CC_LRI_BC
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB43655 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR5462930.fastq.gz | Fastqsanger.gz | |||
| ERR5462940.fastq.gz | Fastqsanger.gz | |||
| ERR5462950.fastq.gz | Fastqsanger.gz | |||
| ERR5462960.fastq.gz | Fastqsanger.gz | |||
| ERR5462970.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 576 |
Similar Datasets
Project description:Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the two groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets predicted genomic instability in our samples. This remarkable congruence suggests a biological dependency of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome. Keywords: disease state analysis 44 samples
2009-11-30 | E-GEOD-11901 | biostudies-arrayexpress
Project description:Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the two groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets predicted genomic instability in our samples. This remarkable congruence suggests a biological dependency of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome. Keywords: disease state analysis
2009-12-01 | GSE11901 | GEO
Project description:We performed Chip-Seq analyses of several DNA binding transcription factors predicted to function as "master transcriptional regulators" (MTRs) of breast cancer prognostic gene signatures in primary human mammary epithelial cells (HMECs). These analyses demonstrated that these factors frequently co–bind the promoters of genes, the expression of which is associated with a proliferative phenotype and poor patient outcome. These data suggest that these predicted MTRs do indeed drive the expression of gene signatures associated with poor patient outcome in breast cancer.
2015-09-30 | GSE62425 | GEO
Project description:Gene-expression patterns of primary breast cancers aid clinicians in predicting the risk of metastatic disease. Some prognostic signatures have recently been prospectively validated, highlighting their clinical value. Such classifiers conceivably comprise biomarker genes that, in fact, functionally contribute to the oncogenic and metastatic properties of the tumors, but this has not been investigated systematically. We previously reported that the transcription factor Fra-1 not only has an essential role in breast cancer, but also drives the expression of a highly prognostic gene set. Here, we systematically perturbed the function of 31 individual Fra-1-dependent poor-prognosis genes and examined their impact on breast cancer growth in vivo. Because of the considerable number of genes in this gene set, we anticipated that the contribution of single genes to breast cancer progression would be limited. In contrast, we find that stable shRNA depletion of each of nine individual signature genes strongly inhibits breast cancer growth and aggressiveness. Several factors within this nine-gene set regulate each other's expression, suggesting that together they form a network. The nine-gene set is regulated by estrogen, ERBB2 and EGF signaling, all established breast cancer factors. We also uncover three transcription factors, MYC, E2F1 and TP53, which act alongside Fra-1 at the core of this network. ChIP-Seq analysis reveals that a substantial number of genes are bound, and regulated, by all four transcription factors. The nine-gene set retains significant prognostic power and includes several potential therapeutic targets, including the bifunctional enzyme PAICS, which catalyzes purine biosynthesis. Depletion of PAICS largely cancelled breast cancer expansion, exemplifying a prognostic gene with breast cancer activity. Our data uncover a core genetic and prognostic network driving human breast cancer. We propose that pharmacological inhibition of components within this network, such as PAICS, may be used in conjunction with the Fra-1 prognostic classifier towards personalized management of poor prognosis breast cancer.
2017-03-23 | GSE95303 | GEO
Project description:Triple-Negative Breast Cancer (TNBC) is a poorly targetable cancer subtype that exhibits a high metastatic potential. To develop new intervention strategies, it is crucial to understand the mechanisms governing migratory and invasive features of TNBC cells. Previously it was shown that high expression of the non-receptor tyrosine kinase FER is an independent prognostic factor that correlates with poor survival in high grade and basal breast cancer. Here, we show that FER controls proliferation and invasion of TNBC cells through the direct substrates MAPK1 and Dynactin 2 (DCTN2; Dynamitin). Our data demonstrate that FER regulates tumor cell proliferation through phosphorylation of MAPK1 on tyrosine 187, and recycling endosome function and subsequent invasion of breast cancer cells through tyrosine 6 residue (Y6) of DCTN2. We show that DCTN2-Y6 is essential for Dynein complex formation and the development of tubular recycling domains at the early endosome. By controlling this crucial step in endosomal recycling, FER drives and sustains adhesion and invasion of (triple negative) breast cancer cells. Our data provide clinical ramification by demonstrating that high grade and basal breast cancers expressing elevated FER levels are more susceptible to taxane-based chemotherapy interventions. In conclusion, our study links FER to kinase-dependent control of intracellular vesicular transport and tumor progression in breast cancer. Importantly, our results indicate that FER represents a predictive and functional biomarker in high grade basal breast cancers.
