Toward an integrated map of genetic interactions in cancer cells

Global analysis of the yeast knockout phenome

Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae, enabled by its knockout collection, have produced the largest, richest, and most systematic phenotypic description of any organism. However, integrative analyses of this rich data source have been virtually impossible because of the lack of a central data repository and consistent metadata annotations. Here, we describe the aggregation, harmonization, and analysis of ~14,500 yeast knockout screens, which we call Yeast Phenome. Using this unique dataset, we characterized two unknown genes (YHR045W and YGL117W) and showed that tryptophan starvation is a by-product of many chemical treatments. Furthermore, we uncovered an exponential relationship between phenotypic similarity and intergenic distance, which suggests that gene positions in both yeast and human genomes are optimized for function.

Prioritization of New Candidate Genes for Rare Genetic Diseases by a Disease-Aware Evaluation of Heterogeneous Molecular Networks

Screening for pathogenic variants in the diagnosis of rare genetic diseases can now be performed on all genes thanks to the application of whole exome and genome sequencing (WES, WGS). Yet the repertoire of gene-disease associations is not complete. Several computer-based algorithms and databases integrate distinct gene-gene functional networks to accelerate the discovery of gene-disease associations. We hypothesize that the ability of every type of information to extract relevant insights is disease-dependent. We compiled 33 functional networks classified into 13 knowledge categories (KCs) and observed large variability in their ability to recover genes associated with 91 genetic diseases, as measured using efficiency and exclusivity. We developed GLOWgenes, a network-based algorithm that applies random walk with restart to evaluate KCs' ability to recover genes from a given list associated with a phenotype and modulates the prediction of new candidates accordingly. Comparison with other integration strategies and tools shows that our disease-aware approach can boost the discovery of new gene-disease associations, especially for the less obvious ones. KC contribution also varies if obtained using recently discovered genes. Applied to 15 unsolved WES, GLOWgenes proposed three new genes to be involved in the phenotypes of patients with syndromic inherited retinal dystrophies.

A phenotype driven integrative framework uncovers molecular mechanisms of a rare hereditary thrombophilia

Antithrombin resistance is a rare subtype of hereditary thrombophilia caused by prothrombin gene variants, leading to thrombotic disorders. Recently, the Prothrombin Belgrade variant has been reported as a specific variant that leads to antithrombin resistance in two Serbian families with thrombosis. However, due to clinical data scarcity and the inapplicability of traditional genome-wide association studies (GWAS), a broader perspective on molecular and phenotypic mechanisms associated with the Prothrombin Belgrade variant is yet to be uncovered. Here, we propose an integrative framework to address the lack of genomic samples and support the genomic signal from the full genome sequences of five heterozygous subjects by integrating it with subjects' phenotypes and the genes' molecular interactions. Our goal is to identify candidate thrombophilia-related genes for which our subjects possess germline variants by focusing on the resulting gene clusters of our integrative framework. We applied a Non-negative Matrix Tri-Factorization-based method to simultaneously integrate different data sources, taking into account the observed phenotypes. In other words, our data-integration framework reveals gene clusters involved with this rare disease by fusing different datasets. Our results are in concordance with the current literature about antithrombin resistance. We also found candidate disease-related genes that need to be further investigated. CD320, RTEL1, UCP2, APOA5 and PROZ participate in healthy-specific or disease-specific subnetworks involving thrombophilia-annotated genes and are related to general thrombophilia mechanisms according to the literature. Moreover, the ADRA2A and TBXA2R subnetworks analysis suggested that their variants may have a protective effect due to their connection with decreased platelet activation. The results show that our method can give insights into antithrombin resistance even if a small amount of genetic data is available. Our framework is also customizable, meaning that it applies to any other rare disease.

Gut microbiota-stem cell niche crosstalk: A new territory for maintaining intestinal homeostasis

Intestinal epithelium undergoes rapid cellular turnover, relying on the local niche, to support intestinal stem cells (ISCs) function and self-renewal. Research into the association between ISCs and disease continues to expand at a rapid rate. However, the detailed interaction of ISCs and gut microbes remains to be elucidated. Thus, this review witnessed major advances in the crosstalk between ISCs and gut microbes, delivering key insights into (1) construction of ISC niche and molecular mechanism of how to jointly govern epithelial homeostasis and protect against intestinal diseases with the participation of Wnt, bone morphogenetic protein, and Notch; (2) differentiation fate of ISCs affect the gut microbiota. Meanwhile, the presence of intestinal microbes also regulates ISC function; (3) microbiota regulation on ISCs by Wnt and Notch signals through pattern recognition receptors; (4) how do specific microbiota-related postbiotics influence ISCs to maintain intestinal epithelial regeneration and homeostasis that provide insights into a promising alternative therapeutic method for intestinal diseases. Considering the detailed interaction is still unclear, it is necessary to further explore the regulatory role of gut microbiota on ISCs to utilize microbes to alleviate gut disorders. Furthermore, these major advances collectively drive us ever closer to breakthroughs in regenerative medicine and cancer treatment by microbial transplantation in the clinic.

Optimal construction of a functional interaction network from pooled library CRISPR fitness screens

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.

PICKLES v3: the updated database of pooled in vitro CRISPR knockout library essentiality screens

PICKLES (https://pickles.hart-lab.org) is an updated web interface to a freely available database of genome-scale CRISPR knockout fitness screens in human cell lines. Using a completely rewritten interface, researchers can explore gene knockout fitness phenotypes across cell lines and tissue types and compare fitness profiles with fitness, expression, or mutation profiles of other genes. The database has been updated to include data from three CRISPR libraries (Avana, Score, and TKOv3), and includes information from 1162 whole-genome screens probing the knockout fitness phenotype of 18 959 genes. Source code for the interface and the integrated database are available for download.

Dynamic rewiring of biological activity across genotype and lineage revealed by context-dependent functional interactions

BACKGROUND

Coessentiality networks derived from CRISPR screens in cell lines provide a powerful framework for identifying functional modules in the cell and for inferring the roles of uncharacterized genes. However, these networks integrate signal across all underlying data and can mask strong interactions that occur in only a subset of the cell lines analyzed.

