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SRP021924
Homo sapiens
5 Downloadable Samples
Illumina HiSeq 2000
Description
We report the application of high-throughput RNA sequencing to the human prefrontal cortex. The brain dataset was obtained by sequencing total RNAs extracted from the dorsolateral prefrontal cortex of five deceased human patients with no apparent pathology, followed by depletion of ribosomal RNA to obtain all non-rRNA coding and non-coding RNAs in the human brain transcriptome. Overall design: Five samples were sequenced, four coming from frozen brain tissue (frontal cortex) of deceased female human patients with no remarkable pathology, and one from a male patient with no remarkable pathology.
Publication Title
HAMR: high-throughput annotation of modified ribonucleotides.
Sample Metadata Fields
Sex, Specimen part, Subject
View Samples- Homo sapiens
- 4 Downloadable Samples
- Illumina Genome Analyzer II
Description
The surprising observation that virtually the entire human genome is transcribed means we know very little about the function of many emerging classes of RNAs, except their astounding diversity. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their ability to classify classes of non-coding RNAs (ncRNAs). To address this, we developed CoRAL, a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length, cleavage specificity, and antisense transcription to distinguish between different ncRNA classes. We evaluated CoRAL using genome-wide small RNA sequencing (smRNA-seq) datasets from two human tissue types (brain and skin [GSE31037]), and were able to classify six different types of RNA transcripts with 79~80% accuracy in cross-validation experiments, and with 71~73% accuracy when CoRAL uses one tissue type for training and the other as validation. Analysis by CoRAL revealed that long intergenic ncRNAs, small cytoplasmic RNAs, and small nuclear RNAs show more tissue specificity, while microRNAs, small nucleolar, and transposon-derived RNAs are highly discernible and consistent across the two tissue types. The ability to consistently annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using smRNA-seq data in less characterized organisms. Overall design: Four samples were sequenced, each one coming from frozen brain tissue (frontal cortex) of a deceased female human patient with no remarkable pathology.
Publication Title
HAMR: high-throughput annotation of modified ribonucleotides.
Sample Metadata Fields
Sex, Specimen part, Disease, Disease stage, Subject
View Samples- Homo sapiens
- 23 Downloadable Samples
Illumina HumanHT-12 V4.0 expression beadchip
Description
Statins, the 3-hydroxy-3-methyl-glutaryl (HMG)-CoA reductase inhibitors, are widely prescribed for treatment of hypercholesterolemia. Although statins are generally well tolerated, up to ten percent of patients taking statins experience muscle related adverse events. Myalgia, defined as muscle pain without elevated creatinine phosphokinase (CPK) levels, is the most frequent reason for discontinuation of statin therapy. The mechanisms underlying statin-associated myalgia are not clearly understood. To elucidate changes in gene expression associated with statin-induced myalgia, we compared profiles of gene expression in the biopsied skeletal muscle from statin-intolerant patients undergoing statin re-challenge versus those of statin-tolerant controls. A robust separation of statin-intolerant and statin-tolerant cohorts was revealed by Principal Component Analysis of differentially expressed genes (DEGs). To identify putative gene expression and metabolic pathways that may be perturbed in skeletal muscles of statin intolerant patients, we subjected DEGs to Ingenuity Pathways (IPA) and DAVID (Database for Annotation, Visualization and Integrated Discovery) analyses. The most prominent pathways altered by statins included cellular stress, apoptosis, senescence and DNA repair (TP53, BARD1, Mre11 and RAD51); activation of pro-inflammatory immune response (CXCL12, CST5, POU2F1); protein catabolism, cholesterol biosynthesis, protein prenylation and RAS-GTPase activation (FDFT1, LSS, TP53, UBD, ATF2, H-ras). Based on these data we tentatively conclude that persistent myalgia in response to statins may emanate from cellular stress underpinned by mechanisms of post-inflammatory repair and regeneration. We also posit that this subset of individuals are genetically predisposed to eliciting altered statin metabolism and/or increased end-organ susceptibility that lead to a range of statin-induced myopathies. This mechanistic scenario further bolstered by the discovery that a number of single nucleotide polymorphisms (e.g., SLCO1B1, SLCO2B1 and RYR2) associated with statin myopathy were observed with increased frequency among statin-intolerant study subjects.
