Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression data from cell lines originating from patients with multiple myeloma, plasmacytoma and diffuse large B-cell lymphoma

ABSTRACT: A predictive gene list for response to high dose melphalan therapy in patients diagnosed with multiple myeloma is generated by combining results from dose response experiments and microarray data using a B-cell line panel and the introduction of multivariate regression techniques. 18 malignant B-cell lines were analyzed by gene expression microarrays and screened for melphalan median dose dependent growth inhibition. Results are used to build a predictive gene list for response to high dose melphalan therapy in multiple myeloma.

ORGANISM(S): Homo sapiens

SUBMITTER: Martin Boegsted

PROVIDER: E-GEOD-22759 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Generation of a predictive melphalan resistance index by drug screen of B-cell cancer cell lines.

Boegsted Martin M Holst Johanne M JM Fogd Kirsten K Falgreen Steffen S Sørensen Suzette S Schmitz Alexander A Bukh Anne A Johnsen Hans E HE Nyegaard Mette M Dybkaer Karen K

PloS one 20110429 4

<h4>Background</h4>Recent reports indicate that in vitro drug screens combined with gene expression profiles (GEP) of cancer cell lines may generate informative signatures predicting the clinical outcome of chemotherapy. In multiple myeloma (MM) a range of new drugs have been introduced and now challenge conventional therapy including high dose melphalan. Consequently, the generation of predictive signatures for response to melphalan may have a clinical impact. The hypothesis is that melphalan s ...[more]

PMID: 21559449

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Project description:Background Toxicity of the oral and gastrointestinal mucosa induced by high-dose melphalan is a clinical challenge with no documented prophylactic interventions or predictive tests. The aim of this study was to describe molecular changes in human oral mucosa and to identify biomarkers correlated with the grade of clinical mucositis. Methods and Findings Ten patients with multiple myeloma (MM) were included. For each patient, we acquired three buccal biopsies, one before, one at 2 days, and one at 20 days after high-dose melphalan administration. We also acquired buccal biopsies from 10 healthy individuals that served as controls. We analyzed the biopsies for global gene expression and performed an immunohistochemical analysis to determine HLA-DRB5 expression. We evaluated associations between clinical mucositis and gene expression profiles. Compared to gene expression levels before and 20 days after therapy, at two days after melphalan treatment, we found gene regulation in the p53 and TNF pathways (MDM2, INPPD5, TIGAR), which favored anti-apoptotic defense, and upregulation of immunoregulatory genes (TREM2, LAMP3) in mucosal dendritic cells. This upregulation was independent of clinical mucositis. HLA-DRB1 and HLA-DRB5 (surface receptors on dendritic cells) were expressed at low levels in all patients with MM, in the subgroup of patients with ulcerative mucositis (UM), and in controls; in contrast, the subgroup with low-grade mucositis (NM) displayed 5–6 fold increases in HLA-DRB1 and HLA-DRB5 expression in the first two biopsies, independent of melphalan treatment. Moreover, different splice variants of HLA-DRB1 were expressed in the UM and NM subgroups. Conclusions Our results revealed that, among patients with MM, immunoregulatory genes and genes involved in defense against apoptosis were affected immediately after melphalan administration, independent of the presence of clinical mucositis. Furthermore, our results suggested that the expression levels of HLA-DRB1 and HLA-DRB5 may serve as potential predictive biomarkers for mucositis severity

Dataset Information

Expression data from cell lines originating from patients with multiple myeloma, plasmacytoma and diffuse large B-cell lymphoma

Publications

Generation of a predictive melphalan resistance index by drug screen of B-cell cancer cell lines.

Similar Datasets

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