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Can a Genetic Model Help Reduce Overuse of Mammography in Women at Low Risk for Breast Cancer?


In a study assessing the use of a genetic risk stratification model to estimate breast cancer risk in over 25,000 women, researchers found that 0.69 percent of women at low genetic risk were diagnosed with breast cancer by the age of 45 and 1.41 percent of this group were diagnosed by the age of 58.

Could the use of genetic risk stratification rein in possible over screening with the advent of recent recommendations to expand regular mammography screening for breast cancer?

In a retrospective case-control study, recently published in JAMA Oncology, researchers examined the use of genetic risk stratification for 25,591 women (mean age of 53.8) in order to determine those who were at low, average, and high risk for breast cancer, and subsequently determined the incidence and age of onset for breast cancer in these groups.

Employing a 313-single-nucleotide variant model to assign polygenic risk scores, the study authors noted that women with a low genetic risk for breast cancer had no pathogenic variants or a variant of uncertain significance in addition to having a polygenic risk score in the bottom 10 percent of the study cohort. The study cohort was ultimately comprised of 22,843 women at average genetic risk for breast cancer, 2,338 women at low risk and 410 women deemed to be at high risk, according to the study.

The researchers found that 0.69 percent of women were diagnosed with breast cancer by the age of 45 in the average genetic risk group and by the age of 51 in the low-risk group. They also noted that 1.41 percent of the average risk group had breast cancer by the age of 50 and that same percentage of diagnosis didn’t occur until the age of 58 in the low-risk group.

“Current screening guidelines do not adequately account for interindividual variability in breast cancer risk, and when they aim to account for interindividual variability, they specifically focus on identifying those at higher risk. The findings of this … study suggest that rare and common variants can also be combined to identify women at lower risk of breast cancer,” wrote Joseph J. Grzymski, Ph.D., a research professor at the Reno School of Medicine at the University of Nevada, and co-director of the Renown Institute for Health Innovation in Reno, Nevada, and colleagues.

Three Key Takeaways

  1. Genetic risk stratification offers potential for personalized breast cancer screening. The study highlights the potential of genetic risk stratification in identifying women at different risk levels for breast cancer. This information could enable more personalized screening approaches, moving beyond a one-size-fits-all model.
  2. Identification of lower-risk women through polygenic risk scoring. The use of polygenic risk scores allowed the identification of women at low genetic risk for breast cancer. This subgroup had a significantly lower incidence of breast cancer at younger ages compared to those at average genetic risk. This information suggests that genetic screening can help target screening efforts more efficiently.
  3. Potential impact on mammography over screening. Extrapolating the findings to current census data, the study suggests that implementing polygenic risk scoring could lead to the identification of a substantial number of women at low genetic risk. This, in turn, could potentially reduce the number of unnecessary mammograms, aligning with efforts to avoid over screening and adhere to updated screening guidelines.

Extrapolating the study findings to current data from the United State Census Bureau, the study authors estimate that polygenic risk scoring may possibly identify 1.3 million women between the ages of 40 and 47 as being at low genetic risk for breast cancer. The researchers emphasized the potential impact for reining in mammography over screening in this population.

“This genetic screening strategy could potentially avoid 650,000 mammograms each year under the new (United States Preventive Services Taskforce) USPSTF guidelines,” maintained Grzymski and colleagues.

(Editor’s note: For related content, see “Expanded Risk Model Identifies Women Who Need Additional Breast Cancer Screening,” “Diagnostic Imaging’s Top Five Mammography Content of 2023” and “What a New Mammography Study Reveals About BMI, Race, Ethnicity and Advanced Breast Cancer Risk.”

In regard to study limitations, the authors conceded that women classified as being at low risk from a genetics assessment for breast cancer in the study may be at elevated risk if they have a family history of breast cancer. The researchers also acknowledged that ongoing research is assessing emerging models that may bolster polygenic risk prediction for breast cancer.

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