Statistical Methods for Integrative Genomics in Cancer

描述（由申请人提供）：我们的目标是开发新的统计方法，以解决癌症遗传流行病学家在“后GWAS”时代面临的一些主要问题，并说明其在各种结直肠癌（CRC）研究中发现新生物学的用途。这些方法利用先前的生物学知识为整合基因组学分析提供信息（项目1），使用系统发育信息推断基因功能，作为我们流行病学建模项目的输入（项目2），模拟微生物组和肿瘤组在癌症风险中的作用（项目3），并利用肿瘤内的异质性来了解体细胞肿瘤的演变以及内部环境如何改变这一过程（项目4）。这四个项目将由一个管理核心和三个共享资源核心提供支持，分别涉及功能注释、高性能计算和软件开发。整个计划的动机是一个总体目标，即提供工具，评估 基于可改变的风险因素的潜在预防或治疗干预措施。具体而言，本研究课题的主要目的是：（1）开发统计分析方法，利用外部信息，将描述构成性和获得性基因组变异以及外部和内部环境测量的多种组学数据整合到综合风险预测模型中;（2）将这些方法应用于CRC病因学和预后的各种研究，以揭示新的关联并开发对可能的原发性，继发性，和三级干预措施;和（3）建立基础设施（行政，生物信息，计算，软件），以支持各种研究项目，并促进使我们的方法更广泛的科学界。这将通过理论发展、与真实的数据项目密切相关的模拟研究、对CRC的几项研究的应用以及供外部调查人员使用的软件分发的组合来实现。除了应用于结直肠癌，我们的方法将广泛适用于其他癌症类型和许多其他慢性疾病。

DESCRIPTION (provided by applicant): We aim to develop novel statistical methods to address some of the major problems facing cancer genetic epidemiologists in the "post-GWAS" era and to illustrate their use for discovery of novel biology in various colorectal cancer (CRC) studies. These methods leverage prior biological knowledge to inform integrative genomics analyses (Project 1), use phylogenetic information to infer gene function as inputs to our epidemiologic modeling projects (Project 2), model the role of the microbiome and the exposome in cancer risk (Project 3), and exploit intra-tumor heterogeneity to learn about somatic tumor evolution and how this process is modified by the internal environment (Project 4). These four projects will be supported by an administrative core and three shared resource cores on functional annotation, high performance computing, and software development. The entire program is motivated by an overall objective of providing tools for evaluating the impact of potential preventive or therapeutic interventions based on modifiable risk factors. Specifically, the aims of the overall program are (1) to develop statistical analysis methods to integrate multiple types of omics data that describe both constitutional and acquired genomic variation as well as measures of the external and internal environment into comprehensive risk prediction models, leveraging external information; (2) to apply these methods to various studies of CRC etiology and prognosis to uncover novel associations and to develop predictive models that would have translational significance for possible primary, secondary, and tertiary interventions; and (3) to establish an infrastructure (administrative, bioinformatic, computational, software) to support the various research projects and facilitate making our methods accessible to the broader scientific community. This will be achieved by a combination of theoretical developments, simulation studies closely keyed to real data projects, applications to several studies of CRC, and distribution of software for use by outside investigators. Beyond applications to colorectal cancer, our methods will be broadly applicable to other cancer types and many other chronic diseases.