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

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Abstract

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.

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References

  1. Anderton JA, Lindsey JC, Lusher ME, Gilbertson RJ, Bailey S, Ellison DW, Clifford SC (2008) Global analysis of the medulloblastoma epigenome identifies disease-subgroup-specific inactivation of COL1A2. Neuro Oncol 10:981–994

    Article  PubMed  CAS  Google Scholar 

  2. Bibikova M, Lin Z, Zhou L, Chudin E, Garcia EW, Wu B et al (2006) High-throughput DNA methylation profiling using universal bead arrays. Genome Res 16:383–393

    Article  PubMed  CAS  Google Scholar 

  3. Brunet J-P, Tamayo P, Golub TR, Mesirov JP (2004) Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci USA 101:4164–4169

    Article  PubMed  CAS  Google Scholar 

  4. Cairns JM, Dunning MJ, Ritchie ME, Russell R, Lynch AG (2008) BASH: a tool for managing BeadArray spatial artefacts. Bioinformatics 24:2921–2922

    Article  PubMed  CAS  Google Scholar 

  5. Cascon A, Robledo M (2012) MAX and MYC: a heritable breakup. Cancer Res 72:3119–3124

    Article  PubMed  CAS  Google Scholar 

  6. Chang CH, Housepian EM, Herbert C Jr (1969) An operative staging system and a megavoltage radiotherapeutic technic for cerebellar medulloblastomas. Radiology 93:1351–1359

    PubMed  CAS  Google Scholar 

  7. Cho YJ, Tsherniak A, Tamayo P, Santagata S, Ligon A, Greulich H et al (2011) Integrative genomic analysis of medulloblastoma identifies a molecular subgroup that drives poor clinical outcome. J Clin Oncol 29:1424–1430

    Article  PubMed  Google Scholar 

  8. Clifford SC, Lusher ME, Lindsey JC, Langdon JA, Gilbertson RJ, Straughton D, Ellison DW (2006) Wnt/Wingless pathway activation and chromosome 6 loss characterize a distinct molecular sub-group of medulloblastomas associated with a favorable prognosis. Cell Cycle 5:2666–2670

    Article  PubMed  CAS  Google Scholar 

  9. Curran EK, Sainani KL, Le GM, Propp JM, Fisher PG (2008) Gender affects survival for medulloblastoma only in older children and adults: a study from the surveillance epidemiology and end results registry. Pediatr Blood Cancer 52:60–64

    Article  Google Scholar 

  10. Diede SJ, Guenthoer J, Geng LN, Mahoney SE, Marotta M, Olson JM, Tanaka H, Tapscott SJ (2010) DNA methylation of developmental genes in pediatric medulloblastomas identified by denaturation analysis of methylation differences. Proc Natl Acad Sci USA 107:234–239

    Article  PubMed  CAS  Google Scholar 

  11. Duan KB, Rajapakse JC, Wang H, Azuaje F (2005) Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Trans Nanobioscience 4:228–234

    Article  PubMed  Google Scholar 

  12. Dunning MJ, Smith ML, Ritchie ME, Tavare S (2007) beadarray: R classes and methods for Illumina bead-based data. Bioinformatics 23:2183–2184

    Article  PubMed  CAS  Google Scholar 

  13. Ellison DW, Dalton J, Kocak M, Nicholson SL, Fraga C, Neale G, Kenney AM, Brat DJ, Perry A, Yong WH, Taylor RE, Bailey S, Clifford SC, Gilbertson RJ (2011) Medulloblastoma: clinicopathological correlates of SHH, WNT, and non-SHH/WNT molecular subgroups. Acta Neuropathol 121:381–396

    Article  PubMed  CAS  Google Scholar 

  14. Ellison DW, Kocak M, Dalton J, Megahed H, Lusher ME, Ryan SL, Zhao W, Nicholson SL, Taylor RE, Bailey S, Clifford SC (2011) Definition of disease-risk stratification groups in childhood medulloblastoma using combined clinical, pathologic, and molecular variables. J Clin Oncol 29:1400–1407

