Tumor Treating Fields (TTFields) are an adjunctive treatment for glioblastoma, isocitrate dehydrogenase wildtype (IDH wt). TTFields indication is not standardized; patient selection is mostly based on a high KPS. Objective: This study aims to identify molecular biomarkers guiding clinical decisions on TTFields initiation. Patients & methods: A retrospective analysis was conducted on 64 patients with newly diagnosed glioblastoma, IDH wt treated with surgery, radiochemotherapy, followed by TTFields. Clinical data (e.g. extent of resection, survival data) were collected. Tumors underwent TSO500 DNA/RNA sequencing and methylation profiling (EPIC). Alterations were analyzed for association with overall survival using Kaplan-Meier as well as univariate and multivariate Cox models. Methylation class, tumor mutional burden, and signaling pathway activation (e.g. PI3K/AKT/mTOR pathway) were also assessed for associations with overall survival. Two TTFields-naïve glioblastoma, IDH wt cohorts served as controls (combined n=175). Molecular profiling revealed 25 alterations occurring in at least 5 patients (e.g., mutations: PTEN, EGFR, deletions: PTEN, CDKN2A/B; amplifications: EGFR). Univariate analyses showed preoperative KPS and MGMT promoter methylation as protective, while EGFR amplification, CDKN2A/B, and PTEN homozygous deletions were linked to worse survival in the TTFields treated cohort. Multivariate analysis confirmed KPS and MGMT as protective and PTEN homozygous deletion as a significant risk factor for worse outcome (HR: 3.86, 95% CI: 1.51–9.87, p=0.0049). Comparative analysis with TTFields-naïve cohorts showed no link between PTEN homozygous deletion and worse outcomes, with homozygous deletion rates comparable across cohorts (controls: 7%, TTFields: 11%). Alongside established protective outcome factors MGMT and KPS, in our cohort of glioblastoma, IDH wt patients treated with TTFields, PTEN homozygous deletion was significantly associated with worse survival. PTEN deletion status may thus predict reduced benefit from TTFields, warranting testing before treatment initiation.

Abstract WHO grade 2 meningiomas exhibit highly heterogeneous clinical courses. While the Ki67 proliferation index is a standard biomarker, its prognostic utility remains limited by methodological inconsistency and potential time-dependent dynamics. We evaluated an automated, artifact-adjusted Ki67 assessment and its integration with molecular risk profiling. 98 WHO grade 2 meningiomas (WHO 2021) were analyzed using an automated QuPath-based pipeline with HistoART for artifact exclusion. Molecular risk was defined by methylation and copy number profiling to calculate the integrated molecular-morphologic risk score by Maas et al. We employed extended Cox models to account for proportional hazards violations. Automated Ki67 values were significantly lower than routine pathological estimates (median 2.91% vs. 10%; p < 0.001) and correlated modestly with integrated risk scores (ρ = 0.26, p = 0.009). We identified a biphasic risk pattern: within the first 38 postoperative months, an automated Ki67 > 3.62% was a strong independent predictor for local recurrence (HR 5.06, p < 0.001) and progression-free survival (HR 4.15, p = 0.002), remaining significant alongside subtotal resection and the integrated risk group. Beyond 38 months, prognostic impact attenuated. Ki67 and the integrated molecular risk score contributed independently in multivariable models, suggesting complementary biological dimensions. Automated, artifact-adjusted Ki67 quantification provides time-dependent, independent prognostic information in WHO grade 2 meningioma, complementary to molecular risk stratification. It may serve as a cost-effective surveillance marker—both as an adjunct to molecular profiling and as a standalone tool where molecular testing is unavailable.

