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1.  A Chemocentric Informatics Approach to Drug Discovery: Identification and Experimental Validation of Selective Estrogen Receptor Modulators as ligands of 5-Hydroxytryptamine-6 Receptors and as Potential Cognition Enhancers 
Journal of Medicinal Chemistry  2012;55(12):5704-5719.
We have devised a chemocentric informatics methodology for drug discovery integrating independent approaches to mining biomolecular databases. As a proof of concept, we have searched for novel putative cognition enhancers. First, we generated Quantitative Structure- Activity Relationship (QSAR) models of compounds binding to 5-hydroxytryptamine-6 receptor (5HT6R), a known target for cognition enhancers, and employed these models for virtual screening to identify putative 5-HT6R actives. Second, we queried chemogenomics data from the Connectivity Map ( with the gene expression profile signatures of Alzheimer’s disease patients to identify compounds putatively linked to the disease. Thirteen common hits were tested in 5-HT6R radioligand binding assays and ten were confirmed as actives. Four of them were known selective estrogen receptor modulators that were never reported as 5-HT6R ligands. Furthermore, nine of the confirmed actives were reported elsewhere to have memory-enhancing effects. The approaches discussed herein can be used broadly to identify novel drug-target-disease associations.
PMCID: PMC3401608  PMID: 22537153
2.  Signature-Based Small Molecule Screening Identifies Cytosine Arabinoside as an EWS/FLI Modulator in Ewing Sarcoma 
PLoS Medicine  2007;4(4):e122.
The presence of tumor-specific mutations in the cancer genome represents a potential opportunity for pharmacologic intervention to therapeutic benefit. Unfortunately, many classes of oncoproteins (e.g., transcription factors) are not amenable to conventional small-molecule screening. Despite the identification of tumor-specific somatic mutations, most cancer therapy still utilizes nonspecific, cytotoxic drugs. One illustrative example is the treatment of Ewing sarcoma. Although the EWS/FLI oncoprotein, present in the vast majority of Ewing tumors, was characterized over ten years ago, it has never been exploited as a target of therapy. Previously, this target has been intractable to modulation with traditional small-molecule library screening approaches. Here we describe a gene expression–based approach to identify compounds that induce a signature of EWS/FLI attenuation. We hypothesize that screening small-molecule libraries highly enriched for FDA-approved drugs will provide a more rapid path to clinical application.
Methods and Findings
A gene expression signature for the EWS/FLI off state was determined with microarray expression profiling of Ewing sarcoma cell lines with EWS/FLI-directed RNA interference. A small-molecule library enriched for FDA-approved drugs was screened with a high-throughput, ligation-mediated amplification assay with a fluorescent, bead-based detection. Screening identified cytosine arabinoside (ARA-C) as a modulator of EWS/FLI. ARA-C reduced EWS/FLI protein abundance and accordingly diminished cell viability and transformation and abrogated tumor growth in a xenograft model. Given the poor outcomes of many patients with Ewing sarcoma and the well-established ARA-C safety profile, clinical trials testing ARA-C are warranted.
We demonstrate that a gene expression–based approach to small-molecule library screening can identify, for rapid clinical testing, candidate drugs that modulate previously intractable targets. Furthermore, this is a generic approach that can, in principle, be applied to the identification of modulators of any tumor-associated oncoprotein in the rare pediatric malignancies, but also in the more common adult cancers.
Todd Golub and colleagues show that a gene expression-based screen of small-molecule libraries can identify candidate drugs that modulate cancer-associated oncoproteins.
Editors' Summary
Cancer occurs when cells accumulate genetic changes (mutations) that allow them to divide uncontrollably and to travel throughout the body (metastasize). Chemotherapy, a mainstay of cancer treatments, works by killing rapidly dividing cells. Because some normal tissues also contain dividing cells and are therefore sensitive to chemotherapy drugs, it is hard to treat cancer without causing serious side effects. In recent years, however, researchers have identified some of the mutations that drive the growth of cancer cells. This raises the possibility of designing drugs that kill only cancer cells by specifically targeting “oncoproteins” (the abnormal proteins generated by mutations that transform normal cells into cancer cells). Some “targeted” drugs have already reached the clinic, but unfortunately medicinal chemists do not know how to inhibit the function of many classes of oncoproteins with the small organic molecules that make the best medicines. One oncoprotein in this category is EWS/FLI. This contains part of a protein called EWS fused to part of a transcription factor (a protein that controls cell behavior by telling the cell which proteins to make) called FLI. About 80% of patients with Ewing sarcoma (the second commonest childhood cancer of bone and soft tissue) have the mutation responsible for EWS/FLI expression. Localized Ewing sarcoma can be treated with nontargeted chemotherapy (often in combination with surgery and radiotherapy), but treatment for recurrent or metastatic disease remains very poor.
Why Was This Study Done?
Researchers have known for years that EWS/FLI expression drives the development of Ewing sarcoma by activating the expression of target genes needed for tumor formation. However, EWS/FLI has never been exploited as a target for therapy of this cancer—mainly because traditional approaches used to screen libraries of small molecules do not identify compounds that modulate the activity of transcription factors. In this study, the researchers have used a new gene expression–based, high-throughput screening (GE-HTS) approach to identify compounds that modulate the activity of EWS/FLI.
What Did the Researchers Do and Find?
The researchers used a molecular biology technique called microarray expression profiling to define a 14-gene expression signature that differentiates between Ewing sarcoma cells in which the EWS/FLI fusion protein is active and those in which it is inactive. They then used this signature to screen a library of about 1,000 chemicals (many already approved for other clinical uses) in a “ligation-mediated amplification assay.” For this, the researchers grew Ewing sarcoma cells with the test chemicals, extracted RNA from the cells, and generated a DNA copy of the RNA. They then added two short pieces of DNA (probes) specific for each signature gene to the samples. In samples that expressed a given signature gene, both probes bound and were then ligated (joined together) and amplified. Because one of each probe pair also contained a unique “capture sequence,” the signature genes expressed in each sample were finally identified by adding colored fluorescent beads, each linked to DNA complementary to a different capture sequence. The most active modulator of EWS/FLI activity identified by this GE-HTS approach was cytosine arabinoside (ARA-C). At levels achievable in people, this compound reduced the abundance of EWS/FLI protein in and the viability and cancer-like behavior of Ewing sarcoma cells growing in test tubes. ARA-C treatment also slowed the growth of Ewing sarcoma cells transplanted into mice.
What Do These Findings Mean?
These findings identify ARA-C, which is already used to treat children with some forms of leukemia, as a potent modulator of EWS/FLI activity. More laboratory experiments are needed to discover how ARA-C changes the behavior of Ewing sarcoma cells. Nevertheless, given the poor outcomes currently seen in many patients with Ewing sarcoma and the historical reluctance to test new drugs in children, these findings strongly support the initiation of clinical trials of ARA-C in children with Ewing sarcoma. These results also show that the GE-HTS approach is a powerful way to identify candidate drugs able to modulate the activity of some of the oncoproteins (including transcription factors and other previously intractable targets) that drive cancer development.
Additional Information.
Please access these Web sites via the online version of this summary at
Cancerquest from Emory University, provides information on cancer biology (also includes information in Spanish, Chinese and Russian)
The MedlinePlus encyclopedia has pages on Ewing sarcoma
Information for patients and health professionals on Ewing sarcoma is available from the US National Cancer Institute
Cancerbackup offers information for patients and their parents on Ewing sarcoma
Wikipedia has pages on DNA microarrays and expression profiling (note that Wikipedia is a free online encyclopedia that anyone can edit)
PMCID: PMC1851624  PMID: 17425403
3.  Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole 
The authors screen for compounds that show synergistic antifungal activity when combined with the widely-used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.
The authors screen for compounds that show synergistic antifungal activity when combined with the widely-used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.
Chemical screens with a library enriched for known drugs identified a diverse set of 148 compounds that potentiated the action of the antifungal drug fluconazole against the fungal pathogens Cryptococcus neoformans, Cryptococcus gattii and Candida albicans, and the model yeast Saccharomyces cerevisiae, often in a species-specific manner.Chemogenomic profiles of six confirmed hits in S. cerevisiae revealed different modes of action and enabled the prediction of additional synergistic combinations; three-way synergistic interactions exhibited even stronger synergies at low doses of fluconazole.The synergistic combination of fluconazole and the antidepressant sertraline was active against fluconazole-resistant clinical fungal isolates and in an in vivo model of Cryptococcal infection.
Rising fungal infection rates, especially among immune-suppressed individuals, represent a serious clinical challenge (Gullo, 2009). Cancer, organ transplant and HIV patients, for example, often succumb to opportunistic fungal pathogens. The limited repertoire of approved antifungal agents and emerging drug resistance in the clinic further complicate the effective treatment of systemic fungal infections. At the molecular level, the paucity of fungal-specific essential targets arises from the conserved nature of cellular functions from yeast to humans, as well as from the fact that many essential yeast genes can confer viability at a fraction of wild-type dosage (Yan et al, 2009). Although only ∼1100 of the ∼6000 genes in yeast are essential, almost all genes become essential in specific genetic backgrounds in which another non-essential gene has been deleted or otherwise attenuated, an effect termed synthetic lethality (Tong et al, 2001). Genome-scale surveys suggest that over 200 000 binary synthetic lethal gene combinations dominate the yeast genetic landscape (Costanzo et al, 2010). The genetic buffering phenomenon is also manifest as a plethora of differential chemical–genetic interactions in the presence of sublethal doses of bioactive compounds (Hillenmeyer et al, 2008). These observations frame the difficulty of interdicting network functions in eukaryotic pathogens with single agent therapeutics. At the same time, however, this genetic network organization suggests that judicious combinations of small molecule inhibitors of both essential and non-essential targets may elicit additive or synergistic effects on cell growth (Sharom et al, 2004; Lehar et al, 2008). Unbiased screens for drugs that synergistically enhance a specific bioactive effect, but which are not themselves individually active—termed a syncretic combination—are one means to substantially elaborate chemical space (Keith et al, 2005). Indeed, compounds that enhance the activity of known agents in model yeast and cancer cell line systems have been identified both by focused small molecule library screens and by computational methods (Borisy et al, 2003; Lehar et al, 2007; Nelander et al, 2008; Jansen et al, 2009; Zinner et al, 2009).
To extend the stratagem of chemical synthetic lethality to clinically relevant fungal pathogens, we screened a bioactive library of known drugs for synergistic enhancers of the widely used fungistatic drug fluconazole against the clinically relevant pathogens C. albicans, C. neoformans and C. gattii, as well as the genetically tractable budding yeast S. cerevisiae. Fluconazole is an azole drug that inhibits lanosterol 14α-demethylase, the gene product of ERG11, an essential cytochrome P450 enzyme in the ergosterol biosynthetic pathway (Groll et al, 1998). We identified 148 drugs that potentiate the antifungal action of fluconazole against the four species. These syncretic compounds had not been previously recognized in the clinic as antifungal agents, and many acted in a species-specific manner, often in a potent fungicidal manner.
