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Homo sapiens
18 Downloadable Samples
Affymetrix Human Genome U219 Array (hgu219)
Description
The tumor suppressor p53 can induce various biological responses. Yet it is not clear whether it is p53 in vivo promoter selectivity that triggers different transcription programs leading to different outcomes. Our analysis of genome-wide chromatin occupancy by p53 using ChIP-seq (deposited in Sequence Read Archive database as SRP007261) revealed p53 default program, i.e. the pattern of major p53-bound sites that is similar upon p53 activation by nutlin3a, RITA or 5-FU in breast cancer cells, despite different biological outcomes triggered by these compounds. Parallel analysis of gene expression allowed identification of 280 previously unknown p53 target genes, including p53-repressed AURKA. The consensus p53 binding motif was present more frequently in p53-induced, than in repressed targets, indicating different mechanisms of gene activation versus repression. We identified several possible cofactors of p53, and found that STAT3 antagonised p53-mediated repression of a subset of genes, including AURKA. Finally, we showed that the expression of the novel p53 targets correlates with p53 status and survival in breast cancer patients.
Publication Title
Insights into p53 transcriptional function via genome-wide chromatin occupancy and gene expression analysis.
Sample Metadata Fields
Cell line, Treatment
View Samples- Homo sapiens
- 12 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
Description
Since its discovery as a tumour suppressor some fifteen years ago, the transcription factor p53 has attracted paramount attention for its role as the guardian of the genome. TP53 mutations occur so frequently in cancer, regardless of patient age or tumour type, that they appear to be part of the life history of at least 50% of human tumours. In most tumours that retain wild-type p53, its function is inactivated due to deregulated HDM2, a protein which binds to p53 and which can inhibit the transcriptional activity of p53 and induce its degradation.
Publication Title
Ablation of key oncogenic pathways by RITA-reactivated p53 is required for efficient apoptosis.
Sample Metadata Fields
Specimen part, Disease
View Samples- Homo sapiens
- 5 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
Description
Targeting oncogene addiction is a promising strategy for anti-cancer therapy. Here, we report a potent inhibition of crucial oncogenes by p53 upon reactivation with small molecule RITA in vitro and in vivo. RITA-activated p53 unleashes transcriptional repression of anti-apoptotic proteins Mcl-1, Bcl-2, MAP4, and survivin, blocks Akt pathway on several levels and downregulates c-Myc, cyclin E and B-catenin. p53 ablates c-Myc expression via several mechanisms at transcriptional and posttranscriptional level. We show that transrepression of oncogenes correlated with higher level of p53 bound to chromatin-bound p53 than transactivation of pro-apoptotic targets. Inhibition of oncogenes by p53 reduces the cells ability to buffer pro-apoptotic signals and elicits robust apoptosis. Our study highlights the role of transcriptional repression for p53-mediated tumor suppression.
Publication Title
Ablation of key oncogenic pathways by RITA-reactivated p53 is required for efficient apoptosis.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 191 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
The process for evaluating chemical safety is inefficient, costly, and animal intensive. There is growing consensus that the current process of safety testing needs to be significantly altered to improve efficiency and reduce the number of untested chemicals. In this study, the use of short-term gene expression profiles was evaluated for predicting the increased incidence of mouse lung tumors. Animals were exposed to a total of 26 diverse chemicals with matched vehicle controls over a period of three years. Upon completion, significant batch-related effects were observed. Adjustment for batch effects significantly improved the ability to predict increased lung tumor incidence. For the best statistical model, the estimated predictive accuracy under honest five-fold cross-validation was 79.3% with a sensitivity and specificity of 71.4 and 86.3%, respectively. A learning curve analysis demonstrated that gains in model performance reached a plateau at 25 chemicals, indicating that the size of the current data set was sufficient to provide a robust classifier. The classification results showed a small subset of chemicals contributed disproportionately to the misclassification rate. For these chemicals, the misclassification was more closely associated with genotoxicity status than efficacy in the original bioassay. Statistical models were also used to predict dose-response increases in tumor incidence for methylene chloride and naphthalene. The average posterior probabilities for the top models matched the results from the bioassay for methylene chloride. For naphthalene, the average posterior probabilities for the top models over-predicted the tumor response, but the variability in predictions were significantly higher. The study provides both a set of gene expression biomarkers for predicting chemically-induced mouse lung tumors as well as a broad assessment of important experimental and analysis criteria for developing microarray-based predictors of safety-related endpoints.
Publication Title
Use of short-term transcriptional profiles to assess the long-term cancer-related safety of environmental and industrial chemicals.
Sample Metadata Fields
Sex, Age, Specimen part, Disease, Subject
View Samples- Mus musculus, Homo sapiens, Rattus norvegicus
- 1314 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2), Affymetrix Rat Genome 230 2.0 Array (rat2302), Affymetrix Human Genome U133A Array (hgu133a), Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
The MAQC-II Project: A comprehensive study of common practices for the development and validation of microarray-based predictive models
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Sex, Age, Specimen part, Race, Compound
View Samples- Homo sapiens
- 549 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a), Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
The multiple myeloma (MM) data set (endpoints F, G, H, and I) was contributed by the Myeloma Institute for Research and Therapy at the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR, USA). Gene expression profiling of highly purified bone marrow plasma cells was performed in newly diagnosed patients with MM. The training set consisted of 340 cases enrolled on total therapy 2 (TT2) and the validation set comprised 214 patients enrolled in total therapy 3 (TT3). Plasma cells were enriched by anti-CD138 immunomagnetic bead selection of mononuclear cell fractions of bone marrow aspirates in a central laboratory. All samples applied to the microarray contained more than 85% plasma cells as determined by 2-color flow cytometry (CD38+ and CD45-/dim) performed after selection. Dichotomized overall survival (OS) and eventfree survival (EFS) were determined based on a two-year milestone cutoff. A gene expression model of high-risk multiple myeloma was developed and validated by the data provider and later on validated in three additional independent data sets.
