Introduction
High throughput platforms for genome wide DNA methylation analysis have allowed establishing the methylome of large series of patient samples. Pattern analysis of respective datasets has identified CpG island methylator phenotypes (CIMP) for several tumor types such as colon cancer [
19,
21,
42] and more recently also glioma (G-CIMP) [
29,
43,
45]. However, classification of samples as being silenced by aberrant methylation for a given gene is not obvious, since the relationship between CpG-methylation at individual sites, the extent of the overall CpG island methylation, and their effect on gene silencing is strongly dependent on the location within the gene [
46]. Promoter methylation of the repair gene O
6-methylguanine-DNA methyltransferase (
MGMT) is a predictive factor for benefit from alkylating agent therapy in glioblastoma patients [
3,
16,
33]. The predictive value of the
MGMT status is supported by recent findings in two clinical trials, comparing radiotherapy versus temozolomide (TMZ) treatment. In these trials for elderly patients, retrospective analysis of the
MGMT methylation status was associated with prediction of good outcome in the TMZ-, but not the RT-arm [
24,
52]. Furthermore, the
MGMT status has been prospectively validated in a phase III trial as biomarker for favorable outcome in glioblastoma patients treated with temozolomide [
12]. Repair by MGMT reverses alkylation at the O
6-position of guanine, one of the most toxic lesions induced by alkylating agents such as temozolomide (TMZ), thereby blunting the treatment effect [
18,
30]. Hence, the
MGMT methylation status has become a biomarker used for patient stratification or patient selection in clinical trials for glioblastoma patients [
12,
39,
50]. Surprisingly, recent studies in anaplastic glioma (WHO grade III) suggest a prognostic value [
44,
51]. In order to investigate pathogenetic and epigenetic features associated with this intriguingly distinct behavior of anaplastic glioma compared to glioblastoma, it is of high interest analyzing large datasets of glioma for which DNA methylome data have been reported, and classifying them by their
MGMT gene promoter methylation status for integration into multi-dimensional molecular and clinical data analysis. Several glioma datasets comprising methylome data obtained on the Infinium HumanMethylation27 (HM
-27K) or HM-450K BeadChip that interrogate at single-nucleotide resolution over 27,000 or 485,000 methylation sites per sample, respectively, have become publicly available [
7,
29,
43,
45]. The most comprehensive glioblastoma dataset with over 200 samples is from The Cancer Genome Atlas [
29,
40], widely used for hypothesis generation and validation in brain tumor research [
17,
48,
53]; however, the
MGMT methylation status has not yet been annotated.
The objective of this study was to propose a model determining the probability of
MGMT promoter methylation allowing classification into methylated and unmethylated samples based on CpG methylation data obtained on the widely used HM-450K or HM-27K BeadChip. The model can be applied to other datasets for example to further investigation of the relationship of
MGMT methylation with CIMP and other molecular and clinical parameters. The basic idea was to train the model using methylation-specific PCR (MSP)-based classification that we have shown to predict favorable outcome in a homogenously and prospectively treated glioblastoma patient population and for which we have obtained HM-450K data [
15,
16]. Standard treatment included the alkylating agent temozomoide (TMZ) concomitant with and adjuvant to radiotherapy [
36,
38]. At the same time, this study allowed investigation of the relationship between location-specific CpG methylation,
MGMT gene expression and outcome, supporting the mechanistic hypothesis that methylation-dependent gene silencing results in loss of expression and subsequently benefit from alkylating agent therapy in glioblastoma.
Discussion
The analysis of the
MGMT gene in glioblastoma using HM-450K methylation data has shown a strong CpG location-dependent effect on patient outcome. To our knowledge, this is the first report describing the spatial relationship of CpG methylation in the
MGMT promoter and the gene body and outcome of patients treated with alkylating agent therapy. Two regions of methylated CpGs with strong association to patient survival were identified (
p < 0.0001 after Bonferroni’s correction) that are separated by a prediction minimum at the TSS. The two identified regions were also associated with the strongest negative correlation to
MGMT gene expression, consistent with CpG methylation-mediated gene silencing and consequent sensitization to alkylating agent therapy due to lack of MGMT-mediated repair in this homogenously treated patient population. The two regions identified encompass the differentially methylated regions 1 and 2 (DMR1 and 2, Fig.
1) proposed by Malley et al. [
23] to be most relevant for gene silencing when methylated in glioblastoma cell lines and xenografts. Shah et al. [
35] defined three relevant regions, of which R2 and R3 encompass DMR1 and 2, and methylation of two of these three regions were associated with favorable progression free survival in their population of 44 glioblastoma patients treated with RT and concomitant and adjuvant TMZ. Most importantly, the region interrogated for diagnostic purposes using MSP [
5,
16] overlaps with the CpGs associated with best outcome prediction identified here. In contrast, none of the 161 CpGs interrogated outside the CpG island, located mostly in the gene body, showed an association with outcome, or a negative correlation with
MGMT expression (Supplementary Figure S2). However, we cannot exclude that other CpGs may be more relevant as the once described here, since the BeadChip array does not interrogate all CpGs of the CpG island encompassing the
MGMT promoter (Supplementary Fig. S1).
The CpG methylation probes present on the HM-450K and HM-27K BeadChip identified to be most relevant for gene silencing and outcome allowed construction of a model for prediction of the
MGMT methylation status. The use of logistic regression provides a simple model to calculate the methylation probability for a new sample based on two probes. Its ability to compute confidence and prediction intervals [
2,
20] may be of particular interest for treatment decisions for patients whose tumors display methylation probabilities close to the cut-off (Figs.
