A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities

Pathway-driven analysis of synthetic lethal interactions in cancer using perturbation screens

Synthetic lethality offers a promising approach for developing effective therapeutic interventions in cancer when direct targeting of driver genes is impractical. In this study, we comprehensively analyzed large-scale CRISPR, shRNA, and PRISM screens to identify potential synthetic lethal (SL) interactions in pan-cancer and 12 individual cancer types, using a new computational framework that leverages the biological function and signaling pathway information of key driver genes to mitigate the confounding effects of background genetic alterations in different cancer cell lines. This approach has successfully identified several putative SL interactions, including KRAS-MAP3K2 and APC-TCF7L2 in pan cancer, and CCND1-METTL1, TP53-FRS3, SMO-MDM2, and CCNE1-MTOR in liver, blood, skin, and gastric cancers, respectively. In addition, we proposed several FDA-approved cancer-targeted drugs for various cancer types through PRISM drug screens, such as cabazitaxel for VHL-mutated kidney cancer and alectinib for lung cancer with NRAS or KRAS mutations. Leveraging pathway information can enhance the concordance of shRNA and CRISPR screens and provide clinically relevant findings such as the potential efficacy of dasatinib, an inhibitor of SRC, for colorectal cancer patients with mutations in the WNT signaling pathway. These analyses revealed that taking signaling pathway information into account results in the identification of more promising SL interactions.

MAGCN: A Multiple Attention Graph Convolution Networks for Predicting Synthetic Lethality

Synthetic lethality (SL) is a potential cancer therapeutic strategy and drug discovery. Computational approaches to identify synthetic lethality genes have become an effective complement to wet experiments which are time consuming and costly. Graph convolutional networks (GCN) has been utilized to such prediction task as be good at capturing the neighborhood dependency in a graph. However, it is still a lack of the mechanism of aggregating the complementary neighboring information from various heterogeneous graphs. Here, we propose the Multiple Attention Graph Convolution Networks for predicting synthetic lethality (MAGCN). First, we obtain the functional similarity features and topological structure features of genes from different data sources respectively, such as Gene Ontology data and Protein-Protein Interaction. Then, graph convolutional network is utilized to accumulate the knowledge from neighbor nodes according to synthetic lethal associations. Meanwhile, we propose a multiple graphs attention model and construct a multiple graphs attention network to learn the contribution factors of different graphs to generate embedded representation by aggregating these graphs. Finally, the generated feature matrix is decoded to predict potential synthetic lethal interaction. Experimental results show that MAGCN is superior to other baseline methods. Case study demonstrates the ability of MAGCN to predict human SL gene pairs.

Destabilized 3'UTR elements therapeutically degrade ERBB2 mRNA in drug-resistant ERBB2+ cancer models

Breast, lung, and colorectal cancer resistance to molecular targeted therapy is a major challenge that unfavorably impacts clinical outcomes leading to hundreds of thousands of deaths annually. In ERBB2+ cancers regardless of the tissue of origin, many ERBB2+ cancers are resistant to ERBB2-targeted therapy. We discovered that ERBB2+ cancer cells are enriched with poly U sequences on their 3'UTR which are mRNA-stabilizing sequences. We developed a novel technology, in which we engineered these ERBB2 mRNA-stabilizing sequences to unstable forms that successfully overwrote and outcompeted the endogenous ERBB2 mRNA-encoded message and degraded ERBB2 transcripts which led to the loss of the protein across multiple cancer cell types both in the wildtype and drug-resistance settings in vitro and in vivo, offering a unique safe novel modality to control ERBB2 mRNA and other pervasive oncogenic signals where current targeted therapies fail.

Analyzing the interactome of human CK2β in prostate carcinoma cells reveals HSP70-1 and Rho guanin nucleotide exchange factor 12 as novel interaction partners

CK2β is the non-catalytic modulating part of the S/T-protein kinase CK2. However, the overall function of CK2β is poorly understood. Here, we report on the identification of 38 new interaction partners of the human CK2β from lysates of DU145 prostate cancer cells using photo-crosslinking and mass spectrometry, whereby HSP70-1 was identified with high abundance. The KD value of its interaction with CK2β was determined as 0.57 μM by microscale thermophoresis, this being the first time, to our knowledge, that a KD value of CK2β with another protein than CK2α or CK2α' was quantified. Phosphorylation studies excluded HSP70-1 as a substrate or activity modulator of CK2, suggesting a CK2 activity independent interaction of HSP70-1 with CK2β. Co-immunoprecipitation experiments in three different cancer cell lines confirmed the interaction of HSP70-1 with CK2β in vivo. A second identified CK2β interaction partner was Rho guanin nucleotide exchange factor 12, indicating an involvement of CK2β in the Rho-GTPase signal pathway, described here for the first time to our knowledge. This points to a role of CK2β in the interaction network affecting the organization of the cytoskeleton.

Synthetic lethal gene pairs: Experimental approaches and predictive models

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.

Homoharringtonine demonstrates a cytotoxic effect against triple-negative breast cancer cell lines and acts synergistically with paclitaxel

The lack of targeted therapies for triple-negative breast cancer (TNBC) contributes to their high mortality rates and high risk of relapse compared to other subtypes of breast cancer. Most TNBCs (75%) have downregulated the expression of CREB3L1 (cAMP-responsive element binding protein 3 like 1), a transcription factor and metastasis suppressor that represses genes that promote cancer progression and metastasis. In this report, we screened an FDA-approved drug library and identified four drugs that were highly cytotoxic towards HCC1806 CREB3L1-deficient TNBC cells. These four drugs were: (1) palbociclib isethionate, a CDK4/6 inhibitor, (2) lanatocide C (also named isolanid), a Na+/K+-ATPase inhibitor, (3) cladribine, a nucleoside analog, and (4) homoharringtonine (also named omacetaxine mepesuccinate), a protein translation inhibitor. Homoharringtonine consistently showed the most cytotoxicity towards an additional six TNBC cell lines (BT549, HCC1395, HCC38, Hs578T, MDA-MB-157, MDA-MB-436), and several luminal A breast cancer cell lines (HCC1428, MCF7, T47D, ZR-75-1). All four drugs were then separately evaluated for possible synergy with the chemotherapy agents, doxorubicin (an anthracycline) and paclitaxel (a microtubule stabilizing agent). A strong synergy was observed using the combination of homoharringtonine and paclitaxel, with high cytotoxicity towards TNBC cells at lower concentrations than when each was used separately.

SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis

Synthetic lethality has been widely concerned because of its potential role in cancer treatment, which can be harnessed to selectively kill cancer cells via identifying inactive genes in a specific cancer type and further targeting the corresponding synthetic lethal partners. Herein, to obtain cancer-specific synthetic lethal interactions, we aimed to predict genetic interactions via a pan-cancer analysis from multiple molecular levels using random forest and then develop a user-friendly database. First, based on collected public gene pairs with synthetic lethal interactions, candidate gene pairs were analyzed via integrating multi-omics data, mainly including DNA mutation, copy number variation, methylation and mRNA expression data. Then, integrated features were used to predict cancer-specific synthetic lethal interactions using random forest. Finally, SLOAD (http://www.tmliang.cn/SLOAD) was constructed via integrating these findings, which was a user-friendly database for data searching, browsing, downloading and analyzing. These results can provide candidate cancer-specific synthetic lethal interactions, which will contribute to drug designing in cancer treatment that can promote therapy strategies based on the principle of synthetic lethality.

Database URL http://www.tmliang.cn/SLOAD/

Computational methods, databases and tools for synthetic lethality prediction

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

Identification of novel genes involved in apoptosis of HIV-infected macrophages using unbiased genome-wide screening

Background

Macrophages, besides resting latently infected CD4+ T cells, constitute the predominant stable, major non-T cell HIV reservoirs. Therefore, it is essential to eliminate both latently infected CD4+ T cells and tissue macrophages to completely eradicate HIV in patients. Until now, most of the research focus is directed towards eliminating latently infected CD4+ T cells. However, few approaches have been directed at killing of HIV-infected macrophages either in vitro or in vivo. HIV infection dysregulates the expression of many host genes essential for the survival of infected cells. We postulated that exploiting this alteration may yield novel targets for the selective killing of infected macrophages.

Methods

We applied a pooled shRNA-based genome-wide approach by employing a lentivirus-based library of shRNAs to screen novel gene targets whose inhibition should selectively induce apoptosis in HIV-infected macrophages. Primary human MDMs were infected with HIV-eGFP and HIV-HSA viruses. Infected MDMs were transfected with siRNAs specific for the promising genes followed by analysis of apoptosis by flow cytometry using labelled Annexin-V in HIV-infected, HIV-exposed but uninfected bystander MDMs and uninfected MDMs. The results were analyzed using student’s t-test from at least four independent experiments.

Results

We validated 28 top hits in two independent HIV infection models. This culminated in the identification of four target genes, Cox7a2, Znf484, Cstf2t, and Cdk2, whose loss-of-function induced apoptosis preferentially in HIV-infected macrophages. Silencing these single genes killed significantly higher number of HIV-HSA-infected MDMs compared to the HIV-HSA-exposed, uninfected bystander macrophages, indicating the specificity in the killing of HIV-infected macrophages. The mechanism governing Cox7a2-mediated apoptosis of HIV-infected macrophages revealed that targeting respiratory chain complex II and IV genes also selectively induced apoptosis of HIV-infected macrophages possibly through enhanced ROS production.

Conclusions

We have identified above-mentioned novel genes and specifically the respiratory chain complex II and IV genes whose silencing may cause selective elimination of HIV-infected macrophages and eventually the HIV-macrophage reservoirs. The results highlight the potential of the identified genes as targets for eliminating HIV-infected macrophages in physiological environment as part of an HIV cure strategy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06346-7.

Graph contextualized attention network for predicting synthetic lethality in human cancers

MOTIVATION

Synthetic Lethality (SL) plays an increasingly critical role in the targeted anticancer therapeutics. In addition, identifying SL interactions can create opportunities to selectively kill cancer cells without harming normal cells. Given the high cost of wet-lab experiments, in silico prediction of SL interactions as an alternative can be a rapid and cost-effective way to guide the experimental screening of candidate SL pairs. Several matrix factorization-based methods have recently been proposed for human SL prediction. However, they are limited in capturing the dependencies of neighbors. In addition, it is also highly challenging to make accurate predictions for new genes without any known SL partners.

RESULTS

In this work, we propose a novel graph contextualized attention network named GCATSL to learn gene representations for SL prediction. First, we leverage different data sources to construct multiple feature graphs for genes, which serve as the feature inputs for our GCATSL method. Second, for each feature graph, we design node-level attention mechanism to effectively capture the importance of local and global neighbors and learn local and global representations for the nodes, respectively. We further exploit multi-layer perceptron (MLP) to aggregate the original features with the local and global representations and then derive the feature-specific representations. Third, to derive the final representations, we design feature-level attention to integrate feature-specific representations by taking the importance of different feature graphs into account. Extensive experimental results on three datasets under different settings demonstrated that our GCATSL model outperforms 14 state-of-the-art methods consistently. In addition, case studies further validated the effectiveness of our proposed model in identifying novel SL pairs.

AVAILABILITY

Python codes and dataset are freely available on GitHub (https://github.com/longyahui/GCATSL) and Zenodo (https://zenodo.org/record/4522679) under the MIT license.

Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers

MOTIVATION

Synthetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to identify specific genes to target at cancer cells without disrupting normal cells. As high-throughput wet-lab settings are often costly and face various challenges, computational approaches have become a practical complement. In particular, predicting SLs can be formulated as a link prediction task on a graph of interacting genes. Although matrix factorization techniques have been widely adopted in link prediction, they focus on mapping genes to latent representations in isolation, without aggregating information from neighboring genes. Graph convolutional networks (GCN) can capture such neighborhood dependency in a graph. However, it is still challenging to apply GCN for SL prediction as SL interactions are extremely sparse, which is more likely to cause overfitting.

RESULTS

In this article, we propose a novel dual-dropout GCN (DDGCN) for learning more robust gene representations for SL prediction. We employ both coarse-grained node dropout and fine-grained edge dropout to address the issue that standard dropout in vanilla GCN is often inadequate in reducing overfitting on sparse graphs. In particular, coarse-grained node dropout can efficiently and systematically enforce dropout at the node (gene) level, while fine-grained edge dropout can further fine-tune the dropout at the interaction (edge) level. We further present a theoretical framework to justify our model architecture. Finally, we conduct extensive experiments on human SL datasets and the results demonstrate the superior performance of our model in comparison with state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION

DDGCN is implemented in Python 3.7, open-source and freely available at https://github.com/CXX1113/Dual-DropoutGCN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Machine Learning to Identify Gene Interactions from High-Throughput Mutant Crosses

Advances in molecular genetics through high-throughput gene mutagenesis and genetic crossing have enabled gene interaction mapping across whole genomes. Detecting gene interactions in even small microbial genomes relies on measuring growth phenotypes in thousands of crossed strains followed by statistical analysis to compare single and double mutants. The preferred computational approach is to use a multiplicative model that factors phenotype scores of single gene mutants to identify gene interactions in double mutants. Here we present how machine learning models that consider the characteristics of the phenotypic data improve on the classical multiplicative model. Importantly, machine learning improves the selection of cutoff values to identify gene interactions from phenotypic scores.

