Haplotype-aware segmentation with HapASeg increases accuracy of detecting homolog-specific somatic copy number alterations

TL;DR

HapASeg improves detection of cancer-related DNA copy number changes in challenging FFPE samples by using haplotype information.

Contribution

HapASeg introduces a haplotype-aware method for sCNA detection that works without normal sample panels, improving accuracy in FFPE and other sample types.

Findings

HapASeg outperforms existing methods in detecting sCNAs in FFPE samples.

The method works across multiple sequencing types without needing normal sample panels.

Haplotype phasing and covariates enhance sCNA estimation accuracy.

Abstract

Somatic copy number alterations (sCNAs) drive cancer initiation, progression, resistance, and metastasis. Furthering our understanding of sCNAs requires substantially larger cohorts. Most tumors available for sequencing are preserved with formalin-fixed, paraffin-embedding (FFPE), which causes DNA cross-linking that distorts coverage profiles and challenges current sCNA estimation methods. Traditional methods denoise data using large panels of similar normal samples, which are impractical to obtain for FFPE cohorts. Here, HapASeg overcomes this limitation by leveraging haplotype phasing and unique covariates to accurately estimate sCNA segments across FFPE, fresh frozen, whole genome sequencing and whole exome sequencing sample types, outperforming current methods without requiring panel-of-normal correction.

The online version contains supplementary material available at 10.1186/s13059-026-03971-w.

Figures