E-GEOD-52194
Ontology highlight
ABSTRACT:
DISEASE(S): Breast Carcinoma,Normal
PROVIDER: E-GEOD-52194 | ExpressionAtlas | 2025-01-24
REPOSITORIES: ExpressionAtlas
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| E-GEOD-52194-analysis-methods.tsv | Tabular | |||
| E-GEOD-52194-analytics.tsv | Tabular | |||
| E-GEOD-52194-atlasExperimentSummary.Rdata | Rdata | |||
| E-GEOD-52194-configuration.xml | Xml | |||
| E-GEOD-52194-g3_g1-mvaPlot.png | Other |
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Project description:Goal: To define the digital transcriptome of three breast cancer subtypes (TNBC, Non-TNBC, and HER2-positive) using RNA-sequencing technology. To elucidate differentially expressed known and novel transcripts, alternatively spliced genes and differential isoforms and lastly expressed variants in our dataset. Method: Dr. Suzanne Fuqua (Baylor College of Medicine) provided the human breast cancer tissue RNA samples. All of the human samples were used in accordance with the IRB procedures of Baylor College of Medicine. The breast tumour types, TNBC, Non-TNBC and HER2-positive, were classified on the basis of immunohistochemical and RT-qPCR classification. Results: Comparative transcriptomic analyses elucidated differentially expressed transcripts between the three breast cancer groups, identifying several new modulators of breast cancer. We discovered subtype specific differentially spliced genes and splice isoforms not previously recognized in human transcriptome. Further, we showed that exon skip and intron retention are predominant splice events in breast cancer. In addition, we found that differential expression of primary transcripts and promoter switching are significantly deregulated in breast cancer compared to normal breast. We also report novel expressed variants, allelic prevalence and abundance, and coexpression with other variation, and splicing signatures. Additionally we describe novel SNPs and INDELs in cancer relevant genes with no prior reported association of point mutations with cancer mRNA profiles of 17 breast tumor samples of three different subtypes (TNBC, non-TNBC and HER2-positive) and normal human breast organoids (epithelium) samples (NBS) were sequenced using Illumina HiSeq.
2013-11-08 | E-GEOD-52194 | biostudies-arrayexpress
Project description:Introduction: Five different molecular subtypes of breast cancer have been identified through gene expression profiling. Each subtype has a characteristic expression pattern suggested to partly depend on cellular origin. We aimed to investigate whether the molecular subtypes also display distinct methylation profiles. Methods: We analysed methylation status of 807 cancer-related genes in 189 fresh frozen primary breast tumours and four normal breast tissue samples using an array-based methylation assay. Results: Unsupervised analysis revealed three groups of breast cancer with characteristic methylation patterns. The three groups were associated with the luminal A, luminal B and basal-like molecular subtypes of breast cancer, respectively, whereas cancers of the HER2-enriched and normal-like subtypes were distributed among the three groups. The methylation frequencies were significantly different between subtypes, with luminal B and basal-like tumours being most and least frequently methylated, respectively. Moreover, targets of the polycomb repressor complex in breast cancer and embryonic stem cells were more methylated in luminal B tumours than in other tumours. BRCA2-mutated tumours had a particularly high degree of methylation. Finally, by utilizing gene expression data, we observed that a large fraction of genes reported as having subtype-specific expression patterns might be regulated through methylation. Conclusions: We have found that breast cancers of the basal-like, luminal A and luminal B molecular subtypes harbour specific methylation profiles. Our results suggest that methylation may play an important role in the development of breast cancers. DNA methylation profiling of breast cancer samples and normal breast tissue samples. The Illumina GoldenGate Methylation Cancer Panel I was used to obtain DNA methylation profiles across approximately 1500 CpGs. Samples included 189 breast cancer samples and 4 normal breast tissue samples.
2010-06-09 | GSE22210 | GEO
Project description:DCIS is a non-invasive precursor lesion to invasive breast carcinoma. We still have no understanding on why only some DCIS lesions evolve to invasive cancer while others appear not to do so during the life span of the patient. Here, we performed full exome (tumor vs. matching normal), transcriptome and methylome analysis of 30 pure high-grade DCIS (HG-DCIS) and 10 normal breast epithelial samples. Sixty two percent of HG-DCIS cases displayed mutations affecting cancer driver genes or potential drivers. Mutations were observed affecting PIK3CA (21% of cases), TP53 (17%), GATA3 (7%), MLL3 (7%) and single cases of mutations affecting CDH1, MAP2K4, TBX3, NF1, ATM and ARID1A. Significantly, 83% of lesions displayed numerous large chromosomal copy number alterations, suggesting they might precede selection of cancer driver mutations. Integrated pathway-based modeling analysis of RNA-seq data allowed us to identify two DCIS subgroups (DCIS-C1 and DCIS-C2) based on their tumor intrinsic subtypes, proliferative, immune scores and in the activity of specific signaling pathways. The more aggressive DCIS-C1 (highly proliferative, basal-like or ERBB2+) displayed signatures characteristic of activated Treg cells (CD4+/CD25+/FOXP3+) and CTLA4+/CD86+ complexes indicative of a tumor-associated immune suppressive phenotype. Strikingly, all lesions showed evidence of TP53 pathway inactivation. Similarly ncRNA and methylation profiles reproduce changes observed post-invasion. Among the most significant findings we observed upregulation of lncRNA HOTAIR in DCIS-C1 lesions and hypermethylation of HOXA5 and specific SOX genes. We conclude that most HG-DCIS lesions, in spite of representing a pre-invasive stage of tumor progression, displayed molecular profiles indistinguishable from invasive breast cancer. RNAs from 25 out of 30 (83%) pure HG-DCIS and 10 normal breast organoids (total 35 samples) were subjected to RNA-Seq analysis by using Illumina HiSeq2000 platform Please note that description of samples employed for the NGS analyses including age, race, ER/PR immunohistochemistry results, ITIL/STIL scores and PAM50 classification is provided the 'Supplementary Data1_Samples data.xlsx' (available on Superseries record)
2015-07-25 | E-GEOD-69240 | biostudies-arrayexpress
Project description:Genome wide DNA methylation profiling of normal and tumoral tissues of the breast. The Illumina Infinium 450k Human DNA methylation Beadchip was used to obtain DNA methylation profiles across approximately 450 thousand CpGs in fresh frozen tissue samples (40 primary breast tumours and 17 normal breast tissues). Samples included morphologically normal samples of each tissue and tumor samples.
