张贝
(2023-06-30 23:14):
#paper DNA methylation-based classification of central nervous system tumours Nature. 2018 Mar 22;555(7697):469-474.doi:10.1038/nature26000
肿瘤的正确诊断对于后期治疗至关重要，然而在已知的近100多种中枢神经系统肿瘤 (central nervous system tumor,CNS tumor)中，相关标准化的诊断面临很大的挑战。为了高效、迅速的对CNS肿瘤进行分类，作者开发了一个机器学习模型，它可以对甲基化数据进行分类。开发出来的程序经过训练后，可以使用甲基化特征鉴定91种CNS肿瘤。训练集采用的参照数据来自约2800名癌症患者。作者在1104例已经经过人工检查的中枢神经系统肿瘤上进行了测试，发现有12%例存在误诊。该模型不仅可以提高诊断准确率，而且还可以鉴定出新型罕见肿瘤。为了让这种新方法得到广泛应用，作者生成了一款免费在线工具 (Molecular Neuropathology 2.0; http://www.kitz-heidelberg.de/molecular-diagnostics)，可以在几分钟内分析上传的数据。自2016年12月上线以来，该工具已被使用逾4500次，用户可以选择分享他们的数据，以便进一步优化算法。作者总结表示，将甲基化特征与脑肿瘤自动分类器整合起来还可以为创造类似的肿瘤分类算法用于诊断其它癌症类型提供一个蓝图。
DNA methylation-based classification of central nervous system tumours
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Abstract:
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.
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