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[Cancer Research 65, 2147-2156, March 15, 2005]
© 2005 American Association for Cancer Research

Molecular Biology, Pathobiology, and Genetics

Molecular Staging of Cervical Lymph Nodes in Squamous Cell Carcinoma of the Head and Neck

Robert L. Ferris1,2, Liqiang Xi2,3, Siva Raja3, Jennifer L. Hunt4, Jun Wang1,2, William E. Gooding2,5, Lori Kelly2, Jesus Ching6, James D. Luketich2,3 and Tony E. Godfrey2,3

1 Departments of Otolaryngology and Immunology, 2 University of Pittsburgh Cancer Institute, Departments of 3 Surgery, 4 Pathology, and5 Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania; and 6 Cepheid, Sunnyvale, California

Requests for reprints: Tony E. Godfrey, Mount Sinai School of Medicine, Box 1079, One Gustave L. Levy Place, New York, NY 10029. Phone: 212-659-9082; Fax: 212-828-4221; E-mail: tony.godfrey{at}mssm.edu or Robert L. Ferris, University of Pittsburgh Cancer Institute, Hillman Research Wing 1.19d, 5117 Centre Avenue, Pittsburgh, PA 15213. Phone: 412-623-7703; E-mail: ferrisrl{at}upmc.edu..


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Clinical staging of cervical lymph nodes from patients with squamous cell carcinoma of the head and neck (SCCHN) has only 50% accuracy compared with definitive pathologic assessment. Consequently, both clinically positive and clinically negative patients frequently undergo neck dissections that may not be necessary. To address this potential overtreatment, sentinel lymph node (SLN) biopsy is currently being evaluated to provide better staging of the neck. However, to fully realize the potential improvement in patient care afforded by the SLN procedure, a rapid and accurate SLN analysis is necessary. We used quantitative reverse transcription–PCR (QRT-PCR) to screen 40 potential markers for their ability to detect SCCHN metastases to cervical lymph nodes. Seven markers were identified with good characteristics for identifying metastatic disease, and these were validated using a set of 26 primary tumors, 19 histologically positive lymph nodes, and 21 benign nodes from patients without cancer. Four markers discriminated between positive and benign nodes with accuracy >97% but only one marker, pemphigus vulgaris antigen (PVA), discriminated with 100% accuracy in both the observed data and a statistical bootstrap analysis. A rapid QRT-PCR assay for PVA was then developed and incorporated into a prototype instrument capable of performing fully automated RNA isolation and QRT-PCR. The automated analysis with PVA provided perfect discrimination between histologically positive and benign lymph nodes and correctly identified two lymph nodes with micrometastatic tumor deposits. These assays were completed (from tissue to result) in ~30 minutes, thus demonstrating the feasibility of intraoperative staging of SCCHN SLNs by QRT-PCR.

Key Words: molecular markers • head and neck/oral cancer • metastasis


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Squamous cell carcinoma of the head and neck (SCCHN) frequently metastasizes to the regional lymph nodes and this is the strongest predictor of disease prognosis and outcome (1, 2). Whereas accurate staging of lymph nodes in the neck is essential for optimal patient management, current preoperative clinical methods, including newer radiographic techniques, are suboptimal and misdiagnose the presence or absence of cervical nodal metastasis in many patients (3–5). Therefore, due to the low sensitivity of detecting nodal metastasis and the poor prognosis when these metastases are missed, the current management of the clinically node negative (cN0) neck commonly includes routine elective neck dissection (END) with pathologic examination of the removed lymph nodes.

END, or cervical lymphadenectomy done at the time of primary surgery for SCCHN for a cN0 neck, is associated with a significantly improved regional recurrence-free survival and lower incidence of distant metastases (6–9). Furthermore, when END is not done, patients often present later with bulky neck metastases and unresectable disease. END not only provides more accurate staging, it also provides objective criteria to decide when to give adjuvant therapies, such as number/levels of cervical lymph nodes involved and the presence of extracapsular spread of tumor. However, upon END and pathologic analysis of neck dissections, only 25% to 30% of clinically negative necks are found to harbor pathologic evidence of disease (10–12), and 15% of clinically positive necks are in fact tumor negative (6, 13). Consequently, lymphadenectomy may represent overtreatment of almost 50% of patients. Even with END, 7% to 15% of patients with no pathologic evidence of cervical lymph node metastases (pN0) will nonetheless suffer disease recurrence in the neck, indicating the limitation of routine pathology for identifying micrometastasis (13–16).

