Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Gene expression profiling of Multiple Myeloma - Validation, INSERM/CHU Montpellier and UK Heidelberg

ABSTRACT: Gene expression profiling of CD138 purified bone marrow plasma cells of normal donnors, previoulsy untreated MGUS and multiple myeloma patients, and myeloma cell lines

ORGANISM(S): Homo sapiens

DISEASE(S): normal

SUBMITTER: Thierry Reme

PROVIDER: E-MTAB-363 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Modeling risk stratification in human cancer.

Rème Thierry T Hose Dirk D Theillet Charles C Klein Bernard B

Bioinformatics (Oxford, England) 20130314 9

<h4>Motivation</h4>Despite huge prognostic promises, gene expression-based survival assessment is rarely used in clinical routine. Main reasons include difficulties in performing and reporting analyses and restriction in most methods to one high-risk group with the vast majority of patients being unassessed. The present study aims at limiting these difficulties by (i) mathematically defining the number of risk groups without any a priori assumption; (ii) computing the risk of an independent coho ...[more]

PMID: 23493321

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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.

Dataset Information

Gene expression profiling of Multiple Myeloma - Validation, INSERM/CHU Montpellier and UK Heidelberg

Publications

Modeling risk stratification in human cancer.

Similar Datasets

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