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. Author manuscript; available in PMC: 2012 Nov 1.
Published in final edited form as: Alcohol. 2010 Dec 18;45(7):689–699. doi: 10.1016/j.alcohol.2010.11.006

Alcohol consumption in relation to aberrant DNA methylation in breast tumors

Meng Hua Tao 1,2, Catalin Marian 3, Peter G Shields 3, Jing Nie 1, Susan E McCann 4, Amy Millen 1, Christine Ambrosone 4, Alan Hutson 5, Stephen B Edge 6, Shiva S Krishnan 3, Bin Xie 3, Janet Winston 7, Dominica Vito 1, Marcia Russell 8, Thomas H Nochajski 9, Maurizio Trevisan 1,10, Jo L Freudenheim 1
PMCID: PMC3137700  NIHMSID: NIHMS256655  PMID: 21168302

Abstract

The mechanism for the observed association of alcohol consumption breast cancer risk is not known; understanding that mechanism could improve understanding of breast carcinogenesis and optimize prevention strategies. Alcohol may impact breast malignancies or tumor progression by altering DNA methylation. We examined promoter methylation of three genes, the E- cadherin, p16, and RAR-β2 genes in archived breast tumor tissues from participants in a population-based case-control study. Real time methylation-specific PCR was performed on 803 paraffin-embedded samples; and lifetime alcohol consumption was queried. Unordered polytomous and unconditional logistic regression were used to derive adjusted odds ratios (OR) and 95% confidence intervals (CI). RAR-β2 methylation was not associated with drinking. Among premenopausal women, alcohol consumption was also not associated with promoter methylation for E- cadherin and p16 genes. In case-case comparisons of postmenopausal breast cancer, compared to lifetime never drinkers, promoter methylation likelihood was increased for higher alcohol intake for E - cadherin (OR = 2.39, 95% CI, 1.15–4.96), in particular for those with ER-negative tumors (OR = 4.13, 95% CI, 1.16–14.72), and decreased for p16 (OR = 0.52, 95% CI, 0.29-0.92). There were indications that the association with p16 was stronger for drinking at younger ages. Methylation was also associated with drinking intensity independent of total consumption for both genes. We found alcohol consumption was associated with DNA methylation in postmenopausal breast tumors, suggesting that the association of alcohol and breast cancer may be related, at least in part, to altered methylation, and may differ by drinking pattern.

Keywords: alcohol consumption, breast cancer, epidemiology, epigenetics, promoter methylation

Introduction

While the risk of breast cancer is associated with alcohol consumption (Hamajima et al., 2002; Key et al., 2006; Petri et al., 2004; Singletary and Gapstur, 2004; Smith-Warner et al., 1998), the mechanism for this association is not well understood. There are several potential hypotheses by which alcohol may contribute to breast malignancies or tumor progression (Castro et al., 2006; Dorgan et al., 2001; Dumitrescu and Shields, 2005; Homann et al., 2005; Rinaldi et al., 2006); one possible mechanism may be the impact of alcohol on one-carbon metabolism and subsequent DNA methylation (Dumitrescu and Shields, 2005; Mason and Choi, 2005). Alcohol affects the bioavailability of the one carbon units, essential to nucleotide synthesis and biological methylation of DNA, RNA and protein, in a number of ways including negative effects on folate absorption, utilization and excretion, and on activity of metabolic enzymes (Choi and Mason, 2002; Dumitrescu and Shields, 2005; Mason and Choi, 2005). Alcohol also can interfere with estrogen pathways and increase the circulation levels of estrogen (Dumitrescu and Shields, 2005); higher estrogens exposure have been suggested to induce aberrant DNA methylation breast carcinogenesis in both in vivo and in vitro studies (Fernandez and Russo, 2010).

There is increasing evidence that aberrant DNA methylation is significant in carcinogenesis (Szyf et al., 2004; Wajed et al., 2001). These alterations are common in breast cancer and include both global hypomethylation and hypermethylation of promoter regions of specific genes (Szyf et al., 2004). DNA promoter hypermethylation is associated with chromatin condensation, delay of replication, inhibition of transcription initiation, and gene silencing in breast carcinogenesis (Esteller et al., 2001; Szyf et al., 2004; Widschwendter and Jones, 2002). Such silencing is significant in that hypermethylation frequently affects genes with key roles contributing to carcinogenesis. Among those that are frequently found to be hypermethylated in breast tumors are genes functionally important in cell adhesion (Graff et al., 1995), cell cycle regulation (Esteller et al., 2001), and hormone and receptor-mediated cell signaling (e.g., E-cadherin, p16 and RAR-β2 (retinoic acid-binding receptor-β2), respectively) (Widschwendter and Jones, 2002).

The E-cadherin gene, a possible tumor suppressor gene, regulates cell adhesion (Graff et al., 1995). Alcohol concentration as low as 0.12% was shown to decrease expression of E-cadherin α, β, and γ, three major catenin proteins important for cell adhesion and tissue integrity (Meng et al., 2000). Loss of expression of E-cadherin caused by promoter methylation occurs frequently in breast cancer (Widschwendter and Jones, 2002), suggesting that alcohol consumption might down-regulate the expression of E-cadherin through promoter methylation.

In addition, promoter hypermethylation of other tumor suppressor genes such as p16 and RAR-β2 genes is a common and critical epigenetic event leading to increased proliferation, genetic instability in human breast cells, and subsequently breast carcinogenesis (Novak et al., 2009). Promoter methylation of E-cadherin, p16 and RAR-β2 has also been shown to occur more frequently in breast tumors than benign or adjacent nonmalignant breast tissue (Krassenstein et al., 2004; Parrella et al., 2004) and is associated with poor differentiation, distant metastasis, and estrogen receptor (ER) status in breast tumors (Li et al., 2006; Mehrotra et al., 2004; Shinozaki et al., 2005; Tao et al., 2008; Yan et al., 2000).

