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dc.contributor.advisorGüre, Ali Osmay
dc.contributor.authorKüçükkaraduman, Barış
dc.date.accessioned2021-10-07T07:13:45Z
dc.date.available2021-10-07T07:13:45Z
dc.date.copyright2021-09
dc.date.issued2021-09
dc.date.submitted2021-10-05
dc.identifier.urihttp://hdl.handle.net/11693/76590
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Ph.D.): Bilkent University, Department of Molecular Biology and Genetics, İhsan Doğramacı Bilkent University, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 124-135).en_US
dc.description.abstractChemotherapy resistance remains one of the major challenges in cancer treatment. Most of the studies on drug resistance have focused on genetic evolution of cancer cells; however, this focus has shifted to non-genetic and epigenetic mechanisms. There is accumulating evidence that mechanisms of drug resistance are not mutually exclusive but instead coexist within a given cancer to develop resistance and therapy failure. Hence, overcoming resistance requires the comprehension of these complex biological processes. Here, we aimed to characterize drug resistance mechanisms by performing both single omics interrogations and multi-omics integrative analysis. For this purpose, we conducted Gene Set Enrichment Analysis (GSEA), functional enrichment analysis on protein-protein interaction (PPI) networks and miRNA-target networks for interpreting gene and miRNA expression data. To gain further biological insights on resistance mechanisms, we focused on identifying a multi-omics molecular signature that discriminates cancer cells based on their drug response profiles. Collectively, these in silico analyses suggested the epithelial-to mesenchymal transition (EMT) as a mediator of 5-FU/irinotecan resistance in colon cancer and irinotecan/gemcitabine resistance in pancreatic cancer. Drug sensitive cancer cells exhibited a more epithelial phenotype with increased cell proliferation. Multi-omics integration analysis revealed some EMT-related genes such as TGM2 and FOSL1, to contribute differential drug response in cancer cells. On the other hand, response of breast cancer cells to doxorubicin exhibited an opposite profile in which mesenchymal phenotype is sensitive while resistant cells have epithelial phenotype. Secondly, we aimed to induce mesenchymal-to-epithelial transition to overcome EMT-mediated drug resistance. We selected eight natural compounds and two re-purposed agents that have been shown to reverse EMT in various studies. We noted transcriptional changes suggesting a shift towards a more epithelial phenotype in 4 out of the 6 cell lines upon treatment with at least one compound tested. None of the natural compounds or re-purposed agents triggered MET in all cancer cells screened. In addition, compounds with clear or slight MET induction did cause these effects in a specific cell line or only in specific cancer type. We investigated next whether the treatment with natural compounds would result in chemosensitization. MET induction by natural compounds is not uniformly related to increased sensitivity to chemotherapeutics but can result in occasional synergistic or additive effects. Lastly, based on cytotoxic activity of a novel c-Src inhibitor 10a in 15 melanoma cells, we report the identification of a new gene signature that can predict chemosensitivity to 10a. Two distinct phenotypes of cells, defined as sensitive and resistant, were further analyzed to reveal an underlying mechanism for this differential response to 10a. We found that proliferative or mesenchymal features of the cells are associated with distinct sensitivity of 10a. Through a protein−chemical interaction network analysis, we identified that three histone deacetylase inhibitors, valproic acid, entinostat, and trichostatin A, were predicted to synergize with 10a. The synergizing effect of valproic acid was validated in vitro. We also aimed to define a minimal number of genes that could be used as biomarkers of 10a sensitivity. We show that the expression level of four genes can be used to predict drug sensitivity against 10a.en_US
dc.description.statementofresponsibilityby Barış Küçükkaradumanen_US
dc.format.extentxviii, 203 leaves : illustrations, charts, graphics (colors) ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectChemotherapy resistanceen_US
dc.subjectNetwork analysisen_US
dc.subject5-FUen_US
dc.subjectIrinotecanen_US
dc.subjectGemcitabineen_US
dc.subjectDoxorubicinen_US
dc.subjectEMTen_US
dc.subjectPlasticityen_US
dc.titleMultiomics approaches to overcome drug resistance in canceren_US
dc.title.alternativeKanserde ilaç direncinin üstesinden gelmek için multi-omik yaklaşımlaren_US
dc.typeThesisen_US
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.publisherBilkent Universityen_US
dc.description.degreePh.D.en_US
dc.identifier.itemidB132683


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