Breast Cancer Research and Treatment

, Volume 133, Issue 1, pp 127–135 | Cite as

Physical activity and breast cancer survival: an epigenetic link through reduced methylation of a tumor suppressor gene L3MBTL1

  • Hongmei Zeng
  • Melinda L. Irwin
  • Lingeng Lu
  • Harvey Risch
  • Susan Mayne
  • Lina Mu
  • Qian Deng
  • Luca Scarampi
  • Marco Mitidieri
  • Dionyssios Katsaros
  • Herbert Yu
Preclinical study


The study was conducted to determine the effect of physical activity on DNA methylation and to predict the consequence of this effect concerning gene expression and breast cancer survival. Blood samples, collected from 12 breast cancer patients who participated in a randomized clinical trial of exercise, were examined for exercise-related changes in DNA methylation using a methylation microarray. Tumor samples of 348 breast cancer patients were analyzed with qRT-PCR and qMSP to determine gene expression and methylation identified in the microarray analysis. Cox regression models were developed to predict survival outcomes in association with gene expression and methylation. After 6 months of moderate-intensity aerobic exercise, changes in DNA methylation (P < 5 × 10−5) in peripheral blood leukocytes were detected in 43 genes from a panel of 14 495. Based on the list, we analyzed gene expression in association with overall survival in breast tumors and found three genes whose methylation was reduced after exercise were favorably in association with overall survival, i.e., higher expression associated with better survival. Of the three genes, L3MBTL1 was a putative tumor suppressor gene with known function to repress chromatin for transcription, which is activated mainly in germline stem cells. Further analyses of tumor features among patients indicated that high expression of L3MBTL1 was associated with low grade and hormone receptor–positive tumors, as well as low risk of disease recurrence and breast cancer death. In conclusion, the study suggests that increasing physical activity after a breast cancer diagnosis may affect epigenetic regulation of tumor suppressor genes, which have favorable impacts on survival outcomes of breast cancer patients.


Physical activity Survival DNA methylation L3MBTL1 Tumor suppressor gene 


Evidence suggests that physical activity after the diagnosis of breast cancer improves survival outcome. Breast cancer patients who report participating in 2–3 h/week of moderate- to vigorous-intensity physical activity have approximately 50% lower risk of breast cancer death compared to those who report very little time, if any, spent in physical activity [1, 2, 3]. This reduction in risk of breast cancer death is independent of risk factors for disease recurrence, including age at diagnosis, disease stage, menopausal status, body mass index, a family history of breast cancer, or treatment.

The beneficial association between physical activity and breast cancer survival is attributed in part to its influence on sex hormones. A randomized clinical trial showed that moderate-intensity exercise lowered circulating levels of estrogens and androgens among overweight postmenopausal women [4, 5]. Physical activity may also improve breast cancer survival via changes in other hormones [6, 7]. Several clinical trials have demonstrated that physical activity can reduce circulating levels of insulin and insulin-like growth factors [8, 9, 10, 11, 12]. Despite increased understanding of physical activity’s contribution to breast cancer survival, the molecular mechanism underlying the link between exercise and tumor progression remains largely unknown.

Epigenetic regulation plays an important role in cancer. As part of the regulation, DNA methylation is involved in tumor progression. A small study of breast specimens found physically active women to have low methylation in the APC gene [13]. As a tumor suppressor gene, an active APC is considered to be beneficial. A similar association has also been observed in gastric cancer, where methylation in the CACNA2D3 gene, a suspected tumor suppressor, was inversely correlated with physical activity [14]. Aside from these observational studies, there has been little experimental evidence suggesting that physical activity may change DNA methylation, further affecting breast cancer survival. Using microarray methods to interrogate blood and tumor specimens of breast cancer patients, we tested the hypothesis that physical activity alters DNA methylation, and in turn, the changed epigenetic regulation may influence the survival outcomes of breast cancer patients.

