Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA306021 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:This experiment comprises 283 CEL files generated on the Affymetrix U133 Plus 2.0 gene expression microarray platform, using patient peripheral blood and bone marrow samples from the first cohort of patients accrued to Children's Oncology Group Study AALL0232. No clinical covariate data is provided at this time as the clinical study is not yet published. Researchers who would like to request outcome or other covariate data are asked to contact Dr. Cheryl Willman, cwillman@unm.edu, 505.272.5622 (University of New Mexico) and Dr. Steven Hunger, Stephen.Hunger@childrenscolorado.org (Children's Oncology Group and Children's Hospital Colorado) to arrange a collaboration.
2015-05-13 | GSE68790 | GEO
Project description:This experiment comprises 283 CEL files generated on the Affymetrix U133 Plus 2.0 gene expression microarray platform, using patient peripheral blood and bone marrow samples from the first cohort of patients accrued to Children's Oncology Group Study AALL0232. No clinical covariate data is provided at this time as the clinical study is not yet published. Researchers who would like to request outcome or other covariate data are asked to contact Dr. Cheryl Willman, cwillman@unm.edu, 505.272.5622 (University of New Mexico) and Dr. Steven Hunger, Stephen.Hunger@childrenscolorado.org (Children's Oncology Group and Children's Hospital Colorado) to arrange a collaboration. EXP-578 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133_Plus_2 Organism: Home sapien (ncbitax)
2015-05-13 | E-GEOD-68790 | biostudies-arrayexpress
Project description:126 infant leukemia cases registered to NCI-sponsored Infant Oncology Group/Children's Oncology Group treatment trials were studied using oligonucleotide microarrays containing 12,625 probe sets (Affymetrix U95Av2 platform)
2016-12-23 | GSE92781 | GEO
Project description:Children's Oncology Group Study 9906 for High-Risk Pediatric ALL
| PRJNA105689 | ENA
Project description:Pediatric adrenocortical tumors (ACT) are rare and often fatal malignancies; little is known regarding their etiology and biology. To provide additional insight into the nature of ACT, we determined the gene expression profiles of 24 pediatric tumors (five adenomas, 18 carcinomas, and one undetermined) and seven normal adrenal glands. Distinct patterns of gene expression, validated by quantitative real-time PCR and Western blot analysis, were identified that distinguish normal adrenal cortex from tumor. Differences in gene expression were also identified between adrenocortical adenomas and carcinomas. In addition, pediatric adrenocortical carcinomas were found to share similar patterns of gene expression when compared with those published for adult ACT. This study represents the first microarray analysis of childhood ACT. Our findings lay the groundwork for establishing gene expression profiles that may aid in the diagnosis and prognosis of pediatric ACT, and in the identification of signaling pathways that contribute to this disease. We used microarrays to explore the expression profiles differentially expressed in childhood adrenocortical tumors and in normal adrenal gland tissues. Pediatric adrenocortical adenoma and carcinoma patients were enrolled on the International Pediatric Adrenocortical Tumor Registry (IPACTR) and Bank protocol. Tumor specimens were harvested during surgery and snap frozen in liquid nitrogen to preserve tissue integrity. Data have been compiled for eight males and 15 females between 0 and 16 years of age. Table 1 (West et al, Cancer Research 67:601-608, 2007) summarizes the primary clinical information for each subject (excluding sample Unk1 with ACT of undetermined histology), including stage of the disease, tumor class, sex, age, relapse-free survival, and overall survival.
2015-11-25 | E-GEOD-75415 | biostudies-arrayexpress
Project description:Pediatric adrenocortical carcinomas (ACCs) are aggressive; the overall survival of pediatric patients with ACCs is 40%-50%. Appropriate staging and histologic classification are crucial as children with incompletely resected tumors or metastatic disease have a dismal prognosis. The clinical course of pediatric adrenocortical tumors (ACTs) is difficult to predict using the current classification schemas, which rely heavily on subjective microscopic and gross macroscopic variables. Recent advances in adult ACT studies have revealed distinct DNA-methylation patterns with prognostic significance that have not been systematically interrogated in the pediatric population.
2020-05-16 | GSE131350 | GEO
Project description:We have previously observed that expression of HLA genes associate with histology of adrenocortical tumors (PMID 17234769). Here, we used gene expression microarrays to associate the diagnostic tumor expression of these genes with outcome among 34 patients treated on the COG ARAR0332 protocol.
2016-12-14 | GSE76019 | GEO
Project 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
2015-05-12 | GSE68735 | GEO
Project 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. Experiment Overall Design: The was a case-controlled, retrospectively designed study. We performed expression profiles on 92 patients with T-ALL treated on Children's Oncology Group studies 8704 (42 patients) and 9404 (50 patients). Experiment Overall Design: Expression profiles were obtained from patients with newly diagnosed T-ALL. NR = no response (induction failure); F= failure (relapse); C= Complete Continous Remission. Experiment Overall Design: Note: Failure (relapse) samples F8,12 and F19 were removed for reasons of failure other than relapse.
2009-02-09 | E-GEOD-14613 | biostudies-arrayexpress
Project 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. Experiment Overall Design: This was a case-controlled, retrospectively designed study. We performed expression profiles on 92 patients with T-ALL treated on Children's Oncology Group studies 8704 (42 patients) and 9404 (50 patients). Experiment Overall Design: Expression profiles were obtained from patients with newly diagnosed T-ALL. NR = no response (induction failure); F= failure (relapse); C= Complete Continous Remission. Experiment Overall Design: NOTE: non-sequential numbers were removed for reasons other than relapse, or poor hybridization during chip preparation.
2009-02-09 | E-GEOD-14615 | biostudies-arrayexpress