Protein Recognition in Signal Transduction

项目摘要/摘要 通过嵌合抗原受体(CARS)识别和杀死癌细胞的T细胞已经成为一种 前景看好，具有潜在变革性的治疗平台。CAR T细胞已被证明是一种极端的 对某些B细胞癌的有效治疗。尽管如此，仍有许多问题需要解决，以便应用工程化 T细胞对更广泛的癌症的作用。首先，目前的CAR T细胞无法区分高T细胞和高T细胞 低抗原表达细胞。因此，靶向抗原在实体瘤细胞中过度表达(例如， HER2或EGFR)可导致对表达较低水平抗原的旁观者组织的致命脱靶杀伤。 在这里，我们建议通过使用合作识别的公共生化机制来解决这个问题 (即，乙状结节阈值)，以设计新的合成T细胞受体或电路，可以感知抗原密度。 其次，理想情况下，我们希望开发T细胞受体变种，当被激活时，将细胞驱动到更 持久有效的状态(例如，Th1或中央内存状态)。识别驾驶的下一代汽车 ，我们开发了一种新的策略来构建和筛选组合 包含合成的细胞内共刺激信号域的CAR条形码文库。我们会放映 这个CAR变异体的文库可以改善T细胞的激活、增殖和分化。我们还将使用 分析文库以确定主要负责的关键信号基序的综合信息 将T细胞反应轨迹引向特定的路径。我们的具体目标是： 目标1.设计合成T细胞受体和电路，可以区分癌症和旁观者 基于抗原密度差异的细胞(使用Her2抗原作为主要测试案例) A.为Her2设计并测试一系列合作低亲和力但高价的汽车 B.设计和测试用于Her2锐阈值检测的协作两步多接收器电路 C.扩展这些密度感知策略以感知EGFR密度 细胞将与表达不同抗原密度的细胞系进行测试；它们将在体内使用 细胞系和PDX小鼠模型；交叉反应将使用人类组织异种移植进行测试。 目的2.构建和筛选用于鉴定合成T细胞受体的组合CAR文库 优化激活、增殖和细胞命运分化 A.开发一种组装合成T细胞受体条形码库的新方法，包括 线性免疫信号基序的组合(即“合成共刺激结构域”) B.筛选具有特定优化或新反应行为的受体文库 我们将评估从这些文库中涌现的新受体候选的治疗功能； 复合分析将被用来更好地理解指导特定类型的主要信号基序 回应。这个项目应该会产生改进的下一代治疗性T细胞。

Project Summary/Abstract T cells engineered to recognize and kill cancer cells via chimeric antigen receptors (CARs) have emerged as a promising and potentially transformative therapeutic platform. CAR T cells have proven to be an extremely effective therapy for certain B cell cancers. Nonetheless, many issues still remain in order to apply engineered T cells to a broader range of cancers. First, current CAR T cells, are not able to discriminate between high and low antigen expressing cells. Therefore, targeting antigens that are overexpressed in solid tumor cells (e.g. Her2 or EGFR) can result in lethal off-target killing of bystander tissues expressing lower levels of the antigen. Here we propose to attack this problem by using common biochemical mechanisms for cooperative recognition (i.e. sigmoidal thresholds) to engineer new synthetic T cell receptors or circuits that can sense antigen density. Second, we would ideally like to develop T cell receptor variants that, when activated, drive the cell to a more persistent and effective state (e.g. Th1 or central memory state). To identify next-generation CARs that drive particular T cell responses, we have developed a new strategy for constructing and screening a combinatorial barcoded library of CARs that contain synthetic intracellular co-stimulatory signaling domains. We will screen this library for CAR variants that improve T cell activation, proliferation, and differentiation. We will also use the composite information from analyzing the library to identify critical signaling motifs most responsible for directing T cell response trajectories towards specific paths. Our specific aims are: Aim 1. Engineer synthetic T cell receptors and circuits that can discriminate cancer and bystander cells based on differences in antigen density (using Her2 antigen as a primary testcase) A. Engineer and test a series of cooperative low affinity but high valency CARs for Her2 B. Engineer and test cooperative two-step multi-receptor circuits for sharp threshold detection of Her2 C. Extend these density sensing strategies to sense EGFR density Cells will be tested against cell lines expressing different antigen densities; They will be evaluated in vivo using cell lines and PDX mouse models; crossreactivity will be tested using human tissue xenografts. Aim 2. Build and screen combinatorial CAR libraries to identify synthetic T cell receptors with optimized activation, proliferation and cell fate differentiation A. Develop a novel method for assembling barcoded library of synthetic T cell receptors containing combinations of linear immune signaling motifs (i.e. “synthetic co-stimulatory domains”) B. Screen receptor libraries for receptors with specific optimized or novel response behaviors We will evaluate the therapeutic function of new receptor candidates that emerge from these libraries; Composite analysis will be used to better understand the dominant signaling motifs that direct particular types responses. This project should yield improved next-generation therapeutic T cells.