# DGKI Methylation Status Modulates the Prognostic Value of MGMT in Glioblastoma Patients Treated with Combined Radio-Chemotherapy with Temozolomide

Amandine Etcheverry, Marc Aubry, Ahmed Idbaih, Elodie Vauleon, Yannick Marie, Philippe Menei, Rachel Boniface, Dominique Figarella-Branger, Lucie Karayan-Tapon, Veronique Quillien, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Amandine Etcheverry, Marc Aubry, Ahmed Idbaih, Elodie Vauleon, Yannick Marie, et al., DGKI Methylation Status Modulates the Prognostic Value of MGMT in Glioblastoma Patients Treated with Combined Radio-Chemotherapy with Temozolomide. PLoS ONE, 2014, 9 (9), pp.e104455. 10.1371/journal.pone.0104455. [hal-01358132](https://hal.sorbonne-universite.fr/hal-01358132v1)

HAL Id: hal-01358132

https://hal.sorbonne-universite.fr/hal-01358132v1

Submitted on 31 Aug 2016

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# DGKI Methylation Status Modulates the Prognostic Value of MGMT in Glioblastoma Patients Treated with Combined Radio-Chemotherapy with Temozolomide

<figure><img src="image_3.png" /></figure>

Amandine Etcheverry<sup>1,2,4,5</sup>, Marc Aubry<sup>1,2,3,6</sup>, Ahmed Idbaih<sup>5,13</sup>, Elodie Vauleon<sup>1,2,6</sup>, Yannick Marie<sup>5</sup>,
Philippe Menei<sup>7</sup>, Rachel Boniface<sup>1,2</sup>, Dominique Figarella-Branger<sup>8,9</sup>, Lucie Karayan-Tapon<sup>10,11,12</sup>,
Veronique Quillien<sup>1,2,6</sup>, Marc Sanson<sup>5,13</sup>, Marie de Tayrac<sup>1,2,4</sup>, Jean-Yves Delattre<sup>5,13</sup>, Jean Mosser<sup>1,2,3,4*</sup>

1 CNRS, UMR 6290, Institut Génétique et Développement de Rennes, Rennes, France, 2 Université Rennes 1, Université Européenne de Bretagne, Biosit, Faculté de Médecine, Rennes, France, 3 Plate-forme Génomique Santé Biogenouest, Biosit, Rennes, France, 4 Centre Hospitalier Universitaire de Rennes, Service de Génétique Moléculaire et Génomique, Rennes, France, 5 Assistance Publique-Hôpitaux de Paris, Service de Neurologie 2 Mazarin, Groupe Hospitalier Pitié-Salpêtrière, Paris, France, 6 Département de Biologie Médicale, Centre Eugène Marquis, Rennes, France, 7 Centre Hospitalier Universitaire d'Angers, Service de Neurochirurgie, Angers, France, 8 Assistance Publique-Hôpitaux de Marseille, Centre Hospitalier Universitaire de la Timone, Service d'Anatomie Pathologie et de Neuropathologie, Université Aix-Marseille, Marseille, France, 9 INSERM U911, Université Aix-Marseille, Marseille, France, 10 INSERM U935, Poitiers, France, 11 Université de Poitiers, Poitiers, France, 12 Centre Hospitalier Universitaire de Poitiers, Laboratoire de Cancérologie Biologique, Poitiers, France, 13 Centre de Recherche de l'Institut du Cerveau et de la Moelle Épinière, INSERM UMRS 975/CNRS UMR 7225/Université Pierre-et-Marie-Curie, Institut du Cerveau et de la Moelle Épinière, Groupe Hospitalier Pitié-Salpêtrière, Paris, France

## Abstract

**Background:** Consistently reported prognostic factors for glioblastoma (GBM) are age, extent of surgery, performance status, *IDH1* mutational status, and *MGMT* promoter methylation status. We aimed to integrate biological and clinical prognostic factors into a nomogram intended to predict the survival time of an individual GBM patient treated with a standard regimen. In a previous study we showed that the methylation status of the *DGKI* promoter identified patients with *MGMT*-methylated tumors that responded poorly to the standard regimen. We further evaluated the potential prognostic value of *DGKI* methylation status.

**Methods:** 399 patients with newly diagnosed GBM and treated with a standard regimen were retrospectively included in this study. Survival modelling was performed on two patient populations: intention-to-treat population of all included patients (population 1) and *MGMT*-methylated patients (population 2). Cox proportional hazard models were fitted to identify the main prognostic factors. A nomogram was developed for population 1. The prognostic value of *DGKI* promoter methylation status was evaluated on population 1 and population 2.

**Results:** The nomogram-based stratification of the cohort identified two risk groups (high/low) with significantly different median survival. We validated the prognostic value of *DGKI* methylation status for *MGMT*-methylated patients. We also demonstrated that the *DGKI* methylation status identified 22% of poorly responding patients in the low-risk group defined by the nomogram.

**Conclusions:** Our results improve the conventional *MGMT* stratification of GBM patients receiving standard treatment. These results could help the interpretation of published or ongoing clinical trial outcomes and refine patient recruitment in the future.

