Pharmacogenetics: Making cancer treatment safer and more effective 904818617X, 9789048186174

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Pharmacogenetics: Making Cancer Treatment Safer and More Effective

William G. Newman Editor

Pharmacogenetics: Making Cancer Treatment Safer and More Effective

123

Editor Dr. William G. Newman St Mary’s Hospital Manchester Academic Health Sciences Centre NIHR Biomedical Research Centre University of Manchester Manchester, UK [email protected]

ISBN 978-90-481-8617-4 e-ISBN 978-90-481-8618-1 DOI 10.1007/978-90-481-8618-1 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2010922454 © Springer Science+Business Media B.V. 2010 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

To all my colleagues in Genetic Medicine in Manchester for support and encouragement

Contents

1 Principles of Cancer Treatment . . . . . . . . . . . . . . . . . . . . William G. Newman and Fiona H. Blackhall

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2 Clinical Pharmacology and Anticancer Drugs . . . . . . . . . . . . Cristina Rodríguez-Antona and Julia Kirchheiner

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3 Principles of Genetics and Pharmacogenetics . . . . . . . . . . . . William G. Newman

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4 Pharmacogenetics in the Management of Breast Cancer . . . . . . Sacha J. Howell

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5 Pharmacogenetics in Colorectal Cancer . . . . . . . . . . . . . . . Roberta Ferraldeschi

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6 Pharmacogenetics in Lung Cancer . . . . . . . . . . . . . . . . . . Fiona Blackhall

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7 Pharmacogenetics and Cancer Treatment in Children . . . . . . . P. Kellie Turner and Gareth J. Veal

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8 Pharmacogenetics in Palliative Care . . . . . . . . . . . . . . . . . Andrew A. Somogyi

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9 Genetic Predictors of Normal Tissue Response to Radiotherapy . . Catharine M.L. West, Gillian C. Barnett, Alison M. Dunning, Rebecca M. Elliott, and Neil G. Burnet

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Cancer Pharmacogenetics in Industry . . . . . . . . . . . . . . . . Mireille Cantarini

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Ethical Issues in Pharmacogenetics . . . . . . . . . . . . . . . . . . Tara Clancy

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Economics of Cancer Pharmacogenetics . . . . . . . . . . . . . . . Katherine Payne

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Contents

Future Advances in Cancer Pharmacogenomics . . . . . . . . . . . William G. Newman

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Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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

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Contributors

Gillian C. Barnett Department of Oncology, University of Cambridge, Cambridge, CB2 0QQ, UK; Department of Oncology, Strangeways Research Laboratories, Cambridge, CB1 8RN, UK, [email protected] Fiona Blackhall Christie Hospital, University of Manchester, Manchester, M20 4BX, UK, [email protected] Neil G. Burnet Department of Oncology, University of Cambridge, Cambridge, CB2 0QQ, UK, [email protected] Mireille Cantarini AstraZeneca, Macclesfield, UK, [email protected] Tara Clancy St Mary’s Hospital, Central Manchester Foundation Trust, University of Manchester, Manchester, M13 9WL, UK, [email protected] Alison M. Dunning Department of Oncology, Strangeways Research Laboratories, Cambridge, CB1 8RN, UK, [email protected] Rebecca M. Elliott Academic Radiation Oncology, Christie Hospital, University of Manchester, Manchester, M20 4BX, UK, [email protected] Roberta Ferraldeschi Christie Hospital Manchester, Manchester, M20 4BX, UK; Department of Medical Genetics, University of Manchester, Manchester, M13 9WL, UK, [email protected] Sacha J. Howell Paterson Institute for Cancer Research, Christie Hospital, University of Manchester, Manchester, M20 4BX, UK, [email protected] Julia Kirchheiner Institute of Pharmacology of Natural Products and Clinical Pharmacology, University of Ulm, Helmholtzstr. 20, 89081 Ulm, Germany, [email protected] William G. Newman St Mary’s Hospital, Manchester Academic Health Sciences Centre, NIHR Biomedical Research Centre, University of Manchester, Manchester, UK, [email protected] Katherine Payne Health Methodology Research Group, University of Manchester, Manchester M13 9PL, UK, [email protected] ix

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Contributors

Cristina Rodríguez-Antona Hereditary Endocrine Cancer Group, Spanish National Cancer Research Center (CNIO), c/ Melchor Fernández Almagro 3, 28029 Madrid, Spain, [email protected] Andrew A. Somogyi School of Medical Sciences, University of Adelaide, Adelaide 5005, SA, Australia, [email protected] P. Kellie Turner Northern Institute for Cancer Research, Paul O’Gorman Building Medical School, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK, [email protected] Gareth J. Veal Northern Institute for Cancer Research, Paul O’Gorman Building Medical School, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK, [email protected] Catharine M.L. West Academic Radiation Oncology, Christie Hospital, University of Manchester, Manchester, M20 4BX, UK, [email protected]

Abbreviations

ADR AUC BSC CTC CTCAE DFS DHPLC EGF ER FA FISH 5-FU Gene-PARE GENEPI GWAS IHC IV LENT-SOMA OS PTT RAPPER RFS SNP SSCP TTP UGT VEGF

adverse drug reaction area under the curve best supportive care Common Toxicity Criteria Common Terminology Criteria for Adverse Events disease free survival denaturing high performance liquid chromatography epidermal growth factor estrogen receptor folinic acid fluorescent in situ hybridisation 5-flurouracil Genetic Predictors of Adverse Radiotherapy Effects GENetic pathways for the Prediction of the effects of Irradiation genome wide association study immunohistochemistry intravenous Late Effects in Normal Tissues – Subjective, Objective, Management and Analytic overall survival protein truncation test Radiogenomics: Assessment of Polymorphisms for Predicting the Effects of Radiotherapy recurrence-free survival single nucleotide polymorphism single strand conformation polymorphism time to progression UDP glucuronosyltransferase vascular endothelial growth factor

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Introduction William G. Newman

Doctors are men who prescribe medicines of which they know little, to cure diseases of which they know less, in human beings of which they know nothing. Voltaire

A clinician when deciding which is the correct antibiotic to use for a patient suffering with a bacterial infection, like pneumonia, will seek information from his/her microbiology colleagues. Crucial will be details on the specific causative organism and the sensitivity of this organism to different antibiotics. Although this information may take a day or two to come back from the laboratory, a strategy of commencing a broad-spectrum antibiotic to cover the most likely causative strain based on the clinical history and presentation is adopted. When the results are available, conversion to a specific antibiotic is considered standard clinical practice. Using laboratory tests to determine, which is the most likely effective drug to treat an individual is therefore not a new phenomenon. However, apart from antibiotics, when most other drugs are prescribed few are selected with the benefit of specific laboratory tests indicating their clinical effectiveness. Most drugs are chosen based on evidence of their overall effectiveness through clinical trials; intuition and experience; physician/pharmacist/individual preference and on the effectiveness of pharmaceutical companies advertising campaigns. This book will consider the value of tests, which use genetic information (pharmacogenetics) to predict the likelihood of response to medication (efficacy) or the likelihood of a significant side effect (adverse drug reaction) to that medication. This science of pharmacogenetics has been in existence for 50 years, but it is only now in the past decade that technological advances, scientific endeavour and clinical practice has seen the translation of this information into clinical decision-making. This book will concentrate on medications used in the treatment of cancer and so will not consider the vast array of medication that can be bought over the counter (OTC). Pharmacogenetics is likely to be especially relevant to improved management of cancer as many of the drugs have a small therapeutic window, that is, at low doses they are ineffective and at higher doses they are more likely to be associated with excessive toxicity. Chemotherapies are generally toxic – associated with side effects including hair loss, mucositis, increased risk of infection through decreased xiii

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white blood cell counts, vomiting and nausea, and a number of newer agents are very expensive and therefore should be directed to those most likely to benefit. In an individual unlikely to respond to a particular medication non-targeted prescription could potentially delay a more appropriate alternative regimen and expose them to unnecessary toxicity. When a genetic test is performed it is often done on a blood sample. DNA is extracted from the white blood cells (leukocytes) and this material is tested to establish is a specific variant is present or not. The DNA contained within the blood cells is considered to be representative of the DNA present in all the other cells in the body (germline), which has been inherited from that individual’s two parents. Cancer tissue is unique in that the cells comprising it have a different genetic makeup to the surrounding tissue due to somatic genetic changes, mutations that persist in the tumour. This creates opportunities to explore whether the genetic differences within the tumour itself alter the response to medication. Somatic changes occur in the cancer tissue, which release it from controls over cell growth (tumour suppressor genes) or stimulate growth (oncogenes) or alter the ability of the tissue to spread (metastasise) to other parts of the body. These gene changes may involve the loss or gain of whole chromosomes or chromosomal segments, point mutations, deletions/insertions or alteration of other factors which control gene expression e.g. epigenetic mechanisms like methylation. Strictly speaking, pharmacogenetics is the study of germline genetic variation resulting in altered absorption, distribution, metabolism and excretion associated with the efficacy or toxicity of a drug. Pharmacogenomics, an allied discipline, is the use of genetic information in a broader sense e.g. considering somatic changes in cancer tissue, the use of gene expression profiles to predict response or the identification of therapeutic targets. Generally, pharmacogenetics has focussed predominantly on reducing adverse drug reactions, whereas pharmacogenomics has principally contributed to improved drug efficacy. For the purposes of this book, both pharmaco-genetic and –genomic approaches to tailor safer and more effective cancer treatment will be considered. The book will introduce the concepts of cancer treatment, clinical pharmacology, genetics and pharmacogenetics, providing an overview of the technologies and strategies available to identify genetic variants associated with outcome to cancer medication. Chapters focussing on the relevance of pharmacogenetics to specific common cancers including breast, colorectal, lung and childhood tumours follow, with a consideration of pharmacogenetics in palliative cancer care. The book concludes with a series of chapters considering an industry perspective on the use of pharmacogenetics plus educational, ethical, economic and the potential use of genetic testing in predicting adverse outcome to radiotherapy. Of course, like any book this one can only reflect the current state of knowledge. However, medical understanding is constantly being updated reassessed, challenged and hopefully improved upon. The book is not an exhaustive catalogue of all pharmacogenetic research studies undertaken to date, but rather an overview of some of the important principles of pharmacogenetics, illustrated by examples of where this is already improving prescription and the potential for further improvements in

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cancer treatment. We conclude with a chapter considering where the major advances will be in cancer pharmacogenomics in the next few years. It is important to state at the outset that pharmacogenetics (the use of genetic information to predict response to medication) should not be considered a panacea to more effective and safer prescribing, but as one of a number of strategies that can improve outcome and reduce adverse reactions. Correct dosage, adherence to medication and treatment schedules, avoidance of drug-drug interactions and correct drug choice all have an important role to compliment pharmacogenetics. So hopefully this book will convince that although there is a grain of truth in what Voltaire says at least now we will be able to say that: Doctors are both men and women, who know something of the drugs they are using to treat cancer, about which they understand increasing amounts, in patients with a specific molecular profile.

Chapter 1

Principles of Cancer Treatment William G. Newman and Fiona H. Blackhall

Abstract The primary treatment options for patients with cancer mainly revolve around three major modalities: surgery, radiotherapy and chemotherapy. The choice between these different options is dependent upon the type of tumour, the extent (stage) of disease, the available evidence regarding effectiveness, clinician and patient preference. This chapter will explain in brief the principles of cancer treatment and how the type and stage of disease influences the goal of treatment and treatment selection. An important consideration for physicians and their patients is balancing the risk of toxicity versus benefit. It is in part the difficulty in predicting treatment toxicity and efficacy that have driven the search for pharmacogenetic tests to improve the likelihood of benefit and minimise the risk of toxicity. In addition, cancer treatment has evolved over recent years to include mechanism based or so-called targeted therapies for which chance of response can be predicted by the presence of specific, activating mutations encoding the target or key components of the pathway. Keywords Adverse drug reaction · Chemotherapy · Response · Toxicity

Contents 1.1 1.2 1.3 1.4 1.5

Introduction . . . . . . . . . . . . . . . Context of Chemotherapy . . . . . . . . Classes of Chemotherapy Drugs . . . . . . Clinical Trials of Chemotherapeutic Agents Defining Outcomes to Treatment . . . . . 1.5.1 Trial Endpoints . . . . . . . . . .

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1.6 What is an Adverse Drug Reaction to Chemotherapy? . . . . . . . . . . . . . . 1.7 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1.1 Introduction Chemotherapy is a mainstay of treatment of most types of cancer. Multiple (combination chemotherapy) or single drugs are used either alone or in combination with surgery and/or radiotherapy (combined modality treatments). As this book has a focus on pharmacogenetics, we will primarily consider the use of drugs in cancer treatment. However, Chapter 9 provides an important overview of some of the genetic variants that may be important in predicting response in terms of efficacy and toxicity to radiotherapy (radiogenomics). Many drugs are active against cancer. Over the last few decades extensive experience has been developed through clinical trials about the optimum drug treatment combinations for certain tumour types. The treatment choices are also dependent upon the stage of the disease, patient well-being (performance status), availability of treatment and patient preference. For many years, the range of drugs has been limited with the major advances in cancer therapy relating to use of different regimens – combining different drugs, at different doses and in different schedules. Recently, there have been significant advances with the discovery of a new class of molecularly targeted drugs. As the function of chemotherapeutic agents is to arrest cell growth (cytostatic) or to kill rapidly dividing cells (cytotoxic), they are invariably associated with a range of toxic side effects due to lack of selectivity for normal compared to cancer cells. These are especially prevalent in cells/tissues with a high turnover, including skin, the gastrointestinal tract and bone marrow, and lead to some of the more common and severe adverse reactions. Hair loss, mucositis, diarrhoea, vomiting, neutropenia leading to sepsis are all common side effects that can have devastating physical and psychological morbidity and can be fatal. In addition, severe side effects can limit the patient’s tolerance of a particular regimen leading to dose delay, dose reduction or drug withdrawal, all of which may compromise optimal dose delivery and result in an inferior outcome.

1.2 Context of Chemotherapy It is important to consider that chemotherapy can be used in different contexts, chiefly curative, palliative and adjuvant. (i) Curative chemotherapy – where the objective is to cure the patient of cancer. Examples where curative intent chemotherapy is given are lymphoma and testicular cancer.

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(ii) Palliative chemotherapy – where cure is not feasible but the goal is to improve symptoms, quality of life and prolong survival through tumour stabilisation or shrinkage e.g. in advanced, metastatic non-small cell lung cancer (NSCLC). In this context, palliative does not mean treatment at the end of life or symptom control e.g. control of cancer related pain or nausea (see Chapter 8). (iii) Adjuvant chemotherapy – where the tumour has been removed by surgery and chemotherapy is given to treat micrometastatic disease and reduce the likelihood of recurrence e.g. indicated for resected breast, colorectal cancer, NSCLC. Variations of these three major treatment contexts exist, including neo-adjuvant chemotherapy, which refers to the use of chemotherapy to reduce the size of the tumour prior to surgery or radiotherapy. The context in which the treatment is given may dictate the amount of toxicity a patient and their physician is prepared to tolerate. For example, when treatment is given with curative intent this may be associated with severe side effects. An individual may be prepared to accept such toxicity if there is a significant likelihood of complete remission and cure. However, an individual undergoing adjuvant treatment to reduce recurrence, once a tumour has already successfully been removed, may feel that the benefit of the additional treatment does not outweigh the morbidity associated with severe side effects. Ideally, all drugs would be designed to have no side effects with maximal efficacy, but in practice this is far from the case and physicians devise strategies to minimise the toxicity while maximising the response. The stage of the disease will often determine whether treatment is given with curative or palliative intent. For example, a patient presenting with a stage 1 (small, non metastatic, with no lymph node involvement) NSCLC may be cured by surgery alone, whereas a patient with stage IV advanced, metastatic NSCLC, unfortunately the more usual presentation, would be a candidate for palliative chemotherapy. Often, it is difficult to determine which agent in a combination regimen has been responsible for a good or poor outcome in terms of efficacy and toxicity. Therefore, studies are designed to compare and contrast different treatments and determine relationships between efficacy and toxicity. Most chemotherapy is given as intravenous treatment in a hospital setting. Therefore, non-adherence to treatment is less of a confounder in explaining treatment outcomes. However, this is not the case with some of the new orally administered targeted agents or some hormonal treatments e.g. tamoxifen used in breast cancer, where non-adherence can be a result of adverse events, including hot flushes or simply individuals forgetting or choosing not to take their medication (Ruddy and Partridge 2009).

1.3 Classes of Chemotherapy Drugs There are relatively few classes of chemotherapy drugs. They can be broadly separated into groups based on the mechanism of action (Table 1.1).

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W.G. Newman and F.H. Blackhall Table 1.1 Classes of chemotherapeutic agent

Drug group

Example

Mechanism of action

Alkylating agents Anthracyclines Vinca alkaloids Anti-metabolites

Cisplatin, carboplatin Doxorubicin Vincristine, vinblastine 6-mercaptopurine (6-MP) 5-fluorouracil, gemcitabine Irinotecan Oestrogen receptor antagonists (tamoxifen), aromatase inhibitors (letrozole, anastrozole) Gefitinib, erlotinib, cetuximab (all against EGFR), imatinib (against CKIT)

Cytotoxic Cytotoxic Cytotoxic Cytotoxic

Topoisomerase inhibitors Hormonal agents

Targeted agents

Cytotoxic Cytostatic

Cytostatic

1.4 Clinical Trials of Chemotherapeutic Agents Clinical trials are well established as standard practice in oncology centres to assess the effectiveness of a new regimen/drug and ultimately compare it to the standard of care such as a different chemotherapy regimen. These studies take place after enough pre-clinical or anecdotal data is collected to demonstrate a reasonable hypothesis regarding potential clinical value and following ethical approval. During development, and before registration, drugs progress through a number of different phases of trial to establish a safe dose for administration, their efficacy and side effect profile. Phase I – safety assessment and dose finding (maximum tolerated dose or recommended phase II dose). Small number of individuals, usually incorporating pharmacokinetic studies. Phase II – efficacy assessment and further safety assessment. Small studies, but larger than in phase I. Subsequent phases of testing are increasingly expensive. Therefore, many drugs do not progress beyond the initial phase I and II stages. Phase III – randomised controlled trials in large patient groups comparing the new treatment or regimen to the current standard practice. These usually involve hundreds or thousands of patients and are the gold standard for proof of the efficacy of the new intervention. They are expensive and complex to co-ordinate often involving many centres across many countries. Standardisation of trial design, analysis and reporting through the CONSORT guidelines (Begg et al. 1996) has improved the quality of clinical trial research. However, deficiencies still exist. Many different trial designs are used within these broad categories to maximize the amount of information that can be collected while optimizing the number of

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study subjects, minimizing costs, and reducing time to trial endpoint. Phase III trials often include quality of life measures and economic assessment. Increasingly molecular correlative studies are undertaken in order to identify objective predictors of benefit. In particular, pharmacogenetics (the use of genetic factors that influence drug metabolism) has been proposed as a mechanism to identify drug responders. Such genetic factors could also facilitate the conduct of smaller and therefore cheaper clinical trials. Drugs developed in this manner could then be targeted to individuals in whom a companion diagnostic pharmacogenetic test had indicated an increased likelihood to respond (see Chapter 10).

1.5 Defining Outcomes to Treatment Determining whether a patient is better or not after they have been treated with a drug would seem a very simple matter. A simple question “Are you feeling better?” should suffice. However, measuring outcome to chemotherapy is a complex and controversial area. Many different measures are used to establish if a treatment is effective against a specific type of cancer. These measures are selected as the endpoints of clinical trials (see Section 1.5.1) to establish an evidence base to advocate the use of one form of treatment over another. Often one measure is used as the primary endpoint in a trial with others being assessed as secondary endpoints. It is vital that the trial endpoints are defined prior to conduct of the study to ensure that bias cannot be introduced. Pre-registration of trials will increase transparency about what work has been undertaken or is ongoing and what the predefined endpoints are. The gold standard measure for approval of a new drug or regimen is usually survival since this is the most objective measure that is not influenced by factors such as the date of assessment of a patient (Booth and Tannock 2008). However where survival is long, progression free survival is increasingly preferred as the primary endpoint in order to reduce the duration before a result from a trial can be reported on. In addition progression free survival is less influenced by subsequent treatments compared to overall survival. Some measures include more subjective endpoints relating to quality of life assessed using validated quantitative scales like EuroQual or FACT. Other outcomes use more objective measures, like the size of the tumour following treatment e.g. RECIST criteria. These criteria can then be used to determine the response of the solid tumour to the treatment in a consistent manner. In 2000, international standardized criteria for measuring tumour response, called the Response Evaluation Criteria in Solid Tumors (RECIST), were published (Therasse et al. 2000). Four major response categories were defined. A complete response (CR) was defined as the disappearance of all tumour. A partial response (PR) was defined as a ≥ 30% decrease in the sum of the longest diameter of the tumours. Progressive disease (PD) was a ≥ 20% increase in the sum of the diameter. Stable disease (SD) defined all situations where there was between a 30% decrease and 20% increase in tumour size. In recent years, there have been concerns voiced about the validity of the RECIST criteria in the light of improved imaging

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techniques that allow smaller changes in tumour size to be detected and the increasing number of drugs that inhibit cell growth (Tuma 2006). In contrast to cytotoxic agents, cytostatic drugs do not shrink the tumour. Often cytostatic drugs demonstrate benefit in terms of progression-free or overall survival (see Section 1.5.1), and if judged by RECIST criteria, these drugs would not show anti-tumour activity (Millar and Lynch 2003). Therefore, the RECIST criteria have recently been updated to address some of these concerns (Eisenhauer et al. 2009).

1.5.1 Trial Endpoints Various endpoints can be used to determine the advantage of one drug or regimen compared to another. These include: Overall survival (OS): denotes the percentage of individuals in the group who are likely to be alive after a particular duration of time. It takes into account death due to any cause – both related and unrelated to cancer. It is the most robust endpoint and the gold standard outcome measure for regulatory authorities such as the Food and Drug Administration (FDA). Progression free survival (PFS): measures the proportion of people among those treated for a cancer whose disease will remain stable (without signs of progression) at a specified time after treatment. Disease-free survival (DFS): the proportion of people among those treated for a cancer who will remain free of disease at a specified time after treatment. Time to recurrence (TTR): the proportion of individuals who have a recurrence of tumour at a defined time point. Relapse free survival (RFS): the time to the first relapse or death from any cause, and not including second primaries or other cancers. Time to progression (TTP): a measure of time after a disease is diagnosed (or treated) until the disease starts to get worse. Which of these measures is selected is dependent upon a number of factors, including the context in which the chemotherapy is given, palliative or adjuvant. For example, in a study of patients undergoing adjuvant treatment for breast cancer a measure like RFS will capture more events than OS and therefore allow earlier analysis with a greater likelihood of detecting a significant difference in outcome. In contrast, in a study of patients undergoing treatment for metastatic pancreatic cancer OS or TTP would be more appropriate. There is much controversy about the correct application of trial endpoints to ensure that meaningful clinical effects are measured (Chua et al. 2005).

1.6 What is an Adverse Drug Reaction to Chemotherapy? Adverse drug reactions (ADRs) can be classified in different ways (Pirmohamed and Park 2003). A Type A reaction is defined as an expected reaction, which is usually dose dependent. Type B ADRs are unexpected idiosyncratic reactions that

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cannot be predicted from the known pharmacology of the drug. The more common Type A ADRs can usually be screened for through the trial development phases, whereas type B reactions are often only detected during post-marketing surveillance (pharmacovigilance). In the UK, pharmacovigilance is co-ordinated through the Governmental organisation, the Medicines and Healthcare products Regulatory Agency (MHRA). In Europe, this role is taken by the European Medicines Agency (EMEA) and in the United States by the Food and Drug Administration (FDA). Reporting of ADRs is a vital component of any clinical trial. Even if a new drug for the treatment of cancer is extremely effective, its use will be curtailed if it is associated with severe toxicity. A classification and reporting scheme for ADRs has been set out in legislation in the UK (The Medicines for Human Use (Clinical Trials) Regulations 2004: SI 2004/1031). Adverse drug reaction (ADR): is defined as any untoward and unintended response in a subject to an investigational medicinal product, which is related to any dose administered to that subject. An unexpected adverse reaction: is defined as an adverse reaction the nature and severity of which is not consistent with the information about the medicinal product in either the product information for a registered drug or in the investigator’s brochure relating to the trial drug in question. Serious adverse events, serious adverse drug reactions or suspected unexpected serious adverse reactions are those that: (a) (b) (c) (d) (e)

result in death, are life-threatening, require hospitalisation or prolongation of existing hospitalisation, result in persistent or significant disability or incapacity, or consist of a congenital anomaly or birth defect.

Introduced in 1983, the National Institute Common Toxicity Criteria (CTC) are a standardised grading and classification scheme for ADRs. The system defines a range of side effect with grades from 1 to 5 with 1 – Mild, 2 – Moderate, 3 – Severe, 4 – Life threatening and 5 – Death. It is now adopted for use in the majority of clinical trials. In 2003, it was updated to version three to include categories regarding late effects, surgical and paediatric effects and multi-modality issues (Trotti et al. 2003) and in 2009 to version four to standardise the terminology with MedDRA (Medical Dictionary for Regulatory Activities) and to reflect the new information pertaining to targeted therapies. For post-marketing surveillance, many schemes are in place. In the UK, a yellow card reporting system is used. Despite allowing broad classes of health care professionals and patients to submit information, there is widespread under-reporting, especially if a drug has been used for a number of years and the side effect has been well characterised.

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1.7 Concluding Remarks A multitude of factors can increase the likelihood of both ADRs and poor response to treatment. Erroneous, inadequate or excessive dosage, incompatible drug combinations and non-adherence are all potentially correctable causes of reduced efficacy or excessive toxicity, which can be minimised by appropriate education and checks (Blackhall et al. 2006). Even with accurate prescription and full adherence, drugs may still be ineffective and/or have significant side effect profiles. Many drugs will be ineffective or have heightened toxicity if given in the context of poor performance status or specific co-morbidities (e.g. renal or gastrointestinal diseases, which will alter excretion and absorption, respectively). In addition to these factors, evidence has emerged that genetic variants both inherited (germline) and somatic can influence response to drugs. These new clinical disciplines of pharmacogenetics and pharmacogenomics are the major focus of this book. The subsequent chapters will detail where application of genetic knowledge can improve outcome in cancer treatment with the aim of making treatment both safer and more effective.

References Begg C, Cho M, Eastwood S et al (1996) Improving the quality of reporting of randomized controlled trials. The CONSORT statement. JAMA 276:637–639 Blackhall FH, Howell S, Newman B (2006) Pharmacogenetics in the management of breast cancer – prospects for individualised treatment. Fam Cancer 5:151–157 Booth CM, Tannock I (2008) Reflections on medical oncology: 25 years of clinical trials–where have we come and where are we going? J Clin Oncol 26:6–8 Chua YJ, Sargent D, Cunningham D (2005) Definition of disease-free survival: this is my truthshow me yours. Ann Oncol 16:1719–1721 Eisenhauer EA, Therasse P, Bogaerts J, et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247 Millar AW, Lynch KP (2003) Rethinking clinical trials for cytostatic drugs. Nat Rev Cancer 3: 540–545 Pirmohamed M, Park BK (2003) Adverse drug reactions: back to the future. Br J Clin Pharmacol 55:486–492 Ruddy KJ, Partridge AH (2009) Adherence with adjuvant hormonal therapy for breast cancer. Ann Oncol 20:401–402 Therasse P, Arbuck SG, Eisenhauer EA et al (2000) New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst 92:205–216 Trotti A, Colevas AD, Setser A, et al (2003) CTCAE v3.0: development of a comprehensive grading system for the adverse effects of cancer treatment. Semin Radiat Oncol 13:176–181 Tuma RS (2006) Sometimes size doesn’t matter: reevaluating RECIST and tumor response rate endpoints. J Natl Cancer Inst 98:1272–1274

Online Resources/Further Reading Common Toxicity Criteria v4: evs.nci.nih.gov/ftp1/CTCAE/About.html Clinical Trial Adverse event reporting in UK www.mhra.gov.uk/Howweregulate/Medicines/ Licensingofmedicines/Clinicaltrials/Safetyreporting-SUSARSandASRs/index.htm

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DeVita VT, Lawrence TS, Rosenberg SA et al (2008) DeVita, Hellman, and Rosenberg’s Cancer: Principles and Practice of Oncology (8th Ed.). Lippincott Williams and Wilkins, New York Meinert CL (1986) Clinical Trials: Design, Conduct, and Analysis. OUP, New York Tannock IF, Hill RP, Bristow RG, Harrington L (2005) The Basic Science of Oncology (4th Ed). McGraw-Hill, New York

Chapter 2

Clinical Pharmacology and Anticancer Drugs Cristina Rodríguez-Antona and Julia Kirchheiner

Abstract Anticancer drugs are usually aggressive and during treatment result in toxicity, not only in the tumoral cells, but also in normal tissues. There exists a large inter-patient variability in human response to chemotherapy, leading to toxicity for some patients, lack of efficacy for others, and a satisfactory response in only a fraction of patients. One of the major objectives of clinical pharmacology is to precisely define the processes responsible for this variability, which could provide a more rational clinical use of drugs. This variability in drug response can be caused by inter-individual variability in drug absorption, distribution, metabolism, and excretion (pharmacokinetics) by altering systemic drug exposure and delivery to its site of action. In addition, differences in the target and effector molecules (pharmacodynamics) can also alter drug effects. Both the genetic profile of the tumour and the genetic background of the patient may affect these processes. Additionally, co-medication, disease, environmental and demographic factors can contribute to the differences between patients. In this chapter, we will focus on the most clinically relevant aspects of drug pharmacokinetics and pharmacodynamics and discuss the most relevant proteins mediating these processes, placing special emphasis on anticancer drugs. Keywords Drug metabolizing enzymes · Drug targets · Drug transporters · Pharmacodynamics · Pharmacokinetics

Contents 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Pharmacokinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Drug Absorption . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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C. Rodríguez-Antona (B) Hereditary Endocrine Cancer Group, Human Cancer Genetics Programme, Spanish National Cancer Center (CNIO), Melchor Fernández Almagro 3, Madrid, 28029, Spain e-mail: [email protected]

11 W.G. Newman (ed.), Pharmacogenetics: Making Cancer Treatment Safer and More C Springer Science+Business Media B.V. 2010 Effective, DOI 10.1007/978-90-481-8618-1_2, 

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2.2.2 Drug Distribution . . . . . . . . . . . . . . . 2.2.3 Drug Metabolism . . . . . . . . . . . . . . . 2.2.4 Drug Excretion . . . . . . . . . . . . . . . . 2.3 Drug Metabolizing Enzymes . . . . . . . . . . . . . 2.3.1 Phase I Drug Metabolism . . . . . . . . . . . . 2.3.2 Phase II Drug Metabolism . . . . . . . . . . . 2.4 Drug Transporters . . . . . . . . . . . . . . . . . . 2.4.1 Multidrug Resistance (MDR1) P-glycoprotein . . 2.4.2 Multidrug Resistance-Associated Proteins (MRPs) 2.5 Pharmacokinetic-Pharmacodynamic Relations . . . . . 2.6 Variability in Drug Response . . . . . . . . . . . . . 2.7 Clinical Pharmacology of Anticancer Drugs . . . . . . 2.7.1 Non-Targeted Chemotherapy . . . . . . . . . . 2.7.2 Targeted Therapies . . . . . . . . . . . . . . . 2.8 Concluding Remarks . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . .

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2.1 Introduction In oncology, anticancer drug treatment represents an important tool to control disease. Ideally, anticancer drugs would be exclusively active on cancer cells without causing toxicity in normal tissues. However, anticancer drugs are usually aggressive and their therapeutic window, separating toxicity from suboptimal treatment, is usually narrow. Clinical use of these drugs involves weighing benefits and toxicity, trying to find a favourable therapeutic index. However, there exists a large variability in human response to anticancer drugs. This can be caused by inter-individual variability in drug absorption, distribution, metabolism and excretion (pharmacokinetics) or by differences in the sensitivity of target tissues (pharmacodynamics). Both the genetic profile of the tumour and the genetic background of the patient may affect the individual outcome and safety of the drugs used (Spear et al. 2001; Evans and McLeod 2003; Evans and Relling 2004; Ingelman-Sundberg 2008). In addition, co-medications (Beijnen and Schellens 2004; Blower et al. 2005), diseases (Rodighiero 1999; Sun et al. 2006), environmental (Zevin and Benowitz 1999; Carrillo and Benitez 2000) and demographic factors (Kinirons and O’Mahony 2004; Anderson 2005; Bartelink et al. 2006; Maitland et al. 2006; Schwartz 2007) can underlie these differences between patients. In this chapter, we will discuss the most clinically relevant points of drug pharmacokinetics and pharmacodynamics in relation to anticancer drug therapy.

2.2 Pharmacokinetics After administration, drugs are absorbed, reach the systemic circulation and distribute into tissues and organs, some of which may have metabolic or excretory activity for eliminating the drug. These sequential events are called the ADME

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Fig. 2.1 Schematic representation of the events between administration of a drug and production of the effects in the body. In the case of anticancer drugs, the antitumoral agents will produce their desired effect in the cancer cells, while in normal cells they will produce toxicity

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Anticancer drugs

PK

In

i.v.

• • • •

oral

Out

Absorption Distribution Metabolism Excretion

PD

• Target binding • Mechanism of action • Drug response (transduction)

Normal cells (toxicity) Cancer cells (efficacy)

processes: absorption, distribution, metabolism, and excretion (Benet and ZiaAmirhosseini 1995; Caldwell et al. 1995; Rowland and Tozer 1995) (Fig. 2.1). The purpose of pharmacokinetics (PK) is to study ADME processes of drugs in the body by examining the time course of drug concentration profiles in body fluids such as blood, plasma, urine, and/or bile. This knowledge can provide the basis for a rational dose selection in therapeutics. Therapeutic drug monitoring (monitoring plasma drug concentrations or monitoring a specific response endpoint) can be used for drugs with narrow therapeutic indexes, in order to optimize efficacy and prevent toxicities. Some drugs frequently monitored are those used in chemotherapy.

2.2.1 Drug Absorption Drug absorption will depend on the administration method. Oral administration is obviously the most convenient and acceptable for the patient, nevertheless, not all drugs can be taken orally (e.g. instability of the drug in the acid medium of the stomach, irritation of the gastric mucosa). Adequate drug concentrations in plasma after oral administration will be dependent on drug dissolution, absorption and absence of significant first-pass effects (Dressman 2000; Kwon 2001; Shargel et al. 2004). The absorption of drugs in the stomach and the small intestine can be dependent on passive diffusion, which depends on the drug physicochemical properties, particularly lipophilicity, or carrier transport. Most drugs are predominantly absorbed from the duodenum. The efflux pump P-glycoprotein (P-gp), constitutively expressed on the luminal surface of most intestinal cells, opposes drug absorption by actively transporting drugs from the intracellular environment to the intestinal lumen (Wacher et al. 2001). Drug absorption through P-gp might be decreased after intake of grapefruit juice, which inhibits the activity of this transporter in the intestinal mucosa (Takanaga et al. 1998).

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2.2.2 Drug Distribution The distribution of drugs in the body is extremely variable. After administration and absorption, drugs present in plasma may partly bind to plasma proteins, consequently, they will be distributed to tissues and organs via blood vessels. 2.2.2.1 Protein Binding Plasma protein binding (a rapidly reversible process) plays an essential role in the transport and distribution of drugs (du Souich et al. 1993). Most drugs are relatively lipid-soluble and poorly soluble in plasma, thus binding to plasma proteins is essential for their transport in plasma. The degree of plasma protein binding ranges from zero to almost 100%, even among closely related drugs. In some instances, unbound pharmacologically active drug concentrations are only a small percentage of the total drug plasma levels. Protein binding is unlikely to restrict hepatic and renal elimination of drugs due to the immediate dissociation of protein-bound drug in order to maintain equilibrium. On the other hand, physiopathological conditions that modify drug binding to plasma proteins (e.g. hypoalbuminaemia, renal disease, trauma or burns) may result in drug toxicity by an increased concentration of the unbound drug (Grandison and Boudinot 2000). 2.2.2.2 Blood–Brain Barrier Many drugs that are widely distributed in most tissues, do not readily enter the central nervous system. This is due to the blood-brain barrier, a dynamic membrane interface between the blood and the brain. To pass from capillary blood to the brain, most drugs have to cross structural barriers: the endothelium, which has overlapping “tight” junctions restricting passive diffusion, the basement membrane and the peripheral astrocytes. In addition, there is a system of cellular transport mechanisms, mainly constituted by active transporters in the capillary endothelial cells (e.g. P-gp) that maintain homeostasis by restricting the entrance of xenobiotics from blood, while allowing the entrance of essential nutrients (Dauchy et al. 2008). 2.2.2.3 Enterohepatic Circulation Many drugs are excreted from hepatocytes through the bile to the gastrointestinal tract unchanged or after glucuronidation or sulphatation. Hydrolysis of the conjugates in the intestine by enzymes from bacteria may subsequently occur. Then, the deconjugated metabolites or unchanged drug may be reabsorbed into the portal circulation, a part of which will reach the systemic circulation again, and the rest become subject to further metabolism in the liver and/or subsequent biliary excretion (Hofmann 2007). This “recycling” process, which affects nearly all drugs is known as enterohepatic circulation. If the intestinal bacterial flora is disturbed, for example by antibiotics, the decrease in enterohepatic circulation could result in a loss of therapeutic effect of some drugs.

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2.2.3 Drug Metabolism Drug metabolism (biotransformation), which mainly occurs in the liver, is the major elimination pathway for most drugs. This process increases the water solubility of drugs and promotes their elimination from the body through urine or bile (Meyer 1996; Sheweita 2000). These reactions can be divided into: (i) Phase I reactions (also known as functionalization reactions) which introduce new, usually hydrophilic, functional groups into the compounds and (ii) Phase II reactions (also known as conjugation reactions) which involve the conjugation of the drug with hydrophilic endogenous substrates (Caldwell et al. 1995; Kwon 2001). In general, drugs are sequentially metabolized by phase I and then phase II reactions, however, some drugs can be almost entirely metabolized by only phase I or phase II reactions or excreted unchanged. Metabolism usually reduces the biological activity of drugs, however, for pro-drugs the process is the opposite. In addition, in some cases drug metabolism can result in the formation of toxic compounds. The enzymes catalyzing these reactions are known as drug metabolizing enzymes (DME) and are discussed in detail in another section of this chapter.

2.2.3.1 First-Pass Metabolism After oral administration, the drug must cross the intestinal epithelium, the portal venous system, and the liver prior to entering the systemic circulation. Some drugs are extensively metabolized by the gut wall or by the liver before they enter the systemic circulation (first-pass metabolism). In these conditions, oral administration may not produce adequate plasma concentrations in the systemic circulation and may result in an impaired response to drugs (Kwan 1997; Zhang and Benet 2001). Thus, drugs with high hepatic extraction might be largely affected by conditions during which the first pass effect is decreased. These conditions include surgical removal of the drug absorbing parts of the intestine, blood flow bypassing the liver or inhibition of liver and intestinal metabolism and drug transport by inhibitors. Several widely used drugs have been described to inhibit P-gp function and CYP3A4 metabolism, thus, potentially leading to relevant drug– drug interactions. They include various antimicrobial agents (e.g., clarithromycin, erythromycin, itraconazole, ketoconazole), calcium antagonists (verapamil, diltiazem, quinidine, quinine, nifedipine, nicardipine), and other compounds such as cyclosporine, tacrolimus. Indeed, inhibition of first pass metabolism and transport has also been used to increase the bioavailability of anticancer drugs for example oral therapy with paclitaxel (Meerum Terwogt et al. 1999).

2.2.4 Drug Excretion Almost all drugs and their metabolites are eventually eliminated from the body in urine or in bile. In general, drugs with low molecular weight, water soluble and slow

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biotransformation by the liver are preferentially excreted in urine, while lipophilic drugs with a higher molecular weight (>500 Da) are eliminated in bile. 2.2.4.1 Renal Excretion The renal elimination of drugs may include any combination of the following: glomerular filtration, active tubular secretion and tubular reabsorption (Masereeuw and Russel 2001; Perri et al. 2003; Lee and Kim 2004). The major driving force for glomerular filtration (mainly occurring for small molecules) is the hydrostatic pressure within the glomerular capillaries and it correlates fairly well with body surface area. Protein-bound drugs behave as large molecules and the glomerular filtration of drugs is directly related to the unbound drug concentration in the plasma. Active tubular secretion is an active transport process that requires energy input (see drug transporters, Section 2.4). The rate of secretion is dependent on renal plasma flow, it is extremely rapid and practically all the drug carried to the kidney is eliminated in a single pass. Drug protein binding has very little effect on the elimination half-life of an actively secreted drug because a drug bound to plasma proteins rapidly dissociates as free drug is secreted by the kidneys. Tubular reabsorption occurs after the drug is filtered through the glomerulus and can be an active or a passive process. Drug reabsorption can significantly reduce the amount of drug excreted, depending on the pH of the urinary fluid and the pK of the drug. Cisplatin is an effective anticancer agent, but severe nephrotoxicity limits its clinical application. It leads to an increased serum creatinine concentration and cisplatin-induced renal injury at the proximal tubule that cause a decrease in the glomerular filtration rate. The most abundant organic cation transporter in the basolateral side of the kidney is OCT2, which mediates the accumulation of cisplatin into the proximal tubular epithelial cells from blood (Urakami et al. 1998). Cimetidine and corticosterone, both OCT2 inhibitors, inhibited the cytotoxicity and the accumulation of cisplatin in HEK293 cells overexpressing OCT2 (Yonezawa et al. 2005). In addition, in vivo evidence showing the importance of rat Oct2 for cisplatin induced nephrotoxicity has been recently reported (Yokoo et al. 2007). 2.2.4.2 Biliary Elimination Biliary excretion can be an important hepatic elimination pathway for many drugs. It is mainly mediated through carrier-mediated processes in the canalicular membrane of the hepatocytes (see Section 2.4).

2.3 Drug Metabolizing Enzymes 2.3.1 Phase I Drug Metabolism Phase I metabolism is dominated by the cytochrome P450 (CYP) superfamily of haem enzymes (http://drnelson.utmem.edu/cytochromeP450.html) (Guengerich et al. 1998; Nebert and Russell 2002; Rendic 2002; Lewis 2004). The reason for this

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is the broad substrate specificity of CYPs compared to other enzymes. It has been estimated that about 75% of all drugs are primarily metabolized by CYPs (Wrighton and Stevens 1992; Shimada et al. 1994; Evans and Relling 1999). The main organ for CYP expression is the liver which contains about 0.5 nmol CYP/ mg microsomal protein, which represents about 5% of the total liver protein (Shimada et al. 1994). However, some CYPs may be predominantly expressed extrahepatically. CYPs are classified according to their amino acid sequence into gene families and subfamilies. Each gene family (CYP1, CYP2 etc.) has a common amino acid sequence of 40% or more, while members of each subfamily (CYP1A, CYP1B etc.) have a sequence similarity of more than 55% (Nelson et al. 1996). The enzymes in families 1–3 are mainly involved in xenobiotic metabolism, while the substrates of other families mainly metabolize endogenous compounds (e.g. steroids, bile acids, cholesterol) (Nebert and Russell 2002). The individual isoforms have different, but overlapping, substrate specificities, metabolize drugs at different rates, and also differ in their susceptibility to enzyme induction and inhibition. CYP3A4, CYP2D6 and CYP2C9 dominate drug metabolism. CYP3A4 has the broadest substrate specificity and is the most abundant CYP in the body. It has been estimated that CYP3A4, CYP2D6 and CYP2C9 participate in the metabolism of about 50, 25 and 15%, respectively, of commercially available drugs (Wrighton and Stevens 1992; Shimada et al. 1994; Evans and Relling 1999). Several xenobiotics can selectively increase the activity of certain CYP isoforms (e.g. polycyclic hydrocarbons induce CYP1 enzymes, while barbiturates, phenytoin, carbamazepine and rifampicin mainly induce CYP2 and CYP3 enzymes), this process is known as “induction” (Okey 1990). In some instances, drugs may induce their own metabolism (autoinduction) or may have secondary effects on other enzyme systems. On the other hand, specific drugs can inhibit some CYP isoforms (Pelkonen et al. 2008). Enzyme inhibition may increase plasma concentrations of other concurrently used drugs, resulting in drug-drug interactions. Induction of the proteins involved in the metabolism and efflux of chemotherapeutic drugs, result in lower drug plasma levels. St John’s wort (SJW) has been shown to induce both CYP3A4 as well as Pgp in vitro and in vivo (Durr et al. 2000; Perloff et al. 2001; Tian et al. 2005). SJW is an alternative treatment very popular among cancer patients because of its supposed activity in mild to moderate forms of depression. Cancer patients using SJW in combination with irinotecan, had plasma levels of SN-38, the active metabolite of irinotecan, 42% lower than patients not taking SJW. The degree of myelosuppression was substantially worse in the group of patients not taking SJW (Mathijssen et al. 2002). Because of the extensive reduction in the plasma levels of SN-38, patients treated with irinotecan should be advised to refrain from SJW use to avoid under-treatment (Mathijssen et al. 2002). The same advice could be given to patients that are going to be treated with the protein-tyrosine kinase inhibitor imatinib. Healthy subjects taking imatinib combined with SJW showed a 43% greater imatinib clearance (Frye et al. 2004). Although this may appear as a modest effect, it could result in plasma concentrations of imatinib below the minimum effective concentration after taking the standard 400 mg dose.

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In addition to CYPs, Phase I metabolism also includes other enzymes such as epoxide hydrolases, flavin-containing monooxygenases, alcohol and aldehyde dehydrogenases, xantine oxidase or esterases (Meyer 1996; Sheweita 2000).

