A practical guide for mutational signature analysis in hematological malignancies

Francesco Maura (), Andrea Degasperi, Ferran Nadeu, Daniel Leongamornlert, Helen Davies, Luiza Moore, Romina Royo, Bachisio Ziccheddu, Xose S. Puente, Herve Avet-Loiseau, Peter J. Campbell, Serena Nik-Zainal, Elias Campo, Nikhil Munshi and Niccolò Bolli ()
Additional contact information
Francesco Maura: Memorial Sloan Kettering Cancer Center
Andrea Degasperi: Wellcome Sanger Institute
Ferran Nadeu: Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)
Daniel Leongamornlert: Wellcome Sanger Institute
Helen Davies: Wellcome Sanger Institute
Luiza Moore: Wellcome Sanger Institute
Romina Royo: Joint BSC-CRG-IRB Research Program in Computational Biology
Bachisio Ziccheddu: Fondazione IRCCS Istituto Nazionale dei Tumori
Xose S. Puente: Universitat de Barcelona
Herve Avet-Loiseau: IUC-Oncopole, and CRCT INSERM U1037
Peter J. Campbell: Wellcome Sanger Institute
Serena Nik-Zainal: Wellcome Sanger Institute
Elias Campo: Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)
Nikhil Munshi: Harvard Medical School
Niccolò Bolli: University of Milan

Nature Communications, 2019, vol. 10, issue 1, 1-12

Abstract: Abstract Analysis of mutational signatures is becoming routine in cancer genomics, with implications for pathogenesis, classification, prognosis, and even treatment decisions. However, the field lacks a consensus on analysis and result interpretation. Using whole-genome sequencing of multiple myeloma (MM), chronic lymphocytic leukemia (CLL) and acute myeloid leukemia, we compare the performance of public signature analysis tools. We describe caveats and pitfalls of de novo signature extraction and fitting approaches, reporting on common inaccuracies: erroneous signature assignment, identification of localized hyper-mutational processes, overcalling of signatures. We provide reproducible solutions to solve these issues and use orthogonal approaches to validate our results. We show how a comprehensive mutational signature analysis may provide relevant biological insights, reporting evidence of c-AID activity among unmutated CLL cases or the absence of BRCA1/BRCA2-mediated homologous recombination deficiency in a MM cohort. Finally, we propose a general analysis framework to ensure production of accurate and reproducible mutational signature data.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-11037-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11037-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-11037-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().