Clinical validation of a novel quantitative assay for the detection of MGMT methylation in glioblastoma patients

Rosas‑Alonsoetal. Clin Epigenet (2021) 13:52 https://doi.org/10.1186/s13148‑021‑01044‑2 RESEARCH Clinical validation ofanovel quantitative assay forthedetection ofMGMT methylation inglioblastoma patients Rocio Rosas‑Alonso1,2*, Julian Colmenarejo‑Fernandez1,2, Olga Pernia1,2, Carlos Rodriguez‑Antolín1,2, Isabel Esteban2,3, Ismael Ghanem4, Dario Sanchez‑Cabrero2, Itsaso Losantos‑Garcia5, Sara Palacios‑Zambrano6, Gema Moreno‑Bueno6,7,8, Javier de Castro2,4, Virginia Martinez‑Marin4 and Inmaculada Ibanez‑de‑Caceres1,2* Abstract Background: The promoter hypermethylation of the methylguanine‑DNA methyltransferase gene is a frequently used biomarker in daily clinical practice as it is associated with a favorable prognosis in glioblastoma patients treated with temozolamide. Due to the absence of adequately standardized techniques, international harmonization of the MGMT methylation biomarker is still an unmet clinical need for the diagnosis and treatment of glioblastoma patients. Results: In this study we carried out a clinical validation of a quantitative assay for MGMT methylation detection by comparing a novel quantitative MSP using double‑probe (dp_qMSP) with the conventional MSP in 100 FFPE glioblastoma samples. We performed both technologies and established the best cutoff for the identification of positive‑methylated samples using the quantitative data obtained from dp_qMSP. Kaplan–Meier curves and ROC time dependent curves were employed for the comparison of both methodologies. Conclusions: We obtained similar results using both assays in the same cohort of patients, in terms of progression free survival and overall survival according to Kaplan–Meier curves. In addition, the results of ROC(t) curves showed that dp_qMSP increases the area under curve time‑dependent in comparison with MSP for predicting progression free survival and overall survival over time. We concluded that dp_qMSP is an alternative methodology compatible with the results obtained with the conventional MSP. Our assay will improve the therapeutic management of glioblas‑ toma patients, being a more sensitive and competitive alternative methodology that ensures the standardization of the MGMT‑biomarker making it reliable and suitable for clinical use. Keywords: MGMT methylation, MSP, Dp_qMSP, Glioblastoma © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Background Epigenetic modifications are a hallmark of human cancers. The reduction of tumor-associated methylation levels which is associated with genomic instability was one of the first epigenetic alterations to be described [1]. However, there are some areas of the genome that increase their methylation levels, which normally correspond with CpG islands of tumor suppressor genes [2–4]. DNA methylation is catalyzed by DNA methyltransferases, which transfer methyl groups from S-adenosylmethionine on CpG dinucleotides at the 5′carbon position of cytosines located at CpG islands. Methyl groups are recognized by Methyl-CpG-binding domain proteins, which interfere with the binding of transcriptional activators of DNA [5]. Open Access *Correspondence: rocio[email protected]g; inma.ibanezca@salud. madrid.org 1 Epigenetics Laboratory. INGEMM, Paseo La Castellana 261. Edificio Bloque Quirúrgico Planta ‑2. University Hospital La Paz, 28046 Madrid, Spain Full list of author information is available at the end of the article Page 2 of 13 Rosas‑Alonsoetal. Clin Epigenet (2021) 13:52 The methylguanine-DNA methyltransferase (MGMT) gene promoter hypermethylation is one of the most studied molecular biomarkers in neuro-oncology. MGMT gene encodes a repair enzyme that removes alkyl groups from the O6 position of guanine and works by antagonizing the cytotoxic effects of alkylating agents [6]. Promoter methylation is the main way of silencing the MGMT gene and predicts a favorable outcome in glioblastoma