Journal
GENOME MEDICINE
Volume 12, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13073-020-00774-x
Keywords
Drug-response biomarkers; Driver co-occurrence networks; Precision oncology
Categories
Funding
- FPI fellowship
- Spanish Ministerio de Economia y Competitividad [BIO2016-77038-R]
- European Research Council [SysPharmAD: 614944]
- Generalitat de Catalunya [VEIS 001-P-001]
- Miguel Servet grant from ISCIII [CPII19/00033]
- AGAUR [2017 SGR 540]
- GHD-Pink (FERO foundation)
- FI-AGAUR
- NIH [P30 CA008748, RO1CA190642]
- Breast Cancer Research Foundation
- Breast Cancer Alliance
- Juan de la Cierva fellowship [MJCI-2015-25412]
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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.
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