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[Cancer Research 66, 2592-2599, March 1, 2006]
© 2006 American Association for Cancer Research

Molecular Biology, Pathobiology, and Genetics

Fibrinogen {gamma} Overexpression in Pancreatic Cancer Identified by Large-scale Proteomic Analysis of Serum Samples

Mark Bloomston1, Jeff X. Zhou3, Alexander S. Rosemurgy4, Wendy Frankel2, Carlos A. Muro-Cacho5 and Timothy J. Yeatman5

1 Division of Surgical Oncology, James Cancer Hospital and Solove Research Institute; 2 Department of Pathology, Ohio State University, Columbus, Ohio; 3 Laboratory of Immunopathology, National Institute of Allergy and Infectious Diseases, NIH, Rockville, Maryland; 4 Department of Surgery, University of South Florida; and 5 Division of Interdisciplinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida

Requests for reprints: Timothy J. Yeatman, H. Lee Moffitt Cancer and Research Institute, 12902 Magnolia Drive, SRB-2, Tampa, FL 33612. Phone: 813-979-7292; Fax: 813-632-1433; E-mail: yeatman{at}moffitt.usf.edu.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Detection of serum markers for pancreatic cancer has been elusive. Although CA 19-9 is most commonly used, its sensitivity and specificity are modest. We used large-scale proteomics to identify potential serum markers for pancreatic cancer. Samples were analyzed using high-resolution two-dimensional gel electrophoresis to identify differentially expressed proteins in 32 normal and 30 pancreatic cancer patients. Up to 1,744 protein spots were resolved for each serum sample. Candidate proteins were identified using mass spectrometry. ANOVA was used to identify proteins that could discriminate cancer from normal sera. Serum fibrinogen level was also measured using enzymatic assay. Immunohistochemistry was used to detect fibrinogen in resected pancreatic cancers. One hundred fifty-four proteins were commonly overexpressed in all pancreatic cancers. Nine protein spots (four with identifications by mass spectrometry) could effectively separate cancer from normal controls using cross-validation. These proteins successfully discriminated all pancreatic cancer samples (30 of 30) and 94% of normal (30 of 32) samples. Prominent among these candidates was fibrinogen {gamma}, which was subsequently confirmed to be overexpressed in pancreatic cancer sera by enzymatic analysis (54.1 ± 64.1 versus 0.0 ± 0.0 mg/dL, P < 0.05) and tissue by immunohistochemistry (67% versus 29%, P < 0.05) relative to normal pancreas. Proteomic analysis combining two-dimensional gel electrophoresis and mass spectrometry successfully identified 154 potential serum markers for pancreatic cancer. Of these, fibrinogen {gamma}, a protein associated with the hypercoagulable state of pancreatic cancer, discriminated cancer from normal sera. Fibrinogen is a potential tumor marker in pancreatic cancer. (Cancer Res 2006; 66(5): 2592-9)


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
The diagnosis of pancreatic cancer is a lethal one with mortality nearly equaling the incidence of 32,000 in the United States (1). Surgical resection is the mainstay of treatment but few patients are amenable to surgery due to local invasion and distant metastasis, which occur very early in its course. Therefore, early diagnosis and treatment are the only chance for long-term survival. The lack of known risk factors for pancreatic cancer makes defining an "at-risk" group of patients difficult, and the limitation in pancreatic cancer-specific serum markers makes accurate screening virtually impossible (2–7). Proteomics promises to produce new markers to assist in the diagnosis, treatment, and prognosis of a variety of malignancies, including pancreatic cancer (8, 9).

Proteomics attempts to link the thousands of proteins detected in various fluids and tissues with the human genome (10). The cornerstone of this approach is mass spectrometry (11), which can be coupled with high-throughput techniques, such as matrix-associated laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF), to identify protein "fingerprints" and new biomarkers for cancer (12). Using proteomics techniques, we hypothesized that serum markers for pancreatic cancer could be identified.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Serum samples. After Institutional Review Board approval from the University of South Florida, serum samples were obtained from 32 patients with pancreatic cancers during preoperative work-up. As a control, serum was obtained from 30 healthy volunteers.