    Article  PubMed  Google Scholar 

  15. Ellison DW, Onilude OE, Lindsey JC, Lusher ME, Weston CL, Taylor RE, Pearson AD, Clifford SC (2005) beta-Catenin status predicts a favorable outcome in childhood medulloblastoma: the United Kingdom Children’s Cancer Study Group Brain Tumour Committee. J Clin Oncol 23:7951–7957

    Article  PubMed  CAS  Google Scholar 

  16. Gajjar A, Chintagumpala M, Ashley D, Kellie S, Kun LE, Merchant TE et al (2006) Risk-adapted craniospinal radiotherapy followed by high-dose chemotherapy and stem-cell rescue in children with newly diagnosed medulloblastoma (St Jude Medulloblastoma-96): long-term results from a prospective, multicentre trial. Lancet Oncol 7:813–820

    Article  PubMed  Google Scholar 

  17. Heagerty PJ, Lumley T, Pepe MS (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56:337–344

    Article  PubMed  CAS  Google Scholar 

  18. Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M et al (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352:997–1003

    Article  PubMed  CAS  Google Scholar 

  19. Jones DT, Jager N, Kool M, Zichner T, Hutter B, Sultan M et al (2012) Dissecting the genomic complexity underlying medulloblastoma. Nature 488:100–105

    Article  PubMed  CAS  Google Scholar 

  20. Kongkham PN, Northcott PA, Croul SE, Smith CA, Taylor MD, Rutka JT (2010) The SFRP family of WNT inhibitors function as novel tumor suppressor genes epigenetically silenced in medulloblastoma. Oncogene 29:3017–3024

    Article  PubMed  CAS  Google Scholar 

  21. Kool M, Korshunov A, Remke M, Jones DT, Schlanstein M, Northcott PA et al (2012) Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas. Acta Neuropathol 123:473–484

    Article  PubMed  CAS  Google Scholar 

  22. Kool M, Koster J, Bunt J, Hasselt NE, Lakeman A, van Sluis P et al (2008) Integrated genomics identifies five medulloblastoma subtypes with distinct genetic profiles, pathway signatures and clinicopathological features. PLoS ONE 3:e3088

    Article  PubMed  Google Scholar 

  23. Lamont JM, McManamy CS, Pearson AD, Clifford SC, Ellison DW (2004) Combined histopathological and molecular cytogenetic stratification of medulloblastoma patients. Clin Cancer Res 10:5482–5493

    Article  PubMed  CAS  Google Scholar 

  24. Langdon JA, Lamont JM, Scott DK, Dyer S, Prebble E, Bown N, Grundy RG, Ellison DW, Clifford SC (2006) Combined genome-wide allelotyping and copy number analysis identify frequent genetic losses without copy number reduction in medulloblastoma. Genes Chromosomes Cancer 45:47–60

    Article  PubMed  CAS  Google Scholar 

  25. Lindsey JC, Anderton JA, Lusher ME, Clifford SC (2005) Epigenetic events in medulloblastoma development. Neurosurg Focus 19:E10

    Article  PubMed  Google Scholar 

  26. Lindsey JC, Lusher ME, Anderton JA, Bailey S, Gilbertson RJ, Pearson AD, Ellison DW, Clifford SC (2004) Identification of tumour-specific epigenetic events in medulloblastoma development by hypermethylation profiling. Carcinogenesis 25:661–668

    Article  PubMed  CAS  Google Scholar 

  27. Lindsey JC, Lusher ME, Anderton JA, Gilbertson RJ, Ellison DW, Clifford SC (2007) Epigenetic deregulation of multiple S100 gene family members by differential hypomethylation and hypermethylation events in medulloblastoma. Br J Cancer 97:267–274

    Article  PubMed  CAS  Google Scholar 

  28. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114:97–109

    Article  PubMed  Google Scholar 

  29. McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM (2005) Reporting recommendations for tumor marker prognostic studies. J Clin Oncol 23:9067–9072

    Article  PubMed  Google Scholar 

  30. Missiaglia E, Williamson D, Chisholm J, Wirapati P, Pierron G, Petel F, Concordet JP, Thway K, Oberlin O, Pritchard-Jones K, Delattre O, Delorenzi M, Shipley J (2012) PAX3/FOXO1 fusion gene status is the key prognostic molecular marker in rhabdomyosarcoma and significantly improves current risk stratification. J Clin Oncol 30:1670–1677

