Prognostic relevance of Wnt-inhibitory factor-1 (WIF1) and Dickkopf-3 (DKK3) promoter methylation in human breast cancer

  • Jürgen Veeck1, 5,

    Affiliated with

    • Peter J Wild2,

      Affiliated with

      • Thomas Fuchs3,

        Affiliated with

        • Peter J Schüffler3,

          Affiliated with

          • Arndt Hartmann4,

            Affiliated with

            • Ruth Knüchel1 and

              Affiliated with

              • Edgar Dahl1Email author

                Affiliated with

                BMC Cancer20099:217

                DOI: 10.1186/1471-2407-9-217

                Received: 08 October 2008

                Accepted: 01 July 2009

                Published: 01 July 2009

                Abstract

                Background

                Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease.

                Methods

                WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses.

                Results

                WIF1 and DKK3 promoter methylation were detected in 63.3% (95/150) and 61.3% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0% (0/19) and DKK3 methylation in 5.3% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97% OS after 10 years (p < 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58%, compared with 78% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95% CI: 1.0–6.0; p = 0.047) in breast cancer.

                Conclusion

                Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.

                Background

                The most common epigenetic alteration in human cancer affecting gene expression is 5'-cytosine methylation within CpG islands in gene promoter regions [1]. Promoter methylation effectively represses RNA transcription and occurs in many genes involved in human cancer development [2]. The majority of these affected genes are potential or known tumor suppressor genes that are regulators of different cellular pathways, such as cell cycle, DNA repair, growth factor signaling or cell adhesion [3]. Wnt signaling is one of the central cellular pathways commonly disrupted in several tumor types, including breast cancer [4, 5]. Unlike colorectal cancer, evidence for genetic alterations of Wnt pathway components in breast cancer, such as adenomatous polyposis coli (APC) mutations, is rare [6]. Several lines of evidence suggest that in breast cancer the Wnt signaling pathway is disrupted predominantly through epigenetic aberrations, most of all by promoter methylation of genes encoding secreted Wnt inhibitory molecules. For instance, genes encoding secreted frizzled-related proteins (SFRP) and Wnt-inhibitory factor-1 (WIF1) were previously reported as frequent targets of epigenetic inactivation in breast cancer [712]. In addition to this, we have recently shown that the putative Wnt signaling inhibitor Dickkopf-3 (DKK3) is functionally inactivated by promoter methylation in more than 60% of tumors from patients with invasive breast cancer [13]. Besides secreted inhibitors, two studies also reported frequent methylation of the APC gene in breast carcinomas [14, 15]. Altogether, this provides strong evidence for an epigenetically disrupted and thereby activated Wnt signaling pathway in the development of human breast cancer.

                There is increasing evidence that promoter methylation of cancer-related genes can be one of the most prevalent molecular markers for human cancer diseases [16]. The potential clinical applications of DNA-methylation biomarkers may include diagnosis of neoplasm, tumor classification, prediction of response to treatment, or patient prognosis [17]. Methylation of particular Wnt pathway genes has already been described as a potential biomarker for unfavorable patient outcome in human cancer. For instance, we have recently shown that methylation of SFRP1 as well as SFRP5 is associated with reduced patient overall survival in breast cancer [7, 10]. In contrast to this, high-frequent methylation of SFRP2 was not prognostically relevant in breast cancer [9], but was shown to comprise a diagnostic value as a sensitive screening marker for the stool-based detection of colorectal cancer and premalignant colorectal lesions [1820]. DKK3 methylation is associated with reduced DFS in acute lymphoblastic leukemia [21], and also with shorter OS in kidney cancer [22] and non-small cell lung cancer [23], as well as very recently reported with OS in gastric cancer [24]. Taken together, promoter methylation of Wnt signaling antagonists appears to provide a rich pool of novel tumor biomarkers in human cancer, potentially useful in the clinical setting by helping to improve management of this disease.

                In the present study, we addressed the question to whether promoter methylation of two Wnt antagonist genes (WIF1 and DKK3), that were previously reported as hypermethylated in breast cancer, provides prognostically relevant information in this tumor entity. In univariate and multivariate analyses we have investigated gene methylation in a large cohort (n = 150) of invasive breast cancer specimens. We here demonstrate for the first time that DKK3 methylation, but not WIF1 methylation, is an independent prognostic factor indicating poor patient survival in human breast cancer.

                Methods

                Patient material

                Surgically resected samples were obtained from 150 unselected breast cancer patients at the Departments of Gynecology at the University Hospitals of Aachen, Jena, Regensburg and Düsseldorf in Germany from 1991 to 2005. For 19 patients, normal breast tissues were available. In all cases, at least two-board certified pathologists agreed on the diagnosis on breast cancer. The samples were recruited in a non-selective, consecutive manner. Cases were not stratified for any known pre-operative or pathological prognostic factor. Inclusion criteria for the study were: Female patients presenting with unilateral, primary invasive breast cancer without individual breast cancer history. Exclusion criteria were: neo-adjuvant chemotherapy prior to surgery, presentation with secondary breast cancer, or peritumorous carcinoma in situ present in the tumor sample. All patients gave informed consent for retention and analysis of their tissue for research purposes and the Institutional Review Boards of the participating centers approved the study. Tumor histology was determined according to the criteria of the WHO (2003), while disease stage was assessed according to UICC [25]. Histological, tumors were graded according to Bloom and Richardson, as modified by Elston and Ellis [26]. Hormone receptor status was assessed according to the scoring system developed by Remmele and Stegner [27]. For 125 patients follow-up data were available with a median time of 64 months (range 1 to 174 months). Patient characteristics of this cohort have been previously described [13].

                Extraction of genomic DNA

                Tumor material was snap-frozen in liquid nitrogen immediately after surgery. Hematoxylin/Eosin-stained sections were prepared for assessing the percentage of tumor cells; only samples with > 70% tumor cells were selected. A total of 20 tissue sections (20 μm each) per specimen was dissected in a cryotom and pooled. Normal breast tissue specimens were prepared likewise. For normal breast samples, the epithelial cell amount had to exceed 30% in order to be selected for further preparation. Samples were dissolved in lysis buffer followed by DNA isolation, using the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's recommendations. The extracted genomic DNA was finally diluted in 55 μl of Tris buffer (10 mM; pH 7.6).

