Abstract
Recent advancements in computational pathology have greatly improved automated histopathological analysis. A compelling question in the field is how morphological traits are associated with genetic characteristics or molecular phenotypes. Here we propose TEMI, a novel framework for molecular subtype classification of cancers using whole-slide images (WSIs), augmented with transcriptomic data during training. TEMI aims to extract molecular-level signals from WSIs and make efficient use of available multimodal data. To this end, TEMI introduces a patch fusion network that captures dependencies among local patches of gigapixel WSIs to produce global representations and aligns them with transcriptomic embeddings attained from a masked transcriptomic autoencoder. TEMI achieves superior performance compared with existing methods in molecular subtype classification, owing to its effective integration of transcriptomic information achieved by the two developed alignment strategies. Guided by discriminative transcriptomic data, TEMI learns invariant WSI representations, while morphological features also enhance gene expression prediction. These findings suggest that histological features encode latent molecular signals, highlighting the interplay between the tumor microenvironment and cancer transcriptomics. Our study demonstrates how multimodal learning can bridge morphology and molecular biology, providing an effective tool to advance precision medicine.