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-

doi:http://dx.doi.org/10.1186/s13073-020-00774-x

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

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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

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.