Dataset Information

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor.

ABSTRACT: Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.

SUBMITTER: Islam SMA

PROVIDER: S-EPMC9646490 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Uncovering novel mutational signatures by <i>de novo</i> extraction with SigProfilerExtractor.

Islam S M Ashiqul SMA Díaz-Gay Marcos M Wu Yang Y Barnes Mark M Vangara Raviteja R Bergstrom Erik N EN He Yudou Y Vella Mike M Wang Jingwei J Teague Jon W JW Clapham Peter P Moody Sarah S Senkin Sergey S Li Yun Rose YR Riva Laura L Zhang Tongwu T Gruber Andreas J AJ Steele Christopher D CD Otlu Burçak B Khandekar Azhar A Abbasi Ammal A Humphreys Laura L Syulyukina Natalia N Brady Samuel W SW Alexandrov Boian S BS Pillay Nischalan N Zhang Jinghui J Adams David J DJ Martincorena Iñigo I Wedge David C DC Landi Maria Teresa MT Brennan Paul P Stratton Michael R MR Rozen Steven G SG Alexandrov Ludmil B LB

Cell genomics 20221109 11

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for <i>de novo</i> extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidat ...[more]

PMID: 36388765

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Project description:BackgroundThe multiple de novo copy number variant (MdnCNV) phenotype is described by having four or more constitutional de novo CNVs (dnCNVs) arising independently throughout the human genome within one generation. It is a rare peri-zygotic mutational event, previously reported to be seen once in every 12,000 individuals referred for genome-wide chromosomal microarray analysis due to congenital abnormalities. These rare families provide a unique opportunity to understand the genetic factors of peri-zygotic genome instability and the impact of dnCNV on human diseases.MethodsChromosomal microarray analysis (CMA), array-based comparative genomic hybridization, short- and long-read genome sequencing (GS) were performed on the newly identified MdnCNV family to identify de novo mutations including dnCNVs, de novo single-nucleotide variants (dnSNVs), and indels. Short-read GS was performed on four previously published MdnCNV families for dnSNV analysis. Trio-based rare variant analysis was performed on the newly identified individual and four previously published MdnCNV families to identify potential genetic etiologies contributing to the peri-zygotic genomic instability. Lin semantic similarity scores informed quantitative human phenotype ontology analysis on three MdnCNV families to identify gene(s) driving or contributing to the clinical phenotype.ResultsIn the newly identified MdnCNV case, we revealed eight de novo tandem duplications, each ~ 1 Mb, with microhomology at 6/8 breakpoint junctions. Enrichment of de novo single-nucleotide variants (SNV; 6/79) and de novo indels (1/12) was found within 4 Mb of the dnCNV genomic regions. An elevated post-zygotic SNV mutation rate was observed in MdnCNV families. Maternal rare variant analyses identified three genes in distinct families that may contribute to the MdnCNV phenomenon. Phenotype analysis suggests that gene(s) within dnCNV regions contribute to the observed proband phenotype in 3/3 cases. CNVs in two cases, a contiguous gene duplication encompassing PMP22 and RAI1 and another duplication affecting NSD1 and SMARCC2, contribute to the clinically observed phenotypic manifestations.ConclusionsCharacteristic features of dnCNVs reported here are consistent with a microhomology-mediated break-induced replication (MMBIR)-driven mechanism during the peri-zygotic period. Maternal genetic variants in DNA repair genes potentially contribute to peri-zygotic genomic instability. Variable phenotypic features were observed across a cohort of three MdnCNV probands, and computational quantitative phenotyping revealed that two out of three had evidence for the contribution of more than one genetic locus to the proband's phenotype supporting the hypothesis of de novo multilocus pathogenic variation (MPV) in those families.

Dataset Information

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor.

Publications

Uncovering novel mutational signatures by <i>de novo</i> extraction with SigProfilerExtractor.

Similar Datasets

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