Background Current staging methods are inadequate for predictingthe outcome of treatment of non–small-cell lung cancer(NSCLC). We developed a five-gene signature that is closelyassociated with survival of patients with NSCLC.
Methods We used computer-generated random numbers to assign185 frozen specimens for microarray analysis, real-time reverse-transcriptasepolymerase chain reaction (RT-PCR) analysis, or both. We studiedgene expression in frozen specimens of lung-cancer tissue from125 randomly selected patients who had undergone surgical resectionof NSCLC and evaluated the association between the level ofexpression and survival. We used risk scores and decision-treeanalysis to develop a gene-expression model for the predictionof the outcome of treatment of NSCLC. For validation, we usedrandomly assigned specimens from 60 other patients.
Results Sixteen genes that correlated with survival among patientswith NSCLC were identified by analyzing microarray data andrisk scores. We selected five genes (DUSP6, MMD, STAT1, ERBB3,and LCK) for RT-PCR and decision-tree analysis. The five-genesignature was an independent predictor of relapse-free and overallsurvival. We validated the model with data from an independentcohort of 60 patients with NSCLC and with a set of publishedmicroarray data from 86 patients with NSCLC.
Conclusions Our five-gene signature is closely associated withrelapse-free and overall survival among patients with NSCLC.
Lung cancer — predominantly non–small-cell lungcancer (NSCLC) — is the most common cause of death fromcancer worldwide.1 The relapse rate among patients with early-stageNSCLC is 40% within 5 years after potentially curative treatment.2The current staging system for NSCLC is inadequate for predictingthe outcome of treatment.
Gene-expression profiling (see Glossary) by means of microarrays3,4and reverse-transcriptase polymerase chain reaction (RT-PCR)5,6is useful for classifying tumors and formulating a prognosisfor patients with various types of cancer,7,8,9 including lungcancer.10,11,12,13,14,15,16 The use of microarrays in clinicalpractice is limited, however, by the large number of genes usedin gene profiling,17 the need for complicated methods, and thelack of both reproducibility and independent validation. Thegenes selected for profiling in studies of lung cancer havevaried considerably; only a few genes have been consistentlyincluded.10,11,12,13 Moreover, gene-expression profiles canvary according to the microarray platform and the analytic strategyused.6
The RT-PCR method can be applied to paraffin-embedded pathologicalspecimens and is reproducible and applicable in clinical practice.However, RT-PCR can be used to analyze only a small number ofgenes.17 In a previous study, our group performed microarrayanalysis of cell lines derived from specimens of invasive NSCLCand identified 672 genes associated with invasive activity.18We also identified genes (CRMP-1 and HLJ1) that are associatedwith clinical outcome of patients with NSCLC.19,20 A recentstudy showed that the results of RT-PCR analysis of eight genescorrelated with the outcomes of patients with adenocarcinomaof the lung.5
In the current study, we examined gene expression in 125 surgicalspecimens of NSCLC, using microarrays and real-time RT-PCR inorder to identify a gene signature that is correlated with theclinical outcome.
Methods
Patients and Tissue Specimens
We used computer-generated random numbers to assign specimensfrom 185 consecutive patients for microarray analysis. We studiedfrozen specimens of lung-cancer tissue from 125 randomly selectedpatients who underwent surgical resection of NSCLC at the TaichungVeterans General Hospital between December 1999 and December2003. Of these 125 specimens, 60 were adenocarcinomas, 52 weresquamous-cell carcinomas, and 13 were other types of cancer.We validated the five-gene risk-prediction model using an independentcohort of 60 randomly selected patients who underwent surgicalresection of NSCLC at the Taichung Veterans General Hospitalbetween November 1999 and December 2003. The patients had notreceived adjuvant chemotherapy. The study was approved by theinstitutional review board of the hospital. Written informedconsent was obtained from all patients.
Microarray Analysis of Complementary DNA
The 672 genes associated with invasive activity, identifiedin a previous study by our group,18 were rearrayed in duplicateon a nylon membrane. We isolated 4 µg of total RNA fromeach specimen, amplified it using an amplification kit (Ambion),and labeled it with digoxigenin during reverse transcription.21The details of target preparation, hybridization, color development,image analysis, and spot quantification have been describedpreviously.18,21,22
RT-PCR Analysis
To validate the levels of expression of genes found on microarrayanalysis, RT-PCR was performed on 16 genes and a control genefor TATA-box–binding protein (TBP), with the use of specificTaqMan probes and primer sets; the transcripts were amplifiedwith reagent (TaqMan One-Step RT-PCR Master Mix Reagent, AppliedBiosystems) and a sequence detection system (ABI Prism 7900HT,Applied Biosystems). Gene expression was quantified in relationto the expression of TBP with the use of sequence detector softwareand the relative quantification method (Applied Biosystems)(for details, see the Methods section of the Supplementary Appendix,available with the full text of this article at www.nejm.org).We chose TBP as the internal control for real-time RT-PCR becauseit is invariant in clinical cancer specimens.23
Statistical Analysis
The 125 specimens were randomly assigned to either the trainingset or the testing set (see Table 1 of the Supplementary Appendix).The average intensity for each gene in the microarray was assessed.To reduce variation among microarrays, the intensity valuesfor samples in each microarray were rescaled by means of a quantilenormalization method.24 To reduce background noise, backgroundintensity values of less than 3000 were assigned the value of3000.22 Each intensity value was then log-transformed to a base-2scale. Genes with coefficients of variation of less than 3%were excluded from further analyses. Finally, the gene-expressionintensity values were transformed to ordinal coding values,according to the ranking of the level of gene expression amongthe 485 genes in 125 patients (60,625 observations). The intensityvalue was coded as 1 for expression levels ranked as at or belowthe 25th percentile of the total gene expression, 2 for levelsabove the 25th and at or below the 50th percentiles, 3 for levelsabove the 50th and at or below the 75th percentiles, and 4 forlevels above the 75th percentile.
Hazard ratios from univariate Cox regression analysis were usedto determine which genes were associated with death from anycause or recurrence of cancer. Protective genes were definedas those associated with a hazard ratio for death of less than1; risk genes were defined as those associated with a hazardratio for death of more than 1. We used univariate Cox proportional-hazardsregression analysis to evaluate the association between survivaland the level of expression of each gene from microarray analysis.25For genes that were significantly correlated with survival,we used a linear combination of the gene-expression coding valuesweighted by the regression coefficients to calculate a riskscore for each patient.6,10
16-Gene Signature
Risk scores were calculated for 16 genes. A patient's risk scorewas calculated as the sum of the levels of expression of eachgene, as measured by microarray analysis, multiplied by thecorresponding regression coefficients (see the Methods sectionof the Supplementary Appendix). Patients were classified ashaving a high-risk gene signature or a low-risk gene signature,with the 50th percentile (median) of the risk score as the thresholdvalue (median, 4.9; range, 1.3 to 21.9). The median risk scorewas chosen as the threshold value to reflect the fact that almosthalf of patients with early-stage NSCLC relapse within 5 yearsafter potentially curative surgery2 and also in order to eliminatethe effect of extreme values in the training cohort by ensuringthat there were equal numbers of patients in the high-risk andlow-risk groups. The risk scores and the threshold value derivedfrom the training cohort were not reestimated but were applieddirectly to the testing cohort.
Five-Gene Signature
The levels of expression of the 16 genes were confirmed by RT-PCRand indexed by Spearman's rank-correlation test.26 From these16 genes, we further identified five genes that were significantlyassociated with survival. The levels of expression of thesefive genes, as measured by RT-PCR, were used to construct therecursive-partitioning decision tree.27,28 Avadis software29,30(Strand Genomic) was then used to classify patients as havinga high-risk gene signature or a low-risk gene signature on thebasis of the decision tree.
