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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.
The developmental complexity of the brain is reflected by the vast array of distinct brain tumour entities defined in the current WHO (World Health Organization) classification of central nervous system (CNS) tumours1. These tumours are clinically and biologically highly diverse, encompassing a wide spectrum from benign neoplasms, which can frequently be cured by surgery alone (for example, pilocytic astrocytoma), to highly malignant tumours that respond poorly to any therapy (for example, glioblastoma). Previous studies have reported substantial inter-observer variability in the histopathological diagnosis of many CNS tumours, for example, in diffuse gliomas2, ependymomas3 and supratentorial primitive neuroectodermal tumours4. To address this, some molecular grouping has been introduced into the update of the WHO classification, but only for selected entities such as medulloblastoma. Furthermore, several single-gene tests based on DNA methylation analysis (for example, MGMT promoter methylation status), fluorescence in situ hybridization (for example, 1p/19q codeletion, EGFR, MYC, MYCN, PDGFRA, 19q13.42) or immunohistochemistry (for example, CTNNB1 and LIN28A) that are required to cover the most important differential diagnoses have been shown to be difficult to standardize. Such diagnostic discordance and uncertainty may confound decision-making in clinical practice as well as the interpretation and validity of clinical trial results.
The cancer methylome is...