Maskarinec, Gertraud

Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene–environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this. We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P < 0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test. After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE = 4.44 × 10−6). In this transcriptome-informed genome-wide gene–environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk. Our study suggests a limited role of gene–environment interactions in breast cancer risk.

Abstract Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10−5). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk. Cancer Res; 75(12); 2457–67. ©2015 AACR.

Abstract Background. Breast density assessed from mammography is a strong predictor of breast cancer risk, but the strength of the association may vary with adiposity. To examine effect modification by adiposity, we combined data from four case-control studies on breast density that represented an ethnically diverse population with a wide variation in level of adiposity as measured by body mass index (BMI, kg/m2). Methods. We combined data from four case-control studies representing different locations: California, Hawaii and Minnesota in the United States, and Gifu in Japan. All studies included incident breast cancer cases diagnosed between 1994 and 2002 and matched controls representing the underlying case population. One mammographic image per subject was selected, specifically the mammogram at diagnosis for the studies from California, Minnesota, and Japan and the closest prediagnostic mammogram for Hawaii. Percent density was measured by one reader, who was blinded to case status, using a computer-assisted method. Self-reported anthropometric measures were used to classify women as normal, overweight, and obese according to ethnic-specific BMI cut points (<23, 23-27.4, and ≥27.5 for Asian women and <25, 25-29.9, and ≥30 for other ethnic groups). Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (95% CI) and to evaluate interactions using the likelihood ratio while adjusting for potential confounders, including age and ethnicity. Heterogeneity across studies was marginally significant as assessed by examining density-by-study interaction (P = 0.09) and, therefore, we also adjusted for study-site. Results. The study included 1,699 cases and 2,422 controls of diverse ethnicity: 45% Caucasian, 40% Asian, 9% African-American, and 7% Other. Of these women, we classified 34% as overweight and19% as obese. Age-adjusted mean percent density was significantly greater for cases than for controls: 31.7% versus 28.9%, respectively (P < 0.001). BMI was inversely associated with breast density; the estimated age-adjusted mean percent density was 36.5%, 26.8%, and 19.3% for normal, overweight, and obese, respectively (Ptrend < 0.001). The overall OR for a 10% higher percent density was 1.15 (95% CI: 1.05, 1.18) with higher estimates in overweight (OR: 1.19, 95% CI: 1.09, 1.29) and obese (OR: 1.25, 95% CI: 1.11, 1.41) than normal BMI (OR: 1.11, 95% CI: 1.05, 1.18). The effect modification by BMI was statistically significant (Pinteraction = 0.01). Conclusions. Our findings confirm that the elevated risk of breast cancer associated with breast density differs by level of adiposity, with a higher risk for overweight and obese than normal BMI. Further research is needed to understand the underlying biological reasons for these noted differences in association by adiposity. Citation Information: Cancer Prev Res 2010;3(12 Suppl):B25.