Materials and methods

Study patients

The investigation included two breast cancer studies, a randomized clinical trial, designed to evaluate the effect of exercise on circulating growth factors and sex hormones, and a clinical follow-up study, conducted to investigate molecular markers for prognosis. Both studies were approved by the institutional review boards at each study side. Detailed information on these studies has been published elsewhere [15, 16]. Briefly, the clinical trial was conducted at Yale University between March 2004 and January 2006. Participants in the trial were physically inactive postmenopausal women diagnosed with stage 0–IIIA breast cancer who had completed adjuvant treatment at least 6 months prior to enrollment. The trial enrolled 75 patients, with 37 randomly assigned to an exercise group and 38 to a usual care group. Women in the exercise group participated in a 6-month exercise intervention consisting of 150 min/week of supervised moderate-intensity aerobic exercise (primarily brisk walking on a graded treadmill). Women in the usual care group were instructed to maintain their regular activities. At baseline and at six months of the trial, participants provided blood samples and detailed information on physical activity. Aerobic activity was determined using an established method [17]. Twelve women, six in each group, were randomly selected for the current study.

The second study was conducted at the University of Turin between January 1998 and July 1999 [16]. Fresh tumor samples were collected from 348 patients who underwent surgery for primary breast cancer. The specimens were examined by pathologists to confirm 80–90% tumor cell contents, snap frozen in liquid nitrogen immediately after resection, and then stored at −80°C until analysis. Clinical and pathological data were obtained from medical records and pathology reports. Of the 348 patients, 302 were followed through February 2007. The median follow-up time was 86.3 months (range 8.2–107.8). During follow-up, 81 developed recurrent disease and 60 succumbed to the disease.

Microarray analysis of DNA methylation

Twenty-four blood samples from 12 trial patients were processed for methylation analysis. Genomic DNA was extracted from buffy coat and treated with sodium bisulfite using a commercial kit (Zymo Research, Orange, CA). The modified DNA was analyzed for methylation using the Infinium HumanMethylation27 BeadChip, which interrogates 27 578 CpG sites in 14 495 genes. For each CpG site, a beta (β) value was calculated using the GenomeStudio (Illumina, CA), which estimates the methylation level based on the signal intensity between methylated and unmethylated alleles. For each subject, a delta beta (∆β = β 6-month − β baseline) was computed to determine changes in methylation before and after the trial.

Microarray analysis of RNA expression

Fresh frozen tumor samples were processed for RNA extraction, followed by DNase treatment. Reverse transcription (RT) was performed using the Cloned AMV First-Strand cDNA Synthesis kit (Invitrogen, CA). Based on RNA quantity, 204 samples were selected for microarray analysis of expression using the Illumina Expression BeadChip, HumanRef-8 v3. The expression data were processed for background correction and normalization using the BeadStudio software (Illumina).

qMSP analysis of L3MBTL1 methylation

DNA was also extracted from the tumor samples and treated with sodium bisulfate for methylation analysis. L3MBTL1 methylation was analyzed using a SYBR Green-based quantitative methylation-specific PCR (qMSP). Two sets of primers were designed, one for unmethylated and one for methylated L3MBTL1. The primer sequences were 5′-GGTTATGGTATTGATTTTGAGATGG (forward) and 5′-CACCATATTCACAACTTATACTCAC (reverse) for unmethylated templates, and 5′-TATGGTATCGATT TCGAGATGG (forward) and 5′-GCCATATTCAACGACTTATACTCG (reverse) for methylated templates. In the PCR (15 μL), 0.5 μL of DNA template was mixed with 7.5 μL of 2 × Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA) and a pair of primers in a final concentration of 50 nmol/L. The PCR conditions included denaturing at 95°C for 10 min and 40 cycles of 15 s denaturing at 95°C and 60 s annealing/extension at 60°C. After PCR, a melting curve was generated to confirm the PCR product. The samples were tested in duplicate, and the analysis was repeated for those with coefficient of variation (CV) > 5%. The percentage of L3MBTL1 methylation was calculated as reported elsewhere [18]. qMSP was also used to analyze L3MBTL1 methylation in the blood samples for confirmation of the microarray results as well as assessment of the rest blood samples.

qRT-PCR analysis of L3MBTL1 expression

A SYBR Green-based quantitative reverse transcriptase PCR (qRT-PCR) was developed to analyze L3MBTL1 expression in tumor samples using GAPDH as reference. The L3MBTL1 primers were 5′-AGAGTGACCCATGTATCTGG (forward) and 5′-AAGCAAATGAGTGGGTAGAG (reverse). In the PCR (15 μL), 0.5 μL of cDNA template was mixed with 7.5 μL of 2 × Power SYBR Green PCR Master Mix (Applied Biosystems) and a pair of primers (either L3MBTL1 or GAPDH) in a final concentration of 100 nmol/L. The PCR conditions and assay procedures were same as for qMSP. An expression index (EI) was calculated for L3MBTL1, which was based on 1,000 × 2(−∆Ct), where ∆Ct = Ct L3BMTL1 − Ct GAPDH.

