Concordance among Gene-Expression–Based Predictors for Breast Cancer
Cheng Fan, M.S., Daniel S. Oh, Ph.D., Lodewyk Wessels, Ph.D., Britta Weigelt, Ph.D., Dimitry S.A. Nuyten, M.D., Andrew B. Nobel, Ph.D., Laura J. van't Veer, Ph.D., and Charles M. Perou, Ph.D.
Background Gene-expression–profiling studies of primarybreast tumors performed by different laboratories have resultedin the identification of a number of distinct prognostic profiles,or gene sets, with little overlap in terms of gene identity.
Methods To compare the predictions derived from these gene setsfor individual samples, we obtained a single data set of 295samples and applied five gene-expression–based models:intrinsic subtypes, 70-gene profile, wound response, recurrencescore, and the two-gene ratio (for patients who had been treatedwith tamoxifen).
Results We found that most models had high rates of concordancein their outcome predictions for the individual samples. Inparticular, almost all tumors identified as having an intrinsicsubtype of basal-like, HER2-positive and estrogen-receptor–negative,or luminal B (associated with a poor prognosis) were also classifiedas having a poor 70-gene profile, activated wound response,and high recurrence score. The 70-gene and recurrence-scoremodels, which are beginning to be used in the clinical setting,showed 77 to 81 percent agreement in outcome classification.
Conclusions Even though different gene sets were used for prognosticationin patients with breast cancer, four of the five tested showedsignificant agreement in the outcome predictions for individualpatients and are probably tracking a common set of biologicphenotypes.
Many studies of gene expression have identified expression profilesand gene sets that are prognostic, predictive, or both for patientswith breast cancer.1,2,3,4,5,6,7,8,9,10,11,12 Comparisons ofthe lists of genes derived from some of these apparently similarstudies show that they overlap only slightly, if at all. Thereasons for this lower-than-expected overlap are not completelyknown, but they probably include differences in the patientcohorts, microarray platforms, and mathematical methods of analysis.An important and unanswered question, however, is whether thesepredictors are actually concordant with respect to their predictionsfor individual patients. Here, we describe our analysis of asingle data set on which five prognostic or predictive gene-expression–basedmodels were simultaneously compared.
Methods
Patients
We used a single data set of breast-cancer samples from 295women. The gene-expression data set was derived by researchersfrom the Netherlands Cancer Institute and Rosetta Inpharmatics–Merckusing oligonucleotide microarrays (Agilent). Data on relapse-freesurvival (defined as the time to a first event) and overallsurvival were available for all patients.2,3,4 The clinicalinformation was obtained from Chang et al.5 Most of the patientshad stage I or II breast cancer; 165 had received local therapyalone, 20 had received tamoxifen only, 20 had received tamoxifenplus chemotherapy, and 90 had received chemotherapy only.
Statistical Analysis
Gene Sets
We used five prognostic or predictive gene sets (and methods)to evaluate the data set. The resulting classifications foreach patient were recorded for each model (Table 1 in the Supplementary Appendix,available with the full text of this article at www.nejm.org).The gene-expression–based profiles used were the 70-genegood-versus-poor outcome model developed by van de Vijver etal. and van't Veer et al.,2,3 the wound-response model developedby Chang et al.,4,5 the recurrence-score model developed byPaik et al.,6 the intrinsic-subtype model (luminal A, luminalB, basal-like, HER2-positive and estrogen-receptor–negative[HER2+ and ER–], and normal breast-like) developed byPerou and colleagues,1,9,12,13 and the two-gene–ratiomodel (the ratio of the levels of expression of homeobox 13[HOXB13] and interleukin 17B receptor [IL17BR]).7 (The predictionsfor each model are presented in the Supplementary Appendix.)The recurrence-score and two-gene–ratio models were originallydesigned to predict the outcomes among patients with ER+ diseasewho were receiving tamoxifen.6,7 We therefore performed separateanalyses for the subgroup of ER+ samples and for the completeset of ER+ and ER– samples combined. A detailed descriptionof how these methods were applied to the 295-sample data setis provided in the Supplementary Appendix.
