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

   


Authors: Mateo, L.; Duran-Frigola, M.; Gris-Oliver, A.; Palafox, M.; Scaltriti, M.; Razavi, P.; Chandarlapaty, S.; Arribas, J.; Bellet, M.; Serra, V.; Aloy, P.
Article Title: Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns
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.
Keywords: precision oncology; driver co-occurrence networks; drug-response biomarkers
Journal Title: Genome Medicine
Volume: 12
ISSN: 1756-994X
Publisher: Biomed Central Ltd  
Date Published: 2020-09-09
Start Page: 78
Language: English
DOI: 10.1186/s13073-020-00774-x
PUBMED: 32907621
PROVIDER: scopus
PMCID: PMC7488324
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090820847&doi=10.1186%2fs13073-020-00774-x&partnerID=40&md5=75e03d1170f04594e633aa9ee6b1e91a
Notes: Article -- Export Date: 1 October 2020 -- Source: Scopus
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MSK Authors
  1. Sarat Chandarlapaty
    277 Chandarlapaty
  2. Maurizio Scaltriti
    169 Scaltriti
  3. Pedram Razavi
    172 Razavi
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