RESULTS

Here, we decipher dynamic functional interactions by identifying significant cellular contexts, primarily by oncogenic mutation, lineage, and tumor type, and discovering coessentiality relationships that depend on these contexts. We recapitulate well-known gene-context interactions such as oncogene-mutation, paralog buffering, and tissue-specific essential genes, show how mutation rewires known signal transduction pathways, including RAS/RAF and IGF1R-PIK3CA, and illustrate the implications for drug targeting. We further demonstrate how context-dependent functional interactions can elucidate lineage-specific gene function, as illustrated by the maturation of proreceptors IGF1R and MET by proteases FURIN and CPD.

CONCLUSIONS

This approach advances our understanding of context-dependent interactions and how they can be gleaned from these data. We provide an online resource to explore these context-dependent interactions at diffnet.hart-lab.org.

Mutational signatures are markers of drug sensitivity of cancer cells

Genomic analyses have revealed mutational footprints associated with DNA maintenance gone awry, or with mutagen exposures. Because cancer therapeutics often target DNA synthesis or repair, we asked if mutational signatures make useful markers of drug sensitivity. We detect mutational signatures in cancer cell line exomes (where matched healthy tissues are not available) by adjusting for the confounding germline mutation spectra across ancestries. We identify robust associations between various mutational signatures and drug activity across cancer cell lines; these are as numerous as associations with established genetic markers such as driver gene alterations. Signatures of prior exposures to DNA damaging agents – including chemotherapy – tend to associate with drug resistance, while signatures of deficiencies in DNA repair tend to predict sensitivity towards particular therapeutics. Replication analyses across independent drug and CRISPR genetic screening data sets reveal hundreds of robust associations, which are provided as a resource for drug repurposing guided by mutational signature markers.

Mutational signatures can reveal the impact of mutagenic processes in cancer, including exposure to therapy. Here, the authors develop an approach that can accurately predict drug responses in cancer using mutational signatures while simultaneously correcting for germline variants with an ancestry matching procedure.

Concurrent outcomes from multiple approaches of epistasis analysis for human body mass index associated loci provide insights into obesity biology

Genome wide association studies (GWAS) have focused on elucidating the genetic architecture of complex traits by assessing single variant effects in additive genetic models, albeit explaining a fraction of the trait heritability. Epistasis has recently emerged as one of the intrinsic mechanisms that could explain part of this missing heritability. We conducted epistasis analysis for genome-wide body mass index (BMI) associated SNPs in Alzheimer’s Disease Neuroimaging Initiative (ADNI) and followed up top significant interacting SNPs for replication in the UK Biobank imputed genotype dataset. We report two pairwise epistatic interactions, between rs2177596 (RHBDD1) and rs17759796 (MAPK1), rs1121980 (FTO) and rs6567160 (MC4R), obtained from a consensus of nine different epistatic approaches. Gene interaction maps and tissue expression profiles constructed for these interacting loci highlights co-expression, co-localisation, physical interaction, genetic interaction, and shared pathways emphasising the neuronal influence in obesity and implicating concerted expression of associated genes in liver, pancreas, and adipose tissues insinuating to metabolic abnormalities characterized by obesity. Detecting epistasis could thus be a promising approach to understand the effect of simultaneously interacting multiple genetic loci in disease aetiology, beyond single locus effects.

A multiscale cell-based model of tumor growth for chemotherapy assessment and tumor-targeted therapy through a 3D computational approach

OBJECTIVES

Computational modeling of biological systems is a powerful tool to clarify diverse processes contributing to cancer. The aim is to clarify the complex biochemical and mechanical interactions between cells, the relevance of intracellular signaling pathways in tumor progression and related events to the cancer treatments, which are largely ignored in previous studies.

MATERIALS AND METHODS

A three-dimensional multiscale cell-based model is developed, covering multiple time and spatial scales, including intracellular, cellular, and extracellular processes. The model generates a realistic representation of the processes involved from an implementation of the signaling transduction network.

RESULTS

Considering a benign tumor development, results are in good agreement with the experimental ones, which identify three different phases in tumor growth. Simulating tumor vascular growth, results predict a highly vascularized tumor morphology in a lobulated form, a consequence of cells' motile behavior. A novel systematic study of chemotherapy intervention, in combination with targeted therapy, is presented to address the capability of the model to evaluate typical clinical protocols. The model also performs a dose comparison study in order to optimize treatment efficacy and surveys the effect of chemotherapy initiation delays and different regimens.

CONCLUSIONS

Results not only provide detailed insights into tumor progression, but also support suggestions for clinical implementation. This is a major step toward the goal of predicting the effects of not only traditional chemotherapy but also tumor-targeted therapies.

Down-regulating Circular RNA Prkcsh suppresses the inflammatory response after spinal cord injury

Circular RNAs (circRNAs) are a class of conserved, endogenous non-coding RNAs that are involved in transcriptional and post-transcriptional gene regulation and are highly enriched in the nervous system. They participate in the survival and differentiation of multiple nerve cells, and may even promote the recovery of neurological function after stroke. However, their role in the inflammatory response after spinal cord injury remains unclear. In the present study, we established a mouse model of T9 spinal cord injury using the modified Allen’s impact method, and identified 16,013 circRNAs and 960 miRNAs that were differentially expressed after spinal cord injury. Of these, the expression levels of circPrkcsh were significantly different between injured and sham-treated mice. We then treated astrocytes with tumor necrosis factor-α in vitro to simulate the inflammatory response after spinal cord injury. Our results revealed an elevated expression of circPrkcsh with a concurrent decrease in miR-488 expression in injured cells. We also found that circPrkcsh regulated the expression of the inflammation-related gene Ccl2. Furthermore, in tumor necrosis factor-α-treated astrocytes, circPrkcsh knockdown decreased the expression of Ccl2 by upregulating miR-488 expression, and reduced the secretion of inflammatory cytokines in vitro. These findings suggest that differentially expressed circRNAs participate in the inflammatory response after spinal cord injury and act as the regulators of certain microRNAs. Furthermore, circPrkcsh may be used as an miR-488 sponge to regulate Ccl2 expression, which might provide a new potential therapy for SCI. The study was approved by the Animal Ethics Committee of Shandong University of China (approval No. KYLL-20170303) on March 3, 2017.

Computational Approaches for Designing Highly Specific and Efficient sgRNAs

The easily programmable CRISPR/Cas9 system has found applications in biomedical research as well as microbial and crop applications, due to its ability to create site-specific edits. This powerful and flexible system has also been modified to enable inducible gene regulation, epigenome modifications and high-throughput screens. Designing efficient and specific guides for the nuclease is a key step and also a major challenge in effective application. This chapter describes rules for sgRNA design and important features to consider while touching upon bioinformatics advances in predicting efficient guides. Computational tools that suggest improved guides, depending on application, or predict off-targets have also been mentioned and compared.

