Establishment of a chemical synthetic lethality screen in cultured human cells

The role of drug-metabolizing enzymes in synthetic lethality of cancer

Drug-metabolizing enzymes (DMEs) have shown increasing importance in anticancer therapy. It is not only due to their effect on activation or deactivation of anticancer drugs, but also because of their extensive connections with pathological and biochemistry changes during tumorigenesis. Meanwhile, it has become more accessible to discovery anticancer drugs that selectively targeted cancer cells with the development of synthetic lethal screen technology. Synthetic lethal strategy makes use of unique genetic markers that different cancer cells from normal tissues to discovery anticancer agents. Dysregulation of DMEs has been found in various cancers, making them promising candidates for synthetic lethal strategy. In this review, we will systematically discuss about the role of DMEs in tumor progression, the application of synthetic lethality strategy in drug discovery, and a link between DMEs and synthetic lethal of cancer.

KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers

Motivation

Synthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer drug targets. Wet-lab screening of SL pairs is afflicted with high cost, batch-effect, and off-target problems. Current computational methods for SL prediction include gene knock-out simulation, knowledge-based data mining and machine learning methods. Most of the existing methods tend to assume that SL pairs are independent of each other, without taking into account the shared biological mechanisms underlying the SL pairs. Although several methods have incorporated genomic and proteomic data to aid SL prediction, these methods involve manual feature engineering that heavily relies on domain knowledge.

Results

Here, we propose a novel graph neural network (GNN)-based model, named KG4SL, by incorporating knowledge graph (KG) message-passing into SL prediction. The KG was constructed using 11 kinds of entities including genes, compounds, diseases, biological processes and 24 kinds of relationships that could be pertinent to SL. The integration of KG can help harness the independence issue and circumvent manual feature engineering by conducting message-passing on the KG. Our model outperformed all the state-of-the-art baselines in area under the curve, area under precision-recall curve and F1. Extensive experiments, including the comparison of our model with an unsupervised TransE model, a vanilla graph convolutional network model, and their combination, demonstrated the significant impact of incorporating KG into GNN for SL prediction.

Availability and implementation

: KG4SL is freely available at https://github.com/JieZheng-ShanghaiTech/KG4SL.

Supplementary information

Supplementary data are available at Bioinformatics online.

SL2MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization

Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms, or cell lines. Therefore, computational prediction methods are needed to address these issues. This paper proposes a novel SL prediction method, named SL² MF, which employs logistic matrix factorization to learn latent representations of genes from the observed SL data. The probability that two genes are likely to form SL is modeled by the linear combination of gene latent vectors. As known SL pairs are more trustworthy than unknown pairs, we design importance weighting schemes to assign higher importance weights for known SL pairs and lower importance weights for unknown pairs in SL² MF. Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO). In particular, we calculate the similarity between genes based on their GO annotations and topological properties in the PPI network. Extensive experiments on the SL interaction data from SynLethDB database have been conducted to demonstrate the effectiveness of SL² MF.

Synthetic lethality: A promising therapeutic strategy for hepatocellular carcinoma

Hepatocellular carcinoma (HCC), the main cause of liver cancer-related death, is one of the main cancers in terms of incidence and mortality. However, HCC is difficult to target and develops strong drug resistance. Therefore, a new treatment strategy is urgently needed. The clinical application of the concept of synthetic lethality in recent years provides a new therapeutic direction for the accurate treatment of HCC. Here, we introduce the concept of synthetic lethality, the screening used to study synthetic lethality, and the identified and potential genetic interactions that induce synthetic lethality in HCC. In addition, we propose opportunities and challenges for translating synthetic lethal interactions to the clinical treatment of HCC.

Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization

BACKGROUND

Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments.

RESULTS

In this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction.

CONCLUSIONS

In this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods.

Function and mechanism of combined PARP-1 and BRCA genes in regulating the radiosensitivity of breast cancer cells

OBJECTIVE

To study the function and mechanism of combined PARP-1 and BRCA genes in regulating the radiosensitivity of breast cancer cells by poly ADP-ribose polymerase-1 (PARP-1) inhibitor 3-amion benzamide (3-AB) onBRCA mutant and non-mutant breast cancer cells.

