DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis

Ki-67 index as a predictive marker of meningioma recurrence following surgical resection

BACKGROUND

Meningiomas are the most common primary intracranial tumors in adults. Although benign in a majority of cases, they have a variable clinical course and may recur even after a thorough surgical resection. Ki-67, a nuclear protein involved in cell cycle regulation, has been widely studied as a marker of cellular proliferation in various cancers. However, the prognostic significance of Ki-67 in meningiomas remains controversial. Here, we investigate the Ki-67 index, as a predictive marker of meningioma recurrence following surgical resection and compare it to established prognostic markers such as WHO grade and degree of resection.

METHODS

The medical records of 451 patients with previously untreated cranial meningiomas who underwent resections from January 2011 to January 2021 at North Shore University Hospital (NSUH) were reviewed. Collected data included WHO grade, Ki-67 proliferative index, degree of resection - gross (GTR) vs subtotal (STR) - as judged by the surgeon, tumor location, and meningioma recurrence. This study was approved by the NSUH Institutional Review Board IRB 21-1107.

RESULTS

There were 290 patients with grade I, 154 with grade II, and 7 with grade III meningiomas. The average post-resection follow-up period was 4 years, and 82 tumors (18 %) recurred. Higher WHO grades were associated with higher rates of recurrence, with rates of 11.4 %, 27.9 %, and 71.4 % for grades 1, 2, and 3, respectively, and subtotal resection corresponded to a higher rate of recurrence than total resection (34.3 % and 13.4 %, respectively). Higher WHO grades also correlated with higher Ki-67 scores (2.59, 10.01, and 20.71) for grades 1, 2, and 3, respectively. A multivariate logistic regression model identified Ki-67 and degree of resection as independent predictive variables for meningioma recurrence, with Ki-67 specifically predicting recurrence in the WHO grade II subset when analyzed separately for WHO grades I and II.

CONCLUSION

Our 10-year retrospective study suggests that the Ki-67 index is an important predictive marker for recurrence of intracranial meningiomas following surgical resection, particularly among patients with WHO grade II tumors. Our findings add to a growing body of data that support inclusion of Ki-67 index in the WHO grading criteria for patients with meningiomas.

Spatial genomic, biochemical and cellular mechanisms underlying meningioma heterogeneity and evolution

Intratumor heterogeneity underlies cancer evolution and treatment resistance, but targetable mechanisms driving intratumor heterogeneity are poorly understood. Meningiomas are the most common primary intracranial tumors and are resistant to all medical therapies, and high-grade meningiomas have significant intratumor heterogeneity. Here we use spatial approaches to identify genomic, biochemical and cellular mechanisms linking intratumor heterogeneity to the molecular, temporal and spatial evolution of high-grade meningiomas. We show that divergent intratumor gene and protein expression programs distinguish high-grade meningiomas that are otherwise grouped together by current classification systems. Analyses of matched pairs of primary and recurrent meningiomas reveal spatial expansion of subclonal copy number variants associated with treatment resistance. Multiplexed sequential immunofluorescence and deconvolution of meningioma spatial transcriptomes using cell types from single-cell RNA sequencing show decreased immune infiltration, decreased MAPK signaling, increased PI3K-AKT signaling and increased cell proliferation, which are associated with meningioma recurrence. To translate these findings to preclinical models, we use CRISPR interference and lineage tracing approaches to identify combination therapies that target intratumor heterogeneity in meningioma cell co-cultures.

Clinical implications of DNA methylation-based integrated classification of histologically defined grade 2 meningiomas

The combination of DNA methylation analysis with histopathological and genetic features allows for a more accurate risk stratification and classification of meningiomas. Nevertheless, the implications of this classification for patients with grade 2 meningiomas, a particularly heterogeneous tumor entity, are only partially understood. We correlate the outcomes of histopathologically confirmed grade 2 meningioma with an integrated molecular-morphologic risk stratification and determine its clinical implications. Grade 2 meningioma patients treated at our institution were re-classified using an integrated risk stratification involving DNA methylation array-based data, copy number assessment and TERT promoter mutation analyses. Grade 2 meningioma cases according to the WHO 2021 criteria treated between 2007 and 2021 (n = 100) were retrospectively analyzed. The median clinical and radiographic follow-up periods were 59.8 and 54.4 months. A total of 38 recurrences and 17 deaths were observed. The local control rates of the entire cohort after 2-, 4-, and 6-years were 84.3%, 68.5%, and 50.8%, with a median local control time of 77.2 months. The distribution of the integrated risk groups were as follows: 31 low, 54 intermediate, and 15 high risk cases. In the multivariable Cox regression analysis, integrated risk groups were significantly associated with the risk of local recurrence (hazard ratio (HR) intermediate: 9.91, HR high-risk: 7.29, p < 0.01). Gross total resections decreased the risk of local tumor progression (HR gross total resection: 0.19, p < 0.01). The comparison of 1p status and integrated risk groups (low vs. intermediate/high) revealed nearly identical local control rates within their respective subgroups. In summary, only around 50% of WHO 2021 grade 2 meningiomas have an intermediate risk profile. Integrated molecular risk stratification is crucial to guide the management of patients with grade 2 tumors and should be routinely applied to avoid over- and undertreatment, especially concerning the use of adjuvant radiotherapy.

Prediction of Meningioma Grade Using Hematological Parameters

OBJECTIVE

Predicting the aggressiveness of meningiomas may influence the surgical strategy timing. Because of the paucity of robust markers, the systemic immune-inflammation (SII) index is a novel biomarker to be an independent predictor of poor prognosis in various cancers including gliomas. We aimed to investigate the value of SII as well as neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) indices in predicting prognosis.

METHODS

Records including demographic, clinical, and laboratory data of patients operated on due to intracranial meningioma in 2017-2023 were retrospectively reviewed.

RESULTS

A total of 234 patients were included in this study. All of SII index, NLR, and PLR values at presentation were significantly higher in grade ≥2 meningiomas. A positive correlation was observed between SII index and Ki67 index (r=0.313; P<0.001); between NLR and Ki67 index (r=0.330; P<0.001); and between PLR and Ki67 index (r=0.223; P<0.01). SII index (optimal cutoff level >618), NLR (optimal cutoff level >3.53), and PLR (optimal cutoff level >121.2) showed significant predictive values.

CONCLUSIONS

This is the first study to assess the prognostic value of the SII index in patients with intracranial meningiomas. Increased SII index, NLR and PLR were correlated with higher grade and higher Ki-67 index. They also harbor the potential to screen patients that may need more aggressive treatments or more frequent follow-up examinations.

Time to Recurrence of Intracranial Meningiomas from a Monoinstitutional Surgical Series

BACKGROUND

Meningiomas show variable tendency to recur. While risk factors of recurrence have been largely investigated in literature, a paucity of data is available on the time to recurrence. Our purpose was to identify main factors affecting the time to recurrence to assist preoperative treatment decision-making strategy and to define a tailored clinical and neuroradiological follow-up.

METHODS

Data of 35 patients with intracranial meningioma recurrences have been retrospectively reviewed. Demographic (patient age at initial diagnosis and sex), radiologic (meningioma location, pattern of regrowth and topography of recurrences at first reoperation), pathologic (WHO grade and Ki67-MIB1 at initial surgery and at first reoperation, progesterone receptor [PR] expression), and surgical (extent of resection at initial surgery according to Simpsons grading system, number of reoperations) factors were analyzed.

RESULTS

Time to recurrence ranged from 20 to 120 months. Extent of resection at initial surgery was Simpson grade I in 7 patients (20%), grade II in 10 (28.5%), grade III in 14 (40%), and grade IV in 4 (11.5%). Longer median time to recurrence was observed for skull base localization (P < 0.01), Simpson grades I and II versus grades III (P = 0.01) and IV (P = 0.02), values of Ki67-MIB1 ≤ 4% (P = 0.001), and PR > 60% (P = 0.03); conversely, sex, age, number of reoperations, unchanged/progression of Ki67, and/or World Health Organization grade between first surgery and reoperation did not correlate in statistically significant way with time to recurrence.