Little is known about the etiology of these alterations in methylation. Alcohol drinking may explain the observed changes, at least in part. To our knowledge, there has been just one study of alcohol examining one-carbon metabolism and breast tumor hypermethylation. In that study of African-American women, there was some evidence of an association between higher alcohol intake and hypermethylation of the ER- α gene in breast tumors in case control comparisons (Zhu et al., 2003). There is some, although not consistent evidence that alcohol consumption may affect promoter methylation for other cancer sites (de Vogel et al., 2008; Hasegawa et al., 2002; Kraunz et al., 2006; Marsit et al., 2006; Puri et al., 2005; Slattery et al., 2006; van Engeland et al., 2003). It is thought that altered methylation is an early event in carcinogenesis. It may be that drinking earlier in life more than recent drinking is related to methylation. Further, in addition to total intake of alcohol, drinking pattern may also impact the biological consequences of ethanol. Drinking large amounts infrequently may be different from consumption of smaller amounts more frequently even though the absolute intakes are the same. Previous studies have not examined drinking pattern in relation to methylation.

We report here on results of a population-based case control study. We evaluated promoter methylation of E- cadherin, p16, and RAR-β2 in breast tumors in relation to alcohol consumption both over the lifetime and during different periods of the lifetime, examining both absolute intakes as well as intensity of consumption.

Materials and Methods

Study population

A population-based case control study of breast cancer, the Western New York Exposures and Breast Cancer Study (WEB Study) was conducted from 1996–2001. All eligible participants were age 35–79, current residents of Erie or Niagara Counties in New York State, with no previous cancer history other than non-melanoma skin cancer. Cases were identified by trained nurses from medical records of hospitals in those counties. All but two hospitals were included in the study. One of those hospitals did not treat breast cancer patients. In the second, 95% of patients were also seen by one practice of breast surgeons who allowed for case ascertainment through their practice. Eligible cases were women diagnosed with primary, histologically confirmed, incident breast cancer. Among 1,638 eligible cases, 1,170 (72%) participated. Controls were randomly selected from the New York State Department of Motor Vehicles driver’s license list (age ≤ 65 years) and the Health Care Finance Administration rolls (age >65 years), and frequency-matched to cases on age and race. Interviews were completed for 2,115 (63%) eligible women. The protocol was approved by the Institutional Review Boards of the University at Buffalo and all participating institutions.

Extensive in-person interviews and self-administered questionnaires were administered to participants including queries regarding demographic factors, medical history, reproductive history and other breast cancer risk factors. The interview focused primarily on lifetime alcohol consumption using the Cognitive Lifetime Drinking History (CLDH) (Russell et al., 1998). Briefly, participants were asked to complete a calendar of important life events (e.g., schooling, marriage, pregnancy). Using that calendar, drinking intervals were identified by asking participants when they began drinking at least once a month for six months and when their drinking pattern changed. For each interval, participants indicated how often they drank and how many drinks they usually had on weekends, weekdays and days when they had more drinks than usual during a four-week period. Information was collected regarding the proportion of each beverage type. Together with data on beverage-specific drink size, we calculated total consumption and consumption for periods up to two years before diagnosis for cases and two years before interview for controls. Time periods included consumption over the lifetime, for the previous 2–10 years, previous 10–20 years, before age 20 and for each decade of age relevant to that participant (i.e., 20–29, 30–39, 40–49, 50–59). In addition, we examined intensity of consumption, that is, the number of drinks consumed per drinking day for those same time periods. The latter analyses were adjusted for total absolute intake. Those who had never had at least 12 drinks in any 12-month period were classified as lifetime non-drinkers.

For breast cancer cases, information on tumor size, histological grade, and cancer stage (as measured by tumor-node-metastasis (TNM) stage) was abstracted from medical charts by trained research nurses using a standardized protocol. Estrogen receptor (ER) status was determined in the Lombardi Cancer Center by a single pathologist by immunohistochemical analysis as described previously (Tao et al., 2008).

Tumor block promoter methylation determination

Among the 1170 breast cancer cases, we were able to obtain archived tumor blocks for 920 (78.6%). Tumor samples were microdissected from fixed microscope slides in order to minimize the inclusion of normal surrounding tissue. Bisulfite modification was performed on 2μg of tumor DNA isolated from the dissected tissue in accordance with methods described previously (Jeronimo et al., 2001; Tao et al., 2008). For determining promoter methylation of E- cadherin, p16, and RAR-β2, we used a fluorescence based version of methylation-specific PCR (MSP) using real time PCR amplification of bisulfate converted DNA in an ABI 7900HT real time PCR system as previously described (Eads et al., 2000; Tao et al., 2008). Briefly, each reaction contained 5 μl of Taqman Universal Master Mix (29), 4.5 μl of bisulfite treated DNA and 0.5 μl of a 60X assay by design premix containing the primers and probes that were designed for each respective gene (Applied Biosystems, Carlsbad CA); primers and probes sequences were published elsewhere (Tao et al., 2008). Thermal cycling started with an initial 10 min denaturation at 95 °C followed by 45 cycles of 95 °C for 15 s and 60 °C for 1 min, with a final extension of 5 min at 72 °C. As a control to check for modified viable DNA, we used an assay for the ACTB gene with primers and probes specifically designed for CpG free sites within the gene sequence, thus amplifying the modified DNA regardless of the methylation status. If the ACTB result was negative (i.e. no amplification signal was detected), the DNA was not used in subsequent assays, and re-modification was attempted; the other 3 genes being assayed only if ACTB was positive. Each individual DNA sample was assayed in triplicate for each gene for quality control purposes. Additionally, as a positive control, universally methylated DNA (CpGenome; Norcross, GA) was used along with water blanks as a negative control. Finally, we had successful promoter methylation results for 803 cases.

