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Decoding the regulatory landscape of medulloblastoma using DNA methylation sequencing

Abstract

Epigenetic alterations, that is, disruption of DNA methylation and chromatin architecture, are now acknowledged as a universal feature of tumorigenesis1. Medulloblastoma, a clinically challenging, malignant childhood brain tumour, is no exception. Despite much progress from recent genomics studies, with recurrent changes identified in each of the four distinct tumour subgroups (WNT-pathway-activated, SHH-pathway-activated, and the less-well-characterized Group 3 and Group 4)2,3,4, many cases still lack an obvious genetic driver. Here we present whole-genome bisulphite-sequencing data from thirty-four human and five murine tumours plus eight human and three murine normal controls, augmented with matched whole-genome, RNA and chromatin immunoprecipitation sequencing data. This comprehensive data set allowed us to decipher several features underlying the interplay between the genome, epigenome and transcriptome, and its effects on medulloblastoma pathophysiology. Most notable were highly prevalent regions of hypomethylation correlating with increased gene expression, extending tens of kilobases downstream of transcription start sites. Focal regions of low methylation linked to transcription-factor-binding sites shed light on differential transcriptional networks between subgroups, whereas increased methylation due to re-normalization of repressed chromatin in DNA methylation valleys was positively correlated with gene expression. Large, partially methylated domains affecting up to one-third of the genome showed increased mutation rates and gene silencing in a subgroup-specific fashion. Epigenetic alterations also affected novel medulloblastoma candidate genes (for example, LIN28B), resulting in alternative promoter usage and/or differential messenger RNA/microRNA expression. Analysis of mouse medulloblastoma and precursor-cell methylation demonstrated a somatic origin for many alterations. Our data provide insights into the epigenetic regulation of transcription and genome organization in medulloblastoma pathogenesis, which are probably also of importance in a wider developmental and disease context.

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Figure 1: Negative correlation between methylation and expression is enriched in extensive regions downstream of promoters.
Figure 2: Differential methylation around LIN28B reveals a novel promoter, tightly correlated with expression in Group 3 and Group 4 medulloblastomas.
Figure 3: Megabase-scale silenced domains and smaller DMVs are associated with distinct histone marks and positive correlation with expression.
Figure 4: Focal LMRs mark binding sites for key transcriptional regulators.

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Gene Expression Omnibus

Data deposits

Short-read sequencing data have been deposited in the European Genome-phenome Archive (http://www.ebi.ac.uk/ega/) under accession number EGAS00001000561. Methylation array data have been deposited in the Gene Expression Omnibus under accession number GSE54880.

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Acknowledgements

We thank the members of the ICGC PedBrain Tumor Project, the German Cancer Research Center (DKFZ) Genomics and Proteomics Core Facility, the European Molecular Biology Laboratory (EMBL) Genomics Core Facility, M. Schick, R. Fischer, M. Bewerunge-Hudler, M. Knopf, R. Kabbe, A. Benner, R. Volckman and P. van Sluis for technical support and helpful discussion. Active Motif, Inc. is acknowledged for ChIP and library preparation. We also thank C. Plass for critical reading of the manuscript. This work was principally supported by the PedBrain Tumor Project contributing to the International Cancer Genome Consortium, funded by German Cancer Aid (109252) and the German Federal Ministry of Education and Research (BMBF, grants #01KU1201A, MedSys #0315416C and NGFNplus #01GS0883). Additional support came from the DKFZ-Heidelberg Center for Personalized Oncology (DKFZ-HIPO), the Dutch Cancer Foundations KWF (2010-4713) and KIKA (M.Ko.), and the German Research Foundation (DFG; grant LA2983/2-1 to P.La.).

