Discovery Predicts Probability of Spreading Breast Cancer

Researchers from Huntsman Cancer Institute at the University of Utah have discovered a new way to model human breast cancer that could lead to new tools for predicting which breast cancers will spread and new ways to test drugs that may stop its spread.

Results were published online in the journal of Nature Medicine.

To create this improved model for breast cancer studies, the researchers grafted tumor tissue from consenting breast cancer patients directly into mouse mammary glands, rather than the traditional approach, where the cancer cells are grown, or cultured, in the laboratory.

They discovered that the grafts remained virtually identical to the original human breast cancer in structure, genetic makeup and behavior, unlike the methods that rely on cell cultures.

The most surprising result was that the tumor grafts spread from the original site, or metastasized, just as they did in the human patients.” – Alana Welm Ph.D, assistant professor Department of Oncological Sciences

For example, grafts of tumor tissue from patients whose cancer had spread to the lung also spread to the lungs of the mice that received them.”

Most breast cancer deaths result from the disease spreading to other areas of the body such as the lymphatic system, lungs, liver, bones or brain.

In addition, researchers found that the successful grafts were nearly all from patients who developed the most aggressive forms of breast cancer and ultimately died of their disease.

This result reveals the modeling method’s potential as a tool that, soon after a breast cancer diagnosis, could identify whether the tumor would be likely to spread, helping doctors select the best treatment approach for an individual patient’s form of the disease.

The study is a cooperative effort of HCI’s Breast Disease Oriented Team, comprised of surgeons, medical and radiation oncologists, pathologists, and laboratory scientists.

Other contributors included HCI’s Comparative Oncology Resource, the Tissue Resource and Application Core, and ARUP Research Institute. The work was supported by funding from the U.S. Department of Defense Breast Cancer Research Program, the American Association for Cancer Research, the Breast Cancer Research Foundation, and Huntsman Cancer Foundation.

 

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