A pan-cancer atlas of driver mutations in >100,000 patients based on a hypothesis-driven combined computational and experimental approach

基于假设驱动的计算和实验相结合的方法，绘制了超过 100,000 名患者的驱动突变泛癌图谱

• 批准号: 10276520
• 负责人: Felix Dietlein
• 金额: $ 13.32万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-16 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10276520
• 关键词: Accounting; Address; Advanced Malignant Neoplasm; Affect; Atlases; Binding; Biological; Biology; CRISPR interference; Cancer Patient; Cells; Chromatin Remodeling Factor; Clinical; Clinical Markers; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Complex; Computer software; Computing Methodologies; Data; Data Set; Development; Development Plans; Diagnostic; Drug Targeting; Environment; Estrogens; Event; Foundations; Future; Genes; Genetics and Medicine; Genets; Genome; Genomics; Goals; Immunotherapy; Individual; Institutes; Lead; Leadership; Malignant Neoplasms; Manuals; Maps; Medicine; Mentors; Methods; Mission; Modeling; Mutation; Oncogenic; Open Reading Frames; Outcome; Pathologic Mutagenesis; Pathway interactions; Patients; Proteins; Public Health; Publications; Research; Role; Scientist; Signal Transduction; Solid; Somatic Mutation; Statistical Algorithm; Statistical Methods; Statistical Models; Structure; System; Techniques; Testing; The Cancer Genome Atlas; Training; United States National Institutes of Health; Untranslated RNA; base; cancer gene expression; cancer genome; cancer genomics; cancer survival; cancer therapy; career development; clinical care; clinical predictors; computerized tools; driver mutation; exome; experience; experimental study; genome editing; genome sequencing; genomic biomarker; improved; innovation; interdisciplinary approach; machine learning method; malignant breast neoplasm; mathematical methods; mathematical model; medical schools; meetings; mid-career faculty; new therapeutic target; novel; novel therapeutics; open source; precision oncology; professor; promoter; skills; symposium; targeted treatment; tool; transcription factor; tumor; tumorigenesis; whole genome

PROJECT SUMMARY Most mutations in cancer genomes are random passengers that do not contribute to oncogenesis, whereas only a few are drivers critical for tumor development. Existing cancer therapies interfere directly with the biology of drivers, which have been characterized extensively in protein-coding regions but remain largely uncharacterized outside coding regions. Most tumors harbor a combination of several driver mutations, but it is unclear how multiple events are coordinated in tumor development. The applicant's long-term goal is to advance cancer medicine by identifying new drug targets and clinical markers for therapies in complex pathways. The overall objectives in this application are to (i) reveal the biological role of noncoding drivers, (ii) capture the coordination of driver events at a pathway level, and (iii) profile the effects of noncoding drivers on cancer gene expression. The central hypothesis is that refining the biological assumptions of computational methods will enhance their statistical power. The rationale is that defining the biology of noncoding drivers and their combination will offer a strong foundation for new therapies. The central hypothesis will be tested in three specific aims: 1) Determine the impact of integrating biological mechanisms into statistical methods for localizing noncoding drivers; 2) Evaluate mechanisms by which promoter mutations increase the expression of cancer genes; and 3) Assess the coordination of multiple driver events in tumor development. The proposed research is innovative, in the applicant's opinion, because it will allow for an unbiased characterization of driver mutations across the entire genome, address the limitations of existing cancer genomics methods in noncoding regions, and facilitate the usage of statistical concepts for non-computational scientists. The proposal is significant because it will enable a systematic interrogation of noncoding drivers and their combinations. Ultimately, this will pave the way for new targeted therapies. Dr. Dietlein will be mentored by Dr. Van Allen, an Associate Professor of Medicine at Harvard Medical School with considerable experience in cancer genomics methods that require statistical innovation for clinically focused questions. His co-mentor, Dr. Meyerson, is a Professor of Genetics and Medicine at Harvard Medical School and a pioneer in developing targeted therapies based on driver mutations. Additional support will be provided by 4 computational and 2 experimental collaborators. Dr. Dietlein's training plan contains four goals, which will be pursued by hands-on experiential training, conference meetings, and structured coursework: 1) Acquire computational skills for interpreting drivers in noncoding regions; 2) Experimental techniques to validate driver mutations by CRISPR interference; 3) Develop professional leadership skills for interdisciplinary teams of scientists; and 4) Use machine-learning methods for interpreting drivers in cancer genomes. Dana-Farber, Harvard Medical School, and the Broad Institute provide an ideal environment to execute the applicant's career development plan.

项目摘要 癌症基因组中的大多数突变是随机的乘客，不会导致肿瘤发生，而 只有少数对于肿瘤发育至关重要。现有的癌症疗法直接干扰 驱动因素的生物学，这些驱动因素在蛋白质编码区域具有广泛的特征，但在很大程度上仍然 未表征的外部编码区域。大多数肿瘤都有几种驱动突变的组合，但它是 尚不清楚如何在肿瘤发展中协调多个事件。申请人的长期目标是 通过确定复杂疗法的新药物靶标和临床标记来预先癌症医学 途径。本应用程序中的总体目标是（i）揭示非编码驱动因素的生物学作用，（ii） 在途径级别捕获驾驶员事件的协调，（iii）介绍了非编码驱动程序对 癌基因表达。中心假设是完善计算的生物学假设 方法将增强其统计能力。理由是定义非编码驱动因素的生物学和 他们的结合将为新疗法提供良好的基础。中央假设将在三个 具体目的：1）确定将生物学机制整合到统计方法中的影响 定位非编码驱动程序； 2）评估启动子突变增加表达的机制 癌基因； 3）评估肿瘤发育中多个驾驶员事件的协调。提议 申请人认为，研究具有创新性，因为它将允许驾驶员的公正表征 整个基因组的突变，解决了现有的癌症基因组方法在非编码中的局限性 地区，并促进非竞争科学家的统计概念的使用。该提议是 意义重大，因为它将能够对非编码驱动程序及其组合进行系统的询问。 最终，这将为新的目标疗法铺平道路。 Dietlein博士将由Van Allen博士指导 哈佛医学院医学副教授在癌症基因组学方面拥有丰富的经验 需要统计创新的方法来解决临床上的问题。他的联合学者Meyerson博士是 哈佛医学院的遗传学和医学教授，以及开发目标疗法的先驱 基于驾驶员突变。 4个计算和2个实验将提供额外的支持 合作者。 Dietlein博士的培训计划包含四个目标，这将由动手体验追求 培训，会议会议和结构化课程：1）获得解释的计算技能 非编码地区的驾驶员； 2）通过CRISPR干扰验证驾驶员突变的实验技术； 3）为科学家的跨学科团队发展专业领导能力； 4）使用机器学习 解释癌症基因组驱动因素的方法。达娜·法伯（Dana-Farber），哈佛医学院和广阔的 研究所提供了执行申请人职业发展计划的理想环境。