Approaches to Investigating the Protein Interactome of PTEN

The tumor suppressor phosphatase and tensin homologue (PTEN) is a redox-sensitive dual specificity phosphatase with an essential role in the negative regulation of the PI3K-AKT signaling pathway, affecting metabolic and cell survival processes. PTEN is commonly mutated in cancer, and dysregulation in the metabolism of PIP₃ is implicated in other diseases such as diabetes. PTEN interactors are responsible for some functional roles of PTEN beyond the negative regulation of the PI3K pathway and are thus of great importance in cell biology. Both high-data content proteomics-based approaches and low-data content PPI approaches have been used to investigate the interactome of PTEN and elucidate further functions of PTEN. While low-data content approaches rely on co-immunoprecipitation and Western blotting, and as such require previously generated hypotheses, high-data content approaches such as affinity pull-down proteomic assays or the yeast 2-hybrid system are hypothesis generating. This review provides an overview of the PTEN interactome, including redox effects, and critically appraises the methods and results of high-data content investigations into the global interactome of PTEN. The biological significance of findings from recent studies is discussed and illustrates the breadth of cellular functions of PTEN that can be discovered by these approaches.

The CINs of Polo-Like Kinase 1 in Cancer

Simple Summary

Many alterations specific to cancer cells have been investigated as targets for targeted therapies. Chromosomal instability is a characteristic of nearly all cancers that can limit response to targeted therapies by ensuring the tumor population is not genetically homogenous. Polo-like Kinase 1 (PLK1) is often up regulated in cancers and it regulates chromosomal instability extensively. PLK1 has been the subject of much pre-clinical and clinical studies, but thus far, PLK1 inhibitors have not shown significant improvement in cancer patients. We discuss the numerous roles and interactions of PLK1 in regulating chromosomal instability, and how these may provide an avenue for identifying targets for targeted therapies. As selective inhibitors of PLK1 showed limited clinical success, we also highlight how genetic interactions of PLK1 may be exploited to tackle these challenges.

Abstract

Polo-like kinase 1 (PLK1) is overexpressed near ubiquitously across all cancer types and dysregulation of this enzyme is closely tied to increased chromosomal instability and tumor heterogeneity. PLK1 is a mitotic kinase with a critical role in maintaining chromosomal integrity through its function in processes ranging from the mitotic checkpoint, centrosome biogenesis, bipolar spindle formation, chromosome segregation, DNA replication licensing, DNA damage repair, and cytokinesis. The relation between dysregulated PLK1 and chromosomal instability (CIN) makes it an attractive target for cancer therapy. However, clinical trials with PLK1 inhibitors as cancer drugs have generally displayed poor responses or adverse side-effects. This is in part because targeting CIN regulators, including PLK1, can elevate CIN to lethal levels in normal cells, affecting normal physiology. Nevertheless, aiming at related genetic interactions, such as synthetic dosage lethal (SDL) interactions of PLK1 instead of PLK1 itself, can help to avoid the detrimental side effects associated with increased levels of CIN. Since PLK1 overexpression contributes to tumor heterogeneity, targeting SDL interactions may also provide an effective strategy to suppressing this malignant phenotype in a personalized fashion.

Predicting synthetic lethal interactions using heterogeneous data sources

MOTIVATION

A synthetic lethal (SL) interaction is a relationship between two functional entities where the loss of either one of the entities is viable but the loss of both entities is lethal to the cell. Such pairs can be used as drug targets in targeted anticancer therapies, and so, many methods have been developed to identify potential candidate SL pairs. However, these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels; and hence are limited in their ability to learn from complex associations in heterogeneous data sources.

RESULTS

In this article, we develop techniques that can seamlessly integrate multiple heterogeneous data sources to predict SL interactions. Our approach obtains latent representations by collective matrix factorization-based techniques, which in turn are used for prediction through matrix completion. Our experiments, on a variety of biological datasets, illustrate the efficacy and versatility of our approach, that outperforms state-of-the-art methods for predicting SL interactions and can be used with heterogeneous data sources with minimal feature engineering.

AVAILABILITY AND IMPLEMENTATION

Software available at https://github.com/lianyh.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

G2G: A web-server for the prediction of human synthetic lethal interactions

Genetic interactions (GIs) are fundamental to our understanding of biological processes in the cell. While GIs have been systematically mapped in yeast, there is scarce information about them in humans. Recently, we have suggested a state-of-the-art hierarchical method that leverages gene ontology information for predicting GIs in yeast. Here, we adapt this method and apply it for the first time to predict GIs in human. We introduce a web service called G2G for this task that is available at http://bnet.cs.tau.ac.il/g2g/.

Link synthetic lethality to drug sensitivity of cancer cells

Synthetic lethal (SL) interactions occur when alterations in two genes lead to cell death but alteration in only one of them is not lethal. SL interactions provide a new strategy for molecular-targeted cancer therapy. Currently, there are few drugs targeting SL interactions that entered into clinical trials. Therefore, it is necessary to investigate the link between SL interactions and drug sensitivity of cancer cells systematically for drug development purpose. We identified SL interactions by integrating the high-throughput data from The Cancer Genome Atlas, small hairpin RNA data and genetic interactions of yeast. By integrating SL interactions from other studies, we tested whether the SL pairs that consist of drug target genes and the genes with genomic alterations are related with drug sensitivity of cancer cells. We found that only 6.26%∼34.61% of SL interactions showed the expected significant drug sensitivity using the pooled cancer cell line data from different tissues, but the proportion increased significantly to approximately 90% using the cancer cell line data for each specific tissue. From an independent pharmacogenomics data of 41 breast cancer cell lines, we found three SL interactions (ABL1-IFI16, ABL1-SLC50A1 and ABL1-SYT11) showed significantly better prognosis for the patients with both genes being altered than the patients with only one gene being altered, which partially supports the SL effect between the gene pairs. Our study not only provides a new way for unraveling the complex mechanisms of drug sensitivity but also suggests numerous potentially important drug targets for cancer therapy.

