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J Clin Oncol. 2010 September 20; 28(27): 4111–4119.
Published online 2010 August 9. doi:  10.1200/JCO.2010.28.4273
PMCID: PMC2953969

Genomic Index of Sensitivity to Endocrine Therapy for Breast Cancer



We hypothesize that measurement of gene expression related to estrogen receptor α (ER; gene name ESR1) within a breast cancer sample represents intrinsic tumoral sensitivity to adjuvant endocrine therapy.


A genomic index for sensitivity to endocrine therapy (SET) index was defined from genes coexpressed with ESR1 in 437 microarray profiles from newly diagnosed breast cancer, unrelated to treatment or outcome. The association of SET index and ESR1 levels with distant relapse risk was evaluated from microarrays of ER-positive breast cancer in two cohorts who received 5 years of tamoxifen alone as adjuvant endocrine therapy (n = 225 and 298, respectively), a cohort who received neoadjuvant chemotherapy followed by tamoxifen and/or aromatase inhibition (n = 122), and two cohorts who received no adjuvant systemic therapy (n = 208 and 133, respectively).


The SET index (165 genes) was significantly associated with distant relapse or death risk in both tamoxifen-treated cohorts (hazard ratio [HR] = 0.70, 95% CI, 0.56 to 0.88, P = .002; and HR = 0.76, 95% CI, 0.63 to 0.93, P = .007) and in the chemo-endocrine–treated cohort (HR = 0.19; 95% CI, 0.05 to 0.69, P = .011) independently from pathologic response to chemotherapy, but was not prognostic in two untreated cohorts. No distant relapse or death was observed after tamoxifen alone if node-negative and high SET or after chemo-endocrine therapy if intermediate or high SET.


The SET index of ER-related transcription predicted survival benefit from adjuvant endocrine therapy, not inherent prognosis. Prior chemotherapy seemed to enhance the efficacy of adjuvant endocrine therapy related to SET index.


Current challenges for adjuvant (postoperative) treatment of patients with hormone receptor–positive breast cancer include the ability to predict benefit from endocrine therapy independently from the natural history after appropriate locoregional treatment (prognosis), to predict sequential synergy from chemotherapy followed by endocrine therapy, and to optimize the selection, duration and sequence of endocrine treatments.14 These challenges are each conceptually related to the function of estrogen receptor α (ER; gene name ESR1) in a patient's breast cancer. ER activates transcription of numerous genes, directly by binding to estrogen response elements within the promoter regions of some gene and secondarily through transcription initiated by ER-dependent transcription factors and cross-talk between ER at the cell membrane and tyrosine kinase signaling pathways.511 Consequently, ER status is a principal determinant of overall gene transcription in breast cancers.6,12,13 We hypothesized that measurement of the level of ER-associated gene expression in a patient's tumor sample would represent the extent of ER transcriptional activity and thus its likely dependence on estrogen stimulation and consequently would predict the intrinsic sensitivity of that tumor to endocrine therapy. We proceeded to identify an ER-related transcriptional signature and derived an index from a large representative breast tumor cohort (unrelated to treatment or outcome), tested the relationship between this index and distant relapse risk in a different sample cohort from tamoxifen-treated patients, developed cutoff points to define three endocrine sensitivity classes, and then tested the predictive performance of these classes in a second independent sample cohort from tamoxifen-treated patients, in two sample cohorts from patients who did not receive any adjuvant systemic therapy, and in a cohort of patients who received neoadjuvant chemotherapy followed by adjuvant endocrine therapy (tamoxifen and/or aromatase inhibition).


Patients and Samples

This research was conducted with approval and waiver of consent from the Institutional Research Board (protocol LAB04-0093). Patient and sample characteristics are summarized in Tables 1 and and2,2, respectively.

