DNA methylation-based profiling for paediatric CNS tumour diagnosis and treatment: a population-based study

Long-read sequencing for brain tumors

Brain tumors and genomics have a long-standing history given that glioblastoma was the first cancer studied by the cancer genome atlas. The numerous and continuous advances through the decades in sequencing technologies have aided in the advanced molecular characterization of brain tumors for diagnosis, prognosis, and treatment. Since the implementation of molecular biomarkers by the WHO CNS in 2016, the genomics of brain tumors has been integrated into diagnostic criteria. Long-read sequencing, also known as third generation sequencing, is an emerging technique that allows for the sequencing of longer DNA segments leading to improved detection of structural variants and epigenetics. These capabilities are opening a way for better characterization of brain tumors. Here, we present a comprehensive summary of the state of the art of third-generation sequencing in the application for brain tumor diagnosis, prognosis, and treatment. We discuss the advantages and potential new implementations of long-read sequencing into clinical paradigms for neuro-oncology patients.

High-throughput molecular assays for inclusion in personalised oncology trials - State-of-the-art and beyond

In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial.

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.

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.

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.

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.

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.

Osteosarcomas With Few Chromosomal Alterations or Adult Onset Are Genetically Heterogeneous

Osteosarcoma is the most common primary bone malignancy, often detected in children and adolescents and commonly associated with TP53 alterations along with a high number of chromosomal rearrangements. However, osteosarcoma can affect patients of any age, and some tumors display less genetic complexity. Besides TP53 variants, data on key driving mutations are lacking for many osteosarcomas, particularly those affecting adults. To detect osteosarcoma-specific alterations, we screened transcriptomic and genomic sequencing and copy number data from 150 bone tumors originally diagnosed as osteosarcomas. To increase the precision in gene fusion detection, we developed a bioinformatic tool denoted as NAFuse, which extracts gene fusions that are verified at both the genomic and transcriptomic levels. Apart from the already reported genetic subgroups of osteosarcoma with TP53 structural variants, or MDM2 and/or CDK4 amplification, we did not identify any recurrent genetic driver that signifies the remaining cases. Among the plethora of mutations identified, we found genetic alterations characteristic of, or similar to, those of other bone and soft tissue tumors in 8 cases. These mutations were found in tumors with relatively few other genetic alterations or in adults. Due to the lack of clinical context and available tissue, we can question the diagnosis only on a genetic basis. However, our findings support the notion that osteosarcomas with few chromosomal alterations or adult onset seem genetically distinct from conventional osteosarcomas of children and adolescents.

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.

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.

Molecular profiling of pre- and post-treatment pediatric high-grade astrocytomas reveals acquired increased tumor mutation burden in a subset of recurrences

Diffuse gliomas are a heterogeneous category of primary central nervous system tumors. Due to their infiltrative growth precluding complete surgical resection, most diffuse high-grade gliomas are treated with adjuvant chemotherapy and radiation. Recurrent/progressive diffuse gliomas may show genetic differences when compared to the primary tumors, giving insight into their molecular evolution and mechanisms of treatment resistance. In adult-type diffuse gliomas with or without isocitrate dehydrogenase gene mutations, tumor recurrence/progression can be associated with mutations in genes encoding DNA mismatch repair proteins, leading to a dramatic increase in tumor mutation burden. This phenomenon is closely linked to treatment with the DNA alkylating agent temozolomide, a mainstay of adult diffuse glioma chemotherapeutic management. Post-treatment mismatch repair deficiency and acquired high tumor mutation burden is relatively unexplored in pediatric patients who have recurrent high-grade gliomas. Here, we report a molecular and histological analysis of an institutional cohort of eleven pediatric patients with paired initial and recurrent high-grade astrocytoma samples with intervening temozolomide treatment. We identified three cases with evidence for increased tumor mutation burden at recurrence, including two cases of diffuse hemispheric glioma H3 G34-mutant (one previously reported). We also show that molecular analysis by next-generation DNA sequencing and DNA methylation-based profiling enabled an integrated diagnosis per 2021 World Health Organization criteria in 10 of 11 cases (91%). Our findings indicate that increased tumor mutation burden at post-treatment recurrence is relevant in pediatric-type diffuse high-grade gliomas. Diffuse hemispheric glioma H3 G34-mutant may be particularly susceptible to this phenomenon.

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.

A Radiologist's Guide to the 2021 WHO Central Nervous System Tumor Classification: Part 2-Newly Described and Revised Tumor Types

The fifth edition of the World Health Organization classification of tumors of the central nervous system (CNS), published in 2021, introduces major shifts in the classification of brain and spine tumors. These changes were necessitated by rapidly increasing knowledge of CNS tumor biology and therapies, much of which is based on molecular methods in tumor diagnosis. The growing complexity of CNS tumor genetics has required reorganization of tumor groups and acknowledgment of new tumor entities. For radiologists interpreting neuroimaging studies, proficiency with these updates is critical in providing excellent patient care. This review will focus on new or revised CNS tumor types and subtypes, beyond infiltrating glioma (described in part 1 of this series), with an emphasis on imaging features.

