An Explanatory Deep Learning Pipeline for the Prediction and Visualization of Spatially Resolved Biomarker Expression in Triple-Negative Breast Cancer.

Abstract

Histopathologic evaluation remains central to cancer diagnosis and treatment planning, yet the molecular programs underlying distinct tissue morphologies aren't 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 morpho-molecular relationships and limiting spatial interpretation of a wider gene/protein expression network. This limitation is particularly significant in triple-negative breast cancer (TNBC), which exhibits pronounced spatial heterogeneity across tumor, stroma, and immune compartments. We developed X-SPATIO, a spatially compatible computational pipeline designed to directly link hematoxylin and eosin (H&E) morphology with region-matched mRNA and protein expression. The model was trained on H&E-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 morpho-molecular associations, generating spatio-morphologic 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 0.79-0.97. 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 TNBC.