew in 52 of 80 mice (65%)

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in a total of 169 of 266 implants (multiple implants were made on the same animal). Tumor examination of the first- and second-generation xenografts confirmed their histological similarity to the original epithelial tumors. Colt et al. commented on the implantation of intact human mesothelial tissue in the pleural space of four athymic nude mice and subcutaneously in another mouse [49]. The s.c. implantation resulted in a progressive tumor growth, with the tumor reaching a size of 10 × 12 mm. Of the four i.p. mice, one died early, another at 162 days, and the remaining two mice were sacrificed at 180 days after implantation. Both of the sacrificed animals demonstrated tumor growth at the implantation site as well as at the visceral, diaphragmatic, and mediastinal pleural surfaces. No metastases to distant organs were noted a feature similar to the natural history of human malignant mesothelioma. In the human tumor, sections of pleural biopsies showed malignant neoplasms composed of epitheloid cells, which on immunohistochemical study demonstrated diffuse and strong positive stainings for cytokeratin, vimentin, and epithelial-membrane antigen, but tested negative for carcinoembryonic antigen and Leu M1. As a result, the neoplasm was classified as monophasic epithelial-type mesothelioma. In the mouse, the immunohistochemical profile characterized by positive stainings for vimentin and cytokeratin, and negative CEA and Leu M1 stainings, strongly pointed to the similarity between the human and animal tumor. Rats have also been a species of choice for mesothelioma-cell transplants. Linden successfully inoculated athymic Rowett rats s.c. with a coarse cell suspension of mesothelioma cells from a human patient [50]. The take-rate was 93% (13 of 14) in the initial passages (P) and 100% (192/192) in P3-P9. There was a decrease in the tumor-volume doubling (TD) time during the serial passage of rats from 6 days in P2 to 3 days in P9, with no further reduction noted in later passages. The average latency, measured by the time needed to reach a specified tumor volume, was found to decrease sharply from P2 to P12, but not thereafter. It took 36 days in P2 to reach a volume of 2 cm3, and only 11–12 days in P10-P12. Morphological examination of the tumors revealed mesothelioma of an epithelial type. The histological pattern of the original tumor was retained in all xenograft generations of rats, with no differentiation noted. Prewitt et al. describe the orthotopic implantation of the tumor-cell line H-Meso 1 in pneumonectomized Fischer nu/nu rats [51]. Tumor reproducibly filled the chest cavity 6 weeks after implantation with 106 tumor cells, and were identical in histologic pattern to epithelioid mesothelioma. There have been reports of transplants of mesothelioma cells from rat to rat [52]. After the successful induction of mesothelioma in F344 rats with crocidolite fibers, the cell lines were cultured in vitro in RPMI-1640 and inoculated intrapleurally in F344 rats. The mesothelial origin of the cells was confirmed by the co-expression of keratin and vimentin, using an alkaline and anti-alkaline phosphatase. Polyclonal rabbit antibodies directed against human 56-kDa cytokeratin and monoclonal mouse anti-swine vimentin 57-kDa were also used. There was a clear-cut dose–response relationship when several concentrations of cells were inoculated, with the largest dose of mesothelioma cells administered

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(5 × 106) able to induce the most tumors, as determined by chest radiographs performed on 15 and 30 days postinoculation. No rats showed abnormalities at 15 days postinoculation but by 30 days, most animals showed features suggesting massive tumor volumes. Most tumors predominantly invaded the mediastinum and pericardium, with diaphragm involvement, but no metastasis was noted. Transplants of mesothelioma cells from hamster to hamster have been performed [53]. In a study designed to test the usefulness of various chemotherapeutic agents against mesothelioma, Smith et al. report successfully transplanting mesothelioma induced by intrapleural injection of tremolite asbestos in one golden Lak:LVG Syrian hamster to others [53]. Ascites were noted 76 days after transplant in the first generation of three hamsters that had received intraperitoneal injections of peritoneal effusions from the original animal, with two of the animals sacrificed on day 76 and the third dying on day 90. The tumor was carried through 39 serial transplant generations by i.p. injections. The average survival time was found to decrease with continuous passage, leading to the death of new hosts within 21–38 days, and an average survival time of 28–32 days depending on the generation examined. The transplantable tumor line was defined as mesothelioma 10–24. Tumors of animals bearing transplants continued to resemble the epithelial nature of the mesothelioma in the original animal.

13.7 Conclusions A number of animal models for the investigation of mesothelioma are now ­available. These models have been used in a number of preclinical models for the treatment of mesothelioma, including gene therapy with suicide genes [54–57], antisense gene therapy [58], re-expression gene therapy [59], photodynamic ­therapy [60, 61], immunotherapy [62–67], and in vivo chemosensitivity [68–71]. Many cell lines are available, and the models are reproducible.

References 1. Antman KH, Pass HI, Schiff PB. Benign and malignant mesothelioma. In: De Vita VT Jr, Hellman S, Rosenberg SA, editors. Cancer: principles and practice of oncology. 5th ed. Philadelphia, PA: Lippincott-Raven; 1997. p. 1853–78. 2. Kaiser LR. New therapies in the treatment of malignant pleural mesothelioma. Semin Thorac Cardiovasc Surg. 1997;9:383–90. 3. Mark EJ, Yokoi T. Absence of evidence for a significant background incidence of diffuse malignant mesothelioma apart from asbestos exposure. In: Landrigen J, Kazemi H, editors. The third wave of asbestos disease: exposure to asbestos in place. NY: NY Academy of Sciences Press; 1960. p. 196–204. 4. Wagner JC, Sleggs CA, Marchand P. Diffuse pleural mesothelioma and asbestos exposure in the North Western Cape Province. Br J Ind Med. 1960;17:260–71.

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53. Smith W, Huhert DD, Holiat SM, Sobel HJ, Davis S. An experimental model for treatment of mesothelioma. Cancer 1981;47:658–63. 54. Smythe WR, Kaiser LR, Hwang HC, Amin KM, Pilewski JM, Eck B, et  al. Successful ­adenovirus-mediated gene transfer in an in  vivo model of human malignant mesothelioma. Ann Thorac Surg. 1994;57:1395–401. 55. Smythe WR, Hwang HC, Elshami AA, Amin KM, Eck SL, Davidson BL, et al. Treatment of experimental human mesothelioma using adenovirus transfer of the herpes simplex thymidine kinase gene. Ann Surg. 1995;222:78–86. 56. Hwang HC, Smythe WR, Elshami AA, Kucharczuk JC, Amin KM, Williams JP, et al. Gene therapy using adenovirus carrying the herpes simplex-thymidine kinase gene to treat in vivo models of human malignant mesothelioma and lung cancer. Am J Respir Cell Mol Biol. 1995;13:7–16. 57. Kucharczuk JC, Elshami AA, Zhang HB, Smythe WR, Hwang HC, Tomlinson JS, et  al. Pleural-based mesothelioma in immune competent rats: a model to study adenoviral gene transfer. Ann Thorac Surg. 1995;60:593–7. 58. Pass HI, Mew DJ, Carbone M, Matthews WA, Donington JS, Baserga R, et al. Inhibition of hamster mesothelioma tumorigenesis by an antisense expression plasmid to the insulin-like growth factor-1 receptor. Cancer Res. 1996;56:4044–8. 59. Frizelle SP, Grim J, Zhou J, Gupta P, Curiel DT, Geradts J, et al. Re-expression of p16INK4a in mesothelioma cells results in cell cycle arrest, cell death, tumor suppression and tumor regression. Oncogene 1998;16:3087–95. 60. Feins RH, Hilf R, Ross H, Gibson SL. Photodynamic therapy for human malignant mesothelioma in the nude mouse. J Surg Res. 1990;49:311–4. 61. Foster TH, Gibson SL, Raubertas RF. Response of Photofrin-sensitised mesothelioma ­xenografts to photodynamic therapy with 514 nm light. Br J Cancer. 1996;73:933–6. 62. Christmas TI, Manning LS, Garlepp MJ, Musk AW, Robinson BW. Effect of interferon-alpha 2a on malignant mesothelioma. J Interferon Res. 1993;13:9–12. 63. Leong CC, Marley JV, Loh S, Milech N, Robinson BW, Garlepp MJ. Transfection of the gene for B71 but not B72 can induce immunity to murine malignant mesothelioma. Int J Cancer. 1997;71:476–82. 64. Caminschi I, Venetsanakos E, Leong CC, Garlepp MJ, Scott B, Robinson BW. Interleukin-12 induces an effective antitumor response in malignant mesothelioma. Am J Respir Cell Mol Biol. 1998;19:738–46. 65. Astoul P, Boutin C. An experimental model of pleural cancer. Value of orthotopic implantation of human tumor tissue in nude mice. Rev Mal Respir. 1997;14:355–62. 66. Bielefeldt-Ohmann H, Fitzpatrick DR, Marzo AL, Jarnicki AG, Musk AW, Robinson BW. Potential for interferon-alpha-based therapy in mesothelioma: assessment in a murine model. J Interferon Cytokine Res. 1995;15:213–23. 67. Ohnuma T, Szrajer L, Holland JF, Kurimoto M, Minowada J. Effects of natural interferon alpha, natural tumor necrosis factor alpha and their combination on human mesothelioma xenografts in nude mice. Cancer Immunol Immunother. 1993;36:31–6. 68. Chahinian AP, Norton L, Holland JF, Szrajer L, Hart RD. Experimental and clinical activity of mitomycin C and cis-diamminedichloroplatinum in malignant mesothelioma. Cancer Res. 1984;44:1688–92. 69. Sklarin NT, Chahinian AP, Feuer EJ, Lahman LA, Szrajer L, Holland JF. Augmentation of activity of cis-diamine-dichloroplatinum (II) and mitomycin-C by interferon in human malignant mesothelioma xenografts in nude mice. Cancer Res. 1988;48:64–7. 70. Roboz J, Chahinian AP, Holland JF, Silides D, Azrajer L. Early diagnosis and monitoring of transplanted human malignant mesothelioma by serum hyaluronic acid. J Natl Cancer Inst. 1989;81:924–8. 71. Chahinian AP, Mandeli JP, Gluck H, Naim H, Teirstein AS, Holland JF. Effectiveness of ­cisplatin, paclitaxel, and suramin against human malignant mesothelioma xenografts in ­athymic nude mice. J Surg Oncol. 1998;67:104–11.

Chapter 14

The Use of Mouse Models to Study Leukemia/Lymphoma and Assess Therapeutic Approaches William Siders

Abstract  The fields of leukemia and lymphoma research have significantly advanced over the recent years as a result of the establishment of cell lines that reflect several aspects of the human disease. These cell lines have been employed in all phases of preclinical research from the immunization of mice to generate new therapeutic antibodies to proof of concept and target validation experiments. In addition, several transgenic mouse or genetically engineered mouse models have been developed that recapitulate many aspects of both leukemia and lymphoma. These models are particularly well suited to the exploration of interactions between tumor and stromal cells and the progression of cancer as it relates to its microenvironment. Therapeutic antibodies including ofatumumab and epratuzumab are currently undergoing clinical trial evaluation based on their activity in models such as these. Xenograft tumor models have been especially instrumental in studies addressing mechanism of action and in evaluating combination therapies. This chapter will primarily explore the use of human cells in xenograft tumor systems as models for evaluating therapeutic approaches. Keywords  Leukemia • Lymphoma • Mouse • Xenograft • Therapeutic

14.1 Introduction The ability to perform in vivo studies using mouse models has greatly enhanced our understanding of the development and progression of both leukemia and lymphoma disorders. Currently the mouse remains the best system for exploring several aspects of lymphomagenesis and therapeutic intervention. Several ­transgenic W. Siders (*) Cancer and Immunotherapy Research Group, Genzyme Corporation, 49 New York Avenue, Framington, MA 01701, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_14, © Springer Science+Business Media, LLC 2011

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(Tg) mouse or genetically engineered mouse models (GEMMs) have been developed that recapitulate many aspects of both leukemia and lymphoma (for review see Refs. [1–5]). These systems often have the advantage of expressing oncogenes or dominant-negative tumor suppressor genes under the control of cell-specific promoters that restrict expression to the tissue of interest. These models are particularly well suited for exploring interactions between tumor and stromal cells and the progression of cancer as it relates to its microenvironment. As genetic manipulation of the mouse genome has become more common, sophisticated techniques have been developed that have allowed the field of cancer biology to advance as a result of being able to control gene expression not only in a cell-type-dependent manner but in a time-dependent manner as well. For instance, using the inducible tetracycline system [6], Huettner et al. [7] have generated a mouse model that conditionally expresses the BCR-ABL fusion protein resulting in the development of leukemia in 100% of the mice. Earlier attempts at creating BCR–ABL Tg mice resulted in embryonic lethality. In this system, expression of BCR–ABL can be induced at any stage of development in the mouse by simply withholding tetracycline administration. However, it is important to understand that these mouse systems still have limitations and that differences do exist in the development of cancer in mice relative to the human disease [8]. In addition to GEMMs, the study of hematologic malignancies and the development of targeted therapies have also been enhanced by the use of human tumor cells in immunodeficient mice as well as the ability to generate cell lines from patient samples. The creation of mouse models that are deficient in T cells (nude), T cells and B cells (SCID), NK cells (beige) or a combination of all three (SCID-beige) has significantly aided these efforts. Where it was once challenging to inject tumor cells from patients into mice and achieve successful engraftment, it is now common practice to develop novel models that in some instances reflect several aspects of the human disease. The extent of engraftment, however, may be dependent not only on the strain of mouse chosen, but also on whether additional immunosuppression such as radiation is required [9]. Cell lines from tumor samples isolated from patients that have been adapted to grow in  vitro have been used extensively for several applications including (1) as immunogens in immunocompetent animals to create therapeutic antibodies; (2) to establish proof of concept for therapeutics targeting specific pathways or antigens in a particular tumor indication prior to the initiation of clinical trials; and (3) to understand the mechanisms underlying tumor cell migration and metastasis formation. This chapter will primarily explore the use of human cells in xenograft tumor systems as models for evaluating therapeutic approaches. Other approaches such as Tg mice that have been used for therapeutic evaluation or understanding the mechanism of action for a particular therapy have also been included. A table of several successfully xenografted human cell lines accompanies each of the primary hematologic indications discussed and serves as a reference point for investigators wishing to develop mouse models for therapeutic analysis.

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14.1.1 Models of Myeloid Leukemia Myeloid leukemia is characterized by the proliferation of myeloid cells such as neutrophils, basophils or monocytes and may present either as an acute or chronic condition. Recent data from the American Cancer Society estimate that approximately 13,000 new cases of acute and 5,000 new yearly cases of chronic myeloid leukemia will be reported in the US. Acute myeloid leukemia (AML) is associated initially with the growth of myeloid progenitors in the bone marrow leading to the appearance of large numbers of immature white blood cells or blasts within the blood. A subtype of AML has also been identified, acute promyelocytic leukemia (APL), that arises from immature cells known as promyelocytes. In contrast, chronic myeloid leukemia (CML) is characterized by the development of progressively larger numbers of normal appearing white blood cells over time in both the bone marrow and the blood. Although genetic abnormalities have been described that result in the development of myeloid leukemia, most cases occur as a result of chromosomal translocations [10]. For example, the initiation event for APL is the t(15,17) translocation that fuses part of the promyelocytic leukemia (PML) gene in frame with part of the retinoic acid receptor a gene resulting in the PML–RARa fusion protein [11]. Likewise, CML development in a large number of patients is driven by the t(9,22) chromosomal translocation forming the “Philadelphia ­chromosome” and resulting in the generation of the BCR–ABL fusion gene. This fusion gene encodes the BCR–ABL protein with constitutive tyrosine kinase ­activity resulting in the activation of multiple signaling cascades leading to cell proliferation, resistance to apoptosis and leukemiagenesis [12]. Therapies that ­target this activity such as the tyrosine kinase inhibitor (TKI) imatinib have resulted in a significant response rate in patients [13]. Several attempts have been made to generate mouse models that recapitulate the onset and progression of myeloid leukemia to aid in studying the process of leukemiagenesis as well as identifying new cancer-specific targets. Initial efforts at engrafting primary human tumor samples from AML patients into SCID mice resulted in variable levels of engraftment and in most instances did not result in the development of reproducible disseminated disease even when the mice were treated with irradiation and immunosuppressive drugs [14, 15]. Although circulating AML cells could be detected in mice for short periods of time following injection, they quickly became undetectable [15]. The use of cytokines such as GM-CSF was shown to enhance engraftment [16–18] but several investigators were eventually successful in the absence of such help including Chelstrom et al. who demonstrated engraftment of primary tumor isolates from pediatric AML patients in sublethally irradiated SCID mice [19]. Given the mixed results with primary tumor samples, the development of myeloid leukemia cell lines that maintained a disease phenotype when injected into mice became paramount to advance the identification of new therapeutic strategies. Beran et al. [20] describe the development of one such line (KBM-5) from a CML patient that contains multiple copies of the Philadelphia chromosome. These cells

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Table 14.1  Myeloid leukemia xenograft cell lines Cell line Dose Route Mouse strain Referencea KBM-3 2e7 Iv SCID [43] KBM-5 1e7 sc, iv, ip SCID [20] HL-60 ³5e6 Sc NOD/ SCID [44] K-562b 2e7 Sc SCID [29] MEG-01 5e7 Sc Nude [45] MV-4–11c 5e6 Sc Nude [42] Ba/F3 1e6 Iv SCID [28] a  Reference cited is not always provided as the originator of the cell line and is included in most instances to provide an example of the tumor growth curve b  Originally considered myeloid leukemia line but now recognized as an erythroid leukemia cell line c  Biphenotypic cell line

were shown to be resistant to NK cell mediated lysis and to engraft tissues in the SCID mouse in a pattern similar to that observed in the human disease. Several cell lines currently exist that have been used initially in vitro and eventually in vivo to screen for the activity of therapeutic candidates including the murine Ba/F3 cell line and the human HL-60 and K-562 cell lines (Table 14.1). For CML treatment, the most successful therapeutic is the tyrosine kinase ­inhibitor imatinib which targets the BCR–ABL protein (for review see Ref. [21]). Imatinib (also known as Gleevec or STI571) was initially identified from a library of compounds screened as inhibitors of both PDGF and v-Abl activity [22]. Ultimately, potent activity against the BCR–ABL protein was also demonstrated in several cell lines both in vitro and in vivo and supported exploring imatinib efficacy in clinical trials of CML [23, 24]. Following accelerated approval in 2001, imatinib has proven to be successful in the treatment of CML [13]. However, imatinib- resistant BCR–ABL mutations have been identified [25] suggesting the need for secondgeneration therapies. To this end, dasatinib (BMS-354825) was developed and has been shown to have not only a significantly enhanced affinity for BCR–ABL over imatinib [26, 27] but also activity against most imatinib-resistant BCR–ABL mutants [28]. Using the murine pro B cell line Ba/F3, Shah and co-workers generated stable cell lines expressing all of the known imatinib-resistant BCR–ABL mutations and demonstrated that all but one of these mutants (T315I) were susceptible to treatment with dasatinib. To explore the in vivo efficacy of dasatinib, SCID mice were injected with the Ba/F3 cells expressing either wild-type BCR–ABL or the imatinib resistant forms along with the firefly luciferase gene. In vivo imaging revealed that treatment with dasatinib resulted in a decrease in bioluminescence that translated into a significant increase in survival indicating inhibition of tumor growth. Similar to the in vitro data, the T315I mutant was not responsive to dasatinib treatment demonstrating good correlation between in  vitro assays and in  vivo SCID mouse models. Pharmacokinetic studies in K-562 bearing SCID mice were also conducted and accurately predicted the clinical exposure required to inhibit BCR–ABL activity in CML patients [29]. Clinical studies with dasatinib have been conducted in both

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BCR–ABL wild-type and imatinib-resistant CML patients leading to its approval for the treatment of CML patients who are resistant to imatinib therapy as well as Philadelphia chromosome positive acute lymphocytic leukemia (ALL). Although xenografts have proven successful as a predictive tool for some therapeutic approaches, more complex systems have also been examined including humanized-SCID mouse models such as the one developed by Kyoizumi et al. [30]. In this system, human fetal bone fragments are implanted into SCID mice to recapitulate the human hematopoietic microenvironment followed by injection of primary myeloid leukemia cells [31]. It was hypothesized that the human bone marrow may provide growth factors and cellular interactions required to support the engraftment and growth of the leukemic cells. Indeed, successful engraftment was observed in approximately 90% of the samples with growth occurring primarily in the human bone marrow compartment and not the mouse bone marrow. Taken together, these results suggest that understanding the interactions between grafted leukemic cells and their environment is crucial not only for the development of therapeutic models but also for understanding the development and progression of leukemia. Recently, studies in xenograft models have begun to explore the interaction between leukemic cells, their environmental niche and the factors that support tumor growth and have implicated the SDF-1/CXCR4 axis as playing an important role in these processes [32]. CXCR4 is expressed on hematopoietic stem cells (HSCs) and its interaction with SDF-1 produced by stromal cells supports HSCs in their bone marrow microenvironment. In addition to HSCs, CXCR4 is also present on some myeloid leukemia cell lines and to varying degrees on AML blasts [33]. However, conflicting data exist as to the role played by CXCR4 in the engraftment process of primary leukemia cells in NOD/SCID mice. Tavor et  al. [34] demonstrated that treatment of mice with an antibody to CXCR4 following immediate injection of leukemic cells significantly interfered with the engraftment of primary AML cells into NOD/SCID mice. In contrast, Monaco et al. [35] observed significant engraftment in NOD/SCID mice in the absence of CXCR4 expression and pretreatment of CXCR4+ blasts with an anti-CXCR4 antibody did not affect the ability of the AML blasts to engraft. This suggests that a strict correlation may not exist between the surface expression of CXCR4 and engraftment and that other characteristics such as the patient’s disease state may also contribute to the engraftment process in NOD/SCID mice [36]. Although the involvement of CXCR4 in AML engraftment remains controversial, more recent studies have suggested that the SDF-1/ CXCR4 axis may play a role in maintaining leukemic cells in the bone marrow creating a protective environment and promoting AML growth and survival. Initial experiments demonstrated that disruption of this interaction by CXCR4 targeting agents such as AMD3100 (Mozobil) could have significant therapeutic implications. Treatment of primary AML samples or myeloid cell lines with AMD3100 in vitro significantly inhibited AML migration and growth in both a dose- and time-dependent manner [37, 38]. Using a mouse model of APL, Nervi et al. [39] explored the utility of AMD3100 as a mobilizing agent to disrupt the SDF-1/CXCR4 interaction of leukemia cells in the bone marrow and spleen. The APL mouse model was generated by knocking in

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the human PML-RARa cDNA leading to the expression of the PML-RARa protein and the development of a fatal myeloid leukemia [40]. Leukemic blasts from these mice can be transferred to a recipient mouse where they migrate to the bone marrow and spleen leading to the rapid development of a fatal leukemia within 3 weeks. Although treatment with a single dose of AMD3100 can mobilize HSCs into the periphery, multiple doses of AMD3100 were required before mobilization of the APL cells was observed. Once in the periphery, the circulating APL cells became more sensitive to the cytotoxic effects of AraC treatment. Combination treatment with AMD3100 and AraC significantly increased median survival of the mice compared to either treatment alone suggesting that targeting the CXCR4 axis with Mozobil results in enhanced chemosensitization. Data from these studies have resulted in the initiation of a Phase I clinical trial in AML exploring Mozobil treatment in combination with chemotherapy. Successful therapies that target CXCR4 may become critically important given the recent finding that treatment with ­imatinib results in the upregulation of CXCR4 on CML cells resulting in their migration to the bone marrow and potentially increased chemoresistance [41].

14.1.2 Models of Acute Lymphocytic Leukemia Acute lymphocytic leukemia (ALL) results from the uncontrolled rapid growth of lymphocytes and is the most common form of leukemia in children under the age of 15. The malignant cells of ALL are thought to originate from early stages of B or T cells. Similar to chronic lymphocytic leukemia (CLL), a majority of cases of ALL express B cell lineage markers and are of B cell origin. Although current treatments have resulted in a 80–90% complete remission of newly diagnosed ALL in children, only 30–40% long-term disease-free survival has been observed in adult ALL patients. The etiology of ALL ranges from (1) aberrant expression of oncogenes, (2) chromosomal translocations such as the BCR/ABL t(9,22) or the TEL/ AML t(12, 21) or (3) hyperdiploidy (for review see Ref. [46]). Given the diversity of the alterations that contribute to the development of ALL, the use of primary leukemic cells isolated form children was viewed as the best approach to develop mouse models of ALL. Uckun et al. [47] initiated a comprehensive study to begin to identify potential correlations between the level of engraftment and treatment outcome of the ALL patients from which the cells were isolated [47]. SCID mice were injected intravenously with leukemic cells from 681 pediatric leukemia patients and monitored to determine the extent of engraftment. Injection of cells from only 104 of the 681 patients was able to engraft and proliferate in SCID mice with primary engraftment occurring within the bone marrow, liver, spleen, thymus and kidney. Although no overall correlation was observed between the extent of engraftment and the survival outcome of the ALL patients, a trend was observed in a subgroup of patients with poor outcome and the extent to which their leukemic cells engrafted in the SCID mice reflecting a more aggressive disease. In similar

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experiments, the level of engraftment of T-ALL in SCID mice was shown to ­correlate with patient survival [48]. Twelve of 19 T-ALL samples resulted in successful engraftment leading to disseminated disease involving the bone marrow spleen and thymus. Interestingly, all of the seven samples that failed attempts at engraftment in SCID mice successfully engrafted into NOD/SCID mice suggesting that this mouse may be more receptive to engraftment of primary ALL cells potentially due to the increased immunodeficiencies present in this mouse. Others have followed up on this observation and demonstrated similar findings in some instances resulting in 100% engraftment of cells from multiple patients with few changes, if any, in the immunophenotype following injection of the cells [49–51]. Primary tumor models such as these are currently being used to evaluate anti-cancer agents such as mTOR inhibitors and compounds that target Bruton’s tyrosine kinase [52, 53]. In view of the limited availability of clinical samples for these types of studies, several investigators continue to pursue the use of ALL cell lines in xenograft models, especially for the validation of new therapeutic approaches (Table 14.2). For example, ABL chromosomal translocations have also been observed in T-ALL patients which involve fusion to the NUP214 gene resulting in increased kinase activity in these patients. Using NUP214–ABL positive models of ALL, QuintasCardama et al. [54] have demonstrated that agents targeting the BCR-ABL protein are similarly effective in this setting. Significant anti-tumor activity was observed with dasatinib in NUP214–ABL positive cell lines in xenograft studies but had little

Table 14.2  Lymphocytic leukemia xenograft cell lines Cell line Dose Route Mouse strain Referencea 380 Iv NOD/SCID [61] ³1e7 697 ³1e7 Iv NOD/SCID [61] BA31 5e6 Ip SCID [88] CCRF-CEM 5e6 Iv SCID [59] 4e7 Sc SCID [100] ED (HTLV)b HPB-ALL 1e7 Sc NOD/SCID [54] JOK-1 5e6 Iv SCID [59] Jurkat 1e7 ip, iv SCID [89] JVM-3 1e7 Iv nude [90] MET-1 (HTLV)b 1.5e7 Ip NOD/SCID [97–99] Molt-3 5e6 Sc NOD/SCID [91] Molt-4 5e6 Sc nude [92] Nalm-6 1e6 Iv SCID [93] RS4;11 5e7 ip, iv SCID [94] SIL-ALL 1e7 Sc NOD/SCID [54] TA27 5e6 Ip SCID [88] WSU-CLL 1e7 Sc SCID [95] a  Reference cited is not always provided as the originator of the cell line and is included in most instances to provide an example of the tumor growth curve b Model of HTLV induced T cell leukemia

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to no effect on NUP214–ABL negative tumor cells suggesting the clinical utility in T-ALL patients who express the NUP214–ABL fusion protein. Targeting leukemic cells in xenograft models through the use of TRAIL specific approaches has also been examined in attempts to overcome defects in the apoptotic pathway noted in lymphocytic leukemia (for review see Ref. [55]). Although TRAIL mediates its effects through ligation of death receptors on cells and triggering of apoptosis, a significant number of leukemic cells from pediatric ALL patients that express the TRAIL receptors have been shown to be refractory to TRAIL mediated apoptosis [56]. To overcome this issue, investigators have examined whether the inclusion of small molecules that target other proteins in the apoptotic pathway may enhance TRAIL mediated killing. Inhibitors of the antiapoptotic protein XIAP were shown to cooperate with TRAIL in vitro and demonstrated significant anti-tumor activity as single agents in  vivo [57]. Treatment resulted in a decrease in tumor burden based on the number of leukemic blasts present in the blood and reduction in spleen weight. Other approaches aimed at enhancing TRAIL mediated killing include the use of a CD19–TRAIL fusion protein to target CD19 positive leukemic cells [58]. This targeted approach has resulted in significant anti-tumor activity and long-term survival in the Nalm-6 B-ALL xenograft model. Preclinical xenograft models of ALL are also being used to explore other therapeutic approaches including targeting cell surface antigens such as CD47 [59], histone deacetylase inhibitors such as vorinostat [60] and the use of CpG oligonucleotides to stimulate the anti-leukemic activity of NK cells [61].

14.1.3 Chronic Lymphocytic Leukemia A majority of the cases of chronic lymphocytic leukemia (CLL) are of the B cell phenotype (~95%) and result in the expansion and accumulation of malignant CD5+ /CD19+/IgM+ B cells in the blood, lymph nodes, spleen and bone marrow [62]. In contrast to other B cell malignancies that arise from chromosomal translocations, B-CLL often arises as a result of deletions with the most common being the 13q14 deletion on chromosome 13 accounting for approximately half of all B-CLL cases [63]. Cell lines from B-CLL patients were initially transformed for propagation in vitro using EBV induced transformation [64]. Similar to other lymphoid models, the study of B-CLL in mouse models was limited by the inefficient engraftment of cells into SCID mice (for review see Ref. [65]) but several groups were eventually successful at inducing engraftment into either SCID or NOD/SCID mice [66–68]. Several chronic T cell leukemias and lymphomas have been shown to be associated with chromosomal translocations at the Tcl1 (T cell leukemia/lymphoma 1) locus whose oncogenic potential is supported by data generated using a Tcl1 Tg mouse model of leukemia [69]. Recently a model of B-CLL was developed expressing the Tcl1 protein under the control of a B cell specific promoter [70]. This model results in the expansion of CD5+/IgM+ B cells and the development of

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CLL like disease in older mice that resembles the human disease. The model was further validated by Johnson et  al. [71] who demonstrated that the B-CLL like ­disorder developed by these mice was sensitive to fludarabine treatment. Recently, Tcl1 was found to function as a transcriptional regulator and its overexpression was shown to drive the progression of B-CLL through enhancement of NF-kappa B activity [72]. Planelles et  al. [73] have described a similar model of B-CLL ­lymphomagenesis for APRIL Tg mice. APRIL (a proliferation inducing ligand) together with BAFF (B cell activating factor of the TNF family) play a role in B cell survival and differentiation and are expressed by B-CLL cells [74, 75]. Initially, APRIL Tg mice display normal B cell development in the spleen and lymph nodes [76]. However, as mice age, a progressive expansion of the B-1 B cell compartment can be observed, most notably in the lymph nodes and Peyers patches, reminiscent of the development of human B-CLL [73]. Therefore, both the Tcl1 and APRIL transgenic mice appear to be suitable models for understanding the lymphomagenesis of B-CLL as well as evaluating novel therapeutic approaches. CLL cells have been shown to be resistant to apoptotic-inducing therapies as a result of the overexpression of anti-apoptotic genes such Bcl-2 and XIAP [77]. Therapeutics such as monoclonal antibodies which target cell surface antigens and mediate their effects through additional activities such as antibody-dependent cellular cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) have proven successful in the treatment of CLL (for review see Ref. [78]). Following binding of the antibody to its target antigen, ADCC is triggered through ­engagement of Fc receptors on effector cells such as NK cells, macrophages and neutrophils. CDC activity is also induced after ligand binding and involves cleavage of C1q and activation of the complement cascade. Immunodeficient mice, in particular the SCID mouse, have proven to be an invaluable tool for evaluating antibody ­therapeutics and understanding their mechanism of action since both ADCC and CDC pathways are present and functional. Alemtuzumab (Campath) is a monoclonal antibody approved for the treatment of B-CLL [79]. Alemtuzumab binds to the CD52 antigen present on T and B cells and can induce their depletion through both ADCC and CDC mechanisms in vitro. Although we and others have demonstrated the efficacy of antibody-mediated targeting of CD52 in xenograft models [80–82], the exact mechanism through which alemtuzumab mediates its activity in  vivo remained undefined. To address this issue, we created a human CD52 (huCD52) Tg mouse that expresses huCD52 in a pattern similar to humans. Treatment of these mice with alemtuzumab resulted in a time- and dose-dependent depletion of CD52+ cells. Interestingly, removal of complement had little or no impact on the lymphocyte-depleting activity of alemtuzumab while removal of NK cells or neutrophils essentially ablated its activity indicating a prominent role for ADCC in lymphocyte depletion [83]. A role for neutrophils in the ADCC activity of alemtuzumab has not been previously described but has been reported as an effector mechanism in the activity of rituximab [84, 85]. Other cell surface antigens currently being explored for the antibody-mediated therapy of CLL include both CD20 targeted by ofatumumab (discussed in detail below) and CD23 targeted by lumiliximab. CD23 functions as a low-affinity

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r­ eceptor for IgE and has been shown to be upregulated in B-CLL [86]. Treatment of primary CLL samples with lumiliximab results in the induction of apoptosis that is accompanied by the down regulation of anti-apoptotic proteins such as Bcl-2 and XIAP [87]. The anti-tumor activity of lumiliximab as a single agent was confirmed in the CD23+ SKW6.4 lymphoma xenograft model and was enhanced by the addition of fludarabine or rituximab.

14.1.4 HTLV-Related T Cell Leukemia/Lymphoma Adult T cell leukemia/lymphoma (ATLL) develops in a small number of people infected with the human T cell lymphotrophic virus (HTLV-1) and is characterized by the increased number of malignant activated T cells expressing several conventional T cell markers including CD2, CD3, CD25 and CD52. Conventional therapy has proven to be largely ineffective at treating ATLL patients suggesting the need for new therapies and models for therapeutic analysis. Using peripheral blood ­lymphocytes from normal donors, Feuer et  al. [96] demonstrated that normal T cells infected with the HTLV-1 virus can grow in SCID mice and represent a potential model for assessing therapeutic intervention. Antibody approaches targeting the cell surface antigens expressed by ATLL cells have also been evaluated in xenograft models. These therapies have largely been evaluated using the MET-1 model developed by the Waldmann lab. The MET-1 model was developed by injection of NOD/ SCID mice with cells from an ATLL patient. Targeting markers present on activated T cells such as CD2 [97], CD25 [98] and CD52 [99] has been shown to provide a therapeutic benefit and to be mediated through killing mechanisms such as ADCC. Other xenograft ATLL models such as the ED model have demonstrated the utility of inhibiting proteasome activity through treatment with bortezomib [100].

14.2 Models of Hodgkin’s Lymphoma It is estimated by the American Cancer Society that approximately 8,500 new cases of Hodgkin’s lymphoma (HL) will be diagnosed in 2009. Classical HL which accounts for ~95% of all HL cases can be divided into four subtypes and is characterized by the presence of rare tumorigenic cells called Hodgkin’s and Reed/Sternberg (HRS) cells. HRS cells typically account for only 1% of the cells in the tumor tissue and in most cases represent transformed B cells that have lost typical B cell phenotypical and functional features (for reviews see Refs. [101, 102]). Although current radiation and polychemotherapeutic treatments for HL have resulted in cure rates of up to 90% [103], patients who do not respond to therapy or who eventually relapse have a poor prognosis with limited therapeutic options. As a result of the rare nature of the HRS cells, the development of HL cell lines has proven to be a difficult and rarely successful effort. Nevertheless, some HL cell lines have been generated that

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Table 14.3  Hodgkin’s lymphoma xenograft cell lines Cell line Dose Route Mouse strain Referencea HDLM-2 Sc SCID [115] ³2e7 Hs445 5e6 Sc SCID [116] KM-H2 ³2e7 Sc SCID [115] L-428 ³2e7 Sc SCID [115] L-540 1e7 sc, iv SCID [107] L540cy 5e6 sc, iv SCID [106] L-1236 2e7 Sc SCID [117] RPMI6666 5e6 Sc SCID [116] a  Reference cited is not always provided as the originator of the cell line and is included in most instances to provide an example of the tumor growth curve

can grow in immunodeficient mice (Table 14.3) and have proven useful for studies of tumor biology and the evaluation of therapeutic opportunities. Culturing of HRS cells resulted in the identification of CD30 as a viable target antigen due to its restricted expression pattern. CD30 is a member to the TNF receptor superfamily and is expressed on activated and virally transformed lymphocytes and is highly expressed by HRS cells [104]. Several anti-CD30 antibody targeting approaches have demonstrated benefit in HL xenograft models initially using antibodies conjugated to immunotoxins such as pseudomonas exotoxin A [105] and with naked antibodies mediating conventional killing activities such as ADCC [106, 107]. Using the unconjugated antibody approach, Wahl et  al. [106] demonstrated that the anti-CD30 antibody SGN-30 could inhibit proliferation of HL cells in vitro and has potent antitumor activity in both subcutaneous and disseminated HL xenograft models. Within the same time frame, Borchmann et  al. [107] described the generation of a fully human anti-CD30 antibody following immunization of the HuMAb mouse (transgenic for the human immunoglobulin genes) with the HL L540 cell line. The 5F11 clone (known as MDX-060) demonstrated strong ADCC activity against HL cell lines in vitro and in vivo. Both antiCD30 antibodies have demonstrated additive or synergistic activity with conventional chemotherapeutic agents in vitro (MDX-060 with gemcitabine and etoposide and SGN-30 with bleomycin and etoposide) with the activity of SGN-30 additionally being confirmed in  vivo in the L540cy HL xenograft model [108, 109]. Using agents to deplete either NK cells or macrophages, the anti-tumor activity of SGN30 in the L540cy model was shown to be largely dependent on the presence of macrophages [110]. Based on the activity in HL xenograft models, both SGN-30 and MDX-060 have been evaluated in HL clinical trials. Following a multi-dose escalation phase I clinical trial [111], SGN-30 was evaluated in a phase II study of both HL and anaplastic large cell lymphoma (ALCL). Although no objective responses were observed in the HL group, 29% of the patients did exhibit stable disease while two complete responses and five partial responses were achieved in the ALCL group [112]. Similar activity with MDX-060 was observed in a phase I/ II clinical trial in HL and ALCL with two complete responses in both the ALCL and HL groups [113]. An antibody drug conjugate approach is also being pursued

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for SGN-30 by coupling it with the tubulin inhibitor auristatin resulting in the SGN-35 antibody construct. Potent anti-tumor activity has been observed in HL xenograft models in combination with several chemotherapeutics including gemcitabine [114].

14.3 Models of Non-Hodgkin’s Lymphoma Non-Hodgkin’s lymphoma (NHL) is the most common type of hematologic malignancy and comprises approximately 35 different subtypes with the most prevalent types being diffuse large B cell lymphoma (DLBL), follicular lymphoma (FL) and mantle cell lymphoma (ML) [118, 119]. Although NHL can arise from both T and B cells, most cases of NHL occur as a result of disregulated B cell growth. Treatments for NHL include both chemotherapy such as CHOP (cyclophosphamide, adriamycin, vincristine and prednisone) and Rituxan which targets CD20 expressed on B cells or a combination of the two known as RCHOP. The success of Rituxan as a stand-alone agent as well in combination with chemotherapeutics has spurred the generation of antibodies that target other B cell specific antigens including CD19 and CD22 (epratuzumab) [120, 121]. In addition, second generation antibodies targeting CD20 are also currently being evaluated including veltuzumab and ofatumumab. Although more than 30 different subtypes of NHL exist, xenograft tumor models have not been generated that represent each subtype (Table 14.4). Instead, investigators have primarily relied on the use of several Burkitt’s lymphoma-­derived lines such as the Raji, Daudi and Ramos models to evaluate therapeutic opportunities. Intravenous injection of these cells results in the development of a disseminated model of lymphoma. Tumors cells seed distal locations, grow and eventually cause the development of hind limb paralysis. To monitor the growth of these cells and understand the variability of tumor seeding, we have generated a Raji cell line that Table 14.4  Non-Hodgkin’s lymphoma xenograft cell lines Cell line Dose Route Mouse strain Referencea BJAB 2e7 Sc SCID [151] Daudi 5e6, 1.5e7 sc, iv SCID [129, 149] DoHH-2 1e7 Sc SCID [152] Granta-19 2e7 Sc SCID [151] Namalwa 5e6 Iv SCID [153] Raji 5e6, 1e6 sc, iv SCID [127, 132] Ramos ³5e6 sc, iv SCID [127, 140] SR 5e6 Sc SCID [154] SU-DHL-4 1e6 Iv SCID [155] WSU-FSCCL 2.5e6 Iv SCID [141] a Reference cited is not always provided as the originator of the cell line and is included in most instances to provide an example of the tumor growth curve

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stably expresses luciferase under the control of the CMV promoter (Raji-luc). Similar to the parental Raji cell line, iv injection of the Raji-luc cells results in the development of hind limb paralysis. These cells can be visualized following an intraperitoneal injection of luciferin and imaging with the IVIS 200 imaging system (Caliper Life Sciences, Hopkinton, MA). Following iv injection, Raji-luc cells were found to disseminate to the lymph nodes, spleen and bone marrow of mice with a significant concentration of cells seeding the spinal cord, which is ultimately responsible for the development of hind limb paralysis (Fig. 14.1). Examination of various organs during necropsy revealed that Raji-luc cells also disseminated to the brain, liver and lung but not consistently in every mouse. Using this type of system, it is possible to begin to examine the effects of therapeutic intervention on tumors growing in multiple locations and determine which factors may influence susceptibility to therapy. In addition to the visual images, a total bioluminescence signal can be measured for each mouse at each time point representing the total tumor burden and establishing the overall efficacy of a therapeutic. The ability to target CD19 in NHL has been explored by several groups in multiple xenograft tumor models and included approaches such as unconjugated antibodies, antibody drug conjugates and bi-specific antibodies. Early proof of concept studies targeting CD19 using an idarubicin-conjugated antibody revealed significant activity in the Nalm-6 tumor model in nude mice [122]. To further validate CD19 as a potential target for antibody mediated therapy and understand its role in B cell development, a human CD19 Tg mouse was created that overexpresses CD19 in a lineage-specific manner [123]. These mice have been crossed with Em-cMyc

Fig. 14.1  Assessment of growth of Raji-luc cells following intravenous injection into SCID mice. SCID mice were injected intravenously with 2e6 Raji-luc cells. On days 14 and 21 post injection, mice were injected intraperitoneally with luciferin at 150 mg/kg, anesthetized and imaged using the IVIS 200 imaging system to visualize the tumor burden in each mouse. Intensity correlates with a higher tumor burden

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transgenic mice [124, 125], which express the c-Myc oncogene under the control of the Ig heavy chain promoter, resulting in the development of B cell derived lymphomas that express human CD19. Yazawa et  al. [126] have ­demonstrated that tumors from these mice can be isolated, characterized in vitro and adoptively transferred into immunodeficient mice resulting in the presence of circulating CD19+ lymphoblasts that are responsive to treatment with an anti-CD19 antibody. Although in this instance, using an unmodified antibody resulted in anti-tumor responses, efforts involving Fc engineering to enhance the cytolytic function of anti-CD19 antibodies have also been pursued. By encoding mutations within the Fc region to increase the affinity for the Fc receptor, a significant increase in ADCC activity was observed in vitro on several NHL cell lines including Raji, Namalwa and SU-DHL-6 [127]. These mutations also translated into enhanced anti-tumor activity in vivo in both the Raji and Ramos xenograft models. Bi-specific antibodies targeting additional cell surface molecules such as CD16 or CD22 in combination with CD19 have also been explored and have shown efficacy in mice bearing Daudi tumors [128, 129]. Experiments have also been conducted to explore targeting of the CD20 antigen expressed on a large majority of NHL tumors [130, 131]. The activity and mechanism of action of Rituxan alone or in combination with other agents has been explored in several xenograft tumor models including Raji [132], Daudi [133], Ramos [134], BJAB and DoHH2 [135]. Although complement appears to play a significant role in Rituxan-mediated cell lysis [136], several studies also suggest that in mouse xenograft models, neutrophils contribute significantly to this activity through ADCC [84, 85]. SCID mice bearing Raji tumors were treated with the GR-1 antibody to selectively depleted neutrophils and investigate their role in Rituxan-mediated anti-tumor protection. In the absence of these cells, Rituxan activity was severely attenuated indicating that in this model, neutrophils are ­crucial in mediating anti-tumor activity. In further support of this hypothesis, increasing the number of circulating neutrophils was also found to enhance Rituxan activity. Based on these findings and the clinical success of Rituxan, several efforts are underway to generate and evaluate second-generation anti-CD20 antibodies with enhanced or altered therapeutic profiles. Similar to the Raji-luc model described above, Bleeker et al. [137] have used the Daudi-luc model to examine the efficacy of targeting CD20 using ofatumumab. This antibody demonstrates significant CDC mediated activity on several NHL cell lines in vitro including SU-DHL-4 and Raji cells and binds to a different epitope of CD20 compared to Rituxan [138]. In the Daudi-luc model, treatment with a single dose of ofatumumab at 0.5 mg/kg, 5 days post tumor cell injection resulted in a significant inhibition of tumor growth which could be observed in individual mice by in  vivo imaging as well as by a decrease in total bioluminescence. Anti-tumor protection was also observed when treatment was delayed until day 14. In addition to ofatumumab, veltuzumab is also being pursued as an alternative anti-CD20 antibody. Single agent activity has been observed in the Raji, Ramos, Daudi and WSU-FSCCL models either as an ­unconjugated antibody or conjugated with a radionuclide [139–142]. Based on their in  vivo efficacy, ofatumumab and veltuzumab are currently being evaluated in

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clinical trials of NHL. Currently, ofatumumab is undergoing FDA review for approval in the treatment of CLL. Unlike CD20, ligand binding to CD22 results in rapid internalization [143, 144], which makes this cell surface marker a good target for antibody–drug conjugate therapy. This property has been exploited in experiments examining the efficacy of the radio-labeled anti-CD22 antibody epratuzumab (for review see Refs. [145, 146]) that was originally created by immunizing mice with the Raji cell line [147]. Treatment of nude mice bearing Ramos subcutaneous tumors with 175 mCi of 90 Y-epratuzumab resulted in a significant degree of tumor regression [148]. However, tumors eventually re-grew suggesting that although treatment with 90Y-epratuzumab may induce tumor regression, it does not result in a durable response. Interestingly, the addition of veltuzumab enhanced the anti-tumor activity of 90Y-epratuzumab and resulted in long-term survival of tumor-free mice suggesting that targeting both CD20 and CD22 may have clinical utility. The approach is being explored further using a bispecific antibody engineered from epratuzumab and veltuzumab which has demonstrated efficacy in the Daudi tumor model [149, 150].

14.4 Models of Multiple Myeloma After non-Hodgkins lymphoma, multiple myeloma (MM) is the second most prevalent hematologic cancer representing ~1% of all cancers. According to the American Cancer Society, >20,000 new cases of MM will be diagnosed and >10,000 deaths will occur within the United State s in 2009. Multiple myeloma is a B cell malignancy characterized by accumulation of cancerous plasma cells in the bone marrow and often leads to the formation of bone lesions as a result of an increase in osteoclast activity [156, 157]. Current therapeutic options include treatment with chemotherapeutics such as thalidomide and lenalidomide, the proteasome inhibitor bortezomib and autologous stem cell transplant. Bisphosphonates are also often included in the therapy to delay the progression of bone lesions [158]. Attempts at modeling MM in mice have primarily involved xenograft experiments and the use of MM cell lines. Cell lines established more than 30 years ago including MC/CAR, IM-9, RPMI-8226 and ARH-77 are still proving useful in understanding the progression of MM as well as for the testing of new therapeutic strategies (Table 14.5). Early models of MM involved intraperitoneal injection of MM cells that resulted in circulating levels of human IgG and engraftment of tumor cells in the peritoneal cavity but did not result in the development of disseminated disease or migration to the bone marrow [159, 160]. In contrast, intravenous injection of the ARH-77 cell line into irradiated SCID mice did result in the development of a disseminated disease similar to MM in humans and was characterized by growth in the bone marrow, brain, kidney and liver [161]. In addition, growth of ARH-77 cells was associated with the development of osteolytic lesions further validating this as an adequate model for MM. Alsina et al. [162] further characterized this model with respect to the development of bone lesions and observed bone

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destruction with increased osteolytic bone resorption in areas adjacent to the myeloma cells. Antisense blockade of MIP-1a in ARH-77 cells resulted in a decrease in bone destruction as well as a decrease in tumor burden within the bone marrow suggesting an involvement of this pathway in the progression of MM potentially by acting as a growth factor or by promoting interaction with stromal cells [163]. Inhibition of bone destruction was also observed in this model using a RANK-Fc fusion protein that blocks the activities of RANK-L which has been shown to play an important role in osteoclastgenesis [164]. Recently it has been suggested that in addition to inhibiting tumor growth, bortezomib therapy can stimulate bone formation [165]. Bortezomib is a proteasome inhibitor currently approved for the treatment of MM. In preclinical models of MM, treatment of mice bearing RPMI-8226 tumors with bortezomib resulted in an increase in apoptosis and a decrease in angiogenesis [166]. Initial observations by Giuliani et al. and others in MM patients indicate that bortezomib treatment could have both direct and indirect effects on bone formation [167]. This has been further explored in a rabbit-SCID mouse model in which primary human MM cells are injected into nonfetal rabbit bone that has been implanted into SCID mice [168]. In this model, treatment with bortezomib inhibited tumor growth and was associated with an increase in bone mineral density and the number of osteoblasts. Similar to myeloid leukemia, the interaction between the tumor cells and their microenvironment plays a key role in the development and progression of MM. Using more sophisticated mouse models, studies have been conducted to begin examining the mechanisms through which myeloma cells home to the bone marrow and interact with stromal cells. Urashima et  al. [169] have developed a bilateral fetal bone hu-SCID mouse model to monitor homing of MM cells and explore which molecules may play a role in this process. Injection of MM cells into the bone marrow of one implant resulted in the migration of cells to the secondary implant but not to mouse bone marrow. Several MM cell lines such as ARH-77 and RPMI-8226 were shown to be able to migrate in this fashion. In vivo imaging techniques are also currently being incorporated into experiments to better visualize the migration and homing patterns of MM cells. Following injection, RPMI-8226 cells

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expressing the green fluorescent protein (GFP) were also shown to migrate to the spine, skull and pelvis of mice leading to the development of osteolytic lesions similar to the pattern observed in MM patients [170]. The development of models such as these will allow for the testing of therapeutic strategies aimed at inhibiting migration or interrupting the tumor cell stromal cell interaction. To this end, Smallshaw et  al. [171] have demonstrated that an antibody targeting ICAM has therapeutic efficacy in the ARH-77 model and results in a significant increase in survival. Other factors such as the SDF-1/CXCR4 axis described earlier are also thought to play an important role in the migration and homing patterns of MM cells. Inhibiting this interaction using AMD3100 reduces the homing of MM.1S cells in NOD/SCID mice [172]. In addition, the SDF-1/CXCR4 pathway may also contribute to the development of osteolytic lesions. Diamond et al. [173] have demonstrated that intra-tibial injection of RPMI-8226 cells resulted in the formation of osteolytic lesions that could be inhibited by systemic administration of agents that affect the SDF-1/CXCR4 interaction [173]. Therefore, disrupting the SDF-1/ CXCR4 interaction may have significant implication not only on the homing of MM cells to the bone marrow but also on the development of bone lesions. The therapeutic potential of targeting cell surface antigens such as CD74, which is expressed on almost 90% of MM tumors [174], has also been evaluated in several MM xenograft models. CD74, also known as the invariant chain, is a type II membrane protein that associates with the MHC class II molecule and is involved in trafficking and antigen loading (for review see Ref. [175]). An antibody targeting CD74 was generated by immunization of mice with the Raji Burkitt lymphoma cell line and was shown to bind to ARH-77 cells [147]. A humanized version of this antibody (hLL1) displays anti-proliferative effects on CD74+ MM cell lines such as ARH-77, MC/CAR and KMS12-PE cells and demonstrates significant anti-tumor activity in the MC/CAR subcutaneous xenograft model [176]. The observation that hLL1 can be rapidly internalized by MM cells following binding to CD74 makes it ideal for an antibody–drug conjugate platform. This potential has been explored in MM xenograft models by coupling hLL1 to doxorubicin [177] or ­ranpirnase, a novel ribonuclease with potent cytostatic activity [178]. Profound activity was observed in the MC/CAR model using the hLL1–doxorubicin conjugate and has resulted in the evaluation of hLL1, now known as milatuzumab, alone or conjugated to doxorubicin in MM clinical trials. Recently, Stein et al. [179] have demonstrated that the therapeutic activity of milatuzumab in several disseminated MM xenograft models such as CAG or KMS11 can be enhanced by the addition of bortezomib suggesting another potential clinical application.

14.5 Conclusions Advancement of the leukemia and lymphoma research fields has been driven in large part by the utilization of mouse models to understand disease progression and identify new therapeutic opportunities. The development of xenograft tumor ­models in immunodeficient mice has allowed for the testing of new antibodies and drug

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combination strategies that are currently undergoing clinical evaluation. These models are crucial for establishing proof of concept data and gauging the efficacy of a therapy on human tumor cells growing in  vivo especially in cases where therapies do not cross-react with the mouse homolog. Using these models, the interaction of tumor cells with their microenvironment as it relates to the growth of the primary tumor as well as the development of metastases are key areas of research for many groups (for review see Refs. [183, 184]). What was once thought of as only a supporting structure is now known to play a critical role in tumor growth and involve a mixture of cytokines and cell types. Therapies that affect this dynamic interaction such as AMD3100 which targets the CXCR4/SDF-1 axis are being tested clinically in combination with both biological and small molecule therapies. Although we continue to advance our ability to explore the targeting of new antigens on leukemia and lymphoma cells through mouse models, it is important to realize that each type of model comes with its own set of limitations. For example, in some instances the evaluation of therapeutics in xenograft models takes place in a setting where the target is only expressed on the xenografted cell line. Therefore, the impact of the therapy being analyzed on other cell types that express the antigen cannot be determined. In addition, it is important to understand the origin of the cell line being utilized as well as verify that it expresses the antigens common to the tumor from which it was developed. Recently Drexler et al. [185] have used DNA profiling to assess the validity of over 500 leukemia and lymphoma cell lines. Their results suggest that in several instances cell lines can be misidentified with respect to the actual tumor type from which it originated. In addition, cross-contamination from other rapidly growing cultures such as K562 cells can also result in the generation of false-positive or mixed cell lines. Therefore, it is important that we understand as much as possible the origin of the reagents being used as well as how well they mimic the human disease. As we continue to experiment and learn more about the progression of leukemia and lymphoma, it is clear that mouse models will play a pivotal role in all aspects of this research. Acknowledgements  I would like to thank Yanping Hu, Robert Fogel and Gary Jacques for experiments involving in vivo imaging of the Raji-luc cell line. I would also like to thank Johanne Kaplan for critically reading the manuscript.

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Chapter 15

Spontaneous Companion Animal (Pet) Cancers David M. Vail and Douglas H. Thamm

Abstract  The inclusion of companion species with naturally occurring tumors provides significant opportunities for optimizing drug development pathways that other model systems cannot provide. Over the past decade, tremendous growth in the field of comparative oncology has occurred, including significant increases in organized consortium infrastructure, availability of investigational reagents and regulatory standardization. These advances are currently being applied to the development of novel cytotoxic, immunologic and biology-based anticancer therapies, innovative drug delivery systems, identification and validation of biological endpoints, noninvasive imaging techniques and surrogate markers critical to the design of Phase I and Phase II human clinical trials. The biotechnology and pharmaceutical industries recognize the utility of the model’s inclusion and several examples exist where they have initiated studies in companion species to assist in drug development. We are clearly at a period in time where the microscope is turned on this model and while currently a theory, the next 5 or 10 years should determine the degree to which information generated through the inclusion of companion animals with cancer is applicable to human cancer drug development. Keywords  Companion animals • Dog • Cat • Canine • Feline • Cancer • Comparative oncology

15.1 Introduction At present, large animal translational models of cancer etiology, biology and ­therapy that serve to bridge more artificial cell and rodent-based models with the human cancer experience are either lacking or underutilized. Spontaneously D.M. Vail (*) Center for Clinical Trials and Research, School of Veterinary Medicine, University of Wisconsin-Madison, 2015 Linden Drive, Madison, WI 53706, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_15, © Springer Science+Business Media, LLC 2011

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o­ ccurring cancers in companion animal (pet) dogs and cats have the potential to serve as important bridging models to enhance both our understanding of cancer and the development of novel therapeutic modalities [1–5]. While rodent models provide important opportunities for investigating specific molecular and genetic pathways, they tend to lack the tumor–host heterogeneity that occurs in people and dogs with spontaneously occurring tumors. Taking a cafeteria approach to tumor modeling, further expanding knowledge gained in rodent models by supplementary investigations in species with spontaneous tumors that better recapitulate the ­heterogeneity of tumor development and progression makes intuitive sense. Due to the recent elucidation of the canine genome and the commitment to cooperative ventures between several academic, federal and private sector development groups, we stand poised at a phenomenal point of opportunity [6, 7]. The inclusion of ­companion animals into the drug development pathway with sufficient resources and organization could provide the mechanism to seize these opportunities. There is now a sufficient body of experience documenting companion animal owners’ willingness to allow their pets’ participation in tumor collections for biospecimen repositories and enrollment in trials to evaluate novel diagnostic and therapeutic modalities that may benefit both the animal patient and, eventually, humans with cancer. The organization of veterinarians, physicians and basic cancer researchers around the goal of including companion animal cancers within the mainstream of cancer research has begun. Programs in Comparative Oncology have increasingly been included within designated Comprehensive Cancer Centers, the pharmaceutical industry and regulatory bodies, including the FDA. These organizations have recognized the value and savings of both patient and financial resources offered through the integration of companion animal clinical trials into drug development paths. Safety and activity data generated can be invaluable to inform physicianbased human clinical trials that may follow. In addition, the National Cancer Institute has initiated the Comparative Oncology Program within the Center for Cancer Research designed to promote and include companion (pet) animals with spontaneous cancer in preclinical oncology investigations. This chapter serves to provide the reader with an overview of companion animal cancer, the potential opportunities and disadvantages of the model, the research communities involved, unique trial design issues including regulatory oversight and examples where ­companion species have been included in the drug development path.

15.2 Overview of Cancer in Companion Animal Species 15.2.1 Cancer Incidence and Availability of Veterinary Medical Care Cancer is the number one cause of morbidity and mortality in the aging pet ­population, as significant atherosclerotic cardiovascular diseases do not exist in the

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Table 15.1  Comparative annual incidence rates (per 100,000) for common sites or types of cancer in dogs, cats, and humans [10–13] Site/type Dog Cat Human Oral 20.4 – 10.3 Skin 90.4 34.7 22.6 Connective tissue 35.8 17.0 3.3 Testes 33.9 – 5.5 Melanoma 25 – 21.1 Mammary/breast 198.8 25.4 123.0 Bone 7.9 4.9 0.5 Non-Hodgkin’s lymphoma 25 125 19.5 Leukemia – 35.6 11.9

companion species. Nearly half of all households in the United States include a companion animal. This places approximately 75 million dogs and 90 million cats at risk for developing cancers in the United States [8]. It is estimated that over 1 million new cases of cancer are diagnosed in pet dogs every year in North America. In a necropsy (autopsy) series of 2,000 dogs, 23% of all dogs, regardless of age, and 45% of dogs 10 years of age or older died of cancer [9]. Estimates of age-adjusted overall cancer incidence rates per 100,000 individuals/year at risk are 381 for dogs and 264 for cats; comparable to approximately 476 for humans (National Cancer Institute SEER Program) [10]. Cancer rates for dogs, cats and people based on site are presented in Table 15.1 [10–13]. For several histotypes, the incidence rates are significantly higher than that for humans and their relative abundance increases their model potential. In particular, the high incidence of canine osteosarcoma, soft-tissue sarcoma, non-Hodgkin’s lymphoma and malignant melanoma provide for a significant comparative population. There exists a growing body of residency-trained veterinary oncology specialists (approximately 250 in the United States) populating a similarly expanding number of active academic and private-sector veterinary oncology practices to meet the challenges of cancer management in veterinary species. Despite the increasing application and availability of treatment modalities used for years in humans, including surgical oncology, chemotherapy, immunotherapy and radiation therapy, highly effective “standards of care” do not exist for many tumor types in companion animals. This, in part, has led to a highly motivated, well-informed pet owning public that actively seeks high-quality medical care, including well-designed clinical trials for the animals under their charge.

15.2.2 Genetic and Molecular Basis of Cancer in Pet Dogs It is well documented that the genetic, epigenetic and molecular basis of cancer in people is as varied as it is multifactoral. While much has been learned over the past

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decades through the use of existing cancer models and investigations in humans with cancer, significant gaps in our knowledge exist. Germane to this chapter, the more we explore the genetic and molecular pathways implicated in cancers of companion species, the more it becomes clear that they share a great deal with human cancers in terms of etiopathogenesis and biology. The publication of the canine genome in 2005, as well as the rapidly accelerating availability of analysis tools (e.g. canine gene microarrays, FISH probes, etc.), has revealed some remarkable similarities between the genetic basis of cancer in dogs and humans [6, 7]. In the canine genome, greater than 1,000 SNPs are identified and microsatellite markers identify 85 different breeds. Canine-specific CGH and gene expression mircoarrays now exist [14, 15]. Comparative expression pathway analysis has documented similarities between human and canine osteosarcoma [1, 16, 17], intracranial tumors [18], hematological tumors [19] and mammary tumors [20, 21]. Several investigations by Matthew Breen’s group and others have found evolutionarily conserved cytogenetic changes in many malignancies of dogs and humans [16–21]. The similarities of many gene families associated with cancer are much more closely related in dogs and humans than are rodents to either species. In fact, cluster analysis of orthologous gene signatures did not segregate human and canine osteosarcoma on the basis of species, illustrating highly similar gene expression patterns [1]. Mutations in many oncogenes and tumor suppressor genes commonly mutated in human cancer, such as p53 [22–31], rB [25], ras [32–34], myc [35, 36], met [16] and bcl-2 [37, 38] have been detected in a variety of canine and feline tumors. Likewise, overexpression of telomerase and matrix metalloproteases have been detected in several canine tumors [39–46]. Additionally, many signal transduction pathways known to be important in human cancers have been documented to play a role in tumor development and progression in companion species with spontaneous tumors. A variety of tyrosine kinase (TK) growth factors and their receptors have been detected in canine and feline tumors, and thus they may serve as pertinent targets for the preclinical development of small molecule inhibitors with relevance to human cancer. The growth factors HGF and IGF-1 and their respective receptors have been detected in a majority of canine osteosarcomas (OSA) [16, 47–49]. HGF was expressed in 17 of 19 clinical samples and c-Met was expressed in all samples evaluated (Fig. 15.1) [47]. Also, the addition of HGF to several human and canine OSA cell lines results in phosphorylation of c-Met, stimulates proliferation, anchorage-independent growth and invasion. In companion animal cats, it has been demonstrated that PDGF induces phosphorylation of PDGFR-b, enhances the proliferation, survival and chemotherapy resistance of several feline vaccine-associated soft tissue sarcoma (VAS) cell lines and that this stimulation can be reversed by the small-molecule TK inhibitor imatinib (Fig. 15.2) [50]. A large number of canine malignant mast cell tumors (MCT) display aberrant expression of c-kit, the receptor for stem cell factor and many canine MCT have mutations in c-kit that confer constitutive activation of the receptor in the absence of ligand binding [51–53]. Indeed, in vitro ligand activation and tumor cell growth can be inhibited in canine MCT cell lines using novel split-tyrosine kinase inhibitors and trials investigating these novel small

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Fig. 15.1  Expression of HGF and c-Met mRNA in canine OSA tumor samples. All samples expressed c-Met and 17/19 expressed HGF

Fig. 15.2  Immunoprecipitates and immunoblots demonstrating that in several feline soft-tissue sarcoma cell lines, Imatinib blocked autophosphorylation induced by PDGF-BB in a dose-dependent manner, with near complete inhibition at a concentration of 2.5 mM

molecule inhibitors in pet dogs with naturally occurring MCT were instrumental in informing similar c-kit inhibitor trials in human clinical trials for treating c-kit tumors (e.g. GIST) [54–56]. Several studies have documented the presence of EGFR in canine normal and tumor tissues [57–61]. In particular, EGFR has been documented in canine mammary tumors, astrocytoma and lung tumors. We have documented EGFR expression in the majority of spontaneously arising canine bladder transitional cell carcinoma tissues we have evaluated (Fig. 15.3a) and in a transitional cell carcinoma cell line established from a dog (Fig. 15.3b). The documentation of drugable molecular targets in tumors from companion species that are nearly identical to those found in human tumors enhances the model potential. Analogs of most major angiogenic growth factors and their receptors also exist in dogs and cats, and elevations in serum or urine VEGF and/or bFGF have been detected in dogs with hemangiosarcoma (HSA) [62, 63], transitional cell carcinoma of the urinary bladder [64, 65] and osteosarcoma [66]. We have also demonstrated the presence and functional activity of several angiogenic growth factor TKs in canine HSA, a vascular endothelial-derived tumor [67]. HSA is a highly malignant and rapidly fatal tumor and may represent one of the most extreme examples of dysregulated angiogenesis. For example, the majority of canine HSA express receptors for bFGF, VEGF and the angiopoietins and inhibition of the VEGF signaling pathway results in a significant decrease in HSA cell growth in vitro [67].

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Fig. 15.3  (a) Immunohistochemistry documenting EGFR expression in canine transitional cell carcinoma (Abcam EGFR antibody #ab2430). (b) Immunocytochemistry demonstrating EGFR expression in a cytospin preparation of a transitional cell carcinoma cell line derived from a dog

Similarly, a small molecule VEGF-receptor inhibitor has shown clinical utility in human angiosarcoma [68].

15.3 Potential Opportunities/Advantages of Including Companion Animals with Cancer as Models Several aspects of companion animal disease make pets attractive comparative models and are summarized in Table 15.2. Many have been touched upon in the preceding section on genetic and molecular similarities between the companion species and humans. Since tumors from companion species often possess the same molecular targets, regardless of histology, they can be readily utilized for proof-ofconcept, proof-of-target analysis. Companion species represent a more natural outbred population than laboratory animals and their malignancies develop spontaneously without experimental exposure to known carcinogens, transplantation or artificially induced immunologic or genetic modifications. Thus, they more closely recapitulate the intratumoral heterogeneity and cell-stromal interactions known to be of great importance in human tumor progression. Incidence rates for certain malignancies in companion species (e.g. canine osteosarcoma, non-Hodgkin’s lymphoma) are higher than those observed in people and provide a ready population for inclusion. The relative cost of veterinary-based trials, while often of the highest caliber, is substantially less than physician-based trials. Companion animal trials can also be performed in the pre-IND setting, which can have several advantages (see Sect. 15.6). Veterinary cancer patients tend to be less heavily pretreated, with better performance status at study entry than are most humans entered into phase I trials. This may allow a clearer understanding of the adverse event profiles of the treatment modalities under investigation as well as representing a more naive population with respect to acquired tumor resistance mechanisms and therefore provide a better measure of antitumor activity than would be observed in a more heavily pretreated

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Table 15.2  Potential advantages of companion species cancer models Advantage Comments Similar genetic etiopathogenesis Greater synteny between dog and human vs. rodent and human Similar gene expression alterations in cancer Similar signal transduction pathways Similar drugable targets identified for TK pathways Similar angiogenic pathways Similar apoptotic pathways Spontaneous tumors in outbreed populations Recaputulates tumor heterogeneity and cell– stromal interactions observed in human tumors High incidence of cancer in companion species Similar to human SEER rates Some histologies more prevalent (e.g. OSA, NHL) Lower relative cost of therapeutic trials GMP quality not always necessary Pre-IND trials possible Less heavily pretreated patient population Higher patient performance scores More accurate AE assessment Less acquired resistance mechanisms Tumor cell kinetics more comparable than Rapid accrual of response assessment rodent systems Larger body size of companion species Similar imaging and treatment (e.g. radiation therapy units) modalities can be applied Greater opportunity for repeated tissue and fluid sampling over time More abundant tissue available for analysis Companion species share our environment Sentinel species Chemoprevention study potential Owner compliance Highly committed clients in the context of lack of financially available standard of care 80–90% necropsy compliance Intact immune system More relevant immunotherapy trial population Similar innate and adaptive immunity cues Similar tumor-associated antigens Minimal disease models (OSA, HSA) Allow for investigation of the metastatic phenotype Antimetastatic therapy models Hepatic enzyme homology more similar than More accurate PK/PD assessment most rodent models More accurate AE assessment Access to both laboratory normal and tumorSpecies-in-kind approach bearing populations More rapid toxicity to activity assessments Organized research efforts Biospecimen consortiums in place Clinical trial consortiums in place TK tyrosine kinase, OSA osteosarcoma, NHL non-Hodgkin’s lymphoma, HSA hemangiosarcoma, AE adverse events

patient. Companion animal cancers are also more comparable to human cancers than are rodent models in terms of size and cell kinetics and they generally progress at a more rapid rate than their human counterparts; therefore, the time course of trials with progression endpoints are of adequate length to allow comparison of

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response times while short enough to ensure rapid accrual of data. For example, 5-year survival or progression-free temporal measures in human osteosarcoma and non-Hodgkin’s lymphoma populations are roughly equivalent to 1-year temporal measures in companion species [1]. The larger body size of companion species also is advantageous and allows sample collection (e.g. serum, urine, bronchoalveolar lavage, biopsies), surgical intervention, radiation therapy and imaging modalities to be more readily applied than in rodent models. Development of advanced radiation therapy delivery systems, such as Tomotherapy®, have taken advantage of this and used companion species with spontaneous tumors early in their clinical development [69]. Similarly, canine trials of inhalational chemotherapy and immunocytokines benefited from the similarities in body size and helped inform subsequent human trials [70, 71]. Additionally, because companion species require anesthesia for most imaging modalities, this allows for more aggressive and repeated serial collections of tumor and normal tissues during the course of treatment than most human clinical trials allow. By way of example, the antiproliferative effect of a novel cytotoxic chemotherapy for non-Hodgkin’s lymphoma was recently validated by correlating 3¢deoxy-3¢-[18F]fluorothymidine (FLT) PET/CT imaging with Ki-67 immunohistochemistry within the context of a canine clinical trial (Fig. 15.4) [72, 73]. Larger body size (and therefore larger tumor size) also allows for more abundant tumor tissue available for bench-derived molecular analysis, and often tissues can be submitted to several investigators concurrently. Companion species share common environmental exposures with people and therefore can act as sentinels or be included in chemoprevention studies related to exposure. Most companion animal owners are highly motivated and actively seek innovative and promising new therapies to treat their companions’ cancer. Compliance with treatment and recheck visits is exceptional, and necropsy (autopsy) compliance approaches 90%, significantly better than most human clinical trials [2]. Companion species also provide an opportunity to add exogenous substances (e.g. Matrigel plugs) to allow mechanistic assessment. Unlike many laboratory animal models, companion species with naturally occurring tumors have intact immune systems and the key players in both the innate and adaptive immune response are similar between companion animal species and humans. This provides inherent advantages for the inclusion of companion species in investigations of novel immunotherapuetics. Collectively, the species immune systems recognize the same pathogen-associated molecular patterns through similar Toll-like and peptidoglycan recognition receptors [74–76]. Modeling of the innate immune stimulant L-MTP-PE in pet dogs with naturally occurring osteosarcoma, hemangiosarcoma and malignant melanoma exemplifies this approach and helped inform the design of a subsequent clinical trial in children with osteosarcoma [77–80]. Canine and feline tumors also share important tumor-associated antigens with human tumors. For example, the expression of canine analogs of gp100, MART-1 and tyrosinase occurs in a majority of canine melanomas, and immunotherapeutic strategies designed to target canine tumor antigens have shown promise in clinical trials in dogs [81–84]. Canine osteosarcoma and malignant

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Fig. 15.4  Representative 3¢-deoxy-3¢-18F-fluorothymidine (FLT) positron emission tomography/ computed tomography (PET/CT) of a dog with non-Hodgkin’s lymphoma before (a) and 5 days after (b) cytotoxic chemotherapy. Ki-67 immunohistochemistry of lymphoma tissue from the prescapular lymph node of a dog before (c) and 4 days after (d) cytotoxic chemotherapy in the same dog (600×. Note the significant decrease in FLT uptake in PET/CT scans following therapy correlates with Ki-67 immunoreactivity; both indicating an antiproliferative effect

melanoma, both extremely common tumors in pet dogs, also provide a unique opportunity to investigate the minimal residual disease setting and the metastatic phenotype. Both represent populations where the primary tumor is readily manageable (i.e., amputation, surgical excision) leaving micrometastatic disease in the majority of cases; in both histologies, while metastasis is not clinically evident at diagnosis, most dogs progress to develop gross metastatic disease within 4–6 months [85]. These minimal residual disease populations have been included in several mechanistic investigations of the metastatic phenotype as well as proof of concept trials investigating novel antimetastatic therapies.

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Importantly, canine hepatic enzyme homology and organ-specific blood flow are more similar to people than are rodents, and this may allow more accurate assessment of pharmacokinetic and pharmacodynamic properties of novel therapeutics under development. For example, pharmacokinetic, safety and activity assessments for a novel chemotherapeutic prodrug requiring intracellular enzymatic activation was not possible in rodents as they, unlike humans or dogs, have high plasma levels of carboxyesterase which rapidly metabolized the drug in the extracellular compartment, effectively precluding rodent preclinical models [72, 86]. In particular, important information regarding the characterization and assessment of adverse event profiles of new agents in the development pathway may be more realistically assessed in companion species with similar metabolic pathways. Dose-limiting toxicities in tumor-bearing pet dogs treated off-label with chemotherapies ­commonly used in people are uniformly identical in both species [87]. Similarly, specific antitumor activity of chemotherapeutics commonly used in people have shown remarkable similarities in companion species tumors and appear more predictive than mouse xenograft models of activity [88]. For example, the most active agents for non-Hodgkin’s lymphoma (cyclophosphamide, doxorubicin, vincristine, prednisone) and osteosarcoma (platinum analogues) in people are the same as those showing the greatest activity in dogs [85, 89]. Likewise, drugs known to be ­relatively inactive for NHL in people (e.g. gemcitabine and cisplatin) are similarly inactive in dogs with NHL [87, 89, 90]. Finally, the fact that the dog is the only species with significant access to both a laboratory-normal population and a highly varied spontaneous tumor-bearing population is, in itself, a major advantage as it allows the evaluation of both safety and activity of novel therapies in the same species. Taking a “species in kind” approach, significant PK/PD, dosing, biomarker validation and adverse event issues can be significantly explored in laboratory dogs prior to moving promising agents into the “veterinary clinic” for assessment of activity and further characterization in the more relevant naturally occurring companion animal population. Data generated in laboratory dogs allow superior design of clinical trials in companion species without having to “best-guess” starting doses, better anticipation and measurement of adverse events and more rational design of PK/PD and biomarker assessment. Conversely, if an interesting finding requiring further characterization occurs during a study in tumor-bearing pet dogs, such as an unexpected adverse event or a potential biomarker is identified, more focused study can then be performed in purpose-bred laboratory dogs.

15.4 Caveats to Inclusion of Pet Dogs with Cancer as Models No one model of cancer is ideal, nor should it be expected to answer all the myriad of questions remaining to be answered in cancer biology and therapeutic ­development. The utility of including companion species in the global effort to understand and manage cancer in people is still theoretical and several caveats exist.

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The greatest potential strength for inclusion of companion species with spontaneous tumors in cancer drug development, the heterogeneity that occurs in the population and the tumors under study, is also one of its weaknesses. Rodent modeling has been the workhorse of preclinical cancer research owing in part to the ability to regulate or restrict the genetic and molecular diversity of the study system and specifically control variables in a laboratory setting. This allows characterization of very specific genetic and epigenetic alterations to be performed, a process that is important and should ensure continued utility of rodent systems. Tumor-bearing companion species are clearly a different preclinical population compared to research animals; however, study entry characteristics (i.e. breed, sex, age, histology and presence of drugable target) can be restricted and ultimately treatment strategies that are further along in the development pathway could benefit from investigations in a model system that more closely recapitulates the tumor heterogeneity and cell–stromal interactions known to be of great importance in human tumor progression. While the incidence of cancer is high in companion species, the prevalence of common histologies is not similar to humans. The most common histologies in companion species include sarcomas and lymphoid malignancies while more common histologies affecting humans (e.g. breast, prostate, gastrointestinal and lung) are less common and require a multicenter approach and more time for accrual [1, 10–13]. That being said, often target trumps histology with respect to comparative importance as evidenced by the c-kit tyrosine kinase pathway which is often mutated in the common canine mast cell tumor (a histology that is exceedingly rare in people) and in human GIST tumors [51–56]. Despite the histological dissimilarity of these two tumors, significant characterization of the pathway, including development of currently available small molecule c-kit inhibitors in physician-based oncology were initially modeled through inclusion of pet dogs with spontaneous mast cell tumors. The availability and development of commercially available biological reagents and technology platforms specific for companion species have lagged behind those of rodent and human tissues. This weakness is diminishing now and the canine genome is known and commercially available products such as Affymetrix® canine gene chips, canine-based ELISA kits and canine tumor-specific antibodies are becoming available. The recent presence of several cooperative consortia (see Sect. 15.5) committed to the development and dissemination of canine- and feline-based reagents, cell lines, tumor tissue arrays and genetic and molecular probes has also served to lessen this caveat. In comparison to rodent modeling, companion species trials tend to have longer timelines and higher costs, albeit less so than similar human trials. While drug investigations in larger species such as dogs require larger quantities and ramping up production of the agent under investigation, this is tempered to a degree as GMP quality drug is not always necessary for companion species trials as long as the composition of matter is sterile, endotoxin free and of high quality and purity. Finally, there has been an historical aversion by biotechnology and pharmaceutical companies to “rock” the traditional development boat [1]. In other words,

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guidelines for the control and reporting of data accrued in companion species trials including adverse event reporting have not been uniformly stated in the past and regulatory oversight and reporting guidelines have not been established for trials run in the pre- and post-IND setting. This has led to some confusion as well as a fear of having human trials stopped or retarded if an unforeseen adverse event is observed in parallel companion species trials. Recently, the interested parties, including the FDA have met to resolve many of these issues (see Sect. 15.6) [91].

15.5 The Vested Communities The concept of inclusion of companion species in drug development is not new and researchers have been involved in such investigations for several decades, dating back to the 1960s with work performed by Rainer Storb’s group on bone marrow transplantation techniques which included pet dogs with non-Hodgkin’s lymphoma [92]. However, such investigations have historically occurred in an isolated environment by a few individuals. In the last 10 years, however, the comparative oncology community has greatly expanded and become organized through several consortia, many through the efforts of the Comparative Oncology Program (COP, http://ccr. cancer.gov/resources/cop/default.asp) within the Center for Cancer Research at the National Cancer Institute (NCI-CCR). The COP, under the leadership of Chand Khanna, has initiated several programs aimed at better integration of current efforts as well as the development of bioresource and technology platforms aimed at comparative research. These include a biospecimen repository, an antibody validation project, gene microarray, tissue microarray and proteomics programs. An offshoot of the COP is the Canine Comparative Oncology and Genomics Consortium (CCOGC, http://www.ccogc.net/), a 501(c)3 not for profit whose primary objective is to facilitate strategic partnerships and collaborations across a diversity of disciplines, focused on the problem of cancer. Priorities of the CCOGC include advocacy for the field of Comparative Oncology, the development of mechanisms to share reagents and resources in the community, and to utilize the release of the Canine Genomics Project to characterize cancers in companion dogs using modern descriptors. The CCOGC leadership has determined that the most essential resource needed to make progress in the companion species comparative oncology field is the development of a well-described repository of tissues from tumor bearing dogs. This recognition is similar to that made in the development of a National Biospecimen Network that collects and distributes tissues from humans with cancer. A repository of well-described tissues provides opportunities not currently available to individual and institutional investigators interested in developing effective new treatments and prevention strategies for cancer. Through funding from Pfizer Inc., the American Kennel Club–Canine Health Foundation and the Morris Animal Foundation, the Pfizer– CCOGC Biospecimen Repostitory has become a reality and is part of the NCI Frederick Central Repository Services. The bioinformatics platform for the repository includes a relational database that ­connects clinical information on ­samples entered with a front and back end retrieval system. Biological data derived from samples in

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the Biospecimen Repository are uploaded into the bioinformatics database and become part of the progressive value of the Repository. Another program to come out of the COP efforts at NCI-CCR is the Comparative Oncology Trials Consortium (COTC, http://ccr.cancer.gov/resources/cop/COTC. asp). The COTC is an active network of 18 academic comparative oncology centers (Fig. 15.5), centrally managed by the COP, that functions to design and conduct clinical trials in pet dogs with cancer to assess novel therapies. One key element of the COTC is a mechanism by which pharmaceutical companies can execute a single contract or memorandum of understanding with several academic trial centers for the conduct of investigative trials. Additionally, COTC trials utilize the same webbased bioinformatics backbone that exists for human trials and comply with NCI technology initiatives, such as caBIG. The overall goal of this effort is to answer biological questions geared to inform the development path of these agents for future use in human cancer patients. Trials conducted by the COTC are pharmacokinetically and pharmacodynamically intensive with the product of this work directly integrated into the design of current human Phase I and II clinical trials (Fig. 15.6). The inaugural pre-clinical COTC trial was recently concluded. This

Fig. 15.5  Comparative Oncology Trials Consortium (COTC) contributing institutions (Auburn University, Colorado State University, Cornell University, Michigan State University, North Carolina State University, Purdue University, Texas A&M University, The Ohio State University, Tufts University, University of California, Davis, University of Florida, University of Georgia, University of Illinois, University of Minnesota, University Of Missouri, University Of Pennsylvania, University Of Tennessee, University of Wisconsin). The COTC is an active network of 18 academic comparative oncology centers centrally managed by the Comparative Oncology Program within the NCI-Center for Cancer Research that functions to design and conduct clinical trials in pet dogs with cancer to assess novel therapies

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Fig. 15.6  An integrated approach to the inclusion of companion animal species in the drug development pathway. Such studies can be performed prior to first-in-man trials (e.g. phase I trials) as well as in parallel to ongoing human clinical trials in order to further provide data necessary for more advanced trials to follow (e.g. phase II–III). Reproduced by permission from Macmillan Publishers Ltd: Nature Rev Cancer [1], copyright 2008

study demonstrated the utility of the COTC infrastructure to inform the development of new cancer drugs within large animal naturally occurring cancer models; specifically, the evaluation of a targeted AAV-phage vector delivering tumor necrosis factor (RGD-A-TNF) to aV integrins on tumor endothelium provided valuable and necessary data to complete the design of first-in-man studies [93].

15.6 Study Design Issues Specific to Companion Species Trials Companion species trials are best suited for proof-of-concept or proof-of-target investigations with an eye to informing future human clinical trials. Such studies can be performed prior to first-in-man trials (e.g. phase I trials) as well as in parallel to ongoing human clinical trials in order to further provide data necessary for more advanced trials to follow (e.g. phase II–III) as illustrated in Fig. 15.6 [1]. Ultimately such trials could help prioritize which agents continue on in the drug development pathway. Suggested guidelines for the performance of translational trials involving

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veterinary patients have come, in large part, out of a meeting entitled “Translation of new cancer treatments from canine to human cancer patients”, sponsored by the National Cancer Institute in Bethesda, MD (June 2008) which was convened to discuss the potential value, opportunity, risks and rewards of an integrated and comparative drug and device development path for new cancer therapeutics that includes naturally occurring cancers in pet animals [91]. A summary of this meeting and subsequent discussion was recently published to provide clarity on the conduct of these studies and will contribute to the evaluation of this novel drug development opportunity [91]. The reader is referred to this review for a more indepth presentation of the suggested guidelines for companion species trials. Ethical considerations: Any trial to be undertaken in companion species must have the humane care of the companion patient as a priority. The ­appropriate use of an accredited institutional animal care and use committee (IACUC) is required and a well thought out informed consent and consenting process should be in place [94]. The scientific and translational motivation of the study must be balanced against the over-riding mandate for animal care. A data safety management function should be included similar to data safety management boards used in human clinical trials to provide protocol design oversight, evaluate progress and protect the data generated. Trial conduct: Clinical trials involving companion species should be designed to answer specific questions of translational interest with a high likelihood of informing future human trials. A priority should be placed on questions that cannot reasonably be fully answered in the context of conventional preclinical models or early human clinical trials. Specific endpoints should be identified and reviewed by persons with considerable background in both veterinary- and physician-based disciplines and trial design such that important preclinical questions can be effectively posed and answered. While GMP quality investigational agents may not be necessary, minimum standards of composition should be determined with respect to purity, sterility and quality. Similarly, trials should follow the spirit of Good Clinical Practice (GCP), which has been described for veterinary species through the VICH GCP and are summarized in Table 15.3 [95]. Table 15.3  Summary of attributes of the VICH GCP procedures and regulations relevant to the conduct of companion species trials Adequately developed study protocol with consent forms and consenting process Systems in place to manage protocol changes and modifications Adequate training of qualified participating investigators on study conduct including relevant standard operating procedures (SOPs) Facilities and institutional inspections necessary for study conduct Contemporaneous entry of data using paper based and/or web-based mechanisms Safety management system that includes monitoring and reporting of AEs and serious AEs to the IACUC, study coordinators, and if applicable, regulatory agencies System to verify the conduct and reporting of data within the study

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Adverse event reporting:  The reporting of adverse events (AE) encountered during trials of companion species has been of considerable concern among the biomedical and pharmaceutical community with regard to their impact on future or concurrent human clinical trials. A clear understanding of the standards for reporting AE data to regulatory authorities is needed and currently represents a concern within the field. While the majority of preclinical AE information comes from purpose-bred research animals, their assessment in tumor-bearing companion dogs should provide additionally valuable drug safety information. On the other hand, based on experience with over 30 human cytotoxic chemotherapeutic agents commonly used off-label to treat pet dogs, no adverse events have been identified in tumorbearing dogs that were not seen in purpose-bred research dogs (http://ccr.cancer. gov/resources/cop/). In the rare and unprecedented circumstance in which unexpected adverse events are defined in the conduct of a tumor-bearing dog study, it is reasonable that additional studies focused on any unexpected AEs should be conducted in either purpose-bred or tumor-bearing dogs before IND filing. Studies in companion species performed prior to Investigational New Drug (IND) applications should not require contemporaneous reporting of AEs to the regulatory authorities, rather they should be maintained as part of the legacy of the agent under development and included in a final study report as part of any IND application package should the agent progress through development. In companion species studies performed with novel human cancer agents in the post-IND setting, regulations already exist regarding AE reporting and are provided by IND Section 312.32 IND Safety Report [96]. Briefly, all serious and unexpected AEs must be reported within 15 days of development, whereas AEs that are either not serious or are expected, based on the protocol and informed consent, do not require expedited reporting. Of course, post hoc reporting of all AEs with attribution is provided with IND updates at the completion of the study. Because of the contemporaneous reporting requirement for post-IND agents, there exists some risk as to the impact AEs in companion species will have on ongoing human trials with the same agent. As discussed earlier, the risk of observing a unique and unexpected AE in tumor-bearing dogs that was not observed in purpose-bred laboratory dog toxicity trials is small [91]. Our current experience suggests that AE observations in companion species have not stopped ongoing human trials; rather, they have led to modifications in human trial design such as changing eligibility and exclusion criteria, additions to monitoring strategies or changes to informed consent.

