Background

Glioblastoma (GBM) is the prevalent malignant brain tumor with worse prognosis and aggressive development without identifiable precursor lesions [1]. Advances have been made in therapeutic strategies after adding of the temozolomide (TMZ) chemotherapy, the maximal safe cancer resection and radiotherapy. However, average survival is still restricted to about 15 months [2, 3]. Thus, there is a great need to unravel oncogenic mechanisms of GBM since there are two types of this kind of malignancy either displayed rapidly de novo with unknown precursor lesion or from low-grade tumor [4] although they cannot be histopathologically distinguished but both with different molecular alterations due to different genes have been reported to be involved in the process of GBM pathogenicity [5,6,7].

In addition to other genetic actions, the most interesting recent output in glioma oncogenic events has been the recognition of IDH1 and IDH2 genetic mutations. IDH1/2 are homodimeric isozymes that rely on NADP + and have a highly similar protein structure and significant sequence similarity [8]. IDH1 is the cytoplasmic constituent that is produced obviously in the liver and other tissues, while IDH2 is completely limited to the mitochondria and displays the considerable expression in heart and lymphocytes, and muscle tissues [9]. Every mutation in IDH1 or -2 can cause enhancing oxidative stress through its mutagenic act that damages the DNA [1]. This event is constant by an enhanced quantity of DNA damage in the IDH1-mutated cancerous glioma cells and in that way IDH1/2 mutations function as driver genetic alterations in glioma carcinogenesis, though their crucial function is still unexplored [1].

Both IDH1/2 genetic alterations are further exclusively linked to glial-type phenotype of brain cancer and are determined in about 5% of primary and around 50% of secondary GBM that has been validated to give an better prognosis [10]. Additionally, The Cancer Genome Atlas (TCGA) has allowed categorization of different molecular alternatives of GBM with several findings with proneural reporting a better prognosis, while neural, mesenchymal and classical display a worse prognosis [1]. Lately, the proneural variation is related to a good response to the antiangiogenic agent as bevacizumab [11].

Section of cancerous gliomas defies the chemotherapeutic drug as temozolomide (TMZ), effective sensitive molecule that results in the cell death [4]. TMZ is considered an alkylating factor that cross-links DNA through the DNA-repair enzyme O6-methylguanine-DNA methyltransferase (MGMT) [12]. MGMT fixes alkylating lesions of the DNA frequently produced by the TMZ [2, 13].

Hypermethylaterd MGMT revealed cbettwer response to TMZ while cells with unmethylated MGMT gene display the resistance to the drug [13, 14].

This procedure is believed to make the MGMT methylation as promising prognostic benefit in glioma cases received alkylating drugs [15, 16]. MGMT promoter methylation is reported in 35–45% of glioma patients (WHO grades III and IV), though it seems in around 80% of gliomas with low-grade (WHO grade II) [17, 18]. This category of glioma patients may have improved sensitivity to the TMZ as result of the enzyme deficiency, which influences its clinical consequence [1, 12].

In the current study, authors aimed to investigate both IDH1 genetic mutation and MGMT epigenetic methylation of MGMT among GB patients and investigate their correlation with each other as well as their relation as predictive prognostic markers among Egyptian patients when tested alone or in combination.

Materials and methods

Patient selection

The current study has been performed in accordance with the Declarations of Helsinki and approved by Medical Ethical Committee (National Research Centre ID#20110), participants who fulfilled the inclusion criteria were recruited from 2020 till 2022. The inclusion criteria were as follows: adult persons (age > 18 years) newly diagnosed GB with performance less than or equal 2 according to the ECOG (Ester Clinical Oncology Group); which assesses disease progression affecting on patient's daily living abilities and patients with non-neuro-oncological diseases and have not reported other malignancies, while GB patients who have not fulfilled these criteria were excluded. Accordingly, GB patients (n = 58) patients were recruited after signing their informed consent. Also, a group of non-neuro-oncological diseases (NND) were recruited (n = 20). After obtaining signed informed consent from all participants, surgically resected tumor tissue samples were taken by stereotactic/open biopsy of brain tumors, then fixed in neutral buffered formalin and embedded in paraffin stained with hematoxylin–eosin (HE) reviewed by neuropathologists (MM) to confirm diagnosis according to WHO classification 2016 [17, 18]. Then, 5–10 sections from FFPE were transferred to Eppendorf tubes for further processing of DNA extraction.

