Approaches for determining the biological relevance of in-silico computed genetic Minimal Cut Sets (gMCSs) in cancer.
There is a need to stop the rise of cancer new cases and cancer deaths. Currently scientific advances regarding targeted therapy, such as genome editing, are improving immensely. For this reason, a lot of effort should be put in the finding of which genes to aim. Focusing on cancer metabolism, the phenomenon of synthetic lethality has been proven to be an efficient alternative in the termination of cells viability and proliferation. It has also been found that synthetic lethal genes exhibit mutual exclusivity. A tool developed by Apaolaza et. al in TECNUN, University of Navarra, called genetic Minimal Cut Sets (gMCSs) was developed to computationally find synthetic lethal genes of any order. One of the aims of this project was to prove the relationship of the gMCSs with experimental SL pairs from SynLethDB (Synthetic lethality database), this was statistically proven. A relationship between the SL gMCS pairs and mutual exclusivity using data built with COSMIC (Catalogue of Somatic Mutations In Cancer) was also intended to be found, however the results were not as expected. Additionally, rankings based on different criteria and using the databases mentioned were created with the aim of highlighting important gMCSs and SL pairs. The SL gMCS pairs and the top ranked gMCSs encountered have the potential of being possible routes for new cancer treatments.
Date: January-September 2020 Advisor: Iñigo Apaolaza Based on: An in-silico approach to predict and exploit synthetic lethality in cancer metabolism