Multiomic neuropathology improves diagnostic accuracy in pediatric neuro-oncology

The large diversity of central nervous system (CNS) tumor types in children and adolescents results in disparate patient outcomes and renders accurate diagnosis challenging. In this study, we prospectively integrated DNA methylation profiling and targeted gene panel sequencing with blinded neuropathological reference diagnostics for a population-based cohort of more than 1,200 newly diagnosed pediatric patients with CNS tumors, to assess their utility in routine neuropathology. We show that the multi-omic integration increased diagnostic accuracy in a substantial proportion of patients through annotation to a refining DNA methylation class (50%), detection of diagnostic or therapeutically relevant genetic alterations (47%) or identification of cancer predisposition syndromes (10%). Discrepant results by neuropathological WHO-based and DNA methylation-based classification (30%) were enriched in histological high-grade gliomas, implicating relevance for current clinical patient management in 5% of all patients. Follow-up (median 2.5 years) suggests improved survival for patients with histological high-grade gliomas displaying lower-grade molecular profiles. These results provide preliminary evidence of the utility of integrating multi-omics in neuropathology for pediatric neuro-oncology.

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.

Molecular diagnostics enables detection of actionable targets: the Pediatric Targeted Therapy 2.0 registry

BACKGROUND

Precision oncology requires diagnostic accuracy and robust detection of actionable alterations. The Pediatric Targeted Therapy (PTT) 2.0 program aims at improving diagnostic accuracy by addition of molecular analyses to the existing histological diagnosis and detection of actionable alterations for relapsed paediatric oncology patients, in cases with limited availability of tumour material.

METHODS

Paediatric patients diagnosed with relapse or progression of a central nervous system tumour (n = 178), a sarcoma (n = 41) or another solid tumour (n = 44) were included. DNA methylation array, targeted gene panel sequencing on tumour and blood (130 genes), RNA sequencing in selected cases and a pathway-specific immunohistochemistry (IHC) panel were performed using limited formalin-fixed paraffin embedded tissue from any disease episode available. The clinical impact of reported findings was assessed by a serial questionnaire-based follow-up.

RESULTS

Integrated molecular diagnostics resulted in refined or changed diagnosis in 117/263 (44%) tumours. Actionable targets were detected in 155/263 (59%) cases. Constitutional DNA variants with clinical relevance were identified in 16/240 (7%) of patients, half of which were previously unknown. Clinical follow-up showed that 26/263 (10%) of patients received mechanism-of-action based treatment matched to the molecular findings.

CONCLUSION

Next-generation diagnostics adds robust and relevant information on diagnosis, actionable alterations and cancer predisposition syndromes even when tissue from the current disease episode is limited.

Review of the Recent Changes in the WHO Classification for Pediatric Brain and Spinal Cord Tumors

BACKGROUND

Periodic updates to the World Health Organization (WHO) classification system for central nervous system (CNS) tumors reflect advances in the pathological diagnosis, categorization, and molecular underpinnings of primary brain, spinal cord, and peripheral nerve tumors. The 5th edition of the WHO Classification of CNS Tumors was published in 2021. This review discusses the guiding principles of the revision, introduces the more common new diagnostic entities, and describes tumor classification and nomenclature changes that are relevant for pediatric neurological surgeons.

SUMMARY

Revisions to the WHO CNS tumor classification system introduced new diagnostic entities, restructured and renamed other entities with particular impact in the diffuse gliomas and CNS embryonal tumors, and expanded the requirements for incorporating both molecular and histological features of CNS tumors into a unified integrated diagnosis. Many of the new diagnostic entities occur at least occasionally in pediatric patients and will thus be encountered by pediatric neurosurgeons. New nomenclature impacts the terminology that is applied in communication between pathologists, surgeons, clinicians, and patients. Requirements for molecular information in tumor diagnosis are expected to refine diagnostic categories while also introducing practical considerations for intraoperative consultation, preliminary histological evaluation, and triaging of neurosurgical tissue samples for histology, molecular testing, and clinical trial requirements.

KEY MESSAGES

Pediatric brain tumor diagnosis and clinical management are a multidisciplinary effort that is rapidly advancing in the molecular era. Interdisciplinary collaboration is critical for providing the best care for pediatric CNS tumor patients. Pediatric neurosurgeons and their local neuropathologists and neuro-oncologists must work collaboratively to put the most current CNS tumor diagnostic guidelines into standard practice.

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.

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.

Pediatric CNS cancer genomics and immunogenomics

Large-scale genomic discovery in pediatric cancers established the importance of multiple platform-based characterizations of DNA and RNA to obtain a complete molecular landscape of these cancers, including actionable variants, diagnostic or prognostic evidence, and germline susceptibility. While these discoveries set the stage for pediatric cancer precision medicine, broad-based implementation has been quite slow compared with the adult-cancer precision medicine setting, due largely to the rarity of pediatric cancer. Here, we survey several large-cohort studies that utilize multiplex clinical characterization, including pediatric patients diagnosed with central nervous system (CNS) malignancies. The reported results demonstrate that molecularly guided precision therapeutics yield clinical benefit for these patients, establishing one important component needed for precision therapeutics to enter the pediatric CNS setting.

