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Molecular & Cellular Proteomics 5:157-171, 2006.
© 2006 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

Biomarker Discovery from Pancreatic Cancer Secretome Using a Differential Proteomic Approach^*,S

Mads Grønborg^,, Troels Zakarias Kristiansen^,, Akiko Iwahori, Rubens Chang^¶, Raghunath Reddy, Norihiro Sato^¶, Henrik Molina^,, Ole Nørregaard Jensen, Ralph H. Hruban^¶,||, Michael G. Goggins^¶,**, Anirban Maitra^,¶,||, and Akhilesh Pandey^,¶,||,

From the McKusick-Nathans Institute of Genetic Medicine and the Department of Biological Chemistry, The Johns Hopkins University, Baltimore, Maryland 21205, the Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark, and the ^¶ Department of Pathology and The Sol Goldman Pancreatic Cancer Research Center and the ^|| Department of Oncology, The Johns Hopkins University, Baltimore, Maryland 21287

	ABSTRACT

TOP ABSTRACT MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can subsequently be tested using other techniques for use in early detection of cancers. Here we demonstrate the use of stable isotope labeling with amino acids in cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5-fold change), several of which were previously reported as either up-regulated (e.g. cathepsin D, macrophage colony stimulation factor, and fibronectin receptor) or down-regulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have not been correlated previously with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin ß1), and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins not described previously to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, apoE, and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly the protein expression data from SILAC were compared with mRNA expression data obtained using gene expression microarrays for the two cell lines (Panc1 and human pancreatic duct epithelial), and a correlation coefficient (r) of 0.28 was obtained, confirming previously reported poor associations between RNA and protein expression studies.

Global profiling of differentially expressed genes and proteins in cancers versus normal tissues has been utilized to identify novel neoplasia-associated biomarkers (8). Over the past 5 years, our group has identified a large number of such differentially expressed transcripts in pancreatic cancers using cDNA or oligonucleotide-based microarray platforms (9–11). Although the global transcript profiling of pancreatic cancers has yielded numerous insights into the biology of pancreatic cancers (12, 13) and novel therapeutic and imaging targets (14–16), the results in terms of biomarker discovery have been less striking. There are two potential causes for the small fraction of differentially expressed transcripts in pancreatic cancer that translate into effective biomarkers. First, there may be significant dissociation between overexpressed transcripts and overexpressed proteins; indeed such a discordance has been reported in comparisons of global transcriptomic and proteomic analyses of other human cancers (17). Second, because most expression techniques use "bulk" homogenates of cell lines and/or tissues, there is no preferential enrichment for the secreted class of proteins (i.e. the cancer "secretome" (18)), the portion of the proteome expected to harbor the most promising biomarkers.

To overcome these major limitations of biomarker discovery, we have focused in recent years on newly emerging proteomic approaches for unraveling the pancreatic cancer secretome. Two-dimensional gel electrophoresis is a widely used and robust method for studying differentially expressed proteins in cancers versus normal tissues (19, 20). Alternative methods like mRNA profiling have also been used in high throughput experiments but do not discriminate between secreted and non-secreted components. Therefore, we have relied principally on technologies that can be used to directly analyze aberrant proteins within clinically relevant body fluids such as serum or pancreatic juice. For example, SELDI-TOF/MS has been used previously to identify peak patterns in the mass spectrum of serum and pancreatic juice that distinguish neoplastic from non-neoplastic samples (21); a common pitfall of SELDI, however, is the inability to determine the actual proteins or peptides that may be contributing to these differential peak patterns. LC-MS/MS is an alternative approach that couples protein fractionation with mass spectrometry to generate a comprehensive catalog of the proteome of analyzed fluid samples such as pancreatic juice and bile (22, 23).

