Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Transcription profiling of bone marrow plasma cells from 320 newly diagnosed multiple myeloma patients

ABSTRACT: In order to identify relevant, molecularly defined subgroups in Multiple Myeloma (MM), gene expression profiling (GEP) was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/ GMMG-HD4 trial using Affymetrix Gene Chip U133 plus 2.0 arrays. Hierarchical clustering identified 10 distinct subgroups. Bone marrow plasma cell samples were obtained from 320 newly diagnosed multiple myeloma patients included in a large multicenter, prospective, randomized phase III trial (HOVON65/GMMG-HD4). Purified myeloma plasma cells samples with a monoclonal plasma cell purity > 80% were used for analysis.

ORGANISM(S): Homo sapiens

SUBMITTER: Annemiek Broyl

PROVIDER: E-GEOD-19784 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients.

Broyl Annemiek A Hose Dirk D Lokhorst Henk H de Knegt Yvonne Y Peeters Justine J Jauch Anna A Bertsch Uta U Buijs Arjan A Stevens-Kroef Marian M Beverloo H Berna HB Vellenga Edo E Zweegman Sonja S Kersten Marie-Josée MJ van der Holt Bronno B el Jarari Laila L Mulligan George G Goldschmidt Hartmut H van Duin Mark M Sonneveld Pieter P

Blood 20100623 14

To identify molecularly defined subgroups in multiple myeloma, gene expression profiling was performed on purified CD138(+) plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/GMMG-HD4 trial. Hierarchical clustering identified 10 subgroups; 6 corresponded to clusters described in the University of Arkansas for Medical Science (UAMS) classification, CD-1 (n = 13, 4.1%), CD-2 (n = 34, 1.6%), MF (n = 32, 1.0%), MS (n = 33, 1.3%), proliferation-associat ...[more]

PMID: 20574050

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Project description:We have used genome-wide methylation microarrays to analyze differences in CpG methylation patterns in cells relevant to the pathogenesis of myeloma plasma cells, including B cells, normal plasma cells, MGUS, presentation myeloma, and plasma cell leukemia (PCL). We show that methylation patterns in these cell types are capable of distinguishing non-malignant from malignant cells and that the main reason for this difference is hypomethylation of the genome at the transition from MGUS to presentation myeloma. In addition, gene-specific hypermethylation was evident at the myeloma stage. Differential methylation was also evident at the transition from myeloma to PCL with re-methylation of the genome, specifically of genes involved in cell-cell signaling and cell adhesion, which may contribute to independence from the bone marrow microenvironment. There was a high degree of methylation variability within presentation myeloma samples and this was associated with the cytogenetic differences between samples. More specifically, we found that methylation subgroups were defined by translocations and hyperdiploidy, with t(4;14) myeloma having the largest impact on DNA methylation. Two groups of hyperdiploid samples were identified, based on unsupervised clustering, which had an impact on overall survival. Overall, DNA methylation has a large impact on disease progression and on myeloma cytogenetic subgroups. Bone marrow aspirates were obtained after informed consent. B cells (n=6) from normal individuals were selected from peripheral blood using CD19. Plasma cells (PC) from non-myeloma patients (normal plasma cell controls, n=3) as well as MGUS (n=4) and presentation myeloma patients (n=161) were selected to a purity of >90% using CD138 microbeads and magnet-assisted cell sorting (Miltenyi Biotech, Bisley, UK). Samples from PCL patients (n=7) were not CD138 selected but contained >90% plasma cell infiltration as determined by microscopy. In order to achieve a sufficient quantity of DNA some normal plasma cell control samples were pooled.

