小柯机器人

研究发现早期肺癌检测新手段
2020-03-28 20:58

美国斯坦福大学Maximilian DiehnAsh A. Alizadeh研究组近日合作取得一项新成果。他们利用整合基因组特征以进行非侵入性早期肺癌检测。相关论文发表在2020325日出版的《自然》杂志上。

他们介绍了通过深度测序(CAPP-Seq)对癌症个性化分析的改进,CAPP-Seq是一种分析循环肿瘤DNActDNA)的方法,以更好地促进筛选应用。他们表示,尽管早期肺癌中的水平非常低,但是在大多数患者中,ctDNA存在于治疗之前,并且存在强烈的预后。

他们还发现,肺癌患者和风险匹配对照的无细胞DNAcfDNA)中的大多数体细胞突变均反映了克隆性造血功能,并且是非复发性的。与肿瘤来源的突变相比,克隆的造血突变发生在更长的cfDNA片段上,并且缺乏与吸烟相关的突变特征。将这些发现与其他分子特征相结合,他们开发并前瞻性地验证了一种称为血浆中肺癌可能性Lung-CLiP)的机械学习方法,该方法可以将早期肺癌患者与风险匹配对照区分开。

该方法可实现与肿瘤信息ctDNA检测相似的性能,并能够调整检测特异性以促进独特的临床应用。他们的发现证实了cfDNA在肺癌筛查中的潜力,并强调了基于cfDNA的筛查研究中风险匹配病例和对照的重要性。

研究人员表示,对高危成年人进行放射学筛查可降低与肺癌相关的死亡率。但是,极少数人在美国进行此类筛查。血液检查的可用性可能会增加筛查人数。

附:英文原文

Title: Integrating genomic features for non-invasive early lung cancer detection

Author: Jacob J. Chabon, Emily G. Hamilton, David M. Kurtz, Mohammad S. Esfahani, Everett J. Moding, Henning Stehr, Joseph Schroers-Martin, Barzin Y. Nabet, Binbin Chen, Aadel A. Chaudhuri, Chih Long Liu, Angela B. Hui, Michael C. Jin, Tej D. Azad, Diego Almanza, Young-Jun Jeon, Monica C. Nesselbush, Lyron Co Ting Keh, Rene F. Bonilla, Christopher H. Yoo, Ryan B. Ko, Emily L. Chen, David J. Merriott, Pierre P. Massion, Aaron S. Mansfield, Jin Jen, Hong Z. Ren, Steven H. Lin, Christina L. Costantino, Risa Burr, Robert Tibshirani, Sanjiv S. Gambhir, Gerald J. Berry, Kristin C. Jensen, Robert B. West, Joel W. Neal, Heather A. Wakelee, Billy W. Loo, Christian A. Kunder, Ann N. Leung, Natalie S. Lui, Mark F. Berry, Joseph B. Shrager, Viswam S. Nair, Daniel A. Haber, Lecia V. Sequist, Ash A. Alizadeh, Maximilian Diehn

Issue&Volume: 2020-03-25

Abstract: Radiologic screening of high-risk adults reduces lung-cancer-related mortality1,2; however, a small minority of eligible individuals undergo such screening in the United States3,4. 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.

DOI: 10.1038/s41586-020-2140-0

Source: https://www.nature.com/articles/s41586-020-2140-0

Nature:《自然》,创刊于1869年。隶属于施普林格·自然出版集团,最新IF:43.07
官方网址:http://www.nature.com/
投稿链接:http://www.nature.com/authors/submit_manuscript.html


本期文章:《自然》:Online/在线发表

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