    Article  PubMed  Google Scholar 

  31. Ning Y, Manegold PC, Hong YK, Zhang W, Pohl A, Lurje G, Winder T, Yang D, LaBonte MJ, Wilson PM, Ladner RD, Lenz HJ (2011) Interleukin-8 is associated with proliferation, migration, angiogenesis and chemosensitivity in vitro and in vivo in colon cancer cell line models. Int J Cancer 128:2038–2049

    Article  PubMed  CAS  Google Scholar 

  32. Northcott PA, Korshunov A, Witt H, Hielscher T, Eberhart CG, Mack S, Bouffet E, Clifford SC, Hawkins CE, French P, Rutka JT, Pfister S, Taylor MD (2011) Medulloblastoma comprises four distinct molecular variants. J Clin Oncol 29:1408–1414

    Article  PubMed  Google Scholar 

  33. Northcott PA, Shih DJ, Remke M, Cho YJ, Kool M, Hawkins C et al (2012) Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathol 123:615–626

    Article  PubMed  CAS  Google Scholar 

  34. Noushmehr H, Weisenberger DJ, Diefes K, Phillips HS, Pujara K, Berman BP et al (2010) Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 17:510–522

    Article  PubMed  CAS  Google Scholar 

  35. Parsons DW, Li M, Zhang X, Jones S, Leary RJ, Lin JC et al (2011) The genetic landscape of the childhood cancer medulloblastoma. Science 331:435–439

    Article  PubMed  CAS  Google Scholar 

  36. Pfister SM, Remke M, Benner A, Mendrzyk F, Toedt G, Felsberg J et al (2009) Outcome prediction in pediatric medulloblastoma based on DNA copy-number aberrations of chromosomes 6q and 17q and the MYC and MYCN loci. J Clin Oncol 27:1627–1636

    Article  PubMed  Google Scholar 

  37. Pizer BL, Clifford SC (2009) The potential impact of tumour biology on improved clinical practice for medulloblastoma: progress towards biologically driven clinical trials. Br J Neurosurg 23:364–375

    Article  PubMed  Google Scholar 

  38. Pugh TJ, Weeraratne SD, Archer TC, Pomeranz Krummel DA, Auclair D, Bochicchio J et al (2012) Medulloblastoma exome sequencing uncovers subtype-specific somatic mutations. Nature 488:106–110

    Article  PubMed  CAS  Google Scholar 

  39. R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

  40. Robinson G, Parker M, Kranenburg TA, Lu C, Chen X, Ding L et al (2012) Novel mutations target distinct subgroups of medulloblastoma. Nature 488:43–48

    Article  PubMed  CAS  Google Scholar 

  41. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

  42. Rutkowski S, von Bueren A, von Hoff K, Hartmann W, Shalaby T, Deinlein F et al (2007) Prognostic relevance of clinical and biological risk factors in childhood medulloblastoma: results of patients treated in the prospective multicenter trial HIT’91. Clin Cancer Res 13:2651–2657

    Article  PubMed  CAS  Google Scholar 

  43. Ryan SL, Schwalbe EC, Cole M, Lu Y, Lusher ME, Megahed H et al (2012) MYC family amplification and clinical risk-factors interact to predict an extremely poor prognosis in childhood medulloblastoma. Acta Neuropathol 123:501–513

    Article  PubMed  CAS  Google Scholar 

  44. Schwalbe EC, Lindsey JC, Straughton D, Hogg TL, Cole M, Megahed H, Ryan SL, Lusher ME, Taylor MD, Gilbertson RJ, Ellison DW, Bailey S, Clifford SC (2011) Rapid diagnosis of medulloblastoma molecular subgroups. Clin Cancer Res 17:1883–1894

    Article  PubMed  CAS  Google Scholar 

  45. Scott DK, Straughton D, Cole M, Bailey S, Ellison DW, Clifford SC (2006) Identification and analysis of tumor suppressor loci at chromosome 10q23.3–10q25.3 in medulloblastoma. Cell Cycle 5:2381–2389

