Acta Neuropathologica

, Volume 125, Issue 3, pp 359–371 | Cite as

DNA methylation profiling of medulloblastoma allows robust subclassification and improved outcome prediction using formalin-fixed biopsies

  • Edward C. Schwalbe
  • Daniel Williamson
  • Janet C. Lindsey
  • Dolores Hamilton
  • Sarra L. Ryan
  • Hisham Megahed
  • Miklós Garami
  • Peter Hauser
  • Bożena Dembowska-Baginska
  • Danuta Perek
  • Paul A. Northcott
  • Michael D. Taylor
  • Roger E. Taylor
  • David W. Ellison
  • Simon Bailey
  • Steven C. CliffordEmail author
Original Paper


Molecular subclassification is rapidly informing the clinical management of medulloblastoma. However, the disease remains associated with poor outcomes and therapy-associated late effects, and the majority of patients are not characterized by a validated prognostic biomarker. Here, we investigated the potential of epigenetic DNA methylation for disease subclassification, particularly in formalin-fixed biopsies, and to identify biomarkers for improved therapeutic individualization. Tumor DNA methylation profiles were assessed, alongside molecular and clinical disease features, in 230 patients primarily from the SIOP-UKCCSG PNET3 clinical trial. We demonstrate by cross-validation in frozen training and formalin-fixed test sets that medulloblastoma comprises four robust DNA methylation subgroups (termed WNT, SHH, G3 and G4), highly related to their transcriptomic counterparts, and which display distinct molecular, clinical and pathological disease characteristics. WNT patients displayed an expected favorable prognosis, while outcomes for SHH, G3 and G4 were equivalent in our cohort. MXI1 and IL8 methylation were identified as novel independent high-risk biomarkers in cross-validated survival models of non-WNT patients, and were validated using non-array methods. Incorporation of MXI1 and IL8 into current survival models significantly improved the assignment of disease risk; 46 % of patients could be classified as ‘favorable risk’ (>90 % survival) compared to 13 % using current models, while the high-risk group was reduced from 30 to 16 %. DNA methylation profiling enables the robust subclassification of four disease subgroups in frozen and routinely collected/archival formalin-fixed biopsy material, and the incorporation of DNA methylation biomarkers can significantly improve disease-risk stratification. These findings have important implications for future risk-adapted clinical disease management.


Subgroups Medulloblastoma Methylation Prognosis Biomarkers 



This work was supported by grants from The Brain Tumour Charity, Cancer Research UK, The Katie Trust and North of England Children’s Cancer Research. Medulloblastomas investigated in this study include samples provided by the UK Children’s Cancer and Leukaemia Group (CCLG) as part of CCLG-approved biological study BS-2007-04. This study was conducted with ethics committee approval from Newcastle/North Tyneside REC (study reference 07/Q0905/71).