DNA extraction

DNA was extracted from FFPE samples using QIAamp FFPE kit (Cat no. 56404) as per manufacturer instructions and both purity and concentration were detected using nano-drop spectrophotometer (Quawell, Q-500, Scribner, USA) by measuring the absorbance at 260 and 280 nm and checked on 1%agarose gel, the extracted DNA samples were stored in − 20 for further processing to detect MGMT and IDH1 mutation.

Detection of MGMT methylation pattern using Methyl II quantitative PCR system

MGMT methylation pattern was detected in DNA extracted samples using the following steps of EpiTect Methyl II quantitative polymerase chain reaction (qPCR) technique (Qiagen, Germany) which relay on assessment of residual DNA input after cleavage by restriction enzyme, and then, the remaining DNA will be quantified by real-time PCR using specific primers for the desired gene that flanks a promoter region of interest. Thus, reaction was performed in two phases with some modifications in our laboratory: phase I: carried out using EpiTect Methyl II DNA Restriction Kit (cat. no. 335452), briefly input genomic DNA was aliquoted into two equal portions into 2 PCR reaction tubes and they were designated as follows: no-enzyme (UD, i.e., no restriction enzyme was added), methylation-sensitive restriction enzyme (D, i.e., restriction enzyme sensitive to methylation hence digest unmethylated DNA) and then, they were incubated for 6 h at 37 °C, afterwards for 20 min at 65 °C for 20 min by thermal cycler (SureCycler 8800, Agilent, Santa Clara, CA, USA). Then, the remaining genomic DNA sample in each tube (UD and D) is quantified through phase II which was carried out using real-time PCR (Max3005P QPCR system; Stratagene, AgilentTechnologies, CA, USA). Briefly, 5ul from the remaining DNA was directly mixed with Master Mix (RT2 qPCRSYBR Green/ROX Master Mix, Cat number 330520) and was distributed into a PCR plate with pre-aliquoted MGMT primer: Left primer 5\-ATTTTTGTGATAGGAAAAGGTATGG-3\Right primer 5\-CTAAAACAATCTACACATCCTCACT-3\. Real-time PCR reaction is done via specified cycling conditions, for 10 min at 95 °C (1 cycle), then for 30 s at 99 °C, and for 1 min at 72 °C (3 cycles), and finally 40 cycles with following conditions: 15 s at 97 °C and for 1 min at 72 °C for 1 min. Finally, the raw ΔCT values were collected for each PCR reaction tube (UD and D) for each sample as shown in Fig. 1A–B. However, in the qPCR reaction the UD was used, and hence, the DNA in which all CpG sites are methylated will be detected by real-time PCR [19] through following equations:

$$\Delta {\text{CT}} = {\text{ digested }}\Delta {\text{CT }}{-}{\text{ undigested }}\Delta {\text{CT}}$$
$${\text{Methylation }}\% = { 2}^{{ - \Delta {\text{CT}}}} {\text{fold change }} \times {1}00$$
Fig. 1
figure 1

Amplification plots of the MGMT qPCR reaction, A: showing plots of 100% unmethylated sample, and B: showing partially methylated sample

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Determination of IDH1 mutation using Cast-PCR technology