Table 1.
Population Characteristics of the Validation Cohorts
Table 2.
Summary of Available Samples and the Total Number of Microarrays Analyzed

A discovery cohort was evaluated to identify genes with expression that is strongly related to ESR1 expression and to describe an index to measure their combined expression, termed the sensitivity to endocrine therapy (SET) index. This cohort consisted of 437 available Affymetrix U133A microarray profiles from patients at The University of Texas M. D. Anderson Cancer Center (M. D. Anderson) who participated in a research protocol to obtain fine-needle aspiration (FNA) of newly diagnosed invasive breast cancer of American Joint Committee on Cancer stage I to III breast cancer (52% ER positive, 22% HER2 positive).19,20

A first validation cohort was studied to test the concept that the SET index would be related to distant relapse-free survival (DRFS) after adjuvant systemic treatment with tamoxifen alone for 5 years and to then identify thresholds that define categories of the SET index (low, intermediate, or high SET). This consisted of frozen tumor tissue from 245 patients with ER-positive invasive breast cancer that were profiled at Institut Jules Bordet (JBI) using Affymetrix U133A or U133Plus2.0 gene expression microarrays (Table 1).14,15 We also evaluated potential inter-platform (U133A v U133Plus2.0) and inter-laboratory (JBI v M. D. Anderson) effects on SET index values using a 2 × 2 factorial study design in which residual cRNA from 17 cancers (representing the spectrum of SET index values) was profiled on both microarray platforms in both laboratories.

A second validation cohort was studied to independently assess the relationship between the predefined categories of the SET index and DRFS after adjuvant systemic treatment with tamoxifen alone for 5 years. This cohort consisted of frozen tumor tissue from 310 patients with ER-positive invasive breast cancer that were profiled at M. D. Anderson (n = 201) or JBI (n = 109) using only Affymetrix U133A gene expression microarrays.

Two different untreated cohorts were also studied to determine whether SET index represents the natural history of ER-positive breast cancer in patients who did not receive any systemic therapy. These consisted of gene expression data from Affymetrix U133A microarrays derived from frozen tumor samples from patients with node-negative, ER-positive breast cancer that were profiled at Veridex (Raritan, NJ; n = 209)16 or JBI (n = 134; Table 1).14,17

We also studied a chemo-endocrine cohort of 131 patients with ER-positive breast cancer and acceptable microarray quality (subset of the discovery cohort) who received uniform neoadjuvant chemotherapy with paclitaxel, fluorouracil, doxorubicin, and cyclophosphamide (T/FAC), of whom 122 (Table 1) subsequently received adjuvant endocrine therapy with tamoxifen (n = 40), an aromatase inhibitor (n = 53), or both in sequence (n = 29).18

Breast cancers were defined as ER positive if nuclear immunostaining was ≥ 10% tumor cells or Allred score was ≥ 3,21 or if enzyme immunoassay identified more than 10 femtomoles of ER/mg protein. The details of our methods for RNA purification and microarray hybridization have been reported previously.14,15,1720 Briefly, a single-round T7 amplification was used to generate biotin-labeled cRNA for hybridization to oligonucleotide microarrays (U133A GeneChip; Affymetrix, Santa Clara, CA). Raw intensity files (.CEL) from each microarray were processed using MAS5.0 (R/Bioconductor, to generate probe-level intensities and normalized to a median array intensity of 600, transformed to log2 values, and scaled by the expression levels of 1,322 breast cancer reference genes within each sample normalized to median values in a reference cohort.

Identification of ER-Related Genes

ER reporter genes were identified by their coexpression with ER gene (ESR1, probe set 205225_at) based on the Spearman's rank correlation coefficient in the discovery cohort (n = 437).22 The size of the ER gene signature was determined, accounting for sampling variability by bootstrap resampling, and pruned to remove probe sets that contained cross-hybridizing probes, mapped to multiple genomic locations, were strongly associated with proliferation, or exhibited significant stromal bias in a set of matched FNAs and core biopsy samples of breast cancer from 38 different patients. The final signature included 106 genes with positive and 59 genes with negative correlation with ESR1 (Data Supplement).

