Acıbadem meme kanseri kohortu RNA-seq verilerini gen imzalari ve klinik/mutasyon verileriyle moleküler alt tiplere göre keşfetmek, betimlemek ve sınıflandırmak için bir web aracı

Transcriptomics-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 retrospective analysis of cohort studies. In this thesis, breast cancer subtypes of Acıbadem Breast Cancer Cohort (ABCC) RNA-seq data were classified with immunohistochemistry (IHC), PAM50, and SCMOD1 as molecular subtype predictors. The results revealed the moderate concordance of the methods across ABCC and selected five other public datasets. In addition, it was shown that the classification of ABCC and TCGA-BRCA RNA-seq data strongly depends on the gene signature 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 importance 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.