Low-grade Meningiomas Research Articles

CD13 is a useful tool in the differential diagnosis of meningiomas with potential biological and prognostic implications

Meningiomas are common tumors of the central nervous system. Although their histological diagnosis is usually straightforward, their differential diagnosis versus other tumors may be challenging at times. The objective of this study is to assess the diagnostic value of CD13 immunoexpression in the differential diagnosis between meningiomas and their morphological mimics. Immunohistochemical analysis for CD13, epithelial membrane antigen, SOX10, and STAT6 was carried out in a large cohort of primary meningeal tumors comprising 225 meningiomas, 15 schwannomas, and 20 solitary fibrous tumor/hemangiopericytomas. Within the meningioma group, the expression of CD13 and epithelial membrane antigen was distinguished in three categories using a semiquantitative score. Most of meningiomas expressed CD13 (94%) and epithelial membrane antigen (96%) while none of the schwannomas nor of the solitary fibrous tumor/hemangiopericytomas was positive for either the two markers. Diffuse positivity for CD13 and epithelial membrane antigen was more common in low-grade meningiomas than in anaplastic ones, which were also more often negative for such markers, especially for CD13 (32%). CD13 is a helpful immunohistochemical marker for the differential diagnosis of meningiomas and their mimics, achieving in combination with epithelial membrane antigen maximal sensitivity (100%) and showing statistically relevant difference of expression in comparison with both schwannomas (p < 0.0001) and solitary fibrous tumor/hemangiopericytomas (p < 0.0001). Furthermore, loss of CD13 expression could be related to outcome as it is associated with worrisome histological findings, mainly in the setting of anaplastic meningiomas.

Comparison of different radiomic models based on enhanced T1-weighted images to predict the meningioma grade

To compare different models in predicting meningioma grade based on enhanced T1-weighted images. One hundred and eighty-eight patients with meningioma were analysed retrospectively. There were 94 high-grade meningiomas which formed the high-grade group comprising 68 World Health Organization (WHO) grade II meningiomas and 26 WHO grade III meningiomas. Ninety-four low-grade meningiomas were selected randomly to form the low-grade group. Least absolute shrinkage and selection operator (LASSO) regression was used to reduce the dimensions of the texture parameters. Support vector machine (SVM), decision tree (DT), conditional inference trees (CIT), random forest (RF), k-nearest neighbours (KNN), back-propagation neural network (BPNet), and Bayes were used to construct models. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) was applied and compared among different models. Every model performed well and had a high area under the ROC curve (AUC; all >0.80). In the seven models, the highest accuracy was obtained with SVM and KNN (0.79), the highest sensitivity was obtained with DT and Bayes (0.85), and the highest specificity was obtained with SVM and CIT (0.83). SVM and RF had the highest AUC (0.884). KNN had the largest net benefit when the threshold probability was <0.50, whereas SVM had the largest net benefit when the threshold probability was >0.50. Different radiomic models based on enhanced T1-weighted images can be used to predict meningioma grade. The model of SVM and KNN performed better than other models with a larger net benefit.

Advanced MRI prediction of meningioma histopathological classification: a literature review and case presentations

Meningioma is not uncommon case; however, the differentiation of high-grade from low-grade meningioma is important. The rate of recurrence of grade I meningioma is 7-20%, but in grade II meningioma is 30-40% and in grade III 50-80%. Non-invasive MRI techniques that can differentiate high-grade from low-grade meningiomas before surgery are useful for surgical planning and subsequent treatment. We present a review article and some case studies of low-grade (WHO grade I) and high-grade (WHO grade II and grade III) meningioma with conventional MRI and continue with advanced MRI; we performed diffusion weighted imaging (DWI) with apparent diffusion coefficient (ADC) value, dynamic susceptibility contrast (DSC), dynamic contrast-enhanced (DCE) magnetic resonance (MR) perfusion and 3D ASL. From these three cases show that advanced magnetic resonance imaging with ADC value, DSC, DCE, and 3D arterial spin-labelling (ASL) is an essential sequence to differentiate high-grade from low-grade meningioma.

Preoperative Prediction of Intracranial Meningioma Grade Using Conventional CT and MRI.

