Quantitative Analysis of Epidermal Growth Factor Receptor Signaling Networks

描述(申请人提供)：表皮生长因子受体(EGFR)和EGFR家族成员的过度表达和突变导致信号转导失调，并与癌症风险增加和癌症患者预后不良相关，原因是发展为更具侵袭性的癌症(即更高的增殖和转移率)。在这里，我们建议建立一个改进的EGFR信号网络的机制模型，从这个模型中，我们将能够识别信号网络中调节下游对激活的ErbB受体酪氨酸激酶的生物反应的关键节点。在这个为期五年的项目中，我们将对EGFR信号网络进行调查、建模和操作，以提高对细胞信号转导的机械理解。在第一阶段，我们将应用质谱学来量化EGFR下游数百个磷酸化位点在各种刺激条件下的时间磷酸化图谱。为了将这些信号数据与生物结果联系起来，我们将获得每种情况的表型(迁移、增殖、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(层次聚类、SOMS、PLSR)来表征项目第一阶段收集的数据。例如，层级聚类和自组织映射图将用于识别协同调节的磷酸化位点，这些位点可能在EGFR信号网络中作为动态模块发挥作用。模块成分的识别将有助于将潜在的生物学功能分配给特征不佳的蛋白质。PLSR将用于将定量磷酸化图谱与下游生物反应数据相关联。这种方法的结果是信号指标(磷酸化位点)和生物结果(增殖、迁移和凋亡)之间的函数关系；这些预测将在实验中得到验证。在该项目的第二阶段中，我们将构建EGFR信令网络的机械模型，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EGFR信号网络对生物操纵的反应来验证模型预测。干扰可能包括使用RNA干扰(RNAi)或小分子激酶抑制剂(如果有)扰乱网络中各种组件的功能，或者通过稳定的转染过度表达感兴趣的蛋白质。这一研究项目的最终成果将是一个更加全面和校准良好的ErbB信号网络的机制模型，这将对我们对肿瘤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)及其家族成员的过度表达和突变与许多不同类型的肿瘤有关，但我们对这些信号网络的了解仍然很不完整。在这里，我们建议使用尖端的分析和建模工具来开发对这些信号网络及其与生物反应的联系的更全面的机制理解。我们将使用这些改进的模型来预测新的治疗干预措施的生物学结果，目标是为癌症治疗建立新的范例。

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.