Genetic Makeup Predicts Recurrence Of Cancer

April 10, 2007 – 7:44 pm | posted in Breast Cancer, Genetics, Women's Health / OBGYN

Recently, there have been several advances in treatment and therapy for breast cancer. However, the rate of recurrence is still unpredictable. Researchers at West Virginia University have discovered a gene pattern for identifying a patient’s odds of breast cancer recurrence.

The newly identified 28-gene signature pattern predicts recurrence and spreading, according to researcher Lan Guo, PhD of WVU’s Mary Babb Randolph Cancer Center.

Guo uses a computer model to analyze gene characteristics of approximately 500 breast cancer patients. The data covers cancers from the earliest to most advanced stages. The 28-gene signature can identify patients who have higher disease-free and overall survival rates.

Guo’s study, “Population-Based Molecular Prognosis of Breast Cancer by Transcriptional Profiling,” is featured as a sidebar story on the cover of the April edition of Clinical Cancer Research.

“This study discusses the gene test, which shows great promise for identifying patients at high-risk for recurring breast cancer,” Guo, who is awaiting patent approval on the signature, said. “It allows physicians to tailor an individual’s treatment.”

Under Guo’s direction, the breast cancer research team at the MBRCC is building a database of breast cancer tumor samples that will allow researchers to develop a clinical protocol, using the gene-signature to predict patients’ outcomes.

“Dr. Guo’s discovery is a perfect example of how translational research can benefit patients,” MBRCC Deputy Director and co-author Dan Flynn, PhD, said. “At the Cancer Center we use knowledge from the lab for clinical application.”

“The findings from this study will help us identify patients who need more aggressive treatment early on,” Jame Abraham, MD, director of the Comprehensive Breast Cancer Program and co-author, said. “We are very excited about expanding our research in this area and look forward to the day when we can use the gene-signature to identify better treatment on a case-by-case basis.”

Researchers contributing to the article also include: James Harner, Department of Statistics chair; Yong Qian, Xianglin Shi and Vincent Castranova of NIOSH; and WVU graduates Yan Man and Liang Wei.

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