用于3型与4型髓母细胞瘤八种分子亚型的跨平台DNA甲基化分类器
Cross-Platform DNA Methylation Classifier for the Eight Molecular Subtypes of Group 3 & 4 Medulloblastoma
AI总结：
本研究针对3型与4型髓母细胞瘤的8种分子亚型，构建了首个基于DNA甲基化的跨平台机器学习分类器，在HM450和EPIC平台测试中均取得优异性能，有望推动相关精准医疗发展。
摘要：
髓母细胞瘤是一种恶性儿童脑肿瘤，分子亚群的发现正推动个性化治疗策略的发展。2019年，学界经共识研究在3型和4型髓母细胞瘤中确定了8种新亚型，每种亚型均表现出异质性特征。分类器可支撑临床试验、个性化疗法的开发与应用以及患者监测，对将这些研究成果转化为临床实践至关重要。本研究提出了一种基于DNA甲基化的跨平台机器学习分类器，能够在HM450和EPIC甲基化芯片样本上区分这些亚型。在两个独立测试集上，该模型的加权F1值为0.95，平衡准确率为0.957，在不同平台间性能一致。作为首个跨平台解决方案，它既具备向后兼容性，又将适用范围扩展至更新的平台，还提升了可及性。按未来计划通过网页应用部署后，它还有望成为首个面向这些亚型的公开可用分类器。总体而言，这项工作朝着推进精准医疗、改善占比最高的髓母细胞瘤亚群（3型和4型）患者的临床结局迈出了步伐。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