2,
3,
4). This allows application of a “safety margin” as we do in EORTC26082 (NCT01019434) that selects unmethylated glioblastoma patients only (cut-off set at lower bound of 95 % CI using a quantitative MSP assay [
49]), since it omits TMZ, thereby limiting the risk to withhold TMZ in patients who may potentially profit from it. The model’s good performance is reflected in similar or improved prediction of OS as compared to the MSP-based classification or MS-PSeq-based prediction in the E-GBM validation set, in accordance with high values for good classification, kappa value, and sensitivity and specificity measures (Figs.
2,
3).
Our model can be used for both the HM-450K and the HM-27K BeadChip. The platform effect was very weak. A higher amplitude of the methylation signal was detected in the low grade and anaplastic glioma samples that may simply reflect the fact that in non-glioblastoma usually both
MGMT copies are present, while in glioblastoma only one is methylated and the other one is lost due to the characteristic high frequency of deletions of chromosome 10 that reached 90 % in the M-GBM samples [
22]. Consequently, the presence of two methylated
MGMT copies will lead to an increase of the ratio methylated to unmethylated alleles. For the model, however, this may generate a bias in the estimation of
MGMT methylation probabilities based on the MGMT-STP27 model for non-glioblastoma tumors. The estimation could be improved by determining new optimal parameters in this population. Nevertheless, despite these limitations classification using the MGMT-STP27-based outcome prediction of the VB-Glioma-III dataset was similar to the one reported by the authors who used another method of
MGMT testing (Fig.
3). Prediction of the MGMT status in the TCGA-GBM confirmed a favorable outcome for patients with
MGMT methylation, although the effect was weaker than in our homogenously treated cohort (M-GBM) and the E-GBM cohort in which all patients were treated with combined chemo-radiotherapy comprising TMZ. This is not surprising, since most patients in the TCGA cohort had not (yet) been treated according to the current standard of care of combined chemo-radiotherapy (collection before 2005), and many different types of therapy were reported for the patients in the respective annotation file [
29].
The annotation of the
MGMT status in the TCGA-GBM dataset according to MGMT-STP27 allowed determining that
MGMT is not a CIMP gene in glioblastoma although CIMP tumors were more likely to be
MGMT methylated. Further, the prevalence of
MGMT methylated glioblastoma is not different in the two non-CIMP methylation clusters defined by Noushmehr et al. [
29], nor are they enriched in any of the expression-based glioblastoma subtypes, suggesting that
MGMT methylation is not associated with a particular pathogenetic mechanism involved in the development of de novo glioblastoma.
This is in contrast to grade II and III glioma (VB-Glioma-III and T-GliomaII/III) in which
MGMT is methylated in basically all CIMP tumors according to our classification model. It has been proposed that
MGMT methylation may represent an epiphenomenon of CIMP in the context of grade III glioma [
45]. This association of CIMP with
MGMT methylation may provide the key to understand why
MGMT methylation is associated with a prognostic and not a predictive value for benefit from alkylating agent containing chemotherapy in anaplastic glioma as suggested by two independent clinical trials [
44,
51]. Most of the VB-Glioma-III samples analyzed here in fact originate from one of these two studies and were characterized for CIMP [
45]. Anaplastic gliomas with CIMP accumulate other known favorable prognostic factors such as mutations of the isocitrate dehydroxygenase (
IDH) genes, 1p/19 co-deletions, and also
MGMT promoter methylation, in addition a plethora of other methylated genes whose contribution to response to therapy remains to be explored and exploited. It has become clear that these CIMP-positive tumors represent a pathogenetically different disease driven by epigenetic alterations mediated in most cases by IDH1/2 mutations [
11,
43]. Interestingly, non-CIMP anaplastic gliomas showed a
MGMT promoter methylation frequency similar to glioblastoma. It remains to be seen if in this CIMP-negative patient subpopulation the
MGMT status is predictive for benefit from alkylating agent therapy like in glioblastoma or has a prognostic value. This question can be addressed in the ongoing CATNON trial (EORTC 26053-22054; NCT00626990) for anaplastic glioma comparing radiation therapy with or without temozolomide. The same question applies to low-grade glioma where radiation versus temozolomide treatment is tested (EORTC 22033-26033, NCT00182819) and the role of CIMP and
MGMT methylation will need to be dissected. Since the HM-450K BeadChip allows the use of paraffin-embedded tumors, comprehensive DNA methylation analysis of samples collected within these clinical trials has become feasible.
The proposed MGMT-STP27 MGMT classification model will allow investigation of distinct epigenetic features associated with MGMT silencing in the context of CIMP-positive or CIMP-negative gliomas by multidimensional analysis of respective molecular and clinical data. Such alterations likely modulate response to therapy and may be exploited for improvement of personalized therapy.
Acknowledgments
We thank all patients who participated in the study and provided informed consent for translational research on their tumor tissues. We acknowledge the great contributions of local pathologists, the physicians and nurses taking care of the patients. We thank Marie-France Hamou for excellent technical support and the genomics platform in Geneva directed by Dr. Patrick Descombes for methylation profiling. Translational research in this study was supported by the Swiss National Science Foundation 3100A0_122557/1 (MEH, MD), the National Center for Competence in Research (NCCR) Molecular Oncology (MEH, MD), and the Brain Tumor Funders Cooperative (MEH).