A Genomic Strategy to Refine Prognosis in Early-Stage Non–Small-Cell Lung Cancer
Anil Potti, M.D., Sayan Mukherjee, Ph.D., Rebecca Petersen, M.D., Holly K. Dressman, Ph.D., Andrea Bild, Ph.D., Jason Koontz, M.D., Robert Kratzke, M.D., Mark A. Watson, M.D., Ph.D., Michael Kelley, M.D., Geoffrey S. Ginsburg, M.D., Ph.D., Mike West, Ph.D., David H. Harpole, Jr., M.D., and Joseph R. Nevins, Ph.D.
Background Clinical trials have indicated a benefit of adjuvantchemotherapy for patients with stage IB, II, or IIIA —but not stage IA — non–small-cell lung cancer (NSCLC).This classification scheme is probably an imprecise predictorof the prognosis of an individual patient. Indeed, approximately25 percent of patients with stage IA disease have a recurrenceafter surgery, suggesting the need to identify patients in thissubgroup for more effective therapy.
Methods We identified gene-expression profiles that predictedthe risk of recurrence in a cohort of 89 patients with early-stageNSCLC (the lung metagene model). We evaluated the predictorin two independent groups of 25 patients from the American Collegeof Surgeons Oncology Group (ACOSOG) Z0030 study and 84 patientsfrom the Cancer and Leukemia Group B (CALGB) 9761 study.
Results The lung metagene model predicted recurrence for individualpatients significantly better than did clinical prognostic factorsand was consistent across all early stages of NSCLC. Appliedto the cohorts from the ACOSOG Z0030 trial and the CALGB 9761trial, the lung metagene model had an overall predictive accuracyof 72 percent and 79 percent, respectively. The predictor alsoidentified a subgroup of patients with stage IA disease whowere at high risk for recurrence and who might be best treatedby adjuvant chemotherapy.
Conclusions The lung metagene model provides a potential mechanismto refine the estimation of a patient's risk of disease recurrenceand, in principle, to alter decisions regarding the use of adjuvantchemotherapy in early-stage NSCLC.
Lung cancer is the leading cause of death from cancer amongboth men and women in the United States, and non–small-celllung cancer (NSCLC) accounts for almost 80 percent of such deaths.1,2The clinical staging system has been the standard for determininglung-cancer prognosis.3,4,5 Although other clinical and biochemicalmarkers have prognostic significance,6,7 none are more accuratethan the clinicopathological stage.8
The current standard of treatment for patients with stage INSCLC is surgical resection, despite the observation that nearly30 to 35 percent will relapse after the initial surgery andthus have a poor prognosis,2,4 indicating that a subgroup ofthese patients might benefit from adjuvant chemotherapy. Similarly,as a population, patients with clinical stage IB, IIA or IIB,or IIIA NSCLC receive adjuvant chemotherapy,9,10,11,12,13 butsome may receive potentially toxic chemotherapy unnecessarily.Thus, the ability to identify subgroups of patients more accuratelymay improve health outcomes across the spectrum of disease.
Previous studies have described the development of gene-expression,protein, and messenger RNA profiles that are associated in somecases with the outcome of lung cancer.14,15,16,17,18,19,20,21,22,23,24However, the extent to which these profiles can be used to refinethe clinical prognosis and the context in which improved prognosticcapability could be used to alter a clinical treatment decisionwere not clear. Thus, we evaluated the use of gene-expressionpatterns as a means of stratifying risk and treatment in NSCLC.
Methods
Patients and Tumor Samples
We analyzed 198 tumor samples from three cohorts of patientswith NSCLC. The training cohort consisted of 89 patients enrolledthrough the Duke Lung Cancer Prognostic Laboratory. The independentvalidation cohorts included patients in two multicenter cooperativegroup trials: 25 patients from the American College of SurgeonsOncology Group (ACOSOG) Z0030 study and 84 from the prospectiveCancer and Leukemia Group B (CALGB) 9761 trial. Table 1 liststhe clinical and demographic characteristics of the patientsin each cohort and their tumors, and complete details are listedin Table 1 of the Supplementary Appendix, available with thefull text of this article at www.nejm.org. All patients wereenrolled according to protocols approved by the institutionalreview board of Duke University, after written informed consenthad been obtained.
