Mucinous ovarian carcinoma (MOC) is an epithelial ovarian cancer subtype that is frequently misclassified as extraovarian mucinous metastasis (EOM) because of overlapping features. To address this diagnostic challenge, we perform genome-wide DNA methylation profiling of 58 MOCs, 38 EOMs, and 18 mucinous borderline ovarian tumors (mBOTs) collected from six institutions. Methylation analysis defines two mBOT groups, one epigenetically similar to normal ovary and one resembling MOC. Unsupervised clustering reveals two distinct MOC methylation subtypes with potential prognostic relevance in the internal cohort. Using these data together with 389 external profiles, we develop and validate a three-step machine-learning classifier that distinguishes MOC from EOM with 95.5% accuracy. External validation of this classifier on 21 MOCs and 24 EOMs yields an accuracy of 91.11% for differentiating MOC from EOM. These findings establish an epigenetic framework for mucinous ovarian tumors and provide a robust clinical classification tool.

Background: Classification of central nervous system (CNS) tumors has become increasingly complex, raising concerns about the sustainability of comprehensive molecular diagnostics. We have evaluated nanopore whole genome sequencing (nWGS) as a single workflow to replace multiple diagnostic assays. Methods: We performed nWGS on DNA extracted from 90 adult CNS tumor samples (58 retrospective, 32 prospective) and compared the results to findings from standard of care (SoC) diagnostic work-up. Analysis was done through an automated workflow that consolidated diagnostically and therapeutically relevant genomic alterations, including copy-number variation, structural, and single-nucleotide variants, chromosomal aberrations, gene fusions, and methylation-based classification. Results: nWGS supported final diagnostic classification in all samples with >15% tumor cell content, requiring ~3 hours of hands-on library preparation, parallel sample processing, and sequencing times within 72 hours. Methylation-based classification was available within 1 hour and was concordant with the integrated final diagnosis in 89% of cases (80/90). All diagnostically relevant copy-number variations, single-nucleotide variants, and gene fusions were concordant with SoC testing. MGMT promoter methylation status matched in 94% of cases. In addition, nWGS identified prognostic and potentially actionable variants that were not reported or covered by SoC. Conclusions: nWGS delivers comprehensive genetic and epigenetic results with a fast turn-around compared to standard methods. This enables efficient, accurate, and scalable molecular diagnostics of CNS tumors using a single platform. This data supports its implementation in routine clinical practice and may be extended to other cancer types requiring complex genomic profiling. ### Competing Interest Statement F.S. is a co-founder and shareholder of Heidelberg Epignostix GmbH. A.P. became a full-time employee of Heidelberg Epignostix GmbH in December 2024. A.P. and F.S. are inventors on a patent application related to a nanopore sequencing-based method for cancer characterization, filed by Deutsches Krebsforschungszentrum (DKFZ), Universitat Heidelberg, and Oxford Nanopore Technologies PLC (patent application number: 18682016). S.H., H.L. and E.O.V.M. have received reimbursement for travel, accommodation and conference fees to speak at events organized by ONT. The other authors declare no competing interests. ### Clinical Protocols ### Funding Statement This work was supported by grants from Norwegian South-Eastern regional health authorities [grant numbers 2021039, 2023059 to S.H., H.L. and E.O.V.M]. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the Regional Ethics Committee for South East Norway, approvals number 388359 and 853700). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

Leptomeningeal disease (LMD) is a devastating manifestation of advanced cancer, marked by rapid neurological decline and limited treatment options. Immune profiling in central nervous system (CNS) neoplasms, including LMD, is critical for understanding disease biology and guiding therapy. Here, we use single-cell RNA and T cell receptor (TCR) sequencing of cerebrospinal fluid (CSF) from patients with CNS lymphoma (CNSL), brain metastases (BrMs), and glioblastoma (GB), alongside deep TCR sequencing of blood and spatial transcriptomics of brain lesions. We uncover distinct, disease-specific CSF immune landscapes: CNSL-associated LMD shows clonal T cell expansion, while BrMs and GB are enriched in blood-derived and resident-like myeloid cells. Spatial analysis confirms transcriptional similarities between CSF and tumor microenvironments. Longitudinal sampling reveals dynamic immune changes and emerging resistant clones. These findings establish the CSF as an immune-active compartment reflecting disease-specific features and highlight the value of CSF liquid biopsy for immune monitoring and therapeutic stratification in LMD.

