Breast Cancer Risk Stratification in Black Women: Current Status and Potential Solutions to Improve Accuracy

Area of Science:

• Oncology
• Genetics
• Public Health

Background:

• Breast cancer risk stratification models aid early screening and improve outcomes.
• Increasing breast cancer rates in younger individuals, especially Black females, necessitate updated risk assessment guidelines.
• Existing models like Gail, BCSC, BOADICEA, and Tyrer-Cuzick may not accurately assess risk in Black women due to data limitations.

Purpose of the Study:

• To highlight the limitations of current breast cancer risk prediction models in non-European ancestry populations, particularly Black women.
• To emphasize the need for inclusive data in developing and validating risk models.
• To underscore the importance of polygenic risk scores and genome-wide association studies (GWAS) in refining breast cancer risk assessment.

Main Methods:

• Review of existing breast cancer risk prediction models and their underlying data.
• Analysis of current evidence on model performance across different ancestries.
• Discussion of the role of monogenic and polygenic risk factors, including GWAS, in breast cancer risk.

Main Results:

• Current breast cancer risk models, primarily developed using data from White women, tend to underpredict risk in Black women.
• Genome-wide association studies (GWAS) data also predominantly feature European ancestry populations, limiting polygenic risk score accuracy for other groups.
• There is a critical need for diverse population representation in genomics research and model development.

Conclusions:

• Accurate breast cancer risk stratification requires models developed with diverse population data.
• Ensuring representation in genomics research and implementation strategies is vital for equitable clinical care.
• Ongoing development and rigorous assessment of new and existing models are necessary for applicability across all populations.