Regulation of Breast Cancer Cells’ Bone-Metastatic Potential by Mechanically Stimulated Osteocytes
Bibliographic record
Abstract
Bone metastasis, the migration of cancers to the bone, occurs in 65-75% of patients with advanced breast cancer and significantly increases patients’ morbidity and mortality. The bone-metastatic cancer cells interact with cells in the bone to disrupt the bone remodeling balance, causing reduced bone quality and other complications while facilitating tumor growth. Bone remodeling cells can be regulated by osteocytes, the major population of cells in the bone that are embedded in the bone matrix, in response to dynamic loading on the bone. Osteocytes also signal to blood vessel-lining endothelial cells that interact closely with cancer cells during early metastasis before a secondary tumor is established in the bone. Therefore, we hypothesized that mechanically stimulated osteocytes may regulate cancer cells directly and via other cells. To investigate, we mimicked what osteocytes experience in vivo during bone-loading activities, such as walking, with oscillatory fluid flow. We observed that factors secreted by flow-stimulated osteocytes increase cancer cell migration and survival. Contrastingly, signaling from flow-stimulated osteocytes through bone-resorbing osteoclasts or endothelial cells to cancer cells were anti-metastatic. Specifically, it reduced cancer cell migration, survival, and invasion. Factors secreted by flow-stimulated osteocytes also reduced cancer cells’ trans-endothelial migration and endothelial monolayers’ permeability and ability to be adhered by cancer cells. These demonstrated the capability of mechanically stimulated osteocytes in reducing the bone-metastatic potential of breast cancer cells by signaling through osteoclasts and endothelial cells. Investigating this regulation further can provide novel insights into the potential of bone-loading exercise in preventing bone metastasis.
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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".