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Cancer pharmacogenomics: strategies and challenges

Key Points

  • Pharmacogenomics aims to understand how genetic variation influences drug efficacy and toxicity and is especially relevant in oncology because failed treatment is often life-threatening. This Review focuses on the challenges involved in studying the influence of germline genetic variation on cancer pharmacogenomic phenotypes.

  • The ideal way of attributing phenotypic effects to a drug of interest is lack of effect in a control group that did not receive the drug and presence of an effect in a treatment group that received a single oncology drug at a standardized dose but is not always attainable in cancer pharmacogenomic studies.

  • Common clinical phenotypes used in cancer pharmacogenomics include toxicity measures, tumour response, progression-free survival and overall survival. Key endophenotypes include drug or metabolite clearance, enzyme activity, gene expression, methylation patterns and serum protein levels.

  • Tumour samples are a mixture of cancer and normal cells and should be avoided as a source of DNA in germline pharmacogenomic studies. Somatic mutations may define disease subtype and can be used as covariates or endophenotypes in germline cancer pharmacogenomic studies.

  • Because of consistent drug dosing and phenotype collection, clinical trials are an ideal infrastructure for pharmacogenomic studies of oncology drugs. However, appropriate replication trials of sufficient sample size are not always feasible, so researchers may need to turn to cell and animal models before and/or after clinical trial studies to generate hypotheses or validate findings.

  • As has been proposed for complex disease susceptibility, cancer pharmacogenomic traits probably have multiple common and rare variants that, when combined, predict response to therapy. Sophisticated analysis tools implemented by statistical geneticists are needed to explore fully the genetics of cancer drug-induced traits.

Abstract

Genetic variation influences the response of an individual to drug treatments. Understanding this variation has the potential to make therapy safer and more effective by determining selection and dosing of drugs for an individual patient. In the context of cancer, tumours may have specific disease-defining mutations, but a patient's germline genetic variation will also affect drug response (both efficacy and toxicity), and here we focus on how to study this variation. Advances in sequencing technologies, statistical genetics analysis methods and clinical trial designs have shown promise for the discovery of variants associated with drug response. We discuss the application of germline genetics analysis methods to cancer pharmacogenomics with a focus on the special considerations for study design.

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Figure 1: Steps in cancer pharmacogenomic study design.
Figure 2: Negative relationship between sample size and drug treatment consistency in cancer pharmacogenomics.

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Acknowledgements

This work is supported by the following US National Institutes of Health grants: U01GM61393, R01CA136765, K23CA124802, T32CA009594 and F32CA165823. In addition, M.J.R. is a recipient of a Conquer Cancer Foundation of ASCO Translational Research Professorship, In Memory of Merrill J. Egorin, MD. Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect those of the American Society of Clinical Oncology or the Conquer Cancer Foundation.

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Correspondence to Mark J. Ratain.

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Mark J. Ratain receives royalties related to the use of UDP glucuronosyltransferase 1 family, polypeptide A1 (UGT1A1) genotyping in conjunction with irinotecan. Heather E. Wheeler, Michael L. Maitland, M. Eileen Dolan and Nancy J. Cox declare no competing financial interests.

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FURTHER INFORMATION

1000 Genomes Project

ClinicalTrials.gov

CPIC: Clinical Pharmacogenetics Implementation Consortium

Gene Ontology

Genomics > Table of Pharmacogenomic Biomarkers in Drug Labels

HapMap Homepage

Imaging Response Criteria — Cancer Imaging Program

KEGG PATHWAY database

Nature Reviews Genetics Series on Study designs

Nature Reviews Genetics Series on Translational genetics

Pharmacogenomics of Anticancer Agents Research Group

The Pharmacogenomics Knowledgebase (PharmGKB)

Protocol Development (Common Terminology Criteria for Adverse Events)

Summary Minutes of the Pediatric Oncology Subcommittee of the Oncologic Drugs Advisory Committee July 15, 2003

Glossary

Efficacy

In oncology, this term refers to measures such as tumour response, progression-free survival and overall survival.

Pharmacokinetics

The effect of the body on the drug: that is, the process by which a drug is absorbed, distributed, metabolized and eliminated by the body.

Pharmacodynamics

The effect of the drug on the body: that is, drug targets and mechanisms of action.

Nested case–control design

A case–control study in which only a subset of controls is compared to the cases by matching controls to the cases on known covariates that associate with the phenotype of interest. It increases efficiency and may reduce genotyping costs.

Adverse events

Toxicities or side effects attributed to the use of a particular drug.

Common Terminology Criteria for Adverse Events

(CTCAE). Organizes adverse events by body system and rates each specific event according to a 1–5 scale: 1, mild but not warranting intervention; 2, moderate with medical intervention or temporary cessation of treatment warranted; 3, severe requiring intensive medical intervention or hospitalization; 4, life-threatening; and 5, death.

Tumour response

How a tumour changes or does not change in size after a particular treatment regimen.

Fixed effects models

A type of meta-analysis that combines the effect sizes (estimates) across studies that each have the same phenotype measured on the same scale and assumes the genetic effects are the same across the different studies.

Random effects models

A type of meta-analysis that combines the effect sizes (estimates) across studies with the same phenotypic measurement, allows the genetic effects to be different across the different studies and provides a measure of heterogeneity across the studies.

Z scores

A statistical measure that quantifies the number of standard deviations that an observed data point is from the expected value under no association.

Bayesian models

A statistical framework that incorporates uncertainty in prior beliefs about parameters such as between-study variance, effect size and genetic model (that is, additive and dominant) into association testing.

Winner's curse phenomenon

Refers to the overestimation of the effect size of a newly identified genetic association because many genome-wide association studies are underpowered for detecting small genetic effects at a stringent genome-wide significance level. It implies that the sample size required for a confirmatory study will be underestimated, resulting in failure to replicate the association.

Censoring

A type of missing data problem that occurs when the value of a measurement is only partially known (for example, in survival analysis, it might be known only that the date of death is sometime after the date of last patient contact).

Extreme phenotype hypothesis

The assumption that individuals with the most severe drug response phenotypes are more likely to carry alleles that associate with the phenotypes.

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Wheeler, H., Maitland, M., Dolan, M. et al. Cancer pharmacogenomics: strategies and challenges. Nat Rev Genet 14, 23–34 (2013). https://doi.org/10.1038/nrg3352

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