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Journal of Clinical Oncology, Vol 23, No 6 (February 20), 2005: pp. 1152-1160 © 2005 American Society of Clinical Oncology. DOI: 10.1200/JCO.2005.09.055 Relationship of Epidermal Growth Factor Receptor Expression to ErbB-2 Signaling Activity and Prognosis in Breast Cancer PatientsFrom the Department of Internal Medicine, Yale Cancer Center, and Department of Pathology, Yale University School of Medicine, New Haven, CT; Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK; and Geraldine Brush Cancer Research Center, California Pacific Medical Center, San Francisco, CA Address reprint requests to Ann D. Thor, MD, Department of Pathology, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd, Rm 451 BMSB, Oklahoma City, OK 73104; e-mail: ann-thor{at}ouhsc.edu
PURPOSE: To examine the relationship of epidermal growth factor receptor (EGFR) expression to ErbB-2 signaling activity in breast cancer and the impact that this interaction has on the prognosis of patients with early-stage breast cancer. PATIENTS AND METHODS: Paraffin tumor sections were collected retrospectively from 807 breast cancer patients diagnosed between 1976 and 1983. Immunohistochemical assays for ErbB-2, phosphorylated (activated) ErbB-2, and EGFR were performed, and the results were correlated with clinicopathologic variables and outcome. RESULTS: EGFR expression was detectable in 15% of 807 invasive breast cancers, including 35% of the 306 ErbB-2–positive patients. Conversely, the majority (87%) of EGFR-positive tumors co-overexpressed ErbB-2. Ninety-seven percent of tumors with phosphorylated ErbB-2 co-overexpressed EGFR. Patients whose cancers demonstrated ErbB-2 phosphorylation or co-overexpression of ErbB-2 and EGFR had the shortest survival. In contrast, patients whose tumors were negative for all three markers and those tumors that expressed only EGFR or only nonphosphorylated ErbB-2 had a relatively favorable outcome. CONCLUSION: These data provide the first clinical evidence that EGFR expression is linked to activation of ErbB-2 in human breast cancers. We have further shown that the adverse prognostic value of ErbB-2 overexpression is observed only when ErbB-2 is in the phosphorylated (activated) state or coexpressed with EGFR. These data suggest that ligand-dependent mechanisms of ErbB-2 activation are important in human breast cancer. These results also suggest that agents targeting EGFR may be useful in the treatment of tumors with activated ErbB-2.
Genes encoding receptor tyrosine kinases of the epidermal growth factor receptor (EGFR) family, including EGFR, ERBB2/HER2/NEU, ERBB3, and ERBB4, are commonly dysregulated in cancer.1,2 ErbB-2 alterations are associated with a poor prognosis and have variable predictive value in breast cancer patients.3,4 Both EGFR and ErbB-2 are targets for anticancer pharmaceuticals. The antibody trastuzumab targets ErbB-2 and is approved for the treatment of patients with advanced breast cancer who demonstrate significant ErbB-2 protein expression or gene amplification.5,6 The precise mechanisms of ErbB-2 activation in human breast cancers may be important because differences in the ensuing signal transduction and biologic behavior likely result. Activation of ErbB receptors by their ligands may induce homodimers or heterodimers with other ErbBs.7 Heterodimer complexity may also extend the repertoire of potential therapeutic targets.2 Alternatively, ligand-independent signaling of ErbB-2 can be modulated by alternative splicing patterns to produce constitutively active8,9 or dominant negative forms,10,11 by proteolytic processing to produce truncated active and dominant negative forms,11 or via a greater frequency of activation as a result of gene amplification with high protein expression. Different modes of ErbB-2 activation may also be associated with variable susceptibility to therapeutic agents. With novel ErbB-directed therapies, it will be important to have analytic tools that identify subsets of patients who will benefit from these drugs. This will likely be dependent on the presence of ligands, coreceptors, and other modulators. One strategy for improvement of the predictive value of ErbB-2 data is to measure the extent of ErbB-2 receptor activation. This can be done using phospho-specific anti–ErbB-2 antibodies.12-14 Only a small subset of ErbB-2–positive breast tumors stains with the phospho-specific antibody PN2A.12-14 This subset showed a significantly poorer prognosis in long-term follow-up, with reduced time to relapse and reduced disease-specific survival (DSS).12 Cotransfection experiments have provided evidence for synergy of these receptors in cellular transformation.15 Using transgenic models, transforming growth factor alpha and ErbB-2 cooperate in the induction of mammary tumors.16 In mammary tumors that arise in ERBB2 (Neu) transgenic mice, some of us have shown that ErbB-2/Neu is always in the phosphorylated state, and the tumors uniformly demonstrate overexpression of endogenous EGFR.15-17 Interactions between EGFR and ErbB-2 for the prognosis of breast cancer patients has been reported.18,19 We hypothesize that EGFR may activate and phosphorylate ErbB-2 in human breast cancers to facilitate an aggressive biology via receptor activation. To evaluate this hypothesis, we studied EGFR expression and the associated patient outcomes in breast carcinomas previously characterized for ErbB-2 and phosphorylated ErbB-2 (P-ErbB-2).
