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Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Gene expression analyses of pediatric lymphoblastic lymphomas and leukemias

ABSTRACT: T-cell acute lymphoblastic leukemia (T-ALL) and lymphoma (T-LBL) share common morphologic and immunophenotypic features and are treated with similar therapeutic approaches. Nonetheless, they show distinct clinical presentations suggesting that they may represent two different biological entities. In order to investigate T-LBL and T-ALL biological characteristics we used transcriptional profiling approache. Genome-wide gene expression profiling, performed on 20 T-LBL and 10 T-ALL diagnostic specimens, showed that the two malignancies shared a large fraction of their transcriptional profile while a subset of genes appeared to be differentially expressed in T-LBL versus T-ALL. This gene signature included genes involved in chemotactic responses and angiogenesis which might play a role in the different tumor cell localization suggesting that T-LBL and T-ALL could be two distinct diseases with unique transcriptional characteristics. diagnostic bone marrow aspirates from 10 T-ALL and 6 common-ALL pediatric patients and tumor biopsies from 20 T-LBL pediatric patients were analyzed by gene expression profiling

ORGANISM(S): Homo sapiens

SUBMITTER: Lara Mussolin

PROVIDER: E-GEOD-29986 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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gene expression analyses of pediatric lymphoblastic lymphomas and leukemias

Project description:T-cell acute lymphoblastic leukemia (T-ALL) and lymphoma (T-LBL) share common morphologic and immunophenotypic features and are treated with similar therapeutic approaches. Nonetheless, they show distinct clinical presentations suggesting that they may represent two different biological entities. In order to investigate T-LBL and T-ALL biological characteristics we used transcriptional profiling approache. Genome-wide gene expression profiling, performed on 20 T-LBL and 10 T-ALL diagnostic specimens, showed that the two malignancies shared a large fraction of their transcriptional profile while a subset of genes appeared to be differentially expressed in T-LBL versus T-ALL. This gene signature included genes involved in chemotactic responses and angiogenesis which might play a role in the different tumor cell localization suggesting that T-LBL and T-ALL could be two distinct diseases with unique transcriptional characteristics.

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Affymetrix SNP array data for acute lymphoblastic leukemia and lymphoblastic lymphoma samples

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Genomic profiling data of 19 pediatric lympohoblastic lymphoma patients

Project description:Lymphoblastic lymphoma (LBL) is one of the most frequent occurring pediatric non-Hodgkin lymphomas. In the WHO classification scheme pediatric LBL is considered to be the same disease entity as pediatric acute lymphoblastic leukemia (ALL). However, it is unclear whether the genetic basis of pediatric LBL development is similar to that of pediatric ALL. We performed genome-wide analyses of copy number aberrations in 12 T-LBL and 7 precursor B-cell LBL pediatric cases using high-resolution SNP-based array CGH. Similar to what previously has been found in T-ALLs, T-LBLs exhibited recurrent deletions of the CDKN2A locus, occurring in 92% of the cases. Additionally, we detected deletions of RB1 (16%), duplications of MYB (16%) and an amplification of ABL1 in one case. These results show that, similar to T-ALL, the genomic alterations in T-LBL predominantly target genes involved in cell cycle progression. The majority of precursor B-cell LBLs was characterized by high-hyperdiploidy (71%), and showed high resemblance with high-hyperdiploid precursor B-cell ALLs. These results show that, similar to T-ALL, the genomic alterations in T-LBL predominantly target genes involved in cell cycle progression and that high-hyperdiploidy is a commen event in B-LBL. Taken together, our data suggest that pediatric LBLs and ALLs exhibit similar genomic abnormalities within confined immunophenotypic and cytogenetic subgroups, but that the representations of these subgroups differs between the two entities. Copy number detection was performed using CNAG2.0 software, Reference genomes are included in this data set Keywords: comparative genome hybridization

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