Validation of the BOADICEA model in a prospective cohort of BRCA1/2 pathogenic variant carriers
BackgroundNo validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting 5-year breast cancer risks in a prospective cohort of BRCA1/2...
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| Published in | Journal of medical genetics Vol. 61; no. 8; pp. 803 - 809 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BMJ Publishing Group Ltd
01.08.2024
BMJ Publishing Group LTD BMJ Publishing Group |
| Series | Original research |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0022-2593 1468-6244 1468-6244 |
| DOI | 10.1136/jmg-2024-109943 |
Cover
| Abstract | BackgroundNo validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting 5-year breast cancer risks in a prospective cohort of BRCA1/2 PV carriers ascertained through clinical genetic centres.MethodsWe evaluated the model calibration and discriminatory ability in the prospective TRANsIBCCS cohort study comprising 1614 BRCA1 and 1365 BRCA2 PV carriers (209 incident cases). Study participants had lifestyle, reproductive, hormonal, anthropometric risk factor information, a polygenic risk score based on 313 SNPs and family history information.ResultsThe full multifactorial model considering family history together with all other risk factors was well calibrated overall (E/O=1.07, 95% CI: 0.92 to 1.24) and in quintiles of predicted risk. Discrimination was maximised when all risk factors were considered (Harrell’s C-index=0.70, 95% CI: 0.67 to 0.74; area under the curve=0.79, 95% CI: 0.76 to 0.82). The model performance was similar when evaluated separately in BRCA1 or BRCA2 PV carriers. The full model identified 5.8%, 12.9% and 24.0% of BRCA1/2 PV carriers with 5-year breast cancer risks of <1.65%, <3% and <5%, respectively, risk thresholds commonly used for different management and risk-reduction options.ConclusionBOADICEA may be used to aid personalised cancer risk management and decision-making for BRCA1 and BRCA2 PV carriers. It is implemented in the free-access CanRisk tool (https://www.canrisk.org/). |
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| AbstractList | No validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting 5-year breast cancer risks in a prospective cohort of BRCA1/2 PV carriers ascertained through clinical genetic centres.BACKGROUNDNo validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting 5-year breast cancer risks in a prospective cohort of BRCA1/2 PV carriers ascertained through clinical genetic centres.We evaluated the model calibration and discriminatory ability in the prospective TRANsIBCCS cohort study comprising 1614 BRCA1 and 1365 BRCA2 PV carriers (209 incident cases). Study participants had lifestyle, reproductive, hormonal, anthropometric risk factor information, a polygenic risk score based on 313 SNPs and family history information.METHODSWe evaluated the model calibration and discriminatory ability in the prospective TRANsIBCCS cohort study comprising 1614 BRCA1 and 1365 BRCA2 PV carriers (209 incident cases). Study participants had lifestyle, reproductive, hormonal, anthropometric risk factor information, a polygenic risk score based on 313 SNPs and family history information.The full multifactorial model considering family history together with all other risk factors was well calibrated overall (E/O=1.07, 95% CI: 0.92 to 1.24) and in quintiles of predicted risk. Discrimination was maximised when all risk factors were considered (Harrell's C-index=0.70, 95% CI: 0.67 to 0.74; area under the curve=0.79, 95% CI: 0.76 to 0.82). The model performance was similar when evaluated separately in BRCA1 or BRCA2 PV carriers. The full model identified 5.8%, 12.9% and 24.0% of BRCA1/2 PV carriers with 5-year breast cancer risks of <1.65%, <3% and <5%, respectively, risk thresholds commonly used for different management and risk-reduction options.RESULTSThe full multifactorial model considering family history together with all other risk factors was well calibrated overall (E/O=1.07, 95% CI: 0.92 to 1.24) and in quintiles of predicted risk. Discrimination was maximised when all risk factors were considered (Harrell's C-index=0.70, 95% CI: 0.67 to 0.74; area under the curve=0.79, 95% CI: 0.76 to 0.82). The model performance was similar when evaluated separately in BRCA1 or BRCA2 PV carriers. The