Combinatorial CRISPR/Cas9 Screening Reveals Epistatic Networks of Interacting Tumor Suppressor Genes and Therapeutic Targets in Human Breast Cancer

The majority of cancers are driven by multiple genetic alterations, but how these changes collaborate during tumorigenesis remains largely unknown. To gain mechanistic insights into tumor-promoting genetic interactions among tumor suppressor genes (TSG), we conducted combinatorial CRISPR screening coupled with single-cell transcriptomic profiling in human mammary epithelial cells. As expected, different driver gene alterations in mammary epithelial cells influenced the repertoire of tumor suppressor alterations capable of inducing tumor formation. More surprisingly, TSG interaction networks were comprised of numerous cliques-sets of three or four genes such that each TSG within the clique showed oncogenic cooperation with all other genes in the clique. Genetic interaction profiling indicated that the predominant cooperating TSGs shared overlapping functions rather than distinct or complementary functions. Single-cell transcriptomic profiling of CRISPR double knockouts revealed that cooperating TSGs that synergized in promoting tumorigenesis and growth factor independence showed transcriptional epistasis, whereas noncooperating TSGs did not. These epistatic transcriptional changes, both buffering and synergistic, affected expression of oncogenic mediators and therapeutic targets, including CDK4, SRPK1, and DNMT1. Importantly, the epistatic expression alterations caused by dual inactivation of TSGs in this system, such as PTEN and TP53, were also observed in patient tumors, establishing the relevance of these findings to human breast cancer. An estimated 50% of differentially expressed genes in breast cancer are controlled by epistatic interactions. Overall, our study indicates that transcriptional epistasis is a central aspect of multigenic breast cancer progression and outlines methodologies to uncover driver gene epistatic networks in other human cancers. SIGNIFICANCE: This study provides a roadmap for moving beyond discovery and development of therapeutic strategies based on single driver gene analysis to discovery based on interactions between multiple driver genes.See related commentary by Fong et al., p. 6078.

Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells

CRISPR knockout fitness screens in cancer cell lines reveal many genes whose loss of function causes cell death or loss of fitness or, more rarely, the opposite phenotype of faster proliferation. Here we demonstrate a systematic approach to identify these proliferation suppressors, which are highly enriched for tumor suppressor genes, and define a network of 145 such genes in 22 modules. One module contains several elements of the glycerolipid biosynthesis pathway and operates exclusively in a subset of acute myeloid leukemia cell lines. The proliferation suppressor activity of genes involved in the synthesis of saturated fatty acids, coupled with a more severe loss of fitness phenotype for genes in the desaturation pathway, suggests that these cells operate at the limit of their carrying capacity for saturated fatty acids, which we confirm biochemically. Overexpression of this module is associated with a survival advantage in juvenile leukemias, suggesting a clinically relevant subtype.

CRISPR-based knockout screens in cancer cells have suggested the existence of proliferation suppressor genes (PSG). Here, the authors develop an approach to systematically identify them, and reveal a PSG module involved in fatty acid synthesis and tumour suppression in acute myeloid leukemia cell lines.

Oxidative damage-induced hyperactive ribosome biogenesis participates in tumorigenesis of offspring by cross-interacting with the Wnt and TGF-β1 pathways in IVF embryos

In vitro fertilization (IVF) increases the risk of tumorigenesis in offspring. The increased oxidative damage during IVF may be involved in tumor formation. However, the molecular mechanisms underlying this phenomenon remain largely unclear. Using a well-established model of oxidatively damaged IVF mouse embryos, we applied the iTRAQ method to identify proteins differentially expressed between control and oxidatively damaged zygotes and explored the possible tumorigenic mechanisms, especially with regard to the effects of oxidative damage on ribosome biogenesis closely related to tumorigenesis. The iTRAQ results revealed that ribosomal proteins were upregulated by oxidative stress through the Nucleolin/β-Catenin/n-Myc pathway, which stimulated ribosomes to synthesize an abundance of repair proteins to correct the damaged DNA/chromosomes in IVF-derived embryos. However, the increased percentages of γH2AX-positive cells and apoptotic cells in the blastocyst suggested that DNA repair was insufficient, resulting in aberrant ribosome biogenesis. Overexpression of ribosomal proteins, particularly Rpl15, which gradually increased from the 1-cell to 8-cell stages, indicated persistent hyperactivation of ribosome biogenesis, which promoted tumorigenesis in offspring derived from oxidatively damaged IVF embryos by selectively enhancing the translation of β-Catenin and TGF-β1. The antioxidant epigallocatechin-3-gallate (EGCG) was added to the in vitro culture medium to protect embryos from oxidative damage, and the expression of ribosome-/tumor-related proteins returned to normal after EGCG treatment. This study suggests that regulation of ribosome biogenesis by EGCG may be a means of preventing tumor formation in human IVF-derived offspring, providing a scientific basis for optimizing in vitro culture conditions and improving human-assisted reproductive technology.

EGFRAP encodes a new negative regulator of the EGFR acting in both normal and oncogenic EGFR/Ras-driven tissue morphogenesis

Activation of Ras signaling occurs in ~30% of human cancers. However, activated Ras alone is insufficient to produce malignancy. Thus, it is imperative to identify those genes cooperating with activated Ras in driving tumoral growth. In this work, we have identified a novel EGFR inhibitor, which we have named EGFRAP, for EGFR adaptor protein. Elimination of EGFRAP potentiates activated Ras-induced overgrowth in the Drosophila wing imaginal disc. We show that EGFRAP interacts physically with the phosphorylated form of EGFR via its SH2 domain. EGFRAP is expressed at high levels in regions of maximal EGFR/Ras pathway activity, such as at the presumptive wing margin. In addition, EGFRAP expression is up-regulated in conditions of oncogenic EGFR/Ras activation. Normal and oncogenic EGFR/Ras-mediated upregulation of EGRAP levels depend on the Notch pathway. We also find that elimination of EGFRAP does not affect overall organogenesis or viability. However, simultaneous downregulation of EGFRAP and its ortholog PVRAP results in defects associated with increased EGFR function. Based on these results, we propose that EGFRAP is a new negative regulator of the EGFR/Ras pathway, which, while being required redundantly for normal morphogenesis, behaves as an important modulator of EGFR/Ras-driven tissue hyperplasia. We suggest that the ability of EGFRAP to functionally inhibit the EGFR pathway in oncogenic cells results from the activation of a feedback loop leading to increase EGFRAP expression. This could act as a surveillance mechanism to prevent excessive EGFR activity and uncontrolled cell growth.

