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Presently, diagnosis and follow-up of patients with oral cancer is based on physical examination and various imaging modalities. These approaches are ...
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Serum Proteomic Approach for the Identification of Serum Biomarkers Contributed by Oral Squamous Cell Carcinoma and Host Tissue Microenvironment† Krikor Bijian,‡,§ Alex M. Mlynarek,‡,§ Richard L. Balys,| Su Jie,‡ Yingjie Xu,‡ Michael P. Hier,‡ Martin J. Black,‡ Marcos R. Di Falco,⊥ Sylvie LaBoissiere,⊥ and Moulay A. Alaoui-Jamali*,‡ Department of Oncology and Medicine, Otolaryngology-Head and Neck Surgery, Lady Davis Institute for Medical Research and Segal Comprehensive Cancer Center, SMBD Jewish General Hospital, McGill University, Montreal, QC, Canada, and McGill University and Genome Quebec Proteomics Platform, Montreal, QC, Canada Received March 4, 2008

The lack of serum biomarkers for head and neck carcinoma limits early diagnosis, monitoring of advanced disease, and prediction of relapses in patients. We conducted a comprehensive proteomics study on serum from mice bearing orthotopic human oral squamous cell carcinomas (OSCC) with distinct invasive phenotypes. Matched established cell lines were transplanted orthotopically into tongues of RAG-2/γ(c) mice and mouse serum was analyzed by 2-dimensional-differential gel electrophoresis(2D-DIGE)/liquid chromatography (LC)-MS/MS and by online 2D-LC-MS/MS of iTRAQ labeled samples. We identified several serum proteins as being differentially expressed between control and cancer-bearing mice and between noninvasive and invasive cancer (p < 0.05). Differentially expressed proteins of human origin included the epidermal growth factor receptor (EGFR), cytokeratins, G-protein coupled receptor 87, Rab11 GTPase, PDZ-domain containing proteins, and PEST-containing nuclear proteins. Identified proteins of mouse origin included clusterin, titin, vitronectin, vitamin D-binding protein, hemopexin, and kininogen I. The levels of serum and cell secreted EGFR were further validated to match proteomic data regarding the inverse correlation with the invasive phenotype. In summary, we report a comprehensive patient-based proteomics approach for the identification of potential serum biomarkers for OSCC using an orthotopic xenograft mouse model. Keywords: head and neck • oral squamous cell carcinoma • serum • proteomics • biomarkers

Introduction Head and neck squamous cell carcinoma (HNSCC) is diagnosed in an estimated 500 000 people each year worldwide. Despite improvements in diagnostic technology and novel therapy regimens, HNSCC remains the sixth most common cause of cancer deaths in the world, and the 5-year survival rates have not changed significantly, remaining at approximately 50%. The presence of cervical lymph node metastasis is presently the most significant prognostic factor of patient survival, but no serum biomarker is currently available.1 Like most solid tumors, HNSCC is dependent on a myriad of intracellular and intercellular transduction signals occurring * To whom correspondence should be addressed. Moulay A. AlaouiJamali, PhD, Lady Davis Institute for Medical Research and, Segal Comprehensive Cancer Center, Rm E525, 3755, Chemin Coˆte Ste-Catherine, Montreal, Quebec, H3T 1E2, Canada. Tel.: 514-340-8260/8222ext. 3438. Fax: 514340-7576. E-mail: [email protected]. † Presented in part at the 60th Annual Meeting of the Canadian Society of Otolaryngology - Head and Neck Surgery in Kelowna, British Columbia, May 14-17, 2006 and at the 42nd Annual Meeting of the American Society of Clinical Oncology in Atlanta, Georgia, June 2-6, 2006. ‡ Department of Oncology and Medicine. § These authors contributed equally to this work. | Otolaryngology-Head and Neck Surgery. ⊥ McGill University and Genome Quebec Proteomics Platform. 10.1021/pr800979e CCC: $40.75

 2009 American Chemical Society

in the tumor microenvironment between the heterogeneous tumor mass and the surrounding stromal and inflammatory cells. Presently, diagnosis and follow-up of patients with oral cancer is based on physical examination and various imaging modalities. These approaches are neither specific nor sensitive, and many patients are diagnosed only late in the progression of the disease. Often, patients require morbid treatments, such as radical surgery, brachytherapy, or high dose radiotherapy, and relapses are frequent. Clearly, clinically useful biomarkers are needed for early detection and efficient management of this complex disease. To date, at least 12 published studies reported proteomic technology to identify biomarkers for HNSCC. Most of these studies reported differential protein expression levels in carcinoma tissues as compared with their paired normal mucosa.2-11 Four studies have used mass spectrometry to analyze serum profiles of HNSCC patients.12-15 These studies found that particular protein peaks had sensitivities between 68 and 83% and specificities between 73 and 90%, but no specific proteins are reported. Moreover, they also mention that the most dominant data points have very low intensity and can be easily mistaken for background noise. In contrast, a study by Gourin et al. 12 reported that proteomic analysis of serum Journal of Proteome Research 2009, 8, 2173–2185 2173 Published on Web 03/14/2009

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Table 1. Patient Characteristics patient 1

Age Gender TNM Surgery

Previous Treatment Past Medical History Meds Habits Invasivenessa Survival postdiagnosis

Chemo-radiotherapy None None Smoker, EtOH ++++ 1.5 years

74 Male T3N1 Hemiglossectomy, bilateral neck dissection Moderately to poorly differentiated SCCa, without vascular and perineural invasion Surgery × 2 None Percocet Smoker, EtOH ++ >5 years

Cell lineb In vitro Invasivenessc

OSCC1 38%

OSCC2 4%

Path

45 Male T4N2b Total glossectomy, Laryngopharyngectomy, bilateral neck dissection Poorly differentiated SCCa, with vascular, lymphatic and perineural invasion

patient 2

a Based on clinical characteristics. b Cell lines isolated from 2 human patients. c Based on the Boyden chamber invasion assay.

