A New Breast Tumor Intrinsic Gene List Identifies Novel Characteristics that are Conserved Across Microarray Platforms
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE1992 | GEO | 2005/12/08
SECONDARY ACCESSION(S): PRJNA91007
REPOSITORIES: GEO
Similar Datasets
Project description:The purpose of the present study was to investigate the association of glutathione S-transferase P1 (GSTP1) expression with resistance to neoadjuvant paclitaxel followed by 5-fluorouracil/epirubicin/cyclophosphamide (P-FEC) in human breast cancers. The relationship of GSTP1 expression and GSTP1 promoter hypermethylation with intrinsic subtypes was also investigated. In this study, primary breast cancer patients (n = 123, stage II-III) treated with neoadjuvant P-FEC were analyzed. Tumor samples were obtained by vacuum-assisted core biopsy before P-FEC. GSTP1 expression was determined using immunohistochemistry, GSTP1 promoter methylation index (MI) using bisulfite methylation assay and intrinsic subtypes using DNA microarray. The pathological complete response (pCR) rate was significantly higher in GSTP1-negative tumors (80.0%) than GSTP1-positive tumors (30.6%) (P = 0.009) among estrogen receptor (ER)-negative tumors but not among ER-positive tumors (P = 0.267). Multivariate analysis showed that GSTP1 was the only predictive factor for pCR (P = 0.013) among ER-negative tumors. Luminal A, luminal B and HER2-enriched tumors showed a significantly lower GSTP1 positivity than basal-like tumors (P = 0.002, P < 0.001 and P = 0.009, respectively), while luminal A, luminal B and HER2-enriched tumors showed a higher GSTP1 MI than basal-like tumors (P = 0.076, P < 0.001 and P < 0.001, respectively). In conclusion, these results suggest the possibility that GSTP1 expression can predict pathological response to P-FEC in ER-negative tumors but not in ER-positive tumors. Additionally, GSTP1 promoter hypermethylation might be implicated more importantly in the pathogenesis of luminal A, luminal B and HER2-enriched tumors than basal-like tumors. Fresh frozen tumor samples obtained by vacuum-assisted core biopsy from one hundred and fifteen patients were subjected to RNA extraction and hybridization on Affymetrix microarrays.
2012-03-20 | E-GEOD-32646 | biostudies-arrayexpress
Project description:Microarrays have shown that gene expression patterns can be used to molecularly classify breast cancers into distinct and clinically significant groups. In order to translate these profiles into routine diagnostics, we have recapitulated a microarray breast cancer classification using real-time quantitative (q)RT-PCR. We performed statistical analyses on multiple independent microarray datasets to select an “intrinsic” gene set that can classify breast tumors into four different subtypes designated as Luminal, Normal-like, HER2+/ER-, and Basal-like. A minimal gene set from the microarray “intrinsic” list, and additional genes important for outcome (e.g., proliferation genes), were used to develop a real-time qRT-PCR assay comprised of 53 classifiers and 3 housekeepers. We prospectively compared the expression data and classifications from microarray and real-time qRT-PCR using 123 unique breast samples (117 invasive carcinomas, 1 fibroadenoma and 5 normal tissues) and 3 cells lines. The overall correlation for the 50 genes in common between microarray and qRT-PCR was 0.76. There was 91% (114/126) concordance in the hierarchical clustering classification of the real-time qRT-PCR minimal “intrinsic” gene set (37 genes) and the larger (402 genes) microarray gene set from which the PCR list was derived. As expected, the Luminal tumors (ER+) had a significantly better outcome than the HER2+/ER- (p=0.043) and Basal-like tumors (p=0.001). High expression of the proliferation genes GTBP4 (p=0.011), HSPA14 (p=0.023), and STK6 (p=0.027) were significant predictors of relapse free survival (RFS) independent of grade and stage. Our study shows that genomic microarray data can be translated into a qRT-PCR diagnostic assay that enhances the ability to predict outcome and may therefore improve the standard of care in breast cancer. Keywords: other
2006-01-31 | GSE2607 | GEO
Project description:Due to their role in tumorigenesis and remarkable stability in body fluids, microRNAs (miRNAs) are emerging as a promising diagnostic tool. The aim of this study was to identify tumor miRNA signatures for the discrimination of breast cancer and the intrinsic molecular subtypes, and the study in plasma of the status of the most significant ones in order to identify potential circulating biomarkers for breast cancer detection. MiRNA expression profiling of 1919 human miRNAs was conducted in 122 FFPE breast tumors (31 luminal A, 33 luminal B, 27 Her2 and 31 triple negative) and 11 normal breast tissues using LNA based miRNA microarrays. Breast tumors were divided into a training (n=61) and a test set (n=61). Both series comprised a similar number of samples from each molecular subtype. Differential expression analysis was performed and microarray classifiers were developed with samples from the training set and validated in samples from the test set. The most relevant miRNAs were validated by quantitative PCR and analyzed in plasma from 36 pretreated patients, 47 postreated patients and 26 healthy individuals. In addition, further validation in 114 pretreated patients and 116 healthy individuals was performed.
