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Original Article |

Use of Combination Proteomic Analysis to Demonstrate Molecular Similarity of Head and Neck Squamous Cell Carcinoma Arising From Different Subsites FREE

Paul M. Weinberger, MD; Mark Merkley, BS; Jeffrey R. Lee, MD; Bao-Ling Adam, PhD; Christine G. Gourin, MD; Robert H. Podolsky, PhD; Bruce G. Haffty, MD; Evangelia Papadavid, MD; Clarence Sasaki, MD; Amanda Psyrri, MD; William S. Dynan, PhD
[+] Author Affiliations

Author Affiliations: Departments of Otolaryngology (Drs Weinberger and Gourin) and Pathology (Dr Lee), Institute for Molecular Medicine and Genetics (Mr Merkley and Drs Lee and Dynan), and Center for Biotechnology and Genomic Medicine (Drs Adam and Podolsky), Medical College of Georgia, Augusta; Charlie Norwood VA Medical Center, Augusta (Dr Lee); Department of Radiation Oncology, University of Medicine and Dentistry of New Jersey–Robert Wood Johnson Medical School, Newark (Dr Haffty); and Departments of Medical Oncology (Drs Papadavid and Psyrri) and Surgery (Dr Sasaki), Yale University, New Haven, Connecticut.


Arch Otolaryngol Head Neck Surg. 2009;135(7):694-703. doi:10.1001/archoto.2009.78.
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Published online

Objective  To evaluate head and neck squamous cell carcinomas (HNSCCs) for differences in protein expression between oral cavity, oropharynx, larynx, and hypopharynx subsites.

Design  Retrospective proteomic analysis using tissue microarray (TMA) and 2-dimensional difference gel electrophoresis (2D-DIGE). For the TMA, automated quantitative protein expression analysis was used to interrogate levels of 4 cell-cycle regulatory proteins chosen for their known roles in cancer (cyclin D1, p53, Rb, and p14). For the 2D-DIGE, lesional and normal adjacent tissues were enriched by laser capture microdissection. Total protein was extracted, analyzed by 2D-DIGE with saturation dye labeling, and evaluated for relative abundance levels of individual protein spots.

Setting  Two tertiary-care academic medical centers.

Patients  Seventy-one patients with HNSCC for TMA, and 14 patients with HNSCC with frozen tumor and normal tissue for 2D-DIGE.

Results  The automated quantitative analysis of protein expression analysis revealed no difference between subsite for cyclin D1, p53, Rb, or p14 expression. The 2D-DIGE study was based on 28 gels (14 cancer gels and 14 adjacent normal gels), and 732 spots were identified as matching across more than 90% of gels. Significance was evaluated based on false discovery rate (FDR) estimated from permuted data sets. There were no significant differences in protein expression between subsites (FDR greater than or equal to 30% in all instances).

Conclusions  Observed differences in outcomes between HNSCCs from different subsites may not reflect differences in tumor biologic characteristics between subsites. Rather, it is possible that observed clinical heterogeneity among HNSCCs may be based on other factors, such as viral vs chemical carcinogenesis.

Figures in this Article

Head and neck squamous cell carcinoma (HNSCC) represents 5% of newly diagnosed cancers in adult patients, with an annual incidence of more than 500 000 new cases worldwide.1 The overall 5-year survival rate for all cases is 64%,1 but it is a heterogeneous disease. Many factors, including disease stage, subsite, age, the presence of high-risk type human papillomavirus (HPV), and other comorbidities, are important prognostic variables. The site of origin within the head and neck is among the most important of these. Five-year survival rates by site range from 91% (lip) to 69% (larynx) to 31% (hypopharynx).1,2 Controversy exists as to whether this reflects physical differences based on anatomic location and lymphatic drainage or fundamental molecular heterogeneity between subsites.

Anatomic differences have been proposed to underlie the clinical disparity observed between tumors arising from different head and neck subsites. Certain subsites, such as the hypopharynx, have increased vascularity and lymphatic drainage compared with other sites, such as the glottic larynx.3,4 This may predispose tumors arising from certain locations to early nodal and distant metastatic spread. In addition, tumors within some subsites are clinically evident at a much earlier stage than other subsites. Lip and most oral cavity cancers are amenable to direct inspection by a general practitioner, general dentist, and sometimes even the patient. Thus, it is probable that cancers in these locations will be diagnosed at an earlier stage than cancers that arise at more distally located subsites (oropharynx, hypopharynx), where direct observation requires specialized examination skills and instruments (indirect mirror laryngoscopy, transnasal flexible fiber optic examination, etc). Examination of these areas is less likely to occur in the course of routine preventive care.

Similarly, cancers of the glottic larynx cause hoarseness as an early symptom, often prompting early evaluation. Early stage (tumor-node-metastasis [TNM] category I/II) HNSCC carries a far better prognosis than does advanced stage (III/IV) disease: 5-year survival rates are approximately 75% for stage I/II disease but are less than 30% for stage III/IV disease.57

Alternately, it has been suggested that fundamental differences in molecular mechanisms underlying cancer progression account for differences in outcome between head and neck subsites. Differential expression of the proteins EGFR, cyclin D1, and MMP2 have been reported by several investigators between different tumor subsites.812 However, other studies have found no molecular differences in the proteins p53, cyclin D1, p21, VEGF, or Rb among HNSCCs from differing subsites.13,14 The extent to which differences in patient populations or methods may explain the conflicting results is unclear.

Quantitative proteomic profiling affords one approach to determine if molecular differences exist between subsites. We sought to determine if molecular differences in protein expression exist between HNSCCs arising from different subsites. We used 2 methods to assess differences in protein expression: automated quantitative analysis of protein expression (AQUA) and 2-dimensional difference gel electrophoresis (2D-DIGE). For the AQUA study, we used antibodies for p14, cyclin D1, pRb, and p53 because previous analyses1014 with these proteins have been inconclusive. The AQUA study allows quantitative determination of protein expression while preserving subcellular localization information. In the 2D-DIGE study, we used laser capture microdissection (LCM) to isolate pure lesional tissue, followed by saturation dye labeling and 2D-DIGE to obtain protein profiles from as little as 1 μg of total protein, a procedure validated previously in cases in which sample abundance was limiting.1520 Proteins of interest are defined using purely statistical criteria, without requiring or using any prior knowledge about their biological function. The 2 approaches are complementary, and in this study we used both to address whether tumors from different subsites within the head and neck differ in their molecular characteristics.

