High-resolution mapping of cancer cell networks using co-functional interactions.
Evan August BoyleJonathan K PritchardWilliam J GreenleafPublished in: Molecular systems biology (2018)
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.
Keyphrases
- genome wide
- dna methylation
- copy number
- high resolution
- single cell
- healthcare
- electronic health record
- cell therapy
- mental health
- endothelial cells
- machine learning
- high throughput
- gene expression
- squamous cell carcinoma
- deep learning
- label free
- magnetic resonance imaging
- risk assessment
- papillary thyroid
- computed tomography
- mass spectrometry
- transcription factor
- saccharomyces cerevisiae
- small molecule
- bone marrow
- genome wide identification
- single molecule
- amino acid
- high speed
- social media
- high density
- squamous cell