Protein Recognition in Signal Transduction

项目概要/摘要 通过嵌合抗原受体（汽车）识别和杀死癌细胞的T细胞已经成为一种新的治疗方法。 有前途和潜在的变革性治疗平台。CAR T细胞已被证明是一种非常有效的方法。 有效治疗某些B细胞癌。尽管如此，为了应用工程化， T细胞对更广泛的癌症。首先，目前的CAR T细胞不能区分高水平和低水平。 低抗原表达细胞。因此，靶向在实体瘤细胞中过表达的抗原（例如， Her 2或EGFR）可导致表达较低水平抗原的旁观者组织的致死性脱靶杀伤。 在这里，我们建议攻击这个问题，使用共同的生物化学机制的合作识别 (i.e. S形阈值）来设计新的合成T细胞受体或可以感测抗原密度的电路。 第二，理想情况下，我们希望开发T细胞受体变体，当激活时，驱动细胞向更高的水平运动。 持久有效状态（例如Th 1或中央存储器状态）。识别下一代汽车， 特别是T细胞反应，我们已经开发了一种新的策略，用于构建和筛选组合 图10示出了包含合成的细胞内共刺激信号传导结构域的汽车的条形码化文库。我们将筛选 该文库用于CAR变体，其改善T细胞活化、增殖和分化。我们还将使用 通过分析文库来识别最重要的信号基序， 引导T细胞反应轨迹朝向特定路径。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Her 2抗原作为主要测试用例） A.工程化并测试一系列针对Her 2的协同低亲和力但高效价汽车 B。设计和测试协作两步多受体电路，用于Her 2的锐阈值检测 C.扩展这些密度传感策略以检测EGFR密度 将针对表达不同抗原密度的细胞系测试细胞;将使用 细胞系和PDX小鼠模型;将使用人组织异种移植物测试交叉反应性。 目标二。构建和筛选组合CAR文库以鉴定合成的T细胞受体， 优化活化、增殖和细胞命运分化 A.开发用于组装合成T细胞受体的条形码化文库的新方法，所述合成T细胞受体包含 线性免疫信号传导基序的组合（即“合成的共刺激结构域”） B。筛选受体库中具有特异性优化或新颖反应行为的受体 我们将评估从这些库中出现的新受体候选物的治疗功能; 复合分析将被用来更好地了解主导信号基序，直接特定类型 应答该项目将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.