UCSC-Buck Inst. Genome Data Analysis Center for TCGA Research Network (GDAC)

UCSC-巴克研究所

• 批准号: 7789014
• 负责人: Christopher Benz
• 金额: $ 100万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-28 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7789014
• 关键词: Animals; Atlases; Biological; Cancer Biology; Categories; Cell Line; Clinical Data; Clinical Trials; Computer Simulation; Coupled; DNA Sequence Rearrangement; Data; Data Analyses; Data Set; Development; Diagnostic; Gene Targeting; Genes; Genome; Genomics; Glean; Human; Human Genome; Imagery; Individual; Institutes; Knowledge; Machine Learning; Malignant Neoplasms; Molecular; Mutation; Pathway interactions; Patients; Pharmaceutical Preparations; Research; Research Personnel; Resources; Sampling; Stratification; Surveys; Technology; Testing; Translational Research; United States National Institutes of Health; base; cancer genome; cancer genomics; cancer type; clinical Diagnosis; clinically relevant; detector; epigenomics; insight; novel therapeutics; outcome forecast; prevent; prognostic; research study; response; tool; tumor

DESCRIPTION (provided by applicant): The Cancer Genome Atlas (TCGA) project holds promise for a comprehensive understanding of human cancer through the application of genomic technologies. However, current cancer genomic analytical and visualization technologies still have many limitations that will likely prevent investigators from taking full advantage of this resource. The proposed UCSC-Buck Institute Genome Data Analysis Center will support an integrative analysis of TCGA data for all surveyed cancer types throughout the project. The major components of the pipeline are a pathway-centric multi-layer machine learning tool called Biolntegrator, a genome rearrangement detector for next-gen sequencing data, and the tightly coupled UCSC browser tool suite. We aim to detect cancer-associated molecular alterations and the biological pathways that are perturbed by them in tumor samples. Samples will then be classified into clinically relevant categories based on pathway perturbations rather than perturbations of individual genes, which we believe will be more robust, biologically meaningful and clinically accurate. Using Biolntegrator and the associated tools, we will further integrate TCGA data with datasets from external studies, including cell line studies, animal studies and clinical trials, to identify (1) cancer-associated molecular alterations; (2) dysregulated pathways and signatures useful in clinical diagnosis, prognosis, and drug response prediction; and (3) gene targets for the development of novel therapeutics. These results will provide the basis for a refined patient stratification in therapy and will generate new hypotheses for translational research. The tightly coupled UCSC browser suite, which will be enhanced to accommodate the needs of the TCGA project, includes the UCSC Cancer Genomics Browser for visualizing TCGA cancer genomics, clinical data, and analysis results; the UCSC Tumor Browser for displaying tumor genome rearrangements and other tumor mutations; and the UCSC Human Genome Browser for integrating the data with human genome annotations and information gleaned from other projects such as ENCODE and the NIH Epigenomics Roadmap Initiative. The browser resource, hosting this rapidly growing body of cancer genomics data, will enable investigators to perform interactive in-silico experiments to test new hypotheses derived from the TCGA data. Collectively, these proposed tools will enable cancer researchers to better explore the breadth and depth of the TCGA resources and to further characterize molecular pathways that influence cellular dynamics and stability in cancer. Ultimately, insights gained by applying these tools will advance our knowledge of human cancer biology and stimulate the discovery of new prognostic and diagnostic markers, leading to new therapeutic and preventative strategies.

描述（由申请人提供）：癌症基因组图谱（TCGA）项目有望通过基因组技术的应用全面了解人类癌症。然而，目前的癌症基因组分析和可视化技术仍然有许多局限性，这可能会阻止研究人员充分利用这一资源。拟议中的UCSC-Buck研究所基因组数据分析中心将在整个项目中支持对所有被调查癌症类型的TCGA数据进行综合分析。该管道的主要组成部分是一个以通路为中心的多层机器学习工具，称为biolintegrator，下一代测序数据的基因组重排检测器，以及紧密耦合的UCSC浏览器工具套件。我们的目标是检测肿瘤样本中与癌症相关的分子改变和受其干扰的生物学途径。然后，样本将根据途径扰动而不是单个基因的扰动分类为临床相关的类别，我们相信这将更加稳健，生物学意义和临床准确。使用biolintegrator和相关工具，我们将进一步整合TCGA数据与来自外部研究的数据集，包括细胞系研究、动物研究和临床试验，以确定(1)癌症相关的分子改变；(2)对临床诊断、预后和药物反应预测有用的异常通路和信号；(3)开发新疗法的基因靶点。这些结果将为治疗中精细的患者分层提供基础，并将为转化研究产生新的假设。紧密耦合的UCSC浏览器套件将被增强以适应TCGA项目的需求，包括用于可视化TCGA癌症基因组学、临床数据和分析结果的UCSC癌症基因组学浏览器；UCSC肿瘤浏览器，用于显示肿瘤基因组重排和其他肿瘤突变；UCSC人类基因组浏览器将数据与人类基因组注释和从其他项目（如ENCODE和NIH表观基因组学路线图倡议）收集的信息整合在一起。该浏览器资源承载着快速增长的癌症基因组数据体，将使研究人员能够进行交互式的计算机实验，以检验从TCGA数据中得出的新假设。总的来说，这些提出的工具将使癌症研究人员能够更好地探索TCGA资源的广度和深度，并进一步表征影响癌症细胞动力学和稳定性的分子途径。最终，通过应用这些工具获得的见解将提高我们对人类癌症生物学的认识，并刺激发现新的预后和诊断标志物，从而产生新的治疗和预防策略。