Statistical Analysis

DRFS was defined as the interval from breast surgery until diagnosis of distant metastasis or death from any cause.23 The dependence of the hazard rate of distant relapse on the continuous SET index was modeled by a smoothing spline approximation with 2 df. The 10-year DRFS was estimated through a Cox proportional hazards model using the spline approximation of the SET index as the only covariate. The baseline cumulative hazard rate was estimated from the Cox model based on the Nelson-Aalen estimator, and the predicted rate of distant relapse was then obtained from the Breslow-type estimator of the survival function. CIs of the survival estimate were calculated based on the Tsiatis variance estimates of the cumulative log hazards.24 Pathologic response to neoadjuvant chemotherapy was defined by the residual cancer burden (RCB).25

SET index values were classified as low, intermediate, or high based on cutoff values determined from the first validation cohort by fitting a Cox model of the trichotomous SET variable versus DRFS using different thresholds. Nontrivial thresholds that jointly maximized the log-profile likelihood surface for this model were selected as most informative cut points for predicting DRFS.26 The same thresholds were maintained for subsequent validation analyses. The independent prognostic value of the SET index was assessed in multivariate Cox regression analyses based on the likelihood ratio test. All statistical computations were performed in R (v. 2.8.1, R Development Core Team, Vienna, Austria, 2008).


Definition of SET Index

Details of the individual ER-related genes, components of the index, and reproducibility of the genomic measurements are presented in the Data Supplement. We developed an index of ESR1-associated transcription from the mean expression levels XP and XN of 106 positively and 59 negatively correlated signature genes in a given sample. An endocrine index, defined as EI = XN + 0.5 (XP − XN), is higher in ER-positive tumors because the mean expression level of the positively correlated genes (XP) is greater than that of the negatively correlated genes (XN). This can be simplified to EI = 0.5 (XN + XP). The EI was further scaled, first linearly to the range of 0 to 10, then through unconditional Box-Cox power transformation to normalize its distribution. The genomic index of sensitivity to endocrine therapy was then calculated as SET = max [0, 10 (EI −9.48)1.24]. SET index values from samples hybridized on U133Plus2 arrays were adjusted for platform effects before further analysis due to bias observed between different Affymetrix microarrays (Data Supplement).

Relationship With Distant Relapse After Adjuvant Tamoxifen Therapy

In the first validation cohort, we observed a significant association between the SET index (continuous) and the risk (hazard rate) for distant relapse or death (P = .003, Fig 1A), but no significant relationship for ESR1 expression (Fig 1B). The marginal (unadjusted) hazard ratio (HR) for the continuous SET index (as a linear term) was 0.70 (95% CI, 0.56 to 0.88; P = .002). The continuous SET index (HR = 0.65; 95% CI, 0.46 to 0.91; P = .013) and tumor size (T2-3 v T1; HR = 2.32; 95% CI, 1.03 to 5.24; P = .041) were independently predictive of DRFS after adjuvant tamoxifen treatment in multivariate Cox analysis adjusted for ESR1, age, nodal status, and grade (Data Supplement).

Fig 1.
Hazard rate for distant relapse or death in 225 estrogen receptor (ER) –positive tamoxifen-treated patients from first validation cohort as a function of the genomic sensitivity to endocrine therapy (SET) index (A) and log2-transformed ESR1 expression ...

Three Classes of Endocrine Sensitivity Defined by SET Index

Having validated the concept that higher SET index (as a continuous measure of ER-related transcription) is associated with improved DRFS after adjuvant tamoxifen, we sought to establish clinically useful categories. Two cut points (corresponding to index values 2.68 and 3.66) were chosen to maximize the association of the trichotomous SET index with distant relapse events or death that occurred within the first 8 years of follow-up (Fig 2A).

Fig 2.
Kaplan-Meier estimates of relapse-free survival in patients treated with adjuvant tamoxifen in (A) the first validation cohort, with follow-up censored at 8 years, to define the thresholds for sensitivity to endocrine therapy (SET) index categories, and ...

Second Validation of Association With DRFS After Adjuvant Tamoxifen Therapy

A significant association between the SET index (continuous) and the hazard rate for distant relapse or death within 10 years was confirmed in the second independent validation cohort of samples from tamoxifen-treated patients (P < .001; Fig 1C), and again there was no significant relationship for ESR1 expression (Fig 1D). The marginal (unadjusted) HR for DRFS of the continuous SET index (as a linear term) was 0.76 (95% CI, 0.63 to 0.93; P = .007). Furthermore, the previously defined category of high SET index in 24% of this cohort was associated with significantly improved DRFS, compared with intermediate or low SET categories (HR = 0.25, 95% CI, 0.10 to 0.63, P < .001; Fig 2B). The point estimates of DRFS for high, intermediate, and low SET index categories in this independent validation cohort at 5 years of follow-up were 94.1% (95% CI, 88.7% to 99.9%), 87.5% (95% CI, 80.5% to 95.1%), and 79.4% (95% CI, 72.9% to 86.5%), respectively, and point estimates at 10 years of follow-up were 92.1% (95% CI, 85.6% to 99.1%), 73.6% (95% CI, 63.5% to 85.3%), and 66.8% (95% CI, 58.8% to 76%), respectively.