Activation of Ras signalling occurs in ~30% of human cancers. However, activated Ras alone is insufficient to produce malignancy. Thus, the discovery of genes cooperating with Ras in cancer is imperative to understand tumoral growth driven by Ras activating mutations. A key output of over-activated EGFR/Ras signalling is the induction of a complex and dynamic set of transcriptional networks leading to changes in gene expression. As a result of these changes, the normal function of some genes can become adjusted in a tumorigenic context. In this work, using the Drosophila wing imaginal disc as model system, we have identified a new EGFR inhibitor, EGFRAP, which function is redundant for proper morphogenesis, yet becomes an important limiter of the overgrowth driven by oncogenic EGFR/Ras activity. We show that the specificity of EGFRAP in cells with high levels of EGFR activity arises from activation of a negative feedback loop resulting in increased EGFRAP levels. This could act to prevent excessive EGFR activity and uncontrolled cell growth. We believe the identification of other factors behaving like EGFRAP, will help in our fight against cancer, as it might lead to the identification of new therapeutic drugs affecting cancer but not normal cells, a top priority in cancer research.

Multi-omics integration of methyltransferase-like protein family reveals clinical outcomes and functional signatures in human cancer

Human methyltransferase-like (METTL) proteins transfer methyl groups to nucleic acids, proteins, lipids, and other small molecules, subsequently playing important roles in various cellular processes. In this study, we performed integrated genomic, transcriptomic, proteomic, and clinicopathological analyses of 34 METTLs in a large cohort of primary tumor and cell line data. We identified a subset of METTL genes, notably METTL1, METTL7B, and NTMT1, with high frequencies of genomic amplification and/or up-regulation at both the mRNA and protein levels in a spectrum of human cancers. Higher METTL1 expression was associated with high-grade tumors and poor disease prognosis. Loss-of-function analysis in tumor cell lines indicated the biological importance of METTL1, an m⁷G methyltransferase, in cancer cell growth and survival. Furthermore, functional annotation and pathway analysis of METTL1-associated proteins revealed that, in addition to the METTL1 cofactor WDR4, RNA regulators and DNA packaging complexes may be functionally interconnected with METTL1 in human cancer. Finally, we generated a crystal structure model of the METTL1–WDR4 heterodimeric complex that might aid in understanding the key functional residues. Our results provide new information for further functional study of some METTL alterations in human cancer and might lead to the development of small inhibitors that target cancer-promoting METTLs.

RNA atlas of human bacterial pathogens uncovers stress dynamics linked to infection

Bacterial processes necessary for adaption to stressful host environments are potential targets for new antimicrobials. Here, we report large-scale transcriptomic analyses of 32 human bacterial pathogens grown under 11 stress conditions mimicking human host environments. The potential relevance of the in vitro stress conditions and responses is supported by comparisons with available in vivo transcriptomes of clinically important pathogens. Calculation of a probability score enables comparative cross-microbial analyses of the stress responses, revealing common and unique regulatory responses to different stresses, as well as overlapping processes participating in different stress responses. We identify conserved and species-specific 'universal stress responders', that is, genes showing altered expression in multiple stress conditions. Non-coding RNAs are involved in a substantial proportion of the responses. The data are collected in a freely available, interactive online resource (PATHOgenex).

A method for benchmarking genetic screens reveals a predominant mitochondrial bias

We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome-wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene-pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria-associated signal within co-essentiality networks derived from these data and explore the basis of this signal. Our analysis and time-resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them.

A genome-wide atlas of co-essential modules assigns function to uncharacterized genes

A central question in the post-genomic era is how genes interact to form biological pathways. Measurements of gene dependency across hundreds of cell lines have been used to cluster genes into 'co-essential' pathways, but this approach has been limited by ubiquitous false positives. In the present study, we develop a statistical method that enables robust identification of gene co-essentiality and yields a genome-wide set of functional modules. This atlas recapitulates diverse pathways and protein complexes, and predicts the functions of 108 uncharacterized genes. Validating top predictions, we show that TMEM189 encodes plasmanylethanolamine desaturase, a key enzyme for plasmalogen synthesis. We also show that C15orf57 encodes a protein that binds the AP2 complex, localizes to clathrin-coated pits and enables efficient transferrin uptake. Finally, we provide an interactive webtool for the community to explore our results, which establish co-essentiality profiling as a powerful resource for biological pathway identification and discovery of new gene functions.

Genome-wide Screens Implicate Loss of Cullin Ring Ligase 3 in Persistent Proliferation and Genome Instability in TP53-Deficient Cells

TP53 deficiency is the most common alteration in cancer; however, this alone is typically insufficient to drive tumorigenesis. To identify genes promoting tumorigenesis in combination with TP53 deficiency, we perform genome-wide CRISPR-Cas9 knockout screens coupled with proliferation and transformation assays in isogenic cell lines. Loss of several known tumor suppressors enhances cellular proliferation and transformation. Loss of neddylation pathway genes promotes uncontrolled proliferation exclusively in TP53-deficient cells. Combined loss of CUL3 and TP53 activates an oncogenic transcriptional program governed by the nuclear factor κB (NF-κB), AP-1, and transforming growth factor β (TGF-β) pathways. This program maintains persistent cellular proliferation, induces partial epithelial to mesenchymal transition, and increases DNA damage, genomic instability, and chromosomal rearrangements. Our findings reveal CUL3 loss as a key event stimulating persistent proliferation in TP53-deficient cells. These findings may be clinically relevant, since TP53-CUL3-deficient cells are highly sensitive to ataxia telangiectasia mutated (ATM) inhibition, exposing a vulnerability that could be exploited for cancer treatment.