15.7 Conclusions In summary, the inclusion of companion species with naturally occurring tumors provides significant opportunities for optimizing drug development pathways that other model systems cannot provide. Over the past decade, tremendous growth in the field of comparative oncology has occurred, including significant increases in

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organized consortium infrastructure, availability of investigational reagents and regulatory standardization. These advances are currently being applied to the ­development of novel cytotoxic, immunologic and biology-based anticancer therapies, innovative drug delivery systems, identification and validation of biological endpoints, noninvasive imaging techniques and surrogate markers critical to the design of Phase I and Phase II human clinical trials. The biotechnology and pharmaceutical industries recognize the utility of the model’s inclusion and several examples exist where they have initiated studies in companion species to assist in drug development. We are clearly at a period in time where the microscope is turned on this model and while currently a theory, the next 5 or 10 years should determine the degree to which information generated through the inclusion of companion animals with cancer is applicable to human cancer drug development.

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Part VI

Genetically Engineered Mouse Models of Cancer

Chapter 16

Genetically Engineered Mouse Models of Pancreatic Ductal Adenocarcinoma Aram F. Hezel and Nabeel Bardeesy

Abstract  Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer death in the United States. While the incidence of PDAC is low ­compared to that of the more common malignancies such as lung, breast, prostate, and colon cancer, it is the disease’s lethality – nearly all patients who develop the disease die from it – which makes it a significant health menace. PDAC is ­characterized by spread to other organs early in the course of disease and a general resistance to chemotherapy. Insight into the molecular pathogenesis of PDAC has come from analysis of the pathologic precursor lesions found adjacent to cancers in resected cases. The identification of KRAS, INK4A, P53, and SMAD4 mutations in these precursor lesions and advanced PDAC has provided the genetic framework for recent efforts to model the disease. The current models offer valuable tools for the study of PDAC and have been used to investigate critical signaling networks, stromal epithelial interactions, and potential cells of origin and early stages of disease [Hezel et al. Genes Dev. 20:1218–49, 2006]. In this chapter we review the development of genetically engineered mouse models (GEMMs) of PDAC and discuss how such models have given insight into disease biology and provided a foundation for preclinical testing. We also discuss emerging improvements in the PDAC models and how these will impact both basic and therapeutic research in the future. Keywords  Pancreatic cancer • Genetics • Pancreas • Mouse models

N. Bardeesy (*) Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, MA 02114, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_16, © Springer Science+Business Media, LLC 2011

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16.1 Pancreas Anatomy, Physiology, and Development The pancreas regulates protein and carbohydrate digestion and glucose homeostasis through both exocrine and endocrine compartments respectively. Acinar cells of the exocrine pancreas secrete digestive enzymes into a ductal network for transport to the GI tract. The endocrine pancreas regulates metabolism through the secretion of insulin and other hormones into the bloodstream and is organized into numerous highly vascularized islets embedded within the exocrine component (normal pancreatic histology is depicted in Fig. 16.1, left panel). In addition to PDAC, the most common and lethal type of tumor of the pancreas, a series of other types of pancreatic cancers occur in humans, including acinar carcinoma and various types of islet cell carcinoma (insulinoma, glucagonoma). Each of these tumor types has a characteristic histopathological progression and profile of oncogenic mutations and it is thought that these tumor types arise from the transformation of distinct cell populations within the pancreas. PDAC has ­generally been thought to arise from the pancreatic ductal cells based on their ­comparable histological features and on the development of PDAC precursor lesions within normal ducts – the issue of PDAC cell-of-origin will be discussed in greater detail further below.

Fig. 16.1  Histological progression of PDAC in genetically engineered mouse models. The histological progression of PDAC in the Pdx1-Cre (or P48-Cre) LSL-KrasG12D mouse model resembles that seen in the human disease. Left panel: normal wild type pancreas. The inset shows high power image of acinar issue (A), a duct (D), and an islet (I). Center panel: PanIN lesion. Activation of Kras promotes the formation of focal PanINs. Right panel: Invasive PDAC. The inactivation of p53 or Ink4a/Arf promotes the progression of PanIN to PDAC. Note that the tumor has invaded the duodenum (Duod.). Lower panel: IPMN. The combined activation of Kras and inactivation of Smad4 promotes cystic tumors resembling IPMN (or in some mouse strain ­backgrounds, resembling MCN). The cystic tumors progress to PDAC through inactivation of either p53 or Ink4/Arf

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All the exocrine and endocrine compartments as well as the ducts arise from a common endodermal progenitor population (reviewed in [1]). These multipotent pancreatic progenitor cells express the pancreatic homeobox transcription factors, Pdx1, and the helix-loop-helix transcription factor, p48/Ptf-1, both of which are required for pancreatic development. The differentiated pancreatic cell types arise from the Pdx1+ and p48+ progenitors through coordinated expression of lineage specific transcriptional factors. In later stages of development and in the adult, Pdx1 becomes restricted to the differentiated beta-islet cells but can be re-activated in ductal and acinar cells following pancreatic injury [2]. p48 is confined to the acinar cells in the adult. Both the p48 and Pdx1 promoters have provided useful tools to drive Cre recombinase for the coordinated mutation of floxed engineered alleles in PDAC modeling efforts discussed in detail below.

16.2 Histological and Molecular Characteristics of Human PDAC The ideal mouse PDAC models should incorporate the gene mutations associated with human PDAC and should recapitulate the histopathologic progression of the human disease. Three types of PDAC precursor lesions have been identified; pancreatic intraepithelial neoplasm, mucinous cystic neoplasm (MCN), and intraductal papillary mucinous neoplasm (IPMN) [3, 4]. Each of these precursors appears to arise in association with the normal pancreatic ducts and can undergo progressive stages of dysplasia leading to PDAC. PanIN (graded from stages I–III), the most common and well-characterized type of precursor lesion, are microscopic and cannot be detected by noninvasive methods [4, 5]. Analysis of autopsy specimens have shown that early stage PanIN are present in ~30% of elderly individuals [6]. Molecular profiling studies have identified a series of common genetic mutations that appear with increasing frequency in progressively higher grade PanINs [7–15]. Activating K-RAS mutations are observed in the earliest PanIN lesions and are present in nearly all PDAC [10, 16, 17]. The tumor suppressors INK4A, ARF, p53, and SMAD4 are all commonly inactivated in PDAC. INK4A, encoding the cyclin-dependent kinase 4/6 inhibitor, p16Ink4a, is inactivated by intragenic point mutation, deletion, or promoter hypermethylation in most advanced PanINs and in ~ 80 to 95% of PDAC [17, 18]. Germline INK4A mutations are also associated with a significantly increased risk of developing PDAC. The ARF tumor suppressor (encoding P14ARF (Alternative Reading Frame) which promotes p53 stabilization by antagonizing MDM2-mediated p53 proteolysis) shares common exons with INK4A (but translated in a different reading frame) and is lost in the ~40% of PDAC that sustain deletion of the INK4 locus. p53 is also inactivated in ~50% of PDAC due to missense mutations of the DNA-binding domain. Finally, SMAD4, a transcription factor that is critical for transforming growth factor beta (TGFb) signaling, is deleted or sustains intragenic point mutations in ~50% of PDAC [19]. p53 and SMAD4 inactivation have also been documented in late stage PanINs [11, 12, 14]. These data collectively support a model whereby activating KRAS mutations ­promote PanIN initiation and that progression of these lesions is facilitated by loss of the

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INK4A, P53, and SMAD4 tumor suppressors. These predictions have been validated in genetically engineered mouse models as discussed in detail below. The other types of PDAC precursors, MCN and IPMN, are large cystic tumors that can be detected radiographically. These tumors can either have a benign course or progress to PDAC; the probability of malignant progression is not certain, although it is estimated that ~30% of IPMN can progress to PDAC [3]. The mutational profiles of MCN and IPMN, and of the PDAC that arise from these cystic tumors, have not been fully defined, however, the existing evidence suggests that mutations in KRAS, INK4/ARF, p53, and SMAD4 also occur in these tumor types. Recurrent, but less prevalent mutations in a number of other tumor suppressors are observed in PDAC. Germline mutations in the familial breast and ovarian cancer genes, BRCA2, are associated with increased PDAC risk, although somatic BRCA2 mutations have not been reported in sporadic tumors [20]. Germline inactivating mutations in the BRCA2 interacting protein, PALB2, are also observed in a subset of familial PDAC cases [21]. The LKB1 tumor suppressor, encoding a serine-threonine kinase implicated in control of both energy metabolism and cell polarity, is mutationally inactivated in a subset of sporadic PDAC and familial PDAC cases [22] and in associated IPMN [23]. There are also a number of mutational events that disable the TGF-beta signaling pathway in PDAC. While SMAD4 mutation as described above the most frequent, recurrent inactivating mutations have been reported in the TGFbeta receptor type II receptor gene and in SMAD2 and SMAD3 (which encode transcriptional regulators that complex with SMAD4) [24]. In addition to the mutations in the well-characterized cancer-related genes ­discussed above, numerous other genetic alterations have been identified in PDAC. PDAC is characterized by centrosome abnormalities and high level of ­chromosomal aberrations and copy number alterations that may point to additional oncogenes/ tumor suppressors [25–35]. Genomic instability appears to occur relatively early in tumor progression and may reflect the capacity of shortened to promote chromosomal aberrations since shortened telomeres and anaphase bridging have been detected in low-grade PanINs [36]. Recent cancer genome sequencing efforts have identified recurrent mutations and copy number alterations in a series of genes whose functional roles in cancer are still under investigation [24]. Notably, while the individual genes are only altered in a small percentage of tumors, grouping them into oncogenic pathways suggests that a set of more than ten signaling ­pathways may be consistently targeted in PDAC. Genetically engineered mouse models should help in the functional analysis of novel candidate PDAC genes and in the study of telomere dynamics and genomic instability in tumor progression.

16.3 Modeling PDAC The development of mouse PDAC models has progressed from early efforts that employed transgenic expression of a series of viral or endogenous oncoproteins, to more recent systems that employed Cre-Lox technology to generate compound

16  Genetically Engineered Mouse Models of Pancreatic Ductal Adenocarcinoma Table 16.1  Transgenic pancreatic cancer models Gene/promoter Phenotype of mouse Transgenics with predominantly acinar phenotypes T-Ag/elastase Acinar cell carcinoma Hras/elastase Acinar cell carcinoma TGF-a/ elastase Acinar cell carcinoma Develop mixed acinar-ductal tumors on a p53+/− background TGF-a/Metallothionein Tubular metaplasia. Develop lesions resembling serous cystadenomas on Ink4a/Arf or p53 null background c-myc/elastase Mixed Acinar-ductal tumors KrasG12D/Mist1 Acinar cell carcinoma Transgenics using the RCAS TVA system c-myc/elastase Islet cell tumors in Ink4a/Arf null mice PyMT/elastase Mixed Acinar-ductal tumors in Ink4a/Arf null mice and p53 null mice Adapted from Hezel et al. [48]

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[43, 44]

[41] [45] [46] [47]

mutant mice with pancreas-specific activation or inactivation of genes specifically implicated in human PDAC. These different approaches targeted a spectrum of oncogenic mutations to selective cell lineages within the pancreas and resulted in numerous different phenotypic outputs. Collectively, these efforts provide a series of tools for studying pancreatic cancer and reveal information about the sensitivity of pancreatic cell types to malignant transformation and into the histopathologic phenotypes of the ensuing tumors (Table 16.1).

16.4 Transgenic Expression of Oncogenes in the Pancreas A number of transgenic models of exocrine pancreatic cancer have been developed. Acinar expression of SV40 large T antigen (T-Ag), activated H-RAS, or c-Myc under the control of the Elastase (Ela) promoter leads mainly to acinar cell carcinomas [37–40]. Mixed acinar–ductal tumors and cystic acinar tumor are induced in mice with p53 deficiency combined with acinar expression of the Epidermal Growth Factor Receptor ligand, TGF-a [42]. Acinar-targeted metallothioneinTGF-a (MT-TGF-a) transgenics with compound inactivating mutations in either p53 or Ink4a/Arf develop benign pancreatic ductal lesions resembling serous ­cystadenomas but do not develop carcinomas [43]. Finally, acinar expression of high levels of activated Kras (Elastase-KrasG12D mice) results in the development of preinvasive lesions with acinar and ductal features [49]. It is important to note that there may be some drawbacks in the use of elastase to drive transgene expression in these models since elastase is normally confined to differentiated acinar cells, and is not expressed in human PDAC. The fact that the tumor phenotype in these mouse models does not closely recapitulate the PanIN-to-PDAC sequence

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characteristic of the human disease may reflect a silencing of the elastase promoter and consequent loss of oncogene expression as acinar differentiation is lost; the neoplastic lesions in these models may be directed to maintain some acinar features to maintain oncogene expression. KrasG12D has also been targeted to the acinar cells by knocking in this mutant allele into the open reading frame of the acinar transcription factor, Mist1. These mice also develop mixed acinar/ductal tumors again, perhaps reflecting the silencing of acinar genes in ductal tumors [45]. Finally, it is notable that the expression of KrasG12D in the differentiated pancreatic ductal cells (cytokeratin-19-KrasG12V) did not lead to any neoplastic change in the pancreas [50]. Since cytokeratin 19 is expressed in all stages of PDAC progression, these results may indicate that differentiated ducts are not readily transformed by KrasG12V although it is possible that the absence of a PDAC prone phenotype reflected aspects of the design of these transgenic mice.

16.5 Viral Delivery of Oncogenes The avian retroviral transduction system, RCAS-TVA, has been used for the somatic activation of oncogene expression in the pancreas [51, 52]. In this system, transgenic expression of TVA, the receptor for the avian leukosis sarcoma virus subgroup A (ALSV-A), under the control of Elastase (Elastase-tva mice) allows somatic delivery of oncogene-expressing avian retroviruses to the acinar cells [46]. Infection of ­neonatal elastase-tva; Ink4a/Arf null mice with RCAS vectors expressing c-Myc or polyoma virus middle T antigen (PyMT) led to islet cell tumors or to tumors of mixed acinar and ductal features, respectively. Furthermore, a subset of the PyMT-transduced mice developed PanIN-like lesions. The diverse representation of carcinomas in these ­models may have reflected targeting of a progenitor cell population since elastase-tva is expressed more broadly in the neonatal pancreas compared with the acinar-specific expression in the adult. p53 deficiency accelerated the development of PyMT-induced ductal and acinar tumors and promoted metastasis [47]. Although RCAS-TVA system has not gained wide usage to date, and the existing models using this system have not produced tumors that closely resemble human PDAC, the system could provide a rapid context for screening novel candidate pancreatic cancer-relevant genes.

16.6 Compound Inducible Mutants As noted above, the Pdx1 and Ptf1-p48 promoters are expressed in the common progenitors of all pancreatic cell lineages [53, 54]. Given that these promoters have relatively restricted expression outside of the pancreas (Pdx1 is also expressed in the developing duodenum and stomach, and Ptf1-p48 is expressed in the cerebellum) the creation of Pdx1-Cre and Ptf1-p48-Cre deleter strains has allowed for targeted somatic mutations of engineered (floxed) alleles in the pancreas.

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An important tool for studying activated Kras in cancer models has been the generation of inducible knock-in- activated Kras alleles, designated LSL-KrasG12D and LSL-KrasG12ViresLacZ [55, 56]. These alleles are engineered to express mutant Kras (KrasG12D or KrasG12V) from the endogenous promoter and therefore at approximately physiological levels. The excision of a LoxP-flanked Stopper element via transgenic expression of Cre recombinase enables activation of the Kras knock-in allele in specific tissues (see Fig. 16.2). The activation of Kras from the endogenous locus recapitulates the acquisition of intragenic point mutations in evolving human cancers. Perhaps surprisingly, activation of LSL-KrasG12D in all pancreatic cells, from the earliest stages of pancreatic development using either Pdx1-Cre or p48-Cre, does not lead to any anomalies in pancreatic development. However, these mice progressively develop PanIN lesions starting in the first ~3 to 6 weeks of life [57, 58]. KrasG12D-mutants demonstrate a gradual progression of lesions resembling human PanINs-I–III (Fig. 16.1). These lesions develop against the backdrop of histologically normal pancreatic tissue despite the fact that all pancreatic cells harbor the activated KrasG12D allele. PDAC eventually develops in these mice although the latency is relatively long (>1 year). Together these observations indicate that Kras activation promotes the formation of PanIN lesions that can proceed to invasive PDAC, however, genetic/epigenetic alterations are required both to incite neoplasia and facilitate malignant progression. Preliminary analyses indicate that spontaneous deletion of Ink4a/Arf contributes to PDAC progression in this model. Pancreatic phenotypes have also been studied in a related knock-in KrasG12V-IRES-lacZ strain via crosses to a CMV-Cre transgene (which is broadly expressed in different tissues but has limited pancreatic ­expression). These mutant mice develop PanIN but only when crossed to mice that also express a Cdk4 mutant (R24C) that is refractory to Ink4a inhibition [55]. The mild pancreatic phenotype of these mice in comparison to the Pdx1-Cre KrasG12D mice may be due to the activation of Kras in fewer pancreatic cells or differences in the engineered KrasG12D knock-in based strategy PDX or p48 promoter

Cre

CRE

STOP

STOP

Ink4a/Arf

KRASG12D

p53

KRASG12D-IRES-LacZ

Smad4 Tgfbr2 STOP

Fig. 16.2  Cre/Lox based models relying on activated Kras alleles

p53R273H

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Kras alleles (e.g., There may be differences in signaling activity between the KrasG12D and KrasG12V alleles or the replacement of the endogenous Kras 3-UTR with iresLacZ could affect expression levels). Irrespective of the different penetrance of neoplasia in these models, the data collectively show that expression of endogenous levels of activated Kras throughout the pancreas leads specifically to PanINs capable of progressing to PDAC. Importantly, there is no evidence of islet cell or acinar cell cancers in these mice. These findings contrast with the development of acinar or mixed acinar/ductal tumors in mice that over-express Kras under acinar-specific promoters.

16.6.1 Cooperation Between Kras and Tumor Suppressors in PDAC The p53 and Ink4a/Arf tumor suppressor genes have been evaluated for their roles in pancreatic development and homeostasis (Figs. 16.1 and 16.2). Inactivation of any of these tumor suppressors alone does not cause any defects in pancreatic development or promote tumorigenesis. However, when these genes are inactivated in combination with Kras activation there is a significant acceleration of PDAC formation, with each model showing differences in the precursor lesions, tumor latency, and resulting histologic phenotype. Pdx1-Cre LSL-Kras mice, with homozygous or heterozygous conditional Ink4a/Arf allele are born normally, but develop rapidly progressive PanIN and succumb to invasive PDAC with greatly decreased latency in comparison to animals with wild type Ink4a/Arf [57, 59]. Inactivation of Ink4a alone also accelerates Kras-driven PDAC but the effects are modest compared to dual Ink4a/Arf inactivation. Rapid PDAC progression also occurs when expression of the KrasG12D allele is combined with inactivation of p53, either through the expression of a mutant p53 knock-in allele (p53R273H) or deletion of a conditional p53 null allele [59, 60]. Together, these data show that p53 and Ink4a/Arf restrain the malignant progression of PanIN to PDAC while not playing a significant role in the initiation of PanIN lesions (Table 16.2). Most importantly, these models appear to accurately recapitulate many histopathologic and clinical features of the human disease. In addition to their evolution from the characteristic PanIN-PDAC progression sequence, the tumors have a similar immunophenotypic profile to human PDAC, including expression of specific pancreatic lineage markers (absence of islet cell markers, e.g., insulin, and acinar markers, e.g., amylase, and expression of ductal markers, e.g., cytokeratin-19), expression of distinct mucins, and activation of a series of relevant developmental/ oncogenic signaling pathways (e.g., Notch, Hedgehog, and EGFR). In terms of other pathologic and clinical features, the tumors show locally invasive growth, as well as regional and distal metastasis, and provoke the formation of a dense stroma (desmoplasia). Hence, these models appear to be well suited for studying many aspects of PDAC biology. The rest of this article will focus on the refinement, study, and translational application of these Kras-driven models.

16  Genetically Engineered Mouse Models of Pancreatic Ductal Adenocarcinoma Table 16.2  Activated Kras knock-in engineered PDAC models Alleles Phenotype of mouse KrasG12DPdx1-Cre Spectrum of PanINs and some mice develop PDAC with long latency Average latency ~12 weeks with micrometastatic KrasG12DPdx1-Cre disease Ink4a/Arf−/− KrasG12DPdx1-Cre Average latency ~4 months with gross metastatic disease. LOH of WT p53 allele in p53+/− model, p53R273Hor p53+/− sporadic loss of Ink4a by methylation or deletion KrasG12DPdx1-Cre Average latency = ~5 to 6 months Ink4a/Arf+/− Gross metastatic disease and LOH of WT Ink4a/Arf allele KrasG12DPdx1-Cre LOH of WT p53 allele and loss of Ink4a expression p53+/−Ink4a+/− KrasG12DPdx1-Cre PDAC arising in setting of IPMN or MCN Smad4−/− KrasG12DPdx1-Cre PDAC TGFbr2−/− KrasG12DPdx1-Cre Latency of ~9 to 12 weeks, significantly accelerated compared with KrasG12DPdx1-CreSmad4−/− model Smad4−/−Ink4a+/− G12V PDAC only observed when Kras activation occurs in Kras -IRESlacZ setting of inflammation Ela-CreER

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[59]

[59] [61–63] [64] [61] [65]

The different models have some distinct features that will influence their utility for various applications. Mice with homozygous mutations of either Ink4a/Arf or p53 develop multifocal PDAC with very short latency (<10 weeks) and do not commonly show gross metastasis probably due to the rapid progression of the primary tumors. These features may make these models less suitable for preclinical therapeutic studies than the models with p53 heterozygosity (of the null or R273H allele) which have more gradual progression and exhibit single tumor foci. The R273H model may be particularly suitable for studying metastasis as recent studies have defined a pathway whereby this mutant p53 allele can promote invasive growth by interfering with p63 function [66]. The mouse models also exhibit some histologic variation. Human PDAC ­characteristically have a ductal histological phenotype (with a range between welland poorly differentiated features). The mouse models described above all recapitulate these histological features, although tumors with histological variants are also observed. Undifferentiated sarcomatoid histology, an uncommon histological ­variant of human pancreatic cancer, is observed in a subset of Kras-Ink4a/Arf mice, and less frequently in Kras-p53 mice. Anaplastic histology (pleomorphic tumor cells, with significant nuclear irregularities) is also a variant observed in a subset of Kras-p53 and Kras-Ink4a/Arf mice. The final highly recurrent mutation in human PDAC is inactivation of Smad4. In mouse models, homozygous inactivation of Smad4 in the pancreas does not lead to development defects or tumor predisposition, but synergistically promotes PDAC development in combination with Kras activation [61–63]. Notably, the course of

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the tumorigenesis is distinct from the models described above. In particular the Kras-Smad4 mutants develop cystic tumors resembling either IPMN or MCN (Fig. 16.1). These tumors can progress to PDAC although with longer latency than the Kras-Ink4/Arf or Kras-p53 models. The PDAC that emerges appears to have sustained spontaneous Ink4a/Arf deletion, and breeding these mice onto a conditional heterozygous Ink4a/Arf background significantly accelerates progression to PDAC without influencing the IPMN phenotype. Mutation of the Type 2 TGF-beta receptor in cooperation with KrasG12D also leads to PDAC; in this model the tumors arise from progressive PanIN lesions rather than IPMN or MCN [64]. It is notable that the PDAC in the Kras-TGFRII model arise more rapidly than those in Kras-Smad4 mice (although the differences in mouse strain backgrounds in these models prevent definitive comparisons). The different phenotypes between the Smad4 and TGFRII mutant models were somewhat unanticipated since Smad4 is a central effecter of TGFbeta signaling. The differences in phenotype are likely due to the capacity of Smad4 to integrate signals from other pathways (activins, BMPs) and for TGFRII to signal independently of Smad4 [67]. In both model ­systems, the PDAC predominantly show well or moderately differentiated ductal histology, which may suggest that active TGFbeta signaling contributes to the loss of epithelial features (i.e., epithelial-to-mesenchymal transition) in PDAC.

16.6.2 Preclinical Studies In addition to their value for studying the role of individual gene mutations in PDAC pathophysiology, the models described above provide value systems for relevant preclinical studies. For example, these mouse models hold considerable promise for testing novel therapeutics, serving as potential intermediate or complimentary system between in vitro and xenograft studies and human clinical trials. Multiple approaches have been employed with varying relevance to human cancer therapeutics. Recent studies have tested the impact of a variety of agents including gamma-secretase inhibitors, COX2 inhibitors, and Hedgehog inhibitors on PanIN progression to PDAC [68–71]. In these studies treatment was initiated at defined time-points to cohorts of animals evaluated how drugs affect the onset and ­incidence of PDAC. Thus, they have provided useful information regarding the pathways that regulate PDAC initiation. It is more of a challenge to address whether a therapeutic approach has an impact on established PDAC – a situation more akin to what occurs clinically. A number of imaging methods have been shown to be effective in detecting PDAC in mouse models, including MRI, FDG-PET, and ultrasound, enabling monitoring responsiveness in the setting of advanced disease (see Fig. 16.3). In this context an intriguing recent finding is that PDAC may be refractory to treatment at least in part due to the pronounced desmoplasia associated with these tumors. In this study, it was shown that there is very poor uptake of gemcitabine in the engineered mouse models [70]. Correspondingly, there is generally a limited clinical response to ­gemcitabine.

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Fig. 16.3  Detection of murine PDAC with MRI. Gadolinium enhanced MRI imaging may be used to detect focal tumors in the Pdx1-Cre LSL-KrasG12D model. Both axial and coronal images are shown here. S and L denote stomach and liver, respectively

Previous studies have provided evidence that stromal proliferation in PDAC is regulated by the Hedgehog signaling pathway. Notably, treatment with inhibitors of Smoothened, a key mediator of Hh signaling reduced PDAC stroma, resulting in increased gemcitabine uptake, transient tumor regression, and increased survival in the GEMM. Since PDAC xenografts were highly responsive to gemcitabine treatment alone and did not exhibit extensive stromal tissue, it appears that GEMM may be more a more relevant model system for predicting clinical responses. Another translational application of these models is in the development of novel approaches for early detection of PDAC. This is particularly important challenge in this disease as most patients have locally advanced or metastatic disease at diagnosis and therefore are not eligible for potentially curative resection. While the identification of serum biomarkers for early PDAC detection is currently an area of significant investigation, there is significant difficulty in obtaining suitable human specimens. The mouse models could facilitate serum biomarker discovery given their predictable course of tumor progression and the capacity to generate tumors with defined genetic alterations and on inbred backgrounds. One study used identified a high-throughput mass spectrometric method, surfaced enhanced laser desorption ionization time-of-flight (SELDI-TOF), to identify a consistent signature of altered peptides in serum from mice harboring PanIN compared to serum from wild type mice [58, 72]. Another study employed protein fractionation approach coupled to mass spectrometry analysis to identify specific proteins overexpressed in mice harboring advanced PanIN or focal PDAC [73]. This approach led to the identification of novel serum biomarkers that are also upregulated in PDAC patients, and therefore may help in the development of early detection tests. In addition to serum biomarkers, the development of imaging probes for ­incipient PDAC could also enable earlier detection. The mouse models have also provided a useful system for the identification of new targets for molecular imaging of PDAC.

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In order to develop novel images probes and identify cell surface proteins that are specifically upregulated in locally advanced PDAC, early PDAC cell lines were generated from the Kras-p53 mouse model and used in subtractive phage display peptide screens using normal mouse pancreatic ductal cells in the subtraction step [74]. These screens resulted in the identification of plectin-1 as a marker specifically upregulated on the cell surface of PDAC cells. When the peptides that target plectin-1 were conjugated to magnetofluorescent nanoparticles, it was found that PDAC in GEM models and human xenograft specimens could be effectively detected in  vivo. This novel imaging probe and the discovery of plectin-1 as a novel biomarker, may have clinical utility in the diagnosis and management of PDAC in humans.

16.7 Ongoing and Future Modeling Efforts More recent modeling efforts have led to refinements in our understanding of PDAC biology and have provided a range of tractable new systems. One series of studies has begun to address (a) whether acute activation of Kras in the adult mouse pancreas can give rise to PDAC and (b) which specific cell types in the pancreas are susceptible to Kras-mediated transformation into PDAC. The pdx-1 and p48 Cre stains used initially in creating PDAC models target all cells within the pancreas. In order to answer these questions additional Cre strains utilizing specific promoters with activity in defined pancreatic subcellular compartments have been used to activate Kras. The Nestin-Cre transgene is active in acinar progenitors and, less efficiently, in duct cells, but not in islet cells or centroacinar cells. Nestin-Cre LSL-Kras animals develop a PanIN phenotype that resembles that seen in Pdx1-Cre LSL-Kras mice [75]. Adult nonductal cells have also been shown to be susceptible to Kras-mediated transformation to PanIN lesions. Specifically, activation of the Kras-G12D allele in adult acinar cells or acinar and centroacinar cells using the Mist1-CreERt and Elastase-CreERt strains, respectively, led to the rapid development of PanINs with comparable features to those seen in the Pdx1-Cre Kras model [76, 77]. Slightly different interpretations were drawn from experiments with the KrasG12V-IresLacZ allele. When expressed in the acinar cells and centroacinar cells during late embryonic development or at early postnatal time points, this allele results in rapid development of PanIN that progress to PDAC by 1 year of age [65]. On the other hand, activation of this allele in the adult pancreatic acinar and ­centroacinar cells failed to result in PanINs unless coupled with pancreatic injury induced by cerulein. While differences in genetic background, recombination ­frequency, specific Kras mutation (G12D vs. G12V), and method of Cre activation (spontaneous, doxycylcine-regulated, or Tamoxifen-induced), prevent direct ­comparisons of these models, some broad themes appear to emerge. In particular, the data indicate that the adult acinar cells, and possibly centroacinar cells, can give rise to PanIN that presumably are capable of progression to PDAC. In addition, pancreatic injury and inflammation can enhance PDAC progression. This idea is also supported by epidemiologic studies that link pancreatic injury and ­inflammation

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with risk of cancer. Overall, it is clear that there is considerable developmental plasticity since acinar cells can be programed to take on an endocrine character in vivo and convert to a ductal phonotype in vitro, and acinar cells can de-differentiate to a duct-like state in vitro and in vivo [78]. Finally, there is evidence that progenitorlike cells can be induced in the pancreas following pancreatic injury [79]. Future studies using additional lineage-specific Cre strains will be needed to determine whether other pancreatic cell types (e.g., ductal cells, centroacinar cells, differentiated endocrine cells, and endocrine precursors) can give rise to PDAC. There are a number of areas where refinements of the mouse models would be advantageous for gaining further insights into PDAC pathogenesis and for having improved systems for preclinical studies. In terms of understanding the molecular pathogenesis of PDAC, refinements of the existing mouse models could enable an improved understanding of the mechanisms regulating multistage progression of PanIN to PDAC. The existing models either undergo gradual PanIN progression through acquiring spontaneous molecular alterations such as Ink4a/Arf deletion, or have simultaneous activation of Kras and tumor suppressor inactivation. A system whereby Kras activation and TSG inactivation could be temporally controlled would provide a valuable context to study the molecular and cellular alterations by which TSG loss facilitates progression. The combined use of the Cre-Lox and FlpFrt recombination systems could be suitable for this application. Inducible transgenic systems have given insights into the role-specific oncogenes in tumor maintenance in other cancers and could be useful in studies of PDAC. The capacity to turn off the expression of these oncogenes at any stage during tumor progression enables an assessment of the biological roles of the oncogene in evolving tumors and to validate whether tumorigenesis requires sustained expression of these genes [80]. In PDAC, it would be of particular importance to assess the requirement of activated Kras for tumor maintenance in vivo. Another area of refinement could be in the development of models in which tumorigenesis occurs focally in the backdrop of genetically wild type pancreas. In the current models, Kras activation and TSG loss is induced either in all cells of the pancreas, or in subsets of acinar cells. In either case, tumorigenesis appears to be multifocal and there is also a very large population of cells at varying decrease of tumor progression as well as in the histologically normal pancreas that harbor the mutant alleles. A model in which mutations are confined to a much smaller fraction of pancreatic cells could provide an improved context for therapeutic studies. In lung cancer, the use of adenoviruses that express Cre recombinase have proven to be effective in inducing tumorigenesis more focally and to be useful for testing therapeutic compounds [81–83]. Such an approach should also be feasible in the studies of PDAC. An additional area of interest for both biological studies and preclinical studies is in developing genomically unstable models of PDAC. As noted above, human PDAC is characterized by extensive genomic instability that may be facilitated by progressive telomere shortening early in tumor progression. Mouse PDAC shows some chromosomal aberrations; however, the degree of such alterations appears to be somewhat less than that observed in human PDAC [59, 60]. Telomeres in the

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normal mouse are much longer than in humans and do not exhibit significant shortening. Crossing of mice with mutations in components of the telomerase enzyme with the PDAC models could provide systems to study genomic instability in the mouse and thereby give insights into how such instability contributes to tumor progression and response to therapeutic agents [84]. These systems may also provide relevant models for cancer gene discovery.

16.8 Conclusions The utility of GEMMs in therapeutic applications and preclinical testing of promising drugs and biologic agents is an emerging field. The shortcomings of studies relying on cell lines is evident by the poor clinical track records of many drugs which initially appeared efficacious. It is clear there are significant advantages to these models over xenografted human cell lines namely, that the tumors occur in the correct cellular context and microenvironment and these models offer control over genetic context [85, 86]. On the other hand, GEMMs also have limitations, which must be recognized. Given the creation of the many models with differing underlying genetics and associated varying degrees of histological cellular atypia and resemblance to human PDAC, a consensus on the pathologic interpretation of these models is emerging [87]. This is critical to the integration of these models into preclinical drug evaluation. Further refinement in these models allowing pinpointed evaluations of drugs in focal lesions at differing stages of the disease from early precursor lesions through the growth of metastatic deposits and across a range of genetic settings will be invaluable. Neither human cell line-based models nor GEMMS will likely be adequate in cancer drug evaluation by themselves, but will be needed to be used in concert to guide clinical development. Ultimately, the success of engineered mouse models in pancreatic cancer therapeutics will be determined by the coming results of clinical trials, now in the early stages, whose design and implementation were guided by such models. Acknowledgments  Aram Hezel is supported by an NCI K08 CDA and HHMI early career award. Nabeel Bardeesy is supported by grants from the NIH and by the AACR-PanCAN award.

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Chapter 17

Transgenic Adenocarcinoma of the Mouse Prostate: A Validated Model for the Identification and Characterization of Molecular Targets and The Evaluation of Therapeutic Agents Sharon D. Morgenbesser Abstract  Prostate cancer remains a serious health issue worldwide. Genetically engineered mouse models have been utilized extensively to better understand human prostate cancer at the genetic and cellular levels, to identify new diagnostic markers, therapeutic targets, and biomarkers, and to evaluate the efficacy of potential therapeutic agents. Of these, the transgenic adenocarcinoma of the mouse prostate (TRAMP) model is extremely well characterized, highly regarded as a model for human prostate cancer, and the most frequently used. This chapter will review the histological and molecular characterization of pathologic progression in TRAMP, and focus on its use to identify genes that regulate multiple transition points in tumor development, to understand the contributions of the androgen-pathway and DNA methylation as regulators of gene expression, to explore the role of numerous proteins in prostate tumorigenesis, and to evaluate a wide variety of anti-tumor agents. The influence of murine genetic background on disease progression, and on gene expression and function, and the utilization of these differences to model the genetic heterogeneity that exists in the human disease are also discussed. Keywords  Prostate cancer • Androgen • Androgen receptor • Castration-resistant • Gene expression profiling • DNA methylation • Therapeutic • Preclinical trials

17.1 Introduction Prostate cancer (PC) continues to be a major problem affecting men world wide. In 2008 in the US, an estimated 186,320 men were diagnosed with the disease, and approximately 28,660 died as a result [1]. Detection and treatment are generally successful when tumors are confined to the prostate and their growth and survival S.D. Morgenbesser (*) Genzyme Corporation, 49 New York Avenue, Framington, MA 01701, USA e-mail: [email protected]

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are androgen-dependent (AD), as radical prostatectomy is generally very effective at early stages. If there is evidence of new tumor growth, or PC is initially detected at an advanced stage, androgen-deprivation therapy (ADT), which involves surgical or chemical castration, is employed and prostate tumors initially regress [2, 3]. However, within 2–3 years, castration-resistant (CR) tumors emerge in most patients, and CR and metastatic prostate tumors are associated with high mortality rates [2]. Therefore, there remains an urgent need for better diagnostic markers, as monitoring prostate-specific antigen (PSA) levels has limited benefit [4, 5], as well as additional therapeutic targets and more effective therapies. For many years, clinical material in the form of established cell lines, tissues propagated in immunodeficient mice (xenografts), and frozen or fixed tumor specimens had been the sources for interrogation. While they have great utility, they are limited by a high degree of genetic heterogeneity, artifacts commonly associated with cell lines that are serially passaged by growing in culture or implanted outside of the prostate microenvironment, and the difficulty of obtaining primary tumor tissues from patients before treatment, as well as in procuring early prostatic intraepithelial neoplasia (PIN) lesions, CR and metastatic tumors, and suitable normal control tissues. To overcome these limitations, nearly 30 genetically engineered mouse models (GEMMs) of PC have been generated by either enforced expression of oncogenes or inactivation of tumor suppressor genes in the mouse prostate [6]. Although these models display many features commonly found in clinical disease, there are some inherent limitations to their utility as the mouse and human prostates possess some differences. For example, the human organ comprises contiguous ­tissue that can be phenotypically resolved to the central, transition, and peripheral zones while the mouse prostate develops as pairs of ventral, dorsal lateral, and anterior lobes. In addition, mice do not carry Kal3, the gene that encodes PSA. Nevertheless, many of the models develop spontaneous and progressive ­autochthonous PC and some display metastatic and CR disease. While each model possesses strengths and weaknesses, the mice known as ­transgenic adenocarcinoma of the mouse prostate (TRAMP) are extremely wellcharacterized histologically and molecularly, and as such these mice are highly regarded as a model for human disease and the most widely used. In particular, TRAMP is one of the few GEMMS to give rise to lymphatic and hematologic ­metastatic disease with reproducible evidence of bone lesions [7, 8]. Moreover, disease progression in TRAMP occurs within a reasonable time frame for experimental research. Three epithelial cell lines derived from TRAMP have also been established [9] and validated as useful models in in vitro assays [10] and in vivo as grafts [11, 12]. This chapter will review the pathologic characterization of the model, and explore its use for the identification and validation of genes and ­pathways involved in prostate tumorigenesis, which may provide useful diagnostic markers and therapeutic targets, and for use as a platform for testing known and potential therapeutic agents and identifying clinically relevant biomarkers. Though studies that employed the TRAMP cell lines have been informative, only those utilizing the authochthonous mouse model are discussed.

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17.2 Generation of the TRAMP Model and Phenotypic Characterization The TRAMP model was generated by utilizing the minimal regulatory elements of the androgen-regulated rat probasin promoter [13] to enforce prostate-specific expression of the potent SV40 T and t oncoproteins in C57BL/6 inbred mice [14]. The large T can inactivate the tumor suppressor proteins pRb and p53 [15], which are deficient in many human PCs [16, 17]. In addition, the large T can also inhibit Bub1 [18], which is a spindle assembly checkpoint protein, and the little t negatively regulates protein phosphatase 2A, a tumor suppressor [19]. Expression of the transgene initiates between 4 and 6 weeks of age and tumors arise spontaneously in the prostatic epithelial compartment with a short latency. TRAMP C57BL/6 mice have been mated to FVB mice, and comparisons demonstrate that the TRAMP phenotype in C57BL/6 mice is more mild than in [C57BL/6 × FVB]F1 mice [8], suggesting the influence of genetic modifiers and indicating that these mice can model the diversity that exists within the human disease. In the [C57BL/6 × FVB] F1 mice, PIN lesions are evident by 8–12 weeks of age, well differentiated (WD) carcinomas appear by 18 weeks, and poorly differentiated (PD) tumors emerge by 18–24 weeks [20]. Metastatic disease can be observed as early as 12 weeks with lesions primarily in the lymph nodes (LN), but also in lung, kidney, and adrenal gland; bone metastases have only been observed in TRAMP mice on the mixed background [7, 8]. TRAMP [C57BL/6 × FVB]F1 and C57BL/6 mice succumb to the disease by 33 and 36–40 weeks, respectively [8, 20, 21]. TRAMP tumor development is initially AD as tumors in males castrated at early stages (e.g., when PIN lesions have appeared) initially undergo massive apoptosis and regress, but CR primary and metastatic tumors eventually emerge in most mice several weeks later [21, 22]. A significant effort has been devoted towards defining the molecular mechanisms leading to CR growth in human PC, particularly in the context of ADT, including analysis of the expression, mutational status, and activity of the androgen receptor (AR), as well as its various co-factors [2]. Upon binding androgens, the AR assumes an active conformation capable of binding to specific response elements in the promoter regions of androgen-responsive genes, which either enhances or represses their transcription. Mutations have been observed in the AR of clinical samples that are associated with enhanced activities, such as the ability to bind other ligands, which may confer tumor growth after ADT. In TRAMP, both transformed epithelial cells and nascent stromal cells express the AR [8], and naturally occurring, somatic AR mutations have been identified in prostate tumors from intact and castrated TRAMP [C57BL/6 × FVB]F1 mice [23]. Notably, all the mutations from the tumors of intact mice were in the ligand-binding domain (LBD), whereas the vast majority of those from castrated mice were derived from the transactivation domain, indicating that ADT can affect the particular type of alterations. Many of these mutations were similar to those observed in man, and functional analyses revealed their enhanced activities. In fact, enforced expression of one variant, AR-E231G, was subsequently shown to be capable of causing PC in

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a GEMM [24]. This phenotype was likely not simply related to over expressing AR since enforced expression of wild-type AR or another mutation in the LBD commonly observed in human cancer cell lines were unable to cause tumors in GEMMs. Clearly, the emergence of CR prostate tumor growth and the acquisition of these AR mutations demonstrate that TRAMP is highly suitable for further investigation into this aspect of human PC. Human prostate tumor progression is enhanced by a reactive stroma that ­provides various extracellular matrix proteins and growth factors that activate key signaling pathways within the epithelial cells [25]. The stromal compartment is nominally AR-positive, and primarily includes fibroblasts, as well as myofibroblasts and smooth muscle cells. In the tumor microenvironment, fibroblasts often increase in number and are genetically unstable. In TRAMP, tumor-associated stromal cells similarly express AR [8], and about 30% are aneuploid and have ­multiple centrosomes [26]. A stromal response in TRAMP is further indicated by a thickening and remodeling of the stromal compartment in PIN lesions, followed by additional thickening and remodeling with hypercellularity in AD and CR tumors [8, 21, 27]. Human prostate tumorigenesis relies on angiogenesis for a sufficient blood ­supply to enable growth of tumors beyond 1 mm3, with signals provided by both epithelial and stromal cells for the formation of new blood vessels from endothelial cells (ECs) [28]. Tumors in TRAMP [C57BL/6 × FVB]F1 mice have a much higher microvessel density (MVD) relative to those in TRAMP C57BL/6 mice, an observation that correlates with their differences in disease progression [8]. The process of angiogenesis has been particularly well-characterized in the TRAMP [C57BL/6 × FVB]F1 mice [29, 30] and demonstrates high similarity to that observed in clinical PC. New vessels first appear in the stromal space in low grade PIN, are more numerous and reside consistently with the infolded mesenchyme adjacent to the epithelium in high grade PIN, and become increasingly more scattered, disorganized, and abundant in both regions as tumors progress through the WD, PD, and CR tumor stages [29]. While pericytes are tightly associated with vessels at the earlier stages, these cells becomes more numerous in WD tumors, and are loosely associated and abnormally shaped in PD lesions [30]. At the molecular level, expression of the pro-angiogenic factors VEGF (vascular endothelial growth factor) and FGF2 (fibroblast growth factor 2) and their cognate receptors arise during the vascularization process. FGF2 and VEGF receptors (VEGFR) are primarily expressed on ECs, suggesting involvement of both axes during transformation. VEGFR1 protein expression coincides with the initiation of the early angiogenic increase in high-grade PIN, whereas VEGF and VEGFR2 protein expression are detected once PD tumors emerge [29]. FGF2 is expressed in PIN lesions and more advanced tumors, FGFRiiib is expressed in the neovasculature in these tumors, and FGFR2iic is elevated in the epithelial cells within PIN [31]. In the prostate, neuroendocrine (NE) cells arise from a common epithelial stem cell, but differentiate into AR-negative cells to influence the growth and differentiation the prostate [32, 33]. The density of NE foci is much higher in PC than in normal prostate tissue, particularly in patients who have undergone ADT, and this NE density correlates with tumor grade, rapid tumor growth, increased angiogenesis,

17  Transgenic Adenocarcinoma of the Mouse Prostate

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loss of androgen sensitivity, and more importantly, a poor prognosis. Within the tumor environment, and particularly in the context of ADT, the expansion of the NE compartment may be driven by differentiation of prostate tumor stem cells and/or by a transdifferentiation of neoplastic epithelial cells. Similarly, NE cells were observed in TRAMP prostate tumors when this model was initially being characterized [20]. Recent studies have examined the precise timing and mechanism of the emergence of these cells. Huss et al. show that in TRAMP [C57BL/6 × FVB]F1 mice with PD tumors, the NE cells derive from malignant progenitor cells as a rare, stochastic, late event [34]. In contrast, Chiaverotti et  al. studied the TRAMP ­phenotype on the C57BL/6 and FVB backgrounds and concluded that NE carcinomas arose from progenitor cells at an early stage of tumorigenesis to constitute the primary type of lethal malignancy that emerges in these mice [35]. These somewhat contradictory findings likely reflect the different mouse strains used and the subjectivity associated with tumor classification as well as other differences. However, both studies underscore that, like the analysis of NE features in human PC, ­additional investigation is warranted and that multiple molecular mechanisms may drive this phenotype.

17.3 Gene Expression Profiling Studies The expression of approximately 24 transcripts and proteins of particular interest in human PC had been studied and contributed to the validation of the model at the molecular level, as the expression patterns were generally similar to those observed in the human disease. The expressed genes studied include E-cadherin [20], insulin growth factor (IGF) and FGF ligands, their receptors, and binding partners [31, 36, 37], cyclins and cyclin-dependent kinases [38], prostate stem cell antigen (PSCA) [39–41], the metastasis-associated protein S100A4 [42], the Aurora kinases [12], mouse telomerase reverse transcriptase (mTERT) [43], and glutathione S-transferases (GSTs) [44]. Gene expression profiling--> studies that can screen thousands of transcripts simultaneously have great utility for further validating GEMMs, as well as for the identification of novel diagnostic markers, and therapeutic targets and pathways. Recently, three comprehensive and complementary studies utilizing a variety of samples, profiling techniques, and bioinformatic analyses have identified hundreds of transcripts and dozens of pathways that are significantly modulated during disease progression in the TRAMP model. First, Morgenbesser et  al. [45] utilized several approaches to study [C57BL/6 TRAMP × FVB]F1 disease, beginning with Serial Analysis of Gene Expression (SAGE) [46] to compare expression profiles between AD and CR primary TRAMP tumors and normal prostate tissue from normal littermates; for each sample type, several specimens were pooled. SAGE is very quantitative and is an open system in that it provides an unbiased accounting of expression as it can detect all expressed transcripts and is not limited to those that have been cloned and presented on an array. This analysis identified several hundred transcripts that were significantly deregulated in all three pair-wise comparisons. The authors validated many of these mRNAs as

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differentially expressed and expanded the study by utilizing microarray hybridization to analyze the aforementioned specimens, as well as PIN lesions and LN metastases, on a commercial array capable of detecting 10,357 cloned mouse transcripts. Hierarchical clustering analysis of the microarray profiles demonstrated that CR primary tumors were highly similar to both AD and CR LN metastases, and 180 transcripts were identified that distinguished these late-stage tumors from the earlystage samples. The expression of eight transcripts was further validated by real-time PCR (rtPCR). To identify genetic pathways that were altered during tumor progression, Gene Set Enrichment Analysis [47] was employed. The early- and late-clusters were characterized by differences in genes involved with oxidative phosphorylation (e.g., ATP/H+ transporting subunits and cytochrome c oxidase subunits), and the cell cycle (e.g., cyclins and cyclin-dependent kinases). Comparisons between AD and CR tumors revealed alterations in inflammatory response genes. The normal prostate was distinguished from the primary AD and CR tumors by differences in oxidative phosphorylation as well as anabolic enzymes, including those that regulate arachidonic acid and prostaglandin metabolism, which promotes tumor cell proliferation and metastasis; this is particularly interesting as subsequent data supported the possibility that phospholipase A2 group IIA (PLA2G2A) is involved in the highly aggressive nature of late-stage prostate tumors, and with the more severe phenotype on the [C57BL/6 × FVB]F1 background. PLA2G2A promotes arachidonic acid release and metabolism [48, 49], and is over-expressed in late-stage, clinical PC [50, 51]. SAGE revealed and rtPCR confirmed that PLA2G2A was only expressed in the late-stage TRAMP tumors. Moreover, C57BL/6 mice have a naturally occurring, inactivating mutation in PLA2G2A52, and the authors showed that FVB mice have wild-type PLA2G2A alleles and that [C57BL/6 TRAMP × FVB]F1 tumors primarily expressed the normal FVB-derived transcript. A role for PLA2G2A in TRAMP tumorigenesis was further supported by the observation that annexins A3 and A4, which negatively regulate PLA2 enzymes [53], were downregulated in the advanced tumors. Subsequently, Haram et  al. [54] performed microarray hybridization on arrays presenting 34,000 murine genes, with RNA derived from eight tumors from C57BL/6 TRAMP mice or nine normal prostate tissues; the samples were studied individually. Several thousand genes were differentially expressed, and hierarchical clustering of the data segregated the samples into two groups consistent with their origin. To determine which biological processes were altered, the authors used the Database for Annotation, Visualization, and Integrated Discovery (DAVID) and identified 185 categories, most notably cell cycle; DNA replication, recombination, and repair; kinase regulation; and nucleotide metabolism. Ingenuity Pathway Analysis was used to place differentially expressed genes into functional categories, which identified many similar pathways as DAVID, but also other metabolic pathways as well as the aryl hydrocarbon receptor signaling pathway and chromatin assembly, segregation, and structural maintenance pathways. The expression of 25 genes was confirmed by rtPCR. Finally, the authors overlaid their expression ­profiles with similar data derived from human samples to identify common alterations, and they identified two mRNAs that were significant upregulated in PC in both species: Tubb2a, a b-tubulin subtype, and Sox4, the sex-determining region Y box 4 transcription factor.

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Most recently, Kela et al. [55] used laser capture microdissection to isolate normal and malignant epithelial cells from 28 [C57BL/6 TRAMP × FVB]F1 prostate samples, including low- and high-grade PIN, WD and PD tumors, and metastases. The mRNAs were hybridized to an array containing 45,035 murine probe sets. Transcripts with the highest variance were used to group the samples, all of which clustered according to their original identities except for the primary and metastatic tumors which grouped together. Additional analyses, including DAVID, were applied to identify functional clusters that characterized the histological subtypes. Low grade PIN were primarily defined by enhanced expression of cell-cycle and immune-related genes, high-grade PIN by an increase in apoptotic mRNAs, and the tumors by up-regulation of cell adhesion and cell cycle, and down-regulation of immune system and pro-apoptotic transcripts. To identify tumor aggressivenessassociated genes shared by mouse and man, seven human prostate array datasets were compared to their TRAMP expression profiles, and the analysis identified a common set of 933 genes that were differential between high-grade PIN and tumors. Further refinement of this gene list was achieved by identifying those transcripts that intersected with a list of 194 genes considered to represent the core expression of human PCs, which resulted in 64 genes that may be important in the acquisition of the aggressive phenotypes in both species. Of these, 27 were upregulated, with the most differential being Bub1, microtubule-associated protein tau, topoisomeraseII-a, Aurora kinase A, EZH2, and cell division cycle 6. Each of these studies identified many genes that that were previously observed to be differentially regulated in TRAMP, such as E-cadherin, cyclins, GSTs, and the Aurora kinases, and there were also many genes and pathways that were identified by more than one study, both of which indicate the validity of each analysis. A list of selected transcripts that were differential in at least two of these reports are presented in Table 17.1. There were also many transcripts that were only identified by one study, which may reflect differing methods for detecting transcripts (only one study used an open system) [45], algorithms for normalization, fold and statistical significance cutoffs, and pathway identification, as well as the tendency of arrays to underestimate differences in expression [56]. In addition, the samples possessed unique qualities related to their disease stage (e.g., only one study incorporated CR tumors) [45], celltype complexity (one study only used epithelial cells) [55], and genetic background (two studies utilized [C57BL/6 × FVB]F1 mice whereas one used C57BL/6). Therefore, transcripts identified by only one study are worthy of further exploration.

17.4 Epigenetic Regulation of Gene Expression In human and TRAMP PC, many mechanisms, including androgen-regulation, control gene expression. Alterations in DNA methylation also contribute to the etiology of PC, and simultaneously involve hypermethylation in the regulatory regions of some genes that affects their transcription, as well as widespread hypomethylation that can cause genomic instability. Hypermethylation usually occurs in

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Table 17.1  Selected differentially expressed transcripts in TRAMP identified by more than one gene expression profiling study Transcript name Transcript symbol References Transcripts that are downregulated during TRAMP tumorigenesis Beta 2 microglobulin B2M [45, 55] Clusterin CLU [45, 55] Defensin beta 1 DEFB1 [45, 55] E-cadherin ECAD; CDH1 [45, 55] Glutathione-S-transferase mu 1 GSTMU1 [45, 55] Glutathione-S-transferase mu 2 GSTMU2 [45, 55] Microseminoprotein MSMB; PSP94 [45, 55] Myosin, light polypeptide kinase MYLK [45, 55] pdz domain containing 1 PDZK1 [45, 54] Probasin PBSN [45, 54] Prostate secretory glycoprotein p12 SPINK3 [45, 55] Serine protease inhibitor kazal-type 5 SPINK5 [45, 55] Smooth muscle myosin heavy chain 11 MYH11 [45, 55] Spermine binding protein SBP [45, 55] Tnf receptor-associated factor 1 TRAF1 [45, 55] Transcripts that are upregulated during TRAMP tumorigenesis Antigen identified by monoclonal antibody Ki-67 Aurora kinase a Aurora kinase b Brain-abundant, membrane attached signal protein 1 Budding uninhibited by benzimidazoles 1 homolog Budding uninhibited by benzimidazoles 1 homolog, beta Cdc2-associated protein CKS2 Cell division cycle 25 homolog C Chromogranin A Cyclin A2 Cyclin B1 Cyclin E1 Dopa decarboxylase Hematological and neurological expressed sequence 1 Ligase I, DNA, ATP-dependent Microtubule-associated protein homolog, transcript variant 3 Minichromosome maintenance homolog 2 Minichromosome maintenance homolog 4 Minichromosome maintenance homolog 6 Mitotic arrest-deficient 2, homolog 1 mutS homolog 2 (E. coli) Nucleoporin 62 Polymerase, DNA, epsilon Proliferating cell nuclear antigen Rac GTPase-activating protein 1 Secretogranin III Sex determining region Y box 4 Stathmin protein (oncoprotein p18)

MKI67 AURKA AURKB BASP1; NAP22 BUB BUB1B CKS2 CDC25C CHGA CCNA2 CCNB1 CCNE1 DDC HN1 LIG1 TPX2 MCM2, mMCM2 MCM4 MCM6 MAD2L1 MSH2 NUP62 POLE PCNA RACGAP1 SCG3 SOX4 STMN1

[45, 55] [54, 55] [54, 55] [45, 55] [54, 55] [45, 54] [54, 55] [45, 54, 55] [45, 55] [45, 54, 55] [54, 55] [54, 55] [54, 55] [45, 54, 55] [45, 54] [54, 55] [45, 54, 55] [54, 55] [54, 55] [54, 55] [45, 55] [54, 55] [54, 55] [54, 55] [45, 54, 55] [45, 55] [54, 55] [45, 55] (continued)

17  Transgenic Adenocarcinoma of the Mouse Prostate Table 17.1  (continued) Transcript name

405

Transcript symbol

References

Survivin (apoptosis inhibitor 4) AIP4 [45, 55] Timeless TIM; TIM1 [54, 55] Topoisomerase (DNA) II alpha TOP2, TOP2A [45, 54, 55] Trophinin (melanoma antigen, family D, 3) TRO, MAGED3 [45, 55] Please see the individual references for additional details, including degree of differential expression

the promoter regions to silence or significantly down-regulate the transcription of some negative tumor regulators including E-cadherin [57, 58], but it can also reside at other locations and correlate with increased expression of some tumor suppressor transcripts, such as p16 [59]. DNA methylation is controlled by three DNA methyltransferase (DNMT) enzymes, most notably DNMT1 whose expression and activity is enhanced in human prostate tumors. The DNMT1 inhibitor 5-aza-2¢deoxycytidine (5-aza) can reverse promoter hypermethylation and result in decreased tumor formation in cell lines and animals. In TRAMP, the E-cadherin protein is expressed in PIN and WD tumors and is reduced or absent in PD tumors [20]; it is also downregulated at the transcriptional level (Table 17.1) [45, 55] suggesting that DNA hypermethylation of this site occurs in TRAMP tumors. Recently, the status and relevance of DNA methylation have been studied in TRAMP by two groups. Day and colleagues initially studied DNMT1 protein expression in the C57BL/6 model, and determined that it was expressed in PIN lesions, and primary and metastatic tumors but not in normal prostate tissues [60]. Treatment of TRAMP C57BL/6 mice with 5-aza from age 6 weeks until 24 weeks resulted in the cessation of tumor progression at the PIN stage and a significant survival benefit. The mechanism of action was confirmed to be reduced DNA hypermethylation. These data suggest that DNA hypermethylation of key tumor suppressor genes is relevant in this model, and can be reversed with a positive preclinical outcome. Karpf and co-workers studied expression of DNMTs and DNA methylation in [C57BL/6 × FVB]F1 mice, primarily in intact (non-castrated) mice [61, 62]. At the PIN stage, they observed an increase in expression of all three DNMT proteins and low level of hypermethylation at specific sites, while PD tumors were characterized by specific hypermethylation events at a high frequency, and hypermethylated loci were remarkably heterogenous in the metastatic samples [61, 62]; the latter was also observed in CR tumors from castrated mice [63]. DNMT activity was increased in primary and metastatic tumors [60]. Nineteen loci were identified as being ­hypermethylated, and several of these were outside of promoter regions, including one downstream of the p16 transcriptional start site [61] that coincides with p16 mRNA overexpression in TRAMP tumors relative to normal controls [45, 62]. These studies indicate that the TRAMP model is valid for studying mechanisms regulating DNA methylation, discovering hypermethylated loci that might serve as biomarkers [64], and evaluating therapies that act by reversing DNA hypermethylation in PC.

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17.5 Validating and Elucidating Gene Function Through Additional Genetic Engineering To determine the role of various genes of interest in prostate tumorigenesis, TRAMP mice have been mated to other genetically engineered mice that possess specific alterations, and these functional analyses have been very informative. For example, two groups have utilized TRAMP to address two very different, but key questions – one on genetic variation, and the other on spatial expression – regarding the role of the AR in the development of PC. Variations within the human AR gene, including length of the glutamine Q-tract in the N-terminus, have been associated with disease risk, though a precise correlation has not been established [65]. To determine whether the length of the Q-tract influences tumor progression, three novel lines of mice carrying humanized AR genes (h/mAR) with 12, 21, or 48 residue Q-tracts on a mixed B6:129/Sv background were mated with TRAMP C57BL/6 mice [66]. There were notable differences in the latency and survival, confirming that Q-tract length influences PC progression and suggesting the utility of the TRAMP model for further dissecting the influence of AR polymorphisms. As indicated earlier, the AR is expressed in epithelial luminal cells and stromal cells [8]. To study the influence of the AR during prostate tumorigenesis, TRAMP FVB mice were crossed with C57BL/6 mice harboring AR knockout (ARKO) alleles under the control of an inducible system (ind-ARKO) to abrogate AR either in both compartments or only in prostatic epithelial cells (pes-ARKO) [67, 68]; in particular, the ind-ARKO mice may provide an environment similar to that achieved during ADT. These studies suggested that the AR plays a proliferative role in the stroma and a tumor suppressive role in the epithelium, and illuminate the mechanisms of recurrent disease following ADT in man. In contrast, this approach has also been used to elucidate the relevance of a protein, PSCA, whose function is unknown [69]. PSCA is a cell surface, epithelialrestricted protein that is highly upregulated in most, but not all, human primary and metastatic tumors [70–73]. Mouse PSCA is expressed at high levels in some, but not all cells, in TRAMP C57BL/6 PIN lesions and WD tumors, and in most metastatic tumors; in some mice, PSCA expression could not be detected, and in others, surface expression was detected, but on a lower proportion of cells than on primary tumors [39–41]. When TRAMP C57BL/6 mice were mated to PSCA +/+, +/–, and –/– mice on a mixed 129/Sv background, the formation of primary tumors was unaffected by PSCA status, while metastatic tumors appeared much more frequently in the TRAMP/PSCA KO mice [69]. Examination of primary tumors identified a correlation between PSCA absence and increased cytoplasmic localization of aurora kinase B and survivin, which could confer elevated mitotic activity and promote their progression to later stages. These data demonstrate that reduced PSCA correlated with more advanced disease in TRAMP. While the functional role of PSCA in human and mouse prostate is undetermined, the differences in its expression in metastases between human samples and the TRAMP studies may reflect the influence of genetic background.

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TRAMP mice have been mated to other mice with targeted gene disruptions to determine the importance of several genes whose expression in TRAMP and/or function in other settings, suggests roles in regulating prostate tumorigenesis. These include mice that are deficient for the PTEN tumor suppressor gene [74], FGF2 [75], the IGF-1 regulator GHRH-R [76], hepatic IGF-1 [77], the AR-regulated SRC-1 and SRC-3 proteins [78, 79], the polyamine biosynthesis regulator arginase II [80], caveolin-1 (which regulates vesicle transport) [81], the aryl hydrocarbon receptor Ahr [82] (which is part of a pathway implicated in TRAMP tumorigenesis by array analysis) [54], and cox-2 (which regulates the conversion of arachidonic acid to prostaglandins [83], an important pathway also implicated by differential gene expression profiling) [45].

17.6 Testing of Known and Putative Therapeutic Agents TRAMP mice have been used extensively to evaluate the efficacy of dozens of androgen regulators, cytotoxic agents, targeted therapies, immunotherapies, and dietary supplements, either as single agents or in combination with other therapies. Some examples of each will be discussed, and these and many others are also summarized in Table 17.2. The pathologic progression of tumorigenesis in TRAMP facilitates prevention trials, beginning when mice are between 4 and 6 weeks old; early or late intervention trials, initiating when mice are approximately 12 weeks or roughly 18 and 24 weeks of age; and regression trials that can also begin when mice are between 18 and 24 weeks of age [20, 84]. Efficacy in the TRAMP model has typically been measured by overall survival (in the case of long-term studies), as well as by assessing the urogenital (UG) weight, the overall burden and pathology of primary and metastatic prostatic lesions, and the number of proliferating and apoptotic cells within the prostate at a few specific times following treatment; the latter three endpoints require sacrificing the mice which limits the analysis of any given animal to a single point in time. Magnetic resonance imaging (MRI) has been used in some studies to measure tumor volume noninvasively, which facilitates the ability to frequently monitor disease progression in the same mice longitudinally and is more economical as fewer animals are needed [22, 85–87]. More recently, improvements in MRI have allowed for tumor grading, which greatly enhances the use of this model for preclinical studies [88], and ultrasound has been used very recently to identify and quantitate tumor burden [89]. In addition, molecular analyses have also been used to determine whether these drugs modulate their targets, or, for agents whose targets are unknown, other markers of PC to determine if their expression patterns were restored to those observed in the normal prostate or earlier-PC stages; in the absence of PSA, such surrogate biomarkers have included IGF-1 and IGFBP3 [86, 89], E-cadherin [10, 42, 90], and the metastasis-associated protein S100A4 [42]. Despite the limited success of ADT in the clinic, as indicated earlier, there are continued efforts to define the mechanisms that allow for CR tumors to emerge,

Immunotherapy

?

? B6 F1

p

r p ei

B6

B6

p

li

F1

ei

Allogeneic cell vaccination p + rIL-2f

CTLA-4 blockade + tumor cell line vaccines (+/- GM-CSF)d mTERT genetic vaccination GM-CSF- and HA -tumor cell vaccines + cyclophosphamided,e HA vaccine + radiotherapyb,e SV40-Tag peptidepulsed DCs IL-2 expressing oncolytic virusf Inhibition of prim and met tumor progression Inhibition of prim and met tumor progression; increased survival Inhibited prostate carcinogenesis

Reduced disease score

Effect on tumorigenesis was not evaluated

Reduced area effected by PIN lesions and tumors Reduced UG weights

Reduced tumor incidence and lowered tumor grade

Table 17.2  Selected therapeutic agents tested preclinically in TRAMP Agent (target/mechanism Type Class/subclass of action) of triala Strainb Effect on prostate tumorigenesisc Cytotoxic drugs Doxirubicin li/r F1 Reduced tumor volume Doxirubicin + NGR-TNF ei B6 Reduced UG weight and disease score li/r B6 Reduced UG weight and disease score; prevented appearance of NE foci and mets; no increase in survival B6 reduced UG and LN weights and R-fluriprofen (proliferative ei incidence of prim and mets arrest and induces apoptosis)

Increased CD4 and CD8+ T-cells

Virus detected

Evidence for primed anti-tumor response SV40 Tag-specific cytolytic activity Virus detected

antigen-specific T-cells infiltrated Treg cell depletion; activated DCs presentf

[116]

[115]

[114]

[113]

[112]

[43]

[111]

[95]

not determined

Inflammatory cells accumulated

References [88] [94]

Effect on target/ surrogate markersc Not determined Not determined Not determined

408 S.D. Morgenbesser

DNA methylation NSAIDsg

HDAC inhibitors

Angiogenesis

Targeted therapies

p

p li p

MS-275

OSU-HDAC42

5aza (DMNT1)

Celecoxib (COX-2)

r

2-Methoxyestradiol

F1

li/r

B6

F1

F1

?

?

B6 B6

F1

p li

F1 F1

r Angiostatin and endostatin p (ECs) ei

2-Methoxyestradiol

F1 F1

ei li

SU5416 (VEGFRs)

Decreased severity of PIN and prevented progression to PD Slowed progression (delayed emergence of CR disease); increased survival Reduced # of PIN lesions; increased apoptosis and decreased proliferation

No effect on onset but slowed progression; decreased proliferation

Reduced UG weight tumors regressed; significantly lowered disease score; decreased proliferation; apoptosis unchanged

No effect on tumor progression Slowed progression to WD but not later progression; PD tumors had increased apoptosis and decreased MVD No effect on tumor progression or survival rate slowed appearance of PIN and WD; decreased proliferation and MVD; increased survival Slowed appearance of PIN and WD; decreased proliferation and MVD; increased survival No effect on tumor progression and no improved survival Reduced UG weight; no indication of neoplasia Dramatic reduction in prostate volume

Lowered COX-2 and downstream targets

No indication of whether target is present Increased histone H3 acetylation Not determined

Not determined Decreased serum testosterone Decreased Sp1 and FLIP

Not determined

VEGFR2 reduced

Not determined VEGFR2 reduced

Not determined Not determined

(continued)

[106]

[101]

[105]

[104]

[100]

[87]

[99]

[84]

17  Transgenic Adenocarcinoma of the Mouse Prostate 409

Dietary supplements

Other

Genistein Green tea polyphenols

R-enantiomer of etodolac (retinoid X-receptor) B6 F1

B6 B6 B6 B6

p p ei li

B6

p p

ei

F1

p

Indomethacin (nonselective COXinhibitor)

F1

p

B6

B6

p/ei

p

Strainb

Type of triala

Exisulind (COX-2)

Table 17.2  (continued) Agent (target/mechanism Class/subclass of action)

Reduced progression to PD Prevent or delay in tumor development; reduced tumor growth, no mets; increased apoptosis and reduced prolif; increased survival Not evaluated; purpose was to evaluate S100A4 and E-cadherin as biomarkers Increased survival; reduced tumor burden; mostly normal with sparse PIN lesions Increased survival; reduced tumor burden; mostly HGPIN with some WD Increased survival; reduced tumor burden; WD and MD only

Reduced average tumor mass and freq of metas; increased apoptosis in prostate

Reduced incidence of palpable tumors and stabilized disease at WD, no mets; reduced prolif and increased apoptosis; increased survival No reduction in incidence of prim and met tumors; no change in time of tumor development or survival Reduced # of PIN lesions; increased apoptosis and decreased proliferation No reduction in incidence of prim and met tumors; no change in time of tumor development or survival

Effect on prostate tumorigenesisc

Moderate IGF-1 reduction

Reduced S100A4; restored E-cadherin Significant IGF-1 reduction Strong IGF-1 reduction

Not determined Lowered IGF-1; IGFB3 restored

[89]

[42]

[123] [86]

[107]

[83]

Not determined

Retinoid X receptor greatly reduced

[106]

[83]

Not determined (see legend) Decreased cox activity

[85]

References

Reduced cox-2 and activity of downstream marker

Effect on target/ surrogate markersc

410 S.D. Morgenbesser

r p

B6 B6

No effect on IGF-1 No effect on survival; no diff in tumor burden Decreased incidence of PIN and WD; reduced ND [122] proliferation and increased apoptosis Diallyl trisulfide p F1 Inhibits progression to PD; reduced pulmonary Decreased NE marker; [90] no effect on mets; decreased proliferation; no effect on E-cadherin apoptosis or angiogenesis [10] Sulforaphane p F1 Reduced incidence of WD, PD and pulmonary No effect on E-cadherin; mets; decreased proliferation; modest increased T-cell increase in apoptosis; no effect on infiltration angiogenesis Briefs details from some pre-clinical trials performed with TRAMP mice; please refer to the individual references for full experimental methods, results, and conclusions, and to the text for any abbreviations and acronyms not defined here a  To indicate the type of trial, the following abbreviations are used: p, prevention; ei, early intervention; li, late intervention; r, regression b  B6, C57BL/6; F1, [C57BL/6 × FVB]F1;?, not clear c  Results presented are relative to age- and strain-matched, untreated or vehicle-control treated TRAMP mice. Abbreviations: prim, primary tumors; mets, metastases; UG, urogenital d  CTLA-4, cytotoxic T-lymphocyte antigen 4; GM-CSF, granulocyte macrophage-colony stimulating factor e  To introduce influenza hemagglutinin (HA) as a tumor antigen in these studies, a variant of TRAMP mice was utilized derived by mating TRAMP mice with Pro-HA mice which express HA under the control of the prostate-specific probasin promoter f IL-2, interleukin-2. Oncolytic viruses are selective for tumor cells g  NSAIDS, non-steroidal anti-inflammatory drugs. The contrasting results with the COX-2 inhibitors correlate with differences in COX-2 expression and activity in the different mouse strains. In the studies in the TRAMP C57BL/6 model, Cox-2 expression and activity were shown to be elevated in the tumors relative to normal prostate tissue at multiple stages [85, 106], but in the TRAMP [C57BL/6 × FVB]F1 model, expression was actually found to be decreased, and knocking out COX-2 by gene deletion did not effect prostate tumorigenesis [106]. These differences are similar to that observed in human, suggesting that genetic heterogeneity plays a role, and may effect the clinical utility of these agents [126]

Inositol hexaphosphatate

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S.D. Morgenbesser

and to develop therapies to counteract aberrant activation of the androgen ­pathway [2, 3]. The observation that castration in TRAMP leads to the quick regression and eventual return of tumors [21, 22] suggests that TRAMP is ­suitable for studying alternative hormonal interventions to identify those that may achieve a positive clinical outcome. Indeed, two of the first therapeutics ­evaluated in TRAMP mice were flutamide (an anti-androgen) and toremifene (an anti-­ estrogen), and both were shown to prevent tumor progression in TRAMP C57BL/6 mice [91, 92]. Cytotoxic agents are also utilized alone or in combination with other therapies to treat men with PC, including doxorubicin which has limited success in the clinic in part because it has a low tumor penetrance and is rapidly cleared from the bloodstream [93]. The efficacy of doxorubicin was evaluated in [C57BL/6 TRAMP × FVB]F1 mice bearing WD and PD tumors, and a reduction in volume of both tumor types was observed [88]. More recently, to improve the delivery of doxorubicin to the TRAMP tumors, the drug was combined with a peptide (NGF-TNF) that is capable of targeting drugs to the tumor vasculature [94]. The NGF peptide binds CD13, an EC surface protein, and TNF (tumor necrosis factor-a) alters vessel permeability. When 12- or 17-week-old TRAMP C57BL/6 mice, which were shown to express CD13 on the vasculature, were treated, the combination therapy was more effective than doxorubicin alone. More details on these studies as well as those with another cytotoxic agent that has been evaluated in autochthonous TRAMP mice can be found in Table 17.2 [95]. A variety of targeted therapies have been evaluated in TRAMP mice that act upon different pathways or cellular processes. As described earlier, active angiogenesis in TRAMP is associated with the expression of master-regulatory proteins that are being targeted clinically [29, 31], making it a suitable model for testing anti-angiogenic agents. SU5416 is a potent small molecule inhibitor of VEGRs that hinders proliferation and tube formation of human [96] and mouse [97] cultured ECs. In one study [C57BL/6 TRAMP × FVB]F1 mice were given SU5416 beginning when they were 10 or 16 weeks of age for a total of 6 weeks at which time they were sacrificed, in early or late intervention trials, respectively, or commencing when they had palpable abdominal masses until they were in distress in a regression trial [84]. In the late intervention trial, which was carried out concomitant with VEGF and VEGFR2 expression, SU5416 slowed the progression of PIN to WD tumors, but did not affect the development of PD and metastatic cancers. Within the tumors, there was increased apoptosis and decreased MVD, suggesting that SU5416 interferes with VEGFR2 signaling in PC. Differences in tumor progression were not detected in the early intervention trial consistent with observations that although VEGFR1 was expressed, VEGF levels were quite low, suggesting that targeted therapy to the VEGF axis would not be relevant at this stage of tumorigenesis. SU5416 was not effective in the regression trial, as similar incidence of metastatic disease and survival rate were observed. Remarkably, this study is consistent with the lack of efficacy observed with SU5416 in clinical trials with PC patients [98]. In addition, TRAMP has been utilized to study other antiangiogenic agents [87, 99, 100] (Table 17.2).

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As discussed earlier, the DNMT1 inhibitor 5-aza (known clinically as decitabine) can reverse DNA methylation and stabilize tumor progression when given to TRAMP mice before PIN lesions appear. In addition, when 5-aza is given to [C57BL/6 TRAMP × FVB]F1 mice simultaneous with castration at 15 weeks, and then for an additional 10 weeks, there was a marked delay in the onset of CR disease with an overall improvement in survival, suggesting potential utility in treating men with this type of advanced PC [101]. Indeed, 5-aza has been evaluated in dozens of clinical trials to treat a wide variety of tumor types, including CR metastatic PC [102]. In this trial, there was a modest clinical benefit, which is promising considering the very advanced state of the disease, and the highly unstable nature of 5-aza in vivo [103]. Continued evaluation of 5-aza may yield improved outcomes, but this data also suggests the potential for other targeted drugs with a similar mechanism of action. Preclinical trialsin TRAMP with targeted therapies directed at other types of targets are summarized in Table 17.2 [83, 85, 104–107]. Immunotherapy, aimed at enhancing the body’s anti-tumor immune response, is also a potential strategy for treating human PC that has received a great deal of attention [108]. One approach involves vaccination against immunogenic PC-specific markers. In this regard, PSCA represents an attractive target based upon its selective over-expression on the surface of most prostate tumors. Indeed, when young TRAMP C57BL/6 mice with PIN lesions were primed with mouse PSCA cDNA and boosted with Venezuelan equine encephalitis virus replicons containing mouse PSCA, there was a protective immune response associated with a significant increase in survival [109]. The TRAMP tumors that were present were WD, had areas with many apoptotic cells, and were infiltrated by T cells, macrophages, and dendritic cells (DCs). The tumor cells had increased MHC class I expression and cytokine production, and decreased PSCA expression. The increase in survival and disease stabilization at the WD stage is interesting given that TRAMP tumors with reduced PSCA as a result of genetic KO were associated with progression to metastatic disease; it is not clear why metastases were not observed in this therapeutic study, but that observation is encouraging and suggests the potential utility for this treatment in man if applied early. Indeed, in a phase I/II clinical trial in which patients with advanced PC were vaccinated with DCs preloaded with PSCA peptides, there was evidence of infiltrating T-cells but with limited clinical benefit [110]. Some other immunologic-based approaches that have been evaluated in TRAMP are listed in Table 17.2 [43, 111–116]. Finally, TRAMP mice have been used extensively to evaluate the effects of various dietary supplements derived from vegetables and plants, mirroring the use of alternative medicines by nearly one-third of all men with PC [117]. The use of such agents, which have been associated with multiple anti-tumor effects including cellular growth arrest and apoptosis, is controversial, in part because these materials are frequently impure, though the approach is considered worthy of continued study given the epidemiologic evidence correlating diet and PC incidence and growth, particularly when the agents are of high purity. For example, silibin, which is isolated from milk thistle seeds and inhibits the growth of PC and other transformed cell lines in culture or propagated as xenografts, was recently tested in

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TRAMP mice by dietary supplementation with highly purified material [118–120]. In a preventative trial, TRAMP C57BL/6 and [C57BL/6 TRAMP × FVB]F1 mice were fed silibin beginning at 4 weeks of age and continuing for 20 weeks [118]; in most mice, PIN lesions were observed with considerable decrease in tumor incidence, and prostate tissues were characterized by decreased proliferative and increased apoptotic rates. In two intervention trials, TRAMP C57BL/6 mice were fed diets supplemented with silibin or silybin-phytosome, which has superior bioavailability, at older ages, when PIN lesions or tumors were already present [119, 120]; tumor growth, progression, and metastasis were perturbed, and anti-proliferative and anti-angiogenic effects were observed. It was well tolerated in a phase I trial of patients with advanced PC [121] and a phase II trial was recently completed with the results pending. Examples of other alternative approaches that have been evaluated for their efficacy in TRAMP are described in Table 17.2 [10, 86, 89, 90, 122, 123]. Some of the preclinical results generated with TRAMP mice have led to further development; for instance the work with CTLA4-blockade and tumor cell vaccination [111] has led to clinical trials [108] and additional experimentation with the TRAMP model has led to the identification of PC T-cell antigens with relevance to the human disease [124]. In addition, the model was used to evaluate drug enhancements (such as to doxorubicin) or to follow negative responses by molecular studies to determine the reasons for failure, such as by determining the relevance of the target in prostate tumorigenesis by gene deletion [83] or by analyzing the expression of other key modulators [125]. In the case of COX-2 and its inhibitors, protein function and therapeutic efficacy were strain-dependent (Table 17.2 and legend). Therefore, the TRAMP model can be utilized to improve agents at the preclinical stage or for patient selection in the clinic. The TRAMP mouse is commercially available, with C57BL/6 mice and a congenic FVB strain that can be obtained from Jackson Laboratories and the National Cancer Institute Mouse Models of Human Cancers Consortium Repository, respectively [35].

17.7 Summary, Conclusions, and Future Directions The TRAMP mouse is a highly suitable model for the analysis of genes and pathways, testing of therapeutic agents, and identification of biomarkers, that have relevance for human PC. Clearly the role and regulation of the AR and its pathway, and of DNA methylation, can be further evaluated and targeted in these mice. In addition, the three recent, comprehensive gene expression profiling studies have provided the research community with hundreds of candidate diagnostic markers and therapeutic target proteins and pathways for further inquiry, including some that appear to play important roles in the progression to the advanced, aggressive stages of the disease, such as PLA2G2A, and arachodonic acid and prostaglandin metabolism. These lists contain proteins expressed on epithelial, stem, NE, EC, and stromal cells, and given their importance in disease progression in this model, there is an opportunity therein

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to study inhibitors directed against any of them. Breeding of TRAMP mice to those mice that have genetically engineered alterations in proteins of interest is a powerful approach for elucidating their relevance in PC. For some proteins, exemplified by PSA, PSCA, PLA2G2A, and COX-2, expression and/or function may exhibit species- or strain-related differences which need to be considered in determining their utility as targets, or in evaluating preclinical efficacy data. Acknowledgments  The author is grateful to Norman Greenberg for helpful discussions and critical review of this chapter.

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83. Wang X, Colby JKL, Yang P, Fischer SM, Newman RA, Klein RD. The resistance to the tumor suppressive effects of COX inhibitors and COX-2 gene disruption in TRAMP mice is associated with the loss of COX expression in prostate tissue. Carcinogenesis. 2008;29:120–8. 84. Huss WJ, Barrios RJ, Greenberg NM. SU5416 selectively impairs angiogenesis to induce prostate cancer-specific apoptosis. Mol Cancer Therapeut. 2003;2:611–6. 85. Gupta S, Adhami VM, Subbarayan M, et  al. Suppression of prostate carcinogenesis by dietary supplementation of celecoxib in transgenic adenocarcinoma of the mouse prostate model. Cancer Res. 2004;64:3334–43. 86. Gupta S, Hastak K, Ahmad N, Lewin JS, Mukhtar H. Inhibition of prostate carcinogenesis in TRAMP mice by oral infusion of green tea polyphenols. Proc Natl Acad Sci USA. 2001;98:10350–5. 87. Garcia GE, Wisniewski H-G, Lucia MS, et  al. 2-methoxyestradiol inhibits prostate tumor development in transgenic adenocarcinoma of mouse prostate: role of tumor necrosis factora-stimulated gene 6. Clin. Cancer Res. 2006;12:980–8. 88. Degrassi A, Russo M, Scanziani E, et al. Magnetic resonance imaging and histopathological characterization of prostate tumors in TRAMP mice as model for pre-clinical trials. Prostate. 2007;67:396–404. 89. Adhami VM, Siddiqui IA, Sarfaraz S, et al. Effective prostate cancer chemopreventive intervention with green tea polyphenols in the TRAMP models depends on the stage of the disease. Clin Cancer Res. 2009;15:1947–53. 90. Singh SV, Powolny AA, Stan SD, et al. Garlic constituent diallyl trisulfide prevents development of poorly differentiated prostate cancer and pulmonary metastasis multiplicity in TRAMP mice. Cancer Res. 2008;68:9503–11. 91. Raghow S, Kuliyev E, Steakley M, Greenberg NM, Steiner MS. Efficacious chemoprevention of primary prostate cancer by flutamide in an authochthonous transgenic model. Cancer Res. 2000;60:4093–7. 92. Raghow S, Hooshdaran MZ, Katiyar S, Steiner MS. Toremifene prevents prostate cancer in the transgenic adenocarcinoma of mouse prostate model. Cancer Res. 2002;62:1370–6. 93. Primeau AJ, Rendon A, Hedley D, Lilge L, Tannock IF. The distribution of the anticancer drug doxorubicin in relation to blood vesels in solid tumors. Clin Cancer Res. 2005;11:8782–8. 94. Bertilaccio MTS, Grioni M, Sutherland BW, et al. Vasculature-targeted tumor necrosis factor-alpha increases the therapeutic index of doxorubicin against prostate cancer. Prostate. 2008;68:1105–15. 95. Wechter WJ, Leipold DD, Murray Jr. ED, et al. E-7869 (R-flurbiprofen) inhibits progression of prostate cancer in the TRAMP mouse. Cancer Res. 2000;60:2203–8. 96. Fong TAT, Shawver LK, Sun L, et al. SU5416 is a potent and selective inhibitor of the vascular endothelial growth factor receptor (Flk-1/KDR) that inhibits tyrosine kinase catalysis, tumor vascularization, and growth of multiple tumor types. Cancer Res. 1999;59:99–106. 97. Walter-Yohrling J, Morgenbesser S, Rouleau C, et al. Murine endothelial cell lines as models of tumor endothelial cells. Clin Cancer Res. 2004;10(6):2179–89. 98. Stadler WM, Cao D, Vogelzang NJ, et al. A randomized phase II trial of the antiangiogenic agent SU5416 in hormone-refractory prostate cancer. Clin Cancer Res. 2004;10:3365–70. 99. Isayeva T, Chanda D, Kallman L, Eltoum I-EA, Ponnazhagen S. Effects of sustained antiangiogenic therapy in multistage prostate cancer in TRAMP model. Cancer Res. 2007;67:5789–97. 100. Ganapathy M, Ghosh R, Jianping X, et  al. Involvement of FLIP in 2-methoxyestradiolinduced tumor regression in transgenic adenocarcinoma of mouse prostate model. Clin Cancer Res. 2009;15:1601–11. 101. Zorn CS, Wojno KJ, McCabe MT, Kuefer R, Gschwend JE, Day ML. 5-aza-2¢-deoxycytidine delays androgen-independent disease and improves survival in the transgenic adenocarcinoma of the mouse prostate mouse model of prostate cancer. Clin Cancer Res. 2007;13:2136–42.