Of note, the 10-year point estimate of DRFS in the high SET group was 100% (95% CI, 100% to 100%; no events) in patients with node-negative disease (Fig 2C) and 80% (95% CI, 65.5% to 97.4%) in patients with node-positive disease (Fig 2D). High SET index and node-negative status were independently predictive of DRFS in a multivariate Cox model that included age, tumor size, grade, and Allred score for ER (Table 3).

Table 3.
Multivariate Cox Regression Analysis of Association With DRFS

Prognosis Without Adjuvant Systemic Therapy

Neither the gene expression level of ER (ESR1) nor the SET index were associated with the 5-year DRFS in two different cohorts of patients with ER-positive, node-negative breast cancer who did not receive any adjuvant systemic therapy (Figs 3A and and33B).

Fig 3.
Kaplan-Meier estimates of relapse-free survival in (A, B) two cohorts of estrogen receptor (ER) –positive, node-negative patients who did not receive any prior hormonal therapy, and (C) in patients with clinically higher-risk ER-positive breast ...

Association With DRFS After Adjuvant Chemo-Endocrine Therapy

Patients with high or intermediate SET index had similar frequency of clinical node-positive status at presentation compared with low SET (12 of 22 v 68 of 100) and similar frequency of pathologic response from neoadjuvant chemotherapy compared with low SET (pathologic complete response in three of 22 v five of 100, pCR or RCB-I in six of 22 v 35 of 100; χ2 tests not significant). Despite this, point estimates of DRFS were 100% (95% CI, 100% to 100%) for high or intermediate, and 82.4% (95% CI, 75.1% to 90.4%) for low SET index categories at 5 years of follow-up (Fig 3C). Both the pathologic response from chemotherapy (RCB index) and the SET index of the tumor at the time of diagnosis were independently predictive of distant relapse risk, and their interaction term was also borderline significant (Table 3). Graphical plots to illustrate this interaction (Fig 3D; Data Supplement) demonstrate that elevated endocrine sensitivity (SET index) seems to be associated with reduced relapse risk when there is less than extensive RCB after chemotherapy and particularly when RCB is low.


This study demonstrates and confirms that SET index (a measure of transcriptional activity related to ER) is predictive of DRFS in tamoxifen-treated patients (Table 3, Figs 1 and and2)2) but is not prognostic in untreated patients (Figs 3A and and3B).3B). We acknowledge there were different age distributions for treated and untreated cohorts (Table 2) because sample collection included an era when the role of adjuvant tamoxifen therapy had not yet been defined for premenopausal women.27 Nevertheless, SET index can be used to estimate DRFS if a patient were to receive adjuvant endocrine therapy alone and in conjunction with other clinicopathologic information to determine whether or not additional treatment might be indicated to further improve the likelihood of cure. For example, patients with node-negative breast cancer and high SET index could reasonably select a standard adjuvant endocrine therapy alone, but others might benefit from additional treatment including chemotherapy or investigational treatments (Fig 2C).

Lymph node status was independently prognostic in the tamoxifen-treated patients (Table 3, Figs 2C and and22D).28 Therefore, it is important to consider whether chemotherapy should be encouraged for patients with node-positive and ER-positive breast cancer or whether a predictive test for endocrine sensitivity could identify node-positive patients with either excellent survival from endocrine therapy alone or for whom added chemotherapy is futile and novel therapies are needed. In two recent reports, patients with node-positive and ER-positive breast cancer had clinically significant (> 10%) risk of relapse for any 21-gene recurrence score class.29,30 In one study, low or intermediate recurrence score identified a subset for whom chemotherapy offered no significant benefit over tamoxifen alone, but recurrence score failed to identify any subset with excellent survival from either treatment arm.29 We note that SET index also failed to identify a node-positive subset with less than 20% risk of distant relapse from adjuvant tamoxifen alone (Fig 2D).

