A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies
(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.
(Less)
- author
- organization
- publishing date
- 2020
- 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
- 2024-10-17 16:37:36
@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}}, }