2015-02-04 | GSE52865 | GEO
Project description:Background Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+. Results Based upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability. Conclusion As a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.
2006-11-09 | E-SMDB-3828 | biostudies-arrayexpress
Project description:Background Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+. Results Based upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability. Conclusion As a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.
2007-01-05 | E-SMDB-3830 | biostudies-arrayexpress
Project description:Background Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+. Results Based upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability. Conclusion As a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.
2006-11-10 | E-SMDB-3829 | biostudies-arrayexpress
Project description:Background Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+. Results Based upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability. Conclusion As a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.
2006-12-10 | E-SMDB-3831 | biostudies-arrayexpress
Project description:DCIS is a non-invasive precursor lesion to invasive breast carcinoma. We still have no understanding on why only some DCIS lesions evolve to invasive cancer while others appear not to do so during the life span of the patient. Here, we performed full exome (tumor vs. matching normal), transcriptome and methylome analysis of 30 pure high-grade DCIS (HG-DCIS) and 10 normal breast epithelial samples. Sixty two percent of HG-DCIS cases displayed mutations affecting cancer driver genes or potential drivers. Mutations were observed affecting PIK3CA (21% of cases), TP53 (17%), GATA3 (7%), MLL3 (7%) and single cases of mutations affecting CDH1, MAP2K4, TBX3, NF1, ATM and ARID1A. Significantly, 83% of lesions displayed numerous large chromosomal copy number alterations, suggesting they might precede selection of cancer driver mutations. Integrated pathway-based modeling analysis of RNA-seq data allowed us to identify two DCIS subgroups (DCIS-C1 and DCIS-C2) based on their tumor intrinsic subtypes, proliferative, immune scores and in the activity of specific signaling pathways. The more aggressive DCIS-C1 (highly proliferative, basal-like or ERBB2+) displayed signatures characteristic of activated Treg cells (CD4+/CD25+/FOXP3+) and CTLA4+/CD86+ complexes indicative of a tumor-associated immune suppressive phenotype. Strikingly, all lesions showed evidence of TP53 pathway inactivation. Similarly ncRNA and methylation profiles reproduce changes observed post-invasion. Among the most significant findings we observed upregulation of lncRNA HOTAIR in DCIS-C1 lesions and hypermethylation of HOXA5 and specific SOX genes. We conclude that most HG-DCIS lesions, in spite of representing a pre-invasive stage of tumor progression, displayed molecular profiles indistinguishable from invasive breast cancer. DNA from 24 out of 30 (80%) HG-DCIS samples and 5 normal breast organoids (total 29 samples) were subjected to reduced representation bisulfite sequencing analysis (RRBS) by using Illumina HiSeq2000 platform. Please note that description of samples employed for the NGS analyses including age, race, ER/PR immunohistochemistry results, ITIL/STIL scores and PAM50 classification is provided the 'Supplementary Data1_Samples data.xlsx' (available on Superseries record)
2015-07-25 | E-GEOD-69993 | biostudies-arrayexpress
Project description:Breast cancer is a difficult disease to manage because it is comprised of a spectrum of tumor subtypes with different biological characteristics. Previous gene expression studies using breast tumor “intrinsic” gene lists identified five distinct subtypes of breast tumors: Luminal A, Luminal B, Normal Breast-like, HER2+/ER- and Basal-like. Using a training data set of 102 unique breast tumors, we derive a new “intrinsic” gene list based upon 26 paired samples. This Intrinsic/UNC gene set recapitulates the old classifications and extends the analysis to include a large proliferation signature, which was lacking before. Using Distance Weighted Discrimination, we next created a true “test set” of 311 tumors by combining together three different publicly available breast tumor microarray data sets (1-3), which was then analyzed using the newly derived Intrinsic/UNC gene set. The test set analysis identified the same five intrinsic subtypes seen before and a new one (Luminal I), and we show that the intrinsic subtype classifications are independent predictors of relapse-free survival when adjusting for age, node status, tumor size, grade and ER status. We also developed a “Single Sample Predictor” that is able to assign an intrinsic subtype to one sample at a time, which was also shown to be predictive of outcomes on another independent test set of tamoxifen-treated ER+ patients. These analyses demonstrate that 1) common patterns of expression can be identified across different microarray platforms, 2) the breast tumor “intrinsic” subtypes are present in all breast tumor data sets studied, and 3) this classification is adding value to the existing repertoire of clinical markers for breast cancer patients. Keywords: parallel sample
2005-12-08 | GSE1992 | GEO