Combinations of Single-Gene Biomarkers Can Precisely Stratify 1,028 Adult Gliomas for Prognostication

Advanced genomic techniques have now been incorporated into diagnostic practice in neuro-oncology in the literature. However, these assays are expensive and time-consuming and demand bioinformatics expertise for data interpretation. In contrast, single-gene tests can be run much more cheaply, with a short turnaround time, and are available in general pathology laboratories. The objective of this study was to establish a molecular grading scheme for adult gliomas using combinations of commonly available single-gene tests. We retrospectively evaluated molecular diagnostic data of 1,275 cases of adult diffuse gliomas from three institutions where we were testing for IDH1/2 mutation, TERTp mutation, 1p19q codeletion, EGFR amplification, 10q deletion, BRAF V600E, and H3 mutations liberally in our regular diagnostic workup. We found that a molecular grading scheme of Group 1 (1p19q codeleted, IDH mutant), Group 2 (IDH mutant, 1p19q non-deleted, TERT mutant), Group 3 (IDH mutant, 1p19q non-deleted, TERT wild type), Group 4 (IDH wild type, BRAF mutant), Group 5 (IDH wild type, BRAF wild type and not possessing the criteria of Group 6), and Group 6 (IDH wild type, and any one of TERT mutant, EGFR amplification, 10q deletion, or H3 mutant) could significantly stratify this large cohort of gliomas for risk. A total of 1,028 (80.6%) cases were thus classifiable with sufficient molecular data. There were 270 cases of molecular Group 1, 59 cases of molecular Group 2, 248 cases of molecular Group 3, 27 cases of molecular Group 4, 117 cases of molecular Group 5, and 307 cases of molecular Group 6. The molecular groups were independent prognosticators by multivariate analyses and in specific instances, superseded conventional histological grades. We were also able to validate the usefulness of the Groups with a cohort retrieved from The Cancer Genome Atlas (TCGA) where similar molecular tests were liberally available. We conclude that a single-gene molecular stratification system, useful for fine prognostication, is feasible and can be adopted by a general pathology laboratory.

Comprehensive Genomic Profiling of High-Risk Pediatric Cancer Patients Has a Measurable Impact on Clinical Care

PURPOSE

Profiling of pediatric cancers through deep sequencing of large gene panels and whole exomes is rapidly being adopted in many clinical settings. However, the most impactful approach to genomic profiling of pediatric cancers remains to be defined.

METHODS

We conducted a prospective precision medicine trial, using whole-exome sequencing of tumor and germline tissue and whole-transcriptome sequencing (RNA Seq) of tumor tissue to characterize the mutational landscape of 127 tumors from 126 unique patients across the spectrum of pediatric brain tumors, hematologic malignancies, and extracranial solid tumors.

RESULTS

We identified somatic tumor alterations in 121/127 (95.3%) tumor samples and identified cancer predisposition syndromes on the basis of known pathogenic or likely pathogenic germline mutations in cancer predisposition genes in 9/126 patients (7.1%). Additionally, we developed a novel scoring system for measuring the impact of tumor and germline sequencing, encompassing therapeutically relevant genomic alterations, cancer-related germline findings, recommendations for treatment, and refinement of risk stratification or prognosis. At least one impactful finding from the genomic results was identified in 108/127 (85%) samples sequenced. A recommendation to consider a targeted agent was provided for 82/126 (65.1%) patients. Twenty patients ultimately received therapy with a molecularly targeted agent, representing 24% of those who received a targeted agent recommendation and 16% of the total cohort.

CONCLUSION

Paired tumor/normal whole-exome sequencing and tumor RNA Seq of de novo or relapsed/refractory tumors was feasible and clinically impactful in high-risk pediatric cancer patients.

Methylation classifiers: brain tumors, sarcomas and what's next

Tumor classification has evolved over the last decades with technical progress contributing much to our current concepts. Among diagnostic hallmark novelties were immunostaining, Fluorescence in situ hybridization, Sanger sequencing followed by massive parallel DNA sequencing and recently, epigenetic analyses have entered the stage. Although each of these techniques was revolutionary and, in some way, also disruptive in certain diagnostic fields, it took years to decades for broad implementation into standard pathological-diagnostic algorithms. In contrast, DNA methylation profiling has been accepted in short time as a game changer with lasting impact on brain tumor classification and with potential for classification of other tumor types. This review provides a brief introduction in DNA methylation-based tumor classification. We present why DNA methylation signatures are attractive diagnostic biomarkers, discuss present achievements and future aims and explain the integration of methylation-based classifiers in diagnostic procedure. Finally, we provide an outlook on the challenges and opportunities associated with DNA methylation-based tumor profiling. This article is protected by copyright. All rights reserved.

Diagnostic test accuracy and cost-effectiveness of tests for codeletion of chromosomal arms 1p and 19q in people with glioma

BACKGROUND

Complete deletion of both the short arm of chromosome 1 (1p) and the long arm of chromosome 19 (19q), known as 1p/19q codeletion, is a mutation that can occur in gliomas. It occurs in a type of glioma known as oligodendroglioma and its higher grade counterpart known as anaplastic oligodendroglioma. Detection of 1p/19q codeletion in gliomas is important because, together with another mutation in an enzyme known as isocitrate dehydrogenase, it is needed to make the diagnosis of an oligodendroglioma. Presence of 1p/19q codeletion also informs patient prognosis and prediction of the best drug treatment. The main two tests in use are fluorescent in situ hybridisation (FISH) and polymerase chain reaction (PCR)-based loss of heterozygosity (LOH) assays (also known as PCR-based short tandem repeat or microsatellite analysis). Many other tests are available. None of the tests is perfect, although PCR-based LOH is expected to have very high sensitivity.

OBJECTIVES

To estimate the sensitivity and specificity and cost-effectiveness of different deoxyribonucleic acid (DNA)-based techniques for determining 1p/19q codeletion status in glioma.

SEARCH METHODS

We searched MEDLINE, Embase and BIOSIS up to July 2019. There were no restrictions based on language or date of publication. We sought economic evaluation studies from the results of this search and using the National Health Service Economic Evaluation Database.

SELECTION CRITERIA

We included cross-sectional studies in adults with glioma or any subtype of glioma, presenting raw data or cross-tabulations of two or more DNA-based tests for 1p/19q codeletion. We also sought economic evaluations of these tests.

