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Int J Radiat Oncol Biol Phys. Author manuscript; available in PMC 2012 October 1.
Published in final edited form as:
PMCID: PMC2992592

Integrating EGFR Assay with Clinical Parameters Improves Risk Classification for Relapse and Survival in Head and Neck Squamous Cell Carcinoma



Epidermal growth factor receptor (EGFR) overexpression has been consistently found to be an independent predictor of local-regional relapse (LRR) after radiotherapy. We assessed the extent by which it can refine risk classification for overall survival (OS) and LRR in patients with head and neck squamous cell carcinoma (HNSCC).


EGFR expression in locally advanced HNSCC was measured by immunohistochemistry (IHC) in a series of patients randomized to receive accelerated or conventional radiation regimens in a phase III trial. Subsequently, data of the two series were pooled (N=533) for conducting a recursive partitioning analysis that incorporated clinical parameters (performance status, primary site, T- and N-categories, etc.) and four molecular markers (EGFR, p53, Ki-67, and microvessel density).


This study confirmed that patients with higher than median levels of tumor EGFR expression had a lower OS (RR: 1.90, P=0.0010) and a higher LRR (RR: 1.91, P=0.0163). Of the four markers analyzed, only EGFR was found to contribute to refining classification of patients into three risk classes with distinct OS and LRR outcomes. The addition of EGFR to three clinical parameters could identify patients having up to a 5-fold difference in the risk of LRR.


Adding pretreatment EGFR expression data to known robust clinical prognostic variables improved the estimation of the probability for OS and LRR after radiotherapy. Its use for stratifying or selecting patients with defined tumor feature and pattern of relapse for enrollment into clinical trials testing specific therapeutic strategy warrants further investigation.

Keywords: EGFR, Risk Classification, Prognosis, HNSCC


Clinical trials of patients with locally advanced head and neck squamous cell carcinomas (HNSCC) have shown that rational modifications of radiation regimen and combinations of radiation and chemotherapy decrease local-regional relapse (LRR) and increase overall survival (OS) rates (1-9). However, both strategies increase acute side effects and concurrent radiation-chemotherapy regimens also worsen late complications (7, 10). These observations combined with better knowledge of tumor biology have increased the need to identify biomarkers for classifying tumors with different prognosis, predicting tumor response to therapy, and developing therapeutic strategies that can selectively enhance tumor response to various therapeutic modalities.

The epidermal growth factor receptor (EGFR) has emerged as both a prognostic-predictive biomarker in HNSCC and as a target for therapeutic intervention. Initial studies have shown that high tumor EGFR expression correlates with poor survival (11, 12). More recent work has demonstrated that high EGFR expression predicts for poorer tumor response to radiotherapy (13-16) or combination of chemotherapy with radiation (17). Concurrently, studies in cell lines and xenografts have shown that inhibition of EGFR by cetuximab, a monoclonal antibody against EGFR, enhances tumor response to single dose or fractionated radiation (18, 19). The biological basis of these observations may be explained by the finding that ionizing radiation triggers localization of EGFR into the nucleus and promotes the repair of DNA double-strand breaks through increasing the activity of DNA-dependent kinase (DNA-PK). Blockade of EGFR by cetuximab abolishes the EGFR nuclear localization, and results in suppression of the radiation-induced activation of DNA-PK, inhibition of DNA repair and enhanced cellular radiation sensitivity (20).

Our previous study of patients enrolled into a phase III trial and treated with standard radiation therapy has shown that patients with higher EGFR-expressing HNSCC (>median value) had significantly higher LRR (HR: 1.95, P=0.002) and lower OS rates (HR: 1.75, P=0.006) relative to those with lower EGFR-expressing tumors (13). Since the methods used for EGFR assay varied amongst laboratories, we conducted this study to assess the reproducibility of image analysis-based assay and to validate its value in predicting tumor response to radiation. In addition to EGFR, three other assays, p53 expression (a surrogate marker of TP53 mutation), Ki-67 (a marker of cell proliferation) and microvessel density (MVD) have been reported to correlate with outcome in some series (21-23); therefore, we examined these three additional biomarkers with EGFR expression. To develop a model for accurate prognosis, we then assessed whether, and the extent to which, incorporation of biological markers with clinical features can improve classification of risk for LRR and OS in a large cohort of patients enrolled into a phase III trial who received well-defined therapy.



