Published OnlineFirst November 6, 2006; DOI: 10.1158/0008-5472.CAN-06-1327 Published Online First on November 6, 2006 as 10.1158/0008-5472.CAN-06-1327
Research Article
New Potential Ligand-Receptor Signaling Loops in Ovarian Cancer Identified in Multiple Gene Expression Studies
Giancarlo Castellano,1 James F. Reid,2,4 Paola Alberti,1 Maria Luisa Carcangiu,3 Antonella Tomassetti,1 and Silvana Canevari1
1Unit of Molecular Therapies, Department of Experimental Oncology, 2Department of Experimental Oncology, and 3Unit of Pathology C, Department of Pathology, Istituto Nazionale Tumori; and 4Molecular Genetics of Cancer Group, Fondazione Istituto FIRC di Oncologia Molecolare, Milan, Italy
Abstract the generated gene lists remains a major challenge, the availability Based on the hypothesis that gene products involved in the of numerous published microarray analyses, rich in the amount of same biological process would be coupled at transcriptional high-quality data (1), and the public access to the original data sets level, a previous study analyzed the correlation of the gene have accelerated the developments of new types of analysis. In fact, expression patterns of ligand-receptor (L-R) pairs to discover the combination of hypothesis- and discovery-based research potential autocrine/paracrine signaling loops in different resulted in the development of new techniques, based on cancers (Graeber and Eisenberg. Nat Genet 2001; 29:295). aggregated gene sets (reviewed in ref. 2), to extract useful By refining the starting database, a list of 511 L-R pairs was information from microarray gene expression data sets (3, 4) and compiled, combined to eight data sets from a single pathology, to interpret genome-wide expression profiles (5). The use of pathway-oriented approaches has enabled the epithelial ovarian cancer, and examined as a proof- of-principle of the statistical and biological validity of the interrogation and dissection of multiple disrupted signaling path- correlation of the L-R gene expression patterns in cancer. ways during oncogenesis. Accordingly, an algorithm was designed Analysis revealed a Bonferroni-corrected significant correla- (6) that is suitable for detecting dysregulation of autocrine/ tion of 105 L-R pairs in at least one data set and, by systematic paracrine ligand-receptor (L-R) signaling loops. This approach analysis, identified 39 more frequently correlated L-R pairs, was based on the hypothesis that two gene products participating in 7 of which were already biologically confirmed. In four data a common function show correlated expression as reflected in their sets examined for an L-R correlation associated with patient correlated transcription levels. However, to date, this algorithm has survival time, 15 L-R pairs were significantly correlated in only been applied in a single study, in which five cancer-based short surviving patients in two of the data sets. Immunohis- gene expression data sets originated from different cancers were tochemical analysis of one of the newly identified correlated analyzed separately (6). In principle, this type of analysis could L-R pairs (i.e., EFNB3-EPHB4) revealed the correlated expres- provide a tool to compare independently derived gene expression sion of ephrin-B3 and EphB4 proteins in 45 of 55 epithelial data sets, even those obtained from different platforms, and to obtain more consistent results than those from single gene analysis. ovarian tumor samples (P < 0.0001). Together, these data not only support the validity of cross-comparison analysis of gene Here, we examined patterns of correlated gene expression of expression data because known and expected correlations ligands and receptors with respect to their role as possible activated were confirmed but also point to the promise of such analysis signaling pathways involved in epithelial ovarian cancer (EOC). The unfavorable statistics in EOC patients reflects, in part, the poor in identifying new L-R signaling loops in cancer. (Cancer Res understanding of the molecular pathogenesis and progression of 2006; 66(22): 10709-19) the disease. As a step toward gaining insight into the mechanisms underlying this pathology and toward identifying potentially Introduction meaningful activated signaling pathways, we exploited a previously Traditional hypothesis-driven strategies for identifying molecu- described L-R database (6) to select frequently correlated L-R pairs lar markers of a disease state were based on individual gene by a ‘‘systematic’’analysis of EOC publicly available data sets of gene analysis. Although useful, these approaches could fail to identify expression. Analysis across eight selected EOC microarray data sets biological relevant differences that are based on subtle but multiple gave 39 L-R pairs with significant and consistent correlation in at and coordinated gene alterations more than on quantitative least three data sets. In four data sets, analysis of samples from EOC expression differences of a single gene. Several recent critical patients with short-term versus long-term survival showed that 15 advances, such as sequencing of the human genome and the L-R pairs were associated to short-term survival in two of the data development of high-throughput techniques for identifying global sets. EFNB3-EPHB4 pair was one of the newly identified L-R pairs gene expression, have dramatically accelerated the speed of and the coexpression was confirmed at the protein level by research. Although extrapolation of biological mechanisms from immunohistochemistry on epithelial ovarian tumors.
Materials and Methods Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/). Public gene expression data. Twenty-five publications on microarray Requests for reprints: Antonella Tomassetti, Unit of Molecular Therapies, analysis of gene expression profiling of EOC samples were recorded from Department of Experimental Oncology, Istituto Nazionale Tumori, 20133 Milan, PubMed5 (from January 2000 to May 2005). Gene expression data were Italy. Phone: 39-02-23902568; Fax: 39-02-23903073; E-mail: antonella.tomassetti@ istitutotumori.mi.it. I2006 American Association for Cancer Research. doi:10.1158/0008-5472.CAN-06-1327 5 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed. www.aacrjournals.org 10709 Cancer Res 2006; 66: (22). November 15, 2006
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Cancer Research
Table 1. Characteristics of the explored EOC data sets
A.