•

Mixed-effect models with MEMcrispR applied to CRISPR screen analyses

•

Knockout of neddylation genes increases persistent proliferation in TP53^−/− cells

•

TP53^−/−,CUL3^−/− cells exhibit persistent proliferation and partial EMT phenotype

•

TP53^−/−,CUL3^−/− cells show increased DNA damage and display sensitivity to ATM inhibition

CRISPR/Cas9 based genome editing for targeted transcriptional control in triple-negative breast cancer

Breast cancer (BC) is the most common type of cancer in women at the global level and the highest mortality rate has been observed with triple-negative breast cancer (TNBC). Accumulation of genetic lesions an aberrant gene expression and protein degradation are considered to underlie the onset of tumorigenesis and metastasis. Therefore, the challenge to identify the genes and molecules that could be potentially used as potent biomarkers for personalized medicine against TNBC with minimal or no associated side effects. Discovery of the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein 9 (Cas9) arrangement and an increasing repertoire of its new variants has provided a much-needed fillip towards editing TNBC genomes. In this review, we discuss the CRISPR/Cas9 genome editing, CRISPR Technology for diagnosis of (Triple-negative breast cancer) TNBC, Drug Resistance, and potential applications of CRISPR/Cas9 and its variants in deciphering or engineering intricate molecular and epigenetic mechanisms associated with TNBC. Furthermore, we have also explored the TNBC and CRISPR/Cas9 genome editing potential for repairing, genetic modifications in TNBC.

FIREWORKS: a bottom-up approach to integrative coessentiality network analysis

Genetic coessentiality analysis, a computational approach which identifies genes sharing a common effect on cell fitness across large-scale screening datasets, has emerged as a powerful tool to identify functional relationships between human genes. However, widespread implementation of coessentiality to study individual genes and pathways is limited by systematic biases in existing coessentiality approaches and accessibility barriers for investigators without computational expertise. We created FIREWORKS, a method and interactive tool for the construction and statistical analysis of coessentiality networks centered around gene(s) provided by the user. FIREWORKS incorporates a novel bias reduction approach to reduce false discoveries, enables restriction of coessentiality analyses to custom subsets of cell lines, and integrates multiomic and drug-gene interaction datasets to investigate and target contextual gene essentiality. We demonstrate the broad utility of FIREWORKS through case vignettes investigating gene function and specialization, indirect therapeutic targeting of "undruggable" proteins, and context-specific rewiring of genetic networks.

Multiple mutations within individual oncogenes

Recent studies of the cancer genome have identified numerous patients harboring multiple mutations (MM) within individual oncogenes. These MM (de novo MM) in cis synergistically activate the mutated oncogene and promote tumorigenesis, indicating a positive epistatic interaction between mutations. The relatively frequent de novo MM suggest that intramolecular positive epistasis is widespread in oncogenes. Studies also suggest that negative and higher-order epistasis affects de novo MM. Comparison of de novo MM and MM associated with drug-resistant secondary mutations (secondary MM) revealed several similarities with respect to allelic configuration, mutational selection and functionality of individual mutations. Conversely, they have several differences, most notably the difference in drug sensitivities. Secondary MM usually confer resistance to molecularly targeted therapies, whereas several de novo MM are associated with increased sensitivity, implying that both can be useful as therapeutic biomarkers. Unlike secondary MM in which specific secondary resistant mutations are selected, minor (infrequent) functionally weak mutations are convergently selected in de novo MM, which may provide an explanation as to why such mutations accumulate in cancer. The third type of MM is MM from different subclones. This type of MM is associated with parallel evolution, which may contribute to relapse and treatment failure. Collectively, MM within individual oncogenes are diverse, but all types of MM are associated with cancer evolution and therapeutic response. Further evaluation of oncogenic MM is warranted to gain a deeper understanding of cancer genetics and evolution.

Complex Networks in Health and Disease

From protein interactions to signal transduction, from metabolism to the nervous system: Virtually all processes in health and disease rely on the careful orchestration of a large number of diverse individual components ranging from molecules to cells and entire organs. Networks provide a powerful framework for describing and understanding these complex systems in a wholistic fashion. They offer a unique combination of a highly intuitive, qualitative description, and a plethora of analytical, quantitative tools. Here we provide a brief introduction to the emerging field of network medicine. After an overview of the core concepts for connecting network characteristics to biological functions, we review commonly used networks, ranging from the molecular interaction networks that form the basis of all biological processes in the cell to the global transportation networks that govern the spread of global epidemics. Lastly, we highlight current conceptual and practical challenges.

A self-consistent probabilistic formulation for inference of interactions

Large molecular interaction networks are nowadays assembled in biomedical researches along with important technological advances. Diverse interaction measures, for which input solely consisting of the incidence of causal-factors, with the corresponding outcome of an inquired effect, are formulated without an obvious mathematical unity. Consequently, conceptual and practical ambivalences arise. We identify here a probabilistic requirement consistent with that input, and find, by the rules of probability theory, that it leads to a model multiplicative in the complement of the effect. Important practical properties are revealed along these theoretical derivations, that has not been noticed before.

Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma

Many gene products exhibit great structural heterogeneity because of an array of modifications. These modifications are not directly encoded in the genomic template but often affect the functionality of proteins. Protein glycosylation plays a vital role in proper protein functions. However, the analysis of glycoproteins has been challenging compared with other protein modifications, such as phosphorylation. Here, we perform an integrated proteomic and glycoproteomic analysis of 83 prospectively collected high-grade serous ovarian carcinoma (HGSC) and 23 non-tumor tissues. Integration of the expression data from global proteomics and glycoproteomics reveals tumor-specific glycosylation, uncovers different glycosylation associated with three tumor clusters, and identifies glycosylation enzymes that were correlated with the altered glycosylation. In addition to providing a valuable resource, these results provide insights into the potential roles of glycosylation in the pathogenesis of HGSC, with the possibility of distinguishing pathological outcomes of ovarian tumors from non-tumors, as well as classifying tumor clusters.

CEN-tools: an integrative platform to identify the contexts of essential genes

An emerging theme from large-scale genetic screens that identify genes essential for cell fitness is that essentiality of a given gene is highly context-specific. Identification of such contexts could be the key to defining gene function and also to develop novel therapeutic interventions. Here, we present Context-specific Essentiality Network-tools (CEN-tools), a website and python package, in which users can interrogate the essentiality of a gene from large-scale genome-scale CRISPR screens in a number of biological contexts including tissue of origin, mutation profiles, expression levels and drug responses. We show that CEN-tools is suitable for the systematic identification of genetic dependencies and for more targeted queries. The associations between genes and a given context are represented as dependency networks (CENs), and we demonstrate the utility of these networks in elucidating novel gene functions. In addition, we integrate the dependency networks with existing protein-protein interaction networks to reveal context-dependent essential cellular pathways in cancer cells. Together, we demonstrate the applicability of CEN-tools in aiding the current efforts to define the human cellular dependency map.