Overcoming cisplatin resistance by targeting the MTDH-PTEN interaction in ovarian cancer with sera derived from rats exposed to Guizhi Fuling wan extract

Background

The well-known traditional Chinese herbal formula Guizhi Fuling Wan (GFW) was recently reported to improve the curative effects of chemotherapy for ovarian cancer with few clinical side effects. The present study aimed to investigate the reversal mechanism of sera derived from rats exposed to Guizhi Fuling Wan extract (GFWE) in cisplatin-resistant human ovarian cancer SKOV3/DDP cells; the proteins examined included phosphatase and tensin homolog (PTEN) and metadherin (MTDH), and the possible protein interaction between PTEN and MTDH was explored.

Methods

GFWE was administered to healthy Wistar rats, and the sera were collected after five days. The PubMed and CNKI databases were searched for literature on the bioactive blood components in the sera. The systemsDock website was used to predict potential PTEN/MTDH interactions with the compounds. RT-qPCR, western blotting, and immunofluorescence analyses were used to analyze the mRNA and protein levels of MTDH and PTEN. Laser confocal microscopy and coimmunoprecipitation (co-IP) were used to analyze the colocalization and interaction between MTDH and PTEN.

Results

Sixteen bioactive compounds were identified in GFWE sera after searching the PubMed and CNKI databases. The systemsDock website predicted the potential PTEN/MTDH interactions with the compounds. RT-qPCR, western blotting, and immunofluorescence analyses showed decreased MTDH expression and increased PTEN expression in the sera. Laser confocal microscopy images and coimmunoprecipitation (co-IP) analyses demonstrated that a colocalization and interaction occurred between MTDH and PTEN, and the addition of the sera changed the interaction status.

Conclusions

GFWE restored sensitivity to cisplatin by inhibiting MTDH expression, inducing PTEN expression, and improving the interaction between MTDH and PTEN in SKOV3/DDP cells, and these proteins and their interaction may serve as potential targets for cancer treatment. The sera may represent a new source of anticancer compounds that could help to manage chemoresistance more efficiently and safely.

Genetic screens in isogenic mammalian cell lines without single cell cloning

Isogenic pairs of cell lines, which differ by a single genetic modification, are powerful tools for understanding gene function. Generating such pairs of mammalian cells, however, is labor-intensive, time-consuming, and, in some cell types, essentially impossible. Here, we present an approach to create isogenic pairs of cells that avoids single cell cloning, and screen these pairs with genome-wide CRISPR-Cas9 libraries to generate genetic interaction maps. We query the anti-apoptotic genes BCL2L1 and MCL1, and the DNA damage repair gene PARP1, identifying both expected and uncharacterized buffering and synthetic lethal interactions. Additionally, we compare acute CRISPR-based knockout, single cell clones, and small-molecule inhibition. We observe that, while the approaches provide largely overlapping information, differences emerge, highlighting an important consideration when employing genetic screens to identify and characterize potential drug targets. We anticipate that this methodology will be broadly useful to comprehensively study gene function across many contexts.

A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin

BACKGROUND

The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance.

METHODS

Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy.

RESULTS

Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context. We analyzed human homologs of yeast genes exhibiting gene-doxorubicin interaction in cancer pharmacogenomics data to predict causality for differential gene expression associated with doxorubicin cytotoxicity in cancer cells. This analysis suggested conserved cellular responses to doxorubicin due to influences of homologous recombination, sphingolipid homeostasis, telomere tethering at nuclear periphery, actin cortical patch localization, and other gene functions.

CONCLUSIONS

Warburg status alters the genetic network required for yeast to buffer doxorubicin toxicity. Integration of yeast phenomic and cancer pharmacogenomics data suggests evolutionary conservation of gene-drug interaction networks and provides a new experimental approach to model their influence on chemotherapy response. Thus, yeast phenomic models could aid the development of precision oncology algorithms to predict efficacious cytotoxic drugs for cancer, based on genetic and metabolic profiles of individual tumors.

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.

Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer

Background

Synthetic lethal interactions (SLIs) that occur between gene pairs are exploited for cancer therapeutics. Studies in the model eukaryote yeast have identified ~ 550,000 negative genetic interactions that have been extensively studied, leading to characterization of novel pathways and gene functions. This resource can be used to predict SLIs that can be relevant to cancer therapeutics.

Methods

We used patient data to identify genes that are down-regulated in breast cancer. InParanoid orthology mapping was performed to identify yeast orthologs of the down-regulated genes and predict their corresponding SLIs in humans. The predicted network graphs were drawn with Cytoscape. CancerRXgene database was used to predict drug response.

Results

Harnessing the vast available knowledge of yeast genetics, we generated a Humanized Yeast Genetic Interaction Network (HYGIN) for 1009 human genes with 10,419 interactions. Through the addition of patient-data from The Cancer Genome Atlas (TCGA), we generated a breast cancer specific subnetwork. Specifically, by comparing 1009 genes in HYGIN to genes that were down-regulated in breast cancer, we identified 15 breast cancer genes with 130 potential SLIs. Interestingly, 32 of the 130 predicted SLIs occurred with FBXW7, a well-known tumor suppressor that functions as a substrate-recognition protein within a SKP/CUL1/F-Box ubiquitin ligase complex for proteasome degradation. Efforts to validate these SLIs using chemical genetic data predicted that patients with loss of FBXW7 may respond to treatment with drugs like Selumitinib or Cabozantinib.

Conclusions

This study provides a patient-data driven interpretation of yeast SLI data. HYGIN represents a novel strategy to uncover therapeutically relevant cancer drug targets and the yeast SLI data offers a major opportunity to mine these interactions.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0554-z) contains supplementary material, which is available to authorized users.

MOB (Mps one Binder) Proteins in the Hippo Pathway and Cancer

The family of MOBs (monopolar spindle-one-binder proteins) is highly conserved in the eukaryotic kingdom. MOBs represent globular scaffold proteins without any known enzymatic activities. They can act as signal transducers in essential intracellular pathways. MOBs have diverse cancer-associated cellular functions through regulatory interactions with members of the NDR/LATS kinase family. By forming additional complexes with serine/threonine protein kinases of the germinal centre kinase families, other enzymes and scaffolding factors, MOBs appear to be linked to an even broader disease spectrum. Here, we review our current understanding of this emerging protein family, with emphases on post-translational modifications, protein-protein interactions, and cellular processes that are possibly linked to cancer and other diseases. In particular, we summarise the roles of MOBs as core components of the Hippo tissue growth and regeneration pathway.

