Correlates and Determinants of Nuclear Epidermal Growth Factor Receptor Content in an Oropharyngeal Cancer Tissue Microarray

Squamous cell carcinoma of the head and neck richly expresses the epidermal growth factor receptor (EGFR), a receptor tyrosine kinase with an extensively described role in mediating pleiotropic cellular responses (1). Upon binding by a number of ligands, the receptor enters into ligand-receptor dimers or oligomers and is autophosphorylated. The phosphorylated receptor then recruits docking proteins and signal transduction molecules, resulting in the activation of cascades that are antiapoptotic and proliferative and that contribute to metastasis and angiogenesis. Overexpression of EGFR, often associated with increased production of ligands such as transforming growth factor-α, has been consistently reported in squamous cell carcinomas of the head and neck (2). Overexpression and/or increased activity of EGFR in tumors have also been linked to chemotherapy and radiation resistance (3-5). Therefore, EGFR has been considered a promising therapeutic target for squamous cell carcinomas of the head and neck. Both inhibition of ligand-driven receptor activation by administration of a chimeric monoclonal antibody cetuximab, which competitively inhibits ligand binding, and inhibition of the kinase activity of the receptors have been studied in head and neck cancer (6-10). Indeed, treatment of patients with locally advanced squamous cell carcinoma of the head and neck with cetuximab combined with radiation significantly prolongs overall survival compared with radiation alone (11).

The presence of EGFR in the nuclei of normal but highly proliferative cells (12) has been reported for more than a decade. However, it was only recently that a potential role of nuclear EGFR in carcinogenesis was showed (13-15). Experiments in A431 squamous cell carcinoma and MDA-MB-468 breast carcinoma cell lines have showed that the levels of EGFR in the nuclear fraction increase on treatment with EGF and that nuclear EGFR is highly tyrosine phosphorylated (14). There is now compelling biological evidence of a nuclear EGFR signaling network that transmits growth factor signals directly from the cytoplasmic membrane to transcriptional targets in the nucleus, bypassing the traditional protein phosphorylation cascades such as those involving phosphatidylinositol-3 kinase, Ras, and phospholipase C-γ. It has been shown that the juxtamembrane region of EGFR harbors a putative nuclear localization sequence that mediates the nuclear localization of EGFR (16). The C-terminus of intact EGFR contains a proline-rich sequence identical to transactivation domains for transcription factors. EGFR lacks a DNA-binding domain; however, nuclear EGFR can physically interact with other transcriptional factors containing DNA-binding domains such as STAT3 and E2F1 (15); it can thereby up-regulate the expression of target genes, including cyclin D1, iNOS, and B-myb (14).

In addition, nuclear EGFR phosphorylates and stabilizes proliferating cell nuclear antigen (PCNA) in its chromatin-bound form (17). PCNA is a cofactor of DNA polymerases that encircles DNA and orchestrates genome duplication and reproduction of chromatin and its epigenetic information by recruiting crucial players to the replication fork (18). PCNA exists in two forms: the detergent-resistant chromatin-bound form, which is involved in DNA replication, DNA damage repair, and mismatch repair; and a chromatin-unbound form not involved in DNA synthesis (19). Inhibition of PCNA chromatin binding leads to cell death (20). PCNA has been recently found to be involved in cell cycle progression, checkpoint control, regulation of gene expression, and cellular differentiation through several binding partners (21-23). For example, PCNA facilitates the interaction between cyclin–cyclin-dependent kinase complexes and their substrates (23). The binding sites of these PCNA-binding proteins contain overlapping sequences, and the different binding partners must bind and dissociate sequentially to function (18, 24, 25). Switching of PCNA partners is regulated by affinity-driven competition, phosphorylation, proteolysis, and PCNA modification by ubiquitin and small ubiquitin-like modifier (SUMO; refs. 20, 22). Wang et al. (17) showed that nuclear EGFR phoshorylates chromatin-bound PCNA on Tyr211, and this phosphorylation is required for maintaining PCNA function on chromatin. In their study (17), PCNA Tyr211 phosphorylation was associated with pronounced increase in cell proliferation and worse survival of breast cancer patients.

