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Hotspot propensity across mutational processes.

Claudia Arnedo-Pac1,2, Ferran Muiños1,2, Abel Gonzalez-Perez3,4

  • 1Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain.

Molecular Systems Biology
|January 4, 2024
PubMed
Summary
This summary is machine-generated.

Investigating mutational processes revealed that mutational signatures 1 and 17 create mutation hotspots. This hotspot propensity offers a new way to understand mutation rate variability at nucleotide resolution.

Keywords:
Mutation Rate VariabilityMutation Rate Variability at Single-nucleotide ResolutionMutational Hotspot PropensityMutational HotspotsMutational Signatures

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Area of Science:

  • Genetics
  • Genomics
  • Cancer Research

Background:

  • Studying mutation rate variability at nucleotide resolution is challenging due to sparse mutation data across tumors.
  • Mutational processes leave distinct patterns, known as mutational signatures, which can inform our understanding of mutagenesis.

Purpose of the Study:

  • To develop a novel method using "hotspot propensity" to assess mutation rate variability at single-base resolution.
  • To identify which mutational processes exhibit the highest hotspot propensity and explore the underlying genomic features.

Main Methods:

  • Assessed "hotspot propensity" as a readout for mutation rate variability.
  • Analyzed mutational signatures 1 and 17 for their propensity to form mutation hotspots.
  • Investigated the influence of trinucleotide mutational probabilities, sequence composition, genomic heterogeneity, and methylated CpG sites on hotspot formation.

Main Results:

  • Mutational signatures 1 and 17 demonstrated the highest hotspot propensity, significantly exceeding other processes.
  • Most signature 17 hotspots (94-95%) remained unexplained by known factors, indicating the importance of local genomic features.
  • For signature 1, methylated CpG sites explained 80-100% of its hotspot propensity, with increased propensity observed in normal tissues and de novo germline mutations.

Conclusions:

  • Hotspot propensity is a valuable metric for evaluating the accuracy of nucleotide-resolution mutation rate models.
  • This approach provides new insights into mutagenesis and opens avenues for somatic and germline mutation studies.