Suppr超能文献

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-乳腺癌消退。

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 (UVA) School of Medicine, Charlottesville, Virginia 22908.

University of Virginia Cancer Center, Charlottesville, Virginia, USA.

出版信息

bioRxiv. 2025 May 10:2025.01.04.631323. doi: 10.1101/2025.01.04.631323.

Abstract

Tumor clearance by T cells is impaired by insufficient tumor antigen recognition, insufficient tumor infiltration, and the immunosuppressive tumor microenvironment (TME). Although targeted T cell therapy circumvents failures in tumor antigen recognition, suppression by the TME and failure to infiltrate the tumor can hinder tumor clearance. Checkpoint inhibitors (CPI) promise to reverse T cell suppression and can be combined with bispecific antibody armed T cell (BATs) therapy to improve clinical outcomes. We hypothesize that adoptively transferred T cell function may be improved by the addition of CPI if the inhibitory pathway is functionally active. This study develops a kinetic-dynamic model of killing of hormone receptor-positive (HR+) 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 HR+ 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 anti-tumor 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(TME)会损害T细胞对肿瘤的清除作用。尽管靶向T细胞疗法可避免肿瘤抗原识别失败,但TME的抑制作用以及无法浸润肿瘤会阻碍肿瘤清除。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的T细胞(BATs)疗法联合使用以改善临床疗效。我们假设,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蛋白数据,建立了一个由BATs介导的激素受体阳性(HR+)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的表达。LAG3、PD-1和TIGIT被确定为细胞毒性效应CD8 BATs在接触HR+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,在多变量统计分析框架中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+ BATs是主要效应细胞,而TIGIT的表达会减弱细胞毒性功能。这些结果为BATs联合治疗的CPI选择提供了依据,并提供了一个使BATs抗肿瘤功能最大化的框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49e1/12248021/006950d43e56/nihpp-2025.01.04.631323v2-f0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验