Biomarkers determined by histopathological analysis of tumor tissue are important factors for stratification and therapy selection in breast cancer. While a plethora of molecular biomarkers have been developed since the initial sequencing of the human genome – predominently using microarray techniques – these methods have not become routine.
In recent years, RNA sequencing (RNA-seq) is increasingly replacing microarrays as the principal method for transcriptome profiling. RNA-seq offers many advantages over previous methods, including greater dynamic range and reproducibility, and detection of de novo transcripts in addition to quantifying known transcripts, as well as the possibility of calling sequence variants. To date RNA-seq has been used in a multitude of research studies. However, in contrast to DNA sequencing, implementation of RNA-seq in the clinical setting is only slowly progressing. Yet, it promises biomarkers that are more precise and reproducible than available today, and the possibility to run a whole array of these biomarker tests on a single RNA-seq analysis.
With my dissertation project I plan to bring the possibilities of RNA-seq closer to clinical application within breast cancer treatment in three ways: 1. by developing predictors for clinically important biomarkers from gene expression profiles; 2. by developing and verifying resources for a large-scale RNA-seq study; 3. by exploring mutation calling in RNA-seq datasets.