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

用于设计胰腺癌联合治疗的上皮-间质转化的最佳控制模型

• 批准号: 10450032
• 负责人: Matthew J Lazzara
• 金额: $ 45.23万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10450032
• 关键词: Adenocarcinoma Cell; Adjuvant; Agonist; Cells; Cessation of life; Chemoresistance; Clinical Trials; Combined Modality Therapy; Complex; Computer Models; Coupled; Data; Data Set; Diagnosis; Disease; Drug Combinations; Elements; Engineering; Epithelial; Feedback; Fibroblasts; Goals; Human; Hypoxia; In Vitro; Least-Squares Analysis; Light; Maintenance; Malignant Neoplasms; Malignant neoplasm of pancreas; Measurement; Measures; Mesenchymal; Methodology; Methods; Modeling; Neoplasm Metastasis; Nucleoside Transporter; Pancreatic Adenocarcinoma; Pancreatic Ductal Adenocarcinoma; Pathologic Processes; Pathology; Pathway interactions; Patients; Pharmaceutical Preparations; Phenotype; Phosphoproteins; Phosphorylation; Phosphotransferases; Primary Neoplasm; Process; Prognosis; Regimen; Regulation; Resectable; Resistance; Response to stimulus physiology; Schedule; Signal Pathway; Signal Transduction; Surgical Oncologist; Survival Rate; System; Systemic Therapy; Systems Analysis; Systems Biology; Testing; Therapeutic; Therapeutic Intervention; Time; Toxic effect; Treatment Efficacy; Tumor Burden; Validation; Work; antagonist; base; chemotherapy; clinical efficacy; combination cancer therapy; computational platform; computer framework; control theory; data-driven model; design; epithelial to mesenchymal transition; experimental study; high dimensionality; in vivo; in vivo evaluation; inhibitor; mouse model; multidisciplinary; neoplastic cell; novel strategies; pancreatic cancer patients; pancreatic ductal adenocarcinoma cell; pancreatic ductal adenocarcinoma model; patient derived xenograft model; pre-clinical; preclinical study; predictive modeling; response; trait; translational potential; treatment response; tumor microenvironment

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.

项目摘要 胰腺导管腺癌（PDAC）是一种高度致命和常见的癌症，总体五年 存活率为6％。造成这种令人沮丧统计的因素之一是观察到上皮 衍生的PDAC细胞有时直接反应治疗，可以将分化为间质状态 它们更具有化学抗性。该观察结果提示了一个问题：应该上皮 - 间质 过渡（EMT）的目标是促进热反应并增加患者的存活？主要障碍 探索这个想法是，我们不知道如何精确地靶向EMT，尤其是鉴于复杂 多变量细胞信号传导动力学驱动EMT并将其保持为对化学疗法的反馈反应。 我们最近进行了一项初步研究，以识别一组可吸毒的细胞信号传导途径 合作驱动PDAC的间质状态。但是，我们当前的翻译潜力 分析受到限制，因为它仅确定了潜在目标。它没有提供任何系统的可行 理解或可检验的预测，如何最好地安排药物组合以及时最大化 治疗效率并最大程度地减少意外毒性。因此，我们现在试图扩展我们的初步 研究开发系统生物学平台以系统地确定计划组合 PDAC的治疗方法旨在在治疗过程中最大程度地抑制EMT。在AIM 1中，我们将 信号通路活动活性和细胞表型的动态测量在用驱动因素处理的PDAC细胞中 EMT，EMT的拮抗剂和化学疗法。我们的测量将涵盖这些途径 在我们的初步工作中确定为EMT的最可能吸毒调节剂，并将包括 缺氧和癌症相关的成纤维细胞，可能影响EMT的肿瘤微环境的元素 规定。目的是获得一个富含信息的数据集，以便随后用于模型标识 和控制计算。在AIM 2中，我们将使用动态数据来开发用于的计算平台 确定最大程度地抑制EMT所需的驱动因素和拮抗剂的最佳变化， 将作为PDAC的计划组合疗法实施。这将通过：（i） 鉴定上皮或间质细胞状态测定的动态模型。 磷蛋白扰动（即定量表征，以计算模型的形式，EMT的形式 对其驱动因素和对手的变化的响应）和（ii）部署模型“反向”以确定通过 最佳控制原则，如何最好地组合和安排药物以最佳维护上皮 表型。在AIM 3中，我们将根据体外测试基于模型的组合疗法的时间表 和体内实验。最终，这些研究将为新策略提供临床前验证 开发针对PDAC中病理过程的理论方案，该方案限制了治疗反应。新的 由于PDAC生存率在近40年内没有改变，因此迫切需要方法。