Our research focuses on the characterization of genetic alterations in cancer cells. We study the mechanisms that lead to cancer cell development and the genes that are involved. Our areas of emphasis include gene expression, gene amplification, and chromosome abnormalities.

Our specific projects center on the identification and physical mapping of amplified DNA in human solid tumors, particularly those of breast and ovarian cancers. Using comparative genomic hybridization, conventional cytogenetic analysis, and chromosome microdissection, we are identifying amplified chromosomal segments and characterizing these segments in relation to the biology of breast and ovarian cancers. This research led to my discovery that two transcriptional coactivators of the estrogen receptor, AIB1 and AIB3, are novel targets of gene amplification in breast cancer.

One of the major challenges of cancer biology is defining the aberrant pattern of gene expression in tumor cells and then relating this pattern to specific genomic alterations that occur during tumorigenesis. We are addressing this issue by using cDNA microarray hybridization to analyze the consequences of chromosome anomalies at the level of gene expression. This technology provides a large-scale view of gene expression. A robotic device prints thousands of cDNA clones on a single microscope slide. Then, fluorescent cDNA probes prepared from any cell or tissue source of interest are hybridized to these arrays. It is proving possible to use this technology to profile individual diseases, and to determine the consequences of a given genetic alteration on gene expression. Our current research employs cDNA microarray hybridization to study model systems carrying alterations in tumor specific genes affected by translocation, amplification or inactivation, and to study models that have distinct biological properties, such as metastasis or responsiveness to hormones. We then integrate the information obtained from these model systems with gene expression profiles derived from the statistical analysis of expression data from tissue specimens. Through this research, we endeavor to improve the classification of cancer, increase understanding of oncogenic mechanisms, and identify novel therapeutic targets.

For more information, visit http://www.genome.gov/10000301.