2.3.2 Phase II Drug Metabolism Phase II drug metabolism mainly involves conjugation (or derivatization) of drugs’ functional groups with endogenous substrates in order to make the drug water-soluble for renal excretion. The conjugates include: glucuronidation by uridine diphosphate glucuronosyltransferase (UGT); sulphatation by sulphotransferase (ST), glutathione conjugation by glutathione S-transferase (GST); N-acetylation by N-acetyltransferase; methylation by methyl transferases and conjugation with amino acids (e.g., glycine, taurine, glutamic acid). Glucuronide conjugation is the most important of these reactions and represents about 40% of all Phase II reactions (Evans and Relling 1999; Burchell 2003). Recently, much attention has been paid to the importance of pharmacogenetic polymorphisms in UGT and their role in toxicity and therapeutic response in cancer patients undergoing treatment with the potent antitumour agent irinotecan. SN-38, the active metabolite of irinotecan and topoisomerase I inhibitor, is glucuronidated to its inactive form by UGT1A1, the UGT isoform, which is also responsible for glucuronidation of bilirubin. Several variant alleles leading to reduced enzymatic UGT1A1 activity have been identified in the UGT1A1 gene. These polymorphisms result in reduced SN-38 glucuronidation rates (Van Kuilenburg et al. 2000). The impact of UGT1A1 polymorphisms on irinotecan toxicity, even in heterozygous carriers, has been shown (Innocenti et al. 2001; Sai et al. 2004; Araki et al. 2006). Consequently, genotyping for UGT1A1 polymorphisms prior to therapy with irinotecan was recommended for patients of all ethnic groups (see Chapter 5 for fuller discussion of UGT1A1 genotyping in colorectal cancer treatment).

2.4 Drug Transporters Drug movement across the membrane of any cell is a combination of passive diffusion and active transport, mediated by specific drug uptake and efflux molecules (Ito et al. 2005). Intestinal transporters are expressed at the brush-border membrane of enterocytes and include the peptide transporter 1 (PEPT1) and the organic anion-transporting polypeptide (OATP) 2B1 (Niemi 2007; Zair et al. 2008). Efflux transporters, also present at the intestinal brush-border membrane, reduce the oral bioavailability of drugs. Efflux transporters typically belong to the ATP-binding cassette (ABC) transporter superfamily and include MDR1, MRP2 and ABCG2 (Chan et al. 2004). Following drug absorption by enterocytes, an important pharmacokinetic determinant is the degree of hepatic extraction, mediated by uptake transporters expressed at basolateral membrane of the hepatocytes: OATP1B1 and OATP1B3 that are hepatic specific; organic anion transporters (OATs) and mainly OAT2, and

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organic cation transporters (OCTs) among which OCT1 is highly expressed in the liver (Jonker and Schinkel 2004; Marzolini et al. 2004; Niemi 2007). Once in the liver, the drugs and their polarized metabolites can be excreted by efflux carriers at the canalicular membrane, such as MDR1, MRP2 and ABCG2, or at the basolateral membrane, such as MRP3, MRP4 and MRP5 (Chan et al. 2004). Renal excretion is a major route of elimination in which drug transporters play a pivotal role (Lee and Kim 2004). Renal tubular epithelial cells express drug transporters at both the basolateral and the apical plasma membrane domains, allowing for the tubular excretion of drugs and drug metabolites. Drug import carriers include OAT1, OAT3 and OCT2 which are highly expressed in the kidney (Jonker and Schinkel 2004). Among efflux pumps, multidrug and toxin extrusion transporter (MATE) 1 and 2, as well as the ABC transporters MDR1 and MRP2, are localized in the brush border membrane of renal cells. In addition to the enterohepatic and renal membrane barriers, transport proteins also exist at the blood-brain, blood-testis and blood-placenta barriers, where their predominant function is to limit the penetration of potentially toxic compounds.

2.4.1 Multidrug Resistance (MDR1) P-glycoprotein The drug transporter most widely studied is the efflux pump P-gp (Sharom 2008). P-gp is encoded by the gene ABCB1 and is a drug efflux pump that lowers the intracellular concentrations of many drugs. Its location is confined in the luminal domains of cells in the liver, intestine, kidney, and brain. There is a significant overlap between CYP3A and P-gp substrates and tissue distribution. This suggests complementary roles for CYP3A and P-gp in drug disposition via metabolism and active secretion of drugs, in the small intestine, where CYP3A and P-gp can act synergistically as a barrier against the oral absorption of drugs and in the liver facilitating biliary excretion. Overexpression of P-gp in cancer cells is one of the causes of multidrug resistance to a wide range of chemotherapeutic agents (Leonard et al. 2003; Sharom 2008). Some P-gp drug substrates are able to inhibit P-gp–mediated transport of other substrates. Indeed verapamil, a weak P-gp substrate but also an inhibitor of P-gp, could reverse P-gp–mediated vincristine resistance in leukaemia cells (Tsuruo et al. 1981). This discovery was followed by the identification of several other P-gp inhibitors that block P-gp activity by competition for drug-binding sites (competitive inhibitors) or by blockade of the ATP hydrolysis process (non-competitive inhibitors).

2.4.2 Multidrug Resistance-Associated Proteins (MRPs) MRP1 and MRP2 tissue distribution is similar to that of P-gp, including the liver canaliculus, erythrocyte membranes, heart, kidneys, intestinal brush border membranes, and lungs. MRP1 and MRP2 have a broad spectrum of substrates and their overexpression can also confer drug resistance in tumour cells (Borst et al. 2000).

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2.5 Pharmacokinetic-Pharmacodynamic Relations Pharmacodynamics (PD) deals with the relationship between drug concentrations and the magnitude of the observed pharmacological effect (Gard 2000). The most direct link between concentration and effect can be made by measurements of drug concentrations at the receptor or target organ (effect site) (Fig. 2.1). However, owing to experimental difficulties, the PD relationship is sometimes investigated when drug concentrations at the effect site are considered to be in equilibrium with those in biological fluids readily available such as plasma, which may also be directly related to the drug effect intensity. For instance, the effect site of antitumoral agents would be the cancer cells and, thus, drug concentrations in the tumour cells might be more relevant in establishing PD relationships than the plasma drug concentrations. The pharmacological activity of a drug generally includes a series of sequential events, i.e. interaction of drug molecules with their action sites or receptors, induction of a stimulus to the effector systems, and subsequent production of the effect (observed pharmacological endpoints). PK and PD modelling contributes to a better understanding of the relationship between drug concentrations in biological fluids, where concentrations are measured, and those in the effect site (Fig. 2.2) (Holford and Sheiner 1981; Schwinghammer and Kroboth 1988). PK-PD models are constructed to relate plasma drug level to drug concentration in the site of action and establish the intensity and time course of the drug. In PK/PD studies of anticancer agents, the majority of analyses were focused on the correlation between PK parameters and toxicities. In addition, new approaches are also required to maximize the systemic drug exposure and develop an individualized chemotherapy. Study of PK/PD responses will facilitate appropriate dosing to achieve the desired therapeutic response with a minimum risk of toxic effects.

B

time

AU AUC

Time

PK/PD relations

Pharmacodynamics

EC 50

drug conc. (log)

Effect

log

Drug conc.

Pharmacokinetics

C

Effect

A

MEC

Time

Fig. 2.2 Pharmacokinetics and pharmacodynamics relationship. (a) Time-plasma concentration curves after a single dose of drug. The area under the concentration vs time curve (AUC) is shown. Inset, The decline of drug concentration over time is linear on a log- plot, characteristic of first-order elimination. (b) Response as a function of the drug concentration. Typical log dose versus pharmacologic response curve. The concentration of drug that produces 50% of the maximal response is referred to as the EC50 (effective concentration for 50% response). (c) Temporal characteristics of drug effect. Following onset of the response the drug continues to be absorbed and distributed, but drug elimination results in a decline in the effect’s intensity. The effect disappears when the drug concentration falls below the minimum effective concentration (MEC) for the desired effect

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2.6 Variability in Drug Response Over the course of therapy, each patient should be given the dose that achieves the best chance of therapeutic benefit, while being exposed to manageable toxicity. However, intra and inter-individual variations frequently cause either a sub-therapeutic drug concentrations (lack of efficacy) or drug concentrations above the minimum toxic concentration (toxicity), which may then require dose adjustment. Drug therapy may be influenced by PK variability (variability in drug uptake, distribution and metabolism) or PD variability (variability in drug target effector molecules or downstream products). Inter-individual differences in drug metabolism may be related to demographic factors such as gender, age, pregnancy (Kinirons and O’Mahony 2004; Anderson 2005; Bartelink et al. 2006; Maitland et al. 2006; Schwartz 2007). Drug metabolism and clearance of drugs can be greatly influenced by drug-drug interactions (Beijnen and Schellens 2004; Blower et al. 2005) or pathological changes such as hepatic, renal and cardiac diseases (Rodighiero 1999; Sun et al. 2006; Hofmann 2007). In addition, diseases affecting protein binding may also alter drug response (Grandison and Boudinot 2000). Concerning genetic factors, PK variability generally reflects inherited (germline) changes affecting the function of genes coding, for example, for drug metabolizing enzymes (Spear et al. 2001; Evans and McLeod 2003; Evans and Relling 2004; Ingelman-Sundberg 2008). In cancer, PD variability often involves somatic (non-germline) genetic changes in tumour tissue (Ikediobi 2008). In addition, the development of cellular drug resistance is a major problem. It can be highly specific for a single drug or a multidrug-resistant phenotype, this latter usually related to the overexpression of P-gp or other drug efflux pumps.

2.7 Clinical Pharmacology of Anticancer Drugs 2.7.1 Non-Targeted Chemotherapy Most cytotoxic drugs are agents designed to attack actively dividing cells, based on the fact that cancer cells divide more rapidly than normal cells. However, chemotherapy is unspecific and also destroys some normal cells, causing unwanted toxic effects. Commonly, these drugs have narrow therapeutic indexes that may overlap with the range of systemic exposure, resulting in severe toxicity in some patients (Fig. 2.3). Their incorporation into treatment protocols is usually based on “trial and error” approaches. These drugs sometimes show large inter-patient differences in systemic exposure, leading to toxicity for some, lack of efficacy for others, and a satisfactory response mainly for those close to the population average drug elimination. Myelosuppression, mucositis or other adverse events are usually the endpoints frequently used to adjust doses. For these drugs, clearance is important because it relates the drug dose to the measured area under the concentration-time curve (AUC), which is a measure of

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Wide therapeutic index

A 100

100

Effect (%)

Effect (%)

Narrow therapeutic index

B

75 50 25

75 50 25

Drug conc. (log)

Drug conc. (log)

Fig. 2.3 Relationship between dose and cumulative probability of a desired or adverse drug effect. (a) A drug with a wide therapeutic index, i.e., a wide separation of the desired effect and adverse effect curves. (b) A drug with a narrow therapeutic index; in this case the probability of adverse effects at therapeutic doses is increased because the curves are not well separated

systemic drug exposure. A first attempt to individualize cancer chemotherapy is to calculate the drug dose based on the body surface area (BSA). However, the relationship between clearance and BSA is often weak. An approach to improve treatment with these drugs is therapeutic drug monitoring. In this approach, the drug dose is adjusted based on the PK data and this might result in improved outcome results (Evans et al. 1998). Associations between inherited genetic variants and the toxicity or efficacy to various cytotoxic drugs have also been demonstrated. Some of the clinically relevant examples of genetic variants effecting differences in are illustrated in the following chapters and include. deficiency of the drug metabolizing enzyme thiopurine methyltransferase (TPMT) related to 6-mercaptopurine toxicity (Weinshilboum and Sladek 1980; Engen et al. 2006; Wang and Weinshilboum 2006) (see Chapter 7) and deficiency of CYP2D6 altering response to tamoxifen treatment in breast cancer (Goetz et al. 2005; Jin et al. 2005; Goetz et al. 2007; Schroth et al. 2007; Kiyotani et al. 2008; Xu et al. 2008) (see Chapter 4).

2.7.2 Targeted Therapies The sequencing of the human genome in 2001 allowed identifying new promising molecular targets for novel anticancer treatments, highly specific to cancer cells. Ideal drugs would have a target restricted to and critical for cancer cells growth and, thus, would not be toxic for the patient’s normal tissues. In this case, the therapeutic index may be wider and fixed dosing, i.e. not adjusted on the basis of surface area, may be used. In addition, the detection of the target in the tumour would vastly increase the likelihood of a clinical response and would dictate patients’ stratification. Such targets may be normal proteins highly expressed in cancerous tissue; or may be the expression of somatically acquired genetic variants found only in the malignant cells (Petros and Evans 2004; Ikediobi 2008).

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The best-known example is the Philadelphia chromosome, a hallmark of chronic myelogenous leukaemia (CML). Imatinib mesylate (Gleevec, Glivec) inhibits Abl kinase activity and is selectively cytotoxic to cells that depend on Bcr/Abl for survival, i.e. CML. Although imatinib is well tolerated, it does produce clinical toxicities in many patients. Thus, for this type of drug, pharmacogenomics tests able to detect the drug target in the tumour cells can be performed to select patients most likely to respond. Other targeted drugs including: gefitinib and erlotinib (epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors), cetuximab (anti-EGFR monoclonal antibody), trastuzumab (epidermal receptor type 2 (HER2) monoclonal antibody), lapatinib (EGFR and HER2 tyrosine kinase inhibitor) and will be considered in the following chapters.

2.8 Concluding Remarks This chapter has outlined the different processes that are responsible for the therapeutic effect of the drugs and the main key proteins involved. An alteration of the pharmacokinetics and/or pharmacodynamics of a drug can result in pharmacotherapy failure. Thus, the application of these principles in the context of the various pathological, physiological and genetic features of a particular patient could increase therapeutic benefit. To incorporate this information into clinical care, prospective randomized trials comparing the standard care with the “personalized” treatment will be needed.

References Anderson GD (2005) Pregnancy-induced changes in pharmacokinetics: a mechanistic-based approach. Clin Pharmacokinet 44:989–1008 Araki K, Fujita K, Ando Y et al (2006) Pharmacogenetic impact of polymorphisms in the coding region of the UGT1A1 gene on SN-38 glucuronidation in Japanese patients with cancer. Cancer Sci 97:1255–1259 Bartelink IH, Rademaker CM, Schobben AF, van den Anker JN (2006) Guidelines on paediatric dosing on the basis of developmental physiology and pharmacokinetic considerations. Clin Pharmacokinet 45:1077–1097 Beijnen JH, Schellens JH (2004) Drug interactions in oncology. Lancet Oncol 5:489–496 Benet LZ, Zia-Amirhosseini P (1995) Basic principles of pharmacokinetics. Toxicol Pathol 23:115–123 Blower P, de Wit R, Goodin S, Aapro M (2005) Drug-drug interactions in oncology: why are they important and can they be minimized? Crit Rev Oncol Hematol 55:117–142 Borst P, Evers R, Kool M, Wijnholds J (2000) A family of drug transporters: the multidrug resistance-associated proteins. J Natl Cancer Inst 92:1295–1302 Burchell B (2003) Genetic variation of human UDP-glucuronosyltransferase: implications in disease and drug glucuronidation. Am J Pharmacogenomics 3:37–52 Caldwell J, Gardner I, Swales N (1995) An introduction to drug disposition: the basic principles of absorption, distribution, metabolism, and excretion. Toxicol Pathol 23:102–114 Carrillo JA, Benitez J (2000) Clinically significant pharmacokinetic interactions between dietary caffeine and medications. Clin Pharmacokinet 39:127–153

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Chan LM, Lowes S, Hirst BH (2004) The ABCs of drug transport in intestine and liver: efflux proteins limiting drug absorption and bioavailability. Eur J Pharm Sci 21:25–51 Dauchy S, Dutheil F, Weaver RJ et al (2008) ABC transporters, cytochromes P450 and their main transcription factors: expression at the human blood-brain barrier. J Neurochem 107:1518–1528 Dressman JB (2000) Oral Drug Absorption: Prediction and Assessment. Marcel Dekker Incorporated, New York du Souich P, Verges J, Erill S (1993) Plasma protein binding and pharmacological response. Clin Pharmacokinet 24:435–440 Durr D, Stieger B, Kullak-Ublick GA et al (2000) St John’s Wort induces intestinal P-glycoprotein/MDR1 and intestinal and hepatic CYP3A4. Clin Pharmacol Ther 68:598–604 Engen RM, Marsh S, Van Booven DJ, McLeod HL (2006) Ethnic differences in pharmacogenetically relevant genes. Curr Drug Targets 7:1641–1648 Evans WE, McLeod HL (2003) Pharmacogenomics–drug disposition, drug targets, and side effects. N Engl J Med 348:538–549 Evans WE, Relling MV (1999) Pharmacogenomics: translating functional genomics into rational therapeutics. Science 286:487–491 Evans WE, Relling MV (2004) Moving towards individualized medicine with pharmacogenomics. Nature 429:464–468 Evans WE, Relling MV, Rodman JH, Crom WR, Boyett JM, Pui CH (1998) Conventional compared with individualized chemotherapy for childhood acute lymphoblastic leukemia. N Engl J Med 338:499–505 Frye RF, Fitzgerald SM, Lagattuta TF, Hruska MW, Egorin MJ (2004) Effect of St John’s wort on imatinib mesylate pharmacokinetics. Clin Pharmacol Ther 76:323–329 Gard PR (2000) Human Pharmacology. CRC Press LLC, New York Goetz MP, Knox SK, Suman VJ et al (2007) The impact of cytochrome P450 2D6 metabolism in women receiving adjuvant tamoxifen. Breast Cancer Res Treat 101:113–121 Goetz MP, Rae JM, Suman VJ et al (2005) Pharmacogenetics of tamoxifen biotransformation is associated with clinical outcomes of efficacy and hot flashes. J Clin Oncol 23: 9312–9318 Grandison MK, Boudinot FD (2000) Age-related changes in protein binding of drugs: implications for therapy. Clin Pharmacokinet 38:271–290 Guengerich FP, Hosea NA, Parikh A et al (1998) Twenty years of biochemistry of human P450 s: purification, expression, mechanism, and relevance to drugs. Drug Metab Dispos 26:1175–1178 Hofmann AF (2007) Biliary secretion and excretion in health and disease: current concepts. Ann Hepatol 6:15–27 Holford NH, Sheiner LB (1981) Understanding the dose-effect relationship: clinical application of pharmacokinetic-pharmacodynamic models. Clin Pharmacokinet 6:429–453 Ikediobi ON (2008) Somatic pharmacogenomics in cancer. Pharmacogenomics J 8:305–314 Ingelman-Sundberg M (2008) Pharmacogenomic biomarkers for prediction of severe adverse drug reactions. N Engl J Med 358:637–639 Innocenti F, Iyer L, Ratain MJ (2001) Pharmacogenetics of anticancer agents: lessons from amonafide and irinotecan. Drug Metab Dispos 29:596–600 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 Jin Y, Desta Z, Stearns V et al (2005) CYP2D6 genotype, antidepressant use, and tamoxifen metabolism during adjuvant breast cancer treatment. J Natl Cancer Inst 97:30–39 Jonker JW, Schinkel AH (2004) Pharmacological and physiological functions of the polyspecific organic cation transporters: OCT1, 2, and 3 (SLC22A1-3). J Pharmacol Exp Ther 308:2–9 Kinirons MT, O’Mahony MS (2004) Drug metabolism and ageing. Br J Clin Pharmacol 57: 540–544 Kiyotani K, Mushiroda T, Kubo M, Zembutsu H, Sugiyama Y, Nakamura Y (2008) Impact of CYP2D6∗ 10 on recurrence-free survival in breast cancer patients receiving adjuvant tamoxifen therapy. Cancer Sci 99:995–999 Kwan KC (1997) Oral bioavailability and first-pass effects. Drug Metab Dispos 25:1329–1336

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Kwon Y (2001) Handbook of Essential Pharmacokinetics, Pharmacodynamics and Drug Metabolism for Industrial Scientists. Kluwer Academic Publishers, Hingham, MA Lee W, Kim RB (2004) Transporters and renal drug elimination. Annu Rev Pharmacol Toxicol 44:137–166 Leonard GD, Fojo T, Bates SE (2003) The role of ABC transporters in clinical practice. Oncologist 8:411–424 Lewis DF (2004) 57 varieties: the human cytochromes P450. Pharmacogenomics 5:305–318 Maitland ML, DiRienzo A, Ratain MJ (2006) Interpreting disparate responses to cancer therapy: the role of human population genetics. J Clin Oncol 24:2151–2157 Marzolini C, Tirona RG, Kim RB (2004) Pharmacogenomics of the OATP and OAT families. Pharmacogenomics 5:273–282 Masereeuw R, Russel FG (2001) Mechanisms and clinical implications of renal drug excretion. Drug Metab Rev 33:299–351 Mathijssen RH, Verweij J. de Bruijn P, Loos WJ, Sparreboom A (2002) Effects of St. John’s wort on irinotecan metabolism. J Natl Cancer Inst 94:1247–1249 Meerum Terwogt, JM, Malingre MM, Beijnen JH et al (1999) Coadministration of oral cyclosporin A enables oral therapy with paclitaxel. Clin Cancer Res 5:3379–3384 Meyer UA (1996) Overview of enzymes of drug metabolism. J Pharmacokinet Biopharm 24: 449–459 Nebert DW, Russell DW (2002) Clinical importance of the cytochromes P450. Lancet 360: 1155–1162 Nelson DR, Koymans L, Kamataki T et al (1996) P450 superfamily: update on new sequences, gene mapping, accession numbers and nomenclature. Pharmacogenetics 6:1–42 Niemi M (2007) Role of OATP transporters in the disposition of drugs. Pharmacogenomics 8: 787–802 Okey AB (1990) Enzyme induction in the cytochrome P-450 system. Pharmacol Ther 45:241–298 Pelkonen O, Turpeinen M, Hakkola J, Honkakoski P, Hukkanen J, Raunio H (2008) Inhibition and induction of human cytochrome P450 enzymes: current status. Arch Toxicol 82:667–715 Perloff MD, von Moltke LL, Störmer E, Shader RI, Greenblatt DJ (2001) Saint John’s wort: an in vitro analysis of P-glycoprotein induction due to extended exposure. Br J Pharmacol 134: 1601–1608 Perri D, Ito S, Rowsell V, Shear NH (2003) The kidney–the body’s playground for drugs: an overview of renal drug handling with selected clinical correlates. Can J Clin Pharmacol 10:17–23 Petros WP, Evans WE (2004) Pharmacogenomics in cancer therapy: is host genome variability important? Trends Pharmacol Sci 25:457–464 Rendic S (2002) Summary of information on human CYP enzymes: human P450 metabolism data. Drug Metab Rev 34:83–448. Rodighiero V (1999) Effects of liver disease on pharmacokinetics. An update. Clin Pharmacokinet 37:399–431 Rowland M, Tozer TN (1995) Clinical Pharmacokinetics: Concepts and Applications. Lea and Febiger, Philadelphia Sai K, Saeki M, Saito Y et al (2004) UGT1A1 haplotypes associated with reduced glucuronidation and increased serum bilirubin in irinotecan-administered Japanese patients with cancer. Clin Pharmacol Ther 75:501–515 Schroth W, Antoniadou L, Fritz P et al (2007) Breast cancer treatment outcome with adjuvant tamoxifen relative to patient CYP2D6 and CYP2C19 genotypes. J Clin Oncol 25:5187–5193 Schwartz JB (2007) The current state of knowledge on age, sex, and their interactions on clinical pharmacology. Clin Pharmacol Ther 82:87–96 Schwinghammer TL, Kroboth PD (1988) Basic concepts in pharmaco-dynamic modeling. J Clin Pharmacol 28:388–394 Shargel L, Wu-Pong S, Yu A (2004) Applied Biopharmaceutics and Pharmacokinetics. McGrawHill, New York

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Sharom FJ (2008) ABC multidrug transporters: structure, function and role in chemoresistance. Pharmacogenomics 9:105–127 Sheweita SA (2000) Drug-metabolizing enzymes: mechanisms and functions. Curr Drug Metab 1:107–132 Shimada T, Yamazaki H, Mimura M, Inui Y, Guengerich FP (1994) Interindividual variations in human liver cytochrome P-450 enzymes involved in the oxidation of drugs, carcinogens and toxic chemicals: studies with liver microsomes of 30 Japanese and 30 Caucasians. J Pharmacol Exp Ther 270:414–423 Spear BB, Heath-Chiozzi M, Huff J (2001) Clinical application of pharmacogenetics. Trends Mol Med 7:201–204 Sun H, Frassetto L, Benet LZ (2006) Effects of renal failure on drug transport and metabolism. Pharmacol Ther 109:1–11 Takanaga H, Ohnishi A, Matsuo H, Sawada Y (1998) Inhibition of vinblastine efflux mediated by P-glycoprotein by grapefruit juice components in caco-2 cells. Biol Pharm Bull 21:1062–1066 Tian R, Koyabu N, Morimoto S, Shoyama Y, Ohtani H, Sawada Y (2005) Functional induction and de-induction of P-glycoprotein by St. John’s wort and its ingredients in a human colon adenocarcinoma cell line. Drug Metab Dispos 33:547–554 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 Urakami Y, Okuda M, Masuda S, Saito H, Inui KI (1998) Functional characteristics and membrane localization of rat multispecific organic cation transporters, OCT1 and OCT2, mediating tubular secretion of cationic drugs. J Pharmacol Exp Ther 287:800–805 Van Kuilenburg AB, Van Lenthe H, Tromp A, Veltman PC, Van Gennip AH (2000) Pitfalls in the diagnosis of patients with a partial dihydropyrimidine dehydrogenase deficiency. Clin Chem 46:9–17 Wacher VJ, Salphati L, Benet LZ (2001) Active secretion and enterocytic drug metabolism barriers to drug absorption. Adv Drug Deliv Rev 46:89–102 Wang L, Weinshilboum R (2006) Thiopurine S-methyltransferase pharmacogenetics: insights, challenges and future directions. Oncogene 25:1629–1638 Weinshilboum RM, Sladek SL (1980) Mercaptopurine pharmacogenetics: monogenic inheritance of erythrocyte thiopurine methyltransferase activity. Am J Hum Genet 32:651–662 Wrighton SA, Stevens JC (1992) The human hepatic cytochromes P450 involved in drug metabolism. Crit Rev Toxicol 22:1–21 Xu Y, Sun Y, Yao L, et al (2008) Association between CYP2D6 ∗10 genotype and survival of breast cancer patients receiving tamoxifen treatment. Ann Oncol 19:1423–1429 Yokoo S, Yonezawa A, Masuda S, Fukatsu A, Katsura T, Inui K (2007) Differential contribution of organic cation transporters, OCT2 and MATE1, in platinum agent-induced nephrotoxicity. Biochem Pharmacol 74:477–487 Yonezawa A, Masuda S, Nishihara K, Yano I, Katsura T, Inui K (2005) Association between tubular toxicity of cisplatin and expression of organic cation transporter rOCT2 (Slc22a2) in the rat. Biochem Pharmacol 70:1823–1831 Zair ZM, Eloranta JJ, Stieger B, Kullak-Ublick GA (2008) Pharmacogenetics of OATP (SLC21/SLCO), OAT and OCT (SLC22) and PEPT (SLC15) transporters in the intestine, liver and kidney. Pharmacogenomics 9:597–624 Zevin S, Benowitz NL (1999) Drug interactions with tobacco smoking. An update. Clin Pharmacokinet 36:425–438 Zhang Y, Benet LZ (2001) The gut as a barrier to drug absorption: combined role of cytochrome P450 3A and P-glycoprotein. Clin Pharmacokinet 40:159–168

Chapter 3

Principles of Genetics and Pharmacogenetics William G. Newman

Abstract This chapter will explain in brief the principles of genetics and genetic variation and how these apply to prediction of adverse drug reactions and treatment efficacy in cancer. The difference between germline (inherited) and somatic (acquired) genetic variation will be explained and its particular importance in cancer. Some of the different technologies that can now be used to detect genetic variation will be explained. This chapter will also examine how pharmacogenetic studies have been conducted and issues relating to study design comparing different approaches. Keywords Genetics · Germline variation · Single nucleotide polymorphism (SNP) · Somatic variation · Pharmacogenetics

Contents 3.1 Genetic Principles . . . . . . . . . . . . . . . . 3.2 DNA, RNA and Proteins . . . . . . . . . . . . . 3.2.1 DNA Replication . . . . . . . . . . . . . 3.2.2 Transcription to Form RNA . . . . . . . . . 3.2.3 Translation to Form Proteins . . . . . . . . 3.3 Types of Genetic Variation . . . . . . . . . . . . 3.3.1 Mutations . . . . . . . . . . . . . . . . . 3.3.2 Polymorphisms . . . . . . . . . . . . . . 3.3.3 Variable Nucleotide Tandem Repeats (VNTR) 3.3.4 Copy Number Variants (CNVs) . . . . . . . 3.3.5 Inversions . . . . . . . . . . . . . . . . . 3.4 Human Genome and HapMap Projects . . . . . . .

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27 W.G. Newman (ed.), Pharmacogenetics: Making Cancer Treatment Safer and More C Springer Science+Business Media B.V. 2010 Effective, DOI 10.1007/978-90-481-8618-1_3, 

28 3.5 Detection of Genetic Variants . . . . . . . . . . . . . . . . 3.5.1 Polymerase Chain Reaction (PCR) . . . . . . . . . . . 3.5.2 Techniques to Use When the Genetic Variant is Unknown 3.5.3 Techniques When the Genetic Variant is Known . . . . . 3.6 Pharmacogenetics . . . . . . . . . . . . . . . . . . . . . 3.6.1 Principles of Pharmacogenetics . . . . . . . . . . . . 3.6.2 Genetic Studies of Drug Response . . . . . . . . . . . 3.6.3 Challenges in Cancer Pharmacogenetics . . . . . . . . 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

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3.1 Genetic Principles One of the difficult things about genetics and pharmacogenetics to the newcomer to this field is learning the somewhat complex terminology – unfamiliar terms are explained in this introduction to genetics and further information is available in the glossary section. In each cell making up the human body (apart from the gametes or reproductive cells), within the nucleus, there are 23 pairs of chromosomes. Twenty two of these pairs are called the autosomes – starting with the largest (chromosome 1) and ending with the smallest (chromosome 22). A male has a further pair of chromosomes – termed the X and Y chromosomes, whereas a woman has two X chromosomes. The Y chromosome is very small and has few genes apart from some controlling spermatogenesis. Each chromosome is composed of thousands of genes (instructions that form the template for protein formation). Changes in the number of chromosomes present in all the cells in the body – too many or too few – is called aneuploidy. Generally speaking this is incompatible with life. A few exceptions exist. An individual with three copies of chromosome 21 will have a condition called trisomy 21 (Down syndrome) characterised by learning disability, recognisable facial features and sometimes a major heart defect. Importantly, in cancer, chromosome aberrations are common, resulting in chromosomal gains and losses confined to the tumour tissue. These somatic changes may have important implications for cancer treatment and relevant examples are considered below and in subsequent chapters.

3.2 DNA, RNA and Proteins 3.2.1 DNA Replication The chromosomes are composed of a complex molecule called deoxyribonucleic acid (DNA). DNA is the essential molecular code found in each cell. It forms two major purposes. Firstly, DNA is the stable molecule in each cell, which can be

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replicated and transferred to progeny cells during cell division to ensure that these cells have an identical genetic code to the parent cell. Secondly, DNA forms a template for the formation of ribonucleic acid (RNA) molecules, which in turn form the template for protein formation. Therefore, each daughter cell of a dividing parent cell will have the ability to make the same proteins. DNA is a molecular strand composed of four nucleotides the purines; adenine (A) and guanine (G), and the pyrimidines; cytosine (C) and thymine (T). They form a double helix of two complimentary strands such that an adenine always binds to thymine and a cytosine to a guanine (G).

3.2.2 Transcription to Form RNA A gene is composed of blocks of DNA sequence (exons), which form the template for messenger RNA (mRNA or transcript) formation (Fig. 3.1). Exons are separated from each other by stretches of DNA of variable size (introns), which do not contribute to formation of the mature mRNA. At one end of the gene, called the 5 prime (5 ) end, lies a promoter – a switch that binds to factors to control the turning on and off of the gene expression. Whereas at the other end (the 3 prime, 3 ) lies a repetitive sequence that provides stability to the transcript and signals where protein formation ceases. When a transcription factor (a protein that binds to DNA) binds to the promoter region of the gene, an enzymatic process of transcription is activated to make mRNA. The chromosomal DNA helix separates to form single strands to which ribonucleotides are added one at a time corresponding to the ACGT sequence with uracil (U) replacing thymidine in the RNA molecule. The mRNA is then processed by removal (splicing) of the intervening intronic regions to form a continuum of the exons, which forms a template for translation (the formation of a protein/peptide). The transcript moves from the nucleus to the ribosomal machinery in the cytoplasm. It is here that the peptides (a chain of amino acids) are produced.

Fig. 3.1 Schematic of gene and transcript structure

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3.2.3 Translation to Form Proteins All peptides are sequences of a combination of 20 amino acids. The peptide undergoes a number of modifications to produce a mature protein. The mRNA transcript in the cytoplasm binds to a combination of proteins (the ribosomes). The ribonucleotide transcript forms a code, which needs to be translated into a peptide. Three ribonucleotides in a row within a gene form a codon. Each codon binds to an amino acid present within the cytoplasm. This process adds an amino acid sequentially and allows the amino acids to bind to each other in a pre-defined manner to form the peptide. The four nucleotides (A, C, G and U) can form 64 possible combinations, the amino acid code (Table 3.1). As there are only 20 amino acids, there is some duplication in the system such that AUU, AUC or AUA can bind an isoleucine residue. Each gene transcript starts with an AUG codon, which binds a methionine, whereas UGA represents a stop codon, indicating where the peptide sequence ends. The naïve peptide sequence is then processed by a number of post-translational modifications, including glycosylation and cleavage of a signal peptide in the Golgi apparatus and endoplasmic reticulum. These steps form a mature protein, which moves to a different compartment within the cell or is transported out of the cell into another area of the tissue or body. Table 3.1 Amino acid code: The various combinations of nucleotides that form templates for amino acids to bind to create peptides Second position

U

First position

C

U Phenylalanine Leucine

C Serine

A Tyrosine

G Cysteine

Leucine

Proline

STOP STOP Histidine

STOP Tryptophan Arginine

Glutamine A

Isoleucine

G

Methionine Valine

Threonine

Alanine

Asparagine

Serine

Lysine

Arginine

Aspartic acid Glutamic acid

Glycine

U C A G U C A G U C A G U C A G

Third position

3.3 Types of Genetic Variation A mutation or polymorphism is a variation in the normal DNA sequence. Mutations and polymorphisms are usually inherited from a parent. However, they can arise as a new event (de novo) in an individual. Every time a cell divides its entire

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compliment of DNA is copied to form an identical copy. This process of replication is susceptible to mistakes, for example a mismatched nucleotide is added and so a potential error occurs. A repair process exists in cells to correct such replication errors or to remove any cell containing multiple mistakes by a process of apoptosis (cell death). Mistakes in DNA replication increase as individual gets older, if they are exposed to radioactivity, carcinogens (cigarette smoke) or if they have a genetic predisposition, which impairs the fidelity of the replication process e.g. inherited forms of bowel cancer like hereditary non polyposis coli cancer (HNPCC) is characterised by mutations in such mismatch repair genes.

3.3.1 Mutations A mutation usually indicates a rare (1% frequency). It can occur anywhere throughout the genome – either in or near a gene or in areas not known to be important for encoding proteins. Single nucleotide polymorphisms (SNPs) are common single base pair changes occurring on average once every 1000 base pairs throughout the genome. Therefore, there are millions of SNPs present in every individual’s genome. Nearly all SNPs are bi-allelic, that is one of two nucleotides can be found at a particular point, so that the maximum minor allele frequency (MAF) is 50%. The MAF is the frequency at which the less common variant is found in a specified population. Large databases cataloguing millions of human SNPs are now publicly available (e.g. dbSNP – http://www.ncbi.nlm.nih.gov/projects/SNP). Importantly, the presence or frequencies of different SNPs can differ dramatically between different ethnic groups. Generally speaking, SNPs that are common across all ethnic groups arose longer ago in evolution compared to those only present in one population. More common SNPs

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Table 3.2 Type of mutation – illustrated with variants in the CYP2D6 gene (see http://www.cypalleles.ki.se/cyp2d6.htm) Type of mutations

Description

Annotated example

Missense

Alteration of a nucleotide (base) resulting in the substitution of one amino acid with another

Nonsense

Conversion of a base to introduce a premature stop codon

Frameshift

Usually the loss or gain of a nucleotide which alters the reading sequence of codons and results in a premature stop codon Can alter the sequence at the exon/intron border disrupting normal sequencing so that an intron is not spliced and a frameshift results Removal of one or multiple base pairs – can involve a whole exon, whole gene, or multiple genes Addition of a single base or more

CYP2D6∗ 10, g.100C>T, in the DNA sequence a thymine replaces a cytosine at position 100) this results in p. P34S (in the peptide squence a proline is substituted for by a serine residue at position 34). This reduces CYP2D6 activity. CYP2D6∗ 8, g.1758G>T (a guanine is replaced by a thymine to create a TGA stop codon (p. G169X) and converts a glycine amino acid to a truncated protein or degraded transcript) CYP2D6∗ 6, g.1707delT, (a thymine base is deleted at position in the DNA sequence) resulting in a p.T118fs – i.e. a shortened protein product is predicted or transcript that is degraded CYP2D6∗ 4, g.1846G>A (the change of a guanine to adenine in position 1846 in the DNA sequence creates a new splice site and results in absent CYP2D6 activity).

Splicing

Deletion

Insertion

Duplication

Ins/Del

Multiple copies of a sequence, whole exon or gene (amplification) – often associated with overexpression of the gene A combination of a deletion of one or more base pairs and the introduction of one or more different base pairs

CYP2D6∗ 9, g.2615_2617delAAG Three bases are deleted in the DNA sequence resulting in p.K281del with an absent lysine amino acid at position 281 in the peptide CYP2D6∗ 40, g.1863_1864ins (TTT CGC CCC)2. 18 nucleotides are inserted (the same 9 in duplicate) resulting in the addition of two copies of phenylalanine, arginine and proline p. 174_175ins(FRP)2 which causes absent enzyme activity CYP2D6∗ 18, g.4125_4133dupGTGCCCACT duplication of 9 nucleotides resulting in the addition of 3 amino acids p.468_470dupVPT (valine, proline and threonine) in this case reducing enzyme activity

A standardised system for annotating genetic variations has been described (den Dunnen and Antonarakis 2001). Unfortunately, this is more complicated in pharmacogenetics because of the parallel use of a ∗ allele system

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are also more likely to be associated with some advantage to the human, for example either in being able to resist infection or detoxify substances present in the diet. Like mutations, SNPs may result in a change within a gene (coding SNP), predicted to alter an amino acid (a non-synonymous SNP). Alternatively, the SNP may alter a nucleotide within a gene, which is not predicted to alter an amino acid (a synonymous SNP). Previously, synonymous SNPs were thought to be unimportant, but recent studies show that some can alter splice sites or alter exon splice enhancers (ESEs) and therefore alter the function of the protein. The vast majority of SNPs (non-coding SNPs) lie in introns or outside genes altogether. These also were considered unimportant until recently, when some non-coding SNPs were shown to change gene expression by altering transcription factor binding or by creating new splice sites.

3.3.3 Variable Nucleotide Tandem Repeats (VNTR) VNTRs are repetitive sequences found throughout the genome. In their simplest form they may represent a string of a single nucleotide e.g. AAAAAA. . . . or commonly, dinucleotides e.g. TATATATA. Some trinucleotide repeats are prone to expansion causing inherited diseases like Huntington’s disease. They may also represent more complex repeating sequences. A dinucleotide TA repeat in the promoter of the UGT1A1 gene has been associated with irinotecan toxicity (see Chapter 5).

3.3.4 Copy Number Variants (CNVs) Recent advances in technology – particularly in large-scale SNP arrays and array comparative genomic hybridisation (aCGH) have revealed the presence of thousands of CNVs throughout the genome. These are relatively large areas (stretching from a few kilobases to a few megabases, ie thousands or millions of nucleotides in length) of duplication or deletion of chromosomal regions. Tumours are especially susceptible to significant chromosomal aberrations with large deletions and amplification of chromosomal loci. Such chromosomal changes may be tumour specific or random events. An important example (considered in detail in Chapter 6) is that multiple copies of the EGFR gene have been identified in the tumours of some patients with NSCLC due to gene amplification or aneuploidy (multiple copies of chromosome 7 containing EGFR). Some studies have demonstrated a relationship between response to tyrosine kinase inhibitors in NSCLC and the number of EGFR copies.

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3.3.5 Inversions Inversions are simply areas of the chromosome, which have flipped around to the reverse orientation. They may be small or encompass many megabases e.g. an inversion of 900 kilobases on chromosome 17 has been associated with steroid responsiveness in asthma (Tantisira et al. 2008).

3.4 Human Genome and HapMap Projects The Human Genome Project was initiated in the early 1990s culminating with the publishing of the entire DNA sequence of the draft human genome in 2001 (Lander et al. 2001; Venter et al. 2001). A number of areas that were difficult to define at the centres and end of chromosomes or where there was lots of repetitive sequence took a little longer to work out. This provided a summary of all the information representing the 30,000 genes that encode proteins in humans. The initial studies showed that there are a large number of genetic variants shared between individuals. The HapMap (www.hapmap.org) project has proceeded to catalogue all of these genetic variants with particular focus on SNPs (International HapMap Consortium 2005; International HapMap Consortium et al. 2007). They have aimed to establish if some variants occur more commonly in combination with other variants i.e. forming haplotypes. Sections of DNA sequence are inherited in blocks between regions of recombination. Recombination occurs to allow swapping of genetic material between sister chromatids during cell division. This process is one of the mechanisms that ensures that variation occurs from generation to generation. Some areas of the genome undergo a number of recombinations as variation is under positive selection, whereas some chromosomal regions contain clusters of genes whose function is so fundamental to life that little or no variation is tolerated and these are passed intact from one generation to the next as a complete block – a haplotype. The entire genome is therefore composed of chromosomes, which in turn are composed of blocks of DNA of varying sizes that can be traced through an individual’s ancestors. These haplotype blocks contain stretches of DNA with variable numbers of genes, SNPs and other genetic variants. SNPs that are inherited with a panel of other SNPs are called tag SNPs (tSNPs). A single SNP may be informative for one other SNP or for hundreds or thousands in a DNA sequence stretching thousands of kilobases. This information is particularly relevant in the design of genome wide association studies (GWAS, see below). SNP allele and haplotype frequencies and the specific tSNPs differ between ethnic groups and this has been catalogued through the HapMap Project.

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3.5 Detection of Genetic Variants 3.5.1 Polymerase Chain Reaction (PCR) The majority of sequence variant detection in the laboratory is based on a process called the polymerase chain reaction (PCR). PCR is the amplification of a specific DNA sequence, usually 100s of base pairs long. PCR requires the design of a pair of oligonucleotide primers, usually about 20 base pairs long. These sit on opposite strands of the double-stranded DNA and match the DNA sequence. Many websites are now available to design primers. Other components of the PCR reaction are Taq polymerase an enzyme, which adds nucleotides to the primer complimentary to the DNA sequence, buffer containing magnesium to promote the catalysis of the reaction and a mixture of dinucleotides (dNTPs), individually called, dATP, dCTP, dGTP and dTTP. The reaction is initially ◦ heated to a high temperature (usually 95 C) to denature the DNA and create sin◦ gle strands. The reaction is cooled to an annealing temperature usually 65–55 C ◦ to allow binding of the primers to the single strands and then warmed to 72 C to allow addition of the nucleotides (extension) until the process is repeated, usually over 30–40 cycles. This process results in an exponential increase in copies of a single specific sequence to make millions of copies, allowing manipulation by other downstream technologies.

3.5.2 Techniques to Use When the Genetic Variant is Unknown 3.5.2.1 DNA Sequencing The main way of identifying unknown genetic variants is by using DNA sequencing technology. This allows the interrogation of a specific piece of DNA, usually a few hundred base pairs long. The reference sequence of the section of DNA under investigation must be known, to provide a comparator for the DNA sequence to be tested against. The standard DNA sequence is available from a number of databases, including the NCBI GenBank (http://www.ncbi.nlm.nih.gov/Genbank/) and Ensembl (www.ensembl.org). The same method of sequencing has been in place for the past 30 years with only minor modifications (Maxam and Gilbert 1977). Essentially, this involves taking a PCR product (multiple copies of a short specific DNA sequence), a sequencing oligonucleotide primer (usually a stretch of about 20 base pairs corresponding to the DNA sequence) a mixture of dNTPs and dideoxynucleotides (ddNTP), Taq polymerase and a buffer. The ddNTPs are tagged with different fluorescent dyes corresponding to the specific nucleotide A, C, G or T. The mixture is heated and cooled over a number of cycles. Nucleotides are added to each strand complimentary to the template. However, when a ddNTP is added this stops further addition of nucleotides. A mixed population of strands of different lengths, terminated by the

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Fig. 3.2 Multiple DNA strands of different length are generated by the sequencing process and separated by capillary electrophoresis to form a chromatogram read out

ddNTPs is created. These strands are then automatically separated by electrophoresis on polyacrylamide in a gel or capillary. The different sized strands travel at different speeds and pass a laser beam, which records the order in which the ddNTPs occur (Fig. 3.2). This is a relatively straightforward process, which can be achieved in hours and the cost of DNA sequencing has dropped dramatically over the past decade making this the method of choice for unknown sequence detection.

3.5.2.2 Alternative Techniques for Mutation Screening Denaturing high performance liquid chromatography (dHPLC) is one of many techniques, including single strand conformational polymorphism (SSCP), which became popular to screen genes for unknown variants when the costs of sequencing remained high. The basic principle behind both techniques is that a PCR product is heated up to form single stranded DNA. On cooling, the single DNA strands reform a double strand. If a SNP or mutation is present then a mixture of matched and mis-matched strands form on cooling. This pool of identical (homoduplex) and mis-matched (heteroduplex) strands have different properties e.g. they run slower in a gel and can be differentiated. These techniques were used as an initial screen to detect variants, but would not identify the precise genetic change. This would then require DNA sequencing in samples different from the normal pattern.

3.5.3 Techniques When the Genetic Variant is Known Multiple technologies are available to detect the presence of a known genetic variant (a SNP or a mutation). These can be classified according the technology underlying the detection process and how many variants can be genotyped in how many samples in a single assay– low, medium or high throughput (Syvänen2001). DNA sequencing can be performed to identify a genetic variant even if the presence of this is already suspected. However, as this was previously expensive, not particularly rapid and generated excess redundant data other strategies have been devised.

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3.5.3.1 Restriction Endonuclease (Enzyme) Digestion Restriction endonucleases are enzymes which are formed by bacteria and have the ability to cut DNA at specific sequence sites e.g. the enzyme EcoR1 always cuts the DNA sequence GAATTC between the G and the A. If a SNP alters the sequence such that it changes the ability of a restriction enzyme to cut a sequence this can indicate the presence/absence of this variant. DNA is amplified by PCR and then is incubated with the enzyme. The digested DNA fragments are then separated (electrophoresed) on an agarose gel. These will be different sizes dependent upon whether the variant is present or absent (Fig. 3.3). Websites e.g. Webcutter 2.0 (http://rna.lundberg.gu.se/cutter2) are available which predict all the enzyme restriction sites of a DNA sequence. The major advantages of this process are that it is simple, highly specific, cheap and does not rely on expensive laboratory equipment. It is limited though by the fact that the majority of SNPs do not alter restriction enzyme sequences and that only a single SNP at a time can be examined.

