Microsatellite-Stable Gastric Cancer Can be Classified into 2 Molecular Subtypes with Different Immunotherapy...

Most patients with gastric cancer (GC) exhibit microsatellite stability, yet comprehensive subtyping for prognostic prediction and clinical treatment decisions for microsatellite-stable GC is lacking. In this work, RNA-sequencing gene expression data and clinical information of patients with microsatellite-stable GC were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We employed several machine learning methods to develop and validate a signature based on immune-related genes (IRGs) for subtyping patients with microsatellite-stable GC. Moreover, 2 deep learning models based on the Vision Transformer (ViT) architecture were developed to predict GC tumor tiles and identify microsatellite-stable GC subtypes from digital pathology slides. Microsatellite status was evaluated by immunohistochemistry, and prognostic data as well as hematoxylin and eosin whole-slide images were collected from 105 patients with microsatellite-stable GC to serve as an independent validation cohort. A signature comprising 5 IRGs was established and validated, stratifying patients with microsatellite-stable GC into high-risk (microsatellite-stable-HR) and low-risk (microsatellite-stable-LR) groups. This signature demonstrated consistent performance, with areas under the receiver operating characteristic curve (AUC) of 0.65, 0.70, and 0.70 at 1, 3, and 5 years in the TCGA cohort, and 0.70, 0.60, and 0.62 in the GEO cohort, respectively. The microsatellite-stable-HR subtype exhibited higher levels of tumor immune dysfunction and exclusion, suggesting a greater potential for immune escape compared with the microsatellite-stable-LR subtype. Moreover, the microsatellite-stable-HR/LR subtypes showed differential sensitivities to various therapeutic drugs. Leveraging morphologic differences, the tumor recognition segmentation model achieved an impressive AUC of 0.97, whereas the microsatellite-stable-HR/LR identification model effectively classified microsatellite-stable-HR/LR subtypes with an AUC of 0.94. Both models demonstrated promising results in classifying patients with microsatellite-stable GC in the external validation cohort, highlighting the strong ability to accurately differentiate between microsatellite-stable GC subtypes. The IRG-related microsatellite-stable-HR/LR subtypes had the potential to enhance outcome prediction accuracy and guide treatment strategies. This research may optimize precision treatment and improve the prognosis for patients with microsatellite-stable GC.