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J Thorac Cardiovasc Surg 2008;135:627-634
© 2008 The American Association for Thoracic Surgery

General Thoracic Surgery

A simple two-gene prognostic model for adenocarcinoma of the lung

Carolyn E. Reed, MDa,*, Amanda Graham, MSa, Rana S. Hoda, MDb, Andras Khoor, MDd, Elizabeth Garrett-Mayer, PhDc, Michael B. Wallace, MDe, Michael Mitas, PhDa

a Department of Surgery, Medical University of South Carolina, Charleston, SC
b Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC
c Department of Biostatics, Bioinformatics & Epidemiology, Medical University of South Carolina, Charleston, SC
d Department of Laboratory Medicine & Pathology, Mayo Clinic, Jacksonville, Fla
e Division of Gastroenterology & Hepatology, Mayo Clinic, Jacksonville, Fla

Received for publication April 23, 2007; revisions received September 3, 2007; accepted for publication October 26, 2007.

* Address for reprints: Carolyn E. Reed, MD, Medical University of South Carolina, 96 Jonathan Lucas St, 418 CSB, Charleston, SC 29425. (Email: reedce{at}musc.edu).


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Objective: We hypothesized that clinical outcome of resected early-stage adenocarcinoma of the lung can be predicted by the expression of a few critically important genes as measured by quantitative real-time reverse-transcriptase polymerase chain reaction in formalin-fixed paraffin-embedded primary tumors.

Methods: Twenty-two prognostic genes for the metastatic phenotype were identified through complementary DNA microarray analysis of 4 cancer cell lines and bioinformatics analysis. Expression levels of a subset of these genes (n = 13) were measured by real-time time reverse-transcriptase polymerase chain reaction in formalin-fixed paraffin-embedded primary adenocarcinoma from patients whose disease recurred within 2 years (n = 9) and in patients who did not have a recurrence (n = 11). Receiver operating characteristic curves were analyzed to establish prognostic values of single genes. The most informative gene was combined with the remaining genes to determine whether there was a particular pair(s) that yielded high diagnostic accuracy. A small validation study was performed.

Results: Receiver operating characteristic curve analysis of the single genes revealed that high expression of CK19 was associated with nonrecurrence (area under the curve = 0.859, confidence interval = 0.651–0.970). The CK19/EpCAM2 gene ratio had the most reproducible prognostic accuracy, followed by the CK19/P-cadherin ratio. A Kaplan–Meier survival analysis generated from the CK19/EpCAM2 ratio resulted in highly significant curves as a function of marker positivity (P = .0007; hazard ratio = 10.7). Significance declined but was maintained in the validation study.

Conclusions: This preliminary study provides evidence that the CK19/EpCAM2 and/or CK19/P-cadherin ratio(s) may be a simple and accurate prognostic indicator of clinical outcome in early-stage adenocarcinoma of the lung. If further validation studies from large patient cohorts confirm the results, adjuvant therapy could be targeted to this high-risk group.

Abbreviations and Acronyms AUC = area under the curve; cDNA = complementary DNA; CI = confidence interval; cRNA = complementary RNA; Ct = cycle of threshold; NSCLC = non–small cell lung cancer; RT-PCR = reverse-transcription polymerase chain reaction


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Despite surgical resection, patients with pathologic stage I non–small cell lung cancer (NSCLC) will have an approximately 30% to 40% incidence of recurrence and those with stage II a 45% to 60% recurrence rate.1Go At present, the standard of care is to administer postoperative adjuvant chemotherapy to those patients with stage II NSCLC.2,3Go Although there was initial enthusiasm for administering adjuvant therapy to patients with resected stage IB NSCLC, recent data do not support this practice.3-5Go However, subsets of patients with stage I disease could potentially benefit from further treatment to prevent recurrence; likewise, a method to predict which patients with stage II disease could avoid the unnecessary toxicity of chemotherapy would be helpful.