Statistical analysis

Characteristics of participating cases with and without promoter methylation of specific gene and controls were compared using ANOVA for continuous variables and the χ2 test for categorical variables. For examination of alcohol consumption in relation to gene promoter methylation, we calculated odds ratios (ORs) and 95% confidence intervals (95% CIs), comparing cases with and without promoter methylation to controls using polytomous logistic regression. Additionally, unconditional logistic regression was used for case-case comparisons of those with and without promoter methylation to estimate ORs and 95% CIs for associations of alcohol consumption with promoter methylation among women with breast cancer. For categorization by alcohol consumption, drinkers were divided into two groups with a cutoff based on the median consumption in controls who were alcohol consumers; non-drinkers were the referent. We also examined tertiles of alcohol consumption among alcohol consumers based on equal distribution of controls. We found similar results for case-control and case-case comparisons and p for trend >0.05. Because of relative low consumption of alcohol in our study population and limited power for stratified analyses, we reported here only those analyses alcohol consumption divided at the median levels. All analyses were adjusted for age, race, education, age at menarche, age at first birth, family history of breast cancer among first degree relatives, history of benign breast disease, body mass index (BMI), smoking status, total caloric intake, folate intake, and age at menopause among the postmenopausal women. Because we had previously found differences in methylation by ER status (Tao et al., 2008), we further adjusted for ER status in the case-case comparisons. Age, age at menarche, age at first birth, BMI, total caloric intake, folate intake, and age at menopause among the postmenopausal women were adjusted in the models as continuous variables, and other factors were adjusted as categorical variables. All statistical tests were based on two-sided probability. Statistical analyses were conducted using SAS Version 9.1 (SAS Institute, Cary, NC).

Results

Demographic characteristics of cases with and without promoter methylation of E - cadherin, p16, and RAR-β2 gene and of controls are shown in Table 1. The frequency of promoter methylation was 20% (161) for E - cadherin, 25.9% (208) for p16, and 27.5% (221) for RAR-β2. Mean lifetime alcohol consumption was similar for cases with methylated genes, those without methylation, and for controls. Cases with promoter methylation of E - cadherin gene reported younger age at first birth than unmethylated cases and controls. Compared to controls, total energy intake was greater for breast cancer cases with or without promoter methylation of E - cadherin or RAR-β2 gene; while intake of energy was lower for cases with promoter methylation of p16 gene than unmethylated cases. For cases without hypermethylation of E - cadherin, p16, or RAR-β2 gene, parity was lower than for controls. Cases either with or without promoter methylation of E - cadherin, p16, or RAR-β2 gene were more likely than controls to have a history of benign breast disease and family history of breast cancer.

Table 1.

Descriptive characteristics of breast cancer cases and controls by promoter methylation status of E-cadherin, p16, and RAR-β2, WEB Study 1996–2001

E-cadherin p16 RAR-β2
M (n = 161)§ UM (n = 642)§ M (n = 208)§ UM (n = 595)§ M (n = 221)§ UM (n = 582)§ Controls
Age, y (mean ± SD) 58.0 ± 11.8 57.4 ± 11.2 58.0 ± 11.2 57.4 ± 11.3 57.4 ± 11.3 57.6 ± 11.3 57.8 ± 11.8
Race/ethnicity
 White 148 (91.9%) 594 (92.5%) 192 (92.3%) 550 (92.4%) 210 (95.0%) a 532 (91.4%) 1910 (90.3%)
 Non-white 13 (8.1%) 48 (7.5%) 16 (7.7%) 45 (7.6%) 11 (5.0%) 50 (8.6%) 205 (9.7%)
Age at menarche, y (mean ± SD) 12.5 ± 1.5 12.6 ± 1.6 12.6 ± 1.6 12.6 ± 1.6 12.6 ± 1.5 12.5 ± 1.6 12.7 ± 1.6
Parity (mean ± SD) † 2.9 ± 1.5 2.8 ± 1.4 2 3.0 ± 1.5 2.8 ± 1.4 2 2.9 ± 1.5 2.8 ± 1.4 2 3.1 ± 1.6
Age at first birth, y (mean ± SD) † 23.4 ± 4.9 a, 3 24.5 ± 4.8 24.0 ± 4.7 24.3 ± 4.9 24.0 ± 4.5 24.3 ± 5.0 24.1 ± 4.5
BMI, kg/m2 (mean ± SD) 28.1 ± 5.6 28.5 ± 6.5 28.5 ± 6.2 28.4 ± 6.3 28.6 ± 6.1 28.4 ± 6.4 28.2 ± 6.3
Family history of breast cancer *
 Yes 33 (22.0%) 1 124 (20.8%) 2 47 (24.5%) 1 110 (19.8%) 2 41 (19.7%) a 116 (21.5%) 2 252 (12.8%)
 No 117 (78.0%) 473 (79.2%) 145 (75.5%) 445 (80.2%) 167 (80.3%) 423 (78.5%) 1712 (87.2%)
History of benign breast disease *
 Yes 61 (38.6%) 1 208 (33.1%) 2 79 (38.7%) 1 190 (32.6%) 2 70 (31.8%) 1 199 (35.1%) 2 457 (21.8%)
 No 98 (61.4%) 420 (66.9%) 125 (61.3%) 393 (67.4%) 150 (68.2%) 368 (64.9%) 1636 (78.2%)
Daily total energy, kcal (mean ± SD) 1538.3 ± 630.5 1 1506.3 ± 566.2 b 1415.5 ± 471.6 3 1546.8 ± 609.3 2 1549.8 ± 657.6 1 1498.7 ± 546.7 1452.0 ± 558.2
Smoking
 Never 75 (46.9%) 290 (45.2%) 100 (48.1%) 265 (44.7%) 102 (46.2%) 263 (45.3%) 1015 (48.3%)
 Ever 85 (53.1%) 351 (54.8%) 108 (51.9%) 328 (55.3%) 119 (53.8%) 317 (54.7%) 1087 (51.7%)
Lifetime alcohol (oz) 3509.3 ± 6278.0 2901.8 ± 4766.5 3049.5 ± 5194.3 3023.3 ± 5104.7 2626.4 ± 3703.2 3177.4 ± 5548.6 3545.6 ± 12546.9
Postmenopausal 111 (68.9%) 455 (70.9%) 147 (70.7%) 419 (70.4%) 157 (71.0%) 409 (70.3%) 1503 (71.1%)
Hormone replacement therapy **
 Never 60 (54.1%) 209 (46.2%) 72 (49.0%) 197 (47.4%) 73 (47.1%) 196 (48.0%) 720 (50.0%)
 Ever 51 (45.9%) 243 (53.8%) 75 (51.0%) 219 (52.6%) 82 (52.9%) 212 (52.0%) 721 (50.0%)
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§