ObjectivePreoperative diagnosis of tumor grade can assist in treatment-related decision-making for patients with intracranial meningioma. This study aimed to distinguish between high-grade and low-grade meningiomas using conventional CT and MRI.MethodologyWe retrospectively analyzed 173 consecutive patients with intracranial meningioma (149 low-grade and 24 high-grade tumors) who were treated surgically at the National Hospital Organization Kyushu Medical Center from 2008 to 2020. Clinical and radiological features, including tumor doubling time (Td) and relative growth rate (RGR), were compared between low-grade and high-grade meningiomas.ResultsMultivariate logistic regression analysis showed that symptomatic tumor (p=0.001), non-skull base location (p=0.006), irregular tumor shape (p=0.043), tumor heterogeneity (p=0.025), and peritumoral brain edema (p=0.003) were independent predictors of high-grade meningioma. In 53 patients who underwent surgery because of tumor progression, progression to symptoms (p=0.027), intratumoral heterogeneity (p<0.001), peritumoral brain edema (p=0.001), larger tumor volume (p=0.005), shorter Td (p<0.001), and higher RGR (P<0.001) were significantly associated with high-grade meningioma. Receiver operating characteristics (ROC) curve analysis showed that the optimal Td and annual RGR cut-off values to distinguish high-grade from low-grade meningioma were 460.5 days and 73.2%, respectively (100% sensitivity and 78.6% specificity).ConclusionBased on our findings, conventional CT and MRI are useful methods to predict meningioma grades before surgery. High-grade lesions are associated with non-skull base location, irregular tumor shape, intratumoral heterogeneity, and peritumoral brain edema. High-grade meningioma should be suspected in tumors that exhibit Td <460.5 days or annual RGR >73.2% or those that develop intratumoral heterogeneity or surrounding brain edema on surveillance imaging.

Apparent Diffusion Coefficient Values and Dynamic Contrast-Enhanced Magnetic Resonance Perfusion are Potential Predictors for Grading Meningiomas.

Purpose: Distinguishing between high-grade and low-grade meningiomas might be difficult but has high clinical value in deciding precise treatment and prognostic factors. Magnetic resonance imaging (MRI) using apparent diffusion coefficient (ADC) values and dynamic contrast enhancement (DCE) may have a significant role in capturing such complexities.Methods: Data from our hospital database on meningioma patients from January 2020 to December 2021 were obtained. The MRI results of all patients were evaluated for mean ADC value and DCE parameters, including time-signal intensity curves (TIC), maximum signal intensity (SImax), time to maximum signal intensity (Tmax), maximum contrast enhancement ratio (MCER), and slope.Results: In this retrospective analysis, 33 individuals were included. Twenty-eight (84.8%) patients were pathologically diagnosed with low-grade meningioma and five (15.2%) patients with high-grade meningioma. There is a crossover between high- and low-grade meningiomas in conventional MRI. Tumor size, location, shape, necrotic/cystic changes, peritumoral edema, and enhancement patterns did not differ substantially between groups (p = 0.39, 0.23, 0.28, 0.57, 0.56, and 0.33, respectively). The mean ADC and Tmax values of high-grade meningiomas were substantially lower than those of low-grade meningiomas (p = 0.002 and 0.02, respectively). An optimal cut-off of 0.87 × 10-3 mm2s-1 for the mean ADC value (area under the curve [AUC] = 0.94, sensitivity = 80%, specificity = 92.8%) and 42 s for Tmax (AUC = 0.84, sensitivity = 80%, specificity = 89.3%) was suggested. High-grade meningiomas had significantly higher TIC, SImax, MCER, and slope than low-grade meningiomas (p = 0.004, < 0.001, 0.01, and 0.001, respectively). Type IV TIC had a sensitivity of 80% and specificity of 89.3% in distinguishing high-grade meningiomas from low-grade meningiomas. Optimal cut-offs of 940.2 for SImax (AUC = 0.98, sensitivity = 80%, specificity = 96.4%), 245% for MCER (AUC = 0.94, sensitivity = 80%, specificity = 85.7%), and 5% per second for slope (AUC = 0.97, sensitivity = 80%, specificity = 96.4%) were estimated.Conclusion: The ADC value and DCE-MRI parameters (TIC, SImax, Tmax, MCER, and slope) are potential predictors for separating high-grade from low-grade meningiomas.

Is There Any Relationship Between Systemic Inflammatory Markers And Meningioma Grade?

