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      A network based approach to drug repositioning identifies plausible candidates for breast cancer and prostate cancer

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          Abstract

          Background

          The high cost and the long time required to bring drugs into commerce is driving efforts to repurpose FDA approved drugs—to find new uses for which they weren’t intended, and to thereby reduce the overall cost of commercialization, and shorten the lag between drug discovery and availability. We report on the development, testing and application of a promising new approach to repositioning.

          Methods

          Our approach is based on mining a human functional linkage network for inversely correlated modules of drug and disease gene targets. The method takes account of multiple information sources, including gene mutation, gene expression, and functional connectivity and proximity of within module genes.

          Results

          The method was used to identify candidates for treating breast and prostate cancer. We found that (i) the recall rate for FDA approved drugs for breast (prostate) cancer is 20/20 (10/11), while the rates for drugs in clinical trials were 131/154 and 82/106; (ii) the ROC/AUC performance substantially exceeds that of comparable methods; (iii) preliminary in vitro studies indicate that 5/5 candidates have therapeutic indices superior to that of Doxorubicin in MCF7 and SUM149 cancer cell lines. We briefly discuss the biological plausibility of the candidates at a molecular level in the context of the biological processes that they mediate.

          Conclusions

          Our method appears to offer promise for the identification of multi-targeted drug candidates that can correct aberrant cellular functions. In particular the computational performance exceeded that of other CMap-based methods, and in vitro experiments indicate that 5/5 candidates have therapeutic indices superior to that of Doxorubicin in MCF7 and SUM149 cancer cell lines. The approach has the potential to provide a more efficient drug discovery pipeline.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12920-016-0212-7) contains supplementary material, which is available to authorized users.

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          Most cited references33

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          A2A adenosine receptor protects tumors from antitumor T cells.

          The A2A adenosine receptor (A2AR) has been shown to be a critical and nonredundant negative regulator of immune cells in protecting normal tissues from inflammatory damage. We hypothesized that A2AR also protects cancerous tissues by inhibiting incoming antitumor T lymphocytes. Here we confirm this hypothesis by showing that genetic deletion of A2AR in the host resulted in rejection of established immunogenic tumors in approximately 60% of A2AR-deficient mice with no rejection observed in control WT mice. The use of antagonists, including caffeine, or targeting the A2 receptors by siRNA pretreatment of T cells improved the inhibition of tumor growth, destruction of metastases, and prevention of neovascularization by antitumor T cells. The data suggest that effects of A2AR are T cell autonomous. The inhibition of antitumor T cells via their A2AR in the adenosine-rich tumor microenvironment may explain the paradoxical coexistence of tumors and antitumor immune cells in some cancer patients (the "Hellstrom paradox"). We propose to target the hypoxia-->adenosine-->A2AR pathway as a cancer immunotherapy strategy to prevent the inhibition of antitumor T cells in the tumor microenvironment. The same strategy may prevent the premature termination of immune response and improve the vaccine-induced development of antitumor and antiviral T cells. The observations of autoimmunity during melanoma rejection in A2AR-deficient mice suggest that A2AR in T cells is also important in preventing autoimmunity. Thus, although using the hypoxia-->adenosine-->A2AR pathway inhibitors may improve antitumor immunity, the recruitment of this pathway by selective drugs is expected to attenuate the autoimmune tissue damage.
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            Discovery of drug mode of action and drug repositioning from transcriptional responses.

            A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compound-targeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).
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              Cell sensitivity assays: the MTT assay.

              The MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay is based on the conversion of MTT into formazan crystals by living cells, which determines mitochondrial activity. Since for most cell populations the total mitochondrial activity is related to the number of viable cells, this assay is broadly used to measure the in vitro cytotoxic effects of drugs on cell lines or primary patient cells. In this chapter the protocol of the assay is described including important considerations relevant for each step of the assay as well as its limitations and possible applications.
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                Author and article information

                Contributors
                rongchen@bu.edu
                dsherr@bu.edu
                zhenjun.hu@gmail.com
                delisi@bu.edu
                Journal
                BMC Med Genomics
                BMC Med Genomics
                BMC Medical Genomics
                BioMed Central (London )
                1755-8794
                30 July 2016
                30 July 2016
                2016
                : 9
                : 51
                Affiliations
                [1 ]Bioinformatics Program, College of Engineering, Boston University, Boston, MA USA
                [2 ]Graduate Program in Translational Molecular Medicine, Boston University School of Medicine, Boston, MA USA
                [3 ]Department of Environmental Health, Boston University School of Public Health, Boston, MA USA
                [4 ]Department of Biomedical Engineering, Boston University, Boston, MA USA
                Article
                212
                10.1186/s12920-016-0212-7
                4967295
                27475327
                b8d23e66-8322-4e9f-bc9f-53a192ed5f71
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 3 March 2016
                : 20 July 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000009, Foundation for the National Institutes of Health;
                Award ID: R01GM103502-05
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Genetics
                computational drug repositioning,drug screening,cancer treatment
                Genetics
                computational drug repositioning, drug screening, cancer treatment

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