Abstract Background Classification of central nervous system (CNS) tumors has become increasingly complex over the past decade, raising concerns about the availability, feasibility and sustainability of comprehensive molecular diagnostics. We have evaluated nanopore whole genome sequencing (nWGS) as a single workflow to replace multiple diagnostic assays. Methods We performed nWGS on DNA extracted from 90 adult CNS tumor samples (58 retrospective, 32 prospective) and compared the results to findings from standard of care (SoC) diagnostic work-up. Analysis was done through an automated workflow that consolidated diagnostically and therapeutically relevant genomic alterations, including copy-number variation, structural, and single-nucleotide variants, chromosomal aberrations, gene fusions and methylation-based classification. Results Nanopore WGS enabled final diagnostic classification in all samples with >15% tumor cell content, requiring ∼3 hours of hands-on library preparation, parallel sample processing, and sequencing times within 72 hours. Methylation-based classification was available within 1 hour and was concordant with the integrated final diagnosis in 89% of cases (80/90). All diagnostically relevant copy-number variations, single-nucleotide variants, and gene fusions were concordant with standard-of-care testing, and MGMT promoter methylation status matched in 94% of cases. In addition, nWGS identified prognostic and potentially actionable variants that were not reported or covered by SoC. Conclusions Nanopore WGS delivers comprehensive genetic and epigenetic results with a fast turn-around compared to standard methods. This enables efficient, accurate, and scalable molecular diagnostics of CNS tumors using a single platform. Its broad applicability supports its implementation in routine clinical practice and may be extended to other cancer types requiring complex genomic profiling.

Human induced pluripotent stem cells (iPSCs) hold great promise for regenerative medicine, disease modelling, and drug discovery, but most downstream applications require differentiation into specialised cell types not covered by current quality control assays. Here, we present “SteMClass”, a proof-of-concept DNA methylation-based classifier that standardises iPSC differentiation state identification across protocols with one test. We curated a reference cohort of 15 iPSC lines differentiated into seven distinct states (n = 97), performed array-based DNA methylation profiling, and trained a random forest model to classify the eight distinct differentiation states. In nested cross-validation, SteMClass achieved a Brier score of 0.0264, and on an independent cohort (n = 58) attained 96.5% accuracy (Cohen’s K = 0.959) with a 3% rejection rate. Applied to external data (n = 241), performance was 76.5% accuracy (Cohen’s K = 0.593) with a 16.6% rejection rate and enabled the detection of potentially inefficient differentiations. SteMClass is compatible with all Illumina methylation array versions, and accessible via an interactive web interface that supports classification and exploration of DNA methylation profiles. By providing a harmonised, single-assay framework for iPSC-derived differentiation state characterisation, SteMClass improves reproducibility and comparability across studies, paving the way for robust quality control standards and accelerating clinical translation. ### Competing Interest Statement The authors have declared no competing interest. German Academic Exchange Service, https://ror.org/039djdh30 Deutschen Konsortium für Translationale Krebsforschung, https://ror.org/02pqn3g31

Assessing anti-tumor immune responses and immune microenvironments in central nervous system (CNS) neoplasms, such as brain tumors and leptomeningeal disease (LMD), provides prognostic insights and predictive biomarkers. Liquid biopsy of the cerebrospinal fluid (CSF) represents a promising minimally-invasive approach, but its ability to reflect immune responses against tumors remains unclear. Here, we used single-cell sequencing of CSF cells and spatial transcriptomics of CNS lesions to compare and contrast LMD patients with CNS lymphoma (CNSL), glioblastoma (GB) and brain metastases (BrM), to neuroinflammatory CNS disorders. We identified disease-specific CSF environments, reflecting parenchymal tumor microenvironment features. CNSL showed robust T cell responses, while BrM and GB were dominated by both blood-derived and tissue-resident myeloid cells. Longitudinal CSF sampling unveiled mechanisms of disease progression and therapy resistance, highlighting the potential of CSF liquid biopsies for uncovering disease biology, discovering cellular biomarkers and developing personalized therapies for CNS neoplasms. ### Competing Interest Statement JPS has received honoraria for lectures, advisory board participation, consulting, and travel grants from Abbvie, Roche, Boehringer, Bristol-Myers Squibb, Medac, Mundipharma, and UCB unrelated to this study. PSZ has received a lecture honorarium from Bristol-Myers Squibb unrelated to this study. HH is co-founder and shareholder of Omniscope, scientific advisory board member of Nanostring and MiRXES, consultant to Moderna and Singularity and has received an honorarium from Genentech. JCN is a scientific consultant to Omniscope. The remaining authors declare that the research was carried out without any commercial or financial relationships that could potentially create a conflict of interest.