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102. Thibault A, Figg WD, Bergan RC, et al. A phase II study of 5-aza-2¢deoxycytidine (decitabine) in hormone independent metastatic (D2) prostate cancer. Tumorigenesis. 1998;84:87–9. 103. Rivard GE, Momparler RL, Demers J, et  al. Phase I study on 5-aza-2’¢-deoxycytidine in children with acute leukemia. Leukemia Res. 1981;5:453–62. 104. Qian DZ, Wei Y-F, Wang X, Kato Y, Cheng L, Pili R. Antitumor activity of the histone deacetylase inhibitor MS-275 in prostate cancer models. Prostate. 2007;67:1182–93. 105. Sargeant AM, Rengel RC, Kulp SK, et al. OSU-HDAC42, a histone deacetylase inhibitor, blocks prostate tumor progression in the transgenic adenocarcinoma of the mouse prostate model. Cancer Res. 2008;68:3999–4009. 106. Narayanan BA, Narayanan NK, Pittman B, Reddy BS. Regression of mouse prostatic intraepithelial neoplasia by nonsteroidal anti-inflammatory drugs in the transgenic adenocarcinoma mouse prostate model. Clin Cancer Res. 2004;10:7727–37. 107. Kolluri SK, Corr M, James SY, et al. The R-enantiomer of the nonsteroidal antiinflammatory drug etodolac binds retinoid X receptor and induces tumor-selective apoptosis. Proc Natl Acad Sci USA. 2005;102:2525–30. 108. Harzstark AL, Small EJ. Immunotherapeutics in development for prostate cancer. The Oncologist. 2009;14:391–8. 109. Garcia-Hernandez MDLL, Gray A, Hubby B, Klinger OJ, Kast WM. Prostate stem cell antigen vaccination induces a long-term protective immune response against prostate cancer in the absence of autoimmunity. Cancer Res. 2008;68:861–9. 110. Thomas-Kaskel AK, Zeiser R, Jochim R, et  al. Vaccination of advanced prostate cancer patients with PSCA and PSA peptide-loaded dendritic cells induces DTH responses that correlate with superior overall survival. Int J Cancer. 2006;119:2428–34. 111. Hurwitz AA, Foster BA, Kwon ED, et al. Combination immunotherapy of primary prostate cancer in a transgenic mouse model using CTLA-4 blockade. Cancer Res. 2000;60:2444–8. 112. Wada S, Yoshimura K, Hipkiss EL, et al. Cyclophosphamide augments antitumor immunity: studies in an authochthonous prostate cancer model. Cancer Res. 2009;69:4309–18. 113. Harris TJ, Hipkiss EL, Borzillary S, et al. Radiotherapy augments the immune response to prostate cancer in a time-dependent manner. Prostate. 2008;68:1319–29. 114. Degl’Innocenti E, Grioni M, Boni A, et al. Peripheral t cell toleratnce occurs early during spontaneous prostate cancer development and can be rescued by dendritic cell immunization. Eur J Immunol. 2005;35:66–75. 115. Varghese S, Rabkin SD, Nielsen GP, MacGarvey U, Liu R, Martuza RL. Systemic therapy of spontaneous prostate cancer in transgenic mice with oncolytic herpes simplex virus. Cancer Res. 2007;67:9371–9. 116. De Giovanni C, Croci S, Nicolett G, et al. Inhibition of prostate carcinogenesis by controlled active immunoprophylaxis. Int J Cancer. 2007;121:88–94. 117. Bhatt RS, Bubley GJ. The challenge of herbal therapies for prostate cancer. Clin Cancer Res. 2008;14:7581–2. 118. Raina K, Blouin M-J, Singh RP, et  al. Dietary feeding of silibin inhibits prostate tumor growth and progression in transgenic adenocarcinoma of the mouse prostate model. Cancer Res. 2007;67:11083–91. 119. Raina K, Rajamanickam S, Singh RP, Deep G, Chittezhath M, Agarwal R. Stage-specific inhibitory effects and associated mechanisms of silibin on tumor progression and metastasis in transgenic adenocarcinoma of the mouse prostate model. Cancer Res. 2008;68:6822–30. 120. Singh RP, Raina K, Sharma G, Agarwal R. Silibin inhibits established prostate tumor growth, progression, invasion, and metastasis and suppresses tumor angiogenesis and epithelialmesenchymal transition in transgenic adenocarcinoma of the mouse prostate model mice. Clin Cancer Res. 2008;14:7773–80. 121. Flaig TW, Gustafson DL, Su L-J, et  al. A phase I and pharmacokinetic study of silybinphytosome in prostate cancer patients. Invest New Drugs. 2007;25:139–46. 122. Raina K, Rajamanickam S, Singh RP, Agarwal R. Chemopreventive efficacy of inositol hexaphosphate against prostate tumor growth and progression in TRAMP mice. Clin Cancer Res. 2008;14:3177–84.

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Chapter 18

The Utility of Transgenic Mouse Models for Cancer Prevention Research Stephen D. Hursting, Laura M. Lashinger, Powel H. Brown, and Susan N. Perkins

Abstract  The development of effective cancer preventive interventions is being enhanced by the use of relevant animal models to confirm, refine and extend potential leads from clinical and epidemiologic studies. In particular, genetically altered mice, with specific cancer-related genes modulated, are providing powerful tools for studying carcinogenesis, as well as important conduits for translating basic research findings from the laboratory bench to the bedside. This review explores the utility of genetically altered mice for developing cancer preventive strategies that can offset increased cancer susceptibility resulting from specific genetic lesions. Examples will focus on preventing prostate, mammary, intestinal and pancreatic cancers by dietary interventions, particularly obesity prevention/ energy balance modulation, as well as chemoprevention, in mice with alterations in genes such as the p53 or Apc tumor suppressors, components of the Wnt, ErbB/Ras oncogenic pathways, and other pathways frequently altered in human cancer.

18.1 Introduction The development of genetically altered mouse models for cancer research over the past three decades has greatly facilitated efforts in understanding tumor biology and identifying the role of specific genes in carcinogenesis as well as in normal development [1]. These models, with specific cancer-related genes altered, also provide an intact and highly relevant biological system for evaluating the efficacy and underlying mechanisms of cancer preventive interventions [2]. There are numerous examples of how the development of effective cancer preventive interventions has been augmented by the use of relevant genetically altered mouse models to confirm, refine, and extend S.D. Hursting (*) Department of Nutritional Sciences, University of Texas at Austin, Austin, TX, USA and Department of Carcinogenesis, University of Texas MD Anderson Cancer Center, Smithville, TX, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_18, © Springer Science + Business Media, LLC 2011

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potential leads from epidemiologic and clinical research. In particular, genetically altered mice are providing powerful tools for the preclinical evaluation of preventive dietary regimens or pharmacological agents (used singly or in combination). In addition, mutant mice engineered to mirror the genetic alterations characteristic of human tumors are facilitating the identification and validation of biomarkers for early detection of cancer. This chapter will discuss commonly used models of mutant mice for developing cancer preventive strategies. Examples will be provided from studies centered on preventing cancer by dietary (particularly energy balance modulation) and chemopreventive interventions, in a variety of strains of mutant mice. These include transgenic mice overexpressing an oncogene or other cancer-related gene; mice with targeted germ-line deletions of tumor suppressors or other key growth regulatory or metabolic genes; and mice with conditional alterations in targeted cancer-related genes.

18.2 Cancer Prevention in Transgenic Mice: Lessons Learned from Commonly Used Models 18.2.1 Mutant Mouse Models for Prostate Cancer Prevention Prostate cancer prevention research has been hampered by the lack of models that develop lesions analogous to those observed in human prostate cancer progression. Fortunately, numerous transgenic and knockout models have now been generated that develop lesions ranging from murine prostatic intraepithelial neoplasia (mPIN) to metastatic disease. In general, multiple signaling pathways must be altered in order to produce invasive lesions (adenocarcinoma) in the mouse, and mutant mouse models of prostate cancer can be broadly divided into two categories: (1) models with genetic modification of pathways implicated in human prostate cancer development, including PTEN-deficient and c-myc transgenic mice; and (2) models in which viral oncogene(s) are expressed in prostate tissues, particularly the Simian Virus (SV) 40 large T antigen. 18.2.1.1 PTEN Mutant Mouse Models PTEN (phosphatase and tensin homolog deleted on chromosome 10) is a tumor suppressor gene that is frequently mutated or lost in human cancers. The PTEN gene encodes for a lipid phosphatase, the major function of which is the dephosphorylation of phosphatidylinositol-3-phosphate, leading to downregulation of Akt/PKB [3] and other signaling molecules [4]. Deletion or mutation of PTEN is one of the most frequently observed genetic alterations in human prostate cancer, affecting up to 63% of metastatic tumors [5]. Attempts to generate knockout mouse models with conventional homozygous mutation of PTEN

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resulted in an early embryonic lethal phenotype, and therefore only heterozygous PTEN-deficient (PTEN+/-) progeny could be assessed for prostate tumor development. PTEN+/- mice exhibit mPIN by 8–10 months of age, but display no progression beyond mPIN. However, PTEN has been shown to interact with other genetic alterations to induce prostatic lesions. Most notable among these are the PTEN+/- x Cdkn1b-/- and PTEN+/- x Nkx3.1-/- mutant mice. The combined heterozygous knockout of PTEN and homozygous knockout of Cdkn1b (p27) results in invasive adenocarcinoma in the prostate, as well as tumors in other tissues [6]. Knocking out Nkx3.1 expression in PTEN heterozygotes similarly causes invasive prostatic adenocarcinoma, primarily in the anterior prostate lobe, and metastasis to regional lymph nodes [7]. PTEN-floxed mice crossed with probasin-CRE transgenic mice results in a mouse line with homozygous deletion of the PTEN gene in prostate cells expressing the Cre transgene [8]. These mice have 100% incidence of mPIN by 6 weeks of age and invasive adenocarcinoma by 12 weeks of age in all prostatic lobes, with an incidence of metastasis to the lymph nodes and lung by 30 weeks of age. Prostate adenocarcinomas in this model are responsive to androgen ablation, suggesting that this model will be particularly useful for prostate cancer prevention studies targeting androgen signaling. 18.2.1.2 c-Myc Transgenic Mice The nuclear protein c-myc is a transcription factor that regulates proliferation and apoptosis. Myc expression is elevated in up to 30% of human prostate tumors. Ellwood-Yen et al. generated two founder lines of transgenic mice overexpressing c-myc. High-expressing (Hi-Myc) and low-expressing (Lo-Myc) mice, generated using the composite probasin promoter to drive expression of c-myc cDNA in the prostate, have similar phenotypes, with the Hi-Myc having a much shorter latency for the onset of preneoplastic and neoplastic changes in the prostate [9]. From 2 to 4 weeks of age, mPIN lesions appear in Hi-Myc mice, and these lesions progress to invasive adenocarcinomas by 3–6 months of age. Tumors occur primarily in the ventral and dorsolateral prostate lobes in these mice. 18.2.1.3 Viral Oncogene Models Prostate tumorigenesis in TRAMP (transgenic adenocarcinoma mouse prostate) mice is driven by prostate-specific expression of SV40 early genes (large and small T antigens). TRAMP mice develop histological mPIN in 100% of males by 8 weeks of age that progresses to poorly differentiated carcinoma with distant site metastasis by 16–32 weeks of age [10, 11]. Metastatic sites include the lymph nodes, liver, and lung and occasionally the kidney, adrenal glands, and spinal column [10]. These tumors are initially sensitive to androgen ablation, but develop androgen independence following castration [12].

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The precisely defined course of disease progression and the overall high incidence of tumors in the TRAMP model allow prevention studies to be conducted in a reasonable length of time with fewer animals. Although the TRAMP model has been criticized as being too aggressive, and therefore perhaps not sufficiently sensitive to the actions of chemopreventive agents, there is little evidence to support this hypothesis. The TRAMP model has been utilized by many investigators for preclinical testing of chemoprevention strategies, including several positive studies with agents such as epigallocatechin gallate (a green tea polyphenolic compound), genistein (a soy isoflavone), toremifene (an estrogen modulator), difluoromethylornithine (DFMO, an irreversible inhibitor of ornithine decarboxylase), and the antiinflammatory drugs R-flurbiprofen and celecoxib [13–18]. Background strain can have a substantial effect on the prostatic phenotype resulting from expression of a transgene. For example, TRAMP mice are typically maintained on a pure C57BL/6 background. When C57BL/6 TRAMP mice are crossed with wild-type FVB mice, the resulting TRAMP[B6xFVB] F1 progeny exhibit an enhanced phenotype compared to the parental C57BL/6 strain [19]. Chemoprevention studies have been carried out using TRAMP mice of either strain background, but the strain used most often is the pure C57BL/6 TRAMP, even though TRAMP[B6xFVB] F1 mice have been more thoroughly characterized and have a number of advantages. Tumors in TRAMP[B6xFVB] F1 mice tend to form well-defined tumor nodules that can be externally palpated and easily dissected from adjacent tissues for weighing and analyses. Tumors in the C57BL/6 TRAMP mice are more diffuse and invade the seminal vesicles at a higher frequency, often resulting in occlusion. In addition, the incidence of phyllodes-like lesions is much higher in C57BL/6 TRAMP mice; these lesions do not appear to be relevant to human prostatic adenocarcinoma. There have been no direct comparisons of chemoprevention agent effects on the two different strain types. Therefore it is not known if there could be substantial differences between the strain types in terms of response to relevant chemoprevention agents. The incidence of early carcinoma in TRAMP is 100% and occurs within a very confined and predictable time period (6–8 weeks of age). Progression to poorly differentiated carcinoma is more stochastic, and occurs over a much broader time period (16–32 weeks of age). Length of treatment varies considerably, with some researchers conducting survival studies and other researchers collecting prostate tissues prior to the development of poorly differentiated tumors [20]. Treatments intended to prevent the progression of mPIN should be started between 4 and 6 weeks of age, while treatments designed to prevent progression of locally invasive carcinoma to poorly differentiated and metastatic disease should be started before 16 weeks of age. Typical studies designed to prevent progression to metastatic disease should include approximately 30 mice per treatment group in order to provide sufficient power to detect differences between treatment groups. The pathogenesis of prostate cancer in the Lady mouse model, or line 12T-10 of a group of long probasin promoter driven large T-antigen transgenic mice, is similar to that observed in the TRAMP model [21]. Lesions progress from mPIN to invasive adenocarcinoma, followed by conversion to metastatic carcinoma. The progression

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from PIN to metastasis occurs at a somewhat lower rate than in the TRAMP model, so the Lady-12T-10 model may be considered slightly less aggressive than the TRAMP model. Venkateswaran and coworkers evaluated the effects of antioxidants and dietary fat levels on prostate cancer progression in Lady mice [21]. Interestingly, a high-fat diet increased the incidence of poorly differentiated tumors at 32 weeks of age from 74 to 100%. Addition of lycopene, selenium, and vitamin E to the diets reduced tumor incidence in response to both the low- and high-fat diets.

18.2.2 Mutant Mouse Models for Mammary Cancer Prevention Numerous transgenes have been used to generate mouse models to mimic human breast cancer. Most transgenic mouse models used for prevention studies were generated through gain of function or knockout of critical components in oncogenic pathways. The most commonly used models for breast cancer target growth factors or their receptors, cell-cycle regulators, signal transduction pathways, cellular differentiation regulators, oncogenes, and tumor suppressor genes. Most of these use tissue-specific promoters, including mouse mammary tumor virus-long terminal repeat (MMTV-LTR), whey acidic protein (WAP), the C(3)1 component of the rat prostate steroid binding protein, or bovine b-lactoglobulin (BLG) to drive mammary gland expression of the transgene. Each promoter has strengths and limitations, and the choice of promoter is dependent on the research question being addressed. 18.2.2.1 TGFa Models The transcription factor TGFa plays an important role in mammary development and is overexpressed in 30–70% of breast cancer cases, as reviewed by Rudland [22]. TGFa expression has been driven under MMTV-LTR, WAP, and metallothionein promoters [23–26]. The WAP-TGFa model has been shown to have diffuse mammary epithelial hyperplasia in pregnancy, multifocal hyperplastic alveolar nodules at latency of 2–6 months, and mammary tumors at 6–12 months [25–27]. Yet, the TGFa-induced mammary tumors are focal and relatively fewer in number [25], indicating additional tumorigenic mechanisms are needed to promote tumor development. 18.2.2.2 ErbB-2/HER2/neu Models ErbB2 (HER2, neu) is one of the most intensively studied genes in breast cancer biology. The gene is amplified in 15–20% of human breast cancer and is overexpressed in approximately 30% of breast cancers [28, 29]. ErbB-2 is an indicator for clinical prognosis, metastasis, and tamoxifen resistance [30–32]. ErbB-2 has been engineered to express under MMTV and WAP promoters. Wild-type and mutated ErbB-2 transgenic mice develop mammary tumors in several strains around

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7 months of latency [33–38]. Multiple transgenic models have confirmed that the early ErbB-2 model carries a valine-to-glutamic acid substitution in the transmembrane domain that confers constitutive activation of the receptor in the absence of ligand [39]. More relevant to human breast pathology, a late wild-type ErbB-2 model develops mammary tumors that carry sporadic mutations in the transgene in the tumor, but not in the adjacent normal mammary tissue [38]. The mammary tumors in ErbB-2 transgenic mice are estrogen receptor (ER) negative, and their pathologic appearance resembles lobular and alveolar phenotypes, found in about 5% of human breast cancers [38]. 18.2.2.3 SV40 T-antigen Transgenic Models SV40 large and small T-antigens (SV40 Tag) induce mammary tumors by inactivating the p53 and Rb tumor suppressor genes. When SV40 Tag is expressed using the promoter C3(1) from the prostate steroid binding protein, the transgene induces mammary carcinomas in 100% of female mice and prostate tumors in all male mice [40–42]. The C3(1) model has several unique characteristics for breast cancer prevention studies. The model mimics a well-defined time course for progressive mammary lesions and tumorigenesis: atypical ductal epithelia at 8 weeks, mammary intraepithelial neoplasia (similar to human ductal carcinoma in situ (DCIS)) at 12 weeks, and invasive carcinoma by 16 weeks of age [40]. Most interestingly, virgin C3(1) mice all develop tumors without the need of additional hormonal stimulation from pregnancy, a superior attribute over several other transgenic models. Another valuable feature of this model is that the C3(1) promoter itself is not stimulated by estrogen or pregnancy, and the tumors are ER negative and estrogen independent. Therefore, the C3(1) model is especially useful for studying ER-negative mammary tumorigenesis. The SV40 Tag has also been expressed using the WAP promoter [43–45]. In this model, all female mice develop mammary tumors by 8–9 months of age. Histologic appearance of the tumor varies from wellto poorly differentiated phenotypes. Pregnancy enhances the tumor development due to the WAP promoter, and the first tumors appear at 6 months of age after one pregnancy. Similar to the C3(1) model, tumor development in the WAP T-antigen model is characterized by three distinct stages: initial proliferation, hyperplasia, and adenocarcinoma [46]. An interesting attribute of this model is the high level of proliferation, apoptosis, and fibrosis in the tumor. This model is potentially useful to explore the early events during mammary tumorigenesis, particularly with respect to cellular proliferation and cell death. 18.2.2.4 p53-mutant Mouse Models Alterations of the p53 tumor suppressor gene are frequently detected in human breast cancer, with up to 40–50% of all human breast cancers having p53 mutations [47]. Mutations or epigenetic alterations of the p53 tumor suppressor gene are ­commonly

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observed in human cancer [48]. Donehower and colleagues [49] first reported in 1992 that homozygous p53-knockout (p53-/-) mice are viable, but ­susceptible to spontaneous tumorigenesis (particularly lymphomas) at an early age. Heterozygous p53-deficient (p53+/-) mice, with only one p53 allele inactivated, have some analogy to humans susceptible to heritable forms of cancer due to decreased p53 gene dosage, such as individuals with Li-Fraumeni Syndrome [50]. We and others have used these mice for evaluating the influence of diet or chemopreventive agents on genetic susceptibility to tumor development due to p53-deficiency ­[51–56]. However, the germline p53-mutant mice only rarely develop mammary tumors. Several animal models have been developed that either overexpress a mutant p53 gene in mammary tissue or have the endogenous p53 gene deleted or disrupted in mammary gland cells [57–59]. Mammary tumors are infrequently observed in homozygous p53-deficient (p53-/-) mice because the mice first develop lymphoma and die of these tumors before development of mammary gland tumors. Medina et al. developed a transplantable BALB/c-p53-/- mammary epithelium and demonstrated that lack of p53 function is sufficient to cause mouse mammary ­tumorigenesis, though hormone stimulation is an effective enhancer for the p53-/--induced tumorigenesis [60]. A WAP-p53172R-H transgenic mouse model was developed in which p53172R-H functions as a dominant-negative mutant [57, 59]. The WAP-p53172R-H mice develop tumors with shorter latency after DMBA treatment. Mice generated from crossing MMTV-ErbB2 with p53172R-H mice show significantly reduced latency [59]. An important characteristic of this model is that it develops mammary tumors similar to human high-grade breast adenocarcinoma in the presence of carcinogens and oncogenes. Thus, WAP-p53172R-H accelerates carcinogen- and oncogenemediated tumorigenesis, and is useful for cancer preventive intervention. 18.2.2.5 MMTV-Wnt-1 Transgenic Mouse Model Wnt-1 was originally found to be activated after MMTV infection, and the resulting mice had a high incidence of mammary tumors [61]. Wnt-1 is a glycoprotein that signals through the b-catenin pathway. Its expression is seen throughout mammary gland development, and deregulation of the downstream effectors in the Wnt-1 signaling pathway is involved in the tumorigenesis of several tumor types, ­including breast cancer [62]. MMTV-Wnt-1 expression causes ductal hyperplasia in late gestation and in prepubertal mice [63]. The Wnt-1 mice develop adenocarcinoma at 6–12 months of age [63, 64]. These tumors demonstrate a moderately ­differentiated ER-negative phenotype and are heterogeneous in ER-positive and/or ER-negative status. The MMTV-Wnt-1 mice have been crossed with MMTV-Fgf3, Sky-/-, p53-/-, ERa-/-, and TGFb mice [61]. There is a synergistic effect between Wnt-1 and Fgf3 [64], as these bigenic animals show shortened latency to ­developing mammary tumors. The female offspring of p53-/- mice bred with Wnt-1 mice develop mammary tumors significantly faster than their p53+/- counterparts [65]. Metastasis in Wnt-1 mice occurs to lymph node and lung, even after the primary tumors are removed. Therefore, the MMTV-Wnt-1 model is highly relevant to human breast cancer in two aspects: stromal signaling, characteristic of the

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MMTV-Wnt-1 model, is important in breast tumorigenesis, since the human mammary gland has a significant proportion of stromal structure; and the metastatic route is similar to that of human breast cancer. In addition, MMTV-Wnt-1 mammary ­adenocarcinomas are not only reflective of human basal-like breast cancer gene signatures and pathologic characteristics, but also are highly enriched in cells expressing the CD44+/CD24low cell surface markers that correspond to mammary tumor-initiating cells in both human tumors and mouse models (Smith, Hursting, et al., personal communication [66, 67]). 18.2.2.6 Ras Mutant Models Ras mutation is infrequent in breast cancer [68]. However, wild-type ras is significantly activated in breast cancers overexpressing epidermal growth factor receptor (EGFR) and/or ErbB-2 [69]. Ras driven by WAP and MMTV is sufficient to induce hyperplasia, adenocarcinoma, accelerated tumorigenesis, and metastatic mammary tumors [70–73]. MMTV-Ha-ras transgenic mice develop mammary tumors from 5 weeks to 6 months of age [74]. 18.2.2.7 c-myc Transgenic Mice c-myc is a transcription factor that dimerizes with Max and regulates target gene promoters. A defined role for c-myc has been shown in cell-cycle regulation and apoptosis. c-myc regulates normal mammary development and hormone-related proliferation, and also controls involution and remodeling [75]. Further, c-myc is deregulated in many human breast cancers. The c-myc gene is amplified in approximately 15–20% of all human breast cancers and is overexpressed in up to 70% of breast cancers [76]. Several c-myc transgenic models have been developed in which the c-myc gene is expressed using MMTV or WAP promoters. The mice for each of these models develop mammary tumors at a high rate [25, 77, 78]. MMTV-c-myc mice develop spontaneous mammary adenocarcinomas within 4–8 months [78]. WAP-c-myc mice develop adenocarcinomas or solid carcinomas in 80% of female transgenic mice after multiparity, with a latency of 5–10 months [25, 77]. These c-myc-induced mammary tumors are ER-negative tumors. It is important to note that, as suggested by the long latency, the c-myc overexpression does not transform all mammary epithelial cells. This suggests that additional events are required for c-myc-induced transformation of mammary cells. In this regard, the c-myc model reflects the attributes of human breast carcinogenesis and hence is a potentially ideal mouse model for cancer preventive intervention. 18.2.2.8 Cyclin D1 Transgenic Mice Cyclin D1 is amplified in about 20% of human breast cancers [79], while the cyclin D1 protein is overexpressed in more than 50% of human breast cancers

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[80–82]. In addition, loss of cyclin D1 interferes with mammary tumorigenesis. Sicinski and coworkers crossed cyclin D1-/- mice to four different mammary oncomice and found that cyclin D1 mediated mammary tumors induced by MMTV-c-neu and MMTV-v-Ha-ras, but not by MMTV-c-myc and MMTV-Wnt-1, suggesting that cyclin D1 is essential for the neu-ras pathway and the tumors dependent on cyclin D1 [83]. Cyclin D1-overexpressing breast cancers have been modeled by Wang et al. who developed an MMTV-cyclin D1 transgenic model. These mice have enhanced proliferation of mammary epithelial cells and develop mammary carcinomas at a mean age of 18 months [84]. Therefore, the cyclin D1 transgenic mouse models a significant proportion of human breast cancers and thus may be useful to study mammary carcinogenesis. 18.2.2.9 Inducible Models Inducible expression of transgenes has been used for mammary gland studies for more than a decade, although Chodosh’s more recent development of a reverse tetracycline-dependent transcriptional activator (rtTA) system with MMTV promoter achieves mammary-specific, tightly regulated homogeneous transgene expression in the presence of tetracycline or its derivative doxycycline. Using this system, the c-myc transgene was specifically induced in mammary epithelial cells [85, 86]. This system, although cumbersome because of the requirement of at least two transgenes, is highly mammary gland specific and inducible and has great potential for future cancer prevention studies. The development of the Cre-loxP strategy as an inducible and regulatable mammary gland-specific expression system is also providing a powerful approach for prevention studies. In this system, the Cre recombinase gene is under the control of MMTV or WAP promoters. Expression of the Cre gene causes conditional deletion of specific target genes. For example, deletion of the Brca1 gene by this system results in abnormal ductal development and activated apoptosis specifically in the mammary gland [87]. 18.2.2.10 Examples of Dietary or Chemopreventive Studies Using Transgenic Mouse Models of Mammary Cancer Selective Estrogen Receptor Modulators While classic selective estrogen receptor modulators (SERMs) such as tamoxifen and raloxifene are now being examined in the NASBP Study of Tamoxifen and Raloxifene (STAR) trial to compare their efficacy and safety profiles [88], other hormoneregulating agents are also being tested in animal models. The human clinical trials show that SERMs are able to prevent only ER-positive tumor formation. However, in preclinical studies using MMTV-ErbB2 mice, mammary tumor incidence was reduced significantly in mice given tamoxifen at an earlier age (8–18 weeks of age)

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[89, 90]. In addition, a combination of tamoxifen and angiostatin achieved greater suppression of tumor growth than tamoxifen or angiostatin alone [91]. A further combination of tamoxifen, angiostatin, and TIMP-2 achieved 90% reduction of tumor incidence in the MMTV-ErbB-2 model [92]. These results suggest that, in some cases, anti-estrogenic SERMs can suppress the development of ER-negative cancers. The underlying mechanism is unknown at this time. Aromatase Inhibitors Aromatase is a key enzyme in the synthesis of endogenous estrogen in peripheral tissue. The transgenic model overexpressing aromatase demonstrates increased tissue estrogenic activity and induction of hyperplastic and dysplastic lesions in mammary glands with or without circulating estrogen [93, 94]. These preneoplastic changes appeared to be further stimulated by the carcinogen dimethylbenz[a] anthracene, leading to an increased incidence of mammary tumors in mice. Lowdose letrozole, an aromatase inhibitor, inhibits expression of ER, progesterone receptor, cell-cycle regulators, and reduces mammary cell hyperplasia and the index of the proliferation marker PCNA [93, 94]. These studies have provided a vivid example of how to use a transgenic mouse model to elucidate important underlying mechanisms of mammary tumorigenesis. Retinoids Retinoids are Vitamin A analogs that mediate transcriptional regulation with their receptors RAR and RXR. Studies in our laboratory have demonstrated that RXRselective retinoids are much less toxic than RAR-selective retinoids. LGD1069 (Bexarotene, Targretin), an RXR-selective retinoid, prevents ER-negative mammary tumors in C3(1) SV40T and MMTV-ErbB-2 transgenic mouse models [95, 96]. Another, newer RXR-selective retinoid, LG100268, has been reported recently by Suh and colleagues to reduce tumor incidence in the NMU-induced mammary cancer rat model by promoting TGFb-dependent apoptosis [97, 98]. The most striking finding in these studies is that when LG 100268 was used in combination with a third-generation SERM, arzoxifene, only very low dosages of both arzoxifene and LG100268 were needed to cause significant reduction of tumor burden [97]. Similar results were obtained using the MMTV-ErbB2 model. Tyrosine Kinase Inhibitors EGFR (HER1, ErbB-1) or other members of its receptor family (HER2, 3, and 4) are overexpressed in a portion of human breast cancers and are highly expressed in ER-negative tumors [99]. Tyrosine kinase inhibitors (TKIs) can effectively block the tumorigenic potentials that arise from the EGF signaling pathway. ZD1839

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(IRESSA) is the prototype of this class of drugs [100]. Recent work in our laboratory has demonstrated that this drug prevents ER-negative tumor formation in MMTV-ErbB2 mice. The median time to tumor formation was approximately 230 days in vehicle-treated mice and more than 310 days in mice treated with ZD1839 at 100 mg/kg (P < 0.001). This effect was achieved by reducing proliferation and increasing expression of the cell-cycle regulator p27 [101]. Nonsteroidal Anti-Inflammatory Drugs/Cyclooxygenase-2 Inhibitors One most promising new class of chemopreventive agents is the cyclooxygenase (COX)-2 inhibitors. COX-2 is one of the rate-limiting enzymes in converting free arachidonic acid to prostaglandin (PG)G2. The two downstream products PGE1 and PGE2 enhance mitogenesis in mammary cells stimulated with EGF [102]. COX-2 is overexpressed in 56% of breast cancers, including DCIS as well as ­infiltrating ductal and lobular carcinomas [102, 103]. Mammary glands from ­transgenic, the MMTV-COX-2 mouse model, demonstrate hyperplasia, dysplasia, and development of metastatic tumors [104]. The specific COX-2 inhibitor, ­celecoxib, is currently being tested for its ability to prevent cancer in humans. When given at 500 ppm, celecoxib significantly suppresses tumor incidence and PGE2 levels in the MMTV-ErbB-2 model [105]. This drug is also under evaluation in our laboratory using other transgenic models. Energy Balance Interventions Obesity is an established risk factor for postmenopausal breast cancer and is ­associated with poor prognosis for both pre- and postmenopausal breast cancers. A number of transgenic mouse models have been used to assess the impact of obesity or energy balance interventions, including calorie restriction or exercise, on ­mammary tumor development or progression, as recently reviewed [106].

18.2.3 Apc-Deficient Models for Intestinal Cancer Prevention Studies The ApcMin mouse model [107] is an excellent example of the utility of animal models in understanding the connection between energy balance and genetic susceptibility to cancer, as well as for testing chemopreventive agents with potential anticancer effects against intestinal cancer. For example, intestinal polyp burden was significantly reduced in ApcMin mice following both a 4-week and a 10-week 40% calorie restriction regimen [108]. Additionally, insulin-like growth factor (IGF-1) levels, as well as inflammatory markers, were reduced in calorie restricted mice [108]. Further, an olive oil based diet high in fruits and vegetables reduced overall polyp numbers

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in the 10-week intervention, suggesting a role for dietary based anti-­inflammatory compounds in cancer prevention [108]. We also showed that running wheel exercise via a treadmill demonstrated no overall effect on polyp burden in ApcMin mice [109]. However, when male and female mice were analyzed separately, running wheel exercise led to fewer intestinal and total polyps in male mice [109]. A prime example of the efficacy of combination therapies is the administration of both celecoxib and immunotherapy, specifically a carcinoembryonic antigen (CEA)-based vaccine, in CEA.Tg/ApcMin mice [110]. A large majority of the CEA. Tg/ApcMin mice receiving the combination intervention remarkably remained polyp free for more than 2 years, resulting in better health status and increased survival [110]. This combination regimen is of particular relevance in high-risk individuals, such as those with the genetic syndromes of familial adenomatous polyposis, a hereditary syndrome in which individuals develop hundreds of adenomatous ­polyps, and hereditary nonpolyposis colorectal cancer. Treatment with celecoxib reduced polyp numbers in approximately 50% of patients with familial adenomatous polyposis studied over a 6-month period [111], but there were many ­nonresponders. The potential to combine celecoxib treatment with a cancer vaccine or other intervention in this high-risk population based on the results from preclinical animal studies is very encouraging. This model appears to be particularly sensitive to the preventive activity of ­anti-inflammatory agents such as nonsteroidal anti-inflammatory drugs (NSAIDs) and selective COX-2 inhibitors. For example, studies with the selective COX-2 inhibitors nimesulide [112] and celecoxib [113] in ApcMin mice demonstrated ­efficacy against intestinal tumorigenesis. Findings from these studies helped ­support human trials of celecoxib in individuals with familial adenomatous polyposis [111], and the combined mouse and human data encouraged the decision by the US Food and Drug Administration to approve this class of agent in persons with familial adenomatous polyposis. The findings that at least some COX-2 inhibitors at high doses increase risk of myocardial infarction has limited the use of these drugs as long-term cancer prevention agents [114]. However, their anti-cancer effects are unquestioned, providing a striking example of the development and translation of preclinical findings in relevant genetically altered mouse models to clinical application.

18.2.4 Emerging Models of Pancreatic Cancer Historically, pancreatic cancer research relied heavily on both orthotopic and chemically induced models of pancreatic cancer. Orthotopic models, unfortunately, constrain the field to investigations examining tumor progression, which are relevant to translational studies, but have limited utility for cancer prevention studies. Chemically induced models rarely develop pancreatic ductal adenocarcinomas (PDAC), the most common histological type of human pancreatic cancer, and thus they can provide little insight into the genetic alterations characteristic of

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PDAC [115]. Early attempts at transgenic modeling of exocrine-specific pancreatic cancer employed promoters that directed acinar-specific transgene expression, i.e., elastase [116, 117] and Mist1 [118]. Although most of these transgenic models produce acinar cell neoplasms, some do develop an acinar-ductal metaplasia that results in mixed acinar ductal neoplasias with some clinical relevance. The subsequent generation of transgenic pancreatic cancer mouse models owes its advent to cumulative breakthroughs, including discovery of the genetic basis of PDAC, identification of pancreatic intraepithelial neoplasia (PanIN) as the neoplastic precursor to PDAC, and more recently the development of promoters specific to particular pancreatic cell lineages [119]. Consequently, clinically relevant mouse models have been developed that exploit the tumorigenic nature of activating K-ras mutations, the defining genetic lesion of PDAC. Hingorani et al. established the KrasG12D Pdx-1Cre model, which results in an array of mouse PanIN (mPanIN) lesions with a protracted latency period of PDAC, nicely mimicking the accumulation of K-ras anomalies in human disease [120]. In the pancreas, coupling K-ras activation with an Ink4a/Arf deletion [121] and/or a p53 point mutation [122] dramatically potentiates the aggressiveness and invasiveness of the extensive mPanIN lesions. In fact, invasive PDAC, which readily metastasizes, develops in KrasG12D Pdx-1Cre Ink4a/Arf-/- mice within 7–11 weeks. Similarly, KrasG12D Pdx-1Cre p53R273H [122] or p53+/- [123] mice form strikingly aggressive lesions with a short latency period. These mouse models have corroborated the sequential nature of genetic anomalies outlined by the step-wise carcinogenesis scheme, making them all extraordinarily relevant models. However, some may be too aggressive to effectively study pancreatic cancer prevention. The longer latency period and age-related progression of the KrasG12D Pdx-1Cre mouse model are characteristics that would offer an opportunity for modulation. In fact, ­investigators showed that a COX-2 inhibitor, nimesulide, hinders progression of mPanIN precursor lesions in these mice [124]. The link between enhanced COX-2 expression and pancreatic cancer is well established and has recently been exploited in the creation of another pertinent mouse model. Transgenic expression of the inflammation-induced enzyme COX-2, driven by the bovine keratin 5 promoter, in the BK5.COX-2 mouse model results in an extensive matrix of fibrosis, inflammatory cell infiltrate, and acinar-ductal metaplasia with 100% penetrance that leads to significant lesion development and the presence of adenocarcinoma [125]. Histological progression strongly recapitulates the evolution of chronic pancreatitis to human pancreatic cancer. Although moribundity occurs in these mice between 6 and 8 months of age, the authors demonstrated that treatment with the selective COX-2 inhibitor celecoxib abrogates the influence of COX-2 overexpression and significantly extends survival [125]. This model has also proven to be exquisitely sensitive to the anti-cancer effects of calorie restriction (Lashinger, personal communication). In conclusion, the development of genetically engineered mouse models applicable to comparisons with the progressive character of human pancreatic cancer provides exciting opportunities for pancreatic cancer prevention studies.

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18.3 Summary and Conclusions Experimental models of cancer have been crucial to advancing our understanding of the tumorigenesis process, and recent progress in the field of molecular carcinogenesis has revealed multiple targets (Fig. 18.1) for the nutritional modulation and chemoprevention of cancer. We must now fully utilize this knowledge base, as well as capitalize on the availability of tools such as transgenic mice, to identify critical, modulatable targets and make important progress towards one of the major goals in contemporary cancer prevention research: the development of effective mechanismbased strategies for preventing human cancer. Successful attainment of this goal will require the integration of the very best science from multiple levels of investigation, including animal studies, clinical and epidemiologic research, and basic molecular and cellular biologic research. All three levels of investigation are essential in this effort, although in our view animal model studies play a critical central role. Thus, the availability of highly relevant animal models will greatly facilitate future progress in cancer prevention research. In this review, examples of ­cancer prevention studies that have utilized genetically altered mouse models were discussed. Numerous mouse models with cancer-related genes overexpressed or inactivated have been developed in recent years and are cataloged in online databases

Fig. 18.1  Molecular targets in mouse models for cancer prevention. Depicted in the cell are s­ everal key genes and their interacting pathways, which when altered in mice represent important models for studying cancer prevention strategies

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such as the Induced Mutation Registry Database (http://www.jax.org/imr /index.html) and the Mouse Genome Informatics site (http:// www.informatics.jax.org ) maintained by the Jackson Laboratory. In addition, a systematic cataloging of potential mouse models with pertinent histopathology and other aspects of characterization has been undertaken under the auspices of the NCI-sponsored Mouse Models of Human Cancer Consortia, which can also be viewed on the Internet (http://emice.nci.nih.gov). Many of these models have been used effectively in studies focusing on toxicology and carcinogenesis, and some of the models are also being used for cancer prevention studies. In conclusion, mice with specific (and human-like) genetic susceptibilities for cancer provide ­powerful tools for developing and testing interventions which may inhibit the process of carcinogenesis in humans.

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Part VII

Metastasis Models

Chapter 19

Models for Evaluation of Targeted Therapies of Invasive and Metastatic Disease Suzanne A. Eccles

Abstract  This chapter will indicate molecular targets that may be appropriate for metastasis therapy and how they may be evaluated in appropriate preclinical models of malignant disease. The focus will be mainly on the evaluation of agents directed against oncogenic signaling pathways, which have provided the majority of new targeted therapies in the last decade. These approaches include small molecules and antibodies with a brief mention of gene therapeutic and immunological approaches. There is also a detailed description of a variety of tumor models (including syngeneic, xenogeneic, transgenic, and orthotopic) and their use in different therapeutic applications, with a brief discussion of various methods for measuring efficacy. Keywords  Metastasis • Invasion • Syngeneic • Xenogeneic • Orthotopic • Transgenic • Receptors • Inhibitors • Cancer therapy • Signaling • Stem cells • Angiogenesis

19.1 Introduction Metastasis is the most frequent cause of cancer death and novel systemic therapies are required to improve patient outcome [1–3]. Unfortunately, preclinical drug development does not routinely include animal tumor models that mimic metastatic human cancer, and truly “adjuvant” preclinical studies are rare. This deficiency has recently been highlighted by the finding that some antiangiogenic therapies may enhance metastasis, emphasizing the need for more rigorous preclinical evaluation of new agents in appropriate models of disseminated disease [4]. Animal models have been criticized for failing to predict responses in clinical trials [5]. Notably,

S.A. Eccles (*) Tumour Biology and Metastasis, Cancer Research UK Centre for Cancer Therapeutics, The Institute of Cancer Research, Cotswold Rd, Belmont, Sutton, Surrey, SM2 5NG, UK e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_19, © Springer Science+Business Media, LLC 2011

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angiostatin was found to be effective in several syngeneic models, but failed in the clinic, possibly because the immunogenicity of the tumor contributed to its ­responsiveness [6]. A second “failure” was of matrix metalloprotease (MMP) inhibitors, now appreciated as being partially due to the inadvertent inhibition of tumor suppressor proteases and the greater dependency of micrometastases on MMPs for neoangiogenesis compared with established tumors [7]. Also, with the growing realization of the importance of cellular context and microenvironment [8], it is clear that the use of inappropriate models or a failure to take into account differences in oncogenic drivers can give misleading results [9]. Cytotoxic therapies, in particular, are likely to be more active in fast growing rodent tumors and the fact that the attrition rates are less for the more recent targeted therapies [10] provides encouragement that animal models are a valuable intermediary between target validation and clinical trials of novel agents. Preclinical models of cancer can contribute enormously to drug development in many ways: firstly by target validation (e.g. by gene knockdown) or in seeking novel targets by interrogating genetically defined cell lines with lentiviral RNAi libraries in a “synthetic lethality” approach. However, complete ablation of a gene is not the same as inhibition of a function (e.g. enzyme activity, ligand binding) with a drug or antibody. Secondly, animal models are invaluable for identifying biomarkers of response (which are now de rigueur in hypothesis-testing clinical trials) and for identifying off-target or potential harmful side effects [11]. The original mainstays of cancer research were often highly antigenic, ­transplanted rodent tumors. A few of these (most notably B16F10 and Lewis lung carcinoma) were used as metastasis models, generally by injecting the cells i.v. to give lung colonies. Later, human tumors were grown s.c. in immunodeprived mice but rarely metastasized. So the advantage of testing drugs against human molecular targets was counterbalanced by the lack of the definitive feature of malignant human cancers, i.e. distant metastasis. Fortunately, when human tumors are grown orthotopically (i.e. in the correct anatomical site) there is a higher probability of metastasis [12]. Athymic and SCID mice, however, have abnormal immune (and endocrine) systems, and therefore limitations for some therapeutic applications. Transgenic technology is finally enabling the development of more “patientlike” models of cancer; where a known genetic aberration is introduced into the germ line and is seen as “self” by the host. However, it remains to be proven in most cases that the strong over-expression of a single oncogene (or “knock out” of a ­suppressor gene) results in cancers that accurately mimic the human malignancy (especially in relation to metastasis) where multiple genetic, epigenetic, and ­environmental factors contribute to progression. To overcome this, double and ­triple transgenic animals are being developed whose tumors show increased ­malignant potential [13]. Also, well-established and/or simpler models still have their place for the high throughput that is required for modern drug development. The art of preclinical evaluation of targeted therapies is choosing the most ­appropriate model for the particular question being addressed. The use of genetically tagged cells now enables earlier detection and more accurate quantitation of micrometastases. However, there can be issues in

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d­ etermining whether the tumor cells detected in blood, marrow, or other sites are clonogenic – and this applies to clinical studies too. In addition, increasingly sophisticated imaging techniques are enabling high resolution detection and ­functional readouts of internal tumors. Many are directly applicable to the clinic and others such as bioluminescence and fluorescence require the use of ­engineered cells or animals expressing the relevant transgene. In addition, methods for reporting on the delivery and activity of agents are being developed, as well as revealing, for example, intratumoral metabolic, angiogenic, and proteolytic functions.

19.2 Therapeutic Strategies for Targeting Metastases It is generally accepted that further genetic or epigenetic changes (beyond those involved in cellular transformation) are required for metastatic competence, although the recognition of metastasis-associated “signatures” in primary tumors has suggested that this may be a relatively early event [14–16]. Experimentally, it has been easier to identify metastasis suppressor genes than those whose activity can potentiate dissemination because the latter may require the activity of complementary genetic changes to manifest their potential. Interestingly, several of the best characterized metastasis suppressors prevent outgrowth of cells at secondary sites rather than inhibiting earlier phases [17]. This may be a reflection of the essential need of multicellular organisms to prevent ectopic growth of any cells that may escape from their normal tissue boundaries.

19.2.1 Target Identification and Validation Drug development is expensive and lengthy, so potential targets must be thoroughly validated [18]. Initial evidence for a “good” target often comes from studies of genetic aberrations in human cancers (translocations such as the Bcr-Abl oncogene and APC or PTEN loss leading to activated Wnt/b-catenin and PI3K pathway signaling, respectively) or mutations (e.g. BRAF in melanoma). Several were first identified in rodent tumors (e.g. c-ErbB2/neu in rats treated with a chemical carcinogen and Wnt from the insertion sites of oncogenic viruses). The process has become much more efficient with the advent of transgenic mice where genes can readily be mutated/hyperactivated and linked to tumor development. Also RNAi technology has provided a means to check the potential effects of pharmacological inhibition of a target (although with caveats) and increasingly as an approach to determine the “Achilles’ heel” of tumors with a specific genetic defect or to identify synergistic combinations. Since human cancers contain multiple genetic mutations, it is critical to discern the “drivers” (directly and causally linked to malignancy) from “passengers” resulting

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from genetic instability and whose presence is incidental. It is also important to determine the role(s) of these gene products before they are considered as targets for therapy. Some molecules are associated with early events (e.g. immortalization, initiation) but may not be essential for maintenance of the malignant phenotype and become redundant once a tumor (or metastasis) is established. Recently, several studies in experimental animal models and human cancers have identified site-specific metastasis signatures, e.g. for lung, bone, and brain [19–21]. This may reflect adaptation of tumor cells to a particular environment or preselection of cells with a survival advantage in specific tissues. Metastases (even those derived from the same primary but developing in different sites) may depend upon different genotypes/phenotypes for their establishment, continued expansion, or further spread. Therefore, although metastatic models may be more technically ­challenging than simple subcutaneous models, they are likely to give more accurate, sensitive, and predictive readouts of novel therapeutic approaches.

19.2.2 Molecular Targets The following section provides examples of established and emerging molecular targets with key roles in metastatic disease. Examples of targets in cancer cells, related to different steps in the metastatic cascade, and associated with specific organ tropisms are shown in Fig. 19.1a–c. 19.2.2.1 Oncogenic Receptor Tyrosine Kinase Signaling Pathways Monoclonal antibodies were initially the mainstays of targeted therapies against oncogenic cell surface receptors, but more recently have been supplemented by small molecule inhibitors, which have the advantage of oral bioavailability. Such drugs have been developed against proteins, which are mutated, overexpressed, or hyperactivated in cancers. Many are targeted against kinases which, because of the structure of their ATP binding sites, lend themselves to inhibition by small ­molecules. Selective drug (and some antibody-based) therapies targeting oncogenic signaling pathways have emerged as important new classes of anticancer agents and are showing activity in the clinic [22]. However, only subsets of patients respond and resistance remains an issue [23]. Key targets include receptor tyrosine kinases (RTK) such as EGFR, ErbB2/ HER2, PDGFR, c-KIT, c-MET, RON, RET, FGFR, IGF1R, etc. on tumor cells and VEGFR2 on vascular endothelial cells. In addition, elements of their downstream signaling pathways including PI3K, MTOR, AKT, BRAF, SRC, RAF, PLCg, etc. are receiving increasing attention [24–27]. Many of these pathways have been implicated in invasion, metastasis, and angiogenesis as well as primary tumor growth (Fig. 19.1a) [28–36]. Unexpected complexities may be uncovered in ­experimental models, for example, AKT1 co-expressed with PyVMT or ErbB2 in

a

RECEPTORS

ADHESION MOLECULES

CD44 Cadherins

INTEGRINS

PROTEASES

c-Kit, TGFβ, GPCR, Fzd Eph receptors Plexins Ion channels

EGFR, HER2 c-Met, RON PDGFR IGF1R FGFR

α6β4 α5β1/2 α9β1

MMPs, ADAMs uPA Cathepsins Heparanase

ECM P

CELL

Rho ROCK

Rac P P

P

P P P

TRANSCRIPTION

HIF1 HDAC AR, ER MYC NFKB, BMI1 Snail, Twist, ZEB1,2

b

gelsonin

HSP90 chaperone

P

SIGNALLING PATHWAYS

Ras, RAF FAK, PAK, ILK PI3-kinase- AKT-mTOR PLCγ, Memo c-Src, β-catenin Rho, Rac, ROCK, ezrin

profilin cofilin

P P

NUCLEUS

PRIMARY TUMOUR

METASTASES

angiogenesis VEGF/VEGFR αVβ3, αVβ5 PDGFR, Tie2 Robo/Slit

EMT

extravasation

c-MET, Wnt Vimentin, N,-cad Snail, Twist

Integrins Proteases COX-2

motility

hypoxia HIF pathway SIAH LOX PI3K

cancer ‘stem’ cells Hh, telomerase Notch, BMI1, CXCR4 Wnt, Kit, CD44 ABC transporters

C-MET, EGFR, HER2, PLCγ, ROCK, Rac, Rho, Src, PAK

intravasation ErbB2, MMP1,2 Epiregulin Cox-2

anoikis resistance TrkB PI3K/AKT

premetastatic niche VEGFR1 Fibronectin CXCR4 MMP9,LOX osteopontin

ectopic growth RTK-ligands GPCR-cytokines Chaperones JNK/p38

Fig. 19.1  (a) Molecular targets for therapy of metastasis, (b) The role of different molecules in specific elements of the metastatic cascade,

452

S.A. Eccles

c

SITES OF METASTASIS Bone

RANK/RANKL PTHrP ET1, BMP c-Src, GM-CSF Fzd-Wnts EphA2 FGFR/bFGF IGF1R/IGF PDGFRα/PDGFβ CXCR4/CXCL12 TGFR/TGFβ Osteopontin MMP2, MMP9

Brain

HER2-NRG IGF1R/IGF EphA2/ephrinA3 IL6R/IL6 EGFR(vIII)/EGF TGFR/TGF β uPA/uPAR/PAI-1

Lung

EGFR/EGF/EREG CXCR4/CXCL12 CCR5/CCL3 CXCR2/CXCL1 CXCR3/CXCL10 MMP1MMP2 , ANGPTL4 COX2 VCAM1 SPARC CD44 HSP27

Liver

EGFR-TGFα C-MET-HGF EphA2/ephrinA1 CXCR5/CXCL13 CXCR4/CXCL12 IGF1R/IGF1/2 PDGFRβ/PDGF CD44-HA MMP7 MMP9 HSP60

Lymph node

VEGFR3/VEGFC CCR7/CCL19/21 CXCR4/CXCL12 EphB4/ephrinB2 PDGFRα/PDGFBB IGF1R/IGF1 IGFBP7 PAX5 α1β5 integrin HSP110 GRP94 GRP78

Fig 19.1  (continued)  (c) Key signaling pathways implicated in site-selective metastasis. Factors released by tumor cells activate osteoclasts and/or osteoblasts. A vicious cycle between these three cell types and growth factors released from the stroma potentiate tumor cell invasion and bone destruction/remodeling. Cytokines and their receptors may also contribute. In the brain, growth factors are produced, notably by astrocytes, which can stimulate the proliferation and invasion of tumor cells expressing the cognate receptors. Angiogenic factors released by tumor cells are also implicated. An important determinant of lung metastasis is the specific ability of tumor cells to effect transmigration of the lung endothelium. Factors implicated include those released by tumor cells which act upon endothelial cells (e.g. EREG and ANGPTL4), and paracrine stimulation of tumor cells mediated by bone marrow stem cell-derived CCL5. Within the lung, growth may be stimulated by CXCR4-CCL12 interactions. Receptors on tumor cells such as EGFR and MET may respond to high levels of their ligands in the liver. Paracrine interactions between tumor cells, host stromal cells, and endothelial cells involving ephrins and chemokines are also evident. The major signaling pathways implicated in lymphatic metastasis are the VEGF-C-VEGFR3 and CCL21-CCR7 systems. PDGF-BB acting through RTK receptors may also play a role

transgenic mice enhanced primary breast cancer growth, whereas AKT2 increased pulmonary metastases [37]. Animal models, primarily human tumor xenografts and some transgenic models have been used to evaluate novel RTK inhibitors (examples are shown in Tables 19.1–19.3) [38]. Tumor models should be selected with knowledge of their molecular pathology, e.g. testing PI3K pathway inhibitors in tumors where activation is induced by loss of PTEN, overexpressed or mutated RTK, or mutant P110a [39, 40]. It is also important, whenever possible, to evaluate novel agents in metastatic models as ­signaling pathway activation, drug access and responses can vary in different sites [3, 34, 41]. Recently, Lapatinib (EGFR/ErbB2 inhibitor) was shown to be effective in

M24met (mouse) B16 sublines (mouse)

Lewis lung 3LL (mouse)

R4OP (mouse)

C26 (mouse)   MC38 (mouse) CC531 (rat)

K7M2 (mouse)

Melanoma

Lung

Pancreatic

Colon

Osteosarcoma

Walker 256 (rat)   HOSP1 (rat)

i.v.

i.v. Spleen, portal vein Spleen Intrahepatic Lung

Lung Liver Liver Liver

Lymph nodes

Lung Lymph nodes

s.c. Lung Pancreas

Lung, liver

Lung Lung Liver Lung

Bone Bone Lung, lymph nodes

Metastases Lung   Bone marrow Bone, lung Lung Local invasion, lung, LN

i.v.

Id i.v. Spleen i.v.

Intracardiac Intratibial Mfp

Table 19.1  Syngeneic metastatic tumor models Tumour type Name (species) Injection site Breast 4T1 (mouse) Mfp     4T1/E/M3 i.v. 4T1 66cl4 Mfp BN472 (rat) Mfp MTLn3 (rat) Mfp

CXCR4 peptide

CXCR3   CEA, TLR4, immunotherapy COX-2 inhibitor celecoxib

aVb3 targeted nanoparticles + doxorubicin

EP4 receptor antagonist ONO-AE3-208 MMPi MM1270 Celecoxib, MM1270

  MM1270 MMPi HMGB1 vaccination Mab DC101 (VEGFR2) + mab TA99 (TYRP-1)

TGFb antibody 1D11, tranilast ALK5 kinase inhibitor SM16   aVb3 uPA inhibitor Multiphoton microscopy to visualize motility and intravasation, EGFR IKB inhibitors celastrol, parthenolide   MMPs batimastat

Therapeutic target

[270]

[62]   [268] [269]

[220]

[267] [221]

[266]

[262] [263] [264] [265]

[261]

(continued)

[203, 209, 260]

Reference(s) [255] [256] [257] [86, 258] [105] [187, 259]

19  Models for Evaluation of Targeted Therapies of Invasive and Metastatic Disease 453

RENCA

Dunning R3327 MATLyLu (rat) MAT-LyLu (rat) Dunning R-3327 AT6.3 (rat)

Kidney

Prostate

Prostate   i.v. s.c.

Kidney

Injection site Bladder wall Lung, lymph nodes Bone Lung  

Lung

Metastases Invasive IL-2 uPa inhibitor B-428   CEP-701 Trk inhibitor

VEGF Trap

Therapeutic target Cathepsin B, MMP9 uPA

Glioma

Hras/SV40T transformed astrocytes (mouse)

Brain

VEGFR2/PDGFR Invasion along blood vessels and leptomeninges C6 (rat) Brain Invasion Anti-HIF (YC-1) Mfp mammary fat pad, i.v. intravenous, Ipro intraprostate, i.c. intracardiac, TYRP-1 tyrosinase-related protein 1

Name (species) MB49-I (mouse)

Tumour type Bladder

Table 19.1  (continued)

[170]

[82, 95, 276]

[273] [274] [275]  

[272]

Reference(s) [271]

454 S.A. Eccles

Breast Bone Lung   Lung Lung, LN, liver Lung Lungs, bone, heart, liver, kidney Brain Lung

Intracardiac, i

i.v.   Mfp Mfp   Mfp Intracardiac

BR (brain) MDA MB 435#

       

Intracardiac s.c.

Bone, brain, lung

  s.c.   s.c./orthotopic   Bone

  LNCaP-H1   LNCaP C4-2b   MDA Pca 2b

MDA MB 231 sublines, notably BO2 and BoM-1833 (bone)

Bone Lung and rib LN, bone     Bone

s.c. or intraprostate   Intracardiac

Metastases Bone LN LN LN LN, lung, kidney, liver, pancreas, adrenal, bone LN, lung   Brain

LAPC-9 CWR22Rv-1 DU145

Table 19.2  Xenogeneic metastatic tumor models Tumour type Cell line Injection site Prostate PC-3M Intra-tibial+ PC-3LN3 Prostate PC-3MM2 Prostate PC-3AR-A1 Prostate ARCaP prostate Prostate

  C-MET + Met decoy, NK4

LOX inhibitor BAPN MMP1i and anti-PAR1peptide RNA aptamer against osteopontin Sunitinib Axl knockdown and antibody CTCE-9908 (CXCR4 inhibitor) Lapatinib    

VEGFR2 or VEGFR3 inhibitors   Ras/RalGef transfected. High EGFR, antiVEGF therapy and MR imaging   Selected by growth in hypoxia   Detected by QRT-PCR Gene therapy, anti-integrins Implanted human bone fragments, high IGF1R KM1468 mab

Molecular features/therapeutic targets uPA+ 17-AAG, dasatinib NVP-AUY922 Dasatinib AR transfected Osteoblastic and osteolytic

[284] (continued)

[279] [21, 99] [19, 83, 213, 280] [47, 59, 281, 282] [42, 211, 283]

[204]

[207, 208, 278]

[277]

[223]

Reference(s) [219] [56] [52] [218] [208]

19  Models for Evaluation of Targeted Therapies of Invasive and Metastatic Disease 455

Colon

Portal vein

Spleen i.v. Cecal wall   Spleen Caecum

Spleen  

LoVo

HT29   TK-4   KM20 KM12 C

COLO 320DM HCT116

 

CN34-BoM2

Bowel Liver Spleen/portal vein

Mfp Mfp, i.v. mfp Intracardiac s.c./mfp Intracardiac

MDA MB 453 LvBr MCF10A+HRAS+BMI1 MDA MB 468LN BT474 GI101 CN34 BrM2

HCT116    

Subrenal Intracardiac Internal carotid

Injection site

    MDA MB 435 Br4

Table 19.2  (continued) Tumour type Cell line

Liver  

Liver Lung Liver     LN, liver

Lung   Liver Liver Liver

Bone

Lung Lung, spleen liver, brain LN Bone Lung, LN Brain

Lung Bone, adrenal, brain, ovary Brain

Metastases

PIK3CA mutant RON+, RON shRNA 17-DMAG BLI Oncolytic adenovirus targeted via A33 antigen RON+, CXCR3+   CXCR2+, SU6668 (RTKi) effects on premet niche CMET+, activated PI3K pathway High TGFa increased VEGF, MMPs, lymphatic density NOG mice, FTI inhibitor CH4512600  

Avastin, PD-0332991, BLI GFP tagged Activated notch, respond to g secretase inhibitor Erlotinib (EGFR) High activated MAPK High OPN Trastuzumab   ST6GALNAC5 potentiated brain extravasation Src dependent survival in bone marrow

Molecular features/therapeutic targets

[195]  

[62]   [113]   [288] [196]

[159]

[193] [53, 287]  

[212] [191] [285] [43] [286] [19, 211]

[283] [205] [198]

Reference(s)

456 S.A. Eccles

DBM2 U251 U87

Glioma

Intracardiac Intracranial (nu rat)

PC14/B CRL-5904

IMR32 CHLA-255 SK-N-BE

Intracardiac

PC9BrM3

Neuro-blastoma

  Intracardiac

H69VP H2030BrM3

i.v./intracranial

Intratibial Intrafemoral i.v.

s.c.

i.v.

SBC-3/DOX

OH1, SW2

i.v.   i.v.

A549 H226 RERF-LC-AI

 

 

SCLC

Lung

Spleen i.v.

LS174T GW-39

Lung/brain invasion

Bone, liver Bone Liver

Lung

Brain Brain

Brain, bone

Brain, bone

Liver, kidneys, nodes

Lung   Multiorgan

 

Liver Lung

[202] [161, 293]  

[188]

[292] [201]

   

[73]

[106]   [291]

Cells selected for lung metastasis showed [200] increased brain invasion. High IL6, IL8, MCP-1, GM-CSF. Treated with 17AAG (continued)

High IGF1R osteolytic NPG targeted osteoprotegerin Sunitinib + rapamycin

More metastases in pfp/rag2 mice than SCID

ONO-4817 MMPi   MMP inhibitors > liver mets via stromal effects Widespread metastases in NK-depleted SCID mice VEGFR2 inhibitor ZD6474 Wnt/TCF-LEF1-HOXB9 associated with brain mets Wnt/TCF-LEF1-HOXB9 associated with brain mets High S100B, VEGF and MMP9 Kca channels influencing BBB

CEA+, mab A5B7 [289] Pretargeted SPECT (anti-CEAmab TF2/anti- [171] HSGbispecific mab + radiolabelled HSG peptide) and PET imaging Labetuzumab anti-CEA mab + GMCSF [290]

19  Models for Evaluation of Targeted Therapies of Invasive and Metastatic Disease 457

686LN

GDC185 OSC19

SN12-VC

SN12-SVR WM239A 113/6-4L

SCCHN

 

Renal ca

Melanoma

131/4-5B WM266.6 MeWo   UISO-Mel6 FEMX-1 SKMEL-28 A375 A375Br

HT1080 SW620

Sarcoma colon ca

NBT-II

Orthotopic (subdermal)   i.v. i.v.   s.c.   i.v. Intracardiac Internal carotid

Orthotopic (kidney)

Orthotopic--> (sublingual) Floor of mouth Tongue

Chick embryo CAM

Brain Lung Lung Lung, brain, kidney, liver Lung   Brain Bone Brain

Liver lungs, LN

Lung, liver, adrenal, pancreas, spleen

LN LN

Lung

Lung, liver

Intracardiac, Bone, soft tissues, lung intratibial, bladder s.c. LN

(T24) TSU-Pr1 series

Lung, bone

Lung Lung

Metastases

Bladder

i.v. Intratibial

Injection site

i.v.

SAOS-LM7 TE85-143B

Ewing’s sarcoma TC-71

Osteo-sarcoma

Table 19.2  (continued) Tumour type Cell line

[60]

[186]

[24]

[296]

[295]

[61] [294]

Reference(s)

  [52] [67, 83]   [300, 301]   [107] [302, 303]  

[220] [299]

aVb3 Metronomic chemotherapy   BRAFmut, HSP90i Hh VEGFR2 CEACAM1+, MRI   Mab L235 to melanotransferrin Laminin antagonist peptide Increasted STAT3, bFGF and VEGF

[298]

CXCR4+, CXCL12, PDGF-D/PDGFR, Gleevec

  [297] DC101 (VEGFR mab), cetuximab (EGFR) [78] + BLI

CXCR4+

High levels of angiogenic factors and MMPs

FGFR+ EMT reversion (MET). High MT1-MMP, MT2-MMP, MMP9 Src

IGF1R+

CXCR3+ Src

Molecular features/therapeutic targets

458 S.A. Eccles

SiHa

Cervix

Cervix

Pancreas     Pancreas   Pancreas Pancreas LN, lung, viscera

LN, liver LN, peritoneum

  Invasion, ascites, liver

LN, spleen, liver, kidneys

17-DMAG, FRNK (FAK antagonist)

Hh+, IPI-269609 (Hhi)+/ - gemcitabine   NK4 and anti-HGF antibody POP33 prodrug activated in hypoxic cells ZD6474+ gemcitabine   Somatostatin receptor+ imaged by microPET and MR [306]

[69]   [214, 215] [304] [305]    

Leukaemia SHI-1 i.v. Bone marrow, brain Detected by RT-PCR and BLI [192, 307] SCCHN squamous cell carcinoma of the head and neck, HGF hepatocyte growth factor, # may be melanoma, FTI farnesyltransferase inhibitor, NOG NOD/ Shi-scid IL2Rgnul, VEGFR2 vascular endothelial growth factor receptor 2 (Kdr/Flk), BLI bioluminescent imaging, PET positron emission tomography, NPG neural progenitor cells

E3LZ10.7 Capan-1   SUIT-2   L3.6pl AR42J

Pancreatic

19  Models for Evaluation of Targeted Therapies of Invasive and Metastatic Disease 459

NRASQ61K, Ink4a− /− HGF+ survivin (+UV) HGF

Genetic manipulation PTEN loss +BRAFv600E

Liver and lung Lung Lung Mainly lung and some LN

                    Lung

Constitutively active RON MMTV-PyVmT   MMTV Neu

MMTV or WAP also used to target Wnt-1, c-Met, Ras, Notch, HGF, IGF2, TGFa FGFs, Cox-2, BRCA1+/-, etc. Also composite transgenics eg PyMT/CD44-/-Neu/TGFb PyVMT/AKT Neu/S100A4 PyVMT/uPA-/Wnt1/p53+/BRCA1/p53+/ErbB2/PTEN-/MMTV-PyVMT + dox inducible TGFb

Pancreatic acinar

Breast

Liver

Elastase-tv/a/p53−/−

 

LN and liver Lymph node Liver

Gut, mesentery

Metastases LN and lung. BRAF alone – benign tumours   LN and lung Liver

RIP1-Tag2 RIP1-Tag2 + IGF1R KPC Kras and p53 mutant

Pancreatic neurendocrine cancer RIP1-Tag2

Tumour type Melanoma

Table 19.3  Transgenic metastatic tumor models

  Dominant negative PLCg Galardin MMPi Mab mu4D5 (trastuzumab precursor) EphA2 mab                     TGFb antisense

 

HSP90 and glycolysis inhibitors Anti-VEGFR mab (DC101)   IPI-926 (Hh) + gemcitabine

Hh inhibitors    

Notes/experimental studies Rapamycin (mTorc1), PD325901 (MEK)

[230] [282] [281] [236] [238] [37] [229] [250] [240] [231] [314]  

[312] [232] [101] [313]

[311]

[310]   [70]

[54, 82]

[67] [308] [309]

Reference(s) [31]

460 S.A. Eccles

MyrAkt, p53-/-

Mutant b1 integrin+ chem. carcinogenesis LN

Apc

SCCHN

SCC (skin)

Colon

TRb(PV/PV)/Pten(+/-)

Apc mut + Smad2+/-, Smad4+/-, Kras mut, EphB2+/Lung

Invasion

LN

N/a  

[246]

[245]

[68]  

[44]

Activated mTOR/AKT

[241]

Mostly adenomas; require [237] additional mutations for invasion, no metastases. COX-2 inhibitors (celecoxib)

NA

NA. PET imaging

Cyclopamine Hh Antag

sunitinib

Retinal-derived tumour TRP-1/SV40 Tag PAI-/Brain Upregulated FGF1 [103] MMTV mouse mammary tumour virus, WAP whey acidic protein, PyVmT polyoma virus middle T, TRb thyroid hormone receptor beta, TRP-1 tyrosinerelated protein 1, MSC mesenchymal stem cells, LN lymph node

Thyroid

Ptc+/-, p53-/ 

Medullo-blastoma

Min/-

Targeted deletion of PTEN and p53 in LN, spleen, liver, diaphragm Dergulated mTOR pathway, [319] bladder epithelium with adeno-Cre virus rapamycin

Bladder

LN

[316] [317] [318] [158] [160]

Kras/LkbL/L

Dominant negative PLCg Silibinin Oncolytic HSV adenovirus MSC targeted TGFb  

Lung

Lung, Liver, kidney Lung Lung  

[315] [169]  

TRAMP/PTEN+/  TRAMP-C2    

Sca1 stem cells PET imaging CD44 knockout

Prostate

Lung Lung Lung, liver

P53/RB mutants MYCN P53tm1+/CD44+/+

Osteo-sarcoma

19  Models for Evaluation of Targeted Therapies of Invasive and Metastatic Disease 461

462

S.A. Eccles

MDA MB 231 breast cancer brain metastases [42]. This is important because HER2 has been linked to an increased frequency of brain metastases and trastuzumab is unable to cross the blood–brain barrier, leading to a high incidence of relapse at this site. Trastuzumab was shown to be effective in a breast cancer xenograft bone ­metastasis model, but effects were limited if administered when lesions were already established [43]. Sunitinib, a multitargeted angiogenesis inhibitor, improved survival of transgenic mice bearing metastatic lung tumors induced by mutant Kras plus knock out of the suppressor Lkb1. However, the incidence of local and distant metastases was not reduced, suggesting that the benefits were primarily due to effects on the primary tumor [44]. Clearly, these data have implications for clinical studies. Increasingly, genetic analyses of metastases from patients and experimental models are yielding potential new targets for therapy [45]. Examples include TRKB, identified in a screen for inhibitors of anoikis [46] and Axl, implicated in metastasis and angiogenesis [47]. These data, combined with elegant validation studies will provide an armamentarium of selective inhibitors whose judicious use should help to overcome target redundancy or escape mechanisms and allow combination molecular therapies to rival and perhaps ultimately replace complex and toxic chemotherapy regimes. 19.2.2.2 HSP90 Chaperone The HSP90 chaperone, responsible for the correct location and folding of cellular ­proteins, has emerged as a key novel therapeutic target, and several drugs are now in clinical trial [48, 49]. Because its client proteins belong to multiple signaling pathways, a single inhibitor can provide the equivalent of “multitargeted” or combinatorial ­therapy, and resistance appears to be a relatively rare event [50]. Many client proteins are key oncogenes (such as mutant BRAF, ErbB2, AKT) and others are important in invasion and angiogenesis (e.g. HIF-1a, FAK, c-MET, VEGFR). Interestingly, extracellular HSP90 has been linked specifically with invasion and metastasis [51]. Several ­inhibitors have shown efficacy in preclinical metastasis models, for example, NVP-AUY922 in orthotopic PC3 prostate carcinoma (lymph nodes), BRAF mutant WM266.4 melanoma (lung) [52], and 17-DMAG in HCT116 colon carcinoma (liver) [53]. Geldanamycin also inhibited RIP-Tag2 liver metastasis as detected by MRI [54]. However, ­surprisingly, in MDA MD 231 [55] and PC3-M xenografts [56], 17-AAG enhanced bone metastases. In the latter case, this was linked to activation of c-Src in osteoclasts. It will be important to determine if nongeldanamycin drug classes have this undesirable profile and whether bone metastasis would be a contraindication in patients. 19.2.2.3 Chemokine Receptors Several chemokine receptors have been implicated in site-specific metastasis [57, 58]. CXCR4 is linked to breast cancer metastasis in nodes – liver, lung, and bone – sites expressing high levels of its ligand SDF-1a/CXCL12. Treatment with the CXCR4 inhibitor CTCE-9908 peptide prior to intracardiac or i.v. injection of MDA MB 231

19  Models for Evaluation of Targeted Therapies of Invasive and Metastatic Disease

463

breast carcinoma cells surprisingly failed to reduce the number of metastases, but decreased their size in all organs studied (e.g. lungs, bone, viscera) [59]. A different peptide (TN14003) inhibited the growth of orthotopic SCCHN xenografts via suppression of angiogenesis and thence reduced lung metastasis [60]. In further reports, AMG487, a small molecule inhibitor of CXCR3 reduced lung metastasis from human osteosarcoma SAOS2 [61], syngeneic C26, and xenogeneic HT29 colon carcinoma but not liver metastases [62]. In the latter case, efficacy was seen when animals were dosed several days after i.v. tumor cell inoculation, suggesting effects on survival/proliferation of extravasated cells, rather than on prevention of anoikis during dissemination [63] or initial homing/seeding. Other examples of chemokine receptors implicated in cancer dissemination include CCR7 (lymphatic metastasis in melanoma, squamous cell carcinoma, GI cancers and others; CNS infiltration of T-cell leukemia) and CCR10 ­(melanoma skin metastases). However, relatively few selective small molecule inhibitors exist and it is important to determine that any such agents inhibit outgrowth of metastases at multiple sites (rather than selectively or prophylactically) to give true survival advantages. Nevertheless, agents such as CXCR4 inhibitors CTCE-9908 and RCP-168 and the CXCR1/2 inhibitor meraxin are currently in clinical trial [64, 65]. 19.2.2.4 BCr-Abl: A Paradigm for Tumor-Specific Therapy The Bcr-Abl translocation is a paradigm for small molecule targeted therapy [66]. The fusion protein expressed from the Philadelphia chromosome initiates and, more importantly, maintains the malignant phenotype in some chronic myeloid leukemias. Imatinib, and later drugs designed to inhibit resistant cells with ­additional mutations, have been the inspiration for further molecularly targeted agents. However, in solid cancers, single, primary driver mutations are rare, and sometimes only the ­consequences of such deregulations (i.e. hyperactivated signaling pathways) are evident. Also, in many cases it is loss of a tumor (or metastasis) suppressor such as BRCA1, P53, PTEN, nm23, BRMS1, etc. that contributes to malignant progression. 19.2.2.5 Hedgehog (Hh) The Hh pathway has been shown to be important in Ras-driven melanomas [67]. Small molecule inhibitors were found to be effective in medulloblastomas in Ptc+/-, p53-/- transgenic mice [68] and inhibited systemic metastasis in orthotopic ­pancreatic cancer xenografts [69]. Interestingly, although primary tumor growth was not ­inhibited, the proportion of putative cancer “stem-like” cells was reduced, reinforcing the notion that Hh represents a key stem cell signaling pathway. Paracrine Hh signaling from tumor cells to stroma has been linked to desmoplasia. In an interesting approach, IPI-926, a drug that depletes tumor stroma by inhibition of the Hh signaling pathway, enhanced delivery of gemcitabine to transgenic KPC mice with pancreatic carcinomas. Improved responses to the cytotoxic agents were obtained with fewer liver metastases. This was linked to increased vascular density and perfusion [70].

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19.2.2.6 Wnt Pathway Wnt signaling plays major roles in stem cell maintenance and has been implicated in metastasis via canonical (b-catenin stabilization) and several noncanonical ­pathways [71]. In an orthotopic model of basal breast cancer, Wnt signaling was linked to epithelial–mesenchymal transition (EMT) and lung metastasis. Inhibiting signaling through LRP6 co-receptors reduced the self-renewal and lung colonization potential and induced differentiation markers [72]. In a lung xenograft tumor model, EMT was not observed, but the development of brain and bone metastases following intracardiac inoculation of cells was linked to WNT/TCF-LEF1-HOXB9 signaling [73]. Wnt signaling has also been suggested to contribute to prostate cancer bone metastasis [74] and to the acquisition of an invasive cancer stem cell phenotype [75]. So far, developing pharmacological inhibitors of the Wnt pathway(s) has proved challenging, but, providing that the function of normal stem cells (e.g. in the gut and bone marrow) can be spared, it could be an interesting therapeutic target. 19.2.2.7 Combination Therapies As with chemotherapy, where multiple drug regimens are the norm, several studies are exploring combination targeted therapy. For example, an angiogenic (aV) ­integrin antagonist plus an antibody–cytokine fusion protein gave synergistic ­activity against spontaneous liver metastases of a mouse neuroblastoma [76]. Similarly, an antiangiogenic urokinase-derived peptide combined with tamoxifen inhibited the growth and lymphatic metastasis of a rat mammary carcinoma better than single agents [77]. Due to the requirement for a reproducibly high incidence of metastases, most such complex studies have been performed in syngeneic systems. However recently, combinations of EGFR and VEGFR2 inhibitors were shown to be effective in an orthotopic model of oral cancer lymph node metastases [78].

19.2.3 Processes Linked to Metastasis 19.2.3.1 Angiogenesis and Hypoxia Neoangiogenesis is generally considered a prerequisite for sustained tumor growth and spread. Many different antagonists including natural inhibitors, antibodies, and soluble decoys targeting VEGF or small molecule inhibitors of VEGFR1 and 2 have been investigated and several have reached clinical trial (reviewed in [79–81]). Of concern is the fact that few antiangiogenic strategies were tested preclinically in metastatic models. It now seems that under certain circumstances, some agents can promote tumor progression. Downregulation of VEGF may lead to compensatory upregulation of alternative angiogenic factors, or successful inhibition (resulting in hypoxia) can unleash transcriptional programs enhancing cell survival, motility, and invasion.

19  Models for Evaluation of Targeted Therapies of Invasive and Metastatic Disease

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Knockout or inhibition of VEGF signaling reduced growth (but enhanced invasion and/or metastasis) of orthotopic gliomas and RIP1-Tag2 transgenic pancreatic ­carcinomas via “adaptive-evasive” responses [82]. Similarly, short-term treatment with sunitinib or DC101 (anti-VEGFR2 antibody) resulted in accelerated multiorgan metastasis from MDA MB 231 cells in spite of potent inhibitory effects on localized tumors [83]. It was hypothesized that upregulation of pro-angiogenic cytokines facilitated an enhanced “premetastatic niche” involving circulating endothelial cell precursors, myeloid progenitors, CXCR4+ and VEGFR1+ bone marrow-derived cells (BMDC). Clinically, antiangiogenic agents have shown little benefit as single agents, but have improved survival when combined with chemotherapy. However, some patients with glioma develop resistance to anti-VEGF therapy and can relapse with extensive tumor spread. Taken together, these preclinical and clinical observations suggest that antiangiogenic agents should be more carefully evaluated in a range of metastatic models before clinical deployment, with a greater emphasis on developing sensitive biomarkers of response (or compensatory/pro-angiogenic rebound) [84]. What is more, it is likely that combinations of inhibitors will be most effective in controlling tumor progression. Antibodies and other inhibitors directed against integrins such as aVb3 or aVb5 expressed on activated endothelial cells have been developed. Again, caution is required since low concentrations of the RGD-mimetic peptide cilengitide targeting aVb3/aVb5 stimulated angiogenesis and growth of B16F10 melanoma or Lewis lung carcinoma [85]. Interestingly, aVb3 exogenously expressed on breast carcinoma cells can promote metastasis to bone [86]. Other integrins have been implicated in lymphangiogenesis and metastasis (notably in the premetastatic niche) and several inhibitors are in clinical development [87]. Also, VEGFR3 is expressed on lymphatic endothelial cells and may promote lymphatic metastasis [88, 89]. Interestingly, a multikinase inhibitor (E7080) targeting VEGFR2 and 3 was able to inhibit both lung and lymph node metastases from orthotopic MDA MB 231, whereas bevacizumab (anti-VEGF) significantly inhibited only lung metastases [90]. Hypoxia has been linked to invasion, metastasis, and resistance to therapy [91] and the HIF-1a pathway has been considered a viable target [92, 93]. Hypoxia increased HT1080 sarcoma lung metastasis by enhancing postextravasation ­survival [94]. HIF-1a can also recruit BMDC to the tumor site, including MMP9+ cells which, in glioblastoma, initiate the angiogenic switch. However, abrogation of HIF1a or VEGF in a mouse model of glioma enhanced deep invasion into the brain parenchyma [95]. Hypoxic cells can be targeted either by prodrugs ­specifically activated under low oxygen conditions, or by hypoxic radiosensitizers such as TX-1877, which showed some efficacy in metastatic orthotopic colon cancer and rectal cancer xenografts (although no prolongation of survival [96]). Recently, lysyl oxidases have been identified as key mediators of hypoxiainduced invasion and metastasis, and in particular are critical to premetastatic niche formation by crosslinking collagen IV and recruiting CD11b+ myeloid cells [97]. Antibodies against LOXL2 inhibited gastric carcinoma xenograft metastasis [98] and a pharmacological inhibitor (BAPN) reduced the development of bone

466

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­ etastases from MDA MB 231 cells injected into the heart. However, there was no m effect on established metastases, which together with Erler’s data suggests that LOX is mainly required for early organ colonization [99]. 19.2.3.2 Proteolysis in Invasion and Angiogenesis Proteolytic enzymes are considered important in tumor cell invasion, extravasation, and angiogenesis, and particular models (including knockout and transgenic animals) have been useful in determining their modes of action. First and second generation MMP inhibitors proved disappointing in the clinic. More extensive studies of the full panoply of tumor (and host) associated proteases have revealed hitherto unsuspected complexity and an appreciation that some proteases are “antitargets” whose inhibition could promote tumor progression. It is now clearer which MMPs (or ADAMs) could make good therapeutic targets, and efforts are underway to develop more selective inhibitors [7] and to measure protease activity noninvasively by imaging [100]. Galardin/GM6001, a broad-spectrum MMP inhibitor, significantly reduced tumor growth by twofold and spontaneous lung metastases by 100-fold [101]. The uPA/uPAR/PAI1 system has been implicated in tumor growth, metastasis, and angiogenesis. Interestingly, uPAR is expressed on disseminated cells in bone marrow and may regulate the shift between dormancy and proliferation via a fibronectin/ integrin-mediated process [102]. Components of the signaling complex are considered promising targets for therapy, but progress has been hampered, as in many protease systems, by the complexity of regulatory networks and apparently contradictory inhibitory or tumor promoting actions of several key players. For example, the uPA inhibitor PAI-1 was shown to contribute to brain metastasis from transgenic retinal tumors [103]. Nevertheless, new insights and structure–function relationships are emerging that will aid drug discovery [104]. WX-UK1, a derivative of 3-aminophenylaniline, has been shown to inhibit lung and lymph node metastases of syngeneic BN472 rat mammary carcinomas [105] and ONO-4817 inhibited experimental lung metastases of a variety of MMP-expressing human tumors [106]. 19.2.3.3 Intravasation and Extravasation Organ tropism is to some extent linked to adhesive interactions between tumor cells and vascular endothelia, but the ability to cross this barrier is essential if overt metastases are to develop. In an orthotopic rat breast cancer model, EGFR ­promoted tumor cell motility and invasion but not intravasation, whereas ERBB2 was required for the latter [35]. The ability of SK-Mel28 human melanoma cells to cross the blood–brain barrier is facilitated by melanotransferrin and prevented by targeted antibodies [107]. In contrast, in a breast cancer xenograft (CN34-BrM2) capable of brain metastasis from either intra-arterial injection or from orthotopic sites, this process was linked to expression of the brain-specific sialyltransferase ST6GALNAC5 [19]. These studies have been extended using an elegant series of organotropic models, mainly based on MDA MB 231 by Massague’s group. Some common factors were found to be utilized

19  Models for Evaluation of Targeted Therapies of Invasive and Metastatic Disease

467

by cancer cells for early steps in ­metastasis, e.g. molecules enhancing vascular ­permeability and mimicking ­lymphocyte extravasation, others were site selective. Molecules such as epiregulin, COX-2, MMP1, and MMP2 were identified from a “lung metastasis signature” and collectively mediate vascular remodeling and ­promote angiogenesis and tumor cell intravasation and extravasation [108]. Also the cytokine angiopoietin-like 4 (ANGPTL4) was specifically linked to lung metastasis (reviewed in [109]). However, although mechanistically interesting, the clinical ­possibilities of ­therapeutic interventions at such early stages are limited and factors that are required for survival and/or expansion of disseminated cells should be the main focus of attention for effective, potentially curative therapy. 19.2.3.4 The Premetastatic Niche It has recently been recognized that establishment of a successful metastasis may depend upon preconditioning of target organs by factors released by the primary tumor and involving recruitment of BMDC to a premetastatic niche [110, 111], elements of which may be targetable to prevent metastases. For example, osteopontin has been implicated in BMDC recruitment and activation of dormant metastases [112] and SU6668, by virtue of its activity against VEGFR1, was shown to decrease CXCL1 levels and neutrophil infiltration in premetastatic liver and to inhibit metastasis of orthotopic TK-4 colon carcinomas [113]. Metastasis of cancer stem-like cells, particularly to bone marrow, has also been linked to the SDF-1-CXCR4 axis [114]. However, again, it is not clear how long disseminated cells are dependent on support from these specialized microenvironments before becoming autonomous.