Statistical analysis

Changes in DNA methylation at each CpG site before and after exercise intervention were compared between the two trial groups using the two-sample t test. Kaplan–Meier survival curves, both death-specific overall survival (OS) and disease-free survival (DFS), were compared using the log-rank test. Spearman correlation coefficients were calculated to assess the correlations between L3MBTL1 methylation and expression, as well as between microarray and qRT-PCR results. Associations of L3MBTL1 methylation and expression with survival outcomes were analyzed using the Cox proportional hazard regression with adjustment for covariates. Chi-square test was used to compare L3MBTL1 expression among patients with different disease features. A P value < 0.05 (two-sided) was considered statistically significant for most of the analyses, except the microarray data on methylation, where a P value < 5 × 10−5 (two-sided) was used to conservatively select genes for further analysis.


Exercise-induced DNA methylation changes

Among patients enrolled in the trial, there were no significant baseline differences between the exercise and usual care groups with regard to demographics, clinical characteristics, body composition, physical activity, and pedometer steps per day. Baseline physical activity levels for both groups were low (mean duration physical activity = 21.8 ± 38.0 min/week). At 6 months, however, the exercise group had a significant increase in moderate- to vigorous-intensity recreational activity compared to the usual care group (129 min/week vs. 45 min/week, P < 0.001), as well as a significant increase in daily pedometer steps per day (1 621 steps vs. 38 steps, P < 0.001; data not shown).

Comparing the methylation profiles before and after exercise intervention, we found 43 genes having significant change in methylation, P < 5 × 10−5 (Table 1). The most significant genes were EPS15 (P = 1.27 × 10−7, 3% increase in methylation after exercise), RP11-450P7.3 (P = 4.65 × 10−7, 4% decrease in methylation), and KIAA0980 (P = 4.676 × 10−6, 2% decrease in methylation). The largest increase in methylation was CXCL10 (P = 2.876 × 10−5, 5% increase), and the largest decrease was ABCB1 (P = 1.64 × 10−5, 8% decrease).
Table 1

Significant changes in methylation after physical exercise


Exercise group

Usual care group

Difference between groups

P value


Average Δ β

Average Δ β

























































































































































































































Survival association with gene expression

Using the list of 43 genes in Table 1, we analyzed patient overall survival in association with gene expression in 204 tumor samples with available microarray expression data. Of the 43 genes, six were significantly associated with survival (Table 2). Among the six, three genes, IFT172, EPS15, and PPP2R3A, were positively associated with overall survival, i.e., higher expression with better survival. These associations appeared to be inconsistent with the direction of correlations between exercise and methylation, where exercise increased methylation, and therefore were not considered for further analysis of relationship with breast cancer. The other three genes were GLUD1, L3MBTL1, and MSX1, whose associations with survival were in agreement with the direction of exercise-induced methylation change, i.e., exercise reducing methylation and higher expression associated with better survival. Of these 3 genes, GLUD1 encodes glutamate dehydrogenase 1, which is a mitochondrial matrix enzyme regulating energy metabolism, and no evidence suggests the involvement of this gene in cancer [19]. L3MBTL1 is a known tumor suppressor gene, and MSX1 has a role in mammary gland development [20, 21]. L3MBTL1 methylation decreased 1.48% in the exercise group, but increased 2.15% in the control group, resulting in an overall difference in methylation after exercise by 3.63% (P = 2.9 × 10−5). MSX1 methylation reduced 2.02% in the exercisers and elevated 2.75% in controls, a net difference of 4.76% in methylation (P = 3.5 × 10−5).
Table 2