M – methylated; UM – un-methylated;

†

Among parous women.

*

Subjects with missing values were excluded from the analysis.

‡

Daily folate intake from diet and supplements.

**

Among postmenopausal women.

a

Comparison of methylated cases to controls, p ≤0.05;

b

Comparison of unmethylated cases to controls, p ≤0.05;

c

Comparison of methylated to unmethylated cases, p ≤0.05.

1

Comparison of methylated cases to controls, p ≤0.01;

2

Comparison of unmethylated cases to controls, p ≤0.01;

3

Comparison of methylated to unmethylated cases, p ≤0.01.

Adjusted ORs and 95% CIs by menopausal status for ever drinking alcohol, for total lifetime alcohol consumption, and for alcohol intake 2–10, and 10–20 years prior to diagnosis/interview are shown in Tables 2–4. Included are comparisons of: 1) cases without promoter methylation to controls; 2) cases with promoter methylation to controls, and; 3) case-case comparisons of those with methylated gene promoter and those without.

Table 2.

Alcohol consumption and promoter methylation of E-cadherin, p16, and RAR-β2 genes in postmenopausal breast tumors: case-control and case-case comparisons

U vs Controls M vs Controls M vs U
Controls M U OR1 (95% CI) 1 OR2 (95% CI) 1 OR3 (95% CI) 2
E-cadherin
Lifetime alcohol intake a
 Lifetime abstainer 260 13 89 1.0 1.0 1.0
 Low alcohol intake 590 48 182 0.96 (0.70–1.30) 1.93 (1.01–3.71) 2.06 (1.02–4.16)
 High alcohol intake 589 49 176 0.92 (0.67–1.26) 2.06 (1.06–4.03) 2.39 (1.15–4.96)
Alcohol consumption previous 2–10 years a
 Lifetime abstainer 260 13 89 1.0 1.0 1.0
 Low alcohol intake 603 44 174 0.87 (0.64–1.19) 1.69 (0.88–3.27) 1.96 (0.97–3.98)
 High alcohol intake 595 53 187 0.92 (0.67–1.26) 2.07 (1.07–4.03) 2.47 (1.18–5.15)
Alcohol consumption previous 10–20 years a
 Lifetime abstainer 260 13 89 1.0 1.0 1.0
 Low alcohol intake 600 49 171 0.86 (0.63–1.18) 1.89 (0.99–3.62) 2.20 (1.09–4.43)
 High alcohol intake 595 48 190 0.95 (0.69–1.30) 1.85 (0.94–3.63) 2.08 (0.99–4.36)
p16
Lifetime alcohol intake a
 Lifetime abstainer 260 36 66 1.0 1.0 1.0
 Low alcohol intake 590 54 176 1.29 (0.93–1.80) 0.69 (0.43–1.09) 0.50 (0.29–0.86)
 High alcohol intake 589 54 171 1.26 (0.89–1.78) 0.69 (0.42–1.12) 0.52 (0.29–0.92)
Alcohol consumption previous 2–10 years a
 Lifetime abstainer 260 36 66 1.0 1.0 1.0
 Low alcohol intake 603 54 164 1.12 (0.82–1.62) 0.64 (0.40–1.03) 0.53 (0.30–0.91)
 High alcohol intake 595 56 184 1.27 (0.90–1.79) 0.68 (0.42–1.11) 0.47 (0.27–0.85)
Alcohol consumption previous 10–20 years a
 Lifetime abstainer 260 36 66 1.0 1.0 1.0
 Low alcohol intake 600 58 162 1.16 (0.83–1.62) 0.69 (0.44–1.10) 0.56 (0.32–0.97)
 High alcohol intake 595 52 186 1.29 (0.92–1.83) 0.64 (0.39–1.04) 0.44 (0.24–0.78)
RAR-β2
Lifetime alcohol intake a
 Lifetime abstainer 260 31 71 1.0 1.0 1.0
 Low alcohol intake 590 65 165 1.11 (0.80–1.54) 1.00 (0.62–1.59) 0.87 (0.51–1.48)
 High alcohol intake 589 58 166 1.13 (0.80–1.58) 0.89 (0.54–1.46) 0.79 (0.45–1.39)
Alcohol consumption previous 2–10 years a
 Lifetime abstainer 260 31 71 1.0 1.0 1.0
 Low alcohol intake 603 62 156 1.01 (0.72–1.42) 0.88 (0.55–1.42) 0.85 (0.49–1.47)
 High alcohol intake 595 63 177 1.15 (0.82–1.62) 0.86 (0.53–1.41) 0.74 (0.42–1.32)
Alcohol consumption previous 10–20 years a
 Lifetime abstainer 260 31 71 1.0 1.0 1.0
 Low alcohol intake 600 60 160 1.05 (0.76–1.47) 0.86 (0.53–1.38) 0.79 (0.46–1.36)
 High alcohol intake 595 65 173 1.12 (0.80–1.58) 0.91 (0.56–1.49) 0.82 (0.47–1.45)
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1

Odds ratios and 95% confidence intervals adjusted for age, race, education, age at menarche, age at first birth, age at menopause, BMI, family history of breast cancer among first degree relatives, history of benign breast disease, energy, folate, smoking status.

