Methylation array profiling of adult brain tumours: diagnostic outcomes in a large, single centre

Reclassification of pineal tumor as high-grade astrocytoma with piloid features through methylation profiling: illustrative case

BACKGROUND

The recent WHO 2021 CNS classification system defines new tumor types and subtypes based on molecular features. Among them, high-grade astrocytoma with piloid features (HGAP) is a glioma subtype whose classification depends on its DNA methylation profiling signature. These tumors can have a varied histological appearance and are often misclassified.

OBSERVATIONS

A 41-year-old male presented with right-sided weakness and headache. MRI detected a heterogeneously enhancing mass in the pineal region. The patient underwent an endoscopic third ventriculostomy, and a biopsy sample was obtained. The pathology was initially consistent with a high-grade neuroepithelial tumor. Repeat imaging 3 weeks postoperatively demonstrated an interval increase in tumor size. He underwent a suboccipital craniectomy for gross-total resection. DNA methylation profiling was performed and was consistent with the diagnosis of HGAP. The patient was given chemoradiotherapy due to the high-grade pathology.

LESSONS

Integration of molecular pathology has led to crucial and clinically relevant changes in CNS tumor classification. Utilizing these advanced diagnostic techniques to classify tumors could lead to changes in management, targeted therapeutics, and enrollment in clinical trials. https://thejns.org/doi/10.3171/CASE24778.

Tumor genomics and clinical methylation

Next-generation sequencing and clinical methylation were explored as evolving necessities for personalized medicine to augment surgery. The routine integration of these technologies into patient care has heralded an age of precision tumor treatment. This personalization of treatment and precision of diagnosis afforded by it will likely to lead to improved outcomes.

Rapid brain tumor classification from sparse epigenomic data

Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer's maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.

Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics

We have recently developed a machine learning classifier that enables fast, accurate, and affordable classification of brain tumors based on genome-wide DNA methylation profiles that is widely employed in the clinic. Neuro-oncology research would benefit greatly from understanding the underlying artificial intelligence decision process, which currently remains unclear. Here, we describe an interpretable framework to explain the classifier's decisions. We show that functional genomic regions of various sizes are predominantly employed to distinguish between different tumor classes, ranging from enhancers and CpG islands to large-scale heterochromatic domains. We detect a high degree of genomic redundancy, with many genes distinguishing individual tumor classes, explaining the robustness of the classifier and revealing potential targets for further therapeutic investigation. We anticipate that our resource will build up trust in machine learning in clinical settings, foster biomarker discovery and development of compact point-of-care assays, and enable further epigenome research of brain tumors. Our interpretable framework is accessible to the research community via an interactive web application ( https://hovestadtlab.shinyapps.io/shinyMNP/ ).

Mapping the developmental trajectory of human astrocytes reveals divergence in glioblastoma

Glioblastoma (GBM) is defined by heterogeneous and resilient cell populations that closely reflect neurodevelopmental cell types. Although it is clear that GBM echoes early and immature cell states, identifying the specific developmental programmes disrupted in these tumours has been hindered by a lack of high-resolution trajectories of glial and neuronal lineages. Here we delineate the course of human astrocyte maturation to uncover discrete developmental stages and attributes mirrored by GBM. We generated a transcriptomic and epigenomic map of human astrocyte maturation using cortical organoids maintained in culture for nearly 2 years. Through this approach, we chronicled a multiphase developmental process. Our time course of human astrocyte maturation includes a molecularly distinct intermediate period that serves as a lineage commitment checkpoint upstream of mature quiescence. This intermediate stage acts as a site of developmental deviation separating IDH-wild-type neoplastic astrocyte-lineage cells from quiescent astrocyte populations. Interestingly, IDH1-mutant tumour astrocyte-lineage cells are the exception to this developmental perturbation, where immature properties are suppressed as a result of D-2-hydroxyglutarate oncometabolite exposure. We propose that this defiance is a consequence of IDH1-mutant-associated epigenetic dysregulation, and we identified biased DNA hydroxymethylation (5hmC) in maturation genes as a possible mechanism. Together, this study illustrates a distinct cellular state aberration in GBM astrocyte-lineage cells and presents developmental targets for experimental and therapeutic exploration.

Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors

Over the past decade, neuropathological diagnosis has undergone significant changes, integrating morphological features with molecular biomarkers. The molecular era has successfully refined neuropathological diagnostic accuracy; however, a substantial number of CNS tumor diagnoses remain challenging, particularly in children. DNA methylation classification has emerged as a powerful machine learning approach for clinical decision-making in CNS tumors. The aim of this study is to share our experience using DNA methylation classification in daily routine practice, illustrated through clinical cases. We employed a classification system to evaluate discrepancies between histo-molecular and DNA methylation diagnoses, with a specific focus on adult versus pediatric CNS tumors. In our study, we observed that 40% of cases fell into Class I, 47% into Class II, and 13% into Class III among the "matched cases" (≥ 0.84). In other words, DNA methylation classification confirmed morphological diagnoses in 63% of adult and 23% of pediatric cases. Refinement of diagnosis was particularly evident in the pediatric population (65% vs. 21% for the adult population, p = 0.006). Additionally, we discussed cases classified with low calibrated scores. In conclusion, our study confirms that DNA methylation classification provides significant added-value for CNS tumors diagnosis, particularly in pediatric cases.

