Mateo, LidiaDuran-Frigola, Miquel, 1985-Gris-Oliver, AlbertPalafox, MartaScaltriti, MaurizioRazavi, PedramChandarlapaty, SaratArribas, JoaquínBellet, MeritxellSerra, VioletaAloy, Patrick, 1972-2022-05-162022-05-162020Mateo L, Duran-Frigola M, Gris-Oliver A, Palafox M, Scaltriti M, Razavi P et al. Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns. Genome Med. 2020 Sep 9;12(1):78. DOI:10.1186/s13073-020-00774-x1756-994Xhttp://hdl.handle.net/10230/53090Identification 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.application/pdfeng© Lidia Mateo et al. 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were madeCàncer -- TractamentOncologiaGenòmicainfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1186/s13073-020-00774-xinfo:eu-repo/semantics/openAccess