Tuong L. Nguyen

Explaining Variance in the Cumulus Mammographic Measures That Predict Breast Cancer Risk: A Twins and Sisters Study

Background: Mammographic density, the area of the mammographic image that appears white or bright, predicts breast cancer risk. We estimated the proportions of variance explained by questionnaire-measured breast cancer risk factors and by unmeasured residual familial factors. Methods: For 544 MZ and 339 DZ twin pairs and 1,558 non-twin sisters from 1,564 families, mammographic density was measured using the computer-assisted method Cumulus. We estimated associations using multilevel mixed-effects linear regression and studied familial aspects using a multivariate normal model. Results: The proportions of variance explained by age, body mass index (BMI), and other risk factors, respectively, were 4%, 1%, and 4% for dense area; 7%, 14%, and 4% for percent dense area; and 7%, 40%, and 1% for nondense area. Associations with dense area and percent dense area were in opposite directions than for nondense area. After adjusting for measured factors, the correlations of dense area with percent dense area and nondense area were 0.84 and −0.46, respectively. The MZ, DZ, and sister pair correlations were 0.59, 0.28, and 0.29 for dense area; 0.57, 0.30, and 0.28 for percent dense area; and 0.56, 0.27, and 0.28 for nondense area (SE = 0.02, 0.04, and 0.03, respectively). Conclusions: Under the classic twin model, 50% to 60% (SE = 5%) of the variance of mammographic density measures that predict breast cancer risk are due to undiscovered genetic factors, and the remainder to as yet unknown individual-specific, nongenetic factors. Impact: Much remains to be learnt about the genetic and environmental determinants of mammographic density. Cancer Epidemiol Biomarkers Prev; 22(12); 2395–403. ©2013 AACR.

Mammographic density and risk of breast cancer in Korean women.

We carried out this study to evaluate the association between mammographic density adjusted for age and BMI and early-onset breast cancer in Asian women. We recruited 213 Korean patients with breast cancer (45% diagnosed before the age of 50 years) and 630 controls matched for age, menopausal status, and examination date. The percentage and absolute size of dense areas on digital mammograms were measured using a computer-assisted thresholding technique (Cumulus). We carried out an analysis using the conditional logistic regression model with adjustment for covariates. An increase by 1 SD in age and BMI-adjusted absolute dense area and percentage dense area was associated with a 1.15-fold (95% confidence interval: 1.03, 1.29) and 1.20-fold (95% confidence interval: 1.06, 1.37) increased risk of breast cancer, respectively. These associations were stronger for premenopausal disease (P=0.07 and 0.01, respectively) and for disease diagnosed before age 50 (P=0.07 and 0.02, respectively) than for postmenopausal disease (P=0.16 and 0.23, respectively) or later onset disease (P=0.10 and 0.10, respectively). There was no difference in the associations with premenopausal versus postmenopausal and early-onset versus late-onset disease. After adjusting for age and BMI, both a greater absolute dense area and a greater percentage dense area were associated with an increased risk of breast cancer, particularly at a young age.

Mammographic density defined by higher than conventional brightness thresholds better predicts breast cancer risk

Abstract Background: Mammographic density defined by the conventional pixel brightness threshold, and adjusted for age and body mass index (BMI), is a well-established risk factor for breast cancer. We asked if higher thresholds better separate women with and without breast cancer. Methods: We studied Australian women, 354 with breast cancer over-sampled for early-onset and family history, and 944 unaffected controls frequency-matched for age at mammogram. We measured mammographic dense area and percent density using the CUMULUS software at the conventional threshold, which we call Cumulus, and at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus, respectively. All measures were Box–Cox transformed and adjusted for age and BMI. We estimated the odds per adjusted standard deviation (OPERA) using logistic regression and the area under the receiver operating characteristic curve (AUC). Results: Altocumulus and Cirrocumulus were correlated with Cumulus (r ∼ 0.8 and 0.6, respectively). For dense area, the OPERA was 1.62, 1.74 and 1.73 for Cumulus, Altocumulus and Cirrocumulus, respectively (all P < 0.001). After adjusting for Altocumulus and Cirrocumulus, Cumulus was not significant (P > 0.6). The OPERAs for percent density were less but gave similar findings. The mean of the standardized adjusted Altocumulus and Cirrocumulus dense area measures was the best predictor; OPERA = 1.87 [95% confidence interval (CI): 1.64–2.14] and AUC = 0.68 (0.65–0.71). Conclusions: The areas of higher mammographically dense regions are associated with almost 30% stronger breast cancer risk gradient, explain the risk association of the conventional measure and might be more aetiologically important. This has substantial implications for clinical translation and molecular, genetic and epidemiological research.

