Integrating genomic features for non-invasive early lung cancer detection

doi:10.1038/s41586-020-2140-0

GSTDTAP > 地球科学

DOI	10.1038/s41586-020-2140-0
	Integrating genomic features for non-invasive early lung cancer detection
	Wang, Qinyang 1; Wang, Yupeng 2,3; Ding, Jingjin 3,4; Wang, Chunhong 1; Zhou, Xuehan 1; Gao, Wenqing 3; Huang, Huanwei 3; Shao, Feng 2,3,5; Liu, Zhibo 1,6
	2020-03-11
发表期刊	NATURE
ISSN	0028-0836
EISSN	1476-4687
出版年	2020
卷号	580 期号:7802 页码:245-+
文章类型	Article
语种	英语
国家	USA
英文关键词	Circulating tumour DNA in blood is analysed to identify genomic features that distinguish early-stage lung cancer patients from risk-matched controls, and these are integrated into a machine-learning method for blood-based lung cancer screening. Radiologic screening of high-risk adults reduces lung-cancer-related mortality(1,2) however, a small minority of eligible individuals undergo such screening in the United States(3,4). The availability of blood-based tests could increase screening uptake. Here we introduce improvements to cancer personalized profiling by deep sequencing (CAPP-Seq)(5), a method for the analysis of circulating tumour DNA (ctDNA), to better facilitate screening applications. We show that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic. We also find that the majority of somatic mutations in the cell-free DNA (cfDNA) of patients with lung cancer and of risk-matched controls reflect clonal haematopoiesis and are non-recurrent. Compared with tumour-derived mutations, clonal haematopoiesis mutations occur on longer cfDNA fragments and lack mutational signatures that are associated with tobacco smoking. Integrating these findings with other molecular features, we develop and prospectively validate a machine-learning method termed ' lung cancer likelihood in plasma' (Lung-CLiP), which can robustly discriminate early-stage lung cancer patients from risk-matched controls. This approach achieves performance similar to that of tumour-informed ctDNA detection and enables tuning of assay specificity in order to facilitate distinct clinical applications. Our findings establish the potential of cfDNA for lung cancer screening and highlight the importance of risk-matching cases and controls in cfDNA-based screening studies.
领域	地球科学 ; 气候变化 ; 资源环境
收录类别	SCI-E
WOS记录号	WOS:000521531000011
WOS关键词	DNA ; PLASMA ; NUMBER
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/281521
专题	地球科学资源环境科学气候变化
作者单位	1.Peking Univ, Coll Chem & Mol Engn, Beijing Natl Lab Mol Sci Radiochem & Radiat Chem, Key Lab Fundamental Sci, Beijing, Peoples R China; 2.Chinese Acad Med Sci, Res Unit Pyroptosis & Immun, Beijing, Peoples R China; 3.Natl Inst Biol Sci, Beijing, Peoples R China; 4.Chinese Acad Sci, Inst Biophys, Natl Lab Biomacromol, Beijing, Peoples R China; 5.Tsinghua Univ, Tsinghua Inst Multidisciplinary Biomed Res, Beijing, Peoples R China; 6.Peking Univ, Peking Tsinghua Ctr Life Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wang, Qinyang,Wang, Yupeng,Ding, Jingjin,et al. Integrating genomic features for non-invasive early lung cancer detection[J]. NATURE,2020,580(7802):245-+.
APA	Wang, Qinyang.,Wang, Yupeng.,Ding, Jingjin.,Wang, Chunhong.,Zhou, Xuehan.,...&Liu, Zhibo.(2020).Integrating genomic features for non-invasive early lung cancer detection.NATURE,580(7802),245-+.
MLA	Wang, Qinyang,et al."Integrating genomic features for non-invasive early lung cancer detection".NATURE 580.7802(2020):245-+.