DNA methylation loss occurs frequently in cancer genomes, primarily within lamina-associated, late-replicating regions termed partially methylated domains (PMDs). We profiled 39 diverse primary tumors and 8 matched adjacent tissues using whole-genome bisulfite sequencing (WGBS) and analyzed them alongside 343 additional human and 206 mouse WGBS datasets. We identified a local CpG sequence context associated with preferential hypomethylation in PMDs. Analysis of CpGs in this context (‘solo-WCGWs’) identified previously undetected PMD hypomethylation in almost all healthy tissue types. PMD hypomethylation increased with age, beginning during fetal development, and appeared to track the accumulation of cell divisions. In cancer, PMD hypomethylation depth correlated with somatic mutation density and cell cycle gene expression, consistent with its reflection of mitotic history and suggesting its application as a mitotic clock. We propose that late replication leads to lifelong progressive methylation loss, which acts as a biomarker for cellular aging and which may contribute to oncogenesis.

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  1. 1.

    Ehrlich, M. & Wang, R. Y. 5-Methylcytosine in eukaryotic DNA. Science 212, 1350–1357 (1981).

  2. 2.

    Feinberg, A. P. & Vogelstein, B. Hypomethylation distinguishes genes of some human cancers from their normal counterparts. Nature 301, 89–92 (1983).

  3. 3.

    Gama-Sosa, M. A. et al. The 5-methylcytosine content of DNA from human tumors. Nucleic Acids Res. 11, 6883–6894 (1983).

  4. 4.

    Goelz, S., Vogelstein, B. & Feinberg, A. Hypomethylation of DNA from benign and malignant human colon neoplasms. Science 228, 187–190 (1985).

  5. 5.

    Hansen, K. D. et al. Increased methylation variation in epigenetic domains across cancer types. Nat. Genet. 43, 768–775 (2011).

  6. 6.

    Berman, B. P. et al. Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina–associated domains. Nat. Genet. 44, 40–46 (2011).

  7. 7.

    Fortin, J.-P. & Hansen, K. D. Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data. Genome Biol. 16, 180 (2015).

  8. 8.

    Weber, M. et al. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat. Genet. 37, 853–862 (2005).

  9. 9.

    Aran, D., Toperoff, G., Rosenberg, M. & Hellman, A. Replication timing–related and gene body–specific methylation of active human genes. Hum. Mol. Genet. 20, 670–680 (2011).

  10. 10.

    Bergman, Y. & Cedar, H. DNA methylation dynamics in health and disease. Nat. Struct. Mol. Biol. 20, 274–281 (2013).

  11. 11.

    Quante, T. & Bird, A. Do short, frequent DNA sequence motifs mould the epigenome? Nat. Rev. Mol. Cell Biol. 17, 257–262 (2016).

  12. 12.

    Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).

  13. 13.

    Timp, W. et al. Large hypomethylated blocks as a universal defining epigenetic alteration in human solid tumors. Genome Med. 6, 61 (2014).

  14. 14.

    Hovestadt, V. et al. Decoding the regulatory landscape of medulloblastoma using DNA methylation sequencing. Nature 510, 537–541 (2014).

  15. 15.

    Baylin, S. & Bestor, T. H. Altered methylation patterns in cancer cell genomes: cause or consequence? Cancer Cell 1, 299–305 (2002).

  16. 16.

    Brennan, K. & Flanagan, J. M. Is there a link between genome-wide hypomethylation in blood and cancer risk? Cancer Prev. Res. 5, 1345–1357 (2012).

  17. 17.

    Ehrlich, M. et al. Amount and distribution of 5-methylcytosine in human DNA from different types of tissues of cells. Nucleic Acids Res. 10, 2709–2721 (1982).

  18. 18.

    Lister, R. et al. Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 471, 68–73 (2011).

  19. 19.

    Hansen, K. D. et al. Large-scale hypomethylated blocks associated with Epstein–Barr virus–induced B-cell immortalization. Genome Res. 24, 177–184 (2014).