DNA methylation-based classification is now central to contemporary neuro-oncology, as highlighted by the World Health Organization (WHO) classification of central nervous system (CNS) tumors. We present the Heidelberg CNS Tumor Methylation Classifier version 12.8 (v12.8), trained on 7,495 methylation profiles, which expands recognized entities from 91 classes in version 11 (v11) to 184 subclasses. This expansion is a result of newly identified tumor types discovered through our large online repository and global collaborations, underscoring CNS tumor heterogeneity. The random forest-based classifier achieves 95% subclass-level accuracy, with its well-calibrated probabilistic scores providing a reliable measure of confidence for each classification. Its hierarchical output structure enables interpretation across subclass, class, family, and superfamily levels, thereby supporting clinical decisions at multiple granularities. Comparative analyses demonstrate that v12.8 surpasses previous versions and conventional WHO-based approaches. These advances highlight the improved precision and practical utility of the updated classifier in personalized neuro-oncology.

Molecular data integration plays a central role in central nervous system (CNS) tumor diagnostics but currently used assays pose limitations due to technical complexity, equipment and reagent costs, as well as lengthy turnaround times. We previously reported the development of Rapid-CNS2, an adaptive-sampling-based nanopore sequencing workflow. Here we comprehensively validated and further developed Rapid-CNS2 for intraoperative use. It now offers real-time methylation classification and DNA copy number information within a 30-min intraoperative window, followed by comprehensive molecular profiling within 24 h, covering the complete spectrum of diagnostically and therapeutically relevant information for the respective entity. We validated Rapid-CNS2 in a multicenter setting on 301 archival and prospective samples including 18 samples sequenced intraoperatively. To broaden the utility of methylation-based CNS tumor classification, we developed MNP-Flex, a platform-agnostic methylation classifier encompassing 184 classes. MNP-Flex achieved 99.6% accuracy for methylation families and 99.2% accuracy for methylation classes with clinically applicable thresholds across a global validation cohort of more than 78,000 frozen and formalin-fixed paraffin-embedded samples spanning five different technologies. Integration of these tools has the potential to advance CNS tumor diagnostics by providing broad access to rapid, actionable molecular insights crucial for personalized treatment strategies. Application of a nanopore sequencing workflow for real-time analysis of brain tumors results in molecular classification within a 30-minute intraoperative window, followed by comprehensive profiling within 24 hours.

DNA methylation-based classification of (brain) tumors has emerged as a powerful and indispensable diagnostic technique. Initial implementations used methylation microarrays for data generation, while most current classifiers rely on a fixed methylation feature space. This makes them incompatible with other platforms, especially different flavors of DNA sequencing. Here, we describe crossNN, a neural network-based machine learning framework that can accurately classify tumors using sparse methylomes obtained on different platforms and with different epigenome coverage and sequencing depth. It outperforms other deep and conventional machine learning models regarding accuracy and computational requirements while still being explainable. We use crossNN to train a pan-cancer classifier that can discriminate more than 170 tumor types across all organ sites. Validation in more than 5,000 tumors profiled on different platforms, including nanopore and targeted bisulfite sequencing, demonstrates its robustness and scalability with 99.1% and 97.8% precision for the brain tumor and pan-cancer models, respectively.

BAP1-deficient meningiomas have a preferential infratentorial or spinal localization and may present with an undifferentiated histology of small or epithelioid cells rather than the meningothelial, rhabdoid or papillary variants. Frequent expression of cytokeratins may be misleading for a metastatic carcinoma but loss of BAP1 immunostaining in tumor cells and a specific methylation class enable the diagnosis. The clinical impact of the histomolecular diagnosis of BAP1-deficient meningioma is the high risk of relapse and a possible underlying BAP1 tumour predisposition syndrome.

Circular extrachromosomal DNA (ecDNA) is a form of oncogene amplification found across cancer types and associated with poor outcome in patients. ecDNA can be structurally complex and can contain rearranged DNA sequences derived from multiple chromosome locations. As the structure of ecDNA can impact oncogene regulation and may indicate mechanisms of its formation, disentangling it at high resolution from sequencing data is essential. Even though methods have been developed to identify and reconstruct ecDNA in cancer genome sequencing, it remains challenging to resolve complex ecDNA structures, in particular amplicons with shared genomic footprints. We here introduce Decoil, a computational method that combines a breakpoint-graph approach withLASSOregression to reconstruct complex ecDNA and deconvolve co-occurring ecDNA elements with overlapping genomic footprints from long-read nanopore sequencing. Decoil outperforms de novo assembly and alignment-based methods in simulated long-read sequencing data for both simple and complex ecDNAs. Applying Decoil on whole-genome sequencing data uncovered different ecDNA topologies and explored ecDNA structure heterogeneity in neuroblastoma tumors and cell lines, indicating that this method may improve ecDNA structural analyses in cancer.

