Breast Tumor Clinical Implications
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE3193 | GEO | 2005/08/25
SECONDARY ACCESSION(S): PRJNA92851
REPOSITORIES: GEO
Similar Datasets
Project description:The purpose of this study was to explore the possibility of classifying breast carcinomas based upon variations in gene expression patterns derived from cDNA microarrays, and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing tumor and normal breast tissues from 78 individuals were analyzed by hierarchical-clustering. As reported previously, we identified a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized lluminal epithelial/ER-positiven group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets; first, a set of 456 cDNA clones previously selected to reflect the lintrinsicn properties of the tumors and, second, a gene set identified that highly correlated with patient outcome. Survival analyses on the sub-cohort of 51 patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.
2005-06-28 | E-SMDB-579 | biostudies-arrayexpress
Project description:The purpose of this study was to explore the possibility of classifying breast carcinomas based upon variations in gene expression patterns derived from cDNA microarrays, and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing tumor and normal breast tissues from 78 individuals were analyzed by hierarchical-clustering. As reported previously, we identified a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized lluminal epithelial/ER-positiven group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets; first, a set of 456 cDNA clones previously selected to reflect the lintrinsicn properties of the tumors and, second, a gene set identified that highly correlated with patient outcome. Survival analyses on the sub-cohort of 51 patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups. Set of arrays organized by shared biological context, such as organism, tumors types, processes, etc. Computed
2005-08-25 | E-GEOD-3193 | biostudies-arrayexpress
Project description:INTRODUCTION: Basal-phenotype or basal-like breast cancers are characterized by basal epithelium cytokeratin (CK5/14/17) expression, negative estrogen receptor (ER) status and distinct gene expression signature. We studied the clinical and biological features of the basal-phenotype tumors determined by immunohistochemistry (IHC) and cDNA microarrays especially within the ER-negative subgroup. METHODS: IHC was used to evaluate the CK5/14 status of 445 stage II breast cancers. The gene expression signature of the CK5/14 immunopositive tumors was investigated within a subset (100) of the breast tumors (including 50 ER-negative tumors) with a cDNA microarray. Survival for basal-phenotype tumors as determined by CK5/14 IHC and gene expression signature was assessed. RESULTS: From the 375 analyzable tumor specimens, 48 (13%) were immunohistochemically positive for CK5/14. We found adverse distant disease-free survival for the CK5/14-positive tumors during the first years (3 years hazard ratio (HR) 2.23, 95% confidence interval (CI) 1.17 to 4.24, p = 0.01; 5 years HR 1.80, 95% CI 1.02 to 3.15, p = 0.04) but the significance was lost at the end of the follow-up period (10 years HR 1.43, 95% CI 0.84 to 2.43, p = 0.19). Gene expression profiles of immunohistochemically determined CK5/14-positive tumors within the ER-negative tumor group implicated 1,713 differently expressed genes (p < 0.05). Hierarchical clustering analysis with the top 500 of these genes formed one basal-like and a non-basal-like cluster also within the ER-negative tumor entity. A highly concordant classification could be constructed with a published gene set (Sorlie's intrinsic gene set, concordance 90%). Both gene sets identified a basal-like cluster that included most of the CK5/14-positive tumors, but also immunohistochemically CK5/14-negative tumors. Within the ER-negative tumor entity there was no survival difference between the non-basal and basal-like tumors as identified by immunohistochemical or gene-expression-based classification. CONCLUSION: Basal cytokeratin-positive tumors have a biologically distinct gene expression signature from other ER-negative tumors. Even if basal cytokeratin expression predicts early relapse among non-selected tumors, the clinical outcome of basal tumors is similar to non-basal ER-negative tumors. Immunohistochemically basal cytokeratin-positive tumors almost always belong to the basal-like gene expression profile, but this cluster also includes few basal cytokeratin-negative tumors. Keywords: Disease state analysis
2007-02-26 | GSE6768 | GEO
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:This SuperSeries is composed of the following subset Series: GSE4335: Norway/Stanford Breast Tumors GSE4336: Breast Tumors Abstract: Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 "intrinsic" genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities. Refer to individual Series