  20. 20.

    Landan, G. et al. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat. Genet. 44, 1207–1214 (2012).

  21. 21.

    Shipony, Z. et al. Dynamic and static maintenance of epigenetic memory in pluripotent and somatic cells. Nature 513, 115–119 (2014).

  22. 22.

    Schroeder, D. I. et al. The human placenta methylome. Proc. Natl Acad. Sci. USA 110, 6037–6042 (2013).

  23. 23.

    Kulis, M. et al. Whole-genome fingerprint of the DNA methylome during human B cell differentiation. Nat. Genet. 47, 746–756 (2015).

  24. 24.

    Durek, P. et al. Epigenomic profiling of human CD4+ T cells supports a linear differentiation model and highlights molecular regulators of memory development. Immunity 45, 1148–1161 (2016).

  25. 25.

    Schultz, M. D. et al. Human body epigenome maps reveal noncanonical DNA methylation variation. Nature 523, 212–216 (2015).

  26. 26.

    Vandiver, A. R. et al. Age and sun exposure-related widespread genomic blocks of hypomethylation in nonmalignant skin. Genome Biol. 16, 80 (2015).

  27. 27.

    Song, Q. et al. A reference methylome database and analysis pipeline to facilitate integrative and comparative epigenomics. PLoS One 8, e81148 (2013).

  28. 28.

    Edwards, J. R. et al. Chromatin and sequence features that define the fine and gross structure of genomic methylation patterns. Genome Res. 20, 972–980 (2010).

  29. 29.

    Gaidatzis, D. et al. DNA sequence explains seemingly disordered methylation levels in partially methylated domains of mammalian genomes. PLoS Genet. 10, e1004143 (2014).

  30. 30.

    Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

  31. 31.

    Farlik, M. et al. DNA methylation dynamics of human hematopoietic stem cell differentiation. Cell Stem Cell 19, 808–822 (2016).

  32. 32.

    Knijnenburg, T. A. et al. Multiscale representation of genomic signals. Nat. Methods 11, 689–694 (2014).

  33. 33.

    Guelen, L. et al. Domain organization of human chromosomes revealed by mapping of nuclear lamina interactions. Nature 453, 948–951 (2008).

  34. 34.

    Lister, R. et al. Global epigenomic reconfiguration during mammalian brain development. Science 341, 1237905 (2013).

  35. 35.

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

  36. 36.

    Rodriguez-Martin, B. et al. Pan-cancer analysis of whole genomes reveals driver rearrangements promoted by LINE-1 retrotransposition in human tumours. Preprint at bioRxiv https://doi.org/10.1101/179705 (2017).

  37. 37.

    Burnet, F. M. A modification of Jerne’s theory of antibody production using the concept of clonal selection. CA Cancer J. Clin. 26, 119–121 (1976).

  38. 38.

    Wu, H. & Zhang, Y. Reversing DNA methylation: mechanisms, genomics, and biological functions. Cell 156, 45–68 (2014).

  39. 39.

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

  40. 40.

    Lee, E. et al. Landscape of somatic retrotransposition in human cancers. Science 337, 967–971 (2012).

  41. 41.

    Tubio, J. M. C. et al. Extensive transduction of nonrepetitive DNA mediated by L1 retrotransposition in cancer genomes. Science 345, 1251343–1251343 (2014).

  42. 42.

    Iskow, R. C. et al. Natural mutagenesis of human genomes by endogenous retrotransposons. Cell 141, 1253–1261 (2010).

  43. 43.

    Howard, G., Eiges, R., Gaudet, F., Jaenisch, R. & Eden, A. Activation and transposition of endogenous retroviral elements in hypomethylation induced tumors in mice. Oncogene 27, 404–408 (2008).

  44. 44.

    Santos, A., Wernersson, R. & Jensen, L. J. Cyclebase 3.0: a multi-organism database on cell-cycle regulation and phenotypes. Nucleic Acids Res. 43, D1140–D1144 (2015).

