Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
Karic, M.
Prediction of gene expression in plasma DNA in patients with metastatic prostate cancer by Nucleosome Position Mapping.
[ Diplomarbeit/Master Thesis (UNI) ] University of Graz; 2020.
FullText
- Autor*innen der Med Uni Graz:
- Betreuer*innen:
- Geigl Jochen Bernd
- Altmetrics:
- Abstract:
- Prostate cancer is the second most common diagnosed cancer worldwide. Due to the widespread use of PSA screening tests, most of the patients are diagnosed at an early stage and are on active surveillance or cured with radical surgery. In fact, up to 40% of pre-treated prostate cancer cases progress to castration resistant prostate cancer (CRPC), which is associated with a very high mortality and limited therapeutic options. These patients are no longer sensitive to androgen deprivation therapy and new drugs such as abiraterone and enzalutamide can only postpone the disease. There is an urgent need for predictive and prognostic biomarkers as well as cancer driver genes that contribute to therapy decisions and outcome and provide information about the molecular landscape and heterogeneity of the tumor. With the emergence of next-generation sequencing and the analysis of cell-free DNA (cfDNA), referred to as liquid biopsy, a powerful tool for the identification of biomarkers and cancer driver genes has become available. Since cfDNA is released into the circulation as nucleosome-protected DNA fragments, our group established a method based on this finding, called Nucleosome Position Mapping (NPM). Using this approach, the gene expression status can be inferred from cfDNA in order to identify cancer driver genes in predisposed regions. Therefore, a high coverage sequencing of cfDNA and data analysis using a bioinformatic gene prediction algorithm is essential. We were able to demonstrate differences and similarities regarding copy number alterations from tissue biopsies vs. liquid biopsies from CRPC patients. Furthermore, we could show the potential of liquid biopsy for disease monitoring and identification of therapy resistance. The accuracy of the NPM algorithm could not be assessed with high confidence. In this regard, further evaluation is necessary.