Identification of multiplicatively acting modulatory mutational signatures in cancer

Dovydas Kičiatovas, Qingli Guo, Miika Kailas, Henri Pesonen, Jukka Corander, Samuel Kaski, Esa Pitkänen*, Ville Mustonen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
42 Downloads (Pure)

Abstract

Background: A deep understanding of carcinogenesis at the DNA level underpins many advances in cancer prevention and treatment. Mutational signatures provide a breakthrough conceptualisation, as well as an analysis framework, that can be used to build such understanding. They capture somatic mutation patterns and at best identify their causes. Most studies in this context have focused on an inherently additive analysis, e.g. by non-negative matrix factorization, where the mutations within a cancer sample are explained by a linear combination of independent mutational signatures. However, other recent studies show that the mutational signatures exhibit non-additive interactions. Results: We carefully analysed such additive model fits from the PCAWG study cataloguing mutational signatures as well as their activities across thousands of cancers. Our analysis identified systematic and non-random structure of residuals that is left unexplained by the additive model. We used hierarchical clustering to identify cancer subsets with similar residual profiles to show that both systematic mutation count overestimation and underestimation take place. We propose an extension to the additive mutational signature model—multiplicatively acting modulatory processes—and develop a maximum-likelihood framework to identify such modulatory mutational signatures. The augmented model is expressive enough to almost fully remove the observed systematic residual patterns. Conclusion: We suggest the modulatory processes biologically relate to sample specific DNA repair propensities with cancer or tissue type specific profiles. Overall, our results identify an interesting direction where to expand signature analysis.

Original languageEnglish
Article number522
Pages (from-to)1-15
Number of pages15
JournalBMC Bioinformatics
Volume23
Issue number1
DOIs
Publication statusPublished - Dec 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Cancer
  • Modulatory processes
  • Mutational signatures

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