SCAN-ACT: adoptive T cell therapy target discovery through single-cell transcriptomics.

SCAN-ACT:通过单细胞转录组学发现过继性T细胞疗法靶点

阅读:12
作者:Testa Stefano, Pal Aastha, Subramanian Ajay, Varma Sushama, Tang Jack Pengfei, Graham Danielle, Arfan Sara, Pan Minggui, Bui Nam Q, Ganjoo Kristen N, Dry Sarah, Huang Paul, van de Rijn Matt, Jiang Wei, Kalbasi Anusha, Moding Everett J
BACKGROUND: The FDA approval of T cell receptor-engineered T cells (TCR-T) for synovial sarcoma demonstrates the potential for adoptive T cell therapies (ACTs) in solid tumors. However, the paucity of tumor-associated targets without expression in normal tissues remains a major bottleneck, especially in rare cancer subtypes. METHODS: We developed a comprehensive computational pipeline called SCAN-ACT that leverages single-cell RNA sequencing and multi-omics data from tumor and normal tissues to nominate and prioritize putative targets for both chimeric antigen receptor (CAR)- and TCR-T cells. For surface membrane targets, SCAN-ACT proposes monospecific targets and potential target pairs for bispecific Boolean logic-gated CAR T cells. For peptide-MHC targets, SCAN-ACT proposes intracellular peptides bound to a diverse set of human leukocyte antigens. Selected targets were validated experimentally by protein expression and for peptide-MHC binding. RESULTS: We applied the SCAN-ACT pipeline to soft tissue sarcoma (STS), analyzing 986,749 single cells to identify and prioritize 395 monospecific CAR-T targets, 14,192 bispecific CAR-T targets, and 5020 peptide-MHC targets for TCR-T cells. Proposed targets and target pairs reflected the mesenchymal, neuronal, and hematopoietic ontogeny of STS. We further validated SCAN-ACT in glioblastoma revealing its versatility. CONCLUSIONS: This work provides a robust data repository along with a web-based and user-friendly set of analysis tools to accelerate ACT development for solid tumors ( https://scanact.stanford.edu/ ).

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。