2015-09-03 | E-GEOD-58606 | biostudies-arrayexpress
Project description:Purpose: A number of microarray studies have reported distinct molecular profiles of breast cancers (BC): 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. Refinement of their molecular definition is therefore needed. Materials and methods: We have previously reported a gene-expression grade index (GGI) which defines histological 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 to previously reported ER-positive molecular classifications. As further validation, we classified 666 ER-positive samples into subtypes and assessed their clinical outcome. Results: Two ER-positive molecular subgroups (high and low genomic grade) could be defined using the GGI. Despite tracking a single biological 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. Conclusions: The use of genomic grade can identify two clinically distinct ER-positive molecular subtypes in a simple and highly reproducible manner across multiple datasets. This study emphasizes the important role of proliferation-related genes in predicting prognosis in ER-positive BC. Experiment Overall Design: dataset of microarray experiments from primary breast tumors used to assess the reationship between GGI, molecular subtypes, and tamoxifen resistance. Experiment Overall Design: No replicate, no reference sample.
2008-06-20 | E-GEOD-6532 | biostudies-arrayexpress
Project description:Breast cancer is a heterogeneous disease comprised of at least five major subtypes. Luminal subtype tumors confer a more favourable patient prognosis, which is in part, attributed to the Estrogen Receptor-alpha (ER) positivity and anti-hormone responsiveness of these tumors. Expression of the forkhead box transcription factor, FOXA1, also correlates with the luminal subtype and patient survival, but is present in a subset of ER-negative tumors. Similarly, FOXA1 is consistently expressed in luminal breast cancer cell lines even in the absence of ER. In contrast, basal breast cancer cell lines do not express FOXA1, and loss of FOXA1 in luminal cells increases migration and invasion, characteristics of the basal subtype. To delineate an ER-independent role for FOXA1 in maintaining the luminal phenotype, and hence a more favourable prognosis, we performed cDNA microarray analyses on luminal FOXA1-positive, ER-positive (MCF7, T47D) and FOXA1-positive, ER-negative (MDA-MB-453, SKBR3) cell lines in the presence or absence of transient FOXA1 silencing. This resulted in three FOXA1 transcriptomes: (1) a luminal-signature (consistent across cell lines), (2) an ER-positive signature (restricted to MCF7 and T47D) and (3) an ER-negative signature (restricted to MDA-MB-453 and SKBR3). Use of Gene Set Enrichment Analyses (GSEA) as a phenotyping tool revealed that FOXA1 silencing resulted in a transcriptome shift from luminal to basal gene expression signatures. FOXA1 binds to both luminal and basal genes within luminal breast cancer cells, suggesting that it not only transactivates luminal genes, but also represses basal-associated genes. From these results we conclude that FOXA1 controls plasticity between basal and luminal cells, playing a dominant role in repressing the basal phenotype, and thus tumor aggressiveness, in luminal breast cancer cells. Although it has been proposed that FOXA1-targeting agents may be useful for treating luminal tumors, these data suggest that this approach may promote transitions toward a more aggressive cancer. FOXA1 siRNA treated breast cell lines compared directly to nonspecific siRNA treated cell lines using Agilent 4X44 microarrays.
