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Background The adaptation of the CRISPR-Cas9 system to pooled library gene knockout screens in mammalian cells represents a major technological leap over RNA interference, the prior state of the art. New methods for analyzing the data and evaluating results are needed. Results We offer BAGEL (Bayesian Analysis of Gene EssentiaLity), a supervised learning method for analyzing gene knockout screens. Coupled with gold-standard reference sets of essential and nonessential genes, BAGEL offers significantly greater sensitivity than current methods, while computational optimizations reduce runtime by an order of magnitude. Conclusions Using BAGEL, we identify ~2000 fitness genes in pooled library knockout screens in human cell lines at 5 % FDR, a major advance over competing platforms. BAGEL shows high sensitivity and specificity even across screens performed by different labs using different libraries and reagents.

Background Identifying essential genes in genome-wide loss-of-function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpin RNA and CRISPR/Cas9 genome-wide genetic screens. Results We introduce an updated version, BAGEL2, which employs an improved model that offers a greater dynamic range of Bayes Factors, enabling detection of tumor suppressor genes; a multi-target correction that reduces false positives from off-target CRISPR guide RNA; and the implementation of a cross-validation strategy that improves performance ~ 10× over the prior bootstrap resampling approach. We also describe a metric for screen quality at the replicate level and demonstrate how different algorithms handle lower quality data in substantially different ways. Conclusions BAGEL2 substantially improves the sensitivity, specificity, and performance over BAGEL and establishes the new state of the art in the analysis of CRISPR knockout fitness screens. BAGEL2 is written in Python 3 and source code, along with all supporting files, are available on github ( https://github.com/hart-lab/bagel ).

Background Pooled library CRISPR/Cas9 knockout screening across hundreds of cell lines has identified genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the number of essential genes detected from these monogenic knockout screens is low compared to the number of constitutively expressed genes in a cell. Results Through a systematic analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we observe that half of all constitutively expressed genes are never detected in any CRISPR screen and that these never-essentials are highly enriched for paralogs. We investigated functional buffering among approximately 400 candidate paralog pairs using CRISPR/enCas12a dual-gene knockout screening in three cell lines. We observe 24 synthetic lethal paralog pairs that have escaped detection by monogenic knockout screens at stringent thresholds. Nineteen of 24 (79%) synthetic lethal interactions are present in at least two out of three cell lines and 14 of 24 (58%) are present in all three cell lines tested, including alternate subunits of stable protein complexes as well as functionally redundant enzymes. Conclusions Together, these observations strongly suggest that functionally redundant paralogs represent a targetable set of genetic dependencies that are systematically under-represented among cell-essential genes in monogenic CRISPR-based loss of function screens.

Despite its importance in human cancers, including colorectal cancers (CRC), oncogenic KRAS has been extremely challenging to target therapeutically. To identify potential vulnerabilities in KRAS-mutated CRC, we characterize the impact of oncogenic KRAS on the cell surface of intestinal epithelial cells. Here we show that oncogenic KRAS alters the expression of a myriad of cell-surface proteins implicated in diverse biological functions, and identify many potential surface-accessible therapeutic targets. Cell surface-based loss-of-function screens reveal that ATP7A, a copper-exporter upregulated by mutant KRAS, is essential for neoplastic growth. ATP7A is upregulated at the surface of KRAS-mutated CRC, and protects cells from excess copper-ion toxicity. We find that KRAS-mutated cells acquire copper via a non-canonical mechanism involving macropinocytosis, which appears to be required to support their growth. Together, these results indicate that copper bioavailability is a KRAS-selective vulnerability that could be exploited for the treatment of KRAS-mutated neoplasms. The oncogene KRAS is frequently mutated in cancer, including colorectal cancer. Here, using a cell-surface proteomics approach, KRAS-mutated colorectal cancer cells are shown to express high levels of the copper transporter ATP7A, which has an essential roles in cancer cell survival and proliferation.

Background Functional interaction networks, where edges connect genes likely to operate in the same biological process or pathway, can be inferred from CRISPR knockout screens in cancer cell lines. Genes with similar knockout fitness profiles across a sufficiently diverse set of cell line screens are likely to be co-functional, and these “coessentiality” networks are increasingly powerful predictors of gene function and biological modularity. While several such networks have been published, most use different algorithms for each step of the network construction process. Results In this study, we identify an optimal measure of functional interaction and test all combinations of options at each step—essentiality scoring, sample variance and covariance normalization, and similarity measurement—to identify best practices for generating a functional interaction network from CRISPR knockout data. We show that Bayes Factor and Ceres scores give the best results, that Ceres outperforms the newer Chronos scoring scheme, and that covariance normalization is a critical step in network construction. We further show that Pearson correlation, mathematically identical to ordinary least squares after covariance normalization, can be extended by using partial correlation to detect and amplify signals from “moonlighting” proteins which show context-dependent interaction with different partners. Conclusions We describe a systematic survey of methods for generating coessentiality networks from the Cancer Dependency Map data and provide a partial correlation-based approach for exploring context-dependent interactions.

