[Other] A trispecific antibody engaging T cells with tumour and myeloid cells augments antitumour immunity

yueli7 Post time 7 day(s) ago | Show all posts |Read mode
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Nat Biomed Eng




. 2026 Apr;10(4):732-752.

doi: 10.1038/s41551-025-01569-4. Epub 2025 Dec 10.
A trispecific antibody engaging T cells with tumour and myeloid cells augments antitumour immunityChangrui Yang # 1 2, Shanni Guo # 1 2, Kaiyan Ye # 1 2, Yizong Ding # 3, Xingzhong Zhao 4 5, Yucheng T Yang 4 5, Bowen Sun 1 2, Shuyi Qian 1 2, Mei-Chun Cai 1 2, Luyan Liu 6, Libing Xiang 7, Xia Yin 1 2, Xing-Ming Zhao 4 5, Jieyi Wang 8, Jiwei Zhang 9, Wen Di 10 11, Guanglei Zhuang 12 13, Fan Yang 14 15


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Abstract
Immunologically unresponsive tumours often resist immune checkpoint inhibitors due to the low abundance of tumour-specific T cells and an immunosuppressive microenvironment, despite pronounced infiltration of non-tumour-specific (bystander) T cells. Here we analysed single-cell RNA sequencing data from 300 patients across 17 tumour types, identifying abundant but functionally restrained bystander T cells in multiple malignancies, including ovarian and colorectal cancer. To enhance antitumour immunity in such contexts, we engineered B7H3xCD3xPDL1, a trispecific immunoglobulin-based T cell engager targeting B7H3, CD3 and PDL1, to redirect T cells while mitigating immunosuppression. Functional validation in co-culture systems, patient-derived tumour suspensions and fragments, and humanized mouse models showed T cell activation and tumour killing. Imaging cytometry and single-cell transcriptomics revealed IFNγ-dependent macrophage reprogramming and IL-15 secretion, establishing a feed-forward loop that augments T cell functionality. A machine learning model trained on ex vivo cytotoxicity and transcriptomic data predicted patient responsiveness, supporting data-driven clinical stratification for solid tumour immunotherapy.



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