Data sets Intersections of L-R pairs in data sets
Code Author (ref.) Platform No. cDNA No. I II III IV V VI VII VIII or probesets samples
I Berchuck et al. (11) Oligo (U133A) 22,283 68 417 II De Cecco et al. (12) cDNA 4,451 81 63 68 III Lancaster et al. (13) Oligo (Hu GeneFL) 7,070 34 318 57 340 IV Spentzos et al. (14) Oligo (U95Av2) 12,625 68 374 62 315 387 V Schaner et al. (15) cDNA 42,000 59 231 65 190 227 259 VI Jazaeri et al. (16) cDNA 7,651 61 105 43 94 109 103 113 VII Schwartz et al. (17) Oligo (Hu GeneFL) 7,070 113 300 52 302 295 117 89 303 VIII Welsh et al. (18) Oligo (Hu GeneFL) 7,070 38 300 52 302 295 177 89 303 303
B.
Clinical characteristics*
Data set No. patients Histotype Grade Stage Treatment Response Outcome (serous/others) (1-2/3-undif) (early/advanced) (yes/no) (short/long survival)
I 65 65/0 39/26 11/54 54 n.a. 30/24 c II 50 25/25 9/38 4/43 41 25/15 24/12 III 31 n.a. n.a. 0/31 n.a. n.a. 16/14 c IV 68 62/6 14/54 3/65 68 60/8 37/31 V 59 39/20 9/10 2/35 n.a. n.a. n.a. VI 61 37/24 18/43 7/54 n.a. n.a. n.a. VII 113 53/60 54/59 37/73 n.a. n.a. n.a. VIII 22 18/4 6/15 2/19 n.a. n.a. n.a.
Abbreviations: Undif, undifferentiated: oligo, oligonucleotide; n.a., not available. *Due to missing values in a few cases, numbers do not add up to the total number of patients in every category. cThe respective authors defined the response to treatment as complete versus partial or no response in data set II and complete and partial versus no response in data set IV.
available in only eight of these publications and used for our analysis gene identification. When more than one cDNA clone or probe set matched (Table 1A). These data sets were generated by hybridization on cDNA a given gene, all possible pairs where considered. Pearson and Spearman and oligonucleotide DNA chips in three and five cases, respectively. No correlation coefficients were computed for each L-R pair across each data additional data manipulation was done to the downloaded processed set; Ps for each correlation were computed using the function cor.test of the gene expression matrices, except for the thresholding of negative values software package R10 and adjusted for multiple testing using the Bonferroni to 0 for MAS4-processed data (data sets III and VII). All probe sets and method. The complete list of the extracted correlations for each data set is cDNA clones from each platform were assigned National Center for available.11 Biotechnology Information (NCBI) gene identifications (7), which were used Tissue samples and study subjects. The pathologic and clinical to match common genes across data sets based on the most recent plat- characteristics of EOC patients (Table 1B) were derived from published form annotation from either NetAffx6 (release 2005-12-20; ref. 8) or Stanford data (11–18) or, for our previous study (data set II), by up-to-date clinical SOURCE7 (release 2005-10-26; ref. 9). All data (clinical, platform annotation information. Data sets I to IV reported clinical information (Table 1B) that and expression) were stored in BioConductor8 (10) expression sets. allowed subgrouping of the samples according to overall survival. Criteria for L-R pair compilation. The list of L-R pairs was manually curated by a subcategorization in short-term and long-term survivors have been reported PubMed search for proteins interacting on the cell surface. The list was for data sets I, III, and IV (11, 13, 14); for data set II (12), the 36 samples integrated to database of L-R partners (DLRP) contained in the database of from patients with available follow-up data were split into two groups to interacting proteins9 (6), yielding 199 ligands and 157 receptors for a total of obtain median overall survival times similar to those reported in ref. 11. 511 L-R pairs. Immunohistochemistry. All clinical specimens used in this study were Correlation analysis of L-R pairs. Expression measurements of each obtained with Institutional Review Board approval and informed consent to L-R pair were extracted from each data set through their respective NCBI use excess biological material for investigative purposes from all participating patients. Immunohistochemistry was done using nine routine
6 http://www.affymetrix.com/analysis/index.affx.org. 7 http://source.stanford.edu/. 8 http://www.bioconductor.org. 10 http://www.R-project.org. 9 http://dip.doe-mbi.ucla.edu/dip/DLRP.cgi. 11 http://pierotti.group.ifom-ieo-campus.it/suppl/LRovca.html.
