Analysis of gene expression in stage I serous tumors identifies critical pathways altered in ovarian cancer

Gynecologic Oncology 114 (2009) 3–11

Contents lists available at ScienceDirect

Gynecologic Oncology j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y g y n o

Analysis of gene expression in stage I serous tumors identifies critical pathways altered in ovarian cancer Jeremy Chien a,⁎, Jian-Bing Fan b, Debra A. Bell c, Craig April b, Brandy Klotzle b, Takayo Ota a, Wilma L. Lingle a, Jesus Gonzalez Bosquet d, Viji Shridhar a, Lynn C. Hartmann e a

Division of Experimental Pathology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA Illumina Inc, San Diego, CA, USA Division of Anatomic Pathology, Mayo Clinic College of Medicine, Rochester, MN, USA d Division of Cancer Epidemiology and Genetics, National Cancer Institute, Gaithersburg, MD, USA e Division of Medical Oncology, Mayo Clinic College of Medicine, Rochester, MN, USA b c

a r t i c l e

i n f o

Article history: Received 26 February 2009 Available online 1 May 2009 Keywords: Stage I serous carcinoma Gene expression Serous borderline tumor p53 E2F

a b s t r a c t Objective. Despite recent advances in the conceptual understanding of the pathogenesis of ovarian cancer, it remains the foremost cause of death from gynecologic malignancies in developed countries. The main reason for such a high rate of mortality is the lack of sensitive and specific biomarkers and imaging techniques for early detection of ovarian cancer. Additional biological insights into early-stage ovarian carcinogenesis are needed to help speed the development of markers for early detection of ovarian cancer. The objective of this study was to characterize differentially expressed genes in high-grade stage I serous carcinoma of the ovary. Methods. We analyzed gene expression in macrodissected formalin-fixed, paraffin-embedded samples from 5 high-grade stage I serous carcinomas and 5 stage I borderline tumors of the ovary using the Illumina Whole Genome DASL assay (cDNA-mediated annealing, selection, extension, and ligation) corresponding to 24,000 genes. Significance Analysis of Microarrays was performed to determine differentially expressed genes in stage I serous carcinoma, and class prediction analysis was performed to determine the predictive value of differentially expressed gene sets to correctly classify serous carcinoma from borderline tumors in 3 independent data sets. Altered transcription factor pathways and biological pathways unique to stage I serous carcinoma were identified through class comparison of differentially expressed genes. Results. Unsupervised cluster analysis of gene expression correctly classified stage I serous carcinomas from serous borderline tumors. Supervised analysis identified several known, as well as novel, genes differentially expressed in stage I ovarian cancer. Using a differentially expressed gene set, class comparison prediction analysis correctly identified serous carcinomas from serous borderline tumors in 3 independent data sets at over 80% accuracy, sensitivity, and specificity. Pathway analysis demonstrated the significance of p53 and E2F pathways in serous carcinogenesis and significant involvements of cell cycle and immune response pathways in stage I serous epithelial ovarian cancer. Conclusion. We have identified differentially expressed genes associated with the carcinogenesis of highgrade stage I serous EOC. Furthermore, integrative analysis of biological and transcription pathway data contributed to the confirmation of important biological pathways and discovery of additional unique genes and pathways that may have potential importance in ovarian pathogenesis and biomarker development. © 2009 Elsevier Inc. All rights reserved.

Introduction Epithelial cancer of the ovary is the most lethal gynecologic malignancy in the United States, with approximately 22,000 new cases and 16,000 deaths occurring annually [1]. Due to the absence of specific signs and symptoms and the lack of effective screening programs, epithelial ovarian cancer (EOC) is diagnosed at advanced stages in most patients, resulting in low overall cure rates. Therefore, ⁎ Corresponding author. Fax: +1 507 284 1678. E-mail address: [email protected] (J. Chien). 0090-8258/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.ygyno.2009.04.002

there is a critical need to improve our understanding of the biology of early-stage epithelial ovarian cancer in order to rationally design experimental approaches and clinical studies to identify and evaluate biomarkers associated with early-stage disease. Epithelial ovarian cancer constitutes the majority of ovarian malignancies and is classified into distinct morphologic categories consisting of serous, mucinous, endometrioid, clear cell, transitional, squamous, mixed, and undifferentiated subtypes [2]. Although the precise origin of EOC is not fully understood, distinct precursor lesions and de novo carcinogenesis have been proposed for the major histologic subtypes of EOC [2–4]. Although Mullerian metaplasia of

4

J. Chien et al. / Gynecologic Oncology 114 (2009) 3–11

ovarian surface epithelium and its inclusion glands is generally considered as the origin of EOC [2], accumulating evidence suggests that fallopian tubal carcinomas may provide an alternative primary site of origin for serous EOC, particularly in BRCA mutation-positive women [5–8]. Thus, EOC may arise as a result of de novo carcinogenesis, tubal carcinoma implants, or progression from borderline tumors [3,9]. In general, EOC can be classified into two types, based on the two main pathways of tumorigenesis: low grade neoplasms that arise in a stepwise manner from borderline tumors are considered type I, whereas high-grade neoplasms without definable precursor lesions are considered type II [4,10]. Among high-grade neoplasms of the ovary, serous carcinoma represents the most common type of EOC frequently diagnosed at advanced stages due in part to rapid progression, narrower detection window, and deficiencies in understanding the biology of early-stage, high-grade serous carcinoma. Loss of p53 function is suggested to be an early molecular event associated with de novo carcinogenesis of type II serous, endometrioid, and clear cell carcinomas of EOC, whereas type I EOCs arising from the progression of borderline tumors frequently contain wildtype p53 and may provide precursor lesions for type I EOC [2–4,11]. BRCA dysfunction is also considered to be an early event associated with de novo carcinogenesis of type II serous EOC [2,3]. Results from recent studies also indicate that alterations in proliferative pathways are often associated with early lesions in serous EOC. For example, gene expression analyses of early-stage disease using frozen specimens indicate deregulation of proliferation pathways [12–14]. Consistent with the results from these studies [12], immunohistochemical studies of early histological tubal lesions in FFPE specimens also indicate changes in proliferative status, as monitored by MiB staining [6]. High-grade stage I serous carcinomas of the ovary are extremely rare, since most patients with high-grade serous carcinomas present with advanced disease. Better definition of genetic alterations in these early-stage, high-grade serous carcinomas is expected to contribute to better insight into the biology of these early diseases which would, in turn, accelerate the discovery of novel biomarkers for screening and detection of early-stage, high-grade EOC. Successful discoveries of novel biomarkers, such as HE4, have been made using high throughput gene expression platform [15], thus representing a proven approach to the identification of additional biomarkers. Toward this goal, we analyzed gene expression of these rare high-grade stage I serous carcinomas by utilizing a newly developed Whole Genome DASL technology, which allows analysis of gene expression from FFPE specimens. To identify genes and pathways associated with serous carcinogenesis, we compared gene expression between high-grade stage I serous carcinomas and stage I serous borderline tumors. Results from our study demonstrated the feasibility of gene expression analysis from FFPE samples using the Whole Genome DASL technology, identified previously known, as well as unknown, biological pathways, and implicated novel biological pathways in carcinogenesis of EOC. Materials and methods Samples A total of 5 serous borderline tumors and 5 high-grade stage I serous carcinoma samples were reviewed by a gynecologic pathologist (D.B.). Samples were collected and used in accordance with the approved institutional review board protocols. One to 8 slides of each ovarian neoplasm were reviewed to confirm the original diagnosis, and all of the slides of the surgical procedure were also reviewed to confirm the absence of extraovarian tumor (to confirm that the cancer was stage I). Areas of highest grade, non-necrotic tumor in the carcinoma cases and areas with the highest epithelial to stromal ratio

