Somatic copy-number alteration signatures reveal sex-divergent aging trajectories accelerated in cancer

Somatic copy-number alterations (CNAs) accumulate with age and contribute to age-related pathologies, but their systematic characterization at single-cell resolution has been limited by the throughput-resolution trade-off in single-cell whole-genome sequencing.

Here, we developed ultra-CNA, a high-resolution single-cell analysis pipeline that extends CNA detection to 10-kb bin resolution and jointly profiles copy-number and single-nucleotide variation (SNV). Re-analyzing the Tasc-WGS dataset (Liu et al., 2022; previously analyzed at 200-kb resolution) of 32,526 lymphocytes from 16 healthy donors aged 0.7 to 79 years, we constructed a multi-dimensional CNA spectrum stratified by chromosomal context, copy-number state, size, and clonality. Small (<1 Mb), rare, predominantly loss-type CNAs accumulated progressively and stochastically with age. Sex-chromosome loss showed divergent kinetics: chromosome X loss cells in females accumulated at +0.10 percentage points per year, versus +0.03 for chromosome Y loss cells in males. Sex chromosome loss also had specific consequences for autosomal SNV burden: in younger donors, loss cells carried fewer autosomal SNVs than non-loss cells, whereas in older donors (>30 years), loss cells exceeded non-loss cells in both sexes. Female X-loss cells additionally exhibited elevated 45S rDNA copy number, supporting biologically distinct consequences of X loss and LOY. Clock-like SBS1 and SBS5 mutational signatures co-accumulated with age across both sexes.

Applying KL-divergence non-negative matrix factorization to the channelized CNA spectra, we constructed an aging clock validated by leave-one-sample-out cross-validation. Applied to a matched esophageal cohort, the clock detected accelerated aging from normal squamous epithelium through Barrett’s esophagus to esophageal adenocarcinoma, with cancer-associated spectra additionally enriched for large, highly clonal events. Ultra-CNA thus provides a scalable framework for quantifying somatic genomic aging from blood and for detecting accelerated aging in cancer.