Prognostic and predictive impact of MGMT promoter methylation in grade 3 gliomas

Treatment advances in high-grade gliomas

High-grade gliomas (HGG) pose significant challenges in modern tumour therapy due to the distinct biological properties and limitations of the blood-brain barrier. This review discusses recent advancements in HGG treatment, particularly in the context of immunotherapy and cellular therapy. Initially, treatment strategies focus on targeting tumour cells guided by the molecular characteristics of various gliomas, encompassing chemotherapy, radiotherapy and targeted therapy for enhanced precision. Additionally, technological enhancements are augmenting traditional treatment modalities. Furthermore, immunotherapy, emphasising comprehensive tumour management, has gained widespread attention. Immune checkpoint inhibitors, vaccines and CAR-T cells exhibit promising efficacy against recurrent HGG. Moreover, emerging therapies such as tumour treating fields (TTFields) offer additional treatment avenues for patients with HGG. The combination of diverse treatments holds promise for improving the prognosis of HGG, particularly in cases of recurrence.

The evaluation of six genes combined value in glioma diagnosis and prognosis

PURPOSE

Glioma is the most common and fatal type of brain tumour. Owing to its aggressiveness and lethality, early diagnosis and prediction of patient survival are very important. This study aimed to identify key genes and biomarkers for glioma that can guide clinicians in making rapid diagnosis and prognostication.

METHODS

Data mining of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data, and Genotype-Tissue Expression Project brain expression data revealed significantly differentially expressed genes (DEGs), and the risk scores of individual patients were calculated. WGCNA was utilized to screen for genes most related to clinical diagnosis. Prognostic genes associated with glioma were selected via combining the LASSO regression with univariate and multivariate Cox regression and protein-protein interaction network analyses. Then, a nomogram was constructed. And CGGA dataset was utilized to validated. The protein expression levels of the signature were detected using the human protein atlas. Drug response prediction was carried out using the package "pRRophetic".

RESULTS

A six-gene signature (KLF6, CHI3L1, SERPINE1, ANGPT2, TGFBR1, and PTX3) was identified and used to stratify patients into low- and high-risk groups. Survival, ROC curve, and Cox analyses clarified that the six hub genes were a favourable independent prognostic factor for patients with glioma. A nomogram was set up by integrating clinical parameters with risk signatures, showing high precision for predicting 2-, 3-, 4-, 5-years survival. In addition, the expression of most genes was consistent with protein expression. Furthermore, the sensitivity to the top ten drugs in the GDSC database of the high-risk group was significantly higher than the low-risk group.

CONCLUSION

Based on genetic profiles and clinicopathological features, including age, grade, isocitrate dehydrogenase mutation status, we constructed a comprehensive prognostic model for patients with glioma. These signatures can be regarded as biomarkers to predict the prognosis of gliomas, possibly providing more therapeutic strategies for future clinical research.

Demographic variations and time to initiation of adjunct treatment following surgical resection of anaplastic astrocytoma in the United States: a National Cancer Database analysis

BACKGROUND AND AIMS

The aim of this study was to analyze the trends, demographic differences in the type and time to initiation (TTI) of adjunct treatment AT following surgery for anaplastic astrocytoma (AA).

MATERIAL AND METHODS

The National Cancer Database (NCDB) was queried for patients diagnosed with AA from 2004 to 2016. Cox proportional hazards and modeling was used to determine factors influencing survival, including the impact of time to initiation (TTI) of adjuvant therapy.

RESULTS

Overall, 5890 patients were identified from the database. The use of combined RT + CT temporally increased from 66.3% (2004-2007) to 79% (2014-2016), p < 0001. Patients more likely to receive no treatment following surgical resection included elderly (> 60 years old), hispanic patients, those with either no or government insurance, those living > 20 miles from the cancer facility, those treated at low volume centers (< 2 cases/year). AT was received following surgical resection within 0-4 weeks, 4.1-8 weeks, and > 8 weeks in 41%, 48%, and 3%, respectively. Compared to patients who received RT + CT, patients were likely to receive RT only as AT either at 4-8 weeks or > 8 weeks after the surgical procedure. Patients who received AT within 0-4 weeks had the 3-year OS of 46% compared to 56.7% for patients who received treatment at 4.1-8 weeks.

CONCLUSION

We found significant variation in the type and timing of adjunct treatment following surgical resection of AA in the United States. A considerable number of patients (15%) received no AT following surgery.

Preoperative Chemoradiotherapy With Capecitabine With or Without Temozolomide in Patients With Locally Advanced Rectal Cancer: A Prospective, Randomised Phase II Study Stratified by O6-Methylguanine DNA Methyltransferase Status: KCSG-CO17-02

AIMS

To evaluate the clinical efficacy of adding temozolomide (TMZ) to preoperative capecitabine (CAP)-based chemoradiotherapy in patients with locally advanced rectal cancer (LARC) and validate O⁶-methylguanine DNA methyltransferase (MGMT) methylation status as a predictive marker for TMZ combined regimens.

