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基于机器学习的体质变体,使用大型NGS面板调用转移性乳腺癌患者的无细胞DNA

Elisabeth M Jongbloed¹, Maurice P H M Jansen¹, Vanja de Weerd¹

¹Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands.

Scientific reports

|June 27, 2023

概括

一个新的机器学习模型准确地识别了转移性乳腺癌 (MBC) 患者的无细胞DNA (cfDNA) 中的瘤特异型变异. 这种方法显著减少了假阳性,改善了治疗监测和治疗选择.

科学领域:

基因组学和生物信息学
癌症研究癌症研究
分子诊断学分子诊断学

背景情况:

下一代无细胞DNA (cfDNA) 测序显示出对转移性乳腺癌 (MBC) 监测和指导治疗的前景.
从测序错误和生殖线DNA中区分真正的瘤变异是一个主要的挑战,特别是在大型基因组中.
高错误发现率阻碍了cfDNA测序在MBC中的临床实用性.

研究的目的:

开发和验证一种机器学习模型,用于准确识别cfDNA中的瘤衍生变异.
提高cfDNA测序的可靠性,用于转移性乳腺癌的治疗监测和治疗选择.
为了减少假阳性变体呼叫,提高体质变体检测的精度.

主要方法:

一个机器学习模型被开发和训练在两个目标测序面板 (Oncomine和Qiaseq) 的共同区域,使用已知的热点突变作为基础真理.
该模型应用于来自70名MBC患者的cfDNA样本,并与生殖线DNA过策略进行比较.
使用灵敏度和精度指标评估性能,并与10名无瘤个体的cfDNA中基本过方法进行比较.

主要成果:

机器学习模型在应用于更大的Qiaseq面板数据时,实现了35%的灵敏度和36%的精度.
与生殖线过 (245种变异) 相比,该模型只发现了7种变异,其中6种被生殖线策略证实.
在没有瘤的个体中,该模型只检测到一种变异,与未过的数据相比,大大减少了假阳性数据的数量 (521种变异).

结论:

开发的机器学习模型有效地区分了来自瘤的体质变异与cfDNA中的测序文物和生殖基因DNA.
这种计算方法显著提高了对转移性乳腺癌管理的cfDNA分析的准确性和可靠性.
该模型有可能通过提供更精确的变异调用来改善治疗监测和治疗选择.

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