Design, selection and optimization of an anti-TRAIL-R2/anti-CD3 bispecific antibody able to educate T cells to recognize and destroy cancer cells

设计、选择和优化能够训练 T 细胞识别和摧毁癌细胞的抗 TRAIL-R2/抗 CD3 双特异性抗体

阅读:6
作者:Alessandro Satta, Delia Mezzanzanica, Francesco Caroli, Barbara Frigerio, Massimo Di Nicola, Roland E Kontermann, Federico Iacovelli, Alessandro Desideri, Andrea Anichini, Silvana Canevari, Alessandro Massimo Gianni, Mariangela Figini

Abstract

Recombinant human tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) or TRAIL-receptor agonistic monoclonal antibodies promote apoptosis in most cancer cells, and the differential expression of TRAIL-R2 between tumor and normal tissues allows its exploitation as a tumor-associated antigen. The use of these antibodies as anticancer agents has been extensively studied, but the results of clinical trials were disappointing. The observed lack of anticancer activity could be attributed to intrinsic or acquired resistance of tumor cells to this type of treatment. A possible strategy to circumvent drug resistance would be to strike tumor cells with a second modality based on a different mechanism of action. We therefore set out to generate and optimize a bispecific antibody targeting TRAIL-R2 and CD3. After the construction of different bispecific antibodies in tandem-scFv or single-chain diabody formats to reduce possible immunogenicity, we selected a humanized bispecific antibody with very low aggregates and long-term high stability and functionality. This antibody triggered TRAIL-R2 in an agonistic manner and its anticancer activity proved dramatically potentiated by the redirection of cytotoxic T cells against both sensitive and resistant melanoma cells. The results of our study show that combining the TRAIL-based antitumor strategy with an immunotherapeutic approach in a single molecule could be an effective addition to the anticancer armamentarium.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。