Adipocyte-Cancer Cell Interactions in the Bone Microenvironment
Bibliographic record
Abstract
When compared to adipocytes in other anatomical sites, the interaction of bone marrow resident adipocytes with the other cells in their microenvironment is less well understood. Bone marrow adipocytes originate from a resident, self-renewing population of multipotent bone marrow stromal cells which can also give rise to other lineages such as osteoblasts. The differentiation fate of these mesenchymal progenitors can be influenced to favour adipogenesis by several factors, including the administration of thiazolidinediones and increased age. Experimental data suggests that increases in bone marrow adipose tissue volume may make bone both more attractive to metastasis and conducive to cancer cell growth. Bone marrow adipocytes are known to secrete a variety of lipids, cytokines and bioactive signaling molecules known as adipokines, which have been implicated as mediators of the interaction between adipocytes and cancer cells. Recent studies have provided new insight into the impact of bone marrow adipose tissue volume expansion in regard to supporting and exacerbating the effects of bone metastasis from solid tumors, focusing on prostate, breast and lung cancer and blood cancers, focusing on multiple myeloma. In this mini-review, recent research developments pertaining to the role of factors which increase bone marrow adipose tissue volume, as well as the role of adipocyte secreted factors, in the progression of bone metastatic prostate and breast cancer are assessed. In particular, recent findings regarding the complex cross-talk between adipocytes and metastatic cells of both lung and prostate cancer are highlighted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".