Originally published as JCO Early Release 10.1200/JCO.2005.03.9115 on February 27 2006
Journal of Clinical Oncology, Vol 24, No 11 (April 10), 2006: pp. 1665-1671
© 2006 American Society of Clinical Oncology.
Multicenter Validation of a Gene Expression–Based Prognostic Signature in Lymph Node–Negative Primary Breast Cancer
John A. Foekens,
David Atkins,
Yi Zhang,
Fred C.G.J. Sweep,
Nadia Harbeck,
Angelo Paradiso,
Tanja Cufer,
Anieta M. Sieuwerts,
Dmitri Talantov,
Paul N. Span,
Vivianne C.G. Tjan-Heijnen,
Alfredo F. Zito,
Katja Specht,
Heinz Hoefler,
Rastko Golouh,
Francesco Schittulli,
Manfred Schmitt,
Louk V.A.M. Beex,
Jan G.M. Klijn,
Yixin Wang
From the Department of Medical Oncology, Erasmus Medical Center, Daniel den Hoed Cancer Center, Rotterdam; Department of Chemical Endocrinology, Radboud University Nijmegen Medical Centre; Department of Medical Oncology, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands; Veridex LLC, San Diego, CA; Frauenklinik und Institut für Allgemeine Pathologie und Pathologische Anatomie, Technische Universität, München, Germany; National Cancer Institute, Bari, Italy; and the Institute of Oncology, Ljubljana, Slovenia
Address reprint requests to John A. Foekens, PhD, Erasmus Medical Center, Josephine Nefkens Institute, Room BE-426, Rotterdam, the Netherlands; e-mail: j.foekens{at}erasmusmc.nl
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ABSTRACT
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PURPOSE: We previously identified in a single-center study a 76-gene prognostic signature for lymph node-negative (LNN) breast cancer patients. The aim of this study was to validate this gene signature in an independent more diverse population of LNN patients from multiple institutions.
PATIENTS AND METHODS: Using custom-designed DNA chips we analyzed the expression of the 76 genes in RNA of frozen tumor samples from 180 LNN patients who did not receive adjuvant systemic treatment.
RESULTS: In this independent validation, the 76-gene signature was highly informative in identifying patients with distant metastasis within 5 years (hazard ratio, [HR], 7.41; 95% CI, 2.63 to 20.9), even when corrected for traditional prognostic factors in multivariate analysis (HR, 11.36; 95% CI, 2.67 to 48.4). The actuarial 5- and 10-year distant metastasis-free survival were 96% (95% CI, 89% to 99%) and 94% (95% CI, 83% to 98%), respectively, for the good profile group and 74% (95% CI, 64% to 81%) and 65% (53% to 74%), respectively for the poor profile group. The sensitivity for 5-yr distant metastasis-free survival was 90%, and the specificity was 50%. The positive and negative predictive values were 38% (95% CI, 29% to 47%) and 94% (95% CI, 86% to 97%), respectively. The 76-gene signature was confirmed as a strong prognostic factor in subgroups of estrogen receptor-positive patients, pre- and postmenopausal patients, and patients with tumor sizes 20 mm or smaller. The subgroup of patients with estrogen receptor-negative tumors was considered too small to perform a separate analysis.
CONCLUSION: Our data provide a strong methodologic and clinical multicenter validation of the predefined prognostic 76-gene signature in LNN breast cancer patients.
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INTRODUCTION
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Application of genomics to diagnosis and management of cancer is gaining momentum as initial discovery and validation studies are completed. In breast cancer,1,2 several key publications have described gene expression-based signatures with apparent utility in predicting disease recurrence in lymph node-negative (LNN) breast cancer patients.3-12 As the field advances, there has been an increasing appreciation of the pitfalls facing additional application of these signatures in clinical practice. Ransohoff13 and Simon et al14 recently described the merit of elimination of bias and critical aspects of molecular marker evaluation. A common unambiguous requirement for broader acceptance of a molecular signature is validation of assay performance in truly independent patients.