DATA COLLECTION AND ANALYSIS

We followed procedures outlined in the Cochrane Handbook for Diagnostic Test Accuracy Reviews. Two review authors independently screened titles/abstracts/full texts, performed data extraction, and undertook applicability and risk of bias assessments using QUADAS-2. Meta-analyses used the hierarchical summary ROC model to estimate and compare test accuracy. We used FISH and PCR-based LOH as alternate reference standards to examine how tests compared with those in common use, and conducted a latent class analysis comparing FISH and PCR-based LOH. We constructed an economic model to evaluate cost-effectiveness.

MAIN RESULTS

We included 53 studies examining: PCR-based LOH, FISH, single nucleotide polymorphism (SNP) array, next-generation sequencing (NGS), comparative genomic hybridisation (CGH), array comparative genomic hybridisation (aCGH), multiplex-ligation-dependent probe amplification (MLPA), real-time PCR, chromogenic in situ hybridisation (CISH), mass spectrometry (MS), restriction fragment length polymorphism (RFLP) analysis, G-banding, methylation array and NanoString. Risk of bias was low for only one study; most gave us concerns about how patients were selected or about missing data. We had applicability concerns about many of the studies because only patients with specific subtypes of glioma were included. 1520 participants contributed to analyses using FISH as the reference, 1304 participants to analyses involving PCR-based LOH as the reference and 262 participants to analyses of comparisons between methods from studies not including FISH or PCR-based LOH. Most evidence was available for comparison of FISH with PCR-based LOH (15 studies, 915 participants): PCR-based LOH detected 94% of FISH-determined codeletions (95% credible interval (CrI) 83% to 98%) and FISH detected 91% of codeletions determined by PCR-based LOH (CrI 78% to 97%). Of tumours determined not to have a deletion by FISH, 94% (CrI 87% to 98%) had a deletion detected by PCR-based LOH, and of those determined not to have a deletion by PCR-based LOH, 96% (CrI 90% to 99%) had a deletion detected by FISH. The latent class analysis suggested that PCR-based LOH may be slightly more accurate than FISH. Most other techniques appeared to have high sensitivity (i.e. produced few false-negative results) for detection of 1p/19q codeletion when either FISH or PCR-based LOH was considered as the reference standard, although there was limited evidence. There was some indication of differences in specificity (false-positive rate) with some techniques. Both NGS and SNP array had high specificity when considered against FISH as the reference standard (NGS: 6 studies, 243 participants; SNP: 6 studies, 111 participants), although we rated certainty in the evidence as low or very low. NGS and SNP array also had high specificity when PCR-based LOH was considered the reference standard, although with much more uncertainty as these results were based on fewer studies (just one study with 49 participants for NGS and two studies with 33 participants for SNP array). G-banding had low sensitivity and specificity when PCR-based LOH was the reference standard. Although MS had very high sensitivity and specificity when both FISH and PCR-based LOH were considered the reference standard, these results were based on only one study with a small number of participants. Real-time PCR also showed high specificity with FISH as a reference standard, although there were only two studies including 40 participants. We found no relevant economic evaluations. Our economic model using FISH as the reference standard suggested that the resource-optimising test depends on which measure of diagnostic accuracy is most important. With FISH as the reference standard, MLPA is likely to be cost-effective if society was willing to pay GBP 1000 or less for a true positive detected. However, as the value placed on a true positive increased, CISH was most cost-effective. Findings differed when the outcome measure changed to either true negative detected or correct diagnosis. When PCR-based LOH was used as the reference standard, MLPA was likely to be cost-effective for all measures of diagnostic accuracy at lower threshold values for willingness to pay. However, as the threshold values increased, none of the tests were clearly more likely to be considered cost-effective.

AUTHORS' CONCLUSIONS

In our review, most techniques (except G-banding) appeared to have good sensitivity (few false negatives) for detection of 1p/19q codeletions in glioma against both FISH and PCR-based LOH as a reference standard. However, we judged the certainty of the evidence low or very low for all the tests. There are possible differences in specificity, with both NGS and SNP array having high specificity (fewer false positives) for 1p/19q codeletion when considered against FISH as the reference standard. The economic analysis should be interpreted with caution due to the small number of studies.

Role of MicroRNAs in the Development and Progression of the Four Medulloblastoma Subgroups

Medulloblastoma is the most frequent malignant brain tumour in children. Medulloblastoma originate during the embryonic stage. They are located in the cerebellum, which is the area of the central nervous system (CNS) responsible for controlling equilibrium and coordination of movements. In 2012, medulloblastoma were divided into four subgroups based on a genome-wide analysis of RNA expression. These subgroups are named Wingless, Sonic Hedgehog, Group 3 and Group 4. Each subgroup has a different cell of origin, prognosis, and response to therapies. Wingless and Sonic Hedgehog medulloblastoma are so named based on the main mutation originating these tumours. Group 3 and Group 4 have generic names because we do not know the key mutation driving these tumours. Gene expression at the post-transcriptional level is regulated by a group of small single-stranded non-coding RNAs. These microRNA (miRNAs or miRs) play a central role in several cellular functions such as cell differentiation and, therefore, any malfunction in this regulatory system leads to a variety of disorders such as cancer. The role of miRNAs in medulloblastoma is still a topic of intense clinical research; previous studies have mostly concentrated on the clinical entity of the single disease rather than in the four molecular subgroups. In this review, we summarize the latest discoveries on miRNAs in the four medulloblastoma subgroups.

