title: Discriminating somatic and germline mutations in tumor DNA samples without matching normals.
abstract: Tumor analyses commonly employ a correction with a matched normal (MN), a sample from healthy tissue of the same individual, in order to distinguish germline mutations from somatic mutations. Since the majority of variants found in an individual are thought to be common within the population, we constructed a set of 931 samples from healthy, unrelated individuals, originating from two different sequencing platforms, to serve as a virtual normal (VN) in the absence of such an associated normal sample. Our approach removed (1) >96% of the germline variants also removed by the MN sample and (2) a large number (2%-8%) of additional variants not corrected for by the associated normal. The combination of the VN with the MN improved the correction for polymorphisms significantly, with up to ∼30% compared with MN and ∼15% compared with VN only. We determined the number of unrelated genomes needed in order to correct at least as efficiently as the MN is about 200 for structural variations (SVs) and about 400 for single-nucleotide variants (SNVs) and indels. In addition, we propose that the removal of common variants with purely position-based methods is inaccurate and incurs additional false-positive somatic variants, and more sophisticated algorithms, which are capable of leveraging information about the area surrounding variants, are needed for optimal accuracy. Our VN correction method can be used to analyze any list of variants, regardless of sequencing platform of origin. This VN methodology is available for use on our public Galaxy server.
authors.affiliation: Department of Urology, Erasmus University Medical Center, Rotterdam 3015 CN, The Netherlands; Department of Bioinformatics, Erasmus University Medical Center, Rotterdam 3015 CN, The Netherlands;
authors.forename: Saskia
authors.initials: S
authors.lastname: Hiltemann
authors.affiliation: Department of Urology, Erasmus University Medical Center, Rotterdam 3015 CN, The Netherlands;
authors.forename: Guido
authors.initials: G
authors.lastname: Jenster
authors.affiliation: Department of Pathology, Erasmus University Medical Center, Rotterdam 3015 CN, The Netherlands.
authors.forename: Jan
authors.initials: J
authors.lastname: Trapman
authors.affiliation: Department of Bioinformatics, Erasmus University Medical Center, Rotterdam 3015 CN, The Netherlands;
authors.forename: Peter
authors.initials: P
authors.lastname: van der Spek
authors.affiliation: Department of Bioinformatics, Erasmus University Medical Center, Rotterdam 3015 CN, The Netherlands;
authors.forename: Andrew
authors.initials: A
authors.lastname: Stubbs
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D019295:Computational Biology / Q000379:methods; D004273:DNA, Neoplasm; D030561:Databases, Nucleic Acid; D005260:Female; D023281:Genomics / Q000379:methods; D018095:Germ-Line Mutation; D006801:Humans; D054643:INDEL Mutation; D008297:Male; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D011471:Prostatic Neoplasms / Q000235:genetics; D015203:Reproducibility of Results; D064878:Web Browser
chemical_list: D004273:DNA, Neoplasm