Cancer-Associated Substitutions in RNA Recognition Motifs of PUF60 and U2AF65 Reveal Residues Required for Correct Folding and 3' Splice-Site Selection

U2AF65 (U2AF2) and PUF60 (PUF60) are splicing factors important for recruitment of the U2 small nuclear ribonucleoprotein to lariat branch points and selection of 3′ splice sites (3′ss). Both proteins preferentially bind uridine-rich sequences upstream of 3′ss via their RNA recognition motifs (RRMs). Here, we examined 36 RRM substitutions reported in cancer patients to identify variants that alter 3′ss selection, RNA binding and protein properties. Employing PUF60- and U2AF65-dependent 3′ss previously identified by RNA-seq of depleted cells, we found that 43% (10/23) and 15% (2/13) of independent RRM mutations in U2AF65 and PUF60, respectively, conferred splicing defects. At least three RRM mutations increased skipping of internal U2AF2 (~9%, 2/23) or PUF60 (~8%, 1/13) exons, indicating that cancer-associated RRM mutations can have both cis- and trans-acting effects on splicing. We also report residues required for correct folding/stability of each protein and map functional RRM substitutions on to existing high-resolution structures of U2AF65 and PUF60. These results identify new RRM residues critical for 3′ss selection and provide relatively simple tools to detect clonal RRM mutations that enhance the mRNA isoform diversity.

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.

Identifying Epistasis in Cancer Genomes: A Delicate Affair

Recent studies of the tumor genome seek to identify cancer pathways as groups of genes in which mutations are epistatic with one another or, specifically, "mutually exclusive." Here, we show that most mutations are mutually exclusive not due to pathway structure but to interactions with disease subtype and tumor mutation load. In particular, many cancer driver genes are mutated preferentially in tumors with few mutations overall, causing mutations in these cancer genes to appear mutually exclusive with numerous others. Researchers should view current epistasis maps with caution until we better understand the multiple cause-and-effect relationships among factors such as tumor subtype, positive selection for mutations, and gross tumor characteristics including mutational signatures and load.

KCML: a machine-learning framework for inference of multi-scale gene functions from genetic perturbation screens

Characterising context-dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large-scale genetic perturbation screens is based on ad hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge- and Context-driven Machine Learning (KCML), a framework that systematically predicts multiple context-specific functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As a proof of concept, we test KCML on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, KCML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and TGFβ and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcomes. These results highlight KCML as a systematic framework for discovering novel scale-crossing and context-dependent gene functions. KCML is highly generalisable and applicable to various large-scale genetic perturbation screens.

Genetic Interaction-Based Biomarkers Identification for Drug Resistance and Sensitivity in Cancer Cells

Cancer cells generally harbor hundreds of alterations in the cancer genomes and act as crucial factors in the development and progression of cancer. Gene alterations in the cancer genome form genetic interactions, which affect the response of patients to drugs. We developed an algorithm that mines copy number alteration and whole-exome mutation profiles from The Cancer Genome Atlas (TCGA), as well as functional screen data generated to identify potential genetic interactions for specific cancer types. As a result, 4,529 synthetic viability (SV) interactions and 10,637 synthetic lethality (SL) interactions were detected. The pharmacogenomic datasets revealed that SV interactions induced drug resistance in cancer cells and that SL interactions mediated drug sensitivity in cancer cells. Deletions of HDAC1 and DVL1, both of which participate in the Notch signaling pathway, had an SV effect in cancer cells, and deletion of DVL1 induced resistance to HDAC1 inhibitors in cancer cells. In addition, patients with low expression of both HDAC1 and DVL1 had poor prognosis. Finally, by integrating current reported genetic interactions from other studies, the Cancer Genetic Interaction database (CGIdb) (http://www.medsysbio.org/CGIdb) was constructed, providing a convenient retrieval for genetic interactions in cancer.

CRISPR/Cas9 as a tool to dissect cancer mutations

The CRISPR/Cas9 system is transforming many biomedical disciplines, including cancer research. Through its flexible programmability and efficiency to induce DNA double strand breaks it has become straightforward to introduce cancer mutations into cells in vitro and/or in vivo. However, not all mutations contribute equally to tumorigenesis and distinguishing essential mutations for tumor growth and survival from biologically inert mutations is cumbersome. Here we present a method to screen for the functional relevance of mutations in high throughput in established cancer cell lines. We employ the CRISPR/Cas9 system to probe cancer vulnerabilities in a colorectal carcinoma cell line in an attempt to identify novel cancer driver mutations. We designed 100 high quality sgRNAs that are able to specifically cleave mutations present in the colorectal carcinoma cell line RKO. An all-in-one lentiviral library harboring these sgRNAs was then generated and used in a pooled screen to probe possible growth dependencies on these mutations. Genomic DNA at different time points were collected, the sgRNA cassettes were PCR amplified, purified and sgRNA counts were quantified by means of deep sequencing. The analysis revealed two sgRNAs targeting the same mutation (UTP14A: S99delS) to be depleted over time in RKO cells. Validation and characterization confirmed that the inactivation of this mutation impairs cell growth, nominating UTP14A: S99delS as a putative driver mutation in RKO cells. Overall, our approach demonstrates that the CRISPR/Cas9 system is a powerful tool to functionally dissect cancer mutations at large-scale.

Mapping the protein-protein and genetic interactions of cancer to guide precision medicine

Massive efforts to sequence cancer genomes have compiled an impressive catalogue of cancer mutations, revealing the recurrent exploitation of a handful of 'hallmark cancer pathways'. However, unraveling how sets of mutated proteins in these and other pathways hijack pro-proliferative signaling networks and dictate therapeutic responsiveness remains challenging. Here, we show that cancer driver protein-protein interactions are enriched for additional cancer drivers, highlighting the power of physical interaction maps to explain known, as well as uncover new, disease-promoting pathway interrelationships. We hypothesize that by systematically mapping the protein-protein and genetic interactions in cancer-thereby creating Cancer Cell Maps-we will create resources against which to contextualize a patient's mutations into perturbed pathways/complexes and thereby specify a matching targeted therapeutic cocktail.

