Int J Cancer Clin Res ISSN: 2378-3419

• Abstract
• Keywords
• Introduction
• Materials and Methods
• Results
• Discussion
• Acknowledgements
• Ethical Statement
• Table 1
• Table 2
• Table 3
• Table 4
• Supplemental Table 1
• Supplemental Table 2
• References

Download PDF
    

International Journal of Cancer and Clinical Research

¹Department of Epidemiology, University of North Carolina at Chapel Hill; Chapel Hill, USA
²Department of Preventive Medicine, Icahn School of Medicine at Mount Sinai, New York, USA
³Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, USA
⁴Department of and Oncological Science, Icahn School of Medicine at Mount Sinai, New York, USA
⁵Research Center for Translational Medicine; Shanghai East Hospital of Tongji University School of Medicine; Shanghai, China
⁶Department of Environmental Health Sciences, Columbia University; New York, USA
⁷Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, USA
⁸Department of Epidemiology, Columbia University, New York, USA
⁹Department of Medicine, Columbia University, New York, USA
¹⁰Department of Pathology, Columbia University, New York, USA

Abstract

Introduction: Breast cancer, the leading cancer diagnosis among American women, is positively associated with postmenopausal obesity and little or no recreational physical activity (RPA). However, the underlying mechanisms of these associations remain unresolved. Aberrant changes in DNA methylation may represent an early event in carcinogenesis, but few studies have investigated associations between obesity/RPA and gene methylation, particularly in postmenopausal breast tumors where these lifestyle factors are most relevant.

Methods: We used case-case unconditional logistic regression to estimate odds ratios (ORs) and 95% Confidence Intervals (CI) for the associations between body mass index (BMI=weight [kg]/height [m²]) in the year prior to diagnosis, or RPA (average hours/week), and methylation status (methylated vs. unmethylated) of 13 breast cancer-related genes in 532 postmenopausal breast tumor samples from the Long Island Breast Cancer Study Project. We also explored whether the association between BMI/RPA and estrogen/progesterone-receptor status (ER+PR+ vs. all others) was differential with respect to gene methylation status. Methylation-specific PCR and the Methy Light assay were used to assess gene methylation.

Results: BMI 25-29.9kg/m², and perhaps BMI ≥ 30kg/m², was associated with methylated HIN1 in breast tumor tissue. Cases with BMI ≥ 30kg/m² were more likely to have ER+PR+ breast tumors in the presence of unmethylated ESR1 (OR=2.63, 95% CI 1.32-5.25) and women with high RPA were more likely to have ER+PR+ breast tumors with methylated GSTP1 (OR=2.33, 95% CI 0.79-6.84).

Discussion: While biologically plausible, our findings that BMI is associated with methylated HIN1 and BMI/RPA are associated with ER+PR+ breast tumors in the presence of unmethylated ESR1and methylated GSTP1, respectively, warrant further investigation. Future studies would benefit from enrolling greater numbers of postmenopausal women and examining a larger panel of breast cancer–related genes.

Key words

Body mass index, Physical activity, Gene methylation, Breast cancer, Epidemiology

Introduction

Breast cancer remains the leading cause of cancer-related illness in the United States (US), and may be influenced by a number of environmental [1], reproductive and lifestyle [2] factors. There is abundant research showing that elevated body mass and physical inactivity are associated with increased risk of postmenopausal breast cancer [3,4], but the mechanisms driving these associations are unresolved [5]. Given the large proportion of women who are inactive in the US [6] and the steadily increasing rates of obesity [7], understanding the underlying mechanism for the observed association between these lifestyle factors and breast carcinogenesis is of paramount importance.

DNA methylation is an epigenetic alteration that can modify gene expression [8] and is known to been related to breast carcinogenesis [9,10]. Specifically, hypermethylation of tumor suppressor genes has been associated with clinical/pathological factors for breast cancer, as well as mortality in our study population [11]. Some investigators have hypothesized that elevated body mass and/or physical inactivity may affect DNA methylation through increased estrogen [12,13] and chronic inflammation [14,15]; but to date, only three studies have examined associations between body mass and gene-specific methylation in breast tumors [16-18]. This previous research was limited by examining a very small (< 5) subset of genes [16,17] and some studies did not stratify by menopausal status [17,18]. No previous study has considered associations between physical activity and gene methylation of breast tumors.

