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: 7617710
• 负责人: Forest M White
• 金额: $ 36.95万
• 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/7617710
• 关键词: Algorithms; Amphiregulin; Apoptosis; Apoptotic; Behavior; Bioinformatics; Biological; Biological Assay; Biological Process; C-terminal; Cancer Patient; Cells; Characteristics; Computer Simulation; Data; Data Set; Development; Dimerization; Docking; EGF gene; Epidermal Growth Factor Receptor; Evaluation; Family member; Generations; Glioblastoma; Goals; Homo; Individual; Ligand Binding; Ligands; Link; Lung; MAP Kinase Gene; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Measures; Methods; Metric; Modeling; Mutation; Neoplasm Metastasis; Oncogenic; Outcome; Pathway Analysis; Pathway interactions; Patients; Phase; Phosphorylation; Phosphorylation Site; Prostate; 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; public health relevance; response; small molecule; tool; tumor

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.

描述（由申请人提供）：表皮生长因子受体（EGFR）和EGFR家族成员的过表达和突变导致信号转导失调，并且与由于发展更具侵略性的癌症的发展而导致癌症的风险增加，癌症患者的预后不良（即更高的增殖和转移率）。在这里，我们建议开发一个改进的EGFR信号网络的机械模型，我们将能够从中识别信号网络中的关键节点，该节点调节对激活的ERBB受体酪氨酸激酶的下游生物学反应。在这个五年的项目中，我们将调查，模型并操纵EGFR信号网络，以提高对细胞信号转导的机械理解。在第一阶段，我们将在各种刺激条件下应用质谱法来量化EGFR下游的数百个磷酸化位点的时间磷酸化谱。为了将这些信号数据与生物学结果联系起来，我们将为每种情况获取表型（迁移，增殖，凋亡）数据。在项目的第二阶段中，我们将实施各种生物信息学算法（分层聚类，SOMS，PLSR），以表征项目第一阶段收集的数据。例如，分层聚类和自组织图将用于识别共同调节的磷酸化位点，这些位点可能充当EGFR信号网络中的动态模块。模块成分的识别将促进潜在的生物学功能分配到较差的蛋白质。 PLSR将用于将定量磷酸化谱与下游生物学反应数据相关联。该方法的结果是信号指标（磷酸化位点）与生物学结果（增殖，迁移和凋亡）之间的功能关系。预测将通过实验测试。在项目的第二阶段，我们将构建一个EGFR信号网络的机械模型，然后可以使用该模型来预测系统的行为。在项目的第三阶段，我们将尝试通过测量对EGFR信号网络的生物操纵的响应来验证模型预测。扰动可能包括在网络中使用RNA干扰（RNAi）或小分子激酶抑制剂（如果有可能的情况）或通过稳定转染使您感兴趣的蛋白质的各种组件的功能。该研究项目的最终产品将是ERBB信号网络的更全面且校准的机械模型，这将对我们对致癌信号网络的理解产生深远的影响。公共卫生相关性：表皮生长因子受体（EGFR）和EGFR家族成员的过表达和突变与许多不同的肿瘤类型有关，但是我们对这些信号网络的理解仍然非常不完整。在这里，我们建议使用最先进的分析和建模工具来对这些信号网络及其与生物学反应的联系进行更全面的机械理解。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，以建立新的癌症治疗范式。