MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status

被引:253
作者
Bady, Pierre [12 ,13 ,14 ]
Sciuscio, Davide [14 ]
Diserens, Annie-Claire [14 ]
Bloch, Jocelyne [14 ]
van den Bent, Martin J. [11 ]
Marosi, Christine [10 ]
Dietrich, Pierre-Yves [9 ]
Weller, Michael [7 ,8 ]
Mariani, Luigi [6 ]
Heppner, Frank L. [5 ]
Mcdonald, David R. [4 ]
Lacombe, Denis [3 ]
Stupp, Roger [14 ]
Delorenzi, Mauro [2 ,12 ,13 ]
Hegi, Monika E. [1 ,2 ,14 ]
机构
[1] Ctr Hosp Univ Vaudois CHUV BH19 110, Lab Brain Tumor Biol & Genet, Dept Neurosurg, CH-1011 Lausanne, Switzerland
[2] ISREC SV EPFL, Natl Ctr Competence Res Mol Oncol, Lausanne, Switzerland
[3] EORTC Headquaters, Brussels, Belgium
[4] Univ Western Ontario, London Reg Canc Program London Hlth Sci Ctr, London, ON, Canada
[5] Univ Zurich Hosp, Dept Neuropathol, CH-8091 Zurich, Switzerland
[6] Inselspital Bern, Dept Neurosurg, CH-3010 Bern, Switzerland
[7] Univ Zurich Hosp, Dept Neurol, CH-8091 Zurich, Switzerland
[8] Univ Tubingen, Dept Neurol, D-7400 Tubingen, Germany
[9] Univ Hosp Geneva, Geneva, Switzerland
[10] Med Univ Vienna, Vienna, Austria
[11] Erasmus MC, Dept Neurol, Rotterdam, Netherlands
[12] Univ Lausanne Hosp, Dept Format & Rech, Lausanne, Switzerland
[13] Swiss Inst Bioinformat, Bioinformat Core Facil, Lausanne, Switzerland
[14] Univ Lausanne Hosp, Dept Clin Neurosci, Lausanne, Switzerland
基金
瑞士国家科学基金会;
关键词
MGMT; DNA methylation; MSP; Infinium methylation platform; Prediction model; PROMOTER METHYLATION; O-6-METHYLGUANINE-DNA METHYLTRANSFERASE; ISLAND HYPERMETHYLATION; ADJUVANT TEMOZOLOMIDE; EXPRESSION; CONCOMITANT; PHENOTYPE; SURVIVAL; IDH1; INACTIVATION;
D O I
10.1007/s00401-012-1016-2
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
The methylation status of the O-6-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Recent studies in anaplastic glioma suggest a prognostic value for MGMT methylation. Investigation of pathogenetic and epigenetic features of this intriguingly distinct behavior requires accurate MGMT classification to assess high throughput molecular databases. Promoter methylation-mediated gene silencing is strongly dependent on the location of the methylated CpGs, complicating classification. Using the HumanMethylation450 (HM-450K) BeadChip interrogating 176 CpGs annotated for the MGMT gene, with 14 located in the promoter, two distinct regions in the CpG island of the promoter were identified with high importance for gene silencing and outcome prediction. A logistic regression model (MGMT-STP27) comprising probes cg1243587 and cg12981137 provided good classification properties and prognostic value (kappa = 0.85; log-rank p < 0.001) using a training-set of 63 glioblastomas from homogenously treated patients, for whom MGMT methylation was previously shown to be predictive for outcome based on classification by methylation-specific PCR. MGMT-STP27 was successfully validated in an independent cohort of chemo-radiotherapy-treated glioblastoma patients (n = 50; kappa = 0.88; outcome, log-rank p < 0.001). Lower prevalence of MGMT methylation among CpG island methylator phenotype (CIMP) positive tumors was found in glioblastomas from The Cancer Genome Atlas than in low grade and anaplastic glioma cohorts, while in CIMP-negative gliomas MGMT was classified as methylated in approximately 50 % regardless of tumor grade. The proposed MGMT-STP27 prediction model allows mining of datasets derived on the HM-450K or HM-27K BeadChip to explore effects of distinct epigenetic context of MGMT methylation suspected to modulate treatment resistance in different tumor types.
引用
收藏
页码:547 / 560
页数:14
相关论文
共 53 条
  • [1] [Anonymous], 2011, R: A Language and Environment for Statistical Computing
  • [2] [Anonymous], 2002, Model selection and multimodel inference: a practical informationtheoretic approach
  • [3] [Anonymous], LANCET ONCO IN PRESS
  • [4] [Anonymous], 2022, Modern Applied Statistics with S
  • [5] [Anonymous], 2002, MODELLING BINARY DAT, DOI DOI 10.1201/B16654
  • [6] [Anonymous], CANC CELL
  • [7] [Anonymous], 2006, C&H TEXT STAT SCI, DOI 10.1201/9781315382722
  • [8] [Anonymous], 1983, Generalized Linear Models
  • [9] Comprehensive genomic characterization defines human glioblastoma genes and core pathways
    Chin, L.
    Meyerson, M.
    Aldape, K.
    Bigner, D.
    Mikkelsen, T.
    VandenBerg, S.
    Kahn, A.
    Penny, R.
    Ferguson, M. L.
    Gerhard, D. S.
    Getz, G.
    Brennan, C.
    Taylor, B. S.
    Winckler, W.
    Park, P.
    Ladanyi, M.
    Hoadley, K. A.
    Verhaak, R. G. W.
    Hayes, D. N.
    Spellman, Paul T.
    Absher, D.
    Weir, B. A.
    Ding, L.
    Wheeler, D.
    Lawrence, M. S.
    Cibulskis, K.
    Mardis, E.
    Zhang, Jinghui
    Wilson, R. K.
    Donehower, L.
    Wheeler, D. A.
    Purdom, E.
    Wallis, J.
    Laird, P. W.
    Herman, J. G.
    Schuebel, K. E.
    Weisenberger, D. J.
    Baylin, S. B.
    Schultz, N.
    Yao, Jun
    Wiedemeyer, R.
    Weinstein, J.
    Sander, C.
    Gibbs, R. A.
    Gray, J.
    Kucherlapati, R.
    Lander, E. S.
    Myers, R. M.
    Perou, C. M.
    McLendon, Roger
    [J]. NATURE, 2008, 455 (7216) : 1061 - 1068
  • [10] MGMT prognostic impact on glioblastoma is dependent on therapeutic modalities
    Criniere, Emmanuelle
    Kaloshi, Gentian
    Laigle-Donadey, Florence
    Lejeune, Julie
    Auger, Nathalie
    Benouaich-Amiel, Alexandra
    Everhard, Sibille
    Mokhtari, Karima
    Polivka, Marc
    Delattre, Jean-Yves
    Hoang-Xuan, Khe
    Thillet, Joelle
    Sanson, Marc
    [J]. JOURNAL OF NEURO-ONCOLOGY, 2007, 83 (02) : 173 - 179