Bone marrow breakout lesions act as key sites for tumor-immune cell diversification in multiple myeloma
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE284727 | GEO | 2025/02/10
REPOSITORIES: GEO
Similar Datasets
Project description:We hypothesized that the immune microenvironment of the bone marrow influences the progression of myeloma outgrowth in the 5TGM1 transfer model of multiple myeloma. Therefore we sorted bone marrow T, NK, and non-hematopoietic stromal cells from control and tumor-bearing C57Bl/6 mice.
2023-02-24 | E-MTAB-12615 | biostudies-arrayexpress
Project description:We hypothesized that the immune microenvironment of the bone marrow influences the progression of myeloma outgrowth in the 5TGM1 transfer model of multiple myeloma. Therefore we sorted bone marrow T, B, NK, neutrophils, and monocytes/macrophages from control and tumor-bearing C57Bl/6 and KaLwRij mice.
2023-02-23 | E-MTAB-12616 | biostudies-arrayexpress
Project description:The myeloma bone marrow microenvironment drives proliferation of malignant plasma cells and promotes resistance to therapy. Interleukin-6 (IL-6) and downstream JAK/STAT signaling are thought to be central components of these microenvironment-induced phenotypes. In a prior drug repurposing screen, we identified tofacitinib, a pan-JAK inhibitor FDA-approved for rheumatoid arthritis, as an agent that may reverse the tumor-stimulating effects of bone marrow mesenchymal stromal cells.Here, we validated both in vitro, in stromal-responsive human myeloma cell lines, and in vivo, in orthotopic disseminated murine xenograft models of myeloma, that tofacitinib showed both single-agent and combination therapeutic efficacy in myeloma models. Surprisingly, we found that ruxolitinib, an FDA-approved agent targeting JAK1 and JAK2, did not lead to the same anti-myeloma effects. Combination with a novel irreversible JAK3-selective inhibitor also did not rescue ruxolitinib effects. RNA-seq and unbiased phosphoproteomics revealed that marrow stromal cells drive a JAK/STAT-mediated proliferative program in myeloma plasma cells, and tofacitinib reversed the large majority of these pro-growth signals. Taken together, our results suggest that tofacitinib specifically reverses the growth-promoting effects of the tumor microenvironment through blocking an IL-6-mediated signaling axis. As tofacitinib is already FDA-approved, these results can be rapidly translated into potential clinical benefits for myeloma patients.
2018-10-24 | PXD006581 | Pride
Project description:By generating a paired single cell RNA-sequencing database of the tumor niche from 10 newly diagnosed MM patients, we created a unique dataset allowing the in-depth analyses of stromal-immune interactions within the tumor microenvironment. Using this database, we identified the presence of inflammatory stromal fibroblasts in the bone marrow of Myeloma patients.The stromal inflammation was associated with NF-κB signaling, and sources of IL-1β or TNFα were specific immune subsets previously shown to be altered in MM, suggesting the presence of an immune cell-mediated feed-forward loop of bone marrow inflammation in MM. By tracking inflammatory signatures over time in individual patients undergoing first-line treatment using bulk RNA sequencing, we show that bone marrow inflammation is not reverted by successful anti-tumor therapy (see related accession number), suggesting a role for stromal fibroblasts and bone marrow inflammation in disease persistence or relapse.
2020-09-30 | E-MTAB-9139 | biostudies-arrayexpress
Project description:By generating a paired single cell RNA-sequencing database of the tumor niche from 10 newly diagnosed MM patients, we created a unique dataset allowing the in-depth analyses of stromal-immune interactions within the tumor microenvironment (see related accession number). Using this database, we identified the presence of inflammatory stromal fibroblasts in the bone marrow of Myeloma patients.The stromal inflammation was associated with NF-κB signaling, and sources of IL-1β or TNFα were specific immune subsets previously shown to be altered in MM, suggesting the presence of an immune cell-mediated feed-forward loop of bone marrow inflammation in MM. By tracking inflammatory signatures over time in individual patients undergoing first-line treatment using bulk RNA sequencing, we show that bone marrow inflammation is not reverted by successful anti-tumor therapy (this dataset), suggesting a role for stromal fibroblasts and bone marrow inflammation in disease persistence or relapse. Raw sequencing data files will be deposited to EGA.
