Abstract 79: From pixels to spatial microenvironments: AI-powered CAF-Epi niche prediction on H&E slides.

Abstract Routine H&E slides remain the most accessible and rapid diagnostic material in clinical practice, particularly for gastrointestinal cancers. However, although H&E effectively distinguishes cancer from non-cancer, it provides insufficient information to predict treatment response. Spatial niches can be conceptualized as multicellular microanatomical units—defined by characteristic patterns of cellular adjacency, interaction and coordinated signaling. Using spatial transcriptomics across multistage esophageal squamous cell carcinoma (ESCC), we identified a CAF-epithelial (CAF-Epi) niche in which cancer-associated fibroblasts and epithelial cells cooperatively shield tumor cells from immune surveillance. In a neoadjuvant immunotherapy cohort, we found CAF-Epi score derived from this niche robustly discriminated responders from non-responders and stratified long-term survival, indicating that this functional niche, rather than subtle changes in cell proportions or morphology, more directly governs treatment outcome. To enable scalable CAF-Epi assessment on routine histopathology, we developed SHEEN-CAF (Spatial H&E-based CAF-Epi niche model), a deep-learning model that identifies ST-defined CAF-Epi niches directly from H&E slides. We assembled a paired H&E-ST atlas of 198 multistage ESCC specimens, totaling 9.6 million cell annotations and 275,753 CAF-Epi-labeled FOVs. SHEEN-CAF learns multi-scale morphological representations and reconstructs whole-slide CAF-Epi distributions. In the test set, it achieved an AUC of 0.91 with cross-center accuracies of 87-94%. In an independent external validation cohort with paired H&E and multiplex immunofluorescence, SHEEN-CAF-derived CAF-Epi scores correlated with immunofluorescence-defined CAF-Epi signals (r = 0.81, P < 0.01) and discriminated high- versus low-CAF-Epi burden lesions (AUC = 0.89). In an ESCC cohort treated with immune checkpoint blockade, non-responders exhibited significantly higher CAF-Epi scores than responders (P < 0.05), linking CAF-Epi burden on routine H&E to immunotherapy resistance. In summary, SHEEN-CAF converts CAF-Epi niche burden—previously measurable only with advanced spatial-omics—into an automated, quantitative biomarker derived solely from routine diagnostic H&E slides. By requiring no additional tissue or sequencing, it provides an efficient and scalable measure of the spatial microenvironment, with potential to refine immunotherapy response assessment and risk stratification in ESCC. Citation Format: Zhixuan You, Qiankun Li, Jiaying Zhu, Guoyu Cheng, Yanrong Shen, Jiang Chang, Chen Wu. From pixels to spatial microenvironments: AI-powered CAF-Epi niche prediction on H&E slides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 79.