**Citation:** Etcheverry A, Aubry M, Idbaih A, Vauleon E, Marie Y, et al. (2014) *DGKI* Methylation Status Modulates the Prognostic Value of *MGMT* in Glioblastoma Patients Treated with Combined Radio-Chemotherapy with Temozolomide. PLoS ONE 9(9): e104455. doi:10.1371/journal.pone.0104455

**Editor:** Marta M. Alonso, University Hospital of Navarra, Spain

**Received** May 14, 2014; **Accepted** July 9, 2014; **Published** September 18, 2014

**Copyright:** © 2014 Etcheverry et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Data Availability:** The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. Data are available upon request because the multicentric cohort is subjected to legal restrictions. To have access to the data, the readers may contact Pr. Jean Mosser (jean.mosser@univ-rennes1.fr).

**Funding:** Ligue contre le Cancer foundation; European Union FEDER; COmité de la REcherche Clinique COREC du CHU de Rennes; INCa DGOS-Inserm 6038; Région Bretagne. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

**Competing Interests:** The authors have declared that no competing interests exist.

* Email: jean.mosser@univ-rennes1.fr

¶ These authors contributed equally to this work.

## Introduction

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults. Its prognosis remains extremely poor, despite multimodal treatment by surgery, radiotherapy, and

temozolomide-based chemotherapy (standard regimen) [1]. The most consistently reported clinical prognostic factors for GBM are age, extent of surgery, and performance status [2,3,4]. The somatic mutation affecting amino acid 132 in the isocitrate
dehydrogenases 1 gene (*IDH1*) is also associated with a better clinical outcome in gliomas, including glioblastoma. However, this mutation is rare in primary GBMs (approximately 6%) [5,6,7]. The methylation status of the O⁶-methylguanine DNA methyl-transferase gene (*MGMT*) promoter is currently the strongest predictive biomarker of outcome and benefit from temozolomide-based treatment of GBM [8]. In 2008, Gorlia *et al*. integrated biological and clinical prognostic factors and their independent and combined predictive powers into nomograms for GBM patients treated with the standard regimen [9]. These nomograms can be used to predict an individual patient's median survival and the probability of survival at two years. These nomograms can be of interest in patient counselling and in the design and interpretation of clinical trials. However, the authors stressed a lack of statistical power in their subgroup analysis of patients who had an available *MGMT* promoter methylation status (n = 103).

In this study, we retrospectively analysed 399 GBM patients treated with the standard regimen (intention-to-treat). We identified the main clinical prognostic factors in this cohort and compared our results with those of the EORTC (European Organisation for Research and Treatment of Cancer) and NCIC (National Cancer Institute of Canada) trial 26981-22981/CE.3. We propose an updated nomogram intended to predict the survival time of an individual GBM patient. In a previous study on 50 GBM patients treated with the standard regimen, we showed that the methylation status of the diacylglycerol kinase iota gene (*DGKI*) promoter identified patients with *MGMT*-methylated tumors that responded poorly to the standard regimen [10]. The role of *DGKI* and the functional consequences of its methylation status have never been investigated in gliomas but *DGKI* regulates Ras signalling, an oncogenic pathway frequently altered in GBM [11,12]. We further evaluated the potential predictive value of *DGKI* methylation status in the context of both *MGMT*-methylated and intention-to-treat populations.

# Materials and Methods

## Patients and tissue samples

This multi-center retrospective cohort included 399 patients treated in the Departments of Neurosurgery/Neuro-oncology of Angers (n = 28), Marseille (n = 52), Paris-Salpêtrière (n = 227), Rennes (n = 50), and Poitiers (n = 42) between 2006 and 2011. The inclusion criteria were as follows: 1) patients aged 18 years or more, 2) diagnosis of a primary GBM (WHO grade IV), 3) detailed clinical information at diagnosis and during follow-up, 4) treatment with radiotherapy and concurrent/adjuvant temozolomide (standard regimen), and 5) availability of tumor tissue with informed consent in accordance with French regulations and the Helsinki Declaration. All patients included in this study fulfilled the inclusion criteria. Particularly, all patients received a radio-chemotherapy regimen in accordance with the standard of care. Follow-up for included patients ranged from 24 days to 5.2 years (median, 15.5 months). Tumor samples were snap-frozen immediately after resection and stored in tumor banks under the following authorization numbers: (Centre de Ressources Biologiques, DC-2011-1467, Angers), 2008/70 (AP-HM Tumor Bank, Marseille), DC 2009-957 (OncoNeuroTheque Salpêtrière, Paris), DHOS/2004/04056 (Hospital Tumor Bank, Poitiers), and AC-2010-77 (Centre de Ressources Biologiques, Rennes). The extent of surgery was evaluated with an enhanced magnetic resonance imaging (MRI) performed within 24 hours after the resection. All samples presented at least 70% of tumor cells. For each tumor sample, DNA was extracted using the NucleoSpin Tissue Kit (Macherey Nagel) according to the manufacturer's instructions.

The quality of the DNA samples was assessed by electrophoresis on a 1% agarose gel. Only high quality genomic DNAs were selected for further analyses.

