Novel Candidate Targets of -Catenin/T-cell Factor ... - Semantic Scholar

[CANCER RESEARCH 63, 2913–2922, June 1, 2003]

Novel Candidate Targets of ␤-Catenin/T-cell Factor Signaling Identified by Gene Expression Profiling of Ovarian Endometrioid Adenocarcinomas1 Donald R. Schwartz,2 Rong Wu,2 Sharon L. R. Kardia, Albert M. Levin, Chiang-Ching Huang, Kerby A. Shedden, Rork Kuick, David E. Misek, Samir M. Hanash, Jeremy M. G. Taylor, Heather Reed, Neali Hendrix, Yali Zhai, Eric R. Fearon, and Kathleen R. Cho3 Comprehensive Cancer Center and Departments of Pathology [D. R. S., R. W., H. R., N. H., Y. Z., E. R. F., K. R. C.], Internal Medicine [E. R. F., K. R. C.], Pediatrics and Communicable Diseases [R. K., D. E. M., S. M. H.], and Biostatistics [C-C. H., J. M. G. T.], School of Medicine, Department of Epidemiology [S. L. R. K., A. M. L.], School of Public Health and Statistics [K. A. S.], College of Literature Science and the Arts, University of Michigan, Ann Arbor, Michigan 48109-0638

ABSTRACT The activity of ␤-catenin (␤-cat), a key component of the Wnt signaling pathway, is deregulated in about 40% of ovarian endometrioid adenocarcinomas (OEAs), usually as a result of CTNNB1 gene mutations. The function of ␤-cat in neoplastic transformation is dependent on T-cell factor (TCF) transcription factors, but specific genes activated by the interaction of ␤-cat with TCFs in OEAs and other cancers with Wnt pathway defects are largely unclear. As a strategy to identify ␤-cat/TCF transcriptional targets likely to contribute to OEA pathogenesis, we used oligonucleotide microarrays to compare gene expression in primary OEAs with mutational defects in ␤-cat regulation (n ⴝ 11) to OEAs with intact regulation of ␤-cat activity (n ⴝ 17). Both hierarchical clustering and principal component analysis based on global gene expression distinguished ␤-cat-defective tumors from those with intact ␤-cat regulation. We identified 81 potential ␤-cat/TCF targets by selecting genes with at least 2-fold increased expression in ␤-cat-defective versus ␤-cat regulation-intact tumors and significance in a t test (P < 0.05). Seven of the 81 genes have been previously reported as Wnt/␤-cat pathway targets (i.e., BMP4, CCND1, CD44, FGF9, EPHB3, MMP7, and MSX2). Differential expression of several known and candidate target genes in the OEAs was confirmed. For the candidate target genes CST1 and EDN3, reporter and chromatin immunoprecipitation assays directly implicated ␤-cat and TCF in their regulation. Analysis of presumptive regulatory elements in 67 of the 81 candidate genes for which complete genomic sequence data were available revealed an apparent difference in the location and abundance of consensus TCF-binding sites compared with the patterns seen in control genes. Our findings imply that analysis of gene expression profiling data from primary tumor samples annotated with detailed molecular information may be a powerful approach to identify key downstream targets of signaling pathways defective in cancer cells.

INTRODUCTION The vast majority of ovarian cancers are derived from epithelial cells. These malignant epithelial tumors (carcinomas) are typically gland-forming and can be divided into four major morphological types, serous, endometrioid, clear cell, and mucinous adenocarcinomas. Recently, we used oligonucleotide microarrays to characterize global gene expression in 113 ovarian adenocarcinomas (1). Our results and those of other studies offer support for the concept that different histological types of ovarian carcinoma likely represent distinct, albeit overlapping, disease entities, with each type exhibiting Received 1/28/03; accepted 4/14/03. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. 1 Supported by funds from the Department of Defense (DAMD 17-1-1-0727), the National Cancer Institute (U19 CA84953, RO1 CA94172, and RO1 CA85463), and in part by the Tissue Core of the University of Michigan Comprehensive Cancer Center (NIH P30 CA46952). 2 Both authors contributed equally to this work. 3 To whom requests for reprints should be addressed, at Department of Pathology, University of Michigan Medical School, 4301 MSRB III, 1150 West Medical Center Drive, Ann Arbor, MI 48109-0638. Phone: (734) 764-1549; Fax: (734) 647-7979; E-mail: [email protected].

specific molecular and pathobiological features [reviewed by Feeley and Wells (2) and Aunoble et al. (3)]. OEAs4 are characterized by frequent (16 –54%) mutations of CTNNB1, the gene encoding ␤-cat, a critical component of the Wnt signaling pathway (4 –9). Wnts are a highly conserved family of secreted growth factors that bind members of the frizzled family of transmembrane receptors and, through downstream signaling, modulate many developmental and adult tissue processes including cell fate specification, proliferation, and differentiation (10). ␤-cat plays a critical role in both cell adhesion and Wnt signaling. The cytoplasmic/ nuclear pool of ␤-cat involved in Wnt signaling is largely regulated by a multiprotein complex consisting of the APC tumor suppressor, AXIN, and GSK3␤ proteins (11–18). In the absence of Wnt signals, this protein complex promotes degradation of free cytosolic ␤-cat via GSK3␤-mediated phosphorylation of NH2-terminal ␤-cat residues and subsequent ubiquitination and degradation of ␤-cat by the proteasome. Wnt ligands, upon binding to a Frizzled-LRP (lipoproteinreceptor-related protein) transmembrane receptor complex, activate a pathway that inhibits GSK3␤ activity, with resultant stabilization and nuclear localization of ␤-cat. Nuclear ␤-cat cooperates with members of the TCF/lymphoid enhancer factor transcription regulator proteins (hereafter referred to collectively as TCFs) to activate transcription of specific target genes. The Wnt pathway is deregulated in many types of human cancers (17), including melanomas (19), hepatoblastomas (20), medulloblastomas (21) and carcinomas of the colon (22), prostate (23, 24), uterine endometrium (25–27), and ovary (4 –9). Presumably, many of the proteins encoded by ␤-cat/TCF transcriptional targets play important roles in effecting neoplastic transformation. Many of the previous studies aimed at identifying novel Wnt pathway target genes have been carried out in developmental systems or using various in vitro or animal tumor models (28 –33). The relatively few studies based on analysis of primary human cancers have focused mainly on colorectal carcinomas, the majority of which manifest Wnt pathway defects (34 –36). We recently found diverse mechanisms of ␤-cat deregulation in approximately one-third of primary OEAs, including frequent mutations of CTNNB1 and, less commonly, mutations of APC, AXIN1, and AXIN2 (9). Expression of six previously reported ␤-cat/TCF-regulated genes (CCND1, c-MYC, MMP-7, CX43, ITF2, and PPAR-␦) was subsequently evaluated in OEAs with and without documented Wnt pathway defects (37). All of these genes except c-MYC were significantly increased in expression in OEAs with deregulated ␤-cat. These studies suggest that comparison of comprehensive gene expression data from primary OEAs with and without deregulated ␤-cat may provide a robust strategy for identifying as-yet-undetermined ␤-cat/TCF target genes with roles in the pathogenesis of OEAs and perhaps other types of human cancers 4 The abbreviations used are: OEA, ovarian endometrioid adenocarcinoma; TCF, T-cell factor; APC, adenomatous polyposis coli; GSK3␤, glycogen synthase kinase 3␤; RT-PCR, reverse transcription-PCR; q-RT-PCR, quantitative RT-PCR; HC, hierarchical clustering; PCA, principal component analysis; PC, principal component; ␤-cat, ␤catenin; CMV, cytomegalovirus; dn-, dominant negative; ChIP, chromatin immunoprecipitation; PI3K, phosphatidylinositol 3⬘-kinase; UTR, untranslated region.

