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Tissue-specific mutation accumulation in human adult stem cells during life

Abstract

The gradual accumulation of genetic mutations in human adult stem cells (ASCs) during life is associated with various age-related diseases, including cancer1,2. Extreme variation in cancer risk across tissues was recently proposed to depend on the lifetime number of ASC divisions, owing to unavoidable random mutations that arise during DNA replication1. However, the rates and patterns of mutations in normal ASCs remain unknown. Here we determine genome-wide mutation patterns in ASCs of the small intestine, colon and liver of human donors with ages ranging from 3 to 87 years by sequencing clonal organoid cultures derived from primary multipotent cells3,4,5. Our results show that mutations accumulate steadily over time in all of the assessed tissue types, at a rate of approximately 40 novel mutations per year, despite the large variation in cancer incidence among these tissues1. Liver ASCs, however, have different mutation spectra compared to those of the colon and small intestine. Mutational signature analysis reveals that this difference can be attributed to spontaneous deamination of methylated cytosine residues in the colon and small intestine, probably reflecting their high ASC division rate. In liver, a signature with an as-yet-unknown underlying mechanism is predominant. Mutation spectra of driver genes in cancer show high similarity to the tissue-specific ASC mutation spectra, suggesting that intrinsic mutational processes in ASCs can initiate tumorigenesis. Notably, the inter-individual variation in mutation rate and spectra are low, suggesting tissue-specific activity of common mutational processes throughout life.

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Figure 1: Age-associated accumulation of somatic point mutations in human ASCs.
Figure 2: Signatures of mutational processes in human ASCs and their tissue-specific contribution.
Figure 3: Non-random genomic distribution of somatic point mutations in ASCs.
Figure 4: Cancer-associated mutation spectra in driver genes and structural variation in normal ASCs.

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Primary accessions

European Nucleotide Archive

Data deposits

The human sequencing data have been deposited at the European Genome-phenome Archive (http://www.ebi.ac.uk/ega/) under accession numbers EGAS00001001682 and EGAS00001000881. The mouse sequencing data have been deposited at the European Nucleotide Archive (http://www.ebi.ac.uk/ena/) under accession number ERP005717.

References

  1. Tomasetti, C. & Vogelstein, B. Cancer etiology. Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science 347, 78–81 (2015)

    CAS  ADS  Article  Google Scholar 

  2. Rossi, D. J., Jamieson, C. H. M. & Weissman, I. L. Stems cells and the pathways to aging and cancer. Cell 132, 681–696 (2008)

    CAS  Article  Google Scholar 

  3. Huch, M. et al. Long-term culture of genome-stable bipotent stem cells from adult human liver. Cell 160, 299–312 (2015)

    CAS  Article  Google Scholar 

  4. Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009)

    CAS  ADS  Article  Google Scholar 

  5. Sato, T. et al. Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett’s epithelium. Gastroenterology 141, 1762–1772 (2011)

    CAS  Article  Google Scholar 

  6. Stratton, M. R., Campbell, P. J. & Futreal, P. A. The cancer genome. Nature 458, 719–724 (2009)

    CAS  ADS  Article  Google Scholar 

  7. Barker, N. et al. Crypt stem cells as the cells-of-origin of intestinal cancer. Nature 457, 608–611 (2009)

    CAS  ADS  Article  Google Scholar 

  8. Milholland, B., Auton, A., Suh, Y. & Vijg, J. Age-related somatic mutations in the cancer genome. Oncotarget 6, 24627–24635 (2015)

    Article  Google Scholar 

  9. Wu, S., Powers, S., Zhu, W. & Hannun, Y. A. Substantial contribution of extrinsic risk factors to cancer development. Nature 529, 43–47 (2016)

    CAS  ADS  Article  Google Scholar 

  10. Hou, Y. et al. Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell 148, 873–885 (2012)

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  12. 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 Reports 3, 246–259 (2013)

    CAS  Article  Google Scholar 

  13. Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015)

    CAS  Article  Google Scholar 

  14. Supek, F. & Lehner, B. Differential DNA mismatch repair underlies mutation rate variation across the human genome. Nature 521, 81–84 (2015)

    CAS  ADS  Article  Google Scholar 

  15. Schuster-Böckler, B. & Lehner, B. Chromatin organization is a major influence on regional mutation rates in human cancer cells. Nature 488, 504–507 (2012)

