Protein Expression Changes in Ovarian Cancer during the Transition

Article pubs.acs.org/jpr

Protein Expression Changes in Ovarian Cancer during the Transition from Benign to Malignant Sofia Waldemarson,*,† Morten Krogh,‡ Ayodele Alaiya,§ Ufuk Kirik,† Kjell Schedvins,∥ Gert Auer,⊥ Karin M. Hansson,† Reto Ossola,# Ruedi Aebersold,#,¶ Hookeun Lee,#,∇ Johan Malmström,† and Peter James† †

Department of Immunotechnology and ‡Department of Computational Biology and Biological Systems, Lund University, BMC D13, 221 84 Lund, Sweden # Department of Biology, Institute for Molecular Systems Biology, ETH Zurich (Swiss Federal Institute of Technology), ETH Hönggerberg, Wolfgang-Pauli-Strasse 16, HPT, CH-8093 Zurich, Switzerland § Proteomics Unit, Stem Cell Therapy Program, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia ⊥ Karolinska Biomic Center, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden ∥ Gynecological Unit, Karolinska University Hospital, Stockholm, Sweden ¶ Faculty of Science, University of Zurich, Switzerland S Supporting Information *

ABSTRACT: Epithelial ovarian carcinoma has in general a poor prognosis since the vast majority of tumors are genomically unstable and clinically highly aggressive. This results in rapid progression of malignancy potential while still asymptomatic and thus in late diagnosis. It is therefore of critical importance to develop methods to diagnose epithelial ovarian carcinoma at its earliest developmental stage, that is, to differentiate between benign tissue and its early malignant transformed counterparts. Here we present a shotgun quantitative proteomic screen of benign and malignant epithelial ovarian tumors using iTRAQ technology with LC−MALDI-TOF/TOF and LC−ESI-QTOF MS/MS. Pathway analysis of the shotgun data pointed to the PI3K/Akt signaling pathway as a significant discriminatory pathway. Selected candidate proteins from the shotgun screen were further confirmed in 51 individual tissue samples of normal, benign, borderline or malignant origin using LC-MRM analysis. The MRM profile demonstrated significant differences between the four groups separating the normal tissue samples from all tumor groups as well as perfectly separating the benign and malignant tumors with a ROC-area of 1. This work demonstrates the utility of using a shotgun approach to filter out a signature of a few proteins only that discriminates between the different sample groups. KEYWORDS: epithelial ovarian cancer (EOC), biomarker, proteomics, mass spectrometry, iTRAQ, multiple reaction monitoring (MRM), selected reaction monitoring (SRM), Ingenuity pathway analysis

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INTRODUCTION Epithelial ovarian cancer (EOC) presents late in the majority of cases, due to the absence of specific symptoms in the early stages and the lack of reliable diagnostic methods.1 The fiveyear survival rate for patients diagnosed with an advanced stage (stage III−IV) disease is poor (∼30%). This is in contrast to patients diagnosed with stage I disease where the survival rate is over 90%.2 The large discrepancy in survival outcome between early- and late-stage disease in EOC clearly defines a need for the development of screening strategies for early detection. On the basis of their histological appearance, EOCs fall into four major subtypes: serous, endometrioid, mucinous and clear cell. The etiology of EOC is today largely unknown but involvement of various genetic alterations have frequently been seen such as inactivation of tumor suppressors, in particular p53, defective retinoblastoma (RB) pathway and activation of oncogenes such as c-myc, K-ras and Akt.3,4 It is unclear if benign adenoma may be the developmental origin of malignant © 2012 American Chemical Society

tumors. Low- and high-grade ovarian serous carcinoma has recently been suggested to arise from the fallopian tube epithelium (benign or malignant) that implants on the ovary, while the origin of mucinous and transitional cell (Brenner) tumors is still not well established.4 The different histological subtypes of EOC do however display the same relationship between tumor stage and survival and identifying the differences between benign and malignant tumors independently of subtype will be useful for monitoring the malignant transformation and for exploring the differentially expressed proteins as diagnostic biomarkers. Proteomic characterization of tumor samples has the potential to measure thousands of proteins in the samples and their relative abundance. Different proteomics workflows measure different subsets of proteins. In a previous study we Received: December 22, 2011 Published: April 3, 2012 2876

dx.doi.org/10.1021/pr201258q | J. Proteome Res. 2012, 11, 2876−2889

Journal of Proteome Research

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analyzed 64 ovarian tumors by two-dimensional gel electrophoresis and fluorescent labeling (2D-DIGE).5 These tissues were classified as normal, benign, borderline and malignant based on immunohistochemical evaluation and pathology. The samples consisted of purified epithelial cells prepared from fresh tumors representing each of the major subtypes of EOC. Using the spot volumes from the DIGE gels, an unsupervised Pearson correlation analysis could perfectly separate the benign and the malignant tumors. This demonstrates that malignancy is one of the strongest signals in this data set and that the benign and malignant samples constitute well-defined groups. Two-dimensional gel electrophoresis (2DE) is a very effective proteomics technology for running large sample sets and comparing many individual samples through the matching of the same gel spots in all samples. However, 2DE measure more highly expressed and soluble proteins and a limited fraction of the proteome is covered. Shotgun proteomics LC− MS/MS based analysis of peptides is in many ways complementary to gel based analysis and can detect lower abundant proteins, and since LC−MS/MS allows harsher sample preparation methods it allows detection of more acidic and basic proteins as well as membrane proteins thus covering a different, and usually larger part of the proteome. To allow relative quantification with LC-based analyses different isotopic tagging techniques have been introduced allowing comparison of individual samples within the LC−MS/MS run.6−9 Here we present a comparative analysis of benign and malignant epithelial ovarian tumors using the N-terminal isobaric multiplexing tagging reagents, iTRAQ7 in combination with LC−MALDI-TOF/TOF MS/MS and LC−QTOF MS/ MS to screen for proteins differentially expressed between the benign and malignant state. The tumor samples were pooled into two different benign pools and two different malignant pools and labeled with one of the four iTRAQ reagents each for quantification. A label swap was made for two different preparations of the samples. The samples were fractionated by strong cation-exchange chromatography (SCX) before repeated analysis using LC−MALDI-TOF-TOF MS/MS and LC−QTOF MS/MS. Twenty-two proteins identified in this study could be confirmed by the previous 2DE-study and foldchanges agreed very well. Ingenuity pathway analysis (IPA) was used to explore the relationship between the regulated proteins and canonical pathways, and the PI3K/Akt signaling pathway was the one most significant to this data set. Many of the differentially expressed proteins can be directly linked to this pathway mainly as downstream targets of p53 and the 14-3-3 proteins. To confirm the findings comparing the benign and malignant tumor pools and to monitor the expression in individual tissue samples, a set of proteins significantly regulated in the iTRAQ analysis were measured with LCMRM (multiple reaction monitoring) analysis of 51 ovarian tissue samples of normal, benign, borderline and malignant origin. A supervised classifier based on the MRM signature obtained separated the normal tissues from the tumor samples with ROC-areas of 1 (normal vs benign), 0.98 (normal vs borderline) and 0.94 (normal vs malignant). Benign and malignant tumors also separated perfectly with a ROC-area of 1. This study demonstrates how different proteomics platforms can be combined to sieve out a protein signature that perfectly separates tumor groups.

