A community challenge for a pancancer drug mechanism of action inference from perturbational profile data
Eugene F. Douglass, Robert J. Allaway, Bence Szalai, Wenyu Wang, Tingzhong Tian, Adrià Fernández‐Torras, Ron Realubit, Charles Karan, Shuyu Zheng, Alberto Pessia, Ziaurrehman Tanoli, Mohieddin Jafari, Fang Wan, Shuya Li, Yuanpeng Xiong, Miquel Duran‐Frigola, Martino Bertoni, Pau Badia-i-Mompel, Lídia Mateo, Oriol Guitart-Pla, Verena Chung, Jing Tang, Jianyang Zeng, Patrick Aloy, Julio Sáez-Rodríguez, Justin Guinney, Daniela S. Gerhard, Andrea Califano
Abstract
drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.