Functional modules predict cancer-relevant genetic interactions in mammalian cells
Genetic interactions can reveal gene function and identify disease-relevant synthetic lethals, but resource-intensive screening in human cells constrains experimental scope and limits our understanding of how generalizable sparse hits are. Here, we leverage principles from yeast genetic networks to identify human gene modules involved in receptor tyrosine kinase signaling and DNA damage response that are predicted to be enriched for genetic interactions, then use our enCas12a-based In4mer combinatorial knockout platform to efficiently screen these modules across diverse cancer cell lines. We identify hundreds of unreported synthetic lethals, including a dense network within the protein N-linked glycosylation machinery that shows both evolutionary conservation and network rewiring, and we confirm that interactions discovered in 2D cell culture are maintained in more physiologically relevant models. Critically, a meta-analysis within and between screens highlights sources of variation in published results, reveals the potential for false positives in screens with many essential genes, and demonstrates the need to move beyond DDR for actionable synthetic lethalities.