CONCLUSIONS

The extent of resection and the Ki67-MIB1 represent the most important factors predicting shorter recurrence time of intracranial meningiomas. Patients with incomplete (Simpson grades III and IV) resection and high Ki67-MIB1 values, especially at non-skull base localization and with low PR values, require a closer short-term clinical and radiologic follow-up in the first years after surgery.

The Evolving Classification of Meningiomas: Integration of Molecular Discoveries to Inform Patient Care

Meningioma classification and treatment have evolved over the past eight decades. Since Bailey, Cushing, and Eisenhart's description of meningiomas in the 1920s and 1930s, there have been continual advances in clinical stratification by histopathology, radiography and, most recently, molecular profiling, to improve prognostication and predict response to therapy. Precise and accurate classification is essential to optimizing management for patients with meningioma, which involves surveillance imaging, surgery, primary or adjuvant radiotherapy, and consideration for clinical trials. Currently, the World Health Organization (WHO) grade, extent of resection (EOR), and patient characteristics are used to guide management. While these have demonstrated reliability, a substantial number of seemingly benign lesions recur, suggesting opportunities for improvement of risk stratification. Furthermore, the role of adjuvant radiotherapy for grade 1 and 2 meningioma remains controversial. Over the last decade, numerous studies investigating the molecular drivers of clinical aggressiveness have been reported, with the identification of molecular markers that carry clinical implications as well as biomarkers of radiotherapy response. Here, we review the historical context of current practices, highlight recent molecular discoveries, and discuss the challenges of translating these findings into clinical practice.

Plasma extracellular vesicles in meningioma patients following radiotherapy as liquid biopsy- a prospective explorative biomarker study (ARO 2023-05/AG-NRO-07)

BACKGROUND

While surgical resection remains the primary treatment approach for symptomatic or growing meningiomas, radiotherapy represents an auspicious alternative in patients with meningiomas not safely amenable to surgery. Biopsies are often omitted in light of potential postoperative neurological deficits, resulting in a lack of histological grading and (molecular) risk stratification. In this prospective explorative biomarker study, extracellular vesicles in the bloodstream will be investigated in patients with macroscopic meningiomas to identify a biomarker for molecular risk stratification and disease monitoring.

METHODS

In total, 60 patients with meningiomas and an indication of radiotherapy (RT) and macroscopic tumor on the planning MRI will be enrolled. Blood samples will be obtained before the start, during, and after radiotherapy, as well as during clinical follow-up every 6 months. Extracellular vesicles will be isolated from the blood samples, quantified and correlated with the clinical treatment response or progression. Further, nanopore sequencing-based DNA methylation profiles of plasma EV-DNA will be generated for methylation-based meningioma classification.

DISCUSSION

This study will explore the dynamic of plasma EVs in meningioma patients under/after radiotherapy, with the objective of identifying potential biomarkers of (early) tumor progression. DNA methylation profiling of plasma EVs in meningioma patients may enable molecular risk stratification, facilitating a molecularly-guided target volume delineation and adjusted dose prescription during RT treatment planning.

Innovation in Non-Invasive Diagnosis and Disease Monitoring for Meningiomas

Meningiomas are tumors of the central nervous system that vary in their presentation, ranging from benign and slow-growing to highly aggressive. The standard method for diagnosing and classifying meningiomas involves invasive surgery and can fail to provide accurate prognostic information. Liquid biopsy methods, which exploit circulating tumor biomarkers such as DNA, extracellular vesicles, micro-RNA, proteins, and more, offer a non-invasive and dynamic approach for tumor classification, prognostication, and evaluating treatment response. Currently, a clinically approved liquid biopsy test for meningiomas does not exist. This review provides a discussion of current research and the challenges of implementing liquid biopsy techniques for advancing meningioma patient care.

Efficacy and toxicity of bimodal radiotherapy in WHO grade 2 meningiomas following subtotal resection with carbon ion boost: Prospective phase 2 MARCIE trial

BACKGROUND

Novel radiotherapeutic modalities using carbon ions provide an increased relative biological effectiveness (RBE) compared to photons, delivering a higher biological dose while reducing radiation exposure for adjacent organs. This prospective phase 2 trial investigated bimodal radiotherapy using photons with carbon-ion (C12)-boost in patients with WHO grade 2 meningiomas following subtotal resection (Simpson grade 4 or 5).

METHODS

A total of 33 patients were enrolled from July 2012 until July 2020. The study treatment comprised a C12-boost (18 Gy [RBE] in 6 fractions) applied to the macroscopic tumor in combination with photon radiotherapy (50 Gy in 25 fractions). The primary endpoint was the 3-year progression-free survival (PFS), and the secondary endpoints included overall survival, safety and treatment toxicities.

RESULTS

With a median follow-up of 42 months, the 3-year estimates of PFS, local PFS and overall survival were 80.3%, 86.7%, and 89.8%, respectively. Radiation-induced contrast enhancement (RICE) was encountered in 45%, particularly in patients with periventricularly located meningiomas. Patients exhibiting RICE were mostly either asymptomatic (40%) or presented immediate neurological and radiological improvement (47%) after the administration of corticosteroids or bevacizumab in case of radiation necrosis (3/33). Treatment-associated complications occurred in 1 patient with radiation necrosis who died due to postoperative complications after resection of radiation necrosis. The study was prematurely terminated after recruiting 33 of the planned 40 patients.

CONCLUSIONS

Our study demonstrates a bimodal approach utilizing photons with C12-boost may achieve a superior local PFS to conventional photon RT, but must be balanced against the potential risks of toxicities.

EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics

DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data. Given these diverse approaches to methylation profiling, to date, there is no single tool that allows (1) classification and interpretation of microarray, nanopore and parallel sequencing data, (2) direct control of nanopore sequencers, and (3) the integration of microarray-based methylation reference data. Furthermore, no software capable of entirely running in routine diagnostic laboratory environments lacking high-performance computing and network infrastructure exists. To overcome these shortcomings, we present EpiDiP/NanoDiP as an open-source DNA methylation and copy number profiling suite, which has been benchmarked against an established supervised machine learning approach using in-house routine diagnostics data obtained between 2019 and 2021. Running locally on portable, cost- and energy-saving system-on-chip as well as gpGPU-augmented edge computing devices, NanoDiP works in offline mode, ensuring data privacy. It does not require the rigid training data annotation of supervised approaches. Furthermore, NanoDiP is the core of our public, free-of-charge EpiDiP web service which enables comparative methylation data analysis against an extensive reference data collection. We envision this versatile platform as a useful resource not only for neuropathologists and surgical pathologists but also for the tumour epigenetics research community. In daily diagnostic routine, analysis of native, unfixed biopsies by NanoDiP delivers molecular tumour classification in an intraoperative time frame.

Increased expression of leucine-rich α-2 glycoprotein 1 as a predictive biomarker of favorable progression-free survival in meningioma

Most meningiomas, which are frequent central nervous system tumors, are classified as World Health Organization (WHO) grade 1 because of their slow-growing nature. However, the recurrence rate varies and is difficult to predict using conventional histopathological diagnoses. Leucine-rich α-2 glycoprotein 1 (LRG1) is involved in cell signal transduction, cell adhesion, and DNA repair and is a predictive biomarker in different malignant tumors; however, such a relationship has not been reported in meningiomas. We examined tissue microarrays of histological samples from 117 patients with grade 1 and 2 meningiomas and assessed their clinical and pathological features, including expression of LRG1 protein. LRG1-high meningiomas showed an increased number of vessels with CD3-positive cell infiltration (P = 0.0328) as well as higher CD105-positive vessels (P = 0.0084), as compared to LRG1-low cases. They also demonstrated better progression-free survival (hazard ratio [HR] 0.11, 95% confidence interval [CI] 0.016-0.841) compared to LRG1-low patients (P = 0.033). Moreover, multivariate analysis indicated that high LRG1 expression was an independent prognostic factor (HR, 0.13; 95% CI, 0.018-0.991; P = 0.049). LRG1 immunohistochemistry may be a convenient tool for estimating the prognosis of meningiomas in routine practice. Further studies are required to elucidate the key role of LRG1 in meningioma progression.

Radiotherapy intensification for atypical and malignant meningiomas: A systematic review

Background

The outcomes of nonbenign (WHO Grades 2 and 3 [G2, G3]) meningiomas are suboptimal and radiotherapy (RT) dose intensification strategies have been investigated. The purpose of this review is to report on clinical practice and outcomes with particular attention to RT doses and techniques.

