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

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来全面了解人类癌症。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，可能会阻碍研究人员充分利用这一资源。拟建的UCSC-Buck研究所基因组数据分析中心将支持对整个项目中所有调查的癌症类型的TCGA数据进行综合分析。该管道的主要组件是一个以路径为中心的多层机器学习工具，称为BiolnIntegrator，一个用于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。我们的目标是在肿瘤样本中检测与癌症相关的分子变化以及受其干扰的生物途径。然后，样本将根据途径的扰动而不是单个基因的扰动被归类为临床相关的类别，我们相信这将更可靠、更有生物学意义和临床准确性。使用BiolnIntegrator和相关工具，我们将进一步将TCGA数据与外部研究的数据集整合在一起，包括细胞系研究、动物研究和临床试验，以识别(1)与癌症相关的分子变化；(2)在临床诊断、预后和药物反应预测中有用的失调途径和信号；以及(3)用于开发新疗法的基因靶点。这些结果将为改善治疗中的患者分层提供基础，并将为转化性研究产生新的假设。紧密耦合的UCSC浏览器套件将进行增强，以适应TCGA项目的需求，其中包括用于可视化TCGA癌症基因组、临床数据和分析结果的UCSC癌症基因组学浏览器；用于显示肿瘤基因组重排和其他肿瘤突变的UCSC肿瘤浏览器；以及用于将数据与人类基因组注释和从其他项目(如ENCODE和NIH表观基因组计划)收集的信息集成在一起的UCSC人类基因组浏览器。托管这一快速增长的癌症基因组数据的浏览器资源将使研究人员能够进行互动的电子实验，以测试从TCGA数据得出的新假说。总的来说，这些建议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，应用这些工具获得的见解将促进我们对人类癌症生物学的了解，并刺激发现新的预后和诊断标记物，导致新的治疗和预防策略。

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.