Breast Cancer Risk Associations with Digital Mammographic Density by Pixel Brightness Threshold and Mammographic System

Purpose To compare three mammographic density measures defined by different pixel intensity thresholds as predictors of breast cancer risk for two different digital mammographic systems. Materials and Methods The Korean Breast Cancer Study included 398 women with invasive breast cancer and 737 control participants matched for age at mammography (±1 year), examination date, mammographic system, and menopausal status. Mammographic density was measured by using the automated Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software and the semiautomated Cumulus software at the conventional threshold (Cumulus) and at increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were Box-Cox-transformed and adjusted for age, body mass index, and menopausal status. Conditional logistic regression was used to estimate risk associations. For calculation of measures of predictive value, the change in odds per standard deviation (OPERA) and the area under the receiver operating characteristic curve (AUC) were used. Results For dense area, with use of the direct conversion system the OPERAs were 1.72 (95% confidence interval [CI]: 1.38, 2.15) for LIBRA, 1.58 (95% CI: 1.27, 1.97) for Cumulus, 2.04 (95% CI: 1.60, 2.59) for Altocumulus, and 3.48 (95% CI: 2.45, 4.47) for Cirrocumulus (P < .001). The corresponding AUCs were 0.70, 0.69, 0.76, and 0.89, respectively. With use of the indirect conversion system, the corresponding OPERAs were 1.50 (95% CI: 1.28, 1.76), 1.36 (95% CI: 1.16, 1.59), 1.40 (95% CI: 1.19, 1.64), and 1.47 (95% CI: 1.25, 1.73) (P < .001) and the AUCs were 0.64, 0.60, 0.61, and 0.63, respectively. Conclusion It is possible that mammographic density defined by higher pixel thresholds could capture more risk-predicting information with use of a direct conversion mammographic system; the mammographically bright, rather than white, regions are etiologically important.

Causes of blood methylomic variation for middle-aged women measured by the HumanMethylation450 array

ABSTRACT To address the limitations in current classic twin/family research on the genetic and/or environmental causes of human methylomic variation, we measured blood DNA methylation for 479 women (mean age 56 years) including 66 monozygotic (MZ), 66 dizygotic (DZ) twin pairs and 215 sisters of twins, and 11 random technical duplicates using the HumanMethylation450 array. For each methylation site, we estimated the correlation for pairs of duplicates, MZ twins, DZ twins, and siblings, fitted variance component models by assuming the variation is explained by genetic factors, by shared and individual environmental factors, and by independent measurement error, and assessed the best fitting model. We found that the average (standard deviation) correlations for duplicate, MZ, DZ, and sibling pairs were 0.10 (0.35), 0.07 (0.21), -0.01 (0.14) and -0.04 (0.07). At the genome-wide significance level of 10−7, 93.3% of sites had no familial correlation, and 5.6%, 0.1%, and 0.2% of sites were correlated for MZ, DZ, and sibling pairs. For 86.4%, 6.9%, and 7.1% of sites, the best fitting model included measurement error only, a genetic component, and at least one environmental component. For the 13.6% of sites influenced by genetic and/or environmental factors, the average proportion of variance explained by environmental factors was greater than that explained by genetic factors (0.41 vs. 0.37, P value <10−15). Our results are consistent with, for middle-aged woman, blood methylomic variation measured by the HumanMethylation450 array being largely explained by measurement error, and more influenced by environmental factors than by genetic factors.

Associations between environmental breast cancer risk factors and DNA methylation-based risk-predicting measures

Background Genome-wide average DNA methylation (GWAM) and epigenetic age acceleration have been suggested to predict breast cancer risk. We aimed to investigate the relationships between these putative risk-predicting measures and environmental breast cancer risk factors. Methods Using the Illumina HumanMethylation450K assay methylation data, we calculated GWAM and epigenetic age acceleration for 132 female twin pairs and their 215 sisters. Linear regression was used to estimate associations between these risk-predicting measures and multiple breast cancer risk factors. Within-pair analysis was performed for the 132 twin pairs. Results GWAM was negatively associated with number of live births, and positively with age at first live birth (both P<0.05). Epigenetic age acceleration was positively associated with body mass index (BMI), smoking, alcohol drinking and age at menarche, and negatively with age at first live birth (all P<0.05), and the associations with BMI, alcohol drinking and age at first live birth remained in the within-pair analysis. Conclusions This exploratory study shows that lifestyle and hormone-related breast cancer risk factors are associated with DNA methylation-based measures that could predict breast cancer risk. The associations of epigenetic age acceleration with BMI, alcohol drinking and age at first live birth are unlikely to be due to familial confounding.

Genome-wide average DNA methylation is determined in utero

Abstract Background Investigating the genetic and environmental causes of variation in genome-wide average DNA methylation (GWAM), a global methylation measure from the HumanMethylation450 array, might give a better understanding of genetic and environmental influences on methylation. Methods We measured GWAM for 2299 individuals aged 0 to 90 years from seven twin and/or family studies. We estimated familial correlations, modelled correlations with cohabitation history and fitted variance components models for GWAM. Results The correlation in GWAM for twin pairs was ∼0.8 at birth, decreased with age during adolescence and was constant at ∼0.4 throughout adulthood, with no evidence that twin pair correlations differed by zygosity. Non-twin first-degree relatives were correlated, from 0.17 [95% confidence interval (CI): 0.05–0.30] to 0.28 (95% CI: 0.08–0.48), except for middle-aged siblings (0.01, 95% CI: −0.10–0.12), and the correlation increased with time living together and decreased with time living apart. Spouse pairs were correlated in all studies, from 0.23 (95% CI: 0.3–0.43) to 0.31 (95% CI: 0.05–0.52), and the correlation increased with time living together. The variance explained by environmental factors shared by twins alone was 90% (95% CI: 74–95%) at birth, decreased in early life and plateaued at 28% (95% CI: 17–39%) in middle age and beyond. There was a cohabitation-related environmental component of variance. Conclusions GWAM is determined in utero by prenatal environmental factors, the effects of which persist throughout life. The variation of GWAM is also influenced by environmental factors shared by family members, as well as by individual-specific environmental factors.

