SEQC Toxicogenomics Study: microarray data set
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ABSTRACT:
ORGANISM(S): Rattus norvegicus
SUBMITTER: Leming Shi
PROVIDER: E-GEOD-47875 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:The comparative advantages of RNA-Seq and microarrays in transcriptome profiling were evaluated in the context of a comprehensive study design. Gene expression data from Illumina RNA-Seq and Affymetrix microarrays were obtained from livers of rats exposed to 27 agents that comprised of seven modes of action (MOAs); they were split into training and test sets and verified with real time PCR. 105 samples were selected from the DrugMatirx tissue/RNA bank that is now owned by the National Toxicology Program (NTP). The samples were split into 2 sets, training and test, to allow for the evaluation of classifiers derived from the data. There were 63 samples in the training set and 42 in the test set. Of the 63 samples in the training set 45 were derived from rats treated with test agent and 18 were control samples (3 sets of 6). 39 of the test set samples were derived from test agent treated animals and 6 were from vehicle and route matched controls. Five MOAs were represented in the training set and 4 MOAs were in the test set. Two of the MOAs were duplicated from the test set and two were without representation in the training set. For each test agent there were three rats treated, in accordance with the common practice in the field of toxicology. For each MOA there were three representative test agents to ensure adequate power for detecting the MOA signatures. 6 samples from the training set had duplicate libraries sequenced and duplicate sequencing runs for the first library. DrugMatrix, National Toxicology program (NTP) Sequencing was carried out in Dr. Charles Wang's Functional Genomics Core at City of Hope Comprehensive Cancer Center, Duarte, CA
2014-08-08 | E-GEOD-55347 | biostudies-arrayexpress
Project description:The comparative advantages of RNA-Seq and microarrays in transcriptome profiling were evaluated in the context of a comprehensive study design. Gene expression data from Illumina RNA-Seq and Affymetrix microarrays were obtained from livers of rats exposed to 27 agents that comprised of seven modes of action (MOAs); they were split into training and test sets and verified with real time PCR. contributor: DrugMatrix, National Toxicology program (NTP)
2014-08-08 | GSE47875 | GEO
Project description:the immoratlized hepatic cell line HepaRG was transduced with a retroviral vector containing the miR-371-3 cluster. As a control, transduction was performed with the empty vector. Transcriptome analysis was performed by using the Affymetrix whole-transcript human gene 1.0 ST array. Each condition was analyzed in triplicate hierarchical unsupervised clustering of HB samples according to their miRNA expression profile identifies 2 tumor subgroups associated with different degree of differentiation, proliferation rate and invasive phenotype. As subset of these tumors is used as training set to build a mir classifier. When the signature is applied to an independent test set, an independent series of HB is classified patients into two groups associated with the clinical features identified in the first set of patients.
2012-12-31 | E-GEOD-21080 | biostudies-arrayexpress
Project description:The comparative advantages of RNA-Seq and microarrays in transcriptome profiling were evaluated in the context of a comprehensive study design. Gene expression data from Illumina RNA-Seq and Affymetrix microarrays were obtained from livers of rats exposed to 27 agents that comprised of seven modes of action (MOAs); they were split into training and test sets and verified with real time PCR.
2014-08-08 | GSE55347 | GEO
Project description:Hepatocellular carcinoma (HCC) is an important cause of morbidity and mortality worldwide. Although the risk factors of human HCC are well known, the molecular characterization of this disease is complex, and treatment options in general remain poor. The use of rodent models to study human cancer has been extensively pursued both through genetically engineered rodents and rodent models used in carcinogenicity and toxicology studies. In particular, the B6C3F1 mouse used in the National Toxicology Program (NTP) 2-year bioassay has been used to evaluate the carcinogenic effects of environmental and occupational chemicals, and other compounds. The high incidence of spontaneous HCC in the B6C3F1 mouse has challenged its use as a model for chemically induced HCC in terms of relevance to the human disease. Using global gene expression profiling, we identify the dysregulation of several mediators similarly altered in human HCC, including re-expression of fetal oncogenes, upregulation of protooncogenes, downregulation of tumor suppressor genes, and abnormal expression of cell cycle mediators, growth factors, apoptosis regulators, and angiogenesis and extracellular matrix remodeling factors. Although important differences in etiology and pathogenesis remain between human and mouse HCC, there are important similarities in global gene expression and the types of signaling networks dysregulated in mouse and human HCC. These data provide further relevance for the use of this model in hazard identification of compounds with potential human carcinogenicity risk, and may help in better understanding mechanisms of tumorigenesis due to chemical exposure in the NTP 2-year carcinogenicity bioassay. Six spontaneous hepatocellular carcinomas and six normal livers (as controls) from 2-year-old B6C3F1 mice.
