Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns

Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients' progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.Funding: L.M. is a recipient of an FPI fellowship. P.A. acknowledges the support of the Spanish Ministerio de Economía y Competitividad (BIO2016-77038-R), the European Research Council (SysPharmAD: 614944), and the Generalitat de Catalunya (VEIS 001-P-001). V.S. is a recipient of a Miguel Servet grant from ISCIII (CPII19/00033) and receives funds from AGAUR (2017 SGR 540). The PDX program is supported by a GHD-Pink (FERO foundation) grant to V.S., A.G.-O. and M.P. received a FI-AGAUR and a Juan de la Cierva (MJCI-2015-25412) fellowship, respectively. M.S., P.R., and S.C. acknowledge the support of the NIH grants P30 CA008748, RO1CA19064