Brain tumour diagnostics using a DNA methylation-based classifier as a diagnostic support tool

Microarray-Based DNA Methylation Profiling: Validation Considerations for Clinical Testing

Microarray-based methylation profiling has emerged as a valuable tool for refining diagnoses and revealing novel tumor subtypes, particularly in central nervous system tumors. Despite the increasing adoption of this technique in clinical genomic laboratories, no technical standards have been published in establishing minimum criteria for test validation. A working group with experience and expertise in DNA-based methylation profiling tests on central nervous system tumors collaborated to develop practical discussion points and focus on important considerations for validating this test in clinical laboratory settings. The experience in validating this methodology in a clinical setting is summarized. Specifically, the advantages and challenges associated with utilizing an in-house classifier compared with a third-party classifier are highlighted. Additionally, experiences in demonstrating the assay's sensitivity and specificity, establishing minimum sample criteria, and implementing quality control metrics are described. As methylation profiling for tumor classification expands to other tumor types and continues to evolve for various other applications, the critical considerations described here are expected to serve as a guidance for future efforts in establishing professional guidelines for this assay.

Ganglioglioma with anaplastic/high-grade transformation: Histopathologic, molecular, and epigenetic characterization of 3 cases

Ganglioglioma (GG) with anaplasia (anaplastic ganglioglioma) is a rare and controversial diagnosis. When present, anaplasia involves the glial component of the tumor, either at presentation or at recurrence. To date, most published cases lack molecular characterization. We describe the histologic and molecular features of 3 patients presenting with BRAF p. V600E-mutant GG (CNS WHO grade 1) with high-grade glial transformation at recurrence. The tumors occurred in pediatric patients (age 9-16 years) with time to recurrence from 20 months to 7 years. At presentation, each tumor was low-grade, with a BRAFV600E-positive ganglion cell component and a glial component resembling pleomorphic xanthoastrocytoma (PXA) or fibrillary astrocytoma. At recurrence, tumors resembled anaplastic PXA or high-grade astrocytomas without neuronal differentiation. CDKN2A homozygous deletion (HD) was absent in all primary tumors. At recurrence, 2 cases acquired CDKN2A HD; the third case showed loss of p16 and MTAP immunoexpression, but no CDKN2A/B HD or mutation was identified. By DNA methylation profiling, all primary and recurrent tumors either grouped or definitely matched to different methylation classes. Our findings indicate that malignant progression of the glial component can occur in GG and suggest that CDKN2A/B inactivation plays a significant role in this process.

Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning

Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven means to enhance diagnostic accuracy but are also time consuming and not widely available. Here, to address these limitations, we developed Deep lEarning from histoPathoLOgy and methYlation (DEPLOY), a deep learning model that classifies CNS tumors to ten major categories from histopathology. DEPLOY integrates three distinct components: the first classifies CNS tumors directly from slide images ('direct model'), the second initially generates predictions for DNA methylation beta values, which are subsequently used for tumor classification ('indirect model'), and the third classifies tumor types directly from routinely available patient demographics. First, we find that DEPLOY accurately predicts beta values from histopathology images. Second, using a ten-class model trained on an internal dataset of 1,796 patients, we predict the tumor categories in three independent external test datasets including 2,156 patients, achieving an overall accuracy of 95% and balanced accuracy of 91% on samples that are predicted with high confidence. These results showcase the potential future use of DEPLOY to assist pathologists in diagnosing CNS tumors within a clinically relevant short time frame.

DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification

The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potential for advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology.

Evaluating cell culture reliability in pediatric brain tumor primary cells through DNA methylation profiling

In vitro models of pediatric brain tumors (pBT) are instrumental for better understanding the mechanisms contributing to oncogenesis and testing new therapies; thus, ideally, they should recapitulate the original tumor. We applied DNA methylation (DNAm) and copy number variation (CNV) profiling to characterize 241 pBT samples, including 155 tumors and 86 pBT-derived cell cultures, considering serum vs serum-free conditions, late vs early passages, and dimensionality (2D vs 3D cultures). We performed a t-SNE classification and identified differentially methylated regions in tumors compared to cell models. Early cell cultures recapitulate the original tumor, but serum media and 2D culturing were demonstrated to significantly contribute to the divergence of DNAm profiles from the parental ones. All divergent cells clustered together acquiring a common deregulated epigenetic signature suggesting a shared selective pressure. We identified a set of hypomethylated genes shared among unfaithful cells converging on response to growth factors and migration pathways, such as signaling cascade activation, tissue organization, and cellular migration. In conclusion, DNAm and CNV are informative tools that should be used to assess the recapitulation of pBT-cells from parental tumors.

Running in the FAMILY: understanding and predicting the intergenerational transmission of mental illness

Over 50% of children with a parent with severe mental illness will develop mental illness by early adulthood. However, intergenerational transmission of risk for mental illness in one's children is insufficiently considered in clinical practice, nor is it sufficiently utilised into diagnostics and care for children of ill parents. This leads to delays in diagnosing young offspring and missed opportunities for protective actions and resilience strengthening. Prior twin, family, and adoption studies suggest that the aetiology of mental illness is governed by a complex interplay of genetic and environmental factors, potentially mediated by changes in epigenetic programming and brain development. However, how these factors ultimately materialise into mental disorders remains unclear. Here, we present the FAMILY consortium, an interdisciplinary, multimodal (e.g., (epi)genetics, neuroimaging, environment, behaviour), multilevel (e.g., individual-level, family-level), and multisite study funded by a European Union Horizon-Staying-Healthy-2021 grant. FAMILY focuses on understanding and prediction of intergenerational transmission of mental illness, using genetically informed causal inference, multimodal normative prediction, and animal modelling. Moreover, FAMILY applies methods from social sciences to map social and ethical consequences of risk prediction to prepare clinical practice for future implementation. FAMILY aims to deliver: (i) new discoveries clarifying the aetiology of mental illness and the process of resilience, thereby providing new targets for prevention and intervention studies; (ii) a risk prediction model within a normative modelling framework to predict who is at risk for developing mental illness; and (iii) insight into social and ethical issues related to risk prediction to inform clinical guidelines.

Pediatric-type high-grade gliomas with PDGFRA amplification in adult patients with Li-Fraumeni syndrome: clinical and molecular characterization of three cases

Li-Fraumeni syndrome (LFS) is an autosomal dominant tumor predisposition syndrome caused by heterozygous germline mutations or deletions in the TP53 tumor suppressor gene. Central nervous system tumors, such as choroid plexus tumors, medulloblastomas, and diffuse gliomas, are frequently found in patients with LFS. Although molecular profiles of diffuse gliomas that develop in pediatric patients with LFS have been elucidated, those in adults are limited. Recently, diffuse gliomas have been divided into pediatric- and adult-type gliomas, based on their distinct molecular profiles. In the present study, we investigated the molecular profiles of high-grade gliomas in three adults with LFS. These tumors revealed characteristic histopathological findings of high-grade glioma or glioblastoma and harbored wild-type IDH1/2 according to whole exome sequencing (WES). However, these tumors did not exhibit the key molecular alterations of glioblastoma, IDH-wildtype such as TERT promoter mutation, EGFR amplification, or chromosome 7 gain and 10 loss. Although WES revealed no other characteristic gene mutations or copy number alterations in high-grade gliomas, such as those in histone H3 genes, PDGFRA amplification was found in all three cases together with uniparental disomy of chromosome 17p, where the TP53 gene is located. DNA methylation analyses revealed that all tumors exhibited DNA methylation profiles similar to those of pediatric-type high-grade glioma H3-wildtype and IDH-wildtype (pHGG H3-/IDH-wt), RTK1 subtype. These data suggest that high-grade gliomas developed in adult patients with LFS may be involved in pHGG H3-/IDH-wt. PDGFRA and homozygous alterations in TP53 may play pivotal roles in the development of this type of glioma in adult patients with LFS.

