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Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig

Rubanova, Yulia ; Shi, Ruian ; Harrigan, Caitlin F ; Li, Roujia ; Wintersinger, Jeff ; Sahin, Nil ; Deshwar, Amit G and Morris, Quaid D (2020) In Nature Communications 11.
Abstract

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... (More)

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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LU ; LU orcid and LU orcid
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Computational Biology/methods, Computer Simulation, Evolution, Molecular, Gene Frequency, Genome, Human, Humans, Mutation, Neoplasms/genetics, Polymorphism, Single Nucleotide, Whole Genome Sequencing
in
Nature Communications
volume
11
article number
731
pages
12 pages
publisher
Nature Publishing Group
external identifiers
  • pmid:32024834
  • scopus:85079071600
ISSN
2041-1723
DOI
10.1038/s41467-020-14352-7
language
English
LU publication?
yes
id
3baf9375-7cda-4017-8dda-26da695bdf11
date added to LUP
2023-01-05 14:23:35
date last changed
2024-06-11 03:11:27
@article{3baf9375-7cda-4017-8dda-26da695bdf11,
  abstract     = {{<p>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.</p>}},
  author       = {{Rubanova, Yulia and Shi, Ruian and Harrigan, Caitlin F and Li, Roujia and Wintersinger, Jeff and Sahin, Nil and Deshwar, Amit G and Morris, Quaid D}},
  issn         = {{2041-1723}},
  keywords     = {{Computational Biology/methods; Computer Simulation; Evolution, Molecular; Gene Frequency; Genome, Human; Humans; Mutation; Neoplasms/genetics; Polymorphism, Single Nucleotide; Whole Genome Sequencing}},
  language     = {{eng}},
  month        = {{02}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Communications}},
  title        = {{Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig}},
  url          = {{http://dx.doi.org/10.1038/s41467-020-14352-7}},
  doi          = {{10.1038/s41467-020-14352-7}},
  volume       = {{11}},
  year         = {{2020}},
}