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

AIMS:DNA methylation profiling, recently endorsed by the World Health Organisation (WHO) as a pivotal diagnostic tool for brain tumours, most commonly relies on bead arrays. Despite its widespread use, limited data exist on the technical reproducibility and potential cross-institutional differences. The LOGGIC Core BioClinical Data Bank registry conducted a prospective laboratory comparison trial with 12 international laboratories to enhance diagnostic accuracy for paediatric low-grade gliomas, focusing on technical aspects of DNA methylation data generation and profile interpretation under clinical real-time conditions. METHODS:Four representative low-grade gliomas of distinct histologies were centrally selected, and DNA extraction was performed. Participating laboratories received a DNA aliquot and performed the DNA methylation-based classification and result interpretation without knowledge of tumour histology. Additionally, participants were required to interpret the copy number profile derived from DNA methylation data and conduct DNA sequencing of the BRAF hotspot p.V600 due to its relevance for low-grade gliomas. Results had to be returned within 30 days. RESULTS:High technical reproducibility was observed, with a median pairwise correlation of 0.99 (range 0.94-0.99) between coordinating laboratory and participants. DNA methylation-based tumour classification and copy number profile interpretation were consistent across all centres, and BRAF mutation status was accurately reported for all cases. Eleven out of 12 centres successfully reported their analysis within the 30-day timeframe. CONCLUSION:Our study demonstrates remarkable concordance in DNA methylation profiling and profile interpretation across 12 international centres. These findings underscore the potential contribution of DNA methylation analysis to the harmonisation of brain tumour diagnostics.

Abstract Introduction: The metastasis of a pancreatic ductal adenocarcinoma (PAAD) is a diagnosis of exclusion and one of the most common causes of cancer of unknown primary (CUP). We have recently developed a genome-wide DNA methylation-based neural network classifier that can accurately differentiate between liver metastasis of a PAAD and intrahepatic cholangiocarcinoma (iCCA) (PAAD-iCCA-Classifier). Therefore, the aim of our study was to test whether our PAAD-iCCA-Classifier can be extended to be used to correctly diagnose PAAD metastases from other sites in CUP setting. Methods: For this purpose, we enhanced the anomaly detection layer of the classifier by incorporating ten mimicker carcinomas to be excluded by this layer. We used a validation set 1 (n=3786) including primary PAAD (n=242), PAAD liver metastases (n=20), iCCA (n=151) and 10 other mimicker carcinomas (n=3373) and a validation set 2 (n=26) including primary PAAD from a real-life clinical cohort from an independent institution to validate the classifier. Next, we tested the classifier on 16 PAAD initially considered CUP samples (test set) from different sites: peritoneum, lung, liver, and lymph node. The clinical history and diagnostic imaging of these samples were used to confirm PAAD as the most probable origin. We further performed differentially methylated probes (DMP) and copy number alterations (CNA) analysis of primary PAAD and metastatic PAAD from different locations. Results: The improved version of the PAAD-iCCA-Classifier achieved an accuracy of 98.43% on the validation set 1 and was able to exclude most of the mimicker carcinomas. On validation set 2, the classifier achieved an accuracy of 88.46%. Medical history, imaging and immunohistochemical analysis of the test set samples confirmed the diagnosis of PAAD. The DNA methylation classifier correctly labeled 15/16 PAAD metastatic samples as PAAD (93.75% accuracy). We observed that the classifier performance was negatively affected by a high CD3+ immune infiltrate and positively affected by high tumor purity and high proliferation rate. CNA revealed that PAAD liver metastases have a distinct CNA profile characterized by chromosome 6, 9 (CDKN2A/B) and 18q (SMAD4) deletions. DMP analysis showed that PAAD liver metastases have global hypomethylation of both promoter- and enhancer-associated CpGs compared to primary PAAD and PAAD peritoneal carcinomatosis. Finally, gene ontology analysis revealed that different epithelial-mesenchymal transition pathways are activated in PAAD liver metastases compared to PAAD peritoneal carcinomatosis. Conclusion: Our tool performs well in classifying metastatic PAAD samples and could be of great clinical use when a PAAD origin is suspected in the case of a CUP. DMP and CNA profiles show that PAAD liver metastases may have a distinct DNA methylation and copy number profile compared to primary and peritoneal carcinomatosis PAAD. Citation Format: Teodor G. Calina, Eilís Perez, Simon Schallenberg, Horst David, Erik Knutsen, David Capper, Mihnea P. Dragomir. Genome-wide DNA methylation classifier diagnoses pancreatic ductal adenocarcinoma in CUP setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4414.