2

Odds ratios and 95% confidence intervals also adjusted for ER status.

a

Cutoff point (oz): 1162 for lifetime alcohol until 2 years prior to diagnosis/interview; 106 for alcohol consumption between 2–10 years previous; 184 for alcohol consumption 10–20 years previous.

Table 4.

Alcohol consumptions by decades of age and likelihood of E-cadherin, p16 and RAR-β2 promoter methylation in breast tumors stratified by menopausal status, case-case comparisons

E-cadherin OR (95% CI) 1 p16 OR (95% CI) 1 RAR-β2 OR (95% CI) 1
N N N N N N
M U M U M U
Premenopausal
Alcohol intake before age 20 a
 Abstainer 7 19 1.0 10 18 1.0 10 18 1.0
 Low 19 83 0.66 (0.23–1.94) 21 81 0.47 (0.17–1.32) 25 77 0.64 (0.23–1.78)
 High 22 82 0.71 (0.23–2.21) 30 74 0.85 (0.28–2.54) 29 75 0.76 (0.26–2.23)
Alcohol intake age 20–30 b
 Abstainer 7 21 1.0 10 18 1.0 10 18 1.0
 Low 18 72 0.76 (0.25–2.35) 20 70 0.49 (0.18–1.41) 22 68 0.59 (0.21–1.70)
 High 24 94 0.70 (0.22–2.21) 31 87 0.61 (0.21–1.78) 32 86 0.55 (0.19–1.62)
Alcohol intake age 30–40 c
 Abstainer 7 19 1.0 9 17 1.0 9 17 1.0
 Low 13 61 0.87 (0.31–2.39) 17 57 0.41 (0.16–1.05) 23 51 1.45 (0.58–3.62)
 High 23 73 1.16 (0.46–2.95) 21 75 0.39 (0.16–0.95) 25 71 1.01 (0.42–2.44)
Postmenopausal
Alcohol intake before age 20 a
 Abstainer 13 89 1.0 36 66 1.0 31 71 1.0
 Low 54 196 2.16 (1.08–4.32) 67 183 0.58 (0.34–0.98) 65 185 0.82 (0.48–1.40)
 High 43 162 2.22 (1.07–4.63) 41 164 0.42 (0.23–0.75) 59 146 0.85 (0.48–1.50)
Alcohol intake age 20–30 b
 Abstainer 13 89 1.0 36 66 1.0 31 71 1.0
 Low 52 185 2.31 (1.13–4.72) 62 175 0.53 (0.31–0.91) 62 175 0.75 (0.43–1.29)
 High 46 176 2.41 (1.13–5.16) 47 175 0.42 (0.23–0.76) 62 160 0.81 (0.46–1.43)
Alcohol intake age 30–40 c
 Abstainer 13 89 1.0 36 66 1.0 31 71 1.0
 Low 50 186 2.25 (1.10–4.61) 63 173 0.54 (0.31–0.92) 62 174 0.74 (0.43–1.28)
 High 48 176 2.52 (1.18–5.37) 47 177 0.42 (0.23–0.75) 63 161 0.81 (0.46–1.44)
Alcohol intake age 40–50 d
 Abstainer 12 85 1.0 35 62 1.0 31 66 1.0
 Low 45 168 1.69 (0.90–3.17) 55 158 0.76 (0.45–1.27) 56 157 0.63 (0.38–1.04)
 High 46 168 1.78 (0.92–3.44) 52 162 0.67 (0.39–1.17) 55 159 0.62 (0.37–1.06)
Alcohol intake age 50–60 e
 Abstainer 9 64 1.0 26 47 1.0 27 46 1.0
 Low 27 102 1.35 (0.72–2.52) 31 98 0.70 (0.40–1.24) 29 100 0.47 (0.26–0.83)
 High 35 107 1.65 (0.89–3.06) 37 105 0.84 (0.48–1.49) 37 105 0.67 (0.38–1.18)
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1

Odds ratios and 95% confidence intervals adjusted for age, race, education, age at menarche, age at first birth, menopausal status, BMI, family history of breast cancer among first degree relatives, history of benign breast disease, energy, folate, smoking status, and ER status.

a

Alcohol intake before age 20 years, cutoff point (oz): 136 premenopausal women, 0 postmenopausal women.

b

Alcohol consumption between age 20–30 years, cutoff point (oz): 400 premenopausal women, 189 postmenopausal women.

c

Alcohol consumption between age 30–40 years, cutoff point (oz): 196 premenopausal women, 177 postmenopausal women.

d

Alcohol consumption between age 40–50 years, cutoff point (oz): 256 premenopausal women, 187 postmenopausal women.

e

Alcohol consumption between age 50–60 years, cutoff point (oz): 147 postmenopausal women.

Results for postmenopausal women are shown in Table 2. Compared to abstainers, higher lifetime alcohol consumption was associated with increased gene promoter methylation for E - cadherin (OR, 2.39, 95% CI, 1.15–4.96 for the case-case comparison). The association was also similar for those with lower alcohol consumption (OR, 2.06, 95% CI, 1.02–4.16). Associations between alcohol intake and promoter methylation were similar though somewhat stronger point estimates for more recent consumption in the period 2–10 years previous: 2.47 (95% CI, 1.18–5.15) than for intake 10–20 years previous 2.08 (0.99–4.36). Compared to abstainers, there was a decrease in the likelihood of p16 promoter methylation among postmenopausal women who drank (OR, 0.50, 95% CI, 0.29–0.86 for lifetime lower alcohol intake; OR, 0.52, 95% CI, 0.29–0.92 for higher alcohol intake). ORs were similar for 2–10, and 10–20 years before diagnosis. There was no association between RAR-β2 methylation with alcohol intake among postmenopausal women. Alcohol consumption was not associated with the likelihood of E - cadherin, p16, or RAR-β2 promoter methylation among premenopausal breast cancer cases (data not shown).Additionally, we evaluated associations for lifetime intake of specific of alcoholic beverages, i.e. beer, liquor or wine. Results were similar to those for total alcohol consumption (data not shown).