in the borderline tumors were identified on the hematoxylin–eosin (H and E) sections. Tumor area on FFPE blocks that correspond to these areas in the H and E sections were marked, and 4 to 6 1-mm core samples were removed from the blocks. For biological replicates, 2 additional FFPE blocks from the same patients (one with serous borderline tumor and another with stage I serous carcinoma) were included. Sample and patient characteristics are described in Table S1. RNA Isolation RNA samples were isolated using the Qiagen Rneasy FFPE Kit (Cat. No. 74404). H and E slides were made for each sample, and areas of interest were identified. Four to six 1-mm cores were removed from each block and placed in 1 mL of Hemo-De. Standard kit protocol was followed, except the samples were incubated overnight in proteinase K. Samples were spun down and placed in a 2 mL tube and run on the Qiagen Qiacube with the Rneasy FFPE Kit protocol for 3 to 8 FFPE tissue sections. The elution volume was 20 μl. Whole Genome DASL microarray analysis The Whole Genome DASL assay, derived from the DASL Assay [16,17], has improved multiplexing capability, thereby greatly increasing the target set of the original DASL assay while retaining the ability to profile degraded samples, http://www.illumina.com/downloads/ WGDASLAssay_Datasheet.pdf. In brief, 200 ng total RNA was converted to cDNA using biotinylated oligo-dT18 and random nonamer primers, followed by immobilization to a streptavidin-coated solid support. The biotinylated cDNAs were then simultaneously annealed to a set of assay-specific oligonucleotides designed to correspond to 24526 probes (18626 unique genes), based on content derived from the National Center for Biotechnology Information Reference Sequence Database (Build 36.2, Release 22). Extension and ligation of the annealed oligonucleotides generated PCR templates that were amplified using fluorescence-labeled and biotinylated universal primers. The labeled PCR products were then captured on streptavidin paramagnetic beads, washed, and denatured to yield single-stranded fluorescent molecules which were hybridized to Whole Genome gene expression BeadChips (HumanRef-8, Illumina) for 16 h at 58 °C. Images were extracted and fluorescence intensities were read on a BeadArray Reader, whereafter scanned data were uploaded into BeadStudio for further analysis. Data analysis Raw data were used in the unsupervised hierarchical cluster analysis to determine whether unprocessed gene expression values could distinguish between 2 classes of tumors and to test for the reproducibility of assays. For subsequent analyses, log2 transformed, fastlo normalized data were used [18]. Supervised analysis Supervised analysis to determine differentially expressed genes in stage I serous carcinoma was performed using Significance Analysis of Microarrays (SAM) [19]. SAM uses the false discovery rate (FDR) and q-value method reported by John D. Storey [20]. For SAM analysis, unpaired sample analysis was used with Delta set to 0.822, resulting in an FDR of less than 5%. Class comparison of gene set expression Using BRB-ArrayTools, the top 10% of genes with the highest log intensity variance across the arrays were selected and analyzed for class comparison of gene set expression. Transcription Factor Target gene set was used for discovery of altered transcription factor

J. Chien et al. / Gynecologic Oncology 114 (2009) 3–11

networks in high-grade stage I serous carcinoma. The LS/KS permutation test, Efron–Tibshirani's GSA “maxmean” test, and Goeman's global test were used to find significant gene sets. The threshold of determining significant gene sets is 0.005. The LS/KS permutation test finds gene sets with more genes differentially expressed among the phenotype classes than expected by chance. Efron–Tibshirani's test uses “maxmean” statistics to identify gene sets differentially expressed. Goeman's global test finds gene sets associated with the phenotype classes. Class comparison and class prediction Class comparison and class prediction were performed using various models provided in BRB-ArrayTools (Version 3.7.0) developed by Dr. Richard Simon and the BRB-ArrayTools Development Team. BRB-ArrayTools is a multifunctional Excel add-in that contains utilities for processing and analyzing microarray data using the R version 2.8 environment (R Development Core Team, 2008). Class comparison was performed using a 2-sample t test with random variance model and a multivariate permutation with a total of 2000 permutations. A random variance t test was selected to permit the sharing of information among probe sets within class variation without assuming that all probe sets possess the same variance, at nominal significance level of 0.005. Leave-one-out cross-validation method was used to compute misclassification rate. Using our data as a training set, a gene list was constructed to be used for validation in 3 independent data sets (testing sets); for this purpose, a level of significance of 0.005 was used, as class prediction is not really about discovering differentially expressed genes. Cluster analysis of samples alone and with genes were performed with centered correlation analysis and with the complete linkage method.

