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细胞在治疗B细胞方面取得了显著成功 对于恶性肿瘤，它们在实体癌症上的应用远没有那么成功。限制的主要挑战之一是 它们的用处在于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点也在较低的 在正常的上皮器官中的水平，导致非肿瘤毒性。尽管如此，我们知道实体肿瘤包括 一种复杂而复杂的组织，具有由恶性细胞、免疫细胞和基质细胞组成的独特生态系统。从… 第一原则，人们会预测，如果可以的话，肿瘤中应该有足够的歧视性信息。 设计治疗性T细胞，可以整合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，可以感知和响应 抗原，即使它们存在于同一组织微环境中不同的细胞上。这些电路 利用synNotch受体来检测启动抗原，进而诱导一种杀死汽车的表达 以致死抗原为基础的细胞。在初步结果中，我们已经表明，具有这种启动和杀伤力的T细胞 电路可以识别相邻细胞的独特组合来诱导杀伤。这些类型的工程T 细胞是第一批已知的治疗剂之一，它可以整合来自不同物种的分子信息 同一组织内的细胞。 在这项提议中，我们假设这种启动和杀死T细胞识别电路可以用于识别固体 基于分布在整个肿瘤生态系统中的信息的肿瘤。具体地说，我们将针对组合 整合存在于癌细胞和癌症相关基质细胞中的信号，它们发挥着重要的作用 在许多实体癌症中起支持作用。作为一个测试案例，我们建议调查来自 癌症相关成纤维细胞可以用来局部启动CAR T细胞，然后基于癌症相关的T细胞进行杀伤 抗原。即使这种癌症相关抗原不是完全特异的(即，它在其他正常组织中表达 组织)，间质和癌细胞信号的组合应该对肿瘤有更高的特异性。之前 使用单一抗原CARS分别靶向间质细胞或癌细胞的努力没有成功，但 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 结果显著提高了识别特异性。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的实体癌症。