Sample preparation. Whole blood was collected from all patients and allowed to clot at room temperature for 60 minutes. Tubes were then centrifuged at 2,000 x g for 10 minutes at 4°C. The supernatant was removed, divided into four aliquots, and stored at –80°C until analysis was undertaken.

Samples were then purified by column immunosubtraction of albumin; {alpha}-1-antitrypsin; {alpha}-2-macroglobulin; immunoglobulins A, M, and G; and transferrin (13). Fifty microliters of sample were applied to a mixed-bed column containing bound antibodies to the above proteins. Flowthrough was collected and used for two-dimensional gel electrophoresis.

Two-dimensional gel electrophoresis. Sample proteins were resolved using IPGphor system (Amersham Pharmacia, Piscataway, NJ) and the DALT-100 components of Large Scale Biology Corporation's fully automated ProGEx system as described previously (13). Briefly, for each sample, 200 µg of solubilized protein were applied to each immobilized PH-gradient (IPG) strip for isoelectrofocusing. Subsequently, the IPG strips were loaded directly onto gradient (8-15% T) slab SDS gels for electrophoresis. The slab gels were fixed and stained in a Coomassie brilliant blue G-250 staining solution. Three gels were run for each of the 62 samples.

Quantitative gel pattern analysis. Stained slab gels were scanned using the Kepler software system as described before. Briefly, a master pattern was constructed from one of the best quality patterns and edited to include spots observed from all of the tissues. An experiment package was constructed using the best two-dimensional gel electrophoresis pattern of each serum sample, and each pattern was matched to the master to establish the correspondence of spots between patterns and to assign master numbers to spots. To correct for differences in loading and staining, the 62 patterns were scaled together using a linear procedure.

Sample preparation for mass spectrometry. Protein spots were excised from Coomassie-stained gels using an automated spot cutter and placed in a 96-well polypropylene microtiter plate for further processing. Sample preparation of gel plugs (destaining, reduction, alkylation, and trypsin digestion) was carried out on a TECAN Genesis Workstation 200 (Tecan, Durham, NC) as described previously (14). After digestion with trypsin, peptides were extracted from the gel plugs and spotted onto MALDI target plates. A fraction of the sample volumes were deposited onto a 384-format Bruker 600-µm AnchorChip MALDI target followed by {alpha}-cyano-4-hydroxycinnamic acid matrix. Samples plus matrix were allowed to dry followed by a wash with 1% trifluoroacetyl or trifluoroacetic acid. The remainder of the samples was prepped for liquid chromatography tandem mass spectroscopy (LC-MS/MS) analysis using a Packard Multiprobe II EX liquid handling system (Perkin-Elmer, Boston, MA).

MALDI-TOF analysis. MALDI targets were automatically run on Bruker Biflex or Autoflex mass spectrometers. Both instrument models were equipped with delayed ion extraction, pulsed nitrogen lasers (10 Hz Biflex, 20 Hz Autoflex), dual microchannel plates, and 2 GHz transient digitizers. All mass spectra represent signal averaging of 120 laser shots. The performance of the mass spectrometers produced sufficient mass resolution to produce isotopic multiplets for each ion species below m/z 3,000. Spectra were internally calibrated using two spiked peptides (angiotensin II and ACTH18-39) and database searched with a mass tolerance of 50 ppm.

