Computational tools to detect signatures of mutational processes in DNA from tumours: A review and empirical comparison of performance
Autoři:
Hanane Omichessan aff001; Gianluca Severi aff001; Vittorio Perduca aff001
Působiště autorů:
CESP (UMR INSERM 1018), Université Paris-Saclay, UPSud, UVSQ, Villejuif, France
aff001; Gustave Roussy, Villejuif, France
aff002; Cancer Epidemiology Centre, Cancer Council Victoria, and Centre for Epidemiology and Biostatistics, Melbourne School for Population and Global Health, The University of Melbourne, Melbourne, Australia
aff003; Laboratoire de Mathématiques Appliquées à Paris 5—MAP5 (UMR CNRS 8145), Université Paris Descartes, Université de Paris, Paris, France
aff004
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0221235
Souhrn
Mutational signatures refer to patterns in the occurrence of somatic mutations that might be uniquely ascribed to particular mutational process. Tumour mutation catalogues can reveal mutational signatures but are often consistent with the mutation spectra produced by a variety of mutagens. To date, after the analysis of tens of thousands of exomes and genomes from about 40 different cancer types, tens of mutational signatures characterized by a unique probability profile across the 96 trinucleotide-based mutation types have been identified, validated and catalogued. At the same time, several concurrent methods have been developed for either the quantification of the contribution of catalogued signatures in a given cancer sequence or the identification of new signatures from a sample of cancer sequences. A review of existing computational tools has been recently published to guide researchers and practitioners through their mutational signature analyses, but other tools have been introduced since its publication and, a systematic evaluation and comparison of the performance of such tools is still lacking. In order to fill this gap, we have carried out an empirical evaluation of the main packages available to date, using both real and simulated data. Among other results, our empirical study shows that the identification of signatures is more difficult for cancers characterized by multiple signatures each having a small contribution. This work suggests that detection methods based on probabilistic models, especially EMu and bayesNMF, have in general better performance than NMF-based methods.
Zdroje
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