Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting.
ABSTRACT: BACKGROUND:Genome editing by CRISPR-Cas9 technology allows large-scale screening of gene essentiality in cancer. A confounding factor when interpreting CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes within copy number amplified regions of the genome. We have developed the computational tool CRISPRcleanR which is capable of identifying and correcting gene-independent responses to CRISPR-Cas9 targeting. CRISPRcleanR uses an unsupervised approach based on the segmentation of single-guide RNA fold change values across the genome, without making any assumption about the copy number status of the targeted genes. RESULTS:Applying our method to existing and newly generated genome-wide essentiality profiles from 15 cancer cell lines, we demonstrate that CRISPRcleanR reduces false positives when calling essential genes, correcting biases within and outside of amplified regions, while maintaining true positive rates. Established cancer dependencies and essentiality signals of amplified cancer driver genes are detectable post-correction. CRISPRcleanR reports sgRNA fold changes and normalised read counts, is therefore compatible with downstream analysis tools, and works with multiple sgRNA libraries. CONCLUSIONS:CRISPRcleanR is a versatile open-source tool for the analysis of CRISPR-Cas9 knockout screens to identify essential genes.
Project description:The CRISPR-Cas9 system has revolutionized gene editing both at single genes and in multiplexed loss-of-function screens, thus enabling precise genome-scale identification of genes essential for proliferation and survival of cancer cells. However, previous studies have reported that a gene-independent antiproliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, thereby leading to false-positive results in copy number-amplified regions. We developed CERES, a computational method to estimate gene-dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy number-specific effect. In our efforts to define a cancer dependency map, we performed genome-scale CRISPR-Cas9 essentiality screens across 342 cancer cell lines and applied CERES to this data set. We found that CERES decreased false-positive results and estimated sgRNA activity for both this data set and previously published screens performed with different sgRNA libraries. We further demonstrate the utility of this collection of screens, after CERES correction, for identifying cancer-type-specific vulnerabilities.
Project description: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.
Project description:BACKGROUND:Probing genetic dependencies of cancer cells can improve our understanding of tumour development and progression, as well as identify potential drug targets. CRISPR-Cas9-based and shRNA-based genetic screening are commonly utilized to identify essential genes that affect cancer growth. However, systematic methods leveraging these genetic screening techniques to derive consensus cancer dependency maps for individual cancer cell lines are lacking. FINDING:In this work, we first explored the CRISPR-Cas9 and shRNA gene essentiality profiles in 42 cancer cell lines representing 10 cancer types. We observed limited consistency between the essentiality profiles of these two screens at the genome scale. To improve consensus on the cancer dependence map, we developed a computational model called combined essentiality score (CES) to integrate the genetic essentiality profiles from CRISPR-Cas9 and shRNA screens, while accounting for the molecular features of the genes. We found that the CES method outperformed the existing gene essentiality scoring approaches in terms of ability to detect cancer essential genes. We further demonstrated the power of the CES method in adjusting for screen-specific biases and predicting genetic dependencies in individual cancer cell lines. INTERPRETATION:Systematic comparison of the CRISPR-Cas9 and shRNA gene essentiality profiles showed the limitation of relying on a single technique to identify cancer essential genes. The CES method provides an integrated framework to leverage both genetic screening techniques as well as molecular feature data to determine gene essentiality more accurately for cancer cells.
Project description:Cell autonomous cancer dependencies are now routinely identified using CRISPR loss-of-function viability screens. However, a bias exists that makes it difficult to assess the true essentiality of genes located in amplicons, since the entire amplified region can exhibit lethal scores. These false-positive hits can either be discarded from further analysis, which in cancer models can represent a significant number of hits, or methods can be developed to rescue the true-positives within amplified regions. We propose two methods to rescue true positive hits in amplified regions by correcting for this copy number artefact. The Local Drop Out (LDO) method uses the relative lethality scores within genomic regions to assess true essentiality and does not require additional orthogonal data (e.g. copy number value). LDO is meant to be used in screens covering a dense region of the genome (e.g. a whole chromosome or the whole genome). The General Additive Model (GAM) method models the screening data as a function of the known copy number values and removes the systematic effect from the measured lethality. GAM does not require the same density as LDO, but does require prior knowledge of the copy number values. Both methods have been developed with single sample experiments in mind so that the correction can be applied even in smaller screens. Here we demonstrate the efficacy of both methods at removing the copy number effect and rescuing hits from some of the amplified regions. We estimate a 70-80% decrease of false positive hits with either method in regions of high copy number compared to no correction.
Project description:Genome-wide CRISPR-Cas9 dropout screens can identify genes whose knockout affects cell viability. Recent CRISPR screens detected thousands of essential genes required for cellular survival and key cellular processes; however discovering novel lineage-specific genetic dependencies from the many hits still remains a challenge.To assess whether CRISPR-Cas9 dropout screens can help identify cancer dependencies, we screened two human cancer cell lines carrying known and distinct oncogenic mutations using a genome-wide sgRNA library. We found that the gRNA targeting the driver mutation EGFR was one of the highest-ranking candidates in the EGFR-mutant HCC-827 lung adenocarcinoma cell line. Likewise, sgRNAs for NRAS and MAP2K1 (MEK1), a downstream kinase of mutant NRAS, were identified among the top hits in the NRAS-mutant neuroblastoma cell line CHP-212. Depletion of these genes targeted by the sgRNAs strongly correlated with the sensitivity to specific kinase inhibitors of the EGFR or RAS pathway in cell viability assays. In addition, we describe other dependencies such as TBK1 in HCC-827 cells and TRIB2 in CHP-212 cells which merit further investigation.We show that genome-wide CRISPR dropout screens are suitable for the identification of oncogenic drivers and other essential genes.
