The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space

Abstract

Glioblastoma is characterized by widespread genetic and transcriptional heterogeneity, yet little is known about the role of the epigenome in glioblastoma disease progression. Here, we present genome-scale maps of DNA methylation in matched primary and recurring glioblastoma tumors, using data from a highly annotated clinical cohort that was selected through a national patient registry. We demonstrate the feasibility of DNA methylation mapping in a large set of routinely collected FFPE samples, and we validate bisulfite sequencing as a multipurpose assay that allowed us to infer a range of different genetic, epigenetic, and transcriptional characteristics of the profiled tumor samples. On the basis of these data, we identified subtle differences between primary and recurring tumors, links between DNA methylation and the tumor microenvironment, and an association of epigenetic tumor heterogeneity with patient survival. In summary, this study establishes an open resource for dissecting DNA methylation heterogeneity in a genetically diverse and heterogeneous cancer, and it demonstrates the feasibility of integrating epigenomics, radiology, and digital pathology for a national cohort, thereby leveraging existing samples and data collected as part of routine clinical practice.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: DNA methylation landscape of glioblastoma disease progression.
Fig. 2: Glioblastoma transcriptional subtypes inferred from DNA methylation profiles.
Fig. 3: DNA methylation and the tumor microenvironment.
Fig. 4: DNA methylation and histopathological tumor characteristics.
Fig. 5: DNA methylation heterogeneity in glioblastoma disease progression.
Fig. 6: DNA methylation differences between primary and recurring tumors.

References

  1. 1.

    Ferlay, J. et al. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11. International Agency for Research on Cancer http://globocan.iarc.fr (2013).

  2. 2.

    Woehrer, A., Bauchet, L. & Barnholtz-Sloan, J. S. Glioblastoma survival: has it improved? Evidence from population-based studies. Curr. Opin. Neurol. 27, 666–674 (2014).

    PubMed  Google Scholar 

  3. 3.

    Chinot, O. L. et al. Bevacizumab plus radiotherapy–temozolomide for newly diagnosed glioblastoma. N. Engl. J. Med. 370, 709–722 (2014).

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Gilbert, M. R. et al. A randomized trial of bevacizumab for newly diagnosed glioblastoma. N. Engl. J. Med. 370, 699–708 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Stupp, R. et al. Cilengitide combined with standard treatment for patients with newly diagnosed glioblastoma with methylated MGMT promoter (CENTRIC EORTC 26071-22072 study): a multicentre, randomised, open-label, phase 3 trial. Lancet Oncol. 15, 1100–1108 (2014).

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Kim, H. et al. Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution. Genome Res. 25, 316–327 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Kim, J. et al. Spatiotemporal evolution of the primary glioblastoma genome. Cancer Cell 28, 318–328 (2015).

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Kumar, A. et al. Deep sequencing of multiple regions of glial tumors reveals spatial heterogeneity for mutations in clinically relevant genes. Genome Biol. 15, 530 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Lee, J. K. et al. Spatiotemporal genomic architecture informs precision oncology in glioblastoma. Nat. Genet. 49, 594–599 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Meyer, M. et al. Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. Proc. Natl. Acad. Sci. USA 112, 851–856 (2015).

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Snuderl, M. et al. Mosaic amplification of multiple receptor tyrosine kinase genes in glioblastoma. Cancer Cell 20, 810–817 (2011).

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Sottoriva, A. et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl. Acad. Sci. USA 110, 4009–4014 (2013).

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Wang, J. et al. Clonal evolution of glioblastoma under therapy. Nat. Genet. 48, 768–776 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Wang, Q. et al. Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell 33, 152 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Capper, D. et al. DNA methylation–based classification of central nervous system tumours. Nature 555, 469–474 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Ceccarelli, M. et al. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 164, 550–563 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Sturm, D. et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 22, 425–437 (2012).

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Brocks, D. et al. Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer. Cell Reports 8, 798–806 (2014).