Capturing Single-Cell Phenotypic Variation via Unsupervised Representation Learning

We propose a novel variational autoencoder (VAE) framework for learning representations of cell images for the domain of image-based profiling, important for new therapeutic discovery. Previously, generative adversarial network-based (GAN) approaches were proposed to enable biologists to visualize structural variations in cells that drive differences in populations. However, while the images were realistic, they did not provide direct reconstructions from representations, and their performance in downstream analysis was poor. We address these limitations in our approach by adding an adversarial-driven similarity constraint applied to the standard VAE framework, and a progressive training procedure that allows higher quality reconstructions than standard VAE's. The proposed models improve classification accuracy by 22% (to 90%) compared to the best reported GAN model, making it competitive with other models that have higher quality representations, but lack the ability to synthesize images. This provides researchers a new tool to match cellular phenotypes effectively, and also to gain better insight into cellular structure variations that are driving differences between populations of cells.

Large-scale datasets uncovering cell signalling networks in cancer: context matters

Cell signaling pathways control the responses of cells to external perturbations. Depending on the cell's internal state, genetic background and environmental context, signaling pathways rewire to elicit the appropriate response. Such rewiring also can lead to cancer development and progression or cause resistance to therapies. While there exist static maps of annotated pathways, they do not capture these rewired networks. As large-scale datasets across multiple contexts and patients are becoming available the doors to infer and study context-specific signaling network have also opened. In this review, we will highlight the most recent approaches to study context-specific signaling networks using large-scale omics and genetic perturbation datasets, with a focus on studies of cancer and cancer-related pathways.

The Causes and Consequences of Genetic Interactions (Epistasis)

The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dependency is known as epistasis. In recent years, there has been a concerted effort to quantify the extent of pairwise and higher-order genetic interactions between mutations through deep mutagenesis of proteins and RNAs. This research has revealed two major components of epistasis: nonspecific genetic interactions caused by nonlinearities in genotype-to-phenotype maps, and specific interactions between particular mutations. Here, we provide an overview of our current understanding of the mechanisms causing epistasis at the molecular level, the consequences of genetic interactions for evolution and genetic prediction, and the applications of epistasis for understanding biology and determining macromolecular structures.

Context-dependent genetic interactions in cancer

Genetic co-dependencies have been found in many contexts, from processes during the development of organisms to many diseases in man, including cancer. Genetic interactions - and in particular synthetic lethal phenotypes - have provided fundamental insights into the genetic architecture of cells and identified potential new opportunities for therapeutic interventions. However, recent studies also demonstrated that genetic interactions are highly context dependent and synthetic lethal interactions in one tumor context might not be translatable to others. Therefore, to better define and understand contexts will be a key challenge for future studies to fully exploit genetic interaction networks for target identification and cancer therapy. In this review, we summarize recent developments in mapping context-specific genetic interaction networks with a particular focus on conceptual and experimental advances in the past years. We then discuss genetic and environmental contexts that influence genetic interaction networks. Finally, we outline challenges of putting genetic interaction networks into context and give an outlook on future directions.

Global Genetic Networks and the Genotype-to-Phenotype Relationship

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

The ATPase module of mammalian SWI/SNF family complexes mediates subcomplex identity and catalytic activity-independent genomic targeting

Perturbations to mammalian SWI/SNF (mSWI/SNF) chromatin remodeling complexes have been widely implicated as driving events in cancer¹. One such perturbation is the dual loss of the SMARCA4 and SMARCA2 ATPase subunits in small cell carcinoma of the ovary, hypercalcemic type (SCCOHT)^2–5, SMARCA4-deficient thoracic sarcomas⁶ and dedifferentiated endometrial carcinomas⁷. However, the consequences of dual ATPase subunit loss on mSWI/SNF complex subunit composition, chromatin targeting, DNA accessibility and gene expression remain unknown. Here we identify an ATPase module of subunits that is required for functional specification of BAF and PBAF subcomplexes. Using SMARCA4/2 ATPase mutant variants, we define the catalytic activity -dependent and -independent contributions of the ATPase module to the targeting of BAF and PBAF complexes on chromatin genome-wide. Finally, by linking distinct mSWI/SNF complex target sites to tumor-suppressive gene expression programs, we clarify the transcriptional consequences of SMARCA4/2 dual loss in SCCOHT.

Modular epistasis and the compensatory evolution of gene deletion mutants

Screens for epistatic interactions have long been used to characterize functional relationships corresponding to protein complexes, metabolic pathways, and other functional modules. Although epistasis between adaptive mutations is also common in laboratory evolution experiments, the functional basis for these interactions is less well characterized. Here, we quantify the extent to which gene function (as determined by a genome-wide screen for epistasis among deletion mutants) influences the rate and genetic basis of compensatory adaptation in a set of 37 gene deletion mutants nested within 16 functional modules. We find that functional module has predictive power: mutants with deletions in the same module tend to adapt more similarly, on average, than those with deletions in different modules. At the same time, initial fitness also plays a role: independent of the specific functional modules involved, adaptive mutations tend to be systematically more beneficial in less-fit genetic backgrounds, consistent with a general pattern of diminishing returns epistasis. We measured epistatic interactions between initial gene deletion mutations and the mutations that accumulate during compensatory adaptation and found a general trend towards positive epistasis (i.e. mutations tend to be more beneficial in the background in which they arose). In two functional modules, epistatic interactions between the initial gene deletions and the mutations in their descendant lines caused evolutionary entrenchment, indicating an intimate functional relationship. Our results suggest that genotypes with similar epistatic interactions with gene deletion mutations will also have similar epistatic interactions with adaptive mutations, meaning that genome scale maps of epistasis between gene deletion mutations can be predictive of evolutionary dynamics.

The effects of mutations often depend on the presence or absence of other mutations. This phenomenon, known as epistasis, has been used extensively to infer functional associations between genes. For example, genes that participate in the same functional module will often show a characteristic pattern of positive epistasis where the knockout of one gene will mask the deleterious effects of knockouts in the other genes. In the context of adaptation, epistasis can cause the outcomes of evolution to depend strongly on the initial genotype. Although studies have found that epistasis is common in laboratory populations, we do not know the extent to which the patterns of epistasis that reveal functional associations overlap with the patterns of epistasis that are important in evolution. Here, by comparing evolution in strains with gene deletions in different functional modules, we quantify the effect of functional epistasis on evolutionary outcomes. We find that mutants with deletions in the same module have more similar evolutionary outcomes, on average, than mutants with deletions in different modules. This suggests that screens for epistasis between gene deletion mutations will not only reveal functional interactions between those genes but may also predict evolutionary dynamics.