Supplementary material

401_2012_1077_MOESM1_ESM.doc (37 kb)
Supplementary Methods (doc 37 kb)
401_2012_1077_MOESM2_ESM.pptx (272 kb)
Supplementary Figure S1. DNA methylation array data quality, reproducibility and quantitative accuracy. a. Intra- and inter- array replicates demonstrate high reproducibility. b. Bland-Altman plot showing direct comparison between bisulfite sequencing estimation of methylation and GoldenGate array-estimated methylation status of 18 samples at 7 loci (ASCL2, CCKAR, COL1A2, HFE, MSH2, NOS2A, SPDEF). The x-axis shows the average score from the two estimations of β-value by bisulfite sequencing and array analysis, and the y-axis shows the difference between them. Horizontal dotted lines are plotted at the mean difference and at 2 standard deviations of the difference. c. Density plot shows distribution of deviation between bisulfite sequencing and array estimates of methylation. A blue line indicates the modal value for deviation between estimates. Estimates more than two standard deviations from the mean deviation are shown in red (PPTX 271 kb)
401_2012_1077_MOESM3_ESM.pptx (81 kb)
Supplementary Figure S2. Identification of DNA methylation-dependent medulloblastoma subgroups using NMF and consensus clustering. a. Flow-chart shows consensus-clustering procedure to identify optimal combinations of metagenes and clusters in the training dataset. b. The average percentage assignment of samples to the same cluster over 100 iterations is shown as a 3D surface plot for each tested combination of metagenes and clusters. Data peaks represent optimal combinations of metagenes and clusters. c. The data shown in the surface plot is tabulated. The chosen optimal number of 4 metagenes / 4 clusters is highlighted red. d. A SVM classifier of the training cohort H matrix perfectly recapitulates the group assignments by k-means clustering (PPTX 80 kb)
401_2012_1077_MOESM4_ESM.pptx (140 kb)
Supplementary Figure S3. Survival relationships for molecular and clinico-pathological variables within the survival cohort ( n =191). Each panel shows a Kaplan-Meier plot, a bar plot showing group membership and an at-risk table. P values are from log-rank tests. PNET3 and age-matched – PNET3, PNET3 trial patients; Age match, age-matched non-trials patients. MYC / MYCN amp – 0, no amplification; 1, amplification of MYC or MYCN. LCA – 1, LCA pathology; 0, non-LCA. M stage – M-, 0; M+, 1; Gender – 0, male; 1, female. WNT – 0, non-WNT subgroup; 1 – WNT subgroup (PPTX 140 kb)
401_2012_1077_MOESM5_ESM.pptx (195 kb)
Supplementary Figure S4. Identification of bi-modal methylation markers for assessment in survival models. a. The bimodality index[52] was applied to identify candidate biomarkers for assessment of their prognostic ability to integrate into existing disease survival models. The first column shows the three most bimodal probes, the second column the three least bimodal probes, for illustration. b. Selection of optimal additional numbers of prognostic methylation probes to a base model consisting of MYC family amplification, metastatic disease and LCA histology. Left hand y axis shows cross-validated AUC for adding 0-5 methylation probes (red). Right-hand y axis shows -log 10 p value for the selected covariates (blue). c. Cross-validated ROC curves for adding two methylation probes to the base survival model. d. Cross-validated ROC curves for adding two binary-classified methylation probes to the base survival model shows equivalent AUC to model using methylation probes as continuous variables (PPTX 194 kb)
401_2012_1077_MOESM6_ESM.pptx (136 kb)
Supplementary Figure S5. Assessment of MXI1 and IL8 methylation status by bisulfite sequencing. a,b. Scatterplots showing estimates of DNA methylation at the MXI1 (P1269; a) and IL8 (P83; b) probe loci, derived by independent bisulfite sequencing and methylation array analysis. c,d. Methylation status of CpG dinucleotides within the MXI1 (c) and IL8 (d) CpG islands determined by bisulfite sequencing analysis. Positions of individual CpG residues and the array-probes are shown; estimated methylation status is represented by lollipop plots (white, <20% methylation; one-quarter black, 20-40%; half black, 40-60%, three-quarter black, 60-80%; black, >80%). For MXI1, the patterns of methylation observed at the array probe site are similar to those across the CpG region assessed. IL8 shows consistency of methylation status across the first two CpG island sites including the array-probe site (PPTX 135 kb)
401_2012_1077_MOESM7_ESM.pptx (199 kb)
Supplementary Figure S6. Development of a cumulative model for medulloblastoma risk-stratification. a. Nomogram of risk-factors identified in a Cox proportional hazards model derived from non-WNT patients (n=163; Figure 4B), demonstrates similar magnitudes of hazards. Risk boundaries are shown (Low, low-risk; Std, standard risk; Poor, poor risk), defined by the total number of points conferred by risk-factor positivity and delineated by blue lines. In the illustrated stratification scheme, the absence of any risk-factor, or positivity for a single risk-factor, would confer membership of the low-risk group. Positivity for any combination of two risk-factors would confer membership of the standard-risk group and tumors with positivity for any combination of three or more risk-factors would be classified as high-risk. b. The number of risk factors in the non-WNT survival cohort (n=163) is associated with survival. Kaplan-Meier curves are shown for each occurrence of risk factor frequency (0, green; 1, dark green; 2, orange; 3, red; 4, dark red). Bar plot showing group membership and at-risk table is shown below Kaplan-Meier plot. For information, the survival for the WNT cohort (n=28) is shown in blue. c. Co-occurrence of risk factors in the non-WNT survival cohort. 5-way Venn diagram shows risk-factor occurrence for 163 tumors. 5/163 (3%) tumors were negative for all risk factors (PPTX 198 kb)