Patient Population The patient population has been described in detail.12 Formalin-fixed, paraffin-embedded invasive breast tumors from 807 patients treated at the Massachusetts General Hospital (Boston, MA) between 1976 and 1983 were used for this correlative study, which was approved by the Institutional Review Board. Lymph node status was determined in 686 patients; of these patients, 50% were node negative. Carcinomas included 96% invasive ductal and 4% invasive lobular types. Tumors were graded using the modified Nottingham combined histologic grading system.20 Estrogen receptor (ER) data derived from either charcoal dextran or immunohistochemical assay and other clinical and histologic data have been previously reported.12 Follow-up intervals were calculated from the date of surgery to the last recorded follow-up date (mean, 15.6 years; median, 16.3 years for all patients). Adjuvant chemotherapy was administered to 38% of the patients. Twenty-one percent of chemotherapy-treated patients received a doxorubicin-containing regimen (generally at doses of 15 to 45 mg/m2).
Immunohistochemistry
Immunoprecipitation and Immunoblotting of EGFR and ErbB-2 Protein concentration of cell lysates were quantified using the Bio-Rad Protein Assay (Bio-Rad, Hercules, CA). ErbB-2 was immunoprecipitated using antibody Neu 9G6: sc-08 from Santa Cruz Biotechnology, and EGFR was immunoprecipitated using antibody 528 (a gift from John Mendelsohn, M.D. Anderson Cancer Center, Houston, TX), via standard methodology. For immunoblotting, primary and secondary antibodies were purchased from Santa Cruz Biotechnology. Anti-EGFR antibody 1005: sc-03 was used at a concentration of 0.2 µg/mL; anti-ErbB-2 monoclonal antibody Neu 9G6: sc-08 was used at a concentration of 1 µg/mL; and antiphosphotyrosine antibody 4G10 (Upstate Biotechnology, Lake Placid, NY) was used at a concentration of 1 µg/mL. Enhanced chemiluminescence was performed as recommended by the manufacturer (Amersham, Arlington Heights, IL).
Characterization of Anti-EGFR Antibody
Statistical Methods Spearman rank correlations were used to describe the associations between ErbB-2, EGFR, and P-ErbB-2.26 Expression data were recorded as continuous variables; although, for specific analyses, cut points were applied as indicated. Clinical and histologic variables were separated into groupings for the purposes of statistical analyses as described in specific tables and associated text. Associations between clinical, histologic, immunohistochemical variables and immunostaining for ErbB-2, P-ErbB-2, and EGFR were calculated using Spearman rank correlations.26 Unpaired t tests were used to describe the associations between ErbB-2, EGFR, and P-ErbB-2 and lymph node status. The number of positive lymph nodes was logarithmically transformed (after adding the constant 1 to avoid taking log of 0, for node-negative patients) because a plot of Martingale residuals against the number of positive nodes suggested that this transformation yields a more linear relationship between risk and nodes. Tumor size, grade, age, apoptotic index, and mitotic index were entered as continuous variables. Biochemically determined ER content was dichotomized with a cut point of either 10 fmol/mg protein or more than 20% of tumor cells positive by immunohistochemistry (institutional cut points). Survival analyses used both univariate and multivariate methods. The outcomes used in this study were disease-free survival (DFS), which was the interval from diagnosis date to the date of the first recorded failure (local, regional, or distant), and DSS, which was the interval from diagnosis date to the date of death as a result of breast cancer (n = 326). Patients who died from causes unrelated to breast cancer were censored at the time of death (n = 115), and deaths from unknown causes (n = 20) were excluded from DSS analysis. Patients who died from unknown causes with no evidence of documented failures (n = 17) were excluded from DFS analyses along with a single patient who was alive but whose failure status was unknown. The log-rank test was used to calculate the statistical significance for univariate analyses. For univariate analyses, the variables were categorized as dichotomous or categoric with an assigned cut point. Although a recognized cut point is available for many variables (for example, age, with 50 years typically used to separate pre- from peri- and postmenopausal women), there is greater ambiguity with ErbB-2, EGFR, and P-ErbB-2. For the purposes of Table 3, we used low (0%) and high (80%) cut points for ErbB-2 data based on the scatterplot (Fig 2) and our previously published data.12 For EGFR and P-ErbB-2, any membranous staining (> 0% of cells with membranous staining) was considered positive. Multivariate Cox proportional hazards models were constructed by first building a best-fit model from factors excluding ErbB-2, EGFR, and