Molecular Classification of Tamoxifen-Resistant Breast Carcinomas by Gene Expression Profiling
To discover a set of markers predictive for the type of response to endocrine therapy with the antiestrogen tamoxifen using gene expression profiling.
The study was performed on 112 estrogen receptor–positive primary breast carcinomas from patients with advanced disease and clearly defined types of response (ie, 52 patients with objective response v 60 patients with progressive disease) from start of first-line treatment with tamoxifen. Main clinical end points are the effects of therapy on tumor size and time until tumor progression (progression-free survival [PFS]). RNA isolated from tumor samples was amplified and hybridized to 18,000 human cDNA microarrays.
Using a training set of 46 breast tumors, 81 genes were found to be differentially expressed (P ≤ .05) between tamoxifen-responsive and -resistant tumors. These genes were involved in estrogen action, apoptosis, extracellular matrix formation, and immune response. From the 81 genes, a predictive signature of 44 genes was extracted and validated on an independent set of 66 tumors. This 44-gene signature is significantly superior (odds ratio, 3.16; 95% CI, 1.10 to 9.11; P = .03) to traditional predictive factors in univariate analysis and also significantly related with a longer PFS in univariate (hazard ratio, 0.54; 95% CI, 0.31 to 0.94; P = .03) as well as in multivariate analyses (P = .03).
Our data show that gene expression profiling can be used to discriminate between breast cancer patients with progressive disease and objective response to tamoxifen. Additional studies are needed to confirm if the predictive signature might allow identification of individual patients who could benefit from other (adjuvant) endocrine therapies.
Resistance to antiestrogens is one of the major challenges in the treatment of breast cancer. For more than 25 years, the golden standard for the endocrine treatment of all stages of estrogen receptor (ER) –positive breast cancer has been tamoxifen.1,2 However, in the advanced setting, approximately half of the patients with ER-α–positive breast tumors will not respond to endocrine treatment, whereas the response rates in patients with ER-α–negative primary tumors are low. Therefore, additional biomarkers are required that can identify patients who will not respond and that can select patients for various tailored treatments.
In the last 20 years, a large number of cell biologic factors, other than steroid receptors, have been reported that identify those patients who will benefit from endocrine therapy or fail to respond (for review, see Klijn et al3). Few of these, however, seemed valuable and useful in daily clinical practice. Furthermore, in these individual studies only a limited number of factors have been evaluated simultaneously.
Breast cancer is known as a heterogeneous and multifactorial disease, with accumulation of (epi)genetic alterations leading to transformation of normal cells into cancer cells. With the advent of high-throughput quantification of gene expression, simultaneous assessment of thousands of genes is now possible in a single experiment.4,5 Gene expression profiling may become a general future strategy for predicting clinical outcome. Indeed, recent gene profiling studies on breast cancer show that the molecular classification of tumors based on gene expression can identify clinically significant subtypes of cancer6,7 and subgroups of patients with different prognosis or disease outcome,8-10 and can also predict therapeutic response to some chemotoxic agents.11
Using high-throughput gene expression profiling of primary breast tumors, we aim to assess a set of new markers that is predictive for the type of response to endocrine therapy with the antiestrogen tamoxifen in recurrent disease. The main clinical end points of this study are the measurable effect of this endocrine therapy on tumor size and on the time until tumor progression (progression-free survival [PFS]). The study was performed on 112 ER-positive primary breast cancer samples from patients who developed advanced disease that showed the most pronounced types of response (objective response [OR] v progressive disease [PD] from the start of treatment). In addition, this study may also identify some of the underlying gene (signaling) pathways, which could lead to novel targets for therapeutic intervention.
