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LAG3+ CD8+ T 细胞亚群驱动双特异性抗体武装活化 T 细胞疗法中 HR+/HER2-乳腺癌的缩减

英文原题:LAG3+ CD8+ T cell subset drives HR+/HER2- breast cancer reduction in bispecific antibody armed activated T cell therapy.

查看英文原题

LAG3+ CD8+ T cell subset drives HR+/HER2- breast cancer reduction in bispecific antibody armed activated T cell therapy.

PubMed 2025/10/01(内容时间) J Immunol Q2 · IF 4(JCR 2025)

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中文摘要

T 细胞对肿瘤的清除受到肿瘤抗原识别不足、肿瘤浸润不足以及免疫抑制性肿瘤微环境的损害。尽管靶向 T 细胞治疗绕过了肿瘤抗原识别的失败,但肿瘤微环境的抑制和无法浸润肿瘤仍可能阻碍肿瘤清除。检查点抑制剂(CPIs)有望逆转 T 细胞抑制,并可与双特异性抗体武装 T 细胞(BAT)疗法联合以改善临床结局。

我们假设,如果抑制性通路在功能上处于活跃状态,那么加入 CPIs 可能会改善过继转移 T 细胞的功能。本研究利用单细胞转录组学和时间蛋白数据,开发了一个由 BATs 介导的激素受体阳性乳腺癌细胞杀伤的动力学-动态模型,以识别 T 细胞表型并量化抑制性受体表达。LAG3、PD-1 和 TIGIT 被鉴定为细胞毒性效应 CD8 BATs 在暴露于激素受体阳性乳腺癌细胞系后表达的抑制性受体。这些数据与实时肿瘤细胞毒性数据在多变量统计分析框架中相结合,以预测表达每种受体的 T 细胞对肿瘤缩减的相关贡献。开发了一个机制性动力学-动态数学模型,并使用蛋白表达和细胞毒性数据进行参数化,以对多变量统计分析的结果进行计算机验证。该模型证实了多变量统计分析的预测,即 LAG3+ BATs 被鉴定为主要效应细胞,而 TIGIT 表达则削弱了细胞毒性功能。这些结果为 BATs 联合治疗的 CPI 选择提供了依据,并提供了一个最大化 BATs 抗肿瘤功能的框架。

展开英文摘要原文

Tumor clearance by T cells is impaired by insufficient tumor antigen recognition, insufficient tumor infiltration, and the immunosuppressive tumor microenvironment. Although targeted T cell therapy circumvents failures in tumor antigen recognition, suppression by the tumor microenvironment and failure to infiltrate the tumor can hinder tumor clearance. Checkpoint inhibitors (CPIs) promise to reverse T cell suppression and can be combined with bispecific antibody armed T cell (BAT) therapy to improve clinical outcomes.

We hypothesize that adoptively transferred T cell function may be improved by the addition of CPIs if the inhibitory pathway is functionally active.

This study develops a kinetic-dynamic model of killing of hormone receptor-positive breast cancer cells mediated by BATs using single-cell transcriptomic and temporal protein data to identify T cell phenotypes and quantify inhibitory receptor expression. LAG3, PD-1, and TIGIT were identified as inhibitory receptors expressed by cytotoxic effector CD8 BATs upon exposure to hormone receptor-positive breast cancer cell lines. These data were combined with real-time tumor cytotoxicity data in a multivariate statistical analysis framework to predict the relevant contributions of T cells expressing each receptor to tumor reduction.

A mechanistic kinetic-dynamic mathematical model was developed and parametrized using protein expression and cytotoxicity data for in silico validation of the findings of the multivariate statistical analysis. The model corroborated the predictions of the multivariate statistical analysis which identified LAG3+ BATs as the primary effectors, while TIGIT expression dampened cytotoxic function. These results inform CPI selection for BATs combination therapy and provide a framework to maximize BATs antitumor function.

论文信息

作者
Barnes RW、Thakur A、Onengut-Gumuscu S、Lum LG、Dolatshahi S
单位
Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, VA, United States.United States
期刊
Journal of immunology (Baltimore, Md. : 1950)2025 Oct 1
原文标识
PubMed 40795300 · DOI 10.1093/jimmun/vkaf155