Fig. 3.3 Figure of a 2% agarose gel electrophoresis. A 100 bp DNA ladder is present in to define the size of the digested PCR products. The PCR product in lane ORK29 has been cut by the enzyme to define the presence of a specific genetic variant. All other lanes have wild type sequence

3.5.3.2 Hybridisation Techniques Hybridisation is the process of DNA binding to another DNA molecule under conditions of varying stringency e.g. an altered temperature or chemical environment. A probe of a few nucleotides in length is designed complimentary to the sequence encompassing the SNP. Two probes are created, one corresponding to each potential nucleotide at the variant site. The probes are then either individually or simultaneously hybridised to the target DNA. Previously, this technology was reliant on radioactive probes, which made it less attractive. However, since the introduction of fluorescent dyes, it has become a mainstay technology for SNP genotyping in pharmacogenetics using real-time PCR like Taqman (Applied Biosystems, www.appliedbiosystems.com). Hybridisation techniques can be scaled up to consider many variants in a single assay. An example of this is the Affymetrix SNP genotyping platform

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Fig. 3.4 The Roche Amplichip for genotyping an extended profile of CYP2D6 variant alleles

(www.affymetrix.com). This relies on the digestion of genomic DNA by a restriction enzyme to form smaller DNA fragments, which can then be amplified by PCR. These fragments are then hybridised to an array containing a vast number of oligonucleotides. For each SNP, there will be oligonucleotides corresponding to each potential SNP variant plus control probes. This technology forms the basis for the Roche P450 Amplichip (de Leon 2006, http://molecular.roche.com), which can be used to genotype multiple variants within the CYP2D6and CYP2C19 genes (Fig. 3.4). The major advantage of this system is that it can reliably genotype up to hundreds of thousands of SNPs in a single assay. This dramatically decreases the time and cost of undertaking this analysis.

3.5.3.3 Single Base-Pair Extension Single base pair extension is a modification of the DNA sequencing method described above. An oligonucleotide primer is designed a single base pair away from the predicted SNP. A mixture of dNTPs and ddNTPs are added. The ddNTPs terminate the extension of the primer complimentary to the DNA template. Pyrosequencing (Qiagen, http://www.pyrosequencing.com) is a technique that

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detects which nucleotide is added in real time using a luminescent enzyme to produce a pyrogram (Fig. 3.5) (King and Scott-Horton 2007). The iPlex by Sequenom (http://www.sequenom.com) is a technique that uses single primer base extension to add nucleotides to a primer and then measures the extended products by mass spectrometry. This technique has the advantage of being able to genotype many SNPs in a single multiplexed assay.

Fig. 3.5 Pyrosequencing KRASmutation assay in colorectal cancer tissue, demonstrating (a) normal (wild type sequence) at codons 12 and 13 and (b) mutation c.35G > T (pGly12Val) i.e. a G nucleotide has been replaced by a T in one copy of the gene resulting in an amino acid change from a glycine to a valine at position 12

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Low Throughput

Medium throughput

High throughput

Technique

Number of samples

Number of SNPs per assay

Restriction enzyme digestion Pyrosequencing

10 s at a time

One per assay

Real time allelic discrimination (e.g. Taqman) Mass spectrometry (Sequenom)

100 s at a time

One per assay

100 s at a time

Multiplex up to 48 per assay

Affymetrix

One per assay

Illumina

One per assay

Up to 900 k with v6.0 >1million (Human1M-Duo BeadChip

One per assay

When designing a study involving SNP genotyping it is important to establish how many SNPs are to be genotyped in how many samples (Table 3.3).

3.6 Pharmacogenetics The classic studies in pharmacogenetics have identified inherited variants in single genes, which have defined a specific drug response. These strong relationships between genetic change and drug outcome form the major basis of this book. At the present time, clinically meaningful pharmacogenetic tests are based on the interpretation of variants in single genes and how they are likely to affect drug response. However, most drug effects are not determined by a single gene, but by many genes (polygenic); these genes may be interacting with each other and often also with environmental factors.

3.6.1 Principles of Pharmacogenetics 1. A single gene encodes a protein e.g. a drug-metabolising enzyme or transporter whose function may be relevant to several drugs. 2. Different variants within the same gene may cause different functional effects, which can result in absence of the protein, reduced or increased activity or altered binding capacity. 3. The same enzyme can have different functional effects on the metabolism of different drugs e.g. it may inhibit the activity of a drug by facilitating its removal

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or activate a drug by catalyzing the conversion of a pro-drug to its active metabolite. 4. Apart from functional variants in genes, many other factors including adherence, co-morbidities, co-medications can influence the metabolism of a drug.

3.6.2 Genetic Studies of Drug Response Many experimental techniques are available to identify genetic variants that predispose to specific diseases or may predict the response of an individual to a drug. 3.6.2.1 Linkage Analysis Initial genetic studies to define gene variants that cause inherited diseases e.g. cystic fibrosis used a technique called linkage analysis. This approach requires multiple family members to be affected by a similar trait and samples to be available from many families. Although this method has identified the causative gene for many inherited conditions, it is unusual for many family members all to have been treated with the same drug and all to have had a similar outcome. Therefore, it has generally not been feasible in pharmacogenetics, apart from conditions like malignant hyperthermia to use linkage approaches. In addition, it is an expensive, time-consuming method and when multiple genes cause (genetic heterogeneity) or contribute (polygenic) to a phenotype, it is not a very effective technique. 3.6.2.2 Candidate Gene Association Studies The most popular approach to identify genes relevant to altered drug efficacy or ADRs has been candidate gene association studies. Genes for investigation are selected on the basis of their known role in the metabolism, uptake, transport or excretion of a specific drug. Variants within the candidate gene are selected based on their known functional effect e.g. they are known to reduce enzyme activity. A more recent approach has been to use the information from the HapMap project to select tSNPs which provide information about most of the variation present in a gene. For example, genotyping two or three tSNPs may be able to establish or refute an association between an entire gene and a particular outcome rather than being able to establish this for just a single variant. The most widespread approach in pharmacogenetics is to conduct a case-control analysis. The selected variants are genotyped in a population of individuals experiencing the poor outcome (either response or ADR) and compared to a population who have been exposed to the medication and had a favourable outcome. Important considerations are to ensure that the populations are of similar background – this usually means of a similar ethnic group as SNP frequencies can

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differ significantly; that each group should have been treated with the same dose of the drug; treated for similar indications; that adherence rates were similar in both groups, genotyped individuals are unrelated to each other, and that age and gender are as close as possible between the groups. Most candidate gene pharmacogenetic studies have focussed on the relationship between variants in a single gene and drug response. However, greater understanding of drug metabolism pathways has revealed multiple candidate genes relevant to each drug’s metabolism. This allied to advances in technology allowing the rapid genotyping of multiple SNPs across many genes has led to the emergence of studies considering genetic variation within entire drug pathways. Most candidate gene association studies have not been validated (positively replicated) because the studies have been poorly designed: either too few individuals have been genotyped (lack of statistical power); the genotyping has not been robust; different outcomes have been considered; different genetic variants have been analysed, or the case and control groups have been poorly matched (population stratification). Further, a phenomenon of winner’s curse exists, in that the first study usually over-estimates the size of the relationship between a genetic variant and the specific outcome. In addition, there is significant publication bias in that positive associations are more likely to be reported in the literature – over emphasising an effect. Some genetic associations are confined to specific ethnic populations e.g. the relationship between a HLA-B∗ 1502 variant and Stevens-Johnson syndrome in Asians taking carbamazepine for epilepsy (Chung et al.2004).

3.6.2.3 Genome Wide Association Studies (GWAS) The Affymetrix and Illumina genotyping systems have allowed the design and conduct of multiple GWAS over the past couple of years. Not many have been performed in pharmacogenetics although this is changing with studies considering warfarin (Takeuchi et al. 2009), adverse reactions to flucloxacillin (Daly et al. 2009) and bisphosphonate induced osteonecrosis of the jaw (Sarasquete et al. 2008). The principle of GWAS is very simple. Hundreds of thousands of SNPs are genotyped in multiple individuals affected by a particular trait and compared to a matched control population, who do not experience the trait. Similar to the recent transformation in genotyping and sequencing technologies has been the development of powerful computer platforms and software to allow the interrogation of the vast datasets generated by such high-throughput genotyping (Crowley et al. 2009). The major advantages of GWAS are that they consider variants across the entire genome without any pre-selected bias based on previous knowledge about a drug’s metabolism. This means that genes, never previously considered relevant, can be identified as having a key role in the response to a drug. The major limits to GWAS are the considerable costs and the availability of large sample biobanks with robust clinical outcome data.

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3.6.3 Challenges in Cancer Pharmacogenetics 3.6.3.1 Prospective vs Retrospective Studies Most pharmacogenetic studies to date have been case controlled retrospective association studies. These have the advantage of being relatively simple to conduct, but have the limitations of potential biases, lack of availability of complete clinical outcome data, incomplete collection of biological samples from all potential participants The optimal strategy to establish the benefit of a pharmacogenetic test is to conduct a randomised controlled clinical trial (RCT) (Pirmohamed and Park 2001). This has the advantage of defining the specific outcome to be assessed, prospectively collecting clinical data and comparing outcome in individuals in whom a pharmacogenetic test is conducted against individuals who do not undergo testing. However, clinical trials cannot also fully replicate clinical practice, and positive results in RCTs do not always translate into meaningful improvement in day to day clinical practice. Some adverse reactions are extremely rare and design of a RCT is not practicable. Furthermore, the relationship between some ADRs and a specific genetic variant is so compelling, defined through a retrospective analysis, that any prospective confirmation of the relationship would be unethical (see Chapter 11). 3.6.3.2 Somatic Mutation Testing Response to a drug in cancer treatment may be due to a change in a gene that is present in every cell in the body (germline change) or just in the tumour tissue (somatic change). The latter situation can be especially challenging as tumour tissue can sometimes be difficult to access e.g. in the lung or brain and only very small biopsy specimens are available for analysis. Tissue sections from tumours may be a heterogeneous mixture of normal and tumour cells and therefore it is important to use techniques that are very sensitive to identify mutations that may be at a very low level within the sample e.g. DxS Therascreen; to ensure that tumour is being analysed rather than normal tissue which requires close liaison between molecular geneticists and histopathologists; to ensure the optimum tissue samples are taken to allow analysis.

3.7 Conclusion This chapter has provided a very brief broad overview of some of the principles in genetics and pharmacogenetics. Significant advances in technology have driven forward an enormous increase in our understanding of the human genome and the variation between individuals and populations. Studies are starting to unravel some of the specific relationships between variations in genes and outcome to drug treatment. The following chapters will explore these relationships in the context of common tumour types and how these are being adopted into clinical practice.

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References Chung WH, Hung SI, Hong HS et al (2004) Medical genetics: a marker for Stevens-Johnson syndrome. Nature 428:486 Crowley JJ, Sullivan PF, McLeod HL (2009) Pharmacogenomic genome-wide association studies: lessons learned thus far. Pharmacogenomics 10:161–163 Daly AK, Donaldson PT, Bhatnagar P et al (2009) HLA-B∗ 5701 genotype is a major determinant of drug-induced liver injury due to flucloxacillin. Nat Genet 41:816–819 de Leon J, Susce MT, Murray-Carmichael E (2006) The amplichip CYP450 genotyping test: integrating a new clinical tool. Mol Diagn Ther 10:135–151. den Dunnen JT, Antonarakis SE (2001) Nomenclature for the description of human sequence variations. Hum Genet 109:121–124 International HapMap Consortium (2005) A haplotype map of the human genome. Nature 437:1299–1320 International HapMap Consortium, Frazer KA, Ballinger DG et al (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449:851–861 King CR, Scott-Horton T (2007) Pyrosequencing: a simple method for accurate genotyping. Methods Mol Biol 373:39–56. Lander ES, Linton LM, Birren B et al (2001) Initial sequencing and analysis of the human. Nature 409:860–921 Maxam AM, Gilbert W (1977) A new method for sequencing DNA. Proc Natl Acad Sci USA 74:560–564 Pirmohamed M, Park BK (2001) Genetic susceptibility to adverse drug reactions. Trends Pharmacol Sci 22:298–305 Sarasquete ME, García-Sanz R, Marín L et al (2008) Bisphosphonate-related osteonecrosis of the jaw is associated with polymorphisms of the cytochrome P450 CYP2C8 in multiple myeloma: a genome-wide single nucleotide polymorphism analysis. Blood 112:2709–2712 Syvänen AC (2001) Accessing genetic variation: genotyping single nucleotide polymorphisms Nat Rev Genet 2:930–942 Takeuchi F, McGinnis R, Bourgeois S, et al (2009) A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet 5:e1000433 Tantisira KG, Lazarus R, Litonjua AA, Klanderman B, Weiss ST (2008) Chromosome 17: association of a large inversion polymorphism with corticosteroid response in asthma. Pharmacogenet Genomics 18:733–737 Venter JC, Adams MD, Myers EW et al (2001) The sequence of the human genome. Science 291:1304–1351

Chapter 4

Pharmacogenetics in the Management of Breast Cancer Sacha J. Howell

Abstract The estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) represent key pharmacogenomic targets for effective therapies in breast oncology. Furthermore, recent advances in genomic approaches to clarify prognosis have refined the use of cytotoxic chemotherapy, although a great many women still receive unnecessary and often ineffective treatment. Pharmacogenetic factors involved in the metabolism of some of these drugs, and in particular the influence of CYP2D6 polymorphisms in the metabolism of tamoxifen, have begun to change clinical practice. The routine genotyping of drug metabolic pathways may well become another standard measure in breast oncology in the near future. Keywords CYP2D6 · Estrogen receptor · Herceptin · Oncotype DX · Tamoxifen

Contents Introduction . . . . . . . . . . . . . . . . . . . Pharmacogenomics and Risk Prediction . . . . . . Pharmacogenomics, Drug Targets and Trastuzumab . Pharmacogenetics, Drug Metabolism and Tamoxifen Concluding Remarks . . . . . . . . . . . . . . . 4.5.1 Case Scenario . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . .

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S.J. Howell (B) Breast Biology Group, Division of Cancer Studies, Paterson Institute for Cancer Research, University of Manchester, Manchester M20 4BX, UK e-mail: [email protected]

45 W.G. Newman (ed.), Pharmacogenetics: Making Cancer Treatment Safer and More C Springer Science+Business Media B.V. 2010 Effective, DOI 10.1007/978-90-481-8618-1_4, 

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4.1 Introduction Breast cancer is the commonest malignancy in the UK, and most economically developed countries, despite being rare in men (Westlake and Cooper 2008). The incidence of breast cancer has increased by more than 50% over the last 25 years due primarily to changes in lifestyle such as increasing age at first full term pregnancy, hormone replacement therapy (HRT) use and obesity along with a reduction in breast feeding. In the UK, the introduction of the National Health Service Breast Screening Programme (NHSBSP) in 1988 resulted in a transient rise in breast cancer incidence, however, breast cancer incidence continues to rise (13% increase over the last 10 years) despite a stable proportion of eligible women entering the NHSBSP. In contrast, the mortality from breast cancer is declining. Since peaking in the late 1980s, breast cancer death rates have fallen by a third. Statistical models have apportioned approximately equal weighting to both earlier detection through screening and better therapy in the adjuvant setting to account for the improved outlook (Berry et al. 2005). Treatment options for breast cancer broadly fall into three categories; • Endocrine therapy where the target is almost universally the estrogen/ER axis with drugs such as tamoxifen and the aromatase inhibitors. • Cytotoxic chemotherapy. • Novel targeted agents such as trastuzumab (Herceptin) directed against HER-2 and bevacizumab (Avastin), which targets tumour angiogenesis. The Scottish surgeon George Beatson was the first to successfully employ endocrine therapy when he demonstrated regression of recurrent and locally advanced breast cancers following oophorectomy (Beatson 1896). Remarkably, it took a further 30 and 90 years respectively before estrogen and the ER were identified (Allen and Doisy 1923; Green et al. 1986). ER expression in breast tumours is now universally recognised as a prerequisite for anti-estrogen therapy and can be considered as one of the first, and certainly the most frequent, pharmacogenomic tests used in oncology practice. Subsequently, pharmacogenomic analyses have identified novel targets for therapy such as HER2, which is discussed below. Furthermore, these analyses have enabled the refinement of treatment strategies, sparing women at very low risk of relapse the toxicities of adjuvant therapy, whilst identifying those with tumours of poor prognostic phenotypes in whom more aggressive therapy and studies of novel agents are warranted. Pharmacogenetics has also begun to impact on treatment decisions in breast cancer therapy. So far, the most convincing data surround endocrine therapy, and more specifically single nucleotide polymorphisms (SNPs) that impact on the metabolism and clinical efficacy of tamoxifen. This situation is discussed in detail below along with a summary of the key SNPs that have been described to impact on the metabolism of cytotoxic drugs, but have not led to changes in practice so far.

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4.2 Pharmacogenomics and Risk Prediction Early successful approaches to refine prognostic stratification and direct the use of cytotoxic therapy employed classical clinicopathological factors such as tumour grade, size and axillary lymph node status (Galea et al. 1992). A more recent model, Adjuvant-online, which has been retrospectively validated and is now used widely, also incorporates tumour ER expression into its prognostic algorithm (Olivotto et al. 2005; Ravdin 1996). This programme allows the operator to modify the prognostic estimates in light of other prognostic factors such as HER2 expression, thus more accurately tailoring prognostication to the individual. Although these models have clearly been of value in directing cytotoxic therapy, it has been estimated that up to 80% of women still receive cytotoxic chemotherapy unnecessarily (van’t Veer et al. 2005). In addition, whilst the ER has excellent negative predictive value, 30–50% of tumours with ER expression are intrinsically resistant to anti-estrogen therapy, demonstrating the need for better prognostic and predictive markers. Multi-gene expression analysis, using 25–33,000 probe gene chips, has been used in attempts to refine prognostic stratification in early breast cancer. Broadly speaking two approaches have been used; unsupervised analysis of tumour microarray data to identify clusters of tumours with similar gene expression profiles, and supervised analysis, where the clinical outcome of the patients is known, to identify gene expression profiles associated with good and bad prognosis. In the first approach, the resulting tumour clusters resembled cell types in the normal breast by gene expression patterns and were labelled accordingly; Luminal A, Luminal B, basal-like, HER2+ and normal breast-like tumour subtypes (Perou et al. 2000; Sorlie et al. 2003). Perhaps, most importantly the Luminal A and Luminal B subtypes, which both express the ER, have distinctly different rates of distant metastasis and overall survival. Although further work is needed it is likely that women with the better prognosis and more endocrine sensitive Luminal A subtypes can be spared cytotoxic therapies (Hu et al. 2006; Sorlie et al. 2003). An important advantage of such genome wide expression studies is the analysis of so many genes in an unselected manner and thus the potential to identify novel targets for tailored therapy in particular tumour clusters. The second approach has resulted in the development of many genomic signatures to categorise women, predominantly with axillary lymph node negative tumours, into good, intermediate or poor prognostic groups (Foekens et al. 2006; Paik et al. 2004; van de Vijver et al. 2002). Two of these signatures (Mammaprint and Oncotype DX) are the focus of large multinational studies (MINDACT and TAILORx respectively) investigating their capacity to outperform standard clinicopathological approaches to prognostication such as Adjuvant-online (Bogaerts et al. 2006; Sparano and Paik 2008). However, by limiting the number of informative genes analysed in these signatures to 70 (Mammaprint) and 16 (Oncotype DX) the capacity to identify novel therapeutic targets is lost. It is also worth noting that the overlap of genes in these signatures is small and that re-analysis of the database from which the Mammaprint signature was generated led to the identification of at

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least seven different 70 gene signatures with equal prognostic power as the original (Ein-Dor et al. 2005). The Oncotype DX assay is, however, worth special attention. This signature was generated by pre-selecting 250 genes of potential prognostic significance from three breast cancer array databases. The 16 genes with the strongest performance at predicting relapse, in women with ER positive and lymph node negative tumours, and five house-keeping genes were selected to generate a 21-gene signature derived by real-time PCR analysis of paraffin embedded tumour tissue (Paik et al. 2004). The 16 genes were categorised into five subgroups: proliferation, HER2, ER, invasion and ‘other’ and prognostic algorithm was constructed to generate a ‘recurrence score’ (RS; 0–100). The RS demonstrated significant independent prognostic power in multivariate analysis of ‘standard’ prognostic factors (HR 2.81 (95%CI 1.7–4.6; p < 0.001)) (Paik et al. 2004). Importantly, subsequent analysis of Oncotype-DX in tumours from a randomised study of tamoxifen +/– cyclophosphamide, methotrexate and fluorouracil (CMF) chemotherapy (NSABP B-20) demonstrated its excellent predictive value for response to and long-term benefit from chemotherapy, with such benefit only seen in women with tumours of high RS (Paik et al. 2006). Numerous other signatures have been developed, in varied disease settings, in an attempt to better predict responsiveness or resistance to particular therapeutic agents. It is beyond the scope of this chapter to discuss the myriad of such signatures and so far none have entered into widespread clinic practice.

4.3 Pharmacogenomics, Drug Targets and Trastuzumab As discussed above, the ER is the most commonly measured pharmacogenomic marker in oncology clinical practice, informing both prognosis and prediction of response to endocrine and chemo-therapy (Albain et al. 2004). However, the ER was identified after endocrine therapy had been widely adopted as a strategy to treat breast cancer. A more logical approach to drug development is to first identify the pharmacogenomic target of demonstrable importance in cancer cell survival, growth or metastasis before rational drug design to target the receptor or signalling pathways involved. Analysis of target expression should then be employed to identify patients suitable for treatment with the novel therapy, thus avoiding the treatment of patients with no chance of benefit and diluting the perceived benefit seen in target positive tumours. Few drugs have been developed in this way in oncology practice, however one very important example of this strategy is the discovery of the HER2 oncogene, the targeting of which has changed the face of breast cancer therapy. The neu oncogene was first discovered during experiments in which DNA derived from nitrosurea induced rat brain tumours was found to induce malignant transformation of mouse fibroblasts (Shih et al. 1981). The human homologue of neu was identified, designated c-erbB-2 or HER-2 (Human Epidermal growth factor Receptor 2) and shown to be amplified in approximately 25% of breast cancers (Coussens et al. 1985; King et al. 1985). Such tumours with gene amplification were shown, using immunohistochemistry (IHC), to have increased cell surface HER2

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protein expression and to be of a significantly worse prognosis than those without gene amplification (Iglehart et al. 1990; Slamon et al. 1987). Importantly, monoclonal antibodies raised against HER2 only inhibited tumour growth in cell lines with HER2 amplification, despite the low level HER2 protein expression in the nonamplified cell lines (Drebin et al. 1985; Drebin et al. 1986; Hudziak et al. 1989; Lupu et al. 1990). The 4D5 clone was subsequently humanised for translation into early clinical studies (Carter et al. 1992) and demonstrated single agent response rates in HER2 positive metastatic breast cancer of 11–15% (Baselga et al. 1996; Cobleigh et al. 1999) and 28% (Vogel et al. 2002) in pre-treated and chemotherapy naïve patients, respectively. Patient selection for these studies was primarily by IHC with women whose tumours scored 2+ or 3+ for HER2 protein expression being considered eligible. This is important as more recent studies have shown that approximately 95% of tumours HER2 3+ by IHC have gene amplification by fluorescence in situ hybridisation (FISH), whereas only 25% of 2+ tumours have amplification (reviewed in Walker et al. 2008). Indeed, in the phase II study of chemotherapy naïve patients, the objective response rate to single agent trastuzumab was 41% when only tumours with gene amplification by FISH were included in the analysis (Vogel et al. 2002). This highlights the importance of accurate definition of the target population and has led to guidelines recommending analysis of HER2 gene amplification in all cases of equivocal HER2 expression (IHC 2+) (Walker et al. 2008). Using such selection criteria, thousands of women with HER2 positive early breast cancer were randomised into four large multinational studies exploring the utility of trastuzumab in combination with or following adjuvant chemotherapy (Romond et al. 2005; Slamon et al. 2006; Smith et al. 2007). All four studies demonstrated a remarkably consistent and impressive improvement in overall survival with the addition of 1 year of trastuzumab to standard therapy, after as little as 2 years median follow up (Hazard Ratios (HR) 0.59–0.66, all p < 0.02). In addition, a smaller Finnish study demonstrated a similar improvement in overall survival (OS) (HR = 0.41; p = 0.07) with the addition of only nine weeks of trastuzumab to standard chemotherapy (Joensuu et al. 2006). The magnitude of this benefit is the largest seen since the introduction of adjuvant tamoxifen and led to the rapid adoption of adjuvant trastuzumab as a standard of care in HER2 positive early breast cancer. In contrast, had trastuzumab been trialled in an unselected population of women, it has been estimated that the HR for disease free survival in the HERA study would have been 0.95 rather than the impressive 0.54 actually reported (Piccart-Gebhart 2005). In this situation trastuzumab would either have been rejected from use in the adjuvant setting or worse still adopted in all patients, for minimal population benefit but at a high cost both in terms of unwarranted toxicity and expenditure.

4.4 Pharmacogenetics, Drug Metabolism and Tamoxifen In contrast to pharmacogenomic changes in the tumour tissue, pharmacogenetics can changes in the patients’ germline have broader effects on the absorption, distribution, metabolism and elimination of a drug and, as a consequence, its

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efficacy and toxicity (Roses 2000). A great deal of research endeavour has been applied to investigate the influence of SNPs in relevant metabolic pathways on the metabolism, efficacy and toxicity of commonly used cytotoxic agents in breast oncology (Blackhall et al. 2006). Genotypes which have been shown to affect the incidence or severity of drug toxicity or clinical efficacy in breast cancer patients undergoing chemotherapy are summarised in Table 4.1. However, interpretation of many of these studies is fraught with small sample sizes and lack of validation in independent cohorts. These facts and the rarity of some of the more important genotypes in the general population, suggest that routine genetic testing to guide chemotherapy drug choice or dose is not currently warranted. A pharmacogenetic story of very high clinical significance, however, is that surrounding the metabolism of tamoxifen. Tamoxifen is a selective ER modulator that, until recently, was considered the ‘gold standard’ when taken for 5 years as adjuvant endocrine therapy by women with ER positive early breast cancer (EBCTCG 2005). More recently the third generation aromatase inhibitors (AIs) anastrazole, letrozole and exemestane have demonstrated modest superiority over tamoxifen, although sequencing strategies using both tamoxifen and an AI are commonly employed in line with the treatment regimens used in randomised clinical studies (Baum et al. 2002; Boccardo et al. 2006; Coates et al. 2007; Coombes et al. 2007; Jakesz et al. 2008). Tamoxifen is a pro-drug that is metabolised to its active metabolites by multiple members of the cytochrome P450 family, predominantly CYP3A4/5 and CYP2D6 (Fig. 4.1; (Desta et al. 2004)). 4-hydroxytamoxifen (4-OHT) and N-desmethyl-4-hydroxytamoxifen (endoxifen) exhibit 30–100 fold greater potency than tamoxifen and N-desmethyltamoxifen (NDT) in suppressing estrogen dependent cancer cell proliferation in vitro, however plasma endoxifen concentrations 5–10 fold greater than those of NDT are observed in patients receiving tamoxifen therapy (Lee et al. 2003; Stearns et al. 2003). However, endoxifen concentrations vary widely in such patients, with lower levels associated with certain CYP2D6 genotypes (see below) and the co-administration of pharmacological inhibitors of CYP2D6 (Jin et al. 2005; Stearns et al. 2003; Table 4.2). Such studies have led to the description of individuals as extensive, intermediate or poor metabolisers (EM, IM and PM respectively) depending on their CYP2D6 genotype and drug history. Important preclinical data suggest that endoxifen may have a novel mechanism of action by targeting the ER for proteosomal degradation and this effect was only observed at concentrations found in EMs of tamoxifen (Wu et al. 2009). CYP2D6 is a highly polymorphic gene with at least 40 allelic variants and significant inter-racial variability. The most common lack of function polymorphisms in European Caucasians, Asians and Africans/African Americans are CPY2D6∗ 4, ∗ 10 and ∗ 17, respectively (Bradford 2002). The CYP2D6 lack of function genotypes and co-treatment with CYP2D6 inhibitors (Table 4.2) have been shown to correlate inversely with plasma endoxifen levels (Borges et al. 2006) and four studies in predominantly Caucasian populations have demonstrated an inferior breast cancer recurrence-free survival (RFS) with tamoxifen therapy in PM +/– IM versus EM +/– IM groups (Goetz et al. 2007; Goetz et al. 2005; Gonzalez-Santiago et al. 2007; Newman et al. 2008; Schroth et al. 2007). However, conflicting data have also been

Taxanes

Anthracyclines

Glutathione S-transferases (GSTM1 and GSTT1)

Polychemotherapy

C3435T and G2677T/A

C3435T (reduced function)

ABCB1 (MDR-1): drug efflux pump

ABCB1 (MDR-1)

PXR∗ 1B haplotype cluster

G11A

Both null genotypes (reduced capacity to clear reactive oxygen species) MnSOD CC and MPO GG (reduced capacity to clear reactive oxygen species) XRCC1-01 (G339A) XRCC3-01 (C241T)

SNP/deficiency

Pregnane X Receptor (PXR)

Manganese superoxide dismutase (MnSOD) and myeloperoxidase (MPO) Single strand (XRCC-1) and double strand (XRCC-3) DNA repair Carbonyl reductase 3 (CBR3)

Functional pathway

Drug

Bewick et al. (2006)

Worse BCSS with high dose chemotherapy in homozygotes Decreased doxorubicin metabolism. Increased tumour response rate and toxicity in MBC. Decreased doxorubicin metabolism and clearance (Chinese/Malay>Indian) Increased clinical response rate to neoadjuvant doxorubicin Uncertain: Increased toxicity, no effect on plasma levels

Sissung et al. (2006)

Kafka et al. (2003)

Sandanaraj et al. (2008)

Fan et al. (2008)

Ambrosone et al. (2005)

Ambrosone et al. (2001)

References

Improved OS after adjuvant polychemotherapy

Improved DFS and OS after adjuvant polychemotherapy

Clinical importance

Table 4.1 Genotypes associated with clinical outcome in breast cancer patients undergoing therapy with cytotoxic chemotherapy. DFS disease free survival, OS overall survival, BCSS breast cancer specific survival, MBC metastatic breast cancer, PFS progression free survival, SNP single nucleotide polymorphism

4 Pharmacogenetics in the Management of Breast Cancer 51

∗3

CYP1B1

– mainly in vitro data

A208G

cytidine deaminase (CDA)

∗ moderate/potent

A2455G and G2464A

ribonucleotide reductase M1 (RRM1)

Gemcitabine

Predominant SNPs IVS14 + 1-G > A (DPYD∗ 2a) inactivates DPYD

3RG3RG homozygotes

Thymidylate Synthase (TS) Dihydropyrimidine Dehydrogenase (DPYD)

(V432L)

SNP/deficiency

Functional pathway

Capecitabine/5-FU

Drug

Table 4.1 (continued)

Uncertain: increased PFS with polychemotherapy including paclitaxel but no effect on paclitaxel clearance Reduced response duration in MBC Severe toxicity in homozygotes (0.1% of population) increased toxicity in heterozygotes (3–5%) 2455G 2464A haplotype: Reduced toxicity and inferior DFS and OS Decreased metabolism and increased toxicity

Clinical importance

Sugiyama et al. (2007)

Rha et al. (2007)

Schwab et al. (2008)

Largillier et al. (2006)

Marsh et al. (2007)

References

52 S.J. Howell

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Fig. 4.1 Chemical structures of tamoxifen and its major metabolites, depicting the predominant enzymes responsible for each conversion step. CYP cytochrome P450

published, with two studies demonstrating no significant impact of genotype on RFS (Nowell et al. 2005; Wegman et al. 2005) and one in which RFS was worse in the EM group (Wegman et al. 2007). Such variability in the results might be explained by unaccounted confounders such as differences in treatment setting (trial vs nontrial), tumour characteristics, additional breast cancer treatments (chemotherapy), adherence to tamoxifen and adequacy of co-prescription recording. Although no clear pattern has emerged, the differences between the studies has been reviewed in detail, and such confounding factors found generally to be better controlled for in studies demonstrating worse outcome with tamoxifen in the PM groups (Dezentje et al. 2009). Furthermore, the implications of a negative association between PM Table 4.2 Major examples of drugs known to inhibit CYP2D6 activity Class

Moderate/Strong Inhibitors

Psychotropics

Fluoxetine, Paroxetine, Bupropion (Zyban), Haloperidol∗ Tricyclic antidepressants∗ Quinidine, Ticlopidine Terbinafine

Cardiac Antifungal ∗ moderate/potent

– mainly in vitro data

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and breast cancer outcome with tamoxifen have been explored by Punglia et al. (2008). In their modelling analysis, the HR for relapse on tamoxifen by CYP2D6 metabolism group (HR∗ 4/∗ 4 = 1.86) from the study by Goetz et al., was applied to the results of the BIG 1–98 study which had randomised nearly 5,000 postmenopausal women with early breast cancer to treatment for 5 years with either the AI letrozole or tamoxifen (Coates et al. 2007; Goetz et al. 2005). The results of the BIG 1–98 study had already demonstrated superior disease free survival (DFS) in favour of letrozole (HR 0.82; p = 0.007), however this advantage was lost completely when the model limited analysis to the proposed effect in the EM group alone (Punglia et al. 2008). These data, and those from discordant studies of tamoxifen outcome according to the CYP2D6∗ 10 genotype in Asian populations have prompted the retrospective analysis of tumour genotypes from the very large adjuvant endocrine therapy trials such as BIG 1–98, ATAC and ABCSG8 (Kiyotani et al. 2008; Okishiro et al. 2009; Xu et al. 2008). Preliminary data from the ABCSG8 study confirm enrichment in the CYP2D6∗ 4 genotype in cases that have relapsed on tamoxifen, but not anastrozole and the full results from these studies are eagerly awaited (Goetz et al. 2008). Should these results confirm equivalent or greater efficacy of tamoxifen over AIs in appropriately selected EM/IM groups then pharmacogenetics will have ‘come of age’ in breast oncology, opening the door to rational and confident choices between endocrine therapies based on efficacy and tolerability in the individual patient.

4.5 Concluding Remarks Breast oncology has perhaps the best examples to date of personalised therapy in ER and HER2 expression, multigene prognostic signatures and now pharmacogenetic analysis of host genotypes. The goals for the future are to expand such analyses but also to synthesise them into meaningful treatment algorithms for individual patients, thus optimising treatment regimens and reducing the unnecessary use of ineffective and often toxic drugs.

4.5.1 Case Scenario A 40 year old premenopausal woman was diagnosed with a 25 mm, grade 2, invasive ductal carcinoma with no axillary lymph node involvement. IHC demonstrated strong expression of the ER and progesterone receptor (PR) in 80–100% of cancer cells and negative staining for HER2. In view of her estimated 10 year survival of >90% by Adjuvant online (Ravdin 1996) and premenopausal status she was treated with tamoxifen alone, with a planned duration of 5 years. After 6 months of tamoxifen therapy severe hot flushes were significantly detracting from her quality of life and had she had developed moderate depressive symptoms. Fluoxetine was commenced to good effect, which reduced the hot flushes and improved her mood (see Table 4.2). 30 months after commencing tamoxifen, she developed pain

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in her back and was diagnosed with metastatic disease in both the spine and liver from which she eventually died 18 months later. Pharmacogenomic and pharmacogenetic approaches that may have improved this woman’s chances of survival are discussed below in two scenarios. 4.5.1.1 Scenario 1: The ER Positive Endocrine Resistant Tumour Although the ER has excellent negative predictive value in breast cancer therapy, up to 50% of ER positive cancers are endocrine resistant with a third relapsing by 15 years despite endocrine therapy (EBCTCG 2005). In young women, as in the case above, a cluster of tumours that express very high levels of ER but with a very poor prognosis have been identified using microarray analysis (Dai et al. 2005). The majority of such tumours were grade 3, but grading is notoriously observer dependent and in genomic analyses the grade 2 classification does not exist, such tumours falling into either genomic grade (GG) 1 or 3 (Loi et al. 2007). Had the GG been assessed and determined to be three in this woman, chemotherapy would have been advised thus reducing her risk of death by approximately one third. Many other genomic approaches are available for this precise setting including the Mammaprint and Oncotype DX assays discussed in the text. Again, a poor prognosis signature or high RS would have led to the use of chemotherapy and the potential to avert her relapse and subsequent death (Paik et al. 2004; van’t Veer et al. 2002). 4.5.1.2 Scenario 2: The ER Positive Endocrine Sensitive Tumour It is entirely possible that this tumour was indeed endocrine sensitive, but that pharmacogenetics was responsible for the lack of efficacy of tamoxifen. Women with hot flushes are more likely to do well on endocrine therapy, although it is not clear whether this can be attributed to CYP2D6 metaboliser status alone in women treated with tamoxifen (Cuzick et al. 2008; Goetz et al. 2007; Lynn Henry et al. 2009). The severity of this lady’s flushes suggest that she was not a PM although without formal genotyping, we cannot be sure. However, the co-prescription of fluoxetine would have inhibited CYP2D6 and therefore the conversion of NDT to endoxifen and is highly likely to have reduced the efficacy of tamoxifen therapy. Meta-analyses have shown that any tamoxifen is better than none, but also that 5 years is better than one or two (EBCTCG 2005). We can infer that this lady had only 6 months of adequate tamoxifen exposure, which is substandard and may have led directly to her early relapse.

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Schwab M, Zanger UM, Marx C et al (2008) Role of genetic and nongenetic factors for fluorouracil treatment-related severe toxicity: a prospective clinical trial by the German 5-FU Toxicity Study Group. J Clin Oncol 26:2131–2138 Shih C, Padhy LC, Murray M, Weinberg RA (1981) Transforming genes of carcinomas and neuroblastomas introduced into mouse fibroblasts. Nature 290:261–264 Sissung TM, Mross K, Steinberg SM et al (2006) Association of ABCB1 genotypes with paclitaxel-mediated peripheral neuropathy and neutropenia. Eur J Cancer 42:2893–2896 Slamon D, Eiermann W, Robert N et al (2006) 2nd interim analysis phase III randomized trial comparing doxorubicin and cyclophosphamide followed by docetaxel (AC→T) with doxorubicin and cyclophosphamide followed by docetaxel and trastuzumab (AC→TH) with docetaxel, carboplatin and trastuzumab (TCH) in Her2neu positive early breast cancer patients. Breast Cancer Res Treat 100(Suppl 1):52 Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL (1987) Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235:177–182 Smith I, Procter M, Gelber RD et al (2007) 2-year follow-up of trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer: a randomised controlled trial. Lancet 369:29–36 Sorlie T, Tibshirani R, Parker J et al (2003) Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA, 100:8418–8423 Sparano JA, Paik S (2008) Development of the 21-gene assay and its application in clinical practice and clinical trials. J Clin Oncol 26:721–728 Stearns V, Johnson MD, Rae JM et al (2003) Active tamoxifen metabolite plasma concentrations after coadministration of tamoxifen and the selective serotonin reuptake inhibitor paroxetine. J Natl Cancer Inst 95:1758–1764 Sugiyama E, Kaniwa N, Kim SR et al (2007) Pharmacokinetics of gemcitabine in Japanese cancer patients: the impact of a cytidine deaminase polymorphism. J Clin Oncol 25:32–42 van’t Veer LJ, Paik S, Hayes DF (2005) Gene expression profiling of breast cancer: a new tumor marker. J Clin Oncol 23:1631–1635 van’t Veer LJ, Dai H, van de Vijver MJ et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536 van de Vijver MJ, He YD, van’t Veer LJ et al (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999–2009 Vogel CL, Cobleigh MA, Tripathy D et al (2002) Efficacy and safety of trastuzumab as a single agent in first-line treatment of HER2-overexpressing metastatic breast cancer. J Clin Oncol 20:719–726 Walker RA, Bartlett JM, Dowsett M et al (2008) HER2 testing in the UK: further update to recommendations. J Clin Pathol 61:818–824 Wegman P, Elingarami S, Carstensen J, Stal O, Nordenskjold B, Wingren S (2007) Genetic variants of CYP3A5, CYP2D6, SULT1A1, UGT2B15 and tamoxifen response in postmenopausal patients with breast cancer. Breast Cancer Res 9:R7 Wegman P, Vainikka L, Stal O et al (2005) Genotype of metabolic enzymes and the benefit of tamoxifen in postmenopausal breast cancer patients. Breast Cancer Res 7:R284–290 Westlake S, Cooper N (2008) Cancer incidence and mortality: trends in the United Kingdom and constituent countries, 1993 to 2004. Health Stat Q 38:33–46 Wu X, Hawse JR, Subramaniam M, Goetz MP, Ingle JN, Spelsberg TC (2009) The Tamoxifen Metabolite, Endoxifen, Is a Potent Antiestrogen that Targets Estrogen Receptor {alpha} for Degradation in Breast Cancer Cells. Cancer Res 69:1722–1727 Xu Y, Sun Y, Yao L et al (2008) Association between CYP2D6 ∗ 10 genotype and survival of breast cancer patients receiving tamoxifen treatment. Ann Oncol 19: 1423–1429

Chapter 5

Pharmacogenetics in Colorectal Cancer Roberta Ferraldeschi

Abstract Colorectal cancer is one of the most common tumours and a major cause of cancer death worldwide. For nearly 50 years, the cornerstone of medical treatment for colorectal cancer has been the fluoropyrimidine, 5-fluorouracil. Following the development of novel chemotherapeutic drugs (e.g. oxaliplatin, irinotecan, capecitabine) and targeted biologic agents (e.g. bevacizumab, cetuximab, panitumumab), the treatment of this disease has changed significantly over the past decade. However, inter-individual differences in response and toxicity are observed and many patients do not benefit from these anticancer agents. Identification of specific markers to maximize efficacy, to minimize toxicity and to facilitate the “individualization” of patient treatment is necessary for ethical and economic reasons. To date, several potential biological predictive markers of efficacy and/or toxicity in colorectal cancer have been identified, but their inconsistent results in different studies complicate their application into clinical practice. Keywords Colorectal cancer · 5-fluorouracil · Irinotecan · Oxaliplatin · Monoclonal antibodies

Contents 5.1 Introduction . . . . . . . . . . . . . . 5.2 Systemic Treatment of Colorectal Cancer 5.3 Pharmacogenetics in Colorectal Cancer . 5.3.1 Fluoropyrimidines . . . . . . . . 5.3.2 Irinotecan . . . . . . . . . . . .

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61 W.G. Newman (ed.), Pharmacogenetics: Making Cancer Treatment Safer and More C Springer Science+Business Media B.V. 2010 Effective, DOI 10.1007/978-90-481-8618-1_5, 

62 5.3.3 Oxaliplatin . . . 5.3.4 Targeted Therapies 5.4 Conclusions . . . . . . 5.5 Case Scenario . . . . . References . . . . . . . .

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5.1 Introduction Colorectal cancer (CRC) is the second most frequent cause of cancer-related death in Europe with an estimated 412,900 cases diagnosed in 2006 and 207,400 deaths (Ferlay et al. 2007). Over the last decades, significant progress in the systemic treatment of this disease has been achieved with the approval of six new therapeutic agents (oxaliplatin, irinotecan, capecitabine, bevacizumab, cetuximab and panitumumab). These compounds have greatly improved the outlook for patients diagnosed with resectable and metastatic disease. Nevertheless, some patients may suffer from severe adverse drug reactions and may not benefit from therapy. Pharmacogenetics has been applied to CRC as a tool for selecting appropriate drugs or dosages for individualized therapeutic plans. This chapter will consider the new strategies for maximizing the chance of efficacy and decreasing the likelihood of severe adverse reactions to treatment in CRC.

5.2 Systemic Treatment of Colorectal Cancer The majority of CRC patients present with apparently resectable localised disease. In these patients, surgery can be curative, but relapses after complete resection are frequent (Desch et al. 2005). Surgery, followed by adjuvant therapy for high-risk patients, will be the optimum curative treatment approach in such cases (van Cutsem et al. 2006, 2008a). Adjuvant chemotherapy with 5-FU modulated by folinic acid, combined with oxaliplatin, is now considered the standard of care in node-positive colon cancer (stage III) (Andre et al. 2004; Kuebler et al. 2007). In contrast, controversy still exists over the role of adjuvant chemotherapy in stage II patients for whom adjuvant treatment should be appropriate in a subset of individuals at high risk for disease recurrence (Merkel et al. 2001; Sobrero and Guglielmi 2004; André et al. 2006; Figueredo et al. 2008). In patients not considered optimal candidates for treatment with oxaliplatin, capecitabine, an oral fluoropyrimidine, has emerged as an alternative to intravenous 5-FU/leucovorin (Twelves et al. 2005). Numerous clinical trials are now examining the value of adding novel targeted agents known to be active in metastatic disease, such as cetuximab and bevacizumab, in the adjuvant setting. The care of rectal cancer patients is more complex and should be coordinated amongst an experienced multidisciplinary team and include integrated treatments (radiotherapy, chemotherapy, surgery) to maximize the chance of cure

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and to minimize both local and distance recurrence and complications of therapy (O’Neil and Tepper 2007). Over the past 15 years, the management of metastatic CRC has been rapidly improved and median overall survival has increased from less than nine months with no treatment to approximately 24 months (Saunders and Iveson 2006), mainly due to the introduction of novel agents (cytotoxics and biological agents). The current management of metastatic CRC uses various active drugs, either in combination or as single agent (5-FU, capecitabine, irinotecan, oxaliplatin, bevacizumab, cetuximab and panitumumab). The choice of therapy is based on timing, the type of prior therapy and drug toxicity profile. The antimetabolite 5-FU, has been the mainstay of chemotherapy treatment for several years and fluoropyrimidine-based chemotherapy still remains a key component of the treatment algorithm for metastatic disease, in both the first- and second-line settings. A meta-analysis of 21 randomized controlled trials revealed that in the first-line setting 5-FU in combination with the biomodulator folinic acid (FA) doubled the response rate compared with 5-FU alone (from 11 to 21%) and prolonged the median survival time by about one month (from 10.5 to 11.7 months) (Thirion et al. 2004). Furthermore, 5-FU administered as a continuous infusion has shown, compared with 5-FU administered as an intravenous bolus, superior efficacy in terms of tumour response (22 vs. 14%), a slight, but significant, increase of overall survival and a better toxicity profile (Meta-analysis Group in Cancer 1998a). In the last decade, the combination of 5-FU with new classes of drugs, such as irinotecan and oxaliplatin has significantly improved the median survival up to approximately 20 months (Grothey et al. 2004) and response rates up to 40–50% (Giacchetti et al. 2000; Saltz et al. 2000; Douillard et al. 2000; de Gramont et al. 2000) and capecitabine has been shown to be at least as effective as infusional 5-FU (Mayo Clinic schedule) as first-line therapy (Hoff et al. 2001; van Cutsem et al. 2001). More recently, the use of novel biological agents, such as the monoclonal antibodies cetuximab (EGFR inhibitors) and bevacizumab (VEGF inhibitor), have been shown to provide additional clinical benefit prolonging the survival by two to three months (Hurwitz et al. 2004; Kabbinavar et al. 2005; Saltz et al. 2007; van Cutsem et al. 2007a).