For each cohort, a single pathologist reviewed all slides todetermine whether they met the histopathological criteria forNSCLC of the World Health Organization, including the subtypeof adenocarcinoma and the degrees of differentiation, lymphaticinvasion, and vascular invasion. Only samples with a tumor-cellcontent of more than 50 percent were used in the analysis.
Gene-Expression Arrays
Total RNA was extracted from the tumor tissue with RNeasy Kits(Qiagen). The RNA quality was assessed with the use of a bioanalyzer(model 2100, Agilent). Hybridization targets were prepared fromthe total RNA according to standard Affymetrix protocols (describedin detail in the Supplementary Appendix, along with the methodsinvolved in the scanning of the arrays and the normalizationof the resulting data). The microarray assays were carried outwith Affymetrix GeneChips (U133 Plus2). All raw data and datatransformed with the use of the robust multiarray average expressionmeasure for the Duke, ACOSOG, and CALGB data sets are availableelsewhere (accession number GSE3593
[NCBI GEO]
in the Gene Expression Omnibusdatabase at www.ncbi.nlm.nih.gov/geo).
Statistical Analysis
We performed statistical analyses using the metagene constructionand binary prediction tree analysis, as described previously25,26,27,28,29and in detail in the Supplementary Appendix. The metagene fora cluster of genes is the dominant singular factor (principalcomponent), as computed with the use of a singular value decompositionof gene-expression levels in the gene cluster in all samples.The metagene represents the dominant average pattern of expressionof the gene cluster across the tumor samples.25
We then used the set of metagenes and the clinical variablespreviously shown to be of prognostic value (age, sex, tumordiameter, stage of disease, histologic subtype, and smokinghistory) in a binary classification-tree analysis to partitionthe samples recursively into smaller subgroups. Within thesesubgroups, predictions of recurrence (with 0 representing 5-yeardisease-free survival and 1 representing death within 2.5 yearsafter the initial diagnosis of NSCLC) were made in terms ofthe estimated relative probabilities.26,30,31 In the analysis,many classification trees were computed, weighed, and integratedto provide overall risk predictions for each patient. The dominantmetagenes that constituted the final model are described inthe Supplementary Appendix.
To compare the prognostic efficacy of the metagene and clinicalstrategies, the clinical variables were treated as factors orprincipal components (similar to the treatment of metagenesin the lung metagene model) in a classification-tree analysisto generate a clinical model. The end result was the probabilityof recurrence, which represents the conglomerate prognosticvalue of the individual clinical variables. Using GraphPad software,we computed a C statistic (comparable to the area under thecurve in a receiver-operating-characteristic curve in the predictionof binary outcomes) for the model that included just the clinicalvariables, a C statistic for a model that included just themetagenes, and a C statistic for a model that included boththe clinical and genomic variables.
The accuracy of each model was defined with the use of a probabilityof 0.5 as a cutoff. An estimated probability of recurrence ofmore than 0.5 was classified as a high risk of recurrence; anestimated probability of recurrence of 0.5 or less was classifiedas a low risk of recurrence.
Simple univariate and multivariate logistic regressions forrecurrence (with and without the metagene-based assessment ofthe risk) were also computed to assess the baseline prognosticvalue of each clinical variable (age, sex, tumor diameter, stageof disease, histologic subtype, and smoking history) in thecohorts. We also calculated the sensitivity, specificity, andpositive and negative predictive values using a probabilityof recurrence of 0.5 as the cutoff value. Standard Kaplan–Meiersurvival curves were generated for the high-risk and low-riskgroups of patients with the use of GraphPad software; the survivalcurves were compared with the use of the log-rank test. Thistest generates a two-tailed P value that tests the null hypothesis,which was that the survival curves were identical among thecohorts.
