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Next generation sequencing guided molecular diagnostic tests in non-small-cell lung cancer

PhD ceremony:Mr J. (Jiacong) Wei
When:November 19, 2019
Supervisors:prof. dr. J.H.M. (Anke) van den Berg, prof. dr. H.J.M. (Harry) Groen
Co-supervisor:dr. K. Kok
Where:Academy building RUG
Faculty:Medical Sciences / UMCG
Next generation sequencing guided molecular diagnostic tests in
non-small-cell lung cancer

The aim of this thesis is to improve diagnostic methods to identify genomic aberrations with clinical significance in non-small cell lung cancer (NSCLC) patients. We tested the feasibility of an all-in-one transcriptome-based assay to simultaneously identify different types of genetic variants with clinical significance in those patients in Chapter 2. An accuracy of 100% can be reached by using criteria for quality control e.g. RNA integrity and total unique reads. Our study showed the potential of application in diagnostic settings. In chapter 3, we analyzed EGFR mutation and amplification status in advanced NSCLC patients using targeted DNA sequencing data available from the routine molecular diagnostics. We showed the feasibility of using the generation sequencing (NGS) data to identify amplifications in genes relevant for daily diagnostics. Amplification of EGFR in patients with EGFR mutations were associated with poor tumor response to EGFR TKI. In chapter 4, we aimed to identify potential novel crizotinib-induced resistance mechanisms in ALK-break positive NSCLC patients. By analyzing whole exome sequencing data on paired pre- and post TKI tissue samples, we found the enrichment of mutations in genes associated with EMT-related pathways, indicating that loss of epithelial differentiation represents a resistance mechanism for crizotinib. In chapter 5, using a cohort of esophageal squamous cell carcinoma patients, we showed that somatic mutations can be detected in pre-surgery cfDNA in early stage patients, and at a lower frequency in post-surgery cfDNA, indicating that cfDNA could potentially be used to monitor disease load.