Survival
To evaluate the prognostic value of each gene-expression–basedmodel, we performed univariate Kaplan–Meier analysis usingthe Cox–Mantel log-rank test in WinStat for Excel (R.Fitch Software). We also used SAS software to perform a multivariateCox proportional-hazards analysis of each model individuallyin a model that included estrogen-receptor status (positivevs. negative), tumor grade (1 vs. 2 and 1 vs. 3), nodal status(no positive nodes vs. one to three positive nodes and no positivenodes vs. more than three positive nodes), age (as a continuousvariable), tumor diameter (2 cm or less vs. more than 2 cm),and treatment received (no adjuvant therapy vs. chemotherapy,hormonal therapy, or both). Relapse-free survival (defined asthe time to a first event) and overall survival were the endpoints. (For multivariate analysis of the intrinsic subtypesand recurrence score, estrogen-receptor status was not includedas a variable because it was based on the same microarray datathat were used in the gene-expression models).
Two-way contingency-table analyses and the calculation of Cramer'sV statistic were performed with WinStat for Excel. Cramer'sV statistic provides a quantitative measure of the strengthof the association between the two variables in a contingencytable (information that cannot be obtained from the P value).The values range from 0 to 1, with 0 indicating no relationand 1 indicating a perfect association. Traditionally, valuesof 0.36 to 0.49 indicate a substantial relation, and valuesof 0.50 or more indicate a strong relation. The V statisticis a generalization of the more familiar phi statistic for non–two-by-twocontingency tables, and for two-by-two tables, the V statisticis equal to the phi statistic.14
Results
Analysis of All Tumors
For all 295 tumors, all gene-expression–based models exceptthe two-gene–ratio model, estrogen-receptor status, tumorgrade, tumor diameter, and nodal status were significant predictorsof relapse-free survival and overall survival, according tounivariate Kaplan–Meier survival analyses (Figure 1 andTable 1). For the four significant models, the groups with apoor outcome were as expected: those with a poor 70-gene profile,an activated wound response, a high recurrence score, and thebasal-like, luminal B, and HER2+ and ER– intrinsic subtypes.
Figure 1. Kaplan–Meier Survival Estimates of Relapse-free Survival and Overall Survival among the 295 Patients, According to the Intrinsic Subtype (Panels A and B), Recurrence Score (Panels C and D), 70-Gene Profile (Panels E and F), Wound Response (Panels G and H), and Two-Gene Ratio (Panels I and J).
P values were obtained from the log-rank test. X denotes observations that were censored owing to loss to follow-up or on the date of the last contact.
Table 1. Classification of the Netherlands Cancer Institute Patient Data Set According to Five Gene-Expression–Based Models.
To evaluate the prognostic value of each gene-expression–basedmodel, we next performed a multivariate Cox proportional-hazardsanalysis — that included estrogen-receptor status, tumorgrade, nodal status, age, tumor diameter, and treatment status— of each model individually (Table 2 in the Supplementary Appendix).The models based on intrinsic subtype, 70-gene profile, woundresponse, and recurrence score were significant predictors ofboth relapse-free survival and overall survival. Thus, eachgene-expression profile (except for the two-gene ratio) addednew and important prognostic information beyond that providedby the standard clinical predictors. In fact, the 70-gene, recurrence-scoreand intrinsic-subtype profiles were the most predictive variablesin each analysis, as reflected by their having the lowest nominalP value.
As a point of reference, we next analyzed each model relativeto the intrinsic-subtype assignments, which were largely basedon an unsupervised analysis of breast-tumor gene-expressionprofiles (Table 2). All 53 basal-like tumors were classifiedas having a high recurrence score and a poor 70-gene profile,and 50 were classified as having an activated wound-responsesignature. A nearly identical finding was observed for the HER2+and ER– subtype, as well as for the poor-outcome luminalB subtype that is defined clinically as ER+. Conversely, thenormal-like and luminal A tumors showed heterogeneity in termsof how they were classified by the other models; however, 62of 70 samples with low recurrence scores were of the luminalA subtype. These data suggest that if a sample is classifiedas basal-like, HER2+ and ER–, or luminal B, then it mostlikely would be in the poor-prognosis groups of the 70-gene,wound-response, and recurrence-score models.
Table 2. Classification of Tumor Samples from All 295 Patients, According to the Model Used.
