PREDICTING CANCER DRUG RESPONSE USING A RECOMMENDER SYSTEM

CHAYAPORN SUPHAVILAI

Publication

PREDICTING CANCER DRUG RESPONSE USING A RECOMMENDER SYSTEM

CHAYAPORN SUPHAVILAI

Abstract

As we move towards an era of precision medicine, the ability to predict patient-specific drug responses in cancer based on molecular information represents both opportunities and challenges. In particular, methods are needed that can accommodate the high-dimensionality of data to learn interpretable models with the goal of providing the right drug for the right patient. We propose a method based on ideas from recommender systems (CaDRReS) that predicts patient-specific drug responses based on learning projections for drugs and cell-lines into a latent pharmacogenomic space. Comparisons with other proposed approaches on large public datasets show that CaDRReS provides consistently good models and robust predictions even across unseen patient-derived cell-line datasets. Also, analysis of the pharmacogenomic spaces inferred by CaDRReS can be used to understand drug mechanisms, identify cellular subtypes, and characterize drug-pathway associations. Finally, we propose a modified version of CaDRReS for single-cell RNA-seq data to investigate intra-patient drug response heterogeneity.