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L-R Signaling Loops in Ovarian Cancer tissue blocks and two commercially available tissue arrays (Ovary cancer, (25)]. Correlations were consistently positive or negative for 36 and AccuMax Array, Petagen and CJ1 Human, Ovary cancer, and Super Bio 3 L-R pairs, respectively, whereas 2 L-R pairs, CCL4-CCR5 and Chips) essentially as described. Fifty-five samples were selected by the TGFB1-TGFBR2, showed a contradictory trend of correlation in pathologist (M.L.C.) and classified in tumors of low malignant potential different data sets and were excluded from further analysis. Among (LMP) and carcinomas of either grade II and III. Conditions for antigen the 39 consistently correlated L-R pairs, 3 pairs are involved in retrieval and dilution for each antibody were optimized as suggested by the manufacturer. For staining with mouse monoclonal anti-EphB4 antibody angiogenesis, 13 are chemokine family members, 9 are cytokine (Zymed, San Francisco, CA), tissue sections were heated for 20 minutes in family members, 11 mediate growth, and 5 regulate cell migration/ citrate buffer (pH 6) in a pressure cooker; for staining with the rabbit adhesion. polyclonal anti-ephrin-B3 antiserum (Zymed), antigen retrieval was by Identification of L-R pairs correlated in EOC subgroups pepsin digestion (0.4% in 0.01 N HCl for 30 minutes at 37jC). Negative according to survival. Not all tested samples were from surgical control slides omitting primary antibody were included in all assays. Two specimens (the number ranged from 22 in data set VIII to 113 in observers (M.L.C. and A.T.) classified independently slides as negative, weak/ data set VII) and the type of clinical information was variable in the moderate, or strong staining based on intensity and percentage of positive different data sets. Based on the available clinical information, EOC cells. Statistical analysis of L-R correlated expression levels in the same sample samples could be subdivided according to histotype, grading, was done using m2 test. Ps < 0.05 (two sided) were considered significant. staging, response to treatment, and outcome (Table 1B). Because the identification of predictive markers of poor outcome is still a Results major challenge in ovarian cancer, we focused our further analysis on data sets I to IV where outcome data were available. The Generation of DLRP-rev1. The original list of L-R pairs present samples used for this analysis were all, but three in data set IV, at in the DLRP database was refined based on a literature search for late stage of the disease and several other clinical variables are proteins interacting on the cell surface and whose interactions known and recorded (see respective references for the criteria for were experimentally proved, which yielded 25 ligands and 33 subcategorization in short-term and long-term survivors and receptors for a total of 42 added L-R pairs. The revised list (DLRP- further clinical information). Samples with complete clinical rev1) used in this study consisted of 199 ligands, 157 receptors, history were classified according to length of survival (Table 3A), and 511 L-R pairs involved in autocrine/paracrine signaling in and the correlation analysis was focused on L-R correlation in eukaryotic cells (Supplementary Table S1). This final set of L-R short-term and long-term survivors. Initially, we selected L-R pairs pairs was subdivided according to functional consequence of the that were significantly correlated in short-term survivors and not L-R interactions [i.e., angiogenesis factor (4%), chemokine (15%), or inversely correlated in long-term survivors from the same data cytokine (23%), growth factor (42%), motility/adhesion factor set. When no correction for multiple test was done, 166 L-R pairs (14%), and others (2%)]. were found significantly correlated (Supplementary Table S3). No Identification of correlated L-R pairs in EOC. Analysis of the L-R pairs showed a significant correlation after Bonferroni eight data sets revealed extensive variability in the L-R pairs correction, probably due to the limited number of samples for coverage according to platform type and time of manufacture each subgroup (range, 16-37). When we arbitrarily based our (Table 1A). The L-R pairs from DLRP-rev1 extractable from the external statistical validation on L-R pairs showing a concordant eight selected EOC microarray data sets (shaded values) ranged correlation in at least two data sets in short-term survivors, the from 68 to 417 (excluding redundant pairs) and represented a number of correlated L-R pairs dropped to 15 pairs (Table 3B). A maximum of 82.5% DLRP-rev1 pairs in data set I to <13.5% DLRP- support to our selection criteria comes from the observation that rev1 pairs in data set II. The matrix measuring the overlap provides in about a third of the pairs significantly correlated in short-term the possible cross-comparisons between the data sets (Table 1A). survivors (LIF-IL6ST, FGF1-FGFR1, FGF4-FGFR3, and FGF7- When multiple probe sets or clones matching a single gene FGFR2) we observed in the long-term survivors from the same (ligand or receptor) were present, each one was considered data set an opposite correlation. The ability to discriminate separately. Both Spearman