2006-03-07 | E-GEOD-4382 | biostudies-arrayexpress
Project description:Purpose HER2 gene amplification or protein overexpression (HER2+) defines a clinically challenging subgroup of breast cancer with variable prognosis and response to therapy. We aimed to investigate the heterogeneous biological appearance and clinical behavior of HER2+ tumors using molecular profiling. Materials and Methods Hierarchical clustering of gene expression data from 58 HER2-amplified tumors of various stage, histological grade and estrogen receptor (ER) status was used to construct a HER2-derived prognostic predictor that was further evaluated in several large independent breast cancer data sets. Results Unsupervised analysis identified three subtypes of HER2+ tumors with mixed stage, histological grade and ER-status. One subtype had a significantly worse clinical outcome. A prognostic predictor was created based on differentially expressed genes between the subtype with worse outcome and the other subtypes. The predictor was able to define patient groups with better and worse outcome in HER2+ breast cancer across multiple independent breast cancer data sets and identify a sizable HER2+ group with long disease-free survival and low mortality. Significant correlation to prognosis was also observed in basal-like, ER−, lymph node positive or high-grade tumors, irrespective of HER2-status. The predictor included genes associated to immune response, tumor invasion and metastasis. Conclusion The HER2-derived prognostic predictor provides further insight into the heterogeneous biology of HER2+ tumors and may become useful for improved selection of patients that need additional treatment with new drugs targeting the HER2 pathway. Array comparative genomic hybridization (aCGH) identified 58 breast tumors with amplification of HER2 from a larger cohort of approx 500 tumors breast. Global gene expression profiles were obtained using 70-mer oligonucleotide microarrays. Unsupervised hierarchical clustering of the 58 tumors, using Pearson correlation and complete linkage, identified three main clusters. One cluster showed significantly poorer clinical outcome. Significance of microarray (SAM) analysis was performed to identify 158 genes separating the poor outcome cluster compared to the other two clusters. Gene expression centroids, based on the 158 genes, were created for each cluster for validation in independent breast cancer data sets.
2010-03-16 | E-GEOD-18328 | biostudies-arrayexpress
Project description:Abstract: Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 "intrinsic" genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities. This SuperSeries is composed of the SubSeries listed below.
2006-03-08 | GSE4382 | GEO
Project description:MicroRNAs (miRNAs) regulate many genes critical for tumorigenesis. We profiled miRNAs from 11 normal breast tissues, 17 non-invasive, 151 invasive breast carcinomas, and 6 cell lines by in-house-developed barcoded Solexa sequencing. miRNAs were organized in genomic clusters representing promoter-controlled miRNA expression and sequence families representing seed-sequence-dependent miRNA-target regulation. Unsupervised clustering of samples by miRNA sequence families best reflected the clustering based on mRNA expression available for this sample set. Clustering and comparative analysis of miRNA read frequencies showed that normal breast samples were separated from most non-invasive ductal carcinoma in situ and invasive carcinomas by increased miR-21 (the most abundant miRNA in carcinomas) and multiple decreased miRNA families (including mir-98/let-7), with most miRNA changes apparent already in the non-invasive carcinomas. In addition, patients that went on to develop metastasis demonstrated increased expression of mir-423, and triple negative breast carcinomas were most distinct from other tumor subtypes due to up-regulation of the mir-17~92 cluster. However, absolute miRNA levels between normal breast and carcinomas did not reveal any significant differences. We also discovered two polymorphic nucleotide variations among the more abundant miRNAs miR-181a (T19G) and miR-185 (T16G), but we did not identify nucleotide variations expected for classical tumor suppressor function associated with miRNAs. The differentiation of tumor subtypes and prediction of metastasis based on miRNA levels is statistically possible, but is not driven by deregulation of abundant miRNAs, implicating far fewer miRNAs in tumorigenic processes than previously suggested. mRNA profiles for 161 normal breast and invasive breast carcinomas using NKI Operon V3 arrays; These samples have matched entries for miRNA profiles also included in this GEO submission.
