Drug-associated Genes Research Articles

Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles

Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor’s (TF) binding strength, affect enhancer’s chromatin activity or interaction, and eventually change expression level of downstream gene. Here, we propose a computational framework, PERD, to Predict the Enhancers Responsive to Drug. A machine learning model was trained to predict the genome-wide chromatin accessibility from transcriptome data using the paired expression and chromatin accessibility data collected from ENCODE and ROADMAP. Then the model was applied to the perturbed gene expression data from Connectivity Map (CMAP) and Cancer Drug-induced gene expression Signature DataBase (CDS-DB) and identify drug responsive enhancers with significantly altered chromatin accessibility. Furthermore, the drug responsive enhancers were related to the pharmacogenomics genome-wide association studies (PGx GWAS). Stepping on the traditional drug-associated gene signatures, PERD holds the promise to enhance the causality of drug perturbation by providing candidate regulatory element of those drug associated genes.

Identification of genes encoding targets associated with adverse events in multiple myeloma.

1556 Background: Despite novel therapeutic options, understanding the toxicity profile of anti multiple myeloma (MM) drug regimens (AMDR) is vital. Data suggests adverse event (AE) profile is orchestrated by certain associated genes. The purpose of this study is to explore the interplay of genes, regimens, AEs and provide genetic evidence for a linkage between regimens and the presence of AEs. Methods: We examined a group of AMDRs retrieved from the FDA Adverse Event Reporting System. Drug-associated genes were obtained from the Drug Gene Interaction Database, DGIdb. Then, genes were imported to the Online Mendelian Inheritance in Man (OMIM) to search for the existing clinical synopses (features) in the peer reviewed biomedical material, or phenotypes when no data was available. Clinical synopses were then linked to MedDRA. Gene gene interaction was also determined through the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). We then constructed a detailed large MM pharmacogenetic network to illustrate the linkage among drugs, genes and MM via various expert integrated resources. AMDR responses were then assessed at the DNA and protein levels by modularizing genetic causation into different scenarios i) a direct drug effect on gene expression; ii) the secondary or tertiary effect of drug(s) on subsequent drug metabolism or disposition altering gene expression pathways; iii) modulation of another biological network that disturbs drug effect on gene expression resulting in exacerbating AE occurrence. Results: We found multiple genes including CTLA4, NFKB1, PSMB8, CCND1 and HLAA were complicit/associated with different AEs including cardiac or hepatobiliary disorders or other serious events. Our results suggested the expression of CTLA4 was influenced by multiple AMDRs from combinations of bortezomib (V), lenalidomide (R), melphalan (M), or dexamethasone (d). When AMDRs involve these agents, the occurrence of pericardial disorders due to the treatment exhibited high confidence. We also found carfilzomib (K) combinations (Kd or KRd) were associated with PSMB8 which was likely to be the genetic factor concomitant with heart failures, cardiac arrhythmias, or hepatobiliary toxicities. Furthermore, our findings suggested NFKB1, thalidomide associated gene interacting with PSMB8, may present the same toxicity events in hepatobiliary disorders. Conclusions: Understanding the genomic basis of specific drug-AE combinations could reveal molecular mechanisms underlying drug-gene-AE associations and recognize the causality of AEs. When considering alternative agents in MM, our study offers insights for predicting treatment responses such as PSMB8 which could potentially cause an off target effect of K on cardiomyocyte 20S beyond MM cells. Our plan is to integrate this novel computational biology approach with translational medicine to further investigate our observation.

A novel risk assessment metric for antimyeloma therapies and drug interactions.

