Predicting Cancer Drug Response Using a Recommender System
Dr Nagarajan, Niranjan, Adjunct Associate Professor, School of Computing
21 Aug 2018 Tuesday, 09:00 AM to 10:30 AM
COM2 Level 4
Executive Classroom, COM2-04-02
As 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 with the goal of providing the right drug for the right patient at the right time.
We 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) 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.
Currently, we are investigating several aspects to improve performance and interpretability of CaDRReS, starting from modifying the model to enhance the predictive performance for unseen cell lines/patients, extending the model by integrating mutation data, and applying the model to a single-cell dataset to study drug response heterogeneity within a tumor.