Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction

Veen, Elke M. van; Brentnall, Adam R.; Byers, Helen; Harkness, Elaine F.; Astley, Susan M.; Sampson, Sarah; Howell, Anthony; Newman, William G.; Cuzick, Jack; Evans, D. Gareth

doi:10.1001/jamaoncol.2017.4881

Cited by 121 publications

(139 citation statements)

References 23 publications

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“…Most studies evaluated the risk prediction models in the general population (24, 53, 55-58, 60, 61, 63, 64, 66-68, 70, 71, 73-76) while others specifically studied non-BRCA mutation carriers (54,62,69), post-menopausal (23,59,65,72) or pre-menopausal women (72). Most studies included only patients with invasive breast cancer as case participants (23, 53, 56-61, 63, 64, 66-70, 73, 74, 76), while six included invasive or in situ breast cancer patients (24,54,55,62,71,75) and two focused on estrogen receptor (ER)-positive patients with breast cancer (65,72).…”

Section: Statistical Analysesmentioning

confidence: 99%

“…Seven models used BCRAT as baseline model (55,59,66,69,75,76) and another four used partial BCRAT (57,63,68,74). 11 models used partial BCRAT with additional risk factors (23,55,62,64,67,(70)(71)(72)(73), four used BCSC (60,61,65), four used IBIS (24,66,69) and eight used other types of baseline models (54,56,58,69). Risk factors included in the various traditional baseline models are summarized in Supplementary Table S6 (Online Supplementary Material).…”

Section: Risk Prediction Model Characteristicsmentioning

confidence: 99%

“…Another three models used a Bayesian approach to calculate the PRS (60,65). In four models (24,70,73,74), a genetic risk score was calculated by other ways to include genetic information in the model (Supplementary Table S4, Online Supplementary Material). Two models (23) used a multiplicative penetrance model and one model (56) used a multiple log-additive model for incorporating the SNPs.…”

Section: Risk Prediction Model Characteristicsmentioning

confidence: 99%

“…SNP-enhanced risk prediction models may be able to estimate breast cancer risk more accurately and can translate into a more efficient risk-based screening program. Potential risk-adapted screening practices include starting screening later in women with lower risk, increasing the recommended screening interval for these women or possibly not inviting them for screening at all (24). High-risk women could be recommended to begin screening at an earlier age, attend annual screening (25) or even engage in supplemental screening beyond mammography (26).…”

Section: Introductionmentioning

confidence: 99%

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Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis

Fung

Wong

Lee

et al. 2019

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background: SNP risk information can potentially improve the accuracy of breast cancer risk prediction. We aim to review and assess the performance of SNP-enhanced risk prediction models.Methods: Studies that reported area under the ROC curve (AUC) and/or net reclassification improvement (NRI) for both traditional and SNP-enhanced risk models were identified. Meta-analyses were conducted to compare across all models and within similar baseline risk models.Results: Twenty-six of 406 studies were included. Pooled estimate of AUC improvement is 0.044 [95% confidence interval (CI), 0.038-0.049] for all 38 models, while estimates by baseline models ranged from 0.033 (95% CI, 0.025-0.041) for BCRAT to 0.053 (95% CI, 0.018-0.087) for partial BCRAT. There was no observable trend between AUC improvement and number of SNPs. One study found that the NRI was significantly larger when only intermediate-risk women were included. Two other studies showed that majority of the risk reclassification occurred in intermediate-risk women.Conclusions: Addition of SNP risk information may be more beneficial for women with intermediate risk.Impact: Screening could be a two-step process where a questionnaire is first used to identify intermediate-risk individuals, followed by SNP testing for these women only.

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Section: Statistical Analysesmentioning

confidence: 99%

Section: Risk Prediction Model Characteristicsmentioning

confidence: 99%

Section: Risk Prediction Model Characteristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis

Fung

Wong

Lee

et al. 2019

Cancer Epidemiology, Biomarkers &Amp; Prevention

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“…Several studies have shown an improved discriminative power between breast cancer cases and controls by combining the PRS with a breast cancer risk prediction tool [60,63,66,69]. In one study [62], new breast cancer lifetime risks for women from breast cancer families were calculated by adding the PRS to family-based risk prediction.…”

Section: Breast Cancer Risk Prediction Modelsmentioning

confidence: 99%

Breast Cancer Susceptibility—Towards Individualised Risk Prediction

et al. 2019

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Purpose of Review Breast cancer is the most common cancer among females in developed countries. Strategies such as early detection by breast cancer screening can reduce the burden of disease but have disadvantages including overdiagnosis and increased cost. Stratification of women according to the risk of developing breast cancer, based on genetic and lifestyle risk factors, could improve risk-reduction and screening strategies by targeting those most likely to benefit. Recent Findings Breast cancer risk is partly determined by genetic factors including rare pathogenic variants in susceptibility genes and common low-risk variants. Other risk factors include alcohol use, smoking, reproductive factors, hormonal factors, family history, mammographic density, BMI, and body height. Ideally, all risk factors are combined into an individual breast cancer lifetime risk score, but this requires knowledge about their interactions as well as accurate effect sizes. A few risk models seem to be sufficiently developed to inform clinical risk management to minimise cancer risk of those at increased risk and avoid overtreatment of those at decreased risk. Summary In this review, we briefly summarise the breast cancer susceptibility factors and discuss avenues towards combining all these factors to create individual risk scores.

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Association of a Polygenic Risk Score With Breast Cancer Among Women Carriers of High- and Moderate-Risk Breast Cancer Genes

et al. 2020

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IMPORTANCE To date, few studies have examined the extent to which polygenic single-nucleotide variation (SNV) (formerly single-nucleotide polymorphism) scores modify risk for carriers of pathogenic variants (PVs) in breast cancer susceptibility genes. In previous reports, polygenic risk modification was reduced for BRCA1 and BRCA2 PV carriers compared with noncarriers, but limited information is available for carriers of CHEK2, ATM, or PALB2 PVs. OBJECTIVE To examine an 86-SNV polygenic risk score (PRS) for BRCA1, BRCA2, CHEK2, ATM, and PALB2 PV carriers.

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Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction

Abstract: SNP18 added substantial information to risk assessment based on the Tyrer-Cuzick model and mammographic density. A combined risk is likely to aid risk-stratified screening and prevention strategies.

Cited by 121 publications

References 23 publications

Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis

Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis

Breast Cancer Susceptibility—Towards Individualised Risk Prediction

Association of a Polygenic Risk Score With Breast Cancer Among Women Carriers of High- and Moderate-Risk Breast Cancer Genes

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