Increased methylation variation in epigenetic domains across cancer types


Tumor heterogeneity is a major barrier to effective cancer diagnosis and treatment. We recently identified cancer-specific differentially DNA-methylated regions (cDMRs) in colon cancer, which also distinguish normal tissue types from each other, suggesting that these cDMRs might be generalized across cancer types. Here we show stochastic methylation variation of the same cDMRs, distinguishing cancer from normal tissue, in colon, lung, breast, thyroid and Wilms' tumors, with intermediate variation in adenomas. Whole-genome bisulfite sequencing shows these variable cDMRs are related to loss of sharply delimited methylation boundaries at CpG islands. Furthermore, we find hypomethylation of discrete blocks encompassing half the genome, with extreme gene expression variability. Genes associated with the cDMRs and large blocks are involved in mitosis and matrix remodeling, respectively. We suggest a model for cancer involving loss of epigenetic stability of well-defined genomic domains that underlies increased methylation variability in cancer that may contribute to tumor heterogeneity.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Increased methylation variance of common CpG sites across human cancer types.
Figure 2: Large hypomethylated genomic blocks in human colon cancer.
Figure 3: Loss of methylation stability at small DMRs.
Figure 4: Adenomas show intermediate methylation variability.
Figure 5: High variability of gene expression associated with blocks.


  1. 1

    Jones, P.A. & Baylin, S.B. The fundamental role of epigenetic events in cancer. Nat. Rev. Genet. 3, 415–428 (2002).

    CAS  Article  Google Scholar 

  2. 2

    Feinberg, A.P. & Tycko, B. The history of cancer epigenetics. Nat. Rev. Cancer 4, 143–153 (2004).

    CAS  Article  Google Scholar 

  3. 3

    Esteller, M. Epigenetics in cancer. N. Engl. J. Med. 358, 1148–1159 (2008).

    CAS  Article  Google Scholar 

  4. 4

    Irizarry, R.A. et al. The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat. Genet. 41, 178–186 (2009).

    CAS  Article  Google Scholar 

  5. 5

    Doi, A. et al. Differential methylation of tissue- and cancer-specific CpG island shores distinguishes human induced pluripotent stem cells, embryonic stem cells and fibroblasts. Nat. Genet. 41, 1350–1353 (2009).

    CAS  Article  Google Scholar 

  6. 6

    Irizarry, R.A. et al. Comprehensive high-throughput arrays for relative methylation (CHARM). Genome Res. 18, 780–790 (2008).

    CAS  Article  Google Scholar 

  7. 7

    Bibikova, M. et al. High-throughput DNA methylation profiling using universal bead arrays. Genome Res. 16, 383–393 (2006).

    CAS  Article  Google Scholar 

  8. 8

    Feinberg, A.P., Gehrke, C.W., Kuo, K.C. & Ehrlich, M. Reduced genomic 5-methylcytosine content in human colonic neoplasia. Cancer Res. 48, 1159–1161 (1988).

    CAS  PubMed  Google Scholar 

  9. 9

    Ehrlich, M. DNA methylation in cancer: too much, but also too little. Oncogene 21, 5400–5413 (2002).

    CAS  Article  Google Scholar 

  10. 10

    Ogino, S. et al. A cohort study of tumoral LINE-1 hypomethylation and prognosis in colon cancer. J. Natl. Cancer Inst. 100, 1734–1738 (2008).

    CAS  Article  Google Scholar 

  11. 11

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

    CAS  Article  Google Scholar 

  12. 12

    Wen, B., Wu, H., Shinkai, Y., Irizarry, R.A. & Feinberg, A.P. Large histone H3 lysine 9 dimethylated chromatin blocks distinguish differentiated from embryonic stem cells. Nat. Genet. 41, 246–250 (2009).

    CAS  Article  Google Scholar 

  13. 13

    Hesselberth, J.R. et al. Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283–289 (2009).