METHODS

Four groups of BRCA mutant cells MDA-MB-436 and BRCA non-mutant cells MDA-MB-231 were divided respectively into control (CTRL), ionizing radiation alone (IR), 3-AB alone (3-AB), and ionizing radiation combined with 3-AB (IR+3-AB) groups. The γ-H2AX foci were detected by immunofluorescence assay to show the DNA double-strand damage. The clonogenic cell survival assay was applied to evaluate the radiosensitivity of breast cancer cells, and flow cytometry was used to assess the percentage of apoptosis cells.

RESULTS

The apoptosis rate of MDA-MB-436 cells was significantly increased compared with MDA-MB-231 cells treated with irradiation, and 3-AB could further enhance the effect. Similarly, the result of γ-H2AX foci detection showed that DNA double-stranded damage of the MDA-MB-436 cells was significantly greater than that of MDA-MB-231 cells (t = 4.57, P < 0.05), and the DNA damage of MDA-MB-436 cells in IR+3-AB group was the most remarkable. The difference was significant (t = 3.26, P < 0.05). In the same group, compared with MDA-MB-231 cells, MDA-MB-436 cells had the significantly greater apoptosis rate (t = 2.96, P < 0.05). The apoptosis rate of MDA-MB-436 cells in the IR+3-AB group showed by flow cytometry was highest (t = 3.81, P < 0.05).

CONCLUSIONS

Compared with non-BRCA mutant MDA-MB-231 cells, the BRCA mutant breast cancer MDA-MB-436 cells could incur significantly greater DNA damage, and therefore the radiosensetivity of MDA-MB-436 cells is higher than that of MDA-MB-231 cells. The inhibitor of PARP-1, which can block the repair of single-strand damage caused by radiation, can further enhance the level of apoptosis and radiosensitivity of BRCA-mutant cells.

Synthetic lethality in lung cancer and translation to clinical therapies

Lung cancer is a heterogeneous disease consisting of multiple histological subtypes each driven by unique genetic alterations. Despite the development of targeted therapies that inhibit the oncogenic mutations driving a subset of lung cancer cases, there is a paucity of effective treatments for the majority of lung cancer patients and new strategies are urgently needed. In recent years, the concept of synthetic lethality has been established as an effective approach for discovering novel cancer-specific targets as well as a method to improve the efficacy of existing drugs which provide partial but insufficient benefits for patients. In this review, we discuss the concept of synthetic lethality, the various types of synthetic lethal interactions in the context of oncology and the approaches used to identify these interactions, including recent advances that have transformed the ability to discover novel synthetic lethal combinations on a global scale. Lastly, we describe the specific synthetic lethal interactions identified in lung cancer to date and explore the pharmacological challenges and considerations in translating these discoveries to the clinic.

A synthetic-lethality RNAi screen reveals an ERK-mTOR co-targeting pro-apoptotic switch in PIK3CA+ oral cancers

mTOR inhibition has emerged as a promising strategy for head and neck squamous cell carcinomas (HNSCC) treatment. However, most targeted therapies ultimately develop resistance due to the activation of adaptive survival signaling mechanisms limiting the activity of targeted agents. Thus, co-targeting key adaptive mechanisms may enable more effective cancer cell killing. Here, we performed a synthetic lethality screen using shRNA libraries to identify druggable candidates for combinatorial signal inhibition. We found that the ERK pathway was the most highly represented. Combination of rapamycin with trametinib, a MEK1/2 inhibitor, demonstrated strong synergism in HNSCC-derived cells in vitro and in vivo, including HNSCC cells expressing the HRAS and PIK3CA oncogenes. Interestingly, cleaved caspase-3 was potently induced by the combination therapy in PIK3CA+ cells in vitro and tumor xenografts. Moreover, ectopic expression of PIK3CA mutations into PIK3CA- HNSCC cells sensitized them to the pro-apoptotic activity of the combination therapy. These findings indicate that co-targeting the mTOR/ERK pathways may provide a suitable precision strategy for HNSCC treatment. Moreover, PIK3CA+ HNSCC are particularly prone to undergo apoptosis after mTOR and ERK inhibition, thereby providing a potential biomarker of predictive value for the selection of patients that may benefit from this combination therapy.

