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HOPE: Interpretable Histology Analysis with Spatial Omics-Derived Signatures for Precision Oncology

Wang, T.; Bieniosek, M.; Krpicak, T. J.; Luan, M.; Ruf, B.; Schürch, C. M.; Mayer, A. T.; Luo, R.; Trevino, A. E.; Wu, Z.

2026-06-06 bioinformatics
10.64898/2026.06.03.729847 bioRxiv
Show abstract

Hematoxylin and eosin (H&E) stained images are fundamental clinical tools for disease assessment. However, even with advanced computational models, their prognostic capabilities remain limited. Spatial omics characterizes tumor microenvironments (TME) in detail yet remains clinically inaccessible due to cost and complexity. In this study, we present HOPE, a lightweight framework that learns TME signatures from paired H&E and spatial omics data during training, then applies these to H&E alone at inference. Leveraging H&E foundation models, HOPE consistently outperforms identical architectures trained without spatial omics guidance across cancer types and cohorts. It further generates interpretable annotations of TME signature on H&E regions, stratifying patients into biologically coherent groups with different prognostic outcomes. HOPE establishes a practical route to translate high-content spatial omics discoveries into scalable, clinically deployable tools.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.