Results
Patient Characteristics
Table 1 lists the demographic and clinical characteristics ofthe patients (and their tumors) used to develop and test theprognostic model (Figure 1).
Figure 1. Development and Validation of the Lung Metagene Model.
Samples were excluded from analyses on the basis of inadequate quality of the messenger RNA.
Use of Gene-Expression Profiles to Improve Prognosis
Lung cancer is a heterogeneous disease resulting from the acquisitionof multiple somatic mutations; given this complexity, it wouldbe surprising if a single gene-expression pattern could effectivelydescribe and ultimately predict the clinical course of the diseasefor all patients. Recognizing the importance of addressing thiscomplexity, we have previously described methods to integratevarious forms of data, including clinical variables and multiplegene-expression profiles, to build robust predictive modelsfor the individual patient.25,26 There are two critical componentsof this methodologic approach. First, we generated a collectionof gene-expression profiles, termed "metagenes" (an exampleis given in Figure 2A), that provide the basis for the predictivemodels. Second, we used classification- and regression-treeanalysis to sample these metagenes and build prognostic models;this approach mines the collection of profiles to predict theclinical outcome best. An example tree (one of many generatedin the analysis) is depicted in Figure 2B.
Figure 2. Clinical and Genomic Prediction of the Risk of Recurrence of NSCLC.
Panel A shows an example of a key metagene profile used in the lung metagene model, with blue and red representing the two extremes of gene expression. Panel B shows an example of a classification tree illustrating the incorporation of metagenes (mgenes) at various levels to predict survival in the Duke training cohort. Numbers and lines in red indicate patients who survived less than 2.5 years after the initial diagnosis of NSCLC, and those in blue represent patients who survived more than 5 years after the initial diagnosis of NSCLC. The left-hand box at each node of the tree shows the number of patients and the total number of patients, and the right-hand box gives (as a percentage) the corresponding model-based point estimate of the probability of recurrence within 2.5 years based on the tree-model predictions for that group. The mean probabilities of recurrence predicted by the lung metagene model (Panel C) and by the clinical model generated with data on age, sex, tumor diameter, stage of disease, histologic subtype, and smoking history (Panel D) in the Duke cohort are also shown. For each patient, the probability of recurrent disease was predicted in an out-of-sample cross-validation based on a model completely regenerated from the data for the remaining patients. I bars represent 95 percent confidence intervals.
The predictive accuracy of each model was initially assessedwith the use of leave-one-out cross-validation, in which theanalysis is performed repeatedly, one sample is removed eachtime, and the probability of recurrence is predicted for thatsample. Because the entire model-building process is repeatedfor each prediction, the reproducibility of the approach isalso evaluated. As a measure of model stability, we generatedmultiple iterations of randomly split training and validationsets from within the Duke cohort; the resulting accuracy ofprognostic capability exceeded 85 percent (data not shown).
The lung metagene model for the prediction of recurrence wassuperior to a predictive model generated with the same methodsbut that included clinical data alone (including age, sex, tumordiameter, stage of disease, histologic subtype, and smokinghistory). In the Duke cohort, the lung metagene model predicteddisease recurrence with an overall accuracy of 93 percent (Figure 2C).The model built with clinical data had an accuracy of only 64percent (Figure 2D). Inclusion of the clinical data with thegenomic data did not further improve the accuracy of the predictionof recurrence over that of the genomic data alone.
The outperformance of the clinical model by the lung metagenemodel in identifying patients at risk for recurrence was alsosupported by the results of Kaplan–Meier analyses. Thelung metagene model identified two distinct groups of patientswith respect to survival (Figure 3A). In contrast, the distinctionwas less clear for each of the models based on clinical predictions(one that combined the clinical variables in a manner similarto the lung metagene model, and another that was based on individualclinical prognostic factors [tumor diameter and stage of diseaseare shown]) (Figure 3B). Univariate and multivariate analyses(with and without the genome-based assessment of the risk ofrecurrence) to assess the relative prognostic value of the individualclinical variables and the lung metagene model showed that thelung metagene model performed significantly better (P<0.001by multivariate analysis) than stage of disease, tumor diameter,nodal status, age, sex, histologic subtype, or smoking history(Table 3 in the Supplementary Appendix).