Article

MATERIALS AND METHODS

Materials

Chemicals and the protein assay kit (Micro Lowry, Peterson’s modification) were bought from Sigma Aldrich (Buchs, Switzerland). Zeba protein desalting spin columns were obtained from Pierce (Boule, Huddinge, Sweden), trypsin (sequencing grade modified) was from Promega Corp, Madison, WI), iTRAQ reagent kit and SCX-cartridges were from Applied Biosystems (Stockholm, Sweden). Synthetic peptides were from JPT Peptide Technologies, Berlin, Germany. Tumour Material

Tissues were collected after surgery at the gynecological unit, Karolinska University Hospital and made anonymous after informed consent and approval by the Ethics committee. The tumor set used has been described in our previous publication.5 Samples were prepared as described. Briefly, the resected sample was put on ice and a pathologist first examined all samples to obtain representative, viable, and non-necrotic tumor tissue. One part of the tissue was used for sample preparation for proteomics analysis and the adjacent tissue was formalin-fixed and paraffin-embedded for histological characterization according to the WHO guidelines. The pathological diagnosis and tumor grade of the subjects included in this study are listed in Table 1. Tumour cells were collected from the cut surface of the tumor tissue by scraping with a sharp scalpel. Cells were collected in ice-cold RPMI-1640 medium. A metal filter with 0.5 mm pore size was used to remove tissue fragments and connective tissue. Cell suspensions were under laid with ice-cold Percoll and centrifuged. Cells at the interface were collected and washed in PBS, centrifuged and frozen until further processing. The cell pellet was thawed on ice and lyophilized in a sample buffer containing 0.2 mM PMSF, 1 mM EDTA, 0.5% NP-40 and 25 mM CHAPS. The protein concentration was determined and the sample buffer was exchanged using Zeba protein desalting spin columns to a 2Dgel sample buffer containing 6 M urea, 30 mM Tris, 5 mM magnesium acetate and 4% CHAPS. iTRAQ Screen Workflow

Sample Preparation and Labeling. To allow iTRAQ labeling, the 2D-gel sample buffer was exchanged to 0.15 M HEPES, 0.5 M urea using Zeba protein desalting spin columns. Protein concentration was measured after the buffer exchange. Equal amounts of protein from each extract were mixed to construct the four pools A−D, where A and B are benign and C and D are malignant pools (Table 1). A and B were composed from 4 tumors each and C and D from 14 and 15 tumors respectively. For each of the four pools 100 μg protein in 40 μL buffer was used and the subsequent peptide labeling was done using the Applied Biosystems iTRAQ Reagent Multi-Plex Kit. To each of the four samples, 1 μL denaturant (2% SDS) and 2 μL reducing reagent (5 mM TCEP) was added. The samples were incubated at 37 °C for 1 h, and the cysteines were blocked by adding 1 μL 10 mM MMTS. The samples were digested at 37 °C using 1 μg trypsin per 100 μg protein overnight (∼ 14 h). Another 1 μg of trypsin was added and the samples were left at 37 °C for 2 more hours. The volume of the peptide mixture was reduced to 30 μL. The four different iTRAQ reagents were resuspended in 70 μL ethanol and each added to one of the four pool samples. The tubes were left at room temperature for 1 h. One-hundred microliters of water was added to each tube 2877



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rinsed using 0.1% TFA and eluted with 0.1% TFA, 80% ACN. The samples were dried and redissolved in 0.1% TFA. HPLC and Spotting for MALDI Analysis. The different sample fractions were loaded onto a RP capillary column (100 μm i.d. × 15 cm length) by a microautosampler from Eksigent (Dublin, CA). The column was in-house packed with Magic C18AQ (200 Å pore, 5 μm diameter, Michrom Bioresources, Auburn, CA) onto the capillary tubing with a borosilicate frit (Integrafrit, New Objective, Cambridge, MA). Chromatographic separations were carried out with a nanoLC pump (Eksigent) at 500 nL/min flow rate using a solvent composition gradient of solvent A (water/ACN/TFA, 98/2/0.1, v/v/v) and B (water/ACN/TFA, 2/98/0.1, v/v/v). A linear binary gradient of 5−40% solvent B was generated over 50 min, followed by 5 min flush at 90% solvent B. The eluting peptides were mixed with MALDI matrix (3 mg/mL of α-cyano-4hydroxycinnamic acid in 70% ACN and 0.1% TFA spiked with angiotensin II, 0.5 pmol/mL (Sigma) and ACTH, 1.25 pmol/ mL (Proteomass)) delivered with a flow rate of 1.4 μL/min and spotted on to the MALDI targets at a 5 s interval by MALDI spotter of Tempo nano LC system (Applied Biosystems) for a total of 616 spots per gradient run. MALDI-TOF/TOF Mass Spectrometry Analysis. MS and MS/MS analysis was performed using a 4800 MALDI TOF/ TOF Analyzer (Applied Biosystems, Foster City, CA). Each spot was first analyzed in MS mode, by accumulating signal with up to 1000 laser shots (20 subspectra of 50 shots) over the mass range 800−4000 Da unless a preset stop criteria of 10 subspectra was reached where the accumulated spectrum contained at least 5 peaks with S/N > 100. Up to 10 ions of each spot giving an MS signal with S/N > 30 were then candidates for further MS/MS analysis, performed in order of increasing precursor intensity. The job-wide interpretation which generated the list of precursor ions and assigned the most intensive spot of a precursor ion for MS/MS analysis was used for each sample, and the acquisition of an MS/MS spectrum was obtained by accumulating 1500 laser shots (30 subspectra of 50 shots) with the collision energy of 1 kV. The source2 air pressure was set to 2.5 × 10−6 Torr for MS/MS analysis and 5 × 10−7 Torr for MS analysis. ESI-QTOF Mass Spectrometry Analysis. LC−MS/MS analysis was performed on an Agilent 1100 nano chip-LC system, coupled with a 6520 QTOF mass spectrometer (Agilent Technologies AG, Basel, Switzerland). The chip incorporates a 40nl precolumn, a 15 cm × 75 μm ID analytical column, ESI spray emitter and a rotary 6-port valve. Both analytical- and precolumns were packed with Zorbax 300SBC18 5um particles. For solvent A, a mixture of 0.1% formic acid in water, and for solvent B, a mixture of 0.1% formic acid and 10% water in ACN were used. The sample was loaded to the precolumn with a flow of 3 μL/min using 3% solvent B. Peptide separation was carried out at a flow of 300 nL/min over a gradient of 3−30% solvent B in 120 min. The separation gradient was followed by a period of 100% solvent B for washing the pre- and analytical-columns. The QTOF was operated in auto-MS/MS mode. For each MS spectrum (250− 2400 m/z, Scan rate 4.2) the 3 most abundant ions were selected for fragmentation by CID (50−3000 m/z, scan rate 2.0, ∼4 AMU isolation width, collision energy settings: slope 4.6 offset 2.0). Database Searches. Data was uploaded and analyzed through the Proteios software environment.10,11 The 4800 MALDI TOF/TOF Analyzer data was searched against the

Table 1. Ovarian Tumour Samples Analyzed with iTRAQ and their Histopathological Classificationa case ID OC38 OC66 OC78B OC86B OC87 OC92B OC95L OC109A OC04A OC06 OC07 OC08 OC09 OC20L OC27 OC30R OC33A OC40L OC43 OC49 OC51D OC55 OC56 OC57 OC60A OC62B OC73 OC74 OC79 OC81B OC84 OC89 OC99 OC111L OC112R-A a

pathological diagnosis and grade Benign Serous cystadenoma IA Mucinous cystadenoma IIA Cystadenoma IA Serous cystadenoma IA Benign Mucinous cystadenoma IIA Cystadenofibroma Serous cystadenoma IA Mucinous cystadenoma IIA Malignant Mixed tumor Clear cell tumor IVC Serous papillary adenocarcinoma IC Serous papillary adenocarcinoma IC Serous papillary adenocarcinoma IC Serous papillary adenocarcinoma IC Clear cell tumor IVC Bilateral serous papillary adenocarcinoma IC Endometrioid carcinoma IIIC Bilateral serous papillary adenocarcinoma IC Bilateral serous papillary adenocarcinoma IC Endometrioid carcinoma IIIC Clear cell tumor IVC Serous papillary adenocarcinoma IC Malignant Endometrioid carcinoma IIIC Serous papillary adenocarcinoma IC Granulosa Cell Tumor Endometrioid carcinoma IIIC Serous papillary adenocarcinoma IC Endometrioid carcinoma IIIC Serous papillary adenocarcinoma IC Mixed tumor Clear cell tumor IVC Serous papillary adenocarcinoma IC Endometrioid carcinoma IIIC Serous papillary adenocarcinoma IC Endometrioid carcinoma IIIC

pool A A A A B B B B C C C C C C C C C C C C C C D D D D D D D D D D D D D

The samples were pooled into four different pools (A−D).