    ADS  Article  Google Scholar 

  16. Lynch, M. Evolution of the mutation rate. Trends Genet. 26, 345–352 (2010)

    CAS  Article  Google Scholar 

  17. Finette, B. A. et al. Determination of HPRT mutant frequencies in T-lymphocytes from a healthy pediatric population: statistical comparison between newborn, children and adult mutant frequencies, cloning efficiency and age. Mutat. Res. 308, 223–231 (1994)

    CAS  Article  Google Scholar 

  18. Martincorena, I. et al. Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin. Science 348, 880–886 (2015)

    CAS  ADS  Article  Google Scholar 

  19. Xie, M. et al. Age-related cancer mutations associated with clonal hematopoietic expansion. Nat. Med. 20, 1472–1478 (2014)

    CAS  Article  Google Scholar 

  20. Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014)

    Article  Google Scholar 

  21. Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014)

    Article  Google Scholar 

  22. Pleasance, E. D. et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463, 191–196 (2010)

    CAS  ADS  Article  Google Scholar 

  23. Dollé, M. E. T., Snyder, W. K., Dunson, D. B. & Vijg, J. Mutational fingerprints of aging. Nucleic Acids Res. 30, 545–549 (2002)

    Article  Google Scholar 

  24. Dollé, M. E., Snyder, W. K., Gossen, J. A., Lohman, P. H. & Vijg, J. Distinct spectra of somatic mutations accumulated with age in mouse heart and small intestine. Proc. Natl Acad. Sci. USA 97, 8403–8408 (2000)

    ADS  Article  Google Scholar 

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

    CAS  ADS  Article  Google Scholar 

  26. Fearon, E. R. Molecular genetics of colorectal cancer. Annu. Rev. Pathol. 6, 479–507 (2011)

    CAS  Article  Google Scholar 

  27. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009)

    CAS  Article  Google Scholar 

  28. Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015)

    CAS  Article  Google Scholar 

  29. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011)

    CAS  Article  Google Scholar 

  30. Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001)

    CAS  Article  Google Scholar 

  31. R Core Team. R: A language and environment for statistical computing ; http://www.r-project.org/ (2015)

  32. Pinheiro J et al. nlme: Linear and Nonlinear Mixed Effects Models. https://cran.r-project.org/web/packages/nlme/nlme.pdf (2016)

  33. ENCODE Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2013)

  34. Rosenbloom, K. R. et al. The UCSC Genome Browser database: 2015 update. Nucleic Acids Res. 43, D670–D681 (2015)

    CAS  Article  Google Scholar 

  35. Cunningham, F. et al. Ensembl 2015. Nucleic Acids Res . 43, D662–D669 (2015)

    CAS  Article  Google Scholar 

  36. Gaujoux, R. & Seoighe, C. A flexible R package for nonnegative matrix factorization. BMC Bioinformatics 11, 367 (2010)

    Article  Google Scholar 

  37. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLOS Comput. Biol. 9, e1003118 (2013)

    CAS  Article  Google Scholar 

  38. Abyzov, A., Urban, A. E., Snyder, M. & Gerstein, M. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res . 21, 974–984 (2011)

    CAS  Article  Google Scholar 

  39. Boeva, V. et al. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics 28, 423–425 (2012)

    CAS  Article  Google Scholar 

  40. Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012)

    CAS  Article  Google Scholar 

  41. Le Tallec, B. et al. Common fragile site profiling in epithelial and erythroid cells reveals that most recurrent cancer deletions lie in fragile sites hosting large genes. Cell Reports 4, 420–428 (2013)

    CAS  Article  Google Scholar 

  42. Jurka, J. Repbase update: a database and an electronic journal of repetitive elements. Trends Genet. 16, 418–420 (2000)

    CAS  Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the gastroenterologists of the UMCU/Wilhelmina Children’s Hospital and Diakonessen Hospital for obtaining human duodenal and colon biopsies and R. Eijkemans for his advice on the statistical analyses. This study was financially supported by a Zenith grant of the Netherlands Genomics Initiative (935.12.003) to E.C., the NWO Zwaartekracht program Cancer Genomics.nl and funding of Worldwide Cancer Research (WCR no. 16-0193) to R.B. We declare no competing financial interests.