PATIENT SELECTION

The tissue microarray (TMA) cohort was assembled from patients with primary HNSCC treated at Yale–New Haven Hospital (New Haven, Connecticut) from 1992 to 1999 who enrolled in a prospective, randomized clinical trial.21 Patients were treated with primary external beam radiotherapy with or without a radiation sensitizer (porfiromycin or mitomycin C). Exclusion criteria from the TMA cohort were presentation with metastatic disease, paranasal sinus cancers, non–squamous cell histologic characteristics, lack of available archival tissue, and failure to receive a full course of radiation therapy. Patients included in the 2D-DIGE cohort were drawn from a prospectively collected cohort of patients with histologically confirmed HNSCC treated at the Medical College of Georgia (Augusta) from 2004 to 2007 who enrolled in a voluntary tissue and tumor banking registry. All patients with available matching tumor and adjacent histologically normal frozen tissue at study inception were included. Complete demographic and treatment information was maintained for all patients included in the registry. All biopsy specimens for both cohorts were obtained before treatment with chemotherapy and/or radiotherapy.

AQUA STUDY

The TMA was constructed as described and included 71 cases.22 Pilot sections from archival paraffin-embedded, formalin-fixed tissue blocks were stained with hematoxylin-eosin and reviewed by a pathologist to select areas of invasive tumor. Core samples were taken using 0.6-mm2 blunt-tip needles and placed on the recipient microarray block using a Tissue Microarrayer (Beecher Instrument, Silver Spring, Maryland). Tumors were represented with 2-fold redundancy, which has been shown to provide a sufficiently representative sample.2325 Five-micron-thick sections were placed on glass slides using an adhesive tape transfer system (Instrumedics Inc, Hackensack, New Jersey) with UV cross-linking. Tissue microarray slides were deparaffinized with xylene followed by ethanol, rehydrated, and processed for antigen retrieval by pressure cooking in 0.1M citrate buffer (pH 6). Slides were incubated in hydrogen peroxide, 0.3%, in methanol for 30 minutes to block endogenous peroxidase, followed by bovine serum albumin, 0.3%, for 30 minutes at room temperature to block nonspecific antibody binding. Slides were incubated separately with the following mouse monoclonal primary antibodies at 4°C overnight: anti-p14 (US Biological, Swampscott, Massachusetts; catalog No. L4050705), anti-cyclin D1 (Abcam, Cambridge, England; catalog No. ab6152), anti-Rb (Thermo-Scientific, Fremont, California), retinoblastoma Ab-1 (clone 1F8), and anti-p53 (DAKO Corp, Carpinteria, California; clone DO7). Slides were incubated with goat anti-mouse secondary antibody conjugated to a horseradish peroxidase–decorated dextran polymer backbone (Envision; DAKO Corp) for 1 hour at room temperature. Tumor cells were identified by use of anticytokeratin antibody cocktail (rabbit antipancytokeratin antibody z0622; DAKO Corp) with subsequent goat anti-rabbit antibody conjugated to Alexa Fluor 546 fluorophore (Invitrogen, Carlsbad, California; catalog No. A11035). Nuclei were counterstained with 4", 6-diamidino-2-phenylindole (DAPI; Fisher Scientific, Pittsburgh, Pennsylvania). Target antigens were visualized with a fluorescent chromogen (Cy5-tyramide; Perkin Elmer Corp, Waltham, Massachusetts). Slides were mounted with a polyvinyl alcohol–containing aqueous mounting media with antifade reagent (n-propyl gallate; Acros Organics, a division of Thermo–Fisher Scientific).

The AQUA study was performed as previously described.26 Monochromatic, 1024 × 1024-pixel, 0.5-μm resolution images were obtained of each histospot using filter cubes specific to the emission/excitation spectra of DAPI (358-nm excitation/461-nm emission), Alexa Fluor 546 (556-nm excitation/573-nm emission), and Cy5 (650-nm excitation/670-nm emission) (Optical Analysis, Nashua, New Hampshire). Tumor and stroma were distinguished by creating a cytokeratin mask based on the Alexa Fluor 546 signal, and a tumor nuclei–specific compartment was defined using DAPI signal within the previously defined tumor mask. Overlapping pixels (to a 99% confidence interval) were excluded from the nuclear compartment.

The AQUA score was expressed on a normalized scale of pixel intensity divided by target area (tumor nuclei compartment). Duplicates were averaged, and scores across subsites were compared by nonparametric Kruskal-Wallis test.27 Comparisons with clinical and pathologic variables (sex, ethnicity, TNM stage, histologic grade, and head and neck subsite) were made using nonparametric Wilcoxon rank sum test (for dichotomous variables) and Kruskal-Wallis test (for ≥3 categorical variables).27,28 Nuclear protein expression for p14, cyclin D1, Rb, and p53 (by AQUA) was compared by nonparametric Spearman rank correlation coefficient.29 All calculations and analyses were performed with SPSS statistical software (version 11.5; SPSS Inc, Chicago, Illinois).

LCM/2D-DIGE STUDY

Frozen sections (5 μm) were stained briefly with nuclear fast red, and LCM was performed using an Arcturus PixCell IIe microscope (Molecular Devices, Sunnyvale, California) as previously described.18 Caps with polymer film and adherent cells were placed onto a microcentrifuge tube containing lysis buffer (7M urea, 2M thiourea, 4% 3-[(3-Cholamidopropyl) dimethylammonio]-1-propanesulfonate hydrate [CHAPS], 0.4mM 4-[2-Aminoethyl] benzenesulfonyl fluoride [AEBSF] [protease inhibitor], 40mM Tris-hydrochloride [pH, 8], and 5mM magnesium acetate). Tubes were inverted to wet the polymer film and incubated for 30 minutes at room temperature. The resulting extracts were sonicated 5 times for 30 seconds each and centrifuged at 14 000g for 15 minutes, and the supernatant was transferred to a fresh tube. Protein concentration was assayed using the 2D-Quant Kit (GE Healthcare, Little Chafont, England). Tris-(2-carboxyethyl)-phosphine (TCEP) was added (0.4 nmol), and the mixture was incubated for 1 hour at 37°C. Cy5 sulfhydryl-reactive dye was added (0.8 nmol; GE Healthcare), and incubation was continued for 30 minutes at 37°C. The reaction was terminated by the addition of an equal volume of 2X sample buffer (7M urea, 2M thiourea, 4% CHAPS, 130mM dithiothreitol, 2% ampholytes). After 15 minutes at 4°C, the sample was diluted to a final volume of 450 μL with rehydration buffer (7M urea, 2M thiourea, 4% CHAPS, 13mM dithiothreitol, 1% ampholytes). A mixed internal standard (IS) was prepared by combining an aliquot of protein lysate from each sample. This mixture was saturation-labeled with Cy3 using the same ratio of dye and TCEP to protein as for the Cy5-labeled samples. Samples were stored frozen at −80°C until use.