to quench the reaction. After 30 min incubation at room temperature, the samples were stored at −20 °C. The labeling and digestion procedure was repeated once where the iTRAQ tags were swapped between the different pools to avoid labeling effects. Sample Fractionation. The four differentially labeled extracts were pooled together and fractionated into five fractions by step-elution using strong-cationic exchange (SCX). Buffers containing 5 mM KH2PO4, 25% ACN with or without KCl were used, and the pH of sample and buffers was adjusted to 2.9. 500 μL fractions of 100, 200, 300, 450, and 600 mM KCl were collected. In the second sample run fractions of 60, 150, 250, 400, and 600 mM KCl were collected. The fraction volume was reduced to around 200 μL to remove all acetonitrile (ACN) prior to C18 spin columns cleanup (C18 ultra micro spin columns, Harvard apparatus, Holliston, MA, USA). Briefly, the columns were activated with 0.1% TFA, 80% ACN and rinsed with 0.1% TFA. The samples were applied and 2878



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and 3 will lower the false discovery rate further, we conclude that the 94 regulated proteins reported here have a false discovery rate of at most 0.004. The analysis was performed using the programming language Perl and the statistics language R.16 Ingenuity Pathway Analysis. Data were analyzed using Ingenuity Pathways Analysis (Ingenuity Systems, www. ingenuity.com). The gene identifiers (Entrez ids) for all proteins identified in the study and corresponding expression values were uploaded into the application. The differentially expressed proteins that met our thresholds (p-value, fold, rank) were compared to the rest of the proteins identified in the data set using a core analysis. One-hundred twenty-six regulated ids were found and compared with 1321 ids in total. The output from a core analysis provides networks of protein relationships, the biological functions over-represented in the subset of selected molecules and canonical pathways most significant to the data set. The PI3K/Akt signaling pathway was identified as the most significant to the data set. The subset of regulated proteins were imported and tested for a direct relation to any of the proteins in the pathway allowing only direct connections and IPA manually curated information to exclude indirect connections and postulated interactions.

Uniprot database (release 2011−11) using the MASCOT (Matrix Science, Boston, MA) and X!Tandem search algorithms. The search was restricted to tryptic peptides with up to one missed cleavages. Cysteine modification with MMTS, iTRAQ (N-term) was set as fixed modification and iTRAQ (K) and methionine oxidation as variable. Precursor error tolerance was set to 1 Da and MS/MS tolerance was 0.5 Da. Charge state +1 was chosen and minimal individual peptide score confidential interval (C.I.) was set to 85% giving a minimal individual ion score of 33 (Mascot). Based on a target-decoy database search strategy, the estimated false protein discovery rate for the entire data set was below 1%.12 The QTOF data was searched using Mascot and X!Tandem against the Uniprot database (release 2011−11) with a peptide mass tolerance of 0.05 Da and an MSMS tolerance of 0.1 Da. Cysteine modification with MMTS, iTRAQ (N-term) was set as fixed modification and the iTRAQ side-reaction of tagging the side chain of lysine and methionine oxidation were set as variable modifications. All database search results were validated through the TransProteomic pipeline (peptide/protein prophet) by first processing using the PeptideProphet program.13 Peptides were assembled into proteins and the probabilities at the protein level were computed by the protein inference program ProteinProphet14,15 at a false discovery rate of less than 1%. iTRAQ Data Analysis. Each identified protein was represented by one or more peptides, and each peptide was measured in one or more spectra. For each spectrum, the areas under the curve of the four iTRAQ channels were recorded by the Libra module within the TransProteomic pipeline.14 Two different iTRAQ experiments were performed, in the first set, samples A, B, C, and D were labeled with mass tags 116, 115, 114 and 117, respectively, and in the second set with 114, 116, 115, and 117 respectively. For each spectrum, the areas were multiplicatively normalized to make the area for sample A equal to one. The normalized areas were log2 transformed and the log2 fold change for malignant versus benign was calculated as the average of samples C and D minus the average of samples A and B. The overall log2 fold change for malignant versus benign for a protein was obtained as the average of the log2 fold changes of all spectra corresponding to that protein. The overall fold change was obtained by exponentiating with base 2. The two-sided p-value for this log2 fold change to be different from zero was obtained by a t test. Furthermore, for each of the samples A, B, C, and D, the average of log2-normalized areas over all spectra was calculated to obtain expression values for the individual samples. Strictly speaking, in the analysis described above, the averages should have been calculated with a linear model taking the protein → peptide → spectrum structure into account. However, because the spectrum to spectrum variation is large, it is approximately equivalent to averaging all spectra corresponding to a protein. We defined a protein to be significantly regulated if the following three conditions were fulfilled: (1) The t test p-value was less than 0.001. (2) The fold change was at least 2 either up or down. (3) The average area for sample A and the average area for sample B were both greater than or both less than the areas for C and D, that is, the ranking of the four samples is consistent with the up or down regulation. Of 2048 identified proteins, 94 proteins fulfilled those three conditions. To estimate a false discovery rate, we considered the first condition alone. Of 2048 proteins, 506 had a p-value below 0.001, corresponding to a false discovery rate of 0.001 × 2048/506 = 0.004. Since conditions 2

MRM Confirmation Workflow

Sample Preparation and Digestion. Fifty-one tumor protein extracts in 2D-gel sample buffer were prepared for MRM analysis. Samples were thawed on ice and prior to insolution digest proteins were precipitated with chloroform/ methanol. The samples were resolubilized in 3 M urea in 50 mM ammonium hydrogen carbonate, reduced with 10 mM DTT for 1 h at 37 °C and alkylated with 50 mM iodoacetamide for 30 min at room temperature in the dark. The samples were digested with 0.5 μg trypsin per 100 μg protein overnight at 37 °C. Protein digests were stopped by adding formic acid to a final concentration of 5%. Desalting and concentration were carried out on UltraMicrospin C18 columns according to the manufacturer’s instructions (the Nest Group, Southborough, MA). The peptides were eluted in 50% ACN in 5% formic acid, dried completely in the speed vacuum centrifuge and resolubilized in 0.1% formic acid to a final concentration of 1 μg/μL. Design of MRM Assay. An MRM assay was developed for 18 proteins using synthetic peptides.17 A set of 61 synthetic peptides (39 “ovarian” + 22 “standard”) (JPT peptides, Berlin, Germany, ca. 90% pure) were diluted in 0.1% formic acid to 25 pmol/μL and mixed (∼500 fmol of each peptide/μL). The peptide mixture was analyzed on a Thermo LTQ Orbitrap mass spectrometer (Thermo Electron, Bremen, Germany) coupled to an Eksigent 2D NanoLC system (Eksigent technologies, Dublin, CA) with a precolumn (Zorbax 300SB-C18 5 × 0.3 mm, 5 μm, Agilent technologies, Wilmington, DE) and a RP analytical column (Zorbax 300SB-C18 150 × 75 μm, 3.5 μm, Agilent technologies) with a flow rate of 350 nL/min. Solvent A consisted of 0.1% formic acid in water and solvent B of 0.1% formic acid in acetonitrile and peptides were eluted with a gradient of 5−60% solvent B over 60 min. Data were acquired using the Xcalibur software (version 2.0.7) and one full MS scan (from 400 to 1600 m/z range) followed by four MS/MS events using data-dependent acquisition. The acquired fragment ion spectra were used to select transitions by choosing the eight most intense y-type ions for each peptide of precursor charge state +2. The precursor-to-fragment ion transitions were 2879