Leveraging genetic interactions for adverse drug-drug interaction prediction

In light of increased co-prescription of multiple drugs, the ability to discern and predict drug-drug interactions (DDI) has become crucial to guarantee the safety of patients undergoing treatment with multiple drugs. However, information on DDI profiles is incomplete and the experimental determination of DDIs is labor-intensive and time-consuming. Although previous studies have explored various feature spaces for in silico screening of interacting drug pairs, their use of conventional cross-validation prevents them from achieving generalizable performance on drug pairs where neither drug is seen during training. Here we demonstrate for the first time targets of adversely interacting drug pairs are significantly more likely to have synergistic genetic interactions than non-interacting drug pairs. Leveraging genetic interaction features and a novel training scheme, we construct a gradient boosting-based classifier that achieves robust DDI prediction even for drugs whose interaction profiles are completely unseen during training. We demonstrate that in addition to classification power—including the prediction of 432 novel DDIs—our genetic interaction approach offers interpretability by providing plausible mechanistic insights into the mode of action of DDIs.

Adverse drug-drug interactions are adverse side effects caused by taking two or more drugs together. As co-prescription of multiple drugs becomes an increasingly prevalent practice, affecting 42.2% of Americans over 65 years old, adverse drug-drug interactions have become a serious safety concern, accounting for over 74,000 emergency room visits and 195,000 hospitalizations each year in the United States alone. Since experimental determination of adverse drug-drug interactions is labor-intensive and time-consuming, various machine learning-based computational approaches have been developed for predicting drug-drug interactions. Considering the fact that drugs effect through binding and modulating the function of their targets, we have explored whether drug-drug interactions can be predicted from the genetic interaction between the gene targets of two drugs, which characterizes the unexpected fitness effect when two genes are simultaneously knocked out. Furthermore, we have built a fast and robust classifier that achieves accurate prediction of adverse drug-drug interactions by incorporating genetic interaction and several other types of widely used features. Our analyses suggest that genetic interaction is an important feature for our prediction model, and that it provides mechanistic insight into the mode of action of drugs leading to drug-drug interactions.

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.

Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments

The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.

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.

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.

Molecular characterization of an MLL1 fusion and its role in chromosomal instability

Chromosomal rearrangements involving the mixed‐lineage leukemia (MLL1) gene are common in a unique group of acute leukemias, with more than 100 fusion partners in this malignancy alone. However, do these fusions occur or have a role in solid tumors? We performed extensive network analyses of MLL1‐fusion partners in patient datasets, revealing that multiple MLL1‐fusion partners exhibited significant interactions with the androgen‐receptor signaling pathway. Further exploration of tumor sequence data from TCGA predicts the presence of MLL1 fusions with truncated SET domain in prostate tumors. To investigate the physiological relevance of MLL1 fusions in solid tumors, we engineered a truncated version of MLL1 by fusing it with one of its known fusion partners, ZC3H13, to use as a model system. Functional characterization with cell‐based assays revealed that MLL1‐ZC3H13 fusion induced chromosomal instability, affected mitotic progression, and enhanced tumorsphere formation. The MLL1‐ZC3H13 chimera consistently increased the expression of a cancer stem cell marker (CD44); in addition, we detected potential collateral lethality between DOT1L and MLL1 fusions. Our work reveals that MLL1 fusions are likely prevalent in solid tumors and exhibit a potential pro‐tumorigenic role.

A Road Map to Personalizing Targeted Cancer Therapies Using Synthetic Lethality

Targeted therapies rely on the genetic and epigenetic status of the tumor cells and are seen as the most promising approach to treat cancer today. However, current targeted therapies focus on directly inhibiting those molecules that are altered in tumor cells. Unfortunately, targeting these molecules, even with specific inhibitors, is challenging as tumor cells rewire their genetic circuitry to eliminate genetic dependency on these targets. Here, we describe how synthetic lethality approaches can be used to identify genetic dependencies and develop personalized targeted therapies. We also discuss strategies to specifically target these genetic dependencies, using small molecule and biologic drugs.

Synthetic lethal combinations of low-toxicity drugs for breast cancer identified in silico by genetic screens in yeast

In recent years, the concept of synthetic lethality, describing a cellular state where loss of two genes leads to a non-viable phenotype while loss of one gene can be compensated, has emerged as a novel strategy for cancer therapy. Various compounds targeting synthetic lethal pathways are either under clinical investigation or are already routinely used in multiple cancer entities such as breast cancer. Most of them target the well-described synthetic lethal interplay between PARP1 and BRCA1/2. In our study, we investigated, using an in silico methodological approach, clinically utilized drug combinations for breast cancer treatment, by correlating their known molecular targets with known homologous interaction partners that cause synthetic lethality in yeast. Further, by creating a machine-learning algorithm, we were able to suggest novel synthetic lethal drug combinations of low-toxicity drugs in breast cancer and showed their negative effects on cancer cell viability in vitro. Our findings foster the understanding of evolutionarily conserved synthetic lethality in breast cancer cells and might lead to new drug combinations with favorable toxicity profile in this entity.

Identification of synthetic lethality based on a functional network by using machine learning algorithms

Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 gene editing, efforts have been made to systematically identify synthetic lethal interactions, especially for frequently mutated genes in cancers. However, elucidating the mechanism of synthetic lethality remains a challenge because of the complexity of its influencing conditions. In this study, we proposed a new computational method to identify critical functional features that can accurately predict synthetic lethal interactions. This method incorporates several machine learning algorithms and encodes protein-coding genes by an enrichment system derived from gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways to represent their functional features. We built a random forest-based prediction engine by using 2120 selected features and obtained a Matthews correlation coefficient of 0.532. We examined the top 15 features and found that most of them have potential roles in synthetic lethality according to previous studies. These results demonstrate the ability of our proposed method to predict synthetic lethal interactions and provide a basis for further characterization of these particular genetic combinations.

Harnessing synthetic lethality to predict the response to cancer treatment

While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi's utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients' drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.

