AbstractThe use of ultra-deep, next generation sequencing of circulating tumor DNA (ctDNA) holds great promise for early detection of cancer as well as a tool 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. It significantly enhances the specificity for downstream ctDNA mutation detection without sacrificing sensitivity. Results on both simulated and real healthy subjects’ data 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 is publicly available at https://github.com/ctDNA/TNER.
Integrated digital error suppression for improved detection of circulating tumor DNA