We next compared the results of the 70-gene, wound-response,recurrence-score, and two-gene models with one another, usingtwo-way contingency-table analyses. For these analyses, we combinedthe low and intermediate recurrence-score categories into asingle group, because their survival curves were not significantlydifferent (Table 2E in the Supplementary Appendix). All thecomparisons yielded significant correlations, with the two-genemodel having the lowest level of correlation. The results ofthe recurrence score, 70-gene, and wound-response models wereall highly correlated (Table 3 in the Supplementary Appendix)(P<0.001 by the chi-square test).
We then assessed the strength of the correlation between themodels using Cramer's V statistic. Comparison of the 70-geneand recurrence-score models yielded a Cramer's V statistic of0.60 (indicating a strong relation), comparisons of the recurrence-scoreand wound-response models yielded a V statistic of 0.42 (indicatinga substantial relation), and comparison of the 70-gene and wound-responsemodels yielded a V statistic of 0.36 (indicating a substantialrelation). Thus, most tumors classified as resulting in a pooroutcome according to one of these three models were also classifiedas such by the other two. With regard to the Cramer's V values,the model showing the best agreement with the other two wasthe recurrence score (i.e., of the three, recurrence score camethe closest to functioning as a consensus predictor). To determinewhether the use of the three models together would result ina better model than the use of any one alone, we derived a singlemodel based on the most common findings of the three models.The performance of this model according to the Kaplan–Meieranalysis was similar to that of each of the three models butwas not noticeably better.
Histologic grade is an important clinical and biologic featureof tumors, especially in a comparison of the clinical characteristicsof grade 1 and grade 3 breast tumors. An often-asked questionregarding these gene-expression–based models is whetherthe predicted prognosis correlates with tumor grade. We thereforeperformed two-way contingency-table analyses comparing tumorgrade and the results of each of four models (70-gene, wound-response,two-gene ratio, and recurrence score [low plus intermediatevs. high]). All four models showed significant correlationswith grade (P<0.001). The 70-gene model was the most highlycorrelated with grade (Cramer's V statistic, 0.52), followedby recurrence score (V statistic, 0.48), wound response (V statistic,0.35), and the two-gene ratio (V statistic, 0.25).
Thus, to varying degrees, all the models correlated with grade,but the 70-gene, recurrence-score, intrinsic-subtype, and wound-responsemodels added prognostic information beyond that provided bythe tumor grade. Moreover, the use of these four models involvedan assay that is objective and quantitative and could be automatedand easily standardized across institutions.
Of the five models, the 70-gene2,3 and recurrence-score6,15models are the most well validated and are beginning to be usedin the clinical setting to assist in treatment decisions. Wetherefore specifically compared these two models in a groupof 295 patients with cancer, using a simple method. We consideredlow and intermediate recurrence scores to be equivalent to agood score on the 70-gene model and a high recurrence scoreto be equivalent to a poor score on the 70-gene model and thendetermined how many scores agreed between the two models. Weobserved agreement in 239 of 295 samples (81 percent). In particular,81 of the 103 samples with a recurrence score of low or intermediatewere classified as having a good 70-gene profile.
In this analysis, we compared the capacity of each model topredict recurrence in a group of patients with either node-negativeor node-positive tumors and with or without adjuvant chemotherapy.However, the profiles were developed to predict the distantmetastasis–free survival among patients with node-negativedisease only, and they are meant to be used either to predictprognosis without adjuvant treatment (70-gene predictor) orwith the use of tamoxifen (recurrence score).
Analysis of Estrogen-Receptor–Positive Tumors
Two of the five models (recurrence score and two-gene ratio)were specifically designed to evaluate outcomes in patientswith ER+ tumors who were treated with tamoxifen. We thereforeperformed the same analyses described above (Table 1) on the225 samples in the 295-sample data set that were classifiedas ER+ on the basis of the level of expression of the estrogen-receptorgene.4 Again, all the gene-expression–based models (exceptfor the two-gene ratio) were significant predictors of relapse-freesurvival and overall survival in univariate Kaplan–Meieranalyses (Figure 2). In multivariate Cox proportional-hazardsanalyses (in which each model was evaluated individually ina model that included the standard clinical variables), the70-gene, wound-response, and recurrence-score models and theluminal A and B intrinsic subtypes added considerable prognosticinformation regarding relapse-free survival and overall survival;each gene-expression–based model typically had the lowestP value as compared with the traditional clinical variables(Table 4 in the Supplementary Appendix). The ER+ samples werealso classified according to intrinsic subtype (Table 3); 7were classified as basal-like and 18 as HER2+ and ER–,suggesting that approximately 10 percent of the ER+ tumors couldbe considered ER–, according to hierarchical clusteringanalysis.
