Is breast cancer a result of epigenetic responses to traffic-related air pollution? A review of the latest evidence

Environmental toxicants can exert adverse health effects via epigenetic regulation. We conducted a review of studies assessing traffic-related air pollution (TRAP) exposure and breast cancer (BC) risk, and the evidence for epigenetic mediation. 14 epidemiological studies demonstrated associations between TRAP exposure and BC risk, in which a total of 26 comparisons were assessed. 11 of these comparisons reported a positive association; whereas 15 comparisons were negative. Five publications linked TRAP exposure to epigenetic alterations in genes that may be related to BC risk. One animal study provided evidence of TRAP-treatment inducing breast tumorigenesis. Associations between TRAP components polycyclic aromatic hydrocarbons (PAH) and nitrogen dioxide (NO2) and BC risk were more consistent. While evidence for epigenetic regulation remains limited, polycyclic aromatic hydrocarbons (PAH) and nitrogen dioxide (NO2) exposures may alter methylation of breast tumorigenic genes (e.g., EPHB2, LONP1). Future epigenomic studies with environmental measures are needed to interrogate the relationship between TRAP and BC risk

The handling of missing data in molecular epidemiologic studies

Background: Molecular epidemiologic studies face a missing data problem as biospecimen data are often collected on only a proportion of subjects eligible for study. Methods: We investigated all molecular epidemiologic studies published in CEBP in 2009 to characterize the prevalence of missing data and to elucidate how the issue was addressed. We considered multiple imputation (MI), a missing data technique that is readily available and easy to implement, as a possible solution. Results: While the majority of studies had missing data, only 16% compared subjects with and without missing data. Furthermore, 95% of the studies with missing data performed a complete-case (CC) analysis, a method known to yield biased and inefficient estimates. Conclusions: Missing data methods are not customarily being incorporated into the analyses of molecular epidemiologic studies. Barriers may include a lack of awareness that missing data exists, particularly when availability of data is part of the inclusion criteria; the need for specialized software; and a perception that the CC approach is the gold standard. Standard MI is a reasonable solution that is valid when the data are missing at random (MAR). If the data are not missing at random (NMAR) we recommend MI over CC when strong auxiliary data are available. MI, with the missing data mechanism specified, is another alternative when the data are NMAR. In all cases, it is recommended to take advantage of MI’s ability to account for the uncertainty of these assumptions. Impact: Missing data methods are underutilized, which can deleteriously affect the interpretation of results

The use of multiple imputation in molecular epidemiologic studies assessing interaction effects

Background: In molecular epidemiologic studies biospecimen data are collected on only a proportion of subjects eligible for study. This leads to a missing data problem. Missing data methods, however, are not typically incorporated into analyses. Instead, complete-case (CC) analyses are performed, which result in biased and inefficient estimates. Methods: Through simulations, we characterized the bias that results from CC methods when interaction effects are estimated, as this is a major aim of many molecular epidemiologic studies. We also investigated whether standard multiple imputation (MI) could improve estimation over CC methods when the data are not missing at random (NMAR) and auxiliary information may or may not exist. Results: CC analyses were shown to result in considerable bias while MI reduced bias and increased efficiency over CC methods under specific conditions. It improved estimation even with minimal auxiliary information, except when extreme values of the covariate were more likely to be missing. In a real study, MI estimates of interaction effects were attenuated relative to those from a CC approach. Conclusions: Our findings suggest the importance of incorporating missing data methods into the analysis. If the data are MAR, standard MI is a reasonable method. Under NMAR we recommend MI as a tool to improve performance over CC when strong auxiliary data are available. MI, with the missing data mechanism specified, is another alternative when the data are NMAR. In all cases, it is recommended to take advantage of MI’s ability to account for the uncertainty of these assumptions

Exposure to polychlorinated biphenyl (PCB) congeners measured shortly after giving birth and subsequent risk of maternal breast cancer before age 50