Wang et al described discovery and single-center validation of a powerful prognostic 76-gene signature for LNN breast cancer patients in Rotterdam.15 We have now completed an independent assessment of the performance of this prognostic signature in an additional series of 180 LNN breast cancer patients obtained from multiple European institutions outside Rotterdam. The patients were of all ages and tumor size groups, and had not received any adjuvant systemic therapy. Our data provide a validation with high confidence by applying the prespecified prognostic gene signature to LNN patients within a multicenter clinical study.
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PATIENTS AND METHODS
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Patient Samples
Frozen tumor specimens were selected from tumor banks from four European institutions, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands (n = 90; period 1992 to 1993), Technical University Munich, Germany (n = 57; period 1989 to 1995), National Cancer Institute, Bari, Italy (n = 40; period 1990 to 1993), and Institute of Oncology, Ljubljana, Slovenia (n = 4;1997). Samples were drawn from the eldest consecutive series available within the periods based on predefined inclusion criteria: histologic diagnosis of LNN breast cancer, informed consent and/or approval of a local ethical committee, more than 5 years of follow-up except for patients who developed distant relapse within 5 years, no evidence of recurrent disease within 1 month after primary surgery, and availability of frozen tissue. Exclusion criteria were: neoadjuvant or adjuvant systemic therapy, history of other primary cancer (except for basal cell carcinoma), less than 50% tumor area on hematoxylin/eosin section at time of extraction of RNA, or poor RNA quality based on predefined criteria. Of the 191 samples processed, seven were rejected based on poor RNA quality, three were rejected on the basis of poor chip quality as defined in Gene Expression Analysis, and one sample had insufficient tumor. One hundred eighty samples were eligible for additional analysis and patient and tumor characteristics are summarized in Table 1. Routine postsurgical follow-up was similar for the multiple participating institutions, and as described for our single-center study.15 Date of diagnosis of metastasis was defined as the date of imaging or histologic confirmation of metastasis after complaints and/or clinical symptoms, or at regular follow-up. The median follow-up period of surviving patients (n = 157) was 100 months (range, 48 to 137 months). Of the 180 patients included, 30 (17%) showed evidence of distant metastasis within 5 years, and seven (4%) thereafter. A total of 37 patients were counted as failures in the Kaplan-Meier analysis of distant metastasis-free survival (DMFS). One patient (1%) died without evidence of disease and was censored at last follow-up in DMFS analysis. Twenty-two patients (12%) died after a previous relapse. Therefore, a total of 23 patients (13%) were failures in overall survival (OS) analysis.
Gene Expression Analysis
Tumor tissues (median, 90 mg; range, 40 to 120 mg) were processed for RNA isolation at the Erasmus MC, Rotterdam, as described before,15 and the total RNA was sent to Veridex LLC (San Diego, CA) for analysis. Median RNA yield was 51 µg (range, 2 to 345 µg). RNA quality was checked with the Agilent BioAnalyzer (Agilent Technologies, Palo Alto, CA), and samples were considered for profiling if there were clear 18S and 28S peaks with no minor peaks present, area under the 18S and 28S peaks was greater than 15% of total RNA area, and the 28S:18S ratio was between 1.2 and 2.0. Biotinylated targets were prepared using published methods (Affymetrix, Santa Clara, CA)17 and hybridized to custom-made VDX2 GeneChips (Affymetrix) containing the 76 genes of the prognosis profile15 and 221 control genes. Chips with average intensity less than 40 or background signal of greater than 100 were rejected. For chip normalization, probe sets were scaled to a target intensity of 600, and scale mask filters were not selected. Arrays were scanned using the standard Affymetrix protocol. Each probe set was considered a separate gene. Expression values for each gene were calculated using Affymetrix GeneChip analysis software MAS 5.0.
Statistical Methods
Based on the results of our previous single-center validation study15 in which a hazard ratio (HR) of 5.67 was reported, and to achieve 80% power and 95% confidence level, we calculated the need of at least 12 distant recurrences within 5 years and 48 nonrecurrences in this study. Since we tried to include as many cases as possible that met our inclusion criteria, 191 samples were included as starting material to give us a large enough sample size.
