Fu - Proj 3

项目总结 了解控制癌症进展的关键机制和 阐明导致治疗失败的通常不为人所知的因素。尽管它们不能治愈大多数患者 对于常见的转移性实体癌(如乳腺癌和肺癌)，免疫疗法对 少数晚期肺癌和黑色素瘤患者。虽然这些具有潜在疗效的癌症疗法 正在快速开发和测试，一个主要障碍是缺乏描述和评估的量化模型 它们的功效。该项目建议探索临床相关的癌细胞数学模型和电子计算机模型。 个性化免疫治疗的动力学。我们将重点关注两个截然不同但又紧密相连的 癌症治疗的方法：(1)过继细胞转移，即体外工程化和个性化的肿瘤- 输注浸润性T细胞以抑制肿瘤生长；以及(2)增强抗肿瘤的检查点抑制剂 效应器免疫细胞的活性。最近，大量与免疫相关的生物标志物数据已经成为 可用的-它们与机械的、数学模型的紧密结合将释放它们的解释性和 对治疗反应和结果的预测能力。在这里，项目3将利用这些生物标志物 用于推断和量化控制癌症-免疫相互作用的关键参数的数据。具体地说，目标一号将 开发基于动力系统方法的定量数学框架，以提供实用的 过继细胞移植方法疗效的临床评估指南。AIM 2将优化治疗 检查点抑制剂及其潜在组合的战略，而目标3将评估和确定 计算模拟与单细胞紧密结合用于黑色素瘤肿瘤的免疫相关生物标志物 对动物模型和临床试验的数据进行测序。本设计将使用理论框架来评估 并比较不同组合的疗效，以及提供最低疗效的指导 以及实现阳性临床结果所需的检查点抑制剂的最佳剂量方案。这 一项提案将开发临床相关的数学和计算机内模型，以促进新的癌症 免疫治疗策略是构思、测试和理解的。由于它们天生的灵活性，这些在- 硅胶模型也可以很容易地与癌细胞水平上的特定癌症特征结合在一起，因此 实现知情的治疗决策，并以个性化的方式预测治疗结果。终极的 目标是使用这些计算机内和数学模型来解释实验室和临床结果，并指导设计 未来实验室实验和临床试验的原则，所有这些都着眼于模型信息个性化 免疫疗法。

PROJECT SUMMARY It is of fundamental importance to understand the key mechanisms that govern the progression of cancer and elucidate the often-unknown factors that account for treatment failures. Although they fail to cure most patients with common metastatic solid cancers (like breast and lung), immunotherapies have had a significant impact in a minority of late-stage lung cancer and melanoma patients. While these potentially curative cancer therapies are being rapidly developed and tested, a major barrier is the lack of quantitative models to describe and evaluate their efficacy. This project proposes to explore clinically relevant math and in-silico models of cancer cell dynamics for personalized immunotherapy. We will focus on two distinct, yet strongly interconnected, approaches of cancer therapy: (1) adoptive-cell transfer, in which in-vitro engineered and personalized tumor- infiltrating T-cells are transfused to suppress tumor growth; and (2) checkpoint inhibitors that boost anti-tumor activities of effector immune cells. Very recently, a wealth of immune-related biomarker data has become available—their close integration with mechanistic, mathematical models would unleash their explanatory and predictive power in treatment response and outcome. Here, Project 3 will take advantage of these biomarker data to infer and quantify key parameters that govern cancer-immune interactions. Specifically, Aim 1 will develop a quantitative mathematical framework based on the dynamical systems approach to provide practical guidance for clinical assessment of the efficacy of adoptive cell transfer approach. Aim 2 will optimize therapeutic strategies for checkpoint inhibitors and their potential combinations, while Aim 3 will evaluate and identify immune-related biomarkers for melanoma cancer by closely integrating computational modeling with single-cell sequencing data from animal models and clinical trials. This design will use a theoretical framework to assess and compare the efficacies of different combinations, as well as to provide guidance on the minimum efficacy and optimal dosage schedule of checkpoint inhibitors required to achieve positive clinical outcomes. This proposal will develop clinically relevant math and in-silico models that will facilitate the way novel cancer immunotherapeutic strategies are conceived, tested, and understood. Owing to their innate flexibility, these in- silico models also can be readily incorporated with the specific cancer profile on the cancer-cell level, and thus enable informed treatment decisions and predict treatment outcomes in a personalized fashion. The ultimate goal is to use these in-silico and mathematical models to interpret lab and clinical results and to guide design principles of future lab experiments and clinical trials, all with an eye toward model-informed personalized immunotherapy.