From breast cancer cell homing to the onset of early bone metastasis: dynamic bone (re)modeling as a driver of metastasis
Bibliographic record
Abstract
Abstract Breast cancer often metastasizes to bone causing osteolytic lesions. Structural and biophysical changes are rarely studied, yet are hypothesized to influence metastatic progression. Here, we developed a mouse model of early bone metastasis and multimodal 3D imaging to quantify cancer cell homing, dynamic bone (re)modeling and onset of bone metastasis. Using 3D light sheet fluorescence microscopy, we show eGFP + cancer cells and small clusters in 3D (intact) bones. We detect early bone lesions using time-lapse in vivo microCT and reveal altered bone (re)modeling in absence of detectable lesions. With a new microCT image analysis tool, we detect and track the growth of early bone lesions over time. We show that cancer cells home in all bone compartments, while osteolytic lesions are only detected in the metaphysis, a region of high (re)modeling. Our study provides novel insights of dynamic bone (re)modeling as a driver during the early phase of 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 | 0.001 | 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".