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.
Drug-response biomarkers; Driver co-occurrence networks; Precision oncology
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;12:78.
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