Associations between E-cadherin, p16 and RAR-β2 promoter methylation and drinking intensity (drinks per drinking day) among postmenopausal women are shown in Table 3. Compared to abstainers, both lower and higher intensity of lifetime alcohol consumption were positively associated with methylation of the E - cadherin, even after adjusting for total alcohol consumption (OR, 2.21, 95% CI, 1.07–4.57; OR, 2.18, 95% CI, 1.00–4.72, respectively). Additionally, drinking intensity was associated with decreased prevalence of p16 promoter methylation: 0.48 (95% CI 0.28–0.84) for lifetime low alcohol drinking intensity and 0.43 (95% CI 0.23–0.80) for high alcohol drinking intensity, again after control for total intake. No association was observed for lifetime drinking intensity and RAR-β2 promoter methylation in postmenopausal women. We further examined the effects of drinking intensity 2–10 years previous and 10–20 years previous on likelihood of promoter methylation. Results were weaker but similar to those for lifetime drinking intensity (data not shown). No associations of drinking intensity with likelihood of promoter methylation with premenopausal breast cancer were observed (data not shown). Additionally, we also conducted analyses on the associations of alcohol consumption and drinking intensity with the likelihood of promoter methylation in at least one of the three genes in tumors among pre- and postmenopausal women. Lifetime alcohol consumption, alcohol intake 2–10, 10–20 years before diagnosis, and drinking intensity were not associated with the likelihood of promoter methylation in at least one gene (data not shown).

Table 3.

Intensity of alcohol (drinks per drinking day) intake and E-cadherin, p16, and RAR-β2 promoter methylation in postmenopausal breast tumors: case-control and case-case comparisons

U vs Controls M vs Controls M vs U
Controls M U OR1 (95% CI) 1 OR2 (95% CI) 1 OR3 (95% CI) 2
E-cadherin
Lifetime No. of drinks per usual drinking day a, 3
 Lifetime abstainer 260 13 89 1.0 1.0 1.0
 Low alcohol intake 588 49 179 0.94 (0.69–1.27) 2.02 (1.06–3.86) 2.21 (1.07–4.57)
 High alcohol intake 587 48 178 0.95 (0.70–1.30) 2.03 (1.04–3.96) 2.18 (1.00–4.72)
p16
Lifetime No. of drinks per usual drinking day a, 3
 Lifetime abstainer 260 36 66 1.0 1.0 1.0
 Low alcohol intake 588 54 174 1.29 (0.92–1.80) 0.69 (0.44–1.10) 0.48 (0.28–0.84)
 High alcohol intake 587 53 173 1.33 (0.94–1.87) 0.65 (0.40–1.05) 0.43 (0.23–0.80)
RAR-β2
Lifetime No. of drinks per usual drinking day a, 3
 Lifetime abstainer 260 31 71 1.0 1.0 1.0
 Low alcohol intake 588 66 162 1.10 (0.79–1.52) 1.03 (0.63–1.61) 0.92 (0.54–1.58)
 High alcohol intake 587 58 168 1.15 (0.82–1.61) 0.92 (0.56–1.50) 0.76 (0.44–1.34)
Open in a new tab
1

Odds ratios and 95% confidence intervals adjusted for age, race, education, age at menarche, age at first birth, age at menopause, BMI, family history of breast cancer among first degree relatives, history of benign breast disease, energy, folate, smoking status.

2

Odds ratios and 95% confidence intervals also adjusted for ER status.

3

Odds ratios and 95% confidence intervals also adjusted for total amount of alcohol consumption.

a

Lifetime no of drinks per usual drinking day, cutoff point: 2; No of drinks per usual drinking day 2–10 years previous, cutoff point: 1; No of drinks per usual drinking day 10–20 years previous, cutoff point: 1.

In addition, we evaluated associations of alcohol consumption before age 20 and by decade of life (20–29, 30–39, 40–49, 50–59, 60–69) with methylation by menopausal status (Table 4). Among premenopausal women, there was evidence of decreased likelihood of p16 methylation associated with drinking at age 30–40 but not with the other age intervals and there were no associations for the other genes for the analysis by age interval. Among postmenopausal women, the association with the likelihood of E - cadherin methylation was similar for the age intervals. There was some evidence that the association of alcohol consumption with likelihood of p16 methylation was stronger for intakes before age 40. There were no associations in the likelihood estimates Methylation of RAR-β2 again was not associated with alcohol consumption in this analysis by age interval drinking.

We further analyzed associations between alcohol consumption and promoter methylation stratified by ER status (Table 5). We found that the observed increased E - cadherin methylation associated with lifetime ever drinking compared to never drinking was limited to women with ER negative tumors (OR, 4.13, 95% CI, 1.16–14.72); there was no association with alcohol consumption for ER positive tumors (OR, 1.33, 95% CI, 0.70–2.53). The reverse association between ever drinking alcohol and p16 promoter methylation was more significant among ER positive cases (OR, 0.47, 95% CI, 0.27–0.81). Case-case comparisons for alcohol consumption and RAR-β2 methylation did not differ by ER status. Because alcohol consumption can affect one-carbon metabolism through its negative impact on folate absorption (Mason JB and Choi SW, 2005), we further examined potential interaction between dietary folate intake and alcohol consumption for each of the specific gene. There was no evidence of greater than multiplicative interaction between dietary folate and alcohol consumption on promoter methylation in breast tumors (data not shown).

