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Structural variation and fusion detection using targeted sequencing data from circulating cell free DNA.

Alexander R Gawroński1, Yen-Yi Lin2,3, Brian McConeghy2,3

  • 1School of Computing Science, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada.

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This summary is machine-generated.

SViCT is a new tool that accurately detects structural variations in circulating tumor DNA (ctDNA) from liquid biopsies. It outperforms existing methods, even at low ctDNA concentrations, aiding cancer progression research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Cancer involves rapidly evolving cells forming multiple clones, necessitating comprehensive tumor DNA sampling.
  • Liquid biopsies using circulating cell-free DNA (cfDNA) offer minimally invasive sampling.
  • Analyzing cfDNA for structural variations (SVs) is challenging due to low ctDNA ratios and multiple tumor origins.

Purpose of the Study:

  • To introduce SViCT, a novel structural variation detection tool specifically designed for cfDNA analysis.
  • To address the challenges of low ctDNA concentrations and multiple tumor clones in SV detection.

Main Methods:

  • SViCT detects SVs by assembling discordant read pairs, one-end anchors, and soft-clipped/split reads into contigs.
  • It uses k-mer indexing for efficient re-mapping to a reference genome.
  • Graph and greedy algorithms are employed to identify SV signatures.

Main Results:

  • SViCT demonstrated superior positive predictive value and sensitivity compared to state-of-the-art tools on simulated cfDNA datasets.
  • The tool maintained reasonable performance down to 0.01% tumor DNA.
  • SViCT successfully detected known SVs in real cfDNA datasets and identified a novel SV in a prostate cancer cohort.

Conclusions:

  • SViCT is an effective tool for detecting structural variations in cfDNA.
  • Its performance is robust even at very low ctDNA levels, making it valuable for cancer research and clinical applications.
  • The tool aids in understanding tumor evolution and heterogeneity through liquid biopsies.