MinerVa——一种用于实体瘤微小残留病灶检测的高性能检测算法  

MinerVa:A high performance bioinformatic algorithm for the detection of minimal residual disease in solid tumors

作　　者：杨飘 张亚晰 夏粱[2] 梅建东[2] 范锐 黄宇 刘伦旭[2] 陈维之 YANG Piao;ZHANG Yaxi;XIA Liang;MEI Jiandong;FAN Rui;HUANG Yu;LIU Lunxu;CHEN Weizhi(Genecast Biotechnology Co.,Ltd,Wuxi,Jiangsu 214000,P.R.China;Department of Thoracic Surgery,West China Hospital,Sichuan University,Chengdu 610041,P.R.China)

机构地区：[1]无锡臻和生物科技有限公司,江苏无锡214000 [2]四川大学华西医院胸外科,成都610041

出　　处：《生物医学工程学杂志》2023年第2期313-319,共7页Journal of Biomedical Engineering

基　　金：国家重点研发计划重点专项(2022YFC2406800)。

摘　　要：如何提高循环肿瘤DNA(ctDNA)信号的获取能力以及判定低频信号的真实性是肿瘤微小残留病灶(MRD)检测的难点问题。本研究开发了多变异联合置信概率分析的MRD生物信息学算法(MinerVa),并在ctDNA标准品和早期非小细胞肺癌患者血浆DNA样本中对算法效能进行评估。结果显示,MinerVa算法对多变异共追踪的特异性稳定在99.62%~99.70%,当追踪30个变异时,可检测低至6.3×10^(-5)变异丰度的变异信号;进一步对27例非小细胞肺癌患者的数据进行分析,ctDNA-MRD动态监测复发的特异性为100%,灵敏度为78.6%。数据表明MinerVa算法可高效地捕获血液ctDNA信号,在MRD检测中具有较好的准确性。How to improve the performance of circulating tumor DNA(ctDNA)signal acquisition and the accuracy to authenticate ultra low-frequency mutation are major challenges of minimal residual disease(MRD)detection in solid tumors.In this study,we developed a new MRD bioinformatics algorithm,namely multi-variant joint confidence analysis(MinerVa),and tested this algorithm both in contrived ctDNA standards and plasma DNA samples of patients with early non-small cell lung cancer(NSCLC).Our results showed that the specificity of multi-variant tracking of MinerVa algorithm ranged from 99.62%to 99.70%,and when tracking 30 variants,variant signals could be detected as low as 6.3×10^(-5)variant abundance.Furthermore,in a cohort of 27 NSCLC patients,the specificity of ctDNA-MRD for recurrence monitoring was 100%,and the sensitivity was 78.6%.These findings indicate that the MinerVa algorithm can efficiently capture ctDNA signals in blood samples and exhibit high accuracy in MRD detection.

关 键 词：循环肿瘤DNA 微小残留病灶 多变异联合置信概率分析 技术噪音基线 单碱基分辨率 

分 类 号：R734.2[医药卫生—肿瘤] TP391.41[医药卫生—临床医学]