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Sci Rep. 2012; 2: 227.
Published online Jan 17, 2012. doi:  10.1038/srep00227
PMCID: PMC3259129

A Gene Signature for Predicting Outcome in Patients with Basal-like Breast Cancer

Abstract

Basal-like breast cancer is a molecular subtype of breast cancer with a poor prognosis. Follow-up studies of long-term outcome in these patients, demonstrates they can be separated into two clinical groups: those who succumb to their disease within the first 5 years and those expected to show excellent long term survival. Currently available clinical/histopathological variables as well as molecular signatures show little capacity to identify basal breast cancer patients with either a high or low risk of disease relapse. Using data derived from 85 basal-like breast cancer patients, we identified a 14-gene signature, which we subsequently validated on an additional 49 basal breast cancer patient set. The ability to distinguish between these two sub-groups of basal breast cancer patients at the time of initial diagnosis would permit tailoring aggressive therapeutic regimens to those patients with a poor prognosis and conversely avoid such therapy in low risk patients.

Traditionally a number of tumor characteristics have been used to determine the prognosis of breast cancer patients. Such factors include tumor size, grade, hormone receptor status, HER2 status, lympho-vascular space invasion and lymph node involvement1,2. More recently whole genome analysis technology (gene expression profiling) has been added to the armamentarium of experimental techniques, thus providing a new molecular classification for breast cancer and contributing to the development of a number of prognostic multi-gene assays including a 21-gene, 70-gene, 76-gene, 77-gene genomic grade profile, wound response signature and others3,4,5,6,7,8,9. One of these assays that is commercially available is Oncotype DX®, a 21-gene quantitative (q)RT-PCR assay, which evaluates expression of 16 genes identified to be of prognostic importance as well as 5 house-keeping genes3. Oncotype DX® predicts the risk of distant recurrence in Estrogen Receptor (ER) positive breast cancers and their responsiveness to CMF (Cyclophosphamide, Methotrexate and 5-Fluorouracil) chemotherapy10. MammaPrint®, a commercially available microarray evaluates the expression of 70 genes using RNA extracted from fresh frozen tumor samples. This assay distinguishes patients that have a good prognosis (no relapse within 5 years) from those that have a poor prognosis (relapse within 5 years)11. Indeed, large clinical trials, such as TAILORx [Trial Assigning Individualized Options for Treatment] and MINDACT [Microarray In Node Negative and 1-3 positive lymph node Disease may Avoid Chemotherapy] are ongoing to evaluate the use of both Oncotype DX® and MammaPrint® in clinical practice.

The term basal-like breast cancer (BLBC) originated in 2000 from gene expression profiling experiments conducted on invasive breast cancers by Perou and colleagues at Stanford University12,13,14. Using hierarchical clustering these investigators identified a new molecular taxonomy for breast cancer based on the relative expression of the ~500 genes, known as the ‘intrinsic’ gene set. These investigators discovered that breast cancers could be classified into five molecular subgroups. Two of these are ER positive whereas three are ER negative. The ER positive subgroups, termed Luminal A and Luminal B, were identified based on their relative expression of the ER gene, ER regulated genes and other genes expressed by normal breast ‘luminal’ cells. The ER negative subgroups were termed HER2 overexpressing (ERBB2+), normal breast-like and BLBC. The HER2 overexpressing subgroup was characterized by the overexpression of the HER-2 and other genes on the 17q amplicon, such as GRB7. The normal breast-like subgroup expresses genes characteristic of adipose tissue suggesting that this subgroup may be a technical artifact resulting from low tumor cellularity. Lastly, the basal-like subgroup represents a distinct class of tumors characterized by the lack of expression of ER, PR and HER2 and the high expression of cytokeratins (CK) 5, and/or CK 17 (amongst other genes), characteristic of the basal/myoepithelial cell layer of the normal breast epithelium. As gene expression studies continued to evolve, new molecular subtypes of breast cancer continued to be discovered; for example in 2007 the claudin low subtype was identified15.

Most importantly the initial gene expression profiling experiments demonstrated that BLBCs together with the HER2 overexpressing subtype were associated with a particularly poor prognosis. By comparison, patients with Luminal A type tumors displayed an excellent prognosis13,14. However, on closer examination these studies additionally demonstrated that the prognosis of patients with BLBCs is highly time dependent. Some patients with BLBCs experience particularly poor survival in the first 3–5 years following diagnosis, but for others their mortality wanes such that at 10 years post diagnosis these patients have a better survival than those with luminal-type (ER+) tumors16,17,18,19. This suggests that patients with BLBCs can be separated into two clinically distinct groups: those likely to experience a recurrence and to succumb to their disease in the first 3–5 years after diagnosis, and those expected to show excellent long term survival.

