Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning
| Autor: | Pancheng Wu, Cheng Huang, Hongsheng Liu, Jiayue Xu, Xin Guo, Zhongxing Bing, Hao Liu, Huiling Chu, Li Li, Han Han-Zhang, Xingyu Yang, Shuai Fang, Lei Cao, Jianxing Xiang, Yanyu Wang, Yingzhi Qin, Zhihong Zhang, Chenyang Wang, Shanqing Li, Naixin Liang, Yijun Wu, Heng Zhao, Jing Su, Chengju Wu, Yushang Cui, Bingsi Li, Zhili Cao, Ziqi Jia, Feng Xu, Tao Zheng, Fujun Qiu |
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| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Oncology medicine.medical_specialty Sequence analysis Biomedical Engineering Medicine (miscellaneous) Bioengineering 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Internal medicine Cancer screening medicine Lung cancer business.industry Case-control study Cancer Methylation medicine.disease Computer Science Applications 030104 developmental biology chemistry DNA methylation business 030217 neurology & neurosurgery DNA Biotechnology |
| Zdroj: | Nature Biomedical Engineering. 5:586-599 |
| ISSN: | 2157-846X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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