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Figure 1. (a) Unsupervised Pearson correlation between benign and malignant ovarian cancer tumors using 2D-DIGE data from the previous gel study demonstrating how the benign and malignant tumors form two different clusters (blue, benign; red, malignant). Purified epithelial cells prepared from 64 fresh ovarian tumors were analyzed by two-dimensional gel electrophoresis with fluorescent labeling (2D-DIGE) from tissues classified as normal, benign, borderline and malignant (9). An unsupervised Pearson correlation was performed using the spot volumes from the DIGE gels. Only gel spots with no missing values were used (342 spots). (b) Sammon map of the two benign (blue) and the two malignant (red) pool samples analyzed in this study by iTRAQ TOF-TOF. The Sammon map is a projection of the samples from the high dimensional peptide space to two dimensions that attempt to preserve (Euclidean) distances. The figure illustrates that the tumors have more similar peptide profiles to other tumors of the same diagnosis than to those of the other diagnosis.

ROC curve. Missing values for the area of the MS intensity curve was set to zero for the classification.

tested by MRM measurements on a TSQ Vantage triple stage quadropole mass spectrometer (Thermo Electron) equipped with a nanoelectrospray ion source (Thermo Electron). Chromatographic separations of peptides were performed on an Eksigent 1D NanoLC system (Eksigent technologies) using the same conditions as described above. The LC was operated with a flow rate of 400 nL/min. The mass spectrometer was operated in MRM mode, with Q1 and Q3 set at unit resolution (fwhm 0.7 Da). A spray voltage of +1700 V was used with a heated ion transfer setting of 270 °C for desolvation. Data were acquired using the Xcalibur software (version 2.1.0). The dwell time was set to 10 ms and the scan width to 0.01 m/z with default settings for collision energy (CE = (Parent m/z) × 0.034 + 3.314). The MRM data was analyzed in the MRM management software Skyline (http://proteome.gs.washington. edu/software/skyline). A refined peptide/transitions list was built by retaining the five most intense transitions. The optimization was carried out in a matrix of normal ovarian tissue extract. The final optimized assay consisted of the top 42 peptides (30 “ovarian” + 12 “standard”), corresponding to 18 different proteins (13 “ovarian” + 5 “standard”) and a total of 205 transitions. At least three transitions per peptide were included in the method. MRM Data Analysis. The MRM data was analyzed by manual inspection in the MRM data management software Skyline. The area under the curves for the detected peaks were exported as a .csv file. A support vector machine was used for pairwise supervised classification between all 6 pairwise combinations of normal, benign, borderline, and malignant samples. Leave one out cross validation was employed to estimate the ability of the support vector machine to classify the samples from the MRM data. Each sample was left out once in the training and the classifier was applied on the left out sample to give a classification value. The classification values were used to construct a ROC curve, and calculate the area under the

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RESULTS AND DISCUSSION Detecting early stage epithelial ovarian cancer (EOC) is challenging since the disease is clinically asymptomatic, yet can be progressing toward the development of metastases. We present a study of ovarian tumors with an initial iTRAQ-LC− MS/MS mass spectrometry screen of well-defined sets of benign and malignant epithelial ovarian tumors that were pooled and analyzed to elucidate which alterations are occurring during the transformation to the malignant state. iTRAQ LC−MS/MS Screen of Benign and Malignant Tumor Groups

The tumor samples analyzed in the iTRAQ screen are listed in Table 1 according to how they were pooled into the two different benign (A, B) and two different malignant pools (C, D). The sample set comprised of the four major subtypes of EOC. A total of 2048 unique proteins were identified when merging the results from the TOF/TOF and the QTOF setup. The entire data set with all identified proteins is provided as Supplemental Table 1 (Supporting Information). Upon unsupervised clustering of samples using all iTRAQ TOFTOF data, the two benign pools separated well from the two malignant pools (Figure 1b) and consequently showed the same clear-cut separation between benign and malignant samples as seen in the 2D-gel study (Figure 1a).5 Four-hundred fourteen proteins were uniquely identified by QTOF while 1383 of the proteins were uniquely identified by TOF/TOF analysis. The relatively low overlap between these two approaches (251 proteins out of 2048) can partially be explained by the lower sampling time in the QTOF analysis. Ninety-four proteins, listed in Table 2, were filtered out as being regulated using three strict conditions: a p-value cutoff of 0.001, a fold-change cutoff of two, and a rank-sum of either 3 or 7, meaning that the two malignant measurements were both 2880


2881

B3KUJ8, P07858, B4DL49, B4DMY4, Q6LAF9 P08311 A8K9U8, Q2TUW9, B2MV13, B3KSL2, B3VMW0, Q5DSM0, Q5EK5l, B7Z4X2, B7ZAL5, P02788

B4DWD1, P04839 B2R5R1, Q14956-1, B4DN79, B4E3D4, B4DLJ7, B4DLL8, Q14956 A8MWX0, Q96HR9, D6W5Z0, B4DF39 P5089455 B3KVN0, Q0P512, D3DPX0, B4DDR6, B4DKWl, C9JIM8, Q59GX2, P11166

P61626 P05164 P04004

P5966455, P59666 P01903 P56470 Q8TDL5-1

P23528 P63241 P06733-1 P06733 PD2671 P02675 P02679 C7DJS1 P09211 P04792 O15230 P07942 Q14887 P98088 A7Y9J9 O95994 P60900 O75372

Uniprot ID

×

×

×

MRM data

Receptor accessory protein 6 Basal cell adhesion molecule Solute carrier family 2, facilitated glucose transporter member 1 Protease and Kinase Activity Cathepsin B Cathepsin G Lactoferrm/Lactotransferrin, Growth-inhibiting protein 12

Cell Adhesion, Proliferation and Anti-Apoptosis Cofilin-1 Eukaryotic translation initiation factor Isoform alpha-enolase of Alpha-enolase Alpha-enolase Fibrinogen alpha chain Fibrinogen beta chain Fibrinogen gamma chain Glutathione S-transferase pi (Fragment) Glutathione S-transferase P Heat shock protein beta-1 Larnimn subunit alpha-S Laminm subunit beta-1 Mucm (Fragment) Mucm-5AC (Fragments) Mucm −5AC, ohgomeric mucus/gel-forming Anterior gradient 2 homologue (Fragment) Proteasome (Prosome, macropam) subunit, alpha type-6 Gastric mucm (Fragment) Immune, Inflammatory Response and Che Neutrophil defensin 1/Neutrophil defensin 3 MHC class II antigen Galectm-4 Isoform 1 of Long palate, lung and nasal epithelium carcinoma-associated protein 1 Lysozyme C Myeloperoxidase Vitronectm Membrane proteins Cytochrome b-245 heavy chain Transmembrane glycoprotem NMB

protein name

0 10 0

0 5 4

0 0

35 9 18

5 0 8 0

11 4 38 0 53 0 52 8 9 12 13 0 67 264 40 0 0 0

number of MALDI peptides

11 0 21

16 0 6

14 9

79 7 18

4 34 0 47

0 8 0 88 64 92 0 0 0 17 0 9 0 321 66 77 4 125

number of QTOF pep peptides

11 10 21

16 5 10

14 9

114 16 36

9 34 8 47

11 12 38 88 117 92 52 8 9 29 13 9 67 585 106 77 4 125

number of peptides identified

2.44 3.36 3.99

0.33 2.11 2.85

2.21 3.97

0.34 2.66 2.56

5.35 2.98 0.48 0.22

2.05 2.41 2.09 2.60 2.13 2.80 2.57 2.57 2.64 2.49 2.03 3.07 0.29 0.30 0.31 0.30 2.08 0.34

fold change malignant/ benign

10−7 10−11 10−14 10−53 10−62 10−57 10−28 10−6 10−7 10−17 10−6 10−7 10−21 10−128 10−20 10−48 10−4 10−20 10−10 10−24 10−4 10−30

