TNER: a novel background error suppression method for mutation detection in circulating tumor DNA

• Published in: BMC bioinformatics Vol. 19; no. 1; pp. 387 - 7
• Main Authors: Deng, Shibing, Lira, Maruja, Huang, Donghui, Wang, Kai, Valdez, Crystal, Kinong, Jennifer, Rejto, Paul A., Bienkowska, Jadwiga, Hardwick, James, Xie, Tao
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.10.2018; BioMed Central; BMC
• Subjects: Analysis; Background noise; Binomial distribution; Bioinformatics; Cancer; Care and treatment; Circulating Tumor DNA - genetics; Computer Simulation; ctDNA; Deoxyribonucleic acid; Development and progression; DNA; DNA Mutational Analysis - methods; DNA sequencing; Error detection; Error suppression; Evolution; Gene mutation; Genetic aspects; Genomes; High-Throughput Nucleotide Sequencing; Humans; Medical errors; Methodology; Mutation; Mutation - genetics; Neoplasms - genetics; Next-generation sequencing; Noise; Noise reduction; Normal Distribution; ROC Curve; Sample size; Single-nucleotide variant; Software; State of the art; Tumors; Variant calling; New York; United States > US; Next-generation sequencing; Single-nucleotide variant; Error suppression; ctDNA; Variant calling
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2428-3

Ultra-deep next-generation sequencing of circulating tumor DNA (ctDNA) holds great promise as a tool for the early detection of cancer and for monitoring disease progression and therapeutic responses. However, the low abundance of ctDNA in the bloodstream coupled with technical errors introduced during library construction and sequencing complicates mutation detection. To achieve high accuracy of variant calling via better distinguishing low-frequency ctDNA mutations from background errors, we introduce TNER (Tri-Nucleotide Error Reducer), a novel background error suppression method that provides a robust estimation of background noise to reduce sequencing errors. The results on both simulated data and real data from healthy subjects demonstrate that the proposed algorithm consistently outperforms a current, state-of-the-art, position-specific error polishing model, particularly when the sample size of healthy subjects is small. TNER significantly enhances the specificity of downstream ctDNA mutation detection without sacrificing sensitivity. The tool is publicly available at https://github.com/ctDNA/TNER .