Understanding drug resistance in cancer is paramount to improving patient outcomes, quality of life and reducing toxicities in patients receiving chemotherapy. Pharmacogenomic methods seek to understand the interaction of genomic variation and response to chemotherapeutic treatment. This chapter presents a workflow to interrogate multiple genomic inputs and individually assess their relationship with the phenotype of drug resistance using hierarchical clustering to determine the set of features that can best describe what features are associated with drug resistance. Then in a gene-centric manner regulatory features such as miRNAs, SNPs, or DNA methylation can be related back to the differential expression of genes to give understanding to the mechanism underlying resistance. In this chapter, we describe a computational method that can be adapted to a number of different diseases and phenotypes in which there are multiple genomic data types available with concordant phenotypic drug resistance information.
Keywords: Computational biology; Drug resistance; Integrative genomics; Pharmacogenomics; Polygenomic analysis.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.