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Cancer Epidemiology, Biomarkers & Prevention
Cancer Epidemiology, Biomarkers & Prevention
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Research Article

CanRisk Tool - A web interface for the prediction of breast and ovarian cancer risk and the likelihood of carrying genetic pathogenic variants.

Tim Carver, Simon Hartley, Andrew Lee, Alex P Cunningham, Stephanie Archer, Chantal Babb de Villiers, Jonathan Roberts, Rod Ruston, Fiona M. Walter, Marc Tischkowitz, Douglas F Easton and Antonis C. Antoniou
Tim Carver
1Centre for Cancer Genetic Epidemiology, University of Cambridge
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  • For correspondence: tjc29@medschl.cam.ac.uk
Simon Hartley
2Centre for Computational Biology, University of Birmingham
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Andrew Lee
1Centre for Cancer Genetic Epidemiology, University of Cambridge
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Alex P Cunningham
1Centre for Cancer Genetic Epidemiology, University of Cambridge
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Stephanie Archer
3Department of Public Health and Primary Care, University of Cambridge
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Chantal Babb de Villiers
3Department of Public Health and Primary Care, University of Cambridge
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Jonathan Roberts
4Clinical Genetics, Cambridge University Hospitals NHS Foundation Trust
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Rod Ruston
5Priory Analysts
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Fiona M. Walter
3Department of Public Health and Primary Care, University of Cambridge
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Marc Tischkowitz
6Department of Medical Genetics, University of Cambridge
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Douglas F Easton
7Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge
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Antonis C. Antoniou
3Department of Public Health and Primary Care, University of Cambridge
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DOI: 10.1158/1055-9965.EPI-20-1319
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Abstract

Background: The CanRisk Tool (https://canrisk.org) is the next generation web interface for the latest version of the BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) state-of-the-art risk model and a forthcoming ovarian cancer risk model. Methods: The tool captures information on family history, rare pathogenic variants in cancer susceptibility genes, polygenic risk scores, lifestyle/hormonal/clinical features and imaging risk factors to predict breast and ovarian cancer risks and estimate the probabilities of carrying pathogenic variants in certain genes. It was implemented using modern web frameworks, technologies and web services to make it extensible and increase accessibility to researchers and third-party applications. The design of the graphical user interface was informed by feedback from healthcare professionals and a formal evaluation. Results: This freely accessible tool was designed to be user-friendly for clinicians and to boost acceptability in clinical settings. The tool incorporates a novel graphical pedigree builder to facilitate collection of the family history data required by risk calculations. Conclusions: The CanRisk Tool provides healthcare professionals and researchers with a user-friendly interface to carry out multifactorial breast and ovarian cancer risk predictions. It is the first freely accessible cancer risk prediction program to carry the CE marking. Impact: There have been over 3100 account registrations, and 98000 breast and ovarian cancer risk calculations have been run within the first 9 months of the CanRisk Tool launch.

  • Received September 7, 2020.
  • Revision received October 8, 2020.
  • Accepted December 14, 2020.
  • Copyright ©2020, American Association for Cancer Research.

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This OnlineFirst version was published on December 17, 2020
doi: 10.1158/1055-9965.EPI-20-1319

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CanRisk Tool - A web interface for the prediction of breast and ovarian cancer risk and the likelihood of carrying genetic pathogenic variants.
Tim Carver, Simon Hartley, Andrew Lee, Alex P Cunningham, Stephanie Archer, Chantal Babb de Villiers, Jonathan Roberts, Rod Ruston, Fiona M. Walter, Marc Tischkowitz, Douglas F Easton and Antonis C. Antoniou
Cancer Epidemiol Biomarkers Prev December 17 2020 DOI: 10.1158/1055-9965.EPI-20-1319

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CanRisk Tool - A web interface for the prediction of breast and ovarian cancer risk and the likelihood of carrying genetic pathogenic variants.
Tim Carver, Simon Hartley, Andrew Lee, Alex P Cunningham, Stephanie Archer, Chantal Babb de Villiers, Jonathan Roberts, Rod Ruston, Fiona M. Walter, Marc Tischkowitz, Douglas F Easton and Antonis C. Antoniou
Cancer Epidemiol Biomarkers Prev December 17 2020 DOI: 10.1158/1055-9965.EPI-20-1319
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