Prognostic and Predictive Impact of MGMT Promoter Methylation Status in High Risk Grade 2 Glioma

BackgroundMGMT promoter methylation has been associated with favorable prognosis and survival outcomes in patients with glioblastoma and grade 3 glioma. However, the effects of promoter methylation of MGMT in patients with grade 2 gliomas have not been established. The purpose of the current study is to evaluate the prognostic impact and predictive values of MGMT methylation in patients with grade 2 glioma.MethodsThe National Cancer Database (NCDB) was queried (2004-2016) for patients with newly diagnosed grade 2 glioma. Demographics and clinical characteristics of these patients were examined. Statistics included Kaplan-Meier overall survival (OS) analysis alongside Cox proportional hazards modeling.ResultsA total of 11,223 patients met the selection criteria; 1,252 patients (11%) had MGMT testing. Of the patients who had MGMT testing, 58.5% were MGMT methylated (mMGMT), and 43.5% were MGMT unmethylated (uMGMT). mMGMT patients had greater median overall survival (77.3 months) than both uMGMT patients (42.6 months) and patients with no MGMT status reported (61.9 months (p<0.001 for both). mMGMT was also associated with improved OS, when compared to patients with uMGMT, for patients receiving adjuvant chemoradiation or adjuvant radiation therapy.ConclusionsThis is the largest study to date demonstrating both the prognostic and predictive impact of MGMT methylation on patients with grade II glioma. The current results show that mMGMT is a prognostic factor and possibly a predictive biomarker for grade II glioma patients. MGMT methylation status can be used to determine and stratify patients by risk levels, and thus select patients for treatment intensification.

Correlations between Clinical Characteristics and Prognosis in Patients with Grade II Glioma

Objective. Grade II gliomas are mostly astrocytomas and oligodendrocytomas. The treatment method is mainly surgery, combined with chemotherapy and radiotherapy. According to statistics, young patients under the age of 40 years with grade II gliomas have a 50% chance of more than 5-year survival through reasonable treatment and normal eating habits. The survival time of middle-aged and elderly patients over 40 years old is about 2-3 years under the same conditions. The study aimed at analyzing the clinical characteristics and prognostic factors of 60 patients with glioma. Methods. A total of 60 patients diagnosed pathologically with grade II glioma according to the World Health Organization (WHO) classification in 2007 admitted into our hospital from January 2016 to December 2016 were retrospectively analyzed. The Kaplan–Meier curve was plotted to reflect 5-year survival according to patients’ clinical characteristics. The Cox regression model was used to analyze prognostic factors of grade II glioma. Results. Preoperative KPS scores <60, 60–80, and >80 accounted for 25.00% (15/60), 40.00% (24/60), and 35.00% (21/60), respectively. The largest tumor diameter LTD was less than 5 cm revealed in 60.00% patients, of which astrocytoma accounted for 65.00%. Subventricular zone (SVZ) expansion occurred in 23.33% of the patients and peritumoral edema occurred in 16.67% of the patients. The median follow-up time was 54 months. 5-year overall survival and progression-free survival rates of all patients were 70.0% and 56.7%, respectively. The Cox regression model indicated SVZ expansion, surgical resections, and recurrence were the independent prognostic factors of grade II glioma. Conclusion. These data suggested that SVZ expansion, surgical resections, and recurrence were independent factors affecting the prognosis of grade II glioma. According to the above clinical indexes of patients, individualized therapies can be established to prolong the survival time of patients.

A Radiomics Model for Predicting Early Recurrence in Grade II Gliomas Based on Preoperative Multiparametric Magnetic Resonance Imaging

ObjectiveThis study aimed to develop a radiomics model to predict early recurrence (&lt;1 year) in grade II glioma after the first resection.MethodsThe pathological, clinical, and magnetic resonance imaging (MRI) data of patients diagnosed with grade II glioma who underwent surgery and had a recurrence between 2017 and 2020 in our hospital were retrospectively analyzed. After a rigorous selection, 64 patients were eligible and enrolled in the study. Twenty-two cases had a pathologically confirmed recurrent glioma. The cases were randomly assigned using a ratio of 7:3 to either the training set or validation set. T1-weighted image (T1WI), T2-weighted image (T2WI), and contrast-enhanced T1-weighted image (T1CE) were acquired. The minimum-redundancy-maximum-relevancy (mRMR) method alone or in combination with univariate logistic analysis were used to identify the most optimal predictive feature from the three image sequences. Multivariate logistic regression analysis was then used to develop a predictive model using the screened features. The performance of each model in both training and validation datasets was assessed using a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsA total of 396 radiomics features were initially extracted from each image sequence. After running the mRMR and univariate logistic analysis, nine predictive features were identified and used to build the multiparametric radiomics model. The model had a higher AUC when compared with the univariate models in both training and validation data sets with an AUC of 0.966 (95% confidence interval: 0.949–0.99) and 0.930 (95% confidence interval: 0.905–0.973), respectively. The calibration curves indicated a good agreement between the predictable and the actual probability of developing recurrence. The DCA demonstrated that the predictive value of the model improved when combining the three MRI sequences.ConclusionOur multiparametric radiomics model could be used as an efficient and accurate tool for predicting the recurrence of grade II glioma.