EPHB6 augments both development and drug sensitivity of triple-negative breast cancer tumours

Triple-negative breast cancer (TNBC) tumours that lack expression of oestrogen, and progesterone receptors, and do not overexpress the HER2 receptor represent the most aggressive breast cancer subtype, which is characterised by the resistance to therapy in frequently relapsing tumours and a high rate of patient mortality. This is likely due to the resistance of slowly proliferating tumour-initiating cells (TICs), and understanding molecular mechanisms that control TICs behaviour is crucial for the development of effective therapeutic approaches. Here, we present our novel findings, indicating that an intrinsically catalytically inactive member of the Eph group of receptor tyrosine kinases, EPHB6, partially suppresses the epithelial–mesenchymal transition in TNBC cells, while also promoting expansion of TICs. Our work reveals that EPHB6 interacts with the GRB2 adapter protein and that its effect on enhancing cell proliferation is mediated by the activation of the RAS-ERK pathway, which allows it to elevate the expression of the TIC-related transcription factor, OCT4. Consistent with this, suppression of either ERK or OCT4 activities blocks EPHB6-induced pro-proliferative responses. In line with its ability to trigger propagation of TICs, EPHB6 accelerates tumour growth, potentiates tumour initiation and increases TIC populations in xenograft models of TNBC. Remarkably, EPHB6 also suppresses tumour drug resistance to DNA-damaging therapy, probably by forcing TICs into a more proliferative, drug-sensitive state. In agreement, patients with higher EPHB6 expression in their tumours have a better chance for recurrence-free survival. These observations describe an entirely new mechanism that governs TNBC and suggest that it may be beneficial to enhance EPHB6 action concurrent with applying a conventional DNA-damaging treatment, as it would decrease drug resistance and improve tumour elimination.

Functional genomics identifies specific vulnerabilities in PTEN-deficient breast cancer

Background

Phosphatase and tensin homolog (PTEN) is one of the most frequently inactivated tumor suppressors in breast cancer. While PTEN itself is not considered a druggable target, PTEN synthetic-sick or synthetic-lethal (PTEN-SSL) genes are potential drug targets in PTEN-deficient breast cancers. Therefore, with the aim of identifying potential targets for precision breast cancer therapy, we sought to discover PTEN-SSL genes present in a broad spectrum of breast cancers.

Methods

To discover broad-spectrum PTEN-SSL genes in breast cancer, we used a multi-step approach that started with (1) a genome-wide short interfering RNA (siRNA) screen of ~ 21,000 genes in a pair of isogenic human mammary epithelial cell lines, followed by (2) a short hairpin RNA (shRNA) screen of ~ 1200 genes focused on hits from the first screen in a panel of 11 breast cancer cell lines; we then determined reproducibility of hits by (3) identification of overlaps between our results and reanalyzed data from 3 independent gene-essentiality screens, and finally, for selected candidate PTEN-SSL genes we (4) confirmed PTEN-SSL activity using either drug sensitivity experiments in a panel of 19 cell lines or mutual exclusivity analysis of publicly available pan-cancer somatic mutation data.

Results

The screens (steps 1 and 2) and the reproducibility analysis (step 3) identified six candidate broad-spectrum PTEN-SSL genes (PIK3CB, ADAMTS20, AP1M2, HMMR, STK11, and NUAK1). PIK3CB was previously identified as PTEN-SSL, while the other five genes represent novel PTEN-SSL candidates. Confirmation studies (step 4) provided additional evidence that NUAK1 and STK11 have PTEN-SSL patterns of activity. Consistent with PTEN-SSL status, inhibition of the NUAK1 protein kinase by the small molecule drug HTH-01-015 selectively impaired viability in multiple PTEN-deficient breast cancer cell lines, while mutations affecting STK11 and PTEN were largely mutually exclusive across large pan-cancer data sets.

Conclusions

Six genes showed PTEN-SSL patterns of activity in a large proportion of PTEN-deficient breast cancer cell lines and are potential specific vulnerabilities in PTEN-deficient breast cancer. Furthermore, the NUAK1 PTEN-SSL vulnerability identified by RNA interference techniques can be recapitulated and exploited using the small molecule kinase inhibitor HTH-01-015. Thus, NUAK1 inhibition may be an effective strategy for precision treatment of PTEN-deficient breast tumors.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-0949-3) contains supplementary material, which is available to authorized users.

Targeting synthetic lethality between the SRC kinase and the EPHB6 receptor may benefit cancer treatment

Application of tumor genome sequencing has identified numerous loss-of-function alterations in cancer cells. While these alterations are difficult to target using direct interventions, they may be attacked with the help of the synthetic lethality (SL) approach. In this approach, inhibition of one gene causes lethality only when another gene is also completely or partially inactivated. The EPHB6 receptor tyrosine kinase has been shown to have anti-malignant properties and to be downregulated in multiple cancers, which makes it a very attractive target for SL applications. In our work, we used a genome-wide SL screen combined with expression and interaction network analyses, and identified the SRC kinase as a SL partner of EPHB6 in triple-negative breast cancer (TNBC) cells. Our experiments also reveal that this SL interaction can be targeted by small molecule SRC inhibitors, SU6656 and KX2-391, and can be used to improve elimination of human TNBC tumors in a xenograft model. Our observations are of potential practical importance, since TNBC is an aggressive heterogeneous malignancy with a very high rate of patient mortality due to the lack of targeted therapies, and our work indicates that FDA-approved SRC inhibitors may potentially be used in a personalized manner for treating patients with EPHB6-deficient TNBC. Our findings are also of a general interest, as EPHB6 is downregulated in multiple malignancies and our data serve as a proof of principle that EPHB6 deficiency may be targeted by small molecule inhibitors in the SL approach.

CD147: a small molecule transporter ancillary protein at the crossroad of multiple hallmarks of cancer and metabolic reprogramming

Increased expression of CD147 in pancreatic cancer has been proposed to play a critical role in cancer progression via CD147 chaperone function for lactate monocarboxylate transporters (MCTs). Here, we show for the first time that CD147 interacts with membrane transporters beyond MCTs and exhibits a protective role for several of its interacting partners. CD147 prevents its interacting partner's proteasome-dependent degradation and incorrect plasma membrane localization through the CD147 transmembrane (TM) region. The interactions with transmembrane small molecule and ion transporters identified here indicate a central role of CD147 in pancreatic cancer metabolic reprogramming, particularly with respect to amino acid anabolism and calcium signaling. Importantly, CD147 genetic ablation prevents pancreatic cancer cell proliferation and tumor growth in vitro and in vivo in conjunction with metabolic rewiring towards amino acid anabolism, thus paving the way for future combined pharmacological treatments.

