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

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.

Virtual multi-institutional tumor board: a strategy for personalized diagnoses and management of rare CNS tumors

PURPOSE

Multidisciplinary tumor boards (MTBs) integrate clinical, molecular, and radiological information and facilitate coordination of neuro-oncology care. During the COVID-19 pandemic, our MTB transitioned to a virtual and multi-institutional format. We hypothesized that this expansion would allow expert review of challenging neuro-oncology cases and contribute to the care of patients with limited access to specialized centers.

METHODS

We retrospectively reviewed records from virtual MTBs held between 04/2020-03/2021. Data collected included measures of potential clinical impact, including referrals to observational or therapeutic studies, referrals for specialized neuropathology analysis, and whether molecular findings led to a change in diagnosis and/or guided management suggestions.

RESULTS

During 25 meetings, 32 presenters discussed 44 cases. Approximately half (n = 20; 48%) involved a rare central nervous system (CNS) tumor. In 21% (n = 9) the diagnosis was changed or refined based on molecular profiling obtained at the NIH and in 36% (n = 15) molecular findings guided management. Clinical trial suggestions were offered to 31% (n = 13), enrollment in the observational NCI Natural History Study to 21% (n = 9), neuropathology review and molecular testing at the NIH to 17% (n = 7), and all received management suggestions.

CONCLUSION

Virtual multi-institutional MTBs enable remote expert review of CNS tumors. We propose them as a strategy to facilitate expert opinions from specialized centers, especially for rare CNS tumors, helping mitigate geographic barriers to patient care and serving as a pre-screening tool for studies. Advanced molecular testing is key to obtaining a precise diagnosis, discovering potentially actionable targets, and guiding management.

Molecular and clinicopathologic characteristics of CNS embryonal tumors with BRD4::LEUTX fusion

Central nervous system (CNS) embryonal tumors are a heterogeneous group of high-grade malignancies, and the increasing clinical use of methylation profiling and next-generation sequencing has led to the identification of molecularly distinct subtypes. One proposed tumor type, CNS tumor with BRD4::LEUTX fusion, has been described. As only a few CNS tumors with BRD4::LEUTX fusions have been described, we herein characterize a cohort of 9 such cases (4 new, 5 previously published) to further describe their clinicopathologic and molecular features. We demonstrate that CNS embryonal tumor with BRD4::LEUTX fusion comprises a well-defined methylation class/cluster. We find that patients are young (4 years or younger), with large tumors at variable locations, and frequently with evidence of leptomeningeal/cerebrospinal fluid (CSF) dissemination. Histologically, tumors were highly cellular with high-grade embryonal features. Immunohistochemically, 5/5 cases showed synaptophysin and 4/5 showed OLIG2 expression, thus overlapping with CNS neuroblastoma, FOXR2-activated. DNA copy number profiles were generally flat; however, two tumors had chromosome 1q gains. No recurring genomic changes, besides the presence of the fusion, were found. The LEUTX portion of the fusion transcript was constant in all cases assessed, while the BRD4 portion varied but included a domain with proto-oncogenic activity in all cases. Two patients with clinical follow up available had tumors with excellent response to chemotherapy. Two of our patients were alive without evidence of recurrence or progression after gross total resection and chemotherapy at 16 and 33 months. One patient relapsed, and the last of our four patients died of disease one month after diagnosis. Overall, this case series provides additional evidence for this as a distinct tumor type defined by the presence of a specific fusion as well as a distinct DNA methylation signature. Studies on larger series are required to further characterize these tumors.

A supervised learning method for classifying methylation disorders

BACKGROUND

DNA methylation is one of the most stable and well-characterized epigenetic alterations in humans. Accordingly, it has already found clinical utility as a molecular biomarker in a variety of disease contexts. Existing methods for clinical diagnosis of methylation-related disorders focus on outlier detection in a small number of CpG sites using standardized cutoffs which differentiate healthy from abnormal methylation levels. The standardized cutoff values used in these methods do not take into account methylation patterns which are known to differ between the sexes and with age.

