Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns
Lídia Mateo, Miquel Duran‐Frigola, Albert Gris‐Oliver, Marta Palafox, Maurizio Scaltriti, Pedram Razavi, Sarat Chandarlapaty, Joaquı́n Arribas, Meritxell Bellet, Violeta Serra, Patrick Aloy
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.