Competitive allele-specific TaqMan PCR (Cast-PCR) technology was used to detect IDH1 mutation as it is sensitive, specific and fast method for detection of mutant allele since it permits not only the discriminating amplification of minor alleles, but it also blocks the amplification of non-mutant allele [20, 21]. Qualitative assessment of six mutations within IDH1 mutation codon 132 (the 2 major R132H and R132C mutations, and 4 “IDH1-other”: R132G, R132S, R132L, R132V), one within IDH1 codon 100 (R100Q), Amplification Refractory Mutation System (ARMS) PCR technology was combined to selectively identify the most frequent IDH1 R132H/R132C. The Master Mix was prepared as recommended by the supplier. A total of 50 ng of gDNA per reaction and the probes described above were used. The cycling conditions were as follows: pre-PCR read 60°C for 30 s; holding stage 50°C for 2 min, 95°C for 10 min; cycling stage 95°C for 15 s, 60°C for 1 min for 40 cycles; and post-PCR 60°C for 30 s. For each of the analyzed IDH1/2 mutations, the limit of detection (LOD) of castPCR TM was determined by constructing dilution curves of samples from patients with and without IDH1/2 gene mutations. Each point was determined using different dilutions (1:1 to 1:50) of the mutated sample and a non-mutated sample (Fig. 2A-B). Sensitivity and specificity of the Cast-PCR for IDH1 R132H (SNVs) allow over 99% confidence of detecting down to 5% mutant DNA in a wild-type background.

Fig. 2
figure 2

Amplification plots of the IDH1 mutation by Cast-PCR reaction, A: showing plots of wild-type sample, and B: showing positive mutation sample

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Histopathologic preparation

This study included formalin-fixed paraffin-embedded tissue blocks from patients with glioblastoma and the histopathologic criteria of glioblastoma according to WHO classification 2016 were cellular features of neoplastic astrocytic cells as marked pleomorphism, hyperchromatic nuclei and abnormal mitoses and scattered apoptosis, as well as microvascular proliferation and tumor necrosis [17, 18]. The inclusion criteria for selecting the tumor tissue blocks are as follows: (1) histopathological diagnosis of glioblastoma with more than 80% viable tumor tissue, (2) available archival paraffin-embedded tissue blocks and (3) available clinical follow-up data. All studied glioblastoma cases and non-neoplastic control cases were subjected to the following: (I) The paraffin-embedded tissue blocks of the studied glioblastoma cases were cut at full sections with a thickness of four microns and stained for routine hematoxylin and eosin H&E stain. The H&E-stained slides of the tissue specimens were prepared to confirm the diagnosis based on the 2016 CNS Tumors WHO classification and to assess viability of the submitted tumor tissue (Figs. 3 and 4). (II) For preparation of PCR testing, freshly cut sections of paraffin-embedded tissue, each with a thickness of up to 10 ums. Up to eight sections, each with a thickness of up to 10 um and a surface area of up to 250 mm2 can be combined in one preparation.

Fig. 3
figure 3

Histopathology of glioblastoma showing: A—High cellularity and foci of palisading tumor necrosis (Arrow), and B—Vascular endothelial proliferation (Arrow), (Hx&E, X100)

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Fig. 4
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Non-neuro-oncological diseases, Reactive gliosis (A and B)

