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accession-icon GSE68735
caArray_willm-00140: TARGET-ALL Expression: Children's Oncology Group Study 9906 for High-Risk Pediatric ALL
  • organism-icon Homo sapiens
  • sample-icon 204 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Gene Expression Classifiers for Minimal Residual Disease and Relapse Free Survival Improve Outcome Prediction and Risk Classification in Children with High Risk Acute Lymphoblastic Leukemia: A Children's Oncology Group Study

Publication Title

Gene expression classifiers for relapse-free survival and minimal residual disease improve risk classification and outcome prediction in pediatric B-precursor acute lymphoblastic leukemia.

Sample Metadata Fields

Specimen part, Disease, Disease stage

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accession-icon GSE11877
Children's Oncology Group Study 9906 for High-Risk Pediatric ALL
  • organism-icon Homo sapiens
  • sample-icon 193 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

PAPER 1:"Identification of novel subgroups of high-risk pediatric precursor B acute lymphoblastic leukemia (B-ALL) by unsupervised microarray analysis: clinical correlates and therapeutic implications. A Children's Oncology Group (COG) study."

Publication Title

Gene expression classifiers for relapse-free survival and minimal residual disease improve risk classification and outcome prediction in pediatric B-precursor acute lymphoblastic leukemia.

Sample Metadata Fields

Sex, Specimen part, Race

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accession-icon GSE36149
Gene expression data from obatoclax-treated SEM-K2 and RS4:11 cell lines
  • organism-icon Homo sapiens
  • sample-icon 11 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Effects of the pan-anti-apoptotic BCL-2 family small molecule inhibitor, obatoclax mesylate (GeminX Pharmaceuticals), on gene expression were evaluated by microarray analysis in order to gain insights into the killing mechanism by this compound in two human MLL-AF4 cell lines. The results of the gene expression profiling substantiated other lines of evidence derived from genetic and chemical cell death pathway inhibition, Western blot analysis, flow cytometric apoptosis assays, and electron microscopic analyses, showing triple apoptosis, autophagy, and necroptosis death pathway activation by this agent. The results also demonstrated modulation of a number of novel targets of obatoclax encoding various cell death factors at the gene expression level.

Publication Title

Potent obatoclax cytotoxicity and activation of triple death mode killing across infant acute lymphoblastic leukemia.

Sample Metadata Fields

Cell line

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accession-icon GSE68720
caArray_EXP-520: Gene Expression Profiles Predictive of Outcome and Age in Infant Acute Lymphoblastic Leukemia: a Children's Oncology Group Study
  • organism-icon Homo sapiens
  • sample-icon 96 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Gene expression profiling was performed on 97 cases of infant ALL from Children's Oncology Group Trial P9407. Statistical modeling of an outcome predictor revealed 3 genes highly predictive of event-free survival (EFS), beyond age and MLL status: FLT3, IRX2, and TACC2. Low FLT3 expression was found in a group of infants with excellent outcome (n = 11; 5-year EFS of 100%), whereas differential expression of IRX2 and TACC2 partitioned the remaining infants into 2 groups with significantly different survivals (5-year EFS of 16% vs 64%; P < .001). When infants with MLL-AFF1 were analyzed separately, a 7-gene classifier was developed that split them into 2 distinct groups with significantly different outcomes (5-year EFS of 20% vs 65%; P < .001). In this classifier, elevated expression of NEGR1 was associated with better EFS, whereas IRX2, EPS8, and TPD52 expression were correlated with worse outcome. This classifier also predicted EFS in an independent infant ALL cohort from the Interfant-99 trial. When evaluating expression profiles as a continuous variable relative to patient age, we further identified striking differences in profiles in infants less than or equal to 90 days of age and those more than 90 days of age. These age-related patterns suggest different mechanisms of leukemogenesis and may underlie the differential outcomes historically seen in these age groups.

Publication Title

Gene expression profiles predictive of outcome and age in infant acute lymphoblastic leukemia: a Children's Oncology Group study.

Sample Metadata Fields

Sex, Age, Specimen part, Treatment, Race

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accession-icon GSE12995
Expression data for diagnosis acute lymphoblastic leukemia samples
  • organism-icon Homo sapiens
  • sample-icon 175 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