patients treated with alkylating drugs. Glioblastoma (GBM) is the most common primary malignant central nervous system tumor in adults and is invariably associated with poor prognosis. Only 33% of patients survive one year and only 5% of patients live more than five years after diagnosis [7–9]. Thus, the methylation status of MGMT is frequently used in the daily clinical routine as a predictive biomarker to classify GBM patients who are more likely to respond to temozolamide. The MGMT CpG island has 98 CpG sites located on chromosome 10q26 that controls the MGMT gene expression. Malley etal. defined a differentially methylated region (DMR2) essential for silencing the MGMT gene. Most of the assays are based on the analysis of the CpG sites 73 to 90 located at the DMR2 area. Throughout this area, the CpGs 83, 86, 87 and 89 have been the best targets for methylation testing [10]. Furthermore, Bady etal. described two CpG sites in the MGMT promoter (cg12434587, chr10:131,265,209–131,265,210 and cg12981137, chr10:131,265,575–131,265,576) that showed the strongest association with overall survival (OS), being cg12981137 the CpG number 84 in the DMR2 area, and supporting the idea proposed by Malley etal. [11]. A wide range of molecular assays are available for qualitative and quantitative MGMT methylation detection. The most commonly used methods are based on bisulfite conversion of unmethylated cytosines into uracil [12]. Examples of methods include methylation-specific PCR (MSP) [6, 13, 14], pyrosequencing [13–15], different variations of real-time PCR [14, 16], digital PCR [17], methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) [13, 18], methylation-specific high-resolution melting (HRM) [19], and combined bisulfite restriction analysis (COBRA) [20]. Other techniques that can evaluate global methylation changes such as next-generation sequencing are currently employed in the field of research but not in the routine clinical practice [21].Currently, MSP and pyrosequencing are the most widely used technical approaches to MGMT methylation analysis, providing information that is useful for clinical decision-making. However, the analytical sensitivity differs considerably among diverse assays and their standardization across a wide range of diagnostic laboratories is lacking [22, 23]. In fact, there is still a lack of consensus on how to interpret the pyrosequencing data [14, 15]. In addition to the method used, other factors such as tumor content, contamination of inflammatory and stromal cells, necrosis, and tumor heterogeneity could affect the methylation results obtained [24]. Due to the increasing interest in molecular biomarkers and their impact in therapeutic management of glioblastoma patients, more sensitive and competitive alternative methodologies are in demand. In this study, we have developed an innovative quantitative methylation specific PCR (dp_qMSP) assay used for the study of MGMT methylation and validated its clinical use by comparing this novel assay with the conventional MSP. Results Clinical data From May 2014 to March 2020, we enrolled 100 patients with newly diagnosed GBM. Among the 100 patients, 42 were women and 58 were men. The average ageat diagnosis was61yearsold (agerange 24–83years). No significant differences were found between patients’ age, sex, type of surgery, ECOG and MGMT promoter methylation assessed with MSP or dp_qMSP. Relevant clinical data of patients are described in Table1. Comparison betweendp_qMSP andMSP methods forMGMT promoter methylation detection ROC curve was performed to determine the cutoff for dp_qMSP. The area under the curve (AUC) was 0.962 (95% CI 0.927–0.998) (Fig. 1). The methylation cutoff pointwas established in 3.75% and was obtained by the formula previously described [25]. Thus, the samples were classified as methylated when the methylation was above the cutoff point of 3.75% and unmethylated when they were less than 3.75%. The sensitivity and specificity for