Abstract PR04: A CRISPR-based genetic interaction map identifies synergistic drug combinations for cancer

Abstract

Identification of effective combination therapies is critical to address the emergence of drug-resistant cancers. Although millions of drug combinations might be created by repurposing existing drugs, direct screening of these combinations is infeasible. Here, we designed a scalable CRISPR-based double knockout (CDKO) system to generate a mammalian genetic interaction (GI) map at unprecedented scale, comprised of 490,000 double-sgRNAs directed against 21,321 pairs of drug targets. We first developed an efficient strategy for cloning and sequencing the libraries, as well as a robust statistical scoring method for calculating GIs from CRISPR-deleted gene pairs. We then extensively validated this system by identifying known and novel genetic interactions in the ricin pathway, and compared the GIs to known protein-protein interactions (PPIs). Using this validated system, we searched for rare synthetic lethal drug target pairs in K562 leukemia cells and identified a number of potent combinations for which corresponding drugs exhibit synergistic killing. Together, this work demonstrates an effective strategy to screen synergistic drug combinations in high throughput, and a powerful CRISPR-based tool to dissect functional genetic interaction networks. Citation Format: Kyuho Han, Edwin Jeng, Gaelen Hess, David Morgens, Amy Li, Michael Bassik. A CRISPR-based genetic interaction map identifies synergistic drug combinations for cancer [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr PR04.

Deubiquitinases Maintain Protein Homeostasis and Survival of Cancer Cells upon Glutathione Depletion

“…This reasoning could explain why compounds that inhibit multiple DUBs scored highly in our pharmacologic screens involving GSH depletion (Ritorto et al, 2014). Genetic deletion of multiple DUBs using combinatorial CRISPR-Cas9 screening techniques (Adamson et al, 2016;Han et al, 2017;Najm et al, 2018) may shed light on which sets of DUBs play more significant roles in buffering the effects of excess protein ubiquitination under conditions of GSH depletion. Interestingly, both genetic and pharmacologic screens revealed that BSO caused an increased sensitivity to the inhibition of de novo lipogenesis controlled by fatty acid synthetase (FASN) (Tables S3 and S4).…”

Systematic Identification of Host Cell Regulators of Legionella pneumophila Pathogenesis Using a Genome-wide CRISPR Screen

“…Validate Host Regulators of L. pneumophila Pathogenesis We next performed a smaller-scale ''batch retest'' screen at higher coverage (4,000 versus 1,000 cells per sgRNA), which we and others have shown can reduce false positives and false negatives (Bassik et al, 2013;Han et al, 2017;Haney et al, 2018;Parnas et al, 2015). For this custom library, we included 1,139 genes consisting of the 186 genes that were hits in the genome-wide screen at 10% FDR, factors previously identified in LCVs isolated from macrophages (Hoffmann et al, 2014), and additional genes of interest, such as genes involved in trafficking (Figure 1E; Table S2).…”

A Three-Way Combinatorial CRISPR Screen for Analyzing Interactions among Druggable Targets

“…Article ll OPEN ACCESS be carried out by targeting two genes simultaneously using dual gRNA expression cassettes (Chow et al, 2019;Du et al, 2017;Han et al, 2017;Najm et al, 2018;Shen et al, 2017;Wong et al, 2016a). Here we evaluated the extensibility of existing methods and other possible toolkits for assembling a threeway combinatorial gRNA library for screening (Table 1).…”

“…This platform is also versatile and can be used together with dCas9based CRISPR interference systems (Qi et al, 2013) to partially lower target gene expressions to mimic drug inhibitor effects and minimize any spurious effects caused by large deletions and genomic rearrangements. This platform could be coupled with other technologies, like single-cell RNA sequencing (RNA-seq), to explore different cell signatures and contribute to generation of druggable gene interaction networks using existing knowledge (Adamson et al, 2016;Bassik et al, 2013;Chow et al, 2019;Du et al, 2017;Han et al, 2017;Shen et al, 2017). The platform presented in this study is easy to implement and will be valuable for perturbing multi-layer genetic networks to understand complex biological systems and design new combination therapies.…”

“…The Combinatorial Genetics En Masse (CombiGEM)-CRISPR platform (Wong et al, 2015(Wong et al, , 2016a) could, in theory, be used to generate high-order combinatorial guide RNA (gRNA) libraries, but the library assembly strategy was not optimized for screening of three or more targets simultaneously. The extensibility of other existing methods for screening high-order genetic combinations is also limited by the relatively low and variable cleavage efficiency for polycistronic systems to express multiple RNAs (Han et al, 2017;Xu et al, 2017), and/or characterization of high-order combinations requires large-scale oligo synthesis and high sequencing costs (elaborated in Design of CombiGEM-CRISPR v.2.0). Mathematical models have been developed for predicting three-way and higher-order drug interactions (Cokol et al, 2017;Wood et al, 2012;Zimmer et al, 2016), but high-throughput methods are needed to experimentally validate sets of potential combinations.…”