19.2.4 Resistance to Therapy Intrinsic or acquired resistance to conventional cytotoxic therapy is common and targeted therapies are not exempt. Both tumor and host factors can contribute via mechanisms including those mediated by cell–stroma and cell–cell interactions. This may be overcome by targeting the stromal cells, their secreted paracrine ­survival factors, or the proteasome (reviewed in [115]). Resistance in a mouse model of Bcr-Abl ALL to imatinib has been shown to be due to cytokines such as IL-7 released from the hematopoietic microenvironment [116]. Although perhaps rarer than resistance caused by secondary mutations or “kinase switching,” such mechanisms are likely to arise as multikinase inhibitors are increasingly deployed. 19.2.4.1 Cancer Stem-Like Cells Recently, much attention has been given to the possibility that tumor relapse, metastasis, and treatment failure may be due to the presence of cancer stem-like progenitor cells [117, 118], which in some cases has been linked to EMT [119]. Thus, methods to assay these cells in human tumor xenografts and other models have been

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d­ eveloped [120]. Invasive prostate cancer cell lines were found to express a stem cell-like phenotype (CD44+ CD24-) [121] and breast cancer cell lines contained a subpopulation of aldefluor+ cells with metastatic potential [122]. MDA MB 468 human mammary carcinoma cells selected for lymphatic metastasis also expressed a “stem cell”-like phenotype with an enhanced ability to survive in ectopic environments [123]. Cancer stem-like cells may have a low proliferative index, enhanced motility, express drug efflux pumps, reside in specialized niches, and are driven by different signaling pathways than more differentiated progeny [124, 299]. Recently, temozolomide was found to enhance the proportion of putative stem cells in a transgenic PTEN null glioma model and these residual cells had enhanced tumorigenic potential [125]. We must consider how such dangerous, residual cells, spared – or possibly even enriched – by conventional cytotoxic therapies, could be eliminated. Interestingly, HER2 overexpression increases the proportion of stem-like cells via Notch activation [126, 127] and thus trastuzumab or lapatinib efficacy may be linked to their ability to target these cells. Telomerase may also provide a suitable tumor stem cell therapeutic target [128], and an antagonist (GRN163L) has shown activity in prevention of A549 lung metastases [129]. Other possibilities include CD44, Hedgehog and Wnt pathways, TGFb, CXCR4, or Notch [117, 130]. 19.2.4.2 Dormant Metastases The presence of single tumor cells (or small clusters) in sites such as nodes or bone marrow suggests that early dissemination may seed micrometastases, which remain dormant until reactivated [124, 131–133]. Such cells may present a risk of recurrence, since if noncycling they are likely to be resistant to most forms of therapy; mechanisms responsible for their awakening therefore need to be identified and targeted. Dormancy may be due to immune restraint, lack of angiogenesis, or the tissue microenvironment failing to provide a congenial “soil” for the metastatic “seed” [134–137]. Genomic profiling of breast carcinoma, glioblastoma, and ­sarcoma xenografts emerging from dormancy recognized a transcriptional switch primarily linked to angiogenesis, but with upregulation of unexpected genes such as EGFR, IGF-1R, TIMP-3, and others [138]. Metastasis suppressor genes have also been implicated [139]. Alternatively, transition from quiescence to proliferation may be partly regulated by microenvironmental cues (e.g. fibronectin) leading to changes in cytoskeletal architecture. In murine mammary carcinomas and osteosarcomas, reactivated proliferation was inhibited by targeting b1 integrin or myosin light chain kinase [140]. In mouse mammary carcinoma models in which liver metastases arise after a long latency, dormant cells were resistant to doxorubicin, although actively growing macrometastases were inhibited [141, 142]. Dormant cells can be labeled with GFP or luciferase to follow their fate in vivo, and this approach (and patient studies) has demonstrated that viable, nonproliferating cells can persist for long periods [143]. Further investigation is required to evaluate the commonest ­mechanisms of dormancy in order to maintain this state or to target vulnerabilities that may differ from those in actively proliferating cells.

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19.2.5 Immunological Approaches 19.2.5.1 Antibody-Based Therapies Immune effectors have exquisite specificity and should (in the absence of untoward cross-reactivity with normal tissues) give minimal toxicity. They are most effective against low tumor burden, i.e. minimal residual disease or micrometastases, as a useful adjunct to conventional debulking cytotoxic therapies. Many tumor antigens including CEA, PSMA, EGFR, c-erbB-2, gangliosides, etc. have been used as ­targets. Antibodies may be used to block growth factor receptors (EGFR, c-erbB-2, VEGFR) and/or to recruit complement or host effectors or to deliver toxins or ­cytotoxic drugs [144]. Radioimmunotherapy using anti-CEA antibodies has been explored in colorectal tumor lung and liver metastasis models [145, 146] and also in a transgenic breast cancer model [147]. Therapeutic effects were observed, although with these direct conjugates, normal tissue toxicity can be an issue. Antibody-directed enzyme prodrug therapy is one example of “pretargeting” (reviewed in [148]) where an antibody conjugate localizes to tumor deposits, then is allowed to clear from the circulation before a second moiety is administered. More complex therapeutic strategies have been devised which require correspondingly elegant models. Chimeric antibodies designed to target a human antigen and to recruit host effector cells have been assayed in a SCID/hu mouse model of neuroblastoma metastatic to liver [149], where the mouse bone marrow is reconstituted with human stem cells capable of maturing into effector cells. Other strategies have combined the targeting ability of antibodies with cytokines in pulmonary and hepatic metastasis models of syngeneic mouse melanomas, neuroblastomas, and colorectal carcinomas [150].

19.2.5.2 Vaccines, Cytokines, and Cell-Mediated Immunotherapy B700 antigen on B16F10 mouse melanoma cells was used in a vaccine that inhibited spontaneous lymph node and lung metastases and combination with cytokines such as IL-2, Il-12, or GM-CSF potentiated the effects. This model, while mimicking the human disease in terms of primary growth, bone marrow invasion, regional node involvement, and distant dissemination, nevertheless could not distinguish antimetastatic effects from indirect effects on the primary tumor [151]. Vaccination against HER2/neu has been tested in rat Mat-Ly-Lu Dunning prostate carcinoma [152] and orthotopic transgenic or xenograft metastatic murine breast tumors [153–155]. Adoptive immunotherapy with IL-2 and human effector cells injected into the tumor site (OSC-19 cells in the floor of the mouth) in athymic mice resulted in fewer lymph node metastases [156]. The main considerations for selecting models for trials of targeted immunotherapeutic agents are an understanding of the antigen(s) involved and their relative

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immunogenicity in rodents and humans. Also, it is important that the interventions are done (if possible) in animals bearing primary tumors (or those which have had them ­surgically removed) because the immunocompetence of naive animals may not equate to that of animals “conditioned” by the presence of a tumor. 19.2.5.3 Targeting Using Vectors or Peptides with Tumor Selectivity Delivery of prodrug activating enzyme genes such as Herpes simplex thymidine kinase, followed by therapy with ganciclovir can inhibit tumor growth and metastasis. However, because of the current difficulties of generating viral or nonviral vectors that are sufficiently stable to survive in the circulation and/ or their intrinsic immunogenicity, most gene therapy studies depend upon local or regional delivery. Oncolytic viral therapy has been achieved in a rat model of hepatic metastasis [157] and in TRAMP transgenic prostate lymphatic metastases [158]. Also, the A33 antigen was used to target virus to LoVo hepatic metastases [159]. Recently, mesenchymal stem cells have been used to target IFNb to lung metastases in the TRAMP model [160], and tumor-tropic neural progenitor cells expressing osteoprotegerin limited development of neuroblastoma bone metastases [161]. Vectors may be targeted to organs using tumor-specific promoters such as AFP (hepatoma), c-erbB-2 (breast, ovarian, and gastric cancer), and CEA (colorectal cancer). Alternatively, ligands or antibody fragments can redirect the vector to receptor-overexpressing tumor cells. Antisense approaches have mainly been directed against molecules associated with invasion and metastasis, e.g. CD44 v6 and matrilysin (MMP-7) in liver metastasis models of colorectal carcinoma. Increasingly, given the utility of siRNA to silence genes in vitro, attempts are being made to achieve in vivo delivery of stable hairpin RNAs or oligonucleotides, with some success in preclinical models [162, 163]. Novel targets have been identified on tumor cells, metastases, and tumor vasculature by selective binding of peptides or antibody fragments. A FITC-conjugated 5-amino acid peptide (TMTP1) homed to and detected PC3 lymph node and MKN gastric cancer liver micrometastases and could serve as a vector for therapeutic isotopes or drugs [164]. Such small peptides readily diffuse and penetrate tissues, are nonimmunogenic and easily synthesized.

19.3 Detection and Quantitation of Metastases and Determination of Therapeutic Benefit The most significant recent developments have been the use of tumor cells ­genetically tagged with fluorescent or luminescent markers, enabling routine localization and sequential measurements of metastases by optical imaging.

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What is more, tumor-host interactions can be discerned by color coding ­different cell populations, including the generation of fluorescent transgenic mice [165]. Individual fluorescent tumor cells can be detected at high resolution by in vivo videomicroscopy [166]. In vivo measurements of gene expression, angiogenesis, and metabolism are possible using multiphoton laser scanning microscopy, and imaging in the skin or skull have extended the range of such intravital technologies [167]. In ­addition, such markers can be used in  reporter assays of target function, for ­example, to indicate the activation status of a signaling pathway [168] or protease activity by using a quenched substrate [100]. Examples of different techniques used experimentally are given in Table 19.4. Other imaging modalities can be used to provide functional information: 18 FDG positron emission tomography (PET) and 18F-fluoride PET identified MCYN ­transgenic osteosarcoma bone and lung metastases. When the transgene was ­inactivated, glucose uptake was reduced and fluoride uptake increased, indicating bone remodeling. Such systems would be invaluable in experimental therapy studies to follow both proliferation and differentiation [169]. FDG-PET detected responses of orthotopic rat C6 gliomas to therapy with temozolomide and hypoxia inhibitors [170] and localized colon carcinoma lung metastases of ~0.3 mm ­diameter [171]. PET was used to track virus delivery to lymph node micrometastases in gene therapy of B16 melanoma [172] and to show downregulation of ErbB2/HER2 oncogene expression in response to an HSP90 inhibitor [173]. A strain of transgenic mouse with a fluorescent reporter has also been developed for studying PI (3,4,5)P(3) metabolism [168]. Magnetic resonance (MR) imaging and Doppler ultrasound are useful for ­measuring vascular density, permeability, and blood flow, and MRI was used to assay responses to Recentin (VEGFR inhibitor) in a DU145 brain metastasis model [174]. The fate of single MDA MB 231BR cells delivered to the brain via the left ventricle has also been demonstrated [175]. Recently, dual-modality uPAR targeted nanoparticles have been developed for molecular imaging (by optical and MR methods) of pancreatic xenografts and metastases [176]. 3D high frequency ­ultrasound is also used to measure tumor volume in liver [177] and also to ablate liver metastases in rat models [178]. With the increasing use of patient-like tumor models and targeted therapies, molecular imaging will become an intrinsic part of drug development, providing not only noninvasive measurements of tumor growth and spread but also enabling interrogation of tumor metabolism, proliferation, and vascularization [179–181]. Also, key functions associated with metastasis such as adhesion, matrix interactions, and even intracellular signaling are becoming accessible [182]. Ex vivo analyses of endothelial precursor cells or tumor cells in circulation, bone marrow, or nodes also add to our understanding of the dissemination and lodgment phases of metastasis and are applicable both to animal models and clinical studies [183, 184].

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19.4 Animal Models for Evaluating Targeted Therapy of Metastasis A multitude of preclinical tumor models are available to facilitate analysis of the molecular mechanisms of metastasis and for evaluating novel therapeutic approaches. Given the huge number (>200) of different human cancers, their intrinsic heterogeneity plus the confounding factors of host genetic backgrounds, no particular model can serve as an appropriate tool for all applications. The following sections illustrate the main types of animal tumor systems available (with specific examples), their strengths and weaknesses, and utilization in different experimental settings.

19.4.1 Syngeneic Rodent Tumor Models The first experimental tumors were chemically induced or arose in cancer prone strains, usually due to oncogenic viruses such as MMTV. Metastases were rare, although ­sometimes manifest if the primary tumor was surgically removed to allow longer ­survival of the host. Sublines of B16 melanoma, Lewis lung (3LL) carcinoma, and the 4T1 breast carcinoma with enhanced and/or organ-selective metastasis were derived. The importance of using strictly syngeneic, inbred animals for transplantation ­studies has been recognized, and indeed has allowed the identification of significant “metastasis modifier” genes in different mouse strains [185]. Most chemically induced tumors are highly immunogenic, unlike those arising spontaneously (in mouse or man). Several molecules first identified in rodent tumors are important in the malignant ­process in human cancers, and in some cases elicit cell-mediated and/or humoral responses, e.g. c-erbB-2/HER2, MUC-1, NG2, MAGE antigens. There are now a wider variety of syngeneic tumor models available (Table 19.1), although there is an increasing trend toward the use of human tumor xenografts and/or transgenic models.

19.4.2 Human Tumor Xenograft Models Table 19.2 illustrates a selection of reliable xenograft tumors, focusing on metastatic models. Although nu/nu or SCID mice hosts are the norm, nu/nu rats, and even more recently chick embryos are also used [186]. Metastatic models in zebra fish embryos are likely to emerge because transformed melanocytes with migratory ability have been generated [71]. SCID mice, lacking both B and T cell immunity, and bg mice with lower NK activity are often more susceptible to metastasis than athymic mice. Orthotopic 4T1 (mouse) and MTLn3 (rat) breast carcinoma metastasis was significantly increased in NK deficient Rag2(-/-)gC(-/-) mice [187]. Similar results were seen using human SCLC cells injected s.c. into pfp/rag2 mice which showed extensive lung metastases [188].

Cervical ca xenograft Transgenic Knockout mice lacking specific enzymes HIF – luc A431

HIF activity/hypoxia Angiogenesis

Application Liver metastasis growth + doxorubicin Liver metastasis, GFP-tagged cells, dormancy Brain metastasis Brain metastasis + anti-VEGF inhibitor AZD2171 Tumour motility and invasion Inhibition of liver and kidney mets by silibinin BM, LN and tumour cells in blood after mfp, i.v. or i.c. injection Lung metastases   LN micrometastases   Downregulation of HER2 detected by (64)Cu-DOTA-trastuzumab Lung metastases – osteopontin aptamers Subrenal – lung metastases – PD-0332991 (CDK4/6), avastin (VEGF) Bone metastases – dasatanib, 17-AAG Liver metastases Lung and CNS metastasis, also detects differentially labelled adoptively transferred NK-T cells Visceral metastases – HSP90 inhibitors Protease activity/inhibition PI3 kinase activity bioprobe [214] [322]

[306] [100, 168]

  [287, 321]  

[169]       [172] [173] [213] [56, 211]

Reference(s) [177] [320] [175] [174] [259] [181, 317, 318] [183]

Mfp mammary fat pad, i.v. intravenous, i.c. intracardiac, PET positron emission tomography, NMR nuclear magnetic resonance, MRI magnetic resonance imaging, HF-VPDU high frequency volumetric power Doppler ultrasound

Luminescence-based Reporter assays HF-VPDU

Fluorescence Functional fluorescent optical imaging

MDA MB 231 breast ca MDA MB 435

Bioluminescence (BLI)

PC-3M HCT116 BCL1 and A20

MYCN transgenic osteosarcoma B16 + oncolytic virus targeting SKOV3 + 17AAG

PET imaging

Table 19.4  Evaluation of invasion, metastases, or response to therapy Method Model High frequency ultrasound B16F1, HT-29, MDA MB 435 Intravital videomicroscopy B16, colon26 MRI MDA MB231BR DU145 Multiphoton confocal microscopy MTLn3 NMR Metabolomics TRAMP FACS/LCS MDA MB 435HAL (GFP)

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Tumors grown s.c, although convenient for readily measuring responses to therapy, are less likely to metastasize than those grown orthotopically (see Sect. 19.4.4.1). Preserving the tissue architecture by implanting tumor fragments, either direct from patients or following a single s.c. passage can also maintain malignant potential better than using cultured cells and several key molecular drivers such as EGFRvIII or Hh may be rapidly lost in vitro. Malignant potential can be enhanced by selection of tumorigenic/metastatic variants and/or co-injection with matrigel, fibroblasts [189], or mesenchymal stem cells [190]. These procedures presumably mimic to some extent the orthotopic microenvironment or at least overcome some of the defects of the xenogeneic/ectopic environment. Metastatic xenograft tumor models have been used for target identification, validation, and evaluation of therapeutic agents. Some cell lines have been “custom built” to explore synergies between oncogenes and to generate more highly malignant tumors. For example, MCF10A mammary epithelial cells were transfected with H-RAS (which transforms cells) and with BMI-1 (which inhibits the INK4/ARF tumor suppressor locus and is implicated in stem cell maintenance). The doubly transfected cells generated both lung and brain metastases, and BMI1 knockdown reversed this trait [191].

19.4.3 Organ Colonization and Site-Selective Metastases The simplest “metastasis” assays, aiming to mimic late stages of metastasis ­(dissemination, extravasation, and colonization), are achieved by inoculation of a bolus of tumor cells directly into the peripheral circulation to give lung colonies. Tumors derived from cells that are naturally migratory (e.g. leukemias, lymphomas, plasmacytomas) more readily colonize multiple downstream sites including ­marrow, spleen, and liver. Xenograft models of CNS and visceral metastases of human monocytic leukemia are now available [192]. Tumor cells can also be directly injected into other vessels (or target organs) to generate additional models of disseminated disease (see below). These models are more widely used now that noninvasive optical imaging of internal tumors is readily achievable. 19.4.3.1 Lung Metastases Although i.v. injection of tumor cells yields “metastases” there is no direct correlation between lung colonizing ability and spontaneous metastatic potential or their responses to therapy. RON RTK was validated as a potential therapeutic target in HCT116 colon carcinoma cells as its knockdown inhibited metastasis from cecum to lung [193]. Minn et al. developed MDA MB 231 breast carcinoma sublines with enhanced pulmonary metastasis and identified a few key genes that either gave growth advantages at both primary and secondary sites (e.g. epiregulin, CXCL1, MMP-1, COX2) or were selective for enhancing growth in the lung microenvironment (e.g. SPARC, MMP2) [194].

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Interestingly, inhibitors of any single target failed to control metastasis, whereas combinations, particularly of cetuximab and celecoxib, with or without the MMP inhibitor GM6001 were highly effective [108]. 19.4.3.2 Liver Metastasis Liver metastases (appropriate mainly for colon or pancreatic carcinomas) can be generated by inoculation of cells into a mesenteric vein. The spleen provides a simpler alternative since cells pass almost immediately into the portal circulation. However, growth and intraperitoneal spread from the primary tumor can be a confounding factor without splenectomy. Such colon liver metastases were used to show efficacy of a farnesyl transferase inhibitor [195]. When limited numbers of colonies are required (e.g. for focussed ultrasound therapy [178] or photodynamic therapy), tumor cells can be injected directly into the liver. Since the hepatic microenvironment reportedly upregulates EGFR and/or its ligands [196], EGFR inhibitors such as cetuximab or erlotinib could be beneficial and clinical trials seem promising [197]. 19.4.3.3 Brain Metastasis Brain metastasis models have recently been developed from the injection of tumor cells either into a carotid artery [198] or the left ventricle of the heart [19] with subsequent harvesting and recycling by the same means [199]. Interestingly, by selecting glioma cells first for lung colonization, sublines were generated which had a greater invasive potential when re-implanted into the brain. These DMB2 tumors were shown to respond to the HSP90 inhibitor 17-AAG, assessed in part by real-time ultrasound imaging [200]. MDA MB 435 LvBr2 and Br4 sublines injected into the carotid artery express high levels of angiogenic factors including angiopoietins and VEGF, and also elevated Notch signaling. Their invasive potential was reduced by g-secretase inhibitors [198]. Direct injection of human SCLC cells into nude rat brains can be used to mimic metastases and to investigate means of ­selectively permeabilizing the blood–brain barrier to enhance selective delivery of therapeutic agents [201].

19.4.3.4 Bone Metastasis Breast, prostate, neuroblastoma, and myeloma models of bone metastasis have been generated from direct implantation into the tibial marrow space [202, 203] into human bone fragments inserted s.c. [204] or by selection from the bone after intracardiac inoculation of cells [205]. Sublines of the syngeneic mouse 4T1 tumor or rat MatLyLu tumors spread to the skeleton after mfp [86] or i.v. injection [206].

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Interestingly, variants of LNCaP, which can metastasize to bone from s.c. orthotopic or intracardiac routes were obtained following various experimental selection procedures including exposure to hypoxia [207] or coculture with osteoblasts [208]. The resultant tumors lost androgen dependence and gained osteomimetic properties. Such tumors have been very instructive in developing novel gene therapies and methods to interfere with tumor–stromal interactions, e.g. with anti-integrin antibodies [208]. Inhibitors of IkB kinase inhibited bone metastases from rat W256 carcinosarcoma cells injected via the left ventricle [209] and in a similar xenograft model (PC3-ML), an anti-PDGFRa antibody inhibited early phases of bone metastasis establishment, prior to osteoclast recruitment [210]. In the case of neuroblastoma bone metastases, IGF-1R was implicated as a major mediator of osteolysis [202]. Latent metastases in bone marrow can be a major problem in breast and other cancers. Recently, using bone-tropic MDA MB 231 and CN34 human tumor xenografts (BoM-1833 and BoM2 sublines), Src was identified as a key factor enabling cells to respond to CXCL12 and resist TRAIL-mediated apoptosis in this microenvironment. Importantly, in clinical samples, a Src gene expression signature correlated with late bone relapse, whereas a TGFb signature was associated with lung metastases. Hence Src inhibition could be a valid therapeutic strategy to inhibit these latent cells [211]. The many models representing different sites of metastasis in both syngeneic and xenogeneic tumor systems have yielded important insights into the molecular mechanisms of organotropism. Such models are now frequently employed in preclinical evaluation of targeted therapies. However, results obtained in “experimental” metastasis assays should be treated with caution and confirmed in spontaneous metastasis assays. Most importantly, therapies for clinical use must be able to inhibit the growth of established metastases, often simultaneously developing in multiple organs. Many studies commence therapy on the same day as (or before) tumor cell inoculation or even pretreat tumor cells with inhibitory compounds or by genetic manipulation. While this is a reasonable strategy for early validation of the role of a specific molecule in particular stages of the metastatic cascade, such approaches cannot be used as a substitute for well-designed studies of therapeutic agents in established malignant disease.

19.4.4 Spontaneous Metastasis Models “Spontaneous” metastasis refers to the seeding of cells from a primary site to generate detectable lesions at distant sites. Several points are worthy of note: firstly, where possible, the primary tumor should be surgically excised to allow time for metastases to develop. Alternatively, micrometastases may be identified using “tagged” cells, although it should be confirmed that these are extravascular and clonogenic rather than simply in transit. Providing that the fluorescent or luminescent signal is stable (and this cannot be assumed), the presence and to some extent the size of metastatic lesions can be determined.

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All suspected metastatic deposits should be confirmed histologically. It is unethical and unscientific to allow primary tumors, particularly those in vital organs, to reach such gross dimensions that “metastasis” into other sites cannot be distinguished from direct extension or transcelomic seeding. Clinically, most ­systemic therapy for metastases (detectable or suspected) is in an adjuvant setting, postsurgical removal or downstaging of the primary tumor. Ideally, animal ­experiments should be similarly designed. If therapy is commenced while the ­primary is in situ, and results in a significant inhibition of tumor growth, it cannot be concluded that an “antimetastatic” effect was not simply a consequence of a lower tumor burden and thus less cell shedding. 19.4.4.1 Orthotopic Implantation Models The orthotopic implantation of breast tumor cells into the mammary fat pad usually encourages the development of lung and/or lymphatic metastases, although spread to other sites common in human disease (e.g. bone, liver, and brain) is rare without further genetic manipulation or selection. Xenografted tumors also give a higher frequency of metastases when implanted orthotopically, and models of human colorectal cancer metastatic to liver, melanoma metastatic to nodes, prostate cancer metastatic to nodes and bone, pancreatic cancer metastatic to liver, and many other models are available (see Tables 19.1 and 19.2). These are increasingly being used in the preclinical workup of novel therapies [32–34]. In an example of adjuvant therapy, the EGFR inhibitor erlotinib was administered to mice following surgical removal of orthotopic MDA-MB-435 LvBr tumors and found to reduce subsequent lung metastasis [212]. In a novel approach, RNA aptamer blockade of osteopontin was shown to inhibit spontaneous lung metastases from orthotopic MDA-MB-231 breast carcinomas [213]. Liver metastasis from orthotopic SUIT-2 pancreatic carcinoma xenografts was inhibited using a prodrug specifically activated in hypoxic cells by caspase 3 activation and induction of apoptosis [214] and also by an anti-MET antibody or ligand antagonist [215]. 19.4.4.2 Lymph Node Metastases Lymphatic metastases are important for clinical staging, can induce significant morbidity, and potentially act as a bridgehead for wider dissemination [216, 217]. Several rat (e.g. HOSP1, MTLn3) and human (e.g. MDA MB 468, MDA MB 435) breast carcinomas give rise to spontaneous lymphatic metastases when grown in mammary fat pads. PTEN null PC3 orthotopic prostate carcinoma xenografts ­reliably metastasize to regional and distant lymph nodes and have been used to demonstrate efficacy of several novel targeted agents including inhibitors of Src (dasatinib) [218], HSP90 (NVP-AUY922) [52], and PI3 kinase (GDC-0941) [40]. Also, radioimmunoconjugates of alpha-emitting radioisotopes targeted to cell

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s­ urface uPAR inhibited primary tumor growth and lymph node metastases [219]. Other models include orthotopic human kidney and mouse pancreatic cancers in which nanoparticle-mediated delivery of doxorubicin targeted to aVb3 on tumor vasculature inhibited lymph node and/or visceral metastases [220]. With the discovery of important cytokine signaling pathways linked to lymphangiogenesis and lymphatic metastasis (e.g. VEGF-C:VEGFR3; CCL19:CCR7) and their consideration as targets for therapy, there is a need to enhance the models available. Lewis lung carcinoma cells express multiple lymphangiogenic cytokines and MMPs. Metastasis from lung to lymph nodes was inhibited by the MMP inhibitor MM1270 [221]. Transfection of VEGF-C into weakly metastatic LAPC-9 human prostate carcinoma cells (or cells with naturally high levels) showed extensive lymphangiogenesis and lymphatic metastasis and responded to antibodies ­targeted to VEGFR3 or a soluble ligand trap [222, 223]. In the same models, VEGFsiRNA or anti-VEGFR2 antibody reduced systemic metastasis but not nodal metastasis. This illustrates the importance of testing novel therapeutic agents against both hematogenous and lymphatic metastasis as they may not be effective in both. Indeed in a study of the antiangiogenic agent AGM-1470, hematogenous metastasis was reduced but lymphatic metastasis increased [224].

19.4.5 Transgenic Models This topic will be covered in Chap. 30, but models suitable for evaluation of ­targeted therapies against metastasis will be discussed briefly. Examples are shown in Table 19.3. Note that different genetic backgrounds (e.g. FVB vs. C57Bl/6J strains) can significantly influence tumor phenotypes and metastatic potential [185, 225]. Several cancer-prone transgenic mouse strains have been produced but development of clinically relevant tumor types and/or metastasis is not assured. Animals expressing a human oncogene such as c-erbB-2, where the gene not only initiates oncogenesis, but is also an ideal target for therapy are of particular value. Early work used the mutant rat neu gene to induce tumors at high frequency, which were metastatic. However, HER-2/neu was generally thought to be oncogenic simply due to gene amplification in humans, although alternatively spliced forms resembling the spontaneously mutated/activated forms have been identified in some human cancers [226]. MMTV-c-erbB-2/HER2 (wild type) transgenic mice develop mammary cancers with long latency that metastasize to the lung, although some carry additional somatic mutations in the transgene. Polyoma virus middle T (PyMT) transgenic mice rapidly develop metastatic mammary carcinomas [227, 228]. Both models have been quite extensively used to explore vaccination strategies, monoclonal antibodies, and targeted drugs [229]. However, in humans, c-erbB-2 is also expressed at low levels in certain normal cells hence tissue damage (e.g. cardiac toxicity with Herceptin) will not be detected. These models are also useful for ­target validation. For example, EphA2 receptor was identified as a promoter of

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metastasis in MMTV-neu tumors by amplifying ErbB2 signaling, but not in MMTV-PyVMT tumors. Such information would help to define the patient population likely to respond to EphA2 inhibitors [230]. Similarly, conditional expression of TGFb in PyVMT models enhanced lung metastasis in the absence of effects on primary tumor growth. TGFb antisense [231] and dominant negative PLCg [232] inhibited lung metastases in PyVMT and TRAMP mice. In both models, AKT2 (but not AKT1) was found to enhance the incidence of metastases in the absence of effects on primary tumor latency [37]. On the other hand, genetic ablation of MMP-3 in PyVMT mammary tumors did not affect tumor growth or metastasis [233]. Interestingly, in an elegant recent study Husemann et al. determined that the initiation of metastasis in both MMTV-HER2 and PyVMT models was much earlier than previously thought, with disseminated cells detectable in blood, marrow, and lungs even before invasion was manifest [14]. Clinical studies also support the possibility that metastasis may be a relatively early event in cancer progression. These findings have implications both for the need to eradicate incipient metastases and show how the intelligent use of animal models may address these issues Table 19.4. The TRAMP model targets simian virus 40 T antigen to the prostate using the rat probasin gene. Mice develop metastases primarily in lung and lymph nodes, with up to 100% incidence by 28 weeks [234]. In a different approach, transgenic mice have been generated with a PSA tissue expression pattern very similar to that in humans [235] rather than confined to the prostate. In these immunocompetent mice, PSA is a normal “self” antigen, and if metastatic, this would represent an ideal model system for testing the feasibility of PSA targeted therapies. The main drawbacks of the transgenic systems for testing targeted therapies are their relatively high variability, long latency, incomplete penetrance, and development of multiple tumors. The frequency (and sites) of metastases may also be limited and/or unpredictable [236]. Indeed, none of the commonly used Apc (Min) mouse strains consistently develop metastases [237]. Alternative strategies have aimed to combine the benefits of controlled transgene oncogenesis with higher throughput and convenience. One approach is to transplant the autochthonous primary tumors or cell lines derived from them into recipient hosts [238]. TRAMP-C cell lines have been used in such models to evaluate therapies in an adjuvant setting [239]. Finally, producing double or multiply transgenic mice has been used to enhance the malignancy of developing tumors and/or to generate tumors with desired therapeutic targets. These approaches have indicated that, in prostate cancers at least, two “hits” are required for progression from benign to invasive tumors, and up to five for metastasis. PTEN deficiency also accelerated metastasis from transgenic MMTV-NIC ErbB2 breast cancers [240] and TRb thyroid cancers [241]. Such manipulations provide both mechanistic insights into tumor progression and also more reliable and realistic models for preclinical drug evaluation. Increasing use of “knock-in” systems is allowing the development of models exemplified by those such as the PB-Cre4 x PTEN(loxP/loxP) mice which provide a continuum from tumor initiation to metastasis [242].

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A further application has been the development of mice in which transgene expression can be controlled by doxycycline. This conditional expression of oncogenes (or knock out of suppressor genes) also overcomes the significant issue of embryonic lethality and enables the transforming effects to be manifest in adult tissues. By this means it was demonstrated that Her2/neu was essential for the maintenance of mammary tumors and lung metastases, not just their initiation, providing confidence that the model was appropriate for evaluation of targeted therapies in established disseminated disease. Nevertheless, Neu-independent tumors eventually emerged, which could provide a useful model of breaking dormancy [243]. New models of pancreatic cancer have been generated with conditional mutations in both p53 and Kras, which show appropriate metastatic patterns. This model was “credentialed” by showing similar gene and protein expression levels and responses to drugs used in the clinic (e.g. gemcitabine) [244]. Recently, additional metastatic transgenic models of different tumor types have been developed. These include squamous cell cancers with lymphatic metastases such as oral cancers driven by MyrAKT/Trp53-/-, which interestingly showed high levels of putative stem cells [245] and skin cancers in which a mutant b1 integrin collaborated with Ras mutations induced by a chemical carcinogen [246]. Several excellent reviews [38, 244, 247–252] discuss the relative merits of various animal models for drug development and are recommended for additional reading.

19.5 Summary and Conclusions Animal models have provided great insights into the process of metastasis, generated ideas for molecular targets, and have subsequently been used for preclinical evaluation of novel therapies. So why are we still faced, for several of the major cancers, with death rates which have changes little, in spite of advances in early detection and therapeutic options? Are the animal models to blame? Clearly, they represent imperfect systems and cannot represent the complexity and heterogeneity of human malignancies. However, there is much still to be learned about their optimization and rational deployment. Factors controlling tumor cell invasion, dissemination, preconditioning of metastatic “niches”, extravasation, and lodgement at secondary sites are of enormous scientific interest, but unless growth of established micrometastases is controlled, cures of disseminated cancer will remain limited. Cytotoxic therapy is designed to attack systemic disease, but fails perhaps because of metastatic heterogeneity [3], innate or acquired resistance, dormancy or the failure to evaluate such agents in appropriate metastatic tumor models. Too often, the claimed inhibition of metastasis in preclinical studies is secondary to reductions in primary tumor growth. In other cases, therapy is commenced before or at the same time as systemic injection of tumor cells. Although studies of metastasis prevention are of interest mechanistically, more attention must be given to the factors controlling ectopic tumor survival

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Part VIII

Normal Tissue Response Models

Chapter 20

Animal Models of Toxicities Caused by Anti-Neoplastic Therapy Stephen T. Sonis, Gregory Lyng, and Kimberly Pouliot

Abstract  Radiation and chemotherapy induce a wide range of acute and chronic toxicities. Not only are these associated with poor health outcomes but they also limit patients’ ability to tolerate treatment and incur significant increases in resource use and cost. Universally, they impair patients’ quality of life (QoL). In addition to hematological complications such as anemia, thrombocytopenia, and neutropenia, cancer patients are also at risk for a wide range of non-hematological taxicities. These may occure during or soon after cancer treatment (acute toxicities), or they may not develop until well after the completion of treatment (# 100 days, late toxicities) and become chronic and linger for years after the patient’s disease is controlled. The overall incidence of some form of treatment toxicity is almost 100%. Toxicities include those that are tissuespecific such as mucosal injury of some or all of the parts of the gastrointestinal tract (mucositis), cutaneous damage (dermatitis), salivary gland dysfunction, and venous thrombosis. Alteratively, patients may develop more systemic forms of toxicity that result in conditions such as fatigue, depression, cognitive impairment, and cachexia. Keywords  Toxicities • Mucositis • Dermatitis • Ostconecrosis • Fatigue • Animal models • Radiction • Chemotherapy

20.1 Introduction Radiation and chemotherapy induce a wide range of acute and chronic toxicities. Not only are these associated with poor health outcomes but they also limit patients’ ability to tolerate treatment and incur significant increases in resource use and cost. Universally, they impair patients’ quality of life (QoL). S.T. Sonis (*) Harvard-Farber Cancer Center, Boston, MA, USA and Biomodels, Watertown, MA, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_20, © Springer Science+Business Media, LLC 2011

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In addition to hematological complications such as anemia, thrombocytopenia, and neutropenia, cancer patients are also at risk for a wide range of non-hematological toxicities. These may occur during or soon after cancer treatment (acute toxicities), or they may not develop until well after the completion of treatment (³100 days, late toxicities) and become chronic and linger for years after the patient’s disease is controlled. The overall incidence of some form of treatment toxicity is almost 100%. Toxicities include those that are tissue-specific such as mucosal injury of some or all of the parts of the gastrointestinal tract (mucositis), cutaneous damage (dermatitis), salivary gland dysfunction, and venous thrombosis. Alternatively, patients may develop more systemic forms of toxicity that result in conditions such as fatigue, depression, cognitive impairment, and cachexia. The clinical ramifications of regimen-related toxicities are diverse and significant. Some, such as oral mucositis, are associated with pain of such intensity as to require opioid analgesics and inability to eat [1]. Others, such as enteritis, cause diarrhea and put patients at risk for bacteremia or sepsis. Almost all negatively impact patients’ QoL and ability to tolerate treatment. Indeed, toxicities often necessitate less than optimal dosing regimens or early termination of treatment. Toxicities are also associated with higher mortality risks. Almost all toxicities adversely affect patients’ QoL and have a major impact on increased healthcare costs. Often patients with acute toxicities require unplanned office and emergency room visits, or hospital admissions for fluid support, pain control, or infection management. The economic impact of this extra use of resources is substantial. For example, the incremental cost of oral mucositis among patients being treated for cancers of the lung or head and neck is in excess of $17,000 [2]. Toxicities that linger in patients who have completed treatment such as fatigue and depression impair the ability to work and otherwise function normally. As a result of their impact on patients’ symptoms and QoL and especially because they indirectly limit an individuals’ treatment tolerance, toxicities are the subject of intense study. The discovery that many toxicities seem to cluster suggests shared pathoetiologies. Investigations defining the biology of toxicities have stimulated the quest for appropriate and effective pharmacologic and biologic interventions. The use of animal models has proliferated and a number of them now exist which mimic toxicities seen in humans and serve as predictive platforms for drug development. This chapter will focus on models of toxicities of epithelial injury and bisphosphonate osteonecrosis.

20.2 Models of Oral Mucositis Induced by Anti-Neoplastic Drugs and Radiation 20.2.1 Overview of the Condition Oral mucositis is one of the best studied acute toxicities of non-surgical cancer therapy. Since it affects about 40% of all patients being treated for non-cutaneous cancers, the need for a successful intervention remains a high priority [1]. At present only a single agent, palifermin, has been approved for this indication in the US and

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palifermin’s applicability is limited to the small cohort of patients receiving stomatotoxic conditioning regimens in preparation for stem cell transplants to treat hematological malignancies (4% of patients at risk for the condition) [3]. Clinically, mucositis occurs with great frequency among patients being treated with radiation therapy, with or without concomitant chemotherapy, for cancers of the head and neck. Virtually 100% of patients with cancers of the mouth or oropharynx will develop mucositis. The incidence is slightly less among individuals being treated for hypopharyngeal or laryngeal tumors. Many of the conditioning regimens for stem cell transplant are stomatotoxic, especially those in which total body irradiation is a component. Lastly, mucositis impacts patients being treated with cycled therapy for the most common solid tumors (breast, colon, rectum, lung). In this group, the overall risk of mucositis in the first cycle of treatment is relatively low (about 15–20%), but if no effort is made to reduce chemotherapy dosing for subsequent cycles, the risk of mucositis increases dramatically, in many cases to more than 60%. The impact of mucositis is profound. Patients suffer marked pain, often requiring opioids, have to modify their diets, lose weight, have increased risk of local and systemic infection, require fluid support, and use consultation and emergency services more than patients who do not develop the condition [4]. Clinically mucositis develops in predictable stages. Initially, the mucosa is thinned and hyperemic. Although the tissue is intact, patients note some discomfort, often described as being analogous to a bad food burn. Symptoms can be reasonably controlled at this stage with a combination of topical analgesics and systemic agents such as acetaminophen of NSAIDs. The development of ulceration occurs next. This is the phase that is most symptomatic. Pain increases dramatically, often requiring morphine or fentynal. Eating a normal diet becomes impossible. Patients are limited to very soft or liquid diets and some may not be able to eat anything. Consequently, it is not unusual for nutrition to have to be provided by feeding tubes (gastrostomy tubes) or total parenteral tuition. In the majority of cases ulceration spontaneously resolves.

20.2.2 The Biology of Mucositis Historically, mucositis was considered to be solely the consequence of nonspecific direct clonogenic damage to basal cells of the oral mucosa. The paradigm held that as these “mother” cells were injured, normal renewal did not take place, the tissue became atrophic and ultimately ulcerated. Results of studies performed over the past decade paint a more complex picture of the biology of regimen-related mucosal injury [5]. A summary of our current understanding of the pathobiology indicates five phases [6]: 1. Initiation: In this phase, radiation or chemotherapy may directly injure DNA causing clonogenic cell death and, more significantly, cause the generation of reactive oxygen species. 2. Primary tissue response: Radiation, chemotherapy, and ROS trigger the activation of a number of transcription factors such as NF-kB, Wnt, and p53. At least 14 canonical pathways play a role in initiating mucosal injury.

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3. Signal amplification: Many of the molecules induced during the primary response phase have the ability to positively or negatively feedback on activated pathways causing amplification of injury. 4. Ulceration: Ulceration is the most relevant clinical endpoint of the process and is responsible for virtually all of the outcomes with significant clinical meaningfulness. Bacterial colonization occurs during this phase with bacterial products percolating into the submucosa to stimulate macrophages to produce pro-inflammatory cytokines. 5. Healing: Signaling from the extracellular matrix guides epithelial healing, including differentiation, migration, and proliferation. This process is probably initiated by the generation of reactive oxygen species by exposure to stomatotoxic stimuli. Activation and expression of proinflammatory cytokines, particularly tumor necrosis alpha (TNF-a) and interleukin b (IL-b) and endothelial damage characterize the inflammatory/vascular phase.

20.2.3 Objectives of Animal Models of Mucositis There are four objectives for an effective animal model of mucositis to provide clinical meaningfulness: 1. The manifestations of mucositis should mimic the condition as it occurs in humans in its course, appearance, resolution, and dose response to stomatotoxic therapy. Its presentation should be robust enough as to not require microscopic or surrogate endpoints. 2. The pathogenesis of mucositis in the model should replicate, at the molecular, cellular, and tissue levels, the events that occur in humans. 3. Concurrent toxicities, especially those in which myelosuppression is an element, should occur in a measurable way. 4. The oral environment, especially the microscopic flora, should resemble that of humans and should respond to stomatotoxic therapy in a way that is the same as humans.

20.2.4 Current Models Three species have been and/or are used for studies of oral mucositis: mice, rats, and hamsters. Murine models have been used to study both radiation- and chemotherapyinduced mucositis. In general, the endpoints used to assess mucositis have relied heavily on histological outcomes since clinical changes tend to be subtle and focus on erythema, rather than ulceration as a primary endpoint. Rats have also been used to assess radiation and chemotherapy-induced mucositis, and both 5-FU and methatrexate have been used to induce mucosal injury, often accompanied by superficial irritation [7, 8]. Lesions in these models tend to be localized.

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A number of studies focusing on the epithelial biology of oral radiation have been performed using murine lip, snout, or tongue models. Xu et al. described the effects of single and fractionated radiation schedules on the lip mucosa of mice. They found that acute reactions of the lip mucosa, i.e. focal desquamation, could be reliably scored [9]. Alternatively, Kilic et al. [10] have used a model in which the ventral surface of the tongues of mice are radiated by guiding the tongues of anesthetized animals through a 3-mm hole in an aluminum block. The dorsal tongue was then fixed with tape and an aluminum plate with a 3 × 3 mm2 window was placed over the target area on the ventral tongue. Importantly, strain-dependent variability in murine vulnerability to radiation injury has been reported. C3H/Neu mice have been used successfully. These models have been useful to define responses to various radiation regimens, including cell repopulation studies, yet the limited anatomic area available for evaluation, challenges associated with the use of topical formulations, and the subtlety of clinical changes have limited their applicability in interventional studies. While the clinical signal noted in murine models may be subtle, the ready availability of syngeneic animals, knock-outs, immune reagents, and gene chips makes the mouse a good choice for answering specific questions associated with the pathogenesis of mucosal injury. Rats have been the species of choice for studies of gastrointestinal mucositis, especially those induced by chemotherapy. Until recently, histological endpoints were mandated. However, we have recently applied endoscopy to assess mucosal injury of the lower GI tract (see section below). The rat has also been effective in studying radiation-induced proctitis. Given the limitations of the murine and rat models for oral mucositis, the hamster was evaluated as a potential species. The selection of the hamster was based on five major factors: 1. The hamster cheek pouch consists of a renewing squamous epithelium, which is similar to humans in many ways and has been studied for a number of other conditions, especially chemically induced carcinogenesis. 2. The cheek pouch mucosa provides a large mucosal surface for study that is easily accessible for examination and an anatomical site to which potential topical therapeutics can be easily applied. The pouch can be easily isolated with a lead shield from the rest of the animal’s head thereby permitting targeted radiation with no systemic toxicity. 3 . The hamster’s oral bacterial flora is similar to that of the humans in that Gram-positive bacteria are the dominant species. The cheek pouch has also been described as useful for the study of fungal infections, something that commonly occurs in humans undergoing cancer therapy. Shifts in hamster oral flora following cancer therapy mirror those described in humans – both quantitatively and qualitatively [11]. 4. The hamster is sensitive to chemotherapeutic agents that elicit toxicity in humans. The hamsters’ marrow response to these agents is similar to humans. Neutropenia develops in essentially the same time course as in humans. Thus the model is informative in studying the relationship between mucositis and myelosuppression.

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5. The pathobiology of hamster mucositis is similar to humans. Immunohistochemistry of developing mucositis is easily done. 6. The size of hamster peripheral blood cells is similar to humans, thereby permitting use of automated, instrument run analyses or peripheral blood. 7. Mucositis development in the hamster is robust and follows a predictable course. Erythema, superficial necrosis, and frank ulceration are followed by spontaneous healing.

20.2.4.1 Screening Models for the Enablement of Pharmaceuticals and Biologicals Background Largely because of their predictive value, hamster models have become a workhorse for screening and efficacy testing for pharmaceuticals and biologicals as potential interventions for the prevention and treatment of mucositis. Four variations of the hamster model have been developed, tested, and validated. History of Radiation Model Development in Hamsters As noted above, the first animal models for radiation injury were based on the human paradigm in which an external radiation source induces intraoral injury. Earlier, we attempted to replicate this approach by radiating the faces and cheeks of mice, rats, and hamsters. The animals were placed in a plastic stint (a modified 50 cc polyethylene centrifuge tube with the conical end removed) and shielded with lead so that only the facial region was exposed. Doses of 25 or 30 Gy resulted in erythema, inflammation, and swelling of the mucosa, but within 1 week following radiation, marked peri-oral inflammatory changes were seen, and animals became moribund and demonstrated severe (>20%) weight loss. We concluded that this approach was unsatisfactory. Consequently, we developed a technique which allowed us to isolate the cheek pouch mucosa. A lead shield was fabricated with a slit at its base in the approximate length of the cheek pouch base. Following anesthesia, the shield was positioned so that only the cheek pouch mucosa protruded, and therefore was the only tissue exposed to radiation. The technique proved to be effective in producing consistent pseudomembranous ulcerative mucositis [12]. 20.2.4.2 The Hamster Model for Acute Radiation-Induced Mucositis Golden Syrian hamsters, aged 5–6 weeks and weighing between 80 and 100 g are obtained from Harlan Sprague Dawley or Charles River Laboratories, housed in small groups, fed standard hamster chow, and watered ad libitum. Mucositis is

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induced with a single dose of radiation (40 Gy/dose) is administered on day 0 with a 160 kV potential (15-ma) Kimtron Polaris source at a focal distance of 40 cm, hardened with a 0.35  mm Al filtration system. Irradiation targets the left buccal pouch mucosa at a rate of 2.5 Gy/min. Prior to irradiation, animals are anesthetized with an intraperitoneal injection of ketamine (160 mg/ml) and xylazine (8 mg/ml). The left buccal pouch is everted, fixed, and isolated using a lead shield. Initial studies focused on establishing an optimal radiation dose that would consistently provide ulcerative mucositis that was clinically obvious, but not so severe that we would not be able to determine if a test drug exacerbated the response or was a radiosensitizer. Dose ranging studies were done in which we compared the time-course and clinical and clinical response from doses of 25–40 Gy. We noted that, even with doses of 25 Gy, mild mucositis, characterized by erythematous changes, were seen by 10 days following radiation. After day 10, dosedependent changes were noted. Earlier studies had demonstrated that a dose threshold of 20 Gy was required to produce consistent injury. A 6-point scale was developed to grade mucositis using outcomes that are analogous to clinical scoring (e.g. the NCI-CTC v3 scale) (Table 20.1 and Fig. 20.1–20.3). Little systemic impact was seen as animals were only radiated on one side, so food intake and

Table 20.1  Grading Scale to Describe Mucositis Severity in Hamster Models 0. 1. 2. 3. 4. 5.

Pouch completely healthy. No erythema or ulceration Erythema and vasodilation, but mucosa intact Severe erythema with superficial mucosal erosion Formation of mucosal ulceration with a cumulative size of about 25% of the pouch’s surface area Ulceration with a cumulative size of about 50% of the pouch’s surface area Contiguous ulceration involving almost the entire surface area of the pouch mucosa

Fig. 20.1  Normal hamster mucosa. Score of 0. The pouch is completely healthy with no erythema or ulceration

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Fig. 20.2  Mild to moderate oral mucositis with a score of 2. Mucosal sloughing is present and erythema is noted. Nonetheless, the epithelial integrity is intact

Fig. 20.3  Severe oral mucositis. Ulceration with necrosis and pseudomembrane formation is clearly seen. The surface area of the ulcer exceeds 25% of the pouch service and pliability of the pouch is reduced. This lesion is scored as a 4

weight were not adversely impacted. This fact was important relative to the models’ acceptability by animal use committees. We have determined that an acute dose of 40 Gy produces the most replicable results and that the ability of test agents to attenuate mucositis at this dose is translatable to clinical efficacy in humans. Clinically relevant mucositis characterized by erythema and superficial sloughing is seen by day 12. Typically, ulceration (the most significant clinically relevant endpoint) is noted around day 15 and lasts for about 5 days. Mucositis spontaneously resolves by day 35. For interventional studies,

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Acute Mucositis - Saline Controls: Combined data from 78 animals/10 studies 5

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Fig. 20.4  Mucositis curve representing meta-analysis of 78 hamsters in 10 experiments following an acute dose of 400% ulcerative mucositis (grade ≥ 3) was seen in 42% of evaluable days

we have found that the most predictive endpoint is not peak mucositis score, but instead the ability of the test compound to favorably impact the number of days that animals demonstrate ulcerative mucositis (scores ³3) [13, 14]. A meta-analysis in which data from 10 experiments of 78 animals treated with saline was evaluated demonstrated that animals demonstrated ulcerative mucositis on 42% of study days (evaluated days 6–28) (Fig. 20.4). No significant impact was seen on survival. The acute model has proven to be most valuable as a screening tool for test compounds. A positive impact (25% or more reduction in ulcerative mucositis days) has been a consistent predictor of clinical effect. The model has also been valuable in optimizing dose and study drug formulation, particularly of topically applied agents. However, there are no clinical protocols in which patients receive such high focal doses of radiation. Consequently, other models were developed to optimize pre-clinical drug development. 20.2.4.3 Non-Clinical Endpoints The hamster cheek pouch has provided significant information relative to the descriptive and quantitative histologies, immunohistochemistry, and gene expression studies [12, 15]. The cheek pouch can be easily excised. For histological and immunohistochemical studies, it is best prepared by surgically opening the pouch to create a single layer of tissue and then using a cassette for fixation.

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20.2.4.4 The Hamster Model for Fractionated Radiation-Induced Mucositis Radiation treatment for human head and neck cancer is a consistent cause of ulcerative mucositis. Radiation in these cases is typically given in small, daily fractionated doses of 2  Gy, 5 days per week, for cumulative doses of 60–70  Gy. This regimen results in a predictable pattern of mucositis with erythematous changes occurring at cumulative doses of 10–20 Gy and ulceration developing at doses of 30 Gy. Ulceration usually persists until 2–4 weeks after the completion of radiation. Although the acute radiation model described above provides an excellent method to assess acute toxicity, it has limited applicability to determine optimal scheduling of investigative agents destined to be applied to the head and neck cancer population. Consequently, we developed a model in which animals were irradiated for four consecutive days with daily doses ranging from 5–15 Gy and cumulative doses of 20–60 Gy per animal [16]. Like patients, mucositis induced by fractionated radiation dosing in hamsters is dose dependent. A reproducible regimen which produces significant ulcerative mucositis can be achieved using a cumulative radiation dose of 60 Gy. To replicate the human dosing schedule, the dose is divided into 7.5 Gy fractions delivered for four consecutive days, followed by a 2-day rest period, after which the 4-day regimen is repeated. The use of two cycles permits the study of interventional agents in schedules that are similar to humans. Since mucositis severity is dependent on the incremental and cumulative dose of radiation, daily fractions >7.5  Gy increase mucosal injury and extend the duration. Alternatively, increasing the number of cycles produces a similar result, but is logistically more taxing.

20.2.4.5 Hamster Models for Chemotherapy-Induced Mucositis With or Without Concomitant Radiation In many instances, including treatment of cancers of the head and neck, radiation is rarely given without concomitant chemotherapy. Since this approach involves agents that are synergistic in their ability to produce mucosal injury, a model combining the two is an important component in the development of any anti-mucositis agent [17].

20.2.4.6 Chemotherapy-Induced Oral Mucositis Oral mucosal toxicitiy is affected by the choice of chemotherapeutic agent, its dose and frequency, interval between dosing, and animal age. A number of preliminary studies have been performed to identify the optimal dose and schedule for 5-FU administration. A single large bolus dose of 5-FU caused mortality without mucositis. Multiple dosing at intervals of 5 days with moderate dosages was stomatotoxic with little mortality. Three intraperitoneal (IP) doses of 60 mg/kg of 5-FU administered

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on days 0, 5, and 10 were initially used. Since the cheek pouch mucosa is anatomically protected from functional trauma as occurs in humans, superficial irritation of the mucosa was performed using an 18-gauge needle on days 1 and 2. Importantly, any evidence of irritation resolved in 24  h in animals not receiving 5-FU. The combination of three doses of 5-FU and superficial irritation consistently produced ulcerative mucositis without significant mortality. Slight, but insignificant, weight loss was noted following the second injection of 5-FU on day 5. Evaluation of peripheral white blood cells (WBC) showed marked myelosuppression on day 10 and second dip in WBC on day 14. No changes in animal activity levels were seen [18]. Early mucositis was noted on day 7, after the initial injection of 5-FU. Marked progressive mucositis characterized by large areas of epithelial disruption and surface necrosis was present in 71% of animals by day 9, and 100% of animals had robust lesions by day 14. Additional studies using this model have demonstrated that the maximum depression of femoral bone marrow cellularity was preceded by mucosal breakdown and that mucosal healing occurred by bone marrow recovery. 20.2.4.7 Concomitant Chemotherapy and Radiation Concomitant chemoradiation is significantly stomatotoxic. Mucositis is induced using 5-FU delivered as single ip doses (60 mg/kg) on days -4 and -2. A single dose of radiation (30 Gy/dose) is administered on day 0 [17].

20.3 Chemotherapy-Induced Mucositis of the GI Tract Chemotherapy-induced mucositis may affect any portion of the gastrointestinal tract. Unlike radiation therapy, which is targeted to the tumor site, chemotherapy is most often administered by intravenous infusion. Consequently, its effects are widespread, but often impact the small and large intestine resulting in tissue injury and symptoms of enteritis (i.e. diarrhea). The time course of small intestinal injury is more acute that oral mucositis and is largely governed by the lack of stratified epithelium.

20.3.1 Models of Intestinal Mucositis Chemotherapy-induced intestinal injury has been readily induced in mice and rats using a variety of drugs including methotrexate, cytarabine, 5-fluorouracil, CPT-11, and doxorubicin [19–22]. Keefe et al. have successfully used irinotecan (CPT-11) in dark agouti rats to study the pathobiology of intestinal mucositis and evaluate potential interventions [23]. In their model, female dark agouti rats (weights between 150 and 170  g) are injected intraperitoneally with 200  mg/kg of irinotecan in a

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sorbitol/lactic acid buffer. Animals also receive 0.01  mg/kg of subcutaneous atropine to reduce the cholinergic reaction [24]. Endpoints with this model include diarrhea severity, measured on a 0 (no diarrhea) to 3 (staining over legs and higher abdomen, often with continual anal leakage), descriptive histology, quantitative histology, and immunohistochemistry. Widespread damage is easily noted. For the induction of intestinal mucositis in mice, the dose and schedule of CPT-11 have been modified. 20.3.1.1 Use of Endoscopy to Assess Chemotherapy-Induced Mucosal Injury In an effort to overcome the necessity of relying on histological endpoints to assess mucosal damage and permit in situ assessment of mucositis progression, we have applied video endoscopy to models of gastrointestinal mucositis. Mucositis is induced in mice via a single 60  mg/kg intraperitoneal injection of the chemotherapeutic, methotrexate, and the colon of each mouse is examined using a small animal video endoscope at 24, 48, and 72 h following treatment. Video endoscopy allows for daily visual assessment of the extent and severity of disease using clinically relevant endpoints as well as the tracking of mucosal healing following any potential therapeutic interventions. To conduct endoscopy the mice are anesthetized with isoflurane and the endoscope is slowly inserted into the rectum while the colon is insufflated with air. This method allows for clear imaging of the colon mucosal surface and both video and still images are recorded to help assess disease severity. Severity of disease is scored on 0–4 scale with 0 = normal, 1 = loss of vascularity, 2 = loss of vascularity and friability, 3 = friability and erosions, and 4 = ulcerations and bleeding. Changes in the colon observed endoscopically include modification of the vascular pattern, often accompanied by friability, erosion, and active bleeding (Fig. 20.5).

Methotrexate-Induced GI Mucositis: Video Endoscopy

Normal Colon

24 hours

48 hours

72 hours

Fig. 20.5  The sequence of development of methotrexate-induced mucosal injury of the colon as observed in the same animal using the video endoscopic technique described in the text. The use of the endoscope to evaluate injury precludes the use of sacrifice and histological sampling to determine the extent of injury. Tissue sampling is possible using the endoscope for access

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20.4 Radiation-Induced Proctitis 20.4.1 Overview of the Condition Radiation-induced proctitis is a common complication associated with radiation directed at the lower abdomen or pelvis. Radiotherapy is usually a major portion of the treatment paradigm for rectal, prostate, or cervical malignancies. There are both acute and delayed forms of radiation proctitis that may present following radiation therapy. The symptoms of acute proctitis have been reported as a complication in as many as 75% of patients undergoing pelvic radiation [25] with symptoms beginning almost immediately following therapy and persisting for up to 3 months. Delayed or late radiation proctitis is a chronic disease that can occur well after the completion of the radiation therapy and result in symptoms that may persist for decades. Disease symptomology usually begins in the acute phase during the first or second week following radiation, with presentation of rectal bleeding that can be associated with diarrhea and/ or discharge of mucus. Late proctitis symptoms appear anywhere from a few months to several years following the radiation therapy and as with the acute disease, chronic rectal bleeding is the most common late symptom. Other late symptoms may include a discharge of mucus from the rectum and more instances of tenesmus, or a feeling or inability to empty the bowel upon defecation. Tenesmus is thought to be a result of extensive rectal tissue fibrosis or possibly the formation of rectal strictures. Diagnosis and treatment of radiation proctitis usually follow a colonoscopy to exclude the possibility of any other underlying pathology. Current treatment options for radiation proctitis vary greatly in method as well as rate of success [26]. While there are few, if any, compelling clinical studies in the treatment of radiation proctitis the majority of therapies are based on the results of small unblinded studies with mixed results. The most common first-line therapies have been adapted from the treatments used in inflammatory bowel disease (IBD), including 5-ASA, steroids, sucralfate, and metronidazole. Increasingly, endoscopic therapies are being employed to control bleeding associated with radiation proctitis which includes heat probes, lasers, and most commonly argon plasma coagulation (APC). APC involves the flow ionized argon gas and can target small or larger areas of bleeding without making physical contact to the tissue. While APC has been shown to be quite effective in a number of small studies [26], there are no large clinical studies to help support these results.

20.4.2 The Biology of Radiation-Induced Proctitis It is likely that the overall pathogenesis of mucositis described for the mouth and GI tract is similar to that of proctitis, although the mechanisms of radiation-induced proctitis have not been aggressively studied. Clearly, vascular changes and fibrosis are seen as end points of the condition. In the days to months following irradiation, the onset of acute proctitis begins with early apoptosis, disruptions of mitosis, and fibroblastic proliferation that leads to swelling and sloughing [27, 28], while the later

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changes are primarily vascular and fibrotic in nature [29]. The neovascularization and telangiectasia that occur in the late phases of disease lead to persistent bleeding while increased fibrosis may lead to ischemia and eventually necrosis of the bowel tissue [29, 30]. While the exact mechanisms for the late changes in vascularity as well as fibrosis have yet to be elucidated, there is evidence that several growth factors including: platelet-derived growth factor, vascular endothelial growth factor, and fibroblast growth factor all have a roll in the pathology [28]. Other studies have shown that radiation-induced increased expression avb3 integrin has potent angiogenic effects and may contribute to the vascular changes observed in radiation proctitis [31]. Another recent study demonstrated that lesions in radiation proctitis are associated with increased angiogenesis and associated increased vascular expression of CD39 may be involved in the long-term sequel of radiation-induced proctitis [32]. Additional studies are needed to further understand the mechanisms of radiation proctitis and provide avenues for effective interventions.

20.4.3 Animal Model of Radiation-Induced Proctitis Recent modifications of rat models of radiation proctitis [33] have proven to be effective in creating an effective translational model of the condition. A single 17.5 Gy dose of radiation is directed at the rectum of a rat to induce proctitis. Critical to the success of the model has been the observation that angle of radiation exposure impact toxicity. Radiation is performed following the administration of ketamine and xylazine anesthesia. Animals are placed on a silicon rubber sheet and lead shielding is used to isolate the lower abdomen. The rectum is then irradiated with a single, acute, 17.5 Gy dose of radiation using a 160 kVp (15-ma) source at a focal distance of 30 cm, hardened with a 0.35 mm Cu filtration system at a rate of 2.5 Gy/min. Video endoscopy is used to assess the levels of disease at multiple time points following radiation (Fig. 20.3). Endosopic correlation with pathologic findings has been confirmed using models for inflammatory bowel disease [34]. While endoscopic data add an important visual and clinical component to the animal models of proctitis, the use of histology can still add valuable confirmatory information several weeks following the study completion (Fig. 20.2). Endoscopy has been valuable in evaluating the efficacy of potential therapeutics in radiation proctitis. 20.4.3.1 Radiation-Induced Dermatitis Overview of the Condition Dermatitis is a side effect of radiation therapy in patients in whom the skin is exposed to the radiation source. Acute dermatitis is most commonly reported in patients with cutaneous neoplasms, and head and neck, and breast cancer and usually begins to appear during the second to third week of fractionated radiation. Five to 10% of patients treated with radiation therapy for breast cancer develop moderate to severe radiation-induced dermatitis [35, 36]. The condition is unpleasant,

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painful, and may contribute to poor QoL. In some cases, radiation dermatitis may become so severe as to necessitate interruption or cessation of radiation therapy. Radiation dermatitis usually begins with reddening of the skin, but may also include the epilation, dry desquamation, wet desquamation, decreased sweating, edema, ulceration, and bleeding. Areas of telangiectasia, hyper- and hypo-pigmentation are also common. Symptom progression depends on both treatment-related factors, such as the total radiation dose, fractionation, total duration of treatment, volume of tissue irradiated, and type of radiation delivered, as well as patient-related factors, such as age menopausal state and pre-disposing genetic factors [37]. Current treatment options for radiation dermatitis are primarily palliative and include a wide variety of products. In general, emollients or hydrating lotions are used during the early stages of the condition. If symptoms progress, however, topical corticosteroids, dressings, and/or radiation dose reduction may be necessary [38, 39]. There are no mechanistically based interventions.

20.4.4 The Biology of Radiation-Induced Dermatitis Although the mechanism has not been well studied, it is likely that the pathoetiology of radiation-induced dermatitis is similar to other epithelial toxicities. DNA damage caused by ionizing radiation generates free radicals, which in turn activate IkB kinase (IkK) inducing NF-kB translocation to the nucleus, and subsequently, transcription of pro-inflammatory mediators. This leads to a complex pattern of tissue injury and recruitment of inflammatory cells [38]. The p53 pathway may be activated, and cytokines such as interleukin 1 (IL-1), interleukin 6 (IL-6), and transforming growth factor b (TGF-b) are secreted by keratinocytes. Histological analysis of the tissue reveals a loss of keratinocyte polarization, a loss of sebaceous and sweat glands, and destruction of hair follicles.

20.4.5 Animal Model of Radiation-Induced Dermatitis We have modified the model originally described by Abe et al. [40]. A single 30 Gy dose of radiation directed at the dorsal skin of the mouse. Two days prior to irradiation, the skin on the backs of animals is prepared by removing the hair covering the entire back of each animal using an electric shaver and depilatory cream. Irradiation is performed under ketamine and xylazine anesthesia. Animals are placed on a silicon rubber sheet and the loose dorsal skin is gently stretched and secured with two 25 gauge needles. A lead shield is then placed over the animal so that an area of skin about 2 cm × 4 cm in size was exposed. The tissue is then irradiated with a single, acute, 30 Gy dose of radiation using a 160 kVp (15-ma) source at a focal distance of 30 cm, hardened with a 0.35 mm Cu filtration system at a rate of 2.5 Gy/min. In this model, dermatitis is scored every other day beginning the day after radiation using a 6-point scale (0–5) in which (Fig. 20.6) a score of 0 is defined by

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Radiation-Induced Dermatitis: Scoring Scale Score = 0 Normal Tissue

Score = 1 Mild Erythema Minimal Dry Desquamation

Score = 2 Mild to Moderate Erythema Slight Desquamation

Score = 3 Moderate Desquamation ≤ 50%

Score = 4 Moderate to Severe Desquamation ≥ 50%

Score = 5 Frank Ulceration of Tissue

Fig. 20.6  BevDermatitis Scoring Scale

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5

Mean Dermatitis Scores (+/-SEM)

Dermatitis30 Gy Radiation 4

3

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30 32 34 36

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Fig. 20.7  Progression of radiation-induced dermatitis in a murine model of the condition. Animals receive a single dose of radiation (30 Gy) on day 0

normal skin, a score of 1 is defined by mild erythema, a score of 2 is defined by moderate to severe erythema with or without slight desquamation, a score of 3 is defined by desquamation of 25–50% of the irradiated area, a score of 4 is defined by desquamation of greater than 50% of the irradiated area, and a score of 5 is defined by frank ulceration of the skin. Onset of dermatitis in this model usually begins around 1 week following radiation and reaches peak severity another 7 days later (Fig. 20.7). One of the major benefits of this model is that dermatitis scores are obtained every other day and the disease persists for several weeks, making it an excellent model system to test potential therapeutics.

20.4.6 Bisphosphonate-Related Osteonecrosis of the Jaws 20.4.6.1 Introduction Bisphosphonates have been shown to be of marked benefit in reducing the bony complications of metastatic disease associated with multiple myeloma and breast cancer by inhibiting osteoclast activity, development, migration, and viability [41– 43]. A recognized side effect of bisphosphonate use is osteonecrosis of the jaws [44–46]. The risk of the condition has been placed somewhere between 1 and 10%. Aside from intravenous bisphosphonate infusion, dental manipulation has been iden-

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tified as a significant risk factor for the condition. Studies of the natural history, pathogenesis, and risk factors for BRONJ had been limited by the need for human material and the lack of clinical predictability. Consequently, an animal model which replicated the condition has been an objective of a number of investigators. 20.4.6.2 Rat Model for Bisphosphonate-Associated Osteonecrosis of the Jaws In 2009, a description of a rat model was published in which BRONJ was elicited following a course of zoledronic acid and dexamethosone prior to tooth extraction [47]. Female Sprague–Dawley rats aged 10–12 weeks and weighing between 200 and 280 g underwent extractions of their left maxillary mandibular and maxillary molars following varying courses of zolendronic acid (7.5 mg/kg subcutaneously) and dexamethasone (1 mg/kg, subcutaneously). Clinical, radiographic, and histological endpoints were assessed. Extractions were performed 8, 15, or 22 days following the initiation of up to three courses of zoledronic acid and dexamethasone (Z/D) and animals were then evaluated 2 or 4 weeks later. The jaws were removed, split longitudinally, and photographed and radiographed using a standard dental X-ray source and digital imaging system. After excess tissue was trimmed and the bone decalcified, it was embedded in paraffin and stained for histological examination. Exposed necrotic bone and ulceration was noted 28 days following extractions in animals treated with the combination of zoledronic acid and dexamethasone, but not in animals treated with zoledronic acid alone (Fig. 20.8). Earlier studies had

Fig. 20.8  Gross observation of BRONJ in the rat model. These representative photographs d­ emonstrate the gross clinical appearance of the maxillary ridges of animals with intact epithelium (panel a – day 28 following extraction), or ulcerated mucosa overlying necrotic bone at days 14 (panel b), and 28 (panel c ) following extraction. Ulcerative areas are characterized by rolled mucosa, lack of drainage, and, by day 28 post-extraction, central areas of yellow/gray necrosis. From Sonis et al. Oral Oncol 2009;45:164–72

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Fig. 20.9  Histology of specimen from not with evidence of osteonecrosis demonstrating fragments of necrotic bone

shown that the administration of dexamethasone did not confer a risk of osteonecrosis in the absence of the bisphosphonate. Histologic evidence of necrosis was also noted in the Z/D animals (Fig. 20.9). Refinement and modification of the model will likely evolve, but it provides a basis for studies relating to the mechanism of BRONJ and a way to evaluate potential interventions.

References 1. Sonis ST, Elting LS, Keefe DM, et al. Perspectives on cancer therapy-induced mucosal injury: pathogenesis, measurement, epidemiology and consequences for patients. Cancer 2004;100 (Suppl 9):1995–2025. 2. Nonzee NJ, Randade NA, Patel U, et  al. Evaluating the supportive care costs of severe radiochemotherapy-induced mucositis and pharyngitis: results of a Northwestern University cost of Cancer Program pilot study with head and neck and nonsmall cell lung cancer patients who received care at a county hospital, a Veteran’s Administration hospital, or a comprehensive cancer center. Cancer 2008;113:1446–52. 3. Blijlevens N, Sonis S. Palifermin (recombinant keratinocyte growth factor-1): a pleiotropic growth factor with multiple biological activities in preventing chemotherapy- and radiotherapyinduced mucositis. Ann Oncol 2007;18:817–26. 4. Elting LS, Keefe DM, Sonis ST, et al. Patient-reported measurements of oral mucositis in head and neck cancer patients treated with radiotherapy with or without chemotherapy: demonstration of increased frequency, severity, resistance to palliation, and impact on quality of life. Cancer 2008;113:2704–13. 5. Sonis ST. The pathobiology of mucositis. Nat Rev Cancer 2004;4:277–84. 6. Sonis ST. Pathobiology of mucositis: novel insights and opportunities. J Support Oncol 2007;5:3–11.

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7. Lee SW, Jung KI, Kim YW, et  al. Effect of epidermal growth factor against radiotherapyinduced oral mucositis in rats. Int J Radiat Oncol Biol Phys 2007;67:1172–8. 8. Vivela-Goulat MG, Teixeira RT, Rangel DC, et  al. Homogenious amniotic membrane as a biological dressing for oral mucositis in rats: histomorphic analysis. Arch Oral Biol 2008;53:1163–71. 9. Xu FX, van der Schueren E, Ang KK. Acute reactions of the lip of mice to fractionated irradiations. Radiother Oncol 1984;1:369–74. 10. Kilic Y, Rajewski K, Dorr W. Effect of post-exposure administration of keratinocyte growth factor (palifermin) on radiation effects in oral mucosa of mice. Radiat Environ Biophys 2007;46:13–19. 11. McMillan CD, Lowell VM. Experimental candidiasis in the hamster cheek pouch. Arch Oral Biol 1985;30:248–55. 12. Sonis ST, Peterson RL, Edwards LJ, et al. Defining mechanisms of action of Interleukin-11 on the progression of radiation-induced oral mucositis in hamsters. Oral Oncol 2000;36:372–81. 13. Murphy CK, Fey EG, Watkins BA, et al. Efficacy of superoxide dismutase mimetic M40403 in attenuating radiation-induced oral mucositis in hamsters. Clin Cancer Res 2008;14:4292–7. 14. Huang D, Popat R, Bragdon C, et al. Effects of ceramide inhibition on experimental radiationinduced oral mucositis. Oral Surg, Oral Med, Oral Pathol, Oral Radiol Endod 2005;100:321–9. 15. Sonis ST, Scherer J, Phelan E, et al. The gene expression sequence of radiated mucosa in an animal mucositis model. Cell Profif 2002;35 Suppl 1:93–102. 16. Ara G, Watkins BA, Zhong H, et al. Valifermin (rhFGF-20) reduces the severity and duration of hamster cheek pouch mucositis induced by fractionated radiation. Int J Radiat Biol 2008;84:401–12. 17. Alvarez E, Fey EG, Valax P, et al. Preclinical characterization of CG53135 (FGF-20) in radiation and concomitant chemotherapy/radiation-induced oral mucositis. Clin Cancer Res 2003;9:3453–61. 18. Sonis ST, Tracey C, Shklar G, et  al. An animal model for mucositis induced by cancer ­chemotherapy. Oral Surg, Oral Med, Oral Pathol 1990;69:437–43. 19. Tooley KL, Howarth GS, Lymn KA, et al. Optimization of the non-invasive (13)C-sucrose breath test in a rat model of methotrexate-induced mucositis. Cancer Chemother Pharmacol 2010; 65:913–21. 20. Cheah KY, Howarth GS, Yazbeck R, et al. Grape seed extract protects IEC-6 cells from chemotherapy-induced cytotoxicity and improves parameters of small intestinal mucositis in rats with experimentally-induced mucositis. Cancer Biol Ther 2009;8:382–90. 21. Stringer Am, Gibson RJ, Logan RM, et al. Gastrointestinal microflora and mucins may play a critical role in the development of 5-fluorouracil-induced gastrointestinal mucositis. Exp Biol Med 2009;234:430–41. 22. Logan RM, Stringer AM, Bowen JM, et  al. Is the pathobiology of chemotherapy-induced alimentary tract mucositis influenced by the type of mucotoxic drug administered? Cancer Chemother Pharmacol 2009;63:239–51. 23. Gibson RJ, Bowen JM, Alvarez E, et al. Establishment of a single-dose irinotecan model of gastrointestinal mucositis. Chemotherapy 2007;53:360–9. 24. Stringer AM, Gibson RJ, Logan RM, et al. Irinotecan-induced mucositis is associated with changes in intestinal mucins. Cancer Chemother Pharmacol 2009;64:123–32. 25. Counter SF, Froese DP, Hart MJ. Prospective evaluation of formalin therapy for radiation proctitis. Am J Surg 1999;177:396–8. 26. Leiper K, Morris AI. Treatment of radiation proctitis. Clin Oncol 2007;19:724–9. 27. Haboubi NY, Schofield PF, Rowland PL. The light and electron microscopic features of early and late phase radiation-induced proctitis. Am J Gastroenterol 1988;83:1140–44. 28. Brunn T, Fletcher CD. Postradiation vascular proliferations: an increasing problem. Histophathology 2006;48:106–14. 29. Fajardo LF. The pathology of ionizing radiation as defined by morphologic patterns. Acta Oncol 2005;44:13–22.

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30. Haselton PS, Carr N, Schofield PF. Vascular changes in radiation bowel disease. Histopathology 1985;9:517–34. 31. Abdollani A, Griggs DW, Zieher H, et  al. Inhibition of alpha (v) beta 3 integrin survival ­signaling enhances antiangiogenic and antitumor effects of radiotherapy. Clin Cancer Res 2005;11:6270–9. 32. Sheth S, Bleibel W, Thukral C, et al. Heightened NTPDase-1/CD39 expression and angiogenesis in radiation proctitis. Purinergic Signalling 2009;5:321–6. 33. Kang S, Chun M, Jin YM, et al. A rat model for radiation-induced proctitis. J Korean Med Sci 2000;15:682–89. 34. Lyng CD, Stevens AC, Gordon GJ, et al. Rodent video endoscopy and biopsy: a new method for the development of novel inflammatory bowel disease therapies. Gastroenterology 2008;134:A262. 35. Rosen EM, Fan S, Rockwell S, et  al. The molecular and cellular basis of radiosensitivity: implications for understanding how normal tissues and tumors respond to therapeutic ­radiation. Cancer Invest 1999;17:56–72. 36. Fujishiro S, Mitumori M, Kokubo M, et al. Cosmetic results and complications after breast conserving therapy for early breast cancer. Breast Cancer 2000;7:57–63. 37. Isomura M, Oya N, Tachiiri S, et al. IL12RB2 and ABCA1 genes are associated with susceptibility to radiation dermatitis. Clin Cancer Res 2008;20:6683–89. 38. Hymes SR, Strom EA, Fife C. Radiation dermatitis: clinical presentation, pathophysiology and treatment. J Am Acad Dermatol 2006;54:28–46. 39. McQuestion M. Evidence-based skin care management in radiation therapy. Semin Oncol Nurs 2006;22:162–73. 40. Abe Y, Urano M. Fraction size-dependent acute skin reaction of mice after multiple twice-aday doses. Int J Radiat Oncol Biol Phys 1990;18:359–64. 41. Dunstan CR, Felsenberg D, Seibel MJ. Therapy insight: the risks and benefits of bisphosphonates for the treatment of tumor-induced bone disease. Nat Clin Pract Oncol 2007;4:42–55. 42. Lipton A. Efficacy and safety of intravenous bisphosphonates in patients with bone metastases caused by metastatic breast cancer. Clin Breast Cancer 2007;7(Suppl 1):S14–25. 43. Luftner D, Henshke P, Possinger K. Clinical value of bisphosphonates in cancer therapy. Anticancer Res 2007;27:1759–68. 44. Marx RE. Pamidronate (Aredia) and zoledronate (Zometa) induced avascular necrosis of the jaws: a growing epidemic. J Oral Maxillofac Surg 2003;61:1115–7. 45. Hellstein JW, Marek CL. Bisphosphonate osteochemonecrosis (Bis-Phossy Jaw): is this Phossy Jaw of the 21st century? J Oral Maxillofac Surg 2005;63:682–9. 46. Ruggiero SL, Merota B, Rosenberg TJ, et al. Osteonecrosis of the jaws associated with the use of bisphosphonates: a review of 63 cases. J Oral Maxillofac Surg 2004;62:527–34. 47. Sonis ST, Watkins BW, Lyng GD, et al. Bony changes in jaws of rats treated with zoledronic acid and dexamethasone before dental extractions mimic bisphosphonate-related ­osteonecrosis in cancer patients. Oral Oncol 2009;45:164–72.

Chapter 21

Bone Marrow as a Critical Normal Tissue that Limits Drug Dose/Exposure in Preclinical Models and the Clinic1 Ralph E. Parchment

Abstract  Mouse models of cancer have played an important role in the discovery and development of cytotoxic and targeted anticancer agents. Initially, discovery models used transplantable syngeneic tumors that were treated with doses maximally tolerated by the normal tissues of the mouse. Thus, experimental compounds were selected for development based on their selectivity for murine malignant tissue over murine normal tissue, and the discovery method assumed that a murine therapeutic index closely approximates a human therapeutic index for most compounds. When mouse modeling migrated to the use of xenografted human malignancies in order that drug efficacy assessment would be more relevant for clinical disease, there was not a corresponding transition to human normal tissue to determine the maximum tolerated dose (MTD) to use in treating the mouse. Consequently, drug discovery in these models has been based on selectivity for human malignant tissue over murine normal tissue. This “xeno-therapeutic index” has an unknown relationship either to a mouse or human therapeutic index – these latter ones being determined by efficacy against cancer at the maximum dose tolerated by normal tissues from the same species of origin as the cancer. The biological process of producing new blood cells is termed hematopoiesis, and this process is a frequent target of toxicity of anticancer drugs, including toxicity that limits dose. Two recently established methods in experimental hematology and hematotoxicology are useful for determining the MTD of human hematopoiesis in the mouse. The methods are suitable for implementation in the drug discovery setting, and therefore could be used to discover new anticancer compounds that exhibit selectivity for malignant human 1  This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. This research was supported [in part] by the Developmental Therapeutics Program in the Division of Cancer Treatment and Diagnosis of the National Cancer Institute. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

R.E. Parchment (*) Laboratory of Human Toxicology and Pharmacology, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_21, © Springer Science+Business Media, LLC 2011

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tissue over normal, human hematopoiesis in mouse models. This chapter provides background information on hematopoiesis, explains why it frequently limits the dose of anticancer drugs, describes the two methods, and then proposes ways in which the methods might contribute to mouse modeling of cancer therapy to improve predictive accuracy for clinical outcome. Keywords  Bone marrow • Hematopoiesis • Progenitor • Stem cell • Hematotoxicology • Neutropenia • Myelosuppression • Dose-limiting toxicity • Adverse drug effect • Therapeutic index • Maximum tolerated dose • Prediction models • CFU-GM • Engraftment

21.1 Introduction Many clinically approved chemotherapeutic agents were discovered with preclinical mouse models using transplantable syngeneic cancers of spontaneous or induced origin. In the discovery arena, the dose of an experimental agent was usually pushed to the level at which toxicity to a normal mouse tissue became lifethreatening, or just below that level. Since both the dose-limiting tissue and the malignancy were murine, experimental agents were prioritized and selected for development based on an approximate murine therapeutic index, i.e. efficacy at maximum tolerated toxicity or the toxic dose divided by the efficacious dose. The assumption behind the use of these murine models was a correlation between murine therapeutic index and human therapeutic index. Once both qualitative and quantitative biochemical differences between murine and human malignancies began to be discovered, there was naturally an impetus to develop murine models harboring human disease targets, either transplantable human tumor lines, primary transplants of surgical specimens, or more recently, genetically engineered mouse models harboring human molecular targets in the context of the mouse genetic background. These models, and their rationale as more relevant and hopefully more accurate models of clinical cancer, are given expert, detailed coverage throughout this volume. Of relevance to this chapter is the importance of noting that this move to “humanize” the mouse models stopped short of being complete. Once mouse models containing human disease targets were developed, there was little effort to humanize any other aspects of mouse modeling. Essentially the murine disease target was replaced with a human one, but the normal tissues that limit dose and make an equal contribution to therapeutic index remained murine. As a result, the murine therapeutic index has not yet been replaced by a human therapeutic index as the basis for selecting and prioritizing experimental compounds for development. Instead, compounds are being evaluated based on a “xeno-therapeutic” index that relates human efficacy to mouse dose tolerance, in essence modeling a situation in which the clinical treatment of human patients is somehow limited by the end organ dose tolerance of therapy in mice being treated in parallel. The current use of a chimeric mouse model – human disease and mouse normal tissue that

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limits dose – makes little sense, because the effectiveness of a drug is determined by its therapeutic index in man and therefore its selectivity for tumor over normal tissues within a species, not between two species. This chapter focuses on methods to get to an approximate human therapeutic index in the preclinical discovery setting using mouse models. Hematotoxicology is a wellestablished field, and both in vitro and in vivo methods are now available for quantifying differences between mouse and man in bone marrow tolerance of anticancer agents. The accepted philosophy of humanizing mouse models is thereby extended to doselimiting normal tissue by methods that replace mouse bone marrow with human bone marrow. Although there is not enough evidence yet to conclude that human therapeutic index-based selection and prioritization of experimental compounds from mouse modeling of human cancer provides a higher success rate in clinical development than murine or xeno-therapeutic index-based decisions, it seems reasonable to explore such modeling with key case studies – especially re-visiting very potent, “highly active” experimental compounds that failed clinical development due to severe clinical toxicity at doses/exposures far below that required for activity in mouse models. In addition, it seems unreasonable to select compounds for development that require doses above what human normal tissue like bone marrow will tolerate, unless they are planned for use only in the stem cell transplant setting. After introducing the field of hematotoxicology as related to cancer therapy, this chapter concludes with two specific methods for quantifying differences between murine and human bone marrow drug tolerance and integrating this information into mouse modeling for discovery and prioritization of new agents for development.