To understand the mechanisms of synergism, we interrogated six syncretic drugs—trifluoperazine, tamoxifen, clomiphene, sertraline, suloctidil and L-cycloserine—in genome-wide chemogenomic profiles of the S. cerevisiae deletion strain collection (Giaever et al, 1999). These profiles revealed that membrane, vesicle trafficking and lipid biosynthesis pathways are targeted by five of the synergizers, whereas the sphingolipid biosynthesis pathway is targeted by L-cycloserine. Cell biological assays confirmed the predicted membrane disruption effects of the former group of compounds, which may perturb ergosterol metabolism, impair fluconazole export by drug efflux pumps and/or affect active import of fluconazole (Kuo et al, 2010; Mansfield et al, 2010). Based on the integration of chemical–genetic and genetic interaction space, a signature set of deletion strains that are sensitive to the membrane active synergizers correctly predicted additional drug synergies with fluconazole. Similarly, the L-cycloserine chemogenomic profile correctly predicted a synergistic interaction between fluconazole and myriocin, another inhibitor of sphingolipid biosynthesis. The structure of genetic networks suggests that it should be possible to devise higher order drug combinations with even greater selectivity and potency (Sharom et al, 2004). In an initial test of this concept, we found that the combination of a non-synergistic pair drawn from the membrane active and sphingolipid target classes exhibited potent three-way synergism with a low dose of fluconazole. Finally, the combination of sertraline and fluconazole was active in a G. mellonella model of Cryptococcal infection, and was also efficacious against fluconazole-resistant clinical isolates of C. albicans and C. glabrata.
Collectively, these results demonstrate that the combinatorial redeployment of known drugs defines a powerful antifungal strategy and establish a number of potential lead combinations for future clinical assessment.
Resistance to widely used fungistatic drugs, particularly to the ergosterol biosynthesis inhibitor fluconazole, threatens millions of immunocompromised patients susceptible to invasive fungal infections. The dense network structure of synthetic lethal genetic interactions in yeast suggests that combinatorial network inhibition may afford increased drug efficacy and specificity. We carried out systematic screens with a bioactive library enriched for off-patent drugs to identify compounds that potentiate fluconazole action in pathogenic Candida and Cryptococcus strains and the model yeast Saccharomyces. Many compounds exhibited species- or genus-specific synergism, and often improved fluconazole from fungistatic to fungicidal activity. Mode of action studies revealed two classes of synergistic compound, which either perturbed membrane permeability or inhibited sphingolipid biosynthesis. Synergistic drug interactions were rationalized by global genetic interaction networks and, notably, higher order drug combinations further potentiated the activity of fluconazole. Synergistic combinations were active against fluconazole-resistant clinical isolates and an in vivo model of Cryptococcus infection. The systematic repurposing of approved drugs against a spectrum of pathogens thus identifies network vulnerabilities that may be exploited to increase the activity and repertoire of antifungal agents.
PMCID: PMC3159983  PMID: 21694716
antifungal; combination; pathogen; resistance; synergism
4.  Chemical combination effects predict connectivity in biological systems 
Chemical synergies can be novel probes of biological systems.Simulated response shapes depend on target connectivity in a pathway.Experiments with yeast and cancer cells confirm simulated effects.Profiles across many combinations yield target location information.
Living organisms are built of interacting components, whose function and dysfunction can be described through dynamic network models (Davidson et al, 2002). Systems Biology involves the iterative construction of such models (Ideker et al, 2001), and may eventually improve the understanding of diseases using in silico simulations. Such simulations may eventually permit drugs to be prioritized for clinical trials, reducing potential risks and increasing the likelihood of successful outcomes. Given the complexity of biological systems, constructing realistic models will require large and diverse sets of connectivity data.
Chemical combinations provide a new window into biological connectivity. Information gleaned from targeted combinations, such as paired mutations (Tong et al, 2004), has proven to be especially useful for revealing functional interactions between components. We have been screening chemical combinations for therapeutic synergies (Borisy et al, 2003; Zimmermann et al, 2007), collecting full-dose matrices where combinations are tested in all possible pairings of serially diluted single agent doses (Figure 1). Such screens yield a variety of response surfaces with distinct shapes for combinations that work through different known mechanisms, suggesting that combination effects may contain information on the nature of functional connections between drug targets.
Simulations of biological pathways predict synergistic responses to inhibitors that depend on target connectivity. We explored theoretical predictions by simulating a metabolic pathway with pairs of inhibitors aimed at different targets with varying doses. We found that the shape of each combination response depended on how the inhibitor pair's targets were connected in the pathway (Figure 2). The predicted response shapes were robust to plausible variations in the simulated pathway that did not affect the network topology (e.g., kinetic assumptions, parameter values, and nonlinear response functions), but were very sensitive to topological alterations in the modelled network (e.g., feedback regulation or changing the type of junction at a branch point). These findings suggest that connectivity of the inhibitor targets has a major influence on combination response morphology.
The predicted shapes were experimentally confirmed in yeast combination experiments. The proliferation experiment used drugs focused on the sterol biosynthesis pathway, which is mostly linear between the targets covered in this study, and is known to be regulated by negative feedback (Gardner et al, 2001). The combinations between sterol inhibitors confirmed expectations from our simulations, showing dose-additive responses for pairs targeting the same enzyme and strong synergies across enzymes of the shape predicted in our simulations for linear pathways under negative feedback. Combinations across pathways showed much more variable responses with a trend towards less synergy on average.
Further experimental support was obtained from human cells. A combination screen of 90 annotated drugs in a human tumour cell line (HCT116) proliferation assay produced strong synergies for combinations within pathways and more variable effects between targeted functions. Synergy profiles (sets of all synergy scores involving each drug) also showed a greater degree of similarity for pairs of drugs with related targets. Finally, the most extreme outliers were dominated by inhibitors of kinases that are especially critical for HCT116 proliferation (Awwad et al, 2003), with effects that are consistent across mechanistic replicates, showing that chemical combinations can highlight biologically relevant cellular processes.
This study demonstrates the potential of chemical combinations for exploring functional connectivity in biological systems. This information complements genetic studies by providing more details through variable dosing, by directly targeting single domains of multi-domain proteins, and by probing cell types that are not amenable to mutagenesis. Responses from large chemical combination screens can be used to identify molecular targets through chemical–genetic profiling (Macdonald et al, 2006), or to directly constrain network models by means of a prediction-validation procedure (Ideker et al, 2001). This initial exploration can be extended to cover a wider range of response shapes and network topologies, as well as to combinations of three or more chemical agents. Moreover, this approach may even be applicable to non-biological systems where responses to targeted perturbations can be measured.
Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.
PMCID: PMC1828746  PMID: 17332758
chemical genetics; combinations and synergy; metabolic and regulatory networks; simulation and data analysis
5.  RNA interference (RNAi) screening approach identifies agents that enhance paclitaxel activity in breast cancer cells 
Paclitaxel is a widely used drug in the treatment of patients with locally advanced and metastatic breast cancer. However, only a small portion of patients have a complete response to paclitaxel-based chemotherapy, and many patients are resistant. Strategies that increase sensitivity and limit resistance to paclitaxel would be of clinical use, especially for patients with triple-negative breast cancer (TNBC).
We generated a gene set from overlay of the druggable genome and a collection of genomically deregulated gene transcripts in breast cancer. We used loss-of-function RNA interference (RNAi) to identify gene products in this set that, when targeted, increase paclitaxel sensitivity. Pharmacological agents that targeted the top scoring hits/genes from our RNAi screens were used in combination with paclitaxel, and the effects on the growth of various breast cancer cell lines were determined.
RNAi screens performed herein were validated by identification of genes in pathways that, when previously targeted, enhanced paclitaxel sensitivity in the pre-clinical and clinical settings. When chemical inhibitors, CCT007093 and mithramycin, against two top hits in our screen, PPMID and SP1, respectively, were used in combination with paclitaxel, we observed synergistic growth inhibition in both 2D and 3D breast cancer cell cultures. The transforming growth factor beta (TGFβ) receptor inhibitor, LY2109761, that targets the signaling pathway of another top scoring hit, TGFβ1, was synergistic with paclitaxel when used in combination on select breast cancer cell lines grown in 3D culture. We also determined the relative paclitaxel sensitivity of 22 TNBC cell lines and identified 18 drug-sensitive and four drug-resistant cell lines. Of significance, we found that both CCT007093 and mithramycin, when used in combination with paclitaxel, resulted in synergistic inhibition of the four paclitaxel-resistant TNBC cell lines.
RNAi screening can identify druggable targets and novel drug combinations that can sensitize breast cancer cells to paclitaxel. This genomic-based approach can be applied to a multitude of tumor-derived cell lines and drug treatments to generate requisite pre-clinical data for new drug combination therapies to pursue in clinical investigations.
PMCID: PMC2917036  PMID: 20576088
6.  Predicting selective drug targets in cancer through metabolic networks 
The authors develop a genome-scale model of cancer metabolism and use it to predict genes that are essential for cancer cell growth. An array of target combinations are then identified that could potentially provide novel selective treatments for specific cancers.
The first genome-scale network model of cancer metabolism is developed and validated by successfully identifying genes essential for cellular proliferation in cancer cell lines.The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new.Combinations of synthetic lethal drug targets are predicted, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection.Potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled.
During tumor development, cancer cells modify their metabolism to meet the requirements of cellular proliferation, thus facilitating the uptake and conversion of nutrients into biomass. Many key metabolic alterations are similar across tumor cells, including changes in glucose metabolism that give rise to the Warburg effect, and an increase in biosynthetic activities (such as nucleotide, lipids and amino-acid synthesis) (DeBerardinis et al, 2008; Tennant et al, 2009; Vander Heiden et al, 2009). The observation that many types of cancer cells adapt their metabolism toward increased proliferation makes flux balance analysis (FBA), a constraint-based modeling (CBM) approach, suitable for modeling cancer metabolism as it assumes that cells are under selective pressure to increase their growth rate (Price et al, 2003).
Building on previous reconstructions of a generic (non-tissue specific) human metabolic network (Duarte et al, 2007; Ma et al, 2007), we develop here the first large-scale FBA model of cancer metabolism that aims to capture the main metabolic alterations that are common across many cancer types. The model reconstruction is based on our recent computational method for the automatic reconstruction of human tissue metabolic models (Jerby et al, 2010), integrating the human metabolic model with cancer gene expression data. The construction of the cancer model focuses on activating a core set of metabolic enzyme-coding genes that are highly expressed across cancer cell lines in the NCI-60 collection, with additional reactions enabling their activation and the biosynthesis of a set of biomass compounds required for cellular proliferation. This generic cancer model enables the successful prediction of the metabolic state of cancer cells across different gene knockdowns and modeling the effects of drug applications on a large scale. As a first demonstration of the predictive performance of the cancer model, we applied it to predict 199 growth-supporting genes whose knockdown is expected to inhibit cellular proliferation, showing that the model predictions indeed match results of shRNA gene silencing experiments (Luo et al, 2008).
