HisToSpatialCNV: an interpretable deep learning method predicting spatial copy number variations from histopathology images

Copy number variations (CNV) are key drivers of cancer progression, yet methods for predicting spatial CNVs directly from haematoxylin and eosin (H&E) images are currently lacking. We introduce HisToSpatialCNV, a multiscale deep-learning framework that integrates histopathological features from H&E-stained images to infer spatial CNV patterns. Combining interpretable feature extraction with graph neural networks and multihead self-attention, our approach captures both local and global tissue contexts. Applied to HER2+ breast cancer, skin cancer and brain cancer datasets, HisToSpatialCNV outperformed existing methods for spatial gene expression inference and showed strong concordance between predicted CNVs and gene expression. In addition, it enabled tumour subclone identification, phylogenetic reconstruction and detection of pathway alterations linked to tumour progression. HisToSpatialCNV generalized across Visium and Xenium spatial transcriptomics platforms, on HER2+ patients. Applying the HisToSpatialCNV HER2+ model to TCGA HER2+ histopathology data identified spatial-molecular subtypes associated with distinct survival outcomes in TCGA HER2+ patients. By connecting histopathology to spatial genomics, HisToSpatialCNV offers a powerful, cost-effective tool for studying intratumour heterogeneity using only routine pathology images.