e24082 Background: New treatments for multiple myeloma (MM) improved both the clinical course and survival. These differ in their mode of action, safety profile, and adverse events (AEs). The goal of this study is to establish the benchmark for an integrated systematic investigation to assess the toxicity of different anti-MM drug regimens (AMDR). Methods: We curated 342,213 MM patient information from 2016–2022 from the FDA Adverse Event Reporting System. A total of 71 AMDR were evaluated in 7 different system organ classes when mapped to MedDRA. Drug-associated genes and AEs were also quantified as genetic evidence of a drug-AE combination. Gene-gene associations were then explored using the STRING database. We then developed a novel risk assessment metric that combined historical data from MM patients with drug-AE scores regarding the AE occurrence, the number of reported AE, and the genetic evidence of drug-gene and gene-AE associations (GAA). This likelihood-based rating metric includes (vH): (very) high MDR risk–(very) frequent AEs and mortality with (very) significant GAA evidence; and similarly computed for M: medium risk; and L: low risk. Results: Our risk assessment findings revealed single agents (carfilzomib (K), bortezomib (V), daratumumab (D), lenalidomide (R), melphalan (Mel), thalidomide (T), ixazomib (Ix), elotuzumab (El), isatuximab (Is) or dexamethasone (d)) illustrated very low profile of toxicity compared to combination regimens. Overall, we found that AMDR profiles showed higher toxicities in 4 different SyOCs (hepatobiliary, cardiac, nervous, renal) with nominal issues in 3 SyOCs (vascular, neoplasm, GI). AMDR toxicity significantly increased when d was added to the treatment compared to AMDR without d. Cardiac and vascular AEs were higher with K-based combinations. Hepatobiliary toxicities were higher with V, K, or T-based combinations. Secondary neoplasms were seen more frequently in patients who received high dose Mel followed by R. Conclusions: Our analysis of large data shows the association of SyOCs and graded toxicity profiles with unique AMDR. The single drug or doublets without dexamethasone seemed to show an improved AE profile compared to those with dexamethasone used as doublets, triplets, or quadruplets. Secondary cancer risk was much higher with high dose Mel followed by R compared with other AMDR. Our assessment system provides a framework for AMDR comparison toward future precision MM therapeutics based on unique drug doses, combination regimens, patient phenotypic risk profiles, secondary cancer surveillance and interplay of genetics and comorbidities. [Table: see text]

Abstract P3-09-17: Systems-scale characterization of the human immunome in breast cancer for precision medicine

Abstract It becomes more clear that many deleterious cancer mutations could exhibit a 'gain-of-function' (GOF) behavior. Systematic investigation of such mutations has been lacking and largely overlooked. Advances in next-generation sequencing have identified thousands of genomic variants that perturb the normal functions of proteins, further contributing to diverse phenotypic consequences in breast cancer. Alterations in immune-related pathways are common hallmarks of cancer. A comprehensive understanding of how cancer mutations rewire immune signaling networks and functional output in breast cancer is instrumental to realize the full potential of immunotherapy. Here, we systematically interrogated somatic mutations involved in immune signaling that alter immune responses in cancer patients. To do so, we developed a network-based integrative model to prioritize potential immune responder genes. Identified mutations were enriched in essential protein domains and genes identified were associated with responsiveness to multiple immunotherapy modalities. These genes were used to devise an interactome network propagation framework integrated with drug-associated gene signatures to identify potential immunomodulatory drug candidates in breast cancer. Together, our systems-level analysis results help interpret the heterogeneous immune responses among patients and serve as a resource for future functional studies and targeted breast cancer therapeutics. This study is significant and innovative, as it demonstrates that integration of multi-omics data can help identify critical molecular determinants for effective targeted therapeutics. Citation Format: Stephen Yi. Systems-scale characterization of the human immunome in breast cancer for precision medicine [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-09-17.

Explainable drug sensitivity prediction through cancer pathway enrichment

Computational approaches to predict drug sensitivity can promote precision anticancer therapeutics. Generalizable and explainable models are of critical importance for translation to guide personalized treatment and are often overlooked in favor of prediction performance. Here, we propose PathDSP: a pathway-based model for drug sensitivity prediction that integrates chemical structure information with enrichment of cancer signaling pathways across drug-associated genes, gene expression, mutation and copy number variation data to predict drug response on the Genomics of Drug Sensitivity in Cancer dataset. Using a deep neural network, we outperform state-of-the-art deep learning models, while demonstrating good generalizability a separate dataset of the Cancer Cell Line Encyclopedia as well as provide explainable results, demonstrated through case studies that are in line with current knowledge. Additionally, our pathway-based model achieved a good performance when predicting unseen drugs and cells, with potential utility for drug development and for guiding individualized medicine.

Integrated Genomic Characterization of the Human Immunome in Cancer.