Two patient populations were considered in this study: the population of all included patients (population 1) and the subgroup of *MGMT*-methylated patients (population 2). Population 1 was used to identify the main clinical prognostic factors and to compare our results with those of the EORTC and NCIC trial 26981-22981/CE.3 [9]. Population 2 was studied to evaluate the strength and importance of these prognostic factors after conventional *MGMT* stratification. The effect of *DGKI* promoter methylation status on the prognosis of GBM patients assigned to standard treatment was also evaluated in *MGMT*-methylated patients. Patients from population 2 were randomly assigned to a training cohort and a validation cohort of equal sizes in a randomized block design stratified by the hospital center. Table 1 shows the demographic and clinical characteristics of the patients included in this study.

## *IDH1* mutation

Tumor DNA was screened for somatic mutations in *IDH1* *codon 132* via exon 4 PCR amplification and direct sequencing as previously described [13]. Because *IDH1* mutation is sufficient to establish the “glioma-CpG island methylator phenotype” (G-CIMP), we did not take this phenotype into account in our analysis [14].

## DNA methylation analysis

DNA was bisulfite-modified using the EZ DNA Methylation Kit (Zymo Research). The methylation percentage (%met) of the *MGMT* promoter was measured using the PyroMark Q96 CpG MGMT kit (Qiagen) (average percentage of the five tested CpG sites). The *DGKI* promoter's %met was measured using VeraCode GoldenGate Methylation technology (Illumina Inc.) or by pyrosequencing. PCR and pyrosequencing primers were designed using the Pyrosequencing Assay Design Software (Qiagen). The primers and PCR conditions are given in Figure S1. The reproducibility of the pyrosequencing assays was assessed on a subset of 21 patients for *MGMT* (Pearson correlation coefficient r = 0.999, p<1e-08) and of 26 patients for *DGKI* (Pearson correlation coefficient r = 0.994, p<1e-08). The consistency between VeraCode GoldenGate Methylation technology and pyrosequencing was assessed on 24 patients for DGKI (Pearson correlation coefficient r = 0.89, p<1e-08). The assessment of MGMT and DGKI %met was conducted at the Rennes hospital. For MGMT, we used the 8% methylation threshold defined on an independent data set by Quillien *et al.* [15]. The DGKI methylation threshold of 28% was determined in the training cohort using the *risksetROC* R package (AUC = 0.61).

## Statistical analyses

Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan-Meier method. Comparisons between survival groups were performed using a log-rank test for binary variables, and a log-rank trend test for ordered categories. Cox proportional hazard models provided estimates of the hazard ratios (HRs). From these tests, variables with *p*-values less than 5% were candidates for the multivariate analyses. In population 1, the Cox proportional hazards model was then used with forward stepwise model selection. We have checked that no evidence of violation of the proportional hazards assumption was found. The probability of inclusion of a factor in the multivariate model was estimated by using the bootstrap resampling technique as
<table><caption>**Table 1.** Patients demographic and clinical characteristics.</caption><thead><tr><th>Characteristics</th><th>Population 1 (n=399)</th><th>EORTC cohort* (n=103)</th><th>Population 2 training (n=86)</th><th>Population 2 validation (n=89)</th></tr></thead><tbody><tr><td>Age (years)</td><td></td><td></td><td></td><td></td></tr><tr><td>Median</td><td>59</td><td></td><td>57</td><td>59</td></tr><tr><td>Range</td><td>21-88</td><td></td><td>29-88</td><td>26-80</td></tr><tr><td>Age - no. (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>≤50</td><td>95 (24)</td><td>44 (43)</td><td>23 (27)</td><td>22 (25)</td></tr><tr><td>51-60</td><td>130 (33)</td><td>40 (39)</td><td>30 (35)</td><td>26 (29)</td></tr><tr><td>>60</td><td>174 (44)</td><td>19 (18)</td><td>33 (38)</td><td>41 (46)</td></tr><tr><td>Sex - no. (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>Women</td><td>161 (40)</td><td>38 (37)</td><td>41 (48)</td><td>41 (46)</td></tr><tr><td>Men</td><td>238 (60)</td><td>65 (63)</td><td>45 (52)</td><td>48 (54)</td></tr><tr><td>KPS (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>Median</td><td>80</td><td></td><td>80</td><td>80</td></tr><tr><td>Range</td><td>40-100</td><td></td><td>40-100</td><td>40-100</td></tr><tr><td>KPS - no. (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>≤70</td><td>37 (9)</td><td></td><td>10 (12)</td><td>10 (11)</td></tr><tr><td>>70</td><td>331 (83)</td><td></td><td>69 (80)</td><td>72 (81)</td></tr><tr><td>Missing</td><td>31 (8)</td><td></td><td>7 (8)</td><td>7 (8)</td></tr><tr><td>Extent of surgery - no. (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>Biopsy</td><td>30 (8)</td><td>0 (0)</td><td>10 (12)</td><td>5 (6)</td></tr><tr><td>Partial resection</td><td>140 (35)</td><td>56 (54)</td><td>25 (29)</td><td>34 (38)</td></tr><tr><td>Complete resection</td><td>220 (55)</td><td>47 (46)</td><td>50 (58)</td><td>49 (55)</td></tr><tr><td>Missing</td><td>9 (2)</td><td>0 (0)</td><td>1 (1)</td><td>1 (1)</td></tr><tr><td>IDH1 mutational status - no. (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>Mutated</td><td>18 (5)</td><td></td><td>5 (6)</td><td>8 (9)</td></tr><tr><td>Wild-type</td><td>364 (91)</td><td></td><td>80 (93)</td><td>77 (87)</td></tr><tr><td>Missing</td><td>17 (4)</td><td></td><td>1 (1)</td><td>4 (4)</td></tr><tr><td>MGMT methylation status - no. (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>Methylated</td><td>175 (44)</td><td>45 (44)</td><td>86 (100)</td><td>89 (100)</td></tr><tr><td>Unmethylated</td><td>224 (56)</td><td>58 (56)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>DGKI methylation status - no. (%)</td><td></td><td></td><td></td><td></td></tr><tr><td>Methylated</td><td>95 (24)</td><td></td><td>22 (26)</td><td>21 (24)</td></tr><tr><td>Ummethylated</td><td>304 (76)</td><td></td><td>61 (74)</td><td>68 (76)</td></tr><tr><td>Overall survival - mo</td><td></td><td></td><td></td><td></td></tr><tr><td>Median</td><td>19.1</td><td></td><td>29.6</td><td>30.2</td></tr><tr><td>95% CI</td><td>17.1-20.8</td><td></td><td>22.5-46.7</td><td>24.1-46.8</td></tr><tr><td>Progression-free survival - mo</td><td></td><td></td><td></td><td></td></tr><tr><td>Median</td><td>10.8</td><td></td><td>15.2</td><td>15.6</td></tr><tr><td>95% CI</td><td>10.1-11.9</td><td></td><td>13.8-19.1</td><td>13.1-23.4</td></tr></tbody></table>