The goals of our study were two-fold. First, we aimed to assess the potential association between body mass index (BMI) or recreational physical activity (RPA) in relation to promoter methylation status, assessed in a panel of 13 breast cancer-related genes measured in tumor tissue (APC, BRCA1, CCND2, CDH1, DAPK1, ESR1, GSTP1, HIN1, CDKN2A, PGR, RARß, RASSF1A and TWIST1). These genes may play an important role in breast carcinogenesis and their promoter regions have been frequently methylated in breast tumors [19]. Second, we explored whether associations between BMI/RPA and breast cancer subtypes, defined by estrogen and progesterone receptor (ER/PR) status, were modified by gene promoter methylation.

We hypothesized that: (1) breast tumors from postmenopausal women with elevated body size/physical inactivity would have a greater prevalence of methylation than tumors from postmenopausal women with lower body mass/high physical activity; and (2) elevated body size/physical inactivity would differentially associate with ER+PR+ breast cancer when we also consider the gene-promoter methylation status of the tumor (methylated vs. unmethylated).

Materials and Methods

We utilized case-only resources from the case-control component of the Long Island Breast Cancer Study Project (LIBCSP), a population-based study. Details of the parent study have been reported previously [20]. Institutional Review Board approval was obtained by all participating institutions.

Study population

Case women were English-speaking female residents of Nassau and Suffolk counties, Long Island, New York (NY), newly diagnosed with a first primary breast cancer between August 1, 1996 and July 31, 1997. Participants were identified using rapid case ascertainment via daily or weekly contact with pathology departments of all 28 hospitals on Long Island, and three tertiary care hospitals in New York City. At diagnosis, participants were aged 20-98 years and 67% were postmenopausal. Approximately 94% of study participants self-reported their race as white, 4% as black, and 2% as other, which was consistent with the underlying racial/ethnic distribution in these two NY counties at the time of data collection.

Data collection

Interviews were completed for 82.1% (n=1508) of eligible cases, and occurred within 3 months of diagnosis (before initiation of chemotherapy) for most case participants [20]. Tumor tissue was excised prior to the initiation of chemotherapy or radiation for all case participants. Written informed consent was obtained from all study participants prior to the study interview.

For LIBCSP cases, study investigators obtained archived pathology blocks for the first primary breast cancer from the 31 hospitals in Long Island and adjacent areas. Tumor blocks were successfully retrieved for 962 women [21] and tumor tissue from 532 postmenopausal participants were available for this study. Cases with tumor blocks available for methylation analysis (vs. those without tumor tissue available) were more likely be older (mean age 59.6 vs. 57.9 years), postmenopausal (70.7% vs. 64.6%), and have an invasive tumor (87.8% vs. 80.1%). Other demographic and clinical/pathological characteristics were similar between the two groups [19].

Body size, physical activity and covariate assessment

Body size and physical activity were assessed as part of the interviewer-administered structured questionnaire that was completed shortly after diagnosis. BMI in the year prior to diagnosis was calculated for each participant based on the following formula: weight (kg)/height (m²). RPA was assessed using a modified instrument developed by Bernstein and colleagues [22]. RPA from menopause to reference date was used to estimate postmenopausal RPA as previously described [23] and defined as inactive, low RPA (≤9.23 hrs/wk) and high RPA (>9.23 hrs/wk) based on the control median.

During the interview participants were also asked about their demographic characteristics; reproductive, environmental, and medical histories (including family history of breast cancer); cigarette smoking and alcohol use; and use of exogenous hormones. Menopausal status was derived using information on the last menstrual period and gynecologic surgeries, combined with data on pregnancy, lactation, and use of hormone replacement therapy as previously described [24].