Whereas several multi-gene signatures exist to predict breast cancer patient prognosis, their prognostic values appears to be mostly derived from their capacity to measure expression of genes associated with proliferation20,21. Because BLBCs are generally highly proliferative, the existing prognostic signatures fail to identify a subset of BLBC with good prognosis22. Some recent work has focused on identifying multi-gene predictors of outcome in triple negative (ER-, PR-, HER2-) and hormone receptor negative breast cancer21,22,23,24,25,26. However, a robust method of distinguishing between BLBCs with good and poor outcome has yet to be developed. To the latter end, we have begun optimizing such a method and report here the identification of a 14-gene signature that is associated with patient outcome in BLBCs.

Results

Compiling multiple gene expression profiles of basal breast tumors

To identify genes whose expression might be associated with the clinical outcome of BLBC patients, we compiled a large collection of human breast tumor gene expression data for which clinical data was also available (n = 995). Hierarchical clustering using the ‘intrinsic’ gene set revealed that many of these tumors (n = 547) clustered into the previously described molecular subtypes12,13,14 (Fig. 1a). Importantly, survival analysis using Kaplan-Meier survival curves revealed distinct differences in clinical outcome among the patients with tumors of different molecular subtypes. As observed previously patients with tumors of the basal-like, ERBB2, claudin-low and luminal B subtypes experienced the poorest 10-year survival, whereas patients with luminal A or normal-like tumors experienced the best 10-year survival13 (Fig. 1b). Interestingly, the 10-year survival rate of patients with basal-like tumors was approximately 60% and very few BLBC patient mortalities occurred after this time (Fig. 1b). The latter findings are consistent with previous observations that the prognosis of BLBC patients is time dependent, where these patients are at highest risk for relapse during the first 5 years post diagnosis and experience a very low risk for relapse 10-years post diagnosis16,17,18.

Figure 1
Human breast tumors cluster into 6 distinct molecular subtypes of breast cancer with differences in patient survival.

Importantly, the BLBC tumor cohort comprised 134 patients with clinical follow-up data, thus providing a fairly large number of basal tumors to identify a genomic predictor that could be used to guide prognosis for patients with basal-like breast tumors.

Training signature

To develop a genomic predictor that could be used to identify BLBC patients who were likely to have either good or poor survival outcomes, we first divided the 134 patient BLBC cohort into a 85 patient training set and a 49 patient validation set. We used binary regression probabilistic models for feature selection to identify genes that had the best prognostic performance among the gene expression profiles derived from the 85 BLBCs of the training set27. For these analysis, <5 year DFS was taken to indicate poor outcome, whereas >5 year DFS was taken to indicate good outcome. Previous studies have shown that the vast majority of disease recurrence among BLBC patients occurs within the first 5 years16,17,18. Starting with a single probe set signature, we iteratively generated signatures by gradually adding probe sets and tested the resulting signature using leave-one-out cross-validation. In this fashion we generated multiple signatures comprising n probe sets, where n = 1,2,3…,50 (Fig. 2A). For each discrete value of n, this technique assigned a probability to every patient within the training set that indicated the likelihood of a patient experiencing disease relapse. To establish a probability cut-point, where patients with higher probability are assigned into the poor prognosis category and patients with lower probability are assigned into the good prognosis category, we used a previously described tertile method28. In this fashion, good prognosis was assigned to patients whose probability score fell in the lowest 1/3 of all probability scores, whereas poor prognosis was assigned to patients whose score fell into the higher 2/3 of probability scores. Indeed, these approximate proportions have been observed in several gene expression based breast cancer prognostication studies4,7,29,30. We therefore took this approach as a relatively non-biased and simple means to divide patients into predicted good and poor outcome groups. To determine which n-element signature had optimal performance we compared the relative risk of relapse for each signature (Fig. 2B, red line: relative risk, black line: LOWESS (LOcally WEighted Scatterplot Smoothing) curve fitted to relative risk data, n = 14 identifies optimal signature length). In this fashion we identified a 14-probe-set (each gene represented by 1 probe set, henceforth called Basal 14 signature) signature, which optimally separated patients into good and poor outcome groups (Table 1).