this cutoff point were 100% (95% CI 88.6–100) and 88.6% (95% CI 79.0–94.1), respectively. MGMT methylation was detected in 30 out of 100 FFPE samples by MSP and 38 out of 100 samples by dp_qMSP (Table2); a representative gel and quantitative amplifications are shown in Fig.2 (see the uncropped gel at Additional file1: Fig.1). We obtained discrepancies in eight samples within both technologies, two of these eight patients present a survival in the mean value of patients harboring a methylated promoter (> 18months) (Patients number 1; 23.4months and patient number 76; 21.6months). Patients number 75 and 100 were alive at their last following-up at our hospital, although unfortunately we lost their follow-up because they changed hospitals. Patients number 28 and 78, with median survival of 8.3months with a incomplete tumor resection and 12,6months with complete resection, respectively present a standard overall survival in this pathology and the last two patients (numbers 89; Page 3 of 13 Rosas‑Alonsoetal. Clin Epigenet (2021) 13:52 4.5months and 91; 4.8months) with the worst survival, were diagnosed with biopsy and they did not underwent a complete resection. The results from those samples by MSP and dp_qMSP together with sample number 11 that presents the lowest percentage of methylation by using dp_qMSP are shown in Additional file 2: Supplementary Fig.2. If we consider the eight positives identified by dp_qMSP to be false positives based on the data obtained using MSP, the specificity achieved by dp_qMSP would be 88.6%. Additionally, we considered of great interest to probe the presence of methylated DNA molecules in these samples, and in fact, in collaboration with the Md Anderson hospital, they were able to amplify 5 of these samples using an alternative MSP technique with different settings and DNA modification procedures (deeply described in Additional file3: supplementary Fig.3) and found a very weak amplification at the methylated reaction in three of the samples, 1, 78 and 100. In none of the three cases, this amplification would have suggested the diagnosis of a methylated sample for the MGMT marker, as it happens with our results using MSP technology, but supports our positive results obtained by dp_qMSP, as these three samples out of the five, are the ones with the highest percentage of methylation when were analysed by dp_qMSP in our laboratory. Examination dp_qMSP andMSP methods forsurvival analysis The multivariable COX regression survival analysis identified significant differences for the variables MSP Table 1 Demographic and clinical data of the study population (n = 100) Characteristic Value Methylation status MSP Methylation status dp_qMSP MSP (p value) dp_qMSP (p value) Average age at sur‑ gery and range 61 (25—84) p = 0.632 p = 0.697 Sex Women 42 15 methylated 27 unmethylated 19 methylated 23 unmethylated p = 0.377 p = 0.218 Men 58 15 methylated 43 unmethylated 19 methylated 39 unmethylated Type of surgery Total resection 51 19 methylated 32 unmethylated 22 methylated 29 unmethylated p = 0.174 p = 0.367 Partial resection 29 8 methylated 21 unmethylated 11 methylated 18 unmethylated Biopsy 20 3 methylated 17 unmethylated 5 methylated 15 unmethylated ECOG 0 59 17 methylated 42 unmethylated 21 methylated 38 unmethylated p = 0.727 p = 0.624 1 24 7 methylated 17 unmethylated 10 methylated 14 unmethylated 2 12 5 methylated 7 unmethylated 6 methylated 6 unmethylated 3 5 1 methylated 4 unmethylated 1 methylated 4 unmethylated Fig. 1 ROC curve for dp_qMSP compared to MSP (n = 100). Area under the curve = 0.962 (95% CI 0.9268–0.998). Blue shade represents the CI Page 4 of 13 Rosas‑Alonsoetal. Clin Epigenet (2021) 13:52 Table 2 Clinical, pathological and methylation data of 100 GBM patients ID Age Sex Type of surgery ECOG MSP %Methylation dp_qMSP 1 57 Female Total resection 0 U 66,6 2 70 Male Biopsy 2 U 0 3 45 Male Partial resection 0 M 65 4 44 Male Partial resection 0 U 0 5 54 Male Partial resection 0 U 0 6 71 