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Breast cancer recurrence, location predicted by molecular data

Molecular data identifies breast cancer subgroups likely to recur decades after successful treatment, predicts probable timing and location of metastases.

Women successfully treated for breast cancer and their clinicians often want to know whether, when and where their tumors are likely to recur. It's a million dollar question with no clear answer. But now cancer geneticist Christina Curtis, PhD, together with colleagues from the Cancer Research UK Cambridge Institute, have shown that a close look at the molecular makeup of the cancer cells can give significant clues — including whether the cancer is likely to recur decades after initial treatment.

To do so, the researchers separated patients with breast cancer into subgroups based on, among other characteristics, whether they express certain molecules on their cell surfaces such as the estrogen receptor or another receptor called HER2. Curtis first defined the distinct subgroups of patients in a study published in Nature in 2012. They published their newest results in Nature.

As Curtis explained in our release:

We found that about 25 percent of women whose tumors express the estrogen receptor and not HER2 have an exceedingly high risk of late distant relapse and account for the vast majority of these events. These are the women who seem to be cured but then present with systemic disease many years later. Until now, there has been no good way to identify this subset of women who might benefit from ongoing screening or treatment.

Curtis and her colleagues, including Cambridge cancer researcher Oscar Rueda, PhD, studied more than 3,000 women diagnosed with breast cancer in the United Kingdom and Canada between 1977 and 2005, categorizing their tumors based on a plethora of genetic and molecular data.

They found that, while certain subgroups had a higher likelihood than others of late recurrence, others, including one subgroup of triple-negative breast cancer, were unlikely to recur after five years. The researchers were also able to correlate subgroup types with the location and timing of metastases experienced by the women.

As Curtis explained:

For the first time, we’ve been able to study the rates and routes of breast cancer metastases at unprecedented resolution. ... Our model uniquely accounts for the chronology of a patient’s disease and is based on a genome-driven classification scheme that can inform personalized therapeutic approaches.

Curtis and her colleagues are developing an online clinical tool that they hope clinicians will one day use to identify women who might benefit from more intensive therapy and long-term monitoring.

Photo by travelnow.or.crylater

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