Wenxuan Wu, Zhenzhen Xun, Yaxuan Han, Qimiao Chen, Shaojun Yu, Yikai Luo, Zhixing Hao, Jing Chen, Yewei Xu, Xiaying Han, Jia Qi, Kai Song, Xiaojing Ma, Yongyuan Chen, Guofeng Chen, Muxing Kang, Xiaoli Jin, Yuan Ding, Zhiqiang Zhu, Can Hu, Xiangdong Cheng, Lie Wang, Pin Wu, Han Liang, Jian Chen
Tertiary lymphoid structures (TLS) are critical components of the tumor microenvironment in gastric cancer, but clinical assessment of TLSs is challenging. The development of automated annotation tools for histopathologic slide analysis could facilitate the identification of TLSs and enhance our understanding of the mechanisms driving TLS maturation. In this study, we generated a transformer-based deep learning model that enables quantitative characterization of TLS maturity from whole-slide images. Application of the model to a large gastric cancer cohort (n = 253) showed that higher TLS maturity correlated with improved patient survival. Integration of single-cell RNA sequencing data from 17 patients with gastric cancer combined with multiplex IHC, flow cytometry, and functional coculture assays identified a key immune circuit in mature TLSs involving CD8+ tissue-resident memory T cells, which recruit activated B cells via the CXCL13-CXCR5 axis to enhance tissue-resident memory T-cell cytotoxicity through granzyme B upregulation. Overall, this study established a clinically applicable artificial intelligence tool and uncovered key immune interactions that regulate TLS maturation and antitumor immunity in gastric cancer. SIGNIFICANCE: A deep learning model demonstrates that higher tertiary lymphoid structure maturity predicts improved gastric cancer patient survival and identifies a key immune circuit, offering a clinically applicable tool that could guide treatment. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.