Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Nature Genetics
  • Analysis
  • Published:

Clock-like mutational processes in human somatic cells

Nature Geneticsvolume 47pages1402–1407 (2015)Cite this article

Subjects

Abstract

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.

This is a preview of subscription content,access via your institution

Access options

Access through your institution

Subscription info for Japanese customers

We have a dedicated website for our Japanese customers. Please go tonatureasia.com to subscribe to this journal.

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

¥ 4,980

Prices may be subject to local taxes which are calculated during checkout

Figure 1: A model for the accumulation of somatic mutations in cancers.
Figure 2: Patterns of mutational signatures 1 and 5.
Figure 3: Correlations between ages of cancer diagnosis and mutations attributed to signatures 1 and 5.

Similar content being viewed by others

References

  1. Stratton, M.R., Campbell, P.J. & Futreal, P.A. The cancer genome.Nature458, 719–724 (2009).

    Article CAS  Google Scholar 

  2. Alexandrov, L.B. & Stratton, M.R. Mutational signatures: the patterns of somatic mutations hidden in cancer genomes.Curr. Opin. Genet. Dev.24, 52–60 (2014).

    Article CAS  Google Scholar 

  3. Helleday, T., Eshtad, S. & Nik-Zainal, S. Mechanisms underlying mutational signatures in human cancers.Nat. Rev. Genet.15, 585–598 (2014).

    Article CAS  Google Scholar 

  4. Alexandrov, L.B. et al. Signatures of mutational processes in human cancer.Nature500, 415–421 (2013).

    Article CAS  Google Scholar 

  5. Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers.Cell149, 979–993 (2012).

    Article CAS  Google Scholar 

  6. Alexandrov, L.B., Nik-Zainal, S., Wedge, D.C., Campbell, P.J. & Stratton, M.R. Deciphering signatures of mutational processes operative in human cancer.Cell Rep.3, 246–259 (2013).

    Article CAS  Google Scholar 

  7. Bell, S.P. & Dutta, A. DNA replication in eukaryotic cells.Annu. Rev. Biochem.71, 333–374 (2002).

    Article CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article CAS  Google Scholar 

  10. Davis, C.F. et al. The somatic genomic landscape of chromophobe renal cell carcinoma.Cancer Cell26, 319–330 (2014).

    Article CAS  Google Scholar 

  11. Welch, J.S. et al. The origin and evolution of mutations in acute myeloid leukemia.Cell150, 264–278 (2012).

    Article CAS  Google Scholar 

  12. Kong, A. et al. Rate ofde novo mutations and the importance of father's age to disease risk.Nature488, 471–475 (2012).

    Article CAS  Google Scholar 

  13. Michaelson, J.J. et al. Whole-genome sequencing in autism identifies hot spots forde novo germline mutation.Cell151, 1431–1442 (2012).

    Article CAS  Google Scholar 

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

    Article CAS  Google Scholar 

  15. Behjati, S. et al. Genome sequencing of normal cells reveals developmental lineages and mutational processes.Nature513, 422–425 (2014).

    Article CAS  Google Scholar 

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

    Article  Google Scholar 

  17. Ju, Y.S. et al. Origins and functional consequences of somatic mitochondrial DNA mutations in human cancer.eLife3 (2014).

  18. Murchison, E.P. et al. Transmissible [corrected] dog cancer genome reveals the origin and history of an ancient cell lineage.Science343, 437–440 (2014).

    Article CAS  Google Scholar 

  19. Nik-Zainal, S. et al. Association of a germline copy number polymorphism ofAPOBEC3A andAPOBEC3B with burden of putative APOBEC-dependent mutations in breast cancer.Nat. Genet.46, 487–491 (2014).

    Article CAS  Google Scholar 

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

    Article CAS  Google Scholar 

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

    Article CAS  Google Scholar 

  22. Wagener, R. 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.Leukemia29, 1612–1615 (2015).

    Article CAS  Google Scholar 

  23. Barnett, V. & Lewis, T.Outliers in Statistical Data (Wiley, 1994).

  24. Holland, P.W. & Welsch, R.E. Robust regression using iteratively reweighted least-squares.Comm. Stat. Theory MethodsA6, 813–827 (1977).

    Article  Google Scholar 

  25. Huber, P.J. & Ronchetti, E.Robust Statistics (Wiley, 2009).

  26. Street, J., Carroll, R. & Ruppert, D. A note on computing robust regression estimates via iteratively reweighted least squares.Am. Stat.42, 152–154 (1988).

    Google Scholar 

  27. Abdullah, M.B. On a robust correlation coefficient.Statistician39, 455–460 (1990).

    Article  Google Scholar 

Download references

Acknowledgements

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 inSupplementary 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

Authors and Affiliations

  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

Authors
  1. Ludmil B Alexandrov
  2. Philip H Jones
  3. David C Wedge
  4. Julian E Sale
  5. Peter J Campbell
  6. Serena Nik-Zainal
  7. Michael R Stratton

Contributions

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.

Corresponding authors

Correspondence toLudmil B Alexandrov orMichael R Stratton.

Ethics declarations

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.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–43 and Supplementary Table 1. (PDF 13389 kb)

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. (XLSX 388 kb)

Supplementary Data Set 2

Number of somatic substitutions per megabase pairs attributed to each signature of operative mutational process in each cancer type. (XLSX 1684 kb)

Supplementary Data Set 3

Evaluation of correlation between age of diagnosis and mutational signatures in each cancer type. (XLSX 69 kb)

Supplementary Data Set 4

Number of C>T mutations at CpG sites and total somatic mutations for each of the 10,250 examined samples. (XLSX 404 kb)

Supplementary Data Set 5

Evaluation of correlation between age of diagnosis and total mutations/C>T mutations at CpG sites in each cancer type. (XLSX 69 kb)

Supplementary Data Set 6

Tissue turnover classification and best estimates for the slopes of signatures 1 and 5. (XLSX 36 kb)

Supplementary Software

MATLAB code for calculating theP values across individual cancer types. (ZIP 447 kb)

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alexandrov, L., Jones, P., Wedge, D.et al. Clock-like mutational processes in human somatic cells.Nat Genet47, 1402–1407 (2015). https://doi.org/10.1038/ng.3441

Download citation

This article is cited by

Access through your institution
Buy or subscribe

Advertisement

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for theNature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly.Sign up for Nature Briefing: Cancer

[8]ページ先頭

©2009-2026 Movatter.jp