PEaRL: Pathway-Enhanced Representation Learning for Gene and Pathway Expression Prediction from Histology
This work addresses the challenge of improving predictive scope and interpretability in computational pathology for cancer research, though it is incremental as it builds on existing multimodal approaches by incorporating pathway activation scores.
The paper tackled the problem of predicting gene and pathway expression from histology by integrating histopathology with spatial transcriptomics, and it resulted in a framework that outperformed state-of-the-art methods with up to 58.9% and 20.4% increases in Pearson correlation coefficient for gene- and pathway-level predictions.
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.