Figure 2. Kaplan–Meier Survival Estimates of Relapse-free Survival and Overall Survival among the 225 Patients with ER+ Disease, According to the Intrinsic Subtype (Panels A and B), Recurrence Score (Panels C and D), 70-Gene Profile (Panels E and F), Wound Response (Panels G and H), and Two-Gene Ratio (Panels I and J).
P values were obtained from the log-rank test. X denotes observations that were censored owing to loss to follow-up or on the date of the last contact.
Table 3. Classification of Tumor Samples from the 225 Patients with ER+ Disease, According to the Model Used.
As for the 295-sample data set, we performed a pairwise comparisonof the 70-gene, wound-response, recurrence-score, and two-generatio assignments for the 225 ER+ samples, using two-way contingency-tableanalyses. All comparisons yielded significant correlations exceptfor the two-gene model (Table 5 in the Supplementary Appendix).The recurrence-score, 70-gene, and wound-response profiles werehighly correlated (P<0.001); the Cramer's V values were 0.54for the 70-gene model as compared with the recurrence-scoremodel, 0.38 for the recurrence-score model as compared withthe wound-response model, and 0.34 for the 70-gene model ascompared with the wound-response model. Thus, recurrence scoreshowed the best agreement with the other two models. We againderived a model based on the most common results for the threemodels, and its performance in Kaplan–Meier analysis wassimilar to that of the three individual models.
When the recurrence scores were compared with the 70-gene profilescores for the 225-sample subgroup as they were for the completedata set, 173 of the 225 samples (76.9 percent) showed agreement.In particular, of the 105 samples with low or intermediate recurrencescores, 83 were classified as having a good 70-gene profile.
We did not perform any multivariate Cox proportional-hazardsanalyses using all predictors simultaneously to identify theoptimal model for either the 225-patient group or the 295-patientgroup. We believed that doing so would not be a fair test forany model for which this group was a true test set (recurrencescore and two-gene ratio) or for those that were developed withthe use of a different platform (recurrence score, two-generatio, and intrinsic subtype).
Discussion
We analyzed a single data set for which enough genes had beenassayed to allow the simultaneous analysis of five gene-expression–basedmodels. Four of these models resulted in similar predictions— for example, each model assigned the same samples tothe poor-outcome groups. Tumors classified as basal-like, HER2+and ER–, and luminal B by the intrinsic-subtype modelwere almost all classified as having a poor outcome (regardlessof estrogen-receptor status) by the 70-gene, recurrence-score,and wound-response models. Only within the luminal A and normal-likeintrinsic subtypes was variability in the outcome predictionsfound.
Of the five models analyzed in our study, only the two-generatio failed to identify significant differences in outcomewithin the data set. In an independent data set of patientswith ER+ disease who were receiving tamoxifen, Reid et al. reportedthat the two-gene model failed to detect differences in outcome.16However, Goetz et al. showed that in women with node-negativedisease from the North Central Cancer Treatment Group Study89-30-52, the two-gene ratio was a significant predictor ofrelapse-free survival and disease-free survival.17 A model basedon the analysis of only two genes is much more likely to besensitive to technical differences in analysis platforms thanone based on many genes, and it is possible that one of thefeatures representing HOXB13 or IL17BR in the Netherlands CancerInstitute data set may not faithfully reflect the values seenby Ma et al.,7 owing to alternative splicing or differencesin probe-hybridization conditions.
