Transcription factors (TFs) control gene expression by binding to highly specific DNA sequences in gene regulatory regions. This TF binding is central to control myriad biological processes. Indeed, transcriptional dysregulation has been associated with many diseases such as autoimmune diseases and cancer. In this thesis, I studied the transcriptional regulation of cytokines and gene transcriptional dysregulation in cancer. Cytokines are small proteins produced by immune cells that play a key role in the development of the immune system and response to pathogens and inflammation. I mined three decades of research and developed a user-friendly database, CytReg, containing 843 human and 647 mouse interactions between TFs and cytokines. I analyzed CytReg and integrated it with phenotypic and functional datasets to provide novel insights into the general principles that govern cytokine regulation. I also predicted novel cytokine promoter-TF interactions based on cytokine co-expression patterns and motif analysis, and studied the association of cytokine transcriptional dysregulation with disease. Transcriptional dysregulation can be caused by single nucleotide variants (SNVs) affecting TF binding sites (TFBS). Therefore, I created a database of altered TFBS (aTFBS-DB) by calculating the effect (gain/loss) of all possible SNVs across the human genome for 741 TFs. I showed how the probabilities to gain or disrupt TFBSs in regulatory regions differ between the major TF families, and that cis-eQTL SNVs are more likely to perturb TFBSs than common SNVs in the human population. To further study the effect of somatic SNVs in TFBS, I used the aTFBS-DB to develop TF-aware burden test (TFABT), a novel algorithm to predict cancer driver SNVs in gene promoters. I applied the TFABT to the Pan-Cancer Analysis of Whole Genomes (PCAWG) cohort and identified 2,555 candidate driver SNVs across 20 cancer types. Further, I characterized these cancer drivers using functional and biophysical assay data from three cancer cell lines, demonstrating that most SNVs alter transcriptional activity and differentially recruit cofactors. Taken together, these studies can be used as a blueprint to study transcriptional mechanisms in specific cellular processes (i.e. cytokine expression) and the effect of transcriptional dysregulation in disease (i.e. cancer).

(EN) Humans and cells in their bodies are exposed to various mutagens in their lifetime that cause DNA damage and mutations, which affect the biology and physiology of the target cell, and can lead to the expansion of an immortalized cell clone. Genome-wide massively parallel sequencing allows the identification of DNA mutations in the coding sequences (whole exome sequencing, WES), or even the entire genome of a tumour. Mutational signatures of individual mutagenic processes can be extracted from these data, as well as mutations in genes potentially important for cancer development ('cancer drivers', as opposed to 'passengers', which do not confer a comparative growth advantage to a cell clone). Many known mutational signatures do not yet have an attributed cause; and many known mutagens do not have an attributed signature. Similarly, it is estimated that many cancer driver genes remain to be identified. This Thesis proposes a system based on immortalization of mouse embryonic fibroblasts (MEF) upon mutagen treatment for modelling of mutational signatures and identification and testing of cancer driver genes and mutations. The signatures extracted from WES data of 25 immortalized MEF cell lines, which arose upon treatment with a variety of mutagens, showed that the assay recapitulates the...

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