Pediatric CNS tumors and 2021 WHO classification: what do oncologists need from pathologists?

The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, established new approaches to both CNS tumor nomenclature and grading, emphasizing the importance of integrated diagnoses and layered reports. This edition increased the role of molecular diagnostics in CNS tumor classification while still relying on other established approaches such as histology and immunohistochemistry. Moreover, it introduced new tumor types and subtypes based on novel diagnostic technologies such as DNA methylome profiling. Over the past decade, molecular techniques identified numerous key genetic alterations in CSN tumors, with important implications regarding the understanding of pathogenesis but also for prognosis and the development and application of effective molecularly targeted therapies. This review summarizes the major changes in the 2021 fifth edition classification of pediatric CNS tumors, highlighting for each entity the molecular alterations and other information that are relevant for diagnostic, prognostic, or therapeutic purposes and that patients' and oncologists' need from a pathology report.

Decoding the DNA methylome of central nervous system tumors: An emerging modality for integrated diagnosis

The definitive diagnosis and classification of individual cancers are crucial for patient care and cancer research. To achieve a robust diagnosis of central nervous system (CNS) tumors, a genotype-phenotype integrated diagnostic approach was introduced in recent versions of the World Health Organization classification, followed by the incorporation of a genome-wide DNA methylome-based classification. Microarray-based platforms are widely used to obtain DNA methylome data, and the German Cancer Research Center (Deutsches Krebsforschungszentrum [DKFZ]) has a webtool for a DNA methylation-based classifier (DKFZ classifier). Integration of DNA methylome will further enhance the precision of CNS tumor classification, especially in diagnostically challenging cases. However, in the clinical application of DNA methylome-based classification, challenges related to data interpretation persist, in addition to technical caveats, regulations, and limited accessibility. Dimensionality reduction (DMR) can complement integrated diagnosis by visualizing a profile and comparing it with other known samples. Therefore, DNA methylome-based classification is a highly useful research tool for auxiliary analysis in challenging diagnostic and rare disease cases, and for establishing novel tumor concepts. Decoding the DNA methylome, especially by DMR in addition to DKFZ classifier, emphasizes the capability of grasping the fundamental biological principles that provide new perspectives on CNS tumors.

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.

Unclassifiable CNS tumors in DNA methylation-based classification: clinical challenges and prognostic impact

DNA methylation analysis has become a powerful tool in neuropathology. Although DNA methylation-based classification usually shows high accuracy, certain samples cannot be classified and remain clinically challenging. We aimed to gain insight into these cases from a clinical perspective. To address, central nervous system (CNS) tumors were subjected to DNA methylation profiling and classified according to their calibrated score using the DKFZ brain tumor classifier (V11.4) as "≥ 0.84" (score ≥ 0.84), "0.3-0.84" (score 0.3-0.84), or "< 0.3" (score < 0.3). Histopathology, patient characteristics, DNA input amount, and tumor purity were correlated. Clinical outcome parameters were time to treatment decision, progression-free, and overall survival. In 1481 patients, the classifier identified 69 (4.6%) tumors with an unreliable score as "< 0.3". Younger age (P < 0.01) and lower tumor purity (P < 0.01) compromised accurate classification. A clinical impact was demonstrated as unclassifiable cases ("< 0.3") had a longer time to treatment decision (P < 0.0001). In a subset of glioblastomas, these cases experienced an increased time to adjuvant treatment start (P < 0.001) and unfavorable survival (P < 0.025). Although DNA methylation profiling adds an important contribution to CNS tumor diagnostics, clinicians should be aware of a potentially longer time to treatment initiation, especially in malignant brain tumors.

Evolutionary gravitational neocognitron neural network optimized with marine predators optimization algorithm for MRI brain tumor classification

Magnetic resonance imaging (MRI) is a powerful tool for tumor diagnosis in human brain. Here, the MRI images are considered to detect the brain tumor and classify the regions as meningioma, glioma, pituitary and normal types. Numerous existing methods regarding brain tumor detection were suggested previously, but none of the methods accurately categorizes the brain tumor and consumes more computation period. To address these problems, an Evolutionary Gravitational Neocognitron Neural Network optimized with Marine Predators Algorithm is proposed in this article for MRI Brain Tumor Classification (EGNNN-VGG16-MPA-MRI-BTC). Initially, the brain MRI pictures are collected under Brats MRI image dataset. By using Savitzky-Golay Denoising approach, these images are pre-processed. The features are extracted utilizing visual geometry group network (VGG16). By utilizing VGG16, the features, like Grey level features, Haralick Texture features are extracted. These extracted features are given to EGNNN classifier, which categorizes the brain tumor as glioma, meningioma, pituitary gland and normal. Batch Normalization (BN) layer of EGNNN is eliminated and included with VGG16 layer. Marine Predators Optimization Algorithm (MPA) optimizes the weight parameters of EGNNN. The simulation is activated in MATLAB. Finally, the EGNNN-VGG16-MPA-MRI-BTC method attains 38.98%, 46.74%, 23.27% higher accuracy, 24.24%, 37.82%, 13.92% higher precision, 26.94%, 47.04%, 38.94% higher sensitivity compared with the existing AlexNet-SVM-MRI-BTC, RESNET-SGD-MRI-BTC and MobileNet-V2-MRI-BTC models respectively.

Refinement of prognostication for IDH-mutant astrocytomas using DNA methylation-based classification

The 2021 World Health Organization (WHO) grading system of isocitrate dehydrogenase (IDH)-mutant astrocytomas relies on histological features and the presence of homozygous deletion of the cyclin-dependent kinase inhibitor 2A and 2B (CDKN2A/B). DNA methylation profiling has become highly relevant in the diagnosis of central nervous system (CNS) tumors including gliomas, and it has been incorporated into routine clinical diagnostics in some countries. In this study, we, therefore, examined the value of DNA methylation-based classification for prognostication of patients with IDH-mutant astrocytomas. We analyzed histopathological diagnoses, genome-wide DNA methylation array data, and chromosomal copy number alteration profiles from a cohort of 385 adult-type IDH-mutant astrocytomas, including a local cohort of 127 cases and 258 cases from public repositories. Prognosis based on WHO 2021 CNS criteria (histological grade and CDKN2A/B homozygous deletion status), other relevant chromosomal/gene alterations in IDH-mutant astrocytomas and DNA methylation-based subclassification according to the molecular neuropathology classifier were assessed. We demonstrate that DNA methylation-based classification of IDH-mutant astrocytomas can be used to predict outcome of the patients equally well as WHO 2021 CNS criteria. In addition, methylation-based subclassification enabled the identification of IDH-mutant astrocytoma patients with poor survival among patients with grade 3 tumors and patients with grade 4 tumors with a more favorable outcome. In conclusion, DNA methylation-based subclassification adds prognostic information for IDH-mutant astrocytomas that can further refine the current WHO 2021 grading scheme for these patients.