Although both SELDI and LC-MS/MS are technically capable of secretome analyses, they remain essentially qualitative approaches (i.e. peak patterns or peptides are identified solely as present or absent in normal and cancerous specimens). However, small quantities of many proteins are likely to be present in body fluids of normal individuals or those with benign pancreatic disorders, even those that demonstrate significant quantitative differences in cancer patients. Quantitative proteomic approaches are therefore required to significantly increase the repertoire of candidate biomarkers, which can then be tested on larger sample sets using traditional ELISA-based approaches. Such newly described technologies include mass tagging (e.g. ICAT) (24, 25), ¹⁸O labeling (26), and stable isotope labeling with amino acids in cell culture (SILAC)¹ (27, 28), all of which can be combined with mass spectrometry to yield powerful tools to quantitatively analyze cancer secretomes. SILAC has been used previously for relative quantitation in several systems such as differential changes in cells upon epidermal growth factor or insulin simulation and dynamics of lipid raft proteins or phosphorylation events (29–32). Most recently, SILAC has also been used for relative quantitation of lysates from prostate cancer cell lines with varying metastatic potential (33).

In this study we used SILAC to compare the secretomes of two human pancreatic cell lines, an immortalized non-neoplastic human pancreatic duct epithelial (HPDE) cell line and a pancreatic ductal adenocarcinoma cell line (Panc1). Three features of our study design increase our yield of potential biomarkers. First, we directly compare a well established in vitro model of normal ductal epithelium (i.e. HPDE (34–36)) with its neoplastic ductal counterpart (i.e. Panc1), circumventing abundant acinar and islet proteins that would be present in bulk pancreatic tissue comparisons. Second, by restricting our analyses to quantitating relative differences in proteins in the secreted compartment alone, we maximize the possibility of identifying candidates that have the greatest potential for translation to fluid-based biomarkers for pancreatic cancer detection. Third, we validate our best biomarker candidates from the proteomic study by immunohistochemical labeling using pancreatic cancer tissue microarrays.

	MATERIALS AND METHODS

TOP ABSTRACT MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Cell Culture and SILAC Reagents—
Custom Dulbecco’s modified Eagle’s medium lacking arginine and lysine was prepared by Invitrogen. [¹³C₆]Arginine (heavy arginine) and [¹³C₆]lysine (heavy lysine) were purchased from Cambridge Isotopes, Inc. (Andover, MA). Panc1 pancreatic cancer cells were obtained from American Type Culture Collection (ATCC, Manassas, VA) and were grown in Dulbecco’s modified Eagle’s medium with a combination of heavy arginine plus lysine with 10% dialyzed fetal bovine serum plus antibiotics. Immortalized non-neoplastic HPDE cells were obtained from Dr. Ming Tsao, Ontario, Canada (35) and grown in defined keratinocyte-serum free medium with supplements (Invitrogen) and antibiotics. We have demonstrated previously that the HPDE cell line represents a valid in vitro surrogate for human ductal epithelium (10, 34, 37–39). For harvesting conditioned media, HPDE and Panc1 cells were grown as described above but in the absence of serum.

Sample Preparation—
Approximately 3 x 10⁷ HPDE or Panc1 cells were grown to 80% confluence, washed six times in serum-free medium, and incubated for 24 h in serum-free medium. The conditioned medium containing the secreted proteins was collected and filtered using a 0.45-µm filter (Millipore, Bedford, MA) and subsequently concentrated using a 3,000-Dalton molecular mass cutoff spin column, Centriprep (Millipore). A dilution series of the concentrated media (after Centriprep concentration) from HPDE and Panc1 cell cultures was run on a 4–12% gradient NuPAGE gel (Invitrogen) and silver-stained. To ensure normalization, three different ratios close to 1:1 were mixed, and five bands (different molecular weights) from each sample were cut out and digested by trypsin as described previously (22). The digested bands were analyzed by LC-MS/MS, and the mixed sample in which the most proteins were in a 1:1 ratio was chosen for the large scale experiment. For the initial small scale normalization experiments, 10 µg (protein concentration measure by DC protein assay, Bio-Rad) of protein from each sample were mixed. For the large scale experiment (used for quantitation and identification), 50 µg of each sample were mixed and run on a NuPAGE 4–12% gradient gel. The gel was subsequently silver-stained, excised into 25 bands, and digested by trypsin. A replicate of the sample was generated by loading the same amount of sample (50 µg of each sample) on another 4–12% NuPAGE gel and digested as described above.