2012-03-09 | E-GEOD-31003 | biostudies-arrayexpress
Project description:The purpose of the present study was to investigate the association of glutathione S-transferase P1 (GSTP1) expression with resistance to neoadjuvant paclitaxel followed by 5-fluorouracil/epirubicin/cyclophosphamide (P-FEC) in human breast cancers. The relationship of GSTP1 expression and GSTP1 promoter hypermethylation with intrinsic subtypes was also investigated. In this study, primary breast cancer patients (n = 123, stage II-III) treated with neoadjuvant P-FEC were analyzed. Tumor samples were obtained by vacuum-assisted core biopsy before P-FEC. GSTP1 expression was determined using immunohistochemistry, GSTP1 promoter methylation index (MI) using bisulfite methylation assay and intrinsic subtypes using DNA microarray. The pathological complete response (pCR) rate was significantly higher in GSTP1-negative tumors (80.0%) than GSTP1-positive tumors (30.6%) (P = 0.009) among estrogen receptor (ER)-negative tumors but not among ER-positive tumors (P = 0.267). Multivariate analysis showed that GSTP1 was the only predictive factor for pCR (P = 0.013) among ER-negative tumors. Luminal A, luminal B and HER2-enriched tumors showed a significantly lower GSTP1 positivity than basal-like tumors (P = 0.002, P < 0.001 and P = 0.009, respectively), while luminal A, luminal B and HER2-enriched tumors showed a higher GSTP1 MI than basal-like tumors (P = 0.076, P < 0.001 and P < 0.001, respectively). In conclusion, these results suggest the possibility that GSTP1 expression can predict pathological response to P-FEC in ER-negative tumors but not in ER-positive tumors. Additionally, GSTP1 promoter hypermethylation might be implicated more importantly in the pathogenesis of luminal A, luminal B and HER2-enriched tumors than basal-like tumors.
2012-03-20 | GSE32646 | GEO
Project description:Breast cancer is a profoundly heterogeneous disease with respect to biological and clinical behavior. Gene expression profiling has been used to dissect this complexity and stratify tumors into intrinsic gene expression subtypes associated with distinct biology, patient outcome and different genomic alterations. Additionally, breast tumors occurring in individuals with germline BRCA1 or BRCA2 mutations typically fall into distinct subtypes. We applied global DNA copy number and gene expression profiling in 359 breast tumors. All tumors were classified according to intrinsic gene expression subtypes and included cases from genetically predisposed women. The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was used to identify significant DNA copy number aberrations and genomic subgroups of breast cancer. We identified 31 genomic regions that were highly amplified in >1% of the 359 breast tumors. Several amplicons were found to co-occur, the 8p12 and 11q13.3 regions being the most frequent combination besides amplicons on the same chromosomal arm. Unsupervised hierarchical clustering with 133 significant GISTIC regions (66 and 67 with DNA copy number gain and loss, respectively) revealed six genomic subtypes, termed: 17q12, basal-complex, luminal-simple, luminal-complex, amplifier and mixed subtype. Four of them had striking similarity to intrinsic gene expression subtypes and showed association to conventional tumor biomarkers and clinical outcome. However, luminal A-classified tumors were distributed in two main genomic subtypes, luminal-simple and luminal-complex, the former group having better prognosis while the latter group included also luminal B and the majority of BRCA2-mutated tumors. The basal-complex subtype displayed extensive genomic homogeneity and harbored the majority of BRCA1-mutated tumors. The 17q12 subtype comprised mostly HER2-amplified and HER2-enriched subtype tumors and had the worst prognosis. The amplifier and mixed subtypes contained tumors from all gene expression subtypes, the former being enriched for 8p12-amplified cases while the mixed subtype included many tumors with predominantly DNA copy number losses and poor prognosis. Genomic profiling of 359 breast tumors using tiling BAC aCGH. A number of cases were hybridized as replicates or replicate as dye-swaps. Gene expression profiling of 359 breast tumors using 55K oligonucleotide microarrays.
