Using computational strategies to predict potential drugs for nasopharyngeal carcinoma.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a unique cancer. Refinement of current therapy by discovering potential drugs may be approached by several computational strategies. METHODS: We collected NPC genes from published microarray data and the literature. The NPC disease network was constructed via a protein-protein interaction (PPI) network. The Connectivity Map (CMap) was used to predict potential chemicals, and support vector machines (SVMs) were further utilized to classify the effectiveness of tested drugs against NPC using their gene expression from CMap. RESULTS: A highly interconnected network was obtained. Several chemically sensitive genes were identified and 87 drugs were predicted with the potential for treating NPC by SVM, in which nearly half of them have anticancer effects according to the literature. The 2 top-ranked drugs, thioridazine and vorinostat, were demonstrated to be effective in inhibiting NPC cells. CONCLUSION: This in silico approach provides a promising strategy for screening potential therapeutic drugs for NPC treatment.

Gene expression-based chemical genomics identifies heat-shock protein 90 inhibitors as potential therapeutic drugs in cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CCA) is an aggressive tumor with a poor prognosis. There is no standard therapy for CCA, and novel drugs for treating refractory CCA need to be identified. METHODS: The authors hypothesized that, if a drug could reverse the gene expression signature of CCA, then it may inhibit the carcinogenesis of CCA and, hence, would be a potential therapeutic agent. Thus, the gene expression signatures from patients with CCA were queried using the bioinformatic method Connectivity Map, resulting in the enrichment of heat-shock protein 90 (HSP90) inhibitors with therapeutic potentials. RESULTS: Two HSP90 inhibitors, 17-AAG (tanespimycin) and the synthetic diarylisoxazole amide resorcinol NVP-AUY922, demonstrated potent antiproliferative activity in in vitro studies. In a thioacetamide-induced animal model, NVP-AUY922 also had antitumor activity and resulted in objective tumor regression. In addition, NVP-AUY922 reduced the expression of client oncoproteins involved in CCA oncogenesis and inhibited downstream proteins of both the phosphatidylinositol 3-kinase catalytic subunit α/v-akt murine thymoma viral oncogene homolog 1 protein kinase (PIK3/AKT) pathway and the v-Ki-ras2 Kirsten rat sarcoma viral oncogene/mitogen-activated protein kinase (KRAS/MAPK) pathway. CONCLUSIONS: Preclinical data from the current study suggest that NVP-AUY922 may be an effective treatment option for patients with CCA.

In-silico drug screening and potential target identification for hepatocellular carcinoma using Support Vector Machines based on drug screening result.

Hepatocellular carcinoma (HCC) is a severe liver malignancy with few drug treatment options. In finding an effective treatment for HCC, screening drugs that are already FDA-approved will fast track the clinical trial and drug approval process. Connectivity Map (CMap), a large repository of chemical-induced gene expression profiles, provides the opportunity to analyze drug properties on the basis of gene expression. Support Vector Machines (SVM) were utilized to classify the effectiveness of drugs against HCC using gene expression profiles in CMap. The results of this classification will help us (1) identify genes that are chemically sensitive, and (2) predict the effectiveness of remaining chemicals in CMap in the treatment of HCC and provide a prioritized list of possible HCC drugs for biological verification. Four HCC cell lines were treated with 146 distinct chemicals, and cell viability was examined. SVM successfully classified the effectiveness of the chemicals with an average Area Under ROC Curve (AUROC) of 0.9. Using reported HCC patient samples, we identified chemically sensitive genes that may be possible HCC therapeutic targets, including MT1E, MYC, and GADD45B. Using SVM, several known HCC inhibitors, such as geldanamycin, alvespimycin (HSP90 inhibitors), and doxorubicin (chemotherapy drug), were predicted. Seven out of the 23 predicted drugs were cardiac glycosides, suggesting a link between this drug category and HCC inhibition. The study demonstrates a strategy of in silico drug screening with SVM using a large repository of microarrays based on initial in vitro drug screening. Verifying these results biologically would help develop a more accurate chemical sensitivity model.

Gene expression-based chemical genomics identifies potential therapeutic drugs in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is an aggressive tumor with a poor prognosis. Currently, only sorafenib is approved by the FDA for advanced HCC treatment; therefore, there is an urgent need to discover candidate therapeutic drugs for HCC. We hypothesized that if a drug signature could reverse, at least in part, the gene expression signature of HCC, it might have the potential to inhibit HCC-related pathways and thereby treat HCC. To test this hypothesis, we first built an integrative platform, the "Encyclopedia of Hepatocellular Carcinoma genes Online 2", dubbed EHCO2, to systematically collect, organize and compare the publicly available data from HCC studies. The resulting collection includes a total of 4,020 genes. To systematically query the Connectivity Map (CMap), which includes 6,100 drug-mediated expression profiles, we further designed various gene signature selection and enrichment methods, including a randomization technique, majority vote, and clique analysis. Subsequently, 28 out of 50 prioritized drugs, including tanespimycin, trichostatin A, thioguanosine, and several anti-psychotic drugs with anti-tumor activities, were validated via MTT cell viability assays and clonogenic assays in HCC cell lines. To accelerate their future clinical use, possibly through drug-repurposing, we selected two well-established drugs to test in mice, chlorpromazine and trifluoperazine. Both drugs inhibited orthotopic liver tumor growth. In conclusion, we successfully discovered and validated existing drugs for potential HCC therapeutic use with the pipeline of Connectivity Map analysis and lab verification, thereby suggesting the usefulness of this procedure to accelerate drug repurposing for HCC treatment.

From NPC therapeutic target identification to potential treatment strategy.

Nasopharyngeal carcinoma (NPC) is relatively rare in Western countries but is a common cancer in southern Asia. Many differentially expressed genes have been linked to NPC; however, how to prioritize therapeutic targets and potential drugs from unsorted gene lists remains largely unknown. We first collected 558 upregulated and 993 downregulated NPC genes from published microarray data and the primary literatures. We then postulated that conversion of gene signatures into the protein-protein interaction network and analyzing the network topologically could provide insight into key regulators involved in tumorigenesis of NPC. Of particular interest was the presence of cliques, called fully connected subgraphs, in the inferred NPC networks. These clique-based hubs, connecting with more than three queries and ranked higher than other nodes in the NPC protein-protein interaction network, were further narrowed down by pathway analysis to retrieve 24 upregulated and 6 downregulated bottleneck genes for predicting NPC carcinogenesis. Moreover, additional oncogenes, tumor suppressor genes, genes involved in protein complexes, and genes obtained after functional profiling were merged with the bottleneck genes to form the final gene signature of 38 upregulated and 10 downregulated genes. We used the initial and final NPC gene signatures to query the Connectivity Map, respectively, and found that target reduction through our pipeline could efficiently uncover potential drugs with cytotoxicity to NPC cancer cells. An integrative Web site (http://140.109.23.188:8080/NPC) was established to facilitate future NPC research. This in silico approach, from target prioritization to potential drugs identification, might be an effective method for various cancer researches.