Integration of Ocogenic Networks in Cancer Phenotypes

拟议的综合癌症生物学计划将联合收割机的资源和专业知识，在杜克大学， Dana Farber癌症研究所、UT西南医学中心和南加州大学将重点关注 开发数据和计算工具，以实现对细胞信号传导的综合和深入理解 - 对控制细胞增殖和致癌过程至关重要的途径。ICBP的工作将侧重于 _n这些致癌信号通路的整合，代表一组相互连接的通路，包括 Ras、Myc和Rb-E2 F途径，然后也连接到p53反应途径。这些途径的活性是 是控制细胞从静止状态通过G1期进入S期的基础和关键， 将细胞增殖过程的活性与细胞命运的决定联系起来。此外，放松对 这些途径是人类癌症发展的核心。我们的目标是开发基因组尺度的基因测量方法， 表达和蛋白质分析，以生成数据集，其分析和解释将成为发展的基础， 全面了解涉及这些途径的复杂调控网络。而不是 开发一个广泛的项目组，我们提出了一个高度集中的计划，涉及多学科团队， 研究人员，将开发和应用新的统计和计算方法，为一组高度 整合的细胞通路通过对这些途径采取非常集中的方法，我们相信我们将能够 深入了解这些途径中的活动和相互作用，并以此为范例， 定义了用于更广泛地应用于细胞信号传导的其他方面的工具。重要的是，我们还将整合 人类乳腺癌研究中的信息，利用详细的数据集为预测模型提供信息， 乳腺癌复发我们相信这是一个独特的机会，汇集致癌途径的基础研究 以一种有助于了解每个过程的方式来研究人类癌症。这些程序也将成为 一个综合和多方面的培训计划，将作为一个培训基地，年轻的调查人员在 生物学、基因组学和计算科学的接口。最后，我们将开发一个基于Web的信息 管理系统，以提供用于数据访问、利用和高阶途径的可视化的工具， 网络数据。网络系统还将作为项目管理资源，将中心的活动联系起来。 这是一个向科学界传播数据和计算工具的机制。

The proposed Integrated Cancer Biology Program will combine the resources and expertise at Duke University, the Dana Farber Cancer Institute, UT Southwestern Medical Center, and the University of Southern California to focus on the development of data and computational tools to achieve an integrative and in-depth understanding of cell signaling _athways that are central to the control of cell proliferation and the oncogenic process. The work of the ICBP will focus _n an integration of these oncogenic signaling pathways, representing a set of interconnected pathways including the Ras, Myc, and Rb-E2F pathway, that then also connect to the p53 response pathway. The activity of these pathways is fundamental and critical for the control of cells moving from a quiescent state, through G1 and into S phase, and that link the activity of the cellular proliferation process with the determination of cell fate. Moreover, the deregulation of these pathways is central to the development of human cancer. We aim to develop genomic-scale measures of gene expression and protein analysis to generate datasets whose analysis and interpretation will underlie the development of a comprehensive understanding of the complex regulatory networks that involves these pathways. Rather than developing a broad group of projects, we propose a highly focused program, involving a multi-disciplinary team of investigators, that will develop and apply novel methods of statistics and computation for modeling a set of highly lintegrated cellular pathways. By taking a very focused approach to these pathways, we believe we will be able to develop an in depth understanding of the activity and interaction within these pathways and use this as a paradigm for defining the tools for broader application to other aspects of cellular signaling. Importantly, we will also integrate this information in the study of human breast cancer, making use of the detailed datasets to inform predictive models of breast cancer recurrence. We believe this is a unique opportunity to bring together basic studies of oncogenic pathways with the study of human cancer in a way that will help to inform each process. These programs will also be the source of an integrated and multi-faceted training program that will serve as a training ground for young investigators at the interface of biology, genomics, and computational sciences. Finally, we will develop a web-based information management system to provide the tools for data access, utilization, and visualization of higher-order pathway and network data. The web-based system will also serve as a project management resource to link the activities of Center investigators as well as a mechanism for dissemination of data and computational tools to the scientific community.