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: 10620844
• 负责人: Felix Dietlein
• 金额: $ 21.45万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 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 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10620844
• 关键词: Accounting; Address; Advanced Malignant Neoplasm; Affect; Algorithms; 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; Drug Targeting; Environment; Estrogens; Event; Foundations; Future; Genes; Genetics and Medicine; Genets; Genome; Genomics; Goals; Immunotherapy; Individual; Leadership; Malignant Neoplasms; Manuals; Maps; Medicine; Mentors; Methods; Mission; Modeling; Mutation; Oncogenic; Open Reading Frames; Outcome; Pathologic Mutagenesis; Pathway interactions; Patients; Public Health; Publications; Research; Role; Scientist; Signal Transduction; Solid; Somatic Mutation; Statistical Methods; Statistical Models; Structure; System; Techniques; Testing; The Cancer Genome Atlas; Training; United States National Institutes of Health; Untranslated RNA; base editing; cancer gene expression; cancer genome; cancer genomics; cancer survival; cancer therapy; cancer type; 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; tumor diagnostic; 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.

项目总结