Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human mononuclear cells from acute megakaryoblastic leukemia with and without Down syndrome

ABSTRACT: mononuclear cells were isolated on a density gradient and RNA extracted using Trizol and the Promega SV column RNA purification. Affymetrix U133A chips were hybridised using standard procedures at the core facility of Dana Farber Cancer Institute. Experiment Overall Design: This series includes samples of acute megakaryoblastic leukemia from 24 patients with Down Syndrome, 28 patients without Down Syndrome, 9 samples with acute myelomonocytic leukemia of patients without Down Syndrome, 8 samples from neonates with Down Syndrome and with transient myeloproliferative disease

ORGANISM(S): Homo sapiens

SUBMITTER: Jean-Pierre Bourquin

PROVIDER: E-GEOD-4119 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Identification of distinct molecular phenotypes in acute megakaryoblastic leukemia by gene expression profiling.

Bourquin Jean-Pierre JP Subramanian Aravind A Langebrake Claudia C Reinhardt Dirk D Bernard Olivier O Ballerini Paola P Baruchel André A Cavé Hélène H Dastugue Nicole N Hasle Henrik H Kaspers Gertjan L GL Lessard Michel M Michaux Lucienne L Vyas Paresh P van Wering Elisabeth E Zwaan Christian M CM Golub Todd R TR Orkin Stuart H SH

Proceedings of the National Academy of Sciences of the United States of America 20060221 9

Individuals with Down syndrome (DS) are predisposed to develop acute megakaryoblastic leukemia (AMKL), characterized by expression of truncated GATA1 transcription factor protein (GATA1s) due to somatic mutation. The treatment outcome for DS-AMKL is more favorable than for AMKL in non-DS patients. To gain insight into gene expression differences in AMKL, we compared 24 DS and 39 non-DS AMKL samples. We found that non-DS-AMKL samples cluster in two groups, characterized by differences in expressi ...[more]

PMID: 16492768

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Project description:INTRODUCTION: Acute lymphoblastic leukemia (ALL) is among the most common cancers in children. With improvements in combination chemotherapy regimens, the overall survival has increased to over 90%. However, the current challenge is to mitigate adverse events resulting from the complex therapy. Several chemotherapies intercept cancer metabolism, but little is known about their collective role in altering host metabolism.OBJECTIVES: We profiled the metabolomic changes in plasma of ALL patients initial- and post- induction therapy.METHODS: We exploited a biorepository of non-fasted plasma samples derived from the Dana Farber Cancer Institute ALL Consortium; these samples were obtained from 50 ALL patients initial- and post-induction therapy. Plasma metabolites and complex lipids were analyzed by high resolution tandem mass spectrometry and differential mobility tandem mass spectrometry. Data were analyzed using a covariate-adjusted regression model with multiplicity adjustment. Pathway enrichment analysis and co-expression network analysis were performed to identify unique clusters of molecules.RESULTS: More than 1200 metabolites and complex lipids were identified in the total of global metabolomics and lipidomics platforms. Over 20% of those molecules were significantly altered. In the pathway enrichment analysis, lipids, particularly phosphatidylethanolamines (PEs), were identified. Network analysis indicated that the bioactive fatty acids, docosahexaenoic acid (DHA)-containing (22:6) triacylglycerols (TAGs), were decreased in the post-induction therapy.CONCLUSION: Metabolomic profiling in ALL patients revealed a large number of alterations following induction chemotherapy. In particular, lipid metabolism was substantially altered. The changes in metabolites and complex lipids following induction therapy could provide insight into the adverse events experienced by ALL patients.

Dataset Information

Transcription profiling of human mononuclear cells from acute megakaryoblastic leukemia with and without Down syndrome

Publications

Identification of distinct molecular phenotypes in acute megakaryoblastic leukemia by gene expression profiling.

Similar Datasets

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