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Originally published as JCO Early Release 10.1200/JCO.2005.03.2755 on February 27 2006 © 2006 American Society of Clinical Oncology. Estrogen-Regulated Genes Predict Survival in Hormone Receptor–Positive Breast Cancers
From the Departments of Genetics, Medicine, and the Pathology & Laboratory Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC Address reprint requests to Charles M. Perou, PhD, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Campus Box 7295, Chapel Hill, NC 27599; e-mail: cperou{at}med.unc.edu
PURPOSE: The prognosis of a patient with estrogen receptor (ER) and/or progesterone receptor (PR) –positive breast cancer can be highly variable. Therefore, we developed a gene expression–based outcome predictor for ER+ and/or PR+ (ie, luminal) breast cancer patients using biologic differences among these tumors. MATERIALS AND METHODS: The ER+ MCF-7 breast cancer cell line was treated with 17ß-estradiol to identify estrogen-regulated genes. These genes were used to develop an outcome predictor on a training set of 65 luminal epithelial primary breast carcinomas. The outcome predictor was then validated on three independent published data sets. RESULTS: The estrogen-induced gene set identified in MCF-7 cells was used to hierarchically cluster a 65 tumor training set into two groups, which showed significant differences in survival (P = .0004). Supervised analyses identified 822 genes that optimally defined these two groups, with the poor-prognosis group IIE showing high expression of cell proliferation and antiapoptosis genes. The good prognosis group IE showed high expression of estrogen- and GATA3-regulated genes. Mean expression profiles (ie, centroids) created for each group were applied to ER+ and/or PR+ tumors from three published data sets. For all data sets, Kaplan-Meier survival analyses showed significant differences in relapse-free and overall survival between group IE and IIE tumors. Multivariate Cox analysis of the largest test data set showed that this predictor added significant prognostic information independent of standard clinical predictors and other gene expression–based predictors. CONCLUSION: This study provides new biologic information concerning differences within hormone receptor–positive breast cancers and a means of predicting long-term outcomes in tamoxifen-treated patients.
Breast cancers are traditionally stratified into hormone receptor–positive and –negative groups to guide patient management. This is because almost all hormone antagonist (ie, tamoxifen) -responsive breast cancers are estrogen receptor (ER) and/or progesterone receptor (PR) -positive.1 However, a subgroup of these patients experiences disease relapse irrespective of standard therapy with tamoxifen.2-4 In many cases, the resistance mechanisms are unknown.5-7 A method for identifying those ER+ and/or PR+ patients who do not experience relapse in the presence of tamoxifen versus those who do would be of significant value. Gene expression profiling is a powerful method for breast cancer prognostication. With gene expression profiling, breast tumors can be classified into five molecular subtypes (basal-like, HER2+/ER-, normal breast–like, and luminal A and B) with significant differences in patient outcome.8,9 The two luminal subtypes are composed almost entirely of ER+ and/or PR+ tumors and are defined by the high expression of a gene set (luminal epithelial ER+ set) that includes ER and GATA3. Compared with luminal A tumors, luminal B tumors have poor outcomes despite being clinically ER+.8,9 To date, several laboratories have developed gene expression–based predictors for ER+ and/or PR+ patients. All used supervised analyses based on patient outcomes/tumor response.10-13 Here, we developed a gene expression–based predictor using an approach based solely on biologic characteristics of the tumors. Because of the importance of estrogen signaling in breast epithelial cell biology, we hypothesized that differential expression of estrogen-regulated genes would be useful in predicting outcome.
Cell Culture and Collection of mRNA MCF-7 cells were maintained as described previously.14 Cells were plated in 150-mm dishes and grown until 50% confluence. Media was changed and cells maintained for 48 hours in estrogen-free medium (phenol red-free RPMI-1640 with 10% charcoal-dextran-stripped fetal bovine serum) before treating for 2,4,8, or 24 hours with 10–6 M 17ß-estradiol (Sigma-Aldrich Inc, St Louis, MO). Cells were harvested, and mRNA isolated using a Micro-FastTrack kit (Invitrogen Corp, Carlsbad, CA). A reference mRNA sample was collected from cells maintained for 48 hours in estrogen-free medium (ie, estrogen-starved cells).
Microarray Experiments
Analysis of Microarray Data to Identify GATA3 and Estrogen-Regulated Genes GATA3-induced genes were identified by microarray experiments on 293T cells transfected with GATA3 gene constructs, as detailed in Usary et al.19 One-class SAM analysis (0.58% FDR) identified 407 genes that were induced in the GATA3 samples (as a single class) relative to empty vector controls.
