Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE15347
PROVIDER: PRJNA116577 | ENA |
REPOSITORIES: ENA
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Project description:In high income countries 90% of the patients achieve complete remission after induction chemotherapy. However, 30-40% of these patients suffer from relapse. These patients face a dismal prognosis, as the majority (>60%) of relapsed patients die within 5 years. As a result, outcome for pediatric acute myeloid leukemia (AML) patients remains poor and has stabilized over the past 15 years. To prevent or better treat relapse of AML is the best option to improve outcome. Despite patient specific differences, most patients do respond to initial therapy. This suggests that at relapse, mechanisms are active that cause the altered response to chemotherapy. Detailed understanding of mechanisms that cause relapse remain largely elusive. To gain insight in the molecular pathways that characterize relapsed AML, we performed genome wide gene expression profiling on paired initial diagnosis and relapsed AML samples of 23 pediatric AML patients. We used pathway analysis to find which molecular pathways are involved in altered gene expression between diagnosis and relapse samples of individual AML patients. 23 paired diagnosis and relapse bone marrow or peripheral blood samples were collected and cryo-preserved. They were later thawed and processed for hybridization to Affymetrix U133 Plus 2.0 arrays.
2015-05-07 | E-GEOD-52891 | biostudies-arrayexpress
Project description:Affymetrix U133A array data for 111 pediatric acute myeloid leukemia (AML) samples at diagnosis
| PRJNA123171 | ENA
Project description:In high income countries 90% of the patients achieve complete remission after induction chemotherapy. However, 30-40% of these patients suffer from relapse. These patients face a dismal prognosis, as the majority (>60%) of relapsed patients die within 5 years. As a result, outcome for pediatric acute myeloid leukemia (AML) patients remains poor and has stabilized over the past 15 years. To prevent or better treat relapse of AML is the best option to improve outcome. Despite patient specific differences, most patients do respond to initial therapy. This suggests that at relapse, mechanisms are active that cause the altered response to chemotherapy. Detailed understanding of mechanisms that cause relapse remain largely elusive. To gain insight in the molecular pathways that characterize relapsed AML, we performed genome wide gene expression profiling on paired initial diagnosis and relapsed AML samples of 23 pediatric AML patients. We used pathway analysis to find which molecular pathways are involved in altered gene expression between diagnosis and relapse samples of individual AML patients.
2015-05-07 | GSE52891 | GEO
Project description:Pediatric acute myeloid leukemia (AML) bone marrow (BM) samples from diagnosis (Dx), end of induction (EOI), and relapse timepoints were analyzed by single-cell RNA sequencing (scRNA-seq). Analysis of matched Dx, EOI scRNA-seq datasets and TARGET AML RNA-seq datasets revealed a novel AML blasts-associated 7-gene signature (CLEC11A, PRAME, AZU1, NREP, ARMH1, C1QBP, TRH) that was validated in two independent datasets. Distinct clusters of relapse- and continuous complete remission (CCR)-associated AML-blasts were observed at Dx, with differential expression of genes associated with survival. At Dx, relapse-associated samples had more exhausted T cells while CCR-associated samples had more inflammatory M1 macrophages. Post-therapy EOI residual blasts overexpressed fatty acid oxidation, tumor growth, and stemness genes. Also, a post-therapy T-cells cluster present in relapse-associated samples exhibited downregulation of MHC Class I and T-cell regulatory genes. Altogether, this study deeply characterizes pediatric AML relapse- and CCR-associated samples to provide novel insights into BM microenvironment landscape.
2023-09-05 | GSE235923 | GEO
Project description:Pediatric acute myeloid leukemia (AML) is a heterogeneous disease characterized by non-random genetic aberrations related to outcome. Detecting these aberrations however still lead to failures or false negative results. Therefore, we focused on the potential of gene expression profiles (GEP) to classify pediatric AML. Gene expression microarray data of 237 children with AML were generated and cases were split into a discovery cohort (n=157) and an independent validation cohort (n=80). Next, a double-loop cross validation approach was used to generate a subtype-predictive GEP in the discovery cohort which was then tested for its true predictive value in the independent validation cohort. 237 bone marrow and peripheral blood samples were collected at diagnosis and frozen. They were later thawed and hybridized to Affymetrix U133 Plus 2.0 arrays.
2010-10-21 | E-GEOD-17855 | biostudies-arrayexpress
Project description:Affymetrix SNP array data for pediatric acute myeloid leukemia (AML) samples at diagnosis: Hind SNP array
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Project description:Affymetrix SNP array data for pediatric acute myeloid leukemia (AML) samples at diagnosis: Xba SNP array
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Project description:Affymetrix SNP array data for pediatric acute myeloid leukemia (AML) samples at diagnosis: Nsp SNP Array
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Project description:Affymetrix SNP array data for pediatric acute myeloid leukemia (AML) samples at diagnosis: Sty2 SNP array
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Project description:Array comparative genomic hybridization data for 372 diagnosis pediatric acute lymphoblastic leukemia samples.
2023-03-13 | GSE184692 | GEO