2011-06-06 | E-GEOD-26538 | biostudies-arrayexpress
Project description:Therapy-related myelodysplasia or acute myeloid leukemia (t-MDS/AML) is a lethal complication of cancer treatment. Although t-MDS/AML development is associated with known genotoxic exposures, its pathogenesis is not well understood and methods to predict risk of development of t-MDS/AML in individual cancer survivors are not available. We performed microarray analysis of gene expression in samples from patients who developed t-MDS/AML after autologous hematopoietic cell transplantation (aHCT) for Hodgkin lymphoma (HL) or non-Hodgkin lymphoma (NHL) and controls that did not develop t-MDS/AML after aHCT. CD34+ progenitor cells from peripheral blood stem cell (PBSC) samples obtained pre-aHCT from t-MDS/AML cases and matched controls, and bone marrow (BM) samples obtained at time of development of t-MDS/AML, were studied. Significant differences in gene expression were seen in PBSC obtained pre-aHCT from patients who subsequently developed t-MDS/AML compared to controls. Genetic alterations in pre-aHCT samples were related to mitochondrial function, protein synthesis, metabolic regulation and hematopoietic regulation. Progression to overt t-MDS/AML was associated with additional alterations in DNA repair and DNA-damage checkpoint genes. Altered gene expression in PBSC samples were validated in an independent group of patients. An optimal 63-gene PBSC classifier derived from the training set accurately distinguished patients who did or did not develop t-MDS/AML in the independent test set. These results indicate that genetic programs associated with t-MDS/AML are perturbed long before disease onset, and can accurately identify those at risk of developing this complication. PBSC samples obtained pre-aHCT and BM samples at the time of development of t-MDS/AML post-HCT were studied. The training set consisted of 18 patients who developed t-MDS/AML (M-bM-^@M-^]casesM-bM-^@M-^]) after aHCT, matched with 37 controls who underwent aHCT, but did not develop t-MDS/AML. One to three controls were selected per case, matched for primary diagnosis (HL/NHL), age at aHCT (M-BM-110years), and ethnicity (Caucasians, African-Americans, Hispanics, other). The length of follow-up after aHCT for controls was longer than the time to t-MDS/AML in the corresponding case. The results of the training set were validated in an independent group of 36 patients (test set) consisting of 16 cases that developed t-MDS/AML post-aHCT and 20 matched controls. In the test set, 55 PBSC samples from 18 cases and 37 matched controls were studied. BM samples from time of development of t-MDS/AML were available for 12 cases, and from 21 matched controls obtained at a comparable time from aHCT. For validation, 36 PBSC samples from 16 cases and 20 matched controls were studied. All samples had been cryopreserved as mononuclear cells. After thawing, samples were labeled with anti-CD34-APC and anti-CD45-FITC and CD34+CD45dim cells were selected using flow cytometry. Total RNA was extracted using the RNeasy kit. RNA from 1000 cells was amplified and labeled using GeneChipM-BM-. Two-Cycle Target Labeling and Control Reagents from Affymetrix. 15 M-BM-5g of cRNA each was hybridized to Affymetrix HG U133 plus 2.0 Arrays. Microarray data were analyzed using R (version 2.9) with genomic analysis packages from Bioconductor (version 2.4). Data for PBSC and BM samples were normalized separately using robust multiarray averages with consideration of GC content (GCRMA). Probesets with low expression or variability were filtered. Expression of genes represented by multiple probesets was set as the median of the probesets. Using conditional logistic model (CLM) to retain matching between cases and controls, we analyzed the magnitude of association [expressed as odds ratio (OR)] between t-MDS/AML and i) gene expression levels in PBSC at the pre-aHCT time point; ii) gene expression levels in BM at time of t-MDS/AML; and iii) change of expression of individual genes from PBSC to time of t-MDS/AML. False discovery rate (FDR) was applied to adjust for multiple testing. Gene set enrichment analysis (GSEA) was performed on ranked lists of genes differentially expressed between cases and controls. Where multiple significant gene sets were related to each other, analysis was performed to identify a subset of common enriched genes. Average gene expression was calculated for each set and heatmaps plotted to show the contrasts between cases and controls. Gene Ontology (GO) and pathway analysis was performed using DAVID 2008 and Ingenuity IPA 7.5 respectively, retaining genes with z-scores M-bM-^IM-%1.8 or M-bM-^IM-$-1.8, and M-bM-^IM-%1.5-fold change in OR between cases and controls. The association between gene expression in the PBSC product and subsequent development of t-MDS/AML identified in the training set was validated in an independent test set of 36 PBSC sample procured from patients who developed t-MDS/AML after aHCT (16 cases) or did not (20 controls). Pre-processing, normalization and filtering procedures for the test set were identical to the training set. Differential expression between cases and controls was analyzed using CLM. GSEA analysis was performed on the ranked list of differentially expressed genes. Prediction analysis of microarray (PAM) was used to derive a prognostic gene signature from the training set to classify patients as case or control. PAM uses the M-bM-^@M-^\nearest shrunken centroidM-bM-^@M-^] approach and 10-fold cross-validation to select a parsimonious gene expression signature that can classify samples with minimal misclassification. PAM was applied to genes common to both datasets. Based on the misclassification error in cross-validation, a 63-gene signature was selected for prediction using the test data.