    Article  PubMed  CAS  Google Scholar 

  46. Simon RM, Subramanian J, Li MC, Menezes S (2011) Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data. Brief Bioinform 12:203–214

    Article  PubMed  Google Scholar 

  47. Tamayo P, Scanfeld D, Ebert BL, Gillette MA, Roberts CWM, Mesirov JP (2007) Metagene projection for cross-platform, cross-species characterization of global transcriptional states. Proc Natl Acad Sci USA 104:5959–5964

    Article  PubMed  CAS  Google Scholar 

  48. Taylor MD, Northcott PA, Korshunov A, Remke M, Cho YJ, Clifford SC et al (2012) Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol 123:465–472

    Article  PubMed  CAS  Google Scholar 

  49. Taylor RE, Bailey CC, Robinson K, Weston CL, Ellison D, Ironside J, Lucraft H, Gilbertson R, Tait DM, Walker DA, Pizer BL, Imeson J, Lashford LS (2003) Results of a randomized study of preradiation chemotherapy versus radiotherapy alone for nonmetastatic medulloblastoma: the International Society of paediatric oncology/United Kingdom Children;s Cancer Study Group PNET-3 Study. J Clin Oncol 21:1581–1591

    Article  PubMed  CAS  Google Scholar 

  50. Taylor RE, Bailey CC, Robinson KJ, Weston CL, Walker DA, Ellison D, Ironside J, Pizer BL, Lashford LS (2005) Outcome for patients with metastatic (M2–3) medulloblastoma treated with SIOP/UKCCSG PNET-3 chemotherapy. Eur J Cancer 41:727–734

    Article  PubMed  CAS  Google Scholar 

  51. Thompson MC, Fuller C, Hogg TL, Dalton J, Finkelstein D, Lau CC, Chintagumpala M, Adesina A, Ashley DM, Kellie SJ, Taylor MD, Curran T, Gajjar A, Gilbertson RJ (2006) Genomics identifies medulloblastoma subgroups that are enriched for specific genetic alterations. J Clin Oncol 24:1924–1931

    Article  PubMed  CAS  Google Scholar 

  52. Wang J, Wen S, Symmans WF, Pusztai L, Coombes KR (2009) The bimodality index: a criterion for discovering and ranking bimodal signatures from cancer gene expression profiling data. Cancer Inform 7:199–216

    PubMed  Google Scholar 

  53. Zervos AS, Gyuris J, Brent R (1993) Mxi1, a protein that specifically interacts with Max to bind Myc-Max recognition sites. Cell 72:223–232

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

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).

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Authors and Affiliations

  1. Northern Institute for Cancer Research, Newcastle University, Sir James Spence Institute Level 5, Royal Victoria Infirmary, Newcastle upon Tyne, NE1 4LP, UK

    Edward C. Schwalbe, Daniel Williamson, Janet C. Lindsey, Dolores Hamilton, Sarra L. Ryan, Hisham Megahed, Simon Bailey & Steven C. Clifford

  2. Department of Paediatrics, Semmelweis University, Budapest, Hungary

    Miklós Garami & Peter Hauser

  3. Department of Pediatric Oncology, The Children’s Memorial Health Institute, Warsaw, Poland

    Bożena Dembowska-Baginska & Danuta Perek

  4. Division of Neurosurgery and The Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, Canada

    Paul A. Northcott & Michael D. Taylor

  5. South Wales Cancer Centre, Singleton Hospital, Swansea, UK

    Roger E. Taylor

  6. Department of Pathology, St. Jude Children’s Research Hospital, Memphis, USA

    David W. Ellison

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  1. Edward C. Schwalbe
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  2. Daniel Williamson
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Supplementary Methods (doc 37 kb)

401_2012_1077_MOESM2_ESM.pptx

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

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

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

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

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

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

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

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

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

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

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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Schwalbe, E.C., Williamson, D., Lindsey, J.C. et al. DNA methylation profiling of medulloblastoma allows robust subclassification and improved outcome prediction using formalin-fixed biopsies. Acta Neuropathol 125, 359–371 (2013). https://doi.org/10.1007/s00401-012-1077-2

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