Figure 3. Kaplan–Meier Survival Estimates for the Duke Training Cohort.
Estimates based on predictions from the lung metagene model demonstrate the value of that approach (Panel A). Panel B shows the estimates based on the clinical model of prognosis, as well as those based on individual clinical characteristics — here, tumor diameter and stage of disease. A high risk of recurrence was defined as a probability of recurrence of more than 0.5, and a low risk of recurrence was defined as a risk of 0.5 or less. P values were obtained with the use of a log-rank test. Tick marks indicate patients whose data were censored by the time of last follow-up or owing to death.
Finally, further confirmation that the lung metagene model representsthe biology of the tumor was provided by the finding that themetagenes with the greatest discriminatory capability in themodel included genes that have previously been shown to haveclinical relevance in NSCLC. In some instances, a metagene representeda single molecular process such as angiogenesis (metagene 19),which is a proven target for therapy in NSCLC. Other key metagenes,such as metagene 41, represented a combination of biologic processes— for example, the BRAF, phosphatidylinositol 3' kinase,TP53, and MYC signaling pathways.
Validation of the Metagene Prognostic Model
Validation across Early Stages and Subtypes of NSCLC
The samples used to devise the prognostic model representedboth the major histologic subtypes of NSCLC (adenocarcinomaand squamous-cell carcinoma) and all the early stages of disease.To assess the general robustness of the prognostic model inthe Duke cohort, we examined the predictions of risk as a functionof these variables. The lung metagene model was consistentlyaccurate across all the early stages of NSCLC (Figure 1 in theSupplementary Appendix) and between the major histologic subtypes(Figure 2 in the Supplementary Appendix), not only in the estimatedrisk of recurrence but also in the results of the Kaplan–Meiersurvival analysis for each stage or subtype.
Validation across Data from Two Multicenter Studies
For a new prognostic model that assesses the risk of recurrenceto be used to inform the decision of whether to administer adjuvantchemotherapy, the model must be shown to be robust when appliedto independent, heterogeneous populations of patients and conditionsof sample acquisition. We therefore evaluated the ability ofthe metagene model generated from the Duke training cohort topredict the risk of recurrence by using samples from two multicenter,cooperative group studies (ACOSOG Z0030 and CALGB 9761) (Figure 1).These sets of samples represented the full spectrum of clinicaloutcomes; the samples were not selected with respect to theduration of survival.
We analyzed 25 samples from the ACOSOG Z0030 trial to validatethe performance of the predictive model of recurrence basedon the Duke training cohort. As was the case with the Duke cohort,for the ACOSOG Z0030 cohort, univariate and multivariate analysesshowed that the metagene model was a significantly more accuratepredictor (P<0.001 by multivariate analysis) than stage ofdisease, tumor diameter, nodal status, age, sex, histologicsubtype, or smoking history (Table 3 in the Supplementary Appendix).The accuracy of the prediction of recurrence in the ACOSOG sampleswas approximately 72 percent (sensitivity, 85 percent; specificity,58 percent; positive predictive value, 69 percent; and negativepredictive value, 78 percent) (Figure 4A). The level of accuracyprovides an assessment of the robustness of the risk predictionsand is substantial, particularly given the heterogeneity ofthe cohort and the fact that the clinical outcomes among thepatients in the ACOSOG cohort are prospective. The Kaplan–Meiersurvival curves, stratified according to the risk predictionsbased on the lung metagene model, provide strong evidence ofthe reliability of those predictions (Figure 4A). In addition,a multivariate analysis showed that in this cohort, the patientspredicted by the lung metagene model to have a probability ofrecurrence of more than 0.5 were more likely to have a recurrencethan those with a predicted probability of recurrence of 0.5or less (adjusted odds ratio, 35.9; 95 percent confidence interval,2.8 to 46.3).