Dual gene activation and knockout screen reveals directional dependencies in genetic networks

Understanding the direction of information flow is essential for characterizing how genetic networks affect phenotypes. However, methods to find genetic interactions largely fail to reveal directional dependencies. We combine two orthogonal Cas9 proteins from Streptococcus pyogenes and Staphylococcus aureus to carry out a dual screen in which one gene is activated while a second gene is deleted in the same cell. We analyse the quantitative effects of activation and knockout to calculate genetic interaction and directionality scores for each gene pair. Based on the results from over 100,000 perturbed gene pairs, we reconstruct a directional dependency network for human K562 leukemia cells and demonstrate how our approach allows the determination of directionality in activating genetic interactions. Our interaction network connects previously uncharacterised genes to well-studied pathways and identifies targets relevant for therapeutic intervention.

The exomic landscape of t(14;18)-negative diffuse follicular lymphoma with 1p36 deletion

Predominantly diffuse t(14;18) negative follicular lymphoma (FL) with 1p36 deletion shows distinctive clinical, morphological and molecular features that distinguish it from classical FL. In order to investigate whether it possesses a unique mutation profile, we performed whole exome sequencing of six well-characterised cases. Our analysis showed that the mutational landscape of this subtype is largely distinct from classical FL. It appears to harbour several recurrent mutations, affecting STAT6, CREBBP and basal membrane protein genes with high frequency. Our data support the view that this FL subtype should be considered a separate entity from classical FL.

"Stealth dissemination" of macrophage-tumor cell fusions cultured from blood of patients with pancreatic ductal adenocarcinoma

Here we describe isolation and characterization of macrophage-tumor cell fusions (MTFs) from the blood of pancreatic ductal adenocarcinoma (PDAC) patients. The MTFs were generally aneuploidy, and immunophenotypic characterizations showed that the MTFs express markers characteristic of PDAC and stem cells, as well as M2-polarized macrophages. Single cell RNASeq analyses showed that the MTFs express many transcripts implicated in cancer progression, LINE1 retrotransposons, and very high levels of several long non-coding transcripts involved in metastasis (such as MALAT1). When cultured MTFs were transplanted orthotopically into mouse pancreas, they grew as obvious well-differentiated islands of cells, but they also disseminated widely throughout multiple tissues in "stealth" fashion. They were found distributed throughout multiple organs at 4, 8, or 12 weeks after transplantation (including liver, spleen, lung), occurring as single cells or small groups of cells, without formation of obvious tumors or any apparent progression over the 4 to 12 week period. We suggest that MTFs form continually during PDAC development, and that they disseminate early in cancer progression, forming "niches" at distant sites for subsequent colonization by metastasis-initiating cells.

Mapping a diversity of genetic interactions in yeast

Genetic interactions occur when the combination of multiple mutations yields an unexpected phenotype, and they may confound our ability to fully understand the genetic mechanisms underlying complex diseases. Genetic interactions are challenging to study because there are millions of possible different variant combinations within a given genome. Consequently, they have primarily been systematically explored in unicellular model organisms, such as yeast, with a focus on pairwise genetic interactions between loss-of-function alleles. However, there are many different types of genetic interactions, such as those occurring between gain-of-function or heterozygous mutations. Here, we review recent advances made in the systematic analysis of such diverse genetic interactions in yeast, and briefly discuss how similar studies could be undertaken in human cells.

Synthetic lethality and cancer

A synthetic lethal interaction occurs between two genes when the perturbation of either gene alone is viable but the perturbation of both genes simultaneously results in the loss of viability. Key to exploiting synthetic lethality in cancer treatment are the identification and the mechanistic characterization of robust synthetic lethal genetic interactions. Advances in next-generation sequencing technologies are enabling the identification of hundreds of tumour-specific mutations and alterations in gene expression that could be targeted by a synthetic lethality approach. The translation of synthetic lethality to therapy will be assisted by the synthesis of genetic interaction data from model organisms, tumour genomes and human cell lines.

TheCellMap.org: A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae. In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner.

Enhancing the throughput and multiplexing capabilities of next generation sequencing for efficient implementation of pooled shRNA and CRISPR screens

Next generation sequencing is becoming the method of choice for functional genomic studies that use pooled shRNA or CRISPR libraries. A key challenge in sequencing these mixed-oligo libraries is that they are highly susceptible to hairpin and/or heteroduplex formation. This results in polyclonal, low quality, and incomplete reads and reduces sequencing throughput. Unfortunately, this challenge is significantly magnified in low-to-medium throughput bench-top sequencers as failed reads significantly perturb the maximization of sequence coverage and multiplexing capabilities. Here, we report a methodology that can be adapted to maximize the coverage on a bench-top, Ion PGM System for smaller shRNA libraries with high efficiency. This ligation-based, half-shRNA sequencing strategy minimizes failed sequences and is also equally amenable to high-throughput sequencers for increased multiplexing. Towards this, we also demonstrate that our strategy to reduce heteroduplex formation improves multiplexing capabilities of pooled CRISPR screens using Illumina NextSeq 500. Overall, our method will facilitate sequencing of pooled shRNA or CRISPR libraries from genomic DNA and maximize sequence coverage.

Genetic interactions and functional analyses of the fission yeast gsk3 and amk2 single and double mutants defective in TORC1-dependent processes

The Target of Rapamycin (TOR) signalling network plays important roles in aging and disease. The AMP-activated protein kinase (AMPK) and the Gsk3 kinase inhibit TOR during stress. We performed genetic interaction screens using synthetic genetic arrays (SGA) with gsk3 and amk2 as query mutants, the latter encoding the regulatory subunit of AMPK. We identified 69 negative and 82 positive common genetic interactors, with functions related to cellular growth and stress. The 120 gsk3-specific negative interactors included genes functioning in translation and ribosomes. The 215 amk2-specific negative interactors included genes functioning in chromatin silencing and DNA damage repair. Both amk2- and gsk3-specific interactors were enriched in phenotype categories related to abnormal cell size and shape. We also performed SGA screen with the amk2 gsk3 double mutant as a query. Mutants sensitive to 5-fluorouracil, an anticancer drug are under-represented within the 305 positive interactors specific for the amk2 gsk3 query. The triple-mutant SGA screen showed higher number of negative interactions than the double mutant SGA screens and uncovered additional genetic network information. These results reveal common and specialized roles of AMPK and Gsk3 in mediating TOR-dependent processes, indicating that AMPK and Gsk3 act in parallel to inhibit TOR function in fission yeast.