LC-MS/MS analysis. Samples that did not get positive identifications by MALDI were subjected to LC-MS/MS analysis using Finnegan LCQ mass spectrometers as described previously. A microelectrospray interface similar to an interface described before (15) was used. Samples were injected from an Endurance autosampler (Spark-Holland, Emmen, the Netherlands) onto a trapping cartridge (CapTrap, Michrom BioResources, Auburn, CA). Seven-minute reversed-phase gradients from pumps A and B eluted peptides off the trap and capillary-LC column and into the MS. Spectra were acquired in automated MS/MS mode with relative collision energy preset to 35%. To maximize data acquisition efficiency, the additional variables of dynamic exclusion, isotopic exclusion, and top three ions were incorporated into the auto-MS/MS procedure. The scan range for MS mode was set at m/z 375 to 1,400. A parent ion default charge state of +2 was used to calculate the scan range for acquiring tandem MS.

MS data analysis. MS data was automatically registered, analyzed, and searched with appropriate public protein/genome databases using RADARS, a separate relational database provided by Proteometrics (acquired by Harvard Biosciences, Holliston, MA). For MALDI peptide mapping, Mascot (Matrix Science, London, United Kingdom) and Profound (Harvard Biosciences) search engines were employed. Identifications are noted when one of the following conditions was met: (a) both Profound and Mascot search results were above the 95th percentile of significance showing the same protein identification (scores of Profound ≥1.65 and Mascot ≥50); (b) one of the two search engines delivered results above the 95th percentile of significance, whereas the other search engine was below it but with the same protein identification as the top hit; (c) one of the two search engines delivered results above the 95th percentile of significance with no corroborative result from the other search engine, where the manually observed spectrum had a peptide fingerprint quality positively identifying the protein. Mascot is used for peptide sequence searching of LC-MS/MS data. Scores above the 95th percentile (Mascot ≥ 50) are noted.

Data analyses. In an unsupervised method to see if the protein spots separated using two-dimensional gels can distinguish cancer samples from normal ones, all the 1,744 protein spots was used for principal component analysis (PCA) using SAS software (SAS Institute, Cary, NC). To identify differentially expressed proteins, ANOVA was used to select a group of protein spots with significant differences between the control and pancreatic cancer groups. These significant protein spots were further used for discriminant analysis (linear model) using SAS software. To validate the results from discriminant analysis, cross-validation method was used. Alternatively, the samples (n = 62) were split into a training set (n = 53) and a test set (n = 9, 4 controls and 5 cancer) for discriminant analysis. Following calibration analysis using the training set, all the four control and five cancer samples in the test set were correctly classified into their respective groups.

Fibrinogen measurement. Fibrinogen levels were measured in all 32 pancreatic cancer serum samples and 10 normal control samples. Samples were run on a Sysmex CA-500 (Diamond Diagnostics, Hollingston, MA) using reagents from Dade Behring, Inc. (Deerfield, IL) in accordance with manufacturer's instructions.

Tissue microarray and immunohistochemistry for fibrinogen. Formalin-fixed, paraffin-embedded tissue blocks from 50 previously characterized pancreatic ductal adenocarcinomas were obtained from the archival files of the Department of Pathology at The Ohio State University Medical Center. Tumor grade and stage were verified by H&E stain, and the sampling area was marked. For each block, 2.0-mm tissue cores were used for tissue microarray. Two tissue cores per block were arrayed using a manual device (Beecher Instruments, Silver Springs, MD). The arrayed tissue was cut at 4 µm and placed on positively charged slides then heated to 40°C for 30 minutes. After leveling paraffin and cores, the array was cooled to 4°C for 15 minutes then inspected.

Immunohistochemical staining was undertaken on the arrayed tissue. Slides were placed in a 60°C oven for 1 hour, cooled, and deparaffinized and rehydrated through xylenes and graded ethanol solutions to water. All slides were quenched for 5 minutes in a 3% hydrogen peroxide solution in water to block for endogenous peroxidase. Antigen retrieval was accomplished by incubating in Proteinase K solution (DAKO Corp., Carpinteria, CA). Slides were then placed on a DAKO Autostainer immunostaining system (DakoCytomation, Carpinteria, CA) for use with immunohistochemistry and incubated with rabbit anti-human polyclonal fibrinogen primary antibody (DakoCytomation #A0080) at 1:300 dilution for 30 minutes. The detection system used was a labeled streptavidin-biotin complex. Tissues were avidin and biotin blocked before the application of the biotinylated secondary reagent. Slides were then counterstained in Richard Allen hematoxylin, dehydrated through graded ethanol solutions, and coverslipped. Immunohistochemical staining was considered positive if >5% of cells stained in either core. The positive control was human tonsil, and it was stained appropriately.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Protein separation and identification. For each serum sample 1,744 protein spots were analyzed. Using MS, 154 proteins were identified (Table 1).