protein profiles can distinguish patients with HNSCC from controls with a high degree of sensitivity and specificity. One study has used mass spectrometry to identify specific serum proteins in a mouse model of tongue cancer, and identified the squamous cell carcinoma antigen 1 as the only protein overexpressed in serum from tumor-bearing mice.16 Likewise, Strati et al. were able to identify minichromosome maintenance protein 7 and p16, as potential biomarkers in a transgenic mouse model for HPV-positive HNSCCs by immunohistochemistry.17 In a previous study, we have developed a RAG2/γ(c) immunocompromised mouse model able to reliably engraft human oral cancer, as well as normal human tongue tissue.18 This model fulfills several advantages for biomarker proteomic discovery, including: the mouse genetic background, which is alike in all respects except for engraftment of the cancer tissue or normal tongue tissue from the same patient; the ability to reproduce repeated sampling on the same mice to demonstrate accuracy of the findings; and the ability to differentiate proteins released from cancer tissue versus host/tumor microenvironment. In the later case, homology search allows discrimination between distinct conserved regions between mouse and human proteins identified. Human proteins will have arisen from the cancer while the mouse proteins reflect host and tumor microenvironment response. The objective of this study was to first establish a reproducible proteomics platform for identifying human serum proteins in our mouse model of oral squamous cell carcinoma, and second to identify specific candidate proteins that can serve as potential biomarkers for the human disease.

Materials and Methods Patients, Orthotopic Xenograft Tumor Induction, and Serum Preparation. Patients with a planned surgical resection of an oral squamous cell cancer were informed of the study and asked to participate (McGill University, protocol no. 04-082). During the operation, we obtained biopsies of oral cancer, and adjacent normal tissue. Tumors were cut into 0.5 mm3 pieces and implanted surgically into the tongues of separate RAG-2/γ(c) mice. Both patients have experienced local 2174

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recurrences and are therefore not de novo OSCCs. Postoperatively, the mice were kept under identical conditions. When the tumor-bearing mice lost 20% of their weight or showed signs of discomfort, they were sacrificed with their respective control mouse. As such, two orthotopic human oral squamous carcinoma tumors were generated. Our previous study has demonstrated the most reliable method of obtaining a pure serum sample (no hemolysis) is through puncture of the inferior vena cava. The serum was then separated into 50 µL aliquots with protease inhibitor cocktail tablets (Roche Diagnostics) and immediately frozen at -80 °C until the time of proteomic analysis. In parallel, tumor tissues were used to establish matched cancer cell lines using enzymatic digestion as we described earlier.19,20 From each patient’s cancer tissue, we isolated matched fibroblast-free cancer cell populations, which we referred to as OSCC1 and OSCC2, for patient 1 and patient 2, respectively. These cells were maintained in RPMI medium supplemented with 5% fetal calf serum and 50 units/mL of penicillin-streptomycin antibiotics. The invasive property of these cells were examined using the Boyden Chamber invasion assay, as described earlier.21 Tumors induced by these cells into the tongue of RAG-2/γ(c) mice were confirmed to maintain the same pathologic, tumorigenic and invasive potential as their original human tissue.18 Proteomic Studies. The serum was processed using an automated workstation and analyzed using a two-arm proteomics approach. Each arm uses different method of protein separation, identification and quantification, combined in a complementary fashion to allow the highest probability of identification of proteins from cancer versus host and tumor microenvironment. The first arm of our model uses DIGE/LC-MS/MS.22 In brief, 50 µg of each control and experimental samples were labeled with different CyDyes (Cy2 and Cy5, respectively; minimal labeling protocol), mixed together and loaded on 18 cm IPG DryStrips (Amersham, pH range 3-10 NL) by active rehydration. Isoelectrofocusing (firstdimension) was carried out on IPGphor (Amersham). Total of 78,000 vhrs was applied. After equilibration with SDS, reduction (2% DTT), and alkylation (2.5% IAA) the strips were put on 20 × 20 cm, 10% Tris-Glycine gels. SDS-PAGE (seconddimension) was done in Protean II xi Cell (Biorad) at 10W/gel. Immediately after the run, images of the gels were acquired on Typhoon Imaging System (Amersham) using dual wavelengths and analyzed using DyCyder software (Ettan DIGE, GE Healthcare). For the purpose of semiquantitation, the analysis was continued with Phoretix 2004 Image Analysis program. Proteins that showed at least 2-fold increase of expression across the samples were cut from the gels, in-gel trypsin digested, and analyzed by LC-MS/MS for identification using a Qtrap 4000 (ABI-Sciex, Concord, Ontario) hybrid linear ion-trap mass spectrometer. Nanoflow chromatography of digested peptides was performed on an Agilent 1100 series nanopump at a flow-rate of 200nl/min. Sample injection and desalting was performed with an Isocratic Agilent 1100 series pump at 15 µL/min for 5min. A trapping column (Agilent) packed with Zorbax 300SB-C18 (5 × 0.3 mm) was used for sample desalting. Peptide separation was performed with a Biobasic C18 (10 × 0.075 mm) reversed phase picofrit column (New Objective). Peptides were eluted using a 20 min gradient with solvent A (0.1% FA) and solvent B (95% ACN:0.1% FA) from 10 to 100% B.

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Figure 1. Representative result from DIGE of serum from mice bearing oral cancer tissue and matched normal tongue tissue from patient 2. Proteins from immunodepleted serum taken from control mice were labeled with Cy2 (Blue) whereas immunodepleted serum proteins from tumor bearing mice were labeled with Cy5 (Red). Expression differences are observed in the overlaid scans shown in the “Combined” panel. Spots for protein identification by LC-MS/MS were taken from a replicate silver stained gel.