We studied a cohort of 221 high-risk pediatric B-progenitor ALL patients that excluded known high risk ALL subtypes (BCR-ABL1 and infant ALL), using Affymetrix single nucleotide polymorphism microarrays, gene expression profiling and candidate gene resequencing. A CNA poor outcome predictor was identified using a semi-supervised principal components approach, and tested in an independent validation cohort of 258 pediatric B-progenitor ALL cases. Over 50 regions of recurring somatically acquired CNA, with the most frequently targeted genes encoding regulators of B-lymphoid development (66.8% of cases; with PAX5 targeted in 31.7% and IKZF1 in 28.6%). A CNA classifier identified a very poor outcome subgroup in the high-risk cohort (P=4.2x10-5) and was strongly associated with the presence of deletions involving IKZF1, which encodes the early lymphoid transcription factor IKAROS. This classifier, and IKZF1 deletions, also predicted poor outcome and elevated minimal residual disease at the end of induction therapy in the validation cohort. The gene expression signature of the poor outcome group was characterized by reduced expression of B lineage specific genes, and was highly related to the expressing signature of BCR-ABL1 ALL, a known high-risk ALL subtype also characterized by a high frequency of IKZF1 deletion.Somatically acquired deletions involving IKZF1 identify a very poor outcome subgroup of pediatric ALL patients. Incorporation of molecular tests to identify IKZF1 deletion in diagnostic leukemic blasts should improve the ability to accurately risk stratify patients for appropriate therapy.

Publication Title

Deletion of IKZF1 and prognosis in acute lymphoblastic leukemia.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP076097
TLR2/1 ligand and IFN-g inducible genes in human monocyte-derived macrophages (MDMs)
  • organism-icon Homo sapiens
  • sample-icon 39 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Transcriptome profiles for innate and adaptive immune stimuli important for host response against mycobacteria. Human monocyte-derived macrophages were stimulated with TLR2/1 ligand and interferon-g, stimuli present during innate and adaptive immune responses, respectively. Overall design: Human monocyte-dervided macrophages from five healthy donors were stimulated with TLR2/1L, IFN-g, or media control for 2, 6, and 24 hours. RNA-sequencing was performed on a total of 45 samples.

Publication Title

S100A12 Is Part of the Antimicrobial Network against Mycobacterium leprae in Human Macrophages.

Sample Metadata Fields

Specimen part, Subject

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accession-icon GSE12831
The role of qseE, qseF and qseG in the regulation of EHEC virulence
  • organism-icon Escherichia coli
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix E. coli Genome 2.0 Array (ecoli2)

Description

Escherichia coli 8624 and the isogenic mutants in qseE, qseF and qseG are compared to determine the role that each of the genes play in regulation of the transcriptome. These results are verified by qRT-PCR and reveal the important role of this three-component signaling system.

Publication Title

The two-component system QseEF and the membrane protein QseG link adrenergic and stress sensing to bacterial pathogenesis.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE90811
Genome-wide profiling of gene expression/splicing patterns in iAs-transformed cells
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Transcriptome Array 2.0 (hta20)

Description

Chronic low dose inorganic arsenic (iAs) exposure leads to changes in gene expression and epithelial-to-mesenchymal transformation. During this transformation, cells adopt a fibroblast-like phenotype accompanied by profound gene expression changes. While many mechanisms have been implicated in this transformation, studies that focus on the role of epigenetic alterations in this process are just emerging. DNA methylation controls gene expression in physiologic and pathologic states. Several studies show alterations in DNA methylation patterns in iAs-mediated pathogenesis, but these studies focused on single genes. We present a comprehensive genome-wide DNA methylation analysis using methyl-sequencing to measure changes between normal and iAs-transformed cells. Additionally, these differential methylation changes correlated positively with changes in gene expression and alternative splicing. Interestingly, most of these differentially methylated genes function in cell adhesion and communication pathways. To gain insight into how genomic DNA methylation patterns are regulated iAs-mediated carcinogenesis, we show that iAs probably targets CTCF binding at the promoter of DNA methyltransferases, regulating their expression. These findings reveal how transcription factor binding regulates DNA methyltransferase to reprogram the methylome in response to an environmental toxin.

Publication Title

Genome-wide DNA methylation reprogramming in response to inorganic arsenic links inhibition of CTCF binding, DNMT expression and cellular transformation.

Sample Metadata Fields

Specimen part, Cell line, Treatment

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accession-icon GSE46449
Expression data from Patients with Bipolar (BP) Disorder and Matched Control Subjects
  • organism-icon Homo sapiens
  • sample-icon 84 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

There are currently no biological tests that differentiate patients with bipolar disorder (BPD) from healthy controls. While there is evidence that peripheral gene expression differences between patients and controls can be utilized as biomarkers for psychiatric illness, it is unclear whether current use or residual effects of antipsychotic and mood stabilizer medication drives much of the differential transcription. We therefore tested whether expression changes in first-episode, never-medicated bipolar patients, can contribute to a biological classifier that is less influenced by medication and could potentially form a practicable biomarker assay for BPD.

Publication Title

Utilization of never-medicated bipolar disorder patients towards development and validation of a peripheral biomarker profile.

Sample Metadata Fields

Sex, Age, Specimen part

View Samples
accession-icon GSE6903
Expression data from high-fat diet feeded WT and LIGHT Tg mice
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

effect of over-expression LIGHT on T cells for the liver gene expression

Publication Title

Lymphotoxin beta receptor-dependent control of lipid homeostasis.

Sample Metadata Fields

No sample metadata fields

View Samples
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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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