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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. [ABSTRACT FROM AUTHOR]

Purpose: Studies suggested the existence of a 'trial effect,' in which for a given treatment, participation in a clinical trial is associated with a better outcome. Sunitinib is a standard treatment for metastatic renal cell carcinoma (mRCC). We aimed to study the effect of clinical trial participation on the outcome of mRCC patients treated with sunitinib, which at present, is poorly defined. Materials and Methods: The records of mRCC patients treated with sunitinib between 2004-2013 in 7 centers across 2 countries were reviewed. We compared the response rate (RR), progression free survival (PFS), and overall survival (OS), between clinical trial participants (n=49) and a matched cohort of non-participants (n=49) who received standard therapy. Each clinical trial participant was individually matched with a non-participant by clinicopathologic factors. PFS and OS were determined by Cox regression. Results: The groups were matched by age (median, 64), sex (male, 67%), Heng risk (favorable, 25%; intermediate, 59%; poor, 16%), prior nephrectomy (92%), RCC histology (clear cell 86%), pre-treatment neutrophil to lymphocyte ratio (> 3 in 55%, n=27), sunitinib induced hypertension (45%), and sunitinib dose reduction/treatment interruption (41%). In clinical trial participants versus non-participants, RR was partial response/stable disease 80% (n=39) versus 74% (n=36), and progressive disease 20% (n=10) versus 26% (n=13) (p=0.63; odds ratio, 1.2). The median PFS was 10 versus 11 months (hazard ratio [HR], 0.96; p=0.84), and the median OS 23 versus 24 months (HR, 0.97; p=0.89). Conclusion: In mRCC patients treated with sunitinib, the outcome of clinical trial participants was similar to that of non-participants who received standard therapy. [ABSTRACT FROM AUTHOR]