21.2 Blood Cytopenia as a Quantifiable Dose-Limiting Toxicity in the Oncology Clinic The blood of adult mammals contains a number of cell types that perform critical functions in health. The blood cells are classified into four major lineages based on morphology and ontogeny: granulocytes, lymphocytes, red blood cells and platelets. Homeostatic maintenance of blood cell concentrations, and in some cases specific ranges, is critical for achieving adequate oxygen delivery to tissue, immunity and hemostasis. Many antineoplastic agents, a number of other pharmaceutical agents, and some food contaminants can adversely affect the concentration of one or more blood cell types as determined by clinical laboratory measurements. The resulting severity of any clinical side effect will range from unremarkable to life-threatening, depending on the extent and duration of the change. It is important to recognize that anticancer therapy is often administered intentionally at maximum tolerated dose (MTD). One important objective of clinical Phase I dose-escalation trials is the identification of the dose that causes dose-limiting toxicity, defined as severe or lifethreatening toxicity caused by the investigational agent. The human MTD is defined as the dose just below that which causes dose-limiting toxicity, as long as patients recover from the moderate toxicity associated with that lower dose in time to maintain treatment schedules (“reversible toxicity”). Formal systems have been developed to

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standardize the classification of adverse toxic effects to a number of organ systems as well as the grading of their severity. The reader is referred to the most current version of the National Cancer Institute’s Common Toxicity Criteria and the Common Terminology Criteria for Adverse Events for classifying and grading severity of adverse events caused by investigational therapy during clinical trials [1]. For many anticancer drugs, the dose-limiting toxicity is a severe or life-threatening adverse effect on the hematologic system, as defined by the CTC. A decrease in a blood cell concentration, known as a cytopenia, is the most commonly encountered adverse effect of oncolytic drug therapy on the hematologic system. There are more specific terms used to describe various cytopenic conditions specifically affecting one blood cell lineage. The term “neutropenia” refers to a decrement in the blood concentration of a prevalent type of granulocyte called a neutrophil. Neutropenia is a subset of the condition known as leukopenia, which is literally a decrease in the blood concentration of all types of white blood cells counted together. Of all the leukocyte subsets that could be monitored during cancer therapy of human patients, neutropenia draws special attention, because decreased neutrophil granulocyte counts of even a few days’ duration are associated with increased risk of infection and septicemia. In the adult human, the normal concentration of neutrophils is 1,800 –7,700 ml–1 [2], and a life-threatening adverse effect of drug therapy (Grade 4) is defined as a drug-related drop in concentration below 500 for a few days or more [1]. The condition of severely decreased neutrophil counts approaching or reaching zero is termed agranulocytosis. Blood concentrations of monocytes may also decrease when neutropenia occurs, but isolated monocytopenia rarely occurs in man [3]. Except for leukemic disease, an overabundance of neutrophils or monocytes in the blood does not usually produce clinical side effects, although overabundant eosinophils are associated with clinical toxicity [3]. Of relevance to veterinary models in cancer drug development, it is important to note that there are substantial inter-species differences in the values of hematology parameters that indicate clinical toxicity. A similar 70–90% decline in the blood concentration of neutrophils, which causes significant clinical risk of infection in humans and dogs, may result in few if any clinical consequences in rodents, which have a much higher lymphocyte:neutrophil ratio than man (or dog) and are often housed in isolator/barrier facilities designed to minimize risk of infection. The white blood cell concentration in the mouse of ~6,000 ml–1 is similar to that in man, but the distribution of the various leukocyte cell types is different: ~75% of the white blood cells in the mouse are lymphocytes, i.e. a lymphocyte concentration of 4,500 ml–1 that is well above the typical human concentration [4]. A second, commonly encountered hematologic toxicity of cancer therapy is “thrombocytopenia,” which refers to a decrease in the blood concentration of thrombocytes (“platelets”). In man, the normal blood concentration of thrombocytes is 150,000–440,000 ml–1 [2], and a life-threatening (Grade 4) adverse effect of drug therapy is defined as a drop in the blood concentration of platelets below 25,000  ml–1 [1]. However, it is recognized that there is not a strong relationship between platelet count and bleeding manifestations, because clinical consequences of platelet counts are tempered by platelet function [5]. In patients with normally functioning platelets, bleeding time correlates with platelet count, whereas patients

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with platelet abnormalities may show prolonged bleeding times despite normal platelet counts [5]. Of note, mouse exhibits normal platelet concentrations of ~1,200,000  ml–1, much higher than in healthy humans, but mouse platelets are smaller in size than human platelets [4]. Cytopenia in the erythroid or lymphoid lineage is a less frequent clinical concern of anticancer therapy than neutropenia and thrombocytopenia. However, chemotherapeutic agents destroy lymphocytes and result in a decreased count in the blood termed a lymphocytopenia. The normal blood lymphocyte concentration is 1,500– 4,000 ml–1, although counts as low as 1,000 ml–1 may be considered normal in adult humans [6, 7]. A Grade 4 adverse effect on the lymphocyte concentration is defined as a decrease to less than 200 ml–1 [1]. In addition, substantial clinical knowledge about immunodeficiency conditions and susceptibility to opportunistic infections has resulted in a specific definition of life-threatening drug toxicity as a decrease in blood concentration of CD4+ lymphocytes to <50 ml–1 [1]. In contrast to the neutrophil lineage, elevated lymphocyte counts are also considered an adverse effect of drug therapy, and a severe adverse effect (Grade 3) is defined as a blood concentration of lymphocytes of >20,000 ml–1 [1]. Erythrocyte counts are usually unaffected by acute dosing schedules of cytotoxic chemotherapy, unless the agents cause hemolysis. In contrast to the other lineages mentioned above, toxicity is not judged solely on the basis of a decreased blood concentration of cells, i.e. “erythropenia.” Any adverse drug effect on the red cell lineage (anemia) is usually described by the magnitude of the resulting drop in hemoglobin concentration or in the hematocrit [1], which is the portion of blood volume occupied by erythrocytes, considering both red cell concentration and cell volume [2]. Hemoglobin concentration and packed red cell volume are relatively uniform across many mammalian species, even though erythrocyte concentrations vary [4].

21.3 Cancer Therapeutics as Toxicants to Highly Proliferative Hematopoietic Cells In the absence of infections, hemorrhagic events, hematologic disease or autoimmune reactions, the blood concentrations of the four hematologic lineages described above remain remarkably stable over many years of life. However, these stable concentrations belie the fact that mature cells are continually lost from the circulation, and new cells must therefore be produced to replace these losses. In fact, loss rates and therefore life-spans of various blood cell types vary widely (Table 21.1), ranging from the very short disappearance half-life of 7 h for neutrophils [8], to a 9–11 day life-span of platelets [9, 10], to a life-span of ~4 months for erythrocytes [11]. The rates of loss of mature blood cells from the circulation can be used to calculate rates of production of new, replacement cells required to maintain blood cell concentrations (Table 21.1). Because both cell concentrations and their specific life-spans in the blood are lineage-specific, the replacement rates also differ between the cell lineages [8, 9, 12]. As is apparent from the magnitude of these rates, there must be highly proliferative tissue(s) that is the source of production of these replacement cells.

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Table 21.1  Hematology values and calculated hematopoietic rates for the three blood cell lineages that are affected adversely by many anticancer agents Loss rate from Human cell type Blood concentration circulation Replacement rate Neutrophil [2, 8] 7-h half-life 0.9–1.6 × 109 kg–1 day–1 1.8-7.7 × 103 ml–1 Erythrocyte [2, 11, 12] 5,000 × 103 ml–1 120-d life span 3 × 109 kg–1 day–1 3 –1 Platelet [2, 9, 10] 10-d life span 3.3 × 109/kg/day 150-440 × 10  ml (5 × 104 µl–1 blood/day)

Fig. 21.1  Hematopoiesis in red marrow and its disruption by potential mechanisms of toxicity. (left) Photomicrograph of a histological section of hematopoietic (red) bone marrow in the mouse (courtesy of Dr. Miriam Anver, Pathology/Histotechnology Laboratory, the National Cancer Institute at Frederick). (right) Progenitor model of bone marrow hematopoiesis. The process begins with a multi-potential progenitor (purple cell) that is quiescent most of the time and therefore more resistance to drug toxicity. Via the influence of the cytokine milieu and interactions with stromal cells, this cell generates progeny committed to a particular blood cell lineage (blue, granulocyte/macrophage; orange, erythrocytes). Committed CFU-GM and CFU-G progenitors (blue circles-low stipples) encountering trophic factors (e.g. colony stimulating factors, blue boxes) survive and divide to produce differentiated, morphologically identifiable daughter cells (myeloblast, promyelocyte, myelocyte; blue circles-high stipples). Proliferation is tightly linked to differentiation to a post-mitotic state via paracrine/autocrine differentiation factors (yellow circles) induced by the trophic factors. Terminal differentiation in the post-mitotic, maturation compartment produces the metamyelocyte, band cell and the polymorphonuclear neutrophil (blue squares). A similar developmental process beginning with the multi-potential progenitor (purple) but under control of different trophic factors (e.g. erythropoietin) progresses through erythropoiesis (orange pathway)

Hematopoiesis is the term given to the renewable process of producing new blood cells from smaller numbers of undifferentiated precursor cells. In adult rodents, dogs and humans, both mature and maturing blood cells are found in the red marrow (Fig. 21.1a) primarily of flat bones (sternum, ribs, vertebrae, skull, pelvis) and in the proximal ends of long bones, whereas the yellow marrow of the

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long bones and elsewhere is composed of adipose and stromal tissues with only rare blood cells [13]. Under certain circumstances, hematopoiesis can also occur in the spleen of rodents [14] and in the lungs of humans [9]. The red marrow contains morphologically identifiable, yet immature precursor cells of each lineage at various stages of phenotypic maturation, as well as a number of undifferentiated cells without any morphological features that could be used to assign them to particular lineages [13]. The renewable nature of blood cell production, in which large numbers of new cells are produced over the life-span of the organism without depleting red marrow cellularity, is consistent with a stem cell population that replenishes itself while producing progeny cells that terminally differentiate into the needed blood cells [15, 16]. In mouse models, some of these undifferentiated cells are capable of reconstituting blood cell production for longer than two life-spans during serial transplantation into marrow-ablated recipients [17]. Thus, the marrow appears to contain a complete system for producing new blood cells as well as for replenishing this capacity. There are cell specializations, and hierarchical relationships between precursors and progeny cells, within the “undifferentiated” population of cells, and methods other than histology are required to distinguish these different cell types. A number of subpopulations, differing in degree of lineage commitment and capacity to proliferate, can be detected, defined functionally, and quantified in vitro, when specific cytokines, cytokine combinations (or conditioned media), or cytokine sequences are used to promote cell survival in liquid cultures [18] or stimulate clonal colony formation in semi-solid media [19–22]. Because they respond to these specific cytokines by producing immature blood cells, these subpopulations are called hematopoietic progenitors, and they are named according to the blood cell lineage they produce. If they form clonal colonies containing recognizable blood cell types in semi-solid media, they are named colony-forming units (CFU) and designated by a suffix to indicate what lineage of blood cells are found in the colonies. For example, CFU-E produces hemoglobinized erythroid daughter cells, whereas CFU-G and CFU-M produce granulocytic and monocytic daughter cells. These CFUs exhibit developmental potential restricted to just one lineage. However, there are other, more immature CFUs that exhibit multi-lineage potential. For example, CFU-GM forms colonies of granulocytes and macrophages, and CFU-GEMM forms mixed colonies of granulocytes, erythrocytes, monocytes and megakaryocytes. The control of hematopoiesis lies with a complex network of cytokines [15, 23] that can act on progenitors individually, as well as synergistically and antagonistically (Fig. 21.1b). Under homeostatic conditions, normal concentrations of the blood cells are maintained by underutilized marrow capacity. Pathophysiological conditions as well as clinical trials of supra-physiological levels of recombinant cytokines have shown that there is marrow reserve that can increase production of new blood cells and thereby increase blood cell concentrations above normal levels, leading to the clinical condition known as “cytosis.” Some cytokines possess the ability to stimulate colony formation by particular CFUs, under culture conditions where there is not any spontaneous colony formation. These soluble proteins are usually known as “colony-stimulating factors” (CSF), except erythropoietin (Epo)

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and Interleukins-3 and -5 that were named based on reasons other than in  vitro colony formation. In general, these CSFs appear to act as trophic factors for the CFUs by binding to cell surface receptors and thereby preventing a default program of apoptosis [24–27]. The surviving cells then pursue their committed developmental programs. Other factors act to modulate response to CSFs, whereas still others act synergistically on early progenitors with multi-lineage potential. Greater complexity is created by some cytokines that can act not only as trophic factors in the progenitor compartments, but also as modulators of the function of mature blood cells. Several cytokines that regulate myelopoiesis are produced by cell types found in the bone marrow and elsewhere in the body and may function both as paracrine factors from the bone marrow stromal and as endocrine factors from remote tissue locations when there is high demand for new blood cells. Epo is released from the kidney in response to hypoxia and is therefore an endocrine factor [28, 29]. Progenitor survival is tightly coupled to differentiation by CSF induction of paracrine/autocrine factors that direct the terminal differentiation program [15]. Cancer therapeutics are intended to interfere with aberrant, poorly controlled proliferation during one or more phases of the cell cycle, and therefore a priori they carry a mechanistic risk of toxicity in highly proliferative tissues, such as bone marrow, gastrointestinal epithelium and oral mucosa. Furthermore, cellular assays and preclinical models with high proliferation rates are often employed in drug discovery for the practical reasons that they are most likely to demonstrate a drug effect and that they provide study data in a much shorter time frame than models resembling clinical disease, where therapeutic outcomes may require months – years to be detectable. Using these models for lead selection further increases the bias toward experimental agents that will affect rapidly proliferating normal tissues. When coupled with the ease and frequency of monitoring blood cell counts during investigational cancer therapy, it is not surprising that adverse hematologic effects, which are usually manifestations of hematopoietic disruption in the bone marrow, are commonly reported toxicities of many classes of anticancer drugs, especially cytotoxic agents. Molecularly targeted agents that interfere with trophic factor signaling pathways (either blocking response or reducing cytokine availability), activate pro-apoptosis pathways, or cause chemical cytotoxicity via off-target effects could inhibit signal transduction and cause adverse hematological effects. The rapidly proliferating myelopoietic system (Table 21.2), including its lineage-committed progenitors, is a frequent target of cancer therapy, because even acute dosage regimens provide a sufficiently long duration of drug exposure to affect a large proportion of precursors in active cell cycle. More immature, multi-potential progenitors, as well as the hematopoietic stem cell, usually tolerate acute dosage regimens, because most of these cells are quiescent and are therefore not susceptible to the cell cycle phase-specific agents. However, the introduction of molecularly targeted agents that require protracted daily dosing brings into play potentially more serious toxicity to these multi-potential progenitors and stem cells. If the initial days’ dosing of the drug causes a loss of committed progenitors in the marrow and a cytopenic event in the blood, the multi-potential progenitors and stem cells may enter the cell cycle to replace the progenitor pool and compensate for the toxicity,

Duration of S-phase and cell cycle (h) Mitotic index

Stage of development Transit time (h) % S-Phase

14, 86 [32]

0.025

–

Myeloblast 23 85

CFU – Human: 26–43 [32–35] Mouse: 25–55 [36–38] – 0.015

–

26 [32]

Promyelocyte 26–78 65

0.011

–

Myelocyte 17–126 33

Metamyelocyte 8–108 Post-Mitotic

Band 12–96

PMN → neutrophil 0–120

Table 21.2  Proliferative properties of bone marrow progenitors, precursors and mature cells of the human granulocyte lineage Stage of development Proliferating (Amplification) compartment Maturation compartment Yield = 32x (~5 divisions) Transit time to blood = 5–7 days

Blood   PMN neutrophil T½ = 7 h

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at which point they could also become susceptible to the antiproliferative effects of the therapy. Studies of protracted daily dosing of different chemotherapeutic agents have shown that these early, multi-potential cells become sensitive to some, but not all, cytotoxic drugs when responding to acute neutropenia caused by previous doses [30, 31]. The result of this study also indicated that dosing of some anticancer drugs can be continued through the cytopenic episode without worsening the condition, but the author is not aware of any test or assay that can substitute for the empirical in vivo study for identifying drugs with this property. The process of generating granulocytes (mostly neutrophils, but also basophils and eosinophils) from committed progenitors in hematopoietic tissue is termed granulopoiesis, and it is frequently a target of toxicity of cancer therapy, both with chemotherapeutics as well as some molecularly targeted agents. Not surprisingly, the cellular hierarchy responsible for producing replacement granulocytes (Table 21.2) is highly proliferative in terms of both a rapid cell cycle time and a high growth fraction [32–38]. Coupled with the rapid rate of loss of granulocytes from the blood stream, it is apparent why disruption of granulopoiesis for even short periods of time leads to neutropenia.

21.4 Bone Marrow as a Critical Normal Tissue that Sets Maximum Human Dose/Exposure and Therefore Should Restrict Dose/Exposure Levels Used in Mouse Modeling Although severe neutropenia is considered a clinically manageable side effect of therapy, it nonetheless increases the risk of life-threatening sepsis and is therefore considered a dose-limiting toxicity [1]. The clinical dose that causes severe neutropenia is the highest possible dose that patients can tolerate without some type of hematopoietic stem cell support (transplantation or growth factor treatment), and this dose is known as the MTD. If pharmacokinetic data are available, a maximally tolerated systemic exposure (area under the plasma C × t curve) associated with the MTD will also be identified. In the absence of intentional support during therapy, the efficacy of an anticancer agent in the dose range above the MTD (exposure) will never be known, or need to be known. Efficacy at these unreachable dose levels is irrelevant to the clinical success or failure of an experimental drug. If the success or failure of an experimental drug in clinical trials is determined by its efficacy at doses/exposures that do not exceed maximum tolerated levels, why would one base preclinical development decisions on, or even want to know about, the efficacy of an experimental compound in mouse models at doses/exposures that lie above what human patients will tolerate? The answer to this question is obviously that one would not, so why hasn’t this philosophy gained traction in mouse modeling? There are significant modeling and technical challenges to identifying what the maximum tolerated human level of an experimental compound will be at the time of preclinical modeling – prior to any human clinical trial experience. Because the actual human maximum tolerated level would not be known until

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early clinical trials are complete, the best one could do at the preclinical discovery/ development stage is to estimate or predict it. Then, this estimate could be incorporated into mouse modeling by treating only at doses (exposures) that do not exceed the predicted human maximally tolerated level. The technical challenge is establishing methodology that accurately predicts human MTD (exposure) early in the discovery setting, so this value is available by the time of mouse modeling. The switch from mice harboring mouse tumors to mice harboring human tumors was a substantial advance in modeling, because it provided an in vivo evaluation of human molecular target response with small quantities of compounds. However, the effort to “humanize” mouse models should not have stopped there, but should have continued into other aspects of animal modeling: replicating medical procedures for assessing tumor response, using anticipated clinical dosage regimens and routes, and limiting dose levels (exposures) to those predicted to be tolerated by normal human tissues. Potency against a human target has been a key factor in selection of experimental agents, and in some programs, potency against the human molecular target is further increased by iterative lead optimization studies. This focus on high human potency tends to create small molecules that are well tolerated in the mouse, either because of species-specific differences in drug binding to target, the functional role of the target, or its expression pattern in normal tissues, including bone marrow. Compounds highly selected for human potency may appear exceptionally active in mouse models, not because they are selective for tumor, but simply because they have very low potency against the mouse counterpart of the human target, or the mouse target is not critical for normal tissue function. Lead optimization of potency against a human molecular target usually does not include assessment of potency on the counterpart target of the mouse that will determine target-dependent organ toxicity, so it is not possible to distinguish tumor-selective from primate-selective agents. In this situation, it is not surprising that doses (exposures) required for efficacy in the mouse model are often not achievable in clinical trials. Since therapeutic index is a major determinant of the clinical success of an investigational agent, it is important that therapeutic index in the mouse models approximates its clinical counterpart. However, the therapeutic index in currently used xenograft mouse models is a highly artificial number: TImouse model = mouse maximum tolerated dose ÷ human efficacious dose The fundamental question is how mouse modeling can be modified to yield more accurate estimates of the actual human therapeutic index that will ultimately determine clinical success or failure: TImouse model = human maximum tolerated dose in mouse ÷ human efficacious dose in mouse Methods for estimating maximum tolerated human dose (exposure), and then limiting the evaluation of experimental compounds in mice to dose levels that do not exceed this maximum, hold promise for more accurate assessment of clinical therapeutic potential in the preclinical setting. Based on mouse modeling studies using xenografted pediatric solid tumors, the Houghton group has found that preclinical efficacy of topoisomerase I inhibitors, ixabepilone and irofulven closely resembles their clinical effectiveness only when the mouse models are treated

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at human tolerated dose levels, or doses that result in human tolerated systemic exposures [39–44]. However, these human dose/exposure levels were not estimate using preclinical methods, but were obtained from results of previous Phase 1 doseescalation clinical trials. This study was retrospective in nature, taking human clinical tolerability data back into the mouse models. Nonetheless, it proves the point that mouse modeling can give a more accurate picture of clinical outcome, if efficacy evaluations are restricted to doses/exposures that do not exceed those reachable in the clinic. It points to the potential payoff of incorporating human tolerated dose/ exposure into mouse modeling of experimental cancer therapeutics, but leaves open the question of how to predict/estimate human dose/exposure well before any human patients are treated. If one posits the reasonable assumption that granulopoiesis will be the clinical dose-limiting toxicity, then one can focus on the use of two emerging methods for predicting the tolerated dose (exposure) of this human tissue renewal process. These methods can be used sufficiently early in preclinical discovery/development to incorporate predicted human tolerated dose/exposure into mouse modeling of drug therapy.

21.5 Using Hematotoxicology to Limit Treatment of Mouse Models to Tolerated Human Doses/Exposures How can the anticipated human MTD/exposure for granulopoiesis be determined in the preclinical arena, prior to human clinical trials, so this information can be used to set the upper limit on dose used to treat mouse models of human cancer? If this is possible, then evaluation of experimental agents in mouse models can emphasize the efficacy of dose levels that do not exceed the predicted maximum tolerated human dose/exposure, without regard for how high a dose the mouse host will tolerate. The following sections describe two methods for determining the dose/exposure in mice that will resemble MTD/exposure in man. The reader will note that much of the discussion is focused on “dose” rather than “exposure” (area under the systemic Cp × T curve), and this is because in many situations, the promise of new agents must be judged by first-in-mouse studies prior to developing the bioanalytical methods to support pharmacokinetic studies. However, the reader should appreciate that maximum tolerated exposure of human hematopoietic tissue could also be used to limit the amount of drug administered to a tumored mouse. When using either of the methods described below, it is important to remember that the drug tolerance of human hematopoietic tissue will set an upper limit on dose/ exposure in the mouse model. Drugs requiring higher doses/exposures than the level tolerated by human marrow to be effective in mouse models would not be expected to be clinically effective. However, drugs that are active in mouse models at or below maximum tolerated human dose/exposure will not necessarily be active in the clinic, because organ systems other than the marrow could be more susceptible to toxicity and therefore limit dose/exposure to even lower levels than the

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marrow can tolerate. Although the accuracy and value of these methods for setting relevant testing conditions in mouse models for a wide range of therapeutic and chemical classes of molecules are not yet established, they are presented as methods most likely to be useful in the immediate future.

21.5.1 Method 1: Treating Mouse Models at Maximum Tolerated Human Dose, Predicted Using CFU-GM Assays As discussed above, hematopoietic tissue contains a continuous spectrum of precursor cells within the granulocyte lineage (Table 21.2). Some precursor cells are morphologically recognizable based on the immature appearance of features found in mature granulocytes. The morphologically recognizable cells include a proliferative compartment containing myeloblasts, promyelocytes and myelocytes as well as a post-mitotic “maturation” compartment of terminal differentiating cells (metamyelocytes, band cells and mature PMN leukocytes). There are also more primitive, undifferentiated marrow precursor cells called “progenitors.” Although the progenitors do not morphologically resemble granulocytes, some nonetheless possess a commitment to produce a supply of myeloblasts, whereas others possess a bi-lineage potential to supply precursors into both granulocyte and monocyte lineages (Fig. 21.1). Not only are these lineage-specific progenitors impossible to recognize morphologically, but they are quite rate in the marrow mononuclear fraction – on the order of 0.01–0.5% – and until recently were not directly quantifiable. Insightful ex vivo studies used clonogenic assays in semi-solid media (agar, methylcellulose, etc) to identify these and other relatively rare progenitor cell populations based on their ability to form colonies containing morphologically recognizable precursor cells and sometimes even mature blood cells [19–22, 45, 46]. A cell whose original presence was proven by the formation of a colony was named a CFU, and pathways of development from more immature, multi-potential CFUs to unipotential CFUs, committed to producing daughter cells of a particular hematologic lineage, were discovered. Using different CFU assays, the cytokines that act alone to stimulate colonies of specific lineages (“colony stimulating factors,” including erythropoietin) were identified and eventually cloned to be available as recombinant proteins for use in the next generation assays. Additional cytokines were discovered and eventually cloned that modulate the CSFs in a network of positive and negative regulators of CFU proliferation, survival and apoptosis, or that are induced by CSFs to promote differentiation in a tightly linked feedback cycle [15, 16, 23]. Using enrichment approaches and complement-mediated destruction of CFUs, cluster of differentiation (CD) cell surface antigens that mark the various CFUs have been identified, and recent advances in cell sorting have made it possible to isolate highly enriched preparations of CFUs using CD markers. Not long after assays for CFUs of the granulocyte/monocyte lineage (CFU-GM, CFU-G, CFU-M) were reported, cancer researchers naturally began investigating the in  vivo effects of cytotoxic chemotherapeutic agents on these cells.

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Chemotherapeutic agents that caused leukopenia and/or neutropenia in the mouse were shown to substantially decrease the number of myeloid CFUs in vivo prior to the onset of neutropenia [47–53]. Additional in vitro studies established that these chemotherapeutic agents were directly toxic to the CFUs outside of the host [54–61]. These data established the CFUs of the myeloid lineage, along with proliferating granulocyte precursors, as important targets of cancer chemotherapy, the inhibition of which leads to marrow hypocellularity and neutropenia. In vitro, drug exposure reduced the number of granulocyte/monocyte colonies in a concentrationdependent manner. Therefore, drug toxicity could be quantified based on the concentration that reduced colony formation by a particular amount, such as IC50 (the concentration required to reduce CFU number by 50% compared to vehicle treated cultures) (Fig. 21.2). In the early 1980s, a new nucleoside analog named fludarabine was advanced into clinical development [62, 63]. Rodent and non-rodent toxicology studies of the clinical dosage regimen indicated that myelosuppression would be dose limiting in man, and that a safe (non-toxic) first-in-human dose for Phase I clinical trials would be 1/10 the mouse LD10, equal to ~112  mg  m–2  day–1 over 5 consecutive days. Despite being non-toxic in a non-rodent animal model, this dose level caused clinically significant neutropenia in the first patients treated in the Phase I trials. The failure of standard toxicology to provide a safe starting dose led to a quest to find additional toxicology tests that could flag such compounds that were singularly

+ drug:

CFUs (% Vehicle Ctl)

+ vehicle:

Drug Concentration

Fig. 21.2  Quantifying drug hematotoxicity using in  vitro CFU assays. A small proportion of mononuclear cells cultivated in semi-solid medium will form clonal colonies over 1–2 weeks when stimulated with cytokines, and the particular blood cell lineage of the colony-forming unit (CFU) is identified by morphologically recognizable daughter cells found in the colony. CFUs from a particular lineage, e.g. CFU-GM of the granulocyte/monocyte lineage, can be specifically stimulated by properly selected cytokine(s). Using this in  vitro method, drug toxicity against myeloid progenitors can be quantified by the concentration that reduces colony formation by a certain level, e.g. IC50 being the drug concentration that reduces the number of CFU-derived colonies by 50% (dashed line). Ideally, treatment with vehicle alone does not affect the number of clonal colonies

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toxic in the human. It was reasonable to compare the in  vitro hematotoxicity of fludarabine to human and rodent CFU-GM progenitors for a number of reasons: granulopoiesis was the target of drug toxicity, the enzyme that activates fludarabine is present at 10-times higher level in human than rodent marrow [64], and as mentioned above, CFU assays were available for all relevant species. As anticipated, human CFU-GM exhibits dramatically greater susceptible to drug toxicity than its murine counterpart (Fig. 21.3). In addition, the quantitative difference between human and mouse IC values closely approximated the difference between human and mouse tolerated dose for this myelosuppressive agent. Because the in  vitro concentration–response curves for human and mouse CFU-GM to fludarabine were similarly shaped and parallel, a comparison of the IC50 values provides the most accurate quantitation of the inter-species difference in susceptibility to toxicity. But what about cases in which concentration–response curves are not parallel (differently shaped) and even cross? A clinical study correlating exposure-dependent CFU-GM toxicity in  vitro with exposure-dependent clinical neutropenia in  vivo revealed that the assay readout that correlates most closely with severe neutropenia is the IC90 rather than the IC50 [65]. Because severe neutropenia is a dose-limiting toxicity that defines MTD, one would anticipate

Fludarabine for Injection

CFU-GM (% vehicle)

125%

100%

75%

50%

25%

Hu 1.1 Hu 1.2 Hu 3.1 Hu 3.2 Mu 4.1 Mu 4.2 Mu 5.1 Mu 5.2 Mu 6.1 Mu 6.2

0% 0.00001 0.0001

0.001

0.01

0.1

Conc (mcg/mL)

1

10

100

Fig. 21.3  An in vitro comparison of the toxicity of the anticancer agent, fludarabine, to human and mouse CFU-GM. The drug was added directly into agarose cultures of human or mouse bone marrow mononuclear cells, which were stimulated to form colonies by species-specific rGM-CSF. After 7 (mouse, blue lines) or 14 (human, red lines) days, the mature colonies were counted, and the data expressed as the number of colonies relative to vehicle controls that were formed as a function of drug concentration. Each individual line indicates the results using an individual bone marrow preparation. Not only did the CFU-GM assay detect the much greater susceptibility of human than rodent granulopoiesis observed clinically but also the quantitative difference between the human and mouse IC50 values closely approximated the difference between human and rodent maximum tolerated dose

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that differences in IC90 values from CFU-GM assays across species should be similar to differences in their MTDs for myelosuppressive compounds. The correlation of the IC90 ratio from the CFU-GM assay with the MTD ratio between two species has been confirmed by a study of camptothecin-class topoisomerase I inhibitors [66] and a natural product cytotoxic compound [67]. The following equation that leads to a prediction model for human MTD in the mouse [68, 69] generally applies to IC90 values obtained from validated CFU assays of mouse and human CFU-GM developed by the US National Cancer Institute [22, 70] and the European Centre for the Validation of Alternative Methods (ECVAM) [71–74]:

IC90human MTD human = IC90mouse MTD mouse

(21.1)

Because all of the values in (21.1) except the human MTD can be obtained by running simple tests for CFU-GM toxicity and mouse MTD, and bioanalytical support is not required, it is readily apparent that the human MTD should be predictable early enough in the discovery setting to incorporate the information into mouse modeling, so to limit, or at least emphasize, efficacy testing at dose levels in the mouse that are predicted to be achievable in man. By rearranging (21.1), these simple preclinical values can be used to obtain a predicted human MTD that can set relevant doses in mouse models [68, 69]:

MTD human = MTD mouse × (IC90human ÷ IC90mouse )

(21.2)

Defining an innovative compound’s value based on efficacy at predicted human marrow MTD instead of efficacy at mouse MTD is a continuation of the philosophy of humanizing the mouse model that began with human tumor xenografting. However, it may be difficult to convince an organization to accept this new testing paradigm, especially when there are highly active compounds that exhibit their high activity only at doses above the predicted human MTD. In the new paradigm, the development of such compounds should be discontinued because they are inactive, rather than proceeding as “high priority.” Naturally, considerable justification needs to be provided for switching to this new paradigm of prioritizing discovery compounds. The reliability of CFU-GM assays to yield IC90 values was recently demonstrated by an international validation study of the analytical performance of SOP-defined mouse and human assays [73, 74]. The study demonstrated robust assay performance during SOP-driven transfers between laboratories. In addition, blinded testing by multiple laboratories yielded consistent human and mouse IC90 values for 9 of 10 chemical toxicants that inhibited CFU-GM colony formation by at least 90%, and 7 of 10 toxicants that were not toxic enough to reach 90% inhibition and needed IC90 values to be estimated. This validation study achieved one more milestone that is relevant for mouse modeling: it demonstrated the accuracy of the simple prediction model for human MTD represented by (21.2) above. “Successful prediction” was defined as a predicted human MTD within ±fourfold of the actual MTD established by Phase I clinical trials. The rationale for the fourfold

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range was that pharmacokinetic differences between the species, which are not accommodated in the CFU-GM assay, could account for this degree of inaccuracy. When combined with the testing results from the prevalidation study, the validated CFU-GM assay correctly predicted the human MTD of 20 of 23 chemicals [74]. The development of a validated CFU-GM assay using public SOPs [75], and a companion method to predict human MTD from the data, creates the opportunity to apply this method to mouse modeling of cancer therapy. Needing only in vivo data from mouse dose-finding studies and in  vitro data from human and mouse CFU-GM assays, it is possible to set a dose for mouse modeling studies that likely (87%) lies within ±fourfold of the eventual MTD that will be encountered in early clinical trials should that compound reach clinical development. Since it is possible to know the doses in the mouse that will also be achievable in the clinic, it seems reasonable to emphasize efficacy at those doses. In the drug discovery setting, it seems reasonable to err on the side of maximizing chances to find efficacious new structures, so the ±fourfold inaccuracy in the prediction model might cause the upper limit on mouse dose levels to be set at fourfold higher than the human MTD predicted by the model. Later in development, when candidate molecules are being prioritized, the requirements might be made more stringent – requiring efficacy at the predicted human MTD, or at that dose plus some fraction of it to find compounds with the widest therapeutic margin. Comparative CFU-GM testing results have just begun to be incorporated into mouse modeling of experimental cancer therapy and preclinical decision-making [76], so more time will be needed to know if this method will eventually improve the success rate of clinical development.

21.5.2 Method 2: Treating Mouse Models at Maximum Tolerated Human Dose/Exposure Determined Empirically Using NOD/SCID Mice Engrafted with Human Granulopoietic Tissue If a normal human dose-limiting tissue could be engrafted into mice, it would then be possible to determine a human MTD in that mouse strain. Using this empirical result to limit dose in efficacy studies would be a major step toward using mouse models to evaluate human therapeutic index, not just efficacy, and there has been considerable interest in creating such models (Table 21.3). Early studies took advantage of in vitro methodology for cultivating bone marrow CFUs in semi-solid media, and implanted diffusion chambers containing human hematopoietic cells embedded in agar into lethally irradiated mice to prevent immune rejection [46]. Although the mice survived only long enough to evaluate acute toxicity of chemotherapeutic agents to human CFUs in  vivo [50], these studies proved that human CFUs can survive in vivo in the mouse, be susceptible to adverse drug effects, and provide a quantitative readout of drug toxicity. This method was extended to a comparison of the susceptibility of human, canine and mouse hematopoietic cells to chemotherapeutic agents [51]. Theoretically, this model could be used with syngeneic mouse tumors

SCID (C.B-17PrkdcSCID)

–(→+)

–(→+) Hi

 

 

 

BM, PB, CB → M,B

Fetal thymus+liver → T

 

 

Table 21.3  Mouse models with potential for evaluating human therapeutic index in the discovery setting Murine immune Co-engrafting system HSC and solid Proven human Human solid T B NK Ig hematopoietic engraftment tumor xenografts tumor Strain           Not engrafted – MNCs loaded   C57BL, CD-1 into diffusion chamber (lethally with semisolid agar irradiated)

Gain lymphocyte function with age Increased activity of alternative Complement pathway Modeling human therapy Low HSC engraftment rate

Mice

Very short-lived (9 day study) Modeling human therapy In vivo CFU assay Compare human and mouse Identified inter-species difference  in drug tolerance

Notes Mice

[77–79]

References [46, 50, 51]

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NOD/LtSz-scid

–(→+)

–(→+) lo

lo

 

Fetal liver CD34+ w/fetal liver/thymus → M,B,T

CB CD34+ following in vitro ChemoRx

BM, CB → M,E,B

 

Human tumor lines (glioma, Ewings sarcoma)

 

Patient specimens   of multiple myeloma into fetal bone

Patient specimens Autologous of breast Ca T-cell therapy of breast Ca

Pre-selected mouse marrow Serially transplantable MM Used in chemoRx studies (BCNU/ O6BG, busulfantreosulfan)  

Radiosensitive Gain lymphocyte function with age C5 deficient Modeling human therapy

Short-lived Early lymphomas

Mice

Needs exogenous cytokines for human hematopoiesis

(continued)

[77–89]

21  Bone Marrow as a Critical Normal Tissue that Limits Drug Dose/Exposure 539

NOD/LtSz-scid IL2R null

Strain

–

–

Murine immune system T B

Table 21.3  (continued)

–

NK

 

Ig

M,E,P,B,T Expanded CB CD34+ → M,E,B (TNFα-induced T-cell function)

Purified CB CD34+ →

G-CSF mob PBMC CD34+ → B,NK,M,T (IL7-induced T-cell phenotypes)

Proven human hematopoietic engraftment

Co-engrafting HSC and solid tumor

Single cells from patient specimens of melanoma

 

 

Canine transmissible venereal tumor Patient specimens   of carcinoma and sarcoma

Human solid tumor xenografts

Poor human T-cell development Low neutrophil level Single cells from patient tumors can be cloned Engrafted tumors from patients are serially transplantable

Long-lived Radioresistant Low lymphoma rate Modeling human therapy

Mice

Notes

[90–96]

References

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–

NOD/LtSzRag1null

–

–

NOD/LtSz-scid- – 2m-/-

Lo

–

–

lo

     

CB CD34+ → M,E,B  

 

 

PBMC → T, B

 

 

PBMC → B,T

CB CD34+ → N (inflammation required for human neutrophil detection)

   

 

 

 

 

Long-lived Later appearing  follicular cell lymphomas Radioresistant

Mice

Shorter-lived than NOD/LtSz-scid High lymphoma rate Modeling human therapy High level of human engraftment Absence of MHC Class I expression Much higher levels of T cell engraftment Normalized CD4+/ CD8+ ratio

Mice

Human leukocytes/ stroma in tumor specimens survive long-term  

(continued)

[77, 99]

[97, 98]

21  Bone Marrow as a Critical Normal Tissue that Limits Drug Dose/Exposure 541

–

–

NK

–

Ig

PBMC → T

Proven human hematopoietic engraftment

 

Human solid tumor xenografts

 

Co-engrafting HSC and solid tumor Notes

Mice

Modeling human therapy Mostly CD8+ T-cells using PBMC [100]

References

CB MNC → T,B

 

 

Designed to eliminate activity of NK Cells Similar life-span to NOD/LtSz-Rag1null mice Modeling human therapy Mostly CD4+ T-cells using PBMC PB peripheral blood; CB cord blood; MNC mononuclear cell; CB cord blood; N neutrophil; T T-lymphocyte; B B-lymphocyte; M myeloid leukocyte; E erythroid; P platelet; HSC hematopoietic stem cell; NK natural killer cell

NOD/LtSz– Rag1nullPfpnull

Table 21.3  (continued) Murine immune system T B Strain

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implanted weeks before, but evaluating efficacy of human tissue-tolerated doses against murine transplantable tumors would have little therapeutic value. The advent of immunocompromised/immunodeficient mice (nu/nu and C.B-17PrkdcSCID) made it possible to xenograft human tumor tissue into subcutaneous and orthotopic sites, and study in vivo drug efficacy against human malignancies in the preclinical setting. Studies of hematopoietic engraftment in C.B-17-PrkdcSCID immunodeficient mice [77–79] found that cord blood, peripheral blood or bone marrow hematopoietic cells could reconstitute the B-lymphoid and myeloid lineages, but the presence of NK cells result in low engraftment efficiency and a lack of T-cell reconstitution, which in turn makes it necessary to provide exogenous cytokine support of human hematopoiesis. C.B-17-PrkdcSCID also exhibit elevated activity in the alternate pathway of complement. The NOD/LtSz-scid strain [80] has very low NK cell activity and is deficient in complement C5 [77–80], and therefore yields more successful and complete hematopoietic engraftment [81–83]. The mouse strain has been used to model T-cell therapy of breast cancer by transplanting surgical tumor specimens, and then subsequently treating the xenografted tumors with autologous lymphocytes from the same patient that had been processed, stored and re-stimulated prior to injection [84]. In addition to human breast cancer, clinical specimens of multiple myeloma [85], Ewing’s sarcoma and glioma human tumor lines [86, 87] and canine primary tumors [88] have all been successfully engrafted in this model. The NOD/scid strain has also been used as an in vivo assay system to quantify the loss of stem cell function following cytotoxic chemotherapy [89]. These data indicate the potential of a single designed mouse strain to host both human hematopoietic and tumor tissue, and in the case of autologous marrow and tumor engraftment, perhaps even the possibility of a reconstituted system that models an individual patient’s drug tolerance and response simultaneously, i.e. a human therapeutic index. However, the model has limited utility because the mice have a “leaky” immune system in which B- and T-cell function gradually appear with age [77–79]. Also, radiation is required to condition the mice for hematopoietic stem cell transplant, and NOD/LtSz-scid mice are radiosensitive, and prone to spontaneous lymphomas [78–80]. Limitations of the C.B-17-PrkdcSCID and nod/SCID strains mentioned above motivated the development of strains with additional defects in the immune system to eliminate residual NK cell activity, increase tolerance of radiation conditioning regimens to prepare the mice for hematopoietic stem cell transplant, and reduce or eliminate spontaneous lymphoma formation. The NOD/LtSz-scid IL2Rγ null strain, which carries a defective gamma chain of the IL-2 receptor, exhibits these properties [90, 91]. This strain affords high rates of multi-lineage hematopoietic engraftment following transplantation of cord blood- or G-CSF mobilized peripheral blood derived-CD34+ stem cells [90–92]. The rate and extent of development of a mature repertoire of human T-cells can be enhanced by treating the mice with interleukin-7 [90] or tumor necrosis factor-α [92]. Although blood cell counts of neutrophils are low, they can be induced to accumulate in areas of induced inflammation [93]. As expected due to negligible levels of NK cell activity, the NOD/LtSz-scid IL2Rγ null strain offers much improved engraftment rates of surgical specimens of human

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carcinoma and sarcoma, resulting in tumor xenografts that are serially transplantable [94]. In fact, the engraftment environment of these mice has supported cloning of patient melanoma specimens from unselected single cells [95]. Importantly, the tissue architecture of non-disrupted surgical specimens of primary human nonsmall cell lung cancer, including accompanying stroma, is preserved for prolonged periods of time after xenografting into these mice [96]. Tumor-derived fibroblasts and leukocytes (both CD3+ T-cells and CD138+ plasma cells) show long-term survival after implantation, and the leukocytes, including T-cells, are able to populate host organs [96]. Additional strains of mice have been produced by introducing defects into other molecular targets that play critical roles in immune function and graft rejection. Using a strategy similar to that resulting in elimination of interleukin-2 receptor γ-chain function in the NOD/LtSz-scid genetic background, genetic crosses between NOD/LtSz mice in which the gene encoding β2-microglobulin has been knocked out and NOD/LtSz-scid mice have yielded mice that are doubly homozymgous for the scid mutation and for the absence of the β2-microglobulin gene [97]. Because the resulting NOD/LtSz-scid-β2m-/- mice lack NK activity as well as impaired lymphocyte functions, they support much higher levels of human lymphomyeloid engraftment from peripheral blood mononuclear cells, including both CD4 and CD8 T-cells in appropriate ratio [98]. Another mouse model has been developed by backcrossing the null allele of the recombination-activating gene Rag1 into the NOD/LtSz background [99]. The resulting NOD/LtSz-Rag1null mice do not gain low level B- and T-cell functionality with age, because the loss of this gene function eliminates any ability to initiate V(D)J recombination of immunoglobulin or T-cell receptor genes. However, the NOD/ LtSz-Rag1null strain retains some residual NK cell activity, and in an effort to eliminate this entirely, Shultz et al. [100] have backcrossed this stain with mice harboring a mutation in the perforin structural gene, which is a key mediator of NK cell cytotoxicity. The resulting NOD/LtSz-Rag1nullPfpnull strain did not exhibit NK cell cytotoxicity and supported high level engraftment of human hematopoietic stem cells. Although NOD/LtSz-scid-β2m–/–, NOD/LtSz-Rag1null, and NOD/LtSz-Rag1null mice strains show promise as long-term models of xenografted human hematopoiesis, the author is not aware of any evaluations of these strains as hosts for human tumor xenografts. But, because these strains reliably support human multi-lineage hematopoietic engraftment, they provide a model for determining the mouse dose which is maximally tolerated by human bone marrow. For example, one can envision conducting a dose-ranging study in NOD/LtSz-scid IL2Rγnull, NOD/LtSzRag1nullPfpnull or NOD/LtSz-Rag1null mice stably engrafted with human hematopoietic tissue, and identifying the dose that causes a severe (90%) drop in human CFU-GM in the marrow, or a dose that causes a 90% drop in circulating human granulocytes if blood cell counts were sufficiently high and stable. Once identified, then additional mice harboring human tumors can be treated at that maximum tolerated human dose in the mouse to evaluate efficacy. It is exciting to think that such strains could be used to propagate both tumor and its autologous hematopoietic tissue from the same patient, so as needed for preclinical drug evaluations, these tissues could

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be transplanted into mice for evaluation of human therapeutic index – the ratio of the toxic dose to the efficacious dose. Because hematopoietic reconstitution involves production of functional B- and T-lymphocytes, it may not be possible to study any hematopoietic tissue with any tumor line. The immune system produced by the engrafted hematopoietic tissue may recognize and reject human tumor tissue derived from different individuals than provided the hematopoietic stem cells.

21.6 Concluding Thoughts on Improving the Predictive Accuracy of Mouse Efficacy Models Using Human Hematotoxicology Data The switch from a murine to a pure human therapeutic index-based evaluation of experimental agents in mouse models of human cancer is certainly a new frontier, but is a logical next step to take in the continued quest to humanize these models and thereby improve their predictive accuracy for clinical outcome. We may need to admit that it may not be possible to design ethical prospective studies to prove that this approach is better at predicting clinical outcome than the xeno-therapeutic index approach currently in use, because it would be difficult to justify moving an experimental agent into clinical trials if it was efficacious only at the highest doses tolerated by the mouse host, but not at predicted maximum tolerated human dose/ exposure. So, we find ourselves in the difficult conundrum of philosophically needing to continue down the path of humanizing the mouse models, without ever being able to prove that this change represents an improvement in modeling and eliminates many clinical failures at an early stage in development. However, human xenograft models were adopted for selecting and prioritizing compounds without proof of superiority over models of syngeneic mouse tumors, despite the fact that the syngeneic models were responsible for the identification of many effective chemotherapeutic agents in clinical use today. The common claim that animal models do not predict clinical outcomes may relate much more to treatment regimens that only hit the clinically-relevant human dose/exposure by chance, than to the response of those human tumor models per se. Incorporating assessments of efficacy against human tumor models and toxicity against human normal tissue models into overall preclinical modeling will achieve an evaluation of human therapeutic index in the mouse and move us toward complete humanization of mouse modeling. What is clear from the fludarabine case study is that a large inter-species difference in dose tolerance does not indicate clinical success or failure, but only requires the drug to possess a favorable therapeutic index, i.e. to be effective against human malignancy despite the much lower dose tolerance of the human patient compared to the mouse models. Naturally, a favorable therapeutic index should be closely tied to clinical effectiveness. Such case studies can provide important justification and scientific foundation for incorporating human dose tolerance into mouse modeling of human malignancy. Houghton and colleagues have already proven that the efficacy of chemotherapeutic agents against human pediatric tumor xenografts in mouse models

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most closely correlates with their clinical effectiveness, when the dosage regimens and exposures in the mouse models replicate those tolerated by pediatric patients affected with the same malignancies [39–44]. Although these studies prove that limiting the doses/exposures used for efficacy assessments in mouse models to those tolerated by human patients improves the correlation between preclinical and clinical outcomes, it is important to note that the Houghton studies obtained the human dose/ exposure data from the results of Phase I clinical trials in patients – data that would not be available at the time of preclinical development studies. The two methods described in this chapter provide ways to estimate what this maximum tolerated human dose/exposure will be prior to the availability of any clinical results in man. Instead of using efficacy data for lead identification and optimization that comes from mouse models treated with doses/exposures that cannot be reached clinically, experimental agents could be compared based on efficacy in mouse models at their predicted human maximum doses/exposure levels. There are two frequent objections to the strategy of incorporating comparative hematoxicology data into preclinical modeling. The first is that hematopoiesis is not the only dose-limiting target of anticancer drugs; hepatic, renal and cardiac functions are all frequent targets for adverse drug effects. Of course, it would be a great advance in mouse modeling to be able to incorporate information about dose tolerance of all of these human organs into the selection of dose/exposure for evaluating efficacy. Only the most susceptible organ system is relevant to dose selection, because it will not be possible to escalate dose above the MTD of the most susceptible organ system. Other organ systems may exhibit even greater susceptibility to drug-induced toxicity than the bone marrow; therefore the maximum dose/exposure tolerated by hematopoiesis may not be reachable because of severe toxicities to these other organ systems that will occur at lower doses. The important point to recognize is that, regardless of which organ system turns out to be dose-limiting, it will not be possible to reach doses/exposures in the human that are higher than those tolerated by the granulopoietic cells because of dose-limiting neutropenia. Because it will not be possible to reach higher doses/exposures than those tolerated by human neutrophil progenitors, there is no need to exceed these levels for evaluating efficacy in mouse models. Although some compounds may be advanced into development based on efficacy at doses/exposures tolerated by human myelopoiesis that later show other organ toxicity at even lower levels, one is certainly avoiding compounds that have no chance at clinical effectiveness, i.e. those active at doses/exposures above marrow tolerated levels. The second common objection is the point that hematotoxicology is not relevant in the new age of targeted therapeutics, which are considered safer than cytotoxic chemotherapeutic agents. In response to this objection, it has been noted that myelosuppression has been identified as the clinical doselimiting toxicity of a number of targeted agents, perhaps not surprising given the fact that molecular pathways related to the biology of proliferating cell types are often targeted, and these pathways might be expected to play a role in the proliferation of normal cell types like hematopoietic progenitors and precursors. On a final note, because there are many examples of drugs where human progenitors show substantially greater susceptibility to toxicity than their mouse counterparts,

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it would be expected that some drugs may show the opposite profile – being more toxic to mouse progenitors than their human counterpart or progenitors from other preclinical species. Since this property is not currently assessed in the discovery setting, many such compounds would be excluded from development because they would be classified as “toxic, inactive.” Only those compounds that possessed a very good therapeutic index would exhibit efficacy despite the dose reductions necessitated by the poor dose tolerance of the mouse. The rewards of developing these compounds could be great, because they will likely exhibit greater than expected clinical effectiveness as a result of being able to administer doses above those evaluated in the mouse models. The CFU-GM assay provides a way to identify those compounds for which the mouse may be the most susceptible species to bone marrow toxicity, and for which some other species may be more appropriate for efficacy studies. Acknowledgments  The author would like to thank Drs. Charles K. Grieshaber, Joseph E. Tomaszewski and Adaline C. Smith for their seminal contributions to the field of in vitro hematotoxicology. The author would also like to acknowledge the critical importance of support of in  vitro hematotoxicology and its application to drug and chemical safety evaluations by the Toxicology & Pharmacology Branch of the US National Cancer Institute and the European Centre for the Validation of Alternative Methods (ECVAM).

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72. Gribaldo L, Casati S, Castoldi AF, Pessina A. Comparison of in  vitro drug-sensitivity of human granulocyte-macrophage progenitors from two different origins: umbilical cord blood and bone marrow. Exp Hematol. 1999;27:1593–8. 73. Pessina A, Albella B, Bueren J, Brantom P, Casati S, Gribaldo L, Croera C, Gagliardi G, Foti P, Parchment R, Parent-Massin D, Sibiril Y, Schoeters G, Van Den Heuvel R. Prevalidation of a model for predicting acute neutropenia by colony forming unit-granulocyte/macrophage (cfu-gm) assay. Toxicol In Vitro 2001;15:729–40. 74. Pessina A, Albella B, Bayo M, Bueren J, Brantom P, Casati S, Croera C, Gagliardi G, Foti P, Parchment R, Parent-Massin D, Schoeters G, Sibiril Y, Van Den Heuvel R, Gribaldo L. Application of the CFU-GM assay to predict acute drug-induced neutropenia: an international blind trial to validate a prediction model for the maximum tolerated dose (MTD) of myelosuppressive xenobiotics. Toxicol Sci 2003;75:355–67. 75. Links to the INVITTOX Protocol and the ESAC Statement dated 21-March-2006 regarding The Colony Forming Unit-Granulocyte/Macrophage (CFU-GM) Assay for predicting acute neutropenia in humans can be found at http://ecvam.jrc.it/. 76. Kurtzberg LS, Battle T, Rouleau C, Bagley RG, Agata N, Yao M, Schmid S, Roth S, Crawford J, Krumbholz R, Ewesuedo R, Yu XJ, Wang F, Lavoie EJ, Teicher BA. Bone marrow and tumor cell colony-forming units and human tumor xenograft efficacy of noncamptothecin and camptothecin topoisomerase I inhibitors. Mol Cancer Ther. 2008;7:3212–22. 77. Gr einer DL, Hesselton RA, Shultz LD. SCID mouse models of human stem cell engraftment. Stem Cells 1998;16:166–77. 78. Macchiarini F, Manz MG, Palucka AK, Shultz LD. Humanized mice: are we there yet? J Exp Med. 2005;202:1307–11. 79. Manz MG, Di Santo JP. Renaissance for mouse models of human hematopoiesis and immunobiology. Nat Immunol. 2009;10:1039–42. 80. Shultz LD, Schweitzer PA, Christianson SW, Gott B, Schweitzer IB, Tennent B, McKenna S, Mobraaten L, Rajan TV, Greiner DL, et al. Multiple defects in innate and adaptive immunologic function in NOD/LtSz-scid mice. J Immunol. 1995;154:180–91. 81. Larochelle A, Vormoor J, Hanenberg H, Wang JC, Bhatia M, Lapidot T, Moritz T, Murdoch B, Xiao XL, Kato I, Williams DA, Dick JE. Identification of primitive human hematopoietic cells capable of repopulating NOD/SCID mouse bone marrow: implications for gene therapy. Nat Med. 1996;2:1329–37. 82. Melkus MW, Estes JD, Padgett-Thomas A, Gatlin J, Denton PW, Othieno FA, Wege AK, Haase AT, Garcia JV. Humanized mice mount specific adaptive and innate immune responses to EBV and TSST-1. Nat Med. 2006;12:1316–22. 83. Lan P, Tonomura N, Shimizu A, Wang S, Yang YG. Reconstitution of a functional human immune system in immunodeficient mice through combined human fetal thymus/liver and CD34+ cell transplantation. Blood 2006;108:487–92. 84. Feuerer M, Beckhove P, Bai L, Solomayer EF, Bastert G, Diel IJ, Pedain C, Oberniedermayr M, Schirrmacher V, Umansky V. Therapy of human tumors in NOD/SCID mice with patientderived reactivated memory T cells from bone marrow. Nat Med. 2001;7:452–8. 85. Huang SY, Tien HF, Su FH, Hsu SM. Nonirradiated NOD/SCID-human chimeric animal model for primary human multiple myeloma: a potential in vivo culture system. Am J Pathol. 2004;164:747–56. 86. Werner S, Mendoza A, Hilger RA, Erlacher M, Reichardt W, Lissat A, Konanz C, Uhl M, Niemeyer CM, Khanna C, Kontny U. Preclinical studies of treosulfan demonstrate potent activity in Ewing’s sarcoma. Cancer Chemother Pharmacol. 2008;62:19–31. 87. Kreklau EL, Pollok KE, Bailey BJ, Liu N, Hartwell JR, Williams DA, Erickson LC. Hematopoietic expression of O(6)-methylguanine DNA methyltransferase-P140K allows intensive treatment of human glioma xenografts with combination O(6)-benzylguanine and 1,3-bis-(2-chloroethyl)-1-nitrosourea. Mol Cancer Ther. 2003;2:1321–9. 88. Harmelin A, Pinthus JH, Katzir N, Kapon A, Volcani Y, Amariglio EN, Rehavi G. Use of a murine xenograft model for canine transmissible venereal tumor. Am J Vet Res. 2001;62: 907–11.

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89. Libura J, Ward M, Solecka J, Richardson C. Etoposide-initiated MLL rearrangements detected at high frequency in human primitive hematopoietic stem cells with in  vitro and in vivo long-term repopulating potential. Eur J Haematol. 2008;81:185–95. 90. Shultz LD, Lyons BL, Burzenski LM, Gott B, Chen X, Chaleff S, Kotb M, Gillies SD, King M, Mangada J, Greiner DL, Handgretinger R. Human lymphoid and myeloid cell development in NOD/LtSz-scid IL2Rγnull mice engrafted with mobilized human hemopoietic stem cells. J Immunol. 2005;174:6477–89. 91. Ishikawa F, Yasukawa M, Lyons B, Yoshida S, Miyamoto T, Yoshimoto G, Watanabe T, Akashi K, Shultz LD, Harada M. Development of functional human blood and immune systems in NOD/SCID/IL2 receptor γ chainnull mice. Blood 2005;106:1565–73. 92. Giassi LJ, Pearson T, Shultz LD, Laning J, Biber K, Kraus M, Woda BA, Schmidt MR, Woodland RT, Rossini AA, Greiner DL. Expanded CD34+ human umbilical cord blood cells generate multiple lymphohematopoietic lineages in NOD-scid IL2rγnull mice. Exp Biol Med. 2008;233:997–1012. 93. Doshi M, Koyanagi M, Nakahara M, Saeki K, Saeki K, Yuo A. Identification of human neutrophils during experimentally induced inflammation in mice with transplanted CD34+ cells from human umbilical cord blood. Int J Hematol. 2006;84:231–7. 94. Fujii E, Suzuki M, Matsubara K, Watanabe M, Chen YJ, Adachi K, Ohnishi Y, Tanigawa M, Tsuchiya M, Tamaoki N. Establishment and characterization of in vivo human tumor models in the NOD/SCID/γcnull mouse. Pathol Int. 2008;58:559–67. 95. Quintana E, Shackleton M, Sabel MS, Fullen DR, Johnson TM, Morrison SJ. Efficient tumour formation by single human melanoma cells. Nature 2008;456:593–8. 96. Simpson-Abelson MR, Sonnenberg GF, Takita H, Yokota SJ, Conway Jr TF, Kelleher Jr RJ, Shultz LD, Barcos M, Bankert RB. Long-term engraftment and expansion of tumor-derived memory T cells following the implantation of non-disrupted pieces of human lung tumor into NOD-scid IL2Rγnull mice. J Immunol. 2008;180:7009–18. 97. Christianson SW, Greiner DL, Hesselton RA, Leif JH, Wagar EJ, Schweitzer IB, Rajan TV, Gott B, Roopenian DC, Shultz LD. Enhanced human CD4+ T cell engraftment in β2microglobulin-deficient NOD-scid mice. J Immunol. 1997;158:3578–86. 98. Glimm H, Eisterer W, Lee K, Cashman J, Holyoake TL, Nicolini F, Shultz LD, von Kalle C, Eaves CJ. Previously undetected human hematopoietic cell populations with short-term repopulating activity selectively engraft NOD/SCID-β2 microglobulin-null mice. J Clin Invest. 2001;107:199–206. 99. Shultz LD, Lang PA, Christianson SW, Gott B, Lyons B, Umeda S, Leiter E, Hesselton R, Wagar EJ, Leif JH, Kollet O, Lapidot T, Greiner DL. NOD/LtSz-Rag1null mice: an immunodeficient and radioresistant model for engraftment of human hematolymphoid cells, HIV infection, and adoptive transfer of NOD mouse diabetogenic T cells. J Immunol. 2000;164:2496–507. 100. Shultz LD, Banuelos S, Lyons B, Samuels R, Burzenski L, Gott B, Lang P, Leif J, Appel M, Rossini A, Greiner DL. NOD/LtSz-Rag1nullPfpnull mice: a new model system with increased levels of human peripheral leukocyte and hematopoietic stem-cell engraftment. Transplantation 2003;76:1036–42.

Chapter 22

Anesthetic Considerations for the Study of Murine Tumor Models Thies Schroeder, Siqing Shan, and Mark W. Dewhirst

22.1 Overview This chapter is to provide researchers with an overview over the requirements, challenges, and current solutions of rodent anesthesia in preclinical cancer research. Since the overwhelming majority of research is currently done in mouse models, rather than rats or other rodents, the review will focus predominantly on mouse strains. We will provide a range of hands-on protocols and suggestions on the application of the most commonly used rodent anesthesia procedures. Our target groups are both scientists that are new to the field of animal research in cancer and need help to establish SOPs, and PIs with previous expertise who wish to update or extend their knowledge about rodent anesthesia in cancer research. Our protocols are compliant with the current Duke University institutional guidelines. The chapter will cover the following sections: 1 . Rationale and requirements for animal anesthesia in cancer research 2. Guidelines of assessing depth and quality of anesthesia 3. Animal support 4. Currently used anesthesia solutions in cancer research

22.2 Rationale and Requirements for Animal Anesthesia in Cancer Research Anesthesia in research animals is carried out for two reasons: M.W. Dewhirst (*) Duke University Medical Center, Department of Radiation Oncology, Durham, NC 27710, USA e-mail: [email protected]

B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_22, © Springer Science+Business Media, LLC 2011

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22.2.1 Humane Reasons Anesthesia is required to eliminate pain and suffering due to invasive procedures, experimentally caused irritation, physical restraint, and certain types of euthanasia.

22.2.2 To Control Motion For most types of surgery, intravenous injections, and for imaging, it is very important to minimize motion during the procedures. Avoiding motion is also important in order to homogenize the response to treatment.

22.3 Special Requirements for Anesthesia in Cancer Research 22.3.1 Non-Survival Surgery One of the most common endpoints in cancer research studies in mice is the rate of delivery of an anticancer agent to the tumor and to organs during pharmacokinetic studies. Another typical endpoint is the effect of an anticancer agent on the representative growth rate of the tumor in a treatment group. These types of studies, typically conducted in mice, require anesthesia as a means of pain relief and physical restraint, enabling the experimentalist to surgically remove the tumor and organs in a humane manner. The requirements for anesthesia are relatively few: the onset of expressional changes of proteins (usually more than 30 min) exceeds the time needed to initiate anesthesia. In addition, considerations about post-surgical pain and distress, or carcinogenicity of the drug do not apply. Typical anesthetics used in this setting are the injectible drugs ketamine/xylazine and pentobarbital.

22.3.2 Survival Surgery Several cancer research applications require temporary anesthesia of the animal, with subsequent recovery: • orthotopic injection of tumor cells (e.g. injection of cells into the mammary fat pad), • permanent insertion of osmotic pumps (“Alzet pumps”) for studies that require steady rates of drug release, and

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• permanent insertion of devices, such as probes and sensors, or constructions for intravital microscopy. Anesthetics used for this purpose must primarily guarantee that the animal experiences no pain during the surgery and that it experiences minimal stress upon recovery. This excludes anesthetics that can cause permanent complications, such as urethane or chloralose. Typical anesthetics for these operations are pentobarbital, ketamine/xylazine, and isoflurane.

22.3.3 Functional Studies Particularly in an academic setting, anesthesia often has to meet additional demands: in order to acquire functional, mechanistic information from a treatment or diagnostic regimen, anesthesia, on one hand, has to provide efficient restraint of spontaneous movement and also must not change the physiology of the animal during data acquisition. Common applications involve functional imaging, using MicroPET, MicroMRI, MicroCT, ultrasound, or Laser Doppler measurements. These studies seek to obtain information about parameters such as tumor perfusion and blood flow, tumor cell proliferation, uptake of glucose and other metabolites, uptake of radiolabeled drugs, tumor oxygenation and hypoxia, extent of tumor necrosis, and expression of cellular markers. Unfortunately, every anesthetic strategy has inherent potential to change the physiologic condition of the tumor. At the same time, choosing anesthetic solutions based on minimal effects on animal physiology can be influenced by the accessibility of the animal during imaging (e.g. MRI). It can also be affected by the price of the setup, as in the case of the inhalable anesthetic isoflurane. Very commonly, injectables are used to maintain anesthesia during functional studies. A problem with this approach is that literally all injectibles exert some effect on the animal’s physiology, as will be discussed below. In addition, injectibles inevitably introduce variation into the study, because of the heterogeneity between individuals of even the same strain, in the reaction to the drug. This pertains in particular to differences in the depth and duration of anesthesia.

22.3.4 Controlled Delivery of Anticancer Treatment Certain anticancer treatments, such as continuously infused drugs, and radiotherapy, also require immobilization. This is because changes in tumor blood flow and oxygenation can influence the efficiency of the therapy.

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22.3.5 Assessment of Anesthetic Depth in Rodents Full anesthesia is defined as a pharmacologically induced, reversible state of amnesia (loss of memory), analgesia (freedom of pain), loss of consciousness, ataxia or chemical restraint (inability to move purposely), and loss of skeletal muscle reflexes. Among Guedel’s stages of anesthesia [1] (I: from induction of anesthesia to consciousness, II: from consciousness to onset of automatic breathing, III: from onset of automatic breathing to respiratory paralysis, VI: respiratory arrest until death), stage III, also called “surgical level of anesthesia,” is required for most applications in cancer research. Anesthetic depth in rodents must be continually monitored, for humane reasons and to allow for intervention if premature recovery from anesthesia threatens to introduce heterogeneity into the study. The following parameters can be used: • Toe pinch: A gentle pinch that does not injure the animal is sufficient. Any observed reaction to the pinch (withdrawing the paw) indicates that the animal is not sufficiently anesthetized. • Jaw retraction: This can serve as an indicator of muscle relaxation. The lower jaw is gently opened to its maximum extent. Any observed closing of the mouth is an indicator that the animal is not yet sufficiently anesthetized to do surgery. • Respiratory rate: This is a good indicator of anesthetic depth. Rapid, shallow breathing usually indicates the animal is above stage III anesthesia. Ventilation frequency can be quantified by observation (can be difficult, because fast), or using an advanced pulse oximeter that recalculates the breathing rate. • Heart rate: An increase in heart rate and/or blood pressure usually indicates a decrease in anesthetic depth. The heart rate and blood oxygenation status can be monitored using a pulse oxymeter, such as MouseOx. Using heart rate as an indicator of anesthetic depth is appropriate, but requires experience with the respective species and strain. However, even without such knowledge, a relative, consistent increase in heart rate during anesthesia can still be an indicator of anesthesia wearing off. • Eye blinking reflex: The medial edge of the cornea is very gently touched with a gauge sponge or cotton tip. Movement of the eyelids is an indication that the depth of anesthesia is not sufficient to do surgery.

22.4 Animal Support 22.4.1 Body Temperature Among all physiological consequences of prolonged anesthesia, hypothermia is the most common and significant side effect. Hypothermia during anesthesia has different reasons: on one hand, most types of anesthesia result in a drop of blood

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pressure, and therefore for less efficient temperature homeostasis. As measurements in awake, restrained mice show, this can directly lead to a loss in tumor temperature [2]. On the other hand, loss of thermoregulation can occur due to infrared radiation to cooler surroundings, conduction to colder objects, through convection to surrounding air, and by surface evaporation of liquids [3]. Accordingly, one of the prime goals of measures of animal support is to maintain animal body temperature. This is best done using a temperature-controlled warm water-circulated rubber pad. The most sophisticated way is a heating device (electronic, water based), that is directly feedback controlled via a rectal thermometer or thermocouple. The use of heat lamps has the inherent danger of overheating the animals, and should therefore not be used, unless the light switch is controlled by a rectal thermal probe. In any case, continuous control of the animal’s body temperature using rectal monitoring is a good idea.

22.4.2 Respiration One of the most advanced developments in animal anesthesia is the control of respiratory movements and concomitant delivery of inhalable anesthesia, using an anesthesia machine and a ventilator. This setup solves numerous problems during functional studies: • The breathing rate and volume is passive, and can therefore be controlled. This homogenizes the conditions of data acquisition. This aspect is very important: less heterogeneity leads to decreased numbers of animals needed to obtain conclusive data. This saves time, effort, and money and avoids unnecessary use of animals. • Anesthetics can be delivered in an even more controlled manner and are therefore safer for the animal. • Animal physiology affecting the tumor is less influenced by the kinetics of anesthetics. • Exact control of respiratory movement is essential for cardiopulmonary imaging, e.g. during metastasis studies [4]. This solution is doubtlessly the best for functional studies. However, it is also expensive and requires skills and experience to set up the necessary facilities. The rat or mouse needs to be intubated with a matching endotracheal catheter that needs to be cut to the proper length. For rats, a 14–18 G catheter is appropriate. For mice, 20–24 G catheters work best. The insertion should be done by the help of an endoscope. Particularly for new users of this technique, it is very important to test whether the trachea, and not the esophagus, was catheterized. This is done by holding the teased end of a cotton swab in front of the opening of the catheter, and verifying its movement during in- and exhalation. It is recommended that after insertion, the catheter is sutured to the lips of the animal, to prevent it from sliding out.

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When purchasing a small animal ventilator, it has to be taken into account that MRI requires a specific setting, where long tubing has to be used, in order to keep the anesthesia machine away from the animal, or MR compatible equipment needs to be used.

22.4.3 Hydration During long periods and after fluid loss, the animals need to be rehydrated. This can be done by injecting milliliter amounts of pre-warmed sterile saline into the peritoneal cavity.

22.4.4 Analgesia It is important to alleviate pain in animals that arises due to an experiment, first and foremost for humane reasons and also to avoid stress-related bias of the experimental outcome. Pain can be most commonly defined as a stimulus that causes withdrawal and evasive action. Several pharmacological analgetic solutions are currently in use to alleviate pain in rodents, e.g. opioids such as Buprenorphine, amides such as Lidocaine and Bupivacaine, and other substances such as Carprofen and Ketoprofen. Buprenorphine is, for most applications in rats and mice, an acceptable analgesic, the dosing of which depends on the severity of the surgical procedure. In most cases, a subcutaneous dose of 0.05–0.1 mg/kg in mice and 0.01–0.05 mg/kg in rats, applied twice after 8–12 and 16–24 h, respectively, is acceptable. For further information on rodent analgesia (and anesthesia), the “Guide for the Care and Use of Laboratory Animals” by the National Research Council is an important resource to have at hand [5].

22.4.5 Inhalable Anesthetics Inhalation anesthetics are commonly used in rodent anesthesia, and are the most ideal choice in most applications in preclinical cancer research. The most widespread agents are currently halothane and isoflurane. Although most inhalable drugs are volatile at room temperature, controlled delivery of these substances to the animal is done using a calibrated vaporizer that allows to control the percentage (vol%) of the drug in the carrier gas, which is usually oxygen. The greatest advantage inhalable anesthetics offer is that their pharmacokinetics allow for the highest degree of user control, in terms of predictability and speed of adjustment, compared with other anesthetic solutions; recovery time is minimal, even after prolonged anesthesia. In addition, anesthesia can be safely delivered over several hours, if the

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Table 22.1  Inhalable anesthetics and their metabolism

Agent

Chemical class

MAC (in % carrier gas) [6]

Halothane

Halogenated alkane

0.96 ± 0.07%

Enflurane

Halogenated methyl ethyl ether Halogenated methyl ethyl ether

1.95 ± 0.16%

Biometabolism (% recovered as metabolites) 15–20% trifluoroacetyl chloride 2.4% fluoride ion

1.34 ± 0.1%

0.17% fluoride ion

Isoflurane

animal is properly monitored and supported. However, inhalant anesthetics can also be delivered in a closed compartment, using the vapor/fluid equilibrium at room temperature. This very short-termed type of anesthesia is typically used for short operations, such as to deliver an injection to a rat. The key to the high user control of many inhalants is the low rate of cellular uptake and metabolism: this has the consequence that the tissue concentration of the drug is largely a function of the level of exposure, or in other words, the concentration of the inhalant in the alveolar space. Thus, an important measure for the anesthetic potency of an inhaled drug is the MAC (minimal alveolar concentration), which is the alveolar partial pressure at which 50% of animals, or humans, will not respond purposefully to a noxious stimulus, such as a surgical incision. An anesthetic dose can be expressed as a multiple of the MAC. Table 22.1 lists several inhalable drugs, together with their MAC and biometabolism. We will, in this chapter, limit ourselves to describing the use of isoflurane, the most important inhalable anesthetic drug in the field.

22.5 Isoflurane 22.5.1 Background About the Drug Isoflurane, a halogenated methyl ethyl ether, is probably the most commonly used inhalable anesthetic in research involving rodents. Its lipophilicity, high potency (MAC50 of 1.2%), low rate of metabolism (0.2%), and relatively low price make it a prime choice for almost any application. Isoflurane anesthesia allows for a high degree of investigator control: initiation of anesthesia normally does not take longer than 2–3 min, and upon withdrawal of anesthesia, animals recover from anesthesia in usually less than a minute. To apply isoflurane in mice, a calibrated vaporizer and flow regulator is necessary, with oxygen as a carrier gas, and a scavenger with filter, for waste gas. Isoflurane has a vasodilatory effect, therefore decreases blood pressure and increases the heart rate.

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The main advantage of using isoflurane in experimental animals is that it undergoes minimal biotransformation and is almost completely eliminated in exhaled air. This suggests that there will be little effect on liver microsomal enzymes and, hence, little interference in drug metabolism or toxicology studies. This characteristic, together with the rapid induction and recovery from anesthesia, has led to the widespread adoption of isoflurane in many research establishments.

22.5.2 Anesthetic Properties Isoflurane leads to analgesia and muscle relaxation [7]. Due to the above-mentioned low rate of metabolism, mice typically wake up within 1–2 min after cessation of isoflurane. It is important to consider the vasodilatory effect of isoflurane, which leads to a decrease in blood pressure, and a compensatory increase of the heart rate [8]. This leads to alterations of drug delivery, as well as loss of body temperature. It is easy to overdose and kill animals with isoflurane anesthesia; therefore, it is essential to observe the stability of respiration and heart rate. This can be done using oxymetric measurements and also by toe pinch and measurements of body temperature with a rectal thermistor.

22.5.3 Typical Applications Because of the high level of user control, isoflurane can be used for virtually any application, including most types of non-survival surgery. However, the proper control of anesthesia depth may require expensive machinery, such as an animal pulse oxymeter (e.g. MouseOx, STARR Life Sciences, Oakmont, PA). In addition, a calibrated anesthesia machine is necessary. Isoflurane is particularly useful in applications where long recovery times would adversely affect the feasibility of the study, such as when working with large group sizes and/or the need for multiple periods of anesthesia in short succession. Isoflurane has proven valuable in applications that require tight control of respiratory movements, such as imaging: MicroPET, MRI, and SPECT. In this case, it is applied over a ventilation machine, which controls respiratory movements of the animal. Isoflurane can be used to initiate anesthesia prior to injecting other drugs into rats, if the injection procedure has proven to involve significant stress for the animal. This will be discussed below. It is important, for reasons of occupational safety, to perform all operations where isofurane is not scavenged under a chemical fume hood.

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22.5.4 Required Equipment • Ventilated box with grid, for initiation of anesthesia • Anesthesia machine: vaporizer with oxygen tank, calibrated flow controller, hose and nose cone, scavenger • Heating pad • Eye ointment

22.5.5 Protocol for Isoflurane in Mice and Rats 1 . Pre-warm the heating device to 37°C. 2. Initiate anesthesia by exposing mice to 2% isoflurane (rats 4%) in an anesthesia box with grid. Immediately after arrival at full anesthesia, transfer mice to 1.5% (rats 1.5–2%) isoflurane, delivered over a nose cone. The animals are placed on the heating device for the rest of the procedures. 3. Lubricate rectal thermistor with vaseline, then insert into the rectum of the animal. 4. Attach oximetry clamp to one of the hind legs. 5. Lubricate eyes. 6. After procedures, return animals to their cages. They should recover within 1–3 min.

22.5.6 Induction of Anesthesia in Rats with Isoflurane, Followed by Injectable Anesthesia The use of injectable anesthesia in rats is challenging because the initial injection, done intraperitoneally below through the hind legs, requires experience and confidence. Isoflurane can be used to induce ketamine and pentobarbital anesthesia even in the absence of an anesthesia machine. For this purpose, a sealable, transparent containment with a gridded bottom, such as a large desiccator, should be used. 1 . Prepare the respective injectable, based on the animal’s body weight. 2. Place the anesthesia container under a chemical fume hood. 3. Place 3–5 cotton sponges under the grid in the container. Soak sponges with isoflurane. Return grid on top of the sponges. 4. Transfer rat to the container and close the lid. Closely observe the animal until it is prone and loses the eye blink reflex. Immediately remove the animal from the containment, verify respiratory movement, and inject the anesthetic drug i.p. It is critical not to wait too long before removing the rat from the isoflurane,

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because there is only a very short time window from onset of isoflurane anesthesia to overdosing. After injection, continue to monitor the animal for anesthetic depth.

22.6 Injectible Anesthetics 22.6.1 Ketamine HCl with Xylazine 22.6.1.1 Background and Biochemistry Ketamine (Ketaset, Fort Dodge Animal Health, Iowa, USA) is a cyclohexamine that enjoys widespread popularity in its use in rodent anesthesia since its development 30 years ago. Parts of its popularity stem from its outstanding safety, efficiency, inexpensiveness, and vast pre-existing documentation. Ketamine causes a dosedependent CNS depression that is termed “dissociative,” meaning deep analgesia and amnesia, but not necessarily loss of consciousness. Ketamine appears to target the thalamoneocortical projection system in the brain, leading to a selective depression of neuronal function of the neocorticothalamic axis and the central nucleus of the thalamus, while it stimulates parts of the limbic system. With ketamine, ocular and pharyngeal reflexes are retained to a higher degree than with other drugs, which can lead to misinterpretation of anesthetic depth through observation of physical signs. Commercial ketamine is a racemic mixture of two optical enantiomers, R(–) and S(+), which differ in anesthetic potency and effect. Ketamine produces a range of pharmacological effects, spanning from interactions with N-methyl d-aspartate (NMDA) and non-NMDA glutamate/nitric oxide/cGMP receptors, as well as nicotinic and muscarinic cholinergic receptors and opioid receptors [9]. It also appears to interact with voltage-dependent Na+ and L-type Ca2+ channels. Ketamine is metabolized extensively by the hepatic cyt p450 system. Norketamine, the primary metabolite, is one-third to one-fifth as potent as ketamine and is excreted by the kidney. Therefore, reduced renal output can result in prolonged ketamine action. While ketamine produces dose-dependent analgesia and sedation/chemical restraint, it causes only little muscle relaxation. Therefore it is usually supplemented with an analgesic and/or sedative, such as the a-2 adrenergic agonist xylazine or a benzodiazepine, such as diazepam. The combination with xylazine provides satisfactory muscle relaxation and visceral analgesia.

22.6.1.2 Anesthetic Properties in Rodents The ratio of ketamine to xylazine is usually 1:20 to 1:10. Dosing in mice provides good results in i.p. injections at ketamine/xylazine at 100/5–10 mg/kg. Anesthesia

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takes effect quickly, usually within 2–8  min after i.p. injection in mice and rats. Anesthesia can be refreshed at one-third of the initial dose, if necessary. Redosing should be done with ketamine only, not xylazine. Animal support must include body temperature management using, e.g. water circulated pads. The depth of anesthesia should be continually monitored by toe pinch and other methods outlined earlier. Recovery from anesthesia is also comparably quick, which is caused by rapid redistribution of ketamine from the CNS to all body tissues (primarily body fat, lung, liver, and kidney).

22.6.1.3 Impact of Ketamine on Rodent Physiology Ketamine alone induces short vasodepression, followed by a long-lasting pressor response [10]. Ketamine–xylazine mixtures generally decrease arterial blood pressure (MAP) and heart rate in both rats and mice [11, 12]. As a result, blood flow in an experimental tumor can decrease [13]. The influence of ketamine in combination with diazepam on heart rate and blood flow is less pronounced [11]. There is evidence that ketamine/xylazine has the potential of decreasing arterial pH slightly in some rat strains and in mice [11, 14]. Combined with diazepam, ketamine appears to decrease blood pH in rats as well [11]. Xylazine, alone or combined with ketamine, also has the potential to increase blood glucose levels [15–18]. This can lead to increased glucose levels and pH acidification in tumors [19]. There is also potential that the increased diuresis, caused by a-2 agonists such as ketamine, leads to an increase in hematocrit.

22.6.1.4 Anesthesia Protocol of Mice Using Ketamine/Xylazine Anesthesia Preparation • Prepare working solution: From 100  mg/ml solutions ketamine (ketaset) and xylazine, transfer 100 ml ketamine and 10 ml xylazine to a 15-ml falcon tube. Add 890 ml of saline. • Pre-heat heating pad to 37°C. • Provide several 1 ml syringes with 25-G needles.

Protocol 1. Measure out the body weight of the mouse. To apply 10/1 mg/kg ketamine/xylazine, multiply by 10 to get the amount of ketamine/xylazine working solution in microliters. For example, a 23-g mouse would require an injection of 230 ml of working solution.

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2 . Inject calculated amount i.p. Return the animal to its cage and wait until asleep. 3. Transfer animal to heating pad. Ensure surgical level anesthesia by toe pinch and corneal reflex. Continually test depth of anesthesia throughout procedures. 4. Redose, if needed, at one-third of the original volume, i.e. 80 ml.

22.7 Pentobarbital 22.7.1 Background and Biochemistry Pentobarbital, as all barbiturates, is a central nervous system depressant that is extensively used in rodent anesthesia. Although it is not a cheap drug any longer, its ongoing popularity roots in the amount of historical data and pre-existing experience with this substance at most research sites, its rapid onset of anesthesia (5–10 min in mice), and in the ease of its use through i.p. injection [20]. Pentobarbital produces deep hypnosis, but relatively poor analgesics. Barbiturates prolong the GABA induced opening of chloride channels in neurons. At anesthetic doses, this leads to the suppression of high-frequency neuronal firing via the inhibition of voltage-dependent Na+ channels [21–23]. Pentobarbital is metabolized by hepatic microsomal enzymes and hydroxylation of the 3-carbon methylbutyl side chain [24].

22.7.2 Anesthetic Properties in Rodents Pentobarbital often causes a mild phase of excitement, before and after it takes its full impact on the animal. This usually appears as an increase in breathing frequency. Another sign of onset or recovery from pentobarbital anesthesia in mice is the (unpurposeful) “stretching” of the animal, which is not an alarming sign. Depth of anesthesia can be readily assessed by observing the breathing frequency and by reaction to toe pinch. Successful pentobarbital anesthesia is also dependent on the quality of animal support: It is very important to provide adequate body temperature maintenance since pentobarbital reduces body temperature. Body temperature failure during pentobarbital anesthesia will lead to greatly extended recovery times (up to several hours), or the death of the animal. Pentobarbital can also be easily overdosed, as it has a narrow safety margin: 50  mg/kg has provided insufficient analgesia in mice, however, 60 mg/kg already shows some rate of mortality [14]. In our experience, 75–80 mg/kg provides safe, surgical level anesthesia between 25 and 45 min in female athymic nude, C57 black, and Balb/C mice, with the greatest risk of mortality due if redosing is necessary. However, the length of anesthesia can vary greatly, depending on mouse strains or genders [25, 26]. Redosing should be done in units of 1  mg/100  ml, and should not happen in intervals shorter than 20  min. Redosing should always be preceeded by adequate testing of anesthesia

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depth, such as by toe pinch or observation of breathing rate. Overdosing usually manifests itself in shallow breathing, long breathing intervals, followed by gasping. The animal loses body temperature very quickly. In this state, which precedes death, it is often possible to rescussitate the animal, by placing it on its back and gently, but abruptly pushing the rib cage between index finger and thump, in series of 5–6. Repeat if necessary. In rats, nembutal at a dose of 40–60 mg/kg i.p. will produce surgical level anesthesia 20–50 min after injection. Redosing should be done only after careful testing for anesthetic depth, at a dose one-third of the original volume. Extended anesthesia for survival procedures has to be accompanied by application of eye ointment.

22.7.3 Physiological Impact of Pentobarbital Pentobarbital exerts strong cardiovascular effects that are less pronounced if administered i.p. than intravenously: blood flow in all organs except the kidney decreases under anesthesia in both rats and mice [12, 27, 28]. Pentobarbital also decreases the respiratory and heart rate in mice [14]. These mechanisms, together with hypothermia, may be responsible for reports of increases in tumor hypoxia [13, 29, 30]. Pentobarbital has consequently demonstrated radioprotective activity in several tumor types [31].

22.7.4 Applications The best application of pentobarbital is in both survival and nonsurvival surgery, where reliable, long anesthesia is required and free handling must not be hampered by attached nose cones and other machinery. Although pentobarbital is also a common choice where accessibility of the animal is hampered, such as during irradiation, or MRI and PET imaging, this application is less than optimal because of the known effects of pentobarbital on the animal physiology.

22.7.5 Protocol for Pentobarbital Anesthesia in Mice 22.7.5.1 Preparation • • • •

Prepare a heating pad, warm to 37°C, cover with fresh absorbent paper. Prepare 1-ml syringes w/25-G needles. Prepare 10 mg/ml nembutal, from 50 mg/ml stock, with saline. Butterfly needles 25  G, with 12¢ polyethylene catheters, and attached 1-ml syringe. Flush with 10 mg/ml nembutal.

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We recommend initiating pentobarbital anesthesia by injecting the appropriate amount of a 10 mg/ml solution intraperitoneally: 80 mg/kg equals the body weight in grams, times eight, in microliters. A 20-g mouse would therefore receive 160 ml of 10 mg/ml pentobarbital.

22.7.5.2 Protocol 1 . Initiate anesthesia i.p. as described. 2. Once the animal does not react to toe pinch, apply the butterfly needle catheter i.p. 3. Place the mouse on the heating pad. The animal should always be lying on its ventral side. 4. Frequently check retraction reflexes to toe pinch. Redose at 100 ml units via the catheter, if necessary. 5. After the procedures, allow the animal to recover on the heating pad, until sternal recumbency. Then transfer the animal back into its cage.

22.7.6 Other Injectibles Alpha-chloralose and urethane are other widespread used rodent anesthetics, often in combination with each other. Alpha-chloralose, a derivative of glucose, is a central nervous system depressant that is also used as a rodenticide and avicide. Its use is limited to non-survival procedures, because its late effect that can lead to significant animal suffering, e.g. due to its immunological effects [32]. Urethane, chemically ethyl carbamate, is an ammonia ethyl ester that produces deep anesthesia and analgesia in rodents, however, its use is limited to non-survival procedures due to its carcinogenicity and anti-proliferative effect [33]. Chloralose and urethane have been used in the following schedules in rats: anesthesia was induced with 4% isoflurane and maintained with alpha-chloralose at 30 mg/kg h [34]. In mice, induction of anesthesia was done with 3% isoflurane and then urethane (1,000 mg/kg) and chloralose (50 mg/kg) [35] were injected intravenously.

