生物
细胞
癌症研究
边距(机器学习)
免疫学
细胞生物学
遗传学
计算机科学
机器学习
作者
Benjamin Ruf,Matthias Bruhns,Sepideh Babaei,Noémi Kedei,Lichun Ma,Mahler Revsine,Mohamed-Reda Benmebarek,Chi Ma,Bernd Heinrich,Varun Subramanyam,Jonathan Qi,Simon Wabitsch,Benjamin L. Green,Kylynda C. Bauer,Yuta Myojin,Layla T. Greten,Justin McCallen,Patrick Huang,Rajiv Trehan,Xin Wei Wang
出处
期刊:Cell
[Cell Press]
日期:2023-08-01
卷期号:186 (17): 3686-3705.e32
被引量:75
标识
DOI:10.1016/j.cell.2023.07.026
摘要
Summary
Mucosal-associated invariant T (MAIT) cells represent an abundant innate-like T cell subtype in the human liver. MAIT cells are assigned crucial roles in regulating immunity and inflammation, yet their role in liver cancer remains elusive. Here, we present a MAIT cell-centered profiling of hepatocellular carcinoma (HCC) using scRNA-seq, flow cytometry, and co-detection by indexing (CODEX) imaging of paired patient samples. These analyses highlight the heterogeneity and dysfunctionality of MAIT cells in HCC and their defective capacity to infiltrate liver tumors. Machine-learning tools were used to dissect the spatial cellular interaction network within the MAIT cell neighborhood. Co-localization in the adjacent liver and interaction between niche-occupying CSF1R+PD-L1+ tumor-associated macrophages (TAMs) and MAIT cells was identified as a key regulatory element of MAIT cell dysfunction. Perturbation of this cell-cell interaction in ex vivo co-culture studies using patient samples and murine models reinvigorated MAIT cell cytotoxicity. These studies suggest that aPD-1/aPD-L1 therapies target MAIT cells in HCC patients.
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