RESULTS

Here we profile genome-wide DNA methylation from blood samples drawn from within a cohort composed of healthy controls of different age and sex alongside patients with Prader-Willi syndrome (PWS), Beckwith-Wiedemann syndrome, Fragile-X syndrome, Angelman syndrome, and Silver-Russell syndrome. We propose a Generalized Additive Model to perform age and sex adjusted outlier analysis of around 700,000 CpG sites throughout the human genome. Utilizing z-scores among the cohort for each site, we deployed an ensemble based machine learning pipeline and achieved a combined prediction accuracy of 0.96 (Binomial 95% Confidence Interval 0.868[Formula: see text]0.995).

CONCLUSION

We demonstrate a method for age and sex adjusted outlier detection of differentially methylated loci based on a large cohort of healthy individuals. We present a custom machine learning pipeline utilizing this outlier analysis to classify samples for potential methylation associated congenital disorders. These methods are able to achieve high accuracy when used with machine learning methods to classify abnormal methylation patterns.

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

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

Molecular pathology as basis for timely cancer diagnosis and therapy

Precision and personalized therapeutics have witnessed significant advancements in technology, revolutionizing the capabilities of laboratories to generate vast amounts of genetic data. Coupled with computational resources for analysis and interpretation, and integrated with various other types of data, including genomic data, electronic medical health (EMH) data, and clinical knowledge, these advancements support optimized health decisions. Among these technologies, next-generation sequencing (NGS) stands out as a transformative tool in the field of cancer treatment, playing a crucial role in precision oncology. NGS-based workflows are employed across a range of applications, including gene panels, exome sequencing, and whole-genome sequencing, supporting comprehensive analysis of the entire cancer genome, including mutations, copy number variations, gene expression profiles, and epigenetic modifications. By utilizing the power of NGS, these workflows contribute to enhancing our understanding of disease mechanisms, diagnosis confirmation, identifying therapeutic targets, and guiding personalized treatment decisions. This manuscript explores the diverse applications of NGS in cancer treatment, highlighting its significance in guiding diagnosis and treatment decisions, identifying therapeutic targets, monitoring disease progression, and improving patient outcomes.

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.

Classification and Grading of Central Nervous System Tumors According to the World Health Organization 5th Edition

The World Health Organization (WHO) released the 5th edition of its classification of central nervous system (CNS) tumors in 2021. Advances in the landscape of molecular tumor pathophysiology prompted major revisions to the previous edition released in 2016, some of which were first introduced by the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy-Not Official WHO (cIMPACT-NOW). The 2021 classification system integrates newly gained molecular insights to guide changes in tumor taxonomy and nomenclature, introduces several new types of tumors, and expands the use of molecular testing for diagnosis and grading, with a particular impact on adult-type and pediatric-type gliomas, ependymomas, and embryonal tumors. These updates aim to promote clear and accurate diagnoses, yield more reliable prognostic information, and enable the selection of optimal therapies. Familiarity with these changes will be of great importance for clinicians involved in the management of CNS tumor patients.

Clinical impact of molecular profiling in rare brain tumors

PURPOSE OF REVIEW

The purpose of this review is to describe the commonly used molecular diagnostics and illustrate the prognostic importance to the more accurate diagnosis that also may uncover therapeutic targets.

RECENT FINDINGS

The most recent WHO Classification of Central Nervous System Tumours (2021) lists over 100 distinct tumor types. While traditional histology continues to be an important component, molecular testing is increasingly being incorporated as requisite diagnostic criteria. Specific molecular findings such as co-deletion of the short arm of chromosome 1 (1p) and long arm of chromosome 19 (19q) now define IDH-mutant gliomas as oligodendroglioma. In recent years, DNA methylation profiling has emerged as a dynamic tool with high diagnostic accuracy. The integration of specific genetic (mutations, fusions) and epigenetic (CpG methylation) alterations has led to diagnostic refinement and the discovery of rare brain tumor types with distinct clinical outcomes. Molecular profiling is anticipated to play an increasing role in routine surgical neuropathology, although costs, access, and logistical concerns remain challenging.

SUMMARY

This review summarizes the current state of molecular testing in neuro-oncology highlighting commonly used and developing technologies, while also providing examples of new tumor types/subtypes that have emerged as a result of improved diagnostic precision.