and Pearson correlation coefficients between patient subsets, as well as the inclusion of the IGF2-IGF2R were computed for each L-R pair in each data set. Because pair already associated with a worse prognosis (23), further correlation coefficients (q and r) and the associated Ps assigned for supported the validity of our selection. Correlations in short-term each L-R pair were very similar (data not shown), only the Pearson survivors were consistently positive or negative for 10 and 5 L-R correlation values were considered. A total of 105 L-R pairs had pairs, respectively. Of the selected L-R pairs, only IGF2-IGF2R and significant correlation after Bonferroni correction in at least one EFNB3-EPHB4 were identified as correlated also in the entire case data set (Supplementary Table S2). These L-R pairs were material (see Table 2), suggesting that the weight of correlation in subsequently analyzed across the other data sets for concordant short-term survivors only was very strong. correlations. After several arbitrary selections (e.g., Bonferroni- Among the 15 correlated L-R pairs, 1 is involved in angiogenesis, corrected significant correlations in two or three data sets and/or 1and2belongtothechemokineandcytokinefamilies, significant correlations in at least half of the informative data sets), respectively, 6 mediate growth, and 5 regulate cell migration/ L-R pairs that showed a significant correlation after Bonferroni adhesion. Furthermore, we found that four of the six growth- correction in at least one data set and have significant correlations, associated L-R pairs exhibited a negative correlation and four of without Bonferroni correction, in at least two other data sets were the five motility/adhesion factors belong to the ephrin-Eph considered potentially meaningful. Forty-one L-R pairs showed a receptor family. significant correlation in at least three data sets (Table 2). This EFNB3-EPHB4 correlated protein expression in epithelial arbitrary cutoff produced a substantial number of L-R pairs shown ovarian tumor specimens. Emerging information on the roles of previously to be implicated in EOC molecular signaling [PDGFA- ephrin proteins and their receptors links them to tumorigenesis PDGFRA (19), CXCL12-CXCR4 (20), CSF1-CSF1R (21, 22), FGF2- and invasion (26). Although the EFNB3-EPHB4 L-R pair is tagged FGFR4 (12), IGF2-IGF2R (23), HGF-MET (24), and PLAU-PLAUR in the original DLRP as experimentally determined, it is not www.aacrjournals.org 10711 Cancer Res 2006; 66: (22). November 15, 2006
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Table 2. L-R pairs with statistically significant correlation coefficient (Pearson r) after Bonferroni correction
Data set
Ligand Receptor I II III IV
rPrPrPrP
Angiogenesis factors ANGPT1 TEK 0.51 0.00001 0.48 0.00445 0.39 0.00111 PDGFA* PDGFRA 0.27 0.02669 0.61 0.00012 0.56 0.00000 PGF FLT1 0.32 0.00866 0.46 0.1280 À0.07 0.70975 0.57 0.00000 Chemokines CCL19 CCR7 0.38 0.00168 0.41 0.01582 0.64 0.00000 CCL2 CCR1 0.61 0.00000 0.29 0.9390 0.42 0.00035 CCL21 CCR7 0.34 0.00629 0.23 0.19880 0.66 0.00000 CCL25 CCR9 0.21 0.9280 0.36 0.3774 0.59 0.00000 CCL3 CCR1 0.46 0.00616 0.64 0.00000 CCL4 CCR1 0.68 0.00000 0.53 0.00120 CCL4* CCR5 0.47 0.00007 0.66 0.00002 CCL5* CCR1 0.65 0.00000 0.63 0.00006 0.51 0.00001 CCL5 CCR5 0.56 0.000000 0.55 0.00081 0.62 0.00000 CCL8 CCR1 0.68 0.00000 0.56 0.00063 0.55 0.00000 CCL8* CCR5 0.47 0.00009 0.56 0.00061 0.45 0.00010 CXCL12 CXCR4 0.23 0.06559 0.40 0.00063 0.15 0.38706 0.36 0.00248 PPBP IL8RB 0.34 0.00516 0.34 0.05235 0.48 0.00004 Cytokines CSF1 CSF1R 0.57 0.00000 0.54 0.00104 0.22 0.06877 EPO EPOR 0.29 0.01721 À0.27 0.12662 0.71 0.00000 IL12B IL12RB2 0.26 0.03760 0.74 0.00000 0.46 0.00007 IL15 IL15RA 0.56 0.00000 0.12 0.49169 0.16 0.36727 0.65 0.00000 IL15 IL2RB 0.32 0.00973 À0.13 0.45284 0.20 0.25731 0.48 0.00004 IL16 CD4 0.51 0.00002 0.56 0.00000 IL6 IL6ST À0.22 0.08025 0.48 0.00369 0.26 0.03214 LIF IL6ST 0.29 0.01964 0.35 0.04139 0.55 0.00000 TNF TNFRSF1B 0.31 0.01291 0.22 0.20230 0.59 0.00000 Growth factors FGF2 FGFR4 0.31 0.01141 0.78 0.00000 0.06 0.73822 0.43 0.00023 FGF4 FGFR4 0.29 0.01739 0.63 0.00006 0.58 0.00000 IGF2 IGF2R 0.26 0.03718 0.94 0.00000 0.45 0.00744 À0.08 0.53725 INHA ACVR1B 0.29 0.01752 0.35 0.04142 0.57 0.00000 INHBA* ACVR1B À0.38 0.00173 0.40 0.01778 0.53 0.00000 JAG1* NOTCH3 À0.37 0.00221 0.35 0.00699 0.42 0.01336 À0.40 0.00077 NRG1* ERBB3 0.38 0.00196 À0.15 0.17804 À0.39 0.02144 0.54 0.00000 TGFB1* TGFBR2 0.49 0.00003 0.06 0.57487 0.30 0.08760 À0.59 0.00000 TGFB1* TGFBR3 À0.15 0.23348 0.38 0.00057 À0.14 0.43299 À0.17 0.16731 TGFB2* TGFBR2 À0.23 0.06019 À0.07 0.54554 0.26 0.14050 À0.28 0.02238 TGFB3* TGFBR3 0.05 0.69678 À0.45 0.00007 À0.07 0.67513 À0.10 0.41893 Motility/adhesion factors EFNB1 EPHB2 À0.08 0.53813 0.15 0.39382 0.17 0.16673 EFNB3 EPHB4 0.16 0.20784 0.39 0.00030 0.16 0.36816 0.26 0.03221 HGF* MET 0.66 0.00000 0.62 0.00000 0.63 0.00007 0.29 0.01634 PLAU PLAUR 0.51 0.00002 À0.22 0.06034 0.56 0.00057 0.57 0.00000 THBS1* CALR À0.38 0.00170 0.12 0.30819 À0.40 0.01854 À0.71 0.00000
NOTE: Shaded areas identify significant correlation (P < 0.05); bold characters identify a significant correlation after Bonferroni correction. *Discordant correlations observed for the indicated L-R pairs in different data sets.