To identify viable anticancer drug targets, we predicted whether the knockdown of the growth-supporting genes is likely to be toxic to normal cells. Out of the 199 genes that are predicted to be growth supporting in the cancer model, 52 are predicted to have negligible effects on energy production in normal cells. However, the knockdown of the majority of the latter is yet predicted to potentially cause damage to proliferation of normal cells, suggesting that the targeting of these genes would cause similar side effects to those observed with current cytostatic drugs (Partridge et al, 2001). Next, we predicted 342 synthetic lethal drug targets, whose predicted synergy was validated based on (i) comparison with genetic interactions between the corresponding yeast orthologs (Costanzo et al, 2010) and (ii) by analyzing the efficacy of metabolic drugs targeting these genes, finding that drugs that target a single gene (participating in a predicted synthetic lethal pair) indeed have higher efficacy in cell lines in which the synergistic gene is lowly expressed. In contrast to the single targets described above, the knockdown of a third of these synergistic pairs is predicted to leave the proliferation of normal cells intact. Most importantly, the specific targeting of a gene participating in a synergistic pair is especially appealing in tumors in which its interacting gene is specifically inactivated—the targeting of such a gene solely is likely to selectively damage the tumor, without affecting the function of healthy tissues in which the interacting gene in the pair is active. We utilized genomic and transcriptomic data to infer gene inactivation across an array of cancers, which has led to the identification of cancer type-specific targets based on the intersection of this data with our predicted synergistic gene pairs.
In summary, the model presented here lays down a fundamental computational approach for interpreting the rapidly accumulating proteomics (Bichsel et al, 2001) and metabolomics (Fan et al, 2009) data characterizing cancer metabolic alterations. We hope that the publication of this first step will spur further studies aimed at obtaining a systems level understanding of cancer metabolism and at designing new therapeutic means that selectively target them.
The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled.
PMCID: PMC3159974  PMID: 21694718
cancer; metabolic; metabolism; modeling; selectivity
7.  A Novel Tumor-Promoting Function Residing in the 5′ Non-coding Region of vascular endothelial growth factor mRNA 
PLoS Medicine  2008;5(5):e94.
Vascular endothelial growth factor-A (VEGF) is one of the key regulators of tumor development, hence it is considered to be an important therapeutic target for cancer treatment. However, clinical trials have suggested that anti-VEGF monotherapy was less effective than standard chemotherapy. On the basis of the evidence, we hypothesized that vegf mRNA may have unrecognized function(s) in cancer cells.
Methods and Findings
Knockdown of VEGF with vegf-targeting small-interfering (si) RNAs increased susceptibility of human colon cancer cell line (HCT116) to apoptosis caused with 5-fluorouracil, etoposide, or doxorubicin. Recombinant human VEGF165 did not completely inhibit this apoptosis. Conversely, overexpression of VEGF165 increased resistance to anti-cancer drug-induced apoptosis, while an anti-VEGF165-neutralizing antibody did not completely block the resistance. We prepared plasmids encoding full-length vegf mRNA with mutation of signal sequence, vegf mRNAs lacking untranslated regions (UTRs), or mutated 5′UTRs. Using these plasmids, we revealed that the 5′UTR of vegf mRNA possessed anti-apoptotic activity. The 5′UTR-mediated activity was not affected by a protein synthesis inhibitor, cycloheximide. We established HCT116 clones stably expressing either the vegf 5′UTR or the mutated 5′UTR. The clones expressing the 5′UTR, but not the mutated one, showed increased anchorage-independent growth in vitro and formed progressive tumors when implanted in athymic nude mice. Microarray and quantitative real-time PCR analyses indicated that the vegf 5′UTR-expressing tumors had up-regulated anti-apoptotic genes, multidrug-resistant genes, and growth-promoting genes, while pro-apoptotic genes were down-regulated. Notably, expression of signal transducers and activators of transcription 1 (STAT1) was markedly repressed in the 5′UTR-expressing tumors, resulting in down-regulation of a STAT1-responsive cluster of genes (43 genes). As a result, the tumors did not respond to interferon (IFN)α therapy at all. We showed that stable silencing of endogenous vegf mRNA in HCT116 cells enhanced both STAT1 expression and IFNα responses.
These findings suggest that cancer cells have a survival system that is regulated by vegf mRNA and imply that both vegf mRNA and its protein may synergistically promote the malignancy of tumor cells. Therefore, combination of anti-vegf transcript strategies, such as siRNA-based gene silencing, with anti-VEGF antibody treatment may improve anti-cancer therapies that target VEGF.
Shigetada Teshima-Kondo and colleagues find that cancer cells have a survival system that is regulated by vegf mRNA and that vegf mRNA and its protein may synergistically promote the malignancy of tumor cells.
Editors' Summary
Normally, throughout life, cell division (which produces new cells) and cell death are carefully balanced to keep the body in good working order. But sometimes cells acquire changes (mutations) in their genetic material that allow them to divide uncontrollably to form cancers—disorganized masses of cells. When a cancer is small, it uses the body's existing blood supply to get the oxygen and nutrients it needs for its growth and survival. But, when it gets bigger, it has to develop its own blood supply. This process is called angiogenesis. It involves the release by the cancer cells of proteins called growth factors that bind to other proteins (receptors) on the surface of endothelial cells (the cells lining blood vessels). The receptors then send signals into the endothelial cells that tell them to make new blood vessels. One important angiogenic growth factor is “vascular endothelial growth factor” (VEGF). Tumors that make large amounts of VEGF tend to be more abnormal and more aggressive than those that make less VEGF. In addition, high levels of VEGF in the blood are often associated with poor responses to chemotherapy, drug regimens designed to kill cancer cells.
Why Was This Study Done?
Because VEGF is a key regulator of tumor development, several anti-VEGF therapies—drugs that target VEGF and its receptors—have been developed. These therapies strongly suppress the growth of tumor cells in the laboratory and in animals but, when used alone, are no better at increasing the survival times of patients with cancer than standard chemotherapy. Scientists are now looking for an explanation for this disappointing result. Like all proteins, cells make VEGF by “transcribing” its DNA blueprint into an mRNA copy (vegf mRNA), the coding region of which is “translated” into the VEGF protein. Other, “noncoding” regions of vegf mRNA control when and where VEGF is made. Scientists have recently discovered that the noncoding regions of some mRNAs suppress tumor development. In this study, therefore, the researchers investigate whether vegf mRNA has an unrecognized function in tumor cells that could explain the disappointing clinical results of anti-VEGF therapeutics.
What Did the Researchers Do and Find?
The researchers first used a technique called small interfering (si) RNA knockdown to stop VEGF expression in human colon cancer cells growing in dishes. siRNAs are short RNAs that bind to and destroy specific mRNAs in cells, thereby preventing the translation of those mRNAs into proteins. The treatment of human colon cancer cells with vegf-targeting siRNAs made the cells more sensitive to chemotherapy-induced apoptosis (a type of cell death). This sensitivity was only partly reversed by adding VEGF to the cells. By contrast, cancer cells engineered to make more vegf mRNA had increased resistance to chemotherapy-induced apoptosis. Treatment of these cells with an antibody that inhibited VEGF function did not completely block this resistance. Together, these results suggest that both vegf mRNA and VEGF protein have anti-apoptotic effects. The researchers show that the anti-apoptotic activity of vegf mRNA requires a noncoding part of the mRNA called the 5′ UTR, and that whereas human colon cancer cells expressing this 5′ UTR form tumors in mice, cells expressing a mutated 5′ UTR do not. Finally, they report that the expression of several pro-apoptotic genes and of an anti-tumor pathway known as the interferon/STAT1 tumor suppression pathway is down-regulated in tumors that express the vegf 5′ UTR.
What Do These Findings Mean?
These findings suggest that some cancer cells have a survival system that is regulated by vegf mRNA and are the first to show that a 5′UTR of mRNA can promote tumor growth. They indicate that VEGF and its mRNA work together to promote their development and to increase their resistance to chemotherapy drugs. They suggest that combining therapies that prevent the production of vegf mRNA (for example, siRNA-based gene silencing) with therapies that block the function of VEGF might improve survival times for patients whose tumors overexpress VEGF.
Additional Information.
Please access these Web sites via the online version of this summary at
This study is discussed further in a PLoS Medicine Perspective by Hughes and Jones
The US National Cancer Institute provides information about all aspects of cancer, including information on angiogenesis, and on bevacizumab, an anti-VEGF therapeutic (in English and Spanish)
CancerQuest, from Emory University, provides information on all aspects of cancer, including angiogenesis (in several languages)
Cancer Research UK also provides basic information about what causes cancers and how they develop, grow, and spread, including information about angiogenesis
Wikipedia has pages on VEGF and on siRNA (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
PMCID: PMC2386836  PMID: 18494554
8.  Disruption of focal adhesion kinase and p53 interaction with small molecule compound R2 reactivated p53 and blocked tumor growth 
BMC Cancer  2013;13:342.
Focal Adhesion Kinase (FAK) is a 125 kDa non-receptor kinase that plays a major role in cancer cell survival and metastasis.
We performed computer modeling of the p53 peptide containing the site of interaction with FAK, predicted the peptide structure and docked it into the three-dimensional structure of the N-terminal domain of FAK involved in the complex with p53. We screened small molecule compounds that targeted the site of the FAK-p53 interaction and identified compounds (called Roslins, or R compounds) docked in silico to this site.
By different assays in isogenic HCT116p53+/+ and HCT116 p53-/- cells we identified a small molecule compound called Roslin 2 (R2) that bound FAK, disrupted the binding of FAK and p53 and decreased cancer cell viability and clonogenicity in a p53-dependent manner. In addition, dual-luciferase assays demonstrated that the R2 compound increased p53 transcriptional activity that was inhibited by FAK using p21, Mdm-2, and Bax-promoter targets. R2 also caused increased expression of p53 targets: p21, Mdm-2 and Bax proteins. Furthermore, R2 significantly decreased tumor growth, disrupted the complex of FAK and p53, and up-regulated p21 in HCT116 p53+/+ but not in HCT116 p53-/- xenografts in vivo. In addition, R2 sensitized HCT116p53+/+ cells to doxorubicin and 5-fluorouracil.
Thus, disruption of the FAK and p53 interaction with a novel small molecule reactivated p53 in cancer cells in vitro and in vivo and can be effectively used for development of FAK-p53 targeted cancer therapy approaches.
PMCID: PMC3712010  PMID: 23841915
Focal adhesion kinase; p53Cancer; Small molecule; p21; Tumor; Apoptosis
9.  An Evolutionarily Conserved Synthetic Lethal Interaction Network Identifies FEN1 as a Broad-Spectrum Target for Anticancer Therapeutic Development 
PLoS Genetics  2013;9(1):e1003254.
Harnessing genetic differences between cancerous and noncancerous cells offers a strategy for the development of new therapies. Extrapolating from yeast genetic interaction data, we used cultured human cells and siRNA to construct and evaluate a synthetic lethal interaction network comprised of chromosome instability (CIN) genes that are frequently mutated in colorectal cancer. A small number of genes in this network were found to have synthetic lethal interactions with a large number of cancer CIN genes; these genes are thus attractive targets for anticancer therapeutic development. The protein product of one highly connected gene, the flap endonuclease FEN1, was used as a target for small-molecule inhibitor screening using a newly developed fluorescence-based assay for enzyme activity. Thirteen initial hits identified through in vitro biochemical screening were tested in cells, and it was found that two compounds could selectively inhibit the proliferation of cultured cancer cells carrying inactivating mutations in CDC4, a gene frequently mutated in a variety of cancers. Inhibition of flap endonuclease activity was also found to recapitulate a genetic interaction between FEN1 and MRE11A, another gene frequently mutated in colorectal cancers, and to lead to increased endogenous DNA damage. These chemical-genetic interactions in mammalian cells validate evolutionarily conserved synthetic lethal interactions and demonstrate that a cross-species candidate gene approach is successful in identifying small-molecule inhibitors that prove effective in a cell-based cancer model.
