We have hypothesized that exposure to estrogenically-active chemicals alone, or in combination with eachother, during early critical periods of development will alter predisposition for breast cancer. Our goal is to investigate the potential ability of environmental chemicals that are known endocrine disruptors to alter morphology and genomic/proteomic expressions that can alter mammary gland differentiation and therefore create a predisposition for breast cancer. To reach this goal, we are studying three compounds:
These compounds were used separately to treat the rats at two early periods of development: Prenatal (while the rats are developing intrauterus, the compound is received through the placenta)and Prepubertal (during the lactation, the compound is received through the milk of the mother). The study of the female offspring was performed when they reached 21, 35, 50 or 100 days old (Figure 1: Click to see).

After the mammary glands of the animals were collected, they were used for morphological analysis using whole mount preparations and cell proliferation studies. In addition, RNA was extreacted from the mammary glands, which was used for gene expression analysis through microarray analysis and real time RT-PCR. Biostatistics and Bioinformatics analysis have been done with the microarray results to determine gene expression profiles of exposure, as well to predict or explain changes in the development or cancer susceptibility (Figure 2: Click to see).

Figure 3: Interactive relationship among the UA and FCCC.
This project requires an interaction between the animal studies performed at the University of Alabama at Birmingham (UA) and the genomic studies performed at the Fox Chase Cancer Center (FCCC). At UA the animals are treated according to the protocol described in Figure 6 and the mammary glands are collected and shipped to the FCCC, where the morphological, cell kinetics and genomic studies are performed. At UA, the proteomics studies are performed. (Figure 2). The informatics cores of the FCCC and the UA are extremely important for the data analysis and interpretation for the genomic and proteomic data respectively (Figure 3).
Main Publications Generated from this Project