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https://doi.org/10.1186/s12916-022-02334-z
Title: | Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification | Authors: | Ho, Peh Joo Ho, Weang Kee Khng, Alexis J Yeoh, Yen Shing Tan, Benita Kiat-Tee Tan, Ern Yu Lim, Geok Hoon Tan, Su-Ming Tan, Veronique Kiak Mien Yip, Cheng-Har Mohd-Taib, Nur-Aishah Wong, Fuh Yong Lim, Elaine Hsuen Ngeow, Joanne Chay, Wen Yee Leong, Lester Chee Hao Yong, Wei Sean Seah, Chin Mui Tang, Siau Wei Ng, Celene Wei Qi Yan, Zhiyan Lee, Jung Ah Rahmat, Kartini Islam, Tania Hassan, Tiara Tai, Mei-Chee Khor, Chiea Chuen Yuan, Jian-Min Koh, Woon-Puay Sim, Xueling Dunning, Alison M Bolla, Manjeet K Antoniou, Antonis C Teo, Soo-Hwang Li, Jingmei Hartman, Mikael |
Keywords: | Science & Technology Life Sciences & Biomedicine Medicine, General & Internal General & Internal Medicine Breast cancer Polygenic risk score Gail model Protein-truncating variants Risk-based screening MAMMOGRAPHY ASSOCIATION WOMEN |
Issue Date: | 26-Apr-2022 | Publisher: | BMC | Citation: | Ho, Peh Joo, Ho, Weang Kee, Khng, Alexis J, Yeoh, Yen Shing, Tan, Benita Kiat-Tee, Tan, Ern Yu, Lim, Geok Hoon, Tan, Su-Ming, Tan, Veronique Kiak Mien, Yip, Cheng-Har, Mohd-Taib, Nur-Aishah, Wong, Fuh Yong, Lim, Elaine Hsuen, Ngeow, Joanne, Chay, Wen Yee, Leong, Lester Chee Hao, Yong, Wei Sean, Seah, Chin Mui, Tang, Siau Wei, Ng, Celene Wei Qi, Yan, Zhiyan, Lee, Jung Ah, Rahmat, Kartini, Islam, Tania, Hassan, Tiara, Tai, Mei-Chee, Khor, Chiea Chuen, Yuan, Jian-Min, Koh, Woon-Puay, Sim, Xueling, Dunning, Alison M, Bolla, Manjeet K, Antoniou, Antonis C, Teo, Soo-Hwang, Li, Jingmei, Hartman, Mikael (2022-04-26). 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). ScholarBank@NUS Repository. 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. | Source Title: | BMC MEDICINE | URI: | https://scholarbank.nus.edu.sg/handle/10635/237405 | ISSN: | 1741-7015 | DOI: | 10.1186/s12916-022-02334-z |
Appears in Collections: | Staff Publications Elements |
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