Global MicroRNA Expression Profiling of High-Risk ER+ Breast Cancers from Patients Receiving Adjuvant Tamoxifen Mono-Therapy: A DBCG Study

PURPOSE: Despite the benefits of estrogen receptor (ER)-targeted endocrine therapies in breast cancer, many tumors develop resistance. MicroRNAs (miRNAs) have been suggested as promising biomarkers and we here evaluated whether a miRNA profile could be identified, sub-grouping ER+ breast cancer patients treated with adjuvant Tamoxifen with regards to probability of recurrence. EXPERIMENTAL DESIGN: Global miRNA analysis was performed on 152 ER+ primary tumors from high-risk breast cancer patients with an initial discovery set of 52 patients, followed by two independent test sets (N = 60 and N = 40). All patients had received adjuvant Tamoxifen as mono-therapy (median clinical follow-up: 4.6 years) and half had developed distant recurrence (median time-to-recurrence: 3.5 years). MiRNA expression was examined by unsupervised hierarchical clustering and supervised analysis, including clinical parameters as co-variables. RESULTS: The discovery set identified 10 highly significant miRNAs that discriminated between the patient samples according to outcome. However, the subsequent two independent test sets did not confirm the predictive potential of these miRNAs. A significant correlation was identified between miR-7 and the tumor grade. Investigation of the microRNAs with the most variable expression between patients in different runs yielded a list of 31 microRNAs, eight of which are associated with stem cell characteristics. CONCLUSIONS: Based on the large sample size, our data strongly suggests that there is no single miRNA profile predictive of outcome following adjuvant Tamoxifen treatment in a broad cohort of ER+ breast cancer patients. We identified a sub-group of Tamoxifen-treated breast cancer patients with miRNA-expressing tumors associated with cancer stem cell characteristics

Gene expression signatures that predict outcome of tamoxifen-treated estrogen receptor-positive, high-risk, primary breast cancer patients: a DBCG study.

BACKGROUND: Tamoxifen significantly improves outcome for estrogen receptor-positive (ER+) breast cancer, but the 15-year recurrence rate remains 30%. The aim of this study was to identify gene profiles that accurately predicted the outcome of ER+ breast cancer patients who received adjuvant Tamoxifen mono-therapy. METHODOLOGY/PRINCIPAL FINDINGS: Post-menopausal breast cancer patients diagnosed no later than 2002, being ER+ as defined by >1% IHC staining and having a frozen tumor sample with >50% tumor content were included. Tumor samples from 108 patients treated with adjuvant Tamoxifen were analyzed for the expression of 59 genes using quantitative-PCR. End-point was clinically verified recurrence to distant organs or ipsilateral breast. Gene profiles were identified using a model building procedure based on conditional logistic regression and leave-one-out cross-validation, followed by a non-parametric bootstrap (1000x re-sampling). The optimal profiles were further examined in 5 previously-reported datasets containing similar patient populations that were either treated with Tamoxifen or left untreated (n = 623). Three gene signatures were identified, the strongest being a 2-gene combination of BCL2-CDKN1A, exhibiting an accuracy of 75% for prediction of outcome. Independent examination using 4 previously-reported microarray datasets of Tamoxifen-treated patient samples (n = 503) confirmed the potential of BCL2-CDKN1A. The predictive value was further determined by comparing the ability of the genes to predict recurrence in an additional, previously-published, cohort consisting of Tamoxifen-treated (n = 58, p = 0.015) and untreated patients (n = 62, p = 0.25). CONCLUSIONS/SIGNIFICANCE: A novel gene expression signature predictive of outcome of Tamoxifen-treated patients was identified. The validation suggests that BCL2-CDKN1A exhibit promising predictive potential

The miRNA-200 family and miRNA-9 exhibit differential expression in primary versus corresponding metastatic tissue in breast cancer

