Supporting oncology professionals through education
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The content on this site is intended for healthcare professionals only
An analysis of nearly 200,000 patients who received mammograms between 2006 and 2015 across three U.S. health systems underscores the importance of understanding the heterogeneity of breast cancer risk factors for women of differing races, ages, and disease subtypes.
The study, led by researchers in the Perelman School of Medicine at the University of Pennsylvania, were published in Cancer Medicine.
The cohort included 29,822 (15 percent) Black women — a group historically understudied in cancer research.
Most strikingly, the researchers found that Black women had nearly a three-fold increased risk of triple negative breast cancers, which have a poor prognosis.
While it is known that Black women have a higher risk of this type of breast cancer, the magnitude of the risk found in this study was impactful, given its comprehensive adjustment for breast cancer risk factors in a screened population.
Additionally, the researchers found that triple negative breast cancers were less likely to be screen detected and more likely than other subtypes to be diagnosed as interval cancers.
Higher breast density was associated with increased risk of all four tumour subtypes, with a stronger association among premenopausal women for ER/PR+HER2- and TNBC.
In a separate study led by the same group, the researchers looked further at risk factor among Black women.
They found that breast density was more strongly associated with TNBC than other subtypes, and obesity was associated with greater risk of TNBC among this group.
Those findings were published in Breast Cancer Research and Treatment.
“The risk prediction models available are about 60 percent accurate for predicting risk of breast cancer,” said Anne Marie McCarthy, PhD, an assistant professor of Epidemiology at Penn. “In our studies, we see clear differences in risk factors across these types of breast cancers, and we need to do a better job of identifying how we can accurately predict risk for women, particularly for women of colour.”
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