cIMPACT-NOW update 9: Recommendations on utilization of genome-wide DNA methylation profiling for central nervous system tumor diagnostics

Genome-wide DNA methylation signatures correlate with and distinguish central nervous system (CNS) tumor types. Since the publication of the initial CNS tumor DNA methylation classifier in 2018, this platform has been increasingly used as a diagnostic tool for CNS tumors, with multiple studies showing the value and utility of DNA methylation-based classification of CNS tumors. A Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) Working Group was therefore convened to describe the current state of the field and to provide advice based on lessons learned to date. Here, we provide recommendations for the use of DNA methylation-based classification in CNS tumor diagnostics, emphasizing the attributes and limitations of the modality. We emphasize that the methylation classifier is one diagnostic tool to be used alongside previously established diagnostic tools in a fully integrated fashion. In addition, we provide examples of the inclusion of DNA methylation data within the layered diagnostic reporting format endorsed by the World Health Organization (WHO) and the International Collaboration on Cancer Reporting. We emphasize the need for backward compatibility of future platforms to enable accumulated data to be compatible with new versions of the array. Finally, we outline the specific connections between methylation classes and CNS WHO tumor types to aid in the interpretation of classifier results. It is hoped that this update will assist the neuro-oncology community in the interpretation of DNA methylation classifier results to facilitate the accurate diagnosis of CNS tumors and thereby help guide patient management.

DNA methylation-based analysis reveals accelerated epigenetic aging in giant cell-enriched adult-type glioblastoma

BACKGROUND

Giant cell (gc)-enriched glioblastoma (gcGB) represents a distinct histological variant of isocitrate dehydrogenase wild-type adult-type glioblastoma with notable enlarged mono- or multinuclear tumor cells. While some studies suggest a survival advantage for gcGB patients, the underlying causes remain elusive. GcGBs are associated with TP53 mutations, and gcs were shown to accumulate DNA double-strand breaks and show deficient mitosis, potentially triggering cellular senescence programs. Epigenetic clocks have emerged as valuable tools for assessing tumor-induced age acceleration (DNAMethAgeAcc), which has lately proved itself as prognostic biomarker in glioblastoma. Our study aimed to comprehensively analyze the methylome and key metabolic proteins of gcGBs, hypothesizing that they undergo cellular aging programs compared to non-gcGBs.

RESULTS

A total of 310 epigenetically classified GBs, including 26 gcGBs, and nine adults with malignant gliomas allocating to pediatric high-grade glioma molecular subclasses (summarized as "pediatric GB") were included. DNAMethAgeAcc was computed by subtraction of chronological patient ages from DNA methylome-derived age estimations and its increase was associated with better survival within gcGB and non-gcGB. GcGBs were significantly more often allocated to the subgroup with increased DNAMethAgeAcc and demonstrated the highest DNAMethAgeAcc. Hypothetical senescence/aging-induced changes of the tumor microenvironment were addressed by tumor deconvolution, which was able to identify a cluster enriched for tumors with increased DNAMethAgeAcc. Key metabolic protein expression did not differ between gcGB and non-gcGB and tumor with versus without increased DNAMethAgeAcc but for elevated levels of one single mitochondrial marker, anti-mitochondrial protein MT-C02, in gcGBs.

CONCLUSIONS

With its sped-up epigenetic aging, gcGB presented as the epigenetic oldest GB variant in our cohort. Whereas the correlation between accelerated tumor-intrinsic epigenetic aging and cellular senescence in gcGB stays elusive, fostering epigenetic aging programs in GB might be of interest for future exploration of alternative treatment options in GB patients.

Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma

BACKGROUND

Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the background coming from embedding media.

METHODS

Spontaneous Raman spectroscopy was used for molecular fingerprinting of FFPE tissue from 46 patient samples with known methylation subtypes. Spectra were used to construct tumor/non-tumor, IDH1WT/IDH1mut, and methylation-subtype classifiers. Support vector machine and random forest were used to identify the most discriminatory Raman frequencies. Stimulated Raman spectroscopy was used to validate the frequencies identified. Mass spectrometry of glioma cell lines and TCGA were used to validate the biological findings.

RESULTS

Here, we develop APOLLO (rAman-based PathOLogy of maLignant gliOma)-a computational workflow that predicts different subtypes of glioma from spontaneous Raman spectra of FFPE tissue slides. Our novel APOLLO platform distinguishes tumors from nontumor tissue and identifies novel Raman peaks corresponding to DNA and proteins that are more intense in the tumor. APOLLO differentiates isocitrate dehydrogenase 1 mutant (IDH1mut) from wild-type (IDH1WT) tumors and identifies cholesterol ester levels to be highly abundant in IDHmut glioma. Moreover, APOLLO achieves high discriminative power between finer, clinically relevant glioma methylation subtypes, distinguishing between the CpG island hypermethylated phenotype (G-CIMP)-high and G-CIMP-low molecular phenotypes within the IDH1mut types.

CONCLUSIONS

Our results demonstrate the potential of label-free Raman spectroscopy to classify glioma subtypes from FFPE slides and to extract meaningful biological information thus opening the door for future applications on these archived tissues in other cancers.

