Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification

Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification

BMC Medicine

10.1186/s12916-022-02334-z

http://dx.doi.org/10.1186/s12916-022-02334-z

Peh Joo Ho, Weang Kee Ho, Alexis J. Khng, Yen Shing Yeoh, Benita Kiat-Tee Tan, Ern Yu Tan, Geok Hoon Lim, Su-Ming Tan, Veronique Kiak Mien Tan, Cheng-Har Yip, Nur-Aishah Mohd-Taib, Fuh Yong Wong, Elaine Hsuen Lim, Joanne Ngeow, Wen Yee Chay, Lester Chee Hao Leong, Wei Sean Yong, Chin Mui Seah, Siau Wei Tang, Celene Wei Qi Ng, Zhiyan Yan, Jung Ah Lee, Kartini Rahmat, Tania Islam, Tiara Hassan, Mei-Chee Tai, Chiea Chuen Khor, Jian-Min Yuan, Woon-Puay Koh, Xueling Sim, Alison M. Dunning, Manjeet K. Bolla, Antonis C. Antoniou, Soo-Hwang Teo, Jingmei Li, Mikael Hartman

General Medicine

26 April 2022

Ho, P. J., Ho, W. K., Khng, A. J., Yeoh, Y. S., Tan, B. K.-T., Tan, E. Y., Lim, G. H., Tan, S.-M., Tan, V. K. M., Yip, C.-H., Mohd-Taib, N.-A., Wong, F. Y., Lim, E. H., Ngeow, J., Chay, W. Y., Leong, L. C. H., Yong, W. S., Seah, C. M., Tang, S. W., … Hartman, M. (2022). Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification. BMC Medicine, 20(1). https://doi.org/10.1186/s12916-022-02334-z

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. 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%. 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. Conclusions Family history and genetic and non-genetic risk stratification tools have the potential to complement one another to identify women at high risk.

Attribution 4.0 International (CC BY 4.0)

This research / project is supported by the National Research Foundation - NRF Fellowship
Grant Reference no. : NRF-NRFF2017-02

This research is supported by core funding from: BMRC - Central Research Fund
Grant Reference no. : NA

This research / project is supported by the National Research Foundation - PRECISE
Grant Reference no. : NA

This research / project is supported by the National University Cancer Institute Singapore - Centre Grant
Grant Reference no. : NMRC/CG/NCIS/2010, NMRC/CG/012/2013 and CGAug16M005

This research / project is supported by the National Medical Research Council - Clinician Scientist Award
Grant Reference no. : NMRC/CSA-SI/0015/2017

This research / project is supported by the National Medical Research Council - Centre Grant
Grant Reference no. : CGAug16M012

This research is supported by NUS, under the following programs: Breast Cancer Prevention Programme [BCPP, awarded to MH], Breast Cancer Screening Prevention Programme [BCSPP, awarded to MH], Asian Breast Cancer Research Fund [awarded to MH]

The Malaysian Breast Cancer Genetic Study was established using funds from the Malaysian Ministry of Science, and the Malaysian Ministry of Higher Education High Impact Research Grant [grant no: UM.C/HIR/MOHE/06]. The Malaysian Mammographic Density Study was established using funds raised through the Sime Darby LPGA tournament and the High Impact Research Grant. Additional funding was received from Newton-Ungku Omar Fund [grant no: MR/P012930/1] and Wellcome Trust [grant no: v203477/Z/16/Z], Yayasan Sime Darby, PETRONAS, Estee Lauder Group of Companies, and other donors of Cancer Research Malaysia. The BRIDGES panel sequencing was supported by the European Union Hori zon 2020 research and innovation program BRIDGES (grant number, 634935) and the Wellcome Trust (v203477/Z/16/Z). Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer UK Grant (1287/A16563) and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. The Singapore Chinese Health Study was supported by the National Medical Research Council, Singapore (NMRC/CIRG/1456/2016) and National Institutes of Health, USA (NCI RO1 CA55069, R35 CA53890, R01 CA80205, and R01 CA144034).

https://oar.a-star.edu.sg/communities-collections/articles/19026

1741-7015

Genome Institute of Singapore