Gene-specific promoter DNA methylation assessment

DNA extraction from the archived tumor tissue was performed as previously described [25]. For methylation analysis, a panel of 13 genes known to be involved in breast carcinogenesis was selected. Promoter methylation of ESR1, PGR and BRCA1 was determined by methylation-specific (MSP)-PCR as described previously [25,26]. The Methy Light assay was used for determining the methylation status of the remaining genes [27,28]. The percentage of methylation was calculated by the 2^-ΔΔCT method, where ΔΔCT = (C_T,Target - C_T,Actin)_sample - (C_T,Target - C_T,Actin)_{fully methylated DNA}[29] and multiplying by 100. The MSP-PCR assay for ESR1, PGR and BRCA1 promoter methylation generated dichotomous outcomes (i.e. methylated vs. unmethylated). Conversely, Methy Light assay yielded percentage of methylation for gene promoters that were subsequently dichotomized into methylated or unmethylated cases using a 4% cut-off as reported in previous literature [30]. The numbers of assayed samples and corresponding methylation frequencies for the selected genes are summarized in Xu et al. [19]. The main reason for missing methylation data was insufficient DNA, primarily due to small tumor size.

Hormone Receptor (HR) subtype assessment

We abstracted data recorded on the medical record to ascertain breast cancer subtype defined by HR status [20]. ER/PR status of the first primary breast cancer was available from the medical record for 65.6% of cases (N=990), of which 67.7% (N=670) were postmenopausal and included in these analyses.

Statistical Methods

All statistical analyses were performed using SAS statistical software version 9.1 (SAS Institute, Cary, NC).

We previously reported the relationship between gene-promoter methylation with demographic and clinical-pathological characteristics of the LIBCSP breast cancer cases by menopausal status [11,31]. The study reported here focuses on: (1) whether BMI and/or RPA are associated with gene methylation in postmenopausal breast tumors; and (2) whether the association between BMI and/or RPA and ER/PR subtype is differential with respect to gene methylation status. To address these aims, we employed a case-case approach, and thus we relied solely upon data collected among postmenopausal case participants of the LIBCSP (n=532) [32].

To assess whether BMI or RPA was associated with gene-specific promoter methylation levels measured in case tumor tissue, we used logistic regression [32] to estimate odds ratios (ORs),and corresponding 95% confidence intervals (CIs) with case groups characterized by tumor methylation status (methylated vs. unmethylated for each marker). With this approach the ORs estimate the likelihood of a case possessing a methylated gene-promoter given their body size/physical activity status.

To determine whether the association between BMI or RPA and ER/PR receptor status was differential with respect to gene-specific promoter methylation, we used logistic regression to estimate ORs (95% CIs) with case groups characterized by both gene methylation status (methylated vs. unmethylated) and ER/PR status (ER+PR+ vs. all others: ER-PR-, ER+PR-, ER-PR+). With this approach the ORs estimate the likelihood of an ER+PR+ case given both gene methylation and body size/physical activity status. If the sample size in any strata of BMI/RPA and gene promoter methylation was less than ≤ 5, the OR (95% CI) was not estimated. In addition to comparing ER+PR+ breast cancer cases to all others, we also considered the comparison of ER+PR+ cases (primarily Luminal A and B subtypes) to ER-PR- cases (exclusively HER2 and triple negative subtypes) to better understand of potential associations with intrinsic subtypes.

We formally assessed evidence for multiplicative interaction using a likelihood ratio test [33], comparing multivariable models with and without cross-product terms to represent the interaction between BMI or RPA and a gene-specific methylation marker (a priori α=0.05). A significant interaction indicates that the odds of a case possessing the ER+PR+ breast cancer subtype, given BMI (or RPA) level, are statistically different across strata of gene-specific methylation.

We identified potential confounders based on the known epidemiology of breast cancer and analysis of causal diagrams [34]. For all models, potential confounders included: race (white/black/other); family history of breast cancer (yes/no); and history of benign breast disease (yes/no). Confounders were added in the model if they their inclusion changed the exposure estimate >10% [35]. None of the covariates assessed resulted in a >10% change in estimate, therefore only 5-year age group remained in our final case-case models.

Results

Associations between postmenopausal BMI and gene promoter methylation for the 13 breast cancer-related genes are shown in Table 1. Women with BMI 25-29.9 kg/m²were more likely to have methylated HIN1 in breast tumors (OR=1.57, 95%CI: 1.03-2.39). Although we observed elevated likelihood of methylated HIN1in breast tumors among women with BMI ≥ 30 kg/m², the estimate was less pronounced and included the null (OR=1.44, 91% CI: 0.94-2.23). The remaining methylated gene promoters did not appear to be associated with postmenopausal BMI. We observed no differences in the likelihood of gene promoter methylation breast cancer in association with postmenopausal RPA for any of the 13 genes examined (Table 2).

