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Methylation‐based algorithms for diagnosis: experience from neuro‐oncology
Author(s) -
Pickles Jessica C,
Stone Thomas J,
Jacques Thomas S
Publication year - 2020
Publication title -
the journal of pathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.964
H-Index - 184
eISSN - 1096-9896
pISSN - 0022-3417
DOI - 10.1002/path.5397
Subject(s) - profiling (computer programming) , dna methylation , histopathology , methylation , pathological , precision medicine , molecular biomarkers , omics , bioinformatics , algorithm , medicine , oncology , biology , pathology , computer science , dna , gene , genetics , gene expression , operating system
Brain tumours are the most common tumour‐related cause of death in young people. Survivors are at risk of significant disability, at least in part related to the effects of treatment. Therefore, there is a need for a precise diagnosis that stratifies patients for the most suitable treatment, matched to the underlying biology of their tumour. Although traditional histopathology has been accurate in predicting treatment responses in many cases, molecular profiling has revealed a remarkable, previously unappreciated, level of biological complexity in the classification of these tumours. Among different molecular technologies, DNA methylation profiling has had the most pronounced impact on brain tumour classification. Furthermore, using machine learning‐based algorithms, DNA methylation profiling is changing diagnostic practice. This can be regarded as an exemplar for how molecular pathology can influence diagnostic practice and illustrates some of the unanticipated benefits and risks. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.