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Microscopic clones with somatic mutations are common in aging tissues and can lead to cancer or disease. A new sequencing method, NanoSeq, accurately detects these mutations, revealing selection landscapes and factors influencing their development.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Cancer Research

Background:

  • Aging tissues accumulate microscopic clones with somatic driver mutations, potentially initiating cancer or contributing to aging.
  • Detecting mutations in small clones is challenging, limiting our understanding of this phenomenon.
  • Somatic mutations play a role in aging and disease, but their precise mechanisms and selection pressures are not fully understood.

Purpose of the Study:

  • To introduce an advanced version of nanorate sequencing (NanoSeq) for sensitive detection of somatic mutations in small clones.
  • To profile large numbers of clones in polyclonal samples with single-molecule sensitivity.
  • To accurately determine mutation rates, signatures, and driver frequencies across various tissues.

Main Methods:

  • Utilized a novel duplex sequencing method (NanoSeq) with an ultra-low error rate (<5 errors/billion base pairs).
  • Applied targeted NanoSeq to 1,042 oral epithelium and 371 blood samples from a twin cohort.
  • Employed multivariate regression models to study the impact of exposures and risk factors on somatic mutation acquisition and selection.

Main Results:

  • Revealed a rich selection landscape in oral epithelium, with 46 genes under positive selection and over 62,000 driver mutations identified.
  • Observed evidence of negative selection in essential genes, indicating constraints on mutation accumulation.
  • Generated high-resolution maps of selection across coding and non-coding sites, akin to in vivo saturation mutagenesis.
  • Demonstrated how factors like age, tobacco, and alcohol influence somatic mutation patterns.

Conclusions:

  • Accurate single-molecule sequencing with NanoSeq is a powerful tool for studying early carcinogenesis and cancer prevention.
  • This technology enables detailed investigation into the role of somatic mutations in aging and disease.
  • The findings provide insights into mutational epidemiology and the selection pressures acting on somatic mutations in vivo.