Clock-like mutational processes in human somatic cells

  • Nature Genetics volume 47, pages 14021407 (2015)
  • doi:10.1038/ng.3441
  • 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.

  • Subscribe to Nature Genetics for full access:



Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.


  1. 1.

    , & The cancer genome. Nature 458, 719–724 (2009).

  2. 2.

    & Mutational signatures: the patterns of somatic mutations hidden in cancer genomes. Curr. Opin. Genet. Dev. 24, 52–60 (2014).

  3. 3.

    , & Mechanisms underlying mutational signatures in human cancers. Nat. Rev. Genet. 15, 585–598 (2014).

  4. 4.

    et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

  5. 5.

    et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).

  6. 6.

    , , , & Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013).

  7. 7.

    & DNA replication in eukaryotic cells. Annu. Rev. Biochem. 71, 333–374 (2002).

  8. 8.

    DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).

  9. 9.

    & Transcription-coupled nucleotide excision repair in mammalian cells: molecular mechanisms and biological effects. Cell Res. 18, 73–84 (2008).

  10. 10.

    et al. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell 26, 319–330 (2014).

  11. 11.

    et al. The origin and evolution of mutations in acute myeloid leukemia. Cell 150, 264–278 (2012).

  12. 12.

    et al. Rate of de novo mutations and the importance of father's age to disease risk. Nature 488, 471–475 (2012).

  13. 13.

    et al. Whole-genome sequencing in autism identifies hot spots for de novo germline mutation. Cell 151, 1431–1442 (2012).

  14. 14.

    et al. Variation in genome-wide mutation rates within and between human families. Nat. Genet. 43, 712–714 (2011).

  15. 15.

    et al. Genome sequencing of normal cells reveals developmental lineages and mutational processes. Nature 513, 422–425 (2014).

  16. 16.

    et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat. Commun. 5, 2997 (2014).

  17. 17.

    et al. Origins and functional consequences of somatic mitochondrial DNA mutations in human cancer. eLife 3 (2014).

  18. 18.

    et al. Transmissible [corrected] dog cancer genome reveals the origin and history of an ancient cell lineage. Science 343, 437–440 (2014).

  19. 19.

    et al. Association of a germline copy number polymorphism of APOBEC3A and APOBEC3B with burden of putative APOBEC-dependent mutations in breast cancer. Nat. Genet. 46, 487–491 (2014).

  20. 20.

    et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225–233 (2014).

  21. 21.

    et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21, 751–759 (2015).

  22. 22.

    et al. Analysis of mutational signatures in exomes from B-cell lymphoma cell lines suggest APOBEC3 family members to be involved in the pathogenesis of primary effusion lymphoma. Leukemia 29, 1612–1615 (2015).

  23. 23.

    & Outliers in Statistical Data (Wiley, 1994).

  24. 24.

    & Robust regression using iteratively reweighted least-squares. Comm. Stat. Theory Methods A6, 813–827 (1977).

  25. 25.

    & Robust Statistics (Wiley, 2009).

  26. 26.

    , & A note on computing robust regression estimates via iteratively reweighted least squares. Am. Stat. 42, 152–154 (1988).

  27. 27.

    On a robust correlation coefficient. Statistician 39, 455–460 (1990).

Download references


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


  1. Search for Ludmil B Alexandrov in:

  2. Search for Philip H Jones in:

  3. Search for David C Wedge in:

  4. Search for Julian E Sale in:

  5. Search for Peter J Campbell in:

  6. Search for Serena Nik-Zainal in:

  7. Search for Michael R Stratton in:


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.