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

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 di...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9040206/
https://www.ncbi.nlm.nih.gov/pubmed/35468796
http://dx.doi.org/10.1186/s12916-022-02334-z
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author 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
author_facet 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
author_sort Ho, Peh Joo
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02334-z.
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spelling pubmed-90402062022-04-27 Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification 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 BMC Med Research Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02334-z. BioMed Central 2022-04-26 /pmc/articles/PMC9040206/ /pubmed/35468796 http://dx.doi.org/10.1186/s12916-022-02334-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
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
Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification
title Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification
title_full Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification
title_fullStr Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification
title_full_unstemmed Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification
title_short Overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification
title_sort overlap of high-risk individuals predicted by family history, and genetic and non-genetic breast cancer risk prediction models: implications for risk stratification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9040206/
https://www.ncbi.nlm.nih.gov/pubmed/35468796
http://dx.doi.org/10.1186/s12916-022-02334-z
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