Methods

The PICO criteria (Population, Intervention, Comparison, and Outcomes) were used to frame the research question, directed at outlining the clinical outcomes in patients with G2-3 meningiomas treated with RT. The same search strategy was run in Embase and MEDLINE and, after deduplication, returned 1 807 records. These were manually screened for relevance and 25 were included.

Results

Tumor outcomes and toxicities are not uniformly reported in the selected studies since different endpoints and time points have been used by different authors. Many risk factors for worse outcomes are described, the most common being suboptimal RT. This includes no or delayed RT, low doses, and older techniques. A positive association between RT dose and progression-free survival (PFS) has been highlighted by analyzing the studies in this review (10/25) that report the same endpoint (5y-PFS).

Conclusions

This literature review has shown that standard practice RT leads to suboptimal tumor control rates in G2-3 meningiomas, with a significant proportion of disease recurring after a relatively short follow-up. Randomized controlled trials are needed in this setting to define the optimal RT approach. Given the increasing data to suggest a benefit of higher RT doses for high-risk meningiomas, novel RT technologies with highly conformal dose distributions are preferential to achieve optimal target coverage and organs at risk sparing.

Head and neck cancer of unknown primary: unveiling primary tumor sites through machine learning on DNA methylation profiles

BACKGROUND

The unknown tissue of origin in head and neck cancer of unknown primary (hnCUP) leads to invasive diagnostic procedures and unspecific and potentially inefficient treatment options for patients. The most common histologic subtype, squamous cell carcinoma, can stem from various tumor primary sites, including the oral cavity, oropharynx, larynx, head and neck skin, lungs, and esophagus. DNA methylation profiles are highly tissue-specific and have been successfully used to classify tissue origin. We therefore developed a support vector machine (SVM) classifier trained with publicly available DNA methylation profiles of commonly cervically metastasizing squamous cell carcinomas (n = 1103) in order to identify the primary tissue of origin of our own cohort of squamous cell hnCUP patient's samples (n = 28). Methylation analysis was performed with Infinium MethylationEPIC v1.0 BeadChip by Illumina.

RESULTS

The SVM algorithm achieved the highest overall accuracy of tested classifiers, with 87%. Squamous cell hnCUP samples on DNA methylation level resembled squamous cell carcinomas commonly metastasizing into cervical lymph nodes. The most frequently predicted cancer localization was the oral cavity in 11 cases (39%), followed by the oropharynx and larynx (both 7, 25%), skin (2, 7%), and esophagus (1, 4%). These frequencies concord with the expected distribution of lymph node metastases in epidemiological studies.

CONCLUSIONS

On DNA methylation level, hnCUP is comparable to primary tumor tissue cancer types that commonly metastasize to cervical lymph nodes. Our SVM-based classifier can accurately predict these cancers' tissues of origin and could significantly reduce the invasiveness of hnCUP diagnostics and enable a more precise therapy after clinical validation.

Noncoding RNA landscape and their emerging roles as biomarkers and therapeutic targets in meningioma

Meningiomas are among the most prevalent primary CNS tumors in adults, accounting for nearly 38% of all brain neoplasms. The World Health Organization (WHO) grade assigned to meningiomas guides medical care in patients and is primarily based on tumor histology and malignancy potential. Although often considered benign, meningiomas with complicated histology, limited accessibility for surgical resection, and/or higher malignancy potential (WHO grade 2 and WHO grade 3) are harder to combat, resulting in significant morbidity. With limited treatment options and no systemic therapies, it is imperative to understand meningioma tumorigenesis at the molecular level and identify novel therapeutic targets. The last decade witnessed considerable progress in understanding the noncoding RNA landscape of meningioma, with microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) emerging as molecular entities of interest. This review aims to highlight the commonly dysregulated miRNAs and lncRNAs in meningioma and their correlation with meningioma progression, malignancy, recurrence, and radioresistance. The role of "key" miRNAs as biomarkers and their therapeutic potential has also been reviewed in detail. Furthermore, current and emerging therapeutic modalities for meningioma have been discussed, with emphasis on the need to identify and subsequently employ clinically relevant miRNAs and lncRNAs as novel therapeutic targets and biomarkers.

Single-cell transcriptome analysis upon ECM-remodeling meningioma cells

Meningiomas are the most common tumours that primarily arise in the central nervous system, but their intratumoural heterogeneity has not yet been thoroughly studied. We aimed to investigate the transcriptome characteristics and biological properties of ECM-remodeling meningioma cells. Single-cell RNA sequencing (ScRNA-seq) data from meningioma samples were acquired and used for analyses. We conducted comprehensive bioinformatics analyses, including screening for differentially expressed genes (DEGs), Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway and Gene Ontology (GO) term enrichment analyses, Gene Set Enrichment Analysis (GSEA), protein-protein interaction (PPI) analysis, and copy number variation (CNV) analysis on single-cell sequencing data from meningiomas. Eighteen cell types, including six meningioma subtypes, were identified in the data. ECM-remodeling meningioma cells (MGCs) were mainly distributed in brain-tumour interface tissues. KEGG and GO enrichment analyses revealed that 908 DEGs were mainly related to cell adhesion, extracellular matrix organization, and ECM-receptor interaction. GSEA analysis demonstrated that homophilic cell adhesion via plasma membrane adhesion molecules was significantly enriched (NES = 2.375, P < 0.001). CNV analysis suggested that ECM-remodeling MGCs showed considerably lower average CNV scores. ECM-remodeling MGCs predominantly localized at the brain-tumour interface area and adhere stably to the basement membrane with a lower degree of malignancy. This study provides novel insights into the malignancy of meningiomas.

Hotspots and Trends in Meningioma Research Based on Bibliometrics, 2011-2021

BACKGROUND

Meningiomas, the most prevalent benign intracranial neoplasms, have been studied extensively for many years, but significant problems remain. To date, there is a scarcity of detailed studies elucidating the hotspots and future directions of meningiomas research.

METHODS

A comprehensive search and screening strategy was used to collect relevant studies published between 2011 and 2021 in the Web of Science Core Collection database. Thorough and systematic coauthorship and co-occurrence keyword maps were generated, and tables of statistics summarizing countries, organizations, authors, and keywords were created.

RESULTS

A total of 1544 articles meeting the screening criteria were collected. The countries producing the most publications between 2011 and 2021 were the United States, Germany, and China, with 586, 244, and 197 records, repectively. The cooperation networks also revolved mainly around these 3 countries, particularly the United States. The most frequently used keyword was "surgery," followed by "recurrence" and "management," with the frequencies of 248, 212, and 163, respectively. The most prominent cluster during the last decade was the #0 methylation cluster, and several keywords, including "survival," "brain invasion," and "magnetic resonance imaging," exhibited significant burst strength.

CONCLUSIONS

This study aimed to provide a comprehensive analysis of the research landscape and to identify potential research directions. Our findings disclose productive individuals and institutions. The current research focuses on the molecular pathology of meningiomas, improvements in techniques, and advances in diagnosis by magnetic resonance imaging. In particular, the improvements in molecular pathology might direct future research directions.

Prognostic and predictive biomarkers in central nervous system tumours: the molecular state of play

Central nervous system (CNS) tumours were one of the first cancer types to adopt and integrate molecular profiling into routine clinical diagnosis in 2016. The vast majority of these biomarkers, used to discriminate between tumour types, also offered prognostic information. With the advent of The Cancer Genome Atlas (TCGA) and other large genomic datasets, further prognostic sub-stratification was possible within tumour types, leading to increased precision in CNS tumour grading. This review outlines the evolution of the molecular landscape of adult CNS tumours, through the prism of World Health Organization (WHO) Classifications. We begin our journey in the pre-molecular era, where high-grade gliomas were divided into 'primary' and 'secondary' glioblastomas. Molecular alterations explaining these clinicopathological observations were the first branching points of glioma diagnostics, with the discovery of IDH1/2 mutations and 1p/19q codeletion. Subsequently, the rigorous characterisation of paediatric gliomas led to the unearthing of histone H3 alterations as a key event in gliomagenesis, which also had implications for young adult patients. Simultaneously, studies investigating prognostic biomarkers within tumour types were undertaken. Certain genomic phenotypes were found to portend unfavourable outcomes, for example, MYCN amplification in spinal ependymoma. The arrival of methylation profiling, having revolutionised the diagnosis of CNS tumours, now promises to bring increased prognostic accuracy, as has been shown in meningiomas. While MGMT promoter hypermethylation has remained a reliable biomarker of response to cytotoxic chemotherapy, targeted therapy in CNS tumours has unfortunately not had the success of other cancers. Therefore, predictive biomarkers have lagged behind the identification of prognostic biomarkers in CNS tumours. Emerging research from new clinical trials is cause for guarded optimism and may shift our conceptualisation of predictive biomarker testing in CNS tumours.

