Predicting Cancer Drug Response Using a Recommender System

Abstract

MotivationAs we move towards an era of precision medicine, the ability to predict patient-specific drug responses in cancer based on molecular information such as gene expression data represents both an opportunity and a challenge. In particular, methods are needed that can accommodate the high-dimensionality of data to learn interpretable models capturing drug response mechanisms, as well as providing robust predictions across datasets.\n\nResultsWe propose a method based on ideas from \"recommender systems\" (CaDRReS) that predicts cancer drug responses for unseen cell-lines/patients based on learning projections for drugs and cell-lines into a latent \"pharmacogenomic\" space. Comparisons with other proposed approaches for this problem based on large public datasets (CCLE, GDSC) shows that CaDRReS provides consistently good models and robust predictions even across unseen patient-derived cell-line datasets. Analysis of the pharmacogenomic spaces inferred by CaDRReS also suggests that they can be used to understand drug mechanisms, identify cellular subtypes, and further characterize drug-pathway associations.\n\nAvailabilitySource code and datasets are available at https://github.com/CSB5/CaDRReS\n\nContactnagarajann@gis.a-star.edu.sg\n\nSupplementary informationSupplementary data are available online.