Optimal control models of epithelial-mesenchymal transition for the design of pancreas cancer combination therapy

项目总结 摘要胰腺导管腺癌是一种高致命性的常见癌症，总的发病时间为五年。 存活率为6%。造成这一令人沮丧的统计数据的因素之一是观察到上皮细胞- 衍生的PDAC细胞，有时对治疗有直接反应，可以去分化为间充质状态 它们对化疗的抵抗力更强。这一观察引发了一个问题：上皮间充质是否应该 过渡(EMT)是否有针对性地促进治疗反应和提高患者存活率？主要障碍是 要探讨这一思想，我们不知道如何准确地定位EMT，特别是在复杂的情况下 驱动EMT并将其维持为对化疗的反馈反应的多元细胞信号动力学。 我们最近进行了一项初步研究，以确定一组可用药的细胞信号通路，它们可能 协同驱动PDAC中的间质状态。然而，我们目前的翻译潜力 分析是有限的，因为它只确定了潜在的目标；它没有提供任何系统的、可操作的 对如何最好地及时安排药物组合以实现最大化的理解，也不是可测试的预测 治疗效果，并将意外毒性降至最低。因此，我们现在寻求延长我们的初步 开发系统生物学平台用于系统测定预定组合的研究 PDAC的治疗方法设计为在治疗期间最大限度地抑制EMT。在目标1中，我们将使 驱动因素对PDAC细胞信号通路活性和细胞表型的动态检测 EMT、EMT的拮抗剂和化疗药物。我们的测量已经覆盖了这些路径 在我们的初步工作中确定为EMT最有可能的可用药调节剂，并将包括 低氧与肿瘤相关成纤维细胞--可能影响EMT的肿瘤微环境因素 监管。目标是获得一个信息丰富的数据集，以便随后用于模型识别 并控制计算。在目标2中，我们将使用动态数据来开发计算平台 确定实现对EMT的最大抑制所需的驱动器和拮抗剂的最佳改变， 将按计划实施PDAC的联合治疗。这将通过以下方式实现：(I) 确定上皮细胞或间充质细胞状态的动态模型 磷蛋白扰动(即，以计算模型、EMT的形式进行的定量表征 对其驱动因素和拮抗剂的变化作出反应)和(2)“反向”部署模型以确定，通过 最优控制原则，如何最好地组合和调度药物以实现最优的上皮维护 表型。在目标3中，我们将在体外序列中测试基于模型的联合治疗方案 以及活体实验。最终，这些研究将为一项新的战略提供临床前验证 开发针对PDAC中限制治疗反应的病理过程的治疗方案。新的 由于PDAC存活率近40年来没有变化，因此迫切需要治疗方法。

PROJECT SUMMARY Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and common cancer, with an overall five-year survival rate of 6%. Among the factors contributing to this dismal statistic is the observation that epithelial- derived PDAC cells, sometimes in direct response to therapy, can de-differentiate to a mesenchymal state in which they are more chemoresistant. This observation prompts the question: should epithelial-mesenchymal transition (EMT) be targeted to promote therapeutic response and increase patient survival? The main barrier to exploring this idea is that we do not know how to target EMT precisely, especially in light of the complex multivariate cell signaling dynamics that drive EMT and maintain it as a feedback response to chemotherapy. We recently undertook a preliminary study to identify a group of druggable cell signaling pathways that may cooperatively drive the mesenchymal state in PDAC. However, the translational potential of our current analysis is limited in that it merely identified potential targets; it does not provide any systematic actionable understanding, nor testable predictions, of how best to schedule combinations of drugs in time to maximize therapeutic efficacy and minimize unintended toxicity. Consequently, we now seek to extend our preliminary studies to develop a systems biology platform for the systematic determination of scheduled combination therapy approaches for PDAC designed to maximally suppress EMT during treatment. In Aim 1, we will make dynamic measurements of signaling pathway activity and cell phenotypes in PDAC cells treated with drivers of EMT, antagonists of EMT, and chemotherapeutics. Our measurements will cover those pathways already identified in our preliminary work as the most likely druggable regulators of EMT, and will include the effects of hypoxia and cancer-associated fibroblasts, elements of the tumor microenvironment that may impact EMT regulation. The goal is to obtain an information-rich data set to be used subsequently for model identification and control computations. In Aim 2, we will use the dynamic data to develop the computational platform for determining optimal changes to the drivers and antagonists required to achieve maximal suppression of EMT, to be implemented as scheduled combination therapies for PDAC. This will be accomplished through: (i) identification of a dynamic model for epithelial or mesenchymal cell state determination in response to phosphoprotein perturbations (i.e., quantitative characterization, in the form of a computational model, the EMT response to changes in its drivers and antagonists) and (ii) deploying the model “in reverse” to determine, via optimal control principles, how best to combine and schedule drugs for optimal maintenance of the epithelial phenotype. In Aim 3, we will test the model-based schedules for combination therapy in a sequence of in vitro and in vivo experiments. Ultimately, these studies will provide pre-clinical validation for a new strategy to develop therapeutic regimens that target a pathological process in PDAC that limits therapeutic response. New approaches are urgently needed, as PDAC survival rates have not changed in nearly 40 years.