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- Title
Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns.
- Authors
Mateo, Lidia; Duran-Frigola, Miquel; Gris-Oliver, Albert; Palafox, Marta; Scaltriti, Maurizio; Razavi, Pedram; Chandarlapaty, Sarat; Arribas, Joaquin; Bellet, Meritxell; Serra, Violeta; Aloy, Patrick
- 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.
- Subjects
PROGRESSION-free survival; CANCER treatment; FORECASTING; XENOGRAFTS; ONCOLOGY; PHARMACOGENOMICS
- Publication
Genome Medicine, 2020, Vol 12, Issue 1, pN.PAG
- ISSN
1756-994X
- Publication type
Article
- DOI
10.1186/s13073-020-00774-x