Back

Nuclear FAK aggravates CD8+ T cell exhaustion via the SP1-IL-6 axis in colorectal cancer

Kong, H.; Wu, X.; Yang, Q.; Wu, C.; Yu, X.; Yang, J.; Xie, D.; Dai, Y.; Chen, L.; Ma, P.; Dai, S.; Huang, L.; Chen, C.; Mi, P.; Peng, Y.; Shi, H.; Zhang, D.; Ke, N.; Hu, B.; Zhou, Z.; Cao, D.; Wang, P.; Chen, L.; Liu, Y.-H.; Jiang, H.

2024-07-24 immunology
10.1101/2024.07.24.604770 bioRxiv
Show abstract

Nuclear abnormalities such as nuclear deformation are hallmarks of many diseases, including cancer. Accumulating evidence suggests that the dense and mechanically stiff tumor microenvironment promotes nuclear deformation in cancer cells. However, little is known about how nuclear deformation in neoplastic cells regulates immune exhaustion in the tumor microenvironment. Here, we found that lamin A/C-mediated nuclear stiffening in neoplastic cells promotes the nuclear translocation of phosphorylated focal adhesion kinase (pFAK), which is strongly correlated with the heterogeneity and exhaustion of CD8+ T cells within the spatial context of the tumor microenvironment in human colorectal cancer. Mechanistically, we revealed that increased nuclear tension within tumor cells promotes pFAK nuclear translocation, where nuclear pFAK was found to regulate SP1/IL-6-mediated T-cell exhaustion and the transcription of proinflammatory cytokines/chemokines. Pharmacological inhibition or disruption of pFAK nuclear translocation enhanced antitumor immune responses and synergistically potentiated PD-1 and TIM-3 immunotherapy by increasing CD8+ T-cell cytotoxicity and restoring exhaustion in preclinical models of colorectal cancer. These findings highlight the pivotal role of nuclear tension-mediated pFAK translocation into the tumor cell nucleus in regulating CD8+ T-cell exhaustion, suggesting that pFAK is a promising target for advancing cancer immunotherapy.

Matching journals

The top 7 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.