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.

项目摘要/摘要 尽管工程嵌合抗原受体（CAR）T细胞在B细胞的治疗中取得了显着成功 Malignancys，他们在固体癌症中的应用远不如成功。限制的主要挑战之一 它们的效用很难识别可用于区分癌症的理想表面抗原 和正常组织 - 在较低的情况下还发现了许多在实体瘤中高度表达的潜在靶标 正常上皮器官的水平，导致肿瘤毒性。尽管如此，我们知道实体瘤构成 一个复杂而复杂的组织，具有不同的恶性，免疫和基质细胞的生态系统。从 第一原则，人们会预测，如果可以的话，肿瘤中应该有充分的歧视性信息 设计理论T细胞可以整合来自肿瘤生态系统中不同细胞的信息。 我们最近开发了新的汽车T细胞识别电路，可以感知并响应 抗原，即使它们存在于同一组织微环境中的不同细胞上。这些电路 利用同步受体检测启动抗原，这反过来诱导了杀死的汽车的表达 基于杀伤抗原的细胞。在初步结果中，我们已经证明了具有这种原始和杀伤的T细胞 电路可以识别相邻细胞的独特组合以诱导杀戮。这些类型的工程 细胞是最早可以从不同的分子信息整合的已知治疗剂之一 细胞在同一组织内。 在此提案中，我们假设该原始和杀性T细胞识别电路可用于识别固体 基于分布在肿瘤生态系统中的信息。具体来说，我们将针对组合 在癌细胞和与癌症相关的基质细胞中存在的信号的整合，这些信号的中心 在许多固体癌症中的支持作用。作为测试案例，我们建议研究是否来自 与癌症相关的成纤维细胞可用于局部质量的汽车T细胞，然后根据相关的癌症杀死 抗原。即使这种癌症与抗原相关的抗原也不是完全特异性的（即，它以其他正常表达 组织），基质和癌细胞信号的组合应该对肿瘤更为特异性。事先的 努力未成功地使用单抗原汽车探索以单独靶向基质细胞，但 在这里，我们测试是否使用癌细胞/基质细胞生态系统的集成组合识别可以 导致明显提高的识别特异性。如果是这样，那么这种整合的肿瘤生态系统 识别可以应用于大量固体癌症。