Pharmacogenomics of anticancer drugs: Personalising the choice and dose to manage drug response

The field of pharmacogenomics has made great strides in oncology over the last 20 years and indeed a significant number of pre-emptive genetic tests are now routinely undertaken prior to anticancer drug administration. Many of these gene-drug interactions are the fruits of candidate gene and genome-wide association studies, which have largely focused on common genetic variants (allele frequency>1%). Examples where there is clinical utility include genotyping or phenotyping forG6PDto prevent rasburicase-induced RBC haemolysis, andTPMTto prevent thiopurine-induced bone marrow suppression. Other associations such as CYP2D6 status in determining the efficacy of tamoxifen are more controversial because of contradictory evidence from different sources, which has led to variability in the implementation of testing. As genomic technology becomes ever cheaper and more accessible, we must look to the additional data our genome can provide to explain interindividual variability in anticancer drug response. Clearly genes do not act on their own and it is therefore important to investigate genetic factors in conjunction with clinical factors, interacting concomitant drug therapies and other factors such as the microbiome, which can all affect drug disposition. Taking account of all of these factors, in conjunction with the somatic genome, is more likely to provide better predictive accuracy in determining anticancer drug response, both efficacy and safety. This review summarises the existing knowledge related to the pharmacogenomics of anticancer drugs and discusses areas of opportunity for further advances in personalisation of therapy in order to improve both drug safety and efficacy.

Automated summary

Pharmacogenomics has made significant progress in oncology in the past 20 years, focusing on common genetic variants such as G6PD and TPMT, as well as controversial genes like CYP2D6 in determining drug efficacy. Investigating the interaction between genetic factors and clinical factors, concurrent drug therapies, and the microbiome can improve the accuracy of predicting anticancer drug response.