Integration of ER-related signaling in breast cancer

乳腺癌中 ER 相关信号的整合

• 批准号: 7879085
• 负责人: Robert R. Clarke
• 金额: $ 149.96万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-29 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7879085
• 关键词: Integration; ER; related; signaling; breast

DESCRIPTION (provided by applicant): 70% of newly diagnosed invasive breast cancers express estrogen receptor-a (ER); advanced ER+ breast cancer remains an incurable disease. We will create a Center for Cancer Systems Biology (CCSB) and build predictive computational and mathematical models of how ER regulates molecular signaling and cellular functions to affect the risk of neoplastic transformation in the normal breast, and responsiveness to endocrine therapies in breast cancer. Robust predictive models will enable a greater understanding of ER action in the regulation of cell fate, leading to discoveries that contribute to reducing breast cancer mortality. A fully integrated and productive group of senior investigators with an established track-record of collaborative peer reviewed funding and publications, and joint education and training activities, will be supported by Core A: Administration, Evaluation and Planning. The critical informatics infrastructure required will be provided by Core B: Bioinformatics Infrastructure and Data Integration. Data will be obtained in a unique series of human breast cancer cells, rodent models, and human breast cancer specimens from women treated with TAM as their only form of adjuvant therapy for invasive breast cancer (Component 1). We will integrate methods from two different fields to model ER-regulated signaling (Component 2) by extracting small, subnetwork topologies by computational bioinformatics and using these models to inform mathematical modeling. Predictions from these models will be validated in vitro and in vivo, with extensive iterative modeling guided by experimental data and the robustness of model predictions.

描述（由申请人提供）：70％的新诊断的入侵性乳腺癌表达雌激素受体A（ER）；晚期ER+乳腺癌仍然是一种无法治愈的疾病。我们将创建一个癌症系统生物学（CCSB）的中心，并建立ER如何调节分子信号传导和细胞功能的预测计算和数学模型，以影响正常乳房中肿瘤转化的风险，以及对乳腺癌内分泌疗法的反应性。强大的预测模型将使对细胞命运的调节中的ER作用有更多的了解，从而导致发现有助于降低乳腺癌死亡率的发现。一个完全融合和富有成效的高级调查员小组，拥有合作的同行审查资金和出版物以及联合教育和培训活动的既定田径记录，将得到核心A：管理，评估和计划的支持。核心B：生物信息学基础架构和数据集成所需的关键信息基础架构将提供。数据将在一系列独特的人类乳腺癌细胞，啮齿动物模型和人类乳腺癌标本中获得，这些女性被用TAM作为其侵入性乳腺癌的唯一形式的辅助治疗形式（成分1）。我们将通过通过计算生物信息学并使用这些模型来告知数学建模，从而将方法从两个不同字段集成到模型的ER调节信号传导（组件2）。这些模型的预测将在体外和体内进行验证，并在实验数据和模型预测的鲁棒性的指导下进行广泛的迭代建模。