The gold standard for precise diagnostic classification of brain tumors requires tissue sampling, which carries relevant procedural risks. Brain biopsies often have limited sensitivity and fail to address tumor heterogeneity, because small tissue parts are being examined. This study aims to explore the detection and quantification of diagnostically relevant somatic copy number aberrations (SCNAs) in cell-free DNA (cfDNA) extracted from cerebrospinal fluid (CSF) in a real-world cohort of patients with defined brain tumor subtypes. A total of 33 CSF samples were collected from 30 patients for cfDNA extraction. Shallow whole-genome sequencing was conducted on CSF samples containing > 3ng of cfDNA and corresponding tissue DNA from nine patients. The sequencing cohort encompassed 26 samples of 23 patients, comprising 12 with confirmed CNS cancer as compared to 11 patients with either ambiguous CNS lesions (n = 5) or non-cancer CNS lesions (n = 6). After mapping and quality filtering SCNAs were called by depth-of-coverage analyses with a binning of 5.5 Mbp. SCNAs were exclusively identified in CSF cfDNA from brain tumor patients (10/12, 83

The introduction of tumour classification by analysis of DNA methylation has changed the landscape of paediatric neuro-oncology. With the launch of Oxford Nanopore sequencing, generation of sequencing data is possible in real-time, including information on base-modifications. Diverse classification tools for tumour tissue have been developed recently. While this is an important step towards optimization of surgical treatment, pre-operative knowledge on tumour entity would further facilitate surgical planning. Therefore, we investigated the possibility of liquid biopsy for rapid pre-operative tumour classification. CSF samples from patients with pre-operative extra-ventricular drain (EVD) (n = 5) as well as samples obtained via Ommaya reservoirs in cases of suspected recurrence (n = 3) were analysed. Additionally, samples from non-tumour patients (epilepsy, inflammation) who had lumbar puncture were available as controls. Samples were sequenced on MinIon devices (Oxford Nanopore) and subsequently classified with previously published tumour tissue classification pipelines (NanoDx, crossNN, Sturgeon). Even though CSF derived from EVD has been stored on room temperature for up to 72 hours before processing, cfDNA isolation was possible with the advantage of higher CSF input amounts. DNA fragments showed a distribution characteristic for cell-free DNA, with a maximum peak at 170bp. The amount of total input DNA ranged from 0.35ng to 12ng, with higher input amounts generating more sequencing data but not resulting in better classification. Classification by sturgeon and crossNN/nanoDx using CSF derived cfDNA was possible in exemplary cases, such as a medulloblastoma patient with a new inoperable lesion, where CSF analysis resulted in medulloblastoma Group 4 within the first hour of sequencing. In total, 38% of samples were correctly classified. Liquid biopsy as a method to pre-operatively classify tumours and thereby enable risk-adjusted surgery is feasible. Ongoing efforts focus on optimisation of ctDNA based tumour classification by extension of the cohort.

DNA methylation-based classification of brain tumors has emerged as a powerful and indispensable diagnostic technique. Initial implementations have used methylation microarrays for data generation, but different sequencing approaches are increasingly used. Most current classifiers, however, rely on a fixed methylation feature space, rendering them incompatible with other platforms, especially different flavors of DNA sequencing. Here, we describe crossNN, a neural network-based machine learning framework which can accurately classify tumor entities using DNA methylation profiles obtained from different platforms and with different epigenome coverage and sequencing depth. It outperforms other deep- and shallow machine learning models with respect to precision as well as simplicity and computational requirements while still being fully explainable. Validation in a large cohort of >1,900 tumors profiled using different microarray and sequencing platforms, including low-pass nanopore and targeted bisulfite sequencing, demonstrates the robustness and scalability of the model.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThis study was funded by The Brain Tumour Charity, UK, grant no. GN-000694. ### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The local ethics committee (Charite Universitaetsmedizin Berlin, Berlin, Germany; EA2/041/18) approved generation of prospective data in the context of this study. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors

Abstract The 2021 WHO classification underscores the importance of molecular data integration in Central Nervous System (CNS) tumor diagnostics. However, currently used assays have disadvantages due to technical complexity, required equipment and reagent cost, as well as lengthy turnaround times. In response to these challenges, we introduce Rapid-CNS2 and MNP-Flex. Rapid-CNS2, an adaptive sampling-based nanopore sequencing workflow, offers real-time methylation classification and DNA copy-number information within a 30-minute window, suitable for intra-operative settings, followed by comprehensive molecular profiling within 24 h, covering the complete spectrum of diagnostically and therapeutically relevant information for the respective entity. We have prospectively validated Rapid-CNS2 in a multi-center setting on 223 samples. For even more widespread use of methylation-based CNS tumor classification, we developed MNP-Flex, a platform-agnostic methylation classifier encompassing 184 CNS tumor classes. MNP-flex achieved 92% accuracy across a global validation cohort of 78,000 samples spanning five different technologies. These innovations represent a significant advancement in CNS tumor diagnostics, making rapid, actionable molecular insights more widely and more rapidly available, which is crucial for personalized treatment strategies. Their integration streamlines the diagnostic process, broadening access to accurate molecular classification and promising improved patient outcomes in neurooncology on a global scale. MNP-Flex is available as a web-service https://mnp-flex.org and the Rapid-CNS2 workflow is available on Github.

Background Although cavitating ultrasonic aspirators are commonly used in neurosurgical procedures, the suitability of ultrasonic aspirator-derived tumor material for diagnostic procedures is still controversial. Here, we explore the feasibility of using ultrasonic aspirator-resected tumor tissue to classify otherwise discarded sample material by fast DNA methylation-based analysis using low pass nanopore whole genome sequencing. Methods ultrasonic aspirator-derived specimens from pediatric patients undergoing brain tumour resection were subjected to low-pass nanopore whole genome sequencing. DNA methylation-based classification using a neural network classifier and copy number variation analysis were performed. Tumor purity was estimated from copy number profiles. Results were compared to microarray (EPIC)-based routine neuropathological histomorphological and molecular evaluation. Results 18 samples with confirmed neuropathological diagnosis were evaluated. All samples were successfully sequenced and passed quality control for further analysis. DNA and sequencing characteristics from ultrasonic aspirator-derived specimens were comparable to routinely processed tumor tissue. Classification of both methods was concordant regarding methylation class in 16/18 (89%) cases. Application of a platform-specific threshold for nanopore-based classification ensured a specificity of 100%, whereas sensitivity was 78%. Copy number variation profiles were generated for all cases and matched EPIC results in 16/18 (89%) samples, even allowing the identification of diagnostically or therapeutically relevant genomic alterations. Conclusion Methylation-based classification of pediatric CNS tumors based on ultrasonic aspirator-reduced and otherwise discarded tissue is feasible using time- and cost-efficient nanopore sequencing.

Primers and annealing temperatures used in quantitative allele-specific real-time PCR.

The mainstay of treatment for adult patients with gliomas, glioneuronal and neuronal tumors consists of combinations of surgery, radiotherapy, and chemotherapy. For many systemic cancers, targeted treatments are a part of the standard of care, however, the predictive significance of most of these targets in central nervous system (CNS) tumors remains less well-studied. Despite that, there is increasing use of advanced molecular diagnostics that identify potential targets, and tumor-agnostic regulatory approvals on targets also present in CNS tumors have been granted. This raises the question of when and for which targets it is meaningful to test in adult patients with CNS tumors. This evidence-based guideline reviews the evidence available for targeted treatment for alterations in the RAS/MAPK pathway (BRAF, NF1), in growth factor receptors (EGFR, ALK, fibroblast growth factor receptor (FGFR), neurotrophic tyrosine receptor kinase (NTRK), platelet-derived growth factor receptor alpha, and ROS1), in cell cycle signaling (CDK4/6, MDM2/4, and TSC1/2) and altered genomic stability (mismatch repair, POLE, high tumor mutational burden (TMB), homologous recombination deficiency) in adult patients with gliomas, glioneuronal and neuronal tumors. At present, targeted treatment for BRAF p.V600E alterations is to be considered part of the standard of care for patients with recurrent gliomas, pending regulatory approval. For approved tumor agnostic treatments for NTRK fusions and high TMB, the evidence for efficacy in adult patients with CNS tumors is very limited, and treatment should preferably be given within prospective clinical registries and trials. For targeted treatment of CNS tumors with FGFR fusions or mutations, clinical trials are ongoing to confirm modest activity so far observed in basket trials. For all other reviewed targets, evidence of benefit in CNS tumors is currently lacking, and testing/treatment should be in the context of available clinical trials.

Telomerase promoter genotype and MGMT status in matched glioblastoma tumor vs. cell line pairs.

Tumor purity of matched tumor/cell line pairs: Tumor purity of GBM bulk tumor samples and matched gliomasphere cell lines was determined either using ESTIMATE for deconvolution of gene expression profiles (A) or pTERT MAF (B, C). Connected dots indicate matched pairs.