Resolving therapy resistance mechanisms in multiple myeloma by multiomics subclone analysis
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ABSTRACT:
PROVIDER: PRJNA987620 | ENA |
REPOSITORIES: ENA
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Project description:Intratumor heterogeneity as a clinical challenge becomes most evident after several treatment lines, when multidrug-resistant subclones accumulate. To address this challenge, the characterization of resistance mechanisms at the subclonal level is key to identify common vulnerabilities. In this study, we integrate whole-genome sequencing, single-cell (sc) transcriptomics (scRNA sequencing), and chromatin accessibility (scATAC sequencing) together with mitochondrial DNA mutations to define subclonal architecture and evolution for longitudinal samples from 15 patients with relapsed or refractory multiple myeloma. We assess transcriptomic and epigenomic changes to resolve the multifactorial nature of therapy resistance and relate it to the parallel occurrence of different mechanisms: (1) preexisting epigenetic profiles of subclones associated with survival advantages, (2) converging phenotypic adaptation of genetically distinct subclones, and (3) subclone-specific interactions of myeloma and bone marrow microenvironment cells. Our study showcases how an integrative multiomics analysis can be applied to track and characterize distinct multidrug-resistant subclones over time for the identification of molecular targets against them.
2023-08-01 | GSE235783 | GEO
Project description:Intratumor heterogeneity as a clinical challenge becomes most evident after several treatment lines, when multidrug-resistant subclones accumulate. To address this challenge, the characterization of resistance mechanisms at the subclonal level is key to identify common vulnerabilities. In this study, we integrate whole-genome sequencing, single-cell (sc) transcriptomics (scRNA sequencing), and chromatin accessibility (scATAC sequencing) together with mitochondrial DNA mutations to define subclonal architecture and evolution for longitudinal samples from 15 patients with relapsed or refractory multiple myeloma. We assess transcriptomic and epigenomic changes to resolve the multifactorial nature of therapy resistance and relate it to the parallel occurrence of different mechanisms: (1) preexisting epigenetic profiles of subclones associated with survival advantages, (2) converging phenotypic adaptation of genetically distinct subclones, and (3) subclone-specific interactions of myeloma and bone marrow microenvironment cells. Our study showcases how an integrative multiomics analysis can be applied to track and characterize distinct multidrug-resistant subclones over time for the identification of molecular targets against them.
| S-EPMC10733835 | biostudies-literature
Project description:Subclone specific microenvironmental impact and drug response in refractory multiple myeloma revealed by single cell transcriptomics
| PRJNA679548 | ENA
Project description:To investigate the relationship between genetic and transcriptional heterogeneity in a context of cancer progression, we devised a computational approach called HoneyBADGER to identify copy number variation and loss-of-heterozygosity in individual cells from single-cell RNA-sequencing data. By combining allele frequency and expression magnitude deviations, HoneyBADGER is able to infer the presence of subclone-specific alterations in individual cells and reconstruct subclonal architecture. Also HoneyBADGER to analyze single cells from a progressive multiple myeloma (MM) patient to identify major genetic subclones that exhibit distinct transcriptional signatures relevant to cancer progression.
2018-05-24 | GSE110499 | GEO
Project description:Virtually all patients with multiple myeloma become unresponsive to treatment over time. Relapsed/refractory multiple myeloma (RRMM) is accompanied by the clonal evolution of myeloma with heterogeneous genomic aberrations and profound changes of the bone marrow microenvironment (BME). However, the molecular mechanisms that drive drug resistance remain elusive. Here, we have analyzed the heterogeneous tumor cell population of 20 RRMM patients and its complex interaction network with the BME by single cell RNA-sequencing before/after treatment. Subclones with chromosome 1q-gain expressed a specific transcriptomic signature and frequently expanded during treatment. Furthermore, RRMM cells shaped an immune suppressive BME by upregulation of inflammatory cytokines and close interaction with the myeloid compartment. It was characterized by the accumulation of PD1+ γδ T-cells and tumor-associated macrophages as well as the depletion of hematopoietic progenitors. Thus, our study resolves transcriptional features of subclones in RRMM and mechanisms of microenvironmental reprogramming with implications for clinical decision-making.
2021-08-18 | GSE161801 | GEO
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>
2022-04-27 | MTBLS676 | MetaboLights
Project description:RNA-seq analysis comparing the transcriptional landscape of PRDM5 silenced vs mock KMS-11 multiple myeloma cell line
2019-11-16 | GSE122638 | GEO
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.
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2024-09-02 | BIOMD0000000804 | BioModels
Project description:In this study we addressed subclonal evolutionary process after treatment and subsequent relapse in multiple myeloma (MM) in a cohort of 24 MM patients treated either with conventional chemotherapy or with the proteasome inhibitor, bortezomib. Because MM is a highly heterogeneous disease coupled with a large number of DNA copy number alterations (CNAs) and loss of heterozygosity (LOH), we focused our study on the secondary genetic events: 1q21 gain, NF-kB activating mutations, RB1 and TP53 deletions, that seem to reflect progression. By using genome-wide high resolution SNP arrays we identified subclones with nonlinear complex evolutionary histories in a third of patients with myeloma, the relapse clone apparently derived from a minor subclone at diagnosis. Such reordering of the spectrum of genetic lesions during therapy is likely to reflect selection of genetically distinct subclones not initially competitive against the dominant population that survived chemotherapy, thrived and acquired new anomalies. In addition we found that emergence of minor subclones at relapse was significantly associated with bortezomib treatment. Altogether, these data support the idea of new strategy of future clinical trials in MM that would combine targeted therapy and subpopulations control to eradicate all myeloma subclones in order to obtain long-term remission. This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2013-02-13 | E-GEOD-37469 | biostudies-arrayexpress