[Show abstract][Hide abstract]ABSTRACT: Epigenetic therapy has had a significant impact on the management of hematologic malignancies, but its role in the treatment of ovarian cancer remains to be defined. The authors previously demonstrated that treatment of ovarian and breast cancer cells with DNA methyltransferase and histone deacetylase (HDAC) inhibitors can up-regulate the expression of imprinted tumor suppressors. In this study, demethylating agents and HDAC inhibitors were tested for their ability to induce re-expression of tumor suppressor genes, inhibiting growth of ovarian cancer cells in culture and in xenografts.
Ovarian cancer cells (Hey and SKOv3) were treated with demethylating agents (5-aza-20-deoxycytidine [DAC] or 5-azacitidine [AZA]) or with HDAC inhibitors (suberoylanilide hydroxamicacid [SAHA] or trichostatin A [TSA]) to determine their impact on cellular proliferation, cell cycle regulation, apoptosis, autophagy, and re-expression of 2 growth inhibitory imprinted tumor suppressor genes: guanosine triphosphate-binding Di-RAS-like 3 (ARHI) and paternally expressed 3 (PEG3). The in vivo activities of DAC and SAHA were assessed in a Hey xenograft model.
The combination of DAC and SAHA produced synergistic inhibition of Hey and SKOv3 cell growth by apoptosis and cell cycle arrest. DAC induced autophagy in Hey cells that was enhanced by SAHA. Treatment with both agents induced re-expression of ARHI and PEG3 in cultured cells and in xenografts, correlating with growth inhibition. Knockdown of ARHI decreased DAC-induced autophagy. DAC and SAHA inhibited the growth of Hey xenografts and induced autophagy in vivo.
A combination of DAC and SAHA inhibited ovarian cancer growth while inducing apoptosis, G2/M arrest, autophagy, and re-expression of imprinted tumor suppressor genes.
[Show abstract][Hide abstract]ABSTRACT: The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis.
The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method (Chou and Talalay, 1984). The Median-Effect Principle/Combination Index method leads to inefficiency by ignoring important sources of variation inherent in dose-response data and discarding data points that do not fit the Median-Effect Principle. Previous work has shown that the conventional method yields a high rate of false positives (Boik, Boik, Newman, 2008; Hennessey, Rosner, Bast, Chen, 2010) and, in some cases, low power to detect synergy. There is a great need for improving the current methodology.
We developed a Bayesian framework for dose-response modeling and drug-drug interaction analysis. First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the case that parametric dose-response models do not fit the data, we developed a practical and flexible nonparametric regression method for meta-analysis of independently repeated dose-response experiments. Third, and lastly, we developed a method, based on Loewe additivity that allows one to quantitatively assess interaction between two agents combined at a fixed dose ratio. The proposed method makes a comprehensive and honest account of uncertainty within drug interaction assessment. Extensive simulation studies show that the novel methodology improves the screening process of effective/synergistic agents and reduces the incidence of type I error.
We consider an ovarian cancer cell line study that investigates the combined effect of DNA methylation inhibitors and histone deacetylation inhibitors in human ovarian cancer cell lines. The hypothesis is that the combination of DNA methylation inhibitors and histone deacetylation inhibitors will enhance antiproliferative activity in human ovarian cancer cell lines compared to treatment with each inhibitor alone. By applying the proposed Bayesian methodology, in vitro synergy was declared for DNA methylation inhibitor, 5-AZA-2'-deoxycytidine combined with one histone deacetylation inhibitor, suberoylanilide hydroxamic acid or trichostatin A in the cell lines HEY and SKOV3. This suggests potential new epigenetic therapies in cell growth inhibition of ovarian cancer cells.
[Show abstract][Hide abstract]ABSTRACT: In this article, we propose a Bayesian approach to dose-response assessment and the assessment of synergy between two combined agents. We consider the case of an in vitro ovarian cancer research study aimed at investigating the antiproliferative activities of four agents, alone and paired, in two human ovarian cancer cell lines. In this article, independent dose-response experiments were repeated three times. Each experiment included replicates at investigated dose levels including control (no drug). We have developed a Bayesian hierarchical nonlinear regression model that accounts for variability between experiments, variability within experiments (i.e., replicates), and variability in the observed responses of the controls. We use Markov chain Monte Carlo to fit the model to the data and carry out posterior inference on quantities of interest (e.g., median inhibitory concentration IC(50)). In addition, we have developed a method, based on Loewe additivity, that allows one to assess the presence of synergy with honest accounting of uncertainty. Extensive simulation studies show that our proposed approach is more reliable in declaring synergy compared to current standard analyses such as the median-effect principle/combination index method (Chou and Talalay, 1984, Advances in Enzyme Regulation 22, 27-55), which ignore important sources of variability and uncertainty.