5

Gene ontology and pathways analysis MetaCore pathway analyses were performed on the differentially expressed genes, consisting of 285 genes, identified by SAM (GeneGo Inc., San Diego, CA). The 24,526 probes from the DASL platform were used as the reference list. Pathways with p values ≤ 0.05 were identified as pathways significantly enriched with differentially expressed genes. Additional gene enrichment analysis, provided by MetaCore, was performed to score and rank the most relevant cellular processes, disease targets, biomarkers, toxicity processes, and molecular functions for the dataset. p53 immunohistochemistry Formalin-fixed paraffin-embedded tissue sections were deparaffinized and antigen retrieval was carried out using EDTA in a preheated 98° steamer for 30 min. Slides were loaded onto the DAKO Autostainer and treated with 3% H2O2 to inactivate endogenous peroxidase followed by incubation with protein block. Mouse monoclonal antip53 (1:200, Clone DO-7, DAKO M7001, DAKO North America, Inc., Carpinteria, CA) was applied for 30 min. Visualization was carried out using Dual+Envision link (K4061, DAKO North America, Inc., Carpinteria, CA) followed by incubation with diaminobenzidine. Sections were counterstained with hematoxylin. Results Five stage I high-grade serous carcinomas from the approximately 30 cases in the surgical pathology files of Mayo Clinic were randomly selected for review, as well as 5 randomly selected stage I serous borderline tumors. The original pathologic findings were reviewed

Fig. 1. Histologic confirmation of tumors included in the studies. After macrodissection of tumors, remaining paraffin blocks were cut and stained with H and E. Microscopic examination of tumor histology is consistent with serous for both borderline and carcinoma. Objective lens magnifications are indicated on top of the micrographs. Rectangular boxes in the left panels indicate areas where corresponding magnified views are shown on the right panels. Holes in the tissues indicate areas where samples were cored out for RNA extraction.

6

J. Chien et al. / Gynecologic Oncology 114 (2009) 3–11

and confirmed. Serous borderline tumors showed characteristic features of branching papillae lined by moderately atypical cells compared to high-grade serous carcinomas, which were irregularly papillary or solid with stromal invasion and marked cellular pleomorphism (Fig. 1). Four of the carcinomas were grade 3 of 3; 1 showed greater papillarity and was grade 2 of 3. Histologic analysis of H and E-stained slides from FFPE blocks, after sample core extraction, confirmed serous histology in the areas where tumor cells were sampled (Fig. 1). Additional sample characteristics are shown in Table 1. The oldest sample block, sample #5, is 17 years old.

Total RNA extracted from FFPE samples were subjected to Whole Genome DASL gene expression profiling. Prior to more detailed analyses, the entire experiment was first assessed for data quality as ascertained by several sample-dependent and -independent control metrics for the Whole Genome DASL assay, which include oligonucleotide annealing, array hybridization, and negative and housekeeping controls. Together, these built-in system [Float1]controls provided a quantitative assessment of data quality and indicated that, overall, the data were of good quality (data not shown), with the exception of sample #5 (see below).

Table 1 Differentially expressed genes in high-grade stage I serous EOC based on SAM analysis. Overexpressed in serous carcinoma (100) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61

Underexpressed in serous carcinoma (185)

Gene ID

Gene name

Fold change

q-value (%)

FOLR3 MT1G KLK5 LOC284739 C3orf9 HIST2H4A OPLAH MT1F RTKN VTCN1 BIRC5 E2F2 TAP1 VPS18 IFI44L HIST1H3C CDC20 MGC61571 TMEM79 CELSR3 C20orf55 FOXM1 HK1 CCDC7 MCM6 NFKBIL2 ABCF1 PARP1 GUF1 RPL39L MGC40579 C1orf135 ZBTB10 HCP5 UCHL5 C1orf37 ATR ABCA4 TTC13 SRPK1 HTATIP COMMD5 ZNF256 UQCRH CACYBP ARL6IP2 E2F3 MCM7 ZXDA EHMT2 MCM7 HIST1H3H AHSA2 CACYBP ERN1 MT1E KLK6 HIST1H3G PSRC1 KIAA0240 OCIAD2

NM_000804.2 NM_005950.1 NM_012427.4 NM_207349.1 NM_020231.3 NM_003548.2 NM_017570.1 NM_005949.1 NM_001015055.1 NM_024626.1 NM_001168.2 NM_004091.2 NM_000593.5 NM_020857.2 NM_006820.1 NM_003531.2 NM_001255.1 NM_182523.1 NM_032323.1 NM_001407.1 NM_001042353.1 NM_021953.2 NM_000188.1 NM_145023.4 NM_005915.4 NM_013432.3 NM_001090.2 NM_001618.2 NM_021927.1 NM_052969.1 NM_152776.1 NM_024037.1 NM_023929.2 NM_006674.2 NM_015984.1 NM_138391.3 NM_001184.2 NM_000350.1 NM_024525.2 NM_003137.3 NM_182710.1 NM_014066.3 NM_005773.2 NM_006004.2 NM_014412.2 NM_022374.1 NM_001949.2 NM_182776.1 NM_007156.3 NM_006709.2 NM_005916.3 NM_003536.2 NM_152392.1 NM_001007214.1 NM_152461.2 NM_175617.3 NM_002774.3 NM_003534.2 NM_001005290.2 NM_015349.1 NM_152398.2

10.26193 6.245833 4.708378 7.677374 3.036577 4.058878 3.069272 5.219116 2.999061 29.67271 4.849515 3.288571 4.939365 3.464108 3.761234 4.385978 3.452183 2.143335 2.37097 2.350353 2.151579 3.541648 4.765039 8.283448 3.269625 4.673593 3.132827 3.84849 2.667419 6.850288 4.82612 7.357366 2.178012 2.453314 3.136538 2.311584 2.957076 2.494087 2.873367 3.797696 2.040446 2.192176 2.137503 2.444097 2.612137 2.096538 2.20745 2.0485 3.349487 2.189663 4.838081 2.517383 2.47489 2.392206 2.358352 2.2263 3.99266 2.46184 3.710093 2.815514 4.562946

