Keywords
MGMT promoter gene methylation; Glioma; Neurological Scale; NANO Scale
This article is included in the Cell & Molecular Biology gateway.
MGMT promoter gene methylation; Glioma; Neurological Scale; NANO Scale
The new version contains a few changes as listed below.
1. In Methods section under the ‘Histopathological and molecular analysis’ heading: The histopathological criteria included in this study and how the pathological examination was performed, were added.
2. In Results section:
a. Multivariate analysis using logistic regression was heeded.
b. A subgroup analysis focused on glioblastoma was added, along with Table 6 and Table 7.
3. In Discussion section:
a. Reason of using a cut-off value decrement ≥1 of the NANO scale as neurological improvement was explained in detail.
b. Hypothesis of why the subgroup analysis shows no significant correlation, was added.
See the authors' detailed response to the review by Young Zoon Kim
See the authors' detailed response to the review by Ery Kus Dwianingsih
Glioma is one of the most common primary brain tumours, in which 80% of the cases are found to be malignant (Alifieris & Trafalis, 2015; Schwartzbaum et al., 2006; Stupp et al., 2010). Grade II glioma has a median survival ranging from four to more than ten years (Bell et al., 2018; Claus & Black, 2006). However, glioblastoma – a grade IV glioma – accounts for the majority of gliomas and carries a poor prognosis with median survival of six to 14 months despite aggressive treatment with radiotherapy plus chemotherapy, and less than 12 months with radiotherapy alone (Stupp et al., 2005).
Molecular characteristics of glioma have undergone extensive research in the last decade, and some biomarkers have even been integrated into the 2016 glioma classification. Isocitrate dehydrogenase (IDH) mutation and O6-Methylguanine-DNA methyltransferase (MGMT) promoter gene methylation has been associated with better survival. Nonetheless, patients’ quality of life and neurological deficit have not been considerably measured and correlated with these prognostic biomarkers.
In 2017, the Neurological Assessment of Neuro-Oncology (NANO) scale was developed to objectively measure neurological deficit in patients with brain tumours (Nayak et al., 2017). Previous studies have shown that a good initial NANO scale score was significantly associated with improvement in neurological deficit two months after surgery (Gunawan et al., 2020) and a more powerful method to predict prognosis during initial diagnosis and disease progression (Lee et al., 2018). In this study, we aimed to elucidate biomarkers that might be associated with neurological scale improvement three months after surgery.
This is a cohort study, with a purposive sampling method. There were 72 suspected brain tumour patients, aged more than ten years old, who presented at three major hospitals in Jakarta and Tangerang over a period of one year (July 2019–July 2020). After a brain MRI, 5 patients were excluded due to tumour location (brainstem or infratentorial). Written consent was obtained from every patient or their caregiver (for patients who are unable to give consent). Karnofsky Performance Scale (KPS) as well as neurological scale were assessed before surgery. Following tumour resection, 62 patients were histologically confirmed as having glioma grade II-IV. Tumour tissue was tested for IDH mutation as well as MGMT promoter gene methylation. Patients underwent standardised therapy, and neurological function was re-assessed three months after surgery. Six patients were deceased before the second neurological scale assessment, and two patients were lost to follow-up, hence were excluded from the study. The study was approved by Medical Ethical Research Committee, Universitas Hasanuddin, No: 1232/UN4.6.4.5.31/PP36/2019.
Histopathological and molecular analysis was carried out in Mochtar Riady Institute Tangerang and Kalgen Innolab Clinical Laboratory, Jakarta. Tissue sample dissected from the tumor was immediately put in formaldehyde 10% buffer. Six to 24 hours later, the desired tumor tissue was stored into cassettes and processed in an automatic tissue processor. After an overnight processing, tissue was put into a mold with paraffin wax. Paraffin block with the embedded tissue was cut using a microtome and placed on glass slides stained with hematoxylin and eosin to determine the diagnosis and grade of malignancy. Histopathological examination was conducted by two neuropathologists and classified according to the 2016 WHO Classification of Tumours of the Central Nervous System (Louis et al., 2016). Diffuse astrocytic and oligodendroglial tumours and other astrocytic tumours WHO grade II-IV were included in the study. Area with the most tumour accumulation along with tumour cell percentage was marked. This area was then scraped for further analysis.