5.3 Pharmacogenetics in Colorectal Cancer In cancer pharmacogenetics, drug responses may be due to variants in the individual’s germ line DNA or somatic (de novo) genetic changes in the tumour. The former are more likely to be relevant in the prediction of adverse reactions whereas the latter may be pertinent in the effectiveness of the response of a tumour to treatment, although the two are not mutually exclusive. Both germline and somatic genetic variants have relevance to the management of CRC and the most clinically relevant examples are presented below.

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5.3.1 Fluoropyrimidines 5-FU and its prodrug, capecitabine, are a mainstay in the treatment of numerous solid tumors, including CRC, alone or as part of combination therapies. The antitumor effect of 5-FU has been ascribed to a number of mechanisms, including competitive inhibition of thymidylate synthase (TS), a key enzyme of pyrimidine de novo synthesis (Santi et al. 1974; Langenbach et al. 1972; Danenberg et al. 1974) (Fig. 5.1). TS protein expression seems to be influenced by different functional polymorphisms in the TS gene (Horie et al. 1995; Kawakami et al. 1999). TS gene contains seven exons and a 5 -flanking untranslated enhancer region containing a 28-bp tandem repeat sequence (Kaneda et al. 1990). Three tandem repeats (3R) represent the wild-type form of this allele, whereas variant forms generally contain only two repeats (2R). Different lengths of the polymorphic repeat sequence can affect translational efficiency (Kawakami et al. 2001). Lower expression levels of TS are associated with the homozygous variant form, estimated to occur with frequencies of 20–40% in Caucasians (Horie et al. 1995; Kawakami et al. 1999; Yu et al. 2008). Data from several series have associated the homozygous variant (2R/2R) with increased response and survival in CRC patients treated with

Fig. 5.1 Metabolism and mechanism of action of 5-fluoruracil (5-FU): 5-Fluorouracil (5-FU), fluorodeoxyuridine (FUdR), 5-fluorodeoxyuridine monophosphate (FdUMP), 5-fluoro-2 deoxyuridine-5 -diphosphate (FdUDP), 5-fluorodeoxyuridine triphosphate (FdUTP), deoxyuridine monophosphate (dUMP), thymidylate (dTMP), dihydropyrimidine dehydrogenase(DPD), dihydrofluorouracil (FUH2), α-fluoro-β-ureido-propanoic acid (FUPA); α-fluoro-β-alanine (FBAL), 5 deoxy-5-fluorocytidine (5 dFCR), 5 deoxy-5-fluorouridine (s dFUR)

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fluoropyrimidines (Pullarkat et al. 2001; Villafranca et al. 2001; Iacopetta et al. 2001; Marsh et al. 2001; Park et al. 2002a; Martinez-Balibrea et al. 2007; Gosens et al. 2008). Moreover, this variant has also been associated with an increased risk of 5-FU toxicity (Lecomte et al. 2004). An additional SNP, a G>C substitution at the twelfth nucleotide of the second repeat of the 3R allele has been described, leading to a tri-allelic locus (2R, 3RG, and 3RC) (Kawakami and Watanabe 2003; Mandola 2003). This polymorphism occurs within the USF consensus element and alters the transcriptional activity of the TS gene. Like the tandem repeat variant, this SNP has been associated with decreased TS expression and the double polymorphism in the TS tandem repeat sequence has been suggested to be a more effective predictor of response to 5-FU treatment (Kawakami and Watanabe 2003; Marcuello et al. 2004a). The 3RC allele occurs in 56%, 28% and 37% of all 3R alleles in Caucasians, African-Americans and Chinese, respectively (Mandola et al. 2003). A 6 bp insertion/deletion in the 3 untranslated region (+6 bp/–6 bp) of the TS gene has also been associated with decreased mRNA stability and TS RNA expression (Mandola et al. 2004). Recently, a novel G>C SNP in the first repeat of the 2R allele has also been found (Lincz et al. 2007; Gusella et al. 2006). TS genotyping has shown to predict clinical outcomes in various studies in CRC patients receiving 5-FU based regimens. However, such an association has not always been detected and some studies failed to demonstrate a significant correlation between TS functional polymorphisms and clinical outcomes (Jakobsen et al. 2005; Ruzzo et al. 2007, Ruzzo et al. 2008; Matsui et al. 2006; Prall et al. 2007). Heterogeneity in clinical experimental conditions, in patient populations, in combination regimens and in sample size of the subsets may explain variable results in these pharmacogenetic studies. Recently, the combination of high TS expression genotypes (2R/3G, 3C/3G and 3G/3G) was demonstrated to have a role in predicting response in a homogeneous and low-burden metastatic disease population (liver-only) exposed to the same regimen of 5-FU (Graziano et al. 2008a). Further large, prospective studies are warranted to better define the effect of TS polymorphisms in patients receiving fluoropyrimidine-based therapy. Numerous serious adverse side effects have been reported with fluoropyrimidine treatment, including myelosuppression, cardiac toxicity, mucositis, hand-foot syndrome (HFS), and diarrhoea. A meta-analysis of 1219 patients with CRC receiving 5-FU, showed that grade 3–4 toxicity was encountered in more than 30% of patients, with 0.5% of the patients experiencing lethal toxicity (Meta-Analysis Group In Cancer 1998b). Dihydropyrimidine dehydrogenase (DPD) deficiency has been recognized as an important risk factor in predisposing patients to the development of severe fluoropyrimidine-associated toxicity (Diasio et al. 1988; Diasio and Johnson 1999; van Kuilenburg 2004). DPD is the initial and rate-limiting enzyme involved in the catabolism of the uracil and thymine into β-alanine and β-aminoisobutyric acid, respectively (Gonzalez and Fernandez-Salguero 1995). More than 80% of the administered 5-FU is catabolised by DPD in the liver (Diasio and Harris 1989). Deficiency in DPD activity leads to the accumulation of 5-FU and can result in severe, life-threatening or fatal toxicity, including mucositis, neutropenia and neuropathy (Wei et al. 1996; Johnson et al. 1999; Milano et al. 1999; Lyss et al. 1993; Fleming et al. 1993). DPYD, the gene encoding DPD, is located within human

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chromosomal region 1p22 and is composed of 23 exons encompassing approximately 950 kb (Wei et al. 1998). To date, at least 17 different functional variants in DPYD have been identified in patients with severe 5-FU toxicity (Innocenti and Ratain 2002). It is estimated that in the general population up to 5% of individuals are heterozygous for alleles with impaired DPYD function and 0.1% homozygous for these alleles (Lu et al. 1993; Ridge et al. 1998). A common mutation in DYPD, c.1905+1G>A (also termed IVS14+1G>A or DPYD∗ 2A), which causes a splicing variant with skipping of exon 14 and production of a non-functional protein, accounts for >50% of relevant variants in DPYD (Wei et al. 1996; van Kuilenburg et al. 1999). Carrying one or more DPYD∗ 2A alleles has been associated with severe toxicity after 5-FU treatment (Johnson et al. 1999; van Kuilenburg et al. 2001). It is estimated that about a quarter of patients suffering from severe 5-FU toxicity have a DPYD∗ 2A polymorphism (van Kuilenburg et al. 2002; Raida et al. 2001). However, the allelic frequency of DPYD∗ 2A is only about 1.8% in European Caucasians, 0.6% in Turks, while it has not been detectable in North African, Taiwanese and Japanese populations (van Kuilenburg et al. 2001; Yamaguchi et al. 2001; Hamdy et al. 2002; Hsiao et al. 2004; Uzunkoy et al. 2007). Many other variants in DPYD have been identified and associated with reduced DPD activity and 5-FU toxicity (van Kuilenburg et al. 2000, 2005; Morel et al. 2007; Ben Fredj et al. 2007). However, variants in DPYD do not account, for all adverse reactions to 5-FU and genotype and phenotype analyses may be discordant and patients with a low DPD phenotype may not have a molecular basis for reduced activity (Collie-Duguid et al. 2000). Methylation of the DPYD promoter has been proposed as an important alternative mechanism for reducing DPYD expression (Ezzeldin et al. 2005), although the evidence supporting the clinical relevance of this has been conflicting (Amstutz et al. 2008). Yen and McLeod (2007) reviewed the performance of assays that assess DPD and DPYD status, with an emphasis on the utility for routine clinical applications and concluded that none of the current strategies are adequate to screen cancer patients with a high level of predictive accuracy. Moreover, the low frequency of DPYD polymorphisms has hampered the translation into routine clinical practice. Further studies, including pharmacoeconomic analyses, are required to establish the true utility of this approach.

5.3.2 Irinotecan The topoisomerase I interactive drug irinotecan was introduced into clinical studies in the early 1990s, and was found to have activity in several malignancies (Pizzolato and Saltz 2003). Added to 5-FU, irinotecan resulted in the first major advance in many years in the treatment of metastatic CRC, and the combined administration of these drugs became the standard of care (Douillard et al. 2000; Saltz et al. 2000). The metabolism of irinotecan is complex; it undergoes extensive metabolism that results in a complex disposition profile (Chabot 1997). Irinotecan is a pro-drug, which is converted to the cytotoxic metabolite SN-38 (7-ethyl-10hydroxycamptothecin), a potent topoisomerase 1 inhibitor (Rivory et al. 1996; Ahmed et al. 1999; Sanghani et al. 2004). SN-38 is inactivated predominantly by

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glucuronidation to SN-38G (10-O-glucuronyl-SN-38), which is then excreted in the bile through the small intestine (Fig. 5.2). This glucuronidation reaction is mediated primarily by hepatic uridine diphosphate-glucuronosyltransferase 1A (UGT1A) phase II enzymes, which are encoded by the UGT1 locus on chromosome 2. The UGT isoforms 1A1, 1A7 and 1A9 are involved in the glucuronidation of bilirubin and SN-38 (Ciotti et al. 1999; Gagne et al. 2002). More than 50 variations in the promoter and coding regions of the gene are known to decrease the enzyme activity

Fig. 5.2 Metabolism and mechanism of action of irinotecan. Carboxylesterases (CES1; CES2); pseudocholinesterase (BChE); solute carrier organic anion transporter family, member 1B1 (SLCO1B1); hepatic UDP glucuronosyl-transferases (UGT) (UGT1A1; UGT1A9; UGT1A10); 7-ethyl-10-hydroxy-camptothecin (SN-38), adenosine-triphosphate binding cassette (ABC) transporter family (ABCB1; ABCC2; ABCG2), cytochome P450 subfamily (CYP3A4; CYP3A5); 7-ethyl-10-hydroxy-camptothecin glucuronide (SN-38G). Courtesy of PharmGKB

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(Kadakol et al. 2000) leading to inherited unconjugated jaundice (Crigler-Najjar or Gilbert’s syndromes). Diarrhoea and myelosuppression are the dose-limiting toxicities of irinotecan and interfere with optimal utilization of this important drug (Innocenti and Ratain 2006). Both irinotecan-related toxicities have been associated with increased levels of SN-38 (Gupta and Lestingi 1994). The pharmacokinetics of irinotecan and SN-38, are determined by numerous drug transporters and metabolizing enzymes. Many pharmacogenetic studies have investigated the influence of genetic variation in these pathways on inter-individual variation in irinotecan toxicity (Marsh and McLeod 2004; Smith et al. 2006; Kweekel et al. 2008a). The most studied polymorphism in “irinogenetics” is a common TA repeat variant in the UGT1A1 promoter. In the population, five, six, seven, and eight TA repeats in the UGT1A1 promoter can be found. UGT1A1 expression in individuals is inversely correlated with the number of TA repeats (Fig. 5.3). In individuals carrying two copies of the seven TA repeat allele have UGT1A1 expression reduced up to 70% (Bosma et al. 1995). The wild-type six repeat allele (TA6) (also classified as UGT1A1∗ 1) is the most common form, the seven TA repeat allele (TA7) (also classified as UGT1A1∗ 28) is the second most common. The frequency of the homozygous variant UGT1A1∗ 28/ ∗ 28 genotype varies depending on ethnic group, with increased prevalence in Africa and the Indian-subcontinent compared to European populations and it is less common in South East Asians (Monaghan et al. 1996; Beutler et al. 1998; Hall et al. 1999; Premawardhena et al. 2003). Based on the findings of four pharmacogenetic trials that found an increased prevalence of irinotecan-induced toxicity in patients homozygous for UGT1A1∗ 28 (Ando et al. 2000; Innocenti et al. 2004; Marcuello et al. 2004b; Rouits et al. 2004), pharmacogenetic information pertaining to irinotecan toxicity has been included in the revised safety drug labelling after the US Food and Drug Administration (FDA) advisory committee meeting in November, 2004. The amended product information now includes the association between the UGT1A1∗ 28 genotype and haematological toxicity and recommend that patients with the UGT1A1∗ 28/ ∗ 28 genotype receive a lower starting dose of irinotecan. Although initial studies found UGT1A1∗ 28 genotype to be strongly associated with risk of toxicity, results of subsequent studies were less compelling. Several studies showed that severe neutropenia (Ando et al. 2000; Iyer et al. 2002; Innocenti et al. 2004; Rouits et al. 2004; McLeod et al.

Fig. 5.3 Fluorescent microsatellite analysis of UGT1A1 to define the UGT1A1∗ 28 genotype

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2006; Roth et al. 2008; Ruzzo et al. 2008) and febrile neutropenia (Kweekel et al. 2008b; Roth et al. 2008) were significantly associated with carriage of two UGT1A1∗ 28 alleles. Other studies showed either association (Marcuello et al. 2004b; Massacesi et al. 2006; Ferraldeschi et al. 2009) or no association (Iyer et al. 2002; Innocenti et al. 2004; Routis et al. 2004; Cote et al. 2007; Toffoli et al. 2006) of UGT1A1∗ 28/ ∗ 28 genotype with severe diarrhoea. Moreover, in some studies the UGT1A1∗ 28/ ∗ 28 genotype was not associated with a higher risk of toxicity (Carlini et al. 2005; Seymour et al. 2006). The conflicting results may result from the relatively low number of patients included in the studies, the different dosing regimens, the patient type, or the use of retrospective analyses. In the largest study conducted prospectively in a homogeneous patient population and single treatment regimen (FOLFIRI) a significant increased risk of developing severe hematologic toxicity (primarily grade 3–4 neutropenia) was observed among patients carrying the UGT1A1∗ 28 allele, for the first treatment cycle, but not throughout the whole treatment period (Toffoli et al. 2006). Hoskins et al. (2007) performed a meta-analysis to assess the association between irinotecan dose and the risk of haematological toxic effects dependent upon UGT1A1∗ 28 genotype. The incidence of grade three or four haematological toxicity in patients homozygous for UGT1A1∗ 28 was increased at high (>250 mg/m2 ) and moderate (150–250 mg/m2 ) irinotecan dosage, whereas no association was found in patients receiving low irinotecan doses (A), have been proposed as better predictors of irinotecan toxicity than UGT1A1∗ 28 (Innocenti et al. 2004; Cote et al. 2007; Ferraldeschi et al. 2009). However, the hypothesis

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that the c.-3156G>A variant is a better predictor of outcome requires further study. Additional research is necessary to better define the clinical utility of UGT1A1 testing. Prospective studies evaluating the effects of modification of dosage based on genotyping results on patients’ outcome (e.g. toxicity, response rate and survival) are warranted.

5.3.3 Oxaliplatin Oxaliplatin, a third generation platinum analogue, is an effective chemotherapeutic agent for the treatment of CRC (Giacchetti et al. 2000; de Gramont et al. 2000). It is a diaminocyclohexane-containing platinum that forms DNA adducts, leading to impaired DNA replication and cellular apoptosis (Raymond et al. 1998). There is growing evidence that polymorphisms in genes encoding DNA repair enzymes, drug transporters and metabolic inactivation routes can contribute to the inter-individual differences in anti-tumour efficacy and toxicity of oxaliplatin (Marsh 2005; Kweekel et al. 2005) In general, the cytotoxic efficacy of platinum compounds in cancer cells can be related to inhibition of DNA synthesis or to saturation of the cellular capacity to repair platinum-DNA adducts. There are a number of enzyme systems in the cell that repair damage to DNA (Wood 1996). Some of these are specific for a particular type of DNA damage (e.g. a particular base modification) while others can repair a range of mutations. The excision repair cross-complementing 1 (ERCC1) gene encodes a protein of 297 amino acids, that is a major component of the nucleotide excision repair (NER) pathway (Wilson et al. 2001). An earlier pharmocogenomic study demonstrated that CRC patients with low intratumoral ERCC1 mRNA expression level have a significantly longer median survival and higher response rates than those with high ERCC1 mRNA expression level when treated with a 5-FU and oxaliplatin combination (Shirota et al. 2001). A silent common polymorphism in codon 118 (c.354C>T) does not alter the asparagine residue (Asp118) (Yu et al. 1997). Interestingly, it has been suggested that this SNP may be associated with a higher ERCC1 mRNA levels as the number of T alleles increase (Park et al. 2002b). Various retrospective studies evaluated the ERCC1 codon 118 polymorphism as a predictor for clinical outcome in metastatic CRC patients treated with oxaliplatin-based chemotherapy, reporting contrasting results (Stoehlmacher et al. 2004; Park et al. 2003; Viguier et al. 2005; Moreno et al. 2006; Parè et al. 2008; Braun et al. 2008). While, a prospective study of 166 patients with advanced CRC treated with firstline oxaliplatin, 5-FU and leucovorin (FOLFOX) chemotherapy reported that the ERCC1 T/T genotype is independently associated with adverse progression-free survival (PFS) (Ruzzo et al. 2007). Recently, Chang et al. analyzed the influence of the c.354C>T polymorphism of ERCC1 on its protein expression levels and outcome of 168 Chinese patients with metastatic CRC that had been treated with first-line FOLFOX chemotherapy (Chang et al. 2008). They found a higher prevalence of the C/C (~50%) genotype in the Asian population, in comparison with Caucasian populations in previous studies (Stoehlmacher et al. 2004; Ruzzo et al. 2007).

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A marked increase of ERCC1 protein expression levels was noted in patients with C/T or T/T genotypes, which was associated with significantly lower response to FOLFOX, and shorter progression-free and overall survival times. Polymorphisms in other repair enzymes (e.g. XRCC1, ERCC2) have also been associated with different responses to platinum-based chemotherapy in CRC (Stoehlmacher et al. 2001; Park et al. 2001; Stoehlmacher et al. 2004; Monzo et al. 2007; Ruzzo et al. 2007; Le Morvan et al. 2007). Variable chemosensitivity to oxaliplatin may also depend on detoxification pathways, including the glutathione S-transferase (GST) family of isoenzymes (Kweekel et al. 2005). The GST supergene family is part of one of the main mechanisms of cellular defence against a wide variety of many xenobiotics, including anticancer drugs (Zhang et al. 1998). Many members of these gene families have polymorphisms that influence their transcription and/or function of their encoded proteins (Lo and Ali-Osman 2007; Robert et al. 2005). The isoenzyme glutathioneS-transferase-P1 (GSTP1) has at least two functional polymorphisms: GSTP1 c.313A>G and c.341C>T. The c.313A>G SNP effects the amino acid substitution Ile105Val and is associated with decreased enzymatic activity (Watson et al. 1998). In some reports, patients with two variant alleles have shown a survival benefit from oxaliplatin-containing combination regimens (Sweeney et al. 2000; Allan et al. 2001; Stoehlmacher et al. 2002, 2004). However, three more recent studies in patients with advanced CRC treated with FOLFOX report no association of GSTP1 genotype with PFS (Le Morvan et al. 2007; Ruzzo et al. 2007; Kweekel et al. 2009). Reversible, cumulative, peripheral sensory neuropathy is the principle dose-limiting factor of oxaliplatin therapy (Culy et al. 2000). Studies analyzing the association of GSTP1 c.313A>G with oxaliplatin-induced cumulative neuropathy were also nonconclusive (Grothey et al. 2005; Lecomte et al. 2006; Ruzzo et al. 2007; Kweekel et al. 2009). Further studies in larger and prospective patient cohorts are warranted to evaluate the role and to establish the real influence of these polymorphisms in CRC patients treated with oxaliplatin-containing combination regimen. Eleven candidate predictive biomarkers of irinotecan and/or oxaliplatin efficacy were recently assessed in 1,628 patients in Fluorouracil, Oxaliplatin, Irinotecan: Use and Sequencing (FOCUS), a large randomized trial of fluorouracil alone compared with fluorouracil and irinotecan and compared with fluorouracil and oxaliplatin in advanced CRC (Braun et al. 2008). The authors developed a two-stage strategy for screening the markers, and ultimately were only able to demonstrate a significant treatment interaction for topoisomerase (Topo) -1 immunohistochemistry. Notable among the negatives were germline DNA polymorphisms in ERCC1 and ERCC2 and GSTP1.

5.3.4 Targeted Therapies Several molecular pathways have been shown to play key roles in the development and progression of CRC. This enhanced understanding of tumour biology has provided the rationale for the design and development of novel agents that are

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directed against important targets, including EGFR and VEGF. In recent years, the combination of traditional chemotherapy drugs with agents that inhibit these specific pathways has led to a significant improvement in survival of metastatic CRC (Hurwitz et al. 2004; Kabbinavar et al. 2005; Saltz et al. 2007; Van Cutsem et al. 2007a). Since the work of Judah Folkman in the 1970s, it is now widely accepted that angiogenesis is essential for tumour growth since tumours cannot grow beyond 2 mm3 without developing a new vasculature (Folkman 1971). The VEGF family of growth factors are key regulators of this process (Ferrara 2004). Bevacizumab is a humanized monoclonal antibody directed against VEGF-A that has been examined in combination with chemotherapy in patients with advanced CRC and is currently under investigation in the adjuvant setting. Bevacizumab improves the outcome in both first-line therapy in combination with, either 5-FU/leucovorin monotherapy, oxaliplatin-based or irinotecan-based doublets (Hurwitz et al. 2004; Kabbinavar et al. 2005; Saltz et al. 2007), and in second-line therapy in combination with oxaliplatin-based doublets in patients previously treated with a fluoropyrimidine and irinotecan (Giantonio et al. 2007). Many predictive biomarkers of angiogenesis have been proposed and intensely investigated, but none have been validated to date (Sessa et al. 2008). The EGFR plays a critical role in the signal transduction pathway for cell proliferation, differentiation, and survival. EGFR protein expression is increased in up to 80% of CRCs (Goldstein and Armin 2001). To date, two anti-EGFR monoclonal antibodies, cetuximab (chimeric antibody) and panitumumab (full human antibody), have shown definitive efficacy in CRC (Messersmith and Hidalgo 2007; Ng and Zhu 2008). Cetuximab and panitumumab as monotherapy have provided significant advantages compared with best supportive care (BSC) in chemotherapy refractory patients (van Cutsem et al. 2007b; Jonker et al. 2007) and cetuximab was shown to restore chemosensitivity in irinotecan-refractory metastatic CRC patients (Cunningham et al. 2004). In addition, cetuximab in combination with FOLFIRI significantly increased response rate and prolonged PFS in the first-line treatment of metastatic CRC patients (van Cutsem et al. 2007a). Various studies have shown that the there is no significant correlation between EGFR immunostaining and response to anti-EGFR antibodies (Cunningham et al. 2004; Saltz et al. 2004; Lenz et al. 2006) and that objective response can be obtained in CRC that does not express EGFR (Chung et al. 2005; Hecht et al. 2008; Hebbar et al. 2006). The reasons why IHC detection of EGFR is a poor indicator may include a variety of biological and technical factors (Valentini et al. 2008). While the identification of EGFR mutations that predicted for response to EGFR tyrosinekinase inhibitors opened up a new avenue for research in non-small cell lung cancer (see Chapter 6), it appears that EGFR gene mutations are rare in CRC and have no clinical relevance with regard to the activity of anti-EGFR therapy (Barber et al. 2004; Nagahara et al. 2005; Ogino et al. 2005; Tsuchihashi et al. 2005). In contrast, it has been suggested that an increased EGFR gene copy number, analyzed by fluorescent in situ hybridization Study (FISH), could be a promising predictor of anti-EGFR responsiveness in CRC patients (Moroni et al. 2005; Sartore-Bianchi et al. 2007; Cappuzzo et al. 2008; Personeni et al. 2008).

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The EGFR gene has several germinal polymorphisms that influence EGFR expression. The EGFR gene contains a highly polymorphic sequence in intron 1, which consists of a variable number of CA dinucleotide repeats (15–22). It has been demonstrated that as the number of (CA)n repeats increases the level of transcription decreases (Gebhardt et al. 1999). The c.497G>A SNP in the extracellular subdomain IV of EGFR and the c.-216G>T EGFR promoter SNP within the Sp-1 binding site, a key regulator of the EGFR promoter activity, have also been associated with EGFR regulation (Lopez et al. 1996; Liu et al. 2005). Functional variants have been described in the ligand, EGF gene e.g. c.61G>A (Shahbazi et al. 2002). More recently, EGFR gene polymorphisms at positions -216G>T and c.497G>A were analyzed along with CA repeat polymorphism in intron 1 and EGFR gene copy number in 80 colorectal primary tumors (Milano et al. 2008). No relationship was observed between any of these EGFR genotypes and EGFR expression (Milano et al. 2008). The effect of these genotypes on clinical outcome in patients treated with anti-EGFR antibody has also been recently evaluated. Individuals homozygous for the c.497G>A variant have a worse clinical outcome and shorter PFS, compared with other genotypes in 130 patients treated with cetuximab (Lurje et al. 2008). Graziano et al. evaluated a number of polymorphisms in 110 patients who underwent cetuximab-irinotecan salvage therapy (Graziano et al. 2008b). A significant association with favourable survival was observed with two copies of EGFR allele containing less than 17 CA repeats and wild type at EGF c.61G>A. CA repeats were also associated with skin toxicity and treatment response. Conversely, Zhang et al. presented a higher overall survival in patients with two copies of the c.61G>A variant (Zhang et al. 2006), which is theoretically in agreement with preclinical studies, indicating that the transcript with the G allele is more active than the A containing transcript and consequently associated with higher EGF level (Shahbazi et al. 2002). More recently, Garm Spindler et al. showed an unfavourable outcome in KRAS wild-type patients heterozygous for c.61G>A (Garm Spindler et al. 2009). The role of a patient’s tumor KRAS mutational status in the treatment of metastatic CRC with anti-EGFR agents has recently become an important area of research and interest. KRAS, a small G-protein downstream of EGFR is a major component of the EGFR signalling pathways. KRAS mutations are found in about 40% of sporadic CRCs (Andreyev et al. 1998, 2001). Specific mutations in KRAS result in constitutive activation, and subsequent signalling events are unregulated and independent of EGFR control (Bos 1989). Several retrospective analyses from single-arm studies have suggested that the presence of KRAS point mutations in codons 12 and 13 predict resistance to anti-EGFR antibodies in metastatic CRC patients (Lievre et al. 2006, 2008; Benvenuti et al. 2007; Di Fiore et al. 2007; De Roock et al. 2008; Frattini et al. 2007; Freeman et al. 2008; Lurje et al. 2008). The first conclusive data demonstrating the relationship between KRAS mutations and panitumumab efficacy was published in 2008 (Amado et al. 2008). In this phase III trial of panitumumab versus BSC in chemotherapy refractory patients, the researchers observed a statistically significant improvement in PFS in the panitumumab arm compared with BSC (van Cutsem et al. 2007b). This effect was more pronounced among patients with KRAS wild-type status (Amado et al. 2008). Conversely, in patients with KRAS mutant status no benefit was observed from

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adding panitumumab to BSC. Subsequently, retrospective subset analyses of the data from two randomized clinical trials investigating cetuximab in the treatment of first-line metastatic CRC highlighted the increased efficacy of cetuximab in patients with wild-type KRAS (van Cutsem et al. 2008b; Bokemeyer et al. 2009). These analyses found that patients with KRAS wild-type tumours had a clinically relevant increase in the chance of response and a decrease in the risk of disease progression in the cetuximab plus chemotherapy arm compared with those in the standard chemotherapy arm. These results have also been confirmed by data from another two retrospective analyses of large randomized studies (Punt et al. 2008; Karapetis et al. 2008). Moreover, two studies raise the possibility that in relation to tumours with mutations in the KRAS gene, the addition of anti-EGFR agents to oxaliplatin-based chemotherapy may impair the efficacy of a cytotoxic component of the combined regimen (Punt et al. 2008; Bokemeyer et al. 2009). Hence, there is a consistent body of data from independent retrospective analyses, across thousands of patients, suggesting that EGFR-targeted monoclonal antibodies therapy is only effective in KRAS wild-type patients. As a result, the European Medicines Agency (EMEA) has restricted approval of cetuximab and panitumumab in CRC to patients with wildtype KRAS tumours. More recently, based on systematic reviews of the relevant literature, the American Society of Clinical Oncology (ASCO) Provisional Clinical Opinion (PCO), has also recommended KRAS mutation testing in all patients with metastatic CRC who are candidates for anti-EGFR antibody therapy (Allegra et al. 2009). KRAS testing should be routinely conducted in all CRC patients before starting patients on therapies involving cetuximab or panitumumab. KRAS mutation status might allow the identification of patients who are likely to benefit from cetuximab and panitumumab and not only spare patients ineffective and toxic therapies, but might also reduce futile costs (Shankaran et al. 2009). However, as only a fraction of patients with CRC tumors that carry a wild-type KRAS allele can achieve a clinical response with EGFR-targeted therapies, the search for additional predictive biomarkers to anti-EGFR antibody therapy remains an important challenge (Heinemann et al. 2008; Wong and Cunningham 2008).

5.4 Conclusions The treatment regimens and options for CRC patients have significantly changed in the last 15 years. The development of the cytotoxic agents such as irinotecan, oxaliplatin and capecitabine and of the monoclonal antibodies against EGFR and VEGF, including bevacizumab, cetuximab and panitumumab have clearly increased the therapeutic options and have improved the outcome for these patients. The ability to tailor anti-cancer therapy based on the status of biomarkers has intrigued cancer researchers in recent years. Much work continues to investigate the critical molecular biomarkers that can be used to predict clinical response as well as to identify which patients might be at increased risk for developing drug-specific side effects.

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Currently, with the exception of KRAS, no other biological markers have been developed for CRC for use in routine clinical practice. Although there are a number of promising pharmacogenetic and pharmacogenomic tests for CRC, their adoption into clinical practice has been hindered by a lack of robust evidence accumulated through non-standardized, studies (e.g. retrospective analysis, small number of patients, several different endpoints, no defined analysis plan, different patient subsets). These promising tests for CRC will need prospective validation if they are to add to KRAS testing as standard in the management of CRC.

5.5 Case Scenario A 40 year old woman without any comorbid illness presented with lower abdominal pain and anal bleeding on defecation. Colonoscopy revealed intraluminal stenosis of the colorectal junction and biopsy specimens were obtained. Histologically, a moderately-differentiated adenocarcinoma was diagnosed. She underwent left hemicolectomy. The tumour invaded to the sub-serosal layer (T3) with moderate lymphatic invasion and mild venous invasion. Nine excised lymph node were negative for metastasis (N0). There was no evidence of distant metastasis, based on the results of abdominal computed tomography (CT) and chest X-ray at the time of surgery and serum carcinoembryonic antigen (CEA) was normal. According to the classification of TNM (tumour, lymph nodes, metastasis), the disease was stage II. Her postoperative course was uneventful, and in she was started on adjuvant chemotherapy, comprised of oxaliplatin i.v. 85 mg/m2 (on day one), folinic acid, 100 mg/m2 (on days one, two), 5-FU i.v., bolus 400 mg/m2 (on days one, two) 5-FU i.v. 22 hours infusion, 600 mg/m2 (on days one, two), every 2 weeks for 12 cycles. After the first cycle of chemotherapy, she presented with moderate diarrhoea and stomatitis, severe neutropenia and mild thrombocytopenia. Cycle two was deferred by two weeks because of prolonged thrombocytopenia and dose reduced to 75%. She underwent cycle three and four with a discrete tolerance and adequate timing (moderate neutropenia and thrombocytopenia, mild stomatitis). However, after cycle five, she presented with prolonged severe neutropenia (>7 days) and she was admitted with fever, diarrhoea and stomatitis. Due to the severe toxicity reported and according to patient preference, she was withdrawn from further adjuvant chemotherapy. She is regularly followed-up, having a chest-X-ray and an abdomen ultrasound every six months and a blood testing including the measurement of serum carcinoembryonic antigen (CEA) level at three-monthly interval. This report describes a 40 year old white woman with stage II colon cancer, who suffered from severe gastrointestinal and haematological toxicity while undergoing 5-FU/folinic acid/oxaliplatin adjuvant treatment. Pre-treatment measurement of DPD activity in peripheral blood mononuclear cells (PBMC) was would have revealed a moderate deficit (70.5 pmo/min/mg protein). Partial or complete deficiency of the rate-limiting enzyme in pyrimidine catabolism, DPD, is increasingly

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being considered as a contributing factor to the occurrence and severity of 5-FU toxicity. Predictive pharmacogenetic testing is considered a potentially powerful approach to individualise cancer chemotherapy. This could be extremely relevant in the adjuvant setting where the goal of treatment is to increase the chance of cure.

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Chapter 6

Pharmacogenetics in Lung Cancer Fiona Blackhall

Abstract Lung cancer is the commonest cause of cancer-related mortality worldwide. Surgery is the mainstay of curative therapy for early stage non-small cell lung cancer (NSCLC) with or without adjuvant chemotherapy to reduce risk of relapse. For inoperable locally advanced NSCLC, and for small cell lung cancer (SCLC) limited to the thorax, chemoradiotherapy is given with curative intent. However, the majority of patients have disseminated, metastatic disease at presentation and palliative chemotherapy with the aims of improving symptoms, optimising quality of life and extending survival may be appropriate. A platinum drug, often in combination, is the mainstay of both curative intent and palliative chemotherapy for SCLC and NSCLC. Recently, epidermal growth factor receptor (EGFR) inhibitors have been licensed for use in NSCLC. This chapter will focus on these new agents and how molecular profiling on EGFR and KRAS mutation is informative in selecting patients most likely to respond. Keywords EGFR (epidermal growth factor receptor) · Erlotinib · Gefitinib · KRAS · Non small cell lung cancer (NSCLC)

Contents 6.1 Epidemiology of Lung Cancer . . . . . . . . . . 6.2 Early Detection, Diagnosis, Classification and Staging 6.2.1 Early Detection . . . . . . . . . . . . . . 6.2.2 Diagnosis . . . . . . . . . . . . . . . . . 6.2.3 Classification and Staging . . . . . . . . . 6.3 Lung Cancer Treatment . . . . . . . . . . . . . 6.3.1 Principles of Lung Cancer Treatment . . . .

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F. Blackhall (B) Christie Hospital Manchester, Manchester M20 4BX, UK; Department of Medical Oncology, University of Manchester, Manchester M20 4BX, UK e-mail: [email protected]

87 W.G. Newman (ed.), Pharmacogenetics: Making Cancer Treatment Safer and More C Springer Science+Business Media B.V. 2010 Effective, DOI 10.1007/978-90-481-8618-1_6, 

88 6.3.2 Standard Chemotherapy in Lung Cancer . . . . . . . . . . . 6.4 EGFRIs: A New Treatment Paradigm for Non-small Cell Lung Cancer 6.4.1 Discovery and Characteristics of EGFR Mutations in NSCLC . 6.4.2 Incidence and Clinical Significance of EGFR-TK Mutations in Patients with NSCLC . . . . . . . . . . . . . . . . . . . . 6.5 K-RAS in NSCLC . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Summary and Perspectives . . . . . . . . . . . . . . . . . . . . 6.7 Case Example . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7.1 Comment . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6.1 Epidemiology of Lung Cancer Lung cancer is responsible for 1.3 million deaths annually and is the commonest cause of cancer-related mortality worldwide (Cancer Research UK 2009a; World Health Organisation 2004). Tobacco exposure is the strongest risk factor with the majority of cases occurring in current or former smokers. Approximately 15% of cases occur in never smokers who are defined as having smoked fewer than 100 cigarettes per lifetime. Other risk factors include environmental exposure to radon gas, industrial carcinogens, air pollution and a positive family history (Cancer Research UK 2009b). The rising incidence of smoking among young women and in Asia and developing countries, dictates that lung cancer will continue to be a significant health and socioeconomic burden for several decades to come, despite global efforts at tobacco control and smoking cessation strategies (World Health Organisation 2008).

6.2 Early Detection, Diagnosis, Classification and Staging 6.2.1 Early Detection There is no proven role for screening to detect early stage lung cancer. A number of studies are underway to determine risk classifiers and assess the value of low dose computed tomography (CT) (Pedersen et al. 2009; Pegna and Picozzi 2009). With respect to genetic testing of high-risk individuals, there is emerging data that may be of relevance to those with multiple affected family members. Notably, a recent study of six multigenerational families with five or more affected members identified a likely candidate gene (RGS17) on chromosome 6q23-25, but further validation is required before genetic testing can be recommended as part of a screening programme (Christiani 2009; You et al. 2009).

6.2.2 Diagnosis Lung cancer is most commonly suspected on the basis of symptoms such as a cough with or without production of blood or sputum, shortness of breath, chest pain or

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weight loss. Investigations include at least a chest radiograph and CT scan of the thorax and upper abdomen. There is increasing use of positron emission tomography (PET) scanning. Histological confirmation is usually obtained via bronchoscopic biopsy, CT guided biopsy, mediastinoscopy or thoracoscopy.

6.2.3 Classification and Staging Lung cancer is classified histologically into either small cell lung cancer (SCLC) or non-small cell lung cancer (NSCLC) subtypes. The latter accounts for at least 80% of cases and is an umbrella term for adenocarcinoma, squamous cell carcinoma, large cell undifferentiated carcinoma and other rarer variants. Staging is according to the International Association for the Study of Lung Cancer (IASLC) tumour, node, metastasis (TNM) classification for NSCLC. For SCLC there is a two stage system that categorises SCLC into limited or extensive disease, defined by the Veterans Administration Lung Study Group (Travis 2009).

6.3 Lung Cancer Treatment 6.3.1 Principles of Lung Cancer Treatment The reader should refer to regularly updated and comprehensive guidelines for detail on lung cancer management and treatment (American Society of Clinical Oncology (ASCO); National Institute for Health and Clinical Excellence (NICE) 2005). In brief, the most important information for treatment selection is the tumour stage, histological classification (whether NSCLC or SCLC), the performance status of the patient and end-organ function. Biochemical factors such as lactate dehydrogenase (LDH) and sodium provide additional prognostic information in patients with SCLC. Surgery is the mainstay of curative therapy for early stage (TNM stage I-IIIA) NSCLC with or without adjuvant chemotherapy to reduce risk of relapse. For inoperable locally advanced NSCLC, and for SCLC limited to the thorax, chemoradiotherapy is given with curative intent although rates of relapse are relatively high at around 75%. The majority of patients have disseminated, metastatic disease at presentation. For these patients, there is no available curative treatment and so the goal of treatment is palliative, to improve symptoms, optimise quality of life and extend survival. A platinum drug is the mainstay of both curative intent and palliative chemotherapy regimens for SCLC and NSCLC. Cytotoxic agents that are currently used in combination with platinum are etopside, gemcitabine, docetaxel, paclitaxel, pemetrexed and vinorelbine. Topotecan is also indicated for relapsed SCLC. In recent years, mechanism based (targeted) therapies designed to inhibit angiogenesis (new blood vessel formation) and epidermal growth factor receptor (EGFR) inhibitors have been licensed for NSCLC. There is no mechanism-based therapy licensed at the time of writing this article for SCLC.

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6.3.2 Standard Chemotherapy in Lung Cancer Standard cytotoxic regimens used for the treatment of lung cancer have response rates ranging from as little as 8% for second line therapy for NSCLC to around 20–30% for first line therapy of NSCLC, and 70% for first line treatment of SCLC. It is evident that a substantial proportion of patients may not derive any benefit from treatment at the risk of significant side effects. Therefore, a major goal is to develop better predictors of benefit from treatment, particularly new treatments designed to target critical molecular pathways. From herein, this chapter will focus on the role of genetic mutations in the selection of treatment for lung cancer. The most clinically advanced area is somatic mutation testing for treatment with EGFR inhibitors.

6.4 EGFRIs: A New Treatment Paradigm for Non-small Cell Lung Cancer The epidermal growth factor receptor (EGFR) is one of a family of four cell membrane spanning receptors identified to regulate cell growth and proliferation. EGFR is normally activated by ligands such as epidermal growth factor (EGF) that trigger receptor dimerisation and activation (by phosphorylation) of the intracellular tyrosine kinase (TK) domain of the receptor. The activated TK domain effects multiple downstream intracellular signalling pathways including the ras-raf-MAPK pathway that is also critical for cell growth (Mendelsohn and Baselga 2006). R , AstraZeneca, UK) and Two orally active drugs; gefitinib (ZD 1839, Iressa R  erlotinib (OSI 774, Tarceva , Genentech, US) reversibly and selectively inhibit EGFR-TK phosphorylation. Erlotinib was licensed in 2005 for second or third line use in patients with NSCLC who have progressive cancer after one or two lines of chemotherapy (Johnson et al. 2005) following the result of the phase III BR21 trial that randomised 731 patients to best supportive care with erlotinib or placebo (Shepherd et al. 2005). Patients on erlotinib had an overall survival of 6.7 months compared to 4.7 months on placebo (hazard ratio, 0.70; p < 0.001). Somatic mutation in the EGFR gene was associated with increased tumour response rate, but this did not translate to a survival benefit (Tsao et al. 2005). Gefitinib was given accelerated FDA approval for patients with NSCLC after promising phase I and II trial results. However, the licence was revoked when gefitinib failed to demonstrate a statistically significant survival benefit compared to placebo in the phase III Iressa Survival Evaluation in Lung Cancer (ISEL) trial (Thatcher et al. 2005). The ISEL trial was similar in design to BR21, but conducted in a larger population of 1692 patients. Despite the negative result overall, patients who were never-smokers and Asian patients were noted to have better tumour response rates and survival when treated with gefitinib compared to placebo, but these data were insufficient for the gefitinib licence to continue in Western countries (Blackhall et al. 2006; Chang et al. 2006). Higher response rates

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were also observed in patients with tumours bearing EGFR mutations, but there was insufficient data to draw conclusions on survival (Hirsch et al. 2006). With respect to toxicity both gefitinib and erlotinib are well tolerated, oral agents that are administered once daily. The commonest side effects are rash and diarrhoea. Rash is acneiform in nature and occurs in 50–70% of patients. Diarrhoea is usually mild and self-limiting with an incidence of 30–50% (Shepherd et al. 2005; Thatcher et al. 2005). Rare, but fatal, cases of interstitial lung disease (Danson et al. 2005) and hepatotoxicity (Ramanarayanan and Krishnan 2008) have also been noted. Erlotinib is now a standard of care for second or third line treatment of NSCLC and, at present, it is prescribed on an empiric basis. A turning point for gefitinib has come from the Iressa Pan Asian Study (IPASS) that was reported in abstract form at the Annual Meeting of the European Society of Medical Oncology in 2008 and recently published in full (Mok et al. 2009). The IPASS trial was conducted exclusively in East Asia and in patients with the adenocarcinoma subtype of NSCLC and either a history of never-smoking (95%) of mutations are located in codons 12 and 13, with less common mutations involving codons 59 and 61. The modified conformation of mutant Ras confers ability to active downstream signals, even in the absence of growth factor stimuli. K-RAS mutations are more common in adenocarcinomas than squamous cell cancers and have been associated with tobacco smoking and asbestos exposure. There is conflicting evidence as to whether K-RAS mutations in tumours confer a poor prognosis in NSCLC patients. Laboratory studies have also associated mutated K-RAS with resistance to cytotoxic chemotherapeutics and radiation, but the clinical significance of mutant K-RAS for prediction of response to treatment is not sufficiently robust to apply in the clinic. Criteria used to select patients, study design, stage of disease and methods of detection have all contributed to inconsistencies in the published literature (Aviel-Ronen et al. 2006). Of interest, mutations in KRAS and EGFR appear to be mutually exclusive (Eberhard et al. 2005; Shigematsu et al. 2005). The presence of a K-RAS mutation appears to be associated with resistance to treatment with EGFR inhibitors (Pao et al. 2005b) and has been included in predictive algorithms for EGFR inhibitors (Califano et al. 2008). Since K-Ras is a downstream component of EGFR, activation of K-RAS by mutation enables signalling independent of EGFR activation. Agents are currently in development to target tumours with a K-RAS mutation (Sebolt-Leopold 2008).

