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 .

Biased and unbiased approaches to develop predictive biomarkers of response to drug treatment will be introduced and their utility demonstrated for cell cycle inhibitors. Opportunities to leverage the growing knowledge of tumors characterized by modern methods to measure DNA and RNA will be shown, including the use of appropriate preclinical models and selection of patients. Furthermore, techniques to identify mechanisms of resistance prior to clinical treatment will be discussed. Prospects for systematic data mining and current barriers to the application of precision medicine in cancer will be reviewed along with potential solutions.

BACKGROUND: Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention. RESULTS: We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC) database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and beta-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors. CONCLUSIONS: Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.