High-Throughput Architecture for Discovering Combination Cancer Therapeutics.

PURPOSE: The amount of available next-generation sequencing data of tumors, in combination with relevant molecular and clinical data, has significantly increased in the last decade and transformed translational cancer research. Even with the progress made through data-sharing initiatives, there is a clear unmet need for easily accessible analyses tools. These include capabilities to efficiently process large sequencing database projects to present them in a straightforward and accurate way. Another urgent challenge in cancer research is to identify more effective combination therapies. METHODS: We have created a software architecture that allows the user to integrate and analyze large-scale sequencing, clinical, and other datasets for efficient prediction of potential combination drug targets. This architecture permits predictions for all genes pairs; however, Food and Drug Administration-approved agents are currently lacking for most of the identified gene targets. RESULTS: By applying this approach, we performed a comprehensive study and analyzed all possible combination partners and identified potentially synergistic target pairs for 38 approved targets currently in clinical use. We further showed which genes could be synergistic prediction markers and potential targets with MAPK/ERK inhibitors for the treatment of melanoma. Moreover, we integrated a graph analytics technique in this architecture to identify pathways that could be targeted synergistically to enhance the efficacy of certain therapeutics in cancer. CONCLUSION: The architecture and the results presented provide a foundation for discovering effective combination therapeutics.