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A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies

Degasperi, Andrea ; Amarante, Tauanne Dias ; Czarnecki, Jan ; Shooter, Scott ; Zou, Xueqing ; Glodzik, Dominik LU ; Morganella, Sandro ; Nanda, Arjun S. ; Badja, Cherif and Koh, Gene , et al. (2020) In Nature Cancer 1(2). p.249-263
Abstract

Mutational signatures are patterns of mutations that arise during tumorigenesis. We present an enhanced, practical framework for mutational signature analyses. Applying these methods to 3,107 whole-genome-sequenced (WGS) primary cancers of 21 organs reveals known signatures and nine previously undescribed rearrangement signatures. We highlight interorgan variability of signatures and present a way of visualizing that diversity, reinforcing our findings in an independent analysis of 3,096 WGS metastatic cancers. Signatures with a high level of genomic instability are dependent on TP53 dysregulation. We illustrate how uncertainty in mutational signature identification and assignment to samples affects tumor classification, reinforcing... (More)

Mutational signatures are patterns of mutations that arise during tumorigenesis. We present an enhanced, practical framework for mutational signature analyses. Applying these methods to 3,107 whole-genome-sequenced (WGS) primary cancers of 21 organs reveals known signatures and nine previously undescribed rearrangement signatures. We highlight interorgan variability of signatures and present a way of visualizing that diversity, reinforcing our findings in an independent analysis of 3,096 WGS metastatic cancers. Signatures with a high level of genomic instability are dependent on TP53 dysregulation. We illustrate how uncertainty in mutational signature identification and assignment to samples affects tumor classification, reinforcing that using multiple orthogonal mutational signature data is not only beneficial, but is also essential for accurate tumor stratification. Finally, we present a reference web-based tool for cancer and experimentally generated mutational signatures, called Signal (https://signal.mutationalsignatures.com), that also supports performing mutational signature analyses.

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organization
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type
Contribution to journal
publication status
published
subject
in
Nature Cancer
volume
1
issue
2
pages
15 pages
publisher
Nature Publishing Group
external identifiers
  • scopus:85083741435
  • pmid:32118208
ISSN
2662-1347
DOI
10.1038/s43018-020-0027-5
language
English
LU publication?
yes
id
715644a1-67d8-440f-924a-dfad70a7dfeb
date added to LUP
2020-12-22 11:44:30
date last changed
2024-06-28 06:52:18
@article{715644a1-67d8-440f-924a-dfad70a7dfeb,
  abstract     = {{<p>Mutational signatures are patterns of mutations that arise during tumorigenesis. We present an enhanced, practical framework for mutational signature analyses. Applying these methods to 3,107 whole-genome-sequenced (WGS) primary cancers of 21 organs reveals known signatures and nine previously undescribed rearrangement signatures. We highlight interorgan variability of signatures and present a way of visualizing that diversity, reinforcing our findings in an independent analysis of 3,096 WGS metastatic cancers. Signatures with a high level of genomic instability are dependent on TP53 dysregulation. We illustrate how uncertainty in mutational signature identification and assignment to samples affects tumor classification, reinforcing that using multiple orthogonal mutational signature data is not only beneficial, but is also essential for accurate tumor stratification. Finally, we present a reference web-based tool for cancer and experimentally generated mutational signatures, called Signal (https://signal.mutationalsignatures.com), that also supports performing mutational signature analyses.</p>}},
  author       = {{Degasperi, Andrea and Amarante, Tauanne Dias and Czarnecki, Jan and Shooter, Scott and Zou, Xueqing and Glodzik, Dominik and Morganella, Sandro and Nanda, Arjun S. and Badja, Cherif and Koh, Gene and Momen, Sophie E. and Georgakopoulos-Soares, Ilias and Dias, João M.L. and Young, Jamie and Memari, Yasin and Davies, Helen and Nik-Zainal, Serena}},
  issn         = {{2662-1347}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{249--263}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Cancer}},
  title        = {{A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies}},
  url          = {{http://dx.doi.org/10.1038/s43018-020-0027-5}},
  doi          = {{10.1038/s43018-020-0027-5}},
  volume       = {{1}},
  year         = {{2020}},
}