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.

Mutational Forks: Inferring Deregulated Flow of Signal Transduction Based on PatientSpecific Mutations The precise mechanism behind treatment resistance in cancer is still not fully understood. Despite advances in precision oncology, there is a lack of tools that help to understand a mechanistic picture of treatment resistance in cancer patients. Existing enrichment methods heavily rely on quantitative data and limited to analysis of differentially expressed genes, ignoring crucial players that might be involved in this process. In order to tackle treatment resistance, the precise identification of deregulated flow of signal transduction is critical. Here, we introduce a bioinformatics framework that is capable of inferring deregulated flow of signal transduction given evidencebased knowledge about pathway topology and patientspecific mutations. While testing the proposed pipeline on a case study, our algorithm was able to confirm findings from biological experiment, where KRAS mutant cells developed treatment resistance to MEK inhibitor. Our model provides a framework for mechanistic understanding of acquired treatment resistance, thus, equipped clinicians with tool for searching more accurate diagnostic clues in patients with nontrivial disease representations.