2011-06-07 | E-GEOD-29174 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the following subset Series: GSE29173: MicroRNA sequence and expression analysis in breast tumors by deep sequencing [miRNA sequence data] GSE29174: MicroRNA sequence and expression analysis in breast tumors by deep sequencing [mRNA expression array data] MicroRNAs (miRNAs) regulate many genes critical for tumorigenesis. We profiled miRNAs from 11 normal breast tissues, 17 non-invasive, 151 invasive breast carcinomas, and 6 cell lines by in-house-developed barcoded Solexa sequencing. miRNAs were organized in genomic clusters representing promoter-controlled miRNA expression and sequence families representing seed-sequence-dependent miRNA-target regulation. Unsupervised clustering of samples by miRNA sequence families best reflected the clustering based on mRNA expression available for this sample set. Clustering and comparative analysis of miRNA read frequencies showed that normal breast samples were separated from most non-invasive ductal carcinoma in situ and invasive carcinomas by increased miR-21 (the most abundant miRNA in carcinomas) and multiple decreased miRNA families (including mir-98/let-7), with most miRNA changes apparent already in the non-invasive carcinomas. In addition, patients that went on to develop metastasis demonstrated increased expression of mir-423, and triple negative breast carcinomas were most distinct from other tumor subtypes due to up-regulation of the mir-17~92 cluster. However, absolute miRNA levels between normal breast and carcinomas did not reveal any significant differences. We also discovered two polymorphic nucleotide variations among the more abundant miRNAs miR-181a (T19G) and miR-185 (T16G), but we did not identify nucleotide variations expected for classical tumor suppressor function associated with miRNAs. The differentiation of tumor subtypes and prediction of metastasis based on miRNA levels is statistically possible, but is not driven by deregulation of abundant miRNAs, implicating far fewer miRNAs in tumorigenic processes than previously suggested. Refer to individual Series
2011-06-07 | E-GEOD-28884 | biostudies-arrayexpress
Project description:MicroRNAs (miRNAs) regulate many genes critical for tumorigenesis. We profiled miRNAs from 11 normal breast tissues, 17 non-invasive, 151 invasive breast carcinomas, and 6 cell lines by in-house-developed barcoded Solexa sequencing. miRNAs were organized in genomic clusters representing promoter-controlled miRNA expression and sequence families representing seed-sequence-dependent miRNA-target regulation. Unsupervised clustering of samples by miRNA sequence families best reflected the clustering based on mRNA expression available for this sample set. Clustering and comparative analysis of miRNA read frequencies showed that normal breast samples were separated from most non-invasive ductal carcinoma in situ and invasive carcinomas by increased miR-21 (the most abundant miRNA in carcinomas) and multiple decreased miRNA families (including mir-98/let-7), with most miRNA changes apparent already in the non-invasive carcinomas. In addition, patients that went on to develop metastasis demonstrated increased expression of mir-423, and triple negative breast carcinomas were most distinct from other tumor subtypes due to up-regulation of the mir-17~92 cluster. However, absolute miRNA levels between normal breast and carcinomas did not reveal any significant differences. We also discovered two polymorphic nucleotide variations among the more abundant miRNAs miR-181a (T19G) and miR-185 (T16G), but we did not identify nucleotide variations expected for classical tumor suppressor function associated with miRNAs. The differentiation of tumor subtypes and prediction of metastasis based on miRNA levels is statistically possible, but is not driven by deregulation of abundant miRNAs, implicating far fewer miRNAs in tumorigenic processes than previously suggested.
2011-06-07 | GSE29174 | GEO