Mechanical regulation of breast cancer migration and apoptosis via direct and indirect osteocyte signaling
Bibliographic record
Abstract
Bone metastases, the migration of cancers to bone, occur in 65-80% of patients with advanced breast cancer. Metastasized cancer cells interact with cells such as the bone-resorbing osteoclasts to alter bone remodeling. Exercise, often suggested as an intervention for cancer patients, regulates bone remodeling via osteocytes. Osteocytes also signal to endothelial cells, which may affect cancer cell extravasation. Therefore, we hypothesize that mechanically stimulated osteocytes can regulate processes in breast cancer bone metastasis. To test this, we exposed osteocytes to oscillatory fluid flow in vitro using parallel-plate flow chambers. We observed that conditioned medium from flow-stimulated osteocytes increased migration (by 45%) and reduced apoptosis (by 12%) of breast cancer cells. Conditioned medium from osteoclasts conditioned in flowed osteocytes' conditioned medium reduced migration (by 47%) and increased apoptosis (by 55%) of cancer cells. Cancer cell trans-endothelial migration was reduced by 34% toward flowed osteocytes' conditioned medium. This difference was abolished with ICAM-1 or IL-6 neutralizing antibodies. Conditioned medium from endothelial cells conditioned in flowed osteocytes' conditioned medium increased cancer cell apoptosis by 29%. To summarize, this study demonstrated mechanically stimulated osteocytes' potential to affect breast cancer cells not only through direct signaling, but also through osteoclasts and endothelial cells. The anti-metastatic potential of the indirect signalings is particularly exciting since osteocytes are further away from metastasizing cancer cells than osteoclasts and endothelial cells. Future studies into the effect of bone mechanical loading on metastases and its mechanism will assist in designing cancer intervention programs that lowers the risk for bone metastases.
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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) | 0.001 | 0.000 |
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".