Pairwise comparisons of the 70-gene, wound-response, recurrence-score,and two-gene models showed that the results of all but the two-genemodel were highly concordant. Comparison of the 70-gene andrecurrence-score models showed that their sample predictionsagreed in 77 percent of patients with ER+ cancer and 81 percentof all patients. These analyses suggest that even though therewas very little gene overlap (the 70-gene and recurrence-scoreprofiles overlapped by only 1 gene: SCUBE2) and different algorithmswere used, the outcome predictions for the majority of patientswith breast cancer would be similar. It is also likely thatthe recurrence-score model, originally developed for patientswith ER+ disease, is accurate for all patients with breast cancer,because almost all (69 of 70) patients with ER– tumorswere classified as having a high recurrence score.
The outcome predictions derived from the various models largelyoverlapped, according to multivariate Cox proportional-hazardsanalyses (the 95 percent confidence intervals of the hazardratios for relapse-free and overall survival are given in Table2 in the Supplementary Appendix). The discordance rate of upto 20 percent among the patients in different categories ledto slight differences in outcome prediction and emphasizes theneed for further validation of this approach. The National CancerInstitute and the European Union have designed randomized clinicaltrials (Trial Assigning Individualized Options for Treatment(Rx) [TAILORx] and Translating Molecular Knowledge into EarlyBreast Cancer Management Building on the Breast InternationalGroup network for Improved Treatment Tailoring [TRANSBIG]-Microarrayin Node-Negative Disease May Avoid Chemotherapy [MINDACT], respectively)that will prospectively address the prognostic and predictivepowers of the recurrence-score and 70-gene models, respectively.
Despite the absence of gene overlap, the different gene modelsyielded similar predictions largely because they reflected commoncellular phenotypes, which encompass the consistent differencesin ER+ (i.e., luminal) breast cancer and ER– (basal-likeand HER2+ and ER–) breast cancers. Although these differencesare correlated with histologic grade, it is clear that theseprofiles provided additional information beyond that providedby grade. Our findings also show that outcomes can readily bepredicted by a large number of genes and that a model that usesa sufficiently representative subgroup of these genes shouldbe effective. This is consistent with an observation made bySon et al., who reported that approximately 19,000 genes aredifferentially expressed in various tissues and that any randomlyselected subgroup that is sufficiently large (approximately100 genes) reproduces the hierarchical clustering obtained withthe use of the full gene set.18
We conclude that overlap in gene identity among gene-expressionprofiles is not a good measure of reproducibility and that theclassification of individual samples is the relevant measureof concordance. Our results are encouraging and can be interpretedto mean that although different gene sets are being used aspredictors, they each track a common set of biologic characteristicsthat are present in different groups of patients with breastcancer, resulting in similar predictions of outcome.
Supported by grants from the National Cancer Institute (RO1-CA-101227-01,to Dr. Perou), the National Cancer Institute Breast SpecializedPrograms of Research Excellence program (P50-CA58223-09A1, tothe University of North Carolina at Chapel Hill), the BreastCancer Research Foundation, the Dutch Cancer Society (DCS-NKI02-2575,to Dr. van't Veer), the Dutch National Genomics Initiative (NGI02-01,to the Cancer Genomics Center), and the National Science Foundation(DMS 0406361, to Dr. Nobel).
Presented in part at the 13th Specialized Programs of ResearchExcellence Investigators Workshop, Washington, D.C., July 9,2005, and at the 4th Annual Future of Breast Cancer Meeting,Bermuda, July 21, 2005.
Dr. van't Veer reports holding equity in Agendia BV. No otherpotential conflict of interest relevant to this article wasreported.
We are indebted to Lisa Carey and Melissa A. Troester for readingand commenting on the manuscript.
Source Information
From the Departments of Genetics (C.F., D.S.O., C.M.P.), Statistics and Operations Research (A.B.N.), and Pathology and Laboratory Medicine (C.M.P.), University of North Carolina at Chapel Hill and Lineberger Comprehensive Cancer Center, Chapel Hill; and the Divisions of Diagnostic Oncology (L.W., B.W., L.J.V.) and Radiotherapy (D.S.A.N.), the Netherlands Cancer Institute, Amsterdam. Drs. Fan and Oh contributed equally to this article.
Address reprint requests to Dr. Perou at Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Campus Box 7295, Chapel Hill, NC 27599, or at cperou{at}med.unc.edu.
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