BRIDGE: Bridging Histology and Spatial Gene Expression with Latent Co-Expression Programs

Predicting spatial gene expression from hematoxylin and eosin (H&E)-stained histology could extend routinely acquired pathology images toward spatial molecular characterization. Existing methods primarily map local morphology to a selected target-gene panel, yet the same visual appearance can accompany different target-gene levels, and similar target expression can occur in morphologically distinct regions. Paired training spatial transcriptomics profiles also measure non-target genes whose coordinated expression provides complementary molecular context for these cases. To use this training signal, we introduce BRIDGE, a histology-based spatial gene expression prediction framework that learns cohort-adaptive co-expression programs from a disjoint support-gene set and converts them into target-specific visual predictors. The framework comprises three complementary modules: the Local Morphology Encoding Module (LME) extracts target-gene-conditioned morphology features; the Co-expression Program Learning Module (CPL) converts support-gene programs into queries for program-specific morphology aggregation; and the Gene-Adaptive Decoding Module (GAD) combines pooled and program-specific morphology with target-gene features through gene-adaptive experts. Across patient-disjoint evaluations, BRIDGE achieves the best MAE and MSE on all three datasets and the highest PCC on ANDERSSON and LUAD. On ANDERSSON, its PCC is 0.734 versus 0.710 for SpaHGC, while its MSE is 0.399 versus 0.497 for MERGE. Controlled comparisons, cross-cohort transfer, and ablations assess the roles of support-derived supervision, program-guided morphology aggregation, and gene-adaptive decoding.