Abstract 2983: In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals novel targeting opportunities
Authors: Rubio-Perez Carlota , Tamborero David , Schroeder Michael P. , Antolin Albert A. , Deu-Pons Jordi , Perez-Llamas Christian , Mestres Jordi , Gonzalez-Perez Abel , Lopez-Bigas Nuria 
Published: August 2015

Recent advances in DNA sequencing technologies provide unprecedented capacity to comprehensively identify the alterations, genes, and pathways involved in the tumorigenic process, raising the hope of extending targeted therapies against the drivers of cancer from a few successful examples to a broader personalized medicine strategy. However, high intertumor heterogeneity is a major obstacle to develop and apply therapeutic targeted agents to treat most cancer patient. In addition, advances in our ability to precisely assign the most effective targeted therapy to each patient based on the genome events driving the tumor are urgently needed.

The present study offers the first comprehensive assessment of the scope of targeted drugs in a large pan-cancer cohort. To pursue this goal, we developed a three-step in silico drug prescription strategy. We first identified the driver genes acting across 6792 tumor samples from 28 different cancer types via an integrated analysis of their mutations, copy number alterations and gene fusions. All information pertaining these driver genes has been compiled in a publicly available Drivers Database. Next, following the rationale that targeted therapies are effective only if they are administered to treat tumors driven by the alterations they are aimed at, we collected all therapeutic agents capable of targeting altered driver genes either directly, indirectly or through gene therapies. The catalog of available therapeutic agents and ancillary information on their application, referred here as Drivers Actionability Database, included FDA (Foods and Drugs Administration Agency) approved drugs, agents undergoing clinical trials, and ligands in pre-clinical stages. Finally, based on the driver alterations in each tumor in the cohort and the rules in the Drivers Actionability Database, we connected each patient to all targeted therapies that could benefit them, thus producing the landscape of utility of targeted therapeutic agents in the cohort.

We found that only a minority of patients could benefit from approved targeted therapy interventions following clinical guidelines (5.9%), while up to 40% could benefit from different types of repurposing opportunities of approve drugs, and up to 78% considering treatments currently under investigation. In addition, we identified 16 therapeutically unexploited cancer genes targeted by small molecules currently in pre-clinical stages, and 66 others structurally suitable for small molecule binding or accessible by antibody targeting. These results highlight the current scope of targeted anti-cancer therapies and its prospects for growth. The application of the strategy to larger cohorts and the continuous update of drug-target interactions information, will improve the in silico prescription rules contained within the two databases, thus enhancing its usefulness within personalized cancer medicine.

Citation Format: Carlota Rubio-Perez, David Tamborero, Michael P. Schroeder, Albert A. Antolin, Jordi Deu-Pons, Christian Perez-Llamas, Jordi Mestres, Abel Gonzalez-Perez, Nuria Lopez-Bigas. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals novel targeting opportunities. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2983. doi:10.1158/1538-7445.AM2015-2983