Pharmacogenomics of chemotherapeutic susceptibility and toxicity

Abstract

The goal of personalized medicine is to tailor a patient's treatment strategy on the basis of his or her unique genetic make-up. The field of oncology is beginning to incorporate many of the strategies of personalized medicine, especially within the realm of pharmacogenomics, which is the study of how inter-individual genetic variation determines drug response or toxicity. A main objective of pharmacogenomics is to facilitate physician decision-making regarding optimal drug selection, dose and treatment duration on a patient-by-patient basis. Recent advances in genome-wide genotyping and sequencing technologies have supported the discoveries of a number of pharmacogenetic markers that predict response to chemotherapy. However, effectively implementing these pharmacogenetic markers in the clinic remains a major challenge. This review focuses on the contribution of germline genetic variation to chemotherapeutic toxicity and response, and discusses the utility of genome-wide association studies and use of lymphoblastoid cell lines (LCLs) in pharmacogenomic studies. Furthermore, we highlight several recent examples of genetic variants associated with chemotherapeutic toxicity or response in both patient cohorts and LCLs, and discuss the challenges and future directions of pharmacogenomic discovery for cancer treatment.

Keywords: International HapMap Project; Pharmacogenomics; chemotherapeutics; clinical translation; genome-wide association studies.

Figure 1

Integration of LCL datasets allows for comprehensive investigation of genotype-phenotype relationships. Genotype information can be found in the International HapMap Project or 1000 Genomes Project databases. Publicly available cytosine modification and microRNA data can be included to identify SNPs associated with these epigenetic factors. Genetics and epigenetics can both influence gene transcriptional activity, which may ultimately lead to variation in pharmacological phenotypes.

Figure 2

Translation between cell-based models and clinical studies is bidirectional. The identification of SNPs associated with drug response from a GWAS in LCLs has to be confirmed in patient studies to determine clinical significance. Conversely, SNPs associated with drug response that are identified in a patient cohort and are confirmed in a validation cohort can be experimentally tested in the LCL model to determine biological significance.