Table 5.

Relationship between alcohol consumptions and promoter methylation of E-cadherin, p16 and RAR-β2 genes stratified by ER status: case-case comparisons

Ecad OR (95% CI) 1 p16 OR (95% CI) 1 RAR-β2 OR (95% CI) 1
N N N N N N
M U M U M U
ER positive
Alcohol drinking
 Never 16 69 1.0 29 56 1.0 28 57 1.0
 Ever 91 350 1.33 (0.70–2.53) 98 344 0.47 (0.27–0.81) 126 315 0.69 (0.41–1.18)
Lifetime alcohol intake a
 Abstainer 16 69 1.0 29 56 1.0 28 57 1.0
 Low 46 158 1.29 (0.66–2.50) 40 164 0.45 (0.25–0.83) 58 146 0.73 (0.41–1.29)
 High 45 191 1.09 (0.54–2.19) 58 178 0.59 (0.32–1.09) 67 169 0.69 (0.38–1.25)
ER negative
Alcohol drinking
 Never 4 33 1.0 15 22 1.0 10 27 1.0
 Ever 41 154 4.13 (1.16–14.72) 55 140 0.59 (0.25–1.39) 44 151 0.81 (0.32–2.07)
Open in a new tab
1

Odds ratios and 95% confidence intervals adjusted for age, race, education, age at menarche, age at first birth, BMI, family history of breast cancer among first degree relatives, history of benign breast disease, energy, smoking status, and menopausal status.

a

Lifetime alcohol until 2 years prior to diagnosis/interview, cutoff point (oz): 1002 premenopausal women, 1162 postmenopausal women.

Discussion

We found that alcohol drinking was associated with the likelihood of promoter methylation in postmenopausal breast cancer for two of the three genes that we examined, positively for E - cadherin and negatively for p16. In addition, drinking pattern appeared to affect methylation; associations were similar for drinks per drinking day for both E - cadherin methylation and p16 methylation after adjusting for total intake. The association of alcohol consumption with E - cadherin was limited to ER-negative tumors, and there was evidence that associations tended to be stronger for alcohol consumption at younger ages for p16 in postmenopausal breast cancer. RAR-β2 methylation did not differ by alcohol consumption.

Frequencies of promoter methylation for p16 and RAR-β2 genes in our sample were similar to previous reports; E-cadherin promoter methylation frequency was somewhat lower than has been reported previously (Li et al., 2006; Parrella et al., 2004; Shinozaki et al., 2005). This variation may be related to characteristics of our sample, sample size in this or other studies or it may depend on the sensitivity of the MSP assay and differences in MSP assay design. In our study, we used the same assay conditions for each tumor DNA sample and positive and negative internal controls; our MSP analysis was reliable.

There are few studies examining a possible association between alcohol consumption and promoter methylation in cancers (Kraunz et al., 2006; Marsit et al., 2006; Puri et al., 2005; Slattery et al., 2006; van Engeland et al., 2003; Zhu et al., 2003), and only one of breast cancer (Zhu et al., 2003). In a case control study of 304 African-American cases and 305 controls, there was a trend toward greater risk of breast cancer with hypermethylation of the ER α gene associated with alcohol consumption (>0.5 drinks/day), but the confidence interval was wide and included the null (Zhu et al., 2003). In studies of other cancer sites, Marsit et al (Marsit et al., 2006) found increased likelihood of SRRP1 hypermethylation with any lifetime alcohol exposure for head and neck squamous cell carcinoma; and van Engeland et al (van Engeland et al., 2003) found a marginally positive association between promoter methylation of at least one out of six genes, and low folate/high alcohol intake for colorectal cancer. Other studies did not find such associations (Hasegawa et al., 2002; Kraunz et al., 2006; Puri et al., 2005; Slattery et al., 2006). However, most of these studies did not evaluate drinking patterns or lifetime total alcohol consumption, and none of them examined alcohol intake at different lifetime periods. Our results indicate that alcohol consumption may affect promoter methylation of genes at earlier age periods (before age 40) among postmenopausal breast cancer patients, also providing evidence that DNA methylation alteration is an early event in carcinogenesis, at least for these genes.

In our study, we found an inverse association between the likelihood of p16 promoter methylation and lifetime alcohol consumption among postmenopausal women with breast cancer; the associations with alcohol were stronger for drinking before the age of 40. Our results were different from two previous studies examining p16 methylation of head and neck squamous cell cancers (Kraunz et al., 2006; Puri et al., 2005), which found no difference in prevalence by alcohol intake. A recent animal study also observed that alcohol consumption did not affect p16 promoter methylation in mice aged either 18 months or 4 months (Sauer et al., 2010). This discrepancy might be partly due to differences in pathways at different cancer sites. It may also be that we were able to detect a difference in our study because of the extensive data regarding alcohol consumption history which we had in our study. However, the biological mechanisms underlying the observed inverse associations are not known.