*EORTC and NCIC trial 26981-22981/CE.3 population 3: GBM patients who underwent partial or complete resection and were assigned temozolomide and radiotherapy in the presence of an MGMT promoter methylation assessment.

doi:10.1371/journal.pone.0104455.t001

described in Gorlia *et al.* [9]. All tests were adjusted for hospital center. Analyses were carried out using the *survival* R package.

A nomogram was developed for population 1 to predict each patient's median survival and probability of survival at two years, taking into account their clinical characteristics. Variables with a probability of inclusion higher than 90% based on 1000 bootstrap samples were included in the final model. The definition of two risk groups (high/low) was based on the value of the linear predictor underlying the nomogram; values greater than or equal

to zero were assigned to the high-risk group, and negative values were assigned to the low-risk group (the total points cut-off between high and low-risk is the value matching a linear predictor value equal to zero). The accuracy of predictions was assessed by estimating the model's calibration and discrimination measured by the Concordance index corrected for optimism (C-index). The nomogram was built using the *rms* R package.
<table><caption>Table 2. Univariate analyses of survival prognostic factors.</caption><thead><tr><th rowspan="3"></th><th colspan="2">Population 1 (n = 399)</th><th colspan="2">Population 2 (n = 175)</th><th colspan="2">Training cohort (n = 86)</th><th colspan="2">Validation cohort (n = 89)</th></tr><tr><th>Median survival, months HR (95% CI)</th><th>HR (95% CI)</th><th>p</th><th>Median survival, months (95% CI)</th><th>HR (95% CI)</th><th>p</th><th>Median survival, months (95% CI)</th><th>HR (95% CI)</th><th>p</th></tr></thead><tbody><tr><td>Age (years)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>≤50</td><td>33.1 (24.2–56.7)</td><td>..</td><td>..</td><td>42.5 (33.1–N)</td><td>..</td><td>0.01</td><td>NA (37.8–N)</td><td>..</td><td>0.01</td></tr><tr><td>51–60</td><td>19.3 (17.1–24.3)</td><td>1.4 (1.2–1.7)</td><td>..</td><td>31.4 (19.7–N)</td><td>1.7 (1.1–2.6)</td><td>..</td><td>30.2 (26.2–N)</td><td>1.8 (1.1–2.8)</td><td>..</td></tr><tr><td>>60</td><td>15.6 (14.0–18.0)</td><td>..</td><td>..</td><td>20.1 (16.3–N)</td><td>..</td><td>..</td><td>16.5 (14.4–37.2)</td><td>..</td><td>..</td></tr><tr><td>Sex</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Women</td><td>18.0 (16.3–26.0)</td><td>1.0</td><td>NS</td><td>34.8 (25.0–N)</td><td>1.0</td><td>NS</td><td>34.4 (17.8–N)</td><td>1.0</td><td>NS</td></tr><tr><td>Men</td><td>19.3 (17.1–21.1)</td><td>1.2 (0.9–1.6)</td><td>..</td><td>24.6 (19.7–N)</td><td>1.6 (0.8–3.3)</td><td>..</td><td>27.5 (21.5–46.8)</td><td>0.9 (0.5–1.7)</td><td>..</td></tr><tr><td>KPS (%)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>≤70</td><td>11.7 (10.2–16.9)</td><td>1.0</td><td>..</td><td>19.3 (9.5–N)</td><td>1.0</td><td>0.002</td><td>13.3 (10.9–N)</td><td>1.0</td><td>0.002</td></tr><tr><td>>70</td><td>19.8 (18.3–24.1)</td><td>0.5 (0.3–0.7)</td><td>..</td><td>33.1 (26.2–52.6)</td><td>0.1 (0.04–0.5)</td><td>..</td><td>34.4 (26.2–N)</td><td>0.2 (0.1–0.6)</td><td>..</td></tr><tr><td>Extent of surgery</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Biopsy</td><td>14.5 (11.3–34.8)</td><td>..</td><td>..</td><td>14.5 (11.4–N)</td><td>..</td><td>0.01</td><td>20.3 (20.3–N)</td><td>..</td><td>NS</td></tr><tr><td>Partial</td><td>19.2 (17.1–24.1)</td><td>0.7 (0.6–0.9)</td><td>..</td><td>19.9 (19.1–N)</td><td>0.6 (0.4–0.9)</td><td>..</td><td>37.8.1 (21.5–N)</td><td>0.8 (0.5–1.5)</td><td>..</td></tr><tr><td>Complete</td><td>19.6 (17.1–22.3)</td><td>..</td><td>..</td><td>33.1 (25.0–N)</td><td>..</td><td>..</td><td>27.5 (22.3–N)</td><td>..</td><td>..</td></tr><tr><td>IDH1 mutational status</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mutated</td><td>56.7 (38.6–N)</td><td>1.0</td><td>..</td><td>56.7 (N–N)</td><td>1.0</td><td>NS</td><td>NA (38.6–N)</td><td>1.0</td><td>NS</td></tr><tr><td>Wild-type</td><td>18.3 (16.6–19.8)</td><td>7.8 (2.5–24.3)</td><td>..</td><td>27.8 (21.1–42.5)</td><td>4.4 (0.6–33.8)</td><td>..</td><td>24.8 (20.3–37.8)</td><td>5.3 (0.7–39.5)</td><td>..</td></tr><tr><td>MGMT methylation status</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Methylated</td><td>30.2 (24.8–37.8)</td><td>1.0</td><td>..</td><td>..</td><td>..</td><td>..</td><td>..</td><td>..</td><td>..</td></tr><tr><td>Unmethylated</td><td>14.9 (13.8–16.9)</td><td>3.1 (2.3–4.2)</td><td>..</td><td>..</td><td>..</td><td>..</td><td>..</td><td>..</td><td>..</td></tr><tr><td>DGKI methylation status</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Methylated</td><td>16.9 (14–19.9)</td><td>1.0</td><td>..</td><td>19.9 (9.9–N)</td><td>1.0</td><td>..</td><td>16.5 (13.3–N)</td><td>1.0</td><td>0.01</td></tr><tr><td>Unmethylated</td><td>19.6 (17.8–24.1)</td><td>0.7 (0.5–0.9)</td><td>..</td><td>34.8 (27.8–52.6)</td><td>0.3 (0.1–0.5)</td><td>..</td><td>37.8 (26.2–N)</td><td>0.4 (0.2–0.8)</td><td>..</td></tr></tbody></table>