Liquid Chromatography—
Each fraction from the in-gel digestion was analyzed by automated nanoflow LC-MS/MS. An Agilent Technologies 1100 series system was used to deliver a flow of 1.5 µl/min during desalting of the sample and 250 nl/min during elution of the peptides into the mass spectrometer. Each sample was loaded onto an on-line analytical fused silica needle column (Proxion Biosystems, Odense, Denmark) packed with 5-µm Vydac C₁₈ resin. Washing and desalting was done with 95% mobile phase A (H₂O with 0.4% acetic acid and 0.005% heptafluorobutyric (v/v)) and 5% mobile phase B (90% acetonitrile, 0.4% acetic acid, 0.005% heptafluorobutyric acid in water). Samples were eluted from the analytical column by a linear gradient of 90% mobile phase A to 60% mobile phase A. A 34-min gradient was used for elution. A potential of 2.8 kV was applied to the emitter (Proxion Biosystems). The spectra were acquired on a Micromass (Manchester, UK) Q-TOF API-US system equipped with an ion source designed by Proxion Biosystems. All data were obtained in positive ion mode. Data-dependent acquisition was used with a ion mass window set to 2.5 Da. MS to MS/MS switch was set to switch on a threshold of 10 counts/s, and MS/MS to MS was set to an intensity below a threshold of 2 counts/s. Charge state recognition was used to estimate the collision energy for the fragmented precursor. Scan time was set to 0.9 s, and interscan time was set to 0.1 s. The number of components (i.e. number of MS/MS per MS scan) was set to three resulting in a total cycle time (one MS and three MS/MS spectra) of 10 s. Data analysis was performed using MassLynx 4.0 software.

Data Analysis, Quantitation, and Database Searching—
Peak lists were generated from the raw data using the following criteria: smooth window, 4.00; number of smooth, 2; smooth mode, Savitzky Golay; and percentage of peak height to calculate centroid spectra, 80% with no base-line subtraction. The generated peak lists (pkl-file) were searched against the RefSeq database (build 33) (www.ncbi.nlm.nih.gov./RefSeq/) using Mascot version 2.0. with a mass accuracy of 1.1 Da for the parent ion (MS) and 0.3 for the fragment ions (MS/MS). The peptides were constrained to be tryptic with a maximum of two missed cleavages. Carbamidomethylation of cysteines was considered a fixed modification, whereas oxidations of methionine residues, "heavy" arginine (+6 Da), heavy lysine (+6 Da), and formation of pyroglutamic acid for peptides containing an N-terminal glutamine were considered as variable modifications. An initial protein list was generated using the following screening procedure. Only proteins containing at least one unique peptide (we refer to a peptide as being unique for a specific protein if the sequence has not been assigned to a different protein) with a Mascot score over 25 were considered in the dataset. The highest scoring peptide was manually interpreted to confirm the predicted sequence by Mascot and to eliminate potential false positives. For all proteins identified based on a single peptide, the corresponding MS/MS spectra are provided in Supplemental Fig. 1. Quantitation was performed on three to four peptides (if available) by comparing the extracted ion chromatogram of the corresponding light and heavy peptides. In addition, the ratios in the MS spectrum of the correlated "light" and heavy peptides were measured. In cases where the ratio of the light and heavy peptides was difficult to determine because of low signal in one of the states, a conservative value of 20 (for up-regulated proteins) or 0.05 ( ) for down-regulated proteins was used.

View larger version (17K): [in this window] [in a new window]   FIG. 1. Summary of changes in abundance of proteins identified by SILAC. A total of 195 proteins was identified and quantitated by SILAC. In cases where the abundance of one of the peptide species (light or heavy) was very low or undetectable, a >20-fold change is indicated as shown.

Western Blot Analysis—
A subset of proteins determined to be differentially expressed by SILAC was confirmed by Western blot analysis; these proteins were selected using a combination of parameters including level of differential expression in cancer versus normal, protein function, and availability of commercial antibodies. Approximately 10 µg of protein form HPDE and Panc1 cells were run on a NuPAGE 4–12% gradient gel (Invitrogen). After gel electrophoresis the resolved proteins were transferred onto nitrocellulose. The membranes were blocked in 5% skimmed milk powder in phosphate-buffered saline containing 0.1% Tween 20 overnight at 4 °C and incubated with the relevant antibodies. The membranes were washed and incubated with horseradish peroxidase-conjugated antibody and developed using enhanced chemiluminescence detection according to the manufacturer’s instructions (Amersham Biosciences).