BackgroundWe have recently constructed a DNA methylation classifier that can discriminate between pancreatic ductal adenocarcinoma (PAAD) liver metastasis and intrahepatic cholangiocarcinoma (iCCA) with high accuracy (PAAD-iCCA-Classifier). PAAD is one of the leading causes of cancer of unknown primary and diagnosis is based on exclusion of other malignancies. Therefore, our focus was to investigate whether the PAAD-iCCA-Classifier can be used to diagnose PAAD metastases from other sites.MethodsFor this scope, the anomaly detection filter of the initial classifier was expanded by 8 additional mimicker carcinomas, amounting to a total of 10 carcinomas in the negative class. We validated the updated version of the classifier on a validation set, which consisted of a biological cohort (n = 3579) and a technical one (n = 15). We then assessed the performance of the classifier on a test set, which included a positive control cohort of 16 PAAD metastases from various sites and a cohort of 124 negative control samples consisting of 96 breast cancer metastases from 18 anatomical sites and 28 carcinoma metastases to the brain.ResultsThe updated PAAD-iCCA-Classifier achieved 98.21% accuracy on the biological validation samples, and on the technical validation ones it reached 100%. The classifier also correctly identified 15/16 (93.75%) metastases of the positive control as PAAD, and on the negative control, it correctly classified 122/124 samples (98.39%) for a 97.85% overall accuracy on the test set. We used this DNA methylation dataset to explore the organotropism of PAAD metastases and observed that PAAD liver metastases are distinct from PAAD peritoneal carcinomatosis and primary PAAD, and are characterized by specific copy number alterations and hypomethylation of enhancers involved in epithelial-mesenchymal-transition.ConclusionsThe updated PAAD-iCCA-Classifier (available at https://classifier.tgc-research.de/) can accurately classify PAAD samples from various metastatic sites and it can serve as a diagnostic aid.

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.

Background Differentiating intrahepatic cholangiocarcinomas (iCCA) from hepatic metastases of pancreatic ductal adenocarcinoma (PAAD) is challenging. Both tumours have similar morphological and immunohistochemical pattern and share multiple driver mutations. We hypothesised that DNA methylation-based machine-learning algorithms may help perform this task. Methods We assembled genome-wide DNA methylation data for iCCA (n = 259), PAAD (n = 431), and normal bile duct (n = 70) from publicly available sources. We split this cohort into a reference (n = 399) and a validation set (n = 361). Using the reference cohort, we trained three machine learning models to differentiate between these entities. Furthermore, we validated the classifiers on the technical validation set and used an internal cohort (n = 72) to test our classifier. Findings On the validation cohort, the neural network, support vector machine, and the random forest classifiers reached accuracies of 97.68%, 95.62%, and 96.5%, respectively. Filtering by anomaly detection and thresholds improved the accuracy to 99.07% (37 samples excluded by filtering), 96.22% (17 samples excluded), and 100% (44 samples excluded) for the neural network, support vector machine and random forest, respectively. Because of best balance between accuracy and number of predictable cases we tested the neural network with applied filters on the inhouse cohort, obtaining an accuracy of 95.45%. Interpretation We developed a classifier that can differentiate between iCCAs, intrahepatic metastases of a PAAD, and normal bile duct tissue with high accuracy. This tool can be used for improving the diagnosis of pancreato-biliary cancers of the liver.