The second arm uses an online two-dimensional liquid chromatography-MS/MS of iTRAQ labeled samples (iTRAQ Reagents, Applied Biosystems). Separate serum samples that were previously affinity depleted for major serum proteins were in-solution trypsin digested, labeled with different iTRAQ reagents and mixed before 2DLC-MS/MS analysis to allow detection of quantitative differences between samples. 2D-LC of tryptic peptide mixtures was carried out by injecting samples onto a Zorbax Bio-SCX 50 × 0.8 mm strong cation exchange (SCX) column (Agilent). Elution from SCX column was done by stepwise injection of 40ul of 12.5, 25, 37.5, 50, 75, 100, 125, 150, 250, and 500 mM NaCl:0.1%FA:3%ACN. Eluted peptides were trapped and analyzed by reversed phase LC-MS/MS as described above using a 90 min gradient from 10 to 100% solvent B. Database searches for peptide identification from 2D-DIGE/ LC-MS/MS experiments were done with Mascot version 2.1 (Matrixscience, Boston, MA) using carboxyamidomethyl-cys as fixed modification, methionine oxidation as variable modification, 1.5 Da precursor and 0.8 Da ms/ms fragment tolerances. Proteins were identified by searching a human and mouse hybrid uniprot nonredundant protein database release from

May 2008 containing 131, 679 protein sequences.23,24 Protein sequence identification redundancy was collapsed using either an in-house developed algorithm25 or the Progroup software (ABI, Foster City, Ca). Proteins were determined to be of either mouse, human or ambiguous origin based on experimentally identified unique and shared peptide sequence evidence. Proteins were considered only if they demonstrated at least 2 peptide identifications. Since 2D-electrophoresis resolves proteins based on both pI and molecular weight, comigration of homologous human and mouse proteins to the same gel spot is very unlikely, unless they are almost completely identical in sequence. Therefore, proteins for which identified peptides could be uniquely assigned to the human sequence where considered to be of human origin and concluded to originate from the tumor, while under the same guidelines proteins determined to be of mouse origin can be contributed by host tissue microenvironment. Identification and quantitation of iTRAQ labeled peptides was done using ProQuant 1.4 software (Applied Biosystems, Foster City, CA). Protein sequence database searches were done using 1.5 Da precursor tolerance and 0.8 Da MS/MS fragment tolerance, N-terminal iTRAQ label and Methylthio-cysteine as fixed modifications and Lysine-iTRAQ, Journal of Proteome Research • Vol. 8, No. 5, 2009 2175

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Table 2. DIGE/PROQUANT Results: Human and Mouse Proteins Identified OSCC 1 accession

difference

ProtScorea

spot #

3.83 3.22

2 1

Human protein None Serum amyloid P Haptoglobin

Mouse protein Downregulated × 2 Downregulated × 2

P12246 Q61646

Shared protein None OSCC 2 accession

a

Human protein Downregulated × 2 Upregulated × 2 Upregulated × 2

Cytokeratin type II Cytokeratin type I DNA polymerase delta-3

P04264 P13645 Q15054

Alpha-2-macroglobulin Alpha-1-antitrypsin 1-2 Hemopexin Complement C3 precursor Ceruloplasmin Alpha-trypsin inhibitor H4 Vitamin D-binding protein Haptoglobin Apolipoprotein A-I Kininogen-1 Murinoglobulin-1 Gelsolin Antithrombin-III Zinc-alpha-2-glycoprotein Contraspin Serine proteinase inhibitor Serum amyloid P Liver carboxylesterase N Alpha-2-HS-glycoprotein Kininogen-II Alpha-1-antitrypsin 1-4 Complement component 8 Hemoglobin subunit beta-1 Alpha-trypsin inhibitor H3 Serine protease inhibitor Beta-2-glycoprotein 1 Glutathione peroxidase 3 Major urinary protein 1 Alpha-1-antitrypsin 1-3 Mannose-binding protein A AMBP Apolipoprotein E Complement factor B Trypsinogen 16 Retinol-binding protein Leucine-rich alpha-2-glycoprotein Apolipoprotein M

Q61838 P22599 Q3UKP2 P01027 Q61147 Q8C7G9 P21614 Q61646 Q00623 O08677 P28665 P13020 P32261 Q9DBB7 P07759 Q3UJ83 P12246 P23953 P29699 Q6S9I0 Q00897 Q8K182 A8DUM2 Q61704 Q91WP6 Q01339 P46412 A2BIN1 Q00896 P39039 Q07456 Q6GTX3 Q3UEG8 Q9Z1R9 Q3TF08 Q91XL1 Q8VC79

Mouse protein Upregulated × 2 Downregulated × Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Upregulated × 2 Downregulated × Upregulated × 2 Downregulated × Downregulated × Downregulated × Upregulated × 2 Downregulated × Downregulated × Downregulated × Upregulated × 2 Downregulated × Downregulated × Downregulated × Downregulated × Upregulated × 2 Downregulated × Downregulated × Downregulated ×

Clathrin heavy chain 1 Plasminogen Thrombospondin 1

Q5SXR6 Q3V1T9 Q3TR40

spot #

4.03 2.23 1.31

12 7 7

2 2 2

32.24 21.94 21.86 21.74 18.44 18.11 18.03 14.45 13.80 12.74 10.17 10.12 9.85 8.63 8.14 8.12 7.18 6.92 6.30 4.97 4.90 4.51 3.95 3.89 3.41 3.17 3.13 2.98 2.93 2.12 2.05 2.01 2.00 2.00 2.00 2.00 1.30

29 30 8 53 2 3 16 34 46 12 1 6 11 22 13 13 38 9 17 12 24 8 44 4 30 15 48 51 27 44 2 23 53 36 50 32 49

Shared protein Upregulated × 2 Upregulated × 2 Downregulated × 2

7.12 2.37 1.85

5 5 39

2

2 2 2 2 2 2 2 2 2 2 2

ProtScore (confidence): >99 (2.0), >95 (1.3), >90 (1.00), >66 (0.47).