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Treatment strategies

The clinical target volume (CTV) was contoured on computerized tomography (CT) and postoperative magnetic resonance imaging (MRI) image fusion and integrated residual tumor mass (T1 gadolinium-enhanced lesion) and/or postoperative cavity (i.e., GTV) plus a 15–20mm margin without reflection for peri-tumoral edema. Volume contouring took into account anatomical barriers, as ventricular spaces, cranial bones and the midline excluding for the region of the corpus callosum. An isotropic margin of 5mm was added around to obtain the planning target volume (PTV-1). Radiotherapy treatment (RT) was delivered with a linear accelerator 6–10 MeV beam and 3D-conformal or intensity modulated techniques up to a planned total dose of 60 Gy and with a standard fractionation (2Gy/day for 5 days per week). All patients received also temozolomide (TMZ), concurrently administered per os during RT, according to Stupp’s protocol (daily TMZ 75mg/m2 during the RT course, for 6 weeks followed by the sequential TMZ schedule (150–200mg/m2 for 5 days every 28 days) until disease progression (PD) or complete response (CR) after 12 cycles. After the completion of RT and concurrent TMZ administration, patients entered a scheduled follow-up program. Brain MRI scans were repeated at 4 weeks, 12–16 weeks, and then every 6 months or in any case showing clinical signs suggesting progressive disease (PD). Taking into account the fact that no patient of this series received antiangiogenic treatment, PD after RT-TMZ treatment was assessed using the RANO Criteria [22]. A diagnosis of pseudoprogression was made in cases showing an increase in tumor size and/or T1-contrast enhancement within 3–6 months after the end of concomitant RT-TMZ, without worsening of neurological status and with stabilization or resolution in subsequent further MRIs studies. Imaging findings suggestive of radionecrosis were recorded. All the MRI examinations were revised for the compilation of this paper by a neuroradiologist (LEA). General and neurological examinations and blood counts and chemistry were obtained every 3 months.

Statistical analysis

Statistical analysis was performed using Statistical Package for Social Sciences (SPSS Inc, Chicago IL, USA). Kaplan–Meier survival curves were done and differences in PFS and OS were tested for statistical significance using the log-rank test which is a statistical methodology used to test whether there is a difference between the distribution of survival times until the occurrence of an event of interest in independent groups. Significance level was set at p < 0.05. Cutoff value for MGMT methylation status was obtained by plotting receiver operating characteristic (ROC) curve by plotting true positive (sensitivity) versus false positive (100-specificity) for investigation of diagnostic efficacy of MGMT by considering GB versus NND. The area under the ROC curve (AUC) assessed the accuracy, and hence: if equals 1 means accurate test; < 1 − 0.8 a good test; < 0.8 – 0.7 a fair test, < 0.7 − 0.6 a poor test, while < 0.5 as worthless test [23].

Results

The current study was carried out on FFPE samples from 20 NND and 58 GB cases, their full clinical data are summarized in Table 1. No significant level was reached when considering gender between the two groups (NND versus GB), while significant level was reached when age of the two groups were considered, for both groups IDH1 mutation and MGMT methylation are as represented in Table 2. To determine the methylation level, ROC curve was plotted and the best cutoff point (methylation percentage) that discriminates between them was 66%, and those reported level of methylation blow the < 66% were represented as low–moderate methylation while individuals reported methylation level above ≥ 66% were highly methylated (Fig. 5). Accordingly, all NND patients were highly methylated (100%) while 30 out of 58 (51.7%) GB cases reported high MGMT methylation and the remaining were low–moderate methylation at significant level P < 0.0001. For IDH1 mutation, it was detected in 15 GB cases (25.9%) while the remaining (43, 74.1%) reported IDH wild type, and all NND patients reported IDH wild type at significant level P = 0.011, as shown in Table 2.

Table 1 Clinical and demographic data for studied cases
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Table 2 Investigated MGMT methylation and IDH1 mutation among studied groups
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Fig. 5
figure 5

Receiver operating characteristic (ROC) curve for MGMT methylation among investigated groups. Arrow contributes to the best cutoff point that discriminates between high methylation (≥ 66%) versus low–moderate methylation at area under the curve (AUC) = 0.837, 95%CI = 0.723–0.917, at P = 0.0001

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Distributions of IDH1 mutation and MGMT methylation among GB cases are presented in Table 3. For IDH1 mutations, significant levels were reported between IDH1 mutation with both age, ECGO and surgical intervention, while for MGMT methylation was revealed significant with other factors apart from age and gender. Patients were treated with standard of care treatment protocol and patients were categorized according to their response to treatment as follows; complete response (CR), partial response (PR), stable disease (SD) and progressed disease (PD). Both IDH1 mutation and MGMT methylation reported higher frequency among those patients with CR as reported in Table 4. When response of GB patients was divided into either responders (CR, PR, SD) (n = 30) versus non-responders (PD) (n = 28) and GB patients with both IDH1 mutations and MGMT methylation were combined in one group (n = 15) versus those GB patients with either mutated, methylated or non in another group (n = 43), significant level was reached as all of GB patients (15/15, 100%) with both IDH1 mutated with MGMT methylated showed response to treatment as reported in Table 5.

