A Mechanistic Model of Chimeric Antigen Receptor (CAR) Signaling Predicts the Effects of Co-Stimulatory Signaling on T cell Activation

描述（由申请人提供）：免疫细胞疗法（ACT）是癌症免疫疗法研究的一个令人兴奋的领域，其中患者自身的免疫细胞从其体内取出，在体外进行修饰，并重新注射以攻击癌症。最成功的修饰形式是用嵌合抗原受体（CAR）蛋白改造患者的T细胞，这种蛋白在识别癌细胞时触发免疫反应。为了激活T细胞，汽车由（1）来源于抗体的胞外结构域和（2）来源于内源性T细胞受体的几个胞内信号传导结构域组成，所述胞外结构域可以结合肿瘤相关抗原，所述内源性T细胞受体可以激活免疫应答。目前已经开发了三代汽车，每一代都增加了CAR上存在的细胞内共刺激结构域的数量。含有CD 3 β信号传导结构域与CD 28或41 BB共刺激结构域组合的汽车在B细胞淋巴瘤的临床试验中显示出一些前景;然而，一些患者对治疗没有反应，而另一些患者经历免疫系统的过度激活，这可能是致命的。目前尚不清楚进一步增加共刺激结构域的数量将如何影响T细胞活化，或者这种增加是否能够改善对CAR治疗的控制。因此，需要更深入地了解导致CAR T细胞活化的细胞信号传导途径，以描述信号传导结构域途径如何整合以影响T细胞活化的各种功能。系统生物学，特别是计算机制建模，提供了一个独特的平台来理解和优化CAR工程化T细胞的激活。该提案旨在通过开发一组可以预测由不同汽车介导的T细胞活化的机械计算模型来探索不同CAR信号传导结构域单独和组合的影响。这些模型将描述单个信号传导结构域的激活（以前没有建模）影响下游蛋白质的机制，这些蛋白质与T细胞激活的特定特性相关，如细胞因子产生、T细胞存活和细胞增殖。该模型将包括从文献中已知的相互作用推导出的常微分方程。他们将接受蛋白质结合和激活的流式细胞术数据的培训，以便模型能够预测CAR刺激的影响。我们将进行计算机模拟实验，以预测单个CAR共刺激结构域的信号传导如何整合以影响T细胞应答的特定方面。例如，我们可以通过使用该模型来确定最佳信号传导结构域组合，以增加与细胞因子产生相关的蛋白质，而不影响与生存相关的蛋白质，从而优化对治疗无反应的患者的CAR治疗。这些假设将通过体外实验进行验证。因此，拟议的研究将提供有价值的信息，以提高CAR工程化T细胞的安全性和有效性，使这种疗法的益处能够扩展到更广泛的患者群体。

DESCRIPTION (provided by applicant): Adoptive cell therapy (ACT) is an exciting area of cancer immunotherapy research in which a patient's own immune cells are removed from their body, modified in vitro, and re-injected to attack the cancer. The most successful form of modification is to engineer T cells from the patient with chimeric antigen receptor (CAR) proteins, which trigger an immune response upon recognition of a cancer cell. To activate the T cells, CARs are composed of (1) an extracellular domain, derived from an antibody, that can bind to a tumor associated antigen and (2) several intracellular signaling domains, derived from endogenous T cell receptors that can activate an immune response. Currently three generations of CARs have been developed, each increasing the number of intracellular co-stimulatory domains present on the CAR. CARs containing the CD3ζ signaling domain in combination with either CD28 or 41BB co-stimulatory domains have shown some promise in clinical trials of B cell lymphoma; however, some patients do not respond to therapy, while others experience over activation of the immune system, which can be deadly. It is not clear how further increasing the number of co-stimulatory domains will affect T cell activation or if the increase will be able to improve the control over CAR therapy. Therefore, a deeper understanding of the cell signaling pathways that lead to CAR T cell activation is needed to describe how signaling domain pathways integrate to affect the various fucntions of T cell activation. Systems biology, specifically computational mechanistic modeling, provides a unique platform to understand and optimize the activation of CAR engineered T cells. This proposal aims to explore the effects of different CAR signaling domains, individually and in combination, by developing a set of mechanistic computational models that can predict T cell activation mediated by different CARs. The models will describe the mechanisms by which activation of individual signaling domains, which have not been previously modeled, affect downstream proteins that correlate to specific properties of T cell activation, such as cytokine production, T cell survival, and cell proliferatin. The models will be comprised of ordinary differential equations derived from known interactions in the literature. They will be trained on flow cytometry data of protein binding and activation such that the models are able to predict the effects of CAR stimulation. We will perform in silico experiments to predict how signaling of individual CAR co-stimulatory domains is integrated to affect particular aspects of the T cell response. For example, we can optimize CAR therapy for patients that do not respond to treatment by using the model to determine the optimal signaling domain combination to increase proteins that correlate to cytokine production without affecting those that correlate to survival. These hypotheses will then be validated by in vitro experiments. Thus, the proposed research will provide valuable information to improve the safety and efficacy of CAR engineered T cells, enabling the benefits of this therapy to be expanded to a wider patient population.