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

2026-01-29

AI Summary

This study addresses the limitations of existing gene expression prediction methods, which are often confined to single cancer types and lack biological evaluation at the functional pathway level. To overcome these challenges, we propose HistoPrism—a lightweight Transformer architecture that enables efficient, end-to-end mapping from routine hematoxylin and eosin (H&E) histopathology slides to gene expression profiles across pan-cancer contexts, achieving high generalizability. Innovatively, we introduce a pathway-level evaluation benchmark grounded in biological prior knowledge, moving beyond conventional gene-variance-based metrics. Experimental results demonstrate that HistoPrism outperforms state-of-the-art models in predicting highly variable genes and significantly improves functional consistency at the pathway level, effectively recovering biologically meaningful transcriptomic patterns.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Machine Learning: Feature Construction/Reformulation
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data

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 , but also 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.

Problem

• gene expression prediction
• pan-cancer
• functional pathway analysis
• histology
• biological coherence

Innovation

• HistoPrism
• pan-cancer
• gene expression prediction
• functional pathway analysis
• transformer architecture