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Departments of 1 Urology, 2 Preventive Medicine, 3 Biochemistry and Molecular Biology, and 4 Surgery, University of Southern California, Keck School of Medicine, Norris Comprehensive Cancer Center, Los Angeles, California, and 5 Department of Obstetrics and Gynecology, University Hospital, University of Innsbruck, Innsbruck, Austria
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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, proved to be the best predictor of progesterone receptor status, whereas methylation of the PGR gene, encoding progesterone receptor, was the best predictor of estrogen receptor status. ESR1 methylation outperformed HR status as a predictor of clinical response in patients treated with the antiestrogen tamoxifen, whereas promoter methylation of the CYP1B1 gene, encoding a tamoxifen- and estradiol-metabolizing cytochrome P450, predicted response differentially in tamoxifen-treated and nontamoxifen-treated patients. High levels of promoter methylation of the ARHI gene, encoding a RAS-related small G-protein, were strongly predictive of good survival in patients who had not received tamoxifen therapy. Our results reveal an as yet unrecognized degree of interaction between DNA methylation and HR biology in breast cancer cells and suggest potentially clinically useful novel DNA methylation predictors of response to hormonal and non-hormonal breast cancer therapy. |
INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Hormone receptor (HR) status, defined as ER and/or PR positivity, has been shown to predict response to tamoxifen treatment (3 , 4) . Interestingly, although tamoxifen is thought to act through the ER, PR status is an independent factor predictive of adjuvant endocrine treatment benefit (4) . Tamoxifen, which is a selective ER modulator, has been shown to dramatically reduce the risk of breast cancer (17) and of breast cancer recurrence (18) . Since its introduction more than 25 years ago, tamoxifen has been the mainstay of the endocrine adjuvant treatment of breast cancer, has become the most widely used anticancer drug, and may be considered one of the first targeted therapies (18) . In this study we found that ESR1 methylation predicts survival only in tamoxifen-treated patients and that ARHI methylation predicts survival only in non-tamoxifen-treated patients, whereas CYP1B1 methylation predicts survival differentially in tamoxifen-treated and nontreated patients. We propose that these differences in DNA methylation profiles reflect alternative pathways of tumorigenesis associated with differences in HR status, possibly due to different originating cell types (9) and/or disease etiology (2) .
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MATERIALS AND METHODS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Histopathological Analyses.
All breast cancer specimens were reviewed by a single pathologist (E. M-H.). HR positivity was defined as presence of ER and/or PR in >10% of tumor cells (immunohistochemistry was done for the 106 breast cancers) or 
15 fmol/mg protein (biochemical assays were performed for 42 breast cancer specimens, which were obtained before immunohistochemistry had been established in our laboratory).
DNA Methylation Analyses.
Genomic DNA was isolated using a QIAmp tissue kit (Qiagen, Hilden, Germany). Sodium bisulfite conversion of genomic DNA was performed as described previously (16)
. DNA methylation analysis was performed by MethyLight (16
, 19)
. Three sets of primers and probes designed specifically for bisulfite-converted DNA were used [a methylated set for the gene of interest and two reference sets, ß-actin (ACTB) and collagen 2A1 (COL2A1)] to normalize for input DNA. The specificity of the reactions for methylated DNA was confirmed separately using SssI (New England Biolabs)-treated human peripheral blood lymphocyte DNA (Promega), which results in near complete methylation of this reference DNA (16)
. The percentage of fully methylated molecules at a specific locus was calculated by dividing the GENE:ACTB ratio of a sample by the GENE:ACTB ratio of SssI-treated sperm DNA and multiplying by 100 and calculated separately by dividing the GENE:COL2A1 ratio of a sample by the GENE:COL2A1 ratio of SssI-treated sperm DNA and multiplying by 100. The mean of these two resulting values was used in subsequent statistical analyses. We use the abbreviation PMR (percentage of fully methylated reference) to indicate this measurement (20)
. The initial 64 methylation markers and the final panel of 35 markers were selected based on published reports demonstrating a role for DNA methylation in breast cancer or due to the fact that they are involved in HR action. Primer and probe sequences are shown in Data Supplement 2.