    CAS  Article  Google Scholar 

  14. 14

    Li, Y. et al. The DNA methylome of human peripheral blood mononuclear cells. PLoS Biol. 8, e1000533 (2010).

    Article  Google Scholar 

  15. 15

    Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).

    CAS  Article  Google Scholar 

  16. 16

    Frigola, J. et al. Epigenetic remodeling in colorectal cancer results in coordinate gene suppression across an entire chromosome band. Nat. Genet. 38, 540–549 (2006).

    CAS  Article  Google Scholar 

  17. 17

    Gal-Yam, E.N., Saito, Y., Egger, G. & Jones, P.A. Cancer epigenetics: modifications, screening, and therapy. Annu. Rev. Med. 59, 267–280 (2008).

    CAS  Article  Google Scholar 

  18. 18

    Yu, A.E., Hewitt, R.E., Connor, E.W. & Stetler-Stevenson, W.G. Matrix metalloproteinases. Novel targets for directed cancer therapy. Drugs Aging 11, 229–244 (1997).

    CAS  Article  Google Scholar 

  19. 19

    Aleman, M.J. et al. Inhibition of Single Minded 2 gene expression mediates tumor-selective apoptosis and differentiation in human colon cancer cells. Proc. Natl. Acad. Sci. USA 102, 12765–12770 (2005).

    CAS  Article  Google Scholar 

  20. 20

    Yeung, H.Y. et al. Hypoxia-inducible factor-1-mediated activation of stanniocalcin-1 in human cancer cells. Endocrinology 146, 4951–4960 (2005).

    CAS  Article  Google Scholar 

  21. 21

    Eurich, K., Segawa, M., Toei-Shimizu, S. & Mizoguchi, E. Potential role of chitinase 3-like-1 in inflammation-associated carcinogenic changes of epithelial cells. World J. Gastroenterol. 15, 5249–5259 (2009).

    CAS  Article  Google Scholar 

  22. 22

    Fischer, H. et al. COL11A1 in FAP polyps and in sporadic colorectal tumors. BMC Cancer 1, 17 (2001).

    CAS  Article  Google Scholar 

  23. 23

    Clark, S.J. Action at a distance: epigenetic silencing of large chromosomal regions in carcinogenesis. Hum. Mol. Genet. 16, R88–R95 (2007).

    CAS  Article  Google Scholar 

  24. 24

    Feber, A. et al. Comparative methylome analysis of benign and malignant peripheral nerve sheath tumors. Genome Res. 21, 515–524 (2011).

    CAS  Article  Google Scholar 

  25. 25

    Feinberg, A.P. & Irizarry, R. Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease. Proc. Natl. Acad. Sci. USA 107, 1757–1764 (2010).

    CAS  Article  Google Scholar 

  26. 26

    Zilliox, M.J. & Irizarry, R.A. A gene expression bar code for microarray data. Nat. Methods 4, 911–913 (2007).

    CAS  Article  Google Scholar 

  27. 27

    Bolstad, B.M., Irizarry, R.A., Astrand, M. & Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

    CAS  Article  Google Scholar 

  28. 28

    Leek, J.T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).

    CAS  Article  Google Scholar 

  29. 29

    Aryee, M.J. et al. Accurate genome-scale percentage DNA methylation estimates from microarray data. Biostatistics 12, 197–210 (2011).

    Article  Google Scholar 

  30. 30

    Bormann Chung, C.A. et al. Whole methylome analysis by ultra-deep sequencing using two-base encoding. PLoS ONE 5, e9320 (2010).

    Article  Google Scholar 

  31. 31

    Deng, J. et al. Targeted bisulfite sequencing reveals changes in DNA methylation associated with nuclear reprogramming. Nat. Biotechnol. 27, 353–360 (2009).

    CAS  Article  Google Scholar 

  32. 32

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

    Article  Google Scholar 

  33. 33

    Loader, C. Local Regression and Likelihood (Springer Verlag, New York, New York, USA, 1999).

  34. 34

    Eckhardt, F. et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nat. Genet. 38, 1378–1385 (2006).

