Multi-Drug Resistance in Cancer [1 ed.] 9781607614159, 1607614154

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METHODS

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MOLECULAR BIOLOGY™

Series Editor John M. Walker School of Life Sciences University of Hertfordshire Hatfield, Hertfordshire, AL10 9AB, UK

For other titles published in this series, go to www.springer.com/series/7651

Multi-Drug Resistance in Cancer Edited by

Jun Zhou Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China

Editor Jun Zhou Department of Genetics and Cell Biology College of Life Sciences Nankai University Tianjin China

ISSN 1064-3745 e-ISSN 1940-6029 ISBN 978-1-60761-415-9 e-ISBN 978-1-60761-416-6 DOI 10.1007/978-1-60761-416-6 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2009938934 © Humana Press, a part of Springer Science+Business Media, LLC 2010 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 Springer is part of Springer Science+Business Media (www.springer.com)

Preface Chemotherapy is one of the major treatment options for cancer patients; however, the efficacy of chemotherapeutic management of cancer is severely limited by multidrug resistance, in that cancer cells become simultaneously resistant to many structurally and mechanistically unrelated drugs. In the past three decades, a number of mechanisms by which cancer cells acquire multidrug resistance have been discovered. In addition, the development of agents or strategies to overcome resistance has been the subject of intense study. This book contains comprehensive and up-to-date reviews of multidrug resistance mechanisms, from over-expression of ATP-binding cassette drug transporters such as P-glycoprotein, multidrug resistance-associated proteins, and breast cancer resistance protein to the drug ratio-dependent antagonism and the paradigm of cancer stem cells. The book also includes strategies to overcome multidrug resistance, from the development of compounds that inhibit drug transporter function to the modulation of transporter expression. In addition, this book contains techniques for the detection and imaging of drug transporters, methods for the investigation of drug resistance in animal models, and strategies to evaluate the efficacy of resistance reversal agents. The book intends to provide a state-of-the-art collection of reviews and methods for both basic and clinician investigators who are interested in cancer multidrug resistance mechanisms and reversal strategies. Tianjin, China

Jun Zhou

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Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2

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Multidrug Resistance in Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bruce C. Baguley Multidrug Resistance in Oncology and Beyond: From Imaging of Drug Efflux Pumps to Cellular Drug Targets . . . . . . . . . . . . . . . . . . . . . . . . . . Wouter B. Nagengast, Thijs H. Oude Munnink, Eli C.F. Dijkers, Geke A.P. Hospers, Adrienne H. Brouwers, Carolien P. Schröder, Marjolijn Lub-de Hooge, and Elisabeth G.E. deVries Studying Drug Resistance Using Genetically Engineered Mouse Models for Breast Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sven Rottenberg, Marina Pajic, and Jos Jonkers Mechanisms of Multidrug Resistance in Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . Jean-Pierre Gillet and Michael M. Gottesman Molecular Mechanisms of Drug Resistance in Single-Step and Multi-Step Drug-Selected Cancer Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anna Maria Calcagno and Suresh V. Ambudkar Pharmacogenetics of ATP-Binding Cassette Transporters and Clinical Implications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ingolf Cascorbi and Sierk Haenisch Flow Cytometric Evaluation of Multidrug Resistance Proteins . . . . . . . . . . . . . . . Adorjan Aszalos and Barbara J. Taylor Targeted Chemotherapy in Drug-Resistant Tumors, Noninvasive Imaging of P-Glycoprotein-Mediated Functional Transport in Cancer, and Emerging Role of Pgp in Neurodagenerative Diseases . . . . . . . . . . . . . . . . . . Jothilingam Sivapackiam, Seth T. Gammon, Scott E. Harpstrite, and Vijay Sharma Epigenetic Regulation of Multidrug Resistance 1 Gene Expression: Profiling CpG Methylation Status Using Bisulphite Sequencing . . . . . . . . . . . . . . Emma K. Baker and Assam El-Osta Expression and Function of P-Glycoprotein in Normal Tissues: Effect on Pharmacokinetics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Frantisek Staud, Martina Ceckova, Stanislav Micuda, and Petr Pavek Molecular Mechanism of ATP-Dependent Solute Transport by Multidrug Resistance-Associated Protein 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiu-bao Chang Impact of Breast Cancer Resistance Protein on Cancer Treatment Outcomes . . . . Douglas D. Ross and Takeo Nakanishi

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Drug Ratio-Dependent Antagonism: A New Category of Multidrug Resistance and Strategies for Its Circumvention . . . . . . . . . . . . . . . . . . . . . . . . . . Troy O. Harasym, Barry D. Liboiron, and Lawrence D. Mayer 14 Reversing Agents for ATP-Binding Cassette Drug Transporters . . . . . . . . . . . . . . Chow H. Lee 15 Overcoming Multidrug Resistance in Cancer: Clinical Studies of P-Glycoprotein Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Helen M. Coley 16 Pharmacokinetic and Pharmacodynamic Implications of P-Glycoprotein Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jeannie M. Padowski and Gary M. Pollack 17 Examination of CYP3A and P-Glycoprotein-Mediated Drug–Drug Interactions Using Animal Models. . . . . . . . . . . . . . . . . . . . . . . . . . . Punit H. Marathe and A. David Rodrigues 18 Generating Inhibitors of P-Glycoprotein: Where to, Now? . . . . . . . . . . . . . . . . . . Emily Crowley, Christopher A. McDevitt, and Richard Callaghan 19 Immunosuppressors as Multidrug Resistance Reversal Agents . . . . . . . . . . . . . . . . Hamid Morjani and Claudie Madoulet 20 Overcoming Multidrug Resistance by RNA Interference . . . . . . . . . . . . . . . . . . . Alexandra Stege, Andrea Krühn, and Hermann Lage 21 Circumventing Tumor Resistance to Chemotherapy by Nanotechnology . . . . . . . Xing-Jie Liang, Chunying Chen, Yuliang Zhao, and Paul C. Wang Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors SURESH V. AMBUDKAR • Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, NIH, DHHS, Bethesda, MD, USA ADORJAN ASZALOS • Laboratory of Cell Biology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA BRUCE C. BAGULEY • Auckland Cancer Society Research Centre, The University of Auckland, Auckland, New Zealand EMMA K. BAKER • Epigenetics in Human Health and Disease Laboratory, Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia ADRIENNE H. BROUWERS • Department of Medical Oncology, University Medical Center Groningen, Groningen, The Netherlands ANNA MARIA CALCAGNO • Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, NIH, DHHS, Bethesda, MD, USA RICHARD CALLAGHAN • Nuffield Department of Clinical Laboratory Sciences, John Radcliffe Hospital, University of Oxford, Oxford, UK INGOLF CASCORBI • Institute of Experimental and Clinical Pharmacology, University of Kiel, Kiel, Germany MARTINA CECKOVA • Department of Pharmacology and Toxicology, Faculty of Pharmacy in Hradec Kralove, Charles University in Prague, Hradec Kralove, Czech Republic XIU-BAO CHANG • Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA CHUNYING CHEN • Key Laboratory of Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanosciences and Technology of China, Beijing, China Key Laboratory of Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China HELEN M. COLEY • Division of Biological Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK EMILY CROWLEY • Nuffield Department of Clinical Laboratory Sciences, John Radcliffe Hospital, University of Oxford, Oxford, UK ELI C.F. DIJKERS • Department of Medical Oncology, University Medical Center Groningen, Groningen, The Netherlands ASSAM EL-OSTA • Epigenetics in Human Health and Disease Laboratory, Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia SETH T. GAMMON • Molecular Imaging Center, Mallinckrodt Institute of Radiology, Washington University Medical School, St. Louis, MO, USA JEAN-PIERRE GILLET • Laboratory of Cell Biology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA MICHAEL M. GOTTESMAN • Laboratory of Cell Biology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA SIERK HAENISCH • Institute of Experimental and Clinical Pharmacology, University of Kiel, Kiel, Germany

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TROY O. HARASYM • Celator Pharmaceuticals Corp., Vancouver, BC, Canada SCOTT E. HARPSTRITE • Molecular Imaging Center, Mallinckrodt Institute of Radiology, Washington University Medical School, St. Louis, MO, USA MARJOLIJN LUB-DE HOOGE • Department of Medical Oncology, University Medical Center Groningen, Groningen, The Netherlands GEKE A.P. HOSPERS • Department of Medical Oncology, University Medical Center Groningen, Groningen, The Netherlands JOS JONKERS • Division of Molecular Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands ANDREA KRÜHN • Charité Campus Mitte, Institute of Pathology, Berlin, Germany HERMANN LAGE • Charité Campus Mitte, Institute of Pathology, Berlin, Germany CHOW H. LEE • Chemistry Program, University of Northern British Columbia, Prince George, BC, Canada XING-JIE LIANG • Key Laboratory of Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanosciences and Technology of China, Beijing, China BARRY D. LIBOIRON • Celator Pharmaceuticals Corp., Vancouver, BC, Canada CLAUDIE MADOULET • Laboratoire de Biochimie, Reims Pharmacy School, Reims Cedex, France PUNIT H. MARATHE • Metabolism and Pharmacokinetics, Bristol-Myers Squibb, Princeton, NJ, USA LAWRENCE D. MAYER • Celator Pharmaceuticals Corp., Vancouver, BC, Canada CHRISTOPHER A. MCDEVITT • Nuffield Department of Clinical Laboratory Sciences, John Radcliffe Hospital, University of Oxford, Oxford, UK STANISLAV MICUDA • Department of Pharmacology and Toxicology, Faculty of Pharmacy in Hradec Kralove, Charles University in Prague, Hradec Kralove, Czech Republic HAMID MORJANI • MEDyC Unité CNRS UMR6237, Reims Pharmacy School, Reims Cedex, France THIJS H. OUDE MUNNINK • Department of Medical Oncology, University Medical Center Groningen, Groningen, The Netherlands WOUTER B. NAGENGAST • Department of Medical Oncology, University Medical Center Groningen, Groningen, The Netherlands TAKEO NAKANISHI • Department of Membrane Transport and Biopharmaceutics, Kanazawa University School of Pharmaceutical Sciences, Kanazawa, Japan JEANNIE M. PADOWSKI • Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA MARINA PAJIC • Division of Molecular Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands PETR PAVEK • Department of Pharmacology and Toxicology, Faculty of Pharmacy in Hradec Kralove, Charles University in Prague, Hradec Kralove, Czech Republic GARY M. POLLACK • Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA A. DAVID RODRIGUES • Metabolism and Pharmacokinetics, Bristol-Myers Squibb, Princeton, NJ, USA DOUGLAS D. ROSS • University of Maryland Greenebaum Cancer Center, University of Maryland School of Medicine, and the Baltimore VA Medical Center, Baltimore, MD, USA SVEN ROTTENBERG • Division of Molecular Biology, The Netherlands Cancer Institute, Amsterdam, The Netherlands

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CAROLIEN P. SCHRÖDER • Department of Medical Oncology, University Medical Center Groningen, Groningen, The Netherlands VIJAY SHARMA • Molecular Imaging Center, Mallinckrodt Institute of Radiology, Washington University Medical School, St. Louis, MO, USA JOTHILINGAM SIVAPACKIAM • Molecular Imaging Center, Mallinckrodt Institute of Radiology, Washington University Medical School, St. Louis, MO, USA FRANTISEK STAUD • Department of Pharmacology and Toxicology, Faculty of Pharmacy in Hradec Kralove, Charles University in Prague, Hradec Kralove, Czech Republic ALEXANDRA STEGE • Charité Campus Mitte, Institute of Pathology, Berlin, Germany BARBARA J. TAYLOR • Laboratory of Cancer Biology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA ELISABETH G.E. DE VRIES • Department of Medical Oncology, University Medical Center Groningen, Groningen, The Netherlands PAUL C. WANG • Laboratory of Molecular Imaging, Department of Radiology, Howard University, Washington, DC, USA YULIANG ZHAO • Key Laboratory of Biomedical Effects of Nanomaterials and Nanosafety, National Center for Nanosciences and Technology of China, Beijing, China Key Laboratory of Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China

Chapter 1 Multidrug Resistance in Cancer Bruce C. Baguley Abstract It is becoming increasingly clear that the proliferation of human tumours is driven by a small proportion of cells, termed tumour stem cells, which have the properties of self-renewal. On analogy with stem cells for normal tissues, there are likely to be multiple mechanisms, involving both intrinsic cellular properties and microenvironmental factors, which enable tumour stem cells to resist potentially genotoxic agents. Intrinsic properties include maintenance of cells in a predominantly non-cycling state, expression of transport proteins such as P-glycoprotein, protection from induced apoptosis or other forms of cell death, and limitation of diffusion of potential cytotoxins from the bloodstream. In addition, tumour stem cells are likely to contain multiple genetic changes that will potentially activate host immune mechanisms, which are designed to respond to such changes, and the methods by which tumours suppress such mechanisms are of great relevance to drug resistance. A number of methods of overcoming intrinsic multidrug resistance of tumours have been developed but methods for overcoming tumour resistance mediated by host cells are still at an early stage and require further research. Key words: Cytokinetics, ABC transporters, Drug diffusion, Apoptosis, Tumour dormancy, Macrophages, Apoptosis, Niche, Microenvironment

1. Introduction Cancer multidrug resistance describes a phenomenon whereby resistance to one anticancer drug is accompanied by resistance to drugs whose structures and mechanisms of action may be completely different. One might consider the following two theoretical examples; in the first, a woman is diagnosed with advanced ovarian cancer. Chemotherapy is commenced using combined carboplatin and paclitaxel and a complete remission is obtained. After an interval of 1 year, an abdominal mass is detected and combination therapy is reinstituted. However, in this case there is no significant reduction in tumour mass and after four cycles, treatment with irinotecan is initiated. No response is obtained and treatment is continued with doxorubicin, again with no response. J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_1, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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In the second example, a patient diagnosed with metastatic pancreatic cancer is treated with the drug gemcitabine. Both the primary tumour and a lymph node metastasis continue to grow, and chemotherapy is changed to a combination of 5-fluorouracil and oxaliplatin, but again with no effect on tumour progression. These examples demonstrate the two main types of multidrug resistance, one acquired during treatment and the other preexisting at the time of diagnosis. Early ideas on the nature of multidrug resistance were strongly influenced by studies on multidrug resistance in bacteria, in which resistant strains with an identified genetic basis for a lack of response to multiple antibiotics and/or chemotherapeutic agents can be characterised (1). Experimental models for tumour growth were based particularly on transplantable murine leukaemias where it was assumed that the majority of transplanted tumour cells were capable of forming tumours and that resistant cells could be identified or selected for. The development of stem cell theory for animal and human tissues, with its subsequent extension to tumour tissue, changed this concept by postulating that survival of normal or tumour tissue is controlled not by the whole population but by a very small proportion of the total cells that have the property of selfrenewal. The tumour stem cell model, which has had increasing general acceptance, implies that the resistance properties of the tumour stem cell population will dictate overall response to therapy. An important facet of this model is that the survival properties of the tumour stem cells are determined from the microenvironment of these cells, which is usually referred to as the niche. This model highlights two principal methodological problems in the investigation of resistance; cancer stem cells within a tumour population cannot easily be directly identified and also cannot be understood adequately when they are separated from the niche environment. Stem cells in normal tissues have multiple resistance mechanisms to preserve their integrity in the face of potential mutagenic mechanisms associated with inflammation, infection, and dietary toxins. The host stromal cells in the niche microenvironment, particularly those of fibroblast origin, provide soluble and matrixlinked factors to inhibit cell division and apoptosis while simultaneously preserving a primitive multipotent phenotype (2). This microenvironment has been particularly well characterised for the bone marrow (3), where the low oxygen tension suggests diminished perfusion of oxygen and probably also diminished perfusion of potential toxins and mutagens (4). Stem cells appear to express multiple transport proteins of ATP-binding cassette (ABC) family excluding toxins and mutagens (5). In addition, stem cells express pathways such as NF-kB and bcl-2 that protect them from the induction of apoptosis (6). Thus, the protection of normal stem cells from death is a combination of intrinsic (cellular) and extrinsic (microenvironmental) factors.

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The aim of this review is to consider mechanisms of cancer multidrug resistance in the context of stem cell theory, and then to consider possible methods to overcome these mechanisms. While most reviews on multidrug resistance have often concentrated on intrinsic multidrug resistance of tumour cells, this review also includes discussion of extrinsic contributions to multidrug resistance. The picture of multidrug resistance that emerges from this discussion is therefore a complex one, and the development of strategies to combat clinical multidrug resistance requires considerable further research and mechanistic insight.

2. Resistance Properties of Tumour Stem Cells 2.1. Cytokinetic Resistance

A salient feature of stem cells in normal tissue is that their specialised microenvironment (niche) maintains proliferation at a low level and therefore protects them from genetic (or epigenetic) damage. As shown in Fig. 1.1, this involves stromal cell production of factors, generally members of the TGFb/BMP superfamily, which act on stem cells through corresponding surface receptors, smad family proteins, and signalling pathways (7) and result in the increased production of cyclin-dependent kinase (cdk) inhibitors such as p15, p16, p21, and p27. Tumour stem cells are likely to be contained in a similar niche microenvironment, but because of genetic or epigenetic changes that reduce or prevent their production their production of cdk inhibitors is defective in many cases. Thus, proliferation of tumour stem cells is inadequately constrained and cells leave the niche to become transit amplifying cells that continue, at least initially, to proliferate.

Fig. 1.1. Simplified examples of control of stem cell proliferation by the niche microenvironment. (a) Control by members of the TGF-b superfamily, thought to be an important mechanism in the stem cell niche of normal tissues. (b) Control by g-interferon, thought to be an important mechanism underlying tumour cell dormancy.

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The loss of control of proliferation of tumour stem cells in the niche should theoretically make them more sensitive than normal tissue stem cells to cytotoxic agents. There is a second potential mechanism, as shown in Fig. 1.1, whereby the proliferation of tumour stem cells may be constrained by the niche microenvironment. The clinical observation that a kidney transplant recipient developed melanoma with the genotype of the kidney donor, even though the donor had been free of melanoma for 16 years (8) as well as other examples (9) point to a mechanism whereby tumour stem cells may be maintained for long periods in a non-proliferating state. A possible explanation for the loss of dormancy following treatment of the recipient patient with immunosuppressive drugs is that such therapy weakens T cell-mediated induction of dormancy. Surgery might in some cases also induce a loss of dormancy (10). A preclinical model for tumour dormancy is provided by a study in which a group of mice were treated with a carcinogen. Under normal conditions, only a small proportion developed tumours, but when tumour-free mice were treated subsequently with antibodies to T cells or to g-interferon a high proportion developed tumours (11). Examination of tumour-free mice before immunosuppressive treatment revealed the presence of microscopic groups of tumour cells associated with T cells. From these observations one can hypothesise that IFNg acts in concert with other cytokines, probably through the induction of STAT1 (12) to constrain tumour cell proliferation (13). Other host cell-mediated mechanisms may also contribute to reduction of tumour cell proliferation, raising the possibility that immune cell-mediated release of cytokines or other factors can augment factors produced by the normal stroma to maintain tumour stem in a non-proliferating and thus drug-resistant state. 2.2. Multidrug Resistance Mechanisms Preventing Drugs from Reaching Target Cells

Regardless of whether they are administered orally, intravenously, intra-arterially, or by other routes, anticancer drugs must diffuse from the bloodstream to individual tumour stem cells. The vascular density of the tumour, which determines the mean diffusion distance from blood supply to the individual tumour cell, will have a major effect on diffusion time for some drugs and can contribute to multidrug resistance. As with the case of normal bone marrow, there is evidence that tumour stem cells may exist in a state of low oxygen tension, suggesting the presence of a perfusion barrier, which could limit the rate of penetration of anticancer drugs (4). Tumour hypoxia may also be either intrinsic (related to vascular geometry) or intermittent (related to temporal changes in tumour blood flow), and both of these states may contribute to drug access and efficacy. Drug diffusion depends not only on tumour architecture and dynamics but also on drug properties such as the molecular weight and degree of protein binding.

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Albumin is a common binding protein that accounts for the low free drug fraction of many drugs, and a-acid glycoprotein, an acute phase protein commonly elevated in cancer patients, can also have a major effect on free drug concentrations (14). The free drug fraction can be quite low ( T2). Technetium-99m methoxyisobutylisonitrile chest single photon emission computed tomography and P-glycoprotein express ion. Oncology 63:173–179 11. Kim IJ, Bae YT, Kim SJ et al (2006) Determination and prediction of P-glycoprotein and multidrug-resistance-related protein expression in breast cancer with double-phase technetium-99m sestamibi scintimammography. Visual and quantitative analyses. Oncology 70:403–410 12. Hendrikse NH, de Vries EG, Franssen EJ, Vaalburg W, van der Graaf WT (2001) In vivo measurement of [11C]verapamil kinetics in human tissues. Eur J Clin Pharmacol 56: 827–829 13. Guhlmann A, Krauss K, Oberdorfer F et al (1995) Noninvasive assessment of hepatobiliary and renal elimination of cysteinyl leukotrienes

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Chapter 3 Studying Drug Resistance Using Genetically Engineered Mouse Models for Breast Cancer Sven Rottenberg, Marina Pajic, and Jos Jonkers Abstract The generation of genetically engineered mouse models (GEMMs) that mimic breast cancer in humans provides new tools to investigate mechanisms of drug resistance in vivo. The advantages are manifold: inbred mice do not have the genomic heterogeneity seen in patients; mammary tumors are superficial and therefore easily accessible for measurement and sampling pre- and posttreatment; tumors can be transplanted orthotopically into syngeneic, immunocompetent animals; and tumor cells can be modified in vitro (e.g., gene overexpression, shRNA knockdown, insertional mutagenesis) prior to transplantation. Here, we provide an overview with experimental details of various approaches to study mechanisms of drug resistance in GEMMs for breast cancer. Key words: GEMM, Breast cancer, Orthotopic transplantation, BRCA1, BRCA2, E-cadherin, ATP-binding cassette (ABC) transporter, FACS sorting, Tumor-initiating cells

1. Introduction In humans, breast cancers display a variety of responses to anticancer drugs, ranging from pathological complete response to progressive disease. In combination with local therapy (radiation or surgical removal) systemic therapy is frequently curative at an early stage of the disease. However, once solid tumors are disseminated and therapeutic success relies on chemotherapy only, complete tumor eradication is unlikely, even if tumors are initially very sensitive to drug. Intrinsic or acquired multidrug resistance of tumor cells eventually results in therapy failure. As underlying cause of (multi)drug resistance a range of different mechanisms have been identified using cell lines derived from human tumors or tumor samples from patients. These mechanisms include alterations in drug accumulation/metabolism, in cellular targets, or in DNA damage repair (1–5). Since one of the ways in which drugs kill J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_3, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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cells is the induction of apoptosis/senescence, it has been postulated that abrogation of cell death pathways may also affect the action of anticancer drugs (6–10). The relevance of each of these mechanisms in real human tumors, however, usually remains to be determined. To study the contribution of specific mechanisms to drug resistance in an in vivo setting, mouse models that mimic human cancer are useful. Xenotransplantation models provide an alternative, but thus far these were rarely successful in the study of drug resistance, as reported elsewhere (11, 12). However, Quintana et al. recently reported a significant optimization in the transplantation of melanoma cells, resulting in tumor outgrowth after transplantation of only a few cells (13). Possibly this approach might also yield higher transplantation take rates for breast cancer xenotransplants. Other improvements are orthotopic grafting of human breast cancer cell lines into “humanized” stroma (14) or xenotransplantation of fresh breast tumor samples (15). Whether these advances will result in better models to study chemotherapy resistance remains to be seen. Hence, the study of anticancer drug resistance in “human-like” spontaneous tumors in genetically engineered mouse models (GEMMs) (12) remains an important alternative. First-generation GEMMs are transgenic mice with tissue-specific expression of oncogenes or dominant-negative tumor suppressor genes (16). More advanced, second-generation GEMMs are based on conditional knock-out and knock-in mice with spatiotemporally restricted targeted mutations in tumor suppressor genes or oncogenes (16, 17). Several GEMMs have been developed and validated to recapitulate key features of their human archetype. An overview of available models can be found under http://emice.nci.nih.gov/mouse_models. For the study of anticancer drug resistance in breast cancer we have used GEMMs for BRCA1- and BRCA2-associated hereditary breast cancer (K14cre; Brca1F/F; p53F/F or K14cre; Brca2F/F; p53F/F) as well as models for E-cadherin-mutated lobular breast cancer (K14cre; EcadF/F; p53F/F and WAPcre; EcadF/F; p53F/F) (18–22). In these models, spontaneously developing mammary tumors resemble the natural history of human tumors, share many histological features of their corresponding human tumors, express similar markers and in the case of the BRCA1/BRCA2 models also possess a high degree of genomic instability. In addition, they are convenient for drug studies since growth of the relatively superficial tumors is easily measured, and tumor samples can be taken before, during, and after drug treatment. In this chapter, we outline various techniques aimed at studying intrinsic or acquired drug resistance in GEMMs for breast cancer. A summary of the different procedures is presented in Table 3.1. Several of these approaches may also be useful for other tumor types generated in GEMMs.

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Table 3.1 Summary of the different procedures for studying intrinsic or acquired drug resistance in GEMMs for breast cancer Opportunities

Hurdles

1. GEMMs bearing spontaneous mammary tumors

Spontaneous tumor development mimicking cancer development in humans

Maximum tolerable dose (MTD) needs to be established Development of multiple tumors Off-target effects of genetic alterations Extensive breeding

2. Syngeneic wild-type female animals bearing mammary tumors through orthotopic grafting of fragments of spontaneous GEMM mammary tumors

Comparison of various drugs/ drug combination schedules using the same original tumor Similar morphology, genomic profile, and treatment response as original tumor

Stromal irritation by transplantation Heterogeneity between tumor fragments derived from different intratumoral locations Alterations through repeated passages

3. Syngeneic wild-type female animals bearing mammary tumors through orthotopic grafting of suspensions of spontaneous GEMM mammary tumors

Comparison of various drugs/ drug combination schedules using the same original tumor Similar morphology, genomic profile and treatment response as original tumor

Stromal irritation by transplantation Dissociation of tumor cells from their stromal niche

4. Syngeneic wild-type female animals bearing mammary tumors through orthotopic grafting of cell lines derived from spontaneous GEMM mammary tumors

Comparison of different drugs/ schedules using the same original tumor

In vitro selection may result in considerable genetic differences to the bulk of cells in the original tumor Morphologic alterations Stromal irritation by transplantation

5. GEMMs carrying orthotopically transplanted tumors derived from GEMMs

Specific alterations of host cells

See 2.-4.

2. Materials 2.1. Cryopreservation and Orthotopic Transplantation of Tumor Fragments or Tumor Cells

1. Dulbecco’s Modified Eagle’s Medium (DMEM) (Gibco/ Invitrogen). 2. Sterile PBS (Gibco/Invitrogen) and 70% ethanol. 3. Freezing medium: 20% DMSO, 60% FCS, 20% DMEM.

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4. Sterile scalpels (Swann-Morton, England). 5. Watchmaker tweezers (Cat. no. 30617.C), anatomic forceps (Cat. no. 30582), and surgical scissors (Cat. no. 30640) (Vos en zoons, Amsterdam, The Netherlands). 6. 100-µl syringe (Hamilton Bonaduz AG, Switzerland). 7. Cotton swaps (SKS Science, England). 8. Matrigel (GF-reduced, BD-Biosciences). 9. Equipment for injection (hypnorm/dormicum) or inhalation (isofluran) anesthesia. 10. Painkiller (e.g., buprenorphine). 2.2. Tumor Cell Dissociation and FluorescenceActivated Cell Sorting

1. Dulbecco’s Modified Eagle’s Medium (DMEM), FCS, and 0.05% trypsin-EDTA from Gibco/Invitrogen. 2. Collagenase/hyaluronidase solution (10×), dispase (5 mg/ ml), and Hanks Balanced Salt Solution (HBSS/HEPES balanced) from Stem Cell Technologies (England). 3. DNAseI and Red Blood Cell Lysing Buffer Hybri-Max from Sigma-Aldrich. 4. 40- and 70-µm cell strainer from BD Falcon. 5. Antibodies: biotinylated antibodies directed against leukocytes (rat antimouse CD45 and rat antimouse Ly6G, eBioscience), erythroid cells (rat antimouse TER-119, eBioscience), endothelial cells (rat antimouse CD31, eBioscience) and fibroblasts (rat antimouse CD140A, eBioscience) followed by streptavidinCy5 (Invitrogen) coupling. FITC- or PE-labeled rat antimouse antibodies against CD24 and CD49f are from BD Pharmingen.

3. Methods Once a GEMM has been chosen to study drug resistance, several approaches can be taken (Table 3.1). Besides using the mice in which spontaneous tumors arise, it might be useful to employ one of the outlined transplantation techniques, depending on the question to be addressed. Since we think that it is important to study drug resistance in the presence of an intact immune system, we have only described the orthotopic transplantation of mammary tumors from GEMMs into syngeneic, immunocompetent mice. Nevertheless, transplantations into immunodeficient hosts may be useful if the role of the immune system in the development of drug resistance is investigated, in case viral oncoproteins (e.g., Polyoma middle-T; PyMT) or oncoproteins from a different species (e.g., rat NEU) are expressed, or if the GEMM is on a mixed genetic background.

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3.1. GEMMs Bearing Spontaneous Mammary Tumors

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Mice are not humans and drug pharmacokinetics may differ significantly between the two species. A clinically relevant dose of a compound may kill a mouse or may be rapidly cleared from the animal body resulting in lower drug plasma levels. Therefore, the maximum tolerable dose (MTD) of single drug or drug combinations needs to be established for the GEMM of interest. Although the MTD of the background strain in which the GEMM is bred gives a first indication, it may differ significantly in the GEMM in case the genetically engineered alterations affect also other tissues besides the mammary gland. For example, different MTDs were observed when DNA-damaging drugs such as doxorubicin or cisplatin were tested in K14cre; Brca1F/F; p53F/F vs. Brca1F/F; p53F/F mice (21). The route of drug administration should mimic the administration of the drug to humans, and here in particular i.v. administration via the tail vein or repeated daily oral dosing requires an experienced investigator. In xenotransplantation studies, treatments are frequently not performed on established tumors but initiated at a predefined time point after transplantation, well before palpable tumors arise. In contrast, it is not practical to choose a specific time point to start therapy in GEMMs with a tumor latency of several months. An early time point before the average tumor latency day would have to be chosen, but then therapy is directed against early (precancerous) lesions rather than a fully developed tumor. We therefore prefer to initiate therapy in GEMMs once tumors are palpable. At this point tumor diameters may vary between 2 and 5 mm, depending on the level of experience of the researcher and the frequency with which animals are palpated. This variation is suboptimal since such differences in size provide another experimental variable that needs to be taken into consideration. More reasonable criteria to start drug administration are predefined tumor volumes (e.g., 200 mm3), but this requires frequent and careful monitoring of animals, otherwise the right time point might be easily missed. Since mammary tumors are frequently egg-shaped, the ellipsoid formula to estimate the volume of an egg (volume = length × width2/2) can be utilized to determine tumor volume using two-dimensional caliper measurements. If the investigator needs additional information such as tumor vascularization or the presence of necrosis, ultrasound (US) imaging is very helpful and also provides a more accurate measurement of tumor volumes. Although considered a high-throughput technique, US imaging takes about 20 min per animal, including anesthesia. In humans, drugs are repeatedly given with a recovery interval of 21 days, which is usually necessary for the bone marrow to recover. In mice, bone marrow toxicity may not be the most important dose-limiting factor for several anticancer drugs, since animals live in relatively sterile environments and the risk to catch opportunistic infections is lower. Instead, gastrointestinal symptoms

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appear to be restrictive for several agents such as platinum drugs, melphalan, thiotepa, or topotecan (Gonggrijp and Rottenberg, unpublished). The recovery time between treatments can therefore be shortened to 1 or 2 weeks. Like in humans, several cycles (e.g., 6) may be given initially. In case tumors are sensitive to the drug treatment, a complicating factor may be that relapsing and often still drug-sensitive tumors can no longer be treated after six initial treatments due to accumulating toxicity of the host or, in case of i.v. injections, tissue damage at the injection site complicating access to the tail vein. We have therefore implemented a different treatment schedule, in which after the recovery time a second treatment is only given in case the tumor volume remains ¢ 50% of the initial size (21). In case the tumor volume drops below 50%, the next treatment is administered when the tumor relapses to 100% of the original volume. This approach appears to be difficult in those first-generation GEMMs that develop multiple mammary tumors at the same time, e.g., models that use MMTV or WAP promoters to overexpress oncogenes like Neu, PyMT, or Wnt. 3.2. Syngeneic Wild-Type Female Mice Bearing Mammary Tumors Through Orthotopic Grafting of Fragments of Spontaneous GEMM Mammary Tumors

A disadvantage of tumor intervention studies in GEMMs is that for several models a complex breeding schedule needs to be maintained (e.g., heterozygosity of the Cre allele, homozygosity of the floxed tumor suppressor gene(s)). Moreover, it often takes several months before “spontaneous” tumors develop in GEMMs. The engineered mutations in GEMMs may also cause unwanted side effects that increase the amount of animals needed. Examples are GEMMs in which the Keratin 14 promoter is used to drive Cre expression in epithelial tissues. These GEMMs develop skin tumors, in addition to mammary tumors (18–20). Finally, several individual tumors per experimental group are required to compensate for the intrinsic tumor heterogeneity in GEMMs. This heterogeneity is a result of the long latency between the initial genetic alteration (e.g., deletion of a tumor suppressor gene) and the development of the final tumor, during which different mutations accumulate. A solution for these complications is the orthotopic transplantation of small tissue pieces from individual GEMM-derived tumors into several syngeneic wild-type female mice (Fig. 3.1a). This approach also resolves the difficulty that the MTD might change due to side effects of the genetically engineered mutation. Importantly, for a limited number of models it has been shown that morphologies, gene expression profiles, and drug responses of the transplanted tumors are remarkably similar to those of the parental tumor. Thus, orthotopic tumor allograft models may be particularly useful for testing various drug regimens on independent outgrowths of the same tumor (21, 23). Practically, small tumor fragments of original GEMM-derived tumors can be frozen like cell lines. This permits cryopreservation of large numbers of primary tumors, which can be characterized

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Fig. 3.1. Strategies for orthotopic transplantation of GEMM-derived mammary tumors to study drug resistance. (a) Upon orthotopic grafting of tumor fragments several drugs, drug combinations, or schedules can be tested on independent outgrowths from the same parental tumor. (b) Crossbreeding of GEMMs for breast cancer with mice deficient in a (candidate) drug resistance gene X permits a comparison of drug responses to results obtained in (a). (c) Mammary tumors generated in GEMMs proficient for gene Y can be transplanted into GEMMs deficient for this gene to test the antitumoral effect of inhibitors of gene product Y, alone or in combination with other anticancer drugs.

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by immunophenotyping and molecular profiling to allow preselection of tumors with high expression of the drug target prior to transplantation and initiation of the intervention study. Cryopreservation of tumor tissues is also very useful to store tumors that have acquired resistance to specific anticancer drugs and need to be transplanted for subsequent experiments to study cross-resistance to other drugs or to explore pharmacological reversal of resistance (21). Here, a risk is that the drug resistance phenotype is not stable and that tumors become again drug sensitive upon transplantation and outgrowth without drug selection. Thus far, we have only observed that as a major complication with topotecan resistance (Zander, Borst and Rottenberg, unpublished). Another powerful though time-consuming approach is to cross a GEMM with mouse strains deficient for a specific gene, which might contribute to drug resistance. Tumors derived from such a compound model can then be transplanted orthotopically into syngeneic wild-type mice and treatment response can be compared to tumors proficient for the (candidate) drug resistance gene (Fig. 3.1b). 3.2.1. Cryopreservation of Mammary Tumor Fragments

1. Sacrifice animal and harvest the tumor in one piece with sterile instruments. Remove as much fat and fibrous tissue as possible. Dip the tumor in 70% ethanol, then in sterile PBS and transfer to Petri dish on ice. 2. Add about 10 ml of PBS to cover the plate. Cut the tumor into small pieces of 1–2 mm in diameter with a scalpel (see Note 1). 3. Using a 25-ml pipette transfer the tumor piece/PBS mixture into a 50-ml Falcon tube and spin for 1 min at 450 × g. 4. Remove supernatant by pipetting and also remove fragments swimming in the supernatant since these represent (mainly) fat or fibrous tissue. Add 5-ml ice-cold DMEM medium; keep on ice. Add dropwise 5 ml of ice-cold freezing medium and mix by swirling the tube. 5. Using a 25-ml pipette (larger diameter of tip) transfer about 1–1.5 ml containing about 10 tumor pieces into 2-ml cryogenic vials on ice and freeze overnight at −80°C in cell freezing devices. Then store in liquid nitrogen.

3.2.2. Thawing of Cryopreserved Tumor Pieces

1. Place cryogenic vial into 37°C water bath. 2. Once only a small ice block remains transfer tumor fragments into 50-ml Falcon tube filled with ice-cold PBS. 3. Spin for 1 min at 450 × g. Remove supernatant by pipetting and repeat wash with ice-cold PBS. 4. Remove supernatant by pipetting and add 10-ml ice-cold PBS. Keep on ice until transplantation.

Studying Drug Resistance Using Genetically Engineered Mouse 3.2.3. Orthotopic Transplantation of Tumor Fragments into Syngeneic Female Animals

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This procedure describes the transplantation of tumor fragments into the fourth right mammary fat pad. This procedure is applicable also to other mammary glands. If the investigator does not want endogenous mammary epithelium to be present, the nippleconnected part distal of the mammary lymph node can be removed from the fat pad by electrocauterization before tumor implantation in 21-day-old female mice. 1. Keep thawed or freshly cut tumor pieces in PBS on ice. 2. Anaesthetize animal for at least 30 min (e.g., by hypnorm/ dormicum injection or isoflurane inhalation) and fix the mouse belly up (see Note 2). 3. Shave belly and right inguinal area and desinfect with 70% ethanol. 4. Make a 1-cm cranio-caudal incision in the right inguinal skin and explore the fourth mammary fat pad using cotton swaps and forceps. 5. Using watchmaker’s forceps insert a small pocket into the fat pad and install a tumor fragment of 1–2 mm at this site. Carefully move the fad pad to its original location and avoid that the tumor fragment dislocates from the pocket. 6. Close the skin by stitching or wound clips (remove after 1 week) and apply painkiller (e.g., 0.1 µg buprenorphine/g mouse).

3.3. Syngeneic Wild-Type Female Mice Bearing Mammary Tumors Through Orthotopic Grafting of Suspensions of Spontaneous GEMM Mammary Tumors

The advantage of this approach lies in a more homogeneous distribution of tumor cells before transplantation. Though clonally related, tumor cell nests located in different parts of the same tumor might have acquired differential (epi-)genetic alterations. This intratumor heterogeneity might result in differences between tumor outgrowths produced by transplantation of different tumor fragments from the same parental tumor. Although homogenization of tumor cells by dissociation might reduce heterogeneity among the resulting outgrowths, a potential risk might be that dissociation of tumor cells from their stromal niche affects paracrine signaling pathways that are required for tumor cell proliferation or survival or for maintenance of the differentiation state.

3.3.1. Transplantation of Mechanically Dissociated Tumor Cells Derived from GEMM Mammary Tumors

Varticovski et al. mechanically dissociated mammary tumors from different genetically engineered mouse models (MMTV-PyMT, MMTV-neu, MMTV-wnt1/p53+/−, BRCA1/p53+/−, and C3(1)T-Ag) by mincing, passaging through a 40-µm mesh, and subsequent passaging through 18- to 25-gauge needles (23). These mechanically dissociated tumor cells can also be cryopreserved in 10% DMSO by stepped rate freezing. Upon transplantation of 1 × 106 tumor cells into syngeneic or immunodeficient mice, mammary tumor outgrowths developed, which showed a high morphologic similarity to the original tumor.

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3.3.2. Tumor Cell Dissociation and Fluorescence-Activated Cell Sorting

Another helpful application is the use of cell surface markers, such as mammary epithelial stem cell markers or putative cancer stem cell markers, for tumor cell dissociation and fluorescence-activated cell sorting (FACS) sorting of dissociated tumor cells in order to investigate whether specific tumor cell subpopulations show increased resistance to anticancer drugs. We provide here a protocol adapted from Stingl et al. (24) to dissociate and purify mouse mammary tumor cells based on the mammary epithelial stem cell markers CD49f and CD24. Such subpopulations may then be used for orthotopic transplantations or for in vitro studies (e.g., cytotoxicity assays or Hoechst dye exclusion). 1. Harvest tumor in a 50-ml Falcon tube, in 3-ml DMEM on ice. Mince tissue thoroughly on ice. 2. Add collagenase/hyaluronidase solution diluted to 1×. 3. Leave in 37°C incubator for 2-h shaking. Every 30 min pipette up and down vigorously (5-ml pipette). 4. At the end of digest, transfer the solution into a new 15-ml Falcon tube, add ~8–9 ml cold HF (HBSS/HEPES + 2%FCS). 5. Pellet the cells at 450 × g for 5 min and discard the supernatant. 6. Resuspend pellet in red blood cell lysing buffer: first add 0.3– 0.4 ml of HF buffer, followed by 2-ml red blood cell lysis buffer; mix gently (with 1-ml filter tip, tip cut off); add 9 ml of HF buffer and centrifuge at 450 × g for 5 min. 7. Discard supernatant and add 2 ml of prewarmed (37°C) 0.05% trypsin-EDTA. Gently pipette up and down (with 1-ml filter tip, tip cut off) for 1–3 min. 8. Add 9-ml cold HF and spin at 450 × g for 5 min. Remove as much of the supernatant as possible. 9. Add 2 ml of prewarmed dispase and 100 µl of 2 mg/ml DNase I; pipette up and down for 2–3 min until clumps are released. 10. Wet the cell strainers (40 and 70 µm) with 1–2 ml HF buffer. 11. Dilute the cell suspension with 5-ml cold HF and filter the cell suspension through a 70-µm strainer followed by filtering through a 40-µm cell strainer into a new 50-ml Falcon tube (add an extra 3–4 ml to wash the remaining cells in the tube and strainers while filtering). Transfer the filtered cells into a new 15-ml Falcon tube and centrifuge at 450 × g for 6 min. 12. Resuspend in HF buffer (about 2–3 ml). 13. Primary antibody incubations are done for 15 min in FACS tubes (5 ml, 12 × 75 mm) at the maximum of 1 × 107 cells/ml. To exclude stromal lineage (Lin+) cells, biotinylated antibodies against CD45, TER119, Ly6G (1:100), CD31, and CD140a

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(1:200) are used. AntiCD24-FITC (1:50) and antiCD49fPE (1:100) are from BD Pharmingen. As secondary antibody to detect Lin+ cells Strep-Cy5 (1:50) is applied for 20 min. Single channel controls (AntiCD24-FITC, antiCD49f-PE, or biotinylated antibodies only) are included for adjusting channel compensation. 14. Add 2-µg propidium iodide (PI) per ml for 2 min on ice. Filter through a 70-µm strainer and centrifuge at 450 × g for 5 min, remove the supernatant, and resuspend cells in HF buffer. PI-positive cells are dead and should be gated out during the FACS procedure. 15. For cell sorting we use a BD FACSaria sorter with a 100-µm nozzle and reduced pressure (20 psi). For transplantation of sorted cells proceed as described under Subheading 3.4. 3.4. Syngeneic Wild-Type Female Animals Bearing Mammary Tumors Through Orthotopic Grafting of Cell Lines Derived from Spontaneous GEMM Mammary Tumors

Alteration of genes that confer drug resistance can be carried out in mammary tumor cell lines derived from GEMMs for breast cancer. Instead of the time- and resource-consuming generation of compound mouse models, one can try to transplant modified cell lines orthotopically into syngeneic hosts. The risk is that the in vitro culture conditions may cause selection of tumor cells with altered biological properties and genomic profiles. This should be checked since it would compromise the use of tumor cell lines as models for the in situ tumors from which they were derived. The procedure for orthotopic transplantation of tumor cells into the mammary fat pad of syngeneic mice is similar to what is described under Subheading 3.2.3. Instead of generating a pocket and inserting a tumor fragment, cells are injected in a maximal volume of 100 µl into the fat pad. In our experience grafting is improved if the cells are resuspended in Matrigel. For this purpose 30 µl of ice-cold Matrigel is added to cells (e.g., 1 × 106) diluted in 30-µl DMEM and the mixture is kept on ice until injection. Before exploring the fat pad of the animal, flip the Eppendorf tube, take the cell suspension up a Hamilton syringe, and leave at RT for up to 5 min until injection into the fat pad.

3.5. GEMMs Carrying Orthotopically Transplanted Tumors Derived from Other GEMMs

Orthotopic transplantation of tumor pieces or cells into genetically engineered animals with defined stromal defects might be useful for investigating the effects of stromal elements on therapy response and drug resistance. This includes immune cells, cancerassociated fibroblasts, and endothelial cells (25–27). To study the effect of inhibitors of ATP-binding cassette (ABC)-dependent drug efflux transporters that are expressed in tumor cells, it may also be convenient to graft ABC transporter proficient tumors into syngenic mice that are ABC transporter-deficient (Fig. 3.1c). ABC transporters are expressed in the gut, liver, kidneys, or brain endothelium and are involved in the clearance of several drugs (2).

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Systematic application of an inhibitor may therefore change the drug pharmacokinetics. If specific inhibitors are used in combination with drugs, it will be easier to evaluate tumor cell-specific inhibition if the tumor is transplanted into a transporter-deficient model, although this requires preliminary adjustment of the drug MTD in the ABC transporter-deficient mouse.

4. Notes 1. In case areas of necrosis (liquefied, calcified, white) or hemorrhage (dark red), which are macroscopically visible, these areas should be removed from the sample. 2. The methods and doses of anesthetics are not elaborated, since these vary between mouse strains and can be deduced from the literature.

Acknowledgments Our work is supported by grants of the Dutch Cancer Society (2006-3566 to Piet Borst, S.R. and J.J.; 2007-3772 to J.J., S.R. and Jan H.M. Schellens) and the European Union (FP6 Integrated Project 037665-CHEMORES to Piet Borst and S. R.). We thank Piet Borst for critical reading of the manuscript. References 1. Gottesman MM (2002) Mechanisms of cancer drug resistance. Annu Rev Med 53:615–627 2. Borst P, Oude Elferink R (2002) Mammalian ABC transporters in health and disease. Annu Rev Biochem 71:537–592 3. Fojo T, Bates S (2003) Strategies for reversing drug resistance. Oncogene 22:7512–7523 4. Szakacs G, Paterson JK, Ludwig JA, BoothGenthe C, Gottesman MM (2006) Targeting multidrug resistance in cancer. Nat Rev Drug Discov 5:219–234 5. Fojo T (2007) Multiple paths to a drug resistance phenotype: mutations, translocations, deletions and amplification of coding genes or promoter regions, epigenetic changes and microRNAs. Drug Resist Updat 10:59–67 6. Lee S, Schmitt CA (2003) Chemotherapy response and resistance. Curr Opin Genet Dev 13:90–96

7. Schmitt CA (2003) Senescence, apoptosis and therapy – cutting the lifelines of cancer. Nat Rev Cancer 3:286–295 8. Shabbits JA, Hu Y, Mayer LD (2003) Tumor chemosensitization strategies based on apoptosis manipulations. Mol Cancer Ther 2:805–813 9. Voorzanger-Rousselot N, Alberti L, Blay JY (2006) CD40L induces multidrug resistance to apoptosis in breast carcinoma and lymphoma cells through caspase independent and dependent pathways. BMC Cancer 6:75 10. Debatin KM (2004) Apoptosis pathways in cancer and cancer therapy. Cancer Immunol Immunother 53:153–159 11. Sharpless NE, DePinho RA (2006) The mighty mouse: genetically engineered mouse models in cancer drug development. Nat Rev Drug Discov 5:741–754 12. Rottenberg S, Jonkers J (2008) Modeling therapy resistance in genetically engineered

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mouse cancer models. Drug Resist Updat 11:51–60 Quintana E, Shackleton M, Sabel MS et al (2008) Efficient tumour formation by single human melanoma cells. Nature 456:593–598 Kuperwasser C, Chavarria T, Wu M et al (2004) Reconstruction of functionally normal and malignant human breast tissues in mice. Proc Natl Acad Sci USA 101:4966–4971 Marangoni E, Vincent-Salomon A, Auger N et al (2007) A new model of patient tumorderived breast cancer xenografts for preclinical assays. Clin Cancer Res 13:3989–3998 Van Dyke T, Jacks T (2002) Cancer modeling in the modern era: progress and challenges. Cell 108:135–144 Jonkers J, Berns A (2002) Conditional mouse models of sporadic cancer. Nat Rev Cancer 2:251–265 Jonkers J, Meuwissen R, van der Gulden H et al (2001) Synergistic tumor suppressor activity of BRCA2 and p53 in a conditional mouse model for breast cancer. Nat Genet 29:418–425 Derksen PW, Liu X, Saridin F et al (2006) Somatic inactivation of E-cadherin and p53 in mice leads to metastatic lobular mammary carcinoma through induction of anoikis resistance and angiogenesis. Cancer Cell 10: 437–449 Liu X, Holstege H, van der Gulden H et al (2007) Somatic loss of BRCA1 and p53 in mice induces mammary tumors with pathologic

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and molecular features of human BRCA1mutated basal-like breast cancer. Proc Natl Acad Sci USA 104:12111–12116 Rottenberg S, Nygren AOH, Pajic M et al (2007) Selective induction of chemotherapy resistance of mammary tumors in a conditional mouse model for hereditary breast cancer. Proc Natl Acad Sci USA 104:12117–12122 Rottenberg S, Jaspers JE, Kersbergen A et al (2008) High sensitivity of BRCA1-deficient mammary tumors to the PARP inhibitor AZD2281 alone and in combination with platinum drugs. Proc Natl Acad Sci USA 105:17079–17084 Varticovski L, Hollingshead MG, Robles AI et al (2007) Accelerated preclinical testing using transplanted tumors from genetically engineered mouse breast cancer models. Clin Cancer Res 13:2168–2177 Stingl J, Eirew P, Ricketson I et al (2006) Purification and unique properties of mammary epithelial stem cells. Nature 439:993–997 de Visser KE (2008) Spontaneous immune responses to sporadic tumors: tumor-promoting, tumor-protective or both? Cancer Immunol Immunother 57:1531–1539 Bergers G, Hanahan D (2008) Modes of resistance to anti-angiogenic therapy. Nat Rev Cancer 8:592–603 Micke P, Ostman A (2005) Exploring the tumour environment: cancer-associated fibroblasts as targets in cancer therapy. Expert Opin Ther Targets 9:1217–1233

Chapter 4 Mechanisms of Multidrug Resistance in Cancer Jean-Pierre Gillet and Michael M. Gottesman Abstract The development of multidrug resistance (MDR) to chemotherapy remains a major challenge in the treatment of cancer. Resistance exists against every effective anticancer drug and can develop by numerous mechanisms including decreased drug uptake, increased drug efflux, activation of detoxifying systems, activation of DNA repair mechanisms, evasion of drug-induced apoptosis, etc. In the first part of this chapter, we briefly summarize the current knowledge on individual cellular mechanisms responsible for MDR, with a special emphasis on ATP-binding cassette transporters, perhaps the main theme of this textbook. Although extensive work has been done to characterize MDR mechanisms in vitro, the translation of this knowledge to the clinic has not been crowned with success. Therefore, identifying genes and mechanisms critical to the development of MDR in vivo and establishing a reliable method for analyzing clinical samples could help to predict the development of resistance and lead to treatments designed to circumvent it. Our thoughts about translational research needed to achieve significant progress in the understanding of this complex phenomenon are therefore discussed in a third section. The pleotropic response of cancer cells to chemotherapy is summarized in a concluding diagram. Key words: Multidrug resistance, Uptake transport, ABC transporters, Drug metabolism, DNA repair, Vaults, Microenvironment, Translational research

1. Introduction Chemotherapy is the treatment of choice for patients diagnosed in the late stages of locally advanced and metastatic cancers. The main challenge is then to administer a drug dosage that maximizes the efficacy and minimizes the toxicity of the treatment. Unfortunately, in a significant number of patients, the tumor does not respond to the therapeutic agents. This impediment to the clinical cure of cancers results from known and yet-to-be determined mechanisms of resistance to chemotherapy. Resistance, either inherent or acquired, exists against every effective anticancer drug and can develop by multiple mechanisms. The overall mechanisms were recently J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_4, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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reviewed in part by Lage (1), Mellor et al. (2), and Mimeault et al. (3). These mechanisms can act individually or synergistically, leading to multidrug resistance (MDR), in which the cell becomes resistant to a variety of structurally and mechanistically unrelated drugs in addition to the drug initially administered. In this chapter, we will first summarize the current knowledge on individual cellular mechanisms responsible for MDR. Since a detailed discussion of all the mechanisms is beyond the scope of this chapter, our aim will be to briefly depict them and provide the reader with the most recent and relevant reviews on the subject. As ATP-binding cassette (ABC) transporters are perhaps the unifying theme of this book, we will emphasize their biology and their role in the MDR process to provide a fairly detailed idea of what we currently know about this family of proteins. Although extensive research has characterized some of the mechanisms involved in MDR in vitro, translating this to the clinic still represents a major challenge. We will therefore briefly summarize our thoughts about translational research that could be done to achieve significant progress in the understanding of this complex phenomenon. Finally, we provide a diagram to summarize the pleotropic response of cancer cells to chemotherapy.

2. Mechanisms Involved in the Resistance of Cells to Chemotherapy 2.1. Drug and Plasma Membrane Interactions 2.1.1. Uptake Transport: Emerging Role of Solute Carriers

Aside from pharmaceutical factors such as drug administration, distribution, metabolism, and excretion, the primary obstacle that prevents a drug from reaching the intracellular compartment is the plasma membrane. Therapeutic agents can react with various molecules, resulting in a complex speciation profile. These species can enter cells by either passive diffusion (4) or facilitated transport (5). Although the exact mechanisms of cellular uptake are poorly understood for most chemotherapeutic drugs, it has been well established that decreased expression of reduced-folate carrier (SLC19A1/hRFC1) and polymorphisms in its gene significantly hamper a patient’s response to methotrexate, a common therapeutic agent (6). The solute carrier (SLC) family comprises approximately 360 uptake transporters classified into 45 gene families, outlined at http://www.bioparadigms.org/ slc/menu.asp. Genes of the SLC superfamily encode passive transporters, ion-coupled transporters, and exchangers (7). We can rationally speculate that observations concerning SLC19A1 could also apply to other members of this large family involved in uptake of anticancer drugs. Transporters of the SLC28 and 29 families mediate equilibrative diffusion of nucleosides across the plasma membrane, contributing to salvage pathways of

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nucleotide synthesis (8, 9). It has also been demonstrated that these transporters mediate the cellular uptake of nucleoside analogs used in the treatment of cancers (10). Mackey et al. have highlighted the role of SLC29A1, A2 and SLC28A1 but not SLC28A2 in gemcitabine transport (11). More recently, the same group reported that SLC29A1 is a predictive marker for overall survival in patients with pancreatic cancer who received gemcitabine (12). One could therefore suggest that tumors with reduced expression of SLC29A1, A2, or SLC28A1 transporters or with mutant transporters might be resistant to this drug. Genetic polymorphisms of these transporters may alter drug pharmacokinetics, triggering interindividual differences in the safety and efficacy of drug therapy. SLCO1B3/OATP1B3 was shown to mediate uptake of paclitaxel (13). Investigation of the functional consequences of mutations in SLCO1B3 showed that paclitaxel pharmacokinetics were not associated with SLCO1B3-344T>G or 699G>A (14). The SLC22A1/OCT1, SLC22A2/OCT2, and SLC22A3/OCT3 transporters may mediate uptake of some platinum anticancer drugs (15). Interestingly, genetic variants of SLC22A1 and SLC22A2 showed altered transport of some of their substrates, such as metformin, which is used in therapy for type 2 diabetes mellitus (16, 17). However, the transport of platinum anticancer drugs by these variant SLC22A1 and A2 transporters has not yet been directly investigated. To date, SLCs have not been intensively focused on as candidate transporters for anticancer drugs. Therefore, the correlation of SLC genetic variants to treatment outcomes still needs to be clarified. An important step toward a better understanding of the role of SLCs in drug transport was made with a recent study released by Okabe et al. who used a bioinformatics approach to identify SLC substrates (18). In that study, mRNA expression of 28 members of the SLCO and SLC22 families in the NCI-60 cell line panel (19) was profiled. By correlating expression profiles with growth inhibitory profiles of 1,429 compounds (including anticancer drugs and drug candidates) tested against the cells (20–23), it was confirmed that SLC22A4 confers sensitivity to doxorubicin in cancer cells (18). 2.1.2. Efflux Transport: Role of ABC and Other Transporters

Drug uptake can also be significantly reduced by ATP-dependent drug efflux pumps. These include ABC transporters (24, 25) such as the extensively studied ABCB1 (26), C1 (27), and G2 (28) transporters. As the main mechanism of resistance discussed in this book, ABC transporters will be thoroughly discussed in Subheading 4.2 of this chapter. ABC transporters are not the only actors in the process of ATP-dependent drug efflux, as other transporters such as RLIP76/RALBP1 (29) and ATP7A/B (30) have also been reported to mediate drug resistance. RLIP76 is a GTPase-activating protein that mediates the export of the GSH-conjugates of chemotherapeutic agents such as melphalan

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(31) as well as doxorubicin, vincristine, and mitomycin-c (32). ATP7A/B are copper efflux pumps that appear to have a role in resistance to platinating agents, as supported by numerous studies reviewed by Hall et al. (15) and Kuo et al. (30). 2.1.3. Lipid Metabolism Affects Biophysical Properties of the Lipid Bilayer as well as Signaling Pathways

Multidrug-resistant cells may have alterations in lipid metabolism (33, 34), which induce modifications in the biophysical properties of the lipid bilayer, consequently influencing drug uptake. Ceramide has been intensively studied concerning its role as the cellular messenger of apoptosis (35). GouazeAndersson et al. recently showed that this lipid has a similar role in the regulation of ABCB1 expression (36). The same group also reported that ceramide plays a major role in acquired resistance to doxorubicin (37). The mechanism they propose is the following: doxorubicin increases ceramide levels by either activation of sphingomyelinase or activation of enzymes of de novo ceramide synthesis, resulting in activation of apoptotic pathways. However, as the natural lipid substrate of glucosylceramide synthase (GCS), ceramide upregulates GCS expression through the Sp1 transcription factor. The authors postulate that this positive feedback cycle is antiapoptotic and drives cellular resistance to ceramide-generating types of chemotherapeutic drugs, speculating further that the characterization of this signal transduction pathway might reveal a way to prevent anthracycline resistance (37).

2.1.4. MDR Mechanisms Related to Drug Entry: A Pleiotropic Phenomenon

The mechanisms mentioned earlier are closely interconnected, and this can be illustrated by the reduced uptake of platinum drugs in cisplatin-resistant cells. Our laboratory has studied extensively the causes underlying resistance to platinum drugs (recently reviewed in Hall et al. (15)). Following a first study reporting the establishment of cisplatin-resistant cell lines and their crossresistance to a wide array of structurally and mechanistically unrelated drugs (38), subsequent extensive work by our laboratory has clearly shown the pleiotropic defect in uptake of cisplatin and unrelated compounds found in cancer cells. Using selected cell lines for cisplatin resistance as models, we have observed decreased expression of SLC19A1 transporter and arsenic-binding proteins (38–40). We later demonstrated that this defective uptake results from DNA hypermethylation (41). This suggests a common mechanism that might also reduce the uptake of platinum drugs, as no ABC transporter efflux pumps have been found to be overexpressed in the models studied. This may be explained by the mislocalization of membrane proteins caused by a defect of plasma membrane protein recycling associated with a cytoskeletal defect (42, 43). Other alterations in the biophysical properties of the lipid bilayer, such as a defective fluid-phase endocytosis (44) and an increase in membrane fluidity have also been detected in cisplatin resistant cells (45). Whether or not this latter defect is a

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primary cause leading to cisplatin resistance, or a secondary effect has yet to be determined. These studies clearly demonstrate the pleiotropic phenomenon underlying multidrug resistance mechanisms and their effect on cellular drug entry. 2.2. Drug Metabolism

Once in the intracellular compartment, drug metabolism enzymes are the second line of cellular resistance. This process involves phase I and II enzymes.

2.2.1. Phase I Metabolic Enzymes

Phase I or oxidative metabolism is mediated mainly by cytochrome P450 enzymes (CYPs) and epoxide hydrolases. CYPs belong to a superfamily of hemoproteins comprising 57 genes classified in 18 families and 44 subfamilies based on their degree of sequence homology. An up-to-date database and nomenclature for these enzymes can be found at: http://drnelson.utmem.edu/ CytochromeP450.html. CYPs are localized in mitochondria and the endoplasmic reticulum (ER) and catalyze the monooxygenase reaction by incorporating one atom of molecular oxygen into the substrate and one into water. This reaction also requires a source of electrons, provided by the NADPH cytochrome P450 reductase in the ER and ferredoxin in the mitochondria (46). While mitochondrial CYPs are involved in the metabolism of endogenous substrates, microsomal CYPs metabolize both endogenous and exogenous compounds. Therapeutic drugs are therefore metabolized by microsomal CYP and epoxide hydrolases, which convert highly mutagenic aromatic metabolites (epoxide) created from the CYP metabolism in a metabolite that can be conjugated by the phase II enzymes and then effluxed by transporters such as the members of the ABCC transporter family (46). Although mainly expressed in the liver, extrahepatic expression of CYPs has been shown in both normal and tumor tissues (47, 48). The CYP3A, 2D6, and 2C families metabolize most chemotherapeutic drugs. These enzymes are genetically highly polymorphic, and the expression of some of their variants has been found to predict treatment outcome (49). This is exemplified by the 516G>T polymorphism in CYP2B6, which is related to an increase in cyclophosphamide metabolism (50, 51). However, the same polymorphism was correlated with a threefold decrease in efavirenz metabolism (52–54). Phase I reactions result mainly in drug detoxification. However, these enzymes can be utilized in a prodrugbased strategy (55). Understanding the effect of polymorphisms on CYP enzyme activity is consequently particularly important. This is well illustrated by the data obtained for the 516G>T polymorphism in CYP2B6, which indicate clearly that the effect of polymorphisms can vary dramatically, depending on the drug administered. Studies have highlighted the synergism between CYP enzymes and ABC transporters that occurs when metabolites produced by CYP enzymes, especially CYP3A4, are better substrates for ABCB1 than the parent compound or when ABCB1 prevents the

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saturation of CYP enzymes by necessitating a subsequent entry of the drug into the cell (56). This process increases exposure to CYP enzymes and can dramatically decrease the efficacy of chemotherapeutic treatment. 2.2.2. Phase II Enzymes

Phase II enzymes are involved in conjugation reactions including glutathionylation (57), glucuronidation (58), and sulfation (59). These enzymes include glutathione-S-transferase (GST) (60), UDPglucuronosyltransferases (UGT) (61), sulfotransferases (62), and arylamine N-acetyltransferases (NAT) (63), which transform the reactive species into hydrophilic nontoxic metabolite conjugates. These conjugated metabolites are then effluxed by members of the ABCC family of ABC transporters (27). Genetic polymorphisms in these families of genes have also been associated with overall survival in cancer patients. Ekhart et al. (64) review the influence of genetic polymorphisms not only in these enzymes but also in phase I enzymes and ABC transporters on survival after chemotherapy.

