Systematic Cpg Islands Methylation Profiling of Genes in the Wnt Pathway in Epithelial Ovarian Cancer Identifies Biomarkers of Progression-Free Survival

Published OnlineFirst April 1, 2011; DOI: 10.1158/1078-0432.CCR-10-3021

Clinical Cancer Imaging, Diagnosis, Prognosis Research

Systematic CpG Islands Methylation Profiling of Genes in the Wnt Pathway in Epithelial Ovarian Cancer Identifies Biomarkers of Progression-Free Survival

Wei Dai1, Jens M. Teodoridis1, Constanze Zeller1, Janet Graham1, Jenny Hersey3, James M. Flanagan1, Euan Stronach2, David W. Millan4, Nadeem Siddiqui5, Jim Paul6, and Robert Brown1,3

Abstract Purpose: Wnt pathways control key biological processes that potentially impact on tumor progression and patient survival. We aimed to evaluate DNA methylation at promoter CpG islands (CGI) of Wnt pathway genes in ovarian tumors at presentation and identify biomarkers of patient progression-free survival (PFS). Experimental Design: Epithelial ovarian tumors (screening study n ¼ 120, validation study n ¼ 61), prospectively collected through a cohort study, were analyzed by differential methylation hybridization at 302 loci spanning 189 promoter CGIs at 137 genes in Wnt pathways. The association of methylation and PFS was examined by Cox proportional hazards model. Results: DNA methylation is associated with PFS at 20 of 302 loci (P < 0.05, n ¼ 111), with 5 loci significant at false discovery rate (FDR) less than 10%. A total of 11 of 20 loci retain significance in an independent validation cohort (n ¼ 48, P 0.05, FDR 10%), and 7 of these loci, at FZD4, DVL1, NFATC3, ROCK1, LRP5, AXIN1, and NKD1 genes, are independent from clinical parameters (adjusted P < 0.05). Increased methylation at these loci associates with increased hazard of disease progression. A multivariate Cox model incorporates only NKD1 and DVL1, identifying two groups differing in PFS [HR ¼ 2.09; 95% CI (1.39–3.15); permutation test P < 0.005]. Methylation at DVL1 and NFATC3 show significant association with response. Consistent with their epigenetic regulation, reduced expression of FZD4, DVL1, and ROCK1 is an indicator of early-disease relapse in an independent ovarian tumor cohort (n ¼ 311, adjusted P < 0.05). Conclusion: The data highlight the importance of epigenetic regulation of multiple promoter CGIs of Wnt pathway genes in ovarian cancer and identify methylation at NKD1 and DVL1 as independent predictors of PFS. Clin Cancer Res; 17(12); 4052–62. 2011 AACR.

Introduction num-based chemotherapy, but there is a low 5-year survival rate of less than 30% (1–5). There is a need to identify key Epithelial ovarian cancer (EOC) is the most lethal of all pathways in ovarian cancer whose activity is associated the gynecologic cancers accounting for 52% of all gyneco- with patient survival to assist our understanding of the logic cancer-related deaths (1). Patients with advanced molecular basis of disease progression and to identify disease have surgical cytoreduction followed by plati- biomarkers of sufficient discriminatory prognostic and predictive power to be of clinical value. The established prognostic factors of EOC at clinical presentation before Authors' Affiliations: 1Epigenetics Unit; 2Ovarian Cancer Action Research primary chemotherapy are residual disease and patient age, Centre, Department of Surgery and Cancer, Imperial College London, as well as histology, stage and grade of tumor (5–7); Hammersmith Hospital, Du Cane Road, London; 3Section of Medicine, 4 however, these clinical factors do not provide insights into Institute for Cancer Research, Sutton; Departments of Pathology and 5Gynaecology, Glasgow Royal Infirmary; and 6Cancer Research UK Clin- biological mechanisms for the clinical progression of ical Trials Unit, The Beatson West of Scotland Cancer Centre, Glasgow, tumors. Although many individual biomarkers in EOC United Kingdom have been investigated (8–14), currently only 2 serum Note: Supplementary data for this article are available at Clinical Cancer biomarkers CA125 and HE4 have been approved by the Research Online (http://clincancerres.aacrjournals.org/). FDA for monitoring patients with EOC (15). Identifying Corresponding Author: Robert Brown, Epigenetics Unit, Ovarian Cancer Centre, 4th Floor IRDB Building, Hammersmith Hospital, Du Cane Road, robust biomarkers of patient survival following conven- London W12 0NN, United Kingdom. Phone: 44 (0) 2075941804; Fax: 44- tional treatment of EOC will also provide a solid basis for 2083835830; E-mail: [email protected] characterizing potential predictive biomarkers to novel doi: 10.1158/1078-0432.CCR-10-3021 treatment strategies and hence for stratification of patients 2011 American Association for Cancer Research. for targeted care in clinical practice.

