CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens.

BACKGROUND: CRISPR-Cas9 genome-wide screens are being increasingly performed, allowing systematic explorations of cancer dependencies at unprecedented accuracy and scale. One of the major computational challenges when analysing data derived from such screens is to identify genes that are essential for cell survival invariantly across tissues, conditions, and genomic-contexts (core-fitness genes), and to distinguish them from context-specific essential genes. This is of paramount importance to assess the safety profile of candidate therapeutic targets and for elucidating mechanisms involved in tissue-specific genetic diseases. RESULTS: We have developed CoRe: an R package implementing existing and novel methods for the identification of core-fitness genes (at two different level of stringency) from joint analyses of multiple CRISPR-Cas9 screens. We demonstrate, through a fully reproducible benchmarking pipeline, that CoRe outperforms state-of-the-art tools, yielding more reliable and biologically relevant sets of core-fitness genes. CONCLUSIONS: CoRe offers a flexible pipeline, compatible with many pre-processing methods for the analysis of CRISPR data, which can be tailored onto different use-cases. The CoRe package can be used for the identification of high-confidence novel core-fitness genes, as well as a means to filter out potentially cytotoxic hits while analysing cancer dependency datasets for identifying and prioritising novel selective therapeutic targets.

Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens.

Low success rates during drug development are due, in part, to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs with genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate cellular drug mechanism-of-action. We observed an enrichment for positive associations between the profile of drug sensitivity and knockout of a drug's nominal target, and by leveraging protein-protein networks, we identified pathways underpinning drug sensitivity. This revealed an unappreciated positive association between mitochondrial E3 ubiquitin-protein ligase MARCH5 dependency and sensitivity to MCL1 inhibitors in breast cancer cell lines. We also estimated drug on-target and off-target activity, informing on specificity, potency and toxicity. Linking drug and gene dependency together with genomic data sets uncovered contexts in which molecular networks when perturbed mediate cancer cell loss-of-fitness and thereby provide independent and orthogonal evidence of biomarkers for drug development. This study illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function screens can elucidate mechanism-of-action to advance drug development.

Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure.

BACKGROUND: Correlation matrices are important in inferring relationships and networks between regulatory or signalling elements in biological systems. With currently available technology sample sizes for experiments are typically small, meaning that these correlations can be difficult to estimate. At a genome-wide scale estimation of correlation matrices can also be computationally demanding. RESULTS: We develop an empirical Bayes approach to improve covariance estimates for gene expression, where we assume the covariance matrix takes a block diagonal form. Our method shows lower false discovery rates than existing methods on simulated data. Applied to a real data set from Bacillus subtilis we demonstrate it's ability to detecting known regulatory units and interactions between them. CONCLUSIONS: We demonstrate that, compared to existing methods, our method is able to find significant covariances and also to control false discovery rates, even when the sample size is small (n=10). The method can be used to find potential regulatory networks, and it may also be used as a pre-processing step for methods that calculate, for example, partial correlations, so enabling the inference of the causal and hierarchical structure of the networks.

DvD: An R/Cytoscape pipeline for drug repurposing using public repositories of gene expression data.

SUMMARY: Drug versus Disease (DvD) provides a pipeline, available through R or Cytoscape, for the comparison of drug and disease gene expression profiles from public microarray repositories. Negatively correlated profiles can be used to generate hypotheses of drug-repurposing, whereas positively correlated profiles may be used to infer side effects of drugs. DvD allows users to compare drug and disease signatures with dynamic access to databases Array Express, Gene Expression Omnibus and data from the Connectivity Map. AVAILABILITY AND IMPLEMENTATION: R package (submitted to Bioconductor) under GPL 3 and Cytoscape plug-in freely available for download at www.ebi.ac.uk/saezrodriguez/DVD/.

Benchmark Software and Data for Evaluating CRISPR-Cas9 Experimental Pipelines Through the Assessment of a Calibration Screen.

Genome-wide genetic screens using CRISPR-guide RNA libraries are widely performed in mammalian cells to functionally characterize individual genes and for the discovery of new anticancer therapeutic targets. As the effectiveness of such powerful and precise tools for cancer pharmacogenomics is emerging, tools and methods for their quality assessment are becoming increasingly necessary. Here, we provide an R package and a high-quality reference data set for the assessment of novel experimental pipelines through which a single calibration experiment has been executed: a screen of the HT-29 human colorectal cancer cell line with a commercially available genome-wide library of single-guide RNAs. This package and data allow experimental researchers to benchmark their screens and produce a quality-control report, encompassing several quality and validation metrics. The R code used for processing the reference data set, for its quality assessment, as well as to evaluate the quality of a user-provided screen, and to reproduce the figures presented in this article is available at https://github.com/DepMap-Analytics/HT29benchmark. The reference data is publicly available on FigShare.