full model identified 5.8%, 12.9% and 24.0% of BRCA1/2 PV carriers with 5-year breast cancer risks of <1.65%, <3% and <5%, respectively, risk thresholds commonly used for different management and risk-reduction options.BOADICEA may be used to aid personalised cancer risk management and decision-making for BRCA1 and BRCA2 PV carriers. It is implemented in the free-access CanRisk tool (https://www.canrisk.org/).CONCLUSIONBOADICEA may be used to aid personalised cancer risk management and decision-making for BRCA1 and BRCA2 PV carriers. It is implemented in the free-access CanRisk tool (https://www.canrisk.org/). Background No validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting 5-year breast cancer risks in a prospective cohort of BRCA1/2 PV carriers ascertained through clinical genetic centres. Methods We evaluated the model calibration and discriminatory ability in the prospective TRANsIBCCS cohort study comprising 1614 BRCA1 and 1365 BRCA2 PV carriers (209 incident cases). Study participants had lifestyle, reproductive, hormonal, anthropometric risk factor information, a polygenic risk score based on 313 SNPs and family history information. Results The full multifactorial model considering family history together with all other risk factors was well calibrated overall (E/O=1.07, 95% CI: 0.92 to 1.24) and in quintiles of predicted risk. Discrimination was maximised when all risk factors were considered (Harrell’s C-index=0.70, 95% CI: 0.67 to 0.74; area under the curve=0.79, 95% CI: 0.76 to 0.82). The model performance was similar when evaluated separately in BRCA1 or BRCA2 PV carriers. The full model identified 5.8%, 12.9% and 24.0% of BRCA1/2 PV carriers with 5-year breast cancer risks of <1.65%, <3% and <5%, respectively, risk thresholds commonly used for different management and risk-reduction options. Conclusion BOADICEA may be used to aid personalised cancer risk management and decision-making for BRCA1 and BRCA2 PV carriers. It is implemented in the free-access CanRisk tool ( https://www.canrisk.org/ ). BackgroundNo validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting 5-year breast cancer risks in a prospective cohort of BRCA1/2 PV carriers ascertained through clinical genetic centres.MethodsWe evaluated the model calibration and discriminatory ability in the prospective TRANsIBCCS cohort study comprising 1614 BRCA1 and 1365 BRCA2 PV carriers (209 incident cases). Study participants had lifestyle, reproductive, hormonal, anthropometric risk factor information, a polygenic risk score based on 313 SNPs and family history information.ResultsThe full multifactorial model considering family history together with all other risk factors was well calibrated overall (E/O=1.07, 95% CI: 0.92 to 1.24) and in quintiles of predicted risk. Discrimination was maximised when all risk factors were considered (Harrell’s C-index=0.70, 95% CI: 0.67 to 0.74; area under the curve=0.79, 95% CI: 0.76 to 0.82). The model performance was similar when evaluated separately in BRCA1 or BRCA2 PV carriers. The full model identified 5.8%, 12.9% and 24.0% of BRCA1/2 PV carriers with 5-year breast cancer risks of <1.65%, <3% and <5%, respectively, risk thresholds commonly used for different management and risk-reduction options.ConclusionBOADICEA may be used to aid personalised cancer risk management and decision-making for BRCA1 and BRCA2 PV carriers. It is implemented in the free-access CanRisk tool (https://www.canrisk.org/). No validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA in predicting 5-year breast cancer risks in a prospective cohort of PV carriers ascertained through clinical genetic centres. We evaluated the model calibration and discriminatory ability in the prospective TRANsIBCCS cohort study comprising 1614 and 1365 PV carriers (209 incident cases). Study participants had lifestyle, reproductive, hormonal, anthropometric risk factor information, a polygenic risk score based on 313 SNPs and family history information. The full multifactorial model considering family history together with all other risk factors was well calibrated overall (E/O=1.07, 95% CI: 0.92 to 1.24) and in quintiles of predicted risk. Discrimination was maximised when all risk factors were considered (Harrell's C-index=0.70, 95% CI: 0.67 to 0.74; area under the curve=0.79, 95% CI: 0.76 to 0.82). The model performance was similar when evaluated separately in or PV carriers. The full model identified 5.8%, 12.9% and 24.0% of PV carriers with 5-year breast cancer risks of <1.65%, <3% and <5%, respectively, risk thresholds commonly used for different management and risk-reduction options. BOADICEA may be used to aid personalised cancer risk management and decision-making for and PV carriers. It is implemented in the free-access CanRisk tool (https://www.canrisk.org/). |