Figure 4. Independent Validation of the Lung Metagene Model with the Use of Data from the ACOSOG Z0030 Study and the CALGB 9761 Study.
The lung metagene model was used to estimate the probabilities of recurrence for the ACOSOG samples (Panel A) and the CALGB samples (Panel B) and to estimate the Kaplan–Meier survival estimates according to the predicted risk of recurrence. For the CALGB cohort, investigators were unaware of the clinical outcomes, and the predictive results were submitted to the CALGB statistical center for the evaluation of performance. I bars represent 95 percent confidence intervals. A high risk of recurrence was defined as a risk of more than 0.5, and a low risk of recurrence was defined as a risk of 0.5 or less. P values were obtained with the use of a log-rank test. Tick marks indicate patients whose data were censored by the time of last follow-up or owing to death.
We analyzed 84 samples from the CALGB 9761 trial as a secondindependent validation cohort. The investigators applying thepredictive model were unaware of the outcomes among these patients;thus, the genome-based predictions of recurrence were submittedto a CALGB statistician for comparison with the true outcomes.Once again, univariate and multivariate analyses showed thatthe lung metagene model predicted outcome significantly better(P<0.001 by multivariate analysis) than the stage of disease,tumor diameter, nodal status, age, sex, histologic subtype,or smoking history (Table 3 in the Supplementary Appendix).The overall predictive accuracy of the model for the CALGB sampleswas 79 percent (sensitivity, 68 percent; specificity, 88 percent;positive predictive value, 79 percent; and negative predictivevalue, 80 percent) (Figure 4A). Again, the Kaplan–Meieranalysis showed a significant difference in the survival ratesof patients with a probability of recurrence of greater than0.5 as compared with 0.5 or less, according to the lung metagenemodel (Figure 4B). Similar to the results seen for the Dukeand ACOSOG data, the adjusted odds ratio for disease recurrencein the CALGB cohort was 16.6 (95 percent confidence interval,4.4 to 62.8) when the model estimate for recurrence was greaterthan 0.5 (Table 3 in the Supplementary Appendix).
We also applied the lung metagene model to another cohort of15 patients with surgically resected stage I squamous-cell lungcancer. Using the lung metagene model, we were able to predictthe outcome accurately in all 5 patients with recurrence andin 7 of 10 patients without recurrence, for an overall accuracyof 80 percent (Figure 3 in the Supplementary Appendix).
Finally, to evaluate the extent to which the metagene modelcould increase the ability of clinicians to estimate prognosis,we computed a C statistic as a measure of the capacity of theclinical or genomic information to identify patients accordingto the risk of recurrence. For the ACOSOG cohort, the C statisticbased on clinical variables alone was 0.67; this value was increasedto 0.84 by the inclusion of genomic data. For the CALGB cohort,inclusion of the genomic data increased the value from 0.73to 0.87. Clearly, the genomic data transformed a limited clinical-basedprognosis to one with substantial capacity to identify patientswho were likely to have disease recurrence.
Application of the Refined Prognosis
Previous studies have shown that 25 percent of patients withstage IA NSCLC will have disease recurrence within five years.Thus, some patients with stage IA NSCLC might be more appropriatelycategorized as being at higher risk than others and might becandidates for adjuvant chemotherapy. We therefore focused onthe 68 patients from the Duke, ACOSOG, and CALGB cohorts whowere classified clinically as having stage IA disease. Kaplan–Meiersurvival curves were generated for the group as a whole, aswell as for the subgroups predicted to be at high or low riskfor recurrence by the lung metagene model. Although the survivalrate for the group was approximately 70 percent at four years,the survival rate for those predicted to be at high risk wasless than 10 percent (Figure 5A), thus identifying the subgroupof patients with stage IA NSCLC at risk for recurrence.
Figure 5. Application of the Lung Metagene Model to Refine the Assessment of Risk and Guide the Use of Adjuvant Chemotherapy in Stage IA NSCLC.
