Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning

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. Deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of early cancers from plasma samples at dilution factors as low as 1/10,000.

Automated summary

Deep methylation sequencing aided by a machine-learning classifier allows for the detection of early cancers from plasma samples at dilution factors as low as 1/10,000, with high sensitivity and specificity. This method accurately identifies patients with lung cancer at different disease stages and outperforms other methods in cancer detection.