0 0 0 0 0 0 0 0 0 0 0 0 1.009078 1.009078 1.009078 1.204383 1.204383 1.204383 1.204383 1.204383 1.204383 1.204383 1.204383 2.074216 2.074216 2.074216 2.074216 2.074216 2.074216 2.765621 2.765621 2.765621 2.765621 2.765621 2.765621 2.765621 2.765621 3.017041 3.017041 3.017041 3.017041 3.017041 3.017041 3.017041 3.140402 3.140402 3.44639 3.44639 3.44639 3.44639 3.44639 3.44639 3.44639 3.44639 3.44639 3.44639 3.44639 3.840263 3.840263 3.840263 3.840263

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61

Gene ID

Gene name

Fold change

q-value (%)

SYNE1 AKAP14 C9orf116 KNDC1 DLEC1 C10orf92 C1orf110 TSGA2 LRRC50 SORBS2 IL6 TNFSF9 DLK1 DNAI1 GAMT TTC25 TEKT1 FRMPD2 CCDC33 KCNRG THRAP4 OR1L6 PPIL6 POFUT2 SPINK4 PAGE4 FLJ41170 DPP10 NRG1 CAPS C1orf173 CGI-38 FLT1 DYNLRB2 BNC1 CYLC1 CHGA FLJ40919 TRIM16L FST COVA1 SCUBE1 FLRT2 LOC90835 ZMYND10 C14orf161 LOC144983 GK2 PTK2B ITSN1 OR52J3 ROPN1L C10orf67 ADRA1A MAGEB6 NALP13 SFRP2 IL4 CACNG6 SPATA22 DEFB105A

NM_033071.1 NM_001008535.1 NM_144654.2 NM_152643.5 NM_007337.2 NM_017609.2 NM_178550.3 NM_080860.2 NM_178452.3 NM_003603.4 NM_000600.1 NM_003811.2 NM_003836.4 NM_012144.2 NM_000156.4 NM_031421.1 NM_053285.1 NM_001018071.2 NM_182791.1 NM_173605.1 NM_014815.3 NM_001004453.1 NM_173672.1 NM_015227.3 NM_014471.1 NM_007003.2 NM_001004332.1 NM_001004360.1 NM_013960.1 NM_004058.2 NM_001002912.2 NM_015964.2 NM_002019.2 NM_130897.1 NM_001717.2 NM_021118.1 NM_001275.2 NM_182508.1 NM_001037330.1 NM_006350.2 NM_006375.2 NM_173050.1 NM_013231.4 NM_001014979.1 NM_015896.2 NM_024764.2 NM_001011724.1 NM_033214.2 NM_173174.1 NM_003024.2 NM_001001916.1 NM_031916.2 NM_153714.1 NM_033303.2 NM_173523.2 NM_176810.1 NM_003013.2 NM_000589.2 NM_031897.2 NM_032598.2 NM_152250.1

0.22486 0.135539 0.197983 0.117442 0.209681 0.106914 0.332288 0.250702 0.132814 0.387319 0.300153 0.298657 0.143389 0.229906 0.345933 0.188621 0.151436 0.314316 0.205151 0.304576 0.137323 0.314987 0.29813 0.457541 0.510757 0.49239 0.374927 0.330477 0.427229 0.247926 0.21817 0.138842 0.350796 0.282821 0.324999 0.513666 0.079867 0.263341 0.409724 0.392445 0.258636 0.364434 0.34448 0.545516 0.283388 0.43814 0.448343 0.363734 0.499986 0.248685 0.245419 0.318651 0.344779 0.345511 0.465687 0.44228 0.318127 0.345649 0.230697 0.41633 0.408029

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.009078 1.009078 1.009078 1.009078 1.009078 1.009078 1.697086 1.697086 1.697086 1.697086 1.697086 1.697086 1.697086 1.697086 1.697086 1.697086 1.697086 1.697086 1.697086 1.697086 1.697086 1.697086 2.074216 2.074216 2.074216 2.074216 2.765621 2.765621 2.765621 2.765621 2.765621 2.765621 3.017041 3.017041 3.017041 3.017041 3.017041 3.017041 3.017041 3.140402 3.140402 3.140402 3.140402

J. Chien et al. / Gynecologic Oncology 114 (2009) 3–11

7

Fig. 2. Unsupervised hierarchical cluster analysis correctly classified serous borderline tumors from serous carcinoma. Unprocessed data from Whole Genome DASL assays were used to perform unsupervised hierarchical cluster analysis. (A) Stage I serous borderline tumor (SBT) clustered separately from high-grade stage I serous carcinoma. Serous carcinoma sample #5 clustered separately. Samples 3 and 4 for borderline and samples 11 and 12 for carcinoma are replicate samples obtained from two different FFPE blocks from the same patients. (B) Distributions of raw data from these studies indicate that sample #5 (asterisk) showed an unusually wide interquartile range, suggesting the possibility of poor sample or assay quality specific to this sample. Coincidentally, sample #5 is the oldest sample in this study, being 17 years old (see Table 1).

Unsupervised cluster analysis of unprocessed data indicates that stage I serous borderline tumors clustered separately from stage I serous carcinomas (Fig. 2A). In addition, biological replicates (RNA extracted from different blocks from the same patients) clustered together (Fig. 2A). These results demonstrate the potential of Whole Genome DASL assays to produce representative gene expression profiles from FFPE samples. Detailed analysis of data distribution of unprocessed data indicated an unusually wide interquartile range for sample #5 (Fig. 2B), suggesting the possibility of poor sample or assay quality. Therefore, this sample was excluded in subsequent normalization and data analysis. To minimize array and assay biases, samples were normalized by log2 transformation and fastlo normalization (Fig. S1). Normalized data were then used for SAM to determine differentially expressed genes. Two class unpaired t tests were performed to identify differentially expressed genes between serous carcinoma and serous borderline tumors. Two hundred eighty-five genes were identified as differentially expressed in serous carcinoma (FDR = 4.98%) (Fig. 3A). Among 285 differentially expressed genes, 100 genes were identified as overexpressed genes in serous carcinomas compared to serous borderline tumors, and 185 genes were identified as underexpressed genes in serous carcinomas. A partial list of differentially expressed genes is shown in Table 1. A complete list of genes is provided as supplementary information. Among the overexpressed genes, folate receptor gamma (FOLR3) tops the list. Although folate receptor alpha (FOLR1) transcript variant (v7) showed overexpression, not all isoforms showed it consistently (Fig. 3B). Low level expression of folate receptor beta was detected in all tumor samples. Additional differentially expressed genes include Survivin, MCMs, E2Fs, VTCN1 (all overexpressed in serous carcinoma) (Fig. 3C) and SYNE1, AKAP14, KNDC1, DLEC1, and several novel genes (all underexpressed in serous carcinoma) (Fig. 3D). To determine whether the differentially expressed gene set could be used to classify tumors as either borderline or carcinoma, we first performed supervised class prediction analysis using our data set. These analyses indicate a gene set consisting of 477 genes that could be useful in classifying tumors into borderline or carcinoma, based on the expression value of these genes. We then performed a validation study using 3 independent data sets obtained from Gene Expression Omnibus. Class prediction analysis, using the Nearest Centroid Classifier method, indicated over 80% accuracy, sensitivity, and specificity in classification of tumors in the 3 data sets (Table 2). Finally, to gain biological insight into carcinogenesis of early-stage ovarian cancer, we performed class comparison of differentially

