GeneRoute: From Gene Knowledge to Histological Evidence for Spatial Gene Expression Prediction

Predicting spatial gene expression from routine H&E histology could make molecular tissue maps widely accessible, but fixed output panels restrict many models to genes observed during training. We introduce GeneRoute, a gene-conditioned framework that uses molecular knowledge to guide multiscale image interpretation. Sequence and functional priors define shared gene representations, while cancer-specific dependencies and molecular programs adapt them to tissue context. These queries retrieve histological features at three scales, and a shared scalar decoder predicts expression without gene-specific output heads. Across five cancers, GeneRoute leads all five macro-averaged metrics on cancer-related genes, improving gene-wise Pearson correlation by 25% over the strongest comparator. For 200 genes excluded from expression supervision and model selection, it also achieves the highest macro-averaged gene-wise Pearson and Spearman correlations. On the six-gene immune-receptor subset, GeneRoute achieves positive mean gene-wise correlations across all five cancers, with predicted IGHG1 enrichment partly overlapping outlined immune infiltrates in a colorectal section.