Immunohistochemical Labeling of Tissue Microarrays—
Immunohistochemical labeling was performed on pancreatic cancer tissue microarrays for the most promising candidates based on biological relevance and/or Western blot analysis; this included six proteins that were confirmed as up-regulated in the Panc1 secretome (CD9, perlecan, SDF4, apoE, fibronectin receptor, and L1CAM). Tissue microarrays of pancreatic cancer were constructed as described previously (40, 41). Briefly archival sections of human pancreatic cancers and adjacent normal pancreata were selected as "donor" blocks from which 1.4-mm cores were retrieved for constructing the arrays. Each case was represented by four cores, which included three cores of each cancer to exclude intratumoral heterogeneity of protein expression and one core of normal pancreatic parenchyma. Immunohistochemical labeling using various antibodies (anti-CD9, anti-perlecan, anti-L1CAM, anti-apoE, anti-SDF4, and anti-fibronectin receptor) was performed as described previously (40, 41). Tissue microarrays were scored by a gastrointestinal pathologist (A. M.) with extensive familiarity in pancreatic cancer morphology and immunohistochemistry. CD9 and fibronectin receptor labeling was scored as positive based on specific membrane localization, whereas perlecan, L1CAM, SDF4, and apoE were scored based on cytoplasmic/membrane localization.

Comparison with Data from DNA Microarrays—
To determine concordance between differential expression of proteins determined using SILAC and differential expression of transcripts determined using a DNA microarray-based platform (Affymetrix), we performed a global expression analysis of Panc1 and HPDE lines (42). Total RNA was extracted from cultured Panc1 and HPDE cells using TRIzol reagent (Invitrogen) and was purified using an RNeasy minikit (Qiagen, Valencia, CA). First and second stranded cDNA was synthesized from 10 µg of total RNA using T7-(dT)₂₄ primer (Genset Corp., South La Jolla, CA) and the SuperScript Choice system (Invitrogen). Labeled cRNA was synthesized from the purified cDNA by in vitro transcription reaction using the BioArray HighYield RNA Transcript Labeling kit (Enzo Diagnostics, Inc., Farmingdale, NY) at 37 °C for 6 h. The cRNA was fragmented at 94 °C for 35 min in a fragmentation buffer (40 mmol/liter Tris acetate (pH 8.1), 100 mmol/liter potassium acetate, 30 mmol/liter magnesium acetate). The fragmented cRNA was then hybridized to the Human Genome U133A chips (Affymetrix, Santa Clara, CA) with 18,462 unique gene/expressed sequence tag transcripts at 45 °C for 16 h. The washing and staining procedure was performed in the Affymetrix Fluidics Station according to the manufacturer’s instructions. The probes were then scanned using a laser scanner, and signal intensity for each transcript (background-subtracted and adjusted for noise) was calculated using Microarray Suite Software 5.0 (Affymetrix). -Fold change analysis of signal intensities obtained from DNA microarrays between Panc1 and HPDE lines was performed using Data Mining Tool software (Affymetrix).

	RESULTS AND DISCUSSION

TOP ABSTRACT MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
A SILAC-based Strategy for Quantitative Secretome Analysis—
A pancreatic adenocarcinoma cell line (Panc1) and an immortalized HPDE cell line served as our model system. HPDE cells were grown in normal medium containing light arginine and lysine, whereas Panc1 cells were grown in medium containing heavy arginine plus lysine. Twenty-four hours prior to harvesting of the conditioned medium, the cells were extensively washed in serum-free medium to remove any "contaminants" originating from serum in the medium. Subsequently the conditioned medium was harvested and filtered. The conditioned medium was desalted and concentrated using a 3,000-Dalton molecular mass cutoff spin column. We used a mixture of heavy arginine and lysine amino acids in this study to maximize the number of labeled peptide pairs for quantitation. The mass spectrometric analysis (LC-MS/MS) was done in duplicate and resulted in identification of 195 proteins in total (see Supplemental Fig. 2). Of these, 145 proteins were found to be differentially regulated (>1.5-fold change) in the secretome of Panc1 cells versus HPDE cells. Fig. 1 shows the distribution of changes in abundance of these proteins. Of these, a total of 68 proteins were up-regulated >1.5-fold, whereas 77 proteins were down-regulated >1.5-fold in cancer (Panc1) compared with normal (HPDE) cells. Twelve (6%) of the overexpressed proteins were highly up-regulated (defined arbitrarily as 20-fold) in cancer compared with normal, whereas 23% of the down-regulated proteins were highly down-regulated (20) Table I and Table II).

