CNVSIM: an interpretable copy-number reference atlas for cancer subtyping

Abstract

Background: Genome-wide copy-number variation (CNV) profiles provide a structural representation of cancer genomes, but interpreting new profiles within a reusable reference remains challenging. Results: We developed CNVSIM, which organizes CNV profiles into cancer root-specific reference spaces and defines reference subtypes annotated with chromosome-scale and gene-level signatures. Applied to the Progenetix dataset, CNVSIM established 112 CNV-defined subtypes across 19 cancer roots, supported by 155,881 analyses from neoplastic samples. Within each cancer root, samples sharing a broad diagnosis separated into distinct CNV subtypes, whereas individual subtypes spanned multiple diagnostic labels. All subtypes included analyses from multiple assay platforms and data sources, and core subtypes remained reproducible after systematic reduction of reference analyses. Calibrated projection assigned 85.7% of held-out CNV profiles while maintaining high accuracy for supported assignments. Projection of 4,605 cell-line CNV profiles identified cell lines representative for 90/112 cancer subtypes while highlighting gaps in cell line representation. Conclusions: CNVSIM provides a reusable and interpretable CNV reference that complements diagnostic classification for profile comparison, projection, and cell line selection.