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

Mateo, Lidia; Duran-Frigola, Miquel, 1985-; Gris-Oliver, Albert; Palafox, Marta; Scaltriti, Maurizio; Razavi, Pedram; Chandarlapaty, Sarat; Arribas, Joaquín; Bellet, Meritxell; Serra, Violeta; Aloy, Patrick, 1972-

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

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dc.contributor.author Mateo, Lidia
dc.contributor.author Duran-Frigola, Miquel, 1985-
dc.contributor.author Gris-Oliver, Albert
dc.contributor.author Palafox, Marta
dc.contributor.author Scaltriti, Maurizio
dc.contributor.author Razavi, Pedram
dc.contributor.author Chandarlapaty, Sarat
dc.contributor.author Arribas, Joaquín
dc.contributor.author Bellet, Meritxell
dc.contributor.author Serra, Violeta
dc.contributor.author Aloy, Patrick, 1972-
dc.date.accessioned 2022-05-16T10:33:56Z
dc.date.available 2022-05-16T10:33:56Z
dc.date.issued 2020
dc.description.abstract 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.
dc.description.sponsorship 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, RO1CA190642
dc.format.mimetype application/pdf
dc.identifier.citation Mateo 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-x
dc.identifier.doi http://dx.doi.org/10.1186/s13073-020-00774-x
dc.identifier.issn 1756-994X
dc.identifier.uri http://hdl.handle.net/10230/53090
dc.language.iso eng
dc.publisher BioMed Central
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/614944
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/BIO2016-77038-R
dc.rights © 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 made
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject.other Càncer -- Tractament
dc.subject.other Oncologia
dc.subject.other Genòmica
dc.title Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns
dc.type info:eu-repo/semantics/article
dc.type.version info:eu-repo/semantics/publishedVersion

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