Diffuse gliomas in adults encompass a heterogenous group of central nervous system neoplasms. In recent years, extensive (epi-)genomic profiling has identified several glioma subgroups characterized by distinct molecular characteristics, most importantly IDH1/2 and histone H3 mutations. A group of 16 diffuse gliomas classified as "adult-type diffuse high-grade glioma, IDH-wildtype, subtype F (HGG-F)" was identified by the DKFZ v12.5 Brain Tumor Classifier . Histopathologic characterization, exome sequencing, and review of clinical data was performed in all cases. Based on unsupervised t -distributed stochastic neighbor embedding and clustering analysis of genome-wide DNA methylation data, HGG-F shows distinct epigenetic profiles separate from established central nervous system tumors. Exome sequencing demonstrated frequent TERT promoter (12/15 cases), PIK3R1 (11/16), and TP53 mutations (5/16). Radiologic characteristics were reminiscent of gliomatosis cerebri in 9/14 cases (64%). Histopathologically, most cases were classified as diffuse gliomas (7/16, 44%) or were suspicious for the infiltration zone of a diffuse glioma (5/16, 31%). None of the cases demonstrated microvascular proliferation or necrosis. Outcome of 14 patients with follow-up data was better compared to IDH-wildtype glioblastomas with a median progression-free survival of 58 months and overall survival of 74 months (both P <0.0001). Our series represents a novel type of adult-type diffuse glioma with distinct molecular and clinical features. Importantly, we provide evidence that TERT promoter mutations in diffuse gliomas without further morphologic or molecular signs of high-grade glioma should be interpreted in the context of the clinicoradiologic presentation as well as epigenetic profile and may not be suitable as a standalone marker for glioblastoma, IDH-wildtype.

Diffuse paediatric-type high-grade glioma, H3-wildtype and IDH-wildtype (pHGG) is a rare and aggressive brain tumor characterized by a specific DNA methylation profile. It was recently introduced in the 5th World Health Organization classification of central nervous system tumors of 2021. Clinical data on this tumor is scarce. This is a case series, which presents the first clinical experience with this entity. We compiled a retrospective case series on pHGG patients treated between 2015 and 2022 at our institution. Data collected include patients' clinical course, surgical procedure, histopathology, genome-wide DNA methylation analysis, imaging and adjuvant therapy. Eight pHGG were identified, ranging in age from 8 to 71 years. On MRI tumors presented with an unspecific intensity profile, T1w hypo- to isointense and T2w hyperintense, with inhomogeneous contrast enhancement, often with rim enhancement. Three patients died of the disease, with overall survival of 19, 28 and 30 months. Four patients were alive at the time of the last follow-up, 4, 5, 6 and 79 months after the initial surgery. One patient was lost to follow-up. Findings indicate that pHGG prevalence might be underestimated in the elderly population.

Background A methylation-based classification of ependymoma has recently found broad application. However, the diagnostic advantage and implications for treatment decisions remain unclear. Here, we retrospectively evaluate the impact of surgery and radiotherapy on outcome after molecular reclassification of adult intracranial ependymomas. Methods Tumors diagnosed as intracranial ependymomas from 170 adult patients collected from 8 diagnostic institutions were subjected to DNA methylation profiling. Molecular classes, patient characteristics, and treatment were correlated with progression-free survival (PFS). Results The classifier indicated an ependymal tumor in 73.5%, a different tumor entity in 10.6%, and non-classifiable tumors in 15.9% of cases, respectively. The most prevalent molecular classes were posterior fossa ependymoma group B (EPN-PFB, 32.9%), posterior fossa subependymoma (PF-SE, 25.9%), and supratentorial ZFTA fusion-positive ependymoma (EPN-ZFTA, 11.2%). With a median follow-up of 60.0 months, the 5- and 10-year-PFS rates were 64.5% and 41.8% for EPN-PFB, 67.4% and 45.2% for PF-SE, and 60.3% and 60.3% for EPN-ZFTA. In EPN-PFB, but not in other molecular classes, gross total resection (GTR) (P = .009) and postoperative radiotherapy (P = .007) were significantly associated with improved PFS in multivariable analysis. Histological tumor grading (WHO 2 vs. 3) was not a predictor of the prognosis within molecularly defined ependymoma classes. Conclusions DNA methylation profiling improves diagnostic accuracy and risk stratification in adult intracranial ependymoma. The molecular class of PF-SE is unexpectedly prevalent among adult tumors with ependymoma histology and relapsed as frequently as EPN-PFB, despite the supposed benign nature. GTR and radiotherapy may represent key factors in determining the outcome of EPN-PFB patients.

