Comprehensive identification of germline-somatic interactions

种系-体细胞相互作用的综合鉴定

• 批准号: 10656304
• 负责人: Ludmil B Alexandrov
• 金额: $ 44.09万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10656304
• 关键词: Address; Adult; Affect; Age; Age of Onset; Attention; Bioinformatics; Cancer Biology; Cell physiology; Clinical; Communities; Computer software; DNA Repair; Data; Data Set; Detection; Development; Diagnosis; Disease; Drug Screening; Early Diagnosis; Event; Evolution; Gene Expression Regulation; Genes; Genetic; Genetic Variation; Genome; Genomics; Germ-Line Mutation; Goals; Heterogeneity; Human; Immune; Immune system; Immunity; Immunologic Surveillance; Immunotherapy; Individual; Inherited; Inter-tumoral heterogeneity; Literature; Malignant Childhood Neoplasm; Malignant Neoplasms; Molecular; Morbidity - disease rate; Mutagenesis; Mutation; Mutation Analysis; Nature; Normal tissue morphology; Oncogenes; Oncogenic; Online Systems; Outcome; Pathway interactions; Patients; Pattern; Penetrance; Population; Prevention therapy; Process; Prognosis; Progression-Free Survivals; Research; Research Personnel; Resources; Retinoblastoma; Role; Sample Size; Selection Criteria; Shapes; Signal Pathway; Somatic Mutation; Source; Statistical Methods; Survival Rate; Technology; Testing; Tumor Biology; Tumor Suppressor Proteins; Validation; Variant; anticancer research; bioinformatics pipeline; bioinformatics tool; cancer genome; cancer genomics; cancer predisposition; cancer prevention; cancer risk; cancer therapy; clinical phenotype; clinically relevant; cloud based; computing resources; design; disorder risk; exome; fitness; genetic profiling; genetic variant; genomic data; genomic profiles; insight; inter-individual variation; loss of function; meetings; molecular phenotype; mortality; neoplastic cell; next generation sequencing; novel; personalized intervention; response; risk variant; success; targeted treatment; tool; treatment response; tumor; tumor progression; tumorigenesis; whole genome

Cancer remains a significant source of morbidity and mortality worldwide. Advances in next generation sequencing technologies have allowed extensive profiling of the genetic variants present in tumors and revealed daunting levels of inter-tumoral heterogeneity. Tumor genomic datasets have been extensively mined to reveal germline risk variants and to characterize heterogeneous patterns of somatic alteration that drive tumor progression. However, little attention has been given to interactions between genetic background and somatic changes, which could represent a major driver of heterogeneity. New evidence suggests that germline-somatic interactions are prevalent and our preliminary data support that some such interactions directly influence individual disease risk and potential to respond to therapies. This proposal will develop computational strategies to identify germline-somatic interactions and to characterize them in the context of molecular and clinical phenotypes, enabling new understanding of their role in inter-tumoral heterogeneity. Germline-somatic interactions have been challenging to study due to the limited amount of available data. To address this challenge, we have compiled tumor genomic data from public sources to boost our sample size to almost 45,000 tumor whole-exome and whole-genome sequences. Our analysis will focus on three major forces that shape the tumor genome: (i) the mutational processes that generate somatic mutations, (ii) the molecular organization of oncogenic pathways which determines the genes that can effectively drive cancer, and (iii) the immune system which acts as a selective force throughout tumor development. We will focus hypothesis testing with strict criteria for selecting germline variants and somatically altered genes likely to interact based on established tumor biology. To identify and characterize germline-somatic interactions we will: 1) Elucidate germline variants affecting the somatic mutational landscapes of human cancers 2) Reveal germline variants that modify somatic activation of hallmark oncogenic pathways 3) Establish the role of pathway-specific variant burden in cancer predisposition, overall survival, and response to immunotherapy Our team of co-investigators includes strong complementary expertise in analysis of mutational processes, genetic variation effects on molecular pathways and immunity, cancer biology, statistical methods and bioinformatic software dissemination. Careful attention will be given to statistical considerations including power, controlling false discovery rates, and validation in independent datasets. This proposal will produce A) novel bioinformatics tools designed specifically to detect and annotate germline-somatic interactions, B) new understanding of the contribution of germline variation to tumor progression, and C) a set of validated germline- somatic interactions affecting cancer risk, tumor evolution and immunotherapy response.

癌症仍然是全球发病率和死亡率的重要来源。下一代的进展 测序技术允许对肿瘤中存在的遗传变异进行广泛的分析， 肿瘤间异质性的惊人水平。肿瘤基因组数据集已被广泛挖掘，以揭示 生殖系风险变异和表征驱动肿瘤的体细胞改变的异质模式 进展然而，很少有人注意到遗传背景和体细胞之间的相互作用 变化，这可能是异质性的主要驱动因素。新的证据表明， 相互作用是普遍的，我们的初步数据支持，一些这样的相互作用直接影响 个体疾病风险和对治疗反应的潜力。这个提议将发展计算策略 确定生殖细胞-体细胞相互作用，并在分子和临床背景下对其进行表征， 表型，使新的理解他们的作用，在肿瘤间的异质性。 由于可用数据有限，生殖系-体细胞相互作用的研究一直具有挑战性。到 为了应对这一挑战，我们从公共来源汇编了肿瘤基因组数据，以增加样本量， 近45，000个肿瘤全外显子组和全基因组序列。我们的分析将集中在三个主要力量 形成肿瘤基因组：（i）产生体细胞突变的突变过程，（ii） 组织致癌途径，其决定可以有效驱动癌症的基因，和（iii） 免疫系统在整个肿瘤发展过程中充当选择性力量。我们将重点假设检验 严格的标准来选择种系变异和体细胞改变的基因， 建立了肿瘤生物学。为了鉴定和表征生殖细胞-体细胞相互作用，我们将： 1)阐明影响人类癌症体细胞突变景观的种系变异 2)揭示改变标志性致癌通路的体细胞激活的种系变异 3)确定通路特异性变异负荷在癌症易感性、总生存期和缓解中的作用 对免疫疗法 我们的合作研究者团队在突变过程分析方面拥有强大的互补专业知识， 遗传变异对分子途径和免疫的影响，癌症生物学，统计方法和 生物信息学软件传播。将仔细注意统计考虑因素，包括功效， 控制错误发现率和独立数据集中的验证。这一建议将产生A）小说 生物信息学工具专门设计用于检测和注释生殖系-体细胞相互作用，B）新 了解生殖系变异对肿瘤进展的贡献，和C）一组经验证的生殖系- 影响癌症风险、肿瘤演变和免疫治疗反应的体细胞相互作用。