22.8 Summary In this article we presented different experimental situations that are typical for cancer research in rodents that require anesthesia. We have explained that anesthetic requirements are very different, depending on whether an organ extraction, a device implantation, or a functional study is conducted. We have listed different anesthetic solutions that are currently used, including benchtop grade protocols and guidelines.

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As animal work in cancer research becomes more established and commercialized, we are currently experiencing an increased use of inhalable anesthetics, particularly isoflurane. The outstanding degree of user control that this drug offers makes it likely that it will take over many areas that are still covered by injectable anesthetics.

References 1. Guedel AE. Inhalation anesthesia. New York: Macmillan, 1951. 2. Pallavicini MG, Hill RP. Effect of tumor blood flow manipulations on radiation response. Int J Radiat Oncol Biol Phys. 1983;9:1321–5. 3. Suit HD, Sedlacek RS, Silver G, Dosoretz D. Pentobarbital anesthesia and the response of tumor and normal tissue in the C3Hf/sed mouse to radiation. Radiat Res. 1985:104;47–65. 4. Hedlund LW, Johnson GA. Mechanical ventilation for imaging the small animal lung. ILAR J. 2002:43;159–74. 5. The National Research Council. Guide for the care and use of laboratory animals. Washington, DC, 1996. 6. Mazze RI, Rice SA, Baden JM. Halothane, isoflurane, and enflurane MAC in pregnant and nonpregnant female and male mice and rats. Anesthesiology. 1985:62: 339–41. 7. Stokes EL, Flecknell PA,, Richardson CA. Reported analgesic and anaesthetic administration to rodents undergoing experimental surgical procedures. Lab Anim. 2009:43;149–54. 8. Clark SC,   MacCannell KL. Vascular responses to anaesthetic agents. Can Anaesth Soc J. 1975:22;20–33. 9. Kohrs R, Durieux ME. Ketamine: teaching an old drug new tricks. Anesth Analg. 1998:87;1186–93. 10. Altura BM, Altura BT, Carella A. Effects of ketamine on vascular smooth muscle function. Br J Pharmacol. 1980:70;257–67. 11. Wixson SK, White WJ, Hughes HC, Jr, Lang CM, Marshall WK. The effects of pentobarbital, fentanyl–droperidol, ketamine–xylazine and ketamine–diazepam on arterial blood pH, blood gases, mean arterial blood pressure and heart rate in adult male rats. Lab Anim Sci. 1987:37;736–42. 12. Yang XP, et al. Echocardiographic assessment of cardiac function in conscious and anesthetized mice. Am J Physiol. 1999:277;H1967–74. 13. Menke, H, Vaupel, P. Effect of injectable or inhalational anesthetics and of neuroleptic, neuroleptanalgesic, and sedative agents on tumor blood flow. Radiat Res. 1988:114;64–76. 14. Erhardt, W, Hebestedt, A, Aschenbrenner, G, Pichotka, B, Blumel, G. A comparative study with various anesthetics in mice (pentobarbitone, ketamine–xylazine, carfentanyl–etomidate). Res Exp Med (Berl). 1984:184;159–69. 15. Aynsley-Green, A, Biebuyck JF, Alberti KG. Anaesthesia and insulin secretion: the effects of diethyl ether, halothane, pentobarbitone sodium and ketamine hydrochloride on intravenous glucose tolerance and insulin secretion in the rat. Diabetologia. 1973:9;274–81. 16. Hsu WH, Hembrough FB. Intravenous glucose tolerance test in cats: influenced by acetylpromazine, ketamine, morphine, thiopental, and xylazine. Am J Vet Res. 1982:43;2060–2061. 17. Reyes Toso CF, Linares LM, Rodriguez RR. Blood sugar concentrations during ketamine or pentobarbitone anesthesia in rats with or without alpha and beta adrenergic blockade. Medicina (B Aires). 1995:55;311–6. 18. Kawai, N, Keep RF, Betz AL. Hyperglycemia and the vascular effects of cerebral ischemia. Stroke. 1997:28;149–54. 19. Pavlovic, M, Wroblewski, K, Manevich, Y, Kim, S, Biaglow JE. The importance of choice of anaesthetics in studying radiation effects in the 9L rat glioma. Br J Cancer Suppl. 1996:27;S222–5. 20. Wixson SK. Anesthesia and analgesia in rodents. In: Kohn DF WS WJ, White GJ, Benson, editors. Anesthesia and analgesia in laboratory animals, pp 165–203 San Diego: Academic Press, 1997.

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21. Macdonald RL, McLean MJ. Anticonvulsant drugs: mechanisms of action. Adv Neurol. 1986:44;713–6. 22. Olsen RW. GABA–drug interactions. Prog Drug Res. 1987:31;223–41. 23. Saunders PA, Ho IK. Barbiturates and the GABAA receptor complex. Prog Drug Res. 1990:34;261–86. 24. Freudenthal RI, Carroll FI. Metabolism of certain commonly used barbiturates. Drug Metab Rev. 1973:2;265–78. 25. Lovell DP. Variation in pentobarbitone sleeping time in mice. 2. Variables affecting test results. Lab Anim. 1986:20;91–6. 26. Lovell DP. Variation in pentobarbitone sleeping time in mice. 1. Strain and sex differences. Lab Anim. 1986:20;85–90. 27. Kawaue Y, Iriuchijima J. Changes in cardiac output and peripheral flows on pentobarbital anesthesia in the rat. Jpn J Physiol. 1984:34;283–94. 28. Tuma RF, Irion GL, Vasthare US, Heinel LA. Age-related changes in regional blood flow in the rat. Am J Physiol. 1985:249;H485–91. 29. Rockwell S, Moulder JE, Martin DF. Tumor-to-tumor variability in the hypoxic fractions of experimental rodent tumors. Radiother Oncol. 1984:2;57–64. 30. Moulder JE, Rockwell S. Hypoxic fractions of solid tumors: experimental techniques, methods of analysis, and a survey of existing data. Int J Radiat Oncol Biol Phys. 1984:10;695–712. 31. Denekamp, J, Terry NH, Sheldon PW, Chu AM. The effect of pentobarbital anaesthesia on the radiosensitivity of four mouse tumours. Int J Radiat Biol Relat Stud Phys Chem Med. 1979:35;277–280. 32. Silverman, J, Muir WW, 3rd. A review of laboratory animal anesthesia with chloral hydrate and chloralose. Lab Anim Sci. 1993:43;210–216. 33. Field KJ, Lang CM. Hazards of urethane (ethyl carbamate): a review of the literature. Lab Anim. 1988:22;255–262. 34. Luckl, J, Keating, J, Greenberg JH. Alpha-chloralose is a suitable anesthetic for chronic focal cerebral ischemia studies in the rat: a comparative study. Brain Res. 2008:1191;157–167. 35. Kazerani HR, Furman BL. Comparison of urethane/chloralose and pentobarbitone anaesthesia for examining effects of bacterial lipopolysaccharide in mice. Fundam Clin Pharmacol. 2006:20;379–384.

Part IX

Experimental Methods and Endpoints

Chapter 23

Preclinical Tumor Response End Points Beverly A. Teicher

Abstract  The first in vivo tumor models were developed in the mid-1960s. These models were mouse leukemia models grown as ascites. The growth pattern was like that of bacteria in vivo and therefore it was possible to apply similar mathematics of growth and response to these tumors as had been worked out for bacteria. Since the development of the murine leukemia models, investigators have devoted a large effort to modeling solid tumors in mice. There are now a variety of models including syngeneic mouse tumors and human tumor xenografts grown as subcutaneous nodules, syngeneic mouse tumors and human tumor xenografts grown in orthotopic sites, models of disseminated disease, ‘‘labeled’’ tumor models that can be visualized using varied technologies, and transgenic tumor models. The value of these models depends upon the application of rigorous experimental design and data analysis. The endpoints used can be in situ or excision. Each of these has advantages and disadvantages to the ‘‘drug hunter’’ searching for improved treatments. Keywords  Tumor growth delay • Xenografts • Isobolograms • Drug distribution • Enzastaurin • Intratumoral vessels

23.1 Introduction The field of cancer research only recently came to the forefront of human scientific endeavor and took advantage of experience gained in studying other disease processes. Over many years prior to the formal investigation of malignant disease, researchers had worked out scientific methodology and recognized the importance of laboratory models for infectious diseases, allowing rapid progress in antibacterial

B.A. Teicher (*) Genzyme Corporation, 49 New York Avenue, Framington, MA 01701-9322, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_23, © Springer Science+Business Media, LLC 2011

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drug development. Cancer research also benefited from the early research of the 1950s and 1960s, which took a very orderly and rigorously scientific approach to the development of in vivo models and to the development of the most informative endpoints available from experiments suing in vivo tumor models.

23.2 Ascites Tumors The science of preclinical modeling of anticancer therapies began in the 1950s, but the establishment of guidelines for experimental quality and end point rigor can be attributed in large part to the group headed by Howard Skipper at the KetteringMeyer Laboratory affiliated with Sloan-Kettering Institute, Southern Research Institute in Birmingham, Alabama. In the mid-1960s, this group published a series of reports on the criteria of “curability” and on the kinetic behavior of leukemia cells in animals and the effects of anticancer chemotherapy. The principles put forth in these reports were derived directly from the behavior of bacterial-cell populations exposed to antibacterial agents, and were based upon experimental findings in mice bearing intraperitoneally implanted L1210 or P388 leukemia [1–15]. The initial assumptions were: (1) one living leukemia cell can be lethal to the host. Therefore, to cure experimental leukemia, it is necessary to kill every leukemia cell in the animal, regardless of the number, anatomic distribution or metabolic heterogeneity, with treatment that spares the host. (2) The percentage, not the absolute number, of in vivo leukemic cell populations of various sizes killed by a given dose of a given anti-leukemic drug is reasonably constant. This phenomenon of a constant fractional (or percentage) drug-kill of a cell population, regardless of the population size, has been repeatedly observed and may be a general phenomenon. (3) The percentage of experimental leukemic cell populations of any size killed by single dose treatment of drug to the host is directly proportional to the dose level of the drug (the higher the dose, the higher than the percentage of cells killed). Thus, it is obviously necessary to kill leukemic cells faster than they are replaced by proliferation of the cells surviving the therapy if a “cure” is to be attempted [10–12]. The exponential killing of cells by drugs with time (mathematically equivalent to “a constant percentage kill of leukemic cells regardless of number”) was observed in bacterial cell populations around 1900, and has been investigated with many antibacterial agents [16–20]. Studies with bacterial cells exposed to anticancer agents confirmed that the first-order kinetics of cell kill by anticancer agents was similar to that of antibacterial agents [12]. The hypothesis that “the percentage, not the absolute number, of cells in populations of widely varying sizes killed by a given dose of a given anticancer drug is reasonably constant” was studied intensively and found, for the most part, to be valid [12]. Skipper and the group at the Kettering-Meyer Laboratory developed the mouse L1210 leukemia [21], as well as the mouse P388 leukemia [22], into highly sensitive and reasonably quantitative in vivo bioassay systems to study anatomic distribution, rate of leukemic cell proliferation, and effects of chemotherapy in tumor-bearing

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mice [14]. These studies were based on the notion that the principal mechanism of drug-induced increase in host life-span was leukemic cell kill, and not “inhibition of growth” of the leukemic cell population [23–26]. Leukemic cells that gained access to the brain and other areas of the central nervous system (CNS) were not markedly affected by certain peripherally administered anti-leukemic drugs. Therefore, if leukemic cells were present in the CNS at the time of treatment initiation, a drug that passes the blood–brain barrier would be required to achieve “cure” [27, 28]. The observation that there was a close relationship between the number of L1210 leukemic cells inoculated into BDF1 mice and the life-span of the mice was critical (Fig. 23.1) [23]. Thus, it was possible to estimate the in vivo doubling (or generation) time and the approximate lethal number of L1210 leukemic cells when L1210 leukemia cells were inoculated into the mice by various routes. By the intraperitoneal route (ip), the average doubling time for the leukemic cells was 0.55 days, and the lethal number of leukemic cells was approximately 1.5 billion [23]. When the L1210 leukemic cells were implanted intravenously (iv) or intracerebrally (ic), the doubling time and lethal number of cells was lower. This knowledge was used to develop an in vivo bioassay by ip implantation of unknown numbers of L1210

IP inoculation: generation time = 0.55 days lethal cell number = 1.5 x 109 IV inoculation: generation time= 0.43 days

IC inoculation generation time = 0.46 days

NUMBER OF LL1210 LEUKE EMIA CELLS IMPLANTED

Mean Survival Time of Mice Inoculated with L1210 Leukemia 1e+6

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Fig. 23.1  Mean survival time of mice inoculated with various numbers of murine L1210 leukemia cells injected intraperitoneally (ip), intravenously (iv) or intracranially (ic). These data form the basis for the in vivo bioassay method for determining the number of L1210 cells surviving after treatment of L1210 tumor-bearing mice with therapy. From these survival curves, it was determined that from: (1) ip inoculation the L1210 cell-generation time is 0.55 days; the lethal number of L1210 cells is 1.5 × 109; (2) iv inoculation the L1210 cell-generation time is 0.43 days, and (3) ic inoculation the L1210 cell-generation time is 0.46 days (adapted from Refs. [14, 23–31])

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leukemic cells isolated from various tissues of chemotherapy-treated L1210-bearing animals into fresh hosts and using the survival time of those mice to estimate the tumor cell killing by the chemotherapy. The estimated experimental error in this bioassay procedure was ±1 log of tumor cells. The method gave an order-of-magnitude estimate of the number of leukemic cells in various tissues and was sensitive to small, absolute numbers of viable L1210 leukemia cells [14]. Antitumor activity endpoints used in these mouse ascitic leukemia models were the percent mean or median increase in life-span (% ILS), net log10 cell kill, and number of long-term survivors [29, 30]. The percent mean or median increase in life-span (% ILS) is the ratio of the survival time of the treated mice (days) compared with the survival time of the untreated control mice (days). Calculation of net log10 cell kill is made from the tumor doubling time determined from an internal tumor titration consisting of implants from serial 10-fold dilutions (Fig. 23.1) [31]. Long-term survivors are excluded from the calculations of %ILS and net log10 tumor cell kill. To assess net log10 tumor cell kill at the end of treatment, the survival time (days) difference between treated and control groups is adjusted to account for re-growth of tumor cell populations that may occur between individual doses [32]. The net log10 cell kill is calculated as:

Net log10 cell kill = [ (T - C ) - (duration of treatment in days )] / 3.32 ´ Td , where (T – C) is the difference in the median day of death between the treated (T) and the control (C) groups, 3.32 is the number of doublings required for a population to increase 1 log10 unit, and Td is the mean tumor doubling time (days) calculated from a log-linear least squares fit of the implant sizes and the median days of death of the titration groups and accounts for any repopulation of the tumor during or after treatment (Fig. 23.1).

23.3 Solid Tumors As solid tumor models were developed, the response endpoints devised were tumor growth delay or tumor control of the implanted tumor. These assays require that drugs be administered at doses producing tolerable normal tissue toxicity, so that the response of the tumor to therapy can be monitored for a relatively long time. Treatment with test compounds can be initiated either prior to tumor development or after a tumor nodule has developed. If treatment begins the day after or on the day of tumor cell implant, the experiment is a tumor growth inhibition study. If treatment begins when an established tumor nodule (50–200 mm3) is present, the experiment is a tumor growth delay study. Activity in a tumor growth delay study is more persuasive than activity in a tumor growth inhibition study, and is a better model of clinical disease. Historically, in primary screening experiments in mouse solid tumor models, the tumor volumes in the treated and control groups were measured with calipers only

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once, usually when the control tumors reached approximately 1 cm3 in volume (1 g by weight), at which time all the mice were sacrificed. Alternatively, the mice in all the groups were sacrificed when the tumors of the untreated or vehicle-treated control group reached approximately 1 cm3 in volume, and the tumors were excised and weighed. This traditional protocol design provided no kinetic data regarding tumor growth and response [33]. A more informative experimental design includes tumor volume measurements and body weight measurements of individual mice twice per week for the duration of the experiment. This experimental design elucidates the growth pattern of the tumor in control mice as well as elucidating the effect of the drug on the tumor growth pattern [33–35]. Tumor volumes are usually estimated from measurements of two diameters of the nodule:

Tumor volume (mm3 ) = (longer diameter ´ shorter diameter 2 ) ´ 0.5 where the diameters are the tumor length and width in mm, usually measured with calipers, respectively. Experiments in which tumor volume measurements are made over a relatively long period of time, until the tumors reach a volume of 1.5–2 cm3, allow the calculation of tumor growth delay and percent T/C at multiple time points and the tumor volume doubling time (Fig. 23.2). Tumor growth delay is the difference in days for treated versus control tumors to reach a specified, usually between 500 mm3 and 2 cm3. Therefore, tumor growth delay is simply T – C in days. T is the mean or median time (in days) required for the treatment group tumors to reach a predetermined size and C is the mean or median time (in days) for the control group tumors to reach the same size. Animals that are tumor-free at the time of the determination of the tumor growth delay are excluded from the calculation. Tumor growth delay may be the most important estimate of antitumor effectiveness, because it mimics most closely clinical endpoints and requires observation of the mice through the time of disease progression. TUMOR GROWTH DELAY 10,000

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The importance of the duration of observation of tumors response is illustrated in Fig. 23.3. Mice bearing the mouse CT-26 colon carcinoma were treated with 10 compounds in a developing structure–activity relationship (SAR) in an effort to provide guidance regarding the relative antitumor activity of the compounds. When the experiment was terminated at 27 days the resolution amongst the compounds is very limited; however, when the tumor response observation time was extended to 48 days differentiation amongst the compounds became clear. If tumor growth is log-linear through the treatment and response phase of the experiment, the data can be used to calculate the log cell kill. The group at the Kettering-Meyer Laboratories used techniques developed in the mouse leukemia models for obtaining order-of-magnitude estimates of the absolute number, percent of

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Fig. 23.3  Growth curves for the murine CT-26 colon carcinoma treated with 10 compounds from a developing structure–activity relationship (SAR). In the upper panel, the observation of tumor response was terminated at 27 days which provides very little guidance regarding the efficacy of the compounds. In the lower panel, the observation of tumor response was extended to 48 days allowing clear delineation of the efficacy of the compounds to be discerned and maximizing the information obtained from the experiment

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viable cancer cells remaining and log10 cell kill methodology for selected experimental solid tumors after a single dose of a drug [13]. The assumptions were that: (1) the mass of the tumor is in direct proportion to the number of malignant cells in the mass. (2) The cells killed by the cytotoxic agent immediately become nonviable. (3) The cells which remain viable despite treatment begin to grow again after a relatively short lag and proliferate at the same rate as tumor cells in untreated control animals. These assumptions appeared to be valid for two of the three tumors studied in the initial report, which included the hamster Plasmacytoma 1 tumor, the mouse Sarcoma 180, and the mouse Adenocarcinoma 755 tumor. Because both Plasmacytoma 1 and Sarcoma 180 control tumors grow logarithmically during the treatment period, a method for estimating cell killing could be applied to these tumors. However, many solid tumors do not maintain log-linear growth and this method cannot be applied to those tumors without modification. Furthermore, many cytotoxic therapies such as radiation therapy do not kill cells destined to die promptly but kill cells over several generations of proliferation and this methodology will not be accurate. Slopes were derived from tumor growth curves for untreated and treated groups and first order rate constants for tumor growth were derived to allow determination of the fraction of tumor cells killed or the fraction of viable cells remaining after the treatment. For subcutaneously growing tumors, the log10 cell kill is calculated as:

Log10 cell kill total (gross) = [T - C value in days / 3.32 ´ Td ] where T – C is the tumor growth delay and Td is the tumor volume doubling time (in days) of the control tumors in exponential growth over a volume range from approximately 100 mm3 to 1 cm3. The conversion of the T – C values to log10 cell kill is possible if the tumor maintains a log-linear growth pattern and if the Td of the tumors re-growing post-treatment approximates the Td values of the tumors in control group. The net log10 cell kill is derived by subtraction of the duration of the treatment period from the T – C value and then dividing by 3.32 × Td [36, 37]. Similarly, Norton and Simon proposed that cytotoxic agents were only active toward the growing tumor cell fraction. With the Norton–Simon model, it is possible to account the dose-dependence of a compound on tumor volume as a function of time [38, 39]. The Norton–Simon hypothesis predicts that the rate of tumor regression increases proportionally with increasing level of the drug. For most, solid tumors volume behavior is not a reliable endpoint with respect to tumor cell kill [4, 40–44]. A drug-induced regression in tumor mass of no more than 50% may represent a 99.99% reduction in clonogenic cells in a solid tumor mass. Many widely used human tumor xenograft models do not conform to the requirement of log-linear growth and many anticancer therapies do not kill promptly with little lag prior to the resumption of log-linear growth in the treated groups (Fig. 23.4). Despite this limitation, the NCI was able to correlate response in human tumor xenograft models with the activity of compounds in phase II clinical trial if the compound was an active anticancer agent in at least 33% of the models tested [45].

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Fig. 23.4  Top: Growth curves for the human SW-2 small cell lung carcinoma and for the human Calu-6 non-small cell lung carcinoma grown as subcutaneous xenograft tumors in nude mice. Although small regions of the tumor growth curves may approach log-linear growth, marked deviations from log-linearity are clear. Bottom: Growth curves from two different studies for the human LNCaP prostate carcinoma grown as a subcutaneous xenograft tumor in nude mice. The control tumors approximate log-linear growth. The drug in the left-hand panel alters tumor growth variously depending upon dose. After the drug treatment in the right-hand panel, the tumors regain log-linear growth parallel to the control tumors after recovery from the regression phase

23.4 Combination Treatments That combination therapy regimens would be required to effectively treat malignant disease was realized in the very early scientific history of cancer research and rationale means of selecting drugs for combination were described. Sequential inhibition is the action of two or more inhibitors on different enzymes of a multi-enzyme pathway; concurrent blockade is the simultaneous inhibition by two or more agents

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of alternative pathways to the same critical end-product and complementary inhibition is the combination of two or more agents that inhibit different loci involved in a critical metabolic process (Fig. 23.5) [46–48]. As it has become evident that “normal” cells involved in the malignant disease process are valid targets for therapeutic attack, horizontal combinations inhibiting different pathways in two or more cell types involved in malignant disease and vertical combinations inhibiting the same or related pathways in two or more cell types involved in malignant disease have been described (Fig. 23.5) [49, 50]. In the study of multimodality therapy or combination chemotherapy, it is important to determine whether the combined effects of two agents are additive or whether the combination is substantially different than the sum of the parts [51, 52]. Several methods have been developed to examine Enz1 A X B I1

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Fig. 23.5  Several rationale methods for developing anticancer drug combinations are shown. Sequential, concurrent and complimentary combinations were developed in the 1950s and 1960s [46–48]. Horizontal and vertical combinations were developed as cells other than the malignant cell were envisioned to be therapeutic targets [49, 50]

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combination therapy regimens for additive, sub-additive or greater than additive effects [53–61]. Among the most widely applicable analysis methods for data from combination regimens is the Combination Index method which requires that the two agents be combined in a constant ratio [55, 59, 61]. The uniform measures and response surface models allow more parameters to be taken into account [53, 56, 57]. Here examples will use isobologram analysis applied to in vivo tumor studies [54, 58]. Conceptual foundations for this analysis were based on the construction of an envelope of additivity in an isoeffect plot (isobologram). This approach provides a rigorous basis for defining regions of additivity, supra-additivity, sub-additivity, and protection [62]. This method of analysis is based on a clear conceptual formulation of the ways that two agents can show additivity [62]. For a selected level of effect (survival) on a log scale, the dose of Agent A to produce this effect is determined from a survival curve. A lower dose of Agent A is then selected, the difference in effect from the isoeffect level is determined and the dose of Agent B needed to make up this difference is determined from a survival curve for Agent B (Fig. 23.6a). For example, 3 mg of Agent A may be needed to produce 0.1% survival (3 logs of kill), the selected isoeffect. A dose of 2.5 mg Agent B produces 1.0% survival (2 logs of kill). The Mode I isoeffect point for Agent B would be the level of Agent B needed to produce 1 log of kill, to result in the same overall effect of 3 logs of kill. In this instance, 4 mg of Agent B are needed to produce 1 log of kill. Mode II additivity is conceptually more complex, and corresponds to the concepts of additivity discussed by Berenbaum [63]. For any given level of effect, the dose of Agent A needed to produce this effect is determined from the survival relationship. The isoeffect dose of Agent B is calculated as the amount of Agent B needed to produce the given effect, determined from the survival relationship. The isoeffect dose of Agent B is calculated as the amount of Agent B needed to produce the given effect, starting at the level of effect produced by Agent A (Fig. 23.6b). For example, 3 mg of Agent A may be needed to produce 0.1% survival (3 logs of kill). A dose of 2.5 mg of Agent A produces 1.0% survival (2 logs of kill). A dose of 6 mg of Agent B is needed to produce 3 logs of kill and 2 logs of kill are obtained with Agent B at 5 mg. Thus, the Mode II isoeffect point with Agent A at 2.5 mg is equal to the amount of Agent B needed to take Agent B from 2 logs of kill to 3 logs of kill (6 mg – 5 mg = 1 mg). This can be conceptualized by noting that Agent A should produce 2 logs of kill and is equal to 5 mg of Agent B. If Agent A + Agent B are identical in their mode of action, then 1 mg more of Agent B should be equivalent in effect to 6 mg of Agent B. Graphically, on a linear dose scale, Mode II additivity is defined as the straight line connecting the effective dose of Agent A alone and the effective dose of Agent B alone. This relationship is also described by the equation: Dose of A/ Ae + Dose of B/Be = 1 where Ae and Be are the doses of Agent A and Agent B, respectively, needed to produce the selected effect.

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Overall combinations that produce the desired effect that are within the boundaries of Mode I and Mode II are considered additive. Those displace to the left are supraadditive and those displace to the right are sub-additive (Fig. 23.6a). Combinations that produce effects outside of the rectangle defined by the intersections of Ae and Be

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are protective. This type of classical isobologram methodology is difficult to use experimentally because each combination must be carefully titrated to produce a constant level of effect. Dewey et al. described a form of analysis for the special case in which the dose of one agent was held constant [64]. Using full survival curves of each agent alone, this method produces envelopes of additive effect for various levels of the variable agent. It is conceptually identical to generating a series of isoeffect curves and then plotting the survivals from a series of these at constant dose of Agent A on a log effect by dose of Agent B coordinate system [65]. This approach can be applied to the experimental situation in a direct and efficient manner and isobolograms can be derived describing the expected effect (Mode I and Mode II) for any level of the variable agent and constant agent combinations. The schedule and sequence of drugs in combination can affect therapeutic outcome. The definition of additivity and therapeutic synergism has evolved with increasing stringency. In the early work of Schabel, Corbett, and Griswold, therapeutic synergism between two drugs was defined to mean that “the effect of the two drugs in combination was significantly greater than that which could be obtained when either drug was used alone under identical conditions of treatment” [66–75]. Using this definition, the combination of cyclophsophamide and melphalan administered simultaneously by intraperitoneal injection every 2 weeks was reported to be therapeutically synergistic in the Ridgeway osteosarcoma growth delay assay [66–70]. Similarly, the combination of cyclophosphamide and melphalan was reported to be therapeutically synergistic in L1210 and P388 leukemias [71]. Cyclophosphamide plus a nitrosourea (BCNU, CCNU or MeCCNU) were also reported to be therapeutically synergistic in increase in lifespan and growth delay assays using this definition [71]. However, in the EMT6 mouse mammary carcinoma, the maximum tolerated combination therapy of thiotepa (5 mg/kg × 6) and cyclophospahmide (100 mg/kg × 3) produced 25 days of tumor growth delay, which was not significantly different than expected for additivity of the individuals drugs by isobologram analysis [44, 51, 52]. The surivival of EMT6 tumor cells after the treatment of the animals with various single doses of thiotepa and cyclophosphamide was assayed. Tumor cell killing by thiotepa produced a very steep and linear survival curve through 5 logs. The tumor cell survival curve for cyclophosphamide up to 500 mg/kg gave linear tumor cell kill through almost 4 logs. In all cases, the combination treatment tumor cell survivals fell well within the envelope of additivity (Fig. 23.7). Both these drugs are somewhat less toxic toward bone marrow cells by the granulocyte-macrophage colony forming unit (CFU-GM) assay method than to tumor cells. The combination treatments were sub-additive or additive in bone marrow CFU-GM killing. When bone marrow is the dosing limiting tissue, there is a therapeutic advantage to the use of this drug combination [51]. The Lewis Lung Carcinoma (LLC) arose spontaneously as a carcinoma of the lung of a C57BL mouse in 1951 in the laboratory of Dr Margaret R. Lewis at the Wistar Institute. The Lewis lung carcinoma was among the earliest transplantable tumors used to identify new anticancer agents. Sugiura and Stock found that the Lewis lung carcinoma produced tumors 100% of the time yielding a highly malignant carcinoma. These investigators used the Lewis lung carcinoma along with

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Fig. 23.7  Isobolograms for the combination treatment of the EMT6 mouse mammary carcinoma treated with 10 or 15 mg/kg of thiotepa and various doses of cyclophosphamide. The survival curve for the EMT6 tumors exposed to cycophosphamide only is shown with open triangles. The dotted lines define the envelope of additivity for the combination treatment. Tumor cell survivals for the combination treatment are shown in solid circles (adapted from Ref. [51])

several other transplantable mouse tumors to determine the antitumor activity of a series of phosphoramides from which the antitumor alkylating agent thiotepa emerged [76–78]. Twenty years later, DeWys standardized techniques for following primary tumor growth by tumor volume measurements and for assessing the response of lung metastases to therapeutic intervention [79]. DeWys observed the Gompertsian pattern of primary tumor growth, effects of tumor burden on therapeutic efficacy, and the effects of the presence of the primary tumor on the growth rate of lung metastases [80]. G. Gordon Steel and co-investigators continued work with the Lewis lung carcinoma and developed culture colony formation techniques, lung colony formation techniques and limiting dilution techniques to assess tumor response to new anticancer drugs and radiation therapy [81, 82]. The syngeneic Lewis lung carcinoma mimics the human disease because the primary tumor metastasizes to lungs, bone, and liver. It is nonimmunogenic and is grown in a host with a fully functional immune system. The rate of tumor growth is relatively rapid, with a tumor volume doubling time of 2.5 days and is lethal in 21–25 days when the tumor cell implant is 106 cells. Although the tumor growth rate is rapid, it is in line with the life-span of the host, which is about 2 years. Gemcitabine (LY18801; 2¢,2¢-difluorodeoxycytidine) is an analog of the natural pyrimidine. The mechanism of action and metabolism of gemcitabine have been well-characterized [83–87]. Gemcitabine is active against many solid tumor models, including the CX-1 human colon cancer xenograft and the LX-1 human lung carcinoma xenograft in nude mice [85–88]. In Phase I clinical trials, gemcitabine was evaluated in a variety of schedules. The greatest efficacy with the least toxicity was obtained with a weekly schedule [83]. In Phase II clinical trials, gemcitabine

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had activity against small cell lung, nonsmall cell lung, breast, ovarian, pancreatic, myeloma, prostatic, renal, and bladder cancer [89, 90]. Gemcitabine has demonstrated a 22% objective tumor response rate in a database of 331 patients diagnosed with nonsmall cell lung cancer (NSCLC) who received the drug on a weekly schedule in a dose range of 800–1,250 mg/m2. Vinorelbine (navelbine) is a semi-synthetic vinca alkaloid with antitumor activity related to microtubule depolymerization which dissolves mitotic spindles [91–98]. In a variety of human tumor cell lines, vinorelbine was cytostatic at nanomolar concentrations that are significantly below achievable plasma levels in patients [92, 96]. In a number of in vivo studies exploring activity in rodent tumor models and human tumor xenografts in athymic mice, vinorelbine demonstrated efficacy against P388, L1210, B16, and M5076 in vivo mouse models and in animals with human tumor xenografts. Phase I clinical trials showed the maximum tolerated dose of vinorelbine was 30 mg/m2 with weekly intravenous administration. Phase II clinical trials, employing weekly schedules of vinorelbine, demonstrated activity against SCLC, NSCLC and ovarian and breast cancer [96]. Single agent vinorelbine was studied in nonrandomized Phase II human trials as first-line therapy in NSCLC using a weekly schedule and showed good activity with 23 responders out of 70 evaluable patients producing a response rate of 32.8%. The median duration of response was 34 weeks [96–99]. Gemcitabine was an active anticancer agent in animals bearing the Lewis lung carcinoma. Gemcitabine was well-tolerated by the animals over the dosage range from 40 mg/kg × 3 to 80 mg/kg × 3 (Fig. 23.8a) [100]. Navelbine was administered in three different well-tolerated regimens with total doses of 10, 15, and 22.5 mg/kg. Both gemcitabine and navelbine produced increasing tumor growth delay with increasing drug dose. To assess the efficacy of the drug combination, the intermediate dosage regimen of navelbine was combined with each gemcitabine dose. These combination regimens were tolerated and the tumor growth delay increased with increasing gemcitabine dose. Isobologram methodology [51] was used to determine whether the combinations of gemcitabine and navelbine achieved additive antitumor activity (Fig. 23.8a). At gemcitabine doses of 40 and 60 mg/kg, the combination regimens achieved additivity, with the experimental tumor growth delay falling within the calculated envelope of additivity. At the highest gemcitabine dose, the combination regimen produced less than additive tumor growth delay [52, 100]. The untreated control animals in this study had a mean number of 35 lung metastases on day 20. Gemcitabine was highly effective against disease metastatic to the lungs, with a mean number of lung metastases on day 20 decreased to 1.0–1.5 or 3–4% of the number in the untreated controls. Each of the navelbine regimens decreased the number of lung metastases on day 20 to 10 or 11, or about 30% of the number in the untreated controls. The combination regimens were highly effective against Lewis lung carcinoma metastatic to the lungs, with a mean number of <1–0 metastases found on day 20. These results support the notion that gemcitabine and navelbine may be an effective anticancer drug combination against NSCLC [100].

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Fig. 23.8  Panel a: Growth delay of the Lewis lung carcinoma produced by a range of doses of gemcitabine (40, 60 or 80 mg/kg) alone administered by intraperitoneal injection on days 7, 10 and 13 post-tumor implant or along with navelbine administered by intraperitoenal injection as 10 mg/kg on day 7 and 5 mg/kg on day 14 (15 mg/kg total dose) post-tumor implant. The points are the mean values of two experiments with five animals per group per experiment; the bars are SEM. The dotted area is the envelope of additivity determined by isobologram analysis (adapted from Ref. [100]). Panel b: Growth delay of human HCT116 colon carcinoma grown as a xenograft in nude mice after treatment with irinotecan (7.5, 15 or 30 mg/kg) administered by intraperitoneal injection on days 7 through 11 after tumor cell implant alone or along with ALIMTA (100 mg/kg) administered by intraperitoneal injection on days 7 through11 and days 14 through 18. The points are the mean values of two experiments with five animals per group per experiment; bars indicate the SEM. The shaded area represents the envelope of additivity by isobologram analysis (adapted from Ref. [101])

The human HCT116 colon carcinoma was selected for the study of ALIMTA (N-[4-[2-(2-amino-3,4-dihydro-4-oxo-7H-pyrrolo[2,3–9]pyrimidin-5-yl)ethyl]benzoyl]-L-glutamic acid; raltitrexed) in combination treatment because the HCT116 tumor is responsive to ALIMTA and because antitumor activity of ALIMTA has been observed in patients with colon cancer [101–105]. Treatment of nude mice bearing subcutaneously implanted HCT116 colon tumors with ALIMTA (100 mg/kg) administered twice daily by intraperitoneal injection 5 days/week for 2 weeks produced a tumor growth delay of 2.7 ± 0.3 days. Irinotecan administered daily for 5 days produced increasing tumor growth delay with increasing drug dose (Fig. 23.8b) [106]. Treatment of HCT116 tumor bearing animals with ALIMTA and irinotecan resulted in greater than additive tumor growth delay for the two drugs, reaching 27 days when the irinotecan dose was 30 mg/kg. No toxicity as determined by body weight loss was observed with this regimen. Irinotecan inhibits the DNA-unwinding enzyme topoisomerase I resulting in DNA strand breaks [107–110]. The combination of ALIMTA and irinotecan resulted in synergistic antitumor effect against the human HCT116 colon carcinoma at all irinotecan doses examined [106].

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23.5 Therapeutic Index Predicting from preclinical studies whether a potential new anticancer agent will have a positive therapeutic index in patients remains a challenge. Mice, the traditional preclinical host for cancer compound testing, tend to be more resilient to many xenobiotics and supra-normal concentrations protein molecules than patients. Translational investigators probing deeper into the genomes of malignant diseases have described gene amplifications and deletions, rearrangements and in many silent and active mutations frequent in cancer [111–116]. The plasticity and instability of the cancer genome is impressive. Most malignant tumors are unlikely to yield to single enzyme-targeted agents. In future, therapeutic regimens may be selected based upon the gene expression pattern or “molecular signature” or the level of specific proteins in an individual tumor, there will continue to be a need for broadly active anticancer agents long-term. The current armamentarium of medical oncology includes many active anticancer agents that are applied across tumor types. None of the broadly active anticancer agents are ideal medications; however, for some diseases they are curative as sole therapy and for some diseases as adjuvant therapy. It is likely that combination regimens including cytotoxic anticancer agents and molecularly targeted agents will comprise the next generation of successful cancer treatments. Bone marrow is critically sensitive to many antineoplastic agents [117]. It is important to understand the toxicity of agents to bone marrow, and to determine whether bone marrow progenitor cells will be more or less sensitive to the agent than human malignant cells. Bone marrow granulocyte-macrophage colony forming unit (CFU-GM) assays comparing the sensitivity of bone marrow cells across species are useful in predicting the blood levels of an agent that might be achieved in patients compared with blood levels tested in preclinical efficacy and safety study species. Mouse bone marrow is less sensitive to many cytotoxic agents than is human bone marrow allowing blood levels in preclinical efficacy testing that are not achieved in patients [117–121]. An efficacious level of a compound with smaller or no differential in bone marrow progenitor sensitivity among species may have a better potential for reaching similar blood levels in patients as in mice. If bone marrow toxicity is dose-limiting in humans, these compounds may be more likely to be successful in reaching therapeutic doses. Pessina et al. [117] suggested that through use of the ratio of mouse/human CFU-GM IC90 values and the mouse maximum tolerated dose that the human maximum tolerated dose of a compound could be predicted and thus the potential for achieving a therapeutic blood level in patients estimated. Excision assays involving the removal of tumor, bone marrow and other tissues from the host after treatment with a therapy to determine the effects of the therapy in an ex vivo assay are an important preclinical tool in assessing the potential therapeutic benefit of the therapy [122]. Unlike standard in situ experimental designs such as increase-in-lifespan, tumor growth delay, and local tumor control, excision assays require removal of the tumor or normal tissue from the environment in

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which the treatment was delivered. This difference leads to several advantages and disadvantages to using excision assays. The ability to measure cell survival directly is an important advantage for both malignant and normal cells. The excision assay allows greater accuracy and discrimination amongst the effects of varied therapeutic regimens. Supra-lethal treatments can be tested allowing more points of observation than can be achieved with in situ assays. Perhaps the greatest disadvantage of excision assays is that extended treatment regimens cannot be studied because cells that are killed by the treatments will lyse and thus be lost to measurement. Thus, an excision assays provides a snapshot of tumor and host response at a short time post treatment [123]. The survival of malignant cells from tumors treated in vivo and then excised is often determined by colony formation (CFU) in cell culture. Similarly the survival of bone marrow cells from treated hosts is most often determined by colony formation and can be refined by use of growth factor supplements to the cell culture media to allow growth of specific progenitor cell types [124].

23.6 In Vivo/Ex Vivo Assay of Primary and Metastatic Disease The tumor cell survival assays has been applied extensively to examine the response of the primary tumor to therapy; however, it can be applied to the detection of metastatic disease and to the response of metastatic disease to therapy [125]. The response of metastatic tumor to antitumor alkylating treatment was carried out in mice bearing subcutaneously implanted EMT-6 mouse mammary carcinoma primary tumors. The tumor-bearing mice were treated with cyclophosphamide (300 or 500 mg/kg) as a single intraperitoneal injection on day 8 postimplantation of 2 × 106 tumor cells from donor tumors. On day 8 the tumor volumes were about 200 mm3. On day 9, the animals were sacrificed and primary tumors, liver, lungs, blood, bone marrow, brain, and spleen were collected. The tissues were minced with crossed scalpels and then treated with DNase and collagenase to disaggregate the tissues into single cells. The enzyme exposure time was varied depending on the tissue to optimize cell yield from each tissue. Known numbers of nucleated cells from each tissue were plated in monolayer culture under conditions suitable for tumor cell proliferation. After 10 days, colonies of EMT-6 tumor cells were stained with crystal violet and counted manually [125]. The data were expressed as tumor cell colonies per 106 nucleated cells plated from each tissue (Fig. 23.9). In the absence of treatment, the primary tumor produced 2 × 104 colonies per 106 cells plated, and about 6,000, 1,000, and 500 tumor cell colonies grew from liver, lungs and blood per 106 nucleated cell plated, respectively. Many fewer colonies, about 25, 2, and 1.5, grew from the bone marrow, brain, and spleen per 106 nucleated cells plated, respectively. These data reflect the relative abundance of viable malignant cells in these normal tissues of the host. The response of the malignant cells to therapy varied markedly depending upon the tissue in which the cells resided. Cyclophosphamide was used as a representative

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tum or

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liver lungs blood

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Cyclophosphamide Dose, mg/kg Fig. 23.9  EMT-6 mouse mammary carcinoma tumor cell colonies per 106 nucleated cell plated from tissues of mice bearing subcutaneous EMT-6 tumors either without treatment or after treatment with a single dose of cyclophosphamide (300 or 500 mg/kg) by intraperitoneal injection. The animals were treated on day 8 post-tumor implant and tissues were collected on day 9 (adapted from Ref. [125])

anticancer agent and was administered as a single dose to the tumor-bearing mice. The blood and spleen are shown only along the axis because no tumor cell colonies grew from these tissues after the host was treated with either dose of cyclophosphamide. Although the number of tumor cells in the brain was relatively few, most of them survived treatment of the host with cyclophosphamide and grew colonies. Tumor metastatic to the liver, bone marrow, and lungs was less responsive to treatment with cyclophosphamide than was the primary tumor as evidenced by the shallower slope of the dose responsive curves for tumor in these organs compared with the slope of the responsive curve for the primary tumor [125]. The reasons for the differential responsiveness of the EMT-6 tumor to cyclophosphamide depending upon the location of the tumor cells in the host are manifold. There is great heterogeneity in drug distribution throughout the host and great variability in the capacity of the surrounding normal tissue of the host to detoxify the drug molecule. There are differences in the gene expression in the tumor cells depending upon the microenvironment in which the tumor cells are residing.

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23.7 In Vivo Resistant Tumors In vivo alkylating agent-resistant EMT-6 mouse mammary tumor lines were developed by treating tumor-bearing animals with cisplatin, carboplatin, cyclophosphamide or thiotepa with transfer to fresh hosts 10-times over 6 months [126]. In spite of the high levels of in  vivo resistance, no difference in responsiveness was observed when cells from the parent and resistant tumors were exposed to the same drugs in monolayer cultures. The pharmacokinetics if cisplatin and cyclophosphamide were altered in mice bearing the respective resistant tumors compared with mice bearing the parental tumor and nontumor-bearing mice. The resistance of these tumor lines decreased over 3–6 months when the tumor lines were passed in animals without further exposure to the drugs. The response of malignant cells throughout the host bearing the in vivo resistant tumors was decreased compared with the parent tumor (Fig. 23.10). The survival of bone marrow granulocyte-macrophage colony-forming units (CFU-GM), an alkylating agent-sensitive normal tissue, was assessed in mice-bearing the EMT-6/parent tumor or in vivo-resistant tumors [126–128]. The survival pattern of the bone marrow CFU-GM recapitulated the survival of the tumor cells, mimicking

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Cyclophosphamide Dose, mg/kg Fig. 23.10  Survival, determined by colony formation, of EMT-6 mouse mammary tumor cells and EMT-6/CTX mouse mammary tumor cells resistant to cyclophosphamide in vivo after treatment of the tumor-bearing mice with a single dose of cyclophosphamide (100, 300 or 500 mg/kg) by intraperitoneal injection. The animals were treated on day 8 post tumor implant and tissues were collected on day 9. Surviving fraction is calculated by comparison with untreated controls (adapted from Ref. [127])

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the development of resistance and reversion to sensitivity on removal of the selection pressure for each of the four alkylating agents. When the EMT-6/parent was implanted in the opposite hind-limb of animals bearing the in vivo-resistant tumors, the survival of the parental tumor cells after the treatment of the animals with appropriate antitumor alkylating agent was enhanced. These results indicated that the presence of an alkylating agent-resistant tumor in a host can affect drug response of tissues distal to the tumor (Fig. 23.10) [126–128]. Understanding the mechanisms involved in the sensitivity/resistance of tumors to chemotherapy, coupled with the development of a clinically relevant means of ensuring tumor sensitivity is an important continuing endeavor [129–131]. To explore the mechanisms of drug resistance, transcriptional profiling was performed on Affymetrix arrays using RNA from the parental and in vivo-resistant tumors [130]. RNA was extracted from three tumors for each line and hybridized to the microarrays independently. Assuming the global mean hybridization intensity from chip to chip was the same, hybridization intensities were normalized to permit direct comparisons between samples. One to two percent of the genes from the in vivo-resistant tumors showed at least twofold changes in mean hybridization intensities compared with the parental tumor. A small subset of genes had significant expression changes across the resistant tumor lines [130, 131]. Transforming growth factor-b (TGF-b), platelet-derived growth factor (PDGFR) and mitogen-activated protein kinase (MAPK) genes were increased in expression in the resistant tumors. Several of these changes were confirmed by Western blot.

23.8 Drug Penetration into Tumor The antiangiogenic combination of TNP-470 and minocycline administered for 2 weeks did not alter the growth of the Lewis lung carcinoma, the EMT-mouse mammary carcinoma, the FSaIIC mouse fibrosarcoma or the rat 9L gliosarcoma [132–138]. However, when TNP-470 and minocycline were added to treatment with cytotoxic anticancer therapies, tumor response was markedly increased. When C3H mice bearing the FSaIIC fibrosarcoma were treated with TNP-470/minocycline for 5 days prior to intravenous injection of the fluorescent dye Hoescht 33342, by fluorescence activated cell sorting, there was a shift of the entire tumor cell population toward greater brightness. The 10% brightest cell and the 20% dimmest cell subpopulations were composed of cells containing much more fluorescent dye that the same subpopulations of cells from the control tumor (Fig. 23.11) [132]. The TNP-470/ minocycline-treated tumors were more easily penetrated by the lipophilic dye. This was the first indication that TNP-470 and minocycline treatment might allow greater distribution of small molecules into tumors. Cyclophosphamide was about six times more toxic toward bright cells than toward dim cells (Fig. 23.12). Treatment of tumor-bearing mice with TNP-470 (3 × 30 mg/kg) or minocycline (5 × 10 mg/kg) along with cyclophosphamide resulted in three times more killing of the bright cell and the dim cell sub-populations compared with cyclophosphamide alone. Treatment with the combination of TNP-470 and minocycline along with cyclophosphamide

Fig.  23.11  Fluorescence distribution, determined by fluorescence activated cell sorting, in FSaIIC tumor cells after intravenous injection of tumor-bearing mice with Hoescht 33342 (2 mg/kg). The data shown are for untreated control tumors and for tumors from mice treated with TNP-470 (3 × 30 mg/kg) by subcutaneous injection and minocycline (5 × 10 mg/kg) by intraperitoneal injection for the 5 days prior to collection of the tumors (adapted from Ref. [132]) 1

Surviving Fraction

Bright cells Dim cells

0.1

0.01

Fig. 23.12  Survival of tumor cell sub-populations based on Hoechst 33342 fluorescence intensity of FSaIIC cells from mice bearing FSaIIC tumors treated with a single dose of cyclophosphamide (150 mg/kg, ip) on day 7 alone or in combination with TNP-470 (3 × 30 mg/kg, sc) on days 3, 5 and 7 and/ or minocycline (5 × 10 mg/kg, ip) on days 3 through 7. The bars show the survival for bright cells and dim cells. Data are the means of three experiments; error bars are SEM (adapted from Ref. [132])

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was most effective resulting in 3.6-times more killing of bright cells and 6.8-times more killing of dim cells compared with cyclophosphamide alone. To explore this observation further, Lewis lung carcinoma-bearing mice were treated for 5 days with TNP-470/minocyline and then injected intraperitoneally with [14C]-cyclophosphamide and 6 h later tissues were collected. There were higher [14C]-levels in the tissues of the mice treated with TNP-470/minocycline than in the tissues of the mice treated with cyclophosphamide alone. The tissues which showed changes were primary subcutaneous tumor, liver, kidney, brain, heart, gut, skin, muscle, and lung. The largest increases were 2.6-fold in the tumor, 2.3-fold in the kidney, 3.2-fold in the heart, 5.6-fold in the gut, and 7.9-fold in skeletal muscle [133]. A similar study was carried out with TNP-470/minocycline along with cisplatin or cisplatin treatment alone. The platinum levels in the tissues were determined by atomic absorption. There were increased levels of platinum in all of the tissues taken from animals treated with TNP-470/minocycline compared with cisplatin only treated animals. The largest increases were 5.2-fold in the tumor, 3.8-fold in the gut, 3.0-fold in the skin, and 2.5-fold in the skeletal muscle [133]. These findings may represent a general mechanism for antiangiogenic agents [134–138]. [14C]Paclitaxel was administered to Lewis lung carcinoma-bearing mice either treated for 5 days with TNP-470/minocyline or not and tissues were collected over a 24-h time course [139]. The pattern of [14C]paclitaxel distribution into tumor and other tissues was similar with higher peak levels of [14C]paclitaxel in the tissues of mice pretreated with TNP-470/minocycline. The highest levels of [14C]paclitaxel were in the lungs of mice that had received TNP-470/minocycline. Other tissues with high paclitaxel concentrations were gut and heart. To determine whether treatment with TNP-470/minocycline would alter the tissue distribution of large molecules into tumors and normal tissues, [14C]albumin was administered to TNP-470/ minocycline-treated mice bearing the Lewis lung carcinoma and untreated animals [139]. There was a two- to threefold higher concentration of [14C]albumin in the tumors and normal tissues of mice treated with TNP-470/minocycline than in untreated mice during the initial hours after [14C]albumin injection. A differential in [14C]albumin tumor and normal tissues levels persisted over the 24-h period post[14C]albumin injection examined with TNP-470/minocycline-treated mice having higher levels. The highest levels of [14C]albumin were in liver and lungs. The Lewis lung tumor growing subcutaneously in C57Bl mice is very hypoxic with 92% of the pO2 measurements £5 mmHg (radiobiologic hypoxia), as determined with a polarographic oxygen electrode [136]. In Lewis lung tumor-bearing mice treated with TNP-470/minocyline for 5 days the percent of pO2 readings £5 mmHg was 75%. When the mice were administered an oxygen delivery agent and allowed to breath a 95% oxygen atmosphere the percent of pO2 readings £5 mmHg was decreased to 45% and the response of the tumors to fractionated radiation therapy markedly increased. There was a linear relationship between the decreases in the percent of pO2 readings £5 mmHg and tumor response at each radiation dose (2, 3, and 4 Gray × 5), indicating that the diminution in tumor hypoxia produced by the treatment may be directly responsible for the increase in the effectiveness of the radiation therapy.

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The effect of treatment with TNP-470/minocycline on the number of countable intratumoral blood vessels was examined in mice bearing the Lewis hung carcinoma. Treatment of the tumor-bearing animals with TNP-470 (30 mg/kg) on alternate days by subcutaneous injection for five doses decreased the number of intratumoral vessels to 51% of the number found in the untreated controls. Interestingly, treatment with TNP-470 and minocycline (10 mg/kg) daily by intraperitoneal injection was somewhat less effective in decreasing the number of intratumoral vessels and resulted in tumors with about 64% of the number of vessels counted in the control tumors. Both cyclophosphamide and cisplatin are cytotoxic through the formation of cross-links in cellular DNA. DNA alkaline elution was performed with DNA isolated from tumors treated in vivo and there was increased DNA cross-linking with increased dose of cyclophosphamide [133]. Treatment of Lewis lung carcinomabearing mice with cyclophosphamide (300 mg/kg) resulted in a cross-linking factor of 4.7. Treatment of the mice with TNP-470/minocycline along with cyclophosphamide resulted in a DNA cross-linking factor of 6.2 which extrapolates to an equivalency of about 650 mg/kg of cyclophosphamide. Increased DNA cross-linking was also detected with administration of cisplatin to mice bearing the Lewis lung carcinoma. Treatment with cisplatin (20 mg/kg) alone resulted in a crosslinking factor of 2.0. Treatment of tumor-bearing mice with TNP-470/minocycline along with cisplatin resulted in a DNA cross-linking factor of 8.9 which extrapolates to an equivalency of about 85 mg/kg of cisplatin. Enzastaurin is a potent inhibitor of PKCb [140, 141]. Exposure to a range of concentrations of enzastaurin (600 nM, 72 h) profoundly inhibited proliferation of VEGF (20 ng/ml)-stimulated HUVEC. Cell culture studies indicate that exposure to enzastaurin can cause growth inhibition and apoptosis in human multiple myeloma cells, diffuse large cell lymphoma and mantle cell lymphoma [142–148]. Administration of enzastaurin orally twice per day on days 1 through 10 postsurgical implant of VEGF impregnated filters resulted in markedly decreased vascular growth in the cornea of Fisher 344 female rats. A dose of 10 mg/kg of enzastaurin or decreased vascular growth to about one-half of the VEGF stimulated controls; while a dose of 30 mg/kg of enzastaurin decreased vascular growth to the level of the unstimulated surgical control (Fig. 23.13) [140]. Administration of enzastaurin (30 mg/kg) orally twice per day on days 1 through 10 postsurgical implantation of bFGF resulted in decreased vascular growth to a level of 26% of that of the bFGF control. To inform the selection of human tumor models in which to study enzastaurin, VEGF secreted into culture medium by 12 human tumor cell lines was measured and compared with VEGF levels in plasma from mice bearing the same 12 human tumor lines grown as xenografts (Fig. 23.14) [149]. In culture VEGF levels were in the range of 0–1064 pg/106 cells, with the highest levels secreted by HS746T gastric carcinoma cells (1064 pg/106 cells) and Caki-1 renal cell cancer cells (452 pg/106 cells). Those secreting the lowest amount of VEGF into the cell culture medium were GC3 colon, SW480 colon, and SW2 small cell lung cancer cells (25 pg/106 cells). The plasma levels of VEGF in mice bearing each of the 12 human tumor lines as xenografts were measured. Nontumor-bearing mice had no detectable plasma VEGF. Plasma levels of VEGF in tumor-bearing mice ranged from 5 to 200 pg/ml,

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Fig. 23.13  Vascular area determined by image analysis and described in pixel number for Fisher 344 female rats implanted with a small filter disc (inside diameter of a 20g needle) impregnated with VEGF or bFGF (except the surgical control). Animals were untreated or treated with enzastaurin (10 or 30 mg/kg) administered orally twice per on days 1–10. Data are the means of four to six determinations from photographs on day 14; bars are SEM (adapted from Ref. [140])

except MDA-MB-468 breast carcinoma. The highest plasma VEGF levels were obtained in mice bearing Caki-1 renal cell, SW2 small cell lung, HT29, and HCT116 colon cancers. These data suggest that there is no direct correlation between in vitro and in vivo tumor-induced VEGF secretion (r = – 0.006) [149]. Nude mice bearing human tumor subcutaneous xenografts were treated with enzastaurin orally twice daily on days 4 through 14 or 14 through 30 post-tumor cell implantation. Tumors were collected and immunohistochemically stained for the expression of endothelial specific markers, either CD105 or CD31. The number of intratumoral vessels in the samples was quantified by counting stained regions in 10 high power microscope fields (200×). The number of intratumoral vessels was decreased to one-half to one-quarter of the controls in animals treated with enzastaurin (30 mg/kg) (Fig. 23.15a) [140, 146–149]. Although some of the tumors responded to enzastaurin as an antiangiogenic agent in no case was angiogenesis completely blocked as in the corneal micropocket neoangiogenesis model. The tumor growth delay in the tested tumors did not correlate with intratumoral vessel decrease (Fig. 23.15b). In most tumor models the tumor growth delay produced by enzastaurin as a single agent was not sufficient to predict single agent activity in the clinic. However, the combination regimens suggested high activity. VEGF plasma levels in mice bearing the human SW2 SCLC and Caki-1 renal cell carcinomas treated or untreated with enzastaurin were obtained every 3 days starting on day 7 postimplantation, through treatment, and after the termination of treatment. Plasma VEGF levels were similar between the treated and untreated groups through day 20 [149–151]. Plasma VEGF levels in the control groups continued to increase throughout the

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Fig. 23.14  Panel a: Mean levels of VEGF secretion by 12 human tumor cell lines into cell culture medium. The bars are means ± SEM from three independent experiments [149]. Panel b: Mean plasma levels of VEGF from nude mice bearing human tumor xenografts of 12 human tumor lines. The numbers indicate the sample size analyzed for each group [149]

study; however, even after the termination of enzastaurin treatment, plasma VEGF levels in enzastaurin-treated mice were significantly decreased [150, 151]. The plasma levels of VEGF in mice bearing the human SW2 small cell lung carcinoma, Caki-1 renal cell carcinoma or HCT116 colon carcinoma treated or untreated with enzastaurin were measured by the Luminex assay [148–150]. Plasma samples were obtained every 3 days starting on day 7 postimplantation and carried through treatment, as well as after the termination of treatment. Plasma VEGF levels were undetectable until tumor volumes were 500–600 mm3 (Fig. 23.16). Plasma VEGF levels were similar between the treated and untreated groups through day 20, when plasma VEGF levels reached 75 pg/ml. Plasma VEGF levels in the SW2 control group continued to increase throughout the study reaching values of 400 pg/ml on day 40 postimplantation. Upon termination of the

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Fig. 23.16  Plasma VEGF levels in nude mice bearing human SW2 SCLC, Caki-1 renal cell carcinoma or HCT116 colon carcinoma xenograft tumors, either untreated controls or treated with enzastaurin orally twice daily day 14–30 (21–39 for Caki-1 bearing mice). The data represents the average results for three trials, with each point being the average of nine individual tumors. Bars represent SEM. Asterisk indicates statically significant differences (P < 0.05) [150]

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Fig. 23.17  Tumor volumes in nude mice bearing human SW2 SCLC, Caki-1 renal cell carcinoma or HCT116 colon carcinoma xenograft tumors, either untreated controls or treated with enzastaurin orally twice daily day 14–30 (21–39 for Caki-1 bearing mice). The data represents the average results for three trials, with each point being the average of nine individual tumors. Bars represent SEM. Asterisk indicates statically significant differences (P < 0.05) [150]

treatment, plasma VEGF levels slightly increased to 100 ng/ml, which were still significantly decreased compared to the untreated control group. The VEGF levels in the control Caki-1 group continued to increase through the study and peaked at 225 pg/ml on day 49 post-tumor implantation. In the treatment group, the plasma levels remained suppressed compared to controls throughout the treatment period (days 21–39). The plasma VEGF levels, reaching a maximum of 37 pg/ml, remained suppressed out to day 53, which was 14 days after terminating treatment [148–150]. Tumor volumes were followed in the same cohorts of mice. Tumor growth did not slow when plasma VEGF levels decreased; however, some decrease in tumor growth was evident in the enzastaurin-treated groups late in the experiment (Fig. 23.17).

23.9 Conclusion The tumor response endpoints described in this chapter are well-established and reflect clinical outcomes in the human disease. Although the value of the particular tumor lines and the value of the mouse as a host remain topic for discussion;

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response endpoints, survival end points and tumor cell killing end points are wellcoordinated with clinical investigations. Whereas the value of the particular tumor lines and the value of the mouse as a host remain the subject of discussion, response end points, survival endpoints, and tumor cell killing end points are well coordinated with clinical investigations. The subcutaneously implanted tumor nodule remains the mainstay of tumor models because it is easy to follow and because it has yet to be shown that orthotopic tumor models would discover drugs missed by subcutaneous tumors. Although all of the types of tumor models discussed are being applied to drug discovery, the study of drug combinations, and to the discovery of biomarkers, transgenic tumor models primarily remain as tools to study tumor biology [152-154]. Most malignant diseases are still without useful biomarkers of response to therapies. Both imaging modalities and plasma/serum markers are being explored in the preclinical setting with hopes of developing clinically useful assays; however, examples of biomarkers/surrogate markers of tumor response remain quite rare. It is important to apply quantitative methods to the assessment of tumor growth and response to therapy and to the determination of the additivity/synergy of combination regimens.

References 1. Skipper HE. Historic milestones in cancer biology: a few that are important in cancer treatment (revisited). Semin Oncol. 1979;6:506–14. 2. Skipper HE. Thoughts on cancer chemotherapy and combination modality therapy. JAMA. 1974;230:1033–5. 3. Skipper HE. Successes and failures at the preclinical level; where now? Seventh Cancer Conference Proc.Philadelphia, PA: JB Lippincott Company; 1973. p. 109–21. 4. Skipper HE. Kinetics of mammary tumor cell growth and implications for therapy. Cancer. 1971;28:1479–99. 5. Skipper HE. Cancer chemotherapy is many things: G.H.A. Clowes memorial lecture. Cancer Res. 1971;31:1173–80. 6. Skipper HE. Improvement of the model systems. Cancer Res. 1969;29:2329–33. 7. Skipper HE. Biochemical, biological, pharmacologic, toxicologic, kinetics and clinical (subhuman and human) relationships. Cancer. 1968;21:600–10. 8. Skipper HE. Criteria associated with destruction of leukemia and solid tumor cells in animals. Cancer Res. 1967;27:2636–45. 9. Himmelfarb P, Thayer PS, Martin H. Growth of colonies of murine leukemia L1210 in vitro. Cancer Chemother Rep 1967;51:451–3. 10. Wilcox WS, Schabel FM, Skipper HE. Experimental evaluation of potential anticancer agents XV. On the relative rates of growth and host kill of “single” leukemia cells that survive in vivo cytoxan therapy. Cancer Res. 1966;26:1009–14. 11. Moore GE, Sandberg AA, Ulrich K. Suspension cell culture and in vivo and in vitro chromosome constitution of mouse leukemia L1210. J Natl Cancer Inst. 1966;36:405–21. 12. Pittlo RF, Schabel FM, Skipper HE. Experimental evaluation of potential anticancer agents. XVI. Basic study of effects of certain anticancer agents on kinetic behavior of model bacterial cell populations. Cancer Chemother Rep. 1965;47:1–26. 13. Wilcox WS, Griswold DP, Laster WR, Schabel FM, Skipper HE. Experimental evaluation of potential anticancer agents. XVII. Kinetics of growth and regression after treatment of certain solid tumors. Cancer Chemother Rep. 1965;47:27–39.

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14. Skipper HE, Schabel FM, Wilcox WS, Laster WR, Trader MW, Thompson SA. Experimental evaluation of potential anticancer agents. XVIII. Effects of therapy on viability and rate of proliferation of leukemia cells in various anatomic sites. Cancer Chemother Rep. 1965;47:92–64. 15. Skipper HE. The effects of chemotherapy of the kinetics of leukemic cell behavior. Cancer Res. 1965;25:1544–50. 16. Chick H. An investigation of the laws of disinfection. J Hyg (London). 1908;8:92–158. 17. McCulloch EC. Disinfection and sterilization, 2nd ed.. Philadelphia, PA: Lea and Febiger; 1945. 18. Davis BD. Bacterial and mycotic infections in man, 3rd ed.. Dubos RJ, editor, Philadelphia, PA: JB Lippincott Company; 1958. 19. Porter JR. Bacterial chemistry and physiology. New York, NY; John Wiley and Sons, Inc.; 1947. 20. Wyss O. Chemical factors affecting growth and death. In: Werkman CH, Wilson PW, editors. Bacterial physiology. New York, NY: Academic Press, Inc.; 1951. 21. Law LW, Dunn TB, Boyle PJ, Miller JH. Observations on the effects of a folic acid antagonists on transplantable lymphoid leukemias in mice. J Natl Cancer Inst. 1949;10:179–95. 22. Evans VJ, LaRock JF, Yoshida TH, Potter M. A new tissue culture isolation and explanation of the P388 lymphocytic neoplasm in a chemically characterized medium. Exp Cell Res. 1963;32:212–7. 23. Skipper HE, Schabel FM, Wilcox WS. Experimental evaluation of potential anticancer agents. XIII. On the criteria and kinetics associated with ‘curability’ of experimental leukemia. Cancer Chemother Rep. 1964;35:1–111. 24. Skipper HE. Perspectives in cancer chemotherapy: therapeutic design. Cancer Res. 1964;24:1295–302. 25. Frei E III. Potential for eliminating leukemic cells in childhood acute leukemia (Abstr). Proc Am Assoc Cancer Res. 1964;5:20. 26. Hananian J, Holland JF, Sheehe P. Intensive chemotherapy of acute lymphocytic leukemia in children (Abstr). Proc Am Assoc Cancer Res. 1965;6:26. 27. Rall DP. Experimental studies of the blood brain barrier. Cancer Res. 1965;25:1572–7. 28. Thomas LB. Pathology of leukemia in the brain and meninges: postmortem studies of patients with acute leukemia and of mice inoculated with L1210 leukemia. Cancer Res. 1965;25:1555–71. 29. Bibby MC. Making the most of rodent tumor systems in cancer Br J Cancer. 1999;79:1633–40. 30. Waud WR. Murine L1210 and P388 leukemias. In: Teicher B, editor. Anticancer drug development guide: preclinical screening, clinical trials and approval. Totowa, NJ; Humana Press Inc.; 1998. p. 59–74. 31. Schabel FM Jr, Griswold DJ Jr, Laster WR Jr, Corbett TH, Lloyd HH. Quantitative evaluation of anticancer agent activity in experimental animals. Pharmacol Ther (A). 1977;1:411–35. 32. Lloyd HH. Application of tumor models toward the design of treatment schedules for cancer chemotherapy. In: Drewinko B, Humphrey RM, editors. Growth kinetics and biochemical regulation of normal and malignant cells. Williams & Wilkins, Baltimore, MD, 1977. p. 455–69. 33. Corbett TH, Valeriote F, LoRusso P, Polin L, Panchapor C, Pugh S et al. In vivo methods for screening and preclinical testing. In: Teicher B, editor., Anticancer drug development guide: preclinical screening, clinical trials and approval. Humana Press Inc., Totowa, NJ, 1998. p. 75–99. 34. Plowman J, Dykes DJ, Hollingshead M, Simpson-Herren L, Ally MC. Human tumor xenograft models in NCI drug development. In: Teicher B, editor. Anticancer drug development guide: preclinical screening, clinical trials and approval. Totowa, NJ: Humana Press Inc.; 1998, p. 101–25. 35. Teicher BA. Preclinical models for high dose therapy. In: Teicher B, editor, Anticancer drug development guide: preclinical screening, clinical trials and approval. Totowa, NJ: Humana Press Inc.; 1998. p. 145–82.

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102. Rinaldi DA, Burris HA, Dorr FA, et al. Initial Phase I evaluation of the novel thymidylate synthase inhibitor, LY231514, using the modfified continual reassessment method for dose escalation. J Clin Oncol. 1995;13:2842–50. 103. McDonald AC, Vasey PA Adams L et al. A phase I and pharmacokinetic study of LY231514, the multi-targeted antifolate. Clin Cancer Res. 1998;4:605–10. 104. Takimoto CH. Antifolates in clinical development. Semin Oncol. 1997;24 (9 suppl 18):40–51. 105. Brandt DS, Chu E. Future challenges in the clinical development of thymidylate synthase inhibitor compounds. Oncol Res. 1997;9:403–10. 106. Teicher BA, Alvarez E, Liu P, Liu K, Menon K, Dempsey J et al. MTA (LY231514) in combination treatment regimens using human tumor xenografts and the EMT6 murine mammary carcinoma. Semin Oncol. 1999;26 Suppl 6:55–62. 107. Giovanella BC. Topisomerase I inhibitors. In: Teicher BA, editor, Cancer therapeutics: experimental and clinical agents.Totowa NJ: Humana Press Inc; 1997. p. 137–52. 108. Chabot GC. Clinical pharmacokinetics of inrotecan. Clin Pharmacokinet. 1997;33:245–59. 109. Aschele C, Baldo C, Sobrero AF, et  al. Schedule-dependent synergism between ZD1694 (ralitrexed) and CPT-11 (irinotecan) in human colon cancer in  vitro. Clin Cancer Res. 1998;4:1323–30. 110. O’Reilly S, Rowinsky EC. The clinical status of irinotecan (CPT-11), a novel water soluble camptothecin analogue. Crit Rev Oncol Hematol. 1996;24:47–70. 111. Weir BA, Woo MS, Getz G, Perner S, Ding L, Beroukhim R, Lin WM, et al. Characterizing the cancer genome in lung adenocarcinoma. Nature. 2007;450:893–8. 112. Wang H, Han H, Mousses S, Von Hoff DD. Targeting loss-of-function mutations in tumorsuppressor genes as a strategy for development of cancer therapeutic agents. Semin Oncol. 2006;33:513–20. 113. Wood LD, Parsons DW, Jones S, Lin J, Sjoblom T, Leary RJ et al. The genomic landscapes of human breast and colorectal cancers. Science. 2007;318:1108–13. 114. Nicolau M, Tibshirani R, Borresen-Dale AL, Jeffrey SS. Disease-specific genomic analysis: identifying the signature of pathologic biology. Bioinformatics. 2007;23:957–65. 115. Chanock SJ, Burdett L, Yeager M, Llaca V, Langerod A, Presswalla S, Kaaresen R, Strausberg RL, Gerhard DS, Kristensen V, Perou CM, Borresen-Dale AL. Somatic sequence alterations in twenty-one genes selected by expression profile analysis of breast carcinomas. Breast Cancer Res. 2007;9:R5. 116. Yosef N, Yakhini Z, Tsalenko A, Kristensen V, Borresen-Dale AL, Ruppin E, Sharan R. A supervised approach for identifying discriminating genotypes patterns and its application to breast cancer data. Bioinformatics. 2007;23:e91–8. 117. Pessina A. Application of the CFU-GM assay to predict acute drug-induced neutropenia: an international blind trial to validate a prediction model for the maximum tolerated dose (MTD) of myelosuppressive xenobiotics. Toxicol Sci. 2003;75:355–367. 118. Kummar S, Gutierrez M, Doroshow JH, Murgo AJ. Drug development in oncology: classical cytotoxics and molecularly targeted agents. Br J Clin Pharmacol. 2006;62:15–26. 119. Masubuchi N. A predictive model of human myelotoxicity using five camptothein derivatives and the in vitro colony-forming unit granulocyte/macrophage assay. Clin Cancer Res. 2004;10:6722–31. 120. Erickson-Miller C. Differential toxicity of camptothecin, topotecan and 9-aminocamptothecin to human, canine, and murine myeloid progenitors (CFU-GM) in  vitro. Cancer Chemother Pharmacol. 1997;39:467–72. 121. Kurtzberg LS, Battle T, Rouleau C, Bagley RG, Agata N, Yao M, Schmid S, Roth S, Crawford J, Krumbholz R, Ewesuedo R, Yo X-J, Wang F, LaVoie E, Teicher BA. Bone marrow and tumor cell CFU and human tumor xenograft efficacy of non-camptothecin and camptothecin topoisomerase I inhibitors. Molec Cancer Therap. 2008;7:3212–22. 122. Teicher BA. Preclinical models for combination therapy. In: Teicher BA, Andrews PA, editors. Anticancer drug development guide. Totowa, NJ: Humana Press; 2004. p. 213–42. 123. Rockwell SC. Tumor-cell survival. In: Teicher BA, editor. Tumor models in cancer research. Totowa, NJ: Humana; 2002. p. 617–32.

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Chapter 24

Tumor Cell Survival Sara Rockwell

Abstract  The development of assays for measuring the survival of tumor cells revolutionized expermental cancer therapy by enabling researchers to move from assessing the gross responses of cell cultures and tumors to measuring the survival of cells in the critical clonogenic tumor cell populations. The development and use of these assays provided the basis for many of our modern concepts of tumor biology by demonstrating logarithmic cell killing after treatment with drugs and radiation, by showing the importance of tumor cell proliferation patterns and of the microenvironment heterogeneity within tumors, and by proving that tumors contain both clonogenic stem cells that contribute to tumor growth, progression, and metastasis and also non-clonogenie cells that have limited proliferative potential and are not important in determining the outcome of therapy. Rigorous measurements of tumor cell survival are essential in evaluating and understanding the effects of novel agents and regimens for the treatment of cancer. Assays for tumor cell survial must measure the long term viability and prolliferative capacity of the clonogenic tumor cells, beacuse these are the cells that are critical in determining the success or failure of cancer therapy. Assays that measure cellular integrity, cell metabolism or cell proliferation soon after treatment and assays that are compromised by the abundant stromal cells within tumors cannot rigorously measure the response to therapy of the critical, clonogenic tumor cells. Many experimental problems, artifacts and analytic problems complicate rigorous measurements of tumor cell survival; these potential problems are discussed in this chapter. Factors that must be considered in desgninig and performing experiments that measure tumor cell survival and in analyzing tumor cell survival data are also discussed.

S. Rockwell (*) Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT 06520-8040, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_24, © Springer Science+Business Media, LLC 2011

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Keywords  Tumor cell survival • tumor cell viability • clonogenic assays • trypan blue assay • MTT assay • proliferation assays • colony formation assays • tumor stem cells

24.1 Introduction The development of assays for measuring the survival of individual tumor cells revolutionized the study of experimental cancer therapy by enabling researchers to move from assessing the gross responses of tumors to measuring the survival of cells in the critical, clonogenic tumor cell populations [1]. The development and use of these assays formed the basis for many of our modern concepts of tumor biology, from the concept of logarithmic cell kill to considerations of cell-proliferation kinetics. The first major step in this revolution in cancer biology was made by Puck and Marcus, who developed a cell culture assay for cloning individual HeLa cells (a cell line derived from a human carcinoma of the cervix) and then used this assay to determine the changes in cell survival in cultures given graded doses of radiation [2, 3]. Assays for measuring the viability of cells suspended from tumors in vivo followed rapidly. The first survival curve for cells from tumors treated in vivo was obtained in 1959 by Hewitt and Wilson, using a quantitative tumor transplantation assay (the TD50 assay, an endpoint dilution technique) to measure the survival of cells harvested from leukemia infiltrates in the livers of mice after treatment with graded doses of radiation [4]. Over the next few years, Hewitt’s TD50 assay was extended and used to study the quantitative transplantation and radiation responses of a wide variety of hematologic malignancies and solid tumors [5, 6]. The techniques were also refined and extended to produce true clonogenic assays for tumor cell survival, in which the clonogenicity of individual tumor cells was tested by preparing tumor cell suspensions, counting the tumor cells, and determining the ability of individual tumor cells to proliferate to form macroscopic clones [7–10]. For some tumors, this can be done by injecting known numbers of tumor cells intravenously into recipient mice, allowing the cells to lodge in the spleen [7] or the lung [8], waiting for the individual cells to grow into macroscopic tumors, and counting the number of tumors. In a few tumor systems, the suspended cells can be plated at low densities in cell culture, so that individual tumor cells will grow into macroscopic colonies, allowing the measurement of cell survival by colony formation in cell culture [9, 10], in assays analogous to those developed by Puck and Marcus. The final step in this revolution in cancer biology came in 1961, when Till and McCulloch [11] described their spleen colony assay for measuring the survival of bone marrow stem cells. (It should be noted that this landmark paper was also the first to report that adult tissues can contain pluripotent stem cells that are capable of restoring many different differentiated cell populations.) Clonogenic assays for other normal cell populations followed [12], allowing the toxicities of antineoplastic agents to be evaluated in terms of the survival and responses of the critical clonogenic stem cell populations within the dose-limiting normal tissues.

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The conceptual basis of all these cell survival assays is simple: the cell population of interest is harvested, and the individual cells are isolated and tested for their ability to proliferate indefinitely. In practice, however, the process of making this measurement is fraught with problems and potential artifacts. The techniques used in these measurements, and their problems and limitations, are the subject of this chapter.

24.2 Selection of Tumor–Host Systems As in all studies with experimental rodent tumors, the tumor–host system used with cell survival assays must be chosen with care to ensure that it provides an appropriate model for the proposed studies [1, 5, 6, 10, 13–15]. Because the assays require quantitative comparisons of cells suspended from treated and control tumors, they require the use of matched tumors grown in carefully matched recipients. For most rodent tumors, the ideal host is the inbred strain and subline in which the tumor originated [5, 14, 15], so that immunologic incompatibilities between tumor and host are minimized. It can sometimes be difficult, or even impossible, to find syngeneic hosts for some tumor lines. Some commonly used tumor lines arose in noninbred mice or in mouse substrains which have been lost [5, 6, 16, 17]. Some of the common, commercially available mouse strains (e.g. Balb/c, C57/BL, and C3H) have been maintained in various places as genetically separate breeding stocks for several decades, and the resultant sublines have diverged over dozens or hundreds of generations to have markedly different phenotypes [16, 17]. Finding an appropriate host is an even more serious problem for the investigator who wishes to use tumors originating in genetically altered mice or who wishes to study tumors transplanted into genetically manipulated hosts, because many transgenic and knockout mouse strains are only partially inbred. Although these mouse strains are well defined at the genetic loci of interest, the individual mice may have other genetic differences that influence the behavior of implanted tumors. For investigators studying human tumors xenografted into immune-deficient rodents [18–20], the choice of host can also be difficult. It must first be remembered that xenografts of established human tumor lines in mice are inevitably imperfect models for the primary human cancers from which the cells were isolated. The tumor cells have been heavily selected for their ability to grow rapidly in vitro or in mice and their patterns of differentiation and proliferation are very different from those in the original tumor. The different drug pharmacokinetics in mice and humans will also produce differences in tumor responses. Moreover, the stromal cells and vasculature within the tumors originate from the host mice, and those tumor responses that reflect the interactions of tumor cells with the stromal elements will therefore reflect the characteristics of the murine host, rather than the characteristics of the human tumor cells. This may raise special problems when human tumors are treated in SCID mice, because the DNA repair deficiency that produces the immunedeficient phenotype of the SCIDs also leads to unusual radiation and drug sensitivity

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in the host and in the tumor bed [20]. The athymic nu/nu (nude) mutation exists in a number of genetic backgrounds, and the commercial nude strains are quite different physiologically. Moreover, the nude mutation produces deficits in only some aspects of the immune response. Nude/beige, NOD/nude, and other strains with multiple immunologic deficits are therefore used by some investigators. Other investigators pretreat nude mice with total body irradiation to provide additional immunosuppression. The presence of genetic heterogeneity in the hosts within or between experiments can result in experimental variability that can compromise the validity and precision of the experiments with transplanted tumors. This problem is even more serious for assays such as the lung colony and spleen colony assays, which use recipient mice as hosts for the analysis of tumor cell survival. Other sources of host variability should also be considered [5, 6, 13–15, 21]. Sex and age may be important. The use of immature animals (less than 2.5 months) can be problematic. The rapid growth and changing physiology of these young animals can result in large experimental variations from small differences in age. Rapidly growing tumors also produce stress and disease in small, fast-growing animals more quickly than in adult animals. Because many vendors ship their mice only a week or two after weaning, the investigator must balance the costs and problems of holding mice to maturity before use with the benefits of the more stable model system that results from this practice. Stress resulting from shipping, recaging, environmental problems, or experimental manipulations can also introduce experimental variability and should be avoided or considered in the experimental design [13, 15, 21]. The health and microbiological status of the animals are also major considerations. Active and prior infections with certain bacteria, viruses, and parasites have been shown to alter tumor transplantation, tumor growth, and tumor responses to therapy [13, 15, 22]. Moreover, many common murine viruses and some bacteria have been found to infect tumor tissue and tumor cells, and can be passaged with the tumor cells in vitro or in vivo. Even subclinical or “nonpathogenic” infections can have effects of considerable experimental significance. Some murine viruses, such as Minute Virus of Mice or Kilham’s Rat Virus, replicate in and selectively kill proliferating cells in  vivo and in cell culture, thereby altering the proliferative patterns of tumor cells, changing their response to cycle-active therapy, and compromising cell survival assays. Tumor cell lines infected with human or murine pathogens have been implicated as a source of active infection in mice inoculated with the tumors, and in some cases have been a source of infections that have spread through animal colonies and resulted in illness or death among people in proximity to the colonies [13, 15, 22]. Pathogens carried by human tumor cell lines can also compromise the use of these tumors in experimental cancer therapy studies, and can offer even greater risks of illness in the immune-deficient murine hosts and for investigators, resulting in the need to handle these cells and tumors under BL2 conditions. Another factor that influences the choice of model system for tumor cell survival studies is the question of whether a human tumor xenograft or a syngeneic animal

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tumor offers a better model system [15, 18–20]. For example, human tumor cells are sometimes critical for studies of species-specific cytokines, antibodies, or gene therapy approaches. In other studies, it may be more important that the tumor and host are syngeneic so that immunological incompatibilities, biochemical differences, or cross-species differences in signaling pathways are avoided; in this case, a murine system will be the better model.

24.3 Cell Survival Assays 24.3.1 Implications of Clonogenic Cell Survival Clonogenic assays measure the ability of individual cells to proliferate indefinitely to form colonies of hundreds or thousands of cells. In tumors, this is the critical metric of cell viability: only a clonogenic cell has the ability to cause a recurrence or to create a metastasis. A cell that is intact and metabolically active but incapable of proliferation is unimportant in cancer therapy, because it will not contribute to the growth, recurrence, or metastasis of the malignancy. Conversely, there are resting, clonogenic cells within tumors, which are not proliferating at the time of treatment but which can later be called back into indefinite proliferation to cause growth, recurrence, or metastases [14, 23–25]. These resting, clonogenic cells must be forced into proliferation during the clonogenic assay for the assay to accurately measure tumor cell survival. Clonogenic assays differ fundamentally from assays that follow tumor growth, that examine cellular integrity (e.g. by Trypan blue exclusion), or that measure cell proliferation directly (by changes in cell number or by incorporation of BUdR or 3HTdR into DNA) or indirectly (by measures of protein content or metabolism as occurs in the MTT or Alamar blue assay). The latter assays consider all of the cells in the tumor (tumor and stroma, clonogenic and nonclonogenic, alive, dying, and sometimes dead), and therefore offer only indirect, and sometimes misleading, measures of the responses of the critical clonogenic cell populations in treated tumors [14, 26–28]. Moreover, these metabolic assays measure endpoints that are sensitive to cell size and metabolic rate, and are compromised by treatments (such as radiation, Fig. 24.1) that cause changes in these parameters [14, 26–28].

24.3.2 Measuring Clonogenicity In a clonogenic assay, individual tumor cells are isolated and individually tested for their ability to proliferate indefinitely. This can be done in several ways. Some tumor cells that have been selected for or adapted to growth in cell culture will form colonies when plated at low densities in vitro [9, 10, 26] Cell culture assays

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Fig. 24.1  Changes in the volume of irradiated cells. Exponentially growing EMT6 cells were irradiated (or sham irradiated) with 250 kV X-rays. Twenty-four hours after irradiation, the cells were suspended and their volumes were measured with a Coulter Counter. The distribution of cell volumes is shown. The dose-dependent increase in the cell volumes after irradiation reflects the progression delays and unbalanced growth of these cells during the first 24 h after irradiation. These changes would complicate interpretation if the results of metabolic assays were used to measure “cell viability”

can be used to assay the clonogenicity of cells suspended from these tumors. When cells from some hematologic malignancies are injected intravenously, the cells lodge in the spleen and the clonogenic cells grow to form small tumors [7]. Similarly, cells of many solid tumors lodge in the lung when injected intravenously, and the clonogenic cells then proliferate to form lung colonies [8]. In vitro colonies, spleen colonies, and lung colonies are the most common assays for clonogenic cell survival. In the end-point dilution technique originally developed by Hewitt and Wilson [4], serial dilutions of tumor cells are injected into animals, and the pattern of tumor development is monitored and used to calculate the TD50, or the number of cells needed to produce tumors in 50% of the sites injected. Although the TD50 technique is in some ways the most physiologic test of the clonogenicity of cells from solid tumors, it also suffers from the limitation of requiring that very large numbers of recipient animals be followed for several months for tumor development [4, 5, 14]. 24.3.2.1 Identifying Clonogenic Cells A primary requirement of a clonogenic assay is that it must distinguish clonogenic cells (cells with a capacity for prolonged or unlimited proliferation) from cells that can undergo only one or a few divisions. This is critical when the assay is used to study the effects of radiation or of drugs that damage DNA and produce delayed mitotic death. Cells killed by these agents will continue to proliferate

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after treatment, with cell-proliferation patterns that are at first indistinguishable from those of the cells that will ultimately survive; eventually all of the cells in these “abortive clones” will cease proliferation, die, and lyse. However, several cell divisions may occur before this happens [29–32]. The lower threshold for colony size must therefore be set high enough that abortive clones are not counted among the surviving cells, even when the dying cells undergo abortive mitoses and become multinucleate “giant cells” that may be many times larger than normal. This is not a problem with the in  vivo assays, which count macroscopic tumors containing millions of cells. However, it can be a problem in cell culture systems. A threshold size of 50 cells is often used for colonies in cell culture assays, because it has been found to distinguish between cells surviving and dying from low doses of X-rays [32]. The assay must consider the fact that treatments with many cytotoxic or cytostatic agents will injure the surviving cells and cause them to proliferate more slowly than untreated cells [29–34]. Colonies developing from heavily treated tumors therefore grow more slowly than control colonies (see Fig. 24.2), and the incubation time and colony size threshold must be chosen to allow detection of all of the slowly growing clonogens in the treated cultures [29–34]. The requirements described here create some stringent requirements for cell culture systems that are used to measure clonogenic cell survival. Additional requirements are imposed by the fact that it is important to detect all of the clonogens, so that quiescent clonogens or slowly growing clonogens are not overlooked [24, 34]. Considerable work may be needed to optimize the cell culture system so that the viability of cells suspended from control and treated tumors can be assayed reproducibly and reliably.