Alterations in immune-related pathways are common hallmarks of cancer. A comprehensive understanding of how cancer mutations rewire immune signaling networks and functional output across cancer types is instrumental to realize the full potential of immunotherapy. Here, we systematically interrogated somatic mutations involved in immune signaling that alter immune responses in patients with cancer. To do so, we developed a Network-based Integrative model to Prioritize Potential immune respondER genes (NIPPER). Identified mutations were enriched in essential protein domains and genes identified by NIPPER were associated with responsiveness to multiple immunotherapy modalities. These genes were used to devise an interactome network propagation framework integrated with drug-associated gene signatures to identify potential immunomodulatory drug candidates. Together, our systems-level analysis results help interpret the heterogeneous immune responses among patients and serve as a resource for future functional studies and targeted therapeutics. SIGNIFICANCE: This study demonstrates that integration of multi-omics data can help identify critical molecular determinants for effective targeted therapeutics.

SCDb: an integrated database of stomach cancer

BackgroundStomach cancer (SC) is a type of cancer, which is derived from the stomach mucous membrane. As there are non-specific symptoms or no noticeable symptoms observed at the early stage, newly diagnosed SC cases usually reach an advanced stage and are thus difficult to cure. Therefore, in this study, we aimed to develop an integrated database of SC.MethodsSC-related genes were identified through literature mining and by analyzing the publicly available microarray datasets. Using the RNA-seq, miRNA-seq and clinical data downloaded from The Cancer Genome Atlas (TCGA), the Kaplan-Meier (KM) survival curves for all the SC-related genes were generated and analyzed. The miRNAs (miRanda, miRTarget2, PicTar, PITA and TargetScan databases), SC-related miRNAs (HMDD and miR2Disease databases), single nucleotide polymorphisms (SNPs, dbSNP database), and SC-related SNPs (ClinVar database) were also retrieved from the indicated databases. Moreover, gene_disease (OMIM and GAD databases), copy number variation (CNV, DGV database), methylation (PubMeth database), drug (WebGestalt database), and transcription factor (TF, TRANSFAC database) analyses were performed for the differentially expressed genes (DEGs).ResultsIn total, 9990 SC-related genes (including 8347 up-regulated genes and 1643 down-regulated genes) were identified, among which, 65 genes were further confirmed as SC-related genes by performing enrichment analysis. Besides this, 457 miRNAs, 20 SC-related miRNAs, 1570 SNPs, 108 SC-related SNPs, 419 TFs, 44,605 CNVs, 3404 drug-associated genes, 63 genes with methylation, and KM survival curves of 20,264 genes were obtained. By integrating these datasets, an integrated database of stomach cancer, designated as SCDb, (available at http://www.stomachcancerdb.org/) was established.ConclusionsAs a comprehensive resource for human SC, SCDb database will be very useful for performing SC-related research in future, and will thus promote the understanding of the pathogenesis of SC.

Role of β-Estradiol in MCF-7 Breast Cancer Cell Line Based on the Bioinformatics Analysis

Background: This study aimed to investigate the action mechanism of β-estradiol in MCF-7 breast cancer (BC) cells. Methods: The cell samples were sequenced using Hiseq 2000, including 2 MCF-7 controls and 2 samples treated with β-estradiol. Differentially expressed genes (DEGs) were screened using the NOISeq package in R, followed by the functions and pathways analyses using Database for Annotation, Visualization and Integrated Discovery. DEGs associated with β-estradiol were selected using the WbeGestalt software, and the corresponding target miRNAs of these genes were analyzed from different miRNA databases. Additionally, protein-protein interaction network of the drug-associated genes was constructed using Cytoscape. Results: A total of 1,835 DEGs in BC samples were screened. Thereinto, DEGs associated with BC (17 upregulated and 28 downregulated DEGs) were involved in the regulation of cell proliferation, response to endogenous stimulus, and response to hormone stimulus, while the genes participated in several significant pathways. Cyclin D1, estrogen receptor 1, catechol-O-methyltransferase, and cathepsin D (CTSD; hub genes) were the predicted new genes associated with β-estradiol. Besides, hsa-miR-140-3p was the only target miRNA of CTSD. Conclusion: β-Estradiol may play a key role in contributing to BC progression and metastasis by regulating the expression of the selected genes.