When do I ask for a DNA methylation array for primary brain tumor diagnosis?

PURPOSE OF REVIEW

Despite remarkable advances in molecular characterization, the diagnosis of brain tumors remains challenging, particularly in cases with ambiguous histology or contradictory molecular features. In this context, DNA methylation profiling plays an important role in improving diagnostic and prognostic accuracy. This review aims to provide diagnostic guidance regarding when DNA methylation arrays represent a useful tool for the diagnosis of primary brain tumors.

RECENT FINDINGS

Large-scale profiling has revealed that DNA methylation profiles of brain tumors are highly reproducible and stable. Therefore, DNA methylation profiling has been successfully used to classify brain tumors and identify new entities. This approach seems to be particularly promising for heterogeneous groups of tumors, such as IDH -wildtype gliomas, and glioneuronal and embryonal tumors, which include a variety of entities that are still under characterization.

SUMMARY

As underlined in the fifth edition of the WHO classification of central nervous system tumors, the diagnosis of brain tumors requires the integration of histological, molecular, clinical, and radiological features. Although advanced imaging and histological examination remain the standard diagnostic tools, DNA methylation analysis can significantly improve diagnostic accuracy, with a substantial impact on patient management.

HOXD12 defines an age-related aggressive subtype of oligodendroglioma

Oligodendroglioma, IDH-mutant and 1p/19q-codeleted has highly variable outcomes that are strongly influenced by patient age. The distribution of oligodendroglioma age is non-Gaussian and reportedly bimodal, which motivated our investigation of age-associated molecular alterations that may drive poorer outcomes. We found that elevated HOXD12 expression was associated with both older patient age and shorter survival in the TCGA (FDR < 0.01, FDR = 1e-5) and the CGGA (p = 0.03, p < 1e-3). HOXD12 gene body hypermethylation was associated with older age, higher WHO grade, and shorter survival in the TCGA (p < 1e-6, p < 0.001, p < 1e-3) and with older age and higher WHO grade in Capper et al. (p < 0.002, p = 0.014). In the TCGA, HOXD12 gene body hypermethylation and elevated expression were independently prognostic of NOTCH1 and PIK3CA mutations, loss of 15q, MYC activation, and standard histopathological features. Single-nucleus RNA and ATAC sequencing data showed that HOXD12 activity was elevated in neoplastic tissue, particularly within cycling and OPC-like cells, and was associated with a stem-like phenotype. A pan-HOX DNA methylation analysis revealed an age and survival-associated HOX-high signature that was tightly associated with HOXD12 gene body methylation. Overall, HOXD12 expression and gene body hypermethylation were associated with an older, atypically aggressive subtype of oligodendroglioma.

A High-Grade Glioma, Not Elsewhere Classified in an Older Adult with Discordant Genetic and Epigenetic Analyses

The World Health Organization's (WHO) classification of central nervous system (CNS) tumors is continually being refined to improve the existing diagnostic criteria for high-grade gliomas (HGGs), including glioblastoma. In 2021, advances in molecular analyses and DNA methylation profiling were incorporated to expand upon the diagnostic criteria for HGG, including the introduction of high-grade astrocytoma with piloid features (HGAP), a new tumor entity for which a match to the HGAP class in DNA methylation profiling is an essential criterion. We present an equivocal case of a 72-year-old male with an HGG exhibiting features of both HGAP and glioblastoma, but which did not conform to any existing 2021 WHO classification of CNS tumor entities. This "no match" in DNA methylation profiling resulted in a final diagnosis of HGG not elsewhere classified (NEC), for which standard treatment options do not exist.

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.

Rapid DNA methylation-based classification of pediatric brain tumors from ultrasonic aspirate specimens

BACKGROUND

Although cavitating ultrasonic aspirators are commonly used in neurosurgical procedures, the suitability of ultrasonic aspirator-derived tumor material for diagnostic procedures is still controversial. Here, we explore the feasibility of using ultrasonic aspirator-resected tumor tissue to classify otherwise discarded sample material by fast DNA methylation-based analysis using low pass nanopore whole genome sequencing.

METHODS

Ultrasonic aspirator-derived specimens from pediatric patients undergoing brain tumor resection were subjected to low-pass nanopore whole genome sequencing. DNA methylation-based classification using a neural network classifier and copy number variation analysis were performed. Tumor purity was estimated from copy number profiles. Results were compared to microarray (EPIC)-based routine neuropathological histomorphological and molecular evaluation.

RESULTS

19 samples with confirmed neuropathological diagnosis were evaluated. All samples were successfully sequenced and passed quality control for further analysis. DNA and sequencing characteristics from ultrasonic aspirator-derived specimens were comparable to routinely processed tumor tissue. Classification of both methods was concordant regarding methylation class in 17/19 (89%) cases. Application of a platform-specific threshold for nanopore-based classification ensured a specificity of 100%, whereas sensitivity was 79%. Copy number variation profiles were generated for all cases and matched EPIC results in 18/19 (95%) samples, even allowing the identification of diagnostically or therapeutically relevant genomic alterations.

CONCLUSION

Methylation-based classification of pediatric CNS tumors based on ultrasonic aspirator-reduced and otherwise discarded tissue is feasible using time- and cost-efficient nanopore sequencing.