In an in vitro study of MCF-7 cells, ethanol down-regulated the expression of E-cadherin, and increase cell invasion and migration in human breast cancer cell lines (Meng et al., 2000), and it was proposed that the alcohol consumption may directly decrease the expression of E-cadherin through an epigenetic mechanism. Meanwhile, the ErbB2/Her2/Neu, a member of the epidermal growth factor receptor (EGFR) family, is over-expressed in 20–30% of invasive breast tumors and is associated with poor prognosis. Previous studies reported that the status of ErbB2 expression determine a cellular response to ethanol exposure and high expression of ErbB2 enhances an ethanol-mediated migration and invasion of breast cancer cells in vitro (Ke et al., 2006; Ma et al., 2003). DNA methylation of CDH13, coding for H-cadherin (a new member of the cadherin superfamily), has been found to be more prevalent in Her2/neu-positive breast tumors (Fiegl et al., 2006). Therefore, it is plausible to postulate that increased EGFR signaling may decrease the expression of E-cadherin through aberrant DNA methylation following the alcohol consumption in breast tumors. No previous studies have evaluated the association of alcohol intake and E-cadherin promoter methylation in breast tumors; a study of head and neck squamous cell carcinoma showed a borderline association of E-cadherin promoter methylation with increased years of drinking (Hasegawa et al., 2002). We found increased prevalence of E-cadherin promoter methylation in breast tumors from postmenopausal drinkers compared with never drinkers. These findings suggest that even the relatively low consumption levels of the study participants may be sufficient to induce aberrant promoter methylation of E-cadherin. We found that the association between alcohol consumption and E-cadherin promoter methylation was limited to ER-negative breast tumors. However, the biological mechanisms underlying the observed differences by menopausal status and ER status are not known. Further studies would be needed to elucidate a mechanism.

To our knowledge, our study is the first to examine the association between RAR-β2 methylation and alcohol consumption in breast tumor, and we found no associations between RAR-β2 methylation with alcohol consumption. In addition to the time period of alcohol consumption, the way alcohol is consumed may also affect methylation. Drinks per drinking day was associated with increased likelihood of E- cadherin and decreased likelihood of p16 promoter methylation, even after adjusting for total alcohol intake. Drinking intensity could affect the biological effects of alcohol. There may be important differences between, for example, drinking seven drinks per week as one drink per day, or as seven drinks on a single day each week. In our study, drinking intensity contributed additional information in explaining the difference in likelihood of methylation of these two genes. However, it is important to note that drinking quantity and intensity were highly correlated so that is difficult to separate these different components of drinking behavior.

As in any study of this kind, the strengths and weakness of the study need to be taken into account when considering the findings. Strengths of this study include the population-based study design and relatively large sample size, leading to more stable risk estimates. Nevertheless, the statistical power for examining of subgroups remained limited due to the low frequencies of the promoter methylation, limiting our ability to identify weak associations. Another strength of this study is the detailed information collected regarding lifetime alcohol consumption. The CLDH used in the study has been shown to have high test-retest reliability for estimates of lifetime alcohol consumption (Russell et al., 1997). While recall bias may be a concern for case control study, there is evidence that there is not much bias in recall of alcohol in case control studies of breast cancer (Friedenreich et al., 1991; Giovannucci et al., 1993). Further, it is unlikely that biased recall of alcohol intake would be related to gene promoter methylation, and thus would not differentially affect the case-case comparisons. Among the limitations, the lack of response among cases and controls has the potential for selection bias. However, it is unlikely that there were difference in participation of cases by methylation status; case-case comparisons would not be affected by this bias. A further concern was that we were unable to obtain paraffin-embedded breast tumor tissue for 21.4% of cases. Compared to those for whom we were unable to obtain tissue, those cases with breast tumor tissue were slightly younger at diagnosis and had a higher TNM stage of breast tumor. They were similar in terms of tumor size, histological grade, nuclear grade, ER and PR status. Further, both age and tumor stage were unrelated to methylation of these genes in this population (Tao et al., 2008); and so selection bias is not a likely explanation for our findings, particularly for the case-case comparisons. Finally, there are concerns with limitations on the outcome measurement of methylation. We examined methylation for three genes that known to be commonly methylated in breast cancer and are known to be significant in three pathways important in breast carcinogenesis. However, clearly we are somewhat limited by the study of a small number of genes. Expansion of our findings to a larger number of genes and genome-wide scale will be important. Further, the methodology used in this study was limited to examination of a single CpG island in the promoter regions. It is assumed that these single regions are sentinels for gene silencing and methylation of other CpG islands, especially in tumors, but it is possible that in some women, these genes are hypermethylated in CpG sequences that we did not study. Finally, in order to assess promoter methylation in this study we used real time MSP that increases the specificity of the MSP by interrogating more than one CpG (Shames et al., 2007). More specifically, we used a fluorescence based version of the MSP technique due to its increased throughput by eliminating the need of gel electrophoresis (Eads et al., 2000). This method has been found to be 10 times more sensitive than the classic MSP method, able to detect methylated sequences from an excess of 10,000-fold unmethylated alleles (Eads et al., 2000). Moreover, due to its increased sensitivity, it can use very small amounts of inferior quality DNA, making it amenable to detect methylation patterns in samples with possible degraded DNA yields and contamination with normal cells, such as archived tumor blocks (Eads et al., 2000). While we are aware of the limitations of this technique given its qualitative nature compared to other quantitative methods such as pyrosequencing, and the fact that it interrogates a limited number of CpG sites, we have followed stringent quality control criteria to ensure confidence in results. Moreover, recent findings show that results from MSP are highly correlated with other methods. By using the highly specific real time MSP, it is likely that our results would be reproduced by other methods (Lee et al., 2008).

In summary, we found an association of alcohol consumption and of drinking pattern with increased promoter methylation of E- cadherin and decreased promoter methylation of p16 genes in postmenopausal breast cancer. The associations were stronger for consumption at younger ages. Thus, our study provides evidence that the observed association of alcohol drinking and breast cancer risk may be related at least in part to alterations in methylation pathways. These findings are important in providing more data to support an etiologic role for alcohol consumption in breast carcinogenesis and for suggesting potential mechanisms for prevention and treatment.

Acknowledgments

This study would not have been possible without the support of all the study participants and the research staff of the WEB Study. This work was supported in part by the National Institute on Alcohol Abuse and Alcoholism (P50-AA09802), the Department of Defense (DAMD 179616202, DAMD 17030446), and the National Cancer Institute (R01CA 092040).

Footnotes

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