Recognizing the tumor ecosystem: Integrating stromal and cancer antigen signals to achieve precision recognition of solid tumors by CAR T cells

识别肿瘤生态系统：整合基质信号和癌抗原信号，实现CAR T细胞对实体瘤的精准识别

• 批准号: 10559489
• 负责人: WENDELL A LIM
• 金额: $ 55.2万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10559489
• 关键词: Antigen Presentation; Antigens; B lymphoid malignancy; Cell Therapy; Cells; Complex; Cytotoxic T-Lymphocytes; Ecosystem; Endothelial Cells; Engineering; Epithelium; Fibroblasts; Goals; Growth; Immune; Immunotherapeutic agent; In Vitro; Individual; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of ovary; Molecular; Normal tissue morphology; Organ; Play; Pre-Clinical Model; Role; Signal Transduction; Solid; Solid Neoplasm; Source; Specificity; Stromal Cells; Stromal Neoplasm; Supporting Cell; Surface Antigens; T-Lymphocyte; Testing; Therapeutic; Therapeutic Agents; Tissues; Toxic effect; Tumor Antigens; VTCN1 gene; Work; Xenograft Model; cancer cell; candidate identification; cell killing; cell type; chimeric antigen receptor; chimeric antigen receptor T cells; combinatorial; cross reactivity; design; efficacy evaluation; engineered T cells; improved; mesothelin; mouse model; neoplastic cell; pancreatic cancer model; receptor; spatial integration; success; tool; tumor; tumor microenvironment; tumor xenograft

Project Summary/Abstract Despite the remarkable success of engineered chimeric antigen receptor (CAR) T cells in the treatment of B cell malignancies, their application to solid cancers has been far less successful. One of the major challenges limiting their utility is the difficulty in identifying ideal surface antigens that can be used to discriminate between cancer and normal tissues – many potential targets that are highly expressed in solid tumors are also found at lower levels in normal epithelial organs, leading to off-tumor toxicity. Nonetheless, we know that solid tumors comprise a complex and sophisticated tissue with a distinct ecosystem of malignant, immune and stromal cells. From first principles, one would predict that there should be ample discriminatory information in the tumor, if one could design therapeutic T cells that could integrate information from across different cells in the tumor ecosystem. We have recently developed new CAR T cell recognition circuits that can sense and respond to combinations of antigens, even if they are present on distinct cells within the same tissue microenvironment. These circuits utilize a synNotch receptor to detect a priming antigen, which in turn induces the expression of a CAR that kills cells based on a killing antigen. In preliminary results, we have shown that T cells with this kind of prime-and-kill circuit can recognize unique combinations of neighboring cells to induce killing. These types of engineered T cells are one of the first known therapeutic agents that can integrate molecular information from across different cells within the same tissue. In this proposal, we hypothesize that this prime-and-kill T cell recognition circuit could be used to recognize solid tumors based on information distributed across the tumor ecosystem. Specifically, we will target combinatorial integration of signals that are present in cancer cells and cancer-associated stromal cells, which play a central supportive role in a number of solid cancers. As a test case, we propose to investigate whether antigens from cancer associated fibroblasts can be used to locally prime CAR T cells to then kill based on a cancer associated antigen. Even if this cancer associated antigen in not perfectly specific (i.e., it is expressed in other normal tissues), the combination of stromal and cancer cell signals should be far more specific for the tumor. Prior efforts have unsuccessfully explored using single antigen CARs to target stromal or cancer cells individually, but here we test whether using integrated combinatorial recognition of the cancer cell/stromal cell ecosystem can result in significantly improved recognition specificity. If so, then this kind of integrated tumor ecosystem recognition could be applied to a large number of solid cancers.

项目概要/摘要 尽管工程嵌合抗原受体 (CAR) T 细胞在治疗 B 细胞方面取得了巨大成功 恶性肿瘤，其在实体癌中的应用远没有那么成功，这是限制其的主要挑战之一。 它们的用途是难以识别可用于区分癌症的理想表面抗原 和正常组织——许多在实体瘤中高表达的潜在靶标也在较低的表达量中被发现 然而，我们知道实体瘤包含正常上皮器官中的水平，导致肿瘤外毒性。 一种复杂而精密的组织，具有独特的恶性细胞、免疫细胞和基质细胞生态系统。 第一个原则，人们会预测肿瘤中应该有足够的歧视性信息，如果可以的话 设计能够整合肿瘤生态系统中不同细胞信息的治疗性 T 细胞。 我们最近开发了新的 CAR T 细胞识别电路，可以感知并响应以下组合： 抗原，即使它们存在于同一组织微环境中的不同细胞上。 利用 synNotch 受体检测启动抗原，进而诱导 CAR 的表达，从而杀死 初步结果表明，T 细胞具有这种“启动并杀死”的能力。 电路可以识别邻近细胞的独特组合以诱导杀伤。 细胞是第一个已知的治疗剂之一，它可以整合不同来源的分子信息 同一组织内的细胞。 在此提案中，我们追求这种引发并杀死 T 细胞识别电路可用于识别固体 具体来说，我们将针对组合性肿瘤进行基于分布在肿瘤生态系统中的信息。 癌细胞和癌症相关基质细胞中存在的信号整合，发挥着核心作用 作为一个测试案例，我们建议研究抗原是否来自于实体瘤。 癌症相关成纤维细胞可用于局部启动 CAR T 细胞，然后根据癌症相关细胞进行杀伤 即使这种癌症相关抗原不具有完全特异性（即，它在其他正常细胞中表达）。 组织），基质和癌细胞信号的组合对于肿瘤应该更具特异性。 已经成功地探索过使用单一抗原 CAR 单独靶向基质细胞或癌细胞，但是 在这里，我们测试使用癌细胞/基质细胞生态系统的集成组合识别是否可以 如果是这样的话，那么这种整合的肿瘤生态系统就会显着提高。 识别可以应用于大量实体癌。