| Author | Yang, Xin Walker, Lisa Zhao, Emily Tischkowitz, Marc Kanani, Farah Barwell, Julian Lesueur, Fabienne Donaldson, Alan van Asperen, Christi J Searle, Claire Leslie, Goska Easton, Douglas F van Gils, Carla H Kast, Karin Nambot, Sophie Spaendonck-Zwarts, Karin Y van Lalloo, Fiona Side, Lucy Engel, Christoph Wevers, Marijke R Noguès, Catherine Musgrave, Hannah Gómez García, Encarna B Oosterwijk, Jan C Antoniou, Antonis C Gallagher, David Jakubowska, Anna Collee, Margriet J Kemp, Zoe Rogers, Mark T Snape, Katie Miedzybrodzka, Zosia van Leeuwen, Flora E Ong, Kai-Ren Morrison, Patrick Murray, Alex Murray, Jennie Kennedy, John Gregory, Helen Andrieu, Nadine Copeland, Harriet Mooij, Thea M Izatt, Louise Davidson, Rosemarie Ausems, Margreet G E M Singer, Christian F. Stoppa-lyonnet, Dominique Brady, Angela Tripathi, Vishakha Cook, Jackie A Rookus, Matti A Eason, Jacqueline Hanson, Helen Tan, Yen Y Berthet, Pascaline New, Rachel Hart Ficorella, Lorenzo Brennan, Paul Ahmed, Munaza Schmidt, Marjanka K Conti, Hector Adank, Muriel A Evans, D. Gareth Lasset, Chris |
| AuthorAffiliation | 3 INSERM U900 , Paris , France 26 Department of Clinical Genetics , Guy's and St Thomas' NHS Foundation Trust , London , UK 15 Devision Laboratories, Pharmacy and Biomedical Genetics, Department of Genetics , University Medical Centre Utrecht , Utrecht , The Netherlands 11 Julius Center for Health Sciences and Primary Care , University Medical Center Utrecht , Utrecht , The Netherlands 17 Department of Clinical Genetics , Erasmus Medical Center , Rotterdam , The Netherlands 4 Institut Curie , Paris , France 39 Université Paris CIté , Paris , France 13 Department of Clinical Genetics , The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital , Amsterdam , The Netherlands 29 Université Claude Bernard Lyon 1 , Villeurbanne , France 35 Département d’Anticipation et de Suivi des Cancers, Oncogénétique Clinique , Institut Paoli Calmettes , Marseille , France 45 Centre for Cancer Genetic Epidemiology, Department of Oncology , University of Cambridge , Cambridge , UK 18 Sheffield Clinical Geneti |
| AuthorAffiliation_xml | – name: 1 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care , University of Cambridge, Strangeways Research Laboratory , Cambridge , UK – name: 7 Center for Hereditary Breast and Ovarian Cancer, Center for Integrated Oncology (CIO) , Medical Faculty, University Hospital Cologne , Cologne , Germany – name: 17 Department of Clinical Genetics , Erasmus Medical Center , Rotterdam , The Netherlands – name: 41 Department of Medical Genetics , National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge , Cambridge , UK – name: 6 PSL Research University , Paris , France – name: 14 Department of Clinical Genetics , Leiden University Medical Center , Leiden , The Netherlands – name: 31 Centre Léon Bérard , Unité de Prévention et Epidémiologie Génétique , Lyon , France – name: 21 The Prevent Breast Cancer Research Unit, The Nightingale Centre , Manchester University NHS Foundation Trust , Manchester , UK – name: 35 Département d’Anticipation et de Suivi des Cancers, Oncogénétique Clinique , Institut Paoli Calmettes , Marseille , France – name: 24 Department of Clinical Genetics , Maastricht University , Maastricht , The Netherlands – name: 33 Centre de Génétique et Centre de Référence Anomalies du Développement et Syndromes Malformatifs , CHU de Dijon, Hôpital d’Enfants , Dijon , France – name: 39 Université Paris CIté , Paris , France – name: 19 Nottingham Clinical Genetics Service , Nottingham University Hospitals NHS Trust , Nottingham , UK – name: 27 Department of Cancer Genetics , Royal Marsden Hospital, NHS Trust , London , UK – name: 42 Clinical Genetics Service , Guy's and St Thomas' NHS Foundation Trust , London , UK – name: 20 Department of Human Genetics , Amsterdam University Medical Centres , Amsterdam , The Netherlands – name: 