The development of metastatic disease is the most common cause of death among patients with NSCLC and results from dissemination of malignant cells. It is now recognized that the ability of cells to gain metastatic potential is an intrinsic property of the primary tumor, which is substantiated by the high correlations between clinical outcome and gene expression profiles of a variety of primary tumors.6,7Go The ability to predict clinical outcome on the basis of analysis of primary tumors would allow patients with cancer to be treated more effectively. However, the problem with many of these expression studies is that they require measurements of large sets of predictive genes using a platform (complementary DNA [cDNA] microarray analysis) that is not well suited to clinical application.

In this pilot study, we hypothesized that clinical outcome of patients with resected early-stage adenocarcinoma of the lung could be predicted by the expression of relatively few, but critically important, genes measured by quantitative real-time reverse-transcription polymerase chain reaction (RT-PCR) in formalin-fixed paraffin-embedded primary tumors. Specifically, we hypothesized that there exists a "good gene" and a "bad gene" such that the ratio of the two is a strong prognostic indicator of clinical outcome.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Identification of 15 Highly Expressed Genes in NSCLC Cell Lines
Expression levels of 22,283 gene transcripts were determined on oligonucleotide microarrays using RNA prepared from 4 NSCLC cell lines (CRL 5807 [bronchoalveolar carcinoma], CRL 5876 [adenocarcinoma derived from metastatic lymph node], A549 [adenocarcinoma], and HTB 177 (large cell carcinoma]), as well as from a pool of 4 normal cervical lymph nodes. Eight micrograms of total RNA per sample were used. First- and second-strand cDNA synthesis, double-stranded cDNA cleanup, biotin-labeled complementary RNA (cRNA) synthesis, cleanup, and fragmentation were performed according to protocols in the Affymetrix GeneChip Expression Analysis technical manual (Affymetrix, Santa Clara, Calif). Microarray analysis was performed by the DNA Microarray and Bioinformatics Core Facility at the Medical University of South Carolina using U133 A GeneChips (Affymetrix). Fluorescent images of hybridized microarrays were obtained by an HP GeneArray scanner (Affymetrix). For normalization, the microarray office suite was used such that all fluorescence values were multiplied by a factor that resulted in a mean fluorescent score for all genes equal to 150. Data for normal lymph nodes were obtained from a previous study.8Go All microarray results were imported into a single Microsoft Excel file. The first algorithm in the selection of highly expressed genes involved elimination of genes from NSCLC cell lines that were expressed in normal lymph nodes (n = 11,326; 50.8% of total [22,283]). Of the remaining 10,957 genes, those that were detected in at least 2 NSCLC cell lines were first selected (n = 1731; 7.7% of total). After this round, genes whose mean fluorescence in all cell lines was greater than 500 were selected (n = 91; 0.41% of total). The final group of 91 genes was sorted according to mean cell line fluorescence/mean fluorescence of normal lymph nodes, and the 15 top genes were selected (Go Table 1).


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  		Table 1 Top 15 most highly overexpressed genes in lung cancer cell lines
 
Bioinformatics Analysis to Identify Potentially Prognostic Genes in NSCLC
Of the 15 most highly expressed genes identified by cDNA microanalysis, it was hypothesized that some were also expressed in other cancers, whereas some genes were specific for NSCLC. To identify genes that were highly expressed in other cancers, we queried the online Comparative Genome Anatomy Project (CGAP) National Cancer Institute 60-gene expression database (URL = http://cgap.nci.nih.gov) using all 15 genes. The output of a given query consists of a list of 10 genes whose expression levels are most highly correlated with the query sequence. Using the output of each gene, we constructed a correlation map such that the appearance of a gene on the map required (1) direct contact with one of the 15 highly expressed genes, (2) contacts with at least 2 genes, (3) that the correlation coefficient of any 2 genes must have a P value < 8 x 10–6, (4) that the relevant gene must be overexpressed in the CGAP SAGE data set in at least two cancers (with respect to normal tissue), and (5) that expression of the relevant gene must be at least 16 to 31 tags/200,000 sequenced tags in at least one cancer tissue. Genes identified from the first set of queries were used as query in a reiterative round of interrogation (data mining).