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Methylation of Wnt Pathway Loci in EOC Associates with PFS

Translational Relevance epigenetic mechanisms, such as promoter hypermethylation (32–34). The Wnt pathway extracellular inhibitors, SFRPs WIF1 Epithelial ovarian cancer is the most lethal of all the such as family and , are frequently silenced by gynecologic cancers. Patients with advanced disease promoter hypermethylation in cancers including EOC have surgical cytoreduction followed by platinum- (33, 35–40). However, promoter methylation of the based chemotherapy, but there is a low 5-year survival Wnt pathway has not been comprehensively studied in rate due to disease progression and the acquisition of EOC. Our aim was to systematically profile DNA methy- resistance to therapy. There is a need for biomarkers to lation at promoter CGIs of Wnt pathway genes and assist our understanding of the molecular basis of evaluate their role in tumor progression by assessing their ovarian cancer progression and of sufficient discrimi- association with patient progression-free survival (PFS) natory prognostic power to be able to aid clinical and overall survival (OS) in EOC in a prospective cohort management of this disease. We have systematically study. examined promoter CpG island (CGI) methylation at Wnt pathway genes by using differential methylation Materials and Methods hybridization of customized microarrays spanning more than 300 loci and identified loci associated with Patients progression-free survival of patients that are indepen- Tumor biopsies were prospectively collected in an dent from known clinical prognostic factors. These data ongoing Scottish Gynaecology Clinical Trial Group show the clinical importance of epigenetic regulation of (SGCTG)/National Cancer Research Institute (NCRI) multiple promoter CGIs at Wnt pathway genes in ovar- cohort study since 1997. Tumors included were restricted ian cancer and their potential as predictive biomarkers to those from patients with confirmed EOC treated with in future clinical studies. cytoreductive surgery and platinum-based chemotherapy, excluding mucinous and clear cell tumors. These latter histologic tumor types were excluded due to their different Aberrant DNA methylation frequently occurs in cancer, clinical outcome from more common serous and endome- particularly at regions generally unmethylated in normal trioid EOC (41, 42). Macroscopically normal ovarian tissue cells, known as CpG islands (CGI). CGIs often colocalize taken adjacent to the tumor and from tissue adjacent to with the promoters of genes and promoter hypermethyla- benign ovarian cysts and tumors were also collected. The tion is associated with repression of gene transcription information about progression and survival status of (16). Several studies have shown that CGI methylation patients is obtained monthly for the first 2 years after has potential as a biomarker for monitoring tumor pro- chemotherapy, subsequently 6 monthly for 5 years and gression (17) and is associated with platinum-based che- annually thereafter. PFS was defined as the time from first- moresistance in EOC (18, 19). However, many of these line chemotherapy to progressive disease or early death due studies are either limited by small sample size or lack of to EOC or other causes. OS was defined as the time from validation of the methylation biomarker as an independent first-line chemotherapy to death due to EOC or other prognostic marker. causes. Response was measured by Response Evaluation The Wnt pathways control a variety of biological pro- Criteria in Solid Tumors (RECIST) 1.0 criteria. The primary cesses including cell proliferation, differentiation, and objective of the cohort study is to prospectively examine the migration and have a crucial role in development and association of DNA methylation with PFS. Secondary tissue homeostatis (20, 21). Wnt signaling is transduced objectives are to examine for association of DNA methyla- through binding of Wnt molecules to the transmembrane tion with OS and response to treatment. The study is frizzled receptor proteins and activation of downstream approved by the MREC (multicentre research ethics com- signaling pathways depending on the interaction between mittees) for Scotland (reference number 01/165). Genomic Wnt molecules and their specific receptors. In the "cano- DNA was extracted for methylation analysis as previously nical" pathway, binding of Wnt molecules to frizzled/LRP described (43). transmembrane receptor complex results in disheveled phosphorylation and inhibition of Axin, Gsk3b, and Differential methylation hybridization APC complex, subsequently allowing b-catenin to accumu- Samples were assayed in duplicates by differential late in the nucleus and interact with transcription factors of methylation hybridization (DMH), as previously described the T-cell–specific transcription factor/lymphoid enhancer (44). Briefly, DNA was digested with MseI, ligated to an binding factor family (22–24). Although mutations of APC end-linker, and divided into 2 aliquots. One aliquot was and b-catenin are frequently observed in colorectal cancer mock treated, the other aliquot was digested with the (25, 26), they are rare in EOC (24, 27), with the exception of methylation-sensitive restriction enzyme McrBC which cuts m m endometrioid subtypes (28). Among numerous noncano- (G/A) CN40–3,000(G/A) C (45, 46). PCR amplification nical pathways, Wnt/planar cell polarity signaling and was conducted with primers binding to the end linkers, þ Wnt/Ca2 signaling are involved in tumorigenesis (29–31). the amplicons were then labeled with Cy3 or Cy5 and In cancers in which Wnt pathway gene mutations are hybridized to the custom-designed 60 mer microarrays rare, the Wnt pathway can become disregulated through fabricated by using Agilent SurePrint Tehcnology (Agilent).