A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization.

Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer dependencies derived from CRISPR-Cas9 screens. We identify dependency-associated gene expression markers beyond driver genes, and observe many gene addiction relationships driven by gain of function rather than synthetic lethal effects. By combining clinically informed dependency-marker associations with protein-protein interaction networks, we identify 370 anti-cancer priority targets for 27 cancer types, many of which have network-based evidence of a functional link with a marker in a cancer type. Mapping these targets to sequenced tumor cohorts identifies tractable targets in different cancer types. This target prioritization map enhances understanding of gene dependencies and identifies candidate anti-cancer targets for drug development.

RNF43/ZNRF3 loss predisposes to hepatocellular-carcinoma by impairing liver regeneration and altering the liver lipid metabolic ground-state.

RNF43/ZNRF3 negatively regulate WNT signalling. Both genes are mutated in several types of cancers, however, their contribution to liver disease is unknown. Here we describe that hepatocyte-specific loss of Rnf43/Znrf3 results in steatohepatitis and in increase in unsaturated lipids, in the absence of dietary fat supplementation. Upon injury, Rnf43/Znrf3 deletion results in defective hepatocyte regeneration and liver cancer, caused by an imbalance between differentiation/proliferation. Using hepatocyte-, hepatoblast- and ductal cell-derived organoids we demonstrate that the differentiation defects and lipid alterations are, in part, cell-autonomous. Interestingly, ZNRF3 mutant liver cancer patients present poorer prognosis, altered hepatic lipid metabolism and steatohepatitis/NASH signatures. Our results imply that RNF43/ZNRF3 predispose to liver cancer by controlling the proliferative/differentiation and lipid metabolic state of hepatocytes. Both mechanisms combined facilitate the progression towards malignancy. Our findings might aid on the management of those RNF43/ZNRF3 mutated individuals at risk of developing fatty liver and/or liver cancer.

Integrated cross-study datasets of genetic dependencies in cancer.

CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.

Analysis of CRISPR-Cas9 screens identifies genetic dependencies in melanoma.

Targeting the MAPK signaling pathway has transformed the treatment of metastatic melanoma. CRISPR-Cas9 genetic screens provide a genome-wide approach to uncover novel genetic dependencies that might serve as therapeutic targets. Here, we analyzed recently reported CRISPR-Cas9 screens comparing data from 28 melanoma cell lines and 313 cell lines of other tumor types in order to identify fitness genes related to melanoma. We found an average of 1,494 fitness genes in each melanoma cell line. We identified 33 genes, inactivation of which specifically reduced the fitness of melanoma. This set of tumor type-specific genes includes established melanoma fitness genes as well as many genes that have not previously been associated with melanoma growth. Several genes encode proteins that can be targeted using available inhibitors. We verified that genetic inactivation of DUSP4 and PPP2R2A reduces the proliferation of melanoma cells. DUSP4 encodes an inhibitor of ERK, suggesting that further activation of MAPK signaling activity through its loss is selectively deleterious to melanoma cells. Collectively, these data present a resource of genetic dependencies in melanoma that may be explored as potential therapeutic targets.

Minimal genome-wide human CRISPR-Cas9 library.

CRISPR guide RNA libraries have been iteratively improved to provide increasingly efficient reagents, although their large size is a barrier for many applications. We design an optimised minimal genome-wide human CRISPR-Cas9 library (MinLibCas9) by mining existing large-scale gene loss-of-function datasets, resulting in a greater than 42% reduction in size compared to other CRISPR-Cas9 libraries while preserving assay sensitivity and specificity. MinLibCas9 provides backward compatibility with existing datasets, increases the dynamic range of CRISPR-Cas9 screens and extends their application to complex models and assays.

Dynamic cell contacts between periportal mesenchyme and ductal epithelium act as a rheostat for liver cell proliferation.