Because of the need to accurately stage the neck and to treat only those most likely to benefit from therapy, much interest has arisen recently to validate the technique of sentinel lymph node (SLN) mapping for SCCHN. This technique has the potential to define those cN0 patients in whom neck dissection is most appropriate (i.e., those who are pathologically node positive), thereby obviating END and its associated morbidities in node-negative patients. Numerous single-institution studies have suggested that SLN mapping in SCCHN accurately predicts the status of the neck (5, 17–21), and this finding is currently being evaluated in a large multicenter validation trial sponsored by the American College of Surgeons Oncology Group (ACOSOG Z0360 trial). When combined with intraoperative analysis of the SLN(s), this approach could allow for definitive staging and surgical treatment in a single procedure. For this goal to be fully realized, however, intraoperative SLN analysis must be both rapid and accurate.

Unfortunately, although final pathology on fixed tissues (with immunostaining if necessary in the case of SLNs) is highly accurate, intraoperative frozen section examination is notoriously insensitive. In breast cancer, reports on intraoperative SLN sensitivity range from 47% to 74% (22, 23) whereas in melanoma the sensitivity is even worse, with reports from 38% to 47% (24, 25). Consequently, many patients have to undergo a second surgical procedure to complete lymph node dissection after definitive pathologic assessment identifies microscopic metastatic disease not evident on intraoperative analysis. Our own work, and that reported by others, suggests that low sensitivity may also be an issue for intraoperative SLN analysis in SCCHN (18, 21). Performing a second surgery on the neck is an undesirable scenario because this would likely increase complications and morbidity and could delay the use of adjuvant therapy. This is in addition to the extra cost, discomfort, and psychological toll on the patient. Such issues may negatively affect the widespread acceptance of SLN biopsy in SCCHN.

Reverse transcription–PCR (RT-PCR) has great theoretical potential for the detection of small tumor deposits in lymph nodes. Studies in many tumor types including esophageal cancer, colon cancer, breast cancer, melanoma, and SCCHN have showed the ability of an RT-PCR–based assay to detect histologically occult micrometastases (7, 9, 26–29). Many of the earlier RT-PCR studies suffered from poor specificity, but the introduction of real-time quantitative RT-PCR (QRT-PCR) has helped overcome this problem and provides the potential for objective and quantitative analysis of lymph nodes (30–33). Whereas we are also interested in detection of occult disease by QRT-PCR, we have recently focused on application of this method to rapid, intraoperative analysis of SLNs. We have shown that RNA isolation and QRT-PCR can be done in a time frame amenable to intraoperative analysis and we have also developed multiplex PCR technology to allow incorporation of necessary controls into rapid QRT-PCR (34, 35) . Unfortunately, these reports used manual RNA isolation and QRT-PCR setup, and this is likely to be too labor intensive, prone to contamination, and variable for true clinical use in an intraoperative setting. This concern has recently been addressed, however, with the development of a fully automated and integrated RNA isolation and quantitative PCR instrument called the GeneXpert (Cepheid, Sunnyvale, CA).7 The GeneXpert is capable of performing RNA isolation from lysed tissue, reverse transcription, and quantitative PCR all in ~30 minutes. Our goals in this study were to identify appropriate QRT-PCR markers for detection of metastasis to lymph nodes and to use the GeneXpert to show the feasibility of automated analysis in an intraoperative time frame. When applied to SLNs from patients with SCCHN, we show that this technology has the potential to accurately stage the neck and allow the surgeon to provide optimal treatment in a single procedure.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Patients
A total of 26 primary tumors and 24 histologically tumor involved nodes were harvested at surgery and immediately snap-frozen at –80°C until RNA extraction was done. Of these specimens, eight sets of paired human head and neck primary tumors and tumor-containing metastatic lymph nodes (from the same 8 patients with SCCHN) were included in this study. Benign lymph nodes (n = 21) were obtained from patients undergoing surgery for benign esophageal disorders (gastroesophageal reflux or achalasia). Institutional review board–approved, written informed consent was obtained from all patients donating specimens for this study, either through the ENT Registry at the Department of Otolaryngology or the Esophageal Risk Registry at the Division of Thoracic and Foregut Surgery, University of Pittsburgh. Clinical and demographic data for fresh tumor/metastasis specimens are shown in Table 1. These patients are similar to historical patients with SCCHN in our practice, with tumors distributed throughout all head and neck subsites. In the 44 patients studied, histologically verified squamous cell carcinoma originated in one of the following primary sites: oral cavity (18), oropharynx (10), larynx (10), hypopharynx (5), and site undetermined (1).