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Mazor, T. et al. DNA methylation and somatic mutations converge on the cell cycle and define similar evolutionary histories in brain tumors. Cancer Cell 28, 307–317 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Hao, J. J. et al. Spatial intratumoral heterogeneity and temporal clonal evolution in esophageal squamous cell carcinoma. Nat. Genet. 48, 1500–1507 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Lin, D. C. et al. Genomic and epigenomic heterogeneity of hepatocellular carcinoma. Cancer Res. 77, 2255–2265 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Li, S. et al. Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia. Nat. Med. 22, 792–799 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Landau, D. A. et al. Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. Cancer Cell 26, 813–825 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Sheffield, N. C. et al. DNA methylation heterogeneity defines a disease spectrum in Ewing sarcoma. Nat. Med. 23, 386–395 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Wöhrer, A. et al. The Austrian Brain Tumour Registry: a cooperative way to establish a population-based brain tumour registry. J. Neurooncol. 95, 401–411 (2009).

    Article  PubMed  Google Scholar 

  28. 28.

    Meissner, A. et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454, 766–770 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Klughammer, J. et al. Differential DNA methylation analysis without a reference genome. Cell Reports 13, 2621–2633 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Veillard, A. C., Datlinger, P., Laczik, M., Squazzo, S. & Bock, C. Diagenode® premium RRBS technology: cost-effective DNA methylation mapping with superior coverage. Nat. Methods 13, 184 (2016).

    Article  CAS  Google Scholar 

  31. 31.

    Bock, C. et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat. Biotechnol. 28, 1106–1114 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Gu, H. et al. Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution. Nat. Methods 7, 133–136 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Stefanits, H. et al. KINFix—a formalin-free noncommercial fixative optimized for histological, immunohistochemical and molecular analyses of neurosurgical tissue specimens. Clin. Neuropathol. 35, 3–12 (2016).

    Article  PubMed  Google Scholar 

  34. 34.

    Bock, C. Analysing and interpreting DNA methylation data. Nat. Rev. Genet. 13, 705–719 (2012).

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Weller, M. et al. MGMT promoter methylation in malignant gliomas: ready for personalized medicine? Nat. Rev. Neurol. 6, 39–51 (2010).

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Bienkowski, M. et al. Clinical Neuropathology practice guide 5-2015: MGMT methylation pyrosequencing in glioblastoma: unresolved issues and open questions. Clin. Neuropathol. 34, 250–257 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Mikeska, T. et al. Optimization of quantitative MGMT promoter methylation analysis using pyrosequencing and combined bisulfite restriction analysis. J. Mol. Diagn. 9, 368–381 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Turcan, S. et al. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 483, 479–483 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Wemmert, S. et al. Patients with high-grade gliomas harboring deletions of chromosomes 9p and 10q benefit from temozolomide treatment. Neoplasia 7, 883–893 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Verhaak, R. G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98–110 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Bowman, R. L., Wang, Q., Carro, A., Verhaak, R. G. & Squatrito, M. GlioVis data portal for visualization and analysis of brain tumor expression datasets. Neuro-oncol. 19, 139–141 (2017).

    Article  PubMed  Google Scholar 

  42. 42.

    Sheffield, N. C. & Bock, C. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics 32, (587–589 (2016).

    Google Scholar 

  43. 43.

    Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Beier, C. P. et al. The cancer stem cell subtype determines immune infiltration of glioblastoma. Stem Cells Dev. 21, 2753–2761 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Strojnik, T. et al. Prognostic impact of CD68 and kallikrein 6 in human glioma. Anticancer Res. 29, 3269–3279 (2009).

    CAS  PubMed  Google Scholar 

  46. 46.

    Prosniak, M. et al. Glioma grade is associated with the accumulation and activity of cells bearing M2 monocyte markers. Clin. Cancer Res. 19, 3776–3786 (2013).

    CAS  Article  PubMed  Google Scholar 

  47. 47.

    Nowosielski, M. et al. Progression types after antiangiogenic therapy are related to outcome in recurrent glioblastoma. Neurology 82, 1684–1692 (2014).

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Gentles, A. J. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21, 938–945 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Louis, D. N. et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 131, 803–820 (2016).