12p gain is predominantly observed in non-germinomatous germ cell tumors and identifies an unfavorable subgroup of central nervous system germ cell tumors

BACKGROUND

Central nervous system (CNS) germ cell tumors (GCTs) are neoplasms predominantly arising in pediatric and young adult populations. While germinomas generally respond to chemotherapy and radiation, non-germinomatous GCTs (NGGCTs) require more intensive treatment. This study aimed to determine whether 12p gain could predict the prognosis of CNS GCTs.

METHODS

Eighty-two CNS GCTs were included in this study. The 12p gain was defined by an additional 12p in the background of potential polyploidy or polysomy. Cases were analyzed using an Illumina methylation 450K array for copy number investigations and validated by fluorescence in situ hybridization (FISH).

RESULTS

A 12p gain was found in 25-out-of-82 cases (30%) and was more frequent in NGGCTs (12% of germinoma cases and 50% of NGGCT cases), particularly in cases with malignant components, such as immature teratoma, yolk sac tumor, choriocarcinoma, and embryonal carcinoma. 12p gain and KIT mutation were mutually exclusive events. The presence of 12p gain correlated with shorter progression-free (PFS) and overall survival (OS) (10-year OS: 59% vs 94%, with and without 12p gain, respectively, P = 0.0002), even with histology and tumor markers incorporated in the multivariate analysis. Among NGGCTs, 12p gain still had prognostic significance for PFS and OS (10-year OS: 47% vs. 90%, respectively, P = 0.02). The 12p copy number status was shared among histological components in mixed GCTs.

CONCLUSIONS

12p gain may predict the presence of malignant components of NGGCTs, and poor prognosis of the patients. It may be associated with early tumorigenesis of CNS GCT.

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.

Low-grade epilepsy-associated neuroepithelial tumours with a prominent oligodendroglioma-like component: The diagnostic challenges

AIMS

We searched for recurrent pathological features and molecular alterations in a retrospective series of 72 low-grade epilepsy-associated neuroepithelial tumours (LEATs) with a prominent oligodendroglioma-like component, in order to classify them according to the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumours.

METHODS

Centralised pathological examination was performed as well as targeted molecular analysis of v-Raf murine sarcoma viral oncogene homologue B (BRAF) and fibroblast growth factor receptor 1 (FGFR1) by multiplexed digital polymerase chain reaction (mdPCR). DNA methylation profiling was performed in cases with sufficient DNA. In cases with no genetic alteration by mdPCR and sufficient material, RNA sequencing was done.

RESULTS

We first reclassified our cohort into three groups: ganglioglioma (GG, n = 14), dysembryoplastic neuroepithelial tumours (DNTs, n = 19) and glioneuronal tumours/paediatric-type low-grade glioma (LGG) not otherwise specified (GNT/PLGG NOS, n = 39). mdPCR found an alteration in 38/72 cases. Subsequent RNA sequencing revealed a fusion transcript involving BRAF, FGFR1/2/3 or neurotrophic tyrosine kinase receptor type 2 [NTRK2] in 9/25 cases. DNA methylation profiling found 12/46 cases with a calibrated score ≥0.9. Unsupervised hierarchical clustering revealed two clusters: Cluster 1 was enriched with cases classified as DNT at histology, belonging to the LGG-DNT methylation class (MC), with haematopoietic progenitor cell antigen (CD34) negativity and FGRF1 alterations; Cluster 2 was enriched with cases classified at histology as GG, belonging to the LGG-GG MC MC, with BRAF V600E mutation and CD34 positivity. The tumours reclassified as GNT/PLGG NOS were equally distributed across both clusters. Interestingly, all polymorphous low-grade neuroepithelial tumour of the young belonged to Cluster 2, whereas diffuse LGG mitogen-activated protein kinase (MAPK) pathway-altered were equally distributed among the two clusters. This led us to build an algorithm to classify LEATs with a prominent oligodendroglioma-like component.

CONCLUSIONS

Integrated histomolecular diagnosis of LEATs with a prominent oligodendroglioma-like component remains challenging. Because these tumours can be split into two major clusters of biological significance, the clinicopathological relevance of the four types recognised by the WHO CNS5 within this spectrum of tumours is questionable.

BRAIN UK: Accessing NHS tissue archives for neuroscience research

The purpose of BRAIN UK (the UK BRain Archive Information Network) is to make the very extensive and comprehensive National Health Service (NHS) Neuropathology archives available to the national and international neuroscience research community. The archives comprise samples of tumours and a wide range of other neurological disorders, not only from the brain but also spinal cord, peripheral nerve, muscle, eye and other organs when relevant. BRAIN UK was founded after the recognition of the importance of this large tissue resource, which was not previously readily accessible for research use. BRAIN UK has successfully engaged the majority of the regional clinical neuroscience centres in the United Kingdom to produce a centralised database of the extensive autopsy and biopsy archive. Together with a simple application process and its broad ethical approval, BRAIN UK offers researchers easy access to most of the national archives of neurological tissues and tumours (http://www.brain-uk.org). The range of tissues available reflects the spectrum of disease in society, including many conditions not covered by disease-specific brain banks, and also allows relatively large numbers of cases of uncommon conditions to be studied. BRAIN UK has supported 141 studies (2010-2020) that have generated 70 publications employing methodology as diverse as morphometrics, genetics, proteomics and methylomics. Tissue samples that would otherwise have been unused have supported valuable neuroscience research. The importance of this unique resource will only increase as molecular techniques applicable to human tissues continue to develop and technical advances permit large-scale high-throughput studies.

