Detecting somatic mutations with high accuracy in plasma cellfree DNA with TNER2 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, noninvasive 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 ultrasensitive detection with high specificity. Previously, we published TNER, an effective noise reducing algorithm based on trinucleotide context (TNC) data from healthy subject samples to polish background noise before calling mutations. It offers great specificity in mutation detection however it might overpolish 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.

Deleterious Impact of Mutational Processes on Transcription Factor Binding Sites in Human Cancer Somatic mutations occurring in many cancer types are associated with wellunderstood processes, such as exposure to tobacco smoking or to ultraviolet (UV) light, but also with mutational processes of so far unknown etiology. Mutational processes can be described in terms of socalled mutational signatures, most often represented as vectors of mutation probabilities which indicate what mutation types are preferentially induced by the mutational processes. In this paper we propose a framework to identify which mutational processes are more likely to harm binding sites of a given transcription factor. Our method starts from the binding site motif and assigns to each mutational signature both a hit score, i.e., the likelihood that the mutational process mutates a binding sequence in at least one nucleotide, and a measure of deleteriousness, i.e., the likelihood that a binding site can be disrupted by mutations belonging to the signature. In a final step, the determined scores can be adjusted according to the strengths with which individual mutational signatures have contributed to the observed mutational load of a tumor. We apply the method to CTCF, a transcription factor that is a core architectural protein dictating the dimensional structure of the genome. Our analysis concentrates on melanoma (skin cancer), for which we show that our framework predicts the disruption of CTCF binding sites by specific UVlight associated mutational signatures, confirming our biological expectations.