6.6 Summary and Perspectives The identification of EGFR mutations as predictors of response to EGFR-TKIs is a major breakthrough in the field. While the question of whether to routinely test or not remains contentious, the shift away from empiric therapy to treatment selection based on predictive DNA based biomarkers has now begun for patients with lung cancer. The concerted efforts of molecular biologists, geneticists and oncologists will now be vital to ensure that tests are developed and applied according to the evidence base and rigorous internationally approved and recognised standards. The evidence for EGFR mutation as a predictor for clinical benefit from erlotinib or gefitinib has been relatively slow to evolve. Improved response rates to EGFRTKIs were observed early on, but compelling evidence to link treatment with an EGFR-TKI and survival and the presence of an activating EGFR mutation proved elusive until the IPASS trial result discussed earlier in this chapter. A logistical problem in the clinical trials before IPASS was lack of statistical power due to insufficient quantities of tumour tissue samples to test. This is because the majority of lung cancer patients are diagnosed from a small biopsy and a substantial proportion are diagnosed from a fine needle aspirate, although the latter is discouraged. Coupled with this, diagnostic biopsies obtained before any treatment may not reflect the tumour mutation status after one or more lines of chemotherapy. While repeat biopsies post treatment may be the ideal, the invasive procedure required in patients

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with a poor life expectancy, and the impact on biopsy services are significant barriers to repeat biopsy. Consequently, techniques that are based on a blood test and less invasive such as analysis of circulating free DNA or DNA from circulating tumour cells are of interest (Maheswaran et al. 2008), but not yet validated sufficiently to replace use of a biopsy. There is also provocative data on serum proteomic profiles assayed using mass spectrometry for prediction of response to an EGFR-TKI (Taguchi et al. 2007). Finally, methodology to identify EGFR mutations is not yet standardised. In the BR21 trial, available tumour was retrospectively analysed for mutation status using two different techniques with different results (Zhu et al. 2008). The experience to date in NSCLC highlights the challenges faced for oncologists and geneticists in determining clinically meaningful predictive DNA based tests. This chapter is not an exhaustive account of genetic biomarkers for selection of treatment and the controversies that have surrounded the empiric versus individualised therapy debate for treating lung cancer patients in the clinic. Suitable alternatives and/or adjuncts to EGFR mutation testing such as EGFR gene copy number analysis or K-RAS mutation, are an ongoing area of research. It is plausible that there may be a panel of potential tests that will be used for selection of an EGFR-TKI depending on the amount of tissue available for analysis, economic considerations, local expertise and the turnaround time of the assay(s). It is true to say that in Western populations the majority of NSCLC cases will not have an EGFR mutation and so genes or other molecular markers that predict for benefit or resistance to cytotoxic chemotherapy are of great interest. The data relating to gene mutations that may be predictive are largely exploratory and therefore beyond the scope of this chapter that aims to highlight clinically proven pharmacogenetic tests for lung cancer. With respect to platinum chemotherapy, there has been a focus on genes involved in DNA repair such as NQO1, ERCC1, RRM1, Beta-tubulin and BRCA1/2 (Danesi et al. 2009). Studies of irinotecan in patients with small cell lung cancer also prompted interest in UGT1A1 polymorphisms to explain disparate results of trials conducted in Japanese compared to North American/European populations with SCLC. Briefly, the JCOG trial demonstrated a significant benefit in survival for irinotecan in combination with platinum compared to standard etoposide and platinum for first line palliative treatment of extensive stage SCLC (Noda et al. 2002). When the study was replicated in a North American/European population no survival benefit was detected and the toxicity profile with respect to diarrhoea differed (Lara et al. 2009). The role of pharmacogenetic testing for irinotecan in the treatment of colon cancer is discussed elsewhere in this book (see Chapter 5). With respect to SCLC, the irinotecan trials have been seminal in highlighting that pharmacogenetic considerations can be crucial for the interpretation of different results observed among different ethnic populations. Pharmacogenetic tests for NSCLC should ultimately expand access to more effective treatments by reducing uncertainty among prescribers and patients regarding chance of benefit versus risk of side effects; a vital consideration for palliative treatment where quality of life is the major goal. The availability of predictive tests

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to identify patients who stand to benefit most may also improve cost-effectiveness and facilitate faster uptake of new drugs for lung cancer by state funded healthcare systems.

6.7 Case Example A 50 year old East Asian lady, who emigrated from Hong Kong to the United Kingdom 20 years ago, presented with a one month history of cough and shortness of breath on exertion. Her symptoms were such that she had to discontinue her work as a physical education teacher. She had never smoked but had been exposed to passive smoking (father and husband). She also had a strong family history of gastric cancer in three first-degree relatives. She attended her GP who performed a chest X-ray after a short course of antibiotics failed to improve her symptoms. The X-ray revealed multiple bilateral pulmonary nodules and so she proceeded to undergo a CT scan and CT guided biopsy that confirmed a diagnosis of pulmonary adenocarcinoma. She was referred to a medical oncologist who recommended standard first line chemotherapy with gemcitabine and cisplatin. She was informed that she had advanced, stage IV NSCLC for which the treatment would not be curative, but chemotherapy treatment could help to palliate symptoms and extend survival. She asked about her life expectancy and was informed that on average, with treatment, this would be on average nine months. She consented to chemotherapy after full explanation of potential risks including life-threatening infection. She noted improvement in symptoms after one cycle of treatment, but experienced fatigue and poorly controlled nausea for several days after each treatment such that she had to remain in bed. A CT scan performed after three cycles of chemotherapy demonstrated stable disease (no objective response). She completed six cycles of treatment and also required a blood transfusion after her fourth cycle of treatment. One month after discontinuing chemotherapy, she demonstrated worsening of cough and shortness of breath so marked that her GP arranged for installation of supplemental oxygen for use at home. She had been intending to travel to Hong Kong to visit elderly relatives in view of her limited life expectancy, but this was not practical. On the clinical assumption that the cancer was progressing only one month after a course of chemotherapy, she underwent a repeat CT scan that confirmed rapid disease progression. Her oncologist recommended second line treatment with erlotinib and counselled her regarding risks of rash and diarrhoea. She explained that the overall response rate was less than 10%, but that never-smokers had a higher chance of response. Four days later the patient received a routine call from the lung nurse specialist to check tolerability of the new medication. The patient reported a remarkable and complete resolution of cough and no longer required supplemental oxygen. She was reconsidering her trip to Hong Kong. Chest X-ray and CT scans confirmed response to erlotinib when performed one month later (Fig. 6.1).

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Fig. 6.1 CT chest scans demonstrating response to gefitinib after 1 month of treatment

She subsequently continued on erlotinib without significant side effects for 16 months during which time she returned to her work as a teacher. On analysis of the diagnostic biopsy for research purposes the presence of an activating p.L858R EGFR mutation was confirmed.

6.7.1 Comment This lady arguably benefited only modestly from first line treatment with chemotherapy and she experienced some toxicities. Her quality of life may have been better on an EGFR-TKI from the outset. This case illustrates how in the future the addition of EGFR mutation status in the diagnostic workup will help to guide selection of either chemotherapy or an EGFR-TKI for first line treatment of advanced NSCLC in patients who have a high chance of an EGFR mutation.

References American Society of Clinical Oncology Clinical Guidelines: Lung cancer. http://www.asco.org/ ASCOv2/Practice+%26+Guidelines/Guidelines/Clinical+Practice+Guidelines/Lung+Cancer. Cited 8 Aug 2009 Aviel-Ronen S, Blackhall FH, Shepherd FS, Tsao MS (2006) K-ras mutations in non-small-cell lung carcinoma: a review. Clin Lung Cancer 8:30–38 Blackhall F, Ranson M, Thatcher N (2006) Where next for gefitinib in patients with lung cancer? Lancet Oncol 7:499–507 Califano R, Blackhall F, Finocchiaro G et al (2008) EGFR tyrosine kinase inhibitors in non-small cell lung cancer patients: how do we interpret the clinical and biomarker data? Target Oncol 3:173–186 Cancer Research UK (2009a) Commonly diagnosed cancers worldwide Cancer Research UK. http://info.cancerresearchuk.org/cancerstats/geographic/world/?a=5441. Cited 8 Aug 2009

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Cancer Research UK (2009b) UK lung cancer incidence statistics. Cancerstats http://info. cancerresearchuk.org/cancerstats/types/lung/incidence. Cited 8 Aug 2009 Chang A, Parikh P, Thongprasert S et al (2006) Gefitinib (IRESSA) in patients of Asian origin with refractory advanced non-small cell lung cancer: subset analysis from the ISEL study. J Thorac Oncol 1:847–855 Christiani DC (2009) Lung cancer genetics: a family affair? Clin Cancer Res 15:2581–2582 Cortes-Funes H, Gomez C, Rosell R et al (2005) Epidermal growth factor receptor activating mutations in Spanish gefitinib-treated non-small-cell lung cancer patients. Ann Oncol 16:1081–1086 Danesi R, Pasqualetti G, Giovannetti E et al (2009) Pharmacogenomics in non-small-cell lung cancer chemotherapy. Adv Drug Deliv Rev 61:408–417 Danson S, Blackhall F, Hulse P, Ranson M (2005) Interstitial lung disease in lung cancer: separating disease progression from treatment effects. Drug Saf 28:103–113 Eberhard DA, Johnson BE, Amler LC et al (2005) Mutations in the epidermal growth factor receptor and in KRAS are predictive and prognostic indicators in patients with non-small-cell lung cancer treated with chemotherapy alone and in combination with erlotinib. J Clin Oncol 23:5900–5999 European Medicines Agency (2009) www.emea.europa.eu/pdfs/human/opinion/Iressa_20075609 en.pdf. Cited 8 Aug 2009 Greulich H, Chen TH, Feng W et al (2005) Oncogenic transformation by inhibitor-sensitive and -resistant EGFR mutants. PLoS Med 2:e313 Hirsch FR, Varella-Garcia M, Bunn PA et al (2006) Molecular predictors of outcome with gefitinib in a phase III placebo-controlled study in advanced non-small-cell lung cancer. J Clin Oncol 24:5034–5042 Inukai M, Toyooka S, Ito S et al (2006) Presence of epidermal growth factor receptor gene T790M mutation as a minor clone in non-small cell lung cancer. Cancer Res 66:7854–7858 Johnson JR, Cohen M, Sridhara R et al (2005) Approval summary for erlotinib for treatment of patients with locally advanced or metastatic non-small cell lung cancer after failure of at least one prior chemotherapy regimen. Clin Cancer Res 11:6414–6421 Kim ES, Hirsh V, Mok T et al (2008) Gefitinib versus docetaxel in previously treated non-small-cell lung cancer (INTEREST): a randomised phase III trial. Lancet 372:1809–1818 Kobayashi S, Boggon TJ, Dayaram T et al (2005) EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N Engl J Med 352:786–792 Lara PN, Natale R, Crowley J et al (2009) Phase III trial of irinotecan/cisplatin compared with etoposide/cisplatin in extensive-stage small-cell lung cancer: clinical and pharmacogenomic results from SWOG S0124. J Clin Oncol 27:2530–2535 Lynch TJ, Bell DW, Sordella R et al (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 350:2129–2139 Maheswaran S, Sequist LV, Nagrath S et al (2008) Detection of mutations in EGFR in circulating lung-cancer cells. N Engl J Med 359:366–377 Marchetti A, Felicioni L, Buttitta F (2006) Assessing EGFR mutations. N Engl J Med 354: 526–528 Mendelsohn J, Baselga J (2006) Epidermal growth factor receptor targeting in cancer. Semin Oncol 33:369–385 Mok T, Wu Y, Thongprasert S et al (2009) Gefitinib or Carboplatin-Paclitaxel in Pulmonary Adenocarcinoma. N Eng J Med 361:947–957 National Institute for Health and Clinical Excellence (2005) Lung Cancer Diagnosis and Treatment. NICE guideline CG24. http://www.nice.org.uk/guidance/CG24. Cited 8 Aug 2009 Noda K, Nishiwaki Y, Kawahara M et al (2002) Irinotecan plus cisplatin compared with etoposide plus cisplatin for extensive small-cell lung cancer. N Engl J Med 346:85–91 Paez JG, Janne PA, Lee JC et al (2004) EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304:1497–1500

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Pao W, Miller V, Zakowski M et al (2004) EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci USA 101:13306–13311 Pao W, Miller VA, Politi KA et al (2005a) Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Med 2:e73 Pao W, Wang TY, Riely GJ et al (2005b) KRAS mutations and primary resistance of lung adenocarcinomas to gefitinib or erlotinib. PLoS Med 2(1):e17 Pedersen JH, Ashraf H, Dirksen A et al (2009) The Danish randomized lung cancer CT screening trial–overall design and results of the prevalence round. J Thorac Oncol 4:608–614 Pegna AL, Picozzi G (2009) Lung cancer screening update. Curr Opin Pulm Med 15:327–333 Ramanarayanan J, Krishnan GS (2008) Review: hepatotoxicity and EGFR inhibition. Clin Adv Hematol Oncol 6:200–201 Rosell R, Taron M, Sanchez JJ, Paz-Ares L (2007) Setting the benchmark for tailoring treatment with EGFR tyrosine kinase inhibitors. Future Oncol 3:277–283 Rosell R, Moran T, Queralt C et al (2009) Screening for epidermal growth factor receptor mutations in lung cancer. N Engl J Med 361:958–967 Sebolt-Leopold JS (2008) Advances in the development of cancer therapeutics directed against the RAS-mitogen-activated protein kinase pathway. Clin Cancer Res 14:3651–3656 Shepherd FA, Rodrigues Pereira J, Ciuleanu T et al (2005) Erlotinib in previously treated nonsmall-cell lung cancer. N Engl J Med 353:123–132 Shigematsu H, Lin L, Takahashi T et al (2005) Clinical and biological features associated with epidermal growth factor receptor gene mutations in lung cancers. J Natl Cancer Inst 97:339–346 Taguchi F, Solomon B, Gregorc V et al (2007) Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: a multicohort cross-institutional study. J Natl Cancer Inst 99:838–846 Thatcher N, Chang A, Parikh P et al (2005) Gefitinib plus best supportive care in previously treated patients with refractory advanced non-small-cell lung cancer: results from a randomised, placebo-controlled, multicentre study (Iressa Survival Evaluation in Lung Cancer). Lancet 366:1527–1537 Travis WD (2009) Reporting lung cancer pathology specimens. Impact of the anticipated 7th Edition TNM classification based on recommendations of the IASLC Staging Committee. Histopathology 54:3–11 Tsao MS, Sakurada A, Cutz JC et al (2005) Erlotinib in lung cancer – molecular and clinical predictors of outcome. N Engl J Med 353:133–144 World Health Organisation (2008) Global Infobase on tobacco. http://apps.who.int/infobase/report. aspx?rid=112. Cited on 8 Aug 2009. World Health Organisation (2004) World Health Organisation Annual Report. http://www.who.int/ whr/2004/en/index.html. Cited 8 Aug 2009 You M, Wang D, Liu P et al (2009) Fine mapping of chromosome 6q23-25 region in familial lung cancer families reveals RGS17 as a likely candidate gene. Clin Cancer Res 15:2666–2674 Zhu CQ, da Cunha Santos G, Ding K et al (2008) Role of KRAS and EGFR as biomarkers of response to erlotinib in National Cancer Institute of Canada Clinical Trials Group Study BR.21. J Clin Oncol 26:4268–4275

Chapter 7

Pharmacogenetics and Cancer Treatment in Children P. Kellie Turner and Gareth J. Veal

Abstract Despite significant improvements in the treatment of childhood cancer over the past several decades, there remains a need for improved cure rates for many tumour types. It is therefore essential that, in addition to the development of novel anticancer drugs, selected on the basis of the molecular and cellular pathology of the target tumour, we continue to explore more effective ways of using conventional chemotherapeutics. Improved knowledge of the clinical pharmacology of an increasing number of chemotherapeutics may facilitate their efficacious use with minimal toxicity and a decreased likelihood of other unwanted long-term effects. In this respect, it is important to consider the many cases where variations in pharmacokinetics may reflect genetic polymorphisms in enzymes involved in drug metabolism and transport. In the same way that inter-patient pharmacokinetic variation has been used successfully to facilitate individualisation of dosing for a number of anticancer drugs, the more recent appeal of pharmacogenetics research has significant potential for improving the clinical treatment of children with cancer. Indeed, pharmacogenetic approaches may allow for the upfront selection of which drugs are likely to be most beneficial, in addition to defining the most appropriate dosing regimens for individual patients. This chapter highlights a number of polymorphisms in genes, which play important roles in determining anticancer drug disposition and response or toxicity in a paediatric oncology setting. Keywords Acute lymphoblastic leukaemia (ALL) · Thiopurine methyltransferase (TPMT) · 6-mercaptopurine (6-MP) · UDP-glucuronosyltransferase (UGT1A1) · Irinotecan · Dihydrofolate reductase (DHFR) · Methotrexate

G.J. Veal (B) Northern Institute for Cancer Research, Paul O’Gorman Building Medical School, Newcastle University, Newcastle upon Tyne NE2 4HH, UK e-mail: [email protected]

101 W.G. Newman (ed.), Pharmacogenetics: Making Cancer Treatment Safer and More C Springer Science+Business Media B.V. 2010 Effective, DOI 10.1007/978-90-481-8618-1_7, 

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Contents 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Cancer Treatment in Paediatrics . . . . . . . . . . . 7.2 Current Dosing Practice in Paediatric Oncology . . . . . . . 7.3 Pharmacokinetic Approaches to Therapy Individualization . . 7.4 Pharmacogenetic Approaches to Therapy Individualization . 7.4.1 Pharmacogenetics of Cancer Chemotherapy in Children 7.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Case Example . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . .

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7.1 Introduction 7.1.1 Cancer Treatment in Paediatrics Significant improvements in the treatment of childhood cancer over the past 40 years have led to marked increases in 5 year survival rates for many malignancies, including a number of tumour types that were previously deemed to be largely ‘incurable’. For example, childhood acute lymphoblastic leukaemia (ALL) is now cured in approximately 80% of cases, with similarly impressive response rates observed in the treatment of Wilms’ tumour (Pui and Evans 2006; Mitchell et al. 2000). However, even with current advances in the utility of chemotherapeutics, there remain a number of malignancies with clear scope for further improvement through the optimisation of treatment regimens. Response rates for tumours such as neuroblastoma have shown more modest improvements, in comparison to success stories such as ALL. Approximately 50% of neuroblastoma patients still succumb to their disease, despite the introduction of new drugs and development of intensive chemotherapy regimens (Matthay et al. 1999). In addition to the need for improved cure rates for those tumour types which remain and for those patients who fail to respond or relapse following treatment for ALL and other malignancies with higher survival rates, it is important to consider the long-term effects of cancer treatment. With a marked increase in the number of childhood cancer survivors, there are now significant concerns over the long-term effects of the chemotherapy used in their treatment. In addition to potentially long-term side effects associated with the use of many anticancer drugs, such as cisplatin-induced ototoxicity (Rademaker-Lakhai et al. 2006) and nephrotoxicity linked to the use of ifosfamide (Skinner 2003), there is increasing evidence supporting a significant occurrence of wide-ranging late-effects of chemotherapy, including increased risks of secondary malignancies (Hudson et al. 2003). Indeed, it could be argued that these problems are somewhat inevitable when we consider the rudimentary mechanisms of action exhibited by many of the current formulary of anticancer drugs and the associated modest specificity for tumour tissue versus host cells. It is therefore essential that in addition to

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the development of new agents, ideally selected on the basis of the molecular and cellular pathology of the target tumour, we continue to explore more effective ways of using conventional chemotherapeutics.

7.2 Current Dosing Practice in Paediatric Oncology In paediatric oncology, the current practice for the vast majority of chemotherapeutics is to dose children based on body size, commonly utilising surface area in older children and body weight in infants and younger children (Ratain 1998). This practice results in considerable heterogeneity in response rates and incidence of toxicity which are often impossible to predict based on our current, and in many cases limited, knowledge of the clinical pharmacology of these drugs (Evans and Relling 1999). Indeed, even for drugs where relatively large numbers of clinical pharmacology studies have been carried out, it has proven difficult to establish more rational approaches to dosing. The heterogeneity of antitumour efficacy and host toxicity presents a particular problem in the field of cancer due to the narrow therapeutic window exhibited by the majority of chemotherapy agents (Kamb et al. 2007). The narrow margins between doses of drugs which are associated with antitumour efficacy and doses associated with side-effects are such that a certain amount of toxicity is deemed to be acceptable, and in some cases even necessary, for successful treatment. More rational approaches to treatment, based on pharmacokinetic and pharmacogenetic factors, have to date been successfully utilised in a relatively limited number of cases to optimise treatment (Veal et al. 2003). However, an improved knowledge of the clinical pharmacology of chemotherapeutics may facilitate their efficacious use with minimal toxicity and a decreased likelihood of other unwanted long-term effects.

7.3 Pharmacokinetic Approaches to Therapy Individualization While the focus of this chapter remains on the pharmacogenetics of cancer treatment in paediatric oncology, it is important to highlight advances that have been made in this area through the implementation of dosing based on an improved understanding of the related discipline of pharmacokinetics. Two of the best examples of anticancer drugs where dose individualisation has successfully been achieved are those of the widely used chemotherapeutics carboplatin and methotrexate. Carboplatin pharmacokinetics have been investigated in a paediatric oncology setting in a large number of studies, with significant inter-patient variation reported in pharmacokinetic parameters such as drug clearance and exposure or AUC. Correlations between carboplatin total drug exposure estimated by area under the curve (AUC) calculations and clinical response and toxicity, observed in both adults and children (Newell et al. 1987; Jodrell et al. 1992), have resulted in a shift from dosing based on body weight or surface area to the dose required to achieve a target

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AUC for a particular tumour type in a particular setting. In the case of carboplatin, the clearance of which is determined largely by the extent of renal elimination, the glomerular filtration rate of the patient is used in combination with the desired target AUC to determine individual patient doses through the use of paediatric dosing equations (Newell et al. 1993). This approach has been shown to result in more consistent exposures to carboplatin than dosing based simply on body weight or body surface area (Thomas et al. 2000). More recently, further advances have been made through the implementation of adaptive dosing based on the real-time monitoring of carboplatin pharmacokinetics. This approach has shown to be useful in the treatment of subpopulations of patients such as anephric patients, infants and children being treated with high-dose carboplatin chemotherapy (Veal et al. 2004; Veal et al. 2007; Picton et al. 2009). The anti-metabolite methotrexate has been used successfully for the treatment of a number of malignancies over the past 50 years and remains a key component of ALL treatment regimens. In addition to the use of therapeutic drug monitoring, based on methotrexate plasma levels determined 24 h after high dose methotrexate, to predict which patients require leucovorin rescue to minimize toxicity (Nirenberg et al. 1997), pharmacokinetically-guided treatment has also been shown to improve clinical response rates in ALL (Evans et al. 1986). In this respect, methotrexate steady state plasma concentrations below 16 µM are associated with increased risk of disease relapse (Evans et al. 1986), and individualised dosing to achieve plasma concentrations above 16 µM has been shown to result in lower relapse rates in patients (Evans et al. 1998).

7.4 Pharmacogenetic Approaches to Therapy Individualization In considering the impact of differences in pharmacokinetics on clinical response and toxicity in children with cancer, in many cases the variations observed between patients will reflect genetic polymorphisms in enzymes involved in drug metabolism and transport. In the same way that inter-patient variation in pharmacokinetics has been utilised to improve dosing, the more recent advances in pharmacogenetics research similarly relate to the potential for individualisation of cancer treatment, with the aim of achieving improved response rates with minimal associated toxicity. While pharmacokinetic approaches are limited to individualising drug doses for patients already on treatment in a real-time setting, pharmacogenetic approaches may allow for the upfront selection of which drugs are likely to be most beneficial, in addition to defining the most appropriate dosing regimens for individual patients. In this way, it may be possible for patients to be more effectively stratified into appropriate treatment groups, based on their genetic profile, prior to any chemotherapy being administered. The following section focuses on some examples of anticancer drugs where genetic polymorphisms have been shown to play a key role in influencing response and toxicity in a paediatric oncology setting. While the number of studies carried out in childhood cancer is limited by the small numbers

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of patients available for recruitment, in comparison with similar studies in an adult setting, ongoing research in this area is likely to lead to further important findings in the near future. Indeed, one major advantage of carrying out studies in childhood cancer is that relatively small patient numbers allow for more practicable implementation of changes to future treatment protocols, based on findings from ongoing pharmacogenetics research.

7.4.1 Pharmacogenetics of Cancer Chemotherapy in Children 7.4.1.1 TPMT and 6-Mercaptopurine in ALL Maintenance therapy for childhood ALL includes the thiopurine drug 6-mercaptopurine (6-MP), which is a prodrug that is activated through a series of steps to thioguanine nucleotides. Thiopurine S-methyl transferase (TPMT) inactivates thiopurine drugs such as 6-MP and azathioprine via S-methylation. TPMT polymorphisms are present in approximately 10% of the population and result in reduced enzymatic activity, impaired inactivation of 6-MP, and increased risk for 6-MP associated toxicity. At least 26 TPMT variant alleles have been identified to date (Wang et al. 2008). The most important TPMT variants are ∗ 2, ∗ 3A, ∗ 3B, ∗ 3C and ∗ 4 (Table 7.1), with the ∗ 2, ∗ 3A, and ∗ 3C alleles accounting for at least 95% of cases of deficiency in TPMT activity (Tai et al. 1996). The reduction in TPMT catalytic activity occurs due to low enzyme expression caused by rapid proteasomal degradation or aberrant splicing of the TPMT transcript (Krynetski et al. 1995; Tai et al. 1997; Tai et al. 1999). 6-MP dosage individualization based on TPMT genotype can minimize the interindividual variation in exposure to thioguanine nucleotides and reduce the risk of toxicity in children with TPMT deficiency (Krynetski et al. 1996; Cheok and Evans 2006). On the Total XII protocol, the requirement for 6-MP dosage reduction was highest in children with TPMT deficiency (homozygous variant) due to the risk of severe myelotoxicity (Relling et al. 1999). TPMT heterozygotes are also at higher risk for dose-limiting toxicity than those with wild-type TPMT. Children who receive full-dose 6-MP and are TPMT deficient are likely to experience toxicity that may prevent administration of other drugs used in ALL therapy, with the corresponding reduction in dose intensity potentially having a negative impact on response rates. Furthermore, pharmacogenetic testing in patients with ALL prior to receiving 6-MP has been reported as cost effective in a pharmacoeconomic analysis (van den Akker-van Marle et al. 2006). Because children with TPMT deficiency are exposed to higher levels of active thioguanine nucleotides, this can also influence response to 6-MP in ALL. On the BFM regimen in which TPMT heterozygotes and wild-type homozygotes received the same 6-MP dosage, TPMT heterozygotes had a threefold lower risk of the presence of measurable minimal residual disease (MRD) on day 78 than those who were homozygous wild-type (Stanulla et al. 2005). In contrast to the Total XII regimen, there was no difference in hematopoietic toxicity between TPMT wild-type

dBSNP

rs1800462

NA

rs1800460

rs1142345

rs1800584

TPMT Variant

∗2

∗ 3A

∗ 3B

∗ 3C

∗4

Ala80Pro

Amino acid change Low expression, due to rapid proteasomal degradation

Change in enzyme compared to wild-type

c. 874A>G

c.615G>A

Alternative mRNA splicing, truncated protein

Tyr240Cys

Ala154Thr

Low

Low expression, due to rapid proteasomal degradation

Low intrinsic stability and rapid proteasomal degradation

c.615G>A Ala154Thr Low expression, due c.874A>G Tyr240Cys to rapid proteasomal degradation

c.238G>C

mRNA Variant

Very low

1.4-fold lower than wild-type

9-fold lower than wild type

Undetectable activity

100-fold lower than wild-type

Enzyme activity compared to wildtype

T variant. In another population of 26 children with ALL or malignant lymphoma receiving high dose methotrexate with leucovorin rescue, MTHFR c.677C>T was not significantly associated with hepatotoxicity (Imanishi et al. 2007). However, the variant allele was associated with methotrexate concentrations ≥1 µM at 48 h. Further clinical studies are required to determine if carriers of the MTHFR c.677C>T allele would benefit from further leucovorin rescue. As with the relationship between irinotecan toxicity and UGT1A1 genotype, the effects of DHFR expression and genotype and MTHFR genotype on methotrexateassociated toxicity and survival may be dependent on dose in addition to the use of leucovorin rescue.

7.5 Summary In summary, this chapter has highlighted a number of polymorphisms in genes, which play important roles in determining drug disposition and response or toxicity in a paediatric oncology setting. While TPMT genetic variation remains the most frequently used example of the importance of developing an increased knowledge of pharmacogenetics, and the utilisation of findings from pharmacogenetic studies in a clinical setting, there are clearly other drugs for which efficacy and toxicity may be influenced by genetic polymorphisms. For those drugs focused on in this chapter, no significant associations between genotype and toxicity have currently been identified in children receiving irinotecan, in contrast with 6-MP, despite polymorphisms in UGT1A1 having previously been correlated with increased toxicity in adults. For enzymes in the folate pathway involved in methotrexate pharmacodynamics, there are currently conflicting reports of the effect of genotype or expression on toxicity or survival in childhood leukaemia. Further clinical studies will be required to evaluate the utility of individualization of therapy based on genotype and/or gene expression analysis for these and other anticancer agents. In this respect, it will be important for these studies to be carried out as large, prospective, multicentre investigations in order to avoid the generation of inconsistent findings from smaller trials involving insufficient numbers of patients. Additional areas of interest, which may have been included in the current chapter, include preliminary findings that polymorphisms in the cytochrome P450 3A family may impact on the risk of toxicity following ALL treatment, with

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chemotherapy, including vincristine, prednisone and cyclophosphamide (Aplenc et al. 2003). Polymorphisms in cytochrome P450 enzymes may also have a role to play in determining the effectiveness of cyclophosphamide in the treatment of non-Hodgkin’s lymphoma in children, where increased relapse rates have been correlated with the formation of inactive cyclophosphamide metabolites (Yule et al. 2004). A study designed to identify polymorphisms in genes encoding phase I and II drug metabolising enzymes associated with relapse rates in neuroblastoma have suggested that presence of the NAT1∗ 11 variant and the GSTM1 wild-type variant contribute to a more favourable outcome (Ashton et al. 2007). A similar study focusing on glutathione S-transferase genes and outcome of treatment for childhood acute myeloid leukaemia (AML) showed greater toxicity and reduced survival rates in children with a GSTT1-negative genotype (Davies et al. 2001). It is hoped that these preliminary findings will be further investigated in a paediatric setting in the near future. Optimisation of childhood cancer treatment through the utilisation of pharmacologically guided drug therapy, to determine the drugs and dosages most likely to produce a therapeutic effect, in conjunction with pharmacogenetic data concerning enzymes involved in drug metabolism, transport and resistance, represents a very realistic and achievable goal for current and future research.

7.6 Case Example A 5 year old boy presented with a white blood cell count of 45,000/µL and was diagnosed with hyperdiploid pre-B ALL. TPMT phenotype was determined at diagnosis, based on TPMT activity in red blood cells, using a HPLC analytical method involving measurement of the production of 6-methylthioguanine from 6-thioguanine. He was found to have a high TPMT activity of >60 nmol 6-MTG/g.Hb/h. In view of this, it was decided to after induction therapy he was started on maintenance therapy incorporating a full dose of 6-mercaptopurine (6-MP) of 75 mg/m2 /day. However, after two weeks of maintenance therapy, the child developed grade 4 neutropenia and sepsis. At this point genotyping for TPMT polymorphisms was carried out and the patient was found to be a TPMT∗ 2 homozygote, therefore having low constitutive levels of TPMT protein and decreased enzyme activity. The child died several weeks after the termination of maintenance treatment due to mercaptopurine-induced toxicity. This case illustrates a number of important points. Firstly, children with TPMT polymorphisms that result in low TPMT enzyme activity are at increased risk of severe mercaptopurine-induced toxicity if they receive full dose 6-MP. In addition, the timing of TPMT phenotyping is clearly an important factor in determining treatment, due to chromosomal aberrations that can result in false positives (high TPMT activity). Therefore, patients are routinely genotyped/phenotyped six weeks after induction therapy, but prior to the initiation of maintenance therapy. The importance of this is highlighted by the fact that as this child was diagnosed with standard risk disease, he was likely to be cured of his disease if he had survived the adverse effects of the treatment administered.

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References Aplenc R, Glatfelter W, Han P, et al (2003) CYP3A genotypes and treatment response in paediatric acute lymphoblastic leukaemia. Br J Haematol 122:240–244 Ashton LJ, Murray JE, Haber M, Marshall GM, Ashley DM, Norris MD (2007) Polymorphisms in genes encoding drug metabolizing enzymes and their influence on the outcome of children with neuroblastoma. Pharmacogenet Genomics 17:709–717 Bomgaars LR, Bernstein M, Krailo M et al (2007) Phase II trial of irinotecan in children with refractory solid tumors: a Children’s Oncology Group Study. J Clin Oncol 25:4622–4627 Cheok MH, Evans WE (2006) Acute lymphoblastic leukaemia: a model for the pharmacogenomics of cancer therapy. Nat Rev Cancer 6:117–1129 Chiusolo P, Reddiconto G, Casorelli I et al (2002) Preponderance of methylenetetrahydrofolate reductase C677T homozygosity among leukemia patients intolerant to methotrexate. Ann Oncol;13:1915–1918. Davies SM, Robison LL, Buckley JD et al (2001) Glutathione S-transferase polymorphisms and outcome of chemotherapy in childhood acute myeloid leukemia. J Clin Oncol 19: 1279–1287 Dulucq S, St-Onge G, Gagne V et al (2008) DNA variants in the dihydrofolate reductase gene and outcome in childhood ALL. Blood 111:3692–3700 Evans W, Crom W, Abromowitch M et al (1986) Clinical pharmacodynamics of high-dose methotrexate in acute lymphocytic leukemia. Identification of a relation between concentration and effect. New Engl J Med 314:471–477 Evans WE, Relling MV, Rodman JH, Crom WR, Boyett JM, Ching-Hon P (1998) Conventional compared with individualised chemotherapy for childhood acute lymphoblastic leukemia. New Engl J Med 338:499–505 Evans WE, Relling MV (1999) Pharmacogenomics: translating functional genomics into rational therapies. Science 286:487–491 Goker E, Waltham M, Kheradpour A et al (1995) Amplification of the dihydrofolate reductase gene is a mechanism of acquired resistance to methotrexate in patients with acute lymphoblastic leukemia and is correlated with p53 gene mutations. Blood 86:677–684 Hon YY, Fessing MY, Pui CH, Relling MV, Krynetski EY, Evans WE (1999) Polymorphism of the thiopurine S-methyltransferase gene in African-Americans. Hum Mol Genet 8:371–376 Hoskins JM, Goldberg RM, Qu P, Ibrahim JG, McLeod HL (2007) UGT1A1∗ 28 genotype and irinotecan-induced neutropenia: dose matters. J Natl Cancer Inst 99:1290–1295 Hudson MM, Mertens AC, Yasui Y et al. (2003) Health status of adult long-term survivors of childhood cancer. A report from the Childhood Cancer Survivor Study. JAMA 290: 1583–1592 Imanishi H, Okamura N, Yagi M et al (2007) Genetic polymorphisms associated with adverse events and elimination of methotrexate in childhood acute lymphoblastic leukemia and malignant lymphoma. J Hum Genet 52:166–1671 Iyer L, Das S, Janisch L et al (2002) UGT1A1∗ 28 polymorphism as a determinant of irinotecan disposition and toxicity. Pharmacogenomics J 2:43–47 Jodrell DI, Egorin MJ, Canetta RM et al (1992) Relationships between carboplatin exposure and tumor response and toxicity in patients with ovarian cancer. J Clin Oncol 10:520–528 Kamb A, Wee S, Lenguar C (2007) Why is cancer drug discovery so difficult? Nature Rev Drug Discov 6:115–120 Kishi S, Griener J, Cheng C et al (2003) Homocysteine, pharmacogenetics, and neurotoxicity in children with leukemia. J Clin Oncol 21:3084–3091 Krynetski EY, Schuetz JD, Galpin AJ, Pui CH, Relling MV, Evans WE (1995) A single point mutation leading to loss of catalytic activity in human thiopurine S-methyltransferase. Proc Natl Acad Sci USA 92:949–953 Krynetski EY, Tai HL, Yates CR et al (1996) Genetic polymorphism of thiopurine S-methyltransferase: clinical importance and molecular mechanisms. Pharmacogenetics 6: 279–290

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Levy AS, Sather HN, Steinherz PG et al (2003) Reduced folate carrier and dihydrofolate reductase expression in acute lymphocytic leukemia may predict outcome: a Children’s Cancer Group Study. J Pediatr Hematol Oncol 25:688–695 Matherly LH, Taub JW, Wong SC et al (1997) Increased frequency of expression of elevated dihydrofolate reductase in T-cell versus B-precursor acute lymphoblastic leukemia in children. Blood 90:578–589 Matheson EC, Hogarth LA, Case MC, Irving JA, Hall AG (2007) DHFR and MSH3 coamplification in childhood acute lymphoblastic leukaemia, in vitro and in vivo. Carcinogenesis 28:1341–1346 Matthay KK, Villablanca JG, Seeger RC et al (1999) Treatment of high-risk neuroblastoma with intensive chemotherapy, radiotherapy, autologous bone marrow transplantation and 13-cis-retinoic acid. N Engl J Med 341:1165–1173 Mitchell C, Jones PM, Kelsey A et al (2000) The treatment of Wilms’ tumour: results of the United Kingdom Children’s Cancer Study Group (UKCCSG) second Wilms’ tumour study. Br J Cancer 83:602–608 Newell DR, Siddik ZH, Gumbrell LA et al (1987) Plasma free platinum pharmacokinetics in patients treated with high dose carboplatin. Eur J Cancer Clin Oncol 23:1399–1405 Newell DR, Pearson ADJ, Balmanno K et al (1993) Carboplatin pharmacokinetics in children: the development of a pediatric dosing formula. J Clin Oncol 11:2314–2323 Nirenberg A, Mosende C, Mehta BM, Gisolfi AL, Rosen G (1997) High-dose methotrexate with citrovorum factoe rescue: predictive value of serum methotrexate concentrations and corrective measures to avert toxicity. Cancer Treat Rep 61:779–783 Otterness DM, Szumlanski CL, Wood TC, Weinshilboum RM (1998) Human thiopurine methyltransferase pharmacogenetics. Kindred with a terminal exon splice junction mutation that results in loss of activity. J Clin Invest 101:1036–1044 Picton S, Keeble J, Holden V, et al (2009) Therapeutic monitoring of carboplatin dosing in a premature infant with retinoblastoma. Cancer Chemother Pharmacol 63:749–752 Pui CH, Evans WE (2006) Treatment of acute lymphoblastic leukemia. N Engl J Med 354:166–178 Rademaker-Lakhai JM, Crul M, Zuur L et al (2006) Relationship between cisplatin administration and the development of ototoxicity. J Clin Oncol 24:918–924 Ratain MJ (1998) Body-surface area as a basis for dosing of anti-cancer agents. J Clin Oncol 16:2297–2298 Relling MV, Hancock ML, Rivera GK et al (1999) Mercaptopurine therapy intolerance and heterozygosity at the thiopurine S-methyltransferase gene locus. J Natl Cancer Inst 91: 2001–2008 Rots MG, Willey JC, Jansen G et al (2000) mRNA expression levels of methotrexate resistancerelated proteins in childhood leukemia as determined by a standardized competitive templatebased RT-PCR method. Leukemia 14:2166–2175 Skinner R (2003) Chronic ifosfamide nephrotoxicity in children. Med Pediatr Oncol 41:190–197 Sorich MJ, Pottier N, Pei D et al (2008) In vivo response to methotrexate forecasts outcome of acute lymphoblastic leukemia and has a distinct gene expression profile. PLoS Med 5:e83 Stanulla M, Schaeffeler E, Flohr T et al (2005)Thiopurine methyltransferase (TPMT) genotype and early treatment response to mercaptopurine in childhood acute lymphoblastic leukemia. JAMA 293:1485–1489 Stewart CF, Panetta JC, O’Shaughnessy MA et al (2007) UGT1A1 promoter genotype correlates with SN-38 pharmacokinetics, but not severe toxicity in patients receiving low-dose irinotecan. J Clin Oncol 25:2594–2600 Szumlanski C, Otterness D, Her C et al (1996) Thiopurine methyltransferase pharmacogenetics: human gene cloning and characterization of a common polymorphism. DNA Cell Biol 15: 17–30 Tai HL, Krynetski EY, Yates CR et al (1996) Thiopurine S-methyltransferase deficiency: two nucleotide transitions define the most prevalent mutant allele associated with loss of catalytic activity in Caucasians. Am J Hum Genet 58:694–702

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Tai HL, Krynetski EY, Schuetz EG, Yanishevski Y, Evans WE (1997) Enhanced proteolysis of thiopurine S-methyltransferase (TPMT) encoded by mutant alleles in humans (TPMT∗ 3A, TPMT∗ 2): mechanisms for the genetic polymorphism of TPMT activity. Proc Natl Acad Sci USA 94:6444–6449 Tai HL, Fessing MY, Bonten EJ et al (1999) Enhanced proteasomal degradation of mutant human thiopurine S-methyltransferase (TPMT) in mammalian cells: mechanism for TPMT protein deficiency inherited by TPMT∗ 2, TPMT∗ 3A, TPMT∗ 3B or TPMT∗ 3C. Pharmacogenetics 9:641–650 Thomas HD, Boddy AV, English MW et al (2000) Prospective validation of renal function-based carboplatin dosing in children with cancer: a United Kingdom Children’s Cancer Study Group trial. J Clin Oncol 18:3614–3621 Ulrich CM, Yasui Y, Storb R et al (2001) Pharmacogenetics of methotrexate: toxicity among marrow transplantation patients varies with the methylenetetrahydrofolate reductase C677T polymorphism. Blood 98:231–234 van den Akker-van Marle ME, Gurwitz D, Detmar SB et al (2006) Cost-effectiveness of pharmacogenomics in clinical practice: a case study of thiopurine methyltransferase genotyping in acute lymphoblastic leukemia in Europe. Pharmacogenomics 7: 783–792. Veal GJ, Coulthard SA, Boddy AV (2003) Chemotherapy individualization. Invest New Drugs 21:149–156 Veal GJ, English MW, Grundy RG, et al (2004) Pharmacokinetically guided dosing of carboplatin in paediatric cancer patients with bilateral nephrectomy. Cancer Chemother Pharmacol 54: 295–300 Veal GJ, Errington J, Tilby MJ, et al (2007) Adaptive dosing and platinum-DNA adduct formation in children receiving high dose carboplatin for the treatment of solid tumours. Br J Cancer 96:725–731 Wang L, Pelleymounter L, Weinshilboum R, Johnson, JA. Annotated PGx Gene Information for TPMT. (2008) [cited 2009 Feb 10]; Available from: http://www.pharmgkb.org /do/serve?objId=PA356&objCls=Gene#tabview=tab1 Yule SM, Price L, McMahon AD, Pearson ADJ, Boddy AV (2004) Cyclophosphamide metabolism in children with non-Hodgkin’s lymphoma. Clin Cancer Res 10:455–460

Chapter 8

Pharmacogenetics in Palliative Care Andrew A. Somogyi

Abstract Analgesic, especially opioids, antiemetic and antidepressant drugs show marked interpatient variability in responses both efficacy and adverse effects. The cytochrome P450 2D6 poor metaboliser phenotype reduces the effects of some opioids, such as tramadol. However, in contrast, in ultrarapid metabolisers adverse effects are seen with codeine and the antidepressants, but enhanced efficacy to tropisetron. The efflux transporter p-glycoprotein located at the blood-brain barrier limits access of these drug classes to the brain; genetic polymorphisms in ABCB1 result in enhanced efficacy, but also adverse effects to many drugs used widely in palliative care. For opioids, a mu receptor polymorphism leads to reduced efficacy and for NSAIDs, CYP2C9 polymorphisms are associated with a higher risk of bleeding. Thus, a major source of interpatient variability in response to these major palliative care classes of drugs can be attributed to the patients’ genetic profiles that control their drug metabolism, transport out of the brain and target site. These might explain at the bedside why some of the drugs “don’t work” or “work too well”. Keywords Analgesics · Antidepressants · Antiemetics · Opioids · Palliative care

Contents 8.1 Overview . . . . . . . . . . . . . . . . . . . . . . 8.2 Pharmacogenetics of Analgesics . . . . . . . . . . . 8.2.1 Opioids . . . . . . . . . . . . . . . . . . . . 8.2.2 Non Steroidal Anti-Inflammatory Drugs (NSAIDs) 8.2.3 Paracetamol (Acetaminophen) . . . . . . . . . . 8.3 Pharmacogenetics of Antiemetics . . . . . . . . . . . 8.3.1 5-HT3 Receptor Antagonists . . . . . . . . . .

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115 W.G. Newman (ed.), Pharmacogenetics: Making Cancer Treatment Safer and More C Springer Science+Business Media B.V. 2010 Effective, DOI 10.1007/978-90-481-8618-1_8, 

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8.1 Overview Palliative care patients receive multiple medications for symptom and disease control, and to attenuate adverse effects from medications. However, until quite recently it was extremely difficult to conduct scientifically rigorous clinical pharmacological studies to determine the contribution of pharmacokinetic (e.g. metabolism) and pharmacodynamic (e.g. transporters and receptors) factors to interpatient variability in response and the potentially pivotal role that could be played by pharmacogenetics. Over the past 5 years, advances in population pharmacokinetics, objective measures of response and, of course, the technology to rapidly and cheaply genotype any drug metabolizing enzyme, transporter and receptor/target has allowed for greater insights into explaining why some patients respond poorly, why some experience unacceptable adverse effects necessitating drug discontinuation, and why dosage requirements vary substantially between patients. In this chapter, the role of pharmacogenetics in explaining the wide range in dosage requirements and the occurrence of adverse effects to analgesics, especially opioids, antiemetics and antidepressants will be reviewed. The pharmacogenetic reasons why a patient may respond differently to a medicine can be due to variants in genes encoding for proteins controlling the enzymatic metabolism of drugs (e.g. CYP2D6 and codeine), the transport of drugs out of their target organ (e.g. p-glycoprotein and fentanyl) and the target receptor (e.g. mu opioid receptor and morphine). It is important to appreciate that in many cases, the phenotypic response may be a combination of one or more different gene variants. In addition, not all gene variants result in a reduced drug effect. Some patients may have multiple copies of the “normal” or wildtype gene of a drug metabolizing enzyme, which, in the case of codeine results in heightened effects (see below), whereas gene variants for efflux transporters at the blood-brain barrier may result in enhanced, and not reduced, effects; these will be explained in greater detail below.