POLR2A Mutation is a Poor Prognostic Marker of Cerebellopontine Angle Meningioma

BACKGROUND AND OBJECTIVES

Recent molecular analyses have shown that the driver genetic mutations of meningiomas were associated with the anatomic location. Among these, POLR2A mutation is common among lesions in the skull base, mainly in the cerebellopontine angle (CPA). The objective of this study was to investigate the efficacy of POLR2A mutation as a prognostic marker for CPA meningiomas.

METHODS

We retrospectively analyzed the clinical data of 70 patients who had World Health Organization grade I CPA meningiomas. Somatic DNA was analyzed by Sanger sequencing and microsatellite array to examine for NF2, AKT1, KLF4, SMO, and POLR2A mutations and 22q loss. Genetic and clinical parameters were analyzed to identify the factors related with tumor recurrence.

RESULTS

We detected clearly the clinical features of the CPA cases with POLR2A mutation. Compared with cases without POLR2A mutation, cases with POLR2A mutation had more meningothelial type (P = 6.9 × 10-4), and higher rate of recurrence (P = .04). We found that the poor prognostic factors associated with the recurrence of CPA meningiomas were POLR2A mutation (P = .03, hazard ratio [HR] 9.38, 95% CI 1.26-70.0) and subtotal resection (STR) (P = 5.1 × 10-4, HR 63.1, 95% CI 6.09-655.0). In addition, in the group that underwent STR, POLR2A mutation was a poor prognostic factor associated with tumor recurrence (P = .03, HR 11.1, 95% CI 1.19-103.7).

CONCLUSION

POLR2A mutation and STR were the poor prognostic markers associated with the recurrence of CPA meningioma. For CPA meningioma cases that underwent STR, only POLR2A mutation was a poor prognostic factor. Detecting POLR2A mutation may be a cost-effective, easy, and useful marker for prognostication.

Molecular neuropathology: an essential and evolving toolbox for the diagnosis and clinical management of central nervous system tumors

Molecular profiling has transformed the diagnostic workflow of CNS tumors during the last years. The latest WHO classification of CNS tumors (5th edition), published in 2021, pushed forward the integration between histopathological features and molecular hallmarks to achieve reproducible and clinically relevant diagnoses. To address these demands, pathologists have to appropriately deal with multiple molecular assays mainly including DNA methylation profiling and DNA/RNA next generation sequencing. Tumor classification by DNA methylation profiling is now a critical tool for many diagnostic tasks in neuropathology including the assessment of complex cases, to evaluate novel tumor types and to perform tumor subgrouping in hetereogenous entities like medulloblastoma or ependymoma. DNA/RNA NGS allow the detection of multiple molecular alterations including single nucleotide variations, small insertions/deletions (InDel), and gene fusions. These molecular markers can provide key insights for diagnosis, for example, if a tumor-specific mutation is detected, but also for treatment since targeted therapies are progressively entering the clinical practice. In the present review, a brief, but comprehensive overview of these tools will be provided, discussing their technical specifications, diagnostic value, and potential limitations. Moreover, the importance of molecular profiling will be shown in a representative series of CNS neoplasms including both the most frequent tumor types and other selected entities for which molecular characterization plays a critical role.

The importance of considering competing risks in recurrence analysis of intracranial meningioma

BACKGROUND

The risk of recurrence is overestimated by the Kaplan-Meier method when competing events, such as death without recurrence, are present. Such overestimation can be avoided by using the Aalen-Johansen method, which is a direct extension of Kaplan-Meier that accounts for competing events. Meningiomas commonly occur in older individuals and have slow-growing properties, thereby warranting competing risk analysis. The extent to which competing events are considered in meningioma literature is unknown, and the consequences of using incorrect methodologies in meningioma recurrence risk analysis have not been investigated.

METHODS

We surveyed articles indexed on PubMed since 2020 to assess the usage of competing risk analysis in recent meningioma literature. To compare recurrence risk estimates obtained through Kaplan-Meier and Aalen-Johansen methods, we applied our international database comprising ~ 8,000 patients with a primary meningioma collected from 42 institutions.

RESULTS

Of 513 articles, 169 were eligible for full-text screening. There were 6,537 eligible cases from our PERNS database. The discrepancy between the results obtained by Kaplan-Meier and Aalen-Johansen was negligible among low-grade lesions and younger individuals. The discrepancy increased substantially in the patient groups associated with higher rates of competing events (older patients with high-grade lesions).

CONCLUSION

The importance of considering competing events in recurrence risk analysis is poorly recognized as only 6% of the studies we surveyed employed Aalen-Johansen analyses. Consequently, most of the previous literature has overestimated the risk of recurrence. The overestimation was negligible for studies involving low-grade lesions in younger individuals; however, overestimation might have been substantial for studies on high-grade lesions.

The Natural History and Treatment of Meningiomas: An Update

Meningiomas are the most frequent nonmalignant tumors of the central nervous system (CNS). Despite their benign nature and slow-growing pattern, if not diagnosed early, these tumors may reach relatively large sizes causing significant morbidity and mortality. Some variants are located in hard-to-access locations, compressing critical neurovascular structures, and making the surgical management even more challenging. Although most meningiomas have a good long-term prognosis after treatment, there are still controversies over their management in a subset of cases. While surgery is the first-line treatment, the use of fractionated radiotherapy or stereotactic radiosurgery is indicated for residual or recurrent tumors, small lesions, and tumors in challenging locations. Advances in molecular genetics and ongoing clinical trial results have recently helped both to refine the diagnosis and provide hope for effective biomolecular target-based medications for treatment. This article reviews the natural history and current therapeutic options for CNS meningiomas.

Grading meningioma resections: the Simpson classification and beyond

Technological (and also methodological) advances in neurosurgery and neuroimaging have prompted a reappraisal of Simpson's grading of the extent of meningioma resections. To the authors, the published evidence supports the tenets of this classification. Meningioma is an often surgically curable dura-based disease. An extent of meningioma resection classification needs to account for a clinically meaningful variation of the risk of recurrence depending on the aggressiveness of the management of the (dural) tumor origin.Nevertheless, the 1957 Simpson classification undoubtedly suffers from many limitations. Important issues include substantial problems with the applicability of the grading paradigm in different locations. Most notably, tumor location and growth pattern often determine the eventual extent of resection, i.e., the Simpson grading does not reflect what is surgically achievable. Another very significant problem is the inherent subjectivity of relying on individual intraoperative assessments. Neuroimaging advances such as the use of somatostatin receptor PET scanning may help to overcome this central problem. Tumor malignancy and biology in general certainly influence the role of the extent of resection but may not need to be incorporated in an actual extent of resection grading scheme as long as one does not aim at developing a prognostic score. Finally, all attempts at grading meningioma resections use tumor recurrence as the endpoint. However, especially in view of radiosurgery/radiotherapy options, the clinical significance of recurrent tumor growth varies greatly between cases.In summary, while the extent of resection certainly matters in meningioma surgery, grading resections remains controversial. Given the everyday clinical relevance of this issue, a multicenter prospective register or study effort is probably warranted (including a prominent focus on advanced neuroimaging).