NA = not available; N = not enough events to calculate upper 95% CI boundary; NS = not significant. Age, KPS and extent of surgery are treated as ordinal variables.
doi:10.1371/journal.pone.0104455.t002
<table><caption>**Table 3.** Multivariate analyses of survival prognostic factors.</caption><thead><tr><th></th><th colspan="2">Population 1 (n = 399)</th></tr><tr><th></th><th>HR (95% CI)</th><th>p (%inclusion)</th></tr></thead><tbody><tr><td>Age (years)</td><td></td><td></td></tr><tr><td>≤50</td><td>..</td><td>0.006 (92)</td></tr><tr><td>51–60</td><td>1.3 (1.1–1.6)</td><td>..</td></tr><tr><td>>60</td><td>..</td><td>..</td></tr><tr><td>Karnofsky performance status (%)</td><td></td><td></td></tr><tr><td>≤70</td><td>1.0</td><td>&lt;0.001 (99)</td></tr><tr><td>>70</td><td>0.4 (0.2–0.6)</td><td>..</td></tr><tr><td>Extent of surgery</td><td></td><td></td></tr><tr><td>Biopsy</td><td>..</td><td>&lt;0.001 (98)</td></tr><tr><td>Partial resection</td><td>0.6 (0.5–0.8)</td><td>..</td></tr><tr><td>Complete resection</td><td>..</td><td>..</td></tr><tr><td>IDH1 mutational status</td><td></td><td></td></tr><tr><td>Mutated</td><td>1.0</td><td>0.02 (94)</td></tr><tr><td>Wild-type</td><td>4.1 (1.3–13.3)</td><td>..</td></tr><tr><td>MGMT methylation status</td><td></td><td></td></tr><tr><td>Methylated</td><td>1.0</td><td>&lt;0.001 (100)</td></tr><tr><td>Unmethylated</td><td>3.0 (2.2–4.2)</td><td>..</td></tr><tr><td>DGKI methylation status</td><td></td><td></td></tr><tr><td>Methylated</td><td>1.0</td><td>0.03 (74)</td></tr><tr><td>Unmethylated</td><td>0.7 (0.5–1.0)</td><td>..</td></tr></tbody></table>

NA = not available; N = not enough events to calculate upper 95% CI boundary; NS = not significant. For ordered categorical factors, the first value is the reference.
doi:10.1371/journal.pone.0104455.t003

# Results

## Survival analysis of all included patients (population 1)

In population 1, univariate Cox analyses showed that age, Karnofsky performance status (KPS), extent of surgery, *IDH1* mutational status, *MGMT* promoter methylation status, and *DGKI* promoter methylation status were significantly associated with OS (Table 2). These variables remained significantly associated with OS in the multivariate Cox analyses (Table 3). The final multivariate Cox model used to build the nomogram included age, KPS, extent of surgery, *IDH1* mutational status, and *MGMT* promoter methylation status. This model was associated with a C-index corrected for optimism of 68%.