Systemic inflammatory markers have been found to be of value in predicting the prognosis in renal cell and gastrointestinal cancers. Recent studies in gliomas correlating with Systemic inflammatory markers (SIMS) have shown promise in predicting survival. Meningiomas are the commonest intracranial primary brain tumors in adults. There is a need to distinguish low and high grade meningiomas preoperatively as they have crucial implications for the radicality of surgical excision and prognostication. A retrospective search was conducted, and the newly diagnosed meningioma cases were studied. The preoperative hematological parameters of the patients and pathological tumor grades were noted. The grade 1 meningioma was defined as low-grade meningioma while grade 2 and 3 meningiomas were defined as high-grade meningioma. Statistical analysis was performed to determine the association between the hematological parameters and tumor grade. Seven hundred and eighty meningioma cases with a mean age of 43.5 years were included. The ANC, NLR, and dNLR were found to be significantly elevated in high-grade meningioma (P = 0.03). Other inflammatory parameters including TLC failed to show a statistically significant difference when compared between the grades of meningioma. ROC analysis further showed limited value of these markers in predicting meningioma grade. However, an elevated ANC (OR = 1.08, 95% CI = 1.02-1.14), male sex (OR = 1.71, 95% CI = 1.13-2.58), and tumor origin from sites other than the skull base or thecal sac (OR = 3.33, 95% CI = 1.93-6.12) were predictive of high-grade meningioma in regression analysis. Preoperative hematological inflammatory parameters have limited value in predicting the grade of tumor in meningiomas. An elevated preoperative ANC, NLR, and dNLR were found to be more frequently associated with high grade of meningiomas.

Proteomic analysis of genetically stratified low-grade meningioma

Abstract Aims Meningioma is the most common primary intracranial tumour. Although ~80% are benign WHO grade I and show high rates of recurrence. Surgery is the main therapeutic approach, yet location can hamper complete resection and chemotherapies are ineffective. Moreover, accurate biomarkers for clinical management are lacking. Approximately 60% sporadic meningiomas harbour mutations in the NF2gene, while mutations in genes including TRAF7, KLF4, AKT1, SMO and PIK3CAhave been identified majority in the NF2-positive low grade-tumours. Moreover, mutations in TRAF7 mostly co-occur with a KLF4K409Q or with AKT1E17K mutation. The mutations and their molecular manifestations consequently affect the signalling pathways at the protein level. The molecular mechanisms behind meningioma tumourigenesis are still obscure and the identification of specific biomarker is necessary to enable their implementation in routine diagnostics and therapeutics. Therefore, we aim to identify novel biomarkers and therapeutic targets of genetically stratified low-grade meningioma by characterising the proteomic landscape. Method Frozen tumour samples have already been analysed for NF2-/- by next generation sequencing and genotyped for common mutational hotspots in non-NF2 meningioma such as TRAF7, KLF4 and AKT1 and grouped in to three different mutational groups: AKT1E17K/TRAF7, KLF4K409Q/TRAF7and NF2-/- and all these mutations will be compared to normal healthy meninges. For global proteomics, proteins were separated by SDS-PAGE followed by in-gel tryptic digestion and sample preparation for LC-MS/MS analysis. Raw mass spectrometry data files were processed by MaxQuant (1.6.2.10) and Perseus software (1.6.1.3). Quantitative phospho-proteomics was performed using TMT 10plex labelling approach followed by motif analysis using motif-X algorithm. GO enrichment analyses were performed using (DAVID) v6.8 against all human proteins. Potential candidates from expression data analysis will be validated via Western Blot and immunohistochemistry. Results We have quantified 4162 proteins across all mutational meningioma subgroups and normal meninges (n=31). Hierarchical clustering analysis showed distinct proteomic profiles of mutational subgroups revealing clusters of differentially expressed proteins. Comparative analysis showed 10 proteins were commonly significantly upregulated (log2 fold-change≥1; p&amp;lt;0.05) among all mutational subtypes vs. normal meninges, indicating proteomic landscapes of mutational subtypes to be highly variable. In contrast, 257 proteins were commonly significantly downregulated (log2 fold-change≤-1; p&amp;lt;0.05) and enriched with molecular functions including aldehyde dehydrogenase and oxido-reductase. Mutational subtype-specific analysis identified 162 proteins significantly upregulated in AKT1E17K/TRAF7 vs. remaining sample groups to be enriched in the oxidative phosphorylation pathway. However, only 14 and 7 proteins were commonly significantly upregulated in KLF4K409Q/TRAF7 and NF2-/- mutant meningioma subtypes respectively. Several of these up-regulated proteins including ANNEXIN-3, CRABP2, CLIC3 and Endoglin were already verified via WB. Lastly, analyses of 6600 phospho-sites (n=8) predicted regulatory kinases including CHEK1, CHEK2 and LCK. Conclusion Global proteomic and phos-phoproteomics analysis has led to the identification of proteins differentially expressed in mutant subtypes. Results of this study to date suggest that a proteomic approach is an effective tool to identify distinct patterns in genetically distinct meningioma subgroups. Further validation and functional verification (with inhibitory or knockdown approaches) of potential candidates will allow us to identify potential drug targets/biomarkers for meningiomas.

