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.

项目摘要/摘要通过嵌合抗原受体（CAR）识别和杀死癌细胞的T细胞已成为一种有希望的和潜在的变革性治疗平台。事实证明，CAR T细胞是极端的某些B细胞癌的有效疗法。尽管如此，仍然存在许多问题以应用工程 T细胞到更广泛的癌症范围。首先，当前的汽车T细胞无法区分高和低抗原表达细胞。因此，靶向在实体瘤细胞中过表达的抗原（例如 HER2或EGFR）可能导致攻击抗原水平较低的旁观者组织致命的脱靶杀死。在这里，我们建议通过使用常见的生化机制进行合作识别来攻击这个问题（即Sigmoidal阈值）用于设计新的合成T细胞受体或可以感觉到抗原密度的电路。其次，我们理想地希望开发T细胞接收器变体，该变体在激活时，将细胞驱动到更多持续有效的状态（例如TH1或中央记忆状态）。识别驾驶的下一代汽车特定的T细胞响应，我们制定了一种新的策略来构建和筛选组合包含合成细胞内共刺激性信号域的汽车的条形码库。我们将筛选该库用于改善T细胞激活，增殖和分化的汽车变体。我们还将使用分析库的综合信息以识别最负责的关键信号图案将T细胞响应轨迹引导到特定路径。我们的具体目的是：目标1。可以区分癌症和旁观者的工程师合成T细胞受体和电路细胞基于抗原密度的差异（使用HER2抗原作为主要测试用柜） A.工程师和测试一系列合作低亲和力，但HER2的高价值汽车 B.工程师和测试教练两步的多对电路，用于HER2的尖锐阈值检测 C.扩展这些密度传感策略以感知EGFR密度细胞将针对表达不同抗原密度的细胞系进行测试；它们将在体内评估细胞系和PDX鼠标模型；交叉反应性将使用人体组织Xenographictic进行测试。 AIM 2。构建和屏幕组合车库，以识别使用合成的T细胞接收器优化的激活，增殖和细胞脂肪分化 A.开发一种新的方法，用于组装包含包含的合成T细胞受体的条形码库线性免疫信号传导基序的组合（即“合成共刺激域”） B.具有特定优化或新颖反应行为的受体的屏幕受体库我们将评估从这些文库中出现的新受体候选者的治疗功能；复合分析将用于更好地理解指导特定类型的主要信号传导图案回答。该项目应产生改善的下一代疗法T细胞。