Project description:The paper describes a model of multiple myeloma.
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This model is described in the article:
A mathematical model of cell equilibrium and joint cell formation in multiple myeloma
M.A. Koenders, R. Saso
Journal of Theoretical Biology 390 (2016) 73–79
Abstract:
In Multiple Myeloma Bone Disease healthy bone remodelling is affected by tumour cells by means of paracrine cytokinetic signalling in such a way that osteoclast formation is enhanced and the growth of osteoblast cells inhibited. The participating cytokines are described in the literature. Osteoclast-induced myeloma cell growth is also reported. Based on existing mathematical models for healthy bone remo- delling a three-way equilibrium model is presented for osteoclasts, osteoblasts and myeloma cell populations to describe the progress of the illness in a scenario in which there is a secular increase in the cytokinetic interactive effectiveness of paracrine processes. The equilibrium state for the system is obtained. The paracrine interactive effectiveness is explored by parameter variation and the stable region in the parameter space is identified. Then recently-discovered joint myeloma–osteoclast cells are added to the model to describe the populations inside lytic lesions. It transpires that their presence expands the available parameter space for stable equilibrium, thus permitting a detrimental, larger population of osteoclasts and myeloma cells. A possible relapse mechanism for the illness is explored by letting joint cells dissociate. The mathematics then permits the evaluation of the evolution of the cell populations as a function of time during relapse.
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Project description:<h4><strong>BACKGROUND:</strong> Multiple myeloma is characterized by clonal proliferation of malignant plasma cells in the bone marrow that produce monoclonal immunoglobulins. N-glycosylation changes of these monoclonal immunoglobulins have been reported in multiple myeloma, but previous studies only detected limited serum N-glycan features.</h4><h4><strong>METHODS:</strong> Here, a more detailed study of the human serum N-glycome of 91 multiple myeloma patients and 51 controls was performed. We additionally analyzed sequential samples from patients (n = 7) which were obtained at different time points during disease development as well as 16 paired blood serum and bone marrow plasma samples. N-glycans were enzymatically released and measured by mass spectrometry after linkage specific derivatization of sialic acids.</h4><h4><strong>RESULTS:</strong> A decrease in both α2,3- and α2,6-sialylation, galactosylation and an increase in fucosylation within complex-type N-glycans were found in multiple myeloma patients compared to controls, as well as a decrease in difucosylation of diantennary glycans. The observed glycosylation changes were present in all ISS stages, including the 'low-risk' ISS I. In individual patients, difucosylation of diantennary glycans decreased with development of the disease. Protein N-glycosylation features from blood and bone marrow showed strong correlation. Moreover, associations of monoclonal immunoglobulin (M-protein) and albumin levels with glycan traits were discovered in multiple myeloma patients.</h4><h4><strong>CONCLUSIONS & GENERAL SIGNIFICANCE: </strong>In conclusion, serum protein N-glycosylation analysis could successfully distinguish multiple myeloma from healthy controls. Further studies are needed to assess the potential roles of glycan trait changes and the associations of glycans with clinical parameters in multiple myeloma early detection and prognosis.</h4>
Project description:Gene Expression profiling of 170 newly diagnosed Multiple Myeloma patients Gene Expression profiling of Multiple Myeloma Cells from Healthy donors and Multiple myeloma patients were profiled using Affymetrix Exon-1.0 ST microarrays
Project description:miRNA profiling in multiple myeloma - microRNAs represent a class of noncoding regulators of gene expression implicated in several biological and pathophysiological processes, including cancer. We investigate here their role in multiple myeloma using miChip-arrays interrogating 559 miRNAs in 92 purified myeloma-, MGUS-, normal plasma cell- and myeloma cell line samples. Impact on gene expression is assessed by Affymetrix U133 2.0 DNA-microarrays in 741 samples including two cohorts of 332 and 345 myeloma patients; chromosomal aberrations are assessed by iFISH, survival for 247 and 345 patients undergoing up-front high-dose therapy and autologous stem cell transplantation. Compared to normal plasma cells, 67/559 (12%) miRNAs are differentially expressed with fold changes of 4.6 to -3.1 in myeloma-, 20 (3.6%) in MGUS-samples, and three (0.5%) between MGUS- and myeloma-samples. Expression of miRNAs is associated with biological and pathophysiological parameters, i.e. proliferation, chromosomal aberrations, e.g. t(4;14), tumor mass, and gene expression-based high-risk scores. This holds true for target-gene signatures of regulated mRNAs. miRNA-expression confers prognostic significance for event-free (72/559) and overall survival (69/559), as do respective target-gene signatures. In conclusion, the miRNome of myeloma confers a pattern of small changes of individual miRNAs compared to normal plasma cells impacting on gene expression, biological functions, and survival.
Project description:Multiple myeloma is a plasma cell malignancy of the bone marrow. Despite therapeutic advances, multiple myeloma remains incurable and better risk stratification as well as new therapies are therefore highly needed. The proteome of multiple myeloma has not been systematically assessed before and holds the potential to uncover additional insight into disease biology and improved prognostic models. Here, we provide a comprehensive multi-omics analysis including deep tandem mass tag (TMT)-based quantitative global (phospho)proteomics, RNA sequencing and nanopore DNA sequencing of 138 primary patient-derived plasma cell malignancies encompassing treatment-naive multiple myeloma patients treated in clinical trials, plasma cell leukemia, and the premalignancy monoclonal gammopathy of undetermined significance (MGUS), as well as healthy controls. We found that the (phospho)proteome of malignant plasma cells is highly deregulated as compared to healthy plasma cells and is both defined by chromosomal alterations and extensive post-transcriptional regulation. A protein signature was identified that is associated with aggressive disease and more predictive for outcome than cytogenetic-based risk assessment in newly diagnosed multiple myeloma. Integration with functional genetics and single-cell sequencing revealed generally and genetic subtype-specific deregulated proteins and pathways in plasma cell malignancies that include novel potential targets for (immuno)therapies. These findings provide new insights in the biology of multiple myeloma and will be a unique resource for investigating new therapeutic approaches.
Project description:Multiple myeloma is a plasma cell malignancy of the bone marrow. Despite therapeutic advances, multiple myeloma remains incurable and better risk stratification as well as new therapies are therefore highly needed. The proteome of multiple myeloma has not been systematically assessed before and holds the potential to uncover additional insight into disease biology and improved prognostic models. Here, we provide a comprehensive multi-omics analysis including deep tandem mass tags (TMT)-based quantitative global (phospho)proteomics, RNA sequencing and nanopore DNA sequencing of 138 primary patient-derived plasma cell malignancies encompassing treatment-naive multiple myeloma patients treated in clinical trials, plasma cell leukemia, and the premalignancy monoclonal gammopathy of undetermined significance (MGUS), as well as healthy controls. We found that the (phospho)proteome of malignant plasma cells is highly deregulated as compared to healthy plasma cells and is both defined by chromosomal alterations and extensive post-transcriptional regulation. A protein signature was identified that is associated with aggressive disease and more predictive for outcome than cytogenetic-based risk assessment in newly diagnosed multiple myeloma. Integration with functional genetics and single-cell sequencing revealed generally and genetic subtype-specific deregulated proteins and pathways in plasma cell malignancies that include novel potential targets for (immuno)therapies. These findings provide new insights in the biology of multiple myeloma and will be a unique resource for investigating new therapeutic approaches.