Discrete windows of susceptibility to toxicants have been identified for the breast, including in utero, puberty, pregnancy, and postpartum. We tested the hypothesis that polychlorinated biphenyls (PCBs) measured during the early postpartum predict increased risk of maternal breast cancer diagnosed before age 50. We analyzed archived early postpartum serum samples collected from 1959 to 1967, an average of 17 years before diagnosis (mean diagnosis age 43 years) for 16 PCB congeners in a nested case–control study in the Child Health and Development Studies cohort (N = 112 cases matched to controls on birth year). We used conditional logistic regression to adjust for lipids, race, year, lactation, and body mass. We observed strong breast cancer associations with three congeners. PCB 167 was associated with a lower risk (odds ratio (OR), 75th vs. 25th percentile = 0.2, 95 % confidence interval (95 % CI) 0.1, 0.8) as was PCB 187 (OR, 75th vs. 25th percentile = 0.4, 95 % CI 0.1, 1.1). In contrast, PCB 203 was associated with a sixfold increased risk (OR, 75th vs. 25th percentile = 6.3, 95 % CI 1.9, 21.7). The net association of PCB exposure, estimated by a post-hoc score, was nearly a threefold increase in risk (OR, 75th vs. 25th percentile = 2.8, 95 % CI 1.1, 7.1) among women with a higher proportion of PCB 203 in relation to the sum of PCBs 167 and 187. Postpartum PCB exposure likely also represents pregnancy exposure, and may predict increased risk for early breast cancer depending on the mixture that represents internal dose. It remains unclear whether individual differences in exposure, response to exposure, or both explain risk patterns observed

Environmental exposures during windows of susceptibility for breast cancer: a framework for prevention research.

BackgroundThe long time from exposure to potentially harmful chemicals until breast cancer occurrence poses challenges for designing etiologic studies and for implementing successful prevention programs. Growing evidence from animal and human studies indicates that distinct time periods of heightened susceptibility to endocrine disruptors exist throughout the life course. The influence of environmental chemicals on breast cancer risk may be greater during several windows of susceptibility (WOS) in a woman's life, including prenatal development, puberty, pregnancy, and the menopausal transition. These time windows are considered as specific periods of susceptibility for breast cancer because significant structural and functional changes occur in the mammary gland, as well as alterations in the mammary micro-environment and hormone signaling that may influence risk. Breast cancer research focused on these breast cancer WOS will accelerate understanding of disease etiology and prevention.Main textDespite the plausible heightened mechanistic influences of environmental chemicals on breast cancer risk during time periods of change in the mammary gland's structure and function, most human studies of environmental chemicals are not focused on specific WOS. This article reviews studies conducted over the past few decades that have specifically addressed the effect of environmental chemicals and metals on breast cancer risk during at least one of these WOS. In addition to summarizing the broader evidence-base specific to WOS, we include discussion of the NIH-funded Breast Cancer and the Environment Research Program (BCERP) which included population-based and basic science research focused on specific WOS to evaluate associations between breast cancer risk and particular classes of endocrine-disrupting chemicals-including polycyclic aromatic hydrocarbons, perfluorinated compounds, polybrominated diphenyl ethers, and phenols-and metals. We outline ways in which ongoing transdisciplinary BCERP projects incorporate animal research and human epidemiologic studies in close partnership with community organizations and communication scientists to identify research priorities and effectively translate evidence-based findings to the public and policy makers.ConclusionsAn integrative model of breast cancer research is needed to determine the impact and mechanisms of action of endocrine disruptors at different WOS. By focusing on environmental chemical exposure during specific WOS, scientists and their community partners may identify when prevention efforts are likely to be most effective

The Handling of Missing Data in Molecular Epidemiology Studies

Molecular epidemiology studies face a missing data problem, as biospecimen or imaging data are often collected on only a proportion of subjects eligible for study. We investigated all molecular epidemiology studies published as Research Articles, Short Communications, or Null Results in Brief in Cancer Epidemiology, Biomarkers & Prevention from January 1, 2009, to March 31, 2010, to characterize the extent that missing data were present and to elucidate how the issue was addressed. Of 278 molecular epidemiology studies assessed, most (95%) had missing data on a key variable (66%) and/or used availability of data (often, but not always the biomarker data) as inclusion criterion for study entry (45%). Despite this, only 10% compared subjects included in the analysis with those excluded from the analysis and 88% with missing data conducted a complete-case analysis, a method known to yield biased and inefficient estimates when the data are not missing completely at random. Our findings provide evidence that missing data methods are underutilized in molecular epidemiology studies, which may deleteriously affect the interpretation of results. We provide practical guidelines for the analysis and interpretation of molecular epidemiology studies with missing data

