INTRODUCTION

Breast cancer risk arises from a complex interaction of genetic, environmental, and physiological factors. Integrating Polygenic Risk Scores (PRS) with clinical risk factors can enhance personalized risk prediction, especially in diverse populations like Colombia.

OBJECTIVE

To evaluate the predictive performance of ancestry-specific PRS combined with clinical and imaging risk factors for breast cancer in Colombian women.

METHODS

We developed and validated ancestry-specific PRS using diverse genetic datasets. A cohort of 1,997 Colombian women, including 510 breast cancer cases (25.5%) and 1,487 controls (74.5%), were recruited. Clinical data, such as breast density and family history, were analyzed for predictive ability using the area under the receiver operating characteristic curve (AUC). Participants were categorized into genetic ancestry groups: Admixed American, African, and European. PRS were applied to the cohort and adjusted for clinical factors to assess risk prediction.

RESULTS

Breast density and family history were the strongest individual predictors, with AUCs of 0.66 and 0.64, respectively. Most participants were of Admixed American ancestry (70% of cases, 73% of controls). The combined PRS showed an Odds Ratio per Standard Deviation of 1.56 (95% CI 1.40-1.75) and an AUC of 0.72 (95% CI 0.69-0.74) when adjusted for family history. Incorporating PRS with clinical and imaging data improved the AUC to 0.79 (95% CI 0.76-0.81), significantly enhancing predictive accuracy.

CONCLUSION

Combining ancestry-specific PRS with clinical risk factors provides a more accurate approach for breast cancer risk stratification in Colombian women. These findings support the development of precise, population-specific risk assessment models.