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）及其家族的过表达和突变 成员导致信号传导失调，并与以下疾病的风险增加相关 癌症和癌症患者由于发展为更侵袭性的癌症而导致的不良预后 (i.e.更高的增殖和转移率）。在这里，我们建议开发一种改进的 EGFR信号网络的机制模型，从中我们将能够识别关键节点 在调节下游对激活的ErbB受体的生物反应的信号网络中 酪氨酸激酶 在这个为期五年的项目中，我们将研究，建模和操纵EGFR信号网络， 发展细胞信号转导的更好的机制理解。在第一阶段， 我们将应用质谱法来定量数百个细胞的时间磷酸化谱， 在各种刺激条件下，EGFR下游的磷酸化位点。为了 将该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， 细胞凋亡）数据。在项目的第二阶段，我们将实施各种 生物信息学算法（分层聚类，SOM，PLSR）来表征收集的数据 在项目的第一阶段。例如，分层聚类和自组织映射将 用于鉴定可作为动态模块发挥功能的共调节磷酸化位点 在EGFR信号网络中。模块组件的识别将有助于分配 潜在的生物学功能的蛋白质。PLSR将用于关联 定量磷酸化曲线与下游生物反应数据。这样做的结果 方法是信号传导指标（磷酸化位点）和 生物学结果（增殖、迁移和凋亡）;将进行测试的预测 实验性的在这个项目的第二阶段，我们将构建一个机械模型， EGFR信号网络，然后可以用于预测系统的行为。第三 在项目的第一阶段，我们将尝试通过测量对 EGFR信号网络的生物学操纵。干扰可能包括干扰 网络中各种组分的功能与RNA干扰（RNAi）或小分子 激酶抑制剂（如果可用），或通过稳定的表达过量表达目的蛋白质， 转染本研究项目的最终产品将是一个更全面，更好的 ErbB信令网络的校准机制模型，这将对 我们对致癌信号网络的理解。表皮生长因子受体（EGFR）及其家族的过表达和突变 成员与许多不同的肿瘤类型有关，但我们对这些肿瘤的理解 信令网络仍然非常不完善。在这里，我们建议使用尖端的分析和 建模工具，以开发对这些信号的更全面的机械理解 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 新的治疗干预的生物学结果，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