Diagnosis of pediatric central nervous system tumors using methylation profiling of cfDNA from cerebrospinal fluid

Pediatric central nervous system tumors remain challenging to diagnose. Imaging approaches do not provide sufficient detail to discriminate between different tumor types, while the histopathological examination of tumor tissue shows high inter-observer variability. Recent studies have demonstrated the accurate classification of central nervous system tumors based on the DNA methylation profile of a tumor biopsy. However, a brain biopsy holds significant risk of bleeding and damaging the surrounding tissues. Liquid biopsy approaches analyzing circulating tumor DNA show high potential as an alternative and less invasive tool to study the DNA methylation pattern of tumors. Here, we explore the potential of classifying pediatric brain tumors based on methylation profiling of the circulating cell-free DNA (cfDNA) in cerebrospinal fluid (CSF). For this proof-of-concept study, we collected cerebrospinal fluid samples from 19 pediatric brain cancer patients via a ventricular drain placed for reasons of increased intracranial pressure. Analyses on the cfDNA showed high variability of cfDNA quantities across patients ranging from levels below the limit of quantification to 40 ng cfDNA per milliliter of CSF. Classification based on methylation profiling of cfDNA from CSF was correct for 7 out of 20 samples in our cohort. Accurate results were mostly observed in samples of high quality, more specifically those with limited high molecular weight DNA contamination. Interestingly, we show that centrifugation of the CSF prior to processing increases the fraction of fragmented cfDNA to high molecular weight DNA. In addition, classification was mostly correct for samples with high tumoral cfDNA fraction as estimated by computational deconvolution (> 40%). In summary, analysis of cfDNA in the CSF shows potential as a tool for diagnosing pediatric nervous system tumors especially in patients with high levels of tumoral cfDNA in the CSF. Further optimization of the collection procedure, experimental workflow and bioinformatic approach is required to also allow classification for patients with low tumoral fractions in the CSF.

Unraveling morphology, methylation profiling, and diagnostic challenges in BRAF-Mutant pediatric glial and glioneuronal tumors

OBJECTIVES

To elucidate the relationship between DNA methylation profiling (DMP) and pathological diagnosis (PD) in pediatric glial and glioneuronal tumors with B-Raf proto-oncogene, serine/threonine kinase (BRAF) mutations, addressing their diagnostic challenges.

METHODS

This retrospective study, conducted in Saudi Arabia, analyzed 47 cases from the Children's Brain Tumor Network online database using scanned images, next-generation sequencing data, and methylation profiles processed using the Heidelberg methylation brain tumor classifiers v12.5 and v12.8. The data was last access on 10 November 2023.

RESULTS

The highest prevalence of BRAF mutations was observed in pilocytic astrocytoma and ganglioglioma. The DMP was consistent with PD in 23 cases, but discrepancies emerged in others, including diagnostic changes in diffuse leptomeningeal glioneuronal tumor and polymorphous low-grade neuroepithelial tumor of the young. A key inconsistency appeared between a pilocytic astrocytoma MC and a glioneuronal tumor PD. Two high-grade astrocytomas were misclassified as pleomorphic xanthoastrocytomas. Additionally, low variant allelic frequency in gangliogliomas likely contributed to misclassifications as control in 5 cases.

CONCLUSION

This study emphasized the importance of integrating DMP with PD in diagnosing pediatric glial and glioneuronal tumors with BRAF mutations. Although DMP offers significant diagnostic insights, its limitations, particularly in cases with low tumor content, necessitate cautious interpretation, as well as its use as a complementary diagnostic tool, rather than a definitive method.

The 2021 World Health Organization Central Nervous System Tumor Classification: The Spectrum of Diffuse Gliomas

The 2021 edition of the World Health Organization (WHO) classification of central nervous system tumors introduces significant revisions across various tumor types. These updates, encompassing changes in diagnostic techniques, genomic integration, terminology, and grading, are crucial for radiologists, who play a critical role in interpreting brain tumor imaging. Such changes impact the diagnosis and management of nearly all central nervous system tumor categories, including the reclassification, addition, and removal of specific tumor entities. Given their pivotal role in patient care, radiologists must remain conversant with these revisions to effectively contribute to multidisciplinary tumor boards and collaborate with peers in neuro-oncology, neurosurgery, radiation oncology, and neuropathology. This knowledge is essential not only for accurate diagnosis and staging, but also for understanding the molecular and genetic underpinnings of tumors, which can influence treatment decisions and prognostication. This review, therefore, focuses on the most pertinent updates concerning the classification of adult diffuse gliomas, highlighting the aspects most relevant to radiological practice. Emphasis is placed on the implications of new genetic information on tumor behavior and imaging findings, providing necessary tools to stay abreast of advancements in the field. This comprehensive overview aims to enhance the radiologist's ability to integrate new WHO classification criteria into everyday practice, ultimately improving patient outcomes through informed and precise imaging assessments.

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.