401_2012_1077_MOESM8_ESM.pptx (216 kb)
Supplementary Figure S7. Development and validation of a minimal DNA methylation signature for assessment of WNT subgroup status. a. Recursive feature elimination SVM[11] identifies smallest cross-validation error to distinguish WNT and non-WNT methylomic subgroups using a 5-probe classifier. b. SVM classifier predicts class membership with high (probability >0.8) confidence in all but one previously-classified samples. NC samples remain difficult to assign. c. Stacked bar plot showing the vote for each class from the 5-probe subgroup classifier. Prior class assignment (Figure 2) is shown at the top of the bar plot; tumors previously classified as WNT are shown in red, non-WNT white. For each sample, the probabilities for membership of each class using the 5-probe signature are colored in the same way. A blue line separates training and test cohorts. d. Confusion matrix demonstrates one false-negative mis-classification error of WNT-subgroup assignment by applying classifier (PPTX 216 kb)
401_2012_1077_MOESM9_ESM.pptx (153 kb)
Supplementary Figure S8. Development and validation of a minimal DNA methylation signature for assessment of medulloblastoma methylation subgroup status. a. Recursive feature elimination SVM[11] identifies smallest cross-validation error to distinguish between 4 methylomic subgroups using a 65 probe classifier. b. SVM classifier predicts class membership with high (probability >0.8) confidence in all previously classified samples. NC samples remain difficult to assign. c. Stacked bar plot showing the vote for each class from the 65-probe subgroup classifier. Prior class assignment (Figure 2) is shown at the top of the bar plot; tumors previously classified as SHH are shown in blue, G3 purple, WNT red and G4 orange. For each sample, the probabilities for membership of each class using the 65-probe signature are colored in the same way. A white line separates training and test cohorts. d. Confusion matrix demonstrates perfect recapitulation of subgroup membership by applying classifier (PPTX 152 kb)
401_2012_1077_MOESM10_ESM.pptx (801 kb)
Supplementary Figure S9. Minimal DNA methylation signatures for medulloblastoma subgroup classification and assessment of risk-stratification markers ( MXI1 / IL8 ). a. Minimal 5-probe methylomic classifier shows different methylation patterns in WNT and non-WNT samples. MXI1 and IL8 probe status are shown as continuous and binarized variables. Binarized variables are shown in black (methylated; beta > 0.67) and white (unmethylated; beta ≤ 0.67). b. PCA of selected probes demonstrates separation of WNT from non-WNT tumors. Covariance spheroids have been plotted at 95% confidence intervals for the assigned groups. c. Minimal sixty-five probe methylomic classifier showing signature methylation patterns across assigned subgroups. d. PCA of selected probes demonstrates separation between the previously assigned classes. Covariance spheroids for the assigned groups are again plotted at 95% confidence intervals. Heatmaps show methylation status (methylated, red; unmethylated, green; partially-methylated, black (PPTX 801 kb)
401_2012_1077_MOESM11_ESM.pptx (66 kb)
Supplementary Table S1. Primer sequences for bisulfite sequencing of differentially methylated and prognostic loci. Sequences are listed 5’ – 3’ (PPTX 65 kb)
401_2012_1077_MOESM12_ESM.pptx (114 kb)
Supplementary Table S2: The top 10 most correlative and anti-correlative probes that define each metagene represent novel biomarkers for the methylation subgroups of medulloblastoma. Anti-correlative probes are shown with a white background. Correlative probes are shown with a gray background. Probe name, gene name, Pearson correlation, β-scores of group and non-group members, as well as the difference in β-values between group and non-group members are given. P values, calculated using Mann-Whitney tests comparing one subgroup versus others, with a Bonferroni correction for multiple hypothesis testing, are shown (PPTX 114 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Edward C. Schwalbe
    • 1
  • Daniel Williamson
    • 1
  • Janet C. Lindsey
    • 1
  • Dolores Hamilton
    • 1
  • Sarra L. Ryan
    • 1
  • Hisham Megahed
    • 1
  • Miklós Garami
    • 2
  • Peter Hauser
    • 2
  • Bożena Dembowska-Baginska
    • 3
  • Danuta Perek
    • 3
  • Paul A. Northcott
    • 4
  • Michael D. Taylor
    • 4
  • Roger E. Taylor
    • 5
  • David W. Ellison
    • 6
  • Simon Bailey
    • 1
  • Steven C. Clifford
    • 1
    Email author
  1. 1.Northern Institute for Cancer ResearchNewcastle UniversityNewcastle upon TyneUK
  2. 2.Department of PaediatricsSemmelweis UniversityBudapestHungary
  3. 3.Department of Pediatric OncologyThe Children’s Memorial Health InstituteWarsawPoland
  4. 4.Division of Neurosurgery and The Labatt Brain Tumour Research CentreHospital for Sick ChildrenTorontoCanada
  5. 5.South Wales Cancer CentreSingleton HospitalSwanseaUK
  6. 6.Department of PathologySt. Jude Children’s Research HospitalMemphisUSA

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