Association between overall survival (OS) and gene expression


P value

mRNA expression and breast cancer overall survival

Changes in methylation after exercise




↑expression↑ survival time

↑ methylation

Inconsistency between methylation and expression



↑expression↑ survival time

↑ methylation

Inconsistency between methylation and expression



↑expression↑ survival time

↓ methylation

No report supported as a candidate gene of cancer



↑expression↑ survival time

↑ methylation

Inconsistency between methylation and expression



expressionsurvival time


A tumor suppressor gene candidate



↑expression↑ survival time

↓ methylation

Very low methylation rate by qMSP

Correlation between L3MBTL1 methylation and expression

Two qMSPs were developed to analyze promoter methylation of L3MBTL1 and MSX1 in tumor samples and their relation to gene expression. Our initial analysis of 30 tumor samples showed that methylation of MSX1 was low in breast tumors, averaging 1.25%. Given the low methylation, no further analysis was performed to examine the relationship of MSX1 methylation with expression. In contrast to MSX1, L3MBTL1 methylation was high in the tumors. The average methylation was 65.6%, ranging from 10.2 to 98.4%. Methylation in L3MBTL1 was inversely correlated with gene expression (r = –0.19, P = 0.006). High level of L3MBTL1 methylation was also related to slightly elevated risk of breast cancer death, although the association was not statistically significant (Table 3).
Table 3

Association of breast cancer survival with L3MBTL1 methylation and expression


HR for DFS (95% CI)a

P valueb

HR for OS (95% CI)a

P valueb


 Low (n = 97)





 Medium (n = 100)

1.26 (0.73–2.18)


1.30 (0.66–2.56)


 High (n = 93)

0.97 (0.55–1.71)


1.45 (0.75–2.77)


 P for trend





mRNA expression

 Low (n = 96)





 Medium (n = 94)

1.04 (0.61–1.75)


1.01 (0.55–1.84)


 High (n = 100)

0.60 (0.33–1.10)


0.37 (0.17–0.80)


 P for trend





aHR hazard ratio, CI confidence interval, DFS disease-free survival, OS overall survival

bAdjusted for age, stage, grade, histology, ER, PR, and adjuvant treatment

Association of L3MBTL1 expression and breast cancer survival

Levels of L3MBTL1 expression measured by expression microarray and qRT-PCR were strongly correlated (r = 0.736; P = 0.015). Given the correlation, we combined and grouped the data sets into high, medium, and low categories based on tertile distributions. Using the categorized data, we confirmed our initial finding of an association between overall survival and L3MBTL1 expression in univariate Cox regression analysis (data not shown) and further demonstrated that the relationship remained after adjustment for patient age at surgery, disease stage, tumor grade, histological type, ER/PR status, and adjuvant treatment. Patients with high L3MBTL1 expression had a greater than 60% reduction in risk of breast cancer death compared to those with low expression, HR = 0.37, 95% CI: 0.17–0.80, P = 0.012 (Table 3). A dose–response relation was also suggested between L3MBTL1 expression and risk of death (P = 0.014). Reduction in risk of tumor recurrence was suggested for patients with high L3MBTL1 (HR = 0.60, 95% CI: 0.33–1.10), though not statistically significant (P = 0.097). Figures 1 and 2 show the overall and disease-free survival curves for patients with high levels of L3MBTL1 expression in comparison to those with low and medium expression.
Fig. 1

Kaplan-Meier overall survival in breast cancer by L3MBTL1 expression

Fig. 2

Kaplan-Meier disease-free survival in breast cancer by L3MBTL1 expression

Association of L3MBTL1 expression with disease features

Table 4 shows L3MBTL1 expression in patients with different clinical and pathological features. The expression did not differ substantially by tumor size, disease stage, or nodal status. However, patients with tumors of low grade, lobular histology, or positive hormone-receptor status had higher expression of L3MBTL1 compared to those with high grade, non-lobular histology, or negative hormone receptors. These findings were consistent with the results of our survival analysis, i.e., high L3MBTL1 expression associated with less aggressive tumors and better survival outcomes.
Table 4

Association of L3MBTL1 expression with features of breast cancer




L3MBTL1 expression

P value

Low (%)

Medium (%)

High (%)

Tumor size (n = 344)


 < 2 cm



61 (30.5)

64 (32.0)