Enhancing mitosis quantification and detection in meningiomas with computational digital pathology

Mitosis is a critical criterion for meningioma grading. However, pathologists' assessment of mitoses is subject to significant inter-observer variation due to challenges in locating mitosis hotspots and accurately detecting mitotic figures. To address this issue, we leverage digital pathology and propose a computational strategy to enhance pathologists' mitosis assessment. The strategy has two components: (1) A depth-first search algorithm that quantifies the mathematically maximum mitotic count in 10 consecutive high-power fields, which can enhance the preciseness, especially in cases with borderline mitotic count. (2) Implementing a collaborative sphere to group a set of pathologists to detect mitoses under each high-power field, which can mitigate subjective random errors in mitosis detection originating from individual detection errors. By depth-first search algorithm (1) , we analyzed 19 meningioma slides and discovered that the proposed algorithm upgraded two borderline cases verified at consensus conferences. This improvement is attributed to the algorithm's ability to quantify the mitotic count more comprehensively compared to other conventional methods of counting mitoses. In implementing a collaborative sphere (2) , we evaluated the correctness of mitosis detection from grouped pathologists and/or pathology residents, where each member of the group annotated a set of 48 high-power field images for mitotic figures independently. We report that groups with sizes of three can achieve an average precision of 0.897 and sensitivity of 0.699 in mitosis detection, which is higher than an average pathologist in this study (precision: 0.750, sensitivity: 0.667). The proposed computational strategy can be integrated with artificial intelligence workflow, which envisions the future of achieving a rapid and robust mitosis assessment by interactive assisting algorithms that can ultimately benefit patient management.

Pan-cancer copy number variant analysis identifies optimized size thresholds and co-occurrence models for individualized risk-stratification

Chromosome instability leading to accumulation of copy number gains or losses is a hallmark of cancer. Copy number variant (CNV) signatures are increasingly used for clinical risk-stratification, but size thresholds for defining CNVs are variable and the biological or clinical implications of CNV size heterogeneity or co-occurrence patterns are incompletely understood. Here we analyze CNV and clinical data from 565 meningiomas and 9,885 tumors from The Cancer Genome Atlas (TCGA) to develop tumor-and chromosome-specific CNV size-dependent and co-occurrence models for clinical outcomes. Our results reveal prognostic CNVs with optimized size thresholds and co-occurrence patterns that refine risk-stratification across a diversity of human cancers.

Epidemiology, Genetics, and DNA Methylation Grouping of Hyperostotic Meningiomas

BACKGROUND AND OBJECTIVES

Meningiomas are the most common primary intracranial tumors and are among the only tumors that can form lamellar, hyperostotic bone in the tumor microenvironment. Little is known about the epidemiology or molecular features of hyperostotic meningiomas.

METHODS

Using a retrospective database of 342 meningiomas treated with surgery at a single institution, we correlated clinical, tumor-related, targeted next-generation DNA sequencing (n = 39 total, 16 meningioma-induced hyperostosis [MIH]), and surgical variables with the presence of MIH using generalized linear models. Meningioma DNA methylation grouping was analyzed on a separate population of patients from the same institution with preoperative imaging studies sufficient for identification of MIH (n = 200).

RESULTS

MIH was significantly correlated with anterior fossa (44.3% of MIH vs 17.5% of non-MIH were in the anterior fossa P < .001, c2) or skull base location (62.5% vs 38.3%, P < .001, c2) and lower MIB-1 labeling index. Gross total resection was accomplished in 27.3% of tumors with MIH and 45.5% of nonhyperostotic meningiomas (P < .05, t test). There was no association between MIH and histological World Health Organization grade (P = .32, c2). MIH was significantly more frequent in meningiomas from the Merlin-intact DNA methylation group (P < .05). Somatic missense mutations in the WD-repeat-containing domain of the TRAF7 gene were the most common genetic alteration associated with MIH (n = 12 of 15, 80%, P < .01, c2).

CONCLUSION

In this article, we show that MIH has a predilection for the anterior skull base and affected tumors are less amenable to gross total resection. We find no association between MIH and histological World Health Organization grade, but show that MIH is more common in the Merlin-intact DNA methylation group and is significantly associated with TRAF7 somatic missense mutations. These data provide a framework for future investigation of biological mechanisms underlying MIH.

Liquid biopsy evaluation of circulating tumor DNA, miRNAs, and cytokines in meningioma patients

Introduction

Liquid biopsy is a non-invasive method used to detect cancer and monitor treatment responses by analyzing blood or other bodily fluids for cancer biomarkers. Meningiomas are the most common primary central nervous system tumors, and biomarkers play a crucial role in their diagnosis, prognosis, and treatment monitoring. The World Health Organization (WHO) classifies meningiomas based on tumor grades and molecular alterations in genes such as in NF2, AKT1, TRAF7, SMO, PIK3CA, KLF4, SMARCE1, BAP1, H3K27me3, TERT promoter, and CDKN2A/B. Liquid biopsy, specifically cell-free DNA (cfDNA) analysis, has shown potential for monitoring meningiomas as it can detect ctDNA release in the blood, unaffected by the blood-brain barrier. MicroRNAs (miRNAs) have also been found to be deregulated in various cancers, including meningiomas, presenting potential as diagnostic biomarkers. Additionally, studying cytokines in the tumor microenvironment may aid in establishing prognostic or diagnostic panels for meningiomas.

Methods

In the present study we analyzed the DNA coming from both the plasma and tumor samples, in addition to analyze miRNA-21 and cytokines in the plasma of 28 meningioma patients.

Discussion and Conclusion

Our findings indicate that the detection of ctDNA in the plasma of meningioma patients is feasible. However, it's important to note that certain challenges persist when comparing plasma DNA analysis to that of tumor tissues. In our study, we observed a paired identification of mutations in only one patient, highlighting the complexities involved. Furthermore, we successfully identified miR-21 and cytokines in the plasma samples. Notably, our analysis of Interleukin 6 (IL-6) unveiled higher expression in the clear cell subtype compared to the other types. Despite the ongoing research, the clinical implementation of liquid biopsy in meningiomas remains somewhat limited. Nevertheless, our promising results underscore the need for further investigation.

Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review

Neuroblastoma, a paediatric malignancy with high rates of cancer-related morbidity and mortality, is of significant interest to the field of paediatric cancers. High-risk NB tumours are usually metastatic and result in survival rates of less than 50%. Machine learning approaches have been applied to various neuroblastoma patient data to retrieve relevant clinical and biological information and develop predictive models. Given this background, this study will catalogue and summarise the literature that has used machine learning and statistical methods to analyse data such as multi-omics, histological sections, and medical images to make clinical predictions. Furthermore, the question will be turned on its head, and the use of machine learning to accurately stratify NB patients by risk groups and to predict outcomes, including survival and treatment response, will be summarised. Overall, this study aims to catalogue and summarise the important work conducted to date on the subject of expression-based predictor models and machine learning in neuroblastoma for risk stratification and patient outcomes including survival, and treatment response which may assist and direct future diagnostic and therapeutic efforts.

Comprehensive DNA Methylation Profiling of Medullary Thyroid Carcinoma: Molecular Classification, Potential Therapeutic Target, and Classifier System

PURPOSE

Medullary thyroid carcinoma (MTC) presents a distinct biological context from other thyroid cancers due to its specific cellular origin. This heterogeneous and rare tumor has a high prevalence of advanced diseases, making it crucial to address the limited therapeutic options and enhance complex clinical management. Given the high clinical accessibility of methylation information, we construct the largest MTC methylation cohort to date.

EXPERIMENTAL DESIGN

Seventy-eight fresh-frozen MTC samples constituted our methylation cohort. The comprehensive study process incorporated machine learning, statistical analysis, and in vitro experiments.

RESULTS

Our study pioneered the identification of a three-class clustering system for risk stratification, exhibiting pronounced epigenomic heterogeneity. The elevated overall methylation status in MTC-B, combined with the "mutual exclusivity" of hypomethylated sites displayed by MTC-A and MTC-C, distinctively characterized the MTC-specific methylation pattern. Integrating with the transcriptome, we further depicted the features of these three clusters to scrutinize biological properties. Several MTC-specific aberrant DNA methylation events were emphasized in our study. NNAT expression was found to be notably reduced in poor-prognostic MTC-C, with its promoter region overlapping with an upregulated differentially methylated region. In vitro experiments further affirmed NNAT's therapeutic potential. Moreover, we built an elastic-net logistic regression model with a relatively high AUC encompassing 68 probes, intended for future validation and systematic clinical application.