expressed gene set analysis using the transcription factor network dataset. These analyses indicated perturbed p53 networks and E2F networks in serous carcinomas compared to serous borderline tumors (Table 3). In addition, we also identified that two unknown pathways, CREB1 and progesterone receptor (PGR), perturbed in serous carcinomas. The analysis of GO pathways associated with differential gene expression in stage I serous carcinoma indicates alterations in pathways associated with cell cycle regulation, cell cycle relatedcytoskeletal signaling, transcription related chromatin modification, and kallikrein-related inflammatory signaling (Fig. S2). Discussion In this study, we described the analysis of gene expression in stage I serous tumors from FFPE samples. Traditionally, these samples contain poor-quality RNA not adequate for expression analysis. However, using newly developed Whole Genome DASL expression profiling, we were able to garner reproducible data and biological insights into the molecular profiles of early-stage serous ovarian cancer. The DASL (cDNA-mediated annealing, selection, extension and ligation) technology is capable of transcriptional profiling in FFPE samples, as this assay displays high specificity and sensitivity in interrogating partially degraded targets. Several published studies have already demonstrated the utility of this platform in analyzing gene expression profiles from archived FFPE samples [16,17,21–25]. The results of our studies, using the Whole Genome DASL assay, http://www.illumina.com/downloads/WGDASLAssay_Datasheet. pdf, provided novel biological insights into rare cases of ovarian cancer and further supported the application of this technology in gene expression analysis of archived FFPE sample. The fact that unsupervised cluster analysis correctly separated stage I serous borderline tumors from stage I serous carcinoma, and that biological replicates from 2 different blocks of the same patient clustered together, also supported the robustness of this assay platform for gene expression analysis of FFPE samples. In this study, we focused on analyzing the gene expression from high-grade stage I serous carcinomas, which represent extremely rare cases of the early-stage of the most common and lethal form of ovarian cancer. Rationale for selecting these tumors for gene expression analysis is that they remain an enigma in terms of understanding the molecular profiles of these tumors. Although sample size in this study is very small, due to limited availability of these rare samples, we were able to produce valuable biological

8

J. Chien et al. / Gynecologic Oncology 114 (2009) 3–11

insights into carcinogenesis of high-grade stage I serous carcinoma. By comparing gene expression profiles between stage I serous borderline tumors and stage I high-grade serous carcinomas, we were able to identify biological processes involved in the carcinogenesis of highgrade stage I serous carcinomas. SAM indicated differentially expressed genes consisting of known cancer genes as well as novel genes. In general, there are more underexpressed genes than overexpressed genes in serous carcinoma compared to borderline tumors. These results are consistent with previous studies using frozen samples and different array platforms [13,14,26]. Genes such as mini-chromosome maintenance genes (MCMs), E2Fs, cyclin E1, and histones are overexpressed in highgrade stage I serous carcinomas compared to stage I serous borderline

tumors, indicating that proliferative and cell cycle pathways are deregulated in high-grade serous carcinomas. These results are consistent with previous reports indicating alterations in these pathways associated with ovarian cancer [6,8,12–14]. It should be noted that current findings are limited to high-grade stage I serous ovarian carcinoma. Additional studies should investigate differential gene expression among different histologic subtypes of epithelial ovarian cancer. In a report by Marchini et al. [12], differentially expressed genes in different histotypes of early-stage ovarian cancer were analyzed using fresh-frozen tissue specimens. Their analysis indicated that CCNE1 and MCM5 were significantly upregulated in relapsing patients compared to non-relapsing ones [12]. These results suggest the role of these 2 genes in tumor

Fig. 3. Significance Analysis of Microarrays (SAM) identifies differentially expressed genes in high-grade stage I serous carcinoma. (A) SAM plot indicating the distribution of genes based on expected and observed score. Genes that fall within upper and lower delta lines are considered not significant. Genes that fall outside the upper and lower delta lines are considered significant. Each dot represents a gene, with green being underexpressed, and red being overexpressed in serous carcinomas compared to serous borderline tumors. False discovery rate in this analysis was 4.98%. (B) Comparison of log2 transformed and fastlo normalized intensity (expression) values for folate receptors. Folate receptor alpha transcript variant v7 (R1v7), shows overexpression in cancer compared to borderline tumors (SBT). However, overexpression was not statistically significant. Two other folate receptor alpha transcript variants, v3 (R1v3) and v1 (R1v1), showed low levels of expression across all tumors. Low levels of folate receptor beta (R2) also were observed in these tumors. However, statistically significant overexpression of folate receptor 3 (R3) was observed in high-grade stage I serous carcinomas compared to stage I serous borderline tumors. Blue symbols indicate intensity values obtained from serous borderline tumors. (C) Scatter plots representing the relative mRNA expression levels of selected overexpressed genes in cancer compared to serous borderline tumors (SBT). (D) Scatter plots representing the relative mRNA expression levels of selected underexpressed genes in cancer compared to serous borderline tumors (SBT).