The correlation map obtained by this bioinformatics data mining approach contained a total of 22 genes (Go Figure 1). Seven of the 22 genes (AGR2, Map7, S100P, CK19, EpCAM1, EpCAM2, and P-cadherin) were derived from the list of 15 most highly expressed genes and are referred to as the primary prognostic genes (underlined in Figure 1). The remaining 15 genes identified from this bioinformatics approach are referred to as the secondary prognostic genes (italicized in Figure 1).


Figure 1
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  		Figure 1. Correlation map of cancer-associated genes. Correlation map of the genes was constructed as described in the text. Genes are positioned in a hypothetical cell to reflect intracellular, membrane-bound, or extracellular localization. The thickness of a solid line connecting a given gene pair is ~proportional to the R 2 value of gene expression, which ranges from 0.91 (P < .0001) for the Spint1/SNC19 pair, to 0.55 (P < .0001) for the TFF1/S100P pair.

 
Identification of Genes of Prognostic Value in Patients With Early-stage NSCLC Adenocarcinoma
To determine whether the genes described above had potential prognostic value, we measured the expression levels by real-time RT-PCR in paraffin-embedded formalin-fixed primary tumors of patients whose adenocarcinoma recurred within 2 years (poor outcome group A; n = 9) and of patients who survived disease-free longer than 4 years (good outcome group B; n = 11). Group A patients included 2 with stage IA, 2 stage IB, and 5 stage IIB disease. Group B patients included 5 with stage IA, 3 stage IB, and 3 stage IIB disease. Genes analyzed included the 7 primary prognostic genes, 6 secondary prognostic genes (Sprint 2, Esx, CEA6, Ma12, GPX2, E-cadherin), as well as µPAR, a gene whose expression has previously been shown to be associated with multiple cancers. The laboratory investigators were initially blinded to the clinical outcome. The study was approved by the Medical University of South Carolina Institutional Review Board.

A small validation study was performed using paraffin sections from patients with early-stage adenocarcinoma who had an early recurrence (n = 10) and survived greater than 2 years (n = 12) undergoing resection at the Mayo Clinic, Jacksonville, Florida.

Real-time RT-PCR of formalin-fixed paraffin-embedded samples was performed according to the method of Sprecht and associates.9Go A 50-µm section was cut from tissue blocks of primary tumor for messenger RNA extraction. For isolation of RNA, paraffin-embedded tissue sections were deparaffinized twice with 1 mL of xylene at 37°C or room temperature for 10 minutes. The pellet was subsequently washed with 1 mL of 100%, 90%, and 70% ethanol and air-dried at room temperature for 2 hours. The pellet was resuspended in 200 µL of RNA lysis buffer (2% lauryl sulfate, 10 mmol/L Tris-HCl [pH 8.0], and 0.1 mmol/L ethylenediaminetetraacetic acid) and 100 µg of proteinase K and incubated at 60°C for 16 hours. RNA was extracted by 1 mL of phenol/chloroform (5:1) solution (Sigma Chemical Company, St Louis, Mo). The aqueous layer containing RNA was transferred to a new 1.5-mL tube. Phenol/chloroform extraction was done a total of 3 times. RNA was precipitated with an equal volume of isopropanol, 0.1 volume of 3 mol/L sodium acetate, and 100 µg of glycogen at –20°C for 16 hours. After centrifugation at 12,000 rpm for 15 minutes (4°C), the RNA pellet was washed with 70% ethanol and air-dried at room temperature for 2 hours. Finally, the pellet was dissolved in 12 µL of diethyl pyrocarbonate water. cDNA synthesis was performed with a panel of truncated gene-specific primers. Real-time RT-PCR was performed on a PE Biosystems Gene Amp 7300 or 7500 Sequence Detection System (PE Biosystems, Foster City, Calif). With the exception of the SYBR Green I master mix (purchased from Qiagen, Valencia, Calif), all reaction components were purchased from PE Biosystems. Standard reaction volume was 10 µL and contained 1X SYBR RT-PCR buffer, 3 mmol/L MgCl2, 0.2 mmol/L each of deoxyadenosine triphosphate, deoxycytosine triphosphate, deoxyguanosine triphosphate, 0.4 mmol/L deoxyuridine triphosphate, 0.1 U UngErase enzyme, 0.25 U AmpliTaq Gold, 0.35 µL cDNA template, and 50 nmol/L of oligonucleotide primer. Initial steps of RT-PCR were 2 minutes at 50°C for UngErase activation, followed by a 10-minute hold at 95°C. Cycles (n = 40) consisted of a 15-second melt at 95°C followed by a 1-minute annealing/extension at 60°C. The final step was a 60°C intubation for 1 minute. All reactions were performed in triplicate. Threshold for cycle of threshold (Ct) analysis of all samples was set at 0.5 relative fluorescence units.

