Transcriptomic Signatures Associated with Brain Metastasis Across Breast Cancer Subtypes
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE345865 | GEO | 2026/09/03
REPOSITORIES: GEO
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Project description:Addressing tumor heterogeneity in breast cancer research is crucial, given the distinct subtypes like triple-negative, luminal A/B, and HER2, requiring precise differentiation for effective treatment. This study introduces a non-invasive method by analyzing post-translationally modified proteins in plasma extracellular vesicles (EVs), which play a role in immune regulation and intercellular communication. Examining modifications like phosphorylation, acetylation, and glycosylation in EVs provides insights into breast cancer dynamics. One hundred one plasma samples from luminal A/B, triple-negative breast cancer, and healthy individuals underwent discovery and validation experiments. The study identified over 28,000 unique non-modified peptides, 5,000 phosphopeptides, 680 acetyl peptides, and 1,300 glycopeptides that were successfully characterized. Bioinformatics analyses revealed significant overexpression of 815 non-modified proteins, 3,958 phosphopeptides, 352 acetyl peptides, and 895 glycopeptides in luminal A/B or triple-negative breast cancer subtypes. Phosphorylated and glycosylated PD-L1 peptides emerged as potential markers for breast cancer, regardless of subtype. Aligning findings with literature and PAM50 gene signatures highlighted markers correlated with lower survival rates. The study also conducted 123 scheduled parallel reaction monitoring (PRM) analyses, leveraging machine learning to pinpoint a panel of specific modification sites with high accuracy in subtype differentiation. This research reveals diagnostic markers and enhances understanding of the molecular landscape, contributing to more effective and personalized breast cancer diagnostics and treatments.
2025-11-01 | PXD057412 | JPOST Repository
Project description:Systems-wide profiling of breast cancer has so far built on RNA and DNA analysis by microarray and sequencing techniques. Dramatic developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analyzed 40 estrogen receptor positive (luminal), Her2 positive and triple negative breast tumors and reached a quantitative depth of more than 10,000 proteins. Comparison to mRNA classifiers revealed multiple discrepancies between proteins and mRNA markers of breast cancer subtypes. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a 19-protein predictive signature, which discriminates between the breast cancer subtypes, through Support Vector Machine (SVM)-based classification and feature selection. The deep proteome profiles also revealed novel features of breast cancer subtypes, which may be the basis for future development of subtype specific therapeutics.
2016-01-05 | PXD002619 | Pride
Project description:Systems-wide profiling of breast cancer has so far built on RNA and DNA analysis by microarray and sequencing techniques. Dramatic developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analyzed 40 estrogen receptor positive (luminal), Her2 positive and triple negative breast tumors and reached a quantitative depth of more than 10,000 proteins. Comparison to mRNA classifiers revealed multiple discrepancies between proteins and mRNA markers of breast cancer subtypes. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell-cell communication. Furthermore, we derived a 19-protein predictive signature, which discriminates between the breast cancer subtypes, through Support Vector Machine (SVM)-based classification and feature selection. The deep proteome profiles also revealed novel features of breast cancer subtypes, which may be the basis for future development of subtype specific therapeutics.
2017-03-29 | MSV000080757 | MassIVE
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.
2006-11-10 | E-SMDB-3829 | biostudies-arrayexpress
Project description:Addressing tumor heterogeneity in breast cancer research is crucial, given the distinct subtypes like triple-negative, luminal A/B, and HER2, requiring precise differentiation for effective treatment. This study introduces a non-invasive method by analyzing post-translationally modified proteins in plasma extracellular vesicles (EVs), which play a role in immune regulation and intercellular communication. Examining modifications like phosphorylation, acetylation, and glycosylation in EVs provides insights into breast cancer dynamics. One hundred one plasma samples from luminal A/B, triple-negative breast cancer, and healthy individuals underwent discovery and validation experiments. The study identified over 28,000 unique non-modified peptides, 5,000 phosphopeptides, 680 acetyl peptides, and 1,300 glycopeptides that were successfully characterized. Bioinformatics analyses revealed significant overexpression of 815 non-modified proteins, 3,958 phosphopeptides, 352 acetyl peptides, and 895 glycopeptides in luminal A/B or triple-negative breast cancer subtypes. Phosphorylated and glycosylated PD-L1 peptides emerged as potential markers for breast cancer, regardless of subtype. Aligning findings with literature and PAM50 gene signatures highlighted markers correlated with lower survival rates. The study also conducted 123 scheduled parallel reaction monitoring (PRM) analyses, leveraging machine learning to pinpoint a panel of specific modification sites with high accuracy in subtype differentiation. This research reveals diagnostic markers and enhances understanding of the molecular landscape, contributing to more effective and personalized breast cancer diagnostics and treatments.
| PXD057449 | panorama
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. Bisulphite converted DNA from the samples were hybridised to the Illumina GoldenGate Methylation Cancer Panel I
2010-06-09 | E-GEOD-22210 | 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: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.
2006-12-10 | E-SMDB-3831 | biostudies-arrayexpress