Male Total resection 0 U 1,6 7 61 Female Total resection 0 M 60,3 8 47 Female Total resection 0 M 66 9 77 Female Biopsy 3 U 0 10 49 Male Biopsy 0 U 0 11 69 Female Partial resection 2 M 5,9 12 67 Male Total resection 0 U 0 13 65 Female Total resection 0 U 0 14 80 Female Total resection 0 U 0 15 66 Female Partial resection 0 U 0 16 65 Female Biopsy 1 U 0 17 81 Female Biopsy 0 U 0 18 72 Male Partial resection 0 M 97,5 19 54 Male Partial resection 3 U 0 20 51 Male Total resection 0 U 0 21 49 Male Total resection 3 U 0 22 79 Female Total resection 2 M 99,9 23 55 Female Partial resection 1 U 0 24 64 Male Total resection 0 M 27,5 25 49 Male Total resection 0 M 94,1 26 62 Female Total resection 0 M 97,6 27 55 Male Partial resection 0 U 0 28 65 Male Partial resection 0 U 64,6 29 58 Male Partial resection 0 U 0 30 71 Female Total resection 0 U 0 31 76 Female Total resection 0 M 17,8 32 73 Female Total resection 0 M 83 33 67 Male Total resection 0 U 0 34 60 Male Total resection 0 U 0 35 50 Female Total resection 0 M 36 36 54 Male Total resection 0 M 96,4 37 62 Male Biopsy 0 U 0 38 76 Female Biopsy 0 M 100 39 76 Female Total resection 1 U 0 40 84 Male Biopsy 0 U 0 41 75 Male Total resection 1 M 100 42 46 Male Total resection 0 U 0 43 55 Female Partial resection 2 U 0 44 80 Female Partial resection 1 U 0 45 29 Male Biopsy 0 M 77 46 56 Male Biopsy 1 U 0 47 61 Male Total resection 1 U 0 48 72 Male Total resection 0 M 92,6 49 48 Female Partial resection 1 M 100 Page 5 of 13 Rosas‑Alonsoetal. Clin Epigenet (2021) 13:52 Table 2 (continued) ID Age Sex Type of surgery ECOG MSP %Methylation dp_qMSP 50 66 Male Total resection 0 U 0 51 70 Male Partial resection 0 U 0 52 82 Male Biopsy 3 M 99,9 53 52 Female Total resection 0 U 0 54 54 Female Total resection 0 M 100 55 68 Female Biopsy 2 U 0 56 60 Male Total resection 0 U 0 57 54 Male Partial resection 0 U 0 58 64 Male Partial resection 0 U 0 59 75 Female Total resection 0 M 88,89 60 68 Female Partial resection 1 U 0 61 73 Female Partial resection 1 U 0 62 51 Male Total resection 1 U 0 63 37 Male Total resection 1 M 100 64 69 Male Biopsy 0 U 0 65 71 Male Total resection 0 M 100 66 67 Female Total resection 1 U 0 67 51 Male Total resection 2 M 99,9 68 50 Male Total resection 0 U 0 69 57 Female Biopsy 0 U 0 70 50 Female Total resection 0 U 0,4 71 61 Male Total resection 1 U 0 72 56 Male Biopsy 0 U 0 73 73 Male Total resection 2 U 0 74 49 Male Total resection 0 U 0 75 63 Female Partial resection 1 U 53,1 76 60 Male Partial resection 0 U 81 77 71 Male Total resection 0 U 0 78 65 Male Total resection 2 U 99,9 79 64 Male Total resection 0 U 0 80 40 Female Partial resection 0 U 0 81 79 Male Total resection 1 U 0 82 44 Female Total resection 2 M 100 83 51 Male Total resection 1 M 48,6 84 62 Male Total resection 0 U 0 85 62 Male Total resection 0 U 0 86 62 Female Partial resection 0 U 0 87 71 Female Biopsy 1 U 0 88 66 Male Total resection 1 U 0 89 57 Male Biopsy 0 U 35 90 69 Male Partial resection 1 M 94,4 91 67 Female Biopsy 1 U 6,9 92 59 Male Partial resection 0 U 0 93 57 Female Total resection 2 U 0 94 52 Male Partial resection 0 U 0 95 50 Male Partial resection 1 M 87,8 96 60 Female Partial resection 2 M 91,3 97 58 Male Biopsy 2 U 0 98 74 Female Partial resection 1 M 47,9 Page 6 of 13 Rosas‑Alonsoetal. Clin Epigenet (2021) 13:52 and dp_qMSP for both PFS (p = 0.001 and p = 0.004) and overall survival (p = 0.008 and p = 0.036) respectively; while no significant differences were found for the clinical variables (type of surgery, age, sex and ECOG). Therefore, we proceeded to study these variables using a univariate model. The median of OS measured by MSP in the group of patients with unmethylated MGMT promoter in our cohort was 11.8months (95% CI 10.4– 13.2) while the median of OS was not reached in the methylated group (Fig.3a). We observed significant differences between unmethylated and methylated groups in terms of OS (p = 0.004, HR = 0.37, 95% CI 0.19–0.72). The rate of OS at two years was only 17% in