Analysis of Primary Breast Tumor Data Using the Estrogen-Induced Gene Set The 383-gene MCF-7 estrogen-induced gene list was used to hierarchically cluster the 65 luminal tumors resulting in two groups, which we called groups I and II. We used a two-class, unpaired SAM analysis (with 1% FDR) to identify 822 genes (referred to as the estrogen-SAM list) that optimally differentiated group I versus group II tumors.20,21 The 65 luminal tumors were then clustered using the 822 genes, and two groups (groups IE and IIE) were evident. By matching UniGene (NCBI, http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unigene) identifiers, data for as many as possible of the 822 estrogen-SAM genes was obtained for ER+ and/or PR+ tumors (classified as provided in the primary publications) from three independent test data sets.8,9,12,22 The Ma et al,12 Sorlie et al,8,9 and Chang et al22 data sets consisted of 60, 90, and 250 ER+ and/or PR+ tumors, respectively. Ma et al tumors were uniformly treated with adjuvant tamoxifen alone. Sorlie et al tumors received adjuvant tamoxifen, with some also receiving neoadjuvant chemotherapy. Chang et al tumors were heterogeneously treated; 24 of the 250 tumors we used for this data set were discussed in earlier publications.23 To remove microarray platform/source systematic biases, we applied distance-weighted discrimination (DWD)24 to the training and test data sets. From the DWD standardized luminal tumor training data set, centroids were created consisting of the average expression of the 822 estrogen-SAM genes for groups IE and IIE. We then classified each ER+ and/or PR+ tumor in the test data sets as group IE or IIE according to each sample’s nearest centroid as determined by Spearman correlation.
Survival Analyses
Identification of Estrogen-Regulated Genes To identify estrogen-regulated genes, we used the ER+ human breast tumor-derived MCF-7 cell line as a model system. A one-class SAM supervised analysis16 with an FDR of 0.04% identified 383 induced and 574 repressed genes in microarray experiments on MCF-7 cells treated with 17ß-estradiol for 2, 4, 8, or 24 hours. Many genes identified were previously known to be ER-regulated including CCND1, PR, RERG, CTSD, and PDZK1.25-28 Using the program EASE (Expression Analysis Systematic Explorer; http://david.niaid.nih.gov/david/ease.htm),29 the gene ontology (GO) categories "sterol metabolism/biosynthesis," "ribosome biogenesis/assembly," and "cytoskeleton structural constituent" were over-represented relative to chance in the set of 383 estrogen-induced genes.
Estrogen and GATA3-Regulated Genes Are Present in the Luminal/ER+ Gene Cluster
Analysis of Luminal Tumors Using Estrogen-Induced Genes We hypothesized that expression differences of estrogen-induced genes may define clinically relevant subgroups within clinically defined ER+ and/or PR+ tumors. To test this hypothesis, we clustered the 65 tumors identified as luminal in Figure 1 (blue dendrogram branch) using the 383 MCF-7 estrogen-induced genes. Two main groups resulted. Group I had higher expression of XBP1, PR, and TFF, which are all known ER targets. Group II had higher expression of a cluster of estrogen-induced genes that included CTPS, E2F6, and FANCA. Kaplan-Meier survival analysis showed that group I patients had significantly better relapse-free survival (RFS) outcomes than group II (P = .0004). To further characterize the differences between group I and II tumors, we performed a supervised analysis (two-class SAM with 1% FDR) using the major dendrogram branch division to define the two supervising groups. This analysis identified 822 genes for which group I and II tumors showed significant differential expression. This gene set, the estrogen-SAM list, was then used to hierarchically cluster the 65 luminal tumors (Fig 2), which as expected, resulted in a very similar grouping of samples when compared with that using the 383 estrogen-induced genes. Kaplan-Meier analysis showed that using the estrogen-SAM list grouped the tumors into two groups (referred to as group IE and IIE) that predicted RFS (P = .019; Fig 3A).
