Homo sapiens
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PROVIDER: PRJNA117985 | ENA |
REPOSITORIES: ENA
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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: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.
2010-03-16 | GSE18328 | GEO
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:A subset of HER2 breast cancers with amplification of the TFAP2C gene locus becomes addicted to AP-2g. We sought to define AP-2g-regulated genes that control growth and invasiveness by comparing HER2 cell lines with differential response to TFAP2C knockdown. A set of 68 differentially expressed genes was identified, which included CDH5 and CDKN1A. Pathway analysis implicated the MAPK13/p38δ and retinoic acid regulatory nodes, which were confirmed to display divergent responses. The AP-2g gene signature was highly predictive of outcome in HER2-positive breast cancer patients. We conclude that AP-2g regulates a set of genes in HER2 breast cancer that drive cancer growth and invasiveness and that the AP-2g gene signature can predict outcome of patients with HER2 breast cancer.
2020-04-13 | GSE126897 | GEO
Project description:Analysis of 143 formalin-fixed, paraffin-embedded (FFPE) primary breast tumors using a Custom Breast Cancer Panel and Human Cancer Panel for the DASL platform. Molecular markers between the pathology defined subtypes of breast cancer were assessed to hypothesize potential therapeutic targets specific to the subtypes Molecular Characterization of 143 primary breast carcinomas including 101 triple negative (TN: ER-, PR-, HER2-), 3 HER2-positive (HER2+: ER-, PR-, HER2+), and 39 hormone receptor-positive (HR+: ER+ and/or PR+)
2009-10-23 | E-GEOD-18539 | biostudies-arrayexpress
Project description:Elevated Met receptor tyrosine kinase (RTK) expression correlates with poor outcome in breast cancer, yet a causal role for Met in the development of breast cancer has not been directly established. To examine this question, we generated a transgenic mouse model that targets expression of an oncogenic Met receptor (MetMut) to the mammary epithelium. We show that MetMut induces mammary tumors with a variety of histopathologies that exhibit gene expression profiles sharing similarities with human basal and luminal breast tumor subtypes. Among all breast cancers, we further demonstrate that the Met receptor is primarily overexpressed in human basal and HER2 positive breast cancers, and that a Met associated gene expression signature identifies patients with poor prognosis. Keywords: Met, mammary, poor outcome, EMT, basal breast cancer
2009-03-30 | GSE10450 | GEO
Project description:Accurate characterization and understanding of the breast cancer subtypes is of crucial clinical importance to the heterogeneity of this disease. Several layers of information, including immunohistochemical markers, mRNA and microRNA expression profiles, and pathway analysis have been used for such purpose in several studies. However, a comprehensively integrative approach is currently missing. This paper provides microRNA and mRNA expression profiles, characterizing four breast tumor subtypes, as defined by four immunohistochemical markers. The defined sets of features were validated in two independent data sets at multiple levels, including unsupervised clustering and supervised classification. Moreover, the gene expression signatures of the tumor subtypes were screened by in-depth analysis of 12 cancer core pathways. We successfully identified and validated a novel breast cancer subtypes gene expression signature composed of 976 mRNAs and 69 miRNAs. Luminal and non-luminal tumors are shown to significantly differ both at the mRNA and miRNA levels. HER2 positive tumors are more closely related to triple negative tumors by mRNA profiling than by miRNA expression. Closely related miRNAs sharing the same targets may exert opposite roles during tumor progression. Besides the core cancer pathways, other pathways such as those controling biomass synthesis are shown to be important to enable the core basal subtype with additional progressive nature compared with the other triple negative tumors. Some therapeutic strategies are proposed for breast cancer treatment, including the combined blockage of MAPK/ERK and PI3K/Pten signalings for tumors with poor clinical outcome, and targeting Wnt and JAK/STAT and/or Hedgehog, depending on tumor subtypes, together with conventional chemotherapy with the purpose of achieving an eradicative outcome. The pathway analysis also reveals that the clinical strategy and pivotal targets need to be tuned according to different tumor subtypes. This study is the first attempt to elucidate breast cancer subtypes by combining microRNA and mRNA expression, immunohistochemical markers, and cancer core pathways. The results can avail the functional studies of the etiology of breast cancer and translated for clinical use given their intrinsic link in terms of immunohistochemistry and survival. This submission consists of microRNA profiles of 115 breast cancer tumors of several subtypes only.
2013-06-01 | E-GEOD-43040 | biostudies-arrayexpress
Project description:Analysis of 97 formalin-fixed, paraffin-embedded (FFPE) primary breast tumors using Illumina DASL microarray technology on a Custom Breast Cancer Panel and the Illumina Human Cancer Panel. Molecular markers between the pathology defined subtypes of breast cancer were assessed to hypothesize potential therapeutic targets specific to the subtypes Molecular Characterization of 97 primary breast tumor formalin-fixed, paraffin-embedded (FFPE) specimens including 24 triple negative (TN: ER-, PR-, HER2-), 9 HER2-positive (HER2+: ER-, PR-, HER2+), and 64 hormone receptor-positive (HR+: ER+ and/or PR+). 91 of the 97 specimens were characterized on the Illumina Human Cancer DASL Panel and 86 of 97 specimens were characterized on a custom Breast Cancer DASL Panel, 80 of these specimens were common to both the Human Cancer DASL Panel and the custom Breast Cancer DASL Panel.
2009-10-23 | E-GEOD-17650 | biostudies-arrayexpress
Project description:Interferons are crucial for adaptive immunity and play an important role in the immune landscape of breast cancer. Using microarray-based gene expression analysis, we examined the subtype specific prognostic significance of interferon-γ (IFN-γ) as a single gene as well as an IFN-γ signature covering the signaling pathway in 461 breast cancer patients. Prognostic significance of IFN-γ as well as the IFN-γ signature for metastasis-free survival (MFS) were examined using Kaplan Meier as well as univariate and multivariate Cox regression analyses in the whole cohort and in different molecular subtypes. Kaplan Meier curves and univariate Cox regression analyses showed that the prognostic significance of IFN-γ as a single gene was limited to basal-like breast cancer (P=0.033). In contrast, the IFN-γ associated gene signature was a significant prognostic factor in the whole cohort (HR 1.554; 95%CI 1.1099-2.199; P=0.013) as well as in the luminal B (P=0.007) and HER2-positive (P=0.033) molecular subtype with borderline significance in basal-like breast cancer(P=0.050). In multivariate analysis, the IFN-γ signature retained its independent prognostic significance (HR 2.287; 95% CI: 1.410-3.633;P<0.001) in the entire cohort. These results underline the subtype-dependent prognostic influence of the immune system in early breast cancer.
2020-09-22 | GSE158309 | GEO