The effects of chemicals present in the environment on human health are poorly understood. Exposure to toxicants has been identified as a major preventable risk factor for breast cancer, the leading cause of cancer-related death among women worldwide. The primary objective of this project is to develop, validate and use a reliable 3D high-throughput screening platform to explore the influence of chemicals on the different stages of breast cancer development. We hypothesize that certain chemicals will affect hormone-responsive mammary epithelial cells differently at each stage of breast cancer. In Aim 1 we will optimize and automate our synchronous microfluidic 3D in vitro breast cancer model to be used for chemical library screening with the Microscale Systems Core and the Synthetic Matrices Core. In Aim 2 we will develop an adverse outcome pathway (AOP) based model of estrogen-receptor (ER) mediated invasive ductal carcinoma (IDC) by utilizing quantitative physiological and molecular endpoints to identify key steps between the initiating event (estrogen receptor ligand binding) and the adverse outcome (IDC) in our microfluidic platform. Then, in Aim 3, we will conduct low and medium-throughput screens using chemicals from the ToxCast library in our organotypic system to identify chemicals that promote ER-mediated and non ER-mediated IDC with the Pathway Analysis Core. Completion of the project as described will produce an organotypic culture model (OCM) of breast cancer compatible with higher throughput screening (HTS) and high-content (HCS) screening approaches to discern toxic effects of chemical substances on breast cancer development and progression.
Prof. David Beebe
Wisconsin Institutes for Medical Research, Room 6009
1111 Highland Drive
Madison, WI 53705
Our goal is to generate a 3D high-throughput microfluidics model that captures the different stages of breast cancer development. We hope to explore how the exposure to various environmental compounds affect the difference stages of breast cancer development through various physological and molecular endpoints. For this purpose we have generated a defined adverse outcome pathway (AOP) to model disease progression, utilized matrices from Synthetic Matrices Core, generated a microfluidics device with the Microscale Systems Core, and studied molecular endpoints with the Pathway Analysis Core.