Whole genome sequencing data of 1110 Mycobacterium tuberculosis isolates identifies insertions and deletions associated with drug resistance

BackgroundDrug resistance in Mycobacterium tuberculosis (MTB) is one of the major challenges in tuberculosis (TB) treatment. However, known mutations cannot explain all of the cases of resistance and little research has focused on the relationship between insertions / deletions (indels) and drug resistance.ResultsHere, we retrieved whole genome sequencing data of 743 drug-resistant MTB strains and 367 pan-susceptible strains from TB patients from the public domain to identify novel genomic markers of drug resistance. A total of 20 region markers containing genes and intergenic regions (IGRs) with significant statistical correlation with antibiotic resistance were revealed, four of which have been previously reported to be associated with drug resistance. In addition, 83 point markers containing frameshift (FS) mutations and IGR indels were also identified independently based on differences in their incidence rates between drug-sensitive and -resistant strains. Among the 83 point markers, eight indels were detected in known drug-associated genes or IGRs. Furthermore, the overlap between 20 region markers and 83 point markers further indicated their associations with drug resistance. The markers identified were involved in essential bacterial metabolic functions, including cell wall and transmembrane transporter functions. A strong correlation between FS mutations and mutations in DNA repair genes including I21V in alkA, R48G in mutT4 and P2R in nth was also found.ConclusionsThis study identified a set of novel genetic markers with FS mutations and IGR indels associated with MTB drug resistance, which greatly broadens the pool of mutations related to MTB drug resistance. This insight may be important in identifying novel mechanisms of drug resistance in MTB.

Characterization of the Same Mutations in FCA1 Gene Associated With 5-FC Resistance of Candida albicans

Background: Infections with 5-Fluorocytosine (5-FC)-resistant Candida albicans isolates in China have rarely been reported in clinical settings. Here, we present two 5-FC-resistant C. albicans (Ca5508 and CaBD4291) strains, separately isolated from two patients in China. Objectives: The main aim of this study was to characterize the key genetic mutations responsible for 5-FC resistance of the two drug-resistant isolates from the first affiliated hospital of Nanchang university. Methods: Multiplex PCR and phenotypic tests were used to confirm the isolates Ca5508 and CaBD4291. The standard micro broth dilution method M27-A3 was used for susceptibility testing of the two isolates. Molecular typing and analysis were performed by the combination of random amplified polymorphic DNA (RAPD), multilocus sequence typing (MLST), amplification and Sanger sequencing of 5-FC resistance-associated genes (FUR1 and FCA1). Results: The two isolates Ca5508 and CaBD4291 were identified as C. albicans by multiplex-PCR and phenotypic tests. The antifungal susceptibility testing showed that both of the isolates were resistant to 5-FC (MIC > 64µg/mL); Ca5508 was also resistant to fluconazole, ketoconazole, and itraconazole whereas CaBD4291 was not. The RAPD analysis demonstrated that the two isolates belonged to the same genotype. MLST typing identified two different sequence types: isolate CaBD4191 for ST732 and isolate Ca5508 for ST2975 (a new ST). Although the two STs were generally related, ST732 differed from ST2975 with 3/7 loci (AAC1, SYA1, VPS13). Surprisingly, the two isolates represented completely identical sequences of the two drug-associated genes FUR1 and FCA1. Compared to the susceptible reference strain SC5314, Ca5508 and CaBD4291 were found to have no mutation in FUR1; however, three missense mutations at positions 31, 83, and 107 of FCA1 were identified. Conclusions: Here, we newly reported two 5-FC-resistant clinical isolates of C. albicans that represented the same three mutations in FCA1 gene contributed to 5-FC resistance of C. albicans.

The extraction of drug-disease correlations based on module distance in incomplete human interactome.

BackgroundExtracting drug-disease correlations is crucial in unveiling disease mechanisms, as well as discovering new indications of available drugs, or drug repositioning. Both the interactome and the knowledge of disease-associated and drug-associated genes remain incomplete.ResultsWe present a new method to predict the associations between drugs and diseases. Our method is based on a module distance, which is originally proposed to calculate distances between modules in incomplete human interactome. We first map all the disease genes and drug genes to a combined protein interaction network. Then based on the module distance, we calculate the distances between drug gene sets and disease gene sets, and take the distances as the relationships of drug-disease pairs. We also filter possible false positive drug-disease correlations by p-value. Finally, we validate the top-100 drug-disease associations related to six drugs in the predicted results.ConclusionThe overlapping between our predicted correlations with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways demonstrate our approach can not only effectively identify new drug indications, but also provide new insight into drug-disease discovery.

