Refinement of breast cancer molecular classification by miRNA expression profiles
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE131599 | GEO | 2019/06/20
REPOSITORIES: GEO
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Project description:Since the early 1980s remarkable progress has been made in understanding the role of the HER2 locus in carcinogenesis, but many details of its regulatory network are still elusive. We recently reported the finding of 367 new human microRNA (miRNA) genes of which one, mir-4728, is encoded in an intron of the HER2 gene. Here, we confirm that the HER2 oncogene is a bi-functional locus encoding the membrane receptor and a functional miRNA gene. We further show that miR-4728-3p has alternative functionalities depending on the region used for interaction with its target; the canonical seed between nucleotides 2-8 or a novel, more internal seed shifted to nucleotides 6-12. Analysis of public data shows that this internal seed region, although rare compared to the far more abundant canonical 2-8 seed interaction, can also direct targeted down-regulation by other miRNAs. Through the internal seed, miR-4728-3p regulates expression of estrogen receptor alpha, an interaction that would have remained undetected if classic rules for miRNA-target interaction had been applied. In summary, we present here an alternative mode of miRNA regulation and demonstrate this dual function of the HER2 locus, linking the two major biomarkers in breast cancer. 6 samples treated with hsa-miR-4728-3p mimic and 6 samples treated with non-targeting control 4 samples treated with hsa-miR-1-3p mimic and 4 samples treated with non-targeting control
2014-08-13 | E-GEOD-55822 | biostudies-arrayexpress
Project description:Since the early 1980s remarkable progress has been made in understanding the role of the HER2 locus in carcinogenesis, but many details of its regulatory network are still elusive. We recently reported the finding of 367 new human microRNA (miRNA) genes of which one, mir-4728, is encoded in an intron of the HER2 gene. Here, we confirm that the HER2 oncogene is a bi-functional locus encoding the membrane receptor and a functional miRNA gene. We further show that miR-4728-3p has alternative functionalities depending on the region used for interaction with its target; the canonical seed between nucleotides 2-8 or a novel, more internal seed shifted to nucleotides 6-12. Analysis of public data shows that this internal seed region, although rare compared to the far more abundant canonical 2-8 seed interaction, can also direct targeted down-regulation by other miRNAs. Through the internal seed, miR-4728-3p regulates expression of estrogen receptor alpha, an interaction that would have remained undetected if classic rules for miRNA-target interaction had been applied. In summary, we present here an alternative mode of miRNA regulation and demonstrate this dual function of the HER2 locus, linking the two major biomarkers in breast cancer.
2014-08-13 | GSE55822 | GEO
Project description:Breast cancer is a profoundly heterogeneous disease with respect to biological and clinical behavior. Gene expression profiling has been used to dissect this complexity and stratify tumors into intrinsic gene expression subtypes associated with distinct biology, patient outcome and different genomic alterations. Additionally, breast tumors occurring in individuals with germline BRCA1 or BRCA2 mutations typically fall into distinct subtypes. We applied global DNA copy number and gene expression profiling in 359 breast tumors. All tumors were classified according to intrinsic gene expression subtypes and included cases from genetically predisposed women. The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was used to identify significant DNA copy number aberrations and genomic subgroups of breast cancer. We identified 31 genomic regions that were highly amplified in >1% of the 359 breast tumors. Several amplicons were found to co-occur, the 8p12 and 11q13.3 regions being the most frequent combination besides amplicons on the same chromosomal arm. Unsupervised hierarchical clustering with 133 significant GISTIC regions (66 and 67 with DNA copy number gain and loss, respectively) revealed six genomic subtypes, termed: 17q12, basal-complex, luminal-simple, luminal-complex, amplifier and mixed subtype. Four of them had striking similarity to intrinsic gene expression subtypes and showed association to conventional tumor biomarkers and clinical outcome. However, luminal A-classified tumors were distributed in two main genomic subtypes, luminal-simple and luminal-complex, the former group having better prognosis while the latter group included also luminal B and the majority of BRCA2-mutated tumors. The basal-complex subtype displayed extensive genomic homogeneity and harbored the majority of BRCA1-mutated tumors. The 17q12 subtype comprised mostly HER2-amplified and HER2-enriched subtype tumors and had the worst prognosis. The amplifier and mixed subtypes contained tumors from all gene expression subtypes, the former being enriched for 8p12-amplified cases while the mixed subtype included many tumors with predominantly DNA copy number losses and poor prognosis. Genomic profiling of 359 breast tumors using tiling BAC aCGH. A number of cases were hybridized as replicates or replicate as dye-swaps. Gene expression profiling of 359 breast tumors using 55K oligonucleotide microarrays.
