Novel Methods for Integrative Analysis of Cancer Genomic Data

描述（由申请人提供）：癌症基因组研究已经广泛使用高通量分析技术进行。从这些研究中确定的分子特征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。尽管有希望取得成功，但这些签名往往缺乏可重复性和可靠性。造成这一问题的一个主要原因是样本量相对较小，因此缺乏个体研究的力量。一个具有成本效益的补救办法是汇集和分析来自多个研究的数据。现有的多数据集分析方法存在严重的缺陷。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。这个项目将是第一个系统地开发和实施综合分析方法的项目。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构高维数据集。他们将能够解释多种基因组测量的联合效应和模拟癌症发展的途径结构，并能够适当地调整临床和环境风险因素。通过开发R包和公共网站进行传播，将使我们的研究能够为一般生物医学界所接受。对多种癌症临床结果的数据分析将有助于确定临床上有用的标志物。具体而言，我们计划(1)开发惩罚边际筛选方法，用于多种异质癌症基因组数据集的综合分析；(2)开发基于个体标记的惩罚方法，对多种异质癌症基因组数据集进行综合分析；(3)开发基于途径的惩罚方法，用于多种异质癌症基因组数据集的整合分析；(4)发展能够适当适应部分线性临床和环境协变量效应的综合分析方法；(5)传播所提出的方法，分析多种癌症的数据，识别癌症标志物。拟议的研究将同样强调新方法的发展及其实际应用。它将对多种异构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。

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.