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with Wnt pathway defects. We applied this approach using gene expression data from oligonucleotide microarrays, and we report here on several novel candidate ␤-cat/TCF transcriptional targets.

MATERIALS AND METHODS Tumor Samples and RNA Isolation. Forty-one snap-frozen primary OEAs were analyzed using oligonucleotide microarrays and/or RT-PCR (37 from the Cooperative Human Tissue Network/Gynecologic Oncology Group Tissue Bank, 2 from the University of Michigan Health System, and 2 from the Johns Hopkins Hospital). Gene expression in 28 of the 41 tumors evaluated with Affymetrix HuGeneFL oligonucleotide microarrays has been reported previously (1, 38). Of these 28 OEAs, 11 have a documented Wnt pathway defect and nuclear localization of ␤-cat protein (9). The subset of 11 Wnt pathway-defective tumors includes 10 tumors (OE-2, -13, -17, -18, -37, -44, -47, -48, -55, and -71) with stabilizing mutation of CTNNB1, and one tumor (OE-32) with biallelic inactivation of APC. The remaining 17 OEAs lacked Wnt pathway defects based on immunohistochemical studies showing lack of nuclear ␤-cat protein and mutational analyses of CTNNB1, APC, AXIN1, and/or AXIN2 (9). Primary tumor tissues were manually microdissected before RNA extraction to ensure each tumor sample contained at least 70% neoplastic cells. Total RNA was extracted from frozen tissue biopsies with Trizol (Life Technologies, Inc., Carlsbad, CA) and then further purified using RNeasy spin columns (Qiagen, Valencia, CA) according to the manufacturers’ protocols. Analysis of tissues from human subjects was approved by the University of Michigan’s Institutional Review Board (IRB-MED #2001-0568). Data Processing. Acquisition and processing of oligonucleotide microarray data from the 28 OEAs described above has been reported previously (1). A detailed description of the methods, as well as the freely available code, can be found online.5 The analyses for the present study included data from 6955 noncontrol probe sets (of 7069 available on the chip), after elimination of probe sets that contained fewer than 8 probe pairs (n ⫽ 57) and those that were invariant (SD ⬍10⫺6; n ⫽ 57). These probe sets represent approximately 5700 unique genes. Statistical Analysis. Gene expression values were log-transformed by logarithm10(max[X ⫹100,0],⫹100). Two-sample t tests of the log-transformed data were used to compare Wnt pathway-defective with pathway-intact samples. Fold change in gene expression is the ratio (Wnt defect samples:Wnt intact samples) of the relative gene expression values, which were computed as the antilogarithm of the mean log-transformed data. We selected candidate Wnt pathway target genes by demanding that their relative gene expression levels yield P ⬍ 0.05 for the t test, as well as giving at least a 2-fold increase in the gene expression ratio. To view relationships of the samples based on global gene expression, we performed both HC (39) and PCA (40) on the log-transformed data from all 6955 probe sets. Both HC and PCA were performed using S-PLUS 2000 software (MathSoft, Inc., Cambridge, MA). HC is a method that finds relationships between samples based on correlation of their gene expression and then displays the sample relationships in a taxonomy-like dendrogram. PCA contracts multidimensional gene expression data into ranked statistically independent projections, called components. The first PC captures the greatest fraction of variance from the expression data compared to any other projection, whereas the second PC captures the greatest variance remaining in the data, independent of the first projection, and so on. Any two PCs can be used to plot a two-dimensional view of the multidimensional gene expression data such that samples located close to each other have more similar gene expression than samples that are further apart. Search for Consensus TCF Sites in Candidate Wnt Pathway Target Genes. We searched for consensus TCF sites (5⬘-WWCAAWG-3⬘), as defined by Roose and Clevers (41), in selected regions of putative Wnt pathway target genes in silico. Similar studies were carried out on control genes insensitive to Wnt pathway status, i.e., genes with a geometric mean gene expression ratio of 1 ⫾ 0.01 when Wnt pathway-deregulated samples were compared with Wnt pathway-intact samples. Genomic sequences of only fully mapped genes were downloaded from the June 2002 version of the University 5

