From breast cancer cell homing to the onset of early bone metastasis: The role of bone (re)modeling in early lesion formation
Bibliographic record
Abstract
Breast cancer often metastasizes to bone, causing osteolytic lesions. Structural and biophysical changes are rarely studied yet are hypothesized to influence metastasis. We developed a mouse model of early bone metastasis and multimodal imaging to quantify cancer cell homing, bone (re)modeling, and onset of metastasis. Using tissue clearing and three-dimensional (3D) light sheet fluorescence microscopy, we located enhanced green fluorescent protein-positive cancer cells and small clusters in intact bones and quantified their size and spatial distribution. We detected early bone lesions using in vivo microcomputed tomography (microCT)-based time-lapse morphometry and revealed altered bone (re)modeling in the absence of detectable lesions. With a new microCT image analysis tool, we tracked the growth of early lesions over time. We showed 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 suggests that higher rates of (re)modeling act as a driver of lesion formation during early 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".