Abstract
The availability of systematic drug response registries for hundreds cell lines, coupled with the comprehensive profiling of their genomes/transcriptomes enabled the development of computational methods that investigate the molecular basis of drug responsiveness. Herein, we propose an automated, multi-omics systems pharmacology method that identifies genomic markers of anti-cancer drug response. Given a cancer type and a therapeutic compound, the method builds two cell line groups on the antipodes of the drug response spectrum, based on the outer quartiles of the maximum micromolar screening concentration. The method intersects cell lines that share common features in their mutation status, gene expression levels or copy number variants, and a pool of drug response biomarkers (core genes) is built, using genes with mutually exclusive alterations in the two cell line groups. The relevance with the drug target pathways is then quantified, using the combined interaction score of the core genes and an accessory protein network having strong, physical/functional interactions. We demonstrate the applicability and effectiveness of our methodology in three use cases that end up in known drug-gene interactions. The method steps into explainable bioinformatics approaches for novel anticancer drug-gene interactions, offering high accuracy and increased interpretability of the analysis results. Availability: https://github.com/PGxAUTH/PGxGDSC.