Artificial intelligence in neuro-oncology

Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intelligence. Although formal work in AI has been slowly advancing for almost 70 years, developments in the last decade, and particularly in the last year, have led to an explosion of AI applications in multiple fields. Neuro-oncology has not escaped this trend. Given the expected integration of AI-based methods to neuro-oncology practice over the coming years, we set to provide an overview of existing technologies as they are applied to the neuropathology and neuroradiology of brain tumors. We highlight current benefits and limitations of these technologies and offer recommendations on how to appraise novel AI-tools as they undergo consideration for integration into clinical workflows.

Alterations in cellular metabolism under different grades of glioma staging identified based on a multi-omics analysis strategy

Glioma is a type of brain tumor closely related to abnormal cell metabolism. Firstly, multiple combinatorial sequencing studies have revealed this relationship. Genomic studies have identified gene mutations and gene expression disorders related to the development of gliomas, which affect cell metabolic pathways. In addition, transcriptome studies have revealed the genes and regulatory networks that regulate cell metabolism in glioma tissues. Metabonomics studies have shown that the metabolic pathway of glioma cells has changed, indicating their distinct energy and nutritional requirements. This paper focuses on the retrospective analysis of multiple groups combined with sequencing to analyze the changes in various metabolites during metabolism in patients with glioma. Finally, the changes in genes, regulatory networks, and metabolic pathways regulating cell metabolism in patients with glioma under different metabolic conditions were discussed. It is also proposed that multi-group metabolic analysis is expected to better understand the mechanism of abnormal metabolism of gliomas and provide more personalized methods and guidance for early diagnosis, treatment, and prognosis evaluation of gliomas.

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.

Genome-Wide DNA Methylation Profiling as Frontline Diagnostics for Central Nervous System Embryonal Tumors in Hong Kong

This paper examines the link between CNS tumor biology and heterogeneity and the use of genome-wide DNA methylation profiling as a clinical diagnostic platform. CNS tumors are the most common solid tumors in children, and their prognosis remains poor. This study retrospectively analyzed pediatric patients with CNS embryonal tumors in Hong Kong between 1999 and 2017, using data from the territory-wide registry and available formalin-fixed paraffin-embedded tumor tissue. After processing archival tumor tissue via DNA extraction, quantification, and methylation profiling, the data were analyzed by using the web-based DKFZ classifier (Molecular Neuropathology (MNP) 2.0 v11b4) and t-SNE analysis. Methylation profiles were deemed informative in 85 samples. Epigenetic data allowed molecular subgrouping and confirmed diagnosis in 65 samples, verified histologic diagnosis in 8, and suggested an alternative diagnosis in 12. This study demonstrates the potential of DNA methylation profiling in characterizing pediatric CNS embryonal tumors in a large cohort from Hong Kong, which should enable regional and international collaboration in future pediatric neuro-oncology research.

Molecular diagnostic tools for the World Health Organization (WHO) 2021 classification of gliomas, glioneuronal and neuronal tumors; an EANO guideline

In the 5th edition of the WHO CNS tumor classification (CNS5, 2021), multiple molecular characteristics became essential diagnostic criteria for many additional CNS tumor types. For those tumors, an integrated, "histomolecular" diagnosis is required. A variety of approaches exists for determining the status of the underlying molecular markers. The present guideline focuses on the methods that can be used for assessment of the currently most informative diagnostic and prognostic molecular markers for the diagnosis of gliomas, glioneuronal and neuronal tumors. The main characteristics of the molecular methods are systematically discussed, followed by recommendations and information on available evidence levels for diagnostic measures. The recommendations cover DNA and RNA next-generation-sequencing, methylome profiling, and select assays for single/limited target analyses, including immunohistochemistry. Additionally, because of its importance as a predictive marker in IDH-wildtype glioblastomas, tools for the analysis of MGMT promoter methylation status are covered. A structured overview of the different assays with their characteristics, especially their advantages and limitations, is provided, and requirements for input material and reporting of results are clarified. General aspects of molecular diagnostic testing regarding clinical relevance, accessibility, cost, implementation, regulatory, and ethical aspects are discussed as well. Finally, we provide an outlook on new developments in the landscape of molecular testing technologies in neuro-oncology.

Implementation of DNA Methylation Array Profiling in Pediatric Central Nervous System Tumors: The AIM BRAIN Project: An Australian and New Zealand Children's Haematology/Oncology Group Study

DNA methylation array profiling for classifying pediatric central nervous system (CNS) tumors is a valuable adjunct to histopathology. However, unbiased prospective and interlaboratory validation studies have been lacking. The AIM BRAIN diagnostic trial involving 11 pediatric cancer centers in Australia and New Zealand was designed to test the feasibility of routine clinical testing and ran in parallel with the Molecular Neuropathology 2.0 (MNP2.0) study at Deutsches Krebsforschungszentrum (German Cancer Research Center). CNS tumors from 269 pediatric patients were prospectively tested on Illumina EPIC arrays, including 104 cases co-enrolled on MNP2.0. Using MNP classifier versions 11b4 and 12.5, we report classifications with a probability score ≥0.90 in 176 of 265 (66.4%) and 213 of 269 (79.2%) cases, respectively. Significant diagnostic information was obtained in 130 of 176 (74%) for 11b4, and 12 of 174 (7%) classifications were discordant with histopathology. Cases prospectively co-enrolled on MNP2.0 gave concordant classifications (99%) and score thresholds (93%), demonstrating excellent test reproducibility and sensitivity. Overall, DNA methylation profiling is a robust single workflow technique with an acceptable diagnostic yield that is considerably enhanced by the extensive subgroup and copy number profile information generated by the platform. The platform has excellent test reproducibility and sensitivity and contributes significantly to CNS tumor diagnosis.

Ultra-fast deep-learned CNS tumour classification during surgery

Central nervous system tumours represent one of the most lethal cancer types, particularly among children1. Primary treatment includes neurosurgical resection of the tumour, in which a delicate balance must be struck between maximizing the extent of resection and minimizing risk of neurological damage and comorbidity2,3. However, surgeons have limited knowledge of the precise tumour type prior to surgery. Current standard practice relies on preoperative imaging and intraoperative histological analysis, but these are not always conclusive and occasionally wrong. Using rapid nanopore sequencing, a sparse methylation profile can be obtained during surgery4. Here we developed Sturgeon, a patient-agnostic transfer-learned neural network, to enable molecular subclassification of central nervous system tumours based on such sparse profiles. Sturgeon delivered an accurate diagnosis within 40 minutes after starting sequencing in 45 out of 50 retrospectively sequenced samples (abstaining from diagnosis of the other 5 samples). Furthermore, we demonstrated its applicability in real time during 25 surgeries, achieving a diagnostic turnaround time of less than 90 min. Of these, 18 (72%) diagnoses were correct and 7 did not reach the required confidence threshold. We conclude that machine-learned diagnosis based on low-cost intraoperative sequencing can assist neurosurgical decision-making, potentially preventing neurological comorbidity and avoiding additional surgeries.

