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

Abstract

Powerful new technologies for perturbing genetic elements have recently 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 correcting genome‐wide screening data, bolstering the sensitivity and specificity of detection for genetic interactions. Applying this strategy to a set of 436 whole genome CRISPR screens, we report more than 1.5 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 complex cellular processes.

Genetic screens in isogenic mammalian cell lines without single cell cloning

“…To understand the generalizability of these novel relationships, we queried the Cancer Dependency Map (DepMap) 34,35 , a compendium of genome-wide RNAi and CRISPR screens performed across hundreds of cancer cell lines. Here, correlation in fitness effects across cell lines suggests a functional relationship between genes [36][37][38] . Focusing on the CRISPR data screened with the Avana library, MARCH5 and MCL1 show a strong co-dependency (R = 0.66); UBE2J2 (R = 0.38) is the second-best correlate of MARCH5 dependency after MCL1, and UBE2K ranks 5th (R = 0.29).…”

“…That co-essentiality data from large-scale genetic screening projects 34,35,39 can be used to generate genetic interaction maps has been demonstrated by several groups [36][37][38] , and these data undoubtedly represent a powerful resource for the scientific community. However, these large-scale screening projects are the result of many dollars and years of effort, and it is not trivial for individual researchers with, for example, a patient-derived cell line or an organoid model, to feed into these pipelines.…”

Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome

“…Thus, we combined BioPlex networks with genome-wide CRISPR co-essentiality profiles measured across hundreds of cancer cell lines through Project Achilles (Meyers et al, 2017). Previously, covarying fitness effects have revealed functional associations within protein complexes (Boyle et al, 2018;Pan et al, 2018) and identified novel complex members (Wainberg et al, 2019). We thus correlated fitness profiles for each interacting protein pair in the combined BioPlex network (Figure 7A, Table S7) and extracted subnetworks corresponding to interactions with either positive (Figure 7B) or negative ( Figure 7C) correlations.…”

Coessential Genetic Networks Reveal the Organization and Constituents of a Dynamic Cellular Stress Response

“…Great insights have been gained from pairwise genetic screens in model organisms, most prominently budding yeast (Costanzo et al, 2016), but the larger human genome has proved challenging for genome-wide combinatorial study of genetic perturbations. With the advent of high quality CRISPR-Cas9 screening libraries in recent years, the coessentiality approach has emerged as an alternative to pairwise genetic perturbations for the discovery of novel genes and genetic interactions (Boyle et al, 2018;Kim et al, 2019;Pan et al, 2018). These first coessentiality studies detail a top-down approach which effectively resolves major protein complexes and identifies functional clusters in the genome.…”

“…If not, what mechanisms underlie the requirement for these factors in some cell lines but not others, and which other genes share this context-specific phenotype? Regarding the latter question, recent work has demonstrated that correlated patterns of gene essentiality ("coessentiality") across many cancer cell lines can indeed identify functional relationships between genes (Boyle et al, 2018;Kim et al, 2019;Pan et al, 2018;Wang et al, 2017). These studies utilize a top-down approach, organizing the genome into coessential clusters which have already proven to yield valuable insights.…”