P-ErbB-2.27 Initially, all univariately significant factors were included in the model. Factors were removed individually based on the Wald statistic for each variable until only statistically significant factors remained (P < .05). The factors of interest (ErbB-2, EGFR, and P-ErbB-2) were added to the best-fit model to assess statistical significance in the presence of other markers. Finally, we constructed a variable that dichotomized ErbB-2, EGFR, and P-ErbB-2 at no positivity versus any positivity. We coded each case depending on the number of these dichotomized variables that were positive (zero, one, two, or three positive variables). This coded variable was used to calculate the combined contributions of ErbB-2, EGFR, and P-ErbB-2, as shown in the multivariate survival tables. Patients with incomplete data for statistically significant factors were excluded from this analysis. The hazard ratios (HRs) of each variable in the multivariate model along with the P value in the best-fit model were calculated using the Cox proportional hazards model. All statistics were carried out using Statview 5.01 software (SAS Institute, Cary, NC).
EGFR, ErbB-2, and P-ErbB-2 Expression in Breast Cancers In the 807 invasive breast carcinomas studied, 306 (38%) were immunopositive for ErbB-2. Of these, 107 (35%) coexpressed EGFR. Nearly all of the 123 EGFR-positive patients (87%) coexpressed ErbB-2. These relationships and the expression of a third variable, activated P-Erb-B2, are summarized in Table 1 and shown graphically by Figure 2. Spearman rank statistical analyses of these relationships are summarized in Table 2 (not previously reported). We have previously reported that P-ErbB-2 positivity was significantly associated with high levels of ErbB-2 overexpression.12 In this study, P-ErbB-2 was also significantly associated with erbB-2 (r = 0.674, P < .0001). P-ErbB-2 was also strongly associated with EGFR expression (r = 0.857, P < .0001). Remarkably, all but one of 37 patients with P-ErbB-2 coexpressed ErbB-2 and EGFR, even though only 15% of all patients and 35% of the ErbB-2–positive breast cancer patients overexpressed EGFR. P-ErbB-2 expression was also associated with lymph node metastasis, younger patient age at diagnosis, larger tumor size, higher histologic tumor grade, ER negativity, and higher mitotic and apoptotic indices (P < .05 for each). Significant, similar associations were also observed for ErbB-2 and EGFR expression (Table 2).
Univariate Analyses of Variables Associated With Prognosis Many of these same factors were significantly associated with a worse DFS or DSS by univariate analysis, including P-ErbB-2 expression (HR = 2.4 and 2.6, respectively), EGFR expression (HR = 1.9 and 2.1, respectively), higher ErbB-2 expression (> 0% and < 80% cellular expression: HR = 0.9 and 0.9;  80%: HR = 2.0 and 1.4, respectively), higher tumor grade (HR = 2.3 and 2.5, respectively), larger tumor size (HR = 1.5 and 1.3, respectively), a greater number of affected lymph nodes or the presence of lymph node involvement (DFS: HR = 2.0 for one to three and 4.0 for four positive lymph nodes; DSS: HR = 2.1 for one to three and 4.7 for four positive lymph nodes), ER negativity (HR = 1.4 and 1.5, respectively), and a high mitotic count (HR = 1.5 and 1.8, respectively) and apoptotic count (HR = 1.4 and 1.5, respectively). For lymph node–positive patients (n = 336), adverse prognostic factors for DFS and DSS included ErbB-2, P-ErbB-2, and EGFR expression and other factors as presented in Table 3. In the lymph node–negative patients (n = 335), significant adverse prognostic factors included only histologic grade and proliferation rate (data not shown). In this subset of patients, however, the numbers of ErbB-2–, EGFR-, or P-ErbB-2–positive patients were small. To illustrate interactions between ErbB-2, EGFR, and P-ErbB-2 and patient outcome, Kaplan-Meier survival curves are shown as Figures 3 and 4. For all patients (n = 786) and for the lymph node–positive patient subset (n = 334), DFS and DSS curves were equally favorable (ie, there was no statistically significant difference in outcome) for the patients that were either negative for all three markers (thin black lines) or positive only for ErbB-2 (hatched black lines) or EGFR expression (thick black lines) as a single variable. The worst outcomes were seen in patients whose tumors were positive for all three markers (gray lines), and patients whose tumors were ErbB-2 positive and EGFR positive but negative for P-ErbB-2 (dotted lines) demonstrated nearly as poor a survival (statistically, there is no difference between these two groups). Hence, the poor prognosis for patients with ErbB-2–positive tumors was only observed in patients with ErbB-2 activation (P-ErbB-2 positive) or EGFR coexpression. For node-negative patients, a group with a worse prognosis did not emerge based on stratification by the three markers.
