Vibrational Stimulation of Osteocytes in Modulating Breast Cancer Bone Metastasis
Bibliographic record
Abstract
Breast cancer is a leading cause of mortality worldwide, with bone being a common site of metastasis. Metastatic cancer cells disrupt bone remodeling, leading to incurable bone lesions. While radiotherapy remains a cornerstone of breast cancer treatment, it can unintentionally damage bone, causing bone loss and pain, with no effective strategies currently available. Consequently, the rate of bone degradation is accelerated in breast cancer patients with bone metastases receiving radiotherapy. Given its safety and efficacy, we investigated the potential of low-magnitude, high-frequency (LMHF) vibration as a noninvasive intervention to protect bone against cancer metastasis and irradiation. We focused on osteocytes, the primary mechanosensors and regulators of bone, whose regulatory functions also extend to modulating breast cancer bone metastasis. We found that LMHF vibration (0.3 g, 60 Hz, 1 hour) activated osteocytes by altering mechanosensitive gene expression and demonstrated the importance of the Piezo1 ion channel in osteocyte mechanotransduction. Following irradiation (8 Gy), we observed that LMHF vibration mitigated osteocyte apoptosis and preserved cytoskeletal integrity. Using a microfluidic platform, we modeled the bone-cancer microenvironment to study breast cancer extravasation. Daily LMHF vibration (0.3 g, 60 Hz, 1 hour/day) over three days reduced breast cancer extravasation via osteocyte signaling. Chemical activation of Piezo1 with Yoda1 in osteocytes further enhanced the vibration-induced effects on cancer extravasation at early time points. In contrast, irradiated osteocytes exhibited a diminished ability to reduce cancer metastasis, leading to increased invasion and extravasation of non-irradiated breast cancer cells. Notably, LMHF vibration restored osteocyte regulation of breast cancer extravasation, potentially through the Wnt signaling pathway based on the RNA-seq analysis. A combined approach integrating vibration with radiotherapy (on both osteocytes and cancer cells) further reduced cancer invasion and extravasation, demonstrating a synergistic effect. These findings highlight the potential of LMHF vibration to reduce breast cancer bone metastasis while preserving osteocyte function following irradiation, underscoring the promise of noninvasive mechanical intervention in maintaining bone health and optimizing cancer treatment outcomes.
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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 | 0.000 |
| 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".