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Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Next-generation sequencing (NGS) is crucial for cancer profiling and variant detection.
  • Current variant callers often identify single-nucleotide variants (SNVs) individually, neglecting multi-nucleotide variants (MNVs).
  • This granular approach can lead to inaccurate interpretations of variant effects on amino acid sequences within codons.

Purpose of the Study:

  • To assess the accuracy of multi-nucleotide variant (MNV) annotation in cancer genomics datasets.
  • To identify the prevalence of incorrectly annotated MNVs in publicly available data.
  • To develop and propose a method for merging single-nucleotide variants (SNVs) into MNVs for improved variant calling.

Main Methods:

  • Analysis of 10,383 variant call files (VCF) from The Cancer Genome Atlas (TCGA).
  • Evaluation of mutation calls in seven commonly mutated genes across 178 cBioPortal studies.
  • Development of a custom script utilizing phasing information to merge adjacent SNVs into MNVs.

Main Results:

  • Identified 12,141 incorrectly annotated MNVs within TCGA data.
  • Found consistent underreporting of MNVs in 20 out of 178 cBioPortal studies.
  • Demonstrated accurate MNV annotation in 15 more recent studies, indicating improving practices.
  • Observed misannotation of the common BRAF V600 locus, with separate variants reported instead of a single merged MNV.

Conclusions:

  • Incorrect MNV annotation is a significant issue in cancer genomics, impacting variant interpretation.
  • A novel method for merging SNVs into MNVs has been developed and validated.
  • Incorporating MNV merging into NGS pipelines is recommended as a best practice for accurate cancer variant analysis.
  • Accurate identification of MNVs, including clinically relevant mutations like BRAF V600 and KRAS G12, is essential for research and clinical decisions.