Browsing by Subject "Shiny"

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    BRCA:Cohort-vs-TCGA: a webtool for comprehensive exploration and comparison of mutational landscape of breast cancer cohorts with TCGA-BRCA
    (2022-08) Ahadli, Farid
    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.
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    SmulTCan: A Shiny application for multivariable survival analysis of TCGA data with gene sets
    (Elsevier Ltd, 2021-10) Özhan, Ayşe; Tombaz, Melike; Konu, Özlem
    Background Survival analysis is widely used in cancer research, and although several methods exist in R, there is the need for a more interactive, flexible, yet comprehensive online tool to analyze gene sets using Cox proportional hazards (CPH) models. The web-based Shiny application (app) SmulTCan extends existing tools to multivariable CPH models of gene sets—as exemplified using the netrins and their receptors (netrins-receptors). It can be used to identify survival gene signatures (GSs) and select the best subsets of input gene, microRNA, methylation level, and copy number variation sets from the Cancer Genome Atlas (TCGA). Objectives To create a tool for CPH model building and best subset selection, using survival data from TCGA with input gene expression files from UCSC Xena. Furthermore, we aim to analyze the input TSV file of netrins-receptors in SmulTCan and discuss our findings. Methods SmulTCan uses Shiny's reactivity with built-in R functions from packages for CPH model analysis and best subset selection including “survminer”, “riskRegression”, “rms”, “glmnet”, and “BeSS”. Results Results from the SmulTCan app with the netrins-receptors gene set indicated unique hazard ratio GSs in certain renal and neural cancers, while the best subsets for this gene set, obtained via the app, could differentiate between prognostic outcomes in these cancers.
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    Survival analysis and its applications in identifying genes, signatures, and pathways in human cancers
    (2021-09) Özhan, Ayşe
    Cancer literature makes use of survival analyses focused on gene expression based on univariable or multivariable regression. However, there is still a need to understand whether a) incorporating exon or isoform information on expression would improve estimation of survival in cancer patients; and b) applying multivariable regression to gene sets would allow to obtain cancer-specific independent gene signatures in cancer. Differential usage of individual exons, as well as transcripts, are phenomena common to cancerous tissue when compared to normal tissue. The glioblastoma, GBM; liver cancer LIHC; stomach adenocarcinoma, STAD; and breast carcinoma, BRCA datasets from The Cancer Genome Atlas (TCGA) were investigated to identify individual exons and transcripts with transcriptome-wide impact and significance on survival. Aggregation analyses of exons revealed the important genes for survival in each dataset, including GNA12 in STAD, AKAP13 in LIHC and RBMXL1 and CARS1 in BRCA. GSEA was applied on gene sets formed from the exon-based analysis, revealing distinct enrichment profiles for each dataset as well as overlaps for certain GO terms and KEGG pathways. In the second focus of this thesis, multivariable analyses on gene sets whose expressions were obtained from UCSC Xena were used to create two Shiny applications: one for dataset-specific analyses and one for analyses across TCGA-PANCAN. The dataset specific SmulTCan application incorporates Cox regression analyses with expressions of input genes of the user’s choice. The SmulTCan application contains additional model validation, best subset selection and prognostic analyses. The ClusterHR application performs clustering analyses with Cox regression results, while it can also be used for bicluster identification and comparison. The axon-guidance ligand-receptor gene sets Slit-Robo, netrins-receptors and Semas-receptors were used for demonstrating the apps. Several hazard ratio signatures and best subsets that can differentiate between prognostic outcomes have been identified from the input gene sets, as well as ligand-receptor pairs with prognostic significance.