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.

翻译：从H&E组织学预测空间基因表达为测序提供了一种可扩展且临床可及的替代方案，但实现临床影响需要能够泛化至多种癌症类型并捕获生物学一致信号的模型。先前研究通常局限于单一癌症场景和基于方差的评估，导致功能相关性探索不足。我们提出HistoPrism，一种基于Transformer的高效架构，用于从组织学进行泛癌基因表达预测。为评估生物学意义，我们引入通路级基准测试，将评估重点从孤立的基因级方差转向一致的功能通路。HistoPrism不仅在高度可变基因上超越先前最先进模型，更重要的是在通路级预测上取得显著提升，证明了其恢复生物学一致转录组模式的能力。凭借强大的泛癌泛化能力和改进的效率，HistoPrism为基于常规可用组织学的临床相关转录组建模确立了新标准。