Figure 1A shows the nomogram for population 1. The total number of points for each patient is obtained by summing the points for each of the individual factors in the nomogram. The median survival and probability of survival at two years for a given patient are obtained by drawing a vertical line from the “total points” axis down to the outcome axes. For example, a 55-year-old patient with a KPS of 80 and a partly resected/*IDH1* wild-type/*MGMT*-methylated tumor has a total prognostic score of 129 and is predicted to have a median survival of approximately 30 months and a 60% probability of surviving two years. Because the cut-off between high and low risk is 165 points, this patient is assigned to the low-risk group. Patients in the low-risk group had a median OS of 29.6 months (95% CI, 26.0–37.7), which was significantly longer than 14.9 months (95% CI, 13.7–16.7) for patients in the high-risk group (p<1e-08). A similar stratification was observed for the PFS (Figure 1B).

## Survival analysis of *MGMT*-methylated patients (population 2)

In population 2, univariate Cox analyses showed that age, KPS, and *DGKI* promoter methylation status were significantly associated with OS in both the training and validation cohorts (Table 2). In the multivariate Cox analyses, *DGKI* promoter methylation status was the only variable with a probability of inclusion higher than 90% in the training cohort. *DGKI* methylation status stratified the *MGMT*-methylated patients into two groups with significant differences in OS (19.9 months vs. 34.8 months, p = 0.008, 16.5 months vs. 37.8 months, p = 1e-4, training and validation, respectively, figure 2A). The *MGMT* %met was not significantly different (Student's t-test) between the *DGKI*-methylated and *DGKI*-unmethylated patients (p = 0.23).

The OS and PFS of the *MGMT*-methylated and *DGKI*-methylated patients were not significantly different from the OS and PFS of the *MGMT*-unmethylated patients (19.7 months vs. 14.9 months, 12.0 months vs. 9.0 months, OS and PFS, respectively, Figure S2).

## Prognostic value of *DGKI* methylation status in risk groups (population 1)

The *DGKI* methylation status stratified the low-risk patients into two groups with significant differences in OS and PFS (37.2 months vs. 19.9 months, 18.4 months vs. 12.5 months, OS and PFS, respectively, figure 2B). The OS of low-risk and *DGKI*-methylated patients was not significantly different from the OS of high-risk patients (19.9 months vs. 14.9 months, p = 0.21). The *DGKI* methylation status did not stratify the subgroup of high-risk patients in the intention-to-treat population.
A

<figure><img src="image_4.png" /></figure>

B

<figure><img src="image_5.png" /><figcaption>**Figure 1.** GBM patients assigned to standard treatment (population 1). (A) Nomogram for predicting median survival, probability of survival at two years, and risk category. The total number of points for each patient is obtained by summing the points for each of the individual factors in the nomogram. The median survival and probability of survival at two years for a given patient are obtained by drawing a vertical line from the "total points" axis down to the outcome axes. The definition of two risk groups (high/low) is based on the value of the linear predictor underlying the nomogram: values greater than or equal to zero were assigned to the high-risk group, and negative values were assigned to the low-risk group. The total point value matching the null value of the linear predictor is 165. Example: a 55-year-old patient with a KPS of 80 and a partly resected/IDH1 wild-type/MGMT-methylated tumor has a total prognostic score of 129 and is predicted to have a median survival of approximately 30 months and a 60% probability of surviving two years. This patient is assigned to the low-risk group. (B) Kaplan-Meier estimation of OS and PFS. M: methylated patients, UM: unmethylated patients, LR: low-risk patients, HR: high-risk patients, mo: month. The difference in survival between groups is reported (log-rank test *p*-value). The size and the median survival of each group are also specified.
doi:10.1371/journal.pone.0104455.g001</figcaption></figure>

Discussion

We studied a retrospective cohort of 399 GBM patients
homogeneously treated with the standard regimen. The higher
OS at 6 and 12 months observed in our cohort in comparison to
the reference cohort of the EORTC trial [16] (Table S1) can be
explained by an improvement of surgical practices (55% of
complete resection in our cohort vs. 39% in EORTC trial),
continuation of standard treatment despite pseudoprogression,
and an earlier and/or easier access to bevacizumab treatment at
recurrence for patients progressing after 2007. This cohort was
used to identify the main clinical prognostic factors and to design a
nomogram intended to predict the survival time of an individual
patient. A nomogram-based stratification of the cohort identified
two risk groups (high/low) with significantly different median

survival. In the low-risk group, the *DGKI* promoter methylation
status identified poorly responding patients.

