CNA detection from spatial transcriptomics using SpaCNA

Spatial transcriptomics (ST) enables genome-wide profiling of gene expression while preserving spatial context, yet accurate detection of copy number alterations (CNAs) in tumor ST data remain challenging. Here we present SpaCNA, a spatial-aware computational framework that integrates multi-modal spatial information to improve CNA detection. SpaCNA constructs adjacency graphs using spatial proximity and H&E stained image similarity, refines the raw gene expressions, and implements a Hidden Markov Random Field model for robust CNA state inference. Through extensive benchmarking on simulated data and multi-cancer cohorts, SpaCNA demonstrates superior accuracy and outperforms existing methods in CNA detection and tumor region identification. In real-world applications to breast cancer and colorectal cancer, SpaCNA reveals spatially distinct subclones with context-dependent interactions within the microenvironment. Additionally, applied to a 3D ST dataset of head and neck squamous cell carcinoma, SpaCNA uncovers clone-specific CNAs associated with therapeutic resistance biomarkers across multiple slices. By facilitating precise spatial mapping of CNAs and tumor architecture, SpaCNA significantly enhances our understanding of intratumoral heterogeneity and spatial evolutionary patterns.