Efficient Measurement and Characterization of Genetic Interactions in Cancer Cells

The concept of synthetic lethality holds great promise in cancer targeting, as a critical principle for conceptualizing how to effectively target tumors with combinatorial therapies. However, the vastness of the pairwise gene search space demands integration of advanced computational and experimental approaches. The complete map of genetic interactions in yeast has yielded insight into the functional organization of the eukaryotic cell and extending this approach into mammalian cells promises to both elucidate the “wiring diagram” of a normal cell and reveal how that network is rewired in cancers and in genetic diseases more broadly. CRISPR technologies have made genetic screens in mammalian cells tractable but, to date, even CRISPR-mediated genetic interaction assays do not scale effectively due to the inherent combinatorial nature of a gene-vs-gene search space. Moreover, the necessity of measuring interactions in different cell types renders the task at least two orders of magnitude larger in human cells than in yeast. To realize the potential of synthetic lethal interactions in cancer, the field requires major improvements in both assay efficiency and in predictive models that narrow the search space. We have developed a CRISPR/Cas12a based multiplexing system called IN4MER that accurately quantitates genetic interactions with five-fold fewer reagents than the current state of the art. In parallel, we reverse engineer the yeast functional interaction network to predict regions of the search space enriched for genetic interactions and apply these findings to human data to guide experimental design. We hypothesize that, by integrating our computational and experimental approaches, will identify biologically relevant functional modules that are enriched for genetic interactions. With our highly efficient IN4MER platform, we can assay all pairwise combinations of hundreds of target genes at a scale comparable to a standard genome-wide monogenic knockout screen. By doing so across dozens of relevant cell lines, we can understand how genetic interaction networks vary across lineage and mutation state, identifying candidate synthetic lethalities for tumor-specific therapeutic exploitation. Further, screening data will feed back into the predictive model, iteratively increasing the efficiency and discovery potential of this approach. This proposal offers the potential for a major advance in understanding the basic biology implications of genetic interactions and the translational opportunities for exploiting context-specific synthetic lethalities in cancer and beyond.