Back to Search
Start Over
Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification.
- Source :
-
BMC medicine [BMC Med] 2022 Apr 26; Vol. 20 (1), pp. 150. Date of Electronic Publication: 2022 Apr 26. - Publication Year :
- 2022
-
Abstract
- Background: Family history, and genetic and non-genetic risk factors can stratify women according to their individual risk of developing breast cancer. The extent of overlap between these risk predictors is not clear.<br />Methods: In this case-only analysis involving 7600 Asian breast cancer patients diagnosed between age 30 and 75 years, we examined identification of high-risk patients based on positive family history, the Gail model 5-year absolute risk [5yAR] above 1.3%, breast cancer predisposition genes (protein-truncating variants [PTV] in ATM, BRCA1, BRCA2, CHEK2, PALB2, BARD1, RAD51C, RAD51D, or TP53), and polygenic risk score (PRS) 5yAR above 1.3%.<br />Results: Correlation between 5yAR (at age of diagnosis) predicted by PRS and the Gail model was low (r=0.27). Fifty-three percent of breast cancer patients (n=4041) were considered high risk by one or more classification criteria. Positive family history, PTV carriership, PRS, or the Gail model identified 1247 (16%), 385 (5%), 2774 (36%), and 1592 (21%) patients who were considered at high risk, respectively. In a subset of 3227 women aged below 50 years, the four models studied identified 470 (15%), 213 (7%), 769 (24%), and 325 (10%) unique patients who were considered at high risk, respectively. For younger women, PRS and PTVs together identified 745 (59% of 1276) high-risk individuals who were not identified by the Gail model or family history.<br />Conclusions: Family history and genetic and non-genetic risk stratification tools have the potential to complement one another to identify women at high risk.<br /> (© 2022. The Author(s).)
Details
- Language :
- English
- ISSN :
- 1741-7015
- Volume :
- 20
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- BMC medicine
- Publication Type :
- Academic Journal
- Accession number :
- 35468796
- Full Text :
- https://doi.org/10.1186/s12916-022-02334-z