Dual-Foundation-Model Ensemble Predicts Gene Expression in Muscle-Invasive Bladder Cancer for Patient Outcome Stratification

Muscle-invasive bladder cancer (MIBC) exhibits significant heterogeneity in clinical outcomes and treatment responses. While gene expression profiling has provided important biological and prognostic insight, transcriptomic assays are not routinely incorporated into standard clinical practice, in part due to cost. AI models have been demonstrated to predict gene expression from H&E-stain whole-slide images (WSIs) potentially offering a scalable approach for gathering transcriptomic information for patient stratification and biomarker discovery. We developed and tested models to predict expression of 857 genes directly from H&E-stained whole-slide images (WSI). The best performing final model was a dual-ensemble combining predictions from models trained on features extracted with different foundation models. The dual-ensemble reached 776/857 predictable genes with an average correlation of 0.43 (cross-validation), 0.45 (holdout) and 0.40 (external Frankfurt cohort). Predicted expression strongly correlates with expression of clinically relevant markers (like NECTIN4). Estimated hazard ratios of predicted expression correlated strongly with those of measured expression and genes associated with overall survival for both predicted and measured expression in both cohorts were identified. AI-predicted basal vs. luminal subtypes were associated with overall survival and aligned with the ground-truth (accuracies > 0.8). Findings demonstrate that gene expressions inferred from routine staining can support biomarker discovery and patient stratification in MIBC.