Our study design was approved by the medical ethical committee of the Erasmus MC Rotterdam, the Netherlands (MEC 02.953). To evaluate the predictive value of gene expression profiling in relation to tamoxifen treatment in patients with recurrent breast cancer, 112 fresh frozen ER-α–positive (≥ 10 fmol/mg of protein) primary breast tumor tissue specimens of patients with primary operable (invasive) breast cancer diagnosed between 1981 and 1992 were included. The median age at time of primary surgery (breast-conserving lumpectomy, 33 patients; modified mastectomy, 79 patients) was 60 years (range, 32 to 89 years). In this retrospective study all patients were selected for development of disease recurrence (14 with local or regional relapse, 86 with distant metastasis) that was treated with tamoxifen (40 mg daily) as first-line treatment. At the start of tamoxifen treatment, the median age was 63 years (range, 33 to 90 years), and 27 patients (24%) were premenopausal. None of the patients had received endocrine (neo)adjuvant systemic therapy, nor were they exposed to any hormonal treatment at an earlier stage.
Eighteen patients (16%) received adjuvant chemotherapy. Of these patients, seven were postmenopausal and 11 were premenopausal at time of surgery. At the start of tamoxifen monotherapy, eight patients were still premenopausal, whereas three patients changed to the postmenopausal status before recurrence. Two of these three patients showed objective response to tamoxifen. Therefore, chemical castration as prior endocrine therapy could not have had a significant impact on our results. The median follow-up of living patients was 94 months (range, 21 to 165 months) from primary surgery, and 53 months (range, 2 to 131 months) from the start of tamoxifen treatment. Tumor progression after tamoxifen occurred in 103 (92%) of the patients. During follow-up, 94 patients (84%) died. After tumor progression during first-line tamoxifen treatment, 69 patients were treated with one or more additional endocrine agents, whereas 64 patients were subsequently treated with one or more regimens of chemotherapy (mainly cyclophosphamide, methotrexate, and fluorouracil, or fluorouracil, doxorubicin, and cyclophosphamide) after the occurrence of hormonal resistance.
Criteria for follow-up, type of response, and response to therapy were defined by standard International Union Against Cancer criteria of tumor response,12 and PFS results were described previously by us.13 Complete and partial responses were observed in 12 and 40 patients, respectively, resulting in 52 patients with an OR; PD within 3 to 6 months from start of treatment was observed in 60 patients. Median PFS time of OR was 17 months, whereas the median PFS time of patients with PD was 3 months.
Total RNA was isolated from 30-μm frozen sections (approximately 20 to 50 mg tumor tissue) with RNABee (Campro Scientific, Veenendaal, the Netherlands). The percentage of tumor cells was determined in two frozen 5-μm sections stained with hematoxylin and eosin, which were cut before and after sectioning for RNA isolation. The tumor samples had a median tumor content of 65%. A T7dT oligo primer was used to synthesize double-stranded cDNA from 3 μg total RNA and to generate antisense RNA by in vitro transcription with T7 RNA polymerase (T7 MEGAscript High-Yield Transcription kit; Ambion Ltd, Huntingdon, United Kingdom). Two micrograms of aRNA was labeled with Cy3 or Cy5 (CyDye, Amersham Biosciences, Roosendaal, the Netherlands) in a reverse transcription reaction. The labeled cDNA probes were purified using Qiagen polymerase chain reaction (PCR) cleanup columns (Westburg BV, Leusden, the Netherlands). Similar to the Stanford protocol, a cell line pool of 13 cell lines derived from different tissue origins was used as reference for all microarray hybridizations (details are available at MIAMExpress [www.ebi.ac.uk/miamexpress/]). Probes of the cell line pool were always labeled with Cy5.
