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.

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Meningiomas are the most frequent primary intracranial tumors. They can follow a wide clinical spectrum from benign to highly aggressive clinical course. No specific therapy exists for refractory cases or cases not amenable to resection and radiotherapy. Identification of risk of recurrence and malignant transformation for the individual patients is challenging. However, promising molecular markers and prognostic subgrouping by DNA methylation are emerging. Still, the biological underpinnings of these diagnostic subgroups are elusive, and, consequently, no novel therapeutic options arise thereof. Here we establish robust subgroups across the full landscape of meningiomas, consistent through DNA methylation, mutations, the transcriptomic, proteomic and phospho-proteomic level. Pronounced proliferative stress and DNA damage repair signals in malignant cells and in clusters exclusive to recurrent tumors are in line with their higher mitotic activity, but also provide an explanation for the accumulation of genomic instability in anaplastic meningiomas. Although homozygous deletion of CDKN2A/B is a diagnostic marker of high-grade meningioma, the expression of its gene product increased from low to non-deleted high-grade cases. Differences between subgroups in lymphocyte and myeloid cell infiltration, representing a majority of tumor mass in low-grade NF2 tumors, could be assigned to cluster-specific interaction with tumor cells. Activation to a more proinflammatory phenotype and decreased infiltration of myeloid cells in high-grade cases correlated with lower expression of CSF1, located on chromosome arm 1p, whose deletion is known as prognostic marker, with no proposed mechanism before. Our results demonstrate a robust molecular subclassification of a tumor type across multiple layers, provide insight into heterogeneous growth dynamics despite shared pathognomonic mutations, and highlight immune infiltration modulation as a novel target for meningioma therapy.

Background: The 2021 WHO classification of central nervous system tumors includes multiple molecular markers and patterns that are recommended for routine diagnostic use in addition to histology. Sequencing infrastructures for complete molecular profiling require considerable investment, while batching samples for sequencing and methylation profiling can delay turnaround time. We introduce RAPID-CNS2, a nanopore adaptive sequencing pipeline that enables comprehensive mutational, methylation and copy number profiling of CNS tumours with a single, cost-effective sequencing assay. It can be run for single samples and offers highly flexible target selection that can be personalized per case with no additional library preparation. Methods: Utilizing ReadFish, a toolkit enabling targeted nanopore sequencing without the need for library enrichment, we sequenced DNA from 22 diffuse glioma samples on a MinION device. Target regions comprised our Heidelberg brain tumor NGS panel and pre-selected CpG sites for methylation classification using an adapted random forest classifier. Pathognomonic alterations, copy number profiles, and methylation classes were called using a custom bioinformatics pipeline. The resulting data were compared to their corresponding standard NGS panel sequencing and EPIC methylation array results. Results: Complete concordance with the EPIC array was found for copy number profiles. The vast majority (94%) of pathognomonic mutations were congruent with standard NGS panel-seq data. MGMT promoter status was correctly identified in all samples. Methylation families from the random forest classifier were detected with 96% congruence. Among the alterations decisive for rendering a WHO 2021 classification-compatible integrated diagnosis, 97% of the alterations were consistent over the entire cohort (completely congruent in 19/22 cases and sufficient for unequivocal diagnosis in all 22 samples). Conclusions: RAPID-CNS2 provides a swift and highly flexible alternative to conventional NGS and array-based methods for SNV/InDel analysis, detection of copy number alterations, target gene methylation analysis (e.g. MGMT) and methylation-based classification. The turnaround time of ~5 days for this proof-of-concept study can be further shortened to < 24h by optimizing target sizes and enabling real-time computational analysis. Expected advances in nanopore sequencing and analysis hardware make the prospect of integrative molecular diagnosis in an intra-operative setting a feasible prospect in future. This low-capital approach would be cost-efficient for low throughput settings or in locations with less sophisticated laboratory infrastructure, and invaluable in cases requiring immediate diagnoses.

