Genomic profiling of neoplasms developed in SEC31A-JAK2 expressing mice
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
SUBMITTER: Julio Finalet
PROVIDER: E-GEOD-22404 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:The genetics of classical Hodgkin lymphoma (cHL) is poorly understood. The finding of a JAK2-involving t(4;9)(q21;p24) in one case of cHL prompted us to characterize this translocation on a molecular level and to determine the prevalence of JAK2 rearrangements in cHL. We showed that the t(4;9)(q21;p24) leads to a novel SEC31A-JAK2 fusion. Screening of 131 cHL cases identified one additional case with SEC31A-JAK2 and two additional cases with rearrangements involving JAK2. We demonstrated that SEC31A-JAK2 is oncogenic in vitro and acts as a constitutively activated tyrosine kinase that is sensitive to JAK inhibitors. In vivo, SEC31A-JAK2 was found to induce a T-lymphoblastic lymphoma or myeloid hyperplasia in a murine bone marrow transplant model. Altogether, we identified SEC31A-JAK2 as a first aberration characteristic for cHL and provide evidence that JAK2 rearrangements occur in a minority of cHL cases. Given the proven oncogenic potential of this novel fusion, our studies provide new insights into the pathogenesis of cHL and indicate that in at least some cases, constitutive activation of the JAK-STAT pathway is caused by JAK2 rearrangements. The finding that SEC31A-JAK2 responds to JAK inhibitors indicates that patients with cHL and JAK2 rearrangements may benefit from targeted therapies.
2010-06-19 | GSE22404 | GEO
Project description:Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of Janus-activated kinase (JAK) / signal transducer and activator of transcription (STAT) signaling pathway. Due to complex non-linear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. To untangle these features, we used dynamic pathway modeling that employs model development and calibration based on extensive quantitative data generation. Quantitative data were collected on JAK/STAT pathway signaling components in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We showed that the amounts of STAT5 and STAT6 are higher whereas the amount of SHP1 is lower in the two lymphoma cell lines compared to B cells from healthy donors. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In our experimental setting interleukin-13 (IL13) stimulation levels remained constant over time. In MedB-1 cells surface IL13 receptor alpha 2 had a strong IL13-sequestering/decoy function. In both lymphoma cell lines we observed IL13-induced activation of interleukin-4 receptor alpha, JAK2 and STAT5, but not of STAT6, which was highly phosphorylated even without stimulus. Furthermore, the known STAT-inducible negative regulators CISH and SOCS3 were up-regulated within 2 hours in MedB-1 but not in L1236 cells. Global transcription profiling revealed 11 early and 16 sustained common genes up-regulated by IL13 in both lymphoma cell lines. Based on this detailed information we established two individual mathematical models, MedB-1 and L1236 model, which were able to describe the respective experimental data. Sensitivity analysis of the model identified six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1 cells. By inhibition of STAT5 phosphorylation we successfully validated one of the predicted targets demonstrating the potential of the approach in guiding target identification for highly deregulated signaling networks in cancer cells. We established mathematical models of the JAK/STAT pathway in two lymphoma cell types (PMBL and cHL), able to reproduce experimental data and to predict possible therapeutic targets. Cells from two lymphoma-derived cell lines, MedB-1 and L1236, were used for a time-course microarray analysis comprising stimulations with IL13 for 0, 0.5, 1, 1.5, 2, 3, 4, 6, 8, 12 h and unstimulated controls (0, 0.5, 1, 1.5, 2, 3, 4, 6, 8, 12 h), for a total of 20 microarrays per cell line.