× × × × × × × × × × × × × × × × × × × × × ×

1.85 × 10−10 1.45 × 10−11 3.57 × 10−23

1.80 × 10−4 1.08 × 10−5 1.45 × 10−11

4.22 × 10−8 4.41 × 10−10

6.74 × 10−20 5.41 × 10−9 1.26 × 10−19

2.20 9.86 5.53 7.11

9.37 1.21 1.44 1.53 4,81 8.30 1.47 4.03 4.59 8.92 7.81 3.52 7.27 8.23 1.31 2.57 6.22 1.56

Wilcoxon p-value fold change malignant/ benign

Table 2. Proteins with Significantly Different Expression Level (2-fold up or down, p-value of 0.001 and Rank Sum 3 or 7) between the Benign and Malignant Tumour Pools Analyzed with iTRAQa

Journal of Proteome Research Article


Uniprot ID

2882

P07737, Q53Y44 Q05707-1 Q05707-l, Q05707-2 D3DTX7 P02452 P08123 A6NHG4, B4DJQ7, B4E259, B5MC82, B7Z522, Q53Y51, P30046 A8K9C4, Q5VTE0, Q6IPN6, Q6IPS9, Q6IPT9, Q9H2I7, Q53G85, Q53GA1, Q53GE9, Q53HM9, Q53HQ7, P68104, Q53HR5 B4DPU3, P13639, D6W618 Q96HE7 B4DGL0, P08238 B4DGL0, B4DMA2, P08238 Q58FF8 P26038, Q6PJT4 B4DPX8, Q8IV28, Q14112-1, Q14112-2 A8K0R3, Q7Z532, B4DI63, P20774 B2R5M9, B4DGN8, B4DR87, Q02809 B3KUY2, B4DP11, B4DP21, Q15185 B4DHQ2, Q3HY29, P23219-1, P23219-2, Q3HY28, B4E2S5

P04080, Q76LAl P01009-1 P01009-l, P01009-2, P01009-3

B7ZW66 P18669, Q6FHU2, Q6FHK8, Q53G35, Q0D2Q6, Q6P6D7 P20142 A2A3L9, B4DVZ3, B4DW12, P20142, Q4VXA6, Q8IUM8 A8K4W6, B7Z7A9, P00558 A8K4W6, P00558, B7Z7A9 P14618-1 B4DNK4, B4DUU6, P14618-1

A8K9U8, B2MV13, Q2TUW9, B3KSL2, B3VMW0, Q5DSM0, Q5EK51, P02788 B2R7D6, B4DVY9 B2R7D6, B4DVY9, B7Z719, B7ZW10, B7ZW16, B7ZW62, P00790

Table 2. continued

×

Protease and Kinase Activity Lactoferrm/Lactotransferrin, Growth-inhibiting protein 12

×

48

0 0 8 31 0 2 0 0 0 4 7 5

Elongation factor 2 EROHike protein alpha Heat shock protein HSP 90-beta Heat shock protein HSP 90-beta Putative heat shock protein HSP 90-beta 2 Moesin Nidogen-2 Osteoglycin (Osteoinductive factor, mimecan) Procollagen-lysme, 2-oxoglutarate 5-dioxygenase 1 Prostaglandin E synthase 3 (Cytosohc) Prostaglandin G/H synthase 1

11 0 0 14 3 6 8 24 0 9 0

0 159 0 0 0 0 0

5 0 95 15 18 14 4

0 0 60

4 0 0 46 0 9 0 45

0 5

0


Protein Binding Pmfilin-1 Isoform 1 of Collagen alpha-l(XIV) chain Isoform 1 of Collagen alpha-l(XIV) chain Collagen, type I, alpha 1, isoform CRA_a Collagen alpha-l(I) chain Collagen alpha-2(I) chain D-Dopachrome decarboxylase-like protein, D-dopachrome tautomerase Elongation factor 1-alpha

13 30 0

12 5 16 0 23 0 44 0

23 0

19

protein name

Pepsmogen 3, group I (Pepsinogen A) Pepsmogen 3, group I (Pepsinogen A), Pepsinogen 4, group I (Pepsinogen A) or Pepsinogen S, group I (Pepsinogen A) Pepsinogen S, group I (Pepsinogen A) Phosphoglycerate mutase 1 Gastricsin Progastncsm (Pepsinogen C) (Fragment) Phosphoglycerate kinase Phosphoglycerate kinase 1 Isoform M2 of Pyruvate kinase isozymes M1/M2 Pyruvate kinase isozymes M1/M2 Protease Inhibitors Cystatm-B Alpha-1-antitrypsin Alpha-1-antitrypsin


MRM data

11 8 31 14 5 6 8 24 4 16 5

48

5 159 95 15 18 14 4

13 30 60

16 5 16 46 23 9 44 45

23 5

19


2.53 2.23 2.19 2.47 2.43 2,20 2.62 0.39 2.60 2.25 2.19

2.21

2.16 0.35 0.37 0.28 0.34 0.40 2.37

2.08 2.27 2.53

0.22 2.72 0.16 0.34 2.52 2.71 2.08 2.57

0.20 0.21

2.58

fold change malignant/ benign

× × × × × × × ×

10−14 10−5 10−17 10−25 10−21 10−10 10−20 10−29

× × × × × × ×

10−5 10−75 10−34 10−10 10−12 10−10 10−4

3.37 3.33 2.83 2.39 1.08 6.56 7.70 5.00 1.55 2.18 1.08

× × × × × × × × × × ×

10−7 10−9 10−18 10−13 10−5 10−4 10−4 10−15 10−4 10−18 10−5

2.23 × 10−29

1.08 2.64 2.09 2.60 4.14 5.56 1.55

5.70 × 10−7 2.37 × 10−17 2.89 × 10−40

1.01 1.08 1.31 8.66 4.47 2.20 6.17 1.02

1.39 × 10−22 1.08 × 10−5

4.00 × 10−10




Uniprot ID

2883

×

MRM data

4 12 22 0 0

Serme hydroxymethyltransferase Transketolase Triosephosphate isomerase Tnosephosphate isomerase Thymidine phosphorylase RNA Binding/Splicing/Transportation A8K538_HUMAN DEAD (Asp-Glu-Ala-Asp) box polypeptlde 3,X-linked Putative RNA-bmdmg protein Luc7-like 2 Poly(A) binding protein, cytoplasmic 4, Polyadenylatebinding protein 1

0 0

0

0 0 0 0 0 4 0

33 0 65 0 8 0 7

0 0 0 11 0 0 20

dehydrogenase dehydrogenase Protein disulfide-isomerase A6 Peptidyl-prolyl cis−trans isomerase A Peroxiredoxm-6 Proteasome activator complex subunit 2 Serme hydroxymethyltransferase L-Lactate