18 Sheffield Clinical Genetics Service , Scheffield Children's Hospital , Sheffield , UK – name: 28 Clinical Genetics Service, Manchester Centre for Genomic Medicine , Manchester University Hospitals Foundation Trust , Manchester , UK – name: 25 South West Thames Regional Genetics Service , St George’s University Hospitals NHS Foundation Trust , London , UK – name: 22 Genomic Medicine, Division of Evolution and Genomic Sciences , The University of Manchester, St Mary’s Hospital, Manchester University NHS Foundation Trust , Manchester , UK – name: 26 Department of Clinical Genetics , Guy's and St Thomas' NHS Foundation Trust , London , UK – name: 13 Department of Clinical Genetics , The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital , Amsterdam , The Netherlands – name: 15 Devision Laboratories, Pharmacy and Biomedical Genetics, Department of Genetics , University Medical Centre Utrecht , Utrecht , The Netherlands – name: 43 Department of Clinical Genetics , Radboud University Medical Center , Nijmegen , The Netherlands – name: 34 Centre de Lutte contre le Cancer Georges François Leclerc , Dijon , France – name: 40 INSERM U830 , Paris , France – name: 44 Division of Molecular Pathology and Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute , Amsterdam , The Netherlands – name: 29 Université Claude Bernard Lyon 1 , Villeurbanne , France – name: 5 Mines ParisTech , Fontainebleau , France – name: 23 Manchester Breast Centre, Oglesby Cancer Research Centre, The Christie , University of Manchester , Manchester , UK – name: 10 Independent Laboratory of Molecular Biology and Genetic Diagnostics , Pomeranian Medical University , Szczecin , Poland – name: 32 Yorkshire Regional Genetics Service , Leeds Teaching Hospitals NHS Trust , Leeds , UK – name: 38 Institut Curie, Service de Génétique , Paris , France – name: 2 Department of Epidemiology , The Netherlands Cancer Institute , Amsterdam , The Netherlands – name: 8 Department of OB/GYN and Comprehensive Cancer Center , Medical University of Vienna , Vienna , Austria – name: 16 Oncogénétique Département de Biopathologie , Centre François Baclesse , Caen , France – name: 36 Aix Marseille Univ, INSERM, IRD, SESSTIM , Marseille , France – name: 3 INSERM U900 , Paris , France – name: 4 Institut Curie , Paris , France – name: 11 Julius Center for Health Sciences and Primary Care , University Medical Center Utrecht , Utrecht , The Netherlands – name: 37 Department of Genetics , University of Groningen, University Medical Center , Groningen , The Netherlands – name: 9 Department of Genetics and Pathology , Pomeranian Medical University , Szczecin , Poland – name: 12 Institute for Medical Informatics, Statistics and Epidemiology , University of Leipzig , Leipzig , Germany – name: 30 CNRS UMR 5558 , Lyon , France – name: 45 Centre for Cancer Genetic Epidemiology, Department of Oncology , University of Cambridge , Cambridge , UK |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38834293$$D View this record in MEDLINE/PubMed https://inserm.hal.science/inserm-04994412$$DView record in HAL |
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| ContentType | Journal Article |
| Contributor | Miedzybrodzka, Zosia Side, Lucy Brady, Angela Walker, Lisa Kanani, Farah Ong, Kai-Ren Barwell, Julian New, Rachel Hart Brennan, Paul Morrison, Patrick Murray, Alex Murray, Jennie Kennedy, John Ahmed, Munaza Gregory, Helen Gallagher, David Copeland, Harriet Donaldson, Alan Searle, Claire Conti, Hector Davidson, Rosemarie Rogers, Mark T Snape, Katie |
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| Copyright | Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ. 2024 Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ . Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Distributed under a Creative Commons Attribution 4.0 International License Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ. 2024 |