    Article  PubMed  Google Scholar 

  50. 50.

    Li, S. et al. Dynamic evolution of clonal epialleles revealed by methclone. Genome Biol. 15, 472 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Aldape, K. et al. GLASS Consortium. Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium. Neuro-oncol. 20, 873–884 (2018).

    Article  Google Scholar 

  52. 52.

    Sahm, F. et al. DNA methylation–based classification and grading system for meningioma: a multicentre, retrospective analysis. Lancet Oncol. 18, 682–694 (2017).

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    McCord, M., Mukouyama, Y. S., Gilbert, M. R. & Jackson, S. Targeting WNT signaling for multifaceted glioblastoma therapy. Front. Cell. Neurosci. 11, 318 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Bock, C. et al. BLUEPRINT consortium. Quantitative comparison of DNA methylation assays for biomarker development and clinical applications. Nat. Biotechnol. 34, 726–737 (2016).

    Article  CAS  Google Scholar 

  55. 55.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Xi, Y. et al. RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing. Bioinformatics 28, 430–432 (2012).

    CAS  Article  PubMed  Google Scholar 

  58. 58.

    Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, (1571–1572 (2011).

    Google Scholar 

  59. 59.

    Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Talevich, E., Shain, A. H., Botton, T. & Bastian, B. C. CNVkit: genome-wide copy number detection and visualization from targeted DNA sequencing. PLOS Comput. Biol. 12, e1004873 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Kuilman, T. et al. CopywriteR: DNA copy number detection from off-target sequence data. Genome Biol. 16, 49 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Li, J. et al. Single-cell transcriptomes reveal characteristic features of human pancreatic islet cell types. EMBO Rep. 17, 178–187 (2016).

    CAS  Article  PubMed  Google Scholar 

  64. 64.

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Glaus, P., Honkela, A. & Rattray, M. Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics 28, 1721–1728 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Sahm, F. et al. Next-generation sequencing in routine brain tumor diagnostics enables an integrated diagnosis and identifies actionable targets. Acta Neuropathol. 131, 903–910 (2016).

    CAS  Article  PubMed  Google Scholar 

  67. 67.

    Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–7 (2016). W1.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Makambi, K. Weighted inverse chi-square method for correlated significance tests. J. Appl. Stat. 30, 225–234 (2003).

    Article  Google Scholar 

  71. 71.

    Assenov, Y. et al. Comprehensive analysis of DNA methylation data with RnBeads. Nat. Methods 11, 1138–1140 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Hinrichs, A. S. et al. The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 34, D590–D598 (2006).

    CAS  Article  PubMed  Google Scholar 

  74. 74.

    Lawson, J.T., Tomazou, E.M., Bock, C. & Sheffield, N.C. MIRA: an R package for DNA methylation-based inference of regulatory activity. Bioinformatics https://doi.org/10.1093/bioinformatics/bty083 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Deroulers, C. et al. Analyzing huge pathology images with open source software. Diagn. Pathol. 8, 92 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Schindelin, J., Rueden, C. T., Hiner, M. C. & Eliceiri, K. W. The ImageJ ecosystem: an open platform for biomedical image analysis. Mol. Reprod. Dev. 82, 518–529 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Ruifrok, A. C. & Johnston, D. A. Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23, 291–299 (2001).

    CAS  PubMed  Google Scholar 

  79. 79.

    Phansalkar, N., More, S., Sabale, A. & Joshi, M. Adaptive local thresholding for detection of nuclei in diversity stained cytology images. In International Conference on Communications and Signal Processing (ICCSP) 218–220 (IEEE, 2011).

  80. 80.

    Vincent, L. & Soille, P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13, 583–598 (1991).

    Article  Google Scholar 

  81. 81.

    Liu, Q. et al. Genetic, epigenetic, and molecular landscapes of multifocal and multicentric glioblastoma. Acta Neuropathol. 130, 587–597 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Porz, N. et al. Multi-modal glioblastoma segmentation: man versus machine. PLoS One 9, e96873 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Wen, P. Y. et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J. Clin. Oncol. 28, 1963–1972 (2010).