J. Chien et al. / Gynecologic Oncology 114 (2009) 3–11

9

Fig. 3 (continued).

progression, and are consistent with our finding that these genes are overexpressed in high-grade serous tumors with high capacity for progression, compared to borderline tumors with low capacity for progression. In addition, several ovarian cancer-associated genes are also identified in our study as overexpressed in stage I serous carcinoma. For example, BIRC5 (Survivin) is upregulated in ovarian cancer, and its upregulation is implicated in the resistance to chemotherapy [27–29]. VCTN1 (B7-H4) is a co-stimulator of T cells and acts as a suppressor of T-cell immunity [30]. It is upregulated in early-stage ovarian cancer, and its overexpression is associated with poor prognosis [31–33]. Ovarian cancer immunogenic antigen domain containing protein 2 (OCIAD2) shares sequence similarity to OCIAD1 that is elevated in the serum of patients with ovarian cancer [34]. Finally, folate receptor 3 is also identified as an overexpressed gene in stage I serous EOC. It is important to note that Whole Genome DASL assay allows expression analysis of genes with alternative spliced mRNAs representing specific protein isoforms. For example, Whole Genome DASL includes probes specific for different isoforms of folate receptor alpha (FOLR1). Our analysis indicates that FOLR1 is also overexpressed in some serous carcinomas, but overexpression is limited to isoforms 1 and 3, and are Table 2 Class prediction analysis (BRB Arraytools) of differentially expressed genes in high-grade stage I serous EOC. Performance of the nearest centroid classifier: GEO DataSet

Class

Sensitivity

Specificity

PPV

NPV

% Accuracy

GSE8842

Borderline Cancer Borderline Cancer Borderline Invasive

0.8 0.809 0.8 0.853 0.889 0.898

0.809 0.8 0.853 0.8 0.898 0.889

0.48 0.948 0.545 0.951 0.571 0.981

0.948 0.48 0.951 0.545 0.981 0.571

81

GSE8841 GSE6822

84 90

Independent data sets obtained from Gene Expression Omnibus are denoted according to GEO identifications (GSE8842, GSE8841, and GSE6822). Percent accuracy column indicates the percentage of tumors correctly classified as either borderline or carcinoma using the gene set trained in our data set.

not consistently overexpressed. Results from previous studies indicate overexpression of folate receptors in ovarian cancer [35–37]. Folate receptors are currently being evaluated as imaging targets and therapeutic targets for ovarian cancer [35,36]. Therefore, our observation that folate receptor gamma (FOLR3) is differentially overexpressed in serous carcinoma is unique and exciting, as it could open a new avenue of research into the utility of this specific receptor as a biomarker for ovarian cancer. Unlike previous studies that used a few selected candidate genes for validation with quantitative PCR or RT-PCR, which could be limited by the number of genes included in the validation, we used a more inclusive approach. Rather than focusing on selected candidate genes, we tested whether differentially expressed genes in our data set can be used to classify tumors in 3 independent data sets. These analyses indicate that differential expression of genes in our data set was appropriate for correctly classifying tumors into 2 groups at over 80% accuracy. These results validate the significance of differentially expressed genes identified in our study as putative signatures for the classification of serous carcinomas from serous borderline tumors. Our GeneGo biological process analyses also indicate perturbations in cell cycle-related processes in stage I serous carcinomas compared to borderline tumors. These results are consistent with previous reports indicating changes in proliferative and cell cycle pathways associated with ovarian cancer [12–14]. The GeneGo pathway analysis also implicates alterations in immune response pathway in stage I serous carcinoma (data not shown). Relevant to this observation is the identification of B7-H4 as an overexpressed gene in serous carcinoma. Recent evidence suggests that B7-H4 acts to suppress T-cell immune response [30,38]. Therefore, it is likely that overexpression of B7-H4 on cancer cells may provide immunosuppressive microenvironment for tumor progression. Analysis of differentially expressed genes in the context of transcription factor networks indicates perturbations in p53 and E2F pathways. These results are consistent with the significance of these 2 pathways in the pathogenesis of ovarian cancer. Mouse models of ovarian cancer can be reliably produced by targeted deletion of p53

10

J. Chien et al. / Gynecologic Oncology 114 (2009) 3–11

Table 3 List of transcription factor networks identified to be perturbed in high-grade stage I serous EOC compared to borderline tumors.

1 2 3 4 5 6 7 8 9 10 11

Transcription factor gene sets

Number of genes

LS permutation

KS permutation

Efron–Tibshirani's GSA test

Goeman's global test

p-value

p-value

p-value

p-value

TP53_T00671 PGR_T0 E2F-4_T01546 CREB1_T00163 SPI1_T02068 E2F-1_T01542 ETS2_T00113 PPARA_T05221 SP3_T02338 WT1_T00899 TFAP2A_T00035

41 5 37 25 8 49 6 9 14 5 38

0.00049 0.00796 0.0252 0.06361 0.07346 0.08496 0.08865 0.15104 0.24084 0.24215 0.24515

0.00013 0.02757 0.00191 0.00693 0.01194 0.10416 0.09363 0.18995 0.0956 0.29296 0.42158

0.07 b 0.005 0.02 b 0.005 0.3 b 0.005 0.06 0.11 0.13 0.02 0.26

0.0021645 0.008658 0.0021645 0.0021645 0.004329 0.0021645 0.004329 0.004329 0.004329 0.0021645 0.0021645

11 gene sets sorted by LS permutation.