Expression Analyses.
RNA isolation and expression analyses were performed as described previously (19)
. Before cDNA synthesis, RNA samples were treated with DNase to ensure removal of contaminating genomic DNA. TATA box-binding protein served as the reference gene. Primer and probe sequences are shown in Data Supplement 3.
Statistics.
To cluster samples and DNA methylation markers, we used agglomerative hierarchical cluster analysis in SPLUS 2000 (Insightful Corp.) Because many of the CpG regions had undetectable methylation, we categorized the PMR values into quartiles (coded 1–4). If >25% but <50% of the samples had undetectable methylation, this resulted in scores of 1 (undetectable methylation), 2 (detectable methylation and 
50th percentile), 3 (51st–75th percentile), and 4 (>75th percentile). If >50% but <75% of the samples had undetectable methylation, the scores were 1, 3, and 4. If >75% of the samples had undetectable methylation, the score was either 1 for undetectable methylation or 4 for detectable methylation. Manhattan distance, the sum of the absolute deviations across methylation markers, was used to measure dissimilarity. The dissimilarity between clusters was measured by the group average method (21)
. We tested the association between (categorized) PMR values and HR status using logistic regression. Separate analyses were conducted for each gene. Multiple linear regression was used to study the relationship between DNA methylation and ESR1 gene expression. A total of 75 samples had ESR1 gene expression measured, but one was omitted from the analyses as an outlier (ESR1 upstream A expression > 3 SDs above the mean). Comparisons between groups of samples and between different genes were simplified by expressing all expression data relative to the mean for the entire set of 74 samples. We used Cox regression to study the association between PMR values and overall (and disease-free) survival, treating PMR quartiles as ordered categorical variables. Using an interaction model, we tested whether the association of PMR values and survival varied by treatment with tamoxifen therapy (received tamoxifen treatment versus did not receive tamoxifen treatment; tamoxifen treatment was defined as 20 mg of tamoxifen daily for 5 years or until recurrence of disease). The analyses were adjusted for nodal status (0, 1–3, and >3) and tumor stage (I, II, and III/IV). Nodal status was coded using indicator variables for two of the three levels. All analyses were age-adjusted.
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Two-dimensional unsupervised hierarchical clustering analysis of cases versus methylation markers revealed that the tumors segregated naturally into groups of cases with distinct methylation profiles (Fig. 1)
. These two major clusters differed significantly in their HR status [P = 0.0011 for cluster 1 (indicated in green; n = 87) versus cluster 2 (indicated in red; n = 56) for ER+ versus ER–; P = 0.0013 for PR+ versus PR–; P = 0.0011 for HR+ versus HR–] and in age (P = 0.0080 for cluster 1 versus cluster 2; mean age, 57 versus 63 years, respectively). We adjusted for age in all subsequent analyses. We did not detect significant clustering of cases by HER2 status (22)
, menopausal status, relapse, death, grade, nodes, stage, or tumor diameter (data not shown).