Genomic DNA was extracted from paraffin-embedded tumor tissues using the QIAamp®DNAMicroKit (QIAGEN, catalogue number 56304) following the manufacturers’ protocol.
IDH mutation evaluation was done using high resolution melting (HRM) analysis and Sanger sequencing of polymerase chain reaction (PCR)-amplified fragments, which were generated during the HRM procedure with the PCR primers. Primers used were: IDH1-forward 5’-CGGTCTTCAGAGAAGCCATT-3’ and IDH1-reverse 5’-GCAAAATCACATTATTGCCAAC-3’ (gBlocks Gene Fragment, Integrated DNA Technologies).
HRM analysis consists of 3 steps: PCR reaction, DNA melting, and data analysis. These analyses were performed using the LightCycler® 480 High-Resolution Melting Master Kit (Roche) according to the manufacturer’s instructions. PCR amplification and HRM analysis were carried out in a LightCycler® 480 real-time PCR system (Roche Diagnostics).
PCR amplification started with an initial denaturation of 95°C for 10 minutes (which included activation of FastStartTM Taq DNA polymerase and denaturation of DNA), followed by 45 cycles of amplification (denaturation at 95°C for 10 seconds, annealing at 60°C for 30 seconds, and extension at 72°C for 12 seconds). Melting was performed with a denaturation step at 95°C for 1 minute, followed by an annealing step at 40°C for 1 minute and a melt from 70 to 97°C at a ramp rate of 0.03°C/second with 18 acquisitions per degree Celsius. These results were analyzed using LightCycler® 480 software version 1.5.0.
Sanger sequencing was conducted using Applied Biosystems 3500 Genetic Analyzer. For each reaction, 200 ng of genomic DNA was amplified with the following PCR conditions: an initial 10-minute denaturation at 95°C followed by 40 cycles of 30 seconds at 95°C; 30 seconds at 60°C; 30 seconds at 72°C, and a final extension of 7 minutes at 72°C. Sequencing were aligned and edited with the BioEdit Software version 7.2.5.
Samples with conflicting results by HRM and sequencing were re-tested and only HRM-positive samples confirmed by sequencing were considered mutated.
DNA extracted first underwent bisulphite treatment to convert all unmethylated cytosine to uracil, leaving 5-methylcytosine unaltered. After being eluted in DNase-free water, methylation analysis was commenced using real-time methylation-specific PCR (MSP). PCR amplification was carried out in a LightCycler® 480 real-time PCR system (Roche Diagnostics). QuantiFast®Multiplex PCR Kit (QIAGEN) was used for PCR and DNA was amplified utilizing HotStarTaq Plus DNA polymerase which was included in the kit. Primers used were: MGMT Methylated-forward 5’-TTTCGACGTTCGTAGGTTTTCGC-3’, MGMT Methylated reverse 5’-GCACTCTTCCGAAAACGAAACG-3’ and MGMT Unmethylated-forward 5’ TTTGTGTTTTGATGTTTGTAGGTTTTTGT-3’, MGMT Unmethylated-reverse 5’- AACTCCACACTCTTCCAAAAACAAAACA-3’. Cycling conditions were 95°C for 5 min, followed by 42 cycles of amplification (denaturation at 95°C for 30 seconds, annealing at 59°C for 30 seconds, and extension at 72°C for 30 seconds) and 72°C for 5 min. PCR reactions (15 μl) were analyzed on a 2% agarose gel stained with SYBR Safe. Commercial methylated DNA and unmethylated DNA (Zymo Research) served as positive controls. Outcomes were classified as either methylated or unmethylated.
