Integrative Network Modeling of the Dasatinib Treatment in Glioblastoma Stem Cells by Gökçe Senger

M.S. Candidate: Gökçe SENGER

Program: Bioinformatics

Date: 26.03.2019 14:30

Place: A-212

Abstract: Glioblastoma (GBM), the most aggressive type of the glial tumours, is thought to be widely promoted by stem-like cells. Although certain cancer types have been radically treated with Receptor Tyrosine Kinases (RTKs) inhibitors, prior studies demonstrate that treatment Glioblastoma Stem Cells (GSCs) with RTK inhibitors led to dynamic interconversion from proliferative to slow-cycling, persistent state.In this work, we use the publicly available RNA-seq and ChIP-seq data in naive patient-derived GBM cell line (GSC8), 12-day and chronic dasatinib treated GSC8 published by Liau et al and apply an integrative approach to develop a further explanation for reversible transition of GSCs and to model the effect of the dasatinib treatment in a network context. We first used the Garnet module in Omics Integrator software which identifies transcription factor binding sites from epigenomic data, relates known and predicted transcription factor binding sites to gene expression and finds the significantly active transcription factors. Then, we used the Forest module of the Omics Integrator software to reconstruct an optimal network for each condition by integrating significantly active transcription factors and a confidence weighted protein interactome. As a result, we obtained three condition specific networks and clustered them based on the topology of these networks. Each module was analysed in terms of pathway enrichments. Then, we compared these networks based on the node, edge and pathway similarities. We reveal that GSCs tend to activate RTK-targeted genes and upregulate neurodevelopmental programs by reorganizing chromatin modifications.