P10.03 Establishment and validation of clinical prediction model in WHO grade II glioma

OBJECTIVE WHO grade II glioma has the characteristics of heterogeneity, and this disease progresses rapidly in some patients, in whom the malignant degree is equivalent to that of high-grade glioma. In order to accurately predict the prognosis of patients, an effective clinical prediction model based on relevant risk factors is needed which could provide a theoretical basis for optimization of clinical individualized treatment. METHODS According to the inclusion and exclusion criteria, eligible patients from January 2010 to December 2018 in our hospital were selected, and those who met the criteria were randomly assigned 4:1 to the training group and the validation group, respectively. The predictors were screened by univariate and multivariate Cox regression analysis, the prediction model was established, and the model was verified and evaluated. RESULTS A total of 258 patients with WHO grade II glioma were recruited, including 208 patients as the training group and 50 patients as the validation group. Six independent risk factors, including patient age, preoperative Karnofsky performance status (KPS) score, preoperative seizure symptoms, surgical resection range, tumor size and IDH status, were selected and included into the prediction model by univariate and multivariate Cox regression analysis, and were visualized in the form of Nomogram. The concordance index (C index) was used to evaluate the predictive ability of the model. Results showed that the C-index was 0.832 in the training group and 0.853 in the validation group, respectively, indicating good performance for the prediction model. The calibration charts were drawn in both groups respectively, which showed that the calibration lines were in good agreement with the standard lines, indicating good consistency between the two groups. CONCLUSIONS In this study, a clinical prediction model for WHO grade II glioma was established, and it was verified that the model has good predictive ability, which may be beneficial for clinical work.

Perfusion imaging with arterial spin labeling (ASL)–MRI predicts malignant progression in low‑grade (WHO grade II) gliomas

Purpose Predicting malignant progression of grade II gliomas would allow for earlier initiation of treatment. The hypothesis for this single-centre, case–control study was that the perfusion signal on ASL-MRI predicts such malignant progression in the following 12 months. Methods Consecutive patients with the following criteria were included: ≥ 18 years, grade II glioma (biopsied or resected) and an ASL-MRI 6–12 months prior to malignant progression (cases) or stable disease (controls). Malignant progression was defined either radiologically (new T1w-contrast enhancement) or histologically (neurosurgical tissue sampling). Three controls were matched with each case. Some patients served as their own control by using earlier imaging. The ASL-MRIs were reviewed by two neuroradiologists and classified as positive (hyper-intense or iso-intense compared to cortical grey matter) or negative (hypo-intense). In patients with epilepsy, a neurologist reviewed clinicoradiological data to exclude peri-ictal pseudoprogression. The statistical analysis included diagnostic test properties, a Cohen’s Kappa interrater reliability coefficient and stratification for previous radiotherapy. Results Eleven cases (median age = 48, IQR = 43–50 years) and 33 controls (43, 27–50 years) were included. Malignant progression appeared at 37 months (median, IQR = 17–44) after first surgery. Thirty ASL-MRIs were assessed as negative and 14 as positive. None of the MRIs showed signs of peri-ictal pseudoprogression. ASL significantly predicted subsequent malignant progression (sensitivity = 73%; specificity = 82%; OR = 12; 95%-CI = 2.4–59.1; p = 0.002). The interrater reliability coefficient was 0.65. In stratified analysis, ASL-MRI predicted malignant progression both in patients with previous radiotherapy and in those without (Mantel–Haenszel test, p = 0.003). Conclusion Perfusion imaging with ASL-MRI can predict malignant progression within 12 months in patients with grade II glioma.

Propolis from Poland versus propolis from New Zealand - chemical composition and antiproliferative properties on glioblastoma cell lines.

Background Several studies have previously reported that propolis and its ingredients inhibit glioma cancer cell lines. The chemical composition and antiproliferative activity of propolis from Poland (PPE) and propolis from New Zealand (MPE) were compared in this study. Methods The chemical composition was investigated by gas chromatography-mass spectrometry. Antiproliferative activity of PPE and MPE was determined by a cytotoxicity test and DNA binding by [ 3 H]-thymidine incorporation on Human Diffuse Astrocytoma cell line (DASC) derived from a patient with a Grade II glioma and glioblastoma multiforme T98G and LN-18 cell lines from American Type Culture Collection. Results The chemical composition of both propolis was comparable, with marginal differences in the amount of some compounds. Flavonoids and chalcones, of which pinocembrin, pinobanksin, pinobanksin 3-acetate, chrysin and galangin showed the highest level, were the main components of both examined propolis (PPE–49.4% and MPE–52.1%). The performed cytotoxicity test showed powerful activity of PPE and MPE propolis on DASC, T98G and LN-18 cells. The degree of the antiproliferative activity was similar in the case of both propolis (viability after 72 h for 30 µg/mL ranged from 22.0% to 51.6% and proliferation inhibition after 72 h approximately was from 18.6% to 75.6%). Conclusions These results are the first to show that propolis from Poland and propolis from New Zealand have a strong cytotoxic and antiproliferative effect on DASC (Grade II glioma) derived from a patient and glioblastoma multiforme T98G and LN-18 cell lines. This activity may be associated with the high content of polyphenolic compounds in both propolis. These findings suggest that Polish and New Zealand propolis shows promising anticancer activity in the treatment of glioblastoma. However, further studies are required.