of California at Santa Cruz working draft of the human genome sequence,6 with highly repetitive sequences masked. Pointers to the transcription start and stop sites provided within the RefSeq portion of the University of California at Santa Cruz human genome browser database were used to extract specific gene sequences for analysis (42). We obtained sequence starting from 5000 bp upstream (5⬘) of the start of transcription to 1000 bp downstream (3⬘) from the stop of translation. For genes with multiple transcripts, only the longest transcript was used for TCF site annotation. In-house software was used to extract genomic DNA sequences and to identify consensus TCF sites within them. We compared the proportion of sites (the number of sites within a gene region divided by the total number of possible sites within the same gene region) within candidate Wnt pathway genes and control genes using a onesided standard normal Z test. The ratio of the proportions of sites in candidate target genes relative to control genes was estimated, and 95% confidence intervals were computed using the delta method. PCR-based Gene Expression Expression Analyses. We used real-time q-RT-PCR analysis to validate differential expression of selected putative Wnt pathway target genes in RNA samples from 31 primary OEAs, including 18 of the 28 OEAs analyzed by oligonucleotide microarrays. Of these 31 tumors, 14 had documented ␤-cat deregulation, and 17 had intact ␤-cat/TCF signaling. The detailed methods and sequences of forward primer (f), reverse primer (r), and probe (p) for the genes validated by q-RT-PCR [CNND1 (cyclin D1), FGF9 (fibroblast growth factor 9), EDN3 (endothelin 3), SFN (stratifin), and HPRT1, which served as an internal control] are available online.5 Differences between tumors with intact versus deregulated TCF/␤-cat were tested using the Student’s t test. Pearson product-moment correlations were used to estimate the degree of association between the microarray and q-RT-PCR data. Semiquantitative multiplex RT-PCR for CST1 was performed as follows. Each PCR reaction contained 2 ␮Ci of [␣-32P]dCTP (ICN, Costa Mesa, CA); 25 ␮M dCTP; 200 ␮M dATP, dGTP, and dDTP; 1.5 ␮M of each primer (both target gene and internal control; see website5 for primer sequences); 1 unit of platinum Taq polymerase (Invitrogen, Carlsbad, CA); and 20 ng of first-strand cDNA. HPRT1 was used as the reference gene. Reactions were performed at 95°C for 3 min; denaturing at 95°C for 30 s, annealing at 58°C for 30 s, and elongation at 72°C for 45 s for 28 cycles; followed by 7 min of extension. PCR products were resolved on 6% denaturing polyacrylamide gels. After vacuum drying, gels were exposed to phosphorimager screens (Amersham Biosciences Corp., Piscataway, NJ). To determine the ratio of target gene amplification in tumors, the values from the target gene (CST1) were normalized with the values of internal control gene (HPRT1) from each sample. All reactions were performed at least twice. Plasmids. Expression constructs for the mutant form of ␤-cat (codon 33 substitution of tyrosine for serine, S33Y) and dn-TCF-4 (TCF-4r N31) have been described previously (43). The reporter constructs pTOPFLASH, which contains three copies of an optimal TCF/lymphoid enhancer factor binding motif (CCTTTGATC), and pFOPFLASH, which contains three copies of a mutant motif (CCTTTGGCC), were generously provided by Bert Vogelstein (Johns Hopkins University) and have been described previously (44). pCH110 (Amersham, Arlington Heights, IL) contains a functional LacZ gene cloned downstream of a CMV early region promoter-enhancer element. DNA fragments containing human CST1 and EDN3 promoter sequences were obtained by PCR amplification of genomic DNA, using primers for CST1 (accession number AL591074) and EDN3 (accession number AL035250) from sequences in GenBank. PCR fragments were cloned into pCR-Blunt II-TOPO (Invitrogen) and then subcloned upstream of the luciferase reporter gene in the pGL3Basic vector (Promega, Madison, WI). Mutations of presumptive TCFbinding sites in the CST1 and EDN3 promoters were obtained in vitro by PCR-based mutagenesis using primers containing the desired mutations. All construct sequences were confirmed by automated sequencing of doublestranded DNA templates. Cell Culture. The cells lines 293 (transformed human kidney epithelium), DLD-1 (colon cancer), SW480 (colon cancer), and TOV112D (OEA) were obtained from the American Type Culture Collection (Manassas, VA). Cell lines with stable expression of dn-TCF-4 (TCF-4⌬N31/pPGS-CMV-CITEneo), DLD-1/dn-TCF, SW480/dn-TCF, and TOV112D/dn-TCF or empty vector (pPGS-CMV-CITE-neo) were generated as described previously (45). All 6

http://dot.ped.med.umich.edu:2000/pub/Ovary/index.html.

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cell lines were grown in DMEM containing 10% fetal bovine serum and penicillin/streptomycin (Life Technologies, Inc., Gaithersburg, MD). Luciferase Reporter Gene Assays. Reporter assays for ␤-cat/TCFmediated transcriptional activity were performed in triplicate as described previously (9, 46). Briefly, cells were plated into 6-well dishes and then transfected with various constructs using FuGENE 6 reagent (Roche, Indianapolis, IN). Cells were co-transfected with 0.5 ␮g of pCH110 (Amersham) to determine transfection efficiency based on ␤-galactosidase expression. After 48 h of incubation at 37°C, transfected cells were washed with PBS and lysed with reporter lysis buffer (Promega). Luciferase assay reagent (Promega) was added to the cell lysates, and then luciferase activity was measured with a luminometer (model TD-20E; Turner Corp., Mountain View, CA). To correct for variation in transfection efficiency, luciferase activity was normalized to ␤-galactosidase activity, which was measured using the ␤-galactosidase enzyme assay system (Promega) and a microplate reader. To assess the response of human CST1 and EDN3 promoter sequences to ␤-cat, 293 cells were cotransfected with pCH110, 0.2 ␮g of pcDNA3/S33Y, and 0.5 ␮g of various CST1 or EDN3 reporter constructs (see Figs. 3A and 4A) or 0.5 ␮g of the pGL3Basic vector as a negative control. As a positive control for ␤-cat activation of a TCF responsive promoter, pTOPFlash or pFOPFlash was cotransfected into 293 cells. Reporter gene assays using dn-TCF-mediated transcriptional repression were performed in triplicate as described previously (45). Cell lines stably expressing dn-TCF (pPGS-CMV-CITE-TCF-4r N31) or empty vector (pPGS-CMV-CITE-neo) were cotransfected (0.5 ␮g) with reporter constructs CST1-A, EDN3-A, or pGL3Basic empty vector as a negative control. Transfection efficiency was assessed as described above. The total mass of transfected DNA in each well was kept constant by adding empty vector plasmid DNA. Fold activation of TCF transcriptional activity was calculated by normalizing the luciferase activity to the empty vector (pGL3Basic) control after adjusting for transfection efficiency. ChIP Assay. ChIP assays were performed according to the manufacturer’s protocol (kit 17-925; Upstate Biotechnology, Lake Placid, NY), with some modifications as reported previously (47). Briefly, 1% formaldehyde was added to the culture medium of 2 ⫻ 106 adherent cells for 10 min at 37°C. Cells were washed twice with ice-cold PBS (Life Technologies, Inc.) containing proteinase inhibitors. Cells were collected and suspended in 200 ␮l of lysis buffer containing proteinase inhibitors, and lysed cells were sonicated to yield 200-1000-bp DNA fragments. After centrifugation to eliminate cell debris, the samples were diluted 1:10 in ChIP dilution buffer containing proteinase inhibitors and 0.1 volume of the diluted sample was removed and saved to assess input DNA. Salmon sperm DNA/protein A-agarose (80 ␮l) was added to 1.8 ml of the diluted sample and gently mixed for 30 min at 4°C to reduce nonspecific binding. Then, 4 ␮l of polyclonal anti-␤-cat antibody (06-734; Upstate Biotechnology) was added to the precleared ChIP solution and incubated overnight at 4°C with gentle agitation, followed by the addition of the salmon sperm DNA/protein A-agarose slurry for 1 h. Immunoprecipitates were washed, and the DNA-protein complexes were eluted as reported previously (47). NaCl (final concentration, 200 mM) was added, and the samples were incubated at 65°C for 4 h to reverse the formaldehyde cross-link. After treatment with proteinase K, DNA was extracted from the samples with phenol/chloroform and precipitated with ethanol. DNA pellets were suspended in 40 ␮l of H2O. The primer pairs used to amplify EDN3 promoter sequences and “irrelevant” DNA downstream and upstream of the EDN3 promoter from the immunoprecipitated chromatin are specified on our website.5

