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Clock-like mutational processes in human somatic cells

Nature Genetics volume 47, pages 14021407 (2015) | Download Citation


During the course of a lifetime, somatic cells acquire mutations. Different mutational processes may contribute to the mutations accumulated in a cell, with each imprinting a mutational signature on the cell's genome. Some processes generate mutations throughout life at a constant rate in all individuals, and the number of mutations in a cell attributable to these processes will be proportional to the chronological age of the person. Using mutations from 10,250 cancer genomes across 36 cancer types, we investigated clock-like mutational processes that have been operating in normal human cells. Two mutational signatures show clock-like properties. Both exhibit different mutation rates in different tissues. However, their mutation rates are not correlated, indicating that the underlying processes are subject to different biological influences. For one signature, the rate of cell division may influence its mutation rate. This study provides the first survey of clock-like mutational processes operating in human somatic cells.

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We would like to thank M.E. Hurles and R. Durbin for early discussions about the analyses performed. We would like to thank The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC) and the authors of all previous studies cited in Supplementary Data Set 1 for providing free access to their somatic mutational data. This work was supported by the Wellcome Trust (grant 098051). S.N.-Z. is a Wellcome-Beit Prize Fellow and is supported through a Wellcome Trust Intermediate Fellowship (grant WT100183MA). P.J.C. is personally funded through a Wellcome Trust Senior Clinical Research Fellowship (grant WT088340MA). J.E.S. is supported by an MRC grant to the Laboratory of Molecular Biology (MC_U105178808). L.B.A. is supported through a J. Robert Oppenheimer Fellowship at Los Alamos National Laboratory. P.H.J. is supported by the Wellcome Trust, an MRC Grant-in-Aid and Cancer Research UK (programme grant C609/A17257). This research used resources provided by the Los Alamos National Laboratory Institutional Computing Program, which is supported by the US Department of Energy National Nuclear Security Administration under contract DE-AC52-06NA25396. Research performed at Los Alamos National Laboratory was carried out under the auspices of the National Nuclear Security Administration of the US Department of Energy.

Author information


  1. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, UK.

    • Ludmil B Alexandrov
    • , Philip H Jones
    • , David C Wedge
    • , Peter J Campbell
    • , Serena Nik-Zainal
    •  & Michael R Stratton
  2. Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, New Mexico, USA.

    • Ludmil B Alexandrov
  3. Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.

    • Ludmil B Alexandrov
  4. Medical Research Council (MRC) Cancer Unit, Hutchison/MRC Research Centre, University of Cambridge, Cambridge, UK.

    • Philip H Jones
  5. MRC Laboratory of Molecular Biology, Cambridge, UK.

    • Julian E Sale
  6. Department of Haematology, University of Cambridge, Cambridge, UK.

    • Peter J Campbell
  7. Department of Medical Genetics, Addenbrooke's Hospital National Health Service (NHS) Trust, Cambridge, UK.

    • Serena Nik-Zainal


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L.B.A. and M.R.S. conceived the overall approach and wrote the manuscript. L.B.A., P.H.J., S.N.-Z. and M.R.S. carried out signature and/or statistical analyses with assistance from D.C.W., J.E.S. and P.J.C.

Competing interests

M.R.S. and P.J.C. are founders, stockholders and consultants for 14M Genomics, Ltd. The remaining authors declare no competing financial interests.

Corresponding authors

Correspondence to Ludmil B Alexandrov or Michael R Stratton.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–43 and Supplementary Table 1.

Excel files

  1. 1.

    Supplementary Data Set 1

    List of cancer samples with their respective cancer types, sequencing types, age of diagnosis and source from which the data were taken.

  2. 2.

    Supplementary Data Set 2

    Number of somatic substitutions per megabase pairs attributed to each signature of operative mutational process in each cancer type.

  3. 3.

    Supplementary Data Set 3

    Evaluation of correlation between age of diagnosis and mutational signatures in each cancer type.

  4. 4.

    Supplementary Data Set 4

    Number of C>T mutations at CpG sites and total somatic mutations for each of the 10,250 examined samples.

  5. 5.

    Supplementary Data Set 5

    Evaluation of correlation between age of diagnosis and total mutations/C>T mutations at CpG sites in each cancer type.

  6. 6.

    Supplementary Data Set 6

    Tissue turnover classification and best estimates for the slopes of signatures 1 and 5.

Zip files

  1. 1.

    Supplementary Software

    MATLAB code for calculating the P values across individual cancer types.

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