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.

World Cancer Day 2021 - Perspectives in Pediatric and Adult Neuro-Oncology

Significant advances in our understanding of the molecular genetics of pediatric and adult brain tumors and the resulting rapid expansion of clinical molecular neuropathology have led to improvements in diagnostic accuracy and identified new targets for therapy. Moreover, there have been major improvements in all facets of clinical care, including imaging, surgery, radiation and supportive care. In selected cohorts of patients, targeted and immunotherapies have resulted in improved patient outcomes. Furthermore, adaptations to clinical trial design have facilitated our study of new agents and other therapeutic innovations. However, considerable work remains to be done towards extending survival for all patients with primary brain tumors, especially children and adults with diffuse midline gliomas harboring Histone H3 K27 mutations and adults with isocitrate dehydrogenase (IDH) wild-type, O6 guanine DNA-methyltransferase gene (MGMT) promoter unmethylated high grade gliomas. In addition to improvements in therapy and care, access to the advances in technology, such as particle radiation or biologic therapy, neuroimaging and molecular diagnostics in both developing and developed countries is needed to improve the outcome of patients with brain tumors.

DNA methylation profiling for molecular classification of adult diffuse lower-grade gliomas

BACKGROUND

DNA methylation profiling has facilitated and improved the classification of a wide variety of tumors of the central nervous system. In this study, we investigated the potential utility of DNA methylation profiling to achieve molecular diagnosis in adult primary diffuse lower-grade glioma (dLGG) according to WHO 2016 classification system. We also evaluated whether methylation profiling could provide improved molecular characterization and identify prognostic differences beyond the classical histological WHO grade together with IDH mutation status and 1p/19q codeletion status. All patients diagnosed with dLGG in the period 2007-2016 from the Västra Götaland region in Sweden were assessed for inclusion in the study.

RESULTS

A total of 166 dLGG cases were subjected for genome-wide DNA methylation analysis. Of these, 126 (76%) were assigned a defined diagnostic methylation class with a class prediction score ≥ 0.84 and subclass score ≥ 0.50. The assigned methylation classes were highly associated with their IDH mutation status and 1p/19q codeletion status. IDH-wildtype gliomas were further divided into subgroups with distinct molecular features.

CONCLUSION

The stratification of the patients by methylation profiling was as effective as the integrated WHO 2016 molecular reclassification at predicting the clinical outcome of the patients. Our study shows that DNA methylation profiling is a reliable and robust approach for the classification of dLGG into molecular defined subgroups, providing accurate detection of molecular markers according to WHO 2016 classification.

Clinical impact of combined epigenetic and molecular analysis of pediatric low-grade gliomas

BACKGROUND

Both genetic and methylation analysis have been shown to provide insight into the diagnosis and prognosis of many brain tumors. However, the implication of methylation profiling and its interaction with genetic alterations in pediatric low-grade gliomas (PLGGs) are unclear.

METHODS

We performed a comprehensive analysis of PLGG with long-term clinical follow-up. In total 152 PLGGs were analyzed from a range of pathological subtypes, including 40 gangliogliomas. Complete molecular analysis was compared with genome-wide methylation data and outcome in all patients. For further analysis of specific PLGG groups, including BRAF p.V600E mutant gliomas, we compiled an additional cohort of clinically and genetically defined tumors from 3 large centers.

RESULTS

Unsupervised hierarchical clustering revealed 5 novel subgroups of PLGG. These were dominated by nonneoplastic factors such as tumor location and lymphocytic infiltration. Midline PLGG clustered together while deep hemispheric lesions differed from lesions in the periphery. Mutations were distributed throughout these location-driven clusters of PLGG. A novel methylation cluster suggesting high lymphocyte infiltration was confirmed pathologically and exhibited worse progression-free survival compared with PLGG harboring similar molecular alterations (P = 0.008; multivariate analysis: P = 0.035). Although the current methylation classifier revealed low confidence in 44% of cases and failed to add information in most PLGG, it was helpful in reclassifying rare cases. The addition of histopathological and molecular information to specific methylation subgroups such as pleomorphic xanthoastrocytoma-like tumors could stratify these tumors into low and high risk (P = 0.0014).

CONCLUSION

The PLGG methylome is affected by multiple nonneoplastic factors. Combined molecular and pathological analysis is key to provide additional information when methylation classification is used for PLGG in the clinical setting.

Advanced molecular pathology for rare tumours: A national feasibility study and model for centralised medulloblastoma diagnostics

Aims

Application of advanced molecular pathology in rare tumours is hindered by low sample numbers, access to specialised expertise/technologies and tissue/assay QC and rapid reporting requirements. We assessed the feasibility of co‐ordinated real‐time centralised pathology review (CPR), encompassing molecular diagnostics and contemporary genomics (RNA‐seq/DNA methylation‐array).

Methods

This nationwide trial in medulloblastoma (<80 UK diagnoses/year) introduced a national reference centre (NRC) and assessed its performance and reporting to World Health Organisation standards. Paired frozen/formalin‐fixed, paraffin‐embedded tumour material were co‐submitted from 135 patients (16 referral centres).

