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Updated: Dec 29, 2025

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Inferring structural variant cancer cell fraction.

Marek Cmero1,2,3,4,5, Ke Yuan6,7, Cheng Soon Ong8,9,10

  • 1Department of Surgery, Division of Urology, Royal Melbourne Hospital and University of Melbourne, Parkville, VIC, 3050, Australia. cmerom@unimelb.edu.au.

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|February 7, 2020
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Summary
This summary is machine-generated.

SVclone accurately infers cancer cell fraction from structural variants (SVs) in whole-genome sequencing. This method improves characterization of intra-tumour heterogeneity and identifies specific cancers with poorer survival due to SVs.

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Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Structural variants (SVs) are critical in cancer development.
  • Accurate inference of cancer cell fraction for SVs is challenging.
  • Understanding intra-tumour heterogeneity is key to cancer progression.

Purpose of the Study:

  • To introduce SVclone, a novel computational method for estimating cancer cell fraction of SV breakpoints.
  • To assess SVclone's performance against existing methods using simulated data.
  • To apply SVclone to a large cancer genomics dataset to uncover novel biological insights.

Main Methods:

  • SVclone determines variant allele frequencies of SV breakends.
  • It simultaneously estimates cancer cell fraction and SV copy number.
  • Performance was validated using in silico mixtures of real patient samples.

Main Results:

  • SVclone achieves performance comparable to single-nucleotide variant methods with less data.
  • Analysis of Pan-Cancer Analysis of Whole Genomes (PCAWG) data revealed subclonally enriched copy-number neutral rearrangements in liver, ovarian, and pancreatic cancers.
  • These rearrangements were associated with decreased overall survival.

Conclusions:

  • SVclone provides an accurate and efficient method for characterizing SVs and intra-tumour heterogeneity.
  • The findings highlight the clinical relevance of subclonally enriched rearrangements in specific cancer types.
  • SVclone facilitates deeper understanding of structural variant evolution in cancer.