Figure 2
14 probe sets optimally separate patients into good and poor survival groups.
Table 1
Features comprising the optimal 14-gene signature

Assessment of Signature Performance

Validation of a gene signature using an independent data set is a more accurate measurement of its prognostic value than using cross-validation on a training data set. Therefore, we tested our Basal-14 signature on an independent cohort of patients with BLBC (n = 49). To learn whether the probability of disease relapse predicted by the Basal-14 signature could be used as a continuous predictor of disease relapse, we calculated the proportion of patients who had experienced disease relapse while increasing the cut-off (decreasing stringency) for assigning a patient into the good outcome group. Indeed, the proportion of patients experiencing disease relapse increased in an approximate linear fashion as the probability assigned for disease relapse by the Basal-14 signature increased (Fig. 3A). To assess the predictive accuracy of the Basal-14 signature, we completed receiver-operator characteristic (ROC) curve analysis. In this fashion, an AUC (Area Under Curve) value of 0.5 indicates predictive performance which is no better than chance, whereas values greater than 0.5 indicate true predictive capacity. The Basal-14 signature produced an AUC that was statistically significantly higher than 0.5 (AUC: 0.76, p = 0.003, Fig. 3B). Taken together, these data demonstrate the capacity for the Basal-14 signature to identify BLBC patients at high risk for disease relapse. To visualize survival differences between groups of patients that were predicted to have either high or low risk for disease relapse, we stratified patients from the validation cohort into good and poor outcome groups using tertiles, and completed Kaplan-Meier survival analysis. Patients whose predicted probability for disease relapse fell within the lowest tertile of predicted probabilities were stratified into the good outcome group, whereas those whose predicted probabilities fell within the upper two tertiles were stratified into the poor outcome group. The Kaplan-Meier estimate for the proportion of patients in the low-risk group who did not experience a disease relapse at 5 years (94%) was significantly greater than the proportion in the poor outcome category (48%) (Table 2, Fig. 3C, HR: 4.7 [CI95: 1.8–12.3], p = 0.0017). Because our overarching objective was to identify patients who could be spared aggressive chemotherapy, we also tested the capacity of our signature to predict the outcome of patients who had not received adjuvant chemotherapy. In this fashion, we were able to test the relationship between the Basal-14 signature and the natural progression of BLBCs without having adjuvant chemotherapy as a potentially confounding variable. 26 patients within the 49 patient validation cohort met this criterion (patients from GSE7390 & GSE2034). We re-tested the predictive capacity of the Basal-14 signature on these 26 chemotherapy naïve patients and observed a statistically significant difference in the survival of patients who were predicted to have either good or poor outcome (Fig. 3D, HR: 4.4 [CI95: 1.1–16.7], p = 0.03, Table 3). The proportion of patients in the chemotherapy naïve validation cohort who were predicted to have good survival and were free of disease at 5 years was 100%, whereas among those patients who were predicted to have poor survival, only 50% were disease free after 5 years. Taken together, these findings demonstrate the capacity of our gene signature to identify patients who have excellent long-term survival even when patients did not receive aggressive adjuvant chemotherapy.

Figure 3
The Basal 14 signature accurately predicts outcome in independent patients with BLBC.
Table 2
Survival characteristics of the 49 patient validation cohort
Table 3
Survival characteristics of the 26 patient chemo-naïve validation cohort