MATERIALS AND METHODS

LARC patients with clinical stage II (cT3-4N0) or III (cT_anyN+) disease were enrolled. They were stratified into unmethylated MGMT (uMGMT) and methylated MGMT (mMGMT) groups by methylation-specific polymerase chain reaction before randomisation and were then randomly assigned (1:1) to one of four treatment arms: uMGMT/CAP (arm A), uMGMT/TMZ + CAP (arm B), mMGMT/CAP (arm C) and mMGMT/TMZ + CAP (arm D). The primary end point was the pathological complete response (pCR) rate.

RESULTS

Between November 2017 and July 2020, 64 patients were randomised. Slow accrual caused early study termination. After excluding four ineligible patients, 60 were included in the full analysis set. The pCR rate was 15.0% (9/60), 0%, 14.3%, 18.8% and 26.7% for the entire cohort, arms A, B, C and D, respectively (P = 0.0498 between arms A and D). The pCR rate was 9.7% in the CAP group (arms A + C), 20.7% in the TMZ + CAP group (arms B + D), 6.9% in the uMGMT group (arms A + B) and 22.6% in the mMGMT group (arms C + D). Grade 1-2 nausea or vomiting was significantly more frequent in the TMZ + CAP treatment groups (arms B + D) than in the CAP treatment groups (arms A + C, P < 0.001) with no difference in grade 3 adverse events. There were no grade 4 or 5 adverse events.

CONCLUSION

The addition of TMZ to CAP-based chemoradiotherapy tended to improve pCR rates, particularly in those with mMGMT LARC. MGMT status may warrant further investigation as a predictive biomarker for chemotherapeutic agents and radiotherapy.

Iron Oxide Nanoparticles Decorated with Functional Peptides for a Targeted siRNA Delivery to Glioma Cells

Glioma is a deadly form of brain cancer, and the difficulty of treating glioma is exacerbated by the chemotherapeutic resistance developed in the tumor cells over the time of treatment. siRNA can be used to silence the gene responsible for the increased resistance, and sensitize the glioma cells to drugs. Here, iron oxide nanoparticles functionalized with peptides (NP-CTX-R₁₀) were used to deliver siRNA to silence O6-methylguanine-DNA methyltransferase (MGMT) to sensitize tumor cells to alkylating drug, Temozolomide (TMZ). The NP-CTX-R₁₀ could complex with siRNA through electrostatic interactions and was able to deliver the siRNA to different glioma cells. The targeting ligand chlorotoxin and cell penetrating peptide polyarginine (R₁₀) enhanced the transfection capability of siRNA to a level comparable to commercially available Lipofectamine. The NP-siRNA was able to achieve up to 90% gene silencing. Glioma cells transfected with NP-siRNA targeting MGMT showed significantly elevated sensitivity to TMZ treatment. This nanoparticle formulation demonstrates the ability to protect siRNA from degradation and to efficiently deliver the siRNA to induce therapeutic gene knockdown.

The diagnostic value of ADC histogram and direct ADC measurements for coexisting isocitrate dehydrogenase mutation and O6-methylguanine-DNA methyltransferase promoter methylation in glioma

Objectives

To non-invasively predict the coexistence of isocitrate dehydrogenase (IDH) mutation and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in adult-type diffuse gliomas using apparent diffusion coefficient (ADC) histogram and direct ADC measurements and compare the diagnostic performances of the two methods.

Materials and methods

A total of 118 patients with adult-type diffuse glioma who underwent preoperative brain magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) were included in this retrospective study. The patient group included 40 patients with coexisting IDH mutation and MGMT promoter methylation (IDHmut/MGMTmet) and 78 patients with other molecular status, including 32 patients with IDH wildtype and MGMT promoter methylation (IDHwt/MGMTmet), one patient with IDH mutation and unmethylated MGMT promoter (IDHmut/MGMTunmet), and 45 patients with IDH wildtype and unmethylated MGMT promoter (IDHwt/MGMTunmet). ADC histogram parameters of gliomas were extracted by delineating the region of interest (ROI) in solid components of tumors. The minimum and mean ADC of direct ADC measurements were calculated by placing three rounded or elliptic ROIs in solid components of gliomas. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the diagnostic performances of the two methods.

Results

The 10th percentile, median, mean, root mean squared, 90th percentile, skewness, kurtosis, and minimum of ADC histogram analysis and minimum and mean ADC of direct measurements were significantly different between IDHmut/MGMTmet and the other glioma group (P < 0.001 to P = 0.003). In terms of single factors, 10th percentile of ADC histogram analysis had the best diagnostic efficiency (AUC = 0.860), followed by mean ADC obtained by direct measurements (AUC = 0.844). The logistic regression model combining ADC histogram parameters and direct measurements had the best diagnostic efficiency (AUC = 0.938), followed by the logistic regression model combining the ADC histogram parameters with statistically significant difference (AUC = 0.916) and the logistic regression model combining minimum ADC and mean ADC (AUC = 0.851).