DNA methylation-based diagnosis confirmation in a pediatric patient with low-grade glioma: a case report

Central nervous system (CNS) tumors in children comprise a highly heterogenous and complex group of diseases. Historically, diagnosis and confirmation of these tumors were routinely based on histological examination. However, recently obtained data demonstrate that such a diagnostic approach is not completely accurate and could lead to misdiagnosis. Also, in recent times, the quantity and quality of molecular diagnostic methods have greatly improved, which influences the current classification methods and treatment approach for pediatric CNS tumors. Nowadays, molecular methods, such as DNA methylation profiling, are an integral part of diagnosing brain and spinal tumors in children. In this paper, we present the case of an infant with a posterior fossa tumor who demonstrated a non-specific morphology and whose diagnosis was verified only after DNA methylation.

A systematic review of neuroimaging epigenetic research: calling for an increased focus on development

Epigenetic mechanisms, such as DNA methylation (DNAm), have gained increasing attention as potential biomarkers and mechanisms underlying risk for neurodevelopmental, psychiatric and other brain-based disorders. Yet, surprisingly little is known about the extent to which DNAm is linked to individual differences in the brain itself, and how these associations may unfold across development - a time of life when many of these disorders emerge. Here, we systematically review evidence from the nascent field of Neuroimaging Epigenetics, combining structural or functional neuroimaging measures with DNAm, and the extent to which the developmental period (birth to adolescence) is represented in these studies. We identified 111 articles published between 2011-2021, out of which only a minority (21%) included samples under 18 years of age. Most studies were cross-sectional (85%), employed a candidate-gene approach (67%), and examined DNAm-brain associations in the context of health and behavioral outcomes (75%). Nearly half incorporated genetic data, and a fourth investigated environmental influences. Overall, studies support a link between peripheral DNAm and brain imaging measures, but there is little consistency in specific findings and it remains unclear whether DNAm markers present a cause, correlate or consequence of brain alterations. Overall, there is large heterogeneity in sample characteristics, peripheral tissue and brain outcome examined as well as the methods used. Sample sizes were generally low to moderate (median nall = 98, ndevelopmental = 80), and attempts at replication or meta-analysis were rare. Based on the strengths and weaknesses of existing studies, we propose three recommendations on how advance the field of Neuroimaging Epigenetics. We advocate for: (1) a greater focus on developmentally oriented research (i.e. pre-birth to adolescence); (2) the analysis of large, prospective, pediatric cohorts with repeated measures of DNAm and imaging to assess directionality; and (3) collaborative, interdisciplinary science to identify robust signals, triangulate findings and enhance translational potential.

Genomic profiles of IDH-mutant gliomas: MYCN-amplified IDH-mutant astrocytoma had the worst prognosis

This study aimed to find any ambiguous genetic outlier for "oligodendroglioma, IDH-mutant and 1p/19q-codeleted (O_IDH_mut)" and "astrocytoma, IDH-mutant (A_IDH_mut)" and to redefine the genetic landscape and prognostic factors of IDH-mutant gliomas. Next-generation sequencing (NGS) using a brain tumor-targeted gene panel, methylation profiles, and clinicopathological features were analyzed for O_IDH_mut (n = 74) in 70 patients and for A_IDH_mut (n = 95) in 90 patients. 97.3% of O_IDH_mut and 98.9% of A_IDH_mut displayed a classic genomic landscape. Combined CIC (75.7%) and/or FUBP1 (45.9%) mutations were detected in 93.2% and MGMTp methylation in 95.9% of O_IDH_mut patients. In A_IDH_mut, TP53 mutations were found in 86.3% and combined ATRX (82.1%) and TERTp (6.3%) mutations in 88.4%. Although there were 3 confusing cases, NOS (not otherwise specified) category, based on genetic profiles, but they were clearly classified by combining histopathology and DKFZ methylation classifier algorithms. The patients with MYCN amplification and/or CDKN2A/2B homozygous deletion in the A_IDH_mut category had a worse prognosis than those without these gene alterations and MYCN-amplified A_IDH_mut showed the worst prognosis. However, there was no prognostic genetic marker in O_IDH_mut. In histopathologically or genetically ambiguous cases, methylation profiles can be used as an objective tool to avoid a diagnosis of NOS or NEC (not elsewhere classified), as well as for tumor classification. The authors have not encountered a case of true mixed oligoastrocytoma using an integrated diagnosis of histopathological, genetic and methylation profiles. MYCN amplification, in addition to CDKN2A/2B homozygous deletion, should be included in the genetic criteria for CNS WHO grade 4 A_IDH_mut.

Newly Recognized CNS Tumors in the 2021 World Health Organization Classification: Imaging Overview with Histopathologic and Genetic Correlation

In 2021, the World Health Organization released an updated classification of CNS tumors. This update reflects the growing understanding of the importance of genetic alterations related to tumor pathogenesis, prognosis, and potential targeted treatments and introduces 22 newly recognized tumor types. Herein, we review these 22 newly recognized entities and emphasize their imaging appearance with correlation to histologic and genetic features.

Utility of genome-wide DNA methylation profiling for pediatric-type diffuse gliomas

Despite the current progress of treatment, pediatric-type diffuse glioma is one of the most lethal primary malignant tumors in the central nervous system (CNS). Since pediatric-type CNS tumors are rare disease entities and highly heterogeneous, the diagnosis is challenging. An accurate diagnosis is essential for the choice of optimal treatment, which leads to precision oncology and improvement of the patient's outcome. Genome-wide DNA methylation profiling recently emerged as one of the most important tools for the diagnosis of CNS tumors, and the utility of this novel assay has been reported in both pediatric and adult patients. In the current World Health Organization classification published in 2021, several new entities are recognized in pediatric-type diffuse gliomas, some of which require methylation profiling. In this review, we investigated the utility of genome-wide DNA methylation profiling in pediatric-type diffuse glioma, as well as issues in the clinical application of this assay. Furthermore, the combination of genome-wide DNA methylation profiling and other comprehensive genomic assays, which may improve diagnostic accuracy and detection of the actionable target, will be discussed.

Medulloblastoma in adults: evaluation of the Dutch society for neuro-oncology treatment protocol

PURPOSE

Medulloblastoma is a rare tumor in adults. The objective of this nationwide, multicenter study was to evaluate the toxicity and efficacy of the Dutch treatment protocol for adult medulloblastoma patients.

METHODS

Adult medulloblastoma patients diagnosed between 2010 and 2018 were identified in the Dutch rare tumors registry or nationwide pathology database. Patients with intention to treat according to the national treatment protocol were included. Risk stratification was performed based on residual disease, histological subtype and extent of disease. All patients received postoperative radiotherapy [craniospinal axis 36 Gy/fossa posterior boost 19.8 Gy (14.4 Gy in case of metastases)]. High-risk patients received additional neoadjuvant (carboplatin-etoposide), concomitant (vincristine) and adjuvant chemotherapy (carboplatin-vincristine-cyclophosphamide) as far as feasible by toxicity. Methylation profiling, and additional next-generation sequencing in case of SHH-activated medulloblastomas, were performed.