Low-grade intracranial meningioma with bilateral pulmonary metastases incidentally detected postpartum: a case report and review of the\xa0literature

IntroductionMeningiomas are the most commonly encountered intracranial tumors, usually showing indolent behavior. Extra-axial spreading and distant metastases are seldom detected in these tumors, and lung metastasis from a low-grade meningioma is a rare event.Case presentationThis case report aimed to present the clinical, imaging, and pathological features of a 37-year-old Caucasian pregnant woman with bilateral lung metastases incidentally detected during preoperative workup ahead of surgery for a primary intracranial meningioma. The possible metastatic routes and risk factors of dissemination to the pulmonary circulation were discussed as well.ConclusionMetastasis must be considered in patients with intracranial meningiomas accompanied by venous sinus invasion and extension through the calvarium. Thorough paraclinical investigations are suggested in such cases.

Discovering the Molecular Landscape of Meningioma: The Struggle to Find New Therapeutic Targets.

Meningiomas are the most common primary CNS tumors. They are usually benign but can present aggressive behavior in about 20% of cases. The genetic landscape of meningioma is characterized by the presence (in about 60% of cases) or absence of NF2 mutation. Low-grade meningiomas can also present other genetic alterations, particularly affecting SMO, TRAF7, KLF4 AKT1 and PI3KCA. In higher grade meningiomas, mutations of TERT promoter and deletion of CDKN2A/B seem to have a prognostic value. Furthermore, other genetic alterations have been identified, such as BAP1, DMD and PBRM1. Different subgroups of DNA methylation appear to be correlated with prognosis. In this review, we explored the genetic landscape of meningiomas and the possible therapeutic implications.

EHealth cognitive rehabilitation for brain tumor patients: results of a randomized controlled trial

BackgroundEvidence-based cognitive rehabilitation programs for brain tumor patients are not widely available, despite the high need. We aimed to evaluate the effects of a tablet-based cognitive rehabilitation program on cognitive performance, cognitive complaints, fatigue, and psychological distress in primary brain tumor patients following neurosurgery. Also, attrition, adherence and patient satisfaction with the program were evaluated.MethodsAdults with presumed low-grade glioma and meningioma were recruited before surgery. Three months thereafter, participants were allocated to the intervention group or waiting-list control group using minimization. The 10-week eHealth app ReMind, based on the effective face-to-face intervention, consisted of psychoeducation, strategy-training and attention retraining. Performance-based cognitive outcomes and patient-reported outcomes were assessed before surgery and 3, 6 and 12 months thereafter. Mean scores, percentages of cognitively impaired individuals and reliable change indices (RCIs) were compared between groups.ResultsSixty-two out of 183 eligible patients were randomized. Of the people who declined, 56% reported that participation would to be too burdensome. All participants found a tablet-app suitable for delivery of cognitive rehabilitation and 90% rated the program as “good” or “excellent”. Performance-based cognitive outcomes and patient-reported outcomes did not significantly differ in group means over time nor RCIs between the intervention (final n = 20) and control group (final n = 25).ConclusionsRecruitment at this early stage was difficult, resulting in limited statistical power. No significant effects were demonstrated, while adherence and satisfaction with the eHealth program were good. In clinical practice, ReMind may be helpful, if timing would be adapted to patients’ needs.