    CAS  Article  Google Scholar 

  35. 35

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

    CAS  Article  Google Scholar 

  36. 36

    Olshen, A.B., Venkatraman, E.S., Lucito, R. & Wigler, M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004).

    Article  Google Scholar 

  37. 37

    Sabates-Bellver, J. et al. Transcriptome profile of human colorectal adenomas. Mol. Cancer Res. 5, 1263–1275 (2007).

    CAS  Article  Google Scholar 

  38. 38

    Gyorffy, B., Molnar, B., Lage, H., Szallasi, Z. & Eklund, A.C. Evaluation of microarray preprocessing algorithms based on concordance with RT-PCR in clinical samples. PLoS ONE 4, e5645 (2009).

    Article  Google Scholar 

  39. 39

    Galamb, O. et al. Reversal of gene expression changes in the colorectal normal-adenoma pathway by NS398 selective COX2 inhibitor. Br. J. Cancer 102, 765–773 (2010).

    CAS  Article  Google Scholar 

  40. 40

    Smith, J.C., Boone, B.E., Opalenik, S.R., Williams, S.M. & Russell, S.B. Gene profiling of keloid fibroblasts shows altered expression in multiple fibrosis-associated pathways. J. Invest. Dermatol. 128, 1298–1310 (2008).

    CAS  Article  Google Scholar 

  41. 41

    Chen, Y. et al. Developing and applying a gene functional association network for anti-angiogenic kinase inhibitor activity assessment in an angiogenesis co-culture model. BMC Genomics 9, 264 (2008).

    Article  Google Scholar 

  42. 42

    Duarte, T.L., Cooke, M.S. & Jones, G.D. Gene expression profiling reveals new protective roles for vitamin C in human skin cells. Free Radic. Biol. Med. 46, 78–87 (2009).

    CAS  Article  Google Scholar 

Download references


We thank C. Adams and Applied Biosystems, Inc. for supplying reagents for the sequencing experiments, B. Vogelstein, F. Giardiello and M. Zeiger for tumor samples and M. Newhouse for computer assistance. This work was supported by US National Institutes of Health grants R37CA054358, R01HG005220, 5P50HG003233, F32CA138111, 5R01GM083084 and R01DA025779 (K.Z.).

Author information




K.D.H. and R.A.I. wrote the DMR finder and smoothing algorithms. W.T. performed and analyzed the arrays with H.C.B., who wrote new software for this purpose. S.S. made the libraries and performed validation. B.L. wrote new methylation sequence alignment software. O.G.M. performed the histopathologic analysis. B.W. and H.W. performed LOCK experiments. Y.L. performed copy number experiments. D.D. and K.Z. performed bisulfite capture. E.B. performed the sequencing. R.A.I. and A.P.F. conceived and led the experiments and wrote the paper with the predominant assistance of K.D.H., W.T., H.C.B. and B.L.

Corresponding authors

Correspondence to Rafael A Irizarry or Andrew P Feinberg.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Note, Supplementary Tables 1, 2, 4–6, 8–10 and 13–19 and Supplementary Figures 1–24. (PDF 15293 kb)

Supplementary Table 3

List of block locations (XLS 2912 kb)

Supplementary Table 7

List of small DMRs (XLS 1969 kb)

Supplementary Table 11

List of genes showing statistically significant over-expression in cancer compared to normal samples and are within 2,000 bp from an outward methylation boundary shift. (XLS 263 kb)

Supplementary Table 12

Genes with higher gene expression variability in cancer compared to normal. (XLS 170 kb)

Supplementary Table 20

As Supplementary Table 3, but for sample-specific blocks. (XLS 8811 kb)

Supplementary Table 21

As Supplementary Table 7, but for sample-specific small DMRs. (XLS 5053 kb)

Supplementary Table 22

Primers used for bisulfite pyrosequencing (XLS 35 kb)

Supplementary Table 23

List of microarrays used to identify tissue-specific genes (XLS 87 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

Further reading