8.2 Pharmacogenetics of Analgesics 8.2.1 Opioids The mu opioid receptor is the prime target for all opioids and although there are multiple variants of its gene OPRM1, the c.118A>G SNP [note this SNP is now

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annotated as c.304A>G] in which the G variant allele has a frequency of 10–15% and results in reduced opioid effects has been the most widely studied. In healthy subjects given the same dose of alfentanil, those with one or two copies of the G variant allele showed a two-fourfold reduced effect to experimentally-induced pain (electrical stimulation) and 10-11-fold reduced respiratory depressant effect (Oertel et al. 2006). However, it is more difficult to systematically quantitate opioid responses in patients experiencing pain and variability is often assessed simply as dosage requirements. In cancer patients given morphine for chronic pain, four patients who had two copies for the G variant gene required a higher dose (225 ± 143 mg/day) compared to 78 patients with none or 17 with one copy of the variant (97 ± 89 and 66 ± 50 mg/day, respectively)(Klepstad et al. 2004). However, the small numbers of patients with both alleles variant (homozygous variant), the large interpatient variability in dosage requirements per se and the non gene-dose effect, limit clinical interpretation. In 175 cancer patients commencing morphine, after dosing for 1 week and although there was no difference in dosage requirements between the three genotype groups, those with two copies of the G variant had a significantly lower change in pain numerical rating scale (mean 0.2), compared to those with one copy (1.8) and those with no copies (3.8). When those with one or two copies of the variant allele were combined, the result was significant in that those with the G variant allele had a much lower change in pain rating (Campa et al. 2008). Thus the mu opioid receptor gene variant c.118A>G leads to a reduced opioid effect. Although not a direct target site for morphine, the COMT enzyme system that metabolises noradrenaline, adrenaline and dopamine can affect morphine dosage requirements. In 207 cancer patients, there was a significant gene-dose effect, in that those with two copies of the COMT A variant at c.474G>A required 95 ± 99 mg/day, those with one copy 117 ± 100 mg/day and those with no variant allele (G wildtype) 155 ± 160 mg/day (Rakvag et al. 2005). Although the mechanism is not completely clear, it appears that the A variant causes upregulation of the mu opioid receptor, so that endogenous opioids have a greater effect, resulting in less dosage required of exogenous opioids (e.g. morphine). The target organ of the opioids is the brain and opioids need to cross the bloodbrain barrier for the majority of their effects. P-glycoprotein is an efflux transporter found on the luminal membrane of many organs and tissues; it is located on the apical membrane of the capillary endothelial cells at the blood-brain barrier and it functions to limit access of substrate drugs to the brain. Reduced function of the transporter allows for more drug to be present in the brain, and in mice this results in enhanced antinociceptive effect. Some opioids such as fentanyl, morphine and methadone are p-glycoprotein substrates. In acute pain, a variant of the gene ABCB1, which encodes for p-glycoprotein, results in enhanced respiratory depression to a single IV dose of fentanyl (Park et al. 2007). Haplotype (multiple SNP) analysis for ABCB1 showed that dosage requirements of methadone in maintenance treatment for opioid dependence was related to the number of copies of the variant haplotype- those with two wildtype alleles required a higher dose than those with both alleles variant (Coller et al. 2006). In the study cited above for morphine by Campa and colleagues (2008), they also genotyped for ABCB1 and found that

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those with two copies of the most common ABCB1 variant allele c.3435C>T had an increased analgesic effect (increase in pain rating scale of 4.4), compared to those with one variant 3.15 and those wildtype 2.31. These findings indicate that drugs such as opioids, several of which are p-glycoprotein substrates (many have not been tested), will produce an enhanced analgesic and adverse effect to standard doses of opioids in patients with variants in ABCB1. It is important to appreciate that the combination of variants in OPRM1 and ABCB1 have opposite effects on response with the former reducing the effect and the latter enhancing the effect. Thus depending on which combination of these two gene variants a patient has will influence their overall response. For example, Campa et al. (2008) showed that the best response to morphine in their cancer patients was in individuals with a combination of wildtype OPRM1 plus ABCB1 variant (numerical pain rating scale 4.8) and the worst response was in those with a combination of OPRM1 variant and wildtype ABCB1 (1.3). In terms of metabolism of the opioids and the role of pharmacogenetics, those opioids such as codeine, tramadol and oxycodone which are O-demethylated to more potent opioid metabolites such as morphine, O-desmethyltramadol and oxymorphone by the highly polymorphic cytochrome P450 CYP2D6 enzyme show reduced effects to the parent drug in subjects with mutations in the CYP2D6 gene, resulting in the poor metaboliser (PM) phenotype. For example, CYP2D6 PMs given codeine show a substantially reduced response to cold pressor pain and reduced respiratory depression and lower psychomotor performance (see Somogyi et al. 2007). However, there have been no clinical pain studies. Similarly with oxycodone, in a small study in palliative care, the one PM patient required the highest dose of oxycodone and greatest number of breakthrough analgesic doses (Maddocks et al. 1996). Finally, for tramadol in postoperative patients following abdominal surgery, there was a significantly higher number of nonresponders in PMs (81%) compared to extensive metabolisers (EMs 17%) (Stamer et al. 2007). In addition to PMs, who comprise about 7% of the Caucasian population, about 2% of Caucasians are ultrarapid metabolisers (UMs) mainly through having multiple copies (up to 13) of the CYP2D6 gene. In such patients, enhanced adverse effects such as euphoria and dizziness have been reported due to increased conversion of codeine to morphine (Dálen et al. 1997). Finally, many of the potent opioids, which contain an hydroxyl group, such as morphine and hydromorphone, are metabolized by glucuronidation, via the enzyme UGT2B7. Although there are multiple polymorphisms in the UGT2B7 gene, these appear to have a relatively minor effect on their pharmacokinetics and no effect on the response to these drugs. The receptor ß-arrestin2 is involved in the internalization of G-protein coupled receptors such as the mu opioid receptor. In 162 patients with severe cancer pain in a palliative care unit, 39 were unable to tolerate morphine and were switched to oxycodone. Carriers of the c.8627T>C variant allele of the ß-arrestin2 gene were significantly more likely to switch from morphine to oxycodone (Ross et al. 2005) In summary, subjects with variants in the mu opioid receptor gene OPRM1 have a reduced response; those with variants in the p-glycoprotein efflux transporter gene ABCB1 an enhanced response; and those with poor metaboliser variants in the

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CYP2D6 gene result in reduced response to some opioids that produce metabolites with substantially enhanced mu opioid activity (Coller et al. 2009).

8.2.2 Non Steroidal Anti-Inflammatory Drugs (NSAIDs) The major polymorphic cytochromeP450s involved in the metabolism of NSAIDs are CYP2C9 and CYP2C8. Almost 20 NSAIDS are substrates but few studies have examined the impact of CYP2C9 genetic polymorphisms (variant allele frequencies: CYP2C9∗ 2 variant 15%; CYP2C9∗ 3 variant 5%) on their pharmacokinetics and pharmacodynamics. In brief, the systemic exposures of flurbiprofen, ibuprofen, diclofenac (minor effect), lornoxicam, piroxicam and celecoxib are increased in subjects with the CYP2C9∗ 3 variant allele, but the magnitude is less than twofold from single doses in most cases (Rollason et al. 2008). For celecoxib, subjects who were homozygous for CYP2C9∗ 3 had a sevenfold increase in drug exposure (Lundblad et al. 2006), but this has not been confirmed in independent studies (Brenner et al. 2003). For the active S-ibuprofen and piroxicam, such increased exposures resulted in greater thromboxane A2 concentrations and it is speculated that for celecoxib, CYP2C9 polymorphisms might reduce its COX-2 selectivity (Kirchheiner et al. 2003). For CYP2C8∗ 3, homozygous variant carriers exposures were on average nine-fold greater than those wildtype and fourfold higher than individuals homozygous for CYP2C9∗ 3 (Garcia-Martin et al. 2004). In terms of NSAID-induced gastrointestinal toxicity, NSAID-induced bleeding is higher in those with the CYP2C9∗ 3 variant allele (Pilotto et al. 2007). Some NSAIDs are also UGT substrates for glucuronidation. Very few studies have examined the impact of UGT polymorphisms on their pharmacokinetics; however diclofenac-induced hepatotoxicity was found to be more common in those with UGT2B7∗ 2 variant alleles compared with controls (Daly. 2007). Several studies have investigated genetic polymorphisms in the COX1 and COX2 enzymes in relation to NSAID effects. In an ex vitro platelet study, healthy subjects who were heterozygous for the c.-842A>G /50C>T haplotype had significantly greater inhibition of prostaglandin H2 formation by acetylsalicylic acid compared to the common homozygous haplotype (Halushka et al. 2003); the clinical significance of this finding is at present unclear. In patients following dental surgery and given rofecoxib, ibuprofen or placebo, those homozygous for the G allele of c.-765G>C of COX2 had a significantly lower pain intensity score from rofecoxib at 48 hours compared with those on ibuprofen, whereas those homozygous or heterozygous for the minor allele variant had a significantly higher pain intensity score compared to the wildtype (Lee et al. 2006). The mechanisms underpinning these findings and their clinical importance remain to be established.

8.2.3 Paracetamol (Acetaminophen) Paracetamol is principally glucuronidated by UGT1A6 and to a lesser extent by UGTs 1A1, 1A9 and 2B15. In subjects with a UGT1A1 promoter variant

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(UGT1A1∗ 28) causing Gilbert’s syndrome, the effect on paracetamol pharmacokinetics is inconclusive. However, in thalassemic patients with a UGT1A6 mutation, the exposure of paracetamol and its glucuronide were lower compared to controls (Tankanitlert et al. 2007); the clinical significance of which is unknown.

8.3 Pharmacogenetics of Antiemetics The major groups of drugs include the selective 5-HT3 receptor antagonists (ondansetron, tropisetron and granisetron), corticosteroids (mainly dexamethasone) and dopamine antagonists (principally, metoclopramide).

8.3.1 5-HT3 Receptor Antagonists 5-HT3 receptor antagonists are primarily eliminated by hepatic metabolism; ondansetron by CYP3A4 and to lesser extent by CYPs 1A2, 2D6 and 2E1; tropisetron by CYP2D6 and to a lesser extent by CYP3A4, and granisetron by CYP3A4. The role of genetic polymorphisms in CYP2D6 gene on the response to these drugs is particularly relevant. Ultrarapid CYP2D6 metabolisers had more chemotherapy-induced vomiting compared with extensive and poor metabolisers when treated with tropisetron and to a lesser extent with ondansetron (Kaiser et al. 2002). Similarly more postoperative vomiting was experienced in ultrarapid metabolisers when given tropisetron (Candiotti et al. 2005). This effect would be predicted as CYP2D6 is the major enzyme involved in tropisetron elimination, whereas for granisetron which is mainly metabolized by CYP3A4, there is no effect of the CYP2D6 ultrarapid metaboliser phenotype on postoperative nausea and vomiting (Janicki et al. 2006). Most of these antiemetics are also substrates of the efflux transporter pglycoprotein cited above with respect to the opioids and located at the blood-brain barrier and elsewhere. Hence, genetic polymorphisms may result in greater efficacy due to greater brain retention. Patients who were ABCB1 c.3435C>T variant heterozygous or homozygous responded better to granisetron (in particular) and tropisetron and ondansetron within the first 24 hours of chemotherapy, however delayed vomiting response was not different (Babaoglu et al. 2005). As these drugs act on the pentameric ion channel 5-HT3 receptor, of which the 5-HT3A and 5-HT3B subtypes appear to be the most important; polymorphisms in these receptors may also affect their response. In patients given prophylactic tropisetron or ondansetron during chemotherapy, those with the – AAG deletion variant in the 5-HT3B gene had more nausea and vomiting (Tremblay et al. 2003) but the same group found no affect of polymorphisms in 5-HT3A . In summary, polymorphisms in drug metabolising enzymes (CYP2D6), transporters (ABCB1) and receptor (5-HT3B ) can clinically significantly affect the response to these drugs (Ho et al. 2006).

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8.3.2 Corticosteroids Very little is known about the effect of pharmacogenetics on the response to corticosteroids, most especially dexamethasone. Nevertheless, these drugs are powerful inducers of CYPs (especially CYP3A4, but not CYP2D6) and efflux transporters such as p-glycoprotein. Therefore, they potentially might affect the response to these drugs, particularly in those who are ABCB1 wildtype and have the normal functioning p-glycoprotein (less brain concentrations and therefore lesser drug response) compared to those with variant alleles. In these, the encoded protein is less likely to be affected by gene expression induction.

8.3.3 Dopamine Antagonists For metoclopramide, there are very few pharmacogenetic studies. Interestingly, acute dystonic reactions have been reported in subjects with two nonfunctioning CYP2D6 alleles as metoclopramide is a CYP2D6 substrate (van der Padt et al. 2006).

8.4 Pharmacogenetics of Antidepressants The drugs in this class that are used in palliative care for neuropathic pain are mainly amitriptyline, nortriptyline and, less commonly, doxepin. They are metabolized by CYP enzymes and for amitriptyline and its active metabolite nortriptyline, CYP2D6 contributes about 50% to their overall metabolism. Hence, loss of function alleles result in an approximate doubling of plasma concentrations (Kirchheiner et al. 2001). The latter authors recommended a 50% dose reduction for both drugs in CYP2D6 PMs. However, it is uncertain to what extent the CYP2D6 phenotype contributes to enhanced efficacy, especially in neuropathic pain. It is generally considered that such reductions are unnecessary and have not been adopted in the psychiatric and pain-palliative care communities. However, in the case of nortriptyline at least, although occurring in about 2% of the Caucasian population, ultrarapid metabolisers with multiple copies of CYP2D6 can have plasma concentrations of only 20–50% of those with two functioning alleles and this could lead to lack of drug efficacy (resistance) (Dálen et al. 1998). There is some evidence that CYP2D6 PMs have increased side effects to antidepressant drugs (Kirchheiner et al. 2004); whether the same hold true in the neuropathic pain setting is unknown. Amitriptyline and nortriptyline are also p-glycoprotein substrates. In patients with depression treated with nortriptyline, those with the ABCB1 c.3435C>T variant allele had a higher incidence (25%) of postural hypotension than those with the wildtype genotype (0%) (Roberts et al. 2002). With respect to pharmacodynamic genetic polymorphisms for antidepressants, in general there are few polymorphisms in candidate genes affecting neurotransmitter receptors and transporters that significantly influence efficacy or side effects.

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Table 8.1. Summary of the influence of pharmacogenetics factors on the efficacy and adverse effects to the analgesic, antiemetic and antidepressant drug classes through effects on drug metabolizing enzymes, transporters and receptors Drug class

Efficacy

Adverse effects

Decrease Increase Decrease

Decrease Increase

Opioids Mu receptor P-glycoprotein CYP2D6a – poor metabolisers CYP2D6a – ultrarapid metabolisers COMT

Increase

NSAIDs CYP2C9/ CYP2C8 COX2

Increase Unclear

Antiemetics 5HT3 antagonists CYP2D6 – ultrarapid metabolisers P-glycoprotein 5HT3 receptor

Decrease Increase Decrease

Increase

Metoclopramide CYP2D6 – poor metabolisers Antidepressants CYP2D6 – poor metabolisers CYP2D6 – ultrarapid metabolisers P-glycoprotein a codeine,

Increase

Increase Increase Decrease

Increase Increase

tramadol and possibly oxycodone

In summary, there are many pharmacogenetics factors that can contribute to the efficacy and adverse effects to analgesics, antiemetics and antidepressants. Table 8.1 summarizes these in terms of drug metabolizing enzymes, transporters and receptors.

8.4.1 Case Scenario A 47 year-old male of North African origin was diagnosed with unresectable gastric cancer. His palliative chemotherapy treatment included the moderately emetogenic anthracycline, epirubicin plus docetaxel and cisplatin. Prior to this therapy, he received the 5HT3 antagonist tropisetron plus dexamethasone to limit any nausea and vomiting. However, the antiemetic therapy did not prevent these symptoms, as he experienced considerable nausea and vomiting, resulting in reduced quality of life and both dose delay and reduction. A pharmacogenetic assessment may explain the lack of efficacy and offer a solution. Trials of chemotherapy plus best supportive care versus supportive care alone demonstrate improved median survival and quality of life in patients with advanced gastric cancer in the group treated with chemotherapy. Hence, the appropriate use

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of chemotherapy in this context. Effectiveness of chemotherapy can be severely limited if it is delayed, discontinued or the dose reduced due to uncontrollable nausea and vomiting. Tropisetron is a highly effective antiemetics. Tropisetron is metabolized by the highly polymorphic CYP2D6 enzyme. In addition to CYP2D6 poor metaboliser status, which occurs at a frequency of about 7% in white subjects, ultrarapid metaboliser status occurs with a frequency of about 20% in subjects of North African origin (2% in white subjects). Hence, it is possible that the subject is an ultrarapid metaboliser of tropisetron such that standard doses do not achieve sufficiently high enough concentrations for the drug to be effective. To overcome this, an increase in its dose would be appropriate or a switch to an alternative antiemetic e.g. ondansetron, which is metabolised mainly by CYP3A4. In addition or alternatively, he may have the deletion polymorphism in the 5HT3B gene, which confers significantly reduced activity. However, as the incidence of the deletion is less than 2% (homozygous), it is less likely to be the cause of the reduced efficacy to tropisetron compared with the CYP2D6 ultrarapid metaboliser status. Acknowledgements The author’s research is supported by the National Health and Medical Research Council of Australia

References Babaoglu MO, Bayare B, Aynacioglu S, et al (2005) Association of the ABCB1 3435C>T polymorphism with antiemetic efficacy of 5-hydroxytryptamine type 3 antagonists. Clin Pharmacol Ther 78:619–626 Brenner SS, Herrlinger C, Dilger K et al (2003) Influence of age and cytochrome P450 2C9 genotype on the steady-state disposition of diclofenac and celecoxib. Clin Pharmacokinet 42:283–292 Campa D, Gioia A, Tomei A, Poli P, Barale R (2008) Association of ABCB1/MDR1 and OPRM1 gene polymorphisms with morphine pain relief. Clin Pharmacol Ther 83:559–566 Candiotti KA, Birnbach DJ, Lubarsky DA et al (2005) The impact of pharmacogenomics on postoperative nausea and vomiting: do CYP2D6 allele copy number and polymorphisms affect the success or failure of ondansetron prophylaxis. Anesthesiology 102:543–549 Coller JK, Barratt DT, Dahlen K, Loennechen MJ, Somogyi AA (2006) ABCB1 genetic variability and methadone dosage requirements in opioid-dependent individuals. Clin Pharmacol Ther 80:682–690 Coller JK, Christrup LL, Somogyi AA (2009) Role of active metabolites in the use of opioids. Eur J Clin Pharmacol 65:121–139 Dálen P, Frengell C, Dahl M-L, Sjöqvist F (1997) Quick onset of severe abdominal pain after codeine in an ultrarapid metaboliser of debrisoquine. Ther Drug Monit 19:543–544 Dálen P, Dahl M-L, Ruiz MLB, Nordon J, Bertilsson L (1998) 10-hydroxylation of nortriptyline in white persons with 0,1,2,3, and 13 functional CYP2D6 genes. Clin Pharmacol Ther 63:444–452 Daly AK, Aithal GP, Leathart JB, Swainsbury RA, Dang TS, Day CP (2007) Genetic susceptibility to diclofenac-induced hepatotoxicity: contribution of UGT2B7, CYP2C8 and ABCC2. Gastroenterology 132:272–281 García-Martín E, Martínez C, Tabarés B, Frías J, Agúndez AG (2004) Interindividual variability in ibuprofen pharmacokinetics is related to interaction of cytochrome P450 2C8 and 2C9 amino acid polymorphisms. Clin Pharmacol Ther 76:119–127 Halushka MK, Walker LP, Halushka PV (2003) Genetic variation in cyclooxygenase I: effects on response to aspirin. Clin Pharmacol Ther 73:122–130

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Ho K-Y, Gan TJ (2006) Pharmacology, pharmacogenetics, and clinical efficacy of 5hydroxytryptamine type 3 receptor antagonists for postoperative nausea and vomiting. Curr Opin Anesthesiol 19:606–611 Janicki PK, Schuler HG, Jarzembowski TM, Ross M (2006) Prevention of postoperative nausea and vomiting with granisetron and dolasetron in relation to CYP2D6 genotype. Anesth Analg 102:1127–1133 Kaiser R, Sezer O, Papies A et al (2002) Patient-tailored antiemetic treatment with 5hydroxytryptamine type 3 receptor antagonists according to cytochrome P-450 2D6 genotypes. J Clin Oncol 20:2805–2811 Kirchheiner J, Brøsen K, Dahl ML et al (2001) CYP2D6 and CYP2C19 genotype-based dose recommendations for antidepressants: a first step towards subpopulation-specific dosages. Acta Psychiatr Scand 104:173–192 Kirchheiner J, Stormer E, Meisel C, Steinbach N, Roots I, Brockmöller J (2003) Influence of CYP2C9 genetic polymorphisms on pharmacokinetics of celecoxib and its metabolites. Pharmacogenetics 13:473–480 Kirchheiner J, Nickchen K, Bauer M et al (2004) Pharmacogenetics of antidepressants and antipsychotics: the contribution of allelic variations to the phenotype of drug response. Mol Psychiatr 9:442–473 Klepstad P, Rakvåg TT, Kaasa S et al (2004) The 118 A>G polymorphism in the human µ-opioid receptor gene may increase morphine requirements in patients with pain caused by malignant disease. Acta Anesthesiol Scand 48:1232–1239 Lee Y-S, Kim H, Wu T-W, Wang X-M, Dionne RA (2006) Genetically mediated interindividual variation in analgesic response to cyclooxygenase inhibitory drugs. Clin Pharmacol Ther 79:407–418 Lundblad MS, Ohlsson S, Johansson P, Lafolie P, Eliasson E (2006) Accumulation of celecoxib with a 7-fold higher drug exposure in individuals homozygous for CYP2C9∗ 3. Clin Pharmacol Ther 79:287–288 Maddocks I, Somogyi A, Abbott F, Hayball P, Parker D (1996) Attenuation of morphine-induced delirium in palliative care by substitution with infusion of oxycodone. J Pain Sympt Manage 12:182–189 Oertel BG, Schmidt R, Schneider A, Geisslinger G, Lötsch J (2006) The µ-opioid receptor gene polymorphism 118A>G depletes alfentanil-induced analgesia and protects against respiratory depression in homozygous carriers. Pharmacogenet Genomics 16:625–36 Park H-J, Shinn HK, Ryu SH, Lee H-S, Parl C-S, Kang J-H (2007) Genetic polymorphisms in the ABCB1 gene and the effects of fentanyl in Koreans. Clin Pharmacol Ther 81:539–546 Pilotto A, Seripa D, Franceschi M et al (2007) Genetic susceptibility to nonsteroidal antiinflammatory drug-related gastroduodenal bleeding: role of cytochrome P450 2C9 polymorphisms. Gastroenterology 133:465–471 Rakvåg TT, Klepstad P, Baar C et al (2005) The Val158Met polymorphism of the human catechol-O-methyltransferae (COMT) gene may influence morphine requirements in cancer pain patients. Pain 116:73–78 Roberts RL, Joyce PR, Mulder RT, Begg EG, Kennedy MA (2002) A common P-glycoprotein polymorphism is associated with nortriptyline-induced postural hypotension in patients treated for major depression. Pharmacogenomics J 2:191–196 Rollason V, Samer C, Piguet V, Dayer P, Desmeules J (2008) Pharmacogenetics of analgesics: toward the individualization of prescription. Pharmacogenomics 9:905–933 Ross JR, Rutter D, Welsh K et al (2005) Clinical response to morphine in cancer patients and genetic variation in candidate genes. Pharmacogenomics J 5:324–336 Somogyi AA, Barratt DT, Coller JK (2007) Pharmacogenetics of opioids. Clin Pharmacol Ther 81:429–444 Stamer UM, Mussoff F, Kobilay M, Madea B, Hoeft A, Stuber F (2007) Concentrations of tramadol and O-desmethyltramadol enantiomers in different CYP2D6 genotypes. Clin Pharmacol Ther 82:41–47

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Tankanitlert J, Morales NP, Howard TA et al (2007) Effects of combined UDPglucuronosyltransferase (UG) 1A1∗ 28 and 1A6∗ 2 on paracetamol pharmacokinetics on ßthalassemia/HbE. Pharmacology 79:97–103 Tremblay PB, Kaiser R, Sezer O et al (2003) Variations in the 5-hydroxytryptamine type 3B receptor gene as predictors of the efficacy of antiemetic treatment in cancer patients. J Clin Oncol 21:2147–2155 van der Padt A, van Schaik RH, Sonneveld P (2006) Acute dystonic reactions to metoclopramide in patients carrying homozygous cytochrome P450 2D6 genetic polymorphisms. Neth J Med 64:160–162

Chapter 9

Genetic Predictors of Normal Tissue Response to Radiotherapy Catharine M.L. West, Gillian C. Barnett, Alison M. Dunning, Rebecca M. Elliott, and Neil G. Burnet

Abstract Radiogenomics is the study of the genetic variation that underlies how a cancer patient responds to radiotherapy. Almost half of all cancer patients undergo radiotherapy at some point in their treatment, many in combination with chemotherapy. It may be necessary, therefore, to consider genetic variation involved in both chemotherapy and radiotherapy toxicity, as well as in tumour response, to provide a comprehensive personalisation of cancer treatment. There is evidence for a genetic basis for individual variation in sensitivity to radiation and in the development of radiotherapy toxicity. Genes linked with the development of toxicity include ATM, XRCC1, and TFGB1. Although a number of studies have investigated associations between single nucleotide polymorphisms in selected genes and a patient’s probability of developing radiotherapy toxicity, results are inconclusive due to small sample sizes. Large studies are now underway collecting several thousands of samples. Keywords Cancer · Radiation · Radiogenomics · Toxicity

Contents 9.1 Introduction . . . . . . . . . . . . . . . . . . . . 9.2 Radiotherapy Toxicity . . . . . . . . . . . . . . . 9.3 The Genetic Basis of Individual Sensitivity to Radiation 9.4 Predicting Radiotherapy Toxicity . . . . . . . . . . 9.5 Genetic Markers for Predicting Radiotherapy Toxicity . 9.6 Radiogenomics and Its Challenges . . . . . . . . . 9.7 Summary . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . .

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127 W.G. Newman (ed.), Pharmacogenetics: Making Cancer Treatment Safer and More C Springer Science+Business Media B.V. 2010 Effective, DOI 10.1007/978-90-481-8618-1_9, 

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9.1 Introduction The role of radiotherapy in cancer management is growing, as the indications increase, and more treatments are given concurrently or sequentially with chemotherapy. Approximately 300,000 people develop cancer in the United Kingdom each year, and of this number about half will require radiotherapy at some point in their illness, with the majority being treated with curative intent. It has been estimated that ∼40% of all cancer survivors have received radiotherapy. The probability of curing a tumour increases with the radiation dose delivered, and what limits this dose is the tolerance of the normal tissues surrounding the tumour. Patients vary in their normal tissue tolerance, so that in a group of patients treated with the same curative dose, a wide range of toxicity is seen, ranging from no observable effect in a minority, through clinically insignificant changes in the majority, to significant toxicity in a few. This toxicity can be very debilitating, it may impact negatively on quality of life and, in extreme cases, is life threatening. The ability to predict a patient’s likelihood of developing toxicity has enormous potential for individualising radiation dose prescriptions, to increase tumour control and decrease the morbidity of cancer patients. Increasing our understanding of the underlying genetic basis for this individual variation will also identify new targets for protection from and amelioration of radiation toxicity.

9.2 Radiotherapy Toxicity An important aspect of radiotherapy toxicity is that it occurs in the area irradiated. This distinguishes it from the toxicities associated with chemotherapy, which are systemic. Radiotherapy toxicity is traditionally classified as acute, occurring during or shortly after, and late, occurring 6 months to many years following, irradiation. Although the mechanistic basis for this separation is no longer valid, it remains useful in clinical practice (Bentzen 2006). Acute effects occur in rapidly proliferating tissues (e.g. skin, gastrointestinal tract, haematopoietic system) and typically involve inflammation (e.g. dermatitis, mucositis, cystitis, proctitis). Radiation toxicity is dependent on the site irradiated and other examples of acute effects are hair loss and bone marrow suppression. Late effects typically occur in slowly proliferating tissues, such as kidney, heart and central nervous system. The pathogenesis includes fibrosis, atrophy and vascular damage involving respectively proliferative response of surviving fibrocytes (e.g. bowel fistulae, breast hardening), fibrocyte loss and collagen absorption (e.g. breast shrinkage) and vascular damage (e.g. skin telangiectasia, bleeding, ischaemia). Other late normal tissue side effects include infertility and second malignancies.

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9.3 The Genetic Basis of Individual Sensitivity to Radiation Individual variation in radiotherapy toxicity has been known for many decades (Burnet et al. 1998; Holthusen 1936). A number of factors are implicated in this variation, which are related to physics (radiation dose, volume and inhomogeneity/ spatial variation in dose), patients (smoking and age are thought to increase toxicity), co-morbid conditions (e.g. diabetes mellitus), treatment (chemotherapy increases radiation toxicity) and Poisson statistics (i.e. the chance that a cell in the irradiated normal tissue sustains lethal damage). For many years an inherited genetic basis underlying individual variation in radiation toxicity was not considered. The first evidence for a genetic basis of individual sensitivity to radiation came from the observation that people with the rare cancer predisposing syndrome ataxia telangiectasia were very sensitive to radiotherapy and fibroblasts cultured from skin samples were also sensitive to radiation in vitro (Taylor et al. 1975). In the 1980s, there were several reports of individuals with hypersensitive reactions to radiation and work showed that fibroblasts from skin samples from those patients were also radiosensitive in vitro (see West and Hendry 1992). By the end of the 1980s, papers were published showing that there was variation in the in vitro radiosensitivity of cells cultured from ‘normal’ individuals i.e. there was variation in sensitivity to radiation in people without any known genetic syndrome (Malaise et al. 1987). In 1996, Turesson and colleagues analysed 402 breast cancer patients who were treated with identical radiotherapy schedules and showed a substantial variation in the degree of acute and late toxicity (Turesson et al. 1996). To identify the cause of the variation, a large number of patient- and treatment-related factors were tested (e.g. age, menopausal status, haemoglobin level, smoking habits, diabetes, hypertension, influence of hormone therapy and chemotherapy, radiation dose). The analysis showed that the factors studied explained, at most, up to 20% of the variance describing the total patient-to-patient variability in toxicity. The remaining 80% of the variability was unexplained, which suggested it could be related to individual differences in radiosensitivity, determined by genetic and epigenetic variations. Evidence for the inheritability of individual radiosensitivity came at the end of the 1990s when Scott and colleagues used a chromosome damage assay to measure the radiosensitivity of first-degree relatives of 16 radiosensitive and eight ‘normal’ breast cancer survivors. Sixty-two percent of the relatives of the sensitive patients were also radiosensitive compared with only 7% of the relatives of ‘normal’ patients (Roberts et al. 1999). The importance of genetics in determining individual radiosensitivity is illustrated by the effect of rare highly penetrant homozygote gene mutations observed in heritable syndromes, such as ataxia telangiectasia, which lead to extreme radiosensitivity. The ATM (mutated in ataxia telangiectasia) gene was cloned in 1995 (Savitsky et al. 1995), and it was thought that individuals with heterozygote mutations might have an increased probability of developing radiotherapy toxicity. A number of studies have been carried out and the results are inconclusive (Table 9.1). However, these

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C.M.L. West et al. Table 9.1 Studies correlating ATM mutation status with radiotherapy toxicity

Cancer

Pts studied

Assay

Association

Reference

Various Breast Various Prostate Breast Various Breast Breast Breast Prostate Breast

23 15 5 17 80 20 46 1100 138 37 41

PTT PTT PTT DNA Sequencing PTT SSCP DHPLC SSCP DNA Sequencing DHPLC DHPLC

No No No Yes No No Yes No No Yes Yes

Appleby et al. (1997) Ramsay et al. (1998) Clarke et al. (1998) Hall et al. (1998) Shayeghi et al. (1998) Oppitz et al. (1999) Iannuzzi et al. (2002) Bremer et al. (2003) Meyer et al. (2004) Cesaretti et al. (2005) Andreassen et al. (2006b)

Pts: patients; PTT = protein truncation test; SSCP = Single-strand conformation polymorphism; DHPLC = denaturing high performance liquid chromatography

rare germline mutations are confined to individual families and are probably of little relevance when assessing the patient-to-patient variability observed in the majority of (non-syndromic) patients receiving radiotherapy. Instead, radiosensitivity is now considered to be an inherited complex polygenic trait dependent on the interaction of many genes/gene products that are implicated in a number of cell processes. The individual variation in radiosensitivity seen in non-syndromic patients is thus considered to be the result of normal variations in genes, known as polymorphisms, rather than abnormal variations, known as mutations.

9.4 Predicting Radiotherapy Toxicity Since the late 1980s there has been interest in exploring the potential of assays to measure radiosensitivity to predict a patient’s likelihood of developing toxicity. This work has involved predominately skin fibroblasts or peripheral blood lymphocytes and irradiation with a test dose of radiation in vitro. Many assays have been explored e.g. clonogenic cell survival; induction of apoptosis, DNA damage and chromosome damage. Some studies showed a relationship between measured cellular radiosensitivity and the development of radiotherapy toxicity e.g. (Burnet et al. 1992; West et al. 2001). However, results from these cellular based assays have not been reproducible (Dikomey et al. 2003; Peacock et al. 2000). Although work continues in this area, it is not possible to draw any firm conclusions regarding any cellular based test of radiosensitivity because too many poorly designed, under-powered studies have been carried out (Bentzen 2008). Interest in this area has now moved from cellular to high-throughput molecular assays, which include assessment of gene expression arrays and analysis of multiple single nucleotide polymorphisms (SNPs).

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9.5 Genetic Markers for Predicting Radiotherapy Toxicity The timescale for radiation effects on tissues ranges from fractions of a second (excitations and ionisation, free radical reactions) to minutes (DNA damage and repair) to hours (chromosome damage) to days (cell kill, mutations) to years (tissue injury, carcinogenesis). The initial deposition of energy to a tissue results in DNA damage, and genes involved in DNA damage sensing and repair have long been studied in radiobiology. More recently it became apparent that radiation initiates cytokine cascades in various cell types including inflammatory, stromal and endothelial cells – for a detailed review of the processes involved see (Bentzen 2006). In particular, there has been considerable interest in the multi-functional cytokine transforming growth factor-ß (TGFß). Therefore, genes involved in DNA damage recognition and repair as well as cytokine response, cell signalling, apoptosis and proliferation are implicated in the development of radiotherapy toxicity. Some of the genes of interest are listed in Table 9.2. Although studies have looked at mutations and microsatellite polymorphisms (Price et al. 1997), most research carried out investigating potential genetic markers to predict a patient’s likelihood of developing radiotherapy toxicity has concentrated on SNPs. All the work has involved selected candidate genes, i.e. those identified in biological pathways implicated in radiation response. A recent review (Alsner et al. 2008) provides a comprehensive summary of the studies carried out. The published candidate gene work has involved only one or a few genes and small numbers of patients; it has lacked the statistical power to draw any firm conclusions. This problem is illustrated by research from a Danish group which showed a significant correlation between SNPS occurring in TGFB1, SOD2, XRCC1, XRCC3, RAD21 and APEX in 41 breast cancer patients and a risk of radiation-induced fibrosis (Andreassen et al. 2003). However, the group’s subsequent validation study involving a further 120 breast cancer cases failed to identify the original radiation risk correlations (Andreassen et al. 2006a). Table 9.2 Some of the genes implicated in the development of radiotherapy toxicity Process

Examples of genes involved

DNA damage recognition DNA repair

MRE11A, RAD50, NBN, H2AFX, TP53BP1, BRCA1, MDC1 XRCC4, XRCC5, XRCC6, PRKDC, LIG4, RAD51, BRCA1, BRCA2, XRCC3, XRCC1, APEX, OGG1 CDKN2C, CCKND2, ATM, ATR, TP53, CHEK2, RB1, CCNE1, CCND1, CCNB1, CDK7, CDK10 TP53, BCL2, CASP3, BIRC5, RHOB, TP53INP1 TNF, IL1A, IL6, NFKB1 TGFB1, TGFB2, TGFB3, CTGF SMAD1, SMAD2, SMAD4, SMAD6, SMAD7 TGFBR1, TGFBR2

Cell cycle Apoptosis Inflammatory response Fibrotic response TGF beta signalling Extracellular matrix and collagen deposition Vascular damage response Antioxidant

VEGF, FGF2, CTGF, HSPB2, ZYX SOD1, SOD2

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9.6 Radiogenomics and Its Challenges Radiogenomics – the study of genetic variation associated with inter-patient variability in radiotherapy response – is starting to move away from a candidate gene approach to consider genome wide association studies (GWASs; Barnett et al. 2009). It is now recognised that candidate gene studies are limited: the precise functions of many of the genes identified by the Human Genome Project remain unknown and the molecular aetiology of normal tissue response following irradiation is complicated and not fully understood. However, it is only recently that GWAS have become affordable. Many thousands of samples are being collected in collaborative studies, e.g. Gene-PARE (Genetic Predictors of Adverse Radiotherapy Effects), GENEPI (GENetic pathways for the Prediction of the effects of Irradiation) and RAPPER (Radiogenomics: Assessment of Polymorphisms to Predict the Effects of Radiotherapy) (Baumann et al. 2003; Burnet et al. 2006; Ho et al. 2006; West et al. 2007). It is anticipated results should emerge from these studies over the next few years. Radiogenomics, however, faces a number of challenges related to measuring the phenotype, the unknown importance of tissue specificity and confounding factors involved in the development of toxicity. Measuring the phenotype, i.e. radiotherapy toxicity, is not straightforward. First, it is not a binary variable, e.g. cancer or not, but a continuous spectrum that is thought to be represented by a Gaussian distribution. Second, toxicity can take years to manifest and it is the late toxicity that is dose-limiting and of most interest. Toxicity data are not collected as part of routine follow-up and so, although many patients undergo radiotherapy, radiogenomic studies must involve patients enrolled in clinical trials or specific studies with late toxicity assessments. Third, there is no standardised approach for collecting toxicity data. Numerous reporting systems exist and they can be site specific and involve both objective and subjective (patient reported outcomes) criteria. Unfortunately, symptoms are not always translated into the same grade of toxicity in different systems. The Late Effects on Normal Tissues – Subjective, Objective, Management and Analytic (LENT-SOMA) system was the first attempt to produce a single comprehensive system for radiotherapy (Pavy et al. 1995). Acute drug toxicity is monitored using the National cancer Institute’s Common Toxicity Criteria (CTC). LENTSOMA items were included in the third version of CTC (CTCAEv3), which was published in 2003 (Trotti et al. 2003) to produce a single system for use in oncology. The increasing use of CTCAEv3 should be an advantage for future combined radio- and pharmacogenetic studies. Regarding tissue specificity, it has been suggested that some genes will affect overall radiosensitivity, i.e. the sensitivity of any or all tissues irradiated, and others are likely to display a high degree of tissue specificity (Alsner et al. 2008; Andreassen et al. 2002). There may not be a single genetic profile that predicts radiotherapy toxicity, but several that are tissue specific. These would be linked with specific cancer sites by virtue of the anatomical location of different tumours. The development of a radiosensitivity signature will depend on not only identifying the individual genes associated with tissue dependent/independent radiotherapy

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toxicity, but also understanding the interactions between multiple gene products and their relevance in the development of such toxicity. A further challenge for radiogenomic studies is to collect information on potential confounding factors. In particular, there is variation in radiotherapy schedules used for different cancers and between centres. Radiation dose, fraction size and the time over which it is delivered are important in the development of radiotherapy toxicity. As considerable variation in radiotherapy toxicity can be produced by these dosimetric and other factors (e.g. use of chemotherapy; smoking habits, age, ethnicity), they must be controlled as much as possible and a large amount of data must be collected in radiogenomic studies.

9.7 Summary The ultimate goal of radiogenomic studies is to develop genetic risk signatures for individualising radiation dose prescriptions. A signature could be used to stratify patients into subgroups with different probabilities of developing toxicity. Patients at both ends of the spectrum of normal tissue radiosensitivity are likely to benefit from the introduction of genetic profiling into routine radiotherapy practice (Burnet et al. 1998). Information from pharmacogenetic studies will also be relevant due to the increasing use of concurrent chemotherapy with radiation. It may be necessary to consider genetic variation involved in both chemotherapy and radiotherapy toxicity, as well as in tumour response (Tu et al. 2008), to provide a comprehensive personalisation of cancer treatment.

References Alsner J, Andreassen CN, Overgaard J (2008) Genetic markers for prediction of normal tissue toxicity after radiotherapy. Semin Radiat Oncol 18:126–135 Andreassen CN, Alsner J, Overgaard J (2002) Does variability in normal tissue reactions after radiotherapy have a genetic basis – where and how to look for it? Radiother Oncol 64:131–140 Andreassen CN, Alsner J, Overgaard M, Overgaard J (2003) Prediction of normal tissue radiosensitivity from polymorphisms in candidate genes. Radiother Oncol 69:127–135 Andreassen CN, Alsner J, Overgaard M, Sorensen FB, Overgaard J (2006a) Risk of radiationinduced subcutaneous fibrosis in relation to single nucleotide polymorphisms in TGFB1, SOD2, XRCC1, XRCC3, APEX and ATM – a study based on DNA from formalin fixed paraffin embedded tissue samples. Int J Radiat Biol 82:577–586 Andreassen CN, Overgaard J, Alsner J et al (2006b) ATM sequence variants and risk of radiationinduced subcutaneous fibrosis after postmastectomy radiotherapy. Int J Radiat Oncol Biol Phys 64:776–783 Appleby JM, Barber JB, Levine E et al (1997) Absence of mutations in the ATM gene in breast cancer patients with severe responses to radiotherapy. Br J Cancer 76:1546–1549 Barnett GC, West CM, Dunning AM et al (2009) Normal tissue reactions to radiotherapy: towards tailoring treatment dose by genotype. Nat Rev Cancer 9:134–142 Baumann M, Holscher T, Begg AC (2003) Towards genetic prediction of radiation responses: ESTRO’s GENEPI project. Radiother Oncol 69:121–125

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Bentzen SM (2006) Preventing or reducing late side effects of radiation therapy: radiobiology meets molecular pathology. Nat Rev Cancer 6:702–713 Bentzen SM (2008) From cellular to high-throughput predictive assays in radiation oncology: challenges and opportunities. Semin Radiat Oncol 18:75–88 Bremer M, Klopper K, Yamini P et al (2003) Clinical radiosensitivity in breast cancer patients carrying pathogenic ATM gene mutations: no observation of increased radiation-induced acute or late effects. Radiother Oncol 69:155–160 Burnet NG, Nyman J, Turesson I et al (1992) Prediction of normal-tissue tolerance to radiotherapy from in-vitro cellular radiation sensitivity. Lancet 339:1570–1571 Burnet NG, Johansen J, Turesson I, Nyman J, Peacock JH (1998) Describing patients’ normal tissue reactions: concerning the possibility of individualising radiotherapy dose prescriptions based on potential predictive assays of normal tissue radiosensitivity. Steering committee of the BioMed2 European Union concerted action programme on the development of predictive tests of normal tissue response to radiation therapy. Int J Cancer 79:606–613 Burnet NG, Elliott RM, Dunning A, West CM (2006) Radiosensitivity, radiogenomics and RAPPER. Clin Oncol (R Coll Radiol) 18:525–528 Cesaretti JA, Stock RG, Lehrer S et al (2005) ATM sequence variants are predictive of adverse radiotherapy response among patients treated for prostate cancer. Int J Radiat Oncol Biol Phys 61:196–202 Clarke RA, Goozee GR, Birrell G et al (1998) Absence of ATM truncations in patients with severe acute radiation reactions. Int J Radiat Oncol Biol Phys 41:1021–1027 Dikomey E, Borgmann K, Peacock J, Jung H (2003) Why recent studies relating normal tissue response to individual radiosensitivity might have failed and how new studies should be performed. Int J Radiat Oncol Biol Phys 56:1194–1200 Hall EJ, Schiff PB, Hanks GE et al (1998) A preliminary report: frequency of A-T heterozygotes among prostate cancer patients with severe late responses to radiation therapy. Cancer J Sci Am 4:385–389 Ho AY, Atencio DP, Peters S et al (2006) Genetic predictors of adverse radiotherapy effects: the Gene-PARE project. Int J Radiat Oncol Biol Phys 65:646–655 Holthusen H (1936) Erfahrungen uber die vertraglichkeitsgrenze fur rontgenstrahlen and deren nutzanwendung zur verhutung von schaden. Strahlentherapie 57:254–269 Iannuzzi CM, Atencio DP, Green S, Stock RG, Rosenstein BS (2002) ATM mutations in female breast cancer patients predict for an increase in radiation-induced late effects. Int J Radiat Oncol Biol Phys 52:606–613 Malaise EP, Fertil B, Deschavanne PJ, Chavaudra N, Brock WA (1987) Initial slope of radiation survival curves is characteristic of the origin of primary and established cultures of human tumor cells and fibroblasts. Radiat Res 111:319–333 Meyer A, John E, Dork T et al (2004) Breast cancer in female carriers of ATM gene alterations: outcome of adjuvant radiotherapy. Radiother Oncol 72:319–323 Oppitz U, Bernthaler U, Schindler D, et al (1999) Sequence analysis of the ATM gene in 20 patients with RTOG grade 3 or 4 acute and/or late tissue radiation side effects. Int J Radiat Oncol Biol Phys 44:981–988 Pavy JJ, Denekamp J, Letschert J et al (1995) EORTC Late Effects Working Group. Late Effects toxicity scoring: the SOMA scale. Int J Radiat Oncol Biol Phys 31:1043–1047 Peacock J, Ashton A, Bliss J, et al (2000) Cellular radiosensitivity and complication risk after curative radiotherapy. Radiother Oncol 55:173–178 Price EA, Bourne SL, Radbourne R et al (1997) Rare microsatellite polymorphisms in the DNA repair genes XRCC1, XRCC3 and XRCC5 associated with cancer in patients of varying radiosensitivity. Somat Cell Mol Genet 23:237–247 Ramsay J, Birrell G, Lavin M (1998) Testing for mutations of the ataxia telangiectasia gene in radiosensitive breast cancer patients. Radiother Oncol 47:125–128 Roberts SA, Spreadborough AR, Bulman B et al (1999) Heritability of cellular radiosensitivity: a marker of low-penetrance predisposition genes in breast cancer? Am J Hum Genet 65:784–794

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Savitsky K, Sfez S, Tagle DA et al (1995) The complete sequence of the coding region of the ATM gene reveals similarity to cell cycle regulators in different species. Hum Mol Genet 4: 2025–2032 Shayeghi M, Seal S, Regan J, et al (1998) Heterozygosity for mutations in the ataxia telangiectasia gene is not a major cause of radiotherapy complications in breast cancer patients. Br J Cancer 78:922–927 Taylor AM, Harnden DG, Arlett CF et al (1975) Ataxia telangiectasia: a human mutation with abnormal radiation sensitivity. Nature 258:427–429 Trotti A, Colevas AD, Setser A et al (2003) CTCAE v3.0: development of a comprehensive grading system for the adverse effects of cancer treatment. Semin Radiat Oncol 13:176–181 Tu HF, Chen HW, Kao SY et al (2008) MDM2 SNP 309 and p53 codon 72 polymorphisms are associated with the outcome of oral carcinoma patients receiving postoperative irradiation. Radiother Oncol 87:243–252 Turesson I, Nyman J, Holmberg E, Oden A (1996) Prognostic factors for acute and late skin reactions in radiotherapy patients. Int J Radiat Oncol Biol Phys 36:1065–1075 West CM, Hendry JH (1992) Intrinsic radiosensitivity as a predictor of patient response to radiotherapy. BJR Suppl 24:146–152 West CM, Davidson SE, Elyan SA et al (2001) Lymphocyte radiosensitivity is a significant prognostic factor for morbidity in carcinoma of the cervix. Int J Radiat Oncol Biol Phys 51:10–15 West CM, Elliott RM, Burnet NG (2007) The genomics revolution and radiotherapy. Clin Oncol 19:470–480

Chapter 10

Cancer Pharmacogenetics in Industry Mireille Cantarini

Abstract The pharmaceutical industry is responsible for the identification of potential targets for drug development and the testing of molecules through both preclinical and clinical trials up to a point where they are registered and marketed to healthcare providers. Beyond this, there is continual monitoring of the drugs as they are used in the clinic up to the time of eventual product withdrawal. The potential advantages of the application of pharmacogenetics is becoming increasingly recognised throughout all these life cycle stages, particularly in oncology. In addition to the pharmaceutical industry using pharmacogenetics to improve drug development, there is an increasing requirement for pharmacogenetic data by the Regulatory Authorities, who control the registration of new drugs, and awareness from the payors who control reimbursement of innovative, but expensive, new treatments. This chapter will explore the application and impact of pharmacogenetics on the pharmaceutical industry. Keywords Clinical trials · Drug discovery · Pharmaceutical industry · Regulatory control · Targeted treatment

Contents 10.1 Introduction . . . . . . . . . . . . . . . 10.2 Pre-clinical Screening . . . . . . . . . . 10.3 Pharmacogenetic Selection in Clinical Trials 10.4 Post-registration Use of Pharmacogenetics . 10.5 Targeted Therapies . . . . . . . . . . . . 10.6 Regulatory Control . . . . . . . . . . . . 10.7 Future Directions . . . . . . . . . . . . . 10.8 Concluding Remarks . . . . . . . . . . . References . . . . . . . . . . . . . . . . . .

