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

Journal: Nature biomedical engineering
Published Date: Jun 15, 2021

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.

Authors

  • Naixin Liang
    Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Bingsi Li
    Burning Rock Biotech, Guangzhou, China.
  • Ziqi Jia
    Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Chenyang Wang
    Burning Rock Biotech, Guangzhou, China.
  • Pancheng Wu
    Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Tao Zheng
    Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, People's Republic of China; Key Laboratory of Renewable Energy, Chinese Academy of Sciences, Guangzhou 510640, People's Republic of China. Electronic address: zhengtao@ms.giec.ac.cn.
  • Yanyu Wang
    Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Fujun Qiu
    Burning Rock Biotech, Guangzhou, China.
  • Yijun Wu
    Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
  • Jing Su
    Indiana University School of Medicine.
  • Jiayue Xu
    Burning Rock Biotech, Guangzhou, China.
  • Feng Xu
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Huiling Chu
    Burning Rock Biotech, Guangzhou, China.
  • Shuai Fang
    Burning Rock Biotech, Guangzhou, China.
  • Xingyu Yang
    School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
  • Chengju Wu
    Department of Industrial Engineering & Operations Research, University of California, Berkeley, Berkeley, CA, USA.
  • Zhili Cao
    Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Lei Cao
    State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, People's Republic of China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, People's Republic of China. University of Chinese Academy of Sciences, Beijing, People's Republic of China.
  • Zhongxing Bing
    Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Hongsheng Liu
    School of Life Science, Liaoning University, Shenyang, 110036, China. liuhongsheng@lnu.edu.cn.
  • Li Li
    Department of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
  • Cheng Huang
    James H. Clark Center, Stanford University, Stanford, California, USA.
  • Yingzhi Qin
  • Yushang Cui
    Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Han Han-Zhang
    Burning Rock Biotech, Guangzhou, China.
  • Jianxing Xiang
    Burning Rock Biotech, Guangzhou, China.
  • Hao Liu
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Xin Guo
    Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong.
  • Shanqing Li
    Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China. lishanqing.pumch@yahoo.com.
  • Heng Zhao
    Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
  • Zhihong Zhang
    School of Materials and Chemical Engineering, Zhengzhou University of Light Industry, No. 136, Science Avenue, Zhengzhou, 450001, China. Electronic address: 2006025@zzuli.edu.cn.

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