SET index did identify patients with high or intermediate SET index who had excellent survival with T/FAC chemotherapy followed by endocrine therapy (Fig 3C). The endocrine predictive utility of SET index was independent of pathologic response from chemotherapy (Table 3). Furthermore, it seems (Fig 3D) that elevated SET index was more strongly associated with reduced relapse risk if there had been some response to prior T/FAC chemotherapy. However, the prognosis of those with chemo-resistant disease (high RCB) remained poor, irrespective of endocrine sensitivity (SET). This supports our interpretation of SET index as an endocrine therapy predictor and also demonstrates that partial or better response to chemotherapy in a tumor with intrinsic endocrine sensitivity can facilitate further benefit from adjuvant endocrine therapy (sequential synergy).

The results of this study challenge a popular view that chemosensitive tumors and endocrine-sensitive tumors tend to be mutually exclusive within ER-positive breast cancer.4,31,32 This view may depend on how chemosensitivity and endocrine sensitivity are predicted. For example, genomic tests that result from empirical methods to train signatures on molecular class or on survival status at a specified time-point after diagnosis tend to rely heavily on measurement of proliferation as an essential prognostic and predictive component.20,29,3336) Proliferation has complex associations with outcome, being associated with poor prognosis and greater likelihood of pathologic response but also with higher risk of distant relapse after chemotherapy, and for neoadjuvant endocrine therapy is only predictive of benefit if suppressed after exposure to preoperative endocrine therapy (pharmacodynamic response).14,15,18,35,37 In contrast, SET index was conceptually derived to address a targeted transcriptional pathway, has less reliance on proliferation genes, and so is probably less subject to the mixed effects of prognosis, chemosensitivity and endocrine sensitivity that would be variably represented in a single composite result from other empirically derived signatures.

We recognize that efforts to directly compare the performance of SET index with other genomic signatures would be severely limited by technical biases. For example, although there was no association between a microarray-based approximation of recurrence score and relapse risk in the second validation cohort (Data Supplement), this score has not been validated against the commercial reverse transcriptase polymerase chain reaction assay.15,36 Furthermore, the presence of systematic bias in SET index values between two different versions of Affymetrix U133 microarrays that have identical oligonucleotide probe sequences (Data Supplement) is cautionary for meta-analyses of microarray data derived using different technical methods.

In this study, approximately 25% of patients with ER-positive node-negative breast cancer had high SET index values and excellent survival from 5 years of endocrine therapy alone. Another 30% of patients with intermediate SET index values might consider sequential chemo-endocrine therapy or prolonged and different endocrine therapy, and the remaining 25% to 50% with low SET index are best advised to consider chemo-endocrine therapy or a clinical trial. In addition, approximately 20% of patients with clinical stage II or III disease had high or intermediate SET index and excellent 5-year DRFS that was independent of their chemotherapy response, but attributable to sequential benefits from chemo-endocrine therapy. We expect that additional future studies that evaluate the predictive performance of SET index would further inform this clinical interpretation.

The clinical relevance of independent prediction of endocrine sensitivity is unlikely to be based on a single test result interpreted in isolation and should not dissuade the use of adjuvant endocrine therapy as a standard treatment for any eligible patient. Rather, SET index results would be better interpreted in the context of nodal status and combined with independent tests for prognosis and chemosensitivity. Reducing the complex puzzle of adjuvant treatment to its main components, with separate tests for each (prognosis and prediction of benefit from endocrine, chemotherapy, and targeted therapies), should become more effective than any single composite test result in realizing the potential of multiplex technologies to provide the next generation of diagnostic tools for personalized cancer treatment planning.35,38 In that context, SET index would be considered as one important piece of a diagnostic puzzle for personalized breast cancer treatment.