Group IE tumors showed high expression of XBP1, FOXA1, PR, and many ribosomal genes (Fig 2B-E). According to EASE, the GO categories "transcriptional regulation," "DNA binding," and "extracellular" were over-represented relative to chance in group IE tumors. Group IIE tumors showed the high expression of a prominent proliferation signature34,35 including Ki-67, MYBL2, Survivin, STK6, and CCNB2 (Fig 2G); these first four genes plus CCNB1 form the basis for the proliferation portion of the Paik et al recurrence score predictor,13 which is a gene expression–based outcome predictor for ER+/node-negative, tamoxifen-treated patients. Recently, Dai et al10 performed a supervised analysis for genes that correlated with outcomes in patients with high ER expression relative to age, and identified this same proliferation signature as the main determinant for predicting patient outcomes; however, they identified few genes associated with good outcomes. Group IIE tumors also showed high expression of a cluster of MAGE-A genes (Fig 2F), which have been associated with an increased recurrence risk36 and poor tumor differentiation.37 Figure 2H shows that group IIE tumors have high expression of genes with functions in the interferon pathway and apoptosis, such as FLIP/CFLAR, which is an inhibitor of tumor necrosis factor receptor–mediated apoptosis.38 Several antiapoptosis genes including FLIP, AVEN, Survivin, and BCL2A1 showed high expression in group IIE, suggesting an impaired ability to undergo cell death. Recent reports have shown that high expression of FLIP39 or BCL2A140,41 can directly contribute to chemoresistance, suggesting that functional inhibition of these proteins may provide a therapeutic target for group IIE patients. According to EASE, the GO categories "cell cycle/mitosis," "antiapoptosis," and "MHC-I" were over-represented relative to chance in group IIE.
Group IE-IIE Classification Predicts Outcome in ER+ and/or PR+ Tumors
Multivariate Analysis
In multivariate analyses that included Chang et al’s22 wound-response signature and van’t Veer et al’s23 70-gene signature along with the clinical variables, the group IE-IIE classification continued to provide significant prognostic power independent of other variables in the model (RFS P = .014; OS P = .042; Table 2). The performance of the 70-gene and wound-response signatures in this multivariate analysis may be optimistically high because a subset of the patients in the Chang et al data set was used to train/optimize these two signatures; therefore, the ability of the group IE-IIE classification to show independent prognostic power in a model containing these two predictors indicates its usefulness in predicting outcomes.
Group IE-IIE Associations With Clinical and Biologic Parameters To examine the hypothesis that group IE may be more differentiated than group IIE tumors, we determined whether an association existed between this classification and histologic grade. Two-way contingency table analysis showed significant association between grade and group IE-IIE class (Appendix Table A2, online only), with grade 1 and 3 tumors more likely to be classified as group IE and IIE, respectively. Cramer’s V statistic, which measures the strength of association between two variables in a contingency table, indicated a substantial association (Cramer’s V > 0.36) between grade and group IE-IIE class for all data sets. For the Sorlie et al data set, p53 mutation data was available, and a two-way contingency table analysis showed a significant association between p53 status and group IE-IIE class, with group IIE more likely to be p53 mutant (P = .0019; Cramer’s V = 0.44).
Comparison of Group IE-IIE Classification to Luminal A/B classification
The search for markers that predict long-term outcomes in hormone receptor-positive tamoxifen-treated patients has been an intense area of study. Genomic analyses have contributed to this area, with the development of several predictive gene sets and assays based on the selection of genes that directly correlate with patient/tumor outcomes.10-13,23 We took a different approach and selected genes using no knowledge of outcomes, and instead selected genes on the basis of regulation by estrogen and their natural patterns of expression in primary tumors. The 822-gene estrogen-SAM list identified many genes that may help explain the outcome differences seen in ER+ and/or PR+ patients. Good-outcome group IE tumors tended to be more differentiated, and highly expressed a subset of estrogen- and GATA3-regulated genes. Conversely, poor-outcome group IIE tumors were more likely to be poorly differentiated. Association of the group IE-IIE profile with grade is expected because grade includes a measure of proliferation, which is an important determinant of outcomes in ER+ and/or PR+ patients.8,10,13,19 However, because the group IE-IIE distinction was significant in a multivariate analysis with grade included, this distinction adds prognostic information beyond what grade provides. We used three published data sets as test sets and confirmed that the Group IE-IIE classification was a significant predictor in ER+ and/or PR+ patients. We note, however, that the relapse rates differed between data sets, and that the Group IE tumors showed 7% to 40% relapse rates depending on the data set (Fig 3). This underscores the fact that relapse rates are dependent on the characteristics of the patient set used. For example, comparing relapse rates in the Chang et al22 data set to those observed in Paik et al13 may not be valid because the Paik et al data set comprised tamoxifen-treated, node-negative patients, whereas the majority of the Chang et al patients received no adjuvant therapy and many were node positive. However, the multivariate analysis we performed on the Chang et al data set indicated that our predictor had significant prognostic value independent of standard clinical factors and other gene expression–based predictors, and a hazard ratio of 2.90 for group IIE versus IE indicates that our predictor has potential clinical utility. By limiting the Chang et al data set to those patients who received adjuvant therapy and were stage I-II, we observed a relapse rate for group IE patients of 12% (P = .007) and significance for overall survival outcomes (data not shown). This indicates that given a patient population similar to Paik et al, our predictor’s "good group" can achieve outcomes similar to the Paik et al low-risk group. An important unanswered question is whether the group IE-IIE distinction predicts pure prognosis, responsiveness to endocrine therapy, or both. From analyses of patient subsets in the Chang et al data set, it is clear that the group IE-IIE distinction predicts outcome in ER+ and/or PR+ patient subsets either receiving or not receiving adjuvant hormone therapy (data not shown). Paik et al observed similar results for their predictor.13 This is not surprising because half (eight of 16) of the Paik et al genes were present in the estrogen-SAM gene set. However, an advantage of our analysis is that it provides additional biologic information (eg, antiapoptosis genes) that the Paik et al and other predictors did not. Paik et al’s finding that their predictor also predicts benefit of chemotherapy42 may also apply to ours. Thus, the most pressing questions remaining regarding the group IE-IIE classification are (1) whether group IE and IIE gain similar benefits from chemotherapy, and (2) because group IIE tumors do poorly in the presence of tamoxifen, might they do better if administered alternative endocrine therapies.
