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Single-cell atlas of multiple myeloma and precursor diseases

ABSTRACT: Single cell profiling of small cohorts of myeloma precursor disease patient samples including monoclonal gammopathy of unknown significance and smoldering multiple myeloma have shown diverse evolutionary patterns and immune changes that occur early in the disease process. Using scRNAseq and scBCRseq in a large cohort of fifty-three patients with myeloma precursor disease in comparison with myeloma and normal donors we describe the early genomic drivers of malignant transformation and describe their divergent clonal expansion in hyperdiploid compared to non-hyperdiploid samples. We describe intra-patient heterogeneity with potential therapeutic implications as well as the distinct evolution patterns (linear/branching) from myeloma precursor disease to myeloma. Finally, we describe the unique adaptations of the microenvironment as a response to distinct genomic changes in myeloma cells. These results further our knowledge to characterize myeloma precursor disease evolution, inform individual patient progression risk stratification and identify potential biomarkers that could be clinically exploited.

PROVIDER: EGAS00001006694 | EGA |

REPOSITORIES: EGA

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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:The paper describes a model of multiple myeloma. Created by COPASI 4.26 (Build 213) 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. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Single-cell atlas of multiple myeloma and precursor diseases

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