Large-Scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines

Perturbation biology is a powerful approach to modeling quantitative cellular behaviors and understanding detailed disease mechanisms. However, large-scale protein response resources of cancer cell lines to perturbations are not available, resulting in a critical knowledge gap. Here we generated and compiled perturbed expression profiles of similar to 210 clinically relevant proteins in >12,000 cancer cell line samples in response to similar to 170 drug compounds using reverse-phase protein arrays. We show that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective drug combinations. We build a systematic map of "proteindrug'' connectivity and develop a user-friendly data portal for community use. Our study provides a rich resource to investigate the behaviors of cancer cells and the dependencies of treatment responses, thereby enabling a broad range of biomedical applications.