Tyrosine-iTRAQ and oxidation of methionine as variable modifications. 2176

ProtScorea

difference

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EGFR Validation. EGFR validation was performed using ELISA, Western blot and immunostaining. Levels of circulating

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Oral Squamous Cell Carcinoma Biomarkers

Figure 2. Venn diagram summary of differentially expressed human proteins.

Figure 3. Venn diagram summary of differentially expressed mouse proteins.

serum EGFR, and EGFR released in conditioned serum-free cell culture medium were measured using an ELISA assay according to the manufacturer specification (Oncogene Science, Bayer Corporation, Cambridge, UK). Mouse serum was prepared as described above and human serum was drawn by forearm venipuncture, centrifuged and serum was stored at -80 °C. To generate cell conditioned medium, cells were cultured in 12 mL complete medium in 150 mm plates until 80% confluence. Cells were then washed with serum-free medium and then maintained in culture in serum-free condition for 24 h. Medium was collected, clarified by centrifugation for 10min at 1000 × g. Collected medium was then concentrated by adding equal volume of 20% TCA in acetone. After 2 h at -20 °C, protein pellet was collected by centrifugation at 15 000 × g for 15 min at 4 °C, washed with ice-cold acetone, and then solubilized in a 8 M urea, 4% w/v CHAPS [3-(3-cloramidopropyl) dimethyl

amino)-1-propanesulfonate], 30 mM Tris pH 8.0. The results are reported per cell protein concentration as determined using Bradford assay. Statistical significance was examined using the Student’s t test. Western blot assay and immunostaining using an anti-EGFR antibody (clone13, Transduction Laboratories) were performed on total cell extracts and 5-µm thick tissue sections, respectively, as previously described.21

Results Our study focused on tumor versus matched normal tissue from two patients with a defined pathology and distinct invasive property, based on clinical observations in humans and mouse-transplanted human cancer (Table 1). Clinically, cancer from patient 1 is highly invasive, while the cancer from Journal of Proteome Research • Vol. 8, No. 5, 2009 2177

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Figure 4. Venn diagram summary of differentially expressed proteins with common human and mouse sequences.

patient 2 is less aggressive when comparing the survival time of the human subjects post diagnosis, as well as survival time of mice post tissue implantation and the presence of lung metastasis at the time of sacrifice. From each patient’s cancer tissue, we isolated fibroblast-free primary oral squamous carcinoma cell (OSCC) populations, which we referred to as OSCC-1 and OSCC-2, for patient 1 and patient 2, respectively. These cells maintained the same pathologic, tumorigenic and invasive potential as their parental tissue when transplanted into tongue of RAG-2/γ(c) mice, and when compared to the original human tissue.18 OSCC-1 cells were more invasive than OSCC2 as determined by the in vitro Boyden chamber assay (Table 1). Serum proteomics using the two-dimensional difference in gel electrophoresis (2D-DIGE) approach revealed 50 distinct spots in serum from mice bearing tissue from patient 1, and 75 spots in mice bearing tissue from patient 2. The majority of the identified protein spots were easily distinguishable. In depth image analysis of the spots revealed 2 and 43 proteins that were differentially expressed in the serum of mice which received the cancer tissue implants, as compared to the corresponding mice which received the normal tissue from patient 1 and 2, respectively (Figure 1 and Table 2). A much larger number of proteins was identified by 2DLC-MS/MS. Using the ProQuant search engine we were able to identify, within a 90% confidence interval, 26 proteins in the cancer from patient 1 that were differentially expressed between control and cancer-bearing mice, and 109 proteins in patient 2. The Mascot search engine, on the other hand, identified 82 and 75 proteins in patient 1 and patient 2 (p < 0.05), respectively (Supplemental Tables S1-S2, Supporting Information). The most significant human and mouse proteins are summarized in Figures 2-4. Human Proteins. Proquant identified 15 unique human proteins which were differentially expressed in cancer-bearing mice vs control (Figure 2 and Table 3). Two human proteins, cytokeratin type I and type 2 were identified using the various proteomic methods and search engines. Other interesting proteins identified included the EGFR, G-protein coupled 2178

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receptor 87, Rab11 GTPase, PDZ-domain containing proteins and PEST-containing nuclear proteins, all of which were upregulated in serum from mice implanted with tissue from patient 2. Another interesting human protein identified in serum to be significantly affected between control and cancerbearing mice included was OBSCN (obscurin, cytoskeletal calmodulin and titin-interacting RhoGEF), which was downregulated in serum from mice implanted with tissue from patient 1 (Table 3). Individual unique human peptides corresponding to the reported human proteins are listed in Table 3B. Mouse Proteins. Deregulated expression of thirty-one of the eighty-nine recognized mouse proteins were identified using the various proteomic methods and search engines. These proteins include several inflammatory and noninflammatoryassociated factors, including: alpha-1 antitrypsin, clusterin, fetuin-B, histidine-rich glycoproteins, serpina1d, serum amyloid P, titin, vitronectin, and vitamin D associated proteins. Twelve mouse proteins were identified in serum from mice implanted with tissues from both patients (Figure 3 and Tables 2-4). Proteins that were up-regulated in both patients include R-2HS-glycoprotein, complement C3 and C4, hemopexin and plasminogen. Proteins found in both patients but with different expressions included R-2-macroglobulin, apolipoproteins, hemoglobin R and β, kininogen 1, murinoglobulin 1 and the serine protease inhibitor A3K. Common Proteins. Five proteins were identified to be differentially expressed among cancer and control groups, however the source of the proteins could not be ascertained due to the homology shared between peptides sequences of human and mouse origin. As such we identified histone-lysineN-methyltransferase (ASH1L), E3 ubiqutin-protein ligase, apoplipoprotein C-I and ataxin-2-like protein. Interestingly, gelsolin was found to be differentially expression in both patients, which was downregulated in patient 1 and upregulated in patient 2 (Figure 4 and Table 5). EGFR Validation. To validate the overall proteomic technology used in this study, we selected EGFR, which has been reported to be frequently deregulated in HNSCC and was found