The authors indicated no potential conflicts of interest.
Centroid: Mean expression profile of a group of samples for a given set of genes.
Cramer's V statistic: This statistic provides a quantitative measure of the strength of association between the two variables in a contingency table (which cannot be obtained from the P value). Cramer's V values range from 0 to 1, with 0 indicating no relationship and 1 indicating perfect association. Traditionally, values between 0.36 and 0.49 indicate a substantial relationship, and values greater than 0.50 indicate a very strong relationship. The V statistic is a generalization of the more familiar DWD (distance-weighted discrimination): A method to identify and adjust systematic biases that are present within microarray datasets. These systematic biases are a result of many different experimental features including different microarray platforms and different production lots of microarrays. DWD is useful in merging/comparing two tumor microarray datasets completed on different microarray platforms. EASE (Expression Analysis Systematic Explorer): A computer program used for rapid biologic interpretation of gene lists that result from the analysis of microarray or other high-throughput genomic data. The main function of EASE is to perform theme discovery for a given list of genes. Gene ontology: Allows for annotating genes and their products with a limited set of attributes, with the three organizing principles being molecular function, biological process, and cellular component. The development of structured, controlled vocabularies (ontologies) that describe gene products in terms of these organizing principles in a species-independent manner is a constantly evolving process. Luminal epithelial tumors: Human breast tumors that are characterized by the expression of genes typically expressed in the cells that line the ducts of normal mammary glands; these genes include the estrogen receptor (ER), GATA3, X-box binding protein 1, FOXA1, and cytokeratins 8 and 18. SAM (significance analysis of microarrays): A statistical technique using established software that determines the significance in changes of gene expression seen in microarray analysis (eg, cDNA and oligonucleotide microarrays), which measures the expression of thousands of genes and identifies changes in expression between different biologic states. On the basis of changes in gene expression relative to the standard deviation of repeated measurements, SAM assigns a score to each gene. When scores are greater than an adjustable threshold, permutations of repeated measurements are used by SAM to estimate the percentage of such genes identified by chance, the false discovery rate (FDR). In addition, SAM correlates gene expression data to a wide range of clinical parameters, including treatment, diagnosis categories, and survival time. Supervised analysis: Analysis of gene expression profiling data in which external information such as survival status is used as a guide to select genes from microarray data.
Two-way contingency table: A tabular representation of categorical data, it shows the frequencies for particular combinations of values of two discrete variables X and Y. Each cell in the table represents a mutually exclusive combination of X-Y values. A
We thank Phil Bernard, MD, Juan Palazzo, MD, and Funmi Olopade, MD, for providing tumor materials and advice, and Lynda Sawyer and Amy Drobish for collection of patient-associated data.
Supported by funds from the National Cancer Institute (Bethesda, MD) Breast SPORE program to the University of North Carolina–Chapel Hill (Chapel Hill, NC; Grant No. P50-CA58223-09A1), National Institutes of Health Grant No. M01RR00046, and the National Institute of Environmental Health Sciences Grant No. U19-ES11391-03; and by University of North Carolina Lineberger Cancer Control Education Program Grant No. R25 CA57726 (M.A.T.). 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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Copyright © 2006 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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ABSTRACT
INTRODUCTION
statistic to non–2 x 2 contingency tables, and for 2 x 2 tables the V statistic is equal to the 
2-based P value can be calculated for a contingency table and is used to determine whether there is a statistically significant association between the two variables in the contingency table. A 2 x 2 contingency table refers to a table in which X and Y are binary variables. 
