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

被引：42

作者：

Boyle, Evan A. ^{[1
]}

Pritchard, Jonathan K. ^{[1
,2
,3
]}

Greenleaf, William J. ^{[1
,4
]}

机构：

[1] Stanford Univ, Dept Genet, Stanford, CA 94305 USA

[2] Stanford Univ, Dept Biol, Stanford, CA 94305 USA

[3] Howard Hughes Med Inst, Stanford, CA USA

[4] Chan Zuckerberg Biohub, San Francisco, CA 94158 USA

来源：

MOLECULAR SYSTEMS BIOLOGY | 2018年 / 14卷 / 12期

基金：

美国国家科学基金会;

关键词：

CRISPR; functional genomics; genetic interactions; genome-wide perturbation; network topology; GENE ONTOLOGY; CRISPR SCREEN; MAP; INHIBITORS; PATHWAYS; YEAST; TOOL; SET;

D O I：

10.15252/msb.20188594

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页数：16

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