Microarray analyses of induction failure in T-ALL
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PROVIDER: PRJNA112317 | ENA |
REPOSITORIES: ENA
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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. This SuperSeries is composed of the SubSeries listed below.
2009-01-29 | GSE14618 | 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.
2009-01-29 | GSE14615 | 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.
2009-01-29 | GSE14613 | GEO
Project description:Host-virus transcriptome dynamics determined from microarray analyses
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Project description:Microarray analyses of mRNA in PCDH1 knockdown cells
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Project description:Microarray analyses of induction failure in T-ALL (COG study 9404)
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Project description:Microarray analyses of induction failure in T-ALL (COG study 8704)
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Project description:We carried out WGS and RNAseq on a cohort of 48 children and young adults with induction failure in T-cell Acute Lymphoblastic Leukemia (T-ALL) to identify genomic drivers of treatment resistance. The study includes WGS for 33 tumour/normal pairs and 15 tumour-only samples. In addition, there is RNAseq data for 37 cases.
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Project description:Microarray Analyses of mRNA in lncRNA IRENA overexpressed macrophages
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Project description:Atherosclerosis and pressure overload are major risk factors for the development of heart failure in patients. Cardiac hypertrophy often precedes the development of heart failure. However, underlying mechanisms are incompletely understood. To investigate pathomechanisms underlying the transition from cardiac hypertrophy to heart failure we used experimental models of atherosclerosis- and pressure overload-induced cardiac hypertrophy and failure, i.e. apolipoprotein E (apoE)-deficient mice, which develop heart failure at an age of 18 months, and non-transgenic C57BL/6J (B6) mice with heart failure triggered by 6 months of pressure overload induced by abdominal aortic constriction (AAC). The development of heart failure was monitored by echocardiography, invasive hemodynamics and histology. The microarray gene expression study of cardiac genes was performed with heart tissue from failing hearts relative to hypertrophic and healthy heart tissue, respectively. The microarray study revealed that the onset of heart failure was accompanied by a strong up-regulation of cardiac lipid metabolism genes involved in fat synthesis, storage and oxidation.
2010-12-03 | GSE25765 | GEO