2010-12-09 | E-GEOD-23591 | biostudies-arrayexpress
Project description:Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of Janus-activated kinase (JAK) / signal transducer and activator of transcription (STAT) signaling pathway. Due to complex non-linear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. To untangle these features, we used dynamic pathway modeling that employs model development and calibration based on extensive quantitative data generation. Quantitative data were collected on JAK/STAT pathway signaling components in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We showed that the amounts of STAT5 and STAT6 are higher whereas the amount of SHP1 is lower in the two lymphoma cell lines compared to B cells from healthy donors. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In our experimental setting interleukin-13 (IL13) stimulation levels remained constant over time. In MedB-1 cells surface IL13 receptor alpha 2 had a strong IL13-sequestering/decoy function. In both lymphoma cell lines we observed IL13-induced activation of interleukin-4 receptor alpha, JAK2 and STAT5, but not of STAT6, which was highly phosphorylated even without stimulus. Furthermore, the known STAT-inducible negative regulators CISH and SOCS3 were up-regulated within 2 hours in MedB-1 but not in L1236 cells. Global transcription profiling revealed 11 early and 16 sustained common genes up-regulated by IL13 in both lymphoma cell lines. Based on this detailed information we established two individual mathematical models, MedB-1 and L1236 model, which were able to describe the respective experimental data. Sensitivity analysis of the model identified six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1 cells. By inhibition of STAT5 phosphorylation we successfully validated one of the predicted targets demonstrating the potential of the approach in guiding target identification for highly deregulated signaling networks in cancer cells. We established mathematical models of the JAK/STAT pathway in two lymphoma cell types (PMBL and cHL), able to reproduce experimental data and to predict possible therapeutic targets.
2010-12-09 | GSE23591 | GEO
Project description:Here we investigated the effects of JAK/STAT pharmacological inhibition on cHL cell models using ruxolitinib, a JAK 1/2 inhibitor. We use five classical Hodgkin lymphoma cell lines: L428, L1236, L540, KMH2, L591
2021-10-15 | GSE185818 | GEO
Project description:MALT lymphoma is characterized by t(11;18)(q21;q21)/API2-MALT1, t(1;14)(p22;q32)/BCL10-IGH and t(14;18)(q32;q21)/IGH-MALT1, which commonly activate the NF-κB pathway. Gastric MALT lymphomas harboring such translocation do not respond to H. pylori eradication, while those without translocation can be cured by antibiotics. To understand the molecular mechanism of these different MALT lymphoma subgroups, we performed gene expression profiling analysis of 24 MALT lymphomas (15 translocation-positive, 9 translocation-negative). Gene set enrichment analysis (GSEA) of the NF-κB target genes and 4394 additional gene sets covering various cellular pathways, biological processes and molecular functions showed that translocation-positive MALT lymphomas are characterized by an enhanced expression of NF-κB target genes, particularly TLR6, CCR2, CD69 and BCL2, while translocation-negative cases were featured by active inflammatory and immune responses, such as IL8, CD86, CD28 and ICOS. Separate analyses of the genes differentially expressed between translocation-positive and negative cases and measurement of gene ontology term in these differentially expressed genes by hypergeometric test reinforced the above findings by GSEA. Finally, expression of TLR6, in the presence of TLR2, enhanced both API2-MALT1 and BCL10 mediated NF-κB activation in vitro. Our findings provide novel insights into the molecular mechanism of MALT lymphomas with and without translocation, potentially explaining their different clinical behaviors. This study compares MALT with other lymphomas, namely follicular lymphomas (FL) and mantle cell lymphomas (MCL), and investigates the molecular mechanisms of the lymphomagenesis between translocation-positive versus -negative MALT lymphoma cases in order to derive the pathways leading to MALT lymphoma pathogenesis. The study uses fresh frozen tissues from 24 MALT lymphoma cases with 7 FL and 7 MCL. Samples were run on the HG-U133A, HG-U133B, and HG-U133 plus2 GeneChips.
2010-10-07 | E-GEOD-16024 | biostudies-arrayexpress
Project description:This is the model of IL13 induced signalling in L1236 cells described in the article:
Dynamic Mathematical Modeling of IL13-Induced Signaling in Hodgkin and Primary Mediastinal B-Cell Lymphoma Allows Prediction of Therapeutic Targets.