CONCLUSIONS

Conducting research on diseases with low incidence poses significant challenges, and we provide a robust resource and comprehensive research framework to assist in ongoing MTC case inclusion and facilitate in-depth dissection of its molecular biological features.

The outcomes measured and reported in intracranial meningioma clinical trials: A systematic review

Background

Meningioma clinical trials have assessed interventions including surgery, radiotherapy, and pharmacotherapy. However, agreement does not exist on what, how, and when outcomes of interest should be measured. To do so would allow comparative analysis of similar trials. This systematic review aimed to summarize the outcomes measured and reported in meningioma clinical trials.

Methods

Systematic literature and trial registry searches were performed to identify published and ongoing intracranial meningioma clinical trials (PubMed, Embase, Medline, CINAHL via EBSCO, and Web of Science, completed January 22, 2022). Reported outcomes were extracted verbatim, along with an associated definition and method of measurement if provided. Verbatim outcomes were deduplicated and the resulting unique outcomes were grouped under standardized outcome terms. These were classified using the taxonomy proposed by the "Core Outcome Measures in Effectiveness Trials" (COMET) initiative.

Results

Thirty published articles and 18 ongoing studies were included, describing 47 unique clinical trials: Phase 2 n = 33, phase 3 n = 14. Common interventions included: Surgery n = 13, radiotherapy n = 8, and pharmacotherapy n = 20. In total, 659 verbatim outcomes were reported, of which 84 were defined. Following de-duplication, 415 unique verbatim outcomes remained and were grouped into 115 standardized outcome terms. These were classified using the COMET taxonomy into 29 outcome domains and 5 core areas.

Conclusions

Outcome measurement across meningioma clinical trials is heterogeneous. The standardized outcome terms identified will be prioritized through an eDelphi survey and consensus meeting of key stakeholders (including patients), in order to develop a core outcome set for use in future meningioma clinical trials.

Molecular prognostication in grade 3 meningiomas and p16/MTAP immunohistochemistry for predicting CDKN2A/B status

Background

The World Health Organization 2021 classification introduces molecular grading criteria for anaplastic meningiomas, including TERT promoter (TERTp) mutations and CDKN2A/B homozygous deletion. Additional adverse prognostic factors include H3K27me3 and BAP1 loss. The aim of this study was to explore whether these molecular alterations stratified clinical outcomes in a single-center cohort of grade 3 meningiomas. Additionally, we examined whether p16 and MTAP immunohistochemistry can predict CDKN2A/B status.

Methods

Clinical and histopathological information was obtained from the electronic medical records of grade 3 meningiomas resected at a tertiary center between 2007 and 2020. Molecular testing for TERTp mutations and CDKN2A/B copy-number status, methylation profiling, and immunohistochemistry for H3K27me3, BAP1, p16, and methylthioadenosine phosphorylase (MTAP) were performed. Predictors of survival were identified by Cox regression.

Results

Eight of 15 cases demonstrated elevated mitotic index (≥20 mitoses per 10 consecutive high-power fields), 1 tumor exhibited BAP1 loss, 4 harbored TERTp mutations, and 3 demonstrated CDKN2A/B homozygous deletion. Meningiomas with TERTp mutations and/or CDKN2A/B homozygous deletion showed significantly reduced survival compared to anaplastic meningiomas with elevated mitotic index alone. Immunohistochemical loss of p16 and MTAP demonstrated high sensitivity (67% and 100%, respectively) and specificity (100% and 100%, respectively) for predicting CDKN2A/B status.

Conclusions

Molecular alterations of grade 3 meningiomas stratify clinical outcomes more so than histologic features alone. Immunohistochemical loss of p16 and MTAP show promise in predicting CDKN2A/B status.

Concurrent gliomas in patients with multiple sclerosis

BACKGROUND

Concurrent malignant brain tumors in patients with multiple sclerosis (MS) constitute a rare but paradigmatic phenomenon for studying neuroimmunological mechanisms from both molecular and clinical perspectives.

METHODS

A multicenter cohort of 26 patients diagnosed with both primary brain tumors and multiple sclerosis was studied for disease localization, tumor treatment-related MS activity, and molecular characteristics specific for diffuse glioma in MS patients.

RESULTS

MS neither predisposes nor protects from the development of gliomas. Patients with glioblastoma WHO grade 4 without isocitratdehydrogenase (IDH) mutations have a longstanding history of MS, whereas patients diagnosed with IDH-mutant astrocytoma WHO grade 2 receive multiple sclerosis diagnosis mostly at the same time or later. Concurrent MS is associated with a lesser extent of tumor resection and a worse prognosis in IDH-mutant glioma patients (PFS 32 vs. 64 months, p = 0.0206). When assessing tumor-intrinsic differences no distinct subgroup-defining methylation pattern is identified in gliomas of MS patients compared to other glioma samples. However, differential methylation of immune-related genetic loci including human leukocyte antigen locus on 6p21 and interleukin locus on 5q31 is found in MS patients vs. matched non-MS patients. In line, inflammatory disease activity increases in 42% of multiple sclerosis patients after brain tumor radiotherapy suggesting a susceptibility of multiple sclerosis brain tissue to pro-inflammatory stimuli such as ionizing radiation.

CONCLUSIONS

Concurrent low-grade gliomas should be considered in multiple sclerosis patients with slowly progressive, expansive T2/FLAIR lesions. Our findings of typically reduced extent of resection in MS patients and increased MS activity after radiation may inform future treatment decisions.

A novel BRAF::PTPRN2 fusion in meningioma: a case report

Gene fusion events have been linked to oncogenesis in many cancers. However, gene fusions in meningioma are understudied compared to somatic mutations, chromosomal gains/losses, and epigenetic changes. Fusions involving B-raf proto-oncogene, serine/threonine kinase (BRAF) are subtypes of oncogenic BRAF genetic abnormalities that have been reported in certain cases of brain tumors, such as pilocytic astrocytomas. However, BRAF fusions have not been recognized in meningioma. We present the case of an adult female presenting with episodic partial seizures characterized by déjà vu, confusion, and cognitive changes. Brain imaging revealed a cavernous sinus and sphenoid wing mass and she underwent resection. Histopathology revealed a World Health Organization (WHO) grade 1 meningioma. Genetic profiling with next generation sequencing and microarray analysis revealed an in-frame BRAF::PTPRN2 fusion affecting the BRAF kinase domain as well as chromothripsis of chromosome 7q resulting in multiple segmental gains and losses including amplifications of cyclin dependent kinase 6 (CDK6), tyrosine protein-kinase Met (MET), and smoothened (SMO). Elevated pERK staining in tumor cells provided evidence of activated mitogen-activated protein kinase (MAPK) signaling. This report raises the possibility that gene fusion events may be involved in meningioma pathogenesis and warrant further investigation.

Prognostic Factors Affecting Postsurgical Outcomes of Adult Patients with Intracranial Meningioma: A Retrospective Study

OBJECTIVE

To identify the prognostic factors of surgical resection for meningioma and their relationship with patient outcomes.

METHODS

This retrospective study included 53 patients (≥16 years), who underwent surgical resection for intracranial meningioma at the King Abdulaziz University Hospital from 2012 to 2022. Data regarding tumor location and size, histopathological type, chief complaint, chief complaint duration, admission date, diagnosis, operation, and discharge date were collected. These data were subjected to univariate and bivariate analyses to investigate the relationship between the postsurgical outcomes of the patients with meningioma and the variables of age at surgery, sex, length of hospitalization, chief complaint, Glasgow outcome score, World Health Organization histopathological classification, body mass index, tumor size, and nature of surgical resection.

RESULTS

The mean age of our study cohort was 49.09 ± 12.64 years, with a female preponderance (75.5%) and mean body mass index of 29.31 ± 5.52 kg/m2. length of hospitalization (mean: 26.92 ± 54.88 days) demonstrated a significant (P = 0.012) impact on prognosis after surgery. In addition, convexity meningiomas (21.2%), which were observed in the maximum number of cases, and mean tumor volume (28.67 ± 48.85 mm) were significantly (P = 0.049) associated with the outcome. Most patients (78.8%) underwent total surgical resection, and histopathological examinations revealed a higher frequency of grade 1 than grade 2 tumors.