the unmethylated group compared with the 53% observed in the methylated group. The median PFS was 7.0 months (95% CI 5.3–8.8) in the unmethylated MSP group and 18.0months (95% CI 9.8–26.1) in the methylated MSP group (Fig.3b). We also observed significant differences Table 2 (continued) ID Age Sex Type of surgery ECOG MSP %Methylation dp_qMSP 99 25 Female Biopsy 3 U 0 100 57 Female Total resection 1 U 100 Age (years), M (methylated MGMT), U (unmethylated MGMT) Fig. 2 MSP and dp_qMSP examples in the analyzed tumor samples for patients 98 and 99. a Example of MGMT promoter methylation in acrylamide gel. The MSP products were loaded and electrophoresed as follows: sample number 98 (lanes 1–4 unmethylated and methylated reactions performed by duplicated), sample number 99 (lanes 5–8, unmethylated and methylated reactions performed by duplicated), lanes 9–10 correspond to unmethylated and methylated reactions using a FFPE negative control sample. Lanes 11 and 12 correspond to unmethylated and methylated reactions using a PBMC control sample. Lane 13 corresponds to PBMC methylated in vitro (IVD) as a positive control, and last line is the water methylation reaction used to discard contamination. b, c. Example of methylated and unmethylated amplification by qMSP. B. Patient number 98, FAM (M) and VIC (U) probes amplified (47.9% methylation). c Patient number 99, only VIC probe amplified (0% methylation). U: Unmethylated. M: methylated. FFPE: Formalin fixed paraffin embedded. PBMCs: Peripheral blood mononuclear cells. NC: negative control. IVD: In vitro Methylated DNA (positive control). NTC (No Template Control) Page 7 of 13 Rosas‑Alonsoetal. Clin Epigenet (2021) 13:52 in terms of PFS regarding the methylation status between groups (p < 0.001, HR = 0.33, 95% CI 0.18–0.61). The rate of PFS at two years was 9.2% in the unmethylated group compared to the 31.5% observed in the methylated group. When using MGMT methylation data obtained by dp_qMSP, the median OS in the unmethylated group was 12.6months (95% CI 10.0–15.1) while this median was not reached in the methylated group (Fig.3c). Consistent with the results obtained by MSP, there were significant differences between unmethylated and methylated groups in terms of OS (p = 0.014, HR = 0.47, 95% CI 0.26–0.86) and PFS (p = 0.001, HR = 0.41, 95% CI 0.24– 0.70). The rate of OS at two years was 19% in the unmethylated group compared to 45% in the methylated group. The median PFS was 7.0months (95% CI 5.6–8.4) in the unmethylated dp_qMSP group and 16.0months (95% CI 11.8–20.3) in the methylated dp_qMSP group (Fig.3d). While the rate of PFS at two years was 10.8% in the unmethylated group compared to the 24.9% observed in the methylated group. Fig. 3 Survival analysis of GBM patients. a Kaplan–Meier OS graph comparing methylation GBM patients to unmethylated classified according to MSP. b Kaplan–Meier PFS graph comparing methylation GBM patients to unmethylated classified according to MSP. c Kaplan–Meier OS graph comparing methylation GBM patients to unmethylated classified according to dp_qMSP. d Kaplan–Meier PFS graph comparing methylation GBM patients to unmethylated classified according to MSP Page 8 of 13 Rosas‑Alonsoetal. Clin Epigenet (2021) 13:52 Comparison betweendp_qMSP andMSP methods forprogression evaluation according toROC (t) We performed ROC(t) curves to compare both MSP and dp_qMSP for predicting PFS and OS in our cohort of GBM patients. The time-dependent area under the curve or AUC(t) for OS was 0.49 when we analyzed the patients with the MSP method and 0.60 in dp_qMSP assay (p = 0. 