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Labeling of DNA, array hybridization and image scanning Sample size and statistical power estimation was done by Oxford Gene Technology (Oxford, UK) The initial screening set consisted of DMH data from 120 according to the standard Agilent aCGH protocol. tumors with 111 subsequently shown to have suitable data The raw signal intensities were extracted by Agilent quality and 102 patients had disease progression (see feature extraction (v9.5.3.1). Probes with high background Results). To estimate approximate statistical power of this noise (about 3%) were treated as missing data points and screening set prior to analysis, we assumed 5% of the loci imputed by KNN algorithm (k ¼ 10; refs. 47, 48). Probes examined in the Wnt pathway were true positives and split with high (>65,000, signal saturation) and low (chromosome 16 and the island within 2 kb of the promoter and these are repre- mitochondrial genome. The unmethylated controls were sented by 302 MseI fragments (130–6,000 bp) analyzed selected from the mitochondrial genome and low CG by DMH. density region from chromosome 16 (59,000,000– 61,000,000) that lacks McrBC recognition site (49). In The cancer genome atlas dataset average, each MseI fragment was covered by 12 probes The level 2 expression dataset on Affymetrix HGU133A and by at least 7 probes. To examine what the percentage of microarrays and level 3 methylation dataset on Illumina tumors in SGCTG cohort are methylated at the loci we HumanMethylation27 Beadchip of serous tumors were examined, 1-sided Mann–Whitney U test was used to obtained from the cancer genome atlas (TCGA) data portal compare the methylation levels of multiple probes target- (http://cancergenome.nih.gov/dataportal), consisting of ing the same MseI fragment to those of unmethylated 311 high-grade serous tumors. controls (n ¼ 93) designed on the microarray. The sig- The expression microarray data have been preprocessed nificance level was set at P < 0.0003 on the basis of and normalized across the samples, and methylation data Bonferroni correction. have been summarized as b value which was calculated as M/(MþU), in which M is the signals of methylation bead Survival analysis type and U is the signals of unmethylation bead type of the The workflow of the survival analysis is illustrated in targeted CpG site (see http://tcga-data.nci.nih.gov/docs/ Supplementary Fig. S1. Cox proportional hazard model TCGA_Data_Primer.pdf and Supplementary Method 2 was used to examine for association between methylation for details). and patient survival. The DMH ratios of multiple probes targeting the same locus (MseI fragment) were averaged. Statistical analysis The mean value of methylation at the locus in duplicates All statistical analyses were done in R (version 2.10.1) was used as a continuous variable in the Cox model. The and SPSS (version 18.0). association between methylation and PFS was examined by

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Methylation of Wnt Pathway Loci in EOC Associates with PFS

univariate Cox model first, and then multivariate analysis analyzed by DMH, 4 samples were excluded from subse- was carried out to evaluate the independence of loci iden- quent analysis due to poor quality of signal intensities in tified from clinical parameters. The variables applied for more than 10% of probes in the duplicates and 5 samples adjustment in multivariate analysis included histologic were excluded as methylation controls did not reach accep- type, International Federation of Gynecology and Obste- tance criteria. Therefore, 111 tumors remained in the trics stage, grade, and age. The significance of estimated analysis. Assuming a HR of 1.75 and FDR (54) less than HRs was calculated by using the score test. 50%, then the estimated average power of the screening To determine the best predictors of PFS in patients with study is 0.8 (Supplementary Method 2). In a subsequent late-stage (stages III and IV) ovarian cancer, a multivariate validation stage, 59 further tumors collected through the Cox model was constructed by using the forward stepwise same protocol were examined. Eleven samples were method based on likelihood ratio statistics with a prob- excluded due to not reaching acceptance criteria, so 48 ability of 0.05 for entry and 0.10 for removal (Supplemen- tumors remained in the analysis. Among the loci with FDR tary Method 4). Among the variables including clinical less than 10% from the screening set the median power of parameters and validated, independent methylation mar- the validation study was about 70%. The genes in the Wnt kers, only 2 methylation markers meet the entry criteria, pathway frequently methylated (>5%, i.e., methylated in thus selected into the model in this study (see Results). more than 5% of tumors) in ovarian cancer and unmethy- Subsequently, a methylation index (MI) was calculated by lated in PBMC are shown in Supplementary Table S1. using the selected covariates from this model. Leave-one- Following the REMARK recommendations (55), we present out cross validation (LOOCV) was done to evaluate the full details of clinical parameters in Supplementary predictive value of this multivariate Cox model (Supple- Table S2 and their relationship to patient outcome in mentary Method 5). Supplementary Tables S3 and S4. Kaplan–Meier curves were used to show the PFS in the patients with high/low methylation at promoter CGI, and Methylation in the Wnt pathway and PFS those with high/low MI. Because average methylation To evaluate the association between methylation at frequency of the Wnt pathway genes observed is approxi- promoter CGIs of the Wnt pathway and tumor progression, mately 25%, the third quartile was used as the cutpoint to we used average DMH ratios as a continuous variable in the define high/low methylation. Kaplan–Meier survival curves univariate Cox model with PFS as the endpoint (see Meth- of 2 groups were compared by the log-rank test (2-sided). ods). In the screening study, we identified methylation of The significance of log-rank test of 2 groups with high/low 20 of 302 loci at promoter CGIs of 17 genes that may be MI determined by the third quartile was assessed by a associated with PFS (P < 0.05), with 5 loci at genes FZD4, permutation test applied to the entire variable selection DVL1, CCND1, CCND3, and NKD1 significant at FDR less and model fitting process. than 10% after multiple test correction (see univariate The correlation between response and promoter methy- survival analysis in Table 1). In an independent patient lation in the Wnt pathway was tested by logistic regression. cohort (n ¼ 48), 11 of 20 loci identified in the screening This is restricted to patients with measurable disease at stage were still prognostic, P 0.05 and FDR 10% baseline. Patients were classified as responders (complete (Table 2). or partial response) or nonresponders (stable disease or The OR of 11 loci identified as having P 0.05 and progressive disease) according to RECIST 1.0 criteria. FDR 10% in univariate analysis from the validation study External validation of prognostic value of biomarkers were adjusted by age, stage, grade, and histologic type and identified from SGCTG cohort was done in TCGA cohort the patients were stratified into 3 groups who either by using methylation level (b value)/expression as a con- received platinum alone, combination of platinum and tinuous variable in univariate analysis, then multivariate taxane, or other platinum-based treatment. Hypermethyla- analysis was conducted to adjust the HR of disease pro- tion at 7 loci was associated with increased hazard of gression/relapse in every unit increase of expression data by disease progression independent from clinical parameters: clinical parameters (stage, grade, and age) in this cohort. CGIs at FZD4, DVL1, AXIN1, and LRP5 (adjusted P < 0.05) The direct association between methylation and expression and CGIs at NKD1, ROCK1, and NFATC3 (adjusted 0.05 < was evaluated by Spearman correlation. P < 0.06; see multivariate survival analysis in Table 3 and Fig. 1A–G). Of the 7 loci, patients with increased methyla- Results tion level at gene DVL1, FZD4, and NKD1 also had higher risk of death (P < 0.05, FDR 20%; Table 4). Patient characteristics DNA methylation at the 6 loci associated with poor PFS Epithelial ovarian tumors prospectively collected were assessed for any relationship with response of the through a cohort study, were analyzed by DMH at 302 patients to first-line platinum-based chemotherapy. To Wnt pathway associated loci, as defined by KEGG (entry maximize the numbers of patients with response data, this ID: hsa04310; ref. 50). Mucinous and clear cell cancers was examined in the screening and validation sets com- were excluded due to their different clinical outcome from bined, therefore, the average DMH ratio of each locus was more common serous and endometrioid EOC (37, 38). In transformed to a Z score (standardized average DMH ratio an initial screening stage, from 120 ovarian tumor samples of each locus within each study). Increased methylation at