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. Fifteen of the 35 genes yielded Ps < 0.05 using methylation values (PMR measurements as described in "Materials and Methods") as predictors of HR (ER+ and/or PR+) status (Table 1
, HR Status Predictors). Since we tested 35 different markers simultaneously, we adjusted for the multiple comparisons by controlling the false discovery rate, which is the expected proportion of false positive tests among all positive tests (see "Materials and Methods"). After this adjustment, three markers (SOCS1, RASSF1A, and BCL2) were significantly associated with HR status (Table 1
, HR Status Predictors). Interestingly, SOCS1 deficiency results in accelerated mammary gland development in mice (23)
and is known to be methylated in human tumors (24)
. Promoter methylation of RASSF1A has previously been reported to be methylated in a large percentage of human breast cancers (25)
, and this methylation can even be detected in epithelial hyperplasia and intraductal papillomas (26)
. Here, we show that RASSF1A promoter methylation is associated with HR status in advanced breast tumors. Our observation of a significant negative association between BCL2 methylation and HR status is consistent with earlier reports that BCL2 expression is positively associated with HR positivity (27)
and that its down-regulation is negatively associated with HR positivity (28)
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was the least associated with HR status. PGR methylation was also not significantly associated with HR status after adjustment for multiple comparisons. Breast tumors are often concordant for ER and PR status. In our study, 127 of 148 breast cancer specimens were either double receptor (ER and PR) positive (n = 86) or double receptor negative (n = 41), whereas only 21 tumors were positive for either just ER (n = 12) or PR (n = 9). This highly significant association (P = 2.6 x 10–16 by 
2) between ER and PR status is attributed to induction of PGR gene expression by activated ER (29
, 30)
. This makes it difficult to separate the effects of the two receptors. We addressed this problem by investigating which methylation markers best predict the status of ER and PR individually, while adjusting for the status of the other receptor and for age (Table 1
, ER Status Predictors and PR Status Predictors). Interestingly, methylation of the 5' CpG island of the PGR gene turned out to be the best predictor of ER status (Table 1
, ER Status Predictors). Positive ER status is inversely associated with PGR CpG island methylation, consistent with the well-established induction of PGR gene expression by activated ER (31)
. On the other hand, ESR1 methylation turned out to be the best predictor of PR status (Table 1
, PR Status Predictors), even though it is the least significant predictor of overall HR status (Table 1
, HR Status Predictors). Thus, whereas neither ESR1 nor PGR methylation marker was a good predictor of overall HR status, each is the best predictor of the status of the other receptor, but not of their own cognate receptor. It is not a priori clear why this would be the case, or why there would be a positive association between PR status and ESR1 methylation (Table 1
, PR Status Predictors) because activated ER is known to induce PGR gene expression. This raises the question of whether ESR1 methylation is truly reflective of reduced ESR1 expression. We analyzed ESR1 expression by quantitative real-time reverse transcription-PCR in 74 samples for which we had frozen tissue available for RNA analysis. We did not find a clear inverse relationship between ESR1 expression levels and quartiled ESR1 methylation levels when the tumors were analyzed collectively (Fig. 2)
. However, PR– tumors did show a statistically significant inverse trend between ESR1 gene expression levels and ESR1 methylation levels (Fig. 2)
. This suggests that PR+ status may confer resistance of the ESR1 gene expression to ESR1 DNA methylation. We have further explored this interesting observation, as described in "Discussion," but we have not yet resolved the mechanism. It should be noted that the levels of methylation that we observed at the ESR1 locus are quite low, with a median PMR of 0.8 (see Data Supplement 4), and may therefore be more of a reflection of chromatin status at the ESR1 locus rather than a major driving force in silencing ESR1 gene expression. Regardless, it is yet another intriguing finding of an interaction between DNA methylation and HR biology.
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. Our results described above suggest a link between HR status and cellular DNA methylation profiles in breast cancer cells. Therefore, we tested whether any of the 35 DNA methylation markers would be better predictors of response to tamoxifen treatment than HR status. We ranked our 35 DNA methylation markers according to their ability to predict response to tamoxifen therapy as measured by a test for interaction in a Cox model with adjustments for treatment-specific effects of HR status. The model was also adjusted for age, stage, and number of positive nodes. Three markers were statistically significant predictors of response to tamoxifen therapy (Table 3)
. Interestingly, we had shown above that ESR1 methylation is a good predictor of PR status. Here, we show that ESR1 methylation outperforms PR status as a predictor of response to tamoxifen (Table 3)
. We further analyzed the relationship between ESR1 methylation and tamoxifen response by comparing the survival curves of patients with above or below median ESR1 methylation levels who either received tamoxifen therapy or did not receive tamoxifen therapy (Fig. 3)
. High ESR1 methylation was a significant predictor of better survival in the tamoxifen-treated group but showed no significant predictive value for the non-tamoxifen-treated group (Fig. 3)
.