A mixture of Cy5-labeled sample and Cy3-labeled IS was loaded into a 24-cm strip holder containing a nonlinear immobilized pH gradient strip (pH, 3-10) and overlaid with Immobiline DryStrip Cover Fluid (GE Healthcare). Rehydration was performed for 15 hours at 20°C with an applied electric field of 30 V. For first-dimension electrophoresis, electric potentials of 500 V for 1 hour, 1000 V for 2 hours, and 8000 V for 7 hours were applied. The strip was removed and equilibrated twice in 6M urea, 100mM Tris-hydrochloride (pH 8), sodium dodecyl sulfate, 2% (SDS), 32.5mM dithiothreitol, and glycerol, 30%, for 15 minutes at room temperature. The strip was applied to the top of an SDS gel, 12.5% (25 × 20 × 0.1 cm), and electrophoresis was performed using 10 mA per gel overnight. The gel was removed and scanned separately for Cy5 and Cy3 fluorescence using a GE Healthcare Typhoon 9400 Series Variable Imager.

Spots were defined using the GE Healthcare DeCyder software package and matched across all gels. Intensity data were log transformed and normalized such that the mean log spot intensities in the Cy5 and Cy3 images of each gel were equal. We calculated an internal ratio (IR) of the normalized volume of each spot in the experimental sample vs the volume of the same spot in the IS: ln (IR)(i,j) = ln [S(i,j)] – ln [IS(i,j)], where S(i,j) is the normalized volume of sample spot i on gel j, and IS(i,j) is the normalized volume of the corresponding IS. This IR is a measure of relative protein abundance in the sample and can be used as the basis for between-sample comparisons. Candidate biomarkers were identified and ranked using the ln (IR)(i,j) values as input for a Significance Analysis of Microarrays (SAM) (version 3.0; free downloadable statistical software available at: http://www-stat.stanford.edu/~tibs/SAM/). For each spot, the SAM analysis provides a relative difference score, d(i) that is calculated based on the mean difference between groups divided by the sum of the spot-specific scatter (variance) and a measure of scatter (variance) common to all proteins.30 The false discovery rate (FDR) for each spot is calculated based on the SAM scores from permutations of the data.31By working with SAM scores, the calculation avoids the task of simulating new data from a population having an unknown correlation structure. The reported q values represent the FDR for the spot list that includes the spot and all spots that are more significant. The q value is used as the measure of significance for the study. Cancer and normal tissue samples from the same location were taken from the same patient, resulting in paired data within each cancer location. As such, all comparisons involving cancer vs normal tissue used paired analyses, whereas comparison of cancer locations used unpaired analyses. Data were further analyzed with the DeCyder Extended Data Analysis (EDA) software (GE Healthcare) to perform a principal component analysis and hierarchical clustering using average linkage.

PATIENT DEMOGRAPHICS

In the AQUA cohort, 71 patients met criteria and were included in the TMA (59 men and 12 women), with age at diagnosis ranging from 36 to 76 years. In the LCM/2D-DIGE cohort there were 14 patients (7 men and 7 women) with age at diagnosis ranging from 45 to 74 years. In both cohorts, patients were classified by sex, primary subsite, TNM stage, histologic grade, tumor type (recurrent vs primary), and treatment (Table 1).

Table Graphic Jump LocationTable 1. Demographic, Clinical, and Pathologic Dataa
AQUA QUANTITATIVE PROTEIN EXPRESSION ANALYSIS

The AQUA study was performed as described in the “Methods” section using antibodies to p14, cyclin D1, Rb, and p53. For each antibody, scores were reported as the mean of 2 duplicate histospots, colocalized to the nuclear subcellular compartment. There was considerable interindividual variation (median coefficient of variation) but no obvious difference between subsites. To explore more thoroughly whether there were any significant differences, protein expression scores were compared between subsites by nonparametric Kruskal-Wallis test. There was no significant difference between subsites for any of the examined proteins (P > .50) (Figure 1 and Table 2). Similar results were obtained after removing the unknown primary patients from the analysis (data not shown).

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Figure 1.

Protein expression between subsites. A, p14; B, cyclin D1; C, Rb; D, p53. Nuclear expression of cell cycle control proteins was compared by anatomic subsite within the head and neck. Data are represented as standard box plots. Boxes indicate the first and third quartiles, with a bar indicating the median. Circles denote outliers, defined as 1.5 times the interquartile range below the first quartile or above the third quartile. Vertical bars denote the highest and lowest values that are not outliers. The unit of measure for the y-axis is arbitrary fluorescence intensity expressed on a scale of 0 to 255. Differences in expression levels between subsites were not significant in panels A-D (P>.50 for all comparisons).

Graphic Jump Location
Table Graphic Jump LocationTable 2. Cell Cycle Control Protein Expression by AQUA Analysis

Secondary analysis was performed to correlate AQUA scores for each protein with pathologic and demographic variables and between AQUA scores. There was a significant positive correlation between nuclear p14 (P = .03) and Rb (P = .04) expression and advanced (stage III or IV) disease. Poorly differentiated tumors had elevated p53 levels compared with well or moderately differentiated tumors (P = .02). There was a significant correlation between p14 and cyclin D1 (P < .001), Rb (P = .01), and p53 (P = .003) expression, and between cyclin D1 and Rb (P = .004) expression by AQUA. There were no other significant correlations between proteins (P > .50). Figure 2 presents graphical scatterplot representations of nuclear protein expression correlations.

Place holder to copy figure label and caption
Figure 2.

