Abstract C060: Spatial cell-identity and gene expression inference directly from histopathology slides in colorectal cancer with Path2SpaceHD

Abstract Background: High-resolution spatial profiling of the tumor microenvironment (TME) offers a promising approach for advancing immunotherapy in solid tumors. However, spatial transcriptomics remains expensive, low-throughput, and difficult to integrate into routine pathology workflows. In contrast, hematoxylin and eosin (H 0.4 between measured and predicted values across spots), including very high prediction accuracy for clinically relevant biomarkers such as CEACAM5, CEACAM6, and EPCAM (correlations 0.8). For cell-type classification, Path2SpaceHD achieved 77% overall accuracy and 89% top-2 accuracy across nine biologically relevant cell types, including T cells, B cells, and myeloid subsets. Notably, the model resolved morphologically similar immune subtypes such as B cells vs. T cells at near single-cell resolution (Figure 1). A simplified 4-class version of the model, trained for head-to-head comparison with CellViT, demonstrated consistent performance advantages across epithelial, neoplastic, inflammatory, and connective tissue classes. Conclusions: Path2SpaceHD delivers robust, high-resolution spatial insights directly from H 2026 Feb 18-21; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2026;14(2 Suppl):Abstract nr C060.