OPTC-1. Proteomic analysis of genetically stratified low-grade meningioma

IntroductionMeningioma are the most common primary intracranial tumour. According to WHO, ~80% tumours are benign grade I. Although, some grade I tumour clinically show aggressive behaviour. Radio-surgery are the main therapeutic approaches, chemotherapies are ineffective. Accurate biomarkers for clinical management are lacking. The mutational profile of low-grade meningioma is well-defined, with non-NF2 mutated tumours harbouring recurrent mutations in genes including TRAF7, KLF4, AKT1 and SMO. Here, we aim to identify novel biomarkers and therapeutic targets of genetically stratified low-grade meningioma by characterising the proteomic landscape.Materials and methodsMeningioma specimens were stratified according to mutational background: AKT1E17K/TRAF7, KLF4K409Q/TRAF7 and NF2-/-. Proteins were separated by SDS-PAGE followed by in-gel tryptic digestion and sample preparation for LC-MS/MS analysis. Raw mass spectrometry data files were processed by MaxQuant and Perseus software. Quantitative phospho-proteomics was performed using TMT-10plex labelling approach followed by motif analysis using motif-X algorithm. GO enrichment analyses were performed using DAVID against all human proteins.Results and ConclusionsWe have quantified 4162 proteins across all mutational meningioma subgroups and normal meninges (n=31). Hierarchical clustering analysis showed distinct proteomic profiles of mutational subgroups revealing clusters of differentially expressed proteins (DEPs). Comparative analysis showed 10 proteins were commonly significantly upregulated (log2 fold-change≥1; p<0.05) among all mutational subtypes vs. normal meninges, indicating proteomic landscapes of mutational subtypes to be highly variable. In contrast, 257 proteins were commonly significantly downregulated (log2 fold-change≤-1; p<0.05) and enriched with molecular functions including aldehyde dehydrogenase and oxido-reductase. Mutational subtype-specific analysis identified 162 proteins significantly upregulated in AKT1E17K/TRAF7 vs. remaining sample groups to be enriched in the oxidative phosphorylation pathway. Lastly, analyses of 6600 phospho-sites (n=8) predicted regulatory kinases including EGFR and PKCα. Several of these up-regulated proteins and kinases already verified via WB. Further validation and functional verification will allow us to identify potential drug targets/biomarkers for meningioma.

The substantial loss of H3K27me3 can stratify risk in grade 2, but not in grade 3 meningioma

Trimethylation of lysine 27 of histone H3 (H3K27me3) has recently emerged as a crucial epigenetic marker in meningioma. The loss of H3K27me3 expression might predict the early recurrence of grade 1 and 2 meningiomas. However, this is controversial in terms of grade 3 meningioma and the effects of H3K27me3 on the overall survival (OS) of patients with low low-grade meningioma have not been studied. Therefore, we immunohistochemically assessed the prognostic implications of H3K27me3 expression in grade 2 and 3 meningiomas. Whole-slide H3K27me3 immunostaining was evaluated for strict quality control and to confirm a significant correlation (P<.0001) with tissue microarray results. The effects of tissue age on H3K27me3 immunostaining were also evaluated, to select an appropriate cohort for survival analysis. Log-rank tests of 115 grade 2 meningiomas and 26 grade 3 meningiomas showed that the loss of H3K27me3 expression was a prognostic factor for early recurrence (P<.0001) and death (P=.00012) in grade 2, but not in grade 3 meningioma. Multivariate analysis revealed that age, recurrent tumor, and loss of H3K27me3 expression (hazard ratio, 1.264-7.510; P=.0133) were significant for recurrentrecurrence-free survival (RFS), and that recurrent tumor and loss of H3K27me3 expression (hazard ratio, 1.717-120.621; P=.0140) were significant for OS. We concluded that H3K27me3 expression is a significant prognostic factor for the RFS and OS of patients with grade 2 meningioma; it should be considered as an ancillary test for risk stratification of this meningioma.

Hydroxyurea for the Treatment of Recurrence and Unresectable Meningiomas: A Systematic Review

BACKGROUND: Meningioma is mostly benign tumor (World Health Organization Grade 1) and surgery remains the best option in treating symptomatic or enlarging meningiomas where total removal of the tumor is the goal of surgery. Radiation therapy has shown to be effective to cease the growth of the tumor, but not in tumor regression. Adjuvant therapy may treat patients with recurrence or unresectable meningiomas yet the uses of hormone therapy, immunotherapy, or chemotherapy had many results and were not consistently effective. Hydroxyurea has promising results in patients with meningiomas.&#x0D; AIM: This study analyzed the efficacy and safety of hydroxyurea for the treatments of recurrence or unresectable meningiomas.&#x0D; MATERIALS AND METHODS: The study adapted PRISMA guidelines by searching electronic databases, PUBMED, Cochrane, and JNS in August 2020 and was full-text observational study or randomized control trial presented as PICO and assessed using the risk-of-bias assessment tool.&#x0D; RESULTS: A total of six articles (157 patients with meningioma) were reviewed from the total of 425. Hydroxyurea was administered orally for 28 days continuously and repeated every 28 days or after recovery with various dosages in six studies.&#x0D; DISCUSSION: Administration of hydroxyurea showed a varied stable disease rate ranging from 30 to 69% with a median progression-free survival med varying between 2 and 27.75 months. The studies performed oral hydroxyurea administration at a dose of 20–30 mg/kg body weight/day or 1000 mg/m2/day. However, the adverse events (AEs) that appear also, based on literature, are not much different from other chemotherapy administrations.&#x0D; CONCLUSION: Patients with unresected and recurrent meningiomas have limited treatment options due to difficulty for surgical management. However, this study offers another perspective addressing the efficacy and safety results with the use of hydroxyurea. Overall, hydroxyurea showed good outcomes, particularly in low-grade meningioma, with relatively low AEs. Further combination treatment may be used as a multimodal therapy.