Patients with AJCC stage III-IV, non-metastatic, squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx, and supraglottic larynx were enrolled into a phase III study comparing the efficacy of three modified fractionation regimens with the conventional schedule. The clinical outcome of this trial was reported earlier (24). Paraffin-embedded blocks or unstained slides submitted to the Radiation Therapy Oncology Group Tissue Repository were retrieved for the assays. The population for EGFR validation study component consisted of 267 patients randomized to receive accelerated fractionation by concomitant boost (AFX-C). Then, the previously published data of 266 patients randomized to standard fractionation (SFX) (13) were added for combined analyses (Table 1).

Table 1
Distribution of Patient and Tumor Characteristics


EGFR assay

The EGFR expression was determined as previously described (13). Briefly, the slides were incubated with mouse monoclonal antibodies (Mab) that is reactive to the peptide backbone of the extracellular domain of the EGFR molecule (31G7, Zymed Laboratories, Inc.) diluted 1:50 in buffer. After incubating with a secondary antibody and counterstaining, slides were mounted for quantitative analysis. The magnitude of EGFR expression was measured by computerized quantitative image analysis using a SAMBA 4000 Cell Image Analysis System (SAMBA Technologies, Meylan, France). Parameters measured were the mean optical density (MOD: optical densities measured over the labeled areas within the structure), staining index (SI: the proportion of stained area relative to the total area of the structures), and the quick score (QS: MOD × SI/100).

p53, Ki-67, and microvessel density assays

The assays were conducted as previously described (21, 22, 25). For the p53 IHC assay, the primary antibody was a mouse Mab directed against p53 protein (DO-7, DAKO Corp., Carpinteria, CA, dilution 1:200). For Ki-67, the slides were incubated with the Ki-67/MIB-1 Mab (DAKO Corp., Carpinteria, CA, dilution 1:100). For MVD, the sections were stained with murine Mab anti-Factor VIII-related antigen (Factor VIII-RA), subsequently treated with biotinylated second antibody and processed.

For p53 and Ki-67, the medians were chosen as cut points and the groups with equal or less than median versus greater than median value of IHC staining were compared for endpoints described below. Sections with MVD results were scanned and the number of stained microvessels within areas of invasive tumor was counted. The mean number of microvessels per field was recorded as the MVD. A prospective MVD of ≥60 was pre-selected as the threshold for high MVD for this study, based on previously published studies of breast and nasopharyngeal cancer (23).


Three endpoints were evaluated: overall survival (OS), progression-free survival (PFS), and time to local-regional relapse (LRR). Failure for OS was death due to any cause. Failure for LRR was local or regional progression, death due to study cancer or unknown causes, non-protocol radiation therapy or chemotherapy, surgery of the primary tumor with disease present or unknown, or surgery of the regional nodes with disease present or unknown > 15 weeks from the end of radiation therapy. Distant metastasis, second primary tumor, and death from other causes were considered as competing risks. Failure for PFS was LRR, distant metastasis, or death due to any cause. Patients were censored for second primary tumor. OS and PFS were estimated using the Kaplan-Meier method (26) and compared by the log-rank test (27). Time to LRR was estimated by the method of cumulative incidence (28) and tested with Gray's test (29).

To confirm the prognostic value of EGFR, only AFX-C patients were used. Cox proportional hazards model was used to estimate the hazard ratio between high and low EGFR after adjustment for known prognostic factors. The magnitude and distribution of pretreatment marker assays were assessed using various descriptive statistics (mean, median, etc.) along with their association with the known tumor- and patient-related prognostic variables for OS, PFS, and LRR. The distributions of EGFR for SFX and AFX-C were compared with the Kolmogorov-Smirnov test (30). To investigate the prognostic value of Ki-67, p53, and MVD, both SFX and AFX-C patients were used. Cox proportional hazards model was used to estimate the hazard ratio between high and low values after adjustment for known prognostic factors, and models were stratified by assigned treatment.

