Breast cancer risk assessment using genetic variants and risk factors in a Singapore Chinese population.

INTRODUCTION: Genetic variants for breast cancer risk identified in genome-wide association studies (GWAS) in Western populations require further testing in Asian populations. A risk assessment model incorporating both validated genetic variants and established risk factors may improve its performance in risk prediction of Asian women. METHODS: A nested case-control study of female breast cancer (411 cases and 1,212 controls) within the Singapore Chinese Health Study was conducted to investigate the effects of 51 genetic variants identified in previous GWAS on breast cancer risk. The independent effect of these genetic variants was assessed by creating a summed genetic risk score (GRS) after adjustment for body mass index and the Gail model risk factors for breast cancer. RESULTS: The GRS was an independent predictor of breast cancer risk in Chinese women. The multivariate-adjusted odds ratios (95% confidence intervals) of breast cancer for the second, third, and fourth quartiles of the GRS were 1.26 (0.90 to 1.76), 1.47 (1.06 to 2.04) and 1.75 (1.27 to 2.41) respectively (P for trend<0.001). In addition to established risk factors, the GRS improved the classification of 6.2% of women for their absolute risk of breast cancer in the next five years. CONCLUSIONS: Genetic variants on top of conventional risk factors can improve the risk prediction of breast cancer in Chinese women.

International Consortium on Mammographic Density: Methodology and population diversity captured across 22 countries.

Mammographic density (MD) is a quantitative trait, measurable in all women, and is among the strongest markers of breast cancer risk. The population-based epidemiology of MD has revealed genetic, lifestyle and societal/environmental determinants, but studies have largely been conducted in women with similar westernized lifestyles living in countries with high breast cancer incidence rates. To benefit from the heterogeneity in risk factors and their combinations worldwide, we created an International Consortium on Mammographic Density (ICMD) to pool individual-level epidemiological and MD data from general population studies worldwide. ICMD aims to characterize determinants of MD more precisely, and to evaluate whether they are consistent across populations worldwide. We included 11755 women, from 27 studies in 22 countries, on whom individual-level risk factor data were pooled and original mammographic images were re-read for ICMD to obtain standardized comparable MD data. In the present article, we present (i) the rationale for this consortium; (ii) characteristics of the studies and women included; and (iii) study methodology to obtain comparable MD data from original re-read films. We also highlight the risk factor heterogeneity captured by such an effort and, thus, the unique insight the pooled study promises to offer through wider exposure ranges, different confounding structures and enhanced power for sub-group analyses.

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

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.