Myeloma Cells Down-Regulate Adiponectin in Bone Marrow Adipocytes Via TNF-Alpha
Bibliographic record
Abstract
Multiple myeloma is caused by abnormal plasma cells that accumulate in the bone marrow and interact with resident cells of the bone microenvironment to drive disease progression and development of an osteolytic bone disease. Bone marrow adipocytes (BMAds) are emerging as having important endocrine functions that can support myeloma cell growth and survival. However, how BMAds respond to infiltrating tumor cells remains poorly understood. Using the C57BL/KaLwRij murine model of myeloma, bone marrow adiposity was found to be increased in early stage myeloma with BMAds localizing along the tumor-bone interface at later stages of disease. Myeloma cells were found to uptake BMAd-derived lipids in vitro and in vivo, although lipid uptake was not associated with the ability of BMAds to promote myeloma cell growth and survival. However, BMAd-derived factors were found to increase myeloma cell migration, viability, and the evasion of apoptosis. BMAds are a major source of adiponectin, which is known to be myeloma-suppressive. Myeloma cells were found to downregulate adiponectin specifically in a model of BMAds but not in white adipocytes. The ability of myeloma cells to downregulate adiponectin was dependent at least in part on TNF-α. Collectively our data support the link between increased bone marrow adiposity and myeloma progression. By demonstrating how TNF-α downregulates BMAd-derived adiponectin, we reveal a new mechanism by which myeloma cells alter the bone microenvironment to support disease progression. © 2019 The Authors. Journal of Bone and Mineral Research published by American Society for Bone and Mineral Research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".