Results

Complete CPR diagnostics were successful for 88% (120/135). Inadequate sampling was the most common cause of failure; biomaterials were typically suitable for methylation‐array (129/135, 94%), but frozen tissues commonly fell below RNA‐seq QC requirements (53/135, 39%). Late reporting was most often due to delayed submission. CPR assigned or altered histological variant (vs local diagnosis) for 40/135 tumours (30%). Benchmarking/QC of specific biomarker assays impacted test results; fluorescent in‐situ hybridisation most accurately identified high‐risk MYC/MYCN amplification (20/135, 15%), while combined methods (CTNNB1/chr6 status, methylation‐array subgrouping) best defined favourable‐risk WNT tumours (14/135; 10%). Engagement of a specialist pathologist panel was essential for consensus assessment of histological variants and immunohistochemistry. Overall, CPR altered clinical risk‐status for 29% of patients.

Conclusion

National real‐time CPR is feasible, delivering robust diagnostics to WHO criteria and assignment of clinical risk‐status, significantly altering clinical management. Recommendations and experience from our study are applicable to advanced molecular diagnostics systems, both local and centralised, across rare tumour types, enabling their application in biomarker‐driven routine diagnostics and clinical/research studies.

The Implementation of DNA Methylation Profiling into a Multistep Diagnostic Process in Pediatric Neuropathology: A 2-Year Real-World Experience by the French Neuropathology Network

DNA methylation profiling has recently emerged as a powerful tool to help establish diagnosis in neuro-oncology. Here we present our national diagnostic strategy as the French neuropathology network (RENOCLIP-LOC) and our current approach of integrating DNA methylation profiling into our multistep diagnostic process for challenging pediatric CNS tumors. The tumors with diagnostic uncertainty were prospectively selected for DNA methylation after two rounds of review by neuropathology experts. We first integrated the classifier score into the histopathological findings. Subsequent analyses using t-SNE (t-Distributed Stochastic Neighbor Embedding) representation were performed. An additional step consisted of analyzing copy-number variation data (CNV). Finally, we combined all data to establish diagnoses and evaluated the impact of DNA methylation profiling on diagnostic and grading changes that would affect patient management. Over two years, 62 pediatric tumors were profiled. (1) Integrating the classifier score to the histopathological findings impacted the diagnosis in 33 cases (53%). (2) t-SNE analysis provided arguments for diagnosis in 26/35 cases with calibrated scores <0.84 (74.3%). (3) CNV investigations also evidenced alterations used for diagnosis and prognostication. (4) A diagnosis was finally established for 44 tumors (71%). Our results support the use of DNA methylation for challenging pediatric tumors. We demonstrated how additional methylation-based analyses complement the classifier score to support conventional histopathological diagnosis.

A rare case of paediatric astroblastoma with concomitant MN1-GTSE1 and EWSR1-PATZ1 gene fusions altering management

In a case of astroblastoma, methylation analysis was uninformative, with no clustering with known CNS-HGNET-MN1 cases. Whole genome sequencing however identified a novel MN1-GTSE1 gene fusion (image), confirming the diagnosis of astroblastoma, as well as an EWSR1-PATZ1 gene fusion. Whole genome sequencing, alongside methylation profiling and conventional neuropathology, will continue to lead to improved diagnostics and prognostication for children with brain tumours.

Intraoperative DNA methylation classification of brain tumors impacts neurosurgical strategy

BACKGROUND

Brain tumor surgery must balance the benefit of maximal resection against the risk of inflicting severe damage. The impact of increased resection is diagnosis-specific. However, the precise diagnosis is typically uncertain at surgery due to limitations of imaging and intraoperative histomorphological methods. Novel and accurate strategies for brain tumor classification are necessary to support personalized intraoperative neurosurgical treatment decisions. Here, we describe a fast and cost-efficient workflow for intraoperative classification of brain tumors based on DNA methylation profiles generated by low coverage nanopore sequencing and machine learning algorithms.

METHODS

We evaluated 6 independent cohorts containing 105 patients, including 50 pediatric and 55 adult patients. Ultra-low coverage whole-genome sequencing was performed on nanopore flow cells. Data were analyzed using copy number variation and ad hoc random forest classifier for the genome-wide methylation-based classification of the tumor.

RESULTS

Concordant classification was obtained between nanopore DNA methylation analysis and a full neuropathological evaluation in 93 of 105 (89%) cases. The analysis demonstrated correct diagnosis in 6/6 cases where frozen section evaluation was inconclusive. Results could be returned to the operating room at a median of 97 min (range 91-161 min). Precise classification of the tumor entity and subtype would have supported modification of the surgical strategy in 12 out of 20 patients evaluated intraoperatively.

CONCLUSION

Intraoperative nanopore sequencing combined with machine learning diagnostics was robust, sensitive, and rapid. This strategy allowed DNA methylation-based classification of the tumor to be returned to the surgeon within a timeframe that supports intraoperative decision making.

CKAP2L Knockdown Exerts Antitumor Effects by Increasing miR-4496 in Glioblastoma Cell Lines

Despite advances in the diagnosis and treatment of the central nervous system malignancy glioma, overall survival remains poor. Cytoskeleton-associated protein 2-like (CKAP2L), which plays key roles in neural progenitor cell division, has also been linked to poor prognosis in lung cancer. In the present study, we investigated the role of CKAP2L in glioma. From bioinformatics analyses of datasets from The Cancer Gene Atlas and the Chinese Glioma Genome Atlas, we found that CKAP2L expression correlates with tumor grade and overall survival. Gene set enrichment analysis (GSEA) showed that MITOTIC_SPINDLE, G2M_CHECKPOINT, and E2F_TARGETS are crucially enriched phenotypes associated with high CKAP2L expression. Using U87MG, U118MG, and LNZ308 human glioma cells, we confirmed that CKAP2L knockdown with siCKAP2L inhibits glioma cell proliferation, migration, invasion, and epithelial-mesenchymal transition. Interestingly, CKAP2L knockdown also induced cell cycle arrest at G2/M phase, which is consistent with the GSEA finding. Finally, we observed that CKAP2L knockdown led to significant increases in miR-4496. Treating cells with exogenous miR-4496 mimicked the effect of CKAP2L knockdown, and the effects of CKAP2L knockdown could be suppressed by miR-4496 inhibition. These findings suggest that CKAP2L is a vital regulator of miR-4496 activity and that CKAP2L is a potentially useful prognostic marker in glioma.

