Background Cell-free DNA (cfDNA) analysis holds great promise for non-invasive cancer screening, diagnosis, and monitoring. We hypothesized that mining the patterns of cfDNA shallow whole-genome sequencing datasets from patients with cancer could improve cancer detection. Methods By applying unsupervised clustering and supervised machine learning on large cfDNA shallow whole-genome sequencing datasets from healthy individuals (n = 367) and patients with different hematological (n = 238) and solid malignancies (n = 320), we identified cfDNA signatures that enabled cancer detection and typing. Results Unsupervised clustering revealed cancer type-specific sub-grouping. Classification using a supervised machine learning model yielded accuracies of 96% and 65% in discriminating hematological and solid malignancies from healthy controls, respectively. The accuracy of disease type prediction was 85% and 70% for the hematological and solid cancers, respectively. The potential utility of managing a specific cancer was demonstrated by classifying benign from invasive and borderline adnexal masses with an area under the curve of 0.87 and 0.74, respectively. Conclusions This approach provides a generic analytical strategy for non-invasive pan-cancer detection and cancer type prediction.

中文翻译：

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通过在大型全基因组无细胞 DNA 测序数据集中挖掘模式进行泛癌检测和分型

背景 无细胞 DNA (cfDNA) 分析为非侵入性癌症筛查、诊断和监测带来了巨大希望。我们假设从癌症患者中挖掘 cfDNA 浅层全基因组测序数据集的模式可以改善癌症检测。方法 通过对来自健康个体 (n = 367) 和不同血液学 (n = 238) 和实体恶性肿瘤 (n = 320) 患者的大型 cfDNA 浅层全基因组测序数据集应用无监督聚类和监督机器学习，我们确定了 cfDNA 特征启用癌症检测和分型。结果 无监督聚类揭示了癌症类型特异性亚组。使用监督机器学习模型进行分类在区分血液和实体恶性肿瘤与健康对照方面的准确率分别为 96% 和 65%。血液肿瘤和实体肿瘤的疾病类型预测准确率分别为 85% 和 70%。通过将良性与侵袭性和边界附件肿块分类，曲线下面积分别为 0.87 和 0.74，证明了管理特定癌症的潜在效用。结论该方法为非侵入性泛癌检测和癌症类型预测提供了通用分析策略。