Raia V, Schilling M, Böhm M, Hahn B, Kowarsch A, Raue A, Sticht C, Bohl S, Saile M, Möller P, Gretz N, Timmer J, Theis F, Lehmann WD, Lichter P and Klingmüller U. Cancer Res. 2011 Feb 1;71(3):693-704. PubmedID: 21127196
; DOI: 10.1158/0008-5472.CAN-10-2987
Abstract:
Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.
All concentrations in the model, apart from IL13, are in molecules/cell. IL13 is given in ng/ml. As the cell volume is not explicitely given in the article, it is just approximately derived from the MW of IL13 (15.8 kDa) and the conversion factor 3.776 molecules IL13/cell = 1 ng/ml to be around 100 fl.
SBML model exported from PottersWheel on 2010-08-10 12:14:57.
Inline follows the original matlab code:
% PottersWheel model definition file
function m = Raia2010_IL13_L1236()
m = pwGetEmptyModel();
%% Meta information
m.ID = 'Raia2010_IL13_L1236';
m.name = 'Raia2010_IL13_L1236';
m.description = '';
m.authors = {'Raia et al'};
m.dates = {'2010'};
m.type = 'PW-2-0-47';
%% X: Dynamic variables
% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)
m = pwAddX(m, 'Rec' , 1.8, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'Rec_i' , 118.598421286338, 'global', 0.001, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'IL13_Rec' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'p_IL13_Rec' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'p_IL13_Rec_i', 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'JAK2' , 24, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'pJAK2' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'SHP1' , 9.4, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'STAT5' , 209, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'pSTAT5' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'CD274mRNA' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
%% R: Reactions
% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)
m = pwAddR(m, {'Rec' }, {'IL13_Rec' }, {'IL13stimulation'}, 'C' , [] , 'k1 * m1 * r1 * 3.776', {'Kon_IL13Rec' });
m = pwAddR(m, {'Rec' }, {'Rec_i' }, { }, 'MA', [] , [] , {'Rec_intern' });
m = pwAddR(m, {'Rec_i' }, {'Rec' }, { }, 'MA', [] , [] , {'Rec_recycle' });
m = pwAddR(m, {'IL13_Rec' }, {'p_IL13_Rec' }, {'pJAK2' }, 'E' , [] , [] , {'Rec_phosphorylation' });
m = pwAddR(m, {'JAK2' }, {'pJAK2' }, {'IL13_Rec' }, 'E' , [] , [] , {'JAK2_phosphorylation' });
m = pwAddR(m, {'JAK2' }, {'pJAK2' }, {'p_IL13_Rec' }, 'E' , [] , [] , {'JAK2_phosphorylation' });
m = pwAddR(m, {'p_IL13_Rec' }, {'p_IL13_Rec_i'}, { }, 'MA', [] , [] , {'pRec_intern' });
m = pwAddR(m, {'p_IL13_Rec_i'}, { }, { }, 'MA', [] , [] , {'pRec_degradation' });
m = pwAddR(m, {'pJAK2' }, {'JAK2' }, {'SHP1' }, 'E' , [] , [] , {'pJAK2_dephosphorylation' });
m = pwAddR(m, {'STAT5' }, {'pSTAT5' }, {'pJAK2' }, 'E' , [] , [] , {'STAT5_phosphorylation' });
m = pwAddR(m, {'pSTAT5' }, {'STAT5' }, {'SHP1' }, 'E' , [] , [] , {'pSTAT5_dephosphorylation'});
m = pwAddR(m, { }, {'CD274mRNA' }, {'pSTAT5' }, 'C' , [] , 'm1*k1' , {'CD274mRNA_production' });
%% C: Compartments
% m = pwAddC(m, ID, size, outside, spatialDimensions, name, unit, constant)
m = pwAddC(m, 'cell', 1);
%% K: Dynamical parameters
% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)
m = pwAddK(m, 'Kon_IL13Rec' , 0.00174086832237195, 'global', 1e-009, 1000);
m = pwAddK(m, 'Rec_phosphorylation' , 9.07540737285078 , 'global', 1e-009, 1000);
m = pwAddK(m, 'pRec_intern' , 0.324132341358502 , 'global', 1e-009, 1000);
m = pwAddK(m, 'pRec_degradation' , 0.417538218767296 , 'global', 1e-009, 1000);
m = pwAddK(m, 'Rec_intern' , 0.259685756311325 , 'global', 1e-009, 1000);
m = pwAddK(m, 'Rec_recycle' , 0.00392430355501153, 'global', 1e-009, 1000);
m = pwAddK(m, 'JAK2_phosphorylation' , 0.300019047540849 , 'global', 1e-009, 1000);
m = pwAddK(m, 'pJAK2_dephosphorylation' , 0.0981610557569751 , 'global', 1e-009, 1000);