Table 3 Distribution of IDH1 mutation and MGMT methylation status among GBM cases
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Table 4 Relation between response to treatment and investigated markers
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Table 5 Distribution of the response of GBM patients when IDH1 mutation and MGMT methylation were combined
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In GBM, PFS and OS are intensely correlated, representing that PFS may be a suitable surrogate for OS. Compared with OS, PFS proposes earlier assessment and advanced statistical power at the time of analysis. Survival patterns (PFS and OS) were calculated using Kaplan–Meier curves as it is the easiest way of analyzing the survival over time in spite of all other difficulties related with subjects or situations. For every time interval, survival probability is computed as the number of subjects surviving divided by the number of patients at risk.

GB patients were followed up for a median of 10 months and the estimated progression-free survival (PFS) was 13 months, while median overall survival (OS) was 16 months. Relation between survival pattern and estimated markers reported significant difference for IDH1 mutation with PFS (log rank X2 = 9.2, P = 0.002) and OS (log rank X2 = 8.99, P = 0.003), as GB patients reported to have IDH1 mutations revealed better PS and OS; similarly, MGMT methylation reported significant with PFS (log rank X2 = 17, P = 0.0001) and OS (log rank X2 = 27, P = 0.0001) as GB patients with methylated MGMT showed better PFS and OS as reported in Fig. 6A–D. Moreover, survival pattern for patients with IDH1 mutation with MGMT methylation was better (mean PFS = 20 months, mean OS 26 months) than patients with either IDH1 mutation or MGMT methylation alone (mean PFS = 10 months, mean OS = 15 months) as plotted in Fig. 7A–B.

Fig. 6
figure 6

A PFS for IDH1 mutation, B OS for IDH1 mutation, C PFS for MGMT methylation, D OS for MGMT methylation

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Fig. 7
figure 7

A PFS or IDH1 mutation with MGMT methylation, B OS for IDH1 mutation with MGMT methylation

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Discussion

Alteration of many genes has been found to be implicated in pathogenesis of GBM; hence, they may play an important role in predicting prognosis and response to treatment strategies [24]. In the current study, the role of IDH1 gene mutation and MGMT promoter methylation status were investigated among Egyptian GB patients as compared to a group of NND. Among the investigated groups, no significant difference was reported between their genders; however, significant difference was reported among their ages as all NND were below 60 years. This result emphasizes the relation between the increase of GBM among elderly which agree with previous reported studies [25,26,27] which may be attributed to the fact that aging may gradually suppress immunosurveillance and hence contributes to GB cell initiation and/or outgrowth [25].

Sanger sequencing is considered the “gold standard” for detection of IDH1 mutations because of its high specificity and low false positive results but with some drawbacks as low sensitivity, consumes time and high-quality tissue samples to perform the reaction in addition needs manual interpretation [28]. As it is significant to detect the occurrence of IDH1 mutations in a rapid method, patients can gain the advantage from targeted therapies. Therefore, authors detected IDH1 mutation using TaqMan™ competitive allele-specific probes (castPCR™) which has high sensitivity over Sanger sequencing (0.1% vs. 10–25%, respectively) [29] and high specificity as minimal quantities of mutated DNA in a sample that have large quantities of normal wild-type DNA [20] since this technique uses oligonucleotides for the mutated allele so as to repress the normal allele [30]. Accordingly, in the current study IDH1 mutation was not detect among patients with NND 0 out of 20 individuals (0%), these results were agreed with previously reported data [31] who reported that detection of IDH1 mutation points to the presence of glioma and it cannot be attributed to non-neoplastic diseases. For GBM cases, IDH1 mutation was detected in 15 out of 58 (25.9%). These results are in concordance with Kalkan and his colleagues [32] who reported the presence of IDH1 mutations in 12.5% primary GB cases which reveal that it is an early consequence in tumor genesis and this due to the fact that mutated IDH1 reduced the action of NADPH which is important for cellular protection against oxidative stress giving rise to tumor genesis because of oxidative DNA damage [33].