In a separate analysis, we also studied the effects of using a cut point of 10% for ErbB-2 because several commercial kits for the immunohistochemical detection of ErbB-2, including one based on the CB11 antibody, use this level to discriminate between negative and positive. Using a cut point of 10%, 224 patients (28%) were positive for ErbB-2, 105 patients (47%) coexpressed EGFR, and 16% of these 224 patients demonstrated ErbB-2 activation. The use of this cut point did not modify the prognostic value of ErbB-2 data for all patients and the node-positive or node-negative subsets.
Multivariate Analyses of Variables Associated With Outcomes
For node-positive patients, the best-fit baseline model for DFS included the number of positive nodes, tumor size, and ER status (Table 4). ErbB-2 added significance when added as a continuous variable and dichotomized at cutoff scores of 0%, 40%, or 80% or trichotomized at 0% v more than 0% and less than 80% v 80% (data not shown). Tumors that expressed ErbB-2 (dichotomized at 0% v > 0%) had a HR of 1.48 (data not shown). P-ErbB-2 individually, but not EGFR alone, added significance to ErbB-2 in the model (data not shown). Having all three variables in the model further enhanced prognostication of the model, with a HR of 2.14 for tumors with two or three positive variables. For node-positive patient DSS, the best-fit model included the number of positive lymph nodes and ER status (Table 4). For these patients, the adverse impact of ErbB-2, P-ErbB-2, and EGFR was similar in DFS. Having all three variables (when each was dichotomized) provided a higher risk of death, with a HR of 3.14. In the node-negative patient subset, the best-fit baseline model for DFS included tumor size and mitosis (data not shown in tabular form). Other variables failed to add significance to this baseline. For DSS, the best-fit model included grade only. ErbB-2, P-ErbB-2, or EGFR, either alone or in combinations, failed to provide significant improvement to this baseline model.
All but one of the 37 patients with P-ErbB-2 positivity coexpressed ErbB-2 and EGFR, even though only 15% of all cancers and 35% of the ErbB-2–positive cancers overexpressed EGFR. This is the first clinical evidence that EGFR overexpression is linked mechanistically to activation of ErbB-2 in human breast cancers. These data suggest that, in patients with coexpression of both receptors, treatment with an EGFR antagonist or a regimen targeting both receptors may be more beneficial in blocking ErbB-2 receptor activation than a single anti–ErbB-2 agent alone. The isolated overexpression of either ErbB-2 or EGFR was not a significant adverse prognostic variable in this study as judged by Kaplan-Meier analysis. In fact, the DFS and DSS of patients with isolated expression of either ErbB-2 or EGFR were statistically indistinguishable from the ErbB-2–negative or triple-negative subsets in long-term follow-up. Patients with tumors positive for both receptors and ErbB-2 phosphorylation demonstrated the worst outcome. Patients whose tumors were positive for both ErbB-2 and EGFR, but that were without detectable P-ErbB-2, had nearly as poor an outcome as the triple-positive tumor-bearing patients. This supports the hypothesis that ErbB-2–positive patients can be subdivided into groups based on coexpression of other receptors or receptor activation. These data may be particularly useful for prognostic or predictive (therapeutic) purposes. Our study included an approximately equivalent mixture of node-positive and node-negative patients. In addition, we used a cohort that had a median follow-up of more than 16 years, which was important for two reasons. First, breast cancer relapses may occur many years after initial treatment. Thus, long-term follow-up is essential in studying the natural history of breast cancer. Second, ErbB-2 has been identified as not only a prognostic factor but also as a predictive factor that can influence the response to specific types of chemotherapy. Therefore the ideal study of ErbB-2 as a prognostic factor would best be analyzed in a population who did not receive systemic chemotherapy, which could confound the results by adding the complexity of the interaction of ErbB-2 with the specific therapy administered. In modern practice, the majority of patients with breast cancer receive some type of adjuvant systemic therapy, even node-negative patients with relatively small tumors. Our study patients were