2010-06-23 | E-GEOD-22133 | biostudies-arrayexpress
Project description:We performed high throughput transcriptome of breast cancer and normal tissues, identifying lincRNA associated molecular subtype with powerful capacity to distinct breast cancer population and predict prognosis. We also identified subtype-specific lincRNAs that may be a useful complement to intrinsic molecular subtype classification when divergence emerges among pathologists.Paired-end transcriptome sequencing were carried out on a cohort of 33 breast tissues from 11 groups including five breast cancer subtypes including luminal A (LA), luminal B (HER2 negative)(LB, HER2-), luminal B (HER2 positive) (LB, HER2+), HER2 and tripple negative breast cancer (TNB), adjacent noncancerous breast tissue (ANT, three samples for each subtype) and the complete normal breast tissues (three samples)
2017-09-06 | GSE71651 | GEO
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: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:Breast cancer is a profoundly heterogeneous disease with respect to biological and clinical behavior. Gene expression profiling has been used to dissect this complexity and stratify tumors into intrinsic gene expression subtypes associated with distinct biology, patient outcome and different genomic alterations. Additionally, breast tumors occurring in individuals with germline BRCA1 or BRCA2 mutations typically fall into distinct subtypes. We applied global DNA copy number and gene expression profiling in 359 breast tumors. All tumors were classified according to intrinsic gene expression subtypes and included cases from genetically predisposed women. The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was used to identify significant DNA copy number aberrations and genomic subgroups of breast cancer. We identified 31 genomic regions that were highly amplified in >1% of the 359 breast tumors. Several amplicons were found to co-occur, the 8p12 and 11q13.3 regions being the most frequent combination besides amplicons on the same chromosomal arm. Unsupervised hierarchical clustering with 133 significant GISTIC regions (66 and 67 with DNA copy number gain and loss, respectively) revealed six genomic subtypes, termed: 17q12, basal-complex, luminal-simple, luminal-complex, amplifier and mixed subtype. Four of them had striking similarity to intrinsic gene expression subtypes and showed association to conventional tumor biomarkers and clinical outcome. However, luminal A-classified tumors were distributed in two main genomic subtypes, luminal-simple and luminal-complex, the former group having better prognosis while the latter group included also luminal B and the majority of BRCA2-mutated tumors. The basal-complex subtype displayed extensive genomic homogeneity and harbored the majority of BRCA1-mutated tumors. The 17q12 subtype comprised mostly HER2-amplified and HER2-enriched subtype tumors and had the worst prognosis. The amplifier and mixed subtypes contained tumors from all gene expression subtypes, the former being enriched for 8p12-amplified cases while the mixed subtype included many tumors with predominantly DNA copy number losses and poor prognosis.
2010-06-24 | GSE22133 | 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:Breast cancer is a heterogeneous disease encompassing a number of phenotypically diverse tumours. Expression levels of the estrogen, progesterone and HER2/neu receptors which characterise clinically distinct breast tumors have been shown to change during disease progression and in response to systemic therapies. Mi(cro)RNAs play critical roles in diverse biological processes and are aberrantly expressed in several human neoplasms including breast cancer, where they function as regulators of tumour behaviour and progression. The aims of this study were to identify miRNA signatures that accurately predict the oestrogen receptor (ER), progesterone receptor (PR) and HER2/neu receptor status of breast cancer patients to provide insight into the regulation of breast cancer phenotypes and progression. Expression profiling of 353 microRNAs was performed in 29 early stage breast cancer specimens. MiRNA signatures associated with ER, PR and HER2/neu status were generated using artificial neural networks (ANN) and expression of specific microRNAs was validated using RQ-PCR. Results: Stepwise artificial neural network (ANN) analysis identified predictive miRNA signatures corresponding with estrogen (miR-342, miR-299, miR-217, miR -190, miR-135b, miR-218), progesterone (miR-520g, miR-377, miR-527-518a, miR-520f-520c) and HER2/neu (miR-520d, miR-181c, miR-302c, miR-376b, miR-30e) receptor status. MiR-342 and miR-520g expression was further analysed in 95 breast tumours. MiR-342 expression was highest in ER and HER2/neu positive luminal B tumours and lowest in triple-negative tumours. MiR-520g expression was elevated in ER and PR negative tumours.
2010-05-18 | E-GEOD-15885 | biostudies-arrayexpress