The prognostic factors identified in our study were age, KPS,
extent of surgery, *IDH1* mutational status, and *MGMT* promoter
methylation status. This result is in agreement with the most
consistently reported prognostic factors for GBM [2,3,4,5,6,8].
From an individual patient's perspective, a nomogram offers a
more tailored approach, taking into account their clinical
characteristics. This can be of interest in patient counselling and
in the design and interpretation of clinical trials. Therefore, we
propose a nomogram based on the prognostic factors identified in
our study. The nomogram can be used to predict an individual
patient's median survival and probability of survival at two years.
The DGKI methylation status was not included in this nomogram
as it did not identify clinically relevant groups of patients in the
A

<figure><img src="image_6.png" /></figure>

B

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

**Figure 2. Prognostic value of DGKI methylation status.** (A) MGMT-methylated GBM patients assigned to standard treatment (population 2). Kaplan-Meier estimation of OS in training and validation cohorts. (B) GBM patients assigned to standard treatment (population 1). Kaplan-Meier estimation of OS and PFS. M: methylated patients, UM: unmethylated patients, LR: low-risk patients, HR: high-risk patients, mo: month. The difference in survival between groups is reported (log-rank test *p*-value). The size and the median survival of each group are also specified.
doi:10.1371/journal.pone.0104455.g002

intention-to-treat population. However, the interaction between MGMT and DGKI in a multivariate Cox model including all significant prognostic factors (age+KPS+surgery+IDH1+MGMT+DGKI+MGMT:DGKI) was significant (p = 0.0007). This indicated that the prognostic value of DGKI was to find in the context of the MGMT methylation status. A nomogram including this interaction can be found in Figure S3.

In 2008, Gorlia *et al.* proposed a nomogram for GBM patients who underwent either a partial or complete resection and were assigned to temozolomide and radiotherapy in the presence of a *MGMT* promoter methylation assessment (population 3 of the EORTC and NCIC trial 26981-22981/CE.3). Their nomogram includes *MGMT* promoter methylation status, Folstein Mini-Mental State Examination (MMSE) score, and WHO performance status [9]. We propose an updated version of this nomogram that includes not only *MGMT* promoter methylation

status and Karnofsky performance score but also age, *IDH1* mutational status, and extent of surgery. In the EORTC trial, elderly patients (>70 years) were excluded, and the prognostic value of *IDH1* was not evident when the trial was designed. Furthermore, the authors discussed the restricted reliability of the extent of surgery in their study. Age and extent of surgery were nonetheless identified by Gorlia *et al.* in the population of GBM patients who underwent partial or complete resection and were assigned to temozolomide and radiotherapy without the knowledge of the *MGMT* promoter methylation status (population 2 of the EORTC trial). Because it was not routine to collect MMSE score in the neurosurgical units involved, we were unable to evaluate the prognostic value of these nomograms on our cohort. However, the nomogram proposed in the present study showed better performance evaluation (AUC) than MGMT status alone (0.71[0.65–0.78] vs. 0.65[0.59–0.71], p<1e-08). The potential
skewing effect of treatment at recurrence on patient survival was not controlled; however, the validity of our PFS results indicated that our findings could be independent of these treatments. A recent study showed that the nomogram designed by Gorlia *et al.* for the intention-to-treat population (radiotherapy only or temozolomide and radiotherapy) was a poor predictor of an individual patient's survival because the standard of care has evolved since the EORTC trial [17]. Unfortunately, the nomograms proposed by Gorlia *et al.* for GBM patients who received the standard treatment (population 2 and population 3 of the EORTC trial) were not evaluated in the study by Parks *et al.* However, our study confirmed the appropriateness of Gorlia *et al.* findings as we also identified *MGMT* methylation status, performance status, age and extent of surgery in the nomogram for the population of GBM patients assigned to standard treatment.

A nomogram-based stratification of our cohort of primary GBM patients treated with the standard regimen identified two risk groups (high/low) with significantly different median survival. The low-risk group was almost exclusively composed of *MGMT*-methylated patients. Interestingly, our previous study showed that the methylation status of the *DGKI* promoter identified GBM patients with *MGMT*-methylated tumors who responded poorly to the standard regimen [10]. In this study, we have validated the prognostic value of *DGKI* methylation status for *MGMT*-methylated patients (population 2). However, this finding could be restricted by the limited size of the training and validation cohorts. We further evaluated the potential predictive value of *DGKI* methylation status on the intention-to-treat population (population 1). The methylation status of the *DGKI* promoter identified 22% of poorly responding patients in the low-risk group but had no prognostic value for high-risk patients. The role of *DGKI* and the functional consequences of its methylation status have never been investigated in gliomas but *DGKI* regulates Ras signalling, an oncogenic pathway frequently altered in GBM [11,12]. Recently, Revill et al. showed that *DGKI* was hypermethylated in primary hepatocellular carcinoma and was reexpressed in liver cancer cell lines after exposure to reagents reversing DNA methylation [18]. This study suggests that *DGKI* expression is regulated by its promoter methylation. In GBM, we observed an anti-correlation between *DGKI* expression and methylation levels, in a private cohort and in the TCGA cohort (data not shown). Further functional studies on *DGKI* are clearly required.

Our results improve the conventional *MGMT* stratification of GBM patients receiving standard treatment. In particular, the

*DGKI* methylation status identified poorly responding patients in the group of low-risk or *MGMT*-methylated patients. A retrospective study precluding the establishment of firm conclusions, these results need to be validated in a prospectively recruited cohort. They could however be of help in the interpretation of published or ongoing clinical trial outcomes and refine patient recruitment in the future.

