Abstract 71: Bridging histopathology and spatial transcriptomics for comprehensive tumor microenvironment profiling.

Abstract Purpose: The tumor microenvironment (TME) critically influences cancer progression, treatment response, and patient outcomes. Histopathology reflects morphological features of TME states but lacks molecular specificity, whereas spatial transcriptomics (ST) provides spatial gene expression yet is costly and impractical for large-scale use. To bridge this gap, we develop a multimodal AI framework that transfers spatial molecular information from ST into histopathology-derived representations, enabling reconstruction of molecular programs, TME phenotypes, and spatial biology directly from routine H PR from 0.792/0.712 to 0.812/0.715; and HER2 from 0.662/0.602 to 0.696/0.634. (2) Spatial spot classification: On the DLPFC dataset (n=12 WSIs), linear probing achieved 71.75% balanced accuracy and 78.15% weighted F1, compared with 55.19% and 63.61% for UNI—improvements of 16.56% and 14.54%. Conclusions: Our multi-scale contrastive alignment framework transfers spatial molecular signals from ST into histopathology-derived representations, improving gene expression prediction, mutation inference, and spatial TME characterization. By enabling sequencing-free reconstruction of molecular and microenvironmental features, this approach offers a scalable solution for large-cohort cancer profiling and may facilitate biomarker discovery, patient stratification, and biologically informed precision oncology. Citation Format: Xiaohan Xing, Lei Xing. Bridging histopathology and spatial transcriptomics for comprehensive tumor microenvironment profiling 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 71.