Author information

Authors and Affiliations

Authors

Contributions

D.T.W.J., S.Pi., W.W., M.S., S.B., J.B., C.C.B., C.v.K., R.V., S.S., S.W., J.F. and P.La performed and/or coordinated experimental work. V.H., M.K., P.A.N., K.S., N.J., H.-J.W., M.Ra., K.K., S.E., C.L., J.E., J.K. and P.La. performed data analysis. M.Ry., T.M., O.W., T.P., S.R., W.S., M.D.T. and A.K. collected data and provided patient materials. V.H., D.T.W.J., M.K., P.A.N., M.Z., B.R., S.M.P. and P.Li. prepared the initial manuscript and figures. D.T.W.J., U.D.W., B.B., G.R., A.B., H.L., R.J.W.-R., R.E., M.-L.Y., A.K., P.La., M.Z., B.R., S.M.P. and P.Li. provided project leadership.

Corresponding authors

Correspondence to Bernhard Radlwimmer, Stefan M. Pfister or Peter Lichter.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Global properties of DNA methylation.

a, Fraction of genome-wide CpGs showing different methylation states, binned into ten windows. The majority of CpGs show a bimodal pattern close to either fully methylated or unmethylated, with few intermediate values. b, Distribution of average methylation rate with varying CpG density, showing that CpG-dense regions are typically unmethylated, with high methylation in CpG-poor regions. c, Genome-wide methylation rates by tumour subgroup or age (for controls). All tumour subgroups show significantly reduced methylation compared with fetal cerebellum (CBM), with the largest reduction in WNT and Group 3 tumours. *P < 0.05, **P < 0.01, ***P < 0.001. d, Minimal non-CpG (CH) methylation is seen in tumours or fetal cerebellum, with levels significantly below that of adult normal cerebellum. ***P < 0.001. e, The distribution of methylation states for non-CpG methylation does not show a similar bimodal pattern to CpG methylation, with a marked shift towards low percentages (indicating heterogeneity of methylation levels within a tissue). The curves are truncated at a lower limit of 0.1 to increase resolution in the higher range, as the vast majority of sites show methylation rates <0.1.

Extended Data Figure 2 Hypermethylation of CGIs is rare in medulloblastoma.

a, Overview of number of genes associated with significantly differentially methylated CGIs across the four medulloblastoma subgroups and control cerebellum, and those that are specifically differentially methylated in a given tumour subgroup. The fraction of differentially methylated promoter CGIs per subgroup that are hypo- (blue) or hypermethylated (red) relative to control cerebellum is also shown. b, Methylation plot for the 5′ end of WNK2, showing a region of methylation extending into the promoter CGI in some WNT, SHH and Group 3 medulloblastomas. c, Methylation of the WNK2 promoter region is negatively correlated with gene expression in the extended, array-based validation cohort. Sample numbers are indicated next to the boxplots (n = 95 overlap). CBM, cerebellum; r, Pearson’s correlation coefficient; p.adj.expr, expression-adjusted P value (analysis of variance (ANOVA), Benjamini–Hochberg adjustment); p.adj.meth, methylation-adjusted P value. d, A summary of methylation levels at all promoter CGIs across medulloblastoma cell lines and tumours, and normal tissues.

Extended Data Figure 3 pdCRs overlap with the H3K4me3 histone mark.

a, Methylation plot for ESYT2, showing hypomethylation of a pdCR in some Group 4 medulloblastomas. The variable methylation states at the boundaries of the pdCR suggest subpopulations of differing values within a tumour. Additionally, the heterogeneity across the subgroup indicates a potential somatic acquisition of this demethylation. b, Methylation of the ESYT2 pdCR is negatively correlated with gene expression in the extended, array-based validation cohort. c, Methylation plot including H3K4me3 ChIP-seq data for the SOAT1 and FSTL1 loci, showing differential pdCRs methylation in two medulloblastoma cell lines. Notably, the pdCR overlaps with the presence of H3K4me3. d, When looking at segments of pdCRs unique to either medulloblastoma cell line, H3K4me3 levels are significantly higher in the cell line showing the extended pdCR. e, Methylation levels at the pdCR of PDLIM3 are negatively correlated with expression in the extended array cohort (see also Fig. 1h). f, Mouse SHH medulloblastomas also show hypomethylation of a pdCR in Pdlim3 that is associated with increased expression, which is not seen in GNPs (that is, somatic in origin, see also Fig. 1i).

