Daosheng Huang, Yi Li, Wei Rui, Keyong Sun, Zhixiao Zhou, Xiachen Lv, Li Yu, Junfan Chen, Jing Zhou, Vincent Liu, Jiasheng Wang, Xun Lan, Yang-Xin Fu, Xueqiang Zhao, Xin Lin
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引用次数: 0
Abstract
Targeting tumor-specific neoantigens holds promise for cancer immunotherapy, but their ultra-low expression on tumor cells poses challenges for T cell therapies. Here, we found that chimeric antigen receptors (CARs) exhibit 10-100 times lower sensitivity than T cell receptors (TCRs) when targeting human leukocyte antigen (HLA) class I-presented p53R175H neoantigen. To enhance CAR functionality, we introduced T cell receptor fusion constructs (TRuCs) and synthetic TCRs and antigen receptors (STARs). Our data show that STARs optimally reproduce TCR antigen sensitivity, outperforming CARs and TRuCs in redirecting CD8+ and CD4+ T cells to recognize HLA class I neoantigens. STAR-T cells demonstrate superior killing of cancer cell lines with low neoantigen density in vitro and improved tumor control in mouse models compared to CAR-T and TRuC-T cells. These findings highlight CAR sensitivity limitations and present STARs as more effective synthetic receptors for T cell-based immunotherapy against tumors with low neoantigen density.
靶向肿瘤特异性新抗原为癌症免疫疗法带来了希望,但它们在肿瘤细胞上的超低表达为T细胞疗法带来了挑战。在这里,我们发现嵌合抗原受体(CAR)在靶向人类白细胞抗原(HLA)I类表达的p53R175H新抗原时,其敏感性比T细胞受体(TCR)低10-100倍。为了增强 CAR 的功能,我们引入了 T 细胞受体融合构建体(TRuCs)和合成 TCR 与抗原受体(STARs)。我们的数据显示,STARs 能以最佳方式再现 TCR 抗原敏感性,在重定向 CD8+ 和 CD4+ T 细胞识别 HLA I 类新抗原方面优于 CARs 和 TRuCs。与 CAR-T 和 TRuC-T 细胞相比,STAR-T 细胞在体外对新抗原密度较低的癌细胞系具有更强的杀伤力,在小鼠模型中也能更好地控制肿瘤。这些发现凸显了 CAR 敏感性的局限性,并将 STAR 作为更有效的合成受体,用于以 T 细胞为基础的针对低新抗原密度肿瘤的免疫疗法。
期刊介绍:
Cell Reports publishes high-quality research across the life sciences and focuses on new biological insight as its primary criterion for publication. The journal offers three primary article types: Reports, which are shorter single-point articles, research articles, which are longer and provide deeper mechanistic insights, and resources, which highlight significant technical advances or major informational datasets that contribute to biological advances. Reviews covering recent literature in emerging and active fields are also accepted.
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