Comparison of the Basal-14 signature with other multigene predictors

Previous studies have reported that many published multigene predictors fail to accurately identify high and low risk patients among patients with ER-negative breast cancer22,24. As the majority of BLBCs are ER-negative, we sought to test whether multiple previously described multigene predictors were prognostic in the context of BLBC. To this end, we measured the association of the Genomic Grade Index5, NKI-70 signature31, Recurrence score3, CSR/Wound response signature6, Triple-negative signature22, MS-14 signature32, as well as the Basal-14 signature in the 49 patient validation cohort by calculating a signature index and completing either Kaplan-Meier survival analysis using tertiles to dichotomize the validation cohort into good and poor outcome groups, or generating ROC curves. Interestingly, other than the Basal-14 signature (Fig. 4A, HR: 4.3 [CI95: 1.6–11.4], p = 0.0032) none of the other signatures identified patient groups with statistically significant differences in survival (Kaplan-Meier: Fig. 4A-F. ROC: supplementary figure. 1A–F). These data suggest that the prognostic capacity of previously reported multigene outcome predictors may be diminished in patients with BLBC. However, it should be noted that the tertile method used to separate patients into good and poor outcome groups may be non-optimal for these signatures. Interestingly, the triple negative signature trended towards significance in the Kaplan-Meier analysis (Fig. 4F, HR: 2.0 [CI95: 0.8–5.4], p = 0.15) and was statistically significant in the ROC curve analysis (Supplementary fig. 1G, AUC: 0.7, p = 0.02). This is likely because the triple negative signature was developed with triple negative breast tumors, which comprises a sub-group that overlaps with the basal-like molecular subtype. Together, these findings underscore the need for prognostic multigene signatures, such as the Basal 14 signature, for guiding therapy choice for breast cancer patients.

Figure 4
Other reported prognostic signatures fail to predict patient outcome in the context of BLBC.

Performance of Basal-14 signature in other molecular subtypes of breast cancer

Previous studies have demonstrated that biological processes that can be linked to breast cancer patient outcome vary among the different molecular subtypes of breast cancer21. In this regard, we sought to test whether the Basal-14 signature could be used to identify high and low risk patients among the other molecular subtypes of breast cancer, or whether its capacity to stratify patients into high and low risks groups was limited to patients with BLBCs. The Basal-14 signature showed no capacity to identify patients at high and low risk for disease relapse among the luminal A (HR: 1.3, p = n.s.), luminal B (HR: 1.2, p = n.s.), claudin low (HR: 1.0, p = n.s.) and normal (HR: 0.4, p = n.s.) molecular subtypes of breast cancer (Fig. 5A–D). Unexpectedly, the Basal-14 signature was also prognostic in the ERBB2 molecular subtype (HR: 2.8 [CI95: 1.3–6.5], p = 0.01). Interestingly, a previously reported prognostic gene signature developed using Her2-positive tumors was also found to be prognostic in BLBCs33. These data suggest that similar biological processes may govern patient outcome in both the basal-like and ERBB2 molecular subtypes of breast cancer. Taken with our previous findings, it appears that transcripts whose expression may be informative for patient prognosis vary between the different molecular subtypes of breast cancer. For example, it appears that signatures that are prognostic in ER-positive breast tumors, such as the Reccurrence score (OncotypeDX®) and the Genomic Grade Index, fail to stratify BLBCs into good and poor outcome groups, whereas the Basal-14 signature is prognostic in basal-like and ERBB2-overexpressing breast cancer, but fails to identify patients in the ER-positive luminal subtypes of breast cancer.

Figure 5
Basal 14 signature is prognostic in the basal and ERBB2 molecular sutbypes of breast cancer.

Discussion

Few, if any, clinical variables show prognostic capacity in the context of BLBC. Therefore, we sought to identify a genomic predictor of patient outcome for patients with BLBC. In the present study, we identified a 14 probe set signature, which we named the Basal 14 signature. We tested the Basal 14 signature on an independent validation cohort of BLBC patients and were able to accurately stratify patients into good and poor outcome groups. Importantly, the difference in risk for disease relapse for patients who were predicted to have either good or poor outcome was both relatively large and statistically significant. Because it was unclear whether the Basal 14 signature was related to the natural progression of BLBCs, tumor response to therapy, or both, we also tested the Basal 14 signature on a smaller group of patients who did not received treatment with adjuvant chemotherapy. In this fashion, we were able to confirm a relationship, albeit in a small number of patients, between the Basal 14 signature and patient survival in chemotherapy naïve patients. Notably, previous reports suggest that immune-based signatures predict response to chemotherapy in triple negative breast cancer patients, suggesting that the Basal 14 signature might also measure treatment response21,34. The relationship between the Basal 14 signature and response to chemotherapy was not examined in this study. Another possibility is that the Basal 14 signature is associated with histological subtypes of BLBC with known good prognosis, such as the medullary subtype35,36. However, the frequency of medullary breast tumors is exceptionally low (2%), suggesting that the Basal 14 signature would also need to identify good prognosis non-medullary BLBCs the achieve the level of accuracy described here. In total, the capacity of the Basal 14 signature to identify BLBC patients with good prognosis is likely multi-factorial, and many additional possibilities remain unexplored.

