Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human breast cancers to define clinically distinct molecular subtypes in estrogen receptor positive breast carcinomas using genomic grade

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

SUBMITTER: Benjamin Haibe-Kains

PROVIDER: E-GEOD-6532 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade.

Loi Sherene S Haibe-Kains Benjamin B Desmedt Christine C Lallemand Françoise F Tutt Andrew M AM Gillet Cheryl C Ellis Paul P Harris Adrian A Bergh Jonas J Foekens John A JA Klijn Jan G M JG Larsimont Denis D Buyse Marc M Bontempi Gianluca G Delorenzi Mauro M Piccart Martine J MJ Sotiriou Christos C

Journal of clinical oncology : official journal of the American Society of Clinical Oncology 20070401 10

<h4>Purpose</h4>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.<h4>Materials and methods</h4>We hav ...[more]

PMID: 17401012

Similar Datasets

Project 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. Methods: We generated gene expression profiles (Affymetrix) and computed the GGI for a matched, case-control sample of patients enrolled in the BIG 1-98 trial from the two hospitals where frozen samples were available. All relapses (cases) were identified from patients randomized to receive monotherapy or from the switching treatment arms for whom relapse occurred before the switch. Each case was randomly matched with four controls based upon nodal status and treatment (T or L). The prognostic value of GGI was assessed as a continuous predictor and divided at the median. Predictive accuracy of GGI was estimated using time-dependent area under the curve (AUC) of the ROC curves. Results: Frozen samples were analyzable for 48 patients (10 cases and 38 controls). Seven of the 10 cases had been assigned to receive L. Cases and controls were comparable with respect to menopausal and nodal status, local and chemotherapy, and HER2 positivity. Cases were slightly older than controls and had a larger proportion of large, poorly differentiated ER+/PgR- tumors. The GGI was significantly and linearly related to risk of relapse: each 10-unit increase in GGI resulted in an increase of approximately 11% in the hazard rate (p=0.02). Within the subgroups of patients with node-positive disease or who were treated with L, the hazard of relapse was significantly greater for patients with GGI at or above the median. AUC reached a maximum of 78% at 27 months. Conclusions: This analysis supports the GGI as a good predictor of relapse for ER-positive patients, even among patients who receive L. Validation of these results, in a larger series from BIG 1-98, is planned using the simplified GGI represented by a smaller set of genes and tested by qRT-PCR on paraffin-embedded tissues. Test whether the Gene expression Grade Index (GGI) is a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1-98 trial. 55 microarray experiments from primary breast tumors of endocrine-treated patients. No replicate, no reference sample.

Project 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. Methods: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identified differentially expressed genes in a training set of 64 estrogen receptor (ER)-positive tumor samples by comparing expression profiles between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to define the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan-Meier analysis. All statistical tests were two-sided. Results: We identified 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1-3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confidence interval = 2.25 to 5.78; P<.001, log-rank test). Conclusions: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value. NB: The patients coming from Uppsala Hospital have been also used in other studies as in GSE3494. You can find the common set of patients in removing the abbreviation "UPP_" from the sample names and compare the results with the "INDEX (ID)" from the GSE3494 series. Experiment Overall Design: 64 microarray experiments from primary breast tumors used as training set to identify genes differentially expressed in grade 1 and 3. Experiment Overall Design: 129 microarray experiments from primary breast tumors of untreated patients used as validation set to validate the list of genes and its correlation with survival. Experiment Overall Design: No replicate, no reference sample. **NOTE** There are some inconsistencies between the sample annotation provided by GEO for this experiment in the GSE2990_family.soft.gz file and the supplementary data file GSE2990_suppl_info.txt. ***

Project 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 IIα (TOP2A) and to develop a gene expression signature to identify those patients who do not benefit from anthracyclines. METHODS: The TOP trial included 149 patients, of which 141 were evaluable for response prediction analyses. The primary endpoint was pathological complete response (pCR). TOP2A and gene expression profiles were evaluated using pre-epirubicin biopsies. Gene expression data from ER-negative samples of the EORTC 10994/BIG 00-01 and MDACC 2003-0321 neoadjuvant trials were used for validation purposes. RESULTS: A pCR was obtained in 14% of the evaluable TOP patients. TOP2A amplification, but not protein overexpression, was significantly associated with pCR (p=0.001 and 0.22). We developed an “anthracycline-based score (A-Score)” that combines three signatures: a TOP2A gene signature and two previously published signatures related to tumor invasion and immune response. The A-Score was characterized by a high negative predictive value (NPV=0.98 [95% CI: 0.90-1.00]) overall, and in the HER2-negative and HER2-positive subpopulations. Its performance was independently confirmed in the anthracycline-based (FAC/FEC) arms of the two validation trials (BIG 00-01: 0.80 [0.61-0.92] and MDACC 2003-0321: 1.00 [0.80-1.00]). CONCLUSION: Given its high NPV, the A-Score could become, if further validated, a useful clinical tool to identify those patients who do not benefit from anthracyclines and could therefore be spared the non-negligible side effects. Predicting the efficacy of anthracyclines (epirubicin) in breast cancer (BC) patients (TOP trial) 120 microarray experiments from primary ER-negative breast tumors of anthracycline-treated patients. No replicate, no reference sample.

Project description:Breast cancer is a heterogeneous disease classified into 3 major subtypes based on the presence or absence of molecular markers for oestrogen receptors (ER), progesterone receptors (PR) and human epidermal growth factor-2 (Her2) . Hormone receptor-positive BC (ER+, PR+ and Her-2-), accounts for approximately 70% of patients. Patients with ER- BC account for approximately 30 % of all cases and commonly have a worse prognosis than ER+ patients (9). However, a significant proportion of ER- cases have good outcomes and could potentially benefit from a less aggressive therapy. Triple negative breast cancer (TNBC, ER-, PR- and Her-2-) accounts for approximately 15% of breast cancers and has the poorest outcomes. An hypoxic microenvironment is an important intrinsic component of solid tumours that can result in rapid proliferation of cancer cells and is associated with the lack of oxgyen and abnormal tumour blood vessels. Hypoxia stimulates the hypoxia inducible factor-1α (HIF-1α) that transactivates genes associated with angiogenesis, tumour growth, metastasis, metabolic reprogramming, and treatment resistance. HIF-1α is recognised to induce the expression of carbonic anhydrase IX (CAIX), an enzyme that has been attributed a central role in pH regulation and cancer progression and is particularly pronounced in peri necrotic tumour areas, high-grade BCs. The adaption to hypoxia is governed by multiple transcriptional and post-transcriptional changes in gene expression. Up to 1.5 % of the human genome is estimated to be transcriptionally responsive to hypoxia. Genes and pathways which have been identified as being responsive to hypoxia may have the potential to be used as prognostic or predictive markers, and furthermore, can help identify novel therapeutic targets. The aim of the present study was to gain a better understanding of the transcriptomic and protein pathways associated with CAIX in ER- BC to identify potential therapeutic targets against this aggressive phenotype.

Dataset Information

Transcription profiling of human breast cancers to define clinically distinct molecular subtypes in estrogen receptor positive breast carcinomas using genomic grade

Publications

Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure