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，以加速我的职业发展，因为我从Michael Snyder博士实验室的博士后研究员转变为独立的终身教授。这个计划的关键要素是：候选人：我在研究生物医学的实验和计算方法方面受过广泛的训练。在K99指导的博士后研究阶段，职业发展的额外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，我将接受实验室管理、指导和负责任的研究方面的培训。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过渡到终身教职人员。环境：我有一个有价值的咨询委员会，由基因组学、遗传学和癌症生物学领域的专家组成，以确保我在这个培训项目中取得成功，并指导我成功获得一份教职工作。其中包括我的导师Michael Snyder博士，我的共同导师James Ford博士和两位顾问，Hanlee Ji博士和Anshul Kundaje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世界级设施、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、创新的。目前大多数癌症基因组学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。本提案特别侧重于研究人类基因组的调控区域，以识别、表征和解释这些调控区域中点突变的影响。该提案的核心假设是，人类基因组调控区域的点突变驱动癌症的形成，这些突变的功能后果可以使用机器学习算法进行预测。Aim 1提出了在癌症样本中突变的调控区域的统计鉴定，Aim 2提出了在Aim 1中鉴定的普遍突变的功能表征，Aim 3通过使用基因组学方法扩展了对全基因组突变影响的表征分析，并提出使用机器学习将新突变分类为破坏、激活或对调控元件活性没有影响。通过将实验数据集与个体癌症变异的功能后果预测模型相结合，本研究将进一步实现癌症治疗的个性化基因组解释目标。

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.