OHSU Center for Specialized Data Analysis as part of the GDAN

项目摘要 最近治疗学的进步提高了许多癌症的存活率。然而，几乎所有的 转移性肿瘤是无法治愈的，对治疗干预的抵抗几乎是普遍存在的。确实有 我们缺乏进展的许多原因--最重要的是缺乏对反应机制的了解 以及抗药性和缺乏标记物来识别理想适合特定治疗的患者亚群。 我们的团队在TCGA和其他多学科协调项目方面拥有丰富的经验，例如 BEAT-AML，以及对抗癌症西海岸前列腺癌梦之队。癌症基因组 阿特拉斯(TCGA)之所以成功，是因为多学科团队通力合作，创造出新的、创新的 我们希望确保GDAN取得同样的成功。在此应用程序中，我们 已经组建了一个由来自TCGA四个不同小组的经验丰富的调查人员组成的团队，以扩展 TCGA在癌症基因组学中心管理的项目中的成功。我们的团队将继续我们的 通过部署数据分析管道在分析和解释癌症基因组数据方面具有出色的能力 以支持网络的关键功能。 除了我们在协调网络研究方面的经验外，我们还带来了临床试验设计方面的经验 和解释。此外，该团队在构建必要的计算基础设施方面拥有深厚的专业知识 为了GDAN的成功。例如，在分布式计算领域，我们部署了用于 在许多地点执行死刑。我们还为综合路径和分析带来了新的方法。最后，我们带来了 我们应对竞争挑战的经验确保我们能够识别和部署最有效的 基因组数据分析的方法。 利用这些优势，我们将支持GDAN和它所服务的分析工作组(AWG 两个主要目标。根据《框架协议》，第一个目标和第二个目标是 创新的生物信息学和计算工具和方法“，这将使我们能够使临床和 生物相关性和“对GCC产生的数据集进行综合分析 每个GDAC开发的生物信息学工具。我们将通过五个具体目标实现这些目标，一个 行政目标，以支持GDAN和四个目标，每个领域一个，我们提出一个 能力。

Project Summary Recent advances in therapeutics have improved survival rates for many cancers. However, nearly all metastatic tumors are incurable, and resistance to therapeutic interventions is nearly universal. There are many reasons for our lack of progress-foremost of which is lack of understanding of mechanisms of response and resistance and lack of markers to identify subsets of patients ideally-suited for specific treatments. Our team brings enormous experience in TCGA and other multi-disciplinary coordinated projects, such as BEAT-AML, and the Stand Up to Cancer West Coast Prostate Cancer Dream Team. The Cancer Genome Atlas (TCGA) was successful because multi-disciplinary teams worked together to create new and innovative knowledge about cancer and we intend to ensure that the GDAN is equally successful. In this application, we have assembled a team of proven investigators from four of the different TCGA groups to extend the successes of TCGA to the projects managed by the Center for Cancer Genomics. Our team will continue our outstanding capabilities at analyzing and interpreting cancer genomic data by deploying data analysis pipelines that support the key capabilities of the network. In addition to the our experience in coordinated network studies, we bring experience with clinical trial design and interpretation. Further, the team has deep expertise at building the computational infrastructure necessary for the GDAN to succeed. For example, in the domain of distributed computing we have deploy pipelines for execution at many sites. We also bring novel methods for integrative pathways and analysis. Finally, we bring our experiences with competitive challenges that ensure that we can identify and deploy the most effective methods for genomic data analyses. Using these strengths we will support the GDAN and the Analysis Working Groups (AWGs) that it serves with two principal objectives. The first objective and second objective, per the RFA, are the “Development of innovative bioinformatics and computational tools and methodologies”, which will allow us to make clinical and biological correlations and to “Conduct Integrative analysis of data sets generated by GCCs using the bioinformatics tools developed by each GDAC.” We will achieve these objectives in five specific aims, one administrative aim to support the GDAN and four aims, one for each of the areas where we propose a competency.