Statistical Methods for Integrative Analysis of Genomics and Proteomics Data

描述(申请人提供)：肿瘤是复杂的生物系统。没有一种单一类型的分子方法可以完全阐明肿瘤的行为，需要在多个水平上进行分析，包括基因组学和蛋白质组学。因此，现在从许多来源收集的不同类型的数据现在在基因组范围内被收集，包括：DNA拷贝数改变、mRNA表达、蛋白质表达测量和许多其他。然而，如果没有有效的统计和计算方法，这些研究中的生物医学信息是不可能实现的。因此，这项研究的长期目标是开发创新的方法，联合对这些不同类型的数据进行建模，以帮助揭示基因和蛋白质相互作用的大规模组织。为了应对这一挑战，这项提议从目标1开始，开发新的统计和计算方法来确定DNA/RNA/蛋白质相互作用。我们建议使用为图形模型开发的工具，并研究具有各种条件相关性的基因/蛋白质之间的条件依赖关系。目的2提出将相互作用网络与疾病表型相结合的新方法，以改进生物标记物的识别和临床结果预测。我们将获得与疾病启动/发展相关的基因/蛋白质模块，并使用Boosting程序将模块信息合并到预测模型中。稀疏回归技术和适当的平滑正则化将被用来处理高维和解释这两个目标的局部相关性。该提案使用了两项乳腺癌研究作为鼓舞人心的例子。但这里开发的工具可以很好地推广到其他疾病。 这项研究的成功将大大改进大规模整合研究的统计方法，从而有助于从机制上加深对基因组/蛋白质组变化对肿瘤生长和进展的贡献的理解，并促进更有效的分子诊断和预后测试的开发。来自两项乳腺癌研究的数据将与广泛的模拟实验一起用于测试和改进方法学，以便在现实世界中应用。

DESCRIPTION (provided by applicant): Tumors are complex biological systems. No single type of molecular approach fully elucidates tumor behavior, necessitating analysis at multiple levels encompassing genomics and proteomics. Therefore different types of data from numerous sources are now collected at a genome-wide scale, including: DNA copy number alterations, mRNA expression, protein expression measurements and many others. However, the full extent of biomedical information in these studies cannot be realized without effective statistical and computational methods. Thus, the long-term goal of this research is to develop innovative methods jointly modeling these different types of data to help uncover the large-scale organization of genes and proteins interacting. To tackle this challenge, this proposal begins in Aim 1 by developing new statistical and computational methods for identifying DNA/RNA/Protein interactions. We propose to use tools developed for graphics models and study conditional dependencies among genes/proteins with various conditional correlations. Aim 2 proposes novel approaches to integrate the interaction network with disease phenotypes to improve biomarker identification and clinical outcome prediction. We will derive modules of genes/proteins which are associated with disease initiation/progression, and use boosting procedures to incorporate the module information into the predictive models. Sparse regression techniques together with proper smooth regularization will be used to handle the high-dimensionality and to account for the local correlation in both aims. The proposal uses two breast cancer studies as motivating examples. But the tools develop here can be well generalized to other disease. Success of this research will result in substantially improved statistical methods for large-scale integration studies, and thus help to increase mechanistic understanding of the contribution of genomic/proteomics alterations to tumor growth and progression, as well as facilitate the development of more effective molecular diagnostic and prognostic tests. Data from the two breast cancer studies will be used together with extensive simulation experiments to test and refine the methodology for real-world application.