Engineering TCR-controlled fuzzy logic into CAR T cells enhances therapeutic specificity

将TCR控制的模糊逻辑工程化引入CAR T细胞可增强治疗特异性

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作者:Taisuke Kondo ,François X P Bourassa ,Sooraj Achar ,Justyn DuSold ,Pablo F Céspedes ,Makoto Ando ,Alka Dwivedi ,Josquin Moraly ,Christopher Chien ,Saliha Majdoul ,Adam L Kenet ,Madison Wahlsten ,Audun Kvalvaag ,Edward Jenkins ,Sanghyun P Kim ,Catherine M Ade ,Zhiya Yu ,Guillaume Gaud ,Marco Davila ,Paul Love ,James C Yang ,Michael L Dustin ,Grégoire Altan-Bonnet ,Paul François ,Naomi Taylor
Chimeric antigen receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of hematological malignancies, but poor specificity has limited its applicability to solid tumors. By contrast, natural T cells harboring T cell receptors (TCRs) can discriminate between neoantigen-expressing cancer cells and self-antigen-expressing healthy tissues but have limited potency against tumors. We used a high-throughput platform to systematically evaluate the impact of co-expressing a TCR and CAR on the same CAR T cell. While strong TCR-antigen interactions enhanced CAR activation, weak TCR-antigen interactions actively antagonized their activation. Mathematical modeling captured this TCR-CAR crosstalk in CAR T cells, allowing us to engineer dual TCR/CAR T cells targeting neoantigens (HHAT(L8F)/p53(R175H)) and human epithelial growth factor receptor 2 (HER2) ligands, respectively. These T cells exhibited superior anti-cancer activity and minimal toxicity against healthy tissue compared with conventional CAR T cells in a humanized solid tumor mouse model. Harnessing pre-existing inhibitory crosstalk between receptors, therefore, paves the way for the design of more precise cancer immunotherapies.

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