Chayaporn Suphavilai1,2, Denis Bertrand2, Niranjan Nagarajan2. 1. Department of Computer Science, School of Computing, National University of Singapore, Singapore. 2. Computational and Systems Biology, Genome Institute of Singapore, Singapore.
Abstract
Motivation: As we move toward 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. Results: 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 and GDSC) show 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. Availability and implementation: Source code and datasets are available at https://github.com/CSB5/CaDRReS. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: As we move toward 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. Results: 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 and GDSC) show 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. Availability and implementation: Source code and datasets are available at https://github.com/CSB5/CaDRReS. Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: Fangfang Xia; Jonathan Allen; Prasanna Balaprakash; Thomas Brettin; Cristina Garcia-Cardona; Austin Clyde; Judith Cohn; James Doroshow; Xiaotian Duan; Veronika Dubinkina; Yvonne Evrard; Ya Ju Fan; Jason Gans; Stewart He; Pinyi Lu; Sergei Maslov; Alexander Partin; Maulik Shukla; Eric Stahlberg; Justin M Wozniak; Hyunseung Yoo; George Zaki; Yitan Zhu; Rick Stevens Journal: Brief Bioinform Date: 2022-01-17 Impact factor: 11.622