Total RNA isolated for the microarray analysis was used to verify the quantity of specific messengers by real-time PCR. The RNA was reverse transcribed and real-time PCR products were generated in 35 cycles from 15 ng cDNA in an ABI Prism 7700 apparatus (Applied Biosystems, Foster City, CA) in a mixture containing SYBR-green (Applied Biosystems) and 330 nmol/L primers for differentially expressed genes (ie, CASP2, DLX2, EZH1, CHD6, MST4, RABEP, SIAH2, and TNC). SYBR-green fluorescent signals were used to generate cycle threshold values from which mRNA ratios were calculated when normalized against the average of three housekeeping genes (ie, hypoxanthine-guanine phospho-ribosyltransferase [HPRT], porphobilinogen deaminase [PBGD], and beta2-microglobulin [B2M]).14
Microarray slides were manufactured at the Central Microarray Facility at the Netherlands Cancer Institute (NKI).15 Sequence-verified clones obtained from Research Genetics (Huntsville, AL) were spotted with a complexity of 19,200 spots per glass slide using the Microgrid II arrayer (Biorobotic, Cambridge, United Kingdom) The geneID list can be found at http://microarrays.nki.nl. Labeled cDNA probes were heated at 95°C for 2 minutes and added to preheated hybridization buffer (Slide hyb buffer 1; Ambion, Huntingdon, UK). The probe mixture was hybridized to cDNA microarrays for 16 hours at 45°C.
Fluorescent images of microarrays were obtained by using the GeneTAC LS II microarray scanner (Genomic Solutions; Perkin-Elmer, Milan, Italy). IMAGENE v5.5 (Biodiscovery, Marina Del Rey, CA) was used to quantify and correct Cy3 and Cy5 intensities for background noise. Spot quality was assessed with the flagging tool of IMAGENE, which in this study was set at R more than 2 for both Cy3 and Cy5. Fluorescent intensities of each microarray were normalized per subgrid using the NKI MicroArray Normalization Tools (http://dexter.nki.nl) to adjust for a variety of biases that affect intensity measurements (eg, color, print tips, local background bias).16 All ratios were log 2 transformed.
Microarray data analyses were performed with the software packages BRB Array Tools, developed by the Biometric Research Branch of the US National Cancer Institute (http://linus.nci.nih.gov/BRB-ArrayTools.html) and Spotfire. BRB was implemented for statistical analysis of microarray data, whereas Spotfire was used for cluster analysis. The class comparison tool in BRB combines a univariate F test and permutation test (n = 2,000) to find discriminating genes and to confirm their statistical significance. In the class comparison a significance level of .05 was chosen to limit the number of false-negative results.
Spotfire was used to perform hierarchical clustering. To analyze microarray data from different batches of slides, genes were Z score normalized per batch. The Z score was defined as (value − mean)/standard deviation. After normalization, microarray data were clustered via complete linkage. The similarity measure for clustering was based on cosine correlation and average value.
Sensitivity, specificity, positive predictive value, negative predictive value, and odds ratios were calculated and presented with their 95% CIs. The performance of the signature in the validation set was determined via the likelihood ratio of the χ2 test. A supervised-learning approach has been applied to reduce our 81 differentially expressed genes to a smaller 44-gene predictive signature. First, all 81 genes were rank ordered on the basis of their significance as calculated with the BRB class comparison tool. Next, starting with the most significant gene, the Pearson correlation coefficient of expression with the other 80 genes was calculated. Succeeding genes were excluded from the signature as long as their expression correlated significantly (P < .05) with the most significant gene. The first gene of the 81-gene profile that did not correlate with expression of the most significant gene was added to the final signature, and the whole procedure of expression correlation analysis with this second gene was repeated with the remaining less significant genes. In this way, genes with overlap in their expression would be removed and the 44-gene predictive signature was derived.
The predictive score for the traditional factors–based model included menopausal status, disease-free interval (DFI > 12 v ≤ 12 months after primary surgery), dominant site of relapse (relapse to viscera or bone v relapse to soft tissue), log ER and log progesterone receptor (PgR) levels. In the analyses of PFS, the Cox proportional hazards model was used to calculate the hazard ratios and their 95% CIs. Survival curves were generated using the method of Kaplan and Meier, and a log-rank test for trend was used to test for differences. Correlation between microarray data and real-time PCR data was determined with Spearman rank correlation test. Computations were performed with the STATA statistical package, release 7.0 (STATA Corp, College Station, TX). All P values are two sided.