A previously specified gene set encoding 24 ribosomal proteins was used to normalize the average intensity (brightness) of the VDX2 array. Briefly, this set of 24 genes showed a low variation in the original data set of 286 patients.15 This was done without using any information on the patients’ clinical outcomes and without using any of the 76 genes of our signature. The average coefficient of variation of this set was 0.2 with the standard deviation of 0.05. In order to normalize the signal of VDX2 array, the average intensity of the 24-gene set for each VDX2 array was calculated. The ratio between the average intensity of the VDX2 array and the average intensity of the same gene set of all the original arrays was determined. This ratio was used as a scaling factor to normalize the custom VDX2 array to ensure that the custom arrays had a similar intensity range as the original data set. The data variability resulting from different protocols for sample handling at individual clinical institutions was minimized by using analysis of variance on the gene expression data. It is of note that in a clinical assay it will be necessary to scale any new data to a predefined training set or a reference intensity target because the chips run in one laboratory may not be as bright as the chips run in another laboratory, even with the same protocol.
Estrogen receptor (ER) status was determined by immunohistochemistry or biochemical assay, with 10% positive tumor cells or 10 fmol/mg cytosolic protein used as cutoff, respectively. Because this study included patient samples from multiple centers, and when including patients in future studies, there could be some variations in the measures of ER by either immunohistochemistry or biochemical assay conducted at individual sites. To avoid this potential variability, we developed an array ER measure from the previous study.15 The array ER cutoff of 1,000 was based on the best correlation between the routinely assessed ER status and the array-measured ER status in our previous data set. Both this array-based cutoff and the original cutoff from routine ER assays were tested in this validation study. The relapse score was used to classify each patient at high or low risk for developing distant metastasis within 5 years. Patients with a relapse score greater than 0 were classified as high risk (poor 76-gene signature), those with a relapse score less than 0 as low risk (good 76-gene signature). The calculation of the relapse score was as follows:
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where
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wi is the standardized Cox regression coefficient for ER + marker xi is the expression value of ER + marker in log2 scale wj is the standardized Cox regression coefficient for ER – marker xj is the expression value of ER – marker in log2 scale Kaplan-Meier survival plots18 and log-rank tests were used to assess the differences in DMFS and OS of the predicted high- and low-risk groups. Sensitivity was defined as the percentage of the patients with distant metastasis within 5 years that were predicted correctly by the 76-gene signature, and specificity was defined as the percentage of the patients free of distant recurrence for at least 5 years that were predicted correctly by the 76-gene signature. Odds ratio (OR) was calculated as the ratio of the odds of distant metastasis between the predicted relapse patients and relapse-free patients.
Both univariate and multivariate analyses were performed using the Cox proportional hazards regression model. The HR and its 95% CI were derived from these results. Likelihood ratio test on the interaction between the gene signature and the institution was used in Cox regression in order to measure the heterogeneity of the HRs derived from the results of different institutions. All statistical analyses were performed using S-Plus 6.1 software (Insightful, Seattle, WA).
Role of Funding Source
This study was supported in part by the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research. This organization had no role in the study design; the collection, analysis, or interpretation of data; writing of the paper; or in decisions relating to publication.
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RESULTS
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Analysis for DMFS and OS in All Patients
Survival analyses were performed as a function of the 76-gene profile. When ER status was determined by immunohistochemistry and biochemical assay (Fig 1), the relapse score calculated from this method correctly predicted 27 of 30 relapses (90% sensitivity) that occurred within 5 years (OR, 8.0; 95% CI, 2.3 to 27.4; P = .0004), and 69 of 147 nonrelapsers (47% specificity). Three of the 150 nonrelapsers had missing ER measures. A poor gene signature was associated with a short DMFS (HR, 6.50; Fig 1A), and an early death (HR, 4.93; Fig 1B). When ER status was determined by array (Fig 2), the relapse score correctly predicted 27 of 30 relapses (90% sensitivity) that occurred within 5 years (OR, 9.0; 95% CI, 2.6 to 31.6; P = .0001), and 75 of 150 nonrelapsers (50% specificity). These data suggest that the two methods of measuring ER status in the prognostic signature produced highly consistent results. A poor gene signature was associated with a short DMFS (HR, 7.41; Fig 2A). The positive and negative predictive values were 38% (95% CI, 29% to 47%) and 94% (95% CI, 86% to 97%) respectively, assuming 25% prevalence of distant recurrence within 5 years. The actuarial 5- and 10-year DMFS were 96% (95% CI, 89% to 99%) and 94% (95% CI, 83% to 98%), respectively, for the good profile group and 74% (64% to 81%) and 65% (95% CI, 53% to 74%), respectively, for the poor profile group of tumors. Furthermore, a poor gene signature predicted a shorter OS (HR, 5.46; Fig 2B).
