High-resolution mapping of cancer cell networks using co-functional interactions

Powerful new technologies for perturbing genetic elements have expanded the study of genetic interactions in model systems ranging from yeast to human cell lines. However, technical artifacts can confound signal across genetic screens and limit the immense potential of parallel screening approaches. To address this problem, we devised a novel PCA-based method for eliminating these artifacts and bolstering sensitivity and specificity for detection of genetic interactions. Applying this strategy to a set of >300 whole genome CRISPR screens, we report ~1 million pairs of correlated \"co-functional\" genes that provide finer-scale information about cell compartments, biological pathways, and protein complexes than traditional gene sets. Lastly, we employed a gene community detection approach to implicate core genes for cancer growth and compress signal from functionally related genes in the same community into a single score. This work establishes new algorithms for probing cancer cell networks and motivates the acquisition of further CRISPR screen data across diverse genotypes and cell types to further resolve the complexity of cell signaling processes.