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  		Table 1. Clinical and demographic data of tumor and lymph node specimens obtained from patients with SCCHN in this study

 
Identification of Potential Markers
An extensive literature and public database survey was conducted to identify any potential markers. Resources for this survey included PubMed, OMIM, UniGene (http://www.ncbi.nlm.nih.gov/), GeneCards (http://bioinfo.weizmann.ac.il/cards), and CGAP (http://cgap.nci.nih.gov). Our survey criteria were somewhat flexible but the goal was to identify genes with moderate to high expression in head and neck cancer and low expression in normal lymph nodes. In addition, genes reported to be up-regulated in head and neck cancer and genes with restricted tissue distribution were considered potentially useful. Finally, genes reported to be cancer specific, such as the cancer testis antigens and hTERT, were evaluated.

Tissues and Pathologic Evaluation
Archived tissue from all patients in this study was reviewed by a specialized head and neck pathologist (J.L.H.), and the diagnosis was reconfirmed histologically. Twenty 5-µm sections were cut from each optimal cutting temperature compound (OCT)–embedded tissue for RNA isolation. In addition, sections were cut and placed on slides for H&E and immunohistochemistry analysis at the beginning, middle (between the 10th and 11th sections for RNA), and end of the sections for RNA isolation. All three H&E slides from each specimen underwent pathologic review to confirm presence of tumor, percentage of tumor, and to identify the presence of any contaminating tissues. All of the unstained slides were stored at –20°. Immunohistochemistry evaluation was done using the antibody pan keratin (AE1/AE3) mixture (DAKO, Carpinteria, CA), and Vector Elite ABC kit and Vector AEC Chromagen (Vector Laboratories, Burlingame, CA). Immunohistochemistry was used as needed to confirm the H&E histology.

Screening Approach
The screening was conducted in two phases. All potential markers entered the primary screening phase and expression was analyzed in 6 primary tumors and 10 benign lymph nodes obtained from patients without cancer (five RNA pools with 2 lymph node RNAs per pool). Markers that showed good characteristics for lymph node metastasis detection (consistent, high expression in tumors and very low expression in benign nodes) passed into the secondary screening phase. The secondary screen consisted of expression analysis on 26 primary tumors, 19 histologically positive lymph nodes, and 21 benign lymph nodes without cancer.

RNA Isolation and cDNA Synthesis
RNA was isolated using the RNeasy minikit (Qiagen, Valencia, CA) essentially as described by the manufacturer. The only modification was that we doubled the volume of lysis reagent and loaded the column in two steps. This was found to provide better RNA yield and purity, probably because of diluting out the OCT in the tissue sections. Reverse transcription was done in 100-µL reaction volumes with random hexamer priming and SuperScript II (Invitrogen, Carlsbad, CA) reverse transcriptase (36). For the primary screen, two reverse transcription reactions were done, each with 1,000 ng of RNA. The cDNAs were combined and quantitative PCR was done using the equivalent of 20 ng RNA per reaction. For the secondary screen, the RNA input for primary tumors and positive nodes was 1200 ng per reverse transcription reaction and 20 ng per quantitative PCR reaction, but this was increased to 2000 ng per reverse transcription reaction and 80 ng per quantitative PCR reaction for the benign nodes in order to improve sensitivity for detection of low background expression.

Quantitative PCR
All quantitative PCR for marker screening was done on the ABI Prism 7700 Sequence Detection Instrument (Applied Biosystems, Foster City, CA). Relative expression of the marker genes was calculated using the {Delta}CT methods previously described (37) and with ß-glucuronidase as the endogenous control gene. All assays were designed for use with 5' nuclease hybridization probes although the primary screening was done using SYBR Green quantification to save cost. Assays were designed using the ABI Primer Express Version 2.0 software and, where possible, amplicons spanned exon junctions in order to provide cDNA specificity. All primer pairs were tested for amplification specificity (generation of a single band on gels) at 60°C, 62°C, and 64°C annealing temperature. In addition, PCR efficiency was estimated using SYBR Green quantification before use in the primary screen. Further optimization and more precise estimates of efficiency were done with 5' nuclease probes for all assays used in the secondary screen.