Practical Application of Deep Learning in Diagnostic Neuropathology-Reimagining a Histological Asset in the Era of Precision Medicine

In the past few decades, neuropathology has experienced several paradigm shifts with the introduction of new technologies. Deep learning, a rapidly progressing subfield of machine learning, seems to be the next innovation to alter the diagnostic workflow. In this review, we will explore the recent changes in the field of neuropathology and how this has led to an increased focus on molecular features in diagnosis and prognosis. Then, we will examine the work carried out to train deep learning models for various diagnostic tasks in neuropathology, as well as the machine learning frameworks they used. Focus will be given to both the challenges and successes highlighted therein, as well as what these trends may tell us about future roadblocks in the widespread adoption of this new technology. Finally, we will touch on recent trends in deep learning, as applied to digital pathology more generally, and what this may tell us about the future of deep learning applications in neuropathology.

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach

BACKGROUND

The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas.

METHODS

This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data.

RESULTS

We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617].

CONCLUSIONS

Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

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.

Distinct MRI characteristics of spinal cord diffuse midline glioma, H3 K27-altered in comparison to spinal cord glioma without H3 K27-alteration and demyelination disorder

BACKGROUND

An applicable magnetic resonance imaging (MRI) biomarker for diffuse midline glioma (DMG), H3 K27-altered of the spinal cord is important for non-invasive diagnosis.

PURPOSE

To evaluate the efficacy of conventional MRI (cMRI) in distinguishing between DMGs, H3 K27-altered, gliomas without H3 K27-alteration, and demyelinating lesions in the spinal cord.

MATERIAL AND METHODS

Between January 2017 and February 2023, patients with pathology-confirmed spinal cord gliomas (including ependymomas) with definite H3 K27 status and demyelinating diseases diagnosed by recognized criteria were recruited as the training set for this retrospective study. Morphologic parameter assessment was performed by two neuroradiologists on T1-weighted, T2-weighted, and contrast-enhanced T1-weighted imaging. Variables with high inter- and intra-observer agreement were included in univariable correlation analysis and multivariable logistic regression. The performance of the final model was verified by internal and external testing sets.

RESULTS

The training cohort included 21 patients with DMGs (13 men; mean age = 34.57 ± 13.489 years), 21 with wild-type gliomas (10 men; mean age = 46.76 ± 17.017 years), and 20 with demyelinating diseases (5 men; mean age = 49.50 ± 18.872 years). A significant difference was observed in MRI features, including cyst(s), hemorrhage, pial thickening with enhancement, and the maximum anteroposterior diameter of the spinal cord. The prediction model, integrating age, age2, and morphological characteristics, demonstrated good performance in the internal and external testing cohort (accuracy: 0.810 and 0.800, specificity: 0.810 and 0.720, sensitivity: 0.872 and 0.849, respectively).

CONCLUSION

Based on cMRI, we developed a model with good performance for differentiating among DMGs, H3 K27-altered, wild-type glioma, and demyelinating lesions in the spinal cord.

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.

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.

Low-Grade Gliomas: Histological Subtypes, Molecular Mechanisms, and Treatment Strategies

Low-Grade Gliomas (LGGs) represent a diverse group of brain tumors originating from glial cells, characterized by their unique histopathological and molecular features. This article offers a comprehensive exploration of LGGs, shedding light on their subtypes, histological and molecular aspects. By delving into the World Health Organization's grading system, 5th edition, various specificities were added due to an in-depth understanding of emerging laboratory techniques, especially genomic analysis. Moreover, treatment modalities are extensively discussed. The degree of surgical resection should always be considered according to postoperative quality of life and cognitive status. Adjuvant therapies focused on chemotherapy and radiotherapy depend on tumor grading and invasiveness. In the current literature, emerging targeted molecular therapies are well discussed due to their succinctly therapeutic effect; in our article, those therapies are summarized based on posttreatment results and possible adverse effects. This review serves as a valuable resource for clinicians, researchers, and medical professionals aiming to deepen their knowledge on LGGs and enhance patient care.

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.

Droplet digital PCR (ddPCR) using FFPE DNA to assess methylation status of MGMT gene among patients with IDH mutant astrocytoma and IDH wild-type glioblastoma

MGMT promoter methylation analysis in formalin-fixed paraffin-embedded (FFPE) tissues can be challenging since the DNA obtained is often fragmented. Bisulfite conversion, which is essential to determine methylation status, further degrades DNA. While conventional methylation-specific PCR (MSP) and pyrosequencing assays have long been used to determine the methylation status of MGMT, this study was designed to determine the utility of one-tube DNA extraction method coupled with a droplet digital PCR (ddPCR) assay, to study the epigenetic changes in the promoter region of the MGMT gene using DNA obtained from FFPE.The FFPE blocks of 30 (n=30) patients with Central Nervous System (CNS) WHO grade 4 tumours, previously tested by MSP (2011-2021) were retrieved; DNA was extracted using one-tube extraction method and bisulfite converted. All converted samples were analyzed for methylation status of the MGMT promoter region with a laboratory designed Methylation-Specific ddPCR (MS ddPCR) using degenerate primers and probes that were labelled with FAM or HEX flurocein dye.Of the 30 cases, 20 cases were MGMT methylated and 10 cases were unmethylated by MS ddPCR. The results of MS ddPCR were then compared with those obtained by MSP and found to be concordant in 93.3% (28/30) of the cases and discordant in 2 cases. The Cohen's kappa coefficient (κ) was 0.84. The sensitivity, specificity, positive predictive value and negative predictive value of the assay in detecting the methylation status was found to be 95%, 90%, 95% and 90%.The results show that MS ddPCR is a valuable tool to detect the methylation status of MGMT in FFPE with high sensitivity. This method is cost-effective and easy to perform and could be an attractive alternative to the routine method of MSP.