Gene expression values were quantified as {Delta}Ct values, which were obtained by subtracting the Ct value of an internal reference control gene (β2-microglobulin, B2M) from the gene of interest. Ct values are inversely proportional to gene expression levels and are based on log2 scale.

The results were internally validated by repeating the real-time RT-PCR process using a new section cut from tissue blocks of the primary tumor. Variability of tumor quantity on the sections was minimized by hematoxylin and eosin comparison performed by a pathologist. A cross-validation procedure was used to determine whether the results were sensitive to the samples included. A leave-one-out procedure was used whereby each sample was systemically removed and the data reanalyzed.

Statistical Analysis
To assess for prognostic accuracy, we analyzed receiver operating characteristic curves on the individual genes normalized to B2M (Med Calc Software, Mariakerke, Belgium). Prognostic gene combinations were tested by subtracting {Delta}Ct values generated by RT-PCR analysis. Subtraction of {Delta}Ct values ({Delta}{Delta}Ct) is equivalent to the log of the ratio of values. In the text, the {Delta}Ctgene A – {Delta}Ctgene B calculation is abbreviated as a gene expression ratio. The value of the 2-gene prognostic assay was further assessed by Kaplan–Meier survival analysis.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
A primary tumor's ability to metastasize requires many genetic events. In this study, we hypothesized that there are relatively few genes that may be critical to the metastatic phenotype, such that high expression of a gene that portends nonrecurrence coupled with the low expression of a gene critical to metastasis would be useful to predict clinical outcome in adenocarcinoma of the lung.

The correlation map illustrated in Figure 1 resulted from a unique bioinformatics analysis that led to a set of genes that had specific structured connections based on a query of 15 genes overexpressed in 4 lung cancer cell lines. Of the 22 identified genes, 7 were in the original query set and were labeled primary prognostic genes. These genes combined with 6 of the most frequently expressed remaining 16 secondary genes constituted the study's test gene set in patients with adenocarcinoma of the lung. This unique approach is somewhat similar to the description of expression profiles in different tumors in terms of behavior modules, sets of genes that are in concert to carry out a specific function.10Go In fact, many of the genes in this study test set were contained in one of the modules (module 180) described by Segal and colleagues.10Go

Area under the curve (AUC) values for the primary and secondary genes are shown in Go Table 2. Receiver operating characteristic curve analysis of the individual genes revealed that high expression of CK19 was associated with nonrecurrence (≥4 years) (AUC = 0.859; 95% confidence interval [CI] = 0.651–0.970); whereas high expression of EpCAM2 was associated with disease recurrence within 2 years (AUC = 0.606; 95% CI = 0.366–0.813).