                In silico promoter analysis

                The WIF1 promoter, located at chromosome position 12q14.2, was investigated according to the contig ENSG00000156076 contained in the Ensembl database [28]. A genomic nucleotide sequence consisting of 1000 bp upstream of the annotated transcription start site (TSS) and 293 bp downstream of the TSS (first exon) was analyzed by methprimer software [29]. Criteria for CpG island prediction were adjusted to the definition from Takai and Jones [30], and included an observed/expected CpG ratio of ≥ 0.7 and a GC content of ≥ 60%. The identified CpG island proximal to the TSS was chosen for promoter methylation analysis. Methylation-specific PCR (MSP) primers were derived from a particular region within this island, which has been analyzed for CpG methylation by bisulfite genomic sequencing (BGS) in a previous study [11]. The DKK3 promoter, located at chromosome position 11p15.3, was investigated according to the contig ENSG00000050165 contained in the Ensembl database. A genomic nucleotide sequence consisting of 1000 bp upstream of the annotated TSS and 1001 bp downstream of the TSS was analyzed by methprimer software. The downstream region covered the first three exons of the gene, since DKK3 was reported to be alternatively transcribed under the control of two distinct promoters [31]. Methprimer software identified the existence of two distinct CpG islands, located proximal to either of the two predicted transcription start sites. The downstream CpG island was chosen for promoter methylation analysis, since the shorter DKK3 transcript was shown to be more commonly distributed in normal human tissues [31].

                Bisulfite-modification and methylation-specific PCR

                Approximately 1 μg of genomic DNA was bisulfite-modified using the EZ DNA Methylation Kit (Zymo Research, Orange, CA) according to the manufacturer's recommendations. The bisulfite-converted DNA was finally eluted in 20 μl of Tris buffer (10 mM; pH 7.6). Methylation-specific PCR was performed according to Herman et al. [32]. In short, 1 μl of modified DNA (~50 ng) was amplified using MSP primers (see Table 1) that specifically recognized either the unmethylated or methylated promoter sequences after bisulfite conversion [32]. Reaction volumes of 25 μl contained 1 × MSP-buffer [33], 400 nM of each primer, and 1.25 mM of each dNTP. One drop of mineral oil was added to each reaction tube. The PCR was initiated as "Hot Start" PCR at 95°C and held at 80°C before the addition of 1.25 units Taq DNA polymerase (Promega, Madison, WI). Cycle conditions were: 95°C for 5 min, 34 cycles of 95°C for 30 sec, 55°C (58°C) for 30 sec, 72°C for 40 sec and a final extension at 72°C for 5 min. Amplification products were visualized on 3% low range ultra agarose gel (Bio-Rad Laboratories, Hercules, CA) containing ethidium bromide and illuminated under ultraviolet light. Specificity of MSP primers in detecting the promoter methylation status were demonstrated by use of universal unmethylated and universal poly-methylated DNA as template (Epi Tect Control DNA Set; Qiagen, Hilden, Germany). Sensitivity of the utilized primers and cycling conditions was defined by use of a dilution series in MSP assays, constituted of methylated DNA diluted with unmethylated DNA (50%, 10%, 1%, 0.1%, 0.01% and 0% methylation).
                Table 1

                Oligonucleotide primers used in the study

                 

                Sequence (5' → 3')

                TA

                [°C]

                Primer

                [nM]

                Product size (bp)

                Methylation-specific PCR

                WIF1 unmethylated

                Forward: GGGTGTTTTATTGGGTGTATTGT

                55

                400

                154

                 

                Reverse: AAAAAAACTAACACAAACAAAATACAAAC

                   

                WIF1 methylated

                Forward: CGTTTTATTGGGCGTATCGT

                55

                400

                145

                 

                Reverse: ACTAACGCGAACGAAATACGA

                   

                DKK3 unmethylated

                Forward: TTAGGGGTGGGTGGTGGGGT

                58

                320

                126

                 

                Reverse: CTACATCTCCACTCTACACCCA

                   

                DKK3 methylated

                Forward: GGGCGGGCGGCGGGGC

                58

                320

                120

                 

                Reverse: ACATCTCCGCTCTACGCCCG

                   

                TA, annealing temperature.

                Statistical evaluations

                Statistical analyses were completed using SPSS 14.0 (SPSS, Chicago, IL). Differences were considered significant when P -values were below 0.05. To study statistical associations between clinicopathological factors and methylation status contingency tables and two-sided Fisher's exact test were accomplished. In case of multiple statistical tests, the false discovery rate controlling procedure was applied. Survival curves were calculated using the Kaplan-Meier method, with significance evaluated by two-sided log-rank statistics. OS (n = 125) was measured from the day of surgery until breast cancer-related death (n = 21) and was censored for patients alive at last contact (n = 91), in case of death unrelated to the tumor (n = 5) or when the death cause was unknown (n = 8). DFS (n = 125) was measured from surgery until local or distant relapse (n = 30) and was censored for patients alive without evidence of relapse at the last follow-up (n = 95). A stepwise multivariate Cox regression model was adjusted, testing the independent prognostic relevance of clinical/investigational factors. The limit for reverse selection procedures was p = 0.1. Only patients for whom the status of all variables was known (n = 103) were included in the proportional hazard models. The proportionality assumption for all variables was assessed with log-negative-log survival distribution functions. The variables tumor size (pT), node status (pN) and histological grade (G) were dichotomised into less and more progressive groups (pT1-2 vs. pT3-4; pN0 vs. pN1-3; G1-2 vs. G3).

                Results

                WIF1 promoter methylation in primary breast carcinomas

                WIF1 promoter methylation in human breast cancer has been previously reported by Ai et al. [11], who demonstrated, by use of MSP, WIF1 methylation in 16 of 24 (67%) breast carcinoma samples. For three breast tumor specimens, these MSP results had also been confirmed by BGS. Unfortunately, in their study the WIF1 promoter regions investigated by MSP and BGS were not matching or overlapping, so we decided to analyze WIF1 promoter methylation in breast cancer by MSP in the particular promoter region that has been covered by BGS in the other study (Figure 1A). Initially, a dilution series of methylated DNA in an excess of unmethylated DNA (Epi Tect Control DNA, bisulfite-converted) was tested by MSP. This experiment determined the sensitivity of the utilized WIF1 MSP assay to be 1.0% in the detection of methylated DNA molecules (~0.1 ng) in a background of unmethylated DNA (~9.9 ng) (Figure 1B). Next, WIF1 promoter methylation was determined by MSP in 150 primary breast carcinoma specimens and also in 19 matching normal breast tissues. In all normal breast tissues, only unmethylated WIF1 promoter sequence could be detected, as indicated by exclusive amplification with primers recognizing the unmethylated DNA sequence (Figure 2). In contrast, 95 of 150 primary breast carcinomas (63.3%) revealed a methylated WIF1 promoter sequence, as indicated by amplification with primers specific to methylated DNA (Figure 2). The remaining 55 tumor specimens (36.7%) revealed solely unmethylated WIF1 promoter sequence. In general, tumor samples, despite methylation, also revealed unmethylated WIF1 promoter sequence, which is likely due to small contaminations with stromal and endothelial cells, as has also been previously described [34].
                http://static-content.springer.com/image/art%3A10.1186%2F1471-2407-9-217/MediaObjects/12885_2008_Article_1551_Fig1_HTML.jpg
                Figure 1