RESULTS

Forty-seven medulloblastoma patients were identified, of whom 32 were treated according to the protocol. Clinical information and tumor material was available for 28 and 20 patients, respectively. The histological variants were mainly classic (43%) and desmoplastic medulloblastoma (36%). Sixteen patients (57%) were considered standard-risk and 60% were SHH-activated medulloblastomas. Considerable treatment reductions and delays in treatment occurred due to especially hematological and neurotoxicity. Only one high-risk patient could complete all chemotherapy courses. 5-years progression-free survival (PFS) and overall survival (OS) for standard-risk patients appeared worse than for high-risk patients (PFS 69% vs. 90%, OS 81% vs. 90% respectively), although this wasn't statistically significant.

CONCLUSION

Combined chemo-radiotherapy is a toxic regimen for adult medulloblastoma patients that may result in improved survival.

Epigenetics applied to child and adolescent mental health: Progress, challenges and opportunities

Background

Epigenetic processes are fast emerging as a promising molecular system in the search for both biomarkers and mechanisms underlying human health and disease risk, including psychopathology.

Methods

In this review, we discuss the application of epigenetics (specifically DNA methylation) to research in child and adolescent mental health, with a focus on the use of developmentally sensitive datasets, such as prospective, population-based cohorts. We look back at lessons learned to date, highlight current developments in the field and areas of priority for future research. We also reflect on why epigenetic research on child and adolescent mental health currently lags behind other areas of epigenetic research and what we can do to overcome existing barriers.

Results

To move the field forward, we advocate for the need of large-scale, harmonized, collaborative efforts that explicitly account for the time-varying nature of epigenetic and mental health data across development.

Conclusion

We conclude with a perspective on what the future may hold in terms of translational applications as more robust signals emerge from epigenetic research on child and adolescent mental health.

BRAF-mediated brain tumors in adults and children: A review and the Australian and New Zealand experience

The mitogen-activated protein kinase (MAPK) pathway signaling pathway is one of the most commonly mutated pathways in human cancers. In particular, BRAF alterations result in constitutive activation of the rapidly accelerating fibrosarcoma-extracellular signal-regulated kinase-MAPK significant pathway, leading to cellular proliferation, survival, and dedifferentiation. The role of BRAF mutations in oncogenesis and tumorigenesis has spurred the development of targeted agents, which have been successful in treating many adult cancers. Despite advances in other cancer types, the morbidity and survival outcomes of patients with glioma have remained relatively stagnant. Recently, there has been recognition that MAPK dysregulation is almost universally present in paediatric and adult gliomas. These findings, accompanying broad molecular characterization of gliomas, has aided prognostication and offered opportunities for clinical trials testing targeted agents. The use of targeted therapies in this disease represents a paradigm shift, although the biochemical complexities has resulted in unexpected challenges in the development of effective BRAF inhibitors. Despite these challenges, there are promising data to support the use of BRAF inhibitors alone and in combination with MEK inhibitors for patients with both low-grade and high-grade glioma across age groups. Safety and efficacy data demonstrate that many of the toxicities of these targeted agents are tolerable while offering objective responses. Newer clinical trials will examine the use of these therapies in the upfront setting. Appropriate duration of therapy and durability of response remains unclear in the glioma patient cohort. Longitudinal efficacy and toxicity data are needed. Furthermore, access to these medications remains challenging outside of clinical trials in Australia and New Zealand. Compassionate access is limited, and advocacy for mechanism of action-based drug approval is ongoing.

Advances on Epigenetic Drugs for Pediatric Brain Tumors

Pediatric malignant brain tumors represent the most frequent cause of cancer-related deaths in childhood. The therapeutic scheme of surgery, radiotherapy and chemotherapy has improved patient management, but with minimal progress in patients' prognosis. Emerging molecular targets and mechanisms have revealed novel approaches for pediatric brain tumor therapy, enabling personalized medical treatment. Advances in the field of epigenetic research and their interplay with genetic changes have enriched our knowledge of the molecular heterogeneity of these neoplasms and have revealed important genes that affect crucial signaling pathways involved in tumor progression. The great potential of epigenetic therapy lies mainly in the widespread location and the reversibility of epigenetic alterations, proposing a wide range of targeting options, including the possible combination of chemoand immunotherapy, significantly increasing their efficacy. Epigenetic drugs, including inhibitors of DNA methyltransferases, histone deacetylases and demethylases, are currently being tested in clinical trials on pediatric brain tumors. Additional novel epigenetic drugs include protein and enzyme inhibitors that modulate epigenetic modification pathways, such as Bromodomain and Extraterminal (BET) proteins, Cyclin-Dependent Kinase 9 (CDK9), AXL, Facilitates Chromatin Transcription (FACT), BMI1, and CREB Binding Protein (CBP) inhibitors, which can be used either as standalone or in combination with current treatment approaches. In this review, we discuss recent progress on epigenetic drugs that could possibly be used against the most common malignant tumors of childhood, such as medulloblastomas, high-grade gliomas and ependymomas.

Impact of new molecular criteria on diagnosis and survival of adult glioma patients

The fifth edition WHO classification of Tumors of the Central nervous system (WHO-CNS5) integrated new molecular parameters to refine CNS tumor classification. This study aimed to reclassify a retrospective cohort of adult glioma patients according to WHO-CNS5, and assess if overall survival (OS) correlated with the revised diagnosis. Further, the diagnostic impact of methylation profiling (MP) was evaluated. Adult gliomas diagnosed according to 2016 WHO-CNS (n = 226) were evaluated according to WHO-CNS5 criteria. All patients had diagnostic NGS performed. 29 patients had 850k MP performed due to challenging tumor cases. OS was analyzed using Kaplan-Meier plots and log-rank test. 19 patients were reclassified. Specifically, diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma (DAG-G) were reclassified as glioblastoma (n = 15). Shifts to glioblastoma were because of TERT promoter (TERT_p) mutation (n = 9), EGFR amplification (n = 2), EGFR amplification and TERT_p mutation (n = 1), and TERT_p mutation with gain of chromosome 7, but uncertain chromosome 10 status due to lack of NGS coverage (n = 3). Lower grade IDH-mutant astrocytomas were reclassified as astrocytoma IDH-mutant, WHO grade 4 due to CDKN2A/B homozygous deletion (n = 4). No significant difference in OS was found for reclassified DAG-G in whole group (p = 0.59) and for TERT_p mutation only (p = 0.44), compared to glioblastoma. MP resulted in revised diagnosis (n = 2), confirmed diagnosis (n = 15) and no match (n = 12). Our study showed similar overall survival for glioblastoma and DAG patients, supporting that isolated TERT_p mutation may have a prognostic role in IDH-wildtype gliomas. Further, our study suggests MP is useful for confirming the diagnoses in challenging tumors.

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Retrospective cohort of adult glioma reclassified using WHO-CNS5 molecular criteria.

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8.4% of the cohort received a new diagnosis and often a higher WHO grade.

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TERT promoter mutation suggested as a prognostic factor in IDH wildtype gliomas.

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DNA methylation profiling useful for diagnostically difficult cases.