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Oral Squamous Cell Carcinoma Biomarkers Table 3. MudPIT/PROQUANT Result Human Proteins Identified (A) human protein

accession

117:114a

ProtScoreb

IGL Inactive ubiquitin carboxyl-terminal hydrolase 53 OBSCN

OSCC 1 Q6GMW4 Q70EK8 A8MRB5

1.44 1.12 0.47

2.01 2.00 1.54

Cytokeratin type II Cytokeratin type I Epidermal growth factor receptor Antithrombin-III PDZ domain-containing protein 2 HEAT repeat-containing protein 4 Rab11 family interacting protein 4 PEST-containing nuclear protein variant IGHM Probable G-protein coupled receptor 87 D-beta-hydroxybutyrate dehydrogenase

OSCC 2 P04264 P13645 A8K2T7 P01008 O15018 Q86WZ0 Q86YS3 Q53GF3 Q6GMY2 Q9BY21 Q02338

1.85 2.22 2.62 9.52 5.40 8.52 2.11 1.86 2.02 1.95 2.56

4.38 2.15 2.01 2.00 2.00 2.00 1.52 1.05 1.05 1.05 1.05

(B) peptidec

Hd

Me

∆MWf

OSCC 1 FSGSGSGNTASLTITGAQAEDEADYYCNSR ATLVCLISDFYPGAVTVAWK GNSCDSSSK TMAIAAQGACR LVVQQAGQADAGEYSCEAGGQRLSFR ALPARFTQDLK AQLLITGATLQDSGRYK LSSSSKVHMEASGYTR VAPVEWK VRIEAAGCMR

1 37 1 7 7 3 7 1 3 7

0 0 0 0 0 0 0 0 0 0

1.00 0.01 -0.34 1.18 1.97 -0.59 1.02 0.35 -1.12 -0.82

OSCC 2 GGGGGGYGSGGSSYGSGGGSYGSGGGGGGGR GSGGGSSGGSIGGR SDQSRLDSELK YEDEINKR YEELQITAGR YEELQITAGRHGDSVR LENEIQTYR TIDDLKNQILNLTTDNANILLQIDNAR YCVQLSQIQAQISALEEQLQQIR KLFGTSGQK GDSFTHTPPLDPQELDILK TIQEVAGYVLIALNTVER LQPLDFK IPEATNR ITDVIPSEAINELTVLVLVNTIYFK RVWELSK EGDEILDVNGIPIK ESDGLGIQVSGGR GDQILEVNSVNVR RPIMAWFK SLGPLGIPTPTMTLASPVKR VTVAGFQPGGAVEKESLGK DLLTHKILK QAKSLQEDVTWELVVLR DQETTAEQALEEEAR DGGLGGLFLPEDK LGSSKPKETVPTLAPK ISKFGFAIGSQTTK SAEEEAADLPTKPTKISK NVPLPVIAELPPK FTISRDNAK IVHDAGFGPWYFK IYYDYTDV QVAEVNLWGTVR VVEIVRSSLK

1 1 1 24 1 1 1 1 1 12 16 10 4 5 4 6 3 3 3 2 2 2 2 2 2 2 3 3 3 14 39 2 2 1 1

0 0 0 20 0 0 4 0 0 0 0 5 2 0 0 2 0 2 0 0 0 0 0 0 1 0 0 4 0 0 19 0 2 0 0

0.15 -0.70 -1.21 -0.43 0.11 -0.19 0.08 0.76 -0.14 0.1 0.16 0.10 0.10 -1.06 -0.07 0.11 0.17 1.79 1.21 0.05 0.53 1.10 -0.05 -0.93 1.48 0.10 1.03 0.05 0.54 0.03 0.55 0.46 -0.12 -1.43 1.33

protein

IGL Inactive ubiquitin carboxyl-terminal hydrolase 53 OBSCN

Cytokeratin type II

Cytokeratin type I Epidermal growth factor receptor Antithrombin-III

PDZ domain-containing protein 2

HEAT repeat-containing protein 4 Rab11 family interacting protein 4 PEST-containing nuclear protein variant IGHM Probable G-protein coupled receptor 87 D-beta-hydroxybutyrate dehydrogenase

a 117:114 ratio: 117: tumor, 114 control; 117:114 > 1 then upregulated; 117:114 < 1 then downregulated. b ProtScore (confidence): >99 (2.0), >95 (1.3), >90 (1.00), >66 (0.47). c Human-specific peptide sequences are bolded. Blast tool used: http://ca.expasy.org/tools/blast/. d H: the number of peptides with 100% identity found in a human database search. e M: the number of peptides with 100% identity found in a mouse database search. f ∆MW: difference between theoretical and experimental MW.

in our proteomic study to be up-regulated in serum of mice bearing the cancer tissue from patient 2 compared to control (Figure 5). As shown in Figure 6A, serum concentration of the extracellular domain of EGFR was significantly higher (p < 0.05)

in independent serums from mice implanted with tissue from Patient 2 compared to serum from mice implanted with tissue from patient 1 or from normal tongue. Interestingly, the levels of circulating serum EGFR from mice bearing oral cancer Journal of Proteome Research • Vol. 8, No. 5, 2009 2179

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Table 4. MudPIT/PROQUANT Result Mouse Proteins Identified mouse protein