Towards a compendium of essential genes - From model organisms to synthetic lethality in cancer cells

Essential genes are defined by their requirement to sustain life in cells or whole organisms. The systematic identification of essential gene sets not only allows insights into the fundamental building blocks of life, but may also provide novel therapeutic targets in oncology. The discovery of essential genes has been tightly linked to the development and deployment of various screening technologies. Here, we describe how gene essentiality was addressed in different eukaryotic model organisms, covering a range of organisms from yeast to mouse. We describe how increasing knowledge of evolutionarily divergent genomes facilitate identification of gene essentiality across species. Finally, the impact of gene essentiality and synthetic lethality on cancer research and the clinical translation of screening results are highlighted.

A high content clonogenic survival drug screen identifies mek inhibitors as potent radiation sensitizers for KRAS mutant non-small-cell lung cancer

INTRODUCTION

Traditional clonogenic survival and high throughput colorimetric assays are inadequate as drug screens to identify novel radiation sensitizers. We developed a method that we call the high content clonogenic survival assay (HCSA) that will allow screening of drug libraries to identify candidate radiation sensitizers.

METHODS

Drug screen using HCSA was done in 96 well plates. After drug treatment, irradiation, and incubation, colonies were stained with crystal violet and imaged on the INCell 6000 (GE Health). Colonies achieving 50 or more cells were enumerated using the INCell Developer image analysis software. A proof-of-principle screen was done on the KRAS mutant lung cancer cell line H460 and a Custom Clinical Collection (146 compounds).

RESULTS

Multiple drugs of the same class were found to be radiation sensitizers and levels of potency seemed to reflect the clinical relevance of these drugs. For instance, several PARP inhibitors were identified as good radiation sensitizers in the HCSA screen. However, there were also a few PARP inhibitors not found to be sensitizing that have either not made it into clinical development, or in the case of BSI-201, was proven to not even be a PARP inhibitor. We discovered that inhibitors of pathways downstream of activated mutant KRAS (PI3K, AKT, mTOR, and MEK1/2) sensitized H460 cells to radiation. Furthermore, the potent MEK1/2 inhibitor tramenitib selectively enhanced radiation effects in KRAS mutant but not wild-type lung cancer cells.

CONCLUSIONS

Drug screening for novel radiation sensitizers is feasible using the HCSA approach. This is an enabling technology that will help accelerate the discovery of novel radiosensitizers for clinical testing.

Synthetic lethal therapy for KRAS mutant non-small-cell lung carcinoma with nanoparticle-mediated CDK4 siRNA delivery

The KRAS mutation is present in ~20% of lung cancers and has not yet been effectively targeted for therapy. This mutation is associated with a poor prognosis in non-small-cell lung carcinomas (NSCLCs) and confers resistance to standard anticancer treatment drugs, including epidermal growth factor receptor tyrosine kinase inhibitors. In this study, we exploited a new therapeutic strategy based on the synthetic lethal interaction between cyclin-dependent kinase 4 (CDK4) downregulation and the KRAS mutation to deliver micellar nanoparticles (MNPs) containing small interfering RNA targeting CDK4 (MNPsiCDK4) for treatment in NSCLCs harboring the oncogenic KRAS mutation. Following MNPsiCDK4 administration, CDK4 expression was decreased, accompanied by inhibited cell proliferation, specifically in KRAS mutant NSCLCs. However, this intervention was harmless to normal KRAS wild-type cells, confirming the proposed mechanism of synthetic lethality. Moreover, systemic delivery of MNPsiCDK4 significantly inhibited tumor growth in an A549 NSCLC xenograft murine model, with depressed expression of CDK4 and mutational KRAS status, suggesting the therapeutic promise of MNPsiCDK4 delivery in KRAS mutant NSCLCs via a synthetic lethal interaction between KRAS and CDK4.