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  		Table 1. Protein identifications by mass spectrometry

 
Data analysis. As an exploratory analysis, all 1,744 protein spots were used for PCA for all samples (Fig. 1). The use of all the protein spots for PCA without any pretreatment guaranteed the unbiased basis. As shown in Fig. 2, PCA can identify the two groups of samples, although an outlier was present in the normal group.


Figure 1
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  		Figure 1. PCA of 62 serum samples. Using 1,744 protein spots from two-dimensional gel electrophoresis, sera from pancreatic cancer patients ({triangleup}) can be distinguished from normal sera ({circ}).

 

Figure 2
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  		Figure 2. Discriminant analysis of pancreatic cancer ({triangleup}) and normal ({circ}) sera using nine protein spots shown in Table 2.

 

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  		Table 2. Protein spots selected for further discriminant analysis

 
To identify protein spots that can distinguish pancreatic cancer sera from normal, ANOVA was used to select a group of protein spots with significant differences between the control and pancreatic cancer groups. Sixteen protein spots were selected using ANOVA at P < 0.0000001, and nine of them passed the visual inspection (Table 2). Of these nine protein spots, four of them had identifications by MS. Using discriminant analysis (linear model) of these nine protein spots, pancreatic cancer sera can be separated from normal (Fig. 2).

To validate the discriminant analysis, a cross-validation method was used. The results showed that 100% (30 of 30) of the normal samples were correctly classified, and 93.8% (30 of 32) of the cancer samples were correctly classified.

Alternatively, the samples (n = 62) were split into a training set (n = 53) and a test set (n = 9, 4 normal and 5 cancer) for discriminant analysis. Following calibration analysis using the training set, all four normal and five cancer samples in the test set were correctly classified into their respective groups (Table 3).


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  		Table 3. Classification of training and test samples using discriminant analysis

 
Fibrinogen expression in pancreatic cancer. Fibrinogen precursors were noted to be significantly and uniquely expressed in pancreatic cancer sera. Fibrinogen {gamma} chain (in two isoforms) represented two of nine protein spots that were used in discriminant analysis to differentiate cancer sera from normal (Table 2), whereas fibrinogen ß chain was seen only in pancreatic cancer sera (MSN 411 in Table 4). To confirm these findings, enzymatic analysis was undertaken to determine fibrinogen activity in the 32 pancreatic cancer serum samples and compared with 10 normal serum samples. No fibrinogen was noted in any normal serum samples. As such, absolute fibrinogen levels and number of samples with fibrinogen activity was significantly greater in pancreatic cancer sera (Table 4).


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  		Table 4. Enzymatic analysis for fibrinogen

 
Fibrinogen immunohistochemistry. Fibrinogen staining was noted in the stroma of all samples, particularly in chronic pancreatitis and pancreatic cancer (Figs. 3 and 4). Pancreatic islets did not stain for fibrinogen in any specimen. Fibrinogen was expressed more commonly in the cytoplasm of pancreatic cancer cells than in normal pancreas (66% versus 29%; P = 0.02, Fisher's exact test), chronic pancreatitis (66% versus 17%, P = 0.003), or colon cancer (66% versus 16%, P = 0.0003). No correlation was seen between fibrinogen expression and tumor pathology (i.e., differentiation, size, invasion, nodal status, stage) or survival.