2180

accession

117:114a

ProtScoreb

Apolipoprotein A-I Complement component 3 Murinoglobulin 1 Serine protease inhibitor A3K Apolipoprotein A-II Hemoglobin alpha, adult chain 1 Alpha-2-macroglobulin Kininogen 1 Hemopexin Alpha-2-HS-glycoprotein Hemoglobin subunit beta-1 Serpina1d protein Plasminogen Transferrin Kallikrein 11 isoform 3 Apolipoprotein C-I PRSS3 Complement component 4 Alpha-1-antitrypsin 1-3

OSCC 1 Q00623 P01027 P28665 P07759 P09813 Q9QWJ3 Q61838 Q32MX7 Q3UKP2 P29699 P02088 Q8VC41 Q3V1T9 Q9DBD0 Q8IXD7 P34928 A1A508 Q3TAT4 Q00896

0.92 1.12 0.92 0.64 0.61 0.16 0.88 0.96 1.11 1.08 0.11 0.70 1.09 0.88 1.17 0.75 1.23 1.15 0.61

19.32 8.18 7.73 6.84 6.01 5.67 5.25 4.38 4.00 3.12 3.10 2.91 2.22 2.15 2.00 2.00 1.53 1.40 1.22

Complement component 3 Alpha-2-macroglobulin Fibronectin 1 Murinoglobulin 1 Ceruloplasmin Complement component 4-B Hemopexin Inter alpha-trypsin inhibitor, heavy chain H4 Alpha-1-antitrypsin 1-2 Liver carboxylesterase N Apolipoprotein A-I Complement factor H Haptoglobin Kininogen 1 Hemoglobin subunit beta-1 Complement factor B Serine protease inhibitor A3K Vitamin D-binding protein Apolipoprotein A-IV Plasminogen Alpha-2-HS-glycoprotein Hemolytic complement Alpha-1-antitrypsin 1-1 Histidine-rich glycoprotein Inter alpha-trypsin inhibitor, heavy chain H3 Alpha-2-antiplasmin Beta-2-glycoprotein 1 Apob protein Antithrombin-III Plasma protease C1 inhibitor Hemoglobin subunit alpha Vitronectin Fetuin-B Serum amyloid P-component Peptidase inhibitor, clade A, member 1e Alpha-1-acid glycoprotein 1 Coagulation factor II Serine protease inhibitor A3N Clusterin Afamin Interalpha trypsin inhibitor, heavy chain H2 Apolipoprotein E Serum amyloid A-2 Hemoglobin subunit beta-2 Alpha-1-acid glycoprotein 2 Lecithin cholesterol acyltransferase Carboxypeptidase N catalytic chain Angiotensinogen Peptidoglycan recognition protein 2 Complement factor I Apolipoprotein C-III Beta-2-microglobulin Interalpha trypsin inhibitor, heavy chain H1 Glutathione peroxidase 3 Apolipoprotein C-II Complement factor D Serum paraoxonase/arylesterase 1 Sulfhydryl oxidase 1 Apolipoprotein A-II Serum amyloid A-4 Serine protease inhibitor A3M Zinc-alpha-2-glycoprotein

OSCC 2 Q80XP1 Q61838 Q3UHL6 P28665 Q61147 P01029 Q3UKP2 Q8C7G9 P22599 P23953 Q00623 P06909 Q3UBS3 O08677 P02088 Q3UEG8 P07759 P21614 P06728 Q3V1T9 Q3UEK5 A2AS36 P07758 Q6YK32 Q61704 Q5ND36 Q01339 Q8CGG8 P32261 A2ATR7 P01942 P29788 Q6YJU1 P12246 Q3UJ83 Q60590 A2AGT7 Q91WP6 Q549A5 O89020 Q8K016 Q3UBS0 P05367 Q54AH9 P07361 Q9CW47 Q9JJN5 P11859 Q8VCS0 Q61129 P33622 P01887 Q91WU7 P46412 Q3UJG0 Q3UP47 P52430 Q8BND5 P09813 P31532 Q03734 Q64726

2.89 2.45 3.12 2.13 2.16 2.13 1.65 2.16 1.95 2.65 2.42 1.99 1.12 2.77 1.28 1.81 1.73 2.04 1.47 2.22 2.14 2.25 1.82 2.48 2.50 1.98 2.12 3.32 2.56 1.79 1.27 2.34 2.88 3.04 1.40 1.91 2.01 1.93 1.64 2.02 4.36 2.02 3.20 1.27 1.60 1.74 1.90 3.68 1.67 2.23 8.52 2.28 1.88 3.29 2.70 2.54 2.75 1.99 2.57 1.80 1.85 1.66

100.75 87.40 65.25 59.99 50.96 47.28 39.09 30.77 26.11 26.06 26.03 22.55 21.61 20.43 19.54 19.15 19.05 17.02 16.10 16.05 15.10 13.94 12.00 11.03 10.96 10.86 10.68 10.29 10.26 10.11 10.00 9.16 8.68 8.12 7.70 7.67 7.40 7.07 6.33 6.08 5.30 4.17 4.14 4.00 4.00 4.00 4.00 4.00 4.00 3.91 3.53 3.52 3.52 3.43 3.10 2.92 2.68 2.66 2.60 2.38 2.16 2.15

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Oral Squamous Cell Carcinoma Biomarkers Table 4. Continued mouse protein

accession

117:114a

ProtScoreb

Leucine-rich alpha-2-glycoprotein Alpha-1-antitrypsin 1-4 Coagulation factor V Hemopexin Serum amyloid A-1 Kininogen Coagulation factor XII Complement component C9 Complement component 4A Alpha 1 microglobulin/bikunin C4b-binding protein alpha-chain Insulin-like growth factor binding protein Protein Z-dependent protease inhibitor Complement component 1 Interleukin-28 ANKRD26-like family C Spectrin alpha 2 Oxysterol-binding protein-related protein 11 Exonuclease 3”-5” domain-like-protein 1 Heparin cofactor 2 RPB11A Zinc fingers and homeoboxes protein 1 Carboxypeptidase N subunit 2 Titin G-protein coupled receptor 98 Klra21 Complement factor C2 Multidrug resistance protein 1a

