Circulating tumor DNA (ctDNA) refers to small fragments of tumor DNA in the total cell free DNA (cfDNA) from cancer patients. Detecting ctDNA in plasma has provided a promising, non-invasive liquid biopsy approach that could be utilized for cancer diagnosis and treatments. Depending on many factors, the quantity of ctDNA can be very low in the total cfDNA especially in early stage cancers, which posted a great challenge for ultra-sensitive detection with high specificity. Previously, we published TNER, an effective noise reducing algorithm based on tri-nucleotide context (TNC) data from healthy subject samples to polish background noise before calling mutations. It offers great specificity in mutation detection however it might over-polish true mutations when ctDNA concentration is very low. To improve its detection sensitivity in samples with low ctDNA concentration, we developed TNER2, a significant extension of TNER with a refined background polishing process by leveraging somatic mutation patterns from known cancer mutation data. Since there is a distinct difference between TNC pattern in cancer and healthy sample, TNER2 enables a more sensitive analysis on ctDNA data for early detection of cancer.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: TNER significantly enhances the specificity of downstream ctDNA mutation detection without sacrificing sensitivity. The tool is publicly available at https://github.com/ctDNA/TNER .

SUMMARY: ACGT (a comparative genomics tool) is a genomic DNA sequence comparison viewer and analyzer. It can read a pair of DNA sequences in GenBank, Embl or Fasta formats, with or without a comparison file, and provide users with many options to view and analyze the similarities between the input sequences. It is written in Java and can be run on Unix, Linux and Windows platforms. AVAILABILITY: The ACGT program is freely available with documentation and examples at website: http://db.systemsbiology.net/projects/local/mhc/acgt/