For development of risk groups, both SFX and AFX-C patients were used. The 533 patients were randomly divided into training (n=267) and test (n=266) sets stratified by assigned treatment. The training data were used to develop models and the test data to validate the results. Comparisons of baseline characteristics for the training and test sets were done using the Pearson chi-square test (31) for unordered contingency tables (gender, race, and primary site), the Kruskal-Wallis test (32) for singly ordered contingency tables (Karnofsky performance status [KPS], T- and N-categories, and AJCC Stage) and the Kolmogorov-Smirnov test of continuous distributions for EGFR expressions and age (30). Cut points for EGFR SI were limited to multiples of five from 50 to 90 and those for age were limited to multiples of five from 50 to 75. For risk classification analysis, we used a nonparametric statistical technique known as recursive partitioning analysis (RPA) with the log-rank splitting method (33). The goal was to produce three RPA classes by combining terminal nodes with similar outcome. In patients without available tumor specimens for laboratory assays, missing values were imputed 10 times and the most frequently occurring model was selected from examining all 10 separate RPA trees. The relative risks comparing the survival classes from the 10 imputations were combined using SAS software and the PROC MI and MIANALYZE procedures (34). Terminal nodes were further combined into classes based on visual inspection of Kaplan-Meier plots. Models with similar discriminatory properties and reproducibility were compared by simplicity (preference given to fewer factors). Cox regression models were utilized to determine hazard ratios (relative risks) and P-values between the identified classes. Only OS was used to develop the model. The resulting classes were then evaluated to see if they yielded similar classifications for PFS and LRR.


Patient characteristics

The 533 eligible patients (SFX arm – 266 and AFX-C arm – 267) were enrolled between 1991 and 1997 to a phase III clinical trial conducted by the Radiation Therapy Oncology Group (RTOG 90-03). Patient and tumor characteristics are summarized in Table 1. The clinical variables did not differ significantly between the training and test sets. EGFR, Ki-67, p53, and MVD values were available for 300, 223, 217, and 213 patients, respectively. The numbers of patients in each staining differed because different numbers of tissue samples and staining data were available for each marker. Detailed patient and tumor characteristics of EGFR, Ki-67 and p53 assays are presented in Supplemental Tables 1-6, and the MVD assay data were previously published (25).

EGFR assay reproducibility and correlations between EGFR expression and treatment outcome

Because the EGFR staining of the samples from SFX and AFX-C arms was performed in two separate batches, we performed comparative analyses of these two data sets to assess experimental variations and reproducibility of the EGFR assay. The continuous distributions of EGFR SI and MOD scores (Figure 1A) found in the samples from AFX-C arm were analogous to our previously reported series (13) from SFX arm (p=0.62 for SI; p=0.85 for MOD). As in our prior study, we found no correlation between EGFR expression and clinical variables including the T- and N- categories, AJCC stage grouping, KPS, age, and gender (Supplemental Table 7). Moreover, in multivariate analyses, high EGFR expression (>median value for SI, MOD, or QS previously determined using samples from prior SFX arm) was also found to be a robust, independent determinant of OS, PFS, and LRR using samples from AFX-C (Supplemental Table 8). The relative risk (RR with the 95% confidence intervals) derived using EGFR-SI for these three outcome endpoints were 1.90 (1.30-2.79, P=0.0010), 1.74 (1.15-2.64; P=0.0086), and 1.91 (1.13-3.24; P=0.0163), respectively. Furthermore, LRR was mostly seen in patients with high EGFR expression assessed by both SI and MOD with high concordance between the sample sets from SFX and AFX-C arms (Figure 1B). Also, regardless of the treatment modalities, the magnitude of separation in the cumulative LR relapse curves depending on the level of EGFR expression were comparable (SFX- Relative Risk 1.67, p=0.0257; AFX-C- RR 1.91, p=0.0163; Figure 1C).

Figure 1
EGFR assay reproducibility and correlations between EGFR expression and treatment outcome. A: EGFR expression measured by mean optical density (MOD - optical densities measured over the labeled areas within the structure) and staining index (SI - the ...

Determination of p53 and Ki-67 expression, and tumor microvessel density

P53 protein expression and cell proliferation as assessed by Ki-67 expression were also determined by IHC. Each marker was assessed separately and summarized in Supplemental Table 9. While the expression of p53 in > 30% (median) of the tumor cells were associated with worse PFS (RR 1.53, 95% CI 1.11-2.09, P=0.0086) and increased rate of LRR (RR 1.58, 95% CI 1.08-2.32, P=0.0186) compared to ≤ 30%, there was no detectable difference in OS (RR 1.24, 95% CI 0.92-1.67, P=0.1608). We could not detect any significant contribution of Ki-67 to outcome. The tumor MVD also did not correlate with any of the survival parameters consistent with previously published data (25).