                Methylation analysis of the humanWIF1promoter. (A) A 1.29 kb genomic sequence of the WIF1 promoter, analyzed by methprimer software [29], revealed the presence of a CpG island (blue) between relative position 604 and 1153. Position 1000 indicates the transcription start site (TSS, arrow). A region of high CpG (red vertical bars) densitiy was chosen for MSP analysis. The black bar indicates the MSP amplicon. (B) Sensitivity of the utilized MSP primers was determined by a dilution series of methylated DNA with unmethylated DNA (Epi Tect control DNA, Qiagen). At least 1% of methylated DNA (~0.1 ng) can be detected with the WIF1 MSP primers. bp, base pair marker; NTC, 'no template control'.

                http://static-content.springer.com/image/art%3A10.1186%2F1471-2407-9-217/MediaObjects/12885_2008_Article_1551_Fig2_HTML.jpg
                Figure 2

                WIF1methylation in primary breast cancer. WIF1 methylation analyses of primary breast cancer specimens. MSP was performed on bisulfite-treated DNA from breast cancer (T) and matching normal primary breast tissues (N). MSP results from three representative matched pairs and eight additional breast carcinomas (#) are shown. DNA bands in lanes labeled with U indicate MSP products amplified with primers recognizing the unmethylated promoter sequence. DNA bands in lanes labeled with M represent amplified MSP products with methylation-specific primers. Peripheral blood lymphocytes (PBL) and breast cancer cell line ZR75-1 served as positive controls for the methylation-specific reaction, respectively. Water was used as template in the 'no template control' (NTC). Note that tumor tissue usually displayed a PCR product in the U-reaction as well, due to contaminating normal tissue (stromal cells, endothelial cells) present in the tumor specimens as has also been described by Suzuki et al. [34].

                DKK3 promoter methylation in primary breast carcinomas

                We have recently reported of frequent DKK3 promoter methylation in human breast cancer [13]. In the respective report, we have demonstrated that DKK3 methylation was present in 61.3% of breast cancer patients (92 of 150), whereas in 19 matching normal breast tissues only one sample (5.3%) revealed faint methylation signals. Ideally, the samples analyzed for WIF1 methylation in the present report were physically identical with the samples previously analyzed for DKK3 methylation. The previously analyzed DKK3 promoter region is pictured in Figure 3A. To allow a direct comparison between methylation of these two genes, we first assayed a dilution series of methylated DNA with unmethylated DNA by MSP using DKK3 methylation-specific primers (see above). This experiment determined the sensitivity of the utilized DKK3 MSP assay to be 1.0% in the detection of methylated DNA molecules (~0.1 ng) in a background of unmethylated DNA (~9.9 ng) (Figure 3B), thus enabling a subsequent correlation analysis in breast cancer employing the methylation results from both genes.
                http://static-content.springer.com/image/art%3A10.1186%2F1471-2407-9-217/MediaObjects/12885_2008_Article_1551_Fig3_HTML.jpg
                Figure 3

                Methylation analysis of the humanDKK3promoter. (A) A 2.0 kb genomic sequence of the DKK3 promoter, analyzed by methprimer software [29], revealed the presence of two CpG islands (blue); one between relative position 834 and 1261 and another one between position 1529 and 1917. Two alternative tissue-specific DKK3 transcripts have been described [30]. Since transcription of the shorter transcript is widely distributed in normal tissues, we chose the region of the second transcription start site (TSS, arrow) for methylation analysis. Position 1000 indicates the alternative transcription start site of the longer transcript (TSS*, arrow). A region of high CpG (red vertical bars) densitiy within the second CpG island was chosen for MSP analysis. (B) Sensitivity of the utilized MSP primers was determined by a dilution series of methylated DNA with unmethylated DNA (Epi Tect control DNA, Qiagen). At least 1% of methylated DNA (~0.1 ng) can be detected with the DKK3 MSP primers. bp, base pair marker; NTC, 'no template control'.

                Association of WIF1 and DKK3 promoter methylation with clinicopathological parameters

                For descriptive data analysis clinicopathological parameters were correlated with the WIF1 and DKK3 promoter methylation status. In a bivariate analysis, WIF1 methylation was not associated with patient age at diagnosis, tumor size, lymph node status, histological grade, histological type, and estrogen or progesterone receptor status (Table 2). DKK3 methylation was associated with advanced patient age at diagnosis (p = 0.007), but not associated with any other of the investigated parameters (Table 2).
                Table 2

                Demographic/clinicopathological parameters in relation to WIF1 and DKK3 promoter methylation

                  

                WIF1 methylation

                DKK3 methylation

                Variable

                Categorization

                n 1

                No (%)

                Yes (%)

                P 2

                n 1

                No (%)

                Yes (%)

                P 2

                Clinicopathological factors

                Age at diagnosis

                 

                <57 years

                74

                32 (43)

                42 (57)

                0.127

                74

                37 (50)

                37 (50)

                0.007

                 

                ≥ 57 years

                76

                23 (30)

                53 (70)

                 

                76

                21 (28)

                55 (72)

                 

                Tumor size3

                 

                pT1-pT2

                129

                48 (37)

                81 (63)

                1.000

                129

                52 (40)

                77 (60)

                0.440

                 

                pT3-pT4

                18

                7 (39)

                11 (61)

                 

                18

                5 (28)

                13 (72)

                 

                Lymph node status3

                 

                pN0

                72

                29 (40)

                43 (60)

                0.606

                72

                32 (44)

                40 (56)

                0.170

                 

                pN1-pN3

                71

                25 (35)

                46 (65)

                 

                71

                23 (32)

                48 (67)

                 

                Histological grade

                 

                G1-G2

                88

                28 (32)

                60 (68)

                0.170

                88

                31 (35)

                57 (65)

                0.313

                 

                G3

                62

                27 (44)

                35 (57)

                 

                62

                27 (44)

                35 (57)

                 

                Histological type

                 

                IDC

                122

                43 (35)

                79 (65)

                 

                122

                45 (37)

                77 (63)

                 
                 

                lobular

                19

                7 (37)

                12 (63)

                0.296

                19

                7 (37)

                12 (63)

                0.236

                 

                other

                9

                5 (56)

                4 (44)

                 

                9

                6 (67)

                3 (33)

                 

                Immunohistochemistry

                Estrogen receptor

                         
                 

                negative (IRS4 0–2)

                47

                21 (45)

                26 (55)

                0.142

                47

                20 (43)

                27 (57)

                0.467

                 

                positive (IRS 3–12)

                98

                31 (32)

                67 (68)

                 

                98

                35 (36)

                63 (64)

                 

                Progesterone receptor

                 

                negative (IRS4 0–2)

                51

                22 (43)

                29 (57)

                0.206

                51

                21 (41)

                30 (59)

                0.593

                 

                positive (IRS 3–12)

                94

                30 (32)

                64 (68)

                 

                94

                34 (36)

                60 (64)

                 

                WIF1 promoter

                 

                unmethylated

                -

                -

                -

                -

                55

                29 (53)

                26 (47)

                0.009

                 

                methylated

                -

                -

                -

                 

                95

                29 (31)

                66 (69)

                 

                1Only female patients with primary, unilateral invasive breast cancer were included.2Fisher's exact test.3According to UICC: TNM Classification of Malignant Tumours [25].4IRS, immunoreactivity score according to Remmele and Stegner [27]. Percentages may not sum to 100 due to rounding. IDC, invasive ductal carcinoma; n.a., not available.

