Engagement of American Indians of Southwestern Tribal Nations in Cancer Genome Sequencing - Diversity Supplement

项目摘要 影响美国印第安人(AI)社区的癌症发病率和相关死亡率显著 比美国其他种族都要高。更具体地说，肾细胞癌(RCC)是最 人工智能人群中常见的癌症形式，与其他癌症相比，确诊年龄较小 比赛。当前用于改进个性化医疗的测序数据集缺乏来自 人工智能癌症样本，因为人工智能缺乏对基因组和基因研究的参与。我们的最终目标是 该项目旨在将我们的发现转化为提高所有人群的精准医疗，了解 AI癌症患者中独特的原因和驱动程序突变是必要的。我们假设在功能上 在AI癌症样本中存在相关和独特的突变特征和分子途径中断 而它们的鉴定将为癌症的发病机制、进展提供重要的见解，还将 帮助提高诊断和治疗水平。为了解决这一假设，我们提出了以下目标：1)。 AI中肾细胞癌(RCC)与西班牙裔和非西班牙裔突变特征的比较 白色肿瘤标本，2)。确定AI RCC中转录途径的中断并与 使用RNA测序技术的西班牙裔和非西班牙裔白人。这个项目将在桥梁方面起到关键作用 通过识别被破坏的基因和分子，精确医学和人工智能癌症结果之间的差距 途径，并将有助于确定治疗管理和改善疾病结局的目标 这些社区因此缩小了RCC的健康差距。

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.