Age-dependent pathogenic variant risk in female breast cancer.

1624 Background: Germline pathogenic and likely pathogenic variants (GPVs) in BRCA1 , BRCA2 , and PALB2 raise female breast cancer risk. Genetic counseling and screening guidelines rely on fixed age estimates. Using two large biobanks, All of Us Research Program (AoURP) and BioVU biobank, we aim to characterize age-dependent breast cancer risk trajectories and associations with family history (FH). Methods: In AoURP, we extracted data on female participants with genetic data to identify those with BRCA1 , BRCA2 , and PALB2 GPVs, FH surveys to identify those with first degree breast cancer FH (BCFH), and electronic health records (EHR) to determine presence of breast cancer diagnosis and diagnosis age. Age served as the time scale, with left truncation at first EHR entry and censoring at last EHR event. Gene-specific breast cancer risk was estimated using Cox models with age-dependent genetic effects, adjusted for genetic ancestry (first five principal components) and FH. Analyses in AoURP were stratified by self-reported race and BCFH. We validated results in BioVU using the same methodology excluding FH which was unavailable as discrete data. Results: In AoURP, 100,916 women with genetic, FH, and EHR data were available. The majority (67%) were White, based on self-reported race, and 5208 had breast cancer. The sample included 291 BRCA1 GPVs, 505 BRCA2 GPVs, and 139 PALB2 GPVs, with breast cancer diagnosed in 36%, 23%, and 19% respectively. BioVU included 127,147 women, of whom 85.5% were White and 5,664 had breast cancer. The sample had 262 BRCA1 GPVs, 515 BRCA2 GPVs, and 210 PALB2 GPVs, with breast cancer diagnosed in 29%, 22%, and 16% respectively. Across the genes, we observed a bimodal distinct age-dependent breast cancer risk pattern. Bimodal peaks for BRCA1 in AoURP were at 26 and 36 years, while in BioVU were at 35 and 62 years. The bimodal peaks for BRCA2 in AoUPR were at 32 and 51 years, while in BioVU were at 32 and 55 years. Finally, bimodal peaks for PALB2 in AoUPR were 35 and 51 years, while in BioVU were at 41 and 64 years. Age-specific risk trajectories also varied by FH where risk peaks for participants with BCFH were earlier by 2-7 years compared to those without BCFH. Conclusions: We presented population based study providing unbiased estimates of breast cancer risk associated with GPVs in BRCA1, BRCA2, and PALB2 in two enrollment-based cohorts. Leveraging these large datasets, we identified novel bimodal age-dependent patterns of breast cancer risk across genes, with consistent peak ages for BRCA2 and PALB2 and cohort specific differences for BRCA1, highlighting the importance of population context. Replication in BioVU confirmed similar bimodal trends with later peak ages, supporting our finding robustness. Our results show that breast cancer risk in GPVs genes is more dynamic than previously recognized. Integrating age and FH risk estimates into genetic counseling and screening may improve personalized risk assessment and clinical decision making.

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Publication Details

Journal
Journal of Clinical Oncology
Published
2026-05-27
DOI
https://doi.org/10.1200/jco.2026.44.16_suppl.1624
Primary Topic
BRCA gene mutations in cancer
Type
article
Field-Weighted Citation Impact
0.00

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article

Age-dependent pathogenic variant risk in female breast cancer.

Qingxia Chen, Andrew Guide, H Chen, 艺豪 杨 et al.
Journal of Clinical Oncology
BRCA gene mutations in cancer
article

Age-dependent pathogenic variant risk in female breast cancer.

Qingxia Chen, Andrew Guide, H Chen, 艺豪 杨, Robert M. Cronin, Lina Sulieman, Douglas Ruderfer, A Bick, Brian Shirts, Lindsey Stolze, Joshua Peterson, Tuya Pal, Quanhu Sheng
article en

Abstract

1624 Background: Germline pathogenic and likely pathogenic variants (GPVs) in BRCA1 , BRCA2 , and PALB2 raise female breast cancer risk. Genetic counseling and screening guidelines rely on fixed age estimates. Using two large biobanks, All of Us Research Program (AoURP) and BioVU biobank, we aim to characterize age-dependent breast cancer risk trajectories and associations with family history (FH). Methods: In AoURP, we extracted data on female participants with genetic data to identify those with BRCA1 , BRCA2 , and PALB2 GPVs, FH surveys to identify those with first degree breast cancer FH (BCFH), and electronic health records (EHR) to determine presence of breast cancer diagnosis and diagnosis age. Age served as the time scale, with left truncation at first EHR entry and censoring at last EHR event. Gene-specific breast cancer risk was estimated using Cox models with age-dependent genetic effects, adjusted for genetic ancestry (first five principal components) and FH. Analyses in AoURP were stratified by self-reported race and BCFH. We validated results in BioVU using the same methodology excluding FH which was unavailable as discrete data. Results: In AoURP, 100,916 women with genetic, FH, and EHR data were available. The majority (67%) were White, based on self-reported race, and 5208 had breast cancer. The sample included 291 BRCA1 GPVs, 505 BRCA2 GPVs, and 139 PALB2 GPVs, with breast cancer diagnosed in 36%, 23%, and 19% respectively. BioVU included 127,147 women, of whom 85.5% were White and 5,664 had breast cancer. The sample had 262 BRCA1 GPVs, 515 BRCA2 GPVs, and 210 PALB2 GPVs, with breast cancer diagnosed in 29%, 22%, and 16% respectively. Across the genes, we observed a bimodal distinct age-dependent breast cancer risk pattern. Bimodal peaks for BRCA1 in AoURP were at 26 and 36 years, while in BioVU were at 35 and 62 years. The bimodal peaks for BRCA2 in AoUPR were at 32 and 51 years, while in BioVU were at 32 and 55 years. Finally, bimodal peaks for PALB2 in AoUPR were 35 and 51 years, while in BioVU were at 41 and 64 years. Age-specific risk trajectories also varied by FH where risk peaks for participants with BCFH were earlier by 2-7 years compared to those without BCFH. Conclusions: We presented population based study providing unbiased estimates of breast cancer risk associated with GPVs in BRCA1, BRCA2, and PALB2 in two enrollment-based cohorts. Leveraging these large datasets, we identified novel bimodal age-dependent patterns of breast cancer risk across genes, with consistent peak ages for BRCA2 and PALB2 and cohort specific differences for BRCA1, highlighting the importance of population context. Replication in BioVU confirmed similar bimodal trends with later peak ages, supporting our finding robustness. Our results show that breast cancer risk in GPVs genes is more dynamic than previously recognized. Integrating age and FH risk estimates into genetic counseling and screening may improve personalized risk assessment and clinical decision making.

Journal of Clinical OncologyVol. 44(16_suppl)
Vanderbilt University (US), Vanderbilt-Ingram Cancer Center, The Ohio State University (US), Vanderbilt University Medical Center (US)
National Institutes of Health
Good health and well-being
Openalex Percentile: Top 6%
BRCA gene mutations in cancer
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