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| References_xml | – volume: 98 start-page: 535 year: 2006 ident: R20 article-title: Pregnancies, breast-feeding, and breast cancer risk in the International BRCA1/2 carrier cohort study (IBCCS) publication-title: J Natl Cancer Inst doi: 10.1093/jnci/djj132 – volume: 22 start-page: 1653 year: 2020 ident: R2 article-title: Scores and breast and epithelial ovarian cancer risks for carriers of BRCA1 and BRCA2 pathogenic variants publication-title: Genet Med doi: 10.1038/s41436-020-0862-x – volume: 2 year: 2018 ident: R4 article-title: Embrace GBHk, Ibccs. oral contraceptive use and breast cancer risk: retrospective and prospective analyses from a BRCA1 and BRCA2 mutation carrier cohort study publication-title: JNCI Cancer Spectr doi: 10.1093/jncics/pky023 – volume: 15 year: 2020 ident: R13 article-title: iCARE: an R package to build, validate and apply absolute risk models publication-title: PLoS One doi: 10.1371/journal.pone.0228198 – volume: 317 start-page: 2402 year: 2017 ident: R1 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prospective analyses from a BRCA1 and BRCA2 mutation carrier cohort study publication-title: JNCI Cancer Spectr doi: 10.1093/jncics/pky023 – ident: 2024102417552450000_61.8.803.12 doi: 10.1101/2024.04.18.24305970 – ident: 2024102417552450000_61.8.803.1 doi: 10.1001/jama.2017.7112 – ident: 2024102417552450000_61.8.803.17 doi: 10.1016/j.ajhg.2018.11.002 – ident: 2024102417552450000_61.8.803.21 doi: 10.1200/JCO.2007.11.1179 – volume: 5 year: 2021 ident: 2024102417552450000_61.8.803.8 article-title: Prospective evaluation of the addition of polygenic risk scores to breast cancer risk models publication-title: JNCI Cancer Spectr doi: 10.1093/jncics/pkab021 – volume: Online First start-page: 1206 year: 2022 ident: 2024102417552450000_61.8.803.7 article-title: Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D BARD1 updates to tumour pathology and cancer incidence publication-title: J Med Genet doi: 10.1136/jmedgenet-2022-108471 – ident: 2024102417552450000_61.8.803.2 doi: 10.1038/s41436-020-0862-x – ident: 2024102417552450000_61.8.803.10 doi: 10.1136/jmg-2022-108806 – volume: 2 year: 2018 ident: 2024102417552450000_61.8.803.5 article-title: The influence of number and timing of pregnancies on breast cancer risk for women with BRCA1 or BRCA2 mutations publication-title: JNCI Cancer Spectr doi: 10.1093/jncics/pky078 – ident: 2024102417552450000_61.8.803.3 doi: 10.1158/1055-9965.EPI-19-0546 – volume: 2 year: 2000 ident: 2024102417552450000_61.8.803.11 article-title: The International BRCA1 /2carrier cohort study: purpose, rationale, and study design publication-title: Breast Cancer Res doi: 10.1186/bcr93 – ident: 2024102417552450000_61.8.803.22 doi: 10.1158/1055-9965.EPI-06-0829 – ident: 2024102417552450000_61.8.803.15 doi: 10.1001/jama.2019.11885 – ident: 2024102417552450000_61.8.803.6 doi: 10.1038/s41436-018-0406-9 – volume: 17 year: 2015 ident: 2024102417552450000_61.8.803.25 article-title: Mammographic density and breast cancer in women from high risk families publication-title: Breast Cancer Res doi: 10.1186/s13058-015-0604-1 |
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| Snippet | BackgroundNo validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV)... No validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in pathogenic variant (PV) carriers to date.... No validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers... Background No validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV)... |
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| SubjectTerms | Adult Age BRCA1 protein BRCA1 Protein - genetics BRCA2 protein BRCA2 Protein - genetics Breast cancer Breast Neoplasms - epidemiology Breast Neoplasms - genetics Calibration Cancer Genetics Decision making Discrimination Disease management Early Diagnosis Epidemiology Family medical history Female Genetic Counseling Genetic Predisposition to Disease Heterozygote Humans Life Sciences Mastectomy Middle Aged Performance evaluation Polymorphism, Single Nucleotide - genetics Prediction models Prospective Studies Public Health Questionnaires Regression analysis Risk Assessment Risk Factors Single-nucleotide polymorphism Surgery Women's Health Womens health |
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| Title | Validation of the BOADICEA model in a prospective cohort of BRCA1/2 pathogenic variant carriers |
| URI | https://jmg.bmj.com/content/61/8/803.full https://www.ncbi.nlm.nih.gov/pubmed/38834293 https://www.proquest.com/docview/3065840590 https://www.proquest.com/docview/3120153496 https://www.proquest.com/docview/3064921364 https://inserm.hal.science/inserm-04994412 https://pubmed.ncbi.nlm.nih.gov/PMC11287562 |
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