A mixture of the Universal Human Reference RNA (Stratagene, La Jolla, CA) and RNAs from human placenta, thyroid, heart, colon, PCI13 cell line (gift of Dr. Theresa Whiteside, University of Pittsburgh Cancer Institute) and SKBR3 cell line served as a universal positive expression control for all the genes in the marker screening process.

Quantification with SYBR Green (Primary Screen)
For SYBR Green I–based quantitative PCR, each 50-µL reaction contained 1x TaqMan buffer A (Applied Biosystems), 300 nmol/L each deoxynucleotide triphosphate, 3.5 mmol/L MgCl2, 0.06 units/µL Amplitaq Gold (Applied Biosystems), 0.25x SYBR Green I (Molecular Probes, Eugene, OR), and 200 nmol/L each primer. The amplification program consisted of two stages with an initial 95°C Taq activation stage for 12 minutes followed by 40 cycles of 95°C denaturation for 15 seconds, 60°C, 62°C, or 64°C anneal/extend for 60 seconds, and a 10-second data collection step at a temperature 2°C to 4°C below the Tm of the specific PCR product being amplified (38). After amplification, a melting curve analysis was done by collecting fluorescence data while increasing the temperature from 60°C to 95°C over 20 minutes.

Quantification with 5' Nuclease Probes (Secondary Screen)
Probe-based quantitative PCR was done as described previously (26, 37). Briefly, reactions were done with a probe concentration of 200 nmol/L and a 60-second anneal/extend phase at 60°C for ß-GUS, CK19, PTHrP, and SCCA1/2, 62°C for pemphigus vulgaris antigen (PVA), and 64°C for carcinoembryonic antigen (CEA) and TACSTD1. The sequences of primers and probes (purchased from IDT, Coralville, IA) for genes evaluated in the secondary screen are listed in Supplementary Table 1. The primer sequences for markers used in the primary screen will be provided upon request.

Data Analysis
In the primary screen, data from the melting curve were analyzed using the ABI Prism 7700 Dissociation Curve Analysis 1.0 software (Applied Biosystems). The first derivative of the melting cure was used to determine the product Tm as well as to establish the presence of the specific product in each sample. In general, samples were analyzed in duplicate PCR reactions and the average Ct value was used in the expression analysis. However, in the secondary screen triplicate runs were done for each individual benign node and the lowest Ct value was used in the calculation of relative expression to obtain the highest value of background expression for the sample.

Statistical Analysis
Generation of Prediction Rules. Six markers that passed the secondary screen were evaluated individually and in combination with other markers. The characteristics used to evaluate markers were sensitivity, specificity, classification, accuracy and the area under the receiver operating characteristic curve. For individual markers, a cutoff value was determined that maximized the classification accuracy (proportion of lymph nodes correctly classified). In cases wherein classification accuracy was 100%, the cutoff was set at the midpoint between the highest expressing negative node and the lowest expressing positive node. Markers were also combined into pairs for lymph node classification and a linear prediction rule was generated for each pair. The rule was equivalent to the linear predictor that equalized the fitted probabilities above and below the linear boundary. That is, points on the boundary line had a predicted probability midway between the numerical scores assigned to positive and negative nodes. For example, if positive nodes were assigned a score of 2 and benign nodes a score of 1, predicted scores >1.5 were classified as positive.

Internal Validation of Prediction Rules. Internal validation of prediction rules was conducted by nonparametric bootstrap resampling using Efron and Tibshirani's improved bootstrap method (39), in which 500 bootstrap samples of lymph nodes are selected from the pool of all positive and negative nodes. The optimism in the original estimates of sensitivity, specificity, and classification accuracy are then calculated as the difference between the bootstrap classification statistic applied to the original data and applied to the bootstrap data. The average difference over all bootstrap samples is computed and reported as the bias in the values derived from the observed data and then subtracted from the original estimates to produce the bootstrap-validated estimates.

