XpressO: An explanatory deep learning pipeline for the prediction and visualization of gene expression heterogeneity in breast tumors

Spatial genetic heterogeneity plays a critical role in tumor evolution and therapeutic resistance, yet traditional histopathological characterization remains challenging and time-consuming. Here, we present an explainable deep learning pipeline “XpressO” that predicts and visualizes gene expression directly from whole slide images (WSIs), providing spatial resolution of tumor transcriptomics. Using histopathological image features of WSIs of invasive breast cancer as data and associated bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) as expression labels, our model forms complex associations between tissue phenotype and gene expression. By generating high-resolution expression maps, our approach reveals both spatial variation and predicted gene activity across tumor samples, capturing patterns that are often lost in bulk profiling. The interpretability framework further highlights histological regions that contribute to specific gene expression signals, bridging the gap between tumor histology and genetic heterogeneity. This method offers a promising tool for integrating imaging and transcriptomics, enabling data-driven biomarker discovery and advancing precision oncology through spatially-informed molecular profiling.