用病理切片预测基因表达，跨癌种通用且能还原生物通路
HistoPrism: Unlocking Functional Pathway Analysis from Pan-Cancer Histology via Gene Expression Prediction
- 基于高效Transformer架构，实现跨癌种基因表达预测
- 在高变基因上超越现有模型，通路层面预测性能显著提升
- 适合肿瘤研究者和临床医生，推动病理图像的生物功能挖掘
从H&E病理切片预测空间基因表达，为测序提供可扩展且临床可用的替代方案，但要实现临床价值，需具备跨癌种泛化能力并捕捉生物上连贯的信号。以往工作多局限于单癌种设置，评估方式以方差为主，忽视功能相关性。我们提出HistoPrism，一种基于Transformer的高效架构，用于跨癌种从组织学图像预测基因表达。为评估生物学意义，引入通路级基准，将评估重点从孤立基因水平的方差转向一致的生物通路。HistoPrism不仅在高度可变基因上超越现有最先进模型，更在通路级预测上取得显著提升，证明其能有效恢复生物上连贯的转录组模式。凭借强跨癌种泛化能力和更高效率，HistoPrism确立了从常规病理切片进行临床相关转录组建模的新标准。
原文摘要 · Abstract (English)
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.
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