For the validation of our 44-gene signature, a classification algorithm (gene prediction tool) was developed that is comparable to the compound covariate predictor from BRB Array Tools. The gene prediction tool applies two cutoff values instead of the midpoint used in the compound covariate predictor tool for classification. The two thresholds are the median values of PD and OR, and are defined in the tumors of our training set. To obtain a robust classification algorithm, genes from the signature become only classifiers whenever the expression values are outside the two thresholds and as a result mainly represent one class, either PD or OR. When the expression level falls between the cutoff values, the gene is excluded as a classifier because the value can represent both response classes (ie, PD and OR). The gene classifiers from the predictive 44-gene signature are identified for each tumor from the validation set using the above-described algorithm. Finally, the ratio between the identified response-predicting genes and resistance-predicting genes determines the predicted signature-based response outcome.
To select discriminatory genes for the type of response to tamoxifen, a training set of 46 tumors was defined that comprised primary tumors of 25 patients with PD and tumors of 21 patients with OR (Fig 1). The tumor RNAs of this training set were hybridized in duplicate, and genes or expression sequence tags that had more than 10% absent calls over the experiments were eliminated. This resulted in 8,555 and 7,087 assessable spots, respectively. Using a significance level of .05 in the BRB class comparison tool, 569 and 449 genes, respectively, were differentially expressed between the PD and OR subsets. The overlap (ie, 81 genes) was designated as the differentially expressed signature. After supervised hierarchical clustering (Fig 2), this discriminatory signature correctly classified 21 of 25 patients with PD (84% sensitivity; 95% CI, 0.63 to 0.95) and 19 of 21 patients with OR (91% specificity; 95% CI, 0.68 to 0.98), with an odds ratio of 49.8 (P < .0001). The positive predictive value and negative predictive value for resistance to tamoxifen was 91% and 83%, respectively.
Additional analysis (rank ordering of genes on the basis of significance level followed by a step-up calculation of correlation coefficient of expression) reduced the initial set of 81 genes to a smaller 44-gene predictive signature with similar accuracy.
In a validation set of 66 tumors, the predictive 44-gene signature correctly classified 27 of 35 patients with PD (77% sensitivity; 95% CI, 0.59 to 0.89) and 15 of 31 patients with OR (48% specificity; 95% CI, 0.31 to 0.67) with an odds ratio of 3.16 (95% CI, 1.10 to 9.11; P = .03). In univariate analysis for response, the predictive signature seemed to be superior (ie, more than two-fold higher odds ratio) to most traditional factors (ie, menopausal status, DFI, first dominant site of relapse, and ER and PgR status), of which only ER level (odds ratio, 1.54; 95% CI, 1.00 to 2.40; P = .05) and PgR level (odds ratio, 1.37; 95% CI, 1.05 to 1.79; P = .02) showed significant predictive value. In multivariate analysis for response, the signature-based classification did not significantly (increase in χ2 = 1.45) add to the traditional factors–based score (data not shown).
In addition, in univariate analysis (Table 1), only the 44-gene signature (hazard ratio, 0.54; 95% CI, 0.31 to 0.94; P = .03) and PgR level (hazard ratio, 0.83; 95% CI, 0.73 to 0.96; P = .01) were significantly related with a longer time to tumor progression, and this was retained for the signature in the multivariable analysis (hazard ratio, 0.48; 95% CI, 0.26 to 0.91; P = .03). PgR is also independent, but with a less striking hazard ratio (0.82; 95% CI, 0.71 to 0.94; P = .01). After addition of the signature-based classification to the traditional factors–based score, the increase in χ2 was 5.18 (df = 1; P = .02), indicating that the predictive signature independently contributed to the traditional predictive factors for PFS.
