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GSE14618
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
- 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
- 44 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
We identified germline single nucleotide polymorphisms (SNPs) associated with childhood acute lymphoblastic leukemia (ALL) and its subtypes. Using the Affymetrix 500K Mapping array and publicly available genotypes, we identified 18 SNPs whose allele frequency differed (P<1x10-5) between a pediatric ALL population (n=317) and non-ALL controls (n=17,958). Six of these SNPs differed (P0.05) in allele frequency among four ALL subtypes. Two SNPs in ARID5B not only differed between ALL and non-ALL groups (rs10821936, P=1.4x10-15, odds ratio[OR]=1.91; rs10994982, P=5.7x10-9, OR=1.62) but also distinguished B-hyperdiploid ALL from other subtypes (rs10821936, P=1.62 x10-5, OR=2.17; rs10994982, P=0.003, OR 1.72). These ARID5B SNPs also distinguished B-hyperdiploid ALL from other subtypes in an independent validation cohort (n=124 children with ALL) (P=0.003 and P=0.0008, OR 2.45 and 2.86, respectively) and were associated with methotrexate accumulation and gene expression pattern in leukemic lymphoblasts. We conclude that germline genomic variations affect susceptibility to and characteristics of specific ALL subtypes.
Publication Title
Germline genomic variants associated with childhood acute lymphoblastic leukemia.
Sample Metadata Fields
Specimen part, Disease
View Samples- Homo sapiens
- 47 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Gene expression profiling (GEP) can reveal characteristic signatures associated with distinct biologic subtypes of acute lymphoblastic leukemia (ALL).
Publication Title
Genomic profiling in Down syndrome acute lymphoblastic leukemia identifies histone gene deletions associated with altered methylation profiles.
Sample Metadata Fields
Specimen part, Disease
View Samples- Homo sapiens
- 3 Downloadable Samples
Description
Patients with Down syndrome (DS) and acute lymphoblastic leukemia (ALL) have distinct clinical and biological features. Whereas most DS-ALL cases lack the sentinel cytogenetic lesions that guide risk assignment in childhood ALL, JAK2 mutations and CRLF2 overexpression are highly enriched. To further characterize the unique biology of DS-ALL, we performed genome-wide profiling of 58 DS-ALL and 35 non-Down syndrome (NDS) ALL cases by DNA copy number, loss of heterozygosity, gene expression, and methylation analyses. We report novel deletions within the 6p22 histone gene cluster as significantly more frequent in DS-ALL, occurring in 12 DS (24%) and only a single NDS case (3%) (Fishers exact p = 0.013). Homozygous deletions yielded significantly lower histone expression levels, and were associated with higher methylation levels, distinct spatial localization of methylated promoters, and enrichment of highly methylated genes for specific pathways and transcription factor binding motifs. Gene expression profiling identified CRLF2 overexpression in nearly half DS-ALL cases, and supervised analysis identified an associated 39-gene signature. However, no expression signature was identified for DS-ALL overall, nor for histone status, suggesting that DS-ALL constitutes several, heterogeneous molecular entities. Characterization of pathways associated with histone deletions and high CRLF2 expression may identify opportunities for novel targeted interventions.
Publication Title
Genomic profiling in Down syndrome acute lymphoblastic leukemia identifies histone gene deletions associated with altered methylation profiles.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 204 Downloadable Samples
- 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
View Samples- Homo sapiens
- 193 Downloadable Samples
- 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
View Samples- Homo sapiens
- 11 Downloadable Samples
- 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
View Samples- Homo sapiens
- 96 Downloadable Samples
- 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
View Samples