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基于甲基化的诊断算法:神经肿瘤学的经验。

Methylation-based algorithms for diagnosis: experience from neuro-oncology.

机构信息

Developmental Biology and Cancer Research & Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK.

Department of Histopathology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.

出版信息

J Pathol. 2020 Apr;250(5):510-517. doi: 10.1002/path.5397. Epub 2020 Mar 10.

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. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

摘要

脑肿瘤是年轻人因肿瘤相关而死亡的最常见原因。幸存者面临严重残疾的风险,至少部分与治疗的影响有关。因此,需要进行精确的诊断,对患者进行分层,以选择最合适的治疗方法,使其与肿瘤的基础生物学相匹配。尽管传统的组织病理学在许多情况下对预测治疗反应非常准确,但分子分析揭示了这些肿瘤分类中以前未被认识到的显著的生物学复杂性水平。在不同的分子技术中,DNA 甲基化分析对脑肿瘤分类的影响最为显著。此外,基于机器学习算法的 DNA 甲基化分析正在改变诊断实践。这可以被视为分子病理学如何影响诊断实践的一个范例,并说明了一些意想不到的好处和风险。© 2020 英国和爱尔兰病理学会。由 John Wiley & Sons, Ltd 出版。

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