m = pwAddK(m, 'STAT5_phosphorylation' , 0.00426766529531612, 'global', 1e-009, 1000);
m = pwAddK(m, 'pSTAT5_dephosphorylation', 0.0116388587580445 , 'global', 1e-009, 1000);
m = pwAddK(m, 'CD274mRNA_production' , 0.0115927572109515 , 'global', 1e-009, 1000);
%% U: Driving input
% m = pwAddU(m, ID, uType, uTimes, uValues, compartment, name, description, u2Values, alternativeIDs, designerProps)
m = pwAddU(m, 'IL13stimulation', 'steps', [-100 0] , [0 1] , [], [], [], [], {}, [], 'protein.generic');
%% Default sampling time points
m.t = 0:1:120;
%% Y: Observables
% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)
m = pwAddY(m, 'Rec + IL13_Rec + p_IL13_Rec' , 'RecSurf_obs' , 'scale_RecSurf' , '0.1 * y + 0.1 * max(y)');
m = pwAddY(m, 'IL13_Rec + p_IL13_Rec + p_IL13_Rec_i', 'IL13-cell_obs', 'scale_IL13-cell', '0.15 * y + 0.05 * max(y)');
m = pwAddY(m, 'p_IL13_Rec + p_IL13_Rec_i' , 'pIL4Ra_obs' , 'scale_pIL4Ra' , '0.10 * y + 0.15 * max(y)');
m = pwAddY(m, 'pJAK2' , 'pJAK2_obs' , 'scale_pJAK2' , '0.1 * y + 0.1 * max(y)');
m = pwAddY(m, 'CD274mRNA' , 'CD274mRNA_obs', 'scale_CD274mRNA', '0.1 * y + 0.1 * max(y)');
m = pwAddY(m, 'pSTAT5' , 'pSTAT5_obs' , 'scale_pSTAT5' , '0.1 * y + 0.1 * max(y)');
%% S: Scaling parameters
% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)
m = pwAddS(m, 'scale_pJAK2' , 0.469836894150194, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_pIL4Ra' , 1.80002942264669, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_RecSurf' , 1, 'fix', 0.0001, 10000);
m = pwAddS(m, 'scale_IL13-cell', 174.726805005048, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_CD274mRNA', 0.110568221201943, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_pSTAT5' , 1, 'fix', 0.001, 10000);
%% Designer properties (do not modify)
m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];
2024-09-02 | BIOMD0000000314 | BioModels
Project description:The pathogenesis of classical Hodgkin lymphoma (cHL), the most common lymphoma in the young, is still enigmatic, largely because its Hodgkin and Reed-Sternberg (HRS) tumor cells are rare in the involved lymph node and therefore difficult to analyze. Here, by overcoming this technical challenge and performing for the first time a genome-wide transcriptional analysis of microdissected HRS cells in comparison to other B-cell lymphomas, cHL lines and normal B-cell subsets, we show that they differ extensively from the usually studied cHL cell lines, that the lost B-cell identity of cHLs is not linked to the acquisition of a plasma cell-like gene expression program, and that Epstein-Barr virus infection of HRS cells has a minor transcriptional influence on the established cHL clone. Moreover, although cHL appears a distinct lymphoma entity overall, HRS cells of its histological subtypes diverged in their similarity to other related lymphomas. Unexpectedly, we identified two molecular subgroups of cHL associated to differential strengths of the transcription factor activity of the NOTCH1, MYC and IRF4 proto-oncogenes. Finally, HRS cells display deregulated expression of several genes potentially highly relevant to lymphoma pathogenesis, including silencing of the apoptosis-inducer BIK and of INPP5D, an inhibitor of the PI3K-driven oncogenic pathway. The present study complements the GSE12453 and GSE14879 records by adding the following 10 samples: 5 primary tumor samples and 5 cell line samples. The 5 primary tumor samples represent 1000-2000 neoplastic cells microdissected from frozen biopsies of 5 cases of primary mediastinal B-cell lymphoma (PMBL). The 5 cell line samples represent 500-1000 living neoplastic cells isolated by fluorescence-activated cell sorting from growing cultures of the classical Hodgkin lymphoma (cHL) cell lines L1236, L428, KMH2 and HDLM2 and the lymphocyte-predominant Hodgkin lymphoma (lpHL) cell line DEV.
