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: 10310406
• 负责人: WENDELL A LIM
• 金额: $ 14.7万
• 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/10310406
• 关键词: Antigens; B lymphoid malignancy; Cell Therapy; Cells; Complex; Cytotoxic T-Lymphocytes; Ecosystem; Endothelial Cells; Engineering; Epithelial; 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; base; cancer cell; cell killing; cell type; chimeric antigen receptor; chimeric antigen receptor T cells; combinatorial; cross reactivity; design; engineered T cells; improved; mesothelin; mouse model; neoplastic cell; pancreatic cancer model; receptor; 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.

项目总结/文摘