KITAHARA Shuji
   Department   Graduate School of Medical Science, Graduate School of Medical Science
   Position   Associate Professor (Fixed Term)
Article types Original article
Language English
Peer review Non peer reviewed
Title A tumor microenvironment-based classification of gastric cancer for more effective diagnosis and treatment.
Journal Formal name：Surgical oncology
Abbreviation：Surg Oncol
ISSN code：09607404/18793320
Domestic / ForeginForegin
Volume, Issue, Page 63,pp.102298
International coauthorship International coauthorship
Author and coauthor Simona O. Dima†, Andrei Sorop, Shuji Kitahara, Namrata Setia, Mihaela Chivu-Economescu, Lilia Matei, Vlad Herlea, Nicolae C. Pechianu, Takenori Inomata, Aya Matsui, Anna Khachatryan, Shuichi Aoki, Gregory Y. Lauwers, Irinel Popescu*, Dan G. Dud*
Publication date 2025/12
Summary With approximately one million diagnosed cases and over 700,000 deaths recorded annually, gastric cancer (GC) is the third most common cause of cancer-related deaths worldwide. GC is a heterogeneous tumor. Thus, optimal management requires biomarkers of prognosis, treatment selection, and treatment response. The Cancer Genome Atlas program sub-classified GC into molecular subtypes, providing a framework for treatment personalization using traditional chemotherapies or biologics. Here, we report a comprehensive study of GC vascular and immune tumor microenvironment (TME)-based on stage and molecular subtypes of the disease and their correlation with outcomes. Using tissues and blood circulating biomarkers and a molecular classification, we identified cancer cell and tumor archetypes, which show that the TME evolves with the disease stage and is a major determinant of prognosis. Moreover, our TME-based subtyping strategy allowed the identification of archetype-specific prognostic biomarkers such as CDH1-mutant GC and circulating IL-6 that provided information beyond and independent of TMN staging, MSI status, and consensus molecular subtyping. The results show that integrating molecular subtyping with TME-specific biomarkers could contribute to improved patient prognostication and may provide a basis for treatment stratification, including for contemporary anti-angiogenesis and immunotherapy approaches.
DOI 10.1016/j.suronc.2025.102298
PMID 41033926