Publication Title
Patients experiencing statin-induced myalgia exhibit a unique program of skeletal muscle gene expression following statin re-challenge.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 77 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2), Affymetrix Human Genome U133A Array (hgu133a)
Description
The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial.
Publication Title
Identification of genomic classifiers that distinguish induction failure in T-lineage acute lymphoblastic leukemia: a report from the Children's Oncology Group.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 53 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Rhabdoid tumors (RT) are aggressive tumors characterized by genetic loss of SMARCB1 (SNF5, INI-1), a component of the SWI/SNF chromatin remodeling complex. No effective treatment is currently available. This study seeks to shed light on the SMARCB1-mediated pathogenesis of RT and to discover potential therapeutic targets. Global gene expression of 10 RT was compared with 12 cellular mesoblastic nephromas, 16 clear cell sarcomas of the kidney, and 15 Wilms tumors. 114 top genes were differentially expressed in RT (p<0.001, fold change >2 or <0.5). Among these were down-regulation of SMARCB1 and genes previously associated with SMARCB1 (ATP1B1, PTN, DOCK4, NQO1, PLOD1, PTP4A2, PTPRK). 28/114 top differentially expressed genes were involved with neural or neural crest development and were all sharply down-regulated. This was confirmed by Gene Set Enrichment Analysis (GSEA). Neural and neural crest stem cell marker proteins SOX10, ID3, CD133 and Musashi were negative by immunohistochemistry, whereas Nestin was positive. Decreased expression of CDKN1A, CDKN1B, CDKN1C, CDKN2A, and CCND1 was identified, while MYC-C was upregulated. GSEA of independent gene sets associated with bivalent histone modification and polycomb group targets in embryonic stem cells demonstrated significant negative enrichment in RT. Several differentially expressed genes were associated with tumor suppression, invasion and metastasis, including SPP1 (osteopontin), COL18A1 (endostatin), PTPRK, and DOCK4. We conclude that RTs arise within early progenitor cells during a critical developmental window in which loss of SMARCB1 directly results in repression of neural development, loss of cyclin dependent kinase inhibition, and trithorax/polycomb dysregulation.
Publication Title
Rhabdoid tumor: gene expression clues to pathogenesis and potential therapeutic targets.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 35 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2), Affymetrix Human Genome U133A Array (hgu133a)
Description
The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial.
Publication Title
Identification of genomic classifiers that distinguish induction failure in T-lineage acute lymphoblastic leukemia: a report from the Children's Oncology Group.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 42 Downloadable Samples
Affymetrix Human Genome U133A Array (hgu133a)
Description
The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial.
Publication Title
Identification of genomic classifiers that distinguish induction failure in T-lineage acute lymphoblastic leukemia: a report from the Children's Oncology Group.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 96 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
The cure rate for childhood ALL has improved considerably in part because therapy is routinely tailored to the predicted risk of relapse. Various clinical and laboratory variables are used in current risk-stratification schemes, but many children who fail therapy lack adverse prognostic factors at initial diagnosis. Using gene expression analysis, we have identified genes and pathways in a NCI high-risk childhood B-precursor ALL cohort at diagnosis that may play a role in early blast regression as correlated with the Day 7 marrow status. We have also identified a 47-probeset signature (representing 41 unique genes) that was predictive of long term outcome in our dataset as well as three large independent datasets of childhood ALL treated on different protocols.
Publication Title
Gene expression signatures predictive of early response and outcome in high-risk childhood acute lymphoblastic leukemia: A Children's Oncology Group Study [corrected].
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 46 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Comparison of miRNA expression profiles in malignant germ cell tumors compared to non-malignant control group.
Publication Title
Malignant germ cell tumors display common microRNA profiles resulting in global changes in expression of messenger RNA targets.
Sample Metadata Fields
Sex, Age
View Samples- Homo sapiens
- 39 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
The goal of this study is to define biologically distinct subsets of Very Low Risk Wilms Tumors (VLRWT) using oligonucleotide arrays.
Publication Title
Subsets of very low risk Wilms tumor show distinctive gene expression, histologic, and clinical features.
Sample Metadata Fields
No sample metadata fields
View Samples