Pathomics-based prediction of natural regulatory T cell infiltration in breast cancer

Abstract Background Natural regulatory T cells (nTregs) are a key immunosuppressive component of the tumor microenvironment (TME), but their assessment typically requires specialized molecular assays. This study aimed to develop and validate a deep learning-based pathomics model to predict nTregs infiltration directly from routine hematoxylin and eosin (H&E)-stained whole slide images (WSI) of breast cancer and evaluate its prognostic significance. Methods Data from 1097 breast cancer patients in The Cancer Genome Atlas (TCGA) were analyzed. A cohort of 928 patients with complete RNA-seq data was used to establish the prognostic value of nTregs (estimated by ImmuneCellAI). A subset of 791 patients with matched high-quality H&E-stained WSI was randomly split into training (n = 633) and validation (n = 158) cohorts. A total of 1488 quantitative pathomic features were extracted. After feature selection via minimum Redundancy Maximum Relevance (mRMR) and Recursive Feature Elimination (RFE), a Gradient Boosting Machine (GBM) classifier was trained to predict high versus low nTregs status, generating a continuous Pathomics Score (PS). The PS was validated against FOXP3 immunohistochemistry (IHC) and evaluated for its association with overall survival (OS). Multi-omics analyses explored the underlying biology of PS-defined groups. Results High nTregs infiltration was an independent predictor of poor OS (HR = 1.58, 95% CI 1.10–2.28, p = 0.013). The GBM model achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.81 (95% CI 0.78–0.85) in the training cohort and 0.72 (95% CI 0.63–0.80) in the validation cohort. The PS showed a strong correlation with FOXP3 + cell density ( p < 0.001) and was independently associated with worse OS (HR = 1.68, 95% CI 1.15–2.46, p = 0.008). Patients with high PS exhibited a distinct transcriptomic signature enriched for immune activation pathways (e.g., estrogen responses) and upregulated immune checkpoint genes (e.g., CD276, TNFSF4, TNFSF9), alongside an immunosuppressive microenvironment characterized by increased nTregs and M2-like macrophage estimates. Conclusion We developed and validated a pathomics model that non-invasively predicts nTregs infiltration and patient prognosis from standard H&E images. The PS serves as a novel, accessible digital biomarker that captures the complexity of an inflamed yet immunosuppressive TME and has the potential to augment clinical decision-making, particularly in resource-limited settings.