Neurological function was assessed using the NANO scale. NANO scale is a simple neurological assessment evaluating patients in nine domains: gait, strength, upper extremity ataxia, sensation, visual fields, facial strength, language, level of consciousness, and behaviour. Each domain was scored from 0 to 2 or 3, with higher score indicating worse neurologic function. Neurological scale was assessed by two individual physicians, and subjective complaints not included in the scale were also noted. Any discrepancies between investigators were discussed and optimal scale were determined. NANO scale was first assessed before surgery and then re-assessed three months later during their routine clinical follow up. Patients who were deceased before the second assessment were excluded from the study. Neurological improvement was defined as decrement in NANO scale score by 1 or more, which was calculated from difference of initial NANO and NANO three months after surgery.
The association between baseline characteristics and biomarkers were analysed. Categorical data are presented as proportions and interpreted using chi-square or Pearson Fisher’s exact test. All calculations were performed using IBM SPSS Statistics version 24. The reported p values are two-sided, and a probability value of <0.05 was considered statistically significant.
In total, 54 patients completed the follow up and were analysed. Half of the cases (50%) were glioma grade IV, followed by grade II (37%) and grade III (13%). Overall mean age was 43.63 (14.723) years old for all gliomas, and mean age for glioblastoma was 50 (12.7) years old. Male to female ratio was 1.57. Median Karnofsky Performance Scale (KPS) score was 55 (30–80). Most of the patients underwent surgery followed by radiation and chemotherapy (79.6%) (Table 1).
The most common presenting symptom was headache (63%). Patients with tumours located in the frontal lobe most commonly presented with headache (58.8%), one-sided weakness (35.3%), seizure (32.4%), cognitive disturbance (20.6%), and aphasia (5.9%).
In 16 patients (29.6%) an IDH1 mutation was found and no IDH2 mutation was found. Younger age and male gender were significantly associated with having an IDH mutation (Table 2).
Variables | IDH mutation | P value | |
---|---|---|---|
IDH mutant | Wild type | ||
Age | |||
≥45 years old | 5 | 23 | 0.049 |
<45 years old | 11 | 15 | |
Gender | |||
Male | 13 | 20 | 0.049 |
Female | 3 | 18 | |
Initial KPS | |||
0–50 | 5 | 22 | 0.074 |
60–100 | 11 | 16 |
There were 33 patients (61.1%) that were MGMT methylated (Table 3). Older patients had a higher tendency to have methylated MGMT than younger patients. Patients with IDH mutations were more likely to harbour MGMT methylation (p=0.049; OR=1.54; 95%CI 1.05-2.26).
The number of patients with MGMT methylation who underwent surgery plus chemoradiation, surgery plus radiation only, and surgery only were 26 patients (78.8%), one patient (3%) and six patients (18.2%), respectively.
Median NANO scale score before surgery was 4 (0–12) and three months after surgery was 3 (0–12). Improvement in neurological function, measured using the NANO scale, was found in 44 (81.5%) of the patients. Age, gender, initial KPS, tumour location, grade of glioma and IDH mutation were not associated with improvement in neurological function. Among patients with MGMT promoter gene methylation, 90.9% showed improvement in neurological function. A multivariate analysis using logistic regression confirmed MGMT methylation as an independent factor towards NANO scale improvement (p=0.035; OR=5; 95%CI 1.122-22.272) (Table 4).
Further analysis shows that coexistence of IDH mutation and MGMT methylation were mostly found in grade II patients (53.8%) and 100% of patients with coexistence of both biomarkers showed improvement in neurological scale (p=0.032) (Table 5).
Biomarker | NANO scale improvement | P value | |
---|---|---|---|
+ | (-) | ||
IDH mutant | |||
MGMT methylated | 13 | 0 | 0.032 |
MGMT unmethylated | 2 | 1 | |
Wild type IDH | |||
MGMT methylated | 17 | 3 | 0.184 |
MGMT unmethylated | 12 | 6 |
We did a subgroup analysis of patients with glioblastoma. Baseline characteristics of patients with glioblastoma were depicted in Table 6. IDH mutation and MGMT methylation did not show any relation towards neurological scale improvement (Table 7).