L-Lactate

Protein Binding S100 calcium binding protein Al Protein S100-A4 S100 calcium binding protein A9 (calgranulm B) (S100A9) S100 calcium binding protein A9 (calgranulm B) (S100A9) 14-3-3 protein epsilon 14-3-3 protein gamma 14-3-3 protein zeta/delta Proteins with catalytic activity Fructose-bisphosphate aldolase A Ceruloplasmin Glyceraldehyde-3-phosphate dehydrogenase Glyceraldehyde-3-phosphate dehydrogenase Glucose-6-phosphate isomerase Glucose-6-phosphate isomerase L-Lactate dehydrogenase

protein name


7 4

8

0 0 0 22 11

11 18 57 51 28 0 5

0 16 0 108 0 21 0

4 13 10 0 59 56 100


7 4

8

4 12 22 22 11

11 18 57 51 28 4 5

33 16 65 108 8 21 7

4 13 10 11 59 56 120


2.34 2.64

2.10

2.78 2.23 2.59 3.46 2.29

2.76 3.29 2.15 2.18 2.86 2.08 2.78

2.50 2.49 2.24 2.99 2.31 2.34 2.24

2.13 2.55 3.46 3.78 2.21 2.00 2.13

fold change malignant/ benign 10−4 10−11 10−10 10−9 10−36 10−35 10−69 10−21 10−13 10−29 10−69 10−6 10−17 10−5 10−13 10−20 10−36 10−34 10−18 10−4 10−5 10−4 10−14 10−12 10−26 10−9

× × × × × × × × × × × × × × × × × × × × × × × × × ×

9.97 × 10−8 1.55 × 10−4

3.99 × 10−8

1.55 6.20 5.61 7.62 3.33

9.51 5.42 1.65 5.07 3.24 1.55 1.08

7.35 1.32 1.75 1.69 4.03 2.66 2.53

1.55 9.28 1.02 4.28 1.27 5.97 9.76


a The table merges the data from the TOF/TOF and the QTOF platform. When the two platforms have identified the same protein but with different subsets of peptides and that therefore are associated with partly different uniprot IDs, this protein is seen as two separate entries in the table. The MRM data column indicates with “×” proteins that were included in the MRM analysis.

B3KRRl, B3KSL5, B7Z4Q3, B7Z500, Q9Y383-l, Q9Y383-2 B1ANRO, Q13310-2, B3KT93, B1ANR1, P11940-l, Q6JQ30, Q13310-1, B4DZW4, P11940-2, Q4VC03

A8K538, B4E3E8, B5BTY4, Q5S4N1, Q59GX6, O00571

P04075 A5PL27, QlL857, B7Z5Q2, A8K5A4, P00450 P04406 P04406, Q2TSD0 B4DVJ0, P06744 B4DE36, B4DG39, B4DVJ0, P06744 A8MXQ4, D3DQY3, D3YTI4, B7Z5E3, C9J4M5, P00338-1, P00338-2 B7Z5E3, P00338-l, P00338-2, D3DQY3, C9J4M5 A8MW50, Q5U077, P07195, C9J7H8 B3KY95, B5MBW7, B7Z254, Q53RC7, Q15084-1, Q15084-2 A8K220, B2RE56, A8K486, P62937 P30041 A8MZ76, Q2TNB3, Q6FHK9, C9JE52, Q9UL46, Q86SZ7 B4DJ63, B4DWA7, B4E1G2, B7Z9F1, Q53ET4, Q5BJF5, Q5HYG8, Q8N1A5, P34897, B4DJQ3, B4DLV4, B4DP88, B4DW25 B7Z9F1, Q5BJF5, Q5HYG8, Q8N1A5, Q53ET4, P34897 B3KSI4, B4DE31, B4DVUl, B4E022, Q53EM5, P29401 D3DUS9, P6017411 B4DUI5, D3DUS9, B7Z5D8, Q53HE2, P6017411 B2RBL3, C9JGI3, P19971

B2R5D9, Q5T7Y6, P23297 D3DV46, P26447 B2R4M6, P06702, D3DV36, C9JlHl B2R4M6, D3DV36, C9JlHl, P06702 D3DTH5, Q4VJB6, P62258 B3KNB4, B4DHC4, P61981 D0PNIl, P63104

Table 2. continued




Article

Figure 2. Comparison of expression ratios between benign and malignant tumors of the proteins overlapping between the 2D-DIGE study5 and present iTRAQ study. The expression ratios agreed very well for all proteins but threelamin B2, lamin A/C and keratin type II, cytoskeletal 7.

protein expression ratio and by immunohistochemistry in the 2D-DIGE study.5 Ingenuity Pathway Analysis. The 94 regulated proteins were compared to the entire data set of identified proteins using Ingenuity pathway analysis. Among the 2048 protein identified, 613 were single hits, that is, the protein was identified with only one peptide in one spectrum. This total data set of 2048 proteins was used as a reference data set in the Ingenuity analysis. We reasoned that this would be a more representative comparison than using all gene IDs in the ingenuity database since our data set takes into account the bias of what type of proteins that are identified with the mass spectrometry technology. The analysis identified the PI3K/Akt signaling pathway to be the pathway in the Ingenuity Pathway Analysis library most significant to the data set. Activation of the PI3K/Akt signaling pathway induces cell proliferation and prolongs cell survival, and activation of this pathway is frequently observed in different solid tumors, for example breast,22,23 pancreatic24 and ovarian tumors.25,26 Two key tumor suppressors, PTEN and p53, both frequently reported to be associated with cancer, are linked through this pathway.27 Somatic mutations of p53 are the most commonly identified alteration in EOCs.28 It is unclear which alternate pathways are important in the development of p53 wild-type ovarian carcinoma. The PIK3 and PTEN genes are known to be

either above or below the two benign measurements. Out of the 94 proteins, 74 were up-regulated in the malignant samples. Overlap between 2DE and iTRAQ Study. Of all proteins identified, 22 proteins overlapped with proteins identified in the previous 2DE-study5 (Figure 2). For the majority of these proteins fold-changes agreed, except for 3 proteins that displayed regulated in opposite directions. These proteins are two different lamins (lamin B2 and lamin A/C) and keratin type II, cytoskeletal 7. These were all found in multiple spots on the gels indicating extensive post-translational modification and a simple explanation to the conflicting regulation of these proteins is that the two techniques detect different isoforms. Most of the proteins that were found in both studies are abundant proteins such as cytoskeletal proteins including lamins, keratin, actin and tubulin. Many of these have been linked to cancer through their involvement in the modification of the cytoskeleton upon apoptosis.18 Chaperones are also relatively abundant proteins and endoplasmic reticulum protein ERp29, 78 kDa glucose-regulated protein (GRP78), Protein disulfide isomerase (PDI) and Calreticulin (CALR) have all been linked to cancer.19,20 Prohibitin (PHB) that is associated with a blockage of the cell cycle and cell survival and has been suggested to possibly contribute to development of drug resistance in ovarian cancer21 was confirmed both by the 2D-gel 2884



Article

Table 3. Proteins and their Corresponding Peptides Measured in the MRM Assay Uniprot ID

protein name

peptide sequence

intended MRM use

P63104 Q8TDL5 Q8TDL5 Q8TDL5 Q8TDL5 Q96HE7 Q96HE7 Q96HE7 Q96HE7 P02788 P08648 P08648 P08648 P08648 P08727 P08727 P05164 P09874 P09874 P46940 P46940 P46940 P21860 P21860 P21860 P04004 P04004 P60709 P60709 P60709 P04406 P00492 P00492 P00492 P08397 P08397 P31040 P31040 P31040 P20226 P62988 P62988