Protein expression correlation by the automated quantitative analysis of protein expression method. Nuclear expression of cell cycle control proteins was correlated by Spearman rank correlation. Panels A-D are plots showing correlation between expression levels of proteins indicated on the x- and y-axes in each panel. Circles represent individual cases. The units of measure for the x- and y-axes are arbitrary fluorescence intensities expressed on a scale of 0 to 255. Curves represent correlation plot (middle curve) and 95% confidence intervals (upper and lower curves).

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LCM/2D-DIGE ANALYSIS

To expand the search for differences in protein expression patterns among subsites, we performed large-scale proteomic profiling. A second cohort was identified for which matched tumor and adjacent normal frozen tissue was available, because proteomic profiling by LCM/2D-DIGE analysis cannot be performed with fixed specimens. Pure cell samples from lesional tissue were obtained by LCM as described in the “Methods” section. Figure 3 depicts tissue sections before and after LCM enrichment, as well as a representative 2D gel that demonstrates the ability of the procedure to separate individual proteins in 2 dimensions. Approximately 5000 cells for each sample were captured. For quantitative analysis, proteins from 28 samples (14 cancer and 14 matched normal samples) were extracted, labeled, and analyzed by 2D-DIGE. An average of 2156 spots was identified by the DeCyder software on each gel. Of these, an average of 1310 was matched to the master spot map. From this group of spots, manual inspection revealed that 732 spots were unequivocally present on more than 90% of gels. We determined an expression level for each spot in each gel with reference to the invariant IS (see the “Methods” section).

Place holder to copy figure label and caption
Figure 3.

Laser capture microdissection (LCM) and 2-dimensional (2D) difference gel electrophoresis. Panels A-E illustrate the process of LCM. A, Hematoxylin-eosin– stained pilot section. B, The nuclear fast red-stained sample prior to LCM. C, The black circles indicate areas where the laser has had an impact on the tissue. D, The leftover tissue that remains on the slide. E, The captured tissue. F, A representative merged image of a 2D gel. Proteins from the captured tissue (tumor labeled with Cy5 and shown in red, mixed internal standard labeled with Cy3 and shown in green) are separated based on isoelectric point and molecular weight. Proteins more abundant in the mixed internal standard are green, proteins more abundant in cancer are red, and equally expressed proteins are yellow.

Graphic Jump Location

The IR values were used as input data for a SAM calculation, which was performed as described in the “LCM/ 2D-DIGE Study” subsection in the “Methods” section. We performed a multiclass SAM analysis based on the difference of cancer- and patient-matched normal expression values for each spot. There were no significant differences by subsite based on a cutoff value for q of less than 10% (all q values were >30%; data not shown). We performed an additional multiclass SAM analysis based on the sum of the cancer and normal expression values for each spot. This analysis was designed to detect differences based on the anatomical location from which the tissue was derived irrespective of whether it was cancerous. Again, all q values were greater than 30% in the subsite comparisons.

To demonstrate that the analytical methods were sensitive and appropriate, we performed a paired analysis of all cancers and all normal tissue, regardless of subsite. Results are plotted in Figure 4A. Spots that fall outside the parallel diagonal lines had q values of less than 10% and are thus candidates for further investigation. Of 732 proteins, 348 (47.5%) met the 10% threshold, and 129 of 732 (17.6%) met a more stringent threshold of zero percent. By contrast, pair-wise comparisons of cancers by subsite (laryngeal cancer vs oral cancer, laryngeal cancer vs oropharyngeal cancer, and oropharyngeal cancer vs oral cancer; the plot sheets shown in Figure 4B-D) showed no proteins that met the q value threshold of 10%.

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Figure 4.

Significance analysis of microarrays (SAM) plot sheets. These panels depict the graphical representation of the SAM analysis. The diagonal lines delineate the bounds for normal variation. Circles that are outside the lines represent proteins that are differentially expressed between samples, with either increased abundance in normal (red), or increased abundance in cancer (green), as in panel A, a comparison of normal and cancer. Circles within the lines represent proteins that are not significantly different in the comparison, as in panels B, C, and D, representing the comparisons between laryngeal vs oral cancer, oropharyngeal vs laryngeal cancer, and oropharyngeal vs oral cancer, respectively.

Graphic Jump Location

A secondary analysis was performed by hierarchical clustering and principal component analysis using DeCyder EDA software. A heat map (Figure 5A) depicted the relative abundance of each protein in each sample using a color scale, with samples grouped using a hierarchical clustering algorithm. Cancer and normal samples clustered spontaneously in 2 groups, but within these groups, specimens did not cluster by anatomical subsite. A similar conclusion was reached by principal component analyses (Figure 5B). Cancer and normal specimens each formed discrete clusters, whereas anatomic subsites were intermingled.

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Figure 5.

Heat map and principal component (PC) analysis. A, Heat map representing the 732 proteins present on more than 90% of all gels. LC indicates laryngeal cancer; LN, laryngeal normal; OC, oral cancer; ON, oral normal; OPC, oropharyngeal cancer; OPN, oropharyngeal normal. The black line in the middle indicates separation of cancer and normal. Red, green, and black squares indicate that the expression of genes is greater than, less than, or equal to the median level of expression across all tissue samples, respectively. B, Principal component analysis derived from the expression levels of the same 732 proteins. PC1 represents the first principal component; PC2, the second principal component.

Graphic Jump Location

Although different areas of benign squamous mucosa have unique histologic appearances, invasive HNSCCs arising at different subsites in the oral cavity and larynx have no characteristic histologic differences. Despite this similarity, it is well established that HNSCCs from different subsites differ in survival and recurrence rates.5 One hypothesis to explain this observation is that there are characteristic molecular alterations particular to tumors arising at different subsites. If so, these should be detectable by proteomic profiling. In the present study, we applied 2 complementary, quantitative profiling methods. The AQUA method is an antibody-based approach that allows measurement of a predetermined set of markers while preserving spatial information. The 2D-DIGE method is a biochemical approach based on separation and quantification of proteins without prior assumptions about which proteins are likely to be important in a given biological process. We found no significant differences at the proteomic level when HNSCCs arising at different anatomical subsites were compared (> .05 for AQUA data; q > 10% for 2D-DIGE data).