Analysis of Prognostic Factors of World Health Organization Grade Ⅲ Meningiomas.

ObjectiveWHO grade III meningiomas are highly aggressive and lethal. However, there is a paucity of clinical information because of a low incidence rate, and little is known for prognostic factors. The aim of this work is to analyze clinical characteristics and prognosis in patients diagnosed as WHO grade III meningiomas.Methods36 patients with WHO grade III meningiomas were enrolled in this study. Data on gender, age, clinical presentation, preoperative Karnofsky Performance Status (KPS), histopathologic features, tumor size, location, radiologic findings, postoperative radiotherapy (RT), surgical treatment, and prognosis were retrospectively analyzed. Progression-free survival (PFS) and overall survival (OS) were evaluated using the Kaplan-Meier method. Univariate and multivariate analysis were conducted by the Cox regression model.ResultsMedian PFS is 20 months and median OS is 36 months in 36 patients with WHO grade III meningiomas. Patients with secondary tumors which transformed from low grade meningomas had lower PFS (p=0.0014) compared with primary group. Multivariate analysis revealed that tumors location (PFS, p=0.016; OS, p=0.013), Ki-67 index (PFS, p=0.004; OS, p<0.001) and postoperative radiotherapy (PFS, p=0.006; OS, p<0.001) were associated with prognosis.ConclusionWHO grade III meningiomas which progressed from low grade meningiomas were more prone to have recurrences or progression. Tumors location and Ki-67 index can be employed to predict patient outcomes. Adjuvant radiotherapy after surgery can significantly improve patient prognosis.

PATH-39. ASSOCIATIONS OF GENOMIC SUBGROUP WITH RECURRENCE IN LOW-GRADE MENINGIOMAS

Abstract Up to 80% of meningiomas are classified as clinically low-grade, however, a subset of these ‘benign’ cases will ultimately recur and require additional treatment. The role of molecular subgroup in meningioma recurrence has not been thoroughly investigated, despite correlations of this variable with other clinical features. Indeed, epigenetic and transcriptional evidence supports involvement of distinct oncogenic processes within each subgroup, and as shown in other brain tumors, this may result in divergent clinical courses. In the present study, we classified 429 meningiomas into six established molecular subgroups based on genomic driver, and investigated associations of each subgroup with tumor recurrence. At two years of follow-up, we observed differences in progression free survival curves among relatively aggressive (NF2-loss, PI3K-activated, Hedgehog-activated) and quiescent (KLF4-mutant, SMARCB1-mutant, POLR2A-mutant) subgroups (log rank p = 4.3 x 10-2), with the former group recurring at a rate 14x higher. We found PI3K-activated meningiomas recurred significantly earlier than other subgroups (average time to recurrence of 19.2 months; p = 2.2 x 10-2), though we observed an intermediate long-term outcome relative to the Hedgehog and NF2 lesions. Overall, Hedgehog tumors recurred significantly more frequently than other low-grade meningiomas (adj. p = 3.1 x 10-2), and this subgroup was found to be an independent predictor of progression free survival using cox proportional hazards modelling (HR = 3.1; p = 2.4 x 10-2). By contrast, the aggressiveness of NF2 meningiomas was found to depend upon gender, WHO grade, and elevated Ki-67 index, and this subgroup was not an independent prognostic predictor. Our results suggest molecular subgroup is predictive of recurrence in low-grade meningiomas, and thus is an important consideration in post-operative management decisions. Routine genotyping to detect Hedgehog and PI3K mutant lesions may identify patients that would benefit from closer follow-up and consideration of adjuvant therapies.