2.3. Drug Sequestration

Recent evidence has led us to consider intracellular drug sequestration as an important mechanism of MDR. Abnormalities in lysosomal function, protein trafficking and secretion have now been clearly identified. It has been shown that cisplatin is sequestered into lysosome, golgi, and secretory compartments and then effluxed from the cell (44, 65–67). A complete profile of the mediators of the intravesicular transport has certainly not yet been developed. We do know that the copper efflux transporters ATP7A/B are colocalized with fluorescent cisplatin analogs in vesicles of the secretory pathway (65, 67, 68). A recent study carried out in our laboratory highlighted the role of melanosomes in cisplatin resistance (69). We showed that cisplatin is sequestered in melanosomes, altering the melanogenic pathway and accelerating extracellular transport of melonosomes that contain cisplatin (69). No transporter responsible for this intracellular transport has yet been identified. However, one could suggest a role of the ABC transporter ABCB5, found to be preferentially expressed in pigment-producing cells (70). ABCA3-mediated intracellular drug sequestration has recently been associated with poor overall survival in acute myeloid leukemia (71, 72). ABCA3 is first localized in lysosomal (73) and late endosomal membranes and then colocalized with daunorubicin (72). Its role in MDR was initially indirectly suggested by Steinbach et al. when they showed a significant effect on cell viability after a combination of ABCA3 gene expression silencing and doxorubicin treatment (71). Direct evidence of its role as mediator of MDR was reported very recently in a study carried out by Chapuy et al., in which the authors demonstrated that ABCA3 mediates resistance to daunorubicin, mitoxantrone, etoposide, Ara-C, and vincristine (72).

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In addition to drug sequestration in intracellular vesicles, “scavenger” metallothioneins (MTs) play a role in ensnaring drugs within a cell. These molecules are cysteine-rich and have high affinity for metal ions. This specificity, along with their ability to ensnare reactive oxygen species strongly suggests them as significant players in resistance to metal-based therapeutic agents and radiation treatment. However, the disparate literature on their role in cancer drug resistance does not allow unequivocal corroboration of this assumption (reviewed by Theocharis et al. (74) and Thirumoorthy et al. (75)). Although important as part of the pleiotropic phenomena underlying MDR, intracellular drug sequestration does not appear to induce resistance to the same extent as that produced by typical efflux transporters. 2.4. DNA-Damage Response Network

A complex network of interacting pathways has evolved to monitor the integrity of a cell’s DNA and govern proper responses to any genetic damage (76). This network includes sensor complexes that detect DNA breaks. It has been shown that Mre11-Rad50-Nbs1 (MRN) recognizes or senses DNA doublestrand breaks (DSBs), while the RPA-ATRIP complex binds to single strand breaks (SSBs). Kinases such as ATM and ATR are then recruited by MRN and RPA-ATRIP, respectively, and phosphorylate/activate a myriad of other proteins including the checkpoint kinases Chk1 and 2, initiating a cascade that results in cell-cycle arrest and DNA repair (Fig. 4.1) (77). However, if the damage is too extensive, rather than repair itself, the cell will enter one of these states: (1) senescence, which is characterized by an irreversible growth arrest, (2) apoptosis, or (3) necrosis (see Subheading 4.2.4). Many chemotherapeutic drugs have been employed to kill proliferating cells, causing extensive DNA damage that ultimately leads to cell cycle arrest and cell death. However, the efficacy of these therapeutic agents such as platinum drugs (78) and alkylating agents (79) can be significantly reduced by the ability of cells to repair DNA. DNA repair involves an intricate network of repair systems that each target a specific subset of lesions. These pathways include (1) the direct reversal pathway (MGMT, ABH2, ABH3), (2) the mismatch repair (MMR) pathway, (3) the nucleotide excision repair (NER) pathway, (4) the base excision repair (BER) pathway, (5) the homologous recombination (HR) pathway, and (6) the nonhomologous end joining (NHEJ) pathway (80). Figure 4.2 summarizes the role of these pathways in the repair of lesions induced by some chemotherapeutic agents (reproduced from Helleday et al. (81)). Studies have reported an inverse correlation of ERCC1 (NER pathways) with either response to platinum therapy or survival in ovarian (82, 83), non-small cell lung cancer (84), and colorectal cancers (85). It has also been shown that MMR deficiency is

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Fig. 4.1. Chk1 and Chk2 kinases are serine/threonine kinases that are activated by the ATM and ATR kinases in response to DNA damage. The checkpoint kinases are transducers of the DNA damage signal and both phosphorylate a number of substrates involved in the DNA damage response. Chk1 and Chk2 share a number of overlapping substrates, although it is clear that they have distinct roles in directing the response of the cell to DNA damage. Current understanding is that the checkpoint kinases are involved not only in cell cycle regulation but also in other aspects of the cellular response to DNA damage. The G1checkpoint is modulated primarily by the ATM-Chk2-p53 pathway, as expression of ATR, Chk1, and Cdc25a is limited until the cell passes this restriction point. At this point, levels of ATR, Chk1, and Cdc25a all increase. If DNA damage is detected, Chk1/Chk2 are activated, Cdc25a is phosphorylated, and thus, destabilized, resulting in a p53-independent S arrest. In S phase, the same cascade can result in an intra-S arrest in response to stalled replication forks. The G2-M checkpoint prevents entry into mitosis with unrepaired DNA lesions. Initiation of this checkpoint is mediated by the ATM/ATR/Chk1/Chk2 cascades as shown, which ultimately suppresses the promitotic activity of cyclin B/cdc2. Along with their pivotal roles in the modulation of the cell cycle checkpoints, Chk1 and Chk2 are also involved in other aspects of the DNA damage response, including DNA repair, induction of apoptosis, and chromatin remodeling. Reproduced from (77), by permission of the American Association for Cancer Research.

associated with cisplatin resistance (86, 87). The MMR mechanism removes the newly inserted intact base instead of the damaged base, triggering subsequent rounds of futile repairs, which can lead to cell death (88). Furthermore, a role in triggering checkpoint signaling and apoptosis was also suggested (89). Resistance to alkylating agents via direct DNA repair by O(6)-methylguanine methyltransferase (MGMT) has been extensively studied and is considered to be a significant barrier to the successful treatment of patients with malignant glioma (90). There is no doubt that other proteins involved in those mechanisms will be revealed in the future through the characterization of MDR tumors.

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Fig. 4.2. Overview of DNA repair pathways involved in repairing toxic DNA lesions formed by cancer treatments. The DNA-damaging agents that are used in cancer treatment induce a diverse spectrum of toxic DNA lesions. These lesions are recognized by a variety of DNA repair pathways that are lesion-specific but are complementary in some respects. (a) Ionizing radiation and radiomimetic drugs induce double-strand breaks (DSBs) that are predominantly repaired by nonhomologous end joining (NHEJ). (b, c) Monofunctional alkylators (b) and bifunctional alkylators (c) induce DNA base modifications, which interfere with DNA synthesis. Lesions produced by some alkylators are processed into toxic lesions in a mismatch repair-dependent manner. The base excision repair (BER) and nucleotide-excision repair (NER) pathways are, together with alkyltransferases (ATs), major repair pathways, whereas other repair pathways repair toxic replication lesions, such as those produced by interstrand crosslinks. (d) Antimetabolites interfere with nucleotide metabolism and DNA synthesis, causing replication lesions that have not yet been characterized. Mismatch repair mediates the toxicity of some antimetabolites (for example, thiopurines). The repair pathways involved in repair of antimetabolite-induced lesions are, apart from BER, poorly characterized. (e) Topoisomerase poisons trap topoisomerase I or II in transient cleavage complexes with DNA, thus creating DNA breaks and interfering with replication. (f) Replication inhibitors induce replication fork stalling and collapse, resulting in indirect DSBs. The relative contributions of the major repair pathways to the respective types of DNA damage outlined are indicated by the sizes of the boxes. This is based on the extent of sensitivity of repair-deficient cells to anticancer drugs in each category. ENDO endonucleasemediated repair, FA Fanconi anaemia repair pathway, HR homologous recombination, O2G DNA dioxygenases, RecQ RecQ-mediated repair, SSBR DNA single-strand break repair, TLS translesion synthesis. Reprinted by permission of Macmillan Publishers Ltd., (81), copyright 2008.

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Cellular death is the underlying pharmacological purpose for chemotherapy. Disruption of apoptotic pathways, the hallmark of cancer, is a major obstacle in the success of chemotherapy. Both apoptotic and nonapoptotic mechanisms can lead to the cell death (91). Nonapoptotic mechanisms include autophagy, mitotic catastrophe, necrosis, and senescence (92) (Table 4.1). The apoptotic cascade can be initiated via two routes, involving either the release of cytochrome c from the mitochondria (intrinsic/ mitochondrial pathway), or activation of death receptors (TNF-tumor necrosis factor) in response to ligand binding (93). The activation of these pathways leads to the activation of a family of cysteine proteases, the caspases, that mediate the cleavage of cellular substrates leading to morphological and biochemical changes that characterize apoptosis. Increasing evidence, however, suggests that caspase-independent pathways also exist (94). Although in vitro studies have shown that nuclear translocation of the mitochondrial flavoprotein apoptosis-inducing factor (AIF) might

2.5. Evasion of Drug-Induced Apoptosis

Table 4.1 Characteristics of different types of cell death Morphological changes Type of cell death

Nucleus

Cell membrane Cytoplasm

Apoptosis

Chromatin condensation; Blebbing nuclear fragmentation; DNA laddering

Autophagy

Partial chromatin condensation; no DNA laddering

Biochemical features

Caspase-dependent Fragmentation (formation of apoptotic bodies)

Blebbing

Increased number of autophagic vesicles

Caspase-independent; increased lysosomal activity

Mitotic Multiple micronuclei; catastrophe nuclear fragmentation

–

–

Caspase-independent (at early stage) abnormal CDK1/ cyclin B activation

Necrosis

Clumping and random degradation of nuclear DNA

Swelling; rupture

Increased vacuolation; organelle degeneration; mitochondrial swelling

–

Senescence

Distinct heterochromatic structure (senescenceassociated heterochromatic foci)

–

Flattening and increased granularity

SA-b-gal activity

CDK1 cycline-dependent kinase 1, MDC monodansylcadaverine, MPM2 mitotic phosphoprotein 2, SA-b-gal senescence-associated b-galactosidase, RB retinoblastoma protein Reprinted by permission of Macmillan Publishers Ltd., (91), copyright 2004

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be a key factor in this pathway (94), the mechanisms governing caspase-independent cell death are little known. Chemotherapy-induced resistance occurs primarily via the mitochondrial pathway, regulated by the Bcl2 family of genes. Therefore, alterations in Bcl2 genes, which are involved in maintenance of the homeostasis of pro- and antiapoptotic factors, may considerably hamper the success of treatment (95, 96). Many researchers have investigated the usefulness of Bcl2 family proteins in the prediction of chemotherapy outcome. Sjöström et al. found that none of the apoptosis-related proteins they investigated (Bcl2, Bax, Bcl-xl, Bag1, FAS, FASL) could predict response to drug treatment for breast cancer (97). This observation was confirmed in two other studies, one in which the Bcl-2 status was not predictive for paclitaxel treatment in patients with breast cancer (98) and another involving vinorelbine plus docetaxel treatment in patients with non-small cell lung cancer (NSCLC) (99). On the other hand, some studies did find a correlation between Bcl2 expression status and chemotherapy response in patients with breast cancer (100, 101). This exemplifies the conflicting literature which to date does not allow us to establish any clear connection between defective apoptotic pathways and treatment failure. 2.6. Vaults

Vaults were first described in 1986 (102). They are the largest ribonucleoprotein particles ever described, with a size of ~42 × 75 nm and a mass of ~13 MDa (103). They were given this name because of their morphological resemblance to vaulted ceilings in gothic cathedrals (104). Vault particles are evolutionary highly conserved, although they are not found in Saccharomyces cerevisiae, Caenorhabditis elegans, or Drosophila melanogaster (105). They form a barrel-shaped structure composed of multiple copies of three proteins including the major vault protein (MVP), the vault poly-ADP-ribose polymerase (VPARP), and the telomeraseassociated protein-1 (TEP1) plus an untranslated vault RNA (vRNA) (106). Vaults, as detected by MVP expression, are ubiquitously expressed, with high levels in tissues that are chronically exposed to xenobiotics such as lung and epithelial cells of the intestine, and in macrophages and dendritic cells (107). They are localized in the cytoplasm, where up to 100,000 particles can be found per cell (108). For detailed information, see Mossink et al. (109) and Steiner et al. (110). Several cellular functions have been proposed for vaults (111). The detection of the MVP protein (initially named LRP) in a multidrug-resistant ABCB1 negative non-small cell lung cancer cell line implied a role for vaults in MDR (112). However, 15 years later, their role is still not clear. In vitro studies have yielded conflicting results; some reports show a direct role of vaults in MDR (113–115), whereas others fail to substantiate such involvement (116–118).

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In the clinic, numerous studies have been undertaken to understand the role of vaults in multidrug resistance and whether they could be used as predictive markers of treatment response. Most of the work has been focused on hematological malignancies, but a few solid tumors have also been analyzed. This research is summarized in Table 4.2. As is the case for other MDR mechanisms, the role of vaults is still the subject of debate. Disappointingly, vault knockout mice do not show any phenotypic defects compared with wild-type mice (119). Moreover, no enhancement of chemosensitivity was observed and no compensation by increased activity of ABC transporters was found following drug treatment (120). It was concluded that at least in mice, vaults are not directly involved in drug resistance. However, analysis of both human and mice promoters seems to indicate that vault-associated MDR may be species specific. Indeed, the mouse promoter lacks a DNA segment found in the human sequence containing lentiviral elements and an inverted CCAAT box (Ybox) and therefore cannot bind to the NF-Y transcription factor. It has been shown that activation of the ABCB1 promoter is dependent on both elements (121). These substantial differences between human and mice promoters may explain these puzzling observations and indicate that mouse models may not be the ideal tool to study the role of vaults in MDR.

Table 4.2 Conflicting clinical data on the association of MVP with response to therapy and patient prognosis MVP expression correlated to response to chemotherapy

MVP expression correlated to prognosis

Type of cancer

Yes

No

Yes

No

AML

(191–194)

(195–199)

(200–202)

(195–199)

ALL

(203)

(203–205)

ATL

(206)

MM

(207–209)

Ovarian cancer

(210–212)a

Breast cancer NSCLC

(219, 220)

Bladder cancer

(224)

Sarcoma

(225)

Testicular germ-cell tumors Borderline

a

(207, 208)

(209)

(213)

(210–212)a

(213)

(214–216)

(214)

(217, 218)

a

(221, 222)

(222, 223)

(226, 227)

(225)

(228)

(228, 229)

(226, 227)

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Solid tumors are heterogeneous structures composed of tumor and stromal cells that are embedded in the extracellular matrix and sustained by an abnormal vasculature network. This tumor microenvironment can influence the tumor cells’ propensity to metastasize (122, 123) and can dramatically affect treatment outcome (124–126). For a general review on this, see Tredan et al. (127). Tumor responsiveness to chemotherapy is highly influenced both by the abnormal vasculature and an elevated interstitial fluid pressure, which dramatically impede the penetration of macromolecules into the tumor (125, 128). Tumor vasculature is characterized by a disorganized architecture composed of dilated and convoluted blood vessels. These vessels may also be compressed by tumor cells (129), leading to the disruption of blood flow (130). Furthermore, the lack of lymphatic vessels in the tumor vasculature network contributes to the increase of interstitial fluid pressure, which hinders the delivery of therapeutic agents by convection (131–133). As a result, metabolites are not cleared and sustaining nutrients cannot be delivered, leading to hypoxia and acidification of the extracellular compartment (124). Tumor cells that lack oxygen (hypoxic cells) fulfill their energetic needs via the glycolytic pathway, which ultimately generates lactic acid, leading to intracellular acidification (124). The cells then maintain their pH homeostasis through the expression of proton (H+) pumps, rendering the extracellular environment highly acidic. This feature may severely hamper chemotherapy. Indeed, according to the ion trapping theory, weakly basic drugs (e.g., doxorubicin, mitoxantrone, vincristine, etc.) are ionized in the acidic compartment, consequently hindering their entry into the cell. Conversely, an increased accumulation of weakly acidic drugs (e.g., chlorambucil, cyclophosphamide, camptothecin, etc.) in the cell will be observed as the plasma membrane is permeable to nonionized molecules (134, 135). Hypoxia plays an important role in MDR (124). It dramatically reduces the effectiveness of chemotherapeutic agents (e.g., bleomycin) that require oxidation to become cytotoxic or enhances the cytotoxicity of other agents (e.g., mitomycin C) that must undergo reduction to form active cytotoxic species (136). Hypoxic cells also display reduced rates of cell proliferation and can avoid apoptosis by triggering Bcl2 family genes, rendering them resistant to many therapeutic agents that target proliferative cells. It has been shown that hypoxia upregulates glutathione, GSH, and metallothionein (MT) protein levels, which reduces significantly the effectiveness of alkylating agents and platinum-based drugs (137, 138). The main mediator of the response to hypoxia is the HIF1 transcription factor, which was also shown to induce the expression of ABCB1, ABCC1, and ABCG2 (139–143). Taken together, the tumor microenvironment not only leads to the development of the pleiotropic mechanisms underlying MDR, but also amplifies or intensifies them.

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2.8. The Cancer Stem Cell Paradigm

3. The ATP-Binding Cassette (ABC) Transporters

Cancer-initiating cells, also known as cancer stem cells (CSCs), might underlie the intractable nature of many human cancers, explaining why conventional cancer therapy fails in many patients (144, 145). These cells have the capacity to initiate and sustain the growth of a heterogeneous cancer through self-renewal and differentiation. Moreover, they acquire most of the MDR mechanisms discussed in this section. CSCs can be isolated based on the expression of cell surface markers associated with immature cell types. However, no unique set of markers has been identified that distinguishes them from normal stem cells (144, 145). ABCB1 and ABCG2 are well-characterized ABC transporters expressed in both cancer and normal stem cells (146–148). Besides these transporters, ABCA3 was also detected in neuroblastoma CSCs along with ABCG2 (149). ABCA3 is localized in membranes of lysosomes and endoplasmic reticulum, suggesting a role in drug sequestration (72). More recently, it was suggested that ABCB5 could be a marker of melanoma cancer-initiating cells (150). Although the concept is exciting, knowledge of ABCB5 from the genomic to the proteomic level is rudimentary and does not support this hypothesis (70, 151, 152). Although there has been a great deal of interest in the elegant CSC paradigm, its technological and experimental challenges are colossal. One of these challenges is the isolation of normal and cancer stem cell populations to identify differences in self-renewal mechanisms. If achieved, this could open new avenues to cancer research.

The ATP-binding cassette (ABC) transporter proteins are a large superfamily of membrane proteins comprising 48 members divided into seven different families based on sequence similarities. The nomenclature for human ABC transporter genes is provided at: http://nutrigene.4t.com/humanabc.htm. These proteins are evolutionary highly conserved and there is a high sequence homology among all the members, especially those within a particular family. The functional protein typically contains two nucleotide-binding domains (NBDs) and two transmembrane domains (TMDs) encoded by a single polypeptide. ABC transporters may also be multicomponent units in which different genes encode each domain or half molecule. ABC transporters can have a wide array of cellular roles (24). They regulate local permeability by being expressed in the blood– brain barrier, blood cerebrospinal fluid, blood–testis barrier, and placenta (153). In the liver, gastrointestinal tract, and kidney,

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ABC transporters excrete toxins, thereby protecting the organism (154). ABC transporters also play an active role in the immune system by transporting peptides into the endoplasmic reticulum that are identified as antigens by class I HLA molecules (e.g., ABCB2/TAP1, ABCB3/TAP2) (155, 156). Furthermore, they play physiological roles in cellular lipid transport and homeostasis (157). Finally, as expected from the diverse functional roles of ABC genes, the genetic deficiencies associated with their mutation also vary widely, as reviewed in Borst and Elferink (154). The first ABC transporter discovered was ABCB1 in 1976 (158). This transporter, called P-glycoprotein, was found to be expressed in Chinese hamster ovary cells selected for colchicine resistance. The authors also discovered that these cells displayed resistance to a variety of structurally and mechanistically unrelated drugs in addition to colchicine (158). Human P-glycoprotein, the product of the MDR1 or ABCB1 gene, was subsequently shown to confer MDR on drug-sensitive cells (159). More than 30 years later, 15 ABC transporters (ABCA2, ABCA3, ABCB1, ABCB4, ABCB5, ABCB11, ABCC1–6, ABCC11–12, and ABCG2) have been associated with drug resistance (Table 4.3). Of these, ABCB1 (160), ABCC1 (161), and ABCG2 (162) have been the most extensively studied. Yet attempts to translate these transporters into clinical targets have so far been unsuccessful (discussed further in Subheading 4 of this chapter). A lot of progress has been made in understanding the molecular mechanisms of ABCB1 through mutagenesis experiments and the resolution of structures of non-mammalian ABC proteins (163). A number of recent reports have addressed genetic polymorphisms in drug transporters; for an excellent review, see Cascorbi (164). Among the 48 ABC transporters, ABCB1 is one of the most thoroughly studied and characterized, with more than 50 SNPs reported (164–166). The correlation of ABC transporter genetic variants to treatment outcomes is gradually being clarified, yet the overall picture is still puzzling, as much of the published data are conflicting. Nevertheless, the many studies reporting correlations between SNPs and clinical outcome indicate the necessity to pursue further investigations; for a detailed review on the role of polymorphisms in ABCB1 drug transport including the role of synonymous polymorphisms in altering ABCB1 transporter function (167), see Fung and Gottesman (168). The regulation of ABCB1 is poorly understood. It has been suggested that therapeutic agents (169, 170) and endogenous stimuli such as hypoxia, acidosis, free radical formation, or glucose deprivation could also induce its expression through multiple signal transduction pathways (26). Thus, the complex network of ABCB1 regulation ensures rapid emergence of pleiotropic resistance in cancer cells.

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Table 4.3 Established anticancer drugs as substrates of ABC transporters Established anticancer drugs (Nonexhaustive list)

Subfamily

Genes

A/ABC1

ABC A2/ABC2/STGD ABCA3/ABC3

Estramustine, mitoxantrone (230, 231) Daunorubicin, Ara-C, mitoxantrone, etoposide (72)

B/MDR-TAP

ABC B1/MDR1/P-gp/PGY1

Anthracyclines, vinca alkaloids, taxanes, etoposide, teniposide, imatinib, irinotecan, SN-38, bisantrene, colchicine, methotrexate, mitoxantrone, saquinivir, actinomycin D, etc. (231, 232) Daunorubicin, doxorubicin, vincristine, etoposide, mitoxantrone (234) Doxorubicin, camptothecin, 10-OH camptothecin, and 5FU (151, 235) Paclitaxel (236)

ABC B4/MDR3/PFIC3/PGY3 ABCB5 ABC B11/BSEP/PFIC2/SPGP C/MRP

ABC C1/MRP1

ABC C2/MRP2/CMOAT

ABC C3/MRP3/CMOAT2 ABC C4/MRP4/MOATB

ABC C5/MRP5/MOATC ABC C6/MRP6/MOATE/PXE ABC C10/MRP7 ABC C11/MRP8 G/WHITE

ABC G2/BCRP/MXR/ABCP

Anthracyclines, vinca alkaloids, methotrexate, antifolate antineoplastic agents, etoposide, imatinib, irinotecan, SN-38, arsenite, colchicine, mitoxantrone, saquinivir, etc. (27, 237) Vinca alkaloids, cisplatin, etoposide, doxorubicin, epirubicin, metotrexatetaxanes, irinotecan, SN-38, topotecan, arsenite, mitoxantrone, saquinivir (238–241) Etoposide, tenoposide, metotrexate (242, 243) 6-Mercaptopurine, 6-thioguanine, irinotecan, SN-38, topotecan, AZT, metotrexate, PMEA (244–247) 6-Mercaptopurine, 6-thioguanine, 5-FU, cisplatin, metotrexate, PMEA, AZT (245, 248) Etoposide, doxorubicin, daunorubicin, teniposide, cisplatin (249) Taxanes, vinca-alkaloids (250, 251) 6-Mercaptopurine, 5-FU, PMEA (252, 253) Mitoxantrone, camptothecin, anthracycline, etoposide, teniposide imatinib, flavopiridol, bisantrene, methotrexate, AZT, etc. (171)

Modified from Gillet et al. (24)

4. Translational Research: From the Lab to the Clinic

Extensive research has characterized some of the mechanisms involved in multidrug resistance in vitro. However, translating this to the clinic still represents a major challenge, as evidenced by the failure of trials to modulate ABCB1 expression (25) and the disputed role in vivo of ABC transporters in MDR (24, 171). Briefly, meta-analyses exploring ABC transporter expression

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profiles in leukemia have highlighted the disparity of the literature (172–174). In contrast to adult acute myeloid leukemia (AML), for which the role of ABCB1 gene expression in the drug resistance of tumors and prognosis of patients is widely accepted (175, 176), the data for adult acute lymphoblastic leukemia (ALL) are conflicting (176–178). In solid tumors, most attention has been directed to the roles played by ABCB1, ABCC1, and ABCG2 in MDR observed in breast cancer. However, it is difficult to decipher their exact role in the clinical drug resistance observed in this disease (179, 180). Various reasons have been suggested for the conflicting reports on the role of ABC transporters in the clinic. The most obvious one is that ABC transporters other than the three usual suspects (ABCB1, C1, and G2) can also influence treatment outcome. As mentioned in Subheading 4.2, 15 ABC transporters have been associated with drug resistance. Moreover, several recent studies suggest that more than 25 ABC transporters can be involved in chemotherapy-induced resistance (71, 181–184). In the previous section, we discussed the pleiotropic response of cancer cells to treatment. Therefore, another explanation for the disparity of the data reported is that studies focus on only one particular mechanism and neglect to investigate the other MDR mechanisms involved. The presence of normal or inflammatory tissues in tumors can also dramatically influence the gene expression profiles generated. Besides these biological factors, there are emerging issues associated with the “-omic” technologies. The variety of experimental systems developed to characterize cancers at a molecular level, such as cDNA expression profiling, comparative genomic hybridization (array CGH), promoter arrays, SNP arrays, etc., has increased dramatically in the last 5 years. In addition, the variety of platforms proposed for each of those analytical systems is remarkable, and numerous proposed normalization processes also render an integrative computational and analytical approach extremely challenging (185). The establishment of standard analytical methods and the development of systems biology/integrative approaches should help to produce a more unified picture of MDR. That could also lead to progress not only in understanding the mechanisms governing multidrug resistance but also in the translation of this knowledge to clinical practice, especially in personalized medicine. In this regard, the recent development of Oncomine, a bioinformatics initiative aimed at collecting, standardizing, analyzing, and delivering cancer transcriptome data to the scientific community is a step in the right direction (186–188). The knowledge acquired these last three decades by our own laboratory on MDR mechanisms has recently been tentatively translated to the clinic. Annereau et al. developed a high-density microarray platform dedicated to multidrug resistance to address the roles of MDR-linked genes in camptothecin resistance using cancer cell lines (189). However, microarrays require relatively

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large amounts of sample and often have poor probe specificity for families of genes with a high degree of homology, such as those involved in MDR. At the same time, Szakacs et al. generated a database to identify lead compounds in the early stages of drug discovery that are not ABC transporter substrates (184). This repository is of great importance to help avoid the use of toxic drugs that ultimately will not provide any benefit to cancer patients. In that study, qRT-PCR using SYBR Green chemistry was used to profile the expression of ABC transporters. Although this platform is more reliable and accurate than microarrays, high-throughput analyses are tedious and require multiple pipetting steps, which can introduce variability. The availability of a reliable, sensitive and specific high-throughput platform that would allow the discrimination of highly homologous genes from small amounts of clinical tissue as opposed to in vitro models is fundamental to achieving any significant enhancement in the understanding of clinical multidrug resistance. In a recent study (190), our laboratory demonstrated the superiority of two platforms over established technologies in assessing ABC transporter expression profiles. This was demonstrated by an improved database that allows a more precise identification of compounds whose resistance is mediated by ABC transporters. In addition, it helps to pinpoint the compounds responsible for collateral sensitivity. We are now applying these platforms to identify genes critical to the development of MDR in cancer.

5. Conclusion For many years, MDR has been explained solely by an overexpression of ABCB1 in the tumor. Since its discovery in the late seventies, extensive research has characterized this ABC transporter-mediated MDR mechanism in vitro. These studies have also highlighted a multiplicity of additional mechanisms governing MDR. Although, these mechanisms can act individually, this concise review underlines the pleiotropic phenomenon of the cell response to drug treatment (Fig. 4.3). Beside the cellular response, we have mentioned the role of the microenvironment on the initiation and maintenance of MDR, which is now supported by strong data. These last years have seen the development of a new paradigm suggesting the role of cancer stem cells in the intractability of cancers. Whether or not cancer stem cells represent one of the mechanism(s) of MDR needs to be unraveled. In this chapter, we have discussed briefly the fundamental need for translational research to confirm MDR mechanisms in human

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Fig. 4.3. Pleotropic mechanisms of multidrug resistance. (1) Drug entry. Aside from pharmaceutical factors, the primary obstacle that prevents a drug from reaching the intracellular compartment is the plasma membrane. Therapeutic agents can react with many molecules resulting in a complex speciation profile. These species represented uniformly in this schema as a hexagonal (D) can enter cells by passive diffusion, endocytosis, or facilitated transport (uptake transporters). However, drug uptake can be significantly reduced by ATP-dependent drug efflux pumps (such as the ABC transporters ABCB1 and G2) and alterations in lipid metabolism (ceramide pathway) usually found in multidrug-resistant cells, which induce modifications in the biophysical properties of the lipid bilayer. (2) Drug metabolism. Once in the intracellular compartment, drug metabolism enzymes are the second line of cellular resistance. This process involves three phases. Phase I, or oxidative metabolism is mediated mainly by cytochrome P450 enzymes (CYPs) and epoxide hydrolases. Drug species are metabolized and converted into highly mutagenic aromatic metabolites (epoxide) that can be conjugated by phase II enzymes including GSTs, UGTs, SULTs, and NATS. These conjugated metabolites are then effluxed by transporters, which can be considered as phase III of drug metabolism. (3) Drug sequestration. Drug species can be trapped in subcellular organelles such as lysosomes and endosomes through ATP7A/B, ABCA3, or ABCB5 influx and then expelled from the cell. “Scavenger” metallothioneins ensnare metal ions and reactive oxygen species, leading to resistance to metal-based therapy and radiation. (4) Mechanisms activated after nuclear entry. Drug species (newly activated in the case of a prodrug-based strategy) that evade the above mechanisms of resistance enter the nucleus, where they encounter several mechanisms of resistance. Drug species can be effluxed via vault proteins into the cytoplasm and be either sequestered in intracellular vesicles or effluxed from the cell via ATP-dependent transport. Some drug species remain in the nucleus and form damaging adducts with DNA. A complex network of interacting pathways is then initiated, leading either to cell-cycle arrest and DNA repair or if the damage is extensive, rather than repair itself, the cell will enter one of these states: (1) senescence, (2) apoptosis, or (3) necrosis. (5) Evasion of drug-induced apoptosis. Disruption of apoptotic pathways, the hallmark of cancer, is a major obstacle to the success of chemotherapy. Blockage of apoptosis can result through the inhibitory effect of glycosylceramide and a myriad of pathways. (6) Microenvironment. Hypoxia upregulates the expression of numerous MDR-linked genes such as ABC transporters, Bcl2 family genes, glutathione, MT, etc., mainly through the activation of the transcription factor HIF1. It also dramatically reduces the effectiveness of chemotherapeutic agents that require oxidation to become cytotoxic or enhances the cytotoxicity of other agents that must undergo reduction to form active cytotoxic species. The acidic extracellular compartment also has important effects on the success of chemotherapy. (7) Signal transduction pathways. Cancer cells have altered signal transduction pathways, governed via

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cancers. Conflicting reports in the literature concerning the role of MDR-linked genes in cancer and recent technological development based on the “-omics” underline the urgency of the establishment of standard analytical methods to characterize tumor biology, especially MDR. We have entered the postgenomic era where scientific knowledge along with technological resources allows the development of systems biology. Important achievements in the understanding of clinical multidrug resistance will depend on our ability to accept these challenges.

Acknowledgments This research was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute. We would like to thank George Leiman for editorial assistance.

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Fig. 4.3. (continued) integrin receptors, growth factor receptors, frizzled receptors, and smoothened-patched receptors. These altered pathways can lead to the blockage of apoptosis and expression of MDR-linked genes such as those involved in DNA repair and drug-efflux pumps. Last but not least, cancer cells often display chromosomal abnormalities that can lead to the overexpression of antiapoptotic genes, etc. SLCs solute carriers, ABCs ATP-binding cassette transporters, SMase sphingomyelinase, GFR growth factor receptor, Wnt wingless, FZD frizzled, Smo smoothened, SHH sonic hedgehog, PTCH patched, MT metallothionein, GSTs glutathione-S-transferases, UGTs UDP-glucuronosyltransferases, SULTs sulfotransferases, NATs arylamine N-acetyltransferases, GCS glucosylceramide synthase, ABCCs ABC transporters subfamily C

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40.

41.

42.

43.

44.

45.

46.

47.

48.

49. 50.

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Chapter 5 Molecular Mechanisms of Drug Resistance in Single-Step and Multi-Step Drug-Selected Cancer Cells Anna Maria Calcagno and Suresh V. Ambudkar Abstract Multidrug resistance (MDR) remains one of the key determinants in chemotherapeutic success of cancer patients. Often, acquired resistance is mediated by the overexpression of ATP-binding cassette (ABC) drug transporters. To study the mechanisms involved in the MDR phenotype, investigators have generated a variety of in vitro cell culture models using both multi-step and single-step drug selections. Sublines produced from multi-step selections have led to the discovery of several crucial drug transporters including ABCB1, ABCC1, and ABCG2. Additionally, a number of mechanisms causing gene overexpression have been elucidated. To more closely mimic in vivo conditions, investigators have also established MDR sublines with single-step drug selections. Here, we examine some of the multi-step and single-step selected cell lines generated to elucidate the mechanisms involved in the development of MDR in cancer cells. Key words: Multidrug resistance, Multi-step selection, Single-step selection, ABC transporter, Gene amplification, Epigenetic changes

1. Introduction Cancer is one of the top ten leading causes of death in the world. In the USA alone, one of every four deaths will be due to cancer in 2008 (1). Advancements in early detection and cancer treatments have yielded significant progress, yet cancer deaths still outnumber deaths due to heart disease in people less than 85 years of age in the USA. A major factor in therapeutic failure for cancer involves the development of drug resistance to a variety of structurally unrelated anticancer drugs, also known as multidrug resistance (MDR) (2). MDR can develop in several different ways, with the predominant mechanism being the overexpression of ATP-binding cassette (ABC) drug transporters on the plasma membrane of tumor J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_5, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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cells. These transporters act as energy-driven pumps (3), and as such, maintain intracellular drug concentrations below toxic levels. Thus, the tumor survives, rendering the treatment ineffective. Tumors can demonstrate intrinsic drug resistance in which the tumor is innately resistant to treatment; this occurs in tumors originating from epithelial cells such as renal or adrenal tumors, which naturally express ABC drug transporters (4). Acquired resistance, on the other hand, arises following therapy, and tumors normally present with the MDR phenotype subsequent to various genetic changes. ABC drug transporters belong to the largest superfamily of transporter proteins (5). Members of this family are recognized by a consensus ATP-binding site from 90 to 110 amino acids in length, which also includes a linker region between two Walker motifs. In addition to two ATP-binding sites, ABC transporters normally possess two transmembrane domains. The 48 human ABC transporter genes are further subdivided into seven subfamilies (A–G) based on similar gene structure, order of the domains, and on sequence homology in their consensus domains (6). ABC drug transporters such as ABCB1, ABCC1, and ABCG2 are expressed in normal and tumor cells and are localized to different plasma membrane surfaces; the normal function of a number of these transporters is to efflux endogenous and xenobiotic metabolites from the cell (7). The substrate specificity for ABCB1, ABCG2, and the various ABCC family members overlaps extensively although the primary sequences of these transporters vary significantly (7). This phenomenon makes treatment of multidrug-resistant cancer unsuccessful in spite of the multitude of drugs available. Reports show that patients with ABCB1-positive tumors are three times more likely to fail a course of therapy than those who have tumors that are ABCB1negative (8). Even more taxing for patients are tumors that express multiple ABC transporters, since overexpression is not mutually exclusive and a tumor can overexpress several MDRlinked ABC transporters in tandem. Although over 12 of these transporters have been linked to MDR (9), little is known about the regulation of these transporters. Often multi-step drug selections have been employed to study the MDR phenotype. Several drawbacks are associated with this technique. The multi-step selections utilize higher concentrations of drug than those found in patients as well as extended periods of exposure. These factors produce pleiotropic effects. To avoid such issues, we recently employed a single-step selection to evaluate ABC transporter regulation. We reported that ABC transporter mRNA expression patterns vary with single vs. multi-step selection with doxorubicin in MCF-7 breast cancer cells (10). In multi-step selections with doxorubicin, ABCB1 is often the dominant ABC transporter causing MDR; we have shown that follow-

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ing a single-step selection using low concentrations of doxorubicin other transporters including ABCC2, ABCC4, and ABCG2 are overexpressed. In this chapter, we will review a number of the multi-step and single-step selected cancer cell lines that have been established and used extensively to investigate MDR (Table 5.1). In addition, we will discuss the mechanisms that have been ascertained in the development of these drug-resistant cell lines.

Table 5.1 List of select multi-step and single-step selected cell lines overexpressing ABC transporters Selection regimen

Cell line

Drug

ABC transporter overexpressed

Multi-step

KB-3-1

Colchicine Doxorubicin Vinblastine

ABCB1 (13) ABCB1 (13) ABCB1 (13)

Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step Multi-step

OVCAR-8 MCF-7 MES-SA MCF-7 MDA-MB-231 MCF-7 MCF-7 NCI-H69 COR-L23 MOR GLC4 MCF-7 NCI-H69 MCF-7 MCF-7 MCF-7 S1 IGROV-1 IGROV-1 SF295 GLC4

Doxorubicin Doxorubicin Doxorubicin Docetaxel Docetaxel Doxorubicin Paclitaxel Doxorubicin Doxorubicin Doxorubicin Doxorubicin Etoposide Etoposide Flavopiridol Doxorubicin and verapamil Mitoxantrone Mitoxantrone Topotecan Mitoxantrone Mitoxantrone Mitoxantrone

ABCB1 (14, 15) ABCB1 (17) ABCB1 (19) ABCB1 (20) ABCB1 (20) ABCB1 and ABCG2 (21, 22) ABCB1 and ABCG2 (21, 22) ABCC1 (23) ABCC1 (24, 25) ABCC1 (24, 25) ABCC1 (26) ABCC1 (27) ABCB1 and ABCC1 (28) ABCG2 (29) ABCG2 (30) ABCG2 (31) ABCG2 (32) ABCG2 (33) ABCG2 (33) ABCG2 (34) ABCA2 (35)

Single-step Single-step Single-step Single-step Single-step Single-step Single-step Single-step

MCF-7 MCF-7 IGROV-1 S1 MES-SA MES-SA NCI-H82 GLC4

Doxorubicin Etoposide Doxorubicin Doxorubicin Doxorubicin Paclitaxel Epirubicin Doxorubicin

ABCC4 and ABCG2a (10) ABCG2 (10) ABCG2 (10) ABCG2 (10) ABCB1 (57) ABCB1 (59) ABCC1 (62) ABCC1 (63)

In all cases the overexpression of an ABC transporter was demonstrated at the functional level and the references are given in the parenthesis a ABCG2 is the ABC transporter responsible for MDR and ABCC4 does not confer resistance to doxorubicin

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2. Multi-Step Selected Cell Lines To study MDR in vitro, investigators have utilized drug selections to generate resistant cell lines for over 30 years. Selections can be performed on individual clones or on mass populations of cells (11). To establish individual clones, the cells must be cloned so that an individual cell is the source for the entire population, which will then be selected with the drug of choice. This technique boosts one major advantage in that a single gene will be responsible for the MDR. Alternatively, multifactorial MDR will result when an entire cell population is selected (11). The selection of a cell population, on the other hand, more closely mimics the clinical situation and can stem from a spectrum of mechanisms. One of the original multi-step selected cell lines was established by the selection of an individual clone of KB epidermoid carcinoma cells with colchicine (12). The subsequent resistant sublines were generated with increasing single-step selections beginning with KB-3-1, an individual clone from a population of the KB cells. The MDR1 (ABCB1) gene was first identified from these cells. Using this same methodology, four additional KB sublines were created with selections in high concentrations of colchicine, vinblastine, or doxorubicin (13). These sublines were selected with a stepwise selection, and all express high levels of ABCB1. Since their establishment, the various resistant sublines of KB cells have been widely used to investigate MDR mediated by ABCB1. In contrast to the KB cells, MCF-7 breast cancer cells were selected with doxorubicin using the mass population method, yet also expressed ABCB1 (14). This original selection was performed with increasing concentrations of doxorubicin beginning with 10 nM. The final resistant subline, MCF-7 AdrR, was capable of surviving in 10 µM doxorubicin. Later these original doxorubicin-resistant MCF-7 cells were determined to actually be OVCAR-8 ovarian cancer cells, which were resistant to doxorubicin (15). Their nomenclature has changed accordingly to NCI/ ADR-Res or OVCAR-8/ADR (16). Other laboratories independently generated doxorubicin-resistant MCF-7 cells, and one such subline was established by culturing MCF-7 cells in 0.025 µg/ ml doxorubicin and increasing the selection pressure by twofold until the cells grew in the presence of 2 µg/ml doxorubicin (17). Interestingly, these resistant cells also overexpressed ABCB1 and were karyotyped to match MCF-7 cells from ATCC (18). Doxorubicin-resistant sarcoma cells (MES-SA/Dx5) were also one of the early MDR models expressing ABCB1; these cells were selected with increasing concentrations of doxorubicin (19). Lastly, MCF-7 and MDA-MB-231 breast cancer cells exposed to increasing concentrations of docetaxel (up to 30 µM), known as

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MCF-7 TAX30 and MDA-MB-231 TAX30, were also found to overexpress ABCB1 (20). Moreover, ABCB1 was involved in MDR in highly resistant cell lines derived from both the individual clone and population selection techniques. Yet in other more recent studies with MCF-7 cells using multi-step selections with lower concentrations of doxorubicin, both ABCG2 and ABCB1 were expressed (21, 22). In these studies concentrations ranging from 9 to 300 nM doxorubicin were employed. Similarly, MCF-7 cells selected with a multi-step selection with paclitaxel, 0.56 nM to a final concentration of 6.6 nM, also express both transporters, but at a lower level than the doxorubicin-selected MCF-7 cells (21, 22). Remarkably, paclitaxel is not a substrate of ABCG2, yet its selection pressure caused the overexpression of ABCG2. Furthermore, the investigators demonstrated that the clones were isogenic and that MDR was a consequence of adaptation and not a selection of a clone within the population. Involvement of other ABC transporters at lower selection concentrations suggests that ABCB1 may be dominant only at the later stages of MDR in particular cell types. ABCC1 was first reported in a resistant cell line produced with a stepwise doxorubicin selection in a small cell lung cancer cell line, NCI-H69; this was called H69AR and did not express ABCB1 (23). Large-cell (COR-L23), small-cell (H69), and adenocarcinoma (MOR) lung tumor lines continuously selected with increasing concentrations of doxorubicin were also found to express ABCC1 (24, 25). Another small cell lung carcinoma cell line, GLC4, was utilized in resistance studies, and ABCC1 was overexpressed when selected with doxorubicin concentrations augmented from 18 to 1,152 nM (26). Surprisingly, etoposideselected MCF-7 cells also showed overexpression of ABCC1 instead of ABCB1. These cells were generated with a stepwise selection in increasing concentrations of etoposide starting with 200 nM up to 10 µM and revertants were prevented by occasional reselection in 4 µM etoposide (27). Investigators reported that etoposide-selected H69 small cell lung cancer cells expressed both ABCB1 and ABCC1 at the mRNA and protein levels (28). During this selection process, ABCC1 expression preceded that of ABCB1. At a moderate level of ABCC1 expression, rather than continue increasing the expression of ABCC1, the cells turned on the ABCB1 gene (28). It also appears that cell type dictates the particular ABC transporter that is induced and that cells can activate more than one ABC transporter. Investigators have also prepared a variety of resistant cell lines overexpressing ABCG2. For instance, MCF-7 cells selected with increasing concentrations of flavopiridol, MCF-7/FLV1000, expressed wild-type ABCG2 (29). MCF-7 cells selected in the presence of both doxorubicin and verapamil also overexpressed ABCG2; these cells are known as MCF-7 AdVp3000 (30). Unlike

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the MCF-7/FLV1000, the MCF-7 AdVp3000 expressed the mutant ABCG2 R482T. MCF-7 cells were also exposed to mitoxantrone and were found to overexpress wild-type ABCG2 (31). When S1 human colon carcinoma cells were selected with mitoxantrone, ABCG2 was also overexpressed (29). Additional sublines were generated when these original S1-M1-3.2 (32) were exposed to higher concentrations of drug, up to a final concentration of 80 µM. These S1 sublines also expressed a mutant R482G ABCG2 protein. Another example of MDR mediated by ABCG2 overexpression occurred in IGROV-1 human ovarian carcinoma cells selected with either topotecan or mitoxantrone (33). Similarly, SF295 human glioblastoma cells showed ABCG2 overexpression when selected in increasing concentrations of mitoxantrone (50–500 nM) (34). Unexpectedly, when a mitoxantrone-resistant small cell lung cancer cell line, GLC4-MITO, was established, ABCA2 upregulation was found (35). In the case of ABCG2overexpressing cell lines in vitro, two gain of function mutations have been identified (R482T and R482G). To our knowledge, no such mutations have been reported in clinical samples positive for ABCG2 to date.

3. Mechanisms of Overexpression in Multi-Step Selected Cell Lines

Gene amplification is the most common method for drug-resistant cells to overexpress a particular ABC transporter. In a comprehensive analysis of 23 drug-resistant cancer cell lines derived from ten different human cancers, it was revealed that changes in gene copy number were implicated in acquired drug resistance (36). Comparative genomic hybridization (CGH) was executed on drug-sensitive and their corresponding drug-resistant sublines, and the regions of gain within the drug-resistant cell lines were consistent with regions encoding ABC transporters in 19 of the 23 cell lines. These changes were further confirmed by fluorescence in situ hybridization (FISH) analysis in these cells. Of particular interest were ABCA3, ABCB1, and ABCC9, which had a greater than twofold increase in copy numbers. Furthermore, gene amplification was in line with gene expression changes present in these resistant cells. Amplified genes are either present in homogeneously staining regions or on extrachromosomal elements such as double-minutes. Investigations of resistant KB cells also showed gene amplification of the ABCB1 gene (37, 38). Double-minute chromosomes were identified in these KB-resistant cell lines. Investigators also determined that KB cells could easily lose their resistance when no selection pressure was present because the gene amplification was only found in the form of double-minutes. Furthermore, in

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this KB resistant series, it was uncovered that in the less-resistant sublines the ABCB1 was activated while in the more highly resistant sublines gene amplification occurred (39). For instance, later studies showed that with the higher colchicine selection pressure, double-minutes were stably maintained even after several months of continuous passaging in culture. The exact formation of the double-minutes in the sequential series of resistant sublines was closely examined using gel electrophoresis. Submicroscopic extrachromosomal circular DNA was revealed in the less-resistant sublines. Consequently, the double-minutes uncovered in the more-resistant sublines were formed by multimerization of these submicroscopic circular DNAs (39). Gene amplification has also been reported for some of the other multi-step selected cell lines previously described. MES-SA/ Dx5, doxorubicin-resistant sarcoma cells, displayed 7q21 alterations and gene amplification (40). However, these cells, unlike the KB-resistant cells, did not possess double-minutes as seen by FISH analysis. In the two breast cancer cell lines selected with docetaxel, MCF-7 TAX30 and MDA-MB-231 TAX30, gene amplification of chromosome 7q in the region that encodes for ABCB1 was discovered using CGH (20). ABCB1 overexpression can also be caused at the transcription level. Investigators have found that in drug-sensitive cells only one transcription start site is used for ABCB1; nonetheless, drug-resistant cells that do not exhibit gene amplification often exploited more than one downstream transcription start site for ABCB1 (41). This substitution of other downstream transcription start sites for ABCB1 within the same cell line was a distinct mechanism that led to the identification of the MED-1 (multiple start site element downstream) in many of the genes with a TATA-less promoter, which have multiple start sites such as ABCB1 (42, 43). This MED-1, GCTCCC/G, was crucial for ABCB1 expression in drug-resistant cells in the cell lines examined. Likewise MEF1, MDR1 promoter-enhancing factor 1, also activated transcription but through an upstream promoter element, −118 to −111 (44). Often drug selections can also cause alterations in genes that appear as gene rearrangements through nonhomologous recombinations. For example, investigators first reported a hybrid ABCB1 mRNA resulting from such a chromosomal rearrangement in a doxorubicin-selected S48-3s human colon adenocarcinoma cell line (45). In these cells, there was a 4;7 translocation resulting with the 3¢ end of ABCB1 adjacent to a transcriptionally active chromosome 4 gene, thus triggering the activation of ABCB1 by the promoter sequences on the adjacent chromosome 4. For this particular gene rearrangement, the subsequent ABCB1 protein structure remained unaltered due to the rearrangement occurring within the 5¢ region of ABCB1. Follow-up studies illustrated that eight other selected cells and two clinical samples had

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gene rearrangements (45, 46). Unexpectedly, these gene hybrids differed, suggesting that ABCB1 merely required a sufficiently active promoter to activate it and that the specific promoter was irrelevant. Other ABCB1 mRNA hybrids have also been reported (47). These hybrids were regulated by nearby genomic sequences with similarity to a retroviral element. Nevertheless, no chromosomal rearrangements were discovered in these hybrids. Epigenetic changes have also been reported to activate ABCB1. In the repressed state, the ABCB1 promoter is methylated and enriched in methyl-CpG binding protein 2 (MeCP2). In MCF-7 cells exposed to a stepwise selection with doxorubicin, ABCB1 is overexpressed and in the resistant cells the ABCB1 promoter is completely unmethylated (48). The promoter methylation status of ABCB1 is inversely correlated to the expression of the ABCB1 gene. The loss of methylation at the promoter facilitates the activation of ABCB1 in these resistant cells. In the resistant cell lines that displayed ABCC1 overexpression, gene amplification was also the most common mechanism. The original ABCC1-overexpressing cell line H69AR demonstrated gene amplification with Southern blot analysis (23). Many doubleminutes of chromosome 16 were discovered in the COR-L23/R cells, while the MOR cells exhibited an enlarged copy of chromosome 16 with homogeneously staining regions (24). As with the cells overexpressing ABCB1, highly resistant cell lines such as GLC4/ADR (26), COR-L23/R, and MOR/R predominantly displayed gene amplification of ABCC1 as the mode of gene overexpression. On the contrary, transcriptional activation of ABCC1 was solely responsible for gene overexpression in the weakly resistant SW-1573 30.3 M subline, which had been selected with low concentrations of doxorubicin (49). Of the highly resistant cell lines, only MOR/R presented a combination of gene amplification and gene activation, whereas gene amplification was the main mechanism for gene overexpression in the GLC4/ADR and COR-L23/R selected cell lines. For resistant cell lines overexpressing ABCG2, Southern analysis of MCF-7 AdVp3000 and S1-M1-80 sublines uncovered that only MCF-7 AdVp3000 had gene amplification (50). In later studies, these cell lines as well as MCF-7/MX were further characterized by CGH, FISH, spectral karyotyping, and Southern blotting (51). No amplification was confirmed in the S1-M1-80 subline, while the other two cell lines showed amplification with multiple translocations of chromosome 4. Other investigators evaluated the MCF-7/MX subline and also showed gene amplification by Southern blot analysis (52). In the SF295 MX selected cells, ABCG2 was found amplified by Southern analysis. The sublines selected with the lower concentrations displayed double-minutes containing the ABCG2 gene when examined with FISH and spectral karyotyping. At 500 nM mitoxantrone selection pressure, a

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homogeneously staining region was found integrated into the chromosome causing ABCG2 overexpression (34). Furthermore, Boonstra et al. also found gene amplification with CGH in the GLC4-MITO cells for ABCA2 on chromosome 9 (35). Analogous to results from ABCB1 promoter studies in drugresistant cells overexpressing ABCB1, ABCG2 has also been shown to have multiple transcription start sites in drug-selected cells (53). Investigators have also reported the expression of novel 5¢UTR variants of transcripts that possess different translation efficiencies. Thus, the ABCG2 protein expression is directed at the posttranscriptional level as a consequence of these 5¢UTR variants. However, no gene rearrangements in the 5¢UTR region were seen. Various epigenetic changes have also been found in multistep selected cell lines that overexpress ABCG2. Chromatin immunoprecipitation (ChIP) has been used to identify histone modifications in these multi-step selected cells. In the S1-M1-80 cells, which show no gene amplification, the ABCG2 proximal promoter displayed histone H3 acetylation (54). Further epigenetic changes were present in this subline as HDAC1 and HDAC3 bound less to the proximal promoter of ABCG2. More importantly, Pol II binding, an indicator of ABCG2 promoter activity, was enhanced in these resistant cells. MicroRNAs (miRNA) have also been implicated in the regulation of genes. Recent reports investigated the effects of miRNA on ABCG2 expression. S1 colon cells possessed a longer 3¢UTR in the ABCG2 mRNA where a putative hsa-miR-519c binding site exists (55). This miRNA binds to this site, causing translational repression and mRNA degradation in the sensitive parental cells. Conversely, the S1-M1-80 cells utilize a noncanonical AUUAAA poly (A) signal to yield a much shorter 3¢UTR lacking this miRNA binding site, thus eluding degradation mediated by hsa-miR-519c. It appears that a combination of epigenetic and miRNA-mediated changes are responsible for the overexpression of ABCG2 in the highly resistant S1-M1-80 cells.

4. Single-Step Selected Cell Lines These and many other multidrug resistant cancer cell lines have been established in vitro through continual drug selection. Although they offer a sufficient means for investigating the regulation of ABC transporter expression and function, rarely do these continual drug selections emulate what is found in the clinical setting. Thus, insight into the development of MDR by ABC transporters at clinically relevant concentrations and ascertaining which factors induce upregulation of ABC transporters during the initial steps of MDR will afford more advantageous measures

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to circumvent MDR. Our goal was to evaluate the expression of ABC transporters following a short low dose selection, which would simulate the conditions encountered in vivo and to compare the gene expression levels of MDR-linked ABC transporters in these sublines selected by a low-dose single step to an established high-dose doxorubicin selected subline (17) and to determine if overexpression of the same ABC transporters occurs. We have recently found that ABC transporter mRNA expression patterns vary with single- vs. multi-step treatment with doxorubicin in MCF-7 breast cancer cells. We established single-step doxorubicin-selected MCF-7 sublines using very low concentrations (14 or 21 nM) (10). Individual clones were selected from a population of 10,000 cells in a 100× 20 mm tissue culture dish exposed to drug for 10 days. Clones were then maintained in drug-free medium following the initial drug selection. We compared these single-step sublines to a previously established multistep doxorubicin-selected MCF-7 subline (17) known to overexpress only ABCB1 at the mRNA and protein levels (Fig. 5.1) due to gene amplification. We evaluated a number of ABC transporters and found that ABCC2, ABCC4, and ABCG2 were overexpressed at the mRNA level in these single-step selected sublines (Fig. 5.2). Yet, only ABCC4 and ABCG2 were overexpressed at the protein level. Both 14 and 21 nM single-step doxorubicin-selected sublines exhibited nearly fivefold resistance to doxorubicin compared with parental MCF-7 cells. However, ABCC4 did not confer resistance to this drug, suggesting that ABCG2 was the major transporter responsible for the development of doxorubicin resistance. Sequencing of ABCG2 in the single-step selected sublines revealed that our in vitro selection resulted in the overexpression of the wild-type ABCG2 and not the gain of function mutations either G or T at amino acid 482. SiRNA studies further confirmed that mainly ABCG2 confered drug resistance in these clones. We also observed that the upregulation of ABCG2 was facilitated by histone hyperacetylation of H3 at the proximal promoter of ABCG2. Similar to what was found in the S1-M1-80 cells, Pol II binding was increased while HDAC1 binding was decreased in the single-step selected sublines. This was the first report of ABCG2 overexpression in MCF-7 cells following a short-term low-dose selection with doxorubicin. To further evaluate if this ABCG2 overexpression was drug and cell line independent, we generated additional sublines of MCF-7 cells with a single-step selection using 300 nM etoposide and two different cancer cell lines, IGROV-1 ovarian cancer cells and S-1 colon tumor cells, with 14 and 21 nM doxorubicin, respectively. To ensure that we were not selecting for a resistant clone, several lower etoposide concentrations, 50, 100, and 200 nM, were first evaluated. These lower selections indicated that all MCF-7 parental cells were able to survive. For IGROV-1

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Fig. 5.1. Analysis of ABC transporter expression and function in the multi-step doxorubicin-selected MCF7/ADR cell line. (a) The delta-delta CT method was used to determine the fold change of ABC transporter gene expression in the multistep doxorubicin-selected cells, MCF7/ADR (17), compared to their parental line, MCF-7. The values represent the mean and standard deviation (n = 2). The overexpression of ABCB1 is highlighted by the black circle. (b) Using C219, the ABCB1-specific monoclonal antibody, the relative quantities of ABCB1 were determined for MCF7/ADR and MCF-7 in whole-cell lysates. Lane 1, MCF-7 control and lane 2, MCF7/ADR, (100,000 cells for all samples). (c) Calcein-AM efflux assays were performed using flow cytometry. Assays compared MCF-7 and MCF7/ADR. Charcoal gray histogram, MCF7/ADR; dark gray histogram, MCF7/ADR cells in the presence of 10 µM cyclosporin A (CSA); black histogram, MCF-7, and light gray histogram, MCF-7 in the presence of cyclosporine A. The schematic on the far left side depicts the multi-step selection with doxorubicin in 0.025 µg/ml doxorubicin and increasing the selection pressure by twofold until the cells grew in the presence of 2 µg/ml doxorubicin.

cells, only the 14 nM doxorubicin selection yielded resistant cells. Five sublines derived from IGROV-1 cells obtained using 14 nM doxorubicin and S-1-resistant clones obtained at a 21 nM doxorubicin concentration were further characterized. Furthermore, we were also able to replicate the upregulation of the same ABC

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Fig. 5.2. Single-step doxorubicin-selected clones overexpress ABCG2. (a) The schematic of the single-step selection for the MCF-7 cells with doxorubicin. (b) Characterization of selected ABC transporter gene expression levels in several single-step clones. Doxorubicin-resistant MCF-7 clones were established employing a single-step selection with either 14 or 21 nM treatment for 10 days followed by culturing continuously in drug-free medium. The average fold change compared to parental MCF-7 cells ± SD (n = 4) was calculated using delta-delta Ct method from real-time RT-PCR data. Reference gene is PMCA4 (64). The key for selected five ABC transporters is given in figure. (c) Western blotting analysis of ABCG2 protein using BXP-21 antibody following no treatment (lane 1), 50 nM negative siRNA treatment (lane 2), and 50 nM G2-2 siRNA treatment (lane 3). (d) Cytotoxicity assays with mitoxantrone evaluating the effect of silencing ABCG2 in the 21 nM single-step clone. Dose–response curves were derived from three independent experiments using the CCK-8 assay. White box, 21 nM cells with 12.5 nM G2-2 siRNA and black triangle, 21 nM cells with 12.5 nM negative siRNA. Error bars indicate standard deviation (n= 3).

transporters in the MCF-7 cells using this single-step selection with 300 nM etoposide. ABCG2 was also the dominant overexpressed ABC transporter for these additional sublines. This suggests

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that even a low-dose selection can bring about MDR and that ABCG2 overexpression mediates the early stages of MDR development in certain cell lines. ABCG2 may be protecting against the cytotoxic effects of drugs in our single-step-selected clones, as it does in stem cells (56). Analysis of other mammary stem cell markers in our single-step sublines demonstrated that we did not enrich for cancer stem cells during the single-step selections of these clones. Taken together with the epigenetic alterations that were discovered in these resistant sublines, adaptation as opposed to selection appears to be the mitigating factor for this selection process. Single-step selections have also been performed with the MES-SA, human sarcoma cell line. This protocol used the mass population selection technique, where cells were first plated in a 96-well plate, treated for 2 weeks with 40 nM doxorubicin, grown drug free for two additional weeks, and then individually harvested from each well (57). As with the MES-SA/Dx5, all clones examined expressed ABCB1. The authors used fluctuation analysis to determine that the doxorubicin-resistant clones were derived due to spontaneous mutations. Additionally, no chromosomal alteration or gene amplification was discovered in these singlestep mutants (40). When either etoposide or paclitaxel was used in single-step selections with MES-SA cells, authors found that either no ABCB1 overexpression occurred (58) or that only 44% of the clones expressed ABCB1 (59), respectively. Etoposideselected MES-SA cells showed a reduction in topoisomerase II but no ABC transporter increases. This suggests that ABCB1 substrates have different effects when selecting for ABCB1expressing clones. Furthermore, a single-step selection with 40 nM doxorubicin in the presence of an ABCB1 inhibitor, PSC833, also produced no detectable levels of ABCB1 but rather decreased levels of topoisomerase II a (60). In recent follow-up studies, the authors found that an increase in acetylated H3 modified the chromatin structure of ABCB1 far upstream, 968-bp proximal to the upstream promoter, and initiated upstream transcripts for these single-step selections (61). Equally important, the authors confirmed that these upstream ABCB1 transcripts were spontaneous in nature given that a clonal variant expanded to several million cells without any drug selection also produced these ABCB1 upstream variants. Other single-step selections have generated sublines that overexpress ABCC1. One such example was the H82, a variant of small cell lung cancer, which was selected for 18 h with 69 nM epirubicin. This initial selection yielded a drug-resistant cell line, which was then subsequently selected for 18 h with 14 nM epirubicin, producing an even more resistant line known as H82/E8 (62). These sublines displayed two- to ninefold resistance. Remarkably, the H82/E8 subline remained stably resistant for

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over 2 years without further drug treatment. Investigators also selected H69 cells with eight treatments of 14 nM epirubicin followed by maintenance in drug-free medium. This subline is referred to as H69/E8. Only the H82/E8 increased ABCC1 expression (62) while neither cell line expressed ABCB1. Other investigators using a 50 nM single-step doxorubicin-selection with GLC4 small cell lung cancer cells attributed an increase in ABCC1 expression to the activation of the JNK pathway (63).

5. Conclusion Drug selections with both clonal and cell populations have aided in the study of MDR mediated by ABC transporters. For instance, these in vitro techniques have led to the identification of at least three of the most influential ABC drug transporters for MDR. The overexpression of a particular ABC transporter during drug selection appears to depend on a multitude of factors, which include but are not limited to the cell type, the selection regimen, the drug used for selection pressure, as well as the concentrations utilized. These factors suggest that a number of ABC transporters should be evaluated following drug selection in addition to ABCB1. The single-step selection is capable of generating sublines with the MDR phenotype at clinically relevant concentrations while eliminating pleiotropic effects due to long-term drug exposure. With advancements in techniques for analysis at the molecular level and better understanding of gene regulation in the presence of drug, future studies should focus on translational research to improve the success rate of cancer therapies.