Author information

Authors and Affiliations

Authors

Contributions

C.L.W., S.M. and E.E.S.N. obtained duodenal biopsies. N.S., M.M., E.E.S.N., M.M.A.V. and J.J. obtained colon biopsies. M.M.A.V., L.J.W.L., J.J. and J.N.M.I. obtained human liver biopsies. M.J., V.S., N.S., M.H., E.K., C.L.W., T.S., G.S. and R.B. performed ASC culturing. M.W. performed cell sorting. S.R., M.R.S., E.C. and R.B. performed sequencing. F.B., J.L., S.B., P.P., I.J.N., I.M. and R.B. performed bioinformatic analyses. F.B, R.G.V., H.C., E.C. and R.B. were involved in the conceptual design of the study. F.B., H.C., E.C. and R.B. wrote the manuscript.

Corresponding author

Correspondence to Edwin Cuppen.

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The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks G. Pfeifer, L. Vermeulen, J. Vijg and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Cataloguing somatic mutation loads in human ASCs.

a, Schematic overview of the experimental setup to determine somatic mutations in individual human ASCs. Colon, small intestine and liver biopsies were cultured in bulk for 1-2 week(s) before single cells were sorted and clonally expanded until enough DNA could be isolated for WGS analysis. WGS of the clonal organoid culture allows for cataloguing of somatic variants in the original ASCs that gave rise to the clonal cultures that were acquired during life and the first 7–14 days of culturing. Biopsy or blood was sequenced as a reference sample. b, Filter steps to obtain somatic mutations in ASCs. c, Number of point mutations that pass each corresponding filter step in a for each ASC culture of donors 5 and 6. d, Independent validations of mutations that were filtered out by amplicon-based resequencing. The asterisk indicates a position that is not located in the surveyed areas of the assessed ASCs in the original experiment, which is corrected for in all analyses. e, Independent validations of mutations that passed all filters by amplicon-based re-sequencing. Confirmed positions are defined as those with a call in the indicated ASC with a VAF ≥ 0.3 and without a call in the corresponding reference sample. f, Qualification of unconfirmed positions based on manual inspection. True-positive positions are positions that were correctly called, but for which the VAF threshold was not met in the validation experiment. False-positive positions are positions without evidence in the validation experiment or are noisy. ‘Missed in germline’ are positions that were called in the reference sample in the validation experiment.

Extended Data Figure 2 Variant allele frequency distribution plot for each assessed ASC.

A distribution plot of the VAFs of all somatic mutations that remain before filtering for the VAF in filter step 6 (Extended Data Fig. 1b). Clonal heterozygous somatic mutations form a peak around VAF = 0.5. A threshold of VAF ≥ 0.3 was used to obtain somatic mutations that were clonal in the organoid cultures and therefore present in the original cloned ASCs (see Methods). Mutations acquired after the single ASC expansion step are subclonal (that is, not present in all cells of the clonal culture) and have lower VAFs. Two samples (donor 14, ASC 14-b and donor 17, ASC 17-c) showed a shift in the main VAF peak to the left, indicating that these cultures did not arise from a single ASC and were therefore excluded from the study.

Extended Data Figure 3 Confirmation rate of somatic point mutations.

a, Overlap of somatic point mutations between the clonal organoid cultures and corresponding subcloned cultures depicted in Extended Data Fig. 6. b, Confirmation rate of point mutations, which were observed in the original cloned culture, in the corresponding subcloned culture. Data are represented as the mean percentage of confirmed point mutations over all clone–subclone pairs indicated in a (n = 10) and error bars represent s.d.

Extended Data Figure 4 Somatic mutation loads in consensus-surveyed area and overlap of point mutations between ASCs from the same donor.

a, Correlation of the number of somatic point mutations per ASC, which were observed in the genomic regions that were surveyed (for example, a base coverage of at least 20× in both the clonal culture and the reference sample; Methods) in all the ASCs, with the age of the donors per tissue indicated. This consensus-surveyed area comprises 38.2% of the non-N autosomal genome. Each data point represents a single ASC. Indicated are the P values of the age effects in the linear mixed model (two-tailed t-test) for each tissue. The sample sizes for colon, small intestine and liver are 6, 9 and 5 donors and 21, 14 and 10 ASCs, respectively. b, Somatic mutation accumulation rate per tissue as estimated by the linear mixed models in a. Error bars represent the 95% confidence intervals of the slope estimates. c, Relative contribution of the indicated mutation types to the point mutation spectra in the consensus-surveyed area per tissue type. Data are represented as the mean relative contribution of each mutation type over all ASCs per tissue type (n = 21, 14 and 10 for colon, small intestine and liver, respectively); error bars represent s.d. The total number of identified somatic point mutations per tissue is shown. d, Overlap of the somatic point mutations between ASCs of the same donor. The number of point mutations, observed in the total surveyed area per ASC, that are shared between the assessed ASCs of the same donor is indicated.

