SVCFit: Inferring structural variant cellular fraction in tumors

Many tumors evolve through cellular mutation and selection, where subpopulations of cells (subclones) with shared ancestry compete for dominance. Introduction of next generation sequencing enables subclone identification using small somatic. However, there are several advantages to marking subclones with structural variants: they have greater functional impact, play a crucial role in late-stage tumors, and provide a more complete view of genomic instability driving tumor evolution. Here, we present SVCFit, a scalable method to estimate the cellular prevalence of somatic deletions, duplications and inversions. We demonstrate that cellular prevalence estimation can be improved by incorporating distinct read patterns for each structural variant type. Additionally, this improvement is achieved without prior knowledge of tumor purity, which is often inaccurate. Using a combination of simulated data and patient-derived metastatic samples with known mixture proportions, we show that our algorithm achieves significantly greater accuracy than state-of-the-art in estimating the structural variants cellular prevalence (p<0.05). The speed of SVCFit estimation from cost-effective bulk whole-genome sequencing (WGS) makes it well-suited for analyzing large cohorts of sequenced tumor samples, enhancing the accessibility of SV-based clonal reconstruction.