Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig

The 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 reliabl...

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প্রধান লেখক: Rubanova, Y, Shi, R, Harrigan, CF, Li, R, Wintersinger, J, Sahin, N, Deshwar, A, PCAWG Evolution and Heterogeneity Working Group, Morris, Q, PCAWG Consortium
অন্যান্য লেখক: Wedge, DC
বিন্যাস: Journal article
ভাষা:English
প্রকাশিত: Springer Nature 2020
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author Rubanova, Y
Shi, R
Harrigan, CF
Li, R
Wintersinger, J
Sahin, N
Deshwar, A
PCAWG Evolution and Heterogeneity Working Group
Morris, Q
PCAWG Consortium
author2 Wedge, DC
author_facet Wedge, DC
Rubanova, Y
Shi, R
Harrigan, CF
Li, R
Wintersinger, J
Sahin, N
Deshwar, A
PCAWG Evolution and Heterogeneity Working Group
Morris, Q
PCAWG Consortium
author_sort Rubanova, Y
collection OXFORD
description The 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.
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spelling oxford-uuid:c0ff6d76-0334-441e-a8e8-694f908541f02022-03-27T05:58:26ZReconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSigJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:c0ff6d76-0334-441e-a8e8-694f908541f0EnglishSymplectic ElementsSpringer Nature2020Rubanova, YShi, RHarrigan, CFLi, RWintersinger, JSahin, NDeshwar, APCAWG Evolution and Heterogeneity Working GroupMorris, QPCAWG ConsortiumWedge, DCDentro, SCThe 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.
spellingShingle Rubanova, Y
Shi, R
Harrigan, CF
Li, R
Wintersinger, J
Sahin, N
Deshwar, A
PCAWG Evolution and Heterogeneity Working Group
Morris, Q
PCAWG Consortium
Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig
title Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig
title_full Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig
title_fullStr Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig
title_full_unstemmed Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig
title_short Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig
title_sort reconstructing evolutionary trajectories of mutation signature activities in cancer using tracksig
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