Optimisation of cell-free DNA analysis methods for solid malignancies: Making more of the data we have

Solid malignancies, such as lung and colorectal cancers, are non-fluid tumour types that pose significant challenges in diagnosis and treatment. Cell-free DNA (cfDNA), obtained through a simple blood draw, often contains tumour-derived DNA fragments carrying valuable genomic information that can guide treatment decisions. However, current methods for analysing cfDNA frequently lack the sensitivity and specificity required to detect the minute fractions of tumour-derived DNA present in early-stage or low-burden disease.
 
This thesis focuses on optimizing cfDNA analysis methods, emphasizing the potential of the extensive data already generated through standard diagnostic and research workflows. We evaluated and compared different analytical approaches, demonstrating the added value of cfDNA-guided treatment selection over traditional tissue-based methods. By improving data analysis procedures for droplet digital PCR and capture-based NGS, we achieved enhanced detection of tumour signals. Ultimately, this work highlights that better utilization of existing data can significantly improve cancer detection using cfDNA.