Feng, Xiaodong_ Xie, Hong-Guang - Applying pharmacogenomics in therapeutics-CRC Press (2016)

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56 Applying Pharmacogenomics in Therapeuticsreflecting cellular functions, is a complex trait itself that may be affected by bothgenetic and nongenetic factors (Figure 3.1), integrating SNP genotypic data withquantitative gene expression levels using the HapMap Project 17,18 samples, a widelyused human genetics model, have demonstrated that common genetic variants, knownas eQTL (expression quantitative trait loci) contributed substantially to gene expressionvariation within and between populations. 20–24 Given that gene expression is sucha fundamental phenotype that may be intermediate to other cellular and whole-bodyphenotypes, it is likely that genetic control of complex traits (drug response) could bethrough eQTL that regulate gene expression in relevant pathways. For the variabilityof drug response, though the extent of genetic contribution is still controversial, atleast some of the individual variations in drug response phenotypes may be attributedto the 0.1% genetic differences between individuals.APPROACHES TO IDENTIFYINGPHARMACOGENOMIC BIOMARKERSIn general, the research approaches used in pharmacogenomic research are similarto those used in other complex trait studies, though investigation of drug responsemay present certain unique challenges in study design and data analysis. An importantlimitation for pharmacogenomic research is that it can be both unethical anddifficult to identify healthy controls in a conventional case–control association study,because many drugs (cytotoxic anticancer drugs) may cause serious harms to healthyindividuals. Therefore, in vitro systems, such as cell lines, are often used in pharmacogenomicdiscovery. For example, for cytotoxicities induced by anticancer agents,the LCLs samples from the HapMap Project 17,18 have been developed as a cell-basedmodel. 25,26 Significant progress in pharmacogenomic discovery for chemotherapiesof various mechanisms, such as cisplatin, 27 etoposide, 28 and daunorubicin, 29 has beenmade using this model by integrating the extensive genotypic and gene expressiondata available for these samples. 30There are two general research frameworks in pharmacogenomic discovery:the candidate-gene approach and the whole-genome approach (Figure 3.3). Studiesapplying the candidate-gene approach focus on selected genes or pathways withknown functions and/or relationships with a drug response phenotype, such as genesknown to be involved in pharmacokinetics and pharmacodynamics. In contrast,studies applying the whole-genome approach aim to identify pharmacogenomicloci associated with drug response through an unbiased, genome-wide scan. A combinationof these two frameworks is often necessary for elucidating the geneticcontribution to a particular drug response phenotype under investigation.Statistically, various analytical approaches and methods have been utilized toidentify genetic loci associated with complex traits. Genome-wide association studies(GWASs) have been used to map genetic loci associated with more than 500 complextraits including common human traits (adult height and skin color) and risks for complexdiseases, such as cancers, cardiovascular diseases, and psychiatric disorders. 19Specifically, genetic association studies, which take advantage of the characteristicsof human genetic variations (linkage between a genotyped variant and an nontyped
Essential Pharmacogenomic Biomarkers in Clinical Practice57Candidate-gene approachWhole-genome approachCandidate genesGenome-wide molecular profilesBioinformatics/biostatisticsCandidate biomarkersBiomarkers of drug responseFIGURE 3.3 General approaches to detecting biomarkers of drug response. Either aknowledge-driven candidate gene or an unbiased, whole-genome approach may be used todetect biomarkers (genetic variants) of drug response phenotypes.causal variant), have been extensively utilized to identify pharmacogenomic biomarkersor primarily genetic variants associated with drug response phenotypes.Some essential pharmacogenomic biomarkers identified from previous associationstudies are introduced in the following sections.PHARMACOGENOMIC BIOMARKERSWITH CLINICAL IMPLICATIONSThe Pharmacogenomics Knowledgebase (PharmGKB) (http://www.pharmgkb.org/) 31 guidelines were followed to obtain a list of essential pharmacogenomic biomarkersfor drugs used to treat common, complex diseases with the strongest levelof evidence for association and clinical implications. Specifically, we focus on essentialpharmacogenomic biomarkers that contain a genetic variant–drug combinationin a CPIC (Clinical Pharmacogenetics Implementation Consortium) 32 or medicalsociety–endorsed pharmacogenomic guideline as well as those implemented at aPGRN (Pharmacogenomics Research Network) 33 site or another major health system.In addition, essential pharmacogenomic biomarkers must contain a variant–drugcombination where the preponderance of evidence shows an association, and mustbe replicated in more than one cohort with significant p-values, and preferably, witha strong effect size. Table 3.1 shows some essential pharmacogenomic biomarkersby disease type. Given the important roles of drug-metabolizing enzymes, many ofthe current pharmacogenomic biomarkers with clinical implementation are geneticvariants located in these enzymes: for example, cytochrome P450 (CYP) genes.
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DedicationWe dedicate this book to
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viiiContentsChapter 10 Pharmacogeno
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EditorsXiaodong Feng, PhD, PharmD,
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ContributorsWeiguo Chen, PhDDepartm
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11 PharmacoeconogenomicsA Good Marr
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BIOMEDICAL SCIENCEApplying Pharmaco
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Feng, Xiaodong_ Xie, Hong-Guang - Applying pharmacogenomics in therapeutics-CRC Press (2016)

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