Giant-Cell Ependymoma of the Cervical Spinal Cord With Syringomyelia and Pathological Presentation: A Case Report and Review of the Literature

Ependymomas are unusual neuroepithelial tumors of the central nervous system that arise from clusters of ependymal cells. In adults, ependymomas are the most common primary spinal cord tumors. Nevertheless, only a few cases of large-cell ependymoma have been documented; these cases often involve the brain. Here, we report the case of a 43-year-old man who had a cervical spinal cord ependymoma with syringomyelia. The giant-cell ependymoma (GCE) in the spinal cord discussed in this case emphasizes the characteristics of GCE and the discrepancy between the pathological appearance, the surgical results, and the clinically good prognosis.

Methylation Profiling in Diffuse Gliomas: Diagnostic Value and Considerations

Diffuse gliomas cause significant morbidity across all age groups, despite decades of intensive research efforts. Here, we review the differences in diffuse gliomas in adults and children, as well as the World Health Organisation (WHO) 2021 classification of these tumours. We explain how DNA methylation-based classification works and list the methylation-based tumour types and subclasses for adult and paediatric diffuse gliomas. The benefits and utility of methylation-based classification in diffuse gliomas demonstrated to date are described. This entails the identification of novel tumour types/subclasses, patient stratification and targeted treatment/clinical management, and alterations in the clinical diagnosis in favour of the methylation-based over the histopathological diagnosis. Finally, we address several considerations regarding the use of DNA methylation profiling as a diagnostic tool, e.g., the threshold of the classifier, the calibrated score, tumour cell content and intratumour heterogeneity.

Sarcoma classification by DNA methylation profiling in clinical everyday life: the Charité experience

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.

Epigenetics and ADHD: Reflections on Current Knowledge, Research Priorities and Translational Potential

Attention-deficit/hyperactivity disorder (ADHD) is a common and debilitating neurodevelopmental disorder influenced by both genetic and environmental factors, typically identified in the school-age years but hypothesized to have developmental origins beginning in utero. To improve current strategies for prediction, prevention and treatment, a central challenge is to delineate how, at a molecular level, genetic and environmental influences jointly shape ADHD risk, phenotypic presentation, and developmental course. Epigenetic processes that regulate gene expression, such as DNA methylation, have emerged as a promising molecular system in the search for both biomarkers and mechanisms to address this challenge. In this Current Opinion, we discuss the relevance of epigenetics (specifically DNA methylation) for ADHD research and clinical practice, starting with the current state of knowledge, what challenges we have yet to overcome, and what the future may hold in terms of methylation-based applications for personalized medicine in ADHD. We conclude that the field of epigenetics and ADHD is promising but is still in its infancy, and the potential for transformative translational applications remains a distant goal. Nevertheless, rapid methodological advances, together with the rise of collaborative science and increased availability of high-quality, longitudinal data make this a thriving research area that in future may contribute to the development of new tools for improved prediction, management, and treatment of ADHD.

DNA methylation profiling improves routine diagnosis of paediatric CNS tumours: a prospective population-based study

AIMS

Paediatric brain tumours are rare and establishing a precise diagnosis can be challenging. Analysis of DNA methylation profiles has been shown to be a reliable method to classify central nervous system (CNS) tumours with high accuracy. We aimed to prospectively analyse CNS tumours diagnosed in Sweden, to assess the clinical impact of adding DNA methylation-based classification to standard paediatric brain tumour diagnostics in an unselected cohort.

METHODS

All CNS tumours diagnosed in children (0-18 years) during 2017-2020 were eligible for inclusion provided sufficient tumour material was available. Tumours were analysed using genome-wide DNA methylation profiling and classified by the MNP brain tumour classifier. The initial histopathological diagnosis was compared to the DNA methylation-based classification. For incongruent results, a blinded re-evaluation was performed by an experienced neuropathologist.

RESULTS

240 tumours with a histopathology-based diagnosis were profiled. A high-confidence methylation score of 0.84 or more was reached in 78% of the cases. In 69%, the histopathological diagnosis was confirmed and for some of these also refined, 6% were incongruent and the re-evaluation favoured the methylation-based classification. In the remaining 3% of cases, the methylation class was non-contributory. The change in diagnosis would have had a direct impact on the clinical management in 5% of all patients.

CONCLUSIONS

Integrating DNA methylation-based tumour classification into routine clinical analysis improves diagnostics and provides molecular information that is important for treatment decisions. The results from methylation profiling should be interpreted in the context of clinical and histopathological information.

DNA methylation alterations across time and space in paediatric brain tumours

DNA methylation is increasingly used for tumour classification and has expanded upon the > 100 currently known brain tumour entities. A correct diagnosis is the basis for suitable treatment for patients with brain tumours, which is the leading cause of cancer-related death in children. DNA methylation profiling is required for diagnosis of certain tumours, and used clinically for paediatric brain tumours in several countries. We therefore evaluated if the methylation-based classification is robust in different locations of the same tumour, and determined how the methylation pattern changed over time to relapse. We sampled 3-7 spatially separated biopsies per patient, and collected samples from paired primary and relapse brain tumours from children. Altogether, 121 samples from 46 paediatric patients with brain tumours were profiled with EPIC methylation arrays. The methylation-based classification was mainly homogeneous for all included tumour types that were successfully classified, which is promising for clinical diagnostics. There were indications of multiple subclasses within tumours and switches in the relapse setting, but not confirmed as the classification scores were below the threshold. Site-specific methylation alterations did occur within the tumours and varied significantly between tumour types for the temporal samples, and as a trend in spatial samples. More alterations were present in high-grade tumours compared to low-grade, and significantly more alterations with longer relapse times. The alterations in the spatial and temporal samples were significantly depleted in CpG islands, exons and transcription start sites, while enriched in OpenSea and regions not affiliated with a gene, suggesting a random location of the alterations in less conserved regions. In conclusion, more DNA methylation changes accumulated over time and more alterations occurred in high-grade tumours. The alterations mainly occurred in regions without gene affiliation, and did not affect the methylation-based classification, which largely remained homogeneous in paediatric brain tumours.

Diagnostic accuracy of 1p/19q codeletion tests in oligodendroglioma: A comprehensive meta-analysis based on a Cochrane systematic review

Codeletion of chromosomal arms 1p and 19q, in conjunction with a mutation in the isocitrate dehydrogenase 1 or 2 gene, is the molecular diagnostic criterion for oligodendroglioma, IDH mutant and 1p/19q codeleted. 1p/19q codeletion is a diagnostic marker and allows prognostication and prediction of the best drug response within IDH-mutant tumours. We performed a Cochrane review and simple economic analysis to establish the most sensitive, specific and cost-effective techniques for determining 1p/19q codeletion status. Fluorescent in situ hybridisation (FISH) and polymerase chain reaction (PCR)-based loss of heterozygosity (LOH) test methods were considered as reference standard. Most techniques (FISH, chromogenic in situ hybridisation [CISH], PCR, real-time PCR, multiplex ligation-dependent probe amplification [MLPA], single nucleotide polymorphism [SNP] array, comparative genomic hybridisation [CGH], array CGH, next-generation sequencing [NGS], mass spectrometry and NanoString) showed good sensitivity (few false negatives) for detection of 1p/19q codeletions in glioma, irrespective of whether FISH or PCR-based LOH was used as the reference standard. Both NGS and SNP array had a high specificity (fewer false positives) for 1p/19q codeletion when considered against FISH as the reference standard. Our findings suggest that G banding is not a suitable test for 1p/19q analysis. Within these limits, considering cost per diagnosis and using FISH as a reference, MLPA was marginally more cost-effective than other tests, although these economic analyses were limited by the range of available parameters, time horizon and data from multiple healthcare organisations.