View larger version (20K): [in this window] [in a new window]   FIG. 2. Validation of protein expression by Western blotting. Conditioned medium from HPDE (Normal) and Panc1 (Cancer) cells was loaded onto SDS-PAGE gels, and the proteins were transferred onto nitrocellulose and probed with the indicated antibodies. A shows some of the proteins that were up-regulated, whereas B shows down-regulated proteins. CTGF, connective tissue growth factor; SLPI, secretory leukocyte protease inhibitor.

Identification of Known Markers of Pancreatic Cancer—
Our approach led to the identification of several known as well as novel proteins that were elevated in cancer compared with normal. Among the proteins previously known to be up-regulated in pancreatic cancer were cathepsin D, Mac-2BP, fibronectin receptor (integrin ß1), macrophage colony stimulation factor, and cathepsin B. These proteins have been validated by several platforms (9, 38, 43, 44) and were also identified to be up-regulated in our proteomic study Table I). Table I provides a partial list of proteins identified along with the heavy/light ratios as well as annotations regarding presence of signal peptide, subcellular localization, and protein function mainly obtained from the Human Protein Reference Database (45). Supplemental Table 1 provides detailed information on all proteins identified in this study including the peptide sequences, Mascot scores, any post-translational modifications, and heavy/light intensity ratios. Mac-2BP is a secreted glycoprotein that was originally found in human breast carcinoma cells and is a member of the scavenger receptor cysteine-rich domain family of proteins (46–48). Mac-2BP is known to bind galectins, ß1 integrins, collagens, and fibronectin and serves as an endogenous ligand for galectin-3 (44, 48, 49). It has been observed to be elevated in serum of patients with different solid tumors, e.g. breast, ovarian, lung, and colorectal cancers, and is often associated with poor survival and metastatic spread in these malignancies (46, 50–53). In our study, it was increased 5-fold in cancer cells (Panc1) as compared with normal cells (HPDE). Several of the cathepsins (e.g. cathepsin B and cathepsin D) were also identified in our study. Cathepsins are lysosomal proteolytic enzymes that function as cysteine proteinases and are known to be present in almost all mammalian cells (54). Cathepsins B and D are both involved in different cancers and were up-regulated 3- and 7-fold, respectively. The up-regulation of these proteins was also confirmed by Western blotting using the conditioned media from the two cell lines (Fig. 2A). Prosaposin, a protein that has been shown to interact with cathepsin D, was also identified in our screen and found to be highly up-regulated (11-fold) in cancer (Panc1) compared with normal (HPDE). Although many proteins known to be elevated in pancreatic cancer were identified in our screen, we still missed a few of the known candidates (e.g. hepatocarcinoma-intestine-pancreas/pancreatitis-associated protein (HIP/PAP) and deleted in malignant brain tumor (DMBT1)).

Down-regulated Proteins That Are Known to Be Involved in Cancer—
Some of the down-regulated proteins identified in our study include members of the insulin-like growth factor-binding protein (IGFBP) superfamily, profilin 1, and one of the most abundant extracellular cysteine proteinase inhibitors, cystatin C. IGFBPs act as both positive and negative regulators in the insulin-like growth factor signaling pathway and play an important role in the development and progression of cancers such as prostate cancer (55). This family has recently been expanded to include insulin-like growth factor binding protein-related proteins (IGFBP-rPs). Two IGFBP-rPs, IGFBP-rP1 and IGFBP-rP2, were identified in our study as strongly down-regulated proteins (>20-fold). IGFBP-rP1, also known as IGFBP-7, has been reported to be significantly down-regulated in malignant leptomeningeal and mammary epithelial cells compared with normal cells (56). A similar down-regulation is seen in the case of prostate cancer, and it has been suggested that this gene might be inactivated by CpG hypermethylation (57). IGFBP-7 was confirmed to be down-regulated by Western blotting (Fig. 2B). One other protein that was found to be significantly down-regulated in cancer is profilin 1 (PEN1). Profilin plays an important role in actin filament assembly (58) and interacts with different ligands including actin-related protein 2 (Arp2), phosphatidylinositol 4,5-bisphosphate, and gephyrin (59, 60). Profilin 1 was identified by differential display to be down-regulated in tumorigenic breast cancer cell lines (61). SPARC was also identified in our study and was found to be down-regulated by SILAC (3-fold) and by Western blotting (Fig. 2B). SPARC is a glycoprotein shown previously to be involved in diverse biological processes including tissue remodeling, wound repair, morphogenesis, cellular differentiation, cell proliferation, and cell migration (62). SPARC is overexpressed in many human malignancies (63). In addition, we have shown recently that SPARC is overexpressed in the stromal fibroblasts immediately adjacent to the neoplastic epithelium in primary pancreatic cancer but rarely in the cancers themselves (64).

