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Engineering Human MAIT Cells with Chimeric Antigen Receptors for Cancer Immunotherapy

Engineering immune cells with chimeric Ag receptors (CARs) is a promising technology in cancer immunotherapy. Besides classical cytotoxic CD8 T cells, innate cell types such as NK cells have also been used to generate CAR-T or CAR-NK cells. In this study, we devised an approach to program a nonclass...

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Published in:The Journal of immunology (1950) 2022-10, Vol.209 (8), p.1523-1531
Main Authors: Dogan, Mikail, Karhan, Ece, Kozhaya, Lina, Placek, Lindsey, Chen, Xin, Yigit, Mesut, Unutmaz, Derya
Format: Article
Language:English
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Summary:Engineering immune cells with chimeric Ag receptors (CARs) is a promising technology in cancer immunotherapy. Besides classical cytotoxic CD8 T cells, innate cell types such as NK cells have also been used to generate CAR-T or CAR-NK cells. In this study, we devised an approach to program a nonclassical cytotoxic T cell subset called mucosal-associated invariant T (MAIT) cells into effective CAR-T cells against B cell lymphoma and breast cancer cells. Accordingly, we expressed anti-CD19 and anti-Her2 CARs in activated primary human MAIT cells and CD8 T cells, expanded them in vitro, and compared their cytotoxicity against tumor cell targets. We show upon activation through CARs that CAR-MAIT cells exhibit high levels of cytotoxicity toward target cells, comparable to CD8 CAR-T cells, but interestingly expressed lower levels of IFN-γ than conventional CAR CD8 T cells. Additionally, in the presence of vitamin B metabolite 5-ARU (5-amino-4-d-ribitylaminouracil dihydrochloride), which is a conserved compound that activates MAIT cells through MHC class I-related (MR1) protein, MAIT cells killed MR1-expressing target breast cancer and B cell lymphoma cell lines in a dose-dependent manner. Thus, MAIT cells can be genetically edited as CAR-T cells or mobilized and expanded by MR1 ligands as an off-the-shelf novel approach to cell-based cancer immunotherapy strategies while being comparable to conventional methods in effectivity.
ISSN:0022-1767
1550-6606
DOI:10.4049/jimmunol.2100856