Conclusion

Both ADC histogram analysis and direct measurements have potential value in predicting the coexistence of IDHmut and MGMTmet in adult-type diffuse glioma. The diagnostic performance of ADC histogram analysis was better than that of direct ADC measurements. The combination of the two methods showed the best diagnostic performance.

A Multimodal Knowledge-Based Deep Learning Approach for MGMT Promoter Methylation Identification

Glioblastoma Multiforme (GBM) is considered one of the most aggressive malignant tumors, characterized by a tremendously low survival rate. Despite alkylating chemotherapy being typically adopted to fight this tumor, it is known that O(6)-methylguanine-DNA methyltransferase (MGMT) enzyme repair abilities can antagonize the cytotoxic effects of alkylating agents, strongly limiting tumor cell destruction. However, it has been observed that MGMT promoter regions may be subject to methylation, a biological process preventing MGMT enzymes from removing the alkyl agents. As a consequence, the presence of the methylation process in GBM patients can be considered a predictive biomarker of response to therapy and a prognosis factor. Unfortunately, identifying signs of methylation is a non-trivial matter, often requiring expensive, time-consuming, and invasive procedures. In this work, we propose to face MGMT promoter methylation identification analyzing Magnetic Resonance Imaging (MRI) data using a Deep Learning (DL) based approach. In particular, we propose a Convolutional Neural Network (CNN) operating on suspicious regions on the FLAIR series, pre-selected through an unsupervised Knowledge-Based filter leveraging both FLAIR and T1-weighted series. The experiments, run on two different publicly available datasets, show that the proposed approach can obtain results comparable to (and in some cases better than) the considered competitor approach while consisting of less than 0.29% of its parameters. Finally, we perform an eXplainable AI (XAI) analysis to take a little step further toward the clinical usability of a DL-based approach for MGMT promoter detection in brain MRI.

Prognostic value of O 6-methylguanine-DNA methyltransferase methylation in isocitrate dehydrogenase mutant gliomas

Background

Patients with isocitrate dehydrogenase (IDH) mutant gliomas have been associated with longer survival time than those that are IDH wild-type. Previous studies have shown the prognostic value of O ⁶ -methylguanine-DNA methyltransferase (MGMT) promoter methylation for glioblastoma multiforme (GBM), which are predominantly IDH wild-type. Little is known of the prognostic value of MGMT methylation status for IDH mutant gliomas.

Methods

We retrospectively identified IDH mutant gliomas patients between 2011 and 2020 that were tested for MGMT promoter methylation. We generated Kaplan-Meier estimator curves and performed Cox proportional hazard models for overall survival (OS) and progression-free survival (PFS) to compare the outcomes of MGMT promoter methylated versus MGMT unmethylated patients.

Results

Of 419 IDH mutant gliomas with MGMT promoter methylation testing, we identified 54 GBMs, 223 astrocytomas, and 142 oligodendrogliomas. 62.3% patients had MGMT methylated tumors while 37.7% were MGMT unmethylated. On Kaplan-Meier analysis, median OS for all MGMT methylated patients was 17.7 years and 14.6 years for unmethylated patients. Median PFS for all MGMT methylated patients was 7.0 years and for unmethylated patients 5.2 years. After univariate subgroup analysis, MGMT methylation is only prognostic for OS and PFS in GBM, and for OS in anaplastic oligodendroglioma and anaplastic oligodendroglioma for OS. In multivariate analysis, MGMT unmethylated GBM patients carry a higher risk of death (HR 7.72, 95% CI 2.10-28.33) and recurrence (HR 3.85, 95% CI 1.35-10.96).

Conclusions

MGMT promoter methylation is associated with better OS and PFS for IDH mutant GBM. MGMT promoter methylation testing for other IDH mutant glioma subtypes may not provide additional information on prognostication.

Prognostic and predictive impact of MGMT promoter methylation status in high risk grade II glioma

BACKGROUND

MGMT promoter methylation has been associated with favorable prognosis and survival outcomes in patients with glioblastoma and WHO grade III glioma. However, the effects of promoter methylation of MGMT in patients with WHO grade II 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 II glioma.

METHODS

The National Cancer Database (NCDB) was queried (2004-2016) for patients with newly diagnosed grade II glioma. Demographics and clinical characteristics of these patients were examined. Statistics included Kaplan-Meier overall survival (OS) analysis alongside Cox proportional hazards modeling.

RESULTS

A total of 11,223 patients met the selection criteria; 1252 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.

CONCLUSIONS

This 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.

IMPORTANCE OF STUDY

The present study is the largest to date examining the prognostic and predictive significance of MGMT methylation (mMGMT) in patients with WHO grade II glioma. The results suggest that mMGMT is prognostic with increasing overall survival rates for patients with mMGMT compared to uMGMT patients. The results also suggest that mMGMT is predictive as shown by improved overall survival in patients receiving gross total resection, adjuvant chemoradiation or adjuvant radiation therapy, but no difference was observed in patients receiving adjuvant chemotherapy or no adjuvant treatment.