Critical determinants of estrogen receptor -mediated agonist activity

Exogenous ligands that bind to the estrogen receptor (ER) exhibit unique pharmacologies distinct from that observed with the endogenous hormone, 17β-estradiol (ED. Differential activity among ER ligands has been observed at the level of receptor binding, promoter interaction and transcriptional activation. Furthermore, xenoestrogens can display tissue-specific agonist activity on the cellular level, functioning as an agonist in one tissue and as an antagonist in another. That the same ligand, functioning through the same receptor, can produce differing agonist responses on the cellular level indicates that there are tissue-specific determinants of agonist activity. In these studies critical molecular determinants of agonist activity were characterized for several cell types. In the normal and neoplastic myometrium a proliferative response was dependent upon activation of AF2 of the ER, functioning as a determinant of agonism in this cell type. Progesterone receptor (PR) ligands transdominantly suppressed ER-mediated transcription and proliferation in uterine leiomyoma cells, indicating that ER/PR cross-talk can modulate agonist activity in a myometrial cell background. In the breast, the agonist response to ER ligands was investigated by employing a functional genomics approach to generate gene expression profiles. Treatment of breast cancer cells with the selective estrogen receptor modulator tamoxifen largely recapitulated the expression profile induced by treatment with the agonist E2, despite the well-characterized antiproliferative effects produced by tamoxifen in this cell type. While the expression of many genes involved in regulating cell cycle progression, including fos, myc, cdc25a, stk15 and cyclin A, were induced by both E2 and tamoxifen in breast cells, treatment with the agonist E2 specifically induced the expression of cyclin D1, fra-1 , and uracil DNA glycosylase. These results suggest that the inability of tamoxifen to transactivate expression of only a few key genes, functioning as cellular gatekeepers, prevent tamoxifen-treated breast cells from entering the cell cycle. Thus, the expression of these agonist-specific marker genes is a potential determinant of agonist activity at the cellular level in the breast. Collectively, studies in the breast and uterine myometrium have identified several mechanisms whereby ER ligands modulate ER-mediated signaling and provide insights into the biology of tissue-specific agonist activity in hormone-responsive tissues. ^

LY2109761, a novel transforming growth factor beta receptor type I and type II dual inhibitor, as a therapeutic approach to suppressing pancreatic cancer metastasis.

Most pancreatic cancer patients present with inoperable disease or develop metastases after surgery. Conventional therapies are usually ineffective in treating metastatic disease. It is evident that novel therapies remain to be developed. Transforming growth factor beta (TGF-beta) plays a key role in cancer metastasis, signaling through the TGF-beta type I/II receptors (TbetaRI/II). We hypothesized that targeting TbetaRI/II kinase activity with the novel inhibitor LY2109761 would suppress pancreatic cancer metastatic processes. The effect of LY2109761 has been evaluated on soft agar growth, migration, invasion using a fibroblast coculture model, and detachment-induced apoptosis (anoikis) by Annexin V flow cytometric analysis. The efficacy of LY2109761 on tumor growth, survival, and reduction of spontaneous metastasis have been evaluated in an orthotopic murine model of metastatic pancreatic cancer expressing both luciferase and green fluorescence proteins (L3.6pl/GLT). To determine whether pancreatic cancer cells or the cells in the liver microenvironment were involved in LY2109761-mediated reduction of liver metastasis, we used a model of experimental liver metastasis. LY2109761 significantly inhibited the L3.6pl/GLT soft agar growth, suppressed both basal and TGF-beta1-induced cell migration and invasion, and induced anoikis. In vivo, LY2109761, in combination with gemcitabine, significantly reduced the tumor burden, prolonged survival, and reduced spontaneous abdominal metastases. Results from the experimental liver metastasis models indicate an important role for targeting TbetaRI/II kinase activity on tumor and liver microenvironment cells in suppressing liver metastasis. Targeting TbetaRI/II kinase activity on pancreatic cancer cells or the cells of the liver microenvironment represents a novel therapeutic approach to prevent pancreatic cancer metastasis.

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.