Glioma remains to be a challenging tumour, with diverse clinical presentation, phenotype and molecular parameters (Louis et al., 2016). It is not unusual for clinicians to encounter cases of higher-grade glioma with longer survival than lower-grade glioma during conventional treatment. Despite survival, clinical and functional status is another concern in treatment initiation and disease progression. KPS has been generally used to evaluate brain tumours’ performance status. However, like tumour grade, there have also been cases in which those with a low KPS score survived longer than those with a high KPS score (Lee et al., 2018).
The NANO scale is a relatively new scale that serve as an objective and quantifiable metric of neurologic function in brain tumour patients (Nayak et al., 2017). A previous study found that performance status estimated by the NANO scale was significantly associated with overall survival, and was a more powerful method to predict the prognosis of GBM than the KPS during both initial diagnosis and disease progression (Lee et al., 2018). It was also found that initial NANO scale possesses a stronger correlation neurological scale improvement than initial KPS towards functional scale improvement (Gunawan et al., 2020). In this study, we used NANO scale to objectively measure neurological scale improvement. A decrement in NANO scale score by 1 or more was used as a cut-off for neurological improvement considering the results and definition used in previous studies. One study showed a mean progression of NANO scale as much as 4.1 points during 18.9 months follow up (Lee et al., 2018) and another study defined neurologic response of NANO scale as ≥2 level of improvement (Nayak et al., 2017). Taking into account that this is a short follow-up study, a decrement of ≥2 will be unlikely, especially if the mean progression of NANO scale is 4.1 points in 18.9 months, which means a progression of 1 point in 4.7 months. Thus, decrement of ≥1 score of NANO scale was considered as seemingly relevant.
From 54 patients included in the study, most of the cases were glioma grade IV, glioblastoma, IDH-wildtype (44.4%), followed by grade II and III. This distribution is similar to previous studies analysing different grades of gliomas (Malueka et al., 2020; Ostrom et al., 2018; Theresia et al., 2020). Mean age in this study was 43.63 years old, and patients with glioblastoma have a mean age of 50 (12.7) years old, which is comparable to previous studies conducted in Asian (Lee et al., 2018; Malueka et al., 2020; Theresia et al., 2020), African (Senhaji et al., 2017), and Amsterdam (Molenaar et al., 2014) populations and lower than mean age of studies in the United Kingdom (Philips et al., 2018; Sehmer et al., 2014), Greece (Alifieris & Trafalis, 2015) and United States (Ostrom et al., 2018; Schwartzbaum et al., 2006). The male to female ratio was 1.57, which is comparable to previous studies (Malueka et al., 2020; Ostrom et al., 2018; Sehmer et al., 2014). The proportion of low and high initial functional status was equivalent. Tumours were mostly located in the frontal lobe, followed by temporal, parietal, and occipital. The most common presenting symptom was headache, and 58.8% of tumours located in the frontal lobe presented with this symptom. Surgery, radiation, and chemotherapy were underwent by 79.6% of patients.
It was found that 29.6% of patients were positive for IDH1 mutation. Being of a younger age and male were factors associated with having an IDH mutant. This is similar to previous studies that found younger age to be associated with IDH mutation in glioblastoma (Molenaar et al., 2014; Songtao et al., 2012). However, a study in Indonesia showed no differences in age and gender towards IDH mutation (Malueka et al., 2020). Dissimilarity may be caused by differences in characteristics of patients included in their study (which also included grade I glioma). Grade I glioma is frequently found in children, with different characteristics, therapeutic interventions, and prognosis. Therefore, in this study, we did not include grade I glioma and excluded patients less than 10 years old.
MGMT methylation was found in 61.1% of patients. Patients with IDH mutation were more likely to have an MGMT methylation as well (p=0.049; OR=1.54; 95%CI 1.05-2.26). These findings are in accordance with previous studies (Molenaar et al., 2014; Songtao et al., 2012).
