ArticleBreast cancer research and treatment2013
Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model.
Article in Breast cancer research and treatment, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis.Breast cancer research : BCR · 2018Pooled it
- The clinical signature of genetic variants and serum levels of macrophage migration inhibitory factor in Egyptian breast cancer patients.Breast cancer research and treatment · 2024Article
- Review
- Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk.Breast cancer research and treatment · 2023Article
- A model for predicting both breast cancer risk and non-breast cancer death among women > 55 years old.Breast cancer research : BCR · 2023Article
- Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention.Medical review (2021) · 2021Review
- Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review.Heliyon · 2020Review
- Clinical applications of polygenic breast cancer risk: a critical review and perspectives of an emerging field.Breast cancer research : BCR · 2020Review
- Article
- Why the Gold Standard Approach by Mammography Demands Extension by Multiomics? Application of Liquid Biopsy miRNA Profiles to Breast Cancer Disease Management.International journal of molecular sciences · 2019Review
- Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model.Breast cancer research : BCR · 2019Article
- Predicting breast cancer risk using personal health data and machine learning models.PloS one · 2019Article
- Modeling risk assessment for breast cancer in symptomatic women: a Saudi Arabian study.Cancer management and research · 2019Article
- Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women.Breast cancer research : BCR · 2018Article
- Review of non-clinical risk models to aid prevention of breast cancer.Cancer causes & control : CCC · 2018Review
- Article
- A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information.PloS one · 2017Article
- Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives.Human genetics · 2016Review
- Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2016Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
It has been shown that, for women aged 50 years or older, the discriminatory accuracy of the Breast Cancer Risk Prediction Tool (BCRAT) can be modestly improved by the inclusion of information on common single nucleotide polymorphisms (SNPs) that are associated with increased breast cancer risk. We aimed to determine whether a similar improvement is seen for earlier onset disease. We used the Australian Breast Cancer Family Registry to study a population-based sample of 962 cases aged 35-59 years, and 463 controls frequency matched for age and for whom genotyping data was available. Overall, the inclusion of data on seven SNPs improved the area under the receiver operating characteristic curve (AUC) from 0.58 (95 % confidence interval [CI] 0.55-0.61) for BCRAT alone to 0.61 (95 % CI 0.58-0.64) for BCRAT and SNP data combined (p < 0.001). For women aged 35-39 years at interview, the corresponding improvement in AUC was from 0.61 (95 % CI 0.56-0.66) to 0.65 (95 % CI 0.60-0.70; p = 0.03), while for women aged 40-49 years at diagnosis, the AUC improved from 0.61 (95 % CI 0.55-0.66) to 0.63 (95 % CI 0.57-0.69; p = 0.04). Using previously used classifications of low, intermediate and high risk, 2.1 % of cases and none of the controls aged 35-39 years, and 10.9 % of cases and 4.0 % of controls aged 40-49 years were classified into a higher risk group. Including information on seven SNPs associated with breast cancer risk, improves the discriminatory accuracy of BCRAT for women aged 35-39 years and 40-49 years. Given, the low absolute risk for women in these age groups, only a small proportion are reclassified into a higher category for predicted 5-year risk of breast cancer.
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Registered trials
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