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10.1 Introduction It takes on average between eight and 12 years to develop a drug from first bench top chemical synthesis to final marketed product (Dickson and Gagnon 2004). This includes initial pre-clinical assessment; phase I, II, and III clinical trials; submission of the regulatory package, and final reimbursement negotiations. The associated costs of this research and development to bring a new drug to the market for its first clinical indication have been estimated to be in the region of $500–$800 millions (DiMasi et al. 2003). Over the years, the timescale and costs of drug development have been escalating in part as a consequence of demands from the Regulatory Authorities for the provision of more comprehensive pre-registration evidence, and the requirement for detailed health economic data to support reimbursement once marketing authorisation has been gained. In order to fund continued innovation and development, the pharmaceutical industry must achieve return on its investment by the sales of approved drugs. The drug registration and reimbursement process can be supported by the application of pharmacogenetics at multiple stages throughout the drug development process (Fig. 10.1) to support the administration of the new drug to the correct patients, with the minimum level of adverse events, or an acceptable benefit: risk relationship. This is especially relevant in the case of drugs used in oncology.

Fig. 10.1 Diagram of drug pipeline – illustrating the relevance of pharmacogenetics at different points in the drug development process

10.2 Pre-clinical Screening Historically, the application of pharmacogenetic knowledge in drug development was focussed upon issues relating to pharmacokinetics i.e. ADME (absorption, distribution, metabolism and elimination), as discussed in Chapter 2 of this book. It is estimated that at least 40% of all drugs in use today are primarily metabolised by CYP3A4. The development a new drug that was also a significant inducer or inhibitor of CYP3A4 would result in a wide range of potential drug-drug interactions. As a result, the product label for this drug would require extensive warnings about potential drug interactions and the necessary precautions to be taken. For example, for drug which induced CYP3A4, warning would need to be given to women of child-bearing potential that use of the oral contraceptive pill alone may not provide full protection against pregnancy and that additional barrier methods

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should be employed (reduction in efficacy). A drug that was a significant inhibitor of CYP3A4 would result in increased exposures of any concomitant medications that were substrates of CYP3A4, leading to an increase exposure related adverse effects. In the oncology field, where patients frequently are older, polypharmacy is common due to multiple therapies; to manage the symptoms of their cancer, to manage toxicities relating to treatment of the cancer, and to manage other chronic medical conditions more common with increased age. An increase in exposure to a cytotoxic drug could in the most extreme cases result in a fatal outcome, since traditionally many oncology drugs are administered at the maximum tolerated dose. Therefore, in modern drug development, it is essential to screen new compounds for CYP450 liability at a very early stage, and reject those with unfavourable profiles if an alternative candidate drug with no effect on CYP3A4 is available. In addition, all drugs are screened at an early stage in preclinical development for liability to prolongation of the QT interval on the electrocardiogram, which results in an increased propensity to develop a ventricular tachyarrhythmia called Torsades de Pointes (Fermini and Fossa 2003). Pharmaceutical companies have the technology to screen hundreds of thousands of compounds each day from their combinatorial chemistry-derived libraries in the process of identifying suitable candidate compounds for further investigation. The automated high throughput screening process includes in vitro assays for CYP450 activity and QT prolongation to enable the rapid exclusion of problematic compounds. Once a compound has cleared this basic level of screening, it will then progress to more complex in vitro and in vivo testing aimed at identifying the major routes of metabolism. As part of this later stage of screening, for example, the use of immortalised cell lines expressing selected CYP450 enzymes can test a limited number of enzymes for quantification of induction and inhibition in each experiment. Subsequent, in vivo testing, such as animal and human hepatocyte preparations, are focussed at identifying the major routes of metabolism of the compound. This work is then supplemented by the administration of radiolabelled compound to animal species and later in clinical development to humans, followed by collection and identification of all radiolabelled metabolite compounds to verify the major routes of metabolism. These tests confirm the major routes of metabolism and excretion and identify if specific drug-drug interaction studies will be required in clinical pharmacology trials prior to registration. Not only does the pharmaceutical industry need to understand the impact of route of metabolism of its drugs, it also needs to relate this information to the target patient population in terms of germline variability in the expression of isoforms of metabolising enzymes. All major pharmaceutical companies would like to simultaneously release a new drug globally. The registration processes, however, may be staggered as a consequence of regulatory requirements in some territories for relevant information for particular ethnic groups, in part due to the geographical variation in the expression of enzyme isoforms. For example, 5–10% of Northern European Caucasians completely lack CYP2D6 activity, whereas 10% of Spanish and Turkish patients have at least duplication of the gene resulting in an ultra-rapid metaboliser status

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(Bradford 2002). If the major metabolising enzymes are significantly polymorphic, the potential implications for these subsets of patients should be documented and preferably quantified with the appropriate posology information inserted into the Product Information in each individual country in which it is licensed, in order to manage the risk of adverse effects of increased systemic exposures or reduced efficacy.

10.3 Pharmacogenetic Selection in Clinical Trials Clinical trials have traditionally been designed to determine the efficacy of a new agent or regimen compared to the accepted standard practice. This has often been done completely independently of any biomarker correlative studies. However, increasingly, both in academic and pharmaceutical company lead trials, biological samples are being collected from patients to facilitate companion pharmacogenetic studies. These studies can potentially define subgroups in whom the drug is more or less effective or in whom different drug combinations or doses may be preferable. This has lead to a natural development where selection into trials using specific biomarkers is becoming more commonly practised (Simon 2008), when there is prior evidence that a particular patient sub-population may benefit. The cost of the “enriched” trial may be decreased by such pre-selection as fewer subjects unlikely to respond are recruited. This may also increase the likelihood of a positive outcome in the trial and drug viability. Testing in a smaller selected sub-population may, however, potentially restrict the potential maximum size of the eventual market. A positive example of such selective trial designs relate to the adjuvant trials of trastuzumab for patients with HER2 positive breast cancer (discussed in Chapter 4). There are currently a number of examples of drugs where a robust diagnostic test to determine drug response has only been identified post-drug registration (e.g. see Chapter 5 regarding KRAS testing and cetuximab) (Roses 2008). Increasingly, positively enriched trials will lead to the requirement for companion pharmacogenetic diagnostics to be available at the time of registration and included in the product label. These companion diagnostics will be delivered by a combination of pharmaceutical companies, healthcare laboratories and biotech diagnostic companies e.g. DxS, who have approved tests for panels of EGFR and KRAS mutations (Little 2003; Webster et al. 2004).

10.4 Post-registration Use of Pharmacogenetics Even after registration, emerging data on pharmacogenetic variability affecting drug efficacy may lead to changes in the label. Warfarin is widely used in oncology patients for management of thromboembolic complications of the disease, but has historically provided a challenge in identifying the optimal dose for each patient. In 2007, the FDA announced the approval of revised labelling for warfarin to reflect

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new pharmacogenetic data. Approximately 35% of the US population have either the CYP2C9∗ 2 or CYP2C9∗ 3 variants resulting in slower metabolism of warfarin (and thus higher exposures for the same dose) (Wadelius and Pirmohamed 2007). In addition, variation in the target gene VKORC1 also contributes to the variability in doses required to achieve adequate anticoagulation. The practical implications of this information being that by screening the patients for VKORC1 and CYP2C9 variants in a clinical algorithm the treating physician may be able to improve the initial estimate for the starting dose (The International Warfarin Pharmacogenetics Consortium 2009). Any such label change would need to be supported by appropriately powered clinical trials, which would add costs to life cycle management for the manufacturing pharmaceutical company. However, in the case of warfarin, the new pharmacogenetic information may make the use of warfarin by the clinician more effective by reducing the effort and resources needed to titrate the starting dose over a period of months as in current clinical practice. It has been estimated that the use of pharmacogenetic testing as routine in warfarin therapy in the US alone could lead to the avoidance of 85,000 serious haemorrhagic events and 17,000 strokes, with a reduced healthcare spending of US $1.1 billion annually (Reynolds et al. 2007).

10.5 Targeted Therapies The application of pharmacogenetics within oncology has been broadened more recently to targeting drugs that act upon specific genetic targets. Somatic genetic alterations occurring within the cell may lead to over proliferation, a change in adhesion control (metastases) and reduction in apoptosis (increase in cell survival) mechanisms, all of which contribute to tumorigenesis. A treatment which is targeted specifically at abnormal somatic gene products may in theory be less selective for normal (wild-type) tissues and hence result in lower toxicity than experienced by patients receiving the classical cytotoxic chemotherapies. The generation of a drug that only affected cells containing an abnormal genetic marker, with low offtarget activity would produce a more attractive drug for both the patient and the clinician. The patient would not experience side effects such as alopecia, severe nausea or bone marrow suppression. The clinician would see a reduction in the level of therapeutic interventions require to manage severe toxicities, such as intensive inpatient therapy with intravenous antibiotics during a febrile neutropaenic episode. The reduction in costs relating to day-to-day management of the patients, inpatient versus day case costs, and the improvement of quality of life for patients receiving these less toxic treatments could be used as evidence to partially offset against the higher development costs of these novel agents. Collection of Quality of Life, Patient Reported Outcome and other pharmacoeconomic data is now an essential component of any drug registration package produced by a pharmaceutical company. Further details of the economic impact of pharmacogenetics in cancer treatment are provided in Chapter 12 of this book.

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The literature on potential genetic targets for the treatment of cancer has been expanding exponentially over the last few years. The pharmaceutical industry has the bioinformatic computing software capability to perform wide scale data mining exercises across multiple information databases in order to determine the strength of evidence for a particular tumour target, or to discover novel structural components in the cancer cell or novel mechanisms/pathways by which cancer could be targeted. Targets selected for recent drug development include products of gene amplification and subsequent overexpression, as described in the HER2 example in breast cancer in Chapter 4 and EGFR in NSCLC detailed in Chapter 6. Research has also centred upon developing drugs to block the activated pathways further downstream of the key molecular change. By unravelling the cell signalling transduction pathways it is now possible to develop drugs targeted at enzymes at numerous stages from the initial transmembrane receptor complexes, via the signal amplification enzymes to transcription factors within the cell or nucleus which result in oncogenic development. Literature mining will provide evidence of relationships between gene mutations over expressed in particular tumours and more importantly any link to prognosis of these tumours. Knowledge of suitable pathway bottleneck molecular targets of oncogenesis can be incorporated into the same type of in vitro screening processes as described earlier in this chapter to identify compound with appropriate selective activity. Subsequent pre-clinical testing can then be focussed upon tumour models most appropriate to the intended clinical indication, using cell lines derived from a particular tumour, or human tumour cell lines xenografted onto mice to investigate the effect of the drug on a tumour mass rather than individual cells. Use of these novel signal transduction pathway agents has also been investigated in patients with tumours linked to inherited conditions such as tuberous sclerosis (Bissler et al. 2008) and neurofibromatosis type 1 (Gudena et al. 2008), where germline mutations resulting in loss of tumour suppressor gene activity result in over-activation of the signalling cascades.

10.6 Regulatory Control There has been an increasing awareness of the application of pharmacogenetics by regulatory authorities in recent years (see FDA Science and Mission at Risk report). A pharmaceutical company is only allowed to actively promote sales of a drug for its approved indication(s). Heavy fines are levied should promotional material, identified from any source such as journal advertisements, materials distributed by sales representatives or presentations at international conferences appear to recommend that the use of a drug in a non-licensed indication is endorsed by the company. Once a drug is licensed for sale, it is very difficult to monitor the off-label prescription of an agent by the treating physician, although this frequently occurs in oncology practice. For example, once a drug receives an indication for third-line therapy in NSCLC, an oncologist who sees a good clinical response in this setting may start to treat second- or first-line lung cancer patients with the same agent, or start to use the drug for adjuvant therapy in other tumours. Once a drug is used off-label in an alternative indication there is no guarantee that the risk-benefit profile of the drug

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will remain the same. Therefore, the manufacturer needs to make every effort to clearly define the patient population for which the therapy is appropriate. Methods for controlling the off-label prescription could be to include use of specific diagnostics as part of the prescription process, such as HercepTestTM for trastuzumab. Once a drug receives the initial marketing approval in an oncology indication, a pharmaceutical company will routinely extend research into additional therapeutic indications such as earlier line treatment, adjuvant therapy and alternative cancers in order to provide the clinician with the appropriate safety and efficacy information in the wider patient population. Further research post-registration may also identify subsets of patients for which the drug is more or less efficacious and/or toxic. A recent example of this being the publication of data identifying resistance to treatment with cetuximab in colorectal cancer patients with KRAS mutation positive tumours (see Chapter 5). Such new evidence may ultimately result in a retrospective change to the product label, limiting the patient population to only patients with specific tumour markers. The justification for such retrospective label changes would be to improve the selection of patients to only those most likely to gain benefit and avoid costly and potentially toxic administration of this treatment to patients unlikely to gain any clinical benefit, and as such may be used to support premium pricing of the agent. The challenge for the pharmaceutical industry will to be to identify these patient groups earlier in the drug development process.

10.7 Future Directions Although pharmacogenetics has focussed on drug response secondary to specific somatic or germline mutations, the scope of research is now expanding to the impact of epigenetic mechanisms including gene methylation. The use of technologies such as siRNA to silence gene expression is a field for current pharmaceutical research and determining patients more or less likely to benefit from these potentially expensive therapies will be a formidable challenge. The pharmaceutical industry is continually seeking novel, selective targets that could potentially treat cancer, in order to develop new approaches to improve the outcomes for patients with these diseases. Furthermore, large industry-academic collaborations to determine the causes of rare serious adverse reactions are being established (Holden 2007). Such studies may go some way in enabling the development of prediction and risk strategies to reduce the likelihood of such devastating events.

10.8 Concluding Remarks The scope of activities covered by major pharmaceutical companies range from the identification of novel targets; identification of novel molecules that hit these targets in preclinical tests; testing the novel molecules in the clinic; successfully

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submitting a registration package; achieving financial reimbursement for sales; continuous responsibility for monitoring the safety, and tolerability profile in all patients treated right up to the time of eventual drug withdrawal. Although historically drug development worked on a “one dose fits all” principle, the awareness of differential responses among sub-populations has lead to the search for means of identifying the patients most likely to benefit, and the creating of the most appropriate dosing information to protect against predictable drug-drug interactions. With respect to these concerns the application of pharmacogenetics can determine the optimum advisory labelling for a novel drug. Currently, some form of pharmacogenetic information is included in about 10% of drug labels (Frueh et al. 2008), but it is anticipated that this will increase significantly over the next few years. Moving forwards the application of knowledge of pharmacogenetics is being used to select highly disease-specific targets, and once candidate drugs have been identified the application of pharmacogenetics knowledge can be used to exclude those compounds with a high liability for drug-drug interactions. In summary, pharmacogenetics is being applied by the pharmaceutical industry across all stages of drug development and life cycle management in order to optimise the benefit to risk balance associated with the administration of any drug to a heterogeneous patient population.

References Bissler JJ, McCormack FX, Young LR et al (2008) Sirolimus for angiomyolipoma in tuberous sclerosis complex or lymphangioleiomyomatosis. N Engl J Med 358:140–151 Bradford LD (2002) CYP2D6 allele frequency in European Caucasians, Asians, Africans and their descendants. Pharmacogenomics 3:229–243 Dickson M, Gagnon JP (2004) The cost of new drug discovery and development. Discovery Medicine 4:172–179 DiMasi JA, Hansen RW, Grabowski HG (2003) The price of innovation: new estimates of drug development costs. J Health Econ 22:151–185 FDA Science and Mission at Risk (2007) http://www.fda.gov/ohrms/dockets/ac/07/briefing/20074329b_02_01_FDA report on science and Technology.pdf (accessed 4 August 2009) Fermini B, Fossa AA (2003) The impact of drug-induced QT interval prolongation on drug discovery and development. Nat Rev Drug Discov 2:439–447 Frueh FW, Amur S, Mummaneni P et al (2008) Pharmacogenomic biomarker information in drug labels approved by the United States Food and Drug Administration: prevalence of related drug use. Pharmacotherapy 28:992–998 Gudena V, Verma N, Post G, Kizziah M, Fenning R, Montero AJ (2008) Metastatic chest wall malignant schwannoma responding to sorafenib. Cancer Biol Ther 7:810–813 Holden A (2007) The innovative use of a large-scale industry biomedical consortium to research the genetic basis of drug induced serious adverse events. Drug Discovery Today: Technologies 4:75–87 Little S (2003) DxS Ltd. Pharmacogenomics 4:97–101 Reynolds KK, Valdes R, Hartung BR, Linder MW (2007) Individualizing warfarin therapy. Personalized Med 4:11–31 Roses AD (2008) Pharmacogenetics in drug discovery and development: a translational perspective. Nat Rev Drug Discov 7:807–817

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Simon R (2008) The use of genomics in clinical trial design. Clin Cancer Res 14:5984–5993 The International Warfarin Pharmacogenetics Consortium (2009) Estimation of the Warfarin Dose with Clinical and Pharmacogenetic Data. N Engl J Med 360:753–764 Wadelius M, Pirmohamed M (2007) Pharmacogenetics of warfarin: current status and future challenges. Pharmacogenomics J 7:99–111 Webster A, Martin P, Lewis G, Smart A (2004) Integrating pharmacogenetics into society: in search of a model. Nat Rev Genet 5:663–669

Chapter 11

Ethical Issues in Pharmacogenetics Tara Clancy

Abstract Pharmacogenetics has been heralded as a new discipline that can significantly improve drug efficacy and limit toxicity. This would advocate its rapid adoption into clinical practice. Closer examination suggests that pharmacogenetics has some limitations and that there are ethical considerations that must be explored before it is routinely applied. Clinical genetic testing has been in place for over three decades and over this time attitudes have developed about the implementation of this information. The overlap between pharmacogenetics and genetic testing and the use of information and protection of individual rights will be considered. Keywords Attitudes · Confidentiality · Consent · Ethics · Ethnicity · Race

Contents 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . 11.2 Privacy and Confidentiality . . . . . . . . . . . . . . . 11.3 Informed Consent . . . . . . . . . . . . . . . . . . . 11.4 The Therapeutic Misconception . . . . . . . . . . . . . 11.5 Pharmacogenetics and Databases . . . . . . . . . . . . 11.6 Returning Results to Research Participants . . . . . . . . 11.7 Public and Professional Attitudes, Education and Resistance 11.8 Clinical Usefulness . . . . . . . . . . . . . . . . . . . 11.9 Clinicians’ Obligations . . . . . . . . . . . . . . . . . 11.10 Pharmacogenetics and Race/Ethnicity . . . . . . . . . . 11.11 Concluding Remarks . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . .

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T. Clancy (B) Regional Genetic Service and Academic Unit of Medical Genetics, St Mary’s Hospital, University of Manchester, Manchester M13 9WL, UK e-mail: [email protected]

147 W.G. Newman (ed.), Pharmacogenetics: Making Cancer Treatment Safer and More C Springer Science+Business Media B.V. 2010 Effective, DOI 10.1007/978-90-481-8618-1_11, 

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11.1 Introduction Pharmacogenetics investigates the extent to which genetic variation underlies the differences between individuals in their response to medication, and why some people suffer more side effects or adverse reactions than others. The concept of pharmacogenetics dates back over 50 years (Motulsky 1957). However; clinicalacademic, industrial and governmental interests have begun to focus on it over the last decade. (Roses 2000). Alongside this, concerns have been expressed about the socio-ethical implications of pharmacogenetics (Buchanan et al. 2002). A number of commentators from an industrial perspective have focused on pharmacogenetics as ‘simply’ being tests to predict the efficacy of treatment and how patients will respond to medicine (Pfost et al. 2000). This partly relates to concerns about public opinion and the extent to which pharmacogenetics will be acceptable to the public.1 Arledge et al. (2000) describe one of the primary goals of the Genetics Directorate at Glaxo Wellcome as the development of the right medication for the right patient . . . to maximise efficacy and minimize the risk of adverse events.

They differentiate between pharmacogenetics to predict response to medicine and other types of genetic testing, arguing that these have different ethical, legal and social implications, because the former provide minimal (if any) information about the patient and his/her condition. Arledge et al. go on to suggest that pharmacogenetic tests to predict response to medicine are similar to, for example, liver function tests used to determine appropriate treatment. So, why is there a need to specifically address the ethical issues in pharmacogenetics? Relling and Hoffman (2007) contend that some pharmacogenetic testing should be incorporated into the drug approval process; they argue that germline DNA should be collected from all trial participants because of the need for replication studies and the difficulty of prospective pharmacogenetic case-control studies. They claim it would be an unethical waste of resources to have to conduct new studies if these could have been answered retrospectively had DNA been stored. They also propose that, in order to minimise adverse events and to maximise efficacy, participants should be genotyped for inactivating polymorphisms if the drug in question or its metabolites are substrates for those gene products. This means that, in the medium- to long-term, pharmacogenetics is likely to lead to the more widespread use of genetic testing in the population. The use of information obtained in pharmacogenetics raises questions related to informed consent, privacy and confidentiality, who has access to the information, and feedback to participants (Robertson 2001). Other issues to consider include: the impact on vulnerable and/or marginalised groups; rare conditions and orphan drugs; and, professional training and clinicians’ obligations.

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See Hedgecoe (2006, pp. 568–569) for a detailed discussion.

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11.2 Privacy and Confidentiality The concepts of privacy and confidentiality raise issues about the limits an individual can place on the use of his/her information. Research has consistently found that most individuals pass genetic information on to all their first degree and many of their more distant relatives (Hughes et al. 2002; Claes et al. 2003). Rather than failing to understand the necessity to disclose genetic information, it seems that individuals more often struggle with the questions of who to disclose the information to, and when and how to do this (Forrest et al. 2003). It is unlikely that patients would refuse to disclose pharmacogenetic information that has serious implications for their relatives, but they may find it difficult to know how to do this. On the other hand, individuals may decline pharmacogenetic testing for fear that third parties outside the family could access the results. Although patients would like better coordination and sharing of their health information, they are often concerned about the disclosure of information to third parties such as employers and insurance companies (Ling and Raven 2006).

11.3 Informed Consent Medical information should not be gathered about people without their consent. However, informed consent can be a problematic concept when thinking about how much information a particular individual needs and wants in order to accept or decline a particular intervention such as a pharmacogenetic test. In pharmacogenetic studies, participants may be asked to consent to three different research studies. Firstly, to the clinical trial; secondly, to a genetic test related to the effect of the drug; and thirdly to unspecified tests in future research (Arledge et al. 2000). Given the limitations to consent being informed prior to participation in clinical trials, patients may not be aware of the risks and benefits of this broad and open consent (Corrigan 2003). Consent may be given (or sought) in various ways: as a one-off blanket consent at the initial stages of research; as presumed consent; as further consent when ‘new’ research is proposed; or, as authorised consent. The UK’s Human Genetics Commission (2002) takes the view that broad consent is acceptable if the aims of the research are described in some detail at the outset. This position was adopted because of concerns expressed by researchers and some patient groups about the feasibility of re-contacting individuals involved in large-scale studies. The main advantage of blanket consent is that it would reduce the need for researchers to have to re-contact participants for further consent, and would reduce the burden on participants in terms of their commitment, thus simplifying the research process. But, in many circumstances consent cannot be both broad and informed because blanket consent is necessarily unspecific (Caulfield et al. 2003). Presumed consent refers to consent given by society, or on behalf of a community, and usually means individuals need to opt-out actively if they do not wish to

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participate (Gulcher and Stefansson 2000). This approach is supported by the argument that the information that will be generated by research is important, and that requiring individual consent would limit its effectiveness. But, community consent is an ambiguous concept, and definitions range from informing the public about what is proposed to engaging with the public so they influence the process (Austin et al. 2003). Even more importantly, opting out does not protect the interests of the most vulnerable individuals (Greely 2000). Seeking consent for research that was unforeseen when the initial consent was obtained is intuitively appealing, but gaining this consent may be either impossible or impractical. Under the authorisation model of consent, individuals can agree to their data being processed and used, and can specify in advance the uses for which they do, and do not, wish to be approached for consent in the future (Upshur and Goel 2001; Arnason 2004). O’Neill’s (2001) concept of ‘trustworthy institutions’ lies on the boundary of community consent and authorisation, and is a very pragmatic approach to resolve many of the issues related to informed consent in research that involves stored samples and databases.

11.4 The Therapeutic Misconception There is a considerable body of evidence that patients participate in clinical trials in the hope of therapeutic benefit from the drug being trialled (Bamberg and Budwig 1992; Dresser 2001; Corrigan 2003). The ‘therapeutic misconception’ that interventions will be personally beneficial may encourage individuals, particularly patients, to agree to participate in research. Patients and the public commonly feel there is a general obligation to be involved in research to help others, but their personal motivations for participation can be different. Many individuals have or would participate in a research project because they anticipate this would improve their own treatment and/or that they would be more closely monitored (Cassileth 1982). Another concern here is that participants seem to have a poor understanding of the disadvantages and risks associated with research. Emotive terminology and the possible misuse of terms (for example ‘therapy’ rather than ‘experiment’) can cause problems in terms of raising patients’ and families’ expectations (Dubowitz 2004). Clinicians and researchers can also have an expectation that research will benefit participants personally (Penman et al. 1984; Tomamichel et al. 2000). Clinicians may recognise that significant medical improvement is unlikely and may have a broad view of what counts as benefit. They may, for example, believe that participants could benefit from feelings of altruism and the maintenance of hope for the future. It is not unusual for patients and families to have unrealistic expectations with regard to treatment options. Clinicians, researchers and commercial interests bear much of the responsibility for this; e.g. pharmacogenetics has been described as ‘personalised medicine’, but it is likely to be some years before this becomes a reality (March et al. 2001). So, individuals’ expectations may be raised inappropriately at this stage.

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11.5 Pharmacogenetics and Databases For pharmacogenetics to make a substantial contribution to health care, researchers will need to develop and have access to large-scale databases. This raises issues around data protection and consent. Both the UK’s House of Lords Select Committee on Science and Technology (2001) and the Human Genetics Commission (2002) have argued for independent oversight of genetic research databases (and DNA collections) One way to minimise problems related to data protection and consent is to establish an independent ethics and governance framework. This approach has been taken by the Wellcome Trust and the Medical Research Council, the funders of UK Biobank2 ; the responsibilities of the independent advisory Ethics and Governance Council include advising on the interests of research participants and the general public in relation to UK Biobank.3

11.6 Returning Results to Research Participants Research participants generally say they would like feedback on the research they contribute to, and they may even assume it will be given (Richards et al. 2003; People Science & Policy Ltd 2002). A proportion of individuals who have taken part in research previously are reluctant to consider doing so again when they have not received feedback (Madsen et al. 1999). Whether or not updates will be offered therefore needs to be made clear (Nuffield Council on Bioethics 2003). However, pharmaceutical companies are concerned about both the practicalities of doing this and whether or not the information will be helpful to research participants. Renegar et al. (2006)4 raise a number of points with regard to returning (pharmaco) genetic research results to study participants. Their particular concern lies not with the fact that the data is genetic, but that it is exploratory. This means that the clinical relevance, i.e. the validity and interpretability, of genotypic information may neither be clear nor agreed. They acknowledge that clinical relevance can change over time, but argue that pragmatic time limits need to be placed on researchers (and sponsors) responsibilities to pass results on to participants. Separately, GSK explains that the results of genetic analysis are not usually provided to the participants or the investigators because they are ‘meaningful only for research, not for clinical use’ (Arledge et al. 2000).

2 UK Biobank is a national database established to study the interactions between genes, environment and lifestyle in health and disease. It will involve around 500 000 participants who will contribute DNA and other samples and provide details about their medical histories and lifestyle http://www.ukbiobank.ac.uk/ 3 UK Biobank Ethics and Governance Council http://www.egcukbiobank.org.uk/ 4 The authors are members of the Pharmacogenetic Working Group, and many are representatives of pharmaceutical companies.

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11.7 Public and Professional Attitudes, Education and Resistance Patients are often positive about the use of pharmacogenetics (Fargher et al. 2007b), but the same cannot be said of clinicians and the public. The latter groups express feelings ranging from ambivalence to anxiety about particular applications of pharmacogenetics. A deficit approach to clinician and public education, i.e. making the assumption that their concerns can be alleviated or even removed by giving them the ‘right’ information, is a common response from supporters of pharmacogenetics. It has generally proved difficult to move pharmacogenetics into clinical practice. Buchanan et al. (2002) and Weinshilboum and Wang (2004) are amongst those who argue that education of the public and of clinicians is needed to promote the acceptability, and greater use, of pharmacogenetics. Education may have a role, but clinicians’ resistance has (also) been informed by their ethical concerns (Hedgecoe 2007). Hedgecoe (2006) uses two case studies to explore clinicians’ attitudes to pharmacogenetics, and he focuses on the context in which ethical decisions are made. The first case relates to the acetyl cholinesterase inhibitors and the APOE gene in Alzheimer’s disease (AD), and the second to trastuzumab (Herceptin) and the HER2 gene in breast cancer. APOE codes for apolipoprotein E, and has three alleles: E2, E3 and E4. Individuals who are homozygous for E4 have an increased risk of developing AD (Saunders et al. 1993). Acetyl cholinesterase inhibitors such as tacrine5 are used to treat patients with AD. It was recognised early on that up to 50% of AD patients do not respond to tacrine (Farlow et al. 1992). When Poirier et al. (1995) looked at APOE status and response to tacrine, they found no improvement or a worsening of the condition in 60% of AD patients with APOE4 alleles. The HER2 oncogene and protein are important in cell division and growth. The protein is over expressed in around 30% of breast cancers. Over expression of HER2 promotes tumour growth and causes more aggressive cancers. Trastuzumab stimulates the immune system to respond to HER2 overexpression, reducing tumour size and improving life expectancy Clinicians commonly view APOE4 testing as neither useful nor ethical because a significant proportion of affected people do not carry the E4 allele and a significant proportion of E4 carriers do not go on to develop the condition.6 The AD clinician-researchers in Hedgecoe’s study identified a number of ethical problems which testing for APOE4 status could lead to in clinical practice: not prescribing acetyl cholinesterase inhibitors to E4 carriers (and 40% of E4 carriers may respond to this treatment); identifying such patients as having a worse prognosis; and, identifying their children as E4 carriers (i.e. as having an increased risk of AD). The breast cancer clinician-researchers did not

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The first AD treatment launched in 1993 and marketed by Parke Davis. see the Consensus report of the Working Group on: “Molecular and Biochemical Markers of Alzheimer’s Disease”. The Ronald and Nancy Reagan Research Institute of the Alzheimer’s Association and the National Institute on Aging Working Group (1998).

6

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have such concerns with regard to HER2 testing; generally this was seen as simply adding to information about the patient’s prognosis. There are two important differences between APOE and HER2 status which help explain the differences between the clinician-researchers’ attitudes to pharmacogenetics. HER2 overexpression is somatic, not inherited, so one woman’s results will not give information about her relatives’ chances of developing a HER2 positive breast cancer, unlike APOE where the genetic change is carried in the germline and has implications for relatives. Secondly, clinical management is directly affected by HER2 status: when a tumour is found to be HER2 positive, then an intervention, trastuzumab, can be used.

11.8 Clinical Usefulness In a more recent paper, Hedgecoe (2008) has shifted to focusing on how the clinical usefulness of pharmacogenetic tests affects their adoption into practice.7 He points out that the AD clinician-researchers in his 2006 study viewed APOE4 as being of minimal clinical usefulness (for reasons including problems replicating Poirier et al.’s results and concerns about denying access to a treatment that may well work). Hedgecoe argues that clinicians also see little point in using pharmacogenetic tests when these would not help to decide between drugs. For example, the degree to which statins affect cholesterol and lipid levels depends on a patient’s genetic profile (Chasman et al. 2004). Trusheim et al. (2007) found that statins are almost invariably prescribed without pharmacogenetic tests. They suggest this is because most statins will lower a given patient’s lipids to some extent and that pharmacogenetic tests add little additional benefit compared with their cost and inconvenience. Over the last decade, certain pharmacogenetic tests have become widely used in clinical practice. Azathioprine is used to treat inflammatory conditions such as Crohn’s disease and to prevent acute rejection after organ transplantation. Many patients experience side-effects from azathioprine, the most significant of which is neutropaenia. Thiopurine methyltransferase (TPMT) is an enzyme in the pathway of azathioprine metabolism; low/absent TPMT activity correlates with the risk of neutropaenia (Lennard et al. 1989), and is due to variations in the TPMT gene (Tai et al. 1996). Approximately 1 in 300 people have variants on both copies of the TPMT gene, and have low/absent activity (see Chapter 7). In 1997, Tan et al. found that none of 370 UK dermatologists used TPMT enzyme level testing prior to prescribing azathioprine. However 10 years later, Fargher et al. (2007a) found that 89/95 (94%) of dermatologists surveyed in England8 used TPMT

7 Hedgecoe has shifted away from the concept of clinicians’ resistance to pharmacogenetics; part of the reason for this is that ‘resistance’ is often associated with blame, and he wants to avoid clinicians being held responsible for the way tests and technologies move/do not move into clinical practice. 8 In total 189/287 (67%) of specialists from dermatology, gastroenterology and rheumatology used TPMT enzyme level testing prior to prescribing AZA.

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enzyme level testing prior to prescribing azathioprine. Fargher et al. (2007b) conducted interviews with 25 patients all of whom had been prescribed azathioprine, and focus groups with 17 healthcare professionals who had limited experience of pharmacogenetics. The healthcare professionals viewed pharmacogenetics positively. However, they were concerned that patients might perceive the results of pharmacogenetic tests as denying access to treatment, and that such tests might reveal susceptibility to other diseases at a later date. The patients were largely positive about pharmacogenetic tests, particularly in terms of getting information about their personal risk of side-effects (although this group of patients will presumably all have had TPMT enzyme level testing, they rarely referred to this in their interviews). Recent work illustrates how pharmacogenetics can inform and improve clinical practice in other areas: the International Warfarin Pharmacogenetics Consortium (2009) found that combining pharmacogenetic data and clinical data gives the best estimate for the appropriate starting dose of the anticoagulant warfarin.9 Such international collaborations hold considerable promise, though they may initially be restricted to commonly used drugs which, like warfarin, are now out of patent.

11.9 Clinicians’ Obligations The idea of pharmacogenetic testing as a pre-requisite for treatment does not sit comfortably with voluntary consent. Also, the results of a test could counter-indicate the use of any existing treatment, in which case the patient would be faced with no treatment options, though off-label prescribing may be possible particularly if the likelihood of adverse reactions is small. Unanticipated findings from pharmacogenetic tests may be difficult to deal with. It is also necessary to consider whether or not the tests will reveal information about the individual additional to, or separate from, the treatment in question and whether the test might reveal information with implications for the patient’s health. Perhaps what is important for pharmacogenetic tests, as for other tests, is whether or not the samples taken or results obtained will be used for other purposes in the future. There is evidence that the CYP2D6 gene is an important predictor of tamoxifen efficacy, and certain CYP2D6 polymorphisms are associated with worse clinical outcomes (Goetz et al. 2007, see Chapter 4). Also, selective serotonin reuptake inhibitors inhibit the CYP2D6 enzyme, and may result in altered tamoxifen activity and poorer outcomes (Stearns et al. 2003). But when should clinical practice change? Hartmann and Helft (2007) point out that the studies to date have been retrospective and based on small numbers of post-menopausal women. They argue for prospectively validated data because: (a) the benefits of tamoxifen are known; (b) there may not be alternative therapies, especially for pre-/perimenopausal women;

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There is a 20-fold variation in the warfarin dose patients need. The initial dose in some patients can be too high and can cause life-threatening bleeding, and in others it can be too low which risks serious thrombo-embolic episodes.

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and, (c) one of our ethical responsibilities is to enable patients to weigh risks and benefits. Gurwitz and Newman (2008) have contested that although the data on tamoxifen pharmacogenetics is still evolving that patients should be informed of the ongoing debate. Newman and Payne (2008) argue that not using pharmacogenetic tests which are known to identify rare severe adverse events is unethical, especially when alternative treatments are available. HLA-B∗ 1502 is associated with Stevens-Johnson syndrome10 in people of Han Chinese, Hong Kong Chinese and Thai origin treated for epilepsy with carbamazepine (Chung et al. 2004). Testing for HLA-B∗ 1502 in people with these origins prior to prescribing carbamazepine has been recommended by Medicines and Healthcare Products Regulatory Agency and the Commission on Human Medicines (2008).

11.10 Pharmacogenetics and Race/Ethnicity Certain groups have experienced stigmatization, discrimination and even harm as the result of participation in biomedical research.11 Another potential problem is whether (apparent) membership of a certain ethnic group will be used as a proxy for pharmacogenetic testing. This means that pharmacogenetics has the potential to interact with and exacerbate existing social stratifications. Having said this, any benefits from pharmacogenetic testing will only be accessible to marginalised and/or vulnerable patients if they are included in research trials. Recognition of a link between race and drug metabolism dates back to the 1940s when: primaquine-induced haemolysis in African-Americans was found to be due to glucose-6-phosphate dehydrogenase (G6PD) deficiency (Hockwald et al. 1952). Data on race in pharmacogenetic research since then can be criticised on a number of grounds: it has generated contradictory results; it has been difficult to replicate; and it has not taken into account other factors such as the environment, education and socioeconomic status (and social factors are commonly influenced by race).12 Also, although differences between racial groups – e.g. between White Europeans and African-Americans – can be identified, the differences within racial groups is greater, as is the overlap between them (Jones and Perlis 2006). There is also a debate to be had over what ‘race’ means, and we often use surrogate markers for race such as skin colour, hair type, national origin and citizenship. In addition, the use of census categories to define race and ethnicity (as happens in the US) is problematic – the US census categories are actually defined as ‘a social-political construct designed for collecting data . . . and are not anthropologically or scientifically based’ (US Census Bureau 2001). As a result, the question of

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Stevens-Johnson syndrome is a serious and potentially life-threatening systemic hypersensitivity disorder. 11 Jones (1993). 12 See Jones and Perlis (2006, pp. 99–103) for a detailed discussion.

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whether or not there is a place for consideration of race/ethnicity in pharmacogenetic research is hotly contested13 : race and ethnicity are socially constructed and therefore not biologically valid (Schwartz 2001; Rothstein and Epps 2001); selfreporting of race/ethnicity as too imprecise to be useful (Foster and Sharp 2002); or, race and ethnicity have biological significance even if they are social constructs (Burchard et al. 2003). BiDil contains a combination of isosorbide dinitrate and hydralazine hydrochloride. It is used to treat heart failure, and was the first race-specific drug. NitroMed initiated the African American Heart Failure trial in 2001, and the US Food and Drug Administration approved BiDil to treat heart failure in African Americans only in 2005. But, BiDil was only tested in self-identified African-Americans. Also, NitroMed’s claim that African- Americans are twice as likely to die from heart failure as white Americans has been challenged as when looking at mortality for all age ranges, the ratio is nearer 1.08:1 (Kahn 2006). Various studies describe racial/ethnic differences in drug related enzymes (Stephens et al. 1994; Solus et al. 2004); in most of these the participants selfidentify their race/ethnicity. In a US study involving 224 participants,14 Condit et al. (2003) found that many (39.6%) did not know all four of their biological grandparents, and so could not confidently identify their ancestors’ geographic origins. Almost half were either very suspicious of the safety of race-labelled drugs (48.5%), and/or very suspicious of their efficacy (41.4%).15 A proportion (13.2%) of African Americans would prefer to take drugs designated for European Americans. Égalité et al. (2007) interviewed genomic scientists16 to investigate their views and perceptions with regard to the use of racial classification in pharmacogenetic research. The participants felt that studies involving racial groups are important to reflect a diverse society, and that stratification by race allows for greater accuracy given the genetic variation between populations, and so would lead to better healthcare outcomes by providing drugs targeted at sub-populations. However, most of the participants were surprised that BiDil is only intended for African-Americans and thought, for example, it was likely to be useful for other racial groups. Despite their positive attitude to stratification by race, the participants felt that race could be ‘a flawed surrogate for complex genetic and environmental factors’. They were apprehensive that racial inequality could lead to black patients particularly not having access to the benefits of pharmacogenetics. Two factors were identified as problematic: racial/socioeconomic inequality and the rarity of some conditions that are (mostly) race-specific which may mean that pharmaceutical companies will not invest in developing drugs for certain populations. The participants were

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See Weijer and Miller (2004, p. 10) for a detailed discussion. over-sampled for minority groups. 15 Participants who identified as European Americans were significantly less suspicious about the safety and efficacy of race-based drugs. 16 The scientists interviewed self-identified as members of particular ethnic groups likely to be approached for race-specific pharmacogenetics research. 14

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also concerned about research linking race to behaviour and other traits, such as intelligence, because this could rationalise racism. Pharmaceutical companies may not invest in the development of medicines that will be useful to a small group of patients only. Pharmacogenetic testing may create novel population stratifications and orphan populations who are screened out during drug development (Smart et al. 2004). It may also lead to the denial of therapies to entire groups. But abandoned drugs may be salvaged if identified as of benefit to a specific (sub-)population.

11.11 Concluding Remarks Many commentators have argued that pharmacogenetics is different from other forms of genetic testing, however this chapter has outlined that there are still ethical considerations to ensure that pharmacogenetic information is used appropriately and to the benefit of as many as possible. The fact that germline genetic tests can potentially provide surrogate information is of concern. Although for the majority of pharmacogenetic tests, there is little or no evidence that the genetic variants predispose to disease or provide prognostic information, genes and their variants have not evolved because of drug exposure and are likely to have multiple complex roles which are not fully understood e.g. cytochrome p450 enzymes have a role in detoxification of dietary plant alkaloids, but are involved in the phase 1 metabolism of 75% of all drugs. The ethical considerations are most likely to be encountered in oncology treatment as pharmacogenetic advances and applications are more likely to impact sooner in this discipline. However, the ethical challenges raised are surmountable and must be at the forefront of future research and the transition into clinical practice.

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Chapter 12

Economics of Cancer Pharmacogenetics Katherine Payne

Abstract The budget for health care service provision is finite and under increasing pressure in part due to technological innovation in healthcare. Newer treatments in oncology are often more expensive than current treatment options and in some cases require additional resources such as molecular tumour testing to determine eligibility or imaging to monitor disease progression and continued eligibility. This chapter describes some key economic concepts that can be applied to help decisionmakers make difficult decisions about which treatments or tests are the best use of healthcare resources. Four methods of economic evaluation are described, briefly. A more detailed description of how to conduct and interpret a cost-effectiveness analysis is then presented using the example of adding CYP2D6 testing to inform prescribing decisions for tamoxifen. The current level of published economic evidence in oncology pharmacogenetics is briefly described. The chapter concludes by mentioning some other economic methods that may prove useful in the field of oncology pharmacogenetics. Keywords Cost effectiveness analysis · Economic evaluation · Efficiency · Opportunity cost · Quality adjusted life years (QALYs)

Contents 12.1 12.2 12.3 12.4 12.5

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12.6 Using Economic Evaluation Information . . . . . . . . . . . . . . . . . . . 12.7 A Final Note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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12.1 Introductory Concepts The budget for health care service provision is finite and under increasing pressure in part due to technological innovation in healthcare. Treatment options in oncology are expanding with the addition of biological agents to existing chemotherapeutic regimens. These newer treatments are often more expensive and, in some cases, require additional resources such as molecular testing of tumours to determine eligibility or imaging to monitor disease progression and continued eligibility. Such innovative treatments potentially offer patients the benefits of improvements in survival and quality of life from fewer side effects. The finite nature of the healthcare budget is more apparent, but not limited to, publicly funded healthcare systems that are generally allocated an annual fixed budget with which to provide a health service and work within a ‘closed system’. In such a ‘closed system’ the inputs (resources) into providing a healthcare service are in limited supply (scarce). In theory, all resources available for that financial year can have alternative uses within this system. When new technologies or services are developed they will have to compete with existing options within the available healthcare budget. Therefore, decision-makers involved with reimbursing or commissioning healthcare services and service providers must make choices and prioritise which treatments should be funded from limited resources.