We hypothesized that postmenopausal BMI or RPA may differentially associate with ER+PR+ breast cancer in strata of gene-promoter methylation. We found that obesity was associated with ER+PR+ breast cancer among women with unmethylated ESR1 (OR=2.63; 95% CI: 1.32-5.25) (Table 3); the corresponding OR among cases with methylated ESR1 was 1.24 (95% CI: 0.62-2.48) (multiplicative p for interaction=0.004). Similarly, we found that high RPA women with methylated GSTP1 were more likely to have ER+PR+ breast cancer (OR=2.33; 95% CI: 0.79-6.84) than high RPA women with unmethylated GSTP1 (OR=1.05; 95% CI: 0.53-2.10) (Table 4). We observed a multiplicative interaction (p=0.03) between GSTP1 promoter methylation, postmenopausal RPA and ER+PR+ breast cancer, but given the small proportion of women with methylated GSTP1 our estimates were imprecise. We were unable to estimate the ORs, due to the low prevalence tumor methylation, in several markers (e.g. CDH1, p16, PR and RASSF1A). With the remaining gene promoters that we considered, we identified no differential associations between BMI or RPA and ER+PR+ breast cancer.

The associations between postmenopausal BMI and breast cancer, defined by ESR1 methylation and estrogen-receptor status, were robust, and remained significant (p=0.019), when we compared ER+PR+breast cancer to ER-PR- breast cancer only (Supplemental Table 1). For postmenopausal RPA and GSTP1 methylation, however, our estimates were less robust, and were of borderline statistical significance (p=0.068) when comparing ER+PR+ breast cancer to ER-PR-breast cancer (Supplemental Table 2).

Discussion

In this population-based study, we found that women with postmenopausal BMI 25-29.9kg/m², and perhaps BMI =30kg/m², were more likely to have methylated HIN1 breast cancer. We also observed a two-fold increase in the likelihood of ER+PR+ breast cancer among postmenopausal obese women with unmethylated ESR1 tumors, and among postmenopausal highly active women with methylated GSTP1 tumors. Our findings are biologically plausible, as discussed below.

Inactivation of tumor suppressor genes by promoter hypermethylation is a common epigenetic alteration in breast carcinogenesis [36,37]. These alterations are known to occur more frequently in breast tumor tissue than adjacent nonmalignant tissue [36,37] and have been associated with the clinicopathologic parameters of breast cancer [10]. Gene-promoter hypermethylation may therefore be an important event in breast carcinogenesis.

Increased BMI and physical inactivity are risk factors for postmenopausal breast cancer [4,38], and their influence on endogenous estrogens are well-documented [39,40]. In vivo and in vitro data suggest estrogen may induce aberrant DNA methylation, altering several genes implicated in breast carcinogenesis [41,42]. Specifically, estrogens were reported to induce promoter hypermethylation of CDH1 and CDKN2A in non-malignant breast cells of humans [43]. In addition to increased levels of estrogen, central adiposity has been associated with chronic low-grade inflammation [44]. Several studies have shown greater frequency of promoter methylation in CDKN2A, CDH1, BRCA1, and MLH1 among patients with chronic inflammatory disease compared with patients without [14,15]. Moreover, clinical data indicate that weight loss (≥ 5% initial body weight) was associated with significantly lower promoter methylation of TNF-α in peripheral blood [45]. Physical activity has similarly been found to reduce levels of pro-inflammatory markers [46].