Contribution of biological markers in refining risk classification

Initial analysis included KPS, primary site, T-category, N-category, age, gender, assigned treatment, EGFR-SI, EGFR-MOD, EGFR-QS, p53, Ki-67, and MVD. Subsequently, EGFR QS and EGFR MOD were removed due to the higher significance of EGFR-SI and the high correlation among these parameters (r=0.71-0.99). In addition, cut points for EGFR were limited to multiples of five so that the resulting model would have a meaningful cut off point rather than a median which may vary depending on samples analyzed. The RPA algorithm chose SI 80 as the best cut point. Of the four markers analyzed, only EGFR showed promise as an important factor in the model, so all other tumor markers were removed from further consideration. A combination of four parameters consisting of KPS, EGFR expression, primary site, and T-category (in decreasing weight) segregated the study population into three distinct, reproducible RPA classes (Class I, II and III) for OS and the final recursive partitioning tree is shown in Figure 2.

Figure 2
Recursive partitioning analysis trees, A: without EGFR, and B: with EGFR.

Three RPA classes have significantly different OS and LRR rates in both training and testing sets (Figure 3). The estimated 5-year OS rates for Classes I, II, and III patients were approximately 50%, 30%, and 10%, respectively, and the corresponding 5-year LRR rates were around 35%, 65%, and 85%. To quantify the contribution of EGFR expression to refining risk classification for LRR, we repeated the RPA analysis with or without this parameter. Table 2 shows that the addition of EGFR expression data improved the definition of risk classes. The risk of patients in Class III (worst prognosis) in experiencing LRR relative to Class I (best prognosis), for example, increased from 3.4 to 5.0.

Figure 3
A: Overall survival curves, and B: cumulative incidences curves for local-regional relapse of three distinct outcome classes (Class I – Best, Class II – Intermediate, and Class III – Worst) derived from RPA analysis in training ...
Table 2
The relative risks for local-regional relapse and class features derived from analysis with or without incorporating EGFR expression data.


The recognition that cancers of the same site, stage and morphology have divergent natural history and respond differently to therapeutic interventions have inspired many to search for predictive markers of response to therapy. Until recently, a large body of research on HNSCC has focused on identifying surgical-pathologic features associated with poor outcome, such as the number and location of lymph node involvement, presence of extra-nodal extension, positive surgical margin, and perineural invasion (35-39). Although clustering of such surgical pathologic adverse features could identify patients who did relatively well after radical surgery alone or followed by adjuvant radiotherapy (40) with or without chemotherapy (6, 7), further work is needed to guide clinical research and practice. Finding prognostic-predictive markers is particularly important for personalizing non-surgical therapy, such as radiation with or without chemotherapy or new class of agents, because surgical-pathologic data are not available in this setting.

Quantification of EGFR expression has been controversial with semi-quantitative scoring system ranging 0-3+. However, our previously study (13) on a well-defined cohort of patients with locally advanced HNSCC randomized to receive standard radiotherapy alone revealed that pretreatment EGFR expression level of the primary tumor measured by image analysis-based IHC assay was a robust predictor for tumor response to radiotherapy, more so than the T-stage. This data is consistent with other image-guided quantitative scoring systems (17). Before promoting any biomarkers for clinical application, it is crucial to determine the reproducibility of the assay, to validate its predictive value for radiation response, and to assess whether and to what extent this assay added value to the known clinical prognostic variables. This follow-up study was conducted using a well-defined group of patients enrolled into the same prospective phase III trial but randomized to receive a different radiotherapy regimen. These results establish that the EGFR assay was remarkably reproducible and confirmed the lack of correlation between the levels of EGFR expression with clinical parameters, and validated its value in predicting tumor response to radiotherapy when measured quantitatively.

Currently, there are many proposed laboratory biomarkers that are prognostic of clinical outcomes. However, there are only limited studies in HNSCC to build upon the clinical prognostic factors that are already known to be robust and often are better than expensive laboratory assays, and few that combine multiple laboratory biomarkers. To examine this issue, we pooled the current clinical and EGFR data with those of the previous series and added the results of additional three molecular assays (p53, Ki-67, and MVD) for further analysis after dividing the study population into training and test sets. Furthermore, with recent data regarding the HPV status as a strong prognostic marker and to put our results in perspective, we assayed the remaining unstained oropharyngeal carcinoma slides for p16, a surrogate marker for HPV, and determined the positivity to be approximately 33% (or approximately 20% of total number of tumor samples), probably due to lower prevalence of HPV infection in the early 1990's (unpublished data). Additional analysis incorporating the HPV data is still ongoing. Out of four markers further analyzed, only EGFR was found to have added value when combined with known robust clinical prognostic factors in identifying three classes of patients with significantly different clinical outcomes. Although p53 staining associated with worse PFS and LRR, it did not significantly contribute to the prognostic model probably because there are biological and clinical differences among the disruptive and non-disruptive mutations within the gene that are not distinguished by IHC staining (41).