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; Fig. 3
). Finally, CYP1B1 methylation was a highly significant predictor of tamoxifen response in the interaction model (Table 3)
. The survival curves reveal that this is due to a differential predictive behavior of this marker in tamoxifen-treated versus nontreated patients (Fig. 3)
. These results show that DNA methylation markers can outperform HR status as predictors of response to tamoxifen therapy and suggest that DNA methylation markers may be of clinical use in directing hormone therapy in breast cancer patients. |
DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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. The lack of observed effect of ESR1 methylation on ESR1 expression in PR+ tumors (Fig. 2)
is interesting. As mentioned earlier, the PMR values obtained for ESR1 methylation are very low, with a median PMR of 0.8, which may not be sufficiently high to cause gene silencing. Moreover, our ESR1 methylation assay is located immediately downstream of the transcription start site for exon 1A of the ESR1 gene (+14 to +114), rather than in the promoter region itself, due to limitations imposed by MethyLight primer design criteria. However, PR– tumors did show the expected inverse relationship between ESR1 methylation and expression with these same assays (Fig. 2)
. Others have shown that ESR1 gene expression can be activated in ER– cell lines by DNA methyltransferase and histone deacetylase inhibitors (32)
. One hypothesis that could reconcile these disparate observations is that PR+ tumors rely more extensively on expression initiated at upstream exons of the ESR1 gene (33)
. Expression driven from these upstream promoters would not be expected to be affected by the DNA methylation that we measure at exon 1A (34)
. Indeed, we identified several putative progesterone response elements located near upstream exon 1C (data not shown). However, we found that the relative utilization of ESR1 exons 1A, 1B, 1C, 1D, and 1E was similar in PR+ and PR– tumors. A summary of this analysis is shown in Data Supplement 5. This interesting difference between PR+ and PR– tumors in the relationship between ESR1 methylation and expression will require further investigation.
Clinical and epidemiological studies in the past have suggested that breast cancer is composed of at least two distinct groups (2
, 35)
. More recently, molecular profiling of breast cancer using gene expression profiles has revealed five distinct clusters composed of one basal-like subgroup, one ERBB2-overexpressing subgroup, two luminal-like subgroups, and one normal breast tissue-like subgroup (9)
. Because we have not performed gene expression microarray experiments on our group of breast tumors, we cannot directly compare our DNA methylation clustering results with the five major groups identified by gene expression profiles. However, it seems likely that the DNA methylation cluster 2, which contains mostly HR+ tumors (Fig. 1)
, overlaps with the two luminal-like subgroups, which contain ER+ tumors (5)
. The DNA methylation cluster 1 contains the majority of HR– tumors and likely overlaps with the other three gene expression subtypes, which tend to be ER– (5)
. It seems likely that the gene expression profile subgroupings represent a much more stable subgrouping because these analyses are based on a much larger number of samples and genes (9)
. Nevertheless, the undirected clustering of our methylation data led us to the identification of an interesting link between DNA methylation patterns and HR biology.
We chose to use MethyLight technology for this study, rather than methylation-specific PCR or the methylation microarray technologies currently under development. One of the unique features of MethyLight technology is that the resulting data are composed of a mixture of discrete and variable measures. The discrete measures arise from the large number of data points with undetectable methylation (PMR values of 0) versus the data points with positive detection of methylation. This type of data structure is similar to that obtained with methylation-specific PCR analysis. On the other hand, the quantitative nature of MethyLight also generates continuous measures for the samples with detectable levels of DNA methylation. We show here that useful information can be extracted from both types of measures. For example, among the methylation markers predictive of response to tamoxifen therapy, CYP1B1 was used as a discrete measure of positive versus negative DNA methylation, similar to methylation-specific PCR analysis. However, a methylation-specific PCR-based approach for the other two markers predictive of treatment response would have been noninformative because ESR1 and ARHI are positive in 100% and 99.3% of the samples, respectively (see Data Supplement 4). The quantitative aspect of MethyLight analysis was required to reveal the association of these methylation markers with response to tamoxifen therapy.
