Development of Integrated Analysis Methods and Applications to TCGA data

TCGA数据综合分析方法及应用的开发

• 批准号: 8786877
• 负责人: Shuangge Ma
• 金额: $ 8.33万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8786877
• 关键词: Adopted; Biological; Cancer Etiology; Cancer Prognosis; Complement; Computational algorithm; Computer software; Copy Number Polymorphism; DNA Methylation; DNA Modification Process; Data; Data Set; Development; Etiology; Future; Gene Expression; Genomics; Goals; Health; Joints; Lead; Literature; Malignant Neoplasms; Measurement; Measures; Messenger RNA; Methods; Methylation; MicroRNAs; Modeling; Outcome; Ovary; Post-Transcriptional Regulation; Property; Prostate Lymphoma; Publishing; Regulation; Research; Sampling; Statistical Methods; Techniques; The Cancer Genome Atlas; Work; anticancer research; base; cancer type; cohort; conditioning; cost; epigenetic regulation; falls; genome-wide; histone modification; insight; malignant breast neoplasm; novel; prototype; routine practice; simulation; user friendly software; web site

DESCRIPTION (provided by applicant): In cancer research, profiling studies have been extensively conducted, measuring genome-wide gene expression levels, DNA modifications, epigenetic regulation, and post-transcriptional regulations. Many studies are "one-dimensional" and restricted to one type of genomic measurement. More recently, "multi-dimensional" studies are becoming more popular. In such studies, the same samples are profiled on multiple layers of genomic activities. A representative example is The Cancer Genome Atlas (TCGA). Multi-dimensional studies offer a unique opportunity to more comprehensively describe the etiology and prognosis of cancer. In the literature, much effort has been devoted to modeling the interconnections among different regulations. In contrast, there are relatively few studies conducting integrated analysis and modeling the associations between multiple types of genomic measurements and cancer outcomes. The existing integrated analysis methods also have serious limitations, which may lead to suboptimal or even biased results. Our goal is to more effectively describe cancer etiology and prognosis by analyzing multi-dimensional genomic data. Motivated by the limitations of existing studies, our first objective is to develop novel statistical methods, effectively integrate multi-dimensional genomic measurements, and establish their associations with cancer outcomes. Such an objective differs significantly from those of published studies. The proposed methods will have significant advantages. They will assume different biological working models, allowing for a direct comparison of these models. They will be applicable to a large number of datasets, can accommodate the joint effects of a large number of markers, and adopt efficient statistical techniques. The second objective is to apply these methods and analyze TCGA data on multiple types of cancers. The specific aims are to (Aim 1) Develop novel statistical methods to integrate multiple types of genomic measurements for cancer outcomes. Three different methods will be developed under different data generating models; (Aim 2) Develop user- friendly software and project website. Analyze TCGA data on multiple types of cancers, particularly including cancers of breast, ovary and prostate and lymphoma. Such data have measurements on gene expression, copy number variation, methylation, microRNA and others available. With the cost of sequencing falling fast, it will soon become a routine practice to profile multi- dimensional genomic characterizations of samples. This study will deliver a new analysis strategy and a set of novel statistical methods. These methods will integrate multiple types of genomic measurements for cancer outcomes and complement the existing methods. The analysis of TCGA data will provide valuable insights into multiple cancers and serve as prototype for future applications.

描述(申请人提供)：在癌症研究中，已经广泛地进行了图谱研究，测量全基因组的基因表达水平、DNA修饰、表观遗传调节和转录后调节。许多研究都是“一维的”，仅限于一种类型的基因组测量。最近，“多维”研究正变得越来越流行。在这样的研究中，相同的样本被描绘在多个基因组活动的层面上。一个有代表性的例子是癌症基因组图谱(TCGA)。多维研究为更全面地描述癌症的病因和预后提供了独特的机会。在文献中，已经投入了大量的努力来建模不同法规之间的相互联系。相比之下，进行综合分析并对多种类型的基因组测量与癌症结果之间的关联进行建模的研究相对较少。现有的综合分析方法也有严重的局限性，可能会导致结果次优甚至有偏差。我们的目标是通过分析多维基因组数据来更有效地描述癌症的病因和预后。由于现有研究的局限性，我们的第一个目标是开发新的统计方法，有效地整合多维基因组测量，并建立它们与癌症结果的关联。这样的目标与已发表的研究有很大不同。所提出的方法将具有显著的优势。他们将采用不同的生物工作模式，以便对这些模式进行直接比较。它们将适用于大量的数据集，能够适应大量标记的联合影响，并采用高效的统计技术。第二个目标是应用这些方法并分析多种癌症的TCGA数据。具体目标是(目标1)开发新的统计方法，以整合癌症结果的多种类型的基因组测量。将在不同的数据生成模式下开发三种不同的方法；(目标2)开发用户友好的软件和项目网站。分析多种癌症的TCGA数据，特别是包括乳腺癌、卵巢癌、前列腺癌和淋巴瘤。这些数据对基因表达、拷贝数变异、甲基化、微RNA和其他可用的数据进行了测量。随着测序成本的快速下降，描绘样本的多维基因组特征将很快成为一种常规做法。本研究将提供一种新的分析策略和一套新的统计方法。这些方法将整合癌症结果的多种类型的基因组测量，并补充现有的方法。对TCGA数据的分析将为多种癌症提供有价值的见解，并作为未来应用的原型。