Quantitative Analysis of Epidermal Growth Factor Receptor Signaling Networks

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，并与癌症患者因发展为更具侵袭性的癌症（即更高的增殖和转移率）而增加的癌症风险和不良预后相关。在这里，我们建议开发一个改进的EGFR信号网络的机制模型，从中我们将能够识别信号网络中的关键节点，这些节点调节下游对活化ErbB受体酪氨酸激酶的生物反应。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，以提高对细胞信号转导的机制理解。在第一阶段，我们将应用质谱法量化在各种刺激条件下EGFR下游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，我们将获取每种情况的表型（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（分层聚类，SOMs， PLSR）来描述项目第一阶段收集的数据。例如，分层聚类和自组织图谱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。模块成分的鉴定将有助于将潜在的生物学功能分配给特征较差的蛋白质。PLSR将用于将定量磷酸化谱与下游生物反应数据相关联。该方法的结果是信号指标（磷酸化位点）和生物学结果（增殖、迁移和凋亡）之间的功能关系；这些预测将被实验验证。在该项目的第二阶段，我们将构建EGFR信号网络的机制模型，然后可用于预测系统的行为。在该项目的第三阶段，我们将尝试通过测量对EGFR信号网络的生物操作的响应来验证模型预测。干扰可能包括用RNA干扰（RNAi）或小分子激酶抑制剂（如有）破坏网络中各种成分的功能，或通过稳定转染过度表达感兴趣的蛋白质。本研究项目的最终成果将是一个更全面和校准良好的ErbB信号网络机制模型，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类型的肿瘤有关，但我们对这些信号网络的理解仍然非常不完整。在这里，我们建议使用尖端的分析和建模工具来对这些信号网络及其与生物反应的联系进行更全面的机制理解。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，目标是建立癌症治疗的新范式。

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.