In this thesis, Van der Pol explored the potential of circulating cell-free DNA (cfDNA) metrics for non-invasive cancer detection and therapy response prediction, leveraging whole genome sequencing (WGS) data. Our research was divided into two parts. In Part I, we focused on developing novel approaches to enhance cancer detection sensitivity. This included combining mutation detection with somatic copy number aberrations (SCNAs) in Chapter 2, investigating cfDNA fragmentomics, and introducing the FrEIA metric in Chapter 3, exploring the use of circulating mitochondrial DNA (mtDNA) as a tumor-derived signal in Chapter 4, and assessing the potential of physically selecting subpopulations of cfDNA to improve signal-to-noise ratio in Chapter 5. These approaches aimed to improve the accuracy and efficiency of non-invasive cancer diagnosis by combining various tumor-derived signals in cfDNA analysis. In Part II, we delved into the reproducibility of cfDNA fragmentomic signals, evaluated their clinical utility for therapy response prediction, and tested a different sequencing platform. Our findings indicated that cfDNA fragmentomics are minimally affected by preanalytical variables. We demonstrated that a combination of cfDNA genomic and fragmentomic parameters is not only effective for cancer detection but is also able to predict treatment response. The utilization of a novel sequencing platform, nanopore-based sequencing, offered a promising path for improved sensitivity in detecting tumor-derived signals. In conclusion, this research showcases the potential of cfDNA metrics generated by WGS as a valuable tool for improving non-invasive cancer diagnosis and treatment response prediction.
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