                Correlation of WIF1 and DKK3 promoter methylation in primary breast carcinoma

                In a bivariate analysis, methylation of the WIF1 promoter was significantly associated with methylation of the DKK3 promoter (p = 0.009) (Table 2). Both gene promoters were mutually unmethylated in tumors from 29 of 150 patients (19.3%) and mutually methylated in tumors from 66 of 150 patients (44.0%) (Figure 4). For 55 of 150 patients (36.7%) the methylation status of the WIF1 and DKK3 promoter differed: WIF1 methylation together with DKK3 non-methylation was detected in 29/250 patients (19.3%), whereas WIF1 non-methylation together with DKK3 methylation was detected in 26 of 150 patients (17.3%).
                http://static-content.springer.com/image/art%3A10.1186%2F1471-2407-9-217/MediaObjects/12885_2008_Article_1551_Fig4_HTML.jpg
                Figure 4

                Distribution ofWIF1andDKK3promoter methylation in primary breast carcinomas. Methylation status of either gene has been determined by MSP in the same tumors. Of n = 150 breast cancer patients, the larger fraction reveals an identical methylation status of both genes (63.3%). In the remaining smaller fraction (36.6%), methylation of only one of the two genes could be detected. In total, WIF1 methylation was significantly associated with DKK3 methylation (p = 0.009; Fisher's exact test).

                Association of WIF1 promoter methylation with patient survival

                Patient OS and DFS were compared between methylated versus unmethylated WIF1 promoter sequence by univariate Kaplan-Meier analysis using log-rank statistics. In this analysis, WIF1 methylation was not significantly associated with patient OS (p = 0.656) or patient DFS (p = 0.154) (Table 3), as also demonstrated by Kaplan-Meier survival curves (Figure 5). As expected, a positive lymph node status (pN1-3) and higher histological grade (G3) were found to be associated with decreased OS (p = 0.002; p = 0.001) and DFS (p < 0.001; p = 0.012).
                Table 3

                Univariate survival analysis of clinicopathological and molecular factors (log-rank test)

                Variable

                Categorization

                Overall survival

                Disease-free survival

                  

                n 1

                events

                P 2

                n 1

                events

                P 2

                Clinicopathological factors

                Age at diagnosis

                 

                <57 years

                64

                7

                0.094

                64

                16

                0.711

                 

                ≥ 57 years

                61

                14

                 

                61

                14

                 

                Tumor size3

                 

                pT1-pT2

                107

                17

                0.372

                107

                25

                0.427

                 

                pT3-pT4

                16

                4

                 

                16

                5

                 

                Lymph node status3

                 

                pN0

                54

                3

                0.002

                54

                5

                <0.001

                 

                pN1-pN3

                65

                18

                 

                65

                24

                 

                Histological grade

                 

                G1-G2

                72

                5

                0.001

                72

                11

                0.012

                 

                G3

                53

                16

                 

                53

                19

                 

                Histological type

                 

                IDC

                101

                19

                0.267

                101

                22

                0.277

                 

                other

                24

                2

                 

                24

                8

                 

                Immunohistochemistry

                Estrogen receptor

                 

                negative (IRS4 0–2)

                40

                9

                0.155

                40

                9

                0.962

                 

                positive (IRS 3–12)

                80

                12

                 

                80

                21

                 

                Progesterone receptor

                 

                negative (IRS4 0–2)

                39

                9

                0.154

                39

                13

                0.087

                 

                positive (IRS 3–12)

                81

                12

                 

                81

                17

                 

                WIF1 promoter

                 

                unmethylated

                47

                9

                0.656

                47

                8

                0.154

                 

                methylated

                78

                12

                 

                78

                22

                 

                DKK3 promoter

                 

                unmethylated

                46

                1

                <0.001

                46

                7

                0.037

                 

                methylated

                79

                20

                 

                79

                23

                 

                1Only female patients with primary, unilateral invasive breast cancer were included.2Log-rank test.3According to UICC: TNM Classification of Malignant Tumours [25].4IRS, immunoreactivity score according to Remmele and Stegner [27]. IDC, invasive ductal carcinoma.

                http://static-content.springer.com/image/art%3A10.1186%2F1471-2407-9-217/MediaObjects/12885_2008_Article_1551_Fig5_HTML.jpg
                Figure 5

                Univariate Kaplan-Meier survival analysis of breast cancer patients in relation toWIF1andDKK3promoter methylation. (A) Overall survival and (B) disease-free survival are not associated with WIF1 promoter methylation in human breast cancer. Solid lines indicate methylated WIF1 promoter; dotted lines indicate unmethylated WIF1 promoter in the tumor. (C) In contrast, methylation of the DKK3 promoter in tumor tissue (solid line) is significantly associated with adverse patient overall survival, whereas patients with an unmethylated DKK3 promoter in the tumor tissue have a very favorable clinical outcome (dotted line) (p < 0.001). (D) In addition, DKK3 -methylated tumors reveal a significant shorter time to recurrence (solid line), as compared to tumors harboring an unmethylated DKK3 promoter (dotted line) (p = 0.037). Vertical tick marks represent censored patients.

