In Silico Analysis of Tumors Identifies Cancer Treatment Strategies

The advent of high-throughput sequencing technology has enabled the compilation of thousands of cancer patient genomes and exomes. These data identify alterations in genes and pathways that contribute to tumorigenesis in various tumor types and may lead to development of personalized therapeutic strategies. Rubio-Perez, Tamborero, and colleagues exploited large pan-cancer patient cohorts and developed a three-step in silico drug prescription strategy that assigns to each patient the targeted therapeutic interventions that would be most beneficial based on cancer driver events. First, analysis of 6,792 tumor samples, many from The Cancer Genome Atlas studies, identified 475 driver genes altered via somatic mutation, copy-number amplification, and/or gene fusions, which were classified as activating or loss-of-function. Second, potential anticancer drug treatments targeting actionable driver genes, including FDA-approved compounds and compounds in clinical or preclinical development, were collected. Lastly, these treatments were in silico–prescribed to individual patient samples based on the respective driver events present in each tumor. Using this strategy, only 5.9% of patients would benefit from FDA-approved therapies. However, tumor type, disease, or off-target repurposing of FDA-approved drugs increased the potential benefit of therapeutic intervention to 40.2% of patients. An additional 33.1% of patients could benefit from drugs currently in clinical trials or in preclinical development, raising the total potential therapeutic benefit to 73.3% of patients. Furthermore, combination therapies would benefit 39% of patients whose tumors harbored multiple driver events. In addition, this in silico analysis identified an additional 80 potentially targetable driver genes not targeted by existing drugs. Overall, this approach provides clinicians with a powerful tool for linking tumor-driving events with therapeutic strategies and highlights the potential for drug repurposing to improve personalized cancer medicine.

Rubio-Perez C, Tamborero D, Schroeder MP, Antolín AA, Deu-Pons J, Perez-Llamas C, et al. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. Cancer Cell 2015;27:382–96.