Rao Chaitra, Huisman Dianna H, Vieira Heidi M, Frodyma Danielle E, Neilsen Beth K, Chakraborty Binita, Hight Suzie K, White Michael A, Fisher Kurt W, Lewis Robert E
Eppley Institute, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27710, USA.
Cancers (Basel). 2020 Oct 27;12(11):3143. doi: 10.3390/cancers12113143.
Genome-wide, loss-of-function screening can be used to identify novel vulnerabilities upon which specific tumor cells depend for survival. Functional Signature Ontology (FUSION) is a gene expression-based high-throughput screening (GE-HTS) method that allows researchers to identify functionally similar proteins, small molecules, and microRNA mimics, revealing novel therapeutic targets. FUSION uses cell-based high-throughput screening and computational analysis to match gene expression signatures produced by natural products to those produced by small interfering RNA (siRNA) and synthetic microRNA libraries to identify putative protein targets and mechanisms of action (MoA) for several previously undescribed natural products. We have used FUSION to screen for functional analogues to Kinase suppressor of Ras 1 (KSR1), a scaffold protein downstream of Ras in the Raf-MEK-ERK kinase cascade, and biologically validated several proteins with functional similarity to KSR1. FUSION incorporates bioinformatics analysis that may offer higher resolution of the endpoint readout than other screens which utilize Boolean outputs regarding a single pathway activation (i.e., synthetic lethal and cell proliferation). Challenges associated with FUSION and other high-content genome-wide screens include variation, batch effects, and controlling for potential off-target effects. In this review, we discuss the efficacy of FUSION to identify novel inhibitors and oncogene-induced changes that may be cancer cell-specific as well as several potential pitfalls within FUSION and best practices to avoid them.
全基因组功能丧失筛选可用于识别特定肿瘤细胞赖以生存的新弱点。功能特征本体论（FUSION）是一种基于基因表达的高通量筛选（GE-HTS）方法，它使研究人员能够识别功能相似的蛋白质、小分子和微小RNA模拟物，从而揭示新的治疗靶点。FUSION利用基于细胞的高通量筛选和计算分析，将天然产物产生的基因表达特征与小分子干扰RNA（siRNA）和合成微小RNA文库产生的特征进行匹配，以识别几种先前未描述的天然产物的假定蛋白质靶点和作用机制（MoA）。我们利用FUSION筛选了Ras激酶抑制因子1（KSR1）的功能类似物，KSR1是Raf-MEK-ERK激酶级联反应中Ras下游的一种支架蛋白，并对几种与KSR1功能相似的蛋白质进行了生物学验证。FUSION纳入了生物信息学分析，与其他利用关于单一途径激活的布尔输出（即合成致死和细胞增殖）的筛选相比，它可能提供更高分辨率的终点读数。与FUSION和其他全基因组高内涵筛选相关的挑战包括变异、批次效应以及控制潜在的脱靶效应。在这篇综述中，我们讨论了FUSION识别新抑制剂和癌基因诱导变化（可能是癌细胞特异性的）的功效，以及FUSION中的几个潜在陷阱和避免这些陷阱的最佳做法。