Hormonal and inflammatory mediators have the capacity to induce and maintain promoter methylation facilitating the growth and survival of tumors, but to our knowledge, few studies have examined associations between body size and methylation status of breast tumors [16-18]. Consistent with our findings, Tao and colleagues [16]observed no association between body size and methylation of CDH1, CDKN2A, and RAR-β2 among postmenopausal case women; associations by ER/PR status were not reported. Naushad and colleagues [17] examined the association between BMI and methylation of Ec-SOD, RASSF1, BRCA1, and BNIP3. BMI was significantly positively associated with Ec-SOD, RASSF1 and BRCA1 methylation but inversely associated with BNIP3. Most recently, Hair and colleagues [18] reported significant associations between BMI and methylation of 2 loci among all breast tumors and 21 loci specific to ER+ tumors, but did not examine menopause-specific associations. The association between body size and breast cancer risk is known to vary by menopausal subgroups [47]. It is therefore likely that obesity-associated methylation sites also differ by menopausal status. While we employed a biologically driven candidate gene approach, our study improves on the prior research by including a larger number of candidate genes, exploring associations by ER/PR status, and focusing on postmenopausal women. Further, it is the first study to consider the association between physical activity and gene methylation in postmenopausal breast tumors.

In our findings reported here, elevated postmenopausal BMI more strongly associated with ER+PR+ breast cancer among women with unmethylated ESR1. The ER protein is coded for by ESR1 and gene silencing of ESR1 by DNA methylation is often observed in breast tissues that do not express ER (e.g. ER-) [48]. Estrogens have long been hypothesized to underlie the positive association between obesity and postmenopausal breast cancer risk [39]. Our observation of stronger and more precise associations between postmenopausal obesity and ER+PR+ breast cancer among women where ESR1 is active (unmethylated) is biologically reasonable and suggests that methylation-mediated silencing of the ESR1 gene may alleviate the role of obesity-related estrogen in postmenopausal breast carcinogenesis.

We similarly found that the odds of being an ER+PR+ breast cancer case was enhanced among women engaging in high postmenopausal RPA in the presence of GSTP1 methylation. GSTP1 is involved in a wide range of detoxification reactions which protect cells from carcinogens [49]. The 5' region of GSTP1 is rich in CpG islands and its methylation has been associated with loss of GSTP1 expression [50] breast, carcinogenesis [51] and ER+PR+ case status [52]. The immediate systemic response to physical activity is an increase in reactive oxygen species production; it is therefore biologically plausible that reduced GSTP1 expression via DNA methylation may enhance risk of breast cancer, specifically ER+PR+ breast cancer.

Strengths of our epidemiologic study include: (1) our novel examination of the potential role of physical activity, as well as obesity, in the association between tumor methylation and breast cancer; (2) restricting eligibility to postmenopausal breast cancer, where the associations with obesity and physical activity are most pronounced; (3) our population-based design, which enhances generalizability and facilitates quantification of any study bias due to subject selection; (4) relatively large sample size, which facilitates examining subgroup associations as we did here; (5) detailed exposure assessment of our anthropometic measures, which reduces the likelihood of random measurement error; (6) our case-case approach, which substantially reduces the likelihood of recall bias given that both the "case" group and our "comparison" group had breast cancer (and it is highly unlikely that misreporting of anthropometric-related information is differential with respect to methylation or HR status[53]); and (7) we only considered associations for which we had a priori strong biologic rationale, mitigating concerns regarding multiple comparisons.

There are also several limitations to consider when examining methylation in tumors in an epidemiologic study. First, we were unable to obtain archived tumor tissue for all LIBCSP case participants, which may result in selection bias; however, we were able to identify and consider potential sources of this error. Second, we were underpowered to explore potential variation by intrinsic subtype (Luminal A, Luminal B, HER2 and triple negative) given our study population primarily consisted of postmenopausal white women with low proportion of HER2- tumors. Third, gene-promoter methylation analyses were constrained by sample size for several of the genes we considered, and thus future studies should consider enlarging study enrollment. Fourth, we had a limited panel of 13 biologically relevant genes for analyses. Although this is four times that of the one previous investigation focused on obesity, gene methylation and postmenopausal breast cancer [16], we were unable to explore all the mechanistic pathways that may be involved in this association. Finally, classification of methylation status is not universally defined and our cutoff of 4% may not be biologically relevant for all the genes assessed.

