A mutation-level covariate model for mutational signatures

Itay Kahane, Mark D M Leiserson and Roded Sharan

PLOS Computational Biology, 2023, vol. 19, issue 6, 1-10

Abstract: Mutational processes and their exposures in particular genomes are key to our understanding of how these genomes are shaped. However, current analyses assume that these processes are uniformly active across the genome without accounting for potential covariates such as strand or genomic region that could impact such activities. Here we suggest the first mutation-covariate models that explicitly model the effect of different covariates on the exposures of mutational processes. We apply these models to test the impact of replication strand on these processes and compare them to strand-oblivious models across a range of data sets. Our models capture replication strand specificity, point to signatures affected by it, and score better on held-out data compared to standard models that do not account for mutation-level covariate information.Author summary: Somatic mutations, caused by processes such as DNA damage and faulty DNA repair, may lead to cancer. Studying the mutational signatures those processes leave behind, provides insights on their activities and can be utilized for personalized therapy. Previous methods for analyzing mutational signatures did not account for the fact that some signatures tend to occur in varying frequencies along the genome, depending on positional factors such as strand identity or genomic region. In this work, we develop new models that account for these factors, and show that exploiting such information improves the inference of mutational signatures and their activities with applications to both basic science and personalized medicine.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1011195

DOI: 10.1371/journal.pcbi.1011195

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