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Table 1. Univariate PFS analysis of screening Table 2. Univariate PFS analysis in the valida- set: loci with P < 0.05 tion set of loci with P < 0.05 in screening set

Genes Univariate PFS analysis (n ¼ 111) Genes Univariate PFS analysis (n ¼ 48)

HRa 95% CIb Pc FDRd HRa 95% CIb Pc FDRd

FZD4 123.3 (8.9–1,701.30) <0.001*** <0.1e FZD4 2.5 (0.9–6.6) 0.034* <0.1e CCND1f 3.9 (1.8–8.3) <0.001*** <0.1e CCND1f 1.4 (0.6–3.4) 0.225 0.2 NKD1 31.4 (3.7–268.3) 0.002** <0.1e NKD1 2.2 (0.9–5.2) 0.039* <0.1e TAF8kCCND3 0.5 (0.3–0.8) 0.002** <0.1e TAF8kCCND3 1.3 (0.9–1.8) 0.929 1 DVL1 12.1 (2.6–55.5) 0.001** <0.1e DVL1 4.3 (1.2–15.0) 0.010* <0.1e FRAT1 0.3 (0.1–0.7) 0.005** 0.2 FRAT1 1.2 (0.7–2.1) 0.787 0.9 ROCK1 2263.1 0.008** 0.3 ROCK1 4.3 (1.0–19.4) 0.027* <0.1e (7.5–681,782.4) PPP2R2B 1.7 (0.1–27.6) 0.357 0.4 PPP2R2B 34.0 (2.4–476.6) 0.009** 0.4 CTBP1f 1.3 (0.7–2.4) 0.176 0.2 CTBP1f 2.3 (1.2–4.3) 0.015* 0.4 CCND1f 3596.2 0.001* <0.1e CCND1f 48.6 (2.1–1,108.6) 0.015* 0.4 (17.0–75,887.8) NKD2 10.6 (1.5–74.1) 0.017* 0.5 NKD2 1.9 (0.8–4.4) 0.062 0.1e FZD9 7.4 (1.4–41.0) 0.021* 0.5 FZD9 1.7 (0.9–3.2) 0.057 0.1e EEFSECkRUVBL1 2.4 (1.2–5.2) 0.021* 0.5 EEFSECkRUVBL1 1.7 (1.0–2.7) 0.016* <0.1e AXIN1 24.5 (1.4–423.3) 0.028* 0.6 AXIN1 2.7 (0.9–7.6) 0.032* <0.1e C4orf42kCTBP1f 1.9 (1.1–3.3) 0.030* 0.6 C4orf42kCTBP1f 1.5 (0.9–2.6) 0.074 0.1e WNT4 41.1 (1.3–1,282.7) 0.034* 0.6 WNT4 2.6 (1.1–6.2) 0.016* <0.1e LRP5 5.7 (1.1–30.2) 0.041* 0.6 LRP5 2.6 (1.0–6.2) 0.020* <0.1e NFATC3 6.4 (1.1–37.6) 0.042* 0.6 NFATC3 5.1 (1.3–20.0) 0.010* <0.1e FRAT1 0.7 (0.6–1.0) 0.040* 0.6 FRAT1 1.2 (0.9–1.5) 0.910 1 SFRP5 51.2 (1.1–2,386.6) 0.045* 0.6 SFRP5 2.2 (0.9–5.3) 0.042* <0.1e

aHR per unit increase in DMH ratio (continuous variable) aHR per unit increase in DMH ratio (continuous variable) estimated from Cox proportional hazard regression model. estimated from Cox proportional hazard regression model. bCI of the estimated HR. bCI of the estimated HR. cP value of score test (2-sided). cP value of score test (1-sided). dMultiple test correction based on 302 loci. dMultiple test correction based on 20 loci. eFDR 0.1; *, P 0.05; **, P 0.01; ***, P 0.001. eFDR 0.1; *, P 0.05; **, P 0.01. fDifferent loci within the promoter region of CCND1/CTBP1 fDifferent loci within the promoter region of CCND1/CTBP1 were identified as fulfilling the definition of a CGI. were identified as fulfilling the definition of a CGI.

