A BAYESIAN APPROACH TO DOSE-RESPONSE ASSESSMENT AND DRUG-DRUG INTERACTION ANALYSIS: APPLICATION TO IN VITRO STUDIES

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.

Alteration of CEACAM1 gene transcription in prostate cancer cells: The role of AR, Sp2, and HDAC

Cell to cell adhesion molecule (CEACAM1), a type II tumor suppressor, has been found to be down-regulated in prostate cancer cells. The mechanism that causes CEACAM1's down-regulation in tumorigenesis is unknown. Here we show that the transcriptional activity of CEACAM1 is decreased in prostate cancer cells. This decrease is not due to methylation of the CEACAM1's promoter, but rather to the alteration of transcription factors regulating CEACAM1 expression. ^ Since androgen/androgen receptors (AR) are potent regulators of prostate growth and differentiation, their role on CEACAM1 gene transcription was examined. The androgen receptor could directly increase CEACAM1 transcriptional activity in a ligand dependent manner by interacting with an AR consensus element that resides in the CEACAM1 promoter. However, AR binding to the CEACAM1 promoter is not related to the loss of CEACAM1 during prostate cancer progression. ^ Further analysis enabled us to determine the particular region in the CEACAM1 promoter that mediates a decrease in CEACAM1 transcriptional activity in prostate cancer cells. Upon further examination, we found that this CEACAM1 promoter region interacts with the Sp1, Sp2, and Sp3 transcription factors. However, only Sp2 expression was found to increase in prostate cancer cells. Inhibiting Sp2 from binding to the CEACAM1 promoter caused an increase in CEACAM1 transcriptional activity in prostate cancer cells. In addition, over-expressing Sp2 in normal prostate cells resulted in a decrease in CEACAM1 transcriptional activity and endogenous protein expression. These observations suggest that Sp2 is a transcription repressor of CEACAM1. Furthermore, prostate cancer cells treated with trichostatin A (TSA), a specific histone deacetylase (HDAC) inhibitor, activated CEACAM1 transcriptional activity. This implies that HDACs are involved in CEACAM1 transcriptional activity. Mutation of the Sp2 DNA binding region on the CEACAM1 promoter inhibited TSA activation of CEACAM1 transcriptional activity. This indicates that HDACs inhibit CEACAM1 transcriptional activity through Sp2. Base on these results, we propose that Sp2 is critical for down-regulating CEACAM1 expression, and one mechanism by which Sp2 represses CEACAM1 expression is by recruiting HDAC to the CEACAM1 promoter in prostate cancer cells. Collectively, these findings provide novel insights into mechanisms that cause the down-regulation of CEACAM1 expression in prostate cancer cells. ^