MSM Mapping and Modeling ErbB Receptor Membrane Topogra

描述（由申请人提供）：我们的目标是了解ErbB信号在子宫内膜癌和乳腺癌中的调控，在这些疾病中，ErbB基因（EGFR, ErbB2, ErbB3）扩增与不良预后相关。新墨西哥大学团队的组成是独特的，具有信号转导，高分辨率显微镜，数学建模和动物模型的专业知识，以及在GOG临床试验中的领导作用。这个多尺度项目从纳米尺度的受体形貌和行为评估开始，使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。创新包括开发用于静息和配体结合受体的单粒子跟踪的单价量子点探针。这些测量结果为使用混合随机/连续方法的数学分析提供了定量信息，旨在评估膜空间组织对ErbB信号传导的贡献。随机平台模拟扩散，集群和内化受体和信号分子使用蒙特卡罗和基于代理的方法。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，该小组在上一个融资周期中发现了一种新的ErbB3突变。我们打算在200多种人类乳腺癌和子宫内膜癌中筛选这种突变，并评估ErbB3的表达水平。关键工具包括携带突变受体或表达特定组合的ErbB受体作为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Erb家族成员之间同性和异性二聚化的当前估计。我们将继续这项令人兴奋的工作，在细胞水平上充分探索ErbB信号传导的空间和时间方面。到项目的第三年，实验人员将在小鼠的异种移植模型中补充他们在细胞和分子尺度上的研究。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推进到毫米和厘米长度尺度。时间尺度也从毫秒和分钟变为小时或天。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。该肿瘤模型将用于预测体内肿瘤生长、血管生成和药物反应的速率。肿瘤模型中的细胞行为将由随机模型的结果控制，从而将考虑明显不同长度和时间尺度的两个数学建模平台连接起来。公共卫生相关性。本项目针对两种女性癌症，乳腺癌和子宫内膜，以及ErbB受体家族。ErbB信号是参与肿瘤生长和生存的基因转录的强大诱导剂，为使用抑制ErbB途径的药物治疗癌症提供了理论依据。个体患者的混合反应表明需要更好地了解患者的初始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，是一个多步骤的发展过程，从局部良性增生开始，到能够转移到其他器官的侵袭性肿瘤结束。肿瘤在被发现时具有遗传异质性，这一特性转化为为特定患者选择适当治疗方法的复杂性。我们的生物学和模型最终都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.