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GSE6532
Homo sapiens
737 Downloadable Samples
Affymetrix Human Genome U133A Array (hgu133a)
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.
Publication Title
Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade.
Sample Metadata Fields
Age, Disease stage
View Samples- Homo sapiens
- 108 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
DNA methylation profiling reveals a predominant immune component in breast cancers.
Sample Metadata Fields
Specimen part, Disease stage, Cell line, Treatment
View Samples- Homo sapiens
- 197 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Background: Recently a 76-gene prognostic signature able to predict distant metastases in lymph node-negative (N-) breast cancer patients was reported. The aims of this study conducted by TRANSBIG were to independently validate these results and to compare the outcome with clinical risk assessment. Materials and Methods: Gene expression profiling of frozen samples from 198 N- systemically untreated patients was performed at the Bordet Institute, blinded to clinical data and independent of Veridex. Genomic risk was defined by Veridex, blinded to clinical data. Survival analyses, done by an independent statistician, were performed with the genomic risk and adjusted for the clinical risk, defined by Adjuvant!Online. Results: The actual 5- and 10-year time to distant metastasis (TDM) were 98% (88%-100%) and 94% (83%-98%) respectively for the good profile group and 76% (68%- 82%) and 73% (65%-79%) for the poor profile group. The actual 5- and 10-year overall survival (OS) were 98% (88%-100%) and 87% (73%-94%) respectively for the good profile group and 84% (77%-89%) and 72% (63%-78%) for the poor profile group. We observed a strong time-dependency of this signature, leading to an adjusted HR of 13.58 (1.85-99.63) and 8.20 (1.10-60.90) at 5 years, and 5.11 (1.57-16.67) and 2.55 (1.07-6.10) at 10 years for TDM and OS respectively. Conclusion: This independent validation confirmed the performance of the 76-gene signature and adds to the growing evidence that gene expression signatures are of clinical relevance, especially for identifying patients at high risk of early distant metastases.
Publication Title
Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series.
Sample Metadata Fields
Age, Disease stage
View Samples- Homo sapiens
- 185 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Background: Histologic grade in breast cancer provides clinically important prognostic information. However, 30%-60% of tumors are classified as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profi les of breast cancers and whether such profi les could be used to improve histologic grading.
Publication Title
Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis.
Sample Metadata Fields
Age, Disease stage
View Samples- Homo sapiens
- 116 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples.
Publication Title
No associated publication
Sample Metadata Fields
Cell line
View Samples- Homo sapiens
- 120 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
PURPOSE: Validated biomarkers predictive of response/resistance to anthracyclines in breast cancer are currently lacking. The neoadjuvant TOP trial, in which patients with estrogen receptor (ER)-negative tumors were treated with anthracycline (epirubicin) monotherapy, was specifically designed to evaluate the predictive value of topoisomerase II (TOP2A) and to develop a gene expression signature to identify those patients who do not benefit from anthracyclines.
Publication Title
Multifactorial approach to predicting resistance to anthracyclines.
Sample Metadata Fields
Disease stage
View Samples- Homo sapiens
- 90 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Breast cancer is a molecularly, biologically and clinically heterogeneous group of disorders. Understanding this diversity is essential to improving diagnosis and optimising treatment. Both genetic and acquired epigenetic abnormalities participate in cancer, but information is scant on the involvement of the epigenome in breast cancer and its contribution to the complexity of the disease. Here we used the Infinium Methylation Platform to profile at single-CpG resolution (over 14,000 genes interrogated) the methylomes of 119 breast tumours. It emerges that many genes whose expression is linked to the ER status are epigenetically controlled (or/ we show that the two major phenotypes of breast cancers determined by ER status are widely involving epigenetic regulatory mechanisms), offering the prospect of a novel approach to treating ER-positive tumours. We have distinguished methylation-profile-based tumour clusters, some coinciding with known expression subtypes but also new entities that may provide a meaningful basis for refining breast tumour typology. We show that methylation patterns may reflect the cellular origins of tumours. Having highlighted an unexpectedly strong epigenetic component in the regulation of key immune pathways, we show that a set of immune genes have high prognostic value in specific tumour categories. By laying the ground for better understanding of breast cancer heterogeneity and improved tumour taxonomy, the precise epigenetic portraits drawn here should contribute to better management of breast cancer patients.
Publication Title
DNA methylation profiling reveals a predominant immune component in breast cancers.
Sample Metadata Fields
Disease stage
View Samples- Homo sapiens
- 77 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Background: Estrogen receptor positive (ER+) breast cancers (BC) are heterogeneous with regard to their clinical behavior and response to therapies. The ER is currently the best predictor of response to the anti-estrogen agent tamoxifen, yet up to 30-40% of ER+BC will relapse despite tamoxifen treatment. New prognostic biomarkers and further biological understanding of tamoxifen resistance are required. We used gene expression profiling to develop an outcome-based predictor using a training set of 255 ER+ BC samples from women treated with adjuvant tamoxifen monotherapy. We used clusters of highly correlated genes to develop our predictor to facilitate both signature stability and biological interpretation. Independent validation was performed using 362 tamoxifen-treated ER+ BC samples obtained from multiple institutions and treated with tamoxifen only in the adjuvant and metastatic settings.
Publication Title
Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen.
Sample Metadata Fields
Age, Disease stage, Treatment
View Samples- Homo sapiens
- 53 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Background: We have previously shown that the Gene expression Grade Index (GGI) was able to identify two subtypes of estrogen receptor (ER)-positive tumors that were associated with statistically distinct clinical outcomes in both untreated and tamoxifen-treated patients. Here, we aim to investigate the ability of the GGI to predict relapses in postmenopausal women who were treated with tamoxifen (T) or letrozole (L) within the BIG 1-98 trial.
Publication Title
The Gene expression Grade Index: a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1-98 trial.
Sample Metadata Fields
Age, Specimen part, Disease stage, Treatment
View Samples- Mus musculus, Homo sapiens
- 22 Downloadable Samples
- Illumina HumanHT-12 V4.0 expression beadchip
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
No associated publication
Sample Metadata Fields
Specimen part, Time
View Samples