Cell-free DNA (cfDNA) sequencing has demonstrated great potential for early cancer detection. However, most large-scale studies have focused only on either targeted methylation sites or whole-genome sequencing, limiting comprehensive analysis that integrates both epigenetic and genetic signatures. In this study, we present a platform that enables simultaneous analysis of whole-genome methylation, copy number, and fragmentomic patterns of cfDNA in a single assay. Using a total of 950 plasma (361 healthy and 589 cancer) and 240 tissue samples, we demonstrate that a multifeature cancer signature ensemble (CSE) classifier integrating all features outperforms single-feature classifiers. At 95.2% specificity, the cancer detection sensitivity with methylation, copy number, and fragmentomic models was 77.2%, 61.4%, and 60.5%, respectively, but sensitivity was significantly increased to 88.9% with the CSE classifier (p value < 0.0001). For tissue of origin, the CSE classifier enhanced the accuracy beyond the methylation classifier, from 74.3% to 76.4%. Overall, this work proves the utility of a signature ensemble integrating epigenetic and genetic information for accurate cancer detection.

游离 DNA (cfDNA) 测序已证明在早期癌症检测方面具有巨大潜力。然而，大多数大规模研究仅关注目标甲基化位点或全基因组测序，限制了整合表观遗传和遗传特征的综合分析。在这项研究中，我们提出了一个平台，可以在一次检测中同时分析 cfDNA 的全基因组甲基化、拷贝数和片段组模式。我们使用总共 950 个血浆（361 个健康样本和 589 个癌症样本）和 240 个组织样本，证明集成所有特征的多特征癌症特征集成 (CSE) 分类器优于单特征分类器。在特异性为 95.2% 时，甲基化、拷贝数和片段组模型的癌症检测灵敏度分别为 77.2%、61.4% 和 60.5%，但使用 CSE 分类器的灵敏度显着提高至 88.9%（ p值 < 0.0001）。对于来源组织，CSE 分类器的准确度比甲基化分类器更高，从 74.3% 提高到 76.4%。总的来说，这项工作证明了整合表观遗传和遗传信息的标志性整体对于准确癌症检测的实用性。