Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank

Peh Joo Ho; Elaine H Lim; Mikael Hartman; Fuh Yong Wong; Jingmei Li

doi:10.1016/j.gim.2023.100917

Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank

Genet Med. 2023 Oct;25(10):100917. doi: 10.1016/j.gim.2023.100917. Epub 2023 Jun 16.

Authors

Peh Joo Ho¹, Elaine H Lim², Mikael Hartman³, Fuh Yong Wong⁴, Jingmei Li⁵

Affiliations

¹ Laboratory of Women's Health and Genetics, Genome Institute of Singapore, A∗STAR Research Entities, Singapore; Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
² Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore.
³ Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore.
⁴ Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore.
⁵ Laboratory of Women's Health and Genetics, Genome Institute of Singapore, A∗STAR Research Entities, Singapore; Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. Electronic address: lijm1@gis.a-star.edu.sg.

PMID: 37334786
DOI: 10.1016/j.gim.2023.100917

Abstract

Purpose: The benefit of using individual risk prediction tools to identify high-risk individuals for breast cancer (BC) screening is uncertain, despite the personalized approach of risk-based screening.

Methods: We studied the overlap of predicted high-risk individuals among 246,142 women enrolled in the UK Biobank. Risk predictors assessed include the Gail model (Gail), BC family history (FH, binary), BC polygenic risk score (PRS), and presence of loss-of-function (LoF) variants in BC predisposition genes. Youden J-index was used to select optimal thresholds for defining high-risk.

Results: In total, 147,399 were considered at high risk for developing BC within the next 2 years by at least 1 of the 4 risk prediction tools examined (Gail_2-year > 0.5%: 47%, PRS_2-yea r > 0.7%: 30%, FH: 6%, and LoF: 1%); 92,851 (38%) were flagged by only 1 risk predictor. The overlap between individuals flagged as high-risk because of genetic (PRS) and Gail model risk factors was 30%. The best-performing combinatorial model comprises a union of high-risk women identified by PRS, FH, and, LoF (AUC_2-year [95% CI]: 62.2 [60.8 to 63.6]). Assigning individual weights to each risk prediction tool increased discriminatory ability.

Conclusion: Risk-based BC screening may require a multipronged approach that includes PRS, predisposition genes, FH, and other recognized risk factors.

Keywords: Breast cancer; Family history; Loss-of-function variants; Polygenic risk scores; Screening.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biological Specimen Banks
Breast Neoplasms* / diagnosis
Breast Neoplasms* / genetics
Female
Genetic Predisposition to Disease
Humans
Risk Assessment
Risk Factors
United Kingdom / epidemiology