Acknowledgments We thank Mr. George Leiman for editorial assistance. This research was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research. References 1. Jemal A, Siegel R, Ward E et al (2008) Cancer statistics. CA Cancer J Clin 58:71–96 2. Gottesman MM (2002) Mechanisms of cancer drug resistance. Annu Rev Med 53:615–627

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29. Robey RW, Medina-Perez WY, Nishiyama K et al (2001) Overexpression of the ATPbinding cassette half-transporter, ABCG2 (MXR/BCRP/ABCP1), in flavopiridol-resistant human breast cancer cells. Clin Cancer Res 7:145–152 30. Lee JS, Scala S, Matsumoto Y et al (1997) Reduced drug accumulation and multidrug resistance in human breast cancer cells without associated P-glycoprotein or MRP overexpression. J Cell Biochem 65:513–526 31. Volk EL, Rohde K, Rhee M et al (2000) Methotrexate cross-resistance in a mitoxantrone-selected multidrug-resistant MCF7 breast cancer cell line is attributable to enhanced energy-dependent drug efflux. Cancer Res 60:3514–3521 32. Rabindran SK, He H, Singh M et al (1998) Reversal of a novel multidrug resistance mechanism in human colon carcinoma cells by fumitremorgin C. Cancer Res 58:5850–5858 33. Maliepaard M, van Gastelen MA, de Jong LA et al (1999) Overexpression of the BCRP/ MXR/ABCP gene in a topotecan-selected ovarian tumor cell line. Cancer Res 59:4559–4563 34. Rao VK, Wangsa D, Robey RW et al (2005) Characterization of ABCG2 gene amplification manifesting as extrachromosomal DNA in mitoxantrone-selected SF295 human glioblastoma cells. Cancer Genet Cytogenet 160:126–133 35. Boonstra R, Timmer-Bosscha H, van EchtenArends J et al (2004) Mitoxantrone resistance in a small cell lung cancer cell line is associated with ABCA2 upregulation. Br J Cancer 90:2411–2417 36. Yasui K, Mihara S, Zhao C et al (2004) Alteration in copy numbers of genes as a mechanism for acquired drug resistance. Cancer Res 64:1403–1410 37. Shen D, Fojo A, Chin J et al (1986) Human multidrug-resistant cell lines: increased mdr1 expression can precede gene amplification. Science 232:643–645 38. Fojo AT, Whang-Peng J, Gottesman MM, Pastan I (1985) Amplification of DNA sequences in human multidrug-resistant KB carcinoma cells. Proc Natl Acad Sci USA 82:7661–7665 39. Schoenlein PV, Shen DW, Barrett JT, Pastan I, Gottesman MM (1992) Double minute chromosomes carrying the human multidrug resistance 1 and 2 genes are generated from the dimerization of submicroscopic circular DNAs in colchicine-selected KB carcinoma cells. Mol Biol Cell 3:507–520 40. Chen GK, Lacayo NJ, Duran GE et al (2002) Preferential expression of a mutant allele of

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Chapter 6 Pharmacogenetics of ATP-Binding Cassette Transporters and Clinical Implications Ingolf Cascorbi and Sierk Haenisch Abstract Drug resistance is a severe limitation of chemotherapy of various malignancies. In particular efflux transporters of the ATP-binding cassette family such as ABCB1 (P-glycoprotein), the ABCC (multidrug resistance-associated protein) family, and ABCG2 (breast cancer resistance protein) have been identified as major determinants of chemoresistance in tumor cells. Bioavailability depends not only on the activity of drug metabolizing enzymes but also to a major extent on the activity of drug transport across biomembranes. They are expressed in the apical membranes of many barrier tissues such as the intestine, liver, blood–brain barrier, kidney, placenta, testis, and in lymphocytes, thus contributing to plasma, liquor, but also intracellular drug disposition. Since expression and function exhibit a broad variability, it was hypothesized that hereditary variances in the genes of membrane transporters could explain at least in part interindividual differences of pharmacokinetics of a variety of anticancer drugs and many others contributing to the clinical outcome of certain leukemias and further malignancies. Key words: ATP-binding cassette, Multidrug resistance, Bioavailability, Efflux transporter, Single nucleotide polymorphisms

1. Introduction A number of drugs are actively transported through intestinal enterocytes into portal blood vessels or back into the gut lumen. Moreover, drug bioavailability at the site of action is influenced by active transport processes, a fact that is well known from drugs having a low bioavailability in the central-nervous system due to active export processes at the blood–brain barrier. Most efflux transporters belong to the ABC (ATP-binding cassette) superfamily of membrane proteins, which may influence the intracellular concentration of numerous compounds in a variety of cells and tissues. These transporters play a major role as defense J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_6, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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mechanism against penetration of xenobiotics such as cytostatics. The energy necessary for the substrate translocation across biomembranes is generated from the hydrolysis of ATP and intermediate phosphorylation of the transporter, enabling active transport of substrates against steep concentration gradients. The ABC transporter P-glycoprotein (Pgp, also known as ABCB1) is one of the best characterized human efflux transporters. There is increasing understanding of its function, regulation, and impact of genetic variants. Aside ABCB1, further members of the ABC transporters related to the phenomenon of multidrug resistance were identified, such as ABCC1 and ABCC2 (multidrug resistance-associated proteins, MRPs) and ABCG2 (breast cancer-related protein, BCRP). To date there are at least 49 members of the ABC transporter family, subdivided into seven subfamilies (http://nutrigene.4t.com/humanabc.htm). The protein size spans 325 amino acids in ABCC13 and up to 5,058 amino acids in ABCA13. In general, most transporters have a size of 1,500 AA. Most ABC transporters are composed of two equal or unequal halves containing a membrane-spanning domain and a nucleotide-binding fold (1).

2. ABCB1 (Pgp) 2.1. Function and Expression

The gene encoding Pgp was multidrug resistance 1 (MDR1, now termed ABCB1), due to the observation that ABC transporters like Pgp were overexpressed in tumor cells conferring to the commonly known phenomenon of multidrug resistance against certain antineoplastic agents (2). Mice have two closely related homologues of ABCB1 (Abcb1a, Abcb1b). Absence of the gene, as being the case in double-knockout mice, is conformable with life. Double-knock-out mice are viable and fertile but are highly sensitive to certain neurotoxins such as ivermectin (3), indicating the important role of ABCB1 in transport across the blood–brain barrier. Interestingly, such knock-out mice develop an inflammatory bowel disease, similar to Crohn’s disease (4). Pgp mediates the apical transport of various hydrophobic substrates including cytostatics such as etoposide, adriamycin, vinblastine as well as lipids, steroids, xenobiotics, and peptides (Table 6.1). It is expressed at the apical side of the brush-border membranes in the intestine, at the canalicular site of hepatocytes, at the apical site of renal tubular cells and of epithelial cells at the blood–brain barrier, protecting against drug penetration into the CNS (5). In general Pgp serves as a functional barrier against drug entry but contributes also to the excretion (Table 6.2) (6–8).

Table 6.1 Typical substrates of the ABC transporters discussed in this chapter ((5, 128–133) and references herein) ABCB1 Drug class MDR1, Pgp

ABCC1

ABCC2

ABCG2

MRP1

MRP2

BCRP

Arsenite, cisplatin, Camptothecin daunoAnticancer Docetacel, doxorubicin, Doxorubicin, rubicin, doxorubicin, doxorubicin, etoposide, drugs etoposide, imatinib, etoposide, gefitinib, etoposide, methotrexate, paclitaxel, teniposide, irinotecan, methoirinotecan, vinblastine, vinblastine, trexate, mitoxanmethotrexate, vincristine vincristine trone, topotecan, vinblastine, vincristine vincristine

Table 6.2 Function and expression of ABCB1, ABCC1, ABCC2, and ABCG2 proteins in human normal tissues (according to (134)) Name

ABCB1

ABCC1

ABCC2

ABCG2

Synonym

MDR1

MRP1

MRP2

BCRP

Locus

Apical

Basolateral

Apical

Apical

Intestinal enterocytes

Vs. gut lumen

Vs. portal blood

Vs. gut lumen

Vs. gut lumen

Duodenum

X

(X)

XX

X

Jejunum

X

(X)

XX

X

Ileum

XX

(X)

(X)

?

Colon

XX

(X)

–

XX

Rectum

X

(X)

?

X

Liver (hepatocytes)

Vs. biliary canaliculi

Vs. blood

Vs. biliary canaliculi

Vs. biliary canaliculi

Blood brain barrier (capillary endothelium)

Vs. blood

Vs. blood

(Vs. blood)

(Vs. blood)

Blood CSF barrier (chorioid plexus epithelium)

Vs. liquor

Vs. blood

Kidney (tubular epithelium)

XX(vs. urinary lumen)

(X)(vs. blood)

Heart (capillary endothelium)

Vs. blood

(X)

Placenta (syncytiotrophoblasts)

Vs. maternal blood

(X)

Testis

Capillary endothelium

(Sertoli cells)

Lymphocytes

X

(X)

Ubiquitous

X

X(vs. urinary lumen) Vs. blood Vs. maternal blood

Vs. maternal blood Capillary endothelium

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ABCB1 is also expressed in lymphocytes, but interestingly to a high extent in hematopoietic stem cells, where it may serve to protect these cells from toxins (3, 9, 10). Moreover, ABCB1 may be involved in the migration of dendritic cells (11). In humans, expression of ABCB1 discloses a broad interindividual variability. In the liver, Pgp concentrations reportedly differ up to 50-fold (12); moreover, Pgp expression and activity are subject to markedly drug–drug interactions (13). For example, the antituberculous agent rifampicin is an effective inducer of ABCB1 due to a responsive element to the nuclear receptor PXR in the ABCB1 promoter region (14). Similarly, the multidrug transporter ABCC2 (MRP2) is upregulated by rifampicin (15, 16). ABCB1 induction was also observed after coadministration of carbamazepine (17) or St. John’s worth (18) also well known as PXR trans-acting ligands (19, 20). On the other hand, competition with verapamil may lead to an inhibition of the transporter activity (21). Although ABCB1 appears to be coregulated in many aspects with cytochrome P450 3A4, there is not always a direct relationship to CYP3A4 expression (22). 2.2. Genetic Variability of ABCB1

Less than 100 SNPs have been identified in the coding region; the absolute number including intronic and the 5¢ and 3¢-regions is several hundreds; a systematic determination of the frequency was, however, performed in a limited number (Table 6.3). Figure 6.1 shows a two-dimensional structure of ABCB1 with locations of amino acid replacements and two frequent synonymous SNPs. The first systematic investigation on ABCB1 SNPs revealed a significant correlation of a silent polymorphism in exon 26 (3435C>T; rs1045642) with intestinal Pgp expression levels and oral bioavailability of digoxin, showing significantly decreased intestinal Pgp expression and increased digoxin plasma levels after oral administration among homozygote 3435T carriers (23). In codon 893 in exon 21, up to six different genotypes exist as a result of combinations of the three alleles 2677G>T/A (rs2032582). Moreover, 2677G>T/A and 3435C>T are in linkage disequilibrium (24–26). The frequency of the putatively most interesting 3435C>T SNP differs significantly between ethnicities. The variant 3435T allele has a prevalence of 0.17–0.27 in African Blacks, 0.41–0.47 in Oriental populations, and 0.52–0.57 among Caucasians (25, 27–30). Such genotypic differences may contribute to interethnic differences of drug responses in certain populations, as far the variants have functional relevance.

2.3. Functional Significance of ABCB1 SNPs

The functional significance of the ABCB1 variants is still discussed controversially. A number of studies indicated a loss of function for the 3435T variant, whereas others did not. Interestingly, there are no confirmations that the 3435C variant would be associated

Table 6.3 Frequency of ABCB1 genetic variants in Caucasians, position on DNA, putative effect, and frequencies (134) Position

Amino acid or effect

Frequency of the variant allele

5¢-Flanking −2903 T>C

0.02a

5¢-Flanking −2410 T>C

0.10a

5¢-Flanking −2352 G>A

0.28a

5¢-Flanking −1910 T>C

0.10a

5¢-Flanking −1717 T>C

0.02a

5¢-Flanking −1325 A>G

0.02a

5¢-Flanking −934 A>G

0.10a

5¢-Flanking −692 T>C

0.10a

5¢-Flanking −41 A>G

0.09b

IVS 1a −145 C>G

0.02b

IVS 1b −129 T>C

0.06b

IVS 1b 12 T>C

0.06c

IVS 2 −1 G>A

0.09d

c. 61 A>G

N21D

0.11d

IVS 5 −35 G>C

Intronic

0.006c

IVS 5 −25 G>T

Intronic

0.16c

IVS 6 +139 C>T

Intronic

0.37d

c. 548 A>G

N183S

0.01e

c. 1199 G>A

S400N

0.05d

c. 1236 C>T

Synonymous

0.41d

IVS 12 +44 C>T

Intronic

0.05d

c. 1474 C>T

R492C

0.01e

IVS 17 −76 T>A

Intronic

0.46d

IVS 17 +137 A>G

Intronic

0.006c

c. 2650 C>T

Synonymous

0.03e

c. 2677 G>T/A

A893S/T

0.42d/0.02d

c. 2956 A>G

M986V

0.005b

c. 3320 A>C

Q1107P

0.002d

c. 3396 C>T

Synonymous

0.03c

c. 3421 T>A

S1141T

0.00c

c. 3435 C>T

Synonymous

0.54d

c. 4030

Synonymous

0.005b

c. 4036

Synonymous

0.30b

References: a[42], b[26], c[25], d[28], e[23]

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Fig. 6.1. Two-dimensional structure of ABCB1 with locations of amino acid replacements and two frequent synonymous SNPs. NBD nucleotide-binding domain (according to (134) ).

with lower activity or expression in Caucasians. The molecular mechanism of how 3435C>T influences Pgp expression is not well understood. Some in vitro studies indicated a lower rhodamine-123 efflux from CD56+ natural killer cells (31), whereas others could not confirm this finding (32, 33). Other methodologies pointed in the same direction. Determination of allelic abundance in genomic DNA and mRNA in human liver samples showed a significantly higher expression of the 3435C allele than that of the 3435T allele. Moreover, increasing 3435C/3435T ratios after cessation of transcription indicated decreased mRNA stability (34). A recent paper discussed with altered drug and inhibitor interactions as the result of altered conformations of the 3435C and T-variant during translation (35). The missense variant 2677G>T/A coding for the three different amino acids A893S/T exhibits altered transport properties in membrane vesicles from Sf9 insect cells, overexpressing human ABCB1. 893T had a higher vmax for the anticancer drug vincristine than 893S, and vmax of 893S was higher than that of the wildtype 893A, whereas KM was higher for 893S than for 893T or A (36). This study corroborated findings from a Japanese group, thoroughly investigating the ATPase activity from ten nonsynonymous SNP in Sf9 insect cells (37). A further rare missense SNP 1199 G>T (frequency 0.05 in Caucasians, (28)) leading to a Ser400Asn amino acid replacement is associated with lower activity and accordingly higher sensitivity against anticancer drugs. In contrast, a later detected 1199G>A variant caused an amino acid exchange to isoleucin at position 400. In vitro transfected HEK cells carrying this variant, however, exhibited an elevated chemoresistance indicating an elevated transport rate of the modified P-glycoprotein (38). A large study on ABCB1 mRNA (n=32) and protein (n=37) expression was performed in German healthy volunteers (39), showing a broad variability but lack of any association to 2677G>T/A or 3435C>T. In contrast, among Japanese, 3435T

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Table 6.4 Functional significance of ABCB1 genetic variants and relevance for clinical outcome Position

Amino acid exchange

Effect of variant

5¢-Flanking −2410 T>C

Decreased mRNAa

5¢-Flanking −692 T>C

Decreased mRNAa

c. 571 G>A

G191R

Reduced chemotherapy resistanceb

c. 1199 G>A

S400N

Elevated activityc

c. 1236 C>T

Synonymous

Increased imatinib disposition and therapy responsed

c. 2677 G>T/A

A893S/T

In vitro increased vmax,q no effect on vincristine,e increased imatinib response in CMLd

c. 3435 C>T

Synonymous

Decreased mRNA and protein expression,f, g decreased in vitro transport,h no effect on expression and bioavailability of talinolol,i no effect on in vitro transport,j, k decreased digoxin bioavailability,l increased etoposid disposition,m no effect on AML or ALL outcome,k better prognosis of multiple myeloma,n better chemotherapy response in breast cancer,o no effect in colon cancerp

References: a[42], b[69], c[38], d[53], e[51], f[23], g[64], h[31], i[39], j[135], k[65–67], l[40, 41], m[52], n[68], o [74], p[70, 71], q[36]

was associated with significantly higher mRNA levels than 3435C, a finding that is in line with the observation that the digoxin bioavailability is lower among Oriental 3435T carriers (24, 40, 41) (Table 6.4). Further genetic variants identified in the 5¢-UTR, particularly −2410T>C, −190T>C, and −692T>C, being in linkage disequilibrium showed a significant association to mRNA levels obtained from Japanese colon cancer specimens (42). Among Japanese liver-transplant recipients, however, there was no correlation to common ABCB1 SNPs, but interestingly there was a correlation to intestinal CYP3A4 expression (43). No association for 3435C>T, but for 2677G>T/A and −129T>C was reported from a Japanese group, who evaluated whether ABCB1 correlates with placenta trophoblast Pgp expression in 100 placentas obtained from Japanese women (26). In contrast, Hitzl et al. showed that the placenta P-glycoprotein expression was lower when both mother and child were carriers of 3435T compared to levels obtained for pairs of 3435C. However, no influence on the mRNA level was observed (31).

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2.4. Association to Drug Bioavailability

As mentioned earlier, the first systematic study on ABCB1 genetic variability and its association to expression and bioavailability was the first one, showing an association of 3435C>T with digoxin plasma levels. TT carriers had higher digoxin levels than CC carriers, a fact that was in line with the findings on Pgp expression levels (23). Numerous studies on the genotype-dependent bioavailability of various drugs, known to underlie Pgp-mediated transport, followed this initial study (for excellent reviews see (44, 45)). Support was given, though less pronounced in subjects being in steady state with 0.25-mg digoxin/day. TT-carriers had a 20% elevated area under the curve (AUC) within the first four hours. Interestingly, the effect was stronger in 2677GG/3435TT carriers (46). A further publication from Europe, summarizing different studies including some African-Blacks, supported these observations with similar differences between 3435TT and CC (47). However, the functional impact of 3435C>T is not consistent in a number of further studies., e.g., the pharmacokinetics of 1 mg orally administered digoxin (the same dose as in the initial study (23)) were not influenced by 3435C>T or 2677G>T/A (48) and a in a Japanese study, the digoxin AUC in the first 4 h was significantly lower in the CC group than in subjects homozygous for TT (41). This opposite tendency was also observed in two further independent Japanese investigations (24, 40, 49). Further thorough investigation on the Pgp substrate talinolol, a b1-blocker, revealed that in Germans carriers of the 2677 TT/TA variants had slightly but significantly elevated drug levels than carriers of at least one wild-type allele (p < 0.05). However, multiple comparisons with combinations of putative functional SNPs did not confirm a significant influence of the ABCB1 genotype on talinolol disposition (39). Conflicting results are reported for the antihistaminic drug fexofenadine (25) and again no effects were observed among Germans (50) (Table 6.4). Few studies on the bioavailability of anticancer drugs also show diverging results. For example, the pharmacokinetics of vincristine were only marginally influenced by the ABCB1 SNPs 2677G>T/A and 3435C>T (51), whereas the disposition of etoposide was apparently influenced by 3435C>T, demonstrating elevated clearance among CC carriers (52). The kinetics of the BCR-ABL inhibitor imatinib was affected by the sense SNP 1236C>T in a study among chronic myelomic leukemia patients (53). The reasons for the discrepancies observed particularly for digoxin are currently unclear. As far as differences of bioavailability for 3435C>T have been found, the association was counterpart in Asians compared with that in Caucasians, suggesting different haplotype constellations among these ethnicities. These effects were more pronounced concerning haplotypes considering 2677G>T/A. However, the majority of studies revealed no differences, and it was concluded that it is unlikely that digoxin

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bioavailability is modulated by ABCB1 polymorphisms (54) due to various hormonal and immunological influences on ABCB1 activity (55–57) and possibly a circadian rhythm as could be demonstrated in a mouse model (58). Moreover, saturation of the intestinal ABCB1 transport capacity may surpass any genetic effects (45). Finally other uptake and efflux transporters may contribute to the disposition of digoxin in humans. 2.5. Association to Treatment Outcome

Drug transporters are expressed in many tissues and tissue barriers. Therefore, the question arises whether changes of transporter activity may contribute to different drug concentrations not only in the plasma but particularly at the site of drug action, e.g., beyond the blood–brain barrier in the treatment of epilepsy, inside lymphocytes in the treatment of HIV infections or lymphatic malignancies, or with tumor masses. Although there is increasing evidence of the importance of P-glycoprotein in the etiology of pharmacotherapy-resistant epilepsy, the data concerning the role of ABCB1 variants are controversial (for review see (59)).

2.5.1. Leukemia

Similar as in HIV therapy, it is obviously clear that the expression of ABCB1 in lymphocyte membrane might be of major importance in the treatment outcome of lymphatic leukemia. Accordingly, a number of studies were performed regarding the contribution of ABCB1 genotypes and indeed, there is an increasing number of studies suggesting an association between ABCB1 genotypes and clinical outcome (60–63). Some functional evidence for a role of ABCB1 for lymphocytic P-glycoprotein expression was given by a study from Seedhouse et al. (64). Investigating lymphocytes from British acute myeloic leukemia (AML) patients, the Pgp expression was significantly higher among the upper percentiles in ABCB1 3435C carriers than T carriers. In a study in 101 Korean AML patients 3435CC was significantly correlated with lower functional ABCB1 function in a daunorubicin intracellular accumulation assay (62). Surprisingly, 3435CC and 2677GG carriers were strongly associated with a higher probability of complete remission and 3-year event-free survival. However, no differences were noted in overall survival according to the ABCB1 MDR1 SNPs. This lack of association was confirmed in a clinical study among elderly AML patients; ABCB1 variants failed to show any association to the treatment outcome or ABCB1 expression and function, as evidenced by rhodamine efflux experiments controlled with the Pgp inhibitor PSC833 (65). In contrast, the molecular response to the BRC-ABL-inhibitor imatinib in French chronic myeloic leukemia (CML) patients was dependent on ABCB1 SNPs (53). Although 3435C>T failed to show a significant influence, patients with the 1236C>T had higher imatinib plasma concentrations and showed also an improved therapy response, whereas presence of the wild-type

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2677G variant worsened the clinical response. In another study in a small sample of CML patients and gastrointestinal stromal tumors (GIST), all “classic” ABCB1 SNPs such as 1236C>T, 2677G>T/A, and 3435C>T led to decreased imatinib clearance among variant carriers. In a Korean study on acute lymphoblastic leukemia (ALL), however, there was lack of association of clinical endpoints such as complete remission rates, or relapse-free and event-free survival rates to ABCB1 variants (66). Only rare ABCB1 haplotypes of 2677G>T/A and 3435C>T differed in a large Hungarian ALL study, but overall the genotype distribution was not statistically different (67). In multiple myelomas treated with dexamethasone, doxorubicin and vincristine, ABCB1 3435CT or TT carriers had a better prognosis than 3435CC carriers (p = 0.02) (68). In a recent study, a novel ABCB1 571G>A missense variant detected in 6.4% of leukemia patients was reported, causing a Gly191Arg amino acid change (69). In a stable recombinant cell model, the anticancer drugs doxorubicin HCl, daunorubicin HCl, vinblastine sulfate, vincristine sulfate, taxanes (paclitaxel), and epipodophyllotoxin (etoposide, VP-16) exhibited selectively reduced degree of Pgp-mediated resistance in 561A carriers. In particular, the ABCB1-dependent resistance on vinblastine, vincristine, paclitaxel, and etoposide was fivefold reduced, indicating lower transport capacity of the 191Arg-variant. This could be proven by determination of intracellular drug concentrations. It was suggested that individuals with the ABCB1 571A genotype may be more sensitive to the specific anticancer drugs that are Pgp substrates but may also exhibit a higher risk of side effects (Table 6.4). 2.5.2. Solid Tumors

3. ABCC1 (Multidrug Resistanceassociated Protein 1)

For colorectal cancer, there are only weak data supporting any evidence of an impact of ABCB1 variants to the risk of cancer and to our knowledge no data on the treatment outcome. Overall, there was lack of association to the risk (70, 71) or only differences in small subgroups requiring confirmation (72, 73). Moreover, in the treatment of solid tumors such as breast cancer, 3435C>T was shown to be associated with the clinical response to preoperative chemotherapy (74) (Table 6.4).

The human ABCC1 protein was first identified in the doxorubicinresistant small cell lung cancer cell line H69AR that did not overexpress Pgp (75). ABCC1 serves as a multispecific organic

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anion transporter for certain drugs such as folate-based antimetabolites, anthracyclines, plant alkaloids, and antiandrogens. Moreover, it is involved in the transport of GSH- and glutathione conjugates (76) (Table 6.1). ABCC1 is expressed ubiquitously in the human body. In polarized epithelial cells, it is localized to basolateral membranes (Table 6.2). A large number of hereditary polymorphisms have meanwhile been identified (77–80). A thorough investigation on the functional significance of ten nonsynonymous SNPs, leading to amino acid changes C43S, T73I, S92F, T117; R230Q, R633Q, R723Q, A989T, C1047S. R1058Q, and S1512L was performed by Létourneau et al. in transfected HEK293T cells (81) (Table 6.5). None of them influenced significantly the expression level, indicating that the amino acid exchanges do not substantially affect the synthesis or stability of the protein. The overall influence on transport capacity of three different substrates such as leukotriene glutathione conjugate LTC4 was moderate. Lowest capacity was found for A989T, caused by a 2965G>A variant. Kinetic analysis revealed a weakened substrate affinity as indicated by an elevated Km value of the 989T-expressing variant (81). In another study, the silent variants 816G>A, 825T>C, 1684T>C, and 4002G>A were investigated for their impact on mRNA expression in peripheral CD4+ cells of German healthy volunteers without obtaining any significant differences (33). In an approach to scan for genetic signatures within the ABCC1 locus in different ethnicities, a haplotype containing a −260G>C SNP in the 5¢-flanking region was identified, associated with diminished activity in a reporter gene assay (82). This SNP has a frequency of 0.23 in European Americans, 0.55 in African Americans, but 0.00–0.05 in Orientals (Table 6.5). 3.1. Susceptibility to Cancer

The risk of lung cancer with respect to variant in ABCB1 and ABCC1 was investigated in a case-control study of 500 patients with incident lung cancer and 517 controls in a Chinese population. Out of three SNPs in the 3¢ untranslated region of ABCC1 (rs3743527, rs212090, and rs212091) the variant rs212090 genotype was more frequent in a recessive model (OR, 1.37; 95% CI, 1.03–1.83) (83) (Table 6.5).

3.2. Clinical Outcome

In a study on genetic determinants of anthracycline-induced cardiomyopathy in non-Hodgkin lymphoma patients, the ABCC1 Gly671Val variant as well as a haplotype of ABCC2 turned out to be significant risk factors. Data on a significant impact of ABCC1 polymorphism on drug bioavailability or further treatment outcome, however, are lacking (84). No effects on the clinical outcome on a platinum- and taxane-based chemotherapy were observed in the Scottish Randomized Trial in Ovarian Cancer (85).

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Table 6.5 Frequency of ABCC1 genetic variants in different populations, position on DNA, putative effect, and frequencies (according to (33, 77–80, 136)) Position

Amino acid or effect

Orientals

Caucasians

Function

c.128G>C

C43S

0.01

–

Elevateda

c. 218C>T

T73I

0.00–0.04

–

c. 257C>T

S92F

0.00

0.00

Decreaseda

c. 350C>T

T117M

–

0.02

(Decreased)a

c. 689G>A

R230N

0.00

0.00

(Decreased)a

c. 816G>A

Synonymous

–

0.04

c. 825T>C

Synonymous

–

0.30

c. 1057G>A

V353M

0.00

0.005

Elevateda

c. 1299G>T

R433S

–

0.01

Elevated vmax of doxorubicin, decreased transport of LTC4a,b

c. 1684T>C

Synonymous

–

0.80

c. 1898G>A

R633Q

–

0.01

(Decreased)a

c. 2012G>T

G671V

–

0.03

Doxorubicine-induced cardiomyopathyc

c. 2168G>A

R723Q

0.01–0.07

–

Decreaseda

c. 2965G>A

A989T

0.00

0.005

(Decreased)a

c. 3140G>C

C1047S

0.00

0.00

c. 3173G>A

R1058Q

0.01

–

c. 4002G>A

Synonymous

–

0.28

c. 4535C>T

S1512L

–

0.03

Decreaseda

References: a[81], b[77], c[84]

4. ABCC2 (Multidrug ResistanceAssociated Protein 2)

The ABCC2 gene encodes for a transmembrane transport pump exhibiting two cytosolic nucleotide binding domains (NBD) and two transmembrane domains (TMD1 and 2) each consisting of six helices. An additional transmembrane domain (TMD0) comprising five helices is located at the N-terminus. The ABCC2 protein is a glycolized protein located in the apical luminal membrane of tissues with function of barriers such as liver, kidney, intestine, placenta, but to newer knowledge weak or even not at the blood–brain barrier (Fig. 6.2). Tumor cells often show

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Fig. 6.2. Apical expression of ABCC2 in the intestine and kidney, as well as canalicular expression in the liver and its role as export pump.

an inducible expression of ABCC2, which contributes also to the phenomenon of drug resistance. The ABCC2 gene is located on chromosome 10q24 and spans a total length of 69 kb. It exhibits 32 exons. After translation, a protein of 1,545 amino acids with a molecular weight of 190 kDa is expressed. As typical for an ABC transporter the spectrum of substrates is wide and partly overlapping with that of other ABC transporters. The substrates are mainly organic anionic sulfate, glutathione or glucuronide conjugates of endogenous compounds such as hormones, leukotrienes, bile acids, and bilirubin. But also conjugated and unconjugated exogenous substances of organic but also inorganic origin are pumped out of the tissue into the lumen (Table 6.1). It is discussed that some substrates require cotransportation with glutathione because of their missing anionic character. Because of the transport of bile acids and glutathione from the hepatocytes into the bile duct, ABCC2 plays physiologically an important role in forming bile flow and potentially in detoxification by delivering glutathione for conjugation of xenobiotics. ABCC2 was for the first time described in man in 1996. Besides other ABC transporters ABCC2 was also found to be overexpressed in cancer cells, which exhibit resistance against antineoplastic drugs such as doxorubicin, cisplatin, etoposid, vincristine, SN38, and

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methotrexate. Some reagents such as probenecid and diclofenac are shown to inhibit the transporter whereas substances such as rifampicin, dexamethasone, phenobarbital, bile acids, and oltipraz induce the expression via nuclear receptors namely PXR (pregnan X receptor), CAR (constitutive androstane receptor), FXR (farnesoid X receptor), or Nrf2 (nuclear factor-erythroid p45 related factor2) (86-89). 4.1. Genetic Variability

Individuals exhibiting the Dubin–Johnson syndrome show different mutations in the ABCC2 gene leading to impairment or complete loss of function of the transporter. The clinical consequence is a benign conjugated hyperbilirubinemia and pigmentation of liver (86–89). A number of polymorphisms have been also found in the normal population by systematically sequencing revealing substantial differences between ethnicities (Table 6.6). For some SNPs evidence for the functional impact is available from in-vitro experiments but also from pharmacokinetic studies. A −24C>T polymorphism in the 5¢-UTR was firstly described by Ito et al. in a Japanese sample (78). It is in a strong linkage disequilibrium with 3972C>T (90, 91) and was reportedly influencing the gene expression but also drug bioavailability, clinical outcome, and toxicity of xenobiotics (92).

4.2. Functional Significance

Renal allograft transplant recipients harboring the −24T allele show a decreased oral clearance for the immunosuppressant mycophenolic acid (MPA), the active metabolite of mycophenolate mofetil. In consequence, these patients are more protected from a decrease in MPA exposure but with a higher association to mild liver dysfunction (93). A similar observation was made for the oral clearance of MPA, suggesting a decreased biliary excretion in −24T allele carriers as far they exhibited the homozygote 334T>G variant of the uptake transporter SLCO1B3 (94). Interestingly, one study showed a significant higher allele frequency of −24T in patients who had undergone renal organ transplantation suggesting a higher risk to develop renal failure probably due to impaired renal excretion and a higher exposure of renal cells to toxic substances derived from nutrition, drugs, and environment (95). In noncancerous renal cortex tissue, the −24C>T showed a decreased mRNA expression. This functional influence could be confirmed in a luciferase assay after transfection of HepG2 cells with reporter gene vector constructs containing the wild type or variant allele (90). Similar results were received in in vitro experiments investigating the −24C>T polymorphism and the polymorphism −1774G/del in the 5¢ flanking region of the ABCC2 gene. Both SNPs that were not linked showed a decreased activity in reporter gene assays. Clinically individuals harboring the deletion variant at −1774 showed a significant

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Table 6.6 Frequency of ABCC2 genetic variants in Japanese (78), Germans (in parentheses) (92), and 72 cell lines* (91), position on DNA, putative effect, frequencies, and function Position

Amino acid or effect

Frequency

−751 T>A

Transcription?

0.01*

−717 C>T

Transcription?

0.01*

−24 C>T

Translation?

0.19 (0.18)

−23 G>A

Translation?

0.01* (0.00)

c. 56 C>T

P19L

0.01*

c. 234 A>G

Synonymous

0.01*

c. 299 G>A

R100Q

0.01*

c. 842 G>A

S281N

0.01*

c. 1249 G>A

V417I

0.13 (0.21)

c. 1446 C>G

(0.01)

c. 1457 C>T

T486I

0.03* (0.00)

c. 2302 C>T

R768W

0.01 (0.00)

c. 2366 C>T

S789F

0.01 (0.00)

c. 2647 G>A

D883N

0.01*

c. 2882 A>G

K961R

0.01*

c. 2934 G>A

Synonymous

0.05*

c. 3039 C>T

Synonymous

0.01*

c. 3057 G>T

Q1019H

0.01*

c. 3321 G>T

Synonymous

0.01*

c. 3521 G>A

R1174H

0.01*

c. 3542 G>T

(0.001)

c. 3561 G>A

(0.00)

c. 3563 T>A

V1188E

0.01* (0.05)

c. 3732 C>T

N1244K

0.01*

c. 3972 C>T

Synonymous

0.22* (0.34)

c. 4100 C>G

S1367C

0.01*

c. 4290 G>T

Synonymous

0.01*

c. 4348 G>A

A1450T

0.01 (0.00)

c. 4488 C>T

Synonymous

0.01*

c. 4544 G>A

C1515Y

0.01* (0.04)

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association to cholestatic or mixed type hepatitis whereas −24T carriers exhibited more often hepatocellular-type hepatitis after intake of drugs or herbal remedies (96). Inline with these reports −24T allele carriers revealed a higher risk to develop a hepatotoxic reaction after intake of diclofenac (97). A missense SNP 1249G>A (Val417Ile) is located in substrate-binding region of the first transmembrane domain and is associated with lower oral bioavailability and increased residual clearance after intravenous administration of the beta-blocker talinolol, indicating a higher activity of the intestinal transporter (92). It was also found to be correlated with renal proximal tubulopathy after treatment with the HIV protease inhibitor tenovir disoproxil fumarate (98), probably due to a toxic concentration of the drug in renal tubular cells after being actively secreted by ABCC2 from the blood into the tubule. It is suggested that the elevated transport capacity toward the substrates 17b-glucuronosyl estradiol, leukotriene C4, and S-glutathionyl 2,4-dinitrobenzene (DNP-SG) is due to elevated protein expression rather than changes of functional properties (99). In methotrexate-treated African American rheumatoid arthritis patients, the 1249A variant allele was associated with higher gastrointestinal toxicity (100). The silent polymorphism 1446C>G is associated with higher ABCC2 mRNA expression in the liver and with a decreased AUC and cmax of the cholesterol lowering drug pravastatin (101) due to an elevated hepatic first pass effect. The SNPs c.3563T>A and c.4544G>A are in a strong linkage and correlated with a higher ABCC2 protein expression in liver (102). 3972C>T was reportedly associated with a fourfold higher risk for occurrence of intrahepatic cholestasis in pregnancy (103). 4.3. Clinical Outcome of Cancer

ABCC2 was shown to be significantly associated with acute doxorubicin toxicity (84). High-dose methotrexate treatment in pediatric ALL induced a two fold higher area under the curve and a ninefold higher risk of intensification of folinate rescue in female patients carrying the −24 variant allele (104). In non-small cell lung cancer patients, ABCC2 −24TT and 3972TT genotypes were associated with higher response rates (p = 0.031 and 0.046, respectively) and longer progression-free survival (p = 0.035 and 0.038, respectively), which was sustained in haplotype analysis, suggesting a more effective exposure to irinotecane (105). However, for haplotype carriers containing the −24C-allele, a less frequent irinotecan-related diarrhea was observed, which was suggested to be the consequence of a less hepatobiliar excretion of the drug (106). In summary there is increasing evidence that polymorphisms of the ABCC2 transporter can influence the first pass effect of the

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Table 6.7 ABCC2 polymorphisms currently described to exhibit a clinical influence ABCC2 polymorphism Effect

Clinical impact on

Function

Reference

−1774 G /del

5¢-flanking

Hepatotoxicity of herbal and conventional drugs

Decreased

[96]

−24C>T

5¢-UTR

Hepatotoxicity of drugs e.g. diclofenac and herbal remedies

Decreased

[96, 97]

Oral clearance of Mycophenolic acid

Decreased

[93, 94]

Risk of renal failure, renal expression

Decreased

[90, 95]

Bioavailability and side effects of methotrexate

Decreased

[104]

Tumor response and side effects of irinotecan

Decreased

[105, 106]

Intestinal activity, bioavailability of talinolol

Increased

[92]

Gastrointestinal toxicity of methotrexate

Increased?

[100]

Proximal tubulopathy of tenovir disproxil fumarate

Increased?

[98]

c.1249G>A

V417I

c.1446C>G

T482T

Bioavailability of pravastatin

Increased

[101]

c.3563T>A

V1188E

Higher protein expression in liver

Increased

[102]

c.3972C>T

I1324I

Intrahepatic cholestasis in pregnancy

Decreased

[103]

c.4544G>A

C1515Y

Higher protein expression in liver

Increased

[102]

liver but also the excretion in the intestine and kidney (Fig. 6.2). These genetically differing transport activities caused by either different expressions and/or affinities to the substrates can be observed as differences in bioavailability, drug response, or toxic side effects. Table 6.7 summarizes the effects of the ABCC2 polymorphisms yet associated with clinical impact on the transporter.

5. ABCG2 (Breast Cancer Resistance Protein) 5.1. Function and Substrate Specificity

ABCG2, also termed breast cancer resistance protein (BCRP) or mitoxantrone-resistant protein, is the second member of the G-family of ABC transporters (ABCG2) (107, 108). The ABCG2 gene is located at 4q22 and encodes a 72-kDa membrane protein

112

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composed of 655 amino acids (109). The protein consists of only one ATP-binding region and one transmembrane domain and is referred to as a half-transporter, and its homodimerization may be necessary to transport substrates (110). In normal human tissues, ABCG2 is highly expressed in the placenta, colon, small intestine, and liver (111, 112). ABCB1 and ABCG2 as well as the major vault protein (MVP) are colocalized in microvascular endothelium of epileptogenic human brain tissue (113) (Table 6.2). Interestingly, ABCG2 expression is upregulated in tissues with a low-oxygen environment (114) and also expressed in endothelial cells of human heart vessels. Strikingly, ventricular samples from cardiomyopathic hearts exhibited significantly increased levels of ABCG2 mRNA (115). Its expression is modulated by EGF by activation of MAPK cascade via phosphorylation of ERK1/2 and JNK/SAPK (112). On the basis of its tissue distribution and findings in knockout mice, ABCG2 is speculated to have a major influence on the pharmacokinetic and pharmacodynamic profiles of certain xenobiotics and endogenous substrates and is believed to contribute to multidrug resistance, since typical substrates are cytostatic drugs such as cisplatin, camptothecin, daunorubicin, doxorubicin, etoposide, methotrexate, mitoxantrone, SN-38, topotecan, and vincristine (111, 116) (Table 6.1). ABCG2 interacts with heme and other porphyrins and protects cells and/or tissues from protoporphyrin accumulation under hypoxic conditions (for review see (114)). 5.2. Genetic Variability of ABCG2

In 2001, Honjo et al. (117) and 1 year later Imai et al. (118) examined cDNA from cancer cell lines for genetic variants. Both identified 34G>A (V12M) and 421C>A (Q141K). Further a deletion at position 944–949 leading to the lack of amino acid residues A315 and T316 was detected. 421C>A ABCG2-transfected PA317 cells showed markedly decreased protein expression and low-level drug resistance compared with wild-type, whereas the other variants showed similar protein expression and drug resistance compared with wild-type ABCG2-transfected cells. Among 124 healthy Japanese volunteers, the frequency of the variant 421A allele, coding for low ABCG2 expression was 27%. This nonsynonymous polymorphism is located near the ATP-binding site of the half transporter. The 376C>T in exon 4, leading to a premature stop codon, was found in only 1.2% (118) (Table 6.8). Currently, more than 80 variants have been detected.

5.3. Association to Activity and Drug Bioavailability

HEK-293 cells, transfected with wild-type or V12N, ABCG2 showed apical staining with an ABCG2 antibody, but high intracellular staining in case of Q141K, suggesting impaired membrane trafficking or incorrect membrane insertion. Moreover, decreased transport rates were found in Sf9 insect cells, transfected with the V12M variant (119). In contrast, in a study by Mizuarai

Pharmacogenetics of ATP-Binding Cassette Transporters and Clinical Implications

113

Table 6.8 Frequency of ABCG2 genetic variants in Japanese, position on DNA, putative effect, and frequencies (according to (137) ) SNP

Amino acid or effect

Frequency of the variant allele

5¢ −20445 C>T

?

0.010

5¢ −20296 A>G

?

0.110

5¢ −19781 A>G

?

0.005

5¢ −19572–69 DCTCA

?

0.235

c. 34 G>A

V12M

0.17 0.04a 0.06b

IVS 2 +16 A>G

?

0.180

IVS 3 +10 A>G

?

0.080

IVS 3 +10 C>T

?

0.005

c. 376 C>T

Q126stop

0.01 0.00a 0.00b

Lack of function

c. 421 C>A

Q141K

0.35 0.11a 0.02b

Reduced activity [120, 124, 137]

IVS 5 −16 A>G

?

0.005

c. 1098 G>A

Synonymous

0.010

IVS 10 +95 T>A

?

0.015

c. 1322 G>A

S441N

0.005

c. 1367 A>G

?

0.165

c. 1465 A>G

F489L

0.005

c.1492 G>C

?

0.335

c. 1515DC

Frame shift

0.005

IVS 13 −42 A>T

?

0.005

IVS 13 −21 C>T

?

0.165

IVS 14 −46 A>G

?

0.500

3¢ 2332 A>TinsA

?

0.070

3¢ 2364 A>C

?

0.005

3¢ 2512 C>T

?

0.005

Caucasians African Americans

a

b

Function

Lack of function

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et al. ABCG2 V12N protein was found to be accumulated intracellulary (120) and always apical expression was observed by Kondo et al. (121). Correlation of the mRNA expression of intestinal samples of 42 patients revealed no significant association to any polymorphism (122). In 15 ventricular (including 10 cardiomyopathic) and 51 auricular heart tissue samples there was also lack of association of 34G>A or 421C>A to ABCG2 mRNA expression (115). The functional impact of various nonsynonymous polymorphisms was also investigated for methotrexate capacity, showing highly varying transport capacities. Interestingly, V12M was associated with elevated activity compared to the wild-type, whereas ABCG2 with premature stop-codon lacked any activity as expected (37). Best evidence was detected for the functional impact of 421C>A, located in the ATP-binding region. The 421A variant showed 1.3-fold decreased ATPase activity than the wild type (120), and the bioavailability of diflomotecan and topotecan was significantly elevated (123, 124) (Table 6.8). The pharmacokinetics of the anticancer drugs 9-nitrocamptothecin (9NC) appear also to be influenced by ABCG2 variants. The ABCG2 421C>A genotype significantly affected the AUC/does ratio of the 9-aminocamptothecin (9AC) metabolite in metastatic colon cancer patients, being 3.6-fold higher in 421CA carriers than in 421CC wild-type carriers (p = 0.032) (125).

6. Conclusion In summary, the physiological consequences of ABC transporter genetic variants are still only partly understood and the current figure of all findings is puzzling. The overall bioavailability of drugs seems to be only moderately influenced by the currently known ABCB1 SNPs, at least as compared to variants of the cytochrome P450 system (126, 127). It is interesting to note that among bioavailability studies performed in Caucasians often 3435T carriers presented higher plasma concentrations, whereas among Orientals this was the case for 3435C subjects, indicating possible different haplotype clusters in these ethnicities. Consequently, due to the large interindividual variability of Pgp expression in the intestine and in the liver that can still not be explained in full, consideration of genetic variants of this transporter does not appear to be a suitable parameter for individualized drug therapy (45). On the other hand, the association of ABCB1 3435T to improved treatment outcome particularly in the field of lymphatic leukemia gives rise to further investigations

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115

on the genetic control of P-glycoprotein concerning intracellular drug disposition. ABCC1 (MRP1) was also shown to be highly polymorphic. Although in vitro data show some variability of substrate specificity dependent on genetic variants, there is lack of evidence for a functional significance for drug bioavailability. Similarly, the current data on ABCC2 (MRP2) suggest that genetic variants not linked to Dubin-Johnson syndrome seem to play a minor part for drug bioavailability. In contrast, in ABCG2 in vitro as well as in vivo findings indicate significant differences of expression and substrate transport capacities. However, more studies have to be performed to clarify the partly divergent results. In conclusion, the current knowledge of the functional significance of genetic variants of ABC membrane transporters does not allow selection of a particular SNP to predict an individual’s pharmacokinetics. However, the large number of studies, achieving associations particularly of ABCB1 variants to clinical outcome, strongly support the necessity of further investigation of the role of these fascinating transporters for intracellular drug bioavailability and clinical outcome, particularly of lymphatic and chronic leukemia. References 1. Borst P, Elferink RO (2002) Mammalian ABC transporters in health and disease. Annu Rev Biochem 71:537–592 2. Juranka PF, Zastawny RL, Ling V (1989) P-glycoprotein: multidrug-resistance and a superfamily of membrane-associated transport proteins. FASEB J 3:2583–2592 3. Schinkel AH, Mayer U, Wagenaar E et al (1997) Normal viability and altered pharmacokinetics in mice lacking mdr1-type (drugtransporting) P-glycoproteins. Proc Natl Acad Sci USA 94:4028–4033 4. Panwala CM, Jones JC, Viney JL (1998) A novel model of inflammatory bowel disease: mice deficient for the multiple drug resistance gene, mdr1a, spontaneously develop colitis. J Immunol 161:5733–5744 5. Fromm MF (2000) P-glycoprotein: a defense mechanism limiting oral bioavailability and CNS accumulation of drugs. Int J Clin Pharmacol Ther 38:69–74 6. Schinkel AH, Wagenaar E, Mol CA, van Deemter L (1996) P-glycoprotein in the blood-brain barrier of mice influences the brain penetration and pharmacological activity of many drugs. J Clin Invest 97:2517–2524 7. Terao T, Hisanaga E, Sai Y, Tamai I, Tsuji A (1996) Active secretion of drugs from

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96. Choi JH, Ahn BM, Yi J et al (2007) MRP2 haplotypes confer differential susceptibility to toxic liver injury. Pharmacogenet Genomics 17:403–415 97. Daly AK, Aithal GP, Leathart JB et al (2007) Genetic susceptibility to diclofenac-induced hepatotoxicity: contribution of UGT2B7, CYP2C8, and ABCC2 genotypes. Gastroenterology 132:272–281 98. Izzedine H, Hulot JS, Villard E et al (2006) Association between ABCC2 gene haplotypes and tenofovir-induced proximal tubulopathy. J Infect Dis 194:1481–1491 99. Hirouchi M, Suzuki H, Itoda M et al (2004) Characterization of the cellular localization, expression level, and function of SNP variants of MRP2/ABCC2. Pharm Res 21:742–748 100. Ranganathan P, Culverhouse R, Marsh S et al (2008) Methotrexate (MTX) pathway gene polymorphisms and their effects on MTX toxicity in Caucasian and African American patients with rheumatoid arthritis. J Rheumatol 35:572–579 101. Niemi M, Arnold KA, Backman JT et al (2006) Association of genetic polymorphism in ABCC2 with hepatic multidrug resistanceassociated protein 2 expression and pravastatin pharmacokinetics. Pharmacogenet Genomics 16:801–808 102. Meier Y, Pauli-Magnus C, Zanger UM et al (2006) Interindividual variability of canalicular ATP-binding-cassette (ABC)-transporter expression in human liver. Hepatology 44: 62–74 103. Sookoian S, Castano G, Burgueno A, Gianotti TF, Pirola CJ (2008) Association of the multidrug-resistance-associated protein gene (ABCC2) variants with intrahepatic cholestasis of pregnancy. J Hepatol 48:125–132 104. Rau T, Erney B, Gores R et al (2006) High-dose methotrexate in pediatric acute lymphoblastic leukemia: impact of ABCC2 polymorphisms on plasma concentrations. Clin Pharmacol Ther 80:468–476 105. Han JY, Lim HS, Yoo YK et al (2007) Associations of ABCB1, ABCC2, and ABCG2 polymorphisms with irinotecanpharmacokinetics and clinical outcome in patients with advanced non-small cell lung cancer. Cancer 110:138–147 106. de Jong FA, Scott-Horton TJ, Kroetz DL et al (2007) Irinotecan-induced diarrhea: functional significance of the polymorphic ABCC2 transporter protein. Clin Pharmacol Ther 81:42–49 107. Allikmets R, Schriml LM, Hutchinson A, Romano-Spica V, Dean M (1998) A human

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108.

109.

110.

111.

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114. 115.

116.

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Cascorbi and Haenisch placenta-specific ATP-binding cassette gene (ABCP) on chromosome 4q22 that is involved in multidrug resistance. Cancer Res 58: 5337–5339 Doyle LA, Yang W, Abruzzo LV et al (1998) A multidrug resistance transporter from human MCF-7 breast cancer cells. Proc Natl Acad Sci USA 95:15665–15670 Bailey-Dell KJ, Hassel B, Doyle LA, Ross DD (2001) Promoter characterization and genomic organization of the human breast cancer resistance protein (ATP-binding cassette transporter G2) gene. Biochim Biophys Acta 1520:234–241 Kage K, Tsukahara S, Sugiyama T et al (2002) Dominant-negative inhibition of breast cancer resistance protein as drug efflux pump through the inhibition of S–S dependent homodimerization. Int J Cancer 97: 626–630 Ito K, Suzuki H, Horie T, Sugiyama Y (2005) Apical/basolateral surface expression of drug transporters and its role in vectorial drug transport. Pharm Res 22:1559–1577 Meyer Zu Schwabedissen HE, Grube M, Dreisbach A et al (2006) Epidermal growth factor (EGF) mediated activation of the MAP kinase cascade results in altered expression and function of ABCG2 (BCRP). Drug Metab Dispos 34:524–533 Sisodiya SM, Martinian L, Scheffer GL et al (2006) Vascular colocalization of P-glycoprotein, multidrug-resistance associated protein 1, breast cancer resistance protein and major vault protein in human epileptogenic pathologies. Neuropathol Appl Neurobiol 32:51–63 Krishnamurthy P, Schuetz JD (2006) Role of ABCG2/BCRP in biology and medicine. Annu Rev Pharmacol Toxicol 46:381–410 Meissner K, Heydrich B, Jedlitschky G et al (2006) The ATP-binding cassette transporter ABCG2 (BCRP), a marker for side population stem cells, is expressed in human heart. J Histochem Cytochem 54:215–221 Jonker JW, Buitelaar M, Wagenaar E et al (2002) The breast cancer resistance protein protects against a major chlorophyll-derived dietary phototoxin and protoporphyria. Proc Natl Acad Sci USA 99:15649–15654 Honjo Y, Hrycyna CA, Yan QW et al (2001) Acquired mutations in the MXR/BCRP/ ABCP gene alter substrate specificity in MXR/BCRP/ABCP-overexpressing cells. Cancer Res 61:6635–6639 Imai Y, Nakane M, Kage K et al (2002) C421A polymorphism in the human breast cancer resistance protein gene is associated

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with low expression of Q141K protein and low-level drug resistance. Mol Cancer Ther 1:611–616 Morisaki K, Robey RW, Ozvegy-Laczka C et al (2005) Single nucleotide polymorphisms modify the transporter activity of ABCG2. Cancer Chemother Pharmacol 56: 161–172 Mizuarai S, Aozasa N, Kotani H (2004) Single nucleotide polymorphisms result in impaired membrane localization and reduced atpase activity in multidrug transporter ABCG2. Int J Cancer 109:238–246 Kondo C, Suzuki H, Itoda M et al (2004) Functional analysis of SNPs variants of BCRP/ABCG2. Pharm Res 21:1895–1903 Zamber CP, Lamba JK, Yasuda K et al (2003) Natural allelic variants of breast cancer resistance protein (BCRP) and their relationship to BCRP expression in human intestine. Pharmacogenetics 13:19–28 Sparreboom A, Gelderblom H, Marsh S et al (2004) Diflomotecan pharmacokinetics in relation to ABCG2 421C>A genotype. Clin Pharmacol Ther 76:38–44 Sparreboom A, Loos WJ, Burger H et al (2005) Effect of ABCG2 genotype on the oral bioavailability of topotecan. Cancer Biol Ther 4:650–658 Zamboni WC, Ramanathan RK, McLeod HL et al (2006) Disposition of 9-nitrocamptothecin and its 9-aminocamptothecin metabolite in relation to ABC transporter genotypes. Invest New Drugs 24:393–401 Cascorbi I (2006) Genetic basis of toxic reactions to drugs and chemicals. Toxicol Lett 162:16–28 Ingelman-Sundberg M (2004) Pharmacogenetics of cytochrome P450 and its applications in drug therapy: the past, present and future. Trends Pharmacol Sci 25:193–200 Ambudkar SV, Dey S, Hrycyna CA et al (1999) Biochemical, cellular, and pharmacological aspects of the multidrug transporter. Annu Rev Pharmacol Toxicol 39:361–398 Dietrich CG, Geier A, Oude Elferink RP (2003) ABC of oral bioavailability: transporters as gatekeepers in the gut. Gut 52: 1788–1795 Ho RH, Kim RB (2005) Transporters and drug therapy: implications for drug disposition and disease. Clin Pharmacol Ther 78:260–277 Sakurai A, Tamura A, Onishi Y, Ishikawa T (2005) Genetic polymorphisms of ATPbinding cassette transporters ABCB1 and ABCG2: therapeutic implications. Expert Opin Pharmacother 6:2455–2473

Pharmacogenetics of ATP-Binding Cassette Transporters and Clinical Implications 132. Sarkadi B, Ozvegy-Laczka C, Nemet K, Varadi A (2004) ABCG2 – a transporter for all seasons. FEBS Lett 567:116–120 133. Suzuki H, Sugiyama Y (2002) Single nucleotide polymorphisms in multidrug resistance associated protein 2 (MRP2/ABCC2): its impact on drug disposition. Adv Drug Deliv Rev 54:1311–1331 134. Cascorbi I (2006) Role of pharmacogenetics of ATP-binding cassette transporters in the pharmacokinetics of drugs. Pharmacol Ther 112:457–473 135. Kimchi-Sarfaty C, Gribar JJ, Gottesman MM (2002) Functional characterization of coding

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polymorphisms in the human MDR1 gene using a vaccinia virus expression system. Mol Pharmacol 62:1–6 136. Moriya Y, Nakamura T, Horinouchi M et al (2002) Effects of polymorphisms of MDR1, MRP1, and MRP2 genes on their mRNA expression levels in duodenal enterocytes of healthy Japanese subjects. Biol Pharm Bull 25:1356–1359 137. Kobayashi D, Ieiri I, Hirota T et al (2005) Functional assessment of ABCG2 (BCRP) gene polymorphisms to protein expression in human placenta. Drug Metab Dispos 33:94–101

Chapter 7 Flow Cytometric Evaluation of Multidrug Resistance Proteins Adorjan Aszalos and Barbara J. Taylor Abstract There are several ways to detect proteins on cells. One quite frequently used method is flow cytometry. This method needs fluorescently labeled antibodies that can attach selectively to the protein to be investigated for flow cytometric detection. Flow cytometry scans individual cells, virtually without their surrounding liquid, and can scan many cells in a very short time. Because of this advantage of flow cytometry, it was adapted to investigate transport proteins on normal and cancerous human cells and cell lines. These transport proteins play important roles in human metabolism. Absorption in the intestine, excretion at the kidney, protection of the CNS compartment and the fetus from xenobiotics, and other vital functions depend on these transporters. However, several transporters are overexpressed in cancer cells. These overexpressed transporters pump out anticancer drugs from the cells and prevent their curative effects. The detection and quantitation of these types of transporters in cancer cells is important for this reason. Here, we review literature on flow cytometric detection of the three most studied transporters: P-glycoprotein, multidrug resistance-associated proteins, and breast cancer resistance protein. Key words: Transport proteins, P-glycoprotein, Multidrug resistance protein, Breast cancer resistance protein, Flow cytometer

1. Introduction Flow cytometric detection and evaluation of three types of transport proteins will be discussed in this chapter: P-glycoprotein (Pgp, ABCB1), multidrug resistance-associated proteins (MRPs, ABCCs), and breast cancer resistance protein (BCRP, ABCG2). These transport proteins play normal physiological roles in the human body but also cause resistance to cancer chemotherapy. The physiological roles of these transporters are many: they include absorption of molecules in the intestine, regulating passage to the CNS compartment at the blood–brain J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_7, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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barrier, passage of metabolites to the urine through the kidney, protecting the placenta from passing toxic agents to the fetus, and other functions at the liver, testis, and the lung. For further details on the expression of the transporters in humans, see refs. (1, 2). Any abnormality in the level of expression of these transporters can result in illness. One reason for resistance to cancer chemotherapy is that cancer cells overexpress some of these transporters and prevent the entry of the chemotherapeutic agents into the cancer cells to exert their curing effects. For these reasons, the analysis of these transporters has become very important in the last two decades. While there are other methods to analyze the presence and amount of these transporters in cells, one of the best methods to analyze, and especially quantitate transporters on the plasma membrane is flow cytometry (3). To distinguish among these transporters by flow cytometric methods, specific antibodies have been developed and specific inhibitors, or modifiers of their function, have been evaluated. Figure 7.1 shows the primary structure of Pgp and the known attachments for three surface antibodies to their epitopes. Antibodies, substrates, and modifiers are listed with the cited methodologies in the text. To distinguish among these three types of transporters, in general, the following molecules could be mentioned. Calcein AM, a fluorescent substrate, is a characteristic substrate for MRP1. Fumitremorgin C (FTC) is a specific inhibitor of BCRP. Rhodamine 123 is not specific but in

Fig. 7.1. Primary structure of P-glycoprotein as positioned in cell membrane, with three antibodies (MRK16, UIC-2, and 17F9) positioned at the known binding epitopes (Adapted from Ambudkar et al. (2003) P-glycoprotein: from genomics to mechanism. Oncogene 22:7468–7485, with permission of the author).

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combination with verapamil, a Pgp substrate, can detect Pgp. Expression of the different transporters on cells can be distinguished with the combination of these agents, as detailed in some of the methodologies described later.

2. Evaluation of Pgp by Flow Cytometry

Several research groups have established protocols for detecting Pgp by flow cytometry for reasons of interest in their laboratories. We will now detail general guidelines provided by each of the main laboratories with established protocols. Next, we describe flow cytometric investigations as applied to some particular purposes. Broxterman et al. analyzed the conditions for determining the amount of Pgp and its functionality on acute myeloid leukemia (AML) cells by flow cytometry (4). Their general recommendation for this evaluation can be summarized as follows. One can estimate the number of Pgp molecules on AML cells by using the antibody (Ab) MRK16, which binds to a surface epitope on the Pgp molecule. Other Abs binding to surface epitopes, such as UIC-2 can also be used. Phycoerythrin (PE)-labeled Abs yield higher sensitivity than fluorescein (FITC)-labeled Abs. The amount of Pgp expressed on AML cells can be determined using cell lines with a known amount of Pgp expressed under the same experimental conditions used for the AML cells. These authors used KB cell lines (KB-3-1, KB8, and KB-8-5), which each express different amounts of Pgp. These reference cell lines should be used fresh from frozen stocks after each 3 or 4 months of culturing. These cells tend to express higher levels of Pgp after longer culturing in the presence of the selecting agents. See Table 7.1 for flow cytometers used. Broxterman et al. provide some general considerations concerning the functional analysis of Pgp expressed on cells (5). Pgp functionality is measured in order to find the transport capacity of the transporters. It also helps to determine the proper modulators for a particular patient. Fluorescent probes for accumulation and efflux studies include rhodamine 123, DiOC2(3), calceinAM, Hoechst 33342, BCECF-AM, Furo-2-AM, Fluo-3-AM, daunorubicin, and doxorubicin. The first three are particularly suitable to study Pgp modulator molecules. All these fluorescent probes have high ratios of active to passive transport and they equilibrate between cells and the medium relatively quickly. Equilibration of some of these probes, however, depends on certain characteristics of the cells studied, such as their membrane

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Table 7.1 Flow cytometers and characteristic substrates used for detection of P-glycoprotein Detectors and filters Cytometer

Laser

FL1

FL2

FL3

FL4

References

FACSCan

488

530

585

650

NA

(5, 7)

FACSCalibur

488

530

585

670

NA

(3, 14)

Epics-XL

488

525

575

620

670

(10)

FL1

FL2

FL3

FL4

Accumulation and efflux

Rhodamine 123 DiOC2(3) Calcein-AM

Doxorubicin Daunorubicin

–

–

Fluorochromes for antibodies

FITC

PE

PE-TRa

–

Viability

–

PI

7-AAD

7-AADc

b

FITC fluorescein, PE phycoerythrin, PE-TR PE-Texas red tandem, PI propidium iodide, 7-AAD 7-aminoactinomycin D a PE-Texas Red (PE-TR) emission is detected suboptimally in the 650 and 670 filters. More suitable fluorochromes may be PerCP and the PE tandem dyes PE-Cy5, PE-Cy5.5, or PE-Cy7 b FACScan and FACSCalibur c Epics-XL

potential, intracellular pH, intracellular Ca2+, and DNA content. It is important that the Pgp modulator does not interfere in any way, such as through fluorescence, with the fluorescence and transport of the fluorescent probe. In this regard, valspodar was found superior to cyclosporin A and verapamil. Because transport is ATP dependent, sufficient glucose should be present in the medium and the cells should be viable during the study. A group in England led by M. Pallis adapted the Dutch protocol with a slight modification that is based on the work of the group of Broxterman et al. (5, 6). The adapted protocol was tested for expression and functionality of Pgp in AML and myelodysplastic syndrome cells in a multicenter trial in England. Leith et al. described a method for functional assay of Pgp in AML and cells of bone marrow origin (7). Differences in the efflux of the fluorescent substrate DiOC2(3) in the presence and absence of the modulator molecule can be measured over a time course according to Krishan (8, 9). In these experiments besides the forward vs. side scatter and forward scatter vs. fluorescence, time vs. fluorescence histograms also could be generated. A multicolor analysis for peripheral blood mononuclear cells (PBMCs) was established by Ford et al. (10). The assay was established for detection of Pgp expression in subsets of blood cells of healthy people.

