A Universal Strategy of Anti‐Tumor mRNA Vaccine by Harnessing “Off‐the‐Shelf” Immunity

Abstract Personalized neoantigen cancer mRNA vaccines are promising candidates for precision medicine. However, the difficulty of identifying neoantigens heavily hinders their broad applicability. This study developed a universal strategy of anti‐tumor mRNA vaccine by harnessing “off‐the‐shelf” immu...

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Bibliographic Details
Main Authors: Jiayan Fu, Shuangqi Wu, Nengcheng Bao, Lili Wu, Huiru Qu, Zhechao Wang, Haiyang Dong, Jian Wu, Yongfeng Jin
Format: Article
Language:English
Published: Wiley 2025-02-01
Series:Advanced Science
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Online Access:https://doi.org/10.1002/advs.202401287
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Summary:Abstract Personalized neoantigen cancer mRNA vaccines are promising candidates for precision medicine. However, the difficulty of identifying neoantigens heavily hinders their broad applicability. This study developed a universal strategy of anti‐tumor mRNA vaccine by harnessing “off‐the‐shelf” immunity to known antigens. First, the model antigen ovalbumin (OVA) is used for mRNA vaccine design. In vitro test indicated that this mRNA vaccine reprogrammed tumor cells that can be recognized and killed by OVA‐specific cytotoxic T lymphocytes (CTLs). In situ mRNA vaccine notably inhibited tumor growth across three subcutaneous solid tumor models in mice. Further single‐cell sequencing analyses revealed that mRNA vaccination act to reshape the immunosuppressive tumor microenvironment (TME) toward more proinflammatory characteristics. Strikingly, this framework of mRNA‐based strategy can be applied to two clinical pathogen antigens, hepatitis B surface antigen (HBsAg), and SARS‐CoV‐2 spike receptor‐binding domain (SRBD). Interestingly, the mRNA‐based strategy largely recapitulated the scenario of spontaneous cancer regression following pathogen infection or vaccination. Collectively, this study provides not only proof of concept for universal anti‐tumor mRNA therapy, but also mechanistic insights in echoing the long‐standing puzzle of spontaneous cancer regression.
ISSN:2198-3844