Mammographic Density and Circulating Sex Hormones: a Cross-Sectional Study in Postmenopausal Korean Women

Mammographic density (MD) is a strong independent risk factor for breast cancer. It has been suggested that breast cancer is related to the exposure to circulating sex hormones. However, relations between MD and hormones have been inconsistent. In addition, such relations are mainly evaluated in Western populations. Therefore, we conducted a cross-sectional study in 396 cancer-free postmenopausal Korean women who had never used hormone replacement therapy. We assayed estradiol, testosterone, and sex hormone-binding globulin (SHBG) levels. We then calculated free testosterone (cFT) levels. Total and dense areas of digital mammogram were measured using a computer-assisted thresholding method, and non-dense area and percent dense area were calculated. Linear mixed model was used for analyses. Estradiol and testosterone levels were not associated with any MD measures after adjusting for reproductive factors and body mass index. However, cFT was persistently associated with non-dense area even after adjusting for covariates, with non-dense area increased by 3.5% per 1 standard deviation increase of cFT. SHBG showed an inverse association with non-dense area, although it showed a positive association with dense area and percent dense area regardless of adjustment for covariates. Non-dense area was decreased by 5.6% while percent dense area was increased by 13.4% per 1 standard deviation increase of SHBG. These findings suggest that SHBG might be related with breast cancer risk, probably through its association with breast density.

Causal effect of smoking on DNA methylation in peripheral blood: a twin and family study

BackgroundSmoking has been reported to be associated with peripheral blood DNA methylation, but the causal aspects of the association have rarely been investigated. We aimed to investigate the association and underlying causation between smoking and blood methylation.MethodsThe methylation profile of DNA from the peripheral blood, collected as dried blood spots stored on Guthrie cards, was measured for 479 Australian women including 66 monozygotic twin pairs, 66 dizygotic twin pairs, and 215 sisters of twins from 130 twin families using the Infinium HumanMethylation450K BeadChip array. Linear regression was used to estimate associations between methylation at ~ 410,000 cytosine-guanine dinucleotides (CpGs) and smoking status. A regression-based methodology for twins, Inference about Causation through Examination of Familial Confounding (ICE FALCON), was used to assess putative causation.ResultsAt a 5% false discovery rate, 39 CpGs located at 27 loci, including previously reported AHRR, F2RL3, 2q37.1 and 6p21.33, were found to be differentially methylated across never, former and current smokers. For all 39 CpG sites, current smokers had the lowest methylation level. Our study provides the first replication for two previously reported CpG sites, cg06226150 (SLC2A4RG) and cg21733098 (12q24.32). From the ICE FALCON analysis with smoking status as the predictor and methylation score as the outcome, a woman’s methylation score was associated with her co-twin’s smoking status, and the association attenuated towards the null conditioning on her own smoking status, consistent with smoking status causing changes in methylation. To the contrary, using methylation score as the predictor and smoking status as the outcome, a woman’s smoking status was not associated with her co-twin’s methylation score, consistent with changes in methylation not causing smoking status.ConclusionsFor middle-aged women, peripheral blood DNA methylation at several genomic locations is associated with smoking. Our study suggests that smoking has a causal effect on peripheral blood DNA methylation, but not vice versa.

Twin birth changes DNA methylation of subsequent siblings

We asked if twin birth influences the DNA methylation of subsequent siblings. We measured whole blood methylation using the HumanMethylation450 array for siblings from two twin and family studies in Australia and Korea. We compared the means and correlations in methylation between pairs of siblings born before a twin birth (BT siblings), born on either side of a twin birth (B/AT pairs) and born after a twin birth (AT siblings). For the genome-wide average DNA methylation, the correlation for AT pairs (rAT) was larger than the correlation for BT pairs (rBT) in both studies, and from the meta-analysis, rAT = 0.46 (95% CI: 0.26, 0.63) and rBT = −0.003 (95% CI: −0.30, 0.29) (P = 0.02). B/AT pairs were not correlated (from the meta-analysis rBAT = 0.08; 95% CI: −0.31, 0.45). Similar results were found for the average methylation of several genomic regions, e.g., CpG shelf and gene body. BT and AT pairs were differentially correlated in methylation for 15 probes (all P < 10−7), and the top 152 differentially correlated probes (at P < 10−4) were enriched in cell signalling and breast cancer regulation pathways. Our observations are consistent with a twin birth changing the intrauterine environment such that siblings both born after a twin birth are correlated in DNA methylation.