RESULTS Wnt Pathway Status Is a Major Determinant of Global Gene Expression in OEAs. We generated gene expression profiles of 28 primary OEAs using Affymetrix HuGeneFL oligonucleotide microarrays. To visualize relationships of tumor samples based on global gene expression, both HC and PCA of the microarray data were performed. The analyses included data from nearly all of the probe sets on the chip (6955 of 7069 noncontrol probe sets as described in “Materials and Methods”) to obtain views of gene expression unbiased by preselection of genes. Fig. 1A shows a HC dendrogram. Notably, all of the OEAs with deregulated ␤-cat are found on one branch of the primary division between the two main clusters. PCA provides an

Fig. 1. Tumor relationships based on global gene expression data from HuGeneFL oligonucleotide microarrays. A, dendrogram of a HC analysis using average linkage. B, PCA showing the first two PCs. The Wnt/␤-catenin pathway status of each tumor is annotated as indicated at the bottom of the figure.

alternative method for viewing relationships between tumors based on multidimensional gene expression data. We have plotted the first two PCs, which represented 23% of the variation in gene expression, in Fig. 1B. Tumor samples with deregulated ␤-cat occupy a space that minimally overlaps the area defined by OEAs without ␤-cat deregulation. The observation that the first PC for this large gene collection is strongly associated with Wnt pathway status was unexpected and suggests that this feature is a critical, but not sole, determinant of gene expression in OEAs. Identification of Candidate Wnt Pathway Target Genes in OEAs. We required that candidate Wnt/␤-cat pathway target genes meet both of the following criteria: (a) expression value increased at least 2-fold in OEAs with deregulated ␤-cat compared with OEAs with apparently intact ␤-cat regulation; and (b) significant Student’s t test (P ⬍ 0.05). Eighty-one genes satisfying these criteria were identified (Table 1). We performed a randomization procedure to demonstrate that 81 genes is more than could be expected by chance alone. Specifically, the Wnt pathway status labels were randomly permuted across the 28 OEA samples 2000 times, and in each case, the number of genes in the randomized data that satisfied our gene selection criteria was determined. In the randomized data, the maximum number of genes satisfying our criteria was 65, the minimum number was 0, and the average number was 6.9. Compared with the number of genes found by chance alone, the observed number (n ⫽ 81) of candidate genes was highly significant (P ⬍ 0.0005). To assess whether the 81 genes are more highly associated with each other in the tumors with deregulated ␤-cat than the other group,

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Table 1 Candidate Wnt pathway target genes in OEA Relative mean gene expression Affymetrix probe set

Gene symbol

Description

Intact Wnt pathway

Deregulated Wnt pathway

Ratio

P

LocusLink

X54667㛭s㛭at U23070㛭at X57348㛭s㛭at M81883㛭at U65932㛭at D89377㛭s㛭at M31682㛭at X52001㛭at L22524㛭s㛭at U14528㛭at S79219㛭s㛭at D14838㛭at K01396㛭at M68516㛭rna1㛭at X68742㛭at M21389㛭at HG880-HT880㛭s㛭at L38517㛭at M55593㛭at U33147㛭at J05257㛭at X91117㛭rna1㛭at U53506㛭at X51441㛭s㛭at X67697㛭at X61118㛭rna1㛭at HG2981-HT3127㛭s㛭at X59798㛭at X16354㛭at L13286㛭at J02611㛭at M21624㛭at D50840㛭at U59321㛭at X13255㛭at X68733㛭rna1㛭at X78706㛭at AB000220㛭at M31994㛭at X07730㛭at M57710㛭at L27479㛭at D42073㛭at M14539㛭at S39329㛭at HG1067-HT1067㛭r㛭at U53347㛭at X68314㛭at U83115㛭at M22490㛭at S62539㛭at L11708㛭at M27492㛭at X76717㛭at M35252㛭at D64109㛭at M97925㛭rna1㛭at U90911㛭at HG2755-HT2862㛭at J03779㛭at X75208㛭at U46689㛭at U73799㛭at X16832㛭at X69111㛭at HG2167-HT2237㛭at U07807㛭at M94151㛭at U35048㛭at J04164㛭at X81892㛭at M14745㛭at U52828㛭s㛭at X71125㛭at D83735㛭at U43148㛭at U73960㛭at M80482㛭at M34455㛭at D86980㛭at D87258㛭at

CST4 NMA SFN GAD1 ECM1 MSX2 INHBB EDN3 MMP7 SLC26A2 PCCA FGF9 SERPINA1 SERPINA5 ITGA1 KRT5 MUC6 IHH MMP2 MGB1 DPEP1 SLC6A2 DIO2 SAA1 SPAG11 LMO2 CD44 CCND1 CEACAM1 CYP24 APOD TRD@ UGCG DDX17 DBH SERPINA3 CRAT SEMA3C ALDH1 KLK3 LGALS3 X123 RCN1 F13A1 KLK2 NULL SLC1A5 GPX2 AIM1 BMP4 IRS1 HSD17B2 IL1R1 MT1L TM4SF3 TOB2 DEFA5 NULL PLS3 MME EPHB3 ALDH10 NULL NULL ID3 LBC NULL CTNNA2 TSC22 IFITM1 GPR64 BCL2 CTNND2 QPCT CNN2 PTCH ARL4 PACE4 INDO KIAA0227 PRSS11