CONCLUSIONS

Short duration of hospitalization and superficial location of the tumor are associated with optimal outcomes after surgical resection for patients with meningioma.

Current status of DNA methylation profiling in neuro-oncology as a diagnostic support tool: A review

Over the last 2 decades, high throughput genome-wide molecular profiling has revealed characteristic genetic and epigenetic alterations associated with different types of central nervous system (CNS) tumors. DNA methylation profiling has emerged as an important molecular platform for CNS tumor classification with improved diagnostic accuracy and patient risk stratification in comparison to the standard of care histopathological analysis and any single molecular tests. The emergence of DNA methylation arrays have also played a crucial role in refining existing types and the discovery of new tumor types or subtypes. The adoption of methylation data into neuro-oncology has been greatly aided by the development of a freely accessible machine learning-based classifier. In this review, we discuss methylation workflow, address the utility of DNA methylation profiling in CNS tumors in a routine diagnostic setting, and provide an overview of the methylation-based tumor types and new types or subtypes identified with this platform.

Postoperative [68Ga]Ga-DOTA-TATE PET/CT imaging is prognostic for progression-free survival in meningioma WHO grade 1

PURPOSE

Tumor resection represents the first-line treatment for symptomatic meningiomas, and the extent of resection has been shown to be of prognostic importance. Assessment of tumor remnants with somatostatin receptor PET proves to be superior to intraoperative estimation with Simpson grading or MRI. In this preliminary study, we evaluate the prognostic relevance of postoperative PET for progression-free survival in meningiomas.

METHODS

We conducted a post hoc analysis on a prospective patient cohort with resected meningioma WHO grade 1. Patients received postoperative MRI and [68Ga]Ga-DOTA-TATE PET/CT and were followed regularly with MRI surveillance scans for detection of tumor recurrence/progression.

RESULTS

We included 46 patients with 49 tumors. The mean age at diagnosis was 57.8 ± 1.7 years with a male-to-female ratio of 1:1.7. Local tumor progression occurred in 7/49 patients (14%) after a median follow-up of 52 months. Positive PET was associated with an increased risk for progression (*p = 0.015) and a lower progression-free survival (*p = 0.029), whereas MRI was not. 20 out of 20 patients (100%) with negative PET findings remained recurrence-free. The location of recurrence/progression on MRI was adjacent to regions where postoperative PET indicated tumor remnants in all cases. Gross tumor volumes were higher on PET compared to MRI (*p = 0.032).

CONCLUSION

Our data show that [68Ga]Ga-DOTA-TATE PET/CT is highly sensitive in revealing tumor remnants in patients with meningioma WHO grade 1. Negative PET findings were associated with a higher progression-free survival, thus improving surveillance. In patients with tumor remnants, additional PET can optimize adjuvant radiotherapy target planning of surgically resected meningiomas.

2021 World Health Organization Classification of Brain Tumors

OBJECTIVE

The classification of brain tumors is a rapidly evolving field that requires extensive integration of molecular diagnostic findings from an expanding set of platforms and assays. This article summarizes the schema presented in the 5th edition of the World Health Organization (WHO) classification of central nervous system (CNS) tumors while highlighting diagnostic molecular findings and discussing the strengths and weaknesses of commonly available testing modalities.

LATEST DEVELOPMENTS

Several major changes in practice were introduced with the 5th edition of the CNS WHO classification, including molecular grading of adult diffuse gliomas, the introduction of many new entities within the spectrum of pediatric gliomas and glioneuronal tumors, and the widespread adoption of methylation classes as useful or even necessary diagnostic criteria. Additionally, several revisions to nomenclature (eg, IDH-mutant gliomas) were introduced for simplicity and to disambiguate from other tumor types.

ESSENTIAL POINTS

The classification of brain tumors continues to grow in complexity alongside our improved understanding of their nuanced molecular underpinnings.

Targeted gene expression profiling predicts meningioma outcomes and radiotherapy responses

Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and indications for postoperative radiotherapy are controversial. Here we develop a targeted gene expression biomarker that predicts meningioma outcomes and radiotherapy responses. Using a discovery cohort of 173 meningiomas, we developed a 34-gene expression risk score and performed clinical and analytical validation of this biomarker on independent meningiomas from 12 institutions across 3 continents (N = 1,856), including 103 meningiomas from a prospective clinical trial. The gene expression biomarker improved discrimination of outcomes compared with all other systems tested (N = 9) in the clinical validation cohort for local recurrence (5-year area under the curve (AUC) 0.81) and overall survival (5-year AUC 0.80). The increase in AUC compared with the standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval 0.07 to 0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% confidence interval 0.37 to 0.78, P = 0.0001) and suggested postoperative management could be refined for 29.8% of patients. In sum, our results identify a targeted gene expression biomarker that improves discrimination of meningioma outcomes, including prediction of postoperative radiotherapy responses.

Domestic Animal Models of Central Nervous System Tumors: Focus on Meningiomas

Intracranial primary tumors (IPTs) are aggressive forms of malignancies that cause high mortality in both humans and domestic animals. Meningiomas are frequent adult IPTs in humans, dogs, and cats, and both benign and malignant forms cause a decrease in life quality and survival. Surgery is the primary therapeutic approach to treat meningiomas, but, in many cases, it is not resolutive. The chemotherapy and targeted therapy used to treat meningiomas also display low efficacy and many side effects. Therefore, it is essential to find novel pharmacological approaches to increase the spectrum of therapeutic options for meningiomas. This review analyzes the similarities between human and domestic animal (dogs and cats) meningiomas by evaluating the molecular and histological characteristics, diagnosis criteria, and treatment options and highlighting possible research areas to identify novel targets and pharmacological approaches, which are useful for the diagnosis and therapy of this neoplasia to be used in human and veterinary medicine.

methylClass: an R package to construct DNA methylation-based classification models

DNA methylation profiling is a useful tool to increase the accuracy of a cancer diagnosis. However, a comprehensive R package specially for it is lacking. Hence, we developed the R package methylClass for methylation-based classification. Within it, we provide the eSVM (ensemble-based support vector machine) model to achieve much higher accuracy in methylation data classification than the popular random forest model and overcome the time-consuming problem of the traditional SVM. In addition, some novel feature selection methods are included in the package to improve the classification. Furthermore, because methylation data can be converted to other omics, such as copy number variation data, we also provide functions for multi-omics studies. The testing of this package on four datasets shows the accurate performance of our package, especially eSVM, which can be used in both methylation and multi-omics models and outperforms other methods in both cases. methylClass is available at: https://github.com/yuabrahamliu/methylClass.

Unveiling a Biomarker Signature of Meningioma: The Need for a Panel of Genomic, Epigenetic, Proteomic, and RNA Biomarkers to Advance Diagnosis and Prognosis

Meningiomas are the most prevalent primary intracranial tumors. The majority are benign but can undergo dedifferentiation into advanced grades classified by World Health Organization (WHO) into Grades 1 to 3. Meningiomas' tremendous variability in tumor behavior and slow growth rates complicate their diagnosis and treatment. A deeper comprehension of the molecular pathways and cellular microenvironment factors implicated in meningioma survival and pathology is needed. This review summarizes the known genetic and epigenetic aberrations involved in meningiomas, with a focus on neurofibromatosis type 2 (NF2) and non-NF2 mutations. Novel potential biomarkers for meningioma diagnosis and prognosis are also discussed, including epigenetic-, RNA-, metabolomics-, and protein-based markers. Finally, the landscape of available meningioma-specific animal models is overviewed. Use of these animal models can enable planning of adjuvant treatment, potentially assisting in pre-operative and post-operative decision making. Discovery of novel biomarkers will allow, in combination with WHO grading, more precise meningioma grading, including meningioma identification, subtype determination, and prediction of metastasis, recurrence, and response to therapy. Moreover, these biomarkers may be exploited in the development of personalized targeted therapies that can distinguish between the 15 diverse meningioma subtypes.

Cytological features of ectopic nasal meningioma. Report of a case diagnosed using FNAC

We describe a case of primary extracranial meningioma of the nasal cavity. FNAC showed loose clusters of epithelioid cells with splayed cytoplasm, focal whorling, and occasional psammoma bodies suggestive of meningioma. This case highlights the importance of FNAC in diagnosing meningioma when encountering similar morphology at a rare location.