Author Summary
Anticancer therapeutic discovery is a major challenge in cancer research. Because cancer is a disease caused by somatic genetic mutations, the search for anticancer therapeutics is often driven by the ability to exploit genetic differences specific to tumor cells. Recently, cancer therapeutic development has sought to exploit synthetic lethality, a situation in which the combination of two independently viable mutations results in lethality. If a compound can be found to selectively kill a specific genotype via inhibition of a specific gene product, this is known as a chemical-genetic interaction, and it mimics a synthetic lethal genetic interaction. The ideal therapeutic would be broad spectrum, that is, active against multiple cancer genotypes within a tumor type and/or across a variety of cancers. We have developed an approach, taking advantage of the evolutionary conservation of synthetic lethal interactions, to identify “second-site” targets in cancer: genes whose chemical inhibition leads to selective killing of tumor cells across a broad spectrum of cancer genotypes. We identified small-molecule inhibitors of one such target, FEN1, and showed that these compounds were able to selectively kill human cells carrying cancer-relevant mutations. This approach will facilitate the development of anticancer therapeutics active against a variety of cancer genotypes.
PMCID: PMC3561056  PMID: 23382697
10.  Cdc25B Dual-Specificity Phosphatase Inhibitors Identified in a High-Throughput Screen of the NIH Compound Library 
The University of Pittsburgh Molecular Library Screening Center (Pittsburgh, PA) conducted a screen with the National Institutes of Health compound library for inhibitors of in vitro cell division cycle 25 protein (Cdc25) B activity during the pilot phase of the Molecular Library Screening Center Network. Seventy-nine (0.12%) of the 65,239 compounds screened at 10 μM met the active criterion of ≥50% inhibition of Cdc25B activity, and 25 (31.6%) of these were confirmed as Cdc25B inhibitors with 50% inhibitory concentration (IC50) values <50 μM. Thirteen of the Cdc25B inhibitors were represented by singleton chemical structures, and 12 were divided among four clusters of related structures. Thirteen (52%) of the Cdc25B inhibitor hits were quinone-based structures. The Cdc25B inhibitors were further characterized in a series of in vitro secondary assays to confirm their activity, to determine their phosphatase selectivity against two other dual-specificity phosphatases, mitogen-activated protein kinase phosphatase (MKP)-1 and MKP-3, and to examine if the mechanism of Cdc25B inhibition involved oxidation and inactivation. Nine Cdc25B inhibitors did not appear to affect Cdc25B through a mechanism involving oxidation because they did not generate detectable amounts of H2O2 in the presence of dithiothreitol, and their Cdc25B IC50 values were not significantly affected by exchanging the dithiothreitol for β-mercaptoethanol or reduced glutathione or by adding catalase to the assay. Six of the nonoxidative hits were selective for Cdc25B inhibition versus MKP-1 and MKP-3, but only the two bisfuran-containing hits, PubChem substance identifiers 4258795 and 4260465, significantly inhibited the growth of human MBA-MD-435 breast and PC-3 prostate cancer cell lines. To confirm the structure and biological activity of 4260465, the compound was resynthesized along with two analogs. Neither of the substitutions to the two analogs was tolerated, and only the resynthesized hit 26683752 inhibited Cdc25B activity in vitro (IC50 = 13.83 ± 1.0 μM) and significantly inhibited the growth of the MBA-MD-435 breast and PC-3 prostate cancer cell lines (IC50 = 20.16 ± 2.0 μM and 24.87 ± 2.25 μM, respectively). The two bis-furan-containing hits identified in the screen represent novel nonoxidative Cdc25B inhibitor chemotypes that block tumor cell proliferation. The availability of non-redox active Cdc25B inhibitors should provide valuable tools to explore the inhibition of the Cdc25 phosphatases as potential mono- or combination therapies for cancer.
PMCID: PMC2956648  PMID: 19530895
11.  A Discovery Funnel for Nucleic Acid Binding Drug Candidates 
Drug development research  2011;72(2):178-186.
Computational approaches are becoming increasingly popular for the discovery of drug candidates against a target of interest. Proteins have historically been the primary targets of many virtual screening efforts. While in silico screens targeting proteins has proven successful, other classes of targets, in particular DNA, remain largely unexplored using virtual screening methods. With the realization of the functional importance of many non-cannonical DNA structures such as G-quadruplexes, increased efforts are underway to discover new small molecules that can bind selectively to DNA structures. Here, we describe efforts to build an integrated in silico and in vitro platform for discovering compounds that may bind to a chosen DNA target. Millions of compounds are initially screened in silico for selective binding to a particular structure and ranked to identify several hundred best hits. An important element of our strategy is the inclusion of an array of possible competing structures in the in silico screen. The best hundred or so hits are validated experimentally for binding to the actual target structure by a high-throughput 96-well thermal denaturation assay to yield the top ten candidates. Finally, these most promising candidates are thoroughly characterized for binding to their DNA target by rigorous biophysical methods, including isothermal titration calorimetry, differential scanning calorimetry, spectroscopy and competition dialysis.This platform was validated using quadruplex DNA as a target and a newly discovered quadruplex binding compound with possible anti-cancer activity was discovered. Some considerations when embarking on virtual screening and in silico experiments are also discussed.
PMCID: PMC3090163  PMID: 21566705
drug discovery; in silico screening; SURFLEX-DOCK; DNA; G-quadruplex; high-throughput screening
12.  Prediction of the P. falciparum Target Space Relevant to Malaria Drug Discovery 
PLoS Computational Biology  2013;9(10):e1003257.
Malaria is still one of the most devastating infectious diseases, affecting hundreds of millions of patients worldwide. Even though there are several established drugs in clinical use for malaria treatment, there is an urgent need for new drugs acting through novel mechanisms of action due to the rapid development of resistance. Resistance emerges when the parasite manages to mutate the sequence of the drug targets to the extent that the protein can still perform its function in the parasite but can no longer be inhibited by the drug, which then becomes almost ineffective. The design of a new generation of malaria drugs targeting multiple essential proteins would make it more difficult for the parasite to develop full resistance without lethally disrupting some of its vital functions. The challenge is then to identify which set of Plasmodium falciparum proteins, among the millions of possible combinations, can be targeted at the same time by a given chemotype. To do that, we predicted first the targets of the close to 20,000 antimalarial hits identified recently in three independent phenotypic screening campaigns. All targets predicted were then projected onto the genome of P. falciparum using orthologous relationships. A total of 226 P. falciparum proteins were predicted to be hit by at least one compound, of which 39 were found to be significantly enriched by the presence and degree of affinity of phenotypically active compounds. The analysis of the chemically compatible target combinations containing at least one of those 39 targets led to the identification of a priority set of 64 multi-target profiles that can set the ground for a new generation of more robust malaria drugs.
Author Summary
There is an urgent need for new antimalarials acting through novel mechanisms of action that can overcome the increasing incidence of resistance observed for currently used drugs. In this respect, drug polypharmacology is emerging as an attractive strategy to reduce the chances of the parasite evolving drug resistance. Although there were close to 20,000 antimalarial hits recently identified from three independent phenotypic screenings, the molecular targets through which most of those active compounds exert their antimalarial action are unfortunately unknown at present. To address this issue, a computational approach was first used to predict their protein targets. Statistical analyses were applied to detect any enrichment of phenotypically active compounds in P. falciparum targets, leading to a final list of 39 putative high-priority targets for malaria drug discovery. The presence of at least one high-priority target in the target profile predicted for the antimalarial hits was then used as a constraint to identify a priority set of 64 multi-target profiles. Multi-target strategies based on such profiles can set the basis for designing a next generation of more robust malaria drugs.
PMCID: PMC3798273  PMID: 24146604
13.  Analysis of multiple compound–protein interactions reveals novel bioactive molecules 
The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases.
We have demonstrated that machine learning of multiple compound–protein interactions is useful for efficient ligand screening and for assessing drug polypharmacology.This approach successfully identified novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein-coupled receptors and protein kinases.These bioactive compounds were not detected by existing computational ligand-screening methods in comparative studies.The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. Perturbations of biological systems by chemical probes provide broader applications not only for analysis of complex systems but also for intentional manipulations of these systems. Nevertheless, the lack of well-characterized chemical modulators has limited their use. Recently, chemical genomics has emerged as a promising area of research applicable to the exploration of novel bioactive molecules, and researchers are currently striving toward the identification of all possible ligands for all target protein families (Wang et al, 2009). Chemical genomics studies have shown that patterns of compound–protein interactions (CPIs) are too diverse to be understood as simple one-to-one events. There is an urgent need to develop appropriate data mining methods for characterizing and visualizing the full complexity of interactions between chemical space and biological systems. However, no existing screening approach has so far succeeded in identifying novel bioactive compounds using multiple interactions among compounds and target proteins.
High-throughput screening (HTS) and computational screening have greatly aided in the identification of early lead compounds for drug discovery. However, the large number of assays required for HTS to identify drugs that target multiple proteins render this process very costly and time-consuming. Therefore, interest in using in silico strategies for screening has increased. The most common computational approaches, ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS; Oprea and Matter, 2004; Muegge and Oloff, 2006; McInnes, 2007; Figure 1A), have been used for practical drug development. LBVS aims to identify molecules that are very similar to known active molecules and generally has difficulty identifying compounds with novel structural scaffolds that differ from reference molecules. The other popular strategy, SBVS, is constrained by the number of three-dimensional crystallographic structures available. To circumvent these limitations, we have shown that a new computational screening strategy, chemical genomics-based virtual screening (CGBVS), has the potential to identify novel, scaffold-hopping compounds and assess their polypharmacology by using a machine-learning method to recognize conserved molecular patterns in comprehensive CPI data sets.
The CGBVS strategy used in this study was made up of five steps: CPI data collection, descriptor calculation, representation of interaction vectors, predictive model construction using training data sets, and predictions from test data (Figure 1A). Importantly, step 1, the construction of a data set of chemical structures and protein sequences for known CPIs, did not require the three-dimensional protein structures needed for SBVS. In step 2, compound structures and protein sequences were converted into numerical descriptors. These descriptors were used to construct chemical or biological spaces in which decreasing distance between vectors corresponded to increasing similarity of compound structures or protein sequences. In step 3, we represented multiple CPI patterns by concatenating these chemical and protein descriptors. Using these interaction vectors, we could quantify the similarity of molecular interactions for compound–protein pairs, despite the fact that the ligand and protein similarity maps differed substantially. In step 4, concatenated vectors for CPI pairs (positive samples) and non-interacting pairs (negative samples) were input into an established machine-learning method. In the final step, the classifier constructed using training sets was applied to test data.
To evaluate the predictive value of CGBVS, we first compared its performance with that of LBVS by fivefold cross-validation. CGBVS performed with considerably higher accuracy (91.9%) than did LBVS (84.4%; Figure 1B). We next compared CGBVS and SBVS in a retrospective virtual screening based on the human β2-adrenergic receptor (ADRB2). Figure 1C shows that CGBVS provided higher hit rates than did SBVS. These results suggest that CGBVS is more successful than conventional approaches for prediction of CPIs.