described as a canonical pair in normal or cancer signaling (27). carried out to determine whether these proteins are coexpressed In our analysis, the EFNB3-EPHB4 pair was significantly in epithelial ovarian tumors. Consistent with previous reports correlated in four of seven EOC data sets and, in two of them, (28, 29), anti-ephrin-B3 antibodies selectively reacted with the correlation was also significant only in the short-term arterioles, whereas they did not stain venous vessels and the survivor subgroup. Thus, immunohistochemical analysis was surrounding stromal cells (Fig. 1A, 1). Anti-EphB4 was strongly
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L-R Signaling Loops in Ovarian Cancer
Table 2. L-R pairs with statistically significant correlation coefficient (Pearson r) after Bonferroni correction (Cont’d)
Data set
Ligand Receptor V VI VII VIII
r PrPrP r P
Angiogenesis factors ANGPT1 TEK 0.12 0.46809 0.11 0.39983 0.15 0.10732 PDGFA* PDGFRA 0.34 0.33128 0.22 0.08806 0.31 0.00069 À0.40 0.00682 PGF FLT1 0.22 0.16112 À0.15 0.11226 0.22 0.15490 Chemokines CCL19 CCR7 0.24 0.01008 0.40 0.00670 CCL2 CCR1 0.34 0.02727 0.08 0.54674 À0.10 0.31264 CCL21 CCR7 À0.03 0.73401 0.32 0.03172 CCL25 CCR9 À0.04 0.65497 0.53 0.00019 CCL3 CCR1 0.41 0.00676 0.11 0.42242 CCL4 CCR1 0.47 0.00000 CCL4* CCR5 0.43 0.00000 À0.49 0.00074 CCL5* CCR1 0.23 0.13944 À0.26 0.04306 0.50 0.000000 CCL5 CCR5 0.50 0.00000 À0.09 0.54865 CCL8 CCR1 0.39 0.23182 0.41 0.00001 CCL8* CCR5 0.36 0.00008 À0.51 0.00045 CXCL12 CXCR4 0.84 0.00114 0.44 0.00043 0.15 0.10563 À0.21 0.17967 PPBP IL8RB 0.30 0.00108 0.47 0.00119 Cytokines CSF1 CSF1R À0.51 0.13434 0.43 0.00000 0.08 0.58686 EPO EPOR À0.24 0.45348 0.10 0.42689 À0.03 0.73626 0.37 0.01296 IL12B IL12RB2 À0.10 0.27461 0.05 0.73704 IL15 IL15RA 0.14 0.38690 0.39 0.00194 0.40 0.00001 0.14 0.34901 IL15 IL2RB 0.13 0.41234 0.40 0.00001 0.03 0.82490 IL16 CD4 0.50 0.00099 À0.07 0.44860 0.11 0.46750 IL6 IL6ST 0.26 0.09457 0.50 0.00006 À0.06 0.53944 0.08 0.61314 LIF IL6ST 0.36 0.01835 0.40 0.00170 0.04 0.63902 0.04 0.78033 TNF TNFRSF1B À0.44 0.15133 0.52 0.00002 0.07 0.44745 À0.21 0.16690 Growth factors FGF2 FGFR4 À0.13 0.39052 À0.18 0.15994 0.11 0.22822 À0.09 0.56650 FGF4 FGFR4 À0.05 0.63463 À0.11 0.46907 IGF2 IGF2R 0.29 0.05498 0.50 0.00004 0.03 0.79188 0.52 0.00032 INHA ACVR1B À0.11 0.25881 0.12 0.44518 INHBA* ACVR1B À0.42 0.00667 0.27 0.00382 À0.25 0.10428 JAG1* NOTCH3 0.32 0.04281 À0.22 0.08996 0.02 0.80421 0.90 0.00000 NRG1* ERBB3 0.41 0.18542 À0.11 0.24770 0.40 0.00713 TGFB1* TGFBR2 0.33 0.03760 À0.36 0.00512 0.25 0.00645 0.16 0.30638 TGFB1* TGFBR3 0.37 0.01582 0.26 0.04996 À0.01 0.92640 À0.43 0.00347 TGFB2* TGFBR2 À0.35 0.02167 À0.04 0.75038 À0.05 0.59681 À0.57 0.00005 TGFB3* TGFBR3 À0.63 0.02884 0.35 0.00900 À0.21 0.02277 À0.27 0.07156 Motility/adhesion factors EFNB1 EPHB2 0.62 0.00002 0.55 0.00000 0.18 0.06284 0.36 0.01616 EFNB3 EPHB4 0.16 0.62466 0.36 0.00009 0.46 0.00171 HGF* MET 0.35 0.02479 À0.29 0.02389 PLAU PLAUR 0.55 0.00018 0.32 0.00056 0.35 0.01933 THBS1* CALR 0.41 0.00554 À0.09 0.47552 À0.16 0.08381 0.10 0.51591 reactive with the cell membrane of a colonic adenocarcinoma cells of all the different histologic types (Fig. 1, 5-7), with the but was negative on stromal cells surrounding the tumor cells exception of clear cell carcinoma where was observed only a (Fig. 1A, 2). Representative staining with anti-ephrin-B3 and anti- weak/moderate cytoplasmic staining of the tumor cells. Basically, EphB4 antibodies on different histotypes of EOCs is shown (Fig. no reactivity was observed with anti-EphB4 antibody on stromal 1B, 1-8). Anti-ephrin-B3 staining was strong and well defined on cells surrounding EOC cells. On the mucinous-type tumor, the the tumor cell membrane of all the different histotypes with some staining with both anti-ephrin-B3 and anti-EphB4 appeared staining in the cytoplasm of tumor and stromal cells; some tumor reduced in some cells because of the cytoplasmic filling by nuclei also showed weak/moderate reactivity (Fig. 1B, 1, 3, and 7). mucous (Fig. 1B, 3 and 4). The anti-ephrin-B3 and anti-EphB4 Anti-EphB4 antibody strongly stained the membrane of tumor staining on 8 LMP ovarian tumors and 47 EOC is summarized in www.aacrjournals.org 10713 Cancer Res 2006; 66: (22). November 15, 2006
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Table 3. L-R pairs differentially correlated between short-term and long-term survivors in at least two EOC data sets
Short-term survivors
I II III IV
A. Clinical information
No. patients 30 24 16 37 Median survival 17.5 33 <24 30
B. Ligand receptor r PrPrPrP
Angiogenesis VEGF* NRP2 À0.47 0.00904 À0.45 0.03113 À0.27 0.12045 Chemokines c CCL11 CCR5 0.37 0.04626 0.64 0.00742 0.37 0.02743 Cytokines IL15 IL2RG 0.53 0.00289 0.24 0.44834 0.41 0.11299 0.50 0.00250 LIF IL6ST 0.41 0.02364 0.42 0.01200 Growth factors FGF1 FGFR1 À0.39 0.03474 0.04 0.86895 À0.43 0.00921 FGF13 FGFR1 0.55 0.00183 À0.42 0.10211 0.37 0.02907 FGF4 FGFR3 À0.38 0.03647 À0.63 0.00845 À0.02 0.89576 c FGF7 FGFR2 À0.44 0.01409 À0.54 0.03016 À0.36 0.03573 FGF8 FGFR3 À0.50 0.00462 À0.59 0.01658 0.12 0.48082 c IGF2 IGF2R 0.37 0.04457 0.92 0.00000 0.75 0.00092 0.14 0.42554 Motility adhesion factors EFNA5* EPHA5 0.39 0.03221 0.62 0.01100 0.21 0.22593 EFNB2 EPHA4 0.41 0.02537 0.15 0.58643 0.59 0.00019 EFNB2 EPHB2 0.38 0.03926 0.47 0.06614 0.41 0.01429 EFNB3 EPHB4 À0.31 0.09764 0.44 0.03028 0.18 0.49978 0.50 0.00245 TNC* ANXA2 0.45 0.01360 0.70 0.00271 À0.09 0.59351