DVL1 and NFATC3 was correlated with poor response. stepwise algorithm from clinical parameters (age, stage, The OR of the patients with progressive or stable disease grade, and histologic type) and promoter methylation at (n ¼ 29) to the patients with partial or complete response FZD4, DVL1, NKD1, ROCK1, LRP5, AXIN1, and NFATC3 (n ¼ 54) in every unit increase of methylation Z score is 1.7 (see Supplementary Method 4). The final model includes (95% CI: 1.1–2.8, P ¼ 0.026, FDR < 10%) and 1.6 (95% CI: DVL1 (HR ¼ 1.24; 95% CI: 1.05–1.46; P ¼ 0.01) and 1.0–2.6, P ¼ 0.032, FDR < 10%) for DVL1 and NFATC3, NKD1 (HR ¼ 1.28; 95% CI: 1.04–1.57; P ¼ 0.02) as the 2 respectively. best predictors. The HR represents the relative risk per unit increase in Z score. The remaining variables including Wnt pathway MI in the late-stage ovarian tumors clinical parameters and 5 methylation biomarkers were To identify the best methylation predictors of PFS in the not selected into the model due to not meeting the entry ovarian tumors from both screening and validation sets, criteria in forward stepwise method (see Methods), that is, the average DMH ratio of each locus was transformed to a Z the association between PFS and methylation at FZD4, score. We excluded 10 stage I tumors from 159 tumors ROCK1, LRP5, AXIN1, NFATC3, and other conventional because these may be biologically distinct entities and prognostic factors: grade, stage, age, and histologic type do patients with early-stage ovarian cancer have much better not provide additional prognostic information beyond that clinical outcome than patients with late-stage cancer. From provided by DVL1 and NKD1. A MI calculated from this the Z scores of 150 patients with late-stage EOC (stages III model (MI ¼ 0:25 ZNKD1 þ 0:22 ZDVL1, Z denotes Z and IV), we constructed a multivariate Cox model, of which score) can identify 2 distinct prognostic groups by using the the covariates were selected by likelihood ratio forward third quartile of the index as the cutoff (HR ¼ 2.09; 95% CI:

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P ¼ < Table 3. Multivariate PFS analysis of loci sig- 0.06, FDR 10%; see univariate PFS analysis in nificantly associated with PFS in univariate ana- methylation in Table 5). lysis Expression of genes associated with methylated loci Genes Multivariate PFS analysis (n ¼ 111) and PFS Promoter hypermethylation is frequently associated with HRa 95% CIb Adjusted Pc gene inactivation (16); therefore, we further examined the association between expression and PFS of the 7 linked FZD4 64.5 (3.4–1,243.8) 0.006* candidate genes as well as direct correlation between NKD1 10.2 (0.9–113.4) 0.059þ expression and methylation. This was examined in the DVL1 8.3 (1.7–39.1) 0.008* TCGA cohort (n ¼ 311). Among the 7 genes, NKD1 is ROCK1 285.4 (1.0–82,977.6) 0.051þ not present on the Affymetrix HGU133A expression micro- CCND1 4.6 (0.5–37.9) 0.161 array and lacks coverage of CpG sites on HumanMethyla- EEFSECkRUVBL1 1.7 (0.8–3.7) 0.184 tion26 BeadChip. For the remaining 6 genes, low AXIN1 44.9 (2.0–1,004.6) 0.016* expression of FZD4, DVL1, and ROCK1 indicated a higher WNT4 15.6 (0.4–670.8) 0.152 risk of recurrent/progressed disease (FZD4:HR¼ 0.8; 95% LRP5 6.8 (1.3–36.6) 0.026* CI: 0.7–0.9; adjusted P ¼ 0.002; DVL1:HR¼ 0.8; 95% CI: NFATC3 6.6 (1.0–44.9) 0.052þ 0.6–1.0; adjusted P ¼ 0.035; ROCK1:HR¼ 0.7; 95% CI: SFRP5 26.8 (0.4–1,774.0) 0.124 0.5–1.0; adjusted P ¼ 0.045), independent from stage, grade, and age (see univariate and multivariate PFS analysis a HR per unit increase in DMH ratio (continuous variable) in expression in Table 5). The reduced expression of FZD4, estimated from Cox proportional hazard regression model. DVL1, and ROCK1 correlating with increased hazard of b CI of the estimated HR. disease progression is consistent with promoter methyla- c P value adjusted by histology, grade, stage, and age. Age tion leading to the downregulation of the genes, although was used as a continuous variable and histology; grade and direct inverse association between expression and methyla- stage were used as categorical variables. þ, P < 0.1; *, P tion was only found at DVL1 (rs ¼0.13, P ¼ 0.023) and 0.05; **, P 0.01; ***, P 0.001. ROCK1 (rs ¼0.25, P < 0.001), but not at FZD4 (rs ¼ 0.02, P ¼ 0.766) by Spearman correlation (see correlation between methylation and expression in Table 5). Again, 1.39–3.15; log-rank test, P ¼ 2.42 10 4; permutation test, consistent with the methylation data is that decreased P < 0.005; Fig. 1H). LOOCV was used to assess the expression of DVL1 was related to poor response to plati- predictive value of the multivariate Cox model in 150 num-based chemotherapy in the TCGA cohort [PD þ SD late-stage EOCs (see Supplementary Fig. S2). (n ¼ 36) vs. PR þ CR (n ¼ 217), OR ¼ 0.5, 95% CI: 0.3–0.9, P ¼ 0.035]. External validation of the biomarkers from Wnt pathways in TCGA study Discussion To validate in a further independent tumor set the prognostic value of the 7 validated methylation biomarkers We examined DNA methylation at CGIs at the promoter that are independent from clinical parameters (at genes regions of Wnt pathway genes, as defined by KEGG (entry FZD4, DVL1, NKD1, ROCK1, AXIN1, LFRP5, and ID: hsa04310; ref. 45), in 159 EOCs. Using average DMH NFATC3), we analyzed associations between PFS and ratio as a continuous variable we identified 7 loci (FZD4, methylation at these loci in data from a large (n ¼ 311) DVL1, ROCK1, NFATC3, AXIN1, LRP5, and NKD1)of independent cohort of high-grade serous ovarian tumors which methylation was significantly associated with PFS (see Method and Supplementary Method 6) available in evaluation and validation tumor cohorts and are inde- through TCGA Pilot study established by the National pendent from known clinical prognostic features. Cancer Institute (NCI) and National Human Genome As with any microarray experiment, systematic bias can Research Institute (NHGRI; unpublished). The methyla- be introduced from any of the multiple steps of DMH: tion data was generated by using a different method from such as, DNA preparation, hybridization and image scan. DMH: analysis of bisulfite modified DNA by Illumina In particular, DMH based on McrBC restriction enzyme, HumanMethylation27 BeadChip. Individual CpG sites, involves extra steps of digestion, ligation, and PCR ampli- either within exactly the same genomic location or within fication. Therefore, the reproducibility of technical the same promoter CGI of the genes identified in our study, duplicates was evaluated by using R2 (coefficient of are present on the HumanMethylation27 BeadChip, except determination, ranging from 0 to 1) to estimate the at NKD1. Among the 6 loci examined, methylation of variations introduced in DMH assays. After background DVL1, ROCK1, LRP5, and AXIN1 were still prognostic in correction, with array normalization and feature selec- this independent patient cohort (1-sided P < 0.05, FDR < tion, the average R2 of duplicate arrays in the screening set 10%, n ¼ 311), and methylation of FZD4 shows marginal (n ¼ 111) and in the following validation set (n ¼ 48) was correlation with PFS in this patient cohort (1-sided very close to 1, 0.92 (n ¼ 111; mean SD: 0.92 0.04)