GeneXpert Analysis. Twenty-four 5-µm sections of OCT-embedded tissue were sectioned into 800 µL of GeneXpert lysis buffer (Cepheid). The lysis buffer was filtered through a 0.22-mm syringe filter (Osmonics Inc, Westborough, MA), and loaded into a GeneXpert cartridge. The automated processes of RNA isolation, reverse transcription, and QRT-PCR on the GeneXpert are described elsewhere.7 Briefly, the filtered tissue lysate is placed into a reservoir on the GeneXpert cartridge (Supplemental Fig. 1, panel 5) and all necessary reagents for RNA isolation, reverse transcription and quantitative PCR are placed in additional reservoirs. The cartridge is placed in the GeneXpert (Supplemental Fig. 1, panel 6) and the remaining steps are fully automated. The tissue lysate is first passed over an RNA isolation resin, washed, and then RNA is eluted into a clean reservoir (~6 minutes). Reverse transcription reagents are added and the mixture is pumped into the integrated PCR tube and heated for cDNA synthesis (~5 minutes). The cDNA is then removed from the PCR tube and PCR reagents (primers, probes, etc.) are added. The mixture is returned to the PCR tube and thermal cycling is done (~20-25 minutes). Probe fluorescence is monitored at each cycle and results are updated on the monitor in real time. For this study, the PCR assay consisted of a multiplex QRT-PCR for PVA and the endogenous control gene, ß-glucuronidase. This assay was optimized to perform in a rapid, multiplex PCR protocol using our previously published methods for rapid PCR and temperature-controlled primer limiting (34, 35).


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Primary Screening. Our literature and database surveys identified 40 genes for evaluation in the primary tumor marker screen. All of these genes were analyzed for expression in 6 primary SCCHN and 10 benign lymph nodes. Resulting data for the 20 genes with the highest median expression in tumors is shown in Fig. 1 and similar data for all genes in the primary screen will be provided upon request. Histograms showing the data from all genes in the primary screen can also be viewed at http://www.mssm.edu/labs/godfrt01/research/charts.htm. Median relative expression in the primary tumors and benign nodes was calculated for each gene in the primary screen and is reported in Table 2. In addition, we also calculated the ratio of relative expression between the lowest expressing tumor and the highest expressing benign node, and between the median expression in tumors and the highest expressing benign node. Some genes had no detectable expression in benign nodes and therefore ratios could not be calculated (see Table 2).



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  		Figure 1. Primary screen data showing the expression profiles of top 20 genes ranked by median expression in 6 primary head and neck tumors and 10 benign lymph nodes from the patients without cancer.

 

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  		Table 2. Relative expression in head and neck tumors and benign lymph nodes for the markers analyzed in the primary screen

 
Using these three parameters (median expression in the tumors, lowest tumor/highest benign node and median tumor/highest benign node ratios), we selected six genes that we judged to have expression characteristics suitable for detection of lymph node metastases. These six genes, PVA, CEA, CK19, PTHrP, SCCA1/2, and TACSTD1, all have median tumor/highest benign node ratios >500 and therefore have the potential to detect small foci of tumor while still discriminating negative nodes. In addition, all genes except CK19 also have lowest tumor/highest benign node ratios >100 indicating that they are expressed at reasonably high levels in all six tumors tested in the primary screen. MAGEA3 and CK7 were omitted from the secondary screen due to a combination of low median tumor/highest benign node expression ratios and relatively low median expression in tumors. Similarly, CK18 was excluded based on a combination of low expression ratios and ITGB4 was excluded due to very low median expression in primary tumors. Finally, EGFR was inadvertently omitted from the primary screen but was included in the secondary screen based on its frequent use as a marker in SCCHN studies (40–42).

Secondary Screen. Histologic evaluation of the 26 primary tumor specimens revealed a median tumor percentage of 40% (range, 5-95%). Similarly, in the 19 histologically positive nodes, the median tumor percentage was 70% (range, 2-90%). The relative expression profiles of the markers selected for the secondary screen are shown in Fig. 2A. The data shows that all markers are expressed in positive lymph nodes as well as in primary tumors indicating that metastatic tumor cells continue to express these genes. Figure 2A also indicates the relative expression cutoff values that provide the most accurate classification of histologically positive and benign nodes. These cutoff values were used to calculate classification characteristics (sensitivity, specificity, area under the receiver operating curve, and overall classification accuracy) for each marker and the results are presented in Table 3.



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  		Figure 2. Secondary screen data: A, expression profiles of selected markers in primary head and neck tumors (T), histologically positive lymph nodes (PN), and benign lymph nodes (BN) from the patients without cancer. B, two marker prediction on histologically positive nodes (red plus sign) and benign lymph nodes (green circle).

