To date, cross-species comparisons of genetic interactomes have been restricted to small or functionally related gene sets, limiting our ability to infer evolutionary trends. To facilitate a more comprehensive analysis, we constructed a genome-scale epistasis map (E-MAP) for the fission yeast Schizosaccharomyces pombe, providing phenotypic signatures for ~60% of the non-essential genome. Using these signatures, we generated a catalogue of 297 functional modules, and assigned function to 144 previously uncharacterised genes, including mRNA splicing and DNA damage checkpoint factors. Comparison with an integrated genetic interactome from the budding yeast Saccharomyces cerevisiae revealed a hierarchical model for the evolution of genetic interactions, with conservation highest within protein complexes, lower within biological processes, and lowest between distinct biological processes. Despite the large evolutionary distance and extensive rewiring of individual interactions, both networks retain conserved features and display similar levels of functional cross-talk between biological processes, suggesting general design principles of genetic interactomes.

Identifying genetic biomarkers of synthetic lethal drug sensitivity effects provides one approach to the development of targeted cancer therapies. Mutations in ARID1A represent one of the most common molecular alterations in human cancer, but therapeutic approaches that target these defects are not yet clinically available. We demonstrate that defects in ARID1A sensitize tumour cells to clinical inhibitors of the DNA damage checkpoint kinase, ATR, both in vitro and in vivo. Mechanistically, ARID1A deficiency results in topoisomerase 2A and cell cycle defects, which cause an increased reliance on ATR checkpoint activity. In ARID1A mutant tumour cells, inhibition of ATR triggers premature mitotic entry, genomic instability and apoptosis. The data presented here provide the pre-clinical and mechanistic rationale for assessing ARID1A defects as a biomarker of single-agent ATR inhibitor response and represents a novel synthetic lethal approach to targeting tumour cells.

SummaryOne approach to identifying cancer-specific vulnerabilities and therapeutic targets is to profile genetic dependencies in cancer cell lines. Here, we describe data from a series of siRNA screens that identify the kinase genetic dependencies in 117 cancer cell lines from ten cancer types. By integrating the siRNA screen data with molecular profiling data, including exome sequencing data, we show how vulnerabilities/genetic dependencies that are associated with mutations in specific cancer driver genes can be identified. By integrating additional data sets into this analysis, including protein-protein interaction data, we also demonstrate that the genetic dependencies associated with many cancer driver genes form dense connections on functional interaction networks. We demonstrate the utility of this resource by using it to predict the drug sensitivity of genetically or histologically defined subsets of tumor cell lines, including an increased sensitivity of osteosarcoma cell lines to FGFR inhibitors and SMAD4 mutant tumor cells to mitotic inhibitors.