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LAG3+ CD8+ T细胞亚群在双特异性抗体武装的活化T细胞疗法中推动HR+/HER2-乳腺癌缩小。

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

作者信息

Barnes Robert Weldon, Thakur Archana, Onengut-Gumuscu Suna, Lum Lawrence G, Dolatshahi Sepideh

机构信息

Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, VA, United States.

University of Virginia Comprehensive Cancer Center, Charlottesville, VA, United States.

出版信息

J Immunol. 2025 Aug 7. doi: 10.1093/jimmun/vkaf155.

Abstract

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.

摘要

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会损害T细胞对肿瘤的清除作用。尽管靶向T细胞疗法规避了肿瘤抗原识别方面的失败,但肿瘤微环境的抑制作用以及无法浸润肿瘤仍会阻碍肿瘤清除。检查点抑制剂(CPI)有望逆转T细胞抑制作用,并且可以与双特异性抗体武装T细胞(BAT)疗法联合使用以改善临床疗效。我们推测,如果抑制途径具有功能活性,那么添加CPI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分辨蛋白质数据建立了一个由BAT介导的激素受体阳性乳腺癌细胞杀伤动力学-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被确定为细胞毒性效应性CD8 BAT在接触激素受体阳性乳腺癌细胞系时表达的抑制性受体。这些数据与实时肿瘤细胞毒性数据相结合,置于多变量统计分析框架中,以预测表达每种受体的T细胞对肿瘤缩小的相关贡献。利用蛋白质表达和细胞毒性数据建立并参数化了一个机制性动力学-动态数学模型,用于对多变量统计分析结果进行计算机模拟验证。该模型证实了多变量统计分析的预测结果,即LAG3+BAT是主要效应细胞,而TIGIT的表达会削弱细胞毒性功能。这些结果为BAT联合疗法的CPI选择提供了依据,并提供了一个使BAT抗肿瘤功能最大化的框架。

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