Improvement in NANO scale within three months after surgery was found in 81.5% of the patients. From the variables studied, only MGMT promoter gene methylations revealed a significant link to NANO scale improvement three months after surgery (p=0.035; OR=5; 95%CI 1.122-22.272).
Although IDH mutation alone was not significantly related to scale improvement, the combination of IDH mutation and MGMT methylation was associated with improvement in neurological function three months later. This might be due to the significant association of MGMT methylation towards NANO scale improvement, which influenced the relation between its combination with IDH mutation towards scale improvement.
MGMT promoter gene methylation has been commonly accepted to confer survival advantage regardless of therapy (Reifenberger et al., 2014). It carries both prognostic and predictive value. In this study, it was found that gliomas with MGMT gene promoter methylation are five times likely to show neurological improvement three months after surgery. However, a subgroup analysis of patients with glioblastoma, dis not show significant association between MGMT methylation status and neurological scale improvement. This might be apparently due to the small sample size of glioblastoma patients in this study.
IDH mutation is one of the molecular tests well known for its prognostic and predictive implications in high-grade as well as some low-grade gliomas. Patients with IDH mutation have been shown to convey a longer overall survival and progression-free survival compared to those in the wild-type group (Malueka et al., 2020; Molenaar et al., 2014; Reifenberger et al., 2014; Songtao et al., 2012). However, it is still unclear if IDH mutational status is a prognostic and predictive measure of response to treatment. Some studies have concluded that IDH mutation is an independent factor towards response to treatment (Hartmann et al., 2011; Houillier et al., 2010), while others linked the relation to other markers such as 1p19q deletion (Iwamoto et al., 2008; Jenkins et al., 2006; Mariani et al., 2006) and G-CIMP phenotype (Reifenberger et al., 2014). Our study found no association between IDH mutation and neurological improvement. This might be due to the difference in study outcome, which is improvement in neurological function rather than survival or progression-free survival. Secondly, since we used the difference between initial NANO scale score and score three months later as a measure for neurological improvement, it is possible that patients with low initial scale scores had a persistent score three months later, which is then assessed as not having improvement after three months. Third, the NANO scale as a measure for neurological improvement does not integrate headaches into the scale, which is the most common presenting symptom in this study.
There are several limitations of this study. First, we did not analyse other predictive biomarkers such as 1p19q deletion and G-CIMP phenotype. Second, the follow up period of three months after surgery might not reveal changes in neurological improvement, since some patients might still be in chemotherapy and radiation therapy. Hopefully, future studies could integrate other biomarkers as well and conduct a longer period of follow up.
MGMT promoter gene methylation as well as coexistence of IDH mutation and MGMT methylation shows a significant link towards improvement in NANO scale score three months after surgery. Glioma patients with MGMT gene promoter methylation are five times more likely to show neurological improvement three months after surgery.
Zenodo: MGMT promoter gene methylation and Neurological Scale Improvement in Glioma. https://doi.org/10.5281/zenodo.4482094 (Gunawan et al., 2021)
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
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Competing Interests: No competing interests were disclosed.
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Molecular Pathology
Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
I cannot comment. A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Are the conclusions drawn adequately supported by the results?
Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Molecular Pathology
Is the work clearly and accurately presented and does it cite the current literature?
Yes
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
I cannot comment. A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: 1. Research Interest and Activity 1) Cancer Biology (Invasion, Metastasis, Oncogenesis) on malignant brain tumors 2) Cancer Epigenentics (Histone modification) on malignant brain tumors 2. Clinical Practices 1) Neurooncological care for the patients with solid cancer (brain metastasis, neurocognitive function, brain tumor related epilepsy) 2) Chemotherapy for brain tumor patients (malignant primary brain tumor, primary CNS lymphoma, brain metastasis) 3) Microscopical neurosurgery for brain tumors 4) Neuroendoscopic surgery for skull base tumors
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