12.2 Making Choices One approach, used by economists, to make these choices is to minimise ‘opportunity cost’. Opportunity cost may be described by thinking about the lost benefits (cost) associated with using one intervention instead of the best alternative use of the resource (budget). All decisions to use a particular healthcare intervention have an opportunity cost. If a choice is made to use one medicine, or diagnostic test or service, over an alternative then there will be (patient) benefits foregone. Economists argue that the ideal way to prioritise treatments or programmes is to aim to minimise opportunity cost, for a given level of benefit (health gain). Or, maximise benefits for a given cost. Using opportunity cost to guide making choices implies any evaluation used to inform the decision must involve a comparison between at least two alternatives and the decision-maker has good quality information on the costs and benefits possible from all treatment options. For example, providing a pharmacogenetic test to predict whether an individual taking a medicine is likely to be a good responder, has an opportunity cost. Implementing the test is likely to be more expensive than current practice, which does not involve using a test to predict response, but may offer reductions in overall

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cost, because more people respond to the medicine and therefore incur lower future treatment costs and also create additional patient benefits by reducing mortality. Being able to predict who will respond well to an often expensive medicine is the dilemma facing national decision-making bodies, such as the National Institute for Health and Clinical Excellence (NICE). A major proportion of NICE’s technology appraisal programme focuses on oncology drugs, where benefits for the whole population may be seemingly relatively small, but it is clear from trials and/or clinical experience that some individuals exhibit a good response, either in terms of progression free or overall survival. NICE quite often does not reach a blanket ‘yes’ or ‘no’ decision for the whole patient population with the condition under appraisal, but suggests criteria to select potential good responders from the whole population. The aim with defining a sub-group of good responders is to spend the available healthcare budget more (cost-) effectively. By diverting scarce resources to people more likely to respond well it is possible to increase the overall health of the patient population. To date, there are few situations where genetic information has explicitly been used by NICE to define the target population but trastuzumab is one example. Both technology appraisals of trastuzumab for early-stage (NICE 2006) and advanced (NICE 2002) breast cancer used HER2 status as the main criteria to identify the target patient population. These appraisals concluded that trastuzumab was a cost-effective use of healthcare resources, which clearly had implications for laboratories providing HER2 testing services, that would subsequently need to expand the current volume of service provision.

12.3 Measuring Efficiency Attaching a true value to the opportunity cost of a decision to use one medicine or test over another is difficult but must be operationalised in such a way that it is useful to inform decision-making. This is where the concept of efficiency becomes important. Efficiency is sometimes mis-interpreted as meaning using an approach to save money. However, economists attach a specific meaning to the term ‘efficiency’ to explicitly consider a direct comparison between the relative costs and benefits of competing health care interventions. Decision-makers who use this information on relative costs and benefits can work towards ensuring that resources are allocated in such a way as to maximise health gains to society. The ‘efficient’ ideal would be achieved if maximum health gain in a defined patient population is attained at the lowest opportunity cost. This may also be viewed as getting the maximum benefit from an intervention given a defined budget.

12.4 Methods of Economic Evaluation Health economics, in general, but more specifically, economic evaluation, aims to provide decision-makers with a theoretical framework to inform resource allocation. Economic evaluations can be used as a tool to identify if efficiency has

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Fig. 12.1 Framework for economic evaluations

been attained. Figure 12.1 gives an overview of the framework of an economic evaluation. Resources are any type of inputs into a service or treatment. Resources are valued in terms of costs by attaching unit prices to the quantity of resource used. The study perspective and time horizon define the type of resources to include in an evaluation. Assuming the societal perspective would mean the economic evaluation should include all relevant societal costs, such as those to the patient, healthcare payer, general practitioner and hospital. Some countries prefer economic evaluations to take the societal perspective but it is sometimes useful to assume an alternative more restrictive perspective. In the UK, the current recommended perspective is that of the National Health Service (NHS) and Personal and Social Services (PSS) because decision-makers are charged with how to spend the budget for a publicly provided health service and an analysis does not include out-of pocket payments made by patients, such as patient travel costs (NICE 2008). The time horizon of an evaluation informs how long the interventions under comparison are followed–up for and will affect how costs and benefits are valued in an evaluation. The ideal time horizon is the patient lifetime, but it is sometimes difficult to get accurate estimates for these data and analysts must extrapolate from clinical trial data collected over shorter time frames. For a pharmacogenetic test, predicting the risk of a side effect, it might seem meaningful for a decision maker to have information describing the impact over a short time frame, because the side effect is more likely to occur in the early stages of treatment. However, experiencing (or avoiding) this side effect could have longer term costs and consequences for a patient and the health service, because it may affect subsequent treatment pathways and possible treatment options. Benefits, sometimes referred to as consequences or outcomes, refer to the effect on the patient, rather than the effect of the intervention on the people providing the service. By using these methods of economic evaluation, health economists assume that the main objective of the health service is to maximise health gain and use measures of outcome that aim to capture health improvements. The potential

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benefits of pharmacogenetic information for oncology agents can be in terms of being able to predict the risk of a side effect, for example the UGT1A1 test for irinotecan, or predict a good responder, such as CYP2D6 testing for tamoxifen. Economic evaluation involves comparison of the costs and benefits associated with alternative programmes or treatments (Elliott and Payne 2005). There are four methods of economic evaluation: cost minimisation analysis; cost effectiveness analysis; cost utility analysis and cost benefit analysis. All four methods use the same approach to identify and measure costs but differ with respect to how the benefits from each intervention under evaluation are measured. Table 12.1 summarises the definitions of each type of economic evaluation. Each of these methods may be used to quantify the costs and benefits attached to a programme or service and provide another type of information to guide the decision-making process. The role of economic evaluation is to aid the decision-making process, not replace it. In general, decisions which compare the costs and consequences of an action should not be made on an individual basis but should be made when policies for populations of individuals are being formulated. These decisions need to be based on accurate, reliable and generalisable effectiveness and economic evidence. The most basic form of economic evaluation is cost-minimisation analysis (CMA). In this form of analysis there must be unrefutable robust evidence that the outcome of each intervention being compared is the same. This is very rarely the case. However, if the analysis were comparing, for example, TPMT enzyme level testing with genotyping, and clinical utility data had proven these methods were Table 12.1 The four methods of economic evaluation Type of economic evaluation Cost–minimisation analysis (CMA)

Cost–effectiveness analysis (CEA)

Costs

Outcome

The outcomes of the Costs may be identified and measured as: (1) direct costs. These may medical (costs service/treatments being compared are assumed to the hospital or health service which are (based on evidence) to be directly associated with treatment, for the same. example, costs of consumables, drugs, testing equipment and staff) or non-medical costs (costs to social services), and (2) indirect costs (costs to society, for example, lost productivity through morbidity and mortality). The outcomes of the service/treatments being compared are measured using a single, natural unit (for example, reduction in number of neutropaenic events or life-years gained). Treatments that are aimed at achieving the same effect can be compared.

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Type of economic evaluation Cost–utility analysis (CUA)

Cost–benefit analysis (CBA)

Costs

Outcome The outcomes of the service/treatments being compared are measured using utility values. The utility is the value attached to a specific health state ideally by the general population and patients. A number of methods are available to measure utility (for example, time trade off, standard gamble). A commonly-used measure is the quality-adjusted life-year (QALY) that takes in to account the effect of treatment on both the quality (utility) and quantity of life. It is possible to compare treatments for different conditions. The outcomes (health and non health) of the service/treatments being compared are measured using monetary values. The outcome measures, for example, how much an improvement risk reduction from side effects is worth? The monetary value is ideally valued by the general population including current and future patients. Contingent valuation (willingness-to-pay) methods are used to attach a monetary value measured in the currency the evaluation is based in.

equally effective at predicting profound neutropaenia, a CMA would be an approach to use. A decision-maker should select the option with the lower total cost. The majority of published economic evaluations use methods of costeffectiveness analysis. In the literature, particularly from North America, the terms cost-effectiveness analysis and cost-utility analysis are used interchangeably. This is

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because the methodological framework for conducting these analyses is effectively the same. They differ in the way the outcome is measured. In cost-effectiveness analysis the unit of outcome is clinical effectiveness. In the case of a pharmacogenetic test that aimed to identify people at risk of neutropaenia, such as the UGT1A1 test for irinotecan, the measure of effectiveness could be number of cases of grade three or four neutropaenia that are avoided. Avoiding neutropaenia will have a subsequent impact on overall mortality for the patient population being offered UGT1A1 testing. So, effectiveness could also be measured in terms of life-years gained. In cost-utility analysis the outcome is the quality adjusted life year (the QALY). The QALY comprises two components: survival and a measure of the quality of the extra life years. Incorporating a measure of the health related quality of life is important for treatments that may offer extra years, or months, of life but at less than perfect health. This may occur with oncology treatments that improve overall survival, but not disease-free survival. To be consistent with this approach, it follows that a pharmacogenetic test that aims to predict whether an oncology treatment is likely to be effective needs to be valued in terms of the subsequent impact on treatment pathways and combined effect on patient survival and health related quality of life. Cost-benefit analysis (CBA) provides a means of capturing benefits from nonhealth and health improvements (Grosse et al. 2008). An example of a non-health benefit might be the value an individual attaches to the additional information provided by a pharmacogenetic test about the level of risk reduction because of the probability the test predicts side effects. In a CBA, benefits are measured in monetary terms. An intervention is the preferred option if net benefit is positive (benefits outweigh the costs). A number of methods exist to measure benefits in monetary terms but economists currently favour using estimates of consumer ‘willingness to pay’ (WTP) (Elliott and Payne 2005). Cost-benefit analysis is not generally used, especially in the UK, to inform decision making because of methodological concerns and difficulties with applying the method in a publicly funded healthcare system. All these methods of economic evaluation provided decision makers with an aggregate measure of the outcome of interest. An alternative approach is to use cost-consequences analysis. Coast (2004) provides an overview of this approach, which has some advantages because it provides a decision-maker with results for a range of outcomes, or consequences, for the interventions under evaluation. In some instances, an intervention may show improvements in one outcome, for example clinical response, but look worse when other outcomes are considered, for example side effects. The clear disadvantage is that a decision-maker must then decide which outcome is most useful to them to inform a decision.

12.5 Collecting and Analysing Cost-Effectiveness Data This section describes briefly how to collect data for a CEA (CUA), analyse and interpret the results of the economic evaluation. The focus is on CEA (CUA) because these are the most common form of published economic evaluations. An example

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case study, CYP2D6 testing for tamoxifen, will be used to provide a useful means of illustrating the key concepts. Any cost-effectiveness analysis involves five key steps (see Box 12.1). Defining the correct research question is vital. This involves identifying the relevant alternatives to be compared in the evaluation. This may not be as simple as it first appears. For our case study of CYP2D6 testing for tamoxifen there are a number of comparisons we could make (see Chapter 4). Aromatase inhibitors are more expensive than tamoxifen, but some trials have suggested them to be more effective and less toxic, but adding CYP2D6 testing to the clinical pathway for tamoxifen could improve its relative effectiveness (Aydiner and Tas 2008). Therefore, we could evaluate the added value of introducing CYP2D6 testing to inform tamoxifen prescribing decisions compared with current care using no pharmacogenetic test. An alternative evaluation could compare tamoxifen plus CYP2D6 testing compared with aromatase inhibitor monotherapy. The research question must be appropriate to the clinical decision problem and include all the relevant comparators.

Key Stages of Conducting a Cost-effectiveness Analysis • • • • •

What is the (research) question? Find the cost and clinical evidence Incremental economic analysis Measure the uncertainty Present the results

Once the research question has been defined there are two main options for finding the clinical and cost evidence to inform the economic analysis. Prospective economic evaluations involve collecting clinical outcome, health status and resource use data alongside a prospective randomised controlled trial (RCT). For our case study, this would involve designing and running an RCT comparing the relevant alternatives, such as an aromatase inhibitor compared with tamoxifen plus CYP2D6 testing, with sufficient statistical power to detect a clinical difference. This approach has the advantage of reduced selection bias and good internal validity because of the randomised nature of the study design. Patient-specific and accurate resource use data may also be collected but the results will not be generalisable outside the trial setting. This may limit the usefulness of the study findings for decision-makers, who may be interested in extrapolating from the trial results, which are specific to the study patient selection criteria and protocol, alternatives included in the RCT and time frame of the follow-up. There are specific challenges to the robust design of an RCT of a pharmacogenetic test to predict the risk of a side effect. Such RCTs will generally require large, sometimes unfeasibly large, sample sizes to detect a statistically significant difference in side effects when the current prevalence of the

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side effect is small. Retrospective modeling studies that use decision-analytic techniques are an alternative, and often preferred, approach to economic evaluations used to inform health policy decisions. Models allow full evaluation of all relevant alternatives and are useful if there is a need to extrapolate beyond the time frame of the clinical trials to take account of the lifetime perspective (Brennan and Akehurst 2000). NICE uses decision-analytic models in its technology appraisal programme (NICE 2008). Building a decision-analytic model involves structuring a model to represent the clinical decision problem and then using available evidence to populate the model. The available evidence should ideally be of good quality and outcome data should come from meta-analysis of relevant RCTs of the alternatives under evaluation. Resource use and the data necessary to estimate improvements in QALYs will come from systematic reviews of the published literature. These data are then used to populate the decision-analytic model, which then estimates the expected costs and benefits for each alternative under evaluation. A number of results scenarios are possible. Figure 12.2 gives an example of a cost-effectiveness plane which can be used to explain the four results possible when comparing two alternatives (an aromatase inhibitor compared with tamoxifen plus CYP2D6). The y-axis indicates if there is an increase or decrease in costs and xaxis shows difference in QALYs. Clearly, the comparator must be defined and in this case might be an aromatase inhibitor. Tamoxifen plus CYP2D6 testing is said to dominate using an aromatase inhibitor if this option has lower costs and increased QALYs and would be the preferred option for the patient population. The situation requires further analysis if tamoxifen plus CYP2D6 testing has both higher expected costs and benefits. In this case it becomes necessary to estimate the incremental

Fig. 12.2 The cost-effectiveness plane

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cost-effectiveness ratio (ICER), which is the difference in costs divided by the difference in QALYs. This ratio then gives a value of the cost per QALY gained. Benefits, in this example could also be measured using life-years gained if no data on quality of life are available. A decision-maker, representing society, must then decide if they are willing to pay the cost for the additional QALY (or life-year) gained given the available healthcare budget. However, in practice, as for example in the case of NICE, the threshold value is used to guide the optimum allocation of a fixed budget rather than assuming the value chosen represents society’s willingness to pay. Furthermore, the NICE appraisal process does not use a particular explicit threshold value, but rather an indicative range between £20,000 and £30,000 per QALY (McCabe et al. 2008). By using a range rather than a specific threshold value implies that an appraisal committee will take into account other factors (value judgments) to decide whether or not to recommend an intervention to be funded once the cost per QALY exceeds £20,000. Examples of value judgments used by NICE include: the degree of uncertainty surrounding the ICER; particular features of the condition and population using the technology; innovative nature of the technology (Rawlins and Cuyler 2004). The vital part of any economic evaluation is to consider and quantify the degree of uncertainty in the results. This part is called the sensitivity analysis. Ideally, a sensitivity analysis should explore four types of uncertainty: methodological, structural, heterogeneity, and parameter (Phillips et al. 2006). A detailed description of conducting a sensitivity analysis is outside the scope of this chapter. In brief, the process involves changing the values of key parameters in the model, identifying the effect on the expected costs and benefits and seeing whether these changes alter the preferred option. Good practice guidelines for conducting economic modeling suggests that ideally a model should use probabilistic sensitivity analysis (PSA) to get a true picture of the impact the uncertainty in parameter estimates will have on the conclusions drawn from the model (Philips et al. 2006). A natural and logical extension of using PSA is to move towards identifying future research priorities and the need for prospective studies to address key uncertainties in the model parameters by using formal value of information analysis methods, such as the expected value of perfect information to guide the type of evidence required (Claxton 1999) or expected value of sample information to guide trial sample size design (Claxton 2005). In summary, it can be seen that a more ‘efficient’ pharmacogenetic test may not necessarily be the least expensive option for the health service. The results of clinical studies may indicate that a pharmacogenetic test offers the potential to increase the health of a defined patient population, compared to current practice. However, the test may result in increased healthcare expenditure, in terms of acquisition price and subsequent effects on treatment pathways, than the existing therapy or diagnostic option. Decision-makers must then, taking the appropriate perspective, evaluate the total incremental cost and benefit associated with using the test. Furthermore, the impact on the drug budget must then be considered. This is where the threshold for a decision maker’s ‘willingness-to-pay’ for a given cost per unit of health gain (ICER) must be defined.

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12.6 Using Economic Evaluation Information Fundamentally, from an economic perspective, pharmacogenetic tests are no different to other diagnostic tests. Laking et al. (2006) provide one possible analytical approach to evaluate the economics of diagnosis. Pharmacogenetic tests and nongenetic based diagnosis use healthcare resources. One aim of a diagnostic test is to correctly identify subsequent treatment options but there may be errors (false negatives and false positives) leading to inappropriate and some unsafe treatments. Therefore, as with other healthcare interventions and diagnostics, it is important to evaluate the incremental costs and benefits of a pharmacogenetic test. One challenge for designing and conducting economic evaluations of pharmacogenetic tests is the nature of the existing regulatory framework that does not encourage the development of a robust clinical evidence base, which in turn limits the capacity to perform economic analyses because of the paucity of sufficient data. To be useful for informed decision-making, an economic evaluation must be robust and use appropriate methods to value costs and outcomes. A number of guidelines exist to critique the quality of published economic evaluations. One example is provided by Drummond and Jefferson (1996) that describes the British Medical Journal guidelines using a checklist approach. Any researcher or reviewer, preparing or reviewing a paper, is advised to use this list to confirm the relevant components of the economic evaluation are present (Drummond and Jefferson 1996). The NHS Economic Evaluation Database (NHSEED) is a useful resource for busy decisionmakers. It is a structured database that contains abstracts of economic evaluations in one of two formats: one for prospective economic evaluations (single study) and one for retrospective (modelling) economic evaluations. Both formats present a critical appraisal of the published study by an experienced health economist and offer a structured overview of the quality of the paper. A number of authors have produced reviews of the economics literature on pharmacogenetic testing. In 2004, Phillips and Van Bebber found only 11 published economic evaluations. More recently, Vegter et al. (2008) conducted a systematic review of economic evaluations of pharmacogenetic tests, and focused on identifying pharmacogenetic tests for inherited rather than somatic mutations. Therefore, of the 20 papers identified, only one was relevant to oncology that evaluated the cost-effectiveness of TMPT for acute lymphoblastic leukaemia in children (van den Akker van Marle et al. 2006). Flowers and Veenstra (2004) offer a framework for the economic evaluation of pharmacogenetic tests, focusing on oncology, because as they suggest oncology is the area more likely to see the most immediate and useful clinical application of using genetic test to inform prescribing. Similarly, Phillips and Van Bebber (2005) developed a resource-allocation framework for assessing the value of pharmacogenetic testing using testing for CYP2D6 variant alleles as an example. Economists are increasingly paying attention to valuing the benefits of pharmacogenetic testing in oncology and impact on healthcare resources. It is likely that the economics literature will see more publications in this area as clinical studies continue to identify future useful practical applications.

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There are currently no prospective economic evaluations of a pharmacogenetic test in oncology. Some published economic modelling studies in oncology pharmacogenetics do exist, but there are very few. Three examples are Hornberger et al. (2005), Obradovic et al. (2008) and Lidgren et al. (2008). Lidgren et al. (2008) designed an economic modeling study to compare five strategies to determine HER2 status for trastuzumab treatment in the Swedish healthcare system (see Chapter 4). The five strategies are: (1) standard care with no HER2 testing and no additional adjuvant trastuzumab treatment; (2) immunohistochemistry (IHC) testing for all patients with 1-year adjuvant trastuzumab for IHC +3 patients and standard care for all other patients; (3) IHC testing for all patients with 1-year adjuvant trastuzumab for IHC +2 and +3 patients and standard care for all other patients; (4) IHC testing for all patients and fluorescent in situ hybridization (FISH) for IHC +2 and +3 patients with 1-year adjuvant trastuzumab for FISH positive patients and standard care for all other patients; (5) FISH testing for all patients with 1-year adjuvant trastuzumab for FISH positive patients and standard care for all other patients. The authors concluded that strategy four is not dominated by any of the treatment strategies and cost C36,000 per QALY compared with standard care. If, however, society is willing to pay C41,500 per QALY then strategy five, FISH testing for all patients, is the preferred option and represents a cost-effective use of healthcare resources in Sweden.

12.7 A Final Note Cost-effectiveness data is a necessary, but not sufficient, requirement to inform the decision about whether to introduce pharmacogenetic tests and associated medicines into drug formularies. This chapter has focused on the potential merits of economic evaluation and provided an overview of how to use methods of economic evaluation. The discipline of health economics can offer methods other than economic evaluation and can provide other insights into the effective use of genetic information to guide prescribing decisions. Such a broad discussion was outside the scope of this chapter, but the interested reader can access other useful perspectives by referring to the following papers. Danzon and Towse (2002) present an analysis of marketplace incentives and the potential effect of pharmacogenetic testing on pharmaceutical companies. Other authors have presented approaches to analyse the concept of value of information. Pharmacogenetic tests have a potential value to society resulting from the reduced uncertainty associated with the additional (probabilistic) information generated from the test result which can subsequently be used to offer better informed prescribing decisions. Shih and Pusztai (2006) present a simple value of information analysis that is based on decision-analytic methods. This analysis shows that it is possible to put an upper bound on the value of tests. Value is a different concept to price or cost and represents what society is willing to pay for the benefits from a pharmacogenetic test. Garrison and Austin (2007) present a further analysis describing how it is necessary to recognise the societal value to the patient of

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reducing uncertainty. Neither of these analyses is specific to pharmacogenetic tests in oncology, but the concepts are easily generalised. If a CEA has suggested a pharmacogenetic test to be of value, and society is willing to pay for the additional QALYs gained, then it is useful to start to think about how to configure pharmacogenetic services. This is where stated preference methods, such as choice experiments, may provide useful information about peoples’ preferences for the characteristics of a service. There are no examples of choice experiments specific to pharmacogenetic testing for oncology, but Phillips et al. (2006) have usefully summarised stated preference studies, using WTP or a choice experiment, of cancer screening programmes. More recently Phillips and colleagues published a WTP study to measure the perceived value of genetic testing for cancer risk in a general population (Van Bebber et al. 2007).

References Aydiner A, Tas F (2008) Meta-analysis of trials comparing anastrozole and tamoxifen for adjuvant treatment of postmenopausal women with early breast cancer. Trials 9:47 Brennan A, Akehurst R (2000) Modelling in health economic evaluation: What is its place? What is its value? Pharmacoeconomics 17:445–459 Centre for Reviews and Dissemination. NHS Economic Evaluation Database Handbook (3rd Ed). April 2007. http://www.york.ac.uk/inst/crd/pdf/nhseed-handb07.pdf (accessed 4 August 2009) Claxton K (1999) The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ 18:341–364 Claxton K, Schulpher M, McCabe C et al (2005) Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra. Health Econ 14:339–347 Coast J (2004) Is economic evaluation in touch with society’s health values? BMJ 329:1233–1236 Danzon P, Towse A (2002) The economics of gene therapy and of pharmacogenetics. Value Health 5:5–13 Drummond MF, Jefferson TO (1996) Guidelines for authors and peer reviewers of economic submissions to the BMJ. The BMJ Economic Evaluation Working Party. BMJ 313:275–283 Elliott RA, Payne K (2005) Essentials of Economic Evaluation in Health Care. The Pharmaceutical Press, London Flowers CR, Veenstra D (2004) The role of cost-effectiveness analysis in the era of pharmacogenomics. Pharmacoeconomics 22:481–493 Garrison LP, Austin MJF (2007) The economics of personalized medicine: a model of incentives for value creation and capture. Drug Inf J 41:501–509 Grosse SD, Wordsworth S, Payne K (2008) Economic methods for valuing the outcomes of genetic testing: beyond cost-effectiveness analysis. Genet Med 10:648–654 Hornberger J, Cosler LE, Lyman GH (2005) Economic analysis of targeting chemotherapy using a 21-gene RT-PCR assay in lymph-node negative, estrogen receptor-positive, early-stage breast cancer. Am J Manag Care 11:313–324 Laking G, Lord J, Fischer A (2006) The economics of diagnosis. Health Econ 15:1109–1120 Lidgren M, Jonsson B, Rehnberg C, Willking N, Bergh J (2008) Cost-effectiveness of HER2 testing and 1-year adjuvant trastuzumab therapy for early breast cancer. Ann Oncol 19:487–495 McCabe C, Claxton K, Culyer AJ (2008) The NICE cost-effectiveness threshold. What it is and what that means. Pharmacoecnomics 26:733–744 National Institute for Health and Clinical Excellence (NICE) technology appraisal guidance 34 (2002) Guidance on the use of trastuzumab for the treatment of advanced breast cancer. NICE, London

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National Institute for Health and Clinical Excellence (NICE) technology appraisal guidance 107 (2006) Trastuzumab for the adjuvant treatment of early-stage HER2-positive breast cancer. NICE, London National Institute for Health and Clinical Excellence (NICE) guide to the methods of health technology appraisal (2008) NICE, London Obradovic M, Mrhar A, Kos M (2008) Cost effectiveness of UGT1A1 genotyping in secondline, high-dose, once every 3-weeks irinotecan monotherapy treatment of colorectal cancer. Pharmacogenomics 9:539–549 Philips Z, Bojke L, Sculpher M, Claxton K, Golder S (2006) Good practice guidelines for decisionanalytic modelling in health technology assessment: a review and consolidation of quality of assessment. Pharmacoeconomics 24:355–371 Phillips KA, Van Bebber SL (2005) Measuring the value of pharmacogenomics. Nat Rev Drug Discov 4:500–509 Phillips KA, Van Bebber S, Marshall D, Walshe J, Thabane L (2006) A review of studies examining stated preferences for cancer screening. Prev Chronic Dis 3:1–8 Rawlins MD, Culyer AJ (2004) National institute for clinical excellence and its value judgments. BMJ 329:224–227 Shih YT, Pusztai L (2006) Do pharmacogenomic tests provide value to policy makers? Pharmacoeconomics 24:1173–1177 Van Bebber SL, Liang S, Phillips KA, Marshall D, Walsh J, Kulin N (2007) Valuing personalized medicine: willingness to pay for genetic testing for colorectal cancer risk. Personalized Med 4:341–350 van den Akker-van Marle ME, Gurwitz D, Detmar SB, et al (2006) Cost-effectiveness of pharmacogenomics in clinical practice: a case study of thiopurine methyltransferase genotyping in acute lymphoblastic leukemia in Europe. Pharmacogenomics 7:783–792 Vegter S, Boersma C, Rozenbaum M, Wilffert B, Navis G, Postma MJ (2008) Pharmacoeconomic evaluations of pharmacogenetic and genomic screening programmes. A systematic review on content and adherence to guidelines. Pharmacoeconomics 26:569–587

Chapter 13

Future Advances in Cancer Pharmacogenomics William G. Newman

Abstract This chapter will summarise some of the recent advances in cancer pharmacogenomics and how new technologies and study designs may provide important insights to provide safer and more effective treatments for cancer patients. Keywords Breath test · High throughput sequencing · Point of care testing

Contents 13.1 Advances in Phenotyping Tests . . 13.2 Point-of-Care Testing . . . . . . . 13.3 Clinical Trials . . . . . . . . . . 13.4 Advances in Genetic Technology . . 13.5 Use of Different Biological Samples 13.6 Concluding Remarks . . . . . . . References . . . . . . . . . . . . . .

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Numerous academic, clinical and commercial research facilities across the world are conducting projects to improve patient response to cancer treatment through the identification of tumour specific and germline molecular profiles. This chapter outlines some of the approaches that are likely to lead to changes in clinical management and advance cancer pharmacogenomics into clinical practice.

W.G. Newman (B) St Mary’s Hospital, Manchester Academic Health Sciences Centre, NIHR Biomedical Research Centre, University of Manchester, Manchester, UK e-mail: [email protected]

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13.1 Advances in Phenotyping Tests Phenotypic alternatives to genotyping tests are being sought to provide rapid and cheap assessments of patients’ risk associated with different drugs. As we have seen in Chapter 5, both complete and partial deficiency of the enzyme, dihydropyrimidine dehydrogenase (DPD) correlate with adverse reactions to 5-FU. Genotyping tests are cumbersome as the gene, DPYD, encoding DPD is large and a number of different mutations and mechanisms, including methylation, reduce enzyme activity (Yen and McLeod 2007). Several phenotyping methods, including high-performance liquid chromatography, mass spectrometry and thin layer chromatography, have also been developed to detect DPD deficiency, but these also have limitations due to complexity. The uracil-breath test (Ura-BT) has been developed using a single time-point determination to rapidly identify DPD-deficient individuals and could be applied in most clinical settings prior to 5-FU therapy (Mattison et al. 2004). Other breath tests are being developed to determine CYP2D6 deficiency (Leeder et al. 2008), which may have particular relevance in response to tamoxifen (see Chapter 4). Phenotyping assays for CYP2D6 are not clinically viable as these involve the determination of urinary metabolite ratios following challenge with a CYP2D6 substrate and genotype-phenotype correlations are complex. Further, a phenotype test may be particularly useful if an individual is taking co-medications which may be acting as CYP2D6 inhibitors (Leeder et al. 2008). Hence, a breath test that is minimally invasive and has the potential to be performed at the bedside with results available in approximately an hour is attractive.

13.2 Point-of-Care Testing Most pharmacogenetic tests require collection of an appropriate biological sample, extraction of DNA and then downstream analysis and interpretation. This process can take hours to weeks and so can lead to a delay in treatment initiation. This is particularly vital in situations where the cancer is rapidly progressing and treatment is to be given with curative intent. An option is to start treatment and then to modify it at a future time point based on pharmacogenetic results. A more attractive option is to have the results immediately available. Point-of-care testing has the potential to provide rapid results. Such tests are available for measurement of electrolytes, pregnancy tests and a range of other indications in clinical practice. Studies are ongoing to develop similar capabilities in pharmacogenetics (Chowdhury et al. 2007). An example of a phenotypic test already available in the point of care setting is the VerifyNow P2Y12 Assay. This is a whole blood assay that can be used to measure the level of platelet P2Y12 receptor blockade which may indicate patients more or less likely to respond to clopidogrel in preventing to prevent thrombotic complications of acute coronary syndromes and percutaneous coronary intervention.

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13.3 Clinical Trials One of the most promising areas in which pharmacogenomic analysis can be applied is in cancer drug development and early-stage clinical trials (Lee et al. 2005). Selective genotyping can be performed in stratifying the trial population (genostratification) to achieve better treatment success in clinical trials. Studies selecting patients with cancer on this basis are already recruiting (see http://www.cancerhelp.org.uk/trials/trials/). Furthermore, a large number of pharmaceutical companies and academic groups that are conducting clinical trials are ensuring that biological samples are collected to facilitate retrospective pharmacogenomic analyses (Mok et al. 2009), which can lead to rapid translation into clinical practise through changes in drug licensing.

13.4 Advances in Genetic Technology New high throughput sequencing platforms have the capacity to generate DNA sequence data such that the whole genome or the entire coded sequence (transcriptome or exome) of patients can be defined. At present these platforms are expensive to buy and operate and restricted to a few centres, but they will be rapidly adopted into the clinical area to form the backbone of routine genetic testing. This whole genome sequence data will allow the production of cancer specific maps of somatic mutations which can identify specific targets for treatment e.g. through the International Cancer Genome Consortium (ICGC) (http://www.icgc.org). A recent report of a patient with adenocarcinoma of the tongue which had metastasized to the lungs was non-responsive to the EGFR inhibitor erlotinib. Whole genome sequencing revealed mutations in PTEN and upregulation of the downstream gene, RET. Interrogation of the DrugBank database (www.drugbank.ca) highlighted the ret protein inhibitor sunitinib, as a potential treatment, which resulted in tumour regression (Check Hayden 2009). At present, this sequencing technology is still very expensive and in the domain of research projects, but refinements will lead to translation into the routine diagnostic setting in the short term. High throughput sequencing also has a higher level of sensitivity for mutation detection than standard sequencing strategies. This has been elegantly demonstrated for EGFR mutation detection in pleural fluid from a patient with a lung adenocarcinoma (Thomas et al. 2006). Family based or large association studies are not feasible to detect rare gene variants causative for severe ADRs and high throughput sequencing has the potential to identify rare genetic variants that are highly penetrant in predisposing to such events. It is likely that tumour expression profiles, already in use in the clinical management of some patients with breast cancer (see Chapter 4) will become more widespread for other tumour types. The decreased cost of microarray analysis and improvements in biocomputing will see further advances in this area.

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Strategies have also been devised to use tumour cell lines with defined somatic mutations, which mirror the changes found in primary chemonaive tumours, to predict response to treatment (Sos et al. 2009).

13.5 Use of Different Biological Samples Somatic mutations can be detected in tumour tissue to predict response to chemotherapy, especially new targeted treatments. However, sampling to provide sufficient tumour material for mutation detection can be problematic. Even in the clinical trial setting, access to tumour tissue from patients to facilitate molecular profiling can be limited (Mok et al. 2009). Different groups have considered many approaches to circumvent this problem by detecting mutations in circulating tumour cells (Maheswaran et al. 2008), free circulating DNA (Board et al. 2008), bile (Chen et al. 2004) and stool (Zou et al. 2009). These less invasive approaches are attractive but require validation before routine adoption to direct treatment decisions.

13.6 Concluding Remarks A greater number of oncology centres are adopting broad pharmacogenomic testing portfolios into the routine management of their cancer patients (Check Hayden 2009). The FDA and EMEA have both considered pharmacogenomic information (see Chapter 10) to make decisions regarding drug licensing and labelling and will continue to do so (Frueh et al. 2008). The requirement of genetic test results before drug prescription will see the capacity and knowledge base around pharmacogenomics increase as it is likely that many future oncology drugs will be licensed on the basis of companion diagnostic tests. Companion tests either in parallel with drug development or that emerge in the post-marketing phase will require close liaison between pharmaceutical companies and diagnostic laboratories to ensure that testing services are available to inform appropriate prescription. In conclusion, the last few years has seen an explosion in the use of pharmacogenomic tests in oncology to tailor treatment. This is likely to continue apace and hopefully will result in “Making cancer treatment safer and more effective.”

References Board RE, Williams VS, Knight L et al (2008) Isolation and extraction of circulating tumor DNA from patients with small cell lung cancer. Ann N Y Acad Sci. 1137:98–107 Check Hayden E (2009) Personalized cancer therapy gets closer. Nature 458:131–132 Chen CY, Shiesh SC, Wu SJ (2004) Rapid detection of K-ras mutations in bile by peptide nucleic acid-mediated PCR clamping and melting curve analysis: comparison with restriction fragment length polymorphism analysis. Clin Chem 50:481–489

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Chowdhury J, Kagiala GV, Pushpakom S et al (2007) Microfluidic platform for single nucleotide polymorphism genotyping of the thiopurine S-methyltransferase gene to evaluate risk for adverse drug events. J Mol Diagn 9:521–529 Frueh FW, Amur S, Mummaneni P et al (2008) Pharmacogenomic biomarker information in drug labels approved by the United States food and drug administration: prevalence of related drug use. Pharmacotherapy 28:992–998 Lee W, Lockhart AC, Kim RB, Rothenberg ML (2005) Cancer pharmacogenomics: powerful tools in cancer chemotherapy and drug development. Oncologist 10:104–111 Leeder JS, Pearce R, Gaedigk A, Modak A, Rosen D (2008). Evaluation of the [13 C]-DM breath test to assess CYP2D6 phenotype. J Clin Pharm 48:2041–2051 Maheswaran S, Sequist LV, Nagrath S et al (2008) Detection of mutations in EGFR in circulating lung-cancer cells. N Engl J Med 359:366–377 Mattison L, Hany Ezzeldin H, Carpenter M, Modak A, Johnson M, Diasio R (2004) Rapid identification of dihydropyrimidine dehydrogenase deficiency by using a novel 2-13 C-Uracil breath test. Clin Can Res 10:2652–2658 Mok T, Wu Y, Thongprasert S et al (2009) Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Eng J Med 361:947–957 Sos ML, Michel K, Zander T et al (2009) Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions. J Clin Invest 119:1727–1740 Thomas RK, Nickerson E, Simons JF et al (2006) Sensitive mutation detection in heterogeneous cancer specimens by massively parallel picoliter reactor sequencing. Nat Med 12:852–855 Yen JL, McLeod HL (2007) Should DPD analysis be required prior to prescribing fluoropyrimidines? Eur J Cancer 43:1011–1016 Zou H, Taylor WR, Harrington JJ et al (2009) High detection rates of colorectal neoplasia by stool DNA testing with a novel digital melt curve assay. Gastroenterology 136:459–470

Glossary: A Number of the Common Terms Used in Pharmacogenetics

ADME absorption, distribution, metabolism and elimination – the sequential processes which a drug undergoes to exert its clinical effect Adverse drug reaction (ADR) any untoward and unintended response in a subject to an investigational medicinal product, which is related to any dose administered to that subject Adjuvant chemotherapy where the tumour has been removed by surgery and chemotherapy is given to treat micrometastatic disease and reduce the likelihood of recurrence Allele an alternative form of a gene (one member of a pair) that is located at a specific position on a specific chromosome Area under the curve (AUC) a measure of drug exposure. It is derived from drug concentration and time so gives a measure how much and how long a drug stays in the body Association study a genetic study which compares the frequencies of genetic variants between affected and unaffected individuals e.g. those experiencing an ADR and those non experiencing an ADR Best supportive care (BSC) treatment administered with the intent to maximize quality of life without including a specific anti-tumour regimen Candidate gene a gene predicted to be associated with a particular trait e.g disease, adverse reaction to a drug Disease free survival (DFS) The length of time after treatment for a specific disease during which a patient survives with no sign of the disease Epigenetic changes in gene expression or phenotype not due to a change in the DNA sequence but caused by other mechanisms affected DNA function e.g. gene silencing by methylation Genetic heterogeneity where variants in many different genes can cause the same phenotype Germline variant

an inherited genetic variant present in all cells in the body

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Haplotype a combination of alleles at multiple loci that are transmitted together on the same chromosome Heterozygous mutant copy)

where two different alleles are present (one wild type and one

Homozygous where two copies of the same allele are present (these may be both wild type or both mutant) Mutation A rare change in a gene sequence resulting in a pathogenic alteration e.g. a disease causing change, altering protein function which can be important following drug exposure Neoadjuvant chemotherapy drug treatment given prior to surgery or radiotherapy, aiming to reduce the size of the cancer before receiving further treatment Overall survival (OS) The length of time people in a study or treatment group remain alive after they were diagnosed with or treated for a disease Pharmacodynamics (PD) the physiological effects of drugs on the body and the mechanisms of drug action and the relationship between drug concentration and effect e.g. effect on receptor binding Pharmacogenetics the study of germline genetic variation resulting in altered absorption, distribution, metabolism and excretion associated with the efficacy or toxicity of a drug Pharmacogenomics the use of genetic information e.g. considering somatic changes in cancer tissue, the use of gene expression profiles to predict drug response or the identification of therapeutic targets Pharmacokinetics (PK) refers to the movement of drug into, through, and out of the body. The time course of its absorption, bioavailability, distribution, metabolism, and excretion Phase I metabolism the introduction or unmasking of polar bodies to activate or inactivate a drug e.g. reactions catalysed by cytochrome P450 system Phase II metabolism the process including conjugation (or derivatization) of drugs’ functional groups with endogenous substrates in order to make the drug water-soluble for renal excretion Polygenic

where variants in muliple genes contribute to a particular phenotype

Polymorphism Posology

a genetic variant e.g. present at >1% allele frequency

study of medical doses

Progression free survival (PFS) measures the proportion of people among those treated for a cancer whose disease will remain stable (without signs of progression) at a specified time after treatment

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Relapse-free survival (RFS) the time to the first relapse or death from any cause, and not including second primaries or other cancers Single nucleotide polymorphism (SNP) an inherited change in the DNA sequence (usually substituting one nucleotide for another) occurring at a frequency of >1% Somatic variant a genetic change present only in a specific tissue in the body, which has not been inherited but occurred during cell division Taqman a fluorescent hybridisation technique to measure gene expression or to determine genotypes Time to progression (TTP) a measure of time after a disease is diagnosed (or treated) until the disease starts to get worse Xenobiotic a chemical (e.g. drug), which is present in an organism but is not normally produced by that organism

Index

A ABCB1, 19, 51, 67, 117–118, 120–121 Absorption, 8, 11–14, 16, 18–19, 49, 128, 138 Acquired resistance, 92 Acute lymphoblastic leukaemia (ALL), 102, 104–105, 107–110, 171 Adjuvant, 3, 6, 46–47, 49–51, 54, 62, 72, 75, 76, 89, 140, 142–143, 172 Adverse drug reaction, 6–7, 62–69 Analgesics, 116, 122 Antidepressants, 53, 115–116, 121–122 Anti-EGFR antibodies, 72–73 Antiemetics, 116, 120, 122–123 Area under the curve (AUC), 20–21, 30, 103–104, 107 Association, 22, 34, 41–43, 53, 65, 68–69, 71, 73, 89, 107–109, 130, 132, 177 Ataxia telangiectasia, 129 Attitudes, 152–153 B Breast cancer, 6, 31, 46–51, 53–55, 129, 131, 140, 142, 152–153, 163, 177 Breath test, 176 C Chemotherapy, 2–6, 12–13, 20–22, 46–53, 55, 62–63, 70–76, 89–97, 102–105, 107, 110, 120, 122–123, 128–129, 133, 178 Circulating DNA, 178 Colorectal cancer, 3, 18, 39, 61–75, 143 Confidentiality, 148–149 Consent, 148–151, 154 Copy number variant, 33 Cost effectiveness analysis, 165–166, 168 Curative, 2–3, 62, 89, 96, 128, 176 CYP2D6, 17, 22, 32, 38, 50, 53–55, 116, 118–123, 139, 154, 165, 168–169, 171, 176 Cytochrome P450, 16, 50, 53, 109, 118, 157

D Dihydrofolate reductase (DHFR), 107–109 Dihydropyrimidine dehydrogenase (DPD), 52, 64–66, 75, 176 Distribution, 11–14, 19, 21, 49, 92, 132, 138 Drug-drug interaction, 15, 17, 21, 138–139, 144 Drug metabolizing enzymes, 15–18, 21, 122 Drug targets, 21, 23, 48–49 Drug transporters, 16, 18–19, 68, 70 E Economic evaluation, 163–172 Efficiency, 64, 163 EGFR (epidermal growth factor receptor), 4, 23, 33, 48, 63, 72–74, 89–95, 97, 140, 142, 177 Endpoints, 5–6, 20–21, 75 ERCC1, 70–71, 95 Erlotinib, 4, 23, 90–92, 94, 96–97, 177 Estrogen receptor, 4, 46–48, 50, 54–55 Ethics, 151 Ethnicity, 133, 155–156 Excretion, 8, 11–16, 18–19, 41, 107, 139 F FISH (fluorescent in situ hybridisation), 49, 72, 93, 172 5-fluorouracil, 4, 64 Food and Drug Administration (FDA), 6–7, 68, 90, 107, 140, 142, 156, 178 G Gefitinib, 4, 23, 90–94, 97 Genetics, 28, 43, 129, 148–149, 151 Genome wide association studies (GWAS), 34, 42, 132 Genotyping, 18, 37–38, 40–42, 55, 65, 69–70, 92, 107, 110, 165, 176–177

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186 Germline mutation, 130, 142–143 GSTP1, 71 H HER2, 23, 46–49, 54, 140, 142, 152–153, 163, 172 High throughput sequencing, 177 5-HT3 receptor antagonists, 120 J Irinotecan, 4, 17–18, 33, 62–63, 66–69, 71–74, 95, 107, 109, 165, 167 K KRAS, 39, 73–75, 140, 143 L Linkage, 41 M 6-mercaptopurine (6-MP), 4, 22, 105, 109–110 Metabolism, 5, 11–19, 21, 40–42, 45–46, 49–52, 64, 66–67, 101, 104, 108, 110, 115–116, 118–121, 138, 141, 153, 155, 157 Methotrexate, 48, 103–104, 107–109 Mu receptor, 122 Mutation, 30–33, 36, 43, 66, 70, 72–74, 88, 90–97, 118, 120, 129–131, 140, 142–143, 171, 176–178 N National Institute for Health and Clinical Excellence (NICE), 89, 163–164, 169–170 Non small cell lung cancer (NSCLC), 3, 33, 72, 89–97, 142 Non steroidal anti-inflammatory drugs (NSAIDs), 119, 122 O Oncotype DX, 47–48, 55 Opioids, 116–120, 122 Opportunity cost, 162–163 OPRM1, 116, 118 Outcome, 2–3, 5–8, 12, 22, 40–43, 47, 53–54, 65, 70, 72–74, 108, 110, 132, 139–141, 143, 154, 156, 164–168, 171 Oxaliplatin, 62–63, 70–72, 74–75 P Palliative, 2, 3, 6, 89, 91, 95, 106, 118, 121–122 Pharmaceutical industry, 138–139, 142–144 Pharmacodynamics, 12, 20, 23, 109, 119 Pharmacogenetics, 2, 5, 8, 27–43, 45–55, 61–75, 87–97, 101–110, 115–123, 137–144, 147–157, 161–173

Index Pharmacokinetics, 11–13, 23, 68, 103–104, 107, 118–120, 138 Phase I metabolism, 16–18 Phase II metabolism, 18 Point of care testing, 176 Q QT, 139 Quality adjusted life years (QALYs), 166–167, 169–170, 172–173 R Race, 155–157 Radiation, 94, 128–133 Radiation toxicity, 128–129 Radiogenomics, 2, 132 Radiosensitivity, 129–130, 132 Registration, 4, 138–141, 144 Resources, 141, 148, 162–164, 171–172 Response, 2–3, 5, 7, 12–13, 15, 18, 20–22, 33, 40–43, 48, 49, 51–52, 63–65, 69–74, 90–97, 101–105, 108–109, 116–121, 128, 131–133, 140, 142–144, 148, 152, 163, 167, 176, 178 S Single nucleotide polymorphism (SNP), 31, 33–42, 46, 50–52, 65, 70–71, 73, 106, 116–117, 130–131 Somatic mutation, 43, 90, 171, 177–178 Stratifying, 133, 177 T Tamoxifen, 3–4, 22, 45–46, 48–50, 53–55, 154–155, 165, 168–169, 176 Thiopurine methyltransferase (TPMT), 22, 105–107, 109–110, 153–154, 165 Topoisomerase, 4, 18, 66, 71 Toxicity, 2–3, 7–8, 12–14, 16, 18, 21–22, 33, 49–52, 63, 66, 68–70, 73, 75–76, 91, 95, 101–105, 107, 109–110, 119, 128–133, 141 Trastuzumab (Herceptin), 23, 46, 48–49, 140, 143, 152–153, 163, 172 U UGT1A1, 18, 33, 67, 68–70, 95, 107, 109, 119, 165, 167 V VEGF, 63, 72, 74, 131 W Warnings, 138