利用共功能相互作用进行癌症细胞网络的高分辨率绘图。

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

机构信息

Department of Genetics, Stanford University, Stanford, CA, USA.

Department of Biology, Stanford University, Stanford, CA, USA.

出版信息

Mol Syst Biol. 2018 Dec 20;14(12):e8594. doi: 10.15252/msb.20188594.

DOI:10.15252/msb.20188594

PMID:30573688

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6300813/

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.

摘要

最近，强大的新型基因元件扰动技术扩展了从酵母到人类细胞系等模型系统中遗传相互作用的研究。然而，技术伪影会混淆遗传筛选中的信号，并限制平行筛选方法的巨大潜力。为了解决这个问题，我们设计了一种基于 PCA 的新方法来纠正全基因组筛选数据，提高遗传相互作用检测的灵敏度和特异性。将该策略应用于一组 436 个全基因组 CRISPR 筛选，我们报告了超过 150 万个相关的“共功能”基因对，这些基因对细胞区室、生物途径和蛋白质复合物提供了比传统基因集更精细的信息。最后，我们采用基因社区检测方法来暗示癌症生长的核心基因，并将来自同一社区中功能相关基因的信号压缩为单个分数。这项工作为探测癌细胞网络建立了新的算法，并促使在不同基因型和细胞类型中获取更多的 CRISPR 筛选数据，以进一步解析复杂的细胞过程。