Methylation status of MGMT is among the most studied molecular biomarkers in neuro-oncology because of its influence in therapeutic management of glioblastoma; thus, its detection has been reported using different techniques [34]. However, debate remains about the most appropriate technique to be used, in the current study authors assessed methylation status using restriction enzyme that cut the unmethylated regions and hence the detected will be the methylated (REF). Although it was previously reported in several neuro-oncological centers as 10% as the biological cutoff [35], others reported that precise cutoff value might reflect their response to treatment [36]. In the current study as for the first time NND were included, the ROC was plotted between both groups as considering NND as reference (control) group; hence, the best cutoff point was 66% methylation (< 66% as low–moderate methylation, ≥ 66% as highly methylated). By using this methylation cutoff, currently studied groups reported all NND patients with high MGMT methylation as compared to GB cases as 51.9% were high MGMT methylation. Methylation of NND patients could be attributed to the previously reported findings of Teuber-Hanselmann and his colleagues that MGMT hypermethylation arises in chronic neurological diseases that are not strictly associated to distinctive pathogens, oncogenic viruses or neoplasms but that lead to destruction of the myelin sheath in several ways [37].

Among the GBM cases; those reported IDH1 mutation were of younger age (less than 60 years) than those with older ages; these results agreed with previously reported study by Kalkan and his colleagues [32], for MGMT methylation; significant levels were reached with factors like tumor size and tumor location which agreed with previous reports [38, 39] as GBM patients with tumor size less than 5 cm reported high methylation than others with mass more than 5 cm; moreover, it is generally recognized that tumor location, as significant image feature related to genetic features, is associated with patient prognosis [39]. Also, both IDH1 mutation and MGMT methylation were reported at significant levels in GBM patients with ECGO < 2 which may indicate their usefulness as prognostication markers among GBM patients.

After patients were treated with standard of care treatment strategy, they were followed up for median 10 months, GBM patients with IDH1 mutations reported better PFS and OS than those with IDH1 wild type. A finding that agreed with previously reported study [32] that IDH1 mutations can be used as a prognostic marker for primary GBM patients since it is primary event in tumorigenesis. Regarding GBM patients with MGMT, high methylation reported better PFD and OS as compared to those with low–moderate methylation, these result in concordance with Radke and his colleagues [36]. When GBM patients with both IDH1 mutations and MGMT high methylation were considered, our results emphasized best PFS (20 months) and OS (25 months), indicating that detection of IDH1 mutation combined with MGMT methylation is a better prognostic marker and estimates response of GBM patients to treatment than any of them alone this was agreed with previously reported finding [40] thus using both combined markers for predicting response to treatment and predicting survival pattern is obviously advised than using any of them alone.

Conclusion

Current study reported the superiority of combined detection of MGMT methylation and IDH1 mutation among GBM as predictive and prognostic markers than using either of them alone as both reported to discriminate between non-neuro-oncological disease from GBM cases with a significant impact on prediction of GBM response to treatment. Moreover, the use of these two markers highlights significant impact as prognostic markers. In addition, the method used for MGMT methylation and IDH1 mutation detection reported to be highly sensitive than previously reported techniques which may imply its applicable to be used in clinical routine for superlative as follow-up markers.