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A FZD4 B NKD1 1.0 1.0 Low methylation Low methylation –4 log-rank test P = 2.50 × 10 High methylation log-rank test P = 4.93 × 10–5 High methylation Low methylation-censored Low methylation-censored 0.8 High methylation [N = 38] High methylation-censored 0.8 High methylation [N = 39] High methylation-censored HR = 2.05 95% CI: 1.38–3.05 HR = 2.21 95% CI: 1.49–3.28

0.6 Low methylation [N = 118] 0.6 Low methylation [N = 117] HR = 1 HR = 1

0.4 0.4

0.2 0.2 Proportion free progression alive Proportion free progression alive

0.0 0.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 C Time (y) since treatment D Time (y) since treatment DVL1 ROCK1 1.0 1.0 Low methylation Low methylation –4 High methylation –3 High methylation log-rank test P = 1.67 × 10 Low methylation-censored log-rank test P = 9 × 10 Low methylation-censored 0.8 0.8 High methylation-censored High methylation-censored High methylation [N = 38] High methylation [N = 38] HR = 2.08 95% CI: 1.40–3.08 HR = 1.68 95% CI: 1.13–2.51 0.6 0.6 Low methylation [N = 118] Low methylation [N = 118] HR = 1 HR = 1 0.4 0.4

0.2 0.2 Proportion free progression alive Proportion free progression alive

0.0 0.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 Time (y) since treatment Time (y) since treatment E AXIN1 F LRP5 1.0 1.0 Low methylation Low methylation High methylation High methylation × –3 log-rank test P = 2.50 × 10–4 log-rank test P = 1.37 10 Low methylation-censored Low methylation-censored 0.8 0.8 High methylation [N = 38] High methylation-censored High methylation [N = 38] High methylation-censored HR = 1.87 95% CI: 1.26–2.76 HR = 2.05 95% CI: 1.38–3.05

0.6 Low methylation [N = 118] 0.6 Low methylation [N = 118] HR = 1 HR = 1

0.4 0.4

0.2 0.2 Proportion free progression alive Proportion free progression alive

0.0 0.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 GHTime (y) since treatment Time (y) since treatment NFATC3 DVL1 + NKD1 (methylation index) 1.0 1.0 Low methylation Low MI High methylation High MI –2 –4 log-rank test P = 3.45 × 10 Low methylation-censored log-rank test P = 2.42 × 10 Low MI-censored 0.8 0.8 Permutation test P < 0.005 High MI-censored High methylation [N = 38] High methylation-censored HR = 1.54 95% CI: 1.03–2.33 High methylation [N = 36] 0.6 0.6 HR = 2.09 95% CI: 1.39– 3.15 Low methylation [N = 118] HR = 1 Low methylation [N = 111] HR = 1 0.4 0.4

0.2 0.2 Proportion free progression alive Proportion free progression alive

0.0 0.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 Time (y) since treatment Time (y) since treatment

Figure 1. Kaplan–Meier plots of PFS. High/low methylation at A, FZD4 CGI; B, NKD1 CGI; C, DVL1 CGI; D, ROCK1 CGI; E, AXIN1 CGI; F, LRP5 CGI; G, NFATC3 CGI. Combined analysis of the evaluation and validation set shows that patients with hypermethylation at these loci have increased HR of disease progression (P < 0.05). The cutoff was determined by the third quartile in 156 patients (n ¼ 159, 3 patients have PFS missing). Time is from the patients received the first line of chemotherapy. H, Kaplan–Meier plots of PFS in the patients with high/low MI estimated from the multivariate Cox model including DVL1 and NKD1 in late-stage ovarian cancer. The cutoff was determined by the third quartile in 147 patients (n ¼ 150, 3 patients have PFS missing). Time is from the patients received the first line of chemotherapy.