Novel Differentially Expressed Proteins in the Pancreatic Cancer Secretome—
A large number of proteins identified in our study are proteins that have not been described previously to be overexpressed in pancreatic cancer Table I). A partial list of proteins found to be down-regulated in the cancer secretome compared with normal is provided in Table II. These proteins facilitate a range of different biological functions including enzymatic reactions (e.g. lysyl oxidase-like 2 (LOXL2), transport (e.g. apolipoprotein E), metabolism (e.g. biotinidase), cell growth/structure (e.g. actinin 4), and signal transduction (e.g. stromal cell-derived factor 4, fibronectin receptor (integrin ß1), and predicted osteoblast protein (FAM3C)). Below we discuss proteins not described previously to be involved in pancreatic cancer in more detail.

Lysyl oxidase (LOX) is an amino oxidase that oxidizes primary amines to reactive aldehydes (65). This family of enzymes is involved in regulation of biogenesis of connective tissue, development, cell growth, and tumor suppression. Recently four new proteins designated lysyl oxidase-like proteins have been described (LOXL, LOXL2, LOXL3, and LOXL4) that are functionally similar but genetically distinct from the LOX family of proteins (65). Several members of the LOXL family have been described to be involved in breast cancer, and LOXL2 has been shown to play an important role in the invasive characteristics of metastatic breast cancer lines (66). We identified LOXL2 to be highly up-regulated (20-fold) in the Panc1 secretome.

ApoE was first identified as a protein component of triglyceride-rich low density lipoproteins. It was later shown that apoE serves as a ligand for low density lipoprotein receptors, which are involved in cholesterol transport and metabolism (67). ApoE also facilitates other biological functions such as stimulation of cholesterol efflux from macrophages, prevention of platelet aggregation, and inhibition of proliferation (68). There are three major isoforms of apoE (apoE4, apoE2, and apoE2) that differ from each other by only a single amino acid. Still the different isoforms of apoE have profound effects at the structural and functional level. ApoE is synthesized in several places including liver, brain, spleen, lung, ovary, kidney, and muscle (69). The liver is responsible for 60–80% of the production of apoE, whereas 20–40% is derived from extrahepatic sources such as macrophages (69). ApoE has been described to be elevated in several cancers including ovarian and prostate cancer (70, 71). In our study, apoE was found to be up-regulated 20-fold in cancer (Panc1) as compared with normal (HPDE).

Several proteins involved in signal transduction were also identified as highly up-regulated in the Panc1 secretome, including fibronectin receptor (integrin ß1). Integrins are a family of heterodimeric transmembrane receptors that mediate interactions with components of the extracellular matrix and other plasma membrane proteins. Integrins are composed of an and ß subunit (72), and each of the individual subunits consists of an extracellular domain, a transmembrane region, and a cytosolic domain (73). They are involved in a number of biological functions including reorganization of the actin cytoskeleton and differentiation (74). Altered expression of integrins plays a crucial role in several cancers including pancreatic, prostate, and colon cancer (73, 75, 76). In our study, fibronectin receptor (integrin ß1) was up-regulated 3.4-fold in Panc1 cells.

Other signaling molecules identified in our study include protein-tyrosine kinase 7 (PTK7) and Ras-related C3 botulinum toxin substrate 2 (Rac-2). PTK7 and Rac-2 were found to be up-regulated 4.6- and 3.5-fold in cancer secretome, respectively Table I). A novel member of a cytokine-like gene family, tentatively designated as predicted osteoblast protein in databases (FAM3C), was identified in our study to be up-regulated 4.2-fold in Panc1 secretome Table I). Northern blot analysis reveals that FAM3C is expressed in several different tissues including pancreas (77).

