Tumor-intrinsic YTHDF1 drives immune evasion and resistance to immune checkpoint inhibitors via promoting MHC-I degradation

The recently described role of RNA methylation in regulating immune cell infiltration into tumors has attracted interest, given its potential impact on immunotherapy response. YTHDF1 is a versatile and powerful m6A reader, but the understanding of its impact on immune evasion is limited. Here, we re...

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Veröffentlicht in:Nature communications 2023-01, Vol.14 (1), p.265-265, Article 265
Hauptverfasser: Lin, Wanzun, Chen, Li, Zhang, Haojiong, Qiu, Xianxin, Huang, Qingting, Wan, Fangzhu, Le, Ziyu, Geng, Shikai, Zhang, Anlan, Qiu, Sufang, Chen, Long, Kong, Lin, Lu, Jiade J.
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Sprache:eng
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Zusammenfassung:The recently described role of RNA methylation in regulating immune cell infiltration into tumors has attracted interest, given its potential impact on immunotherapy response. YTHDF1 is a versatile and powerful m6A reader, but the understanding of its impact on immune evasion is limited. Here, we reveal that tumor-intrinsic YTHDF1 drives immune evasion and immune checkpoint inhibitor (ICI) resistance. Additionally, YTHDF1 deficiency converts cold tumors into responsive hot tumors, which improves ICI efficacy. Mechanistically, YTHDF1 deficiency inhibits the translation of lysosomal genes and limits lysosomal proteolysis of the major histocompatibility complex class I (MHC-I) and antigens, ultimately restoring tumor immune surveillance. In addition, we design a system for exosome-mediated CRISPR/Cas9 delivery to target YTHDF1 in vivo, resulting in YTHDF1 depletion and antitumor activity. Our findings elucidate the role of tumor-intrinsic YTHDF1 in driving immune evasion and its underlying mechanism. YTHDF1 is an m6A reader that binds to methylated RNA and facilitates translation. Here the authors show that tumor intrinsic YTHDF1 promotes tumorigenesis by regulating lysosomal proteolysis of MHC-I and that YTHDF1 targeting boosts anti-tumor immunity and response to immunotherapy in preclinical cancer models.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-022-35710-7