In summary, using data from a large population-based sample, we found that BMI may associate with HIN1 methylation status of postmenopausal breast tumor tissue. Notably, we also observed that both postmenopausal body size and physical activity may increase the likelihood of ER+PR+ breast cancer (which is the most common subtype diagnosed among American women [54]) in the absence and presence of ESR1 and GSTP1 methylation, respectively. While our results require confirmation in larger studies of postmenopausal women with greater number of genes, they suggest that DNA methylation may play an important role in understanding mechanisms underlying the associations between body size, physical activity and postmenopausal breast cancer. Given the plasticity of epigenetic marks in response to cancer-related exposures, additional research is needed to clarify these mechanisms and identify specific changes likely to be involved in the pathogenesis of breast cancer.

Acknowledgements

This work was supported in part by grants from the National Cancer Institute (R25CA057726, UO1CA/ES66572, R01CA109753, 3R01CA109753-04S1); the National Institutes of Environmental Health and Sciences (P30ES009089, P30ES10126); and the Department of Defense (BC031746, BC972772, and W81XWH-06-1-0298).

Ethical Statement

Institutional Review Board approval was obtained by all participating institutions.

References

1. Gammon MD, Sagiv SK, Eng SM, Shantakumar S, Gaudet MM, et al. (2004) Polycyclic aromatic hydrocarbon-DNA adducts and breast cancer: a pooled analysis. Arch Environ Health 59: 640-649.
   


2. Velie EM, Nechuta S, Osuch JR (2005) Lifetime reproductive and anthropometric risk factors for breast cancer in postmenopausal women. Breast Dis 24: 17-35.
   


3. Friedenreich CM (2001) Review of anthropometric factors and breast cancer risk. Eur J Cancer Prev 10: 15-32.
   


4. Friedenreich CM (2011) Physical activity and breast cancer: review of the epidemiologic evidence and biologic mechanisms. Recent Results Cancer Res 188: 125-139.
   


5. Pischon T, Nöthlings U, Boeing H (2008) Obesity and cancer. Proc Nutr Soc 67: 128-145.
   


6. Centers for Disease Control - Office of Surveillance, Epidemiology, and Laboratory Services (2011) Prevalence and Trend Data - Exercise 2010.
   


7. Ogden CL, Carroll MD, Kit BK, Flegal KM (2012) Prevalence of obesity in the United States, 2009-2010. NCHS Data Brief: 1-8.
   


8. Ehrlich M (2002) DNA methylation in cancer: too much, but also too little. Oncogene 21: 5400-5413.
   


9. Wu HC, Delgado-Cruzata L, Flom JD, Perrin M, Liao Y, et al. (2012) Repetitive element DNA methylation levels in white blood cell DNA from sisters discordant for breast cancer from the New York site of the Breast Cancer Family Registry. Carcinogenesis 33:1946-1952.
   


10. Virmani AK, Rathi A, Sathyanarayana UG, Padar A, Huang CX, et al. (2001) Aberrant methylation of the adenomatous polyposis coli (APC) gene promoter 1A in breast and lung carcinomas. Clin Cancer Res 7: 1998-2004.
    


11. Cho YH, Shen J, Gammon MD, Zhang YJ, Wang Q, et al. (2012) Prognostic significance of gene-specific promoter hypermethylation in breast cancer patients. Breast Cancer Res Treat 131: 197-205.
    


12. Cleary MP, Grossmann ME (2009) Minireview: Obesity and breast cancer: the estrogen connection. Endocrinology 150: 2537-2542.
    


13. Hsu PY, Deatherage DE, Rodriguez BA, Liyanarachchi S, Weng YI, et al. (2009) Xenoestrogen-induced epigenetic repression of microRNA-9-3 in breast epithelial cells. Cancer Res 69: 5936-5945.
    


14. Kang GH, Lee HJ, Hwang KS, Lee S, Kim JH, et al. (2003) Aberrant CpG island hypermethylation of chronic gastritis, in relation to aging, gender, intestinal metaplasia, and chronic inflammation. Am J Pathol 163: 1551-1556.
    


15. Liggett T, Melnikov A, Yi QL, Replogle C, Brand R, et al. (2010) Differential methylation of cell-free circulating DNA among patients with pancreatic cancer versus chronic pancreatitis. Cancer 116: 1674-1680.
    


16. Tao MH, Marian C, Nie J, Ambrosone C, Krishnan SS, et al. (2011) Body mass and DNA promoter methylation in breast tumors in the Western New York Exposures and Breast Cancer Study. Am J Clin Nutr 94: 831-838.
    


