BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and non-genetic risk factors

By Andrew Lee, N. Mavaddat, A. Wilcox, A. Cunningham, T. Carver, Simon Hartley, Chantal Babb de Villiers, A. Izquierdo, J. Simard, M. Schmidt and 7 more. Genetics in Medicine, 2019

619 citations recorded by the source corpus.

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). 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. 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). 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.

Subfields: Cancer Research, Genetics, Oncology

View Paper: https://www.semanticscholar.org/paper/bb23f0a3ebc3b4020c195d2ce22b98f28f71035f

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Citations in the corpus

Early detection of cancer (2022, positive): “The strategy is exemplified by breast cancer risk prediction models used to stratify women into higher risk categories and towards genetic testing for inherited cancer susceptibility ( 37, 38 ).”
Artificial intelligence in clinical and genomic diagnostics (2019, background): “Combining these non-genetic risk factors with genetic data significantly improves the accuracy of breast cancer risk models and can inform risk-based mammographic screening strategies [79].”