Patrick M. Gaffney, Principal Investigator

Gene Expression Profiling in Head and Neck Cancers

Cancer of the head and neck is a functionally, economically, and cosmetically devastating disease that accounts for approximately five percent of all cancers diagnosed in the United States. Although treatment with surgery, radiation therapy, or a combination of both can produce excellent results in early stage disease, only about one third of patients have highly confined lesions. The remaining two thirds of patients have local or regionally advanced disease. In spite of optimum local therapy, 50–60% of patients with advanced disease will subsequently develop local disease recurrence and 30% or more will develop distant metastatic disease. Standard chemotherapy in this patient group produces overall response rates of 30–50%, complete response rates of 5–27%, and a median survival of only four to six months.

Although characteristic morphologic features are similar among tumors arising from the same anatomic site, the presence of “molecular heterogeneity” is evident in the variety of genetic abnormalities described in head and neck cancer. These include activation of various oncogenes, tumor suppressor gene inactivation, and loss of heterozygosity at numerous chromosomal locations. The expression patterns of these and many other genes implicated in cancer development and progression, including many sequences of unknown function, will probably be important to understanding the variability in patient outcome experienced in the clinic.

In order to better understand the genes involved in head and neck cancer, these researchers have initiated a gene expression profiling study of squamous cell carcinoma tumors resected at surgery. Thirty-eight specimens have been subjected to microarray analysis using the Affymetrix U133 GeneChip system thus far and a correlation of gene expression data with clinical information is ongoing. The researchers are using the EXPRESSIONIST software package at the Computational Genetics Laboratory to further analyze and interpret the gene expression profiles.