PhD defence by Kristine Degn

On Wednesday 11 September, Kristine Degn will defend her PhD thesis "Classification and annotations of cancer variants using structure-based methods".

Time: 14:00
Place: Building 308, auditorium 11 & zoom: https://dtudk.zoom.us/meeting/register/u5MpceCgrz8tHNdyFcFqfBpwsQ9tzGx7hma_
Please be aware that the PhD defense may be recorded - This will also be informed at the beginning of the PhD defense.

Supervisor: Associate Professor Elena Papaleo
Co-Supervisor: Dr. Matteo Tiberti, Cancer Structural Biology, Danish Cancer Institute
Associate Professor Karin Wadt, Department of Clinical Genetics, Copenhagen University Hospital, Rigshospitalet

Assessment committee:
Associate Professor Carolina Barra Quaglia, DTU Health Tech
Associate Professor Alexander Hauser, Department of Drug Design and Pharmacology, University of Copenhagen
Professor Piero Fariselli, Department of Medical Sciences, University of Torino

Chairperson:
Associate Professor Lars Rønn Olsen, DTU Health Tech

Abstract:
Genetic changes such as mutations, called variants, can be associated with cancer. The understanding of this relationship has advanced significantly in recent years due to the decreasing costs of DNA sequencing and accompanying new techniques for analyzing sequencing data. The progress has led to the discovery of more genetic variants than it is possible to annotate. As a result, many variants get classified as Variants of Unknown Significance (VUS). This classification is often a consequence of conflicting evidence and different clinical judgments for single variants. This PhD project aims to tackle this complexity by studying the variants from a different angle, namely by using modeling of the variants in the translated protein to investigate how these affect the protein and its interaction pattern. Thus, aiming to categorize the variants with unknown significance. The mutated proteins are studied in several ways, such as predicting thermodynamic stability changes, changes in interaction patterns, and conformational changes elsewhere than at the mutation itself. The purpose of the thesis is to create a basis for predicting and understanding the effects of genetic variants in cancer by developing methods and investigating biological phenomena. Overall, the thesis proposes a structure-based framework to comprehensively describe the mutational cancer landscape.