Herbal medicine Guan Chang Fu Fang enhances 5-fluorouracil cytotoxicity and affects drug-associated genes in human colorectal carcinoma cells.

Guan Chang Fu Fang (GCFF) is a natural compound, which is extracted from three medicinal plants, Agrimonia pilosa Ledeb., Patrinia scabiosaefolia and Solanum nigrum L. GCFF has demonstrated clinical efficacy in the treatment of colon cancer. At present, 5-fluorouracil (5-FU) is the primary active chemotherapeutic agent used for treating colon cancer. Using median-effect and apoptosis analyses, fluorescence microscopy and western blotting, the present study analyzed the association between GCFF and 5-FU in the human colon adenocarcinoma LoVo cell line. The effect of GCFF on the expression of chemotherapeutic agent-associated genes was also investigated. The results of the synergistic analysis revealed that GCFF exhibited a significant effect upon 5-FU-associated cytotoxicity within the LoVo cell line. This effect was observed over a broad dose-inhibition range (5–95%), but was particularly significant in the lower concentrations. The flow cytometry results revealed that low doses of GCFF or 5-FU induced S-phase arrest, as did a low-dose combination of the two drugs. After 48 h, GCFF significantly suppressed the expression levels of the chemotherapeutic agent resistance-associated genes within the colon cancer cells. The western blot analysis revealed that the combined effects of 5-FU and GCFF were due to a regulation of the B-cell lymphoma-2 family of proteins. The findings of the present study suggested that GCFF, when combined with 5-FU, has the potential to be a novel, chemotherapeutic compound for the treatment of colon cancer.

Combining automatic table classification and relationship extraction in extracting anticancer drug–side effect pairs from full-text articles

Anticancer drug-associated side effect knowledge often exists in multiple heterogeneous and complementary data sources. A comprehensive anticancer drug–side effect (drug–SE) relationship knowledge base is important for computation-based drug target discovery, drug toxicity predication and drug repositioning. In this study, we present a two-step approach by combining table classification and relationship extraction to extract drug–SE pairs from a large number of high-profile oncological full-text articles. The data consists of 31,255 tables downloaded from the Journal of Oncology (JCO). We first trained a statistical classifier to classify tables into SE-related and -unrelated categories. We then extracted drug–SE pairs from SE-related tables. We compared drug side effect knowledge extracted from JCO tables to that derived from FDA drug labels. Finally, we systematically analyzed relationships between anti-cancer drug-associated side effects and drug-associated gene targets, metabolism genes, and disease indications. The statistical table classifier is effective in classifying tables into SE-related and -unrelated (precision: 0.711; recall: 0.941; F1: 0.810). We extracted a total of 26,918 drug–SE pairs from SE-related tables with a precision of 0.605, a recall of 0.460, and a F1 of 0.520. Drug–SE pairs extracted from JCO tables is largely complementary to those derived from FDA drug labels; as many as 84.7% of the pairs extracted from JCO tables have not been included a side effect database constructed from FDA drug labels. Side effects associated with anticancer drugs positively correlate with drug target genes, drug metabolism genes, and disease indications.

Antipsychotic drug-associated gene–miRNA interaction in T-lymphocytes

Antipsychotic drugs (APDs) can have a profound effect on the human body that extends well beyond our understanding of their neuropsychopharmacology. Some of these effects manifest themselves in peripheral blood lymphocytes, and in some cases, particularly in clozapine treatment, result in serious complications. To better understand the molecular biology of APD action in lymphocytes, we investigated the influence of chlorpromazine, haloperidol and clozapine in vitro, by microarray-based gene and microRNA (miRNA) expression analysis. JM-Jurkat T-lymphocytes were cultured in the presence of the APDs or vehicle alone over 2 wk to model the early effects of APDs on expression. Interestingly both haloperidol and clozapine appear to regulate the expression of a large number of genes. Functional analysis of APD-associated differential expression revealed changes in genes related to oxidative stress, metabolic disease and surprisingly also implicated pathways and biological processes associated with neurological disease consistent with current understanding of the activity of APDs. We also identified miRNA-mRNA interaction associated with metabolic pathways and cell death/survival, all which could have relevance to known side effects of APDs. These results indicate that APDs have a significant effect on expression in peripheral tissue that relate to both known mechanisms as well as poorly characterized side effects.