Of 35 DNA methylation markers tested, three genes showed the potential to serve as independent predictors of clinical response to systemic hormonal therapy with tamoxifen. Two of the three genes (ESR1 and CYP1B1) are known to be intimately involved in the function and metabolism of estradiol. This lends credence to the biological relevance of DNA methylation changes in breast tumors. The third gene (ARHI) encodes a RAS-related small G-protein, which may play a role in the regulation of breast cancer cell growth (36)
. We found that patients with high levels of ARHI methylation had better survival than patients with low levels of ARHI methylation. However, this effect was completely obliterated in the tamoxifen-treated group (Fig. 3)
. This may be due to the ability of antiestrogens such as tamoxifen to block growth factor-induced mitogenesis, possibly involving pathways regulated by ARHI (36
, 37)
.
ESR1 encodes the ER. Patients treated with tamoxifen who had high levels of tumor ESR1 methylation showed better survival than tamoxifen-treated patients with low levels of ESR1 methylation. The survival benefit in patients with high levels of ESR1 methylation may be due, in part, to the positive association between ESR1 methylation and PR status (Table 1
, PR Status Predictors). PR status appears to be a better predictor of response to tamoxifen than ER status (Table 2)
.
CYP1B1 encodes cytochrome P450 1B1, which catalyzes the conversion of 17-ß-estradiol (E2) to the catechol estrogen metabolites 2-OH-E2 and 4-OH-E2. The 2-hydroxylated form of E2 has been shown to have weak ER agonist or antagonist properties (38)
. CYP1B1 is also the principal catalyst of 4-hydroxytamoxifen trans-cis-isomerization, which converts the primary potent antiestrogen trans-4-hydroxytamoxifen to the weak estrogen agonist cis-4-hydroxytamoxifen (39)
. We have not investigated gene expression levels of CYP1B1 as a function of CYP1B1 methylation, and our measured levels of methylation are quite low (see Data Supplement 4). Nevertheless, if patients with positive CYP1B1 methylation do indeed have reduced CYP1B1 expression, then these patients would be expected to have lower rates of 4-hydroxytamoxifen trans-cis-isomerization and would thus retain higher levels of active antiestrogen. This would be consistent with the better survival of these patients in the tamoxifen-treated group (Fig. 3)
. Conversely, in the patients who did not receive tamoxifen therapy, patients with tumor CYP1B1 methylation would have a reduced capacity for conversion of E2 to its weaker catechol derivatives. This would be consistent with the observation that these patients show a worse survival among the group not receiving tamoxifen therapy (Fig. 3)
.
Our results show a level of interaction between DNA methylation changes in breast cancer and HR status or response to hormonal therapy that was not previously appreciated. Because DNA methylation markers rely on DNA as an analyte, as opposed to the more chemically labile RNA molecule, these results suggest exciting opportunities for the development of robust assays for clinical diagnosis and for predicting response to antiestrogen therapy in the adjuvant setting.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org).
Requests for reprints: Peter W. Laird, University of Southern California/Norris Cancer Center, Room 6418, 1441 Eastlake Avenue, Los Angeles, CA 90089-9176. Phone: (323) 865-0650; Fax: (323) 865-0158; E-mail: plaird{at}usc.edu
Received 12/ 9/03. Revised 2/26/04. Accepted 3/23/04.
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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H. Fiegl, S. Millinger, E. Mueller-Holzner, C. Marth, C. Ensinger, A. Berger, H. Klocker, G. Goebel, and M. Widschwendter Circulating Tumor-Specific DNA: A Marker for Monitoring Efficacy of Adjuvant Therapy in Cancer Patients Cancer Res., February 15, 2005; 65(4): 1141 - 1145. [Abstract] [Full Text] [PDF]
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M. G. Friedrich, D. J. Weisenberger, J. C. Cheng, S. Chandrasoma, K. D. Siegmund, M. L. Gonzalgo, M. I. Toma, H. Huland, C. Yoo, Y. C. Tsai, et al. Detection of Methylated Apoptosis-Associated Genes in Urine Sediments of Bladder Cancer Patients Clin. Cancer Res., November 15, 2004; 10(22): 7457 - 7465. [Abstract] [Full Text] [PDF]
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