14-3-3 protein zeta/delta Carcinoma-associated protein 1 Carcinoma-associated protein 1 Carcinoma-associated protein 1 Carcinoma-associated protein 1 EROl-like protein alpha EROl-like protein alpha EROl-like protein alpha EROl-like protein alpha Lactotransferrin Integrin alpha-5 precursor Integrin alpha-5 precursor Integrin alpha-5 precursor Integrin alpha-5 precursor Keratin, type I cytoskeletal 19 Keratin, type I cytoskeletal 19 Myeloperoxidase precursor PARP1 Poly [ADP-ribose] polymerase 1 PARP1 Poly [ADP-ribose] polymerase 1 Ras GTPase-activating-like protein IQGAP1 Ras GTPase-activating-like protein IQGAP1 Ras GTPase-activating-like protein IQGAP1 Receptor tyrosine-protein kinase erbB-3 precursor Receptor tyrosine-protein kinase erbB-3 precursor Receptor tyrosine-protein kinase erbB-3 precursor Vitronectin precursor Vitronectin precursor Actin, cytoplasmic 1 Actin, cytoplasmic 1 Actin, cytoplasmic 1 Glyceraldehyde-3-phosphate dehydrogenase Hypoxanthine-guanine phosphoribosyltransferase Hypoxanthine-guanine phosphoribosyltransferase Hypoxanthine-guanine phosphoribosyltransferase Porphobilinogen deaminase Porphobilinogen deaminase Succinate dehydrogenase [ubiquinone] flavoprotein subunit Succinate dehydrogenase [ubiquinone] flavoprotein subunit Succinate dehydrogenase [ubiquinone] flavoprotein subunit TATA-box-binding protein Ubiquitin Ubiquitin

GIVDQSQQAYQEAFEISK ILTQDTPEFFIDQGHAK IQLMNSGIGWFQPDVLK LEFDLLYPAIK LSFLVNALAK LGAVDESLSEETQK MLLLEILHEIK YLLQETWLEK SFPLHFDENSFFAGDK YLGPQYVAGITNLK EHQPFSLQCEAVYK GLELDPEGSLHHQQK LLESSLSSSEGEEPVEYK VTGLNCTTNHPINPK ALEAANGELEVK GQVGGQVSVEVDSAPGTDLAK SSGCAYQDVGVTCPEQDK EELGFRPEYSASQLK WSEDFLQDVSASTK ATFYGEQVDYYK TEVSLTLTNK VDFTEEEINNMK FQTVDSSNIDGFVNCTK LTQLTEILSGGVYIEK QLCYHHSLNWTK GDVFTMPEDEYTVYDDGEEK GQYCYELDEK DSYVGDEAQSK HQGVMVGMGQK YPIEHGIVTNWDDMEK AGAHLQGGAK FFADLLDYIK NVLIVEDIIDTGK VIGGDDLSTLTGK ENPHDAWFHPK MSGNGNAAATAEENSPK GFHFTVDGNK IDEYDYSKPIQGQQK VPPIKPNAGEESVMNLDK AEIYEAFENIYPILK TITLEVEPSDTIENVK TLSDYNIQK

Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Pot biomarker Int Std Int Std Int Std Int Std Int Std Int Std Int Std Int Std Int Std Int Std Int Std Int Std

mutated in a minority of ovarian carcinomas.26 We have in this study not identified any of these key tumor suppressors as they are short-lived, low-abundance proteins. However, we see the effect on the PI3K/Akt signaling pathway through alternation of many downstream targets that constitute more abundant proteins such as probable ATP-dependent RNA helicase DDX5 and DDX17, Superoxide dismutase (SOD2), Glucose-6phosphate isomerase (GPI), Protein S100-A4, Clusterin (CLU), Solute carrier family 2 (SLC2A1), Poly(ADP-ribose)polymerase 1 (PARP1), Transgelin-2 (TAGLN2), Glutathione S-transferase P (GSTP1), 60 kDa heat shock protein (HSPD1), Clathrin heavy chain 1 (CLTC), Elongation factor 2 (EEF2) and Glyceraldehyde-3-phosphate dehydrogenase (GAPDH). These were all up-regulated in the malignant cases and many of them have been reported to play an important role in cancer.

MRM Analysis to Confirm Protein Expression in Individual Tissue Samples

To confirm our findings from the iTRAQ screen of the tumor groups and further monitor how a subset of the regulated proteins behave in individual samples, a set of 18 proteins listed in Table 3 were selected for MRM analysis. Fifty-one tissue samples of normal (7 samples), benign (4 samples), borderline (16 samples) or malignant origin (24 samples) were analyzed. We focused on 12 proteins that were regulated in the iTRAQ data set that could be interesting as potential biomarkers, together with 6 housekeeping proteins to use as internal standard proteins for normalizing the data (Table 3). The six proteins that met the stringent filtering criteria of regulated proteins in the iTRAQ screen are indicated in Table 2. The remaining 6 proteins of interest were regulated in the iTRAQ data set but failed meeting one of the three filtering criteria on p-value, fold and rank sum (Supplemental Table 1, Supporting 2885



Article

Figure 3. ROC-curve analysis using the MRM-profile of the 18 measured proteins for the 51 individual tumors analyzed. A support vector machine was used for pairwise supervised classification between groups. Leave-one-out cross validation was employed and the classification values were used to construct ROC curves and calculate the area under the ROC curve. Normal tissue could clearly be separated from all the other groups with ROC areas of 1 (normal vs benign), 0.98 (normal vs borderline) and 0.94 normal vs malignant. The malignant and benign tumors were also perfectly separated with a ROC-area of 1 while the ROC analysis placed the borderline tumors closer to the malignant than the benign tumors.

comparing normal tissue to malignant tumors. The highest expression by far however, was seen in the benign tumors, with a decreasing expression to borderline to malignant (Figure 4). The same pattern was seen for both measured peptides. Keratin 19 is abundantly expressed in epithelial tissue and it has been observed that when malignant cells disintegrate, partially degraded Keratin 19 fragments are released into the circulation. This increase of released peptides into blood in serum from cancer patients has been demonstrated to be a result of Caspase 3 dependent breakdown of the protein.29 The specific fragments released can be quantified using various commercially available specific serological assays. Our MRM assay measures peptides from a different region of the Keratin 19 protein. The higher expression level of Keratin 19 measured in benign tumors compared to malignant might be a result of the Caspase 3 degradation in malignant tumors resulting in higher release into serum and consequently lower levels in the tumors. Several of the 14-3-3 proteins were identified as significantly upregulated in the malignant tumor groups in the iTRAQ screen. This is a large group of proteins that are all downstream targets of Akt. These are best known for promoting cell survival through their interactions with signaling proteins, and have been shown to have hundreds of downstream targets.30 The 143-3 sigma subtype has most frequently been related to cancer as it interacts with p53 and this protein was identified as upregulated in the malignant cases in the 2D-DIGE study. In the iTRAQ screen we identified 14-3-3 protein zeta/delta, 14-3-3

Information). The housekeeping proteins did not show any trend in the data set but were in the majority of cases unregulated when comparing the groups and no renormalization was done of the data. The MRM signal was used for supervised classification with a leave one out cross-validated support vector machine. This separated the malignant and benign tumors perfectly with a ROC area of 1 (Figure 3). The normal tissue could also be separated from all the other groups by the support vector machine (ROC area = 1, normal vs benign, ROC area = 0.98, normal vs borderline and ROC area = 0.94, normal vs malignant). To the supervised classifier, borderline samples seemed closer to the malignant than to the benign tumors (ROC area = 0.97, borderline vs benign, ROC area = 0.85, borderline vs malignant). In conclusion, the MRM signature separated all tissue groups almost perfectly except for borderline vs malignant tumors. The proteins with the most distinct expression patterns in the four different sample groups were 14-3-3 protein zeta/delta, ERO1-like protein alpha and Lactotransferrin which all showed an increased expression from normal to benign to borderline to malignant samples (Figure 4). This correlated well with the upregulation between the benign and malignant groups seen also in the iTRAQ data set (Table 2). In addition Keratin 19, that was found upregulated in the malignant samples in the iTRAQ data set but not with a significant p-value, was confirmed in the MRM analysis to have distinct expression differences in the different tissue groups with an increased expression level 2886



