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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

Distribution of predicted 10-year absolute risk for patients (red) and healthy individuals (black) using the three prediction models.As mean breast dense area and GRS are added to the model, the discrimination between cases and non-cases increases. Y-axis is the density which reflects the number of subjects.
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pone.0136650.g002: Distribution of predicted 10-year absolute risk for patients (red) and healthy individuals (black) using the three prediction models.As mean breast dense area and GRS are added to the model, the discrimination between cases and non-cases increases. Y-axis is the density which reflects the number of subjects.

Mentions: From Fig 2 and S2 Fig, we note a greater discrimination between cases and controls in terms of 10-year predicted absolute risk after the addition of mammographic density and GRS. As the risk thresholds (selected a priori) become more stringent from 1% to 3%, the difference in the proportion of patients that are correctly identified between the modified models and the Gail model, increases in general (Table 4, S4 Table). In terms of accurately classifying healthy individuals, all models fared equally well at the first five absolute risk cut-offs, but not at 5.0% and 10.0%.


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)

Distribution of predicted 10-year absolute risk for patients (red) and healthy individuals (black) using the three prediction models.As mean breast dense area and GRS are added to the model, the discrimination between cases and non-cases increases. Y-axis is the density which reflects the number of subjects.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0136650.g002: Distribution of predicted 10-year absolute risk for patients (red) and healthy individuals (black) using the three prediction models.As mean breast dense area and GRS are added to the model, the discrimination between cases and non-cases increases. Y-axis is the density which reflects the number of subjects.
Mentions: From Fig 2 and S2 Fig, we note a greater discrimination between cases and controls in terms of 10-year predicted absolute risk after the addition of mammographic density and GRS. As the risk thresholds (selected a priori) become more stringent from 1% to 3%, the difference in the proportion of patients that are correctly identified between the modified models and the Gail model, increases in general (Table 4, S4 Table). In terms of accurately classifying healthy individuals, all models fared equally well at the first five absolute risk cut-offs, but not at 5.0% and 10.0%.

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