We then evaluated the ability of the CGBVS method to predict the polypharmacology of ADRB2 by attempting to identify novel ADRB2 ligands from a group of G-protein-coupled receptor (GPCR) ligands. We ranked the prediction scores for the interactions of 826 reported GPCR ligands with ADRB2 and then analyzed the 50 highest-ranked compounds in greater detail. Of 21 commercially available compounds, 11 showed ADRB2-binding activity and were not previously reported to be ADRB2 ligands. These compounds included ligands not only for aminergic receptors but also for neuropeptide Y-type 1 receptors (NPY1R), which have low protein homology to ADRB2. Most ligands we identified were not detected by LBVS and SBVS, which suggests that only CGBVS could identify this unexpected cross-reaction for a ligand developed as a target to a peptidergic receptor.
The true value of CGBVS in drug discovery must be tested by assessing whether this method can identify scaffold-hopping lead compounds from a set of compounds that is structurally more diverse. To assess this ability, we analyzed 11 500 commercially available compounds to predict compounds likely to bind to two GPCRs and two protein kinases. Functional assays revealed that nine ADRB2 ligands, three NPY1R ligands, five epidermal growth factor receptor (EGFR) inhibitors, and two cyclin-dependent kinase 2 (CDK2) inhibitors were concentrated in the top-ranked compounds (hit rate=30, 15, 25, and 10%, respectively). We also evaluated the extent of scaffold hopping achieved in the identification of these novel ligands. One ADRB2 ligand, two NPY1R ligands, and one CDK2 inhibitor exhibited scaffold hopping (Figure 4), indicating that CGBVS can use this characteristic to rationally predict novel lead compounds, a crucial and very difficult step in drug discovery. This feature of CGBVS is critically different from existing predictive methods, such as LBVS, which depend on similarities between test and reference ligands, and focus on a single protein or highly homologous proteins. In particular, CGBVS is useful for targets with undefined ligands because this method can use CPIs with target proteins that exhibit lower levels of homology.
In summary, we have demonstrated that data mining of multiple CPIs is of great practical value for exploration of chemical space. As a predictive model, CGBVS could provide an important step in the discovery of such multi-target drugs by identifying the group of proteins targeted by a particular ligand, leading to innovation in pharmaceutical research.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
PMCID: PMC3094066  PMID: 21364574
chemical genomics; data mining; drug discovery; ligand screening; systems chemical biology
14.  Ago HITS-CLIP Expands Understanding of Kaposi's Sarcoma-associated Herpesvirus miRNA Function in Primary Effusion Lymphomas 
PLoS Pathogens  2012;8(8):e1002884.
KSHV is the etiological agent of Kaposi's sarcoma (KS), primary effusion lymphoma (PEL), and a subset of multicentricCastleman's disease (MCD). The fact that KSHV-encoded miRNAs are readily detectable in all KSHV-associated tumors suggests a potential role in viral pathogenesis and tumorigenesis. MiRNA-mediated regulation of gene expression is a complex network with each miRNA having many potential targets, and to date only few KSHV miRNA targets have been experimentally determined. A detailed understanding of KSHV miRNA functions requires high-through putribonomics to globally analyze putative miRNA targets in a cell type-specific manner. We performed Ago HITS-CLIP to identify viral and cellular miRNAs and their cognate targets in two latently KSHV-infected PEL cell lines. Ago HITS-CLIP recovered 1170 and 950 cellular KSHVmiRNA targets from BCBL-1 and BC-3, respectively. Importantly, enriched clusters contained KSHV miRNA seed matches in the 3′UTRs of numerous well characterized targets, among them THBS1, BACH1, and C/EBPβ. KSHV miRNA targets were strongly enriched for genes involved in multiple pathways central for KSHV biology, such as apoptosis, cell cycle regulation, lymphocyte proliferation, and immune evasion, thus further supporting a role in KSHV pathogenesis and potentially tumorigenesis. A limited number of viral transcripts were also enriched by HITS-CLIP including vIL-6 expressed only in a subset of PEL cells during latency. Interestingly, Ago HITS-CLIP revealed extremely high levels of Ago-associated KSHV miRNAs especially in BC-3 cells where more than 70% of all miRNAs are of viral origin. This suggests that in addition to seed match-specific targeting of cellular genes, KSHV miRNAs may also function by hijacking RISCs, thereby contributing to a global de-repression of cellular gene expression due to the loss of regulation by human miRNAs. In summary, we provide an extensive list of cellular and viral miRNA targets representing an important resource to decipher KSHV miRNA function.
Author Summary
Kaposi's sarcoma-associated herpesvirus is the etiological agent of KS and two lymphoproliferative diseases: multicentricCastleman's disease and primary effusion lymphomas (PEL). KSHV tumors are the most prevalent AIDS malignancies and within Sub-Saharan Africa KS is the most common cancer in males, both in the presence and absence of HIV infection. KSHV encodes 12 miRNA genes whose function is largely unknown. Viral miRNAs are incorporated into RISCs, which regulate gene expression mostly by binding to 3′UTRs of mRNAs to inhibit their translation and/or induce degradation. The small subset of viral miRNA targets identified to date suggests that these small posttranscriptional regulators target important cellular pathways involved in pathogenesis and tumorgenesis. Using Ago HITS-CLIP, a technique which combines UV cross-linking, immunoprecipitation of Ago-miRNA-mRNA complexes, and high throughput sequencing, we performed a detailed analysis of the KSHV miRNA targetome in two commonly studied PEL cell lines, BCBL-1 and BC-3 and identified 1170 and 950 putative miRNA targets, respectively. This data set provides a valuable resource to decipher how KSHV miRNAs contribute to viral biology and pathogenesis.
PMCID: PMC3426530  PMID: 22927820
15.  A Genome-Wide Screen for Promoter Methylation in Lung Cancer Identifies Novel Methylation Markers for Multiple Malignancies  
PLoS Medicine  2006;3(12):e486.
Promoter hypermethylation coupled with loss of heterozygosity at the same locus results in loss of gene function in many tumor cells. The “rules” governing which genes are methylated during the pathogenesis of individual cancers, how specific methylation profiles are initially established, or what determines tumor type-specific methylation are unknown. However, DNA methylation markers that are highly specific and sensitive for common tumors would be useful for the early detection of cancer, and those required for the malignant phenotype would identify pathways important as therapeutic targets.
Methods and Findings
In an effort to identify new cancer-specific methylation markers, we employed a high-throughput global expression profiling approach in lung cancer cells. We identified 132 genes that have 5′ CpG islands, are induced from undetectable levels by 5-aza-2′-deoxycytidine in multiple non-small cell lung cancer cell lines, and are expressed in immortalized human bronchial epithelial cells. As expected, these genes were also expressed in normal lung, but often not in companion primary lung cancers. Methylation analysis of a subset (45/132) of these promoter regions in primary lung cancer (n = 20) and adjacent nonmalignant tissue (n = 20) showed that 31 genes had acquired methylation in the tumors, but did not show methylation in normal lung or peripheral blood cells. We studied the eight most frequently and specifically methylated genes from our lung cancer dataset in breast cancer (n = 37), colon cancer (n = 24), and prostate cancer (n = 24) along with counterpart nonmalignant tissues. We found that seven loci were frequently methylated in both breast and lung cancers, with four showing extensive methylation in all four epithelial tumors.
By using a systematic biological screen we identified multiple genes that are methylated with high penetrance in primary lung, breast, colon, and prostate cancers. The cross-tumor methylation pattern we observed for these novel markers suggests that we have identified a partial promoter hypermethylation signature for these common malignancies. These data suggest that while tumors in different tissues vary substantially with respect to gene expression, there may be commonalities in their promoter methylation profiles that represent targets for early detection screening or therapeutic intervention.
John Minna and colleagues report that a group of genes are commonly methylated in primary lung, breast, colon, and prostate cancer.
Editors' Summary
Tumors or cancers contain cells that have lost many of the control mechanisms that normally regulate their behavior. Unlike normal cells, which only divide to repair damaged tissues, cancer cells divide uncontrollably. They also gain the ability to move round the body and start metastases in secondary locations. These changes in behavior result from alterations in their genetic material. For example, mutations (permanent changes in the sequence of nucleotides in the cell's DNA) in genes known as oncogenes stimulate cells to divide constantly. Mutations in another group of genes—tumor suppressor genes—disable their ability to restrain cell growth. Key tumor suppressor genes are often completely lost in cancer cells. But not all the genetic changes in cancer cells are mutations. Some are “epigenetic” changes—chemical modifications of genes that affect the amount of protein made from them. In cancer cells, methyl groups are often added to CG-rich regions—this is called hypermethylation. These “CpG islands” lie near gene promoters—sequences that control the transcription of DNA into RNA, the template for protein production—and their methylation switches off the promoter. Methylation of the promoter of one copy of a tumor suppressor gene, which often coincides with the loss of the other copy of the gene, is thought to be involved in cancer development.
Why Was This Study Done?
The rules that govern which genes are hypermethylated during the development of different cancer types are not known, but it would be useful to identify any DNA methylation events that occur regularly in common cancers for two reasons. First, specific DNA methylation markers might be useful for the early detection of cancer. Second, identifying these epigenetic changes might reveal cellular pathways that are changed during cancer development and so identify new therapeutic targets. In this study, the researchers have used a systematic biological screen to identify genes that are methylated in many lung, breast, colon, and prostate cancers—all cancers that form in “epithelial” tissues.
What Did the Researchers Do and Find?
The researchers used microarray expression profiling to examine gene expression patterns in several lung cancer and normal lung cell lines. In this technique, labeled RNA molecules isolated from cells are applied to a “chip” carrying an array of gene fragments. Here, they stick to the fragment that represents the gene from which they were made, which allows the genes that the cells express to be catalogued. By comparing the expression profiles of lung cancer cells and normal lung cells before and after treatment with a chemical that inhibits DNA methylation, the researchers identified genes that were methylated in the cancer cells—that is, genes that were expressed in normal cells but not in cancer cells unless methylation was inhibited. 132 of these genes contained CpG islands. The researchers examined the promoters of 45 of these genes in lung cancer cells taken straight from patients and found that 31 of the promoters were methylated in tumor tissues but not in adjacent normal tissues. Finally, the researchers looked at promoter methylation of the eight genes most frequently and specifically methylated in the lung cancer samples in breast, colon, and prostate cancers. Seven of the genes were frequently methylated in both lung and breast cancers; four were extensively methylated in all the tumor types.
What Do These Findings Mean?
These results identify several new genes that are often methylated in four types of epithelial tumor. The observation that these genes are methylated in multiple independent tumors strongly suggests, but does not prove, that loss of expression of the proteins that they encode helps to convert normal cells into cancer cells. The frequency and diverse patterning of promoter methylation in different tumor types also indicates that methylation is not a random event, although what controls the patterns of methylation is not yet known. The identification of these genes is a step toward building a promoter hypermethylation profile for the early detection of human cancer. Furthermore, although tumors in different tissues vary greatly with respect to gene expression patterns, the similarities seen in this study in promoter methylation profiles might help to identify new therapeutic targets common to several cancer types.
Additional Information.
