Expression data from mouse T-cell lymphomas
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
SUBMITTER: Adolfo Ferrando
PROVIDER: E-GEOD-33549 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Transgenic expression of TLX1 induces T-cell leukemias in mice. We used microarrays to identify the gene expression signatures associated with TLX1 tumors compared with different genetic models T acute lymphoblastic lymphoma. RNA extraction from T-cell tumors from mice with different T-cell lymphoma-predisposing genetic backgrouds.
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Project description:The TLX1 and TLX3 transcription factor oncogenes play an important role in the pathogenesis of T-cell acute lymphoblastic leukemia (T-ALL)1,2. Here we used reverse engineering of global transcriptional networks to decipher the oncogenic regulatory circuit controlled by TLX1 and TLX3. This Systems Biology analysis defined TLX1 and TLX3 as master regulators of an oncogenic transcriptional circuit governing T-ALL. Notably, network structure analysis of this hierarchical network identified RUNX1 as an important mediator of TLX1 and TLX3 induced T-ALL, and predicted a tumor suppressor role for RUNX1 in T-cell transformation. Consistent with these results, we identified recurrent somatic loss of function mutations in RUNX1 in human T-ALL. Overall, these results place TLX1 and TLX3 atop of an oncogenic transcriptional network controlling leukemia development, demonstrate power of network analysis to identify key elements in the regulatory circuits governing human cancer and identify RUNX1 as a tumor suppressor gene in T-ALL. This SuperSeries is composed of the following subset Series: GSE33539: Expression data obtained from ALLSIL cell line GSE33540: Expression data obtained from HPBALL cell line GSE33549: Expression data from mouse T-cell lymphomas
2011-11-08 | E-GEOD-33550 | biostudies-arrayexpress
Project description:Transgenic expression of TLX1 induces T-cell leukemias in mice. We used microarrays to identify the gene expression signatures associated with TLX1 tumors compared with different genetic models T acute lymphoblastic lymphoma.
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Project description:Transgenic expression of key transcritpion factors inducing T-cell leukemias in mice. We used microarrays to identify the gene expression signatures associated with TLX1transgenic /RUNX1 heterozygous tumors compared with different genetic models T acute lymphoblastic lymphoma.
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Project description:Immunoglobulin gene rearrangement and somatic hypermutation have the potential to create neoantigens in non-Hodgkin B cell lymphoma. However, the presentation of these putative immunoglobulin neoantigens by B cell lymphomas has not been proven. We used MHC immunoprecipitation followed by liquid chromatography and tandem mass spectrometry (LC-MS/MS) to define antigens presented by follicular lymphomas (FL), chronic lymphocytic leukemias (CLL), diffuse large B cell lymphoma (DLBCL) and mantle cell lymphomas (MCL). We found presentation of the clonal immunoglobulin molecule, including neoantigens by both class I and class II MHC, though more commonly in class II MHC. To determine whether B cell activation could promote presentation of immunoglobulin neoantigens, we used a toll-like receptor 9 (TLR9) agonists to upregulate expression of MHC-II. This resulted in enhanced class II MHC presentation of the immunoglobulin variable region including neoantigens. These findings demonstrate that immunoglobulin neoantigens are presented across most subtypes of B cell lymphomas. Activation of lymphoma cells to upregulate antigen presentation boosts presentation of immunoglobulin neoantigens and represents a strategy for augmenting lymphoma immunotherapies.
2019-01-07 | PXD010808 | Pride
Project description:The experiment was designed in order to knock down the expression of TLX1 gene in T-ALL cell line We used microarrays to identify the gene expression signatures associated with TLX1 knock down in ALLSIL ALLSIL cell lines were infected with pLKO.1-shTLX1 and pLKO.1-luc control lentivirus. After 5 days of puromycin selection cells were collected and RNA was extracted in order to analyze the global changes in gene expression by Affymetrix U133 Plus 2 array.
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Project description:Gene expression profiling (GEP) of ARL patient samples was done to determine whether gene expression signatures derived from HIV- lymphomas retained their ability to molecularly classify HIV+ lymphomas. The GEP-based predictors robustly classified ARL tumors, distinguishing molecular Burkitt lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL), as well as activated B-cell-like (ABC) and germinal center B-cell-like (GCB) molecular subtypes of DLBCL. Gene expression profiles were used to identify coordinately regulated gene sets and pathways that differ between HIV+ and HIV- lymphomas of corresponding molecular subtype. Frozen tumor biopsies were obtained from 20 HIV-positive patients with an AIDS-defining lymphoma. Cases were ascertained at the University of Nebraska Medical Center and through the NCI AIDS and Cancer Specimen Resource tumor bank. Sufficient RNA for hybridization to Affymetrix U133 plus 2 arrays was obtained on 17 ARL cases. Details of all 20 HIV-positive patients can be found in the supplementary file linked below.
2010-03-07 | E-GEOD-17189 | biostudies-arrayexpress
Project description:In this work, we generated hundreds of millions of Hi-C paired end sequence reads for three different human cells (RL follicular lymphoma cell line, primary tumor B-cells from an acute lymphoblastic leukemia patient, and MHH-CALL-4 B-cell acute lymphoblastic leukemia cell line) using the Hi-C technique. An in-house Bioinformatics software pipeline was developed and applied to map sequence reads to the human reference genome, producing a large data set of high-quality and high-resolution chromosome contacts. Our computational analysis on these data reveal some interesting properties of human genome conformation, including conformational conservation and variation of the genomes of different cells, intra- and inter-chromosomal interactions, aberrant chromosomal translocation, spatial gene clusters, spatial gene-gene interactions, and spatial gene-regulatory-element interaction. Furthermore, we derived spatial interactions between functional elements (genes, transcription factor binding sites) from the chromosomal interaction data. The data were then used to generate chromosome-/genome-wide gene-gene interaction networks, transcription factor binding site (TFBS) â TFBS networks, and gene-TFBS networks. Remarkably, the connectivity in both networks shows the hallmark features of scale-free networks, suggesting that spatial interactions of gene-gene, gene-TFBS, and TFBS-TFBS in a genome are far from random. Three different human cells (RL follicular lymphoma cell line, primary tumor B-cells from an acute lymphoblastic leukemia patient, and MHH-CALL-4 B-cell acute lymphoblastic leukemia cell line) were analyzed
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