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UBC Theses and Dissertations

Identification of cancer relevant synthetic genetic interactions with cohesin mutations in Saccharomyces cerevisiae Reytan, Sivan


Cancer therapy is changing. Whole genome sequencing technologies are advancing at an unprecedented pace, opening new opportunities for the genotype-driven personalized treatment of cancer. Synthetic Lethality (SL) based therapeutics have emerged as promising approaches to target cancer-specific somatic mutations, by targeting a second gene that is required for viability in the presence of a tumor-specific mutation. The targetable set of SL partner genes can be expanded by screening for a conditional SL interaction, in which loss of function of two genes results in sensitivity to low doses of a DNA-damaging agent, a concept we have called Synthetic Cytotoxicity (SC). SC also has the potential to expand the number of genotypes that can be treated with existing chemotherapeutics and to improve the efficacy of these therapeutics. In contrast to SL and SC negative genetic interactions, Phenotypic Suppression (PS) describes a genetic interaction in which the double mutant cell is more fit than anticipated based on the fitness of each single mutant. The model organism, Saccharomyces cerevisiae was used to screen for SC interactions with cohesin-mutated genes, with the aim of identifying cross-species candidate genes that could be followed up in subsequent studies as SL-based cancer-drug targets. The cohesin complex is frequently mutated across a wide range of tumors and is conserved from yeast to man. We used Synthetic Genetic Array (SGA) technology, a high-throughput genetic method available in yeast, to screen cohesin-mutated strains for synthetic lethal genetic interactions against an array of 310 deletions affecting mainly DNA damage response genes. The screens were done in the presence and absence of four clinically-relevant genotoxic agents. We screened and analyzed 4,650 potential genetic interactions, identifying hundreds of negative and positive interactions, belonging to conserved biological pathways, and potentially relevant to cancer. Using ScanLag, a new validation method, we re-tested and validated several genetic interactions that represent potential therapeutic candidates. These strong SL, SC and PS interactions can be further analyzed in mammalian cells to potentially inform and improve individual cancer therapies as personalized medicine treatments, and lead to the discovery of new pathways or candidates for anti-cancer treatments.

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