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Fig 1. Distant metastasis-free survival (A) and overall survival (B) analysis as a function of the 76-gene profile. Estrogen-receptor status was determined by immunohistochemistry or biochemical assay. Patients at risk are indicated. HR, hazard ratio.
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Fig 2. Distant metastasis-free survival (A) and overall survival (B) analysis as a function of the 76-gene profile. Estrogen-receptor status was determined by array. Patients at risk are indicated. HR, hazard ratio.
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The traditional prognostic factors age, menopausal status, tumor differentiation, and ER level were not significantly associated with the length of DMFS in Cox univariate analysis in this group of 180 patients; the 76-gene signature (Fig 2) and tumor size (Fig 3) were the only significant factors (Table 2). Furthermore, in Cox multivariate analysis for DMFS, the 76-gene signature was the only significant factor (HR, 11.36; Table 2). The likelihood ratio test showed that there is no significant heterogeneity of HRs among different institutions (P = .4159).
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Fig 3. Distant metastasis-free survival (A) and overall survival (B) analysis as a function of tumor size. Patients at risk are indicated. HR, hazard ratio.
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Analysis for DMFS and OS in Subgroups of Patients
The 76-gene profile also represented a strong prognostic factor for the development of distant metastasis in the subgroups of 126 postmenopausal patients (HR, 9.84; 95% CI, 2.31 to 42.0; P = .0020), 54 premenopausal patients (HR, 4.84; 95% CI, 1.08 to 21.7; P = .0394), 164 ER-positive patients (HR, 6.62; 95% CI, 2.34 to 18.8; P = .0004), 95 patients with T1 tumors (HR, 4.27; 95% CI, 1.19 to 15.3; P = .0258), and 84 patients with T2 tumors (HR, 13.6; 95% CI, 1.83 to 101; P = .0108). The ER-negative subgroup with only 16 patients, of whom two relapsed within 5 years (both predicted with a poor signature), was too small for separate analysis.
Comparison With St Gallen and National Institutes of Health Criteria
Using our gene signature, 78 of 180 patients (43%) were classified as low risk for developing distant recurrence. To allow classification of LNN patients into low-, average-, and high-risk groups based on the published 2003 St Gallen1 or 2001 National Institutes of Health (NIH; Bethesda, MD)2 consensus criteria, information for 170 and 169 patients, respectively, was available. Using the St Gallen criteria, 161 of the 170 patients (95%) would have been guided to receive adjuvant systemic therapy, with the NIH criteria 165 (98%). With either of these criteria, 29 of 30 distant relapses (97%) that occurred within 5 years would have been predicted correctly. However, based on the St Gallen criteria 132 of the 140 patients (94%) who did not relapse would have been guided to receive adjuvant systemic therapy as well. Using the NIH criteria, this would have been 136 of 139 (98%). In comparison, our gene signature predicted 27 of 30 distant relapses (90%) that occurred within 5 years, but correctly classified 75 of 150 patients (50%) who did not relapse, as being at low risk for recurrence. This suggests that our gene signature could be useful to identify a significant proportion of low-risk patients who are misclassified by the St Gallen or NIH criteria while maintaining a comparable level of accuracy in identifying the patients who would need adjuvant systemic therapy. Indeed, our 76-gene set could discriminate well between patients at high and low risk for developing distant metastasis in those patients who were candidates for receiving adjuvant systemic endocrine and/or chemotherapy by the St Gallen criteria (Fig 4A) or the NIH criteria (Fig 4B). Employing our gene signature, 40% and 41% of the average- or high-risk patients defined by the two consensus criteria, respectively, would be classified correctly as being at low risk for recurrence (Fig 4).
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Fig 4. Distant metastasis-free survival analysis stratified by a good and poor 76-gene signature in patients guided to receive adjuvant chemo- and/or endocrine therapy according to the St Gallen2 (A) and National Institutes of Health (Bethesda, MD)2 criteria (B). Patients at risk are indicated. HR, hazard ratio.