Cystatin S Putative transmembrane protein Stratifin Glutamate decarboxylase 1 (brain, 67 kDa) Extracellular matrix protein 1 msh (Drosophila) homeo box homologue 2a Inhibin, ␤ B (activin AB ␤ polypeptide) Endothelin 3 Matrix metalloproteinase 7 (matrilysin) Solute carrier family 26 (sulfate transporter), member 2 Propionyl coenzyme A carboxylase, ␣ polypeptide Fibroblast growth factor 9 Serine (or cys) proteinase inhibitor, clade A, member 1 Serine (or cys) proteinase inhibitor, clade A, member 5 Integrin, ␣ 1 Keratin 5 (epidermolysis bullosa simplex) Mucin 6, gastric Indian hedgehog (Drosophila) homologue Matrix metalloproteinase 2 (gelatinase A) Mammaglobin 1 Dipeptidase 1 (renal) Solute carrier family 6 (noradrenalin transporter), member 2 Deiodinase, iodothyronine, type II Serum amyloid A1 Sperm associated antigen 11 LIM domain only 2 (rhombotin-like 1) CD44 antigen (Indian blood group system) Cyclin D1 (PRAD1: parathyroid adenomatosis 1) Carcinoembryonic antigen-related cell adhesion molecule 1 Cytochrome P450, subfamily 24 (vitamin D 24-hydroxylase) Apolipoprotein D T cell receptor ⌬ locus UDP-glucose ceramide glucosyltransferase DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 17 Dopamine ␤-hydroxylase Serine (or cysteine) proteinase inhibitor, clade A, 3 Carnitine acetyltransferase Sema domain, immunoglobulin domain, (semaphorin) 3C Aldehyde dehydrogenase 1, soluble Kallikrein 3, (prostate specific antigen) Lectin, galactoside-binding, soluble, 3 (galectin 3) Friedreich ataxia region gene X123 Reticulocalbin 1, EF-hand calcium binding domain Coagulation factor XIII, A1 polypeptide Kallikrein 2, prostatic Homo sapiens clone 24747 mRNA sequence Solute carrier family 1 (neutral A.A. transporter), 5 Glutathione peroxidase 2 (gastrointestinal) Absent in melanoma 1 Bone morphogenetic protein 4 Insulin receptor substrate 1 Hydroxysteroid (17-␤) dehydrogenase 2 Interleukin 1 receptor, type I Metallothionein 1L Transmembrane 4 superfamily member 3 Transducer of ERBB2, 2 Defensin, ␣ 5, Paneth cell-specific H. sapiens cDNA: FLJ23260 fis, clone COL05804 Plastin 3 (T isoform) Membrane metallo-endopeptidase (neutral, CD10) EphB3 Aldehyde dehydrogenase 10 (fatty aldehyde dehydrogenase) H. sapiens mRNA, (from clone DKFZp434B1620) H. sapiens cDNA: FLJ22499 fis, similar to Hu cathepsin H Inhibitor of DNA binding 3, dominant negative H-L-H protein Lymphoid blast crisis oncogene H. sapiens pseudogene for metallothionein and AG/CT repeat Catenin (cadherin-associated protein), ␣ 2 Transforming growth factor ␤-stimulated protein TSC-22 Interferon induced transmembrane protein 1 (9-27) G protein-coupled receptor 64 B-cell CLL/lymphoma 2 Catenin, ⌬ 2 (neural plakophilin-related arm-repeat protein) Glutaminyl-peptide cyclotransferase (glutaminyl cyclase) Calponin 2 Patched (Drosophila) homologue ADP-ribosylation factor-like 4 Paired basic amino acid cleaving system 4 Indoleamine-pyrrole 2,3 dioxygenase KIAA0227 protein Protease, serine, 11 (IGF binding)

927.1 346.8 483.0 320.0 463.5 246.6 137.8 209.1 1267.0 476.7 1225.0 423.1 1661.8 705.4 448.4 425.5 130.3 176.0 1057.0 570.3 202.0 265.6 109.4 302.1 406.9 469.9 396.9 1390.7 589.7 211.3 787.4 366.1 1613.0 427.9 203.2 1172.8 872.0 1517.5 1522.8 545.9 7561.3 205.3 1952.3 549.7 193.8 125.2 1495.1 245.7 1147.9 840.0 525.5 267.9 1579.7 2966.1 549.6 1082.9 515.6 1311.3 838.7 520.3 1070.5 630.0 115.0 2622.9 1670.5 1455.2 196.8 308.1 3889.6 16201.5 353.8 662.6 335.2 303.5 1416.7 404.5 725.2 891.8 1402.3 288.0 2996.2

9065.9 2835.7 3918.3 2480.9 2712.3 1425.0 770.4 1122.6 6059.6 2263.6 5661.3 1950.8 7340.6 2973.1 1847.2 1688.1 512.2 675.2 3934.7 2117.6 731.4 923.9 376.0 1004.6 1342.0 1507.7 1239.1 4318.4 1734.8 613.9 2285.9 1057.5 4520.1 1177.4 526.3 3010.1 2213.1 3840.1 3809.8 1362.6 18451.6 499.9 4737.2 1327.9 464.0 298.4 3556.5 582.3 2685.3 1950.7 1217.1 616.5 3624.7 6801.2 1254.8 2458.5 1166.1 2946.8 1846.4 1129.1 2304.6 1353.6 245.0 5520.0 3503.4 3028.9 405.5 634.7 7986.1 33164.5 723.9 1355.5 684.4 619.4 2882.3 822.9 1473.4 1800.0 2812.3 576.6 5994.7

9.8 8.2 8.1 7.8 5.9 5.8 5.6 5.4 4.8 4.7 4.6 4.6 4.4 4.2 4.1 4.0 3.9 3.8 3.7 3.7 3.6 3.5 3.4 3.3 3.3 3.2 3.1 3.1 2.9 2.9 2.9 2.9 2.8 2.8 2.6 2.6 2.5 2.5 2.5 2.5 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.2 2.2 2.2 2.2 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000 0.0066 0.0000 0.0000 0.0000 0.0007 0.0060 0.0000 0.0068 0.0038 0.0005 0.0000 0.0009 0.0038 0.0000 0.0004 0.0311 0.0016 0.0000 0.0000 0.0081 0.0010 0.0001 0.0013 0.0005 0.0001 0.0056 0.0029 0.0153 0.0035 0.0038 0.0128 0.0105 0.0006 0.0020 0.0003 0.0098 0.0204 0.0287 0.0009 0.0047 0.0153 0.0002 0.0003 0.0003 0.0019 0.0020 0.0478 0.0007 0.0018 0.0005 0.0276 0.0032 0.0001 0.0000 0.0014 0.0111 0.0014 0.0000 0.0001 0.0154 0.0008 0.0471 0.0313 0.0020 0.0012 0.0001 0.0047 0.0004 0.0003 0.0018 0.0249 0.0000 0.0127

1472 25805 2810 2571 1893 4488 3625 1908 4316 1836 5095 2254 5265 5104 3672 3852 4588 3549 4313 4250 1800 6530 1734 6288 10407 4005 960 893 634 1591 347 6964 7357 10521 1621 12 1384 10512 216 354 3958 9413 5954 2162 3817 NULL 6510 2877 202 652 3667 3294 3554 4500 7103 10766 1670 NULL 5358 4311 2049 224 NULL NULL 3399 3928 NULL 1496 8848 8519 10149 596 1501 25797 1265 5727 10124 5046 3620 23508 5654

a

Bold text indicates genes that have been previously reported as potential ␤-cat/TCF transcriptional targets in other systems.

