PO.CL12.01 · 临床研究
利用CpG甲基化特征实现跨平台的稳健多类别癌症分类
Leveraging CpG methylation signatures for robust multi-class cancer classification across platforms
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：原发灶不明癌（CUP）是一类无法确定原发部位的转移性恶性肿瘤，常导致经验性化疗和不良预后。DNA甲基化谱分析已成为改善肿瘤识别与分类的有力工具。通过利用独特的甲基化特征，该方法可提高诊断准确性并指导个体化治疗策略。
目的：基于一组聚焦的CpG位点，开发并验证用于癌症类型分类的预测模型。
方法：从TCGA及其他公共数据集获取21种癌症类型共7,476例患者的甲基化数据（Infinium HumanMethylation450）。数据被划分为训练队列和测试队列。采用结合Shapley值与梯度提升的混合特征选择方法以识别CpG区域，并在测试队列上评估模型性能。基于所选CpG谱进行Louvain聚类，用于探索肿瘤表型并评估异质性与预测性能之间的关联。使用来自本机构17种癌症类型31例的Infinium MethylationEPIC v2.0数据进行独立验证。
结果：共选出1,000个CpG区域。在所测试的模型中，岭回归（Ridge regression）表现最佳，在训练队列中各类别平均分类准确率（CA）为95.4%、AUC为0.998、F1评分为0.953、Matthews相关系数（MCC）为0.951。测试队列上的性能为CA 94.7%、AUC 0.998、F1 0.945、MCC 0.943，而独立验证的结果为CA 87.1%、AUC 0.9993、F1 0.847、MCC 0.867。无监督分析揭示了20个不同的Louvain聚类，凸显了跨癌症类型的异质性。尽管异质性和纯度与MCC相关，但在校正混杂因素后，回归分析并未证实其具有独立的预测效应。
结论：基于CpG的甲基化特征结合岭回归可实现高度准确的多类别癌症分类，并展现出强大的跨平台泛化能力。这些发现支持甲基化谱分析在CUP诊断和个体化治疗规划中的临床应用价值。
查看英文原文 English abstract
Background: Cancers of unknown primary (CUP) are metastatic malignancies where the primary site cannot be identified, often resulting in empirical chemotherapy and poor outcomes. DNA methylation profiling has emerged as a promising tool for improving tumor identification and classification. By leveraging unique methylation signatures, this approach can enhance diagnostic accuracy and guide personalized treatment strategies.
Objective: To develop and validate a prediction model for cancer type classification based on a focused set of CpG sites.
Methods: Methylation data (Infinium HumanMethylation450) from 7,476 patients across 21 cancer types were obtained from TCGA and other public datasets. Data were divided into training and test cohorts. A hybrid feature selection approach combining Shapley values and gradient boosting was applied to identify CpG regions, and model performance was evaluated on the test cohort. Louvain clustering based on selected CpG profiles was used to explore tumor phenotypes and assess associations between heterogeneity and prediction performance. Independent validation was performed using Infinium MethylationEPIC v2.0 data from 31 cases representing 17 cancer types from our institution.
Results: A total of 1,000 CpG regions were selected. Ridge regression achieved the best performance among tested models, with classification accuracy (CA) of 95.4%, AUC of 0.998, F1 score of 0.953, and Matthews correlation coefficient (MCC) of 0.951 averaged across classes in the training cohort. Performance on the test cohort was 94.7% CA, 0.998 AUC, 0.945 F1, and 0.943 MCC, while independent validation yielded 87.1% CA, 0.9993 AUC, 0.847 F1, and 0.867 MCC. Unsupervised analysis revealed 20 distinct Louvain clusters, highlighting heterogeneity across cancer types. Although heterogeneity and purity correlated with MCC, regression analysis did not confirm an independent predictive effect after adjusting for confounders.
Conclusion: A CpG-based methylation signature combined with ridge regression enables highly accurate multi-class cancer classification and demonstrates strong generalizability across platforms. These findings support the clinical utility of methylation profiling for CUP diagnosis and personalized treatment planning.
利益披露 Disclosure
M. A. De Velasco,
AstraZeneca ).
K. Sakai, None..
D. Nakatsu, None..
S. Mitani, None..
S. Minamoto, None..
T. Haeno, None.
H. Hayashi,
AstraZeneca ), Other, Honoraria.
Bristol Myers Squibb Other, Honoraria.
Chugai Pharmaceutical ).
Ono Pharmaceutical ).
MSD ).
Takeda Pharmaceutical ).
Nippon Boehringer Ingelheim ).
GlaxoSmithKline ).
Sanofi ).
K. Nishio,
Nippon Boehringer Ingelheim ).
Eli Lilly Japan ).
Otsuka Pharmaceutical ).
Chugai Pharmaceutical Other Intellectual Property, Other, Honoraria.