Using a method of automated quantitative protein analysis with subcellular localization on an oropharyngeal cancer tissue microarray, we showed the presence of EGFR in the nuclei of tumor cells (26). In addition, nuclear EGFR levels were significantly associated with increased local recurrence rate and inferior overall survival. Tumors bearing high nuclear EGFR levels had a significantly decreased likelihood of attaining a complete response to treatment.

In the present study, we sought to identify the molecular determinants and correlates of nuclear EGFR content. To accomplish our goal, the oropharyngeal cancer tissue microarray annotated with long-term patient follow-up data, previously analyzed for nuclear EGFR receptor expression, was analyzed for key molecules of the EGFR signaling cascade. We were interested in whether the findings in vivo were consistent with the previously described in vitro model of translocation of ligand-bound phosphorylated EGFR to the nuclear compartment, resulting in the transcription of genes fostering cell proliferation.

The cohort was assembled from patients with primary and recurrent oropharyngeal cancer of squamous cell histology treated at Yale–New Haven Hospital between 1980 and 1999. Patients were treated with external beam radiotherapy or gross total surgical resection and postoperative radiotherapy. Exclusion criteria included presentation with metastatic disease or failure to receive a full course of radiation therapy. After institutional review board approval, the tissue microarray was constructed as previously described and included 94 cases that met inclusion criteria (27). Tissue cores were obtained from paraffin-embedded, formalin-fixed tissue blocks from the Yale–New Haven Hospital Department of Pathology archives. Slides from all blocks were reviewed by a pathologist (D.K.) to select representative areas of invasive tumor to be cored. The cores were placed in duplicate on the tissue microarray using a tissue microarrayer (Beecher Instrument). Cores from eight normal skin samples embedded in paraffin were included for positive controls. Hela cell lines fixed and embedded in paraffin were also included as positive controls. The tissue microarray was cut into 5 μm sections and placed on glass slides using an adhesive tape transfer system (Instrumedics, Inc.) with UV cross-linking.

Tissue microarray slides were deparaffinized and stained as previously described (26, 27). In brief, the slides were deparaffinized with xylene, followed by ethanol. After rehydration in distilled water, antigen retrieval was accomplished by pressure cooking in 0.1 mol/L citrate buffer (pH 6.0). For EGFR, antigen retrieval was accomplished by application of proteinase K and incubation for 30 minutes. Endogenous peroxidase activity was blocked by incubating in 0.3% hydrogen peroxide in methanol for 30 minutes. Nonspecific antibody binding was then blocked with 0.3% bovine serum albumin for 30 minutes at room temperature. After these steps, the slides were incubated with primary antibody to cyclin D1, Ki-67, PCNA, p53, EGFR, phosphorylated-Akt, extracellular signal-regulated kinase (ERK)-2, and PTEN at 4°C overnight. The primary antibodies and concentrations used are summarized in Table 1.

Subsequently, the slides were incubated with goat antimouse 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 antirabbit antibody conjugated to Alexa546 fluourophore (A11035, Molecular Probes). We added 4′,6-diamidino-2-phenylindole to visualize nuclei. Target (EGFR) molecules were visualized with a fluorescent chromogen (Cy-5-tyramide, Perkin Elmer Corp.). Cy-5 (red) was used because its emission peak is well outside the green-orange spectrum of tissue autofluorescence. The slides were mounted with a polyvinyl alcohol–containing aqueous mounting media with antifade reagent (n-propyl gallate, Acros Organics).

Automated image acquisition and analysis using AQUA have been described previously (28). In brief, monochromatic high-resolution (1,024 × 1,024 pixel; 0.5 μm) images were obtained of each histospot using filter cubes specific to the emission/excitation spectra of 4′,6-diamidino-2-phenylindole (358/461 nm), Alexa 546 (556/573 nm), and Cy-5 (650/670 nm, Optical Analysis). We distinguished areas of tumor from stromal elements by creating a mask from the cytokeratin signal (in this case, identified by Alexa 546 signal). A tumor nuclei–specific compartment was created by using 4′,6-diamidino-2-phenylindole signal to identify nuclei within the previously defined tumor mask. Overlapping pixels [to a 95% confidence interval (95% CI)] were excluded from the nuclear compartment. The target signal (AQUA score) was scored on a normalized scale of 1 to 255 expressed as pixel intensity divided by the target area (tumor nuclei compartment). AQUA scores for duplicate tissue cores were averaged to obtain a mean AQUA score for each tumor.

