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

项目摘要 胰腺导管腺癌（PDAC）是一种高致死性和常见的癌症， 存活率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，有时直接响应于治疗，可以去分化为间充质状态， 他们对化疗更有抵抗力。这一观察结果提出了一个问题：上皮间质细胞 过渡期（EMT）的目标是促进治疗反应和增加患者生存？的主要障碍 探索这一想法的关键在于，我们不知道如何精确地瞄准EMT，特别是考虑到复杂的 多变量细胞信号传导动力学驱动EMT并将其维持为对化疗的反馈反应。 我们最近进行了一项初步研究，以确定一组可药物化的细胞信号通路， 协同驱动PDAC中的间充质状态。然而，我们目前的翻译潜力 分析是有限的，因为它只是确定了潜在的目标;它没有提供任何系统的可操作的 了解，也不是可测试的预测，如何最好地安排药物组合的时间，以最大限度地提高 治疗效果和最小化非预期毒性。因此，我们现在寻求扩大我们的初步 研究开发系统生物学平台，用于系统确定预定组合 PDAC的治疗方法旨在最大限度地抑制治疗期间的EMT。在目标1中，我们将 用以下驱动剂处理的PDAC细胞中信号传导途径活性和细胞表型的动态测量： EMT、EMT拮抗剂和化学治疗剂。我们的测量将涵盖这些途径已经 在我们的初步工作中确定为EMT最有可能的药物调节剂，并将包括以下影响： 缺氧和癌症相关的成纤维细胞，可能影响EMT的肿瘤微环境元素 调控目标是获得一个信息丰富的数据集，随后用于模型识别 和控制计算。在目标2中，我们将使用动态数据开发计算平台， 确定实现EMT的最大抑制所需的驱动剂和拮抗剂的最佳变化， 作为PDAC的计划联合治疗实施。这将通过以下方式实现：㈠ 确定响应于以下的上皮或间充质细胞状态的动态模型 磷蛋白扰动（即，定量表征，以计算模型的形式，EMT 响应其驱动因素和拮抗剂的变化），以及（ii）“反向”部署模型，以通过 最佳控制原则，如何最好地联合收割机和时间表药物的最佳维护的上皮细胞 表型。在目标3中，我们将在一系列体外试验中测试基于模型的联合治疗方案。 和体内实验。最终，这些研究将为新的策略提供临床前验证， 开发针对PDAC中限制治疗反应的病理过程的治疗方案。新 由于PDAC的存活率在近40年内没有变化，因此迫切需要采取新的治疗方法。

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.