Synthetic lethality is a phenomenon whereby two genetic perturbations have little impact on cell growth when present individually, but when combined result in cell death. Increasingly, researchers screen for synthetic lethal interactions in cancer cell lines in order to identify potential therapeutics. However, it is unclear whether synthetic lethal interactions observed in one cancer cell line are likely to be observed in others due to extensive genetic differences. I wish to address this issue – what impact does genetic heterogeneity have on identified synthetic lethal interactions? Are some synthetic lethal interactions more robust to genetic heterogeneity than others?
My overall goal is to understand why a synthetic lethal interaction observed in one individual may be absent in another, and to establish any rules that may be used to identify and prioritise those interactions most robust to genetic heterogeneity. My first key goal is to understand what factors might disrupt a given synthetic lethal interaction. I will assess this by using a reverse genetics approach to try to disrupt a well characterised interaction.
My second key goal is to test whether robust synthetic lethal interactions can be identified by combining multiple screens from diverse cell lines. By focussing on the most widely screened oncogene, I will develop data integration methods that may be applicable to other mutations as the data associated with them increases. This will involve computational work followed by experimental validation.
My final key goal is to find out whether certain categories of interactions, such as those between members of the same pathway, are more robust to changes in genetic background than others. Because experimental studies of individual genes or individual synthetic lethal interactions preclude the assessment of such trends, I will use computational modelling to assess a large number of synthetic lethal interactions across diverse backgrounds.