Hidden Markov models lead to higher resolution maps of mutation signature activity in cancer.
Breast cancer
Hidden Markov model
Mutation signature
Mutational process
Journal
Genome medicine
ISSN: 1756-994X
Titre abrégé: Genome Med
Pays: England
ID NLM: 101475844
Informations de publication
Date de publication:
26 07 2019
26 07 2019
Historique:
received:
17
01
2019
accepted:
12
07
2019
entrez:
28
7
2019
pubmed:
28
7
2019
medline:
30
1
2020
Statut:
epublish
Résumé
Knowing the activity of the mutational processes shaping a cancer genome may provide insight into tumorigenesis and personalized therapy. It is thus important to characterize the signatures of active mutational processes in patients from their patterns of single base substitutions. However, mutational processes do not act uniformly on the genome, leading to statistical dependencies among neighboring mutations. To account for such dependencies, we develop the first sequence-dependent model, SigMa, for mutation signatures. We apply SigMa to characterize genomic and other factors that influence the activity of mutation signatures in breast cancer. We show that SigMa outperforms previous approaches, revealing novel insights on signature etiology. The source code for SigMa is publicly available at https://github.com/lrgr/sigma.
Identifiants
pubmed: 31349863
doi: 10.1186/s13073-019-0659-1
pii: 10.1186/s13073-019-0659-1
pmc: PMC6660659
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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