Updates in Pediatric Glioma Pathology

Gliomas are a diverse group of primary central nervous system tumors with astrocytic, oligodendroglial, and/or ependymal features and are an important cause of morbidity/mortality in pediatric patients. Glioma classification relies on integrating tumor histology with key molecular alterations. This approach can help establish a diagnosis, guide treatment, and determine prognosis. New categories of pediatric glioma have been recognized in recent years, due to increasing application of molecular profiling in brain tumors. The aim of this review is to alert pediatric pathologists to emerging diagnostic concepts in pediatric glioma neuropathology, emphasizing the incorporation of molecular features into diagnostic practice.

Correlation among VEGFR3 gene promoter methylation, protein overexpression, and clinical pathology in early gastric cancer

BACKGROUND

The occurrence and development of gastric cancer is a multi-factor, multi-stage, multi-gene abnormal accumulation process. Both genetic and epigenetic mechanisms play an important role in the molecular mechanism of gastric cancer. DNA methylation is one of the most studied epigenetic expression mechanisms. To study the correlation between gene promoter methylation status and protein expression of vascular endothelial growth factor receptor 3 (VEGFR3), as well as their association with clinicopathological features in early gastric cancer (EGC) cases.

METHODS

Immunohistochemical analysis and methylation-specific PCR (MSP) were used to detect the expression of VEGFR3 protein and methylation status of the VEGFR3 promoter in 50 cases of EGC and their paired normal gastric mucosa tissues. The level of DNA methylation of the VEGFR3 promoter, in situ VEGFR3 protein expression, and the clinicopathological characteristics of EGC patients were statistically analyzed.

RESULTS

The positive rate of VEGFR3 protein expression in EGC tumor tissue (60%) was significantly higher than that in the normal gastric mucosa (10%). The detectable methylation frequency of VEGFR3 promoter in EGC tumor tissue (48%) was significantly lower than that in the normal gastric mucosa (85%). As anticipated, the methylation level of the VEGFR3 gene promoter was negatively associated with the overexpression of VEGFR3 protein. In addition, methylation status of the VEGFR3 gene promoter was positively correlated with lymph node metastasis in EGC patients (P<0.05), but was not linked to patients' gender, age, tumor size, degree of differentiation, or tumor invasion depth (P>0.05).

CONCLUSIONS

Hypomethylation of the VEGFR3 gene promoter is one of the major mechanisms underlying VEGFR3 gene overexpression in EGC tumor tissues and is related to lymph node metastasis in EGC patients. DNA methylation of VEGFR3 is expected to become a molecular diagnostic and prognostic biomarker for EGC.

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

AIMS

Methylation profiling (MP) is increasingly incorporated in the diagnostic process of central nervous system (CNS) tumours at our centres in The Netherlands and Scandinavia. We aimed to identify the benefits and challenges of MP as a support tool for CNS tumour diagnostics.

METHODS

About 502 CNS tumour samples were analysed using (850 k) MP. Profiles were matched with the DKFZ/Heidelberg CNS Tumour Classifier. For each case, the final pathological diagnosis was compared to the diagnosis before MP.

RESULTS

In 54.4% (273/502) of all analysed cases, the suggested methylation class (calibrated score ≥0.9) corresponded with the initial pathological diagnosis. The diagnosis of 24.5% of these cases (67/273) was more refined after incorporation of the MP result. In 9.8% of cases (49/502), the MP result led to a new diagnosis, resulting in an altered WHO grade in 71.4% of these cases (35/49). In 1% of cases (5/502), the suggested class based on MP was initially disregarded/interpreted as misleading, but in retrospect, the MP result predicted the right diagnosis for three of these cases. In six cases, the suggested class was interpreted as 'discrepant but noncontributory'. The remaining 33.7% of cases (169/502) had a calibrated score <0.9, including 7.8% (39/502) for which no class indication was given at all (calibrated score <0.3).

CONCLUSIONS

MP is a powerful tool to confirm and fine-tune the pathological diagnosis of CNS tumours, and to avoid misdiagnoses. However, it is crucial to interpret the results in the context of clinical, radiological, histopathological and other molecular information.

Methylation-based algorithms for diagnosis: experience from neuro-oncology

Brain tumours are the most common tumour-related cause of death in young people. Survivors are at risk of significant disability, at least in part related to the effects of treatment. Therefore, there is a need for a precise diagnosis that stratifies patients for the most suitable treatment, matched to the underlying biology of their tumour. Although traditional histopathology has been accurate in predicting treatment responses in many cases, molecular profiling has revealed a remarkable, previously unappreciated, level of biological complexity in the classification of these tumours. Among different molecular technologies, DNA methylation profiling has had the most pronounced impact on brain tumour classification. Furthermore, using machine learning-based algorithms, DNA methylation profiling is changing diagnostic practice. This can be regarded as an exemplar for how molecular pathology can influence diagnostic practice and illustrates some of the unanticipated benefits and risks. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Invited Review: DNA methylation-based classification of paediatric brain tumours

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.