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USA College of Medicine Researchers Examine Early Predictor of Breast Cancer Aggressiveness

Jun 28th, 2004

CONTACT: Paul Taylor 251-461-1509 ptaylor@usouthal.edu

Physicians may be able to make earlier, more appropriate decisions on the best treatment for breast cancer, thanks to research conducted by faculty at the University of South Alabama Center for Lung Biology (CLB) and the USA Cancer Research Institute (CRI). Their new discovery is published in this month's Breast Cancer Research, one of the most prominent breast cancer research journals in the world.

Dr. Judy A. King and her colleagues in the University of South Alabama Department of Pathology found that a gene involved in the adhesion of cells is less active in breast tumors with a poor prognosis than those that are less aggressive.

Measuring the activity of the activated leukocyte cell adhesion molecule (ALCAM) gene in primary breast tumors could provide physicians advanced warning about the likely clinical outcome of the disease. This should help physicians decide whether to prescribe a more aggressive treatment regimen, such as chemotherapy, much earlier in the diagnostic process.

Researchers compared the expression of the gene for ALCAM in normal breast tissue and tissue samples from primary breast tumors. To do this they counted the number of messages from the ALCAM gene in the different samples.

King and her colleagues found that the ALCAM gene was significantly less active in higher-grade tumors compared to lower-grade tumors, and that the gene was less active in tumors with a worse prognosis compared to those with a better prognosis.

"Tumors from patients who died of breast cancer had significantly lower levels of ALCAM transcripts than those with primary tumors but no metastatic disease or local recurrence," write the researchers in the journal article. "The data clearly suggest that decreased ALCAM expression in the primary tumor is of clinical significance in breast cancer, and that reduced expression indicates a more aggressive phenotype and poor prognosis."

They suggest that quantitative polymerase chain reaction (PCR), a method of making a large number of copies of a gene, to measure the number of ALCAM transcripts in a tissue sample from a primary breast tumor, could be used as a diagnostic tool to identify these more aggressive tumors at an earlier stage, providing physicians with more effective treatment options.

As ALCAM is involved in keeping cells together in a clump, the researchers hypothesize that reduced expression of the gene might allow the tumor cells to separate from one another. This would allow cells to enter the circulation and promote the formation of secondary tumors, which makes the cancer more difficult to treat.

This research project was a collaborative effort lead by King, who is associate professor of pathology and pharmacology at USA and a faculty member in the USA Center for Lung Biology. Four other USA faculty participated in the research - Drs. Solomon F. Ofori-Acquah, assistant professor of cell biology and neuroscience; Troy Stevens, director of the USA CLB and professor of pharmacology; Abu-Bakr Al-Mehdi, assistant professor of pharmacology; Oystein Fodstad, and Barbara Colle, Chair and scientific director of the Center for Basic and Translational Sciences at the USA CRI. Dr. Wen G. Jiang of the University of Wales College of Medicine in Cardiff, United Kingdom, also contributed to this project.


This press release is based on the following article:

"Activated Leukocyte Cell Adhesion Molecule in Breast Cancer: Prognostic Indicator" Judy A. King, Solomon F. Ofori-Acquah, Troy Stevens, Abu-Bakr Al-Mehdi, Oystein Fodstad, and Wen G. Jiang Breast Cancer Research (2004) 6:R478-R487 To be published 28 June 2004

Upon publication this article will be available free of charge according to Breast Cancer Research's Open Access policy at: http://breast-cancer-research.com/content/6/5/R478

Prior to publication, journalists can read the full text of this article at: http://www.biomedcentral.com/content/qcpdf/bcr815.pdf