Our rationale for using a decision tree based on RT-PCR ratherthan on microarray analysis was practicality. RT-PCR uses asmall number of genes to capture the relevant covariate structure,especially the complex interaction and nonlinearity of levelsof gene expression.28 In our univariate-splitting tree, onlyone of the five genes was used to make a splitting decisionat each intermediate node. To avoid overfitting, we used a pruningmethod called minimum error (see the Methods section and Figure1 of the Supplementary Appendix).
The Kaplan–Meier method was used to estimate overall survivaland relapse-free survival. Differences in survival between thehigh-risk group and the low-risk group were analyzed with thelog-rank test. Multivariate Cox proportional-hazards regressionanalysis with stepwise selection was used to evaluate independentprognostic factors associated with survival, and the five-genesignature, age, sex, tumor stage, and histologic characteristicswere used as covariates. A P value of less than 0.05 was consideredto indicate statistical significance, and all tests were two-tailed.
We also studied an independent cohort of 60 patients who underwentsurgical resection of NSCLC at the Taichung Veterans GeneralHospital between November 1999 and December 2003. This cohortwas used to validate our five-gene risk-prediction model.
To further validate our model, we applied it to microarray datafrom 86 patients with NSCLC, reported by Beer et al.10 (availableat http://dot.ped.med.umich.edu:2000/ourimage/pub/Lung/index.html).The five genes (and their corresponding Affymetrix probe sets)were DUSP6 (X93920_at), MMD (X85750_at), STAT1 (M97936_at),ERBB3 (S61953_at), and LCK (M26692_s_at); the control gene wasTBP (X54993_s_at). To make the levels of gene expression fromthe microarrays and from RT-PCR comparable, we log-transformedthe microarray data to a base-2 scale after assigning a valueof 1.1 to intensity values of less than 1.1. After log transformation,the levels of expression of the five genes were divided by thelevel of expression of the control gene TBP in order to calculatethe relative level of expression. We applied the decision-treemodel to these relative levels of expression, using the datafrom 86 patients with NSCLC.10 Because the maximum follow-uptime for the survival analysis in our study was 62 months, weused the 5-year survival data for the 86 patients.
Results
The 16-Gene Signature and Survival
On microarray analysis of tumors from the 125 patients, 485of 672 genes had a coefficient of variation greater than 3%and were thus included in the analyses. Hazard ratios from theunivariate Cox regression analysis showed that the levels ofexpression of 16 genes correlated with death from any cause:4 were protective genes (associated with a hazard ratio of lessthan 1) and 12 were risk genes (associated with a hazard ratioof more than 1 (Table 1).
Table 1. Hazard Ratios for Death from Any Cause for the 125 Patients with NSCLC and Results of Validation of the 16-Gene Signature.
Table 1 of the Supplementary Appendix lists the characteristicsof the 125 patients in the first analysis. Among the 63 patientsin the training cohort, tumors with high risk scores expressedrisk genes, whereas tumors with low risk scores expressed protectivegenes (Figure 1A). Patients with a high-risk 16-gene signaturehad a lower median overall survival than those with a low-risk16-gene signature (20 months vs. not reached) (Figure 1B). Tumorswith a high-risk gene signature were associated with a lowermedian relapse-free survival than tumors with a low-risk genesignature (12 months vs. not reached) (Figure 1B). The medianduration of follow-up in the training cohort was 20 months.
Figure 1. The 16-Gene Signature and Survival of 125 Patients with NSCLC.
Panel A shows the gene-expression profiles of the tumor specimens (according to the color scale shown); each column represents an individual patient. The magnitude of the corresponding risk scores is represented by the slope of the red triangle. Also shown are Kaplan–Meier estimates of overall and relapse-free survival according to the 16-gene microarray signature in the training cohort (Panel B) and the testing cohort (Panel C).
