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%新诊断的浸润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中心 (CCSB)，并建立预测计算和数学模型，以了解 ER 如何调节分子信号传导和细胞功能，从而影响正常乳腺肿瘤转化的风险以及乳腺癌对内分泌治疗的反应。稳健的预测模型将使人们更好地了解内质网在细胞命运调节中的作用，从而得出有助于降低乳腺癌死亡率的发现。核心A：管理、评估和规划将支持一个完全整合且富有成效的高级研究人员小组，他们在同行评审资助和出版物以及联合教育和培训活动方面拥有良好的记录。所需的关键信息学基础设施将由核心 B 提供：生物信息学基础设施和数据集成。数据将从一系列独特的人类乳腺癌细胞、啮齿动物模型和来自接受 TAM 治疗的女性的人类乳腺癌样本中获得，作为侵袭性乳腺癌的唯一辅助治疗形式（第 1 部分）。我们将通过计算生物信息学提取小型子网络拓扑并使用这些模型为数学建模提供信息，从而整合两个不同领域的方法来模拟 ER 调节信号传导（组件 2）。这些模型的预测将在体外和体内得到验证，并以实验数据和模型预测的稳健性为指导进行广泛的迭代建模。