Analysis of Gene Expression Data for Precision Medicine in Ulcerative Colitis: Analysis of Gene Expression Data for Precision Medicine in Ulcerative Colitis

Precision medicine seeks to incorporate factors other than the disease diagnosis into treatment decision-making. With the development of new, increasingly rapid and cost-effective RNA and DNA sequencing methods, there are completely new possibilities in the field of precision medicine. The principle is to use genetic markers in treatment decision making to address the genetic predisposition of the patient. This requires research on genetic markers that are associated with treatment response. In this thesis, biomarkers for treatment response for different treatments in ulcerative colitis patients are searched for using a cross-study data analysis. For the past two decades, the development of new antibody-based drugs from the group of biologics has transformed treatment of ulcerative colitis. Some drugs have been better studied while others are still in their infancy. In this work, all publicly available gene expression data that include the response to biologics in ulcerative colitis patients are analysed. The analysis includes eight different datasets and five different drugs and placebo. First, for each drug, differentially expressed genes at baseline between responders and non-responders are analysed. Then, machine learning models are used to find and evaluate predictive genes for treatment response. Lastly, the change in gene expression over the course of treatment between responders and non-responders is analysed. These analyses provide a broad overview of the response of different therapies in Ulcerative Colitis at a gene expression level and provides results that can be further processed upon multiple levels to better understand and treat this complex disease.