Background Sarcomas are a heterogeneous group of rare malignant tumors with more than 100 subtypes. Accurate diagnosis remains challenging due to a lack of characteristic molecular or histomorphological hallmarks. A DNA methylation-based tumor profiling classifier for sarcomas (known as sarcoma classifier) from the German Cancer Research Center (Deutsches Krebsforschungszentrum) is now employed in selected cases to guide tumor classification and treatment decisions at our institution. Data on the usage of the classifier in daily clinical routine are lacking. Methods In this single-center experience, we describe the clinical course of five sarcoma cases undergoing thorough pathological and reference pathological examination as well as DNA methylation-based profiling and their impact on subsequent treatment decisions. We collected data on the clinical course, DNA methylation analysis, histopathology, radiological imaging, and next-generation sequencing. Results Five clinical cases involving DNA methylation-based profiling in 2021 at our institution were included. All patients' DNA methylation profiles were successfully matched to a methylation profile cluster of the sarcoma classifier's dataset. In three patients, the classifier reassured diagnosis or aided in finding the correct diagnosis in light of contradictory data and differential diagnoses. In two patients with intracranial tumors, the classifier changed the diagnosis to a novel diagnostic tumor group. Conclusions The sarcoma classifier is a valuable diagnostic tool that should be used after comprehensive clinical and histopathological evaluation. It may help to reassure the histopathological diagnosis or indicate the need for thorough reassessment in cases where it contradicts previous findings. However, certain limitations (non-classifiable cases, misclassifications, unclear degree of sample purity for analysis and others) currently preclude wide clinical application. The current sarcoma classifier is therefore not yet ready for a broad clinical routine. With further refinements, this promising tool may be implemented in daily clinical practice in selected cases.

High-grade astrocytoma with piloid features (HGAP) is a recently recognized glioma type whose classification is dependent on its global epigenetic signature. HGAP is characterized by alterations in the mitogen-activated protein kinase (MAPK) pathway, often co-occurring with CDKN2A/B homozygous deletion and/or ATRX mutation. Experience with HGAP is limited and to better understand this tumor type, we evaluated an expanded cohort of patients ( n = 144) with these tumors, as defined by DNA methylation array testing, with a subset additionally evaluated by next-generation sequencing (NGS). Among evaluable cases, we confirmed the high prevalence CDKN2A/B homozygous deletion, and/or ATRX mutations/loss in this tumor type, along with a subset showing NF1 alterations. Five of 93 (5.4%) cases sequenced harbored TP53 mutations and RNA fusion analysis identified a single tumor containing an NTRK2 gene fusion, neither of which have been previously reported in HGAP. Clustering analysis revealed the presence of three distinct HGAP subtypes (or groups = g) based on whole-genome DNA methylation patterns, which we provisionally designated as gNF1 ( n = 18), g1 ( n = 72), and g2 ( n = 54) (median ages 43.5 years, 47 years, and 32 years, respectively). Subtype gNF1 is notable for enrichment with patients with Neurofibromatosis Type 1 (33.3%, p = 0.0008), confinement to the posterior fossa, hypermethylation in the NF1 enhancer region, a trend towards decreased progression-free survival ( p = 0.0579), RNA processing pathway dysregulation, and elevated non-neoplastic glia and neuron cell content ( p < 0.0001 and p < 0.0001, respectively). Overall, our expanded cohort broadens the genetic, epigenetic, and clinical phenotype of HGAP and provides evidence for distinct epigenetic subtypes in this tumor type.

