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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

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-susceptibil...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691826/
https://www.ncbi.nlm.nih.gov/pubmed/36162851
http://dx.doi.org/10.1136/jmedgenet-2022-108471
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author 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
author_facet 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
author_sort Lee, Andrew
collection PubMed
description 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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spelling pubmed-96918262022-11-26 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 J Med Genet Cancer Genetics 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. BMJ Publishing Group 2022-12 2022-09-26 /pmc/articles/PMC9691826/ /pubmed/36162851 http://dx.doi.org/10.1136/jmedgenet-2022-108471 Text en © Author(s) (or their employer(s)) 2022. 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/.
spellingShingle Cancer Genetics
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
Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence
title Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence
title_full Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence
title_fullStr Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence
title_full_unstemmed Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence
title_short Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence
title_sort enhancing the boadicea cancer risk prediction model to incorporate new data on rad51c, rad51d, bard1 updates to tumour pathology and cancer incidence
topic Cancer Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691826/
https://www.ncbi.nlm.nih.gov/pubmed/36162851
http://dx.doi.org/10.1136/jmedgenet-2022-108471
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