Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade.
ABSTRACT A number of microarray studies have reported distinct molecular profiles of breast cancers (BC), such as basal-like, ErbB2-like, and two to three luminal-like subtypes. These were associated with different clinical outcomes. However, although the basal and the ErbB2 subtypes are repeatedly recognized, identification of estrogen receptor (ER) -positive subtypes has been inconsistent. Therefore, refinement of their molecular definition is needed.
We have previously reported a gene expression grade index (GGI), which defines histologic grade based on gene expression profiles. Using this algorithm, we assigned ER-positive BC to either high-or low-genomic grade subgroups and compared these with previously reported ER-positive molecular classifications. As further validation, we classified 666 ER-positive samples into subtypes and assessed their clinical outcome.
Two ER-positive molecular subgroups (high and low genomic grade) could be defined using the GGI. Despite tracking a single biologic pathway, these were highly comparable to the previously described luminal A and B classification and significantly correlated to the risk groups produced using the 21-gene recurrence score. The two subtypes were associated with statistically distinct clinical outcome in both systemically untreated and tamoxifen-treated populations.
The use of genomic grade can identify two clinically distinct ER-positive molecular subtypes in a simple and highly reproducible manner across multiple data sets. This study emphasizes the important role of proliferation-related genes in predicting prognosis in ER-positive BC.
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ABSTRACT: 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. 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). A novel gene expression signature predictive of outcome of Tamoxifen-treated patients was identified. The validation suggests that BCL2-CDKN1A exhibit promising predictive potential.PLoS ONE 01/2013; 8(1):e54078. · 3.73 Impact Factor
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ABSTRACT: BACKGROUND: The forkhead transcription factor FOXM1 is a key regulator of the cell cycle. It is frequently over-expressed in cancer and is emerging as an important therapeutic target. In breast cancer FOXM1 expression is linked with estrogen receptor (ERalpha) activity and resistance to endocrine therapies with high levels correlated with poor prognosis. However, the precise role of FOXM1 in ER positive breast cancer is not yet fully understood. RESULTS: The study utilizes chromatin immunoprecipitation followed by high-throughput sequencing to map FOXM1 binding in both ERalpha positive and negative breast cancer cell lines. The comparison between binding site distributions in the two cell lines uncovered a previously undescribed relationship between binding of FOXM1 and ERalpha. Further molecular analyses demonstrated that these two factors can bind simultaneously at genomic sites and furthermore that FOXM1 regulates the transcriptional activity of ERalpha via interaction with the coactivator CARM1. Inhibition of FOXM1 activity using the natural product thiostrepton revealed down-regulation of a set of FOXM1 regulated genes that are correlated with patient outcome in clinical breast cancer samples. CONCLUSIONS: These findings reveal a novel role for FOXM1 in ERalpha transcriptional activity in breast cancer and uncover a FOXM1-regulated gene signature associated with ER positive breast cancer patient prognosis.Genome biology 01/2013; 14(1):R6. · 10.30 Impact Factor
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ABSTRACT: A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that "random" gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets.PLoS Computational Biology 01/2013; 9(1):e1002875. · 4.87 Impact Factor