NOTE: Shaded areas identify significant correlations (P < 0.05) that differentiate short-term from long-term survivors. *Discordant correlations observed for the indicated L-R pairs in different data sets. cDiscordant correlations observed for the indicated L-R pairs in the same data set.
Table 4. Twenty-seven of the 30 serous tumors showed correlated pairs with the aim of discovering potential autocrine/paracrine expression of both ephrin-B3 and EphB4 proteins. Within LMP signaling loops in different cancers (6). Although limited by the serous tumors, one showed weak staining with anti-ephrin-B3 data sets available at the time (one for each pathology) and by antibody and one was not reactive with anti-EphB4 antibody. the relative paucity of L-R pairs extractable from them (range, 33- Within serous EOCs, only one showed no reactivity with either 68), that study identified a large number (>30) of known and new antibody, and two samples with weak reactivity with anti-EphB4 signaling loops as potentially active in diffuse large B-cell antibody showed strong and negative staining with anti-ephrin- lymphoma, leukemia, and colon and breast cancer. The B3 antibody, respectively. In all mucinous (6) and endometroid systematic evaluation of multiple data sets promises to yield (9) tumors, the staining intensities of both antibodies were more reliable and more valid results because it is based on a correlated. By contrast, 4 of 10 clear cell EOCs were not reactive larger number of samples and the effects of individual study- with anti-EphB4 antibody and 4 samples showed strong specific biases are weakened (32). reactivity; only 1 sample showed no reactivity with anti-ephrin- In the present study, we refined the starting list of L-R pairs and B3 antibody. Contingency analysis by m2 test indicated a applied it to numerous data sets from a single pathology, EOC, as significant (P < 0.0001) correlation between ephrin-B3 and EphB4 proof-of-principle that the correlation of gene expression patterns protein expression levels. of L-R pairs is statistically and biologically valid. Integration of the refined DLRP-rev1 database with eight EOC data sets of gene expression matched a larger number of L-R pairs (range, 68-417) Discussion compared with the initial study and enabled the identification of Initial evidence in Saccharomyces cerevisiae (30) indicated that 105 L-R pairs showing significant correlation after Bonferroni genes with similar expression profiles were more likely to encode correction. To select potential candidates, the 105 L-R pairs interacting proteins and, very recently, similar results were identified as significantly correlated in a single data set were obtained for the human genome (31). A pioneering study analyzed across the other data sets to obtain independent analyzed the correlation of the gene expression patterns of L-R statistical validation of their correlation. Several arbitrary selection
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Table 3. L-R pairs differentially correlated between short-term and long-term survivors in at least two EOC data sets (Cont’d)
Long-term survivors
I II III IV
A. Clinical information
No. patients 24 12 14 31 Median survival 107.5 71 >84 >47
B. Ligand receptor r P r PrPrP
Angiogenesis VEGF* NRP2 À0.31 0.13698 À0.16 0.62375 À0.48 0.00467 Chemokines c CCL11 CCR5 0.19 0.38031 0.57 0.02524 0.25 0.15438 Cytokines IL15 IL2RG 0.35 0.09470 0.31 0.61209 0.15 0.58851 0.22 0.21322 LIF IL6ST 0.29 0.16797 À0.71 0.00000 Growth factors FGF1 FGFR1 0.41 0.04807 0.12 0.66879 0.23 0.20495 FGF13 FGFR1 0.27 0.20500 À0.04 0.87376 0.17 0.33862 FGF4 FGFR3 0.33 0.11983 0.65 0.00929 0.37 0.03483 c FGF7 FGFR2 0.60 0.00179 0.31 0.25912 À0.53 0.00142 FGF8 FGFR3 0.26 0.21225 0.39 0.15425 0.38 0.02975 c IGF2 IGF2R 0.22 0.30655 0.80 0.01670 À0.17 0.54510 À0.30 0.09501 Motility adhesion factors EFNA5* EPHA5 0.32 0.12698 0.17 0.53370 0.37 0.03245 EFNB2 EPHA4 0.07 0.74276 0.46 0.08787 0.15 0.39864 EFNB2 EPHB2 0.39 0.06175 0.21 0.45219 À0.31 0.08244 EFNB3 EPHB4 À0.09 0.69181 0.28 0.38473 0.12 0.67314 0.12 0.50100 TNC* ANXA2 0.10 0.63268 À0.46 0.08542 0.39 0.02466 cutoffs were applied and some were also tested experimentally. In L-R correlation with survival, but the limited number of available our analysis, we gaged statistical validation as well as the existing cases precluded a further subcategorization. Thus, several clinical knowledge about EOC biology. For the entire case material, we variables, such as difference in age, histology, residual disease focused on EOC biology, selecting a cutoff based on the correct after debulking, and type of treatment, could confound the identification of all seven L-R pairs already implicated in EOC results. Some of these factors have been already taken into (12, 19–25) and on means of down-weighting data sets representing account in the original articles describing the subcategorization fewer initial L-R pairs and/or samples. Our selection criteria in short-term and long-term survivors (11, 13, 14) and seemed not reduced the potential L-R candidates to 39 pairs, 7 of which already to play an important role in determining patient’s outcome. biologically confirmed. However, the relevance of candidate L-R pairs