001). The AUC(t) for PFS was 0.50 when we analyzed the patients with MSP method and 0.58 in dp_qMSP assay (p = 0.037) (Fig.4). Discussion Food and Drug Administration and National Institutes of Health Biomarker Working Group define validation as a process to establish that the performance of a test is acceptable for its intended purpose [26]. In order to establish if the dp_qMSP test is suitable for MGMT methylation analysis, we performed comparison-of-methods studies between both dp_qMSP and MSP processes. Firstly, we carried out a ROC study establishing the best cutoff methylation point at 3.75% and finding an excellent model (AUC = 0.962). The sensitivity obtained was 100% and we detected eight additional positive samples that were not identified by MSP. Being strict, we considered them as false-positive, decreasing our specificity to 88.6%; although they could certainly be due to an increased sensitivity of our methodology compared to the MSP. In fact, the clinical response in terms of survival in these patients corresponds to the mean of patients harboring a methylated promoter or to the overall mean of survival in this pathology, but not lower; except in two cases that did not could undergo resection surgery and therefore, a worse prognosis was expected, as described in the literature [27–29]. It has been reported that different methodologies could give rise to different results. When Quillen etal.compared five methods to analyze MGMT methylation, found in their study various discrepancies between the different assays used. Methylation-sensitive HRM and MethyLight obtained a weaker predictive value, whereas pyrosequencing was the best among the 5 techniques tested. In addition, Quillen’s study confirmed effectiveness as prognostic value of MGMT promoter methylation assessed by MS-PCR [14]. The subsequent study of Yoshioka etal. confirmed these good results obtained by the MS-PCR [16]. Dp_qMSP is based on MS-PCR, but it is improved by combining the PCR chemistry with amplicon detection by double fluorescence probes with a MGB, which stabilizes the double-stranded probe template structure resulting in improved allele specificity [30]. Moreover, qPCR can exclude ambiguity of interpretation which may cause bias in conventional PCR and it presents an easier workflow [31]. Thus, if we also take into account that qPCR presents higher sensitivity than the SYBERgreen-stained and gel-based detection under ultraviolet light we should not consider the new methylated samples identified by dp_qMSP to be false positives when using dp_qMSP, but rather that they are false negatives when using MSP. Furthermore, these results are supported by the parallel analytical validation of five of these samples performed in the MD Anderson Cancer center by using a modified methodology. Another possible cause that would explain this discrepancy is that the CpG 82 and/or 83 positions where our hydrolysis probe directed, could be methylated. Methylation of these positions would result in a positive result for dp_qMSP but could result in a negative result for MSP since in this methodology, these CpG positions are not considered. With this in mind, we carried out further studies in order to decide the adequacy of both assays in terms of OS and PFS. The Kaplan–Meier analysis showed that patients with MGMT promoter methylation resulted in significantly longer PFS and OS than unmethylated patients, the same results as previously reported Fig. 4 ROC (t) curves predicting OS and PFS. a AUC(t) for OS is higher for dp_qMSP than MSP (p = 0. 001). b AUC(t) for PFS is higher for dp_qMSP than MSP (p = 0.037) Page 9 of 13 Rosas‑Alonsoetal. Clin Epigenet (2021) 13:52 independently of using MSP or dp_qMSP [7, 14]. Therefore, both techniques allow the classification of patients as responders or non-responders in terms of MGMT methylation. However, the question to be addressed is how well does the MGMT methylation biomarker evaluated by dp_ qMSP distinguish between patients who respond to treatment and patients who do not at a given follow-up time. Cancer outcomes are very time-dependent and ROC curves that vary as a function of time may be more useful that the Kaplan–Meier analysis [32]. Therefore, ROC (t) has been used to compare the MSP and dp_qMSP and to establish the one that best fits the survival data. The AUC (t) for OS and PFS obtained was