In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities - PubMed (original) (raw)

In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities

Carlota Rubio-Perez et al. Cancer Cell. 2015.

Free article

Abstract

Large efforts dedicated to detect somatic alterations across tumor genomes/exomes are expected to produce significant improvements in precision cancer medicine. However, high inter-tumor heterogeneity is a major obstacle to developing and applying therapeutic targeted agents to treat most cancer patients. Here, we offer a comprehensive assessment of the scope of targeted therapeutic agents in a large pan-cancer cohort. We developed an in silico prescription strategy based on identification of the driver alterations in each tumor and their druggability options. Although relatively few tumors are tractable by approved agents following clinical guidelines (5.9%), up to 40.2% could benefit from different repurposing options, and up to 73.3% considering treatments currently under clinical investigation. We also identified 80 therapeutically targetable cancer genes.

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Seek and destroy: relating cancer drivers to therapies.
Martinez-Ledesma E, de Groot JF, Verhaak RG. Martinez-Ledesma E, et al. Cancer Cell. 2015 Mar 9;27(3):319-21. doi: 10.1016/j.ccell.2015.02.011. Cancer Cell. 2015. PMID: 25759016
In silico analysis of tumors identifies cancer treatment strategies.
[No authors listed] [No authors listed] Cancer Discov. 2015 May;5(5):OF16. doi: 10.1158/2159-8290.CD-RW2015-054. Epub 2015 Mar 26. Cancer Discov. 2015. PMID: 25813349 No abstract available.
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Cully M. Cully M. Nat Rev Drug Discov. 2015 May;14(5):311. doi: 10.1038/nrd4619. Epub 2015 Apr 24. Nat Rev Drug Discov. 2015. PMID: 25907342 No abstract available.

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In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities - PubMed (original) (raw)