Supplementary Material

Data Supplement:


We thank the patients who participated in this research and the following collaborators for providing samples or data used in this work: Denis Larsimont, MD, and Martine Piccart, MD, at Institut Jules Bordet, Brussels, Belgium; Kurt Zatloukal, MD, at Medical University of Graz, Austria in part supported by the Austrian GENAU project; Elisabeth Müller-Holzner, MD, at Medical University of Innsbruck, Austria; Phillippe Viehl, MD, at The Histocytopathology Unit (Translational Research Laboratory) of Institut Gustave Roussy, Villejuif, France; and Feng Lin, MS, Bin Zeng, MS, and Rebekah Hubbard, BS (Breast Cancer Pharmacogenomics Laboratory), Yun Gong, MD, and Nour Sneige, MD (Cytopathology), and the Departments of Breast Imaging and Breast Medical Oncology at The University of Texas M. D. Anderson Cancer Center, Houston, TX.

Glossary Terms

Gene expression profile:
The expression of a set of genes in a biologic sample (eg, blood, tissue) using microarray, RT-PCR, or other technology capable of measuring gene expression.
The process that tests the performance of a previously defined classifier or prognostic model on a new set of patients. For example, a gene expression signature classifier developed using data from one set of patients might be validated on another, independent set of patients.
Recurrence score:
The Recurrence Score is a number between 0 and 100 that corresponds to a specific likelihood of breast cancer recurrence within 10 years of initial diagnosis. The score is derived from a mathematical function combining the expression values of 16 breast cancer–related genes and five reference genes.
RT-PCR (reverse-transcriptase polymerase chain reaction):
PCR is a method that allows logarithmic amplification of short DNA sequences within a longer, double-stranded DNA molecule. Gene expression can be measured after extraction of total RNA and preparation of cDNA by a reverse-transcription step. Thus, RT-PCR enables the detection of PCR products on a real-time basis, making it a sensitive technique for quantitating changes in gene expression.
Oligonucleotide arrays:
High-density arrays containing in situ synthesized antisense oligonucleotides (an average of 25 bases long) matching thousands of mRNA transcripts sequences.
SET (sensitivity to endocrine therapy) index:
A multigene expression profile that was developed to measure estrogen receptor–related transcription in breast cancer.
RCB (residual cancer burden):
An index to estimate the extent of residual invasive cancer in the breast and regional lymph nodes after neoadjuvant chemotherapy. RCB combines the following parameters derived from the review of routine pathology materials: two-dimensional extent of residual primary tumor, proportion of this primary tumor area that contains cancer cells, proportion of the residual primary cancer that is in situ, the number of involved regional lymph nodes, and the diameter of the largest nodal metastasis.


See accompanying editorial on page 4101

Supported by grants from the Commonwealth Foundation for Cancer Research to W.F.S., the National Cancer Institute (Grant No. 1-R21 CA118156-01A1) to W.F.S., the Breast Cancer Research Foundation to L.P. and W.F.S., the “Fonds National de la Recherche Scientifique” to C.S., the American Society of Clinical Oncology to F.A., the Austrian GENAU project, the McMichael Family Foundation to D.J.B., and the Nellie B. Connally Breast Cancer Research Fund at The University of Texas M. D. Anderson Cancer Center.

Terms in blue are defined in the glossary, found at the end of this article and online at

Data sets for the factorial study and tamoxifen-treated validation cohort are accessible via the GEO repository ( under accession IDs GSE17700 and GSE17705. Other datasets are linked to their original published reports.

Presented in preliminary format at the Breast Cancer Symposium, September 7-8, 2007, San Francisco, CA.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.


Although all authors completed the disclosure declaration, the following author(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a “U” are those for which no compensation was received; those relationships marked with a “C” were compensated. 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.