                Association of DKK3 promoter methylation with patient survival

                Patient OS and DFS were compared between methylated versus unmethylated DKK3 promoter sequence. In contrast to WIF1 methylation, DKK3 methylation was significantly associated with poor OS (5-year survival: 75% for cases with methylated alleles vs. 97% for cases with unmethylated alleles; 10-year survival: 54% vs. 97%; p = 0.0005; Table 3) and shorter DFS (5-year survival: 67% for cases with methylated alleles vs. 84% for cases with unmethylated alleles; 10-year-survival: 58% vs. 78%; p = 0.037; Table 3), as also illustrated by Kaplan-Meier survival curves (Figure 5). Based on a mean OS of 141 months (95% CI: 127–154 months) patients without DKK3 methylation in the tumor tissue revealed much longer mean OS (170 months, 95% CI: 163–177 months) than patients with DKK3 methylation in the tumor tissue (113 months, 95% CI: 95–131 months). Based on a mean DFS of 99 months (95% CI: 90–107 months) patients without DKK3 methylation in the tumor revealed longer mean DFS (110 months, 95% CI: 99–122 months) than patients with DKK3 methylation in the tumor (86 months, 95% CI: 75–97 months). Multivariate Cox regression models were calculated and adjusted to assess factor-related hazard risks and to test for independency of DKK3 methylation as a prognostic factor in patient OS and DFS. The strength of the association between DKK3 methylation and unfavourable patient outcome is presented in Table 4 and Table 5. Multivariate, DKK3 methylation in breast carcinoma represented an independent and strong risk factor for OS (HR: 14.4; 95% CI: 1.9 – 111.6; p = 0.011; Table 4). In DFS the prognostic potency of DKK3 methylation was weaker than in OS (HR: 2.5; 95% CI: 1.0 – 6.0; p = 0.047; Table 5).
                Table 4

                Multivariate Cox regression analysis of DKK3 promoter methylation with regard to overall survival

                   

                Multivariate analysis

                Overall survival

                (global model)

                Multivariate analysis

                Overall survival

                (reverse selection procedure2)

                Variable

                HR

                95% CI 1

                P

                HR

                95% CI 1

                P

                Age at diagnosis

                <57 years

                0

                1.0

                  

                1.0

                  
                 

                ≥ 57 years

                1

                1.78

                0.63 – 4.99

                0.276

                2.27

                0.85 – 6.07

                0.104

                Tumor size

                pT1-2

                0

                1.0

                     
                 

                pT3-4

                1

                0.83

                0.25 – 2.78

                0.766

                   

                Lymph nodes

                pN0

                0

                1.0

                  

                1.0

                  
                 

                pN1-3

                1

                5.47

                1.46 – 20.53

                0.012

                4.50

                1.26 – 15.87

                0.021

                Histological grade

                G1

                0

                1.0

                  

                1.0

                  
                 

                G2-G3

                1

                4.36

                1.54 – 12.40

                0.006

                4.50

                1.57 – 12.87

                0.005

                Histological type

                ductal

                0

                1.0

                     
                 

                other

                1

                0.46

                0.09 – 2.22

                0.330

                   

                Estrogen receptor

                negative

                0

                1.0

                  

                1.0

                  
                 

                positive

                1

                0.51

                0.18 – 1.47

                0.214

                0.43

                0.17 – 1.09

                0.426

                Progesterone receptor

                negative

                0

                1.0

                     
                 

                positive

                1

                0.57

                0.21 – 1.54

                0.270

                   

                DKK3 promoter

                unmethylated

                0

                1.0

                  

                1.0

                  
                 

                methylated

                1

                14.41

                1.86 – 111.56

                0.011

                13.68

                1.77–105.52

                0.012

                1Confidence interval (CI) on the estimated hazard ratio (HR).2Only terms that remained in the model after reverse selection are listed. All variables were stratified binary according to Table 3.

                Table 5

                Multivariate Cox regression analysis of DKK3 promoter methylation with regard to disease-free survival

                   

                Multivariate analysis

                Disease-free survival

                (global model)

                Multivariate analysis

                Disease-free survival

                (reverse selection procedure2)

                Variable

                HR

                95% CI 1

                P

                HR

                95% CI 1

                P

                Age at diagnosis

                <57 years

                0

                1.0

                     
                 

                ≥ 57 years

                1

                0.57

                0.24 – 1.33

                0.191

                   

                Tumor size

                pT1-2

                0

                1.0

                     
                 

                pT3-4

                1

                0.61

                0.22 – 1.72

                0.353

                   

                Lymph nodes

                pN0

                0

                1.0

                  

                1.0

                  
                 

                pN1-3

                1

                4.24

                1.50 – 11.96

                0.006

                4.01

                1.49 – 10.81

                0.006

                Histological grade

                G1

                0

                1.0

                  

                1.0

                  
                 

                G2-G3

                1

                2.01

                0.91 – 4.43

                0.086

                1.93

                0.88 – 4.24

                0.102

                Histological type

                IDC

                0

                1.0

                     
                 

                other

                1

                1.04

                0.42–2.62

                0.929

                   

                Estrogen receptor

                negative

                0

                1.0

                     
                 

                positive

                1

                2.17

                0.78 – 6.03

                0.137

                   

                Progesterone receptor

                negative

                0

                1.0

                  

                1.0

                  
                 

                positive

                1

                0.36

                0.15 – 0.88

                0.025

                0.53

                0.25 – 1.11

                0.091

                DKK3 promoter

                unmethylated

                0

                1.0

                  

                1.0

                  
                 

                methylated

                1

                2.46

                1.01 – 5.97

                0.047

                2.08

                0.88 – 4.88

                0.094

                1Confidence interval (CI) on the estimated hazard ratio (HR).2Only terms that remained in the model after reverse selection are listed. All variables were stratified binary according to Table 3.

                Discussion

                It was previously reported that expression of the Wnt antagonist genes WIF1 and DKK3 is downregulated in several tumor entities as a consequence of epigenetic DNA modification [11, 13, 21, 31, 35, 36]. WIF1 is a conserved Wnt-binding protein that prevents Wnt ligands from interacting with membranous frizzled receptors, thus may inhibit activation of the Wnt/β-catenin signaling cascade [37]. In breast, lung, prostate and bladder cancer, WIF1 expression was found to be frequently downregulated [38], suggesting it might represent a tumor suppressor gene. In breast cancer, this downregulation could be attributed to hypermethylation of the WIF1 promoter [11], as demonstrated both in breast cell lines and in primary breast carcinomas. In our study, methylation of the WIF1 promoter was detected in 63% of invasive tumors from breast cancer patients, thus being in good agreement with previous results from Ai et al. [11], who reported a frequency of 67% for WIF1 methylation in mammary tumors. Differences may arise through different sample sizes (n = 150 and n = 24) as well as different promoter locations assessed in either study. DKK3 is a further secreted inhibitor of Wnt signaling, but in contrast to WIF1 does not sequester Wnt ligands. The actual mechanism by which DKK3 acts inhibitory on Wnt pathway activation has not been identified yet, but suppression of DKK3 increased β-catenin/T-cell factor (TCF)-dependent gene activity in mammary cells [39], cancerous lung cells [40] and glioma [41]. Likewise to WIF1, the DKK3 gene was also reported as a frequent target of epigenetic inactivation in numerous tumor entities, e.g. in lung cancer, prostate cancer and leukemia [21, 31, 42], suggesting that DKK3 may exert tumor suppressive functions. In a recent report, we have demonstrated that DKK3 is frequently inactivated in invasive breast carcinomas by promoter methylation leading to loss of DKK3 expression [13]. This epimutation affected 92 of 150 investigated breast cancer patients (61%). Since these samples were identical to the samples for which we now have determined WIF1 methylation, we were able to perform a combined analysis of both genes' methylation in breast cancer.