  45. 45.

    Baubec, T. et al. Genomic profiling of DNA methyltransferases reveals a role for DNMT3B in genic methylation. Nature 520, 243–247 (2015).

  46. 46.

    Li, E., Bestor, T. H. & Jaenisch, R. Targeted mutation of the DNA methyltransferase gene results in embryonic lethality. Cell 69, 915–926 (1992).

  47. 47.

    Li, Z. et al. Distinct roles of DNMT1-dependent and DNMT1-independent methylation patterns in the genome of mouse embryonic stem cells. Genome Biol. 16, 115 (2015).

  48. 48.

    Jones, P. A. & Liang, G. Rethinking how DNA methylation patterns are maintained. Nat. Rev. Genet. 10, 805–811 (2009).

  49. 49.

    Hermann, A., Goyal, R. & Jeltsch, A. The Dnmt1 DNA-(cytosine-C5)-methyltransferase methylates DNA processively with high preference for hemimethylated target sites. J. Biol. Chem. 279, 48350–48359 (2004).

  50. 50.

    Flynn, J., Azzam, R. & Reich, N. DNA binding discrimination of the murine DNA cytosine-C5 methyltransferase. J. Mol. Biol. 279, 101–116 (1998).

  51. 51.

    Bashtrykov, P., Ragozin, S. & Jeltsch, A. Mechanistic details of the DNA recognition by the Dnmt1 DNA methyltransferase. FEBS Lett. 586, 1821–1823 (2012).

  52. 52.

    Johann, P. D. et al. Atypical teratoid/rhabdoid tumors are comprised of three epigenetic subgroups with distinct enhancer landscapes. Cancer Cell 29, 379–393 (2016).

  53. 53.

    Liang, G. et al. Cooperativity between DNA methyltransferases in the maintenance methylation of repetitive elements. Mol. Cell. Biol. 22, 480–491 (2002).

  54. 54.

    Schermelleh, L. et al. Dynamics of Dnmt1 interaction with the replication machinery and its role in postreplicative maintenance of DNA methylation. Nucleic Acids Res. 35, 4301–4312 (2007).

  55. 55.

    Neri, F. et al. Intragenic DNA methylation prevents spurious transcription initiation. Nature 543, 72–77 (2017).

  56. 56.

    Jones, P. A. The DNA methylation paradox. Trends Genet. 15, 34–37 (1999).

  57. 57.

    Papillon-Cavanagh, S. et al. Impaired H3K36 methylation defines a subset of head and neck squamous cell carcinomas. Nat. Genet. 49, 180–185 (2017).

  58. 58.

    Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013).

  59. 59.

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

  60. 60.

    Slieker, R. C. et al. Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms. Genome Biol. 17, 191 (2016).

  61. 61.

    Knight, A. K. et al. An epigenetic clock for gestational age at birth based on blood methylation data. Genome Biol. 17, 206 (2016).

  62. 62.

    Walsh, C. P., Chaillet, J. R. & Bestor, T. H. Transcription of IAP endogenous retroviruses is constrained by cytosine methylation. Nat. Genet. 20, 116–117 (1998).

  63. 63.

    Bourc’his, D. & Bestor, T. H. Meiotic catastrophe and retrotransposon reactivation in male germ cells lacking Dnmt3L. Nature 431, 96–99 (2004).

  64. 64.

    Trinh, B. N., Long, T. I., Nickel, A. E., Shibata, D. & Laird, P. W. DNA methyltransferase deficiency modifies cancer susceptibility in mice lacking DNA mismatch repair. Mol. Cell. Biol. 22, 2906–2917 (2002).

  65. 65.

    Eden, A. Chromosomal instability and tumors promoted by DNA hypomethylation. Science 300, 455 (2003).

  66. 66.

    Ehrlich, M. DNA hypomethylation in cancer cells. Epigenomics 1, 239–259 (2009).

  67. 67.

    Solyom, S. et al. Pathogenic orphan transduction created by a nonreference LINE-1 retrotransposon. Hum. Mutat. 33, 369–371 (2012).