In Kaplan-Meier analyses, the median difference in PFS time for patients with a favorable and poor response was two-fold longer when the 44-gene signature (Fig 3C) was used in comparison with the traditional factors–based score without (Fig 3A) and with PgR (ie, 11 v 5 months; Fig 3B).
The mRNA expression levels of eight signature genes were also measured by quantitative real-time PCR (ie, CASP2, DLX2, USP9X, CHD6, MST4, RABEP, SIAH2, and TNC). The quantitative PCR data were correlated with the microarray data. Spearman rank correlations were positive for seven of eight genes.
The 81-gene signature consisted of 15 ESTs and 66 known genes (Fig 2). Functional annotation of the genes in our signature showed that they were involved in estrogen action (26%), apoptosis (14%), extracellular matrix formation (9%), and immune response (6%). The remaining genes had a function in glycolysis, transcription regulation, and protease inhibition.
Seventeen genes were regulated by or associated with estrogen (receptor) action, of which nine genes showed upregulation and eight genes showed downregulation in tamoxifen-resistant tumors (Fig 2). Clustering of the signature revealed a cluster of six genes that was associated with the extracellular matrix (ECM; TIMP3, FN1, LOX, COL1A1, SPARC, and TNC), which were overexpressed in patients with resistant disease. In an exploratory analysis to search for associations between genes, using Ingenuity Pathway Analysis tools, seven genes of the signature (IL4R, LDHA, MAP2K4, NPM1, SIAH2, CASP2, and TXN2) were associated with apoptosis, whereas two other signature genes (API5, BNIP3) were related to antiapoptosis processes. Interestingly, four apoptosis genes (API5, NPM1, LDHA, and BNIP3) were upregulated and five genes (IL4R, MAP2K4, SIAH2, CASP2, and TXN2) were downregulated in tumors with resistant disease. A cluster of four genes linked to the immune system (FCGRT, PSME1, HLA-C, and NFATC3) was downregulated in PD.
Notably, the 81-gene signature showed an over-representation of genes located on chromosome 17, but an under-representation of genes located on chromosomes 4, 15, 18, and 21 (Fig 4). In particular, genes localized to cytoband 17q21 to 17q22 seemed to be significantly (P = .03) over-represented (ie, five of 66 informative genes [APPBP2, COL1A1, EZH1, KIAA0563, and FMNL]) in the signature (6.5%) compared with 199 of 12,771 known genes (1.5%) for the whole microarray.
This study shows that expression array technology can effectively and reproducibly classify tumors according to resistance and sensitivity to tamoxifen treatment. We describe a 44-gene signature that predicts antiestrogen therapy outcome in 112 breast cancer patients with ER-positive recurrent disease. Overall, a prediction of tamoxifen resistance was accomplished with an accuracy of 80%. Moreover, the 44-gene signature predicted a significantly longer PFS time, which is superior to the traditional factors–based score. Differences in RNA expression were confirmed by quantitative real-time PCR. In fact, the predictive value of our 44-gene signature compares favorably with and contributes independently to that of traditional prognostic factors, including the ER, which currently is the validated factor for response prediction to hormonal therapy in breast cancer. The ER, which is present in approximately 70% to 75% of breast cancers, correctly predicts response to tamoxifen in approximately 50% to 60% of the patients,2 whereas the gene signature predicts resistance to tamoxifen in 77% of the patients in the validation set. The estrogen-regulated PgR also showed an independent predictive value. We observed that integration of many genes in several pathways would lead to antiestrogen resistance and sensitivity. The observation that patterns of gene expression will be predictive is in concordance with recently published data on breast cancer prognosis and prediction of response to taxanes, for example.6,9,11
We currently have no information on association between signature and stable disease outcomes; however, the present 44-gene signature, owing to its significant association with time to treatment failure, may be able to categorize these patients with stable disease based on time to treatment failure.