2022-04-15 | PXD021307 | Pride
Project description:Transcriptional profiling was conducted on RNA from 23 breast cancer cell lines to identify genes whose expression level correlates with sensitivity of particular drug Experiment Overall Design: Baseline gene expression profiling was performed using 23 breast cancer cell lines to identify genomic signatures highly correlated with in vitro sensitivity to a particular drug
2008-06-15 | E-GEOD-6569 | biostudies-arrayexpress
Project description:Interactions of cancer cells with the vasculature are essential for tumor growth and likely promote metastatic progression. Endothelial cell content and lympho-vascular invasion are generally associated with tumor aggressiveness, however, these features are generally not employed in the clinic. We aimed to determine if endothelial cell gene expression signatures could be utilized to better characterize breast tumor biology, and to establish if vascular cell-derived signatures could provide information to predict tumors likely to metastasize. Here we report on the identification of a gene signature for vascular endothelial cells, and a second for cancer-activated vasculature. Both signatures independently identify subsets of aggressive breast cancers. Interestingly, the vascular content signature and a previously identified hypoxia signature both provide prognostic information beyond currently utilized clinical parameters and intrinsic subtype classifications. In these studies, we also examined the relationship that the breast cancer subtypes have with vascular gene expression profiles, and found that claudin-low tumors and cell lines express vascular gene expression profiles and displayed endothelial-like tube formation when grown in three-dimensions. These findings are directly applicable to clinical care and therapeutic treatment design as they identify highly aggressive subsets of tumors with genetic and morphologic vascular properties. reference x sample
2014-01-01 | E-GEOD-37145 | biostudies-arrayexpress
Project description:MicroRNAs are a class of small non-coding RNAs that control gene expression by targeting messenger RNAs and triggering either translation repression or RNA degradation. Their aberrant expression may be involved in human diseases, including cancer. Indeed, microRNA aberrant expression has been previously found in human chronic lymphocytic leukemias, where microRNA signatures were associated with specific clinico-biological features. Here, we show that, in comparison to normal breast tissue, microRNAs are also aberrantly expressed in human breast cancer. The overall microRNA expression could clearly separate normal versus cancer tissues, with the most significantly deregulated microRNAs being mir-125b, mir-145, mir-21, mir-155. Results were confirmed by microarray and Northern blot analyses. We could identify microRNAs whose expression was correlated with specific breast cancer bio-pathologic features, such as estrogen and progesterone receptor expression, tumor stage, vascular invasion or proliferation index.
2006-01-01 | E-TABM-23 | biostudies-arrayexpress
Project description:Development and pre-clinical testing of new cancer therapies is limited by the scarcity of in vivo models that authentically reproduce tumor growth and metastatic progression. We report new models for breast tumor growth and metastasis, in the form of transplantable tumors derived directly from individuals undergoing treatment for breast cancer. These tumor grafts represent the diversity of human breast cancer and maintain essential features of the original tumors, including metastasis to specific sites. Co-engraftment of primary human mesenchymal stem cells maintains phenotypic stability of the grafts and increases tumor growth by promoting angiogenesis. We also report that tumor engraftment is a prognostic indicator of disease outcome for newly diagnosed women; orthotopic breast tumor grafting marks a step toward individualized models for tumor growth, metastasis, and prognosis. This bank of tumor grafts also serves as a publicly available resource for new models in which to study the biology of breast cancer.
2011-10-23 | GSE32530 | GEO
Project description:Development and pre-clinical testing of new cancer therapies is limited by the scarcity of in vivo models that authentically reproduce tumor growth and metastatic progression. We report new models for breast tumor growth and metastasis, in the form of transplantable tumors derived directly from individuals undergoing treatment for breast cancer. These tumor grafts represent the diversity of human breast cancer and maintain essential features of the original tumors, including metastasis to specific sites. Co-engraftment of primary human mesenchymal stem cells maintains phenotypic stability of the grafts and increases tumor growth by promoting angiogenesis. We also report that tumor engraftment is a prognostic indicator of disease outcome for newly diagnosed women; orthotopic breast tumor grafting marks a step toward individualized models for tumor growth, metastasis, and prognosis. This bank of tumor grafts also serves as a publicly available resource for new models in which to study the biology of breast cancer. Single replicates of genomic DNA from 12 human breast cancer tumors and xenografts of those tumors in immunodeficient mice were hybridized to Affymetrix Human SNP 6.0 genotyping arrays.
2011-10-22 | E-GEOD-32530 | biostudies-arrayexpress