Abstract A021: Development of cross-ancestry polygenic risk. scores for breast cancer in women of African ancestry

Abstract Introduction: Polygenic risk scores (PRSs) predict breast cancer (BC) risk and have shown utility for screening in European ancestry women (EUR). However, PRSs developed for non-European ancestry, particularly African American (AA) women, have limited predictive accuracy. Methods: We analyzed data from 17,454 BC cases and 19,093 controls in the African American Breast Cancer Genetics (AABCG) consortium, split into training (70%), tuning (10%), and testing (20%) sets. Genome-wide association analyses for overall BC, ER-positive BC, ER- negative BC and triple-negative BC (TNBC) were performed in the training dataset, controlling for age, genotyping platform, and the top 10 principal components. We built single-ancestry PRS models using forward stepwise selection (FSS), PRSice-2, PLINK C+T, Lassosum2, and LDpred2-auto. These were combined with published EUR 313-SNP models (330-SNP for TNBC) in the tuning set to obtain cross-ancestry models. Genome-wide cross-ancestry PRS methods, including CT-SLEB and PRScsx, were also used. We created an ensemble model by combining different PRS models across subtypes using ensemble-based approaches. All developed PRS models were then evaluated in the independent testing set. Additional validation was performed using the All of Us (AoU) study with 834 BC cases and 29,122 controls from the AA population. Results: The observed-scale SNP-based heritability was estimated at 0.29 (SE: 0.12) for overall BC risk, 0.43 (SE: 0.18) for ER-positive BC, 0.44 (SE: 0.24) for ER-negative BC, and 1.13 (SE: 0.40) for TNBC. The best models showed good discriminating capacity: ensemble-based models for overall (AUC=0.626) and ER-positive BC (AUC=0.626), and combined cross-subtype and cross-ancestry FSS models for ER-negative (AUC=0.647) and TNBC (AUC=0.654). The odds ratio per standard deviation was 1.63 (95% CI: 1.55, 1.71) for the ensemble-based PRS of overall BC risk, with an estimated life risk of 28% for the top 1%. The cross-ancestry model using CT-SLEB combined with the EUR 313-SNP model was validated (AUC=0.61) among AA women in the AoU study. Conclusion: We developed PRSs with improved discriminating ability for BC risk in women with African ancestry. Cross- ancestry and subtype learning enhanced model performance. These findings indicate common variation has a larger role in BC in AA women than previously reported, warranting further validation in external AA populations and clinical evaluation. Citation Format: James L. Li, Haoyu Zhang, Julian C McClellan, Guimin Gao, Kevin Wang, Julie R. Palmer, Olufunmilayo I. Olopade, Christopher A. Haiman, Dezheng Huo. Development of cross-ancestry polygenic risk. scores for breast cancer in women of African ancestry [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A021.

Authors

Institutions

Publication Details

Journal
Cancer Epidemiology Biomarkers & Prevention
Published
2024-09-21
DOI
https://doi.org/10.1158/1538-7755.disp24-a021
Primary Topic
BRCA gene mutations in cancer
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract A021: Development of cross-ancestry polygenic risk. scores for breast cancer in women of African ancestry

Dezheng Huo, Julian McClellan, Olufunmilayo I. Olopade, Christopher A. Haiman et al.
3 links
Cancer Epidemiology Biomarkers & Prevention
BRCA gene mutations in cancer
article

Abstract A021: Development of cross-ancestry polygenic risk. scores for breast cancer in women of African ancestry

Dezheng Huo, Julian McClellan, Olufunmilayo I. Olopade, Christopher A. Haiman, Guimin Gao, Julie R. Palmer, Haoyu Zhang, James Li, Xiaoyu Wang
article en

Abstract

Abstract Introduction: Polygenic risk scores (PRSs) predict breast cancer (BC) risk and have shown utility for screening in European ancestry women (EUR). However, PRSs developed for non-European ancestry, particularly African American (AA) women, have limited predictive accuracy. Methods: We analyzed data from 17,454 BC cases and 19,093 controls in the African American Breast Cancer Genetics (AABCG) consortium, split into training (70%), tuning (10%), and testing (20%) sets. Genome-wide association analyses for overall BC, ER-positive BC, ER- negative BC and triple-negative BC (TNBC) were performed in the training dataset, controlling for age, genotyping platform, and the top 10 principal components. We built single-ancestry PRS models using forward stepwise selection (FSS), PRSice-2, PLINK C+T, Lassosum2, and LDpred2-auto. These were combined with published EUR 313-SNP models (330-SNP for TNBC) in the tuning set to obtain cross-ancestry models. Genome-wide cross-ancestry PRS methods, including CT-SLEB and PRScsx, were also used. We created an ensemble model by combining different PRS models across subtypes using ensemble-based approaches. All developed PRS models were then evaluated in the independent testing set. Additional validation was performed using the All of Us (AoU) study with 834 BC cases and 29,122 controls from the AA population. Results: The observed-scale SNP-based heritability was estimated at 0.29 (SE: 0.12) for overall BC risk, 0.43 (SE: 0.18) for ER-positive BC, 0.44 (SE: 0.24) for ER-negative BC, and 1.13 (SE: 0.40) for TNBC. The best models showed good discriminating capacity: ensemble-based models for overall (AUC=0.626) and ER-positive BC (AUC=0.626), and combined cross-subtype and cross-ancestry FSS models for ER-negative (AUC=0.647) and TNBC (AUC=0.654). The odds ratio per standard deviation was 1.63 (95% CI: 1.55, 1.71) for the ensemble-based PRS of overall BC risk, with an estimated life risk of 28% for the top 1%. The cross-ancestry model using CT-SLEB combined with the EUR 313-SNP model was validated (AUC=0.61) among AA women in the AoU study. Conclusion: We developed PRSs with improved discriminating ability for BC risk in women with African ancestry. Cross- ancestry and subtype learning enhanced model performance. These findings indicate common variation has a larger role in BC in AA women than previously reported, warranting further validation in external AA populations and clinical evaluation. Citation Format: James L. Li, Haoyu Zhang, Julian C McClellan, Guimin Gao, Kevin Wang, Julie R. Palmer, Olufunmilayo I. Olopade, Christopher A. Haiman, Dezheng Huo. Development of cross-ancestry polygenic risk. scores for breast cancer in women of African ancestry [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A021.

Cancer Epidemiology Biomarkers & PreventionVol. 33(9_Supplement)
Boston University (US), University of Southern California (US), University of Chicago (US), National Cancer Institute (US), Division of Cancer Epidemiology and Genetics
Reduced inequalities
Openalex Percentile: Top 82%
BRCA gene mutations in cancer
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.