Article

Figure 4. Protein 14-3-3 zeta/delta, ERO-like protein alpha, Lactotransferrin and Keratin 19 all showed distinct expression patterns comparing 51 individual tissue samples of normal (green), benign (blue), borderline (magenta) and malignant (red) tumors in the LC-MRM analysis. All four proteins showed an upregulated expression comparing normal tissue and malignant tumors. The first three with a trend of increasing expression going from normal to benign to borderline to malignant. Keratin 19 however showed with two significant peptides the highest expression in benign tumors.

shown to be induced by hypoxia and is the key adaptive response in HIF-1-mediated pathway and has been suggested as a potential antiangiogenic target.32 We measured this protein with four peptides and the two peptides that were successfully detected both demonstrated the same increased expression when going from normal tissue to benign, borderline to malignant tumors (Figure 4). One peptide was not detected at all and one was detected only in the samples with higher concentrations, that is, borderline and malignant samples with the highest signal in the malignant samples. Lactotransferrin is a multifunctional glycoprotein and an essential element of the innate immunity.33 The protein demonstrates a broad spectrum of properties, including regulation of iron homeostasis, host defense against a broad range of microbial infections, anti-inflammatory activity and has the capacity to induce apoptosis and inhibit proliferation in cancer cells. Conversely, iron-saturated lactoferrin has also been suggested to stimulate cell cycle progression through the PI3K/ Akt signaling pathway.34 We found lactotransferrin to have

protein epsilon and 14-3-3 protein gamma as all being significantly up-regulated (Table 2). We could in the MRM analysis confirm the gradual up-regulation of 14-3-3 zeta/delta protein when going from normal to benign to borderline to malignant tumors (Figure 4). 14-3-3 zeta was recently suggested as a potential biomarker for EOC in a study measuring serum levels from early stage xenograft mouse model.31 Many downstream targets of 14-3-3 zeta were also identified as upregulated in the iTRAQ screen including Cofilin 1 (CFL1), Profilin-1 (PFN1), Elongation factor 1-alpha 1 (EEF1A), Elongation factor 2 (EEF2), DEAD box protein 3, Xchromosomal (DDX3X), Triosephosphate isomerase (TP1), Nucleolin (NCL), Peroxiredoxin 1 (PRDX1), Glyceraldehyde3-phosphate dehydrogenase (GAPDH), Phosphoglycerate kinase 1 (PGK1) and Heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1). GAPDH was also measured with MRM but no significant regulation could be confirmed. ERO-like protein alpha is known to oxidize proteins in the ER to form disulfide bonds and acts directly on protein disulfide isomerase (PDI). ERO-like protein alpha has been 2887



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(5) Bengtsson, S.; et al. Large-scale proteomics analysis of human ovarian cancer for biomarkers. J. Proteome Res. 2007, 6, 1440−1450. (6) Gygi, S. P.; et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 1999, 17, 994−999. (7) Ross, P. L.; et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics 2004, 3, 1154−1169. (8) Ong, S.-E.; et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 2002, 1, 376−386. (9) Thompson, A.; et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/ MS. Anal. Chem. 2003, 75, 1895−1904. (10) Häkkinen, J.; Vincic, G.; Månsson, O.; Warell, K.; Levander, F. The proteios software environment: an extensible multiuser platform for management and analysis of proteomics data. J. Proteome Res. 2009, 8, 3037−3043. (11) Levander, F.; et al. Automated reporting from gel-based proteomics experiments using the open source Proteios database application. Proteomics 2007, 7, 668−674. (12) Elias, J. E.; Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 2007, 4, 207−214. (13) Keller, A.; Nesvizhskii, A. I.; Kolker, E.; Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 2002, 74, 5383− 5392. (14) Keller, A.; Eng, J.; Zhang, N.; Li, X. J.; Aebersold, R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol. Syst. Biol. 2005, 1 (2005), 0017. (15) Nesvizhskii, A. I.; Keller, A.; Kolker, E.; Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 2003, 75, 4646−4658. (16) Ihaka, R.a.G., R. R: A language for data analysis and graphics. J. Comput. Gr. Stat. 1996, 5, 299−314. (17) Picotti, P.; et al. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat. Methods 2010, 7, 43−46. (18) Ndozangue-Touriguine, O.; Hamelin, J.; Breard, J. Cytoskeleton and apoptosis. Biochem. Pharmacol. 2008. (19) Cheretis, C.; et al. Expression of ERp29, an endoplasmic reticulum secretion factor in basal-cell carcinoma. Am. J. Dermatopathol. 2006, 28, 410−412. (20) Xing, X.; Lai, M.; Wang, Y.; Xu, E.; Huang, Q. Overexpression of glucose-regulated protein 78 in colon cancer. Clin. Chim. Acta: Int. J. Clin. Chem. 2006, 364, 308−315. (21) Gregory-Bass, R. C.; et al. Prohibitin silencing reverses stabilization of mitochondrial integrity and chemoresistance in ovarian cancer cells by increasing their sensitivity to apoptosis. Int. J. Cancer 2008, 122, 1923−1930. (22) Kirkegaard, T.; et al. AKT activation predicts outcome in breast cancer patients treated with tamoxifen. J. Pathol. 2005, 207, 139−146. (23) Saal, L. H.; et al. Recurrent gross mutations of the PTEN tumor suppressor gene in breast cancers with deficient DSB repair. Nat. Genet. 2008, 40, 102−107. (24) Asano, T.; et al. The PI 3-kinase/Akt signaling pathway is activated due to aberrant Pten expression and targets transcription factors NF-kappaB and c-Myc in pancreatic cancer cells. Oncogene 2004, 23, 8571−8580. (25) Campbell, I. G.; et al. Mutation of the PIK3CA gene in ovarian and breast cancer. Cancer Res. 2004, 64, 7678−7681. (26) Campbell, I. G.; Russell, S. E.; Phillips, W. A. PIK3CA mutations in ovarian cancer. Clin. Cancer Res. 2005, 11, 7042 ; author reply 7043−7043. (27) Mayo, L. D.; Donner, D. B. The PTEN, Mdm2, p53 tumor suppressor-oncoprotein network. Trends Biochem. Sci. 2002, 27, 462− 467.

increased expression levels comparing normal, benign, borderline and malignant samples (Figure 4). In summary, we report a quantitative shotgun proteomics screen of epithelial ovarian tumors of benign and malignant origin. The PI3K/Akt signaling pathway was the canonical pathway most significant to this data set and many downstream targets and effectors of this pathway were identified as significantly regulated. Out of the regulated proteins a subset were confirmed in individual tissues of 51 normal, benign, borderline and malignant origin using LC-MRM. This analysis gave a signature that successfully could separate the different sample groups, many of them with a ROC area of 1. The majority of these differentially expressed proteins have been linked to cancer; a few of them are already targeted for cancer therapy. This work demonstrates the strength of combining different proteomics workflows, utilizing the depth of the shotgun approach to screen proteins of potential interest, followed by the targeted MRM analysis where measurements of specific peptides from selected proteins is collected for each individual sample analyzed.

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ASSOCIATED CONTENT

S Supporting Information *

Supplemental tables and figure. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +46-46-2227469. E-mail: sofia.waldemarson@immun. lth.se. Present Address ∇

Lee Gil Ya Cancer and Diabetes Institute, Gachon University of Medicine and Science, Incheon, Korea. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The study was supported by 31003A_130530 (to R.A.), from the Swiss National Science Foundation, European Research Council (grant #ERC-2008-AdG- 233226).

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ABBREVIATIONS EOC, epithelial ovarian cancer; 2DE, two-dimensional gel electrophoresis; 2D-DIGE, two-dimensional difference in gel electrophoresis; DTT, dithiothreitol; iTRAQ, isobaric tags for relative and absolute quantitation; RP, reversed phase; MRM, multiple reaction monitoring; SRM, selected reaction monitoring.

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REFERENCES

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Protein Expression Changes in Ovarian Cancer during the Transition

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