Prior studies have provided conflicting evidence about whether there are fundamentally different molecular mechanisms underlying cancer progression in varying subsites within the head and neck. An early study by Takes et al10 examined 3 of the same markers characterized herein: cyclin D1, p53, and Rb. They10 reported that cyclin D1 was elevated in pharyngeal cancer compared with other subsites, whereas differences in the other markers were not significant. Consistent with this cyclin D1 finding, 2 studies by Freier et al11,12 reported that cyclin D1 protein was elevated in pharyngeal and laryngeal cancers and that the corresponding CCND1 locus was amplified in pharyngeal cancer relative to other sites. By contrast, Huang et al13 showed that, although the CCND1 locus was often amplified in HNSCC, there were no significant differences between subsites. Similarly, Volavsek et al14 reported no differences in p53, cyclin D1, p21, or Rb levels between cancers of the hypopharynx and larynx subsites. Our immunohistochemical data, acquired using the AQUA method, agree with the latter 2 studies.

Differences in patient populations may provide an explanation for the discrepancies between studies. We suggest that a particularly important difference may be the prevalence of HPV-related vs HPV-unrelated HNSCC. Estimates of the fraction of HNSCC that is HPV-related vary from 20% to 30%.32,33 A multicenter study33 found that although HPV may be present in HNSCCs from all subsites, it is most common in the oropharynx. If the overall presence of HPV-related disease is more common in some populations than in others, it would explain why oropharyngeal HNSCC seems to be molecularly distinct in these populations. This hypothesis remains to be explored.

It is possible that methodological differences or limitations play some role in explaining the differences between studies, for example the use of AQUA vs other scoring methods for immunohistochemical analysis, or the analysis of microdissected tissue vs bulk specimens. The AQUA method has proven useful, however, for detecting prognostically important biomarkers, as well as for performing high-throughput pathway analysis.3436 Similarly, although 2D-DIGE samples only a small fraction of the expressed proteins in a human cell (<1% based on a proteome size of ≥ 1 × 105 proteins),37 several studies18,38 have demonstrated that the combined LCM/2D-DIGE technique used in the current study is sensitive enough to discern differences between cancer stages or subtypes (see also Arnouk et al39).

Our finding of no systematic difference between HNSCCs from various subsites within the head and neck should not be taken to imply a lack of heterogeneity among HNSCCs. By AQUA analysis we found a large median coefficient of variance for each cell cycle protein studied, demonstrating considerable inherent biologic diversity within HNSCCs. There is no a priori reason why similar aerodigestive mucosal surfaces should have fundamentally different carcinogenic pathways based only on anatomic subsite designation, anymore than a basal cell carcinoma (BCC) of the cheek should be molecularly distinct from a BCC arising on the arm. Instead, we propose that observed heterogeneity may reflect divergent etiologic pathways irrespective of subsite.

Correspondence: William S. Dynan, PhD, Institute for Molecular Medicine and Genetics, Medical College of Georgia, 1120 15th St, Room CB-2803, Augusta, GA 30912 (wdynan@mcg.edu).

Submitted for Publication: May 1, 2008; final revision received August 11, 2008; accepted October 6, 2008.

Author Contributions: Dr Weinberger and Mr Merkley share first authorship on this manuscript, had full access to all the data in the study, and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Weinberger, Merkley, Lee, Adam, Sasaki, Psyrri, and Dynan. Acquisition of data: Weinberger, Merkley, Lee, Gourin, and Haffty. Analysis and interpretation of data: Weinberger, Merkley, Podolsky, Papadavid, and Dynan. Drafting of the manuscript: Weinberger, Merkley, Lee, Papadavid, and Dynan. Critical revision of the manuscript for important intellectual content: Weinberger, Merkley, Adam, Gourin, Podolsky, Haffty, Sasaki, Psyrri, and Dynan. Statistical analysis: Weinberger and Podolsky. Obtained funding: Weinberger, Sasaki, and Dynan. Administrative, technical, and material support: Adam, Gourin, Haffty, Papadavid, and Dynan. Study supervision: Gourin, Psyrri, and Dynan.

Financial Disclosure: None reported.

Funding/Support: This work was supported by a National Institutes of Health grant (No. R33 CA95941) (Dr Dynan) and a CORE resident research grant (No. 2006-26357) from the American Academy of Otolaryngology/Head and Neck Surgery (Dr Weinberger).

Previous Presentation: The study was given as an oral presentation at the American Academy of Otolaryngology–Head and Neck Surgery Foundation 112th Annual Meeting and Otolaryngology Expo; September 23, 2008; Chicago, Illinois (abstract RF137).

Additional Information: For this study, Dr Weinberger was selected for a Resident Research Award (second place).

