Quantitative Analysis of Epidermal Growth Factor Receptor Signaling Networks

表皮生长因子受体信号网络的定量分析

• 批准号: 8240079
• 负责人: Forest M White
• 金额: $ 32.19万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-01 至 2013-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8240079
• 关键词: Algorithms; Amphiregulin; Apoptosis; Apoptotic; Behavior; Bioinformatics; Biological; Biological Assay; Biological Process; C-terminal; Cancer Patient; Cells; Characteristics; Computer Simulation; DTR gene; Data; Data Set; Development; Docking; EGF gene; Epidermal Growth Factor Receptor; Evaluation; Family member; Generations; Glioblastoma; Goals; Heterodimerization; Homo; Individual; Ligand Binding; Ligands; Link; MAP Kinase Gene; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of prostate; Maps; Mass Spectrum Analysis; Measures; Methods; Metric; Modeling; Mutation; Neoplasm Metastasis; Oncogenic; Outcome; Pathway Analysis; Pathway interactions; Patients; Phase; Phosphorylation; Phosphorylation Site; Protein Binding; Proteins; Proteomics; RNA Interference; Receptor Protein-Tyrosine Kinases; Receptor Signaling; Reproducibility; Research Project Grants; Role; Serine; Signal Transduction; Site; Study models; Survival Rate; System; Systems Biology; Testing; Therapeutic; Therapeutic Intervention; Threonine; Time; Transfection; Tyrosine; Tyrosine Phosphorylation Site; Validation; analytical method; base; cancer risk; cancer therapy; cell growth regulation; improved; interest; kinase inhibitor; malignant breast neoplasm; migration; novel; novel therapeutic intervention; outcome forecast; overexpression; response; small molecule; tool; tumor

Overexpression and mutation of epidermal growth factor receptor (EGFR) and EGFR family members leads to dysregulated signal transduction and has been correlated with increased risk for cancer and poor prognosis for cancer patients due to development of more aggressive cancers (i.e. higher proliferation and metastasis rates). Here we propose to develop an improved mechanistic model of the EGFR signaling network, from which we will be able to identify key nodes in the signaling network which regulate downstream biological response to activated ErbB receptor tyrosine kinases. In this five-year project we will investigate, model, and manipulate the EGFR signaling network to develop an improved mechanistic understanding of cellular signal transduction. In the first phase, we will apply mass spectrometry to quantify temporal phosphorylation profiles for hundreds of phosphorylation sites downstream of EGFR, under a variety of stimulation conditions. In order to link this signaling data to biological outcome, we will acquire phenotypic (migration, proliferation, apoptosis) data for each condition. In the second phase of the project, we will implement a variety of bioinformatic algorithms (hierarchical clustering, SOMs, PLSR) to characterize the data gathered in the first phase of the project. For instance, hierarchical clustering and self-organizing maps will be used to identify co-regulated phosphorylation sites which may function as dynamic modules within the EGFR signaling network. Identification of module components will facilitate assignment of potential biological function to poorly characterized proteins. PLSR will be used to correlate quantitative phosphorylation profiles with downstream biological response data. The result of this method is a functional relationship between the signaling metrics (phosphorylation sites) and biological outcomes (proliferation, migration, and apoptosis); predictions which will be tested experimentally. In this second phase of the project we will construct a mechanistic model of the EGFR signaling network which may then be used to predict behavior of the system. In the third phase of the project, we will attempt to validate model predictions by measuring the response to biological manipulation of the EGFR signaling network. Perturbations may include disrupting the function of various components in the network with RNA interference (RNAi) or small molecule kinase inhibitors (where available), or overexpressing proteins of interest through stable transfection. The final product of this research project will be a more comprehensive and well calibrated mechanistic model of the ErbB signaling network which will have a profound impact on our understanding of oncogenic signaling networks. Overexpression and mutation of epidermal growth factor receptor (EGFR) and EGFR family members have been implicated in many different tumor types, yet our understanding of these signaling networks is still very incomplete. Here we propose to use cutting-edge analysis and modeling tools to develop a more comprehensive mechanistic understanding of these signaling networks and their linkage to biological response. We will use these improved models to predict biological outcome to novel therapeutic interventions, with the goal of establishing new paradigms for cancer treatment.

表皮生长因子受体（EGFR）和EGFR家族的过表达和突变 成员导致信号转导失调，并与增加的风险相关 癌症和癌症患者的预后不良，这是由于癌症的发展而导致的 （即更高的增殖和转移率）。在这里，我们建议发展进步 EGFR信号网络的机械模型，我们将能够从中识别关键节点 在调节激活ERBB受体下游生物学反应的信号网络中 酪氨酸激酶。 在这个五年的项目中，我们将调查，模型并操纵EGFR信号网络 对细胞信号转导的机械理解有了改进的理解。在第一阶段， 我们将应用质谱法来量化数百个时间磷酸化曲线 在各种刺激条件下，EGFR下游的磷酸化位点。为了 将此信号数据与生物学结果联系起来，我们将获得表型（迁移，增殖， 凋亡）每种情况的数据。在项目的第二阶段，我们将实施一个多样性 生物信息学算法（分层聚类，SOM，PLSR）的表征收集的数据 在项目的第一阶段。例如，分层聚类和自组织地图将 用于识别可能充当动态模块的共同调节的磷酸化位点 在EGFR信号网络中。模块组件的识别将有助于分配 潜在的生物学功能对蛋白质的特征差。 PLSR将用于关联 具有下游生物反应数据的定量磷酸化谱。结果 方法是信号指标（磷酸化位点）和 生物结局（增殖，迁移和凋亡）；预测将要测试 实验。在项目的第二阶段，我们将构建一个机械模型 EGFR信号网络然后可用于预测系统的行为。在第三 项目的阶段，我们将尝试通过衡量对模型的预测来验证模型预测 EGFR信号网络的生物操纵。扰动可能包括破坏 RNA干扰（RNAi）或小分子的网络中各种组件的功能 激酶抑制剂（如果有的话）或通过稳定的过表达感兴趣的蛋白质 转染。该研究项目的最终产品将是一个更全面，良好的 ERBB信号网络的校准机械模型将对 我们对致癌信号网络的理解。表皮生长因子受体（EGFR）和EGFR家族的过表达和突变 成员与许多不同的肿瘤类型有关，但我们对这些肿瘤类型 信号网络仍然非常不完整。在这里，我们建议使用尖端分析和 建模工具以对这些信号开发更全面的机械理解 网络及其与生物反应的联系。我们将使用这些改进的模型来预测 新型治疗干预措施的生物学结果，目的是建立新的范式 用于癌症治疗。

项目成果

Quantifying oncogenic phosphotyrosine signaling networks through systems biology.

• DOI: 10.1016/j.gde.2009.12.005
• 发表时间: 2010-02
• 期刊: CURRENT OPINION IN GENETICS & DEVELOPMENT
• 影响因子: 4
• 作者: Del Rosario, Amanda M.;White, Forest M.
• 通讯作者: White, Forest M.

Integrated data management and validation platform for phosphorylated tandem mass spectrometry data.

• DOI: 10.1002/pmic.200900727
• 发表时间: 2010-10
• 期刊: PROTEOMICS
• 影响因子: 3.4
• 作者: Lahesmaa-Korpinen, Anna-Maria;Carlson, Scott M.;White, Forest M.;Hautaniemi, Sampsa
• 通讯作者: Hautaniemi, Sampsa