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Gene Expression Profile Helps Predict Chemotherapy Response In Ovarian Cancer Patients

Date:
November 7, 2005
Source:
Beth Israel Deaconess Medical Center
Summary:
A newly identified gene expression profile could help predict how patients with advanced ovarian cancer will respond to chemotherapy treatment. Described in a study in the November 1, 2005 issue of The Journal of Clinical Oncology (JCO), the new findings further establish an important role for microarray gene profiling as a predictor of clinical outcome in ovarian cancer, and could eventually provide clinicians with insights into the mechanisms of drug resistance.

A newly identified gene expression profile could help predict how patients with advanced ovarian cancer will respond to chemotherapy treatment. Described in a study in the November 1, 2005 issue of The Journal of Clinical Oncology (JCO), the new findings further establish an important role for microarray gene profiling as a predictor of clinical outcome in ovarian cancer, and could eventually provide clinicians with insights into the mechanisms of drug resistance.

"In many patients with advanced ovarian cancer, post-operative treatment with first-line chemotherapy will result in an excellent clinical response," says senior author Stephen A. Cannistra, MD, director of gynecologic oncology at Beth Israel Deaconess Medical Center (BIDMC) and professor of medicine at Harvard Medical School.

"However," he adds, "due to the lingering presence of chemotherapy-resistant cancer cells, most patients will unfortunately experience a relapse. The goal of our current research is to help determine which patients will relapse and which will not, and to better understand the reasons for this."

Cannistra's group has been working to develop a genetic profile of ovarian cancer that will enable clinicians to more accurately determine a patient's prognosis. As a first step in this process, he and his colleagues last year identified a gene expression profile known as the Ovarian Cancer Prognostic Profile (OCPP), which is predictive of survival in patients with advanced ovarian cancer. (These study results appear in the December 2004 issue of the JCO.)

Their work makes use of a DNA technology known as microarray analysis, in which genes expressed by cancer cells are labeled and applied to a glass slide containing embedded sequences of thousands of known human genes. The genes that are present in the tumor cell bind to their counterpart sequences on the slide and can then be identified through computer analysis.

In this new study, the authors conducted microarray testing on samples from 60 ovarian cancer patients treated at BIDMC and Memorial Sloan-Kettering Cancer Center to determine if tumor tissue obtained at a patient's initial diagnosis expressed a gene profile predictive of findings at second-look surgery. (Second-look surgery is currently the most sensitive investigational approach for detecting residual disease in patients with advanced ovarian cancer who have achieved a complete clinical remission following chemotherapy, explains Cannistra.)

The expression of 93 genes, collectively referred to as the Chemotherapy Response Profile (CRP), was found to predict which patients would experience a complete response to chemotherapy, as defined by the absence of disease at the time of second-look surgery. The CRP also confirmed the importance of genes such as BAX in this process, which regulate the cell's response to chemotherapy agents such as paclitaxel.

The authors then went on to compare the results of the CRP and the OCPP. "We found that together these two gene profiles [CRP and OCPP] are a more powerful predictor of a patient's prognosis than either profile separately," says Cannistra. "This represents the first time that two profiles have been combined to yield such a powerful result in this disease."

One of the most difficult types of cancer to treat, advanced ovarian cancer accounts for approximately 26,000 new cases and 16,000 deaths in the U.S. each year.

"Being able to identify the expression pattern of these genes from the original tumor sample [i.e. whether genes were 'turned on' or 'turned off'] provides us with valuable information about a patient's prognosis as this type of information cannot always be obtained from standard clinical features, such as tumor grade or residual disease status," notes Cannistra. "And with the identification of each new gene expression profile, we come one step closer to eventually being able to develop treatments tailored to individual ovarian cancer patients."

Coauthors of the study include BIDMC investigators Dimitrios Spentzos, MD, Douglas Levine, MD, Towia A. Libermann, PhD, Shakirahimed Kolia and Hasan Out and Jeff Boyd, PhD, of Memorial Sloan-Kettering Cancer Center in New York.

###

Beth Israel Deaconess Medical Center is a patient care, teaching and research affiliate of Harvard Medical School and ranks fourth in National Institutes of Health funding among independent hospitals nationwide. BIDMC is clinically affiliated with the Joslin Diabetes Center and is a research partner of Dana-Farber/Harvard Cancer Center. BIDMC is the official hospital of the Boston Red Sox. For more information, visit www.bidmc.harvard.edu.


Story Source:

The above story is based on materials provided by Beth Israel Deaconess Medical Center. Note: Materials may be edited for content and length.


Cite This Page:

Beth Israel Deaconess Medical Center. "Gene Expression Profile Helps Predict Chemotherapy Response In Ovarian Cancer Patients." ScienceDaily. ScienceDaily, 7 November 2005. <www.sciencedaily.com/releases/2005/11/051104084409.htm>.
Beth Israel Deaconess Medical Center. (2005, November 7). Gene Expression Profile Helps Predict Chemotherapy Response In Ovarian Cancer Patients. ScienceDaily. Retrieved July 23, 2014 from www.sciencedaily.com/releases/2005/11/051104084409.htm
Beth Israel Deaconess Medical Center. "Gene Expression Profile Helps Predict Chemotherapy Response In Ovarian Cancer Patients." ScienceDaily. www.sciencedaily.com/releases/2005/11/051104084409.htm (accessed July 23, 2014).

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