diagnosed in an era when adjuvant chemotherapy was not used nearly as extensively as it is today, especially in the node-negative patients. Only 38% of the patients in our study received adjuvant chemotherapy, and the regimens would be considered suboptimal by contemporary standards, particularly for ErbB-2–positive patients. We believe that our EGFR assay conditions detect only higher levels of EGFR protein expression because normal mammary epithelial cells generally express low levels of EGFR that do not stain. A wide variation in the incidence of EGFR expression in breast cancer has been reported, which is likely a result of methodology including, for immunohistochemistry, choice of antibody. However, even low-level expression of EGFR may still play an important role in the biology of a tumor, especially in the context of expression and overexpression of other ErbB family members and ligands. Some of the signaling pathways activated by these two receptors differ, resulting in additive or synergistic effects promoting aggressive tumor biology. Moreover, each of these receptors enhances signaling by the other receptor; EGF can transactivate ErbB-228 and expression of ErbB-2 may increase the magnitude and duration of the response to EGF.29 Finally, it is possible that novel kinase and phosphorylation site interactions in the cross-phosphorylation reaction lead to recruitment of novel substrates in heteromeric compared with homomeric receptor combinations. Coexpression of both receptors, even in the absence of ErbB-2 phosphorylation detectable by PN2A, may enable EGFR transactivation of ErbB-2. In such cases, phosphorylation may either be below the limit of detection for PN2A or occur at other sites besides Tyr1248, the autophosphorylation site recognized by PN2A. The identification of a subset of breast tumors with site-specific ErbB-2 phosphorylation12 has made it possible to study mechanisms through which ErbB-2 signaling may be regulated. The precise means by which ErbB-2 is activated may have important prognostic and therapeutic implications. The results presented herein suggest that EGFR-dependent signaling accompanies ErbB-2 activation, which is consistent with EGFR-dependent activation as a major, if not the foremost, mechanism for ErbB-2 activation in human breast cancers. Therefore, therapeutics that disrupt EGFR signaling may be useful either alone or in combination with anti–ErbB-2 therapeutics to treat breast cancers expressing these receptors. Early preclinical data using antagonists to EGFR alone30-32 or in combination with antagonists to ErbB-232,33 support this conclusion. In clinical practice, a challenge will be to identify patients most likely to respond to these agents, either alone or in combination. Our data suggests that phospho-receptor analyses, dual assays for EGFR and ErbB-2, or a triple test involving P-ErbB-2, EGFR, and ErbB-2 may be more useful than a single assay in this regard. Ultimately, selection of the most appropriate combinations of novel specific therapeutics for each patient could be facilitated by a more informative analysis encompassing receptors, ligands, and components of signal transduction pathways.
The following authors or their immediate family members have indicated a financial interest. No conflict exists for drugs or devices used in a study if they are not being evaluated as part of the investigation. Employment: Michael P. DiGiovanna, Yale University; David F. Stern, Yale University. Leadership Position: David F. Stern, Phosphoproteomics LLC. For a detailed description of these categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section of Information for Contributors found in the front of every issue.
Supported by grant No. RO1CA82848 (A.D.T. and D.F.S.) from the US Public Health Service/National Cancer Institute, and grant Nos. DAMD17-97-1-7065 and R01CA45708 (M.P.D.) from the US Army Medical Research and Materiel Command Congressionally Directed Medical Research Programs. D.F.S. and M.P.D. have a financial interest in antibody PN2A, and D.F.S. holds intellectual property relevant to phosphor-specific antibodies. M.P.D. and D.F.S. contributed equally to this work. Authors' disclosures of potential conflicts of interest are found at the end of this article.
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Copyright © 2005 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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ABSTRACT
INTRODUCTION