ATRT-SHH comprises three molecular subgroups with characteristic clinical and histopathological features and prognostic significance

Atypical teratoid/rhabdoid tumor (ATRT) is an aggressive central nervous system tumor characterized by loss of SMARCB1/INI1 protein expression and comprises three distinct molecular groups, ATRT–TYR, ATRT–MYC and ATRT–SHH. ATRT–SHH represents the largest molecular group and is heterogeneous with regard to age, tumor location and epigenetic profile. We, therefore, aimed to investigate if heterogeneity within ATRT–SHH might also have biological and clinical importance. Consensus clustering of DNA methylation profiles and confirmatory t-SNE analysis of 65 ATRT–SHH yielded three robust molecular subgroups, i.e., SHH-1A, SHH-1B and SHH-2. These subgroups differed by median age of onset (SHH-1A: 18 months, SHH-1B: 107 months, SHH-2: 13 months) and tumor location (SHH-1A: 88% supratentorial; SHH-1B: 85% supratentorial; SHH-2: 93% infratentorial, often extending to the pineal region). Subgroups showed comparable SMARCB1 mutational profiles, but pathogenic/likely pathogenic SMARCB1 germline variants were over-represented in SHH-2 (63%) as compared to SHH-1A (20%) and SHH-1B (0%). Protein expression of proneural marker ASCL1 (enriched in SHH-1B) and glial markers OLIG2 and GFAP (absent in SHH-2) as well as global mRNA expression patterns differed, but all subgroups were characterized by overexpression of SHH as well as Notch pathway members. In a Drosophila model, knockdown of Snr1 (the fly homologue of SMARCB1) in hedgehog activated cells not only altered hedgehog signaling, but also caused aberrant Notch signaling and formation of tumor-like structures. Finally, on survival analysis, molecular subgroup and age of onset (but not ASCL1 staining status) were independently associated with overall survival, older patients (> 3 years) harboring SHH-1B experiencing relatively favorable outcome. In conclusion, ATRT–SHH comprises three subgroups characterized by SHH and Notch pathway activation, but divergent molecular and clinical features. Our data suggest that molecular subgrouping of ATRT–SHH has prognostic relevance and might aid to stratify patients within future clinical trials.

Diagnostic and Prognostic Implications of GNAS Inactivation in Sonic Hedgehog-Activated Medulloblastoma: Case Report with Comprehensive Molecular Profiling and Review of Literature

SHH medulloblastoma from GNAS mutation (molecular profiling confirmation) with osteoma cutis & syndromic features.

Epigenetic mechanisms in paediatric brain tumours: regulators lose control

Epigenetic mechanisms are essential to regulate gene expression during normal development. However, they are often disrupted in pathological conditions including tumours, where they contribute to their formation and maintenance through altered gene expression. In recent years, next generation genomic techniques has allowed a remarkable advancement of our knowledge of the genetic and molecular landscape of paediatric brain tumours and have highlighted epigenetic deregulation as a common hallmark in their pathogenesis. This review describes the main epigenetic dysregulations found in paediatric brain tumours, including at DNA methylation and histone modifications level, in the activity of chromatin-modifying enzymes and in the expression of non-coding RNAs. How these altered processes influence tumour biology and how they can be leveraged to dissect the molecular heterogeneity of these tumours and contribute to their classification is also addressed. Finally, the availability and value of preclinical models as well as the current clinical trials exploring targeting key epigenetic mediators in paediatric brain tumours are discussed.

Molecular landscape of IDH-wildtype, H3-wildtype glioblastomas of adolescents and young adults (AYA)

OBJECTIVE

We aimed to characterise glioblastomas of adolescents and young adults (AYA) that were IDH wildtype (wt) and H3 wildtype (wt).

MATERIALS AND METHODS

Fifty such patients (aged 16-32) were studied by methylation profiling, targeted sequencing and targeted RNA-seq.

RESULTS

Tumours predominantly clustered into three methylation classes according to the terminology of Capper et al. (2018): (anaplastic) PXA (21 cases), GBM_midline (15 cases) and glioblastoma RTK/mesenchymal (7 cases). Two cases clustered with ANA_PA, 4 cases with LGG classes and 1 with GBM_MYCN. Only fifteen cases reached a calibrated score >0.84 when the cases were uploaded to DKFZ Classifier. GBM_midline-clustered tumours had a poorer overall survival (OS) compared to the PXA-clustered tumours (p=0.030). LGG-clustered cases had a significantly better survival than GBM_midline-clustered tumours and glioblastoma RTK/mesenchymal-clustered tumours. Only 13/21 (62%) of PXA-clustered cases were BRAF V600E mutated. Most GBM_midline-clustered cases were not located in the midline. GBM_midline-clustered cases were characterized by PDGFRA amplification/mutation (73.3%), mutations of mismatch repair genes (40.0%), and all showed H3K27me3 and EZH1P loss, and an unmethylated MGMT promoter. Across the whole cohort, MGMT promoter methylation and wildtype TERT promoter were favourable prognosticators. Mismatch repair gene mutations were poor prognosticators and together with methylation class and MGMT methylation, maintained their significance in multi-variate analyses. BRAF mutation was a good prognosticator in the PXA-clustered tumours.

CONCLUSION

Methylation profiling is a useful tool in the diagnosis and prognostication of AYA glioblastomas and the methylation classes have distinct molecular characteristics. The usual molecular diagnostic criteria for adult IDHwt glioblastoma should be applied with caution within the AYA age group.

DNA Methylation Profiling: An Emerging Paradigm for Cancer Diagnosis

Histomorphology has been a mainstay of cancer diagnosis in anatomic pathology for many years. DNA methylation profiling is an additional emerging tool that will serve as an adjunct to increase accuracy of pathological diagnosis. Genome-wide interrogation of DNA methylation signatures, in conjunction with machine learning methods, has allowed for the creation of clinical-grade classifiers, most prominently in central nervous system and soft tissue tumors. Tumor DNA methylation profiling has led to the identification of new entities and the consolidation of morphologically disparate cancers into biologically coherent entities, and it will progressively become mainstream in the future. In addition, DNA methylation patterns in circulating tumor DNA hold great promise for minimally invasive cancer detection and classification. Despite practical challenges that accompany any new technology, methylation profiling is here to stay and will become increasingly utilized as a cancer diagnostic tool across a range of tumor types. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 17 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

DNA methylation profiling as a model for discovery and precision diagnostics in neuro-oncology

Recent years have witnessed a shift to more objective and biologically-driven methods for central nervous system (CNS) tumor classification. The 2016 world health organization (WHO) classification update ("blue book") introduced molecular diagnostic criteria into the definitions of specific entities as a response to the plethora of evidence that key molecular alterations define distinct tumor types and are clinically meaningful. While in the past such diagnostic alterations included specific mutations, copy number changes, or gene fusions, the emergence of DNA methylation arrays in recent years has similarly resulted in improved diagnostic precision, increased reliability, and has provided an effective framework for the discovery of new tumor types. In many instances, there is an intimate relationship between these mutations/fusions and DNA methylation signatures. The adoption of methylation data into neuro-oncology nosology has been greatly aided by the availability of technology compatible with clinical diagnostics, along with the development of a freely accessible machine learning-based classifier. In this review, we highlight the utility of DNA methylation profiling in CNS tumor classification with a focus on recently described novel and rare tumor types, as well as its contribution to refining existing types.