The association of alcohol consumption with mammographic density in a multiethnic urban population

Background Alcohol consumption is associated with higher breast cancer risk. While studies suggest a modest association between alcohol intake and mammographic density, few studies have examined the association in racial/ethnic minority populations. Methods We assessed dense breast area and total breast area from digitized film mammograms in an urban cohort of African American (42%), African Caribbean (22%), white (22%), and Hispanic Caribbean (9%) women (n = 189, ages 40-61). We examined the association between alcohol intake and mammographic density (percent density and dense area). We used linear regression to examine mean differences in mammographic density across alcohol intake categories. We considered confounding by age, body mass index (BMI), hormone contraceptive use, family history of breast cancer, menopausal status, smoking status, nativity, race/ethnicity, age at first birth, and parity. Results Fifty percent currently consumed alcohol. Women who consumed >7 servings/week of alcohol, but not those consuming ≤7 servings/week, had higher percent density compared to nondrinkers after full adjustments (servings/week >7 β = 8.2, 95% Confidence Interval (CI) 1.8, 14.6; ≤7 β = -0.5, 95% CI -3.7, 2.8). There was a positive association between high alcohol intake and dense area after full adjustments (servings/week >7 β = 5.8, 95% CI -2.7, 14.2; ≤7 β = -0.1, 95% CI -4.4, 4.2). We did not observe race/ethnicity modification of the association between alcohol intake and percent density. In women with a BMI of 7 servings/week of alcohol had a 17% increase in percent density compared to nondrinkers (95% CI 5.4, 29.0) and there was no association in women with a BMI ≥ 25 kg/m2 (BMI ≥ 25-30 kg/m2 > 7 β = 5.1, 95% CI -8.5, 18.7 and BMI > 30 kg/m2 > 7 β = 0.5, 95% CI -6.5, 7.5) after adjusting for age and BMI (continuous). Conclusion In a racially/ethnically diverse cohort, women who consumed >7 servings/week of alcohol, especially those with a BMI < 25 kg/m2, had higher percent density. Keywords: Mammographic breast density; Alcohol consumption; Breast cance

Alcohol consumption and breast cancer-specific and all-cause mortality in women diagnosed with breast cancer at the New York site of the Breast Cancer Family Registry

Purpose Alcohol consumption is an established and important risk factor for breast cancer incidence in the general population. However, the relationship between alcohol and mortality among women with breast cancer is less clear. This study examines the effect of alcohol consumption on mortality in women affected with breast cancer at baseline from a high-risk family breast and ovarian cancer registry. Methods We studied 1116 women affected with breast cancer at baseline from the Metropolitan New York Registry. The examined reported alcohol consumption (total of beer, wine, liquor) was defined as the average number of drinks per week reported from age 12 to age at baseline. We assessed vital status of each participant using participant or family reported data and we used the National Death Index to supplement deaths reported through family updates. We used Cox proportional hazards models to estimate the association between alcohol intake and overall mortality (HRO), breast cancer-specific mortality (HRBC), and non-breast cancer mortality (HRNBC), adjusted for confounders. Results After a mean follow-up of 9.1 years, we observed 211 total deaths and 58 breast cancer deaths. Compared to non-drinkers, we found that both low and moderate to heavy levels of alcohol intake were not associated with greater overall mortality (≤3 drinks/week: HRO: 0.66, 95% CI: 0.38–1.14); > 3 drinks/week: HRO: 1.16, 95% CI: 0.85–1.58), breast cancer–specific mortality (≤ 3 drinks/week: HRBC:0.62, 95% CI: 0.19–2.03; >3 drinks/week: HR BC: 0.96, 95% CI: 0.49–1.89), or non-breast cancer-specific mortality (≤3 drinks/week: HR NBC: 0.73, 95% CI: 0.32–1.6; >3 drinks/week: HRNBC: 1.18, 95% CI: 0.75–1.86). Conclusions Alcohol intake reported from age 12 to age at baseline was not associated with overall or breast cancer-specific mortality in this cohort of affected women with a family history of breast cancer