Landis  SHMurray  TBolden  SWingo  PA Cancer statistics, 1999. CA Cancer J Clin 1999;49 (1) 8- 31
Link to Article
Sasaki  CTJassin  B Cancer of the pharynx and larynx. Am J Med 2001;111 ((suppl 8A)) 118S- 123S
PubMed Link to Article
Sessions  RBForastiere  AA Cancers of the larynx and hypopharynx. De Vita  VTHellman  SRosenberg  SACancer: Principles and Practice of Oncology. 5th ed. New York, NY Lippincott-Raven1997;
Werner  JADunne  AAMyers  JN Functional anatomy of the lymphatic drainage system of the upper aerodigestive tract and its role in metastasis of squamous cell carcinoma. Head Neck 2003;25 (4) 322- 332
PubMed Link to Article
Hoffman  HTKarnell  LHFunk  GFRobinson  RAMenck  HR The National Cancer Data Base report on cancer of the head and neck. Arch Otolaryngol Head Neck Surg 1998;124 (9) 951- 962
PubMed Link to Article
Al-Sarraf  M Treatment of locally advanced head and neck cancer: historical and critical review. Cancer Control 2002;9 (5) 387- 399
PubMed
Goodwin  WJThomas  GRParker  DF  et al.  Unequal burden of head and neck cancer in the United States. Head Neck 2008;30 (3) 358- 371
PubMed Link to Article
Répássy  GForster-Horvath  CJuhasz  AAdany  RTamassy  ATimar  J Expression of invasion markers CD44v6/v3, NM23 and MMP2 in laryngeal and hypopharyngeal carcinoma. Pathol Oncol Res 1998;4 (1) 14- 21
PubMed Link to Article
Lukits  JTimar  JJuhasz  ADome  BPaku  SRepassy  G Progression difference between cancers of the larynx and hypopharynx is not due to tumor size and vascularization. Otolaryngol Head Neck Surg 2001;125 (1) 18- 22
PubMed Link to Article
Takes  RPBaatenburg de Jong  RJSchuuring  ELitvinov  SVHermans  JVan Krieken  JH Differences in expression of oncogenes and tumor suppressor genes in different sites of head and neck squamous cell. Anticancer Res 1998;18 (6B) 4793- 4800
PubMed
Freier  KJoos  SFlechtenmacher  C  et al.  Tissue microarray analysis reveals site-specific prevalence of oncogene amplifications in head and neck squamous cell carcinoma. Cancer Res 2003;63 (6) 1179- 1182
PubMed
Freier  KBosch  FXFlechtenmacher  C  et al.  Distinct site-specific oncoprotein overexpression in head and neck squamous cell carcinoma: a tissue microarray analysis. Anticancer Res 2003;23 (5A) 3971- 3977
PubMed
Huang  QYu  GPMcCormick  SA  et al.  Genetic differences detected by comparative genomic hybridization in head and neck squamous cell carcinomas from different tumor sites: construction of oncogenetic trees for tumor progression. Genes Chromosomes Cancer 2002;34 (2) 224- 233
PubMed Link to Article
Volavsek  MBracko  MGale  N Distribution and prognostic significance of cell cycle proteins in squamous carcinoma of the larynx, hypopharynx and adjacent epithelial hyperplastic lesions. J Laryngol Otol 2003;117 (4) 286- 293
PubMed Link to Article
Shaw  JRowlinson  RNickson  J  et al.  Evaluation of saturation labelling two-dimensional difference gel electrophoresis fluorescent dyes. Proteomics 2003;3 (7) 1181- 1195
PubMed Link to Article
Sitek  BLuttges  JMarcus  K  et al.  Application of fluorescence difference gel electrophoresis saturation labelling for the analysis of microdissected precursor lesions of pancreatic ductal adenocarcinoma. Proteomics 2005;5 (10) 2665- 2679
PubMed Link to Article
Wilson  KEMarouga  RPrime  JE  et al.  Comparative proteomic analysis using samples obtained with laser microdissection and saturation dye labelling. Proteomics 2005;5 (15) 3851- 3858
PubMed Link to Article
Greengauz-Roberts  OStoppler  HNomura  S  et al.  Saturation labeling with cysteine-reactive cyanine fluorescent dyes provides increased sensitivity for protein expression profiling of laser-microdissected clinical specimens. Proteomics 2005;5 (7) 1746- 1757
PubMed Link to Article
Shekouh  ARThompson  CCPrime  W  et al.  Application of laser capture microdissection combined with two-dimensional electrophoresis for the discovery of differentially regulated proteins in pancreatic ductal adenocarcinoma. Proteomics 2003;3 (10) 1988- 2001
PubMed Link to Article
Kondo  TSeike  MMori  YFujii  KYamada  THirohashi  S Application of sensitive fluorescent dyes in linkage of laser microdissection and two-dimensional gel electrophoresis as a cancer proteomic study tool. Proteomics 2003;3 (9) 1758- 1766
PubMed Link to Article
Haffty  BGWilson  LDSon  YH  et al.  Concurrent chemo-radiotherapy with mitomycin C compared with porfiromycin in squamous cell cancer of the head and neck: final results of a randomized clinical trial. Int J Radiat Oncol Biol Phys 2005;61 (1) 119- 128
PubMed Link to Article
Rimm  DLCamp  RLCharette  LACosta  JOlsen  DAReiss  M Tissue microarray: a new technology for amplification of tissue resources. Cancer J 2001;7 (1) 24- 31
PubMed
Hoos  ACordon-Cardo  C Tissue microarray profiling of cancer specimens and cell lines: opportunities and limitations. Lab Invest 2001;81 (10) 1331- 1338
PubMed Link to Article
Camp  RLCharette  LARimm  DL Validation of tissue microarray technology in breast carcinoma. Lab Invest 2000;80 (12) 1943- 1949
PubMed Link to Article
Fernebro  EDictor  MBendahl  POFerno  MNilbert  M Evaluation of the tissue microarray technique for immunohistochemical analysis in rectal cancer. Arch Pathol Lab Med 2002;126 (6) 702- 705
PubMed
Camp  RLChung  GGRimm  DL Automated subcellular localization and quantification of protein expression in tissue microarrays. Nat Med 2002;8 (11) 1323- 1327
PubMed Link to Article
Kruskal  WHWallis  WA Use of ranks in one-criterion variance analysis. J Am Stat Assoc 1952;47 (260) 583- 621
Link to Article
Wilcoxon  F Individual comparisons by ranking methods. Biometrics Bulletin 1945;1 (6) 80- 83
Link to Article
Spearman  C Demonstration of formulae for true measurement of correlation. Am J Psychol 1907;18 (2) 161- 169
Link to Article
Tusher  VGTibshirani  RChu  G Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001;98 (9) 5116- 5121
PubMed Link to Article
Karp  NAMcCormick  PSRussell  MRLilley  KS Experimental and statistical considerations to avoid false conclusions in proteomic studies using differential in-gel electrophoresis. Mol Cell Proteomics 2007;6 (8) 1354- 1364
PubMed Link to Article
Ragin  CCModugno  FGollin  SM The epidemiology and risk factors of head and neck cancer: a focus on human papillomavirus. J Dent Res 2007;86 (2) 104- 114
PubMed Link to Article
Herrero  RCastellsague  XPawlita  M  et al. IARC Multicenter Oral Cancer Study Group, Human papillomavirus and oral cancer: the International Agency for Research on Cancer multicenter study. J Natl Cancer Inst 2003;95 (23) 1772- 1783
PubMed Link to Article
Psyrri  ABamias  AYu  Z  et al.  Subcellular localization and protein levels of cyclin-dependent kinase inhibitor p27 independently predict for survival in epithelial ovarian cancer. Clin Cancer Res 2005;11 (23) 8384- 8390
PubMed Link to Article
Yu  ZWeinberger  PMHaffty  BG  et al.  Cyclin D1 is a valuable prognostic marker in oropharyngeal squamous cell carcinoma. Clin Cancer Res 2005;11 (3) 1160- 1166
PubMed
Yu  ZWeinberger  PMProvost  E  et al.  Beta-catenin functions mainly as an adhesion molecule in patients with squamous cell cancer of the head and neck. Clin Cancer Res 2005;11 (7) 2471- 2477
PubMed Link to Article
Harrison  PMKumar  ALang  NSnyder  MGerstein  M A question of size: the eukaryotic proteome and the problems in defining it. Nucleic Acids Res 2002;30 (5) 1083- 1090
PubMed Link to Article
Hatakeyama  HKondo  TFujii  K  et al.  Protein clusters associated with carcinogenesis, histological differentiation and nodal metastasis in esophageal cancer. Proteomics 2006;6 (23) 6300- 6316
PubMed Link to Article
Arnouk  HMerkley  MAPodolsky  RH  et al.  Characterization of molecular markers indicative of cervical cancer progression. Proteomics Clin Appl In press

Figures

Place holder to copy figure label and caption
Figure 1.