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  		Table 2 Recurrence analysis of pilot study using single markers paired with the internal B2M reference control gene
 
To determine whether the prognostic accuracy of CK19 could be improved by combining it with another gene whose overexpression might be necessary for the metastatic phenotype and therefore low expression be favorable, we subtracted the mean {Delta}Ct values of individual genes as determined by real-time RT-PCR analysis from {Delta}CtCK19. For all potential CK19/gene X combinations, the ratio of CK19/EpCAM2 yielded the highest prognostic accuracy as determined by AUC measurements (Go Table 3). This observation provided evidence that EpCAM2 is a "bad" gene. The CK19/EpCAM2 expression ratio, which was derived from the mean of two experiments, also performed well when data were analyzed from individual experiments. In the first and second experiments, the prognostic accuracy of the CK19/EpCAM2 expression ratio as determined by AUC analysis was 0.91 (95% CI = 0.69–0.99) and 0.84 (95% CI = 0.56–0.97), respectively (data not shown). Of further note is the observation that among the 12 patients with stage I adenocarcinoma, the prognostic accuracy of the CK19/EpCAM2 expression ratio was 92% (11/12).


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  		Table 3 Recurrence and survival analysis of pilot study based on CK19/gene X ratios
 
The cross-validation procedure found no qualitative differences in inferences. For CK19 alone, the range of AUCs found in the cross-validation analyses was (0.87 to 0.92) whereas the AUC when all samples were included was 0.86. Analogous results were found when CK19 was combined with EpCAM2.

To further assess the value of CK19 unpaired and paired with EpCAM2, we performed a Kaplan–Meier survival analysis using data generated from single marker and CK19/gene X analyses. For the single CK19 marker, a {Delta}Ct cutoff of 11.4 was used, which separated the 20 patients into high ({Delta}Ct < 11.4; n = 13) and low ({Delta}Ct > 11.4; n = 7) expressing tumors. A log–ranked test indicated that the two curves generated as a function of marker positivity were different at a P value of .0021 with a hazard ratio of 6.2 (Go Figure 2, A). For the CK19/EpCAM2 ratio, a {Delta}{Delta}Ct cutoff of 7.2 was used, which separated the 20 patients into high ({Delta}{Delta}Ct ≤ 7.2; n = 13) and low ({Delta}{Delta}Ct > 7.2; n = 7) groups that correlated with survival. A log–ranked test indicated that the two curves generated as a function of marker positivity were different at a P value of .0001 with an associated hazard ratio of 10.7 (Figure 2, B). Kaplan–Meier survival analysis of other CK19/gene X pairs is shown in Table 3. The gene pair that yielded the second most highly significant curves was CK19/P-cadherin, with an associated hazard ratio of 8.1.


Figure 2
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  		Figure 2. Kaplan–Meier survival analysis. Data generated from single-marker (A) and CK19/EpCAM2 (B) analyses. HR, Hazard ratio.

 
To determine assay reliability, we applied the 2-gene test to a set of patients (n = 22) who were treated at the Mayo Clinic, Jacksonville, Florida. Twelve patients survived longer than 2 years, whereas 10 patients had recurrence within 2 years. All patients in this data set died by 65 months. We observed that the hazard ratio of CK19/EpCAM2 expression pair decreased to 4.5 but remained significant (P = .007). The CK19/P-cadherin expression ratio also clearly identified patients with longer survival (hazard ratio = 3.24; P = .0029).


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Unfortunately, a large number of patients with resected early-stage lung cancer will have a recurrence within 2 to 3 years. The ability to predict those patients at high risk for recurrence could help direct the possible addition of therapy to improve survival and, vice versa, avoid the toxicity for those at low risk. Many molecular markers that predict patient survival independent of TNM status have been reported.11Go Tools used to predict recurrence have included immunohistochemical analysis,12Go cDNA microarray profiling,13-17Go real-time RT-PCR,6,18-20Go and most recently, proteomics.21Go Many of the methods have been costly, not readily available to the average surgeon, required frozen tissue specimens, and have therefore been difficult to translate from the research laboratory to the clinical arena.

