AUTHOR=Velasco Parra Harvy Mauricio , Cardona Danny Styvens , Buitrago Cesar Augusto , Vanegas Melisa Naranjo , Hincapié Sebastián Gutiérrez , Jaramillo Carolina Jaramillo , Cock-Rada Alicia Maria , Benavides Duque Carolina , Piedrahita Clara Patricia , Bustamante Catalina , González Niño Leonel Andrés , Kintle Jen , Kulm Scott , Bolli Alessandro , Dominico Paolo Di , Botta Giordano , Busby George B. , Valencia-Arango Juan Pablo 

TITLE=Clinical validation of an integrated risk assessment test incorporating genomic and non-genomic data for sporadic breast cancer in Colombia

JOURNAL=Frontiers in Genetics

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1556907

DOI=10.3389/fgene.2025.1556907

ISSN=1664-8021

ABSTRACT=IntroductionBreast 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.ObjectiveTo evaluate the predictive performance of ancestry-specific PRS combined with clinical and imaging risk factors for breast cancer in Colombian women.MethodsWe 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.ResultsBreast 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.ConclusionCombining 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.