 

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  		Table 3. Single- or two-marker prediction characteristics on positive and benign lymph nodes in the secondary screen

 
Of the seven markers analyzed, two (PVA and SCCA1/2) provided perfect classification of positive and benign lymph nodes. PTHrP and TACSTD1 also proved to be very good markers, with classification accuracy of 97.5%. The remaining three markers, however, suffered from poor sensitivity (EGFR and CK19) or poor specificity (CEA and CK19), resulting in overall classification accuracy of 95% or less. For all markers, these estimates of classification accuracy are likely to be optimistic because the cutoff value is being generated and tested on the same data set. In an attempt to address this issue, we did an internal marker validation using nonparametric bootstrap analysis to estimate the optimism in our observed classification accuracy. The results, shown in Table 3, indicate that PVA is the only marker that retained 100% accuracy in the bootstrap analysis. For SCCA1/2, PTHrP, and TACSTD1 the bias, or estimate of optimism in the observed data, was ≤1.3% and these genes are likely to provide classification accuracy >96% if tested in a larger sample set. The bias for CEA, CK19, and EGFR, on the other hand, ranged from 2.2% to 7.8%, and the estimates of true classification accuracy were all <93%. Thus, it seems that PVA is the single best marker identified in this study, with SCCA1/2, PTHrP, and TACSTD1 also being strong individual markers.

In addition to analyzing individual markers, we also evaluated lymph node classification using paired combinations of PVA, SCCA1/2, PTHrP, and TACSTD1. Marker pairs were evaluated by plotting graphs with expression of one marker on each of the axes and then mathematically calculating a linear cutoff that was midway between the positive and negative lymph node distributions (Fig. 2B). Classification accuracy obtained for all marker combinations using this approach is shown in Table 3 (observed data) and all but one combination (TACSTD1/PTHrP) achieved an observed classification accuracy of 100%. As with the individual markers, this is likely to be optimistic, and nonparametric bootstrap analysis was therefore done. We found that classification accuracy dropped <0.8% compared with the observed data (Table 3, nonparametric bootstrap estimates) indicating that marker combinations are likely to provide more robust lymph node classification than single markers alone.

Automated Analysis of Lymph Nodes. To show the potential for rapid and automated QRT-PCR analysis of lymph nodes, we analyzed a set of seven benign and seven histologically positive lymph nodes for expression of PVA using the Cepheid GeneXpert instrument. As in the screening data, we found that PVA expression was almost 2 orders of magnitude higher in all positive nodes than in any benign node, and in most cases was >3 orders of magnitude higher. Interestingly, two lymph nodes in this set were called positive by final pathology (on the lymph node half that was sent for routine pathologic analysis with H&E staining of fixed tissue) but were apparently negative by H&E staining of the frozen sections adjacent to the sections used for RT-PCR analysis. Pan-keratin immunostaining of these sections, however, revealed extremely small foci of positively staining cells (Fig. 3B) and QRT-PCR for PVA on the GeneXpert also identified these nodes as positive (nodes 1957 and 2634 in Fig. 3). These micrometastases would likely have been missed on intraoperative frozen section analysis. Thus, PVA expression measured on the automated GeneXpert instrument clearly differentiated positive and benign nodes in this sample set and correctly identified two nodes with micrometastatic disease. In addition, all assays were done in 30 minutes or less, demonstrating the potential for intraoperative analysis of SLNs in head and neck cancer.



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  		Figure 3. PVA expression in benign nodes and head and neck cancer positive lymph nodes as measured on GeneXpert. A, H&E staining of sample 2,634; B, immunohistochemistry staining of same area in sample 2,634; C, amplification plot of positive node sample 3,032 (positive in QRT-PCR); D, amplification plot of benign node sample 1,513 (negative in QRT-PCR); E, expression of 14 lymph nodes from individual patients.