Genetic interaction networks in cancer cells

The genotype-to-phenotype relationship in health and disease is complex and influenced by both an individual's environment and their unique genome. Personal genetic variants can modulate gene function to generate a phenotype either through a single gene effect or through genetic interactions involving two or more genes. The relevance of genetic interactions to disease phenotypes has been particularly clear in cancer research, where an extreme genetic interaction, synthetic lethality, has been exploited as a therapeutic strategy. The obvious benefits of unmasking genetic background-specific vulnerabilities, coupled with the power of systematic genome editing, have fueled efforts to translate genetic interaction mapping from model organisms to human cells. Here, we review recent developments in genetic interaction mapping, with a focus on CRISPR-based genome editing technologies and cancer.

Time-resolved mapping of genetic interactions to model rewiring of signaling pathways

Context-dependent changes in genetic interactions are an important feature of cellular pathways and their varying responses under different environmental conditions. However, methodological frameworks to investigate the plasticity of genetic interaction networks over time or in response to external stresses are largely lacking. To analyze the plasticity of genetic interactions, we performed a combinatorial RNAi screen in Drosophila cells at multiple time points and after pharmacological inhibition of Ras signaling activity. Using an image-based morphology assay to capture a broad range of phenotypes, we assessed the effect of 12768 pairwise RNAi perturbations in six different conditions. We found that genetic interactions form in different trajectories and developed an algorithm, termed MODIFI, to analyze how genetic interactions rewire over time. Using this framework, we identified more statistically significant interactions compared to end-point assays and further observed several examples of context-dependent crosstalk between signaling pathways such as an interaction between Ras and Rel which is dependent on MEK activity.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

High-resolution mapping of cancer cell networks using co-functional interactions

Powerful new technologies for perturbing genetic elements have recently expanded the study of genetic interactions in model systems ranging from yeast to human cell lines. However, technical artifacts can confound signal across genetic screens and limit the immense potential of parallel screening approaches. To address this problem, we devised a novel PCA-based method for correcting genome-wide screening data, bolstering the sensitivity and specificity of detection for genetic interactions. Applying this strategy to a set of 436 whole genome CRISPR screens, we report more than 1.5 million pairs of correlated "co-functional" genes that provide finer-scale information about cell compartments, biological pathways, and protein complexes than traditional gene sets. Lastly, we employed a gene community detection approach to implicate core genes for cancer growth and compress signal from functionally related genes in the same community into a single score. This work establishes new algorithms for probing cancer cell networks and motivates the acquisition of further CRISPR screen data across diverse genotypes and cell types to further resolve complex cellular processes.

CRISPhieRmix: a hierarchical mixture model for CRISPR pooled screens

Pooled CRISPR screens allow researchers to interrogate genetic causes of complex phenotypes at the genome-wide scale and promise higher specificity and sensitivity compared to competing technologies. Unfortunately, two problems exist, particularly for CRISPRi/a screens: variability in guide efficiency and large rare off-target effects. We present a method, CRISPhieRmix, that resolves these issues by using a hierarchical mixture model with a broad-tailed null distribution. We show that CRISPhieRmix allows for more accurate and powerful inferences in large-scale pooled CRISPRi/a screens. We discuss key issues in the analysis and design of screens, particularly the number of guides needed for faithful full discovery.

Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls

Synthetic lethal interactions, in which the simultaneous loss of function of two genes produces a lethal phenotype, are being explored as a means to therapeutically exploit cancer-specific vulnerabilities and expand the scope of precision oncology. Currently, three Food and Drug Administration-approved drugs work by targeting the synthetic lethal interaction between BRCA1/2 and PARP. This review examines additional efforts to discover networks of synthetic lethal interactions and discusses both challenges and opportunities regarding the translation of new synthetic lethal interactions into the clinic.

RNAi/CRISPR Screens: from a Pool to a Valid Hit

High-throughput genetic screens interfering with gene expression are invaluable tools to identify gene function and phenotype-to-genotype interactions. Implementing such screens in the laboratory is challenging, and the choice between currently available technologies based on RNAi and CRISPR/Cas9 (CRISPR-associated protein 9) is not trivial. Identifying reliable candidate hits requires a streamlined experimental setup adjusted to the specific biological question. Here, we provide a critical assessment of the various RNAi/CRISPR approaches to pooled screens and discuss their advantages and pitfalls. We specify a set of best practices for key parameters enabling a reproducible screen and provide a detailed overview of analysis methods and repositories for identifying the best candidate gene hits.

CRISPR/Cas9 for cancer research and therapy

CRISPR/Cas9 has become a powerful method for making changes to the genome of many organisms. First discovered in bacteria as part of an adaptive immune system, CRISPR/Cas9 and modified versions have found a widespread use to engineer genomes and to activate or to repress the expression of genes. As such, CRISPR/Cas9 promises to accelerate cancer research by providing an efficient technology to dissect mechanisms of tumorigenesis, identify targets for drug development, and possibly arm cells for cell-based therapies. Here, we review current applications of the CRISPR/Cas9 technology for cancer research and therapy. We describe novel Cas9 variants and how they are used in functional genomics to discover novel cancer-specific vulnerabilities. Furthermore, we highlight the impact of CRISPR/Cas9 in generating organoid and mouse models of cancer. Finally, we provide an overview of the first clinical trials that apply CRISPR/Cas9 as a therapeutic approach against cancer.

Impact of Genetic Variation on CRISPR-Cas Targeting

The CRISPR-CRISPR-associated (Cas) nuclease system offers the ability to perform unprecedented functional genetic experiments and the promise of therapy for a variety of genetic disorders. The understanding of factors contributing to CRISPR targeting efficacy and specificity continues to evolve. As CRISPR systems rely on Watson-Crick base pairing to ultimately mediate genomic cleavage, it logically follows that genetic variation would affect CRISPR targeting by increasing or decreasing sequence homology at on-target and off-target sites or by altering protospacer adjacent motifs. Numerous efforts have been made to document the extent of human genetic variation, which can serve as resources to understand and mitigate the effect of genetic variation on CRISPR targeting. Here, we review efforts to elucidate the effect of human genetic variation on CRISPR targeting at on-target and off-target sites with considerations for laboratory experiments and clinical translation of CRISPR-based therapies.