Disputation: Molecular studies of endocrine tumors: Insights from genetics and epigenetics.
- Plats: Zoom: https://uu-se.zoom.us/j/64101626138 Skoogsalen ing 78/79, 1 tr
- Doktorand: Doktorand Samuel Backman
- Kontaktperson: Per Hellman (huvudhandledare)
Samuel Backman försvarar sin avhandling Molecular studies of endocrine tumors: Insights from genetics and epigenetics.
Endocrine tumors may be benign or malignant and may occur in any of the hormone producing tissues. They share several biological characteristics, including a low mutation-burden, and may co-occur in several hereditary tumor syndromes. The aim of this thesis was to identify genetic and epigenetic aberrations in endocrine tumors.
In paper I we performed a comprehensive DNA methylation analysis of 39 pheochromocytomas/paragangliomas as well as 4 normal adrenal medullae on the HumanMethylation27 BeadChip array. We validated two previously described clusters based on DNA methylation with distinct genetic associations.
In Paper II we performed a transcriptomic analysis of 15 aldosterone producing adenomas. CTNNB1-mutated tumors were found to form a distinct subgroup based on gene expression and to share gene expression similarities with non-aldosterone producing adrenocortical tumors with CTNNB1 mutations, including overexpression of AFF3 and ISM1.
In paper III we used whole genome sequencing to identify germline genetic variants in 14 patients with Multiple Endocrine Neoplasia type 1 previously found to be wildtype for the MEN1 gene on routine clinical testing. Three patients were found to carry previously undetected MEN1 mutations. Two patients were confirmed to have phenocopies caused by variants affecting CASR or CDC73. In total 9/14 patients were not found to have a disease-causing germline variant, suggesting that the syndrome may in some cases be due to chance co-occurrence of several sporadic tumors.
In paper IV RNA-Seq and whole genome sequencing of a cohort of SI-NETs selected on the basis of unusually short or long survival was performed in order to identify disease causing genes and potential prognostic factors. We confirmed known genetic aberrations and found rare variants in known cancer driver genes. Based on gene expression two clusters that differ in prognosis were detected. Moreover, through integration of copy number variation data and gene expression, we identied novel potential disease causing genes.