Title: Using interspecies biological networks to guide drug therapy
Description: The use of drug combinations (DCs) in cancer therapy can prevent the development of drug resistance and decrease the severity and number of side effects. Synthetic lethality (SL), a genetic interaction wherein two nonessential genes cause cell death when knocked out simultaneously, has been suggested as a method of identifying novel DCs. A combination of two drugs that mimic genetic knockout may cause cellular death through a synthetic lethal pathway. Because SL can be context-specific, it may be possible to find DCs that target SL pairs in tumours while leaving healthy cells unscathed. However, elucidating all synthetic lethal pairs in humans would take more than 200 million experiments in a single biological context – an unmanageably large search space. It is thus necessary to develop computational methods to predict human SL. In this thesis, we develop connectivity homology, a novel measure of network similarity that allows for the comparison of interspecies protein-protein interaction networks.

We then use this principle to develop Species-INdependent TRAnslation (SINaTRA), an algorithm that allows us to predict SL between species using protein-protein interaction networks. We validate it by predicting SL in S. pombe from S. cerevisiae, then generate over 100 million SINaTRA scores for putative human SL pairs. We use these data to predict new areas of cancer combination therapy, and then test fifteen of these predictions across several cell lines. Finally, in order to better understand synergy, we develop DAVISS (Data-driven Assessment of Variability In Synergy Scores), a novel way to statistically evaluate the significance of a drug interaction.