Identification of synthetic lethal pairs in biological systems through network information centrality

The immense availability of protein interaction data, provided with an abstract network approach is valuable for the improved interpretation of biological processes and protein functions globally. The connectivity of a protein and its structure are related to its functional properties. Highly connected proteins are often functionally cardinal and the knockout of such proteins leads to lethality. In this paper, we propose a new approach based on graph information centrality measures to identify the synthetic lethal pairs in biological systems. To illustrate the efficacy of our approach, we have applied it to a human cancer protein interaction network. It is found that the lethal pairs obtained were analogous to the experimental and computational inferences, implying that our approach can serve as a surrogate for predicting the synthetic lethality.

Harnessing synthetic lethal interactions in anticancer drug discovery

Unique features of tumours that can be exploited by targeted therapies are a key focus of current cancer research. One such approach is known as synthetic lethality screening, which involves searching for genetic interactions of two mutations whereby the presence of either mutation alone has no effect on cell viability but the combination of the two mutations results in cell death. The presence of one of these mutations in cancer cells but not in normal cells can therefore create opportunities to selectively kill cancer cells by mimicking the effect of the second genetic mutation with targeted therapy. Here, we summarize strategies that can be used to identify synthetic lethal interactions for anticancer drug discovery, describe examples of such interactions that are currently being investigated in preclinical and clinical studies of targeted anticancer therapies, and discuss the challenges of realizing the full potential of such therapies.

A genome-wide RNAi screen identifies novel targets of neratinib sensitivity leading to neratinib and paclitaxel combination drug treatments

ErbB2 is frequently activated in tumors, and influences a wide array of cellular functions, including proliferation, apoptosis, cell motility and adhesion. HKI-272 (neratinib) is a small molecule pan-kinase inhibitor of the ErbB family of receptor tyrosine kinases, and shows strong antiproliferative activity in ErbB2-overexpressing breast cancer cells. We undertook a genome-wide pooled lentiviral RNAi screen to identify synthetic lethal or enhancer (synthetic modulator screen) genes that interact with neratinib in a human breast cancer cell line (SKBR-3). These genes upon knockdown would modulate cell viability in the presence of subeffective concentrations of neratinib. We discovered a diverse set of genes whose depletion selectively impaired or enhanced the viability of SKBR-3 cells in the presence of neratinib. We observed diverse pathways including EGFR, hypoxia, cAMP, and protein ubiquitination that, when co-treated with RNAi and neratinib, resulted in arrest of cell proliferation. Examining the changes of these genes and their protein products also led to a rationale for clinically relevant drug combination treatments. Treatment of cells with either paclitaxel or cytarabine in combination with neratinib resulted in a strong antiproliferative effect. The identification of novel mediators of cellular response to neratinib and the development of potential drug combination treatments have expanded our understanding of neratinib's mode-of-action for the development of more effective therapeutic regimens. Notably, our findings support a paclitaxel and neratinib phase III clinical trial in breast cancer patients.

Novel treatment approaches for triple-negative breast cancer

Triple-negative breast cancers share an aggressive biology, marked by increased recurrence risk and poorer survival compared with hormone receptor-positive subtypes. Few therapeutic trials have specifically focused on triple-negative breast cancer, and the treatment of patients with early-stage triple-negative breast cancer has changed little in the past decade. Over this time, however, attention has shifted to treatment approaches based on molecular subtypes of breast cancer, and investigation into the mechanistic underpinnings of these distinct subtypes has exploded. Converging preclinical rationales combined with early provocative clinical efficacy has focused recent attention on strategies targeting DNA repair defects for the treatment of patients with triple-negative and BRCA mutation-associated breast cancers. These developments are very promising and suggest that major advances in the targeted treatment of patients with triple-negative breast cancer are in sight. This review provides an overview of the clinical features of triple-negative breast cancer and current treatment strategies in the adjuvant setting. Mechanisms of DNA repair and the DNA damage response are reviewed to provide background for understanding novel approaches targeting DNA repair defects in this disease with DNA-damaging chemotherapeutic agents and poly(ADP-ribose) polymerase inhibitors. Ongoing studies, including those investigating the role of antiangiogenic therapies, are also reviewed.