                In a bivariate analysis, WIF1 methylation status in breast carcinomas was significantly associated with the DKK3 methylation status. Despite, within the cohort of carcinomas being affected by methylation of either the DKK3 or WIF1 gene, a large fraction (45%) showed methylation in one gene only. This demonstrates that in spite of a statistical association between methylation of the two genes, there is still a large fraction of breast cancer patients with different DKK3/WIF1 methylation pattern. Therefore, it is unlikely that methylation of both Wnt antagonist genes is a mandatory mutual carcinogenic event. In a further correlation analysis, neither of the two genes was associated with relevant clinicopathological features, except for DKK3 methylation being associated with advanced patient age. Age-dependent promoter methylation has been reported previously [43, 44] and may randomly overlay the effects of gene-specific promoter methylation that can lead to the development of distinct cancer subtypes. The absence of an association between WIF1 or DKK3 methylation with important clinicopathological factors like tumor size, histological grade and lymph node invasion strongly suggests that methylation of either gene is an early carcinogenic event in breast cancer development, rather than contributing to tumor progression.

                Most important, major differences between WIF1 and DKK3 methylation arise in their association with breast cancer patient survival. WIF1 methylation showed no significance in clinical patient outcome in contrast to DKK3 methylation, which was tightly associated with adverse patient OS and weaker with short DFS in our study. Patients harboring DKK3 methylation in the tumor had a poor prognosis (54% chance of 10-years OS) in contrast to patients retaining an unmethylated DKK3 promoter, who had a favorable prognosis (97% chance of 10-years OS). This finding was supported by a multivariate Cox regression analysis in which DKK3 -methylated patients revealed a high risk of tumor-related death (HR: 14.4). Hence, this parameter outperformed classical prognostic factors in our patient cohort, i.e. high histological grade (HR: 4.4) or a positive lymph node status (HR: 5.5). Unproportional HRs of high impact were rarely achieved even in studies with very large sample size numbers, neither by strong conventional factors like node status (HR: 2.4) [45] and grade (HR: 5.7) [46] nor by investigational factors like the tissue urokinase-type plasminogen activator/inhibitor (uPA/PAI), which in case of high level exposes patients to a five times greater risk of dying from breast cancer [47]. In conclusion, our results demonstrate that determination of the DKK3 methylation status may provide valuable information to aid prognostication in the clinical management of breast cancer patients. Notably, methylation of the DKK3 promoter was recently shown to be prognosis relevant also in other tumor entities, such as in acute lymphoblastic leukemia, kidney cancer, lung cancer, and gastric cancer [2124], pointing to a potential clinical use of this marker in several cancer diseases.

                Our findings raise expectations towards translation of such methylation markers into clinical practice. As an example, DKK3 may be a prime candidate gene to be incorporated into diagnostic multimarker panels, for its aberrant methylation is specific to malignant cells in breast cancer [13]. Preliminary results from our laboratory revealed that DKK3 methylation can be detected with high clinical sensitivity and specificity in blood serum of breast cancer patients independent of tumor size and node status (unpublished data). The presence of detectable tumor DNA in serum is generally associated with poor prognosis [48, 49], and taken together with its marker performance in solid breast tumor tissue, DKK3 methylation fulfils essential prerequisites as a biomarker in a blood-borne assay, of which we will report in a future study.

                In summary, we here demonstrate that although WIF1 and DKK3 promoter methylation are similar frequent alterations in human breast cancer, only DKK3 methylation appears to be a survival risk factor for breast cancer patients and thus might be useful as prognostic marker in clinical oncology helping to improve patient outcome.

                Conclusion

                This study shows that the Wnt antagonist gene WIF1 is frequently inactivated by promoter hypermethylation in human breast cancer. Although WIF1 is similarly frequent hypermethylated like the Wnt antagonist gene DKK3, and neither gene methylation is associated with relevant clinicopathological factors, DKK3 methylation is an independent prognostic factor in breast cancer patient survival, whereas WIF1 methylation is not. These differences may reflect subtle distinctions in the biological roles of the two related molecules in inhibiting Wnt/β-catenin signaling.

                Abbreviations

                BGS: 

                bisulfite genomic sequencing

                BMBF: 

                Bundesministerium für Bildung und Forschung

                CI: 

                confidence interval

                DFS: 

                disease-free survival

                DKK3: 

                Dickkopf-3

                ER: 

                estrogen receptor

                HR: 

                hazard ratio

                MSP: 

                methylation-specific polymerase chain reaction

                NTC: 

                no template control

                OS: 

                overall survival

                PCP: 

                planar cell polarity pathway

                PCR: 

                polymerase chain reaction

                PR: 

                progesterone receptor

                TCF: 

                T-cell factor

                UICC: 

                International Union Against Cancer

                UMD: 

                universal methylated DNA

                UUD: 

                universal unmethylated DNA

                WHO: 

                World Health Organization

                WIF1: 

                Wnt-inhibitory factor 1

                Declarations

                Acknowledgements

                The expert technical assistance of Sevim Alkaya, Sonja von Serényi and Inge Losen is greatly appreciated. We thank Dr. Dieter Niederacher (Heinrich-Heine University, Düsseldorf, Germany) and Prof. Matthias Dürst (Friedrich-Schiller University, Jena, Germany) for kindly providing patient samples as well as Dr. Monika Klinkhammer-Schalke and Dr. Felicitas Horn (Tumour Registry, Regensburg, Germany) for continuous help in obtaining clinical follow-up data. This work is a research project within the German Human Genome Project and has been supported by the BMBF grant 01KW0401 to ED.

                Authors’ Affiliations

                (1)
                Molecular Oncology Group, Institute of Pathology, University Hospital of the RWTH Aachen
                (2)
                Institute of Surgical Pathology, University Hospital Zürich
                (3)
                Institute for Computational Science and Department of Computer Science, ETH Zürich
                (4)
                Department of Pathology, University of Erlangen
                (5)
                Cancer Epigenetics and Biology Program (PEBC), Bellvitge Institute for Biomedical Research (ICO-IDIBELL), Hospital Duran i Reynals