Results in the testing cohort were similar to those in the trainingcohort. Among the 62 patients, tumors with high risk scoresexpressed risk genes, whereas tumors with low risk scores expressedprotective genes (Figure 1A). Patients with a high-risk 16-genesignature had a lower median overall survival than those witha low-risk gene signature (Figure 1C). Tumors with a high-riskgene signature were associated with a lower median relapse-freesurvival than tumors with a low-risk gene signature (18 monthsvs. not reached) (Figure 1C). The median duration of follow-upin the testing cohort was 18 months. Our entire microarray dataset is available online (www.ncbi.nlm.nih.gov/projects/geo/)under the data series accession number GSE4882
[NCBI GEO]
.
The Five-Gene Signature and Survival
There was a significant correlation between the results of microarrayand RT-PCR analyses for the gene-expression data for 5 of the16 genes in 101 of the 125 tumor specimens (Table 1). Thesefive genes were for dual-specificity phosphatase 6 (DUSP6),monocyte-to-macrophage differentiation-associated protein (MMD),signal transducer and activator of transcription 1 (STAT1),v-erb-b2 avian erythroblastic leukemia viral oncogene homolog3 (ERBB3), and lymphocyte-specific protein tyrosine kinase (LCK).
We identified 59 patients with high-risk gene signatures and42 with low-risk gene signatures, according to gene expressionas measured with RT-PCR and decision-tree analysis (see Figure1 of the Supplementary Appendix). The structure of the decisiontree was based on the threshold of expression of each of thefive genes, as automatically determined according to a recursive-partitionalgorithm. The use of this algorithm resulted in the most accurateseparation of patients with a high-risk signature from thosewith a low-risk signature. Table 2 summarizes the clinical characteristicsof the 101 patients, hereafter called the original cohort, accordingto their five-gene signatures. The five-gene signature was stronglyassociated with overall survival (sensitivity, 98%; specificity,93%; positive predictive value, 95%; negative predictive value,98%; and overall accuracy, 96%).
Table 2. Clinical Characteristics of the Original and Validation Cohorts.
The median follow-up of the 101 patients was 20 months. Thepatients with a high-risk gene signature had a shorter medianoverall survival than the patients with a low-risk gene signature(20 months vs. 40 months, P<0.001 by the log-rank test) (Figure 2A).The high-risk gene signature was associated with a median relapse-freesurvival of 13 months, whereas the low-risk gene signature wasassociated with a median relapse-free survival of 29 months(P=0.002 by the log-rank test) (Figure 2B).
Figure 2. Kaplan–Meier Estimates of Survival of Patients with NSCLC According to the Five-Gene Signatures as Measured by RT-PCR.
Overall survival and relapse-free survival are shown for the 101 patients with NSCLC (Panel A and Panel B, respectively) and for the 59 patients with stage I or II disease (Panel C and Panel D, respectively). Overall survival is also shown for the independent cohort of 60 patients (Panel E), for the 42 patients in this cohort who had stage I or II disease (Panel F), and for the 86 patients described in an independent set of published NSCLC microarray data10(Panel G).
According to Cox multivariate regression analysis, the high-riskfive-gene signature, tumor stage III, and older age were significantlyassociated with death from any cause among the 101 patients(Table 3), and the high-risk five-gene signature and tumor stageIII were significantly associated with recurrence of canceras well (hazard ratio for the high-risk signature vs. the low-risksignature, 1.92; 95% confidence interval [CI], 1.06 to 3.46;P=0.03; hazard ratio for stage III vs. stage I or II disease,2.28; 95% CI, 1.33 to 3.91; P=0.003). In a subgroup analysisof 59 patients with stage I or II disease, those with a high-riskgene signature had a shorter overall survival and a shorterrelapse-free survival than those with a low-risk gene signature(Figure 2C and 2D, respectively).
Table 3. Hazard Ratios for Death from Any Cause Among Patients with NSCLC, According to Multivariate Cox Regression Analysis.
