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

• Published in: Molecular systems biology Vol. 14; no. 12; pp. e8594 - n/a
• Main Authors: Boyle, Evan A, Pritchard, Jonathan K, Greenleaf, William J
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01-12-2018; EMBO Press; John Wiley and Sons Inc; Springer Nature
• Subjects: Algorithms; Cancer; Clustered Regularly Interspaced Short Palindromic Repeats - genetics; Communities; CRISPR; EMBO09; EMBO10; EMBO26; Epistasis, Genetic; functional genomics; Gene mapping; Gene Regulatory Networks - genetics; Genes; genetic interactions; Genetic screening; Genome, Human - genetics; Genomes; genome‐wide perturbation; Genomics - methods; Genotype; Genotypes; Humans; Mapping; Neoplasms - genetics; Neoplasms - pathology; network topology; Networks; New technology; Proteins; Screening; Yeast; CRISPR; functional genomics; genome‐wide perturbation; network topology; genetic interactions
• ISSN: 1744-4292; 1744-4292
• DOI: 10.15252/msb.20188594

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.
Synopsis
Learning signatures of confounding in pooled gene knockout screens increases power to detect co‐essential gene pairs by an order of magnitude. Analysis of hundreds of deconfounded cancer cell line growth screens generates new hypotheses for cancer drug targets and tissue‐specific growth pathways.
Over 1.5 million pairs of correlated gene essentiality profiles are identified.
Co‐essential gene pairs are shown to be co‐functional interactions broadly enriched for known biological pathways.
One‐dimensional genetic screens performed in parallel suffice to build genome‐wide co‐functional networks that exhibit diffusion of signal through gene‐gene edges.
Hijacking of gene modules by cancer driver genes is implicated as the modus operandi for several well‐studied malignancies.
Graphical Abstract
Learning signatures of confounding in pooled gene knockout screens increases power to detect co‐essential gene pairs by an order of magnitude. Analysis of hundreds of deconfounded cancer cell line growth screens generates new hypotheses for cancer drug targets and tissue‐specific growth pathways.