PARP Inhibitors for the Treatment and Prevention of Breast Cancer

Poly (ADP-ribose) polymerase (PARP) inhibitors, a novel class of drugs that target tumors with DNA repair defects, have received tremendous enthusiasm. Early preclinical studies identified BRCA1 and BRCA2 tumors to be highly sensitive to PARP inhibitors as a result of homologous recombination defect. Based on this premise, PARP inhibitors have been tested in early phase clinical trials as a single agent in BRCA1 or BRCA2 mutation carriers and in combination with chemotherapy in triple-negative breast cancer patients. For high-risk populations, use of PARP inhibition as a prevention agent has been postulated, but no robust preclinical or clinical studies exist yet. We review the preclinical and clinical studies in treatment of breast cancer and rationale for use of PARP inhibitors as a prevention agent for high-risk populations. Of significance, PARP inhibitors vary significantly in mechanism of action, dosing intervals, and toxicities, which are highlighted in this review.

Functional genomics to explore cancer cell vulnerabilities

Our understanding of glioblastoma multiforme (GBM), the most common form of primary brain cancer, has been significantly advanced by recent efforts to characterize the cancer genome using unbiased high-throughput sequencing analyses. While these studies have documented hundreds of mutations, gene copy alterations, and chromosomal abnormalities, only a subset of these alterations are likely to impact tumor initiation or maintenance. Furthermore, genes that are not altered at the genomic level may play essential roles in tumor initiation and maintenance. Identification of these genes is critical for therapeutic development and investigative methodologies that afford insight into biological function. This requirement has largely been fulfilled with the emergence of RNA interference (RNAi) and high-throughput screening technology. In this article, the authors discuss the application of genome-wide, high-throughput RNAi-based genetic screening as a powerful tool for the rapid and cost-effective identification of genes essential for cancer proliferation and survival. They describe how these technologies have been used to identify genes that are themselves selectively lethal to cancer cells, or synthetically lethal with other oncogenic mutations. The article is intended to provide a platform for how RNAi libraries might contribute to uncovering glioma cell vulnerabilities and provide information that is highly complementary to the structural characterization of the glioblastoma genome. The authors emphasize that unbiased, systems-level structural and functional genetic approaches are complementary efforts that should facilitate the identification of genes involved in the pathogenesis of GBM and permit the identification of novel drug targets.

Synthetic lethal interaction between oncogenic KRAS dependency and STK33 suppression in human cancer cells

An alternative to therapeutic targeting of oncogenes is to perform "synthetic lethality" screens for genes that are essential only in the context of specific cancer-causing mutations. We used high-throughput RNA interference (RNAi) to identify synthetic lethal interactions in cancer cells harboring mutant KRAS, the most commonly mutated human oncogene. We find that cells that are dependent on mutant KRAS exhibit sensitivity to suppression of the serine/threonine kinase STK33 irrespective of tissue origin, whereas STK33 is not required by KRAS-independent cells. STK33 promotes cancer cell viability in a kinase activity-dependent manner by regulating the suppression of mitochondrial apoptosis mediated through S6K1-induced inactivation of the death agonist BAD selectively in mutant KRAS-dependent cells. These observations identify STK33 as a target for treatment of mutant KRAS-driven cancers and demonstrate the potential of RNAi screens for discovering functional dependencies created by oncogenic mutations that may enable therapeutic intervention for cancers with "undruggable" genetic alterations.

Methodological approaches in application of synthetic lethality screening towards anticancer therapy

A promising direction in the development of selective less toxic cancer drugs is the usage of synthetic lethality concept. The availability of large-scale synthetic low-molecular-weight chemical libraries has allowed HTS for compounds synergistic lethal with defined human cancer aberrations in activated oncogenes or tumour suppressor genes. The search for synthetic lethal chemicals in human/mouse tumour cells is greatly aided by a prior knowledge of relevant signalling and DNA repair pathways, allowing for educated guesses on the preferred potential therapeutic targets. The recent generation of human/rodents genome-wide siRNAs, and shRNA-expressing libraries, should further advance this more focused approach to cancer drug discovery.

