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Background Genome-wide loss-of-function screens using the CRISPR/Cas9 system allow the efficient discovery of cancer cell vulnerabilities. While several studies have focused on correcting for DNA cleavage toxicity biases associated with copy number alterations, the effects of sgRNAs co-targeting multiple genomic loci in CRISPR screens have not been discussed. Results In this work, we analyze CRISPR essentiality screen data from 391 cancer cell lines to characterize biases induced by multi-target sgRNAs. We investigate two types of multi-targets: on-targets predicted through perfect sequence complementarity and off-targets predicted through sequence complementarity with up to two nucleotide mismatches. We find that the number of on-targets and off-targets both increase sgRNA activity in a cell line-specific manner and that existing additive models of gene knockout effects fail at capturing genetic interactions that may occur between co-targeted genes. We use synthetic lethality between paralog genes to show that genetic interactions can introduce biases in essentiality scores estimated from multi-target sgRNAs. We further show that single-mismatch tolerant sgRNAs can confound the analysis of gene essentiality and lead to incorrect co-essentiality functional networks. Lastly, we also find that single nucleotide polymorphisms located in protospacer regions can impair on-target activity as a result of mismatch tolerance. Conclusion We show the impact of multi-target effects on estimating cancer cell dependencies and the impact of off-target effects caused by mismatch tolerance in sgRNA-DNA binding. Electronic supplementary material The online version of this article (10.1186/s13059-019-1621-7) contains supplementary material, which is available to authorized users.

Genome-wide loss-of-function screens using the CRISPR/Cas9 system allow the efficient discovery of cancer cell vulnerabilities. While several studies have focused on correcting for DNA cleavage toxicity biases associated with copy number alterations, the effects of sgRNAs co-targeting multiple genomic loci in CRISPR screens have not been discussed yet. In this work, we analyze CRISPR essentiality screen data from 391 cancer cell lines to characterize biases induced by multi-target sgRNAs. We investigate two types of multi-targets: on-targets predicted through perfect sequence complementarity, and off-targets predicted through sequence complementarity with up to two nucleotide mismatches. We found that the number of on-targets and off-targets both increase sgRNA activity in a cell line-specific manner, and that existing additive models of gene knockout effects fail at capturing genetic interactions that may occur between co-targeted genes. We use synthetic lethality between paralog genes to show that genetic interactions can introduce biases in essentiality scores estimated from multi-target sgRNAs. We further show that single-mismatch tolerant sgRNAs can confound the analysis of gene essentiality and lead to incorrect co-essentiality functional networks.

In response to diverse stimuli, tumor cells can undergo a process resembling the epithelial-mesenchymal transition (EMT) observed during development. During this transition, epithelial-like tumor cells acquire mesenchymal-like physiologic features, become more motile and invasive, and acquire insensitivity to many therapeutic agents. While the importance of this phenomenon to cancer treatment and patient outcomes is well known, considerable heterogeneity exists across experimental models of EMT, and diverse mechanisms have been proposed to explain its various associated malignant phenotypes. Consequently, the extent to which these processes are shared uniformly across instances of EMT, as well as their relative importance in treatment contexts, remains unclear. We have used RNAseq, ATACseq, and parallelized high-throughput cell viability screening to identify differences in gene expression levels, chromatin accessibility, and drug sensitivity that correlate with EMT in a series of cell line models. By contrasting these changes across different genetic backgrounds and mechanisms of EMT induction, we are able to identify sets of elements in the transcribed and noncoding genome that are characteristic of different modes of EMT induction. Then, by comparing these candidate sets with observed differences in acquired drug sensitivity in the respective models, we were able to define a set of genetic elements whose biologic activity is likely to influence the efficacy of these compounds. Finally, we used a series of targeted CRISPR-based knockout screens to assess the relative importance of these elements to the viability of isogenic epithelial-like and mesenchymal-like cells, alone and in the presence of multiple therapeutic drugs. Using this approach, we were able to validate known mechanisms of acquired drug resistance and identify new candidate effectors. Notably, we identify multiple candidate noncoding elements containing CTCF binding sites that appear to meaningfully influence cell viability in a drug-specific manner, potentially implicating genome conformational changes in acquired therapeutic resistance. Citation Format: Russell O. Bainer, Catherine Wilson, Marinella Callow, Siyu Feng, Michael Costa, Colin Watanabe, Oleg Mayba, Eva Lin, Scott Martin, Bob Yauch, Richard Bourgon, Christiaan Klijn. Functional coding and noncoding drivers of EMT-mediated acquired drug resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 398.