Employment or Leadership Position: Christos Hatzis, Nuvera Biosciences (C) Consultant or Advisory Role: W. Fraser Symmans, Nuvera Biosciences (U) Stock Ownership: W. Fraser Symmans, Nuvera Biosciences; Christos Hatzis, Nuvera Biosciences Honoraria: None Research Funding: None Expert Testimony: None Other Remuneration: None


Conception and design: W. Fraser Symmans, Christos Hatzis, Lajos Pusztai

Financial support: W. Fraser Symmans, Christos Sotiriou, Daniel J. Booser, Lajos Pusztai

Administrative support: W. Fraser Symmans, Christos Sotiriou, Fabrice Andre, Florentia Peintinger, Peter Regitnig, Christian Marth, Suzette Delaloge, Vicente Valero, Gabriel N. Hortobagyi

Provision of study materials or patients: Christos Sotiriou, Fabrice Andre, Florentia Peintinger, Peter Regitnig, Guenter Daxenbichler, Christine Desmedt, Julien Domont, Christian Marth, Suzette Delaloge

Collection and assembly of data: W. Fraser Symmans, Christos Sotiriou, Peter Regitnig, Guenter Daxenbichler, Christine Desmedt, Julien Domont, Thomas Bauernhofer

Data analysis and interpretation: W. Fraser Symmans, Christos Hatzis, Lajos Pusztai

Manuscript writing: W. Fraser Symmans, Christos Hatzis, Christos Sotiriou, Fabrice Andre, Florentia Peintinger, Peter Regitnig, Guenter Daxenbichler, Christine Desmedt, Julien Domont, Christian Marth, Suzette Delaloge, Thomas Bauernhofer, Vicente Valero, Daniel J. Booser, Gabriel N. Hortobagyi, Lajos Pusztai

Final approval of manuscript: W. Fraser Symmans, Christos Hatzis, Christos Sotiriou, Fabrice Andre, Florentia Peintinger, Peter Regitnig, Guenter Daxenbichler, Christine Desmedt, Julien Domont, Christian Marth, Suzette Delaloge, Thomas Bauernhofer, Vicente Valero, Daniel J. Booser, Gabriel N. Hortobagyi, Lajos Pusztai