Project description:Objective: Nonalcoholic fatty liver disease (NAFLD) is linked to obesity and diabetes, suggesting an important role of adipose tissue in the pathogenesis of NAFLD. Here we aim to investigate the interaction between adipose tissue and liver in NAFLD, and identify potential early plasma markers that predict NASH. Research Design and Methods: C57Bl/6 mice were chronically fed a high fat diet to induce NAFLD and compared with mice fed low fat diet. Extensive histological and phenotypical analyses coupled with a time-course study of plasma proteins using multiplex assay was performed. Results: Mice exhibited pronounced heterogeneity in liver histological scoring, leading to classification into 4 subgroups: LF-low (LFL) responders displaying normal liver morphology, LF-high (LFH) responders showing benign hepatic steatosis, HF-low (HFL) responders displaying pre-NASH with macrovesicular lipid droplets, and HF-high (HFH) responders exhibiting overt NASH characterized by ballooning of hepatocytes, presence of Mallory bodies, and activated inflammatory cells. Compared to HFL responders, HFH mice gained weight more rapidly and exhibited adipose tissue dysfunction characterized by decreased final fat mass, enhanced macrophage infiltration and inflammation, and adipose tissue remodelling. Plasma haptoglobin, IL-1β, TIMP-1, adiponectin and leptin were significantly changed in HFH mice. Multivariate analysis indicated that in addition to leptin, plasma CRP, haptoglobin, eotaxin and MIP-1α early in the intervention were positively associated with liver triglycerides. Intermediate prognostic markers of liver triglycerides included IL-18, IL-1β, MIP-1γ and MIP-2, whereas insulin, TIMP-1, GCP-2 and MPO emerged as late markers. Conclusions: Our data support the existence of a tight relationship between adipose tissue dysfunction and NASH pathogenesis and point to several novel potential predictive biomarkers for NASH. Keywords: Expression profiling by array Male wildtype C57Bl/6 mice were fed LFD or HFD for 21 weeks. Mice were divided into 4 groups based on liver histology.

Project description:Background Epigenetic changes are involved in the extinction of the B-cell gene expression program of classical Hodgkin lymphoma. However, little is known regarding epigenetic similarities between classical Hodgkin lymphoma and plasma cell myeloma cells, both of which share an extinction of the gene expression program of mature B-cells. Design and methods Global histone H3 acetylation patterns were determined in cell lines derived from classical Hodgkin lymphoma, plasma cell myeloma and B-cell lymphoma by chromatin immunoprecipitation and subsequent hybridization onto promoter tiling arrays. H3K27 trimethylation was analyzed by chromatin immunoprecipitation and real-time DNA-PCR for selected genes. Epigenetic modifications were compared to gene expression data. Results B-cell characteristic genes were hypoacetylated in classical Hodgkin lymphoma and plasma cell myeloma cell lines, as demonstrated by comparison of their histone H3 acetylation patterns to those of B-cell lines. However, the number of genes jointly hyperacetylated and expressed in classical Hodgkin lymphoma and plasma cell myeloma cell lines, such as IFR4/MUM1 and RYBP, is limited. Moreover, H3K27 trimethylation for selected B-cell characteristic genes revealed that this additional epigenetic silencing is much more prevalent in classical Hodgkin lymphoma as compared to plasma cell myeloma. Conclusion Our epigenetic data support the view that classical Hodgkin lymphoma is characterized by an abortive plasma cell differentiation with a down-regulation of B-cell characteristic genes but without activation of most plasma cell typical genes. Gene expression analysis of plasma cell myeloma (PCM) and B-cell lines: Microarray data of the B-cell line Namalwa that was published previously by our group (GEO accession number GSE8388) was analyzed together with newly generated data for the B-cell lines SU-DHL4 and SU-DHL6 (duplicates) and three PCM cell lines (L363, U266, LP1). For all cell lines, RNA was isolated according to standard protocols (Qiagen, Hilden, Germany) and used for Affymetrix GeneChip hybridization (HG-U133A). Microarrays were normalized using RMA, and differential expression was calculated using moderated t-test.

Dataset Information

Transcription profiling of bone marrow plasma cells from 320 newly diagnosed multiple myeloma patients

Publications

Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients.

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