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导师职业发展奖(K01)将专注于肿瘤学领域一个重要但基本上未得到满足的需求。肾癌是排在第八位的常见癌症，也是最致命的泌尿系癌症。对于最初出现转移疾病的25%的患者和手术后复发的30%的患者，需要进行系统的药物治疗。选择使用哪种药物治疗并不是基于肿瘤的生物分子特征，尽管TH治疗的疗效存在很大差异。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，用于研究与治疗成功相关的肾癌分子标志物。我们假设，将工具包构建到现有的、注释良好的临床数据库和组织库中将允许我们(1)创建第一个具有临床、基因组和结果数据的多机构肾癌患者登记，(2)确定现有癌症疗法治疗成功的分子预测因素，以及(3)调查与免疫治疗的治疗反应相关的患者的个性化亚型。了解治疗成功的分子预测因素可以对肾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一名独立研究员取得成功所需的专业知识，通过使用生物医学数据科学来推动肾细胞癌的精准医学。分配给这个项目的5年时间将为真正建立独立研究所需的技能提供充足的时间。我至少50%的时间将专门用于研究，并将进一步补充临床工作，涉及护理肾癌患者。我有一个由多个机构和多学科组成的专家小组，他们将指导我完成这个研究项目，并使用由此产生的工具。我将接受的教育将是培养我使用生物医学“大数据”改善肾癌患者护理的能力所必需的。我的长期职业目标包括作为一名独立研究员寻求NIH的额外资金，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领域追求挑战，并引导他们遵循与我一样的导师传统。

 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.