Systematic epistatic mapping of cellular processes

Genetic screens have identified many novel components of various biological processes, such as components required for cell cycle and cell division. While forward genetic screens typically generate unstructured ‘hit’ lists, genetic interaction mapping approaches can identify functional relations in a systematic fashion. Here, we discuss a recent study by our group demonstrating a two-step approach to first screen for regulators of the mitotic cell cycle, and subsequently guide hypothesis generation by using genetic interaction analysis. The screen used a high-content microscopy assay and automated image analysis to capture defects during mitotic progression and cytokinesis. Genetic interaction networks derived from process-specific features generate a snapshot of functional gene relations in those processes, which follow a temporal order during the cell cycle. This complements a recently published approach, which inferred directional genetic interactions reconstructing hierarchical relationships between genes across different phases during mitotic progression. In conclusion, this strategy leverages unbiased, genome-wide, yet highly sensitive and process-focused functional screening in cells.

Cytokinetic effects of Wee1 disruption in pancreatic cancer

The Wee1 kinase, which is activated in response to DNA damage, regulates exit from G2 through inhibitory phosphorylation of Cdk1/Cdc2, and is an attractive drug target. However, recent work has highlighted effects of Cdk2 phosphorylation by Wee1 on movement through S-phase, suggesting the potential to sensitize to S-phase specific agents by Wee1 inhibitors. In this paper we applied multiparametric flow cytometry to patient-derived pancreatic cancer xenograft tumor cells to study the cell cycle perturbations of Wee1 disruption via the small molecule inhibitor MK-1775, and genetic knockdown. We find that in vitro treatment with MK-1775, and to a lesser degree, Wee1 RNA transcript knockdown, results in the striking appearance of S-phase cells prematurely entering into mitosis. This effect was not seen in vivo in any of the models tested. Here, although we noted an increase of S-phase cells expressing the damage response marker γH2AX, treatment with MK-1775 did not significantly sensitize cells to the cytidine analog gemcitabine. Treatment with MK-1775 did result in a transient but large increase in cells expressing the mitotic marker phosphorylated H3S10 that reached a peak 4 hours after treatment. This suggests a role for Wee1 regulating the progression of genomically unstable cancer cells through G2 in the absence of extrinsically-applied DNA damage. A single dose of 8Gy ionizing radiation resulted in the time-dependent accumulation of Cyclin A2 positive/phosphorylated H3S10 negative cells at the 4N position, which was abrogated by treatment with MK-1775. Consistent with these findings, a genome-scale pooled RNA interference screen revealed that toxic doses of MK-1775 are suppressed by CDK2 or Cyclin A2 knockdown. These findings support G2 exit as the more significant effect of Wee1 inhibition in pancreatic cancers.

A frameshift mutation in MOCOS is associated with familial renal syndrome (xanthinuria) in Tyrolean Grey cattle

Background

Renal syndromes are occasionally reported in domestic animals. Two identical twin Tyrolean Grey calves exhibited weight loss, skeletal abnormalities and delayed development associated with kidney abnormalities and formation of uroliths. These signs resembled inherited renal tubular dysplasia found in Japanese Black cattle which is associated with mutations in the claudin 16 gene. Despite demonstrating striking phenotypic similarities, no obvious presence of pathogenic variants of this candidate gene were found. Therefore further analysis was required to decipher the genetic etiology of the condition.

Results

The family history of the cases suggested the possibility of an autosomal recessive inheritance. Homozygosity mapping combined with sequencing of the whole genome of one case detected two associated non-synonymous private coding variants: A homozygous missense variant in the uncharacterized KIAA2026 gene (g.39038055C > G; c.926C > G), located in a 15 Mb sized region of homozygosity on BTA 8; and a homozygous 1 bp deletion in the molybdenum cofactor sulfurase (MOCOS) gene (g.21222030delC; c.1881delG and c.1782delG), located in an 11 Mb region of homozygosity on BTA 24. Pathogenic variants in MOCOS have previously been associated with inherited metabolic syndromes and xanthinuria in different species including Japanese Black cattle. Genotyping of two additional clinically suspicious cases confirmed the association with the MOCOS variant, as both animals had a homozygous mutant genotype and did not show the variant KIAA2026 allele. The identified genomic deletion is predicted to be highly disruptive, creating a frameshift and premature termination of translation, resulting in severely truncated MOCOS proteins that lack two functionally essential domains. The variant MOCOS allele was absent from cattle of other breeds and approximately 4% carriers were detected among more than 1200 genotyped Tyrolean Grey cattle. Biochemical urolith analysis of one case revealed the presence of approximately 95% xanthine.

Conclusions

The identified MOCOS loss of function variant is highly likely to cause the renal syndrome in the affected animals. The results suggest that the phenotypic features of the renal syndrome were related to an early onset form of xanthinuria, which is highly likely to lead to the progressive defects. The identification of the candidate causative mutation thus enables selection against this pathogenic variant in Tyrolean Grey cattle.

Electronic supplementary material

The online version of this article (doi:10.1186/s12917-016-0904-4) contains supplementary material, which is available to authorized users.

Novel synthetic lethality screening method identifies TIP60-dependent radiation sensitivity in the absence of BAF180

In recent years, research into synthetic lethality and how it can be exploited in cancer treatments has emerged as major focus in cancer research. However, the lack of a simple to use, sensitive and standardised assay to test for synthetic interactions has been slowing the efforts. Here we present a novel approach to synthetic lethality screening based on co-culturing two syngeneic cell lines containing individual fluorescent tags. By associating shRNAs for a target gene or control to individual fluorescence labels, we can easily follow individual cell fates upon siRNA treatment and high content imaging. We have demonstrated that the system can recapitulate the functional defects of the target gene depletion and is capable of discovering novel synthetic interactors and phenotypes. In a trial screen, we show that TIP60 exhibits synthetic lethality interaction with BAF180, and that in the absence of TIP60, there is an increase micronuclei dependent on the level of BAF180 loss, significantly above levels seen with BAF180 present. Moreover, the severity of the interactions correlates with proxy measurements of BAF180 knockdown efficacy, which may expand its usefulness to addressing synthetic interactions through titratable hypomorphic gene expression.