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Sex, Age
View Samples
GSE24363- Rattus norvegicus
- 410 Downloadable Samples
- Affymetrix Rat Genome 230 2.0 Array (rat2302), Affymetrix Human Genome U133A Array (hgu133a)
Description
The NIEHS data set (endpoint C) was provided by the National Institute of Environmental Health Sciences (NIEHS) of the National Institutes of Health (Research Triangle Park, NC, USA). The study objective was to use microarray gene expression data acquired from the liver of rats exposed to hepatotoxicants to build classifiers for prediction of liver necrosis. The gene expression compendium data set was collected from 418 rats exposed to one of eight compounds (1,2-dichlorobenzene, 1,4-dichlorobenzene, bromobenzene, monocrotaline, N-nitrosomorpholine, thioacetamide, galactosamine, and diquat dibromide). All eight compounds were studied using standardized procedures, i.e. a common array platform (Affymetrix Rat 230 2.0 microarray), experimental procedures and data retrieving and analysis processes.
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Sex, Specimen part, Compound
View Samples- Homo sapiens
- 267 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
The human breast cancer (BR) data set (endpoints D and E) was contributed by the University of Texas M. D. Anderson Cancer Center (MDACC, Houston, TX, USA). Gene expression data from 230 stage I-III breast cancers were generated from fine needle aspiration specimens of newly diagnosed breast cancers before any therapy. The biopsy specimens were collected sequentially during a prospective pharmacogenomic marker discovery study between 2000 and 2008. These specimens represent 70-90% pure neoplastic cells with minimal stromal contamination. Patients received 6 months of preoperative (neoadjuvant) chemotherapy including paclitaxel, 5-fluorouracil, cyclophosphamide and doxorubicin followed by surgical resection of the cancer. Response to preoperative chemotherapy was categorized as a pathological complete response (pCR = no residual invasive cancer in the breast or lymph nodes) or residual invasive cancer (RD), and used as endpoint D for prediction. Endpoint E is the clinical estrogen-receptor status as established by immunohistochemistry. RNA extraction and gene expression profiling were performed in multiple batches over time using Affymetrix U133A microarrays. Genomic analysis of a subset of this sequentially accrued patient population were reported previously. For each endpoint, the first 130 cases were used as a training set and the next 100 cases were used as an independent validation set.
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Age, Specimen part, Race
View Samples GSE24061- Mus musculus
- 88 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
The Hamner data set (endpoint A) was provided by The Hamner Institutes for Health Sciences (Research Triangle Park, NC, USA). The study objective was to apply microarray gene expression data from the lung of female B6C3F1 mice exposed to a 13-week treatment of chemicals to predict increased lung tumor incidence in the 2-year rodent cancer bioassays of the National Toxicology Program. If successful, the results may form the basis of a more efficient and economical approach for evaluating the carcinogenic activity of chemicals. Microarray analysis was performed using Affymetrix Mouse Genome 430 2.0 arrays on three to four mice per treatment group, and a total of 70 mice were analyzed and used as the MAQC-II's training set (GEO Series GSE6116). Additional data from another set of 88 mice were collected later and provided as the MAQC-II's external validation set (this Series). The training dataset had already been deposited in GEO by its provider and its accession number is GSE6116.
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Specimen part, Compound
View Samples- Homo sapiens
- 6 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Studies in vitro and in mice indicate a role for Coenzyme Q10 (CoQ10) in gene expression. To determine this function in relationship to physiological readouts, a 2-week supplementation study with the reduced form of CoQ10 (ubiquinol, Q10H2, 150 mg/d) was performed in 53 healthy males. Mean CoQ10 plasma levels increased 4.8-fold after supplementation. Transcriptomic and bioinformatic approaches identified a gene-gene interaction network in CD14-positive monocytes, which functions in inflammation, cell differentiation and PPAR-signaling. These Q10H2-induced gene expression signatures were also described previously in liver tissues of SAMP1 mice. Biochemical as well as NMR-based analyses showed a reduction of LDL cholesterol plasma levels after Q10H2 supplementation. This effect was especially pronounced in atherogenic small dense LDL particles (19-21 nm, 1.045 g/l). In agreement with gene expression signatures, Q10H2 reduces the number of erythrocytes but increases the concentration of reticulocytes. In conclusion, Q10H2 induces characteristic gene expression patterns, which are translated into reduced LDL cholesterol levels and erythropoiesis in humans.
Publication Title
Ubiquinol-induced gene expression signatures are translated into altered parameters of erythropoiesis and reduced low density lipoprotein cholesterol levels in humans.
Sample Metadata Fields
Sex, Disease, Disease stage
View Samples