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Refa

53 52,28 51

49 29,30

48

50

␤-CATENIN/TCF TARGET GENES IN OVARIAN CANCER

Table 2 The distribution of consensus TCF (5⬘-WWCAAWG-3⬘) sites differs between candidate Wnt pathway target genes and control genes Up-regulateda (ur), n ⫽ 67 c

Gene region

N

⫺5000:transcript-start ⫺500:transcript-start ⫺1000:⫺501 ⫺1500:⫺1001 ⫺2000:⫺1501 ⫺3000:⫺2001 ⫺4000:⫺3001 ⫺5000:⫺4001 5⬘-UTR 3⬘-UTR ⫺1000 from translation-stop translation-stop:⫹500 ⫹501:1000

303 32 41 33 24 53 53 67 203 112 72 43 29

T

d

335000 33500 33500 33500 33500 67000 67000 67000 162313 95710 67000 33500 33500

Pur

Controlb (c), n ⫽ 100 e

9.044 9.55 12.239 9.851 7.164 7.91 7.91 10.00 12.51 11.702 10.746 12.836 8.657

95% Confidence interval

N

T

Pc

Pur/Pc

Lower

Upper

Pf

413 42 40 44 53 90 65 79 281 289 135 82 53

500000 50000 50000 50000 50000 100000 100000 100000 289367 245374 100000 50000 50000

8.30 8.40 8.00 8.80 10.60 9.00 6.50 7.90 9.70 11.80 13.50 16.40 10.60

1.09 1.14 1.53 1.12 0.68 0.88 1.22 1.27 1.29 0.99 0.80 0.78 0.82

.93 .61 .86 .61 .35 .58 .78 .85 1.06 .78 .57 .49 .45

1.26 1.66 2.20 1.62 1.00 1.18 1.66 1.68 1.52 1.21 1.02 1.07 1.19

0.115 0.292 0.027g 0.312 0.946 0.772 0.144 0.077 0.003 0.523 0.942 0.904 0.810

a

Genes from Table 1 for which complete genomic sequence was available. Control genes that have a gene expression ratio of 1 ⫾ 0.01 (Wnt deregulated/Wnt intact). N, number of consensus (5⬘-WWCAAWG-3⬘) TCF sites within a specified gene region. d T, total number of possible sites of a 7-mer sequence within the specified gene region. e P, proportion of consensus TCF sites within the specified up-regulated (Pur) or control (Pc) gene region (N/T) ⫻ 104. f P is from one-sided Z test. g Bold indicates significance. b c

we performed a permutation procedure. We randomly selected 81 genes from the set of 6954 genes and calculated the pairwise correlation (Spearman’s rho) coefficient between every pair of 81 genes. We counted the proportion of these correlation coefficients that had an absolute value greater than 0.3. This procedure was repeated 400 times. In the observed data, this proportion is 0.47 for the Wnt pathway-defective group and 0.29 for the Wnt pathway-intact group. In the permutations, the range of proportions was 0.35– 0.45 in the Wnt pathway-defective group and 0.25– 0.32 in the Wnt pathwayintact group. This demonstrates that these 81 genes are more highly associated with each other in tumors with deregulated ␤-cat than in tumors with intact ␤-cat regulation. Importantly, 7 (Table 1, bold text) of the 81 candidate Wnt pathway target genes have been reported previously as potential ␤-cat/TCF transcriptional targets in other systems, namely, BMP4 (48), CCND1 (29, 30), CD44 (49), EPHB3 (50), FGF9 (51), MMP7 (28, 52), and MSX2 (53). To the best of our knowledge, the oligonucleotide microarray used for this study includes probe sets for 34 previously implicated Wnt pathway target genes (represented by 55 probe sets) reported to date (for a list of these genes and references, see our website5 specified in “Materials and Methods”). These previously reported target genes were identified through our review of the literature and from those listed on the Nusse laboratory website (Stanford University).7 Because 7 of the 34 putative Wnt pathway target genes were found on our list of 81 candidate target genes, our list is highly enriched with presumptive Wnt pathway targets (P ⬍ 0.00001, Fisher’s exact test). Notably absent from the list, among others, is c-MYC, which has been reported to be a Wnt pathway target gene in another system (31, 54), but not in OEAs (37). Localization of Consensus TCF-binding Sites in Candidate ␤-Cat/TCF Target Genes. At least some of the genes with increased expression in OEAs with deregulated ␤-cat might be expected to be direct targets of ␤-cat/TCF-mediated transactivation. Presumably, this occurs through sequence-specific binding of the ␤-cat/TCF complex to TCF recognition sites in key regulatory elements, perhaps including those at or near the genes’ proximal promoters. Based on these considerations, we hypothesized that if a significant proportion of the genes on our list were direct ␤-cat/TCF transcriptional targets, then these genes might show differences in TCF-binding site number and/or distribution when compared with control genes (i.e., genes with 7

http://www.stanford.edu/⬃rnusse/wntwindow.html.

invariant expression in OEAs with respect to ␤-cat regulation status). Consequently, we obtained publicly available genomic sequence spanning 5000 bp upstream of the inferred start of transcription to 1000 bp downstream of the translation stop codon for genes in Table 1 as well as 100 randomly selected control genes. We were able to acquire full genomic sequences for 67 of the 81 candidate target genes. We then compared the proportion of consensus TCF-binding sites between the candidate target genes and control genes in various regions, based on annotation in the RefSeq database (Table 2). In candidate ␤-cat/TCF target genes, we found a significantly higher proportion of consensus TCF-binding sites in the region from ⫺501 to ⫺1000 bp with respect to the transcriptional start site and in the 5⬘-UTR. Notably, a recent study has shown the presence of functionally relevant TCF-binding sites outside the promoter (i.e., in the first intron) of the ␤-cat/TCF target gene, AXIN2 (55). Our results show that certain presumptive regulatory regions of our candidate Wnt/ ␤-cat target genes have more consensus TCF-binding sites compared with control genes. As such, the data offer further evidence that our list of candidate target genes contains many genes likely to be directly regulated by ␤-cat/TCF. Validation of Microarray Data. Five of the most highly upregulated candidate genes shown in Table 1 [namely, cyclinD1 (CCND1), cystatin 1/cystatinSN (CST1),8 endothelin 3 (EDN3), fibroblast growth factor 9 (FGF9), and stratifin (SFN, “14-3-3 sigma”)] were selected for analysis. Semiquantitative multiplex RT-PCR (CST1) and real-time q-RT-PCR (CCND1, EDN3, FGF9, and SFN) were used to validate the microarray data (Fig. 2). The microarray data (Fig. 2, A, C, E, G, and I) reveal, not unexpectedly, some heterogeneity of candidate gene expression within the two groups of tumors. Nonetheless, it can be seen that the majority of samples with deregulated ␤-cat express these genes at much higher levels than the majority of the samples with an intact Wnt pathway and, importantly, that the same conclusions can be reached from the RT-PCR data (Fig. 2, B, D, F, H, and J, respectively). Moreover, for all of the genes, the RT-PCR data were highly correlated (P ⬍ 0.01) with the microarray data (r ⫽ 0.9, 0.77, 0.87, 0.65, and 0.58, respectively) as estimated by the 18 samples included in both the PCR and microarray 8 The HuGeneFL microarray does not contain a probe set specific for CST1. We determined that the probe set for CST4 (“X54667_s_at”) was primarily measuring CST1, and not CST4 gene expression in our OEAs [see the website specified in “Materials and Methods” (footnote 5)].