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Feller at al. described a method to analyze Pgp in solid tumor cells (11). Depending on the origin of the cells isolated from solid tumors, the expression of Pgp on the cells as measured by flow cytometry may or may not correspond to the measured Pgp RNA. One reason for this possible discrepancy could be the heterogeneous expression of Pgp in the isolated cells. Aszalos and Weaver described a flow cytometric test for expression of Pgp on cell lines (12). Pgp on cells can be detected with the MRK16 mAb either labeled directly with FITC according to the instructions provided with the labeling kit or by using a secondary FITC Ab. Histograms showing fluorescence intensities when MRK16-FITC, MRK16 plus a secondary FITC Ab and isotype-matched Ab plus anti-isotype FITC Ab were used are shown in Fig. 7.2. Differences in fluorescence shifts between two histograms can be evaluated by the Kolmogorov–Smirnov statistics included with the flow cytometer software. See Table 7.1 for flow cytometers used. Numbers of Pgp molecules on clinical and in vitro drugselected cells can be determined according to Aleman et al. (3). To determine number of Pgp molecules on cells, a series of beads with increasing numbers of fluorescein molecules is used. Standard fluorescent beads with defined numbers of fluorescein molecules were used as follows: 6,318, 15,877, 53,989, 82,914, 123,338, 170,473, 353,992, and 437,815 fluorochromes. Fluorescence intensities of the beads are obtained with a flow cytometer equipped with a 488-nm laser and a 530 emission filter. Beads are shaken well and mixed two intensities per tube for a total of four tubes. A graph is plotted from the means of individual histograms

Fig. 7.2. Differences in histogram intensity after binding anti-mouse FITC, MRK16-FITC, or MRK-16 + anti-mouse FITC to P-glycoprotein-expressing NIH3T3MDR cells (From Aszalos and Weaver (1998) Estimation of drug resistance by flow cytometry. In: Jaroszeski and Heller (eds) Flow cytometry protocols. Humana, Totowa, NJ, pp 117–122, Figure 2, p. 121, with kind permission of Springer Science).

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of the beads. Means of histograms obtained from 104 cells labeled with MRK16 plus antimouse IgG-FITC or another FITC Ab against a surface epitope of Pgp are matched against the graph. Both the beads and the cells are suspended in the same PBS buffer. The graph for the means of the histograms of the beads is plotted on semilogarithmic paper. Alternatively, QuickCal data analysis software (Bangs Laboratories) can be used. Means of histograms of fluorescence intensities obtained from the tested cells are matched against the plot obtained with the beads. See Table 7.1 for flow cytometers used. Beck et al. described a consensus recommendation for detection of Pgp in patient’s tumors (13). A multinational workshop was organized for the detection of Pgp in clinical samples. The aim of this workshop was to standardize factors such as reagents, preparation of samples, detection of end-points, and methodology of analysis, in the determination of the role of Pgp in drug resistance in clinical evaluation of a patient’s treatment. The following recommendations were made: Hematological samples or disaggregated solid tumor cells are best analyzed by flow cytometry when the preparation is fresh. Samples can be cryopreserved at −135°C in 20–90% fetal bovine serum with 10% DMSO. Otherwise samples can be kept on ice for 24 h before analysis. Recommended Abs, such as MRK16, UIC-2, and 4E3 are best to use because they recognize external epitopes on the Pgp molecule. The advantage of using Abs that recognize an external epitope is that in flow cytometric analysis, correlation can be made by multicolor analysis with other surface antigens and with functional measurement of Pgp using dye accumulation/ efflux measurements. For flow cytometric detection of fluorochromes attached to primary or secondary Abs, PE is preferred over FITC. The reason for this recommendation is that PE has a higher quantum efficiency and therefore detection of low levels of Pgp expression is more accurate. Also, autofluorescence is less in the PE detector (585 nm) than the FITC (530 nm), so the signal-to-noise ratio is higher with PE. An isotypically matched Ab with the same fluorochrome should be used as a baseline control. Well-characterized cell lines with a known amount of Pgp expressed, as determined by mRNA and flow cytometric methods, should be used to validate flow cytometric assays in a particular laboratory. A cell line developed by Beck et al. (13), CEM/VLB, or the aforementioned KB cell line by Broxterman et al. could be used. For clinically relevant low-level expression of Pgp, the 8226/Dox6 cell line could be used. Normal cells expressing a low level of Pgp should be electronically gated out from the malignant cells based on surface marker expression. Reporting results can be done by evaluation of the mean channel shift between control and sample by the

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Kolmogorov–Smirnov statistical method provided with most flow cytometers or by continuous variable data without a cut-off point for positivity. Drug efflux measurement is preferred to a drug accumulation test, using cyclosporin A, verapamil, or valspodar as modulators of Pgp function. For drug efflux measurements, the fluorescent substrates DiOC2(3), rhodamine 123, Hoechst 33342 or the drugs daunorubicin and doxorubicin could be used. The dyes have more favorable uptake and efflux kinetics. The flow cytometric efflux studies should be correlated with Ab-mediated Pgp expression determination. The reason for this necessary correlation is that transporters other than Pgp, such as MRPs, may be responsible for the efflux of the substrate. The consensus report indicates that a tumor’s resistance to chemotherapy does not necessarily correlate with Pgp expression. After considering the aforementioned recommendations, one can use materials and techniques found in several of the earlier described detailed analytical methods. Wang et al. described an assay for quantitative determination of modulation of the function of Pgp by compounds (14). The aim of this study is to quantitatively assess the ability of a compound to modulate the function of Pgp. For this purpose, increasing doses of the tested compound are used in the efflux assay and the inhibition of the function of Pgp is determined in relative % to that of ortho vanadate. While Wang et al. used the CR1R12 cell line, other cell lines exclusively expressing the transporter Pgp could be used for this study. Wang et al. found that 2 µM daunorubicin was the optimal concentration of substrate for the cell line they used. Among the tested compounds, cyclosporin A and progesterone gave the most inhibition of Pgp function, 75 and 60%, respectively. Verapamil and terfenidine inhibition were 40–50%, relative to orthovanadate. Note that second- and thirdgeneration Pgp modulators can achieve greater inhibition of Pgp function than the compounds tested by Wang et al. See Table 7.1 for flow cytometers used.

3. Evaluation of MRPs by Flow Cytometry

Several MRPs have been characterized and described in the literature. Later we detail the known flow cytometric evaluation of some of these transport proteins from laboratories involved in their particular research. Janneh et al. evaluated the expression of Pgp and MRPs in peripheral blood mononuclear cells (PBMCs) for the purpose of determining the interaction of various protease inhibitors at the

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level of Pgp and several MRPs (15). In their study, they determined the uptake of (14C) lopinavir into PBMCs and some specific cell lines expressing various transporter proteins, in combination with specific inhibitors of Pgp, MRP1, MRP2, and organic anion transporter protein. Following this determination, they evaluated the modulating effect of several other protease inhibitors at the level of the transport proteins. Flow cytometry served to determine the presence of MRP1 in PBMCs, obtained from patient buffy coats. The specific modulators used by Janneh et al. (15) in their study are worth mentioning despite the fact that they did not use them in the flow cytometric experiment: tariquidar (Pgp specific), MK571 (MRP specific), frusemid (MRP1/2 specific), dipyridamole (MRP1/Pgp specific), and probenecid (MRP2/OATP specific). These specific blockers can be used to differentiate among the transport proteins for the efflux of drugs and compounds from cells. See Table 7.2 for flow cytometers used. Feller at al. evaluated the expression of MRP in several cell lines (11). Their aim was to find the best combination of fluorescent substrate and modifier of the function of MRP. They came to the conclusion that the best probe to detect the specific function of MRP1 by flow cytometry is to use daunorubicin as substrate and genistein as modulator of the function of MRP1. They also concluded that genistein decreases the fluorescence of rhodamine 123 and calcein-AM in sensitive cells, and therefore these fluorescent substrates cannot be used together with the specific modulator of MRP1, genistein. They also found that valspodin and vincristine are not suitable substrates of MRP1. Meaden et al. compared the expression of MRP in PBMCs between HIV-infected and noninfected patients (16). They concluded that the expression of MRP in PBMCs is the same in HIV-infected and noninfected patients. (Pgp expression is less in HIV infected than noninfected patients.) See Table 7.2 for flow cytometers used. Braga et al. compared the expression of MRP1 and Pgp in cells expressing both transporters (17). For detection of MRP1 they used carboxy fluorescein diacetate (CFDA), and for Pgp, rhodamine 123. The CFDA is nonfluorescent as is, but is hydrolyzed in cells by esterases to the fluorescent derivative. The two probes can distinguish between Pgp and MRP1 (18), as rhodamine 123 is a substrate of Pgp and CFDA a substrate of MRP1. Braga et al. analyzed the effect of oleanolic acid on the transport properties of the two transporters. They found that oleanolic acid inhibits the function of MRP1 but not that of Pgp. McAleer et al. studied the characteristics of MRP5 (MOAT-C, ABCC11) and determined the substrate specificity of this transporter by flow cytometry (19). A comparison was made between stably-transfected and nontransfected HEKc10 cells using probes,

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Table 7.2 Flow cytometers and characteristic substrates used for detection of multidrug resistance-associated protein Detectors and filters Cytometer

Laser

FL1

FL2

FL3

FL4

References

FACSCan

488

530

585

650

NA

(11, 19)

FACSCalibur

488

530

585

670

NA

(17)

Epics-XL

488

525

575

620

670

(15)

FL1

FL2

FL3

FL4

Accumulation and efflux

CMFDA CFDA FDA BCECF-AM Calcein-AM

Doxorubicin Daunorubicin TMR

–

–

Fluorochromes for antibodies

FITC

PE

–

–

FITC fluorescein, PE phycoerythrin, CMFDA 5-chloromethyl fluorescein diacetate, CFDA carboxy fluorescein diacetate, FDA fluorescein diacetate, BCECF-AM 2¢,7¢-bis-(2carboxyethyl)-5(and 6-)carboxy fluorescein acetoxymethyl ester, TMR tetra methyl rosamine

5-chloromethyl fluorescein diacetate (CMFDA), fluorescein diacetate (FDA), 2¢,7¢-bis-(2carboxyethyl)-5(and 6-)carboxy fluorescein acetoxymethyl ester (BCECF-AM), daunorubicin, tetra methyl rosamine (TMR), and calcein-AM. Flow cytometric studies indicated that CMFDA is a substrate of MRP5, but daunorubicin, calcein-AM, and TMR are not. They also found by a non-flow cytometric method, fluorometry, that FDA and BCFCF-AM are also substrates of MRP5. See Table 7.2 for flow cytometers used. Leidert et al. analyzed the influence of MRP2 (cMOAT, ABCC2) expression in melanoma cells on platinum and DNA adduct formation (20). They found that an inverse correlation exists between expression of MRP2 and adduct formation. In connection with this study, they performed cell cycle analysis on cisplatin-treated cells. This analysis indicated a cisplatin-triggered G2 arrest in both sensitive and resistant cells. MRP expression was done by Northern blot and RT-PCR analyses and not by flow cytometry. One should mention in connection with this study that flow cytometry would have analyzed MPR1 expression on the cell membrane, while the Northern blot plus RT-PCR analyzed all MRP1s in the membrane plus in the cytoplasm. Other MRPs (MRP3, MRP4, MRP7, and MRP8) have been characterized in membrane vesicles and not by flow cytometry. Antibodies have been developed against two of them, MRP3 and

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MRP6. Provided these antibodies are available, references for MRP3 (21) and MRP6 (22) can be found in the reference section for potential use in flow cytometry.

4. Evaluation of BCRP by Flow Cytometry

BCRP is found in the human placenta, bile canaliculi, the colon, small bowel, and in brain microvessel endothelium. It is overexpressed in breast and leukemia cancer tissues. In normal tissues, this transporter protects the organs from potentially toxic xenobiotics. A complete treatment of this transport molecule, including genetics, chemistry, modulators of BCRP, transported molecules, antibodies to BCRP, mutation variants of BCRP, and physiological function was published by Doyle and Ross (23). BCRP effluxes the substrates mitoxantrone, daunorubicin, bisantrene, prozasin, rhodamine123 (only in BCRP mutation variants), topotecan, and LysoTracker. The Pgp substrates verapamil (low dose), vinblastine, paclitaxel, and the MRP1 substrate calcein are not transported by BCRP. Based on these different transport properties, these transporters can be distinguished from one another by flow cytometry. Any standard flow cytometer with a 488-nm laser can be used for detection of BCRP. For these studies, either a FACSCan or a one- or two-laser FACSCalibur was used. The antibodies available (5D3, BXP-21, BXP-34) can be used with FITC (FL1, 530 nm) or PE (FL2, 575 or 585 nm). Substrates transported by BCRP include topotecan, BODIPY-prazosin, pheophorbide a, and BBR 3390 (all detected in FL1, 530 nm); topotecan, daunorubicin, and doxorubicin (all detected in FL2, 575 or 585 nm); and mitoxantrone (detected in FL3, 650 or 670 nm). For increased sensitivity with mitoxantrone, a 2-laser cytometer with a 633–639 laser can be used for excitation with detection in a 660/20 filter. Flow cytometers equipped with a 488-nm laser and a 355-nm laser may be used to detect Hoechst 33342 with SP (side population) cells detected at two emission wavelengths; blue at 424/44 nm and red at 675 nm LP with the signal split by a 640nm LP dichroic mirror. In the study cited here, a FACS Vantage was used. See Tables 7.3 and 7.4. There is no unified flow cytometric experiment to detect BCRP. Therefore, experiments used to measure BCRP functions in cells for different purposes are detailed later in connection with the aims of the individual investigators. Detailed descriptions of the flow cytometers are given earlier. A group led by a scientist at Roswell Park Cancer Institute did a basic study. Minderman et al. (24) studied the sensitivity of

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Table 7.3 Flow cytometers and characteristic substrates used for detection of breast cancer resistance protein Detectors and filters Cytometer

Laser

FL1

FL2

FL3

FL4

References

FACSCan

488

530

585

650

NA

(24, 25)

FACSCalibur

488, 635

530

585

670

660

(26, 27, 30)

Epics-XL

488

525

575

620

670

(10)

FL1

FL2

FL3

FL4

Accumulation and efflux

Topotecan BODIPYprazosin Pheophorbide a BBR3390 Rhodamine 123c DiOC2(3)c Calcein-AMc

Topotecan Doxorubicin Daunorubicin

Mitoxantronea

Mitoxantroneb

Fluorochromes for antibodies

FITC GFP

PE

–

–

Viability

–

PI

–

–

FITC fluorescein, PE phycoerythrin, PI propidium iodide, GFP green fluorescent protein a FACSCalibur and Epics-XL, 488 laser, emission at 670 (Calibur) or 620 (Epics) b FACSCalibur, 635-nm laser, emission at 660 c These substrates are used to exclude Pgp and MRP1

Table 7.4 Side population cells for detection of breast cancer resistance protein Detectors, emission filters, and dichroic mirrors Cytometer

Lasers

UV–blue

UV–red

Dichroic mirror

PIa

7-AADa

References

FACS Vantage

488, 355

424/44

675LP

640LP

585

650

(28)

Mo-Flo LSRII

488, 355 488, 355

For these cytometers, combinations of parameters as given below in footnote b can be usedb

The viability dyes propidium iodide or 7-aminoactinomycin D may be used; both are excited at 488 nm A number of filter combinations have been used for the measurement of the blue and red emissions of the Hoechst 33342 dye, which is excited at 351–364 nm. These include 440/40, 450/50, and 450/20 bandpass filters for the blue, 670LP for the red, and 600LP, 610LP, or 635LP for the dichroic mirror a

b

two antibodies (Abs) to BCRP in MCF-MX8, MCF-AdVp3000, and 8226MR20 mitoxantrone-resistant cells and in their parental cell lines. Wild type HL60, the Pgp-expressing cell line HL60/ Adr, and the MRP1-expressing cell line A2780/Dx5b were

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included in the study. The BCRPT482 cell line (containing a mutated BCRP in which arginine 482 was replaced with threonine) was also studied. Their aim was to determine the sensitivity of two Abs, BXP-21 and BXP-34, to two epitopes on BCRP. The detection sensitivity was assessed by flow cytometry and immunohistochemistry. The study included different fluorescent molecules as possible substrates and modulators of BCRP function. Among the different fluorescent molecules, only mitoxantrone was found to be a substrate of BCRP, and therefore its uptake and efflux were studied in the presence and absence of the BCRP modulator. The fluorescence histograms obtained with the fluorescently labeled goat anti-mouse secondary Ab were evaluated with the D parameter of the Kolmogorov–Smirnov statistical method that is included in flow cytometry software. Appropriate isotype control Abs (IgG1 and IgG2a) were used to obtain baseline fluorescence. Excitation was at 488-nm wavelengths with an argon laser and detection at 530 nm for the FITC-conjugated Abs. To evaluate the mitoxantrone concentration in cells, two different excitation wavelengths were used: a 635-nm red diode laser with a 661-nm emission filter and a 488-nm laser with a 670-nm emission filter. The two Abs bind only at internal epitopes. For this reason, cells had to be fixed in formaldehyde for 10 min at RT followed by 90% methanol treatment for 10 min to permeabilize the plasma membrane. Cells were then incubated with the primary Abs or the isotype Abs at 4°C for 60 min. After washing with PBS with 0.01% Tween, cells were incubated with the secondary fluorescent Ab for 20 min at 4°C. Results of the flow cytometry indicated that the two Abs bind only to wild type or mutated BCRP but not to Pgp or MRP1 or lung resistant protein (LRP) in cells. The presence of BCRP was verified by statistical evaluation when the D value was higher than 0.2. This cut off point was in agreement with similar flow cytometric analysis for the positive expression of MRP1 and LRP when cells must be permeabilized for Ab binding to internal epitopes (7). Experiments with both primary Abs resulted in qualitatively the same results, although the D values varied somewhat. Interestingly, the mutant BCRP in 8226/MR20 cells could be detected with flow cytometry with the two Abs but not with immunohistochemistry. Uptake and efflux of mitoxantrone, a substrate of BCRP, was studied with or without modulator molecules. Using excitation at 488 nm and emission at >670 nm was sensitive enough to detect mitoxantrone in cells with low expression of BCRP. A mitoxantrone concentration of 0.01 µM is required for the detection with excitation of the 488-nm laser and emission at 635 nm. Efflux studies were done with the substrates mitoxantrone, DiOC2 (3), rhodamine 123, and doxorubicin in Pgp, MRP1, and wild-type and mutant BCRP-expressing cells. The efflux difference between

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measurements in the presence or absence of transporter modulators was recorded. The modulator for Pgp was valspodar 2.5 µM, for MRP1 probenicid 1 mM, and for BCRP fumitremorgin C (FTC) 10 µM. D values were calculated by the Kolmogorov–Smirnov method from histograms obtained with or without the appropriate modulator. The D values were indicative of the extent of modulation of efflux of substrates by the applied modulator of the efflux pump. For example, the D value with mitoxantrone was above 0.29 for all transporter molecules in each cell (on a scale of 0–1), indicating that the selected modulators blocked the function of all the transporters and that mitoxantrone is transported by all these transporters. Contrary to this, D values indicated that efflux of rhodamine123 was substantially blocked in Pgp- and MRP1-expressing cells but only slightly inhibited in BCRP-expressing cells with the appropriate modulators. The method developed by Minderman et al. is well suited to analyzing clinical samples for expression of BCRP because a flow cytometer equipped with the standard 488-nm argon laser could be used (24). Detection of BCRP with the two available Abs would indicate the presence of BCRP even in cells with low levels of expression. Measuring efflux with mitoxantrone can indicate the extent of expression when FTC, a selective modulator of BCRP, is used. Another flow cytometric method was worked out by Kawabata et al. for detecting BCRP in clinical tissues with various levels of the BCRP mRNA (25). The aim of their investigation was to assess how much BCRP expression constitutes drug resistance in lung cancer. They measured BCRP mRNA levels in cell lines known to express various levels of BCRP. They then measured topotecan retention in the same cell lines by flow cytometry and correlated the results of the two methods. The cell lines they used were PC-6/SN2-5, PC-6, NCI-H460, NCI-H441, NCI-H358, and NCI-H69. PC-6 and NCI-H69 did not efflux much topotecan, indicating low levels of BCRP expression. PL-6/SN1-5 effluxed much more topotecan, indicating high levels of expression of BCRP. The other cell lines demonstrated intermediate levels of efflux. Flow cytometry measurements by Kawabata et al. used topotecan as a fluorescent substrate of BCRP because topotecan is a good substrate of even mutant BCRPs. Kawabata et al. obtained excellent correlation between BCRP mRNA expression, as determined by real-time RT-PCR analysis, and fluorescence intensity of cells after efflux of topotecan as measured by flow cytometry. They selected NCI-H441 from among the cell lines as borderline BCRP-expressing cells, as the amount of BCRP in these cells is the amount necessary to confer drug resistance. After establishing this correlation and establishing this base line expression of BCRP, 23 nontreated non-small cell lung carcinoma tissues were examined by the two methods for BCRP expression.

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Their results indicated that 22% of the tumor tissues had higher efflux-related resistance than the NCI-H441 cells, conferring transport protein-based drug resistance. Rachel Ee and colleagues used flow cytometry to assess the results of small interfering RNA (siRNA) treatment of BCRPexpressing cells to suppress the expression of this transport protein (26). They used essentially the same system as Kawabata et al., detailed earlier. They used topotecan as a fluorescent substrate, and its accumulation in cells was assessed by a FACSCalibur instrument using 488-nm excitation wavelength. BeWo choriocarcinoma cells, treated or nontreated with siRNA for 24 h, were incubated with 30 µM/L topotecan for 15 min at 37°C and flow cytometric measurement followed. Rabindran et al. examined the effect of fumitremorgin C (FTC) on the retention of the dye BBR 3390, 5 µM and daunorubicin, 1 µM, in BCRP-expressing MCF-7 cells (27). An interesting study was done by Scharenberg et al. who analyzed the extent of expression of BCRP, Pgp, and MRP1 in lung carcinoma A549, human embryonic kidney HEK293, and several human leukemia cell lines (28). The aim of their study was to characterize the efflux protein expression in a “side population” (SP) of the cell lines as indicated by Hoechst 33342 dye retention and efflux patterns. Hoechst dye excluding cells sorted out from bone marrow and other tissues contain immature stem cells, isolated as CD34+/CD38− or CD34+/KDR+ cells. They wanted to determine how many of these stem cells were among the studied cell lines and what type of transport proteins are expressed in them. To answer these questions, flow cytometric efflux studies were done with the fluorescent Hoechst dye and efflux protein modulators probenicid, verapamil, and FTC to determine the possible existence of the three efflux pumps. They found that FTC blocks most efflux of Hoechst dye, indicating that BCRP is the predominant transporter in A549 cells. RT-PCR experiments supported these findings. They also found that the two other transporters, Pgp and MRP1 are also expressed, but in minor quantities. A characteristic UV–red vs. UV–blue chart indicates the presence of stem cells expressing BCRP (Fig. 7.3). BCRP has several variants. The V12M and Q141K variants were described by Zamber et al. (29). Three other variants, I206L, N590Y, and D620N, were studied by Vethanayagam et al. for their expression levels and functionalities (30). Expression levels were determined by immunoblotting and functionality by efflux measurements with flow cytometry. All three variants transported mitoxantrone, pheophorbide a, and BODIPY-prazosin. Doyle et al. showed that the resistance of BCRP-expressing cells could be partially reversed by the antibiotic novobiocin (31). In their studies, they used the “comparative growth assay” system developed by Hausner et al. (32).

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Fig. 7.3. Influence of inhibitors on the side population of A549 cells stained with Hoechst 33342 dye (From Scharenberg et al. (2002) The ABCG2 transporter is an efficient Hoechst 33342 efflux pump and is preferentially expressed by immature human hematopoietic progenitors. Blood 99:507–512, used by permission).

Acknowledgments This work was funded by the Intramural Research Program of the National Institutes of Health, National Cancer Institute. We would like to thank Dr. Michael Gottesman for his hospitality and encouragement and George Leiman for editorial assistance.

References 1. Schinkel AH, Jonker JW (2003) Mammalian drug efflux transporters of the ATP binding cassette (ABC) family: an overview. Adv Drug Deliv Rev 55:3–29 2. Szakacs G, Paterson JK, Ludwig JA, BoothGenthe C, Gottesman MM (2006) Targeting multidrug resistance in cancer. Nat Rev Drug Discov 5:219–234

3. Aleman C, Annereau JP, Liang XJ et al (2003) P-glycoprotein, expressed in multidrug resistant cells, is not responsible for alterations in membrane fluidity or membrane potential. Cancer Res 63:3084–3091 4. Broxterman HJ, Lankelma J, Pinedo HM et al (1997) Theoretical and practical considerations for the measurement of P-glycoprotein

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Aszalos and Taylor function in acute myeloid leukemia. Leukemia 11:1110–1118 Broxterman HJ, Lankelma J, Pinedo HM (1996) How to probe clinical tumour samples for P-glycoprotein and multidrug resistance-associated protein. Eur J Cancer 32A: 1024–1033 Pallis M, Das-Gupta E (2005) Flow cytometric measurement of functional and phenotypic P-glycoprotein. Methods Mol Med 111:167–181 Leith CP, Kopecky KJ, Chen IM et al (1999) Frequency and clinical significance of the expression of the multidrug resistance proteins MDR1/P-glycoprotein, MRP1, and LRP in acute myeloid leukemia: a Southwest Oncology Group Study. Blood 94:1086–1099 Krishan A (2000) Monitoring of cellular resistance to cancer chemotherapy: drug retention and efflux. In: Darzynkiewicz Z, Crissman HA, Robinson JP (eds) Cytometry, part B, 3rd edn. Academic, San Diego, CA, pp 193–209 Krishan A (2002) Flow cytometric monitoring of drug resistance in human tumor cells. Methods Cell Sci 24:55–60 Ford J, Hoggard PG, Owen A, Khoo SH, Back DJ (2003) A simplified approach to determining P-glycoprotein expression in peripheral blood mononuclear cell subsets. J Immunol Methods 274:129–137 Feller N, Kuiper CM, Lankelma J et al (1995) Functional detection of MDR1/P170 and MRP/P190-mediated multidrug resistance in tumour cells by flow cytometry. Br J Cancer 72:543–549 Aszalos A, Weaver JL (1998) Estimation of drug resistance by flow cytometry. In: Jaroszeski MJ, Heller R (eds) Flow cytometry protocols. Humana, Totowa, NJ, pp 117–122 Beck WT, Grogan TM, Willman CL et al (1996) Methods to detect P-glycoproteinassociated multidrug resistance in patients’ tumors: consensus recommendations. Cancer Res 56:3010–3020 Wang EJ, Casciano CN, Clement RP, Johnson WW (2000) In vitro flow cytometry method to quantitatively assess inhibitors of P-glycoprotein. Drug Metab Dispos 28: 522–528 Janneh O, Jones E, Chandler B, Owen A, Khoo SH (2007) Inhibition of P-glycoprotein and multidrug resistance-associated proteins modulates the intracellular concentration of lopinavir in cultured CD4 T cells and primary human lymphocytes. J Antimicrob Chemother 60:987–993

16. Meaden ER, Hoggard PG, Maher B, Khoo SH, Back DJ (2001) Expression of P-glycoprotein and multidrug resistanceassociated protein in healthy volunteers and HIV-infected patients. AIDS Res Hum Retroviruses 17:1329–1332 17. Braga F, Ayres-Saraiva D, Gattass CR, Capella MA (2007) Oleanolic acid inhibits the activity of the multidrug resistance protein ABCC1 (MRP1) but not of the ABCB1 (P-glycoprotein): possible use in cancer chemotherapy. Cancer Lett 248:147–152 18. Echevarria-Lima J, Kyle-Cezar F, DF PL, Capella L, Capella MA, Rumjanek VM (2005) Expression and activity of multidrug resistance protein 1 in a murine thymoma cell line. Immunology 114:468–475 19. McAleer MA, Breen MA, White NL, Matthews N (1999) pABC11 (also known as MOAT-C and MRP5), a member of the ABC family of proteins, has anion transporter activity but does not confer multidrug resistance when overexpressed in human embryonic kidney 293 cells. J Biol Chem 274:23541–23548 20. Liedert B, Materna V, Schadendorf D, Thomale J, Lage H (2003) Overexpression of cMOAT (MRP2/ABCC2) is associated with decreased formation of platinum-DNA adducts and decreased G2-arrest in melanoma cells resistant to cisplatin. J Invest Dermatol 121:172–176 21. Young LC, Campling BG, Cole SP, Deeley RG, Gerlach JH (2001) Multidrug resistance proteins MRP3, MRP1, and MRP2 in lung cancer: correlation of protein levels with drug response and messenger RNA levels. Clin Cancer Res 7:1798–1804 22. Belinsky MG, Guo P, Lee K et al (2007) Multidrug resistance protein 4 protects bone marrow, thymus, spleen, and intestine from nucleotide analogue-induced damage. Cancer Res 67:262–268 23. Doyle LA, Ross DD (2003) Multidrug resistance mediated by the breast cancer resistance protein BCRP (ABCG2). Oncogene 22:7340–7358 24. Minderman H, Suvannasankha A, O’Loughlin KL et al (2002) Flow cytometric analysis of breast cancer resistance protein expression and function. Cytometry 48:59–65 25. Kawabata S, Oka M, Soda H et al (2003) Expression and functional analyses of breast cancer resistance protein in lung cancer. Clin Cancer Res 9:3052–3057 26. Ee PL, He X, Ross DD, Beck WT (2004) Modulation of breast cancer resistance protein (BCRP/ABCG2) gene expression using RNA interference. Mol Cancer Ther 3:1577–1583

Flow Cytometric Evaluation of Multidrug Resistance Proteins 27. Rabindran SK, Ross DD, Doyle LA, Yang W, Greenberger LM (2000) Fumitremorgin C reverses multidrug resistance in cells transfected with the breast cancer resistance protein. Cancer Res 60:47–50 28. Scharenberg CW, Harkey MA, Torok-Storb B (2002) The ABCG2 transporter is an efficient Hoechst 33342 efflux pump and is preferentially expressed by immature human hematopoietic progenitors. Blood 99:507–512 29. Zamber CP, Lamba JK, Yasuda K et al (2003) Natural allelic variants of breast cancer resistance protein (BCRP) and their relationship to BCRP expression in human intestine. Pharmacogenetics 13:19–28

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30. Vethanayagam RR, Wang H, Gupta A et al (2005) Functional analysis of the human variants of breast cancer resistance protein: I206L, N590Y, and D620N. Drug Metab Dispos 33:697–705 31. Doyle LA, Yang W, Gao Y, Ordonez JV, Ross DD (1996) Novobiocin increases the accumulation of daunorubicin in an atypical multidrug-resistant breast cancer subline. Proc Am Soc Clin Oncol 15:398 32. Hausner P, Venzon DJ, Grogan L, Kirsch IR (1999) The “comparative growth assay”: examining the interplay of anti-cancer agents with cells carrying single gene alterations. Neoplasia 1:356–367

Chapter 8 Targeted Chemotherapy in Drug-Resistant Tumors, Noninvasive Imaging of P-Glycoprotein-Mediated Functional Transport in Cancer, and Emerging Role of Pgp in Neurodagenerative Diseases Jothilingam Sivapackiam, Seth T. Gammon, Scott E. Harpstrite, and Vijay Sharma Abstract Multidrug resistance (MDR) mediated by overexpression of P-glycoprotein (Pgp) is one of the best characterized transporter-mediated barriers to successful chemotherapy in cancer patients and is also a rapidly emerging target in the progression of neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases. Therefore, strategies capable of delivering chemotherapeutic agents into drug-resistant tumors and targeted radiopharmaceuticals acting as ultrasensitive molecular imaging probes for detecting functional Pgp expression in vivo could be expected to play a vital role in systemic biology as personalized medicine gains momentum in the twenty-first century. While targeted therapy could be expected to deliver optimal doses of chemotherapeutic drugs into the desired targets, the interrogation of Pgp-mediated transport activity in vivo via noninvasive imaging techniques (SPECT and PET) would be beneficial in stratification of patient populations likely to benefit from a given therapeutic treatment, thereby assisting management of drug resistance in cancer and treatment of neurodegenerative diseases. Both strategies could play a vital role in advancement of personalized treatments in cancer and neurodegenerative diseases. Via this tutorial, authors make an attempt in outlining these strategies and discuss their strengths and weaknesses. Key words: Multidrug resistance, P-glycoprotein, SPECT, PET, Metal complexes, Gallium-67/68, Technetium-99m/94m, Chemotherapeutics, Drug transport, Blood–brain barrier, Cancer, Neurodegenerative diseases, Alzheimer’s disease, Parkinson’s disease

1. Introduction Resistance to chemotherapy represents a major obstacle in the treatment of cancer. Many tumors are intrinsically resistant to chemotherapy, whereas others initially respond to treatment, but J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_8, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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acquire resistance to selected cytotoxic drugs during chemotherapy. Multidrug resistance (MDR) can be broadly defined as a phenomenon by which tumor cells in vivo and cultured cells in vitro show resistance simultaneously to a variety of structurally and functionally dissimilar cytotoxic and xenobiotic compounds (1–6). While several different genes have been shown to be associated with a MDR phenotype (7–9), MDR mediated by overexpression of the MDR1 gene product, P-glycoprotein (Pgp), represents one of the best characterized barriers to chemotherapeutic treatment in cancer (6, 10). Pgp, a 170-KDa plasma membrane protein, is predicted by sequence analysis to comprise two symmetrical halves that share both homology with a family of ATP-binding cassette (ABC) membrane transport proteins and a common ancestral origin with bacterial transport systems (5, 11). Characterized by 12 transmembrane domains (TMDs) and two nucleotide-binding folds (Fig. 8.1) (3, 5), the protein is thought to hydrolyze ATP to affect outward transport of substrates across or off the cell surface membrane (5, 12). Although the specific protein domains and amino acids involved in substrate recognition continue to be characterized, genetic and biochemical evidence has conventionally been interpreted to show putative membraneassociated domains interacting directly with selected cytotoxic agents to affect transport (10, 13–15). Recently, Pgp from Chinese hamster was purified using dodecyl maltoside, crystals were grown using standard methods, and three-dimensional structure was determined with a resolution limit of 8 Å using cryoelectron microscopy (16). The structure demonstrated that five of the a-helices from each of the TMDs are related by a pseudo-twofold symmetry and two a-helices positioned closest to the axis of symmetry are slightly twisted. The deviation from a true twofold symmetry in the TMD region is likely a consequence of conformational changes induced by the nucleotide binding and results are consistent with the hypothesis that conformational change opens outward a central cavity in TMD region of Pgp and likely mediates transport of its recognized substrates (17).

Fig. 8.1. A cartoon showing a predicted membrane topology of the MDR1 P-glycoprotein wherein protein consists of 12 transmembrane domains, each half contains a nucleotide binding domain (NBD), and both N- and C- terminus are in cytoplasm.

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2. Probable Mechanism(s) for Pgp-Mediated Drug Transport

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Commonly, Pgp has been postulated to act as an ATP-dependent efflux transporter of xenobiotics and chemotherapeutic drugs (6). In addition, several studies provide evidence supporting alternative mechanisms for the diminished Pgp-mediated drug accumulation in MDR cells. For example, a flippase model has been proposed for Pgp (Fig. 8.2) (18). This model suggests that Pgp flips hydrophobic cytotoxic compounds from the inner to outer leaflet of the lipid bilayer wherein the agents can diffuse away. Alternatively, Pgp acts as a phospholipid flippase (Fig. 8.2) altering membrane permeability, thereby accounting for diminished intracellular concentration of drug and accounting for the broad specificity of Pgp toward its recognized substrates. In addition, cells expressing MDR1 may have intracellular compartments that are more acidic than non-Pgp expressing cells (19, 20), altered intracellular distribution of drug (21), or altered membrane permeability resulting in decreased drug influx (22). For example, Piwnica-Worms and coworkers have shown sustained expression of Pgp without use of chemotherapeutic drugs in a line of stable transfectants, MCF-7/MDR1, using a bicistronic vector for selection of cells (23). Electrical current and drug transport experiments demonstrated insignificant variations in membrane potential or membrane conductance between parental MCF-7 and MCF-7/MDR1 cells, but reduced unidirectional influx and steady-state cellular content of Pgp substrates. There was no change in unidirectional efflux of substrates from cells. These authors concluded that the dominant effect of Pgp in this system was reduction of drug influx, possibly through an increase in intramembranous dipole potential (23). Pgp may also interfere with or alter pathways of apoptosis (programmed cell death) (24, 25), therefore offering protection from cytotoxic compounds.

Fig. 8.2. A putative model showing P-glycoprotein as a transporter of its recognized substrates: (a) pumps substrates from one side of the membrane to the other via hydrophilic channel of the transmembrane domains (classic pump), (b) drugs partitioning into the lipid bilayer are excluded extracellularly (vacuum cleaner), and (c) recognized substrates partition into the bilayer, interact with the hydrophobic binding site in the cytoplasmic domain and translocated or flipped to the outer membrane for exclusion into the extracellular space (flippase model).

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Thus, although the exact mechanism remains to be biochemically elucidated, the observed combined net effect is a decreased intracellular concentration of cytotoxic drugs that results from overexpression of Pgp, thereby rendering chemotherapeutic treatment ineffective in cancer.

3. Role of Pgp in Pharmacokinetics and Its Transcriptional Regulation

In addition to its overexpression in tumors, Pgp is normally located in several tissues involved in excretory functions, including the brush border of proximal tubule cells in the kidney, the biliary surface of hepatocytes, and the apical surface of mucosal cells in the small intestine and colon (26, 27). Pgp also is located on the luminal surface of endothelial cells lining capillaries in the brain and in the testis (28–30) as well as on the apical surface of choroid plexus epithelial cells (29). However, despite its widely disseminated expression, the function of Pgp in normal physiology has not been clearly defined, although Pgp could have a role in protection from xenobiotics (31). Piwnica-Worms and coworkers have also suggested a role for Pgp in intracellular cholesterol trafficking (32). Furthermore, inhibition of Pgp with an MDR modulator could provide an effective means for increasing oral absorption of drugs and reducing drug excretion, resulting in decreased dosing requirements for treatment of cancer and infectious diseases. For example, Pgp modulation had been under evaluation as a means to improve oral absorption of chemotherapeutics and HIV-1 protease inhibitors such as indinavir, nelfinavir, saquinavir, and rotonavir (33, 34). Similarly, use of Pgp inhibitors (commonly known as MDR modulators or reversal agents) could allow drug penetration into Pgp protected sites in the body, such as the brain and selected hematopoietic cells, as has been shown for penetration of protease inhibitors into the central nervous system (34). In addition, transgenic expression of the MDR1 gene has been explored for hematopoietic cell protection in the context of cancer chemotherapy (35–37), wherein Pgp could protect hematopoietic progenitor cells from chemotherapy-induced myelotoxicity. Hematopoietic cells transduced via retroviral-mediated transfer of the MDR1 gene have shown preferential survival in vivo after treatment with MDR drugs (37) and data from pilot clinical studies had supported this approach (38). Various pathways and their transcription factors that could potentially regulate the activity of MDR1 promoter have also been studied (39). Among these pathways, the PKC pathway is

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often activated by stress and chemotherapeutics and has been shown to result in increased levels of MDR1 mRNA. Thus, inhibition of PKC by a variety of inhibitors prevents not only increases in mRNA but also the drug-resistant phenotype. This paradigm fits well with the observed induction of MDR1 in tumors after chemotherapy treatment. The MAPK pathway can also induce upregulation of MDR1. This story is complicated by the fact that some small molecules will activate one arm ERK1/2, but others activate the JNK arm of the pathway. Interestingly, both still result in upregulation of the MDR1 gene. The NFkB pathway is also often misregulated in cancer and chronic inflammation (40). Unfortunately, activators of this pathway also are reported to increase expression of MDR1 in many cell lines. The NFkB pathway under specific contexts such as breast cancer cell line will decrease expression of MDR1 (41). The release of NFkB from the promoter of MDR1 actually decreased expression of the MDR1 gene in this case. The cause of this anomaly has yet to be determined. Recent studies have also shown that activation of the Wnt/b-catenin, yet another prosurvival oncogenic pathway, also upregulates MDR1. In both human and rat brain epithelial cells, b-catenin activators and GSK-3 inhibtors upregulate MDR1 expression and activity (42). Critically, b-catenin pathway activation also upregulated other known drug resistance genes such as MRP2, MRP4, and BCRP. This combination of downstream effects makes the b-catenin pathway an attractive target for therapeutic intervention. Additionally, the sequence-specific binding sites of many of the transcriptional effectors at the MDR1 locus have also been determined. The MDR1 locus highlights one of the more confounding issues when studying gene regulation, long-range enhancers. Several of the enhancer binding sites are thousands of basepairs upstream of the start of MDR1 mRNA transcription. For an example, Pregnane X receptor (PXR) nuclear receptor binds in a region between −7817 and −7864 from the start of the transcript. PXR binding enhances transcription of the MDR1 gene as well as transcriptional reporters (43). Though original study found that Rifampin could enhance MDR1 expression through PXR, subsequent studies have found that small molecules found in some foods and herbs such as hyperforin, tangeretin, ginkgolide A, and ginkolide B could induce expression of MDR1 and activate luciferase reporters of MDR1 promoter via activation of PXR (44, 45). Even further upstream, the vitamin D receptor/ RXRa heterodimer binds to MDR1 promoter region and enhances MDR1 expression in the presence of vitamin D (46). The presence of these enhancer elements lends credence to the normal role of MDR1 preventing the absorption and enhancing the excretion of environmental toxins during and after meals. Finally, one of the most studied transcription factor families, the p53

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family also plays a role in regulating the transcription of the MDR1 gene. The mutant p53 strongly upregulates MDR1 expression, reporter assay activity, and enzyme activity. The region of enhancement was found to be between −58 and −74 from the start of transcription (47). In addition a truncated, oncogenic version of p73 is found to inhibit wild-type (WT) p53, an MDR1 expression inhibitor, and thus also upregulates MDR1 and its associated activity (48). Thus, mutations in p53 and oncogenes targeting p53, one of the oldest known tumor suppressors, likely play a critical role in the upregulation of MDR1 in many tumor samples. Regardless of mode of interaction, the net effect is that Pgp reduces the intracellular concentration of substrates in Pgp-expressing multidrug-resistant cells compared with nonPgp-expressing cells. Therefore, various strategies for delivering of chemotherapeutic agents in drug-resistant targets and radiopharmaceuticals as molecular imaging probes for detection of functional Pgp expression in vivo could be expected to play a vital role in systemic biology as personalized medicine gains momentum in the twenty-first century. Herein, we first describe attempts from various laboratories worldwide to devise ways for targeted delivery of drugs.

4. Strategies for Targeted Chemotherapy of Drug-Resistant Tumors 4.1. Transduction Sequences for Delivery of Drugs

Transduction sequences (when covalently linked to the organic scaffold of interest) could be employed as efficient drug delivery systems for enabling or enhancing uptake of candidate molecules into desired cells or tissues for enhanced therapeutic effects, radiotherapy, and molecular imaging applications. It has been shown that the nuclear transcription activator protein (Tat) crosses the plasma membrane of cells and its ability to penetrate cell membranes could be associated with the highly basic sequence of amino acid residues 49–57 (Tat49–57). Significantly, Tat49–57 is also a highly water-soluble polycation yet exhibits the ability to cross the nonpolar membrane of cells. After the discovery of this membrane-permeant motif, several modifications of this transduction sequence have emerged showing better uptake profiles and decreased cytotoxic effects. Covalent attachment of these selected group of amino acids into peptoids, peptides, spaced oligocarbamates, dendrimers, radioisotopes for imaging, and drug delivery enables their benefits as efficient cargo motifs for targeted drug delivery and biomedical imaging applications (49). The FDA-approved drugs currently used for the treatment of HIV are nucleoside reverse transcriptase inhibitors, nonnucleoside

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reverse transcriptase inhibitors, and protease inhibitors (PIs). Among these potent drugs, PIs represent a class of antiretroviral drugs blocking the protease enzyme activity needed for replication of virus. Like any other class of drugs, PIs also encounter pharmacokinetic limitations such as intestinal absorption and insufficient permeability across the blood–brain barrier (BBB) probably due to their recognition as transport substrates of Pgp (50). Thus, strategies to overcome these barriers would be needed and continue to be an area of intense investigations. Specifically, exploiting the peptide sequence comprising 11 amino acids derived from HIV-1 trans-activating transcriptor (TAT) peptide capable of crossing the cell membranes via pathways independent of either existing transporters or receptor-mediated endocytosis, peptide conjugated nanoparticles (NPs) appended to HIV drugs show higher permeability in validated biochemical systems developed for evaluation of the drug transport (51). Using transwell configurations of MDCK (Madine Darby canine kidney) cell monolayers, conjugation of nanoparticles comprising Ritonavir, an HIV drug, to the TAT peptide has been shown to undergo transwell transepithelial transport (apical to basolateral) using MDCK-MDR1 and MDCK WT cell monolayer counterparts. MDCK cells are capable of polarizing growth with formation of tight junctions, which upon proper validation (leak-proof tight monolayers as well as stable expression of functional Pgp) provide efficient and robust biochemical assay for evaluating transporter-mediated efflux of drug candidates. The observed profiles of transepithelial transport suggested the ability of conjugated-NPs containing Ritonavir to undergo 4.4-fold higher transepithelial transwell transport in MDCK-MDR1 (high Pgpexpressing cells) than their WT counterparts, thereby showing the applicability of the strategy for enhanced transport of attached molecules and overcoming resistance pathways. Importantly, the approach has demonstrated more provocative data via the delivery of Tat-conjugated Ritonavir containing NPs across the BBB in FVB male mice (Functional Pgp). Following intravenous injection of free drug, unconjugated nanoparticles, and TAT conjugated NPs containing Ritonavir, the peptide conjugated NPs show highest permeability across the BBB in these mice models, over prolonged period. It has been shown that clearance of Ritonavir over time was consistent with the profile expected for Pgp recognized substrates. However, the uptake of TAT-conjugated NPs containing Ritonavir in FVB mice brains (14th day) was found to be 11-fold higher than that of unconjugated NPs as well as 22-fold higher than that of Ritonavir alone. These data demonstrate versatility of transduction sequence for delivery of PI across the BBB probably via transcytosis across the endothelium of the brain vasculature (51). However, some transduction sequences are known to cause toxicity, thus these TAT peptide conjugates would need to be investigated for other side effects via safety pharmacology studies for

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further validation of the concept. Nevertheless, the strategy (once adequately validated) could allow maintenance of Ritonavir at thereapeutic levels for extended time in reducing the viral load in the CNS, which act as a reservoir for replication of HIV-1 virus. Furthermore, doxorubicin (DOX), a chemotherapeutic agent has also been appended to a TAT peptide fragment (CGGGYGRKKRRQRRR) via a succinimidyl-4-(N-maleimidomethyl) cyclohexane-1-carboxylate (SMCC) linker 1 and evaluated for therapeutic efficacy in drug-resistant MCF7/ADR cells compared with parental therapeutic drug (52). On incorporation of the chemotherapeutic agent to the peptide, the TAT-conjugated DOX was found to be approximately eightfold more active in drug-resistant cells compared with its parental unconjugated counterpart. Additionally, TAT-conjugated DOX was shown to be translocated in the perinuclear region or cytoplasm compared with parental DOX localization in the nuclei of these cells. Although results have been provocative enough to warrant further investigations of this strategy to target drug-resistant tumors, but cytotoxicity data for parental TAT sequence under same conditions would be needed for further rigorous evaluation of the strategy. Finally, PEG-coated liposomes have been designed for formulation of drugs for improved pharmacokinetics and protection from reticO

OH

O OH OH

O

O

OH

S-CGGGYGRKKRRQRRR

O O

O

OH

O N C H

N O

1

uloendothelium for better delivery to the target sites. Despite these modifications, sterically stabilized liposomal DOX has not shown a marked improvement in efficacy against sarcoma and small cell lung cancer. Therefore, strategy of transduction sequence for efficient delivery has also been employed. As an example, DOX encapsulated in liposome and appended to cell penetrating peptide sequence (TAT, or PEN) has been shown to accumulate in A431 cells 12-fold higher compared with nonconjugated liposomal DOX (53). Although, higher delivery of the drug has been obtained in the targeted cells via this transduction sequence, the potency of formulated DOX was not improved indicating the obstacles in release of the drug within the intracellular compartments. Overall, these strategies demonstrate the ability of transduction sequences to carry the covalently bonded drugs into intracellular

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compartments for enhanced delivery of therapeutics, which in turn could lead to enhanced potency of existing chemotherapeutic regimens. However, simple enhanced delivery of drugs within the cells represents a first important component of these strategies. Challenges could arise in terms of selective delivery of drugs in the desired targeted tissues or organs, as systemic biology gains momentum and additional strategies are conceived such as enzyme-cleavable domains within these molecules to allow selective release of drugs to treat malignant cells/tissues compared with their normal counterparts. 4.2. Prodrug Strategy

The advancement and confluence of the disciplines of chemistry and biology have recently opened an even broader range of opportunities at the interface of interdisciplinary sciences wherein scientists could shift emphasis from the total structure of a biologically active molecule to the subset of its functionality that either influences or determines its activity. The central theme behind this strategy involves a slight modification of a chemical entity, which allows an active component of the molecule to reach its target site with comparable or superior function and simultaneously evade interactions with putative transporters and biochemical pathways devised to reduce intracellular concentrations of available drugs thereby impacting efficacy in the targeted tissues. Acting as a microtubule stabilizing agent paclitaxel is a wonderful anticancer drug that has been successfully used for treatment of a variety of tumors, including breast, ovarian, and lung. Unfortunately paclitaxel has been a well-characterized Pgp substrate. Therefore, strategies for modifying the drug to overcome drug resistance pathways have been sought. Several prodrug derivatives of paclitaxel have been developed based on the chemical modification of the hydroxyl groups at position 7 of the baccatin core and position 2¢ of the Taxane side chain (54, 55). Of these prodrugs, 2¢-ethylcarbonate-conjugated TAX has been shown to be accumulated rapidly into the cells. Additionally, chemical modifications in the baccatin portion of TAX also decrease affinity for the Pgp transporter system. Based upon observation that carboxyesterase (CES) isolated from rabbit liver converts the 2¢-ethylester into a parental compound, a gene-directed enzyme-based prodrug therapeutic model was developed (56). The prodrug paclitaxel-2¢-ethylcarbonate 2 was evaluated for its efficacy in taxol-sensitive, SKOV3 cells and Tax-resistant KOC-7c transfected with a plasmid encoding Ra-CES. The uptake levels of the prodrug in SKOV3/TAX60 cells were found to be comparable to that in Pgp-negative SKOV3 cells (56). Additionally, Pgp inhibitor (verapamil)-induced effects were not observed in SKOV3/TAX60 cells indicating ability of the prodrug to overcome efflux pathways of Pgp (56). These results indicated the potential application of GDEPT (GENE Directed Enzyme Prodrug Therapy) strategy for delivery of taxol into cells or tissues

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expressing high levels of Pgp. Although promising data has been generated, these results would need to be rigorously investigated in the presence of Pgp-specific, third-generation inhibitors to validate unambiguously the benefits of the strategy. CH 3COO C 6 H5 CO NH Ph 3' C 2H 5OCOO

18

O 1'

11

O

O OH

19 7

1 14

3

H OH C6 H5 COO CH 3COO

4

O 20

2

Another concept gaining recognition rapidly is an extension of a prodrug strategy, wherein a chemotherapeutic agent of interest is appended via linker to the cargo. Choice of the linker and cargo is of paramount importance. While the linker could allow a selective release of agent in the targeted compartments, the cargo could assist in differentiation of intracellular vs. extracellular targets. Additionally, if the cargo is based upon an encapsulation strategy, it could simultaneously allow an increase in a biological half-life and decreased side effects. These concepts have been validated in an example of mitomycins, antitumor antibiotics produced by Strepromyces caespitosus (57). Mitomycin C (MMC) was linked through a urethane linker to the lipid promoiety (Fig. 8.3). The design of the strategy was based upon hypothesis that most tumors have enriched thiolytic environment. For example, redox enzymes such as glutaredoxin, thiredoxin, and other corresponding reductases are expressed in variety of the tumors, wherein cleavable linker will allow a delivery of the cytotoxic agent. Further, attachment of lipid will enhance the biological half-life by protecting the agent via encapsulation, thereby decreasing systemic toxicity. Thus, the antitumor activity of a cleavable lipid-based prodrug of MMC delivered by STEALTH liposomes (SL) was studied in drug-resistant human ovarian carcinoma A2780/AD model and compared with free MMC including both free as well as SL forms of DOX, an established anticancer drug. It has been shown that SL-prodrug (SL-pMMC) possessed enhanced antitumor activity when compared with the parent MMC, free DOX, and SL-DOX. Therefore, the resultant high antitumor potency of SL-pMMC could arise from its preferential accumulation in the tumor by the enhanced permeability and retention (EPR) effect and suppression of Pgp efflux pathways. Resultantly, the prodrug strategy could also be extended to include other enzyme (caspase 3 or cathepsin D)-induced cleavable domains and more potent cytotoxic agents susceptible to drug-resistant pathways in chemotherapy. Following a similar prodrug strategy (Fig. 8.4), DOX was coupled to a monoclonal antibody directed to the insulin-like

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Fig. 8.3. Schematic of a lipid-based prodrug strategy, wherein thiolytic activation of the prodrug results in a release of mitomycin C (MMC).

Fig. 8.4. Schematic showing the advantages of the receptor-targeted conjugate of a chemotherapeutic drug (doxorubicin) vs. nonconjugated drug. Compared with free drug that gets excluded into the extracellular space resulting in decreased efficacy, the conjugated motif gets selectively internalized, released within lysosomal compartments, and demonstrated enhanced potency.

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growth factor-1 receptor (IGF 1R) (58). The IGF 1R has been shown to be highly overexpressed in most tumors and is known to be a well-validated tumor target (59, 60). For accomplishing the prodrug synthesis, the derivatized MAb a-IR3 antibody possessing reactive aldehyde functionality was reacted with the amino group of the DOX and in turn was reduced in situ to obtain the prodrug conjugate. The prodrug conjugate was shown to be bounded to tumor cells selectively and accumulated efficiently only in receptor-expressing cells. The conjugate was postulated to be processed within the lysosomes to release free DOX inside target cells leading to selective activity. Overall, the strategy lead to approximately >200-fold improved therapeutic index and in vivo reduced tumor load with no systemic toxicity. For further rigorous evaluation of the strategy, the labeled compounds would be expected to offer better visualization of pharmacokinetics, as agents coupled to antibodies normally suffer from poor pharmacokinetics. Nevertheless, the prodrug conjugate was not a Pgp substrate and provocative approach represented a critical step toward development of improved and more selective anticancer agents. Overall, this prodrug strategy could also be extended to include enzyme-cleavable domains to allow delivery of more potent cytotoxic agents into cells or tissues susceptible to drug-resistant pathways in chemotherapy.

5. Chemotherapeutic Drugs Recognized by Pgp (Transport Substrates)

Compounds recognized by Pgp are typically characterized as modestly hydrophobic (octanol/water partitioning coefficient, logP > 1), often contain titratable protons with a net cationic charge under physiological conditions, and are predominately “natural products” with an aromatic moiety (61, 62). In addition, incorporation of methoxy functionalities has been shown to enhance Pgp recognition. O

OH R OH

OR 2 Ph

O

O

OH

O

O NH2 OH

R1

N H

O

O OH

O OH

HO C 6H 5 COO H CH3 COO

O

3 R = COCH 2OH

5 R 1 = COC 6H 5 , R 2 = COCH3 (Paclitaxel)

4 R = COCH 3

6 R 1 = COOC(CH 3) 3, R2 = H (Docetaxel)

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Among an extensive list of conventional cytotoxic compounds, anthracyclines (doxorubicin 3, daunorubicin 4), taxanes (paclitaxel 5, docetaxel 6), Vinca alkaloids (vincristine 7, vinblastine 8, vindesine 9), and etoposides 10 (VP-16) are examples of clinically important chemotherapeutic drugs recognized by Pgp (6, 7, 61, 63).

OH N N N H

H N R3 H R1 HO R2

7 R 1 = CHO, R 2 = COOCH 3, R3 = OCOCH3 (Vincristine) 8 R 1 = CH3, R 2 = COOCH3 , R 3 = OCOCH 3 (Vinblastine) 9 R 1 = CH3, R 2 = CONH 2, R3 = OH

The broad diversity in the scaffolds of these agents emphasizes the key characteristic feature of MDR, i.e., the apparent capacity of Pgp to recognize a large group of cytotoxic compounds sharing little or no structural or functional similarities. Furthermore, even targeted drugs have encountered MDR in clinics (64). H O O HO

O O OH

O

O

O

NH

O O

NH N

O

O

N

N

N

N

OH 10

11

For example, Gleevec 11, a 2-phenylaminopyrimidine derivative (65, 66), while a highly potent inhibitor of receptor tyrosine kinases, such as BCR-Abl and PDGF-R, is also recognized and transported by Pgp (6, 67). Thus, it would appear that even novel targeted therapeutics will remain susceptible to broad specificity of transporter-mediated resistance mechanisms.

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5.1. Pgp Inhibitors

Because clinical studies have documented the poor outcomes associated with Pgp expression in tumors (63, 68), reversal of MDR by nontoxic agents that block the transport activity of Pgp has been an important target for pharmaceutical development. When coadministered with a cytotoxic agent, these nontoxic compounds (MDR reversal agents), enhance net accumulation of relevant cytotoxic drugs within the tumor cells.

O

O

O

O CN N

12

O

R1

O N

N

N

O O

O

O

O

N R2

O

H N

N

O

H N

O N

N H

N

N H

O

13 R1 = OH, R 2 = CH 2CH3 (Cyclosporin A) 16 R1 = =O, R 2 = CH(CH3 )2 (PSC 833)

H

H

O N 14

N

N

HO

N

N CF3

S 15

Many compounds known to have other pharmacological sites of action initially were used to reverse MDR in cancer cells grown in culture and several underwent pilot clinical trials (61). These compounds included verapamil 12, cyclosporin A 13, quinidine 14, trifluperazine 15, and their derivatives (61). However, these agents had limited clinical utility because of unacceptable toxicities at serum levels of drug needed to modulate Pgp (63).

155

Targeted Chemotherapy in Drug-Resistant Tumors O O N

N O

H

O

O

N H 17

F F H

H

N N

N O

N OH

N O

O

O

O O

O O

N 18

19

Ph O

N

O O

N

O

N OH

N H NH

O

Ph

O

O

O

N

N 20

21

Second-generation modulators, such as dexverapamil, an optically pure verapamil (69), and PSC 833 (Valspodar) 16, a cyclic undecapeptide analogue of cyclosporin A (70) were soon developed with improved efficacy. These were followed by third-generation modulators, such as GF120918 17, a substituted isoquinolinyl acridonecarboxamide (71), LY335979 18, a difluorocyclopropyl dibenzosuberane (72), VX710 (biricodar) 19, an amido-keto-pipecolinate (73), XR9576 (tariquidar) 20, an analog of anthranilamide pharmacophore (74–76), and MS209 21, a quinoline derivatized analog (77, 78), that have been developed more recently.

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N N N

N

O O

O

22

Br

Cl

N

Br N

HN

O

O N

N H

NH 2 HN

O 23

24

Finally, R101933 (Lanquidar) 22 (79, 80), SCH66336 23, earlier developed as farnesyl protein transferase inhibitor (Lonafamib) (81), and ONT 093 24 (82, 83) have also been developed as potent Pgp inhibitors. Upon evaluating chemical structures of these Pgp inhibitors, it is obvious that most of the potent molecules have been designed based upon screening of natural products, pharmacophores of the drug-like molecules, and modification of organic scaffolds known to possess Pgp antagonist activity. Some of common features among these molecules include high hydrophobicity, presence of one or more protonable nitrogen under physiological conditions, two or more aromatic rings, and methyl or methoxy substituents on the aromatic rings. With the availability of structural information of Pgp, the rational drug design would likely emerge to make more potent and specific molecules. Given the two-fold pseudo symmetry in the structure of hamster Pgp, it is quite likely that molecules possessing flexible linker flanked by aromatic rings with methoxy- and/ or methyl-substituents as well as basic nitrogen would start emerging as lead molecules. It is quite possible that these molecules due to flexible spacer would have high probability to find high-affinity recognition sites within the transporter. The loss in the binding energy due to high entropy could be compensated by the gain in enthalpy derived from the fact that molecules would have high degree of freedom to associate with most favorable binding sites. Nevertheless, the MDR phenotype may be modulated more effectively with these more selective reversal agents to improve the efficacy of chemotherapeutic agents by delivering an appropriate

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dose at the targeted site. In terms of biomedical imaging applications using Pgp substrates, a noninvasive method of determining Pgp-mediated drug transport activity could be important in the use of new modulators, applications of gene therapy to chemotherapeutic protocols as well as predicting oral absorption, pharmacokinetics, and penetration of MDR drugs into target tissues and brain. Some of the promising radiopharmaceuticals are described in the following section.

6. SPECT and PET Radiopharmaceuticals for Noninvasive Imaging

Increasingly, the choice of systemic therapy for cancer is based on a priori analysis of tumor markers to assess the presence or absence of a molecular pathway or target (such as a key receptor or enzyme activity) for a given therapeutic agent. Identification of tumor markers with diagnostic agents assists in the proper selection of patients most likely to benefit from targeted therapy. Measurement of MDR is one potentially important marker in planning systemic therapy. However, expression of Pgp, as detected at the level of messenger RNA or protein, does not always correlate with the functional assessment of Pgp-mediated transport activity. Because Pgp transport activity is affected by specific mutations as well as the phosphorylation state of the protein (5, 84, 85), altered or less active forms of Pgp may be detected by polymerase chain reaction (PCR) or immunohistochemistry, which do not accurately reflect the status of tumor cell resistance. Thus, methods for functionally interrogating Pgp transport activity have been sought (86). Imaging with a radiopharmaceutical that is transported by Pgp may identify noninvasively those tumors in which the transporter is not only expressed, but functional. Thus, significant effort has been directed toward the noninvasive detection of transporter-mediated resistance using planar scintigraphy or single-photon emission computed tomography (SPECT) employing radiolabeled metal complexes as well as positron emission tomography (PET) radionuclide incorporated organic molecules, characterized as transport substrates for Pgp.