Project description:The CRISPR/Cas9 system has revolutionized mammalian somatic cell genetics. Genome-wide functional screens using CRISPR/Cas9-mediated knockout or dCas9 fusion-mediated inhibition/activation (CRISPRi/a) are powerful techniques for discovering phenotype-associated gene function. We systematically assessed the DNA sequence features that contribute to single guide RNA (sgRNA) efficiency in CRISPR-based screens. Leveraging the information from multiple designs, we derived a new sequence model for predicting sgRNA efficiency in CRISPR/Cas9 knockout experiments. Our model confirmed known features and suggested new features including a preference for cytosine at the cleavage site. The model was experimentally validated for sgRNA-mediated mutation rate and protein knockout efficiency. Tested on independent data sets, the model achieved significant results in both positive and negative selection conditions and outperformed existing models. We also found that the sequence preference for CRISPRi/a is substantially different from that for CRISPR/Cas9 knockout and propose a new model for predicting sgRNA efficiency in CRISPRi/a experiments. These results facilitate the genome-wide design of improved sgRNA for both knockout and CRISPRi/a studies.
Project description:Several groups have used genome-wide libraries of lentiviruses encoding small guide RNAs (sgRNAs) for genetic screens. In most cases, sgRNA expression cassettes are integrated into cells by using lentiviruses, and target genes are statistically estimated by the readout of sgRNA sequences after targeted sequencing. We present a new virus-free method for human gene knockout screens using a genome-wide library of CRISPR/Cas9 sgRNAs based on plasmids and target gene identification via whole-genome sequencing (WGS) confirmation of authentic mutations rather than statistical estimation through targeted amplicon sequencing. We used 30,840 pairs of individually synthesized oligonucleotides to construct the genome-scale sgRNA library, collectively targeting 10,280 human genes (i.e. three sgRNAs per gene). These plasmid libraries were co-transfected with a Cas9-expression plasmid into human cells, which were then treated with cytotoxic drugs or viruses. Only cells lacking key factors essential for cytotoxic drug metabolism or viral infection were able to survive. Genomic DNA isolated from cells that survived these challenges was subjected to WGS to directly identify CRISPR/Cas9-mediated causal mutations essential for cell survival. With this approach, we were able to identify known and novel genes essential for viral infection in human cells. We propose that genome-wide sgRNA screens based on plasmids coupled with WGS are powerful tools for forward genetics studies and drug target discovery.
Project description:CRISPR-Cas9 technology has accelerated biological research becoming routine for many laboratories. It is rapidly replacing conventional gene editing techniques and has high utility for both genome-wide and gene-focussed applications. Here we present the first individually cloned CRISPR-Cas9 genome wide arrayed sgRNA libraries covering 17,166 human and 20,430 mouse genes at a complexity of 34,332 sgRNAs for human and 40,860 sgRNAs for the mouse genome. For flexibility in generating stable cell lines the sgRNAs have been cloned in a lentivirus backbone containing PiggyBac transposase recognition elements together with fluorescent and drug selection markers. Over 95% of tested sgRNA induced specific DNA cleavage as measured by CEL-1 assays. Furthermore, sgRNA targeting GPI anchor protein pathway genes induced loss of function mutations in human and mouse cell lines measured by FLAER labelling. These arrayed libraries offer the prospect for performing screens on individual genes, combinations as well as larger gene sets. They also facilitate rapid deconvolution of signals from genome-wide screens. This set of vectors provide an organized comprehensive gene editing toolbox of considerable scientific value.
Project description:Although millions of transcription factor binding sites, or cistromes, have been identified across the human genome, defining which of these sites is functional in a given condition remains challenging. Using CRISPR/Cas9 knockout screens and gene essentiality or fitness as the readout, we systematically investigated the essentiality of over 10,000 FOXA1 and CTCF binding sites in breast and prostate cancer cells. We found that essential FOXA1 binding sites act as enhancers to orchestrate the expression of nearby essential genes through the binding of lineage-specific transcription factors. In contrast, CRISPR screens of the CTCF cistrome revealed 2 classes of essential binding sites. The first class of essential CTCF binding sites act like FOXA1 sites as enhancers to regulate the expression of nearby essential genes, while a second class of essential CTCF binding sites was identified at topologically associated domain (TAD) boundaries and display distinct characteristics. Using regression methods trained on our screening data and public epigenetic profiles, we developed a model to predict essential cis-elements with high accuracy. The model for FOXA1 essentiality correctly predicts noncoding variants associated with cancer risk and progression. Taken together, CRISPR screens of cis-regulatory elements can define the essential cistrome of a given factor and can inform the development of predictive models of cistrome function.
Project description:The CRISPR/Cas9 system is a powerful method of editing genes by randomly introducing errors into the target sites. Here, we describe a CRISPR-based test for gene essentiality (CRISPR-E test) that allows the identification of essential genes. Specifically, we use sgRNA-mediated CRISPR/Cas9 to target the open reading frame of a gene in the genome and analyze the in-frame (3n) and frameshift (3n?+?1 and 3n?+?2) mutations in the targeted region of the gene in surviving cells. If the gene is non-essential, the cells would carry both in-frame (3n) and frameshift (3n?+?1 and 3n?+?2) mutations. In contrast, the cells would carry only in-frame (3n) mutations if the targeted gene is essential, and this selective elimination of frameshift (3n?+?1 and 3n?+?2) mutations of the gene indicate its essentiality. As a proof of concept, we have used this CRISPR-E test in the model organism Dictyostelium discoideum to demonstrate that Dync1li1 is an essential gene while KIF1A and fAR1 are not. We further propose a simple method for quantifying the essentiality of a gene using the CRISPR-E test.