and Rb or overexpression of SV40 T-antigen, which suppresses both p53 and Rb pathways [39,40]. Rb negatively regulates E2F pathway, and loss of Rb function results in deregulated E2F pathway [41]. In addition, in human samples, high-grade serous tumors frequently contain mutated p53 compared to low grade type I EOC, and this genetic alteration is considered to be one of an early event in the carcinogenesis of type II EOC. Recent studies by Crum and his collaborators [8] have shown that pronounced p53 staining, indicative of p53 mutation, is observed in early tubal lesions in specimens collected from BRCA mutation carriers with histologically normal ovaries. These putative precursor lesions subsequently acquired proliferative function as indicated by MiB1 staining [6]. In this proposed stepwise model of carcinogenesis, alterations in p53 and proliferative pathways contribute to early precursor lesions [6,42]. In light of this proposed model, our observations that p53 and cell proliferative E2F pathways are altered in high-grade stage I serous carcinomas provide additional support for the model. Finally, our transcription factor pathway analysis also indicated the potential roles of progesterone receptor (PGR)- and CREB1-mediated transcription networks in the carcinogenesis of high-grade serous carcinoma. Recent studies by Shaw and her colleagues [43] indicate that gene expression profiles of tubal epithelium collected from women with BRCA mutations in the luteal phase of the reproductive cycle are similar to serous carcinoma of the ovary, suggesting that gene expression in the luteal phase may promote susceptibility to carcinogenesis. It is also likely that predisposed cells (i.e., those cells with altered p53 function and E2F pathways) may become more susceptible to carcinogenesis under the hormonal milieu elaborated during the luteal phase. The principal hormone during the luteal phase is progesterone. In addition, previous studies indicate that expression levels of progesterone receptor B protein are higher in primary ovarian cancer cultures than in normal surface ovarian epithelium cultures [44]. Therefore, it suggests that perturbations in this transcription factor network during the luteal phase may be important in the progression of early-stage ovarian cancer. The luteal phase is preceded by LH and FSH surges, which initiate signal transduction through G-protein coupled receptors and cAMP as a second messenger to ultimately affect cellular response by activating cAMP responsive element-binding protein 1 (CREB1). Our transcription factor network analysis also indicated perturbed CREB1 network, suggesting the possibility that transcription factor networks initiated by reproductive hormones may play a role in the carcinogenesis of ovarian cancer. These initial observations are preliminary and warrant further investigations. Nonetheless, the discovery of these pathways as potential targets of high-grade serous carcinogenesis is quite exciting and consistent with putative roles of the hormones regulating these pathways in ovarian carcinogenesis. Therefore, our studies, although limited by sample size, produce results that confirm the role of p53 and proliferative pathways in high-grade serous carcinogenesis, and identify several known and novel genes and pathways in

carcinogenesis of high-grade serous EOC. Collectively, results from our studies shed new light into carcinogenesis of high-grade serous EOC. Conflict of interest statement Jian-Bing Fan, Craig April, and Brandy Klotzle are employees and shareholders of Illumina, where whole genome DASL assays were conducted. Other authors declare no conflict of interest.

Acknowledgments This work was funded by the Ovarian Cancer Research Fund Liz Tilberis Scholars (J.C.), the Wallace and Evelyn Simmers Career Development award (J.C.), and Andersen Foundation Program Project award (L.C.H.). We acknowledged statistical assistance from Ann Oberg and bioinformatics assistance from Yan Asmann. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.ygyno.2009.04.002. References [1] Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, et al. Cancer statistics, 2008. CA Cancer J Clin 2008;58:71–96. [2] Bell DA. Origins and molecular pathology of ovarian cancer. Mod Pathol 2005;18 (Suppl 2):S19–32. [3] Fukumoto M, Nakayama K. Ovarian epithelial tumors of low malignant potential: are they precursors of ovarian carcinoma? Pathol Int 2006;56:233–9. [4] Shih Ie M, Kurman RJ. Ovarian tumorigenesis: a proposed model based on morphological and molecular genetic analysis. Am J Pathol 2004;164:1511–8. [5] Dubeau L. The cell of origin of ovarian epithelial tumors and the ovarian surface epithelium dogma: does the emperor have no clothes? Gynecol Oncol 1999;72: 437–42. [6] Crum CP, Drapkin R, Miron A, Ince TA, Muto M, Kindelberger DW, et al. The distal fallopian tube: a new model for pelvic serous carcinogenesis. Curr Opin Obstet Gynecol 2007;19:3–9. [7] Kindelberger DW, Lee Y, Miron A, Hirsch MS, Feltmate C, Medeiros F, et al. Intraepithelial carcinoma of the fimbria and pelvic serous carcinoma: evidence for a causal relationship. Am J Surg Pathol 2007;31:161–9. [8] Lee Y, Miron A, Drapkin R, Nucci MR, Medeiros F, Saleemuddin A, et al. A candidate precursor to serous carcinoma that originates in the distal fallopian tube. J Pathol 2007;211:26–35. [9] Li AJ, Karlan BY. Genetic factors in ovarian carcinoma. Curr Oncol Rep 2001;3: 27–32. [10] Kurman RJ, Visvanathan K, Roden R, Wu TC, Shih Ie M. Early detection and treatment of ovarian cancer: shifting from early-stage to minimal volume of disease based on a new model of carcinogenesis. Am J Obstet Gynecol 2008;198: 351–6. [11] Christie M, Oehler MK. Molecular pathology of epithelial ovarian cancer. J Br Menopause Soc 2006;12:57–63. [12] Marchini S, Mariani P, Chiorino G, Marrazzo E, Bonomi R, Fruscio R, et al. Analysis of gene expression in early-stage ovarian cancer. Clin Cancer Res 2008;14:7850–60. [13] Gilks CB, Vanderhyden BC, Zhu S, van de Rijn M, Longacre TA. Distinction between serous tumors of low malignant potential and serous carcinomas based on global mRNA expression profiling. Gynecol Oncol 2005;96:684–94. [14] Bonome T, Lee JY, Park DC, Radonovich M, Pise-Masison C, Brady J, et al. Expression profiling of serous low malignant potential, low-grade, and high-grade tumors of the ovary. Cancer Res 2005;65:10602–12.