17. Naushad SM, Hussain T, Al-Attas OS, Prayaga A, Digumarti RR, et al. (2014) Molecular insights into the association of obesity with breast cancer risk: relevance to xenobiotic metabolism and CpG island methylation of tumor suppressor genes. Mol Cell Biochem 392: 273-280.
    


18. Hair B, Troester MA, Edmiston SN, Parrish EA, Robinson WR, et al. (2015) Body Mass Index is Associated with Gene Methylation in Estrogen Receptor-Positive Breast Tumors. Cancer Epidemiol Biomarkers Prev .
    


19. Xu X, Gammon MD, Jefferson E, Zhang Y, Cho YH, et al. (2011) The influence of one-carbon metabolism on gene promoter methylation in a population-based breast cancer study. Epigenetics 6: 1276-1283.
    


20. Gammon MD, Neugut AI, Santella RM, Teitelbaum SL, Britton JA, et al. (2002) The Long Island Breast Cancer Study Project: description of a multi-institutional collaboration to identify environmental risk factors for breast cancer. Breast Cancer Res Treat 74: 235-254.
    


21. Rossner P Jr, Gammon MD, Zhang YJ, Terry MB, Hibshoosh H, et al. (2009) Mutations in p53, p53 protein overexpression and breast cancer survival. J Cell Mol Med 13: 3847-3857.
    


22. Bernstein M, Sloutskis D, Kumanyika S, Sparti A, Schutz Y, et al. (1998) Data-based approach for developing a physical activity frequency questionnaire. Am J Epidemiol 147: 147-154.
    


23. McCullough LE, Eng SM, Bradshaw PT, Cleveland RJ, Teitelbaum SL, et al. (2012) Fat or fit: the joint effects of physical activity, weight gain, and body size on breast cancer risk. Cancer 118: 4860-4868.
    


24. Eng SM, Gammon MD, Terry MB, Kushi LH, Teitelbaum SL, et al. (2005) Body size changes in relation to postmenopausal breast cancer among women on Long Island, New York. Am J Epidemiol 162: 229-237.
    


25. Xu X, Gammon MD, Zhang Y, Bestor TH, Zeisel SH, et al. (2009) BRCA1 promoter methylation is associated with increased mortality among women with breast cancer. Breast Cancer Res Treat 115: 397-404.
    


26. Liu ZJ, Maekawa M, Horii T, Morita M (2003) The multiple promoter methylation profile of PR gene and ERalpha gene in tumor cell lines. Life Sci 73: 1963-1972.
    


27. Eads CA, Danenberg KD, Kawakami K, Saltz LB, Danenberg PV, et al. (1999) CpG island hypermethylation in human colorectal tumors is not associated with DNA methyltransferase overexpression. Cancer Res 59: 2302-2306.
    


28. Eads CA, Lord RV, Kurumboor SK, Wickramasinghe K, Skinner ML, et al. (2000) Fields of aberrant CpG island hypermethylation in Barrett's esophagus and associated adenocarcinoma. Cancer Res 60: 5021-5026.
    


29. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25: 402-408.
    


30. Ogino S, Kawasaki T, Brahmandam M, Cantor M, Kirkner GJ, et al. (2006) Precision and performance characteristics of bisulfite conversion and real-time PCR (MethyLight) for quantitative DNA methylation analysis. J Mol Diagn 8: 209-217.
    


31. Xu X, Gammon MD, Zhang Y, Cho YH, Wetmur JG, et al. (2010) Gene promoter methylation is associated with increased mortality among women with breast cancer. Breast Cancer Res Treat 121: 685-692.
    


32. Begg CB, Zhang ZF (1994) Statistical analysis of molecular epidemiology studies employing case-series. Cancer Epidemiol Biomarkers Prev 3: 173-175.
    


33. Kleinbaum DG, Klein M (2002) Springer, New York
    


34. Greenland S, Brumback B (2002) An overview of relations among causal modelling methods. Int J Epidemiol 31: 1030-1037.
    


