用通路激活信号提升病理图像与基因表达的关联预测能力
PEaRL: Pathway-Enhanced Representation Learning for Gene and Pathway Expression Prediction from Histology
- 将基因通路激活得分作为转录组表征，结合Transformer建模
- 在三个癌症数据集上基因与通路预测相关系数提升超20%
- 适合计算病理、多模态生物医学研究者参考
将组织病理学与空间转录组学（ST）结合，为连接组织形态与分子功能提供了强大机会。然而，现有大多数多模态方法依赖少量高变基因，限制了预测范围，并忽视了塑造组织表型的协同生物程序。我们提出PEaRL（通路增强表示学习），一种多模态框架，通过ssGSEA计算通路激活得分来表征转录组。利用Transformer编码生物一致的通路信号，并通过对比学习将其与病理特征对齐，实现降维、提升可解释性并强化跨模态对应关系。在三个癌症ST数据集（乳腺、皮肤、淋巴结）上，PEaRL持续优于当前最优方法，基因与通路水平表达预测的皮尔逊相关系数分别提升最高达58.9%和20.4%。结果表明，以通路为基础的转录组表征能生成更符合生物学事实且可解释的多模态模型，推动计算病理学超越基因级嵌入。
原文摘要 · Abstract (English)
Integrating histopathology with spatial transcriptomics (ST) provides a powerful opportunity to link tissue morphology with molecular function. Yet most existing multimodal approaches rely on a small set of highly variable genes, which limits predictive scope and overlooks the coordinated biological programs that shape tissue phenotypes. We present PEaRL (Pathway Enhanced Representation Learning), a multimodal framework that represents transcriptomics through pathway activation scores computed with ssGSEA. By encoding biologically coherent pathway signals with a transformer and aligning them with histology features via contrastive learning, PEaRL reduces dimensionality, improves interpretability, and strengthens cross-modal correspondence. Across three cancer ST datasets (breast, skin, and lymph node), PEaRL consistently outperforms SOTA methods, yielding higher accuracy for both gene- and pathway-level expression prediction (up to 58.9 percent and 20.4 percent increase in Pearson correlation coefficient compared to SOTA). These results demonstrate that grounding transcriptomic representation in pathways produces more biologically faithful and interpretable multimodal models, advancing computational pathology beyond gene-level embeddings.
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