Please access these Web sites via the online version of this summary at
US National Cancer Institute, information for patients on understanding cancer
CancerQuest, information provided by Emory University about how cancer develops
Cancer Research UK, information for patients on cancer biology
Wikipedia pages on epigenetics (note that Wikipedia is a free online encyclopedia that anyone can edit)
The Epigenome Network of Excellence, background information and latest news about epigenetics
PMCID: PMC1716188  PMID: 17194187
16.  Novel Inhibitors Induce Large Conformational Changes of GAB1 Pleckstrin Homology Domain and Kill Breast Cancer Cells 
PLoS Computational Biology  2015;11(1):e1004021.
The Grb2-associated binding protein 1 (GAB1) integrates signals from different signaling pathways and is over-expressed in many cancers, therefore representing a new therapeutic target. In the present study, we aim to target the pleckstrin homology (PH) domain of GAB1 for cancer treatment. Using homology models we derived, high-throughput virtual screening of five million compounds resulted in five hits which exhibited strong binding affinities to GAB1 PH domain. Our prediction of ligand binding affinities is also in agreement with the experimental KD values. Furthermore, molecular dynamics studies showed that GAB1 PH domain underwent large conformational changes upon ligand binding. Moreover, these hits inhibited the phosphorylation of GAB1 and demonstrated potent, tumor-specific cytotoxicity against MDA-MB-231 and T47D breast cancer cell lines. This effort represents the discovery of first-in-class GAB1 PH domain inhibitors with potential for targeted breast cancer therapy and provides novel insights into structure-based approaches to targeting this protein.
Author Summary
In this paper, we described the identification and evaluation of a set of first-in-class potent inhibitors targeting a new cancer target, Grb2-associated binder-1 (GAB1), which integrates signals from different signaling pathways, and is frequently over-expressed in cancer cells. To achieve our goals, we have employed intensive computational modeling to understand the structure of the GAB1 pleckstrin homology (PH) domain and screened five million compounds. Upon biological evaluation, we found that several inhibitors that induced large conformational changes of the target structure exhibited strong selective binding to GAB1 PH domain. Particularly, these inhibitors demonstrated potent and tumor-specific cytotoxicity in breast cancer cells. This represents a groundbreaking discovery in targeting GAB1 signaling which may be used for cancer therapy, especially for triple negative breast cancer patients.
PMCID: PMC4287437  PMID: 25569504
17.  A Small-molecule Inhibitor, 5′-O-Tritylthymidine, targets FAK and Mdm-2 Interaction, and Blocks Breast and Colon Tumorigenesis in vivo 
Focal Adhesion Kinase (FAK) is overexpressed in many types of tumors and plays an important role in survival. We developed a novel approach, targeting FAK-protein interactions by computer modeling and screening of NCI small molecule drug database. In this report we targeted FAK and Mdm-2 protein interaction to decrease tumor growth. By macromolecular modeling we found a model of FAK and Mdm-2 interaction and performed screening of >200,000 small molecule compounds from NCI database with drug-like characteristics, targeting the FAK-Mdm-2 interaction. We identified 5′-O-Tritylthymidine, called M13 compound that significantly decreased viability in different cancer cells. M13 was docked into the pocket of FAK and Mdm-2 interaction and was directly bound to the FAK-N terminal domain by ForteBio Octet assay. In addition, M13 compound affected FAK and Mdm-2 levels and decreased complex of FAK and Mdm-2 proteins in breast and colon cancer cells. M13 re-activated p53 activity inhibited by FAK with Mdm-2 promoter. M13 decreased viability, clonogenicity, increased detachment and apoptosis in a dose-dependent manner in BT474 breast and in HCT116 colon cancer cells in vitro. M13 decreased FAK, activated p53 and caspase-8 in both cell lines. In addition, M13 decreased breast and colon tumor growth in vivo. M13 activated p53 and decreased FAK in tumor samples consistent with decreased tumor growth. The data demonstrate a novel approach for targeting FAK and Mdm-2 protein interaction, provide a model of FAK and Mdm-2 interaction, identify M13 compound targeting this interaction and decreasing tumor growth that is critical for future targeted therapeutics.
PMCID: PMC3625481  PMID: 22292771
Apoptosis; Focal Adhesion Kinase; Mdm-2; Small molecule compound; p53; Tumor growth
18.  Development of a screen to identify selective small molecules active against patient-derived metastatic and chemoresistant breast cancer cells 
High failure rates of new investigational drugs have impaired the development of breast cancer therapies. One challenge is that excellent activity in preclinical models, such as established cancer cell lines, does not always translate into improved clinical outcomes for patients. New preclinical models, which better replicate clinically-relevant attributes of cancer, such as chemoresistance, metastasis and cellular heterogeneity, may identify novel anti-cancer mechanisms and increase the success of drug development.
Metastatic breast cancer cells were obtained from pleural effusions of consented patients whose disease had progressed. Normal primary human breast cells were collected from a reduction mammoplasty and immortalized with human telomerase. The patient-derived cells were characterized to determine their cellular heterogeneity and proliferation rate by flow cytometry, while dose response curves were performed for chemotherapies to assess resistance. A screen was developed to measure the differential activity of small molecules on the growth and survival of patient-derived normal breast and metastatic, chemoresistant tumor cells to identify selective anti-cancer compounds. Several hits were identified and validated in dose response assays. One compound, C-6, was further characterized for its effect on cell cycle and cell death in cancer cells.
Patient-derived cells were found to be more heterogeneous, with reduced proliferation rates and enhanced resistance to chemotherapy compared to established cell lines. A screen was subsequently developed that utilized both tumor and normal patient-derived cells. Several compounds were identified, which selectively targeted tumor cells, but not normal cells. Compound C-6 was found to inhibit proliferation and induce cell death in tumor cells via a caspase-independent mechanism.
Short-term culture of patient-derived cells retained more clinically relevant features of breast cancer compared to established cell lines. The low proliferation rate and chemoresistance make patient-derived cells an excellent tool in preclinical drug development.
PMCID: PMC4028696  PMID: 23879992
19.  Cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action 
Chemogenomic screens were performed in both budding and fission yeasts, allowing for a cross-species comparison of drug–gene interaction networks.Drug–module interactions were more conserved than individual drug–gene interactions.Combination of data from both species can improve drug–module predictions and helps identify a compound's mode of action.
Understanding the molecular effects of chemical compounds in living cells is an important step toward rational therapeutics. Drug discovery aims to find compounds that will target a specific pathway or pathogen with minimal side effects. However, even when an effective drug is found, its mode of action (MoA) is typically not well understood. The lack of knowledge regarding a drug's MoA makes the drug discovery process slow and rational therapeutics incredibly difficult. More recently, different high-throughput methods have been developed that attempt to discern how a compound exerts its effects in cells. One of these methods relies on measuring the growth of cells carrying different mutations in the presence of the compounds of interest, commonly referred to as chemogenomics (Wuster and Babu, 2008). The differential growth of the different mutants provides clues as to what the compounds target in the cell (Figure 2). For example, if a drug inhibits a branch in a vital two-branch pathway, then mutations in the second branch might result in cell death if the mutants are grown in the presence of the drug (Figure 2C). As these compound–mutant functional interactions are expected to be relatively rare, one can assume that the growth rate of a mutant–drug combination should generally be equal to the product of the growth rate of the untreated mutant with the growth rate of the drug-treated wild type. This expectation is defined as the neutral model and deviations from this provide a quantitative score that allow us to make informed predictions regarding a drug's MoA (Figure 2B; Parsons et al, 2006).
The availability of these high-throughput approaches now allows us to perform cross-species studies of functional interactions between compounds and genes. In this study, we have performed a quantitative analysis of compound–gene interactions for two fungal species (budding yeast (S. cerevisiae) and fission yeast (S. pombe)) that diverged from each other approximately 500–700 million years ago. A collection of 2957 compounds from the National Cancer Institute (NCI) were screened in both species for inhibition of wild-type cell growth. A total of 132 were found to be bioactive in both fungi and 9, along with 12 additional well-characterized drugs, were selected for subsequent screening. Mutant libraries of 727 and 438 gene deletions were used for S. cerevisiae and S. pombe, respectively, and these were selected based on availability of genetic interaction data from previous studies (Collins et al, 2007; Roguev et al, 2008; Fiedler et al, 2009) and contain an overlap of 190 one-to-one orthologs that can be directly compared. Deviations from the neutral expectation were quantified as drug–gene interactions scores (D-scores) for the 21 compounds against the deletion libraries. Replicates of both screens showed very high correlations (S. cerevisiae r=0.72, S. pombe r=0.76) and reproduced well previously known compound–gene interactions (Supplementary information). We then compared the D-scores for the 190 one-to-one orthologs present in the data set of both species. Despite the high reproducibility, we observed a very poor conservation of these compound–gene interaction scores across these species (r=0.13, Figure 4A).
Previous work had shown that, across these same species, genetic interactions within protein complexes were much more conserved than average genetic interactions (Roguev et al, 2008). Similarly we observed a higher cross-species conservation of the compound–module (complex or pathway) interactions than the overall compound–gene interactions. Specifically, the data derived from fission yeast were a poor predictor of S. cerevisaie drug–gene interactions, but a good predictor of budding yeast compound–module connections (Figure 4B). Also, a combined score from both species improved the prediction of compound–module interactions, above the accuracy observed with the S. cerevisae information alone, but this improvement was not observed for the prediction of drug–gene interactions (Figure 4B). Data from both species were used to predict drug–module interactions, and one specific interaction (compound NSC-207895 interaction with DNA repair complexes) was experimentally verified by showing that the compound activates the DNA damage repair pathway in three species (S. cerevisiae, S. pombe and H. sapiens).
To understand why the combination of chemogenomic data from two species might improve drug–module interaction predictions, we also analyzed previously published cross-species genetic–interaction data. We observed a significant correlation between the conservation of drug–gene and gene–gene interactions among the one-to-one orthologs (r=0.28, P-value=0.0078). Additionally, the strongest interactions of benomyl (a microtubule inhibitor) were to complexes that also had strong and conserved genetic interactions with microtubules (Figure 4C). We hypothesize that a significant number of the compound–gene interactions obtained from chemogenomic studies are not direct interactions with the physical target of the compounds, but include many indirect interactions that genetically interact with the main target(s). This would explain why the compound interaction networks show similar evolutionary patterns as the genetic interactions networks.
In summary, these results shed some light on the interplay between the evolution of genetic networks and the evolution of drug response. Understanding how genetic variability across different species might result in different sensitivity to drugs should improve our capacity to design treatments. Concretely, we hope that this line of research might one day help us create drugs and drug combinations that specifically affect a pathogen or diseased tissue, but not the host.
We present a cross-species chemogenomic screening platform using libraries of haploid deletion mutants from two yeast species, Saccharomyces cerevisiae and Schizosaccharomyces pombe. We screened a set of compounds of known and unknown mode of action (MoA) and derived quantitative drug scores (or D-scores), identifying mutants that are either sensitive or resistant to particular compounds. We found that compound–functional module relationships are more conserved than individual compound–gene interactions between these two species. Furthermore, we observed that combining data from both species allows for more accurate prediction of MoA. Finally, using this platform, we identified a novel small molecule that acts as a DNA damaging agent and demonstrate that its MoA is conserved in human cells.