Metastases are the major cause of cancer-related deaths, but the mechanisms of the metastatic process remain poorly understood. In recent years, the involvement of microRNAs (miRNAs) in cancer has become apparent, and the objective of this study was to identify miRNAs associated with breast cancer progression. Global miRNA expression profiling was performed on 47 tumor samples from 14 patients with paired samples from primary breast tumors and corresponding lymph node and distant metastases using LNA-enhanced miRNA microarrays. The identified miRNA expression alterations were validated by real-time PCR, and tissue distribution of the miRNAs was visualized by in situ hybridization. The patients, in which the miRNA profile of the primary tumor and corresponding distant metastasis clustered in the unsupervised cluster analysis, showed significantly shorter intervals between the diagnosis of the primary tumor and distant metastasis (median 1.6 years) compared to those that did not cluster (median 11.3 years) (p<0.003). Fifteen miRNAs were identified that were significantly differentially expressed between primary tumors and corresponding distant metastases, including miR-9, miR-219-5p and four of the five members of the miR-200 family involved in epithelial-mesenchymal transition. Tumor expression of miR-9 and miR-200b were confirmed using in situ hybridization, which also verified higher expression of these miRNAs in the distant metastases versus corresponding primary tumors. Our results demonstrate alterations in miRNA expression at different stages of disease progression in breast cancer, and suggest a direct involvement of the miR-200 family and miR-9 in the metastatic process

Heat-map of significantly differentially expressed miRNAs associated with outcome after adjuvant Tamoxifen treatment.

<p>Data is based on the discovery set (p<6.6e-4, FDR 2.5% and variance >0.1). The green symbols above the heat-map indicate samples from patients with no recurrence, whereas the red symbols indicate samples from patients with recurrence. The heat-map is a standardized intensity plot with the intensities ranging from −2 (green) to +2 (red).</p

Characteristics of included patients and their breast cancer tumor (N = 152).

a<p>the average and median were calculated only for the tumors defined as positive, i.e. staining was observed in ≥ 10% of tumor cells by immunohistochemistry.</p>b<p>If the actual percentage was not provided, patients were deemed positive if ER staining was observed in ≥ 10% of tumor cells by immunohistochemistry and/or target protein (ER or PgR) was >10 fmol/mg total protein as determined by biochemistry.</p>c<p>the 5 ER- tumors had a PgR status of 90%, 50%, 90%, 80% and IHC+ (i.e. >10%) respectively.</p><p>Abbreviations: R: patients with recurrence. N: patients without recurrence. Disc.: Discovery set. Test#1: Test set#1. Test#2: Test set#2. Avg: average IDC: invasive ductal carcinoma. ILC: invasive lobular carcinoma. TTR: time to recurrence.</p

Association of miR-7 with tumor grade.

<p>Grade 1 vs. 3 and Grade 2 vs. 3: p = 0.01, and Grade 1 vs. 2: p = 0.02). N = 52 patients (Discovery set).</p

Kaplan-Meier plots of the 10 miRNAs identified in the discovery set.

<p>The bold line represents the patients with a good prognosis, whereas the dotted line represents the poor-prognosis patients. A) Probability of recurrence. B) Probability of overall survival.</p

Genes identified and their expression pattern.

<p>A) The 2-, 8- and 9-gene signatures identified by various statistical analyses. B) ΔΔCt of the genes present in the 2-, 8- and 9-gene signatures. <i>BCL2</i> overlap in all three, whereas <i>CDKN1A</i> is in the 2- and 9-gene signatures, and <i>PRKCE</i> and <i>EGFR</i> are in both the 8- and 9-gene signatures. A positive ΔΔCt_median value denote that the expression of the gene is highest in the tumor sample from patients without recurrence, whereas a negative value means the expression is higher in the tumor samples from patients with recurrence.</p

Performance of the identified genes.

<p>The capabilities of the identified 2-, 8- and 9-gene signatures to predict recurrence was evaluated in 6 independent gene expression datasets. A) Summarized results of accuracy (%), along with sensitivity/specificity in parenthesis (both given as %), of the identified signatures to predict recurrence. B–G) Dot-plots of the identified 2-gene signature (<i>BCL2-CDKN1A)</i> illustrating the probability of recurrence. The vertical line separates the cases, i.e. patients with recurrence (left of the line) from controls (right of the line). The horizontal line refers to the cut-point used, hence the upper left and lower right corners includes the correctly classified patients. X-axis denotes the patient index in the study (same random order as original study). The Y-axis is the SVM probability of recurrence. B) GSE1378 C) GSE1379 D) GSE9893 E) GSE12093 F) GSE6532-GPL96 and G) GSE6532-GPL570.</p

Joint distribution of the ΔΔCt values of <i>BCL2</i> and <i>CDKN1A</i>.

<p>The diagonal line corresponds to the rule determined by conditional logistic regression. Pairs to the right of the line are correctly classified with respect to their outcome (recurrence/non-recurrence) (accuracy of 75%), whereas pairs left of the line are classified incorrectly.</p