Protein expression between subsites. A, p14; B, cyclin D1; C, Rb; D, p53. Nuclear expression of cell cycle control proteins was compared by anatomic subsite within the head and neck. Data are represented as standard box plots. Boxes indicate the first and third quartiles, with a bar indicating the median. Circles denote outliers, defined as 1.5 times the interquartile range below the first quartile or above the third quartile. Vertical bars denote the highest and lowest values that are not outliers. The unit of measure for the y-axis is arbitrary fluorescence intensity expressed on a scale of 0 to 255. Differences in expression levels between subsites were not significant in panels A-D (P>.50 for all comparisons).

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.

Protein expression correlation by the automated quantitative analysis of protein expression method. Nuclear expression of cell cycle control proteins was correlated by Spearman rank correlation. Panels A-D are plots showing correlation between expression levels of proteins indicated on the x- and y-axes in each panel. Circles represent individual cases. The units of measure for the x- and y-axes are arbitrary fluorescence intensities expressed on a scale of 0 to 255. Curves represent correlation plot (middle curve) and 95% confidence intervals (upper and lower curves).

Graphic Jump Location
Place holder to copy figure label and caption
Figure 3.

Laser capture microdissection (LCM) and 2-dimensional (2D) difference gel electrophoresis. Panels A-E illustrate the process of LCM. A, Hematoxylin-eosin– stained pilot section. B, The nuclear fast red-stained sample prior to LCM. C, The black circles indicate areas where the laser has had an impact on the tissue. D, The leftover tissue that remains on the slide. E, The captured tissue. F, A representative merged image of a 2D gel. Proteins from the captured tissue (tumor labeled with Cy5 and shown in red, mixed internal standard labeled with Cy3 and shown in green) are separated based on isoelectric point and molecular weight. Proteins more abundant in the mixed internal standard are green, proteins more abundant in cancer are red, and equally expressed proteins are yellow.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 4.

Significance analysis of microarrays (SAM) plot sheets. These panels depict the graphical representation of the SAM analysis. The diagonal lines delineate the bounds for normal variation. Circles that are outside the lines represent proteins that are differentially expressed between samples, with either increased abundance in normal (red), or increased abundance in cancer (green), as in panel A, a comparison of normal and cancer. Circles within the lines represent proteins that are not significantly different in the comparison, as in panels B, C, and D, representing the comparisons between laryngeal vs oral cancer, oropharyngeal vs laryngeal cancer, and oropharyngeal vs oral cancer, respectively.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 5.

Heat map and principal component (PC) analysis. A, Heat map representing the 732 proteins present on more than 90% of all gels. LC indicates laryngeal cancer; LN, laryngeal normal; OC, oral cancer; ON, oral normal; OPC, oropharyngeal cancer; OPN, oropharyngeal normal. The black line in the middle indicates separation of cancer and normal. Red, green, and black squares indicate that the expression of genes is greater than, less than, or equal to the median level of expression across all tissue samples, respectively. B, Principal component analysis derived from the expression levels of the same 732 proteins. PC1 represents the first principal component; PC2, the second principal component.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Demographic, Clinical, and Pathologic Dataa
Table Graphic Jump LocationTable 2. Cell Cycle Control Protein Expression by AQUA Analysis