higher when we used the dp_qMSP method (0.49 versus 0.60 in OS and 0.50 versus 0.58 in PFS). We found significant differences between MSP and dp_qMSP, suggesting that the dp_qMSP assay might be more effective at detecting the MGMT methylation biomarker than the classic MSP assay. In addition to the aforementioned advantages of using the dp_qMSP method, we may have obtained better results in AUC (t) particularly within the first 5months after diagnosis in dp_qMSP because we are investigating the most important positions that have been shown to have a major impact on MGMT expression. Malley etal.described the methylation status of CpGs 83, 86, 87 and 89 as critical for transcriptional regulation, being the CpG 83 in our hydrolysis probe and CpG 86 and 87 in reverse primer. In addition, we have in our reverse primer the cg12981137, described by Bady etal.as one of the two more essential [10, 11] (Fig.5). Due to the absence of adequately standardized techniques, international harmonization of the MGMT biomarker is still an unmet clinical need. A main difficulty has been the lack of a gold standard for MGMT methylation detection independently of the technology used. This is in part due to the different CpGs interrogated within the same technology, as there is still no consensus on how many CpG sites should be explored. For example, for pyrosequencing the cut-off values range from 2.7 to 35%, and the positions analyzed from four to more than 60 [15]. Several studies reported pyrosequencing as the method of choice for MGMT promoter methylation analysis in routine clinical practice [14, 33, 34] but, the current limitation of pyrosequencing is the absence of a consensus regarding an established cutoff for binary classification and concerning which are the most relevant CpG sites to analyze for clinical practice (as there are several pyrosequencing protocols that differ in regards to the number and position of the studied CpG sites) [15, 35–37]. The cutoff in pyrosequencing is calculated with the average of the different CpG positions analyzed by this technique and in some cases it gives rise to an indeterminate value called “gray zone” that is not capable of dichotomizing the cases, and there is no consensus on which of them most highly correlated with prognosis. In fact a recent article proposes to change this pyrosequencing calculation for a new analysis that could accurately predict the prognosis of patients in this "gray zone" [38] however, these data have not yet been validated. For all these reasons, MGMT methylation status has sometimes suffered from inconsistent results in the same tumor with different methods, mainly due to the lack of methodological standardization. Undoubtedly and regardless of the methodology used, the settings for selecting the cutoff value, need to be identified with specific controls, allowing the results from each laboratory to be adapted according to the methodology used for DNA extraction, DNA bisulfite modification and the subsequent amplification method selected. We used MSP as a reference because it was the first method described and has been repeatedly shown to be of predictive value in randomized clinical trials [39–42]. However, MSP is a not an automatized method, making it difficult to standardize, and results may be influenced by tumor heterogeneity and/or a subjective interpretation. One of the great advantages of dp_qMSP is that the amplification of both methylated and unmethylated reactions, Fig. 5 DNA and CpG island locations throughout the MGMT gene region (NM_002412; Chr10: 131,265,478—131,265,604). The CpG are represented as circles. The green circles symbolize the critical CpGs described by Malley et al. The orange circle symbolizes the cg12981137 described by Bady et al. The red and blue arrows represent M and U primers respectively. Between the primers, the hydrolysis probes labeled with two different reporter fluorochromes specific for recognizing methylated or Unmethylated DNA