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Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence.
Lee, Andrew; Mavaddat, Nasim; Cunningham, Alex; Carver, Tim; Ficorella, Lorenzo; Archer, Stephanie; Walter, Fiona M; Tischkowitz, Marc; Roberts, Jonathan; Usher-Smith, Juliet; Simard, Jacques; Schmidt, Marjanka K; Devilee, Peter; Zadnik, Vesna; Jürgens, Hannes; Mouret-Fourme, Emmanuelle; De Pauw, Antoine; Rookus, Matti; Mooij, Thea M; Pharoah, Paul Pd; Easton, Douglas F; Antoniou, Antonis C.
Afiliação
  • Lee A; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  • Mavaddat N; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  • Cunningham A; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  • Carver T; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  • Ficorella L; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  • Archer S; Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  • Walter FM; Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  • Tischkowitz M; Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
  • Roberts J; Department of Medical Genetics and National Institute for Health Research, Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK.
  • Usher-Smith J; Department of Medical Genetics and National Institute for Health Research, Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK.
  • Simard J; Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  • Schmidt MK; Centre Hospitalier Universitaire de Québec-Université Laval Research Center, Université Laval, Quebec, Quebec, Canada.
  • Devilee P; Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
  • Zadnik V; Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
  • Jürgens H; Epidemiology and Cancer Registry, Institute of Oncology, Ljubljana, Slovenia.
  • Mouret-Fourme E; Clinic of Hematology and Oncology, Tartu University Hospital, Tartu, Estonia.
  • De Pauw A; Service de Génétique, Institut Curie, Paris, France.
  • Rookus M; Service de Génétique, Institut Curie, Paris, France.
  • Mooij TM; Department of Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
  • Pharoah PP; Department of Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
  • Easton DF; Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
  • Antoniou AC; Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
J Med Genet ; 59(12): 1206-1218, 2022 12.
Article em En | MEDLINE | ID: mdl-36162851
BACKGROUND: BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) for breast cancer and the epithelial tubo-ovarian cancer (EOC) models included in the CanRisk tool (www.canrisk.org) provide future cancer risks based on pathogenic variants in cancer-susceptibility genes, polygenic risk scores, breast density, questionnaire-based risk factors and family history. Here, we extend the models to include the effects of pathogenic variants in recently established breast cancer and EOC susceptibility genes, up-to-date age-specific pathology distributions and continuous risk factors. METHODS: BOADICEA was extended to further incorporate the associations of pathogenic variants in BARD1, RAD51C and RAD51D with breast cancer risk. The EOC model was extended to include the association of PALB2 pathogenic variants with EOC risk. Age-specific distributions of oestrogen-receptor-negative and triple-negative breast cancer status for pathogenic variant carriers in these genes and CHEK2 and ATM were also incorporated. A novel method to include continuous risk factors was developed, exemplified by including adult height as continuous. RESULTS: BARD1, RAD51C and RAD51D explain 0.31% of the breast cancer polygenic variance. When incorporated into the multifactorial model, 34%-44% of these carriers would be reclassified to the near-population and 15%-22% to the high-risk categories based on the UK National Institute for Health and Care Excellence guidelines. Under the EOC multifactorial model, 62%, 35% and 3% of PALB2 carriers have lifetime EOC risks of <5%, 5%-10% and >10%, respectively. Including height as continuous, increased the breast cancer relative risk variance from 0.002 to 0.010. CONCLUSIONS: These extensions will allow for better personalised risks for BARD1, RAD51C, RAD51D and PALB2 pathogenic variant carriers and more informed choices on screening, prevention, risk factor modification or other risk-reducing options.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Ovarianas / Neoplasias da Mama Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Ovarianas / Neoplasias da Mama Idioma: En Ano de publicação: 2022 Tipo de documento: Article