NCOG-48. TRANSFORMATION FROM LOW GRADE MENINGIOMA AND PROGNOSTIC FACTORS FOR SHORTER TIME TO RECURRENCE OF WHO GRADE III MENINGIOMA DIAGNOSIS

Abstract BACKGROUND WHO grade III meningiomas are rare, and clinical outcomes data are limited. To address this problem, we analyzed a single institution retrospective cohort of patients with WHO grade III meningiomas. METHODS Patients who underwent resection of WHO grade III meningiomas between 1983 and 2019 with available follow-up data were included. Treatment, local recurrence, and overall survival rates were obtained. Kaplan-Meier analysis, log rank tests, and Student’s t tests were used for statistical analyses. RESULTS Ninety consecutive patients met inclusion criteria. Median follow-up was 3.2 years (IQR: 1.2-7.9), median age was 58 years (IQR: 46-67). Twenty-three patients (26%) underwent resection alone, and 58 (64%) underwent resection with adjuvant radiotherapy. Fifty-one patients developed local recurrences, with median time to recurrence of 1.0 year (IQR: 0.6-2.9) and higher MIB-1 labeling index for tumors that recurred within 1 year (25% versus 17%, p=0.04). There was a median of 2 recurrences per patient (IQR: 1-3), with a median of 2 resections and 2 radiotherapy courses for initial and salvage treatments. Thirty-nine patients with recurrences died, with median overall survival of 3.7 years (IQR: 1.8-8.8). Thirty-seven patients (41%) had prior low grade meningioma diagnoses with subsequent transformation to grade III, including 6 (7%) with grade I, 16 (18%) with grade II, and 15 (17%) with unknown grade. There was a median of 1 low grade recurrence per patient (IQR: 0-3) before transformation to grade III, which occurred a median of 4.3 years (IQR: 2.4-9.9) after initial diagnosis. Prior low grade diagnosis was associated with shorter time to recurrence of WHO grade III meningioma (p=0.01), and worse overall survival (p=0.03). CONCLUSION Patients with WHO grade III meningioma have poor outcomes despite aggressive multimodal therapy, and there is an urgent, unmet need for new high grade meningioma treatments.

Determination of SOX9 Gene Expression In Brain Tumours Among East Coast Malaysia Population

Introduction: SOX9, a members of SOX family, plays a significant roles in developmental processes during embryogenesis, including brain tissue. Few studies have shown that SOX9 has been involved in tumourigenesis of several types of cancer including brain tumour. However, such studies are still lacking in the Malaysian population. The aim of this study was to determine SOX9 expression level in several types of brain tumours in East Coast Malaysia. Materials and Methods: Five formalin-fixed pariffin-embedded brain tumour samples of Malay descendants were sectioned by using microtome. RNA extraction was performed with slight modification by adding Trizol during tissue lysis. The RNA was converted to cDNA using reverse transcription technique before SOX9 expression was detected using RT q-PCR assay in brain tumours normalized to non-neoplastic brain tissues. Results: Overall results displayed that SOX9 gene in all samples were up-regulated. SOX9 overexpression was found in both high and low grade glioma (anaplastic and pilocytic astrocytoma respectively). This is consistence with both low grade (benign) and atypical meningioma. Secondary brain tumour also showed up-regulation when compared to normal brain tissue. Conclusion: Up-regulation in SOX9 expression in selected brain tumours in Malay patients revealed its significant roles in brain tumourigenesis. Functional studies should be carried out to observe the SOX9 functions and mechanism whether they should reflect their diverse roles in Malaysia population.

Integration and Comparison of Transcriptomic and Proteomic Data for Meningioma.

