Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Matthew H. Bailey, Washington University St. Louis
  • ,
  • William U. Meyerson, Yale University
  • ,
  • Lewis Jonathan Dursi, Ontario Institute for Cancer Research, University of Toronto
  • ,
  • Liang Bo Wang, Washington University St. Louis
  • ,
  • Guanlan Dong, Washington University St. Louis
  • ,
  • Wen Wei Liang, Washington University St. Louis
  • ,
  • Amila Weerasinghe, Washington University St. Louis
  • ,
  • Shantao Li, Yale University
  • ,
  • Sean Kelso, Washington University St. Louis
  • ,
  • Rehan Akbani, University of Texas MD Anderson Cancer Center
  • ,
  • Pavana Anur, Oregon Health and Science University
  • ,
  • Matthew H. Bailey, Washington University St. Louis
  • ,
  • Alex Buchanan, Oregon Health and Science University
  • ,
  • Kami Chiotti, Oregon Health and Science University
  • ,
  • Kyle Covington, Baylor College of Medicine, Castle Biosciences Inc
  • ,
  • Allison Creason, Oregon Health and Science University
  • ,
  • Li Ding, Washington University St. Louis
  • ,
  • MC3 Working Group
  • ,
  • PCAWG novel somatic mutation calling methods working group
  • ,
  • PCAWG Consortium

The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Original languageEnglish
Article number4748
JournalNature Communications
Volume11
Issue1
ISSN2041-1723
DOIs
Publication statusPublished - 2020

See relations at Aarhus University Citationformats

ID: 197612197