This type of comprehensive prognostic data is critical for enriching patients with well defined tumor features and patterns of relapse as well as to prioritize clinically relevant laboratory biomarkers for enrollment into clinical trials and select appropriate correlative studies. Class III patients, having a 10% chance of survival and an 85% risk of developing LRR at 5 years, are ideal candidates for testing a vigorously intensified local therapy regimen. On the other hand, given the relatively favorable outcome achieved with radiation alone, it is prudent to test an agent with fairly low toxicity profile in combination with radiation for patients belonging to Class I. In addition to prognosis, this type of analyses can be applied to further examine the biomarkers for predictive potential. To ascertain EGFR expression levels as a potential predictive biomarker for either of the treatment arms (SFX versus AFX-C), we evaluated Cox models including assigned treatment (1 if SFX, 0 otherwise), EGFR (1 if > median, 0 otherwise), and treatment by EGFR interaction (as well as T stage, N stage, KPS, and primary site). The hazard ratios (95% CI for interaction) for OS, PFS, and LRR were 0.75 (0.45-1.25), 0.77 (0.45-1.31), and 0.88 (0.46-1.71), suggesting that patients with EGFR expression levels below the median may receive more benefit from AFX compared to patients with EGFR above the median. Although it did not reach statistical significance, it still generated an interesting hypothesis for future clinical trials. Thus, its use for stratifying or selecting patients for enrollment into clinical trials testing specific therapeutic strategy is recommended.

The results of the current work provide the impetus to perform other molecular marker assays using the samples in this series of patients with mature outcome data and conduct similar types of analyses to further refine risk classification. It is also desirable to incorporate the biomarkers to currently existing clinical prognostic information, and extend this approach to well defined group of patients enrolled into prospective trials testing the combination of radiation with chemotherapy or with novel agents when the clinical outcome data become available in the near future. With this goal in mind, many centers and cooperative groups have increased tissue banking efforts, including collection of samples suitable for high through put assays, for correlative studies. Such integrated investigations are changing the design of trials in the quest for realization of personalized medicine.

Supplementary Material



The immunohistochemical study was supported by grants CA84415 and CA06294, awarded by the National Cancer Institute and supplemented by the Wiegand Foundation and Gilbert H. Fletcher Memorial Distinguished Chair. The data analysis is, in part, funded by R01 DE-017982. The clinical data for correlative analysis came from an RTOG-sponsored phase III trial (RTOG 90-03) supported by grants U10 CA21661, U10 CA37422, and U10 CA32115 awarded by the National Cancer Institute.

We are grateful to all patients and clinical investigators for contributing to trial RTOG 90-03 and providing pretreatment tumor samples. In addition, we wish to acknowledge the dedication and hard work of the statisticians, clinical research associates, dosimetrists, and administrative staff who have contributed to the success of the clinical trial.


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Conflict of Interest Notification: All authors do not have any conflict of interest.