Extended Data Figure 5 Point-mutation spectrum per donor.

Relative contribution of the different types of point mutation to the spectrum of each donor. Data are represented as the mean relative contribution of each mutation type when multiple ASCs were measured per donor (the number n of ASC per donor is depicted for each donor) and error bars represent standard deviation. Indicated are the age of the donors, the total number of point mutations used to determine each spectrum and the tissue type.

Extended Data Figure 6 Mutation patterns associated with long-term in vitro expansion of ASCs.

a, Schematic overview of the experimental setup to catalogue mutations associated with the organoid culture system. Clonal small intestinal and liver organoid cultures (Extended Data Fig. 1a) were cultured for 3–5 months. A second clonal expansion step was subsequently performed, followed by WGS analysis, to catalogue all the mutations that were present in the subcloned ASCs. To obtain mutations that were specifically acquired during culturing, mutations in the original clonal cultures were subtracted from those observed in the corresponding second subcloned cultures. b, Relative contribution of the indicated mutation types to the point mutation spectra specifically observed in vitro per tissue type. Data are represented as the mean relative contribution of each mutation type over all subcloned ASCs per tissue type (n = 6 and 4 for small intestine and liver, respectively) and error bars represent s.d. Indicated are the total number of identified somatic point mutations, which were specifically acquired between the two clonal expansion steps indicated in a, per tissue. c, Relative contribution of the mutational signatures depicted in Fig. 2a, which explain the mutation spectra depicted in b.

Extended Data Figure 7 Non-random distribution of mutational signatures throughout the genome.

a, Context- and replication-timing-dependent mutation spectrum of the three mutational signatures depicted in Fig. 2a. Indicated is the contribution of each trinucleotide to the signatures (order is similar as in ref. 11), subdivided into the fraction of the trinucleotide-change present in early, intermediate or late replicating genomic regions. b, log2 ratio of the observed and expected number of mutations per indicated base substitution (summed over all trinucleotides) in early-, intermediate- and late-replicating genomic regions for each of the signatures depicted in a. log2 ratio indicates the effect size of the bias and asterisks indicate significant DNA-replication-timing bias (P < 0.05, binomial test). c, log2 ratio of the total number of observed and expected mutations in early-, intermediate- and late-replicating genomic regions for each signature depicted in a. log2 ratio indicates the effect-size of the bias and asterisks indicate significant DNA replication timing bias (P < 0.05, binomial test). d, Context- and transcriptional-strand-dependent mutation spectrum of the three mutational signatures depicted in Fig. 2a. Indicated is the contribution of each trinucleotide to the signatures (order is similar to that in ref. 11), subdivided into the fraction of the trinucleotide-change present on the transcribed and untranscribed strand. e, log2 ratio of the number of mutations on the transcribed and untranscribed strand per indicated base substitution for each signature depicted in d. log2 ratio indicates the effect size of the bias and asterisks indicate significant transcriptional strand bias (P < 0.05, binomial test). f, The dN/dS ratio for all protein-coding somatic point mutations observed in all ASCs per tissue type. Error bars indicate 95% confidence intervals (likelihood ratio test).

Extended Data Figure 8 Comparison of mutation loads between intestinal ASCs derived from human and mouse.

a, Mutation frequency in mouse intestinal ASCs is compared to the linear fit, describing the relationship between the mutation frequency in human intestinal ASCs and age of the donor. Indicated by the dotted lines are the mean mutation frequencies over all ASCs per mouse (n = 3) and the corresponding age of human linear fit. b, Relative contribution of the indicated mutation types to the point mutation spectra for all assessed human intestinal ASCs and for each mouse. Data are represented as the mean relative contribution of each mutation type over all the ASCs per indicated category (n = 14, 3 and 3 for human, mouse 1 and mouse 2, respectively), error bars indicate s.d.

Extended Data Table 1 Overview of somatic point mutations detected in ASCs
Extended Data Table 2 Identified somatic structural variations in ASCs

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Blokzijl, F., de Ligt, J., Jager, M. et al. Tissue-specific mutation accumulation in human adult stem cells during life. Nature 538, 260–264 (2016). https://doi.org/10.1038/nature19768

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