학술논문

Methylation-based algorithms for diagnosis: experience from neuro-oncology
Document Type
Report
Source
Journal of Pathology. April 2020, Vol. 250 Issue 5, p510, 8 p.
Subject
Diagnosis
Algorithm
Gliomas
Machine learning
Methylation
Algorithms
Brain tumors
Gliomas -- Diagnosis
Brain tumors -- Diagnosis
Language
English
ISSN
0022-3417
Abstract
Keywords: CNS tumours; medulloblastoma; neuroblastoma; ependymoma; astrocytoma; glioblastoma; Ewing's tumour; classification; pathology; DNA methylation profiling; diagnosis; algorithm Abstract 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. [c] 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. Article Note: No conflicts of interest were declared. Byline: Jessica C Pickles, Thomas J Stone, Thomas S Jacques