2010-06-23 | E-GEOD-22133 | biostudies-arrayexpress
Project description:Basal-like breast cancers have several well-characterized distinguishing molecular features, but most of these are features of the cancer cells themselves. The unique stromal-epithelial interactions, and more generally, microenvironmental features of basal-like breast cancers, have not been well characterized. To identify characteristic microenvironment features of basal-like breast cancer, we performed cocultures of several basal-like breast cancer cell lines with fibroblasts (reduction mammoplasty-derived fibroblast line; RMF) and compared these to cocultures of luminal breast cancer cell lines with fibroblasts. Interactions between basal-like cancer cells and fibroblasts induced expression of numerous interleukins and chemokines, including IL-6, IL-8, CXCL1, CXCL3, and TGFbeta. Under the influence of fibroblasts, basal-like breast cancer cell lines also showed increased migration in vitro. Migration was less pronounced for luminal lines, but these lines were more likely to have altered proliferation. These differences were relevant to tumor biology in vivo, as the gene set that distinguished luminal and basal-like stromal interactions in coculture also distinguishes basal-like from luminal tumors with 98% accuracy in 10-fold CV and 100% accuracy in an independent test set. However, comparisons between cocultures where cells were in direct contact and cocultures where interaction was solely through soluble factors suggest that there is an important impact of direct cell-to-cell contact. The phenotypes and gene expression changes invoked by cancer cell interactions with fibroblasts support the microenvironment and cell-cell interactions as intrinsic features of breast cancer subtypes. 99 samples were hybridized with Stratagene common reference and profiled on Agilent microarrays.
2011-05-26 | E-GEOD-26411 | biostudies-arrayexpress
Project description:Purpose: The biological subtypes of breast cancer designated as Luminal A, Luminal B, HER2+/ER-, and Basal-like are clinically important for prognosis and planning treatment strategies. Recognizing that there is a continuum in both the spectrum of breast cancer disease and the risk of survival, we sought to develop a clinical test for the biological subtypes using a supervised risk classier.Methods: Microarray and real-time quantitative RT-PCR (qRT-PCR) data from 189 samples, procured as fresh-frozen and formalin-fixed, paraffin-embedded tissues, were used to statistically select prototypical samples and genes for the biological subtypes of breast cancer. Predictions for biological subtype and risk of recurrence were determined for different stages of disease, treatments, and across analytical platforms. Results: The biological subtype predictions on a large combined microarray test set showed prognostic significance across all patients (1244 subjects; p<0.0001), on node negative patients with no adjuvant systemic therapy (738 subjects; p<0.0001), and on patients treated with endocrine therapy (404 subjects; p=0.001). Analysis of a neoadjuvant chemotherapy study revealed a high pathologic complete response (pCR) rate in HER2+/ER- and Basal-like patients. The subtype and risk predications were also highly significant when using the qRT-PCR assay from archived FFPE breast cancers. Conclusion: Our risk predictor based on distance to biological subtype centroids provides a continuous risk score that applies to all stages of breast cancer given current therapies. The assay can be performed using archived breast tissues and a real-time qRT-PCR assay, thus facilitating application to retrospective cohorts and clinical samples. Keywords: reference x sample Comparison of reference samples against treatment
2010-06-24 | E-GEOD-10886 | biostudies-arrayexpress
Project description:Molecular subtypes of breast cancer are characterized by patterns of gene expression, which can be used to predict response to therapy and overall clinical outcome. The luminal breast cancer subtypes are defined by the expression of ER-alpha (ERa) and a set of ERa-associated genes. The transcription factor activator protein 2C (TFAP2C, AP-2C, AP-2g) transcription factor plays a critical role in regulating cell growth and differentiation during ectodermal development and has been implicated in the regulation of ERa and other luminal-associated genes in breast cancer. While TFAP2C has been established as a prognostic factor in human breast cancer, the role of TFAP2C in development of the luminal epithelial cells in the normal mammary gland and in breast cancer have remained elusive. Herein, we demonstrate a critical role of TFAP2C in maintaining the luminal differentiation phenotype during normal mammary development and in luminal breast carcinoma cell lines. Total RNA from MCF7 cells with and without knockdown of TFAP2c. 3 biological replicates, with 2 technical replicates each, were performed for each sample type.
2014-02-01 | E-GEOD-44203 | biostudies-arrayexpress