Ahadli, Farid2022-08-222022-08-222022-082022-082022-08-19http://hdl.handle.net/11693/110461Cataloged from PDF version of article.Thesis (Master's): Bilkent University, Department of Molecular Biology and Genetics, İhsan Doğramacı Bilkent University, 2022.Includes bibliographical references (leaves 142-164).Comparisons of mutational landscapes between independent breast cancer (BRCA) cohorts in a comprehensive and statistical manner using online tools are likely to help advance our understanding of the diversity of somatic interactions and mutational signatures. In this thesis I have developed a webtool called BRCA:Cohort-vs-TCGA, which makes the mutational landscape comparison between a breast cancer cohort and TCGA-BRCA cohort accessible to the general public. BRCA:Cohort-vs-TCGA app contains modules to conduct mutational signature identification and comparison, differentially mutated gene and pathway identification and comparison, driver gene identification, and somatic interaction identification and comparison. Among the applications generated for similar purposes in literature, BRCA:Cohort-vs-TCGA provides advantages because of its comparative, statistical, and signature related features. When BRCA:Cohort-vs-TCGA was applied to compare the Gustave Roussy Institute (IGR) metastatic breast cancer cohort with the TCGA-BRCA cohort, a unique to IGR mutational signature related to SBS17b, a reactive oxygen species associated signature, which was missed by the original analysis of the authors, was identified. Interestingly, contribution of SBS17b was significantly higher in TNBC samples when compared to HER+ and HR+ samples. Differentially mutated gene analysis identified four genes, namely, ESR1, FSIP2, ARMCX4 and MALRD4, of which the latter two were unique to re-analysis by BRCA:Cohort-vs-TCGA. Differentially mutated pathway analysis on the other hand pointed to the differential mutation of DNA damage related pathways, NRF2 pathway and WNT pathway. The influence of the hypermutator samples in Fisher's exact test based analyses is well documented, as a result of many spurious mutations these samples contain. Re-analysis of the IGR cohort after removal of the hypermutator samples showed ARMCX4 was no longer differentially mutated anymore. Re-analysis of the differentially mutated pathways resulted in identification of three pathways, namely Mismatch Repair - Core, LIN37 independent P53 Targets, and U5, all of which had an odds ratio of less than one. These results, however, could be biased by the presence of hypermutators in the TCGA-BRCA cohort. In the second case study, I have demonstrated how the extracted somatic interactions for a user selected gene, i.e., CHRNA5 and TP53, can be used to prioritize and filter genes. This was done by merging the somatic interactions with the differential expression profiles of MCF7 cells treated with siRNA against CHRNA5 and TP53. Accordingly, BRCA:Cohort-vs-TCGA can help annotate/enrich other high throughput data with somatic interactions of custom genes.xviii, 183 leaves : illustrations (some color) ; 30 cm.Englishinfo:eu-repo/semantics/openAccessTCGA-BRCABRCATNBCSomatic mutationMAFVCFHypermutationShinyCancer driver genesThesisB161181