J. Chien et al. / Gynecologic Oncology 114 (2009) 3–11 [15] Schummer M, Ng WV, Bumgarner RE, Nelson PS, Schummer B, Bednarski DW, et al. Comparative hybridization of an array of 21,500 ovarian cDNAs for the discovery of genes overexpressed in ovarian carcinomas. Gene 1999;238:375–85. [16] Fan JB, Yeakley JM, Bibikova M, Chudin E, Wickham E, Chen J, et al. A versatile assay for high-throughput gene expression profiling on universal array matrices. Genome Res 2004;14:878–85. [17] Bibikova M, Talantov D, Chudin E, Yeakley JM, Chen J, Doucet D, et al. Quantitative gene expression profiling in formalin-fixed, paraffin-embedded tissues using universal bead arrays. Am J Pathol 2004;165:1799–807. [18] Ballman KV, Grill DE, Oberg AL, Therneau TM. Faster cyclic loess: normalizing RNA arrays via linear models. Bioinformatics (Oxford, England) 2004;20: 2778–86. [19] Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001;98:5116–21. [20] Storey JD. A direct approach to false discovery rates. J R Stat Soc Ser B 2002;64: 479–98. [21] Bibikova M, Chudin E, Arsanjani A, Zhou L, Garcia EW, Modder J, et al. Expression signatures that correlated with Gleason score and relapse in prostate cancer. Genomics 2007;89:666–72. [22] Bibikova M, Yeakley JM, Wang-Rodriguez J, Fan JB. Quantitative expression profiling of RNA from formalin-fixed, paraffin-embedded tissues using randomly assembled bead arrays. Methods Mol Biol 2008;439:159–77. [23] Ravo M, Mutarelli M, Ferraro L, Grober OM, Paris O, Tarallo R, et al. Quantitative expression profiling of highly degraded RNA from formalin-fixed, paraffinembedded breast tumor biopsies by oligonucleotide microarrays. Lab Invest 2008;88:430–40. [24] Nakagawa T, Kollmeyer TM, Morlan BW, Anderson SK, Bergstralh EJ, Davis BJ, et al. A tissue biomarker panel predicting systemic progression after PSA recurrence post-definitive prostate cancer therapy. PLoS ONE 2008;3:e2318. [25] Hoshida Y, Villanueva A, Kobayashi M, Peix J, Chiang DY, Camargo A, et al. Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N Engl J Med 2008;359:1995–2004. [26] Shridhar V, Lee J, Pandita A, Iturria S, Avula R, Staub J, et al. Genetic analysis of early-versus late-stage ovarian tumors. Cancer Res 2001;61:5895–904. [27] Zhang B, Pan JS, Liu JY, Han SP, Hu G, Wang B. Effects of chemotherapy and/or radiotherapy on survivin expression in ovarian cancer. Methods Find Exp Clin Pharmacol 2006;28:619–25. [28] Zaffaroni N, Pennati M, Colella G, Perego P, Supino R, Gatti L, et al. Expression of the anti-apoptotic gene survivin correlates with taxol resistance in human ovarian cancer. Cell Mol Life Sci 2002;59:1406–12. [29] Zhou J, O'Brate A, Zelnak A, Giannakakou P. Survivin deregulation in beta-tubulin mutant ovarian cancer cells underlies their compromised mitotic response to taxol. Cancer Res 2004;64:8708–14.

11

[30] Flies DB, Chen L. The new B7s: playing a pivotal role in tumor immunity. J Immunother 2007;30:251–60. [31] Simon I, Katsaros D, Rigault de la Longrais I, Massobrio M, Scorilas A, Kim NW, Sarno MJ, Wolfert RL, et al. B7-H4 is over-expressed in early-stage ovarian cancer and is independent of CA125 expression. Gynecol Oncol 2007;106:334–41. [32] Simon I, Zhuo S, Corral L, Diamandis EP, Sarno MJ, Wolfert RL, et al. B7-h4 is a novel membrane-bound protein and a candidate serum and tissue biomarker for ovarian cancer. Cancer Res 2006;66:1570–5. [33] Oikonomopoulou K, Li L, Zheng Y, Simon I, Wolfert RL, Valik D, et al. Prediction of ovarian cancer prognosis and response to chemotherapy by a serum-based multiparametric biomarker panel. Br J Cancer 2008;99:1103–13. [34] Sengupta S, Michener CM, Escobar P, Belinson J, Ganapathi R. Ovarian cancer immuno-reactive antigen domain containing 1 (OCIAD1), a key player in ovarian cancer cell adhesion. Gynecol Oncol 2008;109:226–33. [35] Kalli KR, Oberg AL, Keeney GL, Christianson TJ, Low PS, Knutson KL, et al. Folate receptor alpha as a tumor target in epithelial ovarian cancer. Gynecol Oncol 2008;108:619–26. [36] Wang ZJ, Boddington S, Wendland M, Meier R, Corot C, Daldrup-Link H. MR imaging of ovarian tumors using folate-receptor-targeted contrast agents. Pediatr Radiol 2008;38:529–37. [37] Wu M, Gunning W, Ratnam M. Expression of folate receptor type alpha in relation to cell type, malignancy, and differentiation in ovary, uterus, and cervix. Cancer Epidemiol Biomarkers Prev 1999;8:775–82. [38] Ou D, Wang X, Metzger DL, Ao Z, Pozzilli P, James RF, et al. Suppression of human T-cell responses to beta-cells by activation of B7-H4 pathway. Cell Transplant 2006;15:399–410. [39] Flesken-Nikitin A, Choi KC, Eng JP, Shmidt EN, Nikitin AY. Induction of carcinogenesis by concurrent inactivation of p53 and Rb1 in the mouse ovarian surface epithelium. Cancer Res 2003;63:3459–63. [40] Connolly DC, Bao R, Nikitin AY, Stephens KC, Poole TW, Hua X, et al. Female mice chimeric for expression of the simian virus 40 TAg under control of the MISIIR promoter develop epithelial ovarian cancer. Cancer Res 2003;63:1389–97. [41] Polager S, Ginsberg D. E2F-at the crossroads of life and death. Trends Cell Biol 2008;18:528–35. [42] Jarboe E, Folkins A, Nucci MR, Kindelberger D, Drapkin R, Miron A, et al. Serous carcinogenesis in the fallopian tube: a descriptive classification. Int J Gynecol Pathol 2008;27:1–9. [43] Tone AA, Begley H, Sharma M, Murphy J, Rosen B, Brown TJ, et al. Gene expression profiles of luteal phase fallopian tube epithelium from BRCA mutation carriers resemble high-grade serous carcinoma. Clin Cancer Res 2008;14:4067–78. [44] Li AJ, Baldwin RL, Karlan BY. Estrogen and progesterone receptor subtype expression in normal and malignant ovarian epithelial cell cultures. Am J Obstet Gynecol 2003;189:22–7.