in disease Because the case materials analyzed were biased toward progression should be interpreted with caution and await advanced-stage disease (see Table 1B), the identified L-R pairs validation in larger data sets and confirmation in biological/ are likely associated to EOC biology as well as to EOC progression. functional assays. The identification of pathways involved in epithelial ovarian To retrieve further information about the biological significance oncogenesis awaits gene expression analysis of a larger series of of our data, we evaluated the distribution of the identified L-R pairs early-stage case material. in functional classes (Fig. 2). The distribution of the L-R pairs The availability of clinical information about overall survival in potentially correlated to EOC biology after cross-analysis (Fig. 2C), four data sets provided an opportunity to evaluate correlations compared with their relative presence in the DLRP-rev1 database potentially associated with late-stage tumor progression. The and in correlation analysis of single data sets (Fig. 2A), clearly statistical cutoff adopted for the entire case material (correlation indicated a significant increase in L-R pairs involved in chemokine significance after Bonferroni correction) was not informative, signaling (33% versus 15% and 23%, respectively) accompanied by a probably due to the smaller number of patients included in each decrease in growth factors and cognate relevant receptor L-R pairs data set. When a less stringent level of significance (P < 0.05) (28% versus 42% and 37%, respectively). By contrast, analysis of L-R was considered, 166 L-R pairs were identified. To validate these pairs potentially correlated to EOC progression in late stage correlations, possibly including numerous false-positives, due to implicated mainly motility/adhesion signaling molecules [33% after the limited knowledge of EOC progression, we should rely only on cross-analysis (Fig. 2D) versus 14% in DLRP-rev1 database and in validation using statistical independent data sets. Our selected correlation analysis (Fig. 2B)] and suggested a switch toward a cutoff reduced the list of potential candidates to 15 L-R pairs. negative correlation in the class of growth factors (see Table 3). Only advanced stage EOC patients were selected for analysis of These observations are consistent with current hypotheses linking www.aacrjournals.org 10715 Cancer Res 2006; 66: (22). November 15, 2006
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Cancer Research
epithelial ovarian oncogenesis and progression to inflammation (33) and EOC progression to a dysregulation of cell-cell and cell- stroma interactions (34). The relevance of DLRP database revision based on new biological knowledge is supported by the observation that at least two of the significantly correlated L-R pairs, PLAU-PLAUR (identified in the EOC biology analysis) and TNC-ANXA2 (identified in the EOC progression analysis), could be retrieved by implemen- tation of the originally published database. That database was focused mainly on autocrine signaling, although it contained pairs able to signal also or only through paracrine interactions, and our implementation was partially dedicated to increasing identification of motility/adhesion-involved molecules. Our L-R pair selection, together with the observation that the cancer specimens in all data sets contained 70% to 80% tumor cells but were not micro- dissected, enabled retrieval of most of the potential tumor-tumor and tumor-stroma autocrine/paracrine interactions. Despite the improvements relative to the initial study, several potential limitations and bias, as outlined in the original article (6), could also affect our analyses. In fact, the intra-data set evaluation was strongly limited by the number of samples considered (<40 in data sets III and VIII), and the inter-data set comparison might be biased by the type and size of the platform, by the selected genes present on each array, and by the type of samples included in each data set. Both previous (6) and present studies identified some negatively correlated L-R pairs. By cross-comparison analysis of L-R pairs correlated with EOC biology, only 3 pairs with consistently negative correlation versus 36 with consistently positive correlation were observed. When only the L-R pairs correlated with short-term survivors were considered, the percentage of negatively correlated pairs strongly increased (5 of 15). This may reflect either a lack of autocrine/paracrine signaling due to decreased levels of a ligand/ Figure 1. Expression of ephrin-B3 and EphB4 proteins on normal tissues and ovarian tumor specimens. Immunostaining with anti-ephrin-B3 and EphB4 receptor whenever its cognate receptor/ligand is produced or antibodies was done on paraffin-embedded tissues. Positive controls were the transcriptional activation of an alternative ligand/receptor to as follows: A, 1, normal ovarian arterioles (anti-ephrin-B3); 2, colonic compensate for the absence of the physiologic signaling. Biolog- adenocarcinoma (anti-EphB4). B, 1 and 5, serous ovarian carcinoma; 2 and 6, mucinous ovarian carcinoma; 3 and 7, endometroid ovarian carcinoma; 4 and 8, ical/functional validation is necessary to identify the underlying clear cell ovarian carcinoma. Original magnification, Â200. mechanism.