In the present study, we measured the expression of 14 different test genes and one internal reference control gene in primary tumors resected from patients with early-stage NSCLC. Using the B2M gene as an internal reference, we observed that high expression of CK19 was correlated with good clinical outcome (no disease recurrence), whereas high expression of EpCAM2 was correlated with poor clinical outcome (disease recurrence within 2 years). Of all possible 2-gene combinations (n = 105), we further observed that the ratio of CK19/EpCAM2 had the highest accuracy for predicting disease recurrence. The concept of using a 2-gene ratio was previously applied to NSCLC by Gordon and coworkers,19,22Go who identified S100P as 1 of 7 prognostic markers. It should be noted that in the Mayo data set, the marker combination of CK19/S100P yielded results similar to CK19/P-cadherin (data not shown). However, the current study is the first to analyze the expression of genes in paraffin samples. In colon cancer, high expression of EpCAM2 (also known as TROP2) has been shown to be associated with a higher frequency of liver metastasis (P = .005) and more cancer-related deaths (P = .046),23Go a finding that further supports the concept that for early-stage NSCLC, EpCAM2 is a "bad gene."

The gene pair with the second highest prognostic accuracy for disease recurrence was CK19/P-cadherin. Previous studies have shown that expression levels of P-cadherin in primary tumors correlate with tumor grade in ovarian cancer24Go and metastases to the lung in thyroid cancer.25Go Further, overexpression of P-cadherin in vitro results in increased cell motility in pancreatic cancer,26Go a necessary requirement for establishment of distant metastases. Taken together, these results provide evidence that P-cadherin may also serve as a candidate "bad gene" in NSCLC. Regarding CK19, antibodies to the protein encoded by this gene (and/or a combination of other cytokeratin genes) have been used for the detection of circulating tumor cells in breast, lung, colon, and other cancers.27,28Go In the current study, we suspect that CK19 expression levels serve as a reliable indicator of the epithelial content of the primary tumor.

Although there was a recent report of the use of real-time RT-PCR for prognosis of patients with early-stage NSCLC, the current study differs significantly from the approach taken by Chen and colleagues.20Go In this report, patient prognosis was based on a simple calculation of a 2-gene ratio, an approach that contains only one "decision node." In the study of Chen and associates, a 5-gene marker panel was used that required a relatively high number of decision nodes (n = 19). An algorithm that uses such a high number of decision nodes for a low number of genes is less likely to be clinically applicable because of its cumbersome nature. In contrast, the microarray study of Potti and coworkers6Go required only 5 decision nodes, even though 289 genes were involved.

There are several advantages to the technique used in this preliminary study. It is a simple 2-gene model and uses a technology that is relatively inexpensive and is quickly performed once RNA is extracted. Paraffin-embedded tumor tissue can be screened and an appropriate slide(s) could be sent to a reference laboratory. The technique is amenable to small tissue samples, which may be important if preoperative biopsy directs neoadjuvant therapy.

Several limitations of this pilot analysis need to be acknowledged. First, given the small numbers used for the preliminary study, external verification must be performed on a larger data set before definitive statements are made concerning its application as a prognostic tool. Second, given the number of putative genes that could display either a direct or inverse relationship between expression and prognosis, it is possible that another gene ratio or a combination of two ratio sets will be more informative as patients are added. Correlative experiments looking at protein levels in tumor issues should be a future goal.

In summary, a simple 2-gene molecular model has been developed to predict recurrence in patients having resection of early-stage adenocarcinoma of the lung. The model will require further validation and refinement. It is hoped that in the future a relatively easy, cost-effective, clinically relevant molecular model will be used to individualize therapy in early-stage NSCLC.


   Footnotes
 
Read at the Eighty-seventh Annual Meeting of The American Association for Thoracic Surgery, Washington, DC, May 5–9, 2007.


   References
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 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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