Validation of the Five-Gene Signature
The clinical characteristics of the 60 patients in the validationcohort are listed in Table 2. The median duration of follow-upwas 17 months. Patients with a high-risk gene signature hada shorter median overall survival than those with a low-riskgene signature (21 months vs. not reached) (Figure 2E). Accordingto Cox multivariate regression analysis, the five-gene signaturewas significantly associated with overall survival (Table 3).
We analyzed the five-gene signatures in tumor specimens obtainedfrom patients in the validation cohort with stage I or stageII disease both together and separately. Among patients withstage I or II disease combined, those with a high-risk genesignature had a shorter overall survival than those with a low-riskgene signature (Figure 2F). Among patients with stage I disease,low-risk gene signatures were associated with a longer overallsurvival than were high-risk gene signatures (P=0.02 by thelog-rank test). Among patients with stage II disease, overallsurvival did not differ significantly between those with high-riskand those with low-risk gene signatures, probably owing to thesmall number of patients.
We also validated the five-gene signature in an independentset of microarray data from 86 patients from a Western populationwith NSCLC.10 Table 2 of the Supplementary Appendix lists theclinical characteristics of these 86 patients according to theirfive-gene signatures. The patients with high-risk gene signatureshad a shorter overall survival than did those with low-riskgene signatures (Figure 2G) (P=0.06 by the log-rank test). Accordingto Cox multivariate regression analysis, the high-risk five-genesignature and tumor stage III were significantly associatedwith death from any cause (Table 3).
Discussion
NSCLC is a heterogeneous disease. Even in patients with similarclinical and pathological features, the outcome varies: someare cured, whereas in others, the cancer recurs. Staging systemsfor lung cancer that are based on clinical and pathologicalfindings may have reached their limit of usefulness for predictingoutcomes, but molecular methods add value. Gene-expression profilingwith the use of microarrays3,4 or PCR5,6 has been shown to estimatethe prognosis for patients with lung cancer accurately.10,11,12,13,14,15,16However, the use of microarrays in clinical practice is limitedby the large number of genes in the analysis,17 complicatedmethods, lack of reproducibility and independent validationof the results, and the need for fresh-frozen tissue.17 RT-PCRinvolving a small number of genes may be a more clinically usefulmethod. It allows for accurate and reproducible quantificationof results for RNA obtained from small amounts of paraffin-embeddedspecimens.17,31 The results of RT-PCR performed on 8 genes,selected from a total of 45, have recently been shown to correlatewith the outcomes of lung adenocarcinoma.5
We identified an RT-PCR–based five-gene signature (includingDUSP6, MMD, STAT1, ERBB3, and LCK) using risk scores based onmicroarray and decision-tree analyses of 125 frozen tumor specimensfrom patients with NSCLC. The specimens were randomly dividedinto a training set (63 specimens) and a testing set (62 specimens).The presence of a high-risk five-gene signature in the NSCLCtumors was associated with an increased risk of recurrence anddecreased overall survival.
Our selection of genes in the microarray training set was validatedin the microarray testing set, and the patterns of gene expressionfound on microarray analysis were validated by RT-PCR. Our resultswere also validated in an independent cohort of 60 patientswho were treated at the Taichung Veterans General Hospital.These results in our Chinese patients were also validated withthe use of a set of published NSCLC microarray data from patientsfrom a Western population with NSCLC. Thus, we believe thatthe data we obtained using the five-gene signature are reliable.
The identification of five genes that are closely associatedwith the outcomes in patients with NSCLC has clinical implications.Cisplatin-based adjuvant chemotherapy is effective in some patientswith NSCLC.32 We propose that patients who have tumors witha high-risk gene signature could benefit from this type of adjuvanttherapy, whereas those with a low-risk gene signature couldbe spared what may be unnecessary treatment. Prospective, large-scale,multicenter studies are necessary to test this idea.
