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基因拷贝数可预测食管癌发病的可能性
作者:小柯机器人 发布时间:2020/9/9 17:15:10

英国剑桥大学Rebecca C. Fitzgerald和欧洲生物信息学研究所(EMBL-EBI)Moritz Gerstung研究小组合作取得一项新成果。经过不懈努力,他们发现食道癌在转移前几年可通过基因组拷贝数预测。该项研究成果发表在2020年9月7日出版的《自然-医学》上。

研究人员以肿瘤前体病变Barrett食管为例,评估这些基因组信号是否可用于早期检测和先发性癌症的治疗。研究人员从一项长达15年对Barrett食管检测计划中抽取了88名患者进行研究,对777例活检样品进行了浅层全基因组测序,结果表明,即使在进行组织病理学转化10年前,基因组信号也可以将疾病进展与稳定状态区分开来。这些发现在76名和248名患者这两个独立队列中得到了验证。

该方法成本低,适用于标准临床活检样品。与当前基于组织病理学和临床表现的方法相比,基因组分类可以对高危患者进行早期治疗,并减少对不太可能患病患者进行不必要的治疗和监测。

研究人员表示,最近的研究表明,非整倍性和驱动基因突变的发生早于癌症诊断数年。

附:英文原文

Title: Genomic copy number predicts esophageal cancer years before transformation

Author: Sarah Killcoyne, Eleanor Gregson, David C. Wedge, Dan J. Woodcock, Matthew D. Eldridge, Rachel de la Rue, Ahmad Miremadi, Sujath Abbas, Adrienn Blasko, Cassandra Kosmidou, Wladyslaw Januszewicz, Aikaterini Varanou Jenkins, Moritz Gerstung, Rebecca C. Fitzgerald

Issue&Volume: 2020-09-07

Abstract: Recent studies show that aneuploidy and driver gene mutations precede cancer diagnosis by many years1,2,3,4. We assess whether these genomic signals can be used for early detection and pre-emptive cancer treatment using the neoplastic precursor lesion Barrett’s esophagus as an exemplar5. Shallow whole-genome sequencing of 777 biopsies, sampled from 88 patients in Barrett’s esophagus surveillance over a period of up to 15years, shows that genomic signals can distinguish progressive from stable disease even 10years before histopathological transformation. These findings are validated on two independent cohorts of 76 and 248 patients. These methods are low-cost and applicable to standard clinical biopsy samples. Compared with current management guidelines based on histopathology and clinical presentation, genomic classification enables earlier treatment for high-risk patients as well as reduction of unnecessary treatment and monitoring for patients who are unlikely to develop cancer.

DOI: 10.1038/s41591-020-1033-y

Source: https://www.nature.com/articles/s41591-020-1033-y

期刊信息

Nature Medicine:《自然—医学》,创刊于1995年。隶属于施普林格·自然出版集团,最新IF:30.641
官方网址:https://www.nature.com/nm/
投稿链接:https://mts-nmed.nature.com/cgi-bin/main.plex