R R

R

N

N

C C

N C Tc C N R 25

+ R = H2 C C C

N

O

R 25a O

N R R = H2 C

O 25b

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Hexakis(2-methoxyisobutylisonitrile)technetium-99m (commonly known as (99mTc)Sestamibi) 25a, although originally developed as a radiopharmaceutical for myocardial perfusion imaging (87, 88), subsequently was the first metal complex shown to be a Pgp transport substrate (89). Characterized by octahedral geometry around the central technetium(I) core (87, 90), the radiopharmaceutical possesses a cationic charge and modest hydrophobicity similar to many chemotherapeutic agents in the MDR phenotype. In the absence of Pgp expression, this 99mTcisonitrile complex accumulates within the interior of cells in response to the physiologically negative mitochondrial and plasma membrane potentials maintained within cells (91, 92). However, in Pgp-expressing multidrug-resistant tumor cells, net cellular accumulation levels of (99mTc)Sestamibi are inversely proportional to the level of Pgp expression (89, 93–96). Furthermore, complete reversal of the Pgp-mediated exclusion of (99mTc)Sestamibi has been affected by treatment with conventional Pgp inhibitors such as verapamil 12, cyclosporin A 13, and quinidine 14 or newer more potent reversal agents such as PSC 833 16, GF120918 17, LY335979 18, or VX710 19 (32, 62, 89, 93, 94, 97–102), and more recently XR-9576 20 (6, 74). Furthermore, to optimize the transport and Pgp targeting characteristics of 99mTc-isonitrile complexes, several studies investigating structure–activity relationship (SAR) have been performed. In one study, the alkyl chains in (99mTc)Sestamibi were replaced with longer chain ether functionalities. The hexakis(2-ethoxy-isobutylisonitrile)-Tc-99m complex (99mTc-EIBI) was shown to be a transport substrate recognized by Pgp, but with slightly greater nonspecific cell binding than (99mTc)Sestamibi (103) that would result in inferior target/background ratios compared with (99mTc)Sestamibi. PiwnicaWorms and coworkers have also explored substituted aromatic isonitriles (104). A series of substituted arylisonitrile analogs were obtained from their corresponding amines through a reaction with dichlorocarbene under phase transfer catalyzed conditions, and noncarrier-added hexakis(arylisonitrile)-Tc-99m complexes were produced by reaction with pertechnetate in the presence of sodium dithionite (104). SAR studies resulted in a lead compound, 25b, which demonstrated an overall encouraging transport profile in Pgp-expressing cells, but significant nonspecific adsorption to hydrophobic compartments was identified. Nevertheless, results suggested that methoxy substituents, compared with other functionalities, preferentially contributed to enhanced Pgp recognition for this class of compounds. However, none of these radiolabeled complexes exceeded (99mTc)Sestamibi in their Pgptargeting properties. In addition, several entirely different classes of technetium complexes have been identified as Pgp transport substrates. Using a planar Schiff-base moiety and hydrophobic phosphines, nonre-

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ducible Tc(III) monocationic compounds known as “Q-complexes” were developed for applications in myocardial perfusion imaging (105, 106). The lead complex for clinical development was trans((1,2-bis(dihydro-2,2,5,5-tetramethyl-3(2H)furanone4 - methyleneimino)ethane)bis(tris(3-methoxy-1-propyl) phosphine))Tc(III), known as (99mTc)Furifosmin 26 (107). O O

O

O

P N N Tc O O P

O

O

O

P N

N Tc

O

O

O P

O

O

O

O

O O 26

O

O 27

Because the hydrophobicity and Pgp-targeting properties of these complexes could be readily adjusted by varying functionalities on the Schiff base or phosphine moieties independently, a variety of novel 99mTc-Q-complexes with subtle structural variations were synthesized (97, 108). This approach allowed the coordination environment of the Tc(III) metal core to be maintained while altering the overall electronic environment, thereby enabling evaluation of structural features conferring Pgp-mediated transport properties. Ether functionalities can be incorporated into the equatorial Schiff base ligand by condensation of ethylenediamines with ether-containing b-dicarbonyl compounds (109). The presence of gem-dimethyl groups sterically hinders the attack of diamine at the adjacent carbonyl and the strategy results in regioselective condensation at the less hindered carbonyl. Preparation of the tertiary phosphines was accomplished in a two-step, one-pot reaction involving treatment of 1-chloro-3-methoxy-propane with magnesium in tetrahydrofuran and subsequent reaction of the reagent with dimethylchlorophosphines or dichloro-methylphosphines to provide the necessary substituted phosphines with overall yield of 50–70% (108). The desired 99mTc-Q-complexes were then obtained by a two-step synthetic approach using the phosphines as both reductants and ligands (110). From MDR transport assays in vitro, the trans(2,2¢-(1,2ethanediyldiimino)bis(1,5-methoxy-5-methyl-4-oxo-hexenyl)) bis(methyl-bis(3-methoxy-1-propyl)phosphine)Tc(III) complex 27 and the trans(5,5¢-(1,2-ethanediyldiimino)bis(2-ethoxy-2-

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methyl-3-oxo-4-pentenyl))-bis(dimethyl(3-methoxy-1-propyl) phosphine)Tc(III) complex 28 were discovered.

O O

P N

O

Tc O

O P

O

O

O

P O P Tc P O P

N

O

O

O

O O

O 29

28

These complexes mimic (99mTc)Sestamibi for their Pgp recognition properties in vitro (97, 108). In addition, a Tc(V) complex known as (99mTc)Tetrofosmin (111), bis(1,2-bis{bis(2-ethoxyethyl)phosphino}ethane)Tc(V) 29, has been identified as another 99m Tc-complex with highly favorable Pgp-mediated transport properties (108, 112). While these metal complexes do not share any obvious structural homology, they do share the common features of a delocalized cationic charge and modest hydrophobicity. Overall, which of these selected 99mTc-complexes would be most clinically useful in evaluation of the Pgp status of tumors by SPECT imaging continues to be rigorously investigated.

O O

OH 2 O H 2O C Tc H 2O C O C O 30

N

N C C

C

O

Tc

O N

C

C C O

O

31

Another class of technetium-based radiopharmaceuticals has emerged on the basis of pioneering work done on the development of an air- and water-stable organometallic aqua complex (99mTc(OH2)3(CO)3)+ 30 obtained from reaction of pertechnetate in saline under 1 atm of CO (113). Because it was shown that the coordinated water molecules were labile, thus exchangeable with other ligands, complexes with heterogeneous ligands could be generated. Based upon these observations, these water molecules were substituted with methoxy-isobutylisonitrile ligands to

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obtain another novel class of Pgp-targeted radiopharmaceuticals, tris(carbonyl)tris(2-methoxy-isobutylisonitrile)technetium(I); (99mTc(CO)3(MIBI)3)+ 31 (114). Cellular accumulation of 31 in human epidermal carcinoma KB 3-1 (−Pgp) and KB 8-5 (+Pgp) cells demonstrated uptake profiles inversely proportional to Pgp expression indicating it to be recognized as a transport substrate (115).

CH 3 OCH2 CH 2CH 2

N

CH 2 CH 2CH 2OCH 3 N CH2 CH 2 OCH 2 CH 3 P S Tc N S P CH2 CH 2 OCH 2 CH 3 CH 2CH2 CH 2 OCH3 CH 2 CH 2CH 2OCH 3

R 32 R = CH 2CH2 OCH3 33 R = CH 2CH2 OCH2 CH3

Recently, a new class of nitrido technetium-99m-agents of the type (99mTc(N)(DTC)(PNP)), wherein DTC is a dithiocarbamate ligand and PNP an aminodiphosphine ligand, as potential myocardial imaging agents has been developed (116). Within this novel class, two compounds, (99mTc(N)(DBODC)(PNP5))+ 32 and (99mTc(N)(DBODC)(PNP3))+ 33 (DBODC = bis(N-ethoxyethyl) dithiocarbamato; PNP5 = bis (dimethoxypropyl-phosphinoethyl) ethoxyethylamine, PNP3 = bis-(dimethoxypropylphosphinoethyl)methoxyethylamine) have shown interesting pharmacokinetic profiles that could enable these molecules as myocardial perfusion imaging agents. Importantly, following administration of cyclosporine A (13), a first-generation Pgp inhibitor, intravenous injection of (99mTc(N)(DBODC)(PNP5))+ 32 showed a delayed clearance in lungs, liver, kidney, very significant increase in activity in the intestinal tissue, and a simultaneous decrease in endoluminal contents of the tracer. These results are consistent with their postulation of being Pgp-recognized substrates (117). However, cyclosporin A (13) is not an extremely specific Pgp inhibitor, thus results would need to be validated in the presence of more specific inhibitors such as PSC 833 16 or LY 335979 18 in same rat models. Having demonstrated specificity of the agent in presence of more specific inhibitors, the agent could potentially provide an imaging marker for probing functional Pgp expression in cancer. Availability of such a wide selection of chemical structures of technetium complexes has offered a powerful tool for molecular modeling in selecting critical structural characteristics needed in a given chemical entity for their recognition as Pgp substrate. Taking the advantage of this existing armamentarium, genetic algorithms

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(GA) have been used to develop specific technetium metal–ligand force field parameters for the MM3 force field. These parameters have been developed using automated procedures within the program FFGenerAtor from a combination of crystal structures and ab initio calculations (118). These models have been allowed to predict uptake of technetium complexes in the liver and kidney, two critical organs expressing Pgp. Therefore, the development of new metal ligand parameters to model the biological properties of radiolabeled organometallic complexes provides a versatile tool to scientists in the field of biomedical imaging applications. Organic scaffolds capable of coordinating other metals have also been explored. Multidentate ligands with an N4O2 donor core have the ability to form stable monomeric, monocationic, hydrophobic complexes with a variety of main group (119, 120) and transition metals (121–123). Schiff-base Ga(III) complexes were previously reported as potential PET radiopharmaceuticals with utility as myocardial perfusion imaging agents (124, 125). These complexes, exemplified by the lead compound 34, demonstrated pharmacological profiles consistent with their potential utility as PET probes of Pgp activity in tumors (126–128). Substituted salicylaldehydes were obtained by ortho-formylation of phenols and substituted linear tetramine was obtained through a reaction of dibromoethane and 2,2-dimethylpropane-1,3diamine at room temperature. The triaryl precursors containing a central imidazolidine ring were synthesized by condensation of an appropriate substituted linear tetramine with substituted salicylaldehydes. The desired metal(III) complexes were obtained by cleavage of the imidazolidine ring via transmetallation reactions using appropriate metal(III) acetylacetonates. O O

O

O HN

68 Ga

N

O

NH HN

N O

O

67 Ga

N

NH N

O

O 34

O 35

For evaluation of Pgp-targeting properties, these compounds were screened for their cytotoxicity in Pgp-expressing tumor cells and their appropriate control (−Pgp) cells (Fig. 8.5). Selected metal(III) complexes were able to differentiate between human epidermal carcinoma KB 3-1 (−Pgp) and KB 8-5 (+Pgp) cells

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Fig. 8.5. Cell survival studies in parental KB 3-1 (Pgp−) and multidrug-resistant KB 8-5 (Pgp+) cells at increasing concentrations of R-ENBPI gallium(III) (R = 4,6-dimethoxy (a); 3-methoxy (b)) complexes, colchicine (25 mM , positive control). Each point represents the mean of triplicate determinations; bars represent ± SEM when larger than symbol; solid lines are a spline presentation of the data.

through differential cytotoxicity profiles indicating Pgp-mediated transport (127). The active metal complexes containing 4,6-dimethoxy-substituted aromatic rings (Fig. 8.5a) were more potent than their corresponding 3-methoxy analogs (Fig. 8.5b). SAR studies led to discovery of a lead compound, ({bis(3-ethoxy2-hydroxy-benzylidene)-N,N¢-bis(2,2-dimethyl-3-aminopropyl) ethylenediamine}-gallium(III)), (Ga-ENBDMPI)+ 35. The crystal structure provided direct evidence that gallium is coordinated symmetrically and simultaneously to the N4O2 donor core of the ligand. The structure revealed that the central gallium is hexacoordinated, involving two phenoxy oxygens (O1 and O2), two secondary amine nitrogens (N2 and N3), and two imine nitrogens (N1 and N4) with overall trans-pseudooctahedral geometry (Fig. 8.6). For biochemical studies, the ligand was labeled with radioactive gallium-67/68 through a simple transmetallation reaction of 67/68Ga(acetylacetonate)3 and ligand dissolved in ethanol. The radiolabeled compound was purified through a C-18 sep-pack. For analysis of metabolites, the lead SPECT radiotracer, 67Ga-ENBDMPI

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Fig. 8.6. ORTEP drawing of ((3-ethoxy ENBDMPI)Ga)+(ClO4)− showing the crystallographic numbering scheme. Atoms are represented by thermal ellipsoids (20% probability).

was incubated in buffer and calf serum at 37°C for 2 h. In addition, the radiopharmaceutical was extracted from mouse tissues (liver and kidney) 15 min after tail-vein injection. Radio-TLC analysis of tissue fractions indicated primarily a nonmetabolized agent (Fig. 8.7), because net cell contents of hydrophobic and cationic radiopharmaceuticals transported by Pgp are a function of both passive potentialdependent influx and transporter-mediated efflux. Therefore, favorable cationic 67Ga-complexes would likely penetrate KB 3-1 cells as result of the inwardly directed electrochemical driving forces. Furthermore, membrane potential-dependent influx in KB 3-1 cells in 120 mm K+/20 mM Cl− buffer containing the potassium ionophore valinomycin (1 mg/ml) was shown to collapse mitochondrial and plasma membrane potentials toward zero and reduce net tracer uptake of membrane potential-responsive hydrophobic cations (129). Resultantly, net accumulation of the 67Ga-complexes would be reduced in high K+/valinomycin buffer, and furthermore, tracer levels greater than that expected from equilibrium distribution into the water spaces under these conditions would provide one measure of nonspecific adsorption to intracellular hydrophobic compartments. Conversely, Pgp-mediated outward transport of 67Ga-complexes

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Fig. 8.7. Radio-TLC analysis of 67Ga-ENBDMPI 35 in buffer, calf-serum, mouse liver, and mouse kidney.

would be expected to decrease net cellular accumulation in KB 8-5 cells compared with KB 3-1 cells. Following these criteria, 67 Ga-ENBDMPI accumulated in cells showing profiles inversely proportional to Pgp expression (Fig. 8.8). While residual uptake in control KB 3-1 cells in the presence of high K+/valinomycin buffer indicated modest nonspecific interaction, the inhibitor GF120918induced uptake in MDR KB 8-5 cells (Fig. 8.8) demonstrated target specificity (126, 129). Additionally, a gallium(III) complex 36 of the naphthol–Schiff-base ligand has been developed and evaluated in human epidermal carcinoma cells (130). The compound showed selective cytotoxicity against KB 3-1 cells (Pgp−) compared with KB 8-5 cells (Pgp+) indicating its recognition as a transport substrate and thereby exclusion from drug-resistant (Pgp+) cells (Fig. 8.9). These results suggested that radiolabeled analogues of these Ga(III)complexes could also provide templates for 68Ga-PET radiopharmaceuticals to probe Pgp transport activity in tumors (128).

OMe O HN

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Fig. 8.8. Accumulation of 67Ga-ENBDMPI 35 in KB 3-1 cells (Pgp−) and MDR KB 8-5 cells (Pgp+) as indicated. Shown is net uptake (fmol/mg protein/nMo) at 90 min. Each bar represents the mean of four determinations; line above the bar denotes +SEM.

Fig. 8.9. Cell survival studies in parental KB 3-1 (Pgp−) and MDR KB 8-5 (Pgp+) cells at increasing concentrations of gallium(III) complex of naphthol Schiff-base ligand and a colchicine (25 mM; positive control).

While gallium(III) radiopharmaceuticals are undergoing preclinical evaluation, technetium-99m-based radiotracers have been clinically evaluated for assessment of the Pgp-mediated transport activity. Toward this objective, validation of successful inhibition of the transport function is necessary to evaluate the effects of Pgp inhibitors on patient outcomes. Clinical data indicate that (99mTc)Sestamibi can be used to detect inhibition of Pgp-mediated transport function in patients (Fig. 8.10). Overall, pharmacokinetic studies demonstrated retention of activity in the liver and kidney (Pgp positive tissues) of patients treated with PSC 833, (16), 2 h postinjection of the radiopharmaceutical (Fig. 8.10) (99, 100). In addition, the heart a Pgp negative tissue, acting as an internal control tissue, displayed no difference in retention of activity in either pre-PSC 833 (left panel, Fig. 8.10) or post-PSC 833 (right panel, Fig. 8.10). Overall, results indicated the potential

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Fig. 8.10. Effect of the Pgp inhibitor, PSC 833 (16) on pharmacokinetics of (99mTc)Sestamibi in vivo: Whole body posterior planar image obtained at 2 h postinjection of radiopharmaceutical. Left Panel: The pharmacokinetics demonstrated decreased retention of the radiotracer in the liver and kidney, 2 h postinjection of the radiotracer in the absence of PSC 833. Right Panel: Following 24-h treatment with PSC 833, image was obtained 2 h postinjection of radiotracer. Because PSC 833 blocks the Pgp, an efflux transporter, considerable amount of radioactivity was retained in the liver and kidney (right panel). Note that heart, being a Pgp (−ve) organ, retains the same amount of activity in both cases (Pre-PSC 833 and Post-PSC 833; left and right panels).

of (99mTc)Sestamibi to serve as an efficient diagnostic probe of Pgp-mediated transport activity in vivo. PET radiopharmaceuticals offer enhanced spatial resolution and quantification capabilities compared with SPECT agents. It is also noteworthy that gap in terms of resolution and quantification between SPECT and PET detectors has been drastically decreasing with discovery of advanced SPECT cameras. To probe Pgp transport activity, PET-based radiopharmaceuticals have been actively investigated on three fronts: (a) employing SPECT organic ligands capable of accommodating PET radionuclides, (b) bioinorganic radiolabeled complexes, and (c) conventional PET organic medicinals. Among organic scaffolds that coordinate both SPECT and PET radionuclides, two validated examples make use of the PET radionuclides Tc-94m and Ga-68. Thus, the radiosynthesis and biochemical validation of 94mTc-Sestamibi and 68Ga-ENBPI complexes have been reported (126, 131). As expected chemically, the highly favorable Pgp-targeting properties of these metal complexes were retained upon transformation from SPECT agents to PET imaging agents.

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N

N O CuII H N N O

37

On another front, organic scaffolds capable of accommodating PET radionuclides that generate novel metallopharmaceuticals through short synthetic routes have been reported. Thus, based upon rigorous prior contributions (132, 133), a stable, monocationic radiolabeled complex of copper(II) 37 was obtained as a potential 64Cu-radiopharmaceutical (PET) for targeting Pgp (134). The desired diiminedioxime ligand was synthesized from 2,3dimethyl-propane-1,2-diamine and heptane-2,3-dione-3-oxime. Cellular accumulation in MES-SA (−Pgp) and MES-SA (+Pgp) cells demonstrated uptake profiles inversely proportional to Pgp expression. Furthermore, an inhibitor-induced accumulation was observed in Pgp (+) cells. Ph P Cu P Ph Ph Ph

Ph Ph P P Ph Ph

38

Bidentate tertiary phosphine ligands have the ability to generate stable copper(I) complexes through a one-step synthesis in quantative yields (135, 136) and represent another class of potential 64 Cu-radiopharmaceuticals targeting Pgp. These complexes previously demonstrated potent antitumor properties compared with their free ligands alone (137, 138). As with 99mTc-Q-complexes, herein phosphines were exploited as both ligands and reducing agents to generate cationic, hydrophobic, and tetrahedral copper(I) complexes 38 with 1,2-bis(diphenylphosphino)ethane. These potential PET radiopharmaceuticals show evidence of Pgptargeting properties (139, 140). Although several leads exist for a 64 Cu-radiopharmaceutical for interrogation of Pgp by PET, these radiopharmaceuticals show only modest Pgp-targeting properties compared with 94mTc-Sestamibi (25A) or 68Ga-ENBDMPI (35). To demonstrate the potential use of radiolabeled probes as imaging markers of Pgp-mediated transport in vivo, quantitative pharmacokinetic analysis in mice was performed following

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intravenous injection of tracers. Mice have two isoforms of Pgp (mdr1a and mdr1b), which confer MDR (141). Drugtransporting mdr1a Pgp isoform is expressed in capillary endothelial cells of the brain wherein the protein is a major component of the BBB (142). Here, Pgp limits entry of a variety of amphipathic compounds into the central nervous system (141). Drug-transporting Pgp isoforms are also expressed along the biliary cannalicular surface of hepatocytes wherein the transporters function to secrete substrates into the bile (141). In this regard, mdr1a/1b(−/−) gene disrupted (Knockout; KO) mice, which have no drug-transporting Pgp, are a robust model for evaluation of candidate MDR agents by interrogating net tracer accumulation into brain and liver tissues (97). Therefore, analysis of initial tissue uptake and retention of 67Ga-ENBDMPI 35 in mdr1a/1b(−/−) mice in comparison to wild-type (WT) FVB mice was performed (Fig. 8.11). Relative to WT, the KO mice showed tenfold more radiotracer 35 in brain parenchyma 5 min after

Fig. 8.11. Pharmacokinetics of 67Ga-ENBDMPI 35 in brain (a) and liver (b) of FVB mice. Wild-type (WT) and mdr1a/1b (−/−) (183) mice were administered 35 by bolus injection into a lateral tail vein and organs harvested at the indicated times for analysis. Data are expressed as percent of injected dose of radioactivity per gram tissue at each respective time point. Data points represent the mean of 2–4 determinations each; bars represent ±SEM when larger than the symbol.

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injection of the complex (Fig. 8.11a). Furthermore, the AUC5–120 of 35 in mdr1a/1b(−/−) brain was 62.5 ± 16.1 mCi (injected mCi)−1 (g tissue)−1 × 100 × min (n = 8), a value 17-fold greater than that in wild-type mice (p < 0.005). While comparing to FDA-approved radiopharmaceuticals for noninvasive imaging, 67Ga-ENBDMPI showed three- to fivefold greater differences in brain AUC5–120 between WT and KO mice than 99mTc-Sestamibi (143) and 99mTcTetrofosmin (144), further documenting its potential superiority for imaging Pgp activity in vivo. In addition, the liver showed twice the retained activity of 67Ga-ENBDMPI in KO mice compared with WT mice (Fig. 8.11b). Thus, the data further validated 35 as a substrate for Pgp in vivo and showed its potential as a molecular imaging probe of transporter function and inhibition in the living organism. A third focus has been directed toward incorporation of conventional PET radionuclides C-11 or F-18 into small organic medicinals characterized as substrates or inhibitors known to interact with Pgp (145–147). Employing this strategy, various labeled PET agents, including 11C-colchicine, 11C-verapamil, 11 C-daunomycin, and 11C/18F-paclitaxel, have been reported (147–155). While promising, some of these PET agents suffer from modest radiochemical yields and others from complex pharmacokinetics in vivo mediated, at least in part, by rapid metabolism of the radiolabeled compounds. In addition, these 11 C/18F-radiopharmaceuticals are obtained in custom-designed, dedicated facilities located near cyclotrons that produce these isotopes, thereby imposing potential restrictions in their widespread distribution.

7. Emerging Role of Pgp in Progression of Neurodegenerative Diseases

Because of its location in the brain endothelial cells, Pgp plays an important role in brain detoxification processes. Thus, in principle an optimal function of this transporter at the BBB could ensure a healthy brain function. The downregulation of Pgp expression at the BBB has also been postulated to an enhanced influx of toxic compounds in brains of patients leading to symptoms of neuropathological disorders such as Parkinsonian syndrome (156, 157). Importantly and interestingly, patients with Parkinson’s disease who could have been exposed to toxins earlier in life have been shown to have C3435T polymorphisms of MDR1 gene with a related decreased Pgp function (156). Using 11C-Verapamil as a marker of Pgp function, patients with late stages of Parkinson’s disease have shown a higher uptake of the tracer in the brain compared with their normal counterparts (158). However, results in patients with earlier stages of the disease were not conclusive just

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displaying different regional localization of the tracer in diseased subjects compared with their normal controls. It must be noted that verapamil is a modest Pgp substrate that could result in lower overall signal in the brain leading to complications in interpretation of the data at earlier stages of the disease. Furthermore, Pgp has also been known to inhibit the activity of caspases regulating neuronal apoptosis in neurodegenerative diseases such as Alzheimer’s disease (AD) and Creutzfeldt–Jakob disease (25, 159). Pgp has been shown to be present in astrocytes (to a lesser extent) (160) as well as microglia (161) and has been shown to be induced in neurons by epileptic seizures (162, 163). Finally, Pgp has been shown to be downregulated in patients with AD. The AD brain is associated with loss of neurons in regions of the brain responsible for learning and memory. AD involves the appearance of two distinct abnormal proteinaceous deposits: extracellular amyloid plaques, which are characteristic of AD, and intracellular neurofibrillary tangles that also are found in other neurodegenerative disorders (164–166). AD amyloid plaques comprise primarily a family of proteins known collectively as Ab proteins (167), derived from the ubiquitously expressed cell surface amyloid precursor protein (APP). Indeed, several lines of investigation suggest that Ab accumulation is an initiating event in the pathogenic cascade of AD (168–170). Conversion of Ab within the extracellular spaces of the brain into toxic forms is accelerated at higher concentrations, implying that pathways influencing Ab production or elimination could modulate disease progression. Ab is secreted from neurons into brain interstitial fluid, where it is eliminated by proteolytic degradation (171, 172), passive bulk flow (173), and active transport across the BBB (174, 175). The latter, representing efflux across the BBB into the peripheral circulation, appears to be a substantial pathway for elimination of CNS-derived Ab (175). Recent studies demonstrated that low-density lipoprotein receptor-related protein (LRP1) is a major Ab efflux transporter at the BBB (176) but other Ab transporters have been proposed (177, 178). Region-specific levels of Ab deposition in postmortem AD brains are correlated inversely to the local level of Pgp in brain capillaries as assessed by immunohistochemistry (179). Using mdr1a/1b (−/−) double knockout mice, Ab removal from the brain was reported to be mediated at least partially by Pgp activity at the BBB (180). The study showed that acute inhibition of Pgp activity using a selective Pgp modulator increases Ab levels in brain interstitial fluid within hours of treatment. Importantly, mdr1a/1b (−/−) double knockout mice show enhanced Ab plaque formation in a mouse model of AD (180). These data strongly suggested that Pgp normally transports Ab out of the brain and that perturbation of Ab efflux directly affects Ab accumulation within the brain. Thus, there could be a correlation between Pgp and Ab metabolism in vivo such that Pgp activity at the BBB could impact risk for

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developing AD. Recently, these observations have been reinforced by evaluating transepithelial transwell transport of fluorescent Ab1–40/42 using MDR1 transfected LLC (a porcine-derived proximal renal tube epithelial) cells (181). O OH

OH OH

N HO O

O O

O

O

O

O

OH

39

Fortunately, there are several commonly used drugs that could potentially alter Pgp function. For example, dexamethasone, morphine, rifampin, and St. John’s Wort are reported to induce Pgp activity, while verapamil, cyclosporine A, erythromycin 39, progesterone 40, HIV protease inhibitors, and several statins inhibit Pgp activity (6, 182). Typically, these medicinals only show modest impact on Pgp activity in short-term assays at clinically relevant concentrations, but with chronic use or in combination, these drugs may have a greater effect on Pgp function, and, consequently, in Parkinson’s disease or on brain Ab levels in AD. Recently, it has been shown that treatment of AD patients for 3 months with rifampin 41, an inducer of Pgp expression lead to noticeable improvement in their cognitive function.

HO O

CH3 COO O

H H O 40

O

N

H

O O

N

OH O 41

8. Conclusions In summary, Pgp recognizes and outwardly transports a wide variety of bioinorganic complexes with a broad diversity of scaffolds in their chelation cores as well as conventional anticancer drugs.

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With the discovery of transduction sequences and prodrug approaches, chemotherapeutic drugs could possibly be delivered into drug-resistant cells/tumors. In addition to described strategies, future applications could involve enzyme-induced cleavage of the linkers attached to chemotherapeutic drugs for specific treatments. Both small organic medicinals as well as coordination compounds are amenable to incorporation of PET or SPECT radionuclides that may enable diagnostic imaging for functional interrogation of the MDR phenotype in cancer patients. In particular, clinically approved 99mTc-complexes have already shown to be beneficial in the evaluation of functional Pgp transport activity in human tumors in vivo. Therefore, contributions of bioinorganic radiochemistry and conventional organic radiochemistry are rapidly growing to enhance the existing armamentarium of targeted molecular imaging agents that would be capable of functionally analyzing the presence of Pgp in vivo. With the advent of systemic biology and personalized medicine, both targeted chemotherapy as well as biomedical imaging could play a vital role in the near future. Finally, Pgp offers an interesting example of a protein that may unravel unparalleled discoveries at the interface of oncology and neurosciences, wherein a better understanding of the transporter in one discipline could assist the management (both therapeutic and diagnostic components) of diseases in the other.

Acknowledgments The authors are grateful to Prof. David Piwnica-Worms for inspiring and helpful discussions including coworkers (both past and present) for their contributions that led to successful execution of this tutorial. The financial assistance for the educational component of this contribution is also acknowledged in parts from NIH P50 CA94056, AG030498, and American Health Assistance Foundation, A2007-383 grants. References 1. Ling V, Thompson LH (1974) Reduced permeability in CHO cells as a mechanism of resistance to colchicine. J Cell Physiol 83: 103–111 2. Gerlach JH, Endicott JA, Juranka PF et al (1986) Homology between P-glycoprotein and a bacterial haemolysin transport protein suggests a model for multidrug resistance. Nature 324:485–489

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Chapter 9 Epigenetic Regulation of Multidrug Resistance 1 Gene Expression: Profiling CpG Methylation Status Using Bisulphite Sequencing Emma K. Baker and Assam El-Osta Abstract Methylation of CpG dinucleotides is one of the major epigenetic processes involved in the regulation of gene expression. Catalyzed by DNA methyltransferases, hypermethylation of CpG islands in promoter regions is typically associated with gene silencing. DNA methylation plays an important role in normal differentiation, development, and maintenance of genomic stability, with aberrant CpG methylation being linked with a number of disease states. Three CpG islands within a 1.15- kb region characterize the chromatin landscape surrounding the transcriptional start site of the multidrug resistance 1 (MDR1) gene. We and others have demonstrated that hypermethylation of this region is correlated with MDR1 gene silencing and the inability of chemotherapeutic agents to activate MDR1 transcription. The bisulphite sequencing and cloning method allows a precise interpretation of the methylation status of each individual CpG dinucleotide in the MDR1 region. Key words: Epigenetics, Chromatin, MDR1, CpG methylation, CpG island, MeCP2, HDAC, Bisulphite sequencing, Gene silencing

1. Introduction The expression of a gene is inherently determined by chromatin structure and function, and its conformation can be mediated by a number of epigenetic mechanisms. Methylation of the 5-carbon position of CpG dinucleotides catalyzed by DNA methyltransferases is one of the major epigenetic processes involved in controlling chromatin architecture and is biologically important for normal differentiation, development, and maintenance of genomic stability (1). Hypermethylation of CpG clusters, also known as CpG islands, which are associated with the promoter regions of approximately 60% of genes (2), is correlated with J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_9, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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long-term epigenetic silencing. CpG methylation can induce gene silencing by directly interfering with transcription factor binding (3); however, silencing is more commonly brought about by targeted binding of methyl-CpG-binding-domain (MBD) protein complexes that recruit histone deacetylase (HDAC) activity such as the MeCP2 corepressor complex (4, 5). A number of studies have identified that MDR1 transcriptional silencing is correlated with methylation of the promoter (6–10). We have further demonstrated that the methylation-dependent silencing of MDR1 is mediated by the recruitment of the MBD protein, MeCP2, HDAC1, and Brm, the catalytic subunit of the ATPase-dependent chromatin remodeller, SWI/SNF (7, 11). A CpG island is defined by a region of DNA greater than 200 bp, with a G/C content above 0.5 and an observed or expected presence of CpG dinucleotides above 0.6 (12). According to these specifications, the MDR1 gene has three CpG islands within a 1.15-kb region composed of the promoter, exon 1, and intron 1 sequences (Fig. 9.1). The methylation profile of the 66 CpG dinucleotides in the islands (Fig. 9.1) can be precisely determined using the bisulphite conversion technique (Fig. 9.2). The method is dependent upon the ability of sodium bisulphite to distinguish between unmethylated and methylated cytosine residues in single-stranded DNA, unmethylated residues being converted to uracil (13, 14). Amplifying with primers that recognize the modified single-stranded template results in uracil residues being represented as thymine residues in downstream sequencing analyses, allowing the differentiation between methylated and unmethylated cytosine residues. Direct sequencing of the PCR product provides a population average of the CpG dinucleotide methylation status. However, a CpG methylation pattern is not typically uniformally represented in every cell, especially if the DNA sample is derived from a mixed cell population. A more informative approach is to clone and sequence multiple PCR products to yield a very high resolution map of the methylation

Fig. 9.1. Schematic of the MDR1 CpG islands. The sequence of the MDR1 transcriptional start site (−409 to +719 bp) is characterized by three CpG islands within a region encompassing the promoter (black), exon 1 (light gray), and intron 1 (dark gray) (approximately 1.15 kb). The numbering of nucleotides is relative to the transcription initiation start site. 66 CpG dinucleotides within the 1.15-kb region are represented as circles in the schematic. The black arrows indicate the positioning of the three CpG islands, as predicted using the Methprimer software, freely available at http://www.urogene. org/methprimer/.

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Fig. 9.2. Schematic of the sodium bisulphite conversion reaction. (a) Single-stranded DNA (induced by denaturation) is modified with sodium bisulphite converting unmethylated cytosines (C) to uracil (U), leaving methylated cytosines (Cm) unmodified. (b) The bisulphite converted DNA is amplified by PCR using primers designed to the converted sequence and lacking CpG dinucleotides in their recognition sequence. PCR amplification results in uracil (U) being replaced with thymine (T).

profile. The protocol we describe here includes the reagents and practical notes that will allow users to perform the bisulphite conversion and PCR amplification, cloning and sequencing steps with high reproducibility.

2. Materials 2.1. Cell Culture and DNA Extraction

1. RPMI 1640 medium (+l-glutamine) (Gibco Invitrogen, Mount Waverly, VIC, AUS) supplemented with 10% (v/v) FBS (JHR Biosciences, Lenexa, KS, USA) and 16 mg/ml gentamicin (Pizer, New York, NY, USA). Store at 4°C with protection from light. 2. Phosphate buffered saline (without magnesium and calcium) (PBS) was generated by the Baker IDI Heart and Diabetes Institute media kitchen staff for use in all experiments. Can be stored at room temperature for up to 36 months. 3. Cell Scrapers (Sarstedt, Newton, NC, USA). 4. DNeasy® Blood and Tissue Kit (Qiagen, Hilden, Germany). 5. Ethanol Absolute Analytical Grade (LabServ Biolab, Clayton, VIC, AUS).

2.2. Bisulphite Conversion

1. EZ DNA Methylation™ Kit (ZYMO Research, Orange, CA, USA). 2. Nuclease-free water (Ambion, Austin, TX, USA). Store at room temperature.

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2.3. PCR Amplification

1. Resuspend synthetic oligonucleotides in TE buffer at a concentration of 100 rmole/ml (Sigma, St Louis, MS, USA). The sequences of the oligonucleotides used to amplify the MDR1 region from bisulphite converted DNA are as follows: MDR1 external for 5¢-gaaattgttaagtatgttgaag-3¢; MDR1 external rev 5¢-aaattctcctcctttactcctc-3¢; MDR1 internal for 5¢-aaggtgttaggaagtagaaagg-3¢; MDR1 internal rev 5¢-tcttcttctttactcctccatt-3¢. Store at −20°C. 2. Taq polymerase supplied with PCR Taq buffer and 25 mM MgCl2 (Promega, Madison, WI, USA). Taq polymerase should be stored on ice when setting up reactions to maintain its enzymatic activity. Limit its exposure to temperatures greater than −20°C. 3. 2¢-Deoxynucleotide-5¢triphosphates (dNTPs) (100 mM) (Promega, Madison, WI, USA). 10 mM dNTP stocks of each of the four 2¢-deoxynucleotide-5¢triphosphates were diluted in nuclease-free water (Ambion). Store at −20°C. 4. Nuclease-free water (Ambion, Austin, TX, USA). Store at room temperature.

2.4. Restriction Enzyme Digestion and Agarose Gel Electrophoresis

1. BglII and Sau3A restriction enzymes and dedicated buffers (Promega, Madison, WI, USA). 2. Nuclease-free water (Ambion, Austin, TX, USA). Store at room temperature. 3. 3 M NaAc pH5.2 (Amresco, Solon, OH, USA). 4. Ethanol Absolute Analytical Grade (LabServ Biolab, Clayton, VIC, AUS). 5. 70% Ethanol (Absolute Analytical Grade). Diluted in nucleasefree water. 6. Agarose I™(Amresco, Solon, OH, USA). 7. TAE buffer (1×): 40 mM Tris-acetate and 1 mM EDTA. Store at room temperature. 8. 10 mg/ml Ethidium Bromide dissolved in distilled water (Sigma, St Louis, MS, USA). Ethidium bromide is a known mutagen and care should be taken to limit exposure by wearing gloves and disposing of gels containing ethidium bromide in carcinogen biohazard bins marked for incineration. 9. pGEM DNA Marker (Promega, Madison, WI, USA). 10. Amresco® Gel Loading Buffer 4× with Bromophenol Blue (Amresco, Solon, OH, USA).

2.5. PCR Band Excision

1. TAE buffer (1×): 40 mM Tris-acetate and 1 mM EDTA. Store at room temperature.

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2. 10 mg/ml Ethidium Bromide dissolved in distilled water (Sigma, St Louis, MS, USA). Ethidium bromide is a known mutagen and care should be taken to limit exposure by wearing gloves and disposing of gels containing ethidium bromide in carcinogen biohazard bins marked for incineration. 3. pGEM DNA Marker (Promega, Madison, WI, USA). 4. Sterile scalpel blades. 5. Agarose I™(Amresco, Solon, OH, USA). 6. Amresco® Gel Loading Buffer 4× with Bromophenol Blue (Amresco, Solon, OH, USA). 7. Wizard® SV Gel and PCR Clean-up System (Promega, Madison, WI, USA). 2.6. Cloning of PCR Fragments

1. pCR®II-TOPO® vector kit with chemically competent Escherichia coli strain, One Shot® TOP10 cells and S.O.C. medium (Invitrogen, Mount Waverly, VIC, AUS). 2. Ampicillin (100 mg/ml)-selective Luria Bertani (LB) agar plates were prepared beforehand by the Baker IDI Heart and Diabetes Institute media kitchen staff for use in all experiments. Plates should be sealed with parafilm and stored inverted at 4°C to prevent condensation occurring on the agar. 3. 5¢ Bromo-4-chloro-3-indolyl-b-d-galactopyranoside (X-gal) dissolved in N,N-dimethylformamide (Sigma, St Louis, MS, USA) at 40 mg/ml. Store at −20°C protected from light. 4. LB broth was supplied by Baker IDI Heart and Diabetes Institute media kitchen staff and was supplemented with 100 mg/mL ampicillin (Sigma, St Louis, MS, USA). 5. Promega Wizard® Plus SV Miniprep kit (Promega, Madison, WI, USA).

2.7. Sequencing PCR Inserts

1. Big Dye Terminator Chemistry Version 3.1 (Applied Biosystems, Scoresby, VIC, AUS). 2. TOPO M13for (-20) and M13rev sequencing primers for pCR®II-TOPO® vector inserts. Suspend synthetic oligonucleotides in TE buffer at 3.2 rmole/ml (Sigma, St Louis, MS, USA). The sequences of the oligonucleotides are: M13for (-20) 5¢-gtaaaacgacggccag-3¢; M13rev 5¢-caggaaacagctatgac-3¢ (Invitrogen, Mount Waverly, VIC, AUS) (see Note 16). 3. Automated high-throughput capillary electrophoresis system on a 3100 Genetic Analyser (Applied Biosystems, Scoresby, VIC, AUS). 4. All sequencing reactions were carried out by the Baker IDI Heart and Diabetes Institute DNA Sequencing Facility.

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3. Methods Hypermethylation of the MDR1 promoter is correlated with long-term silencing and an inability to induce transcription in response to a number of stimuli including chemotherapeutic agents and HDAC inhibitors (6, 7). Profiling of the methylation status of the promoter is therefore important when examining MDR1 transcriptional response in cell models and the clinical setting, where potential response to drugs is important. Bisulphite sequencing and cloning enables the precise determination of the methylation status of every individual CpG site within a given region. The method is dependent upon the complete conversion of unmethylated cytosine residues to uracil on single-stranded DNA mediated by sodium bisulphite. Following the conversion, the DNA must be purified (desalted), desulphonated, and neutralized (to remove the bisulfite adduct from the uracil ring, desulphonating the uracil sulphonate to uracil) and finally precipitated for use in downstream analyses. An inefficient conversion can lead to false positives preventing accurate methylation profiling. While methodology for non-kit bisulphite conversion is available (not discussed in this review), the EZ DNA Methylation™ Kit simplifies and streamlines the process in three steps. The in-column desulphonation step eliminates tedious precipitation steps that are necessary in non-kit methodology. The EZ DNA Methylation™ Kit also limits the amount of DNA material required for conversion, some non-kit protocols needing up to 20 mg of DNA due to excessive degradation. When the manufacturer’s (ZYMO) recommended DNA input is adhered to, the bisulphite reaction should be efficient, in excess of 99%. Primer design is a critical component in bisulphite sequencing for an unbiased representation of all CpG sites within a given region (see Note 1) and has a number of constraints, including no CG dinucleotides in the primer sequence. In addition, the modified single-stranded DNA template hampers primer selection and amplicon length is limited (optimal is less than 300 bp) due to sodium bisulphite reaction-mediated DNA degradation (see Note 2). Amplicon lengths greater than 300 bp, however, have been successfully designed as discussed in this review, often by implementing a nested PCR step. While direct sequencing of the PCR fragment would provide an average population profiling of every CpG site, cell to cell and copy number variations in individual CpG methylation can be identified by cloning and sequencing of multiple PCR fragments and is recommended for the most accurate representation of the methylation profile in that DNA sample. The following section details

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the methodology used to establish the MDR1 methylation profile in HeLa cells. 3.1. Preparation of DNA

The ZYMO EZ DNA Methylation™ Kit indicates that the optimal DNA input is between 200 and 500 ng, with a maximum input of 2 mg of DNA. The DNA input volume for the kit must be less than 45 ml. Because of the considerable length of the MDR1 amplicon and inherent DNA degradation during sodium bisulphite conversion, 1.5 mg of DNA is used in the reaction to maximize the chances of a successful PCR amplification. We have routinely had successful conversions using 1.5-mg DNA with the kit. 1. In order to extract enough DNA at a sufficient concentration, culture HeLa cells in a 60-mm tissue culture dish in 4-ml RPMI 1640 medium until they are confluent. At confluency, the plate should contain approximately 2.5 × 106 HeLa cells. 2. Wash the cells twice with PBS to remove residual RPMI 1640 medium before harvesting. 3. Harvest the cells by scraping them off the tissue culture dish into 5 ml of PBS using a cell scraper and transfer to a 15-ml falcon tube. Pellet the cells by centrifuging at 1,000 g for 4 min at room temperature and discard the supernatant. 4. Prepare genomic DNA from the cell pellet using the DNeasy® Blood and Tissue Kit according to the manufacturer’s instructions (Qiagen). At the elution step, collect the DNA in two separate elutions, each of 100 ml (see Note 3).

3.2. Bisulphite Conversion of DNA

The ZYMO EZ DNA Methylation™ Kit efficiently bisulphite converts genomic DNA using a simplified and streamlined approach compared to more traditional non-kit methods. 1. Prepare 1.5 mg of genomic DNA in 45 ml of nuclease-free water for bisulphite conversion using the EZ DNA Methylation™ Kit. Carry out the conversion according to the manufacturer’s instructions (ZYMO) (see Note 4). 2. According to the instructions of the EZ DNA Methylation™ Kit, the bisulphite converted DNA is eluted in 10 ml of M-Elution buffer (provided in the kit). Increase this volume to 40 ml with nuclease-free water to provide sufficient template for multiple PCR reactions.

3.3. PCR Amplification of the 1.15-kb MDR1 Fragment

A 1.15-kb region of the MDR1 promoter (Fig. 9.1) is amplified in a nested PCR reaction using the primers detailed in subheading 9.2.3. The fragment is initially amplified to confirm that the bisulphite conversion has been successful using a restriction enzyme digest assay (Subheading 9.3.4). Once confirmation of a successful conversion is obtained, the fragment is amplified again to clone and sequence the MDR1 fragment (Subheading 9.3.5).

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1. Amplify 3 ml of the diluted bisulphite converted DNA (Subheading 9.3.2) in five identical (see Note 5) 50-ml PCR reactions that contain 2.5 units of Taq polymerase, 2 mM MgCl2, 1× PCR Taq buffer, 0.25 mM of each dNTP, and 25 rmole of each of the primers MDR1 external for and MDR1 external rev. Amplify the primary product using the cycling parameters: 1× 95°C/5 min; 35× 95°C/45 s, 55°C/30 s, 72°C/2 min, with a final extension period of 1× 72°C/10 min. Include a negative water control in the PCR assay, consisting only of the reaction components and water as a template to exclude contamination. 2. Combine the five PCR reactions and make a 1/500 dilution with nuclease-free water. Amplify 5 ml of the diluted products in five individual 50-ml nested PCR reactions (see Note 5), using 25 rmole of each of the primers MDR1 internal for and MDR1 internal rev, 2.5 units of Taq polymerase, 2 mM MgCl2, 1× PCR Taq buffer, and 0.25 mM of each dNTP. Amplify the secondary product using the cycling parameters: 1× 95°C/5 min; 35× 95°C/45 s, 55°C/30 s, 72°C/2 min (see Note 6), with a final extension period of 1× 72°C/30 min (see Note 7). Include a negative water control, consisting only of the reaction components and water as a template in the PCR assay to exclude contamination. 3. Place the PCR products designated for gel-extraction purification (Subheading 9.3.5) and cloning (Subheading 9.3.6) on ice (see Note 8). It is not essential to keep PCR products designated for restriction enzyme analyses on ice and these may be stored at room temperature for several hours, or alternatively at −20°C for longer periods of time. 3.4. Restriction Enzyme Digestion and Agarose Gel Electrophoresis

The restriction enzymes BglII and Sau3A contain cytosine residues in their sequence recognition motifs that are not within a CpG sequence. The BglII and Sau3A sequence recognition motifs are found within the 1.15-kb region of the MDR1 promoter. Bisulphite conversion would remove their sequence recognition motifs from the MDR1 fragment, as the cytosine residues would be converted to uracil in the reaction. Therefore, a successful conversion of the DNA would prevent digestion of the MDR1 PCR product with the restriction enzymes BglII and Sau3A. 1. Pool the five PCR products designated for restriction enzyme digestion analyses and precipitate with 1/10 (v/v) 3 M NaAc pH5.2 and 2.5 volumes of ethanol at −20°C for a minimum of 1 h. Collect the precipitated PCR fragments by centrifugation at 15,000 g for 15 min at 4°C. Wash the pellet with 1 ml 70% ethanol and centrifuge at 13,000 rpm for 5 min at 4°C. Remove the supernatant, air dry the pellet for 4–5 min at room temperature, and dissolve in 30-ml nuclease-free water with pipetting.

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2. To confirm that the bisulphite conversion reaction is complete, digest 10 ml of the precipitated PCR products with each of the restriction enzymes BglII and Sau3A in 20-ml reactions for 2 h at 37°C according to the manufacturer’s recommendations (Promega). After the incubation, heat the reactions to 70°C for 20 min to cease the enzymatic activity. 3. PCR product restriction enzyme digests are visualized by agarose gel electrophoresis. In a glass 250-ml conical flask, mix the constituents for a 1% agarose gel (100 ml 1× TAE, 1-g Agarose I™, 10 ml 10 mg/ml ethidium bromide) and heat for approximately 90 s on a medium setting of a microwave to totally resuspend the solution, taking care not to overboil the solution. While in a liquid state, pour the gel solution into a gel tray (150 mm × 100 mm) equipped with a comb with loading well dimensions of 4 mm × 1.5 mm. Incubate at room temperature until the agarose has polymerized. Remove the comb and transfer the gel tray with the agarose gel to an electrophoresis chamber and submerge in 1× TAE buffer. 4. Combine the restriction enzyme digest products (20 ml) with gel loading dye to a final concentration of 1 ml and load into individual wells of the agarose gel. In a separate well, load 1 ml of the pGEM DNA Marker (used to confirm the fragment sizes). Electrophorese the gel at 120 V for approximately 30 min to size fractionate the restriction digest products. 5. We visualize the restriction digest bands using a ChemiDoc XRS (Biorad) (Fig. 9.3). Successful bisulphite conversion is observed in the left hand panel of Fig. 9.3 by the lack of digestion of the 1.15-kb fragment with the enzymes BglII and Sau3A. In the right hand panel of Fig. 9.3, digestion of the 1.15-kb fragment with BglII and Sau3A indicates incomplete conversion of the genomic DNA by sodium bisulphite. 3.5. PCR Band Excision

Once the successful bisulphite conversion has been confirmed by restriction enzyme digestion (Subheading 9.3.4), amplify the bisulphite converted DNA by PCR (Subheading 9.3.3) for a second time and purify by agarose gel extraction for cloning (Subheadings 9.3.5 and 3.6). 1. Pool the five PCR reactions designated for cloning and mix gently. Leave the products on ice until size fractionation by agarose gel electrophoresis (see Note 8). 2. PCR products are separated by agarose gel electrophoresis. In a glass 250-ml conical flask, mix the constituents for a 1% agarose gel (100 ml 1× TAE, 1-g Agarose I™, 10 ml 10 mg/ml ethidium bromide) and heat for approximately 90 s on a medium setting of a microwave to totally resuspend the solution taking care not to overboil the solution. While in a liquid state, pour the gel solution into a gel tray (150 mm × 100 mm)

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Fig. 9.3. Confirmation of successful sodium bisulphite conversion of genomic DNA by restriction enzyme digestion of PCR products. Restriction enzyme-digested PCR products were size fractionated on a 1% TAE agarose gel. A 1.15-kb band indicates nondigestion of the PCR product by the restriction enzymes BglII and Sau3A. Smaller fragments after digestion with BglII and Sau3A are evidence of incomplete conversion of genomic DNA by sodium bisulphite. pGEM® DNA size marker (M).

equipped with a comb with loading well dimensions of 4 mm × 1.5 mm. Incubate at room temperature until the agarose has polymerized. Remove the comb and transfer the gel tray with the agarose gel to an electrophoresis chamber and submerge in 1× TAE buffer. 3. Combine 20 ml of the pooled PCR products with gel loading dye to a final concentration of 1× and load into individual wells of the gel. In a separate well, load 1 ml of the pGEM DNA Marker (used to confirm the PCR product sizes). Electrophorese the gel at 120 V for approximately 30 min to size fractionate the PCR products. 4. Clean the surface of an ultraviolet (UV) light box by wiping with 70% ethanol, and place the agarose gel on the light source. Ethidium bromide fluoresces under UV light when intercalated with nucleic acid polymers, allowing the visualization of the PCR products. Limit skin and eye exposure to the UV light source by using perspex screens or protective equipment. 5. Excise the correctly sized PCR fragments from the gel using sterile scalpel blades. A single blade should be used for each PCR fragment to prevent cross-contamination of samples. While it is essential to excise the entire fragment, limit the amount of

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nonessential agarose in the excision. Try to work as efficiently as possible, as continual exposure of the gel to UV light increases the risk of DNA lesions, damaging the fragments. 6. Recover the DNA from the agarose gel piece using the Wizard® SV Gel and PCR Clean-up System (Promega) as specified by the manufacturer. 7. Place the purified fragment on ice until vector ligation (see Note 8). 3.6. Cloning of PCR Fragments

Cloning the PCR fragments enables the sequencing of individual alleles providing an in-depth representation of the methylation profile in that DNA sample, compared to directly sequencing the PCR fragment, which only portrays an average methylation profile. 1. Agarose gel purification dilutes the PCR product. If excising a very faint band, it will be necessary to use the maximal template volume (4 ml) in the vector ligation. Only minimal template addition (1 ml) in the vector ligation should be necessary if strong PCR bands were evident under UV irradiation. 2. Subclone the gel-purified PCR products into the pCR®IITOPO® vector as specified by the manufacturer (Invitrogen). Include a control ligation, where PCR product template is replaced by water. Extend the benchtop room temperature ligation to 30 min to improve the efficiency of subcloning the large MDR1 fragment. 3. During this time, warm the S.O.C. medium (Invitrogen) to room temperature. Equilibrate a waterbath to 42°C, and warm the ampicillin-selective Luria Bertani (LB) agar plates to 37°C. Working near a blue flame of a Bunsen burner and using aseptic techniques to prevent contamination, spread the warm ampicillin-selective Luria Bertani (LB) agar plates with 40 ml of 40 mg/ml X-gal to enable blue/white colony screening of positive PCR product insert clones (white colonies) (see Note 9). Return the agar plates to 37°C until needed. 4. Transform the PCR product-vector ligations into the chemically competent E. coli strain One Shot® TOP10 cells by heat shock as specified by the manufacturer of the pCR®II-TOPO® vector kit (Invitrogen). We find that the transformation efficiency is improved by extending the 42°C heat shock time to 45 s. A water bath is used for the heat shock incubation to ensure equal tube exposure to heat, which cannot be guaranteed when using a standard dry cell heat block. After heat shock, immediately transfer the tubes to ice. 5. Add 250 ml of S.O.C. medium to the tubes, seal the lids, and shake horizontally (200 rpm) at 37°C for 1 h.

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6. Working near a blue flame of a Bunsen burner and using aseptic techniques to prevent contamination, spread 50 ml of the transformed cells onto the prewarmed ampicillin-selective Luria Bertani (LB) agar plates with X-gal using a sterile plate spreader (see Note 10). Incubate the plates overnight at 37°C inverted, to prevent condensation collecting on the agar. 7. Successful cloning will be evidenced by a lack of colonies on the agar plate spread with the water control transformed cells and a high ratio of white to blue colonies on plates spread with PCR product transformations. Pick single white colonies with sterile pipette tips and inoculate to tubes containing 3 ml of LB broth supplemented with 100 mg/ml ampicillin. Incubate the culture in a shaking incubator overnight at 37°C (approximately 16–18 h). We routinely pick 5–10 colonies for the initial analysis. More clones can be analyzed at a later date if needed, as viable colonies can still be picked from plates stored at 4°C for up to 4 weeks and inoculated directly to LB broth supplemented with 100 mg/ml ampicillin. Seal plates with parafilm, wrap in aluminium foil, and store inverted at 4°C. 8. Recover plasmid DNA containing MDR1 inserts using the Promega Wizard® Plus SV Minipreps as specified by the manufacturer (Promega). Bacterial cells can be pelleted and stored at −20°C indefinitely if the plasmid DNA cannot be recovered immediately. 3.7. Sequencing PCR Inserts

1. 300 ng of purified plasmid DNA (a minimum of 100 ng in a 13-ml volume can be sequenced) is sequenced by the DNA Sequencing Laboratory at the Baker IDI Heart and Diabetes Institute using an automated, high-throughput, capillary electrophoresis system on a 3100 Genetic Analyser (Applied Biosystems). 2. Sequencing reactions are carried out using Big Dye Terminator Chemistry Version 3.1 (Applied Biosystems) with TOPO M13for (-20) and M13rev sequencing primers for pCR®IITOPO® vector inserts (Invitrogen) (see Note 11). 3. Align the bisulphite converted MDR1 sequences against a template of the unconverted genomic DNA sequence to determine the methylation status of each cytosine residue. Examine the bisulphite converted MDR1 sequencing files for cytosine residues in non-CpG contexts. If evident, discard these sequencing files as this is indicative of incomplete bisulphite conversion of the DNA. Align the sequences. 4. Bisulphite sequencing data are often presented in some form of color-coded schematic. Figure 9.4 demonstrates how we would typically present data for the MDR1 gene. Each row of circles should represent a single cloned allele and each circle

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Fig. 9.4. The methylation status of the MDR1 promoter, exon 1 and intron 1 region (−409 to +719 bp) in HeLa cells was analyzed by bisulphite sequencing. Each row of circles represents a single cloned allele and each circle represents a single CpG dinucleotide. White circles – unmethylated CpG dinucleotide and black circles – methylated CpG dinucleotide. (a) Schematic of the MDR1 1.15 kb region, including promoter, exon 1, and intron 1. The numbering of nucleotides is relative to the transcription initiation start site. The black arrows indicate the positioning of the three CpG islands. (b) Methylation profile of the MDR1 1.15 kb region in HeLa cells.

should represent a single CpG dinucleotide. Black circles represent a methylated cytosine and white circles represent an unmethylated cytosine.

4. Notes 1. The primer sequences for amplifying a 1.15-kb region of the MDR1 locus encompassing 3 CpG islands are included in this protocol. If the methylation profiles of different MDR1 regions or different genes were of interest, primers specifically for bisulphite-modified DNA analyses can be designed using Methprimer software, freely available at http://www.urogene. org/methprimer/. 2. DNA degradation in bisulphite conversion reactions has been suggested to be as high as 90%. The high rate of degradation often results in poor PCR amplification and can limit the amplicon size for primer design. The ZYMO EZ DNA Methylation™ Kit advertizes that 80% of the DNA template is recovered. Despite the company recommendation of using between 200 and 500 ng of DNA, 1.5 mg is used to improve the total yield of DNA template recovered, aiding in amplification of the large 1.15-kb MDR1 fragment. 3. Elute the bound DNA into a new Eppendorf tube in two separate 100-ml elutions. The concentration of the eluted DNA should be well within the limits (>0.04 mg/ml) of converting 1.5 mg of DNA in the allowable 45-ml volume.

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4. The Ct conversion reagent liquid solution in the EZ DNA Methylation™ Kit is light sensitive, so minimize its exposure to light by containing in aluminum foil. Unused CT reagent liquid solution can be stored for up to 1 month at −20°C. Warm to 37°C and vortex before use. It is essential that the 50°C incubation is carried out in darkness. Exposure to light will cause incomplete conversion. A heat block can be used for the incubation if the block is covered with aluminum foil. 5. Amplify the primary and secondary PCR reactions in five identical reactions in order to minimize the effects of PCR artifacts. 6. The number of rounds in the secondary PCR may need to be reduced if the product is strongly amplified, potentially within the range of 25 cycles. Additionally, if only very weak products are observed, increase the cycles to 40–45. 7. The final extension time in the secondary PCR method is increased to 30 min because it can improve Taq-polymerasemediated addition of a single deoxyadenosine (A) to the 3¢ ends of PCR products, which is necessary for cloning into the pCR®II-TOPO® vector. Taq is least efficient at adenylating next to another adenosine residue and most efficient at adenylating next to a cytosine residue. Therefore, primer designs typically should not contain 5¢ T residues and would be improved by a 5¢ G residue as recommended by the pCR®IITOPO® vector kit manufacturer, Invitrogen. The current primer designs do not follow these recommendations; however, with care taken to handle the PCR products postamplification and precloning, efficient ligation reactions can be performed. 8. The loss of adenylation at the 3¢ ends of PCR products mediated by Taq-polymerase can be increased by temperature fluctuations and delays in processing. The pCR®II-TOPO® vector is supplied linearized with single 3¢-thymidine (T) overhangs for TA cloning® (Invitrogen), which allows the direct insertion of Taq polymerase-amplified PCR products. Taq-polymerase adds a single deoxyadenosine (A) to the 3¢ ends of PCR products during amplification, which enables the ligation of the PCR products with the pCR®II-TOPO® vector. Delays between amplifying the PCR product and vector ligation can lead to inadvertent loss of the 3¢ A overhang resulting in inefficient cloning. Methods are available to add 3¢ A-overhangs to the PCR products postamplification; however, the cloning reaction is generally not as efficient. 9. X-gal is used to indicate whether E. coli One Shot® TOP10 cells express the b-galactosidase enzyme. The pCR®II-TOPO® vector contains the LacZa gene, which encodes for a truncated

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version of the b-galactosidase enzyme. The multiple cloning site of the pCR®II-TOPO® vector is located within the LacZa gene. When the LacZa gene is intact in these cells, the X-gal is cleaved by b-galactosidase producing an insoluble blue product (5,5¢-dibromo-4,4¢-dichloro-indigo) resulting in a blue colony. When the MDR1 PCR product has been successfully ligated into the multiple cloning site of the pCR®IITOPO® vector, the LacZa gene open reading frame is disrupted, preventing the production of the b-galactosidase resulting in a white colony. 10. A steel plate spreader can be sterilized by dipping it in 100% ethanol and exposing to the blue flame of a Bunsen burner. Make sure the loop is cooled sufficiently before applying to the agar plate, or you risk killing the bacterial cells with excessive heat. Alternatively, single-use sterile plate spreaders can be made by bending a glass Pasteur pipette using the blue flame of the Bunsen burner. 11. Sequencing with the M13rev sequencing primer typically returns good-quality sequencing reads in excess of 700 bp indicating that the Big Dye Terminator Chemistry works well despite typically high C/T contents in bisulphite converted sequences. Sequencing with the M13for (-20) sequencing primer is often compromised by being significantly reduced in read length, suggesting an incompatibility between the Big Dye Terminator Chemistry and high T/G content. The combined read length from the two sequencing reactions is typically long enough to cover the 1.15-kb MDR1 insert. If sequencing coverage is lacking however, internal sequencing primers should be designed to a bisulphite converted MDR1 sequence that lacks CpG dinucleotides. References 1. Bird A (2002) DNA methylation patterns and epigenetic memory. Genes Dev 16:6–21 2. Larsen F, Gundersen G, Lopez R, Prydz H (1992) CpG islands as gene markers in the human genome. Genomics 13:1095–1107 3. Bird AP, Wolffe AP (1999) Methylationinduced repression–belts, braces, and chromatin. Cell 99:451–454 4. Jones PL, Veenstra GJ, Wade PA et al (1998) Methylated DNA and MeCP2 recruit histone deacetylase to repress transcription. Nat Genet 19:187–191 5. Nan X, Ng HH, Johnson CA et al (1998) Transcriptional repression by the methyl-CpGbinding protein MeCP2 involves a histone deacetylase complex. Nature 393:386–389

6. Baker EK, Johnstone RW, Zalcberg JR, El-Osta A (2005) Epigenetic changes to the MDR1 locus in response to chemotherapeutic drugs. Oncogene 24:8061–8075 7. El-Osta A, Kantharidis P, Zalcberg JR, Wolffe AP (2002) Precipitous release of methyl-CpG binding protein 2 and histone deacetylase 1 from the methylated human multidrug resistance gene (MDR1) on activation. Mol Cell Biol 22:1844–1857 8. Kantharidis P, El-Osta A, deSilva M et al (1997) Altered methylation of the human MDR1 promoter is associated with acquired multidrug resistance. Clin Cancer Res 3:2025–2032 9. Kusaba H, Nakayama M, Harada T et al (1999) Association of 5’ CpG demethylation

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and altered chromatin structure in the promoter region with transcriptional activation of the multidrug resistance 1 gene in human cancer cells. Eur J Biochem 262: 924–932 10. Nakayama M, Wada M, Harada T et al (1998) Hypomethylation status of CpG sites at the promoter region and overexpression of the human MDR1 gene in acute myeloid leukemias. Blood 92:4296–4307 11. Harikrishnan KN, Chow MZ, Baker EK et al (2005) Brahma links the SWI/SNF chromatinremodeling complex with MeCP2-dependent

transcriptional silencing. Nat Genet 37: 254–264 12. Gardiner-Garden M, Frommer M (1987) CpG islands in vertebrate genomes. J Mol Biol 196:261–282 13. Clark SJ, Harrison J, Paul CL, Frommer M (1994) High sensitivity mapping of methylated cytosines. Nucl Acids Res 22:2990–2997 14. Frommer M, McDonald LE, Millar DS et al (1992) A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci USA 89:1827–1831

Chapter 10 Expression and Function of P-Glycoprotein in Normal Tissues: Effect on Pharmacokinetics Frantisek Staud, Martina Ceckova, Stanislav Micuda, and Petr Pavek Abstract ATP-binding cassette (ABC) drug efflux transporters limit intracellular concentration of their substrates by pumping them out of cell through an active, energy dependent mechanism. Several of these proteins have been originally associated with the phenomenon of multidrug resistance; however, later on, they have also been shown to control body disposition of their substrates. P-glycoprotein (Pgp) is the first detected and the best characterized of ABC drug efflux transporters. Apart from tumor cells, its constitutive expression has been reported in a variety of other tissues, such as the intestine, brain, liver, placenta, kidney, and others. Being located on the apical site of the plasma membrane, Pgp can remove a variety of structurally unrelated compounds, including clinically relevant drugs, their metabolites, and conjugates from cells. Driven by energy from ATP, it affects many pharmacokinetic events such as intestinal absorption, brain penetration, transplacental passage, and hepatobiliary excretion of drugs and their metabolites. It is widely believed that Pgp, together with other ABC drug efflux transporters, plays a crucial role in the host detoxication and protection against xenobiotic substances. On the other hand, the presence of these transporters in normal tissues may prevent pharmacotherapeutic agents from reaching their site of action, thus limiting their therapeutic potential. This chapter focuses on P-glycoprotein, its expression, localization, and function in nontumor tissues and the pharmacological consequences hereof. Key words: P-glycoprotein, Expression, Function, Localization, Normal tissues, Pharmacokinetics, Drug absorption, Drug distribution, Drug excretion

1. Introduction Drug efflux transporters of the ATP-binding cassette (ABC) family are membrane-embedded proteins that limit intracellular concentration of substrate agents by pumping them out of cell through an active, energy dependent mechanism. Several of these proteins, mainly P-glycoprotein (Pgp, also known as ABCB1), multidrug resistance-associated protein 1 (MRP1, also known as ABCC1), and breast cancer resistance protein (BCRP, also known J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_10, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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as ABCG2, MXR, and ABCP), have been associated with the phenomenon of multidrug resistance in cancer therapy. In addition, these transporters have also been localized in normal tissues where they seem to control body disposition of their substrates, such as brain penetration, intestinal absorption, maternal-fetal transport, or hepatobiliary excretion. Therefore, ABC drug efflux transporters are widely believed to play a crucial role in host detoxication and protection against xenobiotic substances. Pgp, the paradigm ABC drug efflux transporter, is the first detected and to date the best characterized of the family of ABC drug efflux transporters. It was first identified in 1972 by Juliano and Ling (1) as a surface phosphoglycoprotein expressed in drug-resistant Chinese hamster ovary cells. It gained worldwide attention about three decades ago for its role in the phenomenon of multidrug resistance in tumor cells (2–4). Subsequently, constitutive expression of Pgp has been described in a variety of other tissues including the liver, intestine, kidney, pancreas, adrenal, capillary endothelium of blood–brain and blood–testis barrier, choroid plexus, placental trophoblast, and others (Figs. 10.1 and 10.3) (5, 6). The polarized, apical membrane localization of Pgp causes that its substrates are preferentially translocated from basolateral to the apical side of the epithelium. Thus, Pgp limits the influx and facilitates the efflux of its substrates, eventually preventing their intracellular accumulation. Many in vitro and in vivo studies have demonstrated high impact of Pgp on drug pharmacokinetics in these organs (5). It is likely that Pgp and other ABCs have evolved in these “normal” tissues to protect them from potentially damaging effects of toxic compounds. The function of Pgp can be divided, based on its anatomical localization, into three steps: (i) Pgp limits intestinal absorption of drugs; (ii) once the drug is in the systemic circulation, Pgp protects its passage to sensitive organs and tissues; and finally (iii) Pgp also facilitates elimination of drugs and metabolites into bile and urine (7). Therefore, it greatly affects the fate of a drug in the body as well as effectiveness of drug treatment. However, as pointed by Schinkel and Jonker (5), Pgp will only result in noticeable distribution effects if the rate of active transport for a certain compound is substantially relative to passive diffusion rate. If not, the pump activity will be overwhelmed by the passive diffusion of the component.