An obstacle to effective uniform treatment of glioblastoma, especially at recurrence, is genetic and cellular intertumoral heterogeneity. Hence, personalized strategies are necessary, as are means to stratify potential targeted therapies in a clinically relevant timeframe. Functional profiling of drug candidates against patient-derived glioblastoma organoids (PD-GBO) holds promise as an empirical method to preclinically discover potentially effective treatments of individual tumors. Here, we describe our establishment of a PD-GBO-based functional profiling platform and the results of its application to four patient tumors. We show that our PD-GBO model system preserves key features of individual patient glioblastomas in vivo. As proof of concept, we tested a panel of 41 FDA-approved drugs and were able to identify potential treatment options for three out of four patients; the turnaround from tumor resection to discovery of treatment option was 13, 14, and 15 days, respectively. These results demonstrate that this approach is a complement and, potentially, an alternative to current molecular profiling efforts in the pursuit of effective personalized treatment discovery in a clinically relevant time period. Furthermore, these results warrant the use of PD-GBO platforms for preclinical identification of new drugs against defined morphological glioblastoma features.

BACKGROUND The WHO classification 2021 includes multiple molecular markers for routine diagnostics in addition to histology. Sequencing setup for complete molecular profiling requires considerable investment, while batching samples for sequencing and methylation profiling can delay turnaround time. We introduce RAPID-CNS2, a nanopore adaptive sequencing pipeline that enables comprehensive mutational, methylation and copy number profiling of CNS tumours with a single third generation sequencing assay. It can be run for single samples and offers highly flexible target selection requiring no additional library preparation. MATERIAL AND METHODS Utilising ReadFish, a toolkit enabling targeted nanopore sequencing, we sequenced DNA from 22 diffuse glioma patient samples on a MinION device. Target regions comprised our Heidelberg brain tumour NGS panel and pre-selected CpG sites for methylation classification by an adapted random forest classifier. Pathognomonic alterations, copy number profiles, and methylation classes were called using a custom bioinformatics pipeline. Results were compared to their corresponding NGS panel-seq and EPIC array outputs. RESULTS Complete concordance with the EPIC array was found for copy number profiles from RAPID-CNS2. 94% pathognomonic mutations were congruent with NGS panel-seq. MGMT promoter status was correctly identified in all samples. Methylation families were detected with 96% congruence. Among the alterations decisive for rendering a classification-compatible integrated diagnosis, 97% of the alterations were consistent over the entire cohort (completely congruent in 19/22 cases and sufficient for unequivocal diagnosis in all). CONCLUSION RAPID-CNS2 provides a swift and highly flexible alternative to conventional NGS and array-based methods for SNV/Indel analysis, detection of copy number alterations and methylation classification. The turnaround time of ~4 days can be further shortened to &lt;12h by altering target sizes. It offers a low-capital approach that would be cost-efficient for low throughput settings and invaluable in cases requiring immediate diagnoses.

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BACKGROUND The WHO classification 2021 includes multiple molecular markers for routine diagnostics in addition to histology. Sequencing setup for complete molecular profiling requires considerable investment, while batching samples for sequencing and methylation profiling can delay turnaround time. We introduce RAPID-CNS2, a nanopore adaptive sequencing pipeline that enables comprehensive mutational, methylation and copy number profiling of CNS tumours with a single third generation sequencing assay. It can be run for single samples and offers highly flexible target selection requiring no additional library preparation. METHODS Utilising ReadFish, a toolkit enabling targeted nanopore sequencing, we sequenced DNA from 22 diffuse glioma patient samples on a MinION device. Target regions comprised our Heidelberg brain tumour NGS panel and pre-selected CpG sites for methylation classification by an adapted random forest classifier. Pathognomonic alterations, copy number profiles, and methylation classes were called using a custom bioinformatics pipeline. Results were compared to their corresponding NGS panel-seq and EPIC array outputs. RESULTS Complete concordance with the EPIC array was found for copy number profiles from RAPID-CNS2. 94% pathognomonic mutations were congruent with NGS panel-seq. MGMT promoter status was correctly identified in all samples. Methylation families were detected with 96% congruence. Among the alterations decisive for rendering a classification-compatible integrated diagnosis, 97% of the alterations were consistent over the entire cohort (completely congruent in 19/22 cases and sufficient for unequivocal diagnosis in all). CONCLUSIONS RAPID-CNS2 provides a swift and highly flexible alternative to conventional NGS and array- based methods for SNV/Indel analysis, detection of copy number alterations and methylation classification. The turnaround time of ~4 days can be further shortened to &lt; 12h by altering target sizes. It offers a low-capital approach that would be cost-efficient for low throughput settings and invaluable in cases requiring immediate diagnoses.