1. Bourhis J, Overgaard J, Audry H, et al. Hyperfractionated or accelerated radiotherapy in head and neck cancer: a meta-analysis. Lancet. 2006;368:843–854. [PubMed]
2. Pignon J, Bourhis J, Domenge C, et al. Chemotherapy added to locoregional treatment for head and neck squamous-cell carcinoma: three meta-analyses of updated individual data. The Lancet. 2000;355:949–955. [PubMed]
3. Calais G, Alfonsi M, Bardet E, et al. Randomized trial of radiation therapy versus concomitant chemotherapy and radiation therapy for advanced-stage oropharynx carcinoma. [see comments.]. Journal of the National Cancer Institute. 1999;91:2081–2086. [PubMed]
4. Adelstein DJ, Li Y, Adams GL, et al. An intergroup phase III comparison of standard radiation therapy and two schedules of concurrent chemoradiotherapy in patients with unresectable squamous cell head and neck cancer. J Clin Oncol. 2003;21:92–98. [PubMed]
5. Forastiere AA, Goepfert H, Maor M, et al. Concurrent Chemotherapy and Radiotherapy for Organ Preservation in Advanced Laryngeal Cancer. The New England Journal of Medicine. 2003;349:2091–2098. [PubMed]
6. Bernier J, Domenge C, Ozsahin M, et al. Postoperative irradiation with or without concomitant chemotherapy for locally advanced head and neck cancer. N Engl J Med. 2004;350:1945–1952. [PubMed]
7. Cooper JS, Pajak TF, Forastiere AA, et al. Postoperative concurrent radiotherapy and chemotherapy for high-risk squamous-cell carcinoma of the head and neck. N Engl J Med. 2004;350:1937–1944. [PubMed]
8. Posner MR, Hershock DM, Blajman CR, et al. Cisplatin and fluorouracil alone or with docetaxel in head and neck cancer. N Engl J Med. 2007;357:1705–1715. [PubMed]
9. Vermorken JB, Trigo J, Hitt R, et al. Open-label, uncontrolled, multicenter phase II study to evaluate the efficacy and toxicity of cetuximab as a single agent in patients with recurrent and/or metastatic squamous cell carcinoma of the head and neck who failed to respond to platinum-based therapy. J Clin Oncol. 2007;25:2171–2177. [PubMed]
10. Denis F, Garaud P, Bardet E, et al. Late toxicity results of the GORTEC 94-01 randomized trial comparing radiotherapy with concomitant radiochemotherapy for advanced-stage oropharynx carcinoma: comparison of LENT/SOMA, RTOG/EORTC, and NCI-CTC scoring systems. Int J Radiat Oncol Biol Phys. 2003;55:93–98. [PubMed]
11. Maurizi M, Almadori G, Ferrandina G, et al. Prognostic significance of epidermal growth factor receptor in laryngeal squamous cell carcinoma. Br J Cancer. 1996;74:1253–1257. [PMC free article] [PubMed]
12. Grandis J, Melhem M, Gooding W, et al. Levels of TGF-alpha and EGFR protein in head and neck squamous cell carcinoma and patient survival. J Natl Cancer Inst. 1998;90:824–832. [PubMed]
13. Ang KK, Berkey BA, Tu X, et al. Impact of epidermal growth factor receptor expression on survival and pattern of relapse in patients with advanced head and neck carcinoma. Cancer Res. 2002;62:7350–7356. [PubMed]
14. Chua DT, Nicholls JM, Sham JS, et al. Prognostic value of epidermal growth factor receptor expression in patients with advanced stage nasopharyngeal carcinoma treated with induction chemotherapy and radiotherapy. Int J Radiat Oncol Biol Phys. 2004;59:11–20. [PubMed]
15. Eriksen JG, Steiniche T, Askaa J, et al. The prognostic value of epidermal growth factor receptor is related to tumor differentiation and the overall treatment time of radiotherapy in squamous cell carcinomas of the head and neck. Int J Radiat Oncol Biol Phys. 2004;58:561–566. [PubMed]
16. Bentzen SM, Atasoy BM, Daley FM, et al. Epidermal growth factor receptor expression in pretreatment biopsies from head and neck squamous cell carcinoma as a predictive factor for a benefit from accelerated radiation therapy in a randomized controlled trial. J Clin Oncol. 2005;23:5560–5567. [PubMed]
17. Psyrri A, Yu Z, Weinberger PM, et al. Quantitative determination of nuclear and cytoplasmic epidermal growth factor receptor expression in oropharyngeal squamous cell cancer by using automated quantitative analysis. Clin Cancer Res. 2005;11:5856–5862. [PubMed]
18. Milas L, Mason K, Hunter N, et al. In vivo enhancement of tumor radioresponse by C225 antiepidermal growth factor receptor antibody. Clin Cancer Res. 2000;6:701–708. [PubMed]
