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Gene could predict cancer’s return

MICHIGAN STATE (US) (US) — A genetic pathway that affects the development of breast cancer could help predict which patients are at risk of relapse and could lead to individualized treatment.

Activation of the specific gene E2F2 is associated with a higher probability of breast cancer relapse in humans. Using rodent models, researchers found that removing the gene significantly decreases the likelihood of a tumor.

The findings are published online in the journal Cancer Research.

“Genomic signatures—how genes interact and via what pathways—are a rapidly growing and a powerful method to analyze specific genes in the development, recurrence and spread of breast cancer,” says Eran Andrechek, assistant professor pf physiology at Michigan State University.

After identifying which genes are being activated, physicians can tailor treatments for breast cancer and other diseases to individuals with certain genetic makeups.

For example, breast cancer patients with over-expression of a gene called HER2 are currently treated with the antibody Herceptin, which specifically targets the cells over-expressing HER2.

“With personalized medicine, we can use predictions of how genes will interact, and based on that we can make better use of existing treatments that will have more of an impact,” Andrechek says.

Focusing on tumors initiated by Myc, a gene amplified in 15 percent of all human breast cancer cases, researchers analyzed the tumors to test which pathways were critical to tumor growth, first in computer models and then in rodent models.

In addition to the discovery of E2F2’s role in tumor incidence and relapse, the research also revealed the gene was critical for the development of a type of basal tumor similar to the so-called “triple negative tumors” in human breast cancer that are more prevalent among blacks and are much more difficult to treat.

“We want to examine how we can design therapies for specific tumor types by combining genomics and current medicines,” Andrechek says.

“This holds great promise for personalized cancer therapy.”

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