Microsatellite stable gastric cancer can be classified into two molecular subtypes with different immunotherapy response and prognosis based on gene sequencing and computational pathology.

IF 5.1 2区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Zhiyi Ye, Xiaoyang Wu, Zheng Wei, Qiuyan Sun, Yanli Wang, Tan Li, Yuan Yuan, Jingjing Jing
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引用次数: 0

Abstract

Most gastric cancer (GC) patients exhibit microsatellite stability (MSS), yet comprehensive subtyping for prognostic prediction and clinical treatment decisions for MSS GC is lacking. In this work, RNA-sequencing gene expression data and clinical information of MSS GC patients 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 MSS GC patients. Moreover, two deep learning models based on the Vision Transformer (ViT) architecture were developed to predict GC tumor tiles and identify MSS GC subtypes from digital pathology slides. Microsatellite status was evaluated by immunohistochemistry, and prognostic data as well as H&E whole slide images were collected from 105 MSS GC patients to serve as an independent validation cohort. A signature comprising five IRGs was established and validated, stratifying MSS GC patients into high-risk (MSS-HR) and low-risk (MSS-LR) groups. This signature demonstrated consistent performance, with areas under the receiver operating characteristic (ROC) 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 MSS-HR subtype exhibited higher levels of tumor immune dysfunction and exclusion, suggesting a greater potential for immune escape compared to the MSS-LR subtype. Moreover, the MSS-HR/LR subtypes showed differential sensitivities to various therapeutic drugs. Leveraging morphological differences, the tumor recognition segmentation model (TRSM) achieved an impressive AUC of 0.97, while the MSS-HR/LR identification model (MSSIM) effectively classified MSS-HR/LR subtypes with an AUC of 0.94. Both models demonstrated promising results in classifying MSS GC patients in the external validation cohort, highlighting the strong ability to accurately differentiate between MSS GC subtypes. The IRGs-related MSS-HR/LR subtypes had potential in enhancing outcome prediction accuracy and guide treatment strategies. This research may optimize precision treatment and improve the prognosis for MSS GC patients.

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来源期刊
Laboratory Investigation
Laboratory Investigation 医学-病理学
CiteScore
8.30
自引率
0.00%
发文量
125
审稿时长
2 months
期刊介绍: Laboratory Investigation is an international journal owned by the United States and Canadian Academy of Pathology. Laboratory Investigation offers prompt publication of high-quality original research in all biomedical disciplines relating to the understanding of human disease and the application of new methods to the diagnosis of disease. Both human and experimental studies are welcome.
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