2011-12-12 | E-GEOD-23025 | biostudies-arrayexpress
Project description:Eleven NSCLC cell lines with widely divergent gefitinib sensitivities were compared using gene expression. Genes associated with gefitinib response were used to classify additional NSCLC lines with unknown gefitnib sensitivity. A subset of the test set data was tested for gefitinib sensitivity, and results correlated strongly with the gene expression-based predictions All eleven training set lines, and seven test set lines had both HGU133A and B chips done, while other test set lines had only HGU133As. Keywords: cell response comparison
2006-08-31 | GSE4342 | GEO
Project description:Background There is a lack of tools for identifying the site of origin in mucinous cancer. This study aimed to evaluate the performance of a transcriptome-based classifier for identifying the site of origin in mucinous cancer. Methods Transcriptomic data of 1,878 non-mucinous and 82 mucinous cancer specimens, with 7 sites of origin, namely, the uterine cervix (CESC), colon (COAD), pancreas (PAAD), stomach (STAD), uterine endometrium (UCEC), uterine carcinosarcoma (UCS), and ovary (OV), obtained from The Cancer Genome Atlas, were used as the training and validation sets, respectively. Transcriptomic data of 14 mucinous cancer specimens from a tissue archive were used as the test set. For identifying the site of origin, a set of 100 differentially expressed genes for each site of origin was selected. After removing multiple iterations of the same gene, 626 genes were chosen, and their RNA expression profiles, at each site of origin, were used to train the deep neural network classifier. The performance of the classifier was estimated using the training, validation, and test sets. Results The accuracy of the model in the training set was 0.977, while that in the validation set was 0.939 (77/82). In the test set, the model showed an accuracy of 0.857 (12/14). t-SNE analysis revealed that samples in the test set were part of the clusters obtained for the training set. Conclusion Although limited by small sample size, we showed that a transcriptome-based classifier could correctly identify the site of origin of mucinous cancer.
2020-12-15 | GSE163126 | GEO
Project description:The aim of this study is to identify responders to FOLFOX therapy by applying the Random Forests (RF) algorithm to gene expression data. Eighty-three unresectable colorectal cancer (CRC) patients including 42 responders and 41 non-responders were divided into training (54 patients) and test (29 patients) sets. Samples were divided (approximately 2:1 ratio) into training and test sets. As a result, 54 of 83 samples obtained in the first half of this period were selected for the training set, and the remaining 29 samples in the latter half were selected as the test set.
2011-11-21 | E-GEOD-28702 | biostudies-arrayexpress
Project description:Rationale: We previously generated genome-wide expression data in children with septic shock, based on whole blood-derive RNA, having the potential to lead the field into novel areas of investigation. Objective: Herein we seek to validate our data through a bioinformatic approach centered on a validation patient cohort. Methods: Microarray- and bioinformatics-centered analyses involving our previous data as a training data set (n = 42) and a new, validation cohort (n = 30) as the test data set. Measurements and Main Results: Class prediction modeling using the training data set and the previously reported genome-wide expression signature of pediatric septic shock correctly identified 93 to 100% of septic shock patients in the test data set, depending on the class prediction algorithm and the gene selection method. Subjecting the test data set to an identical filtering strategy as that used for the training data set, demonstrated 72% concordance between the two gene lists. Subjecting the test data set to a purely statistical filtering strategy, with highly stringent correction for multiple comparisons, demonstrated less than 50% concordance with the previous gene filtering strategy. However, functional analysis of this statistics-based gene list demonstrated similar functional annotations and signaling pathways as that seen in the learning data set. In particular, we validated that pediatric septic shock is characterized by large scale repression of genes related to zinc homeostasis and lymphocyte function. Conclusions: These data demonstrate that the previously reported genome-wide expression signature of pediatric septic shock is applicable to a validation cohort of patients. Keywords: Inflammation; sepsis; innate immunity; T cells; MHC antigen; zinc
2007-12-01 | GSE9692 | GEO