Nov. 28, 2023
Breast cancer risk has traditionally been categorized broadly, often resulting in standardized screening recommendations primarily based on age. The evolution of genetic modeling tools has shifted the landscape, allowing for a more nuanced approach that takes into account each patient's unique profile.
"The idea that everyone should receive identical screenings at identical intervals is outdated. In our current era, screening strategies should be customized based on the individual's unique risk profile," says Lauren F. Cornell, M.D., a breast medicine specialist at Mayo Clinic Comprehensive Cancer Center in Jacksonville, Florida. Dr. Cornell, in conjunction with her colleagues spanning across all three Mayo Clinic locations, is actively engaged in the optimization of personalized breast cancer risk models.
Several clinical risk assessment models, such as the Gail, BCSC and IBIS, are currently employed to calculate a patient's breast cancer risk. These models consider a range of factors including family history, past biopsy results and reproductive history. While these tools have proved valuable in clinical practice, Dr. Cornell points out their limitations. One significant shortcoming is the lack of representation for diverse populations, as these models have predominantly been developed using data from patients of European ancestry.
Polygenic risk scores: Introducing a genetic element to breast cancer screening
Polygenic risk scores (PRS) evaluate an individual's DNA, specifically analyzing over 300 single nucleotide polymorphisms (SNPs). Each person has a distinct combination of SNPs that contributes to the polygenic risk score. A higher SNP burden is indicative of an elevated breast cancer risk.
Sandhya Pruthi, M.D., a breast medicine specialist at Mayo Clinic Comprehensive Cancer Center in Rochester, Minnesota, has been leading a national clinical study evaluating the clinical impact of PRS, known as the GENRE2 study.
GENRE2: Analyzing the impact of individualized risk assessments
This study is evaluating the influence of personalized risk assessments, including PRS, on the uptake of cancer-preventive medications among high-risk individuals.
Preliminary results from phase 1 of the trial, which were presented at the 2019 Annual Meeting of the American Society of Clinical Oncology, demonstrated that the inclusion of polygenic risk scores significantly impacted breast cancer risk estimates and patient intent to take preventive medication.
Dr. Cornell and her team are now exploring how PRS can complement clinical risk assessment models, particularly for minority populations. Discriminatory accuracy remains a challenge, as there is a more extensive genetic data set available for populations of European descent. "While both our clinical models and current PRS testing have limitations in minority populations, the combination of these tools allows for the most informed risk assessment," says Dr. Cornell.
Improving health disparities with individualized screening plans
Dr. Cornell is spearheading a study that combines clinical risk assessment tools and polygenic risk scores in Black and Hispanic populations, examining whether a more personalized screening plan, derived from the combined tools, enhances uptake to appropriate breast cancer screening protocols.
"We need to do better at estimating risk for minority communities," says Dr. Cornell. "By combining these tools, we're improving standard of care for underrepresented minorities."
Through these studies, Dr. Cornell and her colleagues not only are propelling the medical community toward a more individualized approach to care planning but also are highlighting the critical need for more inclusive genetic resources that cater to people from all ethnic backgrounds. The collaborative efforts across all three Mayo Clinic locations are ensuring that genetic risk assessments are comprehensive and consider the unique needs of each region served.
For more information
Kim J, et al. Impact of a breast cancer (BC) polygenic risk score (PRS) on the decision to take preventive endocrine therapy (ET): The Genetic Risk Estimate (GENRE) trial. Journal of Clinical Oncology. 2019;37:1501.
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