4058 Clin Cancer Res; 17(12) June 15, 2011 Clinical Cancer Research

Methylation of Wnt Pathway Loci in EOC Associates with PFS

¼ Table 4. Univariate OS analysis of loci signifi- with response to chemotherapy (OR 1.1, 95% CI: 0.7– P ¼ cantly associated with PFS 1.6), 0.791). Because it has been shown that transcriptional silencing of SFRP5 is associated with promoter Genes Univariate OS (n ¼ 111) methylation (40), we further examined if expression of SFRP5 was correlated with response to first-line platinum- HRa 95% CIb Pc FDRd chemotherapy in the TCGA dataset, but no significant association was observed. However, we noted response FZD4 49.4 (2.5–964.3) 0.01* 0.1f was measured by both the reduction of tumor size and NKD1 21.3 (1.5–299.2) 0.023* 0.2e CA125 concentration in the previous study, whereas in our DVL1 14.0 (2.6–75.0) 0.002*** <0.1f study response was measured by RECIST 1.0 criteria based ROCK1 8.6 (0–5,642.9) 0.516 1 on the shrinkage of tumor size. Furthermore, different NKD2 1.3 (0.2–1.2) 0.804 1 composition of tumor stage and histology type in the FZD9 3.3 (0.5–23) 0.224 1 cohort studies might also cause the differences. We observe CCND1 33.4 (0.8–1,382.2) 0.065þ 0.5 that the prognostic value of FZD4, DVL1, and ROCK1 at 2 EEFSECkRUVBL1 1.4 (0.6–3.4) 0.505 1 molecular levels, increased methylation and decreased AXIN1 3.1 (0.1–85.4) 0.506 1 expression, are correlated with increased hazard of disease C4orf42kCTBP1 1.4 (0.6–3.4) 0.505 1 progression in EOC. This indicates expression of these 3 WNT4 5.9 (0.1–335.6) 0.388 1 genes might be tightly regulated by promoter methylation. LRP5 4.6 (0.6–33.8) 0.13 0.9 The interaction between individual Wnts and their specific NFATC3 4.5 (0.6–35.2) 0.148 0.9 receptors is thought to dictate the type of downstream SFRP5 1.1 (0–110.1) 0.967 1 signaling pathways that are activated. Accordingly, the

a Wnts have historically been divided into 2 classes: those HR per unit increase of DMH ratio (continuous variable) that signal through the "canonical" (b-catenin dependent) estimated from Cox proportional hazard regression model. b b or the "noncanonical" ( -catenin independent) signaling CI of the estimated HR. pathway (21). FZD4 is a member of the frizzled receptor cP value of score test. d family and plays a crucial role in corpus luteum formation Multiple test correction in the validation study based on 14 and function in mouse ovary (20). FZD4 is required along validated DNA methylation biomarkers. with LRP5 and the absence of ROR2, for Wnt-5a dependent e f þ P P P FDR 0.2; FDR 0.1; , 0.1; *, 0.05; **, 0.01; b-catenin transcriptional activation (21). Three disheveled P ***, 0.001. genes, DVL1, DVL2, and DVL3 have been identified in human. The Wnt3a-sensitive canonical pathway is particularly sensitive to knockdown of either Dvl1 or Dvl3 (56). Thus, promoter CGI methylation of DVL1 and FZD4 lead- and 0.96 (n ¼ 48; mean SD: 0.96 0.02), respectively. ing to downregulation of the expression of these 2 genes This indicates the bias in our DMH assays was small, could suppress the "canonical" signaling pathways in EOC, especially after appropriate data preprocessing. In addi- while potentially activating the noncannonical pathways. tion, before conducting thesurvivalanalysisofthe Although the majority of the samples from SGCTG Wnt pathway, we observed a good correlation between cohort are serous ovarian tumors, a small number of existing bisulfite pyrosequencing methylation data at tumors were endometrioid (n ¼ 12). It has been reported locus (chr14: 60174197–60174329) and average DMH that 16% to 38% of ovarian enometrioid cases are deregu- ratio observed at this locus (Spearman correlation rs ¼ lated in the Wnt pathway because of activating mutation of 0.87, P < 0.01; Supplementary Fig. S3), supporting the b-catenin, which is a key player in the Wnt canonical use of average DMH as a continuous variable in the pathway (57). Given the prognostic value of 5 of 7 inde- methylation analysis. We further examined 2 loci pendent methylation biomarkers found in SGCTG cohort (DVL1 and FZD4) by bisulfite pyrosequencing in the have been confirmed in the TCGA cohort, which only screening set of SGCTG cohort (see Supplementary contain high-grade serous ovarian tumors, we would argue Method 6), and found positive correlation between that the presence of b-catenin mutation in endometrial DMH assay and bisulfite pyrosequencing at these 2 pro- cancers is unlikely to be a major confounding factor. moter CGIs (Spearman correlation: DVL1, rs ¼ 0.35, P ¼ Further investigation is required for the loci that did not 0.001, n ¼ 79; FZD4, rs ¼ 0.19, P ¼ 0.037, n ¼ 91). This show a direct correlation between expression and methyla- indicates the use of DMH assay on custom-designed tion. As well as the limitations of microarray coverage, microarray with intensive coverage at promoter CGIs is limited statistical power, and potential lack of appropriate reliable for biomarker identification. transcription factors at the unmethylated promoters, these SFRP5 has been previously reported to correlate with analyses are mainly confounded by the tumor samples in poor response to platinum-based chemotherapy (40). We the TCGA cohort not being microdissected, which could found methylation at SFRP5 was weakly associated with influence TCGA expression data due to expression of the PFS, but not independent from clinical parameters genes in normal infiltrating tissues. It is also possible that (adjusted P ¼ 0.124, n ¼ 111), and had no correlation the biomarkers identified are heterogeneously methylated

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Dai et al.

