Interaction of breast cancer cells with osteoclasts
Bibliographic record
Abstract
Breast cancer is a major health problem. Metastatic disease is generally incurable. In the majority of patients, the skeleton bears the major metastatic burden. Bony lesions of metastatic breast cancer are usually osteolytic. Osteolytic metastases are formed by the pathological activation of osteoclasts. Anti-osteoclastic drugs are standard of care for patients suffering from breast cancer metastases. We conducted this project to decipher the signalling mechanisms responsible for osteoclasts activation in response to exposure to mediators released from mammary carcinoma cells. We evaluated the apoptotic profiles of osteoclasts in vitro in the presence of soluble factors derived from mammary carcinoma cells cultures. We observed a significant inhibition of osteoclasts apoptosis secondary to exposure to breast cancer cells-derived factors. This effect was not reversed with bisphosphonates. The pro-apoptotic protein BIM in osteoclasts was a target of modulation by breast cancer cells-derived factors. We proceeded to characterize the osteoclasts intracellular signalling pathways modulated by mammary carcinoma cells-derived factors. We identified phospholipase C γ (PLC γ) and the mammalian target of rapamycin (mTOR) as mechanistic meditors of the anti-apoptotic effect of cancer cells on osteoclasts. We tested the therapeutic benefit of rapamycin administration in a mouse model of experimental bone metastases of mammary carcinoma. In this model, rapamycin therapy inhibited metastasis-associated osteolysis, prolonged animal survival and reversed some tumour-induced immune changes in the host. Taken together, these studies provide new insights into the pathophysiology of breast cancer metastasis to bone, and demonstrate that targeting osteoclast signalling mediators, such as mTOR provides therapeutic benefits in experimental bone metastasis model.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".