Interestingly, the Basal 14 signature comprised multiple genes with known roles in cancer. For example, destrin (DSTN) is one of three mammalian actin depolymerisation factors (ADFs). These proteins are fundamental for multiple cellular processes such as cell survival, cytokinesis, as well as cell migration and chemotaxis37, and have been linked as a major determinant of metastasis in cancer patients38,39. Tudor domain containing protein 3 (TDRD3) has previously been linked to outcome in patients with ER-negative breast tumors40, and while being relatively poorly characterized, is thought to play a role in the regulation of cytoplasmic stress granules41. Regulator of G-protein signaling (RGS4) has also been linked to patient outcome in patients with triple negative tumors22. Notably, RGS4 appears to be a key negative regulator of breast cancer cell migration and invasion42. It is therefore somewhat surprising that high levels of RGS4 transcripts are associated with poor outcome. However, it appears that RGS4 function is heavily regulated post-translationally by proteosomal degradation, suggesting that a negative feedback loop occurs where high levels of RGS4 transcripts indicate low levels of RGS4 protein42. Interestingly, proteasome inhibitors are being explored as possible means for cancer therapy43,44. In this regard, BLBC patients may represent a cancer sub-group that might benefit from such a therapeutic approach. Three of the probe sets comprising the Basal 14 signature bind to transcripts that encode ribosomal protein L3 (RPL3). While it seems likely that this gene is involved in mRNA translation, implying that BLBCs with high levels of protein synthesis are associated with poor patient outcome, the role of RPL3 in cancer is uncharacterized. The genes representing transcripts whose expression was related to good survival are largely uncharacterized in regards to roles in tumor cell biology. Lymphocyte cytosolic protein 1 (LCP1), which is likely expressed by tumor infiltrating lymphocytes, might represent a readout of the extent of tumor lymphocyte infiltrate. This suggests that patient outcome may be influenced by host immune response, where infiltrating immune cells, such as lymphocytes, within a tumor indicate a good prognosis. Indeed, similar observations have been made by multiple other groups in the context of ER negative breast tumors22,24. Taken together, these data highlight the diverse biology of the genes comprising the Basal 14 signature and provide a scientific rationale for new lines of research aimed at developing BLBC specific therapies.

Several issues remain to be addressed for the Basal 14 signature to be a useful clinical tool. Our conclusions are based on the analysis of retrospective data, which limits its clinical value. Moreover, the validation cohort we used to test the predictive accuracy of the Basal 14 signature was relatively small. Finally, many of the patients in our data-set had incomplete clinical data, making it impossible to learn whether the Basal 14 signature was independently prognostic in the context of other additional factors such as patient age, tumor size, tumor grade, etc. However, it is important to note that previous reports suggest that factors such as tumor size, tumor grade, extent of vascular invasion, and patient age show little relationship to patient outcome in the context of BLBC especially in lymph-node negative patients45,46. Indeed, the only standard clinical variable that is consistently prognostic in BLBC appears to be nodal status45,47. Interestingly, we found that the Genomic Grade Index, a genomic based measurement of tumor grade showed no capacity to stratify BLBC patients into good and poor outcome groups. Subsequent validation of the Basal 14 signature will need to be completed in larger cohorts of patients that include such multivariate analyses. In this regard, a major focus of our research is the optimization of the Basal 14 signature for use on breast tumor tissue that is routinely available after surgery, such as formalin fixed paraffin embedded tumor blocks.

No rigorously validated assay exists to guide prognosis of patients specifically with BLBC. Indeed, the data we present here suggests that the possibility of developing such a test exists. Future experiments will aim to extend these findings in additional retrospective cohorts of patients with BLBCs and ultimately in a prospective based clinical trial aimed at sparing low risk BLBCs patients from detrimental and unnecessary adjuvant chemotherapy.

Methods

We used a four-step approach to complete proof-of-principle experiments to show that gene-expression signatures can be identified and used to classify patients with BLBCs into good and poor outcome groups.

  1. We assembled a large cohort of 995 breast tumor gene expression profiles for which clinical follow-up data was available.
  2. We classified each tumor on the basis of its ‘intrinsic’ molecular subtype from which we generated a new dataset consisting of only BLBCs.
  3. We used a subset of BLBCs to iteratively identify several prognostic gene signatures, and used cross-validation to identify the optimal signature for patient outcome classification.
  4. We validated our optimized signature prospectively on an independent subset of basal breast tumors with accompanying gene expression profiles and clinical follow-up data.

