Identifying and Exploiting Synthetic Lethality for Cancer Therapeutics
Abstract:
Cancer care has been progressively moving from a one-size-fits-all approach to a more personalized strategy based on patient-specific genetic characteristics. Synthetic Lethality is considered a promising approach for the development of such personalized treatments. A Synthetic Lethal (SL) interaction is a functional relationship between two genes or functional entities where the loss of either entity is viable but the loss of both is lethal. Such pairs can be used to develop targeted anticancer therapies with fewer side effects and reduced overtreatment. However, such SL interactions in humans remain largely unknown and there is a need to discover more such pairs, especially those that are clinically actionable, and to develop tools that can enable us to gain deeper insights into their role in cancer and therapy development.
In this thesis, new methods have been developed to discover SL pairs and to utilize SL pairs in biological network mining and clinical decision support. Two new methods are designed to identify potential SL pairs using multiple genomic data sources, based on statistical hypothesis testing and collective matrix factorization. A new algorithm to mine and analyse a network of pairwise interactions is presented, that can be used to discover relationships between biological pathways that are known to play important roles in cancer and in targeted drug development. Finally, an application in personalized treatment recommendations is explored that uses information on SL pairs and other public genomic databases by integrating them within an integer linear programming framework.