Table 5. Survival analysis in TCGA expression and methylation dataset (n ¼ 311, disease progression/ relapse ¼ 184) of genes in the Wnt pathway associated with CGI loci independently associated with PFS

Genes Univariate PFS analysis (methylation) Correlation between methylation and expression

HRa 95% CIb P (1-sided)c FDRd Spearman r (rho) P

FZD4 2357.6 (0.1 107–3.3 107) 0.056þ <0.1f 0.02 0.677 NKD1 ----- DVL1 29.5 (5.1–172.1) 8.382 105*** <0.01g 0.14 0.011* ROCK1 46370.4 (0.2 109–9.1 109) 0.042* <0.1f 0.14 0.012* AXIN1 45.1 (1.7–1,187) 0.011* <0.1f 0.03 0.558 LRP5 64.4 (0.6–6697) 0.039* <0.1e 0.01 0.831 NFATC3 0.3 (0.1–0.8) 0.495 0.5 0.04 0.391

Genes Univariate PFS analysis (expression) Multivariate PFS analysis (expression) HRa 95% CIb P (2-sided)c FDRd HRa 95% CIb P (2-sided)e

FZD4 0.8 (0.7–0.9) 0.003** <0.01f 0.8 (0.6–0.9) 0.002** NKD1 ----- DVL1 0.8 (0.6–1.0) 0.038* <0.1f 0.8 (0.6–1.0) 0.035* ROCK1 0.7 (0.5–1.0) 0.04* <0.1f 0.7 (0.5–1.0) 0.045* AXIN1 1 (0.7–1.3) 0.833 0.8 0.9 (0.7–1.3) 0.659 LRP5 0.9 (0.7–1.0) 0.115 0.1f 0.9 (0.7–1.1) 0.163 NFATC3 1 (0.8–1.3) 0.887 0.9 1 (0.8–1.3) 0.748

aHR per unit increase in expression estimated from Cox proportional hazard regression model. bCI of the estimated HR. cP value of score test. dMultiple test correction based on 6 loci. eP value adjusted by stage, grade, and age. Age was used as a continuous variable; stage and grade were used as categorical variables. fFDR 0.1; gFDR 0.01; *, P 0.05; **, P 0.01; ***, P 0.001.

in the tumor tissue, that is, differential methylation of those region of the genes. In addition, the advantages of DNA biomarkers only exist in a subset of tumor (or normal methylation over gene or microRNA expression are (i) it is infiltrating) cells, therefore, the changes of expression of fairly stable in vivo (heritable in cell division and do not those genes in a subpopulation are masked by the general fluctuate in response to short-term stimuli) and ex vivo (can expression levels in the entire tumor. A further limitation of survive routine processing for histopathology), (ii) DNA the study is the low average power of the analysis in the methylation is readily detected in tumor DNA from body validation set (see Materials and Methods). However, 11 of fluids, such as plasma, and (iii) it is less influenced by 20 independent biomarkers identified in the first screening normal cell contamination. set have been validated in the second patient cohort. In conclusion, we have shown that methylation of Nevertheless, those loci that have not been validated need multiple promoter CGIs in the Wnt pathway is frequently further investigation in a larger sample set. observed in EOC and identified methylation of key loci as Although multiple types of prognostic biomarkers show significantly associated with PFS (CGIs at FZD4, DVL1, promise in ovarian cancer, such as serum CA125 and HE4 NKD1, ROCK1, AXIN1, LRP5,andNFATC3)thatare (15), plasma lysophosphatidic acid (58), mRNA expres- independent from clinical parameters. We have used sion profiling (59, 60) and microRNAs (61), there are a thesedatatoconstructamultivariateCoxmodelthat number of advantages of using DNA methylation as a incorporates 2 independent CGIs at NKD1 and DVL1, clinical biomarker (62–65). Compared with protein-based which can identify 2 groups of patients with distinct PFS. biomarkers, DNA methylation is amplifiable and can be Methylation changes at the Wnt pathway will be relevant detected easily by PCR-based methods. Contrary to gene for patient stratification in future clinical trials of ovarian mutation, cancer-specific hypermethylation generally cancer, particularly for novel drugs targeting the Wnt occurs in a defined region usually in or near promoter pathway (66).

4060 Clin Cancer Res; 17(12) June 15, 2011 Clinical Cancer Research

Methylation of Wnt Pathway Loci in EOC Associates with PFS

Disclosure of Potential Conflict of Interest Grant Support

No potential conflicts of interest were disclosed. The study was financially supported by Cancer Research UK and Ovarian Cancer Action. The costs of publication of this article were defrayed in part by the Acknowledgment payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate We thank Jan Graham, Liz-Anne Lewsley, members of SGCTG, and this fact. patients for their help in the study. External validation of our candidate genes used expression and methylation data generated by TCGA Pilot Received November 15, 2010; revised March 6, 2011; accepted March 22, Project established by the NCI and NHGRI. 2011; published OnlineFirst April 1, 2011.

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4062 Clin Cancer Res; 17(12) June 15, 2011 Clinical Cancer Research

Systematic CpG Islands Methylation Profiling of Genes in the Wnt Pathway in Epithelial Ovarian Cancer Identifies Biomarkers of Progression-Free Survival

Wei Dai, Jens M. Teodoridis, Constanze Zeller, et al.

Clin Cancer Res 2011;17:4052-4062. Published OnlineFirst April 1, 2011.

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