Brain Tumor Classification by Methylation Profile

The goal of the methylation classifier in brain tumor classification is to accurately classify tumors based on their methylation profiles. Accurate brain tumor diagnosis is the first step for healthcare professionals to predict tumor prognosis and establish personalized treatment plans for patients. The methylation classifier can be used to perform classification on tumor samples with diagnostic difficulties due to ambiguous histology or mismatch between histopathology and molecular signatures, i.e., not otherwise specified (NOS) cases or not elsewhere classified (NEC) cases, aiding in pathological decision-making. Here, the authors elucidate upon the application of a methylation classifier as a tool to mitigate the inherent complexities associated with the pathological evaluation of brain tumors, even when pathologists are experts in histopathological diagnosis and have access to enough molecular genetic information. Also, it should be emphasized that methylome cannot classify all types of brain tumors, and it often produces erroneous matches even with high matching scores, so, excessive trust is prohibited. The primary issue is the considerable difficulty in obtaining reference data regarding the methylation profile of each type of brain tumor. This challenge is further amplified when dealing with recently identified novel types or subtypes of brain tumors, as such data are not readily accessible through open databases or authors of publications. An additional obstacle arises from the fact that methylation classifiers are primarily research-based, leading to the unavailability of charging patients. It is important to note that the application of methylation classifiers may require specialized laboratory techniques and expertise in DNA methylation analysis.

Epigenetic profiling reveals a strong association between lack of 5-ALA fluorescence and EGFR amplification in IDH-wildtype glioblastoma

Background

5-aminolevulinic acid (5-ALA) fluorescence-guided resection increases the percentage of complete CNS tumor resections and improves the progression-free survival of IDH-wildtype glioblastoma patients. A small subset of IDH-wildtype glioblastoma shows no 5-ALA fluorescence. An explanation for these cases is missing. In this study, we used DNA methylation profiling to further characterize non-fluorescent glioblastomas.

Methods

Patients with newly diagnosed and recurrent IDH-wildtype glioblastoma that underwent surgery were analyzed. The intensity of intraoperative 5-ALA fluorescence was categorized as non-visible or visible. DNA was extracted from tumors and genome-wide DNA methylation patterns were analyzed using Illumina EPIC (850k) arrays. Furthermore, 5-ALA intensity was measured by flow cytometry on human gliomasphere lines (BT112 and BT145).

Results

Of 74 included patients, 12 (16.2%) patients had a non-fluorescent glioblastoma, which were compared to 62 glioblastomas with 5-ALA fluorescence. Clinical characteristics were equally distributed between both groups. We did not find significant differences between DNA methylation subclasses and 5-ALA fluorescence (P = .24). The distribution of cells of the tumor microenvironment was not significantly different between the non-fluorescent and fluorescent tumors. Copy number variations in EGFR and simultaneous EGFRvIII expression were strongly associated with 5-ALA fluorescence since all non-fluorescent glioblastomas were EGFR-amplified (P < .01). This finding was also demonstrated in recurrent tumors. Similarly, EGFR-amplified glioblastoma cell lines showed no 5-ALA fluorescence after 24 h of incubation.

Conclusions

Our study demonstrates an association between non-fluorescent IDH-wildtype glioblastomas and EGFR gene amplification which should be taken into consideration for recurrent surgery and future studies investigating EGFR-amplified gliomas.

Cerebrospinal fluid: A unique source of circulating tumor DNA with broad clinical applications

Malignancies involving the central nervous system present unique challenges for diagnosis and monitoring due to the difficulties and risks of direct biopsies and the low specificity and/or sensitivity of other techniques for assessment. In recent years, liquid biopsy of the cerebrospinal fluid (CSF) has emerged as a convenient alternative that combines minimal invasiveness with the ability to detect disease-defining or therapeutically actionable genetic alterations from circulating tumor DNA (ctDNA). Since CSF can be obtained by lumbar puncture, or an established ventricular access device at multiple time points, ctDNA analysis enables initial molecular characterization and longitudinal monitoring throughout a patient's disease course, promoting optimization of treatment regimens. This review outlines some of the key aspects of ctDNA from CSF as a highly suitable approach for clinical assessment, the benefits and drawbacks, testing methods, as well as potential future advancements in this field. We anticipate wider adoption of this practice as technologies and pipelines improve and envisage significant improvements for cancer care.

