Back

Enhancing anti-gastrointestinal cancer activities of CLDN18.2 CAR-T armored with novel synthetic NKG2D receptors Containing DAP10 and DAP12 signaling domains.

Sun, M.; Wang, H.; Hao, R.; Wang, Y.; Li, Y.; Zhong, Y.; Zhang, S.; Zhai, B.; Cheng, Y.

2023-05-20 immunology
10.1101/2023.05.17.541124 bioRxiv
Show abstract

Chimeric antigen receptor (CAR) T therapies have shown remarkable efficacy in hematopoietic malignancies, but their therapeutic benefits in solid tumors have been limited due to heterogeneities in both antigen types and their expression levels on tumor cells. NK group 2 member D ligands (NKG2DLs) are extensively expressed on various tumors and absent on normal tissues, making them a promising target for cellular immunotherapy. DAP10 and DAP12 function as adaptor proteins in NK cells to transduce activating signals, and recent studies have revealed DAP10 and DAP12s additional role as a co-stimulatory signal in T cells. Our pre-clinical data showed that CAR-T targeting CLDN18.2 is highly effective in gastrointestinal (GI) cancers, but the heterogeneous expression of CLDN18.2 poses a treatment challenge. To complement this antigen deficiency, we demonstrated that NKG2DLs were extensively expressed in GI tumor tissues and formed an ideal dual target. Here, we reported a CLDN18.2 CAR design armored with synthetic NKG2D receptors (SNR) containing DAP10 and DAP12 signaling domains. This novel CAR-T showed improved cytotoxicity against tumor cells with heterogeneous expression of CLDN18.2. The possible underlined mechanism is that SNR promotes CAR-T memory formation and reduces their exhaustion, while also enhancing their expansion and ability to infiltrate immune-excluded tumors in vivo. Taken together, SNR with DAP10/12 signaling and their synergistic involvement, increased CAR-T function and overcame the antigen deficiency, providing a novel treatment modality for solid GI tumor.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.