35. Greenland S (1989) Modeling and variable selection in epidemiologic analysis. Am J Public Health 79: 340-349.
    


36. Jin Z, Tamura G, Tsuchiya T, Sakata K, Kashiwaba M, et al. (2001) Adenomatous polyposis coli (APC) gene promoter hypermethylation in primary breast cancers. Br J Cancer 85: 69-73.
    


37. Dammann R, Yang G, Pfeifer GP (2001) Hypermethylation of the cpG island of Ras association domain family 1A (RASSF1A), a putative tumor suppressor gene from the 3p21.3 locus, occurs in a large percentage of human breast cancers. Cancer Res 61: 3105-3109.
    


38. Friedenreich CM, Bryant HE, Courneya KS (2001) Case-control study of lifetime physical activity and breast cancer risk. Am J Epidemiol 154: 336-347.
    


39. Muti P (2004) The role of endogenous hormones in the etiology and prevention of breast cancer: the epidemiological evidence. Ann N Y Acad Sci 1028: 273-282.
    


40. Bertone-Johnson ER, Tworoger SS, Hankinson SE (2009) Recreational physical activity and steroid hormone levels in postmenopausal women. Am J Epidemiol 170: 1095-1104.
    


41. Starlard-Davenport A, Tryndyak VP, James SR, Karpf AR, Latendresse JR, et al. (2010) Mechanisms of epigenetic silencing of the Rassf1a gene during estrogen-induced breast carcinogenesis in ACI rats. Carcinogenesis 31: 376-381.
    


42. Fernandez SV, Snider KE, Wu YZ, Russo IH, Plass C, et al. (2010) DNA methylation changes in a human cell model of breast cancer progression. Mutat Res 688: 28-35.
    


43. Klein CB, Leszczynska J (2005) Estrogen-induced DNA methylation of E-cadherin and p16 in non-tumor breast cells. Proc Am Assoc Cancer Res 46: 2744.
    


44. Monteiro R, Azevedo I (2010) Chronic inflammation in obesity and the metabolic syndrome. Mediators Inflamm 2010.
    


45. Campión J, Milagro FI, Goyenechea E, Martínez JA (2009) TNF-alpha promoter methylation as a predictive biomarker for weight-loss response. Obesity (Silver Spring) 17: 1293-1297.
    


46. Mattusch F, Dufaux B, Heine O, Mertens I, Rost R (2000) Reduction of the plasma concentration of C-reactive protein following nine months of endurance training. Int J Sports Med 21: 21-24.
    


47. Carmichael AR, Bates T (2004) Obesity and breast cancer: a review of the literature. Breast 13: 85-92.
    


48. Lapidus RG, Nass SJ, Butash KA, Parl FF, Weitzman SA, et al. (1998) Mapping of ER gene CpG island methylation-specific polymerase chain reaction. Cancer Res 58: 2515-2519.
    


49. Lu S, Wang Z, Cui D, Liu H, Hao X (2011) Glutathione S-transferase P1 Ile105Val polymorphism and breast cancer risk: a meta-analysis involving 34,658 subjects. Breast Cancer Res Treat 125: 253-259.
    


50. Chan QK, Khoo US, Chan KY, Ngan HY, Li SS, et al. (2005) Promoter methylation and differential expression of pi-class glutathione S-transferase in endometrial carcinoma. J Mol Diagn 7: 8-16.
    


51. Lee JS (2007) GSTP1 promoter hypermethylation is an early event in breast carcinogenesis. Virchows Arch 450: 637-642.
    


52. Saxena A, Dhillon VS, Shahid M, Khalil HS, Rani M, et al. (2012) GSTP1 methylation and polymorphism increase the risk of breast cancer and the effects of diet and lifestyle in breast cancer patients. Exp Ther Med 4: 1097-1103.
    


53. Terry MB, Gammon MD, Zhang FF, Eng SM, Sagiv SK, et al. (2004) Polymorphism in the DNA repair gene XPD, polycyclic aromatic hydrocarbon-DNA adducts, cigarette smoking, and breast cancer risk. Cancer Epidemiol Biomarkers Prev 13: 2053-2058.
    


54. Clarke CA, Keegan TH, Yang J, Press DJ, Kurian AW, et al. (2012) Age-specific incidence of breast cancer subtypes: understanding the black-white crossover. J Natl Cancer Inst 104: 1094-1101.