Recent large-scale genomic studies which have been performed to understand the changes in genomic landscape in disease context, especially the ones carried out within the frame of international consortia for the molecular characterization of cancer provided very important hints for the deregulated genes/pathways in cancer. Among those, one clear theme emerging was the mutation of the genes involved in chromatin regulation. 25%-30% of the cancer driver mutations affect chromatin modifier genes, emphasizing the importance of studying chromatin regulation in disease context. In the recent years, studies performed within ENCODE and Roadmap consortia investigated the chromatin landscape in diverse cell and tissue types mainly via mapping different histone modifications. Results of these studies provided fundamental insights about how gene regulatory networks can be discovered with the use of chromatin state mappings. Nevertheless, these studies were largely restricted to normal tissue and cell types. Number of studies characterizing chromatin regulation on disease-basis is currently very limited and needs to be further extended and developed.
We are mainly interested in understanding how deregulation of chromatin contributes to the differential gene regulation and appearance of novel regulatory networks in disease with a special focus on cancer. For this aim, we utilize computational biology approaches which mainly include integrative analysis of the genomic data generated for cancer tissue. In addition, we are very interested in generating new epigenomic data sets from primary cancer tissue which shows clear deregulation of chromatin modifiers using ChIP-seq, and chromatin accessibility assays. We aim to analyze the resulting epigenomic data by integrating genomic and transcriptomic data, and genomics databases to thoroughly link the chromatin deregulation with genome and transcriptome regulation. As transcription factors are the master regulators of the genome, one of our key priorities is to identify transcriptional factors and their interacting partners responsible for the cancer regulatory networks using proteomic approaches. Our ultimate aim is to discover novel molecular mechanisms important for cancer development, which can be used to develop diagnostic markers or targeted therapeutic tools.
Discovery of transcriptional regulatory networks. TF: Transcription factor. Adapted from Lin et al. 2016. Nature. 530(7588):57-62 with some modifications.
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