The identification of five genes that can predict the clinicaloutcome in patients with NSCLC may reveal targets for the developmentof therapy for lung cancer. STAT1 causes arrested growth andapoptosis in many types of cancer cells by inducing the expressionof p21WAF1 and caspase.33,34 MMD is preferentially expressedin mature macrophages.35 Our group has shown that macrophageactivation promotes cancer metastasis,22 although the functionof the MMD protein is unknown. DUSP6 inactivates extracellularsignal-regulated kinase 2 (ERK2) (also known as mitogen-activatedprotein kinase 1 [MAPK1]), resulting in tumor suppression andapoptosis.36 ERBB3, a member of the epidermal growth factorreceptor family of tyrosine kinases, can shorten cell survival.37LCK, a member of the Src family of protein tyrosine kinases,is expressed mainly in T cells and is one of the first signalingmolecules downstream of the T-cell receptor. It plays a keyrole not only in the differentiation and activation of T cellsbut also in the induction of apoptosis.38 In addition, LCK isexpressed in many cancers and regulates the mobility of cancercells.39,40
In conclusion, the five-gene expression signature we identifiedis closely associated with the clinical outcome in patientswith surgically resected NSCLC. This signature could be usefulin stratifying patients according to risk in trials of adjuvanttreatment of the disease.
Glossary
Decision tree: A statistical tool for predicting which patientbelongs to which specific class (e.g., good or poor clinicaloutcome) on the basis of feature information (gene-expressionlevels), with the use of a recursive-partitioning process andtree-based classification rules.
Gene-expression profiling: Determination of the level of expressionof thousands of genes simultaneously by DNAmicroarray or real-timeRT-PCR.
High-risk gene signature: Aberrant expression of a panel ofgenes in tissue that signifies a high risk of an adverse outcome(relapse or death in patients with cancer).
Independent cohort: An independent group of patients havingclinical characteristics similar to those of an original groupof patients in a study. The independent cohort is used to confirmthe findings of the original study.
Risk gene: A gene for which altered expression in the tissueof interest is associated with an increased risk of an adverseclinical outcome (relapse or death in patients with cancer).
Risk score: A score that predicts the likelihood of an individualpatient's survival on the basis of statistical analysisof riskfactors (the expression levels of risk genes) associated withsurvival.
Supported by grants from the National Research Program for GenomicMedicine of the National Science Council of the Republic ofChina (NSC94-3112-B002-013-Y) and from Advpharma.
Dr. Terng reports being an employee of Advpharma. No other potentialconflict of interest relevant to this article was reported.
Source Information
From National Taiwan University College of Public Health (H.-Y.C., W.J.C.), National Taiwan University College of Medicine (H.-Y.C., S.-L.Y., C.-L.C., C.-H.W., S.-F.K., H.-N.L., S.S., W.J.C., J.J.W.C., P.-C.Y.), Academia Sinica (C.-H.C, P.-C.Y.), National Taiwan University Hospital (A.Y., W.-K.C., P.-C.Y.), and Advpharma (H.-J.T.) — all in Taipei, Taiwan; and Taichung Veterans General Hospital (G.-C.C., C.-Y.C.) and National Chung-Hsing University (G.-C.C., C.-C.L., J.J.W.C.) — both in Taichung, Taiwan. Drs. W.J. Chen, J.J.W. Chen, and P.C. Yang contributed equally to this article.
Address reprint requests to Dr. Yang at the Department of Internal Medicine, National Taiwan University Hospital, No. 7, Chung-Shan S. Rd., Taipei, Taiwan 100, or at pcyang{at}ha.mc.ntu.edu.tw.
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Five-Gene Signature in Non–Small-Cell Lung Cancer
Michiels S., Hill C., Raz D. J., Jablons D. M., Dobbin K. K., Gounaris I., Quintás-Cardama A., Gibbons D. L., Chen H.-Y., Chen W. J., Yang P.-C.
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