Treatment of anaplastic gliomas: evidences and controversies

PURPOSE OF REVIEW

Evolving molecular data have led to a new and advanced grading system of anaplastic glioma. In everyday practice, physicians have to translate evidence from old clinical trials into evidence meeting the reclassified tumor types.

RECENT FINDINGS

New biomarkers allow the identification of anaplastic glioma with relatively poor prognosis and with prognosis similar to glioblastoma. An update with molecular analysis of the phase 3 CATNON trial demonstrates the benefit of adjuvant temozolomide (TMZ) to be dependent on the mutational status of isocitrate dehydrogenase. In the ongoing debate on the optimal chemotherapy regimen, a large retrospective study suggesting a better tumor control with vincristine (PCV) as compared to TMZ is added to the evidence. The best timing for treatment of anaplastic astrocytoma also remains a matter of controversy. A recent study shows that even in selected patients with anaplastic glioma with foci of malignant tumor following (sub)total resection, postponement of medical treatment can be considered.

SUMMARY

In clinical practice, the trade-off between efficacy and (acute and long-term) toxicity of treatments needs to be re-evaluated for the newly (molecularly) defined entities. Updates from past clinical trials on anaplastic glioma with molecular analysis and subgroup analyses are needed to further guide treatment decisions.

Impact of the methylation classifier and ancillary methods on CNS tumor diagnostics

BACKGROUND

Accurate CNS tumor diagnosis can be challenging, and methylation profiling can serve as an adjunct to classify diagnostically difficult cases.

METHODS

An integrated diagnostic approach was employed for a consecutive series of 1,258 surgical neuropathology samples obtained primarily in a consultation practice over 2-year period. DNA methylation profiling and classification using the DKFZ/Heidelberg CNS tumor classifier was performed, as well as unsupervised analyses of methylation data. Ancillary testing, where relevant, was performed.

RESULTS

Among the received cases in consultation, a high confidence methylation classifier score (>0.84) was reached in 66.4% of cases. The classifier impacted the diagnosis in 46.5% of these high-confidence classifier score cases, including a substantially new diagnosis in 26.9% cases. Among the 289 cases received with only a descriptive diagnosis, methylation was able to resolve approximately half (144, 49.8%) with high-confidence scores. Additional methods were able to resolve diagnostic uncertainty in 41.6% of the low-score cases. Tumor purity was significantly associated with classifier score (p = 1.15e-11). Deconvolution demonstrated that suspected GBMs matching as control/inflammatory brain tissue could be resolved into GBM methylation profiles, which provided a proof-of-concept approach to resolve tumor classification in the setting of low tumor purity.

CONCLUSIONS

This work assesses the impact of a methylation classifier and additional methods in a consultative practice by defining the proportions with concordant vs. change in diagnosis in a set of diagnostically challenging CNS tumors. We address approaches to low-confidence scores and confounding issues of low tumor purity.

Analyses of DNA Methylation Profiling in the Diagnosis of Intramedullary Astrocytomas

Intramedullary astrocytomas (IMAs) consist of a heterogeneous group of rare central nervous system (CNS) tumors associated with variable outcomes. A DNA methylation-based classification approach has recently emerged as a powerful tool to further classify CNS tumors. However, no DNA methylation-related studies specifically addressing to IMAs have been performed yet. In the present study, we analyzed 16 IMA samples subjected to morphological and molecular analyses, including DNA methylation profiling. Among the 16 samples, only 3 cases were classified in a reference methylation class (MC) with the recommended calibrated score (≥0.9). The remaining cases were either considered “no-match” cases (calibrated score <0.3, n = 7) or were classified with low calibrated scores (ranging from 0.32 to 0.53, n = 6), including inconsistent classification. To obtain a more comprehensive tool for pathologists, we used different unsupervised analyses of DNA methylation profiles, including our data and those from the Heidelberg reference cohort. Even though our cohort included only 16 cases, hypotheses regarding IMA-specific classification were underlined; a potential specific MC of PA_SPINE was identified and high-grade IMAs, probably consisting of H3K27M wild-type IMAs, were mainly associated with ANA_PA MC. These hypotheses strongly suggest that a specific classification for IMAs has to be investigated.

The 2021 WHO Classification of Tumors of the Central Nervous System: a summary

The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, is the sixth version of the international standard for the classification of brain and spinal cord tumors. Building on the 2016 updated fourth edition and the work of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy, the 2021 fifth edition introduces major changes that advance the role of molecular diagnostics in CNS tumor classification. At the same time, it remains wedded to other established approaches to tumor diagnosis such as histology and immunohistochemistry. In doing so, the fifth edition establishes some different approaches to both CNS tumor nomenclature and grading and it emphasizes the importance of integrated diagnoses and layered reports. New tumor types and subtypes are introduced, some based on novel diagnostic technologies such as DNA methylome profiling. The present review summarizes the major general changes in the 2021 fifth edition classification and the specific changes in each taxonomic category. It is hoped that this summary provides an overview to facilitate more in-depth exploration of the entire fifth edition of the WHO Classification of Tumors of the Central Nervous System.

DNA methylation-based profiling of bone and soft tissue tumours: a validation study of the 'DKFZ Sarcoma Classifier'

Diagnosing bone and soft tissue neoplasms remains challenging because of the large number of subtypes, many of which lack diagnostic biomarkers. DNA methylation profiles have proven to be a reliable basis for the classification of brain tumours and, following this success, a DNA methylation-based sarcoma classification tool from the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg has been developed. In this study, we assessed the performance of their classifier on DNA methylation profiles of an independent data set of 986 bone and soft tissue tumours and controls. We found that the 'DKFZ Sarcoma Classifier' was able to produce a diagnostic prediction for 55% of the 986 samples, with 83% of these predictions concordant with the histological diagnosis. On limiting the validation to the 820 cases with histological diagnoses for which the DKFZ Classifier was trained, 61% of cases received a prediction, and the histological diagnosis was concordant with the predicted methylation class in 88% of these cases, findings comparable to those reported in the DKFZ Classifier paper. The classifier performed best when diagnosing mesenchymal chondrosarcomas (CHSs, 88% sensitivity), chordomas (85% sensitivity), and fibrous dysplasia (83% sensitivity). Amongst the subtypes least often classified correctly were clear cell CHSs (14% sensitivity), malignant peripheral nerve sheath tumours (27% sensitivity), and pleomorphic liposarcomas (29% sensitivity). The classifier predictions resulted in revision of the histological diagnosis in six of our cases. We observed that, although a higher tumour purity resulted in a greater likelihood of a prediction being made, it did not correlate with classifier accuracy. Our results show that the DKFZ Classifier represents a powerful research tool for exploring the pathogenesis of sarcoma; with refinement, it has the potential to be a valuable diagnostic tool.