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.

Pediatric-type high-grade neuroepithelial tumors with CIC gene fusion share a common DNA methylation signature

Pediatric neoplasms in the central nervous system (CNS) show extensive clinical and molecular heterogeneity and are fundamentally different from those occurring in adults. Molecular genetic testing contributes to accurate diagnosis and enables an optimal clinical management of affected children. Here, we investigated a rare, molecularly distinct type of pediatric high-grade neuroepithelial tumor (n = 18), that was identified through unsupervised visualization of genome-wide DNA methylation array data, together with copy number profiling, targeted next-generation DNA sequencing, and RNA transcriptome sequencing. DNA and/or RNA sequencing revealed recurrent fusions involving the capicua transcriptional repressor (CIC) gene in 10/10 tumor samples analyzed, with the most common fusion being CIC::LEUTX (n = 9). In addition, a CIC::NUTM1 fusion was detected in one of the tumors. Apart from the detected fusion events, no additional oncogenic alteration was identified in these tumors. The histopathological review demonstrated a morphologically heterogeneous group of high-grade neuroepithelial tumors with positive immunostaining for markers of glial differentiation in combination with weak and focal expression of synaptophysin, CD56 and CD99. All tumors were located in the supratentorial compartment, occurred during childhood (median age 8.5 years) and typically showed early relapses. In summary, we expand the spectrum of pediatric-type tumors of the CNS by reporting a previously uncharacterized group of rare high-grade neuroepithelial tumors that share a common DNA methylation signature and recurrent gene fusions involving the transcriptional repressor CIC. Downstream functional consequences of the fusion protein CIC::LEUTX and potential therapeutic implications need to be further investigated.

Rare Neuronal, Glial and Glioneuronal Tumours in Adults

Rare glial, neuronal and glioneuronal tumours in adults form a heterogeneous group of rare, primary central nervous system tumours. These tumours, with a glial and/or neuronal component, are challenging in terms of diagnosis and therapeutic management. The novel classification of primary brain tumours published by the WHO in 2021 has significantly improved the diagnostic criteria of these entities. Indeed, diagnostic criteria are nowadays multimodal, including histological, immunohistochemical and molecular (i.e., genetic and methylomic). These integrated parameters have allowed the specification of already known tumours but also the identification of novel tumours for a better diagnosis.

Medulloblastoma: Current Perspectives and Recent Advances

Medulloblastoma is the most common embryonal tumor of the central nervous system in childhood. Combined multimodality approaches, including surgery, radiation, and chemotherapy, have improved the outcome of medulloblastoma. Advances in genomic research have shown that medulloblastoma is not a biologically or clinically discrete entity. Previously, the risk was divided according to histology, presence of metastasis, degree of resection, and age at diagnosis. Through the development of integrated genomics, new biology-based risk stratification methods have recently been proposed. It is also important to understand the genetic predisposition of patients with medulloblastoma. Therefore, treatment goal aimed to improve the survival rate with minimal additional adverse effects and reduced long-term sequelae. It is necessary to incorporate genetic findings into the standard of care, and clinical trials that reflect this need to be conducted.

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.

WHO 2021 and beyond: new types, molecular markers and tools for brain tumor classification

PURPOSE OF REVIEW

The fifth edition of the WHO classification of central nervous system tumors was published in 2021. It implemented major advances in the current diagnostic practice such as DNA methylation profiling. The review addresses how our understanding of the diversity of brain tumors has recently much improved through omics analysis and derived molecular biomarkers.

RECENT FINDINGS

Latest impactful studies identifying new diagnostic or prognostic biomarkers in frequent tumors and describing new rare tumor types are summarized about adult and pediatric gliomas, rare neuroepithelial tumors, ependymomas, medulloblastomas and meningiomas. Some controversies are debated. The role of methylation classes and surrogate immunohistochemical markers is highlighted.

