MSM Mapping and Modeling ErbB Receptor Membrane Topogra

描述（由申请人提供）：我们的目标是了解子宫内膜癌和乳腺癌中ErbB信号传导的调节，这些疾病中ErbB基因（EGFR，ErbB 2，ErbB 3）的扩增与不良结局相关。UNM团队的组成是独特的，在信号转导，高分辨率显微镜，数学建模和动物模型，以及在GOG临床试验的领导作用的专业知识。这个多尺度项目从受体形貌和行为的纳米级评估开始，用创新的电子显微镜、活细胞成像和流式细胞术技术进行测量。创新包括单价量子点探针的开发，用于静息和配体结合受体的单粒子跟踪。这些测量提供了定量信息的数学分析，使用混合随机/continuum的方法，旨在评估膜空间组织的ErbB信号的贡献。随机平台使用蒙特卡罗和基于代理的方法模拟受体和信号分子的扩散，聚集和内化。通过应用最先进的实验和建模方法，我们将特别考虑组合复杂性对信号传播的影响。我们还将讨论受体突变，受体构象状态和临床相关抑制剂的使用。例如，该小组在上一个资助周期中发现了一个新的ErbB 3突变。我们打算在200多例人类乳腺癌和子宫内膜癌中筛选这种突变，并评估ErbB 3表达水平。关键工具包括携带突变受体或表达ErbB受体的特定组合作为VFP融合蛋白的细胞系。通过将我们独特而全面的数据集与随机建模相结合，我们已经挑战了目前对Erb家族成员之间同源和异源二聚化的估计。我们将继续这项令人兴奋的工作，以充分探索ErbB信号在细胞水平上的空间和时间方面。到项目的第三年，实验人员将在细胞和分子水平上用小鼠异种移植模型补充他们的研究。这项工作将允许在体内评估致癌信号，并将项目从纳米和微米长度尺度移动到毫米和厘米长度尺度。时间尺度也从毫秒和分钟移动到小时或天。来自小鼠模型的数据将为新的基于细胞的实体瘤模型提供关键参数。然后，肿瘤模型将用于预测体内肿瘤生长、血管生成和药物反应性的速率。肿瘤模型中的细胞行为将由随机模型的结果控制，桥接考虑明显不同长度和时间尺度的两个数学建模平台。公共卫生相关性。该项目针对两种女性癌症，乳腺癌和子宫内膜癌，以及ErbB受体家族。ErbB信号是参与肿瘤生长和存活的基因转录的强有力诱导剂，为在癌症治疗中使用抑制ErbB通路的药物提供了理论基础。个体患者的混合反应表明，需要更好地理解来预测初始患者反应和复发肿瘤的可能特征。激素反应性癌症，如乳腺癌、卵巢癌和前列腺癌，在多步骤过程中发展，从局部良性增生开始，以能够转移到其他器官的侵袭性肿瘤结束。肿瘤在被发现时是遗传异质性的，这一特性转化为特定患者选择适当治疗方法的复杂性。我们的生物学和建模最终旨在了解个体肿瘤中ErbB表达的异质性，并将这些知识应用于个性化的预测性肿瘤治疗。

DESCRIPTION (provided by applicant): Our goal is to understand the regulation of ErbB signaling in endometrial and breast cancers, diseases where amplification of ErbB genes (EGFR, ErbB2, ErbB3) is associated with poor outcome. The composition of the UNM team is unique, with expertise in signal transduction, high resolution microscopy, mathematical modeling and animal models, as well as leadership roles in GOG clinical trials. This multiscale project begins with nanoscale evaluation of receptor topography and behavior, measured with innovative electron microscopy, live cell imaging and flow cytometry technologies. Innovations include development of monovalent quantum dot probes for single particle tracking of resting and ligand-bound receptors. These measurements provide quantitative information for mathematical analysis using a mixed stochastic/continumum approach that is aimed at evaluating the contributions of membrane spatial organization to ErbB signaling. The stochastic platform simulates diffusion, clustering and internalization of receptors and signaling molecules using Monte Carlo and agent-based methods. By applying state-of-the-art experimental and modeling approaches, we will specifically consider the impact of combinatorial complexity upon signal propagation. We will also address receptor mutations, receptor conformational state, and the use of clinically-relevant inhibitors. For example, the group discovered a new ErbB3 mutation in the previous funding cycle. We intend to screen for this mutation, as well as evaluate ErbB3 expression levels, in over 200 human breast and endometrial cancers. Critical tools include cell lines bearing mutant receptors or expressing specific combinations of ErbB receptors as VFP-fusion proteins. By combining our unique and comprehensive data sets with stochastic modeling, we have already challenged current estimations of homo- and hetero-dimerization between Erb family members. We will continue this exciting work to fully explore the spatial and temporal aspects of ErbB signaling at the cellular level. By year 3 of the project, the experimentalists will complement their studies at the cell and molecular scales with xenograft models in mice. This work will permit evaluation of oncogenic signaling in vivo and move the project from nanometer and micron length scales to millimeter and centimeter length scales. Time scales also move from millisecond and minutes up to hours or days. Data from the mouse model will provide critical parameters for a new a cell-based solid tumor model. The tumor model will then be used to predict rates of tumor growth, angiogenesis and drug responsiveness in vivo. Cell behavior in the tumor model will be governed by outcomes from the stochastic model, bridging the two mathematical modeling platforms that consider markedly different length and time scales. Public Health Relevance. This project targets two women's cancer, breast and endometrium, and the ErbB family of receptors. ErbB signals are powerful inducers of gene transcription involved in tumor growth and survival, providing a rationale for use of drugs that inhibits ErbB pathways in the treatment of cancer. The mixed response of individual patients indicates better understanding is needed to predict initial patient response and the likely characteristics of recurrent tumors. Hormone-responsive cancers, such breast, ovarian and prostate, develop in a multistep process that starts from a local benign hyperplasia and ends with an invasive tumor able to metastasize to other organs. Tumors are genetically heterogenous by the time their presence is discovered, a property that translates to complexity in selecting the proper therapeutics for specific patients. Both our biology and modeling are ultimately aimed at understanding the heterogeneity of ErbB expression in individual tumors and applying that knowledge for personalized, predictive tumor therapy.