Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento 38123, Italy.
Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital-Weill Cornell Medicine.
Bioinformatics. 2020 May 1;36(9):2665-2674. doi: 10.1093/bioinformatics/btaa016.
The use of liquid biopsies for cancer patients enables the non-invasive tracking of treatment response and tumor dynamics through single or serial blood drawn tests. Next-generation sequencing assays allow for the simultaneous interrogation of extended sets of somatic single-nucleotide variants (SNVs) in circulating cell-free DNA (cfDNA), a mixture of DNA molecules originating both from normal and tumor tissue cells. However, low circulating tumor DNA (ctDNA) fractions together with sequencing background noise and potential tumor heterogeneity challenge the ability to confidently call SNVs.
We present a computational methodology, called Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic SNVs in cfDNA. We tested the capability of our method to analyze data generated using different platforms with distinct sequencing error properties and we compared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients sequencing data. Results show that ABEMUS performs better in most of the tested conditions proving its reliability in calling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples.
ABEMUS is cross-platform and can be installed as R package. The source code is maintained on Github at http://github.com/cibiobcg/abemus, and it is also available at CRAN official R repository.
Supplementary data are available at Bioinformatics online.
癌症患者的液体活检可通过单次或多次抽取血液检测，实现对治疗反应和肿瘤动态的非侵入性跟踪。新一代测序检测可同时检测循环无细胞 DNA（cfDNA）中广泛的体细胞单核苷酸变异（SNV）集，cfDNA 是源自正常和肿瘤组织细胞的 DNA 分子的混合物。然而，低循环肿瘤 DNA（ctDNA）分数、测序背景噪声以及潜在的肿瘤异质性，都对可靠地检测 SNV 的能力提出了挑战。
我们提出了一种计算方法，称为超深度测序数据中的自适应碱基错误模型（ABEMUS），该方法结合了特定于平台的遗传知识和经验信号，可轻松检测和定量 cfDNA 中的体细胞 SNV。我们测试了我们的方法分析具有不同测序错误特性的不同平台生成的数据的能力，并在合成和真实癌症患者测序数据上比较了 ABEMUS 与其他流行的 SNV 调用器的性能。结果表明，ABEMUS 在大多数测试条件下表现更好，证明了其在低 ctDNA 水平血浆样本中可靠地检测和定量低变异等位基因频率体细胞 SNV 的能力。
ABEMUS 是跨平台的，可以作为 R 包安装。源代码在 http://github.com/cibiobcg/abemus 上的 Github 上维护，也可在 CRAN 官方 R 存储库中获得。
补充数据可在生物信息学在线获得。