Extended Data Figure 4 The MIR-23b–27b–24-1 cluster is epigenetically regulated in WNT medulloblastoma.

a, An example of a novel first exon within a gene body is seen for C9orf3, which is the primary transcript hosting the MIR-23b–27b–24-1 cluster. bd, Methylation around MIR-23b, MIR-27b and MIR-24-1 is negatively correlated with expression of these miRNAs in an extended validation cohort. e, Negative correlation of methylation and expression is also observed for C9orf3 itself.

Extended Data Figure 5 Members of the LET-7 family negatively correlate with LIN28B expression.

a, Proportion of Group 3 and Group 4 medulloblastomas showing methylation of the canonical LIN28B promoter in the extended validation cohort. Sample numbers are indicated. b, Coverage of RNA-seq reads for exon 1 and 2 of the canonical LIN28B transcript separated by promoter methylation state for Group 3 and 4 medulloblastomas, showing a loss of exon 1 expression in those samples with methylation of the canonical promoter. c, Group 3 and 4 medulloblastomas that are unmethylated at the canonical promoter show even higher LIN28B expression than other samples in these subgroups. d, Expression levels of LET-7 family miRNAs for medulloblastoma subgroups and normal cerebellum. RPM, reads per million. e, Correlation of LIN28B expression with LET-7 family miRNAs within Group 3 and Group 4 medulloblastomas and across all medulloblastoma subgroups. f, Association of LIN28B expression with overall survival in Group 4 medulloblastomas (Kaplan–Meier analysis; P, log-rank test).

Extended Data Figure 6 PMDs are restricted to WNT and Group 3 tumours and are subgroup specific.

a, A global view of genome-wide methylation values, shown in 100 kb bins and sorted according to similarity, gives a clear picture of the PMDs in WNT and Group 3 tumours. Also notable is the extensive hypomethylation in the cell lines. b, Indication of the fraction of total PMD length in each tumour that is covered by genes, showing that PMDs are typically gene-poor regions compared to the genome average (left-most bar). c, Overview of methylation values in WNT PMDs, showing a high similarity between samples (with the exception of ICGC_MB46, which does not show the same pattern). d, An overview of methylation values in Group 3 PMDs, showing some similarity between samples, but with more variation in extent and sites of demethylation compared with WNT tumours. e, Similarity of PMD regions in WNT and Group 3 medulloblastoma samples that show elevated levels of PMDs (samples with a total of >0.2 Gb covered by a PMD). Correlation is higher within rather than between subgroups.

Extended Data Figure 7 PMDs are associated with decreased gene expression.

a, Genes located within WNT PMDs are expressed at a significantly lower level in those WNT tumours that have PMDs compared with normal cerebellum. WNT medulloblastomas lacking clear PMDs (that is, ICGC_MB46) show intermediate expression. Genes not in PMDs show a slightly elevated expression in the tumours. *P < 0.05, **P < 0.01, ***P < 0.001. b, Genes located within Group 3 PMDs are expressed at a significantly lower level in those Group 3 tumours that have PMDs compared with normal cerebellum. Group 3 medulloblastomas lacking clear PMDs show intermediate expression. Genes not in PMDs show a slightly elevated expression in the tumours. *P < 0.05, **P < 0.01, ***P < 0.001. c, An example of a PMD in Group 3 medulloblastoma around the CLMN gene, showing heterogeneous methylation levels within the subgroup. d, Methylation levels of the CLMN PMD are positively correlated with gene expression in Group 3 medulloblastoma (n = 11). e, PMDs are typically associated with H3K9me3 or H3K27me3 histone marks, but less commonly with both.

