Alterations in CIITA constitute a common mechanism accounting for downregulation of MHC class II expression in diffuse large B-cell lymphoma (DLBCL)

Cycon, Kelly A.; Rimsza, Lisa M.; Murphy, Shawn P.

doi:10.1016/j.exphem.2008.10.001

Cited by 40 publications

(46 citation statements)

References 35 publications

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“…We have shown that expression of each of the classical, non-classical, and invariant chain MHCII molecules and CIITA correlate with one another in DLBCL patient samples, that intervening genes are not affected, and that large deletions within MHCII genes do not explain lost expression [9]. Our demonstration that MHCII is consistently downregulated in concert with CIITA suggests that altered transcription of MHCII via the master transactivator CIITA is a mechanism for MHCII down-regulation in DLBCL, a finding confirmed in DLBCL cell culture studies [7]. We have further shown that mutations of CIITA do not explain the coordinate down-regulation [10].…”

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confidence: 59%

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Decreased major histocompatibility complex class II expression in diffuse large B-cell lymphoma does not correlate with CpG methylation of class II transactivator promoters III and IV

Wilkinson

Fernandez

Murphy

et al. 2009

Leukemia & Lymphoma

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Diffuse large B-cell lymphoma (DLBCL) is a disease marked by heterogeneity in clinical presentation, morphology, and underlying biology. Importantly, this translates into variability of patient response to chemotherapy. While a majority of patients can be cured effectively with modern chemotherapy regimens, many present with or acquire resistance to treatment. Characterizing the mechanism(s) responsible for poor patient outcome is critical for identifying future targets to improve treatment. The expression of classical MHCII, non-classical MHCII, and invariant chain molecules are coordinately regulated by the class II transactivator (CIITA), the master regulator of MHCII transcription [5][6][7]. CIITA is essential for MHCII expression; thus, control of MHCII expression depends on control of and by CIITA expression [5,8]. MHCII cell-specific and temporal expression is controlled by the differential activation of four different promoters within the CIITA gene [8]. The CIITA promoters pIII and pIV are important for MHCII expression in B-cells and hematopoietic tumors [6]. We have shown that expression of each

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supporting

confidence: 59%

“…The expression of classical MHCII, non-classical MHCII, and invariant chain molecules are coordinately regulated by the class II transactivator (CIITA), the master regulator of MHCII transcription [5–7]. CIITA is essential for MHCII expression; thus, control of MHCII expression depends on control of and by CIITA expression [5,8].…”

mentioning

confidence: 99%

Decreased major histocompatibility complex class II expression in diffuse large B-cell lymphoma does not correlate with CpG methylation of class II transactivator promoters III and IV

Wilkinson

Fernandez

Murphy

et al. 2009

Leukemia & Lymphoma

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“…The part of this pathway containing selected genes is visualized in Figure 7a. A selected regulator CIITA was shown to regulate two classes of antigens MHC I and II in DLBCL (Cycon et al , 2009). The loss of MHC II on lymphoma cells—including the selected HLA-DMB, -DQB1, -DMA, -DRA, -DRB1, -DPA1, -DPB1 and -DQA1—was shown to be related to poor prognosis and reduced survival in DLBCL patients (Rosenwald et al , 2002).…”

Section: Methodsmentioning

confidence: 99%

Joint network and node selection for pathway-based genomic data analysis

et al. 2013

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Motivation: By capturing various biochemical interactions, biological pathways provide insight into underlying biological processes. Given high-dimensional microarray or RNA-sequencing data, a critical challenge is how to integrate them with rich information from pathway databases to jointly select relevant pathways and genes for phenotype prediction or disease prognosis. Addressing this challenge can help us deepen biological understanding of phenotypes and diseases from a systems perspective.Results: In this article, we propose a novel sparse Bayesian model for joint network and node selection. This model integrates information from networks (e.g. pathways) and nodes (e.g. genes) by a hybrid of conditional and generative components. For the conditional component, we propose a sparse prior based on graph Laplacian matrices, each of which encodes detailed correlation structures between network nodes. For the generative component, we use a spike and slab prior over network nodes. The integration of these two components, coupled with efficient variational inference, enables the selection of networks as well as correlated network nodes in the selected networks.Simulation results demonstrate improved predictive performance and selection accuracy of our method over alternative methods. Based on three expression datasets for cancer study and the KEGG pathway database, we selected relevant genes and pathways, many of which are supported by biological literature. In addition to pathway analysis, our method is expected to have a wide range of applications in selecting relevant groups of correlated high-dimensional biomarkers.Availability: The code can be downloaded at www.cs.purdue.edu/homes/szhe/software.html.Contact: alanqi@purdue.edu

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“…2 The relevance of the microenvironment for tumor control in this disease is exemplified by the selection of recurrent genetic alterations that mediate escape from immune surveillance. 3,4 With regards to patients' outcomes, the contribution of the tumor microenvironment to treatment failure in DLBCL was highlighted for example by a study from the Lymphoma/Leukemia Molecular Profiling Project (LLMPP) in which two gene expression signatures derived from the microenvironment -the "stromal-1" and "stromal-2" signatures -were predictive of favorable or unfavorable survival, respectively, in patients treated with CHOP alone or CHOP in combination with rituximab. 5 The "stromal-1" signature is thought to arise from extracellular-matrix deposition and histiocytic infiltration whereas the "stromal-2" signature reflects tumor blood-vessel density.…”

mentioning

confidence: 99%