Simple SummaryMeningioma are the most common primary intracranial tumour, yet gaps in their molecular characterisation persist and effective treatments for aggressive, recurring, high-grade (WHO grade III) meningioma are lacking. The aim of our study was to outline differences in the molecular landscape between high-grade and low-grade (WHO grade I) meningioma by combining transcriptomic and proteomic data from two former independent studies. We described both concordant and discordant differential expression between grades and validated expression at either the transcript or protein level for genes including MAOB, CST3, LAMP2, PACS1 and HTRA1. Concordantly upregulated transcripts/proteins of high-grade tumours were enriched in biological processes such as oxidative phosphorylation and RNA metabolism. Using an integrated omics approach we have provided a previously unreported, valuable insight into the altered transcript/protein expression profiles between high and low-grade meningioma. In addition, our findings identified molecules that may hold potential as biomarkers or therapeutic targets of high-grade meningioma.Meningioma are the most frequent primary intracranial tumour. Management of aggressive meningioma is complex, and development of effective biomarkers or pharmacological interventions is hampered by an incomplete knowledge of molecular landscape. Here, we present an integrated analysis of two complementary omics studies to investigate alterations in the “transcriptome–proteome” profile of high-grade (III) compared to low-grade (I) meningiomas. We identified 3598 common transcripts/proteins and revealed concordant up- and downregulation in grade III vs. grade I meningiomas. Concordantly upregulated genes included FABP7, a fatty acid binding protein and the monoamine oxidase MAOB, the latter of which we validated at the protein level and established an association with Food and Drug Administration (FDA)-approved drugs. Notably, we derived a plasma signature of 21 discordantly expressed genes showing positive changes in protein but negative in transcript levels of high-grade meningiomas, including the validated genes CST3, LAMP2, PACS1 and HTRA1, suggesting the acquisition of these proteins by tumour from plasma. Aggressive meningiomas were enriched in processes such as oxidative phosphorylation and RNA metabolism, whilst concordantly downregulated genes were related to reduced cellular adhesion. Overall, our study provides the first transcriptome–proteome characterisation of meningioma, identifying several novel and previously described transcripts/proteins with potential grade III biomarker and therapeutic significance.

Prediction value of preoperative findings on meningioma grading using artificial neural network

ObjectivesMeningioma is the most common brain tumor in adults. Grade 1 meningiomas have excellent prognoses, but grades 2 and 3 usually have worse outcomes, higher recurrence rates, and higher mortality rates. Preoperative determination of tumor grade may be helpful in deciding the type of surgery and the rate of resection. Blood markers have been used to predict the rate of malignancy and prognosis of tumors in different regions, including the brain. The current study investigated the use of blood markers on predicting meningioma grade. Patients and methodsPatients with newly diagnosed meningiomas were retrospectively reviewed. Data on the patients’ demographics, tumor locations, blood markers, and tumor pathology grades was extracted. The relationship between preoperative findings and tumor grade was statistically analyzed, and using the same findings and an artificial neural network, the accuracy of tumor grade prediction was evaluated. ResultsThis study included 95 patients, 69 cases (72.4 %) of grade 1, 23 cases of grade 2 (24.4 %) and 3 cases of grade 3 (3.2 %) meningiomas. Monocyte and neutrophil counts as well as lymphocyte-to-monocyte ratio (LMR) were significantly different between low grade and high grade meningiomas, with higher monocyte and neutrophil counts and higher LMR associated with high grade meningiomas (p < 0.05). Evaluation of the data with an artificial neural network using RBF with 5 variables (age, monocyte count, LMR, platelet-to-lymphocyte ratio (PLR), and neutrophil count) indicated that tumor grade can be determined with 83 % accuracy using an artificial neural network. ConclusionA preoperative high monocyte count and high LMR are associated with high grade meningioma. An artificial neural network using preoperative data can acceptably be used to characterize meningioma tumor grades.

Value of MRI Radiomics Based on Enhanced T1WI Images in Prediction of Meningiomas Grade

Different grades of meningiomas require different treatment strategies and have a different prognosis; thus, the noninvasive classification of meningiomas before surgery is of great importance. The purpose of this study was to explore the application value of magnetic resonance imaging (MRI) radiomics based on enhanced-T1-weighted (T1WI) images in the prediction of meningiomas grade. A total of 98 patients with meningiomas who were confirmed by surgical pathology and underwent preoperative routine MRI between January 2017 and December 2019 were analyzed. There were 82 cases of low-grade meningiomas (WHO grade I) and 16 cases of high-grade meningiomas (7 cases of WHO grade II and 9 cases of WHO grade III). These patients were randomly divided into a training group and test group according to 7:3 ratio. The lesions were manually delineated using ITK-SNAP software, and radiomics analysis were performed using the Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. Next, a prediction model was constructed using the Logistic Regression method and receiver operator characteristic was used to evaluate the prediction performance of the model. A radiomics prediction model was constructed based on the selected nine characteristic parameters, which performed well in predicting the meningiomas grade. The accuracy rates in the training group and the test group were respectively 94.3% and 92.9%, the sensitivities were respectively 94.8%, and 91.7%, the specificities were respectively 91.7% and 100%, and the area under the curve values were respectively 0.958 and 0.948. The MRI radiomics method based on enhanced-T1WI images has a good predictive effect on the classification of meningiomas and can provide a basis for planning clinical treatment protocols.