BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors View Full Text


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Article Info

DATE

2019-01-15

AUTHORS

Andrew Lee, Nasim Mavaddat, Amber N. Wilcox, Alex P. Cunningham, Tim Carver, Simon Hartley, Chantal Babb de Villiers, Angel Izquierdo, Jacques Simard, Marjanka K. Schmidt, Fiona M. Walter, Nilanjan Chatterjee, Montserrat Garcia-Closas, Marc Tischkowitz, Paul Pharoah, Douglas F. Easton, Antonis C. Antoniou

ABSTRACT

PURPOSE: Breast cancer (BC) risk prediction allows systematic identification of individuals at highest and lowest risk. We extend the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk model to incorporate the effects of polygenic risk scores (PRS) and other risk factors (RFs). METHODS: BOADICEA incorporates the effects of truncating variants in BRCA1, BRCA2, PALB2, CHEK2, and ATM; a PRS based on 313 single-nucleotide polymorphisms (SNPs) explaining 20% of BC polygenic variance; a residual polygenic component accounting for other genetic/familial effects; known lifestyle/hormonal/reproductive RFs; and mammographic density, while allowing for missing information. RESULTS: Among all factors considered, the predicted UK BC risk distribution is widest for the PRS, followed by mammographic density. The highest BC risk stratification is achieved when all genetic and lifestyle/hormonal/reproductive/anthropomorphic factors are considered jointly. With all factors, the predicted lifetime risks for women in the UK population vary from 2.8% for the 1st percentile to 30.6% for the 99th percentile, with 14.7% of women predicted to have a lifetime risk of ≥17-<30% (moderate risk according to National Institute for Health and Care Excellence [NICE] guidelines) and 1.1% a lifetime risk of ≥30% (high risk). CONCLUSION: This comprehensive model should enable high levels of BC risk stratification in the general population and women with family history, and facilitate individualized, informed decision-making on prevention therapies and screening. More... »

PAGES

1-11

References to SciGraph publications

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41436-018-0406-9

    DOI

    http://dx.doi.org/10.1038/s41436-018-0406-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111333416

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30643217


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