Table 4. Immunostaining results in 55 paraffin-embedded samples from epithelial ovarian tumors
Tumor histotype* Total cases Ephrin-B3 EphB4 (% correlated expression) c c c c Negative Weak/moderate Strong Negative Weak/moderate Strong
Serous 30 (90) LMP 4 — 1 3 1 1 3 b EOC 26 2 6 20 1 6 20 Mucinous 6 (100) LMP 3 — 1 2 — 1 2 EOC 3 — 1 2 — 1 2 Endometrioid 9 (100) LMP 1 —1 —1 EOC 8 1 7 1 7 Clear cell 10 (30) 1 2 7 4 2 4
NOTE: Ephrin-B3 and EphB4 protein expression levels were significantly correlated (P < 0.0001) by m2 test. *Routine tissue blocks and commercially available tissue arrays. cWeak/moderate or strong staining of 80% of tumor cells in the tissue section. bEOCs comprise grade II and III tumors.
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The identification of known and expected correlations in our very little is known about how Eph receptors contribute to the analysis strongly supports the validity of our cross-comparison oncogenic process, EphB4 overexpression has been reported in analysis and potentially implicated the newly identified correlated colon (37), breast (38), and prostate carcinomas (39) and positively L-R pairs in EOC biology and progression. Among the newly associated with malignant potential, clinical grade and stage in identified correlated L-R pairs, we focused on EFNB3-EPHB4. Eph endometrial carcinoma (40, 41). Furthermore, EphB4 de novo receptors, divided into A and B type based on interaction with expression in a breast carcinoma cell line contributes to tumor their ligands, comprise the largest group of membrane tyrosine progression by attracting endothelial cells and inducing neo- kinase receptors, and their ligands, ephrins, are also membrane vascularization, thus promoting tumor cell proliferation and bound (for review, see ref. 26). The paracrine/juxtacrine signaling survival (42). Signaling through a paracrine loop between EphB4 is cell contact dependent and can potentially trigger a bidirec- and any member of B-class ephrins is required for directional tional response leading to either cell repulsion or invasion. At growth of developing vasculature, confirming that the EphB4 present, only the EphA2 protein has been reported to be receptor can interact with and signal through an overexpressed associated with aggressive ovarian carcinomas (35, 36). Although ephrin-B3 ligand (28). Our immunohistochemical analysis showed
Figure 2. Flow chart diagram of the experimental design showing the steps of analysis and the percentage distribution of L-R pairs according to functional consequence of the L-R interaction. After extraction of L-R pairs from EOC data sets, all tumor samples were examined with respect to EOC biology (A and C) and only the samples from patients with short-term survival were used for EOC progression analysis (B and D). A and B, the class distribution of L-R pairs from correlation analysis of eight and four data sets, respectively. C and D, the class distribution of selected L-R pairs after cross-analysis. See results for cutoff criteria.
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Cancer Research the correlated expression of ephrin-B3 and EphB4 in 45 of 55 ephrin-B3-EphB4 in EOC progression. Further studies are needed ovarian tumor specimens. Clear cell was the only EOC histotype, in to address this possibility and their potential usefulness as which the correlation was absent. A potential explanation rests in phenotypic and/or prognostic markers. recent evidence, by gene expression profiling, that EOC clear cell Together, our data point to the feasibility of a cross-platform histotype can be reconducted to normal uterine endometrium analysis of gene expression data to identify L-R signaling loops in (43), instead of ovarian surface epithelium from which the vast other oncotypes, provided that a sufficient number of data sets are majority of EOC originate (44). The correlated expression in all available and that the principle of external validation in indepen- the other histotypes was similar irrespectively of grading and dent data sets is uphold. The proposed bioinformatic systematic malignant potential. Indeed, seven of eight LMP tumors resulted search of L-R coexpression might also prove useful in conjunction to have correlated expression of ephrin-B3/EphB4 and only one with conventional protein-protein interaction methods of predic- did not express EphB4. LMP ovarian tumors represent a subset tion. Once supported and validated by biological/functional assays, of EOC with a very good prognosis, and most of them show the L-R coexpression search might provide insight into the biology molecular characteristics distinct from carcinomas (45, 46). Hence, of a specific oncotype and could open avenue to the design of analysis of a larger number of samples would allow evaluating the specifically targeted new diagnostic and therapeutic tools. significance of ephrin-B3/EphB4 correlated expression in this subset of tumors. The Eph-ephrin signaling occurs at the membrane level, but it was shown recently that the L-R pair Acknowledgments ephrin-B2-EphB4, once activated by cell-cell contact, is endocy- Received 4/17/2006; revised 7/14/2006; accepted 8/30/2006. tosed as a consequence of a cytoskeletal rearrangement that Grant support: Associazione Italiana Ricerca Cancro and CARIPLO Foundation (S. Canevari). requires RAC function (47). Accordingly, the cytoplasmic localiza- The costs of publication of this article were defrayed in part by the payment of page tion of both ephrin-B3 and EphB4 in our analysis might reflect charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. internalization after signaling activation. Overall, our observations We thank M.A. Pierotti for helpful and stimulating discussion, Y. Yarden for critical suggest the involvement of paracrine/juxtacrine signaling through reading of the article, and Gloria Bosco for secretarial assistance.
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New Potential Ligand-Receptor Signaling Loops in Ovarian Cancer Identified in Multiple Gene Expression Studies
Giancarlo Castellano, James F. Reid, Paola Alberti, et al.
Cancer Res Published OnlineFirst November 6, 2006.
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