The patterns of expression of many genes that are associated with tamoxifen resistance and sensitivity are highly complex. Our 81 differentially expressed genes includes, as expected, genes regulated by or associated with estrogen (receptor) action,8,17-21 but also genes involved in extracellular matrix formation and apoptosis. Several of these estrogen-regulated or -coregulated genes (LDHA, TXN2, and SIAH2) have been linked to apoptosis. In addition to cytostatic effects, tamoxifen is known to have cytolytic effects by induction of apoptosis, as reviewed by Mandlekar and Kong.22 Nine genes in our 81-gene signature are related to programmed cell death, of which three genes inhibit apoptosis (API5, NPM1, and TXN2), whereas three other genes induce apoptosis (CASP2, MAP2K4, and SIAH2). Interestingly, the two latter genes (MAP2K4 and SIAH2) induce the apoptotic machinery of fibroblasts. In general, our expression patterns indicate that tamoxifen resistance is mainly associated with inhibition of apoptosis.
None of the ECM genes in our signature is found in the 70-gene classifier for poor prognosis of node-negative breast cancer patients by van‘t Veer et al,8 indicating that our ECM gene cluster may be specific for the prediction of tamoxifen resistance. Furthermore, SPARC (osteonectin), a myoepithelial cell marker that is regulated or coregulated by estrogen, was recently described as an independent marker of poor prognosis in unselected breast cancers.23-25 In addition, a newly discovered cluster of genes linked to the immune system (FCGRT, PSME1, HLA-C, and NFATC3) was downregulated in the patients with PD compared with those with OR. Interestingly, van‘t Veer et al8 also observed a coregulation of one of these genes (PSME1) with the ER, which demonstrated a significant correlation with prognosis. In general, the arrays used in the different studies comprise different genes or ESTs, and of these arrays, approximately half of the genes show overlap. This could result in few overlapping genes in the generated gene-signatures. Therefore, comparison of pathways based on the extracted gene signatures from different studies could be more informative.
At present, none of these differentially expressed genes that are regulated by or associated with estrogen (receptor) action have been directly linked with endocrine resistance in clinical samples. Our present novel information may lead to a better understanding of endocrine resistance and possibly to novel targets for more individualized treatment.
Genomic Health (Redwood City, CA) recently developed the Oncotype DX diagnostic assay based on a candidate gene selection (not genome wide) approach. This test provides a recurrence score for node-negative breast cancer patients with ER-positive tumors who have received adjuvant tamoxifen.26 The multiplex 21-gene test includes genes associated with proliferation, estrogen, and HER2 action, invasion, and five control genes. None of the genes, however, overlaps with our 81-gene signature that we have selected through microarray-based gene expression profiling.
Recently, using microarray analyses, Sgroi et al27 also analyzed tumors from patients receiving adjuvant tamoxifen therapy. They extracted a two-gene ratio that predicts “a tumor's response to tamoxifen or its intrinsic aggressiveness, or both.” Interestingly Sgroi et al27 showed that HOXB13, located to 17q21, was overexpressed in tamoxifen-resistant patients with recurrence after adjuvant tamoxifen. In our 81-gene signature, we observed five genes located on chromosome 17q21 to 17q22, which could be of importance for tamoxifen resistance. In this region, our signature gene COL1A1 was discriminative and highly expressed in our signature. Moreover, HOXB13, like COL1A1, is not positioned in the 17q21 HER2/ERBB2 amplicon,28 but in the second of three regions (ie, 17q12 HER2, 17q21.2 HOXB2-7, 17q23 PPM1D) highly amplified in breast cancer. This suggests that genes other than those of the ERBB2 amplicon region, such as HOXB13 and COL1A1, could be important for resistance to tamoxifen and could be possible therapeutic targets.
The expression of the other four signature genes located on chromosome 17q does not correlate with the ERBB2 expression, given that they (EZH1, FMNL, KIAA0563, and APPBP2) were downregulated in the tamoxifen resistant tumors. Interestingly, this region has been implicated in loss of heterozygosity (LOH) in 30% of breast cancer cases.29 Only recently, JUP (plakoglobin, gamma-catenin) was identified as an LOH candidate,30 whereas LOH of BRCA1 is observed frequently in high-grade tumors.31 Our signature gene (EZH1) that is located between JUP and BRCA1 might, therefore, be another LOH candidate gene.