Identification of transcription factors for drug-associated gene modules and biomedical implications

One of the major findings in systems biomedicine is that both pathogenesis of diseases and drug mode of action have a module basis. However, the transcription factors (TFs) regulating the modules remain largely unknown. In this study, by using biclustering approach FABIA (factor analysis for bicluster acquisition), we generate 49 modules for gene expression profiles on 1309 agent treatments. These modules are of biological relevance in terms of functional enrichment, drug-drug interactions and 3D proximity in chromatins. By using the information of drug targets (some of which are TFs) and biological regulation, the links between 28 modules and 12 specific TFs, such as estrogen receptors (ERs), nuclear factor-like 2 and peroxisome proliferator-activated receptor gamma, can be established. Some of the links are supported by 3D transcriptional regulation data [derived from ChIA-PET (chromatin interaction analysis using paired-end tags) experiments] and drug mode of action as well. The relationships between modules and TFs provide new clues to interpreting biological regulation mechanisms, in particular, the lipid metabolism regulation by ERα. In addition, the links between natural products (e.g. polyphenols) and their associated modules and TFs are helpful to elucidate their polypharmacological effects in terms of activating specific TFs, such as ERs, nuclear factor-like 2 and peroxisome proliferator-activated receptor gamma.

DTome: a web-based tool for drug-target interactome construction

BackgroundUnderstanding drug bioactivities is crucial for early-stage drug discovery, toxicology studies and clinical trials. Network pharmacology is a promising approach to better understand the molecular mechanisms of drug bioactivities. With a dramatic increase of rich data sources that document drugs' structural, chemical, and biological activities, it is necessary to develop an automated tool to construct a drug-target network for candidate drugs, thus facilitating the drug discovery process.ResultsWe designed a computational workflow to construct drug-target networks from different knowledge bases including DrugBank, PharmGKB, and the PINA database. To automatically implement the workflow, we created a web-based tool called DTome (Drug-Target interactome tool), which is comprised of a database schema and a user-friendly web interface. The DTome tool utilizes web-based queries to search candidate drugs and then construct a DTome network by extracting and integrating four types of interactions. The four types are adverse drug interactions, drug-target interactions, drug-gene associations, and target-/gene-protein interactions. Additionally, we provided a detailed network analysis and visualization process to illustrate how to analyze and interpret the DTome network. The DTome tool is publicly available at http://bioinfo.mc.vanderbilt.edu/DTome.ConclusionsAs demonstrated with the antipsychotic drug clozapine, the DTome tool was effective and promising for the investigation of relationships among drugs, adverse interaction drugs, drug primary targets, drug-associated genes, and proteins directly interacting with targets or genes. The resultant DTome network provides researchers with direct insights into their interest drug(s), such as the molecular mechanisms of drug actions. We believe such a tool can facilitate identification of drug targets and drug adverse interactions.

Synergistic anti-proliferative effects of gambogic acid with docetaxel in gastrointestinal cancer cell lines

SummaryBackgroundGambogic acid has a marked anti-tumor effect for gastric and colorectal cancers in vitro and in vivo. However, recent investigations on gambogic acid have focused mainly on mono-drug therapy, and its potential role in cancer therapy has not been comprehensively illustrated. This study aimed to assess the interaction between gambogic acid and docetaxel on human gastrointestinal cancer cells and to investigate the mechanism of gambogic acid plus docetaxel treatment-induced apoptotic cell death.MethodsMTT assay was used to determine IC50 values in BGC-823, MKN-28, LOVO and SW-116 cells after gambogic acid and docetaxel administration. Median effect analysis was applied for determination of synergism and antagonism. Synergistic interaction between gambogic acid and docetaxel was evaluated using the combination index (CI) method. Furthermore, cellular apoptosis was analyzed by Annexin-V and propidium iodide (PI) double staining. Additionally, mRNA expression of drug-associated genes, i.e., β-tublin III and tau, and the apoptosis-related gene survivin, were measured by quantitative reverse transcription polymerase chain reaction (qRT-PCR).ResultsGambogic acid provided a synergistic effect on the cytotoxicity induced by docetaxel in all four cell lines. The combined application of gambogic acid and docetaxel enhanced apoptosis in gastrointestinal cancer cells. Moreover, gambogic acid markedly decreased the mRNA expression of docetaxel-related genes, including β-tubulin III, tau and survivin, in BGC-823 cells.ConclusionsGambogic acid plus docetaxel produced a synergistic anti-tumor effect in gastrointestinal cancer cells, suggesting that the drug combination may offer a novel treatment option for patients with gastric and colorectal cancers.

