In Silico Prescription of Anticancer Drugs to Cohorts of 28 Tumor Types Reveals Targeting Opportunities

• Published in: Cancer cell Vol. 27; no. 3; pp. 382 - 396
• Main Authors: Rubio-Perez, Carlota, Tamborero, David, Schroeder, Michael P., Antolín, Albert A., Deu-Pons, Jordi, Perez-Llamas, Christian, Mestres, Jordi, Gonzalez-Perez, Abel, Lopez-Bigas, Nuria
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 09-03-2015; Elsevier
• Subjects: Antineoplastic Agents; Carcinogenesis - genetics; Clinical Protocols; Clinical Trials as Topic; Cohort Studies; Computational Biology; Decision Making, Computer-Assisted; DNA Mutational Analysis; Drug Repositioning; Humans; Neoplasms - drug therapy; Neoplasms - genetics; Precision Medicine - methods
• ISSN: 1535-6108; 1878-3686
• DOI: 10.1016/j.ccell.2015.02.007

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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•Driver genes are comprehensively identified across a large pan-cancer cohort•In silico prescription links approved or experimental targeted therapies to patients•Up to 73.3% of patients could benefit from agents in clinical stages•80 therapeutically unexploited targetable cancer driver genes are identified
Using a large pan-cancer cohort, Rubio-Perez et al. develop an in silico drug prescription strategy based on driver alterations in each tumor and their druggability options and use it to identify druggable targets and promising repurposing opportunities.