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The data set has been submitted to the National Center for Biotechnology Information/Genbank Gene Expression Omnibus entry database (series entry GSE 3453).
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DISCUSSION
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This multicenter study confirms the prognostic value of our 76-gene signature previously determined,15 and has similar sensitivity (90%) and specificity (50%) without loss of statistical power or drop of the HR. Assuming a 25% prevalence of distant disease recurrence in LNN patients, our prognostic signature will produce a positive predictive value of 38% and a negative predictive value of 94%, also being similar in comparison to our previous study.15 In contrast to our original study,15 grade showed no prognostic value in this study. This could be due to the relatively small sample size and few ER-negative tumors. Furthermore, different grading methods have been used in the participating institutions. Since archived frozen tissues from multiple institutions were used for microarray analysis and paraffin samples are not readily available, a uniform grading score could not be obtained.
Comparison with the St Gallen1 and NIH guidelines2 was again instructive. The use of our 76-gene signature would spare systemic therapy for approximately 40% of the patients who would have been guided to receive adjuvant chemo- and/or endocrine systemic therapy based on the guidelines. Therefore, application of this gene signature could result in a substantial reduction of the number of LNN patients who would otherwise be recommended for unnecessary adjuvant systemic therapy, in particular avoiding overtreatment by chemotherapy.
Studies that are aimed at developing gene expression classifiers should be rigorously validated and cannot be considered for clinical application until the results are properly confirmed and are demonstrated to be strongly reproducible with regard to methodologic, statistical, and clinical aspects. In this respect, several criticisms have been raised concerning published gene-expression profiling studies on issues relating to the omission of independent validation sets, multiple training sets, the sizes of training and testing sets, or possible confounding effects of treatment.13,14,19 Our initial single-center study15 comprised of only LNN breast cancer patients (N = 286) using completely different training and validation sets without overlap. We also addressed the issue of sample size and showed that our signature was robust when starting with an adequate sample size in the training set and subsequent validation of the established signature in an independent group of patients. This study represents the first successful multicenter validation of a prespecified prognostic profile for all untreated LNN breast cancer patients, irrespective of tumor size, tumor grade, and age of the patient. We also confirmed that our 76-gene signature is applicable in the large subgroup of postmenopausal patients (HR, 9.84), however, the ER-negative group of 16 patients, with only two relapses, was too small to allow a separate validation of this subgroup. The strength of our study relied on the diverse groups of patients from multiple participating institutions. Comparing with the patients used for our initial report of the 76-gene signature, the patients in this confirmatory study had a higher frequency of postmenopausal patients (70% v 51%) and a lower frequency of distant metastasis within 5 years (17% v 33%). Forty-three percent of the tumors had a good signature, slightly higher than the 35% in the original study,15 probably as a reflection of the lower frequency of distant metastasis in this study concerning a patient group with a more favorable prognosis (less ER-negative tumors). The tumor specimens were collected and stored according to institutional protocols, and the RNA samples were prepared using easily applicable procedures. Despite these differences in multicenter tissue handling, the 76-gene signature produced results that were highly consistent with those from our initial validation set of 171 patients, in the absence of heterogeneity between the multiple institutions. Most importantly, this demonstrated the strong reproducibility and the robustness of our 76-gene classifier. Furthermore, the results when using the routinely used ER measure and the chip ER value were highly comparable. Therefore, to avoid the variability in ER determinations in different institutions we advocate the use of the array-based ER assay.
Predicting the course of the disease is fundamental to therapeutic decision making in oncology; however, the standard methods of identifying those individuals at risk of developing metastases remain relatively crude. Various standardized algorithms such as St Gallen and NIH consensus guidelines1,2 result in overtreatment of many patients in whom cure would have been achieved by locoregional therapy alone without chemotherapy or endocrine treatment. It should be noted that AdjuvantOnline (http://www.adjuvantonline.com) and the Nothingham Prognostic Index20 provide rather accurate risk assessment. In this respect, we have performed a preliminary analysis. We found that our signature adds to these prognostic parameters, especially with respect to specificity, but this topic will be the subject of a separate study.
In conclusion, the results of this European multicenter validation study strongly confirm the results of our initial study comprising only patients from Rotterdam. The proven strong reproducibility of the results indicates that the 76-gene signature can be recommended for future studies and potentially in daily clinical practice.