2012-09-17 | E-GEOD-40160 | biostudies-arrayexpress
Project description:Janus kinases (Jak) mediate cytokine, hormone and growth factor responses in hematopoietic cells. Jak2 is one of the most frequently mutated genes in the aging hematopoietic system and in hematopoietic cancers. Mutations in Jak constitutively activate downstream signaling and are drivers of myeloproliferative neoplasms (MPN). In clinical use, Jak-inhibitors have incomplete effects on overall disease burden of Jak2 mutated clones, prompting us to investigate the mechanism underlying disease persistence. By in-depth phospho-proteome profiling we here identify proteins involved in mRNA processing as targets of mutant Jak2. Inactivation of the post-translationally modified Jak2-target Ybx1 sensitizes Jak-inhibitor persistent cells to apoptosis and results in RNA mis-splicing, retained intron enrichment and disruption of the transcriptional control of extracellular signal-regulated kinase (ERK) signaling. In combination with pharmacological Jak-inhibition it induces apoptosis in Jak2-dependent murine and primary human cells, leading to in vivo regression of the malignant clones and inducing remission. This identifies and validates a novel cell-intrinsic mechanism how differential protein phosphorylation results in splicing-dependent alterations of Jak2-ERK-signaling and the maintenance of Jak2V617F malignant clones. Therapeutic targeting of Ybx1 dependent ERK-signaling in combination with Jak2-inhibition may eradicate Jak2-mutated cells.
2020-08-20 | PXD006921 | Pride
Project description:Developing targeted therapy for cutaneous T cell lymphoma (CTCL) patients still requires actionable mutated genes and deregulated pathways to be identified. There is increasing evidence that activating mutations in JAK genes and deregulated JAK/STAT signaling are important mechanisms involved in multiple B and T cell malignancies, including CTCL. Therefore, in this study we focused on studying the mutational status of JAK1, JAK2 and JAK3 genes in a series of human CTCL lesions and cell lines using next-generation sequencing (NGS). We found that 7 of 48 (14.7%) of the analyzed cases harbored mutations in the JAK1 and JAK3 genes that mainly affected the pseudokinase domain of the corresponding proteins. On the basis of these results, we used a specific JAK inhibitor (INCB018424) in a series of CTCL cell lines with deregulated JAK/STAT activity. Treatment of CTCL cells with INCB018424 resulted in dose-dependent reduction of activated STAT expression, diminished cell viability, and increased apoptosis. We also studied global changes in gene expression in cells with mutated JAK1 and JAK3 proteins treated with INCB018424 and identified multiple genes that were differentially regulated by JAK/STAT signaling, such as FGF20 (upregulated) and EGR1 (downregulated). Thus, our results show that the detection of deregulated JAK/STAT signaling in CTCL lesions via JAK mutations or other surrogate markers may serve to indicate the clinical use of JAK/STAT inhibitors. 3 replicates of cells treated with DMSO or JAKi during 30 min and 3h
2015-10-31 | E-GEOD-68562 | biostudies-arrayexpress