HistoPrism: Unlocking Functional Pathway Analysis from Pan-Cancer Histology via Gene Expression Prediction

Susu Hu, Qinghe Zeng, Nithya Bhasker, Jakob Nikolas Kather, Stefanie Speidel

Keywords: spatial transcriptomics, pan-cancer modeling, pathway-level coherence, computational efficiency

TL;DR: HistoPrism is a pan-cancer model that predicts spatial gene expression from histology with state-of-the-art accuracy, pathway-level coherence, and computational efficiency, bridging histology and transcriptomics for scalable clinical use.

Abstract: Predicting spatial gene expression from H\&E histology offers a scalable and clinically accessible alternative to sequencing, but realizing clinical impact requires models that generalize across cancer types and capture biologically coherent signals. Prior work is often limited to per-cancer settings and variance-based evaluation, leaving functional relevance underexplored. We introduce HistoPrism, an efficient transformer-based architecture for pan-cancer prediction of gene expression from histology. To evaluate biological meaning, we introduce a pathway-level benchmark, shifting assessment from isolated gene-level variance to coherent functional pathways. HistoPrism not only surpasses prior state-of-the-art models on highly variable genes and, but more importantly, achieves substantial gains on pathway-level prediction, demonstrating its ability to recover biologically coherent transcriptomic patterns. With strong pan-cancer generalization and improved efficiency, HistoPrism establishes a new standard for clinically relevant transcriptomic modeling from routinely available histology.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 8954