Extended Data Figure 8 Positively correlating DMVs overlap regions of differential H3K27 trimethylation.

a, Boxplots of the number of DMVs identified per sample, split by tumour subgroup (or age group for controls, n = 42 samples). b, c, Overview of genes encompassed by a negatively or positively correlating DMV, those that are significantly differentially methylated across the four medulloblastoma subgroups and control cerebellum, and those that are specifically differentially methylated in a given tumour subgroup. The fraction of subgroup-specific, gene-encompassing DMVs that are hypo- (blue) or hypermethylated (red) relative to control cerebellum is also shown. d, Methylation plot for PITX2, located in a DMV that shows differential methylation in a variety of subgroups. This DMV is hypomethylated in control cerebellum, but methylation is re-established in several tumours. In the MED-8A cell line, this locus is hypomethylated and covered by the inactivating H3K27me3 mark, and is not expressed. In D425, in which PITX2 is expressed, re-establishment of methylation is accompanied by loss of H3K27me3 and gain of the active H3K4me3 modification. e, This re-establishment of methylation, representing normalization of chromatin, is positively correlated with increased gene expression in the extended, array-based validation cohort.

Extended Data Figure 9 Systematic analysis of LMRs identifies transcriptional regulators in human and mouse medulloblastoma.

a, Heat-map representation of genomic regions of 3-kb-centred LMRs predicted to contain an OTX2-binding motif. More than 75% of LMRs overlap with a ChIP peak, with strongest binding at those sites where the motif more closely matches the consensus sequence. b, Heat-map representation of a k-means clustering of subgroup-specifically methylated LMRs. A single methylation value per sample and LMR is shown, as used for the clustering. c, Heat-map representation of a k-means clustering of LMRs in GNPs, SHH medulloblastoma mouse model and external data sets. Left, a single methylation level per sample and LMR is shown, as used for the clustering. Right, genomic regions of 3 kb centred around the LMR of selected samples are shown. Selected transcription-factor-binding motifs enriched within specific clusters are indicated. The relevance of this analysis for highlighting transcriptional regulators is further supported by an LMR cluster with specificity for sorted neuronal cells (NeuN+), which showed clear enrichment for Mef2c binding (a key regulator of neuronal cell fate48).

Extended Data Figure 10 Epigenetic regulation of a novel GLI2 transcript variant.

a, Methylation levels at a pdCR in PTCH1 are negatively correlated with expression in the extended array cohort (see also Fig. 4d). b, Mouse SHH medulloblastomas also show hypomethylation of a pdCR in Ptch1 that is associated with increased expression, which is not seen in GNPs (that is, somatic in origin; see also Fig. 4e). c, Methylation plot for GLI2, showing an exon upstream of the annotated transcript in WNT and SHH medulloblastoma and adult cerebellum. RNA-seq data are shown in reads per million (RPM) below the heat map. d, Methylation at the GLI2 upstream exon is negatively correlated with gene expression in the extended, array-based validation cohort.

Supplementary information

Supplementary Information 1

Cohort details and overview of associated datasets. (XLSX 14 kb)

Supplementary Information 2

Whole-genome bisulphite sequencing summary of tumour samples, cell lines and SHH-MB mouse model. (XLSX 27 kb)

Supplementary Information 3

Integrative analysis of promoter CpG islands and promoter downstream correlated regions, differential gene expression and novel first exon analysis. (XLSX 3325 kb)

Supplementary Information 4

Integrative analysis of DNA methylation valleys. (XLSX 599 kb)

Supplementary Information 5

Systematic analysis of lowly-methylated regions and transcription factor binding motif enrichment. (XLSX 6583 kb)

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Hovestadt, V., Jones, D., Picelli, S. et al. Decoding the regulatory landscape of medulloblastoma using DNA methylation sequencing. Nature 510, 537–541 (2014). https://doi.org/10.1038/nature13268

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