PMCID: PMC3018166  PMID: 21179023
chemogenomics; evolution; modularity
20.  Combining structure-based pharmacophore modeling, virtual screening, and in silico ADMET analysis to discover novel tetrahydro-quinoline based pyruvate kinase isozyme M2 activators with antitumor activity 
Compared with normal differentiated cells, cancer cells upregulate the expression of pyruvate kinase isozyme M2 (PKM2) to support glycolytic intermediates for anabolic processes, including the synthesis of nucleic acids, amino acids, and lipids. In this study, a combination of the structure-based pharmacophore modeling and a hybrid protocol of virtual screening methods comprised of pharmacophore model-based virtual screening, docking-based virtual screening, and in silico ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis were used to retrieve novel PKM2 activators from commercially available chemical databases. Tetrahydroquinoline derivatives were identified as potential scaffolds of PKM2 activators. Thus, the hybrid virtual screening approach was applied to screen the focused tetrahydroquinoline derivatives embedded in the ZINC database. Six hit compounds were selected from the final hits and experimental studies were then performed. Compound 8 displayed a potent inhibitory effect on human lung cancer cells. Following treatment with Compound 8, cell viability, apoptosis, and reactive oxygen species (ROS) production were examined in A549 cells. Finally, we evaluated the effects of Compound 8 on mice xenograft tumor models in vivo. These results may provide important information for further research on novel PKM2 activators as antitumor agents.
PMCID: PMC4159224  PMID: 25214764
pharmacophore; molecular docking; pyruvate kinase; virtual screening
21.  A “Genome-to-Lead” Approach for Insecticide Discovery: Pharmacological Characterization and Screening of Aedes aegypti D1-like Dopamine Receptors 
Many neglected tropical infectious diseases affecting humans are transmitted by arthropods such as mosquitoes and ticks. New mode-of-action chemistries are urgently sought to enhance vector management practices in countries where arthropod-borne diseases are endemic, especially where vector populations have acquired widespread resistance to insecticides.
Methodology/Principal Findings
We describe a “genome-to-lead” approach for insecticide discovery that incorporates the first reported chemical screen of a G protein-coupled receptor (GPCR) mined from a mosquito genome. A combination of molecular and pharmacological studies was used to functionally characterize two dopamine receptors (AaDOP1 and AaDOP2) from the yellow fever mosquito, Aedes aegypti. Sequence analyses indicated that these receptors are orthologous to arthropod D1-like (Gαs-coupled) receptors, but share less than 55% amino acid identity in conserved domains with mammalian dopamine receptors. Heterologous expression of AaDOP1 and AaDOP2 in HEK293 cells revealed dose-dependent responses to dopamine (EC50: AaDOP1 = 3.1±1.1 nM; AaDOP2 = 240±16 nM). Interestingly, only AaDOP1 exhibited sensitivity to epinephrine (EC50 = 5.8±1.5 nM) and norepinephrine (EC50 = 760±180 nM), while neither receptor was activated by other biogenic amines tested. Differential responses were observed between these receptors regarding their sensitivity to dopamine agonists and antagonists, level of maximal stimulation, and constitutive activity. Subsequently, a chemical library screen was implemented to discover lead chemistries active at AaDOP2. Fifty-one compounds were identified as “hits,” and follow-up validation assays confirmed the antagonistic effect of selected compounds at AaDOP2. In vitro comparison studies between AaDOP2 and the human D1 dopamine receptor (hD1) revealed markedly different pharmacological profiles and identified amitriptyline and doxepin as AaDOP2-selective compounds. In subsequent Ae. aegypti larval bioassays, significant mortality was observed for amitriptyline (93%) and doxepin (72%), confirming these chemistries as “leads” for insecticide discovery.
This research provides a “proof-of-concept” for a novel approach toward insecticide discovery, in which genome sequence data are utilized for functional characterization and chemical compound screening of GPCRs. We provide a pipeline useful for future prioritization, pharmacological characterization, and expanded chemical screening of additional GPCRs in disease-vector arthropods. The differential molecular and pharmacological properties of the mosquito dopamine receptors highlight the potential for the identification of target-specific chemistries for vector-borne disease management, and we report the first study to identify dopamine receptor antagonists with in vivo toxicity toward mosquitoes.
Author Summary
Mosquitoes and other arthropods transmit important disease-causing agents affecting human health worldwide. There is an urgent need to discover new chemistries to control these pests in order to reduce or eliminate arthropod-borne diseases. We describe an approach to identify and evaluate potential insecticide targets using publicly available genome (DNA) sequence information for arthropod disease vectors. We demonstrate the utility of this approach by first determining the molecular and pharmacological properties of two different dopamine (neurotransmitter) receptors of the yellow fever- and dengue-transmitting mosquito, Aedes aegypti. Next, we tested 1,280 different chemistries for their ability to interact with one of these dopamine receptors in a chemical screen, and 51 “hit” compounds were identified. Finally, we show that two of these chemistries, amitriptyline and doxepin, are selective for the mosquito over the human dopamine receptor and that both chemistries caused significant mortality in mosquito larvae 24 hours after exposure, identifying them as possible “leads” for insecticide development. Our methodology is adaptable for chemical screening of related targets in mosquitoes and other arthropod vectors of disease. This research demonstrates the potential of target-specific approaches that could complement traditional phenotypic screening, and ultimately may accelerate discovery of new mode-of-action insecticides for vector control.
PMCID: PMC3265452  PMID: 22292096
22.  Molecular cloning, genomic characterization and over-expression of a novel gene, XRRA1, identified from human colorectal cancer cell HCT116Clone2_XRR and macaque testis 
BMC Genomics  2003;4:32.
As part of our investigation into the genetic basis of tumor cell radioresponse, we have isolated several clones with a wide range of responses to X-radiation (XR) from an unirradiated human colorectal tumor cell line, HCT116. Using human cDNA microarrays, we recently identified a novel gene that was down-regulated by two-fold in an XR-resistant cell clone, HCT116Clone2_XRR. We have named this gene as X-ray radiation resistance associated 1 (XRRA1) (GenBank BK000541). Here, we present the first report on the molecular cloning, genomic characterization and over-expression of the XRRA1 gene.
We found that XRRA1 was expressed predominantly in testis of both human and macaque. cDNA microarray analysis showed three-fold higher expression of XRRA1 in macaque testis relative to other tissues. We further cloned the macaque XRRA1 cDNA (GenBank AB072776) and a human XRRA1 splice variant from HCT116Clone2_XRR (GenBank AY163836). In silico analysis revealed the full-length human XRRA1, mouse, rat and bovine Xrra1 cDNAs. The XRRA1 gene comprises 11 exons and spans 64 kb on chromosome 11q13.3. Human and macaque cDNAs share 96% homology. Human XRRA1 cDNA is 1987 nt long and encodes a protein of 559 aa. XRRA1 protein is highly conserved in human, macaque, mouse, rat, pig, and bovine. GFP-XRRA1 fusion protein was detected in both the nucleus and cytoplasm of HCT116 clones and COS-7 cells. Interestingly, we found evidence that COS-7 cells which over-expressed XRRA1 lacked Ku86 (Ku80, XRCC5), a non-homologous end joining (NHEJ) DNA repair molecule, in the nucleus. RT-PCR analysis showed differential expression of XRRA1 after XR in HCT116 clones manifesting significantly different XR responses. Further, we found that XRRA1 was expressed in most tumor cell types. Surprisingly, mouse Xrra1 was detected in mouse embryonic stem cells R1.
Both XRRA1 cDNA and protein are highly conserved among mammals, suggesting that XRRA1 may have similar functions. Our results also suggest that the genetic modulation of XRRA1 may affect the XR responses of HCT116 clones and that XRRA1 may have a role in the response of human tumor and normal cells to XR. XRRA1 might be correlated with cancer development and might also be an early expressed gene.
PMCID: PMC194569  PMID: 12908878
23.  The Preclinical Natural History of Serous Ovarian Cancer: Defining the Target for Early Detection 
PLoS Medicine  2009;6(7):e1000114.
Pat Brown and colleagues carry out a modeling study and define what properties a biomarker-based screening test would require in order to be clinically useful.
Ovarian cancer kills approximately 15,000 women in the United States every year, and more than 140,000 women worldwide. Most deaths from ovarian cancer are caused by tumors of the serous histological type, which are rarely diagnosed before the cancer has spread. Rational design of a potentially life-saving early detection and intervention strategy requires understanding the lesions we must detect in order to prevent lethal progression. Little is known about the natural history of lethal serous ovarian cancers before they become clinically apparent. We can learn about this occult period by studying the unsuspected serous cancers that are discovered in a small fraction of apparently healthy women who undergo prophylactic bilateral salpingo-oophorectomy (PBSO).
Methods and Findings
We developed models for the growth, progression, and detection of occult serous cancers on the basis of a comprehensive analysis of published data on serous cancers discovered by PBSO in BRCA1 mutation carriers. Our analysis yielded several critical insights into the early natural history of serous ovarian cancer. First, these cancers spend on average more than 4 y as in situ, stage I, or stage II cancers and approximately 1 y as stage III or IV cancers before they become clinically apparent. Second, for most of the occult period, serous cancers are less than 1 cm in diameter, and not visible on gross examination of the ovaries and Fallopian tubes. Third, the median diameter of a serous ovarian cancer when it progresses to an advanced stage (stage III or IV) is about 3 cm. Fourth, to achieve 50% sensitivity in detecting tumors before they advance to stage III, an annual screen would need to detect tumors of 1.3 cm in diameter; 80% detection sensitivity would require detecting tumors less than 0.4 cm in diameter. Fifth, to achieve a 50% reduction in serous ovarian cancer mortality with an annual screen, a test would need to detect tumors of 0.5 cm in diameter.
Our analysis has formalized essential conditions for successful early detection of serous ovarian cancer. Although the window of opportunity for early detection of these cancers lasts for several years, developing a test sufficiently sensitive and specific to take advantage of that opportunity will be a challenge. We estimated that the tumors we would need to detect to achieve even 50% sensitivity are more than 200 times smaller than the clinically apparent serous cancers typically used to evaluate performance of candidate biomarkers; none of the biomarker assays reported to date comes close to the required level of performance. Overcoming the signal-to-noise problem inherent in detection of tiny tumors will likely require discovery of truly cancer-specific biomarkers or development of novel approaches beyond traditional blood protein biomarkers. While this study was limited to ovarian cancers of serous histological type and to those arising in BRCA1 mutation carriers specifically, we believe that the results are relevant to other hereditary serous cancers and to sporadic ovarian cancers. A similar approach could be applied to other cancers to aid in defining their early natural history and to guide rational design of an early detection strategy.
Please see later in the article for Editors' Summary
Editors' Summary
Every year about 190,000 women develop ovarian cancer and more than 140,000 die from the disease. Ovarian cancer occurs when a cell on the surface of the ovaries (two small organs in the pelvis that produce eggs) or in the Fallopian tubes (which connect the ovaries to the womb) acquires genetic changes (mutations) that allow it to grow uncontrollably and to spread around the body (metastasize). For women whose cancer is diagnosed when it is confined to the site of origin—ovary or Fallopian tube—(stage I disease), the outlook is good; 70%–80% of these women survive for at least 5 y. However, very few ovarian cancers are diagnosed this early. Usually, by the time the cancer causes symptoms (often only vague abdominal pain and mild digestive disturbances), it has spread into the pelvis (stage II disease), into the space around the gut, stomach, and liver (stage III disease), or to distant organs (stage IV disease). Patients with advanced-stage ovarian cancer are treated with surgery and chemotherapy but, despite recent treatment improvements, only 15% of women diagnosed with stage IV disease survive for 5 y.
