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Related Experiment Video

Updated: Jun 7, 2025

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Evaluating Bioinformatics Processing of Somatic Variant Detection in cfDNA Using Targeted Sequencing with UMIs.

Yixin Lin1,2, Mads Heilskov Rasmussen1,2, Mikkel Hovden Christensen1,2

  • 1Department of Molecular Medicine, Aarhus University Hospital, 8200 Aarhus, Denmark.

International Journal of Molecular Sciences
|November 9, 2024
PubMed
Summary
This summary is machine-generated.

Accurately detecting cancer DNA (ctDNA) in blood is hard. This study found shearwater-AND best for tumor mutations and DREAMS-vc for cancer detection, depending on the analysis type.

Keywords:
UMI sequencingbenchmarkingcancer sample classificationcell-free DNAlow-frequency variant calling

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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
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Area of Science:

  • Genomics
  • Molecular Biology
  • Cancer Research

Background:

  • Circulating tumor DNA (ctDNA) shows promise as a cancer biomarker.
  • Detecting low-frequency tumor mutations in cell-free DNA (cfDNA) is difficult due to low variant allele frequencies and sequencing errors.

Purpose of the Study:

  • To benchmark variant callers (Mutect2, VarScan2, shearwater, DREAMS-vc) for cfDNA analysis.
  • To evaluate performance at both mutation and sample classification levels.
  • To investigate the impact of Unique Molecular Identifiers (UMIs) and consensus strategies.

Main Methods:

  • Deep targeted sequencing of cfDNA with UMIs from 111 colorectal cancer patients.
  • Benchmarking of four variant callers: Mutect2, VarScan2, shearwater, and DREAMS-vc.
  • Assessment of mutation-level precision and sample-level classification accuracy (ROC-AUC).
  • Evaluation of UMI grouping and consensus strategies, including network-based methods.
  • Analysis of sequencing depth effects and downsampling strategies.

Main Results:

  • The shearwater-AND method achieved the highest precision for detecting tumor mutations.
  • shearwater-AND reached an ROC-AUC of 0.984 for sample classification in tumor-informed analyses.
  • DREAMS-vc achieved the highest ROC-AUC of 0.808 for sample classification in tumor-agnostic studies.
  • Sequencing depth variations in PBMCs can cause false positives, mitigated by downsampling.
  • Network-based UMI grouping improved performance compared to identical UMI grouping.

Conclusions:

  • The optimal cfDNA variant caller is context-dependent, varying with study goals (mutation vs. sample classification) and approach (tumor-informed vs. tumor-agnostic).
  • shearwater-AND is highly effective for tumor-informed mutation detection and classification.
  • DREAMS-vc excels in tumor-agnostic sample classification.
  • Careful consideration of UMI strategies and sequencing depth is crucial for accurate cfDNA analysis.