Technological advancement has opened the door to systematic genetics in mammalian cells. Genome‐scale loss‐of‐function screens can assay fitness defects induced by partial gene knockdown, using RNA interference, or complete gene knockout, using new CRISPR techniques. These screens can reveal the basic blueprint required for cellular proliferation. Moreover, comparing healthy to cancerous tissue can uncover genes that are essential only in the tumor; these genes are targets for the development of specific anticancer therapies. Unfortunately, progress in this field has been hampered by off‐target effects of perturbation reagents and poorly quantified error rates in large‐scale screens. To improve the quality of information derived from these screens, and to provide a framework for understanding the capabilities and limitations of CRISPR technology, we derive gold‐standard reference sets of essential and nonessential genes, and provide a Bayesian classifier of gene essentiality that outperforms current methods on both RNAi and CRISPR screens. Our results indicate that CRISPR technology is more sensitive than RNAi and that both techniques have nontrivial false discovery rates that can be mitigated by rigorous analytical methods. Synopsis This study provides a gold‐standard set for essential and nonessential human genes in cancer cell lines. The ‘Daisy model’ for core versus context‐specific essentiality provides a method to evaluate data quality in genome‐scale RNAi and CRISPR screens. Gold‐standard reference sets of human essential and nonessential genes are leveraged to improve analyses of RNAi and CRISPR screens. Characteristics of human essential genes are derived from the cumulative analysis of RNAi screens. The Daisy model of gene essentiality is derived from the difference between core and context‐specific cell line essentials. A computational framework is presented for the prediction of human essential genes from reverse genetic screening data. Graphical Abstract This study provides a gold‐standard set for essential and nonessential human genes in cancer cell lines. The ‘Daisy model’ for core versus context‐specific essentiality provides a method to evaluate data quality in genome‐scale RNAi and CRISPR screens.

53BP1 is a chromatin-binding protein that regulates the repair of DNA double-strand breaks by suppressing the nucleolytic resection of DNA termini 1 , 2 . This function of 53BP1 requires interactions with PTIP 3 and RIF1 4 – 9 , the latter of which recruits REV7 (also known as MAD2L2) to break sites 10 , 11 . How 53BP1-pathway proteins shield DNA ends is currently unknown, but there are two models that provide the best potential explanation of their action. In one model the 53BP1 complex strengthens the nucleosomal barrier to end-resection nucleases 12 , 13 , and in the other 53BP1 recruits effector proteins with end-protection activity. Here we identify a 53BP1 effector complex, shieldin, that includes C20orf196 (also known as SHLD1), FAM35A (SHLD2), CTC-534A2.2 (SHLD3) and REV7. Shieldin localizes to double-strand-break sites in a 53BP1- and RIF1-dependent manner, and its SHLD2 subunit binds to single-stranded DNA via OB-fold domains that are analogous to those of RPA1 and POT1. Loss of shieldin impairs non-homologous end-joining, leads to defective immunoglobulin class switching and causes hyper-resection. Mutations in genes that encode shieldin subunits also cause resistance to poly(ADP-ribose) polymerase inhibition in BRCA1-deficient cells and tumours, owing to restoration of homologous recombination. Finally, we show that binding of single-stranded DNA by SHLD2 is critical for shieldin function, consistent with a model in which shieldin protects DNA ends to mediate 53BP1-dependent DNA repair. The 53BP1 effector complex shieldin is involved in non-homologous end-joining and immunoglobulin class switching, and acts to protect DNA ends to facilitate the repair of DNA by 53BP1.

Genetic screens offer a promising strategy for identifying tumor-specific therapeutic targets, but single-gene knockout screens often miss functionally redundant paralogs. Multiplex Cas9 and Cas12a CRISPR systems have been deployed to assay genetic interactions, but analysis pipelines vary considerably. Here we evaluate data from four in4mer CRISPR/Cas12a screens in cancer cell lines, using delta log fold change, Z -transformed dLFC, and rescaled dLFC approaches to identify synthetic lethal interactions. Both ZdLFC and RdLFC provide more consistent identification of synthetic lethal pairs across cell lines compared to the unscaled dLFC method, while ZdLFC benefits from not requiring a training set of known interactors.

Genetic interactions mediate the emergence of phenotype from genotype, but technologies for combinatorial genetic perturbation in mammalian cells are challenging to scale. Here, we identify background-independent paralog synthetic lethals from previous CRISPR genetic interaction screens, and find that the Cas12a platform provides superior sensitivity and assay replicability. We develop the in4mer Cas12a platform that uses arrays of four independent guide RNAs targeting the same or different genes. We construct a genome-scale library, Inzolia, that is ~30% smaller than a typical CRISPR/Cas9 library while also targeting ~4000 paralog pairs. Screens in cancer cells demonstrate discrimination of core and context-dependent essential genes similar to that of CRISPR/Cas9 libraries, as well as detection of synthetic lethal and masking/buffering genetic interactions between paralogs of various family sizes. Importantly, the in4mer platform offers a fivefold reduction in library size compared to other genetic interaction methods, substantially reducing the cost and effort required for these assays. Paralog synthetic lethals have been assessed with multiple CRISPR-based methods, but systematic comparison among these platforms is unavailable. Here, the authors systematically compare combinatorial perturbation platforms and establish the in4mer CRISPR/Cas12a multiplex knockout platform.

Background Chemogenetic profiling enables the identification of gene mutations that enhance or suppress the activity of chemical compounds. This knowledge provides insights into drug mechanism of action, genetic vulnerabilities, and resistance mechanisms, all of which may help stratify patient populations and improve drug efficacy. CRISPR-based screening enables sensitive detection of drug-gene interactions directly in human cells, but until recently has primarily been used to screen only for resistance mechanisms. Results We present drugZ, an algorithm for identifying both synergistic and suppressor chemogenetic interactions from CRISPR screens. DrugZ identifies synthetic lethal interactions between PARP inhibitors and both known and novel members of the DNA damage repair pathway, confirms KEAP1 loss as a resistance factor for ERK inhibitors in oncogenic KRAS backgrounds, and defines the genetic context for temozolomide activity. Conclusions DrugZ is an open-source Python software for the analysis of genome-scale drug modifier screens. The software accurately identifies genetic perturbations that enhance or suppress drug activity. Interestingly, analysis of new and previously published data reveals tumor suppressor genes are drug-agnostic resistance genes in drug modifier screens. The software is available at github.com/hart-lab/drugz .