A Pan-Cancer Multi-Omic Analysis of Copy Number Signature Clusters and Genomic Instability

Copy number alterations (CNAs) are major contributors to genomic instability in cancer, and copy number signatures (CNS) provide a compact representation of the processes shaping CNA landscapes. However, the relationships among existing CNS frameworks and their predictability from molecular data other than whole-genome sequencing remain unclear. Here, we compare three major CNS compendia across more than 5,800 TCGA cancer samples, evaluating their overlap, complementarity, biological relevance, and prognostic associations. Individual signatures showed limited cross-study concordance, whereas signature-derived clusters identified biologically distinct patient groups, including favorable-outcome clusters observed across all frameworks. Using gene expression, DNA methylation, somatic mutation features, age, and tumor purity, XGBoost models predicted cluster membership with framework-dependent performance, achieving high F1-scores for the Drews and Steele compendia but limited performance for Tao. Feature importance analysis highlighted expression-driven predictors and pathways linked to genomic instability. These findings show that current CNS frameworks capture complementary rather than interchangeable dimensions of tumor genome instability and suggest that multi-omic profiles can extend signature-based stratification to cohorts without whole-genome sequencing.