Q91XL1 Q00897 O88783 P02790 P05366 Q7M084 Q6PER0 P06683 Q9Z0D8 Q07456 Q80SX2 Q791Q5 Q8R121 Q8CG14 Q4VK74 Q6S8J3 A3KGU5 Q8CI95 Q8CDF7 Q5FW62 Q9H1A6 P70121 Q642I5 A2AT56 Q8WXG9 Q14B13 P21180 P21447

1.49 1.89 1.83 1.60 3.29 2.53 1.79 3.78 1.98 4.71 1.49 1.30 2.83 2.16 2.57 1.79 1.46 1.48 1.95 2.07 1.65 1.93 1.99 11.68 1.93 15.23 1.65 2.31

2.14 2.12 2.01 2.01 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 1.53 1.53 1.53 1.53 1.53 1.52 1.52 1.41 1.40 1.26 1.10 1.10 1.10 1.05

a 117:114 ratio: 117: tumor, 114 control; 117:114 > 1 then upregulated; 117:114 < 1 then downregulated. > 90 (1.00), >66 (0.47).

Table 5. MudPIT/PROQUANT Result Human and Mouse Shared Proteins Identified shared protein

accession

OSCC 1 Gelsolin P06396 Histone-lysine Q9NR48 N-methyltransferase ASH1L Gelsolin E3 ubiquitin-protein ligase Apolipoprotein C-I Ataxin-2-like protein

OSCC 2 P06396 Q5T4S7 P34928 Q7TQH0

117:114

a

ProtScore

0.75 0.92

2.03 1.17

1.64 1.21 2.81 1.33

8.12 2.04 2.00 1.53

b

a 117:114 ratio: 117: tumor, 114 control; 117:114 > 1 then upregulated; 117:114 < 1 then downregulated. b ProtScore (confidence): >99 (2.0), >95 (1.3), >90 (1.00), >66 (0.47).

tissues, also correlate with the levels of the extracellular domain of EGFR secreted in conditioned cell culture medium (Figure 6B), as well as the expression of EGFR in matched cell lines (Figure 6C) and cancer tissues (Figures 6D). Moreover, these results also correlated with the levels of circulating EGFR in serum from patient 1 and patient 2 (mean ( SD was 49 ( 11, 78 ( 18, and 43 ( 9 for patient 1, patient 2, and normal tongue tissues (n ) 5)).

Discussion Cancer is a heterogeneous disease where genetic background and factors specific to the host and tissue microenvironment play a key role in its development and progression. This has provided a rationale toward development of individualized approaches for cancer diagnostic and prognostic modalities. In this study, we carried out a global proteomic study on serum from mice implanted orthotopically with two clinically relevant human oral cancers with distinct invasive phenotypes and their matched normal tongue tissues. We have reported earlier that this orthotopic xenograft model mimics human disease in term of histopathology, disease grade and invasiveness, and is highly

b

ProtScore (confidence): >99 (2.0), >95 (1.3),

suitable for repeated proteomic studies while minimizing confounding genetic and host variables often seen in individual patients.18 Using a combination of two different proteomics sample fractionation methods and two separate database search tools, we identified over one hundred proteins as being differentially expressed between control and cancer-bearing mice. Several candidate proteins were identified as being selectively associated with oral cancer or contributed by the host (summarized as Venn diagrams in Figures 2-4). Consistency among repeated samples, and the detection of several markers previously reported in the literature as potential cancer biomarkers, support the observed variations in protein expression between mice implanted with tissues from the two patients, as part of the phenotypic and genotypic characteristics of the disease. For instance, proteomic results of circulating EGFR, which is upregulated in serum from mice bearing oral cancer from patient 2 was confirmed in mice serum ELISA assays, as well as in conditioned cell culture media from matched cells. Interestingly, EGFR serum levels correlated with endogenous level of EGFR in matched cancer cell lines and cancer tissues. This indicates that high circulating EGFR is likely the result of proteolytic cleavage of the extracellular binding domain of EGFR from the cell surface of cancer cells. This is in line with the individual unique human peptide sequences identified for EGFR;KLFGTSGDK,GDSFTHTPPLDPQELDILKandTIQEVAGYVLIALNTVER, which correspond to amino acids 479-487, 378-396 and 81-98 of EGFR respectively, mapping to its extracellular domain. Although the importance of serum EGFR as a cancer biomarker is still debated, several studies support our results by reporting an inverse correlation between the level of serum EGFR and invasiveness and/or survival.26-28 EGFR is activated via a complex heterodimerization process with members of the EGFR family (ErbB-2, ErbB-3 and ErbB-4), and is able to crosstalk with other non-ErbB membrane receptors and intracellular signaling molecules.29 Therefore, a multiplex analysis of EGFR and its partners is needed to establish a Journal of Proteome Research • Vol. 8, No. 5, 2009 2181

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Bijian et al.