                References

                1. Esteller M, Corn PG, Baylin SB, Herman JG: A gene hypermethylation profile of human cancer. Cancer Res 2001, 61: 3225–3229.PubMed
                2. Wildschwendter M, Jones PA: DNA methylation and breast carcinogenesis. Oncogene 2002, 21: 5462–5482.View Article
                3. Mulero-Navarro S, Esteller M: Epigenetic biomarkers for human cancer: The time is now. Crit Rev Oncol Hematol 2008, 68: 1–11.View ArticlePubMed
                4. Howe LR, Brown AM: Wnt signaling and breast cancer. Cancer Biol Ther 2004, 3: 36–41.PubMed
                5. Aguilera O, Muñoz A, Esteller M, Fraga MF: Epigenetic alterations of the Wnt/beta-catenin pathway in human disease. Endocr Metab Immune Disord Drug Targets 2007, 7: 13–21.PubMed
                6. Furuuchi K, Tada M, Yamada H, Kataoka A, Furuuchi N, Hamada J, Takahashi M, Todo S, Moriuchi T: Somatic mutations of the APC gene in primary breast cancers. Am J Pathol 2000, 156: 1997–2005.PubMed
                7. Veeck J, Niederacher D, An H, Klopocki E, Wiesmann F, Betz B, Galm O, Camara O, Dürst M, Kristiansen G, Huszka C, Knüchel R, Dahl E: Aberrant methylation of the Wnt antagonist SFRP1 in breast cancer is associated with unfavourable prognosis. Oncogene 2006, 25: 3479–3488.View ArticlePubMed
                8. Lo PK, Mehrotra J, D'Costa A, Fackler MJ, Garrett-Mayer E, Argani P, Sukumar S: Epigenetic suppression of secreted frizzled related protein 1 (SFRP1) expression in human breast cancer. Cancer Biol Ther 2006, 5: 281–286.View ArticlePubMed
                9. Veeck J, Noetzel E, Bektas N, Jost E, Hartmann A, Knüchel R, Dahl E: Promoter hypermethylation of the SFRP2 gene is a high-frequent alteration and tumor-specific epigenetic marker in human breast cancer. Mol Cancer 2008, 7: 83.View ArticlePubMed
                10. Veeck J, Geisler C, Noetzel E, Alkaya S, Hartmann A, Knüchel R, Dahl E: Epigenetic inactivation of the Secreted frizzled-related protein-5 (SFRP5) gene in human breast cancer is associated with unfavorable prognosis. Carcinogenesis 2008, 29: 991–998.View ArticlePubMed
                11. Ai L, Tao Q, Zhong S, Fields CR, Kim WJ, Lee MW, Cui Y, Brown KD, Robertson KD: Inactivation of Wnt inhibitory factor-1 (WIF1) expression by epigenetic silencing is a common event in breast cancer. Carcinogenesis 2006, 27: 1341–1348.View ArticlePubMed
                12. Suzuki H, Toyota M, Caraway H, Gabrielson E, Ohmura T, Fujikane T, Nishikawa N, Sogabe Y, Nojima M, Sonoda T, Mori M, Hirata K, Imai K, Shinomura Y, Baylin SB, Tokino T: Frequent epigenetic inactivation of Wnt antagonist genes in breast cancer. Br J Cancer 2008, 98: 1147–1156.View ArticlePubMed
                13. Veeck J, Bektas N, Hartmann A, Kristiansen G, Heindrichs U, Knuchel R, Dahl E: Wnt signalling in human breast cancer: expression of the putative Wnt inhibitor Dickkopf-3 (DKK3) is frequently suppressed by promoter hypermethylation in mammary tumours. Breast Cancer Research 2008, 10: R82.View ArticlePubMed
                14. Jin Z, Tamura G, Tsuchiya T, Sakata K, Kashiwaba M, Osakabe M, Motoyama T: Adenomatous polyposis coli (APC) gene promoter hypermethylation in primary breast cancers. Br J Cancer 2001, 85: 69–73.View ArticlePubMed
                15. Virmani AK, Rathi A, Sathyanarayana UG, Padar A, Huang CX, Cunnigham HT, Farinas AJ, Milchgrub S, Euhus DM, Gilcrease M, Herman J, Minna JD, Gazdar AF: Aberrant methylation of the adenomatous polyposis coli (APC) gene promoter 1A in breast and lung carcinomas. Clin Cancer Res 2001, 7: 1998–2004.PubMed
                16. Ordway JM, Budiman MA, Korshunova Y, Maloney RK, Bedell JA, Citek RW, Bacher B, Peterson S, Rohlfing T, Hall J, Brown R, Lakey N, Doerge RW, Martienssen RA, Leon J, McPherson JD, Jeddeloh JA: Identification of novel high-frequency DNA methylation changes in breast cancer. PLoS ONE 2007, 2: e1314.View ArticlePubMed
                17. Miyamoto K, Ushijima T: Diagnostic and therapeutic applications of epigenetics. Jpn J Clin Oncol 2005, 35: 293–301.View ArticlePubMed
                18. Müller HM, Oberwalder M, Fiegl H, Morandell M, Goebel G, Zitt M, Mühlthaler M, Ofner D, Margreiter R, Widschwendter M: Methylation changes in faecal DNA: a marker for colorectal cancer screening? Lancet 2004, 363: 1283–1285.View ArticlePubMed
                19. Huang ZH, Li LH, Yang F, Wang JF: Detection of aberrant methylation in fecal DNA as a molecular screening tool for colorectal cancer and precancerous lesions. World J Gastroenterol 2007, 13: 950–954.PubMed
                20. Oberwalder M, Zitt M, Wöntner C, Fiegl H, Goebel G, Zitt M, Köhle O, Mühlmann G, Ofner D, Margreiter R, Müller HM: SFRP2 methylation in fecal DNA-a marker for colorectal polyps. Int J Colorectal Dis 2008, 23: 15–19.View ArticlePubMed
                21. Roman-Gomez J, Jimenez-Velasco A, Agirre X, Castillejo JA, Navarro G, Barrios M, Andreu EJ, Prosper F, Heiniger A, Torres A: Transcriptional silencing of the Dickkopfs-3 (Dkk-3) gene by CpG hypermethylation in acute lymphoblastic leukaemia. Br J Cancer 2004, 91: 707–713.PubMed
                22. Urakami S, Shiina H, Enokida H, Hirata H, Kawamoto K, Kawakami T, Kikuno N, Tanaka Y, Majid S, Nakagawa M, Igawa M, Dahiya R: Wnt antagonist family genes as biomarkers for diagnosis, staging, and prognosis of renal cell carcinoma using tumor and serum DNA. Clin Cancer Res 2006, 12: 6989–6997.View ArticlePubMed
                23. Suzuki M, Shigematsu H, Nakajima T, Kubo R, Motohashi S, Sekine Y, Shibuya K, Iizasa T, Hiroshima K, Nakatani Y, Gazdar AF, Fujisawa T: Synchronous alterations of Wnt and epidermal growth factor receptor signaling pathways through aberrant methylation and mutation in non small cell lung cancer. Clin Cancer Res 2007, 13: 6087–6092.View ArticlePubMed
                24. Yu J, Tao Q, Cheng YY, Lee KY, Ng SS, Cheung KF, Tian L, Rha SY, Neumann U, Röcken C, Ebert MP, Chan FK, Sung JJ: Promoter methylation of the Wnt/beta-catenin signaling antagonist Dkk-3 is associated with poor survival in gastric cancer. Cancer 2009, 115: 49–60.View ArticlePubMed