Fig. 24.2  Petri dishes from an experiment determining a dose–response curve for EMT6 cells treated with radiation. Left: control plate, 150 cells plated. Right: heavily irradiated cells (10 Gy); 10,000 cells plated

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24.3.2.2 Motion Artifacts For colonies developing on the surface of Petri dishes, covered by liquid medium, it is critical that the system be optimized so that the cultures can sit undisturbed for the full duration of the colony formation assay. As cells in these cultures enter mitosis, they become round and nearly detach from the Petri dish (Fig. 24.3); the daughter cells do not reattach firmly to the surface of the dish until they are well into G1 [33]. If the dishes are moved or the medium is changed during the incubation, mitotic cells will be dislodged from the growth surface and may settle elsewhere on the dish, attach, and proliferate to produce smaller “satellite” colonies. The presence of these extraneous colonies can completely invalidate the clonogenic assay. 24.3.2.3 Cell Density Problems The requirements of the cloning assay system become increasingly severe as the efficacy of the treatments being tested increases. In many rapidly growing transplanted mouse tumor lines, a high proportion of cells are clonogenic [4–10, 24, 25, 34–36]. In the EMT6 system, for example, the plating efficiency of cells explanted from solid tumors is generally about 35%, i.e. 35 colonies will form for every 100 cells plated [10]. When untreated EMT6 cells are plated into a 60-mm Petri dish for colony formation, only 250 cells are needed to produce approximately 100 colonies

Fig. 24.3  Scanning electron microscope photo of an EMT6 cell culture. Interphase cells (such as the flat cell seen here) are spread out on and firmly attached to the growth surface. A mitotic cell is also shown; note that this mitosis is rounded and only loosely attached. Moving the cultures during incubation for clonogenic assays results in the release of mitotic cells from the surface of the culture dish; these cells will settle on other areas of the surface, initiating “satellite colonies” that compromise the validity of the clonogenic assay

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in the dish. However, if one wished to assay the survival of cells from tumors treated with a large dose of radiation that produces a surviving fraction of l0 – 4, it would be necessary to plate 2.5 million cells in the dish to obtain 100 colonies. Some tumors contain only small numbers of clonogenic stem cells (as low as one in 1,000 or even one in 100,000 cells) [5, 25]. It may be difficult, or even impossible, to develop reliable clonogenic assays for such tumors. It has been found consistently that the ability of cells to survive, proliferate, and form colonies in vitro varies with the number of cells in the culture. At very high cell densities, the cells will rapidly deplete the medium; as a result, cell proliferation in the cultures will cease. All colony formation assays must be done below this density. For this reason, it is critical that all measurements of cell survival be based on actual colony formation. Estimates of cell survival which are calculated from the fact that, for example, no colonies were formed when 107 cells were plated may only mean that the cell density plated was high enough to preclude cell growth in the cultures. Other technical problems, such as contamination of the suspension with bacteria or other microorganisms, can also prevent colony formation. At very low densities, especially in suboptimal media, some cell types are unable to proliferate to form colonies. Conditioned medium or the use of radiation-sterilized feeder layers can be used to improve the plating efficiency in such cases [2, 3, 10, 32, 35]. However, when this is done, care much be taken to ensure that similar numbers of cells (feeder layer cells plus experimental cells) are plated in all groups to ensure the linearity of the assay. 24.3.2.4 Colony Density Problems The linearity of the assay system also varies with the number of colonies forming in the dish: plating too many live cells may result in a confluent monolayer of cells, in which individual colonies cannot be distinguished. At slightly lower cell numbers, colonies may be visible, but these colonies will overlap and will be difficult to distinguish. The colonies may also be small, because medium exhaustion near the end of the incubation has inhibited their growth. These problems can result in underestimation of the number of clonogenic cells. Each clonogenic assay system must be tested to establish the range of colony numbers over which colony number increases linearly with the number of cells plated. Colony numbers in this range should always be used to analyze cell survival. In practical terms, this means that in many experiments, several different dilutions of the cell suspensions may need to be plated, so that a group with colony numbers lying within the linear range of the assay can be used in the analysis. 24.3.2.5 Counting the Colonies At the end of the incubation period, the colonies are counted. This process is simplified if the colonies can be fixed and stained to allow the colonies to be visualized more readily. Magnifying the culture so that the individual cells in the

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colonies are visible can be valuable, especially in heavily treated cultures. This allows the counter to assess whether a small colony contains 50 or more cells, and to distinguish true colonies from abortive clones containing a few giant cells and from clumps of debris. Size alone may not be sufficient to distinguish true colonies from debris and abortive clones containing giant cells. In such cases, the use of automated image analysis systems may prove problematic. It is also important to remember that the progeny of untreated cells may grow more rapidly than the progeny of heavily treated cells [31, 32, 34]. As a result, colonies in heavily treated cultures may be smaller and more poorly defined than colonies in untreated control cultures (Fig. 24.2). Because some subjectivity is inherent in the enumeration of colonies, it is important that all the colonies within an experiment be counted by a single, objective observer, preferably one who is blinded to the treatments received by the cells in the different Petri dishes.

24.3.3 Tumor Cell Suspensions 24.3.3.1 Preparing the Suspension Preparation of single-cell suspensions is a critical step in the process of performing a clonogenic assay. This process may be as simple as harvesting the fluid containing the tumor cells, in the case of blood-borne leukemias or ascites tumors growing as single cells in the peritoneal cavity. However, preparing a high-quality single-cell suspension may prove very challenging for solid tumors in which the malignant cells are growing within a rich stromal matrix. In this case, it is usually necessary to use a combination of mechanical disruption and enzymatic treatment to release the cells from the surrounding matrix [5, 6, 8–10, 34–41]. In the process used for EMT6 tumors [10], the tumor is removed from the host, using careful sterile techniques to ensure the sterility of the tissue being removed, and placed in a watch glass with a few drops of a calcium- and magnesium-free Hanks’ Balanced Salt Solution (HBSS), containing 0.25% trypsin. The tissue is then minced into a fine brei. In our laboratory, this is done by mincing the tissue with a small pair of curved surgical scissors; it can also be done by chopping the tumor with a razor blade. We then place the brie in a trypsinizing flask with 100 ml of the trypsin solution, at 37°C, and incubate the mixture for 15 mm, using a magnetic stirring apparatus to provide continuous gentle agitation. We then filter the mixture to remove any intact chunks of tumor tissue, and collect the cells by centrifugation, which is performed at 4°C to halt the action of the trypsin. There are many possible variations to this procedure [8–10, 34–41], and considerable experimentation may be necessary to optimize the procedure for the specific tumor used. Various concentrations of trypsin, other proteolytic enzymes (such as pronase or collagenase), or enzyme cocktails have proven optimal in different systems. Some investigators add EDTA to the enzyme cocktail. The duration of the enzymatic treatment may need to be shorter or longer. For some fragile cell lines, the action of the proteolytic enzyme may need to be stopped by the addition of inhibitors or serum at the end of a brief incubation.

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The intensity of the suspension procedure reflects an important balance [38–41]. On one hand it is important to prepare a suspension containing a large population of tumor cells, which is representative of the total tumor cell population. An inadequately intensive procedure will result in low cell yield. On the other hand, both the mincing and enzymatic treatment will kill cells, and too intensive a treatment will decrease the number and viability of cells in the suspension. Because cells in and near necrotic areas may be more readily released, an inadequate suspension protocol may result in suspensions enriched in cells from these populations. Conversely, an overly aggressive protocol may kill the cells suspended early in the process, resulting in a suspension which contains large numbers of dead cells and is enriched in viable cells from the most fibrous regions of the tumor. 24.3.3.2 Counting the Cells The quality of the tumor cell suspension is evaluated during the counting process. In my laboratory, an aliquot of the suspension is counted using a hemacytometer under phase-contrast microscopy. Trypan blue is used to identify those cells that have lost their ability to exclude this vital dye and are in the process of dying. Phase-contrast is used because the edge effects of this imaging approach aid in identifying stained cells, and also aid in distinguishing tumor cells from the blood cells, macrophages, and stromal cells that inevitably contaminate tumor cell suspensions. In some tumors, macrophages comprise the majority of the cells in the tumor cell suspension [42]. As these stromal cells are irrelevant to the clonogenic assay of tumor cell survival, it is important that the cell count be based only on counts of the tumor cells. Trypan blue-stained and Trypan blue-excluding tumor cells should be counted separately, so that the quality of the suspension can be assessed. In our hands, Trypan blue stained cells generally comprise fewer than 10% of the suspended tumor cells: a number higher than this is considered to indicate a potential problem with the preparation of the cell suspension. Although we routinely use a Coulter Counter to count cells in our cell culture and hematology experiments, we do not use this instrument to count primary tumor cell suspensions, because it does not adequately distinguish our tumor cells from macrophages and other stromal cells, and does not provide the quality control inherent in the analysis of Trypan blue staining. As a side comment, this use of Trypan blue demonstrates the inadequacy of Trypan blue exclusion as an assay of tumor cell survival: even when clonogenic cell survival assays show that 1% or less of the cells in irradiated tumors are truly viable, over 90% of the cells exclude Trypan blue soon after treatment. 24.3.3.3 The Importance of Single-Cell Suspensions The tumor cell suspension must also be examined visually and microscopically for the presence of multicell clumps. The rigorous use of a clonogenic assay depends on the fact that the suspension plated for analysis contains only single cells [32, 43, 44]. Small bits of tissue or clumps of cells containing two or more viable tumor cells

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will, of course, form colonies. However, their response to treatment will be different than that of single cells, because it will be necessary to kill every viable cell in the clump to prevent this clump from growing into a colony [32, 43, 44]. Clumps will therefore be dramatically more resistant to treatment with drugs and radiation than single cells would be. If the supposed “single-cell suspensions” prepared from tumors are actually contaminated with multicellular aggregates, the dose–response curves measured using the suspensions will erroneously suggest marked resistance, especially after intensive treatments (Figs. 24.4 and 24.5).

24.3.4 Scheduling Problems in Clonogenic Assays One of the major problems in designing experiments using clonogenic assays is the question of when the assay should be performed [41, 45]. The radiobiologists who originally developed clonogenic assays had a great advantage in this respect, because the time at which a short treatment with X-rays is completed can be defined with certainty as the time the X-ray machine turned off, allowing the assay to be performed, unambiguously, “immediately after treatment.” When clonogenic assays are used to measure the effect of drugs administered to animals, the “end”

Fig. 24.4  Effect of multiplicity on survival curves. The linear survival curve is a model survival curve for a hypothetical cell population, assayed as single cells. The survival curves marked n = 2, n = 4, n = 10, and n = 25 show the theoretical survival curves that would be measured if these cells were assayed in clusters containing 2, 4, 10, or 25 cells, respectively. Reprinted from [43]

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Fig. 24.5  Effect of small numbers of clusters and clumps on a survival curve. The solid line represents the single-cell survival curve replotted from Fig. 24.4. The long dashes show a theoretical survival curves for a suspension composed of 75% single cells, 5% each of clusters containing 2, 4, 10, or 25 cells, and 5% clumps which are large enough to be counted as colonies. The short dashes represent the curve obtained if all large clumps are excluded from the colony count with complete accuracy. Although the majority of the colonies in the untreated cultures arise from single cells, the survival curve at high doses is dominated by the colonies arising from multicell clumps and clusters. Reprinted from [43]

of the treatment is less well defined. The end of the injection of the drug is clearly not the end of treatment. Rather, the duration of the treatment will be determined by the kinetics of the distribution of the drug and the pharmacokinetics of the drug and its active metabolites. For some drugs, the lifetime of the active species is short, and treatment will effectively end minutes after administration of the drug. In other cases, however, cytotoxic species may be present for hours or days, and cells would continue to be killed throughout this period. The decision of when to perform the clonogenic assay is therefore a significant one.

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Complicating this decision is the fact that many different processes occurring in the treated tumors can impact the measurement of tumor cell survival. Cells may repair potentially lethal damage after treatment with radiation or drugs, and cell survival will increase as a result of this repair if the time of assay is delayed for some hours after treatment [46]. Cells will also proliferate after treatment. If the proliferation patterns of the surviving and dying cells are different, the surviving fractions measured by clonogenic assays will be inaccurate when treatment is delayed long enough to allow significant proliferation (24 h or more). Cell death may also complicate measurements of cell survival. For radiation and radiomimetic drugs, the major mechanism of cell death is generally mitotic death, which may occur days after treatment [29–32]. However, in some tumors, apoptosis may be a major cause of death even for these treatments, and loss of these dying cells may take place soon after treatment and complicate cell survival assays [47]. Similarly, it may be difficult to measure the cytotoxicity produced by agents such as hyperthermia or certain drugs that kill cells by rapid pathways involving membrane injury and lysis. With such agents, the dying tumor cells may lyse and disappear from the tumor during or soon after treatment, and the cell suspensions prepared from the tumors would artifactually appear to have high surviving fractions unless careful analyses of the cell yields had been performed to detect the fact that cells had died and disappeared before the suspension was counted [48]. Analyses of the yields of cells from treated and control tumors can help to identify and correct for changes in the number of tumor cells after treatment. However, these analyses also raise experimental problems [48]. First, they depend absolutely on the assumption that the treated and control tumors are the same size and contain the same number of cells at the time of treatment. Therefore, they require the use of very carefully matched tumors throughout the experiments. Second, the cell yield can vary dramatically with small variations in the suspension procedure, and cell yield measurements are quite variable relative to surviving fraction measurements. Moreover, these analyses will not completely identify and account for the various processes occurring in the treated tumors, and therefore cannot completely correct surviving fractions made several hours or days after treatment. These problems raise significant limitations in the use of clonogenic assays for the measurement of cell survival after treatments with agents that produce very rapid cell death or for measurements after prolonged or fractionated treatment regimens that continue over several days. In such cases, tumor growth or tumor-cure assays may be preferable as assays for tumor response [26].

24.4 Analysis of Cell Survival Data Many articles and books have been written on the analysis of data from clonogenic assays and on the analysis and interpretation of the resulting dose–response curves [32, 49, 50]. Much of this discussion is beyond the scope of this chapter, but a few

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important points of experimental design and analyses should be mentioned. The first is the importance of including appropriate untreated controls in each experiment. In most tumors, many of the cells are inherently not clonogenic. As a result, the “plating efficiency” or “cloning efficiency” (colonies/100 cells plated) is generally well below 100%. The plating efficiency may also vary from experiment to experiment, because of differences in techniques, media, sera, reagents, etc. The surviving fractions of cells from treated tumors should always be analyzed by using the plating efficiencies of cells from control tumors assayed on the same day. When the treatment protocol, as well as the treatment agent, may be stressful or toxic or may have physiologic effects that could alter the viability of the tumor cells, experiments should also include sham- or vehicle-treated controls. Another common problem is the inclusion of “surviving fractions” estimated from plates in which no colonies have developed. As described above, there are numerous experimental problems and artifacts that can prevent colony formation, even when clonogenic cells have been plated. All analyses therefore should be based on actual colony counts, from dishes that have colonies. Some common errors occur in the analysis of cell survival data. One lies in the calculation of error limits. All too frequently, investigators assume that error limits can be calculated by considering the counts in the 3–4 replicate Petri dishes plated from a single-cell suspension as if they were independent points. In fact, these dishes do not represent independent observations: the only source of variability in the dishes is the ability of the investigator to plate equal aliquots of cells from the same suspension into different dishes. Variabilities resulting from heterogeneity in tumors, heterogeneity in treatments, or differences in the preparation of the cell suspension or in the counting process are not considered. Error limits should therefore be based on independent observations of the surviving fractions, preferably made in independent experiments. Another common problem is using arithmetic means, rather than geometric means, to calculate the means of the surviving fractions from several experiments. Surviving fractions are ratios and geometric means are the appropriate means for use with ratios [51]. The use of arithmetic means with ratios leads to an analytic artifact frequently seen in the analysis of survival data after relatively ineffective treatments: the calculated mean surviving fractions will artifactually be greater than 1.0 (i.e. greater than the control value). An example of this is provided in Table 24.1. Moreover, in many experimental therapeutic studies, the surviving fraction varies exponentially with the intensity of treatment. In such cases, the individual surviving fractions determined in replicate experiments are generally log-normally distributed, and the geometric means are again the appropriate metric in such cases; use of arithmetic means would overestimate the true surviving fraction. As described here, clonogenic assays are often used to determine dose–response curves that span surviving fractions extending over two or more orders of magnitude. In such cases, the data should be graphed using logarithmic axes to display the surviving fractions, so that the full range of the dose–response relationship can be visualized. Linear plots and linear analyses do not allow adequate display, comparison or analysis of surviving fractions lower than ~20%.

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Table 24.1  Artifact resulting from the use of arithmetic means to analyze ratios Experiment 1 2 3 4 5 6 Colonies/100 cells, control Colonies/100 cells, treated SF (from mean CE) Geometric mean SF Arithmetic mean SF

100 50 0.5

100 50 0.5

100 50 0.5

50 100 2.0

50 100 2.0

50 100 2.0

Mean 75 75 1.0a 1.0b 1.25c

The mean cloning efficiencies for the control cells and treated cells are identical. Calculating the mean of the surviving fractions as an arithmetic mean leads to the erroneous conclusion that the viability of the treated cultures in these experiments was greater than that of control cultures. This error is seen with remarkable frequency in the literature a  Calculated as the ratio of the means of the cloning efficiencies for control and treated cells (75/75 = 1.0) b  Calculated as the geometric mean of the individual surviving fractions c  Calculated as the arithmetic mean of the individual surviving fractions

24.5 Conclusions The use of clonogenic assays that measure the ability of individual cells to undergo indefinite proliferation produced a revolution in experimental cancer therapy, beginning in the 1950s [1]. This revolution began with the demonstration that a single viable cancer cell was sufficient to cause a tumor, a metastasis, or a recurrence [4], a concept that underlies curative cancer therapy. The similarity of the dose–response curves for cells in culture, tumor cells in  vivo, and clonogenic cells from normal tissues in vivo demonstrated the utility of in vitro studies in the evaluation of anticancer modalities. Since that time, cell survival assays have been used widely and effectively to study the effects of antineoplastic therapies on tumors and normal tissues, and to examine their efficacies, toxicities, and therapeutic ratios in animal model systems. Cell survival assays have proved to be invaluable in the development of improved anticancer therapies. The process of performing rigorous, reproducible studies of the survival of cells from solid tumors in vivo is more complicated than it might at first appear. Careful consideration must be given to a variety of technical factors and to many areas of the experimental design when cell survival assays are used to study the effects of radiation, drugs, or other antineoplastic agents.

References 1. Rockwell S. Experimental radiotherapy a brief history. Radiat Res. 1998;150:S157–69. 2. Puck TT, Marcus PI. A rapid method for viable cell titration and clone production with HeLa cells in tissue culture: the use of x-irradiated cells to supply conditioning factors. Proc Natl Acad Sci USA. 1955;41:432–7. 3. Puck TT, Marcus PI. Action of X rays on mammalian cells. J Exp Med. 1956;103:653–66. 4. Hewitt HB, Wilson CW. A survival curve for mammalian cells irradiated in  vivo. Nature. 1959;183:1060–1.

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5. Hewitt HB. The choice of animal tumors for experimental studies of cancer therapy. Adv Cancer Res. 1978;27:149–200. 6. Kallman RF. Methods for the study of radiation effects on cancer cells. Methods Cancer Res. 1968;4:309–54. 7. Bruce WR, Meeker BE, Valeriote FA. Comparison of the sensitivity of normal hematopoietic and transplanted lymphoma colony-forming cells to chemotherapeutic agents administered in vivo. J Natl Cancer Inst. 1966;37:233–45. 8. Hill RP, Bush RS. A lung colony assay to determine the radiosensitivity of cells of a solid tumour. Int J Radiat Biol. 1969;15:435–44. 9. Barendsen GW, Broerse JJ. Experimental radiotherapy of a rat rhabdomyosarcoma with 15 MeV neutrons and 300 kV X-rays. Effects of single exposures. Eur J Cancer. 1969;5:373–91. 10. Rockwell S. In vivo-in vitro tumor systems: new models for studying the response of tumors to therapy. Lab Anim Sci. 1977;27:831–51. 11. Till JE, McCulloch EA. A direct measurement of the radiation sensitivity of normal mouse bone marrow cells. Radiat Res. 1961;14:213–22. 12. Hendry JH, Potten CS, Moore JV, Hune WJ, editors. Assays of normal tissue injury, and their cellular interpretations. Br J Cancer. 1986;53 Suppl. VII. 13. Rockwell S. Maintenance of tumor systems and appropriate treatment techniques for experimental tumors. In: Kallman RF, editor. Rodent tumors in experimental cancer therapy. New York, NY: Pergamon Press; 1987. p. 29–36. 14. Kallman RF, Rockwell S. Effects of radiation on animal tumor models. In: Becker FF, editor. Cancer: a comprehensive treatise, Vol. 6. New York, NY: Plenum Publishing; 1977. p. 225–79. 15. Rockwell S, Rockwell KR. Mouse models for experimental cancer therapy. In: Conn PM, editor. Source book of models for biomedical research. Totawa, NJ: Humana Press Inc.; 2008. p. 623–30. 16. Keeler CE. The laboratory mouse. Its origin, heredity and culture. Cambridge, MA: Harvard University Press; 1931. 17. Foster HL, Small JD, Fox JG, editors. The mouse in biomedical research. IV. Experimental biology and oncology. New York, NY: Academic Press; 1982. 18. Steel GG, Courtenay VD, Peckham MI. The response to chemotherapy of a variety of human tumor xenografts. Br J Cancer. 1983;47:1–13. 19. Rofstad EK. Human tumor xenografts in radiotherapeutic research. Radiother Oncol. 1985;3:35–46. 20. Taghian AG, Suit HD. Animal systems for translational research in radiation oncology. Acta Oncologica. 1999;38:829–38. 21. Pakes SP, Lu YS, Meunier PC. Factors that complicate animal research. In: Fox JG, Cohen BJ, Loew FM, editors. Laboratory animal medicine. Orlando, FL: Academic Press; 1984. p. 649–66. 22. Hotchin I, Sikora E, Kinch W, Hinman A, Woodall J. Lymphocytic choriomeningitis in a hamster colony causes infection of hospital personnel. Science. 1974;185:1173–4. 23. Mendelsohn ML. The growth fraction: a new concept applied to tumors. Science. 1960;132:1496. 24. Barendsen GW, Roelse H, Hermens AF, Madhuizen HT, Van Peperzeel HA, Rutgers DH. Clonogenic capacity of proliferating and non-proliferating cells of a transplantable rat rhabdomyosarcoma in relation to its radiosensitivity. J Natl Cancer Inst. 1973;51:1521–6. 25. Steel GG. Growth kinetics of tumours. Oxford: Clarendon Press; 1977. 26. Moulder JE, Rockwell S. Comparison of tumor assay methods. In: Kallman RF, editor. Rodent tumor models in experimental cancer therapy. New York, NY: Plenum Press; 1987. p. 272–8. 27. Roper PR, Drewinko B. Comparison of in  vitro methods to determine drug-induced cell lethality. Cancer Res. 1976;36:2183–8. 28. Collier AC, Pritsos CA. The mitochondrial uncouplier dicumarol disrupts the MTT assay. Biochem Pharmacol. 2003;66:281–7.

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29. Elkind MM, Han A, Volz KW. Radiation response of mammalian cells grown in culture. IV. Dose dependence of division delay and post-irradiation growth of surviving and non-surviving Chinese hamster cells. J Natl Cancer Inst. 1963;30:705–21. 30. Hurwitz C, Tolmach LJ. Time-lapse cinemicrographic studies of HeLa S3 cells. Biophys J. 1969;9:607–33. 31. Chu K, Teele N, Dewey MW, Albright N, Dewey WC. Computerized video time lapse study of cell cycle delay and arrest, mitotic catastrophe, apoptosis and clonogenic survival in irradiated 14-3-3s and CDKNIA (p21) knockout cell lines. Radiat Res. 2004;162:270–86. 32. Elkind MM, Whitmore GF. The radiobiology of cultured mammalian cells. New York, NY: Gordon and Breach; 1967. 33. Terasima T, Tolmach LJ. Variations in several responses of HeLa cells to x-irradiation during the division cycle. Biophys J. 1963;3:11–33. 34. Kallman RE. The growth kinetics of clonogenic tumor cells that survive radiation therapy. In: Effects of therapy on biology and kinetics of the residual tumor. Part B: clinical aspects. New York, NY: Wiley Liss Inc; 1990. p. 55–65. 35. Martin DF, Rockwell S, Fischer JJ. Development of an in vitro assay for the survival of cells suspended from BAl 112 rat sarcomas. Eur J Cancer Clin Oncol. 1983;19:791–7. 36. Rosenblum ML, Knebel KD, Wheeler KT, Barker M, Wilson CB. Development of an in vitro assay for the evaluation of in  vivo chemotherapy of a rat brain tumor. In Vitro. 1975;11:264–73. 37. Waymouth C. Obtaining cell suspensions from animal tissues. In: Pretlow TG, Pretlow TP, editors. Cell separation: methods and selected applications, Vol. I. New York, NY: Academic Press; 1982. p. 1–29. 38. Rasey JS, Nelson NJ. Effect of tumor disaggregation on results of in vitro cell survival assay after in  vivo treatment of the EMT6 tumors: X-rays, cyclophosphamide, and bleomycin. In Vitro. 1990;16:547–53. 39. Raaphorst GP, Sapareto SA, Freman ML, Dewey WC. Changes in cellular heat and/or radiation sensitivity observed at various times after trypsinization and plating. Int J Radiat Biol. 1979;35:193–7. 40. Pallavicini MG. Characterization of cell suspensions from solid tumors. In: Kallman RF, editor. Rodent tumor models in experimental cancer therapy. New York, NY: Pergamon Press; 1987. p. 76–81. 41. Wheeler KT, Wallen CA. Timing: an important variable in colony formation assays. In: Kallman RF, editor. Rodent tumor models in experimental cancer therapy. New York, NY: Pergamon Press; 1987. p. 84–89. 42. Stewart CC, Beetham KL. Cytocidal activity and proliferative ability of macrophages infiltrating the EMT6 tumor. Intl J Cancer. 1978;22:152–9. 43. Rockwell S. Effects of clumps and clusters on survival measurements with clonogenic assays. Cancer Res. 1985;45:1601–7. 44. Selby P, Buick RN, Tannock I. A critical appraisal of the “human tumor stem cell assay.” N Engl J Med. 1983;308:129–34. 45. Twentyman PR. Timing of assays: an important consideration in the determination of clonogenic cell survival both in vitro and in vivo. Int J Radiat Oncol Biol Phys. 1979;5:1213–20. 46. Hahn GM, Rockwell S, Kallman RF, Gordon LF, Frindel E. Repair of potentially lethal damage in vivo in solid tumor cells after x-irradiation. Cancer Res. 1974;34:351–4. 47. Olive PL, Durand RE. Apoptosis: an indicator of radiosensitivity in vitro? Int J Radiat Biol. 1997;71:695–707. 48. Stephens TC. Measurement of tumor cell surviving fraction and absolute numbers of clonogens per tumor in excision assays. In: Kallman RF, editor. Rodent tumor models in experimental cancer therapy. New York, NY: Pergamon Press; 1987. p. 90–94. 49. Alper T. Cellular radiobiology. Cambridge UK: Cambridge University Press; 1979. 50. Alper T, ed. Cell survival after low doses of radiation: Theoretical and clinical implications. (Proceedings of the 6th LH Gray Conference), New York, NY: Institute of Physics/Wiley; 1975. 51. Zar JH. Biostatistical analysis. Upper Saddle River, NJ: Prentice-Hall; 1984.

Chapter 25

Apoptosis In Vivo L.C. Stephens, L. Milas, K.K. Ang, K.A. Mason, and R.E. Meyn

Abstract  Apoptosis is a complex and highly regulated process with numerous and varied biological consequences, it is typically described as a sequence of morphological events that can be easily recognized histologically. In fact, the initial identification and subsequent characterization of apoptosis were based on microscopic observations of its occurrence in vivo. In the early 1970’s, an experimental pathologist recognized variations in the morphology of dead cells. He deduced from these observations that the mechanisms for cell death could likewise differ. This Australian pathologist, Professor John Kerr, made these seminal observations and with his colleagues also devised the name apoptosis to distinguish the process from necrosis. Kerr’s sound morphological observations and interpretations based on those observations are the foundations for the explosion of apoptosis research that has occurred since his original observations. As will be detailed in this chapter, apoptosis can be quantified as a response of normal and tumor tissues to various cancer therapies in specimens from animals and patients treated in vivo.

Keywords  Apoptosis • Cancer • Radiation • Mouse models • In vivo

25.1 Introduction Although apoptosis is a complex and highly regulated process with numerous and varied biological consequences, it is typically described as a sequence of morphological events that can be easily recognized histologically. In fact, the initial identification and subsequent characterization of apoptosis were based on microscopic

R.E. Meyn (*) Department of Experimental Radiation Oncology, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Unit 66, Houston, TX 77030, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_25, © Springer Science+Business Media, LLC 2011

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observations of its occurrence in vivo. In the early 1970s, an experimental pathologist recognized variations in the morphology of dead cells. He deduced from these observations that the mechanisms for cell death could likewise differ [1]. This Australian pathologist, Professor John Kerr, made these seminal observations and with his colleagues also devised the name apoptosis to distinguish the process from necrosis [2]. Kerr’s sound morphological observations and interpretations based on those observations are the foundations for the explosion of apoptosis research. His remarks published in 1980 in a symposium proceedings were prophetic not only for radiation biologists but for all research in fundamental cellular processes: “it is possible that an understanding of the general controls of initiation and inhibition of apoptosis at the molecular level may help define the critical targets and ultimate effector mechanisms in radiation-induced cell death” [3]. As will be detailed in this chapter, many aspects of that prediction have come true in the intervening years.

25.2 Recognition and Quantification of Apoptosis 25.2.1 Morphological Assessment In Vivo Morphology of in vivo apoptosis, as initially described by Kerr in conventionally prepared tissue sections, is reliable for recognition and quantification of this mode of cell death. Identification of apoptosis in tissue sections has been facilitated by an immunohistochemical method described by Gavrieli et al. [4] in which DNA breaks in apoptotic nuclei are marked by dUTP-biotin transferred to the free 3¢ end of cleaved DNA. Because terminal deoxynucleotidyl transferase is employed to transfer dUPT-biotin by nick end labeling, the convenient acronym, TUNEL, is used to depict this procedure that has become the standard technique for the study of apoptosis in tissue sections. The in vivo morphology of apoptosis is identical regardless of species, cell, or tissue type, and whether it occurs in normal tissue or tumors. The appearance is not influenced by cause, which encompasses apoptosis associated with physiological processes, pathological conditions, and responses to therapeutic modalities including drugs, ionizing radiation, hyperthermia, and gene therapy. Spontaneous and radiation-induced apoptotic bodies appear the same histologically in all of the normal tissues we have studied, including serous gland, thymus, intestine, and mammary gland. As far as tumors are concerned, the morphology of background and induced apoptosis are the same in carcinomas, lymphomas, and sarcomas of man and animals. With the recognition of the regulation of apoptosis by the expression of oncogenes and tumor suppressor genes, studies of these parameters in conjunction with determination of the proliferation and apoptotic indices have provided insights into the interplay of cell proliferation and cell death on in vivo tumorigenesis. To cite some examples from our own prior work, we have examined these

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relationships in tissues such as the human colorectum [5–7] and rodent models of breast cancer [8–10] and lymphoma [11, 12]. Having emphasized the constancy of the morphology of apoptosis in  vivo an additional critical factor relating to the detection of this mode of cell death is timing. Apoptosis is ephemeral, it occurs rapidly and it is removed quickly. An understanding of the kinetics is critical for successful in vivo investigation of apoptosis. We have utilized radiation in studies of the propensity of normal cells and tumor cells to undergo apoptosis in vivo in large part because of the precision of time zero. Implicit in the light microscopic study of in vivo apoptosis is an appreciation of the distribution, size, shape, and staining characteristics of dead cells. Apoptosis can be seen and distinguished from necrosis in sections of tissue fixed in buffered formalin, processed by conventional methods, embedded in paraffin, and stained by hematoxylin and eosin (H&E). Cell death by apoptosis typically involves scattered individual cells as opposed to necrosis, which can involve confluent groups of cells. Cells undergoing apoptosis shrink and lose contact with neighboring cells so that they are often surrounded by a narrow empty halo. Necrotic cells have a ten­ dency to swell. Apoptotic bodies are smaller than normal cells and necrotic cells. Nuclei in both apoptotic and necrotic cells can be characterized as pyknotic and karyorrhexic. Wyllie et al. [13, 14] propose that the commonality of pyknosis and karyorrhexis may have obscured the recognition of apoptosis as a distinctive process. In apoptosis, the condensed and densely stained pyknotic chromatin becomes packed into smooth round or curved profiles situated in close apposition to the nuclear membrane. These cells shrink into a dense, rounded mass becoming a single apoptotic body or the nucleus may break up, which is known as karyorhexis, and the cell emits processes or buds that contain nuclear fragments surrounded by a narrow rim of cytoplasm. These processes tend to break off and become apoptotic bodies, which may remain free or be phagocytized by macrophages or neighboring cells. A routine H&E is superior to a poor TUNEL for the detection of apoptosis in tissue sections. However, a properly prepared TUNEL section that is suitably counterstained allows one to employ the features used with H&E, namely distribution, structure (nuclear fragment surrounded by a narrow rim of cytoplasm), shape, and size, and have the added parameter of the positive peroxidase reaction to draw attention to apoptotic cells. A good counterstain is important so that morphology can be appreciated and with scrutiny of the morphology in TUNEL sections one can differentiate apoptosis from necrotic cells, autolytic cells, and debris that can all show positive reactions [15]. It is helpful to utilize positive controls, e.g. sections of lymph node with follicular hyperplasia, to develop familiarity with the appearance of positive TUNEL staining.

25.2.2 Quantification of Apoptosis In Vitro Quantification of apoptosis in vitro on the basis of morphological assessment can be done by a variety of techniques including those described above for histological

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sections. However, many different staining options are available including a number of fluorescent indicators of either chromatin configuration, Hoechst 33342 or propidium iodide, or fluorescent-based TUNEL detection of fragmented DNA. Cells growing or placed on microscope slides can then be examined under a fluorescent microscope and detached cells, perhaps more conveniently, by flow cytometry [16, 17]. In addition to detecting apoptosis on the basis of changes at the DNA level, cell surface changes indicative of apoptosis can also be used. The early stages of apoptosis are characterized by cell surface membrane blebbing and translocation of phosphatidyl serine (PS) from the inner to the outer surface of the plasma membrane [18]. The externalized PS can be labeled using a fluorescent tagged binding protein, Annexin V, which can be quantified using fluorescent indicators by either microscopy or flow cytometry [19]. Several manufacturers offer kits based on TUNEL or Annexin V for apoptosis quantification. Prior to the advent of these methods, apoptosis in vitro was routinely assessed on the basis of a very characteristic pattern of DNA fragmentation. The DNA of a cell is enzymatically cleaved by a specific endonuclease during apoptosis into oligonucleotides whose sizes are multiples of the internucleosomal distance, 180 base pairs [20]. These produce a “ladder pattern” when separated by agarose gel electrophoresis. Such a pattern is highly diagnostic of apoptosis in vitro. The shortcomings of this method are that it is not very quantitative and some cell systems are not very efficient in producing this type of low molecular weight DNA fragmentation. It is now understood that the endonuclease responsible for this characteristic DNA fragmentation is activated via a cascade of proteolytic steps mediated by a family of cysteine proteases referred to as caspases [21]. Thus, caspase activity itself is now used as a marker for apoptosis in vitro and kits are available for the detection of this activity as well.

25.3 Apoptosis in Tumor Biology 25.3.1 The Role of Apoptosis in Tumor Development The failure of cells to undergo apoptosis is now associated with the pathogenesis of several human diseases including cancer, autoimmune disorders, and certain viral infections [22]. In the case of cancer, tumors are usually clonal in origin, meaning that they arise from normal cells that have acquired a series of mutations in critical genes that control proliferation, survival, adhesion, and mobility [23]. Thus, several sequential mutations are required to convert a normal cell into a tumor cell. A popular idea is that one of the earliest mutations may involve the dysregulation of proliferation which may in turn allow a clonal expansion of cells that are available as targets for further oncogenic lesions. However, abnormal cell proliferation is probably recognized by the host activating a response to eliminate the nascent tumor cell by triggering its apoptosis. In this case, apoptosis may represent a mechanism for

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protecting the host from inappropriate cell expansion thereby maintaining homeostasis [22]. Eventually, the emerging tumor cell counteracts this attack through additional mutations that suppress or inhibit the apoptotic pathway. Indeed, evading apoptosis has been identified as a hallmark of cancer [24]. Therefore, tumor cells are typically characterized with regard to their genetic lesions by one or more mutations in genes involved in the regulation of apoptosis, i.e. many tumor suppressor genes and oncogenes have critical roles in apoptosis.

25.3.2 Genetic Regulation of Apoptosis Bcl-2 was one of the first genes that was shown to have a major regulatory activity in apoptosis. Originally identified as a result of its location at the site of the t(14:18) chromosome translocation present in human B cell follicular lymphoma, overexpression of Bcl-2 in transgenic animal models was shown to mimic the pattern of human lymphoma development [25]. Other studies demonstrated that Bcl-2 acts to promote cell survival rather than cell proliferation and does this by suppressing cell death [26]. More recently it has been learned that Bcl-2 is the prototypic member of a large and growing family of genes. The protein products of these genes share homology in four conserved domains: BH1, BH2, BH3, and BH4. Interestingly, some members of this family of proteins, i.e. Bax, Bak, and Bcl-XS, are pro-apoptotic, and other members of the family, i.e. Bcl-2, Mcl-1, and Bcl-XL, are antiapoptotic [27]. Although these proteins have function as individual proteins, some of their activities are mediated through dimerization at the site of their BH3 domains. These proteins form hetero- and homo-dimmers in the cell and it was originally suggested that their pro- vs. anti-apoptotic activity appears to be dependent on the ratio of Bax-like to Bcl-2-like composition of the dimers [28]. However, this theory has been challenged recently based on the activities of a third class of Bcl-2 family members that only contain the BH3 domain such as Bim, Bid, Bik, Bad, and PUMA [29]. These BH3-only proteins have specialized functions in regulating the activity of the other Bcl-2 family members. In spite of the fact that Bcl-2’s role in apoptosis was discovered more than 30 years ago, its mechanism is still not completely understood. Clues related to its activity have come through analysis of the DNA sequence of the gene encoding Bcl-2. Many Bcl-2 family members have a hydrophobic stretch of amino acids at the C-terminal region suggesting membrane localization. Indeed, Bcl-2 protein appears to be localized in cellular membranes including the endoplasmic reticulum, nuclear envelope, and mitochondrial membrane [27]. Recently, the focus of research has been on Bcl-2’s possible function in the mitochondrial membrane. The importance of mitochondria in apoptosis was not initially appreciated but it is now generally recognized that mitochondria fulfill an essential role in the execution of apoptosis [30]. Mitochondria respond to apoptosis-inducing signals from other parts of the cell by releasing factors that activate caspases, the cysteine proteases that carry out the degradative part of apoptosis [31]. Cytochrome c is one such

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factor released from the mitochondria in cells undergoing apoptosis. Cytochrome c participates in the activation of caspase 3, one of the caspases central to the apoptotic process. Release of cytochrome c and activation of caspase 3 do not take place in Bcl-2 expressing cells [32]. This ability to effectively block apoptosis not only has a profound influence on tumor cell development as introduced above but also influences cancer therapy. Since many therapeutic agents kill cells via apoptosis, Bcl-2 expressing tumor cells are quite resistant to these modalities [33]. Another very important gene, discovered at about the same time as Bcl-2 and also now recognized to play a very centralized role in controlling apoptosis propensity, to be highlighted in this discussion is p53. p53 is a transcription factor that particularly responds to DNA damage [34]. Normally, p53 protein is expressed at very low levels due to targeted degradation, but the levels of protein rise following irradiation or other DNA-damaging insults forcing the cell into either growth arrest or apoptosis [35]. Cells from transgenic animals, in which both alleles have been knocked out, are unable to carry out G1-arrest or apoptosis after irradiation [36, 37]. As a direct effect of the loss of these functions, cells with nonfunctional p53 have increased risk for malignant transformation on exposure to genotoxic/ carcinogenic agents. p53 is the most frequently mutated gene in human cancer and its role in cancer progression when mutated is apparently related to the inability of cells sustaining DNA damage to be eliminated by apoptosis leading to the idea of p53 as “guardian of the genome” [38]. p53’s role in apoptosis was discovered when cells engineered to overexpress p53 underwent spontaneous apoptosis [39] immediately suggesting a gene therapy strategy for cancer that has been exploited to some success [40]. The ability of p53 to exert a growth arrest in G1 phase is most likely due to the transcription of genes that regulate the cell cycle. One such gene is the cyclin-dependent kinase inhibitor p21 [41]. A role for p53-mediated transcriptional regulation of apoptosis is less clear but the expression of at least one gene that promotes apoptosis, Bax, is known to be controlled by p53 [42]. Furthermore, p53 may repress the transcription of anti-apoptotic genes and there is some evidence to suggest that nontranscriptional mechanisms may also be involved [43]. One of these nontranscriptional mechanisms involves a death-signaling induction of p53 localization to the mitochondrial membrane where it participates in the release of the factors that activate caspases [44]. Although the focus of this section on genes that control apoptosis in tumors has been on two of the most universal, p53 and Bcl-2, numerous other tumor suppressor genes and oncogenes can also be involved in specific types of tumor cells. A long list of such genes would include c-myc, Ras, Akt, PTEN, Rb, and Mdm2 [45]. In addition, another entire class of apoptosis-regulating genes, those encoding the socalled inhibitor of apoptosis proteins has been uncovered that is receiving a great deal of attention in cancer therapeutics [46]. Discussion of these other genes is outside the scope of this chapter but it is hopefully apparent that dysregulation of the apoptotic mode of cell death is a critical event in tumor progression, perhaps as important as dysregulation of controls on cell proliferation. Loss of apoptotic propensity confers a subsequent tumor resistance to cancer treatment modalities that kill cells by apoptosis.

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25.4 Apoptosis in Cancer Therapy 25.4.1 Response of Normal Tissue to Cytotoxic Therapy Investigation of apoptotic cell death in vivo in normal organs has been the subject of specific research and as a byproduct of cytotoxic therapy of tumors. For all modalities of cancer treatment, and in particular, localized and systemic cytotoxic therapies, there is significant concern for concurrent injury of normal tissue. It is now well established that death of tumor cells and normal cells by apoptosis is a major response to virtually all cancer therapy modalities including radiotherapy, chemotherapy, immunotherapy, hyperthermia, hormone ablation, photodynamic therapy, and, most recently, gene therapy. However, the contribution of apoptosis in determining the curability of tumors and the sensitivity of normal tissues to exposure to the cytotoxic insults are largely unknown. Better understanding of apoptosis in tumors and normal tissues should lead to improvement of treatment regimens. Chemotherapy or radiation can accentuate the apoptotic elimination of cells from normal tissues comprised either of rapidly proliferating cells or long-lived cell populations. Apoptotic bodies are observed normally in lymphoid germinal centers and in the involuting thymic cortex. Lymphocyte apoptosis is increased greatly by glucocorticoids, chemotherapy, and radiation. Nonlymphoid tissues whose structure is regulated by apoptosis include glandular epithelium in which hormones or growth factors control hyperplasia and involution (e.g. mammary gland, uterus, and prostate), complex differentiated epithelium with long-lived stem cells (e.g. skin and intestine), and rapidly proliferating cell populations (e.g. bone marrow and gonads). These tissues, like lymphoid tissues, display increased apoptosis when exposed to cytotoxic injury. Our initial interest in apoptosis was based on the investigation of a common problem encountered by our clinical colleagues in patients receiving radiotherapy for cancer of the head and neck regions. The acute clinical responses of salivary glands and lacrimal glands are similar. When radiation treatment fields encompass the major salivary glands or lacrimal glands, many patients experience dryness of oral or conjunctival mucous membranes during the first week or two of therapy. The associated complications are distressing and sometimes serious for afflicted patients. The need to understand the pathogenesis of these sequelae in these patients prompted our investigations in a primate model. We found that differentiated serous acinar cells of salivary glands and lacrimal glands die by apoptosis after radiation exposure and loss of these cells by apoptosis is responsible for many of the oral and ocular complications experienced by patients receiving radiotherapy for cancer of the head and neck [47–51]. As we have shown in the major serous glands, it has become well established that enhanced apoptosis, in sites like lymphoid tissues, crypt cells of the gut, hair follicles, bone marrow, and testicles, is responsible for many of the adverse side effects cancer patients experience because it is a certainty that extensive apoptosis is damaging to the normal function of these tissues [52–55].

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The mammary gland is comprised of ducts and acini like salivary glands and lacrimal glands but mammary acinar cells do not display the remarkable extent of apoptosis that serous cells exhibit following irradiation. However, propensity for apoptosis or lack of proclivity to undergo apoptosis is being studied in the mammary gland in the context of tumorigenesis. As mentioned in Sect. 25.3.2, the tumor suppressor gene p53 maintains the integrity of the genome by stimulating apoptosis in cells that have sustained DNA damage. The p53 gene is frequently altered in human cancers, including breast cancer. We used radiation as a DNA-damaging agent to test the role of p53 in controlling apoptosis in preneoplastic mouse mammary glands [8, 9]. The results of in vivo experiments were consistent with the hypothesis that normal p53 function is important if mammary cells with DNA damage are to be deleted by apoptosis. Additional in vivo experiments reinforcing the central roles of p53 and other proteins like p21 in fundamental radiation responses, including cell growth arrest and apoptosis used knockout mice that were null for the p53 or p21 alleles to study the response of skin to radiation [54]. They demonstrated that the radiation-induced apoptosis in hair follicles was fully dependent on p53, and growth arrest in the epidermis was only partially dependent on p53 but fully dependent on p21. If genetic polymorphisms in humans that determine levels of cytotoxic therapyinduced damage to normal tissue were identified, predictive assays might be developed to ascertain which patients are likely to suffer unacceptable injury from standard treatment doses. Identification and quantification of in vivo apoptosis has been the basis of research directed at understanding the effects of genetic polymorphisms on radiosensitivity of the intestine [56] and thymus [57, 58]. Radiation exposure of the intestine results in rapid apoptotic death of cells lining the crypts [52]. This accounts for acute malabsorption syndromes that are regularly encountered following abdominal irradiation in clinical radiation oncology practice. But, as is the case for most normal tissue reactions to radiation, interindividual differences in response and sensitivity are frequently observed. Differences in susceptibility to crypt cell apoptosis between strains of inbred mice indicate that heritability plays a role in the variability of apoptosis in these animals and supports the notion that genetic predisposition influences the response of patients [56]. Subsequent studies identified a chromosome 15 quantitative trait locus that controls levels of radiation-induced apoptosis in the mouse jejunal crypt [59]. Although no adverse clinical syndromes are associated with irradiation of the thymus, study of in  vivo apoptosis in the thymus confirms further the heritability of the propensity for apoptosis in another cell type and indicates the suitability of the murine model for identifying genes controlling apoptotic cell death [57, 58].

25.4.2 Apoptosis in Tumors Responding to Cytotoxic Therapy The occurrence of apoptosis in solid tumors responding to cytotoxic treatments in vivo was initially demonstrated many years ago. Searle et al. reported in 1975

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that certain chemotherapy agents induced apoptosis in model tumors growing in mice [60]. The work in this area prior to 1980 was reviewed by Kerr and Searle and in that article they illustrate examples of apoptosis following irradiation of a model tumor in vivo [3]. These seminal observations by Kerr and his colleagues stimulated our interest in systematically assessing apoptosis in solid tumors treated in  vivo with various cancer therapeutic agents. To that end, we quantified apoptosis in a variety of transplantable murine tumors treated in  vivo with several different chemotherapy agents and ionizing radiation in several different studies. In all of these investigations, the percentage of cells with the features of apoptosis was determined from H&E stained histological sections of the treated tumors using the morphological criteria described in Sect. 25.2.1. Since very little had been done up until the time we initiated our investigations, the intent of our first study was to simply establish whether apoptosis was a feature of irradiated tumors [61]. Thus, two transplantable murine tumors were chosen based on prior data showing that the hepatocarcinoma, HCa-1, was very resistant to radiation, having a TCD50 (the single dose of radiation required to cure 50% of tumors) of >80 Gy, and that an ovarian adenocarcinoma, OCa-1, was moderately sensitive, having a TCD50 of about 53 Gy. Tumors growing in the hind legs of mice were treated with a series of high doses of radiation and followed for relatively long times after irradiation as we had no preconception about the dose response and kinetics for radiation-induced apoptosis in vivo. The results showed that apoptosis occurred in the OCa-1 tumor but not in the HCa-1 tumor. In the OCa-1 tumor, the maximum percentage of apoptosis occurred at 6 h and fell with longer times. The dose response was already on a plateau with the lowest dose used, 25 Gy. The findings prompted a more detailed examination of apoptosis in the OCa-1 model [62]. There we found that the dose response for radiation-induced apoptosis plateaus at about 30–35% apoptotic cells following doses of 7.5  Gy or more. In addition, the apoptosis peaks very soon after irradiation, about 4  h, and then falls off dramatically. These first two series of experiments allowed the following conclusions to be drawn: (a) some tumors are susceptible to apoptosis but others are not; (b) apoptotic index peaks quickly after irradiation and then falls as the apoptotic bodies are phagocytosed; (c) low doses of radiation preferentially induce apoptosis; (d) there is a relatively large proportion of cells that are apparently resistant to apoptosis even within tumors that display apoptosis after irradiation. These conclusions were borne out in an expanded examination of 15 different murine tumors where we wanted to get a clearer picture about the heterogeneity in response [63]. In that study, we confirmed that some types of tumors, namely adenocarcinomas of the mammary gland and ovaries and lymphomas, display an apoptotic response to radiation in vivo whereas other types of tumors, namely squamous cell carcinomas, hepatocarcinomas, and fibrosarcomas, do not. Fortunately, other laboratory data related to the radiation response of these tumors were available allowing us to determine correlations of in vivo apoptotic response to tumor response. This analysis indicated that when radiation-induced apoptosis for all of the tumors was plotted against the respective tumor’s TCD50 value and specific growth delay, those tumors

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that responded by apoptosis tended to have lower TCD50 values (0.1 < P < 0.2) and longer specific growth delays (P < 0.05). Interestingly, the most significant correlation was produced when the spontaneous levels of apoptosis present in untreated tumors were plotted against the levels of radiation-induced apoptosis for each given tumor (P  <  0.001). This suggests that apoptosis levels assessed prior to treatment may predict treatment response and we subsequently tested that hypothesis in other studies of specimens from patients who had received radiotherapy for bladder cancer [64], carcinoma of the cervix [65], or lymphoma [66]. Although, on the surface, these correlations might indicate that apoptosis was important in tumor response to radiation, they do not prove such a relationship. Moreover, as discussed in detail elsewhere, the fact that under the best of conditions solid tumors in this series only achieved 30–35% apoptotic indices indicates that radiation-induced apoptosis cannot account for the sensitivity of the tumors and other modes of cell death must also be involved. This does not rule out a role for apoptosis in tumor response but suggests that apoptosis is perhaps only one of several mechanisms responsible for tumor response to therapy. In light of the interesting results described above for apoptosis assessed in irradiated murine tumors, we focused some attention on apoptosis induced in tumors treated in vivo by chemotherapy agents. In the first of these studies, we examined two murine tumors previously shown above to be sensitive to radiation-induced apoptosis, MCa-4 and OCa-1, for apoptotic response to cyclophosphamide (CY) using the same methodological approach [67]. The kinetics of apoptosis development was determined as a function of time after treatment with single injections of the mice with 200 mg/kg. The apoptotic index peaked between 10 and 18 h in both tumor models and then slowly declined to background levels by five days. The dose–response relationships illustrated that apoptosis could be observed at even much lower doses of CY. A very similar analysis to this was repeated using the same tumor models with another chemotherapy agent, cisplatin (CP) [68]. As with CY, the kinetics of CP-induced apoptosis was very broad, peaking between 10 and 20  h then declining to background levels by five days. For both CY and CP, the dose–response curves for apoptosis induction did not correlate well with the tumor growth delay measurements made on tumors treated with the same doses, i.e. substantial apoptosis was observed under conditions where that dose of drug only produced a slight delay in growth. Higher doses of drug did not greatly enhance apoptosis but did enhance growth delay and cause tumor regression. Thus, we concluded, similar to the case of radiation, that apoptosis may be important in tumor response to these chemotherapy agents but that it may not be the only parameter that governs response. Indeed, we speculated that since the kinetics of apoptosis is spread out over such a long time, factors such as tumor cell proliferation may also come into play. To extend the investigation of apoptosis in vivo as a response to cancer chemotherapy agents, we completed a series of experiments where we compared the apoptotic response of the MCa-4 and OCa-1 tumors to eight different agents [69]. In addition, we compared seven different murine tumors for their apoptotic response to CY, CP, and radiation. The chemotherapy drugs used for the first part of this analysis were CY, CP, adriamycin, 5-FU, ara-c, etoposide, camptothecin, melphalan,

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and fludarabine. All of these agents produced substantial apoptosis in both MCa-4 and OCa-1 tumors at the doses used and times measured, 8 and 24 h. Two of the agents, camptothecin and etoposide, appeared to be especially potent. The other part of the analysis, where the apoptotic responses of seven different tumors were compared, produced a striking pattern. Tumors previously shown to be responsive to radiation, MCa-4, OCa-1, and the TH lymphoma, also had an apoptotic response to CY and CP and tumors previously shown to be resistant to radiation-induced apoptosis, SSC-7, FSa, NFSa, and Sa-NH, were cross-resistant to CY and CP. This observation is certainly consistent with the theory presented in Sect. 25.3.2 suggesting that intrinsic factors, such as expression patterns of tumor suppressor genes and oncogenes control apoptosis propensity and may, thereby, at least partially influence tumor response to therapy. The caveat to this theory was also illustrated in this same paper because several of the tumors that displayed no apoptotic response to either CP or CY did display a growth delay following the same treatments. Thus, other factors in addition to apoptosis must be important in determining tumor response to chemotherapy.

25.4.3 In Vivo Imaging of Apoptosis As mentioned above with regard to our prior in  vivo investigations, it has been difficult to prove that the apoptosis measured in specimens from experimental tumors in mice treated with cancer therapeutics is responsible for the response of the tumors to the therapies. At best, we observed some interesting correlations with radiation response in both model tumors and specimens from human tumors. However, based on the fact that irradiated cells may die by mechanisms other than apoptosis, some have suggested that, except for hematological malignancies, apoptosis may not be playing a critical role in the response of human cancers to radiation [70, 71]. Thus, in a recent review, we reassessed the role of apoptosis in radiation oncology [72]. One conclusion from this review was that, even if apoptosis only correlated with total radiation-induced cell death, it could serve as a useful biomarker for response if it could be assessed noninvasively early in the course of radiotherapy. With this idea in mind, a number of investigators have developed approaches for imaging apoptosis in vivo. Two basic approaches have been applied for noninvasively assessing apoptosis in animal and human models of cancer. As an example of the first approach, stable cell lines of the human D54 glioma were made that had been transfected with a hybrid luciferase reporter construct that is inactive until cleaved by activated caspase 3 [73]. Mice bearing the D54 glioma xenograft tumor were treated with the combination of temozolomide and radiation and the mice were imaged using a very sensitive camera system after luciferin administration. This bioluminescence technique appears to be very sensitive and quantitative but limited to animal models. The second approach involves detecting the binding of Annexin V to cells undergoing apoptosis using 99mTc-labeled Annexin V scintigraphy (TAVS) [74]. This technique

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has been used in several clinical studies of patients treated with chemotherapy, radiotherapy, or combinations [75–77]. Generally, there have been positive correlations between the uptake of Annexin V as measured using TAVS and patient response to the therapies [74, 78]. TAVS also has shown utility in animal studies [79].

25.5 Summary and Conclusions In this chapter, we have presented a broad overview of apoptosis as it relates to in vivo systems with special emphasis on tumor models used in cancer research. The biochemical and molecular aspects of the apoptosis pathway have necessarily been investigated using cultured cell systems growing in vitro. Such mechanistic questions are difficult to address using in vivo models. Thus, in general, much less has been done with regard to assessing apoptosis in vivo. Although we have taken the opportunity to highlight some of our own work in this chapter, cancer researchers now generally recognize the need to extrapolate from the in  vitro studies to the more clinically relevant in  vivo situation. Realizing that apoptosis occurs in response to therapeutic treatments, the critical question remains finding out whether this apoptotic response represents a determinant of therapeutic response. Hopefully, the new techniques of noninvasive in  vivo imaging of apoptosis will ultimately answer this question. Moreover, as we discussed in Sect. 25.3, tumor cells are most likely resistant to apoptosis mediated by cytotoxic agents because they have turned off this pathway for cell deletion as part of their progression to malignancy. Therefore, strategies designed to restore apoptosis to resistant tumors are currently being explored in a number of laboratories. These would include the use of socalled death cytokines such as TRAIL [80], gene-therapy strategies that restore wild-type p53 function [81, 82], or molecularly targeted agents that block the antiapoptotic activity of Bcl-2 to cite just a few examples [83]. When used in combination with conventional therapeutic agents, radiation and chemotherapy, these new strategies may enhance apoptosis in a synergistic manner leading to more efficacious responses to cancer treatments. Acknowledgments  This work was supported in part by grant PO1 CA06294 from the National Cancer Institute, the Wiegand Foundation, the Gilbert H. Fletcher Chair (KKA), the Kathryn O’Connor Research Professorship (REM), and the United Energy Resources Professorship in Cancer Research (LM).

References 1. Kerr JF. Shrinkage necrosis: a distinct mode of cellular death. J Pathol. 1971;105(1):13–20. 2. Kerr JF, Wyllie AH, Currie AR. Apoptosis: a basic biological phenomenon with wide-ranging implications in tissue kinetics. Br J Cancer. 1972;26(4):239–57. 3. Kerr J, Searle J. Apoptosis: its nature and kinetic role. In: Meyn R, Withers H, editors. Radiation biology in cancer research. New York: Raven Press; 1980. p. 367–84.

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4. Gavrieli Y, Sherman Y, Ben-Sasson SA. Identification of programmed cell death in situ via specific labeling of nuclear DNA fragmentation. J Cell Biol. 1992;119(3):493–501. 5. Sinicrope FA, Ruan SB, Cleary KR, Stephens LC, Lee JJ, Levin B. bcl-2 and p53 oncoprotein expression during colorectal tumorigenesis. Cancer Res. 1995;55(2):237–41. 6. Sinicrope FA, Roddey G, McDonnell TJ, Shen Y, Cleary KR, Stephens LC. Increased apoptosis accompanies neoplastic development in the human colorectum. Clin Cancer Res. 1996;2(12):1999–2006. 7. Sinicrope FA, Roddey G, Lemoine M, et  al. Loss of p21WAF1/Cip1 protein expression accompanies progression of sporadic colorectal neoplasms but not hereditary nonpolyposis colorectal cancers. Clin Cancer Res. 1998;4(5):1251–61. 8. Meyn RE, Stephens LC, Mason KA, Medina D. Radiation-induced apoptosis in normal and pre-neoplastic mammary glands in vivo: significance of gland differentiation and p53 status. Int J Cancer. 1996;65(4):466–72. 9. Medina D, Stephens LC, Bonilla PJ, et al. Radiation-induced tumorigenesis in preneoplastic mouse mammary glands in  vivo: significance of p53 status and apoptosis. Mol Carcinog. 1998;22(3):199–207. 10. Sivaraman L, Stephens LC, Markaverich BM, et  al. Hormone-induced refractoriness to mammary carcinogenesis in Wistar-Furth rats. Carcinogenesis. 1998;19(9):1573–81. 11. Marin MC, Hsu B, Stephens LC, Brisbay S, McDonnell TJ. The functional basis of c-myc and bcl-2 complementation during multistep lymphomagenesis in  vivo. Exp Cell Res. 1995;217(2):240–7. 12. Hsu B, Marin MC, el-Naggar AK, Stephens LC, Brisbay S, McDonnell TJ. Evidence that c-myc mediated apoptosis does not require wild-type p53 during lymphomagenesis. Oncogene. 1995;11(1):175–9. 13. Wyllie AH, Kerr JF, Currie AR. Cell death: the significance of apoptosis. Int Rev Cytol. 1980;68:251–306. 14. Wyllie AH. Apoptosis and the regulation of cell numbers in normal and neoplastic tissues: an overview. Cancer Metastasis Rev. 1992;11(2):95–103. 15. Grasl-Kraupp B, Ruttkay-Nedecky B, Koudelka H, Bukowska K, Bursch W, Schulte-Hermann R. In situ detection of fragmented DNA (TUNEL assay) fails to discriminate among apoptosis, necrosis, and autolytic cell death: a cautionary note. Hepatology. 1995;21(5):1465–8. 16. Dive C, Gregory CD, Phipps DJ, Evans DL, Milner AE, Wyllie AH. Analysis and discrimination of necrosis and apoptosis (programmed cell death) by multiparameter flow cytometry. Biochim Biophys Acta. 1992;1133(3):275–85. 17. Gorczyca W, Gong J, Darzynkiewicz Z. Detection of DNA strand breaks in individual apoptotic cells by the in situ terminal deoxynucleotidyl transferase and nick translation assays. Cancer Res. 1993;53(8):1945–51. 18. Fadok VA, Voelker DR, Campbell PA, Cohen JJ, Bratton DL, Henson PM. Exposure of phosphatidylserine on the surface of apoptotic lymphocytes triggers specific recognition and removal by macrophages. J Immunol. 1992;148(7):2207–16. 19. Koopman G, Reutelingsperger CP, Kuijten GA, Keehnen RM, Pals ST, van Oers MH. Annexin V for flow cytometric detection of phosphatidylserine expression on B cells undergoing apoptosis. Blood. 1994;84(5):1415–20. 20. Wyllie AH. Glucocorticoid-induced thymocyte apoptosis is associated with endogenous endonuclease activation. Nature. 1980a;284(5756):555–6. 21. Cohen GM. Caspases: the executioners of apoptosis. Biochem J. 1997;326(Pt 1):1–16. 22. Thompson CB. Apoptosis in the pathogenesis and treatment of disease. Science 1995;267(5203):1456–62. 23. Evan G. Cancer – a matter of life and cell death. Int J Cancer. 1997;71(5):709–11. 24. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100(1):57–70. 25. McDonnell TJ, Korsmeyer SJ. Progression from lymphoid hyperplasia to high-grade malignant lymphoma in mice transgenic for the t(14; 18). Nature. 1991;349(6306):254–6. 26. Vaux DL, Cory S, Adams JM. Bcl-2 gene promotes haemopoietic cell survival and cooperates with c-myc to immortalize pre-B cells. Nature. 1988;335(6189):440–2.

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27. Adams JM. Ways of dying: multiple pathways to apoptosis. Genes Dev. 2003;17(20):2481–95. 28. Korsmeyer SJ, Shutter JR, Veis DJ, Merry DE, Oltvai ZN. Bcl-2/Bax: a rheostat that regulates an anti-oxidant pathway and cell death. Semin Cancer Biol. 1993;4(6):327–32. 29. Cory S, Adams JM. The Bcl2 family: regulators of the cellular life-or-death switch. Nat Rev Cancer. 2002;2(9):647–56. 30. Susin SA, Zamzami N, Kroemer G. Mitochondria as regulators of apoptosis: doubt no more. Biochim Biophys Acta. 1998;1366(1–2):151–65. 31. Liu X, Kim CN, Yang J, Jemmerson R, Wang X. Induction of apoptotic program in cell-free extracts: requirement for dATP and cytochrome c. Cell. 1996;86(1):147–57. 32. Yang J, Liu X, Bhalla K, et al. Prevention of apoptosis by Bcl-2: release of cytochrome c from mitochondria blocked. Science. 1997;275(5303):1129–32. 33. Voehringer DW, Meyn RE. Reversing drug resistance in bcl-2-expressing tumor cells by depleting glutathione. Drug Resist Updat. 1998;1(6):345–51. 34. Lu X, Lane DP. Differential induction of transcriptionally active p53 following UV or ionizing radiation: defects in chromosome instability syndromes? Cell. 1993;75(4):765–78. 35. Kastan MB, Onyekwere O, Sidransky D, Vogelstein B, Craig RW. Participation of p53 protein in the cellular response to DNA damage. Cancer Res. 1991;51(23 Pt 1):6304–11. 36. Lowe SW, Schmitt EM, Smith SW, Osborne BA, Jacks T. p53 is required for radiationinduced apoptosis in mouse thymocytes. Nature. 1993;362(6423):847–9. 37. Clarke AR, Purdie CA, Harrison DJ, et al. Thymocyte apoptosis induced by p53-dependent and independent pathways. Nature. 1993;362(6423):849–52. 38. Lane DP. Cancer. p53, guardian of the genome. Nature. 1992;358(6381):15–6. 39. Yonish-Rouach E, Resnitzky D, Lotem J, Sachs L, Kimchi A, Oren M. Wild-type p53 induces apoptosis of myeloid leukaemic cells that is inhibited by interleukin-6. Nature. 1991;352(6333):345–7. 40. Spitz FR, Nguyen D, Skibber JM, Meyn RE, Cristiano RJ, Roth JA. Adenoviral-mediated wild-type p53 gene expression sensitizes colorectal cancer cells to ionizing radiation. Clin Cancer Res. 1996;2(10):1665–71. 41. el-Deiry WS, Tokino T, Velculescu VE, et al. WAF1, a potential mediator of p53 tumor suppression. Cell. 1993;75(4):817–25. 42. Miyashita T, Reed JC. Tumor suppressor p53 is a direct transcriptional activator of the human bax gene. Cell. 1995;80(2):293–9. 43. Gottlieb TM, Oren M. p53 and apoptosis. Semin Cancer Biol. 1998;8(5):359–68. 44. Green DR, Kroemer G. Cytoplasmic functions of the tumour suppressor p53. Nature. 2009;458(7242):1127–30. 45. Johnstone RW, Ruefli AA, Lowe SW. Apoptosis: a link between cancer genetics and chemotherapy. Cell. 2002;108(2):153–64. 46. Schimmer AD. Inhibitor of apoptosis proteins: translating basic knowledge into clinical practice. Cancer Res. 2004;64(20):7183–90. 47. Stephens LC, King GK, Peters LJ, Ang KK, Schultheiss TE, Jardine JH. Acute and late radiation injury in rhesus monkey parotid glands. Evidence of interphase cell death. Am J Pathol. 1986;124(3):469–78. 48. Stephens LC, King GK, Peters LJ, Ang KK, Schultheiss TE, Jardine JH. Unique radiosensitivity of serous cells in rhesus monkey submandibular glands. Am J Pathol. 1986a;124(3):479–87. 49. Stephens LC, Ang KK, Schultheiss TE, King GK, Brock WA, Peters LJ. Target cell and mode of radiation injury in rhesus salivary glands. Radiother Oncol. 1986b;7(2):165–74. 50. Stephens LC, Schultheiss TE, Price RE, Ang KK, Peters LJ. Radiation apoptosis of serous acinar cells of salivary and lacrimal glands. Cancer. 1991;67(6):1539–43. 51. Stephens LC, Schultheiss TE, Peters LJ, Ang KK, Gray KN. Acute radiation injury of ocular adnexa. Arch Ophthalmol. 1988;106(3):389–91. 52. Hendry JH, Potten CS. Intestinal cell radiosensitivity: a comparison for cell death assayed by apoptosis or by a loss of clonogenicity. Int J Radiat Biol Relat Stud Phys Chem Med. 1982;42(6):621–8.

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53. Cece R, Cazzaniga S, Morelli D, et  al. Apoptosis of hair follicle cells during doxorubicininduced alopecia in rats. Lab Invest. 1996;75(4):601–9. 54. Song S, Lambert PF. Different responses of epidermal and hair follicular cells to radiation correlate with distinct patterns of p53 and p21 induction. Am J Pathol. 1999;155(4):1121–7. 55. Kerr JF, Winterford CM, Harmon BV. Apoptosis. Its significance in cancer and cancer therapy. Cancer. 1994;73(8):2013–26. 56. Weil MM, Stephens LC, Amos CI, Ruifrok AC, Mason KA. Strain difference in jejunal crypt cell susceptibility to radiation-induced apoptosis. Int J Radiat Biol. 1996;70(5):579–85. 57. Weil MM, Amos CI, Mason KA, Stephens LC. Genetic basis of strain variation in levels of radiation-induced apoptosis of thymocytes. Radiat Res. 1996a;146(6):646–51. 58. Weil MM, Xia X, Lin Y, Stephens LC, Amos CI. Identification of quantitative trait loci controlling levels of radiation-induced thymocyte apoptosis in mice. Genomics. 1997;45(3):626–8. 59. Weil MM, Xia C, Xia X, Gu X, Amos CI, Mason KA. A chromosome 15 quantitative trait locus controls levels of radiation-induced jejunal crypt cell apoptosis in mice. Genomics. 2001;72(1):73–7. 60. Searle J, Lawson TA, Abbott PJ, Harmon B, Kerr JF. An electron-microscope study of the mode of cell death induced by cancer-chemotherapeutic agents in populations of proliferating normal and neoplastic cells. J Pathol. 1975;116(3):129–38. 61. Stephens LC, Ang KK, Schultheiss TE, Milas L, Meyn RE. Apoptosis in irradiated murine tumors. Radiat Res. 1991a;127(3):308–16. 62. Stephens LC, Hunter NR, Ang KK, Milas L, Meyn RE. Development of apoptosis in irradiated murine tumors as a function of time and dose. Radiat Res. 1993;135(1):75–80. 63. Meyn RE, Stephens LC, Ang KK, et  al. Heterogeneity in the development of apoptosis in irradiated murine tumours of different histologies. Int J Radiat Biol. 1993;64(5):583–91. 64. Chyle V, Pollack A, Czerniak B, et al. Apoptosis and downstaging after preoperative radiotherapy for muscle-invasive bladder cancer. Int J Radiat Oncol Biol Phys. 1996;35(2):281–7. 65. Wheeler JA, Stephens LC, Tornos C, et al. ASTRO Research Fellowship: apoptosis as a predictor of tumor response to radiation in stage IB cervical carcinoma. American Society for Therapeutic Radiology and Oncology. Int J Radiat Oncol Biol Phys. 1995;32(5):1487–93. 66. Logsdon MD, Meyn RE, Jr., Besa PC, et al. Apoptosis and the Bcl-2 gene family – patterns of expression and prognostic value in stage I and II follicular center lymphoma. Int J Radiat Oncol Biol Phys. 1999;44(1):19–29. 67. Meyn RE, Stephens LC, Hunter NR, Milas L. Induction of apoptosis in murine tumors by cyclophosphamide. Cancer Chemother Pharmacol. 1994;33(5):410–4. 68. Meyn RE, Stephens LC, Hunter NR, Milas L. Kinetics of cisplatin-induced apoptosis in murine mammary and ovarian adenocarcinomas. Int J Cancer. 1995;60(5):725–9. 69. Meyn RE, Stephens LC, Hunter NR, Milas L. Apoptosis in murine tumors treated with chemotherapy agents. Anticancer Drugs. 1995a;6(3):443–50. 70. Brown JM, Wilson G. Apoptosis genes and resistance to cancer therapy: what does the experimental and clinical data tell us? Cancer Biol Ther. 2003;2(5):477–90. 71. Brown JM, Attardi LD. The role of apoptosis in cancer development and treatment response. Nat Rev Cancer. 2005;5(3):231–7. 72. Meyn RE, Milas L, Ang KK. The role of apoptosis in radiation oncology. Int J Radiat Biol. 2009;85(2):107–15. 73. Coppola JM, Ross BD, Rehemtulla A. Noninvasive imaging of apoptosis and its application in cancer therapeutics. Clin Cancer Res. 2008;14(8):2492–501. 74. Tait JF. Imaging of apoptosis. J Nucl Med. 2008;49(10):1573–6. 75. Belhocine T, Steinmetz N, Hustinx R, et al. Increased uptake of the apoptosis-imaging agent (99m)Tc recombinant human Annexin V in human tumors after one course of chemotherapy as a predictor of tumor response and patient prognosis. Clin Cancer Res. 2002;8(9):2766–74. 76. Haas RL, de Jong D, Valdes Olmos RA, et al. In vivo imaging of radiation-induced apoptosis in follicular lymphoma patients. Int J Radiat Oncol Biol Phys. 2004;59(3):782–7. 77. Verheij M. Clinical biomarkers and imaging for radiotherapy-induced cell death. Cancer Metastasis Rev. 2008;27(3):471–80.

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7 8. Blankenberg FG. In vivo imaging of apoptosis. Cancer Biol Ther. 2008;7(10):1525–32. 79. Belhocine T, Steinmetz N, Li C, Green A, Blankenberg FG. The imaging of apoptosis with the radiolabeled annexin V: optimal timing for clinical feasibility. Technol Cancer Res Treat. 2004;3(1):23–32. 80. Belka C, Jendrossek V, Pruschy M, Vink S, Verheij M, Budach W. Apoptosis-modulating agents in combination with radiotherapy-current status and outlook. Int J Radiat Oncol Biol Phys. 2004;58(2):542–54. 81. Vecil GG, Lang FF. Clinical trials of adenoviruses in brain tumors: a review of Ad-p53 and oncolytic adenoviruses. J Neurooncol. 2003;65(3):237–46. 82. Swisher SG, Roth JA, Komaki R, et al. Induction of p53-regulated genes and tumor regression in lung cancer patients after intratumoral delivery of adenoviral p53 (INGN 201) and radiation therapy. Clin Cancer Res. 2003;9(1):93–101. 83. Fesik SW. Promoting apoptosis as a strategy for cancer drug discovery. Nat Rev Cancer. 2005;5(11):876–85.

Chapter 26

Transparent Window Models and Intravital Microscopy: Imaging Gene Expression, Physiological Function and Therapeutic Effects in Tumors Rakesh K. Jain, Lance L. Munn, and Dai Fukumura Abstract  Currently, gene expression, physiological function and treatment efficacy are typically determined by techniques that are either destructive or have poor spatial resolution (millimeter to centimeter). The former have limited ability to provide insight into the dynamics and the latter preclude visualization at the cellular and sub-cellular levels (1-10 micrometers). Intravital microscopy of tumors growing in various organs overcomes these limitations and offers powerful insight into tumor pathophysiology and treatment. Furthermore, the recent availability of in vivo reporters such as GFP as well as transgenic mice and cell lines is likely to present new opportunities for discoveries. There are three broad categories of tissue preparations, which can accommodate intravital microscopopy: chronic-transparent chambers, acute (exteriorized) tissue preparations and in situ preparations. Each of these preparations can be used to study normal tissue, an implanted tumor or a tissue construct containing growth factor(s) or engineered cells to study angiogenesis. In this chapter, we will briefly describe historical perspective, surgical procedures and strengths and weaknesses for various tissue preparations. We then outline various intravital microscopy techniques and the computer-assisted image analyses. Finally, we highlight key insights obtained from such approaches and the possibilities that lay ahead.

26.1 Introduction The past 30 years have witnessed spectacular advances in our understanding of the molecular origins of cancer and other diseases. These advances have led to the identification of various genes associated with angiogenesis and oncogenesis as well

R.K. Jain (*) Edwin L Steele Laboratory, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, 100 Blossom Street, Boston, MA 02114, USA e-mail: [email protected] B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0_26, © Springer Science+Business Media, LLC 2011

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as to the development of a vast array of therapeutic agents. The grand challenges now are (a) to relate the expression of these genes to their function in an intact organism, and (b) to deliver these novel therapeutics to their targets and make them work in vivo [1]. Currently, gene expression, physiological function, and treatment efficacy are usually measured with techniques that are either destructive or have poor spatial resolution (millimeter to centimeter). The former have limited ability to provide insight into the dynamics and the latter preclude visualization at the cellular and subcellular levels (1–10 mm). Intravital microscopy of tumors in various organs growing in transparent windows overcomes these limitations and offers powerful insight into tumor pathophysiology and treatment. Furthermore, the recent availability of in vivo reporters such as GFP as well as transgenic mice and cell lines is likely to present new opportunities for unexpected findings. Depending upon the thickness of the preparation, either trans- or epi-illumination can be used to visualize all of or only superficial regions of tissue. Based on the method, the tissue preparation can be divided into three broad categories: (a) chronictransparent chambers [e.g. rabbit ear chamber; dorsal skinfold chamber in mice, rats, hamsters, and rabbits; cranial windows in mice and rats; hamster-cheek-pouch-window (Fig. 26.1)]; (b) acute (exteriorized) tissue preparations [e.g. hamster cheek pouch; mouse, rat, or rabbit mesentery; mouse or rat liver; mouse or rat pancreas; air sac in mice and rats (Fig. 26.2)]; and (c) in situ preparations [e.g. chick chorioallantoic membrane (CAM); corneal pocket or iris implant in the eye; mouse ear; mouse tail lymphatics (Fig. 26.3)]. Each of these preparations can be used to study normal

Fig. 26.1  Chronic window preparations. Rabbit ear chamber (a), mouse dorsal skin chamber (b), cranial window (c), and mammary fat pad chamber (d) are used for high-resolution longitudinal observation of tumor growth, angiogenesis, physiological processes, and gene expression

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Fig. 26.2  Acute tissue preparations. Mesentery (a), mouse liver (b), pancreas (c), and mammary fat pad models (d) are used for acute observations and/or organ-specific tumor microcirculation. Tumor size, angiogenesis and physiological parameters, and gene expression are determined by intravital microscopy

Fig. 26.3  In situ preparations. Cornea pocket assay (a) and mouse tail lymphatic model (b) are in situ preparations to study angiogenesis and lymphangiogenesis (adapted from [87])

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tissue or an implanted tumor. The tumor source can be a suspension of cancer cells or a fragment of tumor tissue. For some applications, a gel containing defined growth factor(s) or engineered cells can be implanted in these tissue preparations for the purpose of studying angiogenesis. Each preparation has its strengths and weaknesses. Thus, a combination of several methodologies is normally required to examine the effect of tissue microenvironment on gene expression, physiology, and delivery and efficacy of drugs. In this chapter, each section begins with a brief historical perspective followed by brief descriptions of the surgical procedures for making various tissue preparations.1 We then outline various intravital microscopy techniques and the computer-assisted analyses used to extract parameters of interest from acquired images. Finally, we highlight key insights obtained from such approaches and the possibilities that lay ahead.

26.2 Chronic Window Preparations In 1924, Sandison developed the first transparent window (chamber) for implantation in the ear of a rabbit [2]. This chronic preparation allowed continuous, noninvasive, long-term monitoring of angiogenesis during wound healing [3, 4]. Ide et al. [5] were the first to study angiogenesis in this window, using BrownPearce carcinoma. In the 1940s, Algire adapted the Sandison chamber to the dorsal skin in mice and carried out the pioneering studies of angiogenesis during wound healing and tumor growth [6–9]. Similar chronic windows have been developed for the dorsal skin of other rodents (e.g. rats, hamsters), for the hamster cheek pouch, and for the cranium of the mouse and rat (see Table 26.1 for references). Each of these chronic windows has its advantages and disadvantages. For example, the rabbit ear chamber is perhaps the most optically clear. However, rabbits are expensive to purchase and maintain, and the granulation tissue takes 4–6 weeks to mature before a tumor can be implanted in the window. Mice, hamsters, and rats are less expensive and owing to their smaller body weight require smaller quantities of reagents. From the surgical point of view, rats and hamsters are easier to work with compared to mice, but the latter have many advantages. The easy availability of immunodeficient and genetically engineered mice as well as murine reagents has made mice the most commonly used laboratory animals for cancer research. The dorsal chamber in mice used to be the most widely used chamber preparation because the surgery is less involved than some of the other preparations, and because of its longer history. With some modification to the dorsal skin All animal procedures described in this chapter should be performed following the guidelines of Public Health Service Policy on Humane Care of Laboratory Animals including appropriate anesthesia (please refer individual references for the detail) and with full approval by institutional animal care and use committee. During the surgical procedure or intravital microscopy, animal body core temperature should be maintained constant at 36–37°C using a heating device.

1

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Table 26.1  Examples of various intravital preparations for tumor studiesa Models Species Tumor Year Chronic window preparations Ear chamber

Dorsal skin chamber

Mammary fat pad chamber Cranial window

Rabbit

Brown-Pearce CA VX2 CA – intra-arterial injection VX2 CA-multifocal growth Mouse Various CAs, SAs, melanomas Hepatoma 134 Mammary CA Nude mouse Human amelanotic melanoma SCID mouseHuman tumor xenograft Hamster Amelanotic melanoma Rat Ascites hepatoma Rhabdomyosarcoma Rat SA Rat mammary CA Mouse Human and mouse breast cancer

1939 1958 1984 1943 1961 1971 1984 1992 1981 1971 1977 1979 1989 2009

References [5] [137] [11, 109] [6, 7] [138] [139] [140] [108] [141] [142] [143] [107, 144] [106] [13, 14]

Rat and mouse Acute (exteriorized) preparations

Various rodent and human tumors 1994 [112]

Cheek pouch

Hamster

Mesentery

Rabbit Rat Rat Mouse Mouse Mouse Mouse

Chemically induced SAs Human tumors Melanomas, CA, human angiopericytoma Malignant neurilemmoma VX2 CA – intra-arterial injection Murine colon CA Warker 256 CA, chondrosarcoma Human adenocarcinoma Human mammary CA Human pancreatic CA Lewis lung CA

Cremaster muscle Liver Mammary gland Pancreas Lung In situ preparations Eye anterior chamber Anterior chamber/iris assays Corneal micropocket assay Tail lymphatics Ear model Specialized models

Frog Renal CA Guinea pig Human tumor Rabbit Mouse mammary tumor Mouse mammary papilloma Hyperplastic rat mammary grand Rabbit Brown-Pearce CA, VX2 CA Mouse Murine mammary CA and SA Mouse Murine fibrosarcoma Mouse Murine fibrosarcoma, melanoma

Individual microvessel Mouse perfusion Angiogenesis gel assay Mouse

1950 [145] 1952 [146, 147] 1965 [148] 1973 1961 1990 1986 1997 1998 1999 2000

[149] [44] [43, 150] [151] [152] [49] [48, 153] [154]

1939 1952 1976 1977 1977 1974 1979 2000 2006

[155] [156] [54] [55] [56] [58, 59] [61] [74] [73]

Human adenocarcinoma

1996 [157]

Various angiogenesis factors

1996 [158] (continued)

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Tumor

Tissue engineered Mouse Various sources of endothelial vessel model cells and perivascular cells Adipogenesis model Mouse Preadipocytes GFP used to track Mouse CHO cells, murine mammary CA, cancer cells human adenocarcinoma Transgenic Murine mammary and liver CA GFP used as an mouse intravital gene reporter in transparent windows CA carcinoma, SA sarcoma a  This table is updated version of the table originally described in [87]

Year

References

2004 [24] 2003 [34] 1997 [110, 159–161] 1998 [162]

chamber technique, a mouse mammary fat pad chamber model has been developed. This model is suitable for breast cancer studies and has been increasingly used. The cranial window can be kept for up to a year compared to 30–40 days for the dorsal window, and along with the cheek pouch, is an immunoprivileged site. The main disadvantage of the cranial window is that the visualization of microvessels requires, in most cases, epi-illumination and the injection of a contrast agent such as a fluorescent marker.

26.2.1 Procedures 26.2.1.1 Rabbit Ear Chamber Transparent chambers are surgically implanted in the ears of male New Zealand white rabbits (2–3 kg B.W.) using the following procedure [10, 11]. 1. The animal’s ear is shaved and four holes are punched in the ear avoiding large blood vessels. The four holes consist of three outer perforations that are used to position the chamber and a central puncture (5.4-mm diameter) for housing the transparent window with the newly developing tissue. 2. The epidermis on both sides of the ear around the puncture is carefully retracted. A molded plate is placed on the inside of the ear and aligned with the existing holes, while a thin (about 200 µm) cover of mica glass is positioned on the outside of the cartilage. The molded plate and the mica glass, which sandwich the central puncture to form the chamber, are fastened to each other by three threaded rods and six hex nuts. 3. The retracted skin is then pulled taut over the edges of the mica glass and molded plate to protect the exposed area. A light covering of antiseptic is administered and two plastic covers are mounted so as to enclose and protect the chamber.

