The role of redox signaling in the molecular mechanism of tamoxifen resistance in breast cancer.

The emergence of tamoxifen or aromatase inhibitor resistance is a major problem in the treatment of breast cancer. The molecular signaling mechanism of antiestrogen resistance is not clear. Understanding the mechanisms by which resistance to these agents arise could have major clinical implications for preventing or circumventing it. Therefore, in this dissertation we have investigated the molecular mechanisms underlying antiestrogen resistance by studying the contributions of reactive oxygen species (ROS)-induced redox signaling pathways in antiestrogen resistant breast cancer cells. Our hypothesis is that the conversion of breast tumors to a tamoxifen-resistant phenotype is associated with a progressive shift towards a pro-oxidant environment of cells as a result of oxidative stress. The hypothesis of this dissertation was tested in an in vitro 2-D cell culture model employing state of the art biochemical and molecular techniques, including gene overexpression, immunoprecipitation, Western blotting, confocal imaging, ChIP, Real-Time RT-PCR, and anchorage-independent cell growth assays. We observed that tamoxifen (TAM) acts like both an oxidant and an antioxidant. Exposure of tamoxifen resistant LCC2 cell to TAM or 17 beta-estradiol (E2) induced the formation of reactive oxidant species (ROS). The formation of E2-induced ROS was inhibited by co-treatment with TAM, similar to cells pretreated with antioxidants. In LCC2 cells, treatments with either E2 or TAM were capable of inducing cell proliferation which was then inhibited by biological and chemical antioxidants. Exposure of LCC2 cells to tamoxifen resulted in a decrease in p27 expression. The LCC2 cells exposed to TAM showed an increase in p27 phosphorylation on T157 and T187. Conversely, antioxidant treatment showed an increase in p27 expression and a decrease in p27 phosphorylation on T157 and T187 in TAM exposed cells which were similar to the effects of Fulvestrant. In line with previous studies, we showed an increase in the binding of cyclin E-Cdk2 and in the level of p27 in TAM exposed cells that overexpressed biological antioxidants. Together these findings highly suggest that lowering the oxidant state of antiestrogen resistant LCC2 cells, increases LCC2 susceptibility to tamoxifen via the cyclin dependent kinase inhibitor p27.

An agent-based model of team coordination and performance

This research is based on the premises that teams can be designed to optimize its performance, and appropriate team coordination is a significant factor to team outcome performance. Contingency theory argues that the effectiveness of a team depends on the right fit of the team design factors to the particular job at hand. Therefore, organizations need computational tools capable of predict the performance of different configurations of teams. This research created an agent-based model of teams called the Team Coordination Model (TCM). The TCM estimates the coordination load and performance of a team, based on its composition, coordination mechanisms, and job’s structural characteristics. The TCM can be used to determine the team’s design characteristics that most likely lead the team to achieve optimal performance. The TCM is implemented as an agent-based discrete-event simulation application built using JAVA and Cybele Pro agent architecture. The model implements the effect of individual team design factors on team processes, but the resulting performance emerges from the behavior of the agents. These team member agents use decision making, and explicit and implicit mechanisms to coordinate the job. The model validation included the comparison of the TCM’s results with statistics from a real team and with the results predicted by the team performance literature. An illustrative 26-1 fractional factorial experimental design demonstrates the application of the simulation model to the design of a team. The results from the ANOVA analysis have been used to recommend the combination of levels of the experimental factors that optimize the completion time for a team that runs sailboats races. This research main contribution to the team modeling literature is a model capable of simulating teams working on complex job environments. The TCM implements a stochastic job structure model capable of capturing some of the complexity not capture by current models. In a stochastic job structure, the tasks required to complete the job change during the team execution of the job. This research proposed three new types of dependencies between tasks required to model a job as a stochastic structure. These dependencies are conditional sequential, single-conditional sequential, and the merge dependencies.