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Current analysis of lymph nodes with H&E staining and microscopic examination suffers from two major limitations. First, only one or two tissue sections are typically reviewed, leaving the majority of each node unsampled, and second, small foci of tumor cells can be missed. These limitations are even more pronounced for intraoperative frozen section analysis due to poor tissue architecture and time constraints. Whereas it has been shown that serial sectioning can overcome the issue of sampling error (43, 44) and that the addition of immunohistochemistry staining can improve detection of small tumor foci (28), the combination of these methods is too time-consuming for intraoperative lymph node analysis. Because cost is also an issue, this detailed analysis approach is limited to examination of fixed tissues in diseases in which SLN biopsy is done, thus reducing the number of lymph nodes to be examined. Unfortunately, permanent section results are not available until several days after lymphadenectomy and, therefore, for intraoperative decision making, the surgeon currently has to rely on frozen-section H&E analysis. In the treatment of breast cancer and melanoma, in which SLN biopsy is commonly used, the sensitivity of intraoperative frozen-section analysis ranges from 38% to 74% (22–25). Consequently, many patients have to undergo second surgeries to complete lymph node dissection, in cases in which definitive pathologic assessment identifies metastases that were missed on the frozen-section examination. The limited reports on frozen-section sensitivity in SCCHN suggest that a similar scenario is likely to exist if SLN biopsy is done for this tumor type (21). This is a particularly undesirable scenario in SCCHN and could significantly reduce the acceptance of SLN biopsy by head and neck cancer surgeons.

We and others have previously shown that RT-PCR can potentially be more sensitive than routine pathology for analysis of lymph nodes (7, 26, 28). In head and neck cancer specifically, studies on molecular analysis of cervical lymph node metastasis have used a variety of techniques, including PCR amplification to detect p53 mutations (45), immunohistochemistry staining (with or without serial sectioning) and histopathologic examination (14–16), and standard RT-PCR–based analysis of tumor maker gene expression (46). More recently, several groups have used quantitative RT-PCR for detection of cervical lymph node metastases (8, 47) and Nieuwenhuis et al. showed the prognostic value of QRT-PCR in pN0 SCCHN patients (33). This same group also showed the potential for molecular staging of cervical nodes by using tissue obtained via fine-needle aspiration (29). Despite this encouraging work, however, issues remain regarding the most appropriate molecular marker for SCCHN lymph node analysis and the reproducibility and quality control of QRT-PCR in a clinical setting. Furthermore, no studies have thus far addressed the possibility for intraoperative QRT-PCR analysis of cervical lymph nodes in patients with SCCHN. We address all of these issues in this report.

First, we have done a comprehensive marker screen to identify the best mRNA markers for detection of lymph node metastases in SCCHN. This marker screen has identified four (SCCA1/2, PVA, TACSTD1, and PTHrP) extremely robust, tumor-related markers, any one of which might be used in a single-marker assay. Squamous cell carcinoma antigen (SCCA) is a member of the ovalbumin family of serine proteinase inhibitors. The SCCA protein is expressed in neutral and acidic forms, designated as SCCA1 and SCCA2, and is detected in the superficial and intermediate layers of normal squamous epithelium. The expression of SCCA2 in cancer has been associated with an aggressive phenotype and this gene has been used in several studies for detection of squamous cell carcinoma metastases to lymph nodes (48). TACSTD1, also known as EPCAM, EGP-2, KS1/4, GA-733-2, and MIC-18, is a human cell surface antigen that is defined by the monoclonal antibody AUAI. TACSTD1 is also the antigen for the antibody Ber-Ep4 and as such has been used in studies to detect lymph node micrometastases in a variety of tumor types, including SCCHN (41, 49, 50). A recent study has also shown that TACSTD1 is a good marker for RT-PCR–based identification of lymph node micrometastasis in non–small cell lung cancer (9). Parathyroid hormone–related protein (PTHrP, also known as PTHLH and HHM) regulates endochondral bone development and epithelial-mesenchymal interactions during the formation of the mammary glands and teeth, and its expression in SCCHN may represent dedifferentiation commonly seen in epithelial tumors. The gene encoding PTHrP has been mapped to the short arm of chromosome 12 and is known to contain 6 to 7 exons. Alternate RNA splicing of this gene results in heterogeneity of the mRNA species encoded at the 3' ends. Pemphigus vulgaris antigen (PVA), also known as desmoglein 3 (DSG-3), is a 130-kDa surface glycoprotein that is the serologic target in the autoimmune skin disease pemphigus vulgaris. PVA is a member of the desmoglein subfamily of the desmosomal cadherins and the gene encoding this protein has been mapped to the long arm of chromosome 18 and is known to contain 15 exons. To our knowledge, neither PTHrP nor PVA have been used in prior studies to detect lymph node metastases in SCCHN or other tumor types.

From our data it is clear that PVA and TACSTD1 are the best individual markers studied. In all screening phases, PVA and TACSTD1 provided 100% discrimination between positive and benign lymph nodes although PVA was the only individual marker to maintain 100% classification accuracy upon bootstrap analysis. Thus, it seems that a single marker QRT-PCR analysis for PVA could be adequate for staging of cervical lymph nodes in SCCHN.

