肿瘤细胞治疗研究
英文原题：Identification of broadly tumour-reactive γδ TCRs from multiple myeloma.
γδ T细胞因其抗肿瘤能力以及在介导免疫检查点阻断反应中的作用而日益受到重视1-3。
γδ T 细胞因其抗肿瘤能力以及在介导免疫检查点阻断反应中的作用而日益受到重视 1-3。与经典的 αβ T 细胞不同，γδ T 细胞在诱导抗肿瘤反应中对 T 细胞受体（TCR）的依赖程度仍不清楚。将具有肿瘤反应性 TCR 的 γδ T 细胞与旁观者 γδ T 细胞区分开来的挑战，限制了我们对肿瘤反应性 γδ T 细胞生物学的理解以及将其 TCR 转化为免疫治疗药物。在此，我们提出 PreGame，一种能够从单细胞 CITE 测序数据中识别肿瘤反应性 γδ T 细胞的机器学习算法。我们使用 PreGame 从多发性骨髓瘤或其他实体癌患者中识别肿瘤反应性 γδ T 细胞，并确认其 TCR 对肿瘤细胞的特异性。在临床上，我们证明肿瘤反应性 γδ T 细胞的扩增是接受 belantamab mafodotin 联合治疗的多发性骨髓瘤患者应答的早期生物标志物。我们还鉴定了普遍表达的 HLA-C 蛋白中的一个 γδ TCR 表位，以及一个能够实现肿瘤免疫监视的逻辑门。因此，PreGame 是一种多功能工具，可以加速我们对 γδ T 细胞生物学的理解，并促进 γδ TCR 向通用治疗药物的转化。
γδ T cells are becoming increasingly appreciated for their antitumour capacity and role in mediating responses to immune checkpoint blockade 1-3 . Unlike classical αβ T cells, the degree to which γδ T cells rely on their T cell receptors (TCRs) to induce antitumour responses remains unclear. The challenge of distinguishing γδ T cells with tumour-reactive TCRs from bystander γδ T cells limits our understanding of tumour-reactive γδ T cell biology and the translation of their TCRs into immunotherapeutics. Here we present PreGame, a machine-learning algorithm capable of identifying tumour-reactive γδ T cells from single-cell CITE sequencing data. We use PreGame to identify tumour-reactive γδ T cells from patients with multiple myeloma or other solid cancers, and confirm the specificity of their TCRs to tumour cells. Clinically, we demonstrate that expansion of tumour-reactive γδ T cells is an early biomarker of response in patients with multiple myeloma receiving combination therapy with belantamab mafodotin. We also identify a γδ TCR epitope in the ubiquitously expressed HLA-C protein and a logic gate that enables tumour immunosurveillance. Thus, PreGame is a versatile tool that can accelerate our understanding of γδ T cell biology and facilitate the translation of γδ TCRs into universal therapeutics.
MEMBER ACCOUNT
登录成功会直接打开下一页。