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␤-CATENIN/TCF TARGET GENES IN OVARIAN CANCER

Fig. 2. Comparison of CCND1, FGF9, CST1, EDN3, and SFN gene expression from microarray and RT-PCR analyses. For each gene, relative expression based on the microarray data (A, C, E, G, and I) is shown, and mean fold gene expression (normalized to HPRT) based on the q-RT-PCR (B, D, H, and J) or multiplex-RT-PCR (F) is shown. For q-RT-PCR, 17 and 14 OEAs with an intact or deregulated Wnt pathway, respectively, were used in the analysis, and for multiplex-RT-PCR, 9 and 10 OEAs with an intact or deregulated Wnt pathway, respectively, were analyzed. Expression differences for CCND1 (P ⬍ 0.001), FGF9 (P ⬍ 0.026), CST1 (P ⬍ 0.00003), EDN3 (P ⬍ 0.016), and SFN (P ⬍ 0.0004), validated by RT-PCR between tumors with intact versus deregulated ␤-cat/TCF signaling, were readily apparent.

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␤-CATENIN/TCF TARGET GENES IN OVARIAN CANCER

experiments. Validation experiments were conducted only on the five genes specified above. Hence, we found no examples of discordance between the microarray and q-RT-PCR data. The CST1 and EDN3 Proximal Promoters Are Responsive to ␤-Cat/TCF. We sought to demonstrate that some of the novel genes on our target gene list were responsive to ␤-cat/TCF. Two genes, CST1 and EDN3, were selected for further evaluation. Analysis of the sequence of the CST1 (NT_011387) and EDN3 (NT_011362) promoter regions obtained via NCBI Entrez Nucleotide view revealed several potential TCF-binding sites within 2000 bp upstream of the genes’ purported transcriptional start sites. We used PCR to clone promoter sequences from both CST1 and EDN3, and sequence analysis of the cloned fragments confirmed the presence of the potential TCF-binding sites, which are shown schematically in Figs. 3A and 4A. To determine whether these minimal promoter sequences were responsive to ␤-cat, we generated luciferase reporter gene constructs containing the CST1 and EDN3 promoter fragments. For both genes, constructs containing mutations or deletions of the putative TCF recognition sites were also generated (Figs. 3A and 4A). We found that a mutant constitutively active form of ␤-cat (S33Y mutant), when coexpressed with a CST1 or EDN3 reporter construct in 293 cells, strongly stimulated reporter activity (Figs. 3B and 4B). Mutation of all four potential TCF-binding sites in the CST1 promoter (CST1-Am1– 4) completely abrogated the response to ␤-cat (Fig. 3B). The other mutation and deletion constructs all showed reduced luciferase reporter activity compared with the construct containing the intact CST1 promoter fragment. Similar studies using mutated and deleted versions of the EDN3 promoter fragment suggest that the TCF-binding site at –1614 is most critical for imparting TCF responsiveness to the EDN3 promoter (Fig. 4B). These data indicate that the proximal promoters of CST1 and EDN3 are responsive to ␤-cat/TCF and support the notion that both genes are directly activated by deregulated ␤-cat upon its interaction with TCF in OEAs with Wnt pathway defects. The DLD-1 and SW480 colorectal cancer-derived and TOV112D OEA-derived cell lines have deregulated ␤-cat, as a result of biallelic inactivation of APC (DLD-1 and SW480) or activating mutation of CTNNB1 (TOV112D). To further demonstrate that the CST1 as well as EDN3 promoters are responsive to TCF/␤-cat, we established cell lines that stably express a dn-mutant form of TCF (dn-TCF), which should diminish transcriptional activity of TCF-responsive promoters. Transcriptional activity, as measured by luciferase activation of CST1-A (Fig. 3C) and EDN3-A (Fig. 4C) reporter constructs, was reduced in the presence of dn-TCF in all three cell lines. These experiments provide additional evidence that proximal promoter sequences of CST1 and EDN3 are indeed TCF/␤-cat responsive, consistent with the view that CST1 and EDN3 are likely to be directly regulated by ␤-cat and TCF in OEAs. ChIP Assays Demonstrate Interaction of ␤-Cat with an EDN3 Promoter Region Containing Presumptive TCF-binding Sites. To document direct interaction of ␤-cat with sequences in the 5⬘-flanking region of EDN3, we performed ChIP assays using an antibody directed against ␤-cat and chromatin obtained from SW480/neo or SW480/ dn-TCF cells. The positions of the various primers used to amplify and detect specific regions of immunoprecipitated EDN3 promoter fragments are shown in Fig. 4A. TCF-binding site-containing DNA fragments from the EDN3 promoter were readily recovered using primers flanking the two putative TCF-binding sites at –1693 and ⫺1614 and, as expected, were reduced in the presence of dn-TCF (Fig. 4D). Moreover, irrelevant upstream and downstream DNA fragments lacking TCF-binding sites were not recovered after ChIP with ␤-cat (Fig. 4C). These findings support results from the reporter assays suggesting a role for the TCFbinding site at –1614 in mediating TCF responsiveness of the EDN3

Fig. 3. Upstream regulatory sequences of CST1 are TCF responsive. A, schematic diagram of the human CST1 minimal promoter indicating the locations of the transcriptional start, canonical TCF-binding sites, and extent of sequences cloned into pGL3Basic luciferase reporter vector. Designations of the nine CST1 reporter constructs and the status [wild-type or mutant (m)] of candidate TCF-binding sites in the constructs are indicated. The bold/underlined nucleotides within candidate TCF-binding sites indicate the position of the mutations in the mutant reporter constructs. B, effects of ␤-cat on human CST1 promoter reporter constructs in 293 cells. The relative activity of the CST1 constructs shown in A was assessed after transient transfection of the cells with either pcDNA3 (empty vector) or pcDNA3 construct containing constitutively active ␤-cat(S33Y). C, cell lines expressing dn-TCF were transiently cotransfected with CST1-A promoter construct (or empty vector) and pCH110. Luciferase activity was normalized to ␤-galactosidase activity and reported as the relative fold activation in transcriptional activity compared with empty vector (pGL3Basic) control.