SUMMARY

New diagnostic criteria and better definitions of tumor types aim at improving the management of brain tumor patients and at better evaluating new treatments in clinical trials. The rapidly evolving field of brain tumor classification opens exciting perspectives and many challenges to integrate clinical, radiological, histological and molecular information into a framework relevant for care and research.

Major Features of the 2021 WHO Classification of CNS Tumors

Advances in the understanding of the molecular biology of central nervous system (CNS) tumors prompted a new World Health Organization (WHO) classification scheme in 2021, only 5 years after the prior iteration. The 2016 version was the first to include specific molecular alterations in the diagnoses of a few tumors, but the 2021 system greatly expanded this approach, with over 40 tumor types and subtypes now being defined by their key molecular features. Many tumors have also been reconceptualized into new "supercategories," including adult-type diffuse gliomas, pediatric-type diffuse low- and high-grade gliomas, and circumscribed astrocytic gliomas. Some entirely new tumors are in this scheme, particularly pediatric tumors. Naturally, these changes will impact how CNS tumor patients are diagnosed and treated, including clinical trial enrollment. This review addresses the most clinically relevant changes in the 2021 WHO book, including diffuse and circumscribed gliomas, ependymomas, embryonal tumors, and meningiomas.

The role of methylation profiling in histologically diagnosed neurocytoma: a case series

Purpose

To highlight the clinical, neuroradiographic, neuropathologic, and molecular features of histologically identified neurocytoma in a pediatric cohort and highlight the evolving use methylation profiling in providing diagnostic clarity in difficult to diagnosis pediatric brain tumors.

Methods

Five consecutive children (ages 9–13, 2 girls 3 boys) were histologically diagnosed with neurocytoma at Rady Children’s Hospital San Diego from 2012 to 2018. Clinical and molecular features were analyzed with regards to treatment course and outcome.

Results

Presenting symptoms included seizures (n = 2), syncope (n = 1), headache (n = 2), visual disturbances (n = 2) and emesis (n = 2). Tumor location included intraventricular (n = 2), intraventricular with parenchymal spread (n = 1), and extraventricular (n = 2). Magnetic resonance imaging demonstrated reduced diffusivity (2/5), signal abnormality on susceptibility-weighted sequences (3/5), and varying degrees of contrast enhancement (4/5). All patients underwent surgical resection alone. Recurrence occurred in four children that were treated with surgery (4/4), adjuvant radiation (2/4), and chemoradiation (1/4). Neuropathologic features included positivity for GFAP (4/5), synaptophysin (4/5), NSE (2/2), NeuN (4/4), and variable Ki-67 (< 1% to 15%). Next generation sequencing (3/5) and microarray (3/5) collectively were abnormal in four of five tumors. Methylation profiling was successfully performed on four of five samples which led to modification of diagnosis in two patients and the others were either unclassifiable or confirmatory with the histologic diagnosis. Mean time to follow up was 77 months (range 44–112 months). Mean progression free survival and overall survival were 24 months (range 6 to 52 months) and 100% respectively.

Conclusion

Neurocytomas are a rare clinical entity that warrants further investigation into molecular and pathologic prognosticating features. Methylation profiling may aid in differentiation of neurocytoma from other difficult to diagnose tumors who share similar histologic features.

Chromosomal instability in adult-type diffuse gliomas

Chromosomal instability (CIN) is a fundamental property of cancer and a key underlying mechanism of tumorigenesis and malignant progression, and has been documented in a wide variety of cancers, including colorectal carcinoma with mutations in genes such as APC. Recent reports have demonstrated that CIN, driven in part by mutations in genes maintaining overall genomic stability, is found in subsets of adult-type diffusely infiltrating gliomas of all histologic and molecular grades, with resulting elevated overall copy number burden, chromothripsis, and poor clinical outcome. Still, relatively few studies have examined the effect of this process, due in part to the difficulty of routinely measuring CIN clinically. Herein, we review the underlying mechanisms of CIN, the relationship between chromosomal instability and malignancy, the prognostic significance and treatment potential in various cancers, systemic disease, and more specifically, in diffusely infiltrating glioma subtypes. While still in the early stages of discovery compared to other solid tumor types in which CIN is a known driver of malignancy, the presence of CIN as an early factor in gliomas may in part explain the ability of these tumors to develop resistance to standard therapy, while also providing a potential molecular target for future therapies.