75 (37.5)


 2–5 cm



42 (35.3)

45 (37.8)

32 (26.9)


 ≥ 5 cm



10 (40.0)

7 (28.0)

8 (32.0)


Tumor grade (n = 341)

 Well differentiated



13 (23.2)

18 (32.1)

25 (44.6)


 Moderately differentiated



41 (29.1)

42 (29.8)

58 (41.1)


 Poorly differentiated



60 (41.7)

53 (36.8)

31 (21.5)


Histological type (n = 345)




77 (35.3)

75 (34.4)

66 (30.3)





9 (16.1)

23 (41.1)

24 (42.9)





17 (48.6)

6 (17.1)

12 (34.3)





11 (30.6)

12 (33.3)

13 (36.1)


Disease stage (n = 339)


 Stage I



38 (30.9)

40 (32.5)

45 (36.6)


 Stage II



62 (34.3)

64 (35.4)

55 (30.4)


 Stage III/IV



11 (31.4)

11 (31.4)

13 (37.1)


Nodal status (n = 340)





63 (34.8)

58 (32.0)

60 (33.1)





49 (30.8)

57 (35.8)

53 (33.3)


ER status (n = 341)





51 (42.5)

44 (36.7)

25 (20.8)





61 (27.6)

70 (31.7)

90 (40.7)


PR status (n = 340)





61 (37.7)

62 (38.3)

39 (24.1)





51 (28.7)

51 (28.7)

76 (42.7)



Using methylation microarray to interrogate the blood samples collected before and after a six-month exercise intervention, we found that moderate-intensity physical activity induces significant DNA methylation changes in 43 genes (P < 5 × 10−5). Among these genes, methylation was elevated in 24 and declined in 19. Of the genes with decreased methylation, analysis of tumor specimens showed that L3MBTL1 methylation was inversely correlated with gene expression and high expression was associated with low risk of breast cancer recurrence and death. Since L3MBTL1 is a tumor suppressor gene, these findings suggest that exercise may lower DNA methylation in certain tumor suppressor genes. The decrease in methylation results in increased expression of tumor suppressor genes, which may inhibit tumor progression and improve survival. Recently, Nakajima et al. [22] found that exercise increased methylation of a pro-inflammatory gene ASC (apoptosis-associated speck-like protein containing a caspase recruitment domain), suggesting possible suppression of inflammation by physical activity, which may also be in favor of cancer survival [23, 24].

L3MBTL1 (also known as L3MBTL) belongs to the polycomb group (PcG) proteins. Acting as a transcriptional repressor, L3MBTL1 binds to several methylated lysines in H1b, H3, and H4, blocking DNA sequences from access to transcription [25, 26]. L3MBTL1 is encoded by the L3MBTL1 gene, which is located on chromosome 20q12 in a region that is frequently deleted in patients with myeloid hematopoietic malignancies. Homozygous deletion of the gene causes brain tumors in Drosophila. Other members of the L3MBTL family are also found to be related to cancer. Single- or double-strand deletion of L3MBTL2 and L3MBTL3 has been reported to occur in medulloblastoma [27]. L3MBTL4 was recently found to be frequently mutated or deleted with loss of function in breast cancer [28]. Currently, little is known about the role of L3MBTL1 in breast cancer and its relation to lifestyle factors such as physical activity. Given the fact that physical activity has many physiological effects and that L3MBTL1 regulates chromatin activity that involves many genes, a functional connection between exercise and L3MBTL1 activity seems to be plausible. Further understanding of the genes repressed by this protein may provide more insight into the link between physical activity and epigenetic regulation. A recent study indicates that L3MBTL1 suppresses many genes and microRNAs, which express in early stages of germ cells and germline stem cells. These genes are often reactivated during tumor growth, when animals lose the function of L3MBTL1 [29].

