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Hotspot propensity across mutational processes
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DOI：10.1038/s44320-023-00001-w.png)
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Abstract
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The sparsity of mutations observed across tumours hinders our ability to study mutation rate variability at nucleotide resolution. To circumvent this, here we investigated the propensity of mutational processes to form mutational hotspots as a readout of their mutation rate variability at single base resolution. Mutational signatures 1 and 17 have the highest hotspot propensity (5-78 times higher than other processes). After accounting for trinucleotide mutational probabilities, sequence composition and mutational heterogeneity at 10 Kbp, most (94-95%) signature 17 hotspots remain unexplained, suggesting a significant role of local genomic features. For signature 1, the inclusion of genome-wide distribution of methylated CpG sites into models can explain most (80-100%) of the hotspot propensity. There is an increased hotspot propensity of signature 1 in normal tissues and de novo germline mutations. We demonstrate that hotspot propensity is a useful readout to assess the accuracy of mutation rate models at nucleotide resolution. This new approach and the findings derived from it open up new avenues for a range of somatic and germline studies investigating and modelling mutagenesis. The propensity of mutational signatures to leave mutational hotspots serves as an estimate of the variability in their mutational probability at single-nucleotide resolution. Using known influences of the mutation rate at different scales allowed to closely model the observed hotspot propensity of signature 1.The variability in the mutational probabilities at single-nucleotide resolution of different mutational processes (represented by signatures) can be estimated through their propensity to form hotspots across tumours. Mutational signatures 1 and 17 show several-fold higher variability of their mutational probabilities at single-nucleotide resolution than other common somatic mutational processes. The hotspot propensity of signature 17 (and others) cannot be fully explained through the variability of genomic features known to influence the mutation rate at small and large scales. The hotspot propensity of signature 1 can be closely explained through the distribution of mutations at 10 Kbp resolution and methylated CpG sites along the genome. The propensity of mutational signatures to leave mutational hotspots serves as an estimate of the variability in their mutational probability at single-nucleotide resolution. Using known influences of the mutation rate at different scales allowed to closely model the observed hotspot propensity of signature 1.
Keywords:
Mutational Hotspots
Mutational Hotspot Propensity
Mutation Rate Variability
Mutation Rate Variability at Single-nucleotide Resolution
Mutational Signatures
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