19. Huang SM, Harari PM. Modulation of radiation response after epidermal growth factor receptor blockade in squamous cell carcinomas: inhibition of damage repair, cell cycle kinetics, and tumor angiogenesis. Clin Cancer Res. 2000;6:2166–2174. [PubMed]
20. Dittmann K, Mayer C, Fehrenbacher B, et al. Radiation-induced epidermal growth factor receptor nuclear import is linked to activation of DNA-dependent protein kinase. J Biol Chem. 2005;280:31182–31189. [PubMed]
21. Raybaud-Diogene H, Fortin A, Morency R, et al. Markers of radioresistance in squamous cell carcinomas of the head and neck: a clinicopathologic and immunohistochemical study. J Clin Oncol. 1997;15:1030–1038. [PubMed]
22. Couture C, Raybaud-Diogene H, Tetu B, et al. p53 and Ki-67 as markers of radioresistance in head and neck carcinoma. Cancer. 2002;94:713–722. [PubMed]
23. Weidner N, Semple JP, Welch WR, et al. Tumor angiogenesis and metastasis--correlation in invasive breast carcinoma. N Engl J Med. 1991;324:1–8. [PubMed]
24. Fu KK, Pajak TF, Trotti A, et al. A Radiation Therapy Oncology Group (RTOG) phase III randomized study to compare hyperfractionation and two variants of accelerated fractionation to standard fractionation radiotherapy for head and neck squamous cell carcinomas: first report of RTOG 9003. Int J Radiat Oncol Biol Phys. 2000;48:7–16. [PubMed]
25. Calvin DP, Hammond ME, Pajak TF, et al. Microvessel density >or=60 does not predict for outcome after radiation treatment for locally advanced head and neck squamous cell carcinoma: results of a correlative study from the Radiation Therapy Oncology Group (RTOG) 90-03 Trial. Am J Clin Oncol. 2007;30:406–419. [PubMed]
26. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. Journal of American Statistical Association. 1958;53:457–481.
27. Mantel N. Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother Rep. 1966;50:163–170. [PubMed]
28. Kalbfleish J, Prentice R. The statistical analysis of failure time data. John Wiley and Sons; New York: 1980.
29. Gray SJ. Towards a theory of cultural influence on the development of accounting of accounting systems internationally. Abacus. 1988;24:1–15.
30. Hollander M, Wolfe DA. Nonparametric Statistical Methods. John Wiley & Sons; 1973.
31. Pearson K. On the criterion that a given system of deviations from the probable in the case of correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine. 1900;53:157–175.
32. Kruskal W, Wallis WA. Use of ranks in one-criterion variance analysis. American Statistics Association. 1952;47:583–621.
33. Zhang H, Singer B, editors. Recursive Partitioning in the Health Sciences. NY Springer-Verlag New York, Inc.; New York: 1999.
34. Schiattino I, Villegas R, Cruzat A, et al. Multiple imputation procedures allow the rescue of missing data: an application to determine serum tumor necrosis factor (TNF) concentration values during the treatment of rheumatoid arthritis patients with anti-TNF therapy. Biol Res. 2005;38:7–12. [PubMed]
35. Shah JP, Cendon RA, Farr HW, et al. Carcinoma of the oral cavity. factors affecting treatment failure at the primary site and neck. Am J Surg. 1976;132:504–507. [PubMed]
36. Johnson JT, Barnes EL, Myers EN, et al. The extracapsular spread of tumors in cervical node metastasis. Arch Otolaryngol. 1981;107:725–729. [PubMed]
37. Peters LJ, Goepfert H, Ang KK, et al. Evaluation of the dose for postoperative radiation therapy of head and neck cancer: first report of a prospective randomized trial. Int J Radiat Oncol Biol Phys. 1993;26:3–11. [PubMed]
38. Olsen KD, Caruso M, Foote RL, et al. Primary head and neck cancer. Histopathologic predictors of recurrence after neck dissection in patients with lymph node involvement. Arch Otolaryngol Head Neck Surg. 1994;120:1370–1374. [PubMed]
39. Carter RL, Tanner NS, Clifford P, et al. Perineural spread in squamous cell carcinomas of the head and neck: a clinicopathological study. Clin Otolaryngol Allied Sci. 1979;4:271–281. [PubMed]
40. Ang K, Weber R, Vokes E, et al. Radiation Therapy Oncology Group. Research Plan 2002-2006. Head and Neck Cancer Committee. Int J Radiat Oncol Biol Phys. 2001;51:39–43. [PubMed]
41. Poeta ML, Manola J, Goldwasser MA, et al. TP53 mutations and survival in squamous-cell carcinoma of the head and neck. N Engl J Med. 2007;357:2552–2561. [PMC free article] [PubMed]