(PQ3) A functional genomic approach to identification and interpretation of germline-tumor genetic interactions

(PQ3) 识别和解释种系-肿瘤遗传相互作用的功能基因组方法

• 批准号: 9516467
• 负责人: ALEXANDER GUSEV
• 金额: $ 68.65万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-08 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9516467
• 关键词: Affect; Algorithms; CRISPR interference; Cancer Etiology; Cancer Intervention; Cancer Patient; Cell Line; Cell Proliferation; Chromatin; Clinical; Clinical Data; Clinical assessments; Computing Methodologies; Data; Data Analyses; Data Collection; Data Set; Databases; Development; Discipline; Distal; Drug Targeting; Enrollment; Ensure; Epidemiologist; Evaluation; Event; Evolution; Gene Mutation; Genes; Genetic; Genetic Transcription; Genetic Variation; Genome; Genomic approach; Genomics; Genotype; Goals; Human; Immune; Individual; Inherited; Intervention; Knowledge; Lead; Life; Malignant Neoplasms; Mediation; Medical; Medical Records; Medical Research; Methods; Methylation; Modeling; Mutation; Normal tissue morphology; Outcome; Patient risk; Patients; Phenotype; Population; RNA Splicing; Recurrence; Regulator Genes; Regulatory Pathway; Research; Research Personnel; Risk; Risk Assessment; Risk Factors; Savings; Scientist; Software Tools; Somatic Mutation; Statistical Methods; The Cancer Genome Atlas; Tissues; Transcript; Treatment outcome; Tumor Tissue; Untranslated RNA; Validation; Variant; Work; actionable mutation; anticancer research; cancer genetics; cancer risk; case control; clinical phenotype; clinically relevant; cohort; computerized tools; empowered; exome; follow-up; functional genomics; genetic risk factor; genome wide association study; genome-wide; high throughput screening; improved; innovation; insight; novel; novel drug class; precision medicine; programs; research study; response; risk variant; transcriptome sequencing; treatment response; tumor; tumor growth; tumor progression

PROJECT SUMMARY/ABSTRACT Studies of germline genetic variation in cancer cases and controls as well as studies of somatic mutation have transformed our understanding of cancer etiology and lead to the development of life saving cancer interventions. However, even though tumor progression, evolution, and treatment response are influenced by both somatic and germline variation, these data have largely been examined in isolation. In this work, we propose to integrate extensive data collection, novel statistical methods, and cutting-edge functional validation to discover and characterize somatic-germline interactions in a pan-cancer study. Results from our work will significantly benefit both cancer researcher and multiple medical research discipline more broadly. Within the cancer genetics field, identifying somatic-germline interactions will help (i) identify new classes of drugs targets causally upstream of those identified through somatic driver mutations, (ii) precisely treat patients by selecting interventions the basis of germline and somatic genetics as well as tumor RNA- sequencing, (iii) improve risk profiling, especially for tumor recurrence and outcomes, and (iv) develop hypotheses of the germline risk variants mechanism, especially for non-coding variants. To accomplish these goals, we will leverage tumor sequencing from the DFCI Profile Project together with recent innovations in variant imputation to assemble the largest (N>25,000) pan-cancer germline-somatic cohort to date. We will develop novel statistical and computational methods to maximize the value of these data. Because over 90% of germline genetic variation associated with cancer risk and outcomes is in non- coding regions of the genome we especially focus on integration of functional genomic sequencing from both tumor and normal tissues. Our methods will be capable of modelling proximal germline-somatic interactions as well as distal effects of germline variation on trans and global somatic changes. Furthermore, by focusing largely on RNA-sequencing we investigate a gene-centric model that provides specific hypotheses for mechanism that are readily validated via our experimental follow-up of non-coding variation that is otherwise difficult to interpret.

项目概要/摘要 癌症病例和对照中种系遗传变异的研究以及体细胞突变的研究已经 改变了我们对癌症病因学的理解并导致了拯救生命的癌症的发展 干预措施。然而，尽管肿瘤的进展、进化和治疗反应受到以下因素的影响： 无论是体细胞变异还是种系变异，这些数据在很大程度上都是单独检查的。在这项工作中，我们 建议整合广泛的数据收集、新颖的统计方法和尖端功能 验证在泛癌研究中发现和表征体细胞-种系相互作用。结果 我们的工作将使癌症研究人员和多个医学研究学科受益更多 广泛地说。在癌症遗传学领域，识别体细胞-种系相互作用将有助于（i）识别新的 各类药物的靶点是通过体细胞驱动突变确定的药物的上游，(ii) 精确地 通过选择基于种系和体细胞遗传学以及肿瘤 RNA 的干预措施来治疗患者 测序，(iii) 改善风险分析，特别是肿瘤复发和结果，以及 (iv) 开发 种系风险变异机制的假设，尤其是非编码变异。 为了实现这些目标，我们将利用 DFCI Profile Project 的肿瘤测序以及 最近在变异插补方面的创新，组装了最大的（N>25,000）泛癌种系体细胞 迄今为止的队列。我们将开发新颖的统计和计算方法，以最大限度地发挥这些价值 数据。因为与癌症风险和结果相关的种系遗传变异 90% 以上是非 我们特别关注基因组编码区域的功能基因组测序的整合 肿瘤和正常组织。我们的方法将能够模拟近端种系-体细胞相互作用： 以及种系变异对反式和整体体细胞变化的远端影响。此外，通过聚焦 主要基于 RNA 测序，我们研究了一个以基因为中心的模型，该模型为 通过我们对非编码变异的实验跟踪很容易验证这一机制 否则很难解释。