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000177483 037__ $$aDKFZ-2021-02570
000177483 041__ $$aEnglish
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000177483 1001_ $$aRubanova, Yulia$$b0
000177483 245__ $$aReconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig.
000177483 260__ $$a[London]$$bNature Publishing Group UK$$c2020
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000177483 500__ $$asiehe Correction: DKFZ Autoren affiliiert im PCAWG Consortium: https://inrepo02.dkfz.de/record/212434   /    https://doi.org/10.1038/s41467-022-32336-7
000177483 520__ $$aThe type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3-5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes.
000177483 536__ $$0G:(DE-HGF)POF3-312$$a312 - Functional and structural genomics (POF3-312)$$cPOF3-312$$fPOF III$$x0
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000177483 650_2 $$2MeSH$$aComputational Biology: methods
000177483 650_2 $$2MeSH$$aComputer Simulation
000177483 650_2 $$2MeSH$$aEvolution, Molecular
000177483 650_2 $$2MeSH$$aGene Frequency
000177483 650_2 $$2MeSH$$aGenome, Human
000177483 650_2 $$2MeSH$$aHumans
000177483 650_2 $$2MeSH$$aMutation
000177483 650_2 $$2MeSH$$aNeoplasms: genetics
000177483 650_2 $$2MeSH$$aNeoplasms: pathology
000177483 650_2 $$2MeSH$$aPolymorphism, Single Nucleotide
000177483 650_2 $$2MeSH$$aWhole Genome Sequencing
000177483 7001_ $$aShi, Ruian$$b1
000177483 7001_ $$aHarrigan, Caitlin F$$b2
000177483 7001_ $$aLi, Roujia$$b3
000177483 7001_ $$aWintersinger, Jeff$$b4
000177483 7001_ $$aSahin, Nil$$b5
000177483 7001_ $$aDeshwar, Amit$$b6
000177483 7001_ $$0P:(DE-HGF)0$$aPCAWGEvolution$$b7
000177483 7001_ $$aGroup, Heterogeneity Working$$b8
000177483 7001_ $$aMorris, Quaid$$b9
000177483 7001_ $$0P:(DE-HGF)0$$aPCAWGConsortium$$b10
000177483 773__ $$0PERI:(DE-600)2553671-0$$a10.1038/s41467-020-14352-7$$gVol. 11, no. 1, p. 731$$n1$$p731$$tNature Communications$$v11$$x2041-1723$$y2020
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000177483 9141_ $$y2020
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