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors-Dependencies and Novel Pitfalls

Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.

Next generation sequencing in adult patients with glioblastoma in Switzerland: a multi-centre decision analysis

BACKGROUND

Glioblastoma is the most common malignant primary brain tumour in adults and driven by various genomic alterations. Next generation sequencing (NGS) provides timely information about the genetic landscape of tumours and might detect targetable mutations. To date, differences exist in the application and NGS assays used as it remains unclear to what extent these variants may affect clinical decision making. In this survey-based study, we investigated the use of NGS in adult patients with glioblastoma in Switzerland.

METHODS

All eight primary care centres for Neuro-Oncology in Switzerland participated in this survey. The NGS assays used as well as the criteria for the application of NGS in newly diagnosed glioblastoma were investigated. Decision trees were analysed for consensus and discrepancies using the objective consensus methodology.

RESULTS

Seven out of eight centres perform NGS in patients with newly diagnosed glioblastoma using custom made or commercially available assays. The criteria most relevant to decision making were age, suitability of standard treatment and fitness. NGS is most often used in fitter patients under the age of 60 years who are not suitable for standard therapy, while it is rarely performed in patients in poor general health.

CONCLUSION

NGS is frequently applied in glioblastomas in adults in Neuro-Oncology centres in Switzerland despite seldom changing the course of treatment to date.

Global DNA methylation profiling reveals chromosomal instability in IDH-mutant astrocytomas

Diffusely infiltrating gliomas are among the most common central nervous system tumors in adults. Over the past decade, the subcategorization of these tumors has changed to include both traditional histologic features and more recently identified molecular factors. However, one molecular feature that has yet to be integrated is the presence/absence of chromosomal instability (CIN). Herein, we use global methylation profiling to evaluate a reference cohort of IDH-mutant astrocytomas with and without prior evidence of CIN (n = 42), and apply the resulting methylation-based characteristics to a larger test cohort of publicly-available IDH-mutant astrocytomas (n = 245). We demonstrate that IDH-mutant astrocytomas with evidence of CIN cluster separately from their chromosomally-stable counterparts. CIN cases were associated with higher initial histologic grade, altered expression patterns of genes related to CIN in other cancers, elevated initial total copy number burden, and significantly worse progression-free and overall survival. In addition, in a grade-for-grade analysis, patients with CIN-positive WHO grade 2 and 3 tumors had significantly worse survival. These results suggest that global methylation profiling can be used to discriminate between chromosomally stable and unstable IDH-mutant astrocytomas, and may therefore provide a reliable and cost-effective method for identifying gliomas with chromosomal instability and resultant poor clinical outcome.

Molecular Landscape in Infant High-Grade Gliomas: A Single Center Experience

High-grade gliomas (HGG) represent about 15% of all pediatric brain tumors, with a dismal prognosis and survival rates ranging from 15 to 35%. Approximately 10–12% of pediatric HGGs (pHGG) occur in children younger than five years of age at diagnosis, specifically infants (iHGG), with an unexpected overall survival rate (OS) in 60–70% of cases. In the literature, iHGGs include a large variety of heterogeneous lesions with different molecular profiles that likely explain their different outcomes. We report our single-institution experience of iHGG including 11 children under five years of age with newly diagnosed HGG between 2011 and 2021. All patients received surgery and adjuvant chemotherapy; only two patients received radiotherapy because their age at diagnosis was more than four years-old. Molecular investigations, including next generation sequencing (NGS) and DNA methylation, detected three NTRK-fusions, one ROS1-fusions, one MN1-rearrangement, and two PATZ1-fusions. According to the molecular results, when chemotherapy failed to control the disease, two patients benefited from target therapy with a NTRK-Inhibitor larotrectinib, achieving a complete remission and a very good partial response, respectively, and no severe side-effects. In conclusion, molecular investigations play a fundamental role in the diagnostic work-up and also in the therapeutic decision. Their routine use in clinical practice could help to replace highly toxic chemotherapy regimens with a target therapy that has moderate adverse effects, even in long-term follow-up.