Integration of Oncogenic Networks in Cancer Phenotypes

拟议的综合癌症生物学计划将结合联合收割机的资源和专业知识在杜克大学，达纳法伯癌症研究所，UT西南医学中心，和南加州大学，专注于数据和计算工具的发展，以实现细胞信号通路的综合和深入的理解是中央控制细胞增殖和致癌过程。ICBP的工作将侧重于 这些致癌信号通路的整合，代表了一组相互连接的通路，包括Ras，Myc和Rb-E2 F通路，然后也连接到p53反应通路。这些途径的活性对于控制细胞从静止状态通过G1期进入S期是基本和关键的，并且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表达和蛋白质分析的基因组规模的措施，以生成数据集，其分析和解释将成为全面了解涉及这些途径的复杂调控网络的基础。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，涉及多学科的团队， 研究人员，这将开发和应用新的统计和计算方法来模拟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性和相互作用，并将其作为定义更广泛应用于细胞信号传导其他方面的工具的范例。重要的是，我们还将整合 在人类乳腺癌的研究信息，利用详细的数据集为乳腺癌复发的预测模型提供信息。我们相信这是一个独特的机会，将致癌途径的基础研究与人类癌症的研究结合在一起，有助于为每个过程提供信息。这些方案还将成为一个综合和多方面的培训方案的来源，该方案将作为青年调查员在 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，提供数据访问，利用和可视化的高阶途径和网络数据的工具。基于网络的系统还将作为项目管理资源，将中心调查人员的活动联系起来，并作为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 pathways that are central to the control of cell proliferation and the oncogenic process. The work of the ICBP will focus on 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.