We used Spearman rank correlation coefficients (29) to assess associations between variables. We chose to use Spearman rank correlations to protect our inferences from outlying observations that could overly influence other methods of assessing correlation such as Pearson product-moment correlation coefficient (30).

We used multiple linear regressions with robust SE estimation to assess associations that were statistically significant after controlling for the other markers. Multiple linear regression is similar to ANOVA but is a more general method of identifying independent relationships. We used robust SEs to allow for less stringent modeling assumptions. To account for possible missing data bias, we confirmed our regression results using the multiple imputation approach of Raghunathan et al. (31) with 10 imputed data sets. The imputation process assumes that the data are missing at random; under this assumption, the reason for missingness might be associated with observed variables in the data set but is not associated with unobserved variables.

To display the relationship of EGFR in the nucleus with other markers, we used the semiparametric regression method with mixed model representations of penalized splines as described by Ruppert et al. (32) and implemented in the R statistical package.⁷

There were 94 patients with primary oropharyngeal carcinoma who met inclusion criteria for AQUA analysis. Of these, 72 patients had valid EGFR immunostaining information. Of the 72 with EGFR data, 56 were men and 16 were women, with ages ranging from 41 to 79 years old. Six patients (8.3%) were TNM stage II, 20 (27.8%) were stage III, and 46 (63.9%) were stage IV. Oropharyngeal subsites included 35 (48.6%) tonsillar fossae, 32 (44.4%) base of tongue, and 5 (7.0%) other oropharynx or not recorded. Forty-three (59.7%) patients were managed with primary external beam radiotherapy; 28 (38.9%), with surgical excision, followed by postoperative external beam radiotherapy; and 1 (1.4%), not recorded. Fourteen patients (19.4%) were also treated with chemotherapy. For histologic grade, 6 (5.6%) tumors were well differentiated, 34 (47.2%) were moderately differentiated, 25 (34.7%) were poorly differentiated, and 9 (12.5%) were not recorded.

Of the 72 patients with valid EGFR immunostaining information, 42 (58%) were analyzable for all eight biomarkers by AQUA. Tumors with less than 10% tumor area represented on the tissue microarray were excluded from analysis. As visualized by fluorescent immunohistochemistry, EGFR (Fig. 1), phosphorylated-Akt, and ERK-2 displayed mixed cytoplasmic and nuclear expression pattern. PTEN, Ki-67, PCNA, and cyclin D1 displayed a predominantly nuclear expression pattern.

Using Spearman rank correlations (Table 2), we found that nuclear EGFR was associated with membranous/cytoplasmic EGFR levels (ρ = 0.82, P < 0.0001). Nuclear EGFR was also associated with nuclear ERK-2 (ρ = 0.30, P = 0.01) and nuclear PCNA (ρ = 0.36, P = 0.003). Nuclear phosphorylated-Akt, PTEN, p53, and cyclin D1 levels did not correlate with nuclear EGFR. Nuclear EGFR was marginally associated with membranous/cytoplasmic levels of ERK-2 (P = 0.09) and cyclin D1 (P = 0.07); it was also marginally associated with total tumor levels of ERK-2 (P = 0.09), cyclin D1 (P = 0.09), and PCNA (P = 0.06). In Figs. 2 to 4, we present plots of the data with the point estimates from the semiparametric regressions of EGFR regressed on the markers individually.

In Table 3, we present the multiple linear regression of nuclear EGFR regressed on nuclear components of the markers of interest. In the adjusted analysis, nuclear EGFR is still associated with nuclear PCNA (P = 0.01 using the complete data, P = 0.07 using the multiple imputation approach) but not the other markers. The magnitude of the effect is strong. A 1-unit increase in nuclear PCNA is associated with an expected 0.32-unit increase in nuclear EGFR conditional on other covariates in the model when using the complete case data. The effect of PCNA remained statistically significant (P = 0.04) in a model fit with the complete data in which we added the markers of interest plus tumor stage (as a categorical variable), grade (as a categorical variable), sex, and age as covariates. PCNA was not statistically significantly correlated with nuclear EGFR (P = 0.11) when we further added disease site (other oropharynx, palatine tonsil, base of tongue) to the model with the marker, stage, grade, sex, and age characteristics. However, the sample size (35) in the final model was small relative to the number of parameters in the model (16), which could have limited the ability to detect an adjusted association of nuclear PCNA with nuclear EGFR.