Brain tumour genetic network signatures of survival

Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterized by distinct combinations of genetic and epigenetic alterations, resulting in complex interactions across multiple molecular pathways. Predicting disease evolution and prescribing individually optimal treatment requires statistical models complex enough to capture the intricate (epi)genetic structure underpinning oncogenesis. Here, we formalize this task as the inference of distinct patterns of connectivity within hierarchical latent representations of genetic networks. Evaluating multi-institutional clinical, genetic and outcome data from 4023 glioma patients over 14 years, across 12 countries, we employ Bayesian generative stochastic block modelling to reveal a hierarchical network structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH-wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma, IDH-mutant. Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours and show that generative network models reveal distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories.

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.

Optimizing biomarkers for accurate ependymoma diagnosis, prognostication, and stratification within International Clinical Trials: A BIOMECA study

BACKGROUND

Accurate identification of brain tumor molecular subgroups is increasingly important. We aimed to establish the most accurate and reproducible ependymoma subgroup biomarker detection techniques, across 147 cases from International Society of Pediatric Oncology (SIOP) Ependymoma II trial participants, enrolled in the pan-European "Biomarkers of Ependymoma in Children and Adolescents (BIOMECA)" study.

METHODS

Across 6 European BIOMECA laboratories, we evaluated epigenetic profiling (DNA methylation array); immunohistochemistry (IHC) for nuclear p65-RELA, H3K27me3, and Tenascin-C; copy number analysis via fluorescent in situ hybridization (FISH) and MLPA (1q, CDKN2A), and MIP and DNA methylation array (genome-wide copy number evaluation); analysis of ZFTA- and YAP1-fusions by RT-PCR and sequencing, Nanostring and break-apart FISH.

RESULTS

DNA Methylation profiling classified 65.3% (n = 96/147) of cases as EPN-PFA and 15% (n = 22/147) as ST-ZFTA fusion-positive. Immunohistochemical loss of H3K27me3 was a reproducible and accurate surrogate marker for EPN-PFA (sensitivity 99%-100% across 3 centers). IHC for p65-RELA, FISH, and RNA-based analyses effectively identified ZFTA- and YAP-fused supratentorial ependymomas. Detection of 1q gain using FISH exhibited only 57% inter-center concordance and low sensitivity and specificity while MIP, MLPA, and DNA methylation-based approaches demonstrated greater accuracy.

CONCLUSIONS

We confirm, in a prospective trial cohort, that H3K27me3 immunohistochemistry is a robust EPN-PFA biomarker. Tenascin-C should be abandoned as a PFA marker. DNA methylation and MIP arrays are effective tools for copy number analysis of 1q gain, 6q, and CDKN2A loss while FISH is inadequate. Fusion detection was successful, but rare novel fusions need more extensive technologies. Finally, we propose test sets to guide future diagnostic approaches.

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.

Moving towards a unified classification of glioblastomas utilizing artificial intelligence and deep machine learning integration

Glioblastoma a deadly brain cancer that is nearly universally fatal. Accurate prognostication and the successful application of emerging precision medicine in glioblastoma relies upon the resolution and exactitude of classification. We discuss limitations of our current classification systems and their inability to capture the full heterogeneity of the disease. We review the various layers of data that are available to substratify glioblastoma and we discuss how artificial intelligence and machine learning tools provide the opportunity to organize and integrate this data in a nuanced way. In doing so there is the potential to generate clinically relevant disease sub-stratifications, which could help predict neuro-oncological patient outcomes with greater certainty. We discuss limitations of this approach and how these might be overcome. The development of a comprehensive unified classification of glioblastoma would be a major advance in the field. This will require the fusion of advances in understanding glioblastoma biology with technological innovation in data processing and organization.

CRISPR-Cas9 knockout screen identifies novel treatment targets in childhood high-grade glioma

BACKGROUND

Brain tumours are the leading cause of cancer-related death in children, and there is no effective treatment. A growing body of evidence points to deregulated epigenetics as a tumour driver, particularly in paediatric cancers as they have relatively few genomic alterations, and key driver mutations have been identified in histone 3 (H3). Cancer stem cells (CSC) are implicated in tumour development, relapse and therapy resistance and thus particularly important to target. We therefore aimed to identify novel epigenetic treatment targets in CSC derived from H3-mutated high-grade glioma (HGG) through a CRISPR-Cas9 knockout screen.

RESULTS

The knockout screen identified more than 100 novel genes essential for the growth of CSC derived from paediatric HGG with H3K27M mutation. We successfully validated 12 of the 13 selected hits by individual knockout in the same two CSC lines, and for the top six hits we included two additional CSC lines derived from H3 wild-type paediatric HGG. Knockout of these genes led to a significant decrease in CSC growth, and altered stem cell and differentiation markers.

