PURPOSE: Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established ( CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma.METHODS: DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases.RESULTS: Both CNV- and methylation family–based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively).CONCLUSION: Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction.

Inferring a person’s smoking habit and history from blood is relevant for complementing or replacing self-reports in epidemiological and public health research, and for forensic applications. However, a finite DNA methylation marker set and a validated statistical model based on a large dataset are not yet available. Employing 14 epigenome-wide association studies for marker discovery, and using data from six population-based cohorts (N = 3764) for model building, we identified 13 CpGs most suitable for inferring smoking versus non-smoking status from blood with a cumulative Area Under the Curve (AUC) of 0.901. Internal fivefold cross-validation yielded an average AUC of 0.897 ± 0.137, while external model validation in an independent population-based cohort (N = 1608) achieved an AUC of 0.911. These 13 CpGs also provided accurate inference of current (average AUC_{crossvalidation} 0.925 ± 0.021, AUC_{externalvalidation}0.914), former (0.766 ± 0.023, 0.699) and never smoking (0.830 ± 0.019, 0.781) status, allowed inferring pack-years in current smokers (10 pack-years 0.800 ± 0.068, 0.796; 15 pack-years 0.767 ± 0.102, 0.752) and inferring smoking cessation time in former smokers (5 years 0.774 ± 0.024, 0.760; 10 years 0.766 ± 0.033, 0.764; 15 years 0.767 ± 0.020, 0.754). Model application to children revealed highly accurate inference of the true non-smoking status (6 years of age: accuracy 0.994, N = 355; 10 years: 0.994, N = 309), suggesting prenatal and passive smoking exposure having no impact on model applications in adults. The finite set of DNA methylation markers allow accurate inference of smoking habit, with comparable accuracy as plasma cotinine use, and smoking history from blood, which we envision becoming useful in epidemiology and public health research, and in medical and forensic applications.

Risk prediction for meningioma tumors was until recently almost exclusively based on morphological features of the tumor. To improve risk prediction, multiple models have been established that incorporate morphological and molecular features for an integrated risk prediction score. One such model is the integrated molecular‐morphologic meningioma integrated score (IntS), which allocates points to the histological grade, epigenetic methylation family and specific copy‐number variations. After publication of the IntS, questions arose in the neuropathological community about the practical and clinical implementation of the IntS, specifically regarding the calling of CNVs, the applicability of the newly available version (v12.5) of the brain tumor classifier and the need for incorporation of TERT‐promoter and CDKN2A/B status analysis in the IntS calculation. To investigate and validate these questions additional analyses of the discovery (n = 514), retrospective validation (n = 184) and prospective validation (n = 287) cohorts used for IntS discovery and validation were performed. Our findings suggest that any loss over 5% of the chromosomal arm suffices for the calling of a CNV, that input from the v12.5 classifier is as good or better than the dedicated meningioma classifier (v2.4) and that there is most likely no need for additional testing for TERT‐promoter mutations and/or homozygous losses of CDKN2A/B when defining the IntS for an individual patient. The findings from this study help facilitate the clinical implementation of IntS‐based risk prediction for meningioma patients.

MicroRNAs (miRNAs) are small non-coding RNA molecules that post-transcriptionally regulate the translation of messenger RNAs. Given the crucial role of miRNAs in gene expression, genetic variants within miRNA-related sequences may affect miRNA function and contribute to disease risk. Osteoporosis is characterized by reduced bone mass, and bone mineral density (BMD) is a major diagnostic proxy to assess osteoporosis risk. Here, we aimed to identify miRNAs that are involved in BMD using data from recent genome-wide association studies (GWAS) on femoral neck, lumbar spine and forearm BMD. Of 242 miRNA-variants available in the GWAS data, we found rs11614913:C > T in the precursor miR-196a-2 to be significantly associated with femoral neck-BMD (p-value = 9.9 × 10⁻⁷, β = −0.038) and lumbar spine-BMD (p-value = 3.2 × 10⁻¹¹, β = −0.061). Furthermore, our sensitivity analyses using the Rotterdam study data showed a sex-specific association of rs11614913 with BMD only in women. Subsequently, we highlighted a number of miR-196a-2 target genes, expressed in bone and associated with BMD, that may mediate the miRNA function in BMD. Collectively, our results suggest that miR-196a-2 may contribute to variations in BMD level. Further biological investigations will give more insights into the mechanisms by which miR-196a-2 control expression of BMD-related genes.

Classification of tumors in neuro-oncology today relies on molecular patterns (mostly DNA methylation) and their machine learning-supported interpretation. Understanding the process of algorithmic interpretation is essential for safe application in clinical routine. This is paradigmatically true for the most common primary intracranial tumor in adults, meningioma. Here, by applying multiomic profiling and multiple lines of orthogonal computational evaluation in multiple independent datasets, we found that not only tumor cell characteristics but also incremental changes in the tumor microenvironment (TME) have impact on epigenetic meningioma classification and clinical outcome. Besides revealing the decisive role of non-neoplastic cells in the CNS methylation classifier, this challenges the model of distinct meningioma subgroups toward a TME-determined risk continuum. This refines current controversies in molecular meningioma subtyping. In addition, we apply these learnings to devise and validate a simple diagnostic approach for increased clinical prediction accuracy based on immunohistochemistry, which is also applicable in resource-limited settings.

Diffuse infiltrating gliomas are the most common malignant brain tumors in adults. The 2021 World Health Organization classification for central nervous system tumors (CNS5 WHO) has significantly altered the rules for classification and grading of diffuse gliomas. Clinicians, including neurology residents and neurologists, will have to consider the changes that include the introduction of new tumor types, allotting established tumor types to other groups, and substituting previously essential morphological features for additional molecular markers. For example, in the current classification, glioblastoma is defined as isocitrate dehydrogenase (IDH)-wildtype, grade 4. Whereas, a grade 4 IDH-mutated astrocytic glioma is referred to as astrocytoma, IDH-mutated, grade 4. Additionally, potential targeted treatments, based on the underlying molecular alterations, have become therapeutic options for diffuse gliomas. For clinicians, it is important to know the rationale for why these options are only available for specific tumors. Due to the emphasis of molecular markers in the CNS5 WHO classification, interpretation of a pathology report and understanding of its clinical implications can be challenging. This review describes the most important molecular alterations in glioma, summarizes the recent changes in the CNS5 WHO classification for glioma, and presents a stepwise approach for trainees and neurologist to decipher a glioma pathology report.