A web tool to explore, annotate and classify the Acıbadem Breast Cancer Cohort RNA-seq data with gene signatures and clinical/mutation data, according to molecular subtypes

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buir.advisorKarakayalı, Özlen Konu
dc.contributor.authorÇalışır, Kübra
dc.date.accessioned2022-06-07T10:27:46Z
dc.date.available2022-06-07T10:27:46Z
dc.date.copyright2022-06
dc.date.issued2022-06
dc.date.submitted2022-06-06
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Department of Molecular Biology and Genetics, İhsan Doğramacı Bilkent University, 2022.en_US
dc.descriptionIncludes bibliographical references (leaves 124-138).en_US
dc.description.abstractTranscriptomics-based approaches have revealed the molecular heterogeneity and distinct gene expression patterns across breast cancer subtypes since the early 2000s. This led to the usage of molecular subtypes in clinics and translational research in prognostic assessment, therapeutic efficacy prediction, and retrospec- tive analysis of cohort studies. In this thesis, breast cancer subtypes of Acıbadem Breast Cancer Cohort (ABCC) RNA-seq data were classified with immunohisto- chemistry (IHC), PAM50, and SCMOD1 as molecular subtype predictors. The results revealed the moderate concordance of the methods across ABCC and se- lected five other public datasets. In addition, it was shown that the classification of ABCC and TCGA-BRCA RNAseq data strongly depends on the gene sig- nature selection. Further, a machine learning model trained with TCGA-BRCA RNA-seq data and PAM50 genes as predictors showed moderate results for ABCC and MATADOR due to the imbalanced nature of datasets where feature impor- tance revealed a subset of PAM50 genes as predictors. Additionally, the R-Shiny- based classABCC app was developed to facilitate clustering of ABCC with six gene signatures, molecular subtyping of ABCC, and prediction of subtypes with TCGA-BRCA RNA-seq trained machine learning model.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityby Kübra Çalışıren_US
dc.embargo.release2022-12-06
dc.format.extentvii, 156 leaves : illustrations ; 30 cm.en_US
dc.identifier.itemidB161009
dc.identifier.urihttp://hdl.handle.net/11693/80671
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBreast Cancer Cohorten_US
dc.subjectClusteringen_US
dc.subjectGene signatureen_US
dc.subjectImmunohistochemistryen_US
dc.subjectMachine learningen_US
dc.subjectMolecular subtypingen_US
dc.subjectPAM50en_US
dc.subjectRNA-seqen_US
dc.subjectR-shiny appen_US
dc.subjectSCMOD1en_US
dc.titleA web tool to explore, annotate and classify the Acıbadem Breast Cancer Cohort RNA-seq data with gene signatures and clinical/mutation data, according to molecular subtypesen_US
dc.title.alternativeAcıbadem Meme Kanseri Kohortu RNA-seq verilerini gen imzaları ve klinik/mutasyon verileriyle moleküler alt tiplere göre keşfetmek, betimlemek ve sınıflandırmak için bir web aracıen_US
dc.typeThesisen_US

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