Journal
NATURE
Volume 555, Issue 7697, Pages 469-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/nature26000
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Funding
- DKFZ-Heidelberg Center for Personalized Oncology [DKFZ-HIPO_036]
- German Childhood Cancer Foundation, an Illumina Medical Research Grant [DKS 2015.01]
- DKTK joint funding project 'Next Generation Molecular Diagnostics of Malignant Gliomas'
- A Kids' Brain Tumour Cure (PLGA) Foundation
- Brain Tumour Charity (UK)
- Friedberg Charitable Foundation
- Sohn Conference Foundation
- RKA-Forderpool Project [37]
- Stichting Kinderen Kankervrij
- Stichting AMC Foundation
- NIH/ NCI [5T32CA163185]
- NIH/NCI Cancer Center Support Grant [P30 CA008748]
- Luxembourg National Research Fond (FNR PEARL) [P16/BM/11192868]
- National Institute of Health Research (NIHR) UCLH/UCL Biomedical Research Centre
- Russian Science Foundation [18-45-06012] Funding Source: Russian Science Foundation
- MRC [G1100578, MR/N004272/1, G0701018] Funding Source: UKRI
- Cancer Research UK [23536, 13982] Funding Source: researchfish
- Great Ormond Street Hospital Childrens Charity [W1097] Funding Source: researchfish
- The Brain Tumour Charity [8/197, 16/193, GN-000382, 8/152] Funding Source: researchfish
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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 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.
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