The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of tumour-derived signals at dilution factors as low as 1 in 10,000. For a total of 308 patients with surgery-resectable lung cancer and 261 age- and sex-matched non-cancer control individuals recruited from two hospitals, the assay detected 52–81% of the patients at disease stages IA to III with a specificity of 96% (95% confidence interval (CI) 93–98%). In a subgroup of 115 individuals, the assay identified, at 100% specificity (95% CI 91–100%), nearly twice as many patients with cancer as those identified by ultradeep mutation sequencing analysis. The low amounts of ctDNA permitted by machine-learning-aided deep methylation sequencing could provide advantages in cancer screening and the assessment of treatment efficacy.

血浆样本中循环肿瘤 DNA (ctDNA) 的低丰度使得分析 ctDNA 生物标志物以检测或监测早期癌症具有挑战性。在这里，我们展示了由甲基化模式的机器学习分类器辅助的深度甲基化测序能够以低至万分之一的稀释因子检测肿瘤衍生信号。对于从两家医院招募的总共 308 名手术可切除肺癌患者和 261 名年龄和性别匹配的非癌症对照个体，该检测检测了 52-81% 的 IA 至 III 期疾病患者，特异性为96%（95% 置信区间 (CI) 93–98%）。在 115 人的亚组中，该测定以 100% 的特异性（95% CI 91–100%）确定，癌症患者的数量几乎是通过超深突变测序分析确定的患者数量的两倍。机器学习辅助的深度甲基化测序允许的少量 ctDNA 可以为癌症筛查和治疗效果评估提供优势。