promoter fragment and further bolster the case that EDN3 is a direct transcriptional target of ␤-cat/TCF. DISCUSSION The Wnt signaling pathway is frequently deregulated in certain types of human cancer. In tumors with Wnt pathway defects, stabilized ␤-cat interacts with TCF transcription factors to mediate in-

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␤-CATENIN/TCF TARGET GENES IN OVARIAN CANCER

Fig. 4. TCF elements in the promoter of human EDN3 help regulate transcriptional activity and directly interact with ␤-cat. A, schematic diagram of the human EDN3 minimal promoter indicating locations of the transcriptional start, canonical TCF-binding sites, and extent of sequences cloned into pGL3Basic luciferase reporter vector. Designations of the six EDN3 reporter constructs and the status [wild-type or mutant (m)] of candidate TCF-binding sites in the constructs are indicated. The bold/underlined nucleotides within candidate TCF-binding sites indicate the position of the mutations in the mutant reporter constructs. B, effects of ␤-cat on human EDN3 promoter reporter constructs in 293 cells. The relative activity of the EDN3 constructs shown in A was assessed after transient transfection of the cells with either pcDNA3 (empty vector) or pcDNA3 construct containing constitutively active ␤-cat(S33Y). C, cell lines expressing dn-TCF were transiently cotransfected with EDN3-A pGL3Basic luciferase reporter construct (or empty vector) and pCH110. Luciferase activity was normalized to ␤-galactosidase activity and reported as the relative fold activation in transcriptional activity compared with empty vector (pGL3Basic) control. D, ChIP assay. Cross-linked DNA from SW480/neo and SW480/dn-TCF cell lysates were combined with antibody against ␤-cat (or no antibody), and the immunoprecipitate was subjected to PCR to amplify various regions of the EDN3 promoter.

creased expression of specific genes, at least some which likely play critical roles in cancer pathogenesis. Not surprisingly, identification of downstream targets of activated ␤-cat has become a subject of intense interest. Recent studies have identified several putative targets of ␤-cat signaling, primarily through analysis of cultured cells or animal model systems. We have used a novel strategy to discover potential ␤-cat/TCF target genes, comparing gene expression in otherwise similar primary tumors with and without Wnt/␤-cat pathway defects. Our results suggest that the approach is a robust one for identifying novel candidate genes worthy of further investigation as direct ␤-cat/ TCF transcriptional targets with functional roles in cancer development or progression. This type of approach may be more likely to yield useful insights into cancer pathogenesis than simple comparisons of gene expression in tumor cells with their normal cell counterparts, given the substantial variation in many phenotypic features between neoplastic and nonneoplastic cells. Moreover, as is the case for epithelial ovarian cancer (56), the cell of origin for many cancers may be unclear or difficult to obtain in sufficient quantities for global gene expression profiling. By comparing a sufficiently large number of otherwise similar tumors with and without defects in a particular signaling pathway, nonspecific variations in gene expression are minimized, and a significant proportion of the variation under study may

be due to the consequences of the disrupted signaling pathway under scrutiny. Other signaling pathways, such as those mediated by PTEN and, to a lesser degree, KRAS, are likely to be aberrant in some of our OEAs, based on the reported frequency of PTEN and K-RAS mutations in this tumor type (57, 58). Clearly, defects in signaling pathways other than Wnt will contribute to variation of gene expression in individual tumors. Nonetheless, our global gene expression analysis provides strong evidence that Wnt pathway status, in and of itself, is a major determinant of global gene expression in OEAs. In the future, it will be interesting to survey genes in other pathways that may modulate the one mediated by APC/␤-cat/ TCF. For example, the PI3K/Akt pathway has been shown to modulate ␤-cat/TCF-mediated gene expression in prostate cancer cells through phosphorylation and inhibition of GSK3␤, a downstream substrate of PI3K/Akt, resulting in stabilization of ␤-cat (59). PTEN, an inhibitor of the PI3K/Akt pathway, may inhibit nuclear accumulation of ␤-cat in certain settings (60). We recognize that some of the candidate Wnt/␤-cat pathway target genes shown in Table 1 may not be direct targets. However, 2 of the top 10 genes on our candidate list not previously suggested as Wnt/ ␤-cat pathway targets, namely, CST1 and EDN3, were shown to have ␤-cat/TCF-responsive upstream regulatory sequences. The protein

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␤-CATENIN/TCF TARGET GENES IN OVARIAN CANCER

product encoded by CST1, cystatin 1 (Cst1), is one of several salivary cysteine protease inhibitors and a member of a cysteine protease inhibitor superfamily (61). Very little is known about any role Cst1 may have in cancer. Based on serial analysis of gene expression (SAGE), CST1 was determined to be up-regulated in a metastatic colorectal cancer-derived cell line compared with a cell line derived from the matched primary tumor (62). Members of the cystatin superfamily have also been associated with metastasis in other studies (63). However, in B-16 melanoma cells, overexpression of cystatin C significantly reduced metastatic ability (64). Although present in saliva, in vitro, Cst1 was not found to inhibit the activities of the ubiquitously expressed lysosomal cathepsins B, H, and L (65), which are host cysteine proteases that have been shown to play a significant role in the proteolytic events causing periodontal tissue destruction (66, 67) and are also involved in tissue remodeling and turnover of the extracellular matrix, immune system modulation, and cancer (68, 69). What role, if any, CST1 plays in the pathogenesis of OEA will need to be further explored. EDN3 (endothelin 3) is one of six genes that, when mutated, are known to be associated with Hirschsprung’s disease, a genetic disorder of neural crest development characterized by the absence of intramural ganglion cells in the hindgut (70). Edn3 is a member of the endothelin/sarafotoxin family of proteins and is a secreted vasoactive peptide that binds to the endothelin B receptor and is essential in the development of neural crest-derived cell lineages (71). No evidence currently exists that links Edn3 with cancer. We are currently undertaking additional studies to determine the functional significance of CST1, EDN3, and selected other novel Wnt pathway target genes in OEA pathogenesis. Such studies are necessary to help distinguish those genes that play critical versus nominal roles in cellular transformation. Moreover, we recognize that Wnt pathway activation may result in the transcriptional deregulation of different genes in different tumor types with ␤-cat regulation defects (e.g., ovarian endometrioid versus colorectal carcinomas). Clearly, much remains to be learned about the identity and role of various downstream targets of the ␤-cat/TCF signaling pathway in human cancer. Nevertheless, the studies presented here represent a novel line of attack for addressing some of the many unanswered questions in the field.

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