Enhancement of Anticancer Efficacy of Chemotherapeutics by Gambogic Acid Against Gastric Cancer Cells

Gambogic acid (GA), the main active component of gamboge, is well known for its marked antitumor effect in vitro and in vivo. The aim of this study was to assess the natural interaction between GA and chemotherapeutic agents, 5-fluorouracil (5-FU), oxaliplatin (Oxa), and docetaxel (Doc), which are widely used in gastric cancer treatment. This study also investigated the effect of GA on cell apoptosis and drug-associated gene expression for further mechanism research. Synergistic interaction on human gastric cancer BGC-823 cells and MKN-28 cells was evaluated using the combination index (CI) method. The double staining method with Annexin-V-FITC and PI was employed to distinguish the apoptotic cells from others. Expression of drug-associated genes, that is, thymidylate synthase (TS), excision repair cross-complementing (ERCC1), BRCA1, tau, and β-tubulin III, was measured by real-time quantitative RT-PCR. This study found that GA had a synergistic effect on the cytotoxity of chemotherapeutic agents against both cell lines. The combination of GA and chemotherapeutic agents could also induce apoptosis in a synergistic manner. The mRNA levels of TS, ERCC1, BRCA1, tau, and β-tubulin III were suppressed at 0.009, 0.075, 0.140, 0.267, and 0.624-fold, respectively, when cells were exposed to GA at the concentration of 0.25 μM. These data suggest that GA has a promising role in enhancing the efficacy of 5-FU, Oxa, and Doc in the treatment of gastric cancer. The potential mechanism would be their synergistic effects on apoptosis induction and the downregulation of chemotherapeutic agent-associated genes.

Gene Expression Signature-Based Screening Identifies New Broadly Effective Influenza A Antivirals

Classical antiviral therapies target viral proteins and are consequently subject to resistance. To counteract this limitation, alternative strategies have been developed that target cellular factors. We hypothesized that such an approach could also be useful to identify broad-spectrum antivirals. The influenza A virus was used as a model for its viral diversity and because of the need to develop therapies against unpredictable viruses as recently underlined by the H1N1 pandemic. We proposed to identify a gene-expression signature associated with infection by different influenza A virus subtypes which would allow the identification of potential antiviral drugs with a broad anti-influenza spectrum of activity. We analyzed the cellular gene expression response to infection with five different human and avian influenza A virus strains and identified 300 genes as differentially expressed between infected and non-infected samples. The most 20 dysregulated genes were used to screen the connectivity map, a database of drug-associated gene expression profiles. Candidate antivirals were then identified by their inverse correlation to the query signature. We hypothesized that such molecules would induce an unfavorable cellular environment for influenza virus replication. Eight potential antivirals including ribavirin were identified and their effects were tested in vitro on five influenza A strains. Six of the molecules inhibited influenza viral growth. The new pandemic H1N1 virus, which was not used to define the gene expression signature of infection, was inhibited by five out of the eight identified molecules, demonstrating that this strategy could contribute to identifying new broad anti-influenza agents acting on cellular gene expression. The identified infection signature genes, the expression of which are modified upon infection, could encode cellular proteins involved in the viral life cycle. This is the first study showing that gene expression-based screening can be used to identify antivirals. Such an approach could accelerate drug discovery and be extended to other pathogens.

Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance

Drug resistance remains a major obstacle to successful cancer treatment. A database of drug-associated gene expression profiles was screened for molecules whose profile overlapped with a gene expression signature of glucocorticoid (GC) sensitivity/resistance in acute lymphoblastic leukemia (ALL) cells. The screen indicated that the mTOR inhibitor rapamycin profile matched the signature of GC sensitivity. We tested the hypothesis that rapamycin would induce GC sensitivity in lymphoid malignancy cells and found that it sensitized to GC-induced apoptosis via modulation of antiapoptotic MCL1. These data indicate that MCL1 is an important regulator of GC-induced apoptosis and that the combination of rapamycin and glucocorticoids has potential utility in lymphoid malignancies. Furthermore, this approach represents a strategy for identification of promising combination therapies for cancer.