MHC II immunogenicity shapes the neoepitope landscape in human tumors

Despite advances in predicting physical peptide-major histocompatibility complex I (pMHC I) binding, it remains challenging to identify functionally immunogenic neoepitopes, especially for MHC II. By using the results of >36,000 immunogenicity assay, we developed a method to identify pMHC whose s...

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Veröffentlicht in:Nature genetics 2023-02, Vol.55 (2), p.221-231
Hauptverfasser: Kim, Jeong Yeon, Cha, Hongui, Kim, Kyeonghui, Sung, Changhwan, An, Jinhyeon, Bang, Hyoeun, Kim, Hyungjoo, Yang, Jin Ok, Chang, Suhwan, Shin, Incheol, Noh, Seung-Jae, Shin, Inkyung, Cho, Dae-Yeon, Lee, Se-Hoon, Choi, Jung Kyoon
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Sprache:eng
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Zusammenfassung:Despite advances in predicting physical peptide-major histocompatibility complex I (pMHC I) binding, it remains challenging to identify functionally immunogenic neoepitopes, especially for MHC II. By using the results of >36,000 immunogenicity assay, we developed a method to identify pMHC whose structural alignment facilitates T cell reaction. Our method predicted neoepitopes for MHC II and MHC I that were responsive to checkpoint blockade when applied to >1,200 samples of various tumor types. To investigate selection by spontaneous immunity at the single epitope level, we analyzed the frequency spectrum of >25 million mutations in >9,000 treatment-naive tumors with >100 immune phenotypes. MHC II immunogenicity specifically lowered variant frequencies in tumors under high immune pressure, particularly with high TCR clonality and MHC II expression. A similar trend was shown for MHC I neoepitopes, but only in particular tissue types. In summary, we report immune selection imposed by MHC II-restricted natural or therapeutic T cell reactivity. DeepNeo identifies major histocompatibility complex (MHC) I or MHC II neoepitopes that are immunogenically compatible with the T cell repertoire. It can predict neoepitopes most likely to be depleted through spontaneous immunity or through immune checkpoint blockade from untreated and immunotherapy-treated tumor datasets.
ISSN:1061-4036
1546-1718
DOI:10.1038/s41588-022-01273-y