                25. Sobin LH, Wittekind C, eds: TNM classification of malignant tumors. 5 Edition New York: Wiley Liss 1997.
                26. Elston EW, Ellis IO: Method for grading breast cancer. J Clin Pathol 1993, 46: 189–190.View ArticlePubMed
                27. Remmele W, Stegner HE: Recommendation for uniform definition of an immunoreactive score (IRS) for immunohistochemical estrogen receptor detection (ER-ICA) in breast cancer tissue. Pathologe 1987, 8: 138–140.PubMed
                28. Ensembl Genome Browser [http://​www.​ensembl.​org/​index.​html]
                29. Li LC, Dahiya R: MethPrimer: designing primers for methylation PCRs. Bioinformatics 2002, 18: 1427–1431.View ArticlePubMed
                30. Takai D, Jones PA: Comprehensive analysis of CpG islands in human chromosomes 21 and 22. Proc Natl Acad Sci USA 2002, 99: 3740–3745.View ArticlePubMed
                31. Kobayashi K, Ouchida M, Tsuji T, Hanafusa H, Miyazaki M, Namba M, Shimizu N, Shimizu K: Reduced expression of the REIC/Dkk-3 gene by promoter-hypermethylation in human tumor cells. Gene 2002, 282: 151–158.View ArticlePubMed
                32. Herman JG, Graff JR, Myohanen S, Nelkin BD, Baylin SB: Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc Natl Acad Sci USA 1996, 93: 9821–9826.View ArticlePubMed
                33. Galm O, Herman JG: Methylation-specific polymerase chain reaction. Methods Mol Med 2005, 113: 279–291.PubMed
                34. Suzuki H, Watkins DN, Jair KW, Schuebel KE, Markowitz SD, Chen WD, Pretlow TP, Yang B, Akiyama Y, Van Engeland M, Toyota M, Tokino T, Hinoda Y, Imai K, Herman JG, Baylin SB: Epigenetic inactivation of SFRP genes allows constitutive WNT signaling in colorectal cancer. Nat Genet 2004, 36: 417–422.View ArticlePubMed
                35. Mazieres J, He B, You L, Xu Z, Lee AY, Mikami I, Reguart N, Rosell R, McCormick F, Jablons DM: Wnt inhibitory factor-1 is silenced by promoter hypermethylation in human lung cancer. Cancer Res 2004, 64: 4717–4720.View ArticlePubMed
                36. Taniguchi H, Yamamoto H, Hirata T, Miyamoto N, Oki M, Nosho K, Adachi Y, Endo T, Imai K, Shinomura Y: Frequent epigenetic inactivation of Wnt inhibitory factor-1 in human gastrointestinal cancers. Oncogene 2005, 24: 7946–7952.View ArticlePubMed
                37. Hsieh JC, Kodjabachian L, Rebbert ML, Rattner A, Smallwood PM, Samos CH, Nusse R, Dawid IB, Nathans J: A new secreted protein that binds to Wnt proteins and inhibits their activities. Nature 1999, 398: 431–436.View ArticlePubMed
                38. Wissmann C, Wild PJ, Kaiser S, Roepcke S, Stoehr R, Woenckhaus M, Kristiansen G, Hsieh JC, Hofstaedter F, Hartmann A, Knuechel R, Rosenthal A, Pilarsky C: WIF1, a component of the Wnt pathway, is down-regulated in prostate, breast, lung, and bladder cancer. J Pathol 2003, 201: 204–212.View ArticlePubMed
                39. Wang XY, Yin Y, Yuan H, Sakamaki T, Okano H, Glazer RI: Musashi1 modulates mammary progenitor cell expansion through proliferin-mediated activation of the Wnt and Notch pathways. Mol Cell Biol 2008, 28: 3589–3599.View ArticlePubMed
                40. Yue W, Sun Q, Dacic S, Landreneau RJ, Siegfried JM, Yu J, Zhang L: Downregulation of Dkk3 activates beta-catenin/TCF-4 signaling in lung cancer. Carcinogenesis 2008, 29: 84–92.View ArticlePubMed
                41. Mizobuchi Y, Matsuzaki K, Kuwayama K, Kitazato K, Mure H, Kageji T, Nagahiro S: REIC/Dkk-3 induces cell death in human malignant glioma. Neuro Oncol 2008, 10: 244–253.View ArticlePubMed
                42. Lodygin D, Epanchintsev A, Menssen A, Diebold J, Hermeking H: Functional epigenomics identifies genes frequently silenced in prostate cancer. Cancer Res 2005, 65: 4218–4227.View ArticlePubMed
                43. Waki T, Tamura G, Sato M, Motoyama T: Age-related methylation of tumor suppressor and tumor-related genes: an analysis of autopsy samples. Oncogene 2003, 22: 4128–4133.View ArticlePubMed
                44. Li LC, Shiina H, Deguchi M, Zhao H, Okino ST, Kane CJ, Carroll PR, Igawa M, Dahiya R: Age-dependent methylation of ESR1 gene in prostate cancer. Biochem Biophys Res Commun 2004, 321: 455–461.View ArticlePubMed
                45. Kato T, Kameoka S, Kimura T, Soga N, Abe Y, Nishikawa T, Kobayashi M: Angiogenesis as a predictor of long-term survival for 377 Japanese patients with breast cancer. Breast Cancer Res Treat 2001, 70: 65–74.View ArticlePubMed
                46. Warwick J, Tabar L, Vitak B, Duffy SW: Time-dependent effects on survival in breast carcinoma: results of 20 years of follow-up from the Swedish Two-County Study. Cancer 2004, 100: 1331–1336.View ArticlePubMed
                47. Ferrero-Poüs M, Hacène K, Bouchet C, Le Doussal V, Tubiana-Hulin M, Spyratos F: Relationship between c-erbB-2 and other tumor characteristics in breast cancer prognosis. Clin Cancer Res 2000, 6: 4745–4754.PubMed
                48. Silva JM, Dominguez G, Garcia JM, Gonzalez R, Villanueva MJ, Navarro F, Provencio M, San Martin S, España P, Bonilla F: Presence of tumor DNA in plasma of breast cancer patients: clinicopathological correlations. Cancer Res 1999, 59: 3251–3256.PubMed
                49. Lecomte T, Berger A, Zinzindohoue F, Micard S, Landi B, Blons H, Beaune P, Cugnenc PH, Laurent-Puig P: Detection of free-circulating tumor-associated DNA in plasma of colorectal cancer patients and its association with prognosis. Int J Cancer 2002, 100: 542–548.View ArticlePubMed
                50. Pre-publication history

                  1. The pre-publication history for this paper can be accessed here:http://​www.​biomedcentral.​com/​1471-2407/​9/​217/​prepub

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                © Veeck et al. 2009

                This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.