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000180877 037__ $$aDKFZ-2022-01626
000180877 041__ $$aEnglish
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000180877 1001_ $$aCmero, Marek$$b0
000180877 245__ $$aInferring structural variant cancer cell fraction.
000180877 260__ $$a[London]$$bNature Publishing Group UK$$c2020
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000180877 500__ $$asiehe Correction: DKFZ Autoren affiliiert im PCAWG Consortium:https://inrepo02.dkfz.de/record/212436   /   https://doi.org/10.1038/s41467-022-32338-5
000180877 520__ $$aWe present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone's performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity.
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000180877 650_2 $$2MeSH$$aAlgorithms
000180877 650_2 $$2MeSH$$aComputational Biology: methods
000180877 650_2 $$2MeSH$$aComputer Simulation
000180877 650_2 $$2MeSH$$aDNA Copy Number Variations
000180877 650_2 $$2MeSH$$aFemale
000180877 650_2 $$2MeSH$$aGene Frequency
000180877 650_2 $$2MeSH$$aGenome, Human
000180877 650_2 $$2MeSH$$aHumans
000180877 650_2 $$2MeSH$$aLiver Neoplasms: genetics
000180877 650_2 $$2MeSH$$aLiver Neoplasms: pathology
000180877 650_2 $$2MeSH$$aMale
000180877 650_2 $$2MeSH$$aNeoplasms: genetics
000180877 650_2 $$2MeSH$$aNeoplasms: pathology
000180877 650_2 $$2MeSH$$aOvarian Neoplasms: genetics
000180877 650_2 $$2MeSH$$aOvarian Neoplasms: pathology
000180877 650_2 $$2MeSH$$aPancreatic Neoplasms: genetics
000180877 650_2 $$2MeSH$$aPancreatic Neoplasms: pathology
000180877 650_2 $$2MeSH$$aProstatic Neoplasms: genetics
000180877 650_2 $$2MeSH$$aProstatic Neoplasms: pathology
000180877 650_2 $$2MeSH$$aSensitivity and Specificity
000180877 650_2 $$2MeSH$$aWhole Genome Sequencing
000180877 7001_ $$aYuan, Ke$$b1
000180877 7001_ $$aOng, Cheng Soon$$b2
000180877 7001_ $$aSchröder, Jan$$b3
000180877 7001_ $$aPCAWG, Evolution$$b4
000180877 7001_ $$0P:(DE-HGF)0$$aHeterogeneity Working, Group$$b5
000180877 7001_ $$aCorcoran, Niall M$$b6
000180877 7001_ $$aPapenfuss, Tony$$b7
000180877 7001_ $$aHovens, Christopher M$$b8
000180877 7001_ $$aMarkowetz, Florian$$b9
000180877 7001_ $$aMacintyre, Geoff$$b10
000180877 7001_ $$0P:(DE-HGF)0$$aPCAWGConsortium$$b11
000180877 773__ $$0PERI:(DE-600)2553671-0$$a10.1038/s41467-020-14351-8$$gVol. 11, no. 1, p. 730$$n1$$p730$$tNature Communications$$v11$$x2041-1723$$y2020
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