Novel Methods for Integrative Analysis of Cancer Genomic Data

描述(由申请人提供)：癌症基因组研究已广泛使用高通量分析技术进行。从这些研究中确定的分子标记已被用于辅助临床实践，包括诊断、预后预测和治疗方案的选择。尽管取得了有希望的成功，但这些签名往往缺乏重复性和可靠性。造成这一问题的一个主要原因是样本量相对较小，因此缺乏个别研究的力量。一种经济有效的补救方法是汇集和分析来自多项研究的数据。现有的分析多个数据集的方法存在严重缺陷。迫切需要一种新的统计方法，能够有效地分析和提取多种癌症基因组研究中的有用信息。该项目将是第一批系统开发和实施综合分析方法的项目之一。所提出的方法将能够有效地分析来自多个癌症基因组研究的异质高维数据集。他们将能够在模拟癌症发展过程中考虑到多种基因组测量和途径结构的联合影响，并能够针对临床和环境风险因素进行适当的调整。通过开发R包和公共网站进行传播，将使普通生物医学界能够访问我们的研究。对多种癌症临床结果的数据分析将导致确定临床有用的标记物。具体地说，我们计划(1)开发针对多个异质癌症基因组数据集的综合分析的惩罚边缘筛选方法；(2)针对多个异质癌症基因组数据集的综合分析开发基于单个标记物的惩罚方法；(3)针对多个异质癌症基因组数据集的整合分析开发基于路径的惩罚方法；(4)开发能够适当地适应部分线性临床和环境协变量影响的综合分析方法；(5)传播所提出的方法，分析多个癌症的数据并识别癌症标记物。拟议的研究将同时强调开发新的方法及其实际应用。它将对多个异质数据集的综合分析方法做出重大贡献，并使研究人员能够更有效地从癌症基因组研究中提取有用的信息。 公共卫生相关性：这项研究将是第一批系统地开发和实施新的综合分析方法的研究之一，该方法可以有效地分析多个异质和高维的癌症基因组研究。它将丰富综合分析的方法学家族，使研究人员能够更有效地从现有数据中提取有用的信息，并导致对癌症基因组学的更好理解。所提出的方法的应用将导致识别临床上有用的癌症标记物。

DESCRIPTION (provided by applicant): Cancer genomic studies have been extensively conducted using high-throughput profiling techniques. Molecular signatures identified from these studies have been used to assist clinical practice including diagnosis, prognosis prediction, and selection of treatment regimens. Despite promising successes, these signatures often suffer from a lack of reproducibility and reliability. A major cause of this problem is the relatively small sample sizes and hence lack of power of individual studies. A cost-effective remedy is to pool and analyze data from multiple studies. Available methods for analyzing multiple datasets have serious drawbacks. There is an urgent need for novel statistical methodologies that can effectively analyze and extract useful information from multiple cancer genomic studies. This project will be among the first to systematically develop and implement integrative analysis methodologies. The proposed methods will be able to effectively analyze heterogeneous high-dimensional datasets from multiple cancer genomic studies. They will be able to account for the joint effects of multiple genomic measurements and the pathway structure in modeling cancer development, and be able to properly adjust for clinical and environmental risk factors. Dissemination through the development of R package and public website will make our research accessible to the general biomedical community. Analysis of data on multiple cancer clinical outcomes will lead to identification of clinically useful markers. Specifically, we plan to (1) Develop penalized marginal screening methods for integrative analysis of multiple heterogeneous cancer genomic datasets; (2) Develop individual-marker based penalization methods for integrative analysis of multiple heterogeneous cancer genomic datasets; (3) Develop pathway based penalization methods for integrative analysis of multiple heterogeneous cancer genomic datasets; (4) Develop integrative analysis methods that can properly accommodate partially linear clinical and environmental covariate effects; (5) Disseminate the proposed methods, analyze data on multiple cancers, and identify cancer markers. The proposed study will emphasize equally development of novel methodologies and their practical applications. It will make significant contributions to methodologies for integrative analysis of multiple heterogeneous datasets, and enable researchers to more efficiently extract useful information from cancer genomic studies. PUBLIC HEALTH RELEVANCE: This study will be among the first to systematically develop and implement novel integrative analysis methods, which can effectively analyze multiple heterogeneous and high-dimensional cancer genomic studies. It will enrich the family of methodologies for integrative analysis, enable researchers to more efficiently extract useful information from existing data, and lead to a better understanding of cancer genomics. Applications of the proposed methods will lead to identification of clinically useful cancer markers.