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Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction.

Lee CP, Choi H, Soo KC, Tan MH, Chay WY, Chia KS, Liu J, Li J, Hartman M - PLoS ONE (2015)

Bottom Line: We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population.The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22-2.10), 2.20 (1.65-2.92), 2.33 (1.71-3.20), 2.12 (1.43-3.14), and 3.27 (2.24-4.76) for the corresponding mammographic density categories: 11-20cm2, 21-30cm2, 31-40cm2, 41-50cm2, 51-60cm2, and 1.10 (1.03-1.16) for GRS.At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively.

View Article: PubMed Central - PubMed

Affiliation: NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.

ABSTRACT

Introduction: Known prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population.

Methods: We estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman's genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values.

Results: During 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22-2.10), 2.20 (1.65-2.92), 2.33 (1.71-3.20), 2.12 (1.43-3.14), and 3.27 (2.24-4.76) for the corresponding mammographic density categories: 11-20cm2, 21-30cm2, 31-40cm2, 41-50cm2, 51-60cm2, and 1.10 (1.03-1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively.

Conclusion: Mammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore.

No MeSH data available.


Related in: MedlinePlus

Three receiver operating characteristic (ROC) curves for predicting breast cancer: vGail + BMI (black), vGail + BMI + mean breast dense area (red), vGail + BMI + mean breast dense area + GRS (green).For the model with GRS, the average of 1000 ROC curves is drawn. Areas under the curves (AUCs) are 0.63, 0.66 and 0.68 respectively. The straight dashed line represents the ROC curve expected by chance only.
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pone.0136650.g001: Three receiver operating characteristic (ROC) curves for predicting breast cancer: vGail + BMI (black), vGail + BMI + mean breast dense area (red), vGail + BMI + mean breast dense area + GRS (green).For the model with GRS, the average of 1000 ROC curves is drawn. Areas under the curves (AUCs) are 0.63, 0.66 and 0.68 respectively. The straight dashed line represents the ROC curve expected by chance only.

Mentions: Performance in the three risk prediction models was examined by plotting ROC curves and comparing their areas under the curve. As genotypes were simulated, the average of 1,000 ROC curves for the model with GRS is reported in Fig 1. The model including Gail predictors, BMI, and mean dense area reported an area under the curve of 0.66 (0.64–0.68), while an inclusion of GRS reported 0.68 (0.66–0.69). A similar observation in model performance was observed for the same model using percent density instead (S1 Fig). Table 3 and S3 Table show the concordance probabilities for the respective models, which did not differ greatly regardless of whether mean dense area or percent density was used.


Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction.

Lee CP, Choi H, Soo KC, Tan MH, Chay WY, Chia KS, Liu J, Li J, Hartman M - PLoS ONE (2015)

Three receiver operating characteristic (ROC) curves for predicting breast cancer: vGail + BMI (black), vGail + BMI + mean breast dense area (red), vGail + BMI + mean breast dense area + GRS (green).For the model with GRS, the average of 1000 ROC curves is drawn. Areas under the curves (AUCs) are 0.63, 0.66 and 0.68 respectively. The straight dashed line represents the ROC curve expected by chance only.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581713&req=5

pone.0136650.g001: Three receiver operating characteristic (ROC) curves for predicting breast cancer: vGail + BMI (black), vGail + BMI + mean breast dense area (red), vGail + BMI + mean breast dense area + GRS (green).For the model with GRS, the average of 1000 ROC curves is drawn. Areas under the curves (AUCs) are 0.63, 0.66 and 0.68 respectively. The straight dashed line represents the ROC curve expected by chance only.
Mentions: Performance in the three risk prediction models was examined by plotting ROC curves and comparing their areas under the curve. As genotypes were simulated, the average of 1,000 ROC curves for the model with GRS is reported in Fig 1. The model including Gail predictors, BMI, and mean dense area reported an area under the curve of 0.66 (0.64–0.68), while an inclusion of GRS reported 0.68 (0.66–0.69). A similar observation in model performance was observed for the same model using percent density instead (S1 Fig). Table 3 and S3 Table show the concordance probabilities for the respective models, which did not differ greatly regardless of whether mean dense area or percent density was used.

Bottom Line: We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population.The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22-2.10), 2.20 (1.65-2.92), 2.33 (1.71-3.20), 2.12 (1.43-3.14), and 3.27 (2.24-4.76) for the corresponding mammographic density categories: 11-20cm2, 21-30cm2, 31-40cm2, 41-50cm2, 51-60cm2, and 1.10 (1.03-1.16) for GRS.At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively.

View Article: PubMed Central - PubMed

Affiliation: NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.

ABSTRACT

Introduction: Known prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population.

Methods: We estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman's genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values.

Results: During 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22-2.10), 2.20 (1.65-2.92), 2.33 (1.71-3.20), 2.12 (1.43-3.14), and 3.27 (2.24-4.76) for the corresponding mammographic density categories: 11-20cm2, 21-30cm2, 31-40cm2, 41-50cm2, 51-60cm2, and 1.10 (1.03-1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively.

Conclusion: Mammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore.

No MeSH data available.


Related in: MedlinePlus