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Stem Cells, Helicobacter pylori, and Mutational Landscape: Utility of Preclinical Models to Understand Carcinogenesis and to Direct Management of Gastric Cancer
Gastroenterology  (IF22.682),  Pub Date : 2021-12-21, DOI: 10.1053/j.gastro.2021.12.252
Dylan Liabeuf, Masanobu Oshima, Daniel E. Stange, Michael Sigal

Several genetic and environmental factors increase gastric cancer (GC) risk, with Helicobacter pylori being the main environmental agent. GC is thought to emerge through a sequence of morphological changes that have been elucidated on the molecular level. New technologies have shed light onto pathways that are altered in GC, involving mutational and epigenetic changes and altered signaling pathways. Using various new model systems and innovative approaches, the relevance of such alterations for the emergence and progression of GC has been validated. Here, we highlight the key strategies and the resulting achievements. A major step is the characterization of epithelial stem cell behavior in the healthy stomach. These data, obtained through new reporter mouse lines and lineage tracing, enabled insights into the processes that control cellular proliferation, self-renewal, and differentiation of gastric stem cells. It has become evident that these cells and pathways are often deregulated in carcinogenesis. Second, insights into how H pylori colonizes gastric glands, directly interacts with stem cells, and alters cellular and genomic integrity, as well as the characterization of tissue responses to infection, provide a comprehensive picture of how this bacterium contributes to gastric carcinogenesis. Third, the development of stem cell– and tissue-specific reporter mice have driven our understanding of the signals and mutations that promote different types of GC and now also enable the study of more advanced, metastasized stages. Finally, organoids from human tissue have allowed insights into gastric carcinogenesis by validating mutational and signaling alterations in human primary cells and opening a route to predicting responses to personalized treatment.