Molecular Analysis and Precision Medicine in Renal Cell Carcinoma

 描述（由申请人提供） 我提议的生物医学大数据科学（K01）指导职业发展奖将关注肿瘤学领域内一个重要但基本上未满足的需求。肾细胞癌是第8位最常见的癌症，也是最致命的泌尿系统癌症。25%的患者最初患有转移性疾病，30%的患者在手术后复发，需要全身药物治疗。选择使用哪种药物治疗不是基于肿瘤的生物分子特征，尽管治疗效果存在很大的差异。直到最近，随着测序技术的改进，这些分子分析才成为可能。本申请中提出的研究目标结合了联合收割机生物信息学、生物统计学和分子生物学，以建立一个信息学工具包，用于研究肾细胞癌的分子标志物，因为它们与治疗成功有关。我们假设，在现有的、注释良好的临床数据库和组织库中构建工具包将使我们能够（1）创建第一个具有临床、基因组和结局数据的肾细胞癌患者多机构登记，（2）确定现有癌症治疗成功的分子预测因子，和（3）研究与对免疫疗法的治疗反应相关的患者的个性化亚型。对治疗成功的分子预测因子的理解可以对肾细胞癌的决策产生重大影响，并为未来的临床试验建立假设。 在这个生物医学大数据科学指导职业发展奖的过程中，我的目标是从我的导师那里获得作为独立研究者成功所需的专业知识，通过使用生物医学数据科学推进肾细胞癌的精准医学。分配给这个项目的5年将提供充足的时间来真正建立独立研究所需的技能。我至少有50%的时间将专门用于研究，这将进一步补充临床工作，涉及肾细胞癌患者的护理。我有一个多机构和多学科的专家小组，他们将指导我完成这个研究项目，并使用由此产生的工具。我将接受的教育将培养我使用生物医学“大数据”的能力，以改善肾细胞癌患者的护理。我的长期职业目标包括作为一名独立的研究人员寻求额外的NIH资金，并领导一个多学科团队推进数据驱动的医学。通过这样做，我希望激励未来的科学家在生物医学数据科学方面追求挑战，并以给予我的导师传统指导他们。

 DESCRIPTION (provided by applicant) My proposed Mentored Career Development Award in Biomedical Big Data Science (K01) will focus an important but largely unmet need within the field of oncology. Renal cell carcinoma is the 8th most common cancer and the most lethal of the urologic cancers. Systemic medical therapy is required for the 25% of patients who initially present with metastatic disease and 30% of patients who recur following surgery. The selection of which medical treatment to use is not based on biomolecular characteristics of the tumor, despite large variability in the efficacy of th treatments. Only recently, with improvements in sequencing technologies, have these molecular analyses been possible. The research goals proposed within this application combine bioinformatics, biostatistics, and molecular biology to establish an informatics toolkit for investigating the molecular markers of renal cell carcinoma as they relate to treatment success. We hypothesize that building a toolkit into an existing, well-annotated clinical database and tissue repository will allow us to (1) create the first multi-institutional registry of renal cell carcinoma patients with clinical, genomic, and outcomes data, (2) identify molecular predictors of treatment success for existing cancer therapies, and (3) investigate personalized subtypes of patients that correlate with treatment response to immunotherapy. An understanding of molecular predictors of treatment success can have a major impact on decision-making within renal cell carcinoma and establish hypotheses for future clinical trials. Over the course of this Mentored Career Development Award in Biomedical Big Data Science, my goal is to acquire the expertise from my mentors that is required to succeed as an independent investigator, advancing Precision Medicine in renal cell carcinoma through the use of biomedical data science. The 5 years allotted for this project will provide ample time to truly establish the skill necessary for independent research. At least 50% of my time will be devoted solely to research, which will be further supplemented by clinical work involving care for patients with renal cell carcinoma. I have a multi-institutional and multi-disciplinary panel of experts who will guide me through this research project and use the resultant tools. The education that I will receive will b essential in cultivating my ability to use biomedical "big data" towards improving care in renal cell carcinoma patients. My long-term career goals include pursing additional NIH funding as an independent investigator and lead a multi-disciplinary team in advancing data-driven medicine. In doing so, I hope to inspire future scientists to pursue challenges in biomedical data science and guide them in the same tradition of mentorship that is being given to me.