首个跨平台甲基化分类器，可精准区分儿童脑瘤8种亚型。
Cross-Platform DNA Methylation Classifier for the Eight Molecular Subtypes of Group 3 & 4 Medulloblastoma
- 基于甲基化数据的机器学习模型，兼容HM450与EPIC芯片
- 跨平台测试中准确率达95.7%，F1值为0.95
- 未来将公开部署，助力精准诊疗
髓母细胞瘤是常见的儿童恶性脑瘤，2019年共识鉴定出3、4组中的八个新分子亚型，具有异质性特征。分类器对临床试验、个体化治疗和患者监测至关重要。本研究提出一种基于DNA甲基化的跨平台机器学习分类器，可在HM450和EPIC甲基化芯片数据上准确区分这些亚型。在两个独立测试集中，模型加权F1得分达0.95，平衡准确率为0.957，跨平台表现一致。作为首个跨平台解决方案，该模型兼具向后兼容性与新平台扩展能力，显著提升应用可及性。未来计划通过网络应用公开发布，有望成为首个针对这些亚型的公开可用分类器。本工作推动了精准医学发展，旨在改善3、4组髓母细胞瘤患者的临床结局。
原文摘要 · Abstract (English)
Medulloblastoma is a malignant pediatric brain cancer, and the discovery of molecular subgroups is enabling personalized treatment strategies. In 2019, a consensus identified eight novel subtypes within Groups 3 and 4, each displaying heterogeneous characteristics. Classifiers are essential for translating these findings into clinical practice by supporting clinical trials, personalized therapy development and application, and patient monitoring. This study presents a DNA methylation-based, cross-platform machine learning classifier capable of distinguishing these subtypes on both HM450 and EPIC methylation array samples. Across two independent test sets, the model achieved weighted F1 = 0.95 and balanced accuracy = 0.957, consistent across platforms. As the first cross-platform solution, it provides backward compatibility while extending applicability to a newer platform, also enhancing accessibility. It also has the potential to become the first publicly available classifier for these subtypes once deployed through a web application, as planned in the future. This work overall takes steps in the direction of advancing precision medicine and improving clinical outcomes for patients within the majority prevalence medulloblastoma subgroups, groups 3 and 4.
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