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

Papillary glioneuronal tumors: histological and molecular characteristics and diagnostic value of SLC44A1-PRKCA fusion.

CERVOXY COLLInternational audienc

Critical role for NF-κB-induced JunB in VEGF regulation and tumor angiogenesis

Regulation of vascular endothelial growth factor (VEGF) expression is a complex process involving a plethora of transcriptional regulators. The AP-1 transcription factor is considered as facilitator of hypoxia-induced VEGF expression through interaction with hypoxia-inducible factor (HIF) which plays a major role in mediating the cellular hypoxia response. As yet, both the decisive AP-1 subunit leading to VEGF induction and the molecular mechanism by which this subunit is activated have not been deciphered. Here, we demonstrate that the AP-1 subunit junB is a target gene of hypoxia-induced signaling via NF-κB. Loss of JunB in various cell types results in severely impaired hypoxia-induced VEGF expression, although HIF is present and becomes stabilized. Thus, we identify JunB as a critical independent regulator of VEGF transcription and provide a mechanistic explanation for the inherent vascular phenotypes seen in JunB-deficient embryos, ex vivo allantois explants and in vitro differentiated embryoid bodies. In support of these findings, tumor angiogenesis was impaired in junB(−/−) teratocarcinomas because of severely impaired paracrine-acting VEGF and the subsequent inability to efficiently recruit host-derived vessels

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 challengingwith 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

MYB-QKI rearrangements in Angiocentric Glioma drive tumorigenicity through a tripartite mechanism

Angiocentric gliomas are pediatric low-grade gliomas (PLGGs) without known recurrent genetic drivers. We performed genomic analysis of new and published data from 249 PLGGs including 19 Angiocentric Gliomas. We identified MYB-QKI fusions as a specific and single candidate driver event in Angiocentric Gliomas. In vitro and in vivo functional studies show MYB-QKI rearrangements promote tumorigenesis through three mechanisms: MYB activation by truncation, enhancer translocation driving aberrant MYB-QKI expression, and hemizygous loss of the tumor suppressor QKI. This represents the first example of a single driver rearrangement simultaneously transforming cells via three genetic and epigenetic mechanisms in a tumor