Figure 3
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  		Figure 3. Immunohistochemical staining for fibrinogen. Strong staining is seen in the stroma of all specimens. In normal pancreas (A and B) and chronic pancreatitis (C and D), ductal cells (small arrows) did not stain.

 

Figure 4
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  		Figure 4. Immunohistochemical staining for fibrinogen in pancreatic cancer. Stroma stains intensely for fibrinogen. A, strong cytoplasmic staining is seen in ductal carcinoma cells (large arrows). B, although stroma stains intensely, no fibrinogen is expressed by these pancreatic cancer cells.

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
The complexity of the human genome coupled with new, sensitive technologies designed to allow for the detection of minute mutations found in malignancy make interpretation of the role of various genes to cancer progression difficult. This is particularly relevant in pancreatic cancer, where early progression and late presentation result in death in nearly all patients diagnosed. Recent advancements in proteomic techniques, including MS, are beginning to shed some light on the importance of various proteins in oncogenesis, be it through genetic mutations or posttranslational modifications. Applying these techniques to serum samples obtained from pancreatic cancer patients, we have identified a protein profile that may be helpful in the diagnosis of pancreatic cancer as well as identifying a common protein, fibrinogen {gamma}, which may be important in the pathogenesis and progression of pancreatic cancer.

Sixty-two serum samples obtained from 32 patients with pancreatic cancer and 30 healthy volunteers were used to create protein profiles using high-resolution two-dimensional gel electrophoresis and MALDI-TOF MS and/or LC MS/MS. From 1,744 protein spots analyzed, 154 differentially expressed proteins were identified, which could reliably discriminate pancreatic cancer serum samples from normal in a PCA model. Through statistical modeling, we were able to identify four named proteins of nine that can separate pancreatic cancer from normal sera. This separation was then confirmed using leave-one-out cross-validation with 100% of normal samples and 94% of cancer samples being correctly classified.

To confirm the differential expression of fibrinogen, an enzymatic assay was used to show significantly increased fibrinogen relative to normal control. Still, the question remained if this fibrinogen overexpression was related to inadequate serum separation in our pancreatic cancer samples or simply overexpression secondary to the pancreatitis commonly seen with pancreatic cancer. To address this issue and to clearly delineate the source of fibrinogen, immunohistochemistry was undertaken in 48 additional pancreatic cancer tumor specimens and compared with 12 chronic pancreatitis specimens and 14 normal pancreas samples obtained from different patients without pancreatic cancer. Although fibrinogen was ubiquitously expressed in stroma of all specimens, it was seen to be uniquely expressed by ductal carcinoma cells.

The role of fibrinogen and fibrinogen degradation products in carcinogenesis has been suggested previously for other tumor types (16–18). In a lung carcinoma model using fibrinogen-deficient mice, for example, cancer progression was diminished in the absence of fibrinogen (19). Fibrin/fibrinogen deposition induces fibrinolytic activity, mainly by plasmin, resulting in extracellular matrix degradation providing fertile ground for tumor cell invasion and metastasis as well as having a direct mitogenic effect (16, 19, 20). Interestingly, plasminogen was also identified as a differentially expressed protein in our pancreatic cancer samples by MALDI-TOF. Although these findings have been associated with various malignancies, a specific link between hereditary pancreatic cancer and fibrinogen storage disease has also recently been reported, further supporting our observations (21).

The suggestion of a link between pancreatic cancer and dyscrasia of the coagulation cascade dates back to the 19th century with first description of migratory thrombophlebitis by Trousseau (22). Nearly a century and a half later, using advanced large-scale proteomic techniques, we are just now beginning to elucidate the mechanism involved in the hypercogulable state of cancer prophetically described in 1865.


   Acknowledgments
 
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.


   Footnotes
 
Note: M. Bloomston and J.X. Zhou contributed equally to this work.

Received 10/11/05. Revised 12/ 2/05. Accepted 1/ 6/06.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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