Combining drugs can enhance their clinical efficacy, but the number of possible combinations and inter-tumor heterogeneity make identifying effective combinations challenging, while existing approaches often overlook clinically relevant activity. We screen one of the largest cell line panels (N = 757) with 51 clinically relevant combinations and identify responses at the level of individual cell lines and tissue populations. We establish three response classes to model cellular effects beyond monotherapy: synergy, Bliss additivity, and independent drug action (IDA). Synergy is rare (11% of responses) and frequently efficacious (>50% viability reduction), whereas Bliss and IDA are more frequent but less frequently efficacious. We introduce “efficacious combination benefit” (ECB) to describe high-efficacy responses classified as either synergy, Bliss, or IDA. We identify ECB biomarkers in vitro and show that ECB predicts response in patient-derived xenografts better than synergy alone. Our work here provides a valuable resource and framework for preclinical evaluation and the development of combination treatments.

Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancerdriven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully
recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance
of tissue lineage in mediating drug response. Logicbased modeling uncovers combinations of alterations that sensitize to drugs, while machine learning
demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.