Identification, Characterization, and Prediction of Cancer Driver Mutations in Regulatory Regions

描述(由申请人提供)：拟议的研究培训计划的目标是为我(Collin Melton博士)提供额外的培训，这些培训将在我从Michael Snyder博士实验室的博士后研究员过渡到独立终身教职教授的过程中加速我的职业发展。这项计划的关键要素是：应聘者：我在学习生物医学的实验和计算方法方面接受过广泛的培训。在K99指导的博士后研究阶段，职业发展的其他重点领域包括获得额外的实验技能和癌症生物学、人类遗传学、人类基因组学、应用统计学和并行计算方面的补充培训。此外，我还将接受实验室管理、指导和负责任的研究指导方面的培训。这个全面的计划将为我提供一套技能，使我能够轻松地从博士后研究员过渡到终身教职。环境：我有一个由基因组学、遗传学和癌症生物学领域的专家组成的宝贵的咨询委员会，以确保我在这次培训计划中取得成功，并指导我成功地获得一份教职。这些人包括我的导师迈克尔·斯奈德博士、我的共同导师詹姆斯·福特博士以及两位顾问季汉礼博士和安舒尔·昆达杰博士。斯坦福大学斯奈德实验室和遗传学系的环境促进了生产力和与世界一流的设施、资源和研究人员的合作。研究：我提出的癌症基因组学研究计划是及时的、相关的和创新的。目前的大多数癌症基因组研究在理解基因组编码区发生的相关DNA变异方面取得了突破性进展；然而，人类基因组的97-98%不编码蛋白质。这项建议特别侧重于研究人类基因组的调节区，以识别、表征和解释这些调节区中的点突变的影响。这一提议的中心假设是，人类基因组调节区的点突变会导致癌症的形成，这些突变的功能后果可以使用机器学习算法来预测。目标1建议对癌症样本中突变的调节区进行统计识别，目标2建议对目标1中确定的流行突变进行功能表征，目标3通过使用基因组学方法扩展对全基因组突变影响的分析，并建议使用机器学习将新的突变分类为干扰、激活或对调节元件活性没有影响。通过使用实验数据集和预测个体癌症变异的功能后果的模型，这项研究将进一步实现癌症治疗的个性化基因组解释的目标。

DESCRIPTION (provided by applicant): The goal of the proposed research training program is to provide me (Dr. Collin Melton) with additional training in areas that will accelerate my career development as I transition from a post-doctoral fellow in Dr. Michael Snyder's lab to an independent tenure track professor. The key elements of this plan are: Candidate: I have extensive training in experimental and computational approaches to studying biomedicine. Areas of additional focus for career development during the K99 mentored post-doctoral research phase include the acquisition of additional experimental skills and supplemental training in cancer biology, human genetics, human genomics, applied statistics, and parallel computing. Additionally, I will receive training in laboratory management, mentorship, and responsible conduct of research. This well-rounded plan will provide me with a skill set that will enable a facile transition from postdoctoral fellow to tenure track faculty. Environment: I have a valuable advisory committee with experts in the areas of genomics, genetics, and cancer biology to ensure my success in this training program and to guide me through the successful acquisition of a faculty job. These include my mentor Dr. Michael Snyder, my co-mentor Dr. James Ford and two advisors, Dr. Hanlee Ji and Dr. Anshul Kundaje. The environment at Stanford University in the Snyder lab and department of Genetics fosters productivity and collaboration with word class facilities, resources, and researchers. Research: My proposed research plan in cancer genomics is timely, relevant, and innovative. The majority of current research in cancer genomics has made groundbreaking progress in understanding the relevant DNA variation that occurs in coding regions of the genome; however, 97-98% of the human genome does not code for protein. This proposal focuses specifically on studying the regulatory regions of the human genome to identify, characterize, and interpret the impact of point mutations in these regulatory regions. The central hypothesis of this proposal is that point mutations in regulatory regions of the human genome drive cancer formation and the functional consequences of these mutations can be predicted using machine learning algorithms. Aim 1 proposes the statistical identification of regulatory regions which are mutated across cancer samples, Aim 2 proposes functional characterization of the prevalent mutations identified in Aim 1, and Aim 3 extends the analysis of characterizing the effects of mutations genome-wide through use of genomics approaches and proposes the use of machine learning to classify novel mutations as either disrupting, activating, or having no effect on regulatory element activity. Through its use of experimental datasets combined with predictive models for functional consequences of individual cancer variation, this research will further the goal of personalized genome interpretation for cancer therapy.