CONCLUSIONS

The screen robustly identified essential genes known in the literature, but also many novel genes essential for CSC growth in paediatric HGG. Six of the novel genes (UBE2N, CHD4, LSM11, KANSL1, KANSL3 and EED) were validated individually thus demonstrating their importance for CSC growth in H3-mutated and wild-type HGG. These genes should be further studied and evaluated as novel treatment targets in paediatric HGG.

DNA methylation-based classification of glioneuronal tumours synergises with histology and radiology to refine accurate molecular stratification

AIMS

Glioneuronal tumours (GNTs) are poorly distinguished by their histology and lack robust diagnostic indicators. Previously, we showed that common GNTs comprise two molecularly distinct groups, correlating poorly with histology. To refine diagnosis, we constructed a methylation-based model for GNT classification, subsequently evaluating standards for molecular stratification by methylation, histology and radiology.

METHODS

We comprehensively analysed methylation, radiology and histology for 83 GNT samples: a training cohort of 49, previously classified into molecularly defined groups by genomic profiles, plus a validation cohort of 34. We identified histological and radiological correlates to molecular classification and constructed a methylation-based support vector machine (SVM) model for prediction. Subsequently, we contrasted methylation, radiological and histological classifications in validation GNTs.

RESULTS

By methylation clustering, all training and 23/34 validation GNTs segregated into two groups, the remaining 11 clustering alongside control cortex. Histological review identified prominent astrocytic/oligodendrocyte-like components, dysplastic neurons and a specific glioneuronal element as discriminators between groups. However, these were present in only a subset of tumours. Radiological review identified location, margin definition, enhancement and T2 FLAIR-rim sign as discriminators. When validation GNTs were classified by SVM, 22/23 classified correctly, comparing favourably against histology and radiology that resolved 17/22 and 15/21, respectively, where data were available for comparison.

CONCLUSIONS

Diagnostic criteria inadequately reflect glioneuronal tumour biology, leaving a proportion unresolvable. In the largest cohort of molecularly defined glioneuronal tumours, we develop molecular, histological and radiological approaches for biologically meaningful classification and demonstrate almost all cases are resolvable, emphasising the importance of an integrated diagnostic approach.

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.

Events in CNS Tumor Pathology Post-2016 WHO CNS: cIMPACT-NOW Updates and Other Advancements: A Comprehensive Review Plus a Summary of the Salient Features of 2021 WHO CNS 5

Introduction

The 2016 World Health Organization Classification (WHO) of Tumors of the Central Nervous System (CNS) represented a major change. It recommended an "integrated diagnosis" comprising histologic and molecular information facilitating a more precise diagnosis of specific CNS tumors. Its goal was to provide greater diagnostic precision and reproducibility resulting in more clinical relevance and predictive value, ultimately leading to better patient care. Advances in molecular classification, mostly resulting from DNA methylation array profiling of CNS tumors, were occurring at a very rapid pace and required more rapid integration into clinical practice.

Methods

cIMPACT-NOW updates and other recent papers plus salient features of 2021 WHO CNS5 in this comprehensive write-up were reviewed.

Results

CNS tumor classification needs to be updated at a rapid pace and mechanisms put into place to guide diagnosticians and clinicians in the interim period if major changes in the classification of tumor types came to light. Recognizing the need to integrate these into clinical practice more rapidly and without inordinate delay, the International Society of Neuropathology (ISN) 2016 sponsored an initiative called cIMPACT-NOW.

Discussion and/or Conclusion

Goal of cIMPACT-NOW was to provide clarification regarding contentious issues arising in the wake of the 2016 WHO CNS update as well as report new advancements in molecular classification of CNS tumors and new tumor entities emerging as a result of these advancements. cIMPACT-NOW updates: It thus laid the foundation for the 5th edition of the WHO Classification of CNS tumors (2021 WHO CNS 5). We have discussed cIMPACT updates in detail in this review. In addition, molecular diagnostics including DNA methylation-based classification of CNS tumors and the practical use of molecular classification in the prognostication and treatment of CNS tumors is discussed. Finally, the salient features of the new CNS tumor classification are summarized.

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.

DNA methylation profiling of meningiomas highlights clinically distinct molecular subgroups

BACKGROUND

Introduction of the classification of brain tumours based on DNA methylation profile has significantly changed the diagnostic approach. Due to the paucity of data on the molecular profiling of meningiomas and their clinical implications, no effective therapies and new treatments have been implemented.

METHODS

DNA methylation profiling, copy number analysis, targeted sequencing and H3K27me3 expression was performed on 35 meningiomas and 5 controls.

RESULTS

Unsupervised hierarchical clustering (UHC) analysis revealed four distinct molecular subgroups: Malignant; Intermediate; Benign A, and Benign B. Molecular heterogeneity was observed within the same grade as the Intermediate, Benign A, and Benign B subgroups were composed of WHO grade 1 as well as grade 2 cases. There was association of mutations with distinct methylation subgroups (NF2, AKT1, SMO, TRAF7 and pTERT). Loss of chromosome 22q was observed across all subgroups. 1p/14q co-deletion was seen in 50% of malignant and intermediate while CDKN2A loss was predominantly observed in malignant subgroup (50%). Majority of malignant (75%) and a small proportion of other subgroups (Intermediate: 25%, Benign A: 38.5%, and Benign B: 20%) harboured H3K27me3 loss. 38,734 genes were dysregulated amongst the four subgroups. DKFZ classified 71% cases with acceptable score. On survival analysis, methylation profiling had significant impact on progression-free-survival in WHO grade1 and 2 meningiomas (p = 0.0051).