1. Winer EP, Hudis C, Burstein HJ, et al. American Society of Clinical Oncology technology assessment on the use of aromatase inhibitors as adjuvant therapy for postmenopausal women with hormone receptor-positive breast cancer: Status report 2004. J Clin Oncol. 2005;23:619–629. [PubMed]
2. Winer EP. Optimizing endocrine therapy for breast cancer. J Clin Oncol. 2005;23:1609–1610. [PubMed]
3. Swain SM. A step in the right direction. J Clin Oncol. 2006;24:3717–3718. [PubMed]
4. Berry DA, Cirrincione C, Henderson IC, et al. Estrogen-receptor status and outcomes of modern chemotherapy for patients with node-positive breast cancer. JAMA. 2006;295:1658–1667. [PMC free article] [PubMed]
5. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747–752. [PubMed]
6. van 't Veer L, Dai H, van de Vijver M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–536. [PubMed]
7. Nicholson RI, McClelland RA, Robertson JF, et al. Involvement of steroid hormone and growth factor cross-talk in endocrine response in breast cancer. Endocr Relat Cancer. 1999;6:373–387. [PubMed]
8. Atanaskova N, Keshamouni VG, Krueger JS, et al. MAP kinase/estrogen receptor cross-talk enhances estrogen-mediated signaling and tumor growth but does not confer tamoxifen resistance. Oncogene. 2002;21:4000–4008. [PubMed]
9. Johnston SRD, Head J, Pancholi S, et al. Integration of signal transduction inhibitors with endocrine therapy: An approach to overcoming hormone resistance in breast cancer. Clin Cancer Res. 2003;9:524S–532S. [PubMed]
10. Razandi M, Pedram A, Park ST, et al. Proximal events in signaling by plasma membrane estrogen receptors. J Biol Chem. 2003;278:2701–2712. [PubMed]
11. Shou J, Massarweh S, Osborne CK, et al. Mechanisms of tamoxifen resistance: Increased estrogen receptor-HER2/neu cross-talk in ER/HER2-positive breast cancer. J Natl Cancer Inst. 2004;96:926–935. [PubMed]
12. Gruvberger S, Ringner M, Chen Y, et al. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res. 2001;61:5979–5984. [PubMed]
13. Pusztai L, Ayers M, Stec J, et al. Gene expression profiles obtained from fine needle aspirations of breast cancer reliably identify routine prognostic markers and reveal large-scale molecular differences between estrogen-negative and estrogen-positive tumors. Clin Cancer Res. 2003;9:2406–2415. [PubMed]
14. Sotiriou C, Wirapati P, Loi S, et al. Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 2006;98:262–272. [PubMed]
15. Loi S, Haibe-Kains B, Desmedt C, et al. Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. J Clin Oncol. 2007;25:1239–1246. [PubMed]
16. Wang Y, Klijn JG, Zhang Y, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005;365:671–679. [PubMed]
17. Desmedt C, Piette F, Loi S, et al. Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin Cancer Res. 2007;13:3207–3214. [PubMed]
18. Liedtke C, Hatzis C, Symmans WF, et al. Genomic grade index is associated with response to chemotherapy in patients with breast cancer. J Clin Oncol. 2009;27:3185–3191. [PMC free article] [PubMed]
19. Hess KR, Anderson K, Symmans WF, et al. Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. J Clin Oncol. 2006;24:4236–4244. [PubMed]
20. Gong Y, Yan K, Lin F, et al. Determination of oestrogen-receptor status and ERBB2 status of breast carcinoma: A gene-expression profiling study. Lancet Oncol. 2007;8:203–211. [PubMed]
21. Harvey JM, Clark GM, Osborne CK, et al. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J Clin Oncol. 1999;17:1474–1481. [PubMed]
22. Kendall G, Gibbons JD. Rank Correlation Methods (ed 5) New York, NY: Oxford University Press; 1990.
23. Hudis CA, Barlow WE, Costantino JP, et al. Proposal for standardized definitions for efficacy end points in adjuvant breast cancer trials: The STEEP system. J Clin Oncol. 2007;25:2127–2132. [PubMed]
24. Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model. New York, NY: Springer-Verlag; 2000.
25. Symmans WF, Peintinger F, Hatzis C, et al. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol. 2007;25:4414–4422. [PubMed]
26. Tableman M, Kim JS. Survival Analysis Using S: Analysis of Time-to-Event Data. Boca Raton, FL: Chapman & Hall/CRC; 2004.
27. Bramwell VH, Pritchard KI, Tu D, et al. A randomized placebo-controlled study of tamoxifen after adjuvant chemotherapy in premenopausal women with early breast cancer (National Cancer Institute of Canada–Clinical Trials Group Trial, MA. 12) Ann Oncol. 2010;21:283–290. [PMC free article] [PubMed]
28. Goldstein LJ, Gray R, Badve S, et al. Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features. J Clin Oncol. 2008;26:4063–4071. [PMC free article] [PubMed]
29. Albain KS, Barlow WE, Shak S, et al. Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: A retrospective analysis of a randomised trial. Lancet Oncol. 2010;11:55–65. [PMC free article] [PubMed]
30. Dowsett M, Cuzick J, Wale C, et al. Risk of distant recurrence using Oncotype DX in postmenopausal primary breast cancer patients treated with anastrozole or tamoxifen: A TransATAC study. Proc 31st San Antonio Breast Cancer Symposium; September 10-14, 2008; San Antonio, TX. abstr 53.
31. Hayes DF, Thor AD, Dressler LG, et al. HER2 and response to paclitaxel in node-positive breast cancer. N Engl J Med. 2007;357:1496–1506. [PubMed]
32. Andre F, Broglio K, Roche H, et al. Estrogen receptor expression and efficacy of docetaxel-containing adjuvant chemotherapy in patients with node-positive breast cancer: Results from a pooled analysis. J Clin Oncol. 2008;26:2636–2643. [PubMed]
33. Desmedt C, Haibe-Kains B, Wirapati P, et al. Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. Clin Cancer Res. 2008;14:5158–5165. [PubMed]
34. Paik S, Tang G, Shak S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol. 2006;24:3726–3734. [PubMed]
35. Symmans WF. A pathologist's perspective on emerging genomic tests for breast cancer. Semin Oncol. 2007;34:S4–S9. [PubMed]
36. Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med. 2006;355:560–569. [PubMed]
37. Ellis MJ, Tao Y, Luo J, et al. Outcome prediction for estrogen receptor-positive breast cancer based on postneoadjuvant endocrine therapy tumor characteristics. J Natl Cancer Inst. 2008;100:1380–1388. [PubMed]
38. Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med. 2009;360:790–800. [PubMed]

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