An Explanatory Deep Learning Pipeline for the Prediction and Visualization of Spatially Resolved Biomarker Expression...

Histopathologic evaluation remains central to cancer diagnosis and treatment planning, yet the molecular programs underlying distinct tissue morphologies are not routinely accessible in clinical workflows. Spatial transcriptomic/proteomic platforms provide region-specific molecular measurements but are limited by cost, throughput, and scalability. Most computational pathology models rely on either bulk tissue-based gene expression or a focused gene/protein expression-panel prediction, thereby obscuring subregion-specific morphologic-molecular relationships and limiting spatial interpretation of a wider gene/protein expression network. This limitation is particularly significant in triple-negative breast cancer, which exhibits pronounced spatial heterogeneity across tumor, stroma, and immune compartments. This study developed X-SPATIO, a spatially compatible computational pipeline designed to directly link hematoxylin and eosin morphology with region-matched mRNA and protein expression. The model was trained on hematoxylin and eosin-defined regions of interest paired with spatially resolved omics data obtained from GeoMx Digital Spatial Profiling. Using a multiple-instance learning approach, X-SPATIO captures morphologic-molecular associations, generating spatiomorphologic attention maps that indicate predictive tissue regions. X-SPATIO demonstrated strong performance across biologically relevant spatial biomarkers, achieving area under the curve values ranging from 0.79 to 0.97. Attention maps revealed spatial patterns consistent with known biology, indicating alignment between learned features and tissue organization. By integrating spatial molecular ground truth with routine histopathology, X-SPATIO enables cost-effective inference of spatial biomarker expression and establishes a foundation for biologically grounded discovery and precision oncology in triple-negative breast cancer.