2. Substrates, Inhibitors, and Inducers of P-glycoprotein

Pgp transports an extremely wide variety of chemically and structurally diverse compounds. Pgp substrates are usually organic molecules ranging in size from about 200 Da to over 1,000 Da.

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Fig. 10.1. Localization of P-glycoprotein in human nontumor tissues; see text for detailed description. For P-glycoprotein localization in the placenta see Fig. 10.3.

Although the structure-activity relationship for Pgp substrates has not been fully elucidated to date, it seems that both lipophilicity and number of hydrogen bonds are the most important properties for Pgp affinity (8). Thus, most substrates are uncharged or weakly basic in nature, but some acidic compounds can also be transported. It is of clinical importance that a large number of drugs, or their metabolites, of various pharmacotherapeutic groups have been recognized as Pgp substrates (5, 7). The list of substrates and inhibitors is constantly growing and includes, for example, cytotoxic drugs, HIV protease inhibitors, antibiotics, opioids, antiemetics, as well as diagnostic dyes rhodamine 123 or Hoechst 33342 (see Table 10.1). In addition, considerable overlap in substrates between Pgp and CYP3A4 is often discussed, pointing out synergistic effect of CYP3A4 and Pgp in detoxication processes, mainly in the small intestine (see Subheading 4.1) (9).

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Table 10.1 List of Pgp substrates, inhibitors, and inducers. Based on [5, 12, 16, 42, 136] Pgp substrates

Pgp inhibitors

Pgp inducers

Analgesics: morphine, methadone Anthelmintics: ivermectin, abamectin Antibiotics: erythromycin, tetracyclines, fluoroquinolines Anticancer drugs: vinblastine, vincristine, paclitaxel, doxorubicin, daunorubicin, mitoxantrone, etoposide, methotrexate, topotecan Antidiarrheal agents: loperamide Antiemetics: domperidone, ondansetron Antiepileptic drugs: phenytoin, carbamazepine, lamotrigine, phenobarbital, felbamate, gabapentin, topiramate Anti-gout agents: colchicine Calcium channel blockers: verapamil Cardiac glycosides: digoxin Corticoids: dexamethasone, hydrocortisone, corticosterone, cortisol, aldosterone, triamcinolone Diagnostic dyes: rhodamine 123, Hoechst 33342 HIV protease inhibitors: saquinavir, ritonavir, indinavir Immunosuppressive agents: cyclosporine, sirolimus, tacrolimus Psychotropic drugs: chlorpromazine, clozapine, desipramine, domperidone, flupentixol, imipramine, nortryptiline, sertaline, amitryptiline, doxepin, venlafaxine, paroxetine

1st Generation verapamil, cyclosporine, quinidine, quinine, amiodarone, cremophore EL 2nd Generation PSC-833 (valspodar), GF120918 (elacridar), VX-710 (biricodar), dexverapamil 3rd Generation OC 144-093 (ONT-093), LY335979 (zosuquidar), XR9576 (tariquidar), R101933 (laniquidar)

verapamil midazolam rapamycin reserpine rifampicin phenobarbitol St John’s Wort clotrimazole

The presence of Pgp in normal tissues protects the cells against harmful compounds but, on the other hand, may prevent pharmacotherapeutic agents from entering systemic circulation (Pgp in the small intestine) or their site of action (Pgp in the brain). Therefore, Pgp inhibitors have been searched for to improve pharmacotherapy, for example, to overcome multidrug resistance in anticancer treatment (10), and to enable drug penetration behind the blood–tissue barriers such as the intestine (to increase bioavailabilty of orally administered drugs), to the brain (to increase availability of CNS acting compounds) or fetus (for in utero fetal therapy) (11). Apart from low-molecular

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inhibitors of three generations, other tools for Pgp inhibition have been exploited including monoclonal antibodies (MRK16) or RNA interference (12). Several compounds have been found to upregulate the expression and increase the amount and transport activity of Pgp in tissues. Substrates, inhibitors, and inducers of Pgp are listed in Table 10.1. In clinical practice, Pgp-mediated drug–drug interactions are likely to occur if Pgp substrates/inhibitors/inducers are administered to a patient at the same time as described, for example, for digoxin-quinidine, digoxin/talinolol-rifampin, or digoxin-St John’s Wort (7). In their comprehensive review, Lin et al. summarize in detail drug–drug interactions mediated by inhibition and induction of Pgp and their effect on drug absorption, distribution, and elimination (13).

3. ABCB1 Gene Structure, Regulation, and Polymorphisms

Human Pgp is coded by ABCB1 (MDR1) gene located on chromosome 7q21.1. The gene consists of 29 exons, numbered −1 to 28, spanning more than 200 kb of genomic DNA; however, only 27 exons code for Pgp protein. ABCB1 gene possesses two distinct promoter regions, including an upstream promoter at the beginning of exon −1 and the more preferred downstream promoter located within exon 1. The ATG translation initiation codon is located within exon 2. Unlike its murine analogue, the ABCB1 promoter lacks a consensus TATA box (core DNA sequence 5¢-TATAAA-3¢) within the proximal promoter region. However, like many other TATA-less promoters, basal transcription is directed by an initiator (Inr) sequence that encompasses the transcription start site (+1). Sequences between −6 and +11 are sufficient for proper initiation of transcription and are likely acting as the binding sites of the RNA Pol II transcriptional complex (Fig. 10.2a) (14, 15). Controlling the expression of ABCB1 in both tumor and normal tissues is a multifactorial event that has not been fully elucidated to date (16). Studies of the ABCB1 promoter and regulation of ABCB1 transcription have shown that transcription is under control of the complex regulatory network. In addition to transcriptional regulation by transcription factors such as those of the Sp family, NF-Y, AP-1, the tumor suppressor protein p53 and nuclear receptors (Pregnane X receptor, Vitamin D receptor, and Constitutive androstane receptor), ABCB1 transcription is also influenced by epigenetic modification such as DNA methylation and histone acetylation and via different signal transduction and

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Fig. 10.2. Structure, genomic organization, transcriptional regulation, and major single nucleotide polymorphisms of human ABCB1 (MDR1) gene. (a) Genomic, mRNA and protein structure. (b) Transcriptional regulation of the MDR1 gene.

mitogen-activate cascades such as p38, JNK, ERK, PKC and NF-kB. In addition, many pathophysiological and stress conditions such as inflammation and hypoxia affect transactivation and gene expression of ABCB1 illustrating the complexity of regulatory mechanisms (16–18) (Fig. 10.2b). Genetic polymorphisms of drug transporters and metabolizing enzymes, mainly CYP450, represent a source of interindividual variability in drug pharmacokinetics, drug effectiveness and/or toxicity. Genetic polymorphisms of ABCG1 gene have been reported both in animals (19) and in human (20). Over 50 single nucleotide polymorphisms (SNPs) have been identified so far (21) including coding and noncoding regions of the gene, and it can be predicted that many more will be found in future. Majority of coding region SNPs are nonsynonymous. However, allele frequency for most of the coding region SNPs is low ( that of normal bone marrow – Observed in only 7% of patients – Did not correlate with clinical outcome BCRP protein expressed in small subpopulations of AML cells-?primitive cells

BCRP mRNA exp – higher in those with no CR – higher at time of relapse

BCRP exp – Correlated with functional assay – Did not increase in relapse samples – Was high in cells with immature phenotype (CD34+ cells)

BCRP mRNA – Increased in rel/RD samples – Correlated with Pgp expression (r = 0.44)

BCRP exp correlated with in vitro sensitivity to daunorubicin but not to doxorubicin or mitoxantrone. No diff between chemo naïve and treated samples

Relatively high BCRP exp in 33%; fair correlation of BCRP with Pgp expression (r = 0.66)

Findings/Conclusions

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51 at diagnosis 31 at diagnosis

AML

AML

AML

AML

AML

AML

Galimberti 2005 (134)

Suvannasankha 2004 (118)

Benderra 2004 (123)

Uggla 2005 (125)

Raaijmakers 2005 (138)

Benderra 2005 (124) 85 de novo patients

22 patients 7 normal donors

40, mostly de novo at diagnosis

149 de novo patients at diagnosis

21 at diagnosis, RD or relapse

AML

Nakanishi 2003 (116)

45 at diagnosis and at time of relapse or MRD

AML

van der Pol 2003 (128)

Protein (FC, BXP-34 MoAb); FA

Protein (FC, BXP-21 MoAb); FA

mRNA (qRT-PCR)

mRNA (qRT-PCR); FA

mRNA (qRT-PCR) Protein (FC, BXP-34, BXP-21, anti-ABCG2 MoAbs); FA

mRNA (qRT-PCR)

mRNA (qRT-PCR)

Protein (FC, BXP-34 and BXP-21 MoAbs); FA; Assays for MRD

(continued)

– BCRP, Pgp, and MRP3 protein/ function correlated with CR, OS – Lower CR, DFS, OS in patients with high expression of 2 or more of these transporters – CD34+ correlated with Pgp but not with BCRP – Higher MRP3 in M5 subtype

BCRP expression and function – Highest in CD34+/CD38− cells in both AML and normal blasts – Ko143 did not increase apoptosis in CD34+/CD38− AML cells

BCRP mRNA at diagnosis – Was not predictive of CR – High expression assoc with shorter OS

BCRP, Pgp prognostic factors for achieving CR. High BCRP expression assoc with lower DFS, OS. Coexpression of Pgp and BCRP had lowest OS, DFS

Poor concordance of BCRP mRNA, protein, and function in AML samples; BCRP found in small subpopulations Wild type sequence found at codon 482

Pgp and BCRP are frequently coexpressed (r = 0.9)

High BCRP mRNA expression correlated with resistance to flavopiridol; no mutations of codon 482 observed

No increase in ABC transporter function at relapse or emergence of MRD. No subpopulations of resistant cells Impact of Breast Cancer Resistance Protein on Cancer Treatment Outcomes 261

AML

Normal karyotypes AML

AML in elderly

AML stem cells (LSC)

Childhood AML

Wilson 2006 (190)

Damiani 2006 (132)

Van den Heuvel-Eibrink 2007 (136)

De Figueiredo-Pontes 2008 (133)

Shman 2008 (139) 19 at diagnosis and at Rel Additional 10: 8 de novo, 2 at Rel

26 de novo AML 8 non-AML marrows (4 ITP, 4 orthopedic surg)

mRNA (qRT-PCR)

Protein (FC, BXP-21 MoAb)

mRNA (qRT-PCR)

Protein (FC, BXP-34 MoAb)

Gene expression profiling

Parameter, Methodology

154 age > 60, at diagnosis

73 consecutive de novo patients ad diagnosis

170 elderly patients at diagnosis

Number studied

Disease

Lead Author, year, citation

Table 12.1 (continued)

52% of relapse cases had increase in % CD34+ cells. Trend, but no statistically significant greater expression of BCRP or Pgp expression in CD34+ versus CD34− cells. Did not test for CD38 expression

– LSCs (CD34+/CD38−/CD123+ cells) had higher Pgp and BCRP protein expression – No difference in Pgp, BCRP, MRP1, or LRP expression among WHO classification subtypes

BCRP mRNA correlated with – Secondary AML, lower WBC – MDR1/ABCB1 mRNA expression CD34 expression and MDR1/BCRP coexpression associated with lower CR, DFS, OS

– High BCRP expression in 33% – BCRP and Pgp frequently coexpressed – High BCRP assoc. with risk of relapse and lower DFS

Genes segregated into 6 prognostic clusters; The cluster containing ABCG2 and ABCB1 had highest RD frequency

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AML

AML

Childhood ALL

ALL

ALL, adults and children

Adult ALL

Childhood ALL

Ho 2008 (135)

Saraiya 2008 (191)

Sauerbrey 2002 (141)

Stam 2004 (143)

Plasschaert 2003 (117)

Suvannasankha 2004 (142)

Kourti 2007 (144)

49 newly diagnosed

30: 17 B-lineage, 9 B-myeloid, 4 T-lineage

46: 23 B-lineage, 23 T-lineage

13 Infants, 13 Children – all at diagnosis

67: -47 at diagnosis, -20 at relapse

31 with elevated blast counts at diagnosis

34 adult de novo cases, all ages

mRNA (RT-PCR, 35 cycles)

mRNA (qRT-PCR) Protein (FC, BXP-34, BXP-21, 5D3 MoAbs); FA

Protein (FC, BXP-34 MoAb); FA

mRNA (qRT-PCR) in vitro drug sensitivity assays

mRNA (qRT-PCR)

Protein (Western blots, anti-ABCG2 MoAb)

mRNA (qRT-PCR) for all human ABC transporters

(continued)

– MDR1 but not BCRP mRNA expression correlated with the probability of EFS – Frequency of high BCRP expression low compared with expression of MDR1, MRP1, and LRP

– 37% to 47% stained + by one antibody – BCRP function and mRNA levels correlated poorly with antibody staining – BXP-21 staining may predict shorter DFS – No codon 482 mutations found

– Higher BCRP expression and function in B-lineage ALL – No codon 482 mutations found

BCRP mRNA exp – Lower in infant ALL – Correlated with in-vitro ara-C resistance – ara-C not a BCRP substrate Concluded that MDR proteins are noncontributory to resistance of infant ALL

BCRP mRNA exp – Not of prognostic significance – Was not increased at relapse – Was lowest in T-ALL

– In 2 of 4 evaluable patients, BCRP expression increased during a 5-day IV infusion of topotecan and cytarabine

– Transporter expression in total blast population did not predict response – Nonresponders had higher BCRP and/or MDR1 expression in the CD34+/CD38− population

Impact of Breast Cancer Resistance Protein on Cancer Treatment Outcomes 263

Disease

CML

CML

CML

Lead Author, year, citation

Jordanides 2006 (151)

Jiang 2007 (152)

Wang 2008 (154)

Table 12.1 (continued)

70: 50 CP, 19 AP, 1 BC. All imatinib-naïve Peripheral blood

28: 18 CP, 8 AP, 2 BC, all imatinib-naive Peripheral blood, bone marrow

CCR, PFS, OS – Correlated with pretreatment exp of OCT1 – Did not correlate with Pgp or BCRP exp

CML stem cells (lin− CD34+CD38−) compared with more mature CML cells – Have >tenfold higher proliferation under low growth factor conditions – Produce IL-3 and G-CSF – Express Pgp and BCRP – Have low OCT1 expression – Have higher expression and activity of p210BCR/ABL – Resist killing by imatinib Conclusion: CML stem cells have multiple innate resistance mechanisms to imatinib

mRNA (qRT-PCR)

mRNA (qRT-PCR)

– Functional BCRP is expressed in CD34+ CML cells – BCRP expression in CD34+ normal hematopoietic cells is less than in CML (2 patients) – BCRP does cause imatinib resistance in CD34+ CML cells – Imatinib is an inhibitor of but not a substrate for BCRP in CD34+ CML cells

Findings/Conclusions

mRNA (qRT-PCR); FA

Parameter, Methodology

7, newly diagnosed LPB from chronic phase patients at diagnosis, preRx

Number studied

264 Ross and Nakanishi

CML

11 CP

FA-Dasatinib and imatinib IUR

– Dasatinib is a substrate for BCRP and Pgp – Dasatinib cellular uptake not affected by OCT1

AML acute myelogenous leukemia, ALL acute lymphoblastic leukemia, CML chronic myelogenous leukemia, RT-PCR reverse transcription polymerase chain reaction, qRTPCR quantitative real time RT-PCR, IHC immunohistochemistry, MoAb monoclonal antibody, exp expression, diff difference, FC flow cytometry, FA functional assay, preRx pretreatment, CR complete remission, Rel relapse, RD refractory disease, CCR complete cytogenetic response, PFS progression-free survival, OS overall survival, MRD minimal residual disease, LSC leukemia stem cell, HSC hematopoietic stem cell, CP chronic phase of CML, AP accelerated phase of CML, BC CML in blast crisis, LPB leukapheresed peripheral blood, IUR intracellular uptake and retention

Hiwase 2008 (155)

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et al. (123) evaluated 149 AML patients prior to treatment, using quantitative RT-PCR to detect BCRP mRNA and functional assays to detect Pgp. In contrast to the Abbott study (122), these investigators found that BCRP mRNA expression was a prognostic factor for achieving a complete remission. Furthermore, they found that high BCRP mRNA expression correlated with lower disease-free survival and OS. Patients with both high BCRP mRNA and high Pgp function had the worst prognosis (123). In a follow-up to their initial study of 149 AML patients, Benderra et al. studied an additional 85 de novo AML patients at time of diagnosis for the protein expression and function of Pgp, MRP1, MRP2, MRP3, MRP5, and BCRP (124). The expression or function of BCRP, Pgp, and MRP3 were found to correlate with complete response (CR) rate, and OS. In patients with high expression of two or more of these transporters, lower rates of CR, disease-free survival (DFS), and OS were seen. A study conducted by Uggla et al. on 40 mostly de novo AML patients (median age 57 years) found no effect of BCRP mRNA expression on CR rate, but BCRP expression was associated with shorter OS (125). With the advent of antibodies to BCRP, studies began to appear relating BCRP protein expression to clinical outcome. A study conducted by Sargent et al. on BCRP protein expression in 20 adult AML patients by Sargent et al., using the newly produced BXP34 antibody and immunohistochemical methods, found that BCRP expression correlated with in vitro blast cell sensitivity to daunorubicin, but not to mitoxantrone or doxorubicin (126). However, no difference in BCRP expression was found between samples from chemotherapy naïve and previously treated patients (126). Since studies of AML patients to date find the wild-type sequence at codon 482 (described above (116–118)), this finding is a bit puzzling, since the native R482 BCRP protein is generally thought to efflux mitoxantrone more efficiently than daunorubicin (62). Using the BXP21 and BXP34 antibodies and functional assays, van der Kolk et al. studied 20 paired AML blast cell specimens obtained at diagnosis and at time of relapse or resistant disease (127). In this study, BCRP protein expression did correlate with the functional assays, and was found in subpopulations of cells with an immature phenotype (CD34+); however, there was no increase in these BCRP-positive subpopulations at time of relapse, leading the authors to conclude that “BCRP was not consistently upregulated in relapsed/refractory AML.” If one considers the possibility that these subpopulations may represent leukemia stem cells, it would not be necessary for the population to expand at the time of relapse, but merely persist. A somewhat larger study of paired samples was published the next year by van der Pol et al. wherein 45 AML patients were studied at diagnosis

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and at time of relapse or development of minimal residual disease (MRD) using functional assays and the BXP34 and BXP21 antibodies to detect BCRP protein expression (128). No increase in BCRP, Pgp, or MRP1 expression or function was found in the relapse samples or at time of MRD. Unlike the study by van der Kolk (127), these investigators did not find that BCRP expression was confined to subpopulations of resistant cells (128). A study by Suvannasankha et al. investigated pretreatment blast cells from 31 AML patients by measuring BCRP mRNA using real-time RT-PCR, BCRP function, and BCRP protein using flow cytometry and three antibodies to BCRP available at the time (BXP21, BXP34, and 5D3) (118). In cell line controls, expression of BCRP was concordant by all assays employed; however, in the patient samples, BCRP mRNA expression correlated poorly with BCRP protein expression and function. BCRP expression in patient samples was found only in small subpopulations of cells. All mRNA in patient samples had the wild-type sequence at codon 482. It was concluded that the discordance of assays in the patient samples reflected complex biology of BCRP in AML that was not reflected by cell lines. Indeed, a number of biological processes have recently come to light, which may influence the activity– protein ratio of BCRP in living cells. In multidrug resistant human prostate cancer cell lines, the Pim-1 L kinase is upregulated along with BCRP and phosphorylates the threonine of BCRP at codon 362, which results BCRP transporter activation by multimerization of BCRP and translocation of BCRP to the plasma membrane (129). Monomeric or cytoplasmic BCRP may possibly be detected by flow cytometry and internal epitope-recognizing antibodies such as BXP21 or BXP34. Similarly, in murine systems, AKT/PI3K pathway signaling is necessary for BCRP translocation to the plasma membrane, and hence for transporter activity (130). In human CML cells, however, inhibition of AKT/PI3K signaling results in posttranscriptional down regulation of BCRP protein expression (131). Hence, the impact of BCRP phosphorylation by Pim-1 L kinase or oncogenic signaling such as the AKT/PI3K pathway may need to be evaluated in future studies of BCRP expression and activity in human cancers. More recent studies of BCRP expression in AML seem to confirm the notion that BCRP is often coexpressed with Pgp, and connotes a worse prognosis (132–136); furthermore, BCRP and Pgp expressions appear to be associated with subpopulations of cells with primitive characteristics such as expression of CD34 but not CD38 (133, 136–138), although two studies that measured CD34 expression (but not CD38) did not find a statistically significant correlation of BCRP expression with that of CD34 (124, 139). Coexpression of Pgp with BCRP was found in a study 73 consecutive patients with de novo AML and normal karyotype

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by Damiani et al. (132). High expression of BCRP protein was found in approximately one third of these patients. BCRP expression was associated with an increased risk of relapse and lower DFS. A study conducted by van den Heuvel-Eibrink on the expression of BCRP and MDR1 mRNA in 154 previously untreated de novo or secondary AML patients over the age of 60 found a high degree of BCRP and MDR1 mRNA coexpression (136). BCRP and particularly MDR1 mRNA expression correlated with CD34 expression. The most significant poor prognostic indicator was MDR1/BCRP coexpression, which was associated with a lower CR rate. A number of recent studies evaluated BCRP expression in AML blast cell subpopulations with primitive characteristics. Raaijmakers et al. studied BCRP protein expression and function in blast cells from 21 AML patients at the time of diagnosis, and compared these with hematopoietic cells in normal marrow (138). They found the highest BCRP expression and function in the CD34+/CD38− cells in both normal and AML marrows. Interestingly, the BCRP inhibitor Ko143 increased mitoxantrone accumulation but not cytotoxicity in the leukemic CD34+/CD38− cells, leading these investigators to conclude that “selective modulation of BCRP is not sufficient to circumvent resistance of leukemic CD34+/CD38− cells,” suggesting that factors in addition to BCRP may contribute to the drug resistance displayed by these cells. A recent study by Ho et al. evaluated the mRNA expression of the entire family of human ABC transporter genes in malignant blast cells from 18 AML patients who achieved CR and from 13 AML patients who were refractory to induction chemotherapy (135). No difference in ABC transporter expression was observed between the CR and refractory groups; however, when transporter expression was evaluated based on expression of CD34 and CD38, the nonresponders had significantly higher expression of BCRP and/or MDR1 mRNA in their CD34+/ CD38− cells. Finally, de Figueriedo-Pontes et al. isolated subsets of primitive cells from 26 de novo CD34+ AML cases (133). They found that “leukemia stem cells,” defined as CD34+/CD38−/ CD123+ cells, had higher expression of BCRP (BXP21 antibody) and Pgp than the other subsets, leading these authors to conclude that the presence of BCRP and Pgp in leukemia stem cells “reinforces that (multidrug resistance) is one of the mechanisms of treatment failure.” The expression of BCRP, Pgp, MRP1, or LRP did not differ among the various World Health Organization subgroups of AML (133, 140). ALL

Fewer studies have been done in ALL on the impact of BCRP on clinical outcomes than what have been done in AML. No definite trends correlating BCRP expression with prognosis have been found, although it appears that BCRP expression is highest in

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B-lineage ALL (117, 141). Two studies evaluated ALL blast cells for mutations at codon 482: no mutations were found (117, 142). A study by Sauerbrey et al. evaluated BCRP mRNA expression in 67 children with ALL, 47 at diagnosis, and 20 at relapse (141). BCRP mRNA was not increased in the specimens from the children in relapse. There was no relationship of BCRP mRNA expression with relapse-free survival in this study. Stam et al. studied BCRP mRNA and in vitro drug sensitivity in 13 infants and 13 children with ALL at the time of diagnosis (143). Surprisingly (since ara-C is not known to be a BCRP substrate), blast cell samples with high BCRP mRNA expression had the highest in vitro resistance to ara-C. Hence, BCRP may be a marker for ara-C resistance in this case, but not a direct cause of the resistance per se. A study by Kourti et al. found that MDR1 mRNA but not BCRP mRNA expression correlated with shorter eventfree survival in 49 newly diagnosed cases of childhood ALL; high BCRP mRNA expression was infrequent, compared to the mRNA expression of MDR1, MRP1, and LRP (144). Using three BCRP antibodies, Suvannasankha et al. studied thirty adult ALL cases for BCRP function, and mRNA and protein expression. A relatively high frequency of positivity of staining (37–47%) for each antibody was found; although there was poor concordance of antibody staining, mRNA expression, and functional assays in this study, positive staining with the BXP21 antibody was predictive of a shorter DFS (142). CML

Highly effective tyrosine kinase inhibitors (TKI) such as imatinib and dasatinib that are relatively selective for the chimeric BCRABL tyrosine kinase crucial for CML pathogenesis have revolutionized the current treatment for CML. Unfortunately, molecular CRs with imatinib treatment occur in only 35% of chronic phase patients, casting doubt on whether such patients can be cured by TKI therapy alone. It is known that chronic phase CML samples contain quiescent, Philadelphia chromosome positive stem cells that are resistant to imatinib (145). Since normal hematopoietic and certain other stem cells can be identified by their ability to exclude Hoechst 33342 dye by means of expression of BCRP and perhaps other ABC transporters (73), it is reasonable to hypothesize that BCRP expression in CML stem cells may cause efflux of imatinib and hence may be a reason for failure to obtain molecular remissions of CML with TKI therapy (Fig. 12.1). A number of studies suggest that imatinib may be a substrate for and/or inhibitor of BCRP (63, 64, 146–149), although one study did not find resistance to imatinib in osteosarcoma cells transfected to express BCRP (149). Studies in our own laboratory using K562 CML cells transduced to overexpress BCRP found that the BCRPexpressing cells were indeed resistant to imatinib, but to a lesser

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Fig. 12.1. CML stem cell hypothesis. Primitive, self-renewing Philadelphia chromosome positive CML stem cells express BCRP and possibly other mechanisms of resistance to TKI treatment (indicated by granular surface of stem cells), but the more mature CML progenitor cells remain sensitive to TKIs. TKI treatment would result in death of the CML progenitor and mature cell populations, but persistence of the TKI-resistant stem cell population resulting in hematologic (and possibly cytogenetic) CR but not a molecular CR. Acquisition of TKI resistance mutations of BCR-ABL by the stem cell or early progenitor compartment would then result in relapse of the chronic phase, and possibly the appearance of the accelerated or blastic phase.

extent than to mitoxantrone, a well-known substrate for BCRP (131). Imatinib resistance in BCRP-transduced K562 cells was confirmed by Brendel et al. who also found that BCRP-transduced K562 cells were resistant to nilotinib, a novel inhibitor of BCRABL, and were protected from imatinib- and nilotinib-induced downregulation of CRKL phosphorylation (150). Given the CML stem cell hypothesis (Fig. 12.1), a crucial question is whether functional BCRP is expressed in Philadelphia chromosome positive imatinib resistant CML stem cells, and whether BCRP is responsible for the imatinib resistance in these cells. This was investigated by Jordanides et al. who studied CD34+ CML mononuclear cells obtained by leukapheresis from seven chronic-phase CML patients and found that functional BCRP was expressed in these cells; however, BCRP did not cause imatinib resistance in these stem cells. Instead, imatinib was found to be an inhibitor of and not a substrate for BCRP in these cells, since the BCRP inhibitor FTC enhanced mitoxantrone accumulation but not imatinib accumulation in these CML stem cells (151). The multidrug resistance phenotype of CML stem cells was investigated in lin−/CD34+/CD38− cells collected from 28 CML patients prior to imatinib treatment by Jiang et al. (152).

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Compared with more mature CML cells, the CML stem cells had a higher proliferative capacity, produced IL-3 and G-CSF, expressed Pgp and BCRP, had low expression of human organic cation transporter 1 (OCT1, the transporter for cellular uptake of imatinib), higher expression and activity of the p210BCR/ABL protein, and resisted killing by imatinib. These authors concluded that CML stem cells have multiple mechanisms of resistance to imatinib. Low OCT1 activity in CML cells has been reported to result in a suboptimal response to imatinib (153). High expression of OCT1 mRNA in cells from pretreatment peripheral blood of 70 CML patients was found by Wang et al. to predict a higher complete cytogenetic response rate, longer DFS and OS in response to imatinib treatment compared with patients with low OCT1 mRNA expression (154). These authors found that pretreatment expression of MDR1, BCRP, or MRP1 mRNA did not correlate with imatinib therapeutic outcome; however, these measurements were not made in the CML stem cell compartment. Nevertheless, the authors concluded that “the expression of OCT1, but not efflux transporters, is important in determining the clinical response to imatinib.” Using peripheral blood mononuclear cells from 11 CML patients in chronic-phase, Hiwase et al. found Dasatinib, a second-generation TKI that is effective against BCRABL mutations that cause resistance to imatinib, to be a substrate for Pgp and BCRP (155). In contrast to imatinib, the cellular uptake of Dasatinib was not found to be affected by OCT1 in these patient samples. 3.2.2. Solid Tumors, Lymphomas, and Myeloma

A synopsis of literature relating the expression and function of BCRP with outcomes in solid tumors is given in Table 12.2. Initial immunohistochemical studies with an anti-BCRP monoclonal antibody (BXP-34), using a panel of human tumors, showed BCRP to be low or undetectable except for one case of small intestine adenocarcinomas (156); however, subsequent investigation by the same investigators (Diestra et al.), using a newer monoclonal antibody (BXP-21) in formalin-fixed paraffinembedded specimens, demonstrated a high frequency of BCRP immunoreactivity among a panel of 150 untreated human solid tumors comprising 21 tumor types (157). These authors reported that BCRP expression “was seen in all tumor types, but it seemed more frequent in adenocarcinomas of the digestive tract, endometrium, lung, and melanoma” (157). In selected cases, the immunohistochemical data were validated by Western blots. The authors were careful to exclude BCRP in noncancerous tissues, such as the expression of BCRP in venules. As will be seen from the ensuing discussion, many human solid tumors such as lung and esophageal cancers and some lymphomas appear to express BCRP, and frequently, this expression is correlated with adverse prognostic significance. In contrast

Disease

21 tumor types

Aggressive mantle cell lymphoma

Mature T/NK lymphomas

Multiple myeloma

Multiple myeloma

Lead Author, year, citation

Diestra 2002 (157)

Galimberti 2007 (158)

Saglam 2008 (159)

Raaijmakers 2005 (160)

Turner 2006 (161)

mRNA (qRT-PCR) Protein (FC, antiABCG2 MoAb)

31 patients studied before, during, and at relapse after treatment including topotecan FA

Protein (BXP21 MoAb) FA

Protein -IHC (MoAb type not stated)

mRNA (qRT-PCR)

Protein -IHC (BXP-21 MoAb)

Parameter, Methodology

10 newly diagnosed patients Normal bone marrow

119, archival review

20, prior to treatment with R-hyperCVAD

150 untreated human solid tumors

Number studied

– BCRP mRNA and protein expression was higher after treatment or at relapse compared to pretreatment – BCRP promoter was methylated in pretreatment samples

BCRP protein and functional expression higher in normal plasma cells than in myeloma plasma cells

– Relatively high BCRP expression (3-4+) in 78% of samples – MRP1, Pgp, LRP also frequently expressed

BCRP expression – Associated with worse PFS – Correlated with MRD status

Widespread expression of BCRP/ABCG2 in a variety of untreated human solid tumors

Findings/Conclusions

Table 12.2 Expression and consequences of BCRP/ABCG2 in human solid tumors, lymphomas, and myeloma

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Hepatoblas toma

Testicular tumors

Ovarian Cancer

Esophageal squamous cell carcinoma

Locally Advanced Bladder Cancer

NSCLC

Vander Borght 2008 (162)

Bart 2004 (163)

Nakayama 2002 (174)

Tsunoda 2006 (164)

Diestra 2003 (175)

Kawabata 2003 (165)

mRNA (qRT-PCR) Protein -IHC (BXP-21 MoAb) FA

(continued)

22% of lung cancer samples expressed relatively high expression of BCRP

– High levels of BCRP expression found in 28% of the cases – BCRP expression had no prognostic impact – Pgp expression correlated with shorter PFS, but not with OS

Protein (IHC)

83 patients treated with neoadjuvant chemotherapy

23

– BCRP detectable in 61% of cases – Detectable BCRP expression correlated with poorer OS following surgery – BCRP expression an independent prognostic factor

mRNA (qRT-PCR) (N = 33) Protein – IHC, BXP-21 MoAb (N = 100)

100 paraffin sections; 33 frozen tissues. Treatment varied; about 30% rec’d platinum based regimens

Pgp, BCRP expression in germ-cell and testicular lymphomas may contribute to chemoresistance

Protein -IHC (BXP-21 MoAb)

– BCRP expression was not a prognostic indicator – Copper transporter ATP7B expression correlated with poor prognosis

BCRP (but not Pgp) expression increased in all posttreatment samples; BCRP expression highest in areas of hypoxia

Protein -IHC (BXP-21 MoAb)

mRNA (RT-PCR)

82 sampled before cisplatinbased therapy

10 Nonseminoma 10 seminoma 9 testicular lymphoma 10 normal

7 patients sampled pretreatment and at tumor resection, posttreatment with cisplatin, carboplatin and/or doxorubicin

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Disease

NSCLC

NSCLC

NSCLC

NSCLC

Lead Author, year, citation

Yoh 2004 (166)

Chen 2008 (170)

Surowiak 2008 (169)

Ota 2008 (167)

Table 12.2 (continued)

156 stage IV patients, prior to platinum-based therapy

32 stage IIIB and IV

10, stages IA to IIIB

72 with stage IIIb or IV disease, prior to platinum-based treatment regimens

Number studied

Protein -IHC

Protein -IHC

Protein -IHC (MoAb type not stated)

Protein -IHC (BXP-21 MoAb)

Parameter, Methodology

High BCRP expression correlated with lower OS, but showed no response to chemotherapy

No prognostic or predictive significance of BCRP expression. BCRP expression correlated with expression of COX2 and Pgp

Isolated CD133+ and CD133− LC cells LC-CD133+ cells had – Stem cell properties, with greater proliferative and tumorigenic capacity – High expression of BCRP – Resistance to chemo and radiotherapy – Dependence on the Oct-4 transcription factor for stem cell phenotype

BCRP expression associated with – Lower response rate – Shorter OS, PFS – Independent prognostic variable for PFS MRP2 expression associated with lower OS No association of Pgp, MRP1, or MRP3 expression with response to treatment or OS

Findings/Conclusions

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NSCLC SCLC

SCLC

Colon Cancer

Colon Cancer Dukes C

Colon Cancer

Melanoma; Neuroendocrine (Merkel) carcinoma

Melanoma

Muller 2009 (112)

Kim 2008 (168)

Candeil 2004 (171)

Glasgow 2005 (173)

Gupta 2006 (172)

Deichmann 2005 (178)

Monzani 2007 (180)

mRNA (qRT-PCR)

(continued)

Human melanomas were found to contain cancer stem cells with enhanced tumorigenic potiential (when transplanted into NOD-SCID mice). These stem cells express CD133, ABCG@/BCRP, notch 4 and other stem cell markers

– BCRP mRNA not higher in melanomas compared to acquired melanocytic nevi – BCRP protein not detected by IHC in melanomas – 3 of the neuroendocrine tumors showed some positive immunostaining for BCRP mRNA (qRT-PCR) Protein, IHC (BXP21 MoAb)

66 melanomas (RNA extracted from 18) 29 neuroendocrine carcinomas of the skin

7 skin biopsies

– Lower BCRP expression in cancers compared to normal tissues

mRNA (qRT-PCR, northern blots) Protein, IHC (antiABCG2 MoAb)

– 13 colon cancer – 1 hepatic met. from colon cancer Prior to any treatment

BCRP mRNA expression is higher in metastases after irinotecan treatment than in metastases from irinotecan-naïve patients

BCRP expression correlated with poorer response and shorter PFS

Worse OS seen BCRP 421A hetero- or homozygotes in patients treated with platinum-based chemotherapy

– No difference in BCRP mRNA expression between mucinous and nonmucinous tumors – Mucinous tumors overexpressed markers of resistance to 5-FU and oxaliplatin

mRNA (qRT-PCR)

Protein -IHC

mRNA (qRT-PCR), melting curve analysis

mRNA (qRT-PCR)

21 mucinous 30 non-mucinous

42 patients; Liver metastases sampled pre- and post- irinotecan treatment

130 prior to platinum-based therapy

187 NSCLC 161 SCLC 1 mixed

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Childhood and adult CNS tumors

Retinoblastoma

Breast Cancer

Breast Cancer

Wilson 2006 (177)

Kanzaki 2001 (181)

Faneyte 2002 (182)

Disease

Valera 2007 (176)

Lead Author, year, citation

Table 12.2 (continued)

25 chemotherapy naïve 27 after anthracycline treatment

43 untreated patients 38 subsequently received anthracycline-based adjuvant therapy after surgery

16 patients after primary enucleation

21 astrocytoma I and II 7 astrocytoma III 21 glioblastomas 17 medulloblastomas; 8 ependymomas; 6 oligodendroglioma

Number studied

BCRP mRNA expression – varied greatly among tumor specimens – not associated with decreased response or survival – no different between chemotherapy naïve and treated groups BCRP protein (IHC) – detected in blood vessels and normal breast epithelium – not detected in breast cancer tumor cells

– BCRP mRNA expression low, with little variability compared to the other genes studied – BCRP mRNA expression did not correlate with the expression of the other genes

mRNA (semi-quantitative RT-PCR) for BCRP, MDR1, MRP1 and LRP

mRNA (qRT-PCR, northern blots) Protein, IHC (BXP34 and BXP21 MoAbs)

– BCRP was detected in vascular endothelium but not in tumor cells in all of the specimens studied

– Higher MDR1 and BCRP mRNA expression in glial tumors than in embryonic tumors – No impact of transporter expression on OS of medulloblastomas or high grade gliomas following multimodality treatment

Findings/Conclusions

Protein: Tissue microarray IHC (BXP-21 MoAb)

mRNA (qRT-PCR) of microdissected samples

Parameter, Methodology

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Breast Cancer

Breast Cancer

Fazeny-Dorner 2003 (192)

Park 2006 (184) 21 pretreatment biopsies prior to neoadjuvant chemotherapy

4 cases following neoadjuvant chemotherapy

59 primary tumor samples obtained prior to chemotherapy

mRNA expression profiling of ABC transporters (GeneChip®)

CGH, cytogenetics; BCRP mRNA, protein or function was not studied

mRNA (qRT-PCR) for BCRP, LRP, MRP1, MRP2 and MDR1

– BCRP/ABCG2 not mentioned as a gene being differentially expressed in responders versus resistant disease group

– 2 patients had amplification in the long arm of chromosome 4 (4q22), the region harboring BCRP

Overall response to anthracycline-based therapy – Significant inverse correlation with MDR1 mRNA expression – Trend to inverse correlation with BCRP mRNA expression – Stronger negative correlation between BCRP mRNA expression and response rate or PFS in patients treated with anthracyclines-based regimens than those treated with CMF

IHC immunohistochemistry, PFS progression-free survival, OS overall survival, MRD minimal residual disease, SCLC small cell lung cancer, NSCLC nonsmall cell lung cancer, preRx pretreatment, LC lung cancer, COX-2 cyclooxygenase-2, Pgp p-glycoprotein, the product of the MDR1 gene, MDR1 multidrug resistance gene 1, MRP1 multidrug-resistance associated protein-1, MRP2 multidrug-resistance associated protein-2, LRP lung resistance protein, 5-FU 5-fluorouracil

Breast Cancer

Burger 2003 (183)

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to the study by Diestra et al., low expression of BCRP was found in malignant melanoma. Furthermore, little or no prognostic value of BCRP expression was found in ovarian cancer, locally advanced bladder cancer, colon cancer (some reports), and ironically, breast cancer. Because of the nature of solid tumor specimens, many of these studies did not evaluate BCRP expression in subpopulations such as the side population; however, when such correlations were made (e.g. in the case of melanoma), BCRP expression was found in subpopulations with enhanced selfrenewal capacity, often with coexpression of CD133, a putative stem cell marker. Such delineations may be important to perform in future studies relating BCRP expression to clinical outcome. Galemberti et al. studied BCRP mRNA expression in 20 patients with aggressive mantle cell lymphoma prior to treatment, using qRT-PCR (158). BCRP mRNA expression correlated with worse DFS, finding minimal residual disease. In mature T/NK lymphomas, Sagalam et al. evaluated 119 archival cases using immunohistochemistry (IHC) (159). Relatively high BCRP staining was seen in most of the samples; expression of Pgp, MRP1, and LRP was also seen frequently. Expression of these MDR proteins was not related to survival in this study. In multiple myeloma, a study of 10 newly diagnosed patients prior to treatment by Raaijmakers et al. found that BCRP function and protein expression were lower in multiple myeloma plasma cells compared with bone marrow plasma cells obtained from normal donors (160). A study of 31 multiple myeloma patients by Turner et al. confirmed low expression of BCRP expression in pretreatment samples, in which the BCRP promoter was found to be methylated (161). In contrast, BCRP mRNA and protein expression were higher after treatment or at time of relapse following a topotecan-based treatment regimen. A study of BCRP protein expression in seven infants or children with hepatoblastoma by Vander Borght et al. found that BCRP expression (but not expression of Pgp, MRP1, MRP2, or MRP3) increased in all samples following treatment with a regimen containing cisplatin or carboplatin and/or doxorubicin (162). Interestingly, expression of BCRP was highest in areas of the tumor that appeared to be hypoxic, consistent with the known hypoxia response element in the BCRP promoter region (34, 66). BCRP expression was evaluated in a variety of testicular tumors by Bart et al., using IHC (163). BCRP and Pgp expression were found in germ cell tumors, testicular lymphomas, and newly formed tumor blood vessels, and “may contribute to chemoresistance.” BCRP mRNA and protein expression were found to be independent adverse prognostic indicators in esophageal squamous cell carcinoma in a study of 133 paraffin or frozen sections by

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Tsunoda et al. (164). Treatment in this study varied; approximately 30% received a platinum-based regimen. One of the solid tumors most extensively studied for BCRP expression is lung cancer, particularly NSCLC. In 2003, BCRP expression in lung tumors was confirmed by Kawabata, who used qRT-PCR to detect high levels of BCRP in 6 of 8 nonsmall cell lung cancer cell lines and 5 of 23 (22%) of nonsmall cell lung tumor tissues tested (165). Topotecan efflux in the lung cancer cell lines correlated with the levels of BCRP mRNA expressed. The following year, Yoh et al. published a study of BCRP protein expression in 72 NSCLC patients studied prior to treatment with platinum-based regimens (166). BCRP expression was found to correlate with lower response rate and shorter OS, and was an independent prognostic variable for DFS. In contrast, the study by Ota et al., using IHC in 156 stage IV NSCLC patients prior to treatment with platinum containing regimens, on BCRP protein expression did not find a correlation of BCRP expression with response rate; however, there was a correlation of high BCRP expression and lower OS (167). In SCLC, Kim et al. found a correlation between BCRP protein expression by IHC and poor response and shorter DFS in 130 patients studied prior to treatment with platinum-containing regimens (168). In contrast to these studies, a recent publication by Surowiak et al. found no prognostic or predictive significance of BCRP protein expression (IHC) among 32 patients with stage IIIB or IV NSCLC (169). In this study, BCRP expression correlated with that of cyclooxygenase-2 and Pgp. A recent study by Chen et al. found evidence for the presence of subsets of cells with stem-cell properties in NSCLC, based on CD133 expression in tumor tissue samples obtained from ten patients (170). The lung cancer CD133+ cells had stem cell properties, with greater proliferative and tumorigenic capacity, high expression of BCRP, resistance to chemo and radiotherapy (including cisplatin, etoposide, doxorubicin and paclitaxel), and a dependence on the Oct-4 transcription factor for stem cell phenotype. The drug resistance profile of these cells includes drugs that are not classical substrates for BCRP (cisplatin, etoposide, paclitaxel). Irinotecan and its active metabolite SN-38 are known substrates for BCRP. A study of clinical biopsy samples of hepatic metastases from 42 patients with colon carcinoma by Candeil et al. demonstrated almost a tenfold increase in BCRP mRNA in metastases obtained postirinotecan treatment compared to metastases obtained pretreatment, or posttreatment with non-BCRP substrate drugs (171). Despite high expression of BCRP in the apical surface of normal small bowel and colon epithelial cells, pretreatment specimens of colon cancer and a hepatic metastasis had lower BCRP mRNA and protein expression compared with normal colon in a study of 13 patients by Gupta et al. (172).

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Similarly, in 145 paired samples, down-regulation of BCRP expression was seen in cervical carcinoma by these investigators, and also in cancers from breast, lung, ovary, kidney, liver, uterus, rectum, thyroid, testis, and small intestine, compared with normal tissue counterparts. Glasgow et al. studied BCRP mRNA expression in 21 mucinous compared with 30 nonmucinous Dukes C colon cancer specimens (173) Although mucinous tumors have a worse prognosis than nonmucinous ones, there was no difference in BCRP mRNA expression between mucinous and nonmucinous tumors; however, mucinous tumors overexpressed markers of resistance to 5-FU and oxaliplatin in this study. Together, the above studies imply that BCRP expression in colon carcinoma is low prior to treatment, but may increase following treatment with BCRP substrate drugs. A number of studies do not find a relationship with BCRP expression and adverse outcome in human solid tumors. Nakayama et al. sampled tumor from 82 patients with ovarian cancer prior to cisplatin-based therapy (174). BCRP mRNA expression was not found to be a prognostic indicator. Diestra et al. studied 83 patients with locally advanced bladder cancer treated with neoadjuvant therapy; although high levels of BCRP protein expression were found in 28% of the cases, BCRP expression had no prognostic impact (175). BCRP mRNA expression in microdissected samples of childhood and adult CNS tumors were investigated by Valera et al. (176). Higher BCRP and MDR1 mRNA expression was found in glial tumors compared with embryonic tumors, but no impact of transporter expression was found on OS of medulloblastomas versus high grade gliomas following multimodality treatment. Wilson et al. used tissue microarrays and IHC to study tumor from 16 patients with retinoblastoma after primary enucleation (177). BCRP was detected in vascular endothelium but not in tumor cells in all the specimens studied. Although the early study by Diestra et al. (157) found BCRP expression in melanoma cells, a subsequent paper by Deichmann et al. found no expression of BCRP protein by IHC in biopsy specimens from 66 melanoma patients (178). Though Deichmann et al. concluded that chemoresistance of melanomas cannot be explained by BCRP expression, subsequent work (e.g. Monzani et al. and others (179, 180)) has found that a subset of CD133+/ABCG2+ cells is present in melanoma specimens, and that this subset expresses angiogenic and lymphangiogenic markers and has increased tumorigenicity when transplanted into NOD-SCID mice. Despite its original isolation from multidrug resistant human breast cancer cells, the level of BCRP expression in clinical breast cancer cases appears low, at least in the “whole cell” population: Kanzaki et al. studied BCRP, MDR1, MRP1, and LRP mRNA expression in breast cancers form 43 patients prior to treatment (181). Thirty eight of these went on to receive anthracycline-

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based adjuvant therapy following surgery. BCRP expression was low, had little variability compared to the other drug resistance markers studied, and did not correlate with the epression of MDR1, MRP1, or LRP. BCRP mRNA and protein expression in breast cancers from 25 chemotherapy naïve patients and 27 patients biopsied following anthracycline treatment were reported by Faynette et al. (182). These investigators found that although BCRP mRNA expression varied greatly among tumor specimens, it was not associated with decreased response or survival. There was no difference in BCRP expression between chemotherapy naïve and treated groups. BCRP protein (assessed by IHC) was detected in blood vessels and normal breast epithelium, but was not detected in breast cancer tumor cells. Burger et al. studied BCRP, MDR1, MRP1, MRP2, and LRP mRNA expression in 59 primary breast tumor samples obtained prior to chemotherapy and found a significant inverse correlation with MDR1 mRNA expression but only a trend to inverse correlation with BCRP mRNA expression with the overall response to anthracyclinebased therapy (183). There was a stronger negative correlation between BCRP mRNA expression and response rate or DFS in patients treated with anthracycline-based regimens compared with those patients treated with CMF (cytoxan, methotrexate, fluorouracil). A study by Park et al. of mRNA expression profiling in 21 pretreatment breast biopsies prior to neoadjuvant chemotherapy did not mention BCRP among the genes differentially expressed in responders versus nonresponders (184). A number of caveats should be noted in interpreting the above studies of BCRP expression in breast cancer. Some of the patients enrolled in these studies may have experienced antiestrogen treatment, which may affect BCRP expression: recent studies indicate the presence of an estrogen response element in the BCRP promoter, and that antiestrogens such as tamoxifen can oppose transcriptional activation of BCRP expression by estrogen (185). Hence, future studies of BCRP in breast cancers should take into account treatment with antiestrogens at the time of tissue biopsy. Furthermore, SP cells with high drug efflux capacity have been observed in breast cancer and other solid tumors (186), implying a possible role of BCRP and possibly other ABC transporters in breast cancer resistance. 3.3. BCRP Expression as a Manifestation of the Activity of Metabolic and Signaling Pathways That Impart a Poor Prognosis to Cancers

Thirty years ago, the discovery that a membrane ABC transporter (e.g., Pgp) could cause resistance to multiple drugs raised the vain hope that such a transporter could be the sole cause of clinical multidrug resistance. It is now becoming apparent that BCRP and Pgp expression in human cancers could occur as a component of a much larger cancer cellular orchestration of evolution, through acquired genomic instability, toward a genotype of perpetual immortality, proliferation, invasion, and resistance

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to programmed cell death. For example, Chiou et al. found that BCRP was upregulated in oral cancer stem-like cells, along with other prosurvival genes – Oct-4, CD133, CD117, and Nanog (187). BCRP in cancer SP cells (and possibly other cancer stem cells) might aid these cells from evading damage from BCRP substrate drugs; furthermore, via its transport of protoporphyrin IX, BCRP may help cells evade damage because of the reactive oxygen species generated by hypoxia and perhaps other cellular damaging agents (34, 36, 66, 188). It is likely that certain cells with self renewal capacity do not overexpress BCRP, since one study found that BCRP+ and BCRP− cells are similarly tumorigenic (189). BCRP may be just one factor in a host of other determinants of resistance, and inhibition of BCRP alone will probably not be sufficient to sensitize cells with multiple additional resistance mechanisms in place, consistent with the argument made by Raaijmakers et al. that despite showing BCRP overexpression in leukemia stem cells, “selective modulation of BCRP is not sufficient to circumvent resistance of leukemic CD34+/38− cells” (138). There is considerable evidence that BCRP is upregulated along with other drug resistance genes: Wilson et al. evaluated 170 elderly AML patients before treatment, using gene expression profiling (190). They identified six clusters of genes that varied in disease outcome parameters. The highest rate of resistant disease was found in patients expressing the cluster, which contained BCRP and MDR1. Hence, BCRP expression – along with the expression of other genes marking resistant, self-renewing cancer stem- or initiating-cells – may help lead us to the isolation and characterization of resistant, cancer perpetuating subpopulations, and to discover their unique vulnerabilities, since there will be a need to focus on defeating cancer stem cells without severe damage to normal stem cells. References 1. Ross DD, Doyle LA, Schiffer CA et al (1996) Expression of multidrug resistance-associated protein (MRP) mRNA in blast cells from acute myeloid leukemia (AML) patients. Leukemia 10:48–55 2. Chen YN, Mickley LA, Schwartz AM et al (1990) Characterization of adriamycin-resistant human breast cancer cells which display overexpression of a novel resistance-related membrane protein. J Biol Chem 265:10073–10080 3. Doyle LA, Yang W, Abruzzo LV et al (1998) A multidrug resistance transporter from human MCF-7 breast cancer cells. Proc Natl Acad Sci U S A 95:15665–15670 4. Allikmets R, Schriml LM, Hutchinson A, Romano-Spica V, Dean M (1998) A human pla-

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Chapter 13 Drug Ratio-Dependent Antagonism: A New Category of Multidrug Resistance and Strategies for Its Circumvention Troy O. Harasym, Barry D. Liboiron, and Lawrence D. Mayer Abstract A newly identified form of multidrug resistance (MDR) in tumor cells is presented, pertaining to the commonly encountered resistance of cancer cells to anticancer drug combinations at discrete drug:drug ratios. In vitro studies have revealed that whether anticancer drug combinations interact synergistically or antagonistically can depend on the ratio of the combined agents. Failure to control drug ratios in vivo due to uncoordinated pharmacokinetics could therefore lead to drug resistance if tumor cells are exposed to antagonistic drug ratios. Consequently, the most efficacious drug combination may not occur at the typically employed maximum tolerated doses of the combined drugs if this leads to antagonistic ratios in vivo after administration and resistance to therapeutic effects of the drug combination. Our approach to systematically screen a wide range of drug ratios and concentrations and encapsulate the drug combination in a liposomal delivery vehicle at identified synergistic ratios represents a means to mitigate this drug ratio-dependent MDR mechanism. The in vivo efficacy of the improved agents (CombiPlex formulations) is demonstrated and contrasted with the decreased efficacy when drug combinations are exposed to tumor cells in vivo at antagonistic ratios. Key words: Multidrug resistance, Synergy, Antagonism, Ratiometric, Drug delivery, Liposomes, Drug screening, Median effect analysis

1. Introduction Combination chemotherapy has been the cornerstone of cancer therapy for over 40 years. Improvements in outcomes for childhood leukemia highlight how development of combination treatments has led to dramatic increases in efficacy over single agents. From response rates of 40% and no cures with methotrexate alone, greater than 95% complete response and 75–80% cure rates could be achieved when methotrexate was administered in combination with J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_13, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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asparaginase, daunorubicin, and cytarabine (1, 2). Yet in contrast to these advancements in leukemia, achieving a cure for the majority of solid tumors remains elusive (3). Response rates of pancreatic, esophageal, and recurrent ovarian cancer are still below 20% despite the massive efforts and resources that have been expended to develop superior combination therapies for these indications (4). The failure of many combinations to achieve complete remissions in the majority of cases is frequently attributed to multidrug resistance (MDR), a phenomenon that allows tumor cells to survive and/or flourish under considerable challenge from exogenous cytotoxic agents. Much attention has been paid to identified resistance factors such as P-glycoprotein (Pgp) and multidrug resistance-associated protein (MRP), altered apoptosis mechanisms as well as modifications in enzyme activity (e.g., topoisomerase II, glutathione-Stransferase) in an attempt to elucidate and mitigate these mechanisms in order to improve efficacy (5–8). Efforts to specifically target and neutralize these mechanisms, however, have been largely ineffective (9, 10). Thus, development of new treatments, identification of synergistic drug interactions, and refinement of treatment protocols remain the main strategies for mitigating the effects of MDR and improving the treatment outcomes of cancer patients.

2. Drug–Drug Antagonism: A New Form of MDR

The discovery of favorable drug–drug interactions in combination chemotherapy remains an active field of basic and clinical research. Some researchers have successfully combined agents with different mechanisms of action in which multiple sites in biochemical pathways are attacked, resulting in synergy (11), while others have demonstrated synergy by combining agents that target the same pathway(s) (12–14). Identification and characterization of synergistic drug interactions therefore remains a very active area of research and has resulted in the introduction of several new combination chemotherapies in the past decade (15–18). Intrinsic in the discovery of drug:drug synergy, yet often ignored in these studies, is the identification of drug:drug antagonism, in which the efficacy of the two agents is impaired such that the cytotoxicity of the combined agents is less than what would be expected for the additive activities of the individual agents (19, 20). Extolling the discovery of drug–drug synergy while ignoring the existence of drug–drug antagonism under certain conditions for a given combination can, in fact, lead to compromised efficacy. Exposure of tumor cells to two drugs in combination at a certain ratio and concentration can lead to one of three outcomes: synergistic,

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additive, or antagonistic activity. Thus, while the literature is quick to identify drug combinations that have augmented activity in vitro, little attention is paid to whether the conditions used in vitro (e.g., drug ratio and concentration) are pharmacologically relevant in vivo. Consequently, when synergy is observed, this represents ratios at which tumor cells are susceptible to the combination, but historically identification and avoidance of antagonistic ratios at which tumor cells are resistant to the combined agents is largely ignored. From an empirical perspective, drug:drug antagonism is a form of MDR in that the cellular response is inexplicably less than what would be minimally expected (i.e., additivity); it affects multiple structurally disparate chemotherapeutic agents and it is known to occur across a wide variety of tumor types. For reasons likely related to the interconnected nature of pathway biochemistry and cellular biology, a particular cell line may be less susceptible to a specific drug combination presented at a certain ratio, yet this resistance mechanism can frequently be bypassed through exposure of the same drugs at a different ratio. Further complicating this phenomenon is that some studies reveal that activity of a particular drug ratio may also be concentration-dependent; exhibiting synergy at one total drug concentration and producing additive or antagonistic results at other concentrations. Since drug concentrations decrease over time after in vivo administration, it is important to identify and utilize ratios of drugs that behave synergistically over a broad range of drug concentrations, while avoiding those combinations that exhibit either broad ratio or concentration-specific antagonism. While these ratios can be readily identified through a variety of drug screening techniques, the translation of such information from in vitro cytotoxicity studies to in vivo efficacy studies is difficult to achieve using the current treatment paradigm for antitumor combination chemotherapy. 2.1. Current Combination Therapy Development

The empiric process used to advance new combinations in the clinic has evolved little since the concept of combination chemotherapy was pioneered by Frei and coworkers in the 1960s (21). In this process, individual drugs in a combination are escalated to the maximum dose where aggregate toxicity is tolerable with the expectation that maximum therapeutic activity will be achieved at the maximum dose of each agent. Conventional combination therapy, however, frequently fails to account for disparate physiochemical properties of each drug component that will typically result in the rapid and independent distribution and elimination of each agent. Without a means to control the pharmacokinetics (PKs) of each drug, their unique clearance properties will lead to rapidly changing and uncontrolled drug:drug ratios after administration. Therefore, over a

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short period of time, combinations administered as an unbridled cocktail can be present at synergistic, additive, and/or antagonistic ratios if the combination exhibits drug ratio-dependent interactions. Consequently, failure to account for and control drug ratios in the application of drug combinations in vivo could lead to the exposure of tumor cells to antagonistic drug ratios, leading to empirical MDR and corresponding loss of therapeutic activity. Encouragingly, the role of synergistic and antagonistic drug interactions in the mitigation and emergence of MDR is gaining interest in the current literature for a variety of disease states (22–26). 2.1.1. Case Study: Gemcitabine and Cisplatin Pharmacokinetics

The combination of gemcitabine and cisplatin is instructive in highlighting the changes in plasma concentrations and drug ratios that occur following coadministration of the two drugs, using pharmacokinetic parameters presented in Table 13.1. Gemcitabine is infused first over a period of 30 min at a total dose of 1,250 mg/ m2, followed by a 1-h infusion of saline to minimize renal toxicity from cisplatin. Cisplatin is then administered at 100 mg/m2 in 1 L of saline over a 1-h infusion time. The average terminal half-lives of gemcitabine and cisplatin are 69 and 33 min, respectively (product monographs; Eli Lilly 1999, Mayne Pharma, 2003), which represents a 2.1-fold difference in plasma elimination rates. Using the aforementioned PK parameters, volumes of distribution (Table 13.1) and accounting for the differences in both administration start times and durations between gemcitabine and cisplatin, the drug concentrations and associated drug:drug ratios can be estimated. Upon completion of the cisplatin infusion,

Table 13.1 A typical treatment regimen using the combination of cisplatin and gemcitabine for advanced non-small cell lung cancer (NSCLC) Volume of distribution (L/m2)

Terminal half-life (~min)b

50

42–96

41 Prehydrate with 1,000 mL NS over 60 min then cisplatin IV in 1,000 mL NS over 60 min.

20–45

Drug dosea (mg/m2/day)

Administration

Gemcitabine

1,250 on days 1 and 8

IV in 250 mL of NS over 30 min.

