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

监管区域癌症驱动突变的识别、表征和预测

• 批准号: 8931936
• 负责人: Collin Melton
• 金额: $ 11.61万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-23 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8931936
• 关键词: Address; Advisory Committees; Algorithms; Alleles; American; Apoptosis; Area; Cancer Biology; Cancer Patient; Cancer cell line; Cause of Death; Cell Line; ChIP-seq; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Collaborations; DNA; Data; Data Set; Development; Disease; Distal; Elements; Encyclopedia of DNA Elements; Ensure; Environment; Evaluation; Faculty; Fostering; Future; Gene Targeting; Genes; Genetic; Genome; Genomic approach; Genomics; Goals; Health; Hela Cells; Human; Human Genetics; Human Genome; Individual; Laboratories; Machine Learning; Malignant Neoplasms; Mentors; Mentorship; Mutate; Mutation; Normal Cell; Nucleic Acid Regulatory Sequences; Occupations; Phase; Point Mutation; Postdoctoral Fellow; Productivity; Proteins; Regimen; Regulatory Element; Research; Research Personnel; Research Training; Resources; Role; Sample Size; Sampling; Site; The Cancer Genome Atlas; Therapeutic; Training; Training Programs; United States; Universities; Untranslated RNA; Variant; Vision; base; cancer genome; cancer genomics; cancer therapy; cancer type; carcinogenesis; career development; epigenomics; genome sequencing; genome-wide; innovation; interest; migration; mutant; novel; parallel computer; predictive modeling; professor; responsible research conduct; skills; statistics; success; trend; tumor progression

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.

描述（由申请人提供）：拟议的研究培训计划的目标是为我（柯林梅尔顿博士）提供额外的培训，这些培训将加速我的职业发展，因为我从博士迈克尔斯奈德实验室的博士后研究员过渡到独立的终身教授。这个计划的关键要素是：候选人：我有广泛的训练，在实验和计算方法来研究生物医学。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行为的培训。这个全面的计划将为我提供一套技能，使我能够从博士后轻松过渡到终身教职。工作环境：我有一个有价值的咨询委员会与基因组学，遗传学和癌症生物学领域的专家，以确保我在这个培训计划的成功，并通过成功获得教师工作指导我。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·福特博士和两位顾问，韩莉·吉博士和安舒尔·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文字类设施，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，97-98%的人类基因组不编码蛋白质。该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。这一提议的核心假设是，人类基因组调控区的点突变驱动癌症形成，并且可以使用机器学习算法预测这些突变的功能后果。目标1提出了在癌症样品中突变的调控区的统计学鉴定，目标2提出了在目标1中鉴定的普遍突变的功能表征，目标3通过使用基因组学方法扩展了对全基因组突变的影响进行表征的分析，并提出了使用机器学习将新突变分类为破坏、激活、或者对调节元件活性没有影响。通过使用实验数据集结合预测模型来预测个体癌症变异的功能后果，这项研究将进一步实现癌症治疗个性化基因组解释的目标。