## Supporting Information

<caption>**Figure S1 Primers and cycling conditions for the pyrosequencing analysis of DGKI_7.**
(TIFF)</caption>

<caption>**Figure S2 Prognostic value of DGKI methylation status for GBM patients assigned to standard treatment (population 1).** Kaplan-Meier estimation of OS and PFS. M: methylated patients, UM: unmethylated patients, mo: month. The difference in survival between groups is reported (log-rank test *p*-value). The size and the median survival of each group are also specified.
(TIFF)</caption>

<caption>**Figure S3 Nomogram including the interaction between MGMT and DGKI (population 1).**
(TIFF)</caption>

<caption>**Table S1 OS and PFS - % (95% IC).**
(DOC)</caption>

## Acknowledgments

We gratefully acknowledge the help of Dr Karima Mokhtari, Dr Sophie Michalack, Dr Audrey Rousseau, Dr Dan Christian, Dr Stephan Saikali, Dr Anne Clavreul and the Tumor Banks from Angers, Marseille, Paris, Poitiers and Rennes for their constant support in the collection, processing and histological analysis of tumor samples. We are most grateful to Frédérique Guénot, Cyrille Surbled, the Biogenouest Genomics core facility (Rennes Villejean, France) and the Genotyping GENTYANE core facility (Clermont-Ferrand, France) for their technical support. We thank Anais Peyre for her help in formatting the figures.

## Author Contributions

Conceived and designed the experiments: MA AE MdT JM. Performed the experiments: AE RB. Analyzed the data: MA AE MdT. Contributed reagents/materials/analysis tools: EV YM PM DFB LKT VQ MS JYD JM AI. Contributed to the writing of the manuscript: MA AE.

## References

1. Wen PY, Kesari S (2008) Malignant gliomas in adults. N Engl J Med 359: 492-507.

2. Siker ML, Wang M, Porter K, Nelson DF, Curran WJ, et al. (2011) Age as an independent prognostic factor in patients with glioblastoma: a Radiation Therapy Oncology Group and American College of Surgeons National Cancer Data Base comparison. J Neurooncol 104: 351-356.

3. Stummer W, van den Bent MJ, Westphal M (2011) Cytoreductive surgery of glioblastoma as the key to successful adjuvant therapies: new arguments in an old discussion. Acta Neurochir (Wien) 153: 1211-1218.

4. Filippini G, Falcone C, Boiardi A, Broggi G, Bruzzone MG, et al. (2008) Prognostic factors for survival in 676 consecutive patients with newly diagnosed primary glioblastoma. Neuro Oncol 10: 79-87.

5. Ichimura K, Pearson DM, Kocialkowski S, Backlund LM, Chan R, et al. (2009) IDH1 mutations are present in the majority of common adult gliomas but rare in primary glioblastomas. Neuro Oncol 11: 341-347.

6. Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, et al. (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360: 765-773.

7. Sanson M, Marie Y, Paris S, Idbaih A, Laffaire J, et al. (2009) Isocitrate dehydrogenase 1 codon 132 mutation is an important prognostic biomarker in gliomas. J Clin Oncol 27: 4150-4154.

8. Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, et al. (2009) Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 10: 459-466.

9. Gorlia T, van den Bent MJ, Hegi ME, Mirimanoff RO, Weller M, et al. (2008) Nomograms for predicting survival of patients with newly diagnosed glioblastoma: prognostic factor analysis of EORTC and NCIC trial 26981-22981/CE.3. Lancet Oncol 9: 29-38.

10. Etcheverry A, Aubry M, de Tayrac M, Vauleon E, Boniface R, et al. (2010) DNA methylation in glioblastoma: impact on gene expression and clinical outcome. BMC Genomics 11: 701.

11. Regier DS, Higbee J, Lund KM, Sakane F, Prescott SM, et al. (2005) Diacylglycerol kinase iota regulates Ras guanyl-releasing protein 3 and inhibits Rap1 signaling. Proc Natl Acad Sci U S A 102: 7595-7600.

12. The Cancer Genome Atlas Network (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455: 1061-1068.

13. Kang MR, Kim MS, Oh JE, Kim YR, Song SY, et al. (2009) Mutational analysis of IDH1 codon 132 in glioblastomas and other common cancers. Int J Cancer 125: 353-355.
14. Turcan S, Rohle D, Goenka A, Walsh LA, Fang F, et al. (2012) IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 483: 479-483.

15. Quillien V, Lavenu A, Karayan-Tapon L, Carpentier C, Labussiere M, et al. (2012) Comparative assessment of 5 methods (methylation-specific polymerase chain reaction, methylight, pyrosequencing, methylation-sensitive high-resolution melting, and immunohistochemistry) to analyze O6-methylguanine-DNA-methyltransferase in a series of 100 glioblastoma patients. Cancer.

16. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, et al. (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352: 987-996.

17. Parks C, Heald J, Hall G, Kamaly-Asl I (2013) Can the prognosis of individual patients with glioblastoma be predicted using an online calculator? Neuro Oncol.

18. Revill K, Wang T, Lachenmayer A, Kojima K, Harrington A, et al. (2013) Genome-wide methylation analysis and epigenetic unmasking identify tumor suppressor genes in hepatocellular carcinoma. Gastroenterology 145: 1424-1435 e1421-1425.