Structural robustness of metabolic networks with respect to multiple knockouts

We present a generalised framework for analysing structural robustness of metabolic networks, based on the concept of elementary flux modes (EFMs). Extending our earlier study on single knockouts [Wilhelm, T., Behre, J., Schuster, S., 2004. Analysis of structural robustness of metabolic networks. IEE Proc. Syst. Biol. 1(1), 114-120], we are now considering the general case of double and multiple knockouts. The robustness measures are based on the ratio of the number of remaining EFMs after knockout vs. the number of EFMs in the unperturbed situation, averaged over all combinations of knockouts. With the help of simple examples we demonstrate that consideration of multiple knockouts yields additional information going beyond single-knockout results. It is proven that the robustness score decreases as the knockout depth increases. We apply our extended framework to metabolic networks representing amino acid anabolism in Escherichia coli and human hepatocytes, and the central metabolism in human erythrocytes. Moreover, in the E. coli model the two subnetworks synthesising amino acids that are essential and those that are non-essential for humans are studied separately. The results are discussed from an evolutionary viewpoint. We find that E. coli has the most robust metabolism of all the cell types studied here. Considering only the subnetwork of the synthesis of non-essential amino acids, E. coli and the human hepatocyte show about the same robustness.

Chemical genetics: an evolving toolbox for target identification and lead optimization

Chemical genetics combines chemistry with biology as a means of exploring the function of unknown proteins or identifying the proteins responsible for a particular phenotype. Chemical genetics is thus a valuable tool in the identification of novel drug targets. This chapter describes the application of chemical genetics in traditional and systems-based approaches to drug target discovery and the tools/approaches that appear most promising for guiding future pharmaceutical development.

The concept of synthetic lethality in the context of anticancer therapy

Two genes are synthetic lethal if mutation of either alone is compatible with viability but mutation of both leads to death. So, targeting a gene that is synthetic lethal to a cancer-relevant mutation should kill only cancer cells and spare normal cells. Synthetic lethality therefore provides a conceptual framework for the development of cancer-specific cytotoxic agents. This paradigm has not been exploited in the past because there were no robust methods for systematically identifying synthetic lethal genes. This is changing as a result of the increased availability of chemical and genetic tools for perturbing gene function in somatic cells.

shRNA-mediated RNA interference as a tool for genetic synthetic lethality screening in mouse embryo fibroblasts

Previously, we demonstrated the establishment of synthetic lethality screening in cultured somatic human cells, or mouse embryo fibroblasts (MEFs), for chemicals or mutant genes synergistically lethal with a mutated gene of interest. Here, we show in MEFs that the usage of RNA interference-based genetic suppressor elements encoding short hairpin RNAs (shRNAs) enables for genetic synthetic lethality screening at a frequency much higher than that achieved before with short truncated sense and antisense RNAs. These findings open up the possibility of using in mammalian cells genome-wide shRNA libraries for genetic synthetic lethality screening at the multi-gene level.

A Flexible Data Analysis Tool for Chemical Genetic Screens

High-throughput assays generate immense quantities of data that require sophisticated data analysis tools. We have created a freely available software tool, SLIMS (Small Laboratory Information Management System), for chemical genetics which facilitates the collection and analysis of large-scale chemical screening data. Compound structures, physical locations, and raw data can be loaded into SLIMS. Raw data from high-throughput assays are normalized using flexible analysis protocols, and systematic spatial errors are automatically identified and corrected. Various computational analyses are performed on tested compounds, and dilution-series data are processed using standard or user-defined algorithms. Finally, published literature associated with active compounds is automatically retrieved from Medline and processed to yield potential mechanisms of actions. SLIMS provides a framework for analyzing high-throughput assay data both as a laboratory information management system and as a platform for experimental analysis.

From large networks to small molecules

Large-scale analysis of genetic and physical interaction networks has begun to reveal the global organization of the cell. Cellular phenotypes observed at the macroscopic level depend on the collective characteristics of protein and genetic interaction networks, which exhibit scale-free properties and are highly resistant to perturbation of a single node. The nascent field of chemical genetics promises a host of small-molecule probes to explore these emerging networks. Although the robust nature of cellular networks usually resists the action of single agents, they may be susceptible to rationally designed combinations of small molecules able to collectively shift network behavior.