Whereas the marker screening phase of this study was done using routine QRT-PCR techniques, we have previously published work showing that it is possible to perform QRT-PCR assays in an intraoperative time frame (~25 minutes; refs. 34, 35). Given the potential utility of an intraoperative SLN analysis in SCCHN, we therefore applied our rapid, multiplex QRT-PCR techniques to develop such an assay for PVA. This assay was incorporated into a completely automated RNA isolation and QRT-PCR instrument (the GeneXpert) developed by Cepheid for molecular diagnostic testing. The GeneXpert is an automated cartridge processor with integrated real-time PCR capability. Each single-use GeneXpert cartridge consists of multiple reagent reservoirs, a syringe barrel, and a valve mechanism that allows transfer of reagents between reservoirs. In addition, the cartridges used in this study have a solid-phase matrix for nucleic acid purification and isolation. Finally, each cartridge has a PCR tube that can be accessed from specific reagent reservoirs to allow loading of reaction components into the PCR tube for reverse transcription and/or quantitative PCR. The GeneXpert instrument itself interfaces with the cartridge to control reagent movement and also houses heat plates and optical excite/detect blocks to facilitate real-time PCR. The details of RNA isolation and QRT-PCR using the GeneXpert are to be published elsewhere.

Although the speed of the GeneXpert allows for intraoperative analysis of SLNs there may also be additional clinical value if QRT-PCR eventually proves superior to routine pathology, as suggested by Nieuwenhuis et al. (33). The detection of occult lymph node disease, and subsequent improved patient staging (28), could have significant consequences for the treatment of SCCHN. For example, patients with multiple positive nodes, or extracapsular extension of tumor, are often referred for radiation or chemoradiation therapy, to reduce the high risk of locoregional failure. Although QRT-PCR cannot distinguish extracapsular extension, it may well identify additional positive lymph nodes leading to upstaging, and more appropriate adjuvant treatment. Furthermore, surgical approach and extent of lymph node dissection may also be determined by the lymph node status. Many surgeons advocate planned lymph node dissection in N2 patients who undergo primary chemoradiation because the available data indicate that 20% to 30% of complete responders (based on clinical modalities) may actually have residual tumor in the neck (3). Evaluation of suspicious postradiation cervical nodules could potentially be done via fine-needle aspirate and QRT-PCR, or by intraoperative QRT-PCR, allowing the surgeon to determine the necessary extent of resection before or during the surgery. Thus, the detection of occult lymph node metastasis could play an important role in future staging and treatment options for pathologically node-positive as well as node-negative patients.

In conclusion, we have showed that QRT-PCR can detect, with high sensitivity and specificity, metastatic disease in lymph nodes of patients with SCCHN. In addition, we have showed the feasibility of automated, intraoperative staging of cervical lymph nodes and the possibility that such an approach may eventually prove superior to conventional pathology. Staging of the cN0 neck is currently a topic of intense interest in the head and neck oncologic community, with the goal that therapeutic surgical and adjuvant treatment be administered to those most likely to benefit from it. Whereas the ACOSOG Z0360 trial is powered to validate the multiple single-institution studies that suggest the utility of SLN mapping for staging the cN0 neck in SCCHN, it is unlikely that SLN biopsy will be widely accepted without a rapid, accurate, and standardized method of staging the SLN(s). Our development of such an assay and identification of discriminatory marker genes provides the pilot data necessary for the incorporation of QRT-PCR into future clinical studies applying SLN mapping to clinical practice for patients with this disease.


   Acknowledgments
 
Grant support: NIH/National Cancer Institute CA90665-01 research grant (T.E. Godfrey), University of Pittsburgh Oral Cancer Center of Discovery pilot grant (R.L. Ferris and T.E. Godfrey), and a cooperative research and development agreement with Cepheid (T.E. Godfrey).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


   Footnotes
 
Note: R.L. Ferris and L. Xi contributed equally to this work.

L. Xi and T.E. Godfrey are currently at Mount Sinai School of Medicine, New York, New York.

Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/) and at http://www.mssm.edu/labs/godfrt01/publications/supp.htm.

7 Manuscript submitted for publication. Back

Received 10/19/04. Revised 12/14/04. Accepted 1/ 6/05.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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