CONCLUSION

Genome-wide DNA methylation profiling highlights clinically distinct molecular subgroups and heterogeneity within the same grade of meningiomas. Molecular profiling can usher in a paradigm shift in meningioma classification, prognostic prediction, and treatment strategy.

The 2021 WHO classification of central nervous system tumors: what neurologists need to know

PURPOSE OF REVIEW

The recently published WHO Classification of Tumours, Central Nervous System Tumours, Fifth Edition (WHO CNS-5) introduces substantial clinically relevant changes based on improved understanding of the molecular underpinnings of brain tumor types as biological entities. This review highlights pertinent changes for practicing neurologists.

RECENT FINDINGS

Diffuse gliomas are now divided into adult and pediatric types. Adult types are greatly simplified, being classified into three groups based on IDH and 1p/19q status, with molecular grading criteria now included. Pediatric types are divided into low-grade or high-grade and further classified based on molecular features corresponding to clinical behavior. While still recognizing previous morphological subtypes, meningioma is now a single tumor type, with greatly advanced correlations between molecular alterations, locations, morphologic subtypes, and grades. For the first time, ependymomas are classified based on integration of anatomical location, histopathology, and molecular alterations. Importantly, WHO CNS-5 includes a number of new tumor types that have similar clinicopathologic features and are grouped together by their distinctive molecular characteristics.

SUMMARY

The classification of CNS tumors according to objective, reproducible molecular genetic alterations, provides greater opportunity for neurologists to offer individualized treatment options, enroll homogenous patient populations into clinical trials, and ultimately discover novel therapeutics.

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.

Evaluation of DNA Methylation Array for Glioma Tumor Profiling and Description of a Novel Epi-Signature to Distinguish IDH1/IDH2 Mutant and Wild-Type Tumors

Molecular biomarkers, such as IDH1/IDH2 mutations and 1p19q co-deletion, are included in the histopathological and clinical criteria currently used to diagnose and classify gliomas. IDH1/IDH2 mutation is a common feature of gliomas and is associated with a glioma-CpG island methylator phenotype (CIMP). Aberrant genomic methylation patterns can also be used to extrapolate information about copy number variation in a tumor. This project's goal was to assess the feasibility of DNA methylation array for the simultaneous detection of glioma biomarkers as a more effective testing strategy compared to existing single analyte tests.

METHODS

Whole-genome methylation array (WGMA) testing was performed using 48 glioma DNA samples to detect methylation aberrations and chromosomal gains and losses. The analyzed samples include 39 tumors in the discovery cohort and 9 tumors in the replication cohort. Methylation profiles for each sample were correlated with IDH1 p.R132G mutation, immunohistochemistry (IHC), and previous 1p19q clinical testing to assess the sensitivity and specificity of the WGMA assay for the detection of these variants.

RESULTS

We developed a DNA methylation signature to specifically distinguish a IDH1/IDH2 mutant tumor from normal samples. This signature is composed of 11 CpG sites that were significantly hypermethylated in the IDH1/IDH2 mutant group. Copy number analysis using WGMA data was able to identify five of five positive samples for 1p19q co-deletion and was concordant for all negative samples.

CONCLUSIONS

The DNA methylation signature presented here has the potential to refine the utility of WGMA to predict IDH1/IDH2 mutation status of gliomas, thus improving diagnostic yield and efficiency of laboratory testing compared to single analyte IDH1/IDH2 or 1p19q tests.

Leveraging extrachromosomal DNA to fine-tune trials of targeted therapy for glioblastoma: opportunities and challenges

Glioblastoma evolution is facilitated by intratumour heterogeneity, which poses a major hurdle to effective treatment. Evidence indicates a key role for oncogene amplification on extrachromosomal DNA (ecDNA) in accelerating tumour evolution and thus resistance to treatment, particularly in glioblastomas. Oncogenes contained within ecDNA can reach high copy numbers and expression levels, and their unequal segregation can result in more rapid copy number changes in response to therapy than is possible through natural selection of intrachromosomal genomic loci. Notably, targeted therapies inhibiting oncogenic pathways have failed to improve glioblastoma outcomes. In this Perspective, we outline reasons for this disappointing lack of clinical translation and present the emerging evidence implicating ecDNA as an important driver of tumour evolution. Furthermore, we suggest that through detection of ecDNA, patient selection for clinical trials of novel agents can be optimized to include those most likely to benefit based on current understanding of resistance mechanisms. We discuss the challenges to successful translation of this approach, including accurate detection of ecDNA in tumour tissue with novel technologies, development of faithful preclinical models for predicting the efficacy of novel agents in the presence of ecDNA oncogenes, and understanding the mechanisms of ecDNA formation during cancer evolution and how they could be attenuated therapeutically. Finally, we evaluate the feasibility of routine ecDNA characterization in the clinic and how this process could be integrated with other methods of molecular stratification to maximize the potential for clinical translation of precision medicines.