Project Summary Cancer incidence and associated death rates affecting American Indian (AI) communities are significantly higher than other races in the United States. More specifically, renal cell carcinoma (RCC) is the most common form of cancer in AI populations with diagnoses occurring at a younger age when compared to other races. Current sequencing datasets that are used for improving personalized medicine lacks information from AI cancer samples due to the lack of AI involvement in genomic and genetic research. Our ultimate goal for this project is to translate our findings to improve precision medicine for all populations, understanding the unique causes and driver mutations in AI cancer patients is a necessity. We hypothesize that functionally relevant and unique mutational signatures and molecular pathway disruption exist in the AI cancer samples and their identification will provide significant insights into cancer pathogenesis, progression, and will also help to improve diagnoses and treatment. To address this hypothesis, we propose the following aims; 1). compare mutational signatures identified in renal cell carcinoma (RCC) in AI to Hispanic and non-Hispanic White tumor samples, and 2). determine transcriptional pathway disruption in AI RCC and compare to Hispanic and non- Hispanic White using RNA sequencing techniques. This project will be crucial in bridging the gap between precision medicine and AI cancer outcomes by identifying disrupted genes and molecular pathways and will help to identify targets for therapeutic management and improving the disease outcome in these communities thereby reducing RCC health disparities.