  68. 68.

    Helman, E. et al. Somatic retrotransposition in human cancer revealed by whole-genome and exome sequencing. Genome Res. 24, 1053–1063 (2014).

  69. 69.

    Amendola, M. & van Steensel, B. Nuclear lamins are not required for lamina-associated domain organization in mouse embryonic stem cells. EMBO Rep. 16, 610–617 (2015).

  70. 70.

    Hiratani, I. et al. Genome-wide dynamics of replication timing revealed by in vitro models of mouse embryogenesis. Genome Res. 20, 155–169 (2010).

  71. 71.

    Xi, Y. & Li, W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics 10, 232 (2009).

  72. 72.

    Liu, Y., Siegmund, K. D., Laird, P. W. & Berman, B. P. Bis-SNP: combined DNA methylation and SNP calling for Bisulfite-seq data. Genome Biol. 13, R61 (2012).

  73. 73.

    Triche, T. J. Jr., Weisenberger, D. J., Van Den Berg, D., Laird, P. W. & Siegmund, K. D. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 41, e90 (2013).

  74. 74.

    Zhou, W., Laird, P. W. & Shen, H. Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes. Nucleic Acids Res. 45, e22 (2017).

  75. 75.

    Hansen, R. S. et al. Sequencing newly replicated DNA reveals widespread plasticity in human replication timing. Proc. Natl Acad. Sci. USA 107, 139–144 (2010).

  76. 76.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

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We thank T. Hinoue and H. Noushmehr for help selecting TCGA WGBS samples based on analysis of other TCGA data types and H. Goodridge and D.-C. Lin for useful discussions and comments. We thank The Cancer Genome Atlas Research program office, especially K. Shaw, for helping to get the WGBS project up and running. We also thank the members of the TCGA Research Network along with the dozens of other research groups that generated the published datasets that were used here to gain new insights. This project was supported by the Van Andel Research Institute, the Cedars-Sinai Center for Bioinformatics and Functional Genomics and the Samuel Oschin Comprehensive Cancer Institute, and the University of Southern California USC Epigenome Center. The work was funded by the following grants: National Institutes of Health/National Cancer Institute grants U24 CA143882 (P.W.L., B.P.B., H.Q.D., and H.S.); R01 CA170550 (P.W.L.); U01 CA184826 (B.P.B.); U24 CA210969 (P.W.L., B.P.B., and H.S.), Ovarian Cancer Research Fund Grant 373933 (H.S.), and National Institutes of Health/National Human Genome Research Institute grant R01 HG006705 (B.P.B.).

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Author notes

  1. These authors contributed equally: Wanding Zhou and Huy Q. Dinh. These authors jointly directed this work: Hui Shen, Peter W. Laird and Benjamin P. Berman.


  1. Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI, USA

    • Wanding Zhou
    • , Hui Shen
    •  & Peter W. Laird
  2. Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    • Huy Q. Dinh
    •  & Benjamin P. Berman
  3. Van Andel Institute, Grand Rapids, MI, USA

    • Zachary Ramjan
  4. USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA

    • Daniel J. Weisenberger
    •  & Charles M. Nicolet


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H.S., P.W.L., and B.P.B. conceived the study. C.M.N., P.W.L., and B.P.B. oversaw the data generation and data quality control, with assistance from D.J.W. Z.R. automated the next-generation sequencing analysis and quality control steps, and submission of data to NCI repositories. W.Z., H.Q.D., H.S., and B.P.B. performed computational analysis and produced figures. W.Z., H.S., P.W.L., and B.P.B. wrote the manuscript, with significant contributions from H.Q.D. H.S., P.W.L., and B.P.B. supervised the project.

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The authors declare no competing interests.

Corresponding authors

Correspondence to Hui Shen or Peter W. Laird or Benjamin P. Berman.

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  3. Supplementary Table 1

    Meta-information and sources of WGBS samples reported and analyzed

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