DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

Microcystic Cerebral Neoplasm in a Nilgai Antelope (Boselaphus tragocamelus): Putative Microcystic Meningioma

Tumours of the nervous system are rare in wild and captive mammals. In this report, we describe an intracranial, solid, space-occupying lesion originating from the meninges in a Nilgai antelope (Boselaphus tragocamelus). Histologically, the tumour had a conspicuous microcystic appearance with features similar to the histological subtype of microcystic meningioma described in humans. This is the first such tumour reported in this species

An atypical teratoid/rhabdoid tumor (AT/RT) with molecular features of pleomorphic xanthoastrocytoma (PXA) in a 62-year-old patient

Atypical teratoid/rhabdoid tumors (AT/RT) are aggressively growing malignant embryonal neoplasms of the central nervous system (CNS), which mainly affect young children. Loss of SMARCB1/INI1 (or SMARCA4 in rare cases) is recognized as the genetic hallmark of AT/RTs and these tumors can be distinguished into three distinct DNA-methylation based molecular subgroups (i.e. -MYC, -SHH and -TYR). While most AT/RTs are considered to occur de novo, previous studies have recognized secondary SMARCB1/INI1-deficient rhabdoid tumors arising from other low grade CNS tumors in young patients. Three AT/RTs, which harbor epigenetic and mutational characteristics of pleomorphic xanthoastrocytoma (PXA), while being entirely void of nuclear SMARCB1/INI1 expression, were recently described in older children. We here report the first case of an AT/RT with molecular features of PXA in a senior patient.

Diverse imaging features of adolescent glioblastoma

We highlight an unusual case of multifocal glioblastoma in an adolescent patient, manifesting as four discrete brain lesions, each distinct in appearance. Familiarity with the diverse imaging features of glioblastoma can reduce misdiagnosis and avoid treatment delays

Spatial concordance of DNA methylation classification in diffuse glioma.

BACKGROUND: Intratumoral heterogeneity is a hallmark of diffuse gliomas. DNA methylation profiling is an emerging approach in the clinical classification of brain tumors. The goal of this study is to investigate the effects of intratumoral heterogeneity on classification confidence. METHODS: We used neuronavigation to acquire 133 image-guided and spatially separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma (7 IDH-wildtype and 2 IDH-mutant glioblastoma, 6 diffuse astrocytoma, IDH-mutant and 1 oligodendroglioma, IDH-mutant and 1p19q codeleted), which we characterized using DNA methylation arrays. Samples were obtained from regions with and without abnormalities on contrast-enhanced T1-weighted and fluid-attenuated inversion recovery MRI. Methylation profiles were analyzed to devise a 3-dimensional reconstruction of (epi)genetic heterogeneity. Tumor purity was assessed from clonal methylation sites. RESULTS: Molecular aberrations indicated that tumor was found outside imaging abnormalities, underlining the infiltrative nature of this tumor and the limitations of current routine imaging modalities. We demonstrate that tumor purity is highly variable between samples and explains a substantial part of apparent epigenetic spatial heterogeneity. We observed that DNA methylation subtypes are often, but not always, conserved in space taking tumor purity and prediction accuracy into account. CONCLUSION: Our results underscore the infiltrative nature of diffuse gliomas and suggest that DNA methylation subtypes are relatively concordant in this tumor type, although some heterogeneity exists