| Literature DB >> 34131323 |
Naixin Liang1,2, Bingsi Li3, Ziqi Jia1,2, Chenyang Wang3, Pancheng Wu1,2, Tao Zheng3, Yanyu Wang1,2, Fujun Qiu3, Yijun Wu1,2, Jing Su3, Jiayue Xu3, Feng Xu3, Huiling Chu3, Shuai Fang3, Xingyu Yang3, Chengju Wu4, Zhili Cao1,2, Lei Cao1,2, Zhongxing Bing1,2, Hongsheng Liu1,2, Li Li1,2, Cheng Huang1,2, Yingzhi Qin1,2, Yushang Cui1,2, Han Han-Zhang3, Jianxing Xiang3, Hao Liu3, Xin Guo4, Shanqing Li5,6, Heng Zhao7, Zhihong Zhang8.
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.Entities:
Year: 2021 PMID： 34131323 DOI： 10.1038/s41551-021-00746-5
Source DB: PubMed Journal: Nat Biomed Eng ISSN： 2157-846X Impact factor: 25.671