Ultrasensitive Detection of Circulating Tumour DNA Via Deep Methylation Sequencing Aided by Machine Learning

Overview

Journal Nat Biomed Eng

Publisher Springer Nature

Specialty Biomedical Engineering

Date 2021 Jun 16

PMID 34131323

Citations 49

Authors

Naixin Liang

Bingsi Li

Ziqi Jia

Chenyang Wang

Pancheng Wu

Tao Zheng

Yanyu Wang

Fujun Qiu

Yijun Wu

Jing Su

Jiayue Xu

Feng Xu

Huiling Chu

Shuai Fang

Xingyu Yang

Chengju Wu

Zhili Cao

Lei Cao

Zhongxing Bing

Hongsheng Liu

Li Li

Cheng Huang

Yingzhi Qin

Yushang Cui

Han Han-Zhang

Jianxing Xiang

Hao Liu

Xin Guo

Shanqing Li

Heng Zhao

Zhihong Zhang

Affiliations

Soon will be listed here.

Abstract

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.

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