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REVIEW article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1500921
This article is part of the Research Topic Precision Medicine and Targeted Therapies in Gastrointestinal and Genitourinary Solid Tumors View all 3 articles

Helicobacter pylori-Targeted AI-Driven Vaccines: A Paradigm Shift in Gastric Cancer Prevention

Provisionally accepted
Zhiwei Tu Zhiwei Tu Youtao Wang Youtao Wang Junze Liang Junze Liang Jinping Liu Jinping Liu *
  • Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China

The final, formatted version of the article will be published soon.

    Helicobacter pylori (H. pylori), a globally prevalent pathogen Group I carcinogen, presents a formidable challenge in gastric cancer prevention due to its increasing antimicrobial resistance and strain diversity. This comprehensive review critically analyzes the limitations of conventional antibiotic-based therapies and explores cutting-edge approaches to combat H. pylori infections and associated gastric carcinogenesis. We emphasize the pressing need for innovative therapeutic strategies, with a particular focus on precision medicine and tailored vaccine development. Despite promising advancements in enhancing host immunity, current Helicobacter pylori vaccine clinical trials have yet to achieve long-term efficacy or gain approval regulatory approval. We propose an paradigm-shifting approach leveraging artificial intelligence (AI) to design precision-targeted, multiepitope vaccines tailored to multiple H. pylori subtypes. This AI-driven strategy has the potential to revolutionize antigen selection and optimize vaccine efficacy, addressing the critical need for personalized interventions in H. pylori eradication efforts. By leveraging AI in vaccine design, we propose a revolutionary approach to precision therapy that could significantly reduce H. pylori -associated gastric cancer burden.

    Keywords: gastric cancer, Helicobacter pylori, precision medicine, targeted therapy, Neoantigen vaccines, artificial intelligence

    Received: 24 Sep 2024; Accepted: 08 Nov 2024.

    Copyright: © 2024 Tu, Wang, Liang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jinping Liu, Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.