Purpose High-grade astrocytoma with piloid features (HGAP) is a recently described brain tumor entity defined by a specific DNA methylation profile. HGAP has been proposed to be integrated in the upcoming World Health Organization classification of central nervous system tumors expected in 2021. In this series, we present the first single-center experience with this new entity. Methods During 2017 and 2020, six HGAP were identified. Clinical course, surgical procedure, histopathology, genome-wide DNA methylation analysis, imaging, and adjuvant therapy were collected. Results Tumors were localized in the brain stem (n = 1), cerebellar peduncle (n = 1), diencephalon (n = 1), mesencephalon (n = 1), cerebrum (n = 1) and the thoracic spinal cord (n = 2). The lesions typically presented as T1w hypo- to isointense and T2w hyperintense with inhomogeneous contrast enhancement on MRI. All patients underwent initial surgical intervention. Three patients received adjuvant radiochemotherapy, and one patient adjuvant radiotherapy alone. Four patients died of disease, with an overall survival of 1.8, 9.1, 14.8 and 18.1 months. One patient was alive at the time of last follow-up, 14.6 months after surgery, and one patient was lost to follow-up. Apart from one tumor, the lesions did not present with high grade histology, however patients showed poor clinical outcomes. Conclusions Here, we provide detailed clinical, neuroradiological, histological, and molecular pathological information which might aid in clinical decision making until larger case series are published. With the exception of one case, the tumors did not present with high-grade histology but patients still showed short intervals between diagnosis and tumor progression or death even after extensive multimodal therapy.

Pituicytoma (PITUI), granular cell tumor (GCT), and spindle cell oncocytoma (SCO) are rare tumors of the posterior pituitary. Histologically, they may be challenging to distinguish and have been proposed to represent a histological spectrum of a single entity. We performed targeted next-generation sequencing, DNA methylation profiling, and copy number analysis on 47 tumors (14 PITUI; 12 GCT; 21 SCO) to investigate molecular features and explore possibilities of clinically meaningful tumor subclassification. We detected two main epigenomic subgroups by unsupervised clustering of DNA methylation data, though the overall methylation differences were subtle. The largest group ( n = 23) contained most PITUIs and a subset of SCOs and was enriched for pathogenic mutations within genes in the MAPK/PI3K pathways (12/17 [71%] of sequenced tumors: FGFR1 (3), HRAS (3), BRAF (2), NF1 (2), CBL (1), MAP2K2 (1), PTEN (1)) and two with accompanying TERT promoter mutation. The second group ( n = 16) contained most GCTs and a subset of SCOs, all of which mostly lacked identifiable genetic drivers. Outcome analysis demonstrated that the presence of chromosomal imbalances was significantly associated with reduced progression-free survival especially within the combined PITUI and SCO group ( p = 0.031). In summary, we observed only subtle DNA methylation differences between posterior pituitary tumors, indicating that these tumors may be best classified as subtypes of a single entity. Nevertheless, our data indicate differences in mutation patterns and clinical outcome. For a clinically meaningful subclassification, we propose a combined histo-molecular approach into three subtypes: one subtype is defined by granular cell histology, scarcity of identifiable oncogenic mutations, and favorable outcome. The other two subtypes have either SCO or PITUI histology but are segregated by chromosomal copy number profile into a favorable group (no copy number changes) and a less favorable group (copy number imbalances present). Both of the latter groups have recurrent MAPK/PI3K genetic alterations that represent potential therapeutic targets.

DNA methylation-based machine learning algorithms represent powerful diagnostic tools that are currently emerging for several fields of tumour classification. For various reasons, paediatric brain tumours have been the main driving forces behind this rapid development and brain tumour classification tools are likely further advanced than in any other field of cancer diagnostics. In this review, we will discuss the main characteristics that were important for this rapid advance, namely the high clinical need for improvement of paediatric brain tumour diagnostics, the robustness of methylated DNA and the consequential possibility to generate high-quality molecular data from archival formalin-fixed paraffin-embedded pathology specimens, the implementation of a single array platform by most laboratories allowing data exchange and data pooling to an unprecedented extent, as well as the high suitability of the data format for machine learning. We will further discuss the four most central output qualities of DNA methylation profiling in a diagnostic setting (tumour classification, tumour sub-classification, copy number analysis and guidance for additional molecular testing) individually for the most frequent types of paediatric brain tumours. Lastly, we will discuss DNA methylation profiling as a tool for the detection of new paediatric brain tumour classes and will give an overview of the rapidly growing family of new tumours identified with the aid of this technique.