In the liver, ductal cells rarely proliferate during homeostasis but do so transiently after tissue injury. These cells can be expanded as organoids that recapitulate several of the cell-autonomous mechanisms of regeneration but lack the stromal interactions of the native tissue. Here, using organoid co-cultures that recapitulate the ductal-to-mesenchymal cell architecture of the portal tract, we demonstrate that a subpopulation of mouse periportal mesenchymal cells exerts dual control on proliferation of the epithelium. Ductal cell proliferation is either induced and sustained or, conversely, completely abolished, depending on the number of direct mesenchymal cell contacts, through a mechanism mediated, at least in part, by Notch signaling. Our findings expand the concept of the cellular niche in epithelial tissues, whereby not only soluble factors but also cell-cell contacts are the key regulatory cues involved in the control of cellular behaviors, suggesting a critical role for cell-cell contacts during regeneration.

CELLector: Genomics-Guided Selection of Cancer In Vitro Models.

Selecting appropriate cancer models is a key prerequisite for maximizing translational potential and clinical relevance of in vitro oncology studies. We developed CELLector: an R package and R Shiny application allowing researchers to select the most relevant cancer cell lines in a patient-genomic-guided fashion. CELLector leverages tumor genomics to identify recurrent subtypes with associated genomic signatures. It then evaluates these signatures in cancer cell lines to prioritize their selection. This enables users to choose appropriate in vitro models for inclusion or exclusion in retrospective analyses and future studies. Moreover, this allows bridging outcomes from cancer cell line screens to precisely defined sub-cohorts of primary tumors. Here, we demonstrate the usefulness and applicability of CELLector, showing how it can aid prioritization of in vitro models for future development and unveil patient-derived multivariate prognostic and therapeutic markers. CELLector is freely available at https://ot-cellector.shinyapps.io/CELLector_App/ (code at https://github.com/francescojm/CELLector and https://github.com/francescojm/CELLector_App).

Characteristics and homogeneity of N6-methylation in human genomes.

A novel DNA modification, N-6 methylated deoxyadenosine (m6dA), has recently been discovered in eukaryotic genomes. Despite its low abundance in eukaryotes, m6dA is implicated in human diseases such as cancer. It is therefore important to precisely identify and characterize m6dA in the human genome. Here, we identify m6dA sites at nucleotide level, in different human cells, genome wide. We compare m6dA features between distinct human cells and identify m6dA characteristics in human genomes. Our data demonstrates for the first time that despite low m6dA abundance, the m6dA mark does often occur consistently at the same genomic location within a given human cell type, demonstrating m6dA homogeneity. We further show, for the first time, higher levels of m6dA homogeneity within one chromosome. Most m6dA are found on a single chromosome from a diploid sample, suggesting inheritance. Our transcriptome analysis not only indicates that human genes with m6dA are associated with higher RNA transcript levels but identifies allele-specific gene transcripts showing haplotype-specific m6dA methylation, which are implicated in different biological functions. Our analyses demonstrate the precision and consistency by which the m6dA mark occurs within the human genome, suggesting that m6dA marks are precisely inherited in humans.

Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets.

Genome-scale CRISPR-Cas9 viability screens performed in cancer cell lines provide a systematic approach to identify cancer dependencies and new therapeutic targets. As multiple large-scale screens become available, a formal assessment of the reproducibility of these experiments becomes necessary. We analyze data from recently published pan-cancer CRISPR-Cas9 screens performed at the Broad and Sanger Institutes. Despite significant differences in experimental protocols and reagents, we find that the screen results are highly concordant across multiple metrics with both common and specific dependencies jointly identified across the two studies. Furthermore, robust biomarkers of gene dependency found in one data set are recovered in the other. Through further analysis and replication experiments at each institute, we show that batch effects are driven principally by two key experimental parameters: the reagent library and the assay length. These results indicate that the Broad and Sanger CRISPR-Cas9 viability screens yield robust and reproducible findings.

Bioinformatics challenges and perspectives when studying the effect of epigenetic modifications on alternative splicing.

It is widely known that epigenetic modifications are important in regulating transcription, but several have also been reported in alternative splicing. The regulation of pre-mRNA splicing is important to explain proteomic diversity and the misregulation of splicing has been implicated in many diseases. Here, we give a brief overview of the role of epigenetics in alternative splicing and disease. We then discuss the bioinformatics methods that can be used to model interactions between epigenetic marks and regulators of splicing. These models can be used to identify alternative splicing and epigenetic changes across different phenotypes.This article is part of a discussion meeting issue 'Frontiers in epigenetic chemical biology'.