Protein predicts breast cancer prognosis

Guideline update leaves out novel tests in breast cancer
Guideline update leaves out novel tests in breast cancer

A protein that may help predict breast cancer prognosis has been identified, with the potential to relieve thousands of women at low risk from having to undergo painful, often debilitating, therapies, while insuring the most successful treatments for those at high risk.

Using bioinformatics techniques, researchers showed that the levels of expression of some 1,200 genes that are directly controlled by the enzyme EZH2 correlate with the aggressiveness of breast cancer cases. Their study was published in Molecular and Cellular Biology (2013; doi:10.1128/MCB.00426-13).

"The analysis pipeline that we developed will be useful for stratification of breast cancer patients," said Elizaveta V. Benevolenskaya, PhD, of the University of Illinois at Chicago, Illinois, a researcher on the study. "That stratification will enable clinicians to accurately predict breast cancer progression. The level of expression of a subgroup of EZH2-bound genes could have further predictive value, indicating, for example, that a specific treatment regime is needed."

In the study, she and her collaborators generated breast cancer cells in which they could dampen expression of EZH2 using a technique called RNA inhibition. Inhibiting EZH2 expression reactivated the genes this enzyme controls, which resulted in less aggressive cancer phenotypes with decreased cellular proliferation and improved cellular adhesion.

In addition to predicting aggressiveness, Benevolenskaya said that small-molecule inhibitors of EZH2, which have recently become available, could be developed as therapeutic drugs for breast cancer. The advantage of small molecules is that they are cheaper to manufacture and generally can be taken by mouth, unlike larger molecules, which must be given by injection.

Besides breast cancer, EZH2 overexpression appears to be associated with a worse prognosis in prostate, endometrial, and melanoma tumors. The computational analysis used in their research could also be helpful for predicting the aggressiveness of these and other cancers, said Benevolenskaya.
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