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Abstract

cirDNA has long been known to be present in the blood. In cancer patients, levels of cirDNA are higher and they rise further in patients with advanced stages of disease. Importantly cirDNA in cancer patients contains mutated fragments originating from the tumor. As monitoring the mutational patterns of solid tumors during cancer therapy is an unmet need in oncology, analysis of mutations in circulating DNA offers a non-invasive approach to detect mutations that may be prognostic for disease survival or predictive for primary or secondary drug resistance. A main challenge for the application of cirDNA as a diagnostic tool is the diverse mutational landscape of cancer. Here, a flexible end-to-end experimental and bioinformatic workflow to analyze mutations in cirDNA is established using custom amplicon sequencing. This approach relies on open-software tools to select primers suitable for multiplex PCR using minimal cirDNA as input. In addition, a robust linear model to identify specific genetic alterations from sequencing data of cirDNA was developed. This workflow was applied to design a custom amplicon panel suitable for detection of hotspot mutations relevant for colorectal cancer and analyzed mutations in serial cirDNA samples from a pilot cohort of 34 patients with advanced colorectal cancer. This thesis could show, that in patients with gastrointestinal cancers cirDNA-levels are higher in advanced stages of disease and that cirDNA-levels positively correlate to LDH and CEA in patients with colorectal cancer. However, cirDNA-levels underlie multiple influencing factors and thus impedes to draw individual conclusions. The mutation analysis showed that, recurrent and patient-specific mutational patterns could be detected in most patients. Furthermore, dynamic changes of mutant allele frequencies in cirDNA were shown to correlate well with disease progression. Finally, sequencing of cirDNA proved to reveal mechanisms of resistance to anti-Epidermal Growth Factor Receptor (EGFR) antibody treatment. Thus, this approach offers a simple and highly customizable method to explore genetic alterations in cirDNA.