Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples
Bailey, Matthew H. (Washington University School of Medicine. Department of Medicine (USA))
Meyerson, William U. (Yale University. Program in Computational Biology and Bioinformatics (USA))
Dursi, Lewis Jonathan (Ontario Institute for Cancer Research. Computational Biology Program (Canada))
Wang, Liang-Bo (Washington University School of Medicine. Department of Medicine (USA))
Dong, Guanlan (Washington University School of Medicine. Department of Medicine (USA))
Liang, Wen-Wei (Washington University School of Medicine. Department of Medicine(USA))
Weerasinghe, Amila (Washington University School of Medicine. Department of Medicine (USA))
Li, Shantao (Yale University. Program in Computational Biology and Bioinformatics (USA))
Kelso, Sean (Washington University School of Medicine. Department of Medicine (USA))
Saksena, Gordon (Broad Institute of MIT and Harvard)
Ellrott, Kyle (Oregon Health and Science University. Biomedical Engineering (USA))
Wendl, Michael C. (Washington University School of Medicine. Department of Genetics (USA))
Wheeler, David A. (Baylor College of Medicine. Department of Molecular and Human Genetics (USA))
Getz, Gad (Broad Institute of MIT and Harvard)
Simpson, Jared T. (Ontario Institute for Cancer Research. Computational Biology Program (Canada))
Gerstein, Mark B. (Yale University. Program in Computational Biology and Bioinformatics (USA))
Ding, Li (Washington University School of Medicine. Department of Medicine (USA))

Data:	2020
Resum:	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.
Nota:	L'Institut Català de Paleontologia Miquel Crusafont forma part del PCAWG Consortium
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Cancer genomics ; Communication and replication ; Comparative genomics ; Genetic databases
Publicat a:	Nature communications, Vol. 11 (September 2020) , art. 4748, ISSN 2041-1723

DOI: 10.1038/s41467-020-18151-y
PMID: 32958763

27 p, 2.4 MB

Supplementary information 12 p, 776.7 KB

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