Frontiers in immunology · 2026
AI-driven spatial analysis of tumor-infiltrating lymphocytes predicts chemo-immunotherapy response in triple-negative breast cancer.
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Liang Hai, Zhu Hejie, Wang Liwei, Yu James, Feng Ru, Yan Xiaoshuang, Shi Liang, Xiao Jiayi, Zhou Huirong, Chen Peilin, Liu Hao, Zheng Linfeng
Аннотация
INTRODUCTION: Triple-negative breast cancer (TNBC) is an aggressive subtype with limited therapeutic options due to lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. While neoadjuvant immunotherapy shows promise, the impact of spatial organization of tertiary lymphoid structures (TLS) and tumor-infiltrating lymphocytes (TILs) on treatment efficacy remains incompletely characterized. This exploratory study aims to establish an AI-based quantitative framework for analyzing tumor-immune spatial interactions and develop a predictive model for chemo-immunotherapy response.
METHODS: We developed an integrated AI pipeline for automated analysis of hematoxylin-eosin (HE)-stained breast cancer samples. The framework incorporates: 1) TLS detection and classification, 2) TIL quantification, and 3) spatial relationship mapping between lymphocytes and tumor cells. Biomarkers associated with Miller-Payne (MP) response grades were identified using multivariate statistics, enabling construction of a random forest prognostic model.
RESULTS: The TLS recognition model demonstrated substantial agreement with pathologists (κ=0.73), while the TIL classifier achieved 0.92 accuracy. Analysis of 32 HE images from triple-negative breast cancer (TNBC) cases treated with neoadjuvant chemo-immunotherapy revealed significant associations: stromal lymphocyte density and percentage positively correlated with MP grades (p<0.05); average cell counts in 2-cell and 3-cell lymphocyte aggregates showed no significant correlations; shorter mean minimum distances between lymphocytes and tumor cells within 10-30 μm radius range were inversely associated with MP grades (p<0.05). The spatial-feature-based prediction model achieved an AUC of 0.81 (95% CI: 0.76-0.86).
CONCLUSIONS: This study established an AI-driven HE analysis pipeline that precisely quantified spatial determinants of tumor-immune interactions. Lymphocyte spatial organization, particularly proximity to tumor cells and lymphocyte aggregation patterns, serves as a critical predictor of chemo-immunotherapy response beyond density metrics. The validated MP-grade prediction model demonstrates translational potential for clinical decision-making in TNBC management, although external validation in larger cohorts is warranted.
METHODS: We developed an integrated AI pipeline for automated analysis of hematoxylin-eosin (HE)-stained breast cancer samples. The framework incorporates: 1) TLS detection and classification, 2) TIL quantification, and 3) spatial relationship mapping between lymphocytes and tumor cells. Biomarkers associated with Miller-Payne (MP) response grades were identified using multivariate statistics, enabling construction of a random forest prognostic model.
RESULTS: The TLS recognition model demonstrated substantial agreement with pathologists (κ=0.73), while the TIL classifier achieved 0.92 accuracy. Analysis of 32 HE images from triple-negative breast cancer (TNBC) cases treated with neoadjuvant chemo-immunotherapy revealed significant associations: stromal lymphocyte density and percentage positively correlated with MP grades (p<0.05); average cell counts in 2-cell and 3-cell lymphocyte aggregates showed no significant correlations; shorter mean minimum distances between lymphocytes and tumor cells within 10-30 μm radius range were inversely associated with MP grades (p<0.05). The spatial-feature-based prediction model achieved an AUC of 0.81 (95% CI: 0.76-0.86).
CONCLUSIONS: This study established an AI-driven HE analysis pipeline that precisely quantified spatial determinants of tumor-immune interactions. Lymphocyte spatial organization, particularly proximity to tumor cells and lymphocyte aggregation patterns, serves as a critical predictor of chemo-immunotherapy response beyond density metrics. The validated MP-grade prediction model demonstrates translational potential for clinical decision-making in TNBC management, although external validation in larger cohorts is warranted.