T Cells Expressing CD19/CD20 Bispecific Chimeric Antigen Receptors Prevent Antigen Escape by Malignant B Cells.
T Cells Expressing CD19/CD20 Bispecific Chimeric Antigen Receptors Prevent Antigen Escape by Malignant B Cells.
复制标题
DOI：
10.1158/2326-6066.cir-15-0231
复制
发表时间：
2016-06
影响因子：
10.1
通讯作者：
Chen YY
中科院分区：
文献类型：
--
作者：
Zah E;Lin MY;Silva-Benedict A;Jensen MC;Chen YY
过继转移表达抗CD19嵌合抗原受体(CARS)的T细胞已显示出显著的治疗晚期B细胞恶性肿瘤的潜力，但多项试验也报道了由于CD19阴性白血病细胞的出现而导致患者复发。在这里，我们报告了单链、双特异性CARS的设计和优化，该CARS对表达CD19或CD20的靶细胞具有强大的细胞毒作用，这两个靶细胞是B细胞恶性肿瘤的临床验证靶点。我们确定了有效识别双抗原所需的结构参数，并证明了优化的双特异性CARS在体内可以同样有效地控制野生型B细胞淋巴瘤和CD19−突变。据我们所知，这是第一个能够通过对临床相关的一对肿瘤相关抗原进行真正的或门信号计算来防止抗原逃逸的双特异性CAR。CD19-OR-CD20汽车与现有的T细胞制造程序完全兼容，并可通过当前的临床方案实施。这些结果为CD19CAR T细胞治疗中抗原逃逸的挑战提供了有效的解决方案，并突出了基于结构的合理设计在开发具有更高水平复杂性的受体中的作用。
The adoptive transfer of T cells expressing anti-CD19 chimeric antigen receptors (CARs) has shown remarkable curative potential against advanced B-cell malignancies, but multiple trials have also reported patient relapses due to the emergence of CD19-negative leukemic cells. Here, we report the design and optimization of single-chain, bi-specific CARs that trigger robust cytotoxicity against target cells expressing either CD19 or CD20, two clinically validated targets for B-cell malignancies. We determined the structural parameters required for efficient dual-antigen recognition, and we demonstrate that optimized bi-specific CARs can control both wild-type B-cell lymphoma and CD19− mutants with equal efficiency in vivo. To our knowledge, this is the first bi-specific CAR capable of preventing antigen escape by performing true OR-gate signal computation on a clinically relevant pair of tumor-associated antigens. The CD19-OR-CD20 CAR is fully compatible with existing T-cell manufacturing procedures and implementable by current clinical protocols. These results present an effective solution to the challenge of antigen escape in CD19 CAR T-cell therapy, and they highlight the utility of structure-based rational design in the development of receptors with higher-level complexity.