Numerous reports have described that ERBB2 amplification and overexpression in ER-positive patients is associated with a reduction in response rate to first-line hormone therapy.32-34 Given that the expression patterns of the five signature genes on 17q21 to 17q22 are not significantly correlated with ERBB2 expression in this array study, this indicates that another, yet unknown, mechanism may be activated. This will be a subject for future studies.
In conclusion, three major findings are reported in this pilot study. First, we have developed a 44-gene signature that predicts antiestrogen therapy resistance and time to progression in ER-positive breast cancer patients with recurrent disease, respectively. Second, this gene signature has a significantly better performance than the commonly used traditional clinical predictive factors in uni- and multivariate analyses. Third, in contrast to the traditional factors, that is site of relapse and DFI, the prediction of response can be derived from the gene expression profile of the primary tumors.
The aim of various endocrine therapies, including the recently used aromatase inhibitors, is to withdraw estrogens from the ER. However, aromatase inhibitors,35 tamoxifen, other selective ER modulators, and progestins showed different antitumor efficacy. Future studies will determine whether the proposed profile is able to predict failure of treatment with other endocrine agents. Moreover, it will be important to discover whether the signature can also be applied for early-stage patients who are eligible to receive adjuvant endocrine treatment. Possible clinical implications for patients predicted to have a poor response to tamoxifen therapy are that these patients should be candidates for other treatments or novel therapies, on the basis of different targets present in their tumor profiles. This will reduce the use of ineffective treatments.
The authors indicated no potential conflicts of interest.
|Factor||Univariate (n = 66)||Multivariate (n = 66)|
|HR||95% CI||P||HR||95% CI||P|
|Menopausal status*||1.07||0.57 to 2.00||.83||1.16||0.58 to 2.33||.67|
|Dominant site of relapse|
|Bone to soft tissue||1.56||0.70 to 3.47||.28||1.80||0.76 to 4.26||.19|
|Viscera to soft tissue||1.26||0.47 to 2.79||.57||1.42||0.60 to 3.34||.42|
|DFI†||0.92||0.53 to 1.57||.75||1.08||0.61 to 1.90||.80|
|Log ER||0.83||0.66 to 1.06||.13||0.88||0.68 to 1.14||.33|
|Log PgR||0.83||0.73 to 0.96||.01||0.82||0.71 to 0.94||.01|
|44-gene signature‡||0.54||0.31 to 0.94||.03||0.48||0.26 to 0.91||.03|
Abbreviations: PFS, progression-free survival; HR, hazard ratio; DFI, disease-free interval; ER, estrogen receptor; PR, progesterone receptor.
*Post- v premenopausal.
†> 12 v ≤ 12 months.
‡Sensitive v resistant.
Supported by grant DDHK 2364 of the Dutch Cancer Society, Amsterdam, the Netherlands, and in part by the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research.
Presented in part at the San Antonio Breast Cancer Conference (oral presentation), San Antonio, TX, December 5, 2003; and at the American Association for Cancer Research 2004 (poster presentation), Orlando, FL, March 29, 2004.
Authors' disclosures of potential conflicts of interest are found at the end of this article.
We thank Guido Jenster, Antoine van Veldhoven, and Dennis van der Vlies for their contributions to the article and for stimulating discussions. We gratefully thank the surgeons, pathologists, and internists of the St Clara Hospital, Ikazia Hospital, St Franciscus Gasthuis at Rotterdam, and Ruwaard van Putten Hospital at Spijkenisse for the supply of tumor tissues and/or for their assistance in the collection of the clinical follow-up data. Analyses were performed using BRB ArrayTools developed by Richard Simon and Amy Peng.
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