Perlecan (heparan sulfate proteoglycan 2 (HSPG2)) and CD9 were also identified to be elevated in cancer versus normal by a factor of 5.1 and 8.0, respectively (Fig. 3). Perlecan gene is located on chromosome 1 (78) and is a major component of the basement membranes and extracellular matrices (79–81). Perlecan is 440 kDa, which makes it one of the larger gene products in humans (82). It contains five distinct domains of which only one is unique to perlecan (the heparan sulfate binding region) (80). Perlecan transcription is up-regulated by transforming growth factor-ß (83) and is thought to play a role in angiogenesis and growth by acting as a co-receptor for basic fibroblast growth factor (FGF2) (84). Increased levels of perlecan have been observed in breast carcinoma (85) and metastatic melanoma (86). CD9 (originally called motility-related protein 1, MRP-1) is a membrane protein with a molecular mass of 24 kDa and is expressed on the surface of human platelets (87). It was originally identified as a surface antigen of hematopoietic cells (88) but has subsequently been detected in a wide variety of non-hematopoietic cells including central and peripheral nervous system (89, 90). It is a member of the transmembrane 4 superfamily that comprises a group of cell surface proteins characterized by four hydrophobic transmembrane domains (91). CD9 is involved in many cellular functions such as adhesion, migration, signal transduction, and differentiation (92). CD9 protein expression has been shown to be lower in metastatic lymph nodes than the corresponding primary breast tumors (93) and associated with high tumor grade and lymph node metastasis in pancreatic cancer (94). In addition, the level of CD9 has been to be shown to be elevated in the pancreatic stroma (95). Because several of the differentially expressed proteins identified in our study were not previously described to be elevated in pancreatic cancer (e.g. perlecan, CD9, fibronectin receptor, apoE, and SDF4), we decided to carry out validation studies by immunohistochemistry (IHC) using pancreatic cancer tissue microarrays (TMAs).

View larger version (29K): [in this window] [in a new window]   FIG. 3. Quantitation by SILAC. A and B show the MS and MS/MS spectra of one of the peptides derived from CD9 antigen and perlecan (HSPG2), respectively. Both proteins were also validated as up-regulated proteins in cancer versus normal tissue by IHC (Fig. 4).

View larger version (89K): [in this window] [in a new window]   FIG. 4. Validation of differential expression of five up-regulated SILAC proteins by immunohistochemistry on pancreatic cancer tissue microarrays. A, membranous CD9 expression in an infiltrating pancreatic cancer gland. Adjacent non-neoplastic parenchyma is essentially negative (arrowhead) except for interspersed neutrophils. B, high power view of a pancreatic cancer with robust membranous CD9 expression including luminal accentuation and labeling of intraluminal contents. C, low power view of perlecan expression in a pancreatic cancer with absence of labeling in adjacent normal ducts (arrowheads). D, high power view demonstrates cytoplasmic/membranous labeling with luminal accentuation and labeling of intraluminal contents. E, expression of SDF4 in pancreatic cancer, demonstrating robust cytoplasmic expression in infiltrating nests of cancer cells (left of field), whereas normal ducts are essentially negative (arrowheads). Note that SDF4 is also expressed in admixed inflammatory cells in the stroma. F, membranous expression of fibronectin receptor (integrin ß1) in pancreatic cancer, whereas adjacent normal epithelium (arrowhead) does not label. G, robust cytoplasmic expression of apoE in pancreatic cancer, including occasional labeling of intraluminal secretions.

View larger version (15K): [in this window] [in a new window]   FIG. 5. Correlation of mRNA and protein expression levels. The -fold changes obtained by proteomic experiments (SILAC) were compared with mRNA expression data (DNA microarray) for Panc1 versus HPDE cells. The -fold changes at the protein level are marked in red and at the mRNA level are marked with blue. The correlation coefficient (r) between the -fold changes at protein and mRNA levels was 0.28.