Cisplatin

100 on day 1

Drug

Note: Gemcitabine is administered first a Drug dose and treatment schedule from BC Cancer Agency chemotherapy protocol (LUAVPG) available online at http://www.bccancer.bc.ca/HPI/ChemotherapyProtocols/Lung/default.htm b From BC Cancer Agency drug manual and Gemzar and Platinol-AQ product monographs

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the respective gemcitabine and cisplatin plasma concentrations are 20 and 2 mM, reflecting a 10:1 molar ratio. At 30 min after completion of the cisplatin infusion, only 50% of the postdistribution cisplatin concentration (~1 mM) and 72% of the postdistribution gemcitabine concentration (~14 mM) is expected to remain in the plasma. Hence, the rapidly decreasing plasma drug concentrations will quickly lead to levels that fall below effective cellular cytotoxic values for cisplatin (e.g., the concentration of drug required to kill 50% of target cells in vitro, IC50 value, for gemcitabine and cisplatin in a A549 human non-small cell lung cancer cell line is 0.007 and 4.2 mM, respectively), highlighting that increased drug elimination results in decreased cell kill fractions. Furthermore, given that approximately 50% of the cisplatin and 28% of the gemcitabine is eliminated from the plasma every 30 min postadministration, the gemcitabine:cisplatin ratio increases by 1.44-fold over that time period. Specifically, at t = 0 the molar ratio of gemcitabine:cisplatin is approximately 10:1, whereas after 30 and 60 min, the gemcitabine:cisplatin molar ratio would increase to approximately 14:1 and 21:1, respectively, reflecting a doubling of the original gemcitabine:cisplatin ratio. By 4 h, the gemcitabine:cisplatin ratio will be approximately 185:1, an increase nearly 20-fold from the starting drug ratio. In view of the evidence of drug ratio-dependent synergy in vitro and in vivo for several drug combinations (13, 14, 27–29), careful consideration should be given to ensure that the in vitro methodologies utilized to evaluate drug combinations for synergy take these pharmacological properties into account. Failure to do so could result in concluding that a given drug combination is synergistic when, in fact, drug ratios and concentrations reflecting those exposed to tumor cells in vivo could be highly antagonistic and susceptible to drug ratio-dependent MDR.

3. In Vivo Avoidance of Drug RatioDependent MDR: The CombiPlex Approach

We have developed an approach in which we identify antagonistic drug:drug ratios for a particular drug combination in vitro and subsequently package the two drugs in a drug delivery vehicle such that a concentration-independent synergistic ratio of the two drugs is delivered directly to the tumor site and antagonistic ratios are avoided. Subsequent in vivo efficacy studies are used to confirm the translation of in vitro drug synergy relationships to actual efficacy improvements over the individual agents, the free drug cocktail and, most importantly, significant increases in efficacy over the two drugs delivered in a vehicle at an antagonistic ratio.

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This review will detail the most common algorithms used to assess ratio-dependent drug:drug synergy/antagonism as well as methods to fix desirable drug ratios in vivo and evaluate the efficacy of fixed ratio combination agents in a variety of tumor xenografts. This ratiometric dosing approach to combination chemotherapy represents a means to augment the in vivo efficacy of combination therapies by maximizing tumor exposure to drugs at their synergistic ratios and avoidance of antagonistic ratios at which the ratio-dependent MDR mechanisms are active. 3.1. Drug Synergy/ Antagonism Screening: Experimental Design Considerations

The most efficient and accurate means to evaluate drug interactions is through in vitro cytotoxicity assays, and several approaches can be taken when structuring how agents are combined experimentally in order to evaluate drug combinations for synergy in vitro. In general, these approaches evaluate drug combinations based upon (1) a nonconstant ratio design where the drug concentrations are chosen arbitrarily based on the features such as the relative in vitro antitumor potencies of the individual agents or plasma concentrations achieved clinically (30, 31), or (2) constant ratio designs where drug concentrations are chosen based on an equipotent activity (i.e., the ratio of the IC50s) and serial dilutions are prepared to obtain a dose–effect (or concentration) range for a given ratio (32, 33). A list of methods used to evaluate drug interactions is found in Table 13.2. Clearly, considerable effort has been given to accurately quantify drug:drug synergy; however, this review will describe the methods most commonly employed in the literature. The most common nonconstant ratio approach to evaluate drug combinations for synergy employs a checkerboard drug combination design. In this method, the drug concentrations and drug ratios are varied. For example, in a 7 × 7 checkerboard layout for a two-drug combination each drug is diluted to generate seven different concentrations. Ideally each individual drug dilution will provide concentrations that provide the full range of tumor cell growth inhibition (e.g., £20% to ³90%). In this design, drug A is diluted vertically and drug B is diluted horizontally in a standard multi-well plate format and combining the two dilution groups results in 49 distinct combinations of the two drugs in addition to the seven concentrations of each individual drug. For most two-drug combinations, basing the dilutions on drug concentrations that span the entire cytotoxicity curve for the individual agents will lead to a rather ad hoc assortment of drug ratios and effective concentrations. A major disadvantage to the checkerboard approach is the large number of fixed ratios that are evaluated at a limited number of effect levels (concentrations). For example, applying the 7 × 7 matrix design described previously to gemcitabine and cisplatin tested in the A549 non-small cell lung cancer cell line results in

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Table 13.2 Various drug combination interaction methods used for evaluating synergy (adapted from (37)) Evaluation models

References

Isobologram (1870)

(84, 85)

Loewe additivity (1926)

(86–89)

Bliss independence response surface approach (1939)

(90)

Fractional product method of Webb (1963)

(91)

Multivariate linear logistic model (1970)

(92)

Approach of Gessner (1974)

(93)

Method of Valeriote and Lin (1975)

(94)

Method of Drewinko et al. (1976)

(95)

Interaction index calculation of Berenbaum (1977) (35) Method of Steel and Peckman (1979)

(36)

Median-effect method of Chou and Talalay (1984) (41) Method of Berenbaum (1985)

(96)

Method of Greco and Lawrence (1988)

(97)

Method of Pritchard and Shipman (1990)

(98–100)

Bivariate spline fitting (Sühnel 1990)

(101)

Models of Greco et al. (1990)

(38, 102, 103)

Models of Weinstein et al. (1990)

(39)

33 different gemcitabine:cisplatin molar ratios spanning a range from 1:5 to 1:100,000 (Table 13.3). Note that drug concentrations were chosen such that each drug spans its own dose–response curve, from low cell growth inhibition to high cell growth inhibition. The IC50 values for gemcitabine and cisplatin in A549 cells are 0.007 and 4.2 mM, respectively, a difference of approximately 600-fold. At an IC50 matched ratio (1:600) the nearest ratios evaluated by this checkerboard design are 1:500 and 1:750, indicating that only a few ratios are tested near the steepest and most sensitive region of the dose–response curves (i.e., at the IC50 value). Further, of the 33 ratios generated, only 8 are evaluated at more than 1 effect level or concentration (gemcitabine:cisplatin ratios of 1:50, 1:100, 1:200, 1:500, 1:1,000, 1:2,000, 1:5,000, and 1:10,000), with the 1:500 ratio being evaluated at a maximum of only 4 effect levels. Therefore, using a conventional checkerboard

0.5 mM

0.1 mM 1:200 1:100 1:50 1:20 1:12.5 1:10 1:5

Drug Dose

0.0005 mM

0.001 mM

0.002 mM

0.005 mM

0.008 mM

0.01 mM

0.02 mM

Gemcitabine 100% (IC50 ~ 0.007 mM) 97.4%

88.7%

66.9%

36.2%

29.8%

20.9%a

1:50

1:100

1:125

1:200

1:500

1:1,000

1:2,000

1.0 mM

77.5%

1:200

1:400

1:500

1:800

1:2,000

1:4,000

1:8,000

4 mM

51.3%

1:300

1:600

1:750

1:1,200

1:3,000

1:6,000

1:12,000

6 mM

37.5%

a

Maximum effect was observed at the 0.02 mM dose; higher concentrations resulted in no further reduction in cell viability

1:25

1:50

1:62.5

1:100

1:250

1:500

1:1,000

96.5%

99%

Viability

Cisplatin (IC50 ~ 4.2 mM)

1:500

1:1,000

1:1,250

1:2,000

1:5,000

1:10,000

1:20,000

10 mM

8.3%

1:2,500

1:5,000

1:6,250

1:10,000

1:25,000

1:50,000

1:100,000

50 mM

3.4%

Table 13.3 The gemcitabine and cisplatin ratios obtained when using a checkerboard design in the A549 cell line. The individual drug concentrations were chosen to obtain effect levels that spanned the dose–response curves

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approach limits the evaluation of the concentration dependency for any particular gemcitabine:cisplatin ratio. Drug combination schemes where drug:drug ratios are maintained by equivalent dilutions of drug A and drug B in a checkerboard design can be utilized; however, this often results in one of the drugs failing to span the full dose–response curve and the corresponding loss of synergy information in this portion of the dose–response curve. This can be particularly important if missing data occurs at high tumor growth inhibition, considering that for chemotherapeutics to be clinically effective multilog tumor cell kill is required. Thus, the synergy results obtained at high cell kill values are likely to be much more relevant than observations at low effect levels such as an effective dose (ED) of 20% or 30% (ED20 and ED30 reflect concentrations that result in 20% and 30% tumor cell growth inhibition, respectively). 3.1.1. Fixed Ratio In Vitro Synergy Analysis: Isobologram, Surface Response, and MedianEffect Methods

In vitro synergy analysis based on fixed drug ratios offers an alternative approach to identifying synergistic drug combinations. The most notable of these are the isobologram, surface response, and median-effect analysis methods, which are described in greater detail below. A constant-ratio approach has the benefit of evaluating the drug combination at a fixed drug:drug ratio over the full range of fraction of affected cells (fa; equivalent to effective dose, ED, the percent tumor growth inhibition relative to control cells) and therefore one can assess whether changes in synergy/antagonism occur for a particular ratio as drug concentrations vary. Isobolograms (or Isobol) are prepared for each fixed ratio from equally effective dose pairs for a single effect level. It should be noted that if one of the individual dose–response curves do not attain the chosen effect level, an isobol cannot be evaluated at that effect level and a lower effect level must be chosen or higher drug concentrations must be evaluated. Figure 13.1 shows an isobologram for the irinotecan:floxuridine combination adapted from Harasym et al. (34) at an ED75 (effective dose required to achieve 75% tumor cell growth inhibition). The ED75 isobol is generated from the dose of irinotecan required to elicit an ED75 plotted on the y-axis and the dose of floxuridine required to generate an ED75 plotted on the x-axis. The straight line joining the two data points on each of the axes is the line of additivity. For experimental combinations the drug concentrations required to generate an ED75 response are then plotted. Data points that lie below the line of additivity are considered synergistic, on the line as additive, and above the line as antagonistic. In this example, five fixed ratios were evaluated at an ED75 and, with the exception of the 10:1 ratio, the four remaining ratios were synergistic. Isobols are simple to generate and are visually easy to interpret; however, the isobols are not readily evaluated statistically and it is

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Fig. 13.1. Isobologram of irinotecan and floxuridine in the HCT-116 colorectal cell line (adapted from data presented in [34]. (a) An ED75 isobol including five fixed ratio data points, which were the drug doses of irinotecan:floxuridine required to achieve an ED75. (b) Three isobols are displayed on a single figure, and fixed ratio data are not shown, for clarity.

difficult to quantify the magnitude of the observed synergistic/ antagonistic response (i.e., the perpendicular distance from the line of additivity). Although onerous to generate, several modified approaches from the original isobologram methodology do allow for more statistical rigor and provide quantitative measures of synergy (35, 36). Further, when required to evaluate large data sets multiple effect level isobolograms where more than three isobols are plotted (i.e., the simultaneous plots of ED50, ED60,

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ED70 isobols on the same figure) become cumbersome and difficult to visualize due to data congestion (Fig. 13.1b). Thus, for large quantities of data, alternative methods that generate similar results are preferred. Several zero interaction response surfaces (surface response) analysis methods have been described that present data as threedimensional (3-D) concentration effect curves (37–39). The concentrations of drugs A and B are generally plotted on the x-axis and y-axis, and the fa (or other observed effect) is plotted on the z-axis. The 3-D surface that is generated (usually derived from Loewe additivity and/or Bliss independence principles) defines the predicted results of no interaction (zero effect or additivity) for the drug combination. Zero interaction response surfaces have several advantages including: (1) the evaluation of combinations throughout the complete dose range, (2) analysis when one drug dose is fixed and the second drug dose is varied, and (3) analysis when both drugs are simultaneous varied to keep the dose at a fixed ratio. The latter two points represent cross sections through the zero response surfaces. However, disadvantages of surface response analysis include the need for a large number of regularly dispersed data points, the complexity in implementation, the deficiencies in quantifying the measure of the interaction, and the inability to measure statistical uncertainty (33). As an example, Fig. 13.2 shows cytarabine and daunorubicin viability data obtained using the CCRF-CEM leukemia cell line plotted in relationship to a zero response surface (CombiTool, IMB-Jena, adapted from data presented in (40)). Data points shown above the surface are synergistic, near the surface indicate zero interaction (additivity), and below the surface are antagonistic. The 5:1 molar ratio of cytarabine:daunorubicin was readily identified as synergistic due to its noticeable separation from the zero interaction surface compared to the other ratios that were either additive or antagonistic. What is most evident from attempting to analyze this type of data format is the difficultly in identifying the synergism, additivity, or antagonism by visual inspection. To analyze the five fixed ratios in the graph visually it is necessary to rotate the graph to inspect under the surface. Further, it is difficult to quantify the extent of synergy (the distance from the zero response surfaces) and the relative response level (fa) at which synergy/antagonism occurred. Therefore, in the analysis of this data set, surface response analysis failed to efficiently identify important quantitative differences in synergy in a manner that is amenable to high data throughput. Constructing drug combinations for in vitro synergy analysis based on fixed drug ratios has been driven largely by the development of a third method, the median-effect analysis by Chou et al. (33, 41, 42), and this principle has been widely used for combination

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Fig. 13.2. Zero interaction response surfaces (surface response) obtained for cytarabine and daunorubicin in the CCRFCEM cell line. Data were generated from CombiTool. Data points above the zero interaction surfaces indicate synergy and data points below the surface indicate antagonism (adapted from data presented in (40).)

assessment for numerous years (19, 43–45). In this approach, the ratio of two drugs combined has historically been selected based on their relative IC50 values, and serial dilutions of this drug combination are prepared to span the entire in vitro dose–response curve. The degree of tumor cell growth inhibition is compared to that for the individual agents across the same concentration range used in the combination. The approach has the benefit of evaluating the combination at a given fixed drug:drug ratio over the full range of fraction of affected cells and therefore one can assess whether changes in drug concentrations affect whether or not a particular drug ratio is synergistic or antagonistic. Although this information is extremely valuable, the drug ratios reflected by IC50 values of the individual drugs may not reflect those ratios exposed in vivo after systemic administration of a drug combination. Consequently, in order to assess the pharmacological implications of decreasing drug concentrations and changing drug:drug ratios that occur after in vivo administration of conventional drug combinations, multiple ratios must be systematically evaluated in vitro. Historically, this parameter has rarely been investigated in vitro and when it has, the implications of the results obtained with different drug:drug ratios (typically only 2–3) on the in vivo activity of the combination have been unrecognized.

Drug Ratio-Dependent Antagonism 3.1.2. In Vitro Synergy Analysis Guided by In Vivo Parameters: Combination Index-Fraction Affected Analyses

303

The median-effect equation of Chou and Talalay yields identical conclusions for both isobologram and Combination Index (CI)fa data (discussed further later) with the advantage of ease of operation, the ability to analyze large data sets, and graphical representations that allow dose–response interpretations. Specifically, where isobolograms are dose oriented, the CI-fa analyses are effect oriented. Briefly, from dose–response data the median effect (13.1) is log transformed to a linearized equation and thus takes the form of a straight line equation y = mx + b (13.2). m

fa  D  = . f u  Dm 

(13.1)

 f  log  a  = m log( D) − m log( Dm ),  fu 

(13.2)

where fa = fraction of cells affected, fu = fraction of cells unaffected, fa + fu = 1, D = dose of drug, Dm = the median-effect dose (signifies potency, and is usually near the IC50), and m = signifies the shape (sigmodicity) of the dose–effect curve. The latter equation is used to convert monotonic dose–response curves (usually sigmoidal) into straight lines whereby the slope (m) and the x-intercept (log Dm) and hence the Dm value can be obtained and extrapolated for any dose and effect. Using the Combination Index (CI) (13.3) and using the m and Dm values from above the CI value at any effect level (fa) can be determined where: CI =

( D)1 ( D2 ) + , ( Dx )1 ( Dx ) 2

(13.3)

where CI = combination index; CI < 1, = 1, and >1 indicates synergy, additivity, or antagonism, respectively, (D)1 and (D)2 = the drug concentrations in the combination required to inhibit X%, and (Dx)1 and (Dx)2 = the doses of the individual drugs alone required to inhibit X%.It should be noted that the median-effect CI equation is equivalent to the Loewe additivity principle I = da/ DA + db/DB) for mutually exclusive agents (33, 37). The most efficient design for applying the median effect analysis method is to choose a specified fixed ratio as described earlier in the experimental design section, and then perform a series of serial dilutions to obtain a dose–response curve. We use the median-effect equation as our primary method for synergy analysis as it allows for (1) the evaluation of fixed-drug combination ratios at multiple concentrations, (2) the correlation of synergy to the fraction of cells affected, thereby facilitating synergy analysis at high cell kill values, and (3) straightforward generation of synergy

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data through the availability of commercially available software (CalcuSyn, BioSoft). We have utilized the median-effect analysis method to assess the in vitro drug ratio dependency of synergy for several clinically relevant drug combinations (27, 40, 46, 47). Figure 13.3 presents the in vitro synergy results evaluated in this manner for the combination of irinotecan:floxuridine exposed to HCT-116 human colorectal cancer cells (adapted from (34) and (27)). Irinotecan and fluorinated pyrimidine combination therapy are standard of care in metastatic colorectal cancer (48, 49). Taking the traditional approach to analyzing such a combination would result in the generation of a single CI vs. fa curve using an irinotecan:floxuridine ratio that would reflect their relative potencies in vitro (10:1 molar ratio in this case; see Fig. 13.3a). These data indicate that this drug ratio exhibits significant concentration-dependent synergy since at low fa (corresponding to low concentrations of the 10:1 ratio), the combination is nearly additive; however, the combination shifts to strong antagonism when the drug concentration is increased to achieve tumor growth inhibition greater than an fa value of 0.5. Higher fa values (³0.5) are most relevant for cancer applications and under these conditions the 10:1 irinotecan:floxuridine molar ratio was highly antagonistic. Based on these limited data, one could conclude that this combination is, in fact, not desirable from the perspective of drug synergy. However, when four additional drug ratios were evaluated, we observed that other ratios of the same two agents could be identified where strong synergy was obtained over broad drug concentrations reflecting the full range of fa values (Fig. 13.3b). Notably, irinotecan:floxuridine ratios of 1:1, 1:5 and 1:10 were synergistic between fa values of 0.2 and 0.8. We then compared drug ratio-dependent synergy for the different drug ratios by plotting the CI value as a function of drug ratio for an fa value reflecting high cell kill (e.g., fa = 0.8; see Fig. 13.3c). These data highlight the fact that as irinotecan:floxuridine concentrations and drug ratios change (as they would following in vivo administration of conventional aqueous-based drug combinations, see ref. (34)) conditions would likely occur where the two drugs were exposed at antagonistic drug ratios, and this would compromise therapeutic activity. 3.1.3. Automated Screening Methodology

The in vitro evaluations of drug ratio-dependent synergy detailed earlier were performed using five different drug:drug molar ratios ranging from 10:1 to 1:10 in 3–4 cell lines. While this process led to the identification of synergistic drug ratios that could be exploited in vivo via drug delivery systems (see later), the intervals between the different drug ratios are relatively large (up to fivefold) and the screening matrix must be tailored to each particular combination in the context of drug concentrations employed.

Fig. 13.3. Irinotecan and floxuridine analysis using the median effect principle and combination index (CI) values in the HCT-116 colorectal cell line (adapted from data presented in [34]). (a) CI vs. the fraction affected (fa) at the 10:1 ratio. (b) CI vs. fa for five ratios, with the CI values highlighted at the ED80 for each of the five ratios. (c) The indicated CI values from the ED80 for each of the five fixed ratios. CI < 1.0 indicates synergism, CI = 1 indicates additivity, and CI > 1 indicates antagonism.

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Manual cell culture procedures are capable of managing the analysis of five fixed ratios plus two individual dose–response profiles using eight serial drug dilutions, triplicate assays, and a minimum of three repeats of each experiment (each experiment represents 630 individual assays). Ideally, however, one would test more drug:drug ratios with smaller intervals in a wider range of tumor cell lines in order to improve the robustness, reliability, and predictability of drug ratio-dependent synergy trends (50– 54). Furthermore, a single drug combination matrix would facilitate the application of robotic liquid handling, thereby increasing throughput and allowing virtually any drug combination to be readily examined for in vitro synergy over a wide range of drug ratios and concentrations. To achieve this goal, we have expanded the number of drug ratios from 5 to 18 where each ratio reflects a twofold increment from its nearest neighbor, have increased the number of concentrations evaluated for each ratio from 8 to 16, have increased the number of experiment repeats from 3 to 4 while maintaining the triplicate replicates within each experiment, and have expanded the number of cell lines screened from 3–4 to 10–20 comprising 5–7 tumor types. In cases where drug combinations are approved for patient use, the in vitro screen is enriched in cell lines representing the clinical indication. The data generated in this process reflect 96,000 individual assays (1,600, 96-well plates). Figure 13.4 presents a schematic illustration of this semiautomated process for systematically evaluating drug combinations for drug ratio-dependent synergy. The process is semiautomated as it relies upon numerous liquid handling robots and workstations (multiplate washers, multiplate readers, etc.) to perform all of the required steps of a cell viability assay. The method evaluates the 18 fixed ratios from a fixed ratio range of 64:1 to 1:2,048. This broad ratio range allows for the collection of fixed-ratio dose–response profiles for drug combinations from agents with similar potencies to those with large differences in potencies (i.e., IC50 differences of approximately 2,000). The viability assay used is the colorimetric tetratzolium assay, MTT (55); however, with minimal programming and procedural adjustments the methodology can be readily adapted to other colorimetric, fluorescent, or luminescent based assays. The liquid handling robots are used to plate cells into 96-well plates, prepare the required drug master plates plus the required serial dilutions, and to add drugs to the cell containing plates. The cell viability results generated are entered into a dose– response matrix for each fixed ratio (see Fig. 13.4; horizontal rows indicate the 18 fixed ratios and the left most vertical column indicates the cell lines). From the dose–response matrix various data analysis options are available to analyze for combination effects, such as

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Fig. 13.4. Flow diagram of the in vitro drug screening methodology for the identification of a synergistic fixed-ratio range for a two drug combination in a panel of tumor cell lines.

isobologram, zero interaction (surface response), or median effect. As an example, the median effect analysis leads to the median effect matrix, a summation of data that relates the CI vs. fa data sets. Finally, the data are compiled to show the drug combination ratio identification matrix where the combination effect at a defined effect level (i.e., ED80) is displayed. To graphi-

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cally highlight the observed pattern of synergism, antagonism, or additive affects the numeric CI values are color coded. CI values between 0 and 0.9 are synergistic (green), values between 0.9 and 1.1 are additive (yellow), and values greater than 1.1 are antagonistic (red). This “ratio heat map” assists in the identification of regions of consistent synergy and antagonism across multiple tumor cell lines at a defined effect level. It should be noted that the broad drug ratio range typically leads to a significant number of data points off the sensitive range of the dose–response curve (e.g., 90% cell growth inhibition) for given combinations. Consequently, these data points must be “truncated” in order to avoid erroneous skewing of the synergy analysis using the median-effect algorithm. Table 13.4 presents a “ratio heat map” for the gemcitabine: cisplatin combination. It represents a succinct compilation of the comprehensive data set generated through the automated screening methodology conducted using nine different cell lines and 18 drug ratios. The inclusion of the added cell lines further defines the optimal synergy ratio range near a gemcitabine:cisplatin molar ratio of 1:2, as synergy was observed in all cell lines at this ratio. Higher ratios such as 1:4 for the A2780 ovarian cell line are antagonistic, with widespread drug:drug antagonism observed in several cell lines beyond a drug ratio of 1:128 gemcitabine:cisplatin. Indirectly the tabulated data reveal the different responses of individual cell lines to the gemcitabine:cisplatin combination and such detailed information could be useful for mechanistic correlations. Lastly, the automated screening methodology can be used to generate heat maps for each of the 18 fixed ratios from effect levels of ED5 to high effect levels of ED95 to examine the drug combination effects at any desired effect level. Our primary focus is to identify drug combination synergies at high effect levels (>ED75) and thus at high tumor cell kill. However, this full data set allows synergy to be evaluated at lower effect concentrations, which will be experienced as drugs are eliminated from the body to ensure that significant antagonism does not arise for combinations that may otherwise display synergy at only very high effect levels. 3.2. Translating Drug Ratio-Dependent Synergy In Vivo Using Nanoscale Drug Delivery Systems

The implication of the drug-dependent antagonism (MDR) revealed by the ratiometric screening results described earlier and elsewhere for other drug combinations (27, 47) is that attention must be given to the concentration and ratio of drug combinations that occur over time after systemic administration in vivo. Basing in vitro synergy testing conditions on pharmacologically relevant drug concentrations and drug ratios observed in vivo may increase our understanding of how conventional drug combinations interact therapeutically. More importantly however, this approach has opened the possibility to exploit drug ratio-dependent synergy

1.10

1.10

0.88

1.30

A2780

BxPC-3 Pancreatic 1.30

H460

1.10

9.00

3.00

0.80

Lung

H1299 Lung

IGROV- Ovarian 1

MCF-7 Breast

Ovarian

1.10

A549(3) Lung

0.50

0.79

1.70

0.90

1.10

1.70

H1299 Lung

1.30

32:1

1.80

64:1

Colon

HT-29

Cell line Tumor

1.00

2.90

0.62

0.85

0.83

0.82

0.85

3.60

4.50

16:1

1.10

1.40

0.51

1.40

0.52

0.96

1.10

9.70

2.30

8:1

1.10

1.10

0.49

1.20

0.54

0.73

0.90

0.74

0.62

4:1

1.00

0.70

0.86

0.65

0.94

0.87

0.85

0.95

0.85

2:1

1.00

1.30

0.66

0.96

0.68

0.76

0.88

0.40

0.69

1:1

0.84

0.65

0.62

0.82

0.52

0.76

0.81

0.44

0.50

1:2

0.96

0.87

0.34

0.91

0.38

1.40

0.55

0.58

0.33

1:4

0.81

0.41

0.55

1.10

0.23

1.20

0.71

0.44

0.16

1:8

CI @ ED80

0.42

0.31

0.49

0.67

0.35

3.20

0.41

0.55

0.15

1:16

0.44

0.57

0.27

0.71

0.31

8.30

0.51

0.64

0.14

1:32

0.39

0.64

0.69

1.30

0.48

2.90

0.67

0.18

0.03

1:64

0.38

1.10

2.50

0.76

0.58

4.30

1.20

0.24

0.03

0.81

1.20

1.80

0.92

1.10

3.40

1.90

0.27

0.01

1.30

1.30

4.70

0.93

1.10

2.30

2.60

0.54

0.02

2.20

1.50

0.63

0.86

2.00

6.20

3.40

0.62

0.01

2.90

1.20

3.30

1.00

0.90

5.00

3.80

0.53

0.00

1:128 1:256 1:512 1:1024 1:2048

Table 13.4 A gemcitabine and cisplatin “ratio heat map” displaying the CI values at an ED80 for the standard 18 ratios screened, data from nine cell lines are shown. CI values between 0 and 0.89 are synergistic (green), values between 0.9 and 1.1 are additive (yellow), and values greater than 1.1 are antagonistic (red)

309

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relationships and avoid drug ratio-dependent MDR antagonism through the use of nanoscale drug delivery systems. This is due to the fact that small (20–200 nm diameter) drug delivery vehicles such as liposomes and nanoparticles do not readily distribute into healthy tissues after systemic administration and can be designed to control the rate at which encapsulated drugs are released from the carrier (56, 57). Consequently, such delivery systems can be constructed to deliver drug combinations in vivo such that the formulated drug:drug ratio can be maintained in the body for extended times after i.v. administration (34, 40, 46, 58, 59). An added advantage of nanoscale particulate carriers is that they display enhanced penetration and retention (EPR) effects in solid tumors due to the associated leaky vasculature and poor lymphatic drainage, which results in selective accumulation of the delivery vehicles and their encapsulated contents in sites of tumor growth (56, 60). The ideal drug vehicle for multiple, synergistic agents must coordinate the pharmacokinetics and biodistribution of both drug agents to ensure delivery of both drugs to the tumor site at the desired synergistic ratio. Dissimilar drug leakage rates or tumor distributions between the two drugs could lead to compromised efficacy via drug ratiodependent MDR antagonism. Lastly, the drug carrier should have a relatively prolonged plasma half-life compared to the free agents to allow time for the delivery vehicle to extravasate into tumor tissue and exploit the EPR effect for small particulate drug carriers. Delivery of two agents at a defined ratio through use of a drug delivery vehicle can use one of two strategies. The simplest approach is to formulate each drug component into a separate carrier and combine the two formulated drugs at the desired ratio (Fig. 13.5a). This method has the advantage of facile generation of the desired ratio; however, one must ensure that the drug release rates for both agents are identical and that the biodistribution of both carriers is unaffected by the encapsulation of different agents. These concerns can be eliminated through encapsulation of both active species in a single carrier system (Fig. 13.5b). Such a system presents added complexity to the formulation of a fixed ratio combination agent; iterative variation of internal buffer components and carrier composition is used to coordinate the release of both drugs so that the plasma drug elimination kinetics is matched. For this purpose, liposome technology is better developed than other nanoscale platforms (e.g., nanoparticles, micelles, nanospheres); therefore, the following discussion will focus on development of liposomal carriers. 3.2.1. Designing Liposomes to Deliver Fixed-Ratio Drug Combinations

Liposome drug delivery technology has advanced considerably over the past 25 years to the point where there are now several approved liposomal products of single anticancer agents (e.g., DepoCyt (cytarabine), Doxil (doxorubicin), DaunoXome (daunorubicin)). Liposomes are typically formulated with near equimolar amounts of inert, uncharged lipids such as phosphatidylcholine

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311

Fig. 13.5. Formulation strategies for encapsulation of two drug species at a fixed drug ratio in vivo. (a) Mixing of two individual encapsulated agents; (b) Coencapsulation of two agents within a single drug vehicle.

and cholesterol. Addition of polyethyleneglycol (PEG) to create PEG-coated “stealth” liposomes (61) or negatively charged lipids (e.g., distearoylphosphatidylglycerol (DSPG) (62)) greatly enhances the plasma circulation time of the liposomes by increasing stability and preventing opsonization by the immune system. The lipid composition also plays a major role in the retention of drugs encapsulated within the liposome (47). The phase transition temperature of saturated lipids increases with acyl chain length (e.g., from C14 to C18) accompanied by a concomitant increase in drug retention properties (62, 63). Cholesterol composition can also be manipulated to tune drug release rates in vivo (64, 65). We have noted that liposomes comprising high phase transition temperature lipids containing relatively low amounts of cholesterol (£20% mole ratio) are particularly well suited to coordinated retention of multiple drug agents due to their stable gel phase state in vivo. Drug encapsulation of liposomes is achieved through either passive or active loading. Passive loading is conducted by extrusion of the liposomes in the presence of the drug (40, 66, 67). Drug loading efficiencies are usually low (90%) and rapid. An appropriate transmembrane gradient (pH, ion, metal) is established that typically causes a chemical or physical change in the drug once encapsulated. As an example, pH gradients typically cause deprotonation or protonation of a weak acid/base moiety of the drug molecule upon encapsulation such that the drug becomes charged

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within the liposome and thus far less membrane-permeant after encapsulation (66, 68–70). Other mechanisms related to this concept include intraliposomal drug precipitation (71), metal complexation (72–75), and antiport exchange of drugs and excipients (76). Development of a coencapsulated liposomal formulation of floxuridine and irinotecan (CPX-1) illustrates the intricate design– function relationships inherent in delivery of fixed ratio chemotherapeutic agents via a drug delivery vehicle (46). Figure 13.6 depicts the release of irinotecan and floxuridine from several liposomal formulations containing different amounts of cholesterol (Chol) in distearoylphosphatidylcholine (DSPC)/distearoylphosphatidylglycerol (DSPG) liposomes. For encapsulated irinotecan (Fig. 13.6a), as the cholesterol component is increased from 0 to 15% mole ratio (of the total lipid), the plasma half-life increases dramatically from less than 2 h for 80:0:20 DSPC:Chol:DSPG to ~15 h for 65:15:20 (DSPC:Chol:DSPG). This sensitivity of irinotecan release to cholesterol is contrasted by the insensitivity of the second drug floxuridine over this cholesterol range (Fig. 13.6b). Floxuridine leakage rates from the four different liposome formulations are virtually independent of liposome cholesterol content between 0 and 20 mole percent cholesterol. In this case, the 70:10:20 DSPC:Chol:DSPG formulation was selected to coencapsulate irinotecan and floxuridine as it coordinated the plasma PK properties of the two active agents. The internal buffer composition was also instrumental in controlling the drug release properties of CPX-1. The in vitro release rate of irinotecan was found to be sensitive to the presence of copper in the internal liposomal buffer of CPX-1, while floxuridine release was largely insensitive to copper. Intensive biophysical characterization of both formulations revealed that the aggregation state of irinotecan was modulated by the encapsulated copper gluconate-triethanolamine buffer system; irinotecan formed higher order aggregates in the absence of copper than led to a slower, uncoordinated release relative to floxuridine (77). Careful formulation of multiple drug agents to maintain a particular ratio in vivo requires iteration of encapsulation techniques to optimize drug encapsulation efficiency, drug retention, and biodistribution properties. The use of in vivo data, particularly pharmacokinetic evaluation of plasma clearance rates of both drugs, is essential to guide the formulation process and lock in the desired drug ratio within the carrier. While coformulation of two active agents into drug vehicles is gaining interest in the literature (3, 40, 46, 78–82), attention must be paid to the release properties of both drugs to ensure that the encapsulated drug ratio is not changing over time. Several recent efforts either failed to measure for drug release rates (79, 81, 82) or to maintain the starting drug ratio (72). Use of drug delivery vehicles that successfully coordinate the delivery of the two active agents at a

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313

Fig. 13.6. The in vivo retention of irinotecan (a) and floxuridine (b) coformulated in various liposomal formulations. Liposomes composed of DSPC:Chol:DSPG (65:15:20 molar ratio); (open rectangle), DSPC:Chol:DSPG (70:10:20 molar ratio); (filled down triangle), DSPC:Chol:DSPG (75:5:20 molar ratio); (open circle), and DSPC:DSPG (80:20 molar ratio); (filled circle) (Reprinted from [46] with permission from Elsevier).

predictable, nonantagonistic ratio is essential to this methodology of mitigating drug ratio-dependent MDR antagonism. 3.3. In Vivo Evaluation of Fixed Ratio Combination Therapies

The importance of avoiding exposure of tumor cells to antagonistic drug ratios is elucidated through in vivo comparisons of antitumor activity of the fixed synergistic ratio combination formulation against the combination in which the ratio is uncontrolled (i.e., a free drug cocktail), and most importantly against a fixed liposome-encapsulated antagonistic ratio of the two active species. The goal of such studies is to establish the efficacy superiority of

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the fixed synergistic ratio formulation of the two agents by ensuring that (1) a contribution to the observed efficacy is made by both agents and (2) the exposure of tumor cells to a synergistic drug ratio leads to improved efficacy over formulations in which either the ratio is uncontrolled or set within an antagonistic range. Therefore, evaluation of in vivo efficacy of fixed-ratio combination agents (e.g., CombiPlex formulations) must include assessment of its efficacy against the free drug cocktail, the singly formulated liposomal agents, and finally against a formulation in which the drug ratio is fixed in a region previously identified as antagonistic. Typically, mouse models of cancer are employed for this analysis. Prior to formal efficacy experiments, the maximum tolerable doses of each agent are determined, defined as a single dose or series of doses that results in less than 15% of total body weight loss and no toxicity-related mortality for any one dose or schedule of doses. Human xenograft solid tumors subcutaneously grown in immune-compromised mice may be utilized and selected on the basis of defined genetics and growth attributes. Tumor cells utilized in these experiments can be genetically manipulated or selected to express preferable properties and are injected into mice. Once the tumors have grown to a palpable (measurable) size, delivery vehicle and free drug compositions can be administered, preferably intravenously, and their effects on tumor growth are monitored. It is not readily possible to analyze efficacy data for synergistic or antagonistic interactions by median effect, surface response, or isobologram analysis in an in vivo model. Generation of an appropriate (i.e., statistically robust) data set using animal models would require a dose titration of each individual liposomal agent and the combined agents at several ratios and using multiple experimental repeats: clearly the number of animals required for such a study would be prohibitive and the reproducibility between experiments would be far less than for in vitro experiments. The efficacy of the synergistic CombiPlex formulation is therefore determined across multiple xenograft tumor models to ensure that the superior in vitro efficacy of the synergistic drug ratio translates to in vivo efficacy improvements. Secondly, the efficacy of the competing free drug cocktail is optimized by determining the most favorable dose and treatment schedule so that free cocktail in vivo efficacy represents the best possible performance of an unrestricted drug ratio formulation. Doses of all agents are then administered at or near MTD to account for differences in therapeutic index. It is worth noting that this frequently requires somewhat lower doses of the combined CombiPlex agent relative to the individual liposomal drugs due to the aggregate toxicity of the combined agents.

Drug Ratio-Dependent Antagonism

315

Assessment of antitumor activity is most conveniently measured through volume measurement of subcutaneous xenograft tumors or by survival studies for nonsolid tumors. Several methods are available for statistical comparisons between treatment groups. Tumor growth delay (T − C) is measured as the median time in days for a treated group (T) to reach an arbitrarily determined tumor size (for example, 400 mg) minus median time in days for the control group to reach the same tumor size while tumor regression as a result of treatment may also be used as a means of evaluating a tumor model. Results are expressed as reductions in tumor size (mass) over time. The preferred method of calculated cell kill for solid tumor model evaluation involves measuring tumors repeatedly by calipers until all exceed a predetermined size (e.g., 400 mg). The tumor growth and tumor doubling time can then be evaluated. Log10 cell kill parameters can be calculated by (13.4a–13.4c): log10 cell kill (T − C ) , = Dose (3.32) (Td )(No. of doses)

(

)

log10 cell kill (total) =

log10 cell kill (net) =

(T − C ) , 3.32 (Td )

(

)

((T − C ) − (duration of Rx )), (3.32 (Td ))

(13.4a)

(13.4b)

(13.4c)

where (T − C) = tumor growth delay, Td = Tumor doubling time. Evaluation of nonsolid tumors include measurement of increase in life-span (ILS%), tumor growth delay (T − C, as above) or longterm survivors (cures). Increase in lifespan is calculated by dividing the median survival time of the treatment group by the median survival time of the control group. Long-term survivors are identified as subjects that survive up to and beyond three times the survival of the untreated group. The efficacy advantages of the CombiPlex platform to avoid exposure of tumors to antagonistic drug ratios that may lead to MDR are exhibited in Figs. 13.7 and 13.8. The CombiPlex formulation CPX-1, a liposomal formulation of irinotecan and floxuridine coencapsulated at the synergistic ratio of 1:1, was evaluated for antitumor activity (cell line: pancreatic Capan-1) against the free drug cocktail, the individual liposomal components, and a second coencapsulated formulation of irinotecan and floxuridine at an antagonistic ratio of 1:10 (Fig. 13.7, adapted from data presented in (27)). CPX-1, at a dose of 37 mmol/kg of each agent, showed superior efficacy over all other groups with a calculated log cell kill (LCK) value of 1.8, besting the LCK efficacy of

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high-dose liposomal irinotecan (37 mmol/kg) of 1.4 by nearly 2.5 times, and the LCK of liposomal floxuridine (37 mmol/kg) of 0.3 by ~62 times. CPX-1 is also clearly superior to both free drugs dosed at their MTD; irinotecan (148 mmol/kg) and floxuridine (1,000 mmol/kg) achieved LCK values of only 0.6 and 0.4, respectively. A 1:1 free drug cocktail formulation of irinotecan and floxuridine (148:148 mmol/kg) was also inferior (LCK value 0.65, a factor of 14 times less than CPX-1), demonstrating the value of encapsulating drug-ratio dependent agents in a drug delivery vehicle that controls drug ratios. Finally, encapsulation of antagonistic drug ratios led to decreased efficacy despite the addition of more drug. A dose of liposomal irinotecan at 7.4 mmol/kg led to an observed LCK of 1.1, while liposomal irinotecan:floxuridine at a dose of 7.4:74 mmol/kg had an LCK of 0.55, nearly 3.6 times less activity despite an identical dose of irinotecan and addition of 74 mmol/kg of floxuridine. This result clearly demonstrates the deleterious effects on in vivo efficacy that occur when tumors are exposed to cytotoxic drugs at an antagonistic ratio (27). Later tumor distribution studies confirmed that the augmented activity of CPX-1 and decreased activity of the 1:10 irinotecan:floxuridine formulation was due to synergistic and antagonistic drug interactions at the tumor site, as both formulations delivered the two drugs at their respective ratios to the solid tumors (34). The drug ratio-dependent antitumor activity of a liposomal formulations of cytarabine:daunorubicin (CPX-351) was demonstrated in P388 ascites tumor-bearing BDF-1 mice (40). The 55-day survival percentages of several liposomal formulations of the two drugs are shown in Fig. 13.8. The highest survival percentage of 100% was observed for the 5:1 ratio of cytarabine and daunorubicin, previously demonstrated to be synergistic in vitro. Survivors steadily decreased for all other formulations, dropping to 83% for a 12:1 ratio formulation (despite 50% more cytarabine and only 25% less daunorubicin), and, strikingly, only 50% survival for a 3:1 cytarabine:daunorubicin formulation that actually administered the same amount of cytarabine (10 mmol/kg) as CPX-351, but nearly twofold more daunorubicin (7.4 mmol/kg for the 3:1 formulation vs. 4 mmol/kg for CPX-351). In this instance, more drug actually led to less activity, despite tumor biodistribution studies demonstrating drug delivery at the established ratios in both cases. Clearly, deviation from synergistic ratio toward ratios known to be antagonistic can lead to compromised efficacy. Further translation of in vitro drug screening informatics to in vivo efficacy is provided by a study by Abraham et al. (72). Previous in vitro cytotoxicity studies had demonstrated that coadministration of doxorubicin and vincristine was antagonistic, although detailed studies to identify drug ratio regions of synergy/

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Fig. 13.7. Quantitative analysis of CPX-1 in vivo activity against Capan-1 pancreatic xenograft tumor model (adapted from data presented in [27]). Numbers in brackets represent dose of irinotecan:floxuridine or single agents in mmol/kg. CPX-1 liposomal formulation of irinotecan:floxuridine at synergistic 1:1 molar ratio, ITN irinotecan, Flox floxuridine, Lipo liposomal, antagonistic liposomal formulation of irinotecan:floxuridine at antagonistic 1:10 molar ratio.

Fig. 13.8. Survival of BDF-1 mice bearing P388 ascites tumors at day 55 following Q3Dx3 treatment with saline or coencapsulated liposomal cytarabine:daunorubicin at different drug:drug ratios (adapted from data presented in (40)).

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antagonism were absent. Drug:drug antagonism was confirmed in subsequent in vivo efficacy studies, which demonstrated equivalent efficacy for liposomal doxorubicin:vincristine vs. the single agent of liposomal doxorubicin. This result agreed with an earlier report of antitumor efficacy of liposomal vincristine coadministered with the liposomal doxorubicin agent Doxil. In this study the efficacy of the combined agents was actually less than that observed for Doxil alone, despite the efficacy of both individual liposomal agents being superior to the free drug (83). In both cases, it should be noted that exquisite in vivo control of drug ratios was lacking: in the former case; the plasma drug:drug ratio changed from the initial 4:1 vincristine:doxorubicin ratio to over 20:1 in 24 h (72) while the pharmacokinetic differences between the individual liposomal formulations of vincristine and doxorubicin were not examined. These studies, while examples of incomplete consideration of drug ratio analysis, demonstrate the translation of in vitro antagonism leading to compromised in vivo efficacy likely due to a drug ratio-dependent MDR mechanism.

4. Conclusion MDR continues to be a significant impediment to improving the outcomes of cancer patients to chemotherapy treatment. Current combination therapies, while typically more effective than single agent treatments, can be subject to a newly identified form of MDR that manifests itself as a resistance to drugs presented at discrete ratios and concentrations when administered concurrently. In the current treatment paradigm, multiple agents are administered in saline-based cocktail that cannot account for the disparate pharmacokinetics of each individual agent, which generally leads to rapidly and widely changing drug ratios postadministration. Therefore, plasma elimination of free drug cocktails can result in tumor exposure to antagonistic drug ratios and corresponding compromised efficacy. In this review, we have detailed methods by which this drug-ratio dependent MDR mechanism can be identified and bypassed, resulting in improved antitumor efficacy. Screening for ratio-dependent drug interactions is best achieved through an approach that assesses drug interactions across a wide range of drug ratios and effect levels. Using these techniques has led to identification of drug ratio-dependent synergistic and antagonistic interactions for numerous drug combinations. Given the apparent broad applicability of drug ratiodependent synergy and the ability to exploit such information

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in vivo by delivering fixed drug ratios in particulate drug carriers, we have demonstrated the application of automated, ratiometric drug combination screening in order to facilitate the systematic evaluation of multiple drug combinations at numerous ratios under pharmacologically relevant conditions with high throughput. Liposomal- and nanoparticle-based delivery technologies can be developed to deliver synergistic drug combinations at fixed drug ratios and avoid tumor exposure to antagonistic ratios. Codelivery and coordinated release of synergistic drug combinations facilitate the achievement of maximum efficacy by maintaining synergy throughout the pharmacodynamic (dose–response cytotoxicity) profile and avoidance of antagonistic ratios in vivo. References 1. Frei EI, Freireich EJ (1964) Leukemia. Sci Am 210:88–96 2. Freireich EJ, Frei EI (1964) Recent advances in acute leukemia. Prog Hematol 27:187–202 3. Ramsay EC, Dos Santos N, Dragowska WH, Laskin JJ, Bally MB (2005) The formulation of lipid-based nanotechnologies for the delivery of fixed dose anticancer drug combination. Curr Drug Del 2:341–351 4. Ewesuedo RB, Ratain MJ (2003) Principles of cancer therapeutics. In: Vokes EE, Golomb HM (eds) Oncologic therapies. Springer, Secaucus, NJ, pp 19–66 5. Shabbits JA, Krishna R, Mayer LD (2001) Molecular and pharmacological strategies to overcome multidrug resistance. Expert Rev Anticancer Ther 1:89–98 6. Coley HM (2008) Mechanisms and strategies to overcome chemotherapy resistance in metastatic breast cancer. Cancer Treat Rev 34:378–390 7. Shabbits JA, Hu Y, Mayer LD (2003) Tumor chemosensitization strategies based on apoptosis manipulations. Mol Cancer Ther 2: 805–813 8. Zhou SF, Wang LL, Di YM et al (2008) Substrates and inhibitors of human multidrug resistance associated proteins and the implications in drug development. Curr Med Chem 15:1981–2039 9. Perez-Tomas R (2006) Multidrug resistance: retrospect and prospects in anti-cancer drug treatment. Curr Med Chem 13:1859–1876 10. Nobili S, Landini I, Giglioni B, Mini E (2006) Pharmacological strategies for overcoming multidrug resistance. Curr Drug Targets 7:861–879 11. Shah MA, Schwartz GK (2001) Cell cycle-mediated drug resistance: an emerging concept in cancer therapy. Clin Cancer Res 7:2168–2181

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Chapter 14 Reversing Agents for ATP-Binding Cassette Drug Transporters Chow H. Lee Abstract The multidrug resistance (MDR) phenotype exhibited by cancer cells is believed to be the major barriers to successful chemotherapy in cancer patients. The major form of MDR phenotype is contributed by a group of ATP-binding cassette (ABC) drug transporters which include P-glycoprotein, multidrug resistance-associated protein 1, and breast cancer resistance protein. There has been intense search for compounds which can act to reverse MDR phenotype in cultured cells, in animal models, and ultimately in patients. The ongoing search for MDR modulators, compounds that act directly on the ABC transporter proteins to block their activity, has led to three generations of drugs. Some of the third-generation MDR modulators have demonstrated encouraging results compared to earlier generation MDR modulators in clinical trials. These modulators are less toxic and they do not affect the pharmacokinetics of anticancer drugs. Significant numbers of natural products have also been identified for their effectiveness in reversing MDR in a manner similar to the MDR modulators. Other MDR reversing strategies that have been studied quite extensively are also reviewed and discussed in this chapter. These include strategies aimed at destroying mRNAs for ABC drug transporters, approaches in inhibiting transcription of ABC transporter genes, and blocking of ABC transporter activity using antibodies. This review summarizes the development of reversing agents for ABC drug transporters up to the end of 2008, and provides an optimistic view of what we have achieved and where we could go from here. Key words: Multidrug resistance, ABC drug transporters, P-glycoprotein, MDR modulators, Reversing agent, mRNA degradation, Transcriptional inhibition

1. Introduction The ATP-binding cassette (ABC) transporters are a large group of membrane proteins found virtually in all species. They are capable of transporting a variety of compounds which include peptides, lipids, and anti-cancer drugs. The common feature amongst the ABC transporters is their ability to transport substrates against a concentration gradient utilizing energy from ATP hydrolysis. The J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_14, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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human genome has 48 ABC genes which are further categorized into seven distinct subfamilies, ABCA to ABCG, based on sequence homology and domain organization. Three members of the ABC transporter family, P-glycoprotein (Pgp, also known as ABCB1), multidrug resistance-associated protein 1 (MRP1, also known as ABCC1), and breast cancer resistance protein (BCRP, also known as ABCG2), have so far been associated with the multidrug resistance (MDR) phenotype in cancer cells. These transporters, especially Pgp, are believed to be one of the major causes for failure in chemotherapy in cancer patients. Hence, there has been tremendous amount of studies directed at understanding the structure and function of these drug transporters, as well as in finding the best strategy and drugs that can truly reverse MDR in patients. This review chapter summarizes our current knowledge on the development of inhibitors of ABC drug transporters and the strategies in reversing MDR. This review focuses more on Pgp since there are more studies done on this ABC drug transporter. The obstacles to success in reversing clinical MDR will be discussed and opinions concerning the future of this issue presented.

2. Reversing MDR A number of cellular mechanisms are known to lead to the development of drug resistance (1). However, it is now clear that increased drug efflux by overexpression of ABC drugs transporters from cancer cells is the most common mechanism that reduces the effectiveness of anti-cancer drugs due to the reduced accumulation of drug levels in these cells (1). Furthermore, there is evidence to link the ABC drug transporters, especially Pgp, to clinical drug resistance. For instance, overexpression of Pgp correlates with drug resistance in several forms of cancers (2), and expression of Pgp in some tumors predicts poor chemotherapeutic responses in patients (3). Naturally, these observations had sparked intense search for both compounds and new ways to overcome the MDR phenotype in cancer cells. This section summarizes and provides an update up to the end of 2008 on the development of reversing agents for the ABC drugs transporters. 2.1. Inhibition of ABC Transporters Function as a Mean to Sensitize Multidrug-Resistant Cancer Cells

The most logical step in developing or discovering compounds that can reverse MDR phenotype is finding molecules that can directly block ABC drug transporter activity. In fact, most ABC reversing agents discovered or developed to date fall within this category. The following two subsections summarize the historical development of the so-called MDR modulators and the current status of clinical studies of some of these compounds. The intent is not to exhaustively discuss all of the studied compounds but to

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focus on selected ones which demonstrated somewhat promising results. For a complete list of compounds that have earlier shown to reverse MDR (first- and second-generation MDR modulators), please refer to other earlier reviews (4, 5). 2.1.1. Development of First and Second-Generation MDR Modulators

Verapamil, a calcium channel blocker, was one of the first-generation MDR modulators to be discovered to have the ability to reverse MDR (6). It was found to enhance intracellular accumulation of many anti-cancer drugs in various cell lines. Other MDR modulators were soon discovered and amongst these, cyclosporine A was the most effective and best studied (7). Cyclosporine A was able to completely resensitize a drug-resistant human T-cell acute lymphatic leukemia cell line to anti-cancer drugs and was also effective against doxorubicin resistance in solid tumors (8). Both verapamil and cyclosporine A had entered clinical trials but side effects were seen in patients due to the fact that high doses of the drugs were required (7, 9). The failure of verapamil and cyclosporine A led to the development of second-generation MDR modulators which are mostly derivatives of first-generation MDR modulators with improved efficacy and reduced side effects. Amongst this group of compounds, the cyclosporine A analog SDZ PSC833 (valspodar) was by far the most exciting. SDZ PSC833 was 10- to 20-fold more potent than its predecessor in reversing MDR in cell lines (10, 11), and highly effective against in vivo ascites models and solid tumor MDR models in animals (12). Unfortunately, clinical trials showed that SDZ PSC833 can seriously impair drug metabolism and elimination, resulting in patients overexposing to increased serum concentrations of cytotoxic drugs (13, 14). SDZ PSC833 has recently been tested on patients with multiple myeloma but again it failed to demonstrate any usefulness (15).

2.1.2. Development of Third-Generation MDR Modulators

The third-generation MDR modulators were developed to overcome the limitations that the second-generation MDR modulators exhibited. Most of these drugs which are effective at nanomolar concentration range were developed using structure– activity relationships and combinatorial chemistry. They are not metabolized by cytochrome P450 and they do not affect the pharmacokinetics of anti-cancer drugs. The third-generation MDR modulators which are in clinical trials include LY335979 (Zosuquidar), GF120918 (Elacridar), CBT-1, and XR9576 (Tariquidar). LY335979 (Zosuquidar) is one of the most potent MDR modulator known to date with Ki of 59 nM (16). Studies using cell lines and membrane vesicles have confirmed that LY335979 is not a modulator of either MRP or BCRP, but a highly specific MDR modulator of Pgp (4). In preclinical studies, the compound significantly enhance survival rate and reduce tumor mass of mice

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engrafted with MDR-bearing human tumors (16). LY335979 had no effect on the pharmacokinetics of anti-cancer drugs which may in part explained by its lack of inhibitory effect on cytochrome P450 isozymes (16). Clinical studies on LY335979 have been quite promising. A 75% response rate was observed among 16 AML patients who were given LY335979 in combination with daunorubicin and cytarabine, suggesting the possibility of giving LY335979 to AML patients in combination with induction doses of conventional cytotoxic drugs (17). In a recent Phase I/II clinical trial, LY335979 was shown to have little effect on the pharmacokinetics of anti-cancer drugs in patients with non-Hodgkin’s lymphoma (18). LY335979 is therefore suitable for further Phase III clinical trials. GF120918 is another third-generation MDR modulator which has exhibited some promising properties. Unlike LY335979, GF120918 is not specific for Pgp as the drug can completely reverse mitoxantrone resistance in cells overexpressing BCRP (19). However, GF120918 does share one important property with other third-generation MDR modulators, in that it has minimal interactions with anti-cancer drugs. In a recent Phase I clinical trial, coadministration of GF120918 with oral topotecan resulted in complete apparent oral bioavailability of topotecan (20), suggesting that GF120918 should be suitable for further Phase II or III studies. CBT-1 is a bisbenzylisoquinoline plant alkaloid and a relatively new drug developed against Pgp (21). In Phase I trials, CBT-1 showed no effect on the pharmacokinetics of doxorubicin or paclitaxel (22, 23). The drug is currently in Phase II and III trials (21). XR9576, an anthranilamide derivative, demonstrated high potency both in vitro and in vivo studies. In mice carrying the intrinsically resistant colon tumors, XR9576 potentiated the antitumor activity of doxorubicin without significant toxicity (24). In addition, XR9576 fully restored the anti-tumor activity of several anti-cancer drugs against two highly resistant MDR human tumor xenografts in nude mice (24). Phase I trials showed that XR9576 had no effect on the pharmacokinetics of paclitaxel, vinorelbine, or doxorubicin when it was administered to patients with solid tumors (25, 26), and it was tolerable to patients at concentrations effective in inhibiting Pgp (27). Unfortunately, Phase II and III trials with XR9576 have not been very encouraging. Studies in patients with non-small-cell lung cancer were terminated due to chemotherapy-related toxicity in patients administered with the drug (28). For a complete list and updates on the third-generation MDR modulators, please refer to Table 14.1.

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Table 14.1 Third-generation MDR modulators

2.1.3. Natural Products as MDR Modulators

MDR modulator

Targeted ABC transporter(s)

Current stage of studies

References

CBT-1

Pgp

III

(29)

Tesmilifene

?

III

(30)

MS209 (Dofequidar)

Pgp, MRP1

III

(31)

PSC833 (Valspodar)

Pgp

III

(15)

ONT-093

Pgp

II

(32)

Annamycin

?

II

(33)

Mitotane

Pgp

II

(34)

R101933 (Laniquidar)

Pgp

II

(35)

VX710 (Biricodar)

Pgp, MRP1

II

(36, 37)

LY335979 (Zosuquidar)

Pgp

I, II

(18)

XR9576 (Tariquidar)

Pgp, MRP1

I, II

(36, 38)

GF120918 (Elacridar) Pgp, BCRP

I

(20)

Sulindac

MRP1

I

(39, 40)

S9788

Pgp

I

(41)

The search for MDR modulators has also extended to the natural products. The rationale is that natural products and their derivatives will be less toxic and more potent than the disappointed first- and second-generation MDR modulators. A very large number of varying sources of natural products capable of reversing MDR phenotype have been found and continued to be found. A list of this group of MDR modulators discovered in the last 3 years is shown in Table 14.2. Perhaps, the most widely studied amongst this group of compounds is curcumin (21, 42). Curcumin and its derivatives can inhibit the function of all three major ABC transporters, Pgp, MRP, and BCRP. Its low bioavailability when given orally and its rapid metabolism have prompted investigators to assess the effect of encapsulating curcumin with liposome. Apparently, liposomal curcumin can overcome its bioavailability problems when given intravenously (21). Its toxicity is relatively low and comparable to third-generation MDR modulators, and it has been shown to be effective anti-tumor activity in animal studies

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Table 14.2 Natural product MDR modulators Year

MDR modulators

Targeted ABC transporter(s)

References

2005

5-Bromo tetrandine

Pgp

(43)

Kavalactones

Pgp

(44)

Curcumin

MRP1

(45)

Flavonoids

MRP1

(46)

Myricetin

MRP1

(47)

6-Prenylchrysin

BCRP

(48)

Piperazinobenzopyranones and BCRP phenylalkylaminobenzopyrazones

(49)

Stilbenoids

BCRP

(50)

Tectochrysin

BCRP

(48)

Coumarins

Pgp

(51)

Diterpenes, cycloartane triterpenes, carotenoids

Pgp

(52)

Curcumin

BCRP

(53)

Eupatin

BCRP

(54)

Ginsenosides

BCRP

(55)

Curtisii root extract

Pgp, MRP1

(56)

Deoxyschizandrin

Pgp

(57)

Kaempferia parviflora extracts

Pgp, MRP1

(58)

Schisandrol A

Pgp

(59)

Tryptanthrin

Pgp

(60)

Vitamin E TPGS

Pgp

(61)

Dihydro-b-agarofuran sesquiterpenoids

Pgp

(62)

3¢,4¢,7-Trimethoxyflavone

BCRP

(63)

Rotenoids

BCRP

(64)

Tetrahydrocurcumin

BCRP

(65)

N-hexane root extracts

Pgp

(66)

2006

2007

2008

Chokeberry and mulberry leaves Pgp, MRP1

(67)

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331

(21). With such encouraging results, it would not be a surprise that curcumin may be assess for ability to reverse clinical MDR in the very near future. 2.2. Degradation of mRNA as a Mean to Suppress Expression of ABC Transporters

Targeting specific mRNA for degradation as a mean to inactivate gene expression has been long realized as a potential powerful therapeutic approach. mRNAs for MDR-associated genes have been targeted as a mean to reverse the MDR phenotype. However, unlike the more rigorous search for agents that could directly modulate ABC-transporter’s activity, much less studies focused on discovering agents capable of reversing MDR via destruction of mRNAs for ABC-drug transporters. This is in large due to our lack of basic understanding on cellular components which control the degradation of mammalian mRNAs. I anticipate that as we increase our basic knowledge in this field, especially in identifying key enzymes that control mammalian mRNA decay, new technologies aimed at manipulating mRNA levels will emerge. The approaches that have been used to target mRNAs for ABC-drug transporters are antisense oligonucleotides, ribozymes, and more recently the small interfering RNA (siRNA).

2.2.1. Antisense Oligonucleotides and Ribozymes

Antisense oligonucleotide (AON) was the first mRNA degradation technology to be developed and therefore it is not surprising that it was first to be assessed for efficacy in reversing the MDR phenotype. The earlier version of AON such as phosphorothioate oligonucleotide is believed to function through RNase H-mediated degradation of complementary mRNA. The AON has worked remarkably well in reducing Pgp expression and chemosensitizing drug-resistant cells in culture (4, 68). AONs have also been successful against MRP- and BCRP-mediated drug resistance in cell lines (69–71). Several other newer generation of AONs have been developed and these include modifications at 2' OH, locked nucleic acids, peptide nucleic acids, morpholino compounds, and hexitol nucleic acids, all of which do not utilize the activity of RNase H. Amongst these, peptide nucleic acid (72) and AON with methoxyethoxy group substitution at 2' position (73) have demonstrated modest improvements over phosphorothioate AON against Pgp in cell lines. A recent report showed that when conjugated to doxorubicin, AON was able to further increase drug accumulation in cells as compared to doxorubicin alone or the AON alone (74). This suggests that conjugated AON in combination with anti-cancer drugs may offer a more powerful treatment for MDR. Catalytic RNA hammerhead ribozyme was the second mRNA degradation technology that was used against mRNA for ABC transporters. It is designed to endonucleolytically cleave a specific mRNA at a defined position in trans-containing a NUX motif, in which N is any nucleotide and X is A, C, or U (75).

