Protein Recognition in Signal Transduction

信号转导中的蛋白质识别

基本信息

批准号：
10460232
负责人：
WENDELL A LIM
金额：
$ 33.83万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10460232
关键词：
Affinity Antibodies Antigen Targeting Antigens B-Lymphocytes Bar Codes Biochemical Cell Density Cell Line Cells Complex Detection Disease Engineering Epidermal Growth Factor Receptor Grant Immune Immune signaling Lead Libraries Logic Malignant Neoplasms Memory Methods Mus Proteins Series Signal Pathway Signal Transduction Solid Neoplasm T cell response T-Cell Activation T-Cell Receptor T-Lymphocyte Testing Therapeutic Therapeutic Agents Tissues Variant Xenograft procedure anti-cancer base behavioral response cancer cell cancer immunotherapy cell killing chimeric antigen receptor chimeric antigen receptor T cells combinatorial density design effective therapy engineered T cells extracellular human tissue improved in vitro testing in vivo in vivo evaluation member mouse model neoplastic cell next generation novel novel therapeutics overexpression receptor response screening

项目摘要

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.

项目概要/摘要经过改造的 T 细胞通过嵌合抗原受体 (CAR) 识别并杀死癌细胞，已成为一种 CAR T 细胞已被证明是一个极具前景和潜在变革性的治疗平台。然而，为了应用工程化治疗，仍然存在许多问题。首先，目前的 CAR T 细胞无法区分高癌症和高癌症。因此，针对实体瘤细胞中过度表达的抗原（例如， Her2 或 EGFR）可导致表达较低水平抗原的旁观组织发生致命的脱靶杀伤。在这里，我们建议通过使用常见的生化机制进行协作识别来解决这个问题（即 S 形阈值）来设计能够感知抗原密度的新合成 T 细胞受体或电路。其次，我们理想地希望开发出 T 细胞受体变体，当其被激活时，可以驱动细胞更加活跃。持久且有效的状态（例如 Th1 或中央记忆状态）来识别驱动的下一代 CAR。针对特定的 T 细胞反应，我们开发了一种新策略来构建和筛选组合我们将筛选包含合成细胞内共刺激信号传导域的 CAR 条形码库。我们还将使用这个用于改善 T 细胞活化、增殖和分化的 CAR 变体的库。通过分析文库来综合信息，以识别最负责的关键信号基序将 T 细胞反应轨迹引导至特定路径。目标 1. 设计能够区分癌症和旁观者的合成 T 细胞受体和电路基于抗原密度差异的细胞（使用 Her2 抗原作为主要测试用例） A. 为 Her2 设计并测试一系列合作性低亲和力但高价的 CAR B. 设计和测试用于 Her2 尖锐阈值检测的协作两步多受体电路 C. 扩展这些密度传感策略以传感 EGFR 密度将针对表达不同抗原密度的细胞系对细胞进行测试；将使用它们进行体内评估；细胞系和 PDX 小鼠模型；将使用人体组织异种移植物测试交叉反应性。目标 2. 构建并筛选组合 CAR 文库，以识别合成 T 细胞受体优化的激活、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，其中包含线性免疫信号基序的组合（即“合成共刺激结构域”） B. 筛选受体库以寻找具有特定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候选受体的治疗功能；复合分析将用于更好地理解指导特定类型的主导信号基序该项目应该会产生改进的下一代治疗性 T 细胞。