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4. Granulation tissue grows in the chamber (thickness, 40 ± 5 µm; diameter, 5.4 mm) at an average of eight days and reaches maturity at approximately 40 days postoperation. At this time, the chamber is ready for normal (granulation) tissue study or tumor implantation. 5. For tumor implantation [11], the cover glass that forms the top plate of the transparent chamber is carefully removed and a tumor, excised from the flank of a tumor-bearing host, is minced and placed in 0.9% NaCl solution and spread uniformly over the cover glass. 6. The cover glass is replaced flush against the intact normal tissue. 7. If this procedure causes tissue damage, the damaged tissue should be excluded from the study. Angiogenic response is observed 3–4 days postimplant and tumor-bearing chamber is ready for intravital microscopy approximately ten days postimplant. 8. For intravital microscopy, the animal is placed in a dorsal recumbent position in a cradle that restricts head movement while still maintaining proper circulation to the chamber. The ear containing the chamber is extended horizontally to the specimen plane of an intravital microscope. The chamber is secured to the microscope stage with an aluminum adapter [11]. 26.2.1.2 Dorsal Skin Chamber Preparation Dorsal skin chambers are implanted in mice using the following procedure [12]. 1. Prior to chamber implantation, the entire back of the animal is shaved and depilated and two symmetrical titanium frames (weight 3.2 g), mirror images of each other, are used to sandwich the extended double layer of skin. 2. One layer of skin is removed in a circular area approximately 15 mm in diameter, and the remaining layer, consisting of epidermis, subcutaneous tissue, and striated muscle, is covered with a glass coverslip incorporated into one of the frames. 3. Following implantation of the transparent access chamber, animals are allowed to recover from microsurgery and anesthesia for 48 h before tumor implantation or in vivo microscopy studies. 4. For implantation of tumor cells/tissue or matrix gel, the animals are positioned in a transparent polycarbonate tube (inner diameter 25 mm). 5. The coverslip of the chamber is carefully removed, and 2 µl of dense tumor cell suspension (~2 × 105 cells), a small piece (1 mm in diameter) of tumor tissue, or 20 µ1 of matrix gel is implanted at the center of the dorsal chamber. A new coverslip is then placed in the chamber. 6. The growth of the tumor and angiogenesis are monitored on a regular basis after implantation. The measurements of functional parameters are made when tumors have reached the desired size. 7. To obtain microcirculatory parameters, the mouse is positioned in a polycarbonate tube of approximately 25 mm inner diameter and tumors are observed with an intravital fluorescence microscope, confocal laser-scanning microscope, multiphoton laser-scanning microscope, and optical frequency domain imaging.

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26.2.1.3 Mammary Fat Pad Chamber Preparation Mammary fat pad chambers are implanted in mice using the following procedure [13, 14]. The surgical procedure for this chamber is comparable to the dorsal skin chamber, although the location is different. The site of tumor growth is also essentially same as in the classical mammary tumor model. As opposed to the mammary fat pad flap model (an acute preparation described in Sect. 26.3.1.4), the mammary fat pad chamber allows repeated observations. 1. Prior to chamber implantation, the entire lateral flank of the animal is shaved and depilated and the third nipple from the top is identified. 2. Two symmetrical titanium frames (weight 2.5 g; APJ trading Co, CA), which are mirror images of each other, are implanted to sandwich the extended double layer of skin. 3. One layer of skin is removed in a circular area approximately 15 mm in diameter, and the remaining layer, consisting of epidermis with nipple, subcutaneous tissue, and mammary fat pad, is covered with a glass coverslip incorporated into one of the frames. 4. Following implantation of the transparent access chamber, animals are allowed to recover from surgery and anesthesia for 48 h before tumor implantation. Mice with mammary fat pad chambers are placed one per cage and feeding pellets are placed on the cage floor. 5. Tumor implantation and observation procedures are similar to the dorsal skin chamber.

26.2.1.4 Cranial Window Preparation Cranial windows are implanted in mice using the following procedure [15]. 1 . The head of the animal is fixed by a stereotactic apparatus. 2. A longitudinal incision of the skin is made between the occiput and forehead. Then the skin is cut in a circular manner on top of the skull, and the periosteum underneath is scraped off to the temporal crests. 3. A 6-mm circle is drawn over the frontal and parietal regions of the skull bilaterally. Using a high speed drill with a burr tip, 0.5 mm in diameter, a groove is made on the margin of the drawn circle. This groove is made thinner by cautious and continuous drilling of the groove until the bone flap becomes loose. 4. Using a malis dissector, the bone flap is separated from the dura mater underneath. After removal of the bone flap, gelfoam is placed on the cutting edge and the dura mater is continuously superfused with physiological saline. 5. A nick is made close to the sagittal sinus. Iris microscissors are passed through the nick. The dura and arachnoid membranes are cut completely from the surface of both hemispheres, avoiding any damage to the sagittal sinus. Then, the window

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is sealed with a 6  –7 mm cover glass that is glued to the bone with a histocompatible cyanoacrylate glue. Leptomeningeal model (metastasis, tissue engineered model) 6. At least after a week from cranial window implantation, the cover glass is removed. A non-necrotic piece of the tumor tissue, 0.5–1.0 mm in diameter or 20 µl of matrix gel, is cut and put on the pial surface. The window is then resealed with a 6–7 mm cover glass by adhering to the bone using histocompatible cyanoacrylate glue. Intraparenchymal model (primary and metastasis model) 7. For animals with cranial window, the cranial window is removed. A small chunk of tumor tissue is implanted superficially into a channel prepared with a 30-gauge needle; the tumor depth below brain surface is approximately 0.4  mm. Alternatively, 3–5 µl of a thick single-cell suspension (1 × 105 to 5 × 105 cells/µl) are implanted with a 28-gauge microsyringe (10 µl, Hamilton, Reno, NV). For that purpose, mouse heads are fixed in a stereotaxic holder (Small Animal Stereotaxic Instrument with Mouse Adaptor, David Kopf Instruments, Tujunga, CA), the needle tip is positioned to an angle of 55° and depth of 1.75 mm, and cells are injected slowly over 10 min. This injection technique ensures implantation of a sufficient number of cells into the superficial mouse brain cortex. For animals without cranial window, the part of left scalp is cut and the exposed skull is being drilled as semicircular fashion with a drill, the underlying part of brain is being exposed prior to the implantation of tumor cells. Tumor cell pellet (1 × 106 in 4 µl of Hank’s solution) is injected into the exposed parenchymal space at about 0.1-mm depth. Tumor will grow to several millimeter size within 2–3 weeks for the treatment or intravital imaging. The surgery takes about 45 min in each case and is followed by positioning of the animals on a heating pad (37°C) until they recover from the anesthesia. 8. The growth of tumor and angiogenesis is monitored on a regular basis after implantation. 9. The measurements of functional properties are made when tumors have reached the desired size. The animals are anesthetized and put on a polycarbonate plate. The surface of cranial window is adjusted to be flat and perpendicular to the objective lens. 26.2.1.5 Angiogenesis Gel Assay and Tissue Engineered Vessel Model Instead of using tumor fragments, a polymer matrix (gel/sponge) containing a known amount of angiogenic factor(s) or cells can be glued/attached to a vascular bed [16–24]. The structure and function of new vessels penetrating the matrix can be measured in a variety of ways [23–26]. The matrix implant technique combined with the microcirculatory preparations in the dorsal skinfold chambers and the cranial windows in immunodeficient mice allows investigation into the effect of various growth factors sequestered in the matrix on angiogenesis (angiogenesis gel

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assay) as well as the ability of vascular endothelial cells and perivascular cells derived from different sources to induce angiogenesis and vessel maturation (tissue engineered vessel model) [21, 24, 27–30]. This method allows noninvasive and realtime measurement of structure and function of angiogenic vessels in the matrix. As a direct application of the angiogenesis assay, we demonstrated that the angiogenesis in these gels placed in the cranial windows can be suppressed by a tumor grown elsewhere in the animal [31–33]. We also successfully created long-lasting functional vessels by co-implantation of vascular endothelial cells and perivascular cell precursors, and subsequently demonstrated that clinically accessible cell sources such as cord blood endothelial progenitor cells, mesenchymal stem cells, and embryonic stem cells can participate in vessel formation in this model (Fig. 26.4) [24, 27, 29, 30]. Furthermore, by implanting preadipocytes, angiogenesis and subsequent vessel maturation during adipose tissue formation can be observed as well [34].

Fig. 26.4  Tissue engineered blood vessels. HUVECs and 10T1/2 cells or HUVECs alone were seeded in the three-dimensional constructs and implanted in the animals. (a, b). Three-dimensional intravital MPLSM images of engineered vessels (EGFP expressing HUVEC, green; functional blood vessels contrast-enhanced with rho-dextran, red). (a) Four days after implantation of HUVEC + 10T1/2 construct. Large vacuoles in the tubes resemble the lumens of capillaries (arrows) but they are not perfused (no red). (b) Four months after implantation of HUVEC + 10T1/2 construct. Engineered vessels are stable and functional. (c) Temporal changes in functional density of engineered vessels (total length of perfused vessel structure per unit area). N = 4, mean ± SEM; *P < 0.05 between the two groups (Mann–Whitney U-test). (d) Three-dimensional image of engineered vessels, 4 weeks after implantation of HUVEC + EGFP-expressing 10T1/2 construct. Scale bar: 50 µm (reproduced from [24])

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A common problem associated with various assays of vascularization into matrix implants is the nonspecific host response to the matrix implantation. A significant angiogenic response has been observed without any stimulation with exogenous growth factors [21, 22]. Therefore, caution should be exercised when interpreting the data. Only the amount of angiogenesis relative to the controls should be considered as the response to the exogenous angiogenic factors. The following procedure describes the angiogenesis gel assay [35]: 1. A known amount of growth factor (e.g. 60 ng of human recombinant bFGF or VEGF) is mixed with 2.4 µl of 0.1% bovine serum albumin, 6.5 mg sucralfate, 17.6 µl vitrogen 100 (a type 1 collagen), neutralized to pH 7.4 by addition of 1 part sodium bicarbonate solution (11.76 g/dl) and 1 part 10× minimal essential medium to 8 parts vitrogen. 2. Twenty microliters of this mixture (3,000 ng/ml bFGF or VEGF) is sandwiched between two nylon meshes (3 × 3 mm2). 3. Eight to ten days following cranial window implantation, the cover slip is removed, and the collagen gel is transferred onto the pial surface. The cranial window is closed again with a glass coverslip, avoiding pressure on the gel and air bubbles in the preparation. 4. For quantification of angiogenesis in the gel, mice are anesthetized with ketamine/xylazine (90 mg/9 mg per kg body weight) and positioned on a stereotactic apparatus. 5. Under a dissecting microscope, the number of squares in the top nylon mesh containing at least one vessel is counted with a 2× objective. Angiogenic response is determined as the number of squares containing at least one vessel divided by the total number of squares. The following procedure describes the tissue engineered vessel model (Fig. 26.4) [36]: 1. 800,000 EGFP labeled HUVECs and 200,000 DsRed labeled 10T1/2 cells are suspended in a collagen-fibronectin solution (1.5 mg of rat-tail type 1 collagen and 90 mg of human plasma fibronectin in HEPES buffered EGM medium, total volume 1  ml). The cell suspension is then added into one well of a 12-well plate and incubated at 37°C and 5% CO2 for 24  h. This allows the cells to acclimate to the collagen scaffold before being implanted into a cranial window. 2. A skin biopsy punch (4-mm diameter) is used to cut the collagen scaffold into circular pieces that are 4 mm in diameter. The cover glass is removed from the cranial window (8–10 days following window implantation). Using a fine forceps, a circular piece of collagen scaffold is carefully placed on top of the pia mater, then the window is resealed. 3. Vessel network formation by the implanted fluorescent cells and their perfusion, function, and maturation (perivascular cell investment and stabilization) are monitored by multiphoton laser-scanning microscopy.

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26.3 Acute (Exteriorized) Preparations For visualization of tumors grown in internal organs, it is necessary to surgically exteriorize these organs to place them on a microscope stage. The mesentery of mice, rats, and cats has been extensively used for intravital microscopy of microcirculation [37–42]. Because only two layers of thin membrane cover the microvessels in the mesentery, this model provides the best optical quality for in vivo microcirculation studies. On the other hand, preparation of intact mesenteric microcirculation is not trivial, requiring extremely careful technique and much experience, because the mesentery is quite vulnerable to physical stress. Furthermore, repeated or long-term observation is not feasible. In addition to normal vessels, mesentery can be used to study peritoneal dissemination of tumors [43, 44]. It is generally accepted that the host microenvironment influences tumor biology including gene expression, angiogenesis, physiological function, tumor growth, invasion, metastasis, and responses to antitumor treatments. Therefore, the use of orthotopic tumor models is necessary to obtain rigorous understanding in tumor pathophysiololgy and design of antitumor treatments [45]. Orthotopic tumor models include the liver [46, 47], pancreas [48], and the mammary gland [49, 50]. These preparations have provided unprecedented insights into the effect of host–tumor interactions on tumor biology and response to therapy.

26.3.1 Procedures 26.3.1.1 Mesentery The following procedure can be used for mice and rats [51]. 1 . Animals are fasted for 24 h prior to the observation. 2. After the anesthesia and hair removal of abdominal skin, the abdomen is opened via midline incision. The ileocecal portion is exteriorized and the intestinal loop is gently developed onto the thin glass part of a polyacrylate stage using a saline immersed cotton swab, avoiding direct contact with and tension to the mesentery. 3. The intestine is gently straightened and fixed by cotton sponges immersed in warmed saline, so that the mesentery does not unfold. The mesentery is kept moist and warm by superfusion with warm saline (37°C). 4. The mesenteric microcirculation is observed with an inverted or upright intravital microscope using a ~20–40× objective lens with transillumination or epifluorescence illumination in combination with appropriate tracers. 26.3.1.2 Liver Tumor Preparation The liver is the most common and critical site for distant metastasis of colorectal carcinomas. The liver tumor metastasis model is prepared by performing a splenic injection of tumor cells [52].

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1. A small incision is made in the left lateral flank, the spleen exteriorized and tumor cells (~1–5 × 106 cells in 100 µl) injected into the spleen just under the capsule. 2. The spleen is replaced into the peritoneal cavity. The two layers of incision (skin and abdominal wall) are closed with metal wound clips. 3. The metal clips are removed a week later. 4. Three to four weeks after the tumor cell injection, the abdominal wall is opened via a midline incision for examination. The functional parameters are measured on tumor foci of about ~3–5 mm in diameter. 5. The main liver lobe with metastatic tumors is gently exteriorized and held by a liver support device. This liver support allows adjustment of the three-dimensional position and angle of the top surface. 6. A circular glass coverslip is fixed by cyanoacrylate adhesive onto the bottom surface of the liver lobe, and this cover glass is fixed to the liver support with denture adhesive cream. 7. The top tissue surface is adjusted flat and perpendicular to the objective lens. 8. The circular glass coverslip, attached to the metal ring support, is gently applied onto the top surface of the tumor or normal liver tissue. 26.3.1.3 Pancreatic Tumor Preparation Pancreatic cancer has a poor prognosis, and treatment strategies conducted from preclinical research have not succeeded in extending patient’s survival appreciably. This newly developed abdominal window allows both direct intravital microscopy and chronic observation during pancreas tumor growth and the response to treatment [48]. 1. For tumor implantation, a portion of the skin and the abdominal wall of the left flank are cut in a linear manner, and a small left lateral laparotomy is performed matching the position of pancreas and avoiding damage to the vasculature. 2. The tail and body of pancreas are gently exteriorized from the abdominal cavity. 3. A small piece of tumor is fixed to the serosal side of the pancreas with a 5–0 prolene suture. 4. The abdominal wall and then the skin are sutured and closed. 5. When tumors become 6–8 mm in diameter (approximately 4 weeks after tumor implantation), an abdominal wall window is implanted to observe tumor microcirculation. 6. To implant the abdominal wall window, the skin and abdominal wall over the pancreas are re-opened, and the tail and body of the pancreas with a growing tumor (located close to the spleen) are gently exteriorized. 7. A portion of normal pancreas is sutured to the outer side of abdominal wall with 5–0 prolene to keep the pancreas and the tumor outside the abdominal cavity. 8. A titanium ring with eight holes around the edge is attached with sutures to the abdominal wall. This holds the pancreas and tumor inside the window.

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9. A circular glass coverslip (11  mm in diameter) is placed on top to seal the window. 10. For the subsequent intravital microscopy, mice are anesthetized and the tail vein cannulated for intravenous injection of fluorescent tracers. 11. The mice are placed inside a plastic tube (25 mm inside diameter) with a slit of 14 mm × 37 mm width. 12. The abdominal window fits in the slit of the plastic tube and is fixed by an adhesive tape.

26.3.1.4 Mammary Fat Pad Tumor Preparation Breast cancer is a leading cause of death in women. The mouse mammary fat pad serves as an orthotopic site for breast cancer (Fig. 26.2) [49, 50]. 1. Breast carcinoma cells are injected into the mammary fat pad just inferior to the nipple of female mice. 2. Four to six weeks later, tumors grow to ~5–8 mm in diameter. A midline incision is made through skin and fascia and a flap is gently elevated by blunt dissection, not disrupting the vasculature and avoiding irritation of the tumor vessels. 3. The flap is then placed on a specially designed stage developed for the liver preparation and a glass coverslip is placed over the tumor to allow intravital microscopy and analysis of microvascular parameters.

26.4 In Situ Preparations The anterior chamber of the eye is a natural site for observing tumor growth, and implantation on the iris and in a corneal pocket are two assays used extensively for this purpose [53–61]. Of the two assays, the corneal pocket is more widely used (for review see [57]). Due to the three-dimensional nature of vessel growth it is difficult to quantify the vascular response except in the early stages when the vessel length/number can be assessed. Some investigators have quantified the vascular response by perfusing the cornea with colloidal carbon and then measuring vascular length using computer-assisted image analysis [57]. Originally developed for rabbits, the corneal micropocket assay (Fig. 26.3) has also been adapted for rats and mice [61, 62]. Although it is less expensive to use rats/mice compared to rabbits, surgery becomes more difficult as the size of the eye gets smaller. Because the rat/mouse cornea is thinner than rabbit’s, the threedimensional growth of vessels is limited in these rodent models. Chick CAM is also commonly used with the shell either intact, or partially or completely removed [22, 63–65]. This is an inexpensive, and hence, widely used angiogenic assay. To eliminate the inflammatory response developed in the seven- to eight-day-old CAM, the vitelline membrane of four-day-old chick embryo has also been used [66]. Due to the difficulty in precisely quantifying newly formed vessels,

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the CAM assay has been used primarily for screening purposes. However, Nguyen et al. have modified this assay for easy quantification [22]. The CAM has also proved useful for analyzing the efficiency of metastatic cell extravasation and colonization [67] and for studying the kinetics of gene expression in metastasizing cells [68]. Finally, lymphatic microvasculature has been studied by adapting lymphangiography to the mouse tail and ears [69–73]. In this technique, a high molecular weight tracer bound to FITC is injected into the interstitial compartment of the tip of tail or ear tissue (Fig. 26.3). The tracer is absorbed by the local lymphatic vessels and carried proximally to the base of the tail or ear and subsequently to the regional lymph nodes. By implanting the tumor in the tail or ear, the structure and function of the lymphatics at the tumor periphery as well as steps of lymphatic metastasis can be monitored [73, 74].

26.4.1 Procedures 26.4.1.1 Corneal Pocket Assay Following is the procedure for the corneal pocket assay in rabbits [58] and can be used for mice with modifications [61]: 1. After anesthesia and retrobulbar infiltration with 2% xylocaine, the eye is moved forward and secured in position by a fold clamped in the lower lid. 2. A superficial incision, 1.5-mm long, is made with a surgical blade in the corneal dome to one side of its center. Intraocular tension is reduced by draining a small amount of aqueous humor from the anterior chamber through a 27-gauge needle. 3. A malleable iris spatula (1.5-mm width) is inserted and an oblong pocket fashioned within the corneal stroma. Peripheral pockets end ~1–2 mm from the limbus. 4. A small piece of minced tumor or gel (~5–10 µl) is deposited in the bottom of the pocket, which then seals spontaneously. 5. Eyes with corneal implants are observed with a stereomicroscope. Tumor and new vessel growth are measured en face with an ocular micometer at 10×. A green filter allows clearer definition of fine vascular channels developing within the cornea. 26.4.1.2 Chick Chorioallantoic Membrane 1. Fertilized White Leghorn chick eggs are incubated at 37°C ~60% relative humidity for ~8–12 days [22, 68, 75, 76]. 2. To apply tumor cells or a matrix gel implant, large vessels in the CAM are identified, a window (~1 cm2) is cut with an electric drill in the shell over the CAM and either a small section or the entire shell is removed, leaving the CAM intact. In studies where the CAM is removed entirely from the shell, it is carefully placed in a petri dish. 3. Tumor cells [75] or matrix implant [22] are placed on top of the CAM or fluorescence-labeled tumor cells are injected into CAM vein (~1–4 × 105 cells in 0.1 ml) [68, 76].

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4. Angiogenesis in the gel or tumor growth is determined with a dissecting microscope [22, 75]. 5. Surviving tumor cells are visualized by intravital microscopy with epifluorescence illumination [68, 76]. 26.4.1.3 Tail Lymphatics Female Nu/Nu mice are used for normal and tumor lymphangiography as aggressiveness of this gender (fewer tail wounds, which interrupt the lymphatic network). In the Nu/Nu mice, there is also no need for hair removal prior to observation. As a precaution, however, the animals are housed separately prior to observation in order to prevent bite wounds in the tail. 1. For studies of tumor growth in the tail model, a single-cell suspension of tumor cells must be prepared: tumor tissue is harvested, minced, and digested by trypsin until a uniform solution is formed. 2. For tumor implantation, a 26-gauge needle is used to inject the single-cell suspension, intradermally puncturing the tail skin about 1 cm from the distal tip. Great care is taken to not lacerate the tail veins. Approximately 0.2 ml of singlecell suspension is injected into the tail. 3. The mouse is monitored for tumor growth in the tail (~4 weeks, depending on growth rate). 4. For microlymphangiography, the mouse is placed on cover sponges and its tail taped to a metal board using double stick tape. A strip of tape is place over the hips to secure the mouse to the metal board. 5. A 30-gauge needle connected to a constant pressure source is used to inject FITC dextran (2 million MW) into interstitial space of the distal tail. The injection is made superficially into the skin above the tumor. Using a syringe, the pressure can be increased to allow filling of peri-tumor lymphatics. 6. The mouse tail is visualized by fluorescence intravital microscopy. The images are captured, digitized, and analyzed. 26.4.1.4 Ear Model For the ear model, lymphangiography is performed by slow injection in the interstitial tissue of the peripheral ear and angiography is performed by intravenous injection in the tail vein [73]. Lymphangiography with Evan’s blue dye or FITCdextran (2.5%, MW 2 million) reveals a dense auricular network of lymphatic capillaries, draining to a larger vessel at the ear base and, subsequently, to the exposed superficial cervical lymph node (Fig. 26.5). 1. To establish ear tumors 50 µl of tumor cell suspension (containing 5 × 106 cells) is injected in the peripheral ear. Tumor cell suspension is created from source tumors grown in the flank of 4–6 mice. Excised tumors are minced with scissors, treated with

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Fig. 26.5  Ear tumor model of lymphangiography and lymphatic metastasis. (a) Schematic of ear tumor model showing the peritumor lymphatics, the lymphatic vessels of the ear base, and the afferent lymphatic vessel of the cervical lymph node. (b) Lymphangiography of peri-tumor lymphatics around T241-VEGF-C tumor. Scale bar = 1 mm. Overexpression of VEGF-C induces hyperplasia of peri-tumor lymphatics and increases lymph flow rate. (c) GFP+ tumor cells (green) traveling through a lymphatic vessel (orange; TMR lymphangiography). (d) MPLSM imaging of cervical lymph node. Using the second harmonic generation microscopy, the collagen (blue) in the capsule of the lymph node can be detected. Red, afferent lymphatic vessel. Green, metastasizing T241-VEGF-C-GFP tumor cells (adapted from [73])

trypsin (0.0125% trypsin in Hank’s balanced salt solution), and centrifuged. A dense cell pellet is collected and injected in the tip of the ear with a 30-gauge needle. 2. When tumor volumes reached 150 mm3, mice are anesthetized and placed on a small plate allowing immobilization of the ear. Two microliters of FITC-dextran is injected in the surface of the tumors (or skin if no tumors are implanted). 3. Ear lymphatics are observed with epifluorescence intravital microscopy or multiphoton intravital microscopy. Lymphangiography images are captured and lymphatic diameters were measured using Image J software (http://rsb.info.nih. gov/ij/). The maximum diameter of each lymphangion (a segment of lymphatics between two valves) is measured. Lymphatics within 700 µm from the edge of the tumor are defined as peritumor lymphatics and ones farther from the tumor

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as ear base lymphatics. The afferent lymphatic to the cervical lymph node is observed in the exposed lateral neck area (Fig. 26.5) [73].

26.5 Intravital Microscopy and Image Analysis 26.5.1 Intravital Microscopy Work Station 26.5.1.1 Conventional Single-Photon Microscopy The standard microscopy workstation consists of an upright or inverted microscope equipped with transillumination and fluorescence epi-illumination, a flashlamp excitation device, two independent outlet ports, two separate eye-piece units, a motorized X–Y stage with a ±1.0-µm lateral resolution, a set of fluorescence filters, a motorcontrolled filter wheel, a CCD camera, an intensified CCD camera, a video monitor, a photomultiplier tube, a dual-trace digital oscilloscope, a video recorder, and a frame grabber board for image digitization (Fig. 26.6a) [47, 77–79].

26.5.1.2 Multiphoton Laser-Scanning Microscopy The MPLSM consists of a mode-locked Ti:Sapphire laser and a laser scan-head purchased either as part of a multiphoton system or as a confocal system with further modifications for good I.R. transmission. The laser beam first passes through a Pockels Cell, which allows rapid (~1 ms) modulation of laser intensity, and then is directed by the scan head into the side- or top-entry port of an upright epifluorescence microscope. Nondescanned photomultiplier tubes are used for imaging through significant depths of scattering tissue and should be introduced into the beam path via a dichroic beamsplitter located in the beam path between the scan head and the objective lens (Fig. 26.6b) [80]. To obtain tumor size, we measure tumor dimensions at low magnification. To obtain microcirculatory parameters, randomly selected areas (3–6 locations/tumor or animal) are investigated using long working distance objective with appropriate magnification. The parameters we can routinely measure include: tumor size, angiogenesis, hemodynamics, vascular permeability, leukocyte endothelial interactions, interstitial pH, interstitial and microvascular pO2, gene promoter activity, interstitial diffusion, convection and binding, and collagen structure and dynamics [79]. 26.5.1.3 Optical Frequency Domain Imaging We recently implemented optical frequency domain imaging (OFDI) as an intravital microscopic tool that circumvents the technical limitations of multiphoton microscopy [14]. The OFDI provides high resolution imaging of the elastic light scattering

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Fig. 26.6  Intravital microscopy work stations. Mouse tumor models are observed using conventional intravital microscopy (a) or multiphoton laser-scanning microscopy (b). With appropriate tracer molecules and/or engineered vectors/cells and computer-assisted image analysis, one can monitor tumor size, vessel density, vessel diameter, RBC velocity, leukocyte endothelial interaction, vascular permeability, tissue pO2, pH, nitric oxide, gene promoter activity, enzyme activity, and delivery of drugs, including genes (Panel B. adapted from [80])

properties of a sample in three dimensions. We have adapted Doppler-OFDI to permit rapid (~10  min) three-dimensional imaging of entire tumors (4 by 5  mm) in our mouse window models at depths up to 4 mm [14]. The depth resolution for this technique is 5 mm with an axial resolution of ~10 mm. Because the scattered signal that is used to create images is based upon intrinsic motion of the blood, no external contrast agent is required and it is thus a truly noninvasive technique. As a result, OFDI provides

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Fig. 26.7  Principles of in vivo multiparametric imaging with optical frequency domain imaging (OFDI). (a) An optical beam is focused into the tissue. The light reflected across all depths is combined with a reference beam and the interference signal is recorded as a function of light wavelength from 1,220 to 1,360 nm. The amplitude and phase of the reflected light as a function of wavelength is used to localize the reflected signal as a function of depth. At a given depth, the amplitude and phase of the reflected signal as a function of time is used to derive the optical scattering properties and thereby the tissue structure and function. (b) The depth-projected OFDI angiography within the first 2 mm of mouse brain bearing U87 human glioma. Depth is denoted by color: yellow (superficial) to red (deep). Scale bar 500 mm (reproduced from [14])

unprecedented access to previously unexplored, critically important aspects of tissue biology. Using OFDI-based techniques, we can quantify tumor angiogenesis, lymphangiogenesis, and both vascular and cellular responses to therapy (Fig. 26.7).

26.5.2 Tumor Growth and Regression To measure two-dimensional tumor size, low power transillumination or epi-illumination images are digitized and analyzed using an image processing system [81]. Tumor volume is calculated from the two-dimensional tumor size and depth if available (Fig. 26.8) [82].

26.5.3 Vascular Parameters 26.5.3.1 Angiogenesis and Hemodynamics To visualize microvessels, 100  µl of FITC-Dextran (MW 2 million, 10  mg/ml) is injected into the tail vein of mice. During each observation period, FITC-fluorescence

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Fig. 26.8  Tumor growth and angiogenesis in a human colon carcinoma (a–d) and regression in a Shionogi mouse mammary carcinoma in the dorsal skin chamber (e–h). Panels a–d: At day 5 after tumor cell implantation, enlargement of host vessels is observed and by day 10, occasional hemorrhage and sprout formation occurs. At day 15, tumor growth and further angiogenesis become apparent. By day 20, the tumor is fully vascularized (adapted from [12]). Panels e–h: (e) 12-day tumor prior to orchiectomy; three days (f) and nine days (g) after orchiectomy, the tumor vessels regress and the tumor shrinks. A second wave of angiogenesis is evident in (h) (adapted from [81])

images are recorded for 60  s and the videotapes are analyzed offline using the following methods. Single-Photon Microscopy Procedure 1. The vessel diameter in µm (D) is measured using an image-shearing device [83]. 2 . The red blood cell velocity (VRBC) is measured by temporal correlation velocimetry using a four-slit apparatus connected to a personal computer [84]. 3. The mean blood flow rate of individual vessels (Q) is calculated using D and the mean VRBC (Vmean). Q = p/4 × Vmean × D2, where Vmean = VRBC/a (a = 1.3, for blood vessels  <  10  µm; linear extrapolation 1.3  <  a  <  1.6 for blood vessels 10 and 15 µm; and a = 1.6 for blood vessels >15 µm) [85]. 4. The functional vessel density, a measure of angiogenesis, defined as the total length of vessels per unit area (cm/cm2), and the tortuosity, defined as the mean length of unbranching segment (µm), are analyzed using an image processing system [52, 86, 87]. Multiphoton Laser-Scanning Microscopy Procedure 1. The vessel diameters in µm (D) are measured from the image stack using image analysis software [88, 89].

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2. The transverse red blood cell velocity (VTRBC) is measured by the line-scan technique [80]. This technique produces the red blood cell velocity tangential to the direction of the line scan. The absolute red blood cell velocity (VRBC) is subsequently calculated by determining the angle q between the line scan and the axis of the vessel using the three-dimensional image stack. The true red blood cell velocity is then VRBC = VTRBC  /cos q. 3. As described for single-photon microscopy, the mean absolute blood flow rate of individual vessels (Q) is calculated from D and VRBC. 4. The three-dimensional functional vessel density, a measure of angiogenesis, defined as the total length of vessels per unit volume (cm/cm3), and the branching index, defined as the mean length of unbranched segments (µm), are analyzed from the three-dimensional image stack using an image processing algorithm in MATLAB [88, 89]. 26.5.3.2 Vascular Permeability The effective vascular permeability (P) can be measured using the following procedure [15, 86, 90]. Single-Photon Microscopy Procedure 1. After bolus injection of a fluorescent tracer (e.g. Rho or Cy5-labeled bovine serum albumin, 10 mg/ml; 0.1 ml/25 g body weight), the fluorescence intensity of the tumor tissue is intermittently measured for 20 min. 2. The value of P is calculated as P = (1 - HT) V/S [1/(I0 - Ib) × dI/dt + 1/K], where I is the average fluorescence intensity of the whole image, I0 is the value of I immediately after the filling of all vessels by the fluorescent tracer, and Ib is the background fluorescence intensity. The average hematocrit (HT) of tumor vessels is estimated independently [91]. V and S are the total volume and surface area of vessels within the tissue volume covered by the surface image, respectively. The time constant of clearance of the tracer from plasma (K) is measured independently [15]. Multiphoton Laser-Scanning Microscopy Procedure The vascular permeability (P) of individual vessel segments within ~600 mm of the tumor/window interface can be measured by MPLSM using the following procedure [80]. 1. After bolus injection of a fluorescent tracer (e.g. TMR-labeled bovine serum albumin or other molecule of interest, 10 mg/ml; 0.1 ml/25 g body weight), the fluorescence intensity of a single optical section containing the vessel segment of interest is recorded periodically for up to 20 min using an MPLSM. TMRlabeled BSA is imaged with 840-nm excitation and a 550DF150 emission filter.

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2. The fluorescence intensity F(r) along a line perpendicular to the vessel is measured in each image using image processing software and the permeability P of the vessel segment is given by ∞



F (r )rdr ∂ r =∫R P= ∂t (Fv − Fi ) R where R is the radius of the vessel segment Fv is the fluorescent signal from the plasma in the vessel and Fi is the fluorescence signal immediately outside the vessel.

3. The value of P is calculated from the t = 0 limit of the fluorescence distribution to minimize errors which can result from a finite integration length and due to fluorescence contribution from material leaking out of nearby vessels.

26.5.3.3 Leukocyte Endothelial Interactions The flux of leukocytes, as well as the number of rolling and adhering leukocytes, is measured as follows [80, 92]: Single-Photon Microscopy Procedure [92] 1. Mice are injected with a bolus (20  µl) of 0.1% rhodamine-6G in 0.9% saline through the tail vein and leukocytes are visualized via an intensified CCD camera and recorded on S-VHS tape. 2. The numbers of rolling (Nr) and adhering (Na) leukocytes are counted for 30 s along a 100-µm segment of a vessel. The total flux of cells for 30 s is also measured (Nt). 3. The equations for calculating the ratio of rolling cells to total flux (rolling count), the density of adhering leukocytes (adhesion density), and the shear rate for each vessel are as follows: rolling count (%) = 100 × Nr/Nt, adhesion density (cells/mm2) = 106 × Na/(p × D × 100 µm), shear rate = 8 × Vmean/D.

Multiphoton Laser-Scanning Microscopy Procedure [80] 1. Mice are injected with a bolus (20  µl) of 0.1% rhodamine-6G in 0.9% saline through the tail vein and leukocytes are visualized via an MPLSM using 810-nm excitation and a 550DF150 emission filter. 2. A series of 10–15 high temporal resolution (two or more frames per second) images are generated of a given vessel, covering a total duration of 30 s. The numbers of rolling (Nr) and adhering (Na) leukocytes are counted, as well as the total flux of cells (Nt). 3. Rolling count and adhesion density are calculated as described above.

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26.5.4 Extravascular Parameters 26.5.4.1 Interstitial pH Measurements Fluorescence ratio imaging microscopy for pH, its implementation, application to thick tissues, and calibration are performed as previously described (Fig. 26.9) [77, 93].

Fig. 26.9  Noninvasive, intravital pO2 and pH measurements. (a) Trans-illumination image of a LS174T tumor grown in the dorsal skin chamber; (b) Corresponding interstitial pH (open symbols) and pO2 (closed symbols) profiles; and (c) average profiles of interstitial pO2 and pH when moving away from the blood vessel wall (SEM shown, n = 24 profiles, N = 7 tumors). Solid bar in (a) represents the scanning line (400 µm) (adapted from [77])

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1. The cell-impermeant form of the pH-sensitive fluorochrome 2¢,7¢-bis-(2carboxyethyl)-5,6-carboxyfluorescein (BCECF; 0.7 mg/kg i.v.) is used. 2. Emission intensities (570 nm) are imaged through the CCD camera port following sequential excitations at 440 and 495 nm. A sampling depth of £25 µm and a lateral spatial resolution of 5 µm are obtained using a 400-µm pinhole in the light excitation pathway and a 40× water-immersion objective. 3. The X–Y stage is cycled through the same locations for dynamic measurements. 4. The ratio of intensities is converted into pH using the calibration curves. 26.5.4.2 Interstitial and Microvascular pO2 Measurements The technique is based on the O2-dependent phosphorescence quenching of albuminbound palladium meso-tetra (4-carboxyphenyl) porphyrin [77, 94–96]. 1 . The porphyrin probe is injected (600 mg/kg) into the mouse tail vein. 2. The phosphorescence signal resulting from flashlamp excitation (540 nm) of the tissue is detected at ³630 nm using the photomultiplier tube and averaged on the oscilloscope prior to computer storage. 3. The X–Y stage is cycled through the same tumor locations used for the pH measurements. 4. The illumination field is reduced to a 100-µm spot and a 10  ×  10  µm2  slit is placed in the light emission pathway. This reduces the sampling depth to £25 µm and gives a lateral spatial resolution of 10 µm, similar to the pH measurements. 5. A second eyepiece, placed between the slit and the collecting tube, allows re-focusing on the region of interest prior to measurements of phosphorescence decay. 6. Phosphorescence measurements are valid within interstitial spaces as well as blood vessels. 7. Data are converted to pO2 values according to a standard calibration method [97]. Calibration tests have revealed an excellent linearity (r2  ³  0.99) between pO2 (0–60  mmHg) and the inverse of lifetime values. Moreover, the pO2 calibration curves do not show any dependence on the pH of the solution (pH range 6.60–7.40), which makes this porphyrin an ideal probe for use in tumors. We have also shown that sequential measurements of pH and pO2 in tissues in vivo are possible (Fig. 26.9), because the presence of the pH probe (BCECF) does not affect lifetime measurements of the pO2 probe (porphyrin) [77]. 26.5.4.3 Tissue Nitric Oxide Distribution Tissue distribution of nitric oxide (NO) is visualized using MPLSM and an NO-sensitive fluorescence probe 4,5-diaminofluorescein (DAF-2) as described in [98]. NO converts DAF-2 to 4,5-diaminofluorescein triazolium (DAF-2T) increasing fluorescence by a factor of 200. 1. The NO-sensitive fluorescence probe DAF-2 is injected (0.5 mg) into the mouse tail vein.

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Fig. 26.10  Nitric oxide (NO) imaging. Tissue distribution of NO in U87 tumors grown in the cranial window in Rag-1–/– mice was visualized by means of NO sensitive DAF-2T fluorescence imaging using MPLSM. Parental U87MG (top row) or nNOS-silenced-U87 (bottom row) tumors were studied. (Left) Microangiography using tetramethylrhodamine-dextran (2,000 kDa). (Middle) Representative DAF-2T microfluorography captured 60 min after the loading of DAF-2 in tumors. (Right) Pseudocolor representation of DAF-2T microfluorographs. Color bar in the right shows calibration of the fluorescence intensity with known concentrations of DAF-2T (DAF-2Tapp). The bar indicates 100 µm (reproduced from [98])

2. The DAF-2 associated fluorescence images are captured 60 min after i.v. injection. A calibration curve is generated using known concentrations of DAF-2T (Fig. 26.10). 26.5.4.4 Interstitial Diffusion, Convection, and Bindings To measure interstitial diffusion coefficients, various advance intravital microscopy techniques have been developed. Single-photon fluorescence recovery after photobleaching (FRAP) with spatial Fourier analysis, after proper calibration, allows determination of interstitial diffusion [78, 99]. Following the development of MPLSM, multiphoton fluorescence recovery after photobleaching (MP-FRAP) has been established to improve sensitivity and spatial resolution of diffusion measurements [100, 101]. Finally, multiphoton fluorescence correlation spectroscopy (MP-FCS) is the most advanced, ultra-sensitive technique, permitting multicomponent diffusion analysis [102]. Single-Photon Fluorescence Recovery After Photobleaching Procedures 1. A fluorescently labeled molecule of interest is infused into the tumor interstitium either via extravasation or low pressure microinfusion.

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2. A brief (~milliseconds) flash of focused laser light bleaches out a subpopulation of the fluorescent molecules. 3. Consecutive images of the bleached region are generated via epifluorescence and captured on the CCD camera as unbleached fluorophore diffuse back into the bleached region. During the bleaching flash the camera is shuttered to avoid damage to the electronics. 4. Spatial Fourier analysis of the fluorescence recovery images is performed as described in [78, 99, 103].

Multiphoton Fluorescence Recovery After Photobleaching Procedures [101] 1. A FITC-labeled molecule of interest is infused into the tissue interstitium either via extravasation from the vasculature or low pressure microinfusion. Vessels can be highlighted with an injection of 100 µl of TMR-labeled Dextran (MW 2 million, 10  mg/ml) into the tail vein. Fluorescence is generated with 840-nm excitation and collected with 535DF40 (FITC) and 610DF75 (TMR) emission filters and a 570LP dichroic mirror. 2. A location of interest is identified from MPLSM images of blood vessels. The multiphoton focal volume is parked at the location using the LSM control software and a brief (~submillisecond) flash of high intensity laser light bleaches out a subpopulation of the fluorescent molecules. 3. The bleached region is monitored by the same laser beam, greatly attenuated. The recovery in fluorescence of the bleached region as unbleached fluorophores diffuse back into the bleached region is recorded with a multichannel scaler. 4. Mathematical analysis of the fluorescence recovery curve is performed to extract the diffusion coefficient of the labeled molecules.

Multiphoton Fluorescence Correlation Spectroscopy Procedures [102] 1. The MP-FCS setup is constructed as an integral part of a custom-built MPLSM. Tracer-conjugated rhodamine is excited at 840 nm. Fluorescence emission from the excitation volume collected by a high-numerical aperture water-immersion objective travel through a dichroic mirror (690/DRLP) and detection filter (D605/55) into a GaAsP photodetector. Transistor-transistor logic (TTL)converted photon counts are subsequently fed into a counter-timer board (10-s bins). Data acquisition times typically last 50 s. 2. The resulting photon-count time series are autocorrelated offline and fit to a multicomponent diffusion model MATLAB (The MathWorks). Using a nonlinear least squares fitting routine, the correlation functions are fitted to available models including single-component free diffusion and two-component diffusion.

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26.5.4.5 Gene Expression: Promoter Activity via GFP Imaging To monitor gene promoter activity in stromal and tumor cells, a fluorescent reporter gene driven by the promoter of interest is introduced into mice [104] and/ or tumor cells [105]. Once the gene is activated, the corresponding cells become fluorescent and the fluorescence intensity is measured. Currently, the most commonly used reporter gene is green fluorescent protein (GFP). However, as other variants of GFP become available the possibility of monitoring multiple genes at once or gene activity in different cell populations will become practical. The GFP fluorescence (emission 509  nm) is imaged through the intensified CCD camera following excitation at 488 nm (Fig. 26.11). In order to obtain quantitative data on the promoter activity in vivo, a deconvolution algorithm must be used to account for the kinetics of GFP decay and the relationship between protein levels and the fluorescence emitted by GFP.

Fig. 26.11  Vascular endothelial growth factor (VEGF) promoter activity during wound healing and tumor growth. Transillumination (a) and green fluorescent protein (GFP) fluorescence image (b) of a wound 2 weeks after the wound creation in the ear of VEGFEGFP mice; and GFP images of murine hepatoma HcaI grown in the dorsal skin chamber in the VEGFEGFP mice 2 weeks (c) and 3 weeks (d) after the implantation. Mice bearing VEGFEGFP transgene show cellular green fluorescence around the healing margins and throughout granulation tissue of superficial ulcerative wounds (a, b). Implantation of solid tumors in the transgenic mice leads to an accumulation of green fluorescence, resulting from tumor induction of VEGF promoter activity in host stromal cells. With time, the fluorescent cells invade the tumor and can be seen throughout the tumor mass (c, d). In both wound and tumor models, the predominant GFP-positive cells are fibroblasts [104]. The finding that the VEGF promoter of nontransformed cells is strongly activated by the tumor microenvironment points to a need to analyze and understand stromal cell collaboration in tumor angiogenesis. The bars in panels a and c indicate 500 µm for A/B and C/D, respectively (adapted from [162])

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26.6 Novel Insights Our laboratory has a long-standing tradition of developing techniques to measure physiological parameters from intravital microscopy. Some of these developments are listed in Table 26.2, and the resulting novel insights are summarized in the subsequent discussion. The ability to deliver therapeutic agents to all regions of a tumor is governed by the tumor blood supply. Using in vivo microscopy, we and others have unequivocally demonstrated that the structure and function of tumor vessels is heterogeneous [79, 106–109], and suggested the possibility that the presence of cancer cells of the vessel wall may contribute to this heterogeneity [110, 111]. Furthermore, our finding Table 26.2  Examples of noninvasive techniques developed in authors’ laboratory Technique References Vascular parameters [80] MPLSM angiography, RBC velocity, microvascular permeability, and leukocyte–endothelial interaction measurement Tracer free OFDI angiography [14] Microvascular permeability of normal and neoplastic tissues [163] [164] Microvascular permeability of albumin, tumor vascular surface area, and vascular volume measured in the dorsal chamber Pore cutoff size of tumor vessels [165, 166] Perfusion of single tumor microvessels: application to [157, 167] vascular permeability measurement Effect of RBCs on leukocyte–endothelial interactions [168] Extravascular parameters Fluorescence ratio imaging measurement of pH gradients: [169, 170] calibration and application in normal and tumor tissues [94] Noninvasive measurement of microvascular and interstitial oxygen profiles in a human tumor in SCID mice Simultaneous high-resolution measurements of interstitial [77] pH and pO2 gradients in solid tumors in vivo: tissue NO distribution [103] Direct measurement of interstitial diffusion and convection of albumin in normal and neoplastic tissues using fluorescence photobleaching Fluorescence photobleaching with spatial Fourier analysis [78, 99] for measurement of diffusion and binding in tumors MPLSM FRAP [101] MPLSM FCS [102] Flow velocity in the superficial lymphatic network of the [69–71] mouse tail Tracer free OFDI lymphangiography [14] Gene expression using intravital reporter [104] Cell identification using endogenous GFP [110] Tissue viability [14]

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that cancer cells co-opt the host cells into making VEGF, a potent angiogenic molecule, reveals the importance of host cells [82, 104]: host cells are not passive bystanders but active participants in tumor angiogenesis, growth, and response to therapy. These concepts pull together a number of key observations made in our laboratory: (a) angiogenesis, pO2, pH, permeability, and pore cutoff size in tumor vessels vary from one tumor to the next, from one region to the next within the same tumor, from one day to the next, and from one anatomical site to next [47, 77, 112]. (b) The production of angiogenic inhibitors, similar to angiogenic stimulators, is dependent on the site of primary tumor growth [32] and changes in response to treatment [33]. (c) Surprisingly, in a hormone-dependent tumor, hormone withdrawal causes apoptosis of endothelial cells prior to that of cancer cells by downregulating the production of VEGF by cancer cells. A second wave of angiogenesis is then driven by VEGF, presumably, from host cells [81]. (d) One would expect antiangiogenic therapy to impair drug delivery by inducing vessel regression. However, in the initial phases, these therapies may prune immature vessels and induce a normal vascular network with more mature vessels, thus explaining the potential synergism between antiangiogenic and cytotoxic therapies [81, 113, 114]. Lymphangiography of fibrosarcomas in the mouse tail and ear models have shown that the lymphatics are impaired within the tumor mass and yet enlarged at the tumor periphery [73, 74]. The former contributes to interstitial hypertension in tumors, a barrier to drug delivery [115]. The latter, induced by VEGF-C, facilitates lymphatic metastasis [72, 73]. The impairment of intra-tumor lymphatics results from solid stress generated by proliferating cancer cells [116, 117]. Releasing this solid stress therefore opens lymphatics, lowers pressure, and increases delivery of agents across tumor blood vessels [117, 118]. Once a therapeutic agent has leaked from a blood vessel, it must migrate through the interstitial matrix to reach cancer cells [119]. Using FRAP and FCS, our lab has provided the first measurements of interstitial diffusion, convection, and binding in  vivo [78, 101–103]. Furthermore, we showed that the anomalous assembly of collagen in tumors can prevent the penetration of therapeutic agents [120]. This finding identified collagen synthesis as a potential target for improving the delivery of macromolecules [101]. In an attempt to understand heterogeneous localization of activated lymphocytes to tumor vessels, we discovered that angiogenic agents regulate adhesion molecules on the vasculature. This finding provided the first link between the disparate fields of angiogenesis and adhesion. For example, we showed that VEGF upregulates while bFGF downregulates adhesion molecules on the vasculature [121, 122]. This is one of the rare occasions where two molecules act synergistically for one function but antagonistically for another. This finding also provides a new mechanism of immune evasion by bFGF. Intravital microscopy of structure and function of tumor vessels during antiangiogenic therapy provided powerful insight into how antiangiogenic therapy might work. These studies showed that the agents originally designed to destroy tumor vessels can paradoxically “normalize” their structure and function and improve the tumor microenvironment [123–126].

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26.7 Future Perspectives As discussed in this chapter, intravital microscopy has provided useful insight into angiogenesis and tumor biology [79, 127–130]. However, there are two key challenges: first, the most widely used microscopy techniques are surface weighted. Given the heterogeneous nature of tumors we need dynamic information about the internal milieu of tumors. Confocal laser-scanning microscopy can provide images up to a few hundred micrometers in depth [131, 132]. The advent of multiphoton laser-scanning microscopy (MPLSM) changed this limit to over 500 mm, depending on the tissue and tracer used [80, 133, 134]. Another type of optical method, optical coherence tomography (OCT), can image deeper regions [135, 136]. Recently, the development of second generation OCT–OFDI has provided unprecedented imaging capacity [14]. With this novel technique, we are now able to obtain dynamic images of the entire tumor with high spatial resolution. However, the ability of OFDI to image without an exogenous tracer is also a drawback – the method is not able to image fluorescently labeled cells or molecules. Therefore, it would be ideal to combine the OFDI and MPLSM techniques. The development of such a combined system would open the door to previously unexplored, yet critically important aspects of tumor biology. The second limitation of window models is that they are currently restricted to the study of transplanted (as opposed to spontaneous) tumors. In principle, it should be possible to place windows on spontaneous tumors, but this has not been done to date. Alternatively, mice could be engineered with tissue-specific promoters so that tumors spontaneously arise in regions where the windows described in this chapter can be used. With these improvements in microscopy and animal models, in vivo microscopy will continue to offer new opportunities for unexpected discoveries in tumor biology as well as cancer detection and treatment. Acknowledgments  The work described here was supported by grants from the National Institutes of Health, National Science Foundation, American Cancer Society, the Department of Defense, United States Army, National Foundation for Cancer Research, and the Whitaker Foundation. This chapter is based on six previous related reviews [51, 87, 171–174].We thank the publishers for allowing us to reproduce the relevant material.

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Index

A Actinomycin D-resistant P388 leukemia (P388/ACT-D), 36–37 Acute lymphoblastic leukemia (ALL) xenograft models EFS T/C value, 205 event-free survival, 204 tumor growth delay value, 205 tumor volume T/C value, 205 Acute myeloid leukemia (AML), 327 Acute (exteriorized) preparations liver tumor, 652–653 mammary fat pad tumor, 654 mesentery, 652 pancreatic tumor, 653–654 Acute promyelocytic leukemia (APL), 327 ADC. See Apparent diffusion coefficient ADCC. See Antibody dependent cellular cytotoxicity ADT. See Androgen-deprivation therapy Adult T cell leukemia/lymphoma (ATLL), 334 ALCL. See Anaplastic large cell lymphoma Alkylating agents, 34–36 Alternative reading frame (ARF), 379 AML. See Acute myeloid leukemia Amsacrine-resistant P388 leukemia (P388/ AMSA), 37, 38 Anaplastic large cell lymphoma (ALCL), 335 Androgen-deprivation therapy (ADT), 398 Animal models human tumor xenograft, 472–474 organ colonization and site-selective metastases bone, 475–476 brain, 475 liver, 475 lung, 474–475

spontaneous metastasis lymph node, 477–478 orthotopic implantation, 477 syngeneic rodent tumor, 472 transgenic models, 478–480 Antibody-based therapies, 469 Antibody dependent cellular cytotoxicity (ADCC), 333 Anti-neoplastic drugs and radiation chemotherapy-induced oral mucositis, 507–508 concomitant chemotherapy, 508 fractionated radiation dosing, 507 grading scale, 505–506 hamster model, 504–505 mucositis, 501, 502 murine and rat models, 502–503 non-clinical endpoints, 506 non-surgical cancer therapy, 500 oral radiation, 502 radiation treatment, 506 screening models, 503–504 without concomitant chemotherapy, 507 Antitumor activity percent increase life span, 73 tumor-cell-kill calculation, 73–74 tumor-growth delay, 73 APC. See Argon plasma coagulation Apc-deficient models, 433–434 Apoptosis in vivo biochemical and molecular, 636 cancer therapy CY and CP, 635 cytotoxic treatments, 632, 634 imaging, 635–636 murine tumors, 633 normal tissue, 631–632

B.A. Teicher (ed.), Tumor Models in Cancer Research, Cancer Drug Discovery and Development, DOI 10.1007/978-1-60761-968-0, © Springer Science+Business Media, LLC 2011

681

682 Apoptosis in vivo (cont.) OCa-1 tumor, 633 radiation-induced apoptosis, 634 characterization, 625–626 dead cells, 626 recognition and quantification morphological assessment, 626–627 quantification, 627–628 tumor biology genetic regulation, 629–630 tumor development, 628–629 Apparent diffusion coefficient (ADC), 229 ARF. See Alternative reading frame Argon plasma coagulation (APC), 510 Arterial spin labeling (ASL), 229 Asbestos-induced mesothelioma fiber types, 308–309 inhalation Guinea pigs and rats, 313 Syrian golden hamsters, 314 intraperitoneal injections murine animal model, 311 rat model, 310–311 ultra-structural analysis, 311 intrapleural injection, 312–313 Ascites tumors assumptions, 572 L1210 leukemic cells, 572–573 tumor cell kill, 574 ATLL. See Adult T cell leukemia/lymphoma B Basic fibroblast growth factor (bFGF), 294 B16 cell line antimetastatic therapies, 272–273 intraperitoneal (ip) injection, 274 pulmonary metastases, 273 subclones, 272 Bioluminescence imaging (BLI), 220, 221, 230 Bisphosphonates, jaws bevdermatitis scoring scale, 514, 515 BRONJ, 514, 516 rat model, 514 Blood oxygen level dependant (BOLD), 230 B16 murine melanoma feeder effect, 54 historical context bony metastasis, 81 C954 liver carcinoma, 82, 83 Cloudman’s method, 82 contemporary knowledge, 80 C198 reticuloendothelioma, 82

Index embolus-host interaction, 81 iv injections, 83 microcinematic techniques, 83–84 periodic survey, 80 process of extravasation, 82 SC implantations, 82–83 “seed and soil” concept, 81 immune-suppression, 56 neoplastic cells, 86 parabiosis systems, 90 radioactive label, 85 syngeneic tumors, 79 transcorneal implantation, 89 in vivo-in vitro selection process, 87–88 Bone marrow blood cytopenia classification, 523 lymphocytopenia, 525 neutropenia, 524 thrombocytes, 524–525 cancer therapeutics adverse hematological effects, 528 blood cell lineages, 526 CFUs, 527–528 granulopoiesis, 530 hematologic lineages, 255 proliferative properties, 529 red marrow, 527 stem cell, 528 hematotoxicology CFU-GM assays, 533–537 human granulopoietic tissue, 537–545 human hematotoxicology data, 545–547 mouse modeling doses/exposures, 530 harboring human tumors, 531 human potency, 531 human tolerated dose levels, 531–532 murine therapeutic index, 522 myelosuppression, 546 preclinical mouse models, 522 Bone marrow-derived cells (BMDC), 465 C Camptothecin-resistant P388 leukemia (P388/ CPT), 37, 38 Cancer Chemotherapy National Service Center (CC-NSC), 6, 24 Cancer therapy CY and CP, 635 cytotoxic treatments, 632, 634 murine tumors, 633 normal tissue, 631–632

Index OCa-1 tumor, 633 radiation-induced apoptosis, 634 in vivo imaging, 635–636 Canine melanomas, 263–264 Castration-resistant (CR), 398 CC-NSC. See Cancer Chemotherapy National Service Center CDC. See Complement dependent cytotoxicity Cell-mediated immunotherapy, 469–470 Central nervous system (CNS), 573 CFU. See Colony-forming units Chemogenomics, 12 Chemoprevention protocol, 249–250 Chemotherapy human pancreatic ductal adenocarcinoma, 64, 65 leukemias, L1210/0, 72 log-kill (gross), 66 MX-1 mammary tissue, 68 NCI cutoff, 65 SC mammary adenocarcinoma 16/c, 67 take-rates, 60 Chronic lymphocytic leukemia (CLL), 330 Chronic myeloid leukemia (CML), 327 Chronic window preparations advantages and disadvantages, 644–645 angiogenesis gel assay, 649–651 cranial window, 648–649 dorsal skin chamber, 647 mammary fat pad chamber, 648 rabbit ear chamber, 646–647 tissue engineered vessel model, 649–651 CLL. See Chronic lymphocytic leukemia Clonogenic assays culture methods, 174–175 measurement cell density problems, 614–615 colonies process, 615–616 colony density problems, 615 identifying clonogenic cells, 612–613 motion artifacts, 614 radiobiologists, 618 single cell suspensions, 174 treatment, 619–620 CML. See Chronic myeloid leukemia Colony-forming units (CFU), 527, 582 Colony-stimulating factors (CSF), 527 Color-coded imaging gene transfer, 149, 151 noninvasive, 143, 144, 146 transgenic nude mouse, 139–142 tumor microenvironment, 143 Companion animal (pet) cancers adverse events, 368

683 animal translational models, 353 ethical considerations:, 367 genetic and molecular canine OSA cell lines, 356, 357 dogs and humans, 356 HSA, 357 human clinical trials, 366 pet dogs, 362–364 potential opportunities/advantages canine and feline, 360–361 chemotherapeutics, 362 companion species cancer models, 358 non-Hodgkin’s lymphoma, 362 rodent models, 354 trial, 367 vested communities CCOGC leadership, 364 COP, 364 COTC trials, 365–366 veterinary medical care, 354–355 Comparative oncology program (COP), 364 Comparative oncology trials consortium (COTC), 365 Complement dependent cytotoxicity (CDC), 333 Compound inducible mutants vs. Kras and tumor Ink4a/Arf tumor, 384 mouse models, 385 TGFRII model, 386 preclinical Hedgehog inhibitors, 386 serum biomarkers, 387 Copy number alterations (CNA), 199 CSF. See Colony-stimulating factors Cyan fluorescent protein (CFP), 141–142 D 3¢Deoxy-3¢-18F-fluorothymidine (FLT), 360 Diffusion magnetic resonance imaging (dMRI), 229 7,12-Dimethylbenz(a)anthracene (DMBA), 246 Doxorubicin-resistant P388 leukemia (P388/ ADR), 37 Drug penetration [14C]albumin, [14C]paclitaxel, 592 enzastaurin, 593, 594 FSaIIC tumor cells, 590, 591 plasma VEGF levels, 595–597 TNP-470/minocycline, 590, 592 treatment, 593 VEGF levels, 593–594

684 Drug-resistant leukemias alkylating agents, 34–36 antimetabolites, 36 DNA-and tubulin-binding agents, 36–38 Drug testing, patient explants chemotherapy, 172 clonogenic assay culture methods, 174–175 single cell suspensions, 174 evaluation parameters, 173 gene signatures, 175–176 molecular target characterization genomic profiling, 173 tissue microarrays, 173–174 study design, 171–172 Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), 13, 228 E Epidermal growth factor receptor tyrosine kinase (EGFR-TK), 109 Etoposide-resistant P388 leukemia (P388/ VP-16), 37, 38 European Centre for the Validation of Alternative Methods (ECVAM), 536 F 18 F-Fluoro-thymidine (18F-FLT), 227 Fibroblast growth factor 2 (FGF2), 400 Fluorescence imaging (FLI), 220–221 Fluorescent proteins advantages, 135, 137 antiangiogenetic agents, 162 bacterial cancer therapy strategy, 132, 134, 135 blood vessels, 146 clonality of metastasis, 149 color-coded imaging noninvasive, 143, 144, 146 tumor host interaction, 139–142 imaging apparatus, 152–155 immunohistochemical staining, 162–163 INDEC FluorVivo, 161 lateral gene transfer, 149, 151–152 lymphatic vessels, 148 Olympus OV100, 161 surgical orthotopic implantation colon cancer, 156–157 lung cancer, 156 ovarian cancer, 155–156 prostate cancer, 156

Index tumor tissue sampling, 162 UVP iBOX, 161–162 viral labeling, 137–138 in vivo revolution bone metastases, 128 brain metastasis, 129 colon cancer progression, 131–132 dual-color cancer cells, 127–128 gene transduction and histone H2B gene, 127 melanoma bone and organ metastasis, 129 multi-organ metastases, 129–130 pancreatic cancer progression, 130–131 prostate cancer progression, 131 regional lung tumor metastases, 128 retrovirus production, 126–127 visceral metastasis, 128–129 whole-body optical imaging, 130 Freiburg xenograft system cytotoxic drugs, 178–179 in vivo efficacy, 185–187 G Genetically engineered mouse models (GEMMs), 326, 380, 387, 398 Granulocyte-macrophage (GM), 582 Green fluorescent protein (GFP), 341 future occurrence of metastasis, 137–138 gene transduction and histone H2B-GFP vector, 127 luciferase, 107 melanoma bone and organ metastasis, 129 multi-organ metastases, 129–130 orthotopic models bone metastases, 128 brain metastasis, 129 prostate-cancer bone and visceral metastasis, 128–129 regional lung tumor metastases, 128 retrovirus production, 126–127 transgenic nude mouse, 139–140 whole-body imaging, 131–132 H Hank’s balanced salt solution (HBSS), 289 Hemangiosarcoma (HSA), 357 Hematopoietic stem cells (HSCs), 329 Hematotoxicology CFU-GM assays clinical development, 537 granulocyte and monocyte lineages, 533 human and mouse, 535

Index human MTD, 537 IC90 values, 536 marrow precursor cells, 533 prediction models, 536 human granulopoietic tissue B-and T-lymphocytes, 544–545 chemotherapeutic, 537 hematopoietic engraftmen, 543 human therapeutic index, 537, 538 human tumor xenografts, 544 NOD/LtSz-scid, 544 Herceptin, 230 High throughput screening (HTS), 11 Histone deacetylase (HDAC), 290 History and evolution boundary conditions and expectations, 16–17 correlative studies, 16 early screening models, 6–8 Ehrlich’s concept, 5 initial rodent pharmacology and model selection, 16 maximum tolerated dose, 4 National Cancer Institute, 6, 9 non-mammalian models multicellular, 9–11 unicellular, 8–9 rodent and human drug features, 4 sample size and randomization, 16 target effect, 15 technology-intensive screening chemogenomics, 12 HTS, 11 nanotechnology, 12–13 proteome and kinome, 12 RNA interference, 13 in vitro models cell-based, 13 cytotoxic vs. targeted, 15 integrin inhibitors, 14 qualification of compound, 15 Hodgkin’s and Reed/Sternberg (HRS), 334 Hodgkin’s lymphoma (HL), 334 HTS. See High throughput screening Human T cell lymphotrophic virus (HTLV-1), 334 Human-tumor xenografts. See also Acute lymphoblastic leukemia (ALL) xenograft models animals, 171 molecular target characterization genomic profiling, 173 tissue microarrays, 173–174 tumor growth measurements, 171

685 I IkB kinase (IkK), 512 Imaging efficacy oncology models anatomical imaging, 222–224 CT, 218–219 functional imaging, 224–231 MRI, 217–218 PET, 219 SPECT, 220 ultrasound, 221–222 in vivo optical imaging, 220–221 preclinical imaging, 216–217 Inducible nitric oxide synthase (iNOS), 297 Inflammatory bowel disease (IBD), 509 Intraperitoneal injections murine animal model, 311 rat model, 310–311 ultra-structural analysis, 311 Intravital microscopy and image analysis conventional single-photon microscopy, 658 extravascular parameters gene expression, 668 interstitial and microvascular pO2 measurements, 665 interstitial diffusion, convection, and bindings, 667–668 interstitial pH measurements, 664–665 tissue nitric oxide distribution, 665–666 multiphoton laser-scanning microscopy, 658 optical frequency domain imaging, 658–660 tumor growth and regression, 660 vascular parameters angiogenesis and hemodynamics, 660–662 leukocyte endothelial interactions, 663 vascular permeability, 662–663 Invasive and metastatic disease animal models human tumor xenograft models, 472–474 organ colonization and site-selective metastases, 474–476 spontaneous metastasis models, 476–478 syngeneic rodent tumor models, 472 transgenic models, 478–480 detection and quantitation, 470–471 fluorescent tumor cells, 470–471 PET, 471 preclinical evaluation, 447 therapeutic strategies angiogenesis and hypoxia, 464–466 cancer stem-like cells, 467–468 dormant metastases, 468 immunological approaches, 469–470

686 Invasive and metastatic disease (cont.) intravasation and extravasation, 466–467 invasion and angiogenesis, 466 molecular targets, 450–464 premetastatic niche, 467 target identification and validation, 449–450 transgenic technology, 448 Investigational new drug (IND), 368 In vitro models cell-based, 13 cytotoxic vs. targeted, 15 integrin inhibitors, 14 qualification of compound, 15 In vivo models blood vessels, 146 bone metastases, 128 brain metastasis, 129 clonality of metastasis, 149 colon cancer progression, 131–132 dual-color cancer cells, 127–128 fluorescence dissecting microscope, 153 histone H2B gene and gene transduction, 127 LED flashlight and filters, 152 lymphatic vessels, 148 melanoma bone and organ metastasis, 129 multi-organ metastases, 129–130 occurrence of metastasis, 137–138 pancreatic cancer progression, 130–131 prostate cancer progression, 131 regional lung tumor metastases, 128 retrovirus production, 126–127 telomerase-dependent adenovirus, 138 variable magnification imaging chamber, 153–154 visceral metastasis, 128–129 whole-body optical imaging, 130 Isoflurane anesthetic properties, 560 drug, 559–560 equipment, 561 induction, 561–562 mice and rats, 561 typical applications, 560 L LBD. See Ligand-binding domain Leukemia/lymphoma, therapeutic approaches acute lymphocytic leukemia models B cell, 330 NUP214-ABL fusion protein, 331–332 SCID mice, 330–331 TRAIL mediates, 332

Index BCR-ABL, 326 chronic lymphocytic leukemia ADCC and CDC mechanisms, 333 B-CLL patients, 332 CD23 functions, 333–334 Tcl1, 333 Hodgkin’s lymphoma models ALCL group, 335–336 HRS cells, 334 SGN-30, 335 HTLV-related T cell, 334 mouse models, 326 multiple myeloma models bone marrow, 339, 340 bortezomib therapy, 340 CD74, 341 MC/CAR model, 341 sophisticated mouse models, 340 myeloid leukemia model AML, 327 APL mouse model, 329–330 BCR-ABL mutants, 328 characterized, 327 CML treatment, 328 mouse bone marrow and NOD/SCID mice, 329 SCID mice, 327 SDF-1/CXCR4 and xenograft models, 329 non-Hodgkin’s lymphoma models CD19, 337 CD20 and CD22, 339 Daudi-luc model, 338 Raji-luc cells, 337 T and B cells, 336 xenograft tumor models, 338 Lewis lung carcinoma (LLC), 49, 582 Ligand-binding domain (LBD), 399 M Magnetic resonance imaging (MRI), 407 Magnetic resonance spectroscopy (MRS), 226 Mammary cancer prevention c-myc, 430 cyclin D1, 430–431 dietary, 431–433 ErbB-2/HER2/neu models, 427–428 inducible models, 431 MMTV-Wnt-1, 429–430 p53-mutant mouse models, 428–429 ras mutant models, 430 SV40 T-antigen transgenic models, 428 TGFa models, 427

Index Mammary carcinogenesis, rats DMBA, 246 3-MC, 246 1-methyl-1-nitrosurea advantage, 253 biological characteristics, 251–252 chemoprevention protocol, 249–250 dose and age, 248–250 induction, 247–248 therapeutic protocol, 251 Mammary tumor virus-long terminal repeat (MMTV-LTR), 427 Man-made vitreous fiber 21 (MMVF 21), 315 Mast cell tumors (MCT), 356 Matrix metalloprotease (MMP), 448 Maximum tolerated dose (MTD), 4, 15, 523 Melanomas canine, 263–264 Monodelphis domestica dendritic melanocytes, 262 PRL, 261–262 murine models B16 cell line, 271–274 Cloudman line and Harding-Passey cell line, 271 nude mouse models, 276–277 physical agents, induction, 266–268 SCID mouse models, 277–279 transgenic mice, 268–270 Sinclair swine QTL mapping, 265 RARRES1, 266 stage I and stage II lesions, 265 tumor regression, 266 Xiphophorus macromelanophores, 261 Tu genes, 260 Xmrk gene, 260–261 Mesotheliomas asbestos-induced fiber types, 308–309 inhalation, 313–314 intraperitoneal asbestos injection, 310–312 intrapleural asbestos injection, 312–313 chemical, 314–315 immunohistochemical localization, 314 man-made fibers, 315 Nf2, Ink4a/ARF, and P53, 318–319 orthotopic transplants, 319–321 SV40 transgenic mouse models, 318 viral hamster models, 316–318 xenograft, 319–321

687 Metastasis, therapeutic strategies angiogenesis and hypoxia lysyl oxidases, 465–466 VEGF, 464–465 cancer stem-like cells, 467–468 dormant metastases, 468 immunological approaches, 469–470 intravasation and extravasation, 466–467 invasion and angiogenesis, 466 molecular targets BCr-Abl, 463 chemokine receptors, 462–463 combination therapies, 464 Hedgehog (Hh), 463 HSP90 chaperone, 462 oncogenic receptor, 450–462 Wnt pathway, 464 premetastatic niche, 467 target identification and validation, 449–450 3-Methylcholanthrene (3-MC), 246, 314 1-Methyl-1-nitrosurea (MNU) biological characteristics, 251–252 induction, 247–248 single i.p. injection, 50 mg MNU/ kg, 250 typical animal protocols chemoprevention, 249–250 therapeutic, 251 Microvessel density (MVD), 400 Mitogen-activated protein kinase (MAPK), 590 Mitoxantrone-resistant P388 leukemia (P388/ DIOHA), 37, 38 Molecular characterization copy number alterations, 199 correlation of DNA copy, 200 model fidelity, 201 primary screening, 202 solid tumors, 203 Monitoring tumor progression GFP, 107 in-vivo techniques, 107 in situ caliper measurements, 106 test agents, 106 therapeutic response, 107 Monodelphis domestica dendritic melanocytes, 262 PRL, 261–262 Murine L1210 leukemia characteristics DBA/2 mice, 25 net log10 cell kill, 26 Southern Research Institute, 25 clinical agents, 26–32 drug-resistant leukemias, 34–38

688 Murine L1210 leukemia (cont.) drug screening CCNSC, 24 primary screen, 24, 25 United States Congress, 24 predictive value, 32–33 Murine models B16 cell line antimetastatic therapies, 272–273 intraperitoneal (ip) injection, 274 pulmonary metastases, 273 subclones, 272 Cloudman line and Harding-Passey cell line, 271 nude mouse models, 276–277 physical agents, induction K1735 cell line, 267 UVR, 267–268 SCID mouse models, 277–279 transgenic mice cdk4 and cdk6, 269 HGF/SF and MT-RET, 270 T antigen, 269 Murine syngeneic renal adenocarcinoma bFGF, 294 Fas/FasL pathway, 292 HBSS, 289 HDAC inhibitor, 290–291 IL-4 and IFNg, 291 immunotherapy approaches, 289 intratumoral injection, 293 transfection and VEGF, 294 Murine tumor models animal anesthesia, 553–554 animal support analgesia, 558 body temperature, 556–557 hydration, 558 inhalable anesthetics, 558–559 respiration, 557–558 cancer research anticancer treatment, 555 assessment, 556 functional studies, 555 non-survival surgery, 554 survival surgery, 554–555 injectible anesthetics biochemistry, 562 ketamine/xylazine, 563–564 properties, 562–563 rodent physiology, 563 isoflurane anesthetic properties, 560 drug, 559–560

Index equipment, 561 induction, 561–562 mice and rats, 561 typical applications, 560 pentobarbital anesthetic properties, 564–565 applications, 565 biochemistry, 564 injectibles, 566 physiological impact, 565 preparation, 565–566 protocol, 566 Myelosuppression, 534 N National Cancer Institute (NCI), 6, 9, 44, 196 Neuroendocrine (NE), 400 Non-mammalian models multicellular avian embryo, 10 Caenorhabditis elegans, 10 drosophila strains, 9 xenopus tadpole models, 10 zebrafish, 9, 11 unicellular, 8–9 Nonsmall cell lung cancer (NSCLC), 584 Norton-Simon model, 577 O OCT. See Optical coherence tomography OFDI. See Optical frequency domain imaging Oncology models anatomical imaging, 222–224 computed tomography, 218–219 functional imaging apoptosis, 230–231 blood flow, 227–229 cell proliferation, 227 gene expression, 229–230 pathways, 231 permeability, 227–229 proteolytic activity, 231 receptor occupancy, 229–230 tissue hypoxia and pH, 230 tumor cellularity, 229 tumor metabolism and metabolite levels, 224–226 tumor stem cells, 231 vascular imaging, 227–229 magnetic resonance imaging, 217–218 positron emission tomography, 219

Index single photon emission computed tomography, 220 ultrasound, 221–222 in vivo optical imaging, 220–221 Optical coherence tomography (OCT), 671 Optical frequency domain imaging (OFDI), 658, 660 Orthotopic models bone metastases, 128 brain metastasis, 129 prostate-cancer bone and visceral metastasis, 128–129 regional lung tumor metastases, 128 Osteosarcomas (OSA), 356 P Paclitaxel-resistant P388 leukemia (P388/ PTX), 37, 38 Pancreatic cancer, 434–435 Pancreatic ductal adenocarcinoma (PDAC) compound inducible mutants vs. Kras and Tumor, 384–386 preclinical, 386–388 histological and molecular characteristics ARF tumor, 379 BRCA2, 380 PanIN, 379 tumor progression, 380 types, 379 modeling PDAC, 380–381 ongoing and future modeling efforts Cre activation, 388 Kras-mediated transformation, 388 lung cancer, 389 Nestin-Cre transgene, 388 telomeres, 389–390 tumor maintenance, 389 pancreas anatomy, physiology, and development, 378–379 transgenic models, 381–382 viral delivery oncogenes, 382 Patient tumor explants chemotherapy, 172 clonogenic assay culture methods, 174–175 single cell suspensions, 174 cytotoxic agents drug response, 177–178 Freiburg xenograft panel, 178–179 evaluation parameters, 173 future impact, 23 gene signatures, 175–176 historical perspective, 167–168

689 human-tumor xenografts, 169, 171 molecular target characterization anticancer therapy, 182–183 genomic profiling, 173 target prevalence, 184–185 tissue microarrays, 173–174, 183–184 rates and growth behavior, 176–177 strength of human models, 168–169 study design, 171–172 targeted agents drug response, 178 Freiburg xenograft panel, 179–181 target-oriented approach, 188 in vivo efficacy, 185–187 Pediatric Drug Development Group (PedDDG), 211 Pediatric Oncology Preclinical Protein-Tissue Array Project (POPP-TAP), 197 Pediatric preclinical testing program (PPTP) ALL xenograft models EFS T/C value, 205 event-free survival, 204 tumor growth delay value, 205 clinical evaluation, 212 combination drug testing, 209–210 data presentation, 206–207 integrating molecular data, 210–211 model fidelity, 201 National Cancer Institute, 196 primary screening, 202 secondary models, 210 secondary screening, 208–209 selection clustering of xenografts, 198 POPP-TAP, 197 solid tumors, 203 Pentobarbital anesthetic properties, 564–565 applications, 565 biochemistry, 564 injectibles, 566 physiological impact, 565 preparation, 565–566 protocol, 566 Phosphatidyl-serine (PS), 230–231 PIN. See Prostatic intraepithelial neoplasia Platelet-derived growth factor (PDGFR), 590 P388 leukemia characteristics, 25–26 clinical agents, 26–32 drug-resistant leukemias alkylating agents, 34–36 antimetabolites, 36 DNA-and tubulin-binding agents, 36–38

690 P388 leukemia (cont.) drug screening, 24–25 predictive value, 32–33 PML. See Promyelocytic leukemia Polyoma virus middle T (PyMT), 478 Positron emission tomography (PET), 219, 471 PPTP. See Pediatric preclinical testing program Preclinical tumor response ascites tumors assumptions, 572 L1210 leukemic cells, 572–573 tumor cell kill, 574 combination treatments Agent A and B, 580 cyclophosphamide and melphalan, 582 EMT6 mouse, 583 gemcitabine, 583–584 HCT116 colon carcinoma, 585 isobologram, 581, 582 Lewis lung carcinoma, 582–583, 585 lung metastases, 584 malignant disease process, 578–579 vinorelbine, 584 drug penetration [14C]albumin and [14C]paclitaxel, 592 enzastaurin, 593, 594 FSaIIC tumor cells, 590, 591 plasma VEGF levels, 595–597 TNP-470/minocycline, 590, 592 treatment, 593 VEGF levels, 593–594 malignant diseases, 598 primary and metastatic disease, 587–588 solid tumors cell killing, 577 measurements, 575 murine CT-26 colon carcinoma, 576 Norton-Simon model, 577 tumor growth, 574–575 therapeutic index, 586–578 in vivo resistant tumors, 589–590 Promyelocytic leukemia (PML), 327 Prostate cancer prevention c-Myc transgenic mice, 425 PTEN mutant mouse models, 424–425 viral oncogene models mPIN, 426–427 TRAMP mice, 425–426 Prostate-specific antigen (PSA), 398 Prostatic intraepithelial neoplasia (PIN), 398

Index Q Quality of life (QoL), 499 Quantitative trait loci (QTL) mapping, 265 R Radiation-induced dermatitis biological process, 512 dorsal skin, 512 murine model, 513 pathoetiology, 512 side effect, 511 proctitis biological process, 510 diagnosis and treatment, 509–510 rat models, 510 symptoms, 509 Red fluorescent protein (RFP) gene transduction, 127 retrovirus production, 157–158 transgenic nude mouse, 140–141 whole-body imaging, 130–131 Renal cell carcinoma (RCC) pathogenesis debulking nephrectomy, 288 Eker rat model, 295–296 properties, 288 Renca model bFGF, 294 Fas/FasL pathway, 292 HBSS, 289 HDAC inhibitor, 290–291 IL-4 and IFNg, 291 immunotherapy approaches, 289 intratumoral injection, 293 transfection, 294 VEGF, 294 Wistar-Lewis rat renal adenocarcinoma, 13–14 xenografts IFNb, 297 KCI-18, 298 RTK inhibitor, 299 VEGFR-2 signaling pathway, 300 RFP. See Red fluorescent protein Rhabdomyosarcoma, 196 S Severe combined immune deficiency (SCID) HTLV-1 virus, 334 immunodeficient mice, 102–103, 277–279 transplanted human tumors, 48 Simian virus (SV), 424

Index Single photon emission computed tomography (SPECT), 220 Solid tumors cell-kill calculation, 73–74 cell killing, 577 chemotherapy, 72–73 leukemia L1210/0, 74–75 measurements, 575 murine CT-26 colon carcinoma, 576 Norton-Simon model, 577 percent increase life span, 73 tumor growth, 574–575 tumor-growth delay, 73 X-irradiation, 72 Structure-activity relationship (SAR), 576 Subcutaneous (SC) implantations, 82–83 Superparamagnetic iron oxide contrast agents (SPIOs), 228 Surfaced enhanced laser desorption ionization time-of-flight (SELDI-TOF), 387 Surgical orthotopic implantation (SOI), 104 colon cancer colonic transplantation, 156–157 intrahepatic transplantation, 157 lung cancer, 156 ovarian cancer, 155–156 prostate cancer, 156 SV40 models transgenic mouse, 318 viral hamster, 316–318 T Technology-intensive screening chemogenomics, 12 HTS, 11 nanotechnology, 12–13 proteome and kinome, 12 RNA interference, 13 Toxicities, animal models anti-neoplastic drugs and radiation current models, 502–508 mucositis, 501, 502 non-surgical cancer therapy, 500 bisphosphonates, jaws BevDermatitis scoring scale, 514, 515 BRONJ, 514, 516 rat model, 514 chemotherapy-induced mucosal injury, 508–509 clinical ramifications, 499 radiation-induced dermatitis dorsal skin and biological process, 512 murine model, 513

691 pathoetiology, 512 side effect, 511 radiation-induced proctitis biological process, 510 diagnosis and treatment, 509–510 rat models, 510 symptoms, 509 Transforming growth factor-b (TGF-b), 512, 590 Transgenic adenocarcinoma mouse prostate (TAMP), 425 Transgenic adenocarcinoma of the mouse prostate (TRAMP) definition and cell lines, 398 DNA methylation, 414 gene expression vs. AD and CR tumors, 402 biological processes, 402 epigenetic regulation, 403–405 PIN, 403 SAGE, 401 phenotypic characterization androgen receptor, 399 angiogenesis, 400 C57BL/6 mice, 399 FGF2 and VEGF receptors, 400 neuroendocrine (NE) cells, 400–401 prostate cancer, 397 putative therapeutic agents, testing doxorubicin, 412 immunotherapy, 413 MRI, 407 PIN lesions, 414 preclinical trialsin, 413 PSCA, 413 validating and elucidating gene function, 406–407 Transgenic mouse models Apc-deficient models, 433–434 biological system, 423 mammary cancer prevention c-myc, 430 cyclin D1, 430–431 dietary, 431–433 ErbB-2/HER2/neu models, 427–428 inducible models, 431 MMTV-Wnt-1, 429–430 p53-mutant mouse models, 428–429 ras mutant models, 430 SV40 T-antigen transgenic models, 428 TGFa models, 427 pancreatic cancer, 434–435 prostate cancer prevention c-Myc transgenic mice, 425 PTEN mutant mouse models, 424–425 viral oncogene models, 425–427

692 Transparent window models and intravital microscopy acute (exteriorized) preparations liver tumor, 652–653 mammary fat pad tumor, 654 mesentery, 652 pancreatic tumor, 653–654 angiogenesis and oncogenesis, 641 categories, 642 chronic window preparations advantages and disadvantages, 644–645 angiogenesis gel assay and tissue engineered vessel model, 649–651 cranial window, 648–649 dorsal skin chamber, 647 mammary fat pad chamber, 648 rabbit ear chamber, 646–647 intravital microscopy and image analysis conventional single-photon microscopy, 658 extravascular parameters, 664–668 multiphoton laser-scanning microscopy, 658 optical frequency domain imaging, 658–660 tumor growth and regression, 660 vascular parameters, 660–663 novel insights, 669–670 in situ preparations CAM assay, 654–655 chick chorioallantoic membrane, 655–656 corneal pocket assay, 655 ear model, 656–657 tail lymphatics, 656 Transplantable syngeneic rodent tumors adenocarcinoma-755, 54 cell culture, 51–52 characterization, 69–70 chemotherapy human pancreatic ductal adenocarcinoma, 64, 65 leukemias, L1210/0, 72 log-kill (gross), 66 MX-1 mammary tissue, 68 NCI cutoff, 65 SC mammary adenocarcinoma 16/c, 67 colon adenocarcinoma-36, 59 evidence and consequences factors, 45, 47 lack of invasion and metastasis, 50 Lewis lung carcinoma, 49 thymic nude mice and SCID mice, 47, 48

Index feeder effect, 54, 55 first-order kinetic curves, 55, 57 immune-deficient mice, 50–51 in-vivo activity, 46 maintenance, 71 non-curability of IV, 64 origins, 71–72 quality-control monitoring, 69–70 solid tumors cell-kill calculation, 73–74 leukemia L1210/0, 74–75 percent increase life span, 73 tumor-growth delay, 73 take-rates colon adenocarcinoma-10, 53 mammary adenocarcinoma-16/C, 52, 62–63 T/C value, 75 X-irradiation, 55, 58 Trypan blue assay, 617 Tuberous sclerosis gene 2 (Tsc2), 295–296 Tumor biology genetic regulation Bcl-2, 629 cytochrome c and p53, 630 tumor development, 628–629 Tumor cell survival analysis, 620–621 clonogenic assays radiobiologists, 618 treatment, 619–620 counting process, 617 implications, 611 measuring clonogenicity cell density problems, 614–615 colonies process, 615–616 colony density problems, 615 identifying clonogenic cells, 612–613 motion artifacts, 614 preparation, 616–617 quantitative tumor transplantation assay, 608 single-cell suspensions, 617–618 stem cell, 608 tumor-host systems human tumor xenografts, 609 immature animals, 610 nonpathogenic, 610 rodent tumors, 609 Tumor-host systems human tumor xenografts, 609 nonpathogenic and immature animals, 610 rodent tumors, 609 Tumor necrosis factor-a (TNF), 412 Tyrosine kinase inhibitor (TKI), 327, 356

Index V Vascular endothelial growth factor (VEGF), 229, 400 Vincristine-resistant P388 leukemia (P388/ VCR), 37, 38 W White blood cells (WBC), 507 Whole-body imaging colon cancer progression, 131–132 imaging chambers, 154–155 pancreatic cancer progression, 130–131 prostate cancer progression, 131 Wistar-Lewis rat renal adenocarcinoma, 13–14 X Xenograft efficacy models cultured tumor cells vs. tumor fragments cryo-preserved cell, 103 solid tumors, 104 future perspectives NCI-60, 115 preclinical tumor models, 116 immunodeficient mice, 102–103 monitoring tumor progression GFP and in-vivo techniques, 107 in situ caliper measurements, 106 test agents, 106 therapeutic response, 107

693 pharmacology and pharmacokinetic correlations anticancer drugs, 114 biomarkers, 114–115 pros and cons genetically engineered models, 112 orthotopic procedures and mutational changes, 113 single agent and preclinical trials anti-EGFR, 109 clinical responses, 110–111 EGFR-TKI, 109 gefitinib, 109 hollow fiber assays, 109 subcutaneous vs. orthotopic transplantation hormone supplements, 105 SOI models, 104 tumor metastasis injection of cells, 105 inoculation, 105, 106 Xenograft tumor models, 341 Xiphophorus macromelanophores, 261 Tu genes, 260 Xmrk gene (check), 260–261 Z Zoledronic acid and dexamethasone (Z/D), 514