References

Landis  SHMurray  TBolden  SWingo  PA Cancer statistics, 1999. CA Cancer J Clin 1999;49 (1) 8- 31
Link to Article
Sasaki  CTJassin  B Cancer of the pharynx and larynx. Am J Med 2001;111 ((suppl 8A)) 118S- 123S
PubMed Link to Article
Sessions  RBForastiere  AA Cancers of the larynx and hypopharynx. De Vita  VTHellman  SRosenberg  SACancer: Principles and Practice of Oncology. 5th ed. New York, NY Lippincott-Raven1997;
Werner  JADunne  AAMyers  JN Functional anatomy of the lymphatic drainage system of the upper aerodigestive tract and its role in metastasis of squamous cell carcinoma. Head Neck 2003;25 (4) 322- 332
PubMed Link to Article
Hoffman  HTKarnell  LHFunk  GFRobinson  RAMenck  HR The National Cancer Data Base report on cancer of the head and neck. Arch Otolaryngol Head Neck Surg 1998;124 (9) 951- 962
PubMed Link to Article
Al-Sarraf  M Treatment of locally advanced head and neck cancer: historical and critical review. Cancer Control 2002;9 (5) 387- 399
PubMed
Goodwin  WJThomas  GRParker  DF  et al.  Unequal burden of head and neck cancer in the United States. Head Neck 2008;30 (3) 358- 371
PubMed Link to Article
Répássy  GForster-Horvath  CJuhasz  AAdany  RTamassy  ATimar  J Expression of invasion markers CD44v6/v3, NM23 and MMP2 in laryngeal and hypopharyngeal carcinoma. Pathol Oncol Res 1998;4 (1) 14- 21
PubMed Link to Article
Lukits  JTimar  JJuhasz  ADome  BPaku  SRepassy  G Progression difference between cancers of the larynx and hypopharynx is not due to tumor size and vascularization. Otolaryngol Head Neck Surg 2001;125 (1) 18- 22
PubMed Link to Article
Takes  RPBaatenburg de Jong  RJSchuuring  ELitvinov  SVHermans  JVan Krieken  JH Differences in expression of oncogenes and tumor suppressor genes in different sites of head and neck squamous cell. Anticancer Res 1998;18 (6B) 4793- 4800
PubMed
Freier  KJoos  SFlechtenmacher  C  et al.  Tissue microarray analysis reveals site-specific prevalence of oncogene amplifications in head and neck squamous cell carcinoma. Cancer Res 2003;63 (6) 1179- 1182
PubMed
Freier  KBosch  FXFlechtenmacher  C  et al.  Distinct site-specific oncoprotein overexpression in head and neck squamous cell carcinoma: a tissue microarray analysis. Anticancer Res 2003;23 (5A) 3971- 3977
PubMed
Huang  QYu  GPMcCormick  SA  et al.  Genetic differences detected by comparative genomic hybridization in head and neck squamous cell carcinomas from different tumor sites: construction of oncogenetic trees for tumor progression. Genes Chromosomes Cancer 2002;34 (2) 224- 233
PubMed Link to Article
Volavsek  MBracko  MGale  N Distribution and prognostic significance of cell cycle proteins in squamous carcinoma of the larynx, hypopharynx and adjacent epithelial hyperplastic lesions. J Laryngol Otol 2003;117 (4) 286- 293
PubMed Link to Article
Shaw  JRowlinson  RNickson  J  et al.  Evaluation of saturation labelling two-dimensional difference gel electrophoresis fluorescent dyes. Proteomics 2003;3 (7) 1181- 1195
PubMed Link to Article
Sitek  BLuttges  JMarcus  K  et al.  Application of fluorescence difference gel electrophoresis saturation labelling for the analysis of microdissected precursor lesions of pancreatic ductal adenocarcinoma. Proteomics 2005;5 (10) 2665- 2679
PubMed Link to Article
Wilson  KEMarouga  RPrime  JE  et al.  Comparative proteomic analysis using samples obtained with laser microdissection and saturation dye labelling. Proteomics 2005;5 (15) 3851- 3858
PubMed Link to Article
Greengauz-Roberts  OStoppler  HNomura  S  et al.  Saturation labeling with cysteine-reactive cyanine fluorescent dyes provides increased sensitivity for protein expression profiling of laser-microdissected clinical specimens. Proteomics 2005;5 (7) 1746- 1757
PubMed Link to Article
Shekouh  ARThompson  CCPrime  W  et al.  Application of laser capture microdissection combined with two-dimensional electrophoresis for the discovery of differentially regulated proteins in pancreatic ductal adenocarcinoma. Proteomics 2003;3 (10) 1988- 2001
PubMed Link to Article
Kondo  TSeike  MMori  YFujii  KYamada  THirohashi  S Application of sensitive fluorescent dyes in linkage of laser microdissection and two-dimensional gel electrophoresis as a cancer proteomic study tool. Proteomics 2003;3 (9) 1758- 1766
PubMed Link to Article
Haffty  BGWilson  LDSon  YH  et al.  Concurrent chemo-radiotherapy with mitomycin C compared with porfiromycin in squamous cell cancer of the head and neck: final results of a randomized clinical trial. Int J Radiat Oncol Biol Phys 2005;61 (1) 119- 128
PubMed Link to Article
Rimm  DLCamp  RLCharette  LACosta  JOlsen  DAReiss  M Tissue microarray: a new technology for amplification of tissue resources. Cancer J 2001;7 (1) 24- 31
PubMed
Hoos  ACordon-Cardo  C Tissue microarray profiling of cancer specimens and cell lines: opportunities and limitations. Lab Invest 2001;81 (10) 1331- 1338
PubMed Link to Article
Camp  RLCharette  LARimm  DL Validation of tissue microarray technology in breast carcinoma. Lab Invest 2000;80 (12) 1943- 1949
PubMed Link to Article
Fernebro  EDictor  MBendahl  POFerno  MNilbert  M Evaluation of the tissue microarray technique for immunohistochemical analysis in rectal cancer. Arch Pathol Lab Med 2002;126 (6) 702- 705
PubMed
Camp  RLChung  GGRimm  DL Automated subcellular localization and quantification of protein expression in tissue microarrays. Nat Med 2002;8 (11) 1323- 1327
PubMed Link to Article
Kruskal  WHWallis  WA Use of ranks in one-criterion variance analysis. J Am Stat Assoc 1952;47 (260) 583- 621
Link to Article
Wilcoxon  F Individual comparisons by ranking methods. Biometrics Bulletin 1945;1 (6) 80- 83
Link to Article
Spearman  C Demonstration of formulae for true measurement of correlation. Am J Psychol 1907;18 (2) 161- 169
Link to Article
Tusher  VGTibshirani  RChu  G Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001;98 (9) 5116- 5121
PubMed Link to Article
Karp  NAMcCormick  PSRussell  MRLilley  KS Experimental and statistical considerations to avoid false conclusions in proteomic studies using differential in-gel electrophoresis. Mol Cell Proteomics 2007;6 (8) 1354- 1364
PubMed Link to Article
Ragin  CCModugno  FGollin  SM The epidemiology and risk factors of head and neck cancer: a focus on human papillomavirus. J Dent Res 2007;86 (2) 104- 114
PubMed Link to Article
Herrero  RCastellsague  XPawlita  M  et al. IARC Multicenter Oral Cancer Study Group, Human papillomavirus and oral cancer: the International Agency for Research on Cancer multicenter study. J Natl Cancer Inst 2003;95 (23) 1772- 1783
PubMed Link to Article
Psyrri  ABamias  AYu  Z  et al.  Subcellular localization and protein levels of cyclin-dependent kinase inhibitor p27 independently predict for survival in epithelial ovarian cancer. Clin Cancer Res 2005;11 (23) 8384- 8390
PubMed Link to Article
Yu  ZWeinberger  PMHaffty  BG  et al.  Cyclin D1 is a valuable prognostic marker in oropharyngeal squamous cell carcinoma. Clin Cancer Res 2005;11 (3) 1160- 1166
PubMed
Yu  ZWeinberger  PMProvost  E  et al.  Beta-catenin functions mainly as an adhesion molecule in patients with squamous cell cancer of the head and neck. Clin Cancer Res 2005;11 (7) 2471- 2477
PubMed Link to Article
Harrison  PMKumar  ALang  NSnyder  MGerstein  M A question of size: the eukaryotic proteome and the problems in defining it. Nucleic Acids Res 2002;30 (5) 1083- 1090
PubMed Link to Article
Hatakeyama  HKondo  TFujii  K  et al.  Protein clusters associated with carcinogenesis, histological differentiation and nodal metastasis in esophageal cancer. Proteomics 2006;6 (23) 6300- 6316
PubMed Link to Article
Arnouk  HMerkley  MAPodolsky  RH  et al.  Characterization of molecular markers indicative of cervical cancer progression. Proteomics Clin Appl In press

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