Collecting Microarray Data

We analyzed the gene expression profiles of 5 independent external datasets, obtained using Affymetrix HG-U133A GeneChips arrays, which have been deposited in the Gene Expression Omnibus (GEO); accession numbers GSE1456, GSE2034, GSE3494, GSE6532, and GSE7390. Together these datasets provided expression profiles of 1,077 human breast tumor samples. All gene expression profiles were normalized with frozen Robust Multi-Array Analysis (fRMA), a procedure that allows one to pre-process microarrays individually or in small batches and to then combine the data into a single comparable dataset for further analyses48. To remove batch effect from the combined dataset, we used the ComBat method, which uses an Empirical Bayes method to adjust for potential batch effects in the dataset49 (http://genepattern.broadinstitute.org), and computed Pearson correlation coefficients for pair-wise comparisons of samples using 68 house-keeping probe sets; only samples exhibiting correlations higher than 0.95 with at least half of the dataset were selected for further classification. The latter filtering method yielded a dataset comprising 995 human breast tumor samples.

Tumor Classification

Each of the selected 995 samples described above, were classified as basal-like, HER2+, Luminal A, Luminal B, claudin-low or normal-like by assigning it to a cluster representing the subtype to which it had the highest Pearson correlation12,13,15. The correlation was computed using the subset of 1,500 averaged and median-centered ‘intrinsic’ genes50common to both our dataset (Affymetrix Human Genome U133A Array) and the dataset used by Parker et al. (Stanford Microarray). For robustness, only tumors exhibiting a correlation higher than 0.3 with any of the molecular subtypes were used for further analysis. This led to the classification of 137 breast tumors into the basal-like molecular subtype yielding a group of 134 tumors with useable clinical follow-up data. We randomly separated the 134 patients with basal breast tumors; approximately 2/3 (n = 85) were taken for signature training purposes (training set), whereas and the remaining 1/3 (n = 49) was used as an independent validation set.

Binary regression

Identification of the prognostic signature was completed using the Bayesian binary regression algorithm BinReg ver2.0. The binary regression software (BinReg2.0) was downloaded from http://web.duke.edu/~dinbarry/BINREG/ and was used as a MATLAB plug-in27. In most cases, we used disease free survival (DFS) as the relevant clinical variable, however, in some cases only distant metastasis free survival (DMFS) was available within a patient's clinical annotation. In these cases we counted DMFS as DFS. We used 5 years DFS as the clinical endpoint for these studies.

Assessing signature performance

Survival differences between predicted good and poor outcome groups were evaluated with Kaplan-Meier survival curves and a log-rank test for significance. Many standard prognostic clinical variables (node, grad, size, age…, etc) were unavailable in the GEO files associated with the patients used in this study, thus a limitation of this study is that we were not able to test the capacity of the Basal 14 signature to remain prognostic in the context of standard prognostic factors.

Comparison of the Basal 14 signature with other genomic based predictors

We tested multiple additional prognostic signatures on the 49 patient validation cohort: Genomic Grade5, NKI-7031, Recurrence score3, Wound response6, Triple negative22 and MS-1432. For cross platform comparisons with other gene signatures, signature elements were mapped by Unigene IDs to Affymetrix HG-U133A GeneChip arrays for testing in the 49 patient validation set. The expression values for each gene were transformed such that the mean was 0 and the standard deviation was 1. A signature index was calculated for each patient as follows:

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Object name is srep00227-m1.jpg

Where x is the transformed expression, n is the number of genes that could be mapped to the Affymetrix HG-U133 arrays, P is the set of probes with reported positive correlation to poor outcome, and N is the set of probes with reported positive correlation to good outcome. For each signature, patients were divided into high and low signature index groups using tertiles28.

Author Contributions

RMH, ADG, AB, JAH, conception of project. RMH & ADG performed research. RMH, ADG, AB, JAH, wrote manuscript.

Supplementary Material

Supplementary Information:

Supplementary Figure 1

Acknowledgments

This work was generously supported by grants from the Canadian Stem Cell Network, the Ontario Institute for Cancer Research and the Canadian Breast Cancer Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Footnotes

The authors have filed a provisional patent application on using the described gene signature for prognosis of breast cancer patients.

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