An animal experiment has found that in comparison to matched sedentary controls, 6-week-old male Wistar rats that completed stress-free voluntary treadmill exercise for 12 weeks experienced substantial changes in gene expression in colonic mucosa. One of the genes showing significant changes in expression was the betaine-homocysteine methyltransferase 2 (BHMT2) gene that encodes a methylation enzyme involved in DNA methylation [30]. Few studies have investigated the relationship between physical activity and DNA methylation in cancer patients. Two small observational studies found possible associations between physical activity and DNA methylation of the tumor suppressor genes APC and CACNA2D3 [13, 14]. Our study is the first to demonstrate the effect of exercise on DNA methylation in a randomized clinical trial. Aside from the randomized experimental design of our study, our results show the clinical relevance of our finding by linking the methylation to gene expression in tumor samples and demonstrating an association between the expression and patient survival.

In our study, exercise-induced DNA methylation changes appeared to be small. The largest increase in methylation was 5.5% in the CXCL10 gene, which encodes CXC motif chemokine 10, also known as interferon gamma–induced protein 10 kDa (IP-10) or small-inducible cytokine B10 [31, 32]. The greatest decrease in methylation was 7.8% in the ABCB1 gene that encodes a membrane-associated protein named ATP-binding cassette sub-family B member 1 (also known as P-glycoprotein) acting as ATP-dependent drug efflux pump implicated in multidrug resistance [33]. The most significant change in methylation, i.e., the smallest P value (1.27 × 10−7), was the EPS15 gene, which had a 3% increase in methylation. The EPS15 gene encodes epidermal growth factor receptor substrate 15, which is involved in the EGFR signal pathway [34]. One may wonder whether these small changes, though statistically significant, have adequate biological significance. To address this issue, we first tried to confirm the validity of our microarray results by retesting the samples with qMSP. Analysis of L3MBTL1 and MSX1 methylation in 42 blood samples showed a strong correlation between qMSP and microarray (r = 0.787, P < 0.001). Based on this, we believe that our findings are reliable. Second, similar methylation changes have also been seen in blood samples analyzed by pyrosequencing in a 6-month non-randomized exercise trial. Nakajima et al. [22] found 6.29% methylation in ASC among 153 exercisers compared to 5.33% among the non-exercisers (n = 230). Third, all three genes mentioned above, CXCL10, ABCB1, and EPS15, are known to have functional or biological connections to cancer. Forth, we analyzed all the blood samples before and after exercise with qMSP and found that exercise was strongly associated with declined methylation in L3MBTL1 (OR = 2.67, P = 0.079), when adjusting for other covariates and confounding factors. Finally, since all of these changes are assessed in the blood and the exact amount of alteration in local tissue is unknown, we cannot rule out the possibility that the changes in methylation may be different in tissues. Furthermore, changes in DNA methylation may be greater with higher intensity exercise conducted for a longer duration.

Although our initial observation of exercise-induced methylation changes in L3MBTL1 was based on blood samples, subsequent analyses showed that the changes were related to gene expression in tumor samples, and the expression was associated with patient survival, suggesting that epigenetic changes in the blood may be clinically relevant. Since it is impossible to investigate the effect of physical activity on epigenetic regulation directly in the target tissue (breast) using a randomized experimental design, blood samples are used as a surrogate. Our study provides evidence in support of this approach as a possible alternative. In summary, we used data and specimens from experimental and observational studies to demonstrate that exercise may improve breast cancer survival through its influence on epigenetic regulation of tumor suppressor genes.


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Copyright information

© Springer Science+Business Media, LLC. 2011

Authors and Affiliations

  • Hongmei Zeng
    • 1
    • 2
  • Melinda L. Irwin
    • 1
  • Lingeng Lu
    • 1
  • Harvey Risch
    • 1
  • Susan Mayne
    • 1
  • Lina Mu
    • 3
  • Qian Deng
    • 1
  • Luca Scarampi
    • 4
  • Marco Mitidieri
    • 4
  • Dionyssios Katsaros
    • 4
  • Herbert Yu
    • 1
  1. 1.Department of Epidemiology and Public HealthYale Cancer Center, Yale University School of Public HealthNew HavenUSA
  2. 2.Department of Cancer Epidemiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education)Peking University School of Oncology, Beijing Cancer Hospital and InstituteBeijingChina
  3. 3.Department of Social and Preventive Medicine, School of Public Health and Health ProfessionsUniversity at Buffalo, The State University of New YorkBuffaloUSA
  4. 4.Department of Obstetrics and Gynecology, Gynecologic Oncology and Breast Cancer UnitUniversity of TurinTurinItaly

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