Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research

项目摘要 随着来自基因组、染色体组和表型组的大量数据在人口和临床研究中的快速获得， 数据科学已经变得至关重要，并为新的 癌症的发现NCI杰出研究者奖（R35）的竞争性续期申请 旨在开发和应用可扩展，可解释和可转移的统计和机器学习（ML） 大规模生殖系全基因组测序（WGS）和体细胞全基因组测序的整合分析方法 外显子组测序（WES）数据、流行病学和临床数据，在大规模多种族生物库中， 癌症的人群和临床研究，实验细胞特异性多组学功能数据，如 单细胞RNA/ATAC-seq数据。我们的最终目标是使用先进的数据科学方法和不同的 人口类型，临床和实验数据，以加速癌症基因进展 映射到癌症预防和医学的机制，发现新的有效的跨种族精确 癌症预防和治疗战略，并减少癌症遗传研究中的健康差距。这 该应用旨在满足癌症研究中对海量数据分析的迫切定量需求。 具体而言，（A）对于遗传癌症流行病学，我们将开发可扩展的，可解释的和可转移的 统计学和ML方法用于（1）通过整合基于人群的WGS和实验性的罕见变异分析 单细胞功能数据;（2）从具有未知因果关系和生物学的相关变体发展到因果关系 使用因果中介分析和孟德尔随机化， 遗传、细胞特异性组学、生物标志物和表型数据;（3）估计可转移的跨种族多基因 风险评分（PRS）和遗传性使用常见和罕见的变异，通过整合WGS数据与实验 硅内细胞特异性功能注释和非遗传数据，用于可操作的预防策略;（3） 大型WGS中的联合和可转移的跨种族单一表型和表型全遗传分析 研究和生物银行。(B)对于癌症遗传医学，我们将开发可扩展和可解释的统计数据， 和机器学习方法，用于（1）联合分析生殖系WGS和肿瘤体细胞WES数据，以识别 易患癌症亚型的遗传变异;（2）肿瘤体细胞WES数据的综合分析， 临床病理学特征，以鉴定用于改善免疫疗法功效的患者概况;（3） 分析克隆性造血、线粒体功能障碍、白细胞端粒长度的影响，称为 来自肿瘤体细胞事件、癌症预后和对免疫疗法的反应的生殖系WGS数据。我们 将把所提出的方法应用于肺癌和乳腺癌的遗传流行病学和临床研究 和生物银行我们将开发这些方法和数据的开放访问集群和基于云的软件 资源，并将其提供给癌症研究社区的NIH数据共享。

Project Summary With massive data from genome, exposome and phenome rapidly available in population and clinical studies, data science has emerged to be critically important and provides unprecedented opportunities for new discoveries in cancer. This competing renewal application of an NCI Outstanding Investigator Award (R35) aims at developing and applying scalable, interpretable and transferable statistical and machine learning (ML) methods for integrative analysis of massive germline whole genome sequencing (WGS) and somatic whole exome sequencing (WES) data, epidemiological and clinical data, in large-scale multi-ethnic biobanks, population and clinical studies of cancer, with experimental cell specific multi-omic functional data, such as single cell RNA/ATAC-seq data. Our ultimate goal is to use advanced data science methods and different types of population, clinical, and experimental data to accelerate progress in advancing from cancer gene mapping to mechanisms to cancer prevention and medicine, discover new effective trans-ethnic precision cancer prevention and treatment strategies, and reduce health disparities in cancer genetic research. This application aims to meet the pressing quantitative needs for the analysis of massive data in cancer research. Specifically, (A) for genetic cancer epidemiology, we will develop scalable, interpretable and transferable statistical and ML methods for (1) rare variant analysis by integrating population-based WGS and experimental single cell functional data; (2) advancing from associated variants with unknown causality and biology to causal variants, genes and pathways using causal mediation analysis and Mendelian Randomization by integrating genetic, cell-specific omic, biomarkers and phenotype data; (3) estimating transferable trans-ethnic polygenetic risk scores (PRSs) and heritability using common and rare variants by integrating WGS data with experimental in-silicon cell-specific functional annotations and non-genetic data, for actionable prevention strategies; (3) federated and transferable trans-ethnic single phenotype and phenome-wide genetic analysis in large WGS studies and biobanks. (B) For cancer genetic medicine, we will develop scalable and interpretable statistical and machine learning methods for (1) joint analysis of germline WGS and tumor somatic WES data to identify genetic variants that predispose to cancer subtypes; (2) integrative analysis of tumor somatic WES data and clinicopathological characteristics to identify patient profiles for improved efficacy of immunotherapies; (3) analysis of the effects of clonal hematopoiesis, mitochondrial dysfunctions, leukocyte telomere length called from germline WGS data on tumor somatic events, cancer prognosis and responses to immunotherapies. We will apply the proposed methods in lung cancer and breast cancer genetic epidemiological and clinical studies and biobanks. We will develop open access cluster and cloud-based software of these methods and data resources and make them available at NIH Data Commons to the cancer research community.