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Catalytic RNAs have been successfully used to reverse MDR phenotypes in Pgp-, MRP-, and BCRP-overexpressing cancer cell lines (76–79). In addition, multitarget multiribozyme containing all trans-acting ribozymes against all three ABC transporters were highly successful in decreasing the expression of ABC transporters and in reversing the drug-resistance phenotype that the transporters conferred (80). To date, there are no reports on the use of AON- and ribozyme-based gene therapy against ABC transporters in preclinical and clinical trials. Such studies would most likely proceed if promising results arise from clinical studies on bcl-2 and VEGF mRNAs using AON and ribozyme, respectively (75). Thus, the clinical benefit of reversing MDR using AON and ribozyme remains unknown. 2.2.2. siRNA and Short-Hairpin RNA

The latest mRNA degradation technology uses the RNA interference (RNAi) posttranscriptional mechanism. RNAi is mediated by double-stranded RNA, which is cleaved by the endoribonuclease DICER into 21- to 23-nucleotide duplexes known as siRNAs. The antisense strand of siRNA duplexes, which is part of the RNA-induced silencing complex (RISC), then guides RISC to destroy the target RNA by the second endoribonuclease, Argonaute2. Since 2003, there have been numerous reports on the use of siRNAs which have transient effect, as well as the plasmid expression vector-based short-hairpin RNAs (shRNAs) which have long-term effect, against ABC transporters. For instance, siRNAs and shRNAs have been successfully used to target Pgp, MRP, and BCRP in various cell types (81–85). The use of siRNA and shRNA in reversing MDR has been extended to animal models (86–89) using innovative nucleic acid delivery system. For instance, Stein et al. (86) recently showed significant suppression of Pgp mRNA and reversal of MDR phenotype in vivo using an intratumoral jet-injection delivery of shRNA-expressing vector against Pgp. Using a mouse model-bearing human tongue squamous cell cancer, Jiang et al. (88) reported successful inhibition of Pgp expression and reversing drug resistance by delivering siRNA against Pgp using attenuated Salmonella typhimurium. Significant reversal of MDR phenotype has also been reported in animal models when retroviral-mediated shRNA and electric pulse delivery of siRNA against Pgp were used (87, 89). It does appear that it is feasible to reverse MDR in vivo using mRNA degradation technology. However, such approach has several challenges (90) and foremost would rely heavily on an efficient and harmless mode of delivery system.

2.3. Other Approaches in Reversing MDR

Many other approaches have been assessed for feasibility in reversing MDR. These have been discussed in some detail in recent reviews (5, 21). In this section, I discuss two approaches which have been

Reversing Agents for ATP-Binding Cassette Drug Transporters

333

studied more extensively and that have shown some degree of promises. 2.3.1. Inhibition at the DNA Level

Several methods to inhibit the transcription of Pgp as a mean to reverse MDR have been reported. The transcriptional decoy strategy has been used against Pgp. The 12 nucleotides, double stranded, transcriptional decoy which corresponds to MED-1 sequence element located upstream of MDR1 gene has been shown to sensitize drug-resistant leukemic cell line against vinblastine (91). Others have shown that LANCL2 can be used to transcriptionally suppress Pgp promoter activity leading to reduction in Pgp mRNA and protein levels (92). Another approach utilized transcriptional regulator containing Cys2-His2 type zinc finger (Zif) which recognizes Pgp promoter in addition to Zifs from the SP1 or Zif268 transcription factors (93). The five Zif chimeric proteins were able to suppress Pgp promoter activity, reduce Pgp expression, and sensitize cells to doxorubicin (93, 94). Significant inhibition on Pgp transcription followed by restoration of drug sensitivity has also been demonstrated with b–naphthoflavone (95) and with a natural marine product Et743 (96). To date, there are no reports on the use of transcriptional inhibition strategy as a mean to reverse MDR in animal studies.

2.3.2. Antibodies

To date, only antibodies against Pgp have been assessed for ability to reverse MDR phenotype. Tsuruo et al. demonstrated the ability of Pgp-specific monoclonal antibody MRK16 to inhibit tumor growth in athymic mice (97). This antibody which recognizes an external epitope of human Pgp was later shown to inhibit Pgpmediated MDR in cell line and transgenic mice expressing human Pgp (98, 99). Monoclonal antibodies against Pgp in combination with other MDR modulators (100, 101) or conjugated to cytotoxic agent (102) have also shown successes in inhibiting Pgpmediated MDR. Interestingly, palmitoylated synthetic peptides against extracellular loops of Pgp reconstituted in liposomes were able to induce production of specific autoantibodies and improved chemotherapy in mice (103). The sera from immunized mice were also effective in reducing cellular resistance to vinblastine and doxorubicin (103). Antibodies against ABC transporters have not been assess for ability to reverse MDR in clinical trials.

3 . Major Obstacles to Success in Reversing Clinical MDR

The obstacles confronting development of drugs to reverse clinical MDR are no different from any drug development program against any other diseases. In many respects, the problems are not

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unique. Nonspecific toxicity of MDR modulators to cancer patients is a common problem. Another common obstacle is the unexpected and undesired pharmacokinetic interactions between the modulators and the anti-cancer drugs used for the treatment of patients. Often enough, the modulators blocked the clearance and/or metabolism of anti-cancer drugs resulting in accumulation of the drugs in plasma and hence enhanced toxicity to cancer patients. It is unfortunate that animals or preclinical studies are not good predictor or first round screen for these potential obstacles. Consequently, when tested in clinical trials, a high number of MDR modulators would proved to be disappointment. Another common barrier is the lack of drug delivery system that would target drugs to specific cells, tissues, or organs. The expectation of modern medicine is high. Given that we are able to find the molecular targets and design drugs that would counter attack targeted protein or mRNA, the drug would be useless if it is not able to be delivered to the target cells. Another hurdle which has only recently emerged is the discovery of single nucleotide polymorphisms (SNPs) amongst the ABC drug transporters (104, 105). Genetic polymorphisms in Pgp have been shown to change the mRNA expression, protein expression, and function of the protein, and this is believed to contribute to variations in drug responses observed in different individuals and ethnic groups. The presence and abundance of multiple ABC drug transporters pose another hurdle. Similarly, the presence of MDR mechanisms other than that conferred by the ABC drug transporters would pose further complications and challenges.

4. Conclusion It appears to be daunting when one took a first glance at the problems discussed above and at the rather slow progress that we have made over the last 20 years in finding effective drugs for reversing clinical MDR. However, over the years of experience we have understand the problems and we have taken initiatives to overcome these problems. For instance, we have become smarter in designing drugs that are less toxic and those that do not affect the pharmacokinetics of anti-cancer drugs. This is evidence from the higher rate of success amongst the third-generation MDR modulators. We have become aware of the existence of multiple ABC drug transporters and SNPs amongst the ABC drug transporters, and the potential problems that we faced. It would not be unusual to administer more than one drug to reverse clinical MDR should there be more than one ABC transporters present

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or other MDR mechanisms exist in a particular patient. In fact, it has becoming clearer that successful practice in clinical oncology would usually rely on the use of a combination of drugs as exemplified by the use of antiangiogenic drugs (106). Variant oncoproteins for Bcr-Abl which are resistant to Gleevec and variants for EGF-R which are resistant to Iressa are known to exist (107). New drugs have also been developed against these variants with considerable success. Thus, any variants for ABC transporters, as a result of SNPs, which are resistant to conventional MDR modulators can be potentially counter attack by development of new drugs. As with any other drug development program, one of the most important feature to successful clinical MDR is the ability to deliver MDR modulators, whether it be nucleic acid- or nonnucleic acid-based, to target cells. Research into drug delivery is actively on-going (108, 109) and improvements in this area can potentially overcome some of the obstacles discussed above. In summary, there is no doubt that our knowledge on how to develop better MDR modulators has increased considerably. We have become aware of the existing and future potential problems and how to counter attack these problems. This has and will continue to lead us to better wisdom in developing a wide range of new and better MDR modulators. References 1. Cole SPC, Tannock IF (2005) Drug resistance. In: Tannock IF, Hill RP, Bristow RG, Harrington L (eds) The basic science of oncology. McGrawHill, New York, pp 376–399 2. Goldstein LJ, Galski H, Fojo A et al (1989) Expression of multidrug resistance gene in human cancers. J Natl Cancer Inst 81: 116–124 3. Chan HS, Haddad G, Thorner PS et al (1991) P-glycoprotein expression as a predictor of the outcome of therapy for neuroblastoma. N Engl J Med 325:1608–1614 4. Lee CH (2004) Reversing agents for ATP-binding cassette (ABC) transporters: application in modulating multidrug resistance (MDR). Curr Med Chem Anticancer Agents 4:43–52 5. Wu C-P, Calcagno AM, Ambudkar SV (2008) Reversal of ABC drug transporter-mediated multidrug resistance in cancer cells: evaluation of current strategies. Curr Mol Pharmacol 1:93–105 6. Tsuruo T, Iida H, Tsukagoshi S, Sakurai Y (1981) Overcoming of vincristine resistance in P388 leukemia in vivo and in vitro through enhanced cytotoxicity of vincristine and vinblastine by verapamil. Cancer Res 41:1967–1972

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Chapter 15 Overcoming Multidrug Resistance in Cancer: Clinical Studies of P-Glycoprotein Inhibitors Helen M. Coley Abstract Chemotherapy remains the mainstay in the treatment and management of many cancers. However, this treatment modality is fraught with difficulties associated with toxicity and also the emergence of chemotherapy resistance is a considerable problem. Cancer scientists and oncologists have worked together for some time to find ways of understanding anticancer drug resistance and also to develop pharmacological strategies to overcome that resistance. The greatest focus has been on the reversal of the multidrug resistance (MDR) phenotype by inhibition of the ATP-binding cassette (ABC) drug transporters. Inhibitors of ABC transporters – termed MDR modulators – have in the past been numerous and have occupied industry and academia in drug discovery programs. The field has been fraught with difficulties and disappointments but, nonetheless, we are currently considering the fourth generation of MDR modulator development with much data pending from the clinical trials with the third-generation modulators. Firstgeneration MDR modulator compounds were very diverse and broad spectrum pharmacological agents which fuelled the excitement surrounding the research into the MDR phenotype in cancer at the time. Second-generation agents were very heavily evaluated in mechanistic studies and formed the basis for a number of oncology portfolios of big pharmaceutical companies. Given this input, a number of clinical trials were carried out, the results of which were somewhat disappointing. Even with the modest evidence of active combinations, trial data were considered promising enough to warrant development of the third-generation of modulators. A number of key molecules have been identified with potent, long lasting MDR reversal properties, and minimal pharmacokinetic interaction with the co-administered cytotoxic agent. The results from a number of these trials are eagerly awaited and there are many in the cancer research community who remain committed to this area of anticancer drug discovery. Key words: MDR modulator, ABC transporters, P-glycoprotein, Pharmacokinetic interaction

1. Introduction The notion of anticancer drug resistance in the clinical and laboratory setting goes back as far as the 1950s, a few years following the first use of chemotherapeutic drugs. Elucidation into the underlying mechanisms began to emerge in the 1970s (1). What J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_15, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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remains clear is that the adaptive response of cancer cells to produce a drug-resistant phenotype is a complex process that involves numerous pathways. Indeed, it should be noted that drug resistance in a cancer cell line model, or ultimately in the cancer patient, is likely to be due to multifactorial and complex mechanisms, rather than due to a single mechanism. To consider this in more detail, the molecular biology of the cancer cell has recently revealed to us the complexity of their associated survival pathways and their interactions. Together with its intrinsic genetic instability and the genotoxic nature of the cancer drugs themselves, we can only expect the underlying basis of anticancer drug resistance to be a result of a number of mechanisms. In order to ensure the effectiveness of an anticancer drug, a number of processes should be considered: (a) cellular pharmacokinetics e.g. uptake and retention, (b) evasion of sequestration or metabolic inactivation, (c) interaction with the drug target, (d) evasion of DNA repair pathways (particularly relevant for alkylating agents), and (e) effective induction of cell death (e.g. via apoptosis). Anticancer drug resistance may impede one or more of these processes – but this list is not exhaustive and we must consider anticancer drug resistance as something multifactorial and still awaiting full characterisation. There may be altered drug pharmacokinetic properties (e.g. distribution, uptake, metabolism, and elimination) either at the intra-tumour or at a cellular level. Further, the pharmacodynamic properties of anticancer drugs may be attenuated to prevent the desired drug action (e.g. alteration in the drug target). Finally, downstream signalling of drug actions to elicit an apoptotic response may be dampened in resistant tissue e.g. a lack of the mismatch repair enzyme gene hMLH1 has been shown to reduce the apoptotic response in cells treated with cisplatin (2). One of the biggest problems within the field of anticancer drug resistance has been the poor translation of the key mechanisms identified by in vitro studies to the clinical scenario. Drug resistance may be regarded as a multifaceted and dynamic phenotype which ultimately results in enhanced tumour cell survival and reduced chemo-responsiveness, regardless of the specific mechanism(s) involved. In specific instances, there will be tissue- or drug-dependent predisposing influences towards particular resistance mechanisms. For example, the activity of enzymes such as methyl-guanine-methyl transferase (MGMT) is crucial in the responsiveness of glioma and other brain tumours to the chloroethyl-nitrosourea group of compounds (e.g. CCNU, BCNU). Indeed, competitive inhibition of the enzyme can be shown to bring about enhanced chemo-responsiveness in the clinical setting (3). A specific form of anticancer drug resistance is termed multidrug resistance (MDR) and it is considered by many to represent a significant obstacle to the success of chemotherapy in many cancers

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in the clinical setting. MDR is a phenomenon best described as tumour cells in vitro that have been exposed to one cytotoxic agent (e.g. doxorubicin) developing cross-resistance to a range of structurally and functionally unrelated compounds, but usually of natural product origin (e.g. epipodophyllotoxins, taxanes, anthracyclines, etc). The common correlate in MDR cell lines was shown some time ago to be increase (or over-expression) of membrane glycoproteins, the ATP-binding cassette (ABC) transporters. The most widely described and first to be associated with anticancer drug resistance was the 170 kDa P-glycoprotein (Pgp) (1). Drug resistance in the form of MDR may occur intrinsically in some cancers without previous exposure to chemotherapy agents (4). As previously stated, the cytotoxic drugs that are most frequently associated with MDR are hydrophobic, amphipathic natural products, such as the taxanes (paclitaxel, docetaxel), vinca alkaloids (vinorelbine, vincristine, vinblastine), anthracyclines (doxorubicin, daunorubicin, epirubicin), mitoxantrone, epipodophyllotoxins (etoposide, teniposide, topotecan), dactinomycin, and mitomycin C. Pgp belongs to the ABC family of transporters, which are associated with several (in excess of 40) family members that share sequence and structural homology (see Fig. 15.1 for a simplified schematic diagram of Pgp). It is believed that, while this class of transporters have a large number of members, only ten or so are reported to confer the drug-resistant phenotype. These transporters use the energy that is released when they hydrolyse ATP to drive the movement of various (exogenous and endogenous) molecules across the cell membrane. In addition to their physiological expression in normal tissues, many are shown to be expressed, and, importantly, over-expressed, in human tumours. A number of ABC transporters and the chemotherapy drugs to which they have been shown to confer resistance are listed in Table 15.1. In addition to cytotoxic drugs, Pgp also transports several other classes of pharmacological agents including digoxin, opiates,

Fig. 15.1. Simplified schematic diagram of P-glycoprotein.

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Table 15.1 Anticancer drugs known to be substrates of P-glycoprotein and other efflux transporters Anticancer agents

Pgp [ABCB1]

MRP1 [ABCC1]

BCRP (MXR) [ABCG2]

Anthracyclines (doxorubicin, daunorubicin, epirubicin, mitoxantrone)

+

+

+

Topoisomerase inhibitors (etoposide, teniposideazatoxin)

+

+

Topoisomerase inhibitors (topotecan, irinotecan, SN-38, indolocarbazole NB-506, indolocarbazole J-107088)

Vinca alkaloids (vincristine, vinblastine)

+

+

−

Alkaloids (cepharanthine, homoharringvtonine)

+

−

−

Taxanes (paclitaxel, docetaxel)

+

−

−

Antitumor antibiotics (actinomycin D, mitomycin C)

+

+

−

Antimetabolites (cytarabine)

+

(Methotrexate)

(Methotrexate)

Acridines (amsacrine)

+

Anthracenes (bisantrene)

−

−

+

Flavopiridol

−

−

+

Pgp P-glycoprotein, MRP multidrug resistance protein, BCRP breast cancer resistance protein

polycyclic aromatic hydrocarbons, and technetium (Tc-99m) sestamibi that has been used in imaging techniques involving MDR modulators (5). In cancerous tissue, the expression of Pgp is usually highest in tumours that are derived from tissues that normally express Pgp, such as epithelial cells of the colon, kidney, adrenal, pancreas, and liver, resulting in the potential for resistance to some cytotoxic agents before chemotherapy is initiated. In other tumours, the expression of Pgp may be low at the time of diagnosis, but may be induced after exposure to chemotherapy agents, thereby resulting in the development of MDR in those cells (4). There is a body of evidence, albeit at times inconsistent, that links the failure of certain chemotherapeutic agents to the expression of Pgp (6–8). Moreover, the induction of MDR1 RNA can be very rapid following exposure of tumour cells to chemotherapy (9).

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2. Generations of Pgp Inhibitors The 30-year period since the discovery of Pgp has seen an enormous effort to generate clinically applicable inhibitors to restore sensitivity of cancer cells to chemotherapy. Inhibitor development has been through three distinct generations, classified according to the strategy employed in their discovery. 2.1. First-Generation Pgp Inhibitors

Many agents that modulate the Pgp transporter were identified in the 1980s, including verapamil, cyclosporin A (CsA), tamoxifen, and several calmodulin antagonists (10). These agents often produced disappointing results in vivo because their low binding affinities necessitated the use of high doses, resulting in unacceptable toxicity (10, 11). Many of the first chemosensitizers identified were themselves substrates for Pgp and thus worked by competing with the cytotoxic compounds for efflux by the Pgp pump; therefore, high serum concentrations of the chemosensitizers were necessary to produce adequate intracellular concentrations of the cytotoxic drug (12). In addition, many of these chemosensitizers are substrates for other transporters and enzyme systems, and their use results in unpredictable pharmacokinetic interactions in the presence of chemotherapy agents. To overcome these limitations, several novel analogues of these early chemosensitizers were tested and developed, with the aim of finding Pgp modulators with less toxicity and greater potency. Verapamil was rapidly entered clinical trials in 1985 in spite of a lack of knowledge of Pgp and verapamil interactions (13). These clinical trials were complicated by the cardiotoxicity produced by verapamil as a function of its ability to produce hypotension through calcium channel blockade. The immunosuppressant CsA, known to possess effects on the cell membrane, was also considered as an MDR modulator in the 1980s. CsA was shown to potentiate the cytotoxicity of vincristine and doxorubicin via a mechanism that increased the intracellular concentration of anthracyclines (14, 15) and the vinca alkaloids (16). The mechanism underlying the inhibition appeared to be by reducing the membrane interaction of anticancer drugs with Pgp (16, 17) and also via competitive transport since the Pgp expressing cells also showed reduced CsA accumulation (18). CsA was shown to bind to an identical site to vinblastine (16) using a method of azidopine labelling (17). The results from those studies provided the first evidence for multiple sites of drug interaction on Pgp. Perhaps, the biggest impetus for pursuing the early use of MDR modulators in the clinical setting was provided by the work of the Toronto group headed by Chan et al. who first showed that the expression of Pgp was a significant prognostic marker in certain childhood malignancies (19, 20). This group then went on

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to use CsA in combination with chemotherapy in retinoblastoma patients and achieved a high cure rate (91% of previously untreated patients remained relapse-free, with salvage therapy combining CsA and chemotherapy prolonging survival in those previously untreated with CsA) (21). Although these trials were limited in size, they raised substantial interest in the cancer research community. The mounting enthusiasm for pharmacological modulator use enabled rapid progression of CsA to phase I clinical trials (22, 23). The most salient points from those trials were that considerable pharmacokinetic changes were seen when CsA was given in combination with agents such as etoposide and highlighted the difficulties in managing the consequences of this. Several drug classes were examined for the ability to behave as chemosensitizing agents in combination with chemotherapy drugs in cultured cancer cell lines. The anti-oestrogen tamoxifen was considered in this regard and this was of considerable interest as it clearly has implications for the treatment of breast cancer (24–27). The overall conclusion following the clinical trials of the firstgeneration MDR modulators was that their clinical potency was too low to act as effective modulating agents, and this was further complicated by the considerable pharmacokinetic interaction of modulating agents with chemotherapeutic agents. 2.2. Second-Generation Pgp Inhibitors

The experiences gained from studies of the first-generation inhibitors provided the grounds for more rational drug discovery programs to search for new MDR modulators. However, the first-generation agents such as verapamil were used as lead compounds in drug design. The calcium channel blockers, which include verapamil, were amongst the classes of drugs that were focused on. A standard clinical preparation of verapamil is composed of a racemic mix of L- and D-forms and thus it was decided to investigate the MDR reversing capacity of the individual isomers. The MDR-reversing capacity of the racemic and individual isomers was shown to be equal (28, 29). Perhaps, the most salient point to be gained form these studies was that the D-isomer of verapamil – dexverapamil – has a tenfold lower calcium channel blocking activity than the L-isomer and for this reason shows significantly less cardiotoxicity. The toxicity profile of dexverapamil in a phase I/II trial combining vinblastine in renal cell carcinoma patients still indicated cardiotoxicity but to a more manageable level (30, 31). However, clinical studies failed to produce any responses in cancer patients treated with drug combinations involving dexverapamil and thus this approach was not pursued any longer (30, 31). Following the success of the first-generation Pgp inhibitor CsA, particularly in the trials from the Toronto group (see above), the search continued for related compounds or derivatives as

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potential new MDR modulators. As the immunosuppressive activity was not a prerequisite for inhibition of Pgp, a number of derivatives devoid of immunosuppressive activity were examined for the ability to overcome resistance to anticancer drugs in vitro (32). The cyclosporin derivative PSC-833 (subsequently named Valspodar™) and the cyclopeptolide SDZ280-446 emerged as lead compounds from these drug discovery programs (33–36). The reversal of drug resistance was observed using a wide range of anticancer drugs including vinca alkaloids, anthracyclines, colchicine, and paclitaxel in combination with the cyclosporins. Pgp inhibition via PSC-833 pulse dosage was pronounced and long-lived, still evident at 48 h (37). The circumvention of drug resistance was shown to be related to an enhancement in drug accumulation and cellular retention of anticancer drugs in cell lines (38), but the effects were also observed in monolayer polarised intestinal cells (39). Such effects have clear implications for the pharmacokinetics of orally administered drugs. Further studies showed that PSC-833 was relatively unselective in its inhibition of ABC transporters (40) and this was considered to be a potential disadvantage as normal tissues may be affected due to their relative and specific expression levels of various ABC family members. However, for some this was considered to be a possible advantage as multiple ABC transporters may coexist in high amounts in some tumours. Valspodar was by far the most rigorously researched of the second-generation MDR modulators, with trials being conducted through phase I–III. Certain patients with acute myeloid leukaemia (AML) emerge as the group that may gain most benefit from MDR modulation as a treatment modality, as witnessed in a number of phase III trials. A trial incorporating daunorubicin, cytarabine and etoposide with valspodar in elderly chemotherapy-naïve AML patients failed to show any benefit (41). The results from that study were mirrored in a very similar trial with the same drug combinations and the same patient group (>65 years of age) (42). An interesting study by Kolitz et al. (43) indicated that a combination of daunorubicin, etoposide and cytarabine with valspodar in previously untreated patients less than 45 years of age provided a survival benefit. Another trial in previously treated AML patients (with no specific age cohort considered) suggested that valspodar in combination with mitoxantrone, etoposide, and cytarabine was ineffective (44). Ovarian cancer patients showing chemorefractory disease were treated with a combination of paclitaxel with valspodar but no clinical benefit was demonstrated (45). Some drug discovery programs focused around computational investigations in an attempt to identify particular affinities of Pgp for chemical moieties, and information was gained regarding the physical and chemical character necessary for drug recognition (46, 47). Several second-generation inhibitors including

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the triazine-aminopiperidine S9788, resulted from such studies (48, 49). However, again the clinical efficacy of such agents was never proven, with cardiac toxicity being noted when used in combination with doxorubicin (50). VX-710 (Biricodar) was developed through synthetic chemistry to interact with the FK-506 (Tacrolimus) binding protein and also demonstrated the ability to reverse MDR (51). VX-710 was shown to reverse resistance to doxorubicin, vincristine, etoposide, and paclitaxel in vitro. The mechanisms underlying the chemosensitisation were shown to be via competitive binding with Pgp-mediated drug efflux, as demonstrated by photoaffinity labelling (51). Subsequent studies with a range of resistant cancer cell lines demonstrated that VX-710 was not selective for Pgp (ABCB1). Both MRP (ABCC1) (52) and BCRP (ABCG2) (53) are also inhibited by VX-710 in cell lines thereby promoting this compound as a “broad-spectrum” modulator of efflux-mediated resistance pathways. A phase II trial in small cell lung cancer patients using doxorubicin and vincristine showed promising results as the combinations indicated only mild or low inherent toxicity of VX-710 (54). The second-generation Pgp inhibitors yielded more information into the mechanisms underlying MDR modulation and of course built upon the knowledge gained from the first-generation modulator studies. The significant advances were the identification and development of compounds with high potency and the ability to reverse the MDR phenotype, with associated lower (but not negligible) toxicity. The identification of the affinity of MDR modulators for multiple ABC transporters began to be considered as problematic, however, as this highlighted the potential for more pharmacokinetic interactions. The potential problems associated with MDR modulator strategies were at this stage becoming more apparent from the reports of the clinical trials emerging in the mounting literature on MDR research. 2.3. Third-Generation Pgp Inhibitors

The development of third-generation of Pgp inhibitors used combinatorial chemistry approaches with lead compounds derived from pharmacological information already available on drug–Pgp interaction. More rational design was possible at this stage of MDR modulator development based on data from the previous generations of drug development. A number of criteria were identified as being essential features for new reversal agents; these included reversal of the resistant phenotype at low dose levels with a high level of affinity interaction with Pgp. Other features required for candidate molecules were an indication of selectivity for a particular ABC transporter (e.g. ABCB1 vs. ABCG2) and a reduced susceptibility for metabolic biotransformation by the cytochrome P450 (CYP) isoform CYP3A4.

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Elacridar – GF120918/GG918 – was the first of this new generation of compounds, developed by Glaxo laboratories from a combinatorial chemistry program (55). The structure of the compound is an acridonecarboxamide which in vitro demonstrated reversal of MDR by chemosensitisation in combination with both vincristine and doxorubicin (55). Reversal of drug resistance was accompanied by enhancement of drug accumulation of anticancer with the potency of elacridar to reverse drug resistance in the nanomolar range. Moreover, the potency of elacridar was also borne out in studies using cell lines with very high levels of drug resistance (56). The inhibition of anticancer drug efflux was proposed to be via allosteric binding on Pgp (57). Elacridar was shown to enhance the cytotoxicity of anticancer drugs in cells expressing BCRP and with similar potency to that observed with Pgp (58). The ability of Elacridar to interact with BCRP was demonstrated in experiments where topotecan accumulation was enhanced (59). Ex vivo restoration of chemosensitivity to anthracyclines was shown in clinical samples of acute myelogenous leukaemia (AML) (60) and subsequently a number of in vivo studies were undertaken with the drug. The development of a number of modulators was accompanied by the development of so-called “functional assays” to look for demonstration of MDR modulation in an ex vivo setting. One approach was to analyse of the CD56+ subset of peripheral blood lymphocytes by making use of their intrinsic functional Pgp expression and their reduced ability to accumulate the fluorescent dye rhodamine123, using rapid flow cytometric analysis. Ex vivo data revealed that incubation of cancer cells with elacridar increased rhodamine123 accumulation and these results can be regarded as a surrogate marker for Pgp activity in vivo (61). In vivo studies showed that IV administration of elacridar demonstrated its good bioavailability and reversed doxorubicin resistance (55). Importantly, ex vivo analysis in myeloma and AML samples indicated the concentrations of the modulator necessary to inhibit Pgp were clinically attainable and thus provided the rationale for further studies (62). A drug discovery program carried out by Xenova PLC (Slough, UK) based on a diketopiperazine compound produced a potent MDR modulator XR9051 (63). However, there were shortcomings in the physico-chemical properties of this molecule (e.g. hydrophobicity) and subsequently a series of modifications to the anthranilimide nucleus were undertaken (64). The most potent of the resultant derivatives was XR9576 (tariquidar), which was able to completely overcome resistance to doxorubicin, paclitaxel, etoposide, and vincristine at a concentration range of 25–80 nM in a number of MDR cell lines (65). Resistance was overcome through inhibition of efflux as demonstrated by the ability of tariquidar to increase the cellular accumulation of the Pgp substrates rhodamine123 and [3H]-daunomycin. A very

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important feature of tariquidar was that its pharmacological effects persisted for longer than 22 h following its removal from the assay system, which compares favourably with the persistence of cyclosporine and verapamil, being around 1 h. The antitumour activities of paclitaxel, vincristine, and etoposide were restored in whole mice, in highly resistant tumour xenografts (65) and in a tumour spheroid model (66). The high potency of tariquidar interaction with Pgp was demonstrated by a KD ~ 5 nM and moreover, the drug did not appear to act as a substrate for Pgp itself (67). The ability of tariquidar to inhibit vinblastine and paclitaxel transport was shown to occur via allosteric reductions in the binding affinity of anticancer drugs on Pgp (68). The interaction of tariquidar with Pgp was shown in some elegant studies to be characterised by a slow rate of dissociation and perturbation of cellular drug compartmentalization (68). The awareness of the role of the hepatic CYP3A4 isoform in the pharmacokinetic profiles seen in trials incorporating MDR modulators and cytotoxic drugs became a specific and important issue; it was shown that it shared a considerable substrate specificity with Pgp. Significantly, tariquidar was shown to not be subjected to metabolism via the CYP3A4 isoform but involvement of the 1A2, 2C8, and 2C9 isoforms was reported (69). This was heralded as a considerable advantage and positioned tariquidar very favourably compared with the majority of other MDR modulators. BCRP interaction was noted but the affinity of tariquidar for this transporter was shown to be considerably lower than for Pgp (70). The first phase I study using tariquidar indicated no effect on the pharmacokinetics of vinorelbine and no novel toxicities were reported (5). Twenty-five patients enrolled in this trial, of which 13 experienced disease stabilization and 2 had a partial response. Interestingly, the maximum tolerated dosage for vinorelbine administered alone is 30 mg/m2, but in combination with tariquidar it was shown to be 22.5 mg/m2. This reduction may occur as a result of a pharmacodynamic interaction (possibly at the level of Pgp in the bone marrow) that led to increased toxicity of the drug without increase in systemic exposure. In other phase I/II studies combining tariquidar with paclitaxel or doxorubicin for the treatment of solid tumours, no adverse pharmacokinetic interactions were reported (71, 72). However, in spite of the promising pre-clinical and phase I clinical trial data, subsequent trials with tariquidar were terminated owing to toxicities (serious in some cases) associated with the chemotherapy in the tariquidar arm (73). This may have been a reflection on the previous pharmacodynamic effects suggested in the phase I trial with vinorelbine (5). There has been some discussion as to why vinorelbine was administered at a slightly elevated starting dose of 25 mg/m2, and paclitaxel dose at a higher than usual dose was 200 mg/m2 (74). In spite of these

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initial setbacks, the National Cancer Institute (NCI) began further phase I/II trials and these remain described as ongoing status (at the time of going to press). A phase I study of tariquidar plus chemotherapy in treating children with various solid tumours has finished patient enrolment, and results are pending. There are two phase II trials, one combining tariquidar with docetaxel in patients with lung, ovarian, cervical, or kidney cancer and another with combination chemotherapy and surgical resection in the treatment of adrenocortical cancer are both still recruiting. Another third-generation MDR modulator is LY335979, otherwise known as zosuquidar, which was based on the quinoline MS-073, previously shown to be an effective modulator of Pgp (75). In vitro studies showed that zosuquidar restored sensitivity to vinblastine, doxorubicin, etoposide, and paclitaxel in drug-resistant CEM/VLB100 cells (76) and these effects were apparent following removal of the modulator from the culture environment. Zosuquidar was shown to directly inhibit Pgp and it also showed excellent potency some 500- to 1500-fold greater than that seen for CsA and verapamil (77), with a binding affinity KD of 73 nM (78). It appears that zosuquidar is not transported by Pgp but it can show effects on accumulation of anticancer drugs possibly via allosteric pathways (79). Importantly, zosuquidar did not demonstrate binding to ABCC1 or ABCG2 (78–81) with a lower affinity for CYP3A compared to that for Pgp (78). These data suggest therefore that zosuquidar is selective for Pgp, with low potential for interfering with the metabolism of anticancer drugs. In vivo studies have demonstrated that zosuquidar does not alter the pharmacokinetics of etoposide or doxorubicin in mice (76, 79), with small changes to the pharmacokinetics of paclitaxel (82, 83). In a phase I clinical trial orally administered zosuquidar in combination with doxorubicin was given to patients with advanced non-haematological malignancies (84). The maximum tolerated dose of the inhibitor was 300 mg/m2 (administered every 12 h for 4 days) with doxorubicin doses at either 45 or 75 mg/m2. There were no reported toxicities attributable to zosuquidar, nor was there any alteration in the pharmacokinetics of doxorubicin. Another phase I study administered the inhibitor intravenously, again, with doxorubicin. In this case, the maximal doses administered were 640 and 75 mg/m2 f or zosuquidar and doxorubicin, respectively. At the higher doses of the inhibitor (exceeding 500 mg), modest pharmacokinetic interactions were seen with a decreased doxorubicin clearance (c. 20%) (85). Although the reduced clearance was associated with an enhanced leucopaenia and thrombocytopenia, this effect was not considered clinically significant. Docetaxel chemotherapy combined with zosuquidar was also well-tolerated, with minimal increases in docetaxel plasma peak concentrations and AUC levels. Based on those results, a

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phase II trial using the same combination treatment has been started in breast carcinoma (86), the results of which are eagerly awaited. A phase I trial in AML patients concluded that zosuquidar could be given safely in combination with induction doses of daunorubicin and cytosine arabinoside (87). Importantly, 11 out of the 16 patients in this study achieved a complete remission. In contrast, a phase I trial of zosuquidar in combination with vinorelbine in patients with solid tumours showed no objective responses, and the inhibitor reduced the clearance of vinorelbine (88). Results from the majority of phase I/II trials, have shown an absence, or only modest effects, of zosuquidar on the pharmacokinetics of anti-cancer drugs, supporting its progression into phase III trials. However, to date, only one such trial has been initiated looking at AML and high-risk myelodysplastic syndrome. A total of 442 patients were randomized to standard chemotherapy (cytosine arabinoside and daunorubicin) with or without zosuquidar at 550 mg/m2. Disappointingly, treatment with the inhibitor did not provide any benefit, even in the subset of patients with measurable Pgp expression, with overall survival in the experimental and treatment arm being 8 and 9 months, respectively (89). Recently, the MDR modulators tariquidar, along with elacridar were shown to be very effective in modulating the bloodbrain-barrier in a glioblastoma tumour model treated with a combination involving paclitaxel (90), which suggest there may be a chance for the use of such modulating agents in more specific clinical scenarios. Another significant member of the first-MDR modulators is OC144-093 (ONT-093) (91). This compound is based on a diarylimidazole structure and it was identified as a potent Pgp modulator using high throughput cell screening. ONT-093 showed activity in MDR Pgp expressing cells of breast, ovarian, uterine, lymphoma, and colorectal cancer lines. Effective chemosensitisation via incubation of cells with ONT-093 in combination with either doxorubicin, paclitaxel or vinblastine was achieved at a dose of only 30 nM. The inhibition of Pgp is reversible, but long-lived (>12 h) which is a common feature in the first-generation inhibitors. In pharmacokinetic studies, ONT-093 was shown to be primarily converted to an O-de-ethylated metabolite by liver microsomes, but importantly, this was shown not to occur via the CYP3A4 isoform (92). Administration of paclitaxel with ONT-093 co-administered via the oral route enhanced paclitaxel bioavailability, which is consistent with inhibition of Pgp in the GI tract. In summary, the third-generation inhibitors represent significant improvements on the previous generation compounds showing enhanced potency with minimal pharmacokinetic interactions. Definitive clinical trial data are awaited in order for possible clinical development of this treatment modality.

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2.4. Fourth Generation: Natural Sources

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An increased pharmacological characterisation of multidrug efflux transporters has revealed their important role in the pharmacokinetics of a large number of prescribed medications, quite aside from their role in the pharmacokinetics of cancer chemotherapy. This knowledge has stemmed from the expression of ABC transporters in normal tissues, particularly those involved in drug absorption and excretory pathways. In addition, it is well known that components of food can also interfere with the oral bioavailability of many drugs and that the drug–food interactions may involve Pgp. A number of established foods, in particular fruits such as orange, grapefruit, and strawberry, can inhibit Pgp function (93, 94) and have been shown to effect the transport of drugs such as vinblastine in Caco-2 cells. Generally, the potencies of natural extracts for MDR reversibility are low (i.e. high micromolar) and therefore these compounds are unlikely candidates for clinical modulation of Pgp activity. However, even given the low potency there is the opportunity that active components will influence drug bioavailabilities and other pharmacokinetic parameters. Natural products such as curcumin and the flavonoids kaempferol and quercetin have been shown to influence Pgp function (95) in human cancer cell lines with the MDR phenotype. Flavonoid dimers have been the subject of some more recent drug discovery programs (96). Mechanistic studies have shown that developmental flavonoids can stimulate the ATPase activity of Pgp. Competition for transport or inhibition of the process would influence drug absorption or elimination at various tissues in the body. The characterisation of natural product MDR modulators is currently not a high priority for the cancer research community. However, the active components of food/plant extracts already identified could be exploited as lead compounds for chemical modification to generate novel, selective, and high affinity Pgp inhibitors.

3. Final Comments and Conclusions With a complex and sometimes fraught past history, the state of clinical development of Pgp inhibitors is currently relatively inactive. Considerable resources and time have been spent on the development of the third-generation inhibitors. However, these efforts have failed to produce clinical trial data with the desired outcomes, due to issues with pharmacokinetic or pharmacodynamic interactions and toxicities. Unfortunately, there has been poor dissemination of trial results by the pharmaceutical industry which conflicts with fruitful interaction between industry and academic groups. In future trials there should be more rigour associated with clinical trial design with more use of surrogate

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markers and imaging techniques. In spite of the issues MDR modulator development poses, the problem of clinical anticancer drug resistance remains a significant issue and thus we should continue our efforts to overcome this. References 1. Juliano RL, Ling V (1976) A surface glycoprotein modulating drug permeability in Chinese hamster ovary cell mutants. Biochim Biophys Acta 455:152–162 2. Plumb JA, Strathdee G, Sludden J, Kaye SB (2000) Reversal of drug resistance in human tumour xenografts by 2¢-deoxy-5-azacytidineinduced demethylation of the hMLH gene promoter. Cancer Res 60:6039–6044 3. Marathi UK, Dolan ME (1994) Anti-neoplastic activity of sequenced administration of O6-benzylguanine, streptozotocin and 1, 3-bis(2-chlroethy)-1-nitrosourea in vitro and in vivo. Biochem Pharmacol 48:2127–2134 4. Scotto KW (2002) Transcriptional regulation of ABC transporters. Oncogene 22:7496–7511 5. Agrawal M, Abraham J, Balis FM et al (2003) Increased 99mTc-sestamibi accumulation in normal liver and drug resistant tumours after the administration of the glycoprotein inhibitor, XR9576. Clin Cancer Res 9:650–656 6. Linn SC, Pinedo HM, van Ark-Otte J (1997) Expression of drug resistance proteins in breast cancer, in relation to chemotherapy. Int J Cancer 71:787–795 7. Rudas M, Filipits M, Taucher S (2003) Expression of MRP1, LRP and Pgp in breast carcinoma patients treated with preoperative chemotherapy. Breast Cancer Treat 81: 149–157 8. Mechetner E, Kyshtoobayeva A, Zonis S (1998) Levels of multidrug resistance (MDR1) P-glycoprotein expression in human breast cancer correlate with in vitro resistance to Taxol and doxorubicin. Clin Cancer Res 4:389–398 9. Abolhoda A, Wilsin AE, Ross H et al (1999) Rapid activation of MDR1 gene expression in human metastatic sarcoma after in vivo exposure to doxorubicin. Clin Cancer Res 5: 3352–3356 10. Krishna R, Mayer LD (2000) Multidrug resistance (MDR) in cancer. Mechanisms, reversal using modulators of MDR and the role of MDR modulators in influencing the pharmacokinetics of anticancer drugs. Eur J Pharm Sci 11:265–283 11. Ferry DR, Trauneker H, Kerr DJ (1996) Clinical trials of P-glycoprotein reversal in solid tumours. Eur J Cancer 32A:1070–1081

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MDR in cancer chemotherapy, OC144-193, by human liver microsomes. Eur J Drug Metab Pharmacokinet 26:273–282 Deferme S, Van Gelder J, Augustijns P (2002) Inhibitory effect of fruit extracts on P-glycoprotein-related efflux carriers: an in-vitro screening. J Pharm Pharmacol 54:1213–1219 Honda Y, Ushigome F, Koyabu N et al (2004) Effects of grapefruit juice and orange juice components on P-glycoprotein- and MRP2mediated drug efflux. Br J Pharmacol 143:856–864 Limtrakul P, Khantamat O, Pintha K (2005) Inhibition of P-glycoprotein function and expression by kaempferol and quercetin. J Chemother 17:86–95 Chan KF, Zhao Y, Burkett BA et al (2006) Flavonoid dimmers as bivalent modulators for P-glycoprotein-based multidrug resistance: synthetic apigenin homodimers linked with defined-length poly(ethylene glycol) spacers increase drug retention and enhance chemosensitivity in resistant cancer cells. J Med Chem 49:6742–6759

Chapter 16 Pharmacokinetic and Pharmacodynamic Implications of P-Glycoprotein Modulation Jeannie M. Padowski and Gary M. Pollack Abstract Modulation of P-glycoprotein (Pgp)-mediated transport has significant pharmacokinetic implications for Pgp substrates. Pharmacokinetic alterations may be at the systemic (blood concentrations), regional (organ or tissue concentrations), or local (intracellular concentrations) level. Regardless of the particular location of Pgp modulation, changes in substrate pharmacokinetics will have the potential to alter the magnitude and duration of pharmacologic effect (pharmacodynamics). It is important to understand each of the aspects of Pgp modulation for a given Pgp substrate in order to predict the degree to which Pgp modulation may affect that substrate, to minimize untoward effects associated with that modulation, or to exploit that modulation for specific therapeutic advantage. Key words: P-glycoprotein, Substrate, Transport, Pharmacokinetics, Pharmacodynamics

1. Introduction Transport proteins that operate in the efflux direction (against substrate uptake into a particular organ or tissue) have garnered significant attention in recent years within the pharmaceutical sciences community, and have been the subject of numerous comprehensive reviews (1–3). These proteins can act at one or more steps in the cascade of pharmacokinetic and pharmacodynamic events that ultimately lead to the biologic response, either beneficial or detrimental, to a pharmacologic agent. Transport proteins operating in the efflux direction in the gastrointestinal tract can impede absorption (4), decreasing the rate and, potentially, the extent of presentation of the substrate to the systemic circulation. Outwardly directed transport in organs of elimination, primarily the liver (5) and kidney (6), can facilitate the irreversible removal of substrate molecules from the systemic circulation. J. Zhou (ed.), Multi-Drug Resistance in Cancer, Methods in Molecular Biology, vol. 596, DOI 10.1007/978-1-60761-416-6_16, © Humana Press, a part of Springer Science + Business Media, LLC 2010

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Finally, and perhaps most importantly from a biologic response standpoint, efflux proteins can serve as barriers to substrate distribution into organs (7), tissues (8), or cellular spaces (9) that represent important pharmacologic targets. Such barrier functions may appear at the interface between the organ and the vasculature, or may be present on the cell surface. In either case, the net result of efflux transport is to diminish the amount of substrate available at the site of action to interact with the biologic receptor. Although a variety of efflux transport proteins are expressed in mammalian tissues, the most well-characterized, and perhaps the most important, of these systems is P-glycoprotein (Pgp). Pgp is a 170-kDa protein that is a member of the ABC superfamily of energy-dependent transport systems (10). It is localized to cellular membranes, and originally was identified by its ability to confer multidrug resistance to mammalian cells (11). Although Pgp often is viewed in terms of its ability to transport bulky hydrophobic cations, it is in actuality a relatively indiscriminate system that can interact with compounds from a wide range of chemical classes and with a large degree of structural and physicochemical dissimilarity (12). Indeed, attempts to develop structure–transport relationships for Pgp have yielded mixed, generally negative, results due the lack of well-defined structural specificity. This lack of specificity may be due, in part, to the presence of multiple substrate recognition sites on the protein (13). In addition to complexities associated with substrate recognition, the location of the binding sites on Pgp relative to the cellular membrane has been debated. Although this is a biologically subtle point, it is of critical importance to the kinetics of substrate transport, as it will determine the apparent driving-force concentration for the efflux process (Fig. 16.1). Although both extracellular and intracellular locations have been suggested, the most widely accepted model for Pgp-mediated transport is with the

Fig. 16.1. Depending on the degree of intra- versus extracellular drug sequestration and the rate and extent of drug equilibration across each leaflet of the cell membrane, the driving force for Pgp-mediated flux may be the extracellular (C1), intramembrane (C2), or intracellular (C3) drug concentration.

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substrate-binding site localized within the cellular membrane (14). From a pharmacokinetic standpoint, the proximate driving-force concentration for substrate flux may be the extracellular concentration (e.g., if binding to extracellular proteins is low and if the extracellular concentration is in rapid equilibrium with substrate concentration in the cell membrane) or the intracellular concentration (if sequestration within cellular organelles is minimal and if the cytosolic substrate concentration is in rapid equilibrium with substrate concentration in the cell membrane). It also is conceivable that neither the extracellular nor the intracellular concentration will appear to be the driving force for Pgp-mediated flux if neither locale serves as an adequate surrogate for concentrations in the cellular membrane. Given these brief introductory comments, the purpose of this review is to focus on the pharmacokinetic and pharmacodynamic implications of modulation of Pgp-mediated transport. Approaches or mechanisms through which Pgp activity may be modified will be considered first; the impact of those changes on drug disposition then will be reviewed. Finally, the outcome of altered substrate flux in terms of biologic response will be discussed with respect to several different types of drug response.

2. Modulation of Pgp Activity Changes in substrate flux by Pgp may be the result of chemical modulation (inhibition of transport activity or induction of expression) or due to genetic polymorphisms. When considering the potential outcome of Pgp modulation, it is useful to keep in mind that the effects of chemical inhibitors/inducers or genetic changes may not be specific. For example, inhibitors of Pgp may also modulate other transport proteins; genetic differences in Pgp may result in changes in other pharmacokinetic mechanisms or pathways. The most common and well-studied chemical modulation of Pgp is inhibition of transport. Pgp inhibition has received substantial attention within the pharmaceutical industry as a means to enhance pharmacologic response to existing drugs or new chemical entities that may be significant substrates for this transport protein (15). The initial impetus for identifying or developing compounds that would effectively inhibit Pgp was to reverse multidrug resistance in cancer cells, thereby enhancing antitumor effects (16). Several existing therapeutic agents (e.g., quinidine, verapamil, and cyclosporine) were identified as Pgp inhibitors. Subsequently, several new compounds (e.g., PSC833 and GF120918) were developed for the express purpose of limiting Pgp-mediated flux. Despite major efforts to develop a Pgp inhibitor with clinical efficacy, to

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date no such compound has become part of the therapeutic armamentarium for cancer or any other disorder. The inability to translate this fundamental idea into a clinically useful approach is largely due to difficulties associated with targeting delivery of potential inhibitors to the appropriate site of Pgp-mediated transport (i.e., the desired target tissue, if Pgp serves as a barrier transporter for the therapeutic agent in question; an organ of elimination, if the intent is to prolong residence time in the systemic circulation; or the gastrointestinal tract, if the goal was to enhance absorption). Because Pgp is an inhibitable transporter, because it can affect substrate disposition at a variety of pharmacokinetic steps, and because it interacts with such a wide variety of substrates, drug–drug interactions at the level of Pgp-mediated transport are a likely phenomenon (17, 18). The high likelihood of unintended inhibition of Pgp has led the pharmaceutical industry to screen new compounds for their Pgp transport and inhibition properties, in much the same way that compounds are routinely screened for lability in the presence of various isoforms of cytochrome P450 (19). Specific examples of drug–drug interactions in Pgp transport will be considered in subsequent sections of this review. Although the focus of much less attention than inhibition, the potential induction of Pgp by drugs or other xenobiotics also represents a potential form of drug–drug interaction that could have pharmacokinetic or pharmacodynamic consequences (20, 21). The first suggestion that Pgp might evidence biologically significant induction resulting in overt pharmacokinetic and/or pharmacodynamic changes was a report that expression of Pgp in rat brain tissue was increased following subchronic exposure to morphine (22). Morphine and many other opioids are substrates for Pgp, albeit to varying degrees (23, 24). One logical hypothesis, therefore, was that induction of Pgp by morphine at the blood–brain barrier would decrease concentrations of morphine in brain tissue, leading to the emergence of functional tolerance to the antinociceptive effects of the drug. It is currently unclear as to what extent Pgp is generally inducible and, if so, in what organ systems such induction might occur. This topic will be touched upon briefly in the section on pharmacodynamics later in this review. Significant recent attention has been paid to genetic polymorphisms of Pgp, and how those polymorphisms may influence drug disposition or action in vivo. This complicated line of investigation has yielded contradictory results. To date, there is little consensus on whether genetic polymorphisms in humans result in a consistent and clinically relevant change in Pgp-mediated transport (25). This issue also will be discussed in more detail in the following sections of this review.

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3. Pharmacokinetic Consequences of Pgp Modulation 3.1. Gastrointestinal Absorption

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Pgp is expressed on the apical membrane of intestinal epithelial cells, oriented in a manner to secrete substrates from the epithelial cell into the intestinal lumen. As such, Pgp serves as a functional barrier to absorption of a variety of substrates across the intestinal wall, including the cardiovascular agents digoxin, quinidine, verapamil, and talinolol, and the opioid loperamide (26). The expression of Pgp appears to vary along the length of the human intestine in a manner somewhat inverse to that of the primary intestinal isoform of cytochrome P450, CYP3A4 (27). Pgp and CYP3A4 have common substrates and inhibitors, and there is evidence that the two proteins may act in a concerted fashion to limit systemic absorption of some compounds (28). Orally administered drugs that are substrates of both Pgp and CYP3A4 have three possible fates: they may cross the gut wall and reach the systemic circulation, they may be biotransformed by CYP3A4 in the enterocyte, or they may be effluxed by Pgp back into the intestinal lumen. Of course, molecules that reappear in the lumen of the intestine may be reabsorbed distally, with subsequent opportunities to be absorbed, metabolized, or effluxed. The entry and exit of a compound across the intestinal epithelium multiple times prior to either absorption into the systemic circulation or metabolism in the gut wall constitutes a lumen-to-enterocyte recycling process (27) which results in an increased luminal mean residence time and a decrease in the overall rate of intestinal absorption. In general, intestinal Pgp appears to influence the peak concentration of an orally administered substrate in the systemic circulation (Cmax) more significantly than overall systemic exposure (area under the concentration–time curve or AUC) (29). Pgp-mediated efflux will decrease the extent of systemic absorption if the substrate also is metabolized in the intestinal epithelium (by CYP3A4 or another enzyme) or if absorption of the substrate occurs only at a specific site in the intestine. There is limited but compelling evidence that intestinal Pgp can serve an excretory rather than absorptive barrier function, and can actually facilitate flux of substrates from the systemic circulation into the intestinal lumen (30, 31). Thus, Pgp in the intestine will play a dual role for some substrates, by limiting flux from the intestinal lumen into blood and by stimulating flux from the blood into the intestinal lumen. Several lines of evidence suggest that drug–drug interactions at the level of Pgp in the gut may be biologically significant. For example, the calcium channel blocker verapamil, one of the first compounds shown to inhibit Pgp-mediated flux, increased systemic exposure in rats to orally administered talinolol, a model Pgp substrate (32). Because talinolol is not metabolized extensively,

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it was suggested that this interaction occurred at the level of efflux transport. A similar interaction between digoxin and quinidine (increased systemic exposure to digoxin in the presence of quinidine) in humans has been known for many years, but only recently was attributed to Pgp (33). In addition to drug–drug interactions based on inhibition of Pgp, the potential for drug-associated induction of intestinal Pgp also has been examined. For example, subchronic oral administration of rifampin decreased the ability of oral verapamil to permeate the rat small intestine (34). This interaction was interpreted as induction of intestinal Pgp, resulting in increased secretion of verapamil from the enterocyte into the intestinal lumen. In a commonly cited clinical study, the single-dose pharmacokinetics of digoxin was compared before and after administration of rifampin in healthy volunteers (35). As illustrated in Fig. 16.2, the systemic exposure to digoxin, expressed as the AUC in blood, was reduced during rifampin treatment, in part due to an increase in digoxin biliary clearance, and in part due to a trend toward increased digoxin secretion from blood into the gut lumen. Rifampin increased intestinal Pgp (protein content); among individual subjects, the rifampin-associated decrease in AUC correlated with intestinal Pgp content after oral, but not intravenous, digoxin. A similar study design was used to examine the pharmacokinetics of talinolol in the presence or absence of rifampin administration (36). Rifampin decreased systemic exposure to oral talinolol by approximately 33%, and increased the systemic clearance of intravenous talinolol by nearly 30%. These observations suggest that Pgp-mediated drug–drug interactions at the level of intestinal absorption may be relatively common.

Fig. 16.2. Digoxin nonrenal clearance (block bars) and AUC0-96 h (gray bars) in humans following a 1 mg dose of digoxin, either alone (before rifampin) or with concomitant administration of 600 mg/day rifampin for 14 days (during rifampin). Bars represent means ± SD. Data obtained from ref. (35).

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3.2. Biliary Excretion

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Pgp is one of several transport proteins located on the canalicular membrane of hepatocytes that serve to mediate substrate flux from hepatocytes into bile. Several compounds of clinical importance undergo Pgp-mediated biliary excretion, including anticancer agents such as doxorubicin and paclitaxel (37), cardiac agents such as digoxin and quinidine (38), the immunosuppressive agent cyclosporine (39), and antivirals such as ritonavir and saquinavir (40). Drug–drug interactions at the level of Pgp-mediated biliary excretion are also of potential clinical importance. For example, quinidine has been shown to decrease the biliary excretion of digoxin in humans (Fig. 16.3) by nearly 50% (41), and the Pgp inhibitor GF120918 significantly reduced the biliary clearance of doxorubicin in an isolated perfused rat liver model (42). Any change in the excretory transport of a compound undergoing substantial biliary clearance would, by definition, alter the systemic exposure to this compound. In addition, modulation of excretion into bile would change substrate accumulation in the hepatocellular compartment, a site of potential toxicity or, in a limited number of cases, the site of the intended pharmacologic response. Finally, altered biliary clearance would change substrate presentation to the intestinal lumen, another site of potential drug toxicity, via the common bile duct. If the compound undergoes enterohepatic recycling, the change in luminal exposure may be substantial. Most drugs are metabolized in the liver. In some cases, metabolism produces toxic species that may be eliminated into bile. If the metabolite was a substrate for Pgp, inhibition of this protein would decrease metabolite excretion, potentially resulting in hepatotoxicity. Due largely to difficulties in obtaining bile from humans, the clinical studies evaluating the role of Pgp in biliary excretion have been limited. Numerous in vitro and preclinical in vivo studies have been conducted, and have led to an increased

Fig. 16.3. Influence of quinidine on the biliary clearance of digoxin in humans, based upon pooled data from two clinical studies. Bars represent means ± SD. Data obtained from ref. (41).

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Fig. 16.4. Influence of dexamethasone pretreatment on the rate of rhodamine-123 biliary clearance in rats. Data obtained from ref. (44).

understanding of the possible mechanisms underlying some clinically important drug–drug interactions. Induction of Pgp on the canalicular membrane of hepatocytes may increase the rate of biliary clearance for some drugs. For example, rifampin pretreatment enhanced the biliary clearance of digoxin in humans, presumably through increased expression of Pgp (35), and pretreatment of rats with phenothiazine to induce hepatic Pgp expression stimulated the rate of biliary excretion of the peptide octreotide and the antineoplastic vincristine (43). As shown in Fig. 16.4, dexamethasone pretreatment in rats increased the biliary clearance of rhodamine-123, a Pgp substrate, by nearly fourfold (44). Tamoxifen and its biotransformation products are excreted into bile; tamoxifen pretreatment increased biliary excretion of tamoxifen and its metabolites approximately sixfold, and increased the expression of mRNA which encodes Pgp in the rat (mdr1b), suggesting that tamoxifen displays autoinduction of Pgp-mediated biliary excretion (45). 3.3. Urinary Excretion

A significant body of evidence suggests that Pgp can mediate substrate secretion into urine, and therefore can be a determinant of renal clearance for some drugs such as digoxin (46). Pgp is expressed on the luminal surface of cells lining the proximal tubule of the kidney, the region of the nephron in which most active secretion in the blood-to-urine direction occurs. Evidence for Pgp-mediated secretory clearance in the kidney has been generated with the Pgp substrate digoxin in combination with known modulators of Pgp. Quinidine decreased the renal secretion of digoxin in isolated dog (47) and rat (48) models of renal excretion. Consistent with these observations in preclinical species, coadministration of quinidine with digoxin in humans increased digoxin serum concentrations and correspondingly decreased digoxin renal clearance (49, 50) (Fig. 16.5). A similar interaction between quinidine and digoxin was observed in vitro

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Fig. 16.5. Plasma concentration–time profiles for [3H]-digoxin in a representative patient before (open circles) and during (closed circles) quinidine administration. Data obtained from ref. (49).

(38). Clinical studies also have demonstrated drug–drug interactions with digoxin at the level of Pgp-mediated renal secretion. For example, coadministration of PSC 833 (valspodar), a Pgp inhibitor, to healthy volunteers decreased the renal clearance of digoxin by approximately 75% (51). Ritonavir, an inhibitor of Pgp in vitro, markedly decreased renal, as well as nonrenal, clearance of digoxin in human subjects (52). The Pgp inhibitor cyclosporine also decreased renal secretion of digoxin in isolated perfused rat kidneys (53). The role of Pgp in this interaction was confirmed using Pgp overexpressing LLC-PK1 renal epithelial cells (54). Clarithromycin (55) and itraconazole (54) also have been shown to inhibit Pgp-mediated renal excretion of digoxin. While it is clear, based upon the aggregate results of studies of digoxin renal clearance, that Pgp is involved in the renal excretion of this cardiac glycoside, these efforts also suggest an important and clinically relevant conclusion. Digoxin has a narrow therapeutic window, and even modest changes in bioavailability or systemic clearance can lead to untoward side effects. A multitude of drug–drug interactions with digoxin have been cataloged over the years, and yet the proximal mechanism(s) underlying these interactions remained elusive until fairly recently. It is now clear that modulation of Pgp-mediated transport in the kidney is one explanation for drug–drug interactions involving alterations in the renal clearance of digoxin. Because Pgp expressed in the intestine (which limits the rate and extent of digoxin absorption) and the liver (which mediates biliary excretion of digoxin) also determines systemic exposure to digoxin, it is obvious that global modulation of Pgp function can have significant effects on the disposition and action of this important cardiac agent. 3.4. Extravascular Distribution

Pgp serves as a barrier to the flux of substrates from the systemic circulation into protected organs or tissues. These so-called “sanctuary sites” include the placenta (56), testes (57), and brain (58). The protective role of Pgp relative to tissue exposure has been most

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well-studied in the central nervous system. Arguably, the most significant biologic role of Pgp, at least relative to protecting mammalian systems from chemical insult, is as a functional component of the blood–brain barrier. The structural and biochemical features of the blood–brain interface, particularly with respect to drug delivery to central nervous system (CNS) targets, have been the subject of numerous reviews (19, 59, 60). The blood–brain barrier is composed of a single layer of endothelial cells with intimate cell-to-cell communication afforded by complex tight junctions. These tight junctions limit paracellular permeability of hydrophilic molecules and bulky compounds. Substrate penetration of the blood–brain barrier therefore requires flux through the luminal membrane of capillary endothelia, the endothelial cytoplasm, and the abluminal membrane prior to entry into the extracellular fluid compartment of the brain. The close association of pericytes and astrocytic foot processes with the abluminal endothelial membrane presents an additional barrier to xenobiotic flux. The endothelial cells lining the brain microvasculature also contain systems that serve as functional, rather than structural, impediments to substrate translocation into the central nervous system (2). Cellular components of the blood–brain barrier express metabolic enzymes that biotransform drugs, limiting brain exposure to the intact parent (61). However, enzyme expression (at the BBB or in the brain as a whole) does not provide a significant route of drug elimination or a general barrier to substrate influx into brain for most substrates. More importantly, the blood–brain barrier endothelial cells express numerous membrane transport proteins that mediate substrate influx into and/or efflux from the brain. These various transport systems have been reviewed previously (62). From the standpoint of drug delivery to the brain, transport proteins mediating influx could be used to facilitate uptake of hydrophilic compounds across the blood–brain barrier. These systems mediate nutrient (glucose, for example) uptake into brain; attempts to engineer drug delivery modalities to make use of these systems are ongoing (63). Protein-mediated efflux, on the other hand, serves to limit brain exposure primarily to lipophilic substrates that otherwise would permeate the blood–brain barrier readily. Pgp, the primary barrier transporter at the blood–brain interface, is expressed on the luminal surface of brain capillary endothelial cells (58). However, other structural models of Pgp localization have been suggested. For example, Golden and Pardridge demonstrated that Pgp is expressed on foot processes of astrocytes in close proximity to capillary endothelium (i.e., on the abluminal side of endothelial cells) (64). As discussed earlier in this review, although the difference in anatomical location is minor, the driving forces responsible for Pgp-mediated transport will be markedly different between these two sites (Fig. 16.1), resulting in differ-

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ences in substrate transport kinetics between the brain tissue and the systemic circulation. Experiments performed in mice lacking Pgp expression, including genetic knockouts and naturally occurring Pgp-deficient animals have provided voluminous evidence that Pgp can significantly impede drug translocation across the blood–brain interface (2, 65). Pharmacokinetic experiments in transport-deficient mice form the foundation of the current understanding of attenuated blood–brain barrier translocation by Pgp. This experimental model will be highlighted in the subsequent section on pharmacodynamics. The fact that Pgp can mediate efflux of lipophilic molecules of varying pharmacologic classes and chemical structures complicates the design of effective CNS-targeted drugs. The ability of numerous lipophilic molecules to diffuse through the blood–brain barrier is counterbalanced by Pgp, resulting in low brain penetration despite otherwise favorable physicochemical characteristics. Several studies have demonstrated the role of Pgp in the exposure of brain tissue to pharmacologically relevant compounds. One of the earliest examples focused on the opioid peptide [D-penicillamine2,5] enkephalin (DPDPE). Although DPDPE enters the brain rapidly (66), only a limited fraction of the administered dose (tenfold), loss of Pgp function had a substantial impact on the magnitude and duration of antinociception. In contrast, the Pgp effect for morphine was modest (