    Article  PubMed  Google Scholar 

  84. 84.

    Nowosielski, M. et al. Radiologic progression of glioblastoma under therapy-an exploratory analysis of AVAglio. Neuro. Oncol. 20, 557–566 (2018).

    Article  PubMed  Google Scholar 

  85. 85.

    Crammer, K. & Singer, Y. On the algorithmic implementation of multiclass kernel-based vector machines. J. Mach. Learn. Res. 2, 265–292 (2002).

    Google Scholar 

  86. 86.

    Gentleman, R. & Temple Lang, D. Statistical analyses and reproducible research. Bioconductor project working papers. Working paper 2. http://biostats.bepress.com/bioconductor/paper2 (2004).

Download references

Acknowledgements

We thank all patients who have donated their samples for this study. We also thank G. Wilk, M. Muck, S. Schmid, and U. Andel for technical assistance with immunohistochemical stainings, macrodissection, and tumor tissue shavings; S. Mages for contributing to the interactive data visualization; the Biomedical Sequencing Facility at CeMM for assistance with next-generation sequencing; and all members of the Bock lab for their help and advice. The study was funded in part by an Austrian Science Fund grant (FWF KLI394) to A.W., a Marie Curie Career Integration Grant (European Union’s Seventh Framework Programme grant agreement no. PCIG12-GA-2012-333595) to C.B., an ERA-NET project grant (EpiMark FWF I 1575-B19) to C.B., an Austrian Science Fund grant (FWF I2714-B31) to G.L. and K.-H.N, and an ERC Starting Grant (European Union’s Horizon 2020 research and innovation programme, grant agreement no. 640396) to B.B. Moreover, C.B. is supported by a New Frontiers Group award of the Austrian Academy of Sciences and by an ERC Starting Grant (European Union’s Horizon 2020 research and innovation programme, grant agreement no. 679146). Activities of the Austrian Brain Tumor Registry are supported by unrestricted research grants of Roche Austria to J.A.H. and the Austrian Society of Neurology to S.O. Some of the samples used for this research project were kindly provided by Biobank Graz.

Author information

Affiliations

Authors

Contributions

J. Klughammer, A.W., and C.B. designed the study. B.K., T.R., K.-H.N., J.F., N.P., M.N., M.A., M.M., T.S., G.L., B.B., J.A.H., and A.W. established and annotated the cohort. A.N. and P.D. performed DNA methylation profiling. D.A. performed low-coverage whole-genome sequencing. M.S. performed RNA-seq. J. Klughammer performed the data analysis. N.F., N.C.S, and B.E. contributed to data analysis. P.M., C.F.F., J. Kerschbaumer, C.T., A.E.G., G.S., M.K., S.O., F.M., S.W., J.T., J.B., J. Pichler, J.H., S.K., K.M.A., G.v.C., F.P., C.S., J. Preiser, T.H., P.A.W., W.K., F.W., T.B.-K., M.S., S.S., K.D., M.P., E.K., G.W., and C.M. contributed tumor samples and clinical data. J. Klughammer, A.W., and C.B. wrote the manuscript with contributions from all authors.

Corresponding author

Correspondence to Adelheid Woehrer.

Ethics declarations

Competing interests

The optimized RRBS protocol that was used in this study has been licensed to Diagenode s.a. (Liège, Belgium) and commercialized as a kit and service.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13

Reporting Summary

Supplementary Table 1

Patient summary table

Supplementary Table 2

RRBS summary table

Supplementary Table 3

Survival analysis summary table

Supplementary Table 4

Association analysis summary table

Source Data Figure 1

Source Data Figure 1

Source Data Figure 2

Source Data Figure 2

Source Data Figure 3

Source Data Figure 3

Source Data Figure 4

Source Data Figure 4

Source Data Figure 5

Source Data Figure 5

Source Data Figure 6

Source Data Figure 6

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Klughammer, J., Kiesel, B., Roetzer, T. et al. The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space. Nat Med 24, 1611–1624 (2018). https://doi.org/10.1038/s41591-018-0156-x

Download citation

Further reading