Why Was This Study Done?
Most deaths from ovarian cancer are caused by serous ovarian cancer, a tumor subtype that is rarely diagnosed before it has spread. Early detection of serous ovarian cancer would save the lives of many women but no one knows what these cancers look like before they spread or how long they grow before they become clinically apparent. Learning about this occult (hidden) period of ovarian cancer development by observing tumors from their birth to late-stage disease is not feasible. However, some aspects of the early natural history of ovarian cancer can be studied by using data collected from healthy women who have had their ovaries and Fallopian tubes removed (prophylactic bilateral salpingo-oophorectomy [PBSO]) because they have inherited a mutated version of the BRCA1 gene that increases their ovarian cancer risk. In a few of these women, unsuspected ovarian cancer is discovered during PBSO. In this study, the researchers identify and analyze the available reports on occult serous ovarian cancer found this way and then develop mathematical models describing the early natural history of ovarian cancer.
What Did the Researchers Do and Find?
The researchers first estimated the time period during which the detection of occult tumors might save lives using the data from these reports. Serous ovarian cancers, they estimated, spend more than 4 y as in situ (a very early stage of cancer development), stage I, or stage II cancers and about 1 y as stage III and IV cancers before they become clinically apparent. Next, the researchers used the data to develop mathematical models for the growth, progression, and diagnosis of serous ovarian cancer (the accuracy of which depends on the assumptions used to build the models and on the quality of the data fed into them). These models indicated that, for most of the occult period, serous cancers had a diameter of less than 1 cm (too small to be detected during surgery or by gross examination of the ovaries or Fallopian tubes) and that more than half of serous cancers had advanced to stage III/IV by the time they measured 3 cm across. Furthermore, to enable the detection of half of serous ovarian cancers before they reached stage III, an annual screening test would need to detect cancers with a diameter of 1.3 cm and to halve deaths from serous ovarian cancer, an annual screening test would need to detect 0.5-cm diameter tumors.
What Do These Findings Mean?
These findings suggest that the time period over which the early detection of serous ovarian cancer would save lives is surprisingly long. More soberingly, the authors find that a test that is sensitive and specific enough to take advantage of this “window of opportunity” would need to detect tumors hundreds of times smaller than clinically apparent serous cancers. So far no ovarian cancer-specific protein or other biomarker has been identified that could be used to develop a test that comes anywhere near this level of performance. Identification of truly ovarian cancer-specific biomarkers or novel strategies will be needed in order to take advantage of the window of opportunity. The stages prior to clinical presentation of other lethal cancers are still very poorly understood. Similar studies of the early natural history of these cancers could help guide the development of rational early detection strategies.
Additional Information
Please access these Web sites via the online version of this summary at
The US National Cancer Institute provides a brief description of what cancer is and how it develops and information on all aspects of ovarian cancer for patients and professionals. It also provides a fact sheet on BRCA1 mutations and cancer risk (in English and Spanish)
The UK charity Cancerbackup also provides information about all aspects of ovarian cancer
MedlinePlus provides a list of links to additional information about ovarian cancer (in English and Spanish)
The Canary Foundation is a nonprofit organization dedicated to development of effective strategies for early detection of cancers including ovarian cancer.
PMCID: PMC2711307  PMID: 19636370
24.  Analysis of high-throughput RNAi screening data in identifying genes mediating sensitivity to chemotherapeutic drugs: statistical approaches and perspectives 
BMC Genomics  2012;13(Suppl 8):S3.
High-throughput RNA interference (RNAi) screens have been used to find genes that, when silenced, result in sensitivity to certain chemotherapy drugs. Researchers therefore can further identify drug-sensitive targets and novel drug combinations that sensitize cancer cells to chemotherapeutic drugs. Considerable uncertainty exists about the efficiency and accuracy of statistical approaches used for RNAi hit selection in drug sensitivity studies. Researchers require statistical methods suitable for analyzing high-throughput RNAi screening data that will reduce false-positive and false-negative rates.
In this study, we carried out a simulation study to evaluate four types of statistical approaches (fold-change/ratio, parametric tests/statistics, sensitivity index, and linear models) with different scenarios of RNAi screenings for drug sensitivity studies. With the simulated datasets, the linear model resulted in significantly lower false-negative and false-positive rates. Based on the results of the simulation study, we then make recommendations of statistical analysis methods for high-throughput RNAi screening data in different scenarios. We assessed promising methods using real data from a loss-of-function RNAi screen to identify hits that modulate paclitaxel sensitivity in breast cancer cells. High-confidence hits with specific inhibitors were further analyzed for their ability to inhibit breast cancer cell growth. Our analysis identified a number of gene targets with inhibitors known to enhance paclitaxel sensitivity, suggesting other genes identified may merit further investigation.
RNAi screening can identify druggable targets and novel drug combinations that can sensitize cancer cells to chemotherapeutic drugs. However, applying an inappropriate statistical method or model to the RNAi screening data will result in decreased power to detect the true hits and increase false positive and false negative rates, leading researchers to draw incorrect conclusions. In this paper, we make recommendations to enable more objective selection of statistical analysis methods for high-throughput RNAi screening data.
PMCID: PMC3535706  PMID: 23281588
25.  A Mouse to Human Search for Plasma Proteome Changes Associated with Pancreatic Tumor Development 
PLoS Medicine  2008;5(6):e123.
The complexity and heterogeneity of the human plasma proteome have presented significant challenges in the identification of protein changes associated with tumor development. Refined genetically engineered mouse (GEM) models of human cancer have been shown to faithfully recapitulate the molecular, biological, and clinical features of human disease. Here, we sought to exploit the merits of a well-characterized GEM model of pancreatic cancer to determine whether proteomics technologies allow identification of protein changes associated with tumor development and whether such changes are relevant to human pancreatic cancer.
Methods and Findings
Plasma was sampled from mice at early and advanced stages of tumor development and from matched controls. Using a proteomic approach based on extensive protein fractionation, we confidently identified 1,442 proteins that were distributed across seven orders of magnitude of abundance in plasma. Analysis of proteins chosen on the basis of increased levels in plasma from tumor-bearing mice and corroborating protein or RNA expression in tissue documented concordance in the blood from 30 newly diagnosed patients with pancreatic cancer relative to 30 control specimens. A panel of five proteins selected on the basis of their increased level at an early stage of tumor development in the mouse was tested in a blinded study in 26 humans from the CARET (Carotene and Retinol Efficacy Trial) cohort. The panel discriminated pancreatic cancer cases from matched controls in blood specimens obtained between 7 and 13 mo prior to the development of symptoms and clinical diagnosis of pancreatic cancer.
Our findings indicate that GEM models of cancer, in combination with in-depth proteomic analysis, provide a useful strategy to identify candidate markers applicable to human cancer with potential utility for early detection.
Samir Hanash and colleagues identify proteins that are increased at an early stage of pancreatic tumor development in a mouse model and may be a useful tool in detecting early tumors in humans.
Editors' Summary
Cancers are life-threatening, disorganized masses of cells that can occur anywhere in the human body. They develop when cells acquire genetic changes that allow them to grow uncontrollably and to spread around the body (metastasize). If a cancer is detected when it is still small and has not metastasized, surgery can often provide a cure. Unfortunately, many cancers are detected only when they are large enough to press against surrounding tissues and cause pain or other symptoms. By this time, surgical removal of the original (primary) tumor may be impossible and there may be secondary cancers scattered around the body. In such cases, radiotherapy and chemotherapy can sometimes help, but the outlook for patients whose cancers are detected late is often poor. One cancer type for which late detection is a particular problem is pancreatic adenocarcinoma. This cancer rarely causes any symptoms in its early stages. Furthermore, the symptoms it eventually causes—jaundice, abdominal and back pain, and weight loss—are seen in many other illnesses. Consequently, pancreatic cancer has usually spread before it is diagnosed, and most patients die within a year of their diagnosis.
Why Was This Study Done?
If a test could be developed to detect pancreatic cancer in its early stages, the lives of many patients might be extended. Tumors often release specific proteins—“cancer biomarkers”—into the blood, a bodily fluid that can be easily sampled. If a protein released into the blood by pancreatic cancer cells could be identified, it might be possible to develop a noninvasive screening test for this deadly cancer. In this study, the researchers use a “proteomic” approach to identify potential biomarkers for early pancreatic cancer. Proteomics is the study of the patterns of proteins made by an organism, tissue, or cell and of the changes in these patterns that are associated with various diseases.
What Did the Researchers Do and Find?
The researchers started their search for pancreatic cancer biomarkers by studying the plasma proteome (the proteins in the fluid portion of blood) of mice genetically engineered to develop cancers that closely resemble human pancreatic tumors. Through the use of two techniques called high-resolution mass spectrometry and acrylamide isotopic labeling, the researchers identified 165 proteins that were present in larger amounts in plasma collected from mice with early and/or advanced pancreatic cancer than in plasma from control mice. Then, to test whether any of these protein changes were relevant to human pancreatic cancer, the researchers analyzed blood samples collected from patients with pancreatic cancer. These samples, they report, contained larger amounts of some of these proteins than blood collected from patients with chronic pancreatitis, a condition that has similar symptoms to pancreatic cancer. Finally, using blood samples collected during a clinical trial, the Carotene and Retinol Efficacy Trial (a cancer-prevention study), the researchers showed that the measurement of five of the proteins present in increased amounts at an early stage of tumor development in the mouse model discriminated between people with pancreatic cancer and matched controls up to 13 months before cancer diagnosis.
What Do These Findings Mean?
These findings suggest that in-depth proteomic analysis of genetically engineered mouse models of human cancer might be an effective way to identify biomarkers suitable for the early detection of human cancers. Previous attempts to identify such biomarkers using human samples have been hampered by the many noncancer-related differences in plasma proteins that exist between individuals and by problems in obtaining samples from patients with early cancer. The use of a mouse model of human cancer, these findings indicate, can circumvent both of these problems. More specifically, these findings identify a panel of proteins that might allow earlier detection of pancreatic cancer and that might, therefore, extend the life of some patients who develop this cancer. However, before a routine screening test becomes available, additional markers will need to be identified and extensive validation studies in larger groups of patients will have to be completed.
Additional Information.
Please access these Web sites via the online version of this summary at
The MedlinePlus Encyclopedia has a page on pancreatic cancer (in English and Spanish). Links to further information are provided by MedlinePlus
The US National Cancer Institute has information about pancreatic cancer for patients and health professionals (in English and Spanish)
The UK charity Cancerbackup also provides information for patients about pancreatic cancer
The Clinical Proteomic Technologies for Cancer Initiative (a US National Cancer Institute initiative) provides a tutorial about proteomics and cancer and information on the Mouse Proteomic Technologies Initiative
PMCID: PMC2504036  PMID: 18547137

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