View larger version (41K): [in this window] [in a new window]   FIG. 6. Distribution of the proteins on the basis of correlation between protein expression changes (SILAC) and mRNA expression changes (DNA microarray). As seen in the pie chart 13% of the entries were found to be up-regulated at both the mRNA and protein levels, whereas 21% were found to be down-regulated at the mRNA and protein levels. Sixteen percent of the entries did not show any change at either the mRNA or protein expression level. Many entries (50%) did not correlate at the mRNA and the protein level. These include (a) discordant entries, (b) no change in mRNA level but up- (1.5) or down-regulation (1.5) at the protein level, and (c) no change in proteins level but up- (1.5) or down-regulation (1.5) at the mRNA level.

Conclusions—
The use of mass spectrometry and quantitative proteomics has proven to be a promising strategy for identifying potential biomarkers for a number of cancers. In this study we used SILAC as an initial screening tool to identify proteins that are differentially expressed in the pancreatic cancer secretome. By restricting our analysis to quantitating relative differences in the secreted compartment alone, we increased our chances of identifying a fluid-based biomarker for pancreatic cancer. In total, we identified 195 proteins of which 68 proteins were up-regulated >1.5-fold in cancer. Several of these proteins have been described previously to be elevated in pancreatic cancer (e.g. Mac-2BP, MCP-1, aminopeptidase N, lysyl oxidase-like, and cathepsin D), validating our SILAC approach as a screening tool to identify novel biomarkers for pancreatic cancer. A large set (40%) of proteins were also found to be down-regulated >1.5-fold in cancer of which several were described previously to be down-regulated in pancreatic cancer (e.g. IGFBP-7, profilin 1, and SPARC). The expression profile for a subset of the up- and down-regulated proteins (11 proteins) was also verified by Western blot analysis (Fig. 2) validating the quantitation data obtained by SILAC. Several proteins not described previously to be elevated in pancreatic cancer were also identified in our study including CD9, perlecan, apoE, SDF4, and fibronectin receptor. These proteins were subsequently validated by immunohistochemical labeling on pancreatic cancer microarrays. CD9 and fibronectin receptor showed robust expression in the membranes in 39 and 56%, respectively, of the cancers tested by TMA, whereas perlecan, SDF4, and apoE showed cytoplasmic/membrane labeling in 72, 90, and 78%, respectively, of the cancers tested by TMA. By using a rational combination of quantitative proteomic profiling and antibody-based validation techniques, we identified a number of potential clinically useful biomarkers for pancreatic cancer that could be pursued in larger studies for validation purposes. This approach could be extended to most cancers for which cell line models are available.

	FOOTNOTES

 
Received, June 9, 2005, and in revised form, October 6, 2005.

Published, MCP Papers in Press, October 8, 2005, DOI 10.1074/mcp.M500178-MCP200

¹ The abbreviations used are: SILAC, stable isotope labeling with amino acids in cell culture; HPDE, human pancreatic duct epithelial; SPARC, secreted protein acidic and rich in cysteine; SDF4, stromal cell-derived factor 4; ER, endoplasmic reticulum; Mac-2BP, Mac-2-binding protein; IGFBP, insulin-like growth factor-binding protein; IGFBP-rP, insulin-like growth factor binding protein-related protein; LOX, lysyl oxidase; LOXL, lysyl oxidase-like; apoE, apolipoprotein E; PTK7, protein-tyrosine kinase 7; HSPG2, heparan sulfate proteoglycan 2; IHC, immunohistochemistry; TMA, tissue microarray; MCP-1, monocyte chemotactic protein 1; L1CAM, L1 cell adhesion molecule isoform 1.

^* This work was supported in part by National Institutes of Health Grant P50-CA62924.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

^S The on-line version of this article (available at http://www.mcponline.org) contains supplemental material.

^** Supported by the Michael Rolfe Foundation.

Supported by an American Association for Cancer Research Pancreatic Cancer Action Network Career Development Award; a grant from the Lustgarten Foundation for Pancreatic Cancer Research; NCI, National Institutes of Health Grant 1R01CA113669; and the Michael Rolfe Foundation.

Supported by a Beckman Young Investigator Award, a Sidney Kimmel Scholar Award, and National Institutes of Health Grant U54 RR020839. To whom correspondence should be addressed. Tel.: 410-502-6662; Fax: 410-502-7544; E-mail: pandey{at}jhmi.edu

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