Panel A shows the Kaplan–Meier survival estimates for a group of patients with stage IA disease from the Duke, ACOSOG, and CALGB cohorts and the subgroups predicted to have either a high probability (>0.5) or a low probability (0.5) of recurrence. P values were obtained with the use of a log-rank test. Tick marks indicate patients whose data were censored by the time of last follow-up or owing to death. Panel B illustrates the possible design of a planned prospective, phase 3 clinical trial involving patients with stage IA NSCLC to evaluate the performance of the metagene model.
Discussion
Although gene-expression profiles that can classify patientswith cancer according to their risk of recurrence have beendescribed in many instances, the prognostic tool we devisedcould be used to change a clinical decision. In particular,the guidelines for the treatment of patients with stage I NSCLCprovide an opportunity to use an improved prognostic model torefine the currently imprecise assessment of risk and the decisionregarding whom to treat, and thus potentially leading to morepersonalized cancer treatment. In this case, the refinementof prognosis with the use of the metagene model provides theopportunity for a prospective, randomized, phase 3 clinicaltrial that would evaluate the benefit of the identificationof a subgroup of patients with stage IA disease estimated tobe at high risk for recurrence (Figure 5B). Patients initiallyclassified as having clinical stage IA disease would undergosurgery, and the metagene model would then be applied to identifythe patients predicted to be at high risk for recurrence. Patientsat high risk would then be randomly assigned to observation(the current standard of care for stage IA disease) or adjuvantchemotherapy, in order to evaluate the extent to which the useof genomic reclassification improves survival. Our study isa critical first step in the use of genomic tools as a strategyto refine the prognosis and improve the selection of patientsappropriate for adjuvant chemotherapy.
Drs. Nevins, West, and Dressman report holding equity in ExpressionAnalysis, a DNA microarray service provider established by DukeUniversity. Drs. Nevins, West, Dressman, and Ginsburg reporthaving served on the advisory board of Expression Analysis.Dr. Dressman reports having served as a paid consultant to ExpressionAnalysis, which carried out the microarray assays with AffymetrixGeneChips (U133 Plus2). Dr. Harpole reports having served onthe advisory board of Genentech (OSI Pharmaceuticals). No otherpotential conflict of interest relevant to this article wasreported.
We are indebted to the participants of the ACOSOG Z0030 andCALGB 9761 studies; to Mark Allen, principal investigator ofthe ACOSOG Z0030 study; to Michael Maddaus, principal investigatorof the CALGB 9761 study; to Xiaofei Wang, statistician for theCALGB 9761 study, who was also responsible for the blinded validationof the model predictions; to David Beer, at the University ofMichigan, for the array data on the CALGB 9761 data set; andto Kaye Culler for her assistance with the preparation of themanuscript.
Source Information
From the Institute for Genome Sciences and Policy (A.P., S.M., H.K.D., A.B., J.K., G.S.G., M.W., J.R.N.) and the Institute of Statistics and Decision Sciences (S.M., M.W.), Duke University; and the Departments of Medicine (A.P., J.K., M.K., G.S.G.), Surgery (R.P., D.H.H.), and Molecular Genetics and Microbiology (H.K.D., A.B., J.R.N.), Duke University Medical Center — both in Durham, N.C.; the Department of Medicine, University of Minnesota, Minneapolis (R.K.); and the Department of Pathology and Immunology, Washington University School of Medicine, St. Louis (M.A.W.).
Address reprint requests to Dr. Nevins at the Duke Institute for Genome Sciences and Policy, Duke University, 101 Science Dr., Box 3382, Durham, NC 27708, or at nevin001{at}mc.duke.edu.
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A correction has been published: N Engl J Med 2007;356(2):201.
Previous
0.5) of recurrence. P values were obtained with the use of a log-rank test. Tick marks indicate patients whose data were censored by the time of last follow-up or owing to death. Panel B illustrates the possible design of a planned prospective, phase 3 clinical trial involving patients with stage IA NSCLC to evaluate the performance of the metagene model.