Figure 5. Representative MS/MS result showing the fragment sequence assignment of an EGFR iTRAQ labeled (*) peptide with sequence *KLFGTSGQK* and EGFR up-regulation demonstrated by the reporter ion intensity differences (insert panel, 117:tumor, 114:control) in serum from a mouse bearing an orthotopic oral cancer from patient 2.

meaningful interface between circulating EGFR levels and the disease phenotype. Nevertheless, EGFR inhibitors are being tested as an alternative therapeutic strategy for head and neck cancers.30-35 In addition to EGFR, other serum proteins reported earlier as candidate cancer biomarkers include cytokeratin type 1 and Rab 11 GTPase. Cytokeratin type 1 proteins are intermediate filament proteins of the epithelial cells involved in cell motility and cell differentiation; cytokeratins have been identified as squamous cell carcinoma progression prognostic markers.36,37 Rab GTPases are involved in tethering and fusion of membrane vesicles, as well as in transport of them and associated effector (cargo) proteins through direct or indirect interactions with microtubules or Actin-based motor proteins. Rab11 has been demonstrated to regulate the dynamics of recycling endosomes to and from trans-Golgi network to plasma membrane, particularly involved in the plasma membrane recycling of proteins such as β1 integrins.38 Several Rab family members have been implicated in contributing to the aggressiveness of epithelial cancers.39 Our study also identified several proteins known to play a role in acute phase response. These include gelsolin, and group-specific component protein also known as vitamin D-binding protein. These proteins are abundant components of plasma and both have been reported to bind to G-Actin with high affinity, thus preventing Actin filament repolymerization, increasing their clearance from circulation, and preventing deleterious effects of long cytoskeletal polymers released during normal or pathological cell death.40 Gelsolin has been implicated in EGFR-associated cell motility, and is regulated by osteopontin and integrins, which play a role in cancer metastases. In addition gelsolin regulates cancer-associated signaling by interacting with proteins such as Src and PI-3K.41,42 2182

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Previous studies report gelsolin to be up-regulated in lung cancer cells,43 and renal cancer cells;44 and down-regulated in ovarian cancer cells,45 and in serum of patients with pancreatic cancer.46 However, the correlation between serum gelsolin and the susceptibility to inflammatory reactions versus cancer are still debated. In our study, human and mouse gelsolin was found to be down-regulated in patient 1, and up-regulated in patient 2. The fact that gelsolin was identified in both human and mouse forms suggest that gelsolin is contributed by cancer cells as well as the inflammatory host response. With regards to vitamin D binding proteins, we have reported previously that vitamin D3 analogs have therapeutic benefit for head and neck carcinoma.47 Of significance to the host-tumor microenvironment are the thirty-one mouse proteins identified using the various proteomic methods and search engines. These mouse proteins likely represent a host response to the cancer. In light of the critical role of the tumor microenvironment in tumor development and progression, we believe these proteins are potential biomarkers that need to be carefully examined. Among these proteins, proteinase inhibitors, such as serine proteinase inhibitor, alpha-1 antitrypsin, Serpina1d and antithrombin, were found to be deregulated in all samples. All four proteins have a significant homology, however it is difficult to draw conclusions from these findings, because all the serum samples have been trypsin digested prior to the mass spectral analysis. Other mouse proteins we found in serum from mice implanted with oral cancer tissues included vitronectin, titin and clusterin. Vitronectin, which was found to be upregulated in our study, is an extracellular matrix protein that alters the strength of cellular adhesions, and has recently been reported to bind proteins important in the carcinogenesis of lingual carci-

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Oral Squamous Cell Carcinoma Biomarkers

Figure 6. Validation of EGFR. (A) Serums from 5 independent mice bearing oral squamous cell carcinoma tissue from patient 1 (OSCC1), oral squamous cell carcinoma tissue from patient 2 (OSCC2), or transplanted with normal human tongue tissue were used to determine serum EGFR levels by ELISA. Circulating levels of EGFR extracellular domain (ECD) for nontumor bearing mice was found to be very low (average: 0.26 ( 0.10 ng/ml, N ) 5), as compared to values obtained from mice bearing normal tongue tissues (Control; average: 2.14 ( 0.53 ng/mL, N ) 5) and mice bearing cancer tissues (OSCC1 and OSCC2, averages 2.45 ( 0.72 ng/ml and 13.68 ( 2.02 ng/ml, respectively, N ) 5). (B) Cell culture conditioned media from OSCC1 and OSCC2 cells established from matched oral squamous cell carcinoma tissue from patient 1 and patient 2, respectively, were used to determine serum EGFR levels by ELISA assay. Normal human epithelial oral tongue cells were used as control. (C) Total cell extracts from OSCC1 and OSCC2 cells were used to determine endogenous levels of EGFR by Western blot analysis. The membrane was stripped and reprobed with β-Actin as internal control for protein loading. (D) Immunohistochemical staining. Tumors induced by OSCC 1 or OSCC2 cells implanted into the mouse tongue were removed when they reached a size of 0.5-0.7 cm3, fixed in formalin, and tissue sections were stained with hematoxylin-eosine (H&E) or immunostained with anti-EGFR or anti-PCNA. Control slides (-) were treated in the same manner as EGFR and PCNA groups with the exception that anti-EGFR and anti-PCNA antibodies were omitted.

noma.48,49 Titin is a giant elastic protein important in muscle function and development. It has recently been reported as a possible biomarker for lung adenocarcinoma found in plasma and pleural effusions.50 Clusterin is a ubiquitous secretory sulfated glycoprotein implicated in cell aggregation, inhibition of complement-mediated cytotoxicity, lipid transport, and antiapoptotic functions. It has been reported as a potential biomarker for prostate cancer recurrence.51 In summary, the proteomic approach reported here addresses a step toward individualized proteomic screening and identifies several cancer and host associated biomarkers for HNSCC. Our current challenge with this enormous data set is the appropriate validation and correlation with the disease in a large number of patients.

Acknowledgment. We thank Dr. Naciba Benlimame for her contribution to tissue immunohistochemistry. This study has been supported by McGill University Head and Neck Cancer Fund, and in part by the Canadian Institutes for Health Research, the Canadian Breast Cancer Research Alliance, and the Cancer Research Society. MAAJ is a FRSQ Scholar a recipient of Dundi and Lyon Sachs Distinguished Scientist Award. Supporting Information Available: Supplemental Tables S1-S2. This material is available free of charge via the Internet at http://pubs.acs.org.

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