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.

Learning mutational graphs of individual tumour evolution from singlecell and multiregion sequencing data A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multiregion sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multiregion and singlecell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to dataspecific errors and computational complexity compared to competing methods. We show that the application of TRaIT to singlecell and multiregion cancer datasets can produce accurate and reliable models of singletumour evolution, quantify the extent of intratumour heterogeneity and generate new testable experimental hypotheses.