The aim of the present study was to identify molecular correlates and determinants of nuclear EGFR receptor content in vivo using AQUA on an oropharyngeal cancer tissue microarray. We found that nuclear EGFR protein level is significantly associated with nuclear PCNA. As discussed previously, chromatin-bound PCNA protein plays an important role in DNA replication and damage repair. These results are consistent with preclinical data showing that EGFR may function as tyrosine kinase in the nucleus (17). Wang et al. (17) showed that nuclear EGFR phosphorylates PCNA at Tyr211 and stabilizes its chromatin-bound active form. The authors also showed that phospho-Tyr211 PCNA correlated strongly with nuclear EGFR in hyperproliferative liver tissues and in a breast cancer cohort. Our findings support the association of nuclear EGFR with nuclear PCNA.

Lin et al. (14) showed that EGFR translocates to the nuclei from the cell surface after stimulation with EGF. We found a significant association between nuclear EGFR and membranous/cytoplasmic EGFR. This finding opens up important questions in defining the EGFR phenotype in cetuximab- or erlotinib-treated patients. Because the nuclear EGFR pathway mediates cell proliferation and radioresistance, its inhibition seems crucial for improving patient outcome. The effect of EGFR inhibitors on the nuclear EGFR pathway will be determined by measuring EGFR nuclear and membranous/cytoplasmic levels in patient tumors treated with cetuximab or gefitinib and correlating results with response to treatment. The nuclear EGFR pathway has been studied in relation to treatment resistance. Cordero et al. (33) showed that 1,25 dihydroxyvitamin D inhibited EGF-induced EGFR nuclear transport. Erlotinib would be predicted to inhibit EGFR phosphorylation in the nuclear and the membranous compartment, but to our knowledge, this has not been confirmed. Furthermore, the previously described model of translocation of phosphorylated EGFR to the nucleus, which we have confirmed in this oropharyngeal cancer cohort, would predict that cetuximab-induced inhibition of ligand-driven EGFR phosphorylation would result in a reduction of nuclear EGFR; this effect of cetuximab has been showed in an in vitro model of radiation-induced EGFR nuclear translocation (34). The reliance on measuring reductions in ERK phosphorylation as a surrogate for the efficacy of cetuximab or the tyrosine kinase inhibitors (TKI) gefitinib and erlotinib may be inadequate in tumors with significant nuclear EGFR content or where nuclear translocation of EGFR occurs in response to cytotoxic therapy or radiation, and the interaction of nuclear EGFR with PCNA may represent a novel therapeutic target if persistent nuclear EGFR after cetuximab or TKI therapy is showed.

In our study, nuclear EGFR protein levels were assessed by AQUA. Our analysis shows the power of AQUA to define subclasses of tumors not achievable using standard pathologist-based assessment. This technology allows the measurement of protein expression within subcellular compartments and generates a result directly proportional to the number of molecules expressed per unit area (the concentration). It then quantifies the amount of protein expressed within the compartment by colocalization with fluorescent antibodies to compartment-specific molecules. Therefore, this technology permits preservation of tissue morphology while quantifying protein expression in paraffin-embedded tissue. This is not feasible with methods such as conventional immunohistochemistry (IHC) or ELISA. Using AQUA, we were able to show an association between nuclear EGFR expression levels and PCNA in vivo, a finding consistent with preclinical data showing that nuclear EGFR phosphorylates and stabilizes PCNA.

One limitation of this study is the small sample size, which limits the power of the study and, thus, the ability to use negative findings as strong evidence of true absence of an association. Thus, whereas there is strong evidence in our data that nuclear EGFR is associated with nuclear PCNA in adjusted analyses, we do not have equivalently strong evidence that the other components are not associated with nuclear EGFR. For example, EGFR in the nucleus has been shown to function as a transcriptional activator of the cyclin D1 gene, leading to an increase in the proliferative potential of cells (14). In the present study, we did not find an unadjusted association between cyclin D1 protein levels and nuclear EGFR level (P = 0.09). It is possible that our negative findings are related to the small sample size, rather than a lack of a true relationship. A validation of our findings in a larger cohort in samples from other subsites of head and neck cancer and in patients with advanced disease is appropriate.

No potential conflicts of interest were disclosed.

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