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Authors' Disclosures of Potential Conflicts of Interest
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Although all authors completed the disclosure declaration, the following authors or their immediate family members indicated a financial interest. No conflict exists for drugs or devices used in a study if they are not being evaluated as part of the investigation. For a detailed description of the disclosure categories, or for more information about ASCO’s conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.
| Authors |
Employment |
Leadership |
Consultant |
Stock |
Honoraria |
Research Funds |
Testimony |
Other |
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| John A. Foekens |
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Johnson & Johnson (B) |
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| David Atkins |
Veridex (N/R) |
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Johnson & Johnson (B) |
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| Yi Zhang |
Veridex (N/R) |
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Johnson & Johnson (A) |
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| Yixin Wang |
Veridex (N/R) |
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Johnson & Johnson (A) |
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Dollar Amount Codes (A) < $10,000 (B) $10,000-99,999 (C)  $100,000 (N/R) Not Required
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Author Contributions
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| Conception and design: John A. Foekens, David Atkins, Dmitri Talantov, Jan G.M. Klijn, Yixin Wang
Provision of study materials or patients: Fred C.G.J. Sweep, Nadia Harbeck, Angelo Paradiso, Tanja Cufer, Paul N. Span, Vivianne C.G. Tjan-Heijnen, Alfredo F. Zito, Katja Specht, Heinz Hoefler, Rastko Golouh, Francesco Schittulli, Manfred Schmitt, Louk V.A.M. Beex, Yixin Wang
Collection and assembly of data: John A. Foekens, Yi Zhang, Fred C.G.J. Sweep, Nadia Harbeck, Angelo Paradiso, Tanja Cufer, Anieta M. Sieuwerts, Dmitri Talantov, Paul N. Span, Vivianne C.G. Tjan-Heijnen, Alfredo F. Zito, Katja Specht, Heinz Hoefler, Rastko Golouh, Francesco Schittulli, Manfred Schmitt, Louk V.A.M. Beex, Jan G.M. Klijn, Yixin Wang
Data analysis and interpretation: John A. Foekens, David Atkins, Yi Zhang, Anieta M. Sieuwerts, Dmitri Talantov, Jan G.M. Klijn, Yixin Wang
Manuscript writing: John A. Foekens, David Atkins, Jan G.M. Klijn, Yixin Wang
Final approval of manuscript: John A. Foekens, David Atkins, Yi Zhang, Fred C.G.J. Sweep, Nadia Harbeck, Angelo Paradiso, Tanja Cufer, Anieta M. Sieuwerts, Dmitri Talantov, Paul N. Span, Vivianne C.G. Tjan-Heijnen, Alfredo F. Zito, Katja Specht, Heinz Hoefler, Rastko Golouh, Francesco Schittulli, Manfred Schmitt, Louk V.A.M. Beex, Jan G.M. Klijn, Yixin Wang
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GLOSSARY
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Cox proportional hazards regression model: The Cox proportional hazards regression model is a statistical model for regression analysis of censored survival data. It examines the relationship of censored survival distribution to one or more covariates. It produces a baseline survival curve, covariate coefficient estimates with their standard errors, risk ratios, 95% CIs, and significance levels.
Negative predictive value: The probability of a negative test result being truly negative.
Positive predictive value: The probability of a positive test result being truly positive.
Target intensity: A predefined average intensity signal that is used to scale and normalize the actual signals of all gene sequencing chips in an assay.
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ACKNOWLEDGMENTS
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We thank Fei Yang, Mieke Timmermans, Anita Mangia, Roberto Rodriguez-Garcia, Miranda Arnold, Anneke Goedheer, and Anita Trapman-Jansen for technical assistance, and Marion Meijer-van Gelder for handling of the clinical data.
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NOTES
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Supported in part by the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research.
Presented as an abstract at the San Antonio Breast Cancer Symposium, San Antonio, TX, December 8-11, 2005.
Terms in blue are defined in the glossary, found at the end of this article and online at www.jco.org.
Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.
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REFERENCES
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Submitted August 19, 2005;
accepted December 15, 2006.
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TOP
ABSTRACT
INTRODUCTION