2021-02-01 | E-MTAB-9285 | biostudies-arrayexpress
Project description:Multiple myeloma is a fatal hematological malignancy. In order to develop effective therapeutic approaches, it is critical to understand the pathogenesis of myeloma. The Radl 5T model of multiple myeloma is a clinically relevant murine model where myeloma spontaneously occurs in aged, in-bred C57BlKalwRij mice and can be propagated by intravenous inoculation of 5T myeloma cells into mice of the same strain. Importantly inoculation of 5T myeloma cells into C57Bl6 mice does not result in myeloma, demonstrating that the bone marrow (BM) microenvironment of the C57BlKalwRij strain provides a unique and permissive milieu for myeloma development. We hypothesized that cells of the BM microenvironment may provide essential stimuli for the development of multiple myeloma in vivo. We aim to determine the differences in expression within the bone marrow of C57Bl/KalwRij mice. Comparison of C57Bl/KalwRij mouse bone marrow to C57BL6 mouse bone marrow
2011-08-18 | E-GEOD-31532 | biostudies-arrayexpress
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:This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo.
Biology Direct2010 Apr 20;5(28).
20406449,
Abstract:
BACKGROUND:
Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.
RESULTS:
Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.
CONCLUSIONS:
The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.
Note:
The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model without Tumour.
Typos in the publication:
Equation (4): The first term should be (β1/α1)^(g12/Γ) and not (β2/α2)^(g12/Γ)
Equation (14): The first term should be (β1/α1)^(((g12/(1+r12))/Γ) and not (β2/α2)^(((g12/(1+r12))/Γ)
Equation (13): The first term should be (β1/α1)^((1-g22+r22)/Γ) and not (β1/α1)^((1-g22-r22)/Γ)
All these corrections has been implemented in the model, with the authors agreement.
Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.
2024-09-02 | BIOMD0000000401 | BioModels
Project description:This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo.
Biology Direct2010 Apr 20;5(28).
20406449,
Abstract:
BACKGROUND:
Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.
RESULTS:
Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.
CONCLUSIONS:
The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.
Note:
The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model with Tumour.
Typos in the publication:
Equation (4): The first term should be (β1/α1)^(g12/Γ) and not (β2/α2)^(g12/Γ)
Equation (14): The first term should be (β1/α1)^(((g12/(1+r12))/Γ) and not (β2/α2)^(((g12/(1+r12))/Γ)
Equation (13): The first term should be (β1/α1)^((1-g22+r22)/Γ) and not (β1/α1)^((1-g22-r22)/Γ)
All these corrections has been implemented in the model, with the authors agreement.
Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.
2024-09-02 | BIOMD0000000402 | BioModels
Project description:This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo.
Biology Direct2010 Apr 20;5(28).
20406449,
Abstract:
BACKGROUND:
Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.
RESULTS:
Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.
CONCLUSIONS:
The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.
Note:
The paper describes three models 1) Zero-dimensional Bone Model without Tumou
r, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bo
ne Model with Tumour and Drug Treatment.
Typos in the publication:
Equation (4): The first term should be (β1/α1)^(g12/Γ) and not (β2/α2)^(g12/Γ)
Equation (14): The first term should be (β1/α1)^(((g12/(1+r12))/Γ) and not (β2/α2)^(((g12/(1+r12))/Γ)
Equation (13): The first term should be (β1/α1)^((1-g22+r22)/Γ) and not (β1/α1)^((1-g22-r22)/Γ)
All these corrections has been implemented in the model, with the authors agreement.
Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.
2024-09-02 | BIOMD0000000403 | BioModels