Fractionated radiotherapy for surgically resected intracranial meningiomas: A multicentre retrospective cohort study

BACKGROUND

Aside from surgical resection, the only standard of care treatment modality for meningiomas is radiotherapy (RT). Despite this, few studies have focused on identifying clinical covariates associated with failure of fractionated RT following surgical resection (fRT), and the timing of fRT following surgery still remains controversial (adjuvant versus salvage fRT). We assessed the outcomes of the largest, multi-institutional cohort of surgically resected meningiomas treated with subsequent adjuvant and salvage fRT to identify factors associated with local freedom from recurrence (LFFR) over 3-10 years post-fRT and to determine the optimal timing of fRT.

METHODS

Patients with intracranial meningiomas who underwent surgery and fRT between 1997 and 2018 were included. Primary endpoints were radiographic recurrence/progression and time to progression from the completion of fRT.

RESULTS

404 meningiomas were included for analysis. Of these, 167 (41.3%) recurred post-fRT. Clinical covariates independently associated with worse PFS post-fRT included receipt of previous RT to the meningioma, having a WHO grade 3 meningioma or recurrent meningioma, the meningioma having a higher MIB1-index or brain invasion on pathology, and older patient age at diagnosis. Subgroup analysis identified higher MIB1-index as a histological factor associated with poorer LFFR in WHO grade 2 meningiomas. 179 patients underwent adjuvant RT shortly after surgery whereas 225 patients had delayed, salvage fRT after recurrence/progression. Following propensity score matching, patients that underwent adjuvant fRT had improved LFFR post-fRT compared to those that received salvage fRT.

CONCLUSION

There is a paucity of clinical factors that can predict a meningioma's response to fRT following surgery. Adjuvant fRT may be associated with improved PFS post-fRT compared to salvage fRT. Molecular biomarkers of RT-responsiveness are needed to better inform fRT treatment decisions.

A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-up

Purpose

The selection of patients for further therapy after meningioma surgery remains a challenge. Progress has been made in this setting in selecting patients that are more likely to have an aggressive disease course by using molecular tests such as gene panel sequencing and DNA methylation profiling. The aim of this study was to create a preselection tool warranting further molecular work-up.

Methods

All patients undergoing surgery for resection or biopsy of a cranial meningioma from January 2013 until December 2018 at the University Hospital Zurich with available tumor histology were included. Various prospectively collected clinical, radiological, histological and immunohistochemical variables were analyzed and used to train a logistic regression model to predict tumor recurrence or progression. Regression coefficients were used to generate a scoring system grading every patient into low, intermediate, and high-risk group for tumor progression or recurrence.

Results

Out of a total of 13 variables preselected for this study, previous meningioma surgery, Simpson grade, progesterone receptor staining as well as presence of necrosis and patternless growth on histopathological analysis of 378 patients were included into the final model. Discrimination showed an AUC of 0.81 (95% CI 0.73 - 0.88), the model was well-calibrated. Recurrence-free survival was significantly decreased in patients in intermediate and high-risk score groups (p-value < 0.001).

Conclusion

The proposed prediction model showed good discrimination and calibration. This prediction model is based on easily obtainable information and can be used as an adjunct for patient selection for further molecular work-up in a tertiary hospital setting.

Spheno-Orbital Meningioma - Treatment Outcomes and Factors Influencing Recurrence

PURPOSE

To determine treatment outcomes, recurrence rates, and predictors of recurrence, to inform future therapeutic approaches for spheno-orbital meningiomas (SOM).

METHODS

A retrospective single-center study of SOM treated from 1990 to 2021 was conducted with comprehensive neuro-ophthalmologic follow-up at Columbia University Medical Center (CUMC). Recurrence requiring reintervention was defined clinically as worsening of visual acuity, visual field defect, or ocular motility after an initial period of stabilization or 6 months of improvement following treatment, or radiologically as either a regrowth with an increase in tumor size by 20% at the site of previous growth or a new region of tumor growth.

RESULTS

In total 46 patients met the inclusion criteria. The mean follow-up was 106 months (range 1-303). Dictated by the phenotype of the disease, patients underwent either gross- (50%), near- (17%), or subtotal resection (26%). Removal of the anterior clinoid process (ACP) was performed in 52% of patients. Nine patients (20%) required an enucleation or exenteration. Radiotherapy was employed at some point of treatment in 50% of cases. Inherited cases (24%) were referred to CUMC for treatment following 1 or more recurrences. The total recurrence rate, including inherited cases, was 54%, occurring at a mean interval of 43 months. The recurrence rate of patients treated solely at CUMC was 40%, occurring at a mean interval of 41 months. A subset of patients (32%) had 2 or more recurrences. Histopathology at the first surgery was WHO grade I (87%) and II (13%) and at the final surgery was WHO grade I (74%), II (21%), and III (4%). A subset of grade I tumors that received radiotherapy (35%) evolved to a higher grade or developed multiple recurrences without a change in histologic grade I. Grade II tumors and treatment with radiotherapy increased the odds of recurrence. Removal of the ACP and gross total resection decreased the odds of recurrence.

CONCLUSION

Due to the routinely long interval to tumor recurrence, lifelong surveillance of patients with SOM is prudent. ACP resection and gross total resection, where possible, reduce tumor recurrence and the need for further treatment. Radiotherapy should be reserved for higher-grade meningiomas and select grade I tumors.

The role of m6A RNA methylation regulator in meningioma

Meningiomas are common intracranial tumors, and the effect of surgical resection is often unsatisfactory. N6-Methyladenosine (m6A)-related regulator expression levels are related to cancer occurrence and development. This study aimed to investigate the roles of m6A RNA methylation regulators in meningiomas, as these are currently unclear. Two m6A methylation-regulated genes (METTL3 and IGF2BP2) were identified as survival-associated linear models for RiskScore through bioinformatics analysis. Univariate and multivariate Cox regression analyses showed that the overall survival of patients with meningioma in the high-risk group was substantially shorter than that in the low-risk group. Weighted gene co-expression network analysis constructed a co-expression network based on the m6A methylation model (RiskScore). Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analyses identified the biological processes of hub module gene behavior, and Cytoscape constructed an m6A methylation-related gene regulatory network. In vitro experiments verified that the mRNA and protein expression levels of METTL3 and IGF2BP2 were lower in meningioma cells than in normal meningioma cells. Therefore, central regulators of m6A methylation (METTL3 and IGF2BP2) could potentially serve as novel therapeutic targets in meningioma. Subsequently, a novel methylation signature (RiskScore) was developed for prognostic prediction in patients with meningioma.

Leveraging Attention-Based Convolutional Neural Networks for Meningioma Classification in Computational Histopathology

Convolutional neural networks (CNNs) are becoming increasingly valuable tools for advanced computational histopathology, promoting precision medicine through exceptional visual decoding abilities. Meningiomas, the most prevalent primary intracranial tumors, necessitate accurate grading and classification for informed clinical decision-making. Recently, DNA methylation-based molecular classification of meningiomas has proven to be more effective in predicting tumor recurrence than traditional histopathological methods. However, DNA methylation profiling is expensive, labor-intensive, and not widely accessible. Consequently, a digital histology-based prediction of DNA methylation classes would be advantageous, complementing molecular classification. In this study, we developed and rigorously assessed an attention-based multiple-instance deep neural network for predicting meningioma methylation classes using tumor methylome data from 142 (+51) patients and corresponding hematoxylin-eosin-stained histological sections. Pairwise analysis of sample cohorts from three meningioma methylation classes demonstrated high accuracy in two combinations. The performance of our approach was validated using an independent set of 51 meningioma patient samples. Importantly, attention map visualization revealed that the algorithm primarily focuses on tumor regions deemed significant by neuropathologists, offering insights into the decision-making process of the CNN. Our findings highlight the capacity of CNNs to effectively harness phenotypic information from histological sections through computerized images for precision medicine. Notably, this study is the first demonstration of predicting clinically relevant DNA methylome information using computer vision applied to standard histopathology. The introduced AI framework holds great potential in supporting, augmenting, and expediting meningioma classification in the future.