Complex phenotypic assays in high-throughput screening

High-throughput screening (HTS), systematically testing thousands of small molecules to find candidates for lead optimization, primarily involves exposure of purified proteins to arrayed collections of small molecules. More complex phenotypic assays, such as cell-based or whole-organism assays, traditionally have flanked HTS, preceding it to validate new therapeutic targets, and following it to characterize new lead compounds in cellular contexts. Recently, however, cell- and organism-based phenotypic assays have increasingly been adopted as a primary screening platform for annotating small molecules.

Identification of genotype-selective antitumor agents using synthetic lethal chemical screening in engineered human tumor cells

We used synthetic lethal high-throughput screening to interrogate 23,550 compounds for their ability to kill engineered tumorigenic cells but not their isogenic normal cell counterparts. We identified known and novel compounds with genotype-selective activity, including doxorubicin, daunorubicin, mitoxantrone, camptothecin, sangivamycin, echinomycin, bouvardin, NSC146109, and a novel compound that we named erastin. These compounds have increased activity in the presence of hTERT, the SV40 large and small T oncoproteins, the human papillomavirus type 16 (HPV) E6 and E7 oncoproteins, and oncogenic HRAS. We found that overexpressing hTERT and either E7 or LT increased expression of topoisomerase 2alpha and that overexpressing RAS(V12) and ST both increased expression of topoisomerase 1 and sensitized cells to a nonapoptotic cell death process initiated by erastin.

Genetic synthetic lethality screen at the single gene level in cultured human cells

Recently, we demonstrated the feasibility of a chemical synthetic lethality screen in cultured human cells. We now demonstrate the principles for a genetic synthetic lethality screen. The technology employs both an immortalized human cell line deficient in the gene of interest, which is complemented by an episomal survival plasmid expressing the wild-type cDNA for the gene of interest, and the use of a novel GFP-based double-label fluorescence system. Dominant negative genetic suppressor elements (GSEs) are selected from an episomal library expressing short truncated sense and antisense cDNAs for a gene likely to be synthetic lethal with the gene of interest. Expression of these GSEs prevents spontaneous loss of the GFP-marked episomal survival plasmid, thus allowing FACS enrichment for cells retaining the survival plasmid (and the GSEs). The dominant negative nature of the GSEs was validated by the decreased resident enzymatic activity present in cells harboring the GSEs. Also, cells mutated in the gene of interest exhibit reduced survival upon GSE expression. The identification of synthetic lethal genes described here can shed light on functional genetic interactions between genes involved in normal cell metabolism and in disease.

Constitutive signaling by Kaposi's sarcoma-associated herpesvirus G-protein-coupled receptor desensitizes calcium mobilization by other receptors

We coexpressed Kaposi's sarcoma-associated herpesvirus G protein-coupled receptors (KSHV-GPCRs) with thyrotropin-releasing hormone (TRH) receptors or m1-muscarinic-cholinergic receptors in Xenopus oocytes and in mammalian cells. In oocytes, KSHV-GPCR expression resulted in pronounced (81%) inhibition (heterologous desensitization) of Ca(2+)-activated chloride current responses to TRH and acetylcholine. Similar inhibitions of cytoplasmic free Ca(2+) responses to TRH were observed in human embryonic kidney HEK 293 EM cells and in mouse pituitary AtT20 cells. Further study of oocytes showed that this inhibition was partially reversed by interferon-gamma-inducible protein 10 (IP-10), an inverse agonist of KSHV-GPCR. The basal rate of (45)Ca(2+) efflux in oocytes expressing KSHV-GPCRs was 4.4 times greater than in control oocytes, and IP-10 rapidly inhibited increased (45)Ca(2+) efflux. In the absence of IP-10, growth-related oncogene alpha caused a further 2-fold increase in (45)Ca(2+) efflux. In KSHV-GPCR-expressing oocytes, responses to microinjected inositol 1,4,5-trisphosphate were inhibited by 74%, and this effect was partially reversed by interferon-gamma-inducible protein 10. Treatment with thapsigargin suggested that the pool of calcium available for mobilization by TRH was decreased in oocytes coexpressing KSHV-GPCRs. These results suggest that constitutive signaling by KSHV-GPCR causes heterologous desensitization of responses mediated by other receptors, which signal via the phosphoinositide/calcium pathway, which is caused by depletion of intracellular calcium pools.