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GSE11482
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
- 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- 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
- 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
- 175 Downloadable Samples
- 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
View Samples- Homo sapiens
- 29 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Hepatoblastoma, the most common pediatric liver cancer, is tightly linked to excessive Wnt/�-catenin signaling. Microarray analysis identified two tumor subclasses resembling distinct phases of liver development, and a 16-gene signature discriminated invasive and metastatic hepatoblastomas, and predicted prognosis with high accuracy. <br></br>
Publication Title
Hepatic stem-like phenotype and interplay of Wnt/beta-catenin and Myc signaling in aggressive childhood liver cancer.
Sample Metadata Fields
Sex, Age, Specimen part, Disease, Disease stage, Subject
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
- 26 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
To compare the transcriptome profiles of the two principal histological variants of malignant germ cell tumor that occur in childhood
Publication Title
Pediatric malignant germ cell tumors show characteristic transcriptome profiles.
Sample Metadata Fields
No sample metadata fields
View Samples